THE METABOLIC PATHWAY ENGINEERING HANDBOOK Tools and Applications
The Metabolic Pathway Engineering Handbook, 1st Edition The Metabolic Pathway Engineering Handbook: Fundamentals The Metabolic Pathway Engineering Handbook: Tools and Applications
THE METABOLIC PATHWAY ENGINEERING HANDBOOK Tools and Applications
Edited by
Christina D. Smolke
CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2010 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works Printed in the United States of America on acid-free paper 10 9 8 7 6 5 4 3 2 1 International Standard Book Number-13: 978-1-4200-7765-0 (Hardcover) This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, please access www.copyright.com (http:// www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging-in-Publication Data The metabolic pathway engineering handbook : tools and applications / editor, Christina D. Smolke. p. ; cm. Includes bibliographical references and index. ISBN 978-1-4200-7765-0 (alk. paper) 1. Genetic engineering--Handbooks, manuals, etc. 2. Biosynthesis--Handbooks, manuals, etc. I. Smolke, Christina D. II. Title. [DNLM: 1. Genetic Engineering--methods. 2. DNA--metabolism. 3. Genetic Techniques. 4. RNA--metabolism. QU 450 M587 2010] TP248.6.M48 2010 660.6’5--dc22 Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com
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Contents
Introduction................................................................................................... ix Editor.. ............................................................................................................xv Contributors................................................................................................ xvii
Section I Evolutionary Tools in Metabolic Engineering Claudia Schmidt-Dannert
1 Evolutionary Engineering of Industrially Important
Microbial Phenotypes...................................................................................... 1-1 Stephen S. Fong
2
3 Engineering DNA and RNA Regulatory Regions through Random
Improving Protein Functions by Directed Evolution.................................... 2-1 Nikhil u. Nair and Huimin Zhao
Mutagenesis and Screening.. ........................................................................... 3-1 Ichiro Matsumura, Sean A. Lynch, and Justin P. Gallivan
4 Evolving Pathways and Genomes for the Production of Natural and
Novel Compounds.. ......................................................................................... 4-1
5
Ethan T. Johnson and Claudia Schmidt-Dannert
Models Predicting Optimized Strategies for Protein Evolution.. .................. 5-1 Jonathan J. Silberg and Peter Q. Nguyen
Section II Gene Expression Tools for Metabolic Pathway Engineering Christina D. Smolke
6
Low-Copy Number Plasmids as Artificial Chromosomes.. ........................... 6-1
7
Chromosomal Engineering Strategies............................................................. 7-1
Kristala L. Jones Prather Kenan C. Murphy
v
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8 Regulating Gene Expression through Engineered RNA
Technologies.................................................................................................... 8-1
9
Maung Nyan Win and Christina D. Smolke
Tools Designed to Regulate Translational Efficiency.................................... 9-1 Claes Gustafsson
10 Metabolic Engineering of the Secretory Processing Pathway
in Eukaryotes................................................................................................. 10-1
Mohak Mhatre, Maira P. Pellegrini, and Michael J. Betenbaugh
11 Engineering Multifunctional Enzyme Systems for Optimized
Metabolite Transfer between Sequential Conversion Steps.. ........................ 11-1
Robert J. Conrado, Thomas J. Mansell, and Matthew P. DeLisa
12 Practical Pathway Engineering—Demonstration in
Integrating Tools.. .......................................................................................... 12-1
Sung Kuk Lee and Jay D. Keasling
Section III Application of Emerging Technologies to Metabolic Engineering Jay D. Keasling
13 Genome-Wide Technologies: DNA Microarrays, Phenotypic
Microarrays, and Proteomics........................................................................ 13-1
14
Seh Hee Jang, Mee-Jung Han, Sang Yup Lee, Jong Hyun Choi, and Xiao Xia Xia
Monitoring and Measuring the Metabolome................................................ 14-1 Maria Rowena N. Monton and Tomoyoshi Soga
Section IV Future Prospects in Metabolic Engineering Bernhard ø. Palsson and Sang Yup Lee
15 Systems Biology, Genome-Scale Models, and
Metabolic Engineering.. ................................................................................. 15-1
16 17
Sang Yup Lee, Hyun Uk Kim, Hongseok Yun, Seung Bum Sohn, Jin Sik Kim, Bernhard Ø. Palsson, Markus J. Herrgård, and Vasiliy A. Portnoy
Cell-Free Systems for Metabolic Engineering............................................... 16-1 Kara A. Calhoun and James R. Swartz
In Silico Models for Metabolic Systems Engineering.. .................................. 17-1
Kumar Selvarajoo, Satya Nanda Vel Arjunan, and Masaru Tomita
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Section V Tools for Experimentally Determining Flux through Pathways Ralf Takors
18
19
GC–MS for Metabolic Flux Analysis.. ........................................................... 18-1
Christoph Wittmann
Tools for Measuring Intermediate and Product Formation......................... 19-1
Marco Oldiges
20 Use of AEX-HPLC-ESI-MS for 13C-Labeling Based Metabolic Flux
Analysis in Saccharomyces cerevisiae and Penicillium chrysogenum.......... 20-1
Wouter A. van Winden, Roelco J. Kleijn, Walter M. van Gulik, and Joseph J. Heijnen
Section VI Future Applications of Metabolic Engineering Brian F. Pfleger
21 Energy and Cofactor Issues in Fermentation and
Oxyfunctionalization Processes.................................................................... 21-1
Bruno Bühler, Lars M. Blank, Birgitta E. Ebert, Katja Bühler, and Andreas Schmid
22 Microbial Biosynthesis of Fine Chemicals:
An Emerging Technology.............................................................................. 22-1
Zachary L. Fowler and Mattheos Koffas
23 Applications of Metabolic Engineering for Natural
Drug Discovery.............................................................................................. 23-1
24
Yi Tang, Suzanne Ma, and Wladyslaw A. Wojcicki
Metabolic Engineering for Alternative Fuels.. .............................................. 24-1
Yandi Dharmadi and Ramon Gonzalez
Index.. ............................................................................................................ I –1
Introduction
Progression of Biological Synthesis Methods toward Commercial Relevance The advent of recombinant DNA in the 1970s brought transformative technologies for the synthesis and manipulation of artificial genetic material. The ability to amplify, cut, and piece together fragments of DNA outside of a cell and to get (or transform) that DNA into a cell of interest resulted in a set of molecular cloning tools that enabled the field of genetic engineering. In genetic engineering, foreign DNA that encodes for new or altered functions or traits is inserted into an organism of interest. Many early applications of recombinant DNA technology focused on heterologous protein production in microbial hosts. The first medicine made through recombinant DNA technology that was approved by the United States Federal Drug Administration was the synthesis of synthetic “human” insulin in Escherichia coli. This was an important early application of recombinant DNA technology, as the success of producing a safe and effective synthetic hormone in a bacteria led to the widespread acceptance of the technology and significant resources and funding to be directed to its support and advancement. As the technologies in support of synthesizing and manipulating artificial DNA matured and advanced, so did the applications to which they were applied. The early successful applications of recombinant DNA technology resulted in alternative routes to the synthesis of medicines, such as insulin, human growth factor, and erythropoietin, vaccines, and even genetically modified organisms, including crops that exhibit more desirable traits. Technologies were developed for the manipulation of artificial DNA in both prokaryotic and eukaryotic host organisms, including mammalian and plant cells. In addition, inspired by the diversity of natural products, chemicals, and materials synthesized by biological systems that are observed in the natural world, researchers began to look beyond applications that were limited to the synthesis of a single heterologous protein product in a cellular host to more complicated engineering feats. In particular, these new applications focused on the manipulation of sets or combinations of proteins, or enzymes, that acted in conjunction in a cell, within metabolic pathways, to convert energy and precursor chemicals into desired natural and non-natural products. The production of chemicals, materials, and energy through biology presents an alternative to traditional chemical synthesis routes. While the development of chemical synthesis methods for the production of valuable chemicals and small molecule pharmaceuticals is a more mature field and has demonstrated significant successes, many chemicals remain difficult to be synthesized through such strategies, particularly those with many chiral centers. Biological catalysts, or enzymes, have demonstrated remarkable adeptness at the synthesis of very complex molecules. In addition, cellular biosynthesis strategies offer several advantages over traditional chemical synthesis strategies in that the former is often conducted under less harsh conditions, thereby enabling “green” synthesis strategies that are associated with the production of fewer toxic by-products. In addition, cellular biosynthesis ix
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Introduction
takes advantage of the cell’s natural ability to replenish enzymes and cofactors and to provide precursors from often inexpensive and renewable starting materials. Such advantages are particularly compelling in light of the global challenges we face today in energy, the environment, and sustainability. However, new challenges are presented when manipulating the metabolic pathways in cellular hosts that link energy sources and starting materials to products of commercial interest. The unique challenges faced in engineering metabolic pathways, when compared to the early genetic engineering applications of heterologous protein production, require the development of new enabling technologies, spanning experimental and analytical techniques and computational tools.
The Field of Metabolic Engineering Metabolic engineering is a field that includes the construction, redirection, and manipulation of cellular metabolism through the alteration of endogenous and/or heterologous enzyme activities and levels to achieve the biosynthesis or biocatalysis of desired compounds. Researchers in metabolic engineering often view the biological system as a chemical factory that is converting starting materials to different value-added products. Because the yield or productivity of the process is linked to its commercial viability, the ability to precisely regulate the flow of energy and materials through different cellular pathways becomes critical to the optimization of the overall process, drawing parallels to the more traditional engineering discipline of chemical process design. The basic tenet of metabolic engineering, the use of biology as a technology for the conversion of energy, chemicals, and materials to value-added products, has a long history. Early applications can be cited, even prior to the development of recombinant DNA technology, in the food and beverage industry where more traditional methods of strain development based on evolution, mating, and selection strategies were used to develop more desired production hosts for particular applications. However, recombinant DNA technology enabled the capability to introduce new enzymatic activities and pathways into production hosts allowing access to different energy resources and starting materials and to the production of different chemicals and materials. Such technologies support the forward design of more complex synthetic pathways in host organisms or the targeted manipulation of endogenous pathways, enabling more directed manipulation of the cellular host. Current metabolic engineering efforts are focused on the synthesis of products such as chemical commodities, small molecule drugs, and alternative energy sources including biofuels. In addition, significant effort is also directed to the engineering of host metabolisms to utilize renewable, low cost energy resources. Many of the challenges faced in metabolic engineering are related to the engineering of energy and material flow within complex systems. More specifically, metabolic pathways make up complex interconnected networks in cells, which can rarely be manipulated in isolation of the rest of the network. Highlighting the interconnections between cellular metabolites is the fact that all metabolites are made from a set of 12 common precursors. In addition, the flow of metabolites through a network of enzymes, and in the background of other cellular enzymes that may exhibit activity on these metabolites, is often controlled through layered processes that act at different time scales, implement dynamic feedback control, and utilize localization and transport. Metabolic engineering requires a breadth of skill sets to tackle different points of system design and as a result has developed into a very interdisciplinary field. Researchers with expertise spanning a variety of disciplines, including chemical engineering, biological engineering, environmental engineering, biochemistry, molecular biology, cell biology, bioinformatics, and control theory, are working in different areas of metabolic engineering. However, as an academic endeavor, metabolic engineering has remained an interdisciplinary research discipline with courses covering aspects of the field depending on the expertise of the department in which it is taught. As it has matured, metabolic engineering has gained greater industrial significance. Initial industrial interest was directed to the synthesis of chemical commodities in microorganisms largely at groups within larger chemical companies. However, many smaller startup companies have developed in recent years that are focused on the synthesis of specialty chemicals such as pharmaceuticals and biofuels, on
Introduction
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the development of computational and modeling programs to direct metabolic engineering efforts, and on the discovery and development of new enzyme activities in support of engineering new synthetic pathways into host organisms. The intersection of metabolic engineering, with other emerging areas of systems and synthetic biology, presents exciting opportunities to develop solutions to many of the global challenges we face in energy, the environment, health and medicine, resources, and sustainability, and will likely continue to fuel a significant sector of the biotechnology industry in future years.
An Overview of The Metabolic Pathway Engineering Handbook The purpose of The Metabolic Pathway Engineering Handbook is to provide a thorough overview of the field of metabolic engineering. Each section provides an overview of different aspects of a particular topic that is a central component of the field by experts in that area. Sections are introduced by section editors to provide a perspective on the topic and a description of how the chapters in that section link together to form an integrated overview of that particular topic. The sections are split into two books, where the content of the first book focuses on “fundamentals” or basic principles of metabolic engineering and the second book focuses on “tools and applications” in metabolic engineering. Due to its organization, the handbook can be used as a reference book and read for individual sections or chapters, or it can be used as a book for advanced courses in metabolic engineering. Section I in The Metabolic Pathway Engineering Handbook: Fundamentals provides an overview of the basic processes that support cellular metabolism. The boundary of a cell is defined by its cellular membrane, which acts to separate cellular constituents from the environment. Metabolism begins with systems that allow the import of nutrients and starting materials across the cellular membrane and efforts to engineer transport systems for particular chemicals have been important strategies in enabling cells to convert those chemicals to desired products. Once inside the cell, nutrients are broken down into common precursors for metabolic syntheses, which provide the energy and reducing power necessary for cell survival. In addition, precursors are channeled into the synthesis of important building blocks that the cell then utilizes to build larger macromolecules, including lipids, nucleic acids, and proteins. An understanding of the central metabolic pathways and the general flow of metabolism through a small number of common precursors and carriers is critical to being able to effectively link new synthetic nutrient or product pathways to endogenous metabolisms. Finally, the wealth of untapped diversity in nature, particularly in the microbial biosphere, provides significant opportunities in harvesting new enzymatic activities from nature that can be applied to the production of new chemicals and materials in engineered hosts. Section II provides an overview of mass balances and reaction models applied to predicting product formation and microbial growth in fermentation processes. Various models have been proposed and utilized in the field that exhibit varying levels of detail to provide predictions of product yield and cell growth. Conversion rates are calculated from mass balances and rate equations that take into account the basic nutrients and constituents of cellular systems. Different models, such as those based on thermodynamic or metabolic network constraints, can be utilized to predict product yield and cell growth in fermentation processes. Different models may be more or less appropriate based on the specifics of the fermentation. The application of such models to experimental systems can allow minimization of error in detection strategies resulting in optimized control schemes for fermentations based on such experimental measurements. Section III provides an overview of transcriptional regulation of metabolic pathways in bacterial systems. Bacterial cells use a variety of mechanisms to regulate the transcription of enzymes involved in primary and secondary metabolisms. Transcriptional regulatory strategies exist that regulate a small set of genes in response to specific environmental chemicals, such as operon-specific regulation and two-component systems. However, other strategies exist that regulate larger sets of genes in response to significant environmental changes such as heat shock or nitrogen starvation, through sigma factors and global transcriptional factors. An understanding of the strategies used to regulate the expression of
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enzymes in a cellular host is critical in metabolic engineering to developing effective strategies to alter the expression of endogenous enzymes and to design synthetic systems that exhibit more sophisticated regulatory schemes to balance and coordinate the expression of multiple enzymes to ultimately optimize flux through desired pathways. Section IV is an overview of modeling tools that have been developed for metabolic engineering applications. Earlier modeling and computation efforts that resulted in tools for metabolic flux analysis (MFA) and metabolic control analysis (MCA) have been very powerful for the elucidation of fluxes and control strategies in metabolic networks given partial sets of data. Computation tools based on network and graph concepts have enabled structure and flux analyses that provide optimization tools for metabolic engineering. In addition, metabolic network reconstruction and modeling efforts have resulted in genome-scale models of cellular metabolism for specific organisms based on sets of constraints that enable prediction of flux distributions under different conditions. Whereas multi-scale modeling tools are extending current predictive capabilities by integrating stoichiometry, kinetics, and regulatory and control responses in metabolic networks, such multi-scale tools can be utilized by metabolic engineers to predict the dynamic metabolic response. Section V provides an overview of common cellular hosts that are used in metabolic engineering applications. In particular, the bacterial hosts Escherichia coli, Bacillus subtilis, and Streptomyces have been utilized in various metabolic engineering applications, with E. coli being the most well-developed and utilized host largely due to the genetic tools available for manipulating pathways in this host organism. In addition, two lower eukaryotic hosts, yeast and filamentous fungi, have been utilized in various metabolic engineering applications for the production of natural products or for pathway enzymes that are more readily expressed in functional forms in eukaryotic organisms. Finally, much effort has also been put toward the development of mammalian cell culture hosts for the production of metabolites and products that are more readily produced in mammalian cells. Each host may present advantages and disadvantages in the synthesis of a desired chemical based on the genetic tools available for manipulating pathways and the endogenous metabolism and processing pathways present in that organism, such that the selection of a suitable host is driven largely by the properties of the pathway of interest. Section I in The Metabolic Pathway Engineering Handbook: Tools and Applications provides an overview of the evolutionary tools widely in use in the engineering of metabolic enzymes and networks. Evolutionary strategies have been traditionally used in metabolic engineering to select for desired phenotypes in host organisms. As biological organisms naturally undergo processes of evolution and selection, design strategies that integrate evolutionary engineering objectives with metabolic engineering objectives may result in a more robustly performing engineered cellular system. Directed evolution is a laboratory tool that is used to mimic the evolutionary process in a test tube, by generating diversity in cellular components and then screening or selecting through this diversity for optimized component properties. Various experimental strategies have been utilized for generating and screening through component diversity. In addition, computational tools have been developed that optimize the design of laboratory evolution strategies. These experimental and computational tools have been applied to the directed evolution of enzymes, regulatory systems, pathways, and whole genomes for the optimization of flux through targeted metabolic pathways. Section II provides an overview of gene expression tools that have been utilized in metabolic engineering applications. Various tools have been developed that regulate DNA copy number and enable chromosomal engineering in host organisms. In addition, a variety of other genetic tools have been developed that precisely regulate gene expression levels through post-transcriptional and translational mechanisms. Still other tools have been developed that regulate the activity of enzymes through posttranslational engineering strategies. The application of the tools described in this section is critical to balancing the expression of multiple enzymes, such that individual conversion steps do not limit product yield, toxic intermediates do not accumulate, and cellular resources and energy are efficiently utilized by the host cell. Several examples exist of engineered systems that have utilized such genetic tools for the optimization of flux through metabolic pathways.
Introduction
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Section III provides an overview of emerging technologies and their application to metabolic engineering. Genome-wide technologies that allow global profiling of cellular transcripts, proteins, metabolites, and phenotypes are critical for efficient troubleshooting and debugging of engineered systems. Bioinformatics tools that allow for management and analysis of the vast amounts of data collected from these techniques are also critical. As these technologies mature and become more available, their implementation as standard techniques in metabolic engineering will improve our understanding of the engineered system response and result in efficient troubleshooting and optimization strategies. Section IV provides an overview of key future prospects in metabolic engineering. The integration of new computational tools, such as genome-scale models, and new technologies for analyzing and understanding complex systems, such as systems biology, with metabolic engineering are rapidly advancing the success with which metabolic networks can be forward engineered. In addition, alternative strategies to cellular biosynthesis that remove complications associated with engineering living, evolving systems, such as cell-free synthesis systems, have demonstrated impressive successes. Finally, the modeling and optimization of engineered metabolic pathways in silico, prior to construction and characterization, will significantly transform the field of metabolic engineering and integrate advances in computational modeling, systems biology, and engineering design. Section V provides an overview of common tools that are utilized to determine flux through metabolic pathways. Various types of isotope flux labeling strategies have been widely used to monitor flux through metabolic pathways, where the data from such experiments are typically integrated into the modeling tools described in Section IV. In addition, various analytical strategies are utilized to profile cellular metabolites, where current and future efforts have been focused on developing strategies to profile and quantify global metabolite levels. Section VI provides an overview of various metabolic engineering application areas. One broad application area is focused on the engineering and regulation of the energy state, cofactor supply, and redox balance of cellular hosts. This is a challenge that affects most if not all metabolic engineering applications, where the introduction of new pathways or the manipulation of endogenous pathways can result in imbalances in cellular pathways and stress responses. Metabolic engineering applications are generally directed toward the synthesis of commercially relevant molecules including specialty or commodity chemicals, small molecule drugs, or alternative energy sources. Each of these application areas of metabolic engineering presents distinct challenges that must be addressed in the process design based on chemical and pathway complexity, market cost of the product, volume demand of the product, end use of the product, and purity requirements.
Metabolic Engineering: Looking toward the Future Metabolic engineering as a field has evolved significantly over the past 10 to 15 years in large part due to the scientific and technological advances made during this time frame in support of this application area. The future prospects of metabolic engineering are extremely exciting, and as other supporting scientific and engineering fields mature it is likely to see transformative advances that direct it further toward an engineering discipline. There are several key supporting fields that will aid in directing this transformation. First, enzyme engineering and enzyme discovery will be critical to expanding the diversity of natural and non-natural products that can be produced in engineered organisms. Much of the living world has not been cultured and characterized. Even in those organisms that have been cultured, we do not have genome sequence information, have not mapped functions to many of the sequenced genes, or have not characterized many of the enzyme activities in these organisms. For example, many pathways in plants responsible for the synthesis of diverse pharmacologically relevant molecules have not been elucidated, although many of these activities and their corresponding genes are currently present in large expressed sequence tag (EST) libraries. Because we cannot forward design enzymes to exhibit specific catalytic
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activities, the existing limitations in characterized enzyme activities severely limit the pathways that we can reconstruct in organisms. In addition, programs that will allow us to predict and design enzyme function from sequence will be critically enabling for the design of new activities that have not been recovered from natural systems. Second, because metabolic engineering is largely a systems engineering challenge, continued advances in systems biology will provide important insights into the function of biological systems that will inform engineering design and strategies directed at manipulating metabolic pathways. Many analytic techniques in support of systems biology, including strategies that allow global profiling of transcript, protein, and metabolite levels, are providing vast amounts of information regarding levels of cellular constituents under different conditions. In addition, computational tools are being developed to process the vast amounts of data coming from these techniques. Newer and future efforts in systems biology must focus on taking the information coming from these techniques and abstracting from it the organizing principles governing cellular metabolism and regulation. An understanding of how cells generally layer metabolic pathways with different regulatory strategies will allow engineers to design more robustly performing synthetic pathways that are better integrated with the endogenous metabolic pathways. In addition, such understanding will allow better identification of manipulation points in endogenous networks to alter flux through pathways. Third, the integration of information theory and control theory with systems biology and metabolic engineering will likely have a significant impact on our understanding of biological systems. Such tools will enable a deeper understanding of architectures and properties of complex networks that support robustness, evolvability, and fragility of the system, providing a conceptual framework to systems biology. In addition, such tools will allow researchers to more quantitatively examine models of control schemes around metabolic pathways to better elucidate the design principles around regulating flux through metabolic pathways. Such tools can also be used to examine synthetic network and control scheme designs and guide the more effective design of engineered systems. Finally, metabolic engineering is seeing a transformation with the emerging field of synthetic biology. Synthetic biology is the design, construction, and characterization of biological systems using engineering design principles. To support a framework for engineering biology, synthetic biology is rooted in foundational technologies that enable the construction of more complex, heterologous networks in living systems. With advances in DNA sequencing and synthesis it is becoming common practice to synthesize entire genes and pathways from scratch, no longer limiting researchers to the physical DNA that they obtain from natural organisms. In addition, abstraction frameworks have been proposed to enable rapid assembling and reassembling of basic biological components (or parts) into larger networks (or devices) and systems, supporting the rapid prototyping and troubleshooting and reliable construction of complex metabolic pathways in cellular hosts (or chassis). An example of a synthetic biology approach to the rapid prototyping of a metabolic pathway in Escherichia coli was recently described (http://parts. mit.edu/wiki/index.php/MIT_2006). There are also efforts directed to the engineering of specific chassis, or cellular hosts, optimized for metabolic engineering applications. Finally, enabling genetically encoded technologies are being developed for use in precise and quantitative manipulation of pathway components such as enzymes.
Christina D. Smolke
Editor-in-Chief
Editor
Christina Smolke is an assistant professor in the Department of Bioengineering at Stanford University. She graduated with a BS in chemical engineering with a minor in biology from the University of Southern California in 1997. She conducted her graduate training as a National Science Foundation Fellow in the Chemical Engineering Department at the University of California at Berkeley and earned her PhD in 2001. Christina conducted her postdoctoral training as a National Institutes of Health Fellow in cell biology at UC Berkeley. She started her independent research program as an assistant professor in the Division of Chemistry and Chemical Engineering at the California Institute of Technology from 2003– 2008. She has pioneered a research program in developing foundational technologies for the design and construction of engineered ligand-responsive RNA-based regulatory molecules, their integration into molecular computation and signal integration strategies, and their reliable implementation into diverse cellular engineering applications. These technologies are resulting in scaleable platforms for the construction of molecular tools that work across many cellular systems and allow regulation of targeted gene expression levels in response to diverse endogenous or exogenous molecular ligands. Her research is rapidly advancing current capabilities of noninvasive detection of cellular state and programming cellular function. In particular, her laboratory is examining the application of these tools to the optimization of metabolic pathway engineering strategies in organisms such as yeast. Dr. Smolke’s innovative research program has recently been recognized with the receipt of a National Science Foundation CAREER Award, a Beckman Young Investigator Award, an Alfred P. Sloan Research Fellowship, and the listing of Dr. Smolke as one of Technology Review’s Top 100 Young Innovators in the World. She is also a member and adjunct faculty of the Comprehensive Cancer Center’s Cancer Immunotherapeutics Program at the City of Hope, where she has several translationally oriented collaborative projects exploring the clinical applications of these technologies. She is the inventor of over nine patents and serves on the Scientific Advisory Board of Codon Devices. Dr. Smolke is currently serving as the President of the Institute of Biological Engineering. She is a member of AIChE, ACS, the RNA Society, and IBE.
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Contributors
Satya Nanda Vel Arjunan Institute for Advanced Biosciences Keio University Tsuruoka, Japan
Michael J. Betenbaugh Department of Chemical and Biomolecular Engineering Johns Hopkins University Baltimore, Maryland
Lars M. Blank
Chemical Biotechnology TU Dortmund and ISAS-Institute for Analytical Sciences Dortmund, Germany
Bruno Bühler
Chemical Biotechnology TU Dortmund Dortmund, Germany
Katja Bühler
Chemical Biotechnology TU Dortmund Dortmund, Germany
Kara A. Calhoun
Department of Chemical Engineering Stanford University Stanford, California
Jong Hyun Choi
Department of Chemical and Biomolecular Engineering BioProcess Engineering Research Center Bioinformatics Research Center Institute for the BioCentury Korea Advanced Institute of Science and Technology Daejeon, Korea
Robert J. Conrado
School of Chemical and Biomolecular Engineering Cornell University Ithaca, New York
Matthew P. DeLisa
Stephen S. Fong
Department of Chemical and Life Science Engineering Virginia Commonwealth University Richmond, Virginia
Zachary L. Fowler
Department of Chemical and Biological Engineering State University of New York at Buffalo Buffalo, New York
Justin P. Gallivan
School of Chemical and Biomolecular Engineering Cornell University Ithaca, New York
Department of Chemistry and Center for Fundamental and Applied Molecular Evolution Emory University Atlanta, Georgia
Yandi Dharmadi
Ramon Gonzalez
Department of Chemical and Biomolecular Engineering Rice University Houston, Texas
Birgitta E. Ebert
Chemical Biotechnology TU Dortmund Dortmund, Germany
Department of Chemical and Biomolecular Engineering Rice University Houston, Texas
Claes Gustafsson DNA2.0, Inc. Menlo Park, California
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Mee-Jung Han
Department of Chemical and Biomolecular Engineering BioProcess Engineering Research Center Bioinformatics Research Center Institute for the BioCentury Korea Advanced Institute of Science and Technology Daejeon, Korea
Joseph J. Heijnen
Bioprocess Technology Group Department of Biotechnology Delft University of Technology Delft, the Netherlands
Markus J. Herrgård Department of Bioengineering University of California San Diego, California
Seh Hee Jang
Contributors
Jin Sik Kim
Department of Chemical and Biomolecular Engineering Center for Systems and Synthetic Biotechnology Institute for the BioCentury Korea Advanced Institute of Science and Technology Daejeon, Korea
Roelco J. Kleijn
Bioprocess Technology Group Department of Biotechnology Delft University of Technology Delft, the Netherlands
Mattheos Koffas
Department of Chemical and Biological Engineering State University of New York at Buffalo Buffalo, New York
Department of Chemical and Biomolecular Engineering BioProcess Engineering Research Center Bioinformatics Research Center Institute for the BioCentury Korea Advanced Institute of Science and Technology Daejeon, Korea
Sang Yup Lee
Ethan T. Johnson
Sung Kuk Lee
Department of Biochemistry, Molecular Biology and Biophysics University of Minnesota St. Paul, Minnesota
Department of Chemical and Biomolecular Engineering Center for Systems and Synthetic Biotechnology Institute for the BioCentury Korea Advanced Institute of Science and Technology Daejeon, Korea
School of Nano Bio Chemical Engineering Ulsan National Institute of Science and Technology (UNIST) Ulsan, Korea
Jay D. Keasling
Berkeley Center for Synthetic Biology University of California Berkeley, California
Hyun Uk Kim
Department of Chemical and Biomolecular Engineering Center for Systems and Synthetic Biotechnology Institute for the BioCentury Korea Advanced Institute of Science and Technology Daejeon, Korea
Sean A. Lynch
Department of Chemistry and Center for Fundamental and Applied Molecular Evolution Emory University Atlanta, Georgia
Suzanne Ma
Department of Chemical and Biomolecular Engineering University of California Los Angeles, California
Thomas J. Mansell
School of Chemical and Biomolecular Engineering Cornell University Ithaca, New York
Ichiro Matsumura
Department of Biochemistry and Center for Fundamental and Applied Molecular Evolution Rollins Research Center Emory University School of Medicine Atlanta, Georgia
Mohak Mhatre
Department of Chemical and Biomolecular Engineering Johns Hopkins University Baltimore, Maryland
Maria Rowena N. Monton Institute for Advanced Biosciences Keio University Tsuruoka, Japan
Kenan C. Murphy
Molecular Genetics and Microbiology University of Massachusetts Medical School Worcester, Massachusetts
Nikhil U. Nair
Department of Chemical and Biomolecular Engineering University of Illinois at UrbanaChampaign Urbana, Illinois
Peter Q. Nguyen
Department of Biochemistry and Cell Biology Rice University Houston, Texas
Marco Oldiges
Institute of Biotechnology Forschungszentrum Jülich GmbH Jülich, Germany
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Contributors
Bernhard Ø. Palsson Department of Bioengineering Univerisity of California San Diego, California
Maira P. Pellegrini
Department of Chemical Engineering/COPPE Federal University of Rio de Janeiro Rio de Janeiro, Brazil
Brian F. Pfleger
Department of Chemical and Biological Engineering University of Wisconsin Madison, Wisconsin
Vasiliy A. Portnoy
Department of Bioengineering University of California San Diego, California
Kristala L. Jones Prather Department of Chemical Engineering Massachusetts Institute of Technology Cambridge, Massachusetts
Andreas Schmid
Chemical Biotechnology TU Dortmund and ISAS-Institute for Analytical Sciences Dortmund, Germany
Claudia Schmidt-Dannert Department of Biochemistry, Molecular Biology, and Biophysics University of Minnesota St. Paul, Minnesota
Kumar Selvarajoo
Institute for Advanced Biosciences Keio University Tsuruoka, Japan
Jonathan J. Silberg
Wouter A. van Winden
Christina D. Smolke
Maung Nyan Win
Department of Biochemistry and Cell Biology Rice University Houston, Texas
Division of Chemistry and Chemical Engineering California Institute of Technology Pasadena, California
Tomoyoshi Soga
Institute for Advanced Biosciences Keio University Tsuruoka, Japan
Seung Bum Sohn
Department of Chemical and Biomolecular Engineering Center for Systems and Synthetic Biotechnology Institute for the BioCentury Korea Advanced Institute of Science and Technology Daejeon, Korea
James R. Swartz
Department of Chemical Engineering Stanford University Stanford, California
Ralf Takors
Evonik Degussa GmbH– Health & Nutrition KÜnsebeck, Germany
Yi Tang
Department of Chemical and Biomolecular Engineering University of California Los Angeles, California
Masaru Tomita
Institute for Advanced Biosciences Keio University Tsuruoka, Japan
Walter M. van Gulik
Bioprocess Technology Group Department of Biotechnology Delft University of Technology Delft, the Netherlands
Bioprocess Technology Group Department of Biotechnology Delft University of Technology Delft, the Netherlands
Division of Chemistry and Chemical Engineering California Institute of Technology Pasadena, California
Christoph Wittmann Biochemical Engineering Institute Technische Universitāt Braunscheweig Braunscheweig, Germany
Wladyslaw A. Wojcicki Department of Chemical and Biomolecular Engineering University of California Los Angeles, California
Xiao Xia Xia
Department of Chemical and Biomolecular Engineering BioProcess Engineering Research Center Bioinformatics Research Center Institute for the BioCentury Korea Advanced Institute of Science and Technology Daejeon, Korea
Hongseok Yun
Department of Chemical and Biomolecular Engineering Center for Systems and Synthetic Biotechnology Institute for the BioCentury Korea Advanced Institute of Science and Technology Daejeon, Korea
Huimin Zhao
Department of Chemical and Biomolecular Engineering Institute for Genomic Biology University of Illinois at UrbanaChampaign Urbana, Illinois
Evolutionary Tools in Metabolic Engineering
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Claudia Schmidt-Dannert University of Minnesota
1 Evolutionary Engineering of Industrially Important Microbial Phenotypes Stephen S. Fong.....................................................................................................1-1 Introduction to Evolutionary Engineering • Microbial Evolution • In Vitro Evolution Systems • Evolutionary Engineering Results
2 Improving Protein Functions by Directed Evolution Nikhil U. Nair and Huimin Zhao............................................................................................................................ 2-1 Introduction • Tools of Directed Evolution: Techniques for Generating Mutant Libraries • Tools of Directed Evolution: Screens, Enrichments, and Selections • Successes and Applications of Directed Evolution • Patent and Licensing Issues • Outlook
3 Engineering DNA and RNA Regulatory Regions through Random Mutagenesis and Screening Ichiro Matsumura, Sean A. Lynch, and Justin P. Gallivan....................................................................................................3-1 Introduction • Promoters, Operators, and Enhancers • Practical Approaches to Promoter Mutagenesis and Cloning • Review of Engineered RNA-Based Regulatory Systems • Principles and Protocols for Creating Synthetic Riboswitches • Conclusions
4 Evolving Pathways and Genomes for the Production of Natural and Novel Compounds Ethan T. Johnson and Claudia Schmidt-Dannert...................................4-1 Introduction • Initial Pathway Design and Integration into Host Metabolic Network Optimization of Metabolic Enzyme Levels Using Evolutionary Design Evolving Metabolic Pathways for the Production of New Compounds • Addressing Complex Traits Using Evolutionary Methods • Screening Technologies Conclusions and Future Directions
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5 Models Predicting Optimized Strategies for Protein Evolution Jonathan J. Silberg and Peter Q. Nguyen....................................................................................5-1 Introduction • Random Mutagenesis • Directed Mutagenesis • Recombination Optimizing Chimeric Libraries • Conclusions
T
he development of recombinant DNA technologies in the 1980s enabled the first rational approaches to manipulate the metabolic capabilities of microbial cells. Initial strategies were directed at the manipulation of native pathways in microbial production hosts by overexpression or deletion of individual enzymes. Further developments in the field of genetic engineering combined with an increasing understanding of metabolic processes on the molecular level has enabled more sophisticated metabolic engineering strategies including, for example, the assembly of multienzyme pathways composed of genes selected from different organisms. The analysis of metabolic fluxes and their control has provided means to direct rationally and optimize a desired metabolic output by an engineered cell. Presently, the increasing availability of genomic information provides a seemingly limitless resource for new metabolic functions to be explored and implemented into engineered metabolic pathways for increased production levels and/or synthesis of new compounds. Despite the tremendous advances made in metabolic engineering during the last two decades, rational design efforts are still challenged by the complexity and redundancy of the cellular metabolic network as well as the lack of structural information and biochemical understanding of the metabolic enzyme(s). Recognizing these challenges, engineers have turned to nature as a guide for the development of new design strategies that mimic evolutionary mechanisms. Section I will introduce strategies and applications of evolutionary engineering as they apply to engineering of metabolic enzymes and networks. Evolutionary engineering strategies were first used in metabolic engineering to evolve and select for desired traits or phenotypes in industrially relevant microorganisms. Chapter 1 describes evolutionary engineering strategies applied to whole cell design and contrasts the objectives of such strategies with the objectives of standard metabolic engineering strategies. Whole cell evolutionary engineering strategies attempt to incorporate the natural processes of evolution and selection present in biological organisms into the engineered system design so that a more reliable and stable engineered cellular host will be generated through the combined strategies. The concept of evolutionary engineering has its roots in the development of laboratory evolution strategies, termed directed evolution or in vitro evolution, that aimed to improve the function of individual enzymes and proteins. This approach relies on iterative rounds of mutation and selection for protein variants with desired functional properties. A variety of techniques and strategies have been developed for directed protein evolution, which are now widely applied for the optimization of industrially relevant biocatalysts and proteins. Chapter 2 provides an introduction to the various laboratory evolution methods and critically discusses their strengths and weaknesses. Most evolutionary engineering is focused on optimizing catalytic activities and expression levels of individual enzymes in engineered pathway. However, the optimization of a metabolic pathway not only depends on the manipulation of individual metabolic activities, but also requires the control of metabolic activities along an engineered pathway as well as the integration of such a pathway into the metabolic network of the engineered host cell. Increasingly, methods and approaches are now being developed that allows a recombinant pathway to become part of a heterologous metabolic network. Chapter 3 describes evolutionary engineering strategies that target the regulation of gene expression. The above discussed strategies of directed evolution initially were applied to the optimization of individual enzyme and protein functions but quickly have been extended to the evolution of metabolic enzyme functions and pathways, which are discussed in Chapter 2. The complexity of the cellular metabolic network soon prompted the development of more sophisticated evolutionary strategies that
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would introduce multiple mutations throughout the genome of a host to elicit global metabolic changes. Laboratory evolution applied to individual metabolic enzymes, metabolic pathways and eventually whole genomes is discussed in Chapter 4 to show how evolutionary strategies have been successfully utilized to optimize complex cellular machineries for metabolite production. Evolutionary engineering strategies require the generation of mutational diversity and screening and/or selection of variant libraries represent therefore the most laborious and time consuming steps of this approach. The practical feasibility and success of many directed evolution applications is critically dependent on the quality of the library, meaning the fraction of functional variants present in the library, to be screened. As a consequence, a number of quantitative and predictive models have been developed during the last years for the optimization of directed evolution strategies. Chapter 5 therefore surveys and compares computational models and approaches developed for optimizing protein evolution strategies.
1 Evolutionary Engineering of Industrially Important Microbial Phenotypes 1.1 1.2
Introduction to Evolutionary Engineering...................................1-1 Microbial Evolution �������������������������������������������������������������������������1-3
1.3 1.4
In Vitro Evolution Systems ��������������������������������������������������������������1-7 Evolutionary Engineering Results..................................................1-8
The Evolutionary Process • Evolutionary Genotype Changes • Evolutionary Phenotype Changes
Stephen S. Fong Virginia Commonwealth University
Stress Tolerance • Substrate Utilization • Concurrent Selection of Multiple Objectives • Developing New Selection Criteria
References ��������������������������������������������������������������������������������������������������� 1-11
1.1 Introduction to Evolutionary Engineering Evolutionary engineering is an interesting scientific endeavor blending aspects of biological evolution and directed manipulation. In its application to metabolic engineering, this combination seeks to make use of what a cell inherently knows how to do (grow and evolve) and directs human intervention to achieve some metabolic engineering goal. Throughout this chapter the term “cellular objective” will be used to generically describe the functional purposes inherent in a cell. This is contrasted with a “metabolic engineering objective” that describes the function that we as engineers would like to introduce or improve in a cell. The term evolutionary engineering was first born from a study that used a continuous culture experiment to select for streptomycin-resistant mutants of Streptomyces griseus.1 This study demonstrates some of the key components of evolutionary engineering (Figure 1.1). For evolutionary engineering to work successfully, the natural cellular objective (usually growth for microbes) and the metabolic engineering objective should overlap. This area of overlap provides the basis for implementing an experimental system that provides an appropriate selection pressure during evolution. In the case of the Streptomyces griseus study,1 the cellular objective for growth/survival overlapped with the metabolic engineering objective of antibiotic resistance such that evolution selected for mutants that were able to grow in the presence of streptomycin. Successful implementation of evolutionary engineering for a metabolic engineering application is often difficult as the cellular objective and metabolic engineering objective not only may not overlap, but may be in direct conflict. For most microbes, their most fundamental cellular objectives are to grow and reproduce. Cellular functions associated with growth and reproduction utilize cellular resources to produce biomass components. Thus, if a microbe is trying to grow and/or reproduce it will consume nutrients with the goal of optimizing production of biomass components. This is directly at odds with 1-1
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Problem Cellular objective
Metabolic engineering objective
Experimental design
Overlap between cellular and metabolic engineering objectives
Selection of experimental evolution system
Result
Evolutionary selection of desired phenotype
Figure 1.1 Schematic depicting steps involved in the evolutionary engineering process. Identification of overlap between cellular and metabolic engineering objectives leads to selection of an experimental evolution system to produce the desired results.
many metabolic engineering applications where the objective may not be to produce cells. For example, a typical metabolic engineering goal may be to overproduce a chemical compound. Furthermore, it is often the case where the target chemical compound for production is usually not a critical or essential biomass component. In this situation, as the cell attempts to satisfy its cellular objective by focusing on producing biomass components, the amount of resources used for the metabolic engineering objective decrease. Consider a typical metabolic engineering production strain design to illustrate this point of potentially conflicting objectives. The most common production strains are generated using recombinant DNA technology to delete or add genes. If a total of ten genetic manipulations (gene deletions and/ or additions) were introduced to achieve the metabolic engineering objective, this leaves thousands of native genes intact and functional within the cell. While the introduced genetic changes may initially lead to a desired phenotype, the remainder of the cellular network is still focused on achieving its cellular objective (growth/reproduction). Given enough time (evolution) a metabolic network may adjust and compensate for the ten introduced genetic manipulations resulting in a loss of the phenotype desired for the metabolic engineering objective. If this is the case where cellular objectives and metabolic engineering objectives conflict, then why couple evolution to a metabolic engineering objective? The main advantage of evolutionary engineering is that when properly employed, evolutionary engineering results in a stable, whole-cell metabolic engineering design. The difference between this and other metabolic engineering approaches is really a matter of scale. As in the example given above, metabolic engineering designs often involve a small number of directed genetic alterations that largely leaves the remainder of the metabolic network unchanged. This type of design may not be stable as the cell may adjust and compensate for the introduced changes and the scope of the cellular changes is limited to the small number of changes that were directly introduced. In the case of evolutionary engineering, all cellular components are potentially subjected to modification and beneficial modifications will be selected during evolution. In an ideal sense, this means that evolution leads to a phenotype where every cellular function has been optimized. Furthermore, since evolutionary changes are implemented by the cell itself and have been selected during evolution, they are more likely to be retained and stable. Thus, the result of successful evolutionary engineering is an evolutionarily stable strain where potentially all cellular components have been optimized for a certain function. Another advantage of evolutionary engineering is that evolutionary selection will help to screen large numbers of mutants and primarily retain those with beneficial characteristics. This aspect of the evolutionary process is remarkably similar in principle to the iterative process of strain improvement often undertaken in metabolic engineering (Figure 1.2). Metabolic engineering strain improvement is normally a cyclic process2 involving an introduced genetic manipulation, characterization of the effects,
Evolutionary Engineering of Industrially Important Microbial Phenotypes Metabolic engineering
Evolution
Genetic manipulation
Spontaneous mutation
Phenotype testing
Evolutionary selection
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Figure 1.2 Comparison of basic steps involved in metabolic engineering and evolution.
and reevaluation of the design before implementing another genetic change into the strain. Evolution uses similar mechanisms in that genetic changes are introduced via mutations, effects of the mutations are manifested in phenotype, and selection of the fittest mutations occurs as new genetic variants arise starting another cycle. Thus, the principle steps employed in metabolic engineering strain design occur naturally during evolution. The benefit of evolution is that combinatorial genetic changes are naturally introduced resulting in a larger array of genetic variants being examined. In addition, the cyclic selection process occurs during each generation of cells produced, so all of the genetic variants produced are also efficiently screened. Given the potential advantages of whole-cell designs resulting from evolutionary engineering and the similarities in approach between metabolic engineering and the process of evolution, there is a great deal of promise in evolutionary engineering. One of the keys to successful evolutionary engineering is to learn and to understand basic evolutionary principles. This is a critical component in the design phase of any evolutionary engineering project. The remaining sections of this chapter will discuss different aspects central to evolutionary engineering. First, concepts of microbial evolution will be presented to provide background into cellular objectives (and associated mechanisms). Experimental systems commonly used to conduct evolution experiments will be discussed to demonstrate different types of selection pressure that can be imposed experimentally. Finally, several recent examples of evolutionary engineering studies will be presented with an emphasis placed on design aspects. Evolutionary engineering is an approach to strain improvement for metabolic engineering applications that utilizes an organism’s natural ability to grow and evolve. Design of an evolutionary engineering experiment involves determining areas of overlap between a cell’s normal functional objective (often growth) and the metabolic engineering objective. An appropriate experimental evolution system is then chosen to direct evolution toward the target phenotype through evolutionary selection. In this scenario, cellular modifications and evaluation of phenotypes are part of the normal evolutionary process. The engineering input is to design and implement the appropriate evolutionary environment.
1.2 Microbial Evolution The first stage of an evolutionary engineering project is to carefully consider the problem and goal of the project. Metabolic engineering projects are normally started with a specific production problem or goal in mind, however evolutionary engineering projects add another dimension. The native cellular objectives must also be taken into consideration at the beginning of the project as this will affect both the implementation and results of the project. In evolutionary engineering, the cellular objective is as important to generating an improved strain as the metabolic engineering objective. Both objectives must be accounted for when designing an evolution experiment, so in this section aspects of microbial evolution and associated cellular objectives will be discussed.
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1.2.1 The Evolutionary Process
Fitness
Generally speaking, evolution is the process by which something changes or adapts over time. Usually implied in this term is a sense that the change is beneficial and represents a new stable functional state. In biology, beneficial changes are those that confer some phenotypic advantage, and they are stably maintained through genetic changes. The process of evolution has often been studied using microbes as an in vitro model due to the numerous advantages associated with microbial growth.3 Microbial systems allow the process of evolution to be studied in real time such that the dynamics, mechanisms, and outcomes of evolution can be monitored. Central to studying microbial evolution is the interplay between the two key components of our definition, the changes in phenotype and genotype. Natural selection (or “survival of the fittest”) largely describes evolution at the level of phenotype change. As a change in behavior or function arises, it will be positively selected and maintained if it is beneficial to the organism. The principle means by which beneficial traits are maintained is at the level of the genotype. However, the phenotype does not dictate genotype changes, rather it is the genotype that specifies the phenotype. Thus, the process can really be seen to originate from genetic changes that lead to altered phenotypes that are selected via natural selection. By this process changes in an organism during evolution are thought to occur in a discrete, stepwise manner over time. Genetic variation will naturally occur within a population in the form of point mutations or genomic rearrangements. Some of the genetic changes may result in a phenotypic change that is incrementally beneficial within the growth environment. This method of incremental phenotypic improvement is often conceptualized as a fitness landscape. A fitness landscape4 is used to describe all of the possible phenotypic variation in an organism. This is typically visualized in three dimensions with valleys representing areas of low fitness and peaks signifying areas of high fitness (Figure 1.3). This means of conceptualizing evolution also helps clarify the possibility that evolution may not always lead to a single global maximum of fitness. This may be illustrated by thinking of the fitness landscape as a mountain range. Your intention is to climb to the highest point possible (maximum fitness) but you cannot see. Regardless of your starting point, the way to get to the highest point would be that every step you take will be up. This will eventually take to you the top of some peak, but you will never know if another mountain exists that is taller. The mechanism by which an organism traverses the fitness landscape is determined by genetic changes and the associated phenotypic changes. Both of these are equally important aspects of the evolutionary process. The following sections will discuss each separately.
O
X
Figure 1.3 Schematic depiction of a phenotypic fitness landscape. The X–Y plane illustrates phenotypic variation and the Z-axis is phenotypic fitness. Peaks indicate areas of highest fitness and valleys indicate areas of low fitness. A sample evolutionary path has been shown using arrows from the starting point (X) to the ending point (O).
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1.2.2 Evolutionary Genotype Changes Genetic changes are the foundation and driving force for changes that occur during evolution. There are many different types of genetic changes that can occur during evolution. Each genetic change also can vary greatly in its effect on an organism’s phenotype. This section will discuss some types of genetic changes that can occur during evolution. Genetic changes that occur during evolution can be broadly categorized as either single nucleotide polymorphisms (SNPs) or genomic rearrangements. SNPs involve changes in a single base in the DNA sequence. Genomic rearrangements are characterized by genetic changes involving a group of nucleotides that change location on the chromosome, are duplicated, or are deleted. 1.2.2.1 Single Nucleotide Polymorphisms SNPs occur at various rates in vivo due to mismatches that occur during DNA replication. Each organism has a basal mutation rate that is due to the fidelity of an organism’s DNA polymerase and also the presence or absence of DNA proof-reading mechanisms. It has been estimated that the typical error rate (mutations per base pair per genome replication) varies from 0.5 × 10 -10 in Homo sapiens to 7.2 × 10 -11 in Neurospora crassa.5 These estimates of DNA replication error rates allow for the quick estimation of the number of expected point mutations that would occur per cell per generation and thus can give an indication of what changes would be expected during evolution. For example, if the bacterium Escherichia coli has an estimated error rate of 5.4 × 10 -10 mutations per base pair per genome replication5 and a genome size of 4.6 × 106 bases, it can be expected that there would be approximately 0.0025 mutations in each genome per genome replication.5 If we have a culture that reaches a density of 5 × 1011 cells each day and allow evolution to occur for 100 days, we would expect that at least 1.25 × 1011 mutations would have occurred in the evolving population. In this case, over the course of evolution it would be expected that each site on the genome would have experienced, on average, 27,000 mutations (1.25 × 1011/4.6 × 106). A number of different types of SNPs can occur. One of the broad classifications for SNPs is whether the base change is a transition (purine to purine or pyrimidine to pyrimidine) or a transversion (purine to pyrimidine or pyrimidine to purine). Another means of classifying SNPs as either synonymous or nonsynonymous is more directly related to functional implications of the SNP. Since specific codons (groups of three nucleotides) designate specific amino acids in the translation process, a change in a codon that changes the coded amino acid would result in a potentially large change in the resulting protein. A SNP that does not change the amino acid coded for by a specific codon is termed synonymous and will preserve the original protein sequence. A SNP that causes an amino acid change and thus a change in the protein sequence is called a nonsynonymous mutation. 1.2.2.2 Genome Rearrangements In addition to SNPs, large-scale changes in a genome can be found during evolution where discrete segments of DNA are modified. These large-scale changes are normally mediated by transposons, insertion sequence (IS) elements, or native recombinases. Both transposons and IS elements are segments of DNA that are capable of replication and relocation within a chromosome. The main difference is that IS elements are thought to only contain genes associated with transposition and regulation6 whereas transposons can contain genes with additional functions. These mechanisms typically mediate genomic changes such as deletions, duplications, transpositions, and inversions. The size of genomic segments affected by these mechanisms is normally on the order of hundreds to thousands of DNA bases. While SNPs constitute the means of changing the specific base content of DNA, genomic rearrangements can be thought to alter the order of the existing DNA content. Given these two potential mechanisms of genomic change, the relative contributions of each in generating genetic variability is an important and unanswered question. While there is a growing body of evidence that genomic
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rearrangements do contribute to genetic diversity during evolution and can confer an adaptive advantage, the specific means by which a genomic rearrangement conveys this advantage may not be entirely clear. However, it is possible that SNPs and rearrangements contribute to genetic variation at different stages of cellular growth as the occurrence of SNPs is directly related to the fidelity of DNA polymerases during DNA replication whereas mechanisms associated with rearrangements are not constrained by any cell replication machinery. 1.2.2.3 Mutator Cells Outside of SNPs and genomic rearrangements, another factor that can affect the generation of genetic variation is a change in a cell’s mutation rate. It has been observed that cells can adjust the rate at which mutations occur as an adaptive response. Changes in mutation rate are normally associated with modifications to different aspects of DNA replication, DNA repair, or elevated transposon activity.3,7 DNA replication can be affected by a change in the concentration of different DNA polymerases being utilized as some polymerases are more prone to replication errors than others. For example, E. coli is known to have at least five different active DNA polymerases involved in DNA replication that greatly vary in their replication fidelity. Under normal growth conditions, Pol II, which is relatively accurate, is the dominant DNA polymerase but the replication errors can start to be introduced if Pol III, Pol IV, or Pol V are induced.7 In addition to the fidelity of the DNA polymerase being used, mutations or changes in the methyl-directed mismatch repair system can also result in higher frequencies of mutation (as much as a 100-fold increase in mutation rate). The activity of transposons or recombination-related mutations can also play a significant role, especially in non-growing populations of cells where DNA replication is not active. The frequency of these recombination-related events is dictated by the activity of recombinases inside the cell, and it has been found that recombinases can be induced under a number of different conditions.7
1.2.3 Evolutionary Phenotype Changes Genetic changes are the starting point for changes that occur during evolution, but evolutionary selection is conducted based upon manifested phenotypes. Regardless of the genetic mechanism behind a phenotypic change, an organism with a large phenotypic advantage is likely to prosper. In evolutionary engineering, the way in which a cellular objective is manifested as a phenotype is of fundamental importance to the design of any project. Thus, it is important to understand what phenotypic traits are beneficial and how these characteristics are brought about during evolution. The most obvious and commonly observed phenotype change is an improvement in cellular growth rate. It is intuitive that evolutionary changes that bring about an increased growth rate would confer an advantage to any cell within a population of growing cells. If a cell grows faster than its neighbors, its progeny will eventually outnumber the slow-growing cells until the slow-growing cells become extinct. This type of phenotype improvement is the most ubiquitous phenotype improvement and represents a dominant selection pressure in microorganisms. A second closely related type of phenotype improvement is an improvement in biomass yield (or metabolic efficiency). The contrast between growth rate and biomass efficiency was studied explicitly by Helling8 using competing parallel pathways for glutamate synthesis in E. coli. In situations where nutrients are finite and limited, having improved biomass efficiency can be advantageous. As a finite nutrient becomes depleted, a cell that can satisfy all of its growth requirements using the smallest amount of input would survive longer than one that requires larger amounts of nutrient. The vast majority of phenotype improvements that are experimentally observed during evolution can be categorized as either growth rate or biomass efficiency improvements. Another broad category of evolutionary changes that occur during evolution can be termed either generalist or specialist changes. When applied to phenotype changes, a generalist phenotype improvement is one where the phenotype change is beneficial to the organism in more than one environment.
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A specialist phenotype improvement is one where the phenotype change is only confers a benefit in one specific environment and may even be detrimental in other environments. The concept of a specialist phenotype is very closely linked to the genetic concept of Muller’s ratchet.9 The concept of Muller’s ratchet is that deleterious mutations arise as often (if not far more often) as beneficial mutations. Deleterious mutations over time accumulate within a population due to random drift that causes the irreversible loss of clones that do not house deleterious mutations. Thus, as a population continues to grow in an environment, it remains viable in that environment, but the accumulation of background deleterious mutations becomes sufficient that the strain is effectively crippled for growth in any other environment and is therefore highly specialized and only suited to growth in a single environment. Experimental determination of these types of phenotype improvements is highly dependent upon the diversity of conditions used as phenotype changes may exist but are not reflected in the conditions tested and thus remain “silent phenotypes.”10–13 Thus, a range of experimental results have been generated in this area14 with supporting studies for both generalist improvements15,16 and specialist improvements.17–19 An additional category of adaptations that are found in experimental evolution experiments are cross-feeding adaptations. Cross-feeding is the term given to the situation where a population is composed of at least two different subpopulations. If subpopulation A and subpopulation B existed concurrently in the same environment, cross-feeding would be demonstrated by an interconnectedness between the subpopulations where subpopulation A would consume the primary carbon source in the environment and secrete excess carbon as a metabolic by-product. Subpopulation B would then manifest an adaptation where it would fulfill at least a portion of its metabolic needs by consumption of the by-product secreted by subpopulation A. This type of adaptation has been demonstrated for evolution experiments of E. coli grown on glucose where subpopulations were found that were adapted to better utilize acetate that was being secreted by other cells.20,21 Microbial evolution is a natural process that occurs over time to adjust and refine an organism’s functionality in response to its environment. Genomic changes in the form of SNPs and genomic rearrangements generate genetic variation. Genetic changes can lead to changes in manifested cellular phenotypes, and it is the phenotype that is selected during evolution. Selection and improvement occur in a step-wise fashion that preserves beneficial traits. The most common selection pressures are associated with basic growth behaviors. Besides growth rate, other evolutionarily beneficial traits can include improved biomass efficiency and cross-feeding adaptations.
1.3 In Vitro Evolution Systems In previous sections material has been presented on mechanisms and consequences of evolutionary change at both the genotypic and phenotypic levels. All of these changes occur over time in response to environmental stimulus and selection. Thus, the environment and its influence on cellular properties is a critical component in understanding how and why evolution occurs and is an important factor in evolutionary engineering. There is an inherent level of interconnectedness between the environment, a cell’s genotype, and a cell’s phenotype. To study and understand evolutionary changes experimentally, one of the challenges is to establish controlled environments to stimulate consistent cellular changes. In this section, different experimental systems will be presented as means of conducting evolution experiments and imposing different evolutionary selection pressures. The simplest form of cell culture system and environmental selection pressure occurs during batch culture growth. Batch cultures are instituted by aliquoting a finite amount of medium into a culture vessel, inoculating the vessel, and allowing growth to proceed until all of the nutrients are depleted. Evolution experiments can be conducted in this manner by allowing growth to proceed in the batch culture then transferring some of the culture into a new culture vessel containing fresh medium.22 This process of serial transfer continues for the duration of the evolution experiment and the number of generations occurring during growth in each culture vessel is dictated by the size of the inoculum. During each phase of growth, cellular growth rate is not externally constrained until the nutrients are depleted.
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This means of conducting evolution experiments tends to apply a growth rate selection pressure on the population. As there is a finite amount of nutrients in each vessel, cells can potentially experience different phases of cellular growth beginning with lag phase, exponential growth phase, and stationary phase as the nutrients become depleted. It has been shown that cellular responses differ greatly between exponential growth and stationary phase, so evolution experiments utilizing serially transferred batch cultures can occur in two different manners. Evolving cultures can be permitted to growth through all phases of growth (including stationary phase) before being passed into fresh medium23 or prolonged exponential growth can be achieved by passing growing cultures into fresh medium before stationary phase is reached.24 A second, and perhaps more commonly used, system for conducting experimental evolution experiments is a chemostat. Developed in 1950, 25 the chemostat is culture system that is designed to maintain a chemically constant growth environment. Fresh medium is continuously added to the culture vessel and cells adjust their growth rate until they reach a state of equilibrium where their growth occurs at a constant rate that is dictated by the limiting concentration of nutrient influx. To maintain stability in the vessel, a constant efflux of medium and cells is also employed. This experimental system has been used in a number of different studies20,26–29 and has the advantage of producing a constant, stable environment for long cultivation periods with a high degree of control over the growth rate of the cells being evolved. In this situation, since the evolving cells are kept at a constant growth rate (dictated by the rate of medium influx) the evolutionary selection pressure is not growth rate. In this environment, cells that are efficient in biomass generation would have a competitive advantage as would any cell that develops a means of avoiding being washed out in the effluent stream (thus, increasing their residence time in the culture vessel). One other point to consider in chemostat cultures is that the growth rate that is set by the influx medium is always below the maximum growth rate of the cells (to avoid washout) and thus since the cells are operating at a submaximal condition, there is an increased possibility that cells would operate using different alternate functional states as they are not as highly constrained by the growth requirements. A third apparatus that has been used to study evolution experimentally is called an auxostat, the most common of which is a turbidostat. This system is similar to a chemostat in that is designed to be continuous cell culture system, however the mechanism by which the culture is maintained is quite different. Turbidostats are designed to monitor cell density through measuring the turbidity of the culture and the culture is kept in growth phase by adding fresh medium to the vessel after the turbidity of the culture has reached a threshold value.30 In this sense, a turbidostat is can be likened to an automated batch culture evolution as the periods of growth are more like that in a batch culture rather than a chemostat. While this type of culture system should impose a relatively similar stringent selection pressure for cellular growth with the advantage of being automated, this system has not been used as extensively as the two previously mentioned culture methods. This is largely due to technical complications involved in maintaining the turbidostat in a good functional condition, as they are highly prone to fouling which makes the turbidity measurements inaccurate.31 After gaining a sense of a cellular objective and determining how to overlap a metabolic engineering objective to the cellular objective, it is necessary to determine how the experiment will actually be carried out. Different systems for conducting evolution experiments all have subtle differences that may influence experimental results. Since evolutionary changes are a biological response to the surrounding environment, the environmental conditions and the means of controlling the environmental conditions are important. Batch cultures, chemostats, and turbidostats all have their advantages and disadvantages which must be carefully weighed before choosing and implementing a system.
1.4 Evolutionary Engineering Results In the previous sections, basic concepts of evolution and experimental evolution systems were discussed. Knowledge in these two areas provides the basis for evolutionary engineering. Understanding of the
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atural evolutionary process gives insight into mechanistic changes and cellular objectives that will n result from evolution. Different types of experimental evolution systems provide different types of selection pressure. The effective combination of evolutionary principles and appropriate experimental evolution and selection are key components of evolutionary engineering. In this section examples of evolutionary engineering will be presented. A number of examples of evolutionary engineering studies have been previously reviewed. 32 The studies discussed here will focus on recent progress and developments in several key areas of evolutionary engineering. Overviews of four different areas of evolutionary engineering will be presented along with examples for each case.
1.4.1 Stress Tolerance Evolutionary engineering has its roots in the application of developing strains with better tolerance to stress conditions. Environmental stress and metabolic stress constitute the two major categories32 of conditions that can be addressed by environmental engineering. Environmental stresses include variables external to a cell that affect growth behavior. Examples of environmental stress include antibiotics, toxic chemicals, extreme temperatures, and nutrient limitation. Metabolic stresses occur inside a cell and can include intracellular accumulation of cytotoxic chemicals or increased metabolic burdens often associated with heterologous expression or overexpression of proteins. Either type of stress can be addressed using evolutionary engineering with the majority of the work to date focused on environmental stresses. One recent example of using evolutionary engineering to improve cellular function in the presence of an environmental stress used continuous cultures to study acetate tolerance in Acetobacter aceti and Escherichia coli.33 Acetate is a common metabolic by-product and extracellular acetate accumulation is inhibitory to cellular growth and productivity. Biotechnology processes often involve interconversion of chemicals at high concentrations and build-up of acetate can impede the process. The design process for this study can be viewed within the framework presented earlier. The metabolic engineering objective in this case is to produce a strain that has increased tolerance to extracellular acetate such that high concentrations of acetate will not inhibit growth or productivity. The cellular objective in this case is growth and the overlap between the two objectives is that cells that acquired increased tolerance to acetate will grow better in environments containing high concentrations of acetate. This area of overlap dictates that an appropriate evolution experiment should provide prolonged exposure to high concentrations of acetate. In this case, cultures of A. aceti and E. coli were grown in continuous culture for more than 100 generations in media containing high acetate concentrations. This prolonged exposure to acetate allowed cells with improved acetate tolerance to out-compete cells with poor acetate tolerance. Thus, by the end of the experiment the process of evolution had helped to select cells with improved acetate tolerance.
1.4.2 Substrate Utilization A second area of research where evolutionary engineering is commonly employed is improvement of strain substrate utilization. The approach used to improve substrate utilization is very similar to that taken to improve stress tolerance as the substrate utilization problem can be viewed as a variant of nutrient limitation stress. Strains at the beginning of an evolutionary engineering project to improve substrate utilization typically natively possess the capability to process the target substrate at a slow rate or have been engineered to consume the substrate. Evolutionary engineering is used to increase the rate of substrate consumption and also to help ensure that the substrate consumption is a stable component of the phenotype. The most prevalent reason for targeting substrate utilization in evolutionary and metabolic engineering is to reduce the cost of the starting material for a process. One substrate that has been focused upon
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is the five carbon sugar xylose. Xylose is an inexpensive carbon substrate as it is found in abundance in plants, however not all organisms process xylose as efficiently as other sugars. Thus, xylose utilization is a common target for evolutionary engineering. Many efforts have been made to improve the ability of the yeast Saccharomyces cerevisiae to utilize xylose34 primarily to reduce the cost of producing ethanol. Evolutionary engineering is well-suited to this application, and recent work on S. cerevisiae has focused on improving the ability to utilize xylose under different growth conditions.35,36 The metabolic engineering objective in this situation is to improve the growth characteristics in environments containing xylose. As with stress tolerance, the cellular objective that will be satisfied is cellular growth. The overlap between these two objectives gives a situation where cells will grow better if they are able to utilize xylose so they will out-compete cells that utilize xylose poorly. Continuous cultures are most often used for this application. Most experiments involving substrate utilization begin with strains that can utilize the substrate to some extent so this problem is meant to improve the rate of utilization. Thus, a series of continuous cultures are often employed with incremental changes in the growth condition to progressively improve the phenotype.
1.4.3 Concurrent Selection of Multiple Objectives To this point, examples of evolutionary engineering have focused on metabolic engineering objectives that are directly related to the cellular objective of growth. If evolutionary engineering can only be utilized in these circumstances, application of evolutionary engineering is very limited in scope. The major challenge in expanding evolutionary engineering to additional applications is the problem of identifying and designing overlap between cellular objectives and the target metabolic engineering objective. Often these objectives can have little in common and in the worst case, may be diametrically opposed to each other. In the examples of stress tolerance and substrate utilization, evolutionary engineering used cellular growth objectives to produce as an end product a strain. In both cases the cellular objective of growth was strongly selected during evolution and led to the desired strain improvements. Given the success of experimentally implementing evolution and growth selection for strain improvement, the question is if the cellular growth objective can be made to overlap with a broader spectrum of metabolic engineering objectives. One method that has been developed that broadens the application of evolutionary engineering while still focusing on the cellular objective of growth is a computational design approach called OptKnock. 37 This approach (and other subsequent variants of it 38,39) is built upon a genome-scale metabolic reconstruction.40 These metabolic reconstructions can be used to simulate and predict growth behaviors.16,24,41,42 The OptKnock design algorithm is a bi-level optimization scheme that concurrently optimizes two different objectives (cellular and metabolic engineering) by implementing gene deletions. The OptKnock algorithm was originally designed to optimize both a cellular objective of growth and a metabolic engineering objective of chemical overproduction such that the chemical production rate would increase proportionally with increases in growth rate (Figure 1.4). The OptKnock algorithm was initially tested for overproduction of lactic acid in E. coli.43 Three different gene deletion designs predicted by OptKnock were constructed and evolved. In these experiments, evolution was conducted by maintaining E. coli in prolonged exponential growth by serial passage of batch cultures. All three strain designs secreted lactic acid, showed stable production of lactic acid throughout the duration of evolution (~1,000 generations), and exhibited increased lactic acid secretion rates as growth rates increased. Thus, the OptKnock design algorithm represents one means of intentionally designing overlap between cellular growth and chemical overproduction such that evolutionary improvements in growth rate will also result in improvement in chemical production rates.
Evolutionary Engineering of Industrially Important Microbial Phenotypes (b)
Coupled production
Chemical secretion rate
Uncoupled production
Chemical secretion rate
(a)
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Growth rate
Growth rate
Figure 1.4 Schematic depiction of relationship between metabolite secretion and growth rate as growth rates increase during evolution. Shaded areas represent feasible metabolic phenotypes. (a) Uncoupled phenotypes utilize metabolic resources to increase growth rate causing a decrease in target metabolite secretion. (b) OptKnock designs stoichiometrically couple metabolite secretion to growth causing increased secretion as growth rate increases.
1.4.4 Developing New Selection Criteria All of the examples described above build upon the cellular objective of growth. This is largely because all microbes naturally have a growth objective and also because it is relatively easy to experimentally impose an evolutionary selection pressure for growth. To further expand the application of evolutionary engineering, methods for applying different evolutionary selection pressures need to be developed. In cases where the metabolic engineering objective is overproduction of a chemical compound, there is often a trade-off between the metabolic engineering objective and the cellular objective of growth. There is a metabolic tug-of-war over cellular resources that is normally biased toward growth. To partially overcome this problem, a new evolutionary engineering strategy has been developed to select for metabolically active quiescent cells. Nondividing (quiescent) cells do not have the same metabolic demands as actively growing cells but still has the capacity to perform regular metabolic functions. Thus, a quiescent cell may be a more efficient biocatalyst than a growing cell. The problem with quiescent cells is how to select for them and also how to improve their functionality. Both of these problems were addressed through the use of short-term ammonium-limited chemostat cultures.44 By setting the dilution rate very low and limiting the amount of ammounium available, the chemostat created a growth condition near the transition from exponential growth to stationary phase. Coupling this system with a means of testing for metabolic activity allowed cells to be isolated that had high metabolic activity but were quiescent. These results demonstrate a new experimental selection pressure and also provide a useful tool in metabolic engineering. Evolutionary engineering can be successfully employed for a metabolic engineering purpose through carefully constructed experiments that account for both cellular and metabolic engineering objectives. The most common evolutionary engineering experiments build upon the growth cellular objective as it is ubiquitous and easily controlled experimentally. Recent developments have expanded the scope of evolutionary engineering applications and provided new avenues from metabolic engineering.
References 1. Butler, P. R., Brown, M., and Oliver, S. G. Improvement of antibiotic titers from Steptomyces bacteria by interactive continuous selection. Biotechnol. Bioeng., 49, 185–96, 1996. 2. Petri, R. and Schmidt-Dannert, C. Dealing with complexity: evolutionary engineering and genome shuffling. Curr. Opin. Biotechnol., 15, 298–304, 2004.
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3. Elena, S. F. and Lenski, R. E. Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nat. Rev. Genet., 4, 457–69, 2003. 4. Wright, S. Character change, speciation, and the higher taxa. Evolution, 36, 427–43, 1982. 5. Drake, J. W. The distribution of rates of spontaneous mutation over viruses, prokaryotes, and eukaryotes. Ann. N. Y. Acad. Sci., 870, 100–7, 1999. 6. Schneider, D. and Lenski, R. E. Dynamics of insertion sequence elements during experimental evolution of bacteria. Res. Microbiol., 155, 319–27, 2004. 7. Foster, P. L. Adaptive mutation: implications for evolution. Bioessays, 22, 1067–74, 2000. 8. Helling, R. B. Speed versus efficiency in microbial growth and the role of parallel pathways. J. Bacteriol., 184, 1041–45, 2002. 9. Muller, H. J. The relation of recombination to mutational advance. Mutat. Res., 106, 2–9, 1964. 10. Bouche, N. and Bouchez, D. Arabidopsis gene knockout: phenotypes wanted. Curr. Opin. Plant Biol., 4, 111–17, 2001. 11. Raamsdonk, L. M. et al. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nature Biotechnol., 19, 45–50, 2001. 12. Thorneycroft, D., Sherson, S. M., and Smith, S. M. Using gene knockouts to investigate plant metabolism. J. Exp. Botany, 52, 1593–1601, 2001. 13. Weckwerth, W., Loureiro, M. E., Wenzel, K., and Fiehn, O. Differential metabolic networks unravel the effects of silent plant phenotypes. Proc. Natl. Acad. Sci. USA, 101, 7809–14, 2004. 14. Kassen, R. The experimental evolution of specialists, generalists, and the maintenance of diversity. J. Evol. Biol., 15, 173–90, 2002. 15. Fong, S. S., Marciniak, J. Y., and Palsson, B. O. Description and interpretation of adaptive evolution of Escherichia coli K-12 MG1655 by using a genome-scale in silico metabolic model. J. Bacteriol., 185, 6400–8, 2003. 16. Fong, S. S. and Palsson, B. O. Metabolic gene-deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes. Nat. Genet., 36, 1056–58, 2004. 17. Cooper, V. S. Long-term experimental evolution in Escherichia coli. X. Quantifying the fundamental and realized niche. BMC Evol. Biol., 2, 12, 2002. 18. Cooper, V. S., Schneider, D., Blot, M., and Lenski, R. E. Mechanisms causing rapid and parallel losses of ribose catabolism in evolving populations of Escherichia coli B. J. Bacteriol., 183, 2834–41, 2001. 19. Lenski, R. E. et al. Evolution of competitive fitness in experimental population of E. coli: What makes one genotypes a better competitor than another? Antonie van Leeuwenhoek, 73, 35–47 1998. 20. Helling, R. B., Vargas, C. N., and Adams, J. Evolution of Escherichia coli during growth in a constant environment. Genetics, 116, 349–58, 1987. 21. Treves, D. S., Manning, S., and Adams, J. Repeated evolution of an acetate-crossfeeding polymorphism in long-term populations of Escherichia coli. Mol. Biol. Evol., 15, 789–97, 1998. 22. Atwood, K. C., Schneider, L. K., and Ryan, F. J. Periodic selection in Escherichia coli. Proc. Natl. Acad. Sci. USA, 37, 146–55, 1951. 23. Lenski, R. E. and Travisano, M. Dynamics of adaptation and diversification: a 10,000-generation experiment with bacterial populations. Proc. Natl. Acad. Sci. USA, 91, 6808–14, 1994. 24. Ibarra, R. U., Edwards, J. S., and Palsson, B. O. Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature, 420, 186–89, 2002. 25. Novick, A. and Szilard, L. Description of the chemostat. Science, 112, 715–16, 1950. 26. de Crecy-Lagard, V. A., Bellalou, J., Mutzel, R., and Marliere, P. Long term adaptation of a microbial population to a permanent metabolic constraint: overcoming thymineless death by experimental evolution of Escherichia coli. BMC Biotechnol., 1, 10, 2001. 27. Dykhuizen, D. E., Dean, A. M., and Hartl, D. L. Metabolic flux and fitness. Genetics, 115, 25–31, 1987. 28. Notley-McRobb, L. and Ferenci, T. Adaptive mgl-regulatory mutations and genetic diversity evolving in glucose-limited Escherichia coli populations. Environ. Microbiol., 1, 33–43, 1999.
Evolutionary Engineering of Industrially Important Microbial Phenotypes
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29. Weikert, C., Sauer, U., and Bailey, J. E. Use of a glycerol-limited, long-term chemostat for isolation of Escherichia coli mutants with improved physiological properties. Microbiology, 143, 1567–74, 1997. 30. Northrop, J. H. Apparatus for maintaining bacterial cultures in the steady state. J. Gen. Physiol., 38, 105–15, 1954. 31. Sorgeloos, P., Vanoutryve, E., Persoone, G., and Cattoirreynaerts, A. New type of turbidostat with intermittent determination of cell density outside culture vessel. Appl. Environ. Microbiol., 31, 327–31, 1976. 32. Sauer, U. Evolutionary engineering of industrially important microbial phenotypes. Adv. Biochem. Eng. Biotechnol., 73, 129–69, 2001. 33. Steiner, P. and Sauer, U. Long-term continuous evolution of acetate resistant Acetobacter aceti. Biotechnol. Bioeng., 84, 40–44, 2003. 34. Hahn-Hagerdal, B. et al. Metabolic engineering of Saccharomyces cerevisiae for xylose utilization. Adv. Biochem. Eng. Biotechnol., 73, 53–84, 2001. 35. Kuyper, M. et al. Evolutionary engineering of mixed-sugar utilization by a xylose-fermenting Saccharomyces cerevisiae strain. FEMS Yeast Res., 5, 925–34, 2005. 36. Sonderegger, M. and Sauer, U. Evolutionary engineering of Saccharomyces cerevisiae for anaerobic growth on xylose. Appl. Environ. Microbiol., 69, 1990–98, 2003. 37. Burgard, A. P., Pharkya, P., and Maranas, C. D. Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng., 84, 647–57, 2003. 38. Patil, K. R., Rocha, I., Forster, J., and Nielsen, J. Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinformatics, 6, 308, 2005. 39. Pharkya, P., Burgard, A. P., and Maranas, C. D. OptStrain: a computational framework for redesign of microbial production systems. Genome Res., 14, 2367–76, 2004. 40. Price, N. D., Papin, J. A., Schilling, C. H., and Palsson, B. O. Genome-scale microbial in silico models: the constraints-based approach. Trends Biotechnol., 21, 162–69, 2003. 41. Covert, M. W., Knight, E. M., Reed, J. L., Herrgard, M. J., and Palsson, B. O. Integrating highthroughput and computational data elucidates bacterial networks. Nature, 429, 92–96, 2004. 42. Edwards, J. S., Ibarra, R. U., and Palsson, B. O. In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nature Biotechnol., 19, 125–30, 2001. 43. Fong, S. S. et al. In silico design and adaptive evolution of Escherichia coli for production of lactic acid. Biotechnol. Bioeng., 91, 643–48, 2005. 44. Sonderegger, M., Schumperli, M., and Sauer, U. Selection of quiescent Escherichia coli with high metabolic activity. Metab. Eng., 7, 4–9, 2005.
2 Improving Protein Functions by Directed Evolution 2.1 2.2
Introduction ��������������������������������������������������������������������������������������2-1 ools of Directed Evolution: Techniques for Generating T Mutant Libraries �������������������������������������������������������������������������������2-3
Random Mutagenesis • Gene Recombination • Semirational Approaches
2.3
ools of Directed Evolution: Screens, Enrichments, and T Selections �����������������������������������������������������������������������������������������2-17
Cartesian Co-Localization • Compartmentalization Physical Linkage
2.4
Nikhil U. Nair University of Illinois at Urbana-Champaign
Huimin Zhao University of Illinois at Urbana-Champaign
Successes and Applications of Directed Evolution....................2-22
Altering Catalytic Activity • Improving Enzyme Stability Evolving Proteins with Therapeutic Value • Understanding Natural Evolution
2.5 Patent and Licensing Issues ����������������������������������������������������������2-26 2.6 Outlook ��������������������������������������������������������������������������������������������2-26. Acknowledgments �������������������������������������������������������������������������������������2-27 References ���������������������������������������������������������������������������������������������������2-27
2.1 Introduction While the concept of Darwin’s evolution has yet remained only a theory, its impact has led to the birth of a field we now call directed evolution. Directed evolution, or perhaps more aptly premeditated synthetic evolution, is undertaken in the laboratory with a desired end. This, perhaps more than anything else, differentiates contemporary directed evolution from natural evolution. Synthetic evolution is usually undertaken at the level of a single gene, which is primarily due to current technological limitations rather than constraints of its definition; although several successes have already emerged in the synthetic evolution of complete pathways and organisms [1,2]. Application of directed evolution promises to answer various industrial, medicinal, and perhaps even anthropological evolutionary questions. Interest in evolving biological systems stems from the fact that they can be highly efficient microfactories able to metabolize and produce a variety of natural and unnatural compounds and their use in everyday life even predates the notion of quantized, cellular life. While uses of biological systems in fermentation, baking, and composting are commonplace, their value in chemical and therapeutic industries spurred extensive research in new ways to harness their power. Conceptually, directed evolution is as simple as it is ingenious. Its advantage is that very little knowledge of the system to be improved is required a priori. Crystal structures and mechanistic data—vital 2-1
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Evolutionary Tools in Metabolic Engineering
for the traditional, rational design of proteins—are unnecessary. In spite of recent advances enabling rational design with relatively rapid elucidation of crystal structures by NMR, x-ray crystallography, or homology modeling, the initial investment of time is significant. Even empowered with such data, our limited understanding of factors determining tertiary protein structure and the inability to accurately predict critical molecular interactions does not allow us to find distant allosterically linked residues for rational design. However, random mutagenesis has been shown to identify and target such sites with remarkable success [3–8]. One of the first uses of in vitro evolution technology can be attributed to Mills et al. [9] as early as 1967, but it did not gain widespread acceptance as a bona fide field until the 1990s when molecular biology techniques had matured to allow for its commercial applications. Most successes have focused on evolving single enzymes for higher stability or activity, but recent research has addressed more complex systems such as therapeutic proteins [10–13], viruses [14–17], pathways [18–22], and even complete genomes [1,2] with properties like decreased immunogenicity, increased therapeutic potency, propensity to utilize and produce novel compounds, or for bioremediation. Amongst the less product-oriented applications is its use to understand and trace the natural evolution of proteins by mimicking natural selective pressure [23,24]. Directed evolution starts by prudently picking a target—a single gene, multiple genes, pathways, or organisms, and a desired goal. An iterative process of creating mutant libraries and choosing desired phenotypes over a synthetic fitness landscape is then begun until the goal is achieved or the desired property cannot be further improved (Figure 2.1). Various tools are currently available for each step in this process that can be categorized under relatively few general themes. For mutant library creation, random mutagenesis, gene recombination, and semirational mutagenesis techniques exist. These methods are near-blind processes providing an overwhelming number of possibilities and require an additional influence to guide them to the desired goal. This influence is crucial, and while in nature, there is really only one primary pressure—survival—the goals of directed evolution experiments are often not easily aligned with such a goal. Thus, two additional strategies available are enrichments and high-throughput screens, the latter of which is akin to the oft-stated “looking for a needle in a haystack” analogy. In the absence of a selection scheme or an enrichment strategy, the primary bottleneck in most directed evolution experiments remains the library screening. Several improvements have been made Target gene(s)/pathway(s)/organism(s)
Iterative rounds
Random mutagenesis gene recombination semi-rational mutagenesis A library of mutants
Selection or screening Functionally improved mutants
Goal achieved
Figure 2.1 General scheme of directed evolution.
Improving Protein Functions by Directed Evolution
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to make this step quicker and less labor-intensive, yet it remains the weakest link in directed evolution. Rapid growth of genome and protein sequence data, along with bioinformatic tools such as BLAST searches and multiple sequence alignments (MSA), and protein modeling tools are able to assist screening by reducing the library size. Such a strategy is often dubbed semirational or computationally aided directed evolution. The aim of this chapter is to describe the current status of directed evolution of single proteins. This includes techniques and strategies for library creation, screening, selection, enrichment, as well as successes, issues and future prospects. Improvement of nucleic acids, pathways, and organisms via directed evolution will not be discussed here.
2.2 Tools of Directed Evolution: Techniques for Generating Mutant Libraries The natural wilderness is a harsh place, and in a state of constant flux. Organisms are exposed to stresses and strains, forcing them to adapt, which in turn alters their environment, again forcing different stresses on their progeny, thus continuing the never-ending spiral of change and adaptation. The nature of a stress determines the extent of mutation. Minimal and gradual stresses lead to incidental mutations during vegetative genome replication, whereas abrupt changes such as viral infections or conjugations induce more drastic genome rearrangements. The primary forms of mutations are point mutations, insertions, deletions, recombinations, substitutions, duplications, inversions, and translocations. In any case, detrimental or beneficial mutations determine the competence of the organism to survive in its environment. Neutral mutations are allowed to accumulate, which may later alter the organism’s propensity for engendering certain mutations, thus conditioning divergent evolution from common ancestry. However, natural evolution is a slow and complex process, and the rate of incidental mutations decreases drastically in higher organisms with robust DNA replication mechanisms. It should therefore be no surprise that the natural evolution of innumerable distinct species has taken billions of years. In every case, selection is preceded by mutation for diversity generation. The larger the number of distinct mutants, the higher is the number of beneficial mutations represented [25]. Random, higher complexity mutations such as duplications, inversions, and translocations usually lend themselves to exponentially diminishing probabilities of providing beneficial phenotypes. Such dwindling returns on investment are acceptable if the timescale of an experiment is in millions of years, as in nature. However, the success of a directed evolution experiment is only meaningful if results can be obtained on a reasonable timescale. Thus, investigators currently prefer simpler forms of mutagenesis such as point mutation and recombination, although insertions and deletions have also been applied with some success [26–30]. Development of new techniques for efficiently generating meaningful mutant libraries of manageable size via more complex techniques is an area of active research. Popular mutagenesis techniques generally fall under three primary categories—random mutagenesis, gene recombination, and semirational mutagenesis. Random mutagenesis produces mutations throughout the target gene and includes: (i) point mutations (transitions and transversions); (ii) insertions; (iii) deletions; (iv) inversions; and (v) frameshift mutations. Gene recombination is a “sexual” mutagenesis technique involving exchange of gene fragments between two or more genes. This itself can be subdivided into four major categories: (i) homologous recombination, where recombination occurs between genes with high sequence identity; (ii) nonhomologous recombination, where recombination occurs between genes with low sequence similarity; (iii) reciprocal recombination, in which a symmetrical exchange of genetic material occurs between two DNA strands; and (iv) site-specific recombination, where recombination occurs between two strands sharing little homology at a target site. The third technique, semirational mutagenesis is, a cross between site-directed mutagenesis and random mutagenesis. Specific sites for mutation are rationally determined by crystal structure, mechanistic data, or insights from bioinformatic analysis, and are then individually or combinatorially randomized to all
2-4 (a)
Evolutionary Tools in Metabolic Engineering (b)
(c)
Figure 2.2 Mutagenesis techniques (a) random, (b) recombination, and (c) semirational approaches.
20 amino acids. Figure 2.2 illustrates the general principle of each of the three techniques. While the list of techniques discussed below is extensive, it is not exhaustive. There are several proprietary techniques that engineer proteins via directed evolution. Many of these techniques will not be discussed here— interested readers can obtain more information about these techniques directly from the companies or from the patents themselves.
2.2.1 Random Mutagenesis Although geneticists have used several techniques to induce random mutagenesis in vivo, directed evolution generally prefers the simplicity of in vitro mutagenesis. In vivo techniques generally rely on the template strand damage, base-analogs, mutator strains, or transposition events to induce mutations during replication. Template strand damage can be induced by chemical mutagens, free radicals, or UV irradiation, whereas mutator strains deficient in high fidelity replication systems (such as E. coli XL1Red®, Stratagene, or E. coli mutD5 strains) can generate random mutations on average 1 bp per 1000 bp [8,31–34]. More recently, hypermutating B cells have also been identified as a tool for generating mutations in vivo [5]. Transposon kits are commercially available (Finnzymes, Epicenter, Invitrogen) to introduce mutations by insertions and inductive excision. However, the major drawback of transposition is that the location of insertion cannot be easily targeted in the desired gene. Transposition events are particularly suited to genetic studies such as gene knockout and mapping studies, but do not lend themselves well to the random mutagenesis required for directed evolution studies. Mutator strains have the added complexity of background mutations, thus requiring cloning of the desired gene into a mutationfree strain to ascertain the nature of the mutation(s) on the target gene. Yet another less intrusive genetic technique to generate mutants is adaptive mutagenesis, which closely resembles natural evolution, and as a result is considerably more time consuming. However, this technique is more useful for directed evolution of complete organisms rather than single genes, and will thus not be discussed here. In vitro techniques are much easier to control since they avoid the complexity of a cellular environment. UV irradiation [35,36] and popular chemical mutagens such as base analogs (5-bromo-deoxyuridine [36], 2-aminopurine [37], hydroxylamine [35]), alkylators (ethyl methane sulfonate [38], nitrosoguanidine [39], diethylsulfate), oxidizing agents (nitrous oxide [35], sodium bisulfite [40]), and intercalating agents (ethidium bromide [41] and acridines) can also be used in vitro with low fidelity polymerases; however, these methods are generally biased toward certain types of mutations. For example, UV irradiation tends to favor mutations across pyrimidine repeats whereas intercalating agents primarily cause frameshift mutations. With such biases, it is impossible to obtain a nonbiased randomly mutated library of the template gene. Several other techniques have thus been developed, the most popular of which are discussed in further sections.
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2.2.1.1 Error-Prone Polymerase Chain Reaction (epPCR) Truly the poster child of random mutagenesis techniques, the effectiveness and simplicity of this technique has deterred the development of other random point mutagenesis techniques. EpPCR [42–45] relies on the fallibility of DNA replication, and promotes point mutations by providing DNA polymerase nonideal reaction conditions in vitro (Figure 2.3). Base analogs and alkylating agents can be used in conjunction with low fidelity or mutagenic polymerases, but in general such techniques suffer from severe biases and are generally not used. The most favored protocols utilize an Mg2+ -dependent thermostable polymerase (such as Taq or Mutazyme®, Stratagene) that lacks exonuclease activity and substitute Mn2+ for Mg2+, as well as unequal amounts of the four precursor deoxynucleotidetriphosphates [44,46]. These nonstandard conditions further increase the rate of inaccurate base-pairing during primer elongation. The primary advantage of this technique is that the rate of mutagenesis can be conveniently controlled by varying the concentration of Mn2+ in the reaction mix or the number of cycles of amplification. In general, one to five base mutations/kbp target DNA resulting in one to three amino acid substitutions is not exceeded because high mutagenic rates are often disruptive to enzyme function [47–51]. Additionally, as already mentioned, high mutation rates generate very large libraries and as a result, the probability of finding a functional mutant without extensive screening is diminishingly small. However, in cases where a good selection scheme or robust high-throughput screen is available, high mutation rates have been [49,52] and should be applied [25]. There are several drawbacks to epPCR, the dominant one being that mutagenesis is not completely random. In an ideal situation, the probability of any position on the protein being randomized to all 20 amino acids should be equal. But, since mutagenesis is performed on the gene and not the protein itself, the occurrence of such an ideal distribution is improbable due to the redundancy of the genetic code. Since 64 codons code for 20 amino acids and three stop codons, a mutation at the wobble position is frequently neutral, and mutations at two adjacent positions by epPCR is highly infrequent on average, only about six amino acids are substituted at each position [53]. Even then many mutations are semiconservative with amino acids having similar physiochemical properties [53]. A second limitation is that Taq and Mutazyme®, the most common polymerases used in epPCR, tend to favor transition mutations [53]. This issue has been addressed to a certain degree by Stratagene’s Mutazyme II®, which claims to induce mutations across all four nucleotides evenly. However, polymerases still tend to favor mutating certain regions of a gene, designated “hot spots.” The last major issue with epPCR is the fact
(a) (e)
(f)
(d)
(b)
(c)
Figure 2.3 Scheme of error-prone PCR (epPCR). Point mutations are introduced in replicas during primer extension during a PCR amplification of parental template. (a) Primer extension under “error-prone” condition, (b) introduction of point mutations, (c) completion of first strand synthesis (d) second strand synthesis, (e) repeat for 10–20 cycles, (f) library of mutants.
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Evolutionary Tools in Metabolic Engineering
that it is a PCR reaction, and thus relies on replicating replicas. Therefore, any mutations introduced early on will be propagated with higher frequency, skewing the distribution of mutation rates. This can be addressed to some degree by pooling several small reaction products together to create the library, or by first using only linear amplification with a mutagenic polymerase to produce single-stranded mutant DNA and then generating the second strand via a high-fidelity polymerase. The first alternative can be easily incorporated into experiments, while the second alternative requires additional work and is thus not often used. Of course, several of these issues would be addressed if mutations could be introduced at the amino acid level rather than nucleotide level; however, no known PCR equivalent exists for amino acids. Error-prone Rolling Circle Amplification (epRCA [54]) is claimed to be an alternative to epPCR by its inventors, however, it more closely resembles an in vitro mutagenic strain. Using random primers and a low-fidelity polymerase at isothermal conditions, whole plasmid amplicons are transformed into cells without the requirement of any ligation steps. While its advantage includes negation of specific primers, thermocyclers, and general cloning procedures, its limitations are similar to those of mutagenic strains. The inability to distinguish incidental mutations on the plasmid from phenotypic change due to target gene mutation requires recloning of the gene into a mutation-free vector and reassessment, thus essentially negating the advantage of skipping general cloning steps. 2.2.1.2 Sequence Saturation Mutagenesis (SeSaM) SeSaM [53] was also developed as an alternative approach for epPCR that overcomes several barriers faced by its predecessor. This technique is a true random point mutagenesis technique able to substitute any amino acid with another without any mutational bias. This is achieved by incorporating an alkaline labile analog, α-phosphothionate nucleotide, into the gene and then hydrolyzing it to produce fragments of random length. After treatment with a terminal deoxynucleotidyl transferase (TdT) to incorporate a random number of universal bases at the 3ʹ-terminus, these fragments are used as megaprimers for unidirectional extension into full-length genes. Random point mutations are thus introduced opposite universal bases on further PCR amplification of single-stranded DNA. The authors have shown that SeSaM produces unbiased point mutations and codon mutations throughout a gene, which translates into exchange of any amino acid at any residue with any of the 19 others. However, the number of supplementary steps required, in addition to the complexity of introducing two different types of base analogs, and use of biotinylated primers for purification is nontrivial. In general, epPCR coupled with subsequent saturation mutagenesis, where identified functional important residues are mutagenized into all 20 amino acids in search of the most beneficial mutation, has been able to produce similar results [55]. 2.2.1.3 Indel Mutagenesis Insertion and deletion (indel) mutagenesis opens an area for random mutagenesis inaccessible to substitution mutagenesis alone. By randomly inserting and deleting sections of a gene, thereby altering its size, one can generate a diverse mutant library. Transposable elements are naturally suited for such mutagenesis since their insertion rates are controllable and they usually carry selective phenotypic markers. Use of transposable elements has been quite common at the organism level, but at the single gene level, its application has been less successful. Pentapeptide scanning mutagenesis [56] uses a transposable element Tn4430 that inserts randomly in the target and is then excised leaving behind a 15 bp in-frame fragment. This effectively adds a five amino acid fragment at a random location in the protein. However, there are several drawbacks to using this technique. It can only insert five amino acids, four of which are predetermined due to the cognate target site duplication by Tn3-based transposons and preset restriction site sequence. The fifth codon is variable only at the wobble position, and thus does not contribute significantly to diversity. Therefore, this technique cannot easily generate the diverse and unbiased library desired for directed evolution.
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Another technique developed by Murakami et al. [29] called random insertion/deletion (RID) addresses several issues of transposon-based mutagenesis at the single gene level. Using this technique, the authors were able to delete up to 16 consecutive bases from random locations in a gene and insert a specific or random sequence of arbitrary length at the same location. Not only can this technique produce a large diversity unattainable by epPCR, it can also randomize any residue in a protein to all 20 amino acids by deleting a codon and then inserting another one in its place. As appealing as this sounds, this method is relatively complex, involving eight major steps. A technique for randomly introducing indel mutations called Random Insertional-deletional Strand Exchange (RAISE), [27] has recently been introduced, and is a much simpler technique combining the use of Terminal deoxynucleotidyl Transferase (TdT) for random nontemplated insertions with DNA shuffling technology (see Section 2.2.2.1). DNaseI fragmented genes with random nucleotides incorporated at their 3ʹ-ends are conjoined in a primerless PCR during which imperfect annealing of TdTextended bases introduces random insertions and deletions into final products. A technology that has seemingly swum against the current of the generally accepted dogma of diversity generation has been developed by Kashiwagi et al. [57]. Frameshifts are usually disfavored because they are liable to introduce premature nonsense mutations more often than beneficial mutations, thus encoding truncated, inactive proteins. Frame shuffling [57] however, utilizes three to six of the unutilized reading frames using a microgene technology [58] that minimizes the introduction of nonsense mutations. The introduction of nonsense codons cannot, however be completely eliminated because of the possibility of their introduction by random point mutations. This technology could open a new domain of sequence space hitherto purposely avoided, if the authors can demonstrate the applicability of this technique to naturally occurring genes that do have stop codons in their various reading frames. All random mutagenesis techniques have one common drawback—the generated mutants are functionally similar to the parent, i.e., the evolutionary progression is small. Multiple iterations are therefore required to make significant changes to the protein. While the best mutant may seem like a prudent choice as template for the next iteration, its potential for improvement cannot be guaranteed. On the other hand, if high mutagenic rates are used, disruptive mutations tend to accumulate more frequently than beneficial ones. As a result, mutations with synergistic effect are rarely discovered [59]. Still, drastic changes such as those required for novel functions are rarely discovered even after several rounds of mutagenesis. Readers interested in studying further limitations of random mutagenesis are referred to some excellent published reviews [25,60,61]. Table 2.1 provides a comparative view of the aforementioned techniques. By combining the diversity present on multiple templates, recombination techniques can circumvent this demerit of random mutagenesis.
2.2.2 Gene Recombination Comparable to sexual recombination, these techniques rely on exchange of genetic materials between two or more genes to generate sequence diversity. In nature, gene recombination has been one of the most efficient techniques for generating competent progeny. Certain flowering plants such as petunias have developed a highly polymorphic gene called S-RNase, which encodes for what effectively acts as a self-incompatibility agent thereby minimizing self-pollination, and promoting out-crosses [62,63]. Similarly, in prokaryotes antibiotic resistance tends to be more easily acquired by horizontal gene transfer, rather than adaptive, random mutagenesis. Directed evolution experiments utilizing recombination technique in vitro are able to effectively produce drastic changes in phenotypes, such as those required for novel functions, by taking giant steps across the evolutionary sequence space. Parental templates chosen for recombination are usually fully functional proteins, and as a result, a significant fraction of progeny produced with such techniques tends to be functional as well. Simultaneous backcrossing with excess of parental templates
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Table 2.1 Summary of Random Mutagenesis Techniques Technique
Advantages
Chemical mutagens
• Simplicity
Mutator strains
• Simplicity
epPCR
• • • •
epRCA
SeSaM
RID
RAISE
Frame shuffling
• • • • •
Simplicity Tunable mutation rate Simplicity Specific primers not needed Tunable mutation rate Cloning steps skipped Isothermal Unbiased mutagenesis Codon randomization possible
• Random insertions and deletion • Large diversity possible • Codon randomization possible • Random insertions and deletion • Codon randomization possible • Can utilize three to six unused frames
Disadvantages
Key Factor(s)
• Biased mutagenesis • Required handling of toxic chemicals • Background mutations • Need to retransform mutants into mutation-free strain • Mutagenesis rate not tunable • Low mutation rate • Limited amino acid substitution • Biased mutagenesis
• Identity and concentration of mutagen
• Background mutations • Need to retransform mutants into mutation-free strain • Biased mutagenesis
• Mutation rate
• 2–3 days to perform • Several steps, reagents and enzymes required • Special primers required • Several purification steps involved • Several steps, reagents and enzymes required • Frameshift mutations possible
• Duration of incubation with TdT • Choice of analogs and universal base
• Frameshift mutations possible • DNaseI digestion bias
• Duration of incubation with TdT
• Applicable only to synthetic genes • Synthetic genes are tandem repeats with no stop codons in any reading frame • Applicability to functional genes not demonstrated
• Frameshift introduction frequency by polymerase
• Generations grown • Mutation rate of host
• Mutation rate
• Duration of random ssDNA cleavage • Efficiencies of intra- or interstrand ligations
also tends to minimize accumulation of neutral and deleterious mutations. While four types of gene recombination are possible (homologous recombination, nonhomologous recombination, reciprocal recombination, and site-specific recombination), only the former two are widely used in directed evolution. 2.2.2.1 Homology-Dependent Gene Recombination As the name suggests, homologous recombination requires a certain degree of sequence similarity, or homology, between DNA strands. In general, as with random mutagenesis, in vitro techniques are preferred due to their simplicity, although recently in vivo techniques have garnered some interest primarily due to limitations of in vitro DNA ligations and subsequent transformations. In such cases, the high-efficiency in vivo homologous recombination is exploited to generate molecular diversity.
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(a)
(b)
(d)
(c)
(e)
Figure 2.4 Scheme of DNA shuffling. Chimeric progeny of two or more parental templates are created by random fragmentation and reconstruction of full-length genes using primerless PCR. (a) DNaseI fragmentation, (b) denaturation and annealing, several cycles of (c) extension and (d) denaturation and annealing, to yield (e) fulllength hybrids.
2.2.1.2 Primerless Fragment-Reassembly DNA shuffling, developed by Stemmer et al. [64], was the pioneering and breakthrough technique for in vitro recombinant mutagenesis and diversity generation. Thus, subsequently developed techniques are invariably juxtaposed against it to glean for advantages. As shown in Figure 2.4, DNaseI first digests parental templates randomly to small fragments and then they are reassembled via a primerless PCR reaction. Denatured fragments from different genes anneal to each other based on sequence homology and are extended via a DNA polymerase to full-length fragments. The method can be used to recombine the most promising mutations from random mutagenesis, thus advantageously combining beneficial mutations and displacing deleterious ones, or to shuffle distinct, but related genes. DNA shuffling in combination with a low fidelity DNA polymerase such as Taq can introduce additional mutations if desired, but a high fidelity DNA polymerase can also be utilized to minimize incidental mutations. A similar technique called family shuffling [65] was later introduced that is conceptually similar to DNA shuffling, but uses naturally occurring related genes as templates rather than mutants of a single parent.
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This technique has been shown to expedite access to larger diversity compared to DNA shuffling [65]. While DNA shuffling and family shuffling are powerful tools, they are not without limitations. These techniques rely on homology between genes for recombination, and as a result, crossovers tend to accumulate at certain “hotspots” of high homology and more frequently between templates with higher sequence similarity [66]. In addition, most chimeras tend to contain only one crossover region, thus omitting significant theoretically accessible sequence space [66]. In cases where sequence homology is low among parental templates, most reassembled genes tend to be parental genes instead of hybrids [67]. Bias during DNaseI digestion tends to carry over during chimera reassembly, thus affecting diversity [68]. In an effort to mitigate some of these biases, Kikuchi et al. [69] proposed a modified family shuffling technique that replaces DNaseI with restriction endonucleases. The mixed fragments are amplified by PCR, again relying on homology between genes for reassembly. While they indicated an increased frequency in crossover rate, the technique still relies heavily on sequence homology during gene reassembly and can potentially succumb to similar pitfalls of over-representative recombination regions. In addition, fragmentation is not random thus excluding certain blocks from recombining. To address the low crossover rate issue associated with DNA shuffling, Coco et al. developed a new method called random chimeragenesis on transient templates (RACHITT, [70]). A parental template containing uracil is made single-stranded to serve as a scaffold for hybridization of second-strand fragments from a collection of homologous genes. 5ʹ- and 3ʹ-overhangs are degraded using Taq and Pfu DNA polymerases, respectively, the latter of which also fills in gaps on the transient template. Ligation of nicks is followed by treatment with uracil-DNA-glycosylase that degrades the scaffold. Finally, PCR is used to amplify the chimerical product for cloning. While this procedure shows a drastic improvement in crossover rate, averaging 14 per progeny, it is also more labor intensive and time consuming than DNaseI-based DNA shuffling. Optimization of reaction temperatures and time and strict control of substrate quality are crucial to a successful application of RACHITT. Another alternative technique to traditional DNA shuffling is NExT DNA Shuffling (nucleotide exchange and excision technology, [71]), which uses “exchange nucleotides” (dUTP or 8-oxo-guanine) to dictate fragmentation pattern of amplified templates, thus ignoring the accepted doctrine favoring randomly generated fragments. First, the exchange nucleotides are incorporated into genes and they are then excised by a nucleotide-specific glycosylase. Piperidine is utilized to fragment these genes and finally, internal self-priming is used to reassemble full-length hybrid genes. A computer program also developed by the authors is able to predict fragment size distribution, crossover locations and frequencies based on the standard nucleotide to exchange nucleotide ratio. 2.2.1.3 Oligonucleotide- and Primer-Dependent Reassembly The above-described techniques rely on self-priming of gene fragments based on homology. Other techniques have been developed that required end-primers or the addition of several oligonucleotides to encourage frequent crossover between templates. Random priming in vitro recombination (RPR, [72]), staggered extension process (StEP, [73]), synthetic shuffling [74] and degenerate oligonucleotide gene shuffling (DOGS, [67]) are some of the most well-established and popular techniques. RPR is similar to DNA shuffling but utilizes random primers in place of DNaseI fragmented parental templates. Primers anneal to random locations along the length of genes and when extended, produce fragments similar to those produced by DNaseI digestion in DNA Shuffling. However, the omission of DNaseI reduces the bias of nonrandom fragmentation that frequently plagues standard DNA shuffling. In StEP, full-length chimeras are produced during PCR with extremely short extension and annealing steps. Short steps extend end-primers in small increments with frequent denaturation steps enabling the annealing to different templates in subsequent annealing and extension cycles (template-switching), thereby producing crossovers. A related technique to DNA shuffling, synthetic shuffling is entirely devoid of the use of full-length parental genes for amalgamation. Diversity data between designated “templates” is obtained from bioinformatic analysis and encoded combinatorially into a large number of synthetic degenerate oligonucleotides. These oligonucleotides containing overlapping ends are then extended and amplified
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into full-length composite genes. Since the entireties of the full-length genes are oligonucleotide-based, codon-optimizations, or site-directed mutations can be directly incorporated into the oligonucleotides. Assembly of designed oligonucleotides (ADO, [75]) is a very similar strategy but can additionally use ligation to join nonhomologous regions rather than relying completely on overlap of oligonucleotide ends. DOGS utilizes degenerate internal primers for conserved motifs in genes to be shuffled to create a library of gene fragments, which are subsequently assembled by overlap-extension PCR. The use of degenerate oligonucleotides allows this technique to bias products toward a specific parent or parents and shuffle genes with differing G + C contents much more efficiently than other techniques. 2.2.1.4 In Vivo Techniques In the rush to improve on the shortcomings of in vitro mutagenesis techniques, the prospect of developing in vivo protocols for diversity generation has been largely overlooked. One of the early utilizations of combined in vitro and in vivo recombination events to generate libraries was in the yeast Saccharomyces cerevisiae. In combinatorial libraries enhanced by recombination in yeast (CLERY, [76]), parental genes cloned into plasmids are fragmented randomly using DNaseI and then reassembled using a primerless “progressive hybridization” PCR program. Instead of using only one annealing step, this program uses nine steps with progressively decreasing temperature to facilitate annealing of low-homology genes. The reassembled products are then reamplified using flanking primers and full-length genes along with linearized plasmids are transformed into competent cells. Increased genetic diversity and plasmid circularization is realized by in vivo homologous recombination in the transformants. This diversity was found to be higher than the number of colonies selected as retransformation of rescued plasmids from a single clone into E. coli indicated that the yeast clone contained multiple plasmids variants. In E. coli, a method called heteroduplex recombination [77] uses in vivo mismatch repair on heteroduplex DNA created from strands of two different but homologous genes to create a library of chimerical genes. Single-stranded DNA is prepared from each gene either by unidirectional amplification or exonuclease degradation, mixed, and quenched on ice to enhance heteroduplex formation. After cloning into a plasmid vector and transformation, the system relies on the cells to create hybrid homoduplexes, as mentioned earlier. While this technique does do away with PCR- and DNaseI-based biases, it suffers from a major limitation in being able to use only two parental templates, thus restricting molecular diversity. Another technique utilizing recA-mediated homologous recombination in a recBC sbcA E. coli mutant was recently published [78]. While this technique also has the limitations of heteroduplex recombination, in that it can exploit only two parental templates and produces chimerical genes with only two c rossovers, it nevertheless provides the initial steps toward the avenue of in vivo diversity generation. In this technique, a linearized plasmid is ligated at each end to parental templates that are rendered disjointed in between by virtue of dephosphorylated ends. Homologous recombination rectifies the inability of the plasmid to replicate by circularization in vivo creating single crossover progeny. Using different progeny as parental stands, the process is repeated to produce a second generation with two crossovers. 2.2.2.5 Homology-Independent Gene Recombination Homology-dependent techniques provide a significant advantage over random mutagenesis techniques by making larger leaps across diversity sequence space [64], and in some cases, without much additional procedural complexity. However, in situations where recombination among genes with low homology or even those with no homology is desired, the previously described techniques prove extremely inefficient [79]. With the increasing number of available crystal structures of various proteins, it is easy to see why such a need could arise. A noticeable number of enzymes sharing little sequence homology often fold into similar three-dimensional structures. The (βα)8-barrel structure is a prime example of such a case with about 10% of all enzymes with known structure folding into this configuration [80]. This structure has also been shown to be resistant to large insertion mutations [81], justifying the significant interest it has garnered among protein engineers. Hybridization, swapping, or grafting amongst modular protein domains could also benefit significantly from homology-independent recombination. Several in vitro techniques have been developed in answer to these prospects.
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2.2.2.5.1 Two-Parent Nonhomologous Gene Recombination Some of the earliest attempts at homology-independent recombination could utilize only two parental templates. They are the fancifully named incremental truncation for the creation of hybrid enzymes (ITCHY), SCRATCHY, and sequence homology-independent protein recombination (SHIPREC). ITCHY introduced by Ostermeier et al. [79] randomly fuses truncated fragments from two different genes to create hybrids. Controlled truncation from opposite ends on each gene using exonuclease III under nonideal conditions is followed by removal and reaction quenching of aliquots at various timepoints. This generates collections of truncated genes that are intermixed and then blunt-end ligated together forming genes of various lengths. Optimization and control of exonuclease digestion is key to the success of this technique, and it therefore makes ITCHY difficult and time consuming to perform. The library of hybrids is not full-length and the two genes are not necessarily fused at structurally related sites [82], potentially leading to a large number of inactive clones. Finally, only a fraction of the fusions occur where the gene sequences align [82] and the number of crossovers is limited to one. A modified version of ITCHY introduced later incorporates α-phosphothioate nucleotide analogs at low frequency in genes. Since nuclease activity of exonuclease III is inhibited at sites of analog incorporation, removal of aliquots is not required, thus making the process significantly less labor-intensive. As an added bonus, if incorporation of α-phosphothioate is during PCR amplification, use of error-prone conditions can simultaneously introduce point mutations. This modified technique is called THIO-ITCHY [83]. Another variation that combines ITCHY with DNA Shuffling is called SCRATCHY (Figure 2.5) and was also described by Ostermeier et al. [84]. Yet another improvement called enhanced-crossover SCRATCHY [85] was developed in order to further increase the number of crossovers in hybrid genes.
(a)
(a)
(b)
(b)
(c)
(c)
(d)
(d)
(e)
(e)
(f)
(f)
(g)
Figure 2.5 Scheme of SCRATCHY. Creation of hybrids by combining incremental truncation (ITCHY) and DNA shuffling. (a) Cloning both genes into vectors, (b) restriction digestion at linker, (c) incremental truncation from ends, (d) recircularization, (e) isolation of chimeric genes, (f) DNaseI fragmentation, (g) reassembly by PCR.
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To do so, ITCHY hybrids created from parental genes are subsequently amplified in blocks using skewed primers, i.e., forward primers from the first gene and reverse primers from the second. This step selectively amplifies genes with crossover point, thus enriching them in the pool. Shuffling of pooled hybrid blocks create the library of multicrossover mutants. Soon after the introduction of ITCHY, Sieber et al. introduced an independent method called SHIPREC [82] to address some of the issues associated with the ITCHY method. In SHIPREC, the parental genes are fused together with a linker containing a unique restriction site and then fragmented by controlled DNaseI digestion. Those fragments corresponding to the length of either parent are isolated, polished at the termini to create blunt-ends, and ligated to create covalently closed circular gene hybrids. The circular hybrids are then linearized at the unique restriction site in the linker. Although SHIPREC does produce more functional hybrids due to fragment purification based on gene length, it still can produce only one crossover event. However, if combined with improvements offered by enhanced-crossover SCRATCHY, hybrids could have a larger number of crossover points. 2.2.2.5.2 Multiple-Parent Nonhomologous Gene Recombination In spite of the advantages provided by ITCHY, SCRATCHY and SHIPREC, the inability to recombine more than two genes is a serious hindrance in the search for more random and larger functional libraries. Multiple-parent gene recombination is therefore the next logical step. Whilst some of the multiparent nonhomologous gene recombination techniques have analogs in homologous recombination techniques, the extension to, and advantage of extending the techniques to low-homology systems is definitely not trivial. Exon shuffling [86] was introduced by Kolkman and Stemmer in the same issue of Nature Biotechnology as SHIPREC, signifying the interest in, and need for homology-independent recombination techniques. Exon shuffling, however, takes advantage of the intron–exon organization of eukaryotic genes to first break down genes into libraries of domain-encoding fragments and then reassemble them combinatorially. To join together various domain-encoding fragments, a large number of oligonucleotides that contain crossover points are used to amplify the exons, and then self-primed overlap-extension PCR amplified full-length genes. The gene length, functionality, and order can be easily controlled by permuting the domain crossovers via oligonucleotide sequences. Thus, unlike the previously described homology-independent techniques that ligate genes at random, Exon Shuffling grafts entire functional domains together to form a hybrid that is hopefully greater than the sum of its parts. Random multirecombinant polymerase chain reaction (RM-PCR, [87]) is similar to exon shuffling, but does not require preorganization of genes into domain encoding exons, and is thus generalized toward prokaryotic and eukaryotic genes alike. The reassembly of blocks into full-length genes is enabled by overlap-extension PCR through oligonucleotides that contain crossovers. Degenerate homoduplex recombination (DHR, [88]) utilizes a set of degenerate top-strand oligonucleotides that combinatorially incorporated among them the diversity information of the genes to be shuffled. Gaps between top-strand oligonucleotides are filled in by templating with dephosphorylated bottom-strand oligonucleotides, and then ligated together. This provides a highly polymorphic set of genes with predefined cap on diversity. Since this technique is similar to synthetic shuffling in several aspects, the requirement for a certain degree of homology is required, or the number of degenerate oligonucleotides required will be formidable. Nonhomologous random recombination (NRR, [89]) employs ligation of DNaseI fragmented genes polished to remove 3ʹ-overhangs and amplify 5ʹ-overhangs. Added hairpin oligonucleotides cap the ends of genes, and preferential intermolecular ligation is performed in presence of PEG. The stoichiometric ratio of added hairpins controls the overall length of the hybrid genes, with higher concentrations favoring shorter length chimeras. The presence of hairpins with known sequence allows for amplification of the library for cloning. This process can therefore produce multiple crossover hybrids without the use of large numbers of oligonucleotides. An issue with this method is the production of a significant number of frameshift mutants with premature stop codons, which limited the functional diversity. This was
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addressed with the addition of a CAT fusion [90], which allows protein NRR to simultaneously select against truncated and insoluble proteins. A rather different, albeit more involved, approach to shuffling is Y-ligation-based block shuffling (YLBS, [91]). A Y-structure created by hybridizing complementary sequences of oligonucleotides has blocks of parental genes at their 5ʹ- or 3ʹ-ends. The hybridized section of the oligonucleotides forms the stem and noncomplimentary gene blocks along with different linkers containing different restriction sites constitute the branches. Single-stranded branch ends are ligated together to create a stem-loop structure that is then denatured and amplified by stem-specific biotinylated primers to produce double-stranded DNA with fused blocks. These are restriction digested in separate reactions with different enzymes to reintroduce distinct phosphorylated 5ʹ-ends. Single-stranded DNA with biotinylated ends purified from one reaction hybridizes with nonbiotinylated 5ʹ-phosphorylated purified single strands from the other reaction to recreate the Y-structures. Several iterations of hybridization, ligation, amplification, digestion, and purification can exponentially increase the number of crossovers. However, the elaborate scheme deters multiple iterations and makes the process generally unfeasible to produce a large number of crossovers. Gene recombination—whether homology-dependent or independent—has the major advantage that it can take leaps across sequence space far quicker than simple random mutagenesis. In addition, gene recombination holds the possibility of uncovering novel, but foreseeable functions that are rarely discovered by just random mutations. Predictable or guided outcomes are possible only if there exists similarity amongst diversity. By combining natural and unnatural evolutionary diversity, either based on sequence identity, functional similarity, or structural compatibility, for generation of further diversity, recombination techniques are truly evolution building on evolution. For readers interested in more critical assessments of the aforementioned techniques, recommended reading is referenced [66,92–94]. Table 2.2 and Table 2.3 provide an abstract view of the homology-dependent and independent techniques, respectively. Table 2.2 Homology-Dependent Mutagenesis Techniques Technique
Advantages
Disadvantages
Key Factor(s)
DNA shuffling
• Robust, flexible • Back-crossing to parent removes nonessential mutations • Synergistic/additive mutations can be found
• DNaseI digestion bias • Biased to crossovers in high homology regions • Low crossover rate • High percentage of parent
• Homology of parents • Fragmentation pattern
Family shuffling
• Exploits natural diversity • Accelerated phenotype improvement
• DNaseI digestion bias • Biased to crossover in high homology regions • Need high sequence homology in family • Low crossover rate • High percentage of parent
• Homology of parents • Fragmentation pattern
Family shuffling with restriction endonucleases
• Exploits natural diversity • Accelerated phenotype improvement • No DNAseI bias
• Biased to crossover in high homology regions • Need high sequence homology in family • Nonrandom fragmentation pattern predefined
• Homology of parents • Fragmentation pattern
RACHITT
• No parent genes in shuffled library • Higher rate of recombination • Recombine genes of low sequence homology
• Several steps, reagents and enzymes required • Requires synthesis and fragmentation of singlestranded complement DNA
• Reaction time and temperature • Purity of ssDNA scaffold
(continued)
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Improving Protein Functions by Directed Evolution Table 2.2 Homology-Dependent Mutagenesis Techniques (Continued) Technique
Advantages
Disadvantages
Key Factor(s)
NExT DNA shuffling
• Predictable fragmentation pattern
• Nonrandom fragmentation • Several steps, reagents and enzymes required • Toxic piperidine used
• dUTP:dTTP concentration
RPR
• No DNAseI bias • Independent of template length • Specific primers not required • Isothermal reaction
• Biased point mutations also occur • Low crossover rate
• Homology of parents • Duration of primer elongation • Primers:templates concentration
StEP
• Simplicity
• Need high homology • Low crossover rate • Need tight control of PCR
• Annealing and extension times
Synthetic shuffling
• Greater flexibility • Increased diversity • Parental genes not required
• Required synthesis of many degenerate oligonucleotides • Gene sequences required
• Quality and number of oligonucleotides
DOGS
• Greater flexibility • Parental genes required • Low percent of parents in products
• Required synthesis of many degenerate oligonucleotides • Bias representation possible
• Designed primers encoding crossover regions
ADO
• Greater flexibility • Increased diversity • Parental genes not required
• Required synthesis of many degenerate oligonucleotides • Gene sequences required
• Quality and number of oligonucleotides
CLERY
• Not limited by ligation efficiency of gene into vector
• Transformants contain more than one mutant, so rescue and retransformation required • Long PCR program for reassembly • DNaseI digestion bias • Background mutation in plasmid possible • Limited diversity
• Progressive hybridization temperatures • Frequency of homologous recombination in vivo
Heteroduplex recombination
• No fragmentation bias
• Limited to only two genes • Still limited by ligation efficiency • Limited diversity
In vivo homologous recombination in E. coli
• No fragmentation bias
• Limited to only two genes • Limited to one crossover per iteration • Complex, labor intensive • Limited diversity
• Heteroduplex formation frequency/stability • Frequency of homologous recombination • Frequency of homologous recombination
2.2.3 Semirational Approaches The generally accepted idea and basis for rational enzyme design is that certain types of mutations at specific regions of enzymes have a higher probability of yielding beneficial effects than those at other sites [95,96]. For example, to increase activity, alter substrate specificity, or product selectivity, mutations near the active site may be more beneficial than those on the surface of an enzyme [96]. These sites can be identified readily from crystal structures, homology models, or other experimental data such
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Table 2.3 Homology-Independent Mutagenesis Techniques Technique ITCHY
THIO-ITCHY
SCRATCHY
Enhanced crossoverSCRATCHY SHIPREC
Exon Shuffling
RM-PCR
DHR
Advantages
Disadvantages
Key Factor(s)
• Eliminates recombination bias • Structural knowledge not needed • Completely homologyindependent • Same advantages as ITCHY • Combines recombination and random mutagenesis • Simplified ITCHY method • Eliminates recombination bias • Structural knowledge not needed • Multiple crossovers possible • Same advantages as SCRATCHY • Enriched in recombinants • Crossovers occur at structurally related sites • Larger pool of active recombinants • Preserves exon function • Larger pool of active recombinants
• Limited to two parents • One crossover per iteration • Significant fraction of progeny out-of-frame • Complex, labor-intensive • Single crossovers • Same disadvantages as ITCHY • Incorporated dNTP analogs may complicate further experimentation • Limited to two parents • Significant fraction of progeny out-of-frame • Complex, labor-intensive • DNaseI digestion bias • Same disadvantages as SCRATCHY
• Exonuclease digestion and quenching times
• • • • • •
• DNaseI digestion time
• Unbiased incorporation of variable size DNA fragments • Useful for prokaryotic and eukaryotic genes
• •
• High recombination rate • Eliminates recombination bias • Parental genes not required
• •
• • • • • •
Limited to two parents One crossover per iteration Complex, labor-intensive DNaseI digestion bias Limited to eukaryotic genes Requires known intron–exon organization Limited diversity Required synthesis of many oligonucleotides Frameshifts may occur Mutants may be longer or shorter than expected Requires insights into locations of beneficial crossover points Complex, labor intensive Limited diversity Required synthesis of many oligonucleotides Gene sequences needed Limited diversity
NRR
• Large number of crossovers
• DNaseI digestion bias • Complex, labor-intensive
Protein-NRR
• Same as NRR • Larger pool of soluble and in-frame recombinants • Recombines variable size DNA fragments • Shuffles large fragments such as exons or domains • Parental genes not required
• Same as NRR
YLBS
• • • • • •
One crossover per iteration Frame shifts may occur Gene sequences needed Low product recovery Complex, labor-intensive Requirement for biotinylated primers
• Frequency of analog incorporation
• Fragmentation pattern
• Fragmentation pattern
• Quality and number of oligonucleotides
• Quality and number of oligonucleotides • Location of defined crossovers
• Quality of dephosphorylated bottom strand • Quality and number of oligonucleotides • Concentration of hairpin oligonucleotides • Efficiency of intramolecular ligation • Same as NRR
• Efficiency of ssDNA ligation • Quality of oligonucleotides
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as alanine-scanning mutagenesis or mechanistic studies. The vast number of crystal structures and sequence data that are readily accessible have made such data mining possible. At the advent of directed evolution, or in the pre- “omics” era, vast sequence and crystallographic data was largely unavailable, thus the “blind is better” credo [97] for mutagenesis and extensive screening was justifiable. The choke in the flow of directed evolution experiments was, and still is, the library screening, and any effort to mitigate it would be beneficial. Therefore, to discount physiochemical or simulation data today and adhere only to brute-force random mutagenesis and screening would be imprudent. By first identifying protein regions with higher functional significance, one can avoid the need to screen prohibitively large libraries for elusive beneficial mutations. The combination of these two orthogonal techniques, rational design and directed evolution, in a paradoxical semirational approach can be formidable. 2.2.3.1 Targeted Randomization Since the effect of mutating a particular residue to another cannot be accurately assessed a priori, saturation mutagenesis, which uses degenerate codons (NNN, NNS, NNK, or NNB) to mutate a particular residue in a protein to all 20 amino acids, can be used in a semirational approach to search for most beneficial mutation. Although another technique, maximum efficiency (MAX, [98]) randomization, removes the genetic redundancy by utilizing only one codon per amino acid, resultantly requiring a smaller library for equivalent completeness [99], saturation mutagenesis is the currently preferred technique due to its simplicity. Coupled with information on functionally important residues, saturation mutagenesis has been used individually [100] or combinatorially [101,102] to create mutant libraries in a semirational approach. Even in the absence of information on functionally important residues, saturation mutagenesis can been used to generate mutant libraries. Gene site saturation mutagenesis (GSSM, [103]) is one such technique that mutates every amino acid in a sequence to all others at the genetic level using degenerate codons to create libraries of mutants. Combinatorial consensus mutagenesis (CCM, [104]) uses multiple sequence alignments of the family of the gene of interest to locate residues in the target that differ from the consensus. Targeted residues are combinatorially mutagenized to the consensus amino acid identity to yield mutants with beneficial properties. 2.2.3.2 Guided Recombination Libraries created by techniques that recombine sequences with low homology, such as ITCHY, SCRATCHY, and SHIPREC, usually contain a large number of inactive variants, primarily because the locations of beneficial recombination are hard to predict. While exon shuffling has locations of crossovers predetermined, RM-PCR, a close relative also utilizing user-defined oligonucleotides to specify crossover locations, could use additional insight into determining effective recombination spots. Computational methods such as SCHEMA [105], FamClash [106], and SCOPE [107] can be used to supplement recombination techniques, either homology-dependent or independent, to reduce sequence space, thereby increasing chances of finding positive mutants. Sequence-independent site-directed chimeragenesis (SISDC, [108]) is another block shuffling technique. Similar to RM-PCR, the sites of recombination in SISDC are defined by inserted sequence tags determined with the help of SCHEMA. The inclusion of a type IIb restriction enzyme recognition site within the tag and engineered consensus sequences at the cleavage sites allow for serial extension of shuffled blocks after digestion and purification from tags. To ensure the success of reassembly and to correct reading frame orientation during cloning careful design of cleavage site sequences is essential.
2.3 Tools of Directed Evolution: Screens, Enrichments, and Selections The generation of a large number of mutants necessitates high throughput assays to find the variant(s) with the desired properties. The challenge in designing such an assay is two-fold. The first is the design of the actual assay, which can be implemented in a high-throughput fashion while remaining
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sensitive, economical, and compatible with the protein-DNA tagging. An assay here refers to screens, enrichments, or selection—although currently, enrichments are generally grouped with screens. Thus, a screen involves scrutinizing individual mutants for the desired property, while in a selection mutants deficient in the property of interest are eliminated and only the mutants with the desired property are allowed to propagate. The second challenge is posed by the need to tag proteins with the DNA encoding them in order to facilitate further rounds of mutagenesis. Since manipulating DNA is a much simpler task than proteins, due in part to their inherent stability, and part in view of ease of modification by in vitro techniques such as PCR, all mutagenesis in directed evolution studies is toward DNA alone. While mRNA may also serve as a tag for protein, DNA is still widely preferred for the reasons mentioned above. The mode of tagging DNA to its protein is perhaps the most intuitive way to categorize assay techniques. While direct physical linkage may seem to be the most obvious form of tagging, its general use in directed evolution is still relatively novel. Spatial co-localization is the most commonly used technique, and among them, cellular compartmentalization is the most popular choice as a simultaneous tag and expression host. However, the transformation efficiency of cells is a limiting factor in how many mutants can be assayed by this technique. In order to increase efficiency of space utilization for even higher throughput assay, several in vitro techniques have now shown great promise, and have the potential to screen libraries large enough to where the amount of DNA is limiting ~1014 molecules corresponding to 1 mg of plasmid DNA. Advances in mass-spectrometry (MS) have also shown excellent potential with applicability to detection of very low protein quantities via matrix-assisted laser desorption-ionization (MALDI) or electrospray ionization (ESI). While theoretically, MS can overcome the DNA barrier limit; the logistics of adapting it to a high throughput screen for different types of assays still needs significant work. An added advantage of using MS is that with direct protein sequencing by tandem MS, the requirement to tag a protein molecule to its DNA counterpart is obviated. It may be enticing to use the highest throughput assay for all studies, but this is usually not required. The choice of assay for any particular case depends on two major factors. The first is dependent on the throughput required. To improve physical properties like stability at extreme temperatures, pH, or in organic solvents, screening of about 104 variants is generally sufficient to find a clone with the desired properties. Thus, installation of infrastructure for extremely high-throughput techniques is unnecessary. The second is dependent of the property of the protein to be examined. Display technologies lend themselves naturally to affinity or binding assays, whereas enzymatic reactions are generally more adaptable to microtiter plate assays. Considering the ease of creating large mutant libraries, there must be screening and selection methods available. This section will describe the various technologies currently available to directed evolution. The various sections are thus divided based on the method used to tag a gene to its product. Table 2.4 gives a summary of the techniques and their basic characteristics, in addition to types of assays they are primarily used for.
2.3.1 Cartesian Co-Localization In this technique, the protein and its associated DNA are given a common spatial “address”, and are confined to a restricted area unable to diffuse and mix with other DNA-protein pairs. Microtiter plates are the most common among these, although protein-derivatized solid supports in a chip format, analogous to DNA microchips, have recently made some headway. 2.3.1.1 Microtiter Plates Practically just scale-down versions of test tubes, microtiter plates have been a favorite of immunologists for enzyme-linked immunosorbent assay (ELISA), [109] studies, as well as of drug discoverers for quite some time. They are the most versatile assay technologies and are thus easily adaptable to most types of assays—from enzymatic activity to binding-based assays. However, at the cost of versatility
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Improving Protein Functions by Directed Evolution Table 2.4 Screening and Selection Techniques Technique
Linkage Type
Characteristics
1. Microtiter plates
Cartesian colocalization
• Labor intensive • Low throughput (<104) • Adaptable to many assays
2. Protein chips
Cartesian colocalization
3. In vivo compartmenttalization
Compartmentalization
• • • • • •
4. In vitro compartmenttalization
Compartmentalization
5. Phage display
Physical linkage
6. Cell-surface display 7. Ribosome and mRNA-display
Physical linkage
8. Plasmid display
Physical linkage
Physical linkage
Purified proteins required Difficult to create chips Limited application as screening technique Limited by transformation efficiency (<1012) Adaptable to many assays Selection possible
• Not limited by transformation efficiency • Selection possible • Post-translational modification of proteins not possible • Limited by transformation efficiency (<1012) • Tunable valency of proteins displayed • Protein expressed as fusions • Limited by transformation efficiency (<1012) • Protein expressed as fusions • Not limited by transformation efficiency • Decreased efficiency for large proteins and polypeptides • Protein expressed as fusions • Limited by transformation efficiency (<1012) • Protein expressed as fusions
Assays Used • UV/Vis/IR Absorbance/ transmittance • Fluorescence • Binding • Binding
• Colorimetric • Auxotrophy complementation • Fluorescence • PCR based
• Binding
• Binding • Fluorescence • Binding
• Binding
comes relatively low throughput. Each well has to be attended to individually with components and either purified proteins or crude cell extracts. And although plates with up to 9600 wells are commercially available, the 96-well plate is most synonymous with microtiter plates. UV/Vis absorbance [110], chemiluminescence [111], scintillation proximity [112], and fluorescence [113,114], with variations like Förster resonance energy transfer (FRET), [115] and fluorescence polarization assays are most popularly associated with microtiter plates. Another drawback of these assays is that most of the time, they are only applicable to reactions which can be linked to spectrophotometric properties such as absorbance and fluorescence. 2.3.1.2 Protein Chips The success of DNA microchips has inspired efforts into development of protein chips. In this case, purified protein products are immobilized in a high-density format on solid support such as polyvinylidene difluoride membranes [116], nitrocellulose membranes [117], glass slides [118,119], or polyacrylamide gels [120]. Although protein chips are currently used primarily for proteomic studies, once larger varieties of immobilized proteins on chips become available, their application in binding-based screens [117,120,121] for directed evolution should become more widespread. A clever strategy to adapt protein chips to a wider variety of enzymatic assays is to immobilize proteins in nanowells [122], thus melding microtiter plate technology with protein chips. However, protein chips still face several challenges. First, it is only applicable to proteins that are stable outside the cell and are amenable to rough manipulation such as printing or covalent modifications. Second, the high level of accuracy required to utilize protein chips necessitates robotic precision. The widespread use of protein chips in directed evolution may not become reality in the near future, but its advantage and applicability cannot, and should not, be discounted.
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2.3.2 Compartmentalization Closely related to Cartesian colocalization, compartmentalization also restricts movement of proteinDNA pairs disallowing comingling with other pairs. However, instead of providing spatially addressable Cartesian coordinates, here the compartments are distributed randomly in a three-dimensional volume. The most well known of these compartments is a cell itself, with DNA libraries introduced as a ligation mixture or plasmids, thereby providing an expression host as well as a segregated compartment. However, the number of diverse sequence easily generated by mutagenesis dwarfs the limitation of introducing plasmids with a maximal efficiency of 1010 colony forming units (cfu) per µg DNA by electroporation. In vitro oil-in-water emulsion techniques have the ability to overcome this cap by providing minimalist “cells” such that encapsulations are created around DNA, rather than introducing DNA into compartments. 2.3.2.1 In Vivo Compartmentalization Geneticists have been using cell-based screens and selection for a long time, and there is much that can be directly carried over to directed evolution. Cells are natural compartments as DNA-protein tags, and with techniques like electroporation and transformation of plasmids or ligation mixtures, the transfer of single DNA variants to each cell, and subsequent isolation using a variety of commercially available kits have become routine. Cellular assays are highly suited for selections where the property of interest is phenotypically linked by complementation to an essential metabolic function in an auxotrophic strain. The ease of implementing cellular assays makes it the first choice in any directed evolution study where <1010 clones need to be examined. Cellular assays have been applied to improving protein properties such as resistance to antibiotics [123], increased soluble expression levels [124–126], or ability to metabolize certain carbon sources [127,128]. The development of two-hybrid systems has increased the repertoire of selectable properties to encompass protein-DNA, protein-RNA, proteinsmall molecule interactions, and even catalytic activity [129–135]. However, other properties such as activity at extreme temperatures or pH, and altering substrate specificity are not directly amenable to selections. In such cases, clever techniques can be devised for selection, but usually when implementation of a negative selection is not possible, screens on solid media using visually identifiable markers such as fluorescence and colorimetry, colony-lift methods, or supplementation with microtiter plates may be required. 2.3.2.2 In Vitro Compartmentalization (IVC) By encapsulating only DNA and the transcriptional-translational machinery to encode protein in a water-in-oil emulsion, Tawfik and Griffiths [136] provided a minimalist cell within which to conduct screens or selections (Figure 2.6). Since the compartments are created entirely in vitro, several steps involved in general cloning, such as restriction digests, vector ligations, transformations, etc. are altogether avoided. Not only does this expedite experiments, it has the ability to raise the cap of the number of maximum screenable mutants to where the amount of DNA is limiting. Selecting for properties that directly affect the translation/transcription process [137–141] by IVC seems feasible; however, its adaptation to other types of assays is its next major challenge.
2.3.3 Physical Linkage Perhaps the simplest form of tagging a DNA molecule to the protein it encodes for is by physically linking the two, either directly—as in the case of ribosome- or mRNA-display—or indirectly by phage- or cell-surface display. While it may be tempting to compare phage-display and cell-surface display to compartmentalization, the major difference between the two technologies is that the proteins here are free to interact with each other. Ribosome and mRNA display are in vitro techniques, thus potentially limited only by the amount of DNA produced in the laboratory. Phage display and
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(a) (b)
(e) (c) (f)
(d)
Figure 2.6 Scheme of in vitro compartmentalization (IVC). Compartments for individual genes and translation/transcription system are created in an oil-water emulsion for screening or selection. (a) Emulsification of library tagged with substrate, (b) expression of gene, (c) conversion of substrate into product by protein, (d) capture of gene using product tag, (e) repeat till (f) desired goal is achieved.
cell-surface display are still limited by transformation efficiency of host cells. The applicability of these techniques is primarily limited to binding assays, although recently, usefulness to enzymatic assays has been shown. 2.3.3.1 Phage Display The first display technology introduced, and therefore the most well studied [142–147], phages provide the simplest scaffolds for protein display via fusions to their coat proteins. The filamentous phages M13 has been the most popular host for this technology, with coat proteins pIII [146,148,149], pVII [150,151], pVIII [150,152], or pIX [150,153] serving as the mounts for the encoded proteins. Phage display has been especially prominent in antibody engineering, particularly as a simpler alternative to the time consuming hybridoma technology [154,155]. Additionally, engineering catalytic antibodies [156–158] protein-DNA interactions [159–161], and protein-small molecule interactions [162,163] have already been demonstrated. Enzyme catalysis is more challenging for phage display, but several successes and strategies have already been reported [164–168]. Phage display is a powerful system, capable of multivalent display of multiple proteins, but is ultimately limited by transformation efficiency of the host cells required to express proteins and assemble phage particles. In any case, the ability of phage display to screen a wide variety of enzymatic reactions will eventually ascribe its usefulness in directed evolution, since enzymes are the major targets for engineering. 2.3.3.2 Cell-Surface Display As in phage display, here proteins are expressed and displayed on the outer surface of the cell, thus not requiring cell disruption to detect activity. Bacteria are used to display proteins using an LppOmpA (lipoprotein-outer membrane protein A) fusion [169], although yeasts and mammalian cell display technologies have also been developed [170,171]. Applications of cell-surface display have been, unsurprisingly, similar to phage display, used on peptides [172], antibodies [173,174] and T-cell receptors
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[175], and more recently to engineer nuclease activity [176]. Affinity-based assay are easily applicable to this technology, although FACS is also generally quite popular. Like phage display, the application of cell-surface technology to a wide repertoire of enzymatic activities would require some clever manipulations. 2.3.3.3 Ribosome and mRNA Display Chloramphenicol is known to inhibit cell growth by causing cessation of ribosomal protein translation. Mattheakis et al. [177] observed that in vitro, the ribosome, mRNA, and polypeptide remain associated after chloramphenicol addition. Taking advantage of this, they developed a ribosome-display based enrichment, where the incompletely translated polypeptide is bound to an affinity matrix, after which the complex is dissociated and mRNA reverse transcribed into encoding DNA. While this technology was found effective for short polypeptides, efficiency drops considerably for larger proteins [178]. In addition, the ternary complex was found to be unstable, and these shortcomings eventually inspired the development of mRNA display in which the mRNA is covalently linked to the growing peptide [179,180]. The antibiotic puromycin is an analog for the aminoacyl end of tRNA and is covalently bound to a linker DNA at the 3ʹ-end of the mRNA. Upon reaching the ribosome, puromycin is added to the growing C-terminus of the encoded polypeptide, thus physically linking the mRNA to the translated protein. Unlike the previous two display technologies, these two are not limited by the transformation efficiency of cells. Once extended to different enzyme chemistries, it should prove to be a very powerful screening tool. 2.3.3.4 Plasmid Display Taking DNA-protein tagging literally, plasmid display uses a DNA-binding protein to noncovalently link the encoded protein to its plasmid. Cull et al. [181] used a Lac repressor-peptide fusion to bind lacO DNA sequences on the plasmid, and then screened peptides based on binding to an immobilized antibody. While this is conceptually novel, its application has thus far been limited to only affinity based screens of short peptides, although it should easily lend to the same assays as the previously mentioned physical linkage techniques. A critical shortcoming this technique shares with phage and cell-surface display is that the library size it can screen is limited to the transformation efficiency of the cell. However, it is conceivable that in the future, application of plasmid display in conjunction with in vitro compartmentalization can be used to overcome barriers faced by both techniques. Available techniques to screen or select mutagenic libraries vary over a wide range of applicability and maximal throughput, with one usually coming at the cost of the other. More seasoned techniques like cellular compartmentalization and microtiter plates have already found use in almost every facet of protein phenotype improvement. Screens in such cases have usually been limited to <105 clones, whereas selections have been able to examine larger libraries, however still capped by the electroporation efficiency of cells at <1012. The more novel techniques like in vitro compartmentalization and display techniques are very promising for protein engineering, albeit still in their nascent stage with limited applications. Inventive modifications and utilization in improvement of different types of enzymatic reactions will eventually determine their usefulness and longevity as tools in directed evolution and protein engineering. While these techniques are able to screen larger libraries more efficiently, the cap of maximum producible DNA in a laboratory limits the maximum screenable library to 1014 distinct sequences (~1 mg of plasmid DNA). The major drawback is thus the need to tag DNA to a protein. Mass spectroscopy can therefore overcome this limitation by directly sequencing proteins. Potentially, armed with techniques that mutagenize at the protein- or mRNA-level instead of the DNA level, it could effectively screen a much larger sequence space.
2.4 Successes and Applications of Directed Evolution Synthetic chemistry is a much better-established field than biocatalysis, and has been very successful in the past in catalyzing difficult reactions. Additionally, synthetic catalysts are a more cost-effective means of increasing reaction rates than enzymes because they do not require time-consuming cell
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culturing and low-throughput, low-yield batch chromatographic purification steps. Why, then is any need or interest in utilizing biocatalysts? In general, for any chemical process, the most expensive step is the purification, and the most efficient means to mitigate purification costs is to minimize formation of side-products during reaction. This is the primary reason for employing enzymes—as conduits to desired products—and orders of magnitude enhancement in reaction rate is an added bonus. The challenges faced in designing synthetic catalysts to produce stereochemically pure reaction products are the same as those faced in the rational design of enzyme active sites. Catalytic sites, either synthetic or enzymatic are difficult to rationally mold for a desired mode of action. The advantage of using proteins is that they are innate catalysts and can be adapted to serve even unnatural purposes by genetic manipulation using tools like directed evolution. Several classes of enzymes are already being used at the industrial scale to produce chemicals, and readers interested in learning about those enzymes, are referred to several excellent reviews already published on the subject [182–189]. This section will use examples as a tool to exemplify the vast variety of protein properties directed evolution has been used to improve, which goes beyond just modifying enzymes for industrial catalysis. Proteins are also potent therapeutic agents, and directed evolution has been used to design novel vaccines, gene therapeutics, and potent antibodies. In a more novel application, directed evolution, being an imitation of natural evolution, has been used to shed some light on the latter process.
2.4.1 Altering Catalytic Activity Enzymes are excellent at directing reactions toward certain products, however, they might not do so at high rates. Thus, many of the earliest examples of directed evolution are aimed at increasing the catalytic efficiency of these enzymes. Qian and Lutz [190] increased catalytic efficiency of Candida antarctica Lipase B toward p-nitrophenol butyrate and 6,8-difluoro-4-methylumbelliferyl octanoate over 12- and 28-fold, respectively, using circular permutations of the wild-type protein with a library of about 5 × 105 mutants. In another impressive example, Castle et al. [191] improved the catalytic efficiency of a glyphosate N-acetyltransferase (GAT) toward the herbicide glyphosate by four orders of magnitude. To do so, they screened 5,000 colonies in each of 11 rounds of iterative DNA shuffling with genes from different variants of Bacillus licheniformis. Several classes of enzymes are known to accept a large number of substrates, particularly those involved in primary metabolism. In such a case, the catalytic efficiency toward certain substrates may be less than optimal for a desired purpose, thus there may be a need to broaden enzyme specificity to catalyze reactions with additional substrates with higher efficiency. As an example of this, Fong et al. [192] used epPCR and DNA shuffling to broaden the substrate specificity of D-2-keto-3-deoxy6-phosphogluconate (KDPG) aldolase to accept both d- and l-sugars in nonphosphorylated forms for reversible aldol reactions. Cho et al. [193] used two rounds of DNA shuffling and screened about 12,000 mutants to improve the hydrolytic activity of an organophosphorus hydrolase (OPH) 725-fold toward the toxin chlorpyrifos. Raillard et al. [194] used DNA shuffling on two genes encoding s-triazine hydrolyzing enzymes and screened about 1,600 variants for activity against 15 different triazines. Their best mutant was able to hydrolyze five triazines that were not substrates for either of the wild-type enzymes. In other cases, with promiscuous enzymes, there may be an interest in narrowing substrate specificity, particularly when formation of undesired side-products is an issue. In general, a larger library needs to be screened to find enzymes with increased specificity, although, there have been cases where beneficial mutants have been found in smaller libraries. Antikainen et al. [195] did just this by altering the substrate specificity of phospholipase C from Bacillus cerus (PLCBc) by saturation mutagenesis of three residues in the substrate-binding pocket. The wild-type enzyme had a similar preference for the watersoluble substrate C6PC (1, 2-dihexanoyl-sn-glycero-3-phosphocholine) and C6PE (1, 2-dihexanoylsn-glycero-3-phosphoethanolamine) with kcat/K M ratio = 0.7; a double mutant had narrowed substrate preference with 29.0 for the same ratio, thereby indicating a large preference for C6PE over C6PC, albeit
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at the cost of decreased activity. Mutants were found by screening a library of only 6,000 members. Rothman et al. [196] performed a single round of DNA shuffling on an E. coli aspartate aminotransferase (AATase) mutant, HEX [197], with tyrosine aminotransferase (TATase) activity and increased its activity as a TATase at the cost of AATase activity. Regulatory proteins, unlike those involved in primary metabolism, tend to be very specific in their choice of substrates. Like narrowing substrate specificity, altering substrate specificity requires more drastic changes, and thus screening of a larger library. Focusing initially on the substrate binding pocket in such cases tends to yield quicker results, as demonstrated by the following examples. Chockalingam et al. [100] used the ligand binding domain (LBD) of one such protein, the human estrogen receptor α (hERα), which has a very specific and high affinity for 17β-estradiol (E2), and drastically shifted its affinity to accept the synthetic ligand 4, 4ʹ-dihydroxybenzil (DHB) in its stead, with >107 fold shift in preference. To do so, they used several individual rounds of saturation mutagenesis on 14 ligand-contacting residues followed by epPCR on the entire LBD. Santoro et al. [198] created an orthogonal aminoacyltRNA synthetase and tRNA pair that cannot interact with their endogenous counterparts in E. coli. To do so, they performed combinatorial saturation mutagenesis on five residues thought to constitute the binding pocket, and selected mutants from a library of almost 109 mutants. Altering the substrate-binding pocket is one way to decrease the formation of side-products. Since enantiomeric products are resultants of the same substrate, enantiopure yields cannot be obtained by altering the substrate affinities. To approach this problem, one needs to widen the enzyme “conduit” toward the desired product, thus preferentially producing only that product. The role of substrate contacting residues on enantioselectivity of the enzyme may not be direct, and therefore random mutagenesis is generally preferable to alter this property. In one such study, van Loo et al. [199] increased the enantioselectivity of Agrobacterium radiobacter epoxide hydrolase by up to 20-fold using epPCR followed by DNA shuffling. A total of 40,000 mutants were screened as produced by epPCR and another 20,000 as created by DNA shuffling. In another example, Leibeton et al. [55] increased the enantioselectivity of Pseudomonas aeruginosa lipase by almost 24-fold by epPCR followed by saturation mutagenesis. Each generation of epPCR required screening of only 1,000–7,000 mutants to identify beneficial mutations.
2.4.2 Improving Enzyme Stability Most enzymes are suited to react in physiological conditions, which are quite mild except in extremophiles. Physiological conditions are not optimal for most large-scale reactions, and in such a case, it is simpler to alter the enzyme involved to cater to the needs of the process rather than altering the process itself. The major targets for enzymatic stability improvement are thermostablity, pH insensitivity, and resistance to denaturation in organic solvents. To one such goal, Johannes et al. [200] improved the half-life of NAD(P)H oxidizing phosphite dehydrogenase from Pseudomonas stutzeri by over 7,000-fold at 45°C by introducing 12 mutations over three rounds of epPCR. Each round required the screening of <10,000 clones to find mutants with significant improvement in stability. Giver et al. [201] increased the melting temperature (Tm) of p-nitrobenzyl esterase by over 14°C with six rounds of epPCR and a single round of DNA shuffling, screening <2,000 mutants per library. Sriprapundh et al. [202] increased the stability of Thermotoga neopolitana xylose isomerase at acidic pH using two rounds of epPCR and screening under 1,500 clones per round without sacrificing any activity. Hao and Berry [203] evolved fructose bisphosphate aldolases using four rounds of epPCR followed by DNA shuffling to yield mutants with increased thermostabilty in addition to stability in organic solvents. In a particularly impressive example, Ness et al. [204] improved all the aforementioned properties of the protease subtilisin using family shuffling on 26 homologous subtilisin genes. Stable, soluble expression in heterologous hosts is important for a number of reasons; the most prominent may be for ease of study, and economic large-scale production and purification. Phospho triesterase from Pseudomonas diminuta is one such protein that is extremely efficient at hydrolyzing
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organophosphorus compounds [205] but is difficult to express heterologously. Rooseveldt and Tawfik [206] identified a variant with 20-fold increased functional expression in E. coli from a library of mutants created by epPCR. Sieber et al. [82] produced a soluble mutant of a membrane-associated human cytochrome P450 by creating a hybrid with a soluble bacterial P450 using SHIPREC.
2.4.3 Evolving Proteins with Therapeutic Value As mentioned before, directed evolution has application outside of improving enzymes for industrial catalysis. Several properties of proteins with therapeutic value can be improved using directed evolution, and several reviews have been published which provide more details [207–211]. Increased potency and affinity for targets, decreased immunogenicity, enhanced stability, and improved pharmacokinetics are some of the properties focused on by researchers. Shuffling techniques have shown particular promise [210,212], and several display technologies have been applied successfully [213–216]. Antibodies are a class of proteins with major significance as protein therapeutics and several are commercially available in the market to treat a variety of diseases. Directed evolution has been used to increase the affinity of an antibody by Boder et al. [174] using several rounds of DNA shuffling and epPCR coupled with yeast display to screen 105–107 mutants. A mutant showing >1,000 fold increased affinity was obtained after several rounds of mutagenesis and screening. Hanes et al. [217] used mRNA and ribosome display to screen for single-chain antibody fragments (scFv) mutants from a synthetic library with increased affinity. Selected mutants showed >40-fold increased affinity for their antigen compared to any of the progenitors. Another class of protein therapeutics—engineered cytokines—are beneficial for treatment of hepatitis C viral infections, hairy cell leukemia, and genital warts [218]. However, toxicity limits the dosage and thus, the efficacy of treatments. In an attempt to mitigate dosage limitations, Chang et al. [219] shuffled 20 human cytokines interferon-αs (IFN-αs) to achieve enhanced activity in murine cell-based assay for protection against a viral challenge. Screening fewer than 2,000 clones resulted in identification of chimeras with these beneficial properties. Enzymes have also been used as therapeutic agents in conjunction with reductive prodrugs for cancer chemotherapy. One such enzyme, YieF from E. coli, was identified and Barak et al. [220] improved its reductive activity on the prodrugs mitomycin C and CB 1954 using epPCR thereby enhancing its capacity to kill HeLa cells by over five-fold. Directed evolution has also been used in developing gene delivery vectors. The most successful applications have been on coat proteins of viral vectors, improving their target specificity as well as pharmacokinetic properties. Soong et al. [221] shuffled envelope protein encoding genes of six murine leukemia virus (MLV) strains to confer them with CHOK1 (Chinese Hamster Ovary) cell tropism, which the parent viruses lacked. Powell et al. [222] also used DNA shuffling on six MLV strains, creating a library of 5 × 106 mutants, and isolated mutants with 30- to 100-fold increased stability to ultracentrifugation. Maheshri et al. [223] used epPCR followed by StEP to mutate the coat proteins of an adeno-associated virus 2 (AAV2) to alter its receptor-binding properties. This allowed the mutants to evade neutralizing host antibodies in vivo and in vitro, increasing the potential of recombinant AAV2s as gene delivery vehicles.
2.4.4 Understanding Natural Evolution Since directed evolution is an imitation of natural evolution, albeit at an extremely accelerated rate, should it not be able to enlighten us on the nature of natural evolution? Currently, directed evolution has been used to produce progeny that are only slightly different from their parents, especially evident in comparison to the myriad of divergent enzymatic functions present in Nature. However, the large number of protein crystal structures have shown that these differing functions are built on the scaffold of only a few thousand platforms. This only reaffirms the fact that most novel functions are probably
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derived by evolution of existing proteins, rather than de novo in “ junk DNA”. The generally accepted idea now is that the first step to acquiring a novel functions is to first evolve and procure promiscuous activity, duplicate, and then specialize to a different function [24,224–228]. Aharoni et al. [24] demonstrated this pathway using directed evolution with three different types of enzymes—a serum paranoxase, a phosphotriesterase, and a carbonic anhydrase II. By selecting for mutants with cross-reactivity, they found that loss of natural function is not required. This in itself was not particularly surprising, since there have already been several reports of successful directed evolution of enzymes for broader range of activities, without a selective pressure to retain original function. However, further selection for improvement of the promiscuous activity led indirectly to specialization by decreased efficiency of natural function. Along the same reasoning, Chen and Zhao designed a technique called in vitro coevolution, as a stepwise and iterative process of selective induction of alternating promiscuity and specificity to a parental enzyme to develop novel functions [229]. Yoshikuni et al. [230] reported the divergent evolution of several novel highly active and specific enzymatic functions from a promiscuous parental enzyme by recombining mutations at key sites that conferred functional plasticity. More recently, Peisajovich et al. [23] demonstrated a pathway to the evolution of novel protein topologies from existing structures using directed evolution techniques. The authors showed that mutagenesis and gene rearrangements could systematically lead to the evolution of functional enzyme homologs, especially during relaxed selective pressures, without substantial loss of the host organism’s fitness. Their study also suggested that point mutations that may have little effect on the functionality of parental enzyme, if allowed to accumulate, could yield beneficial effects in evolved progeny.
2.5 Patent and Licensing Issues Most uses of directed evolution are primarily commercially focused, that is, most improvements are generally made to proteins that have commercial value, and improvements are made to those properties that limit their industrial usage. It should therefore not be surprising that several of the protocols discussed here are protected under patents. Up until March 2006, Hoffman-La Roche held key patents on PCR, that covers its use as the foundation for most of these techniques described here (US4683195, US4683202, and US4965188). Patents for certain instruments, reagents, etc. are still valid for several more years, however. Nevertheless, PCR is an indispensable tool in molecular biology and biotechnology, and has been used extensively, especially since the use of Mn2 + as a low fidelity inducing agent was not covered under patents; neither was the use of several commercial polymerases such as Vent (New England BioLabs) and the Mutazyme® I and II systems (Stratagene). Affymax Technologies hold the patents US5605793 and US5830721 for DNA shuffling. Maxygen, Inc. holds US6506603, also for DNA shuffling, as well as US6521453 for synthetic shuffling. Finally, California Institute of Technology holds the patents for StEP (US6153410 and US6177263). MAX, an alternative to saturation mutagenesis, is protected by patents WO00/15777, held by Aston University and Amersham Pharmacia Biotech, and also WO03/106679, held by Aston University. Diversa Corporation claims the patents (US6562594, US6171820) to the use of complete set of primers to mutagenize every position in a gene, as required by GSSM. The ITCHY family of methods, the most popular techniques for homology-independent recombination, are justifiably so, because they are not protected by any patents. RID and ADO are also not protected by patents.
2.6 Outlook Directed evolution has proven to be a powerful tool with applications for more than just engineering preexisting enzymes to industrial requirements. Although its primary focus is to tailor enzymes and proteins for commercial purposes, directed evolution has also been used to understand natural
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evolution and develop novel functions, which makes it an invaluable tool in molecular, genetic, and evolutionary biology. The successes have also focused primarily on only a few techniques in the toolbox available—epPCR and/or DNA shuffling for diversity generation, and microtiter plates and/or cellular compartmentalization for assay systems—but the use of novel tools should be accompanied by novel applications. In vitro display technologies such as ribosome/mRNA display have shown promise to overcome the limitations placed by maximum transformation efficiency of cells. However, this again is still a brute-force technique just circumventing, rather than overcoming, the problem of screening large libraries. In order to achieve quicker results, there is the need to start with a mutagenic technique that can create a larger fraction of improved mutants. With recent advances in computational approaches, semi-rational design may help achieve this goal. Of course, the final goal would be to know beforehand exactly the mutations that would provide the desired goal, and that would require absolute foreknowledge, something that would require outsmarting Mother Nature herself. The chances of this are slim, even in the distant future, so until then directed evolution and computational design will have to work synergistically to develop innovative new technologies and products in the future.
Acknowledgments We gratefully acknowledge financial support from Biotechnology Research and Development Consortium (BRDC) (Project 2-4-121), Office of Naval Research (N00014-02-1-0725), National Science Foundation (BES-0348107), Department of Energy, and the DuPont Company.
References 1. Patnaik, R., et al. Genome shuffling of Lactobacillus for improved acid tolerance. Nat. Biotechnol., 20(7), 707–12, 2002. 2. Zhang, Y.X., et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 415(6872), 644–46, 2002. 3. Kim, D. and F.P. Guengerich. Selection of human cytochrome P450 1A2 mutants with enhanced catalytic activity for heterocyclic amine N-hydroxylation. Biochemistry, 43(4), 981–88, 2004. 4. Kumar, S., et al. Directed evolution of mammalian cytochrome P450 2B1: mutations outside of the active site enhance the metabolism of several substrates, including the anticancer prodrugs cyclophosphamide and ifosfamide. J. Biol. Chem., 280(20), 19569–75, 2005. 5. Wang, C.L., D.C. Yang, and M. Wabl. Directed molecular evolution by somatic hypermutation. Protein Eng. Des. Sel., 17(9), 659–64, 2004. 6. Rajagopalan, P.T., S. Lutz, and S.J. Benkovic. Coupling interactions of distal residues enhance dihydrofolate reductase catalysis: mutational effects on hydride transfer rates. Biochemistry, 41(42), 12618–28, 2002. 7. Iffland, A., et al. Directed molecular evolution of cytochrome c peroxidase. Biochemistry, 39(35), 10790–98, 2000. 8. Horsman, G.P., et al. Mutations in distant residues moderately increase the enantioselectivity of Pseudomonas fluorescens esterase towards methyl 3bromo-2-methylpropanoate and ethyl 3phenylbutyrate. Chemistry, 9(9), 1933–39, 2003. 9. Mills, D.R., R.L. Peterson, and S. Spiegelman. An extracellular Darwinian experiment with a selfduplicating nucleic acid molecule. Proc. Natl. Acad. Sci. USA, 58(1), 217–24, 1967. 10. Chang, C.C., et al. Evolution of a cytokine using DNA family shuffling. Nat. Biotechnol., 17(8), 793–97, 1999. 11. Xu, L., et al. Directed evolution of high-affinity antibody mimics using mRNA display. Chem. Biol., 9(8), 933–42, 2002.
2-28
Evolutionary Tools in Metabolic Engineering
12. Leong, S.R., et al. Optimized expression and specific activity of IL-12 by directed molecular evolution. Proc. Natl. Acad. Sci. USA, 100(3), 1163–68, 2003. 13. Midelfort, K.S. and K.D. Wittrup. Context-dependent mutations predominate in an engineered high-affinity single chain antibody fragment. Protein Sci., 15(2), 324–34, 2006. 14. Maheshri, N., et al. Directed evolution of adeno-associated virus yields enhanced gene delivery vectors. Nat. Biotechnol., 24(2), 198–204, 2006. 15. O’Loughlin T, L., D.N. Greene, and I. Matsumura. Diversification and specialization of HIV protease function during in vitro evolution. Mol. Biol. Evol., 23(4), 764–72, 2006. 16. Perabo, L., et al. Combinatorial engineering of a gene therapy vector: directed evolution of adenoassociated virus. J. Gene Med., 8(2), 155–62, 2006. 17. Kather, I., C.A. Bippes, and F.X. Schmid. A stable disulfide-free gene-3-protein of phage fd generated by in vitro evolution. J. Mol. Biol., 354(3), 666–78, 2005. 18. Mijts, B.N., P.C. Lee, and C. Schmidt-Dannert. Identification of a carotenoid oxygenase synthesizing acyclic xanthophylls: combinatorial biosynthesis and directed evolution. Chem. Biol., 12(4), 453–60, 2005. 19. Lee, P.C., et al. Directed evolution of Escherichia coli farnesyl diphosphate synthase (IspA) reveals novel structural determinants of chain length specificity. Metab. Eng., 7(1), 18–26, 2005. 20. Lee, P.C., et al. Alteration of product specificity of Aeropyrum pernix farnesylgeranyl diphosphate synthase (Fgs) by directed evolution. Protein Eng. Des. Sel., 17(11), 771–77, 2004. 21. Umeno, D. and F.H. Arnold. Evolution of a pathway to novel long-chain carotenoids. J. Bacteriol., 186(5), 1531–36, 2004. 22. Umeno, D. and F.H. Arnold. A C35 carotenoid biosynthetic pathway. Appl. Environ. Microbiol., 69(6), 3573–79, 2003. 23. Peisajovich, S.G., L. Rockah, and D.S. Tawfik. Evolution of new protein topologies through multistep gene rearrangements. Nat. Genet., 38(2), 168–74, 2006. 24. Aharoni, A., et al. The ‘evolvability’ of promiscuous protein functions. Nat. Genet., 37(1), 73–76, 2005. 25. Drummond, D.A., et al. Why high-error-rate random mutagenesis libraries are enriched in functional and improved proteins. J. Mol. Biol., 350(4), 806–16, 2005. 26. Park, H.S., et al. Design and evolution of new catalytic activity with an existing protein scaffold. Science, 311(5760), 535–38, 2006. 27. Fujii, R., M. Kitaoka, and K. Hayashi. RAISE: a simple and novel method of generating random insertion and deletion mutations. Nucleic Acids Res., 34(4), e30, 2006. 28. Osuna, J., et al. Protein evolution by codon-based random deletions. Nucleic Acids Res., 32(17), e136, 2004. 29. Murakami, H., T. Hohsaka, and M. Sisido. Random insertion and deletion of arbitrary number of bases for codon-based random mutation of DNAs. Nat. Biotechnol., 20(1), 76–81, 2002. 30. Hayes, F., B. Hallet, and Y. Cao. Insertion mutagenesis as a tool in the modification of protein function. Extended substrate specificity conferred by pentapeptide insertions in the omega-loop of TEM-1 beta-lactamase. J. Biol. Chem., 272(46), 28833–36, 1997. 31. Bornscheuer, U.T., J. Altenbuchner, and H.H. Meyer. Directed evolution of an esterase for the stereoselective resolution of a key intermediate in the synthesis of epothilones. Biotechnol. Bioeng., 58(5), 554–59, 1998. 32. Selifonova, O., F. Valle, and V. Schellenberger. Rapid evolution of novel traits in microorganisms. Appl. Environ. Microbiol., 67(8), 3645–49, 2001. 33. Coia, G., et al. Use of mutator cells as a means for increasing production levels of a recombinant antibody directed against Hepatitis B. Gene, 201(1–2), 203–9, 1997. 34. Henke, E. and U.T. Bornscheuer. Directed evolution of an esterase from Pseudomonas fluorescens. Random mutagenesis by error-prone PCR or a mutator strain and identification of mutants showing enhanced enantioselectivity by a resorufin-based fluorescence assay. Biol. Chem., 380(7–8), 1029–33, 1999.
Improving Protein Functions by Directed Evolution
2-29
35. Botstein, D. and D. Shortle. Strategies and applications of in vitro mutagenesis. Science, 229(4719), 1193–201, 1985. 36. Djordjevic, B. and O. Djordjevic. Chromosomal aberrations in synchronized mammalian cells treated with 5-bromo-deoxyuridine and irradiated by ultra-violet light. Nature, 206(989), 1165–66, 1965. 37. Pitsikas, P., J.M. Patapas, and C.G. Cupples. Mechanism of 2-aminopurine-stimulated mutagenesis in Escherichia coli. Mutat. Res., 550(1–2), 25–32, 2004. 38. Lai, Y.P., et al. A new approach to random mutagenesis in vitro. Biotechnol. Bioeng., 86(6), 622–27, 2004. 39. Reznikoff, C.A. and R. DeMars. In vitro chemical mutagenesis and viral transformation of a human endothelial cell strain. Cancer Res., 41(3), 1114–26, 1981. 40. Shortle, D. and D. Botstein. Directed mutagenesis with sodium bisulfite. Methods Enzymol., 100, 457–68, 1983. 41. Tripathi, A.K. and H.D. Kumar. Mutagenesis by ethidium bromide, proflavine and mitomycin C in the cyanobacterium Nostoc sp. Mutat. Res., 174(3), 175–78, 1986. 42. Beckman, R.A., A.S. Mildvan, and L.A. Loeb. On the fidelity of DNA replication: manganese mutagenesis in vitro. Biochemistry, 24(21), 5810–17, 1985. 43. Cadwell, R.C. and G.F. Joyce. Mutagenic PCR. PCR Methods Appl., 3(6), S136–40, 1994. 44. Cadwell, R.C. and G.F. Joyce. Randomization of genes by PCR mutagenesis. PCR Methods Appl., 2(1), 28–33, 1992. 45. Fromant, M., S. Blanquet, and P. Plateau. Direct random mutagenesis of gene-sized DNA fragments using polymerase chain reaction. Anal. Biochem., 224(1), 347–53, 1995. 46. Leung, D.W., E. Chen, and D.V. Goeddel. A method for random mutagenesis of a defined DNA segment using a modified polymerase chain reaction. Technique, 1, 11–15, 1989. 47. Arnold, F.H. Enzyme engineering reaches the boiling point. Proc. Natl. Acad. Sci. USA, 95(5), 2035–36, 1998. 48. Suzuki, M., et al. Tolerance of different proteins for amino acid diversity. Mol. Divers, 2(1–2), 111–18, 1996. 49. Daugherty, P.S., et al. Quantitative analysis of the effect of the mutation frequency on the affinity maturation of single chain Fv antibodies. Proc. Natl. Acad. Sci. USA, 97(5), 2029–34, 2000. 50. Shafikhani, S., et al. Generation of large libraries of random mutants in Bacillus subtilis by PCRbased plasmid multimerization. Biotechniques, 23(2), 304–10, 1997. 51. Bloom, J.D., et al. Thermodynamic prediction of protein neutrality. Proc. Natl. Acad. Sci. USA, 102(3), 606–11, 2005. 52. Kunichika, K., Y. Hashimoto, and T. Imoto. Robustness of hen lysozyme monitored by random mutations. Protein Eng., 15(10), 805–9, 2002. 53. Wong, T.S., et al. Sequence saturation mutagenesis (SeSaM): a novel method for directed evolution. Nucleic Acids Res., 32(3), e26, 2004. 54. Fujii, R., M. Kitaoka, and K. Hayashi. One-step random mutagenesis by error-prone rolling circle amplification. Nucleic Acids Res., 32(19), e145, 2004. 55. Liebeton, K., et al. Directed evolution of an enantioselective lipase. Chem. Biol., 7(9), 709–18, 2000. 56. Hallet, B., D.J. Sherratt, and F. Hayes. Pentapeptide scanning mutagenesis: random insertion of a variable five amino acid cassette in a target protein. Nucleic Acids Res., 25(9), 1866–67, 1997. 57. Kashiwagi, K., et al. Frame shuffling: a novel method for in vitro protein evolution. Protein Eng. Des. Sel., 19(3), 135–40, 2006. 58. Shiba, K., Y. Takahashi, and T. Noda. Creation of libraries with long ORFs by polymerization of a microgene. Proc. Natl. Acad. Sci. USA, 94(8), 3805–10, 1997. 59. Zaccolo, M. and E. Gherardi. The effect of high-frequency random mutagenesis on in vitro protein evolution: a study on TEM-1 beta-lactamase. J. Mol. Biol., 285(2), 775–83, 1999. 60. Wong, T.S., et al. A statistical analysis of random mutagenesis methods used for directed protein evolution. J. Mol. Biol., 355(4), 858–71, 2006.
2-30
Evolutionary Tools in Metabolic Engineering
61. Bosley, A.D. and M. Ostermeier. Mathematical expressions useful in the construction, description and evaluation of protein libraries. Biomol. Eng., 22(1–3), 57–61, 2005. 62. Steinbachs, J.E. and K.E. Holsinger. S-RNase-mediated gametophytic self-incompatibility is ancestral in eudicots. Mol. Biol. Evol., 19(6), 825–29, 2002. 63. Wang, Y., et al. S-RNase-mediated self-incompatibility. J. Exp. Bot., 54(380), 115–22, 2003. 64. Stemmer, W.P. Rapid evolution of a protein in vitro by DNA shuffling. Nature, 370(6488), 389–91, 1994. 65. Crameri, A., et al. DNA shuffling of a family of genes from diverse species accelerates directed evolution. Nature, 391(6664), 288–91, 1998. 66. Joern, J.M., P. Meinhold, and F.H. Arnold. Analysis of shuffled gene libraries. J. Mol. Biol., 316(3), 643–56, 2002. 67. Gibbs, M.D., K.M. Nevalainen, and P.L. Bergquist. Degenerate oligonucleotide gene shuffling (DOGS): a method for enhancing the frequency of recombination with family shuffling. Gene, 271(1), 13–20, 2001. 68. Sambrook, J., E.F. Fritsch, and T. Maniatis. Molecular Cloning: A Laboratory Manual. Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press, 1989. 69. Kikuchi, M., K. Ohnishi, and S. Harayama. An effective family shuffling method using singlestranded DNA. Gene, 243(1–2), 133–37, 2000. 70. Coco, W.M. RACHITT: gene family shuffling by random chimeragenesis on transient templates. Methods Mol. Biol., 231, 111–27, 2003. 71. Muller, K.M., et al. Nucleotide exchange and excision technology (NExT) DNA shuffling: a robust method for DNA fragmentation and directed evolution. Nucleic Acids Res., 33(13), e117, 2005. 72. Shao, Z., et al. Random-priming in vitro recombination: an effective tool for directed evolution. Nucleic Acids Res., 26(2), 681–83, 1998. 73. Zhao, H., et al. Molecular evolution by staggered extension process (StEP) in vitro recombination. Nat. Biotechnol., 16(3), 258–61, 1998. 74. Ness, J.E., et al. Synthetic shuffling expands functional protein diversity by allowing amino acids to recombine independently. Nat. Biotechnol., 20(12), 1251–55, 2002. 75. Zha, D., A. Eipper, and M.T. Reetz. Assembly of designed oligonucleotides as an efficient method for gene recombination: a new tool in directed evolution. Chembiochem. 4(1), 34–39, 2003. 76. Abecassis, V., D. Pompon, and G. Truan. High efficiency family shuffling based on multi-step PCR and in vivo DNA recombination in yeast: statistical and functional analysis of a combinatorial library between human cytochrome P450 1A1 and 1A2. Nucleic Acids Res., 28(20), e88, 2000. 77. Volkov, A.A., Z. Shao, and F.H. Arnold. Recombination and chimeragenesis by in vitro heteroduplex formation and in vivo repair. Nucleic Acids Res., 27(18), e18, 1999. 78. Xu, S., et al. Directed evolution of extradiol dioxygenase by a novel in vivo DNA shuffling. Gene, 368, 126–37, 2006. 79. Ostermeier, M., J.H. Shim, and S.J. Benkovic. A combinatorial approach to hybrid enzymes independent of DNA homology. Nat. Biotechnol., 17(12), 1205–9, 1999. 80. Hocker, B. Directed evolution of (betaalpha)(8)-barrel enzymes. Biomol. Eng., 22(1–3), 31–38, 2005. 81. Farber, G.K. and G.A. Petsko. The evolution of alpha/beta barrel enzymes. Trends Biochem Sci, 15(6), 228–34, 1990. 82. Sieber, V., C.A. Martinez, and F.H. Arnold. Libraries of hybrid proteins from distantly related sequences. Nat. Biotechnol., 19(5), 456–60, 2001. 83. Lutz, S., M. Ostermeier, and S.J. Benkovic. Rapid generation of incremental truncation libraries for protein engineering using alpha-phosphothioate nucleotides. Nucleic Acids Res., 29(4), e16, 2001. 84. Lutz, S., et al. Creating multiple-crossover DNA libraries independent of sequence identity. Proc. Natl. Acad. Sci. USA, 98(20), 11248–53, 2001.
Improving Protein Functions by Directed Evolution
2-31
85. Kawarasaki, Y., et al. Enhanced crossover SCRATCHY: construction and high-throughput screening of a combinatorial library containing multiple non-homologous crossovers. Nucleic Acids Res., 31(21), e126, 2003. 86. Kolkman, J.A. and W.P. Stemmer. Directed evolution of proteins by exon shuffling. Nat. Biotechnol., 19(5), 423–28, 2001. 87. Tsuji, T., M. Onimaru, and H. Yanagawa. Random multi-recombinant PCR for the construction of combinatorial protein libraries. Nucleic Acids Res., 29(20), e97, 2001. 88. Coco, W.M., et al. Growth factor engineering by degenerate homoduplex gene family recombination. Nat. Biotechnol., 20(12), 1246–50, 2002. 89. Bittker, J.A., B.V. Le, and D.R. Liu. Nucleic acid evolution and minimization by nonhomologous random recombination. Nat. Biotechnol., 20(10), 1024–29, 2002. 90. Bittker, J.A., et al. Directed evolution of protein enzymes using nonhomologous random recombination. Proc. Natl. Acad. Sci. USA, 101(18), 7011–16, 2004. 91. Kitamura, K., et al. Construction of block-shuffled libraries of DNA for evolutionary protein engineering: Y-ligation-based block shuffling. Protein Eng., 15(10), 843–53, 2002. 92. Ostermeier, M., A.E. Nixon, and S.J. Benkovic. Incremental truncation as a strategy in the engineering of novel biocatalysts. Bioorg. Med. Chem., 7(10), 2139–44, 1999. 93. Ostermeier, M. Theoretical distribution of truncation lengths in incremental truncation libraries. Biotechnol. Bioeng., 82(5), 564–77, 2003. 94. Firth, A.E. and W.M. Patrick. Statistics of protein library construction. Bioinformatics, 21(15), 3314–15, 2005. 95. Dalby, P.A. Optimising enzyme function by directed evolution. Curr. Opin. Struct. Biol., 13(4), 500– 5, 2003. 96. Park, S., et al. Focusing mutations into the P. fluorescens esterase binding site increases enantioselectivity more effectively than distant mutations. Chem. Biol., 12(1), 45–54, 2005. 97. Arnold, F.H. When blind is better: protein design by evolution. Nat. Biotechnol., 16(7), 617–18, 1998. 98. Hughes, M.D., et al. Removing the redundancy from randomised gene libraries. J. Mol. Biol., 331(5), 973–79, 2003. 99. Patrick, W.M., A.E. Firth, and J.M. Blackburn. User-friendly algorithms for estimating completeness and diversity in randomized protein-encoding libraries. Protein Eng., 16(6), 451–57, 2003. 100. Chockalingam, K., et al. Directed evolution of specific receptor-ligand pairs for use in the creation of gene switches. Proc. Natl. Acad. Sci. USA, 102(16), 5691–96, 2005. 101. Schmitzer, A.R., F. Lepine, and J.N. Pelletier. Combinatorial exploration of the catalytic site of a drug-resistant dihydrofolate reductase: creating alternative functional configurations. Protein Eng. Des. Sel., 17(11), 809–19, 2004. 102. Reetz, M.T., et al. Expanding the range of substrate acceptance of enzymes: combinatorial active-site saturation test. Angew Chem. Int. Ed. Engl., 44(27), 4192–96, 2005. 103. Kretz, K.A., et al. Gene site saturation mutagenesis: a comprehensive mutagenesis approach. Methods Enzymol., 388, 3–11, 2004. 104. Amin, N., et al. Construction of stabilized proteins by combinatorial consensus mutagenesis. Protein Eng. Des. Sel., 17(11), 787–93, 2004. 105. Voigt, C.A., et al. Protein building blocks preserved by recombination. Nat. Struct. Biol., 9(7), 553–38, 2002. 106. Saraf, M.C., et al. FamClash: a method for ranking the activity of engineered enzymes. Proc. Natl. Acad. Sci. USA, 101(12), 4142–47, 2004. 107. O’Maille, P.E., M. Bakhtina, and M.D. Tsai. Structure-based combinatorial protein engineering (SCOPE). J. Mol. Biol., 321(4), 677–91, 2002. 108. Hiraga, K. and F.H. Arnold. General method for sequence-independent site-directed chimeragenesis. J. Mol. Biol., 330(2), 287–96, 2003.
2-32
Evolutionary Tools in Metabolic Engineering
109. Voller, A., et al. A microplate method of enzyme-linked immunosorbent assay and its application to malaria. Bull World Health Organ., 51(2), 209–11, 1974. 110. Moore, J.C. and F.H. Arnold. Directed evolution of a para-nitrobenzyl esterase for aqueous-organic solvents. Nat. Biotechnol., 14(4), 458–67, 1996. 111. Bruno, J.G. and J.L. Kiel. In vitro selection of DNA aptamers to anthrax spores with electrochemiluminescence detection. Biosens. Bioelectron., 14(5), 457–64, 1999. 112. Bosworth, N. and P. Towers. Scintillation proximity assay. Nature, 341(6238), 167–68, 1989. 113. Pope, A.J., U.M. Haupts, and K.J. Moore. Homogeneous fluorescence readouts for miniaturized high-throughput screening: theory and practice. Drug Discovery Today, 4(8), 350–62, 1999. 114. Crameri, A., et al. Improved green fluorescent protein by molecular evolution using DNA shuffling. Nature Biotechnol., 14(3), 315–19, 1996. 115. Grunewald, J., et al. Fluorescence resonance energy transfer as a probe of peptide cyclization catalyzed by nonribosomal thioesterase domains. Chem. Biol., 12(8), 873–81, 2005. 116. Lueking, A., et al. Protein microarrays for gene expression and antibody screening. Anal. Biochem., 270(1), 103–11, 1999. 117. Ge, H. UPA, a universal protein array system for quantitative detection of protein-protein, proteinDNA, protein-RNA and protein-ligand interactions. Nucleic Acids Res., 28(2), e3, 2000. 118. MacBeath, G. and S.L. Schreiber. Printing proteins as microarrays for high-throughput function determination. Science, 289(5485), 1760–63, 2000. 119. Rowe, C.A., et al. Array biosensor for simultaneous identification of bacterial, viral, and protein analytes. Anal. Chem., 71(17), 3846–52, 1999. 120. Arenkov, P., et al. Protein microchips: use for immunoassay and enzymatic reactions. Anal. Biochem., 278(2), 123–31, 2000. 121. Schweitzer, B., et al. Inaugural article: immunoassays with rolling circle DNA amplification: a versatile platform for ultrasensitive antigen detection. Proc. Natl. Acad. Sci. USA, 97(18), 10113–19, 2000. 122. Zhu, H., et al. Analysis of yeast protein kinases using protein chips. Nat. Genet., 26(3), 283–89, 2000. 123. Tomatis, P.E., et al. Mimicking natural evolution in metallo-beta-lactamases through second-shell ligand mutations. Proc. Natl. Acad. Sci. USA, 102(39), 13761–66, 2005. 124. Waldo, G.S., et al. Rapid protein-folding assay using green fluorescent protein. Nature Biotechnol., 17(7), 691–95, 1999. 125. Aharoni, A., et al. Directed evolution of mammalian paraoxonases PON1 and PON3 for bacterial expression and catalytic specialization. Proc. Natl. Acad. Sci. USA, 101(2), 482–87, 2004. 126. van den Berg, S., et al. Improved solubility of TEV protease by directed evolution. J. Biotechnol., 121(3), 291–98, 2006. 127. McLoughlin, S.Y., et al. Growth of Escherichia coli coexpressing phosphotriesterase and glycerophosphodiester phosphodiesterase, using paraoxon as the sole phosphorus source. Appl. Environ. Microbiol., 70(1), 404–12, 2004. 128. Sakamoto, T., et al. Laboratory evolution of toluene dioxygenase to accept 4-picoline as a substrate. Appl. Environ. Microbiol., 67(9), 3882–87, 2001. 129. Drees, B.L. Progress and variations in two-hybrid and three-hybrid technologies. Curr. Opin. Chem. Biol., 3(1), 64–70, 1999. 130. McNabb, D.S. and L. Guarente. Genetic and biochemical probes for protein-protein interactions. Curr. Opin. Biotechnol., 7(5), 554–59, 1996. 131. Warbrick, E. Two’s company, three’s a crowd: the yeast two hybrid system for mapping molecular interactions. Structure, 5(1), 13–17, 1997. 132. Brachmann, R.K. and J.D. Boeke. Tag games in yeast: the two-hybrid system and beyond. Curr. Opin. Biotechnol., 8(5), 561–68, 1997. 133. Vidal, M. and P. Legrain. Yeast forward and reverse ‘n’-hybrid systems. Nucleic Acids Res., 27(4), 919–29, 1999.
Improving Protein Functions by Directed Evolution
2-33
134. Colas, P. and R. Brent. The impact of two-hybrid and related methods on biotechnology. Trends Biotechnol., 16(8), 355–63, 1998. 135. Lin, H. and V.W. Cornish. In vivo protein-protein interaction assays: beyond proteins. Angew Chem. Int. Ed. Engl., 40(5), 871–75, 2001. 136. Tawfik, D.S. and A.D. Griffiths. Man-made cell-like compartments for molecular evolution. Nat. Biotechnol., 16(7), 652–56, 1998. 137. Ghadessy, F.J., J.L. Ong, and P. Holliger. Directed evolution of polymerase function by compartmentalized self-replication. Proc. Natl. Acad. Sci. USA, 98(8), 4552–57, 2001. 138. Cohen, H.M., D.S. Tawfik, and A.D. Griffiths. Altering the sequence specificity of HaeIII methyltransferase by directed evolution using in vitro compartmentalization. Protein Eng. Des. Sel., 17(1), 3–11, 2004. 139. Bernath, K., S. Magdassi, and D.S. Tawfik. Directed evolution of protein inhibitors of DNA-nucleases by in vitro compartmentalization (IVC) and nano-droplet delivery. J. Mol. Biol., 345(5), 1015–26, 2005. 140. Levy, M., K.E. Griswold, and A.D. Ellington. Direct selection of trans-acting ligase ribozymes by in vitro compartmentalization. RNA, 11(10), 1555–62, 2005. 141. Sepp, A. and Y. Choo. Cell-free selection of zinc finger DNA-binding proteins using in vitro compartmentalization. J. Mol. Biol., 354(2), 212–19, 2005. 142. Smith, G.P. and V.A. Petrenko. Phage Display. Chem. Rev., 97(2), 391–410, 1997. 143. Sidhu, S.S. Phage display in pharmaceutical biotechnology. Curr. Opin. Biotechnol., 11(6), 610–16, 2000. 144. Rodi, D.J. and L. Makowski. Phage-display technology—finding a needle in a vast molecular haystack. Curr. Opin. Biotechnol., 10(1), 87–93, 1999. 145. Forrer, P., S. Jung, and A. Pluckthun. Beyond binding: using phage display to select for structure, folding and enzymatic activity in proteins. Curr. Opin. Struct. Biol., 9(4), 514–20, 1999. 146. Smith, G.P. Filamentous fusion phage: novel expression vectors that display cloned antigens on the virion surface. Science, 228(4705), 1315–17, 1985. 147. Sblattero, D. and A. Bradbury. Exploiting recombination in single bacteria to make large phage antibody libraries. Nat. Biotechnol., 18(1), 75–80, 2000. 148. Parmley, S.F. and G.P. Smith. Filamentous fusion phage cloning vectors for the study of epitopes and design of vaccines. Adv. Exp. Med. Biol., 251, 215–18, 1989. 149. Crameri, R. and M. Suter. Display of biologically active proteins on the surface of filamentous phages: a cDNA cloning system for selection of functional gene products linked to the genetic information responsible for their production. Gene, 137(1), 69–75, 1993. 150. Gao, C., et al. A cell-penetrating peptide from a novel pVII-pIX phage-displayed random peptide library. Bioorg. Med. Chem., 10(12), 4057–65, 2002. 151. Kwasnikowski, P., P. Kristensen, and W.T. Markiewicz. Multivalent display system on filamentous bacteriophage pVII minor coat protein. J. Immunol. Methods, 307(1–2), 135–43, 2005. 152. Deng, Q., et al. Screening for PreS specific binding ligands with a phage displayed peptides library. World J. Gastroenterol., 11(26), 4018–23, 2005. 153. Gao, C., et al. A method for the generation of combinatorial antibody libraries using pIX phage display. Proc. Natl. Acad. Sci. USA, 99(20), 12612–16, 2002. 154. Hoogenboom, H.R. and P. Chames. Natural and designer binding sites made by phage display technology. Immunol. Today, 21(8), 371–8, 2000. 155. Dall’Acqua, W. and P. Carter. Antibody engineering. Curr. Opin. Struct. Biol., 8(4), 443–50, 1998. 156. Yu, J., et al. A glycosidase antibody elicited against a chair-like transition state analog by in vitro immunization. Proc. Natl. Acad. Sci. USA, 95(6), 2880–84, 1998. 157. Baca, M., et al. Phage display of a catalytic antibody to optimize affinity for transition-state analog binding. Proc. Natl. Acad. Sci. USA, 94(19), 10063–68, 1997.
2-34
Evolutionary Tools in Metabolic Engineering
158. Janda, K.D., et al. Chemical selection for catalysis in combinatorial antibody libraries. Science, 275(5302), 945–48, 1997. 159. Choo, Y. and A. Klug. Toward a code for the interactions of zinc fingers with DNA: selection of randomized fingers displayed on phage. Proc. Natl. Acad. Sci. USA, 91(23), 11163–67, 1994. 160. Jamieson, A.C., S.H. Kim, and J.A. Wells. In vitro selection of zinc fingers with altered DNA-binding specificity. Biochemistry, 33(19), 5689–95, 1994. 161. Rebar, E.J. and C.O. Pabo. Zinc finger phage: affinity selection of fingers with new DNA-binding specificities. Science, 263(5147), 671–73, 1994. 162. Beste, G., et al. Small antibody-like proteins with prescribed ligand specificities derived from the lipocalin fold. Proc. Natl. Acad. Sci. USA, 96(5), 1898–903, 1999. 163. Schlehuber, S., G. Beste, and A. Skerra. A novel type of receptor protein, based on the lipocalin scaffold, with specificity for digoxigenin. J. Mol. Biol., 297(5), 1105–20, 2000. 164. Soumillion, P., et al. Selection of beta-lactamase on filamentous bacteriophage by catalytic activity. J. Mol. Biol., 237(4), 415–22, 1994. 165. Demartis, S., et al. A strategy for the isolation of catalytic activities from repertoires of enzymes displayed on phage. J. Mol. Biol., 286(2), 617–33, 1999. 166. Pedersen, H., et al. A method for directed evolution and functional cloning of enzymes. Proc. Natl. Acad. Sci. USA, 95(18), 10523–28, 1998. 167. Atwell, S. and J.A. Wells. Selection for improved subtiligases by phage display. Proc. Natl. Acad. Sci. USA, 96(17), 9497–502, 1999. 168. Ponsard, I., et al. Selection of metalloenzymes by catalytic activity using phage display and catalytic elution. Chembiochem, 2(4), 253–59, 2001. 169. Francisco, J.A., C.F. Earhart, and G. Georgiou. Transport and anchoring of beta-lactamase to the external surface of Escherichia coli. Proc. Natl. Acad. Sci. USA, 89(7), 2713–17, 1992. 170. Boder, E.T. and K.D. Wittrup. Yeast surface display for screening combinatorial polypeptide libraries. Nat. Biotechnol., 15(6), 553–57, 1997. 171. Ernst, W., et al. Baculovirus surface display: construction and screening of a eukaryotic epitope library. Nucleic Acids Res., 26(7), 1718–23, 1998. 172. Christmann, A., et al. The cystine knot of a squash-type protease inhibitor as a structural scaffold for Escherichia coli cell surface display of conformationally constrained peptides. Protein Eng., 12(9), 797–806, 1999. 173. Daugherty, P.S., et al. Antibody affinity maturation using bacterial surface display. Protein Eng., 11(9), 825–32, 1998. 174. Boder, E.T., K.S. Midelfort, and K.D. Wittrup. Directed evolution of antibody fragments with monovalent femtomolar antigen-binding affinity. Proc. Natl. Acad. Sci. USA, 97(20), 10701–5, 2000. 175. Kieke, M.C., et al. Selection of functional T cell receptor mutants from a yeast surface-display library. Proc. Natl. Acad. Sci. USA, 96(10), 5651–56, 1999. 176. Olsen, M.J., et al. Function-based isolation of novel enzymes from a large library. Nat. Biotechnol., 18(10), 1071–74, 2000. 177. Mattheakis, L.C., R.R. Bhatt, and W.J. Dower. An in vitro polysome display system for identifying ligands from very large peptide libraries. Proc. Natl. Acad. Sci. USA, 91(19), 9022–26, 1994. 178. Hanes, J. and A. Pluckthun. In vitro selection and evolution of functional proteins by using ribosome display. Proc. Natl. Acad. Sci. USA, 94(10), 4937–42, 1997. 179. Roberts, R.W. and J.W. Szostak. RNA–peptide fusions for the in vitro selection of peptides and proteins. Proc. Natl. Acad. Sci. USA, 94(23), 12297–302, 1997. 180. Nemoto, N., et al. In vitro virus: bonding of mRNA bearing puromycin at the 3’-terminal end to the C-terminal end of its encoded protein on the ribosome in vitro. FEBS Lett., 414(2), 405–8, 1997.
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181. Cull, M.G., J.F. Miller, and P.J. Schatz. Screening for receptor ligands using large libraries of peptides linked to the C terminus of the lac repressor. Proc. Natl. Acad. Sci. USA, 89(5), 1865–69, 1992. 182. Bloom, J.D., et al. Evolving strategies for enzyme engineering. Curr. Opin. Struct. Biol., 15(4), 447–52, 2005. 183. Parales, R.E. and J.L. Ditty. Laboratory evolution of catabolic enzymes and pathways. Curr. Opin. Biotechnol., 16(3), 315–25, 2005. 184. Hibbert, E.G., et al. Directed evolution of biocatalytic processes. Biomol. Eng., 22(1–3), 11–19, 2005. 185. Chica, R.A., N. Doucet, and J.N. Pelletier. Semi-rational approaches to engineering enzyme activity: combining the benefits of directed evolution and rational design. Curr. Opin. Biotechnol., 16(4), 378–84, 2005. 186. Powell, K.A., et al. Directed evolution and biocatalysis. Angew Chem. Int. Ed. Engl., 40(21), 3948–59, 2001. 187. Otten, L.G. and W.J. Quax. Directed evolution: selecting today’s biocatalysts. Biomol. Eng., 22(1–3), 1–9, 2005. 188. Hibbert, E.G. and P.A. Dalby. Directed evolution strategies for improved enzymatic performance. Microb. Cell. Fact, 4, 1–6, 2005. 189. Williams, G.J., A.S. Nelson, and A. Berry. Directed evolution of enzymes for biocatalysis and the life sciences. Cell. Mol. Life Sci., 61(24), 3034–46, 2004. 190. Qian, Z. and S. Lutz. Improving the catalytic activity of Candida antarctica lipase B by circular permutation. J. Am. Chem. Soc., 127(39), 13466–67, 2005. 191. Castle, L.A., et al. Discovery and directed evolution of a glyphosate tolerance gene. Science, 304(5674), 1151–54, 2004. 192. Fong, S., et al. Directed evolution of D-2-keto-3-deoxy-6-phosphogluconate aldolase to new variants for the efficient synthesis of D- and L-sugars. Chem. Biol., 7(11), 873–83, 2000. 193. Cho, C.M., A. Mulchandani, and W. Chen. Altering the substrate specificity of organophosphorus hydrolase for enhanced hydrolysis of chlorpyrifos. Appl. Environ. Microbiol., 70(8), 4681–85, 2004. 194. Raillard, S., et al. Novel enzyme activities and functional plasticity revealed by recombining highly homologous enzymes. Chem. Biol., 8(9), 891–98, 2001. 195. Antikainen, N.M., et al. Altering substrate specificity of phosphatidylcholine-preferring phospholipase C of Bacillus cereus by random mutagenesis of the headgroup binding site. Biochemistry, 42(6), 1603–10, 2003. 196. Rothman, S.C., M. Voorhies, and J.F. Kirsch. Directed evolution relieves product inhibition and confers in vivo function to a rationally designed tyrosine aminotransferase. Protein Sci., 13(3), 763–72, 2004. 197. Onuffer, J.J. and J.F. Kirsch. Redesign of the substrate specificity of Escherichia coli aspartate aminotransferase to that of Escherichia coli tyrosine aminotransferase by homology modeling and sitedirected mutagenesis. Protein Sci., 4(9), 1750–57, 1995. 198. Santoro, S.W., et al. An efficient system for the evolution of aminoacyl-tRNA synthetase specificity. Nat. Biotechnol., 20(10), 1044–48, 2002. 199. van Loo, B., et al. Directed evolution of epoxide hydrolase from A. radiobacter toward higher enantioselectivity by error-prone PCR and DNA shuffling. Chem. Biol., 11(7), 981–90, 2004. 200. Johannes, T.W., R.D. Woodyer, and H. Zhao. Directed evolution of a thermostable phosphite dehydrogenase for NAD(P)H regeneration. Appl. Environ. Microbiol., 71(10), 5728–34, 2005. 201. Giver, L., et al. Directed evolution of a thermostable esterase. Proc. Natl. Acad. Sci. USA, 95(22), 12809–13, 1998.
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202. Sriprapundh, D., C. Vieille, and J.G. Zeikus. Directed evolution of Thermotoga neapolitana xylose isomerase: high activity on glucose at low temperature and low pH. Protein Eng., 16(9), 683–90, 2003. 203. Hao, J. and A. Berry. A thermostable variant of fructose bisphosphate aldolase constructed by directed evolution also shows increased stability in organic solvents. Protein Eng. Des. Sel., 17(9), 689–97, 2004. 204. Ness, J.E., et al. DNA shuffling of subgenomic sequences of subtilisin. Nat. Biotechnol., 17(9), 893–96, 1999. 205. Dumas, D.P., et al. Purification and properties of the phosphotriesterase from Pseudomonas diminuta. J. Biol. Chem., 264(33), 19659–65, 1989. 206. Roodveldt, C. and D.S. Tawfik. Directed evolution of phosphotriesterase from Pseudomonas diminuta for heterologous expression in Escherichia coli results in stabilization of the metal-free state. Protein Eng. Des. Sel., 18(1), 51–58, 2005. 207. Vasserot, A.P., et al. Optimization of protein therapeutics by directed evolution. Drug Discov. Today, 8(3), 118–26, 2003. 208. Kurtzman, A.L., et al. Advances in directed protein evolution by recursive genetic recombination: applications to therapeutic proteins. Curr Opin Biotechnol, 12(4), 361–70, 2001. 209. Delagrave, S. and D.J. Murphy. In vitro evolution of proteins for drug development. Assay Drug Dev. Technol., 1(1 Pt 2), 187–98, 2003. 210. Marshall, S.H. DNA shuffling: induced molecular breeding to produce new generation long-lasting vaccines. Biotechnol. Adv., 20(3–4), 229–38, 2002. 211. Vellard, M. The enzyme as drug: application of enzymes as pharmaceuticals. Curr. Opin. Biotechnol., 14(4), 444–450, 2003. 212. Patten, P.A., R.J. Howard, and W.P. Stemmer. Applications of DNA shuffling to pharmaceuticals and vaccines. Curr. Opin. Biotechnol., 8(6), 724–33, 1997. 213. Ballinger, M.D., et al. Selection of heregulin variants having higher affinity for the ErbB3 receptor by monovalent phage display. J. Biol. Chem., 273(19), 11675–84, 1998. 214. Yang, J.H., et al. Enhancing the anticoagulant potency of soluble tissue factor mutants by increasing their affinity to factor VIIa. Thrombosis and Haemostasis, 87(3), 450–58, 2002. 215. Wu, H.R., et al. Stepwise in vitro affinity maturation of Vitaxin, an alpha(v)beta(3)-specific humanized mAb. Proc. Natl. Acad. Sci. USA, 95(11), 6037–42, 1998. 216. Pearce, K.H., Jr., et al. Growth hormone binding affinity for its receptor surpasses the requirements for cellular activity. Biochemistry, 38(1), 81–89, 1999. 217. Hanes, J., et al. Picomolar affinity antibodies from a fully synthetic naive library selected and evolved by ribosome display. Nat. Biotechnol., 18(12), 1287–92, 2000. 218. Blatt, L.M., et al. The biologic activity and molecular characterization of a novel synthetic interferon-alpha species, consensus interferon. J. Interferon Cytokine Res., 16(7), 489–99, 1996. 219. Chang, C.C.J., et al. Evolution of a cytokine using DNA family shuffling. Nat. Biotechnol., 17(8), 793–97, 1999. 220. Barak, Y., et al. New enzyme for reductive cancer chemotherapy, YieF, and its improvement by directed evolution. Mol. Cancer Ther., 5(1), 97–103, 2006. 221. Soong, N.W., et al. Molecular breeding of viruses. Nat. Genet., 25(4), 436–39, 2000. 222. Powell, S.K., et al. Breeding of retroviruses by DNA shuffling for improved stability and processing yields. Nat. Biotechnol., 18(12), 1279–82, 2000. 223. Maheshri, N., et al. Directed evolution of adeno-associated virus yields enhanced gene delivery vectors. Nat. Biotechnol., 24(2), 198–204, 2006. 224. Jensen, R.A. Enzyme recruitment in evolution of new function. Ann. Rev. Microbiol., 30, 409–25, 1976. 225. Copley, S.D. Enzymes with extra talents: moonlighting functions and catalytic promiscuity. Curr. Opin. Chem. Biol., 7(2), 265–72, 2003.
Improving Protein Functions by Directed Evolution
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226. James, L.C. and D.S. Tawfik. Conformational diversity and protein evolution—a 60-year-old hypothesis revisited. Trends Biochem. Sci., 28(7), 361–68, 2003. 227. Gerlt, J.A., P.C. Babbitt, and I. Rayment. Divergent evolution in the enolase superfamily: the interplay of mechanism and specificity. Arch. Biochem. Biophys., 433(1), 59–70, 2005. 228. O’Brien, P.J. and D. Herschlag. Catalytic promiscuity and the evolution of new enzymatic activities. Chem. Biol., 6(4), R91–R105, 1999. 229. Chen, Z. and H. Zhao. Rapid creation of a novel protein function by in vitro coevolution. J. Mol. Biol., 348(5), 1273–82, 2005. 230. Yoshikuni, Y., T.E. Ferrin, and J.D. Keasling. Designed divergent evolution of enzyme function. Nature, 440(7087), 1078–1082, 2006.
3 Engineering DNA and RNA Regulatory Regions through Random Mutagenesis and Screening 3.1
Introduction ��������������������������������������������������������������������������������������3-1
3.2 3.3
Promoters, Operators, and Enhancers...........................................3-2 Practical Approaches to Promoter Mutagenesis and Cloning ���������������������������������������������������������������������������������������3-3
Why Control Gene Expression? • Challenges • Outline of Chapter
Randomization of Promoter by Whole Plasmid PCR • Chromosomal Integration
Ichiro Matsumura Emory University School of Medicine
Sean A. Lynch Emory University
Justin P. Gallivan Emory University
3.4
Review of Engineered RNA-Based Regulatory Systems............ 3-8
3.5
rinciples and Protocols for Creating Synthetic P Riboswitches �����������������������������������������������������������������������������������3-12
Ligand-Independent RNA Regulatory Systems • Ligand-Dependent RNA Regulatory Systems
Aptamer Selection • Design Considerations for Creating Synthetic Riboswitches • Developing a High Throughput Screening Method— General Considerations • Creating Synthetic Riboswitches Using High Throughput Screening
3.6 Conclusions �������������������������������������������������������������������������������������3-16 References ���������������������������������������������������������������������������������������������������3-16
3.1 Introduction 3.1.1 Why Control Gene Expression? The general goal of metabolic engineering is to use living cells to synthesize one or more product molecules. Often, novel secondary metabolic pathways are expressed within recombinant cells. The yield of the desired product(s) is optimized by up-regulating the expression of foreign and native genes, and/ or down-regulating the expression of others. The secondary metabolic pathways of wild-type cells are usually regulated, thus enabling homeostasis and responses to internal and external stimuli. In contrast, human-made gene expression systems, if they are regulated at all, are generally designed to respond to an external stimulus (e.g. isopropyl-β-d-thiolgalactopyranoside, galactose, tetracycline). Until recently, few artificial expression systems enabled the “fine tuning” of transcription rates, and virtually none 3-1
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were designed for self-regulation. Here we review techniques that enable fine-tuning and the control of gene expression with novel effector molecules.
3.1.2 Challenges Metabolic engineers must precisely regulate the expression of multiple genes to optimize the productivity of the cell. Excessive expression of any gene can be toxic to the engineered cell, and could t herefore reduce yield. The expression of multiple genes encoding the enzymes of a metabolic pathway must be coordinated in order to avoid bottlenecks.1,2 This “fine tuning” problem is complicated by the context-dependence of complex molecular systems. All biological systems are complex, which means that interactions between their components create emergent properties. A promoter that produces moderate steady-state concentrations of one transcript may produce too much or too little of another. A chromosomal promoter that is tightly repressed can be leaky when cloned into a high copy number plasmid. A protein that is essential to one cell type can be toxic to another. Another general challenge is that of effector recognition. The rational recombination of effector-dependent repressors, activators, and operators can enable the regulation of any gene by a variety of naturally occurring inducers. One goal of metabolic engineering, however, is the biosynthesis of unnatural compounds. It would therefore be advantageous to fabricate gene regulation systems that are activated or repressed by any arbitrarily chosen compound. This will require the engineering of existing regulatory molecules3 or the selection of entirely new ones. These techniques can be applied to direct the evolution of novel metabolic pathways, or to assemble novel regulatory pathways (synthetic biology).
3.1.3 Outline of Chapter We begin with a review of promoters and promoter mutagenesis studies. High throughput assays to assess the strengths of wild-type and mutant promoters are also discussed. Many expression cassettes (promoter-gene) are genetically unstable when cloned into multicopy plasmids, so approaches to chromosome engineering are also considered. Next we review reports of riboswitches, which are liganddependent RNA sequences that control the expression of metabolic genes. The selection of aptamers, the ligand-binding component of riboswitches, is briefly reviewed, and practical approaches to riboswitch design and construction are discussed.
3.2 Promoters, Operators, and Enhancers Genes are regulated at several levels: transcription, posttranscriptional mRNA modification, translation and posttranslational protein modification. In eukaryotes, the localization of mRNAs and proteins also play important roles in gene regulation. Here we focus upon the regulation of transcription and translation in Eschericia coli and Acinetobacter baylyi sp. ADP1. Transcription is catalyzed by RNA polymerases, which bind upstream of the gene at sites called promoters. Differences in promoter sequence lead to vast differences in transcription initiation efficiencies (vide infra). The control of transcription is mediated by DNA-binding proteins called repressors, which prevent RNA polymerase from binding the promoter, and activators, which potentiate the interaction. Repressors bind elements outside the promoter called operators, while activators bind other (nonpromoter) sites called enhancers. The rational recombination of promoters, operators, and enhancers can switch the control of transcription to different inducers. For example, the chimeric tac promoter contains the strong trp promoter and the operator region of the lac promoter. The tac promoter is 5–10 fold stronger than the lac promoter, but is derepressed by isopropyl-β-d-thiogalactopyranoside (IPTG) in lacI + E. coli cells.4 Sequence comparisons showed that two regions −35 and −10 base pairs upstream of the RNA start site are conserved;5 stronger promoters tended to show greater similarity to a consensus sequence. The functions of sequences within the promoter were studied through random mutagenesis and high throughput clone analysis; 75% of all mutations that affect promoter function fall within these −35 or −10 regions.6
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Mutations in the −35 region are generally very deleterious with respect to transcription initiation, while those in the −10 region can have mild to strong effects.7 Site saturation mutagenesis of the −35 and −10 regions and genetic selection led to the identification of promoter sequences that are stronger than the consensus, at least within the context of particular promoters on particular multicopy plasmids.8,9 Many workers have applied random promoter mutagenesis and high throughput screening to optimize gene expression. The mutagenesis has been applied in three different ways with broadly similar results. We replace the six nucleotide −10 region with random sequence to achieve the widest range of promoter strengths within the smallest populations.10,11 Miksch and coworkers similarly “randomize” the nucleotides upstream of the −10 region.12,13 We presume that many of the mutant promoters will be weaker than the wild-type, and that this result is desirable for otherwise strong promoters on high copy number plasmids. Conversely, Jensen and his colleagues randomize the sequences outside the conserved −35 and −10 regions, presumably to achieve a more gradual variation of promoter strengths.14–17 Stephanopoulos and his colleagues use error-prone PCR to introduce random mutations throughout the promoter.18,19 The promoter libraries are usually cloned into plasmids upstream of reporter genes.11,12,16,19 The reporter genes associated with selected promoter variants can later be replaced with metabolic genes.19 In some cases, the gene is cloned next to a reporter gene so that both are expressed as a polycistronic message (promoter library-ribosome binding site-metabolic gene-ribosome binding site-reporter geneterminator).16,20 E. coli are transformed with these constructs, and those that exhibit the desired phenotypes (reporter gene expression levels) are identified in high throughput screens. The best screens are sensitive, high in throughput, broad in dynamic range and precise (such that isogenic clones exhibit little phenotypic variance). Candidate expression plasmids should always be re-evaluated (using the high throughput screen) several times to demonstrate that they consistently produce the desired phenotype. Multi-copy plasmids are most often employed because they enable the highest cloning efficiencies, and therefore the most diverse libraries. Unfortunately, they are also most prone to genetic instability. When plasmids do not segregate evenly between daughter cells, and when the constitutive or leaky expression of a gene imparts a growth disadvantage, individual cells within a genetically homogeneous population will express different amounts of protein.21 This problem can be overcome in several ways, each with its own advantages and disadvantages. Lower copy vectors, such as those based upon pACYC184, pCDF, pCOLA (Novagen), are usually more stable than ultra-high copy number pUC-type plasmids.22 Singlecopy BACs should be the most stable, but are infrequently used as expression vectors because their low purification yields make them inconvenient. Expression vectors with tightly regulated promoters are usually more stable than those that are leaky or constitutive. Another way to stabilize an expression construct (promoter-gene) is to integrate it into the bacterial chromosome. Alper et al. have randomly mutated promoters, cloned them into plasmids upstream of a reporter gene (GFP), and identified constitutive promoter variants that produce different amounts of GFP. The GFP genes were replaced with those encoding selected metabolic enzymes, and the new expression constructs were integrated into the E. coli chromosome using the Red/ET recombination system19 (Section 3.3.2.1). It might also be possible to integrate libraries of expression constructs (mutant promoter-reporter gene-selectable marker) into the chromosome using “gene gorging,” which utilizes self-linearizing plasmids as templates for recombination.23 Alternatively, expression construct libraries can be integrated into the chromosome of Acinetobacter baylyi sp. ADP1 (Section 3.3.2.2). This organism is naturally competent and integrates foreign DNA into its chromosome much more efficiently than E. coli.24
3.3 Practical Approaches to Promoter Mutagenesis and Cloning 3.3.1 Randomization of Promoter by Whole Plasmid PCR We can randomize the six nucleotides in the −10 region by: (a) synthesis of oligonucleotides encoding degenerate sequences at the 5ʹ ends, (b) long PCR amplification of the entire expression vector, (c) purification of the PCR product, (d) self-ligation to recircularize the plasmid, and (e) transformation of E. coli.
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PCRs often fail when the reaction conditions are too stringent (such that the primers never anneal) or too relaxed (primers or partially extended products anneal to the wrong sequences). The latter problems become pronounced when the sequence becomes very long, when the base composition is imbalanced (GC-rich or AT-rich) or when repeating sequences exist within the region to be amplified. In our hands, the well characterized long PCR protocol developed by Suzanne Cheng25–27 has proven most reliable for plasmid constructs up to 10 kb (amplification of products up to 20 kb is possible). The Tth/Vent polymerase mixture used in this protocol is modestly mutagenic28 and recombinagenic.29 These side-effects are acceptable, even desirable, for libraries that are later evaluated in high throughput screens. The newer high processivity DNA polymerase variants30 should reduce the mutation and recombination rates when the throughput of the screen is low. 3.3.1.1 Primer Design The primers are designed to amplify the entire expression vector EXCEPT the −10 region. One primer should also have six degenerate nucleotides (NNNNNN, where N is an equimolar mixture of G, A, T and C) at its 5ʹ end to replace those in the −10 region (Figure 3.1). For long PCR, Cheng and her colleagues recommend high Tm primers (62–70°C) and higher annealing temperatures to maximize specificity.25 In addition, we recommend the following design rules: (1) Avoid ending your oligonucleotide with a run of three or more identical bases. (2) Primers containing restriction sites at the 5ʹ end should also contain three to six additional nucleotides at the extreme 5ʹ end. (3) Always test your primers “in silico” using Amplify 1.2 (Apple Macintosh) or FastPCR (WindowsXP) to eliminate primer dimers and secondary binding sites. (4) Try to keep primers shorter than 45 nucleotides long to reduce the proportion of those containing deletions, and to eliminate the need for purification. 3.3.1.2 Polymerase Mixtures Applied Biosystems sells a mixture of Tth and Vent polymerases in its GeneAmp XL PCR kit, which comes with 3.3x PCR buffer. However, we choose to mix separately purchased enzyme preparations (13.33 units of Epicenter Tth DNA polymerase: 1 unit NEB Vent polymerase in 50% glycerol, 20 mM Tris pH 8.0, 100 mM KCl, 0.1 mM EDTA, 0.5% Tween 20). We also prepare our own 5x long PCR buffer (125 mM Tricine, pH 8.7, 425 mM K-acetate, 40% glycerol, 5% DMSO). Both the enzyme and buffer are stored at −20°C. 3.3.1.3 Hot Start Thermostable polymerases are often active at mesophilic temperatures, so the mixing of the PCR components at room temperature can lead to the extension of misannealed primers.31 We recommend incubation of the incomplete mixture to at least 80°C before the addition of 0.5 μL Tth/Vent DNA polymerase mixture (1 unit). The completed reaction should be mixed in the thermocycler by pipetting 10 μL up and down to ensure that glycerol-containing DNA polymerases do not settle on the bottom of the PCR tube. The PCR cycling program should then be initiated before the tube has a chance to cool. 5’-GTGTGGAATTGTGAGCGGATAACAATTT-3’ (-35 region) (-10 region) (f-Met) CAGGCTTTACATTTATGCTTCCGGCTCGTATGTTGTGTGGAATTGTGAGCGGATAACAATTTCACACAGGAAACAGCTATG GTCCGAAATGTAAATACGAAGGCCGAGCATACAACACACCTTAACACTCGCCTATTGTTAAAGTGTGTCCTTTGTCGATAC 3’-CGAAATGTAAATACGAAGGCCGAGCNNNNNN-5’
Figure 3.1 “Randomization” of the −10 region of the lac promoter. Both strands of the lac promoter sequence are shown. The primers above and below the double-stranded DNA can be used to PCR amplify the entire plasmid. Self-ligation of the PCR product produces a plasmid in which each nucleotide in the −10 region has been replaced by an equimolar mixture of G, A, T, and C.
Engineering DNA and RNA Regulatory Regions
3-5
3.3.1.4 Purification of Long PCR Product The purification of the product away from the polymerase is a cumbersome but necessary step for efficient ligation. The failure to completely eliminate the polymerase can lead to undesirable modification of the ends of the DNA, so we use a combination of proteinase K, silica-based DNA affinity chromatography, restriction digestions, and extractions with phenol and chloroform. Neither proteinase K nor restriction enzymes work well in XL PCR buffer, presumably due to the DMSO, so we first use the Promega Wizard PCR prep procedure to eliminate primers and exchange buffers. Any other silicabased DNA purification, protocols should work as well, but such methods apparently fail to completely eliminate thermostable polymerases bound to DNA ends.32 The partially purified PCR products are partially digested with DpnI. This restriction enzyme specifically cleaves methylated DNA from dam + E. coli strains, and should thus eliminate wild-type plasmid template. The thermostable polymerases are then eliminated by reaction with 0.5% SDS, 20 mM EDTA and 50 μg/mL proteinase K. These reactants are subsequently eliminated by phenol/chloroform extraction, ethanol precipitation, and a Qiagen PCR purification (again, any silica-based DNA affinity purification protocol will do). 3.3.1.5 Self-ligation of PCR Product The 5ʹ ends of PCR product are phosphorylated with T4 Polynucleotide Kinase, “polished” (made blunt) with the thermolabile T4 DNA polymerase, and self-ligated using T4 DNA ligase. The ligase is heat-killed, and the reaction is desalted.33 E. coli cells are transformed by chemical transformation34 or electroporation35 of DNA. The resulting colonies should be evaluated in high throughput screens (see Section 3.4).
3.3.2 Chromosomal Integration 3.3.2.1 Red/ET Recombination (E. coli) Homologous recombination rates are normally low in wild-type E. coli, but can be increased through the bacteriophage λ Red/ET system.36 The recBC nuclease normally catalyzes the degradation of double-stranded DNA, so this activity must be eliminated through mutation or inhibition. The expression of the Rac prophage recE and recT genes promote recombination. These genes encode a nuclease and single-stranded DNA binding protein, respectively, that in combination with endogenous polymerases, and ligases mediate homologous recombination. Mutations in sbcA lead to the over-expression of the recE and recT genes in a prophage, so recBC - sbcA- strains such as JC8679 are constitutively recombinogenic. Alternatively, inducible vectors for the over-production of the λ Red operon can be used to increase recombination rates in any strain. This operon encodes the Gam protein, which inhibits the recBC nuclease, and the Redα and Redβ proteins (functionally equivalent to the recE and recT proteins, respectively). High Red/ET recombination frequencies (>1,000 recombinants) have been reported,37 but we have been unable to produce libraries using this approach. It seems likely that we were expressing the Gam, Redα and Redβ proteins at suboptimal levels,38 and that recombination frequency is sequencedependent. One possible solution would be to apply the aforementioned “gene gorging” technique.23 Stephanopoulos and his colleagues use plasmids to identify candidate promoter variants and Red/ET recombination to integrate individual expression cassettes into the E. coli chromosome.19 3.3.2.1.1 Natural Transformation (Acinetobacter baylyi sp. ADP1) Acinetobacter baylyi sp. ADP1 is naturally recombinogenic, so we are currently integrating libraries of uncloned promoters into its chromosome. It is a Gram-negative γ-proteobacterium that possesses two advantages over E. coli: natural competence and efficient homologous recombination. Up to ~25% of the A. baylyi cells in a mid-logarithmic culture can be transformed simply by coincubation with DNA
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complementary to the chromosomal target site.39 The recombination efficiency is sufficient to generate libraries.40 A. baylyi cells grow in Luria Broth (LB), but three functional differences should be kept in mind. E. coli-specific promoters are generally less efficient in Acinetobacter, so broad-spectrum promoters, such as those derived from bacteriophage T5 and antibiotic resistance genes, should be employed. A. baylyi is naturally resistant to ampicillin and chloramphenicol, so markers that confer resistance to spectinomycin, kanamycin, and tetracycline should be employed instead.24 Finally, the effective transformation efficiency is directly correlated with the lengths of the homologous flanking sequences up to at least 5 kb.39 The integration of foreign DNA into A. baylyi ADP1 is straightforward, but the creation of recombinant integration constructs (A. baylyi chromosome sequence 1-promoter-ribosome binding site-gene-selectable marker-A. baylyi chromosome sequence 2) requires some effort. The design and fabrication of multi-component DNA sequences can be streamlined by the adoption of standard restriction enzyme sites. For example, the “BioBricks” standard41 is based upon modules flanked by standard restriction sites (EcoRI-NotI-XbaI-“content”-SpeI-NotI-PstI, http://openwetware.org/wiki/Synthetic_ Biology:BioBricks). The content DNA must not contain EcoRI, NotI, XbaI, SpeI, or PstI sites; these must be removed during the initial cloning of the BioBrick. Two BioBricks can be combined by digesting the donor with EcoRI and SpeI, digesting the recipient with EcoRI and XbaI and ligating them together (XbaI and SpeI cut to form compatible sticky ends). The construction of BioBrick versions of A. baylyi chromosome sequences and selectable markers (reported elsewhere) facilitate the combinatorial assembly of integration cassettes. 3.3.2.1.2 Primer Design for Overlap PCR Alternatively, integration constructs can be assembled in a series of long PCRs. Two nearby 5 kb fragments of the A. baylyi chromosome, hereafter called the left and right flanks, are amplified in the first two PCRs. The expression/selection cassette (T5 promoter-gene-selectable marker) is prepared by traditional cloning techniques and amplified in a third PCR. Finally, the left flank, expression/selection cassette and right flank are recombined in an overlap PCR (Figures 3.2 and 3.3). In this reaction, the strands of the original PCR products are denatured and reannealed. The “top” strand of one PCR product will 5´ 3´
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Figure 3.2 Overlap PCR. Two PCR products are represented as gray and black strands, respectively. The sequence at the extreme right side of the gray PCR product is identical to that of the left site of the black PCR product. When the two are combined in a single PCR, the top strand of the gray product can anneal to the bottom strand of the black product. The polymerase-catalyzed extension of this mega-primer dimer produces a single recombined product.
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5´ 3´
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cgc gt gg ca -3’ 5’-g gc ca tg ac tc cc ca at at t gattagtagatcacaatggcggagctgaattacattcccaacc tta at gt aa gg gt tg gc gc ac cg t -5’ cg gt a ctgaggggttataactaatcatctagtgttaccgcctcgact 3’-c Primer 2
Figure 3.3 Assembly of integration constructs by overlap PCR. (Top) The acinetobacter chromosome sequences (left and right “flanks”) can be fused to the expression construct (promoter-gene-marker) by overlap PCR. (Bottom) Composite primers are designed to amplify each segment, and to create complementary ends for overlap PCR.
anneal to the “bottom” strand of a different PCR product if their ends are complementary. Extension of the giant “primer dimer” creates a double-stranded fusion of the two products, which in turn can be amplified in a regular PCR with external primers.42 We presently use inexpensive and/or free software (Clone Manager, Sci Ed Central or Vector NTI, Invitrogen; Molecular BioComputing Suite;43 FastPCR, http://www.biocenter.helsinki.fi/bi/Programs/ fastpcr.htm) to design the primers. First, we identify a chromsomal target in the A. baylyi genome sequence (http://www.cns.fr/externe/English/Projets/Projet_DY/DY.html); we generally target apparently nonessential sites such as putative antibiotic resistance markers or prophage regions.44 Second, we copy and paste the desired left flank, expression/selection cassette and right flank sequences together in a word processing program. Third, we use sequence analysis software to visualize the top and bottom strands of the virtual construct. Fourth, the primers are copied and pasted from the sequence analysis software back into the word processing file. In general, the primers for the amplification of the flanks should be long because the Acinetobacter chromosome is AT rich.44 Primer 1 is identical to the top strand (Tm = 80°C according to the formula Suggs et al.45) of the left flank (Figure 3.3). Primer 2 is identical to the bottom strand at the intersection of the left flank (Tm = 80°C) and the expression/selection cassette (Tm = 40°C). Primer 3 is the top strand at the intersection of the left flank (Tm = 40°C) and the expression/selection cassette (Tm = 80°C). The top strand of the first PCR product (left flank) is thus complementary (Tm = 80°C) to the bottom strand of the second PCR product (expression/selection cassette). 3.3.2.1.3 Long PCR and Overlap PCR The three long PCRs are similar in procedure to the whole plasmid PCR, using the Tth/Vent DNA polymerase mixture (Section 3.3.2.1.4). Whole A. baylyi ADP1 cells can be used as the template for amplification of the left flank (Figure 3.3, primers 1 and 2) and the right flank (primers 5 and 6). We are currently using expression vectors with a T5 promoter, the gene of interest and a spectinomycin resistance marker (PT5-pCDF) as the template for amplification of the expression/selection cassette (primers 3 and 4). We usually gel purify the PCR products to eliminate the templates and PCR sideproducts that could interfere with the overlap PCR. The overlap PCR is simply another long PCR using the three products (left flank, expression/selection cassette, right flank) as templates and the outside
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oligonucleotides (1 and 6) as primers. The extension of annealed templates is likely catalyzed by the Vent polymerase, which has strong strand displacement activity (www.neb.com). 3.3.2.1.4 Transformation of Acinetobacter baylyi sp. Strain ADP1 A. baylyi ADP1 is generally propagated in LB medium at 30°C.24 It is an obligate aerobe,44 and we find that significant agitation (small liquid culture volumes, large vessels, >250 rpm shakers) is necessary to achieve maximum growth rates. Saturated cultures are diluted 1/15-fold (20 μl of culture into 300 μl of media) and shaken vigorously for 2 hours at 30°C. The overlap PCR product (~1 μg in 1–50 μL ) is added directly from the thermal cycler to the mid-logarithmic culture. The culture should be shaken for an additional 3 hours at 30°C and spread on LB agar plates supplemented with spectinomycin and a histochemical marker (such as 5-bromo-4-chloro-4-indolyl-β-d-glucuronide, X-gluc, for the detection beta-glucuronidase activity). Colonies form during overnight incubations at 37°C.
3.4 Review of Engineered RNA-Based Regulatory Systems While RNA occupies the center position of the central dogma of molecular biology, it was long thought to play only a passive role in the transmission of information from DNA to proteins. Over the past few decades, RNA has been shown to play myriad functional roles in the cell, ranging from catalysis to gene regulation. The versatility of RNA derives from its ability to fold upon itself to form complex structures, as well as its ability to interact with DNA, proteins, small molecules, and other RNAs. Because many RNA-based regulatory systems including microRNAs46 and small interfering RNAs47 have been reviewed extensively elsewhere, we will limit the discussion primarily to prokaryotic systems with a special emphasis on systems that can be engineered to regulate gene expression in a metabolite-dependent fashion.
3.4.1 Ligand-Independent RNA Regulatory Systems One of the most straightforward methods to fine tune the expression of prokaryotic genes in a ligandindependent fashion is through the introduction of engineered RNA secondary structure. RNA secondary structure can influence gene expression through a variety of mechanisms, including regulating mRNA stability and modulating the rate of translation initiation. de Smit and van Duin have extensively studied the effects of engineered secondary structure near the ribosome binding site (RBS) on the rates of translation initiation of bacterial mRNA transcripts.48–52 Specifically, they demonstrated that increasing the stability of a hairpin structure near the RBS reduced the translation rate of an mRNA in a manner proportional to the strength of the hairpin. Originally this behavior was explained using a thermodynamic model in which mRNA structures with paired and unpaired RBSs were in rapid equilibrium with one another, but only the fraction of the mRNA pool with unpaired RBSs could initiate translation. In the thermodynamic model, increasing the strength of the engineered secondary structure reduced the population of free RBSs and reduced translation proportionally.48,49 Although this model satisfactorily explained a variety of data, a further analysis based on more recent observations of the kinetics of RNA folding led to a refinement of the model, but did not alter the fundamental relationship between mRNA secondary structure near the RBS and translational efficiency.52 Living systems also take advantage of secondary structure near the RBS to regulate translation in a temperature-sensitive fashion, whereby increases in temperature can “melt” the secondary structure and increase translation.53–59 A beautiful example of translational thermoregulation occurs in the expression of the rpoH gene in E. coli.55 The rpoH gene encodes the σ32-transcription factor protein, which activates the expression of several genes involved in the heat-shock response. An increase in temperature from 30°C to 42°C reduces the amount of base-pairing near the RBS of the rpoH mRNA and increases the expression of the σ32-transcription factor 3.5-fold.55 While a variety of other genes are regulated in a similar fashion, the significance of thermosensing is often less clear.
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In addition to affecting the rate of translation initiation, mRNA secondary structure can also affect transcript stability. RNA hairpins located either 5ʹ- or 3ʹ- to a coding region can often produce increased levels of protein expression by protecting a transcript from degradation by ribonucleases. Smolke and Keasling have demonstrated how introduction of mRNA secondary structure can control the levels of expression from poly-cistronic mRNAs for metabolic engineering applications.60–64 In a very nice example,62 the introduction of a ribonuclease cleavage site between two coding regions of a transcript resulted in two secondary transcripts upon RNA cleavage. The expression levels of each of the secondary transcripts could be tuned independently by engineering the stability of RNA hairpins that flanked the coding region. Additional modifications in secondary structure near the RBS could further regulate the protein expression levels. More recently, a number of groups have developed trans-acting riboregulators that conditionally regulate gene expression.65–67 The groups of Ptashne68 and Liu69 both adapted a yeast-three hybrid approach to transcriptional activation by incorporating a protein-binding domain into a library of trans-acting RNA sequences and selecting for variants that could recruit the yeast transcriptional machinery and activate the transcription of an essential gene. Buskirk and Liu extended this approach by including an RNA aptamer sequence that recognized the small molecule tetramethylrosamine in the library and screening for ligand-dependent activation of the reporter gene.69 Collins and coworkers65 have engineered cis-acting RNA sequences that repress translation of a reporter gene in bacteria by pairing to regions near the RBS; addition of a complementary RNA sequence in trans disrupts the base pairing near the RBS and activates translation of the reporter gene. Bayer and Smolke66 extended this approach by including a theophylline-binding RNA aptamer in the trans-acting sequence, which made gene expression theophylline-dependent in yeast. These and other systems that involve trans-acting RNAs have been reviewed extensively elsewhere,67 so we will focus primarily on ligand-dependent RNA-based regulatory systems that act in cis.
3.4.2 Ligand-Dependent RNA Regulatory Systems A primary goal of metabolic engineering is to enable the efficient multi-step synthesis of compounds from inexpensive feedstocks. The efficiency of any synthetic process depends on appropriately balancing the concentrations of the reactants, intermediates, products, and catalysts. This is particularly true in metabolic processes where intermediates may be toxic, act as inhibitors, or be diverted to other pathways if not acted on readily. Furthermore, protein synthesis places an additional burden on the cell and ill-timed production of an enzyme can lead to an inefficient process. Ideally, all components in a multi-step synthesis would be present at the right concentration and at the right time. Such control thus requires the ability to dynamically tune the level of enzyme activity, for example by increasing activity when a substrate is plentiful, and decreasing activity when a substrate is scarce, or when the immediate downstream product is abundant. Living systems have evolved a variety of mechanisms to regulate metabolism based on the demands of the cell. Many of these mechanisms rely on ligand-induced allostery, where binding of a metabolite regulates function. Allosteric systems operate in a variety of contexts ranging from the regulation of the activity of a single metabolic enzyme, such as the regulation of aspartate transcarbamoylase by both ATP and CTP, to the transcriptional control of entire metabolic pathways, exemplified by the (allo) lactose-induced transcription of genes within the lac operon. In addition to these “textbook examples” in which small molecules interact with proteins to regulate metabolism, it is now recognized that small molecules can interact directly with mRNA sequences to regulate metabolism through a mechanism now known as riboswitch control.70–79 3.4.2.1 Natural Riboswitches Riboswitches are cis-acting RNA elements that regulate gene expression in a ligand-dependent fashion.70–81 Riboswitches are comprised of an aptamer domain, which recognizes the ligand, and an
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“expression platform” that regulates gene expression in one of several ways. As of this writing (mid2006), riboswitches that couple ligand binding to changes in transcription,72, 82–84 translation,70,80 or mRNA structure85 have been discovered in a variety of organisms. As will be discussed in Section 3.4.2.2, engineered riboswitches that operate via mechanisms such as alternative splicing are known,86 and it is not unreasonable to suspect that living systems may also employ such mechanisms to regulate gene expression. Furthermore, while most of the known natural riboswitches have been discovered in prokaryotes, a eukaryotic riboswitch from A. thaliana has been identified and its structure determined by crystallography,87 and it is extremely likely that other riboswitches will be discovered in a variety of organisms and pathways. 3.4.2.1.1 Mechanisms of Natural Riboswitches: Regulation of Translation Initiation As discussed in Section 3.4.1, one of the simplest mechanisms to regulate the initiation of translation is to introduce secondary structure near the RBS of an mRNA. Many riboswitches, particularly those found in Gram-negative bacteria, couple metabolite binding to changes in RNA secondary structure to regulate translation initiation. Most often, these riboswitches act to decrease the rate of translation initiation upon ligand binding by occluding access to the RBS. This mechanism of action is observed for riboswitches that regulate the expression of thiamine pyrophosphate (TPP) synthesis80 and coenzyme-B12 transport in E. coli,81 as well as the expression of a putative riboflavin transporter in B. subtilis.70 Because engineered riboswitches can activate translation in a ligand-dependent fashion (Section 3.4.2.2),88,89 the tendency of natural riboswitches to repress translation in a metabolite-dependent fashion likely results from evolutionary pressure to reduce metabolite synthesis when these compounds are plentiful, and not from any fundamental limitation of the mechanism. 3.4.2.1.2 Mechanisms of Natural Riboswitches: Regulation of Transcription In addition to regulating translation, riboswitches also employ small molecule-RNA interactions to control the fate of transcription. Like riboswitches that control translation, those that regulate transcription most often decrease gene expression in a metabolite-dependent fashion. Typically, the mechanism for this decrease involves a ligand-mediated early termination of transcription. In the absence of a ligand, transcription occurs normally, however, the presence of a ligand during transcription can favor the formation of an intrinsic terminator structure that leads to the premature dissociation of the RNA polymerase and a nonfunctional RNA transcript. Ligand-dependent transcriptional attenuation has been implicated as a mechanism of action for riboswitches that respond to S-adenosylmethionine,71,76,90–93 flavin mononucleotide, lysine,73,94 TPP,80 and guanine.72,83 While most riboswitches down-regulate gene expression, some such as the adenine riboswitch,84,95 up-regulate gene expression in a metabolite-dependent fashion. The adenine riboswitch is particularly interesting because the aptamer core that recognizes the ligand is nearly identical to that found in the guanine riboswitch.* However, unlike the guanine riboswitch, ligand binding to the adenine riboswitch increases the level of transcription. This activation is believed to occur by the disruption of a transcriptional terminator upon adenine binding, which leads to the production of a full-length mRNA transcript.84 Mechanistic studies performed by Wickiser et al.95 suggest that this process may be kinetically controlled, and that the “decision” of whether to terminate transcription is likely made before equilibrium binding is achieved. Thus, although full-length riboswitch transcripts produced in vitro may be able to equilibrate between two structures upon ligand binding, it is unclear whether this is relevant in vivo.95 Although many of the finer mechanistic details of riboswitch function must still be established, it is already clear that riboswitches employ a variety of control mechanisms to balance the thermodynamic and kinetic considerations of metabolite binding with other concurrent cell processes such as the rates of transcription, translation, and mRNA decay. * A single mutation in the guanine riboswitch (C to U) changes the ligand specificity from guanine to adenine.84
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3.4.2.1.3 Mechanisms of Natural Riboswitches: Regulation of RNA Cleavage and Splicing Recent studies have shown that riboswitches can also act as metabolite-sensitive ribozymes that cleave an mRNA upon ligand binding.85,96 The first example of this mechanism was observed for the glmS riboswitch, which controls the expression of an enzyme that synthesizes glucosamine-6-phosphate (GlcN6P) in B. subtilis and other Gram-positive bacteria.85,96 Binding of GlcN6P to a region in the 5ʹ-UTR of the glmS mRNA reduces the half-life of a self-cleavage reaction from 4 hours to 15 seconds in vitro.85 In vivo, the addition of ligand reduces the expression of a reporter construct downstream of the riboswitch possibly by creating a nonfunctional transcript or by influencing the rate of RNA degradation, though the exact mechanism remains to be established. Finally, although there are examples of riboswitches that appear to effect ligand-dependent RNA splicing in eukaryotes,97 engineered RNA-based regulation systems based on this mechanism are not generally applicable in prokaryotes, which do not perform RNA splicing. From this short discussion, it should be clear that nature uses a variety of RNA-based mechanisms to regulate metabolic pathways in a ligand-dependent fashion. However, the explosion of new gene sequences and rapid advances in protein engineering now enable the heterologous expression of entirely novel metabolic pathways. As such, there is an increasing demand for new ligand-dependent gene control systems. In the following sections, we will describe how powerful in vitro selection techniques coupled with high throughput screening methods enable the creation of riboswitches that respond to entirely new compounds. Such riboswitches may become useful tools for controlling engineered metabolic pathways. 3.4.2.2 Synthetic Riboswitches The development of engineered ligand-regulated RNA expression systems actually presaged the confirmation of the natural riboswitch control mechanism by several years. In 1998, Werstuck and Green demonstrated that an RNA aptamer sequence that had been selected to bind to a small molecule in vitro could be cloned upstream of a reporter gene and repress protein translation in a small molecule-dependent fashion in eukaryotic cells.98 In this original study, aptamers that recognized the antibiotics kanamycin and tobramycin, and the Hoescht dyes 33258 and 33342 were all able to repress the translation of a reporter gene in a dose-dependent fashion when cloned upstream of the initiation codon.98 Subsequent studies have demonstrated that aptamers that bind to malachite green,99 biotin,100 theophylline,100 and tetracycline101–104 can also be cloned into the 5ʹ-untranslated region of a gene to repress translation in a small molecule-dependent fashion in eukaryotic translation systems. However, because translation initiation in eukaryotes differs substantially from that in prokaryotes, many of the strategies used to create these ligand-regulated systems, such as the use of multiple aptamer sequences,98,100 are not generally applicable to producing riboswitches that function in prokaryotic systems. Though most of the known riboswitches have been discovered in prokaryotes, there are relatively few examples of engineered riboswitches that control gene expression in these organisms. In 2004, Suess et al.89 and our group88 both reported riboswitches that controlled bacterial gene expression in a theophylline-dependent fashion. The Suess group created their riboswitch using a rational approach, in which the theophylline aptamer was cloned upstream of the RBS of a xylose repressor gene in B. subtilis.89 In the absence of theophylline, the RNA was proposed to adopt a structure that prevented access to the RBS. Addition of theophylline was proposed to cause a slipping of the helix near the RBS that granted access to the ribosome and induced the translation of the xylose repressor. Expression levels of the xylose repressor protein were determined by monitoring the theophylline-dependent repression of a separate reporter gene under the control of two xyl operator sequences.89 In our earlier study,88 we attempted to create a theophylline responsive riboswitch by cloning a t heophylline-binding aptamer into several locations of the 5ʹ-UTR of a lacZ reporter gene. Based on the earlier work of de Smit and van Duin (Section 3.4.1 and Refs 48–51), we hypothesized that simply having a theophylline-binding aptamer near the RBS might be sufficient to reduce the rate of translational
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initiation upon theophylline binding, as ligand binding would strongly stabilize the aptamer structure. To our surprise, we discovered that theophylline activated gene expression in a dose-dependent fashion. Although we were able to establish that the riboswitch acted post-transcriptionally and appeared to be under thermodynamic control, we were unable to determine the precise mechanism of activation.* Nevertheless, we were able to show that this riboswitch could be used to distinguish between two closely related compounds, which is important in metabolic engineering applications that require discriminating between substrates and products. In addition, the riboswitch could effect the ligand-dependent expression of different genes, including an antibiotic resistance gene that coupled cell growth to the presence of a nonmetabolite, which suggested that cell growth could eventually be tied to the product of a biotransformation. While early studies of engineered and synthetic riboswitches have been promising, for riboswitches to become a standard feature of the metabolic engineer’s toolbox, it will be critical to develop methods to rapidly produce riboswitches that respond to new compounds in a selective, tunable, and predictable fashion. In the following sections, we will describe techniques that our lab has developed to create riboswitches with improved performance characteristics, which have also resulted in a better understanding of how riboswitches function.
3.5 Principles and Protocols for Creating Synthetic Riboswitches Fundamentally, a riboswitch couples the binding of a ligand by an aptamer to a change in gene expression. To create a riboswitch that can effectively regulate an engineered metabolic pathway, it is important to discover aptamers that selectively recognize a desired compound while discriminating against closely related compounds (e.g. precursors, or downstream products).
3.5.1 Aptamer Selection The fundamental principles of aptamer selection were reduced to practice in the early 1990s by the groups of Jack Szostak105 and Larry Gold,106 who demonstrated that RNA sequences that bind tightly and specifically to small molecule-targets could be discovered by subjecting libraries of random RNA sequences to iterative rounds of affinity chromatography and amplification. Over the past several years, these “in vitro selection” or “SELEX” methods have been refined in a variety of ways,107 including the introduction of counter-selection techniques that enable the isolation of aptamers that discriminate between related structures,108 and techniques based on allosteric ribozymes, which enable the selection of aptamers without affinity chromatography.109,110 While there are a variety of techniques to select aptamers, all benefit from the remarkable ability of RNA to recognize small molecules, the ability of RNA libraries to cover complete areas of sequence space,† and the ability of PCR to amplify rare sequences from large populations. This has allowed the discovery of aptamers that recognize a wide variety of compounds, including amino acids, alkaloids, and polyketides, and discriminate on the basis of subtle modifications in structure or stereochemistry.107 A variety of methods for aptamer selection are now available.105–107,109,110 Some of the best-established selection methods use affinity chromatography to partition binding sequences from nonbinders.106,108 In these selections, the target is typically immobilized to a solid support through either covalent or noncovalent interactions. Libraries of random RNA sequences are passed over the support to discover sequences that bind to the target. Extensive washing can eliminate nonspecific binding sequences, and washing with structurally related ligands can be used to eliminate nonselective binders. Repeated * We thus considered this riboswitch to be “synthetic” rather than “engineered”, as engineering typically implies the use of design principles that result in a desired outcome, even if the specific details remain murky. † A fully randomized library of 25 nucleotides (N = 4 25 ≈ 1015 possibilities) can be sampled in a single experiment. 25
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rounds of amplification and selection can yield tight binding aptamers that often display impressive selectivities.108 One of the challenges of using affinity chromatography-based selection methods to isolate aptamers that recognize small molecules is that the target molecule must be attached to the support. Thus, targets must often be decorated with functionality that may not only require additional synthesis, but may also eliminate some potential binding modes to the RNA. An alternative method for selecting aptamers is through allosteric selection,109,110 where a randomized RNA library is inserted into a region of a self-cleaving RNA enzyme, such as the hammerhead ribozyme. Using iterative rounds of gel-based selection, where sequences that cleave in the presence of a desired ligand are amplified, and those that cleave in its absence are discarded, it is possible to discover new ligand-dependent aptamers. An advantage of allosteric selection is that the ligands are used in solution in an unmodified form, which eliminates the need for attaching them to a solid support.* Detailed protocols for allosteric selection have been published recently.111
3.5.2 Design Considerations for Creating Synthetic Riboswitches Though riboswitches likely represent a very early form of genetic control, they are a relatively recent addition to the genetic engineer’s toolbox. Thus, it is too early to evaluate the promise of any single strategy for creating new riboswitches, and certainly new strategies (both rational and evolution-based) will emerge in the coming years as our understanding of riboswitch mechanisms improves. That said, it is worth briefly discussing the history of engineered riboswitches to see where we are and where metabolic engineers may want to go. As described in Section 3.4.2.2, most synthetic riboswitches have been created in eukaryotes by cloning one or more copies of an aptamer in the 5ʹ-UTR of a gene. Most of these switches are thought to repress protein translation by introducing an additional barrier to translation upon ligand binding which promotes the dissociation of the ribosome and the termination of protein synthesis. In prokaryotes, there are fewer examples of synthetic riboswitches, and the two best known examples88,89 respond to the same molecule (theophylline), but were created using different strategies and act via different mechanisms. What is clear, however, is that all of these riboswitches were created from pre-existing aptamers that were selected for another purpose. Thus, one the one hand, all of these examples have benefited from the availability of these aptamer structures and the extensive body of knowledge about their structures and binding mechanisms. On the other hand, all of these studies have been constrained to the same set of building blocks that were in most cases, selected for their ability to bind ligands tightly and specifically, and not to control gene expression. To control new metabolic pathways using synthetic riboswitches, it is clear that aptamers that recognize the various metabolites will have to be selected. As discussed in Section 3.5.1, there are several ways of selecting aptamers, and all begin with extremely large combinatorial libraries that are typically winnowed down to a manageable number of clones that can be studied individually. Historically, aptamers have been selected primarily for their ability to bind tightly, however, riboswitches must not only bind ligands, but also transmit signals, and it is not at all clear whether the tightest binding aptamers will make the best riboswitches. Given these uncertainties and the lack of general riboswitch design principles, we currently favor high-throughput combinatorial screening approaches to riboswitch discovery. We have found that combinatorial approaches not only readily identify robustperforming synthetic riboswitches, but also that the sequences of these new constructs provide significant insights into riboswitch function that we hope will provide general principles for riboswitch design.
* Another potential advantage of allosteric selection is that aptamers produced in this manner have already been selected for their ability to participate in context where ligand binding (thermodynamics) and switching (kinetics) are both important. Thus, it is tempting to speculate that these aptamers may be particularly suitable for incorporation into a riboswitches, where ligand-mediated allostery is critical for function, though this notion remains to be tested.
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3.5.3 Developing a High Throughput Screening Method—General Considerations To discover new riboswitches or promoters from large libraries requires an efficient high throughput screening or selection method. Since engineered promoters and riboswitches ultimately control gene expression, a wide variety of well-characterized reporter gene systems are available. In vivo selections, in which cell survival depends on the controlled expression of an essential gene (e.g. antibiotic resistance, production of an essential metabolite), are straightforward to perform and extremely highthroughput.* However, selections can be relatively insensitive to small changes in activity and operate in a narrow dynamic range. Fluorescence activated cell sorting (FACS), in which the activity of a fluorescent reporter gene (e.g. GFP) is determined on a cell-by-cell basis is also extremely high throughput, and offers improvements in sensitivity and dynamic range over selections. However, FACS systems require significant investments in equipment and maintenance. Spectrophotometric assays performed in microtiterplates offer high sensitivity and broad dynamic range, but are more modest in throughput than genetic selection or FACS. To screen for the activity of new promoters (Section 3.3), we generally rely upon histochemical assays performed on agar plates, such as those based on the hydrolysis of 5-bromo-4-chloro-3-indolyl-β-dgalactopyranoside (X-gal) by the enzyme β-galactosidase. This approach is precise, sensitive, relatively high in throughput (~10,000 colonies/plate) and very affordable. We generally employ lower throughput microplate screens to quantitatively evaluate candidate clones.
3.5.4 Creating Synthetic Riboswitches Using High Throughput Screening Screening for new riboswitches adds additional complexity because it is important to assay potential switches in both the presence and absence of a desired ligand. Ideally, a riboswitch will display an extremely low level of gene expression in the absence of the ligand, and display a large, reproducible increase in expression upon addition of the ligand.† To discover new synthetic riboswitches with large dynamic ranges, we developed a two-stage semi-automated screen in which diverse libraries of sequences are first assayed for their abilities to repress protein expression in the absence of the ligand.112 Candidate sequences from the first screen are then assayed for their ability to activate protein expression in the presence of the desired ligand. 3.5.4.1 Creating Libraries of Candidate Riboswitches Controlling the initiation of translation is likely the most straightforward mechanism of engineering riboswitch function because it only requires the formation of structures that affect ribosome binding, and it does not require the engineering of a terminator sequence. As such, we typically adopt a strategy in which an aptamer produced by in vitro selection (Section 3.5.1) is cloned at a location four to ten bases 5ʹ- to the RBS of a reporter gene.‡112 We then create libraries in which the region between the aptamer and RBS is randomized using PCR. An advantage of working with RNA libraries is that the number of potential variants is relatively small (N4 = 4 4 = 16; N8 = 4 8 = 65,536) and nearly all sequence space can be sampled using a small number of screens. Furthermore, candidate riboswitches identified by screening can be sequenced and their folds predicted using established * Often, selections are limited only by the transformation efficiency of the organism, which for E. coli approaches 109 transformants per experiment. † Although for clarity we will refer only to screens for ligand-dependent activation of expression, one can just as easily screen for riboswitches that decrease gene expression in a ligand-dependent fashion. ‡ We typically use β-galactosidase, though we have successfully used GFP, dsRED, β-glucuronidase, and chloramphenicol acetyl transferase as reporters.
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methods113 to generate mechanistic hypotheses112 that may also suggest rational strategies for further improvements. 3.5.4.2 Sample Screening Protocol Here we describe a sample screening protocol that was used to identify synthetic riboswitches that activate protein translation in the presence of theophylline.112 However, we have used this protocol with appropriate modifications to discover riboswitches that respond to a variety of ligands beginning with other aptamers and using other reporter genes (Gallivan et al., unpublished). The number of transformants needed in the initial screening will depend on the size of the library; using a β-galactosidase reporter gene, we typically screen from 1,000 colonies (N4 library) to 100,000 colonies (N8 library) on agar plates to identify candidates.
1. Transform E. coli with library of constructs by electroporation and grow overnight on LB-agar plates supplemented with antibiotic and X-gal. 2. Pick the 96 whitest colonies from the plate and inoculate them in a 96-well plate with each well containing 200 μL of LB supplemented with antibiotic. (This assay can be performed repeatedly using multiple 96-well plates if there are a large number of white colonies.) 3. Incubate the 96-well plate overnight with shaking (37°C, 180 rpm). 4. The following day, inoculate four 96-well plates (two sets of two) with 5 μL of the overnight culture. Each well of the first set of plates should contain 200 μL of LB supplemented with an appropriate antibiotic. Wells in the second set of plates should contain 200 μL of LB supplemented with both antibiotic and ligand (500 μL). 5. Incubate the four 96-well plates for approximately 2.5 hours at 37°C with shaking (210 rpm) until appropriate OD600 is reached (0.3–0.5 when corrected to a 1-cm path length cuvette). Record OD600 for each culture. 6. Lyse cultures with 21 μL of Pop Culture® solution from Novagen (10:1, Pop Culture : lysozyme (4 U/mL)) and mix by pipetting up and down. Allow cultures to rest for 5 min at room temperature to ensure complete lysis. 7. In a new 96-well plate, combine 15 µL of lysed culture with 132.25 μL of Z-buffer (60 mM Na 2HPO4, 40 mM NaH2PO4, 10 mM KCl, 1 mM MgSO4, 50 mM β-mercaptoethanol, pH = 7.0). 8. Add 29 μL of o-nitrophenylgalactoside (ONPG, 4 mg/mL in 100 mM NaH2PO4) to each well containing lysed culture and buffer. Note the time of substrate addition. Allow ONPG to hydrolyze for approximately 20 min or until faint yellow color is observed. 9. Quench the reaction by adding 75 μL of Na 2CO3 (1 M) to each reaction. Record the hydrolysis time and the OD420 for each well measured. 10. Calculate Miller Units for each well using the following formula: Miller Units = OD420/(OD600 × hydrolysis time × relative volume of cell lysate). 11. Compare the ratios of the Miller units for cultures grown in the presence of ligand to those grown in the absence of ligand (the “activation ratio”) to identify functional switches.
3.5.4.2.1 Notes on Screening Protocol We identify candidate switches as clones that show an activation ratio >2.0 in two separate determinations.112 Because ratios of signal to background can be misleading in cases where both the signal and the background are low, we eliminate candidates that do not display a minimum activity in the presence of ligand (an OD420 ≥ 0.04). As a final check, visually inspect the data for aberrations, such as cultures that grew especially slowly or quickly (as represented by OD600), or for cultures with dramatically different results between the two plates. Candidate switches can be subcultured from the original overnight culture and assayed individually following the protocol of Jain and Belasco.114
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3.5.4.3 Individual Assay for Riboswitch Activity
1. Using 5 μL of selected culture, inoculate 5 mL of LB supplemented with the appropriate antibiotic. Incubate overnight at 37°C with shaking. 2. The following day, using 5 μL of fresh overnight culture, inoculate two new tubes. One tube should contain 1.5 mL of LB supplemented with the appropriate antibiotic. The second tube should contain LB supplemented with the appropriate antibiotic plus ligand. 3. Incubate cultures at 37°C with shaking until an OD600 of 0.3–0.5 is reached. Place tubes on ice for 20 min. Measure OD600. 4. To a separate glass culture tube, add 200 μL of culture to 800 μL of Z-buffer (60 mM Na 2HPO4, 40 mM NaH2PO4, 10 mM KCl, 1 mM MgSO4, 50 mM β-mercaptoethanol, pH = 7.0), 20 μL of chloroform and 10 μL of 0.1% sodium dodecyl sulfate. Vortex for 15 seconds. Incubate tubes for 5 min in a 28°C water bath for 5 min. 5. Add 200 μL of ONPG and allow substrate to hydrolyze until a faint yellow color is observed. 6. Quench reaction with 500 μL of Na 2CO3 (1 M). Measure OD420 of reaction measure and calculate Miller Units using the above equation.
3.6 Conclusions The general goal of metabolic engineering is to modify cells to optimize the biosynthesis of desired molecules. The heterologous expression of metabolic enzymes must be coordinated in order to avoid potentially toxic bottlenecks. Gene expression must be mediated by precursor and product molecules, or by other regulators. Nature manages biological complexity by generating sequence diversity and selecting haplotypes associated with the highest organismal fitness. We emulate natural selection by (1) randomizing promoters and riboswitch linkers, (2) cloning the resulting libraries into plasmids or integrating them into bacterial chromosomes, and (3) identifying clones that produce appropriate levels of a coupled reporter gene. These techniques can easily be generalized, and should allow the metabolic engineer to focus on the problem of designing novel pathways.
References 1. Koffas, M. and Stephanopoulos, G. Strain improvement by metabolic engineering: lysine production as a case study for systems biology. Curr. Opin. Biotechnol., 2005, 16, 361–16. 2. Parikh, M. R., Greene, D. N., Woods, K. K., and Matsumura, I. Directed evolution of RuBisCO hypermorphs through genetic selection in engineered E. coli. Protein. Eng. Des. Sel., 2006, 19, 113–19. 3. Schwimmer, L. J., Rohatgi, P., Azizi, B., Seley, K. L., and Doyle, D. F. Creation and discovery of ligand-receptor pairs for transcriptional control with small molecules. Proc. Natl. Acad. Sci. USA, 2004, 101, 14707–12. 4. de Boer, H. A., Comstock, L. J., and Vasser, M. The tac promoter: a functional hybrid derived from the trp and lac promoters. Proc. Natl. Acad. Sci. USA, 1983, 80, 21–25. 5. Harley, C. B. and Reynolds, R. P. Analysis of E. coli promoter sequences. Nucleic Acids. Res., 1987, 15, 2343–61. 6. Rosenberg, M. and Court, D. Regulatory sequences involved in the promotion and termination of RNA transcription. Ann. Rev. Genet., 1979, 13, 319–53. 7. Youderian, P., Bouvier, S., and Susskind, M. M. Sequence determinants of promoter activity. Cell, 1982, 30, 843–53. 8. Horwitz, M. S. and Loeb, L. A. Promoters selected from random DNA sequences. Proc. Natl. Acad. Sci. USA, 1986, 83, 7405–09. 9. Oliphant, A. R. and Struhl, K. Defining the consensus sequences of E. coli promoter elements by random selection. Nucleic Acids Res., 1988, 16, 7673–83.
Engineering DNA and RNA Regulatory Regions
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10. Matsumura, I. and Rowe, L. A. Whole plasmid mutagenic PCR for directed protein evolution. Biomol. Eng., 2005, 22, 73–79. 11. Matsumura, I., Olsen, M. J., and Ellington, A. D. Optimization of heterologous gene expression for in vitro evolution. Biotechniques, 2001, 30, 474–76. 12. Miksch, G., Bettenworth, F., Friehs, K., and Flaschel, E. The sequence upstream of the -10 consensus sequence modulates the strength and induction time of stationary-phase promoters in Escherichia coli. Appl. Microbiol. Biotechnol., 2005, 69, 312–20. 13. Miksch, G., Bettenworth, F., Friehs, K., Flaschel, E., Saalbach, A., Twellmann, T., and Nattkemper, T. W. Libraries of synthetic stationary-phase and stress promoters as a tool for fine-tuning of expression of recombinant proteins in Escherichia coli. J. Biotechnol., 2005, 120, 25–37. 14. Hammer, K., Mijakovic, I., and Jensen, P. R. Synthetic promoter libraries—tuning of gene expression. Trends. Biotechnol., 2006, 24, 53–55. 15. Jensen, P. R. and Hammer, K. Artificial promoters for metabolic optimization. Biotechnol. Bioeng., 1998, 58, 191–95. 16. Solem, C. and Jensen, P. R. Modulation of gene expression made easy. Appl. Environ. Microbiol., 2002, 68, 2397–403. 17. Jensen, P. R. and Hammer, K. The sequence of spacers between the consensus sequences modulates the strength of prokaryotic promoters. Appl. Environ. Microbiol., 1998, 64, 82–87. 18. Fischer, C. R., Alper, H., Nevoigt, E., Jensen, K. L., and Stephanopoulos, G. Response to Hammer et al.: Tuning genetic control—importance of thorough promoter characterization versus generating promoter diversity. Trends Biotechnol., 2006, 24, 55–56. 19. Alper, H., Fischer, C., Nevoigt, E., and Stephanopoulos, G. Tuning genetic control through promoter engineering. Proc. Natl. Acad. Sci. USA, 2005, 102, 12678–83. 20. Jorgensen, C. M., Hammer, K., Jensen, P. R., and Martinussen, J. Expression of the pyrG gene determines the pool sizes of CTP and dCTP in Lactococcus lactis. Eur. J. Biochem., 2004, 271, 2438–45. 21. Keasling, J. D. Gene-expression tools for the metabolic engineering of bacteria. Trends. Biotechnol., 1999, 17, 452–60. 22. Vieira, J. and Messing, J. The pUC plasmids, an M13mp7-derived system for insertion mutagenesis and sequencing with synthetic universal primers. Gene, 1982, 19, 259–68. 23. Herring, C. D., Glasner, J. D., and Blattner, F. R. Gene replacement without selection: regulated suppression of amber mutations in Escherichia coli. Gene, 2003, 311, 153–63. 24. Metzgar, D., Bacher, J. M., Pezo, V., Reader, J., Doring, V., Schimmel, P., Marliere, P., and de Crecy-Lagard, V. Acinetobacter sp. ADP1: an ideal model organism for genetic analysis and genome engineering. Nucleic Acids. Res., 2004, 32, 5780–90. 25. Cheng, S. Longer PCR amplifications. In PCR Strategies, Innis, M. A., Gelfland, D. H., and Sninsky, J. J., Eds. Academic Press: San Diego, CA, 1995, 313–24. 26. Cheng, S., Chang, S. Y., Gravitt, P., and Respess, R. Long PCR. Nature, 1994, 369, 684–85. 27. Cheng, S., Fockler, C., Barnes, W. M., and Higuchi, R. Effective amplification of long targets from cloned inserts and human genomic DNA. Proc. Natl. Acad. Sci. USA, 1994, 91, 5695–99. 28. Stewart, A. C., Gravitt, P. E., Cheng, S., and Wheeler, C. M. Generation of entire human papillomavirus genomes by long PCR: frequency of errors produced during amplification. Genome Res., 1995, 5, 79–88. 29. Shafikhani, S. Factors affecting PCR-mediated recombination. Environ. Microbiol., 2002, 4, 482–86. 30. Wang, Y., Prosen, D. E., Mei, L., Sullivan, J. C., Finney, M., and Vander Horn, P. B. A novel strategy to engineer DNA polymerases for enhanced processivity and improved performance in vitro. Nucleic Acids Res., 2004, 32, 1197–207. 31. D’Aquila, R. T., Bechtel, L. J., Videler, J. A., Eron, J. J., Gorczyca, P., and Kaplan, J. C. Maximizing sensitivity and specificity of PCR by pre-amplification heating. Nucleic Acids Res., 1991, 19, 3749. 32. Wybranietz, W. A. and Lauer, U. Distinct combination of purification methods dramatically improves cohesive-end subcloning of PCR products. Biotechniques, 1998, 24, 578–80.
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Evolutionary Tools in Metabolic Engineering
33. Thomas, M. R. Simple, effective cleanup of DNA ligation reactions prior to electro-transformation of E. coli. Biotechniques, 1994, 16, 988–90. 34. Inoue, H., Nojima, H., and Okayama, H. High efficiency transformation of Escherichia coli with plasmids. Gene, 1990, 96, 23–28. 35. Dower, W. J., Miller, J. F., and Ragsdale, C. W. High efficiency transformation of E. coli by high voltage electroporation. Nucleic Acids Res., 1988, 16, 6127–45. 36. Muyrers, J. P., Zhang, Y., and Stewart, A. F. Techniques: Recombinogenic engineering—new options for cloning and manipulating DNA. Trends Biochem. Sci., 2001, 26, 325–31. 37. Zhang, Y., Muyrers, J. P., Testa, G., and Stewart, A. F. DNA cloning by homologous recombination in Escherichia coli. Nat. Biotechnol., 2000, 18, 1314–17. 38. Nakayama, M. and Ohara, O. Improvement of recombination efficiency by mutation of red proteins. Biotechniques, 2005, 38, 917–24. 39. Palmen, R. and Hellingwerf, K. J. Uptake and processing of DNA by Acinetobacter calcoaceticus—a review. Gene, 1997, 192, 179–90. 40. Buchan, A. and Ornston, L. N. When coupled to natural transformation in Acinetobacter sp. strain ADP1, PCR mutagenesis is made less random by mismatch repair. Appl. Environ. Microbiol., 2005, 71, 7610–12. 41. Endy, D. Foundations for engineering biology. Nature, 2005, 438, 449–53. 42. Temesgen, B. and Eschrich, K. Simplified method for ligase-free cloning of PCR products. Biotechniques, 1996, 21, 828–832. 43. Muller, P. Y., Studer, E., and Miserez, A. R. Molecular Biocomputing Suite: a word processor add-in for the analysis and manipulation of nucleic acid and protein sequence data. Biotechniques, 2001, 31, 1306, 1308, 1310–13. 44. Barbe, V., Vallenet, D., Fonknechten, N., Kreimeyer, A., Oztas, S., Labarre, L., Cruveiller, S., Robert, C., Duprat, S., Wincker, P., Ornston, L. N., Weissenbach, J., Marliere, P., Cohen, G. N., and Medigue, C. Unique features revealed by the genome sequence of Acinetobacter sp. ADP1, a versatile and naturally transformation competent bacterium. Nucleic Acids Res., 2004, 32, 5766–79. 45. Suggs, S. V., Hirose, T., Miyake, T., Kawashima, E. H., Johnson, M. J., Itakura, K., and Wallace, R. B. In Developmental Biology Using Purified Genes (ICN-UCLA Symposia on Molecular and Cellular Biology), Brown, D. D. and Fox, C. F., Eds. Academic Press: New York, NY, 1981, Vol. 23, 683–93. 46. He, L. and Hannon, G. J. Micro RNAs: Small RNAs with a big role in gene regulation. Nat. Rev. Genet., 2004, 5, 522–31. 47. Matzke, M. A. and Birchler, J. A. RNAi-mediated pathways in the nucleus. Nat. Rev. Genet., 2005, 6, 24–35. 48. de Smit, M. H. and van Duin, J. Secondary structure of the ribosome binding site determines translational efficiency: A quantitative analysis. Proc. Natl. Acad. Sci. USA, 1990, 87, 7668–72. 49. de Smit, M. H. and van Duin, J. Control of translation by mRNA secondary structure in Escherichia coli: A quantitative analysis of literature data. J. Mol. Biol., 1994, 244, 144–50. 50. Klovins, J., Tsareva, N. A., de Smit, M. H., Berzins, V., and van Duin, J. Rapid evolution of translational control mechanisms in RNA genomes. J. Mol. Biol., 1997, 265, 372–84. 51. de Smit, M. H. and van Duin, J. Translational initiation on structured messengers: Another role for the shine-dalgarno interaction. J. Mol. Biol., 1994, 235, 173–84. 52. de Smit, M. H. and van Duin, J. Translational standby sites: How ribosomes may deal with the rapid folding kinetics of mRNA. J. Mol. Biol., 2003, 331, 737–43. 53. Narberhaus, F., Kaser, R., Nocker, A. and Hennecke, H. A novel DNA element that controls bacterial heat shock gene expression. Mol. Microbiol., 1998, 28, 315–23. 54. Mathews, D. H., Sabina, J., Zuker, M., and Turner, D. H. Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J. Mol. Biol., 1999, 288, 911–940. 55. Morita, M. T., Tanaka, Y., Kodama, T. S., Kyogoku, Y., Yanagi, H., and Yura, T. Translational induction of heat shock transcription factor sigma32: evidence for a built-in RNA thermosensor. Genes Dev., 1999, 13, 655–65.
Engineering DNA and RNA Regulatory Regions
3-19
56. Johansson, J., Mandin, P., Renzoni, A., Chiaruttini, C., Springer, M., and Cossart, P. An RNA thermosensor controls expression of virulence genes in Listeria monocytogenes. Cell, 2002, 110, 551–61. 57. Waldminghaus, T., Fippinger, A., Alfsmann, J., and Narberhaus, F. RNA thermometers are common in alpha- and gamma-proteobacteria. Biol. Chem., 2005, 386, 1279–86. 58. Chowdhury, S., Maris, C., Allain, F. H., and Narberhaus, F. Molecular basis for temperature sensing by an RNA thermometer. EMBO J., 2006, 25, 2487–97. 59. Narberhaus, F., Waldminighaus, T., and Chowdhury, S. RNA thermometers. FEMS Microbiol. Rev., 2006, 30, 3–16. 60. Smolke, C. D., Carrier, T. A., and Keasling, J. D. Coordinated, differential expression of two genes through directed mRNA cleavage and stabilization by secondary structures. Appl. Environ. Microbiol., 2000, 66, 5399–5405. 61. Smolke, C. D., Khlebnikov, A., and Keasling, J. D. Effects of transcription induction homogeneity and transcript stability on expression of two genes in a constructed operon. Appl. Environ. Microbiol., 2001, 57, 689–96. 62. Smolke, C. D., Martin, V. J. J., and Keasling, J. D. Controlling the metabolic flux through the carotenoid pathway using directed mRNA processing and stabilization. Metabolic Eng., 2001, 3, 313–321. 63. Smolke, C. D. and Keasling, J. D. Effect of gene location, mRNA secondary structures, and RNase sites on expression of two genes in an engineered operon. Biotechnol. Bioeng., 2002, 80, 762–76. 64. Smolke, C. D. and Keasling, J. D. Effect of copy number and mRNA processing and stabilization on transcript and protein levels from an engineered dual-gene operon. Biotechnol. Bioeng., 2002, 78, 412–24. 65. Isaacs, F. J., Dwyer, D. J., Ding, C., Pervouchine, D. D., Cantor, C. R., and Collins, J. J. Engineered riboregulators enable post-transcriptional control of gene expression. Nat. Biotech., 2004, 22, 841–847. 66. Bayer, T. S. and Smolke, C. D. Programmable ligand-controlled riboregulators of eukaryotic gene expression. Nat. Biotech., 2005, 23, 337–343. 67. Isaacs, F. J., Dwyer, D. J., and Collins, J. J. RNA synthetic biology. Nat. Biotechnol., 2006, 24, 545–54. 68. Saha, S., Ansari, A. Z., Jarrell, K. A., and Ptashne, M. RNA sequences that work as transcriptional activating regions. Nucleic Acids Res., 2003, 31, 1565–70. 69. Buskirk, A. R., Landrigan, A., and Liu, D. R. Engineering a ligand-dependent RNA transcriptional activator. Chem. Biol., 2004, 11, 1157–1163. 70. Winkler, W. C., Cohen-Chalamish, S., and Breaker, R. R. An mRNA structure that controls gene expression by binding FMN. Proc. Natl. Acad. Sci. USA, 2002, 99, 15908–13. 71. Epshtein, V., Mironov, A. S., and Nudler, E. The riboswitch-mediated control of sulfur metabolism in bacteria. Proc. Natl. Acad. Sci. USA, 2003, 100, 5052–56. 72. Mandal, M., Boese, B., Barrick, J. E., Winkler, W. C., and Breaker, R. R. Riboswitches control fundamental biochemical pathways in Bacillus subtilis and other bacteria. Cell, 2003, 113, 577–86. 73. Rodionov, D. A., Vitreschak, A. G., Mironov, A. A., and Gelfand, M. S. Regulation of lysine biosynthesis and transport genes in bacteria: yet another RNA riboswitch? Nucleic Acids Res., 2003, 31, 6748–57. 74. Sudarsan, N., Barrick, J. E., and Breaker, R. R. Metabolite-binding RNA domains are present in the genes of eukaryotes. RNA, 2003, 9, 644–47. 75. Wade C. and Winkler, R. R. B. Genetic control by metabolite-binding riboswitches. ChemBioChem, 2003, 4, 1024–32. 76. Winkler, W. C., Nahvi, A., Sudarsan, N., Barrick, J. E., and Breaker, R. R. An mRNA structure that controls gene expression by binding S-adenosylmethionine. Nat. Struct. Biol., 2003, 10, 701–7. 77. Breaker, R. R. Complex riboswitches. Mol. Biol. Cell, 2004, 15, 357a–58a. 78. Mandal, M. and Breaker, R. R. Gene regulation in riboswitches. Nat. Rev. Mol. Cell. Biol., 2004, 5, 451–463. 79. Nudler, E. and Mironov, A. S. The riboswitch control of bacterial metabolism. Trends Biochem. Sci., 2004, 29, 11–17. 80. Winkler, W., Nahvi, A., and Breaker, R. R. Thiamine derivatives bind messenger RNAs directly to regulate bacterial gene expression. Nature, 2002, 419, 952–956.
3-20
Evolutionary Tools in Metabolic Engineering
81. Nahvi, A., Sudarsan, N., Ebert, M. S., Zou, X., Brown, K. L., and Breaker, R. R. Genetic control by a metabolite binding mRNA. Chem. Biol., 2002, 9, 1043–1049. 82. Serganov, A., Yuan, Y. R., Pikovskaya, O., Polonskaia, A., Malinina, L., Phan, A. T., Hobartner, C., Micura, R., Breaker, R. R., and Patel, D. J. Structural basis for discriminative regulation of gene expression by adenine- and guanine-sensing mRNAs. Chem. Biol., 2004, 11, 1729–41. 83. Batey, R. T., Gilbert, S. D., and Montange, R. K. Structure of a natural guanine-responsive riboswitch complexed with the metabolite hypoxanthine. Nature, 2004, 432, 411–15. 84. Mandal, M. and Breaker, R. R. Adenine riboswitches and gene activation by disruption of a transcription terminator. Nat. Struct. Mol. Biol., 2004, 11, 29–35. 85. Winkler, W. C., Nahvi, A., Roth, A., Collins, J. A., and Breaker, R. R. Control of gene expression by a natural metabolite-responsive ribozyme. Nature, 2004, 428, 281–86. 86. Kim, D.-S., Gusti, V., Pillai, S. G., and Gaur, R. K. An artificial riboswitch for controlling pre-mRNA splicing. RNA, 2005, 11, 1667–77. 87. Thore, S., Leibundgut, M., and Ban, N. N. Structure of the eukaryotic thiamine pyrophosphate riboswitch with its regulatory ligand. Science, 2006, 312, 1208–11. 88. Desai, S. K. and Gallivan, J. P. Genetic screens and selections for small molecules based on a synthetic riboswitch that activates protein translation. J. Am. Chem. Soc., 2004, 126, 13247–54. 89. Suess, B., Fink, B., Berens, C., Stentz, R., and Hillen, W. A. Theophylline responsive riboswitch based on helix slipping controls gene expression in vivo. Nucleic Acids Res., 2004, 32, 1610–14. 90. Corbino, K. A., Barrick, J. E., Lim, J., Welz, R., Tucker, B. J., Puskarz, I., Mandal, M., Rudnick, N. D., and Breaker, R. R. Evidence for a second class of S-adenosylmethionine riboswitches and other regulatory RNA motifs in alpha-proteobacteria. Genome Biol., 2005, 6, R70. 91. Fuchs, R. T., Grundy, F. J., and Henkin, T. M. The S-MK box is a new SAM-binding RNA for translational regulation of SAM synthetase. Nat. Struct. Mol. Biol., 2006, 13, 226–33. 92. McDaniel, B. A., Grundy, F. J., Kurlekar, V. P., Tomsic, J., and Henkin, T. M. Identification of a mutation in the Bacillus subtilis S-adenosylmethionine synthetase gene that results in derepression of S-box gene expression. J. Bacteriol., 2006, 188, 3674–81. 93. Montange, R. K. and Batey, R. T. Structure of the S-adenosylmethionine riboswitch regulatory mRNA element. Nature, 2006, 441, 1172–75. 94. Sudarsan, N., Wickiser, J. K., Nakamura, S., Ebert, M. S., and Breaker, R. R. An mRNA structure in bacteria that controls gene expression by binding lysine. Genes Dev., 2003, 17, 2688–97. 95. Wickiser, J. K., Cheah, M. T., Breaker, R. R., and Crothers, D. M. The kinetics of ligand binding by an adenine-sensing riboswitch. Biochemistry, 2005, 44, 13404–14. 96. McCarthy, T. J., Plog, M. A., Floy, S. A., Jansen, J. A., Soukup, J. K., and Soukup, G. A. Ligand requirements for glmS ribozyme self-cleavage. Chem. Biol., 2005, 12, 1221–26. 97. Kubodera, T., Watanabe, M., Yoshiuchi, K., Yamashita, N., Nishimura, A., Nakai, S., Gomi, K., and Hanamoto, H. Thiamine-regulated gene expression of Aspergillus oryzae thiA requires splicing of the intron containing a riboswitch-like domain in the 5’-UTR. FEBS Lett., 2003, 555, 516–20. 98. Werstuck, G. and Green, M. R. Controlling gene expression in living cells through small moleculeRNA interactions. Science, 1998, 282, 296–98. 99. Grate, D. and Wilson, C. Inducible regulation of the S. cerevisiae cell cycle mediated by an RNA aptamer-ligand complex. Bioorg. Med. Chem., 2001, 9, 2565–2570. 100. Harvey, I., Garneau, P., and Pelletier, J. Inhibition of translation by RNA-small molecule interactions. RNA, 2002, 8, 452–63. 101. Hanson, S., Berthelot, K., Fink, B., McCarthy, J. E., and Suess, B. Tetracycline-aptamer-mediated translational regulation in yeast. Mol. Microbiol., 2003, 49, 1627–37. 102. Suess, B., Hanson, S., Berens, C., Fink, B., Schroeder, R., and Hillen, W. Conditional gene expression by controlling translation with tetracycline-binding aptamers. Nucleic Acids Res., 2003, 31, 1853–58. 103. Hanson, S., Bauer, G., Fink, B., and Suess, B. Molecular analysis of a synthetic tetracycline-binding riboswitch. RNA, 2005, 11, 503–11.
Engineering DNA and RNA Regulatory Regions
3-21
104. Muller, M., Weigand, J. E., Weichenrieder, O., and Suess, B. Thermodynamic characterization of an engineered tetracycline-binding riboswitch. Nucleic Acids Res., 2006, 34, 2607–17. 105. Ellington, A. D. and Szostak, J. W. In vitro selection of RNA molecules that bind specific ligands. Nature, 1990, 346, 818–22. 106. Tuerk, C. and Gold, L. Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase. Science, 1990, 249, 505–10. 107. Wilson, D. S. and Szostak, J. W. In vitro selection of functional nucleic acids. Annu. Rev. Biochem., 1999, 68, 611–47. 108. Jenison, R. D., Gill, S. C., Pardi, A., and Polisky, B. High-resolution molecular discrimination by RNA. Science, 1994, 263, 1425–29. 109. Koizumi, M., Soukup, G. A., Kerr, J. N., and Breaker, R. R. Allosteric selection of ribozymes that respond to the second messengers cGMP and cAMP. Nat. Struct. Biol., 1999, 6, 1062–71. 110. Robertson, M. P., and Ellington, A. D. In vitro selection of an allosteric ribozyme that transduces analytes to amplicons. Nat. Biotechnol., 1999, 17, 62–66. 111. Roth, A. and Breaker, R. R. Selection in vitro of allosteric ribozymes. In Methods in Molecular Biology: Ribozymes and siRNA Protocols, 2nd ed. Sioud, M., Ed. Humana Press: Totowa, NJ, 2004, Vol. 252, 145–64. 112. Lynch, S. A., Desai, S. K., Sajja, H. K., and Gallivan, J. P. A high throughput screen for synthetic riboswitches reveals mechanistic insights into their function. Chem. Biol., 2007, 14, 173–184. 113. Zuker, M. Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res., 2003, 31, 3406–15. 114. Jain, C. and Belasco, J. C. Rapid genetic analysis of RNA-protein interactions by translational repression in Escherichia coli. Methods Enzymol., 2000, 318, 309–32.
4 Evolving Pathways and Genomes for the Production of Natural and Novel Compounds
Ethan T. Johnson University of Minnesota
Claudia SchmidtDannert University of Minnesota
4.1 Introduction ��������������������������������������������������������������������������������������4-1 4.2 Initial Pathway Design and Integration into Host Metabolic Network �����������������������������������������������������������������4-2 4.3 Optimization of Metabolic Enzyme Levels Using Evolutionary Design ������������������������������������������������������������ 4-4 4.4 Evolving Metabolic Pathways for the Production of New Compounds.................................................... 4-6 4.5 Addressing Complex Traits Using Evolutionary Methods........ 4-8 4.6 Screening Technologies �����������������������������������������������������������������4-10 4.7 Conclusions and Future Directions..............................................4-12 References ���������������������������������������������������������������������������������������������������4-13
4.1 Introduction Microorganisms synthesize many useful compounds and humans have exploited their metabolic versatility for thousands of years. The discovery of the first microbial fermentation processes in the nineteenth century was the starting point for the development of industrial biotechnology. Biotechnologies introduced during the first half of the twentieth century allowed the mass production of citric acid and penicillin through the fermentation of sugars by specific strains of molds. At the beginning, microbial production of molecules depended entirely upon the exploitation of native biosynthetic pathways in microbial organisms. Microbial strains producing a desired compound were first identified by screening and production levels were then optimized using classical strain improvement strategies involving chemical mutagenesis and selection. While strain selection and screening sometimes did yield impressive increases in production levels, the improvements were not the result of directed engineering and the mechanism of the improvements remained obscure. As recombinant DNA technologies developed, engineering of specific metabolic traits became possible. By the 1980s it was possible to insert or delete enzymes in a microbial genome, and in one of the first examples of this technology the production of cellular carbon from methanol for animal feed was enhanced by replacing the glutamate synthase gene from the bacterium Methylophilus methylotrophus with a glutamate dehydrogenase from E. coli.1 With the sophistication of genetic methods for the manipulation of microbial cells and with increasing knowledge about metabolic processes on a molecular level, it became feasible to not only manipulate metabolic pathways for increased production levels in a native host, but also to combine genes from 4-1
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Evolutionary Tools in Metabolic Engineering
different organisms into new multistep pathways for the production of natural and novel compounds. The genomics era provides an ever increasing resource of DNA sequence to be used for “bioprospecting”, to produce new classes of compounds, industrial fine chemicals and antibiotics and pharmaceuticals.2–5 Manipulation of biosynthetic activities through rational protein design methods facilitated further increases in production levels, enabled synthesis of desired pathway intermediates and new molecules. However, this approach relies on the availability of structural information and an understanding of the function of the biosynthetic enzyme(s) to be manipulated. Protein structures are available only for a small number of biosynthetic enzymes and our ability to correctly predict mutations that would result in a variant enzyme with the desired properties is rather limited. Recognizing these difficulties, scientists began to take nature as a guide for developing new design strategies that mimic evolutionary mechanisms and this strategy has been termed evolutionary engineering. The mechanisms of evolution have been used for thousands of years for plant domestication and breeding, but only recently have been adapted to the molecular scale. Strategies and applications of evolutionary engineering of individual proteins through directed or in vitro evolution is described in more detail elsewhere in this section. Metabolic pathways composed of multiple enzymatic steps that require coordination of their catalytic activities for optimal function represent a higher level of complexity. Individual biosynthetic enzymes within a pathway have been subjected to directed evolution for pathway optimization and diversification of pathway products. Manipulation of individual pathway enzymes via in vitro evolution or rational protein design may not always be sufficient to obtain superior production strains and it may be necessary to introduce multiple changes in the genome that influence the flux through a pathway at different levels. Different evolutionary engineering methods currently are being developed that aim to elicit more global metabolic changes. Natural mechanisms of pathway evolution again serve as a guide for these approaches. Comparison of enzymes and metabolism from many organisms has highlighted how genes are transferred in the community and how new functions develop from existing biochemical pathways. Transferable genetic elements are a main mechanism for driving evolution by sharing large sequences of DNA through a process known as horizontal gene transfer.6–8 Genomic analysis has revealed that individual genes such as ribosomal proteins and entire operons such as the leucine/isoleucine and tryptophan biosynthetic operons are transferred between organisms.6,8 Recombination processes incorporate the acquired DNA into the organism’s genome and new activities and functions develop. Gene duplication, deletion, inversion, and displacement facilitate the evolution of new metabolic pathways, and after assembly of the pathway it can be optimized by point mutations in the regulatory or coding regions of the enzyme to fine tune enzyme expression and activity. Changes throughout the pathway and the genome integrate the new pathway into the complex metabolism that occurs in the organism such that the metabolic systems are efficient with a minimum of overlap between different pathways. The evolution of the pathway optimizes the flux through the system so that intermediates do not build up, and regulatory elements develop to control the function of many pathways that all use the same substrates molecules. In this chapter, we first describe current design principles of complex metabolic pathways and briefly illustrate frequent problems encountered when engineering heterologous pathways. Following this we discuss how evolutionary design strategies have been applied to find solutions to those problems, optimize production levels, and synthesize new compounds.
4.2 Initial Pathway Design and Integration into Host Metabolic Network Recombinant metabolic pathways for the biosynthesis or biodegradation of specific compounds can be constructed in a specific host strain from a native metabolic pathway transferred from a heterologous source, or they can be assembled from a heterologous set of enzymes from different organisms or different pathways. Mixing and matching enzyme functions from different sources and pathways
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may increase production levels and diversity of products. Combinations of enzyme functions into new biosynthetic pathways rely on the compatibility of product and substrate molecules of the combined enzymes. This approach, also termed combinatorial biosynthesis, is described elsewhere in this handbook in more detail. An excellent illustration of the construction of a complex metabolic pathways in microbial hosts is the manipulation of the ergosterol biosynthetic pathway in S. cerevisiae.9 In this study, 13 genes assembled from yeast, mammalian, mitochondrial, and plant pathways were inserted into both chromosomal and plasmid DNA and, working together, produced the steroid hydrocortisone from a simple carbon source. This study also is significant because it recreates a mostly membrane-bound mammalian pathway in a microbial host. In other work, plant pathways have been incorporated into microbial systems to produce carotenoids, terpenes, and flavonoids. Several studies describe the production of naringenin, a flavone that can be derivatized further by a host of enzymes, using a combination of yeast, bacterial, and plant genes10 and also by a set of genes entirely from Arabidopsis thaliana.11 High-level production of porphyrin molecules has been obtained through overexpression of seven genes from E. coli, Synechocystis, and B. subtillis.12 Despite many successes, engineered metabolic pathways are subject to many potential problems. Intermediate compounds may arise because individual enzymes have low activity caused by poor gene expression and protein folding, low-catalytic activity, or low affinity with nonideal substrates. The flux through an assembled recombinant pathway may further be limited by the availability of precursor compounds, enzyme cofactors or, as more recently realized for some biosynthetic pathways (especially from plants), lack of interactions between enzymes to allow substrate channeling between subsequent pathway enzymes. For example, Yan et al.13 describe the expression of a plant-specific anthocyanin pathway in E. coli and despite strong expression levels for each enzyme, significant levels of precursor metabolites accumulated and the product yield remained low. It was suggested that enzyme complexes may not be formed properly in E. coli, limiting the channeling of substrate along the pathway. To aid substrate channeling, consecutive enzymes in a pathway have been fused to improve interactions for efficient substrate channeling and this method has increased the rate of formation of epi-aristolochene in vitro by fusing together two proteins in the terpene biosynthetic pathway.14 Depending on the problem, pathway optimization strategies may require manipulations of the host’s metabolic network and individual recombinant biosynthetic genes. Numerous examples for increasing the activity of rate-limiting steps can be found in the literature and in this book, and only will be discussed in this chapter as they relate to evolutionary engineering. Examples illustrating the manipulation of the host’s metabolic network to accommodate an engineered pathway to provide an initial framework for further optimization by rational or evolutionary design also are described. A recombinant pathway needs to be connected to the host’s metabolic network for the supply of pathway precursors and cofactors. It may be necessary for the precursor routes to be engineered. Like the assembly of the recombinant pathway, initial integration into the host metabolic network is done by rational design. However, evolutionary design methods increasingly are used for further optimization of precursor and cofactor supply. A recent example for the engineering of new precursor routes in E. coli is that of the production of polyketide antibiotics in E. coli. Apart from achieving functional expression of the large polyketide synthases in E. coli, it was necessary to introduce into E. coli the ability for the synthesis of the diverse starter and extender units (propionyl- , malonyl- , and methylmalonyl-CoA) for polyketide chain synthesis.15,16 Another challenge using E. coli for the synthesis of complex natural products, such as polyketide antibiotics, is that E. coli does not synthesize many of the sugars that decorate these compounds and are critical for biological activity. Production of glycosylated natural products in E. coli therefore requires the installment of sugar pathways and overexpression of cognate glycosyltransferases able to transfer the specific sugars to the antibiotic molecule.17 Lombo et al.18 have constructed sugar biosynthetic cassettes to endow the organism the capability to produce branched deoxysugars and have used the “flexible”
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elloramycin glycosyltransferase to generate two tetracenomycin derivatives. The sugar cassettes have been extended to the antibiotic staurosporine, a potent protein kinase inhibitor,19 and future directions of this work include combining novel deoxysugars with aglycones using glycosyltransferases that accept many deoxysugars as substrates.20 Several glycosylated derivatives of erythromycin have been produced in an E. coli strain able to produce 6-deoxyerhthronolide B (6dEB).21 The engineered pathway included various sugar cassettes based upon the megalomicin gene cluster to generate the specific deoxysugars, a glycosyltransferase to transfer this moiety to the completed polyketide and a rRNA methyltransferase to confer resistance to the host cell from the mature antibiotic. In many instances, levels of precursor compounds in a production host limit the flux through an engineered recombinant pathway. Supplementation of the host with an alternative heterologous precursor pathway may therefore become necessary.12 Terpenoid production in E. coli was achieved by overexpressing the yeast isoprenoid precursor pathway (mevalonate pathway).22 Previous efforts to increase isoprenoid precursor production in E. coli by manipulating gene expression levels of its native mevalonate-independent pathway failed as this pathway appeared to be strictly regulated and perturbations of gene expression levels proved detrimental for E. coli. Intermediate molecules can accumulate if cofactor molecules required for subsequent reactions are not present. This was observed during the overproduction of porphyrin compounds in engineered E. coli cells where intracellular iron concentrations appeared to be limiting. Despite high levels of the enzyme ferrochelatase, protoporphyrin IX was not converted into the iron containing tetrapyrrole heme. To overcome intracellular iron limitation the iron transporter zupT was overexpressed to improve the uptake of iron and heme concentrations doubled.23 The intracellular pool of reducing equivalents in a cell defines production levels of many engineered pathways. For example, the availability of reducing equivalents was found to limit the production of succinate from glucose and Sanchez et al.24 have approached the problem by minimizing the NADH requirement by opening up the flow of acetyl-CoA through the glyoxylate pathway and increase production of succinate from this nontraditional pathway.
4.3 Optimization of Metabolic Enzyme Levels Using Evolutionary Design Once the initial framework of a recombinant pathway has been established in a host, production levels and diversity of products can be optimized by engineering specific genes or regulatory elements in the recombinant pathway or host metabolic network (Figure 4.1). This strategy follows the natural evolution of biosynthetic pathways which involves recruitment of duplicated genes for new biosynthetic functions (pathway assembly) followed by optimization of biosynthetic activities.6,25–27 Optimization of protein expression levels of both the enzymes directly involved in the pathway and the enzymes to generate the precursor molecules is a major design goal for improving flux through an engineered pathway. The relative activities of the enzymes in the pathway lead to different intermediate compound concentrations and the targets for protein design often can be determined by identifying bottlenecks in the pathway using metabolic flux analysis. Increasing the activity or expression of the enzyme downstream of the bottleneck can optimize flux through the pathway and improve the overall production. As it is difficult to predict mutations in coding or regulatory regions that would yield optimal enzyme expression levels, in vitro evolution strategies initially developed for individual enzymes are used for the optimization of protein expression levels. Evolutionary design of enzymes uses in vitro evolution methods that are reviewed in depth elsewhere in this book. Briefly, directed evolution selects for enzymes having a specific trait from a “library” of protein sequences that include random mutations or combinations of recombined genes from several related enzymes. Error-prone PCR and saturation mutagenesis methods can be used to generate libraries of random mutations, and DNA shuffling and PCR assembly of genes are efficient methods for making many correlated changes in the overall sequence of an enzyme. The library of enzymes is screened
Evolving Pathways and Genomes for the Production of Natural and Novel Compounds
1. Site-directed mutagenesis 2. Rational protein design 3. Directed-evolution of protein function
Individual enzymes
4. Assembly of artificial pathways 5. Protein expression levels 6. Promoter evolution 7. Cofactor regulation 8. Transporter proteins 9. Precursor supply
Assembled biosynthetic pathway
Microbial host genome
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Host genome
10. Host strain selection 11. Flux analysis 12. Classical strain improvement 13. Whole genome shuffling
Figure 4.1 Optimization strategies for small-molecule production in microbial hosts expressing heterologous biosynthetic pathways. Methods are available that address enzyme regulation and activity at the level of the individual enzymes, assembled biosynthetic pathways and the genome.
for the desired activity using absorbance, fluorescence or cell-growth assays, which often take advantage of recent developments in high-throughput techniques. Directed evolution of protein functions has been a powerful method to quickly generate desired protein variants without the necessity for structural information and frequently results in mutations that would not have been predicted by rational design methods. Optimization of metabolic protein expression levels can occur at many levels including the copy number of the plasmid, 28 the regulation and strength of the promoter, and the efficiency of translation of the mRNA product into protein. Tao et al. 28 have explored the effects of the plasmid copy number on heterologous protein expression by generating a library of sequences for the regulatory protein that controls plasmid replication. Measurements on the levels of product formation and realtime PCR confirmed that the copy number of the plasmid increased proportionally to the amount of product. Continuous variation in gene expression can be obtained by creating a library of promoter and ribosome binding sites that have different degrees of transcription efficiency. Meynial-Salles et al.29 describe a library of expression cassettes that have variation in the sequences for the promoter, ribosome-binding site and mRNA-stabilizing regions. When recombined into the E. coli chromosome, these sequences provided a 200-fold change in enzyme activity when using the model enzyme β-galactosidase. E. coli cells expressing recombinant carotenoid pathways frequently have been used as a model system to explore pathway evolution and optimization strategies. Synthesis of colored carotenoid pigments in E. coli provides a convenient screen with which to identify E. coli clones in a library that produce different production levels and novel chromophores (see below). For example, Kim et al.30 used the carotenoid pathway as a system to report on the effects of copy number and find that the araBAD promoter on medium-copy number plasmids provided the highest production of lycopene in E. coli. Directed evolution of the first committed carotenoid enzyme, geranylgeranyldiphosphate synthase, and the upstream promoter region identified mutations that increase the promoter and enzyme activity which was reflected by an increase in carotenoid production.31 In a systematic study of promoter strength, Alper et al.32 used error-prone PCR and a GFP fluorescence measurement to generate a series of promoters with different promoter-strengths. The series of promoters then were used to drive expression of deoxy-xylulose-P synthase (dxs), an enzyme at the beginning of the isoprenoid precursor pathway of carotenoid biosynthesis, and the amount of the carotenoid lycopene was measured. When enzymes downstream of dxs were expressed at low levels and the dxs promoter strength varied, lycopene production peaked at an
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Evolutionary Tools in Metabolic Engineering
optimal expression level for dxs and further increases in the promoter strength decreased the lycopene production, suggesting that intermediate products had accumulated and presumably had toxic effects. When the downstream enzymes were overexpressed, lycopene production followed the dxs promoter strength suggesting that dxs was rate limiting. The degradation of toxic chemicals by a series of enzymatic reactions is also an area of considerable interest.33 Organophosphates have been used as pesticides and chemical warfare agents and are contaminants in many environments. Phosphotriesterases catalyze the hydrolytic detoxification of these compounds and are targets for directed evolution to improve the ability of these enzymes to hydrolyze more diverse chemicals. Directed evolution of these enzymes has been difficult because of the lack of suitable high-throughput methods for library screening or selection. McLoughlin et al. 34 have improved the screens by engineering a strain of E. coli to express a phosphodiesterase from E. aerogenes which breaks down the phosphotriesterase product into methyl phosphate which E. coli then can use as a phosphorous source. Subsequently, McLoughlin et al.35 used directed evolution and were able to improve protein expression of the phosphotriesterase and increase the growth rate of E. coli when grown using methyl paraoxon as a sole phosphorous source. In another example, DNA shuffling of the enzymes that catalyze the hydrolysis of s-triazines by dechlorination or deamination produced a library of enzymes that transforms an increased range of substrates with reaction rates up to 150-fold higher than the parent enzymes.36 The toxicity of the substrates and products often prevents efficient bioremediation. DNA shuffling has improved the activity of toluene ortho-monooxygenase toward a range of chlorinated ethenes37; however, the toxic epoxides continue to limit growth. Rui et al.38 reasoned that increased glutathione levels would improve biodegradation since glutathione is involved in the biotransformation of chlorinated ethenes in higher organisms. Holding true to this hypothesis, a strain of E. coli overexpressing glutathione degraded chlorinated ethenes two to four-fold faster. Entire operons that confer resistance to a particular chemical also can be optimized by evolutionary methods. Crameri et al.39 have used DNA shuffling to introduce point mutations throughout a Staphylococcus aureus arsenate resistance operon and obtained cells able to grow in 0.5 M arsenate, a 40-fold increase in resistance. While it is possible to regulate the expression and activity of specific enzymes, the optimization of a multienzyme pathway is complex and it is difficult to predict the ideal activity of each enzyme. Computational methods have been developed to address these problems and a current review is included in this book. Experimentally, Jung et al.40 have reconstructed the entire trehalose biosynthetic pathway on an immobilized substrate by using mRNA-protein fusions. The relative concentration of each enzyme component is varied by controlling the amount of capture DNA bound to the substrate and the concentrations of five enzymes were varied systematically and an optimal ratio of the rate limiting enzymes was determined. Recently, Pfleger et al.41 have generated a combinatorial library of tunable intergenic regions containing control elements that include mRNA secondary structures, RNase cleavage sites and ribosome binding sequences that lead to 100-fold changes in activity in a two-gene reporter system using GFP expression, and a seven-fold increase in the mevalonate system which includes three coordinated genes.
4.4 Evolving Metabolic Pathways for the Production of New Compounds Biosynthesis of new compounds can be achieved by (i) combining enzymes from different sources and pathways into new biosynthetic reaction sequences; (ii) altering the product and substrate spectrum of individual enzymes in an assembled pathway; or (iii) a combination of both processes. In all three strategies, an efficient reaction sequence from precursor to product may require adaptation of the substrate and product spectra of enzymes located next to the key diversity generating enzyme(s), and in many cases the catalytic promiscuity of the enzymes is important for generating the new substrate and product spectra.42,43 Many biosynthetic enzymes, especially those from secondary metabolic pathways,
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exhibit some flexibility in their substrate and product molecules, as it allows an organism to produce a series of related bioactive compounds which may provide an advantage during growth in altered environmental conditions. In vitro evolution methods can be applied to increase conversion of a selected substrates from a range of related substrates to a desired new product, or to decrease catalytic promiscuity such that only one particular product is formed. The biosynthesis of carotenoids provides a detailed example of using directed evolution together with gene combination to create product diversity.44 Carotenoids are colored pigments that play important biological functions in photosynthesis, coloration, and are antioxidants and radical scavengers. These pigments are derived from the general isoprenoid pathway by the head-to-head condensation of either two C15 farnesyl diphosphate molecules or two C20 geranylgeranyl diphosphate molecules to generate C30 or C 40 carotenoids, respectively. Following the condensation reaction, a desaturase introduces double bonds along the carotenoid backbone to generate a chromophore that absorbs in the visible range. Modifications, such as cyclization and various oxidation reactions at both ends of the linear carotenoid create diverse cyclic and linear carotenoid structures (Figure 4.2). A strategy termed “molecular pathway breeding”, involving first assembly of a series of genes in a microbial host and then in vitro evolution of key enzymes, has been used to synthesize novel carotenoid compounds in engineered, noncarotenogenic E. coli. New carotenoid structures with fully conjugated electron systems have been obtained by screening a library of desaturase variants for mutants that would synthesize carotenoids with extended chromophores.45,46 DNA shuffling of phytoene desaturases from the genus Erwinia led to a desaturase variant which introduces six double bonds, compared to the typical four double bonds, into the backbone of the noncolored carotenoid phytoene, producing the f ully conjugated carotenoid tetradehydrolycopene. The evolved pathway then was extended with a library of cyclase mutants and screened for variants capable of cyclizing the end-groups of carotenoids with
Diversity-oriented directed evolution of carotenoid pathways
C30 OPP C35
C15PP OPP
C40
C20PP
O O OH
OPP C25PP
C45
C50 Diphosphate precursors
Carotenoid backbones
Terminal groups
Figure 4.2 Pathway engineering of carotenoid biosynthesis has improved production levels and has generated novel and diverse types of carotenoids. Condensation of two diphosphate precursor molecules produces f ull-length natural (C30 and C 40) and novel (C35,C 45 and C50) carotenoid backbones (unsaturated molecules are shown). Desaturase variants introduce double bonds into the carotenoid backbone and subsequent enzymes including monooxygenases, hydroxylases, glucosylases, and ketolases modify the carotenoid scaffold. The solid arrow represents the enzymatic condensation reaction to form the carotenoid backbone and the dashed arrow represents one or more biosynthetic steps to produce a family of diverse molecules (only a few examples are shown).
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extended chromophores. A mutant cyclase was identified in the library that produced a novel carotenoid compound, torulene, in E. coli. Carotenoid enzymes located downstream of the desaturase and cyclase possess sufficient catalytic flexibility to accept related carotenoid substrates, and the two in vitro evolved carotenoid pathway have been extended with additional carotenoid-modifying enzymes such as carotenoid monooxygenase, hydroxylase, glucosylase, or ketolase to synthesize a number of structurally novel carotenoid compounds in E. coli.47 Further, the diversity of carotenoids was increased by a carotenoid desaturase that generated more polar products.48 The chain length of carotenoids also was modified by directed evolution of the enzymes for these pathways. The C30 carotenoid synthase from Staphylococcus aureus was evolved to produce C 40 carotenoids,49 and further rounds of directed evolution exploited the substrate plasticity of the carotenoid synthases to produce C 45 and C50 carotenoids, previously unknown molecules.50 Another study showed that when the C30 carotene synthase is exposed to both C30 and C 40 precursor molecules, a novel asymmetric C35 carotenoid is produced.51 Additionally, the downstream enzymes continue to act on the respective half of the molecule: the cyclase from the C 40 pathway continues to act on that half of the C35 carotenoid. With the success of modifying carotenoids of various lengths, the new backbone structures opens up a wide variety of new compounds. The multiple mutations discovered by directed evolution reveal the enzymology of the chain elongation and has been explored through a series of enzymes with increased activities.52,53
4.5 Addressing Complex Traits Using Evolutionary Methods Activity outside the engineered pathway also has an influence on the growth characteristics and product yields. In the carotenoid pathway, potential bottlenecks in the precursor molecule source are related to the distribution of pyruvate and glyceraldehyde-3-phosphate molecules.54 Farmer et al.55 designed a control element that stimulated expression of two carotenoid genes in times of excess glycolytic flux increasing carotenoid production when there was high concentration of substrate, while also reducing the stress of the external pathway during the periods when the cells are limited by nutrients. Kang et al.56 identified genes that enhance the production of lycopene by screening a shotgun library of E. coli chromosomal DNA. Six clones containing 13 genes were isolated that had increased lycopene production, several of which had not been known to affect lycopene production. The genes included one that directly increases the precursor supply and several that indirectly affect metabolism by regulating processes related to stationary phase growth and transcription factors that regulate anaerobic growth and curli surface fibers. In a complementary study, all genes were subject to a systematic in silico model to identify genes that increase lycopene production.57 Of the several genes identified all but one were directly involved in production of precursor molecules, and the other increased the concentration of NADPH. Extending this work to include a combinatorial method to identify genes that enhance lycopene production, transposon mutagenesis was applied and lycopene production was measured.58 These methods identified two sets of genes that alter product formation by precursor availability or through kinetic and regulatory mechanisms, and a metabolic landscape for a small subset of genes was generated that describes lycopene biosynthesis. An engineered pathway stresses the limited resources of the host as precursor molecules are diverted from natural metabolic pathways, and requires energy resources for reactions such as protein synthesis and plasmid replication. To address the stresses and energy requirements of a population of E. coli cells expressing the lactose operon, the relative growth rate of induced cells versus noninduced cells was measured to determine the cost associated with expressing proteins for enzymatic function. Dekel et al.59 measured the cost of expressing the lactose operon in the absence of lactose and the benefit as a function of lactose concentration. The data allow them to predict the optimal enzyme activity for a given level of lactose, and they observe that E. coli growing in constant lactose for a few hundred generations optimize their use of lactose, most likely by altering the promoter and ribosome binding sequences. Additionally,
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expression of plasmids also strains the host machinery for DNA replication and causes the cells to grow slowly and as many microbial biotechnologies rely on coexpression of genes it is important to identify and reduce these stresses. To study this effect, a high-copy plasmid with an inducible promoter expressing glucose-6-phosphate dehydrogenase was transformed into E. coli.60 Induction of the dehydrogenase led to an increase in growth rate, suggesting that engineering the pentose-phosphate pathway increases the precursor supply for the building blocks necessary for plasmid replication. The genome of the microbial host responds to stresses and evolves to adapt to new metabolic activity and culture conditions; however, many of these mutations may not be predicted and it even may be difficult to know which metabolic systems require changes to improve production. In many cases, it is efficient to recast the problem from one of rationally engineering an optimized pathway to one of introducing random mutations into the genome and screening for the desired phenotype. Strain improvement methods generally subject an initial strain to a mutagen and the resulting phenotypes are screened for improvements in cell growth in specific environments or production of an engineered product. In classical strain improvement strategies further improvements of the strain can be attained by iteration of the mutation and selection strategy, at each point taking the best mutant as the starting point for next round of mutation. Strain improvement strategies recently have addressed the problem that production of an engineered chemical often is linked with cell growth, and that once stationary growth rates are obtained the production is diminished. To overcome this limitation, Sonderegger et al.61 developed a screen to select for cells able to continue to live without spending energy on replication and growth processes in ammonia- and glycerol-limited conditions. A metabolically active quiescent bacterial strain was developed by selecting for increased glucose uptake immediately after a nutrient limited environment. The entry to stationary phase was impaired such that the E. coli strain retained metabolic vigor even when on the verge of starvation, and that the metabolic activity was channeled toward production of the engineered product. Toxic chemicals present during fermentation often limit the growth and efficiency of the microbe. The chemicals may be the desired final product as in ethanol or phenol bioproduction, or may be the target of biodegradation through enzymatic reactions. In either case, a strain resistant to the toxic chemical will improve the reaction. Wierckx et al.62 have started to engineer a host for the bioproduction of phenol from glucose. To produce phenol from glucose and avoid the toxicity of phenolic aromatic compounds, they introduced a tyrosine phenol lyase into the solvent-resistant microbe Pseudomonas putida S12 and used a combination of rational and random engineering and high-throughput screening to optimize production of phenol. Whole genome shuffling approaches have been developed to incorporate multiple changes in the genome that improve the microbe as a host for the engineered biosynthetic pathway. This method is more efficient than the classic strain improvement and depends only on one round of mutation and selection followed by successive rounds of genomic recombination. In whole genome shuffling, a population of cells is subjected to a chemical mutagen to introduce random mutations and the fittest strains are identified by a high-throughput screening strategy. Using recombination techniques known as protoplast fusion, the genomes of the fittest strains are shuffled to create strains that have combinations of the beneficial mutations. In this manner, after only a few rounds of selection the final strains have incorporated many beneficial changes and are significantly fitter than strains developed using classical strain improvement. The original development of genome shuffling focused on increasing the production of a complex polyketide antibiotic.63 Strains isolated after only two rounds of genome shuffling matched the production levels of strains developed over 20 years and 20 rounds of classical improvement methods. In a second study, genome shuffling was applied to growth of Lactobacillus at low pH.64 A mutagenized population of Lactobacillus was screened for growth on plates with a pH-dependent gradient and five rounds of genome shuffling isolated strains thriving at pH 4 that produce lactate at elevated levels. Genome shuffling also has been used to improve the degradation of the pesticide pentachlorophenol
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(PCP) by Sphingobium chlorophenolicum.65 Successive rounds of protoplast fusion and selection lead to strains that constitutively express the PCP degrading enzymes, have increased growth rate and are resistant to the toxic effects of PCP. Recently, Dai et al.66 improved protoplast fusion in E. coli by a factor of 104 and have opened doors to new evolutionary strategies in this powerful genetic host.
4.6 Screening Technologies Evolutionary strategies applied to metabolic pathways or whole genomes are limited by the ability to screen large libraries. In many instances, screening methods have to directly detect the product molecules in lysates obtained from whole cells without the amplification common to high-throughput assays used for the directed evolution of enzymes. The detection strategies are based on a primary detection scheme that uses physical properties of the molecule to quantify its concentration in the sample; alternatively, it is possible to modify chemical groups such as aldehydes to make the molecules visible to a simple screen. Each engineered metabolic pathway requires a specific screen and creativity in development of the screen often plays a large role. Many methods in evolutionary pathway engineering have capitalized on high-throughput technologies developed for drug discovery schemes. Many screening technologies for metabolites are based on absorbance or fluorescence properties of the compound: the absorbance properties of carotenoids have made them ideal for developing methods to design and evolve metabolic pathways, and fundamental insights into the biological processes that govern transcription and translation of enzymes can be monitored by GFP fusion proteins. Screening methods based on absorbance and fluorescence can be done in microplates using robotic devices and are compatible with a variety of extraction and filtering protocols. Additionally, fluorescence-activatedcell sorting is a technique that can screen individual whole cells and has been used in conjunction with directed evolution of a metabolic pathway.23 Recent years have brought improvements in technologies for analysis of metabolic profiles from plants and microbes (Figure 4.3).67–69 High-throughput detection of secondary metabolites depends on fast, parallel, or multiplexed high-resolution chromatographic separations and a strategy for detection. The gold standard for metabolic studies is gas chromatography-mass spectrometry (GC-MS) because of its ability to identify many compounds in a single experiment. Fiehn et al.70 have identified more than 300 secondary metabolites from leaf extracts of Arabidopsis thaliana, and Villas-Boas et al.71 have applied a derivatization strategy in conjunction with high-throughput detection to measure intracellular central carbon metabolism and amino acid biosynthesis. GC-MS has also been used to measure metabolic flux by following 13C through a series of metabolites.72,73 Liquid chromatography is a complementary strategy that often improves metabolite separation because nonvolatile molecules can be detected and sample preparation is simple.74 Reverse-phase, ion-exchange and other types of separations can be coupled to detection using a mass spectrometer. Capillary electrophoresis has also been used to analyze tissue from Arabidopsis for metabolites.75 In a recent study on polyketides, Kittell et al.76 used parallel capillary electrophoresis to screen a library of Aspergillus terreus for production of lovastatin compounds and increased the screening throughput by 50-fold compared to previous methods. In some applications it may be possible to avoid using separation methods and inject the sample directly into the mass spectrometer using electrospray ionization or laser desorption/ionization.77–79 Many clever detection strategies that do not depend on mass spectrometry will contribute to advances in strain improvement. Advances in FTIR and NMR have been applied toward screening metabolites in engineered organisms.80,81 Selections based on chemical gradients in solid media have been used for selection of cell growth at low pH and can be extended to other types of specific culture conditions.64 Chip-based screens for measuring ligand binding may be used for screening and a scintillation proximity assay advances the high-throughput screening for protein ligands and may be extended to quantify production of small molecules.82
Evolving Pathways and Genomes for the Production of Natural and Novel Compounds
Growth characteristics – assay for growth on solid media using pH or chemical gradients
(a) 1 2 Counts
Cells have selectable phenotype Physical characteristics – Cell sorting methods based on absorption or fluoresence
4-11
3
(b) 1
Pathway products can be extracted for analytical characterization Detection strategies include GC-MS, LC-MS, absorption and fluoresence assays.
2
R1
100 101 102 103 Fluoresence intensity (FLI)
Assembled pathway
104
Host genome Functional genomics links phenotype and genotype Transcription measurements, proteomics and flux analysis can determine specific mutations responsible for changes in pathway response.
Figure 4.3 Evolutionary strategies applied to metabolic pathways or whole genomes are limited by the ability to screen large libraries. Screening methods used to detect small-molecule production include high-throughput absorbance or fluorescence based assays, analytical separations, and mass spectrometry. Functional genomics methods have become increasingly important for identification of mutations that improve production levels.
The specific genomic changes that confer improved strain selection also can be identified by advances in functional genomics methods that increase the ability of matching a phenotype to genotype. This becomes important when searching for the mutations identified in strain selection strategies that affect production of secondary metabolites.83–85 A few genes have been identified by various strain selection techniques, but a truly genomic analysis will have great impact on inverse metabolic engineering, the process by which pathways are assembled and engineered in a rational and intentional manner. The techniques used to identify genes involved in the metabolic network include sequencing the DNA of a selected set of genes from several strains to identify key enzymes, hybridization analysis of entire genome, transcriptional profiling, proteomics, metabolic profiling, and flux analysis. For example, transcript profiling improved galactose uptake by identifying that phosphoglucomutase plays a key role in the regulation of the flux of the metabolites.86,87 In addition to advances in analytical chemistry, simple pooling strategies also increase the throughput of screening methods by analyzing multiple samples in a single experiment. For example, the samples in a single 96-well plate can be analyzed by combining aliquots from wells in a single row or column. After analyzing all of the rows and columns, the data can be deconvoluted to determine the individual wells that demonstrate higher product yield by finding the intersections of the rows and columns that have higher product yield. In this example, the number of runs required to identify a single well that
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Evolutionary Tools in Metabolic Engineering
has improved product production is reduced from 96 experiments to 21 (eight rows, 12 columns, and one individual well). For screening strategies with sufficient sensitivity, larger numbers of wells can be combined to reduce further the number of analyses.
4.7 Conclusions and Future Directions Rational construction of metabolic traits in a microbial host began with the introduction of a few recombinant genes and progressed to the assembly of complex heterologous pathways and development of analytical and modeling tools for metabolic flux analysis. Researchers have gained increasing insight into the design principles and complexity of cellular metabolic networks, which has led to the development of the metabolic engineering strategies discussed in this chapter that utilize evolutionary strategies to optimize complex cellular machineries for metabolite production. Initially, evolutionary engineering focused on optimizing catalytic activities and expression levels of individual enzymes in engineering pathways. It has become clear that in order to achieve optimal production levels, an engineered pathway needs to be integrated into the metabolic network of the heterologous host. Of particular importance is the development of regulatory circuits that allow a recombinant pathway to become part of a heterologous metabolic network. Evolving regulatory circuits (or genetic circuits) that self-regulate gene expression and enzyme activity in response to precursor supply is therefore an important next step in the field of metabolic engineering.88,89 The field of synthetic biology recently has gained attention and holds promise to develop tools and methods for the redesign of metabolic networks.3,90,91 Within this field progress has been made at understanding and designing synthetic genetic networks. In early examples of the engineering of genetic networks, the periodic expression of GFP was controlled by a set of three transcription factors 92 and a bistable network that switched between states after transient chemical or thermal induction was constructed from two repressible promoters.93 The stochastic nature of protein expression has been measured in single cells94 and mathematical representations of the stochastic behavior of the genetic circuits has improved to predict the behavior of increasingly complex combinations of activator and repressor elements.95 The majority of the studies on the design of genetic networks involve transcriptional elements that have been characterized in detail. How can the experience gained from the model systems lead to design of industrially important biosynthetic pathways? The best design may arise through a combination of rational methods that provide an original design of the network, followed by evolutionary methods that select for network behavior. The modular nature of the regulatory circuits allows for the evolution of new elements and functions from existing regulatory circuits. Protein networks can evolve new functions while maintaining original functions during selection under varying environmental conditions; however, under static conditions the modularity is lost and the evolved network may be trapped in a local minimum.96 Notable achievements in this area include combinatorial synthesis of regulatory circuits using variations of promoters and transcription factors assembled randomly to generate circuits with 13 different types of connections between the elements of the circuit and lead to unique responses to environmental conditions.97 Directed evolution has been used to fine-tune the control region of a promoter to match protein levels so that the circuit can function properly. Using this strategy, a nonfunctional circuit was converted into one that is functional and the main mutation that made it work was a truncation of one of the genes to generate a different allosteric response.27 Further advances in synthetic biology also may arise from a simplified host genome. Fewer metabolic and regulatory systems may decrease the number of unintended interactions and may provide efficient biocatalytic pathways for natural and novel compounds. To this end, a fully synthetic genome with a characterized list of genes may be possible for applications in bioenergy or biosynthesis or, alternatively, the reduction of the size of the genome to the essential features may provide a route to the ideal microbial host.98–100
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References 1. Windass, J. D. et al. Improved conversion of methanol to single-cell protein by Methylophilus methylotrophus. Nature, 287, 396, 1980. 2. Hibbert, E. G. et al. Directed evolution of biocatalytic processes. Biomol. Eng., 22, 11, 2005. 3. McDaniel, R. and Weiss, R. Advances in synthetic biology: on the path from prototypes to applications. Curr. Opin. Biotechnol., 16, 476, 2005. 4. Koffas, M. Evolutionary metabolic engineering. Metabol. Eng., 7, 1, 2005. 5. Chatterjee, R. and Yuan, L. Directed evolution of metabolic pathways. Trends Biotechnol., 24, 28, 2006. 6. McAdams, H. H., Srinivasan, B., and Arkin, A. P. The evolution of genetic regulatory systems in bacteria. Nat. Rev. Genet., 5, 169, 2004. 7. Brown, J. R. Ancient horizontal gene transfer. Nat. Rev. Genet., 4, 121, 2003. 8. Omelchenko, M. V. et al. Evolution of mosaic operons by horizontal gene transfer and gene displacement in situ. Genome Biol., 4, R55, 2003. 9. Szczebara, F. M. et al. Total biosynthesis of hydrocortisone from a simple carbon source in yeast. Nat. Biotechnol., 21, 143, 2003. 10. Hwang, E. I. et al. Production of plant-specific flavanones by Escherichia coli containing an artificial gene cluster. Appl. Environ. Microbiol., 69, 2699, 2003. 11. Watts, K. T., Lee, P. C., and Schmidt-Dannert, C. Exploring recombinant flavonoid biosynthesis in metabolically engineered Escherichia coli. Chembiochem, 5, 500, 2004. 12. Kwon, S. J. et al. High-level production of porphyrins in metabolically engineered Escherichia coli: Systematic extension of a pathway assembled from overexpressed genes involved in heme biosynthesis. Appl. Environ. Microbiol., 69, 4875, 2003. 13. Yan, Y. J. et al. Metabolic engineering of anthocyanin biosynthesis in Escherichia coli. Appl. Environ. Microbiol., 71, 3617, 2005. 14. Brodelius, M. et al. Fusion of farnesyldiphosphate synthase and epi-aristolochene synthase, a sesquiterpene cyclase involved in capsidiol biosynthesis in Nicotiana tabacum. Eur. J. Biochem., 269, 3570, 2002. 15. Pfeifer, B. et al. Process and metabolic strategies for improved production of Escherichia coli-derived 6-deoxyerythronolide B. Appl. Environ. Microbiol., 68, 3287, 2002. 16. Pfeifer, B. A. et al. Biosynthesis of complex polyketides in a metabolically engineered strain of E. coli. Science 291, 1790, 2001. 17. Rodriguez, L. et al. Engineering deoxysugar biosynthetic pathways from antibiotic-producing microorganisms. A tool to produce novel glycosylated bioactive compounds. Chem. Biol., 9, 721, 2002. 18. Lombo, F. et al. Engineering biosynthetic pathways for deoxysugars: branched-chain sugar pathways and derivatives from the antitumor tetracenomycin. Chem. Biol., 11, 1709, 2004. 19. Salas, A. P. et al. Deciphering the late steps in the biosynthesis of the anti-tumour indolocarbazole staurosporine: sugar donor substrate flexibility of the StaG glycosyltransferase. Mol. Microbiol., 58, 17, 2005. 20. Luzhetskyy, A. and Bechthold, A. It works: combinatorial biosynthesis for generating novel glycosylated compounds. Mol. Microbiol., 58, 3, 2005. 21. Peiru, S. et al. Production of the potent antibacterial polyketide erythromycin C in Escherichia coli. Appl. Environ. Microbiol., 71, 2539, 2005. 22. Martin, V. J. J. et al. Engineering a mevalonate pathway in Escherichia coli for production of terpenoids. Nat. Biotechnol., 21, 796, 2003. 23. Kwon, S. J. et al. A high-throughput screen for porphyrin metal chelatases: application to the directed evolution of ferrochelatases for metalloporphyrin biosynthesis. Chembiochem, 5, 1069, 2004.
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24. Sanchez, A. M., Bennett, G. N., and San, K. Y. Novel pathway engineering design of the anaerobic central metabolic pathway in Escherichia coli to increase succinate yield and productivity. Metabol. Eng., 7, 229, 2005. 25. McAdams, H. H. and Arkin, A. Gene regulation: towards a circuit engineering discipline. Curr. Biol., 10, R318, 2000. 26. Hasty, J. Design then mutate. Proc. Natl. Acad. Sci. USA, 99, 16516, 2002. 27. Yokobayashi, Y., Weiss, R., and Arnold, F. H. Directed evolution of a genetic circuit, Proc Natl. Acad Sci USA, 99, 16587, 2002. 28. Tao, L., Jackson, R. E., and Cheng, Q. Directed evolution of copy number of a broad host range plasmid for metabolic engineering. Metabol. Eng., 7, 10, 2005. 29. Meynial-Salles, I., Cervin, M. A., and Soucaille, P. New tool for metabolic pathway engineering in Escherichia coli: one-step method to modulate expression of chromosomal genes. Appl. Environ. Microbiol., 71, 2140, 2005. 30. Kim, S. W. and Keasling, J. D. Metabolic engineering of the nonmevalonate isopentenyl diphosphate synthesis pathway in Escherichia coli enhances lycopene production. Biotechnol. Bioeng., 72, 408, 2001. 31. Wang, C. W., Oh, M. K., and Liao, J. C. Directed evolution of metabolically engineered Escherichia coli for carotenoid production. Biotechnol. Prog., 16, 922, 2000. 32. Alper, H. et al. Tuning genetic control through promoter engineering. Proc. Natl. Acad. Sci. USA, 102, 12678, 2005. 33. Parales, R. E. and Ditty, J. L. Laboratory evolution of catabolic enzymes and pathways. Curr. Opin. Biotechnol., 16, 315, 2005. 34. McLoughlin, S. Y. et al. Growth of Escherichia coli coexpressing phosphotriesterase and glycerophosphodiester phosphodiesterase, using paraoxon as the sole phosphorus source. Appl. Environ. Microbiol., 70, 404, 2004. 35. McLoughlin, S. Y. et al. Increased expression of a bacterial phosphotriesterase in Escherichia coli through directed evolution. Protein Exp. Purif., 41, 433, 2005. 36. Raillard, S. et al. Novel enzyme activities and functional plasticity revealed by recombining highly homologous enzymes. Chem. Biol., 8, 891, 2001. 37. Canada, K. A. et al. Directed evolution of toluene ortho-monooxygenase for enhanced 1-naphthol synthesis and chlorinated ethene degradation. J. Bacteriol., 184, 344, 2002. 38. Rui, L. Y. et al. Metabolic pathway engineering to enhance aerobic degradation of chlorinated ethenes and to reduce their toxicity by cloning a novel glutathione S-transferase, an evolved toluene o-monooxygenase, and gamma-glutamylcysteine synthetase. Environ. Microbiol., 6, 491, 2004. 39. Crameri, A. et al. Molecular evolution of an arsenate detoxification pathway DNA shuffling. Nat. Biotechnol., 15, 436, 1997. 40. Jung, G. Y. and Stephanopoulos, G. A functional protein chip for pathway optimization and in vitro metabolic engineering. Science, 304, 428, 2004. 41. Pfleger, B. F. et al. Combinatorial engineering of intergenic regions in operons tunes expression of multiple genes. Nat. Biotechnol., 24, 1027, 2006. 42. Aharoni, A. et al. The ‘evolvability’ of promiscuous protein functions. Nat. Genet., 37, 73, 2005. 43. Kazlauskas, R. J. Enhancing catalytic promiscuity for biocatalysis. Curr. Opin. Chem. Biol., 9, 195, 2005. 44. Umeno, D., Tobias, A. V., and Arnold, F. H. Diversifying carotenoid biosynthetic pathways by directed evolution. Microbiol. Mol. Biol. Rev., 69, 51, 2005. 45. Schmidt-Dannert, C., Umeno, D., and Arnold, F. H. Molecular breeding of carotenoid biosynthetic pathways. Nat. Biotechnol., 18, 750, 2000. 46. Wang, C. W. and Liao, J. C. Alteration of product specificity of Rhodobacter sphaeroides phytoene desaturase by directed evolution. J. Biol. Chem., 276, 41161, 2001.
Evolving Pathways and Genomes for the Production of Natural and Novel Compounds
4-15
47. Lee, P. C. et al. Biosynthesis of structurally novel carotenoids in Escherichia coli. Chem. Biol., 10, 453, 2003. 48. Mijts, B. N., Lee, P. C., and Schmidt-Dannert, C. Identification of a carotenoid oxygenase synthesizing acyclic xanthophylls: combinatorial biosynthesis and directed evolution. Chem. Biol., 12, 453, 2005. 49. Umeno, D., Tobias, A. V., and Arnold, F. H. Evolution of the C30 carotenoid synthase CrtM for function in a C40 pathway. J. Bacteriol., 184, 6690, 2002. 50. Umeno, D. and Arnold, F. H. Evolution of a pathway to novel long-chain carotenoids. J. Bacteriol., 186, 1531, 2004. 51. Umeno, D. and Arnold, F. H. A C-35 carotenoid biosynthetic pathway. Appl. Environ. Microbiol., 69, 3573, 2003. 52. Lee, P. C. et al. Directed evolution of Escherichia coli farnesyl diphosphate synthase (IspA) reveals novel structural determinants of chain length specificity. Metabol. Eng., 7, 18, 2005. 53. Lee, P. C. et al. Alteration of product specificity of Aeropyrum pernix farnesylgeranyl diphosphate synthase (Fgs) by directed evolution. Protein Eng. Des. Sel., 17, 771, 2004. 54. Farmer, W. R. and Liao, J. C. Precursor balancing for metabolic engineering of lycopene production in Escherichia coli. Biotechnol. Prog., 17, 57, 2001. 55. Farmer, W. R. and Liao, J. C. Improving lycopene production in Escherichia coli by engineering metabolic control. Nat. Biotechnol., 18, 533, 2000. 56. Kang, M. J. et al. Identification of genes affecting lycopene accumulation in Escherichia coli using a shot-gun method. Biotechnol. Bioeng., 91, 636, 2005. 57. Alper, H. et al. Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli. Metabol. Eng., 7, 155, 2005. 58. Alper, H., Miyaoku, K., and Stephanopoulos, G. Construction of lycopene-overproducing E-coli strains by combining systematic and combinatorial gene knockout targets. Nat. Biotechnol., 23, 612, 2005. 59. Dekel, E. and Alon, U. Optimality and evolutionary tuning of the expression level of a protein. Nature, 436, 588, 2005. 60. Flores, S. et al. Growth-rate recovery of Escherichia coli cultures carrying a multicopy plasmid, by engineering of the pentose-phosphate pathway. Biotechnol. Bioeng., 87, 485, 2004. 61. Sonderegger, M., Schumperli, M., and Sauer, U. Selection of quiescent Escherichia coli with high metabolic activity. Metabol. Eng., 7, 4, 2005. 62. Wierckx, N. J. P. et al. Engineering of solvent-tolerant Pseudomonas putida S12 for bioproduction of phenol from glucose. Appl. Environ. Microbiol., 71, 8221, 2005. 63. Zhang, Y. X. et al. Genome shuffling leads to rapid phenotypic improvement in bacteria. Nature, 415, 644, 2002. 64. Patnaik, R. et al. Genome shuffling of Lactobacillus for improved acid tolerance. Nat. Biotechnol., 20, 707, 2002. 65. Dai, M. H. and Copley, S. D. Genome shuffling improves degradation of the anthropogenic pesticide pentachlorophenol by Sphingobium chlorophenolicum ATCC 39723. Appl. Environ. Microbiol., 70, 2391, 2004. 66. Dai, M. H. et al. Visualization of protoplast fusion and quantitation of recombination in fused protoplasts of auxotrophic strains of Escherichia coli. Metabol. Eng., 7, 45, 2005. 67. Dunn, W. B. and Ellis, D. I. Metabolomics: current analytical platforms and methodologies. Trac. Trends Anal. Chem., 24, 285, 2005. 68. Kell, D. B. et al. Metabolic footprinting and systems biology: the medium is the message. Nat. Rev. Microbiol., 3, 557, 2005. 69. Kell, D. B. et al. Metabolic footprinting: a high-throughput, high-information approach to cellular characterisation and functional genomics. Yeast, 20, S335, 2003.
4-16
Evolutionary Tools in Metabolic Engineering
70. Fiehn, O. et al. Metabolite profiling for plant functional genomics. Nat. Biotechnol., 18, 1157, 2000. 71. Villas-Boas, S. G. et al. High-throughput metabolic state analysis: the missing link in integrated functional genomics of yeasts. Biochem. J., 388, 669, 2005. 72. Wittmann, C., Kim, H. M., and Heinzle, E. Metabolic network analysis of lysine producing Corynebacterium glutamicum at a miniaturized scale. Biotechnol. Bioeng., 87, 1, 2004. 73. Fischer, E., Zamboni, N., and Sauer, U. High-throughput metabolic flux analysis based on gas chromatography-mass spectrometry derived C-13 constraints. Anal. Biochem., 325, 308, 2004. 74. Villas-Boas, S. G. et al. Mass spectrometry in metabolome analysis. Mass. Spectrom Rev., 24, 613, 2005. 75. von Roepenack-Lahaye, E. et al. Profiling of Arabidopsis secondary metabolites by capillary liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry. Plant Physiol., 134, 548, 2004. 76. Kittell, J. et al. Parallel capillary electrophoresis for the quantitative screening of fermentation broths containing natural products. Metabol. Eng., 7, 53, 2005. 77. Allen, J. et al. High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. Nat. Biotechnol., 21, 692, 2003. 78. Vaidyanathan, S., Gaskell, S., and Goodacre, R. Matrix-suppressed laser desorption/ionisation mass spectrometry and its suitability for metabolome analyses. Rapid Commun. Mass. Spectrom, 20, 1192, 2006. 79. Vaidyanathan, S., O’Hagan, S., and Goodacre, R. Direct infusion electrospray ionization mass spectra of crude cell extracts for microbial characterizations: influence of solvent conditions on the detection of proteins. Rapid Commun. Mass Spectrom, 20, 21, 2006. 80. Kaderbhai, N. N. et al. Functional genomics via metabolic footprinting: monitoring metabolite secretion by Escherichia coli tryptophan metabolism mutants using FT-IR and direct injection electrospray mass spectrometry. Comp. Funct. Genom., 4, 376, 2003. 81. Lindon, J. C., Holmes, E., and Nicholson, J. K. So whats the deal with metabonomics? Metabonomics measures the fingerprint of biochemical perturbations caused by disease, drugs, and toxins. Anal. Chem., 75, 384A, 2003. 82. Sun, S. X. et al. Assay development and data analysis of receptor-ligand binding based on scintillation proximity assay. Metabol. Eng., 7, 38, 2005. 83. Bro, C. and Nielsen, J. Impact of ‘ome’ analyses on inverse metabolic engineering. Metabol. Eng., 6, 204, 2004. 84. Gill, R. T. Enabling inverse metabolic engineering through genomics. Curr. Opin. Biotechnol., 14, 484, 2003. 85. Gill, R. T. Special issue on inverse metabolic engineering. Metabol. Eng., 6, 175, 2004. 86. Gill, R. T. et al. Genome-wide screening for trait conferring genes using DNA microarrays. Proc. Natl. Acad. Sci. USA, 99, 7033, 2002. 87. Bro, C. et al. Improvement of galactose uptake in Saccharomyces cerevisiae through overexpression of phosphoglucomutase: Example of transcript analysis as a tool in inverse metabolic engineering. Appl. Environ. Microbiol., 71, 6465, 2005. 88. Endy, D. Foundations for engineering biology. Nature, 438, 449, 2005. 89. Chin, J. W. Modular approaches to expanding the functions of living matter. Nat. Chem. Biol., 2, 304, 2006. 90. Benner, S. A. and Sismour, A. M. Synthetic biology. Nat. Rev. Genet., 6, 533, 2005. 91. Sismour, A. M. and Benner, S. A. Synthetic biology. Expert Opin. Biol. Ther., 5, 1409, 2005. 92. Elowitz, M. B. and Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature, 403, 335, 2000. 93. Gardner, T. S., Cantor, C. R., and Collins, J. J. Construction of a genetic toggle switch in Escherichia coli. Nature, 403, 339, 2000.
Evolving Pathways and Genomes for the Production of Natural and Novel Compounds
4-17
94. Cai, L., Friedman, N., and Xie, X. S. Stochastic protein expression in individual cells at the single molecule level. Nature, 440, 358, 2006. 95. Guido, N. J. et al. A bottom-up approach to gene regulation. Nature, 439, 856, 2006. 96. Kashtan, N. and Alon, U. Spontaneous evolution of modularity and network motifs. Proc. Natl. Acad. Sci. USA, 102, 13773, 2005. 97. Guet, C. C. et al. Combinatorial synthesis of genetic networks. Science, 296, 1466, 2002. 98. Smith, H. O. et al. Generating a synthetic genome by whole genome assembly: phi X174 bacteriophage from synthetic oligonucleotides. Proc. Natl. Acad. Sci. USA, 100, 15440, 2003. 99. Posfai, G. et al. Emergent properties of reduced-genome Escherichia coli. Science, 312, 1044, 2006. 100. Glass, J. I. et al. Essential genes of a minimal bacterium. Proc. Natl. Acad. Sci. USA, 103, 425, 2006.
5 Models Predicting Optimized Strategies for Protein Evolution 5.1 5.2
Introduction ��������������������������������������������������������������������������������������5-1 Random Mutagenesis ����������������������������������������������������������������������5-3
5.3
Directed Mutagenesis ����������������������������������������������������������������������5-7
5.4
Recombination ��������������������������������������������������������������������������������5-10
5.5
Optimizing Chimeric Libraries....................................................5-16
Choosing Parental Proteins • Identifying a Protocol • Caveats
Combinatorial Consensus Mutagenesis (CCM) • Structure-Guided Saturation Mutagenesis • Structure-Guided Consensus Mutagenesis The Schema Algorithm • Residue Clash Maps (RCM) The FamClash Algorithm • Statistical Coupling Analysis (SCA)
Jonathan J. Silberg Rice University
Peter Q. Nguyen Rice University
Tunable Parameters during Library Construction • Calibrating the Disruptive Nature of Recombination • Recombination as a Shortest Path Problem (RASPP) • Optimal Pattern of Tiling for Combinatorial Libraries (OPTCOMB) • Practical Considerations for Library Synthesis • Estimating Chimeric Library Diversity
5.6 Conclusions �������������������������������������������������������������������������������������5-23 References ���������������������������������������������������������������������������������������������������5-24
5.1 Introduction Naturally occurring enzymes are widely used for metabolic engineering, but they frequently do not exhibit the exact functional properties desired for a given application (e.g., they lack the desired stability, expression level, catalytic efficiency, or substrate-specificity). Within the colossal space of possible polypeptide sequences, proteins are thought to exist with functions that meet the specifications for almost any engineering goal that you can imagine. In some cases, knowledge-based design can find proteins with structures and functions distinct from those observed in nature [1–6]. However, our understanding of protein sequence-structure-function relationships is not yet sophisticated enough to consistently alter enzyme functions in a desired manner, especially when the design goal is to optimize a preexisting property. Directed evolution, in contrast, has repeatedly proven quite effective at optimization when applied alone or when used in tandem with knowledge-based mutagenesis [7]. By coupling mutagenic and selection (or screening) strategies as occurs in nature, this approach sieves through libraries of variants to filter out the desired ones with improved fitness. Directed evolution is restricted in its scope for many engineering goals. The number of variants that can be evaluated in a given experiment is minuscule (at best 1013 variants; see Ref. [8]) compared with the number of possible sequence variants that exist for an average size protein (~20300 in prokaryotes; see 5-1
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Evolutionary Tools in Metabolic Engineering
Fraction of variants that are functional
1
% Chimera from parent 1
% Chimera from parent 2
Recombination
0.01 10–4 10–6 10–8
Mutation
10–10 10–12 10–14
0
20 40 60 80 Amino acid substitutions (relative to parent 1)
100
Figure 5.1. Amino acid substitutions created by recombination are more conservative than those created randomly. Random mutations yield variants whose function decreases exponentially with increasing amino acid substitutions m; this trend is approximated by νm, where ν is the fraction of single mutations that do not disrupt function [13]. The fraction of functional proteins remaining after recombination of structurally related proteins is approximated by ρm(D–m)/(D-1), where ρ is the fraction of single amino acid substitutions created by recombination that do not alter function, and D is the sequence distance between the recombined parents. For the example shown, D = 100 and ν and ρ represent the values experimentally determined for lactamases, 0.54 and 0.79, respectively. (Adapted from Drummond, D.A., Silberg, J.J., Meyer, M.M., Wilke, C.O., and Arnold, F.H., Proc. Natl. Acad. Sci. USA, 102, 5380, 2005.)
Ref. [9]) and the frequency with which functional proteins are thought to occur in this sequence space (~1 in 1012 to 1 in 1077; see Refs. [10,11]). In addition, the most commonly used methods for creating sequence diversity (mutation and recombination) create a limited fraction of variants that are functional (see Figure 5.1), a prerequisite for evolving improved fitness. With mutation, this arises because random amino acid substitutions are deleterious to function nearly half of the time [12,13]. On average, the effects of multiple substitutions are statistically independent, leading to an exponential decline in the probability of finding anything functional in a combinatorial library [13,14]. Recombination of structurally related proteins is more efficient at finding functional proteins with a given level of amino acid substitution [15]. Single substitutions created by recombination are more conservative on average than those created randomly, and the probability of retaining function with increasing substitutions does not decrease as dramatically relative to random mutation [16]. Because screening capacity is limited and adaptive mutations are rare, the fraction of folded and functional proteins found in a library can influence the strides you make during laboratory evolution. One way to optimize an experiment is to increase the fraction of folded and potentially interesting variants in your library. This can be done by infusing into your library design some knowledge about the protein(s) being evolved. This can draw from our understanding of protein stability, family sequences, structure-function relationships, or previous laboratory evolution experiments. In this chapter, we survey models that can enrich the number functional proteins found in combinatorial libraries that have unique polypeptide sequences. We primarily focus our discussion on computational approaches that have been directly tested using in vitro evolution experiments (see Table 5.1). In many cases, it remains unclear which strategy is best suited for a given design goal, since these models have not yet been rigorously benchmarked against each other. With continually accruing experimental data, however, we
5-3
Models Predicting Optimized Strategies for Protein Evolution Table 5.1 Summary of Models for Guiding Directed Evolution Strategy
Applications
Thermodynamic predictions
Combinatorial consensus mutagenesis
Finds optimal parents for random mutagenesis Identifies optimal protocols for random mutagenesis Predicts libraries that are enriched in stabilized variants
Structure-guided saturation mutagenesis Structure-guided consensus mutagenesis
Enriches libraries in variants with altered activity Enriches libraries in variants with improved activity
Schema
Anticipates disruption for a single chimera
Residue clash maps
Anticipates disruption for a single chimera Anticipates disruption for a single chimera Identifies networks of residues that specify a proteins fold Finds chimeric libraries that minimize calculated disruption Finds chimeric libraries that minimize calculated disruption
Experimental Evidence
Citation
MUTATION-BASED APPROACHES
EP-PCR simulations
Accelerated evolution of cytochromes P450 function Predicted qualities of scFv EP-PCR libraries Accelerated lactamase and lactamase-antibody stabilization Altered the specificity of various biocatalysts Improved the specific activity of lactate dehydrogenase
[12,25] [19] [35,38]
[42–45] [48]
RECOMBINATION-BASED APPROACHES
FamClash Statistical coupling analysis Recombination as the shortest path problem Optimal pattern of tiling for library design
Active lactamase chimeras have lower disruption than non-functional variants Correlated with previously published chimera data Correlated with DHFR chimera activity Accelerated discovery of WW proteins with diverse function Accelerated discovery of folded cytochromes P450 Not yet experimentally validated
[55,56]
[57] [58] [39,66] [51] [52]
should begin to glean which methods are optimal for a given design goal and how the existing strategies can be used in concert to further enhance the efficiency of directed evolution.
5.2 Random Mutagenesis Typically the first approach used for any laboratory evolution goal is random mutagenesis, in which an error-prone polymerase chain reaction (EP-PCR) is used to create mutations in a gene [14]. This method is easy and useful for optimizing a preexisting function in an enzyme of interest (e.g., increasing the stability, catalytic efficiency, or altering the substrate specificity profile), even in the absence of structural or mechanistic information [17]. By using protocols that produce mutations at a low frequency, EP-PCR creates variants that have a reasonable probability of retaining parental-like structure, a prerequisite for improved fitness. When screening relatively small libraries, beneficial mutations can frequently be identified that yield small increases in protein fitness. Larger improvements can be achieved by performing multiple rounds of EP-PCR, using the best variants from each round of screening [18]. The real challenge in creating optimal libraries is to maximize the number of protein variants with unique sequences that are folded and functional [19]. Library size, mutation rate, and mutation spectrum dictate the number of distinct sequences evaluated in an experiment. The number of unique and functional proteins Uf analyzed also depends on the probability that each variant will retain function, which decreases approximately exponentially with increasing numbers of amino acid substitutions [12–14]. In this section, we review models that have been reported for optimizing random mutagenesis. The first uses a thermodynamic approach to find optimal parents [12], and the latter can be used to simulate EP-PCR reactions and identify protocols that maximize Uf [19].
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Evolutionary Tools in Metabolic Engineering
5.2.1 Choosing Parental Proteins Bloom and coworkers developed a simple thermodynamic model for estimating the fraction of proteins with m nonsynonymous mutations that retain parent-like function Pf(m) after random mutagenesis [12]. Their theory was motivated by the observations that the free energy of stability ∆G f for many proteins is similar in magnitude to the average stability change caused by single amino acid substitution [20,21], and also by the reports that multiple amino acid substitutions have approximately additive thermodynamic effects on protein stability [13,14,22–24]. Their model predicts that at low mutation levels, homologous proteins with differing stabilities exhibit a distinct fraction of functional proteins Pf(m) at each level of m (see Figure 5.2). As the level of m increases for structurally related proteins, the average effects of each additional random mutation begin to have similar average effects on protein function, and Pf(m) is approximately equal to νm, where ν is the average neutrality of each additional nonsynonymous mutation. Neutrality is a fundamental measure of a protein’s tolerance to mutation in terms of the fraction of proteins that retain wild-type structure after a single substitution [12–14]; this parameter is thought to depend largely on protein structure. Bloom’s model suggests that among protein homologs, those with higher stability yield EP-PCR libraries that are enriched in the number of unique, functional and potentially interesting proteins. Thus, when you have a choice of multiple proteins as parents for random mutagenesis, the model predicts that you should choose the parent that is more thermostable, provided that all else is equal. Two reports have recently provided direct experimental support of this hypothesis. In the first, the effects of random mutagenesis on β-lactamase function was measured for TEM-1 and a M182T mutant of TEM-1, which is 2.7 kcal/mol more stable than the native protein [12]. For each parent, identical mutagenesis conditions were used to create five distinct pairs of libraries with a range of mutation levels. The libraries derived from the M182T mutant consistently contained 15–60% more functional variants than those created from TEM-1. A recent study has also provided evidence that increases in protein stability promote the creation of new and improved cytochromes P450 [25]. As with the lactamases, a library derived from a more stable P450 encoded a greater fraction of variants with improved fitness properties. Protein variants created from a more thermostable P450 were ~two-fold more likely to be folded, as assessed by cofactor incorporation, and ~three-fold more likely to have new functions [26]. Provided that structural coordinates are available, Bloom’s theory can be used to estimate the effects of nonsynonymous mutations on protein function for any protein without creating a combinatorial library. This is done via a simple calculation of the probability that a sequence is still folded and functional Pf(m) after m nucleotide mutations as - ∆Gfextra
Pf (m ) =
∫
pm ( ∆∆G m )d ( ∆∆G m )
(5.1)
-∞
where ∆G fextra is the difference between the stability of the parental protein and the minimal threshold required to maintain its structure, and pm(∆∆Gm) is the probability distribution that a random mutation causes a stability change of ∆∆G [12]. The probability distribution can be obtained for all single amino acid substitutions by calculating ∆∆G1 using available algorithms [27,28], weighting these values by the probability that they are introduced by a single-nucleotide mutation during a random mutagenesis protocol, and assigning a zero ∆∆G value to all synonymous mutations and a large ∆∆G value to insertions and deletions (25 kcal/mol) [12]. The distribution pm(∆∆Gm) is calculated using an m-fold convolution of p1(∆∆G) as described [29]. Typically we do not know ∆G fextra. However, you can still estimate ν for any protein without this parameter by calculating the effects of a tenth mutation on folded variants that already contain nine mutations [12], i.e., the fraction of folded proteins with nine mutations that are inactivated by addition of another mutation. ∆G fextra can be calculated by fitting Bloom’s model to experimentally determined values for Pf(m) [12].
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Models Predicting Optimized Strategies for Protein Evolution
m=0
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Figure 5.2 Parental stability affects the fraction of functional proteins obtained upon random mutation. On average, random single mutations yield a higher fraction of folded and functional proteins for homologs with increased stability; compare m = 1 for P1 and P2 (F = folded and U = unfolded). In the case of the marginally stable P1, the incorporation of single random mutations into the F1 variants yields a similar fraction of functional variants with m = 2 as observed in the m = 1 distribution. However, in the case of the P2 homolog with increased stability, subsequent mutation of F1 variants yields a reduced fraction of functional variants (compare m = 1 and m = 2 distributions for P2). As shown for m = 10, a single round of mutation to the functional P1 and P2 progeny yield similar distributions of functional variants at high m. This results in the convergence of P1 and P2 to a similar exponential decline in function with increasing mutation level. (Adapted from Bloom, J.D., Silberg, J.J., Wilke, C.O., Drummond, D.A., Adami, C., and Arnold, F.H., Proc. Natl. Acad. Sci. USA, 102, 606, 2005. Adapted from Bloom, J.D., Labthavikul, S.T., Otey, C.O., and Arnold, F.H., Proc. Natl. Acad. Sci. USA, 103, 5869, 2006.)
5.2.2 Identifying a Protocol Several strategies have been described for altering the spectrum of mutations created during EP-PCR, including increasing Mg2 + concentrations over those used in standard PCR reactions [30], doping reactions with Mn2 + [14], varying the relative concentrations of nucleotides [31], and using DNA polymerases with different biases in the types of errors that they create [32]. Typically, EP-PCR is optimized for a
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Evolutionary Tools in Metabolic Engineering
given application by screening a set of conditions and empirically evaluating each based on the number of variants identified with increased fitness [14,31]. In a few cases, there has been evidence that protocols yielding high mutation frequencies are better for the acquisition of improved or novel functions [33]. These findings have recently been rationalized by a model described by Drummond and coworkers [19], which posits that optimal libraries maximize the number of unique functional variants Uf. Drummond’s model is by far the best available for optimizing EP-PCR protocols in cases where you have some information about: (i) the number of thermal cycles used for PCR n, (ii) the probability that DNA strands are duplicated during PCR λ, (iii) the average nucleotide substitution frequency created by a PCR protocol <mnt>, (iv) the probability that a base pair mutation is nonsynonymous pns, (v) the probability that a nonsynonymous mutation will truncate and inactivate a protein ptr, and (vi) the average fraction of functional protein variants with one amino acid substitution ν. Many of these parameters can easily be obtained through simple experiments. The probability of strand duplication λ = d/n can be calculated by measuring the number of doublings d that occur in n PCR cycles using DNA agarose electrophoresis. Sequencing a small number of variants in a library provides information on the <mnt>, pns, and ptr for a given protocol [12,16]. Furthermore, ν can be measured directly by creating a set of libraries, measuring the fraction functional and <mnt> in each library, and fitting the data as previously described [16]. Alternatively, ν can be estimated as described in Section 5.2.1 [12]. Using these parameters, you first calculate the distribution of nonsynonymous mutations Pr(m) in your library as
Pr (m ) = (1 + λ )-n
n
∑ k =0
n k ( ky ) e - ky k λ m! m
(5.2)
where y = <mnt>pns(1 + λ)/(nλ), and m is the total number of nonsynonymous mutations in a variant. You should avoid assuming that you have a Poisson-distributed library as previously described [14]. This assumption does not hold when high-frequency mutation rates are generated through a small number of PCR thermal cycles [19]. Variants created by EP-PCR have a significant probability of containing nonsynonymous mutations that are insertions, deletions, and premature stop codons [12,16]. These mutations typically disrupt protein function, so it is important to account for them when calculating Uf. In Drummond’s model [19], the number of sequences that lack indels and premature stops at each level of nonsynonymous mutation Nm is N·Pr(m)·(1-ptr/pns)m, where N is the number of transformants analyzed in experiment. The number of possible unique sequences at any level of m is limited by the conservative nature of the genetic code and given by L/3 Mm = 5.7m m
(5.3)
the number of unique, functional sequences Uf in your library is L /3
uf =
∑M
m
(1 - e- N
m / Mm
) νm
(5.4)
m=0
and the fraction of unique functional sequences in a library is Uf/N. Upon establishing Uf /N for a set of libraries, you simply choose the library that maximizes this value to optimize your directed evolution experiment.
5.2.3 Caveats Random mutagenesis can frequently optimize a preexisting enzymatic function through multiple rounds of evolution. However, this approach can be arduous and limited in the types of functional changes that
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can be accomplished. The fraction of functional variants that EP-PCR creates decreases exponentially with increasing mutation frequencies (see Figure 5.1), greatly restricting the number of potentially folded and functional variants evaluated when screening a library with multiple mutations. In addition, libraries created by EP-PCR contain less than a third of all possible variants with a single mutation. Nonsynonymous base pair mutations created by EP-PCR are only capable of producing at best an average of six out of 19 possible amino acid mutations at any given residue position. Because DNA polymerases like Taq exhibit biases in mutation frequency, an even more restricted sequence diversity can be encountered [14,31]. Such biases can be minimized by using strategies that produce a more uniform mutational spectrum than traditional approaches [14,31], e.g., using Strategene’s GeneMorph II kit.
5.3 Directed Mutagenesis Many engineering goals necessitate the incorporation of amino acid substitutions at specific sites that cannot be accessed by random mutagenesis. For example, alterations in cofactor or substrate specificity frequently require multiple substitutions at key sites that are unlikely to occur during EP-PCR [34]. Family sequence or biophysical information can be used to identify these key sites and direct mutations during laboratory evolution. In addition, results from laboratory evolution experiments can be used to guide the design of second-generation libraries, in which mutations are created that have an increased probability of creating variants with increased fitness. In this section, we review approaches that constrain the location and/or the types of mutations that are allowed. These strategies have had success increasing the frequency of finding variants with improved thermostability [35–38], improving protein expression [38], and accelerating the discovery of proteins with altered functions [39].
5.3.1 Combinatorial Consensus Mutagenesis (CCM) The herculean efforts in protein sequencing have yielded a tremendous number of homologous sequences for almost any protein that you might choose to evolve. There is emerging evidence that this information alone can direct mutations during library synthesis. One of the simpler methods that uses sequence information for library design is CCM, which creates mutations in a protein that correspond to highly conserved amino acids within its protein family [35,38]. CCM is derived from the idea that stabilizing residues are more highly conserved than other residues at a given position within a protein family. To perform CCM, two sequential libraries are created. In the initial library, all possible consensus mutations are generated with identical probabilities. Some of these mutations are destabilizing, so a small set of variants is screened for stability improvements, and the relative contribution that each possible mutation makes to stability is calculated using a simple model. Mutations that are predicted to be stabilizing are used to create a second library that is further enriched in variants with improved stability properties [35,38]. Laboratory evolution studies have provided evidence that CCM can accelerate the discovery of stabilizing mutations [35,38]. When screening a small pool of β-lactamase variants from a library created with 29 possible consensus mutations (229 = 108 variants), almost one quarter of all isolates had improved stability compared to the parent [35]. In a second optimized library, created using the subset of the consensus mutations that were calculated to be stabilizing, a great majority of the variants had stability that surpassed the parent. In fact, a screen of just a few hundred variants from this library identified a protein with a mid-point for thermal denaturation that was 9°C higher than the parent. In a more recent study, CCM has been used to optimize the expression and thermostability of a lactamase-antibody fusion, which is being developed as a prodrug therapy [38]. CCM is relatively easy to perform (see Figure 5.3). ClustalW is used to calculate a consensus sequence from a family sequence alignment [40], and an initial library is then synthesized using QuikChange multi site-directed mutagenesis, a method that requires one mutant primer for each possible consensus mutation [41]. The sequence and fitness of a small pool of variants (~100) in this library is evaluated, and the relative contribution that each consensus mutation k makes to fitness P k is calculated using
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Family sequences
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Evolutionary Tools in Metabolic Engineering
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Figure 5.3 Optimizing libraries using combinatorial consensus mutagenesis. A family sequence alignment is used to identify mutation sites and amino acid substitutions for creating an initial library. The sequence and relative fitness (Ri) is determined for i variants in this library, the contribution that each mutation k makes to fitness Pk is calculated, and a further optimized library is created using only those mutations that are predicted to improve fitness, i.e., those with Pk>0. (Adapted from Amin, N., Liu, A.D., Ramer, S., Aehle, W., Meijer, D., Metin, M., Wong, S., Gualfetti, P., and Schellenberger, V., Protein Eng. Des. Sel., 17, 787, 2004. Adapted from Roberge, M., Estabrook, M., Basler, J., Chin, R., Gualfetti, P., Liu, A., Wong, S.B., Rashid, M.H., Graycar, T., Babe, L., and Schellenberger, V., Protein Eng. Des. Sel., 19, 141, 2006.)
screening and sequencing data [35,38,41]. The second-generation library is then created using only those sites k where Pk>0, i.e., by including only those mutations that are found to be stabilizing in nature. To determine Pk, a matrix Mki is created where the possible mutation sites k in each characterized variants i are represented as a 1 if they are present (otherwise = 0). Stability contributions of each mutation are assumed to be additive, and the remaining activity Ri of each protein variant after heat treatment is defined as: m
log Ri =
∑M P +C ki k
(5.5)
k =1
where C is a constant that equals the remaining activity in the parental enzyme after a similar heat treatment. Finally, the set of values for Pk is calculated by minimizing the sum of the differences between the measured and calculated Ri for all n chimeras as follows:
n
min
∑ log( R )
i obs
- log ( Ri )calc
(5.6)
i =1
This minimization can be performed using the solver function of Microsoft Excel, as was described for the optimization of the lactamase CCM library [35].
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5.3.2 Structure-Guided Saturation Mutagenesis Saturation mutagenesis is one of the oldest strategies available for optimizing combinatorial libraries. In this approach, select codons within a gene are randomized to yield the full spectrum of possible amino acids at those positions. Saturation mutagenesis is extremely limited in the number of sites that can be effectively targeted and thoroughly screened for fitness improvements. When m sites are targeted, the ensuing sequence diversity = 20m. Thus, in libraries with just four or five mutations, the number of unique sequences is a staggering 105–106. In some cases, structural information has been used to guide saturation mutagenesis. Such experiments have had the most success when the design goal is to alter the substrate selectivity of an enzyme. In these cases, structural information is used to target key sites that appear to directly mediate protein substrate specificity, e.g., those directly contacting specific functional groups on your ligand of interest. One of the most notable early successes with this approach was the expansion of the genetic code [42]. In this study, an aminoacyl-tRNA synthetase was created that charges a tRNA with the non-natural amino acid O-methyl-L-tyrosine. To alter the amino acid specificity for this tRNA synthetase, variants were created by performing saturation mutagenesis at residues that were within 6.5 Å of the aryl ring of tyrosine [42]. Since this initial success, similar structure-guided approaches have been successfully used to create altered substrate specificity in diverse enzymes, including cytochromes P450 [43], N-acetylneuraminic acid lyase [44], and β-galactosidase [45]. Due to the structure of the genetic code, randomized codons (NNN, where N can be A, C, G, or T) produce amino acids at varying frequencies. This occurs because the number of codons encoding each amino acid varies, e.g., tryptophan is encoded by one codon and serine is encoded by six. If only one position is randomized, the differences in the ratio of the most common variant and the rarest in a library is not that large. However, this ratio increases exponentially ( = 6 m) with the number of codons m that are randomized. Thus, when even a small number of sites are targeted, e.g., m = 5, the most common variant is 7,776 times more likely to be encountered than the rarest one. Randomized codons also produce a significant number of variants with premature stop codons. The genetic code contains three stop codons, and the fraction of variants in a library that lack stop codons is (61/64)m. Thus, for the example above of a library with five randomized sites, approximately 22% of the protein variants are truncated. One way to minimize these issues is to use a biased nucleotide mixture when synthesizing each codon [46,47]. If you create a randomized codon using a nucleotide mixture that corresponds to NNX (where X can be C, T, or G but not A), then the fraction of variants that lack stop codons in increased to (47/48)m; with this mutation approach only 10% of variants in a m = 5 library are truncated. Biased nucleotide mixtures can also be used to reduce library sequence biases, e.g., the ratio of the most common variant and the rarest, as described in Ref. [46].
5.3.3 Structure-Guided Consensus Mutagenesis A modified consensus mutagenesis approach has been described that may accelerate the discovery of proteins with altered function [48]. This strategy borrows on concepts from CCM and site-saturation mutagenesis, in which key residues are mutated in a protein to try to improve the probability of making large fitness jumps. As with CCM, this strategy uses a family protein sequence alignment to determine the natural variability of residues at each position within the family. However, this approach also uses structural information to limit the possible mutations to sites proximal to the ligand-binding site being modified, unlike CCM. Only those residues that lie within 4 Å of the relevant cofactor (or substrate) are mutated. Residues are partially randomized at each site such that those positions can encode the wild type residue or any of the amino acids that were observed within the protein family at that position. Structure-guided CCM has been used to improve the specific activity of lactate dehydrogenase [48]. In this study, the PredictProtein server was used to find similar sequences to LDH from Bacillus
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Evolutionary Tools in Metabolic Engineering
stearothermophilus [49], and MaxHom was used to determine the sequence variability at each postion within the family alignment [50]. Five of the 17 residues proximal to NAD + in the LDH structure exhibited sequence variability and were targeted for mutation. The combinatorial library was then synthesized using PCR with oligonucleotide primers that encoded a programmed set of codons at each of the five positions [48]. The codons were designed to maximize the number of variants that exhibited protein family diversity at those positions. For each mutated codon, a minimal set of base pairs was chosen that encoded the parental residue and phylogenetically prevalent amino acids. It should be emphasized that this type of codon programming can at times generate undesired biases or substitutions at low frequencies in addition to the desired residues. As described in the previous section, the biases in programmed codon mixtures can be evaluated in silico to determine which best suits your sequence diversity goals.
5.4 Recombination The best strategy for creating functional proteins with large numbers of amino acid substitutions is protein recombination, in which structurally related polypeptides are exchanged among homologous proteins [15]. Studies investigating the effects on protein structure of amino acid substitutions created in this way have shown that recombination can be up to 1016-fold more effective than EP-PCR at finding functional proteins with high levels of substitution [16]. While the substitutions created by recombination are inherently more conservative than those created randomly, libraries containing chimeras with high levels of substitutions still contain a significant fraction of variants with disrupted structure and function. Several algorithms have been reported that use sequence and/or structural information to anticipate chimera disruption in a library. Typically these algorithms cannot be used alone for library optimization. They must be applied in concert with the models described in Section 5.5 to design optimized libraries for laboratory evolution, e.g., RASPP or OPTCOMB [51,52]. More sophisticated structure-based models have been described for anticipating the folding of protein variants created through mutation or recombination [53,54]. However, it remains unclear how well these approaches anticipate chimera disruption compared with the simpler models reviewed herein.
5.4.1 The Schema Algorithm The first major innovation in structure-guided recombination was the development of the Schema algorithm [55]. When swapping structurally related polypeptides among protein homologs, Schema posits that the best way to maximize your probability of conserving structure and function is to minimize the number of pairwise residue-residue interactions in the parental structures that are altered by recombination. Interactions are simply defined as any pair of residues that are within a defined cutoff distance. One of the major advantages of Schema is its simplicity and ease of use. As described in subsequent sections, additional models have been developed to anticipate chimera structural disruption. However, the ability of these methods to enrich libraries in folded and functional chimeras has not yet been rigorously tested, or benchmarked against Schema for a range of protein families. Laboratory evolution studies have shown that Schema disruption E is a useful metric for anticipating if a chimeric protein will retain function. In a well-defined library of β-lactamase chimeras, bacterial selections revealed that variants with low E were functional more frequently than expected if there was no correlation between E and the conservation of function [56]. Schema-guided recombination of cytochromes P450 have also yielded an amazing array of folded and functional chimeras with high levels of amino acid substitution [26]. In well-defined library, created by recombining three cytochrome P450 heme domains (~60% pairwise sequence identity), approximately half of the variants were correctly folded. The folded chimeras differed from their parents by an average of 73 amino acid substitutions, and a great majority of these variants had some catalytic function.
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Models Predicting Optimized Strategies for Protein Evolution (a) SCHEMA
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Residues 5 and 9 clash 0 charge in both parents –2 charge in chimera 1 2 3 4 5 6 7 8 9 10 Residue
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Figure 5.4 Using structural coordinates to calculate the disruption of a chimera. (A) To calculate the Schema disruption E for a chimera arising from the recombination of two homologous proteins, a matrix representing all interacting residue-residue pairs within the parents is created using the PDB coordinates of one parent (top panel), residue-residue pairs that cannot be altered by recombination are removed from this matrix (middle panel), and the number of remaining interactions that are broken by recombination are counted (thick lines, bottom panel). (B) To calculate RCM clashes E clash for the same chimera, coordinates are used to generate a contact matrix (top panel), residue-residue pairs that are proximal and not present in either of the parents are defined as potentially disruptive (thick lines, middle panel), and parental sequence data is used to determine which of these pairs create a clash upon recombination (bottom panel). A proximal residue-residue pair that alters the physiochemical properties of that pair compared to the parents is considered a clash, e.g., the chimera shown in the bottom panel contains a new residue-residue pair (E5–E9) whose charge is distinct from the structurally related pairs found in both of the parents (R5-E9 and E5-R9). (Adapted from Voigt, C.A., Martinez, C., Wang, Z.G., Mayo, S.L., and Arnold, F.H., Nat. Struct. Biol., 9, 553, 2002. Adapted from Saraf, M.C., and Maranas, C.D., Protein Eng., 16, 1025, 2003.)
To calculate E for a single chimera (see Figure 5.4a), you first identify all pairwise residue-residue interactions in the structure of one parent. In the simplest case of recombining two parents, the contacts are scaled by the sequence identity of the proteins being recombined, i.e., all contacts that cannot be broken by recombination are ignored. The E of a chimera is simply calculated by counting the number of residue-residue contracts broken when that chimeric protein inherits portions of its sequence from different parents. It should be noted that Schema does not differentiate between chimeras in a library that have identical crossover locations but different polypeptide inheritance, e.g., AA-BB versus BB-AA chimeras, even though these types of chimeras often exhibit distinct functional properties [56]. This can be contrasted with other metrics, such as FamClash and residue clash maps (RCM), which at times assign different calculated disruption values to chimeras with identical crossover locations [57,58].
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When recombining sequence elements from homologous proteins to create a single, monomeric chimera, si is used to indicate the parent incorporated at each position i in the chimeric sequence (e.g., s1 = 1 if the first residue in the chimera is inherited from the first parent, s2 = 2 if the second residue is from the second parent, etc.). The calculated disruption E of that chimera is N
E=
N
∑ ∑C ∆ ij
ij
(5.7)
i =1 j =i +1
where N is the number of residues that have well-defined coordinates in the structure used for calculations, and Cij designates whether two residues are contacting ( = 1 if residues i and j are within the cutoff distance, otherwise Cij = 0). In some cases a pairwise contact (i.e., Cij = 1) cannot be broken by recombination because some exchanged polypeptides do not effectively disrupt pairwise interactions observed in the parents; they don’t create residue-residue pairs that are distinct from those that are observed in one or more of the parents. For this reason, the delta function ∆ij uses a parental sequence alignment to indicate which pairwise interactions in the chimera are distinct from those present at structurally related positions in either parent. If the pairwise interactions between two amino acids i and j in the chimeric sequence are present in structurally related positions of either parent, then ∆ij = 0 (otherwise ∆ij = 1). It remains unclear whether a particular distance cutoff is optimal for calculating Cij and anticipating the structural disruption in a chimera. Studies investigating the quality of Schema predictions have used a distance cutoff equal to 4.5 Å [26,55,56,59], and these studies have typically excluded hydrogen, backbone nitrogen, and backbone oxygen atoms from the calculation of E. In studies using Schema to recombine cofactor-containing proteins, interactions between atoms in the cofactors and residues in the proteins are excluded from the calculation of E [26,59]. Homologous proteins typically bind and use the same cofactor, so any interactions between the cofactor and protein cannot be broken upon recombination. Provided that PDB coordinates are available for all the parents recombined, a structure-based alignment of the parental sequences should be performed to ensure that structurally related residues are numbered identically. Several free software packages can do this, including SwissProt and the combinatorial-extension algorithms [60–62]. If multiple conformational states are available for one or more of the parent proteins (e.g., structures in the presence and absence of substrates) you should assess E using all available coordinates to ensure that both conformational states of the chimeras are likely to exhibit similar disruption [59]. In cases where the structure of only one parent is available, a parental sequence alignment is generated using any multiple sequence alignment program, and all pairwise contacts Cij are identified using the available structure. When no direct structural information is available for any of the parents, a structural model can be used to identify residue-residue contacts in the parents. Structural models can be created using Swiss-Model [63], a fully automated protein structure homology-modeling server. It remains unclear how useful this latter approach is for optimizing libraries, although it seems likely that it will decrease as the parental sequences and the structural template used for modeling become more divergent. Protein homologs often differ in length, and alignment of the parents requires the insertion of gaps within the primary amino acid sequence of one or more of the parents. Such gaps should be accounted for during the calculation of E. When gaps are introduced into the parent whose structural coordinates are being used to generate the contact matrix Cij, the residues found in the other parents are ignored when calculating E because there is no corresponding structural information. In contrast, when gaps occur in any parent other than the one used for structural information, they should be treated like real residues that differ in identity from the residues in the other parents. Some proteins require oligomerization for their stability and function. In cases where such oligomereic proteins are recombined and your design goal is to create oligomeric chimeras, the number of broken interactions within a monomer may not be sufficient to account for all broken interactions. In
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addition to calculating the number of residue-residue interactions broken by recombination as described above (E), you must account for interactions that are disrupted between each of the peptide chains in the oligomer. The equation used to calculate interfacial disruption Einterface of an oligomeric chimera is the same as for calculating disruption in a monomer, except that Cij designates whether residue i from chain A is contacting residue j from chain B. The total calculated disruption for an oligomeric chimera Etotal = E + Einterface.
5.4.2 Residue Clash Maps (RCM) Conceptually, RCM uses a similar approach as Schema [57]. Parental sequences and structures are used to identify and count potentially disruptive residue-residue interactions that occur in chimeras created by recombining structurally related proteins. Whereas Schema treats all broken pairwise interactions as having a similar probability of disrupting function, RCM only considers interactions that significantly alter charge, volume, or hydrogen bonding properties. To date, there have been no reports of libraries optimized using RCM as a guide. It remains unclear how well this algorithm can design libraries de novo that maximize the number of sequences with low disruption (and a user defined level of sequence diversity). However, comparisons of RCM predictions with published experimental results for a small number of recombination studies indicate that functional chimeras have fewer RCM clashes than would be observed if clash maps were randomly generated [57], i.e., if clashes were not selectively restricted to residue-residue pairs that alter charge, volume, or hydrogen bonding properties. In RCM (see Figure 5.4b), the number of clashes in a chimera Eclash is N
Eclash =
N
∑ ∑C ∆ ρ ij
ij ij
(5.8)
i =1 j =i +1
where N is the number of residues that have well-defined coordinates in the structure used for calculations, and Cij designates whether two residues are contacting. Cij = 1 if the C β carbon in residues i and j are within 8 Å (if a residue lacks C β, then C α carbon is used), otherwise Cij = 0 [57]. As with Schema, the delta function ∆ij uses a parental sequence alignment to determine which pairwise interactions in the chimera are distinct from those present at structurally related positions of either parent. If the pairwise interactions between amino acids in the chimera are present in structurally related positions of either parent, then ∆ij = 0 (otherwise ∆ij = 1). Alignments are performed as outlined for Schema. The rho function ρij indicates which pairwise interactions in the chimera significantly alter the charge, volume, or hydrogen bonding properties of that pair from that observed in the parental proteins. ρij = 1 (otherwise ρij = 0) for any residues i and j that: (i) contain repulsive charges that are not observed in the parents (e.g., if two basic or acidic residues are placed in contacting positions within a chimera), (ii) do not create the number of satisfied hydrogen bonds that are detected in the parents, (iii) are within the core of the protein and have a volume change score Sij>15 Å3, or (iv) are surface exposed, have a additive volume greater that of either of the parents at those positions, and have a volume change score Sij>30 Å3 [57]. A residue-residue pair is only considered within the core of a protein if the accessible surface area of the side chain is <8 Å 2. The WHATIF software package can be used to calculate accessible surface area for a 1.4-Å water probe and identify hydrogen bonds [64]. In RCM [57], cavities created by recombination, i.e., residue pairs i and j created with additive volumes (Vi + Vj) that are less than the average additive volume Vp observed for the parents at those positions, have an Sij = |(Vi + Vj)-Vp-Dij|. In cases where steric hinderence arises from recombination (Vi + Vj>Vp), the Sij = |(Vi + Vj)-
+ Dij|. The Dij parameter accounts for level of tolerable changes in volume that occur within the parental proteins. Dij equals |(Vi + Vj)Parent1-(Vi + Vj)Parent2|, provided that it is greater than 0.1 Vp, otherwise Dij = 0.1 Vp to prevent artificially inflated scores.
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Evolutionary Tools in Metabolic Engineering
5.4.3 The FamClash Algorithm FamClash was developed to guide the recombination of homologous proteins using family sequence data alone [58]. FamClash posits that chimeras that retain the observed consensus physiochemical properties of residue-residue pairs within a protein family are most likely to retain parent-like function. The number of FamClash predicted residue-residue incompatibilities (designated clashes) appears to be related to the specific activity of functional chimeras [58]. A strong inverse correlation was reported between the number of calculated clashes and the specific activity of functional dihydrofolate reductase (DHFR) chimeras present in a combinatorial library, which was created by swapping single polypeptides between E. coli and B. subtilis DHFR. In FamClash (see Figure 5.5), each pair of sequence positions i and j in the protein family is initially designated as conserved (or not) based on the relative variation of additive charge (c), volume (v), and hydrophobicity (h) for those positions throughout the family [58]. For each sequence m in the protein family, matrices representing the additive charge (Cij = ci + cj), hydrophobicity (Hij = hi + hj), and volume (Vij = vi + vj) are calculated for related pairs of positions i and j using published values of c, v, and h [58]. The values obtained for all m sequences at a given pair of positions are partitioned into bins φpqr that subdivide the possible combinations of charge (p), hydrophobicity (r), and volume (q) property ranges. In the case of charge, p = Cij, yielding five possible integer values, -2, -1, 0, 1, and 2. Additive hydrophobicity and volume, in contrast, are broken into ten bins each, which divide their possible continuous range of values (q = 0-300 Å3 and r = -2.3-3.7 kcal/mol) into equal sized bins. Any pair of sequence positions i and j where ≥20% of the m naturally occurring pairs reside in one of the 600 possible φpqr bins is designated as potentially conserved. A threshold level of occupancy in a bin φpqr is not sufficient to designate a given pair of sequence positions as conserved by FamClash [58]. In addition, the identity of the amino acid at the first position
Homolog
C1,2
H1 H2 H3 H4 . .
0 1 0 0 . .
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0 0 0 –1 . .
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100 –2 kcal/mol 45 Å3 –1.4 kcal/mol –2 kcal/mol 100 Å3 90 Å3 –1.9 kcal/mol . . . . V1,3 220 Å3 145 Å3 180 Å3 175 Å3 . .
H2
Sort parents into φpqr bins H1 H3 H4
r = hydrophobicity
q = volume p
H1,3 –1 kcal/mol 1.8 kcal/mol 0.4 kcal/mol 0.5 kcal/mol . .
Residue pair 1,2 = potentially conserved
p = charge
H1
H2
H3 H4
q
r Residue pair 1,3 = not conserved
Figure 5.5 Finding residue pairs in a family with conserved physiochemical properties. In FamClash, a family sequence alignment is used to generate matrices that describe the additive physiochemical properties (charge Cij, volume Vij, and hydrophobicity Hij) of every pair of positions i and j in a protein. For each pair of positions, homologs are sorted into φpqr bins based on these properties, and the relative occupancy of the bins is used to establish whether a given pair of positions in the family displays conserved physiochemical properties. Pairs are considered as potentially conserved if a single bin has sufficient occupancy (>20% of the pairs). In the example shown, residue pair 1,2 is potentially conserved, but residue pair 1,3 is not conserved and is no longer considered in the calculation of chimera clashes. (Adapted from Saraf, M.C., Horswill, A.R., Benkovic, S.J., and Maranas, C.D., Proc. Natl. Acad. Sci. USA, 101, 4142, 2004.)
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Models Predicting Optimized Strategies for Protein Evolution
within the pair that occupies a highly populated bin must exhibit a defined level of dependence on the identity of the amino acids found at the second position. This relative level of dependence is given by the mutual information index score Mijpqr as
Mijpqr =
∑ ∑ P (a ,a ).log P (a( ) P (a) ) ik
k
jl
2
P aik , a jl ik
l
(5.9)
jl
where aik is the frequency of residue k at position i, ajl is the frequency of residue l at position j, P(aik,ajl) is the joint probability that k and l are observed at positions i and j at the same time in the parents, P(aik) P(ajl) is the product of the individual probabilities of occupancy for residues k and l at positions i and j. A pair of positions is considered conserved if the mutual information index is greater than a threshold value Mc. Mc is calculated using a bootstrap replicate analysis [58]. In this procedure, the vectors encoding residues i and j are copied from the parental sequence alignment, and 10,000 copies are made by randomly permuting the residues found in the original vectors. For each possible φpqr, mutual information index scores are generated using the bootstrap replicates. A Mc is chosen using the obtained score distributions for each φpqr, such that only 0.5% of the φpqr bins have scores that are greater than that value; that is, only three out of the 600 bins scores are greater than Mc. In a chimera, two residues are considered as having a clash if that pair of residues i and j is designated as conserved in the family, the amino acids k and l participating in that pair are inherited from different parents p1 and p2, and if any of the following four conditions occurs: (i) the additive charge of residues i and j in the chimera differs from the average additive charge obtained for all sequences in the protein family, (ii) the absolute value of the additive hydrophobicity of residues i and j in the chimera (hi + hj) is greater than the average additive hydrophobicity obtained for all sequences in the protein family plus a cutoff value δhij, (iii) the additive volume of residues i and j in the chimera (vi + vj) is greater than the average additive volume obtained for all sequences in the protein family plus a cutoff value δvij, or (iv) the additive volume of residues i and j in the chimera (vi + vj) is less than the average additive volume obtained for all sequences in the protein family minus a cutoff value 2δvij [58]. The cutoff values used for δvij and δhij are
{
}
{
}
δ vij = max Vijp1 - Vijp2 , Vij / 10 , δ hij = max Hijp1 - Hijp2 , Hij / 10
(5.10)
5.4.4 Statistical Coupling Analysis (SCA) SCA is an area of great excitement in sequence-guided protein design [39,65,66]. Like FamClash, SCA posits that if you have enough sequence information for a particular protein family, you can identify networks of coevolving residues that must be inherited from the same parent in order to conserve protein structure. Residues are defined as coevolving if the amino acids found at two positions in the protein family are observed to change in concert during sequence evolution [65]. Protein variants that maintain parent-like coupling are predicted to be the most likely to retain structure and be functional. It should be noted that SCA provides a binary answer about whether a particular variant is expected to retain parent-like coupling (and structure), unlike FamClash which predicts the extent to which protein structure is likely to be disrupted by recombination [58]. There is emerging evidence that for small single-domain proteins, SCA yields much of the information required for specifying protein fold and function [39,66]. Experiments analyzing the structures of 43 artificial WW domains with conserved coupling found that approximately one third of the variants retained parent-like structures [66]. In contrast, a similar sized set of WW variants that did not conserve coupling contained zero proteins with parent-like structures [66]. Subsequent functional analysis of the coupled variants further showed that these designed proteins exhibit functional properties similar to
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Evolutionary Tools in Metabolic Engineering
those observed in naturally occurring WW proteins [39]. Thus, it appears that in at least some protein families, this strategy can recapitulate the functional diversity observed in nature in a small combinatorial library. To perform SCA for a particular family, you must first generate a multiple sequence alignment. Ranganthan and coworkers have noted that such an alignment must be evolutionarily well sampled, such that the removal or addition of sequences does not significantly alter the amino acid distribution at each position [65]. Using the abundance of each amino acid x at each position j from this alignment, the binomial probability of observing an amino acid x at each position j(Pxi) is calculated as
Pjx =
N! N -n pxnx (1 - px ) x nx !( N - nx )!
(5.11)
where N is the number of sequences in the family alignment, nx is the number of sequences with amino acid x at position j, and px is the mean of all amino acids x in all proteins [65]. This information is converted into statistical energies ∆Gxj for each amino acid x at site j as
∆G xj = kT ln
Pjx x PMSA
(5.12)
where P is the probability of observed amino acid x in the family sequence alignment [65]. The statistical coupling between two sites in the protein family i and j (∆Gi,jstat) is given as
∆Gistat , j = kT
∑ ln( ∆G
x i δj
- ∆Gix
)
2
(5.13)
where ∆Gixδ j is the statistical energy for amino acid x at site j derived from the perturbation at i [65]. In cases where two residues are not coupled, the statistical coupling is zero. SCA has not yet been used to design proteins with >100 residues or create large combinatorial libraries for laboratory evolution, e.g., those with ≥102 variants [39,66]. However, statistical coupling has been observed in proteins significantly larger than WW domains, including retinoid X receptors [67] and PDZ domains [65], suggesting that this theory will be useful for diverse protein engineering goals. One can easily envision using SCA concomitantly with other optimization strategies for site-directed recombination. SCA could be used to identify coupled residues within biocatalysts, and library optimization strategies like Schema and RASPP could be used to find the libraries that minimize the structural disruption of chimeras which conserve coupled residues [51,55].
5.5 Optimizing Chimeric Libraries In cases where you want to recombine two parents at a couple of sites, it is typically easy to exhaustively enumerate all possible libraries of that type on a desktop computer and identify those that are optimally enriched in chimeras of low calculated disruption. However, when recombining multiple proteins at a large number of sites, exhaustive enumeration cannot calculate the disruption of all chimeras in all possible libraries of a given size for a set of parents. Consider the recombination of two average size bacterial proteins (~300 residues in length), which exhibit 30% amino acid sequence identity. If you created a library by allowing ten crossover sites between two parents (i.e., allowing inheritance of eleven different polypeptides) that library would contain a total of 211 unique variants. While complete enumeration of a single library is trivial, there are ~1015 possible libraries that exist for the two parents in this example; far too many for complete enumeration on a reasonably fast desktop computer. In these cases, the optimization strategies described below should be used for library design.
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Before implementing the algorithms reviewed in this section, you must first identify your diversity goal and choose a theoretical model to use for structural disruption, e.g., FamClash, Schema, and Residue clash maps [55,57,58]. Diversity goals could be as simple as creating a particular number of unique folded chimeras, or generating a specified number of folded chimeras with desired sequence properties, e.g., a particular average level of amino acid substitution. We still know very little about what constitutes optimal diversity when using recombination for laboratory evolution. There is emerging evidence that high levels of amino acid substitution may at times be better for creating chimeras with altered biocatalytic properties [26,59]. Once your goals have been established, you should calibrate the relationship between the theoretical predictions of your models and experimental reality. The models described in previous sections cannot estimate the fraction of folded and functional chimeras in a library a priori. They only generate noncalibrated estimates for the relative structural disruption of two or more chimeras. You must also have some prior knowledge about the disruptive nature of amino acid substitutions arising from recombination of your proteins of interest. Small experimental data sets, comprised of a handful of chimeras, can be used to establish this relationship and calibrate model predictions. With this data in hand, the algorithms described in this section can be used concomitantly with those described in Section 5.4 to rapidly identify near-optimal libraries that achieve your diversity goals.
5.5.1 Tunable Parameters during Library Construction Chimeric libraries optimized using computational approaches are typically created using site-directed recombination [26,56,68]. This experimental approach affords tight control over a number of library characteristics. The number of unique chimeras can easily be specified by how you build your library, since this parameter depends on the number of parents and crossover sites you use to build your library. Upon recombining p parents at n sites, pn + 1 unique variants are created in cases where there is no bias in the polypeptide inheritance (see Figure 5.6). The amino acid substitution level accessible in your chimeras can also be adjusted through your parental choice. The accessible substitution level increases as the sequence identity among the parents decreases, and also as the number of parents recombined increases. In cases where your optimization goal is to create a library with chimeras exhibiting a defined number of amino acid substitutions, it is best to recombine the most closely related proteins that will yield your desired goal [16]. Libraries created in this way are predicted to contain a higher fraction of functional variants than libraries created using more distantly-related parents [16]. Finally, the fraction of folded (and functional) proteins in your library can be maximized by choosing crossovers that maintain the calculated disruption below a threshold value. The design flexibility associated with the use of site-directed recombination for computation-guided library design is much greater than that of traditional strategies. In site-directed protein recombination, libraries of well-defined composition can be created using any parents that you can imagine. In contrast, annealing based strategies, like DNA shuffling [15], staggered extension process [69], or in vivo methods [70], are limited to recombining proteins with >70% sequence identity because these strategies can only create crossovers in regions where the parental genes exhibit high sequence identity. These restrictions not only limit the choice of parents, but they also limit the number of amino acid substitutions that can be incorporated into a given chimera, relative to the parents used for recombination. For example, when recombining two Class A β-lactamases [26,56], site-directed recombination can generate chimeras with >70 amino acid substitutions, but annealing-based strategies are limited to ≤40 substitutions. The sequence-independent methods SHIPREC, ITCHY, and SCRATCHY can also recombine more distantly related parents and access greater sequence diversity than annealing-based strategies [71–73]. However, these approaches cannot control crossover location, and they are grossly inefficient at recombining structurally related proteins; they frequently create insertions and deletions. In addition, they are limited to making chimeras with only a handful of crossover sites.
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Figure 5.6 Polypeptide inheritance in libraries. Sequence elements encoding structurally related fragments are swapped at user-defined locations during site-directed recombination. In this approach, structurally related fragments can be inherited with equal frequency (unbiased) or with defined biases.
5.5.2 Calibrating the Disruptive Nature of Recombination The effects on protein structure of a particular level of calculated disruption is expected to vary dramatically from experiment to experiment, as observed when comparing recombination studies with β-lactamases and cytochromes P450 [26,55,56,59]. Structural disruption can be influenced by a number of factors. First of all, protein topology and parental sequence identity affect the deleterious nature of amino acid substitutions created by recombination [16]. Second, the thermodynamic stability of the parent proteins is expected to influence the deleterious nature of recombination. Amino acid substitutions are predicted to be less deleterious when incorporated into proteins of higher thermostability, since the deleterious nature of random mutations created by EP-PCR decreases as the thermostability of the parents increases [12]. Protein expression is also expected to vary between recombination experiments, due to changes in chimera mRNA sequence. Some chimeric genes are expected to have altered mRNA half-life or secondary structure from the parental transcripts. Before going to the great effort of building a chimeric library, you should always ascertain the effects of amino acid substitutions by recombining your proteins of interest. In this way you can estimate a priori the fraction of folded (and potentially interesting) chimeras that are likely to be present in your combinatorial libraries. A quick way to perform such a calibration is to construct a small set of chimeras (~20), with varying levels of substitution and calculated disruption, and evaluate the structural conservation of those chimeras. In proteins that contain a cofactor, chimera folding can often be rapidly assessed by investigating whether the cofactor is incorporated [59]. In cases where the proteins being recombined do not contain a cofactor, retention of parent-like enzymatic activity can be used to assess structural conservation, as was reported with β-lactamases [55,56]. Several alternative screens have also been described for rapidly assessing protein solubility and stability [74,75]. Calibration experiments have had great success in estimating library quality and aiding in library design [59]. In the case of cytochromes P450, an initial calibration was performed on a small set of
Models Predicting Optimized Strategies for Protein Evolution
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chimeras by monitoring the incorporation of heme cofactors through CO-difference spectra [59]. The results from this study suggested that three distantly related P450s (60% sequence identity) could be recombined to create a library where almost half of the highly substituted chimeras (average mutation level = 76) folded. In fact, a subsequent study where this library was created and screened showed that 47% of the chimeras fold [26]. While the functional diversity in this library has not yet been established, it is expected to have rich functional diversity as observed in a small set of P450 chimeras derived from the same parents [59].
5.5.3 Recombination as a Shortest Path Problem (RASPP) RASPP is used for optimization in libraries where there are no selective restrictions imposed on the inheritance of polypeptide fragments [51], i.e., chimeras have an equal probability of obtaining each of their polypeptide fragments from the different parents (see Figure 5.6). For a given set of parents, RASPP uses dynamic programming to identify crossover locations that minimize the average energy of each library subject to constraints on the length of the fragments exchanged. This approach yields a set of libraries over the range of possible average amino acid substitution levels, which are near optimal compared with all possible libraries that can be enumerated. RASPP can be used with any metric of chimera structural disruption that involves pairwise-decomposable, residue-based energy functions. These include metrics as simple as SCHEMA calculated disruption [55], as well as rotamer-based energies that have been averaged to derive residue-residue interactions [53]. Several parameters must be defined when using RASPP, including: (i) the parents that will be recombined, (ii) the number of crossovers n, and (iii) the energy function that will be used for quantifying structural disruption (herein we use SCHEMA disruption for illustration purposes). Once these constraints have been established, a directed graph is created which represents every feasible library with n crossovers made from parents of length N (see Figure 5.8). In this graph, there are k columns representing each possible crossover choice in a library. The nodes shown in each column designate the possible recombination sites X within the primary sequence of the parents for each distinct crossover. In a library with n crossovers, the first node Xk that can be visited in any column is equal to the column number k; the last accessible node has a value = N-n + (k-1). A single library is represented by selectively connecting the nodes on adjacent columns with arcs, such that the node visited in column Xk represents the residue after which a recombination site occurs (see Figure 5.7). To calculate the disruptive nature of chimeras in a particular library, arc lengths are assigned such that the length of the total trajectory through the graph represents the average calculated disruption <E> of all chimeras within that library. Figure 5.7 shows that the arcs connecting node 0 to a node within the first column, designated A(0,X1), are simply given a length that corresponds to the average calculated disruption <E>(X1) of the chimeras found in a one-crossover library where the first crossover occurs after residue X1. Arcs between subsequent columns are given a length A(Xk-1,Xk) that corresponds to the incremental energy change associated with swapping the polypeptide defined by that arc. Figure 5.8 illustrates how an optimal library is identified by determining the shortest path through a directed graph representing all feasible three-crossover libraries. There is no need to optimize the arcs connecting node 0 to nodes in the first column because there is only one path to each node in column 1, i.e., all A(0,X1) are optimal. Optimal two-crossover libraries are identified by finding the shortest path to each node in the second column. All arcs representing suboptimal paths from node 0 to each relevant node in column 2 are removed. This process is continued to find the length of the shortest path U from node 0 to node j in column k using the shortest paths from node 0 to all nodes in column k-1:
u kj = min (u kj -1 + A(i , j )) i
(5.14)
The <E> of chimeras in a library is intimately linked to the <m> of those variants. For this reason, you cannot use a single directed graph representing all feasible n-crossover libraries to find a near-optimal
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Evolutionary Tools in Metabolic Engineering
A(0
,2)
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,4)
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A(0,2) = <E>CO1
1
5)
A(2,4)
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<E>CO1,CO2
<E>CO1
Figure 5.7 Directed graph representation of a single library. Top panel: a single library is represented by a set of arcs that form a continuous path through the graph. Each column k represents a crossover, and the node Xk within a given column designates the residue after which a crossover occurs. In this example, three arcs visit the nodes 21, 42, and 53, designating the four swapped polypeptides as those comprised of residues 1–2, 3–4, 5, and 6–10. Bottom: Arc weights are designated such that the complete path through the graph (representing a particular library) is equal to the average calculated disruption of chimeras defined by that library. In the case of arc A(0,2), the average weight is simply the average calculated disruption of chimeras created by allowing this crossover. In the cases of A(2,4) and subsequent arcs, the assigned weight is equal to the average disruption of the chimeras generated by each additional crossover. (Adapted from Endelman, J.B., Silberg, J.J., Wang, Z.G., and Arnold, F.H., Protein Eng. Des. Sel., 17, 589, 2004.)
library with high <m>. To find near-optimal libraries over a range of <m> values, the shortest paths through a set of directed graphs satisfying all feasible constraints on the length L of the swapped polypeptide fragments are calculated. The ranges of possible length constraints are: Lmin = 1 to N/(n + 1) and Lmax = N/(n + 1) to N-nLmin. For the three-crossover example shown in Figure 5.7, there are 11 different sets of possible Lmin and Lmax combinations (Lmin, Lmax = 1,2; 1,3; 1,4; 1,5; 1,6; 1,7; 2,2; 2,3; 2,4; 2,5; 3,3), so the shortest paths through eleven different graphs must be determined. Even for relatively long proteins, optimization problem can be calculated quickly since all arc lengths are initially determined; they do not have to be recalculated when finding the shortest path through directed graphs corresponding to each set of length constraints. Once the set of shortest-path libraries are identified for all combinations of Lmin and Lmax for a given optimization problem, the <E> and <m> is calculated for each RASPP library identified. A “RASPP curve” is then generated that shows the lowest-energy RASPP libraries over the range of all possible <m> values. These libraries represent the optimal tradeoff surface for library design, i.e., the libraries that maximize the fraction of folded and functional variants over a range of <m> values [51]. In previous studies seeking to find optimal libraries with four-crossovers between the β-lactamases PSE-4 and TEM-1, the optimal energy-diversity tradeoff surface identified by RASPP was almost identical to that found by enumerating all four-crossover libraries created from these parents [51].
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Models Predicting Optimized Strategies for Protein Evolution Col1 1 2 3 4 5 6 7 8 9 10
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0
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Col2 1 2 3 4 5 6 7 8 9 10 Col2 1 2 3 4 5 6 7 8 9 10
Col3 1 2 3 4 5 6 7 8 9 10
Figure 5.8 Finding libraries enriched in low disruption chimeras. Top: length constraints Lmin and Lmax dictate the arcs that are allowed when building a directed graph. Bottom: for a given set of length constraints, there is only one possible path from node 0 to each node in the first column, so all paths are retained. In the case of the paths from node 0 to each node in the second column, there are typically many possible paths. All arcs representing suboptimal paths from node 0 to each relevant node in column 2 are removed. This process is then repeated for all subsequent columns. To perform RASPP, the shortest paths are solved for directed graphs representing all possible combinations of constraints. (Adapted from Endelman, J.B., Silberg, J.J., Wang, Z.G., and Arnold, F.H., Protein Eng. Des. Sel., 17, 589, 2004.)
Figure 5.9 illustrates the libraries identified by RASPP when recombining adenylate kinases from Escherichia coli and Bacillus subtilis. By varying the length constraint for all libraries containing three crossovers, we generated 4,187 libraries using RASPP, 484 of which were unique. RASPP identified libraries over nearly the full range of possible <m> which are enriched in low disruption chimeras. A number of gaps do occur in the RASPP curve, however, where no libraries are obtained with a particular <m>. For example, no libraries were identified with <m> = 22-24. Gaps in a RASPP curve are common, since RASPP finds optimal libraries subject to length constraints, not average amino acid substitution level [51], as would be the case of an ideal optimization algorithm. One way to overcome gaps that are present in a RASPP curve is to choose alternate sets of parents for recombination.
5.5.4 Optimal Pattern of Tiling for Combinatorial Libraries (OPTCOMB) OPTCOMB is an approach for library optimization that uses pairwise-decomposable, residue-based energy functions to identify optimal crossover sites for recombination of structurally related proteins [52]. For a given set of parents, OPTCOMB minimizes the number of disruptive interactions (or clashes) present in a library that meets user-defined constraints on the length and number of exchanged polypeptides. By varying these constraints, OPTCOMB establishes the tradeoff between library size and library quality, i.e., average number of clashes per chimera. In a theoretical study evaluating DHFR recombination, OPTCOMB consistently identified libraries that were enriched in low clash chimeras compared to what is found on average in randomly generated
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35
Average calculated disruption
30 25 20 15 10 5 0
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10 15 20 25 30 Average amino acid substitution level
35
Figure 5.9 Evidence that RASPP curves approximate the optimal tradeoff surface. All possible three-crossover libraries were enumerated for the recombination of Bacillus subtilis and Escherichia coli adenylate kinase (N = 111 nonconserved residues), resulting in approximately 1.5x10 6 libraries (gray boxes). RASPP optimized libraries were calculated, yielding 4,187 libraries. Of these, 484 were unique, and this set of libraries exhibited only 62 distinct pairs of values for <E> and <m> (see black boxes). The black line represents the optimal energydiversity tradeoff surface. (Adapted from Endelman, J.B., Silberg, J.J., Wang, Z.G., and Arnold, F.H., Protein Eng. Des. Sel., 17, 589, 2004.)
libraries of similar size [52]. OPTCOMB displayed the most dramatic enrichment of low clash DHFR chimeras when creating small libraries (<104 variants). In these libraries, OPTCOMB consistently identified chimeric libraries with 50% fewer clashes (as defined by FamClash) than found in randomly chosen libraries. However, the extent to which OPTCOMB enriches combinatorial libraries in folded and functional proteins compared to the true optimum remains unclear for libraries with a particular level or sequence diversity. The initial report of OPTCOMB used the CPLEX solver for optimization [52]. CPLEX is a commercially available software package for mixed-integer linear programming that employs branch-andbound (and cut) techniques for optimization (see www.ilog.com/products/cplex/). Several parameters must be defined to use OPTCOMB, including: (i) the energy function that will be used to score structural disruption (e.g., FamClash or Schema disruption), (ii) the set of parents that will be recombined, (iii) the minimum number of exchanged polypeptides, and (iv) the minimum and maximum length constraints on the size of the swapped polypeptides. Once these constraints are defined, libraries of a given size that minimize the number of clashes are simply obtained by running the CPLEX solver for the set of OPTCOMB objective functions and mathematical constraints described [52]. The greatest feature of OPTCOMB is its ability to minimize the average number of clashes present in libraries that are generated with biased polypeptide inheritance (see Figure 5.6). This can be contrasted with RASPP, which can only optimize the design of libraries with unbiased polypeptide inheritance. However, unlike RASPP, OPTCOMB does not identify the optimal tradeoff surface for a given library size as shown in Figure 5.9. In theory, such a tradeoff surface could be obtained for OPTCOMB by identifying optimal libraries for all sets of possible polypeptide length constraints.
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5.5.5 Practical Considerations for Library Synthesis The best in vitro approaches for recombining distantly related proteins, sequence-independent sitedirected chimeragenesis (SISDC) and complete chemical synthesis [56,68], create chimeric libraries through the ligation of gene fragments that contain single-stranded overhangs. SISDC is typically preferred over complete chemical synthesis of a library, even though the latter approach is simpler. The gene fragments recombined during SISDC are derived from plasmids whose sequences can be verified before fragment assembly [68]. If any of your SISDC building blocks contain undesired mutations in the parent plasmids, you can remove them before assembling your combinatorial library. This minimizes random mutations which can significantly reduce the fraction of folded and functional variants in a library [16]. SISDC has one major limitation. As sequence identity among the parents decreases, you become increasingly limited in where you can select recombination sites without introducing amino acids that are not found in either parent. This happens because single-stranded overhangs (3–5 base pair sticky ends) are used at the crossover boundaries to mediate fragment association before ligation. In cases where the parents do not exhibit sufficient identity at the desired crossover locations, synonymous mutations can often be introduced to allow recombination at that site. If synonymous mutations are not sufficient to avoid mutation, then alternative libraries with different crossover positions should be chosen.
5.5.6 Estimating Chimeric Library Diversity When performing site-directed recombination, libraries can be generated where chimeras have a nearequal probability of inheriting polypeptides from any of the parents [16,56]. In such libraries, it is reasonable to assume that the probability of observing one chimera is independent from the number of occurrences of any other chimera, and the frequency at which each variant occurs can be approximated by a Poisson distribution. For this reason, the same approaches that are used to assess the statistics of libraries created by directed mutagenesis can be applied to those created by site-directed recombination [76]. The number of distinct chimeras C that are present in a library containing L clones and V possible total variants is V·(1- e -L/V). The fractional completeness F ( = C/V) in such a library is (1- e -L/V). F can be used to calculate how many clones L must be screened to sample a desired percentage of the V possible variants, i.e., L = -V·ln(1-F). Often when screening well-defined libraries of variants, it is more useful to determine the probability Pc that every chimera is present in your library [76]. In these cases, the number of clones L that must be screened to yield the desired probability Pc is given by
(
L = -V ln -
)
ln Pc V
(5.15)
5.6 Conclusions There is now extensive evidence that theoretical models can be used to optimize directed evolution. Current algorithms can be easily implemented on desktop computers to accelerate the discovery of proteins with improved functional properties. In most cases, existing models seek to increase the frequency at which folded variants are observed in combinatorial libraries, since folding is typically a prerequisite for acquisition of new enzyme functions. However, these strategies still remain somewhat limited in their information content, i.e., their ability to consistently predict which protein variants are functional and which ones are not. New models that use a combination of sequence, structure, and biophysical knowledge will most certainly extend the fitness steps that can be achieved in a single round of screening or selection.
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Evolutionary Tools in Metabolic Engineering
References 1. Allert, M., Rizk, S.S., Looger, L.L., and Hellinga, H.W. Computational design of receptors for an organophosphate surrogate of the nerve agent soman. Proc. Natl. Acad. Sci. USA, 101, 7907, 2004. 2. Dwyer, M.A., Looger, L.L., and Hellinga, H.W. Computational design of a biologically active enzyme. Science, 304, 1967, 2004. 3. Korkegian, A., Black, M.E., Baker, D., and Stoddard, B.L. Computational thermostabilization of an enzyme. Science, 308, 857, 2005. 4. Kuhlman, B., Dantas, G., Ireton, G.C., Varani, G., Stoddard, B.L., and Baker, D. Design of a novel globular protein fold with atomic-level accuracy. Science, 302, 1364, 2003. 5. Park, H.S., Nam, S.H., Lee, J.K., Yoon, C.N., Mannervik, B., Benkovic, S.J., and Kim, H.S. Design and evolution of new catalytic activity with an existing protein scaffold. Science, 311, 535, 2006. 6. Shifman, J.M. and Mayo, S.L. Exploring the origins of binding specificity through the computational redesign of calmodulin. Proc. Natl. Acad. Sci. USA, 100, 13274, 2003. 7. Arnold, F.H. Advances in Protein Chemistry, Vol. 55, 55 Edition. San Diego: Academic Press, 2001. 8. Takahashi, T.T., Austin, R.J., and Roberts, R.W. mRNA display: ligand discovery, interaction analysis and beyond. Trends Biochem. Sci., 28, 159, 2003. 9. Netzer, W.J. and Hartl, F.U. Recombination of protein domains facilitated by co-translational folding in eukaryotes. Nature, 388, 343, 1997. 10. Keefe, A.D. and Szostak, J.W. Functional proteins from a random-sequence library. Nature, 410, 715, 2001. 11. Axe, D.D. Estimating the prevalence of protein sequences adopting functional enzyme folds. J. Mol. Biol., 341, 1295, 2004. 12. Bloom, J.D., Silberg, J.J., Wilke, C.O., Drummond, D.A., Adami, C., and Arnold, F.H. Thermodynamic prediction of protein neutrality. Proc. Natl. Acad. Sci. USA, 102, 606, 2005. 13. Guo, H.H., Choe, J., and Loeb, L.A. Protein tolerance to random amino acid change. Proc. Natl. Acad. Sci. USA, 101, 9205, 2004. 14. Shafikhani, S., Siegel, R.A., Ferrari, E., and Schellenberger, V. Generation of large libraries of random mutants in Bacillus subtilis by PCR-based plasmid multimerization. Biotechniques, 23, 304, 1997. 15. Crameri, A., Raillard, S.A., Bermudez, E., and Stemmer, W.P. DNA shuffling of a family of genes from diverse species accelerates directed evolution. Nature, 391, 288, 1998. 16. Drummond, D.A., Silberg, J.J., Meyer, M.M., Wilke, C.O., and Arnold, F.H. On the conservative nature of intragenic recombination. Proc. Natl. Acad. Sci. USA, 102, 5380, 2005. 17. Bloom, J.D., Meyer, M.M., Meinhold, P., Otey, C.R., MacMillan, D., and Arnold, F.H. Evolving strategies for enzyme engineering. Curr. Opin. Struct. Biol., 15, 447, 2005. 18. Glieder, A., Farinas, E.T., and Arnold, F.H. Laboratory evolution of a soluble, self-sufficient, highly active alkane hydroxylase. Nat. Biotechnol., 20, 1135, 2002. 19. Drummond, D.A., Iverson, B.L., Georgiou, G., and Arnold, F.H. Why high-error-rate random mutagenesis libraries are enriched in functional and improved proteins. J. Mol. Biol., 350, 806, 2005. 20. Bava, K.A., Gromiha, M.M., Uedaira, H., Kitajima, K., and Sarai, A. ProTherm, version 4.0: thermodynamic database for proteins and mutants. Nucleic Acids Res., 32, D120, 2004. 21. Fersht, A.R. Structure and Mechanism in Protein Science. New York: Freeman, 1999. 22. Serrano, L., Day, A.G., and Fersht, A.R. Step-wise mutation of barnase to binase. A procedure for engineering increased stability of proteins and an experimental analysis of the evolution of protein stability. J. Mol. Biol., 233, 305, 1993. 23. Wells, J.A. Additivity of mutational effects in proteins. Biochemistry, 29, 8509, 1990. 24. Zhang, X.J., Baase, W.A., Shoichet, B.K., Wilson, K.P., and Matthews, B.W. Enhancement of protein stability by the combination of point mutations in T4 lysozyme is additive. Protein Eng., 8, 1017, 1995.
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5-25
25. Bloom, J.D., Labthavikul, S.T., Otey, C.O., and Arnold, F.H. Protein stability promotes evolvability. Proc. Natl. Acad. Sci. USA, 103, 5869, 2006. 26. Otey, C.O., Landwehr, M., Endelman, J.B., Hiraga, K., Bloom, J.D., and Arnold, F.H. Structureguided recombination creates an artificial family of cytochromes P450. PLoS Biol., 4, e112, 2006. 27. Guerois, R., Nielsen, J.E., and Serrano, L. Predicting changes in the stability of proteins and protein complexes: a study of more than 1000 mutations. J. Mol. Biol., 320, 369, 2002. 28. Gilis, D. and Rooman, M. PoPMuSiC, an algorithm for predicting protein mutant stability changes: application to prion proteins. Protein Eng., 13, 849, 2000. 29. Van Kampan, N.G. Stochastic Processes in Physics and Chemistry. Amsterdam: Elsevier, 1992. 30. Cadwell, R.C. and Joyce, G.F. Mutagenic PCR. PCR Methods Appl., 3, S136, 1994. 31. Fromant, M., Blanquet, S., and Plateau, P. Direct random mutagenesis of gene-sized DNA fragments using polymerase chain reaction. Anal. Biochem., 224, 347, 1995. 32. Vanhercke, T., Ampe, C., Tirry, L., and Denolf, P. Reducing mutational bias in random protein libraries. Anal. Biochem., 339, 9, 2005. 33. Daugherty, P.S., Chen, G., Iverson, B.L., and Georgiou, G. Quantitative analysis of the effect of the mutation frequency on the affinity maturation of single chain Fv antibodies. Proc. Natl. Acad. Sci. USA, 97, 2029, 2000. 34. Arnold, F.H. Fancy footwork in the sequence space shuffle. Nat. Biotechnol., 24, 328, 2006. 35. Amin, N., Liu, A.D., Ramer, S., Aehle, W., Meijer, D., Metin, M., Wong, S., Gualfetti, P., and Schellenberger, V. Construction of stabilized proteins by combinatorial consensus mutagenesis. Protein Eng. Des. Sel., 17, 787, 2004. 36. Lehmann, M., Kostrewa, D., Wyss, M., Brugger, R., D’Arcy, A., Pasamontes, L., and van Loon, A.P. From DNA sequence to improved functionality: using protein sequence comparisons to rapidly design a thermostable consensus phytase. Protein Eng., 13, 49, 2000. 37. Lehmann, M., Loch, C., Middendorf, A., Studer, D., Lassen, S.F., Pasamontes, L., van Loon, A.P., and Wyss, M. The consensus concept for thermostability engineering of proteins: further proof of concept. Protein Eng., 15, 403, 2002. 38. Roberge, M., Estabrook, M., Basler, J., Chin, R., Gualfetti, P., Liu, A., Wong, S.B., Rashid, M.H., Graycar, T., Babe, L., and Schellenberger, V. Construction and optimization of a CC49-Based scFvb-lactamase fusion protein for ADEPT. Protein Eng. Des. Sel., 19, 141, 2006. 39. Russ, W.P., Lowery, D.M., Mishra, P., Yaffe, M.B., and Ranganathan, R. Natural-like function in artificial WW domains. Nature, 437, 579, 2005. 40. Chenna, R., Sugawara, H., Koike, T., Lopez, R., Gibson, T.J., Higgins, D.G., and Thompson, J.D. Multiple sequence alignment with the Clustal series of programs. Nucleic Acids Res., 31, 3497, 2003. 41. Hogrefe, H.H., Cline, J., Youngblood, G.L., and Allen, R.M. Creating randomized amino acid libraries with the QuikChange Multi Site-Directed Mutagenesis Kit. Biotechniques, 33, 1158, 2002. 42. Wang, L., Brock, A., Herberich, B., and Schultz, P.G. Expanding the genetic code of Escherichia coli. Science, 292, 498, 2001. 43. Kubo, T., Peters, M.W., Meinhold, P., and Arnold, F.H. Enantioselective epoxidation of terminal alkenes to (R)- and (S)-epoxides by engineered cytochromes P450 BM-3. Chemistry, 12, 1216, 2006. 44. Williams, G.J., Woodhall, T., Nelson, A., and Berry, A. Structure-guided saturation mutagenesis of N-acetylneuraminic acid lyase for the synthesis of sialic acid mimetics. Protein Eng. Des. Sel., 18, 239, 2005. 45. Parikh, M.R. and Matsumura, I. Site-saturation mutagenesis is more efficient than DNA shuffling for the directed evolution of beta-fucosidase from beta-galactosidase. J. Mol. Biol., 352, 621, 2005. 46. Patrick, W.M. and Firth, A.E. Strategies and computational tools for improving randomized protein libraries. Biomol. Eng., 22, 105, 2005. 47. Bosley, A.D. and Ostermeier, M. Mathematical expressions useful in the construction, description and evaluation of protein libraries. Biomol. Eng., 22, 57, 2005.
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48. Flores, H. and Ellington, A.D. A modified consensus approach to mutagenesis inverts the cofactor specificity of Bacillus stearothermophilus lactate dehydrogenase. Protein Eng. Des. Sel., 18, 369, 2005. 49. Rost, B., Yachdav, G., and Liu, J. The PredictProtein server. Nucleic Acids Res., 32, W321, 2004. 50. Sander, C. and Schneider, R. Database of homology-derived protein structures and the structural meaning of sequence alignment. Proteins, 9, 56, 1991. 51. Endelman, J.B., Silberg, J.J., Wang, Z.G., and Arnold, F.H. Site-directed protein recombination as a shortest-path problem. Protein Eng. Des. Sel., 17, 589, 2004. 52. Saraf, M.C., Gupta, A., and Maranas, C.D. Design of combinatorial protein libraries of optimal size. Proteins, 60, 769, 2005. 53. Gordon, D.B., Marshall, S.A., and Mayo, S.L. Energy functions for protein design, Curr. Opin. Struct. Biol., 9, 509, 1999. 54. Moore, G.L. and Maranas, C.D. Identifying residue-residue clashes in protein hybrids by using a second-order mean-field approach. Proc. Natl. Acad. Sci. USA, 100, 5091, 2003. 55. Voigt, C.A., Martinez, C., Wang, Z.G., Mayo, S.L., and Arnold, F.H. Protein building blocks preserved by recombination. Nat. Struct. Biol., 9, 553, 2002. 56. Meyer, M.M., Silberg, J.J., Voigt, C.A., Endelman, J.B., Mayo, S.L., Wang, Z.G., and Arnold, F.H. Library analysis of SCHEMA-guided protein recombination. Protein Sci., 12, 1686, 2003. 57. Saraf, M.C. and Maranas, C.D. Using a residue clash map to functionally characterize protein recombination hybrids. Protein Eng., 16, 1025, 2003. 58. Saraf, M.C., Horswill, A.R., Benkovic, S.J., and Maranas, C.D. FamClash: a method for ranking the activity of engineered enzymes. Proc. Natl. Acad. Sci. USA, 101, 4142, 2004. 59. Otey, C.R., Silberg, J.J., Voigt, C.A., Endelman, J.B., Bandara, G., and Arnold, F.H. Functional evolution and structural conservation in chimeric cytochromes p450: calibrating a structure-guided approach. Chem. Biol., 11, 309, 2004. 60. Shindyalov, I.N. and Bourne, P.E. Protein structure alignment by incremental combinatorial extension (CE) of the optimal path. Protein Eng., 11, 739, 1998. 61. Guda, C., Lu, S., Scheeff, E.D., Bourne, P.E., and Shindyalov, I.N. CE-MC: a multiple protein structure alignment server. Nucleic Acids Res., 32, W100, 2004. 62. Guex, N. and Peitsch, M.C. SWISS-MODEL and the Swiss-PdbViewer: an environment for comparative protein modeling. Electrophoresis, 18, 2714, 1997. 63. Schwede, T., Kopp, J., Guex, N., and Peitsch, M.C. SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res., 31, 3381, 2003. 64. Vriend, G. WHAT IF: a molecular modeling and drug design program. J. Mol. Graph., 8, 52, 1990. 65. Lockless, S.W. and Ranganathan, R. Evolutionarily conserved pathways of energetic connectivity in protein families. Science, 286, 295, 1999. 66. Socolich, M., Lockless, S.W., Russ, W.P., Lee, H., Gardner, K.H., and Ranganathan, R. Evolutionary information for specifying a protein fold. Nature, 437, 512, 2005. 67. Shulman, A.I., Larson, C., Mangelsdorf, D.J., and Ranganathan, R. Structural determinants of allosteric ligand activation in RXR heterodimers. Cell, 116, 417, 2004. 68. Hiraga, K. and Arnold, F.H. General method for sequence-independent site-directed chimeragenesis. J. Mol. Biol., 330, 287, 2003. 69. Zhao, H., Giver, L., Shao, Z., Affholter, J.A., and Arnold, F.H. Molecular evolution by staggered extension process (StEP) in vitro recombination. Nat. Biotechnol., 16, 258, 1998. 70. Shao, Z., Zhao, H., Giver, L., and Arnold, F.H. Random-priming in vitro recombination: an effective tool for directed evolution. Nucleic Acids Res., 26, 681, 1998. 71. Lutz, S., Ostermeier, M., Moore, G.L., Maranas, C.D., and Benkovic, S.J. Creating multiple-crossover DNA libraries independent of sequence identity. Proc. Natl. Acad. Sci. USA, 98, 11248, 2001. 72. Ostermeier, M., Shim, J.H., and Benkovic, S.J. A combinatorial approach to hybrid enzymes independent of DNA homology. Nat. Biotechnol., 17, 1205, 1999.
Models Predicting Optimized Strategies for Protein Evolution
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73. Sieber, V., Martinez, C.A., and Arnold, F.H. Libraries of hybrid proteins from distantly related sequences. Nat. Biotechnol., 19, 456, 2001. 74. Waldo, G.S., Standish, B.M., Berendzen, J., and Terwilliger, T.C. Rapid protein-folding assay using green fluorescent protein. Nat. Biotechnol., 17, 691, 1999. 75. Philipps, B., Hennecke, J., and Glockshuber, R. FRET-based in vivo screening for protein folding and increased protein stability. J. Mol. Biol., 327, 239, 2003. 76. Patrick, W.M., Firth, A.E., and Blackburn, J.M. User-friendly algorithms for estimating completeness and diversity in randomized protein-encoding libraries. Protein Eng., 16, 451, 2003.
Gene Expression Tools for Metabolic Pathway Engineering
II
Christina D. Smolke California Institute of Technology
6 Low-Copy Number Plasmids as Artificial Chromosomes Kristala L. Jones Prather..................................................................................................................6-1 Introduction • Development of Low- and Multicopy Plasmid Expression Vectors • The Metabolic Burden Effect, Plasmid Instability, and the Impact of Both on Engineered Metabolic Systems • Applications of Low-Copy Plasmids to Metabolic Engineering • Opportunities for Artificial Chromosome Use in Metabolic Engineering • Summary
7 Chromosomal Engineering Strategies Kenan C. Murphy........................................ 7-1 Introduction • Strategies of λ Red-Promoted and SSR-Mediated Modification of the Bacterial Chromosome • Large Insertions • Applications of Red/ET Recombineering Technology for Metabolic Engineering • Summary
8 Regulating Gene Expression through Engineered RNA Technologies Maung Nyan Win and Christina D. Smolke.........................................8-1 Introduction • Basic RNA Regulatory Elements • RNA Sensory Elements • Natural and Engineered Riboswitches • Applications of RNA Control Elements in Metabolic Network Engineering • Enabling Technologies in Support of Constructing Integrated Network and Control Systems • Future Applications of Advanced RNA-Based Control Systems • Conclusions
II-1
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Gene Expression Tools for Metabolic Pathway Engineering
9 Tools Designed to Regulate Translational Efficiency Claes Gustafsson......................9-1 Introduction • Synthetic Genes • Synthetic Genes for Synthetic Biology • Codon Usage in Different Hosts • Improving Expression by Modifying the Host • Improving Expression by Modifying the Gene • Codon Optimization Using the CAI = 1 Algorithm • Codon Optimization by Probability Score • DNA Sequence Features to Eliminate, Add, or Modify during Design • Incorporating Tools for Translational Control in Metabolic Engineering
10 Metabolic Engineering of the Secretory Processing Pathway in Eukaryotes Mohak Mhatre, Maira P. Pellegrini, and Michael J. Betenbaugh............. 10-1 Introduction • Chaperones and Folding Catalysts • Glycoengineering • γ-Carboxylation • Furin Cleavage • Conclusion
11 Engineering Multifunctional Enzyme Systems for Optimized Metabolite Transfer between Sequential Conversion Steps Robert J. Conrado, Thomas J. Mansell, and Matthew P. DeLisa..........................................................................................................................11-1 Introduction • Enzyme-to-Enzyme Channeling • Metabolic Channeling in Primary Metabolism • Metabolic Channeling in Secondary Metabolism • Advantages Conferred from Multifunctional Enzyme Systems • Engineering Multifunctional Enzymes • Engineering Metabolic Channels de Novo • Concluding Remarks
12 Practical Pathway Engineering—Demonstration in Integrating Tools Sung Kuk Lee and Jay D. Keasling...................................................................................... 12-1 Introduction • Gene Discovery • Protein Engineering • Metabolic Pathway Regulation • Pathway Optimization Using Functional Genomics • Improvement of Cellular Properties • Perspective
M
etabolic engineering is the construction, redirection, and manipulation of cellular metabolism through the alteration of enzyme activities and levels to achieve the biosynthesis or biocatalysis of desired compounds.1 The optimization of production or consumption of a target metabolite often requires precisely controlled expression of several natural and/or heterologous pathway enzymes such that individual conversion steps in the pathway do not limit the desired product yield (based on different K m and kcat values), and that cellular resources and energy are not being inefficiently utilized by the cell host. Therefore, genetically-encoded tools that enable the control of enzyme expression levels and activities are required for the optimization of pathway fluxes. Metabolic engineering is a larger application area within biological engineering, in which cells are used to process chemicals, materials, and energy. The strategies utilized in accomplishing these tasks are based on mutagenesis and screening techniques, genetic engineering strategies, and recombinant DNA techniques. The field of metabolic engineering is seeing a transformation with the emerging field of synthetic biology. Synthetic biology is the design, construction, and characterization of biological systems using engineering design principles.2,3 To support a framework for engineering biology, synthetic biology is rooted in foundational technologies that enable the construction of more complex, heterologous networks in living systems. With advances in DNA sequencing and synthesis it is becoming common practice to synthesize entire genes and pathways from scratch, no longer limiting researchers to physical DNA they obtain from organisms. In addition, abstraction frameworks have been proposed to enable rapid assembling and reassembling of basic biological components (or parts) into larger networks (or devices) and systems, supporting the reliable construction of complex metabolic pathways in cellular hosts (or chassis).3 There is also discussion in the field around the engineering of specific chassis, or cellular hosts, optimized for metabolic engineering applications.4,5 Finally, enabling geneticallyencoded technologies are being developed for use in precise and quantitative manipulation of biological components such as enzymes. An example of a synthetic biology approach to the construction of a metabolic pathway in Escherichia coli was recently described.6
Gene Expression Tools for Metabolic Pathway Engineering
II-3
Section II provides an overview of some of the more important molecular tools for regulating enzyme expression levels and activities in different cellular hosts in metabolic engineering applications. The chapter topics have been selected to cover different mechanisms of regulation with a particular emphasis on tools that focus on regulation at different points along the gene expression pathway. It should be noted that the successful implementation of these strategies to a target pathway of interest will depend on the particulars of the pathway of interest, such that no one strategy will apply to every pathway and that some level of knowledge about the pathway enzymes and their activities in the host cell are needed in order to design a successful regulation strategy. In addition, these tools are not meant to be utilized in exclusivity or in the absence of one another and the most effective strategies will be those that integrate different tools effectively to optimize regulation of enzyme levels and activities across different steps in the gene expression pathway.7,8 In addition, this section does not cover all molecular tools that are important to optimizing flux through metabolic pathways in engineered hosts. For example, tools that allow transcriptional control, i.e., engineered promoter-based systems, are conspicuously absent from this section. The vast majority of metabolic engineering efforts to-date have focused pathway design strategies at the level of transcriptional regulation. In addition, researchers have developed libraries of engineered promoter systems for fine-tuning expression levels in different cellular hosts.9–11 Due to the prevalence of this expression tool, there are many good reviews on promoter-based systems in the literature,12,13 and a chapter on tools that regulate at the level of transcription is left out from this edition of the handbook. Chapters 6 and 7 look at tools that regulate DNA copy number. DNA copy number becomes an important consideration in minimizing the metabolic burden placed on the host cell. Overexpression of enzyme levels can sometimes kill or significantly reduce the growth rate of an engineered host cell by pulling too much of the precursor or energy resources from other pathways required for cell growth. In addition, DNA copy number can also be an important consideration to the stability of the synthetic genetic constructs in the host cell. Two different tools are discussed that allow for lower copies of genetic constructs in host cells: low- or single-copy plasmid systems (Chapter 6) and genome engineering systems (Chapter 7). Both of these systems allow for the expression of pathway enzymes in a host cell without the need for selective maintenance pressure, significantly lowering the costs associated with performing large-scale fermentations with the antibiotics that are typically used for plasmid maintenance. The choice of expressing a synthetic pathway from a single-copy plasmid versus from the chromosome of your host organism will likely be determined by the specifics of the pathway and process stage. Chapters 8 and 9 look at tools that regulate gene expression levels through post-transcriptional and translational mechanisms. Tools based on modulating post-transcriptional and translational processes can provide powerful strategies in precisely regulating enzyme expression levels to avoid overexpression and balance flux through pathways to avoid intermediate bottlenecks. Two different tools are discussed that ultimately allow one to tune enzyme expression levels: RNA-based regulatory systems (Chapter 8) and strategies that modify codon usage of the expressed genes (Chapter 9). Engineered RNA-based regulatory systems offer advantages in genetic control based on their tunability, programmability, and ease of implementation particularly for differential expression of multi-gene control.14,15 In addition, new advances in RNA engineering are enabling the implementation of metabolite-responsive control systems for optimizing metabolic pathways. The optimization of codon usage is becoming a standard strategy in conjunction with gene synthesis. If the genes encoding the enzymes of interest are taken from organisms that exhibit significantly different codon usage than the host cell, synthesis of that gene with a codon set optimized for that host cell can significantly improve the expression of that enzyme. Chapters 10 and 11 look at tools that regulate the activity of enzymes through post-translational engineering strategies. Cells use many different types of post-translational mechanisms to modify the activities of proteins and to increase the specificity of and flux through metabolic pathways. Two different tools are discussed that allow one to regulate enzyme activity: strategies for engineering post-translational modifications on proteins (Chapter 10) and strategies for coupling enzymes to form
II-4
Gene Expression Tools for Metabolic Pathway Engineering
engineered enzyme complexes (Chapter 11). Cells, and in particular eukaryotic organisms, modify proteins post-translationally with a variety of different functional groups to alter and regulate protein activity. The ability to engineer different post-translational modifications is critical to the functional heterologous expression of certain classes of enzymes. General strategies for building engineered enzyme complexes offers several advantages in metabolic engineering including providing methods to reduce intermediates in a pathway that may be toxic to the cell and to optimize the flux through a metabolic pathway through enzyme channeling and increasing effective intermediate concentrations. Both strategies can provide regulatory properties to different pathways that the other strategies that result in the regulation of enzyme levels cannot accomplish. Chapter 12 looks at the integration of different types of tools with different metabolic pathways. The chapter highlights cases in which the implementation of different molecular tools resulted in significant changes to flux or product accumulation in engineered metabolic pathways. The case studies serve to highlight the importance of molecular tools that enable the regulation of enzyme levels and activities and draw attention to the unique challenges posed in metabolic engineering, where often times different levels of enzymes in the metabolic pathway are needed in order to optimize cellular resources and energy usage.
References 1. Khosla, C. and Keasling, J.D. Metabolic engineering for drug discovery and development. Nat. Rev. Drug Discov., 2, 1019–1025, 2003. 2. Voigt, C.A. Genetic parts to program bacteria. Curr. Opin. Biotechnol., 17, 548–557, 2006. 3. Endy, D. Foundations for engineering biology. Nature, 438, 449–453, 2005. 4. Sharma, S.S., Blattner, F.R., and Harcum, S.W. Recombinant protein production in an Escherichia coli reduced genome strain. Metab. Eng., 9, 133–141, 2007. 5. Posfai, G. et al. Emergent properties of reduced-genome Escherichia coli. Science, 312, 1044–1046, 2006. 6. http://parts.mit.edu/wiki/index.php/MIT_2006. 7. Smolke, C.D. and Keasling, J.D. Effect of gene location, mRNA secondary structures, and RNase sites on expression of two genes in an engineered operon. Biotechnol. Bioeng., 80, 762–776, 2002. 8. Smolke, C.D. and Keasling, J.D. Effect of copy number and mRNA processing and stabilization on transcript and protein levels from an engineered dual-gene operon. Biotechnol. Bioeng., 78, 412–424, 2002. 9. Nevoigt, E. et al. Engineering of promoter replacement cassettes for fine-tuning of gene expression in Saccharomyces cerevisiae. Appl. Environ. Microbiol., 72, 5266–5273, 2006. 10. Jensen, P.R. and Hammer, K. Artificial promoters for metabolic optimization. Biotechnol. Bioeng., 58, 191–195, 1998. 11. Jensen, P.R. and Hammer, K. The sequence of spacers between the consensus sequences modulates the strength of prokaryotic promoters. Appl. Environ. Microbiol., 64, 82–87, 1998. 12. Tyo, K.E., Alper, H.S., and Stephanopoulos, G.N. Expanding the metabolic engineering toolbox: more options to engineer cells. Trends Biotechnol., 25, 132–137, 2007. 13. Hammer, K., Mijakovic, I., and Jensen, P.R. Synthetic promoter libraries—tuning of gene expression. Trends Biotechnol., 24, 53–55, 2006. 14. Smolke, C.D., Martin, V.J., and Keasling, J.D. Controlling the metabolic flux through the carotenoid pathway using directed mRNA processing and stabilization. Metab. Eng., 3, 313–321, 2001. 15. Pfleger, B.F., Pitera, D.J., Smolke, C.D., and Keasling, J.D. Combinatorial engineering of intergenic regions in operons tunes expression of multiple genes. Nat. Biotechnol., 24, 1027–1032, 2006.
6 Low-Copy Number Plasmids as Artificial Chromosomes 6.1 6.2 6.3 6.4
Introduction ��������������������������������������������������������������������������������������6-1 evelopment of Low- and Multicopy D Plasmid Expression Vectors ������������������������������������������������������������6-2 The Metabolic Burden Effect, Plasmid Instability, and the Impact of Both on Engineered Metabolic Systems..................... 6-4 Applications of Low-Copy Plasmids to Metabolic Engineering ������������������������������������������������������������������� 6-5
Motivation for Reducing the Expression Level • Case Studies of Low-Copy Plasmids in Metabolic Engineering
6.5
Kristala L. Jones Prather Massachusetts Institute of Technology
pportunities for Artificial Chromosome Use in O Metabolic Engineering ������������������������������������������������������������������6-10
Minimal Perturbation of Host Metabolism: An Opportunity for Novel Metabolic Control Systems • Maintenance of Large DNA Fragments: Pathway Engineering and Discovery
6.6 Summary �����������������������������������������������������������������������������������������6-13 References ���������������������������������������������������������������������������������������������������6-13
6.1 Introduction In the early 1970s, Stanley Cohen et al. reported the production of a new, autonomously replicating DNA molecule that had been constructed in vitro.1 It was produced by digesting separate tetracyclineresistant (pSC101) and kanamycin-resistant (pSC102) DNA fragments with the restriction endonuclease EcoRI and then covalently linking the two fragments with DNA ligase. Transformation of competent Escherichia coli with the resulting ligation mixture yielded clones with resistance to both tetracycline and kanamycin localized on a single contiguous DNA molecule (pSC105). This was the first published report of recombinant DNA technology. Since that time, recombinant DNA molecules have been used to produce a variety of both small molecules and proteins in host organisms ranging from prokaryotic microbes to animal cells. Such productivity is based on the promiscuous nature of molecular biology— based on a lack of fidelity to the genome—and the demonstration by Cohen and colleagues that these recombined genes could come from a variety of sources and be readily transcribed in a bacterial host.1–3 Thus, the biotechnology revolution that began with these first experiments was predicated on the ability to introduce and maintain heterologous DNA in a host organism. The carriers of foreign DNA, or vectors, may be thought of as “artificial chromosomes,” enabling the in vivo expression of genes that are maintained separately from the genome.
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Gene Expression Tools for Metabolic Pathway Engineering
Cohen’s vector of choice for many of these initial experiments—and for several experiments by other researchers that followed—was the R factor-derived plasmid pSC101, a 9.2 Kb plasmid with a single EcoRI site for gene insertion.4,5 Vector pSC101 is a low-copy plasmid, existing at ~five copies per genome in a bacterial host.6 As the field of biotechnology advanced, plasmids remained the vectors of choice for recombinant gene expression. However, the low-copy numbers of pSC101 and other naturally occurring replicons, such as the single-copy F factor of E. coli, proved to be suboptimal for many applications. As a result, the most widely available and widely used vectors to-date continue to be those derived from plasmids with much higher copy numbers. Yet an examination of the impact of high-copy number plasmids on microbial cell physiology, and a review of several examples in the literature, suggests that the use of such vectors can have a negative impact for the objectives of metabolic engineering. Indeed, the low-copy number plasmids may prove to be an advantage for this field. This chapter (i) provides an overview of the evolution of several representative plasmids that have found use in metabolic engineering; (ii) discusses the “metabolic burden effect” observed with multicopy plasmids and its significance for metabolic engineering; (iii) reviews examples of the use of lowcopy plasmids for metabolite production in engineered systems; and (iv) presents opportunities for the unique application of low-copy plasmids as artificial chromosomes. For the purposes of this discussion, the low-copy plasmids include both the pSC101-type R-derived plasmids that exist at less than ten copies per cell, and the F-factor-derived plasmids that exist at one to four copies per cell.
6.2 Development of Low- and Multicopy Plasmid Expression Vectors The most widespread and consistent use of expression vectors has been for the purpose of producing recombinant proteins. (The other significant use is for the replication of DNA fragments for sequencing.) Instinctively, one would believe that a reasonable strategy for improving the yield of a recombinant protein is to increase the dosage of the recombinant gene by using plasmids with higher copy number. However, most naturally occurring replicons exist at low to moderate copy numbers.7,8 The highest of these are the ColE1-type plasmids, which naturally exist at growth-rate-dependent copy numbers of ~40–55 molecules per cell.9 This represents a significant increase in gene dosage over the pSC101-derived and similar plasmids. Yet the copy number of these plasmids can be increased even further. The ColE1-type plasmids (ColE1, pMB1) require only plasmid-encoded proteins for in vivo maintenance and can replicate in the absence of active protein synthesis.10–12 The plasmid-encoded elements include two RNA transcripts, RNAI and RNAII, and a protein called Rop or Rom (for repressor of primer and RNA one modulator, respectively). Exclusion of the Rop/Rom protein from the plasmid results in an elevated copy number, yielding ~150 plasmid molecules per cell.13–15 Among these derivatives are the well-known and often-utilized pUC18 and pUC19 plasmids.16 However, the pUC plasmids were determined to have an additional modification relative to other Rop/Rom-deficient vectors, a G→A point mutation that maps to the RNAII sequence.17 This mutation results in a further amplification of the plasmid copy number, to ~500–700 copies per cell.18 As new vectors were being created with higher plasmid copy number, modifications to the basic genetic sequence were also being made to facilitate the cloning and expression of recombinant genes. A few key characteristics that are consistently included in new cloning vectors are • Constitutive or inducible promoter to eliminate the need for promoters to be carried with the recombinant open reading frame, and to enable high-level (and possibly controlled) expression in the bacterial host; • Multiple cloning site to provide multiple sites for insertion of the cloned gene behind the promoter; and • Selectable marker for identification of cells that have been successfully transformed by the vector (with or without an insertion), which is typically an antibiotic resistance gene.
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These features provide both greater consistency and flexibility in introducing a cloned gene into a vector and even moving the gene to other vectors. Many vectors include an additional feature to facilitate screening of clones for the presence of an inserted fragment. Typically, this is achieved by locating the multiple cloning site within the open reading frame of the lacZα gene fragment, encoding the alpha peptide of the enzyme β-galactosidase. Screening of the clones in the presence of IPTG, an inducer of the lac and related promoters, and X-gal, a chromogenic substrate for β-galactosidase, enables simple identification of clones carrying an inserted fragment based on visual inspection of the cells. Those without an insert will produce functional β-galactosidase, cleave the X-gal substrate, and become blue. Successful insertion will cause disruption of the lacZα fragment and prevent expression of the β-galactosidase enzyme. Such clones will remain white. Low-Copy plasmids with some or all of these same characteristics have also been developed. Several cloning vectors have been developed from the pSC101-type replicons, including pLG338/pLG339,19 pHSG575/pHSG76,20 pCL1920/pCL1921,21 pWSK29/pWSK30 and pWSK129/pWSK130.22 pLG338 and pLG339 do not contain a multiple cloning site, but differ in the number of unique sites available for insertion of a cloned gene. Each pair of the other vectors represents similar constructs with inverted orientations of the recognition sequences in the multiple cloning site. All in this latter group of vectors have the multiple cloning site located within the lacZα gene fragment to enable blue/white selection, and differ primarily in the antibiotic resistance gene (chloramphenicol, spectinomycin/streptomycin, ampicillin, and kanamycin, respectively), and all allow expression of the plasmid-encoded genes from a lac promoter. A representative pSC101-based low-copy plasmid is shown in Figure 6.1a. While in much of the literature, “low-copy plasmids” most often refers to pSC101-derived vectors, there are plasmids that exist at even lower copy numbers. In particular, the mini-F plasmids derived from the f5 fragment of the naturally occurring F factor of E. coli, are stably maintained at one to two copies per genome.23 This stability at such a low-copy number is achieved from specialized systems for timing replication precisely with the cell cycle24 and for segregating replicated plasmids into daughter cells at division.25 A back-up “kill” function results in the inhibition of cell division in plasmid-free cells that would arise from a failure of the replication or segregation systems.26 The extreme stability of the low-copy plasmids makes them especially attractive for metabolic engineering purposes, as will be discussed in more depth in the next section. However, these features, particularly stable maintenance at copy numbers equal or close to the genome, make mini-F plasmids true “artificial chromosomes.” (a)
repA
pCL1920 4549 bp
Plac HindIII PstI SalI XbaI BamHI SmaI KpnI SacI lacZα
(b) araC
pKLJ12 12396 bp
NheI EcoRI PBAD SalI SphI rrnB term. AmpR
Spc/StrR
mini-F (f5) fragment
Figure 6.1 (See color insert following page 13-20.) Representative low-copy plasmids. (a) Plasmid pCL1920, derived from the R-factor replicon pSC101. (b) Plasmid pKLJ12, derived from the mini-F replicon pML31 (f5 fragment of the F factor plasmid). In both diagrams, the restriction sites that constitute the multiple cloning site are shown. (From Cohen, S. N. and Chang, A. C. Y. Proc. Nat. Acad. Sci. USA 70 (5), 1293–1297, 1973a; Jones, K. L. and Keasling, J. D. Biotechnol. Bioeng. 59, 659–665, 1998. With permission.)
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Gene Expression Tools for Metabolic Pathway Engineering
Fewer cloning vectors derived from the mini-F plasmids are available than those from either pUC or pSC101. Two vectors that maintain each of the stability/maintenance functions described above have been constructed.27,28 Both contain a multiple cloning site (though limited in scope) behind an inducible promoter and the ampicillin resistance gene, bla. These two vectors differ in the choice of promoter, with one carrying the IPTG-inducible tac promoter (pKLJ03) and the other carrying the arabinose-inducible araBAD promoter (pKLJ12). A representative mini-F plasmid is shown in Figure 6.1b.
6.3 The Metabolic Burden Effect, Plasmid Instability, and the Impact of Both on Engineered Metabolic Systems Plasmids with such high-copy numbers as the ColE1-type pUC vectors do indeed typically result in a higher level of recombinant protein expression relative to their low-copy counterparts. A cursory review of the offerings of the major suppliers of biological research materials will reveal that most of the commercially available expression vectors are directly or indirectly derived from the high-copy pUC plasmids. As a consequence, these vectors are often the first chosen for applications in metabolic engineering. However, it should be noted that the use of such vectors carries some disadvantages. Principal among these are metabolic burden and plasmid instability. Introduction of plasmid DNA into a cell affects metabolism due to the need to both replicate plasmid DNA and to express plasmid-encoded proteins. Because these tasks utilize cellular machinery (DNA and RNA polymerases, ribosomes) and cellular building blocks (nucleotides, amino acids), the availability of the machinery and building blocks for normal cellular metabolism is altered. This effect has been termed “metabolic load” or “metabolic burden” and has been studied extensively, particularly with regards to limitations on recombinant protein production.28–35 Key findings related to the metabolic burden effect in E. coli include the following: (i) mRNA synthesis rates of a recombinant gene increase with increasing copy number, but the maximum level of recombinant protein is observed at intermediate plasmid copy numbers36,37; (ii) translation, and not transcription, is the bottleneck for recombinant protein synthesis with high plasmid copy numbers37,38; (iii) the total protein content of plasmid-bearing cells is lower than that of plasmid-free cells37; and (iv) high-level expression of recombinant proteins induces the heat-shock response.34,39 At the most basic level, plasmid-mediated metabolic burden can be thought of as any perturbation in the normal metabolism of the host cell as a result of plasmid presence. However, metabolic burden is most often quantified at a macroscopic level by measuring the growth rate. Several studies have shown that for plasmids with relaxed replication, including the ColE1-type vectors, plasmid copy number and growth rate are inversely correlated.9,29,40–42 It has also been shown that although plasmids at moderate copy numbers do cause a shift in the relative abundance of many proteins in the cell, a significant impact on growth rate is not observed until the copy number exceeds ~60 copies/cell.34 In other words, the cell is able to compensate for the extra resources allocated toward replication of plasmid DNA and expression of plasmid-encoded genes up to this point. In this report, the only plasmid-encoded gene was a constitutively expressed bla encoding for ampicillin-resistance. The effect of plasmid presence on growth rate is much more pronounced when high-level expression of a recombinant protein is achieved. In this case, a reduction in growth rate is often seen following the induction of a strong promoter. In one example, a reduction in growth rate of ~25% was observed when lacZ carried on a high-copy plasmid (~100 copies/cell) was expressed from an inducible promoter.27 When a low copy, mini-F plasmid was used instead, the reduction in growth rate was less than 10%. Since the culture growth rate affects the overall process productivity, minimizing this perturbation is often the goal for processes that involve recombinant protein production. In the case of metabolic engineering, however, one must be concerned with perturbations to metabolite flow, since most often, the precursors for the desired end products are provided from cellular metabolism. If the growth rate is maintained, but the fluxes toward these precursors are reduced as a result of increasing the plasmid copy number, then the impact on end metabolite production is likely to be
Low-Copy Number Plasmids as Artificial Chromosomes
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greater than that on recombinant protein production alone. Unfortunately, knowledge of the impact of plasmid presence on the metabolome profile is limited. In the next section, examples of such an effect, i.e., a decrease in metabolite productivity as the result of an increase in plasmid copy number, are described. The second primary disadvantage of multicopy plasmids, plasmid instability, may be observed in two different contexts: structural instability and segregational instability. Structural instabilities arise when inserted gene fragments are either partially or fully excluded from the vector, or disrupted through insertion sequences, following transformation of a host strain. Such instabilities may be observed if expression of the recombinant gene causes undue stress upon the host cell,43 for example, because of toxicity or unbalanced GC content, or if the inserted fragment is too large.44 Structural instabilities are likely to arise early in the strain development cycle; however, it is difficult to determine a priori whether a particular insertion will cause such instabilities.45 Segregational instabilities are the result of the failure of plasmid DNA to be maintained in every cell of a population. These instabilities arise, first, from the uneven distribution of plasmids into daughter cells following cell division.46,47 As discussed previously, growth rate and plasmid copy number are inversely correlated; therefore, plasmid-free cells will exhibit a faster growth rate. Competition will eventually result in the dominance of plasmid-free cells in the population. As stated previously, growth rate differences are more pronounced with high-level expression of a cloned gene from the vector. Similarly, the rates of plasmid loss under these circumstances are also much more pronounced.27 As with structural instability, prediction of which inserts are more likely to induce segregational instability is also quite difficult.48,49 Structural instabilities usually have to be resolved by a modification of the DNA. Segregational instability is most often resolved through the use of selective pressure. Since most cloning vectors include antibiotic resistance genes for selection of transformed cells, it is common to grow the culture in the presence of an antibiotic to suppress the growth of plasmid-free cells. This method is effective on a small scale; however, metabolic engineering frequently targets large-scale chemical production in which cost becomes a critical factor for process feasibility.50 The use of antibiotics in this case is not favorable. The low-copy plasmids, especially those derived from the F-factor, are extremely stable in the absence of antibiotics, even with maximum expression of a cloned gene product.27 The low-copy plasmids are therefore an attractive alternative to multicopy ones for large-scale processes.
6.4 Applications of Low-Copy Plasmids to Metabolic Engineering Although expression of recombinant proteins from multicopy plasmids can have a significant impact on the health and growth of the bacterial host, various control strategies can be used to balance growth and production. This is less true if the recombinant protein is inherently toxic to the cell. Inducible promoters are known to be leaky, with measurable and sometimes significant production of a protein or metabolite in the absence of the inducer.27,28 Reducing the gene dosage also reduces the background activity. Thus, one of the earliest (and continued) uses of low-copy plasmids was for the expression of toxic proteins in E. coli.51 As the field of metabolic engineering has grown, attention has shifted from the ability to produce a single desired protein to the ability to produce a single desired metabolite as the result of the production of several proteins. As discussed in the previous section, the impact of metabolic burden, and attempting to alleviate it, must therefore be considered to a greater extent. Equally important is the issue of vector stability. Metabolic engineering projects are more likely to be employed for large scale, high-volume products than small scale, low-volume ones; therefore, long-term stability of the recombinant strain is critical.
6.4.1 Motivation for Reducing the Expression Level The goal of most metabolic engineering projects remains the construction of a highly productive strain, and in many cases, this will be accomplished by overexpressing a pathway gene. What must be determined then is the expression level that is most appropriate for maximizing not protein activity but
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Gene Expression Tools for Metabolic Pathway Engineering
product formation. Several examples indicate that there are circumstances under which maximum product formation is not correlated with maximum gene expression. In one example, the biopolymer poly-3-hydroxybutyrate (PHB) was produced in E. coli.52 Both the plasmid copy number (~12 and >100 copies/cell) and type of expression (constitutive vs. inducible) were varied to determine the impact on overall product yield and molecular weight distribution. The results indicate that the highest productivity and molecular weight were achieved with induced expression from the lower copy plasmid. In a second example, a pathway was constructed for the production of plant flavonols.53 Completion of the pathway required the expression of a plant P450 hydroxylase. The P450 was introduced on plasmids with two different copy numbers (~10–12 and ~20–40 copies/cell). It was observed that growth of the culture carrying this enzyme on the higher copy plasmid was less stable, resulting in lower productivity. While protein activity does not linearly increase with increases in gene dosage, 36 it is still true that multicopy plasmids will almost always produce higher levels of protein than low-copy counterparts. In both of the previous examples, positive results were obtained by lowering the plasmid copy number and/ or expression level; however, the final vectors employed were still multicopy (≥10 copies/cell). It is therefore appropriate to ask: can low-copy plasmids meet the needs of the metabolic engineer? To answer this question, consider first the stability of maintenance and inheritance and the metabolite productivity as a result of the enzymatic activities that are encoded by the genome, a single copy vector. A healthy bacterial cell produces a variety of metabolic products in a variety of concentrations, mostly with single-copy genes. One approach to metabolic engineering is to increase the flux through an existing pathway in order to increase productivity. In this case, it may appear as if multicopy plasmids are the only effective means of boosting the levels of a particular enzyme. However, expression levels of genome-encoded enzymes can be altered through chromosomal engineering. Frost and coworkers used this approach to improve the synthesis of aromatics from glucose using an E. coli host.54,55 The choice of chromosomal manipulation to engineer the system was made deliberately in order to avoid metabolic burden effects from the expression of multiple genes on multiple plasmids. To increase the activity of enzymes in the biosynthetic pathway for aromatic amino acids, single additional copies of the aroB, aroA, and aroC genes placed behind the strong tac promoter were inserted into the chromosome.54 This strain was compared to others in which the same genes were encoded on multicopy plasmids (tens of copies/cell) and was found to produce an equivalent amount of the aromatic end products phenylalanine, phenyllactate, and prephenate. Measures of enzyme activity revealed that increases of 3.6–11-fold were observed for the three genes following chromosomal insertion. Interestingly, the addition of a fourth and fifth gene for increasing upstream pathway activities (tktA and aroF) on a ColE1-type multicopy plasmid (~40 copies/cell) reduced the aroB, aroA, and aroC activities by two-fold, presumably due to metabolic burden effects. Using the same chromosomal engineering strategy in a different host enabled the high-level production of the aromatic compound p-hydroxybenzoic acid.55 In another example of chromosomal engineering, the objective was the enhancement of β-carotene production in a modified strain of E. coli.56 Protein products of native genes, including dxs, idi, ispA, and ispD, participate in the production of isopentenyl diphosphate (IPP), the universal precursor for isoprenoids. Several researchers had previously increased the copy number of one of more of these genes with multicopy plasmids and observed a corresponding increase in carotenoid production.57,58 However, the approach taken by Yuan et al. was to replace the native promoters of several of the pathway genes with the strong bacteriophage T5 promoter.56 In doing so, β-carotene production increased 6.3-fold over the strains carrying the native promoters. It was also observed that expression of only the dxs gene from the T5 promoter increased β-carotene production by 2–3.3-fold. This increase was comparable to that observed in another carotenoid, lycopene, when dxs was overexpressed on multicopy plasmids.28 Finally, consider a third example where the native promoter was replaced, but rather than testing a single replacement, a promoter library of varying strengths was created, enabling a range of expression levels.59 With this system two case studies were examined: (i) the effect of altered expression of ppc on biomass yield on glucose; and (ii) the effect of dxs expression on lycopene production. In both cases, it
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was determined that the best phenotypes were observed with intermediate expression levels, similar to an observation made several years prior with respect to protein activity.36 The response of dxs to the promoter library changed, however, in a strain background where the expression of downstream pathway genes ispFD and idi had been increased through chromosomal engineering. In this case, lycopene increased nearly monotonically with increasing promoter strength, reflecting a new bottleneck in the pathway. Recall as well that flux through this carotenoid pathway was further modified through additional chromosome engineering.56 From these examples, it can be seen that there are cases in which a reduction in plasmid copy number is a competitive advantage. However, it is worth noting another motivation for using low-copy plasmids, namely, the need to express multiple genes in a single host. Because metabolic engineering is concerned with pathways and systems, it is rare that overexpression of a single gene will result in an optimally productive host. This is certainly the case with transference of entire pathways between hosts. Instead, and as has been demonstrated in the previous examples, multiple genes are required to assemble, first, a fully functional pathway, and second, an optimized one. It is certainly possible to introduce multiple genes on one vector,53–55,60 but reduction to a single vector (plasmid or genome) carrying the entire pathway of interest with all of its modifications is usually a long-term approach. In the short term, researchers are much more likely to clone the desired genes onto several different vectors and simultaneously introduce them into the same cell for coexpression. In order to achieve this, the various vectors must belong to different incompatibility groups. Because the pSC101 and mini-F plasmids belong to incompatibility groups that are both distinct from one another and from the commonly used multicopy plasmids, they are easily employed to co-express genes from multiple plasmids.
6.4.2 Case Studies of Low-Copy Plasmids in Metabolic Engineering Applications of low-copy plasmids in metabolic engineering case studies are summarized in Table 6.1. As shown, in three of these cases, the use of a low-copy vector seemed to be driven primarily by the need to utilize several different plasmid vectors in the same cell.57,61,62 However, in three other cases, use of a low-copy plasmid provided a clear advantage over multicopy counterparts. These three cases will be discussed in more detail below. Table 6.1 Applications of Low-Copy Plasmids in Metabolic Engineering Product
Replicon
Comments
Reference
Lycopene
pSC101
Vector apparently chosen due to need to express multiple plasmid-encoded genes in a single host Uninduced expression of dxs from low-copy plasmid with tac promoter resulted in moderate improvement over expression from a high-copy plasmid (2.9- vs. 2.4-fold over wild type); however, induced expression suppressed productivity when high-copy plasmids were used Comparable production of steady-state levels of polyphosphate with ppk gene present on low- or high-copy plasmids on a per cell basis; higher cell density leads to higher volumetric yields with low-copy plasmid Vector apparently chosen due to need to express multiple plasmid-encoded genes in a single host Low-Copy expression of guaBA gave ~60% higher yield of BH4 compared to expression of the same genes from a high-copy plasmid Vector apparently chosen due to need to express multiple plasmid-encoded genes in a single host
57,75
Mini-F
Polyphosphate
Mini-F
Indigo
pSC101
Tetrahydrobiopterin (BH4)
pSC101
Chitinbiose
pSC101
Note: The host organism in each of these examples is E. coli.
28
28
61 67
62
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Gene Expression Tools for Metabolic Pathway Engineering
6.4.2.1 Enhanced Production of Polyphosphate through Overexpression of ppk from a Mini-F Plasmid (Figure 6.2a) 28 Polyphosphate (polyP) molecules are produced by the action of the enzyme polyphosphate kinase (PPK) in a one-step mechanism in which the terminal phosphate from an ATP molecule is transferred to a growing polyP chain, producing an n + 1 length chain and ADP (Figure 6.2a). PPK can also operate reversibly to synthesize ATP from ADP and a polyP chain. The gene is naturally transcribed as part of an operon that includes the enzyme polyphosphatase (PPX), which processively degrades polyP to inorganic phosphate. PolyP accumulates in E. coli in response to a shift from low Pi to high Pi medium.63 The production of polyP can also be easily enhanced by introducing additional copies of the ppk gene into the host cell, performing the Pi-shifts, and inducing expression of the recombinant genes following the shift to low Pi. These shift experiments were conducted with both the chromosomal copies of ppk and ppx, and plasmid-based copies of ppk at either low- or high-copy number. Since polyP is a storage polymer, steady-state levels were measured 24 hours following the shift to excess phosphate. The results indicate that the amount of Pi incorporated into polyP at steady-state was ~200 µmol/g DCW for both the lowand high-copy strains (Table 6.2). However, the observed maximum cell density was ~30% lower for the multicopy plamids. This is an indication of the metabolic burden effects that can be observed with (a) PPK
polyPn ATP (b)
polyPn+1
PPX
O
polyPn+ Pi
-O
P
O
O-
P
O-
On–2 O
SPR O
H 2N
N
CH3
H2N
N
H N
O
C10, C15, C20 H2N
IMP
OH
N
HN GCHI
N
H2N PPP O
N
N O
OPPP N
(d)
CH3
O
OH
HN
OH
NH
PTPS
OPP DMAPP (C5)
polyP
BH4
O
NH
idi
HN
O
H N
HN
DXP
Lycopene
O
(c)
Glucose
OPP
O
O-
ADP
Pyruvate + G3P dxs
IPP (C5)
P
O
OH
NH
guaB
XMP
guaA
OH OH
GMP
gmk
GDP
GTP
Figure 6.2 (See color insert following page 13-20.) Metabolic pathways in which the use of low-copy plasmids improved production. (a) One-step pathway for the production of polyphosphates from ATP. PPK = polyphosphate kinase, PPX = polyphosphatase. (b) Abbreviated pathway for lycopene formation. dxs = DXP synthase, idi = IPP isomerase; G3P = glyceraldehydes-3-phosphate, IPP = isopentenyl diphosphate, DMAPP = dimethylallyl diphosphate; C10, C15, C20 symbolize 10-, 15-, and 20-carbon chain length intermediates. (c) Tetrahydrobiopterin (BH4) biosynthetic pathway from GTP (guanosine 5′-triphosphate). GCHI = GTP cyclohydrolase I; PTPS = 6-pyruvoly-tetrahydropterin synthase; SPR = sepiapterin reductase. (d) Abbreviated pathway for GTP synthesis from IMP. guaB = IMP dehydrogenase; guaA = GMP synthase; gmk = GMP kinase. IMP = inosine 5′-monophosphate; XMP = xanthosine 5′-monophosphate; GMP = guanosine 5’-monophosphate; GDP = guanosine 5′-diphosphate; GTP = guanosine 5′-triphosphate.
Low-Copy Number Plasmids as Artificial Chromosomes
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Table 6.2 Comparison of Productivities Observed with the Use of High- and Low-Copy Plasmids in Metabolic Engineering Case Studies Low-Copy Plasmids
Multicopy Plasmids Case 1: Polyphosphate28
219 µmol Pi/g DCW (1X)
223 µmol Pi/g DCW (1X)
164 µmol/L (1.3X)
127 µmol/L (1X) Case 2: Lycopene28
tac promoter (0 mM IPTG) 8.5 mg/L (1.2X) tac promoter (0.1 mM IPTG) 8.5 mg/L (7.1X) araBAD promoter (13.3 mM arabinose) 7.5 mg/L (0.7X)
tac promoter (0 mM IPTG) 6.9 mg/L (1X) tac promoter (0.1 mM IPTG) 1.2 mg/L (1X) araBAD promoter (13.3 mM arabinose) 10.3 mg/L (1X)
Case 3: Tetrahydrobiopterin (BH4)*,67 ~120 AU (1.6X)
~75 AU (1X)
Note: Enhancement factors relative to the multicopy plasmids are given in parentheses next to the relevant data points. *For the tetrahydrobiopterin, reported product concentrations are estimated from three data points presented in a scatter plot in the original reference.67 Units were originally reported as “relative intensity” and are given here as “arbitrary units” (AU).
engineered microbes. Because the cell densities differed so significantly, the volumetric yield of polyP was ~30% higher in the culture that utilized low-copy plasmids. 6.4.2.2 Enhanced Lycopene Production by Overexpression of dxs from a Mini-F Plasmid (Figure 6.2b) 28 The production of lycopene, a carotenoid, has already been presented in previous examples. Briefly, E. coli contains a mevalonate-independent pathway for the production of IPP, the universal precursor for the isoprenoids. In this pathway, the first step toward IPP is the condensation of pyruvate and glyceraldehyde-3-phosphate to form 1-deoxyxylulose 5-phosphate (DXP).64 The gene responsible for the DXP synthase activity is dxs.65,66 Introduction of the carotenoid synthesis genes crtEBI from Erwinia herbicola completes the pathway for lycopene synthesis in E. coli. As described previously, increased expression of the dxs gene results in elevated levels of lycopene. 56,58,59 In this case, lycopene levels were measured as a function of plasmid copy number, comparing a mini-F-derived vector to a high-copy ColE1-type plasmid. Additionally, two different promoters were examined, tac and araBAD. Use of the strong tac promoter resulted in increased levels of lycopene for both low- and multicopy plasmids, but only in the absence of any inducer (Table 6.2). The leaky expression was sufficiently high to improve product formation. However, the addition of even a small amount of IPTG resulted in a significant inhibition of growth in the multicopy system, yet another sign of metabolic burden. The loss of cell growth correlated with a loss of productivity, such that an induced tac promoter is overwhelmingly more desirable when used on a low-copy plasmid versus a multicopy one. Because the tac promoter is known to be strong, the experiment was repeated using the weaker, arabinose-inducible araBAD promoter. With this promoter fully induced, increased lycopene production was observed from both low- and multicopy plasmids. Here, the low-copy plasmid was about 30% less productive than its multicopy counterpart. It is interesting to note that reduction of the promoter strength on the multicopy plasmid improved lycopene production; whereas, the same reduction in a
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low-copy plasmid had only a very small effect (Table 6.2). This suggests once again that alteration of the promoter strength may be an effective means of altering gene expression from low-copy vectors, including the genome,56 while minimizing metabolic burden effects. 6.4.2.3 De Novo Biosynthesis of Tetrahydrobiopterin (BH4) by Overexpression of guaBA from a pSC101-Derived Plasmid (Figure 6.2c and d)67 The third example involves the synthesis of tetrahydrobiopterin (BH4), an essential cofactor found in higher organisms.67 BH4 is used in human therapeutics to treat natural deficiencies, which are implicated in some neurological diseases. Currently, BH4 is chemically synthesized, but the presence of three chiral centers in the molecule makes this difficult. The compound can be biologically synthesized in three steps from GTP using mammalian enzymes, all of which have been cloned (Figure 6.2c). Yamamoto and coworkers succeeded in introducing all three of these enzymes into E. coli to construct a pathway for BH4 production.67 To further increase the flux to BH4, the researchers decided to increase the flux to GTP. Two genes were chosen for expression from the purine nucleotide synthesis pathway (Figure 6.2d). Overexpression of the gene gmk, which converts GMP to GDP, the immediate precursor to GTP, had no effect on BH4 levels. However, overexpression of guaB and guaA in an operon did lead to enhancements in BH4. These two enzymes are upstream of GMP in the purine nucleotide catabolic pathway and convert inosine 5′-monophosphate (IMP) to xanthosine 5′-monophosphate (XMP), then XMP to GMP, respectively. In this case, expression of the operon from a low-copy plasmid resulted in the production of 60% more BH4 relative to the high-copy plasmids. It should be noted that addition of additional copies of gmk to a strain overexpressing guaBA did not lead to further increases, indicating that the GMP kinase is not rate controlling. 6.4.2.4 Summary of Case Studies These three cases provide illustrations of the benefits of low-copy plasmids over multicopy ones, and also highlight the potential impact of metabolic burden. However, they do not provide enough information to allow one to predict which systems are more likely to suffer from adverse metabolic burden effects if expression levels are too high. Both polyp and BH4 production consume nucleotide cofactors, ATP and GTP, so perhaps this is a key indicator of vulnerable systems. However, lycopene synthesis has no explicit requirement for a nucleotide cofactor. On the other hand, siphoning of pyruvate and glyceraldehyde-3-phosphate into DXP prevents their entry into the energy-generating pathways of the TCA cycle and glycolysis, respectively. ATP is also the primary “energy currency” of the cell, which suggests that compounds involved in energy generation are critical. Yet one counter-example is the production of a monoterpene, another isoprenoid, in E. coli.68 This pathway uses the same mevalonate route to IPP as the lycopene case above; however, the use of low-copy (pSC101-derived) vectors resulted in very lowexpression levels and poor product yields. And, although the use of a reduced copy number vector did result in an improvement in both yield and product quality for PHB production in E. coli in one case,52 expression of the pha operon from a single-copy plasmid produced no measurable amounts of product in another.69 Hence, it is clear that although low-copy plasmids can perform as well as or better than high-copy ones for metabolic engineering, there is no guaranteeing that they will. Further, there is little understanding of how to determine which systems are most amenable to low-copy expression. Clearly, there is much work yet to be done in uncovering the intricate balance between maximum expression and minimum burden effects that must be achieved to reach optimal product yields.
6.5 Opportunities for Artificial Chromosome Use in Metabolic Engineering While the above examples provide an “existence proof” for the usefulness of low-copy plasmids in metabolic engineering applications, it is worthwhile to consider some aspects of metabolic engineering for which the low-copy plasmids are uniquely qualified. In particular, there are two properties of these plasmids that are worth exploring and exploiting further in this context: (1) the ability to minimally perturb
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the host cell upon introduction and expression of foreign genes; and (2) the ability to stably maintain extremely large fragments of DNA.
6.5.1 Minimal Perturbation of Host Metabolism: An Opportunity for Novel Metabolic Control Systems As discussed previously, altering the dosage of a recombinant gene or changing the promoter that controls its expression are both effective ways of influencing the amount of the corresponding recombinant protein that is produced. Another method that has been investigated involves the manipulation of the messenger RNA transcript. Engineered secondary structures, particularly in the 5′ untranslated region of the transcript, can influence the stability (half-life) of mRNA.70 Based on the central dogma of molecular biology, it is therefore expected that increasing the half-life of a message would increase the average concentration of the molecule within the cell and by extension, increase the amount of the corresponding protein that is produced. This was observed in the case of hairpins applied to a lacZ transcript.71 Several hairpins of varying strength were subsequently tested with inducible expression from either ColE1-type high-copy plasmids (~100 copies/cell) or mini-F-derived low-copy plasmids (~1 copy/ cell).72,73 This experiment revealed that, in the case of high-copy plasmids, stabilizing the transcript resulted in increased β-galactosidase activity only at low to moderate induction levels. At higher induction levels, the activities were comparable between stabilized and nonstabilized transcripts, indicating that transcript level was no longer rate-controlling in the system. Additionally, the growth rate of the cultures was significantly reduced with high-level expression from the high-copy plasmids. By contrast, while the low-copy plasmids gave lower expression than their higher copy counterparts across the various stabilizing structures examined, expression from stabilized transcripts was always higher than the nonstabilized transcripts. The effect on growth rate was minimal across all expression levels. This resulted in a library of structures that gave a range of activities as a function of both inducer concentration and secondary structure, with minimal perturbation to native metabolism. This work was extended to examine the expression of genes from a synthetic two-gene operon composed of lacZ and gfp in which a 5′ hairpin was inserted before the downstream gene.74 Although the results indicate that a number of factors ultimately influence gene expression due to the complexity of the systems examined (i.e., changing copy number, mRNA secondary structure, and relative position of the two genes), the general trend of low-copy plasmids responding to changes in hairpin structure over a wider range of inducer controls was still observed. In the case of the expression of the lacZ gene, the operons also appeared to be better insulated against downstream effects when low-copy plasmids were used. For example, a decrease in the β-galactosidase activity was observed when lacZ was in the upstream position and a hairpin was inserted in front of the downstream gfp gene on a high-copy plasmid. By contrast, there was no significant impact on upstream lacZ expression when hairpins were inserted in front of the downstream gene in low-copy plasmids. This suggests, again, that precise, even predictable, control of gene expression may be more easily achieved with low-copy vectors. This ability to minimally perturb the host (e.g., to minimally impact growth rate) while controlling gene expression may find other uses in metabolic engineering as well. Recently, more researchers have begun to examine regulatory networks to both co-opt existing systems75 and design new ones for novel purposes.76 (Much of this work falls under the heading of “synthetic biology,” yet it still involves a manipulation of the cell’s metabolism through heterologous gene expression and is, therefore, still rightly viewed as an aspect, perhaps even outgrowth, of metabolic engineering.) In at least one example, the integration of regulation and metabolism produced a cell with an oscillatory metabolic state.77 As such systems become more complex and more integrated—for example, engineering both gene expression levels for optimal flux through a key pathway step and the regulation of expression relative to cellular metabolism—utilizing expression vectors that have minimal impact on basic cell metabolism is likely to produce more stable, reproducible, and predictable systems. Indeed, it may be considered an engineering “fail-safe” for many applications in metabolic engineering.
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6.5.2 Maintenance of Large DNA Fragments: Pathway Engineering and Discovery The ability of low-copy plasmids, particularly those of the mini-F variety, to stably replicate large DNA fragments has been appreciated for quite some time.78,79 Reports of the stable maintenance of 300-Kb fragments of human DNA led to interest in the use of these “bacterial artificial chromosomes (BACs)” for large-scale sequencing projects, especially during the period of the Human Genome Project.80 BACs became a viable alternative to yeast artificial chromosomes (YACs) because they can maintain fragment sizes that, though smaller, begin to approach those maintained in YACs (>300-Kb vs. >1,000-Kb) and that are much larger than what can be maintained in cosmids (~50-Kb). Additionally, BACs appear not to carry the difficulties associated with YACs, including the formation of chimeras from noncontinguous fragments81 and the contamination of recovered insert DNA with host chromosomal DNA.82 The recent ability to construct large DNA fragments through primarily chemical (as opposed to biological) means83 warrants a new look at the low-copy plasmids as vectors for metabolic engineering. This new technology can produce DNA segments that are on the order of tens to 100’s of Kb. It is very unlikely that high-copy plasmids could sustain such large fragments with high fidelity.84 Further, it is quite likely that introduction of multiple copies of large DNA fragments into the host organism would have deleterious effects. By contrast, the low-copy plasmids have the demonstrated capacity to maintain >300-Kb insert fragments, which should be sufficient to carry entire metabolic pathways. Additionally, as described in the previous section, the use of low-copy vectors enables the use of other technologies for regulating the expression of multiple genes.72 Other improvements in expression methods, including the incorporation of strong promoter sequences56,85 and the optimization of codon usage in heterologous genes to favor the biosynthetic machinery of the host60 also make it more likely that expression levels from low- and even single-copy vectors would be sufficient to achieve an efficiently engineered system. Carrying such large pathways on artificial chromosomes instead of inserting them into the genome also avoids the potential for long-range epigenetic effects or polar mutations that may result from the incorporation of such a large amount of DNA into the host genome. With the assembly of “mega” fragments now possible, the challenge now shifts to the introduction of such large pieces of DNA into the organism. It has been suggested that the current limitation on the size of BAC libraries is not due to any inherent limitation in the carrying capacity of the vector but rather in the ability to transform the microbial host using existing technologies.84 Indeed, F-prime factors naturally carry chromosomal DNA that ranges from <1 to >30% of the E. coli genome.86 One way to address the challenge of introduction of large DNA fragments into the host cell is through in vivo assembly. Such an approach has been described with F-derived low-copy plasmids.44 Although this specific demonstration resulted in the production of a ~125-Kb plasmid, there is no obvious reason why in vivo methods could not create F-based plasmids of the size naturally observed with the F-primes. It should also be noted that more recently, low-copy vectors with copy numbers closer to that of the pSC101 replicons (four to five copies per cell) have also shown the capability of stably maintaining insert DNA fragments of >300-Kb.84 Conventional vectors with higher copy numbers (30–40 copies/cell) were not stably maintained. However, although vectors with several copies per cell may be able to stably maintain large fragments, an in vivo assembly approach is likely to be less effective with such vectors because of the presence of multiple targets for homologous recombination. The ability of low-copy plasmids to stably maintain large DNA fragments may prove to be a significant advantage for future applications of metabolic pathway engineering, especially for the optimization of existing pathways. This characteristic also provides an interesting opportunity for the discovery of novel enzymes for use in pathway design and construction, or the discovery of entire pathways. BAC libraries have been used to profile “metagenomes,” the composite genomes of bacterial consortia.87,88 These metagenomic libraries provide access to microbial diversity while circumventing the need to culture individual species, akin to 16S RNA genotyping of consortia.89 However, such libraries provide a further opportunity to screen by phenotype, providing clues to the functional diversity of the system as
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well. This technique was first tested with a BAC library of a single genome, in which the expression of Bacillus cereus DNA in E. coli was analyzed by screening for established phenotypes, including ampicillin resistance and orange pigment production.90 The use of BACs has since been expanded to profile antibacterial, lipase, amylase, nuclease, and hemolytic activities in soil bacteria,87 and to isolate a novel alcohol/aldehyde deydrogenase from the microorganisms in a waste water treatment plant.88 While one objective of metabolic engineering is to enable the efficient production of natural products in scaleable processes,60 achieving this goal requires the identification and recruitment of the associated enzyme activities from the native host. In the case of bacteria, functional genomics in BAC libraries could significantly accelerate this process, leading to the identification of new activities and/or whole pathways.
6.6 Summary Until very recently, the low-copy plasmids have been used almost exclusively for the following purposes: (i) low-level expression of toxic genes, (ii) cloning of large eukaryotic DNA fragments for sequencing (i.e., in BACs); and (iii) to enable the expression of multiple genes from independent, compatible plasmids. The metabolic burden effect of high-copy plasmids has been well-documented and appreciated in the context of recombinant protein production, with the protein as the final product of interest. In this case, it is easy, and perhaps warranted, to think of the metabolic burden effect as an engineering challenge that can be addressed in the context of bioprocess development if large quantities of the product are desired, for example, as a biopharmaceutical. However, the focus of metabolic engineering on the production of small molecules—metabolites—requires one to consider the metabolic burden effect to a greater extent. The few examples discussed here of superior performance achieved through the use of a low-copy plasmid rather than a high-copy one to overexpress a gene of interest are unlikely to be the only cases for which this phenomenon is true. Unfortunately, at the current time, there is no simple way to predict which systems will be affected the most by plasmid-mediated metabolic stresses. As a deeper understanding of the effects of high-copy number plasmid maintenance on the metabolome of the cell emerges, perhaps such predictions may become possible. It is also worth noting the unique features of low-copy plasmids that can be exploited in metabolic engineering. The desire to create increasingly sophisticated microbes has resulted in increasing interest in engineering the regulatory network of a cell.75–77 As demonstrated in the case of regulating gene expression at the transcript level, it is very likely that such engineered systems will behave more reproducibly and with tighter control if the vector causes a minimum perturbation to the native metabolism of the host cell. Finally, the ability of low-copy vectors to maintain very large DNA fragments truly warrants their designation as “artificial chromosomes.” Such chromosomes are capable of carrying entire metabolic pathways—with individual promoters, in natural or synthetic operons, or both—for the production of novel compounds in the microbial host through a parallel metabolic network.
References 1. Cohen, S. N., Chang, A. C. Y., Boyer, H. W., and Helling, R. B. Construction of biologically functional bacterial plasmids in vitro. Proc. Nat. Acad. Sci. USA 70 (11), 3240–3244, 1973b. 2. Chang, A. C. Y. and Cohen, S. N. Genome construction between bacterial species in vitro: replication and expression of Staphylococcus plasmid genes in Escherichia coli. Proc. Nat. Acad. Sci. USA 71 (4), 1030–1034, 1974. 3. Morrow, J. F., Cohen, S. N., Chang, A. C. Y., Boyer, H. W., Goodman, H. M., and Helling, R. B. Replication and transcription of eukaryotic DNA in Escherichia coli. Proc. Nat. Acad. Sci. USA 71 (5), 1743–1747, 1974. 4. Cohen, S. N. and Chang, A. C. Y. Recircularization and autonomous replication of a sheared R-factof DNA segment in Escherichia coli transformants. Proc. Nat. Acad. Sci. USA 70 (5), 1293–1297, 1973a.
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5. Cohen, S. N. and Chang, A. C. Y. Revised interpretation of the origin of the pSC101 plasmid. J. Bacteriol. 132 (2), 734–737, 1977. 6. Cabello, F., Timmis, K., and Cohen, S. N. Replication control in a composite plasmid constructed by in vitro linkage of two distinct replicons. Nature 259, 285–290, 1976. 7. Keasling, J. D. Gene-expression tools for the metabolic engineering of bacteria. TIBTECH 17, 452– 460, 1999. 8. Prather, K. J., Sagar, S., Murphy, J., and Chartrain, M. Industrial scale production of plasmid DNA for vaccine and gene therapy: plasmid design, production, and purification. Enzyme Microb. Tech. 33, 865–883, 2003. 9. Lin-Chao, S. and Bremer, H. Effect of the bacterial growth rate on replication control of plasmid pBR322 in Escherichia coli. Mol. Gen. Genet. 203, 143–149, 1986. 10. Davison, J. Mechanism of control of DNA replication and incompatibility in ColE1-type plasmids— a review. Gene 28, 1–15, 1984. 11. Lacatena, R. M., Banner, D. W., Castagnoli, L., and Cesareni, G. Control of initiation of pMB1 replication: purified Rop protein and RNA I affect primer formation in vitro. Cell 37, 1009–1014, 1984. 12. Tomizawa, J-i. and Som, T. Control of ColE1 plasmid replication: enhancement of binding of RNA I to the primer transcript by the Rom protein. Cell 38, 871–878, 1984. 13. Balbás, P., Soberón, X., Merino, E., Zurita, M., Lomeli, H., Valle, F., Flores, N., and Bolivar, F. Plasmid vector pBR322 and its special-purpose derivatives—a review. Gene 50, 3–40, 1986. 14. Twigg, A. J. and Sherratt, D. Trans-complementable copy-number mutants of plasmid ColE1. Nature 283, 216–218, 1980. 15. Vieira, J. and Messing, J. The pUC plasmids, an M13mp7-derived system for insertion mutagenesis and sequencing with synthetic universal primers. Gene 19, 259–268, 1982. 16. Norrander, J., Kempe, T., and Messing, J. Construction of improved M13 vectors using oligodeoxynucleotide-directed mutagenesis. Gene 26, 101–106, 1983. 17. Lin-Chao, S., Chen, W-T., and Wong, T-T. High copy number of the pUC plasmid results from a Rom/Rop-suppressible point mutation in RNA II. Mol. Microbiol. 6 (22), 3385–3393, 1992. 18. Chambers, S. P., Prior, S. E., Barstow, D. A., and Minton, N. P. The pMTL nic- cloning vectors. I. Improved pUC polylinker regions to facilitate the use of sonicated DNA for nucleotide sequencing. Gene 68, 139–149, 1988. 19. Stoker, N. G., Fairweather, N. F., and Spratt, B. G. Versatile low-copy-number plasmid vectors for cloning in Escherichia coli. Gene 18, 335–341, 1982. 20. Takeshita, S., Sato, M., Toba, M., Masahashi, W., and Hashimoto-Gotoh, T. High-copy-number and low-copy-number plasmid vectors for lacZα-complementation and chlromaphenicol or kanamycin-resistance selection. Gene 61, 63–74, 1987. 21. Lerner, C. G. and Inouye, M. Low copy number plasmids for regulated low-level expression of cloned genes in Escherichia coli with blue/white insert screening capability. Nucleic Acids Res. 18 (15), 4631, 1990. 22. Wang, R. F. and Kushner, S. R. Construction of versatile low-copy-number vectors for cloning, sequencing, and gene expression in Escherichia coli. Gene 100, 195–199, 1991. 23. Kline, B. C. Mechanism and biosynthetic requirements for F plasmid replication in Escherichia coli. Biochemistry 13, 139–146, 1974. 24. Keasling, J. D., Palsson, B. O., and Cooper, S. Cell-cycle-specific F plasmid replication: regulation by cell size control of initiation. J. Bacteriol. 173 (8), 2673–2680, 1991. 25. Ogura, T. and Hiraga, S. Partition mechanism of F plasmid: two plasmid gene-encoded products and a cis-acting region are involved in partition. Cell 32, 351–360, 1983. 26. Hiraga, S., Jaffe, A., Ogura, T., Mori, H., and Takahashi, H. F plasmid ccd mechanism in Escherichia coli. J. Bacteriol. 166 (1), 100–104, 1986. 27. Jones, K. L. and Keasling, J. D. Construction and characterization of F plasmid-based expression vectors. Biotechnol. Bioeng. 59, 659–665, 1998.
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28. Jones, K. L., Kim, S-W., and Keasling, J. D. Low-Copy plasmids can perform as well as or better than high-copy plasmids for metabolic engineering of bacteria. Metabolic Eng. 2, 328–338, 2000. 29. Seo, J-H. and Bailey, J. E. Effects of recombinant plasmid content on growth properties and cloned gene product formation in Escherichia coli. Biotechnol. Bioeng. 27, 1668–1674, 1985. 30. Bailey, J. E., Silva, N. A. d., Peretti, S. W., Seo, J.-H., and Srienc, F. Studies of host-plasmid interactions in recombinant microorganisms. Ann. NY Acad. Sci. 469, 194–211, 1986. 31. Mason, C. A. and Bailey, J. E. Effects of plasmid presence on growth and enzyme activity of Escherichia coli DH5α. Appl. Microbiol. Biotechnol. 32, 54–60, 1989. 32. Betenbaugh, M. J., Beaty, C., and Dhurjati, P. Effects of plasmid amplification and recombinant gene expression on the growth kinetics of recombinant E. coli. Biotechnol. Bioeng. 33, 1425–1436, 1989. 33. Betenbaugh, M. J. and Dhurjati, P. Effects of promoter induction and copy number amplification on cloned gene expression and growth of recombinant cell cultures. Ann. NY Acad. Sci. 589, 111–120, 1990. 34. Birnbaum, S. and Bailey, J. E. Plasmid presence changes the relative levels of many host cell proteins and ribosome components in recombinant Escherichia coli. Biotechnol. Bioeng. 37, 736–745, 1991. 35. Glick, B. R. Metabolic load and heterlogous gene expression. Biotechnol. Adv. 13 (2), 247–261, 1995. 36. Peretti, S. W., Bailey, J. E., and Lee, J. J. Transcription from plasmid genes, macromolecular stability, and cell-specific productivity in Escherichia coli carrying copy number mutant plasmids. Biotechnol. Bioeng. 34, 902–908, 1989. 37. Wood, T. K. and Peretti, S. W. Depression of protein synthetic capacity due to cloned-gene expression in E. coli. Biotechnol. Bioeng. 36, 865–878, 1990. 38. Vind, J., Sorensen, M. A., Rasmussen, M. D., and Pedersen, S. Synthesis of proteins in Escherichia coli is limited by the concentration of free ribosomes. J. Mol. Biol. 231, 678–688, 1993. 39. Dong, H., Nilsson, L., and Kurland, C. G. Gratuitous overexpression of genes in Escherichia coli leads to growth inhibition and ribosome destruction. J. Bacteriol. 177 (6), 1497–1504, 1995. 40. Seo, J-H. and Bailey, J. E. Continuous cultivation of recombinant Escherichia coli—existence of an optimum dilution rate for maximum plasmid and gene-product concentration. Biotechnol. Bioeng. 28 (10), 1590–1594, 1986. 41. Klotsky, R-A. and Schwartz, I. Measurement of cat expression from growth-rate-regulated promoters employing β-lactamase activity as an indicator of plasmid copy number. Gene 55, 141–146, 1987. 42. Reinikainen, P. and Virkajärvi, I. Escherichia coli growth and plasmid copy numbers in continuous cultivations. Biotechnol. Lett. 11 (4), 225–230, 1989. 43. Nakamura, K. and Inouye, M. Inactivation of the Serratia marcescens gene for the lipoprotein in Escherichia coli by insertion sequences, IS1 and IS5; sequence analysis of junction points. Mol. Gen. Genet. 183, 107–114, 1981. 44. O’Connor, M., Peifer, M., and Bender, W. Construction of large DNA segments in Escherichia coli. Science 244, 1307–1312, 1989. 45. Balbas, P. and Bolivar, F. Design and construction of expression plasmid vectors in Escherichia coli. In Methods in Enzymology, Goeddel, D. D. Academic Press, Inc., San Diego, CA, 1990, 14–37. 46. Summers, D. K. The kinetics of plasmid loss. TIBTECH 9, 273–278, 1991. 47. Summers, D. Timing, self-control and a sense of direction are the secrets of multicopy plasmid stability. Mol. Microbiol. 29 (5), 1137–1145, 1998. 48. Noack, D., Roth, M., Geuther, R., Muller, G., Undisz, K., Hoffmeier, C., and Gaspar, S. Maintenance and genetic stability of vector plasmids pBR322 and pBR325 in Escherichia coli K12 strains grown in a chemostat. Mol. Gen. Genet. 184, 121–124, 1981. 49. Jones, S. A. and Melling, J. Persistence of pBR322-related plasmids in Escherichia coli grown in chemostat cultures. FEMS Microbiol. Lett. 22, 239–243, 1984.
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50. Chotani, G., Dodge, T., Hsu, A., Kumar, M., LaDuca, R., Trimbur, D., Weyler, W., and Sanford, K. The commercial production of chemicals using pathway engineering. Biochim. Biophys. Acta 1543, 434–455, 2000. 51. Shi, J. and Biek, D. P. A versatile low-copy-number cloning vector derived from plasmid F. Gene 164, 55–58, 1995. 52. Kahar, P., Agus, J., Kikkawa, Y., Taguchi, K., Doi, Y., and Tsuge, T. Effective production and kinetic characterization of ultra-high-molecular-weight poly[(R)-3-hydroxybutyrate] in recombinant Escherichia coli. Polym. Degrad. Stab. 87, 161–169, 2005. 53. Leonard, E., Yan, Y., and Koffas, M. Functional expression of a P450 flavonoid hydroxylase for the biosynthesis of plant-specific hydroxylated flavonols in Escherichia coli. Metab. Eng. 8, 172–181, 2005. 54. Snell, K. D., Draths, K. M., and Frost, J. W. Synthetic modification of the Escherichia coli chromosome: enhancing the biocatalytic conversion of glucose into aromatic chemicals. J. Am. Chem. Soc. 118, 5605–5614, 1996. 55. Barker, J. L. and Frost, J. W. Microbial synthesis of p-hydroxybenzoic acid from glucose. Biotechnol. Bioeng. 76 (4), 376–390, 2001. 56. Yuan, L. Z., Rouviere, P. E., LaRossa, R. A., and Suh, W. Chromosomal promoter replacement of the isoprenoid pathway for enhancing carotenoid production in E. coli. Metab. Eng. 8, 79–80, 2006. 57. Wang, C-W., Oh, M.-K., and Liao, J. C. Engineered isoprenoid pathway enhances astaxanthin production in Escherichia coli. Biotechnol. Bioeng. 62 (2), 235–241, 1999. 58. Kim, S-W. and Keasling, J. D. Metabolic engineering of the nonmevalonate isopentenyl diphosphate synthesis pathway in Escherichia coli enhances lycopene production. Biotechnol. Bioeng. 72 (4), 408–415, 2001. 59. Alper, H., Fischer, C., Nevoigt, E., and Stephanopoulos, G. Tuning genetic control through promoter engineering. Proc. Natl. Acad. Sci. USA 102 (36), 12678–12683, 2005. 60. Martin, V. J. J., Pitera, D. J., Withers, S. T., Newman, J. D., and Keasling, J. D. Engineering a mevalonate pathway in Escherichia coli for production of terpenoids. Nature Biotech. 21 (7), 796–802, 2003. 61. Berry, A., Dodge, T. C., Pepsin, M., and Weyler, W. Application of metabolic engineering to improve both the production and use of biotech indigo. J. Ind. Microbiol. Biotechnol. 28, 127–133, 2002. 62. Cottaz, S. and Samain, E. Genetic engineering of Escherichia coli for the production of NI,NIIdiacetylchitobiose (chitinbiose) and its utilization as a primer for the synthesis of complex carbohydrates. Metab. Eng. 7, 311–317, 2005. 63. Sharfstein, S. T. and Keasling, J. D. Polyphosphate metabolism in Escherichia coli. Ann. NY Acad. Sci. 745, 77–91, 1994. 64. Harker, M. and Bramley, P. M. Expression of prokaryotic 1-deoxy-D-xylulose-5-phosphatases in Escherichia coli increases carotenoid and ubiquinone biosynthesis. FEBS Lett. 448, 115–119, 1999. 65. Sprenger, G. A., Schörken, U., Wiegert, T., Grolle, S., Graaf, A. A. d., Taylor, S. V., Begley, T. P., Bringer-Meyer, S., and Sahm, H. Identification of a thiamin-dependent synthase in Escherichia coli required for the formation of the 1-deoxy-D-sylulose 5-phosphate precursor to isoprenoids, thiamin, and pyridoxol. Proc. Natl. Acad. Sci. USA 94, 12857–12862, 1997. 66. Lois, L. M., Campos, N., Putra, S. R., Danielsen, K., Rohmer, M., and Boronat, A. Cloning and characterization of a gene from Escherichia coli encoding a transketolase-like enzyme that catalyzes the synthesis of D-1-deoxyxylulose 5-phosphate, a common precursor of isoprenoid, thiamin, and pyridoxol biosynthesis. Proc. Natl. Acad. Sci. USA 95, 2105–2110, 1998. 67. Yamamoto, K., Kataoka, E., Miyamoto, N., Furukawa, K., Ohsuye, K., and Yabuta, M. Genetic engineering of Escherichia coli for production of tetrahydrobiopterin. Metab. Eng. 5, 246–254, 2003. 68. Carter, O. A., Peters, R. J., and Croteau, R. Monoterpene biosynthesis pathway construction in Escherichia coli. Phytochem. 64, 425–433, 2003. 69. Fidler, S. and Dennis, D. Polyhydroxybutyrate production in recombinant Escherichia coli. FEMS Microbiol. Rev. 103, 231–36, 1992.
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70. Carrier, T. A. and Keasling, J. D. Controlling messenger RNA stability in bacteria: strategies for engineering gene expression. Biotechnol. Prog. 13, 699–708, 1997. 71. Carrier, T. A. and Keasling, J. D. Engineering mRNA stability in E. coli by the addition of synthetic hairpins using a 5’ cassette system. Biotechnol. Bioeng. 55 (3), 577–580, 1997. 72. Carrier, T., Jones, K. L., and Keasling, J. D. mRNA stability and plasmid copy number effects on gene expression from an inducible promoter system. Biotechnol. Bioeng. 59 (6), 666–672, 1998. 73. Carrier, T., Jones, K. L., and Keasling, J. D., unpublished results. 74. Smolke, C. D. and Keasling, J. D. Effect of copy number and mRNA processing and stabilization on transcript and protein levels from an engineered dual-gene operon. Biotechnol. Bioeng. 78 (4), 412–424, 2002. 75. Farmer, W. R. and Liao, J. C. Improving lycopene production in Escherichia coli by engineering metabolic control. Nat. Biotechnol. 18, 533–537, 2000. 76. Elowitz, M. B. and Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature 403, 335–338, 2000. 77. Fung, E., Wong, W. W., Suen, J. K., Bulter, T., Lee, S-g., and Liao, J. C. A synthetic gene-metabolic oscillator. Nature 435, 118–122, 2005. 78. Hosoda, F., Nishimura, S., Uchida, H., and Ohki, M. An F factor based cloning system for large DNA fragments. Nucleic Acids Res. 18 (13), 3863–3869, 1990. 79. Shizuya, H., Birren, B., Kim, U-J., Mancino, V., Slepak, T., Tachiri, Y., and Simon, M. Cloning and stable maintenance of 300-kilobase-pair fragments of human DNA in Escherichia coli using an F-factor-based vector. Proc. Natl. Acad. Sci. USA 89, 8794–8797, 1992. 80. Anderson, C. Genome shortcut leads to problems. Science 259, 1684–1687, 1993. 81. Green, E. D., Riethman, H. C., Dutchik, J. E., and Olson, M. V. Detection and characterization of chimeric yeast artificial-chromosome clones. Genomics 11 (3), 658–669, 1991. 82. Zhang, H-B. and Wing, R. A. Physical mapping of the rice genome with BACs. Plant Mol. Biol. 35, 115–127, 1997. 83. Tian, J., Gong, H., Sheng, N., Zhou, X., Gulari, E., Gao, X., and Church, G. Accurate multiplex gene synthesis from programmable DNA microchips. Nature 432, 1050–1054, 2004. 84. Tao, Q. and Zhang, H-B. Cloning and stable maintenance of DNA fragments over 300kb in Escherichia coli with conventional plasmid-based vectors. Nucleic Acids Res. 26 (21), 4901–4909, 1998. 85. Chang, T-S., Wu, W-J., Shiu, T-R., and Wu, W-T. High-level expression of a IacZ gene from a bacterial artificial chromosome in Escherichia coli. Appl. Microbiol. Biotechnol. 61, 234–239, 2003. 86. Holloway, B. and Low, K. B. F-prime and R-prime factors. In Escherichia coli and Salmonella: Cellular and Molecular Biology, 2d ed., Neidhardt, F. C., III, R. C., Ingraham, J. L., Lin, E. C. C., Low, K. B., Magasanik, B., Reznikoff, W. S., Riley, M., Schaechter, M., and Umbarger, H. E. Eds. ASM Press, Washington, DC, 1996, 2413–2420. 87. Rondon, M. E., August, P. R., Bettermann, A. D., Brady, S. F., Grossman, T. H., Liles, M. R., Loiacono, K. A., Lynch, B. A., MacNeil, I. A., Minor, C., Tiong, C. L., Gilman, M., Osburne, M. S., Clardy, J., Handelsman, J., and Goodman, R. M. Cloning the soil metagenome: a strategy for accessing the genetic and functional diversity of uncultured microorganisms. Appl. Environ. Microbiol. 66 (6), 2541–2547, 2000. 88. Wexler, M., Bond, P. L., Richardson, D. J., and Johnston, A. W. B. A wide host-range metagenomic library from a waste water treatment plant yields a novel alcohol/aldehyde dehydrogenase. Environ. Microbiol. 7 (12), 1917–1926, 2005. 89. Lane, D. J., Pace, B., Olsen, G. J., Stahl, D. A., Sogin, M. L., and Pace, N. R. Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses. Proc. Nat. Acad. Sci. USA 82, 6955–6959, 1985. 90. Rondon, M. E., Raffel, S. J., Goodman, R. M., and Handelsman, J. Toward functional genomics in bacteria: analysis of gene expression in Escherichia coli from a bacterial artificial chromosome library of Bacillus cereus. Proc. Natl. Acad. Sci. USA 96, 6451–6455, 1999.
7 Chromosomal Engineering Strategies 7.1
Introduction �������������������������������������������������������������������������������������� 7-1
7.2
S trategies of λ Red-Promoted and SSR-Mediated Modification of the Bacterial Chromosome...............................7-12
Mechanism of Red/ET Recombination and Recombineering • Development of Recombineering Technology • Materials and Methods • Recombineering with Single-Stranded DNA Oligos • Counter-Selection Schemes • Flp and Cre Site-Specific Recombination (SSR) Systems
Gene or Operon Replacement • SSR-Mediated Excision of Drug Markers • Large Deletions • Seamless Deletions Insertions • Duplications and Inversions • Reporter Fusions Random Mutagenesis of Chromosomal Genes Promoter Engineering • Genome Reduction
7.3 7.4
Kenan C. Murphy University of Massachusetts Medical School
Large Insertions ������������������������������������������������������������������������������ 7-19 Applications of Red/ET Recombineering Technology for Metabolic Engineering ������������������������������������������������������������������ 7-21 7.5 Summary �����������������������������������������������������������������������������������������7-22 References ���������������������������������������������������������������������������������������������������7-23
7.1 Introduction Central to the concept of metabolic engineering is the modification of a microorganism’s genome to help overproduce a target metabolite, inhibit the formation of an undesirable byproduct, and/or to improve the fitness of the microorganism for industrial applications. In the early years of metabolic engineering, genetic modification was carried out randomly by chemical or transposon mutagenesis, followed by screening or selecting for a desired phenotype. With the onset of recombinant DNA technology, the addition, deletion, or modification of specific genes or operons began to be carried out in a systematic manner. Since the early 1990s, researchers employing Saccharomyces cerevisiae have enjoyed a distinct advantage in chromosomal engineering strategies, since the high rate of homologous recombination in yeast had afforded the means to generate gene deletions via transformation of a PCR product [1–3]. Alterations to bacterial genomes, especially with non-E. coli bacteria, were not as easily performed. Bacterial gene replacements typically involved transformation of a nonreplicating plasmid containing a deleted or modified gene, followed by a low-frequency plasmid integration event into the chromosome. Selection or screening for resolution events was followed by phenotypic screens and/or Southern gels to identify gene replacement candidates. Studies of homologous recombination pathways in E. coli lead to the development of strains that were genetically modified to promote transformation with linear DNA via the RecBC(D –), RecE, or RecF pathways [4–7]. However, recombinant transformants required large amounts of homology to the 7-1
7-2
Gene Expression Tools for Metabolic Pathway Engineering 3KO
5KO Drug marker
Drug marker
PCR product
+ Chromosome
Target gene
Red recombination Modified chromosome
Drug marker
U
M
D
Figure 7.1 Red/ET recombineering. A PCR product is generated by primers 3KO and 5KO that contains a drug marker flanked by 40 bp of target sequence. The PCR product is purified and electroporated into E. coli containing the λ Red + Gam (or RecET + λ Gam) recombination system. After growing the cells for 1–2 hours, the culture is plated on antibiotic-selection media for growth of the drug resistant transformant. The gene replacement can be verified phenotypically, or by a PCR using primers upstream (U), downstream (D), or within the drug marker (M).
target gene (1–2 kb) and often did not occur at high frequency. Furthermore, transformation of linear DNA substrates were restricted to these recombination-proficient strains; wild type E. coli could not be efficiently transformed with linear DNA fragments, let alone small PCR-generated substrates. What is it about wild type E. coli that prevents efficient transformation with linear DNA? The destruction of linear DNA substrates is accomplished by the RecBCD dsDNA exonuclease [8,9]. Paradoxically, this enzyme is the major component in E. coli’s principle pathway of recombination (the RecBCD pathway), but only works efficiently on DNA substrates of 50–100 kb (the typical size of DNA transferred during conjugation and transduction). Modification of RecBCD from a destructive exonuclease into a recombination-promoting RecA-loading helicase depends on its interaction with Chi sites (crossover hotspot instigator, GCTGGTGG) that occur on average every 5 kb in the E. coli genome. Small linear DNA substrates of 1 kb (like PCR products) are unlikely to have either the right number of Chi sites (at least one on each end) or in the right orientation (Chi is active in only one orientation) to be activated for recombination by RecBCD. Interestingly, if one designs linear DNAs to contain such properly orientated Chi sites, gene replacement using such substrates is elevated to levels reaching one event per 105 cells, as was shown by Dahlbert and Smith [10]. Starting nine years ago, however, a new technology for highly efficient gene replacement in E. coli was initiated by the recognition of the fact that the bacteriophage λ Red and RecET recombination systems are very efficient for crossing small linear substrates into the E. coli chromosome [11–17]. These systems are highlighted by their ability to cross PCR-generated substrates bearing small regions of homology to the target gene (40–50 bp) into bacterial chromosomes, allowing bacterial geneticists to perform PCR-mediated gene replacement that yeast geneticists have enjoyed for years (see Figure 7.1). The use of
Chromosomal Engineering Strategies
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phage recombination systems acting on PCR-generated substrates to promote engineering of bacterial chromosomes and BACS has been termed recombineering [18] and presents new options for the cloning and manipulation of DNA [19]. It has altered the way investigators approach chromosomal engineering strategies, and is the focus of the methodology described in this chapter.
7.1.1 Mechanism of Red/ET Recombination and Recombineering Two phage recombination systems have been used to promote recombineering in E. coli and related bacteria: the λ Red system and the rac prophage RecET system. Hence, the term Red/ET recombineering is used here to denote use of either system to promote chromosomal gene replacements and alterations. The λ Red system (initially identified by recombination defective λ phage) consists of three genes: exo, bet and gam (originally described as redα, redβ, and redγ) [20]. The exo gene encodes for λ exonuclease , a trimeric 5′-3′ dsDNA exonuclease that generates a 3′ ended ssDNA tail [21,22], a common intermediate in virtually all recombination pathways. The Bet protein, which forms multimeric rings (12–18 subunits per ring), binds ssDNA, promotes the annealing of complementry ssDNA strands, and can promote limited strand exchange [23–26]. The Exo and Bet proteins form a complex in vitro and presumably work in concert in vivo to prepare dsDNA ends for recombination. The λ Gam protein plays an important role in recombineering by binding to RecBCD and preventing it from binding to dsDNA ends [27], thus inhibiting all of the enzyme’s activities [28,29]. This inactivation of RecBCD allows Exo and Bet to work unimpeded, which in turn promotes high-frequency linear DNA recombination. The RecE and RecT rac prophage recombination functions are biochemically similar to λ Exo and Bet, respectively. The demonstration of gene replacement in E. coli using PCR products with short regions of homology was first demonstrated with RecET [12], but the λ Red functions have been claimed to be up to three-fold more efficient when compared to RecET [19]. RecT and λ Bet singlestrand annealing proteins are members of the same superfamily [30], but share little sequence similarity. RecE exonuclease (originally identified as ExoVIII of E. coli) and λ Exo both degrade the 5′ strand of dsDNA ends and prefer dsDNA to ssDNA. However, the proteins share no sequence similarity, are immunologically distinct [31], and differ in size: λ Exo has a monomer MW of 25.9 kD and has been crystallized as a trimer [22], whereas RecE exonuclease is a monomer of 140 kD [32]. Genetic analyses of phage λ crosses have demonstrated that Red acts at dsDNA breaks in vivo, and have led to a model whereby Bet acts on the 3′ ssDNA generated by Exo and promotes either strand invasion and/or strand annealing, depending on the nature of the substrates involved, the presence or absence of RecA, and whether or not replication is ensuing [33–37]. These studies have led to two models of Redpromoted recombination. In Figure 7.2a, Red-promoted recombination is assisted by RecA (as well as a number of other RecF pathway and phage functions, not shown) and involves 3′ ssDNA invasion into a homologous duplex. Branch migration proteins (RuvAB/RecG) lead to the formation of a classical Holliday junction, which serves as a substrate for RuvC resolvase. In the absence of replication, RecA is strictly required. The role of Red in this pathway may simply be to provide the ssDNA for RecA. An alternative model of Red recombination (Figure 7.2b) is the ssDNA-annealing pathway. In this pathway of recombination, λ Red acts on dsDNA ends containing overlapping sequences (such that might occur on the ends of circularly permuted phage chromosomes). In this scenario, the processive action of Exo degrades the 5′ strand at the sites of entry, and Bet binds (or is loaded by Exo) to the ssDNA ends generated. Bet then aligns and anneals the ssDNA substrates containing homologous regions of the overlap. In this case, RecA is not required (as no strand invasion is necessary). This model of λ Red recombination is based largely on the in vitro properties of Exo and Bet proteins [21,23,24]. Key elements of this model were demonstrated by Red-dependent packaging of phage following transfection of recB spheroplasts with sheared (half-length) molecules of the λ chromosome [38], and by physical analysis of recombinant products generated by cutting two parental nonreplicating λ chromosomes at nonallelic sites in recA hosts [37]. However, attempts to reconstitute this reaction in vitro have failed to
7-4
Gene Expression Tools for Metabolic Pathway Engineering
(a)
(b)
(c)
Exo Bet
RuvAB
RecA
RuvC
X
Figure 7.2 Three pathways of λ Red-promoted recombination. (a) RecA-dependent pathway. At a dsDNA end, λ Exo (toroid) digests the 5´ strand of dsDNA duplex. RecA (triangle) promotes strand invasion of the 3´ ssDNA into an homologous duplex. The exact role of Bet (circle) is not known, but is thought to protect the 3´ ends generated by λ Exo. Bet is then (presumably) replaced by RecA in a RecFOR dependent mechanism. Branch migration and resolution of Holliday junction is carried out by host recombination functions (e.g., RuvABC). Only one resolution product is shown. (b) Single-stranded DNA annealing (SSA) pathway. Bet and Exo act on dsDNA ends that share terminal homology. Exo produces 3´ ssDNA tails and Bet anneals the ssDNA overlapping regions; PolI then fills the gaps and DNA ligase seals the nicks. (c) Red-promoted recombineering. λ Exo’s 5´ nuclease activity generates ssDNA, which serves as a substrate for Bet-promoted annealing to ssDNA in the lagging strand of a replication fork. The “x” in the recombinant represents a deletion, an insertion, or a point mutation. The model shown is a proposed mechanism for oligo-mediated recombineering. The mechanism for recombineering with substrates containing a non-homology (e.g., a PCR product containing a drug marker flanked with 50 bp of target DNA) is not known, but is thought to target a replication fork by a similar type of mechanism.
provide strong biochemical support for this model (Poteete and Murphy, unpublished observations). Instead, an alternative model suggests that Red generates a dsDNA end with a Bet-coated ssDNA tail, which then serves as a substrate for the phage chromosome to invade the replication fork and promote strand switching by DNA polymerase [39]. This model was inspired by the observation by Ellis et al. [40] who showed that Bet can promote annealing of ssDNA oligos to the E. coli chromosome. In that study, oligos that targeted the lagging strand of the replication fork were more efficient for oligomediated repair relative to those that targeted the leading strand, suggesting that ssDNA regions of the replication fork are targets for Bet-promoted ssDNA annealing activity. Recombineering with PCR substrates containing short regions of homology is independent of many of the host recombination functions (including RecA) and thus cannot proceed via the model shown in Figure 7.2a. While the actual mechanism is not known, Figure 7.2c suggest a model whereby Exo generates a fully ssDNA substrate coated with Bet (in this case, the action of Exo is limited to one end) or a dsDNA substrate containing Bet-coated ssDNA tails (with Exo acting at both ends of the linear substrate—not shown). In either case, Bet-ssDNA complexes are targeted to regions of the replication fork where they get incorporated at high frequency. In this case, the “x” in the recombinant (Figure 7.2c) represents a deletion, an insertion, or point mutation. This model is supported by the recent finding that
Chromosomal Engineering Strategies
7-5
a biotinylated single-stranded DNA oligo is directly incorporated into a “repaired” plasmid following λ Red-mediated repair of a defective chlorampenicol acetyl transferase (cat) gene [41].
7.1.2 Development of Recombineering Technology The early years of λ red genetics, and development of the break-join model of λ Red recombination, is nicely reviewed by Stahl [42]. That same year, chromosomal gene replacement using the λ red and gam functions was first demonstrated using drug markers flanked by large regions of flanking homology (~1 kb) [11]. A strain that had the recBCD chromosomal region replaced with a Plac-bet-exo operon generated recombination with linear DNA substrates at a rate 50-fold higher than the standard recombination-proficient strain JC9387 (recBC sbcCD), a RecF pathway E. coli strain typically used at the time for gene replacement. (In my laboratory, the use of the RecF pathway to generate the ∆recBCD::Plac-bet-exo allele was the last time the RecF pathway was used for gene replacement.) Zhang and coworkers, using the Rac prophage RecET recombination system (accompanied by λ Gam) were the first to recognize that PCR-generated substrates with as little as 42 bp of homology could serve as appropriate substrates for recombination [12]. They demonstrated targeting of both plasmid and chromosomal DNA in E. coli, as well as the use of sitespecific recombinases Flp and Cre to generate marker free deletions. The Stewart lab has gone on to show the RecET can be used to modify bacterial artificial chromosomes (BACs) [19], promote DNA cloning of chromosomal fragments [13] (an extension of the RecET-promoted in vivo cloning of PCR products described by Oliner et al. [43]) and the use of λ phage annealing functions to promote single-stranded oligo (SSO) mediated repair in E. coli and mouse ES cells [44]. Concentrating more on the λ Red system, Datsenko and Wanner generated plasmid pKD46, which drives the red and gam functions from the controllable PBAD promoter, and also described the use of FRT sites to generate markerless deletions [15]. Yu et al. [14] developed a Red recombineering strain where the red and gam functions are expressed from a defective prophage (where lysis, replication and structural functions of the λ prophage have been removed). The defective prophage carries the temperature-sensitive cI repressor cI857; thus, induction of red and gam is done by heating to 42° for 15 minutes during preparation of the cells for electroporation. The tight control of the red and gam functions in this system has the potential to better control their expression to a “recombinogenic window,” preventing the generation of unwanted recombinational events, especially with highly susceptible BACs that contain large DNA inserts. In addition, the heating step, besides allowing for controlled expression of the red and gam functions, offers an advantage for the recovery of recombinants. Murphy and Campellone have found that heat shocking E. coli cells at 42oC for 15 minutes (in this case where plasmid-borne red and gam are expressed from the Ptac promoter) generates levels of total recombinants that are two-to ten-times higher [45]. It may be that the heatshock step prepares the cells for the electroporation shock, allowing higher rates of cell survival and thus higher numbers of total recombinants, though its actual effect is not known. The prophage system for λ Red delivery has been used to generate precise and unmarked modification of BACs [46], to demonstrate the efficacy of Bet-promoted single-stranded oligo (SSO) DNA-mediated chromosomal modification [40], and to identify mismatch repair-deficient genetic backgrounds (e.g., mutS) as hosts that generate high levels of SSO-mediated DNA repair (up to 25% of cells surviving electroporation—see below) [47]. Many researchers have used the Red/RecET recombineering technology to manipulate bacterial chromosomes in a variety of interesting ways. What follows is a basic description of the materials and methods used for Red/RecET recombineering, and examples of how the technology has been (and can) be used to generate specific types of chromosomal modifications.
7.1.3 Materials and Methods 7.1.3.1 Source of λ Red By far, the simplest way to deliver the λ red and gam functions is by expression from a controllable low copy-number plasmid containing a temperature-sensitive origin of replication. The red and gam functions should be inducible since constitutive expression of Red and Gam proteins at high levels can be toxic
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Gene Expression Tools for Metabolic Pathway Engineering
Table 7.1 Plasmids Expressing λ red and gam Plasmid pKD46 pSIM5 pKM208 pRed/ET pTP223 pKOBEG-SacB
Operon PBAD-gam-bet-exo PL-gam-bet-exo Ptac-gam-bet-exo PBAD-gam-bet-exo-recA Plac-gam-bet-exo PBAD-gam-bet-exo-sacB
Drug Marker
Reference
Source*
AmpR CamR AmpR TetR TetR AmpR
[15] [48] [44] [49] [119] [120]
http://cgsc.biology.yale.edu/ [email protected] www.addgene.org www.genebridges.com www.addgene.org [email protected]
*Other plasmid constructs, with different drug markers, are also available from most of these resources.
and mutagenic [45,48]. The temperature-sensitive ori serves as a means to cure the host of the Red plasmid following gene replacement of the target gene. Table 7.1 lists six sources of red- and gam- expressing plasmids from various labs. Plasmid pKD46 (Wanner lab) has been used extensively and contains the red and gam genes under control of PBAD; the red and gam are thus inducible with arabinose [15]. In addition, pKD46 expresses araC, the repressor/activator for PBAD, thus allowing control of red and gam gene expression in various backgrounds. The pSIM5 vector (Court lab) was generated from λ phage sequences and contains the red and gam functions driven by the endogenous (strong) PL promoter [49]. The plasmid contains λ sequence devoid of genes between N and kil (including the transcription terminators) making expression of red and gam wholly dependent on the thermo-labile cI857 λ repressor. Thus, the recombination functions are highly suppressed at 30°C and induced by a 15-minute incubation at 42°C. In side-byside comparisons, pSIM5 generated nine-fold higher recombinants/108 viable cells relative to a pKD46 derivative, pKD119 [49]. In plasmid pKM208 (Murphy lab), the red and gam functions are driven by Ptac, and thus inducible with IPTG [45]. pKM208 worked better in pathogenic strains of E. coli relative to pKD46, probably because of more efficient expression of Ptac relative to PBAD in this background (unpublished observation). Also, pKM208 contains the lacI repressor gene, allowing for IPTG-controlled expression in various host backgrounds. The pRed/ET vector (Stewart lab) contains red and gam driven by PBAD and araC, but in addition, expresses the E. coli recA function [50]. The transient expression of recA allows for increased viability, and thus increased recovery of total recombinants when working in recA host backgrounds. Such backgrounds are typically used when engineering BACs in order to prevent unwanted DNA rearrangements. Two other plasmids used for recombineering and listed in Table 7.1 are noteworthy. The plasmid pTP223 (Poteete lab) is a multi-copy vector (colE1 ori) that drives red and gam from the Plac promoter, is TetR and expresses lacI. While it generates lower frequencies of recombineering relative to the other plasmids (perhaps because it makes too much Red proteins), it was able to transform enteropathogenic E. coli (when pSC101 replicons could not) and promoted low but measurable recombineering events in this pathogen with short homology substrates [45]. pKOBEG-sacB has been used for recombineering of Yersinia pseudotuberculosis [51]. Gene replacement occurred readily with plasmid-derived linear substrates containing 0.5 kb of flanking target homology, but with low frequency with short homology substrates. In this bacterium, temperature-sensitive pSC101 replicons could not be easily cured, so this plasmid includes sacB to allow curing by growth on sucrose. 7.1.3.2 Preparation of Recombineering Substrates The extreme ease and simplicity of recombineering technology is based on the use of PCR products as gene replacement substrates. A number of different E. coli drug markers (see Table 7.2) can serve as templates for a PCR. Template sequences can also include site-specific recombination sites (e.g., FRT or loxP sites) for removal of the drug marker following chromosomal incorporation [15], or origins of transfer (oriT) for moving modified plasmid or cosmid constructs into non-E. coli hosts [52]. Typically, two 60 mer primers are used to amplify a drug marker. The drug marker (including its promoter) is targeted
7-7
Chromosomal Engineering Strategies Table 7.2 Drug Cassettes Used for λ Red Recombineering
Antibiotic
Gene(s)
Primer Pair
Tn903 aph (typeI) CACGTTGTGTCTCAAAATCTC TACAACCAATTAACCAATTCTG Kanamycin Tn5 aph (typeII) TATGGACAGCAAGCGAACCG TCAGAAGAACTCGTCAAGAAG Ampicillin Tn3 bla CGCGGAACCCCTATTTGTTT GGTCTGACAGTTACCAATGC Tetracycline Tn10 tetRA CTCGACATCTTGGTTACCGT CGCGGAATAACATCATTTGG Chloramphenicol Tn9 cat TGAGACGTTGATCGGCACGT ATTCAGGCGTAGCACCAGGC Spectinomycin Tn21 aadA AAACGGATGAAGGCACGAA TTATTTGCCGACTACCTTGG Gentamicin Tn1696 aacC CGAATCCATGTGGGAGTTTA TTAGGTGGCGGTACTTGGGT Kanamycin
Suggested Template (Source)
Drug Conc. (µg/ ml)
Cassette Length (Base Pairs)
20
944
20
949
50
975
7
1996
10
822
20
1080
10
616
pTP858 (www.addgene.com) pBBR1MCS-2 [121] pBR322 (New England Biolabs) pTP857 (www.addgene.com) pACYC184 (New England Biolabs) pGB2 (www.addgene.com) strain TP997 (www.addgene.com)
Source: Poteete, A.R., C. Rosadini, and C. St. Pierre, Biotechniques, 41, 261–264, 2006. With permission.
with 20 bases at the 3′ end of these primers. The 5′ tails of the primers (~40 bases) contain sequences upstream and downstream of the target gene (see Figure 7.1). The PCR product, which has the drug marker flanked by 40 bases upstream and downstream of the gene of interest, is purified using a PCR cleaning kit, suspended in a small volume of TE buffer or water, and electroporated into an E. coli strain that expresses the λ red and gam functions (as described below). 7.1.3.3 Preparation of Electrocompetent/Recombinogenic E. coli Cells Below is the protocol we have used for preparing electrocompetent cells containing pKM208, and can be modified accordingly for the various red and gam-expressing constructs listed in Table 7.1. We typically grow 20 ml of cells containing pKM208 (enough for two electroporations) in a 125-ml flask at 30°C; multiple flasks are used dependent upon the number of electroporations required. A single colony (or 100 µl of an overnight culture) is used to inoculate 20 ml of LB containing 100 µg/ml ampicillin; the culture is incubated at 30°C with shaking. When the culture reaches 107 cells/ml (slight cloudiness around 0.5–1 hour prior to collection), IPTG is added to a final concentration of 1 mM. When the culture reaches 0.5–1 × 108, the cells are heat shocked for 15 minutes by swirling at 42°C, transferred to an icewater bath for 10 min with swirling, then collected by centrifugation. The cells are resuspended in 1 ml of ice-cold 20% glycerol–1 mM MOPS (unbuffered), transferred to a 1.5 ml sterile Eppendorf tube, and spun in a microfuge for 30 seconds (moderate speed). The supernatant is removed by decanting without disturbing the pellet, followed by removal of residual buffer with a pipet tip. The cells are resuspended in 1 ml 20% glycerol–1 mM MOPS and recentrifuged. This step is repeated. The cells are finally resuspended in 90–100 µl of ice-cold 20% glycerol–1 mM MOPS. This protocol is a variation of descriptions for the preparation of eletrocompetent cells for recombineering that use either 10% glycerol [12,15] or ice cold water [53]. We have found that the use of water in preparing cells for electrocompetence sometimes leads to partial cell lysis, resulting in a low percentage of or lack of recombinants. 7.1.3.4 Electroporation and Plating A 50 µl sample of cells is electroporated with 1–5 µl of DNA (0.1–0.5 µg) in TE or water (2000 volts in 0.1 cm cuvettes). Include a control electroporation that contains no DNA. Cuvettes are cooled in ice-water for at least 10 minutes prior to use. DNA and cells are mixed in a 1.5 ml Eppendorf tube,
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Gene Expression Tools for Metabolic Pathway Engineering
transferred to the cuvette, and incubated on ice for 1 minute. The cuvette is thoroughly (but quickly) dried, and the cells are shocked. Immediately following electroporation, the cells are resuspended in 0.5 ml of LB, diluted into 2.5 ml of LB, and grown in a roller for 1.5–2 hours at 37oC. The culture is plated directly, or concentrated by a brief microcentrifugation step, and then plated on appropriate antibiotic plates. In some cases (e.g., counterselection with sacB), one should let the culture grow for several hours (or even overnight) prior to plating to allow cells that contain several copies of their chromosomes (only one of which is likely targeted) to replicate and segregate their DNA. 7.1.3.5 Selection and Verification of Recombinants In most cases, the recombineering substrate is simply a drug cassette flanked by 40–50 bp of target DNA (see substrate preparation above). Thus, selection of recombinants is performed by plating the electroporated cells on antibiotic-containing plates. One must remember that targeting the chromosome will involve selection of the drug marker at single copy. This usually means (though not always) selecting recombinants at drug concentrations much lower than one would typically use for growing the same marker on a multicopy plasmid. Drug markers we have used for generating recombineering substrates, and the concentrations of the drugs used to select recombinants, are listed in Table 7.2. In practice, it is best to use the lowest concentration of drug that will allow the selection of recombinants, but that still efficiently suppresses the growth of nonrecombinants. Restreak transformants on fresh drug-selection plates, applying ~40 stabs per plate, especially if multiple-sized and colored colonies are evident. Sometimes, false-positive KanR and CamR colonies arise among the “true recombinants.” True recombinants are identified by their ability to grow well after restreak (relative to the false-positives) on fresh antibiotic-containing plates. At this time, one can also streak onto ampicillin or tetracycline plates (depending on the Red plasmid used), to identify candidates that have spontaneously lost the Red-producing plasmid. We have found that Red plasmids pKM208 and pTP223 are lost at variable frequencies (10–50%) during competent cell preparation and electroporation. A minilysate preparation can be used to identify if the strain is truly cured, or if a deletion variant of the Red plasmid that lost the drug marker is still present. For most of the plasmids listed in Table 7.1, growth at 42°C (pKD46 or pKM208) or growth at 37oC (pSim5) can be used to cure the recombinant of the Red-producing plasmid. Alternatively, P1 transduction (if possible) of the marked deletion or insertion to a clean genetic background is usually the best policy. Phenotypic screens (if available) and/or PCR analysis can be used to verify the presence of the gene deletion or insertion. For PCR analysis, one typically uses primers that target the recombination substrate (i.e., the drug marker) and a region of the chromosome not present in the electroporated substrate (see Figure 7.1). Alternatively, one can target regions flanking the deletion or insertion site, and look for differences in the restriction enzyme pattern or size of the PCR product (relative to PCR product generated from the unmodified chromosome). 7.1.3.6 Other Considerations for Recombineering Success
a. Design the primers to place the drug marker colinear with genes surrounding your target gene (if possible). If one orientation doesn’t work, try the other. We have seen context effects on drug expression from the chromosome (i.e., the drug marker works well in one direction, but not at all when turned around). b. Don’t use intact plasmids for PCR templates. The plasmids (even at low concentration) transform at a high efficiency during electroporation creating false-positives. If one uses a drug marker from a plasmid as a PCR template, gel purify a fragment of the plasmid that contains the target sequence (but does not contain the origin of replication) prior to PCR. Better yet, use a colony or an overnight culture containing a chromosomal drug marker for the template (5 µl of overnight culture in a 100 µl PCR works well), or a use a target contained within a nonreplicative plasmid. If a plasmid is used as a template, digestion with the DpnI (which requires a methylated GATC target site for efficient restriction) prior to electroporation can also be used to reduce plasmid transformants [12].
Chromosomal Engineering Strategies
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c. If there are problems obtaining recombinants, follow cell survival after electroporation by plating the culture on LB plates. In the final 3 ml culture, a survival count of 107–8/ml or more is desired. Cell survivals of less than 106 indicate excess killing. Since frequencies of gene replacement are typically in the range of 10 –4–10 –5/ survivor, excess killing will prevent successful recovery of recombinants. If this happens, search for possible salt contamination in the electrocompetent cell preparation or DNA substrate, lower the electroporation voltage and/or decrease or eliminate the heat-shock exposure.
7.1.4 Recombineering with Single-Stranded DNA Oligos In the reaction of Red proteins with a dsDNA linear substrate, Exo binds to a dsDNA end and generates 3′ ssDNA end, which then serves as the substrate for binding by Bet protein. It was reasoned that if one started with ssDNA (say a DNA oligo), then λ Exo might not be required to generate a recombination event. This idea was verified by Ellis et al. [40] who showed that Bet alone could promote the annealing of a 70-base ssDNA oligo (SSO) to the E. coli chromosome. Targeting an amber stop codon within the chromosomal galK locus, they generated Gal+ recombinants by electroporation with a 70-base oligo that contained the wild-type sequence of the galK gene. The frequency of recombination was 2 × 105 recombinants per 108 viable cells, a higher rate than typically seen with similar dsDNA substrates. They showed that Bet is required, Gam is stimulatory (five-fold), and Exo and RecA are not necessary for SSO-mediated recombineering. Oligos containing 40–60 bases resulted in a five-fold drop in recombination. The small size of the oligo means it cannot carry a selection marker, limiting the usefulness of ssDNA oligo-mediated recombineering in many cases. However, if a phenotype can be selected or screened, it offers the easiest way to make single and multiple base pair changes in the E. coli chromosome. Furthermore, performing recombineering in hosts deficient for MMR (see below) can greatly increase the frequency of recombinants (in some cases, up to 25% of the viable cells). The procedure for SSO-mediated recombineering is identical to that described above for PCRgenerated dsDNA substrates, except one electroporates with a ssDNA oligo. The salt-free oligo (70 bp is optimum) should be dissolved in TE buffer to a final concentration of 1 mM and stored at –20 oC. A working solution of the oligo is made (10 µM); 0.5–1.0 µl of this dilution is mixed with 50 µl of electrocompetent cells and shocked as described above. The base change desired should be positioned toward the middle of the oligo, allowing the 5′ and 3′ ends to become fully annealed to the target site. Either DNA strand of a target site can be the source for the oligo, but oligos that are complementary to the lagging strand of the replicating target site display a higher frequency of recombination relative to oligos that target the leading strand. It is based on this observation that Ellis et al. [40] suggested that the Bet protein targets the replication fork during SSO-mediated recombineering. Once annealed to ssDNA regions of the replication fork, an oligo designed to introduce a base change forms a base-pair mismatch, which serves as a substrate for the E. coli MutSLH MMR system [54,55]. MMR will specifically remove the oligo-directed base change, since it resides in the newly synthesized unmethylated strand. (The unmethylated strand is the signal that directs the MMR system to identify which of the mismatched bases is the “error”.) Thus, MMR should be inhibitory to Red-mediated SSO-promoted recombineering. Constantino and Court showed this to be the case, demonstrating that SSO-recombineering frequencies were increased by a factor of 100 or more when performed in cells deficient for MMR [47]. The problem with this scheme, of course, is that working in a MMR deficient genetic background can leads to the generation of unintended mutations, so the target region of the recombinant will need to be sequenced. Also, post modification, the altered target should be moved to a nonmutagenic background. As an alternative to the use of MMR repair mutants, incubation with 75 µg/ ml 2-aminopurine has been shown to increase the levels of SSO-mediated recombineering in wild type hosts [47], but not to the levels seen in MMR-deficient strains. By careful design of the SSO, however, one can increase the frequency of SSO-mediated recombineering in wild type cells by recognizing that certain mismatches are not repaired well. The E. coli MMR
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system repairs the mismatches in the following order: G/T, A/C, A/A, G/G > A/G, T/C, T/T > C/C. In fact, C/C mismatches are not repaired at all, meaning that G to C changes can be made at high frequency in wild type cells by the use of ssDNA oligos [47]. Also, the efficiency of E. coli’s MMR system is limited to small unrepaired regions between one and three mismatches. Thus, by including in the oligo a sequence that generates a series of four to six consecutive mismatched bases, one can avoid interference of gene modification by MMR. In a two-step method to generate a single base pair change using SSOmediated recombineering, one can design an oligo to make six changes in a small region of the target gene at high frequency. In the second recombineering step, all of them are changed back to the original sequence, with the exception of the desired base pair change [56].
7.1.6 Counter-Selection Schemes Selecting for the absence of a particular gene (counter-selection) has been a popular way to introduce an unmarked mutant gene or engineered operon into bacterial chromosomes. By far, the most popular counter-selection function for this purpose is sacB, a gene from B. subtilits that encodes a levansucrase that transfers fructosyl residues from sucrose to various cellular constituents [57]. Expression of sacB kills E. coli, and many other bacteria, in the presence of sucrose. When sacB is placed alongside an antibiotic drug marker (e.g., cat), one can select for both insertion (chloramphenicol resistance) and removal of DNA containing this cassette (sucrose resistance). The removal of a cat-sacB cassette (and insertion of the DNA of interest) is accompanied by conversion to chloramphenicol sensitivity. Such a cassette containing a kan resistance marker and sacB was first described by Ried and Collmer [58] for use in E. chrysanthemi. While this scheme has been used for years in conventional gene replacement strategies, its use in conjunction with Red recombineering technology has allowed for quick and easy construction of marker-free BAC and chromosomal gene replacements and modifications [16,45,46]. The cat-sacB cassette is amplified by PCR with primers that contain target DNA sequences on their 5′ ends, as described above for single drug cassettes. One disadvantage of this cassette is its rather large size (~3 kb) making for larger PCR products that maybe more difficult to amplify relative to single drug cassettes. However, high fidelity/processive polymerases have made this less of a problem in recent years. After electroporation of the PCR product into Red/ET-expressing cells, one selects for CamR colonies (screening them for sucrose sensitivity), as well as for retention of the AmpR Red-producing plasmid since a second recombineering step is required. In the next step, electroporation of the desired construct is performed (prepared by PCR or from a plasmid digest), which replaces the cassette to generate SucR CamS colonies. While SucS cells are known to revert to SucR at high frequency (1 × 10 –4), these spontaneous revertants can be distinguished from true gene replacements by their resistance to chloramphenicol. In most cases, the frequency of Red recombination is at least as high as (and many times higher than) spontaneous reversion to SucR, such than many recombinants can be easily found. Importantly in this process, the cells must be allowed to grow 4–5 hours (or overnight) following electroporation to segregate the chromosomes that have lost sacB from the ones that still retain sacB. This step is critical, since as far as sucrose sensitivity is concerned, sacB + chromosomes are dominant to sacB- chromosomes. Other counterselection schemes used previously for plasmid counterselection have recently been used for λ Red-promoted chromosomal manipulations [59,60]. In these cases, the genes galK and thyA are used for both the initial selection (by growth on minimal media) and counterselection (by growth in the presence of 2-deoxy-galactose and trimethoprim, respectively). A disadvantage with these cassettes is that they can only be used in strains that have been previously deleted of galK (or thyA), thus creating the auxotrophy that is used in the first selection step. Nonetheless, they are powerful tools to make seamless modifications of BAC constructs and bacterial chromosomes. Another counterselection scheme that has been examined for use in Red recombineering is the rpsL gene (Poteete and Murphy, unpublished observations). The rpsL gene for E. coli encodes the ribosomal
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protein S12. A mutant version of the gene encoding a K43R mutant protein confers resistance to streptomycin (rpsL31). When present in the chromosome, the streptomycin resistance conferred by the mutant allele can be suppressed by the wild type version of rpsL, making cells sensitive to streptomycin. [61] A cat-rpsL allele has been constructed, that contains a strong artificial IPTG-dependent promoter that allows for overexpression of wild type rpsL. When placed into the chromosome of an E. coli (rpsL31) strain, it suppresses the streptomycin resistance conferred by this allele. However, in wild type cells, the cassette is lost spontaneously at high frequency following induction with IPTG, probably as a result of high expression the artificial promoter at the genetic loci tested. Nonetheless, when used in cells overexpressing the LacI repressor (where it is assumed the promoter is more moderately expressed) it has been used successfully to replace lacZ with an inactive version of the tet gene (Murphy, unpublished). Also, a “green cat” cassette has been constructed that contains the chloramphenicol resistance gene and the green fluorescent protein (Poteete, unpublished results). Insertion of the cassette is selected by both CamR and green colored colonies, and the loss of the cassette is monitored by loss of GFP and CamS. Using this cassette, an ampicillin enrichment protocol [16] was used to find a markerless deletion of the E. coli recG gene (Poteete, unpublished results).
7.1.7 Flp and Cre Site-Specific Recombination (SSR) Systems Flp and Cre are members of the λ integrase family of site-specific recombinases, and have been used in bacteria, yeast, plants, Drosophila and mammalian cells over the last 15 years [62–69]. The Flp recombinase (from the Saccharomyces cerevisiae 2 µM plasmid) and the Cre recombinase (from bacteriophage P1) recombine their target sequences (FRT and loxP, respectively) at high efficiency and without the need of endogenous host factors. It is these properties that allow Flp and Cre to be used in a variety of different organisms to create insertions, deletions, inversions, duplications, and translocations. Their use has actually been more prolific in mammalian systems, where they are the key genetic tools for the construction of conditional mouse mutations [68,69]. Flp and Cre, both serine recombinases, share a common mechanism of recombination where all four strands of the recombination targets are cut prior to strand exchange and religation [70]. The target sites FRT (Flp recognition target) and loxP (locus of crossover X in P1) are different sequences, though they share common structural motifs which include 13 bp inverted repeats separated by an 8 bp asymmetric spacer sequence. These 34 bases are the minimal functional sites for both these recombinases (see Table 7.3). Because of the asymmetry in the spacer regions, the relative orientation of the target sites to one another defines the type of chromosomal rearrangement. Recombination between two directly repeated target sites generates a deletion of the intervening DNA and a circular molecule, whereas recombination between two inverted target sites generates an inversion of the sequence between the sites. The first event is essentially irreversible (due to loss of the excised circle), while the latter event is fully reversible (since both target sites are maintained on the same linear DNA). The use of these sitespecific recombination systems, in combination with Red/ET recombineering, is described in various chromosomal engineering strategies as outlined in the next section.
Table 7.3 Site-Specific Recombination Systems Used for Drug Marker Eviction in E. coli System
Target Site
Flp/FRT
GAAGTTCCTATTC (N)8 GTATAGGAACTTC
Cre/loxP
ATAACTTCGTATA (N)8 TATACGAAGTTAT
Plasmids pCD20 PBAD33-Flp 1921-cIFLP pJW168 PBAD75Cre 1921-cICre
Details
Reference
λ PR cI857 Rep Amp Cam PBAD R1 origin KanR λ PR cI857 SpecR PlacUV5 Repts AmpR PBad Repts CamR λ PR cI857 SpecR ts
R
R
[122] [123] [124] [125] [126] [124]
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7.2 Strategies of λ Red-Promoted and SSR-Mediated Modification of the Bacterial Chromosome 7.2.1 Gene or Operon Replacement Red/ET recombineering is most commonly used to generate simple gene replacements, substituting a drug marker in place of the gene of interest. Given the simplicity of the process, it is often just as easy to order the primers one needs to make a knockout, even though a mutant allele of the gene of interest already exist. When an existing mutant allele contains a drug marker that is not compatible with the genetic system under study, the easiest route is to simply recombineer the gene knockout with the marker of one’s choice. (Also, ordered primers typically arrive long before requested materials.) Since most of the drug markers used in recombineering tend to be 1 kb or less (see Table 7.2), gene replacements usually involve deleting a typically sized gene of 1–2 kb with a similar-sized fragment. The size of the deletion seems to have little effect on success of the gene or operon replacement using a 70-bp ssDNA oligo, Ellis et al. generated both a 3,300 bp deletion and a single base pair change with equal frequencies [40]. In enterohemorrhagic E. coli (EHEC), Campellone et al. generated multiple deletions of pathogenicity islands ranging from 5 to 45 kb in size [71]. The method of λ Red-mediated disruption is now the method of choice for E. coli gene knockouts, and has led to the systematic knockout of 3985 of 4288 targeted E. coli genes (the Keio collection) [72]. This collection, where the open reading frames were replaced with a kanamycin marker flanked by FRT sites, is now available from the E. coli Genetic Stock Center (http://cgsc2.biology.yale.edu). The straightforward replacement of a target gene by a drug marker is depicted in Figures 7.1 and 7.3a.
7.2.2 SSR-Mediated Excision of Drug Markers The scheme for excising drug markers with Flp is outlined in Figure 7.3b. This is most easily done by using a drug marker cassette that is flanked by FRT sites as a template for PCR. (The drug marker is said to be “flexed”; if loxP sites were used instead, the marker is “floxed.) Alternatively, the FRT sites can be included within the primer sequences used for the PCR. Following target gene replacement, the drug marker can be removed by transformation with a Flp-expressing plasmid that contains a temperaturesensitive origin of replication. Induction of Flp excises the drug marker, and the drug-free recombinant can be cured of the Flp plasmid by growth at the nonpermissive temperature. This procedure then allows the same drug to be used to target a different location in the chromosome. The sequence of FRT and loxP target sites and plasmids that expressed Flp and Cre recombinases are listed in Table 7.3. A word of caution is noted with this procedure. Once the Flp-promoted recombination event has occurred, a single FRT site is left behind. Together with any exogenous primer sequences, the FRT site represents a “scar” that is left over within the chromosomal deletion. This scar can have two general (unintended) effects. Once inserted into the target gene (depending on its orientation), the scar could have polar effects on the expression of genes downstream of the target gene. In recognition of this fact, Datsenko and Wanner included an idealized ribosome binding site and start codon within one scar to prevent such polar effects [15]. The second concern is that if the excision of a drug marker were used in a reiterative fashion, multiple scars could become targets for the Red recombinase, generating large unintended deletions or inversions in the bacterial chromosome. Given the small sequence of the scar, the frequency of such events is likely low and would depend on the number and proximity of multiple scar sites.
7.2.3 Large Deletions An efficient way to generate large precise deletions involves the use of site-specific recombinases Flp or Cre. Ayres et al. [73] have shown that loxP sites transferred to the broad host range plasmid RK2 could be used to generate precise deletions in RK2 following expression of Cre. More recently, others have
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Chromosomal Engineering Strategies Chromosomal manipulation
Starting or intermediate structure
Final product
(a) Marked gene deletion (b) Unmarked gene deletion
(c) Chromosomal deletion 1 (d) Chromosomal duplication
(e) Chromosomal inversion -
1
2
2 P
P
(f ) Reporter fusion
(g) Promoter engineering
Pm
Po
*
or * Pm
Figure 7.3 Chromosomal manipulations promoted by Red/ET and Cre/Flp site-specific recombination (SSR) systems. Chromosomal target genes (dark gray boxes); drug markers (light gray boxes); loxP or FRT sites (black triangles); PCR primers used for recombineering (black arrows); reporter gene (hatched box). (a) Generation of a simple marked gene deletion. (b) Unmarked deletion—site-specific recombination (SSR) sites flank the drug marker, allowing for removal of the drug cassette by in vivo expression of the SSR recombinase following Red/ET-promoted gene replacement. (c) Chromosomal deletion—two SSR target sites are placed in the same orientation at different regions of the chromosome by Red/ET-promoted recombineering (via incorporation of 2 different drug markers). In vivo expression of the SSR recombinase generates a large deletion of the chromosomal region between the 2 SSR target sites, including the drug markers. (d) Gene duplication—an amplified drug marker is flanked by chromosomal sequences defined by primers 1 and 2. Red/ET recombination between the sequences defined by primer #1 is followed by a second recombination event at sequences defined by primer #2, generating a duplicated region of the chromosome. Growth of the transformant on antibiotic-selection plates ensures the presence of the duplication. (e) Chromosomal inversion—two SSR target sites are placed in the opposite orientation at different regions of the chromosome by Red/ET-promoted recombineering (via incorporation of 2 different drug markers). In vivo expression of the SSR recombinase generates a large inversion of the chromosomal region between the 2 SSR target sites. (f) Reporter fusions—a cassette containing a reporter gene (hatched box) and a drug marker (light grey box) is amplified by PCR, and recombineered in place of the target gene. The cassette can contain SSR target sites to create an unmarked fusion (if so desired). (g) Promoter engineering—a drug marker is incorporated just upstream of the promoter region (Po) of a target gene by Red/ET recombineering. One primer (denoted by asterisk) is degenerate at selected positions to create a variety of promoter mutations on the chromosome (top picture). In this case, however, one might interfere with regulatory features upstream of the promoter under study. As an alternative (see bottom picture) one could use the degenerate primer to amplify the target operon. The PCR product is designed to overlap a second PCR product containing a selectable drug marker. Overlap PCR between the two PCR products would generate the recombineering substrate containing variable promoter mutants that can be crossed into the chromosome.
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modified the system by delivery of loxP sites to the E. coli chromosome by Tn5 transposons [74] or λ Red recombineering [75]. The random insertion of loxP sites into the chromosome by transposons, followed by Cre expression, is useful to assess the essentiality of various regions of the genome, though it takes time to identify the endpoints of the deletions. Alternatively, the placement of loxP sites at defined regions of the chromosome by λ Red recombineering allows one to design precise chromosomal deletions in a systematic manner. In this latter scheme (diagrammed in Figure 7.3c), a loxP site can be engineered to be adjacent to a drug marker, which is then selectively targeted by Red to a specific region of the chromosome. A second loxP site (in the same orientation as the first) is recombineered to a second site using a different drug marker. The arrangement is set up so that one or both drug markers lie in between the two loxP sites, depending on whether one wishes to have a marked or unmarked deletion. Once isolated, the strain is transformed with an AmpR Cre-expressing plasmid, transformants are allowed to form colonies on Amp plates, and the deletion-containing drug-free segregants are found by plating (and verified by PCR). Fukiya et al. [75] have reported that, following isolation of the double-loxP containing intermediate, 117- and 165-kbp deletions were formed in this manner in E. coli with 100% efficiency an indication of the high efficiency of the Cre-loxP recombination system.
7.2.4 Seamless Deletions The use of the Flp and Cre to remove drug markers is a highly efficient way to generate marker-free deletions. However, as discussed above, the site-specific target site is left over as a “scar.” Many times, one desires an in-frame deletion in the gene of interest in order to prevent polar effects on genes downstream. For the generation of a precise deletion in the E. coli chromosome, a two-step method that includes the use of a counter-selection step is required. Any one of the counter-selection markers discussed above is crossed into the template site by Red recombineering. Once selected via the drug marker, the presence of the counter-selection maker is verified (e.g., sucrose sensitivity with sacB). In the second step, replacement of the counter-selection marker with a specifically designed DNA fragment that contains the precise deletion, insertion, or point mutation is carried out. Verification of the chromosomal modification is done by PCR size analysis with primers flanking the target site, and/or by sequencing of the PCR product. Typically, for a simple clean deletion, we have used the cat-sacB cassette for the first step, then crossed a DNA fragment that contains the first three codons fused to the last three codons of the target gene [16]. This convention allows for precise deletion of the gene of interest without generating any polar effects on the expression of downstream genes. Alternatively, a 70-base ssDNA oligo can be used which contains 3′ and 5′ sequences that flank the sequence to be deleted, as describe Section 7.1.4 and by Ellis et al. [40]. Such a deletion can be generated in one step, though the frequency would have to high enough (~1 in 102–103 events) to screen for the desired deletion (i.e., one would need a PCR-based screen or an expected phenotype in order to find the transformants containing the deletion). A second way to generate markerless gene replacements in E. coli was first described by Posfai et al. using suicide plasmids [76], and further developed by Kolisnychenko et al. using linear DNA substrates and Red recombineering [77]. This scheme is outlined in Figure 7.4 and takes advantage of I-SceI meganuclease restriction enzyme, which recognizes an 18 bp restriction site not present in the E. coli chromosome. In this procedure, an initial marked deletion is generated in the chromosome using Red as described above, with two modifications. First, the target for the PCR is a drug marker flanked by two I-SceI sites. Secondly, primer 1 is composed of sequences that fuse region A (which defines the left side of the final deletion) with region B (which defines the right side of the final deletion—see Figure 7.4). A PCR product generated with primers 1 and 2, after crossed into the chromosome (shaded region in Figure 7.4), generates a 40–60 bp duplication of chromosomal DNA (marked as box B in Figure 7.4). The strain carrying the deletion is isolated, verified, and electroporated with a SceI-expressing plasmid that carries a temperature-sensitive origin of replication. The SceI sites flanking the drug marker are cut, generating two broken ends in the chromosome. The dsDNA break is repaired via RecA-mediated
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Chromosomal Engineering Strategies
I
A
C
B
Red recombineering SceI
1. II
A
B
SceI Drug marker
2. C
B
SceI cutting III
A
B
C
B
RecA recombinational repair IV
A
B
Figure 7.4 Markerless gene replacement in E. coli using Red/ET recombineering and the meganuclease I-SceI. (I) Region of chromosome to be deleted (sequence between A and B). (II) Primers 1 and 2 are used to generate a PCR product that is crossed into the chromosome by Red/ET recombineering. The drug marker used as the template for the PCR is flanked by SceI sites. The endpoints of the deletion are ultimately defined by the sequence of primer #1 (see text for details). (III) In vivo expression of SceI restriction enzyme generates a dsDNA break. (IV) RecA-promoted recombinational repair (between sequences denoted by B) occurs at high efficiency to generate a precise (unmarked) deletion.
recombination (which occurs at high frequency since nonrepair is lethal) using the B box sequences. The intramolecular recombinational repair event results in an unmarked chromosomal deletion whose endpoints are defined by the sequence within primer 1. Primer 1 (100–140 bp) is actually a composite primer, made by the annealing of two overlapping oligos, followed by a filling-in reaction. Using this method, Kolisnychenko et al. [75] deleted 0.37 Mb of E. coli DNA consisting of cryptic prophages, transposons, damaged genes and genes of unknown function. This strain MDS12 resulted in a 8.3% reduction of genome content (see genome reduction in Section 7.2.10 below). This method for generating seamless knockouts was later employed for use in BAC vectors [78].
7.2.5 Insertions With recombineering, various antibiotic and counter-selection cassettes (between 0.8 and 3.5 kb in size) have been typically used as foreign DNA inserts into the E. coli chromosome (most often replacing an endogenous gene). While there have been no direct comparisons, large insertions (greater than 4 kb) using λ Red/ET technology tend not to be as successful as large deletions. The failure to generate large insertions via recombineering may have to do with the difficulty in coordinating the annealing of two spatially distant dsDNA ends to a replication fork, though the exact mechanism of this process is not yet known. However, other nonrecombineering schemes for generating large insertions into bacterial chromosomes are described in Section 7.3 below.
7.2.6 Duplications and Inversions Duplications and inversions of defined sequences within the E. coli chromosome can be constructed by classical genetic means [79,80], but the use of λ Red/ET recombineering has allowed such manipulations to be made very easily and precisely. For generating duplications of a region of the E. coli chromosome,
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a proposed mechanism of duplication formation in S. typhimurium by Galitski and Roth [81] can be engineered by λ Red to promote such duplications at high frequency. As shown in Figure 7.3d, a drug marker is generated by PCR with flanking sequences as shown. A recombination event occurring in the interval represented by arrow #1 generates a dsDNA break that has as a terminus the sequence represented by arrow #2. Subsequent λ Red-promoted DNA repair, via recombination at the sequence represented by arrow #2 on a sister chromosome, generates a duplicated region of the chromosome. The duplicated regions are separated by the drug marker, so that growth of the cells in the presence of the drug selects against loss of the duplication. It was just this sort of event that allowed Poteete et al. [82] to identify cryptic prophage sequences in the E. coli chromosome, following the electroporation of PCR substrates that contained homology to lacZ on only one side of a cat marker. The size of the duplications allowed by such a mechanism has not been fully investigated, but duplications over 1 Mbp have been observed [82]. For inversions, the easiest method is a two-step process. In the first step, the region to be inverted is deleted and replaced with a counter-selectable marker. In the second step, primers are selected that target the region to be inverted, with flanking homologous sequences that result in inversion of the segment following excision of the counter-selectable marker. For inversion of much larger regions (3 kb or more), λ Red technology can be combined with site-specific recombination systems (SSRs). In this method, one places an SSR target site (e.g., FRT) at the endpoints of the region one desires to invert, as described above for creating large deletions, with the exception that the two loxP sites are placed in inverted orientation (see Figure 7.3e). Again, two different drug markers are used to precisely place loxP sites at the endpoints of the region to invert. Transformation of the double loxP transformant with a Cre-expressing plasmid results in inversion of the region between the loxP sites. Following curing of the strain by the Cre plasmid (typically by growth at 42oC), the inversion event is verified by PCR analysis. Since inversion is 100% reversible, only half of the cells will contain an inversion of the target sequence.
7.2.7 Reporter Fusions The construction of transcriptional and translation fusions to chromosomal genes has been key to the study of gene regulation and expression in E. coli and other bacteria. Methods for the construction of genetic fusions have been described that place gene fusions on multi-copy plasmids, or in single copy within the chromosome via plasmid-chromosomal integrants [83–86], at phage-attachment sites [87,88], or by the use of the site-specific Flp recombinase system [64]. Transposons have also been widely used to generate genetic fusions [86], which by necessity are randomly generated and generally create null mutations in the target gene. The onset of λ Red/ET recombineering has made the construction of genetic fusions a straightforward manipulation, though the full potential of λ Red for such constructions has yet to be fully exploited. Uzzau et al. have shown that epitope tags can be easily placed into the C-terminal encoding regions of a number of chromosomal genes in Salmonella enterica serovar Typhimurium [89]. FLAG tag sequences were incorporated into primers that targeted a Cam or Kan drug cassette flanked by FRT sites. The 5′ regions of one of the PCR primers targeted the translation stop signal of the target gene. The chromosomal fusion was then generated by λ Red recombineering; Flp recombinase could be used (if needed) to remove the downstream drug marker. Ellermeier et al. [90] used the combination of FRT sites and λ Red system to target single copy lac fusions to the E. coli chromosome. In this method, a drug marker flanked by FRT sites is placed downstream of the promoter of interest, followed by excision of the drug marker by Flp recombinase. This single chromosomal FRT site then becomes the substrate for recombination with FRT-containing replication-deficient plasmids that carries lacZ and lacY, designed in such a way to create either a transcriptional or translational fusion to the promoter of interest. No molecular cloning is necessary. In another study, λ Red recombineering has been used to recombineer the GFP+ -encoding sequence into the proV gene of Salmonella strain LT2 to follow expression of the
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osmotically regulated proU gene [91]. The use of the GFP+ variant increased the sensitivity of the assay, allowing the chromosomal gfp fusion to work as well as an identically located lacZ fusion for measuring proU expression effects due to different salt concentrations. In the same study, Hautefort and colleagues also showed that λ Red can be used to easily place promoter-gfp fusions anywhere in the chromosome by recombineering. To do this, the promoter region of interest was generated by PCR, then cloned into a plasmid downstream of the T1 terminator and upstream of a promoterless gfp. This segment, including an adjacent cat gene, was then amplified by primers that contained 50 nucleotide tails homologous to the chromosomal target site. The transcriptional fusions were placed in single copy at a site different from the gene under study (in this case, the putPA genes, a site not important for Salmonella infectivity). In this scenario, the target genes themselves are left intact at their endogenous positions, so that studies geared toward the regulation of virulence genes can be performed without attenuation of the pathogen. In a two-step process utilizing an rpsL-tetA counter-selection marker, Dolphin and Hope recently constructed a marker-free gfp translational fusions in a C. elegans fosmid clone, which was subsequently used to generate transgenic worms for expression analysis [92]. Gerlach, et al. [93] have described a set of plasmids containing various reporter functions (including luciferase, DsRed and phoA) adjacent to a flexed aph gene (KanR) that can be used as templates for PCR to generate recombineeing substrates for the construction of transcriptional or translational fusions on bacterial chromosomes. As such, fusions can simply be engineered by recombineering the reporter-drug cassette into the region just downstream of the Shine-Delgarno ribosome binding site of the target gene (for transcriptional fusions), or within the coding region of the target gene (for translational fusions) (see Figure 7.3f).
7.2.8 Random Mutagenesis of Chromosomal Genes Random mutagenesis a specific gene or promoter has generally been done with plasmids. Chemical mutagenesis of a plasmid containing the gene of interest, passage of the plasmid through a mutagenic host (e.g., mutD), or cloning of a gene following PCR mutagenesis are typical ways in which random mutations are generated. Characterization of the plasmid-encoded mutated gene typically follows, which can at times (because of overexpression) leads to erroneous conclusions regarding the nature of the mutant protein. For this reason, investigators cross mutated genes into the bacterial chromosome in place of the wild type gene, so that phenotypic comparisons to the wild type host can be made directly. These laborious substitutions are usually done one at a time. Random mutagenesis of a chromosomal gene can be performed by the use of λ Red/RT recombineering technology. De Lay and Cronin recently described mutagenesis of the acpP (acyl carrier protein) gene in the E. coli chromosome [94]. The authors derived three temperature-sensitive mutants in acpP using this technique (where no conditional lethal acpP mutants had been found previously). The process entails a combination of error-prone PCR mutagenesis [95], overlap extension PCR [96] and λ Red recombineering. In the first step, mutagenic PCR is carried out targeting the gene or region of interest. A second nonmutagenic PCR is performed on an adjacent chromosomal sequence that contains a drug marker (previously generated by recombineering). Primers for both PCRs are designed so that the products contain a 20 bp overlap, and fusion of the PCR products is carried out by overlap extension PCR. In the final step, the overlap extention product is electroporated into λ Red expressing hosts. Mutagenic screens can be employed on drug-resistant transformants to identify chromosomal mutants in the gene of interest. It is presumed, though not reported, that chromosomal random mutagenesis could be performed without the need to incorporate a selectable marker if the mutated PCR product were transformed into a MMR-deficient host (i.e., E. coli mutS) expressing red and gam functions. In the absence of MMR, mutated sequences would be incorporated at high frequency, allowing screens or selections to be simply applied to the survivors following electroporation.
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7.2.9 Promoter Engineering The protocol for chromosomal random mutagenesis described above could also be applied to promoters of genes within a metabolic pathway (and other regulatory elements). However, a more systematic approach (for example, increasing metabolic flux or biological yield) would be to replace key genes within a pathway with one or more variant promoters. This method to fine-tune genetic control of a metabolic process by altering the promoters of genes in the pathway is known as promoter engineering. In this scheme, libraries of artificial promoters are created and transferred to the chromosome to test their effects on the metabolic flux of a pathway of interest. Jensen and Hammer [97] described a library of artificial promoters (in Lactococcus lactis) by using PCR mutagenesis to vary the spacer region between the –10 and –35 regions of a consensus promoter, though unintended mutations in the consensus regions (in some of their clones) resulted in a library with promoter strengths covering three orders of magnitude. While initial characterization of these promoters was done with plasmid constructs, the real value of such a collection is evident when the promoters can be engineered into target genes on the chromosome, where their effects on metabolic flux and yield can be determined. This process can be a tedious task when done by the classical route of gene replacement via plasmid integration and resolution [98]. More recently, the λ Red recombination system has made this approach more feasible, allowing chromosomal promoters to be easily replaced with artificial constructs. Meynial-Salles et al. reported the construction of a small library of clones that express the lacZ gene from three short glucose isomerase promoters of different strengths [99]. They demonstrated a range of in vivo β-galactosidase activities ranging from two-fold higher to four-fold lower relative to the level seen with the wild type lacZ locus. This group went on to show that other regulatory elements, such as the Shine-Delgarno sequence (ribosome binding site) and mRNA stabilizing sequences could be randomly modified to create chromosomal operons exhibiting an even wider range of lacZ expression. The authors also generated a library of E. coli clones that expressed glyceraldehyde-3-phosphate dehydrogenase at different levels, and subsequently screened for the clone with the highest level of glycerol production. A diagram for this type of protocol is presented in Figure 7.3g. In a different study [100], Alper et al. used mutagenic PCR of λ PL driving GFP to generate an artificial promoter library. For chromosomal promoter delivery, overlap extention PCR was used to generate a fusion between a dozen selected promoter mutants and a selectable marker (kan), and λ Red recombineering was used to transfer the linear substrates onto the chromosome in place of two endogenous promoters (to the ppc and dxs genes). In this study, using λ Red, there were able to transfer twelve well-defined promoters, which varied step-wise over the entire range of expression levels (325-fold when analyzed at the transcriptional level) to two different genes in three different hosts. The authors went on to find an optimum level of production of phosphoenolpyruvate carboxylase (ppc) for growth yield using glucose as the sole carbon source, as well as the optimum level of dsx expression for the production of lycopene in both wild type and an engineered strain of E. coli.
7.2.10 Genome Reduction One area of chromosomal engineering that will have a direct impact on metabolic engineering studies is the development of strains with reduced genome content. Bacteria possess many genes that play important roles for survival in the environment, but are dispensable when grown in the laboratory. Among these nonessential elements (imbedded throughout bacterial genomes) are cryptic prophages, transposons, insertion sequence elements, damaged genes, and genes of unknown function. Since E. coli and other bacteria are critical for the production of metabolites and proteins of commercial value, it is of interest to create strains that are deleted of as many genes as possible without hindering growth and metabolic efficiency. As noted by Kolisnychenko et al. [78], the simple reduction in the content of needless protein contaminants in the host makes purification of recombinant proteins that much easier. Also, metabolic energy is not wasted maintaining and expressing functions that are not useful,
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and instead is directed more to the synthesis of the desired metabolite. Finally, genome reduction may decrease redundancies in gene function and regulatory mechanisms, making it easier to manipulate and design improvements in metabolic pathways. Using Red/ET recombineering and RecA-mediated dsDNA break repair (see method described in Figure 7.4), the labs of Gyorg Posfai and Frederick Blattner engineered a reduced E. coli genome by identifying and removing nonessential genes, mobile elements, and cryptic phage sequences [78,101]. They did this by comparison of genomic sequences between E. coli K-12 and five other E. coli species, and identified DNA segments present in K-12 but absent from the other genomes. These segments were removed from E. coli K-12 generating a strain that had over 15% of its genome removed (708,267 bp–743 genes). These multiple-deleted strains (MDS41–43) grew similar to starting strain MG1655, possessed a 100-fold increase in electroporation efficiency, and expressed recombinant proteins as well as MG1655. These strains were designed to be free of all insertion sequences (IS) that would be expected to decrease the number of spontaneous transpositional mutagenic events. The reduced genome resulted in a strain that displayed an increase in plasmid stability and a 20% drop in spontaneous mutation rates. Clearly, these attributes are attractive for strains to be used for metabolic engineering applications. Other nonrecombineering approaches have also been used to generate minimized genomes of E. coli K-12. Yu et al. [75] have generated and precisely mapped two libraries of independent transposon mutants (400 per library) using two different selection markers (Cam and Kan). The modified transposons contain loxP sites. Individual members from each library were selected and combined into one strain by P1 transduction. Subsequently, the region between the insertion sites was deleted by Cre recombinase. Using this method, this group generated an E. coli strain with a reduced genome containing a total deletion of 313 kbp (287 orfs). Similar to the reduced MDS strains described above, the cumulative-deletion mutant exhibited no growth defect when grown under standard laboratory conditions. Out of 13 deletions attempted, only six were generated in this study. The seven deletions not obtained suggest these regions likely contain one or more essential functions. Thus, the inability to obtain a deletion of a chromosomal region offers an informative approach for studies designed to identify essential genes, and thus regions of bacterial chromosomes that may contain potential drug targets for antibiotic development. B. subtilis has also been a target of genome reduction. Using plasmid-based chromosomal integrationexcision systems, Westers et al. [102] made a strain that was deleted of two prophages, the prophage-like elements, and a polyketide synthase operon that represented a 7.7% reduction (320 kb) of the B. subtilis chromosome. As these authors pointed out, the deletion of phage and phage-like elements removes potential lysins that might interfere with the growth of bacteria during industrial fermentations. Much like what was found for other reduced genomes discussed above, the reduced genome of B. subtilis had no effect on growth rates and biomass yields. A further examination of the properties of this reduced genome strain was performed using metabolic flux analysis, proteomics, and a variety of physiological assays. The study found that the parent and deleted strains of B. subtilis exhibited similar levels of carbon metabolism, protein secretion, competence, and sporulation, with some measurable changes in bacterial motility observed in the presence of different concentrations of agarose. It had yet to be demonstrated whether this deleted strain represented an improved strain for industrial fermentation.
7.3 Large Insertions When considering the engineering of a metabolic pathway, it is often necessary to transfer all the genes of that pathway from an exogenous source into the target strain chromosome. Since genes within a metabolic pathway tend to be clustered, this process can involve the insertion of a very large DNA segment into the bacterial chromosome. As mentioned above, recombineering does not work efficiently with large DNA inserts (~5 kb and greater). However, a number of nonrecombineering approaches to the insertion of foreign DNA into the E. coli chromosome have been described, protocols that include the use of site-specific recombinases, transposons, and general homologous recombination.
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A method for DNA insertion into the E. coli chromosome described by Haldimann and Wanner uses a series of plasmid-host systems for the introduction of multiple genes into the same cell at single copy [88]. This method should be useful for the development of strains for metabolic engineering that requires multiple genes to be inserted, with various regulatory regions governing each of the inserts. The conditional-replication, integration, and modular (CRIM) plasmids are a series of plasmids that include the attB and attP sites for phages λ, HK022, φ80, P21, and P22, that can be used in conjunction with a series of different promoters for ectopic expression of cloned inserts. The plasmids replicate in a pir-dependent manner (a gene that encodes the trans-acting Π protein for the R6K origin of replication) allowing these plasmids to be grown under either low or high copy-number conditions [103]. The integration events are governed by a series of Int (Integrase) helper plasmids that promote integration of the attP-containing plasmids into the attB site of the chromosome of non-pir hosts (hosts that cannot support replication of the plasmids). Following integration, the inserts are stably maintained in the absence of antibiotic selection. The modular design of these plasmids allows different inserts to be placed at alternate positions in the chromosome, and expressed by various promoters. An alternative method of chromosomal DNA insertion involves the use of the yeast FLP recombinase system. Huang et al. [64] have described the FLIRT system (Flp-mediated DNA integration and rearrangement at prearranged genomic targets). In this method, FRT sites are delivered to various locations in the E. coli genome by a modified Tn5 transposon. Plasmids that contain a cloned DNA insert (and TetR marker) flanked by FRT sites is processed by restriction enzyme digestion to remove the origin of replication. Following treatment of the linearized DNA with Flp in vitro, the circular product is purified and electroporated into E. coli containing the Flp-expressing plasmid pLH29. Site-specific recombination promotes insertion of the cloned DNA into the FRT site on the chromosome, generating TetR colonies. The targeted integrants are stable in the absence of Flp expression. Clearly, as an addendum to this technology, Red/ET recombineering could be used to deliver FRT sites (and subsequently the insert) to any desired location in the chromosome. A third method for chromosomal insertion takes advantage of the site-specific recombination mechanism of transposon Tn7 [104–107]. In this scheme, the DNA insertion is cloned into a multiple cloning site of a plasmid, where it is thus flanked by the left and right ends of Tn7. The nonreplicative plasmid is then transformed, conjugated or electroporated into a target bacterium, along with a helper plasmid expressing the Tn7 transposase functions. The cloned insert is transposed to the attTn7 attachment site, a region downstream of the glnS gene (encoding glucosomin-6-phosphate synthetase). Insertion into this site does not interfere with any gene function, and thus is a neutral site for insertion elements. A broad host range Tn7-based system, utilized in E. coli, Pseudomonas aeruginosa, Pseudomonas putida, and Yersinia pestis, has been described [108]. A system based on mini-Tn5 transposition has also been described [109], but in this case the transposition is random, and thus requires characterization of the insertion site prior to analysis of the transformant. This system is more applicable for insertional mutagenesis and promoter probing applications. In the most recent description of the Tn7 method [107], the miniTn7 plasmid pGRG25 contains a multiple cloning site flanked by Tn7 ends, the trnABCD genes under control of the PBAD promoter, araC (for controlled expression of trnABCD), the bla gene (for selection with ampicillin), and a temperature– sensitive origin of replication (allowing for curing of the plasmid following the transposition event). Thus no helper plasmid is required. Following cloning of the insert into pGRG25 and transformation of the plasmid into E. coli, a cell population is grown up at 32oC in the presence of ampicillin. The culture is then grown nonselectively to saturation in the presence of arabinose to induce transposition, then plated out at 42oC for colonies. Colonies are screened for the loss of the temperature sensitive plasmid (AmpS) and for the presence of the insertion by PCR across the Tn7 insertion site (attTn7). The frequency of insertion is reported to be as high as 79%, making it the most efficient method of transferring insertions to the E. coli chromosome. Given this high frequency of insertion, a selection scheme is not required to identify insertion events. The high frequency of this system likely comes from the outgrowth of the culture following transformation of the mini-Tn7 plasmid, ensuring that a majority of the cells will be subject to the transposition reaction.
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A method for inserting very large regions of exogenous DNA (~100 kb) into the E. coli chromosome that takes advantage of the endogenous RecA pathway of recombination has been described [110]. Two large regions (5–6 kb) of defined E. coli sequence were cloned into a BAC. The two genetic segments, the gpt region (~5.5 kb) and the lacI region (~6.5 kb) are separated by a polylinker for cloning exogenous DNA and the cat gene for genetic selection of the insertion. Following random cloning of L. lactis DNA into the cloning site, two clones were selected for transfer of the inserts to the intergenic region between gpt and lacI in E. coli, a nonessential region of the E. coli chromosome (~110 kb). Following linearization of the clones and electroporation into E. coli, transformants were selected (and verified) that had transferred either 20 kb (17 orfs) or 11 kb (12 orfs) of L. lactis DNA into the E. coli chromosome. A further development of this protocol might include construction of an E. coli strain that has a counter-selectable marker already in place of the chromosomal gpt-lacI interagenic region (e.g., a ∆gpt-lacI::cat sacB strain). One then would not need to incorporate a selectable marker into the BAC clone to select for the insert.
7.4 Applications of Red/ET Recombineering Technology for Metabolic Engineering Red/ET recombineering is now the preferred method for making gene knockouts in E. coli, simplifying the generation of bacterial mutants. The simplicity, speed, and accuracy of the method virtually ensure finding the desired chromosomal alteration in most cases. In fact, this technology has been used to generate a gene knockout in every nonessential gene in the E. coli chromosome [72]. In most recent developments, Red/ET recombineering has been carried out in a 96-well format for the construction of conditional knockout targeting vectors for the generation of mutant mice [111], and been used to fluorescently label numerous genes within the E. coli genome for visualization of the E. coli proteome [112]. Red/ET technology is also moving into the field of bacterial pathogenesis, allowing investigators to make gene insertions and deletions in a day or two that would have normally taken two or more weeks [45,113]. For metabolic engineering, recombineering is now being recognized as a critical tool in the search for novel pharmaceutical compounds, where, for example, one might want to manipulate large biosynthetic gene clusters for expression in heterologous hosts, or for greater flexibility in the use of combinatorial biosynthetic technologies. Many of the bacterial species used by metabolic engineers possess genetic systems that are either time-consuming (e.g., Pseudomonas) or nonexistent (Mycobacteria). Many investigators are cloning the DNA of interest into BAC vectors, manipulating its genes using Red/ET recombineering in E. coli, then incorporating mobilization determinants into these vectors for transfer to the original (or heterologous) recipients. A good example of this gene replacement method was described by Gust et al. [52] where cosmid clones of Streptomyces coelicolor DNA were modified in E. coli by Red/ET recombineering. The inclusion of FRT sites flanking the drug marker allowed marker-free in-frame deletions of the gene of interest to be generated. The clone contained an origin of transfer (oriT) that allowed the investigators to transfer the modified DNA into S. coelicolor where it was integrated into the chromosome. This method has been used to generate numerous gene deletions in S. coelicolor. The value of recombineering for metabolic engineering is also evident in a report by R. Muller and colleagues [114]. In their study, the authors were interested in cloning a large biosynthetic gene cluster from the myxobacterium Stigmatella aurantiaca and expressing it in Pseudomonas putida. The gene cluster, which encodes a PKS/NRPS hybrid that synthesizes various forms of the cyclic peptide myxochromide S, was first cloned into a BAC vector. The vector was then modified by Red/ET recombineering that included the insertion of the following elements: oriT (for transfer in P. putida), a tetracyclineresistance gene (for selection in P. putida) and the P. putida trpE gene (for integration of the vector into the P. putida chromosome by homologous recombination). These steps were followed by the addition of a (missing) thioesterase domain to one of the nonribosomal peptide synthetases(NPSs), and the insertion of the toluic acid-inducible promoter (Pm) to drive expression of the myxochromide S gene cluster.
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These manipulations were carried out by methods described above in Section 7.2 (and outlined in Figure 7.3). The final construct (43 kbp), containing the complete biosynthetic cluster expressed from Pm, was transferred to P. putida by triparental conjugation [115] and integrated into the chromosome. The P. putida strain containing the myxochromide S gene cluster generated a maximum yield of myxochromide S five-fold greater that the natural producing S. aurantiacat strain. The manipulations of such large DNA regions with conventional restriction enzyme technology would have been extremely difficult if not impossible. With Red/ET recombineering, the modifications were made easily and quickly allowing a novel approach to heterologous expression of synthetic genes in Pseudomonads. In another study, Eustaquio et al. [116] used Red recombineering to place the attachment site (attP) and integrase gene (int) of phage ΦC31 into cosmids containing the biosynthetic gene clusters of novobiocin (from Streptomyces spheroids) and clorobiocin (from Streptomyces roseochromogenes), enabling them to place these constructs into the genomes of heterologous hosts S. coelicolor and S. lividans (hosts which can be easily genetically manipulated). In E. coli, the cosmids were modified by λ Red to remove nonessential regions of both genes cluster prior to heterologous expression. The use of Red/ET recombineering for combinatorial biosynthesis has recently been reported. Nguyen et al. [117] generated a library of novel daptomycin derivatives using λ Red technology. Daptomycin, used for the treatment of skin infections caused by gram-positive pathogens, is a cyclic anionic 13-amino acid lipopeptide produced in Streptomyces roseosporus by a NPS. Using λ Red recombineering, these authors exchanged modules of the DptBC subunit, replaced them with other NRPSs, combined them with DptD subunit modules, inactivated a Glu12 methylase activity, and generated variations of its lipid tail to generate 30 novel NRPS biosynthetic pathways. Though none of the active antibiotics produced were superior to daptomycin, several were as equally potent. This study led to an understanding of what structural features of daptomycin were important of its clinical potency, and demonstrated the usefulness of combinatorial biosynthesis for the generation of antibiotic derivatives that cannot be effectively generated by chemical modification. Red/ET recombineering technology has also been used to make simple gene knockouts or replacements in a variety of metabolic engineering applications. Kim et al. [118] describe a method for the rapid and efficient generation of a library of hybrid type I polyketide synthetases for pikromycin production. In this scheme, the plasmid sequence encoding the loading domain (ATo-ACPo) was replaced with sacB, which was subsequently replaced by a library of chimeric loading domains fragments via Red/ ET recombineering. These authors described a rapid, precise, and efficient integration of shuffled DNA into a PKS complementation plasmid that generated a library of modified polyketide synthetases. Other investigators have reported using Red/ET recombineering for engineering of the geldanamycin biosynthesis pathway [119], the engineering of a biofilm-deficient E.coli strain for biotechnology applications [120], and for optimization of biomass yield of E. coli using inverse metabolic engineering [121]. Clearly, the efficiency and simplicity of the Red/ET recombineering system is an important and versatile tool for metabolic engineers, allowing investigators to make changes in large gene clusters that otherwise could not have been easily done with classical techniques of gene manipulation.
7.5 Summary The process of DNA insertions, knockouts, or replacement of gene sequences has always been the cornerstone to understanding the biochemistry and physiology of microorganisms. The development and use of a myriad of antibiotic markers, counter-selection schemes, phage-integration sites, and transposons have been developed over the past few decades to remove, introduce, or rearrange genes (and operons) in the bacterial chromosome. These same techniques of genetic manipulation are now being used by the metabolic engineer to develop strains that are more fit, less toxic and rationally designed for production of a particular metabolite of interest. More recently, the development of the highly efficient Red/ET recombineering technology, together with the use of the SSRs Cre and Flp, has given the investigator/engineer an almost limitless ability to modify bacterial genomes. In particular, recombineering
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changes the way research is performed by simplifying the methods of chromosomal manipulation (i.e., use of a PCR product rather than a plasmid construct), encouraging the investigator to create DNA constructs of increasing complexity in a shorter amount of time. The use of E. coli-borne BAC vectors to house the genetic elements of bacteria of interest to the metabolic engineer, and the subsequent modification of these sequences for transfer to the original or heterologous host, should become a standard method for metabolic engineering of bacterial chromosomes. In time, it is possible that phage elements within the meta-genomic universe might be found to contain Red or RecET-like recombination systems that may be used directly to recombineer the chromosomes of non-E. coli bacterial hosts used in metabolic engineering.
References 1. Baudin, A., et al. A simple and efficient method for direct gene deletion in Saccharomyces cerevisiae. Nucleic Acid Res., 21, 3329, 1993. 2. Lorenz, M.C., et al. Gene disruption with PCR products in Saccharomyces cerevisiae. Gene, 158, 113, 1995. 3. Wach, A., et al. New heterologous modules for classical or PCR-based gene disruptions in Saccharomyces cerevisiae. Yeast, 10, 1793, 1994. 4. Jasin, M. and P. Schimmel. Deletions of an essential gene in Escherichia coli by site-specific recombination with linear DNA fragments. J. Bacteriol., 159, 783, 1984. 5. Marinus, M.G., et al. Insertion mutations in the dam gene of Escherichia coli K-12. Molec. Gen. Genet., 191, 288, 1983. 6. Russell, C.B., D.S. Thaler, and F.W. Dahlquist. Chromosomal transformation of Escherichia coli recD strains with linearized plasmids. J. Bacteriol., 171, 2609, 1989. 7. Winans, S.C., et al. Site-directed insertion and deletion mutagenesis with cloned fragments in Escherichia coli. J. Bacteriol., 161, 1219, 1985. 8. Spies, M. and S.C. Kowalczykowski. Homologous recombination by RecBCD and RecF pathways. In The Bacterial Chromosome, N.P. Higgins, Editor. ASM Press: Washington, DC, 2005, 389. 9. Kuzminov, A. Recombinational repair of DNA damage in Escherichia coli and bacteriophage lambda. Microbiol. Mol. Biol. Rev., 63 (4), 751, 1999. 10. Dabert, P. and G.R. Smith. Gene replacement with linear DNA fragments in wild type Escherichia coli: enhancement by chi sites. Genetics, 145, 877, 1997. 11. Murphy, K.C. Use of bacteriophage λ recombination functions to promote gene replacement in Escherichia coli. J. Bacteriol., 180, 2063, 1998. 12. Zhang, Y., et al. A new logic for DNA engineering using recombination in Escherichia coli. Nat. Genet., 20 (2), 123, 1998. 13. Zhang, Y., et al. DNA cloning by homologous recombination in Escherichia coli. Nat. Biotechnol., 18 (12), 1314, 2000. 14. Yu, D., et al. An efficient recombination system for chromosome engineering in Escherichia coli. Proc. Natl Acad. Sci. USA, 97 (11), 5978, 2000. 15. Datsenko, K.A. and B.L. Wanner. One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. Proc. Natl. Acad. Sci. USA, 97 (12), 6640, 2000. 16. Murphy, K.C., K.G. Campellone, and A.R. Poteete. PCR-mediated gene replacement in Escherchia coli. Gene, 246, 321, 2000. 17. Muyrers, J.P., Y. Zhang, and A.F. Stewart. ET-cloning: think recombination first. Genet. Eng. (NY), 22, 77, 2000. 18. Copeland, N.G., N.A. Jenkins, and D.L. Court. Recombineering: a powerful new tool for mouse functional genomics. Nat. Rev. Genet., 2 (10), 769, 2001. 19. Muyrers, J.P., et al. Rapid modification of bacterial artificial chromosomes by ET-recombination. Nucleic Acids Res., 27 (6), 1555, 1999.
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20. Signer, E.R. and J. Weil. Recombination in bacteriophage λ. Mutants defective in general recombination. J. Mol. Biol., 34, 261, 1968. 21. Cassuto, E. and C.M. Radding. Mechanism for the action of lambda exonuclease in genetic recombination. Nat. New Biol., 229 (1), 13, 1971. 22. Kovall, R. and B.W. Matthews. Toroidal structure of lambda-exonuclease. Science, 277 (5333), 1824, 1997. 23. Kmiec, E. and W.K. Holloman. Beta protein of bacteriophage lambda promotes renaturation of DNA. J. Biol. Chem., 256 (24), 12636, 1981. 24. Muniyappa, K. and C.M. Radding. The homologous recombination system of phage lambda. Pairing activities of beta protein. J. Biol. Chem., 261 (16), 7472, 1986. 25. Li, Z., et al. The beta protein of phage lambda promotes strand exchange. J. Mol. Biol., 276 (4), 733, 1998. 26. Passy, S.I., et al. Rings and filaments of beta protein from bacteriophage lambda suggest a superfamily of recombination proteins. Proc. Natl. Acad. Sci. USA, 96 (8), 4279, 1999. 27. Murphy, K.C. The lambda Gam protein inhibits RecBCD binding to dsDNA ends. J. Mol. Biol., 371 (1), 19, 2007. 28. Karu, A.E., et al. The gamma protein specified by bacteriophage gamma. Structure and inhibitory activity for the recBC enzyme of Escherichia coli. J. Biol. Chem., 250 (18), 7377, 1975. 29. Murphy, K.C. Lambda Gam protein inhibits the helicase and chi-stimulated recombination activities of Escherichia coli RecBCD enzyme. J. Bacteriol., 173 (18), 5808, 1991. 30. Iyer, L.M., E.V. Koonin, and L. Aravind. Classification and evolutionary history of the single-strand annealing proteins, RecT, Redbeta, ERF and RAD52. BMC Genomics, 3 (1), 8, 2002. 31. Gottesman, M.M., et al. Characterization of bacteriophage lambda reverse as an Escherichia coli phage carrying a unique set of host-derived recombination functions. J. Mol. Biol., 88 (2), 471, 1974. 32. Kushner, S.R., H. Nagaishi, and A.J. Clark. Isolation of exonuclease VIII: the enzyme associated with sbcA indirect suppressor. Proc. Natl. Acad. Sci. USA, 71 (9), 3593, 1974. 33. Thaler, D.S., M.M. Stahl, and F.W. Stahl. Double-chain-cut sites are recombination hotspots in the Red pathway of phage l. J. Mol. Biol., 195, 75, 1987. 34. Stahl, F.W., et al. Break-join recombination in phage l. Genetics, 125, 463, 1990. 35. Poteete, A.R. and A.C. Fenton. Efficient double-strand break-stimulated recombination promoted by the general recombination systems of phages l and P22. Genetics, 134, 1013, 1993. 36. Hill, S.A., M.M. Stahl, and F.W. Stahl. Single-strand DNA intermediates in phage λ's Red recombination pathway. Proc. Natl. Aad. Sci. USA, 94, 2951, 1997. 37. Stahl, M.M., et al. Annealing vs. invasion in phage lambda recombination. Genetics, 147 (3), 961, 1997. 38. Wackernagel, W. and C.M. Radding. Transfection by half-molecules and inverted molecules of λ DNA: requirement for exo an b-promoted recombination. Virology, 52, 425, 1973. 39. Poteete, A.R. Involvement of DNA replication in phage lamda Red-mediated recombination. Mol. Microbiol., 68(1), 66, 2008. 40. Ellis, H.M., et al. High efficiency mutagenesis, repair, and engineering of chromosomal DNA using single-stranded oligonucleotides. Proc. Natl Acad. Sci. USA, 98, 6742, 2001. 41. Huen, M.S., et al. The involvement of replication in single stranded oligonucleotide-mediated gene repair. Nucleic Acids Res., 34 (21), 6183, 2006. 42. Stahl, F.W. Recombination in phage λ: one geneticist's historical perspective. Gene, 223, 95, 1998. 43. Oliner, J.D., K.W. Kinzler, and B. Vogelstein. In vivo cloning of PCR products in E. coli. Nucleic Acids Res., 21 (22), 5192, 1993. 44. Zhang, Y., et al. Phage annealing proteins promote oligonucleotide-directed mutagenesis in Escherichia coli and mouse ES cells. BMC Mol. Biol., 4 (1), 1, 2003.
Chromosomal Engineering Strategies
7-25
45. Murphy, K. and K. Campellone. Lambda Red-mediated recombinogenic engineering of enterohemorrhagic and enteropathogenic E. coli. BMC Mol. Biol., 4, 11, 2003. 46. Lee, E.C., et al. A highly efficient Escherichia coli-based chromosome engineering system adapted for recombinogenic targeting and subcloning of BAC DNA. Genomics, 73 (1), 56, 2001. 47. Costantino, N. and D. Court. Enhanced levels of lambda Red-mediated recombinants in mismatch repair mutants. Proc. Natl. Acad. Sci. USA, 100, 15748, 2003. 48. Sergueev, K., et al. Cell toxicity caused by products of the p(L) operon of bacteriophage lambda. Gene, 272 (1–2), 227, 2001. 49. Datta, S., N. Costantino, and D.L. Court. A set of recombineering plasmids for gram-negative bacteria. Gene, 379, 109, 2006. 50. Wang, J., et al. An improved recombineering approach by adding RecA to lambda Red recombination. Mol. Biotechnol., 32 (1), 43, 2006. 51. Derbise, A., et al. A rapid and simple method for inactivating chromosomal genes in Yersinia. FEMS Immunol. Med. Microbiol., 38 (2), 113, 2003. 52. Gust, B., et al. PCR-targeted Streptomyces gene replacement identifies a protein domain needed for biosynthesis of the sesquiterpene soil odor geosmin. Proc. Natl. Acad. Sci. USA, 100 (4), 1541, 2003. 53. Yu, D., et al. Recombineering with overlapping single-stranded DNA oligonucleotides: testing a recombination intermediate. Proc. Natl. Acad. Sci. USA, 100, 7207, 2003. 54. Modrich, P. Methyl-directed DNA mismatch correction. J. Biol. Chem., 264 (12), 6597, 1989. 55. Lobner-Olesen, A., O. Skovgaard, and M.G. Marinus. Dam methylation: coordinating cellular processes. Curr. Opin. Microbiol., 8 (2), 154, 2005. 56. Yang, Y. and S.K. Sharan. A simple two-step, ‘hit and fix’ method to generate subtle mutations in BACs using short denatured PCR fragments. Nucleic Acids Res., 31 (15), e80, 2003. 57. Gay, P., et al. Cloning structural gene sacB, which codes for exoenzyme levansucrase of Bacillus subtilis: expression of the gene in Escherichia coli. J. Bacteriol., 153, 1424, 1983. 58. Ried, J.L. and A. Collmer. An nptI-sacB-sacR cartridge for constructing directed, unmarked mutations in Gram-negative bacteria by marker exchange-eviction mutagenesis. Gene, 57, 239, 1987. 59. Warming, S., et al. Simple and highly efficient BAC recombineering using galK selection. Nucleic Acids Res., 33 (4), e36, 2005. 60. Wong, Q.N., et al. Efficient and seamless DNA recombineering using a thymidylate synthase A selection system in Escherichia coli. Nucleic Acids Res., 33 (6), e59, 2005. 61. Russell, C.B. and F.W. Dahlquist. Exchange of Chromosomal and plasmid alleles in Escherichia coli by selection for loss of a dominant antibiotic sensitivity maker. J. Bacteriol., 171, 2614, 1989. 62. Cox, M.M. The FLP protein of the yeast 2-microns plasmid: expression of a eukaryotic genetic recombination system in Escherichia coli. Proc. Natl. Acad. Sci. USA, 80 (14), 4223, 1983. 63. Huang, L.C., E.A. Wood, and M.M. Cox. A bacterial model system for chromosomal targeting. Nucleic Acids Res., 19 (3), 443, 1991. 64. Huang, L.C., E.A. Wood, and M.M. Cox. Convenient and reversible site-specific targeting of exogenous DNA into a bacterial chromosome by use of the FLP recombinase: the FLIRT system. J. Bacteriol., 179 (19), 6076, 1997. 65. Siegal, M.L. and D.L. Hartl. Application of Cre/loxP in Drosophila. Site-specific recombination and transgene coplacement. Methods Mol. Biol., 136, 487, 2000. 66. Schweizer, H.P. Applications of the Saccharomyces cerevisiae Flp-FRT system in bacterial genetics. J. Mol. Microbiol. Biotechnol., 5 (2), 67, 2003. 67. Gilbertson, L. Cre-lox recombination: Cre-ative tools for plant biotechnology. Trends Biotechnol., 21 (12), 550, 2003. 68. Branda, C.S. and S.M. Dymecki. Talking about a revolution: The impact of site-specific recombinases on genetic analyses in mice. Dev. Cell, 6 (1), 7, 2004.
7-26
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69. Schnutgen, F., et al. Engineering embryonic stem cells with recombinase systems. Methods Enzymol., 420, 100, 2006. 70. Grindley, N.D., K.L. Whiteson, and P.A. Rice. Mechanisms of site-specific recombination. Ann. Rev. Biochem., 75, 567, 2006. 71. Campellone, K.G., D. Robbins, and J.M. Leong. EspFU is a translocated EHEC effector that interacts with Tir and N-WASP and promotes Nck-independent actin assembly. Dev. Cell, 7 (2), 217, 2004. 72. Baba, T., et al. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol. Syst. Biol., 2, 2006 0008, 2006. 73. Ayres, E.K., et al. Precise deletions in large bacterial genomes by vector-mediated excision (VEX). The trfA gene of promiscuous plasmid RK2 is essential for replication in several gram-negative hosts. J. Mol. Biol., 230 (1), 174, 1993. 74. Yu, B.J., et al. Minimization of the Escherichia coli genome using a Tn5-targeted Cre/loxP excision system. Nat. Biotechnol., 20 (10), 1018, 2002. 75. Fukiya, S., H. Mizoguchi, and H. Mori. An improved method for deleting large regions of Escherichia coli K-12 chromosome using a combination of Cre/loxP and lambda Red. FEMS Microbiol. Lett., 234 (2), 325, 2004. 76. Posfai, G., et al. Markerless gene replacement in Escherichia coli stimulated by a double-strand break in the chromosome. Nucleic Acids Res., 27 (22), 4409, 1999. 77. Kolisnychenko, V., et al. Engineering a reduced Escherichia coli genome. Genome Res., 12 (4), 640, 2002. 78. Tischer, B.K., et al. Two-step red-mediated recombination for versatile high-efficiency markerless DNA manipulation in Escherichia coli. Biotechniques, 40 (2), 191, 2006. 79. Schmid, M.B. and J.R. Roth. Genetic methods for analysis and manipulation of inversion mutations in bacteria. Genetics, 105 (3), 517, 1983. 80. Segall, A., M.J. Mahan, and J.R. Roth. Rearrangement of the bacterial chromosome: forbidden inversions. Science, 241 (4871), 1314, 1988. 81. Galitski, T. and J.R. Roth. Pathways for homologous recombination between chromosomal direct repeats in Salmonella typhimurium. Genetics, 146 (3), 751, 1997. 82. Poteete, A.R., A.C. Fenton, and A. Nadkarni. Chromosomal duplications and cointegrates generated by the bacteriophage lamdba Red system in Escherichia coli K-12. BMC Mol. Biol., 5 (1), 22, 2004. 83. Hand, N.J. and T.J. Silhavy. A practical guide to the construction and use of lac fusions in Escherichia coli. Methods Enzymol., 326, 11, 2000. 84. Silhavy, T.J. and J.R. Beckwith. Uses of lac fusions for the study of biological problems. Microbiol. Rev., 49 (4), 398, 1985. 85. Simons, R.W., F. Houman, and N. Kleckner. Improved single and multicopy lac-based cloning vectors for protein and operon fusions. Gene, 53 (1), 85, 1987. 86. Slauch, J.M. and T.J. Silhavy. Genetic fusions as experimental tools. Methods Enzymol., 204, 213, 1991. 87. Platt, R., et al. Genetic system for reversible integration of DNA constructs and lacZ gene fusions into the Escherichia coli chromosome. Plasmid, 43 (1), 12, 2000. 88. Haldimann, A. and B.L. Wanner. Conditional-replication, integration, excision, and retrieval plasmid-host systems for gene structure-function studies of bacteria. J. Bacteriol., 183 (21), 6384, 2001. 89. Uzzau, S., et al. Epitope tagging of chromosomal genes in Salmonella. Proc. Natl. Acad. Sci. USA, 98, (26), 15264, 2001. 90. Ellermeier, C.D., A. Janakiraman, and J.M. Slauch. Construction of targeted single copy lac fusions using lambda Red and FLP-mediated site-specific recombination in bacteria. Gene, 290 (1–2), 153, 2002.
Chromosomal Engineering Strategies
7-27
91. Hautefort, I., M.J. Proenca, and J.C. Hinton. Single-copy green fluorescent protein gene fusions allow accurate measurement of Salmonella gene expression in vitro and during infection of mammalian cells. Appl. Environ. Microbiol., 69 (12), 7480, 2003. 92. Dolphin, C.T. and I.A. Hope. Caenorhabditis elegans reporter fusion genes generated by seamless modification of large genomic DNA clones. Nucleic Acids Res., 34 (9), e72, 2006. 93. Gerlach, R.G., et al. Rapid engineering of bacterial reporter gene fusions by using Red recombination. Appl. Environ. Microbiol., 73 (13), 4234, 2007. 94. De Lay, N.R. and J.E. Cronan. Gene-specific random mutagenesis of Escherichia coli in vivo: isolation of temperature-sensitive mutations in the acyl carrier protein of fatty acid synthesis. J. Bacteriol., 188 (1), 287, 2006. 95. Cadwell, R.C. and G.F. Joyce. Randomization of genes by PCR mutagenesis. PCR Methods Appl., 2 (1), 28, 1992. 96. Horton, R.M., et al. Engineering hybrid genes without the use of restriction enzymes: gene splicing by overlap extension. Gene, 77 (1), 61, 1989. 97. Jensen, P.R. and K. Hammer. The sequence of spacers between the consensus sequences modulates the strength of prokaryotic promoters. Appl. Environ. Microbiol., 64 (1), 82, 1998. 98. Solem, C. and P.R. Jensen. Modulation of gene expression made easy. Appl. Environ. Microbiol., 68 (5), 2397, 2002. 99. Meynial-Salles, I., M.A. Cervin, and P. Soucaille. New tool for metabolic pathway engineering in Escherichia coli: one-step method to modulate expression of chromosomal genes. Appl. Environ. Microbiol., 71 (4), 2140, 2005. 100. Alper, H., et al. Tuning genetic control through promoter engineering. Proc. Natl. Acad. Sci. USA, 102 (36), 12678, 2005. 101. Posfai, G., et al. Emergent properties of reduced-genome Escherichia coli. Science, 312 (5776), 1044, 2006. 102. Westers, H., et al. Genome engineering reveals large dispensable regions in Bacillus subtilis. Mol. Biol. Evol., 20 (12), 2076, 2003. 103. Metcalf, W.W., W. Jiang, and B.L. Wanner. Use of the rep technique for allele replacement to construct new Escherichia coli hosts for maintenance of R6K gamma origin plasmids at different copy numbers. Gene, 138 (1–2), 1, 1994. 104. Bao, Y., et al. An improved Tn7-based system for the single-copy insertion of cloned genes into chromosomes of gram-negative bacteria. Gene, 109 (1), 167, 1991. 105. Koch, B., L.E. Jensen, and O. Nybroe. A panel of Tn7-based vectors for insertion of the gfp marker gene or for delivery of cloned DNA into Gram-negative bacteria at a neutral chromosomal site. J. Microbiol. Methods, 45 (3), 187, 2001. 106. Choi, K.H., et al. A Tn7-based broad-range bacterial cloning and expression system. Nat. Methods, 2 (6), 443, 2005. 107. McKenzie, G.J. and N.L. Craig. Fast, easy and efficient: site-specific insertion of transgenes into enterobacterial chromosomes using Tn7 without need for selection of the insertion event. BMC Microbiol., 6, 39, 2006. 108. Choi, K.H. and H.P. Schweizer. An improved method for rapid generation of unmarked Pseudomonas aeruginosa deletion mutants. BMC Microbiol., 5 (1), 30, 2005. 109. de Lorenzo, V., et al. Mini-Tn5 transposon derivatives for insertion mutagenesis, promoter probing, and chromosomal insertion of cloned DNA in gram-negative eubacteria. J. Bacteriol., 172 (11), 6568, 1990. 110. Rong, R., et al. Engineering large fragment insertions into the chromosome of Escherichia coli. Gene, 336 (1), 73, 2004. 111. Chan, W., et al. A recombineering based approach for high-throughput conditional knockout targeting vector construction. Nucleic Acids Res., 35 (8), e64, 2007.
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112. Watt, R.M., et al. Visualizing the proteome of Escherichia coli: an efficient and versatile method for labeling chromosomal coding DNA sequences (CDSs) with fluorescent protein genes. Nucleic Acids Res., 35 (6), e37, 2007. 113. van Kessel, J.C. and G.F. Hatfull. Recombineering in Mycobacterium tuberculosis. Nat. Methods, 4 (2), 147, 2007. 114. Wenzel, S.C., et al. Heterologous expression of a myxobacterial natural products assembly line in Pseudomonas via red/ET recombineering. Chem. Biol., 12 (3), 349, 2005. 115. Hill, D.S., et al. Cloning of genes involved in the synthesis of pyrrolnitrin from Pseudomonas fluorescens and role of pyrrolnitrin synthesis in biological control of plant disease. Appl. Environ. Microbiol., 60 (1), 78, 1994. 116. Eustaquio, A.S., et al. Heterologous expression of novobiocin and clorobiocin biosynthetic gene clusters. Appl. Environ. Microbiol., 71 (5), 2452, 2005. 117. Nguyen, K.T., et al. Combinatorial biosynthesis of novel antibiotics related to daptomycin. Proc. Natl. Acad. Sci. USA, 103 (46), 17462, 2006. 118. Kim, B.S., D.H. Sherman, and K.A. Reynolds. An efficient method for creation and functional analysis of libraries of hybrid type I polyketide synthases. Protein Eng. Des. Sel., 17 (3), 277, 2004. 119. Vetcher, L., et al. Rapid engineering of the geldanamycin biosynthesis pathway by Red/ET recombination and gene complementation. Appl. Environ. Microbiol., 71 (4), 1829, 2005. 120. Sung, B.H., et al. Development of a biofilm production-deficient Escherichia coli strain as a host for biotechnological applications. Appl. Environ. Microbiol., 72 (5), 3336, 2006. 121. Trinh, C.T., et al. Design, construction and performance of the most efficient biomass producing E. coli bacterium. Metab. Eng., 8 (6), 628, 2006. 122. Poteete, A.R. and A. Fenton. λ red dependent growth and recombination of phage P22. Virology, 134, 161, 1984. 123. Chaveroche, M., J. Ghigo, and C. d’Enfert. A rapid method for efficient gene replacement in the filamentous fungus Aspergillus nidulans. Nucleic Acids Res., 28, E97, 2000. 124. Kovach, M.E. et al. Four new derivatives of the broad-host-range cloning vector pBBR1MCS, carrying different antibiotic-resistance cassettes. Gene, 166, 175, 1995. 125. Cherepanov, P.P. and Wackernagel, W. Gene disruption in Escherichia coli: TcR and KmR cassettes with the option of Flp-catalyzed excision of the antibiotic-resistance determinant. Gene, 158, 9, 1995. 126. Voziyanov, Y, Stewart , A.F., and Jayaram, M. A dual reporter screening system identifies the amino acid at position 82 in Flp site-specific recombinase as a determinant for target specificity. Nucl. Acids Res. 30, 1656, 2002. 127. Buchholz, F., et al. Different thermostabilities of FLP and Cre recombinases: implications for applied site-specific recombination. Nucl. Acids Res., 24, 4256, 1996. 128. Palmeros, B. et al. A family of removable cassettes designed to obtain antibiotic-resistance-free genomic modifications of Escherichia coli and other bacteria. Gene, 247, 255, 2000. 129. Bigger, B.W. et al. An araC-controlled bacterial cre expression system to produce DNA minicircle vectors for nuclear and mitochondrial gene therapy. J. Biol. Chem., 276, 23018, 2001.
8 Regulating Gene Expression through Engineered RNA Technologies 8.1 8.2
Introduction ��������������������������������������������������������������������������������������8-1 Basic RNA Regulatory Elements.....................................................8-3
8.3
RNA Sensory Elements �������������������������������������������������������������������8-9
8.4
Natural and Engineered Riboswitches........................................8-11
RNA Elements That Direct Translation Initiation • RNA Elements That Direct RNase Activity • RNA Elements That Direct Processing and Degradation of Transcripts through Direct Cleavage • RNA Elements That Regulate Gene Expression through Antisense Mechanisms • RNA Elements That Regulate Gene Expression through the RNAi Pathway RNA Elements That Serve as Thermosensors • RNA Elements That Bind Nucleic Acids • RNA Elements That Bind Molecular Ligands
General Composition and Conformational Dynamics of Riboswitches • Mechanisms of Ligand-Controlled Gene Regulation by Riboswitches • Riboswitch Targets and Implementation in Metabolic Networks • Synthetic Riboswitches That Control Target Gene Expression Levels
8.5
Maung Nyan Win California Institute of Technology
Christina D. Smolke California Institute of Technology
pplications of RNA Control Elements in Metabolic A Network Engineering ��������������������������������������������������������������������8-23 8.6 Enabling Technologies in Support of Constructing Integrated Network and Control Systems...................................8-25 8.7 Future Applications of Advanced RNA-Based Control Systems ����������������������������������������������������������������������������� 8-26 8.8 Conclusions �������������������������������������������������������������������������������������8-27 References ���������������������������������������������������������������������������������������������������8-27
8.1 Introduction Much effort has traditionally been directed to the development of organic synthesis methods for the production of valuable chemicals and small molecule pharmaceuticals. However, despite the significant achievements in this field many compounds remain difficult to be synthesized through such means. Advances in enzyme and biochemical pathway engineering have pushed biological synthesis strategies as alternate and viable options to chemical synthesis for certain compounds. Cellular biosynthesis strategies have several advantages over traditional chemical synthesis strategies in that the former is often 8-1
8-2
Gene Expression Tools for Metabolic Pathway Engineering
conducted under less harsh conditions, thereby enabling “green” synthesis strategies that are associated with the production of fewer toxic byproducts. In addition, cellular biosynthesis takes advantage of the cell’s natural ability to replenish enzymes and cofactors and to provide precursors from often inexpensive and renewable starting materials. Metabolic engineering is the construction, redirection, and manipulation of cellular metabolism through the alteration of enzyme activities and levels to achieve the biosynthesis or biocatalysis of desired compounds.1 The optimization of production or consumption of a target metabolite often requires precisely controlled expression of several natural and/or heterologous pathway enzymes such that individual conversion steps in the pathway do not limit the desired product yield (based on different K m and kcat values), and that cellular resources and energy are not being inefficiently utilized. Therefore, programmable and tunable expression technologies that enable the reliable and dynamic control of enzyme levels, including coordinated and differential regulation of multiple gene products, are required for the optimization of pathway fluxes. Technologies have been developed that target many aspects of gene expression such as gene copy number (plasmid and chromosome engineering), transcriptional regulation (promoter engineering), and post-transcriptional regulation (targeted RNA processing and decay). The vast majority of metabolic engineering efforts to-date have focused network design strategies at the level of transcriptional regulation. However, recent scientific and technological advances in RNA biology and engineering have highlighted the significant and diverse roles that RNA plays in controlling gene expression in both prokaryotic and eukaryotic organisms. Through specific binding interactions with a wide range of biological molecules such as DNA, transcripts, proteins, and metabolites, RNA has been shown to direct a variety of complex cellular functions. The traditional model of RNA’s role in cellular processes has been that of a passive messenger of genetic information between the genome and the proteome in all living systems. However, there have been an increasing number of examples of naturally occurring RNAs that act as regulatory elements in the gene expression pathway, thereby revealing it as a functionally versatile molecule. Antisense RNAs,2,3 ribozymes,4,5 riboswitches,6–11 and small interfering and microRNAs (siRNAs and miRNAs, respectively)12–15 are examples of RNA elements that exert their regulatory effects at different levels of the gene expression pathway. Unlike messenger RNAs (mRNAs), these regulatory RNAs are often noncoding RNAs (ncRNAs) or do not encode protein information. In addition, these regulatory elements are implemented through diverse physical compositions that can be grouped more generally into cisand trans-acting elements. In the former composition, the regulatory element is integrated within the transcript that harbors the target gene, whereas in the latter composition the regulatory element is a separate RNA molecule that acts on the transcript harboring the target gene through RNA–RNA binding interactions between the two individual molecules. RNA exhibits a wide variety of functional properties, including catalytic, gene regulatory, and ligandbinding activities. In addition, integrated RNA regulatory molecules have been characterized that achieve more sophisticated control over the expression of target proteins through a combination of these functional properties. The functional properties are encoded within the nucleotide sequence of an RNA molecule, which subsequently dictates its secondary and tertiary structure and ultimately its function. RNA adopts different conformations by folding into complex secondary and tertiary structures, which interact with various cellular constituents such as DNA, proteins, small molecules, and other RNA molecules.16,17 Furthermore, RNA molecules exhibit structural flexibility, which enable them to dynamically adopt different conformations. The binding of cellular and environmental molecules to particular conformations has been demonstrated to regulate the equilibrium distribution between stable conformational states.18,19 Unlike larger biomolecules such as proteins, the functional activity of RNA is more directly defined by its secondary structure. This relationship between RNA secondary structure and function, in combination with predictive RNA secondary structure/energetic folding programs, has recently been used by molecular engineers to construct synthetic “designer” regulatory RNA elements.19–21 Recent foundational advances in RNA engineering have demonstrated the programming of regulatory properties through alteration of nucleotide composition and ultimately RNA
Regulating Gene Expression through Engineered RNA Technologies
8-3
s tructure-function relationships. In addition, technological advances in RNA engineering have made it possible to generate desired regulatory properties through rational and/or combinatorial design strategies.19–21 Recent research in RNA biology and engineering support the model of RNA as a versatile molecule possessing biologically relevant gene regulatory properties. Recent advances in RNA engineering demonstrate that the design strategies connecting RNA structure and function are well-developed and provide technologies for the construction of tailored gene regulatory systems. This chapter provides an overview of several classes of naturally occurring RNAs that are involved in gene expression regulation and their synthetic counterparts that have been generated through various molecular design strategies. In addition, examples of applications of these engineered regulators in metabolic pathway engineering will be described. However, recent technological advances in RNA engineering will allow researchers to more broadly implement RNA elements as basic regulatory systems and to develop more sophisticated regulatory systems that involve integrated designs of multiple functional domains. These synthetic regulatory systems enable target gene expression to be precisely and dynamically controlled in response to changing intracellular environments and represent powerful tools that will advance current capabilities in metabolic pathway engineering. Such RNA-based tools can be implemented to redirect pathway fluxes for optimum metabolic yield, tune the expression levels of enzymes involved in a particular metabolic pathway for optimum energy usage, and establish general screens for noninvasively detecting accumulation of key metabolites.
8.2 Basic RNA Regulatory Elements RNA plays a critical role in the control of protein expression and achieves its regulatory properties through diverse mechanisms that act at each stage along the gene expression pathway. Many regulatory RNAs are cis-acting elements that are commonly located in the 5′ or 3′ untranslated regions (UTRs) of transcripts harboring the target gene(s), whereas others are trans-acting elements that are transcribed independently and act on appropriate transcript targets through intermolecular binding events. 21 These RNA regulatory elements typically lack a protein-coding capacity and most examples characterized to-date act at the post-transcriptional level.22 Over the past few years an increasing number of these regulatory RNAs have been discovered and characterized, and their specific roles in the control of gene expression have been demonstrated in both prokaryotes and eukaryotes, including mammals.22 Five major functional classes of RNA regulatory elements are described in this section and are categorized by their mechanism of action: translation initiation, RNase activity, ribozyme, antisense, and RNA interference (RNAi). These classes are selected for their utility and prevalence in use as synthetic RNA regulatory systems.
8.2.1 RNA Elements That Direct Translation Initiation The first class of RNA elements is associated with the regulation of the efficiency of translation initiation. These elements regulate the efficiency of translation through their sequence and structure.23,24 In prokaryotes, mRNAs contain a region in the 5′ end of transcripts called the ribosome binding site (RBS) consisting of a three to nine nucleotide region upstream of the start codon (Figure 8.1A). This sequence is complementary to and interacts with a sequence near the 3′ end of the 16S rRNA (3′-AUUCCUCCA-5′). Translation efficiency is dictated by the number of base-pairs involved in the interaction and the location of these base-pairs in relation to that of the start codon. The sequence, 5′-AGGAGG-3′, is a wellknown consensus sequence called the Shine–Dalgarno (SD) sequence that directs efficient translation in prokaryotes.25 Four to five base-pairing interactions is often strong enough to mediate efficient translation in most prokaryotic mRNAs. The spacing between the RBS sequence and the start codon is also essential, and while it typically ranges between five to eight nucleotides, the optimum spacing has been shown to be five.26
8-4
Gene Expression Tools for Metabolic Pathway Engineering Translation
A
7mG-cap
Ribosome 5´
SD
Translation
B
AUG
Prokaryotic mRNA
Ribosome IRES
5´
AUG
IRES-bearing eukaryotic mRNA
Figure 8.1 RNA elements that enable sequence- or structure- specific recognition by the ribosome within the 5′ UTR of (A) prokaryotic and (B) eukaryotic mRNAs for ribosomal loading and subsequent translation initiation.
While prokaryotic mRNAs contain an internal region for ribosome binding to initiate translation, eukaryotic mRNAs are capped at their 5′ ends with a 7-methyl guanosine (7mG) and this serves to recruit the translational machinery. Most translation is cap-dependent in eukaryotes, where translation is initiated by recruitment of the 40S ribosomal subunit to the 5′ cap region of the transcript.27,28 However, there are some eukaryotic mRNAs where translation is initiated in a cap-independent manner through specific RNA sequences located upstream of the coding region. These sequences are called internal ribosome entry sites (IRESes) (Figure 8.1B) and were first discovered in viral transcripts that lack the cap structure29,30 and subsequently identified in cellular transcripts.27 Typical IRES elements are large, highly structured, cis-acting RNA sequences, analogs to the 5′ cap structure, that are capable of recruiting the 40S ribosomal subunit, or other translation initiation factors, through binding upstream of a translational initiation codon.27 IRES elements characterized to-date are diverse and can range from nine nucleotides to several hundred nucleotides.31,32 IRESes have been demonstrated to facilitate translation initiation during stress conditions such as cell cycle, apoptosis, or hypoxia.28 Researchers have demonstrated that short synthetic RNA elements that mediate translation initiation events can be generated. Synthetic RBS sequences have been generated with varying strengths through mutagenesis and screening procedures in various bacterial systems.33–35 In addition, IRES sequences have been selected from random sequence libraries in yeast36 and mammalian systems.37 These short synthetic IRES elements are assumed to function similar to bacterial RBS sequences through direct hybridization to regions of the 18s rRNA.36,37 These IRES elements have been shown to exhibit varying translation initiation activities and increased function when present in multiple copies. RNA elements that enable internal ribosome entry and regulate translation, such as RBS and IRES sequences, are attractive tools for pathway engineering in that they enable coordinated production of multiple gene products from a single transcript when placed within intercistronic regions. Furthermore, different RBS and IRES sequences have been shown to have varying translational efficiencies28,36–38 and can also be genetically manipulated to exhibit differential activities.27 This tunability is a valuable feature for metabolic engineering, as RNA elements that impart different translational efficiencies may be used to control the differential expression of several enzymes encoded within a multicistronic transcript, so that balanced enzyme expression levels can be achieved, which will enable optimized yield of the desired metabolite product.
8.2.2 RNA Elements That Direct RNase Activity The next class of regulatory RNAs is associated with the mediation of RNase activity on cellular transcripts. RNases can function through endoribonuclease activity, where cleavage occurs internal to the transcript, and exoribonuclease activity, where degradation occurs processively from one end of the transcript. RNA elements can affect the rates of transcript cleavage and processing by these enzymes. The RNase III family of endoribonucleases are found in both prokaryotic and eukaryotic organisms and are known to cleave hairpin structures or double-stranded regions within RNA.39–42 Substrate recognition, sequence dependence, and cleavage activity of various RNase III endonucleases have been characterized and demonstrated to vary among different organisms.43–46 In addition to being present within ncRNA molecules, examples of RNase III substrates have been discovered within mRNAs where they serve as cleavage sites for an RNase III family enzyme such as Rnt1p, the yeast orthologue of RNase III,
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and silence target gene expression through rapid transcript degradation following cleavage.47,48 These examples support that RNase III substrates can be engineered into transcripts to potentially regulate gene silencing effects. Endoribonucleases have also been characterized that cleave single-stranded sequences within cellular RNAs. One example of this is RNase E, which is responsible for bulk mRNA processing in Escherichia coli and cleaves AU-rich sequences.49–53 Some studies have indicated that a free 5′ end is required for RNase E to bind to the mRNA before subsequent cleavage at sites internal to the transcript.54–56 Two types of RNA elements can be used to modulate RNase E activity on cellular transcripts. The first element is an RNase E substrate site, which is a single-stranded AU-rich sequence that directs cleavage at a particular location in the transcript. The second element is a hairpin structure, located close to (and no more than five nucleotides from) the 5′ end of the transcript.41,55 Such elements have been shown to inhibit cleavage activity of RNase E on the transcript. The strengths of both of these elements can be altered by sequence and structural modification, respectively.57,58 Exonucleases are responsible for bulk mRNA degradation, and these enzymes generally degrade cellular RNAs processively starting from one end of the transcript.59 RNA elements that form hairpins and are located at the 3′ end of cellular transcripts have been shown to inhibit degradation by exoribonucleases.41 These RNA elements stabilize the transcript through their secondary structure, which potentially serves as a barrier to exonucleolytic activities.53 The strengths of these elements can be altered by structural modification.60
8.2.3 RNA Elements That Direct Processing and Degradation of Transcripts through Direct Cleavage The third class of RNA regulatory elements mediates direct cleavage of cellular transcript targets w ithout assistance from protein factors. Ribozymes are catalytic RNAs that were discovered over 20 years ago.61–65 They are naturally found in plant RNA viruses, satellite RNAs, and viroids66 and most commonly catalyze the cleavage and/or ligation of RNA molecules.67 There are four types of self-cleaving ribozymes:4 hammerhead, hairpin, hepatitis delta virus, and Varkud satellite. Self-cleavage occurs at a specific phosphodiester bond via an internal phosphoester transfer reaction68 in a divalent metal ion-dependent manner.69 Each class of natural ribozymes exhibits a highly conserved catalytic core sequence. The hammerhead ribozyme has been extensively studied and most commonly used for its potential significance in targeting messages in biotechnological and biomedical applications due to its small size, ease of design, and rapid kinetics.67,70 Hammerhead ribozymes are generally known to cleave any 5′-NUX-3′ triplets of an RNA sequence 3′ to the X where N is any nucleotide, U is uridine, and X is any nucleotide except guanosine (Figure 8.2). The sequence 5′-GUC-3′ has been the conventional and most commonly found cleavage triplet in nature.69,71 Two of the hammerhead ribozyme’s stems are closed by a loop, and the majority of the hammerhead ribozymes are embodied within viral RNA sequences through stem III (Figure 8.2A).72 The presence of loops closing stem I and stem II has been shown to be critical to the catalytic activity of the hammerhead ribozyme in cellular environments, suggesting possible loop I–loop II interactions.72–74 Naturallyoccurring hammerhead ribozymes catalyze self-cleavage in cis; however, synthetic ribozymes that cleave in trans have been constructed by eliminating one of the two stem loops. Such genetic manipulations result in three possible designs, depending on which strands of the stems are targeted to bind the target transcript for cleavage.69 In one example, the loop sequence of stem I is removed, and the stem I and stem III strands are used as targeting arms to bind a transcript (Figure 8.2B).71 Ribozymes are attractive molecular tools in metabolic engineering for their potential applications in regulating enzyme expression in diverse cellular systems. For cis-acting regulation systems, a hammerhead ribozyme can be integrated into a noncoding region of the target transcript, resulting in self-cleavage and subsequent degradation of the transcript, resulting in lowered expression levels of the target enzyme(s). Alternatively, trans-acting ribozymes can regulate gene expression through
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Figure 8.2 Structures of (A) a self- or cis-cleaving hammerhead ribozyme. (Adapted from Salehi-Ashtiani, K. and Szostak, J.W. Nature 414, 82–84, 2001.) (B) A trans-cleaving ribozyme. The conserved catalytic core sequence of the ribozyme is shown; N represents any nucleotide. The arrow indicates the cleavage site. Stems I and II are enclosed by loop sequences in the cis-cleaving ribozyme and therefore the cleavage is intramolecular, whereas only one of the stems is enclosed by a loop sequence in the trans-cleaving ribozyme and therefore the cleavage is intermolecular. Nonconserved loop sequences are illustrated as circular lines.
identical mechanisms, but they must be engineered to bind to the target transcript in such a way that satisfies requirements for catalytic activity of the regulatory element. One basic strategy is to target regions of transcripts that harbor an “NUX” triplet, which defines the cleavage site on the transcript, and to design a ribozyme carrying the catalytic core and targeting arm sequences that are complementary to the appropriate region of the target transcript for sequence-specific base-pairing (Figure 8.2B).65 In vitro selection processes have been used to generate self-cleaving hammerhead ribozymes with different kinetic properties.75,76 Such engineered ribozymes may be potentially useful in metabolic pathway engineering, as they can be used as regulatory elements to obtain varying enzyme levels through their different cleavage activities. Similarly, trans-cleaving hammerhead ribozyme variants exhibiting varying kinetic properties have been developed in vitro.77–79 The in vivo regulatory activities of both cis- and trans-cleaving hammerhead ribozymes have been demonstrated in various cellular systems.72,80–87
8.2.4 RNA Elements That Regulate Gene Expression through Antisense Mechanisms The fourth class of RNA regulatory elements mediates gene expression through antisense mechanisms. The regulation of gene expression by an antisense RNA was first discovered as a natural process in prokaryotes.88 Antisense RNA molecules are single-stranded, trans-acting regulatory elements that bind to their target transcripts through a sequence-specific hybridization event, resulting in the inhibition of expression from the bound mRNA through one of the two mechanisms.89, 90 The first mechanism involves targeting by double-stranded RNA(dsRNA)-cleaving enzymes such as the RNase III family of endoribonucleases, previously described (Figure 8.3A). The second mechanism involves the binding of the regulatory element to the target transcript around the translation start site or the 5′ cap region such that access or scanning of the ribosome is inhibited, thereby interfering with the translation initiation process (Figure 8.3B). Since their initial identification in bacteria,88 these regulatory elements have been characterized in many cellular processes including metabolism. As one example, SgrS is an antisense RNA that down-regulates the expression of the glucose transporter through base-pairing with the ptsG transcript when localized to the cell membrane, thereby lowering the accumulation of toxic phosphosugar metabolites.91,92
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Figure 8.3 Two major mechanisms through which antisense RNA mediates inhibition of target gene expression. (A) RNase III type nuclease-mediated cleavage of double-stranded RNA molecules. (B) Antisense-directed steric hindrance based on RNA-RNA interactions to ribosomal loading or scanning on the target mRNA for proper translation. (Adapted from Kurreck, J. Eur. J. Biochem., 270, 1628–1644, 2003.)
Antisense RNA molecules are powerful regulatory elements and are potentially useful molecular tools in metabolic engineering where these elements can be designed to target specific metabolic genes to alter cellular enzyme levels and pathway fluxes. Synthetic RNA elements have been designed to regulate target gene expression through the antisense pathway. These regulatory elements can be synthesized intracellularly from a plasmid and have been shown to selectively target and inhibit a variety of genes. As one example, Bunch and Goldstein demonstrated that the induction of antisense RNAs expressed from a promoter targeting the alcohol dehydrogenase gene significantly reduced the expression level of the target gene in cultured Drosophila cells.93 In a similar example, Blomberg et al. expressed a natural antisense RNA from a plasmid and demonstrated translational inhibition of its target transcript in bacteria where the antisense RNA binds to the leader region of the mRNA.94 In another example, Bonoli et al. employed a long antisense RNA regulatory element complementary to the 5′ UTR of a target transcript and demonstrated effective gene silencing in the budding yeast S. cerevisiae.95 Antisense regulatory elements have also been shown to silence gene expression through different mechanisms. As one example, Novick et al. showed that antisense RNAs inhibited the expression of a target mRNA encoding the plasmid replication initiator protein by inducing the formation of a terminator hairpin upstream of the start codon, and that in the absence of these antisense RNAs an upstream sequence hybridizes with part of the terminator hairpin, thereby preventing termination and allowing transcription of the target gene.96 In another example, a sequence segment of a short RNA molecule (DsrA) was proposed to act as an antisense RNA that suppresses gene expression by RNA–RNA basepairing interactions with the start and stop codon regions of a target transcript, while another segment of this short RNA molecule enhances the translation of a different target mRNA by releasing the translation initiation region initially sequestered by a cis-acting antisense sequence.97–99
8.2.5 RNA Elements That Regulate Gene Expression through the RNAi Pathway The final class of RNA regulatory elements mediates gene expression through the RNAi pathway. RNAi is a highly evolutionally conserved RNA-directed gene-silencing pathway.100 This pathway was originally observed in plants,101,102 but first demonstrated by Fire and Mello in the nematode worm Caenorhabditis elegans,103 and later found in a wide variety of metazoans.12 This silencing pathway is triggered by dsRNAs, which cause sequence-specific degradation of target transcripts when introduced into a cell (Figure 8.4).13 The dsRNA substrates can be generated from numerous sources including viruses, overlapping transcripts, and transposons.12,104 These RNA regulatory elements are processed into short RNA duplexes known as siRNAs by a RNase III-type enzyme called Dicer.105 The resulting siRNAs are generally 21–23 nucleotides-long dsRNAs with two nucleotide overhangs at the 3′ ends with 5′ phosphate and 3′ hydroxyl groups.12,13 These cleaved products are subsequently unwound and incorporated into endoribonuclease-containing complexes known as RNAi-induced silencing complexes (RISCs).106 The
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Figure 8.4 A schematic diagram of the RNA interference (RNAi) pathway. dsRNAs introduced artificially or generated inside the cell can induce sequence-specific gene silencing through the RNAi pathway. These molecules are initially processed into siRNA duplexes by Dicer, followed by unwinding of these duplexes and incorporation of one of the duplex strands into RISC. The functional RISC carries the antisense strand of the duplex, which mediates gene silencing of the target mRNA through cleavage. Primary miRNA transcripts (pri-mRNAs) are processed by Drosha in the nucleus to yield miRNA precursors (pre-miRNAs or shRNAs), which are exported into the cytoplasm by Exportin-5. Once in the cytoplasm, the pre-miRNA is further processed by Dicer to yield siRNA-like duplexes known as miRNAs. The duplex is unwound, assembled into miRNP/RISC, and directs translational repression or cleavage of the target mRNA depending on the degree of complementarity between the miRNA antisense strand and its targeting transcript sequence. (Adapted from Meister, G. and Tuschl, T. Nature, 431, 343–349, 2004.)
functional RISC contains the antisense strand and acts as a multiple-turnover enzyme complex.107 The antisense strand hybridizes in a sequence-specific manner to the target mRNA, which subsequently leads to ATP-dependent cleavage of the transcript by endonucleases within RISC at a single site near the center of the siRNA strand.108,109 In some organisms, RNAi-mediated gene-silencing involves enzymes known as RNA-dependent RNA polymerases (RdRPs), which are thought to amplify the silencing effect by replicating siRNAs.110,111 This amplification also has been shown to enable the silencing effect to be spread across cells.12,14 RNAi was initially recognized as an innate host defense mechanism to RNA viruses and transposable elements.12,112 However, certain genes in plants and animals naturally encode RNA hairpins113–115 that are approximately 70 nucleotides in length.12–14,104 These endogenous structural elements are the precursors of another class of regulatory elements known as miRNAs that have been shown to result in sequence-specific gene silencing (Figure 8.4).116–118 The first step in miRNA maturation is the nuclear cleavage of the primary hairpin transcript (pri-miRNA)119,120 by a second RNase III-type endonuclease called Drosha.120 The cleaved hairpin has a 5′ phosphate and a two nucleotide 3′ overhang remaining
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at the base of the stem.120,121 This cleaved hairpin product, known as the pre-miRNA, is subsequently exported to the cytoplasm by the RanGTP-dependent nuclear export receptor, Exportin-5.122–124 The pre-miRNA is processed in the cytoplasm by Dicer, following processing steps similar to that described for siRNAs above.12,104 Once loaded into the ribonucleoprotein complex (miRNP),125 the single-stranded miRNA can direct silencing through two different mechanisms: cleavage or translational repression.12,104 The miRNA-guided cleavage mechanism is similar to that of siRNAs, whereas miRNA-directed translational inhibition occurs through imperfect binding of miRNAs to their target transcripts in the 3′ UTR.12 Researchers have taken advantage of the effectiveness of RNAi-mediated gene silencing to construct synthetic RNA regulatory elements that silence target genes through this pathway. dsRNA elements have been engineered through a variety of methods to down-regulate expression of various target genes.13 In vitro-synthesized dsRNAs have been shown to be processed by Dicer into siRNAs resulting in target gene silencing when introduced into cells.108,109 It has also been demonstrated that chemicallysynthesized siRNAs, that bypass the Dicer cleavage event and load directly into the RISC complex, effectively suppress gene expression.126,127 This strategy eliminates potential induction of the interferon response by dsRNAs longer than 30 nucleotides.126 However, other studies have demonstrated that slightly larger RNA duplexes (~27-mers) can effectively target sites that are refractory to silencing by 21-mer siRNAs without inducing the interferon response.128 Synthetic siRNAs expressed in vivo from RNA polymerase III (pol III) promoters, where individual sense and antisense strands of each siRNA are separately synthesized and assembled in trans, have also been demonstrated to silence target gene expression in vivo.129,130 As synthetic RNAi substrates gain recognition as powerful research and application-based tools and as our understanding of the pathway mechanism deepens, the molecular design strategies guiding the construction of these regulatory elements has evolved. As an example, short hairpin RNAs (shRNAs) have been constructed to produce Dicer-processable substrates through in vitro or in vivo transcription events, and have been demonstrated to induce efficient gene silencing.131–135 In addition, miRNA-based hairpins, with either perfect or imperfect complementarity to their target transcripts, have been constructed and expressed as part of longer transcripts from an RNA polymerase II (pol II) promoter and shown to effectively silence target gene expression.136 Compared with earlier designs, this implementation more closely mimics that of natural miRNAs, as miRNA precursors are usually encoded within the context of longer transcripts.
8.3 RNA Sensory Elements RNA molecules are functionally diverse and structurally flexible, and exhibit a wide range of regulatory properties such as catalytic, interactive, and allosteric binding properties. Another unique property of RNA molecules is their sensing capability to various types of inputs ranging from temperature to varied molecular ligands. Similar to other regulatory properties, RNA employs this property in exerting its diverse functional roles.
8.3.1 RNA Elements That Serve as Thermosensors RNA secondary structure is known to be highly dependent on temperature such that RNA assumes different structures in response to changes in temperature. Examples of such temperature-responsive RNA elements have been described.8 For instance, genes that encode small heat-shock proteins and regulators of heat shock-responsive genes were found to contain sequences in their 5′ UTRs that are capable of sequestering the RBS sequence and the start codon. The regulation of adoptable secondary structures at different temperatures is used to modulate the accessibility of these sequences and subsequently modulate target gene expression levels, thereby enabling these RNA elements to serve as thermosensing gene expression regulators.
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8.3.2 RNA Elements That Bind Nucleic Acids Instances where RNA molecules exert their regulatory activities through base-pairing interactions with other RNA molecules have been described above. Another good example of RNA–RNA interactions through which a regulatory function was achieved was described by Isaacs et al.137 In their engineered system, an RNA sequence segment was integrated into a location upstream of the RBS of a reporter gene to serve as a nucleic acid-sensing domain. This segment sequesters the RBS in the absence of the target RNA molecule, thereby suppressing the expression of the reporter gene. In the presence of the target RNA molecule, the sensor domain becomes bound to the target through RNA–RNA base-pairing interactions and releases the RBS for efficient translation, thereby serving as a nucleic acid-binding sensor domain.
8.3.3 RNA Elements That Bind Molecular Ligands The ability of RNA structural elements to bind specific molecular ligands has been characterized in several natural systems. However, researchers have also generated many examples of synthetic RNA ligandbinding elements, referred to as aptamers, in the laboratory. Synthetic aptamers are generated through in vitro selection or Systematic Evolution of Ligands by EXponential enrichment (SELEX) processes (Figure 8.5).138,139 SELEX provides a very powerful selection method through which nucleic acid molecules exhibiting rare and specific binding properties to a ligand of interest can be generated de novo by selecting for functional binding activities from large randomized nucleic acid pools through iterative in vitro selection and amplification cycles. In vitro aptamer selection schemes begin with a large pool of single-stranded Random sequence library
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Figure 8.5 A schematic illustration of an in vitro selection process known as SELEX. The process starts with a large randomized pool of single-stranded RNA molecules transcribed from their DNA templates. The RNA pool is then incubated with target molecules of interest followed by separation of the target-bound pool, which is reverse-transcribed to cDNA and amplified for the next selection cycle. The selection cycles are repeated typically for eight to 15 cycles.
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RNA molecules generated through in vitro transcription from a DNA library. Aptamer pools are usually c omposed of 30–70 randomized nucleotides and encompass an initial sequence diversity between 1014 and 1015 molecules.140 The pool is incubated with the target ligand of interest and subject to a partitioning event to separate bound members from unbound members. The most commonly used partitioning schemes are based on affinity chromatography. Bound (functional) members are recovered and then amplified through reverse transcription and polymerase chain reaction (PCR) to yield a pool enriched for target binding. This enriched pool will become the input pool for the next round of selection. SELEX is particularly powerful in that RNA sequences that bind particular ligands can be generated de novo and the binding properties of the resulting aptamers can be programmed as desired. Specifically, aptamer binding properties such as affinities and specificities can be programmed by tailoring the stringency and counter-selections during each cycle. Aptamer affinities are tailored through the stringency of each selection cycle, normally by modifying wash volumes and target concentrations, whereas aptamer specificities are tailored through performed counter-selections with molecular analogs to the target. Typically eight to fifteen selection cycles are required to generate aptamers with high binding affinities and specificities. Recent work has demonstrated that protein aptamer selection schemes can be automated using standard robotics.141–143 In addition, partitioning schemes for protein aptamer selections based on capillary electrophoresis have been recently developed that provide several advantages over conventional affinity-based partitioning schemes.144–146 In particular, the efficiency of separation between the bound and unbound pools is significantly greater such that aptamers can be generated in fewer selection cycles. Synthetic aptamers have been generated to a wide range of target ligands including small molecules, antibiotics, carbohydrates, amino acids, peptides, proteins,147 and even organelles such as phospholipid bilayers,148,149 indicating that synthetic aptamers can be potentially generated to targets of interest for metabolic engineering applications.
8.4 Natural and Engineered Riboswitches Cells must regulate the expression of various genes in a controlled manner in response to diverse intracellular and extracellular signals such as changes in metabolic demands and environmental fluctuations.10 This degree of regulation requires highly responsive genetic sensors and regulators that can detect and quantify the magnitude of a particular signal and subsequently modulate, as necessary, the levels and activities of appropriate gene products. Traditionally, proteins have been recognized as the molecules responsible for performing the molecular sensing and actuation functions; however, recent discoveries have revealed that elements within mRNAs, called riboswitches, are also capable of performing biological sensor-actuator functions. Riboswitches are cis-acting RNA elements that modulate the expression of target genes through integrated sensor and regulatory domains. This integration scheme enables riboswitches to sense their target ligands, typically cellular metabolites, through direct binding interactions and thus autonomously mediate their own functional activity in response to changing metabolite levels. While the majority of riboswitches characterized to-date have been discovered in bacteria, it has been shown more recently that these complex RNA regulatory elements are also present in eukaryotes.9,150
8.4.1 General Composition and Conformational Dynamics of Riboswitches Riboswitches are naturally occurring, metabolite-responsive gene control elements primarily located within the 5′ UTRs of cellular transcripts.10 A riboswitch is typically composed of two domains: the ligand-binding or sensor domain known as the aptamer domain, and the gene regulatory domain known as the expression platform (Figure 8.6).10 Both domains are structurally flexible and capable of adopting different conformations. Riboswitches accomplish ligand-controlled regulation of gene expression through targeted dynamic switching between two primary conformations at equilibrium: one in which the regulatory domain is active and the other in which it is inactive. One of these conformational states
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Figure 8.6 A schematic diagram of a typical riboswitch composed of two distinct domains: the ligand-binding domain known as the aptamer domain and the regulatory domain known as the expression platform. Metabolite binding to the aptamer domain enables the stabilization of the rearranged conformation of the riboswitch (right), resulting in a shift in the equilibrium distribution between the two regulatory conformations and metabolitedependent regulation of target gene expression. (Adapted from Winkler, W.C. and Breaker, R.R. Chembiochem, 4, 1024–1032, 2003.)
is associated with the formation of the ligand-binding pocket within the aptamer domain, whereas the other carries an incorrectly formed binding pocket. Therefore, riboswitches can either repress or activate the expression of the target gene by assuming an appropriate combination of different conformational states adopted by the aptamer and regulatory domains. Ligand binding to the riboswitch shifts the distribution between these stable conformations to favor the ligand-bound form, thereby resulting in an allosteric gene regulation event.
8.4.2 Mechanisms of Ligand-Controlled Gene Regulation by Riboswitches Riboswitches regulate target gene expression in cis in response to changing metabolite levels through different mechanisms including transcription termination,151,152 translation initiation,153 mRNA processing,154 and splicing150 (Figure 8.7). Transcription termination takes place through the mediated formation of a rho-independent terminator stem, which is usually GC rich, thereby destabilizing the transcription elongation complex.151 Regulation can also target the disruption of the formation of a terminator stem upon metabolite binding, which allows proper transcription and thus up-regulation of target expression levels.152 Riboswitches can also mediate translation initiation by adopting a secondary structure that interferes with ribosomal access to the target gene, such as sequestering the RBS sequence in prokaryotic cells.153 Regulation targeting transcript processing or deactivation can be achieved through expression platforms composed of self-cleaving ribozymes, where the target transcripts undergo a ligand-directed cleavage event.154 Regulation through splicing has recently been demonstrated in a filamentous fungus, in which metabolite binding to its riboswitch can either repress or activate the expression of the main protein product by modulating the splice site choice through structural rearrangements.150 Metabolite-binding domains have also been found within the 3′ UTRs of transcripts in certain organisms, suggesting that riboswitch-mediated gene control may also occur through the regulation of mRNA stability.9 Riboswitches exhibiting unique mechanistic properties have also been recently identified, such as cooperative binding of the target metabolite162 and a Boolean two-input NOR gate behavior.165 Different classes of natural riboswitches including those with unique properties and their functional roles in metabolic networks in various organisms are discussed in the following section.
8.4.3 Riboswitch Targets and Implementation in Metabolic Networks Most of the riboswitches characterized to-date have been found in bacteria. Cells employ these elements as genetic regulators in many fundamental metabolic pathways in response to changing metabolite levels. Target metabolites include various classes of small molecules such as amino acids, nucleotide bases,
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Figure 8.7 A schematic illustration of mechanisms through which riboswitches achieve gene expression regulation in response to binding their target metabolites. Ligand-regulated mechanisms involve (A) the formation of a transcription terminator stem, (B) sequestering the RBS and inhibiting translation initiation, (C) mRNA processing through catalytic cleavage of the transcript, and (D) alternative splicing using different sets of splice sites, I and II, respectively. (A and B adapted from Nudler, E. and Mironov, A.S. Trends Biochem. Sci., 29, 11–17, 2004.)
and cofactors. The presence of the integrated sensor domain enables riboswitches to sense intracellular metabolite concentrations through specific binding interactions and subsequently regulate expression levels of the associated gene product through allosteric conformational changes. Typically, this gene product is an enzyme directly involved in the biosynthesis, biodegradation, and/or transport of the target metabolite.7 This mode of regulation provides a direct dynamic relationship between the intracellular metabolite concentration and the expression levels of the enzyme responsible for the metabolism, catabolism, and transport of the target metabolite. In addition, riboswitches are capable of binding their
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target metabolites with specificities and affinities appropriate to the intracellular environment. In this section, several different classes of natural riboswitches, their corresponding target metabolites, and functional roles are described. 8.4.3.1 Thiamine Pyrophosphate (TPP) Riboswitch TPP is a cofactor of decarboxylase enzymes involved in carbohydrate metabolism and derived from the precursor thiamine also known as vitamin B1.6 This riboswitch class is the most widespread in nature, and has been identified in bacteria, archaea, fungi, and plants where they have been shown to regulate the expression levels of enzymes responsible for the biosynthesis of thiamine and thus its derivative TPP.9 The sensor domain, referred to as “thi box”, present in the thiC gene of E. coli recognizes its target TPP with an apparent Kd of ~100 nM and discriminates against a precursor by ~1000-fold through the pyrophosphate moiety of TPP.153 This riboswitch employs various mechanisms in gene expression regulation, including transcription termination and translational initiation in bacteria,153,155 alternative splicing in fungi,150 and proposed mRNA processing in plants.9 While most TPP riboswitches are composed of a single metabolite-binding domain and a single expression platform, a recent study revealed that the TPP riboswitch system of the bacterium Bacillus anthracis contains two complete tandem riboswitches that respond independently to changing concentration levels of the same metabolite.156 8.4.3.2 Adenosylcobalamin (Coenzyme B12) Riboswitch This class of riboswitch is widespread across diverse bacterial species and binds coenzyme B12, a derivative of vitamin B12.6 Studies revealed that the riboswitch recognizes its target metabolite through the adenosyl moiety, the dimethylbenzimidazole moiety, and the cobinamide ring.157 In addition, this riboswitch regulates the expression levels of the cobalamin biosynthetic and transport genes through two different mechanisms: transcription termination and translation initiation.157,158 8.4.3.3 Flavin Mononucleotide (FMN) Riboswitch FMN is an essential cofactor for many redox-active enzymes 6 and derived from the precursor riboflavin, or vitamin B2. The FMN riboswitch is another riboswitch class widespread in bacteria. Comparative sequence analysis has revealed that a conserved metabolite-binding domain called the RFN element is commonly found in the untranslated leader regions of mRNAs encoding enzymes involved in the biosynthesis of riboflavin, FMN, and their transport.159 This element, located in the leader region of the ribDEAHT operon of Bacillus subtilis encoding the FMN biosynthetic genes, recognizes FMN with an apparent Kd of ~5 nM and exhibits a molecular discrimination against riboflavin, which lacks a single phosphate group, by almost a 1000-fold (~3 µM).159 Through this precise molecular recognition, this riboswitch regulates gene expression in response to changing levels of its target metabolite through either transcription termination (ribDEAHT) or translation initiation (ypaA).159 8.4.3.4 S-Adenosylmethionine (SAM) Riboswitch SAM is an essential coenzyme derived from methionine by SAM synthetase and serves as a source of methyl groups for methylase enzymes.6,160 The aptamer domain of this riboswitch class, called the S-box, is a highly conserved RNA domain and is located in the leader sequences of genes involved in sulfur metabolism and the biosynthesis of cysteine, methionine, and SAM.160 This riboswitch has been mostly found in gram-positive bacteria where it down-regulates target expression through transcription termination in response to SAM binding with high affinity (Kd of SAM to the S-box in B. subtilis ~4 nM) and molecular discrimination against metabolite analogs that lack a single methyl or methylene group.151 8.4.3.5 Lysine Riboswitch The lysine riboswitch contains a conserved aptamer domain known as the L-box that serves as a sensor in detecting the levels of the target amino acid lysine and subsequently regulates its biosynthesis.161
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The L-box of the B. subtilis lysine riboswitch, located in the 5′ UTR of the lysC gene, encoding the first enzyme in the biosynthetic pathway of L-lysine from L-aspartic acid, binds L-lysine with an apparent Kd of ~1 µM and subsequently down-regulates the expression of this enzyme through transcription termination.161 This aptamer domain was shown to exhibit high target specificity by discriminating closely related analogs including D-lysine.161 8.4.3.6 Glycine Riboswitch This amino acid-responsive riboswitch162 is found in a wide variety of bacteria and regulates the levels of the target amino acid glycine. While most riboswitch classes characterized to-date contain a single conserved metabolite-binding domain, two similar and highly structured aptamer domains connected through a short linker are often found in this class of riboswitch. In B. subtilis these conserved domains are located in the leader region of the gcvT operon, encoding proteins involved in the glycine cleavage system, and up-regulates the expression of target genes as the amino acid becomes present in excess and binds to the riboswitch. Both aptamer domains are specific to glycine and exhibit molecular discrimination against closely related analogs such as alanine and serine. Strikingly, these two domains exhibit cooperative binding to their target, where binding of glycine to the first aptamer enhances the second aptamer’s target affinity, resulting in a more digital gene control response. This property ensures that glycine is sufficiently available for protein synthesis before the amino acid gets degraded upon activation of its cleavage system by this sophisticated genetic ON riboswitch. 8.4.3.7 Guanine and Adenine Riboswitches These purine-responsive riboswitches exhibit conserved sequences and structures of the binding domains for their corresponding target metabolites termed the G-box.6 The guanine-specific riboswitch class was first identified in several operons of B. subtilis, encoding genes mainly involved in purine biosynthesis and transport.163 The G-box of the xpt-pbuX operon selectively binds guanine and hypoxanthine with apparent Kds of ~5 nM and ~50 nM, respectively, and has been shown to down-regulate target gene expression through transcription termination in response to ligand binding. Many purine analogs fail to bind this G-box; for example, adenine binds with an affinity approximately five orders of magnitude lower (Kd ~300 µM) than that of guanine.163,164 Interestingly, the G-box located upstream of the ydhL gene, which encodes a putative purine efflux pump,164 is specific for adenine (Kd ~300 nM) and discriminates against guanine (Kd > 10000 nM).152 Comparative sequence analysis reveals that the aptamer core of this G-box differs from that of the previous G-box by a single nucleotide (C for guanine and U for adenine), indicating that this single nucleotide is the determinant in discriminating between the two target ligands.152 In addition unlike the guanine riboswitch, the adenine riboswitch up-regulates target gene expression through inhibition of transcription termination in response to adenine binding.152 8.4.3.8 Glucosamine-6-Phosphate (GlcN6P) Riboswitch This riboswitch class was identified in gram-positive bacteria and is the only riboswitch class discovered to-date that employs a unique regulatory mechanism—ribozyme-based self-cleavage. The ribozyme is responsive to GlcN6P and located upstream of the glmS gene, which encodes an enzyme responsible for the biosynthesis of GlcN6P from fructose-6-phosphate and glutamine, and down-regulates target gene expression through transcript cleavage activated in response to ligand binding.154 8.4.3.9 SAM-Coenzyme B12 Riboswitch The riboswitch system of B. clausii contains two tandem riboswitches that respond to two different metabolites, SAM and coenzyme B12.165 This tandem genetic control element located in the 5′ region of the metE gene serves as a two-input NOR logic gate, where each metabolite can bind to its aptamer, induce transcription termination, and independently repress the expression of the target gene. The
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genes metE and metH encode for proteins that independently catalyze the biosynthesis of methionine from homocysteine, and the latter requires a cofactor that is a derivative of coenzyme B12. Therefore, when coenzyme B12 is abundant, only the expression of metE should be decreased, as cells can synthesize methionine and its derivative SAM more efficiently through the gene product of metH, which uses the cofactor derived from coenzyme B12. This level of metabolite resource exploitation may explain why such a sophisticated genetic control element is required and employed by cells.
8.4.4 Synthetic Riboswitches that Control Target Gene Expression Levels Riboswitches are sophisticated gene control elements that achieve regulation by direct sensing of target metabolite levels and exhibit molecular recognition, high affinities, and precise control. Examples of the level of complexity achieved by these genetic regulatory elements include the self-cleaving ability of the glmS riboswitch,154 the alternative splicing control of the TPP riboswitch,156 the cooperative binding property of the glycine riboswitch,162 and the NOR gate signal processing behavior of the SAMcoenzyme B12 riboswitch,165 demonstrating that riboswitches are powerful sensor-actuator systems for autonomous gene expression control. Thus, engineered riboswitch control elements are attractive molecular tools for applications in synthetic metabolic network design, by directing pathway fluxes through differential and/or coordinated regulation of a set of associated gene products in response to specific metabolites. Many recent engineering efforts have focused on the construction of such synthetic, ligand-controlled, RNA-based gene regulatory elements through the integration of sensor and regulatory domains. The flexibility in RNA regulatory systems, the programmability inherent in RNA design strategies, and the ability to generate sensor domains to potentially any molecular ligand of interest, enable such synthetic riboswitch systems to hold significant promise in transforming our ability to engineer cellular metabolic networks. 8.4.4.1 Riboswitch Construction Based on Aptamer Insertion within a Transcript Synthetic riboswitches have been constructed by inserting an aptamer or multiple aptamers directly into the 5′ UTR of a target mRNA in eukaryotes (Figure 8.8). Although the insertion location may be any region in the 5′ UTR, it is often chosen to be in the vicinity of the cap region or the start codon of the transcript. This insertion strategy is a trail-and-error strategy, as the inserted aptamer(s) often does not result in ligand-mediated regulation of gene expression and may even cause substantial knockdown of the target gene in the absence of ligand. This strategy requires that the insertion of the aptamer itself and its associated secondary structure does not interfere with translation in the absence of the target ligand. The binding of ligand to the aptamer results in structural stabilization due to the molecular binding interaction between the aptamer and its target.147,166 Similar to binding of a protein to the 5′ UTR,167,168 this stabilized secondary structure can repress translation presumably by interfering with ribosomal scanning or ribosome-mRNA interactions required for effective translation.169 Werstuck and Green constructed the first examples of such riboswitches by inserting small molecule-binding RNA aptamers into the 5′ UTR of transcripts.170 Translation was demonstrated to be repressed by the addition of the appropriate ligands both in vitro and in vivo in mammalian cells. Following this initial work, different research groups have constructed synthetic riboswitches through this design strategy using theophylline-,171 biotin-,172 and tetracycline-binding173 aptamers and demonstrated similar ligand-controlled gene regulation in different systems, including wheat germ extracts,174 Xenopus oocyte,174 and the budding yeast S. cerevisiae.175,176 Synthetic riboswitches have also been constructed to regulate translation of target genes in prokaryotes through a similar aptamer insertion strategy. Although still located in the 5′ UTR of the target transcript, prokaryotes do not exhibit the same type of ribosomal scanning as eukaryotic organisms. Therefore, the physical implementation of these switches requires slightly different design strategies.
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Translation ON AUG
5´
Target mRNA
AAAAA 3´
Ligand Aptamer bound Interrupted ribosome scanning 5´
Translation OFF AUG
Target mRNA
AAAAA 3´
Figure 8.8 A schematic illustration of riboswitches constructed through methods based on aptamer insertion within a target transcript. An aptamer or multiple aptamers can be inserted into the 5′ UTR of a transcript near the 5′ cap region or the start codon. Such an insertion may allow the aptamer-fused transcript region to adopt two primary conformations, one in which the aptamer binding pocket is disrupted (top) and the other in which the aptamer is correctly formed to bind its ligand (bottom), and ligand binding shifts the equilibrium toward the latter conformation by stabilizing this conformation. This insertion strategy requires that the former conformation not introduce significant steric hindrance to the ribosome for proper translation and that the ligand-bound conformation effectively inhibit translation through its stabilized structure.
In bacteria, the sequence distance between the RBS and the start codon is relatively short and varies between five and 13 nucleotides.24 As a result, targeted insertion of an aptamer in this region to interfere with ribosomal scanning through a ligand-induced secondary structure is generally not applicable without the addition of a peptide fusion to the 5′ end of the coding region.177 In most bacteria, translation initiation relies on ribosomal accessibility to the RBS and the start codon, and thus mRNA secondary structure in this translation initiation region can dictate the efficiency of translation.178–180 Desai and Gallivan developed a synthetic riboswitch system in E. coli where they inserted the theophylline aptamer171 to a location five base-pairs upstream of the RBS to modulate ribosomal access to the RBS through ligand binding.181 The theophylline-dependent up-regulation of gene expression by this synthetic riboswitch was demonstrated through plate-based screening and liquid culture assays. In addition, the extent of up-regulation from this synthetic riboswitch was observed to be dramatically affected when the aptamer was moved to a slightly different location, two or eight base-pairs upstream of the RBS, indicating the functional sensitivity of this riboswitch system to the aptamer insertion location. 8.4.4.2 Riboswitch Construction Based on Direct Attachment of the Aptamer to a Regulatory Element Synthetic riboswitches have been constructed by directly attaching an aptamer to the regulatory domain, such that ligand binding to the aptamer inhibits the activity of the regulatory domain through some mechanism (Figure 8.9). This construction strategy is also a trial-and-error strategy, since the desired ligand-responsive regulatory activity may be highly specific to the location of attachment, the mechanism of action, and the specific aptamer-regulator pair. A riboswitch system based on this strategy was developed by An et al. to modulate Dicer processing of an shRNA molecule through a small molecule ligand-aptamer interaction within this RNAi substrate.182 In this example, the theophylline aptamer171 was directly fused to an shRNA molecule, in place of the loop sequence. This shRNA construct silenced reporter gene expression in mammalian cells in a
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Gene Expression Tools for Metabolic Pathway Engineering Partner regulator for regulatory element (Dicer, Spliceosome, etc.)
Direct attachment
+ Aptamer
Regulatory element (shRNA, splice site, etc.)
Aptamer-fused regulatory element
Partner regulator loaded
Partner regulator unable to load
Regulatory activity enabled
Regulatory activity inhibited
Figure 8.9 A schematic illustration of riboswitches constructed through methods based on direct attachment between the aptamer and regulatory domains. An aptamer is directly attached to the regulatory element in such a way that the ligand binding pocket within the aptamer is sufficiently close to the regulatory element. In the absence of ligand, the partner regulator is capable of loading onto this element and enabling the corresponding regulatory event to occur. In the presence of ligand, ligand binding to the aptamer and residing within the binding pocket results in steric hindrance for the partner regulator loading onto this element, resulting in the inhibition of the normal regulatory event.
theophylline dose-dependent manner. Dicer cleavage of the aptamer-fused shRNA for subsequent generation of siRNAs was demonstrated to be modulated in vitro and in vivo by theophylline. This ligandmediated regulation of Dicer processing was likely achieved due to locating the ligand-binding site of the aptamer sufficiently close to the Dicer processing site, such that theophylline binding to its aptamer blocks Dicer cleavage of the shRNA molecule, resulting in regulatable siRNA-based gene silencing. This proposed mechanism is supported by the observation that the ligand-mediated regulatory effect was abolished when the shRNA stem was extended by one or two base-pairs, resulting in a small shift in the aptamer fusion point compared to the initial design. This direct attachment strategy has also been employed in constructing a synthetic riboswitch that regulates gene expression at the level of splicing. This riboswitch was designed by insertion of the t heophylline aptamer171 near the 3′ consensus splice site region of a model pre-mRNA to modulate the splicing of the pre-mRNA through ligand-aptamer complex interactions.183 The addition of theophylline was shown to repress the in vitro splicing of the pre-mRNA harboring the aptamer and have no regulatory effect on the pre-mRNA without the aptamer. In addition, the aptamer’s effect on splicing was demonstrated to be location-dependent and explained by modulating the spliceosome’s accessibility to the splice site. The pre-mRNA harboring an aptamer with a stable base stem, inserted to encompass the 3′ splice site AG within the ligand-binding sequence exhibited the most efficient splicing inhibition, as the splice site becomes less accessible when theophylline resides in the aptamer binding pocket.
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Synthetic riboswitches constructed through the direct attachment strategy between the aptamer and regulatory domains exhibit functional dependence on the attachment location of the aptamer to the regulatory domain. This is because the ligand-mediated regulatory mechanism relies solely on the efficacy of the ligand-aptamer complex interaction in affecting the functional activity of the regulatory domain. This mechanism is not standardized and is highly specific to the particular aptamer-regulator pair. Therefore, such engineered riboswitches lack a reliable composition framework for integrating sensor and regulatory domains that results in allosteric binding properties. 8.4.4.3 Riboswitch Construction Based on an Evolved Linker between the Aptamer and Regulatory Domains Synthetic riboswitches have also been constructed by using a linker region that couples the aptamer and regulatory domains and serves as an element that translates the ligand-binding event in the aptamer domain to the adjacent regulatory domain. Early examples of evolved linker regions implemented a mechanism of information transmission known as “helix slipping”, in which a nucleotide shift event within the element is translated to a small-scale change in the conformation of the regulatory domain in a ligand-dependent manner.184 Such functional elements are often evolved through in vitro selection procedures and referred to as “communication modules.”184 These dynamic elements are typically three to five base-pairs long and often contain non-Watson-Crick base-pairing. Significant effort has been directed toward the construction of communication module-based riboswitches that use a hammerhead ribozyme as the regulatory domain (Figure 8.10), as ribozymes have proven to be a powerful platform for controlling gene expression. Several research groups have engineered a class of in vitro riboswitches called allosteric ribozymes.184–190 Allosteric ribozymes resemble allosteric enzymes in that binding of specific effectors, typically small molecule ligands, modulate the functional activities of the molecule.191 An allosteric ribozyme contains two separate domains, a catalytic, or regulatory, domain and a ligand-binding, or aptamer, domain, which interact in a liganddependent manner to control the catalytic activity of the molecule.191 Thus, the allosteric property of these ribozymes enables their catalytic activity to be regulated through specific ligands, and therefore may represent a modular design platform that can directly make use of different ligand-aptamer pairs. Different strategies including rational design, library screening, and combinatorial approaches have been employed to generate allosteric hammerhead ribozymes.191,192 Rational design strategies involve integration of an existing aptamer domain directly to the catalytic domain of the ribozyme through different linkers, followed by examination of the activities of the resulting integrated constructs.185–187 Library screening strategies involve screening randomized sequence libraries for novel aptamer domains (sometimes including communication modules) that function allosterically with the attached catalytic domain.193,194 Finally, the combined approach involves integration of an existing aptamer domain to the ribozyme’s catalytic domain through a randomized linker region, and screening for functional linker sequences from this library that result in allosteric binding (Figure 8.10).184,188,189 The majority of synthetic allosteric hammerhead ribozymes constructed to-date are responsive to small molecule ligands such as theophylline,171,184 adenosine triphosphate (ATP),185,195 and FMN,165,196 indicating that these riboswitches may be adapted to respond to metabolites, and thus hold potential for implementation in metabolic networks as allosteric gene control elements. While allosteric hammerhead ribozymes have been mostly demonstrated in vitro, Win and Smolke recently specified a set of design principles that enable these engineered allosteric ribozymes to function in the cellular environment.197 The critical design strategy that enabled translation of in vitro allosteric function to in vivo environments addressed the coupling method of the ribozyme and aptamer domains. In earlier systems, one of the loop and/or stem sequences was replaced by part of the aptamer domain, eliminating the tertiary interactions between loop I–II that had been demonstrated to be essential for the in vivo cleavage activity of the ribozyme.72 In the coupling strategy proposed by Win and Smolke, neither of the two loop or stem sequences of the ribozyme was replaced by the aptamer domain, allowing these tertiary interactions to be maintained, and therefore cleavage activity
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A
Targeted random library regions for communication module and ligand binding Stem loops I-II interactions Loop replacement Stem III Regulatory domain (hammerhead ribozyme)
Aptamer
with aptamer
Aptamer-coupled ribozyme (loop I-II interactions abolished)
Functional screening of communication modules
B
Randomize
Targeted random library region for communication module
NNNN NNNN
Functional screening
Helix slipping
In the absence of ligand
In the presence of ligand
Figure 8.10 A schematic illustration of riboswitches constructed based on an evolved linker between the aptamer and regulatory domains. An in vitro functional communication module-based allosteric hammerhead ribozyme system is shown as an example. The ribozyme’s cleavage site is indicated by an arrow. In general, an aptamer is attached to the regulatory domain through a linker (top), whose function is evolved to be ligand-dependent through selection from a random sequence library (bottom). The functional linkers are called “communication modules,” which employ the helix slipping mechanism in mediating the activity of the regulatory domain. An existing functional linker can also be used to mediate the activities of other regulatory platforms such as the RBS. In addition, an existing functional linker can also be used to couple a regulatory domain to an aptamer domain comprised of a random sequence library to evolve the latter to bind a new ligand of interest and function in this ligand-dependent manner (top). In this particular example of the in vitro allosteric ribozyme system, part of the aptamer domain replaces one loop of the ribozyme domain, thereby abolishing loop I-II interactions required for in vivo functionality.
of the ribozyme in vivo. Following these composition strategies, the authors coupled the aptamer and ribozyme domains through a third domain, the information transmission domain, within which previously in vitro selected communication modules were placed and screened for their in vivo functional activities. One quarter of the tested communication modules were demonstrated to be functional in this in vivo system, indicating that in vitro functionality of these elements is selectively translated to in vivo activity. In addition, these riboswitches did not exhibit aptamer-domain modularity in vivo. The select functionality and lack of modularity are presumably due to the weak non-Watson-Crick basepairs comprising these linkers that are likely sensitive to surrounding sequences. This system may be used to screen for new linker sequences that function with specific aptamer-ribozyme pairs to generate new ribozyme-based switches. Researchers have also developed communication module-based riboswitches using a different regulatory platform such as the RBS. Suess et al. engineered a synthetic riboswitch composed of a theophylline aptamer171 and a previously developed communication module191 placed at a position proximal to the RBS.198 This linker element had been proposed to perform helix slipping by one nucleotide between the ligand-bound and unbound states.191 In their design, the communication module served as a helix bridge between the aptamer and the RBS such that binding of theophylline to its aptamer causes a singlenucleotide shift in the communication module, thereby enabling ribosome binding to the RBS without steric interference, and thus efficient translation in the presence of theophylline. This design scheme is similar to a direct coupling design between a theophylline aptamer and RBS described above, except that a distinct communication module was incorporated between the aptamer and regulatory domains.
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Linker regions have also been evolved that implement a different mechanism of information transmission known as “strand displacement”, a functionally similar mechanism to “helix slipping.” Gallivan and colleagues developed a second riboswitch system as an extension of their initial direct attachment riboswitch design, using a combined rational and library screening design strategy.199 A linker region adjoining the theophylline aptamer and the RBS was randomized, and sequences that translated a ligand-binding event in the aptamer domain to a structural change in the RBS, thereby regulating ribosomal access to the RBS, were screened through plate-based assays. These sequences are functionally similar to communication modules in that they translate ligand-binding events at the aptamer domain to the regulatory domain, but they are compositionally and mechanistically distinct. Gene expression regulation through this second class of linker regions takes place through the strand-displacement mechanism instead of a helix slipping mechanism. The functional sequences are complementary to regions of the theophylline aptamer such that base-pairing with a region of the aptamer sequesters the RBS and thus inhibits ribosome access to the RBS, whereas binding of theophylline to its aptamer disrupts this conformation and releases the RBS, resulting in up-regulation of target gene expression. As such, this regulatory mechanism is specific to the theophylline aptamer employed in this system and is not functionally independent. Therefore, this riboswitch system is not readily amenable to the insertion of different aptamers and thus lacks modularity, such that new linker regions would need to be generated by screening for specific aptamer-regulator pairs. 8.4.4.4 A General Framework for the Construction of Modular, Extensible Riboswitches Allosteric hammerhead ribozymes with in vivo functionality are highly attractive gene control elements due to their general applicability to a broad range of organisms, as their regulatory mechanism does not require cell-specific machinery and thus represent a universal platform for controlling gene expression. The modular, in vivo functional, allosteric hammerhead ribozyme system developed by Win and Smolke, called ribozyme switches,197 contains distinct functional domains: a ligand-binding or sensor domain, composed of an RNA aptamer, and a regulatory or actuator domain, composed of a hammerhead ribozyme (Figure 8.11). The authors specified key design strategies in the development of this gene regulatory platform. First, the aptamer sequence is directly coupled to the ribozyme through one of two stem loops without removing or replacing the loop sequences, which have previously been shown to form tertiary interactions essential for in vivo functionality by stabilizing the ribozyme’s active conformation (Figure 8.11A). Second, the ribozyme constructs are integrated into the 3′ UTRs of target transcripts through stem III (Figure 8.11A), maintaining loop I–II interactions, removing regulatory artifacts that can be observed when placing RNA secondary structures in the 5′ UTR due to non-specific effects of these elements on translation initiation, and implementing a universal mechanism through which to destabilize transcripts across bacterial and mammalian cells (cleavage in the 3′ UTR). Third, the ribozyme switch was flanked by spacer sequences to insulate the control system from surrounding sequences (Figure 8.11A). Finally, a standardized information transmission domain was rationally constructed, composed of competing strands that are independent of the other domains within the switch molecule, in order to implement a modular strand displacement-based mechanism (Figure 8.11). This domain serves as a computation element capable of transmitting binding information in the aptamer domain to the activity of the ribozyme domain. The strand-displacement regulatory mechanism is based on the conformational dynamics characteristic of RNA molecules that enables them to distribute between two primary conformations at equilibrium. In one conformation, the competing strand is not base-paired or base-paired such that the ligand-binding pocket is not formed, and in the other conformation, the competing strand is basepaired with the aptamer base stem, displacing the switching strand and thus allowing the formation of the ligand-binding pocket. Binding of ligand to the latter conformation shifts the equilibrium distribution to favor the aptamer-bound form as a function of increasing ligand concentration. Strand displacement results in the disruption (ON switch, Figure 8.11A, bottom) or restoration (OFF switch,
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A Stem loops I-II interactions
Stem loops I-II interactions retained Competing strand
Direct coupling Stem III Regulatory domain (sTRSV ribozyme)
Sensor domain (aptamer)
integration Aptamer-coupled ribozyme
Aptamer-coupled ribozyme ON switch platform
Insertion into the 3´ UTR of a target gene through stem III
AAAAA GFP Aptamer bound, ribozyme inactive conformation, allowing gene expression
Ligand
AAAAA GFP Aptamer unbound, ribozyme active conformation, suppressing gene expression
B
AAAAA GFP Aptamer unbound, ribozyme inactive conformation, allowing gene expression
Ligand
AAAAA GFP Aptamer bound, ribozyme active conformation, suppressing gene expression
Figure 8.11 (See color insert following page 13-20.) General compositional framework and design strategy for engineering universal, ligand-controlled cis-acting hammerhead ribozyme-based regulatory systems. The color scheme is as follows: catalytic core, purple; loop sequences, blue; aptamer sequence, brown; competing strand, green; switching strand, red; spacer sequences, orange; cleavage site, brown arrow. Modular strategies for coupling the aptamer and regulatory domains and systematic integration of the coupled control molecule comprising these domains into a target mRNA are shown. An aptamer is directly attached to the ribozyme through one of its loops without replacing any part of the ribozyme, thereby maintaining loop I-II interactions required for in vivo functionality. Spacer sequences are included on both ends of the control molecule to insulate from non-specific interactions with the surrounding sequences. A competing strand, whose sequence is similar to that of the switching strand, is integrated into the aptamer-coupled ribozyme, which enables the control molecule to adopt two primary conformations through the strand displacement mechanism, as the competing strand displaces the switching strand. Through this mechanism, (A) an ON switch, in which ligand binding stabilizes the conformation with the disrupted catalytic core, and (B) an OFF switch, in which ligand binding stabilizes the conformation with the restored catalytic core, are constructed. (Adapted from Win, M.N. and Smolke, C.D. Proc. Natl. Acad. Sci. USA, 104, 14283–14288, 2007.)
Figure 8.11B) of the ribozyme’s catalytic core, thereby enabling up- or down-regulation of target gene expression. The authors demonstrated that both ON and OFF switches can be tuned to exhibit different dynamic ranges of ligand-dependent responses. The authors demonstrated the tunability of this platform targeted to the information transmission domain through alteration of the nucleotide composition of this domain to affect the stabilities of individual switch constructs and the energies required for the construct to switch between the two primary adoptable conformations. In addition, the modularity of the stranddisplacement switch platform was demonstrated by direct replacement of the aptamer domain with another aptamer sequence, resulting in a new switch that maintains similar switch response properties to a new ligand (Figure 8.12). In addition to exhibiting aptamer domain modularity, this standardized platform also demonstrates modularity in the regulatory domain. For instance, Smolke’s group has also
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Aptamer I
ON switch I, active ribozyme
AAAAA
Aptamer II
Sensor domain replacement
ON switch II, active ribozyme
AAAAA
ON switch II, inactive ribozyme
AAAAA
Figure 8.12 (See color insert following page 13-20.) Modular design strategies for the construction of new ribozyme switches comprising aptamer domains responsive to diverse ligands. The color scheme corresponds to that used in Figure 8.11. An ON switch platform is used for illustration where aptamer I (left dashed box) is directly replaced with aptamer II (right dashed box) to construct an ON switch II. (Adapted from Win, M.N. and Smolke, C.D. Proc. Natl. Acad. Sci. USA, 104, 14283–14288, 2007.)
developed another class of switches, called antiswitches, constructed through a similar compositional strategy, where an antisense RNA was employed as the regulatory domain in place of the hammerhead ribozyme.200 This class of synthetic riboswitches also enables ligand-dependent up- or down-regulation of target gene expression. Finally, the versatility of this switch platform was demonstrated by implementing several ribozyme switches in an application-specific control system where an amino acid biosynthesis gene responsible for cell growth was regulated in a ligand-dependent manner. Cells harboring switches with different dynamic response ranges exhibited corresponding extents of cell growth in response to the presence of ligand. In addition to demonstrating the first examples of small molecule-responsive, in vivo functional allosteric hammerhead ribozymes, this work describes a modular and extensible RNA-based framework for the reliable construction of ligand-controlled gene regulatory systems that is portable across diverse organisms. This engineering framework provides a flexible and programmable platform in which the aptamer and regulatory domains can be modularly coupled to one another, enabling a plug-and-play type capability for the rapid design and construction of new switches.
8.5 Applications of RNA Control Elements in Metabolic Network Engineering RNA plays a critical role in regulating gene expression and significant effort has been directed toward the design of synthetic RNA elements that function as engineered gene regulatory systems. Engineered biological systems design has traditionally focused on transcriptional regulation schemes, without much effort being directed to the design and integration of RNA-based control systems. However, several recent examples demonstrate the application of these engineered regulators to metabolic pathway engineering. Such RNA-based tools have been implemented to redirect pathway fluxes and to tune the expression levels of enzymes involved in a particular metabolic pathway for optimal energy usage. Combinatorial implementation of RNA regulatory elements has been used to achieve precise control over the differential expression levels of multiple enzymes. Bacteria often locate multiple related
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pathway genes into operons to coordinate the levels of the encoded enzymes. RNA regulatory elements can be used to layer additional control on to the system and achieve differential expression of the genes encoded in that operon construct.41,201,202 Therefore, synthetic RNA regulatory elements such as endonuclease cleavage elements and hairpin elements that result in transcript stabilization (as previously described) have been placed within intergenic regions of a polycistronic transcript to direct the processing and stability of different regions of the polycistronic transcript to result in differential expression of the genes encoded in that operon construct.203,204 In one example, Smolke et al. developed an engineered operon system where multiple genes were placed under the control of a single promoter and the encoded proteins were produced at different ratios depending on the combination of control elements used.58,60,205 In this system, an RNase E cleavage site was placed between two coding regions to direct cleavage in this location and to generate two secondary transcripts, and novel hairpin structures were incorporated into the 5′ and 3′ ends of the coding regions to protect the resulting transcripts from exoribonuclease activity. This RNA regulatory system was implemented to control the metabolic flux through a carotenoid pathway, where the production of the product β-carotene was modulated up to 300-fold relative to levels of an intermediate metabolite in the pathway, lycopene.206 Accumulation levels of these and other intermediates in the pathway were varied with different combinations of synthetic RNA regulatory elements, demonstrating the ability of these systems to precisely regulate pathway enzyme levels and balance the production of the intermediate metabolites. A similar system, based on engineering RNA regulatory elements into intergenic regions of operon constructs, was developed through combinatorial assembly strategies.207 A library of integenic regions was generated through combinatorial assembly of oligonucleotide sequences and a screening method was developed based on fluorescent activated cell sorting (FACS) to generate tunable intergenic regions (TIGRs) consisting of combinations of RNA regulatory elements that result in desired expression ratios.207 Functional TIGRs were demonstrated to vary the relative expression of two reporter genes over a 100-fold range. This combinatorial library generation method was applied to a three-gene operon that encodes a heterologous mevalonate pathway and a biosensor assay was used to screen for high mevalonate producers. Several selected intergenic regions optimized the relative expression of the three pathway enzymes resulting in a seven-fold increase in mevalonate production. This system provides another example of the regulatory capabilities of these RNA-based systems as powerful tools in metabolic pathway engineering. Synthetic regulatory elements that act through the RNAi pathway have significant application in plant metabolic engineering systems.208–211 In one recent example, RNAi substrates were employed to induce gene-silencing in Papaver somniferum,212 the opium poppy, to alter accumulation of benzylisoquinoline alkaloid (BIA) metabolites in the plant. A hairpin RNA (hpRNA) construct was designed to target codeinone reductase (COR), encoded by a multi-gene family consisting of seven members. COR is an enzyme responsible for codeine biosynthesis and the penultimate enzyme for morphine biosynthesis from codeine. Expression of this RNA regulatory element in P. somniferum resulted in the unexpected accumulation of an early BIA metabolite, reticuline, at the expense of later metabolites such as thebaine, oripavine, morphine, and codeine. Reticuline is a non-narcotic alkaloid that is synthesized seven enzymatic steps upstream of COR and also a valuable common precursor metabolite that is shared by other alkaloid pathways. Although it is currently unclear how silencing of the downstream enzyme affected metabolic processing to result in accumulation of an intermediate metabolite synthesized far upstream in the pathway, this example highlights RNAi-based regulatory elements as effective tools in plant metabolic pathway engineering. Efforts in metabolic engineering have been primarily directed to the development of gene regulatory tools and the application of these tools to optimize the regulation of the expression levels of metabolic genes of interest with the goal of maximizing the production of desired metabolites. However, tools that provide general strategies to detect key metabolite accumulation levels without mechanically disrupting the cells, providing methods for non-invasive sensing of metabolite production are critical to advancing metabolic engineering efforts. Recently, Win and Smolke developed a
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modular platform for constructing molecular sensors to noninvasively detect biosynthesis of a target metabolite in real-time through a fluorescent reporter signal.197 This platform provides a framework for the construction of ribozyme switches, discussed earlier as extensible ligand-responsive gene regulation systems. The application of these switches to detecting the biosynthesis of a metabolite xanthine from a precursor fed to cells was demonstrated. Ribozyme switches constructed with a sensor domain responsive to xanthine were demonstrated to up-regulate reporter gene expression as the metabolite accumulated in the cell, thereby serving as non-invasive molecular sensors of metabolite production.
8.6 Enabling Technologies in Support of Constructing Integrated Network and Control Systems RNA engineering is a rapidly developing field that holds great promise for implementation of synthetic RNA regulatory systems toward the construction of reliable and robust engineered cellular systems for various biotechnological applications including metabolic engineering. Advances in RNA biology and engineering have enabled researchers to develop numerous regulatory platforms, which will be expanded on to develop more sophisticated systems that will enable the design and construction of biological control systems tailored to application-specific performance requirements. The development of engineered riboswitches highlights such efforts to develop sophisticated control systems, where various regulatory elements such as RBS sequences, antisense RNAs, siRNAs, alternative splice sites, and ribozymes have been employed in combination with a second functional element (aptamers) capable of binding specific ligands. While RNA regulatory elements are generally designed through well-defined forward design principles, the aptamer or sensor elements are generated through standardized in vitro selection processes known as SELEX. Technological advances have resulted in SELEX being adapted to automated, high throughput platforms.141–143 In addition, more efficient SELEX strategies have been developed by modifying the standard affinity-based partitioning schemes to partitioning through capillary electrophoresis.144–146 Selections performed with this partitioning strategy can be conducted in fewer selection cycles to attain higher affinity aptamer than is required in conventional SELEX, due to the substantial enhancement in partitioning target-bound and unbound aptamers. Although these improved aptamer selection schemes have been demonstrated for the generation of aptamers to protein ligands, in principle these schemes can be adapted to the selection of aptamers to small molecule ligands, which are more relevant sensor components to metabolic engineering efforts. Synthetic riboswitches composed of distinct, modular ligand-binding domains are excellent candidates for the construction of metabolite-specific gene control systems. For example, the previously described strand displacement-based ribozyme switch platform is modular in design, tunable in regulation, and specific in target recognition, and thus represents a versatile foundational technology that can be applied to the generation of tailor-made synthetic riboswitches. In addition, as the regulatory mechanism employed by this control system does not require cell-specific machinery, it is therefore applicable to diverse organisms offering platform portability. Since the functional domains of the switch are distinct and exhibit sequence independence from each other, they enable component reuse strategies where domains can be integrated into the platform without disrupting the switch activity. This property potentially enables the construction of higher-order genetic devices and signal processing systems that involve extended integrated design schemes. For instance, tailor-made synthetic riboswitches that exhibit logical processing of multiple inputs can be potentially engineered through tandem ribozyme switch design. Integration schemes may also implement two different sensor domains within a single switch—tandem sensor domain systems—to exhibit higher order signal processing properties. Such higher order signal processing systems will enable more sophisticated control of desired metabolic genes in a pathway, which otherwise cannot be achieved with single riboswitch systems.
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8.7 Future Applications of Advanced RNA-Based Control Systems Riboswitches are sophisticated RNA-based control systems capable of mediating gene expression autonomously without additional aid from auxiliary protein components, and provide a direct link between key metabolites and their biosynthetic genes. Therefore, application of engineered riboswitches in synthetic metabolic networks will enable such autonomous regulation in a specific metabolite-dependent manner. While other synthetic RNA regulatory elements have been employed in metabolic engineering applications, implementation of engineered riboswitches as gene regulatory tools in rewiring metabolic fluxes has not yet been demonstrated. This is because the development of engineered riboswitch systems is recent and most of these systems were constructed as artisanal examples to demonstrate the gene regulatory capabilities of these synthetic systems. As a result, most of these designs do not support modularity and portability and present challenges in adaptation to practical applications. Nevertheless, novel design strategies for engineering adaptable, scalable systems are increasingly emerging. The first example of employing synthetic riboswitches in an application associated with metabolic engineering was recently demonstrated, in which ribozyme switches were implemented as non-invasive sensors for monitoring the biosynthesis of a target metabolite as previously described.197 In addition to serving as cellular biosensors, engineered riboswitches can also be implemented as metabolite-specific gene regulatory tools controlling target enzyme levels in response to key metabolites. A schematic illustration of the application of such advanced RNA control systems in a metabolic network is shown in Figure 8.13, where pathway fluxes are directed and controlled through metabolite-dependent dynamic regulatory schemes. This type of control scheme requires integration of specific aptamers domains into the engineered switch platform. While SELEX has been a powerful technique in generating diverse ligand-binding domains in vitro, it does not assure that these in vitro selected ligand-binding domains will function in vivo within the context of the integrated switch platform. Therefore, a technique complementary to SELEX that enables the generation of new in vivo functional ligand-binding domains that are compatible with integration into the switch platform is needed. Closely related aptamer domains have been evolved in vitro from existing ones through small
S2 M1
E12
M2
S7 E23
E34
M3 E45 M8
M4 S4
E45
M5
E56
E67
M6
M7
E34 M9
Figure 8.13 An abstracted scheme of metabolite-specific RNA switch-based directed gene expression regulation where various switches control the regulation of specific pathway genes and direct metabolic fluxes. Abbreviations are as follows: M, metabolite; E, enzyme; S, switch. S2 up-regulates the downstream enzyme E23 in response to M2 accumulation to conserve cellular energy. S4 up-regulates E 45 in response to M4 accumulation, which is converted from M3, to minimize an undesired conversion of M3 to M8 by E 45. In response to certain levels of M7 accumulation, S7 down-regulates the immediate upstream enzyme E67, which is the biosynthetic enzyme for M7, allowing the metabolite to self-regulate its own production levels. This feedback regulation scheme can be used to specify set points for M7 accumulation.
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sequence libraries placed within the latter (“doped libraries”), 213,214 supporting that the generation of orthogonal aptamers to metabolite families through in vivo selection strategies is feasible. Ribozyme switches, or similar engineered switch platforms, may provide the basis and thus serve as a platform for such in vivo strategies to generate new aptamer domains from smaller randomized libraries. This in vivo screening technique is potentially complementary to standard SELEX techniques such that in vitro selection can be initially performed on larger switch libraries for fewer selection rounds, which will then be subsequently subject to in vivo screening on these partially enriched smaller libraries, thereby enabling the generation of RNA aptamers for building synthetic riboswitches that are functional in the cellular environment.
8.8 Conclusions In natural bacterial and eukaryotic systems, many metabolic pathways have been identified that employ RNA elements such as riboswitches in controlling the expression levels of enzymes responsible for the biosynthesis, biodegradation, and transport of key metabolites in these pathways, indicating that RNA is a well-suited regulatory platform for metabolic networks. Naturally occurring riboswitches control gene expression by regulating the efficiency of transcriptional and post-transcriptional processes upon binding small molecule ligands through different mechanisms that act through base-pairing interactions (terminator formation, RBS sequestering), transcript processing (ribozyme cleavage), and mRNA maturation (splicing). Building on these natural examples and exploiting the interdependent relationship between RNA sequence, structure, and function, various synthetic RNA regulatory systems including engineered riboswitches have been developed and their use as gene control elements has been successfully demonstrated. These natural and engineered RNA-based regulatory systems have highlighted the design flexibility and functional versatility of RNA, and demonstrated their unique and powerful capabilities as tools in metabolic engineering applications.
References 1. Khosla, C. and Keasling, J.D. Metabolic engineering for drug discovery and development. Nat. Rev. Drug Discov., 2, 1019–1025, 2003. 2. Simons, R.W. Naturally occurring antisense RNA control—a brief review. Gene, 72, 35–44, 1988. 3. Inouye, M. Antisense RNA: its functions and applications in gene regulation—a review. Gene, 72, 25–34, 1988. 4. Doudna, J.A. and Cech, T.R. The chemical repertoire of natural ribozymes. Nature, 418, 222–228, 2002. 5. Fedor, M.J. and Williamson, J.R. The catalytic diversity of RNAs. Nat. Rev. Mol. Cell Biol., 6, 399–412, 2005. 6. Mandal, M. and Breaker, R.R. Gene regulation by riboswitches. Nat. Rev. Mol. Cell. Biol., 5, 451–463, 2004. 7. Winkler, W.C. and Breaker, R.R. Regulation of bacterial gene expression by riboswitches. Ann. Rev. Microbiol., 59, 487–517, 2005. 8. Lai, E.C. RNA sensors and riboswitches: self-regulating messages. Curr. Biol., 13, R285–291, 2003. 9. Sudarsan, N., Barrick, J.E., and Breaker, R.R. Metabolite-binding RNA domains are present in the genes of eukaryotes. RNA, 9, 644–647, 2003. 10. Winkler, W.C. and Breaker, R.R. Genetic control by metabolite-binding riboswitches. Chembiochem, 4, 1024–1032, 2003. 11. Tucker, B.J. and Breaker, R.R. Riboswitches as versatile gene control elements. Curr. Opin. Struct. Biol., 15, 342–348, 2005. 12. Meister, G. and Tuschl, T. Mechanisms of gene silencing by double-stranded RNA. Nature, 431, 343–349, 2004.
8-28
Gene Expression Tools for Metabolic Pathway Engineering
13. Dykxhoorn, D.M., Novina, C.D., and Sharp, P.A. Killing the messenger: short RNAs that silence gene expression. Nat. Rev. Mol. Cell. Biol., 4, 457–467, 2003. 14. Novina, C.D. and Sharp, P.A. The RNAi revolution. Nature, 430, 161–164, 2004. 15. Tang, G. siRNA and miRNA: an insight into RISCs. Trends Biochem. Sci., 30, 106–114, 2005. 16. Breaker, R.R. Natural and engineered nucleic acids as tools to explore biology. Nature, 432, 838–845, 2004. 17. Schroeder, R., Barta, A., and Semrad, K. Strategies for RNA folding and assembly. Nat. Rev. Mol. Cell Biol., 5, 908–919, 2004. 18. Soukup, J.K. and Soukup, G.A. Riboswitches exert genetic control through metabolite-induced conformational change. Curr. Opin. Struct. Biol., 14, 344–349, 2004. 19. Bauer, G. and Suess, B. Engineered riboswitches as novel tools in molecular biology. J. Biotechnol., 124, 4–11, 2006. 20. Isaacs, F.J., Dwyer, D.J., and Collins, J.J. RNA synthetic biology. Nat. Biotechnol., 24, 545–554, 2006. 21. Davidson, E.A. and Ellington, A.D. Engineering regulatory RNAs. Trends Biotechnol., 23, 109–112, 2005. 22. Erdmann, V.A. et al. The non-coding RNAs as riboregulators. Nucleic Acids Res., 29, 189–193, 2001. 23. Kozak, M. Regulation of translation via mRNA structure in prokaryotes and eukaryotes. Gene, 361, 13–37, 2005. 24. Kozak, M. Initiation of translation in prokaryotes and eukaryotes. Gene, 234, 187–208, 1999. 25. Shine, J. and Dalgarno, L. Determinant of cistron specificity in bacterial ribosomes. Nature, 254, 34–38, 1975. 26. Chen, H., Bjerknes, M., Kumar, R., and Jay, E. Determination of the optimal aligned spacing between the Shine-Dalgarno sequence and the translation initiation codon of Escherichia coli mRNAs. Nucleic Acids Res., 22, 4953–4957, 1994. 27. Hellen, C.U. and Sarnow, P. Internal ribosome entry sites in eukaryotic mRNA molecules. Genes Dev., 15, 1593–1612, 2001. 28. Bonnal, S., Boutonnet, C., Prado-Lourenco, L., and Vagner, S. IRESdb: the Internal Ribosome Entry Site database. Nucleic Acids Res., 31, 427–428, 2003. 29. Pelletier, J. and Sonenberg, N. Internal initiation of translation of eukaryotic mRNA directed by a sequence derived from poliovirus RNA. Nature, 334, 320–325, 1988. 30. Jang, S.K. et al. A segment of the 5′ nontranslated region of encephalomyocarditis virus RNA directs internal entry of ribosomes during in vitro translation. J. Virol., 62, 2636–2643, 1988. 31. Chappell, S.A., Edelman, G.M., and Mauro, V.P. A 9-nt segment of a cellular mRNA can function as an internal ribosome entry site (IRES) and when present in linked multiple copies greatly enhances IRES activity. Proc. Natl. Acad. Sci. USA, 97, 1536–1541, 2000. 32. Kieft, J.S., Zhou, K., Grech, A., Jubin, R., and Doudna, J.A. Crystal structure of an RNA tertiary domain essential to HCV IRES-mediated translation initiation. Nat. Struct. Biol., 9, 370–374, 2002. 33. Chapon, C. Expression of malT, the regulator gene of the maltose region in Escherichia coli, is limited both at transcription and translation. EMBO J., 1, 369–374, 1982. 34. De Boer, H.A., Comstock, L.J., Hui, A., Wong, E., and Vasser, M. A hybrid promoter and portable Shine-Dalgarno regions of Escherichia coli. Biochem. Soc. Symp., 48, 233–244, 1983. 35. Rackham, O. and Chin, J.W. A network of orthogonal ribosome x mRNA pairs. Nat. Chem. Biol., 1, 159–166, 2005. 36. Zhou, W., Edelman, G.M., and Mauro, V.P. Isolation and identification of short nucleotide sequences that affect translation initiation in Saccharomyces cerevisiae. Proc. Natl. Acad. Sci. USA, 100, 4457– 4462, 2003. 37. Owens, G.C., Chappell, S.A., Mauro, V.P., and Edelman, G.M. Identification of two short internal ribosome entry sites selected from libraries of random oligonucleotides. Proc. Natl. Acad. Sci. USA, 98, 1471–1476, 2001.
Regulating Gene Expression through Engineered RNA Technologies
8-29
38. Borman, A.M., Bailly, J.L., Girard, M., and Kean, K.M. Picornavirus internal ribosome entry segments: comparison of translation efficiency and the requirements for optimal internal initiation of translation in vitro. Nucleic Acids Res., 23, 3656–3663, 1995. 39. Nicholson, A.W. Function, mechanism and regulation of bacterial ribonucleases. FEMS Microbiol. Rev., 23, 371–390, 1999. 40. Lamontagne, B., Larose, S., Boulanger, J., and Elela, S.A. The RNase III family: a conserved structure and expanding functions in eukaryotic dsRNA metabolism. Curr. Issues Mol. Biol., 3, 71–78, 2001. 41. Carrier, T.A. and Keasling, J.D. Controlling messenger RNA stability in bacteria: strategies for engineering gene expression. Biotechnol. Prog., 13, 699–708, 1997. 42. Saunders, L.R. and Barber, G.N. The dsRNA binding protein family: critical roles, diverse cellular functions. FASEB J., 17, 961–983, 2003. 43. Rotondo, G. and Frendewey, D. Purification and characterization of the Pac1 ribonuclease of Schizosaccharomyces pombe. Nucleic Acids Res., 24, 2377–2386, 1996. 44. Zhang, K. and Nicholson, A.W. Regulation of ribonuclease III processing by double-helical sequence antideterminants. Proc. Natl. Acad. Sci. USA, 94, 13437–13441, 1997. 45. Nagel, R. and Ares, M., Jr. Substrate recognition by a eukaryotic RNase III: the double-stranded RNA-binding domain of Rnt1p selectively binds RNA containing a 5′-AGNN-3′ tetraloop. RNA, 6, 1142–1156, 2000. 46. Lamontagne, B. and Elela, S.A. Evaluation of the RNA determinants for bacterial and yeast RNase III binding and cleavage. J. Biol. Chem., 279, 2231–2241, 2004. 47. Ge, D., Lamontagne, B., and Elela, S.A. RNase III-mediated silencing of a glucose-dependent repressor in yeast. Curr. Biol., 15, 140–145, 2005. 48. Larose, S. et al. RNase III-dependent regulation of yeast telomerase. J. Biol. Chem., 282, 4373–4381, 2007. 49. Lundberg, U., von Gabain, A., and Melefors, O. Cleavages in the 5′ region of the ompA and bla mRNA control stability: studies with an E. coli mutant altering mRNA stability and a novel endoribonuclease. EMBO J., 9, 2731–2741, 1990. 50. Taraseviciene, L., Miczak, A., and Apirion, D. The gene specifying RNase E (rne) and a gene affecting mRNA stability (ams) are the same gene. Mol. Microbiol., 5, 851–855, 1991. 51. McDowall, K.J., Lin-Chao, S., and Cohen, S.N. A+U content rather than a particular nucleotide order determines the specificity of RNase E cleavage. J. Biol. Chem., 269, 10790–10796, 1994. 52. Zubiaga, A.M., Belasco, J.G., and Greenberg, M.E. The nonamer UUAUUUAUU is the key AU-rich sequence motif that mediates mRNA degradation. Mol. Cell. Biol., 15, 2219–2230, 1995. 53. Alifano, P., Bruni, C.B., and Carlomagno, M.S. Control of mRNA processing and decay in prokaryotes. Genetica, 94, 157–172, 1994. 54. Emory, S.A. and Belasco, J.G. The ompA 5′ untranslated RNA segment functions in Escherichia coli as a growth-rate-regulated mRNA stabilizer whose activity is unrelated to translational efficiency. J. Bacteriol., 172, 4472–4481, 1990. 55. Emory, S.A., Bouvet, P., and Belasco, J.G. A 5′-terminal stem-loop structure can stabilize mRNA in Escherichia coli. Genes Dev., 6, 135–148, 1992. 56. Hansen, M.J., Chen, L.H., Fejzo, M.L., and Belasco, J.G. The ompA 5′ untranslated region impedes a major pathway for mRNA degradation in Escherichia coli. Mol. Microbiol., 12, 707–716, 1994. 57. Carrier, T.A. and Keasling, J.D. Library of synthetic 5′ secondary structures to manipulate mRNA stability in Escherichia coli. Biotechnol. Prog., 15, 58–64, 1999. 58. Smolke, C.D., Carrier, T.A., and Keasling, J.D. Coordinated, differential expression of two genes through directed mRNA cleavage and stabilization by secondary structures. Appl. Environ. Microbiol., 66, 5399–5405, 2000. 59. Zuo, Y. and Deutscher, M.P. Exoribonuclease superfamilies: structural analysis and phylogenetic distribution. Nucleic Acids Res., 29, 1017–1026, 2001.
8-30
Gene Expression Tools for Metabolic Pathway Engineering
60. Smolke, C.D. and Keasling, J.D. Effect of gene location, mRNA secondary structures, and RNase sites on expression of two genes in an engineered operon. Biotechnol. Bioeng., 80, 762–776, 2002. 61. Zaug, A.J. and Cech, T.R. In vitro splicing of the ribosomal RNA precursor in nuclei of Tetrahymena. Cell, 19, 331–338, 1980. 62. Cech, T.R., Zaug, A.J., and Grabowski, P.J. In vitro splicing of the ribosomal RNA precursor of Tetrahymena: involvement of a guanosine nucleotide in the excision of the intervening sequence. Cell, 27, 487–496, 1981. 63. Kruger, K. et al. Self-splicing RNA: autoexcision and autocyclization of the ribosomal RNA intervening sequence of Tetrahymena. Cell, 31, 147–157, 1982. 64. Guerrier-Takada, C., Gardiner, K., Marsh, T., Pace, N., and Altman, S. The RNA moiety of ribonuclease P is the catalytic subunit of the enzyme. Cell, 35, 849–857, 1983. 65. Symons, R.H. Small catalytic RNAs. Ann. Rev. Biochem., 61, 641–671, 1992. 66. Forster, A.C., Jeffries, A.C., Sheldon, C.C., and Symons, R.H. Structural and ionic requirements for self-cleavage of virusoid RNAs and trans self-cleavage of viroid RNA. Cold Spring Harb. Symp. Quant. Biol., 52, 249–259, 1987. 67. Sun, L.Q., Cairns, M.J., Saravolac, E.G., Baker, A., and Gerlach, W.L. Catalytic nucleic acids: from lab to applications. Pharmacol. Rev., 52, 325–347, 2000. 68. Tang, J. and Breaker, R.R. Structural diversity of self-cleaving ribozymes. Proc. Natl. Acad. Sci. USA, 97, 5784–5789, 2000. 69. Birikh, K.R., Heaton, P.A., and Eckstein, F. The structure, function and application of the hammerhead ribozyme. Eur. J. Biochem., 245, 1–16, 1997. 70. Jen, K.Y. and Gewirtz, A.M. Suppression of gene expression by targeted disruption of messenger RNA: available options and current strategies. Stem Cells, 18, 307–319, 2000. 71. Vaish, N.K., Kore, A.R., and Eckstein, F. Recent developments in the hammerhead ribozyme field. Nucleic Acids Res., 26, 5237–5242, 1998. 72. Khvorova, A., Lescoute, A., Westhof, E., and Jayasena, S.D. Sequence elements outside the hammerhead ribozyme catalytic core enable intracellular activity. Nat. Struct. Biol., 10, 708–712, 2003. 73. De la Pena, M., Gago, S., and Flores, R. Peripheral regions of natural hammerhead ribozymes greatly increase their self-cleavage activity. EMBO J., 22, 5561–5570, 2003. 74. Blount, K.F. and Uhlenbeck, O.C. The structure-function dilemma of the hammerhead ribozyme. Ann. Rev. Biophys. Biomol. Struct., 34, 415–440, 2005. 75. Eckstein, F., Kore, A.R., and Nakamaye, K.L. In vitro selection of hammerhead ribozyme sequence variants. Chembiochem, 2, 629–635, 2001. 76. Salehi-Ashtiani, K. and Szostak, J.W. In vitro evolution suggests multiple origins for the hammerhead ribozyme. Nature, 414, 82–84, 2001. 77. Ishizaka, M., Ohshima, Y., and Tani, T. Isolation of active ribozymes from an RNA pool of random sequences using an anchored substrate RNA. Biochem. Biophys. Res. Commun., 214, 403–409, 1995. 78. Conaty, J., Hendry, P., and Lockett, T. Selected classes of minimised hammerhead ribozyme have very high cleavage rates at low Mg2+ concentration. Nucleic Acids Res., 27, 2400–2407, 1999. 79. Persson, T., Hartmann, R.K., and Eckstein, F. Selection of hammerhead ribozyme variants with low Mg2+ requirement: importance of stem-loop II. Chembiochem, 3, 1066–1071, 2002. 80. Marschall, P., Thomson, J.B., and Eckstein, F. Inhibition of gene expression with ribozymes. Cell Mol. Neurobiol., 14, 523–538, 1994. 81. Fujita, S. et al. Discrimination of a single base change in a ribozyme using the gene for dihydrofolate reductase as a selective marker in Escherichia coli. Proc. Natl. Acad. Sci. USA, 94, 391–396, 1997. 82. Kawasaki, H. et al. Selection of the best target site for ribozyme-mediated cleavage within a fusion gene for adenovirus E1A-associated 300 kDa protein (p300) and luciferase. Nucleic Acids Res., 24, 3010–3016, 1996. 83. Sakamoto, N., Wu, C.H., and Wu, G.Y. Intracellular cleavage of hepatitis C virus RNA and inhibition of viral protein translation by hammerhead ribozymes. J. Clin. Invest., 98, 2720–2728, 1996.
Regulating Gene Expression through Engineered RNA Technologies
8-31
84. Gavin, D.K. and Gupta, K.C. Efficient hammerhead ribozymes targeted to the polycistronic Sendai virus P/C mRNA. Structure-function relationships. J. Biol. Chem., 272, 1461–1472, 1997. 85. Kijima, H. et al. Hammerhead ribozymes against gamma-glutamylcysteine synthetase mRNA down-regulate intracellular glutathione concentration of mouse islet cells. Biochem. Biophys. Res. Commun., 247, 697–703, 1998. 86. Kim, Y.K. et al. Repression of hepatitis B virus X gene expression by hammerhead ribozymes. Biochem. Biophys. Res. Commun., 257, 759–765, 1999. 87. Yen, L. et al. Exogenous control of mammalian gene expression through modulation of RNA selfcleavage. Nature, 431, 471–476, 2004. 88. Green, P.J., Pines, O., and Inouye, M. The role of antisense RNA in gene regulation. Ann. Rev. Biochem., 55, 569–597, 1986. 89. Crooke, S.T. Progress in antisense technology. Ann. Rev. Med., 55, 61–95, 2004. 90. Kurreck, J. Antisense technologies. Improvement through novel chemical modifications. Eur. J. Biochem., 270, 1628–1644, 2003. 91. Kawamoto, H., Morita, T., Shimizu, A., Inada, T., and Aiba, H. Implication of membrane localization of target mRNA in the action of a small RNA: mechanism of post-transcriptional regulation of glucose transporter in Escherichia coli. Genes Dev., 19, 328–338, 2005. 92. Vanderpool, C.K. and Gottesman, S. Noncoding RNAs at the membrane. Nat. Struct. Mol. Biol., 12, 285–286, 2005. 93. Bunch, T.A. and Goldstein, L.S. The conditional inhibition of gene expression in cultured Drosophila cells by antisense RNA. Nucleic Acids Res., 17, 9761–9782, 1989. 94. Blomberg, P., Nordstrom, K., and Wagner, E.G. Replication control of plasmid R1: RepA synthesis is regulated by CopA RNA through inhibition of leader peptide translation. EMBO J., 11, 2675–2683, 1992. 95. Bonoli, M., Graziola, M., Poggi, V., and Hochkoeppler, A. RNA complementary to the 5′ UTR of mRNA triggers effective silencing in Saccharomyces cerevisiae. Biochem. Biophys. Res. Commun., 339, 1224–1231, 2006. 96. Novick, R.P., Iordanescu, S., Projan, S.J., Kornblum, J., and Edelman, I. pT181 plasmid replication is regulated by a countertranscript-driven transcriptional attenuator. Cell, 59, 395–404 (1989). 97. Lease, R.A., Cusick, M.E., and Belfort, M. Riboregulation in Escherichia coli: DsrA RNA acts by RNA:RNA interactions at multiple loci. Proc. Natl. Acad. Sci. USA, 95, 12456–12461, 1998. 98. Majdalani, N., Cunning, C., Sledjeski, D., Elliott, T., and Gottesman, S. DsrA RNA regulates translation of RpoS message by an anti-antisense mechanism, independent of its action as an antisilencer of transcription. Proc. Natl. Acad. Sci. USA, 95, 12462–12467, 1998. 99. Lease, R.A. and Belfort, M. A trans-acting RNA as a control switch in Escherichia coli: DsrA modulates function by forming alternative structures. Proc. Natl. Acad. Sci. USA, 97, 9919–9924, 2000. 100. Sharp, P.A. RNA interference—2001. Genes Dev., 15, 485–490, 2001. 101. Napoli, C., Lemieux, C., and Jorgensen, R. Introduction of a chimeric chalcone synthase gene into Petunia results in reversible co-suppression of homologous genes in trans. Plant Cell., 2, 279–289, 1990. 102. van der Krol, A.R., Mur, L.A., Beld, M., Mol, J.N., and Stuitje, A.R. Flavonoid genes in petunia: addition of a limited number of gene copies may lead to a suppression of gene expression. Plant Cell., 2, 291–299, 1990. 103. Fire, A. et al. Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature, 391, 806–811, 1998. 104. Bartel, D.P. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell, 116, 281–297, 2004. 105. Bernstein, E., Caudy, A.A., Hammond, S.M. and Hannon, G.J. Role for a bidentate ribonuclease in the initiation step of RNA interference. Nature, 409, 363–366, 2001. 106. Hammond, S.M., Bernstein, E., Beach, D., and Hannon, G.J. An RNA-directed nuclease mediates post-transcriptional gene silencing in Drosophila cells. Nature, 404, 293–296, 2000.
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107. Hutvagner, G. and Zamore, P.D. A microRNA in a multiple-turnover RNAi enzyme complex. Science, 297, 2056–2060, 2002. 108. Elbashir, S.M., Lendeckel, W., and Tuschl, T. RNA interference is mediated by 21- and 22-nucleotide RNAs. Genes Dev., 15, 188–200, 2001. 109. Zamore, P.D., Tuschl, T., Sharp, P.A., and Bartel, D.P. RNAi: double-stranded RNA directs the ATPdependent cleavage of mRNA at 21 to 23 nucleotide intervals. Cell, 101, 25–33, 2000. 110. Wassenegger, M. and Pelissier, T. A model for RNA-mediated gene silencing in higher plants. Plant Mol. Biol., 37, 349–362, 1998. 111. Sijen, T. et al. On the role of RNA amplification in dsRNA-triggered gene silencing. Cell, 107, 465–476, 2001. 112. Waterhouse, P.M., Wang, M.B., and Lough, T. Gene silencing as an adaptive defence against viruses. Nature, 411, 834–842, 2001. 113. Lee, R.C., Feinbaum, R.L., and Ambros, V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell, 75, 843–854, 1993. 114. Reinhart, B.J. et al. The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature, 403, 901–906, 2000. 115. Mello, C.C. and Conte, D., Jr. Revealing the world of RNA interference. Nature, 431, 338–342, 2004. 116. Hutvagner, G. et al. A cellular function for the RNA-interference enzyme Dicer in the maturation of the let-7 small temporal RNA. Science, 293, 834–838, 2001. 117. Ketting, R.F. et al. Dicer functions in RNA interference and in synthesis of small RNA involved in developmental timing in C. elegans. Genes Dev., 15, 2654–2659, 2001. 118. Grishok, A. et al. Genes and mechanisms related to RNA interference regulate expression of the small temporal RNAs that control C. elegans developmental timing. Cell, 106, 23–34, 2001. 119. Lee, Y., Jeon, K., Lee, J.T., Kim, S., and Kim, V.N. MicroRNA maturation: stepwise processing and subcellular localization. EMBO J., 21, 4663–4670, 2002. 120. Lee, Y. et al. The nuclear RNase III Drosha initiates microRNA processing. Nature, 425, 415–419, 2003. 121. Basyuk, E., Suavet, F., Doglio, A., Bordonne, R., and Bertrand, E. Human let-7 stem-loop precursors harbor features of RNase III cleavage products. Nucleic Acids Res., 31, 6593–6597, 2003. 122. Yi, R., Qin, Y., Macara, I.G., and Cullen, B.R. Exportin-5 mediates the nuclear export of premicroRNAs and short hairpin RNAs. Genes Dev., 17, 3011–3016, 2003. 123. Lund, E., Guttinger, S., Calado, A., Dahlberg, J.E., and Kutay, U. Nuclear export of microRNA precursors. Science, 303, 95–98, 2004. 124. Bohnsack, M.T., Czaplinski, K., and Gorlich, D. Exportin 5 is a RanGTP-dependent dsRNA-binding protein that mediates nuclear export of pre-miRNAs. RNA, 10, 185–191, 2004. 125. Mourelatos, Z. et al. miRNPs: a novel class of ribonucleoproteins containing numerous microRNAs. Genes Dev., 16, 720–728, 2002. 126. Elbashir, S.M. et al. Duplexes of 21-nucleotide RNAs mediate RNA interference in cultured mammalian cells. Nature, 411, 494–498, 2001. 127. Caplen, N.J., Parrish, S., Imani, F., Fire, A., and Morgan, R.A. Specific inhibition of gene expression by small double-stranded RNAs in invertebrate and vertebrate systems. Proc. Natl. Acad. Sci. USA, 98, 9742–9747, 2001. 128. Kim, D.H. et al. Synthetic dsRNA Dicer substrates enhance RNAi potency and efficacy. Nat. Biotechnol., 23, 222–226, 2005. 129. Miyagishi, M. and Taira, K. U6 promoter-driven siRNAs with four uridine 3′ overhangs efficiently suppress targeted gene expression in mammalian cells. Nat. Biotechnol, 20, 497–500, 2002. 130. Lee, N.S. et al. Expression of small interfering RNAs targeted against HIV-1 rev transcripts in human cells. Nat. Biotechnol., 20, 500–505, 2002. 131. Brummelkamp, T.R., Bernards, R., and Agami, R. A system for stable expression of short interfering RNAs in mammalian cells. Science, 296, 550–553, 2002.
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8-33
132. Paddison, P.J., Caudy, A.A., Bernstein, E., Hannon, G.J., and Conklin, D.S. Short hairpin RNAs (shRNAs) induce sequence-specific silencing in mammalian cells. Genes Dev., 16, 948–958, 2002. 133. Yu, J.Y., DeRuiter, S.L., and Turner, D.L. RNA interference by expression of short-interfering RNAs and hairpin RNAs in mammalian cells. Proc. Natl. Acad. Sci. USA, 99, 6047–6052, 2002. 134. Paul, C.P., Good, P.D., Winer, I., and Engelke, D.R. Effective expression of small interfering RNA in human cells. Nat. Biotechnol., 20, 505–508, 2002. 135. McManus, M.T., Petersen, C.P., Haines, B.B., Chen, J. and Sharp, P.A. Gene silencing using microRNA designed hairpins. RNA, 8, 842–850, 2002. 136. Zeng, Y., Wagner, E.J., and Cullen, B.R. Both natural and designed micro RNAs can inhibit the expression of cognate mRNAs when expressed in human cells. Mol. Cell., 9, 1327–1333, 2002. 137. Isaacs, F.J. et al. Engineered riboregulators enable post-transcriptional control of gene expression. Nat. Biotechnol., 22, 841–847, 2004. 138. Tuerk, C. and Gold, L. Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase. Science, 249, 505–510, 1990. 139. Ellington, A.D. and Szostak, J.W. In vitro selection of RNA molecules that bind specific ligands. Nature, 346, 818–822, 1990. 140. Rimmele, M. Nucleic acid aptamers as tools and drugs: recent developments. Chembiochem, 4, 963–971, 2003. 141. Cox, J.C. and Ellington, A.D. Automated selection of anti-protein aptamers. Bioorg. Med. Chem., 9, 2525–2531, 2001. 142. Cox, J.C. et al. Automated acquisition of aptamer sequences. Comb. Chem. High Throughput Screen, 5, 289–299, 2002. 143. Cox, J.C. et al. Automated selection of aptamers against protein targets translated in vitro: from gene to aptamer. Nucleic Acids Res., 30, e108, 2002. 144. Berezovski, M. et al. Nonequilibrium capillary electrophoresis of equilibrium mixtures: a universal tool for development of aptamers. J. Am. Chem. Soc., 127, 3165–3171, 2005. 145. Drabovich, A., Berezovski, M., and Krylov, S.N. Selection of smart aptamers by equilibrium capillary electrophoresis of equilibrium mixtures (ECEEM). J. Am. Chem. Soc., 127, 11224–11225, 2005. 146. Krylov, S.N. Nonequilibrium capillary electrophoresis of equilibrium mixtures (NECEEM): A novel method for biomolecular screening. J. Biomol. Screen, 11, 115–122, 2006. 147. Hermann, T. and Patel, D.J. Adaptive recognition by nucleic acid aptamers. Science, 287, 820–825, 2000. 148. Khvorova, A., Kwak, Y.G., Tamkun, M., Majerfeld, I., and Yarus, M. RNAs that bind and change the permeability of phospholipid membranes. Proc. Natl. Acad. Sci. USA, 96, 10649–10654, 1999. 149. Vlassov, A., Khvorova, A., and Yarus, M. Binding and disruption of phospholipid bilayers by supramolecular RNA complexes. Proc. Natl. Acad. Sci. USA, 98, 7706–7711, 2001. 150. Cheah, M.T., Wachter, A., Sudarsan, N., and Breaker, R.R. Control of alternative RNA splicing and gene expression by eukaryotic riboswitches. Nature, 447, 497–500, 2007. 151. Winkler, W.C., Nahvi, A., Sudarsan, N., Barrick, J.E., and Breaker, R.R. An mRNA structure that controls gene expression by binding S-adenosylmethionine. Nat. Struct. Biol., 10, 701–707, 2003. 152. Mandal, M. and Breaker, R.R. Adenine riboswitches and gene activation by disruption of a transcription terminator. Nat. Struct. Mol. Biol., 11, 29–35, 2004. 153. Winkler, W., Nahvi, A., and Breaker, R.R. Thiamine derivatives bind messenger RNAs directly to regulate bacterial gene expression. Nature, 419, 952–956, 2002. 154. Winkler, W.C., Nahvi, A., Roth, A., Collins, J.A., and Breaker, R.R. Control of gene expression by a natural metabolite-responsive ribozyme. Nature, 428, 281–286, 2004. 155. Mironov, A.S. et al. Sensing small molecules by nascent RNA: a mechanism to control transcription in bacteria. Cell, 111, 747–756, 2002.
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156. Welz, R. and Breaker, R.R. Ligand binding and gene control characteristics of tandem riboswitches in Bacillus anthracis. RNA, 13, 573–582, 2007. 157. Nahvi, A. et al. Genetic control by a metabolite binding mRNA. Chem. Biol., 9, 1043–1049, 2002. 158. Nahvi, A., Barrick, J.E., and Breaker, R.R. Coenzyme B12 riboswitches are widespread genetic control elements in prokaryotes. Nucleic Acids Res., 32, 143–150, 2004. 159. Winkler, W.C., Cohen-Chalamish, S. and Breaker, R.R. An mRNA structure that controls gene expression by binding FMN. Proc. Natl. Acad. Sci. USA, 99 15908–15913, 2002. 160. Nudler, E. and Mironov, A.S. The riboswitch control of bacterial metabolism. Trends Biochem. Sci., 29, 11–17, 2004. 161. Sudarsan, N., Wickiser, J.K., Nakamura, S., Ebert, M.S., and Breaker, R.R. An mRNA structure in bacteria that controls gene expression by binding lysine. Genes Dev., 17, 2688–2697, 2003. 162. Mandal, M. et al. A glycine-dependent riboswitch that uses cooperative binding to control gene expression. Science, 306, 275–279, 2004. 163. Mandal, M., Boese, B., Barrick, J.E., Winkler, W.C., and Breaker, R.R. Riboswitches control fundamental biochemical pathways in Bacillus subtilis and other bacteria. Cell, 113, 577–586, 2003. 164. Johansen, L.E., Nygaard, P., Lassen, C., Agerso, Y., and Saxild, H.H. Definition of a second Bacillus subtilis pur regulon comprising the pur and xpt-pbuX operons plus pbuG, nupG (yxjA), and pbuE (ydhL). J. Bacteriol., 185, 5200–5209, 2003. 165. Sudarsan, N. et al. Tandem riboswitch architectures exhibit complex gene control functions. Science, 314, 300–304, 2006. 166. Patel, D.J. et al. Structure, recognition and adaptive binding in RNA aptamer complexes. J. Mol. Biol., 272, 645–664, 1997. 167. Stripecke, R., Oliveira, C.C., McCarthy, J.E., and Hentze, M.W. Proteins binding to 5′ untranslated region sites: a general mechanism for translational regulation of mRNAs in human and yeast cells. Mol. Cell. Biol., 14, 5898–5909, 1994. 168. Paraskeva, E., Atzberger, A., and Hentze, M.W. A translational repression assay procedure (TRAP) for RNA-protein interactions in vivo. Proc. Natl. Acad. Sci. USA, 95, 951–956, 1998. 169. Pelletier, J. and Sonenberg, N. Insertion mutagenesis to increase secondary structure within the 5′ noncoding region of a eukaryotic mRNA reduces translational efficiency. Cell, 40, 515–526, 1985. 170. Werstuck, G. and Green, M.R. Controlling gene expression in living cells through small moleculeRNA interactions. Science, 282, 296–298, 1998. 171. Jenison, R.D., Gill, S.C., Pardi, A. and Polisky, B. High-resolution molecular discrimination by RNA. Science, 263, 1425–1429, 1994. 172. Wilson, C., Nix, J. and Szostak, J. Functional requirements for specific ligand recognition by a biotin-binding RNA pseudoknot. Biochemistry, 37, 14410–14419, 1998. 173. Berens, C., Thain, A. and Schroeder, R. A tetracycline-binding RNA aptamer. Bioorg. Med. Chem., 9, 2549–2556, 2001. 174. Harvey, I., Garneau, P. and Pelletier, J. Inhibition of translation by RNA-small molecule interactions. RNA, 8, 452–463, 2002. 175. Suess, B. et al. Conditional gene expression by controlling translation with tetracycline-binding aptamers. Nucleic Acids Res., 31, 1853–1858, 2003. 176. Hanson, S., Berthelot, K., Fink, B., McCarthy, J.E., and Suess, B. Tetracycline-aptamer-mediated translational regulation in yeast. Mol. Microbiol., 49, 1627–1637, 2003. 177. Topp, S. and Gallivan, J.P. Riboswitches in unexpected places—a synthetic riboswitch in a protein coding region. RNA, 14, 2498–503, 2008. 178. de Smit, M.H. and van Duin, J. Control of prokaryotic translational initiation by mRNA secondary structure. Prog. Nucleic Acid Res. Mol. Biol., 38, 1–35, 1990. 179. de Smit, M.H. and van Duin, J. Secondary structure of the ribosome binding site determines translational efficiency: a quantitative analysis. Proc. Natl. Acad. Sci. USA, 87, 7668–7672, 1990.
Regulating Gene Expression through Engineered RNA Technologies
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180. de Smit, M.H. and van Duin, J. Control of translation by mRNA secondary structure in Escherichia coli. A quantitative analysis of literature data. J. Mol. Biol., 244, 144–150, 1994. 181. Desai, S.K. and Gallivan, J.P. Genetic screens and selections for small molecules based on a synthetic riboswitch that activates protein translation. J. Am. Chem. Soc., 126, 13247–13254, 2004. 182. An, C.I., Trinh, V.B., and Yokobayashi, Y. Artificial control of gene expression in mammalian cells by modulating RNA interference through aptamer-small molecule interaction. RNA, 12, 710–716, 2006. 183. Kim, D.S., Gusti, V., Pillai, S.G., and Gaur, R.K. An artificial riboswitch for controlling pre-mRNA splicing. RNA, 11, 1667–1677, 2005. 184. Soukup, G.A. and Breaker, R.R. Engineering precision RNA molecular switches. Proc. Natl. Acad. Sci. USA, 96, 3584–3589, 1999. 185. Tang, J. and Breaker, R.R. Rational design of allosteric ribozymes. Chem. Biol., 4, 453–459, 1997. 186. Araki, M., Okuno, Y., Hara, Y., and Sugiura, Y. Allosteric regulation of a ribozyme activity through ligand-induced conformational change. Nucleic Acids Res., 26, 3379–3384, 1998. 187. Soukup, G.A. and Breaker, R.R. Design of allosteric hammerhead ribozymes activated by ligandinduced structure stabilization. Structure, 7, 783–791, 1999. 188. Soukup, G.A., Emilsson, G.A., and Breaker, R.R. Altering molecular recognition of RNA aptamers by allosteric selection. J. Mol. Biol., 298, 623–632, 2000. 189. Kertsburg, A. and Soukup, G.A. A versatile communication module for controlling RNA folding and catalysis. Nucleic Acids Res., 30, 4599–4606, 2002. 190. Link, K.H. et al. Engineering high-speed allosteric hammerhead ribozymes. Biol. Chem., 388, 779–786, 2007. 191. Soukup, G.A. and Breaker, R.R. Nucleic acid molecular switches. Trends Biotechnol., 17, 469–476, 1999. 192. Wilson, D.S. and Szostak, J.W. In vitro selection of functional nucleic acids. Ann. Rev. Biochem., 68, 611–647, 1999. 193. Koizumi, M., Soukup, G.A., Kerr, J.N., and Breaker, R.R. Allosteric selection of ribozymes that respond to the second messengers cGMP and cAMP. Nat. Struct. Biol., 6, 1062–1071, 1999. 194. Piganeau, N., Thuillier, V., and Famulok, M. In vitro selection of allosteric ribozymes: theory and experimental validation. J. Mol. Biol., 312, 1177–1190, 2001. 195. Sassanfar, M. and Szostak, J.W. An RNA motif that binds ATP. Nature, 364, 550–553, 1993. 196. Burgstaller, P. and Famulok, M. Isolation of RNA apatmers for biological cofactors by in vitro selection. Angew. Chem. Int. Ed. Engl., 33, 1084–1087, 1994. 197. Win, M.N. and Smolke, C.D. From the cover: A modular and extensible RNA-based generegulatory platform for engineering cellular function. Proc. Natl. Acad. Sci. USA, 104, 14283– 14288, 2007. 198. Suess, B., Fink, B., Berens, C., Stentz, R., and Hillen, W. A theophylline responsive riboswitch based on helix slipping controls gene expression in vivo. Nucleic Acids Res., 32, 1610–1614, 2004. 199. Lynch, S.A., Desai, S.K., Sajja, H.K., and Gallivan, J.P. A high-throughput screen for synthetic riboswitches reveals mechanistic insights into their function. Chem. Biol., 14, 173–184, 2007. 200. Bayer, T.S. and Smolke, C.D. Programmable ligand-controlled riboregulators of eukaryotic gene expression. Nat. Biotechnol., 23, 337–343, 2005. 201. Eddy, C.K. et al. Segmental message stabilization as a mechanism for differential expression from the Zymomonas mobilis gap operon. J. Bacteriol., 173, 245–254, 1991. 202. Mejia, J.P. et al. Coordination of expression of Zymomonas mobilis glycolytic and fermentative enzymes: a simple hypothesis based on mRNA stability. J. Bacteriol., 174, 6438–6443, 1992. 203. Higgins, C.F. Stability and degradation of mRNA. Curr. Opin. Cell Biol., 3, 1013–1018, 1991. 204. Keasling, J.D. Gene-expression tools for the metabolic engineering of bacteria. Trends Biotechnol., 17, 452–460, 1999.
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205. Smolke, C.D. and Keasling, J.D. Effect of copy number and mRNA processing and stabilization on transcript and protein levels from an engineered dual-gene operon. Biotechnol. Bioeng., 78, 412–424, 2002. 206. Smolke, C.D., Martin, V.J., and Keasling, J.D. Controlling the metabolic flux through the carotenoid pathway using directed mRNA processing and stabilization. Metab. Eng., 3, 313–321, 2001. 207. Pfleger, B.F., Pitera, D.J., Smolke, C.D., and Keasling, J.D. Combinatorial engineering of intergenic regions in operons tunes expression of multiple genes. Nat. Biotechnol., 24, 1027–1032, 2006. 208. Hamilton, A.J. and Baulcombe, D.C. A species of small antisense RNA in posttranscriptional gene silencing in plants. Science, 286, 950–952, 1999. 209. Smith, N.A. et al. Total silencing by intron-spliced hairpin RNAs. Nature, 407, 319–320, 2000. 210. Wesley, S.V. et al. Construct design for efficient, effective and high-throughput gene silencing in plants. Plant J., 27, 581–590, 2001. 211. Wang, M.B. and Waterhouse, P.M. Application of gene silencing in plants. Curr. Opin. Plant Biol., 5, 146–150, 2002. 212. Allen, R.S. et al. RNAi-mediated replacement of morphine with the nonnarcotic alkaloid reticuline in opium poppy. Nat. Biotechnol., 22, 1559–1566, 2004. 213. Geiger, A., Burgstaller, P., von der Eltz, H., Roeder, A., and Famulok, M. RNA aptamers that bind L-arginine with sub-micromolar dissociation constants and high enantioselectivity. Nucleic Acids Res., 24, 1029–1036, 1996. 214. Mannironi, C., Scerch, C., Fruscoloni, P., and Tocchini-Valentini, G.P. Molecular recognition of amino acids by RNA aptamers: the evolution into an L-tyrosine binder of a dopamine-binding RNA motif. RNA, 6, 520–527, 2000.
9 Tools Designed to Regulate Translational Efficiency 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 9.9
Introduction ��������������������������������������������������������������������������������������9-1 Synthetic Genes ��������������������������������������������������������������������������������9-2 Synthetic Genes for Synthetic Biology...........................................9-2 Codon Usage in Different Hosts.....................................................9-3 Improving Expression by Modifying the Host............................ 9-4 Improving Expression by Modifying the Gene............................9-5 Codon Optimization Using the CAI = 1 Algorithm................... 9-6 Codon Optimization by Probability Score................................... 9-6 DNA Sequence Features to Eliminate, Add, or Modify during Design �����������������������������������������������������������������������������������9-7
GC Content • Avoiding mRNA Sequence Motifs • Avoiding mRNA Secondary Structure • Downstream Region • Homology to Wild-Type Sequence • Codon Context
Claes Gustafsson DNA2.0, Inc.
9.10 I ncorporating Tools for Translational Control in Metabolic Engineering ��������������������������������������������������������������������9-9 Acknowledgments �������������������������������������������������������������������������������������9-10 References ���������������������������������������������������������������������������������������������������9-10
9.1 Introduction The genetic information encoded in an open reading frame (ORF) goes far beyond simply stating the order of amino acids in the protein. Information about temporal and spatial expression levels, DNA tertiary and quaternary structure, nucleosome positioning, and much more is all intertwined with the protein coding information. It is estimated that 40–60% of all human multiexon genes have alternative splicing, antisense transcription occurs in 10–20% of all genes, mRNA editing is common (at least in neural cells), cis-regulatory elements are abundant and mRNA degradation signals are identified in genes throughout the human genome. As we start to peel through the different layers of complex and integrated information present within the coding regions of DNA, we can start to make more informed decisions on how to design genes and genetic networks. The design and use of synthetic genes offers a mechanism by which researchers can assume much greater control over protein expression and regulation in the cellular environment. Codon biases can be manipulated, peptide tags can be added, splice sites removed, and restriction sites placed as desired. The cost, speed and fidelity of gene synthesis appears to be following a trajectory similar to that seen for synthetic oligonucleotides over the past two decades, making their use increasingly time- and cost-effective [1–4]. This trend allows the scientist and metabolic engineer to focus on designing the end product 9-1
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Gene Expression Tools for Metabolic Pathway Engineering
instead of spending time and resources in obtaining the tools with which to do the work. Direct synthesis of genes is now the most efficient way to make functional genetic constructs and enables applications such as codon optimization [5], making RNAi resistant genes [6] and protein engineering [7].
9.2 Synthetic Genes The technology to produce synthetic genes predates both polymerase chain reaction (PCR) and cloning from cDNA libraries. The first synthetic gene, a gene encoding the yeast alanine tRNA, was synthesized already in 1970 [8]. The first gene encoding a translated product, a 14-amino acid residue hormone, was made in 1977 by Genentech and their academic collaborators [9]. A few years later the synthesis of the first full length gene encoding a protein was accomplished by the synthesis of the 166 codons long alpha interferon gene [10]. In 2002 came the first report of synthesizing an entire genome, the 7.4 kb poliovirus genome [2]. Today, there is no technical upper limit to the length of synthetic DNA fragments that can be synthesized. It is common for DNA2.0 (www.dna20.com, a gene synthesis company based in Menlo Park, CA) to receive orders for DNA fragments of 20 kb and larger. Several alternative methods for gene synthesis have been developed over the years including gene synthesis on solid phase in several related formats [11–14], FokI based synthesis [15], polymerase cycling assembly [16,17], ligase chain reaction [18], shotgun synthesis [19], synthesis by serial cloning [20], and insertional gene synthesis [21]. The many methods available each have their distinctive advantages and disadvantages. Some methods are better for GC rich sequences, some for highly repetitive sequences. Some methods are faster and some have higher accuracy. There is no “best method”, but instead the many methods complement each other giving the user a plethora of gene synthesis tools to choose from. With the appearance of several commercial gene synthesis vendors, such as DNA2.0, most scientists can now quickly and routinely order any synthetic DNA fragment without having to ponder the alternative synthesis methods. With the many rapidly advancing gene synthesis technologies available, essentially any sequence can be synthesized independent of sequence characteristics and requirements.
9.3 Synthetic Genes for Synthetic Biology It is difficult to imagine conducting molecular biology research today without access to custom oligonucleotides. Similarly, it is becoming increasingly hard to envision creating artificial metabolic pathways and other synthetic biology applications without the access to synthetic genes. Natural, nonsynthetic, genes encode not only the protein sequence of the gene of interest but also encode a complete evolutionary history of the gene and the organism encoding it [22]. This interpretive proteomics as revealed by cis-regulatory nucleotide sequences, alternative splicing, codon bias, etc., can be very useful for analyzing ancient metabolic pathways and regulatory networks and other features under various degrees of selective pressure. The wild-type DNA sequence, however, is not useful when the purpose of manipulating the gene is to express the protein in a new context, under non-native conditions, as defined by the experimenter. Furthermore, the wild-type genes are rarely selected by evolution to express at high levels even in their native organism. Instead, the expression levels of the wild-type gene are finely tuned both by transcriptional and translational features as well as regulatory circuits to express the gene product at the evolutionarily favored levels and at the evolutionary favored conditions. The construction of novel genetic pathways, regulatory circuits, and designed organisms require biological building blocks that solely contain the information content needed for the relevant application, and not any spurious information content that may or may not be known, and may or may not interfere with the designed application. Just like it is crucial for the software engineer to only use clean blocks of programming code where each block encapsulate one function, it is important for the genetic engineer to use clean blocks of DNA sequence to ensure each function that is introduced into the system is a known entity and only encodes the function required.
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9.4 Codon Usage in Different Hosts The genetic code uses 64 nucleotide triplets (codons) to encode 20 amino acids and stop. Each amino acid is encoded by on average three codons that are “read” during translation by tRNAs charged with the cognate amino acid. The degeneracy of the genetic code enables many alternative nucleotide sequences to encode the same protein. The frequencies with which different codons are used by different organisms and different types of genes vary significantly [23] and are correlated to the concentration of the corresponding tRNA population in the cell [24]. Rare codons are not only strongly associated with low levels of protein expression due to ribosome stalling and abortive translation [25], but also implicated in frameshift and amino acid misincorporation [26,27]. Codon usage has been identified as the single most important factor in increasing prokaryotic gene expression [28]. Principal component analysis (PCA) can be used to compress high-dimensional information into lower dimensional maps. PCA can be applied to compress large codon usage tables for a multitude of organism into a two-dimensional graph that captures the majority of the codon distribution information. This type of PCA plot provides a convenient way to visualize differences in codon preferences between different organisms. Figure 9.1 shows the codon preferences of ten common hosts for heterologous protein expression. The 62-dimensional information is derived from the codon usage for 62 codons (Trp and Met are excluded as they are encoded by one codon, i.e., distribution bias is one). The multidimensional data is projected down to a new coordinate system so that the maximum variance is captured by the first coordinate. In this example, 42% of all variance is captured by the first coordinate (Principal Component 1) and 27% of the variance is captured by the second coordinate (Principal Component 2). Accordingly, 69% of all variance present in the 62-dimensional dataset can be visualized in only two-dimensions. The codon usage of human, mouse, and CHO cells map very closely in the lower right quadrant. This is consistent with all three organisms being mammals and of close evolutionary origin. Codons in the same lower right quadrant such as GAG and CCC are preferentially favored by the mammals, whereas codons in the opposite (upper left) quadrant such as GTA and TTA are preferentially avoided compared to the other hosts in the graph. Figure 9.1 also makes immediately obvious the considerable divergence between E. coli and human codon preferences. This confirms what many researchers have learned through trial and error. E. coli is not the optimal host for expressing proteins encoded by human codon usage profile. The “Coli 2” table is derived from a subset of highly expressed E. coli genes [29]. This codon table, also often called “Coli class II table”, is the most extreme codon table present in Figure 9.1, as is evident in the far top right location of the graph. This result is consistent with the finding that codon bias is usually increased in highly expressed genes [23,24,30]. This may be a direct correlation between expression levels and codon bias, or more likely an indirect correlation as both expression levels, and codon bias rely on other evolutionary constraints [31]. Codons in the lower left quadrant are significantly underrepresented and upper right quadrant codons overrepresented in the “Coli 2” table. From Figure 9.1 it can be deduced that using the “Coli 2” codon usage table is not advisable if the designed gene is to be expressed in any other host than E. coli. Pichia pastoris located orthagonally to “Coli 2” in the figure would be the worst host of the organism depicted here for expressing a “Coli 2” gene. Combined codon usage tables are useful in cases where a gene is initially screened for expressing a protein with a defined activity in a high throughput system such as E. coli and later transferred to a more rigorous low throughput system such as CHO-cells for biochemical characterization. The Sf9/Human table is an example of a combined codon usage table. For this codon usage table the codon usage for human and Sf9 baculovirus are averaged and any codon that is below 10% in either organism is set below the threshold of the combined table. The Sf9/Human codon table is a reasonable compromise for design of genes that are intended to be expressed in both baculovirus and mammalian expression systems. For some host pairs such as Arabidopsis thaliana and Streptomyces coelicolor, combined codon usage tables are impossible to create without severely
9-4
Gene Expression Tools for Metabolic Pathway Engineering 0.5
Coli 2
Coli 0.4
B. subtilis
0.3
PC 2 (27%)
0.2
Yeast
0.1 0
Pichia
–0.1 –0.2 –0.3 –0.4 –0.8
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AAA + CCG ++GCG GAA GTA + ++ + +CGT ATT+TCG + +ACG ++ GTT GAT +CGC +TAT GGT TTA+ +CTG + CAT+ GCA+ +GGC TTT TCT + + +ACC CAA AGC + AAC TGT +TGC + +ATC AAT ++ +CTT +CAG +TTGACT TAT TTC AGA++CTA+ +GGG + ++ GCT +CGG ACA TCCCAC TAC + ++ ++CGA TGA ATA+CCA + GTC + + +GAC + AGT + GTC + GGA + GCC ++ +GAG CCT TAG Sf9 + + CTC + CCC AGG AAG Sf9/Human Human CHO Mouse –0.2 0 PC 1 (42%)
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Figure 9.1 Graphical representation of “codon usage space”. The codon bias for each of the 64 codons in ten common codon usage tables were compressed into two-dimensions using PCA where each codon is a vector. Two of the codons, ATG and TGG, are omitted since the bias is 1.0 (only one option) for all organisms. Principal Component 1 (PC 1) captures 42% of all information and Principal Component 2 (PC 2) captures 27%, i.e., 69% of all codon distribution information from the ten organisms × 62 codons can be visually inspected in the graph. “Yeast” denotes the codon distribution for all ORF in Saccharomyces cerevisiae, “Pichia” denotes the codon distribution for all sequenced open reading frames in Pichia pastoris, “B. subtilis” denotes the codon distribution for all ORF in Bacillus subtilis, “Coli” denotes the codon distribution for all ORF in Escherichia coli, “CHO” denotes the codon distribution for all sequenced ORF in Cricetulus griseus (from where CHO cells are derived), “Human” denotes the codon distribution for all ORF in Homo sapiens, “Mouse” denotes the codon distribution for all ORF in Mus musculus. “Sf9” denotes the codon distribution for all sequenced open reading frames in Spodoptera frugiperda, “Coli 2” denotes the codon distribution for highly expressed E. coli genes (see text), “Sf9/Human” denotes the codon distribution in a combined Sf9/Human table (see text). The genome codon usage distributions are derived from the codon usage database (http://www.kazusa.or.jp/codon).
adjusting the threshold levels. The only Arg codons above the 10% threshold in S. coelicolor are CGG and CGC, whereas in Arabidopsis thaliana both those codons are below the 10% threshold (9 and 7%, respectively).
9.5 Improving Expression by Modifying the Host If the negative effect of different codon usage biases on heterologous gene expression results from different tRNA levels, one simple solution appears to be to expand the intracellular tRNA pool of the host. This can be accomplished by overexpressing genes encoding the rare tRNAs. For E. coli, the primary targets to facilitate expression of human genes are the argU gene encoding the minor tRNA Arg4 that reads rare AGG and AGA codons, tRNA Ile2 that reads AUA, tRNALeu3 that reads CUA and CUG, and tRNAPro2 that reads CCC and CCU [27]. E. coli strains overexpressing these tRNA genes are commercially available from companies such as Stratagene and Novagen. Several laboratories have shown that
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expression yields of proteins whose genes contain rare codons can be improved when the cognate tRNA is increased within the host [27]. Even though tRNA overexpression initially appears to be an attractive solution, there are several caveats. Different tRNAs need to be overexpressed for genes originating from different organisms, and the strategy is less appealing for hosts that are more difficult to manipulate than E. coli. There are also general pleiotropic effects of changing the concentrations of charged tRNA in the cell [32]. Perhaps the most important concern with expanding the intracellular tRNA pool is the question of how increasing the tRNA concentration will affect amino acylation and tRNA modifications and thus if the composition of the overexpressed protein will be consistent. Transfer RNA molecules are extensively processed before amino-acylation and participation in the translational process. More than 30 modified nucleotides have been found in E. coli tRNAs; some are present at the same position for all tRNAs, others are found in one or a few different tRNAs [33]. Many of the tRNA modifications scattered throughout the tRNA molecule, and especially those located in the anticodon loop, have been shown to improve reading frame maintenance [34]. One purpose of these modifications is to reduce levels of translational frameshift. The lack of some tRNA modifications have been experimentally linked to missense and nonsense errors during translation [33], for example tRNAs lacking methylation of tRNA at the N-1 position of guanosine (m1G) at position 37 result in translational frameshift [35]. Thus, one problem with the tRNA overexpression strategy is that producing a fully functional tRNA requires other cellular components that might be in limiting supply when the tRNA alone is overproduced. When tRNALeu1 is overexpressed in E. coli, the tRNA is significantly undermodified in at least two ways. m1G at position 37 and pseudouridine (Ψ) at position 32. Only 40% of the tRNALeu1 molecules are amino-acylated, the strain grows very slowly and the ribosomal step time is reduced two- to three-fold [36]. Similarly, overexpressed tRNATyr at least results in a decrease of the 2-methylthio-N 2 6-isopentenyl adenosine (ms2i6A) modification at position 37 and a tRNA that is less efficient in vitro [37]. Loss of the ms2i6A modification following tRNAPhe overexpression led to decreased fidelity of translation [38,39]. Translational missense substitution frequencies can increase by more than an order of magnitude as a function of underacetylated tRNA. One particular concern over such loss of fidelity is the possibility that the resulting heterogeneous mixture of proteins might induce an immune response if introduced into vertebrates [40,41]. In addition to translational fidelity and host metabolic load issues, the tRNA overexpression strategy is not very flexible. It is much more difficult to engineer fungal or mammalian host cells than E. coli. In eukaryotic cells, the tRNA expression is primarily driven by copy number, not promoter strength, further complicating the issue. For some applications such as the emerging field of DNA vaccines, host engineering is quite out of the question.
9.6 Improving Expression by Modifying the Gene Given the constraints of modifying the host, a more plausible way of controlling the functional characteristics of the engineered biological system is to modify the gene itself. By having access to synthetic genes, the exact nucleotide sequence of the synthetic construct is only limited by the users’ gene design creativity. Any sequence designed in silico now can quickly and efficiently be converted into a physical gene, operon or genome. But how does one design a synthetic gene? In 1977, when Genentech scientists and their academic collaborators produced the first human protein (somatostatin) in a bacterium [9], expression of proteins in heterologous hosts played a crucial role in the launch of the entire biotechnology industry. At the time, only the amino acid sequence of somatostatin was known, so the Genentech group synthesized the 14-codon long somatostatin gene using oligonucleotides instead of cloning it from the human genome. Itakura and coworkers designed these oligonucleotides based on three criteria; First, codons favored by the phage MS2 were used preferentially—very little of the E. coli genome DNA sequence was known at the time, but the MS2 phage had just been sequenced and was assumed to provide a good guide to the codons used in highly expressed
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E. coli genes. Second, care was taken to eliminate undesirable inter- and intra-molecular pairing of the overlapping oligonucleotides as this would compromise the gene synthesis process and as it would also avoid any mRNA repeats or inverted repeats, i.e., secondary structures. Third, sequences that were GC-rich followed by AT-rich were avoided as it was believed such sequences could terminate transcription. The result was the first production of a functional polypeptide from a synthetic gene. Even for the tiny somatostatin ORF, the number of alternative nucleotide sequence solutions available for synthesis is a staggering five million that all code for the same 14 amino acids long somatostatin. Which of the five million solutions is “best”?
9.7 Codon Optimization Using the CAI = 1 Algorithm An often used way to design a DNA sequence from an amino acid sequence is to assign the most abundant codon to all instances of that amino acid in the sequence. Codon usage preference in a gene can be measured by Codon Adaptation Index (CAI score) [42]. The CAI score for such a construct is 1.0, i.e., in each case only the most abundant codon is used. This “one amino acid—one codon” or “CAI = 1.0” approach is very simple and straight forward, but unfortunately has several drawbacks. Strongly transcribed mRNA from a CAI = 1.0 gene will generate high codon concentrations for a subset of the tRNA populations, resulting in imbalanced tRNA pool, skewed codon usage pattern and increased translational error [40,41]. Heterologously expressed proteins may be produced at levels as high as 80% of total cell mass, making an imbalance tRNA pool a significant problem with severe pleiotropic effects [43,44]. Furthermore, protein expression levels are not necessarily directly correlated to the CAI score. Silent mutations in CAI = 1 genes have been shown to lead to increased protein expression yield [45]. A CAI = 1.0 design also offers no flexibility in adjusting the nucleotide sequence. It is impossible to avoid repetitive elements and mRNA secondary structures in the gene if the codons are fixed to only one option. It is often desirable to incorporate or exclude sequence elements such as restriction sites from the sequence to facilitate subsequent manipulations. These modifications are impossible to accommodate if the codon usage is rigidly fixed. Other sequences such a cis-regulatory signals, internal ribosome binding sites, splicing sites, codon context effects, etc. cannot be avoided if the nucleotide is solely defined based on the CAI = 1 principle.
9.8 Codon Optimization by Probability Score Contrary to the “CAI = 1.0” method, an alternative codon optimization algorithm matches the codon distribution profile with a predetermined probability score table [46]. Each codon is given a probability score based on its abundance in, e.g., a set of highly expressed genes, or alternatively derived from the genome of the host organism of interest [5]. For E. coli expression, it is common practice to use the “Coli 2” table which is derived from a collection of highly expressed E. coli genes [29]. Typically any codon below a given threshold value of say 10% is not used and the remaining codons are used according to their defined codon usage probability. The threshold level directly reflects the codons that are omitted from the design of the synthetic genes. At the lowest level (10%), any codon that is present in the codon bias table 10% for any given amino acid can not be used for the design. The threshold variable is strongly correlated with CAI score. The higher the threshold, the higher the CAI scores. Genes designed using probability score ensures that the “fingerprint” of the synthetic gene looks like that of the host organism (or of the genes that were used to build the codon usage table) and the synthetic gene will utilize the tRNA pool of the host at the same relative frequency as genes from the host organism. The probability score method further ensures that there is no one sequence that is “the best” sequence, but that there are many, many sequences that all fulfill the constraints of the algorithm. Candidate sequences can be enumerated in silico using a Monte Carlo algorithm. The algorithm selects codons based on the probabilities obtained from the codon usage table and combines the codons into
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a proposed optimized sequence. Depending on design constraints, computing power, and length of protein sequence, millions of in silico sequences can quickly be generated where each sequence is equally good. Each designed sequence can then be passed through subsequent filters to ensure a match with any additional design criteria. The in silico filters can include filtering out mRNA secondary structures and DNA repeats, eliminating or incorporating restriction sites or other sequence motifs, and avoiding methylation sites that overlap methylation sensitive restriction sites, etc [5]. Commercial software for the design of expression-optimized synthetic gene constructs includes Gene Composer from Emerald BioSystems [47], and the free alternative Gene Designer from DNA2.0 [48], and software referenced therein.
9.9 DNA Sequence Features to Eliminate, Add, or Modify during Design Given the vast number of possible nucleotide solutions to even stringently constrained short proteins, the possibilities to modify a synthetic gene without altering the protein sequence are endless. A convenient way to approach the design is to generate thousands of in silico sequences and sort them based on various criteria.
9.9.1 GC Content It is often desirable to constrain both the local and global GC content of a synthetic gene to fall relatively close to the GC content of the host organism. By in silico generating a multitude of alternative solutions based on the same constraints, the sequences can be sorted by GC content and the sequence closest the preferred GC percentage can be synthesized. However, to a large degree the GC content will be determined by the codon usage table applied in combination with the degree of abundance for certain amino acids. A glycine rich gene (codons GGC, GGG, GGA, and GGT in order of abundance in mammalian codon usage table) optimized for expression in mammalian cells for example will be relatively GC rich, no matter how much the design is focused on avoiding high GC content. Runs of C and/or G in an ORF have been implicated in ribosomal slippage and frameshift of the ribosome during translation [49,50]. We generally filter out any runs of CG longer than six nucleotides to ensure uniform GC content variance during the translational process. In mammalian systems, both correlation analysis [51] and experimental studies [52] suggests a weak but consistent correlation between GC in the 3′ wobble position of translated codons and increased expression levels. This is in direct contradiction to other studies showing no correlation between codon usage and expression levels in mammalian systems [53].
9.9.2 Avoiding mRNA Sequence Motifs Intragenic motifs such as cryptic Shine–Dalgarno sequences, splice site motifs, IRE elements, internal Kozak sequences etc. can all be screened for and removed from the synthetic gene design. For some motifs, such as internal Shine Dalgarno it is clear that avoiding this motif in the synthetic gene design will increase the level of gene expression in E. coli [54]. For many other motifs, however, the data is often limited as to the effect the motif may have for expression. Furthermore, the functional effect of the motifs will vary between host systems, e.g., the presence of a splice site motif in a synthetic gene will not have any effect on protein expression in E. coli. Thousands of short regulatory mRNA motifs have been identified and more are identified every day. Many of these identified sequences are short and have a promiscuous consensus sequences g uaranteeing that any designed synthetic gene will encode one or more of these motifs. For those who cannot resist the urge to analyze their designed synthetic gene sequences against all possible eukaryotic mRNA sequence motifs, we recommend the online resource “Regulatory RNA Motifs and Elements Finder” (RegRNA
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database) [55]. The results derived from searches like these should however be treated with caution; the motifs are often not experimentally validated, are often unique to a particular organism or family of organisms, or part of a multicomponent network. For genes to be expressed in the standard laboratory work horse E. coli, we typically avoid motifs such as internal Shine–Dalgarno sequences in all three reading frames and runs of five or more of a single nucleotide. Depending on applications, it may also be relevant to specifically avoid endoribonuclease target sequences for RNase III [56], and the less defined RNase E [57]. For genes to be expressed in other hosts, other host specific motifs have to be scanned for and avoided during synthesis.
9.9.3 Avoiding mRNA Secondary Structure Much controversy has been generated in the literature on the inhibitory effects by secondary RNA structure elements on translational processivity. It is generally assumed that mRNA secondary structures should be avoided whenever possible. However, during translation a significant stem-loop structure in the coding part of the mRNA does not seem to hinder the progress of the translational machinery. It has been shown that actively translating ribosomes can break up such structures, either by the energy driven translation process itself or by the support of RNA helicases [58,59]. It has even been suggested that mRNA secondary structures could be desired to incorporate in synthetic genes. Analysis of coding regions in bacteria show that RNA secondary structures are overrepresented within the ORF and suggested to have important functions in RNA processing, regulation of mRNA stability, and translational control [60]. There are numerous publicly available software programs that predicts RNA secondary structures, of which mFold [61] is probably the most popular. These types of software are designed to calculate RNA secondary structures for naked RNA and works reasonably well for short nontranslated RNA such as tRNA. The translated mRNA within an ORF, however, is densely covered by ribosomes, protecting the mRNA from long distance cis or trans annealing. Chemical footprinting of mRNA-ribosome complexes show that up to 20 codons (60 nucleotides) are covered by a single translating ribosome [62], and the ribosomes are translating at ~18 codons (54 bp)/second with one ribosome every 2 seconds [63] leaving only ~50 nucleotides available between translating ribosomes for folding an mRNA secondary structure. It is often recommended to avoid mRNA secondary structures, even though the experimental data supporting that conclusion is weak. In any case, any mRNA secondary structure requiring components outside of a 50 nucleotides sliding window are likely existing only in the virtual world, and not in actively translating mRNA.
9.9.4 Downstream Region The region stretching from the initiation fMet-ATG codon to approximately codon ~10 has long been implicated in controlling protein expression levels. The existence and importance of the downstream region has been experimentally verified for many bacterial genes and seems to be a generic bacterial translation feature [64–70]. Despite the widespread acceptance of the downstream region, the mechanistic explanation for the importance of this region is unclear. Several studies point at the unique codon usage in the downstream box [71–73], whereas other experiments suggest that the regulation of translational initiation/efficiency of the region is due to secondary structure formation [74,75], peptidyl-tRNA drop-off [76,77], avoidance of NGG codon [78,79], or base pairing to 16S [64,66]. Whatever the reason, special caution needs to be addressed when designing the first ten codons. We are constantly monitoring the literature as well as our own experimental data to capture as much information constrain as possible about the downstream region and incorporate it into the publicly available Gene Designer software.
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During the first translated codons of an ORF the ribosome is much more prone to premature t ermination and the mRNA-ribosome initiation complex is not stabilized until codon 10–15. It has been suggested that the complex is stabilized once the nascent synthesized peptide is extruding from the ribosome.
9.9.5 Homology to Wild-Type Sequence Many genes encode known and unknown cis-regulatory elements within the ORF. This has been suggested to be more common for viral genes as these are much more densely packed in the viral genomes. By choosing a codon optimization solution that is as distant as possible from the wild-type sequence it can be inferred that the presence of any cis-regulatory elements have been removed and elements such as feedback loops etc no longer exists. Depending on gene sequence, the level of identity between the wild-type gene and the synthetic can be as low as ~65% while still have the exact amino acid sequence of the designed protein and codon bias consistent with the host organism. This feature has also been successfully used to design RNAi-resistant genes [6]. For many optimizations, we routinely take advantage of the ability to identify sequences that are as far away as possible from the wild-type sequence. On other instances (e.g., when optimizing a set of variant genes), we optimize the sequences to be as close as possible to a given reference sequence.
9.9.6 Codon Context The local context of a codon can sometimes influence the protein expression levels. Already in the early 1980s it was shown that the efficiency of the UGA stop codon in E. coli is decreased if followed by an adenine [80,81]. Since then, a multitude of experimentally validated codon contexts have been shown to affect ribosomal frameshift, missense, and nonsense incorporations and translational efficiency [82–85]. Separate from the experimentally validated cases of codon context effect on protein expression levels, there are also several publications where codon context effects have been proposed based on in silico analysis of genomes [86–88]. The absence of certain codon contexts in the analysis of entire genomes does not necessarily reflect that the identified sequences affect protein expression of a recombinant gene when grown in rich media, but more likely is a consequence of other evolutionary pressures such as facilitating DNA replication, mutational bias, expression during starvation, intrinsic metabolic regulation etc. [89]. In at least one case [90], the predicted codon pair bias effect on protein expression could not be experimentally validated [91].
9.10 Incorporating Tools for Translational Control in Metabolic Engineering Fifty years ago, upon unveiling the structure of DNA, Francis Crick and James Watson modestly stated “this structure has novel features which are of considerable scientific interest.” Since then, there has been an abundance of masterful artistry based on DNA which has further revealed and exploited its many secrets. As a sculptor chisels wood, the genetic engineer manipulates DNA, both must work with the many peculiarities of their respective natural substrates. However, unlike the sculptor, who can easily find a knot-free piece of wood, genetic engineers have had to accept their raw materials as they come from nature, despite features that may be incompatible with his or her vision and tools. Thanks to the convergence of molecular biology, computational sciences, and manufacturing engineering, the genetic engineer is no longer confined to building things based on natures leftover DNA. Instead by using the tools developed over the last few years, single genes, operons, complete metabolic pathway as well as entire chromosomes and genomes can now be designed, synthesized and assembled into fully functional units starting from only the simplest of phosphoamidites.
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DNA is the information that defines the scope and extension of the application, just like software code is the information that defines the scope and extension of any application requiring computation (such as writing this text in Microsoft Word). The applications made possible by gene synthesis and metabolic engineering are only constrained by the imagination and creativity of the user.
Acknowledgments Jeremy Minshull, Louise Rafty, Sridhar Govindarajan, and Jon Ness all at DNA2.0, are gratefully acknowledged for providing technical, linguistic and moral support. Magda Bartilson for editing. DNA2.0 is acknowledged for providing financial support.
References 1. Casimiro D.R., Wright P.E., and Dyson H.J. PCR-based gene synthesis and protein NMR spectroscopy. Structure, 1997, 5:1407–1412. 2. Cello J., Paul A.V., and Wimmer E. Chemical synthesis of poliovirus cDNA. generation of infectious virus in the absence of natural template. Science, 2002, 297:1016–1018. 3. Prodromou C. and Pearl L.H. Recursive PCR: a novel technique for total gene synthesis. Protein Eng., 1992, 5:827–829. 4. Withers-Martinez C., Carpenter E.P., Hackett F., Ely B., Sajid M., Grainger M., and Blackman M.J. PCR-based gene synthesis as an efficient approach for expression of the A + T-rich malaria genome. Protein Eng., 1999, 12:1113–1120. 5. Gustafsson C., Govindarajan S., and Minshull J. Codon bias and heterologous protein expression. Trends Biotechnol., 2004, 22:346–353. 6. Kumar D., Gustafsson C., and Klessig D.F. Validation of RNAi silencing specificity Using synthetic genes: Salicylic acid-binding protein 2 is required for plant innate immunity. Plant J., 2006, 45:863–868. 7. Gustafsson C., Govindarajan S., and Minshull J. Putting engineering back into protein engineering. bioinformatic approaches to catalyst design. Curr. Opin. Biotechnol., 2003, 14:366–370. 8. Agarwal K.L., Buchi H., Caruthers M.H., Gupta N., Khorana H.G., Kleppe K., Kumar A., Ohtsuka E., Rajbhandary U.L., Van de Sande J.H., Sgaramella V., Weber H., and Yamada T. Total synthesis of the gene for an alanine transfer ribonucleic acid from yeast. Nature, 1970, 227:27–34. 9. Itakura K., Hirose T., Crea R., Riggs A.D., Heyneker H.L., Bolivar F., and Boyer H.W. Expression in Escherichia coli of a chemically synthesized gene for the hormone somatostatin. Science, 1977, 198:1056–1063. 10. Edge M.D., Green A.R., Heathcliffe G.R., Meacock P.A., Schuch W., Scanlon D.B., Atkinson T.C., Newton C.R., and Markham A.F. Total synthesis of a human leukocyte interferon gene. Nature, 1981, 292:756–762. 11. Beattie K.L. and Fowler R.F. Solid-phase gene assembly. Nature, 1991, 352:548–549. 12. Richmond K.E., Li M.H., Rodesch M.J., Patel M., Lowe A.M., Kim C., Chu L.L., Venkataramaian N., Flickinger S.F., Kaysen J., Belshaw P.J., Sussman M.R., and Cerrina F. Amplification and assembly of chip-eluted DNA (AACED): a method for high-throughput gene synthesis. Nucleic Acids Res., 2004, 32:5011–5018. 13. Tian J., Gong H., Sheng N., Zhou X., Gulari E., Gao X., and Church G.M. Accurate multiplex gene synthesis from programmable DNA microchips. Nature, 2004, 432:1050–1054. 14. Zhou X., Cai S., Hong A., You Q., Yu P., Sheng N., Srivannavit O., Muranjan S., Rouillard J.M., Xia Y., Zhang X., Xiang Q., Ganesh R., Zhu Q., Matejko A., Gulari E., and Gao X. Microfluidic PicoArray synthesis of oligodeoxynucleotides and simultaneous assembling of multiple DNA sequences. Nucleic Acids Res., 2004, 32:5409–5417.
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15. Mandecki W. and Bolling T.J. FokI method of gene synthesis. Gene, 1988, 68:101–107. 16. Dillon P.J. and Rosen C.A. A rapid method for the construction of synthetic genes using the polymerase chain reaction. Biotechniques, 1990, 9:298–300. 17. Jayaraman K., Fingar S.A., Shah J., and Fyles J. Polymerase chain reaction-mediated gene synthesis. synthesis of a gene coding for isozyme c of horseradish peroxidase. Proc. Natl. Acad. Sci. USA, 1991, 88:4084–4088. 18. Au L.C., Yang F.Y., Yang W.J., Lo S.H., and Kao C.F. Gene synthesis by a LCR-based approach. high-level production of leptin-L54 using synthetic gene in Escherichia coli. Biochem. Biophys. Res. Commun., 1998, 248:200–203. 19. Grundstrom T., Zenke W.M., Wintzerith M., Matthes H.W., Staub A., and Chambon P. Oligonucleotide-directed mutagenesis by microscale “shot-gun” gene synthesis. Nucleic Acids Res., 1985, 13:3305–3316. 20. Hayden M.A. and Mandecki W. Gene synthesis by serial cloning of oligonucleotides. DNA, 1988, 7:571–577. 21. Ciccarelli R.B., Loomis L.A., McCoon P.E., and Holzschu D.L. Insertional gene synthesis, a novel method of assembling consecutive DNA sequences within specific sites in plasmids: Construction of the HIV-1 tat gene. Nucleic Acids Res., 1990, 18:1243–1248. 22. Benner S.A., Caraco M.D., Thomson J.M., and Gaucher E.A. Planetary biology—paleontological, geological, and molecular histories of life. Science, 2002, 296:864–868. 23. Gouy M. and Gautier C. Codon usage in bacteria. correlation with gene expressivity. Nucleic Acids Res., 1982, 10:7055–7074. 24. Ikemura T. Correlation between the abundance of Escherichia coli transfer RNAs and the occurrence of the respective codons in its protein genes. a proposal for a synonymous codon choice that is optimal for the E. coli translational system. J. Mol. Biol., 1981, 151:389–409. 25. Hayes C., Bose B., and Sauer R. Stop codons preceded by rare arginine codons are efficient determinants of SsrA tagging in Escherichia coli. Proc. Natl. Acad. Sci. USA, 2002, 99:3440–3445. 26. McNulty D., Claffee B., Huddleston M., Porter M., Cavnar K., and Kane J. Mistranslational errors associated with the rare arginine codon CGG in Escherichia coli. Protein Exp. Purif., 2003, 27:365–374. 27. Kane J.F. Effects of rare codon clusters on high-level expression of heterologous proteins in Escherichia coli. Curr. Opin. Biotechnol., 1995, 6:494–500. 28. Lithwick G. and Margalit H. Hierarchy of sequence-dependent features associated with prokaryotic translation. Genome Res., 2003, 13:2665–2673. 29. Henaut A. and Danchin A. Analysis and predictions from Escherichia coli sequences. In Escherichia coli and Salmonella typhimurium Cellular and Molecular Biology. Neidhardt F.C., Curtiss R.I., Ingraham J., Lin E., Brooks Low K., Magasanik B., Reznikoff W., Riley M., M.S., and Umbarger H. Editors. ASM Press: Washington, DC, 1996, 2047–2066. 30. Ikemura T. Correlation between the abundance of yeast transfer RNAs and the occurrence of the respective codons in protein genes. Differences in synonymous codon choice patterns of yeast and Escherichia coli with reference to the abundance of isoaccepting transfer RNAs. J. Mol. Biol., 1982, 158:573–597. 31. Drummond D.A., Bloom J.D., Adami C., Wilke C.O., and Arnold F.H. Why highly expressed proteins evolve slowly. Proc. Natl. Acad. Sci. USA, 2005, 102:14338–14343. 32. Sorensen M.A. Charging levels of four tRNA species in Escherichia coli Rel(+) and Rel(-) strains during amino acid starvation. a simple model for the effect of ppGpp on translational accuracy. J. Mol. Biol., 2001, 307:785–798. 33. Björk G.R. Stable RNA modification. In Escherichia coli and Salmonella. Cellular and Molecular Biology. Neidhardt F.C., Curtiss III R., Ingraham J.L., Lin ECC., Low Jr. K.B., Magasanik B., Reznikoff W.S., Riley M., Schaechter M., and Umbarger H.E., Editors. ASM Press: Washington, DC, 1996, 861–886.
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34. Urbonavicius J., Qian Q., Durand J.M., Hagervall T.G., and Bjork G.R. Improvement of reading frame maintenance is a common function for several tRNA modifications. EMBO J., 2001, 20:4863–4873. 35. Li J.N. and Björk G.R. 1-methylguanosine deficiency of tRNA influences cognate codon interaction and metabolism in Salmonella typhimurium. J. Bact., 1995, 177:6593–6600. 36. Wahab S.Z., Rowley K.O., and Holmes W.M. Effects of tRNA(1Leu) overproduction in Escherichia coli. Mol. Microbiol., 1993, 7:253–263. 37. Gefter M.L. The in vitro synthesis of 2′-omethylguanosine and 2-methylthio 6N (gamma,gamma, dimethylallyl) adenosine in transfer RNA of Escherichia coli. Biochem. Biophys. Res. Commun., 1969, 36:435–441. 38. Wilson R.K. and Roe B.A. Presence of the hypermodified nucleotide N6-(delta 2-isopentenyl)-2methylthioadenosine prevents codon misreading by Escherichia coli phenylalanyl-transfer RNA. Proc. Natl. Acad. Sci. USA, 1989, 86:409-413. 39. Smith D.W. Problems of translating heterologous genes in expression systems. the role of tRNA. Biotechnol. Prog., 1996, 12:417-422. 40. Kurland C. and Gallant J. Errors of heterologous protein expression. Curr. Opin. Biotechnol., 1996, 7:489–493. 41. Rosenberger R.F. Translational errors during recombinant protein synthesis. Dev. Biol. Stand., 1994, 83:21–26. 42. Sharp P.M. and Li W.H. The codon Adaptation Index—a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res., 1987, 15:1281–1295. 43. Gong M. and Gong F.C.Y. Overexpression of tnaC of Escherichia coli inhibits growth by depleting tRNA2Pro availability. J. Bacteriol., 2006, 188:1892–1898. 44. Farabaugh P.J. and Björk G.R. How translational accuracy influences reading frame maintenance. EMBO J., 1999, 18:1427–1434. 45. Klasen M. and Wabl M. Silent point mutation in DsRed resulting in enhanced relative fluorescence intensity. BioTechniques, 2004, 36:236–237. 46. Tamura T., Holbrook S.R., and Kim S.H. A Macintosh computer program for designing DNA sequences that code for specific peptides and proteins. Biotechniques, 1991, 10:782–784. 47. Stewart L. and Burgin A.B. Whole gene synthesis: A Gene-O-Matic future. Front. Drug Design Discovery, 2005, 1:297–341. 48. Villalobos A., Ness J.E., Gustafsson C., Minshull J., and Govindarajan S. Gene Designer: A synthetic biology tool for constructing artificial DNA segments. BMC Bioinformatics, 2006, 7:285. 49. Björk G.R., Wikström P.M., and Byström A.S. Prevention of translational frameshifting by the modified nucleoside 1-methylguanosine. Science, 1989, 244:986–989. 50. O’Connor M. Imbalance of tRNA(Pro) isoacceptors induces +1 frameshifting at near-cognate codons. Nucleic Acids Res., 2002, 30:759–765. 51. Versteeg R., van Schaik B.D., van Batenburg M.F., Roos M., Monajemi R., Caron H., Bussemaker H.J., and van Kampen A.H. The human transcriptome map reveals extremes in gene density, intron length, GC content, and repeat pattern for domains of highly and weakly expressed genes. Genome Res., 2003, 13:1998–2004. 52. Kudla G., Lipinski L., Caffin F., Helwak A., and Zylicz M. High guanine and cytosine content increases mRNA levels in mammalian cells. PLoS Biol., 2006, 4:e180. 53. Semon M., Mouchiroud D., and Duret L. Relationship between gene expression and GC-content in mammals. statistical significance and biological relevance. Hum. Mol. Genet., 2005, 14:421–427. 54. Jin H., Zhao Q., Gonzalez de Valdivia E., Ardell D.H., Stenström M., and Isaksson L.A. Influences on gene expression in vivo by a Shine–Dalgarno sequence. Mol. Microbiol., 2006, 60:480–492. 55. Huang H.Y., Chien C.H., Jen K.H., and Huang H.D. RegRNA. An integrated web server for identifying r egulatory RNA motifs and elements. Nucleic Acids Res., 2006, 34:W429–434.
Tools Designed to Regulate Translational Efficiency
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56. Pertzev A.V. and Nicholson A. Characterization of RNA sequence determinants and antideterminants of processing reactivity for a minimal substrate of Escherichia coli ribonuclease III. Nucleic Acids Res., 2006, 34:3708–3721. 57. Ehretsmann C.P., Carpousis A.J., and Krisch H.M. Specificity of Escherichia coli endoribonuclease RNase E. in vivo and in vitro analysis of mutants in a bacteriophage T4 mRNA processing site. Genes Dev., 1992, 6:149–159. 58. Takyar S., Hickerson R.P., and Noller H.F. mRNA helicase activity of the ribosome. Cell, 2005, 120:49–58. 59. Iost I. and Dreyfus M. mRNAs can be stabilized by DEAD-box proteins. Nature, 1994, 372:193–196. 60. Katz L. and Burge C.B. Widespread selection for local RNA secondary structure in coding regions of bacterial genes. Genome Res., 2003, 13:2042–2051. 61. Zuker M. Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res., 2003, 31:3406–3415. 62. Green R. and Noller H. Ribosomes and translation. Ann. Rev. Biochem., 1997, 66:679–716. 63. Ingraham J.L. and Maaloe F.C.N. Growth rate as a variable. In Growth of the Bacterial Cell. Ingraham JL, Editor. Sinauer Associates Inc.: Sunderland, MA, 1983, 267–315. 64. Sprengart M.L., Fatscher H.P., and Fuchs E. The initiation of translation in E. coli. apparent base pairing between the 16srRNA and downstream sequences of the mRNA. Nucleic Acids Res., 1990, 18:1719–1723. 65. Ito K. and Kawakami K.Y.N. Multiple control of Escherichia coli lysyl-tRNA synthetase expression involves a transcriptional repressor and a translational enhancer element. Proc. Natl. Acad. Sci. USA, 1993, 90:302–306. 66. Faxen M., Plumbridge J., and Isaksson L.A. Codon choice and potential complementarity between mRNA downstream of the initiation codon and bases 1471–1480 in 16S ribosomal RNA affects expression of glnS. Nucleic Acids Res., 1991, 19:5247–5251. 67. Nagai H., Yuzawa H., and Yura T. Interplay of two cis-acting mRNA regions in translational control of sigma 32 synthesis during the heat shock response of Escherichia coli. Proc. Natl. Acad. Sci. USA, 1991, 88:10515–10519. 68. Stenstrom C.M., Holmgren E., and Isaksson L.A. Cooperative effects by the initiation codon and its flanking regions on translation initiation. Gene, 2001, 273:259–265. 69. Stenstrom C.M., Jin H., Major L.L., Tate W.P., and Isaksson L.A. Codon bias at the 3′–side of the initiation codon is correlated with translation initiation efficiency in Escherichia coli. Gene, 2001, 263:273–284. 70. Stenstrom C.M. and Isaksson L.A. Influences on translation initiation and early elongation by the messenger RNA region flanking the initiation codon at the 3’ side. Gene, 2002, 288:1–8. 71. Sorensen M.A., Kurland C.G., and Pedersen S. Codon usage determines translation rate in Escherichia coli. J. Mol. Biol., 1989, 207:365–377. 72. Bulmer M. Codon usage and intragenic position. J. Theor. Biol., 1988, 133:67–71. 73. Robinson M., Lilley R., Little S., Emtage J.S., Yarranton G., Stephens P., Millican A., Eaton M., and Humphreys G. Codon usage can affect efficiency of translation of genes in Escherichia coli. Nucleic Acids Res., 1984, 12:6663–6671. 74. de Smit M.H. and van Duin J. Control of translation by mRNA secondary structure in Escherichia coli. A quantitative analysis of literature data. J. Mol. Biol., 1994, 244:144–150. 75. de Smit M.H. and van Duin J. Secondary structure of the ribosome binding site determines translational efficiency. a quantitative analysis. Proc. Natl. Acad. Sci. USA, 1990, 87:7668–7672. 76. Karimi R. and Ehrenberg M. Dissociation rate of cognate peptidyl-tRNA from the A-site of hyperaccurate and error-prone ribosomes. Eur. J. Biochem., 1994, 226:355–360. 77. Dincbas V., Heurgue-Hamard V., Buckingham R.H., Karimi R., and Ehrenberg M. Shutdown in protein synthesis due to the expression of mini-genes in bacteria. J. Mol. Biol., 1999, 291:745–759.
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78. Gonzalez de Valdivia E.I. and Isaksson L.A. A codon window in mRNA downstream of the initiation codon where NGG codons give strongly reduced gene expression in Escherichia coli. Nucleic Acids Res., 2004, 32:5198–5205. 79. Gonzalez de Valdivia E. and Isaksson L.A. Abortive translation caused by peptidyl-tRNA drop-off at NGG codons in the early coding region of mRNA. FEBS J., 2005, 272:5306–5316. 80. Bossi L. and Roth J.R. The influence of codon context on genetic code translation. Nature, 1980, 286:123–128. 81. Engelberg-Kulka H. UGA suppression by normal tRNA Trp in Escherichia coli. codon context effects. Nucleic Acids Res., 1981, 9:983–991. 82. Murgola E., Pagel F.T., and Hijazi K.A. Codon context effects in missense suppression. J. Mol. Biol., 1984, 175:19–27. 83. Carrier M.J. and Buckingham R.H. An effect of codon context on the mistranslation of UGU codons in vitro. J. Mol. Biol., 1984, 175:29–38. 84. Bouadloun F., Srichaiyo T., Isaksson L.A., and Bjork G.R. Influence of modification next to the anticodon in tRNA on codon context sensitivity of translational suppression and accuracy. J. Bacteriol., 1986, 166:1022–1027. 85. Hagervall T. and Bjork G. Undermodification in the first position of the anticodon of supG-tRNA reduces translational efficiency. Mol. Gen. Genet., 1984, 196:194–200. 86. Shpaer E.G. Constraints on codon context in Escherichia coli genes. Their possible role in modulating the efficiency of translation. J. Mol. Biol., 1986, 188:555–564. 87. Gouy M. Codon contexts in enterobacterial and coliphage genes. Mol. Biol. Evol., 1987, 4:426–444. 88. Gutman G.A. and Hatfield G.W. Nonrandom utilization of codon pairs in Escherichia coli. Proc. Natl. Acad. Sci. USA, 1989, 86:3699–3703. 89. Moura G., Pinheiro M., Silva R., Miranda I., Afreixo V., Dias G., Freitas A., Oliveira JL., and Santos M.A. Comparative context analysis of codon pairs on an ORFeome scale. Genome Biol., 2005, 6:R28. 90. Irwin B., Heck J.D., and Hatfield G.W. Codon pair utilization biases influence translational elongation step times. J. Biol. Chem., 1995, 270:22801–22806. 91. Cheng L. and Goldman E. Absence of effect of varying Thr-Leu codon pairs on protein synthesis in a T7 system. Biochemistry, 2001, 40:6102–6106.
10 Metabolic Engineering of the Secretory Processing Pathway in Eukaryotes Mohak Mhatre Johns Hopkins University
Maira P. Pellegrini Federal University of Rio de Janeiro
Michael J. Betenbaugh Johns Hopkins University
10.1 Introduction ������������������������������������������������������������������������������������10-1 10.2 Chaperones and Folding Catalysts...............................................10-2 Protein Disulfide Isomerase (PDI) • Binding Protein • Calnexin (CNX) Signal Peptidase
10.3 Glycoengineering �������������������������������������������������������������������������� 10-6 Glycoengineering in Mammalian Cells • Glycoengineering in Insects • Glycoengineering in Plants • Glycoengineering in Yeast
10.4 γ-Carboxylation ����������������������������������������������������������������������������10-13 10.5 Furin Cleavage ������������������������������������������������������������������������������10-16 10.6 Conclusion �������������������������������������������������������������������������������������10-16 References �������������������������������������������������������������������������������������������������10-17
10.1 Introduction There is a great demand in the pharmaceutical and biotechnological industries to synthesize proteins at very high production rates. Although mammalian hosts would create proteins that most resembled human proteins, mammalian cell lines are expensive to culture and often do not produce proteins at the desired rate. Consequently, researchers have also turned to nonmammalian hosts, such as insect, plant, and fungal cells, to produce recombinant proteins at high levels and at relatively low cost. However, researchers have discovered that the secretory pathway—the designated steps for proteins destined to leave the cell—have numerous spots along the way that limit the production of recombinant proteins in all these expression systems. These rate-limiting steps can lead to inactive or misfolded proteins, protein aggregation, and ultimately, protein degradation. These bottlenecks are mainly due to the many post-translational modifications that a protein must undergo before it can leave the cell. Researchers are attempting to overcome these bottlenecks through metabolic engineering of the secretory pathway, specifically by overexpressing or manipulating the levels of expression of various enzymes involved in protein processing. In this chapter, we will discuss the research contributions to metabolic engineering of protein folding and assembly in the endoplasmic reticulum (ER) (protein disulfide isomerase (PDI), binding immunoglobulin protein (BiP), calnexin (CNX), and signal peptidase); N-site occupancy and processing of the N-glycosylation pathway; and gamma-carboxylation and furin cleavage.
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Gene Expression Tools for Metabolic Pathway Engineering
S
PDI SH
SH
SH
SH
S S
PDI
S
S
S
S
S
Inactive protein
Active protein
Inactive protein
Figure 10.1 Schematic of the catalytic oxidation and isomerase functions of protein disulfide isomerase (PDI).
10.2 Chaperones and Folding Catalysts 10.2.1 Protein Disulfide Isomerase (PDI) PDI is an enzymatic protein involved in the protein folding and assembly process that occurs in the cell’s ER. This enzyme is specifically responsible for catalyzing the formation and degradation of disulfide bonds on a protein until the most stable conformation is obtained (Figure 10.1). Disulfide bonds increase the stability of the folded protein and allow it to be secreted in its proper conformation. When a protein is incorrectly folded, the unfolded protein response (UPR) may be triggered, resulting in aggregation and subsequent degradation of the protein. This problem may be especially evident with the expression of recombinant proteins in a host. Currently, researchers are engineering changes in the secretory pathways of a variety of cell hosts, in hopes to overcome UPR and increase protein secretion. Significant work has examined increasing secretion levels of heterologous proteins in Saccharomyces cerevisiae. One group found that the overexpression of PDI caused an improvement in secretion of human platelet derived growth factor B homodimer by a factor of ten.1 The group also found that overexpression of PDI quadrupled the secretion of Schizosaccharomyces pombe acid phosphatase.1 The results show that the overexpression of PDI can significantly improve secretion rates of recombinant proteins. In addition to improving protein secretion levels, researchers are attempting to use PDI to decrease UPR.2 One group studied the effect of the overexpression of the chaperone BiP and PDI in S. cerevisiae on the expression of a single-chain antibody.2 Their results demonstrate that while the UPR was not lowered by the overexpression of BiP alone, the overexpression of PDI alone or in conjunction with BiP led to a decrease in the UPR. In addition, the pairing of the two proteins caused the highest level of antibody secretion.2 This appears to show that PDI plays a pivotal role in both increased protein secretion and decreased UPR, at least for this host. Besides working with yeast, researchers are also interested in the effects of overexpressing single chaperones and pairs of chaperones in Pichia pastoris on recombinant protein secretion. One study revealed that the overexpression of PDI, Ssa1p, and other chaperones such as Kar2p, the BiP homologue
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in yeast, increase heterologous protein secretion by four to seven fold.3 Ssa1p is an Hsp70 chaperone protein found in the cytosol of yeast whose primary functions involve assistance in protein folding and translocation.4,5 The study also evaluated the effects of pairs of these chaperones, and found that YDJ1p/ PDI improved secretion more than pairs of chaperones that did not include PDI.3 Thus, the significance of PDI alone and its combination with chaperone proteins in alleviating the bottleneck effect cannot be ignored. These results show that chaperones and folding enzymes can couple to improve output of heterologous proteins.3 Another study worked with P. pastoris to increase secretion of recombinant antibody fragments. The study noted that while there was an accumulation of heavy chain antibody fragments inside the host cells, whole antibodies, and single light chain fragments were present only in the supernatant.6 The group engineered the host cell to upregulate both PDI and the UPR transcription factor HAC1.6 PDI caused an improvement in antibody secretion almost twice as great as HAC1, demonstrating that formation of disulfide bonds during protein folding significantly affects the production and secretion rates of Fab.6 The applications of PDI overexpression have also been focused on increasing secretion of a potential vaccine against hookworm infection—Necator americanus secretory protein (Na-ASP1) in P. pastoris.7 The group had initially attempted to increase Na-ASP1 secretion by augmenting the number of Na-ASP1 genes in the host cell line, but clones with multiple gene copies showed a reduction in protein secretion.7 To overcome this disparity, the group overexpressed PDI at variable levels, and studied the resulting amount of Na-ASP1 secreted. The results demonstrate that while there was still an aggregation of Na-ASP1 inside the host cells, the protein secretion level did improve with multiple gene copies of PDI.7 The problem of producing recombinant antibodies has also been studied in insect and mammalian cells. The expression of PDI was found to salvage insoluble immunoglobulins in a form that allowed the immunoglobulin monomers to assemble and be secreted into the medium with higher protein yields.8 The original PDI work was then further expanded in another paper 9 in which particular thioredoxinlike domains within PDI were found to facilitate immunoglobulin assembly. A study with a Chinese Hamster Ovary (CHO) cell line found that the cell line did not augment its amount of PDI and BiP with the rising production of the recombinant human antibody.10 The findings also demonstrate that antibody secretion levels are enhanced with increased expression of light chain, and that aggregation of heavy chain occurs when there is not enough light chain present—this may be another significant bottleneck in the pathway.10 To alleviate this bottleneck, PDI and BiP were introduced into the host cells both separately and together.10 The overexpression of BiP alone and paired with PDI caused a decrease in the rate of antibody secretion, while the overexpression of PDI alone enhanced the level of secretion.10
10.2.2 Binding Protein Another ER protein that has been overexpressed to enhance post-translational processing in a variety of expression systems is BiP, also known as the ER glucose regulated protein (GRP78) of the heat shock protein 70 family. Researchers have investigated how BiP’s association with proteins affects the proteinfolding and assembly pathway (Figure 10.2). Early research efforts investigated the effect of overexpressing Kar2p, the BiP homologue in yeast, on the secretion levels of heterologous proteins from Saccharomyces cerevisiae. Researchers found that Kar2p increased secretion rates of single chain antibody fragments (scFv) by two to eight times.11 These researchers also wanted to understand the specific role that BiP played in the processing of secreted proteins. It was hypothesized that higher amounts of BiP would either improve secretion levels by enhancing the solubility of the heterologous protein, or reduce secretion rates by preventing improperly folded proteins from leaving the cell.12 One study created a strain of S. cerevisiae in which the level of BiP could be adjusted from 5 to 250% of the normal expression level.12 They studied the effect of different expression levels of BiP on the secretion rates of bovine pancreas trypsin inhibitor, Schizosaccharomyces
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Gene Expression Tools for Metabolic Pathway Engineering Ribosome 5´
3´
ER membrane BiP Polypeptide
Figure 10.2 Schematic of the polypeptide binding function of the chaperone, immunoglobulin binding rotein (BiP). p
pombe acid phosphatase, and human granulocyte colony-stimulating factor.12 Researchers found that when BiP was underexpressed, there was a correlating decrease in secretion levels from all three proteins12 to suggest that BiP’s role is primarily for solubilizing of proteins rather than being involved in the aggregation of recombinant proteins.12 On the other hand, the overexpression of BiP did not significantly affect the secretion rates in all cases, suggesting that BiP plays various roles in the solubilization and secretion of different proteins. Indeed, in mammalian cell lines, the overexpression of BiP had a variable and often negative effect on the secretion of the proteins von Willebrand factor (vWF), factor VIII, and macrophage colony-stimulating factor (M-CSF).13 BiP was found to associate with vWF and factor VIII, and its overexpression was found to inhibit their secretion. On the other hand, BiP did not associate with M-CSF, and thus, did not affect the secretion of that protein.13 In addition, a reduction in BiP by antisense RNA was observed to increase the secretion of tissue plasminogen activator (t-PA) in mammalian cells due to a reduction in the association of BiP with the heterologous protein.14 However, more recent proteomics and genomics studies have suggested that increases in BiP and other folding catalysts, such as PDI, often correlate with increased levels of IgG antibody production in mammalian cell lines such as NS0.15,16 Interestingly, however, the overexpression of BiP was observed to decrease secretion of an IgG antibody in mammalian CHO cells.10 In another study, one of the central regulators of ER processing capacity—the transcription factor XBP-1, which controls the expression of endogenous BiP and other ER chaperones—was upregulated in CHO-K1 cells, leading to an expansion in the ER and Golgi and an increase in the production of multiple secretory products.17 Production of recombinant proteins in insect cells had also come to a roadblock in the early 1990s, as researchers were obtaining high yields of intracellular proteins, but only small amounts of some secreted heterologous proteins. Our research group suspected that post-translation modification steps, such as folding and assembly, were the rate-limiting steps in the secretory pathway, and thus, represented a bottleneck that could potentially be overcome by metabolic cell engineering.18,19 These researchers expressed BiP in both Sf-9 and Trichoplusia ni (High Five) cells using the baculovirus vector, and observed that BiP interacted with the immunoglobulin chains in cells and increased the intracellular solubility of IgG.20 In High Five cell lines, the overexpression of BiP led to a 90% improvement in the secretion rates after 3½ days.20 These findings demonstrated that BiP was not an antagonist of protein folding and secretion; instead, it could significantly enhance secretion levels and reduce the accumulation of soluble protein within the cell.20,21 Thus, the particular cell engineering strategy used likely depends on the specific cell type and microenvironment encountered, and may require either increasing or decreasing the levels of certain chaperones or folding enzymes in the cells.
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Folded protein leaves CNXCRT cycle to continue on secretory pathway S
S Erp57 Calnexin
EDEM
Translocon complex
ERAD for degradation
Figure 10.3 Simplified schematic of the calnexin (CNX)-calreticulin (CRT) chaperone cycle along with the disulfide isomerase (Erp57), EDEM, and the ER associated degradation (ERAD) pathway.
10.2.3 Calnexin (CNX) Another resident ER chaperone protein is CNX, which works with calreticulin (CRT) to ensure proper folding of glycoproteins (Figure 10.3). CNX and CRT are carbohydrate-specific chaperones in that they recognize glycoproteins that have one glucose residue attached to the end of the N-glycan.22 CNX and CRT work to prevent the accumulation of misfolded proteins and inhibit the UPR.22 In order to interact with glycoproteins, CNX and CRT first bind with the unfolded glycoprotein and then create a complex with ERp57, a member of the DPI family.23 This complex assists the glycoprotein to fold into the correct configuration.23–27 Glucosidase II is an enzyme that allows the glycoprotein to leave the CNX–CRT complex by removing the one remaining glucose from the glycoprotein.23,28–31 If the glycoprotein is not folded correctly at this dissociation, UDPglucose:glycoprotein glucosyltransferase (GT) will reattach the glucose residue and the cycle begins again.23,31–33 Once the protein has folded properly, it leaves the cycle and moves on to the next steps in the processing pathway 23 (Figure 10.3). If, after many CNX–CRT cycles, the glycoprotein is unable to fold into its native conformation, the polypeptide will be retrotranslocated through the translocon complex, deglycosylated by a cytosolic glycoamidase, ubiquitinated, and undergo proteolysis through the ER-associated degradation (ERAD) pathway23,30,34–38 (Figure 10.3). EDEM (or Htm1/Mln1) is another lectin-binding protein that assists in identifying glycoproteins that need to be destroyed. EDEM recognizes an N-glycan with eight mannose residues in order to target the glycoprotein for degradation (Figure 10.3). One study found that overexpression of EDEM caused an increase in ERAD for improperly folded glycoproteins but did not increase the rate of proteolysis of nonglycosylated proteins.23,39–41 Furthermore, the coexpression of EDEM and a mannosidase enzyme accelerated the degradation of unfolded proteins in human embryonic kidney (HEK) 293 cell lines.23,42 Thus, the glycoprotein has the opportunity to attain its proper conformation with the assistance of CNX and CRT, but, failing to achieve this conformation, lectin-binding proteins will also ensure that the polypeptide will be removed from the ER and degraded. Researchers attempted to pinpoint where CNX has the most influence in the protein-folding pathway. In the study, researchers overexpressed CNX or CRT in a mammalian cell line expressing human thyroperoxidase (hTPO).43 Although the presence of the chaperone proteins appeared to increase the rate of the initial steps in the hTPO-folding pathway, there was no significant rise in hTPO at its final processing steps.43 These findings demonstrate that while CNX and CRT played an important role initially, other chaperone proteins are likely involved in the later stages of the protein-folding pathway.43 In order to distinguish the roles of BiP and CNX in the folding pathway, researchers performed pulsechase radiolabeling studies on mammalian cells expressing hTPO.44 CHO cells that overexpressed BiP
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had a reduced level of properly folded hTPO, consistent with the results of previous studies,13,14 while cells overexpressing CNX and Erp57 had increased levels of partially folded hTPO.44 These results support the hypothesis that the CNX and BiP influence protein folding differently and the levels of these chaperones can alter the folding rates significantly in either a positive or negative fashion.44 Researchers have also explored the overexpression of CNX and CRT to improve productivity levels of other proteins, such as secreted thrombopoeitin (TPO), in CHO cells. First, they engineered a cell line that would allow control of CNX and CRT levels through varying the culture medium’s level of doxycycline.45 When there was no doxycycline present, the levels of CNX and CRT almost tripled, and caused the productivity of TPO to double.45 These findings suggest that high levels of CNX and CRT can improve the productivity of recombinant proteins in mammalian cell lines.45 Researchers have also studied the effects of CNX overexpression in insect cells in order to improve the production of membrane proteins, such as the serotonin transporter (SERT).46 Researchers discovered that the overexpression of recombinant SERT resulted in high amounts of improperly folded and inactive protein, and only low levels of properly folded and active transporter.46 These findings suggested that there was a bottleneck due to the lack of chaperones, and thus, the researchers overexpressed recombinant SERT along with CNX, CRT, BiP, and Erp57.46 The expression of CNX led to a tripling in the production of active transporter with lesser increases facilitated by BiP and CRT.46 CNX has also been found to improve levels of properly folded Shaker H4 potassium channel proteins in insect cells.47 In fungal cell lines such as Aspergillus niger, the production of heme-containing peroxidases is inefficient, perhaps due to a post-translational processing bottleneck.48 Researchers evaluated the coexpression of CNX as a way to enhance processing and found that the levels of extracellular manganese peroxidase increased four to five fold.48 In contrast, the coexpression of BiP resulted in lower levels of peroxidase. Addition of heme to the medium negated these effects but its presence was also found to increase the intracellular expression of BiP and CNX.48
10.2.4 Signal Peptidase The initial ER post-translational modification is the cleavage of the signal peptide of a polypeptide by signal peptidase as it passes into the ER compartment (Figure 10.4). The signal peptide is used as a marker for proteins that should be directed to the secretory pathway, and researchers hypothesized that this step may represent another potential bottleneck in the secretory pathway. In one study, multiple protein bands were observed when a scFv was expressed in insect cells using the baculovirus expression vectors.49 The researchers hypothesized that the multiple bands may represent incomplete processing of the signal peptide.49 The coexpression of wild-type SipS, a signal peptidase from Bacillus subtilis, eliminated the presence of multiple protein bands in the cell lysates while the expression of a mutant SipS did not affect the presence of the protein bands.49 This study indicated that signal peptide processing can be a bottleneck in the insect cell expression system. Although wild-type SipS did increase the amount of scFv in the medium, the rate of secretion of the protein was still low, suggesting that subsequent processing bottleneck situations limited secretion of the protein from insect cells.49
10.3 Glycoengineering In the ER, another protein post-translational modification occurs—glycosylation, which is the addition of sugars onto proteins. There are two forms of glycosylation: N-linked and O-linked. For N-linked processing, enzymes add carbohydrates to asparagine residues, while enzymes for O-linked processing glycosylate serine or threonine side chains. This chapter will focus on the metabolic engineering research contributions concerning N-glycosylation. In most eukaryotic cells, the process of N-glycosylation starts with the addition of Glc3Man9GlcNAc2 onto an asparagine side chain by the oligosaccharyltransferase (OST) complex to generate a glycoprotein 50 (Figure 10.5).
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mRNA
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Signal peptidase Signal peptide
Polypeptide
Figure 10.4 Schematic of the cleavage of signal peptides by the ER-associated membrane protein, signal peptidase.
O
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Figure 10.5 Transfer of Glc3Man9GlcNAc2 from lipid or dolichol-linked oligasaccharide onto the asparagine residue of a growing polypeptide in the endoplasmic reticulum by the multisubunit enzyme, oligosaccharide transferase (OST).
During its travel through the ER and Golgi apparatus, the glycoprotein is subject to processing, as various enzymes will both remove and add specific sugars onto the oligosaccharide. These changes will ultimately shape the final structure of the sugars on the glycoprotein; many different sugar structures may be generated for a single glycoprotein. This N-glycan attachment plays a significant role in a glycoprotein’s stability, folding capabilities, secretion rate, and in vivo biological activity.50
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Gene Expression Tools for Metabolic Pathway Engineering Legend GlcNAc Man Fuc Xyl Gal Sialic acid P Phosphate
Insect
Plant P
Mammalian
Yeast
Figure 10.6 Typical N-linked oligosaccharides produced in the insect, plant, and yeast hosts and the desirable mammalian oligosaccharide researchers are attempting to generate in these hosts through glycoengineering.
In N-glycan processing in the ER, insects, plants, and yeasts share many similarities with mammals, particularly in the transformation of the Glc2Man9GlcNAc2 oligosaccharide to Man8GlcNAc2.51 However, in the Golgi apparatus, N-glycan processing varies significantly among the species (Figure 10.6). Insect cells often eliminate most mannose sugars to form paucimannosidic N-glycans, while yeast cells often add at least 50 mannose residues during processing steps. Plants will often eliminate mannose residues, frequently attach β(1,2)-linked Xyl and α(1,3)-linked Fuc residues, and sometimes create GlcNAc or Gal complexes to be added to the oligosaccharide.23,51 The nonmammalian cell hosts are of significant pharmaceutical interest because of their relatively low cost, easy and safe use, and their adaptable system of expressing recombinant proteins at high levels. 52 The problem with producing these heterologous proteins in nonmammalian hosts is that the differences in the N-glycans are potentially immunogenic to humans and can cause the recombinant glycoprotein to be quickly removed from the patient’s circulatory system. Researchers are trying to engineer insect, plant, and yeast cells to create sialylated or galactosylated N-glycans in order to produce glycoproteins similar to those of mammalian strains so that they can be used in a clinical setting and with increased biological activity in vivo.51
10.3.1 Glycoengineering in Mammalian Cells Researchers have begun engineering glycosylation changes in mammalian cells, which would create glycoproteins that more closely resemble human proteins. A current roadblock with these cells is their lower production levels in some cases and their expensive culture costs.53 Nonetheless, researchers have made significant progress in this area, particularly in genetically manipulating mammalian cells to add N-glycans onto EPO.53 One study investigated the role of site occupancy in human erythropoeitin (hEPO) on biological activity in vivo and in vitro. EPO has three sites for N-glycosylation. While unglycosylated EPO derivatives were comparably active in vitro with respective to wild-type EPO, the study revealed that the elimination of these sites significantly reduced the in vivo activity.54,55 This shows that N-linked oligosaccharides are essential for in vivo activity.55 Another study focused on the effect of
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N-glycans on EPO secretion in BHK cells, where EPO mutants with all three glycosylation sites removed had a 90% reduction in production.54 The researchers then investigated the effect of engineering glycosylation consensus sequences into proteins such as recombinant human erythropoeitin (rHuEPO).56 In this case, the researchers modified the genes so that the glycoproteins included two additional target sites for N-glycosylation (Asn-X-Ser/Thr); the protein then would include five N-glycans attached rather than three. They discovered that these sequences caused a significant improvement of in vivo activity and longevity of action.56 In particular, glycoengineered rHuEPO has had clinical success, resulting in fewer required doses of the drug for each patient.56 These findings demonstrate that glycoengineering could be a potential solution to the dosage problem of some protein therapeutics. Transferrin, another protein with two N-glycosylation sites, has also been used in studies concerning the effect of site occupancy in protein secretion. One study created BHK cells producing recombinant transferrin. While wild-type transferrin had a secretion level of 125 mg/L, BHK cells expressing mutant transferrin—which had the glycosylation sites removed—had a decrease in protein secretion to only 25 mg/L.57 N-glycosylation can also be clinically relevant in congenital disorders of glycosylation (CDGs). CDGs involve genetic deficiency in the enzymes responsible for site occupancy or glycan processing.58 CDGs have a variety of clinical features, including developmental delays, seizures, organ failure, and death.23 The diseases can be classified into two groups: CDG-I, where there is a defect in the production of Glc3Man9GlcNAc2-P-P-Dol, and CDG-II, where problems arise in the later series of processing the sugars attached to the protein.58 The most prevalent indicator for CDG-I is the aggregation of improperly glycosylated forms of transferrin in the serum and cerebrospinal fluid of the patient.23 Clinical studies have demonstrated that healthy humans generate human transferrin (hTf) with both of its N-glycosylation site occupied, while CDG patient have a higher level of hTf with only either N-glycan site occupied.59 In addition, researchers have been engineering mammalian cells to create N-glycans for monoclonal antibodies that target tumor cells and trigger the immune system to respond.53 The ability of the antibody to bind to the tumor cell is affected by the sugars attached on the antibody, particular the ones on the heavy chains in the Fc region of the antibody53 (Figure 10.7). Researchers manipulated the glycosylation pathway by eliminating fucose, and subsequently increased binding affinities by 50 fold to Fc receptors on immune cells.53 To do this fucose elimination, some groups have expressed an enzyme, β(1,4)-N-acetylglucosaminyltransferase III (GnT-III), that adds a GlcNAc residue to compete and inhibit fucosylation with native mammalian fucosyltransferase, while other researchers knocked out fucosyltransferase from the mammalian host to produce a similar reduction in fucosylation on the monoclonal antibody.53,60 These glycoengineered antibodies exhibit enhanced antibody-dependent cellular cytoxicity (ADCC).60 In addition, the researchers have observed that changing the intracellular location of the GnT-III domain in the Golgi apparatus can enhance
Fv region Fc region
Figure 10.7 Schematic of an antibody with glycans attached to its fragment crystallizable (Fc) region near the carboxyl terminus of the heavy chain.
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its ability to inhibit fucosylation of the antibodies and enhance ADCC.60 Coexpressing GnT-III and mannosidase II (ManII) in concert was found to increase the number of complex-type oligosaccharides relative to the expression of GnT-III alone.60
10.3.2 Glycoengineering in Insects The difference in N-glycans between insect and mammalian cells results from the lack of enzymes involved in creating sugars and adding these sugars to the N-glycan structure.52 Research has indicated that insect cells may express these enzymes early in development but that cell lines seem to have insufficient expression. When manipulating the glycosylation pathway in insect cells, researchers had to engineer enzymes to cover many steps that are found in the mammalian pathway. First, researchers must inhibit or minimize the effect of β-N-acetylglucosaminidase (GlcNAcase), which removes a terminal GlcNAc on the Manα(1,3)-branch present on mammalian glycoproteins.23 Researchers discovered that if a cell line does not contain GlcNAcase, or an inhibitor to this enzyme cannot be found, other options include the overexpression of N-acetylglucosamine Transferase I (GnT-I) or Galactose Transferase (GalT).23 This approach will either compete against the GlcNacase (GnT-I) or cap the GlcNac residue (GalT) in order to prevent removal of the GlcNac residue. Indeed, expressing mammalian GalT genes in insect cells has been shown to increase the level of galactosylated glycans from 0% to 13% and provide a simultaneous rise from 6.5% to 15.7% in N-glycans having a terminal GlcNAc on the Manα(1,3)branch.23,61 In this way, overexpressing GalT both suppressed GlcNAcase activity and enhanced the number of galactosylated N-glycans produced by insect cells. Unfortunately, the galactose residues were present on only one of the biantennary N-glycan branches (Manα(1,3)-branch), and there were only a few GlcNAc additions to the alternate Manα(1,6)-branch of the N-glycan. In order to obtain Gal additions on both branches, it was necessary to co-express both GalT and N-acetylglucosamine Transferase II (GnT-II).23 One study showed that expression of GnT-II alone in Tn-5B1-4 cells created only 0.7% of biantennary N-glycans on a heterologous transferrin, while the coexpression of GalT and GnT-II resulted in heterologous transferrin with 50% of its N-glycans containing Gal on both branches.23,52 In order to create glycoproteins on therapeutic drugs such as EPO and t-PA with more than two branches, the mammalian genes of GnT-III, IV, and V will also need to be genetically engineered into insect cells.23 The terminal step of the mammalian glycosylation processing pathway is typically the addition of sialic acid onto both branches of the N-glycan—a step that is not replicated in insect cells (Figure 10.6). The attachment of sialic acid plays a significant role in the functional activity and in vivo circulatory half-life of recombinant glycoproteins.62 For the addition of this molecule, cytidine monophospho-sialic acid (CMP-SA) is generated and transferred to an oligosaccharide by the enzyme sialyltransferase (SiaT). Insect cells typically lack or have very low levels of CMP-Neu5Ac, the single most ubiquitous sialylation substrate. Consequently, researchers have been working to engineer changes in the glycosylation processing pathway in insect cells so that they can generate the proper CMP-sialic acid molecule for sialylation. Initially, researchers engineered the enzymes required to generate CMP-sialic acid from the intermediate, N-acetylmannosamine (ManNAc), by expressing sialic acid phosphate synthase (SAS) and CMPsialic acid phosphate synthase (CMP-SAS) (Figure 10.8). When the researchers expressed both CMP-SAS and SAS and included a large amount of N-acetylmannosamine, the levels of CMP-Neu5Ac increased by 30 fold.62 In subsequent studies, researchers further engineered the sialic acid processing pathway of insects. The researchers expressed SAS, UDP-GlcNAc 2-epimerase/ManNAc kinase (EK)—the enzyme that converts UDP-GlcNAc to ManNAc-9-Phosphate—and CMP-SAS and found a significant increase in CMP-Neu5Ac without the need for ManNAc supplementation to the media.63 Furthermore, by mutating the EK gene to eliminate feedback inhibition of the enzyme by CMP-sialic acid, the researchers were able to increase CMP-sialic acid levels four fold.64 Researchers also found that supplying GlcNAc enhanced levels of CMP-Neu5Ac
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Figure 10.8 Biochemical pathway for the synthesis of CMP-N-acetylneuraminic acid (CMP-Neu5Ac), also known as CMP-sialic acid from UDP-N-acetylglucosamine (UDP-GlcNAc).
even more than that which could be obtained by feeding ManNAc alone. These results suggested a limitation in the transport and phosphorylation of ManNAc in the cells.63 Such a strategy of generating CMP-Neu5Ac by metabolic engineering eliminated the need to add serum, which includes sialic acid, to the insect cell culture medium. Unfortunately, some insect cell lines also add a potentially immunogenic α(1,3)-Fucose to some N-glycans through the expression of the α(1,3) fucosyltransferase (α3FucT) gene, implying that future metabolic engineering strategies may require the need to restrict the expression of this gene. Alternatively, researchers have also utilized particular strains, such as Sf9, Ea4, and gypsy moth cells that have low native levels of α3FucT genes.
10.3.3 Glycoengineering in Plants The use of plant cells for producing heterologous glycoproteins is now beginning to be explored. As with other nonmammalian expression systems, the need for glycosylation engineering in plants is imperative because plants add fucose and xylose sugars that can trigger an immune response in humans53 (Figure 10.6). To alleviate this problem, researchers have identified the genes responsible for enzymes that attach xylose and fucose to glycoproteins, and have knocked them out. In one study, both genes were eliminated from a strain of Arabidopsis thaliana, resulting in synthesized glycoproteins that had no β(1,2)-xylosylation and core α(1,3)-fucosylation.23,65 Similar glycoengineering results were obtained when the genes were knocked out of Physcomitrella patens.23,66 Another strategy for restricting the addition of xylose and fucose is to prevent glycoproteins from entering the Golgi apparatus, where these glycosyltransferases are enzymatically active. In order to retain glycoproteins in the ER, researchers attached a KDEL sequence to the glycoprotein and observed that no immunogenic fucoses were attached to the N-glycan of a monoclonal antibody fragment retained in the ER.23,67 Researchers have also explored the possibility of overexpressing GalT to enhance galactosylation on plant N-glycans. In one study, tobacco plants were engineered to express a hybrid GalT that was made of the N-terminal domain of Arabidopsis core XylT and the catalytic domain of human β4GalT.23,68 The expression of this recombinant gene caused a 60% decrease in the level of glycoproteins with xylose and fucose residues but the structures that were obtained included Gal on only one branch of these so-called hybrid N-glycans; no di-galactosylated structures were obtained with Gal attached to both branches of the N-glycan.23,68 Another study knocked out the XylT and FucT genes in plant moss Physcomitrella patens and coexpressed the β4GalT gene to secrete recombinant glycoproteins lacking the immunogenic Xyl and Fuc attachments while including attached Gal residues.23,69 In order to generate fully humanized glycoproteins, plants must also be able to add the terminal sialic acid sugar onto recombinant glycoproteins. Unfortunately, plants have few, if any, of the many enzymes responsible for sialylation. However, researchers have begun to address this roadblock by engineering
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plants to express mammalian SiaT.23 These studies must also address the inability of most plants to produce CMP-SA, a substrate limitation similar to that observed in insect cell lines.23,70
10.3.4 Glycoengineering in Yeast Researchers have also applied yeast as a lower cost method for generating recombinant glycoproteins. However, natural yeast oligosaccharides do not resemble those of humans, and therefore, could trigger an immune reaction in patients53 (Figure 10.6). Consequently, researchers have engineered yeast to express recombinant enzymes needed for glycosylation, such as glycosyltransferases, and restricted the yeast’s own undesirable glycosylation processing enzymes.53 Another advantage of this system is that yeast can potentially be engineered to generate homogenously N-glycosylated proteins similar to those found in humans.53 Such control of glycosylation may have a positive effect on therapeutic activity— one study demonstrated that an engineered yeast version of a commercial antibody was ten times more efficient in attaching to the Fc target receptor in receptor binding assays than the commercial drug.71 The success of this study suggests that other glycoforms found in humans may be produced in yeast cells in the future. Unlike mammals, yeast add significant numbers of mannose residues onto glycoproteins. In order for yeast to synthesize humanlike glycoproteins, polymannosylation must be restricted, and the yeast must be engineered to create the Man5GlcNAc2 acceptor found in humans.23 Researchers have been able to create N-glycans with Man5GlcNAc2 in S. cerevisiae by inhibiting the functions of some mannosyltransferase genes and, alternatively, by retaining A. saitoi α(1,2)-mannosidase in the ER using the ER retention signal HDEL23,72 in order to increase mannosidase activity. However, the production levels of the target Man5GlcNAc2 N-glycan structure were low. To improve these results, researchers examined combinatorial libraries of the α(1,2)-mannosidase gene fused to fungal membrane leaders in order to increase processing of N-glycans to include more than 90% of the desired precursor N-glycan, Man5GlcNAc2. The inclusion of a UDP-GlcNAc transporter and a GnT I fused to a membrane leader resulted in a strain of P. pastoris that could create large amounts of the hybrid N-glycan, GlcNAcMan5GlcNAc2.23,73 Subsequent genetic engineering removed an additional two mannose residues using α-mannosidase II, and co-expression of N-acetylglucosamine transferase II (GnT-II) led to a P. pastoris mutant that produced the complex-type N-glycan GlcNAc2Man3GlcNAc2 with GlcNAc sugars on both branches.23,74 Another strategy developed by researchers for creating Man5GlcNAc2 is to inhibit the formation of Glc3Man9GlcNAc2-PP-Dolichol, which would be modified to the high mannose forms in the yeast glycosylation pathway. Researchers discovered S. cerevisiae and P. pastoris mutants missing the mannosyltransferase gene, Alg3p, that produced Man5GlcNAc2-PP-Dol,23,75 and this mutant subsequently adds only shortened Man5GlcNAc2 structures to the acceptor polypeptide.23,76,77 These modified N-glycans will not allow the subsequent modification of the N-glycan to generate undesirable high mannose glycoforms. Consequently, endoH resistant sugar structures can be generated in this mutant 23,78,79 by expressing α(1,2)-mannosidase23 in order to generate the trimannosyl Man3GlcNAc2 core structure. Researchers have also explored how to obtain effective galactosylation in yeast, which involves the transfer of a Gal sugar onto the acceptor GlcNAc by the GalT enzyme. Unlike the efficient transfer of galactose in insect cells after expressing recombinant mammalian GalT23,61,80–83 yeast cells were not able to efficiently galactosylate GlcNAc residues, signifying that UDP-galactose or its transporter is lacking in yeast.23 Researchers overcame this limitation by introducing a fusion protein of UDP-galactose-4epimerase—which transforms UPD-Glc to UDP-Gal—the catalytic domain of human β4GalT I and a yeast leader sequence into the Golgi apparatus of an engineered P. pastoris strain that synthesized GlcNAc2Man3GlcNAc2.23,84 Since P. pastoris include significant levels of UDP-Glc sugar in the Golgi, the inclusion of this fusion protein resulted in the production of glycoproteins with galactose at the ends of both branches.23,85 Alternatively, production of monoclonal antibodies containing the fully galactosylated structure, Gal2GlcNAc2Man3GlcNAc2, was also achieved by expressing the UDP-Gal4-epimerase in the cytosol along with a UDP-Gal transporter and GalT in the Golgi apparatus of a
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similarly engineered P. pastoris strain.23,71 Another study demonstrated that the presence of both GalT and a UDP-Gal transporter produced high levels of galactosylated N-glycans in S. cerevisiae.23,86 Like insects and plants, yeast are unable to create sialylated glycoproteins because they are unable to produce CMP-sialic acid. They cannot transport CMP-sialic acid from the cytoplasm into the Golgi apparatus, and they do not have the enzyme necessary to transfer sialic acid onto the last galactose residue of an acceptor N-glycan.23 Researchers recently were able to overcome these obstacles and produce recombinant EPO containing N-glycans terminating in sialic acid on both branches by introducing human SAS, human CSAS, mouse CMP–sialic acid transporter, and mouse SiaT into the engineered P. pastoris strain producing the proper Gal2GlcNAc2Man3GlcNAc2 acceptor.23,87 These results and all the studies listed above illustrate convincingly the potential of metabolic engineering for remodeling the glycan structures in yeast, insect cells, and plants to forms that are similar to those obtained from mammalian hosts.
10.4 γ-Carboxylation Vitamin K-dependent (VKD) proteins require an unusual post-translational modification for their biological activity, namely γ-carboxylation. This modification consists of the addition of an extra carboxyl group onto the γ-carbon of specific glutamyl (Glu) residues, converting them to γ-carboxylated glutamyl (Gla) residues (Figure 10.9a). The carboxylation confers metal binding properties onto these proteins. In the presence of calcium ions, mature VKD proteins undergo a conformation change that leads to the ability to interact with anionic phospholipids exposed on the surfaces of cells or hydroxy-apatite present in the extracellular matrix. Until the 1980s, this specialized post-translational modification was considered significant only for hemostatic proteins (prothrombin, factors II, VII, IX, and X, and proteins C, S, and Z). However, other VKD proteins are now known to play a role in growth control, bone development, and regulation of biomineralization and signal transduction.88,89 The enzyme responsible for the conversion of Glu to Gla residues is the γ-glutamyl carboxylase, also known as VKD-carboxylase or GGCX. This enzyme is an integral membrane enzyme that resides in the ER and requires the energy of the reduced hydroquinone form of vitamin K (KH2) as the active cofactor.90 Generally, mammalian VKD proteins contain a highly conserved propeptide sequence located after the signal peptide.91 This propeptide acts as a recognition sequence for the γ-carboxylase to bind and perform γ-carboxylation.92 The cluster of Glu residues to be modified is found in a region called the Gla domain, which is located adjacent to the propeptide region. VKD proteins are carboxylated during their secretory pathway to the cell surface. Subsequent to γ-carboxylation, the propeptide is cleaved from the VKD proteins in a late Golgi event.92 To date, γ-carboxylase activity has only been detected in multicellular organisms, i.e., mammals, Drosophila, and the marine snail Conus. Homology searches have also revealed carboxylase orthologs in bacteria, but whether they have activity or not is unknown.90 The enzymatic reaction in the Conus system has been shown to have many striking similarities to mammalian systems, such as the requirement for reduced vitamin K, the presence of a γ-carboxylation recognition site on the propeptide, and the presence of a propeptide adjacent to the Glu residues undergoing carboxylation.93 Despite these similarities, the mammalian and Conus propeptides are not homologous.94 In addition, substrates from one organism are not or are poorly carboxylated by γ-carboxylases from another phyla.93,95–99 Drosophila VKD proteins have not been isolated yet, so the role of γ-carboxylation in this organism is unknown. Many of the recombinant VKD (r-VKD) proteins have been expressed in mammalian cells. However, the production and secretion of r-VKD proteins is complicated by the limited γ-carboxylation capacity observed in mammalian cell systems. At low levels of VKD protein production, the protein is secreted as fully γ-carboxylated and biological active proteins. However, when expression levels are increased, γ-carboxylation is impaired. Consequently, only a small fraction of fully γ-carboxylated proteins is obtained, while uncarboxylated and undercarboxylated forms are observed as well.90 Individual VKD proteins respond differently to this overexpression—secreted factor X is a mixture of uncarboxylated
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(a) H N ...
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Figure 10.9 The γ-carboxylation system. (a) The γ-carboxylase converts Glu to Gla residues in VKD-proteins by the addition of CO2 to Glu in a reaction requiring O2 and reduced vitamin K (KH2). This reaction results in oxidation of the KH2 to KO. The VKOR enzyme recycles back KO to KH2. There is also a second route for KH2 generation, via one or more quinone reductases (QR) that convert vitamin K to KH2. This cyclic interconversion of vitamin K metabolites constitutes the vitamin K cycle. (b) Hypothetical model of the γ-carboxylation system. γ-carboxylase and VKOR are parts of a supramolecular assembly of proteins responsible for γ-carboxylation in the ER membrane. VKD-proteins contain propeptides (indicated in black) that can bind to the γ-carboxylase, resulting in the conversion of multiples Glus to Glas (). Each Glu to Gla conversion requires one reduced vitamin K (KH2), which is recycled from KO by VKOR.
and fully carboxylated forms, but not partially γ-carboxylated proteins, while secreted factor IX comprises of all three forms.100,101 To improve γ-carboxylation of these systems, researchers overexpressed γ-carboxylase in CHO, BHK and 293 cell lines expressing recombinant factor IX (r-fIX). However, this overexpression reduced the release of r-fIX from these cells and did not improve the specific activity of r-fIX, which suggests that the γ-carboxylase is not the limiting factor.102–104 Researchers have shown that although r-fIX was fully γ-carboxylated, 95% of the r-fIX remained associated with the γ-carboxylase.103 They also suggested that the availability of the reduced vitamin K cofactor limits γ-carboxylation in mammalian cells.103 The VKD γ-carboxylation system does not consist of only γ-carboxylase; rather, it is a multicomponent enzyme system in the ER. The modification is carried out by (i) the γ-carboxylase, which requires the KH2 vitamin cofactor, CO2 and O2 and (ii) the vitamin K epoxide reductase (VKOR), which produces the KH2 cofactor. Concomitantly with γ-carboxylation, the vitamin KH2 is converted to the metabolite vitamin K epoxide (KO), which is reduced back to the vitamin KH2 cofactor by VKOR. Thus, VKOR recycles vitamin KH2 cofactor; and this interconversion of vitamin K metabolites is known as the vitamin K cycle.88,89,105 It is also known that lipids from ER membrane are essential for the operation of this
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system, probably by protecting vitamin KH2 from the oxidative environment of ER compartment.106 Thus, VKOR and γ-carboxylase must be positioned together closely enough in the membrane for a vitamin KH2 cofactor transfer from VKOR to γ-carboxylase (Figure 10.9). One study has shown, in an in vitro preparation, that VKOR and not the γ-carboxylase is the ratelimiting step of the γ-carboxylation system.107 Support of these results in vivo has been hampered because the components of the VKOR lipid–enzyme complex have not been fully identified, which impairs its expression in cell lines. Recently, the gene that encodes a proposed subunit (VKORC1) of the VKOR complex was identified.108,109 Some researchers have been able to clone and express VKORC1 in BHK cell line.110 These stably transfected cells, containing VKORC1 gene or double transfected γ-carboxylase and VKORC1 genes, showed increased activity of the γ-carboxylation system for a synthetic γ-carboxylase peptide substrate FLEEL. The expression of r-fIX in these engineered BHK cell lines showed that overexpression of VKORC1 increases the level of γ-carboxylated r-fIX secreted in the medium.104 Overexpression of VKORC1 alone and a combination of VKORC1 and γ-carboxylase resulted in 50 and 34%, respectively, of γ-carboxylated r-fIX found in the medium—a significantly higher level compared to 18% found in the parent BHK cells.104 The researchers suggest that overexpression of γ-carboxylase is not needed for increased production of r-VKD proteins, rather what is needed is a sufficient supply of its cofactor.104 Although engineered BHK cell lines have demonstrated an increase in γ-carboxylation, the specific activity of r-fIX did not change significantly between the engineered and parent cell lines. In a similar study, other researchers also reported a 2.2-fold increase in γ-carboxylated r-fIX being secreted from BHK overexpressing VKORC1 cells,101 which is in agreement with the 2.9fold reported by the first study.104 This study also measured the overexpression of VKORC1 and reported it to be 15-fold.101 Thus, the increases in γ-carboxylation were significantly smaller than the amount of VKORC1 overexpressed. This data indicates that the effect of VKORC1 is limited, possibly due to a saturation of at least one additional factor that is also required for VKD protein carboxylation. Since VKOR is oxidized and inactivated during each conversion of KO to KH 2 , and the active enzyme is regenerated by a redox protein, the component could be a redox protein. Researchers have also demonstrated that calumenin, an ER chaperone of the CREC family, is an inhibitory protein of γ-carboxylase and is associated with γ-carboxylase in the ER.111 Recently, these researchers eliminated calumenin expression in BHK cell lines expressing r-fIX and both r-fIX and VKORC1 by siRNA silencing. The production yield of functional r-fIX was 80%, in contrast to the 18 and 50% for BHK cell lines expressing r-fIX only and BHK coexpressing r-fIX and VKORC1, respectively.112 Nevertheless, it is not known whether cell lines engineered to stably overexpress calumenin siRNA would be viable, since gene deletions of members of the CREC family of proteins have been shown to be lethal.113 Although mammalian VKD propeptides share sequence homology, as mentioned previously, their relative affinities for the γ-carboxylase vary considerably, with the propeptide of factor X (fX) binding with the highest affinity, followed by the propeptides of fVII, protein S, fIX, protein C, and prothrombin.114 One study constructed a chimeric fX cDNA harboring the prothrombin propeptide and stably transfected into HEK-293 cells.115 They demonstrated that the expression of r-fX with the prothrombin propeptide could yield 70–90% of fully γ-carboxylated material, compared to 20–45% when using native r-fX construction—depending on the expression level of the recombinant protein.115 They hypothesized that the rate of substrate turnover by the γ-carboxylase may be influenced by the affinity of the propeptide, with high-affinity binding propeptides having lowest substrate turnover.115 These results agree with observations that expression of r-prothrombin and r-protein C result in fully γ-carboxylated proteins in CHO and HEK 293 cells, respectively.116–121 In a later study, this group also overexpressed the γ-carboxylase alone, VKORC1 alone, and a combination of them in a HEK-293 cell line expressing the chimeric r-fX.100 In the cell line expressing only the chimeric r-fX, 52% of it was carboxylated. When coexpressing with γ-carboxylase alone, VKORC1 alone, and γ-carboxylase/VKORC1, the fraction of fully γ-carboxylated r-fX increased to 57%, 92%, and 100%, respectively.100 They also observed that an overexpression of the γ-carboxylase alone did not affect the release of r-fX from cells, in contrast to
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reports for r-fIX mentioned above. These results support the idea that the mechanism for processing each VKD protein may have slight differences and result in different yields from overexpression of either γ-carboxylase or VKORC1. These studies have shown that γ-carboxylation can be a limiting step in the production of several heterologous proteins. However, the particular bottleneck may exist at the enzymatic activity step or in the turnover of the cofactors. A complete understanding of the γ-carboxylation system at the molecular level and the identification of all components involved during the process will be essential for the optimal synthesis of competent r-VKD-proteins in mammalian cell lines.
10.5 Furin Cleavage Like γ−carboxylation, furin, and its related proprotein convertases, play an important role in the transGolgi network by transforming many different protein precursors into their active forms.122 These enzymes typically cleave at the consensus basic amino acid sequence Arg-X-(Lys/Arg)-Arg.122 One limitation encountered when engineering mammalian cells to produce high levels of recombinant proteins is the low native levels of furin available in the cells.123 The low levels of furin create a bottleneck that limits the amount of active protein secreted from the engineered cell line.123 Researchers have attempted to overcome this bottleneck by overexpressing furin in CHO cells in combination with the production of recombinant proteins such as vWF and transforming growth factor beta 1 (TGFβ1).123 When furin is coexpressed in animal cell lines, active and matured forms of both recombinant proteins are found at higher levels, demonstrating the positive effect of overexpressing furin in protein production. Researchers also investigated the possibility of using furin to transform recombinant prosomatostatin (PSS) into the end products of somatostatin 28 (SS-28) in COS-7.124 In this study, engineered COS-7 cell lines expressing rPSS had varying levels of furin.124 When the levels of furin doubled, there was a correlating rise in SS-28 expression and a correlating reduction in the amount of whole rPSS.124 The intracellular location and level of furin appear to be important factors in the processing of proproteins.125 One study found that α-1-microglobulin-bikunin was processed equally in both wild-type CHO cells and RPE.40 cells, a mutated CHO cell line which likely has a nonfunctional furin, suggesting that furin did not cleave the target protein.125 However, when researchers overexpressed the processing enzyme and α-1-microglobulin-bikunin in COS, CHO, and RPE.40 cells, processing of the target protein was enhanced, indicating that both the cellular compartment and levels of intracellular protease are important for enhancing the cleavage of precursors.125 Researchers have also evaluated the effects of furin expression on precursor processing in insect cells. One study involved production of simian-human immunodeficiency virus-like particles (SHIV VLPs) in baculovirus-infected insect cells. These cells produced VLP containing primarily the precursor envelope (Env) protein, gp160.126 However, when researchers coexpressed recombinant furin in the insect cell lines, there was an increase in the cleavage of gp160 into the processed gp120 Env protein.126 Researchers also attempted to increase the production levels of TGFβ1 in insect cells by overexpressing furin. To do so, the precursor form of the growth factor and human furin convertase were both expressed in High Five insect cells.127 This coexpression resulted in a nearly eight-fold increase in the production of active growth factor as compared to its level in the absence of furin coexpression.127 Thus, this study and similar ones described above demonstrate that furin and other convertases can represent a critical rate-limiting step in the production of complex proteins requiring protease processing for generating proper activity.
10.6 Conclusion In this chapter, we have discussed the various studies that have brought insight to a range of posttranslational modifications involved in the secretory pathway. These discoveries have led to improved efficiencies in recombinant protein processing and production of heterologous protein in mammalian
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and nonmammalian hosts. As genomic and cell biology studies increase our understanding of the secretory pathway of eukaryotes, many more metabolic engineering enhancements are likely in the future in order to improve the processing capacities of these hosts and to augment their pathways with new capabilities not possible in the native organisms.
References 1. Robinson, A.S., Hines, V., and Wittrup, K.D. Protein disulfide isomerase overexpression increases secretion of foreign proteins in Saccharomyces cerevisiae. Biotechnology (NY), 12, 381, 1994. 2. Xu, P., et al. Analysis of unfolded protein response during single-chain antibody expression in Saccaromyces cerevisiae reveals different roles for BiP and PDI in folding. Metab. Eng., 7, 269, 2005. 3. Zhang, W., et al. Enhanced secretion of heterologous proteins in Pichia pastoris following overexpression of Saccharomyces cerevisiae chaperone proteins. Biotechnol. Prog., 22, 1090, 2006. 4. Wegele, H., Haslbeck, M., and Buchner, J. Recombinant expression and purification of Ssa1p (Hsp70) from Saccharomyces cerevisiae using Pichia pastoris. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci., 786, 109, 2003. 5. Ngosuwan, J., et al. Roles of cytosolic Hsp70 and Hsp40 molecular chaperones in post-translational translocation of presecretory proteins into the endoplasmic reticulum. J. Biol. Chem., 278, 7034, 2003. 6. Gasser, B., et al. Engineering of Pichia pastoris for improved production of antibody fragments. Biotechnol. Bioeng., 94, 353, 2006. 7. Inan, M., et al. Enhancement of protein secretion in Pichia pastoris by overexpression of protein disulfide isomerase. Biotechnol. Bioeng., 93, 771, 2006. 8. Hsu, T.A., et al. Rescue of immunoglobulins from insolubility is facilitated by PDI in the baculovirus expression system. Protein Exp. Purif., 7, 281, 1996. 9. Whiteley, E.M., Hsu, T.A., and Betenbaugh, M.J. Thioredoxin domain non-equivalence and antichaperone activity of protein disulfide isomerase mutants in vivo. J. Biol. Chem., 272, 22556, 1997. 10. Borth, N., et al. Effect of increased expression of protein disulfide isomerase and heavy chain binding protein on antibody secretion in a recombinant CHO cell line. Biotechnol. Prog., 21, 106, 2005. 11. Shusta, E.V., et al. Increasing the secretory capacity of Saccharomyces cerevisiae for production of single-chain antibody fragments. Nat. Biotechnol., 16, 773, 1998. 12. Robinson, A.S., et al., Reduction of BiP levels decreases heterologous protein secretion in Saccharomyces cerevisiae. J. Biol. Chem., 271, 10017, 1996. 13. Dorner, A.J., Wasley, L.C., and Kaufman, R.J. Overexpression of GRP78 mitigates stress induction of glucose regulated proteins and blocks secretion of selective proteins in Chinese hamster ovary cells. EMBO J., 11, 1563, 1992. 14. Dorner, A.J., Krane, M.G., and Kaufman, R.J. Reduction of endogenous GRP78 levels improves secretion of a heterologous protein in CHO cells. Mol. Cell. Biol., 8, 4063, 1988. 15. Smales, C.M., et al. Comparative proteomic analysis of GS-NS0 murine myeloma cell lines with varying recombinant monoclonal antibody production rate. Biotechnol. Bioeng., 88, 474, 2004. 16. Alete, D.E., et al. Proteomic analysis of enriched microsomal fractions from GS-NS0 murine myeloma cells with varying secreted recombinant monoclonal antibody productivities. Proteomics, 5, 4689, 2005. 17. Tigges, M. and Fussenegger, M. Xbp1-based engineering of secretory capacity enhances the productivity of Chinese hamster ovary cells. Metab. Eng., 2006. 18. Hsu, T.A., Eiden, J.J., and Betenbaugh, M.J. Engineering the assembly pathway of the baculovirusinsect cell expression system. Ann. NY Acad. Sci., 721, 208, 1994. 19. Ailor, E. and Betenbaugh, M.J. Modifying secretion and post-translational processing in insect cells. Curr. Opin. Biotechnol., 10, 142, 1999.
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20. Hsu, T.A. and Betenbaugh, M.J. Coexpression of molecular chaperone BiP improves immunoglobulin solubility and IgG secretion from Trichoplusia ni insect cells. Biotechnol. Prog., 13, 96, 1997. 21. Whiteley, E., Hsu, T.A., and Betenbaugh, M.J. Modeling assembly, aggregation, and chaperoning of immunoglobulin G production in insect cells. Biotechnol. Bioeng., 56, 106, 1997. 22. Hebert, D.N., Foellmer, B., and Helenius, A. Calnexin and calreticulin promote folding, delay oligomerization and suppress degradation of influenza hemagglutinin in microsomes. EMBO J., 15, 2961, 1996. 23. Betenbaugh, M.J., Tomiya, N., and Narang, S. Glycoengineering: recombinant glycoproteins. In: Comprehensive Glycoscience. Elsevier, Amsterdam, NL, 2007. 24. Huppa, J.B. and Ploegh, H.L. The eS-Sence of -SH in the ER. Cell, 92, 145, 1998. 25. Molinari, M., and Helenius, A. Glycoproteins form mixed disulphides with oxidoreductases during folding in living cells. Nature, 402, 90, 1999. 26. Oliver, J.D., et al. ERp57 functions as a subunit of specific complexes formed with the ER lectins calreticulin and calnexin. Mol. Biol. Cell, 10, 2573, 1999. 27. Oliver, J.D., et al. Interaction of the thiol-dependent reductase ERp57 with nascent glycoproteins. Science, 275, 86, 1997. 28. Helenius, A. and Aebi, M. Intracellular functions of N-linked glycans. Science, 291, 2364, 2001. 29. Ellgaard, L. and Helenius, A. ER quality control: towards an understanding at the molecular level. Curr. Opin. Cell Biol., 13, 431, 2001. 30. Ellgaard, L. and Helenius, A. Quality control in the endoplasmic reticulum. Nat. Rev. Mol. Cell Biol., 4, 181, 2003. 31. Parodi, A.J. Role of N-oligosaccharide endoplasmic reticulum processing reactions in glycoprotein folding and degradation. Biochem. J., 348 (1), 1, 2000. 32. Ritter, C. and Helenius, A. Recognition of local glycoprotein misfolding by the ER folding sensor UDP-glucose:glycoprotein glucosyltransferase. Nat. Struct. Biol., 7, 278, 2000. 33. Trombetta, E.S. and Helenius, A. Conformational requirements for glycoprotein reglucosylation in the endoplasmic reticulum. J. Cell Biol., 148, 1123, 2000. 34. Hammond, C. and Helenius, A. Quality control in the secretory pathway. Curr. Opin. Cell Biol., 7, 523, 1995. 35. Tsai, B., Ye, Y., and Rapoport, T.A. Retro-translocation of proteins from the endoplasmic reticulum into the cytosol. Nat. Rev. Mol. Cell Biol., 3, 246, 2002. 36. Brodsky, J.L. and McCracken, A.A. ER protein quality control and proteasome-mediated protein degradation. Semin. Cell Dev. Biol., 10, 507, 1999. 37. Jarosch, E., et al. Protein dislocation from the endoplasmic reticulum—pulling out the suspect. Traffic, 3, 530, 2002. 38. Spiro, R.G. Role of N-linked polymannose oligosaccharides in targeting glycoproteins for endoplasmic reticulum-associated degradation. Cell Mol. Life Sci., 61, 1025, 2004. 39. Hosokawa, N., et al. A novel ER alpha-mannosidase-like protein accelerates ER-associated degradation. EMBO Rep., 2, 415, 2001. 40. Mast, S.W., et al. Human EDEM2, a novel homolog of family 47 glycosidases, is involved in ER-associated degradation of glycoproteins. Glycobiology, 15, 421, 2005. 41. Molinari, M., et al. Role of EDEM in the release of misfolded glycoproteins from the calnexin cycle. Science, 299, 1397, 2003. 42. Hosokawa, N., et al. Enhancement of endoplasmic reticulum (ER) degradation of misfolded Null Hong Kong alpha1-antitrypsin by human ER mannosidase I. J. Biol. Chem., 278, 26287, 2003. 43. Fayadat, L., et al. Calnexin and calreticulin binding to human thyroperoxidase is required for its first folding step(s) but is not sufficient to promote efficient cell surface expression. Endocrinology, 141, 959, 2000. 44. Le Fourn, V., et al. Competition between calnexin and BiP in the endoplasmic reticulum can lead to the folding or degradation of human thyroperoxidase. Biochemistry, 45, 7380, 2006.
Metabolic Engineering of the Secretory Processing Pathway in Eukaryotes
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45. Chung, J.Y., et al. Effect of doxycycline-regulated calnexin and calreticulin expression on specific thrombopoietin productivity of recombinant Chinese hamster ovary cells. Biotechnol. Bioeng., 85, 539, 2004. 46. Tate, C.G., Whiteley, E., and Betenbaugh, M.J. Molecular chaperones stimulate the functional expression of the cocaine-sensitive serotonin transporter. J. Biol. Chem., 274, 17551, 1999. 47. Higgins, M.K., Demir, M., and Tate, C.G. Calnexin co-expression and the use of weaker promoters increase the expression of correctly assembled Shaker potassium channel in insect cells. Biochim. Biophys. Acta, 1610, 124, 2003. 48. Conesa, A., et al. Calnexin overexpression increases manganese peroxidase production in Aspergillus niger. Appl. Environ. Microbiol., 68, 846, 2002. 49. Ailor, E., et al. A bacterial signal peptidase enhances processing of a recombinant single chain antibody fragment in insect cells. Biochem. Biophys. Res. Commun., 255, 444, 1999. 50. Jones, J., Krag, S.S., and Betenbaugh, M.J. Controlling N-linked glycan site occupancy. Biochim. Biophys. Acta, 1726, 121, 2005. 51. Betenbaugh, M.J., et al. Biosynthesis of human-type N-glycans in heterologous systems. Curr. Opin. Struct. Biol., 14, 601, 2004. 52. Tomiya, N., et al. Comparing N-glycan processing in mammalian cell lines to native and engineered lepidopteran insect cell lines. Glycoconj. J., 21, 343, 2004. 53. Borman, S. Glycosylation Engineering: controlling personalities tame wild sugars on proteins and natural products. Chem. Eng. News, 84, 13, 2006. 54. Yamaguchi, K., et al. Effects of site-directed removal of N-glycosylation sites in human erythropoietin on its production and biological properties. J. Biol. Chem., 266, 20434, 1991. 55. Higuchi, M., et al. Role of sugar chains in the expression of the biological activity of human erythropoietin. J. Biol. Chem., 267, 7703, 1992. 56. Elliott, S., et al. Enhancement of therapeutic protein in vivo activities through glycoengineering. Nat. Biotechnol., 21, 414, 2003. 57. Mason, A.B., et al. Expression of glycosylated and nonglycosylated human transferrin in mammalian cells. Characterization of the recombinant proteins with comparison to three commercially available transferrins. Biochemistry, 32, 5472, 1993. 58. Eklund, E.A. and Freeze, H.H. The congenital disorders of glycosylation: a multifaceted group of syndromes. NeuroRx, 3, 254, 2006. 59. Wada, Y., et al. Structure of serum transferrin in carbohydrate-deficient glycoprotein syndrome. Biochem. Biophys. Res. Commun., 189, 832, 1992. 60. Ferrara, C., et al. Modulation of therapeutic antibody effector functions by glycosylation engineering: influence of Golgi enzyme localization domain and co-expression of heterologous beta1, 4-N-acetylglucosaminyltransferase III and Golgi alpha-mannosidase II. Biotechnol. Bioeng., 93, 851, 2006. 61. Ailor, E., et al. N-glycan patterns of human transferrin produced in Trichoplusia ni insect cells: effects of mammalian galactosyltransferase. Glycobiology, 10, 837, 2000. 62. Lawrence, S.M., et al. Cloning and expression of human sialic acid pathway genes to generate CMPsialic acids in insect cells. Glycoconj. J., 18, 205, 2001. 63. Viswanathan, K., et al. Engineering sialic acid synthetic ability into insect cells: identifying metabolic bottlenecks and devising strategies to overcome them. Biochemistry, 42, 15215, 2003. 64. Viswanathan, K., et al. Engineering intracellular CMP-sialic acid metabolism into insect cells and methods to enhance its generation. Biochemistry, 44, 7526, 2005. 65. Strasser, R., et al. Generation of Arabidopsis thaliana plants with complex N-glycans lacking beta1, 2-linked xylose and core alpha1,3-linked fucose. FEBS Lett., 561, 132, 2004. 66. Koprivova, A., et al. Targeted knockouts of Physcomitrella lacking plant-specific immunogenic N-glycans. Plant Biotechnol. J., 2, 517, 2004. 67. Ko, K., et al. Function and glycosylation of plant-derived antiviral monoclonal antibody. Proc. Natl. Acad. Sci. USA, 100, 8013, 2003.
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68. Bakker, H., et al. An antibody produced in tobacco expressing a hybrid beta-1,4-galactosyltransferase is essentially devoid of plant carbohydrate epitopes. Proc. Natl. Acad. Sci. USA, 103, 7577, 2006. 69. Huether, C.M., et al. Glyco-engineering of moss lacking plant-specific sugar residues. Plant Biol. (Stuttg), 7, 292, 2005. 70. Wee, E.G., et al. Targeting of active sialyltransferase to the plant Golgi apparatus. Plant Cell, 10, 1759, 1998. 71. Li, H., et al. Optimization of humanized IgGs in glycoengineered Pichia pastoris. Nat. Biotechnol., 24, 210, 2006. 72. Chiba, Y., et al. Production of human compatible high mannose-type (Man5GlcNAc2) sugar chains in Saccharomyces cerevisiae. J. Biol. Chem., 273, 26298, 1998. 73. Choi, B.K., et al. Use of combinatorial genetic libraries to humanize N-linked glycosylation in the yeast Pichia pastoris. Proc. Natl. Acad. Sci. USA, 100, 5022, 2003. 74. Hamilton, S.R., et al. Production of complex human glycoproteins in yeast. Science, 301, 1244, 2003. 75. Huffaker, T.C. and Robbins, P.W. Yeast mutants deficient in protein glycosylation. Proc. Natl. Acad. Sci. USA, 80, 7466, 1983. 76. Aebi, M., et al. Cloning and characterization of the ALG3 gene of Saccharomyces cerevisiae. Glycobiology, 6, 439, 1996. 77. Sharma, C.B., Knauer, R., and Lehle, L. Biosynthesis of lipid-linked oligosaccharides in yeast: the ALG3 gene encodes the Dol-P-Man:Man5GlcNAc2-PP-Dol mannosyltransferase. Biol. Chem., 382, 321, 2001. 78. Verostek, M.F., Atkinson, P.H., and Trimble, R.B. Glycoprotein biosynthesis in the alg3 Saccharomyces cerevisiae mutant. I. Role of glucose in the initial glycosylation of invertase in the endoplasmic reticulum. J. Biol. Chem., 268, 12095, 1993. 79. Verostek, M.F., Atkinson, P.H., and Trimble, R.B. Glycoprotein biosynthesis in the alg3 Saccharomyces cerevisiae mutant. II. Structure of novel Man6-10GlcNAc2 processing intermediates on secreted invertase. J. Biol. Chem., 268, 12104, 1993. 80. Hollister, J.R., Shaper, J.H., and Jarvis, D.L. Stable expression of mammalian beta 1,4-galactosyltransferase extends the N-glycosylation pathway in insect cells. Glycobiology, 8, 473, 1998. 81. Jarvis, D.L. and Finn, E.E. Modifying the insect cell N-glycosylation pathway with immediate early baculovirus expression vectors. Nat. Biotechnol., 14, 1288, 1996. 82. Wolff, M.W., et al. Electrophoretic analysis of glycoprotein glycans produced by lepidopteran insect cells infected with an immediate early recombinant baculovirus encoding mammalian beta1, 4galactosyltransferase. Glycoconj. J., 16, 753, 1999. 83. Breitbach, K. and Jarvis, D.L. Improved glycosylation of a foreign protein by Tn-5B1-4 cells engineered to express mammalian glycosyltransferases. Biotechnol. Bioeng., 74, 230, 2001. 84. Davidson, R.C., et al. Functional analysis of the ALG3 gene encoding the Dol-P-Man: Man5GlcNAc2PP-Dol mannosyltransferase enzyme of P. pastoris. Glycobiology, 14, 399, 2004. 85. Bobrowicz, P., et al. Engineering of an artificial glycosylation pathway blocked in core oligosaccharide assembly in the yeast Pichia pastoris: production of complex humanized glycoproteins with terminal galactose. Glycobiology, 14, 757, 2004. 86. Kainuma, M., et al. Coexpression of alpha1,2 galactosyltransferase and UDP-galactose transporter efficiently galactosylates N- and O-glycans in Saccharomyces cerevisiae. Glycobiology, 9, 133, 1999. 87. Hamilton, S.R., et al. Humanization of yeast to produce complex terminally sialylated glycoproteins. Science, 313, 1441, 2006. 88. Oldenburg, J., et al. Vitamin K epoxide reductase complex subunit 1 (VKORC1): the key protein of the vitamin K cycle. Antioxid. Redox Signal, 8, 347, 2006. 89. Stafford, D.W. The vitamin K cycle. J. Thromb. Haemost., 3, 1873, 2005. 90. Berkner, K.L. The vitamin K-dependent carboxylase. Ann. Rev. Nutr., 25, 127, 2005.
Metabolic Engineering of the Secretory Processing Pathway in Eukaryotes
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91. Furie, B. and Furie, B.C. Molecular basis of vitamin K-dependent gamma-carboxylation. Blood, 75, 1753, 1990. 92. Furie, B., Bouchard, B.A., and Furie, B.C. Vitamin K-dependent biosynthesis of gamma-carboxyglutamic acid. Blood, 93, 1798, 1999. 93. Walker, C.S., et al. On a potential global role for vitamin K-dependent gamma-carboxylation in animal systems. Evidence for a gamma-glutamyl carboxylase in Drosophila. J. Biol. Chem., 276, 7769, 2001. 94. Bush, K.A., et al. Hydrophobic amino acids define the carboxylation recognition site in the precursor of the gamma-carboxyglutamic-acid-containing conotoxin epsilon-TxIX from the marine cone snail Conus textile. Biochemistry, 38, 14660, 1999. 95. Stanley, T.B., et al. Identification of a vitamin K-dependent carboxylase in the venom duct of a Conus snail. FEBS Lett., 407, 85, 1997. 96. Bandyopadhyay, P.K., et al. Conantokin-G precursor and its role in gamma-carboxylation by a vitamin K-dependent carboxylase from a Conus snail. J. Biol. Chem., 273, 5447, 1998. 97. Li, T., et al. Identification of a Drosophila vitamin K-dependent gamma-glutamyl carboxylase. J. Biol. Chem., 275, 18291, 2000. 98. Bandyopadhyay, P.K., et al. gamma-Glutamyl carboxylation: an extracellular posttranslational modification that antedates the divergence of molluscs, arthropods, and chordates. Proc. Natl. Acad. Sci. USA, 99, 1264, 2002. 99. Czerwiec, E., et al. Expression and characterization of recombinant vitamin K-dependent gammaglutamyl carboxylase from an invertebrate, Conus textile. Eur. J. Biochem., 269, 6162, 2002. 100. Sun, Y.M., et al. Vitamin K epoxide reductase significantly improves carboxylation in a cell line overexpressing factor X. Blood, 106, 3811, 2005. 101. Hallgren, K.W., et al. r-VKORC1 expression in factor IX BHK cells increases the extent of factor IX carboxylation but is limited by saturation of another carboxylation component or by a shift in the rate-limiting step. Biochemistry, 45, 5587, 2006. 102. Rehemtulla, A., et al. In vitro and in vivo functional characterization of bovine vitamin K-dependent gamma-carboxylase expressed in Chinese hamster ovary cells. Proc. Natl. Acad. Sci. USA, 90, 4611, 1993. 103. Hallgren, K.W., et al. Carboxylase overexpression effects full carboxylation but poor release and secretion of factor IX: implications for the release of vitamin K-dependent proteins. Biochemistry, 41, 15045, 2002. 104. Wajih, N., et al. Increased production of functional recombinant human clotting factor IX by baby hamster kidney cells engineered to overexpress VKORC1, the vitamin K 2,3-epoxide-reducing enzyme of the vitamin K cycle. J. Biol. Chem., 280, 31603, 2005. 105. Wallin, R. and Hutson, S.M. Warfarin and the vitamin K-dependent gamma-carboxylation system. Trends Mol. Med., 10, 299, 2004. 106. Cain, D., Hutson, S.M., and Wallin, R. Assembly of the warfarin-sensitive vitamin K 2,3-epoxide reductase enzyme complex in the endoplasmic reticulum membrane. J. Biol. Chem., 272, 29068, 1997. 107. Wallin, R., Sane, D.C., and Hutson, S.M. Vitamin K 2,3-epoxide reductase and the vitamin K-dependent gamma-carboxylation system. Thromb. Res., 108, 221, 2002. 108. Rost, S., et al. Mutations in VKORC1 cause warfarin resistance and multiple coagulation factor deficiency type 2. Nature, 427, 537, 2004. 109. Li, T., et al. Identification of the gene for vitamin K epoxide reductase. Nature, 427, 541, 2004. 110. Wajih, N., et al. Engineering of a recombinant vitamin K-dependent gamma-carboxylation system with enhanced gamma-carboxyglutamic acid forming capacity: evidence for a functional CXXC redox center in the system. J. Biol. Chem., 280, 10540, 2005. 111. Wajih, N., et al. The inhibitory effect of calumenin on the vitamin K-dependent gammacarboxylation system. Characterization of the system in normal and warfarin-resistant rats. J. Biol. Chem., 279, 25276, 2004.
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112. Wajih, N., Hutson, S.M., and Wallin, R. siRNA silencing of calumenin enhances functional factor IX production. Blood, 108, 3757, 2006. 113. Honore, B. and Vorum, H. The CREC family, a novel family of multiple EF-hand, low-affinity Ca(2 +)-binding proteins localised to the secretory pathway of mammalian cells. FEBS Lett., 466, 11, 2000. 114. Stanley, T.B., et al. The propeptides of the vitamin K-dependent proteins possess different affinities for the vitamin K-dependent carboxylase. J. Biol. Chem., 274, 16940, 1999. 115. Camire, R.M., et al. Enhanced gamma-carboxylation of recombinant factor X using a chimeric construct containing the prothrombin propeptide. Biochemistry, 39, 14322, 2000. 116. Jorgensen, M.J., et al. Expression of completely gamma-carboxylated recombinant human prothrombin. J. Biol. Chem., 262, 6729, 1987. 117. Huber, P., et al. Identification of amino acids in the gamma-carboxylation recognition site on the propeptide of prothrombin. J. Biol. Chem., 265, 12467, 1990. 118. Yan, S.C., et al. Characterization and novel purification of recombinant human protein C from three mammalian cell lines. Biotechnology (NY), 8, 655, 1990. 119. Zhang, L. and Castellino, F.J. A gamma-carboxyglutamic acid (gamma) variant (gamma 6D, gamma 7D) of human activated protein C displays greatly reduced activity as an anticoagulant. Biochemistry, 29, 10828, 1990. 120. McClure, D.B., Walls, J.D., and Grinnell, B.W. Post-translational processing events in the secretion pathway of human protein C, a complex vitamin K-dependent antithrombotic factor. J. Biol. Chem., 267, 19710, 1992. 121. Sugiura, T. and Maruyama, H.B. Factors influencing expression and post-translational modification of recombinant protein C. J. Biotechnol., 22, 353, 1992. 122. Nakayama, K. Furin: a mammalian subtilisin/Kex2p-like endoprotease involved in processing of a wide variety of precursor proteins. Biochem. J., 327 (3), 625, 1997. 123. Ayoubi, T.A., et al. Production of recombinant proteins in Chinese hamster ovary cells overexpressing the subtilisin-like proprotein converting enzyme furin. Mol. Biol. Rep., 23, 87, 1996. 124. Galanopoulou, A.S., Seidah, N.G., and Patel, Y.C. Direct role of furin in mammalian prosomatostatin processing. Biochem. J., 309 (1), 33, 1995. 125. Bratt, T., Cedervall, T., and Akerstrom, B. Processing and secretion of rat alpha 1-microglobulinbikunin expressed in eukaryotic cell lines. FEBS Lett., 354, 57, 1994. 126. Yao, Q., et al. Production and characterization of simian-human immunodeficiency virus-like particles. AIDS Res. Hum. Retroviruses, 16, 227, 2000. 127. Laprise, M.H., Grondin, F., and Dubois, C.M. Enhanced TGFbeta1 maturation in high five cells coinfected with recombinant baculovirus encoding the convertase furin/pace: improved technology for the production of recombinant proproteins in insect cells. Biotechnol. Bioeng., 58, 85, 1998.
11 Engineering Multifunctional Enzyme Systems for Optimized Metabolite Transfer between Sequential Conversion Steps
Robert J. Conrado Cornell University
Thomas J. Mansell Cornell University
Matthew P. DeLisa Cornell University
11.1 11.2 11.3 11.4 11.5
Introduction ������������������������������������������������������������������������������������11-1 Enzyme-to-Enzyme Channeling..................................................11-2 Metabolic Channeling in Primary Metabolism.........................11-3 Metabolic Channeling in Secondary Metabolism.....................11-5 Advantages Conferred from Multifunctional Enzyme Systems �����������������������������������������������������������������������������11-7 11.6 Engineering Multifunctional Enzymes.......................................11-7 11.7 Engineering Metabolic Channels de Novo..............................11-9 11.8 Concluding Remarks ������������������������������������������������������������������� 11-11 References ������������������������������������������������������������������������������������������������� 11-11
11.1 Introduction A growing body of evidence indicates that many cellular metabolic pathways are catalyzed not by free-floating “soluble” enzymes, but via one or more membrane-associated multienzyme complexes. This type of macromolecular organization has important implications for the overall efficiency, specificity, and regulation of metabolic pathways. An ever-increasing number of biochemical and genetic studies on primary and secondary metabolism have laid a solid foundation for this model, providing compelling evidence in favor of the so-called channeling of intermediates between enzyme active sites and co-localization of enzymes inside of a cell. Information from these studies offers new insights into the structuring of biosynthetic pathways within cells, which should lead to more effective means for engineering the production of valuable metabolites with medical and industrial importance. To gain an appreciation for how such complexes may have arisen, one need look no further than “under the hood” of any living cell. The aqueous interior of a cell (hereafter cytoplasm) is a non-Newtonian fluid full of solutes and macromolecules solvated by water. The cytoplasm is bound by a lipid bilayer and is composed of a heterogeneous mixture of carbohydrates, proteins, and lipids. In Escherichia coli, the concentration of protein, DNA, 11-1
11-2
Gene Expression Tools for Metabolic Pathway Engineering
and RNA can reach 300–400 g/l with a significant volume fraction of 20–30% (Ellis, 2001; Ellis and Minton, 2003; Elowitz et al., 1999; Fulton, 1982). Despite this dense macromolecular presence, the bulk fluid-phase cytoplasm viscosity remains similar to that of water at 1.2–1.4 cP with little spatial variation (Fushimi and Verkman, 1991; Kao et al., 1993; Luby-Phelps et al., 1993). Similar to protein crystals, however, this macromolecular crowding leads to two phases of water, bulk water and water of hydration, the latter of which encompasses the macromolecules and is osmotically inactive (Fulton, 1982). This reduced volume of bulk water lowers the cytoplasm solvent capacity, which the cell must work to conserve (Mendes et al., 1992). Living cells have evolved to maintain a cytoplasm dense with macromolecules and it is this crowded environment that dominates the thermodynamic and entropic energies that drive much of cellular chemistry including movement, kinetics, and interactions. As a result of the crowded interior, the translational diffusion of small molecules and macromolecules differ greatly. The combination of increased viscosity, transient interactions, and hindered movement reduces small molecule diffusion three to four fold in the cytoplasm as compared with pure water, while this effect drops off steeply with increasing molecular weight (Arrio-Dupont et al., 2000; Kao et al., 1993). Enzymes such as green fluorescent protein (GFP) experience an 11-fold reduction in their diffusion in the E. coli cytoplasm (Elowitz et al., 1999) while β-galactosidase is slowed more than 1000 fold in cultured muscle cells (Arrio-Dupont et al., 2000). As a result of the macromolecular confinement within cells, there is a large thermodynamic driving force to minimize volume. The impact of this is seen with an exponential increase in the reaction rate with increased crowding, as governed by the thermodynamic activity of the cell constituents. For enzymatic reactions in the cell that are diffusion limited, this molecular crowding dramatically slows the rate, while reaction limited steps proceed up to 50–100 fold more quickly in a crowded environment (Ellis, 2001; Minton, 2001). The energetic and entropic driving force to reduce volume within the cytoplasm also drives enzyme associations in both a specific (e.g., protein folding and assembly) and a nonspecific manner (e.g., protein misfolding and aggregation). For example, the activity coefficient for a 50 kDa subunit of a tetrameric protein may increase 100 fold in a crowded environment, and therefore homodimer equilibrium can shift eight to 40 fold while tetramerization becomes 103–105 times more favorable (Ellis, 2001; Zimmerman and Trach, 1991). However, molecular confinement can also enhance strongly the propensity of incompletely folded polypeptides to misfold and aggregate (Eggers and Valentine, 2001; van den Berg et al., 1999), a process requiring the assistance of powerful molecular chaperone machines (e.g., Hsp60/GroEL) to be prevented or reversed (Hartl and Hayer-Hartl, 2002). Thus, it is clear from the above examples that the extreme protein crowding in the cytoplasm promotes many unique protein interactions that can be productive or nonproductive with respect to cellular function.
11.2 Enzyme-to-Enzyme Channeling One remarkable instance where macromolecular crowding plays an important and oftentimes unexpected role is in cellular metabolism, specifically in the association of metabolic enzymes (Jorgensen et al., 2005; Ovadi and Srere, 2000; Srere, 1987; Winkel, 2004) (Figure 11.1). Enzyme-to-enzyme channeling (a.k.a. metabolic channeling) and compartmentalization of biochemical reaction modules have long been suggested as key components of cellular metabolism. The first direct evidence of this behavior came in the 1940s when David Green isolated all of the Krebs tricarboxylic acid cycle (TCA) enzymes as an aggregated system, which he termed a “multienzyme complex” (Green, 1957). Meanwhile, cell centrifugation techniques were coming online as a means to isolate cell organelles. Studies of whole cell Neurospora (Zalokar, 1960) and Euglena (Kempner and Miller, 1968) demonstrated that the stratified layers that result from centrifugation correspond roughly to the varying cellular constituents. Remarkably, Kempner and Miller reported that “there may be no free or unbound protein in Euglena,” in agreement with Zalokar’s results a decade earlier. Biochemical studies served to corroborate these ideas of multienzyme complexes, as Yanofsky reported that no free intermediate was detected within the bifunctional tryptophan synthase system (Yanofsky, 1960). Despite the mounting evidence for multifunctional enzymes and enzyme systems, Yanofsky’s finding was not widely accepted until X-ray crystallographic evidence from Salmonella revealed a physical tunnel that connects the active sites of the tryptophan synthase complex and serves
11-3
Engineering Multifunctional Enzyme Systems
RuBisCO
R5PI
PGK
PRK GPD
Figure 11.1 Schematic of complex assembly of known Calvin cycle enzyme interactions. An efficient multienzyme system is formed in vivo by sequential metabolic enzyme associations between ribose-5-phosphate isomerase (R5PI), phosphoribulokinase (PRK), ribulose-bisphosphate carboxylase/oxygenase (RuBisCO), phosphoglycerates kinate (PGK), and glyceraldehyde-phosphate dehydrogenase (GPD).
to prevent diffusion of intermediate into the bulk (Hyde et al., 1988). With the more recent development of the yeast two-hybrid system (Fields and Song, 1989), further protein interaction studies have emerged that demonstrate specific interactions between sequential enzymes in glycolysis and the TCA cycle (Srere, 1987; Sullivan et al., 2003; Winkel, 2004). This progression of ideas has not developed without controversy as it has been argued that diffusion of intermediates is sufficiently fast to remove the need for channeling within living cells (Cornish-Bowden, 1991a,b; Cornish-Bowden and Cardenas, 1993) while others argue that the kinetic data used to support the notion of channeling between sequential enzymes in vivo has been misinterpreted (Petersson, 1991; Wu et al., 1991). Nonetheless, a thorough analysis of the temporal behavior of metabolite pools and cellular control mechanisms demonstrate that channeling between enzymes is important in at least a subset of cases (Kholodenko et al., 1996; Welch and Easterby, 1994).
11.3 Metabolic Channeling in Primary Metabolism Despite the controversy over the evolutionary basis for channeling in terms of kinetics and cellular control, numerous multifunctional enzyme systems have become manifest, spanning a multitude of organisms and impacting both primary and secondary metabolism. Examples of channeling and of multienzyme systems, termed metabolons (Srere et al., 1987), abound in primary metabolism including, but not limited to: glycolysis (Malaisse et al., 2004), fatty acid oxidation (Ishikawa et al., 2004), the TCA cycle (Beeckmans and Kanarek, 1987), pyruvate dehydrogenase (Aevarsson et al., 1999) and glycine decarboxylase (Douce et al., 2001) activities, the carboxysome (Kerfeld et al., 2005), the proteasome (Schmidt et al., 2005), the Calvin cycle (Suss et al., 1993), and the biosynthesis of aromatic amino acids (Welch and Gaertner, 1980). The latter two cases, as well as bacterial carboxysomes and the urea cycle, are discussed in detail below. From the standpoint of primary metabolism, the integration of CO2 into carbohydrate structures through the Calvin cycle in plants demonstrates a crucial, highly conserved sequence of enzymes, with many glycolytic enzyme homologs in prokaryotes and other eukaryotic systems. This coordinated enzyme system provides an experimental model for direct enzyme interaction between both consecutive and nonconsecutive catalytic steps. There is strong evidence from both pea and spinach chloroplasts that demonstrate an isolated multienzyme complex comprising as many as ten enzymes of the Calvin cycle (Anderson et al., 1996; Gontero et al., 1988; Rault et al., 1993). When carefully performed, the isolation of this multienzyme system reveals that these enzymes are bound to the stroma-faced thylakoid membrane in situ (Suss et al., 1993), which provides direct access to a necessary ATP pool generated by the electron-transport chain. Kinetic data further demonstrates that enzymatic activity increases when the individual enzymes are studied as an aggregated system (Gontero et al., 1993). The observation that this multienzyme system could be functionally reconstituted in vitro from individually purified enzymes (Graciet et al., 2003) provides the final evidence for the direct interaction between Calvin cycle proteins.
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The polyaromatic pathway, ubiquitous in microorganisms and higher plants, yields additional examples of multifunctional enzyme systems that operate in primary metabolism. Specifically, production of tryptophan requires 13 total enzyme reactions, seven of which constitute the polyaromatic branch, including the production of phenylalanine and tyrosine as aromatic amino acids. In Neurospora and higher fungi, a coordinated and highly efficient multifunctional enzyme has evolved to couple five distinct enzymatic activities in a single polypeptide. This enzymatic aromatic complex (or AROM protein) is encoded by the arom locus, a continuous 4,812-bp open reading-frame without introns that arose by multiple gene fusions (Charles et al., 1986). The output of this locus is a single mRNA transcript that specifies a pentafunctional polypeptide catalyzing five consecutive steps leading to the production of 5-enolpyruvylshikimate 3-phosphate in the shikimate pathway (Hawkins and Smith, 1991; Hawkins et al., 1993; Welch and Gaertner, 1980). Interestingly, the same enzymatic activity in prokaryotes arises from five uncoupled and monofunctional enzymes (Hawkins, 1987). Evidence that the five active sites of the AROM protein behave as a coordinated multienzyme system come from kinetic studies, where higher catalytic activity and lower Km values were observed for the individual catalytic sites within wild-type complexes over complexes with mutations in another active sites (Welch and Gaertner, 1980). Similar studies demonstrated that the AROM protein has a low-level channeling function probably as a result of the close juxtaposition of five active sites and that this channeling function is only physiologically significant when carbon sources are not constant, which organisms frequently experience in situ (Lamb et al., 1992). Another multifunctional enzyme in the polyaromatic pathway of bacteria, fungi, and plants is the tryptophan synthase complex, which is known to exhibit channeling behavior (Srere, 1987). Tryptophan synthase, an α2β2 complex, is a classic example of an enzyme that is thought to “channel” a metabolic intermediate (indole) from the active site of the α subunit to the active site of the β subunit (Figure 11.2). As mentioned earlier, a physical tunnel with a diameter matching that of the indole intermediate exists within the αββα tryptophan synthase complex which allows indole to pass between active sites without diffusing into the bulk (Houben and Dunn, 1990; Hyde et al., 1988; Matchett, 1974). The use of a stochastic simulation to model tryptophan synthase activity verified this channeling phenomenon and found that the free cellular indole concentration remained well below 1 µM, further demonstrating the kinetic advantage of channeling indole (Degenring et al., 2004). In addition to channeling indole, the reaction rates of the tryptophan synthase complex increase one to two orders of magnitude when the α and β subunits combine over the uncomplexed subunits (Hyde et al., 1988; Yanofsky, 1960). The use of rapid quench methods combined with channel-impaired tryptophan synthase mutants provides additional direct evidence for a substrate channeling mechanism (Anderson et al., 1991; Anderson et al., 1995; Schlichting et al., 1994). Multienzyme systems in primary metabolism are not limited to the plant kingdom. Single celled organisms have evolved several types of polyhedral protein-based organelles in which highly organized enzyme sequestration is employed for carbon fixation, and 1,2-propanediol and ethanolamine degradation (Bobik, 2006). Of these organelle activities, the carboxysome, which can achieve
N H
Figure 11.2 Tryptophan synthase αββα complex. A physical tunnel of 25 Å in length (dotted lines) channels indole (C8H7N) from the active site of the α subunit (dark gray) to that of the β subunit (light gray). Each α/β pair act as a functional unit. (Adapted from Hyde, C.C., Ahmed, S.A., Padlan, E.A., Miles, E.W., and Davies, D.R., J. Biol. Chem., 263: 17857–17871, 1988.)
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rudimentary compartmentalization for CO2 fixation at low CO2 levels, represents the first (Gantt and Conti, 1969) and best-documented example of macromolecular protein organelles in bacteria, notably chemoautotrophs (e.g., H. neopolitanus) and cyanobacteria (e.g., Synechocystis). Here, interacting coat proteins, in the absence of any lipid bilayer, encapsulate the enzymatic activity of the carboxysome, forming complexes approaching 200 nm in diameter. Crystal structure analysis of two such coat proteins from Synechocystis carboxysomes, CcmK4 and CcmK2, revealed a hexamer structure, which when packed into sheets leave only 6 Å gaps between enzyme subunits (Kerfeld et al., 2005). Under low CO2 levels, these hexamers form capsids that enclose most, if not all, of cellular ribulose bisphosphate carboxylase oxygenase (RuBisCO). RuBisCO activity fixes carbon through the incorporation of CO2 into ribulose bisphosphate to form 3-phosphoglycerate (Bobik, 2006), which can then be consumed through glycolysis. Furthermore, CcmK4 and CcmK2 coat proteins have evolved a central pore, critical to controlling metabolite flow, rather than merely serving to localize carbon fixing activity in RuBisCO (Kerfeld et al., 2005). In a final example, the urea cycle, which spans the cytoplasmic and mitochondrial domains, provides a compelling case for enzyme compartmentalization along this multienzyme pathway. Radioactive labeling experiments, both from permeabilized cells and in situ, revealed a high degree of channeling between argininosuccinate synthetase, argininosuccinate lyase, and arginase, the three cytoplasmic reactions in the pathway (Cheung et al., 1989). Mathematical simulations validated these hypotheses as this reaction network was modeled with varying degrees of channeling between the cytoplasmic enzymes. The simulation results from the fully channeled case matched the experimental metabolite concentrations most accurately, from the radioactive labeling experiments, while results deviated as the simulated channeled system became increasingly leaky between the cytoplasmic enzymes (Maher et al., 2003).
11.4 Metabolic Channeling in Secondary Metabolism The underpinnings of metabolic channels come from observations over the past 50 years of primary metabolism in microorganisms, plants, and animals; however, more recent studies have documented that multienzyme systems are also important in secondary metabolism. In contrast to the high-affinity protein interactions typical of the Calvin cycle and the tryptophan biosynthetic machinery, channeling in secondary metabolism is often characterized by dynamic, low-affinity enzyme interactions. An illustrative example of this comes from bacteria where chemically and structurally diverse polyketide molecules are synthesized by modular multifunctional polyketide synthases, highly organized enzyme structures that are tethered to cell membranes (Cane et al., 1998; Pfeifer and Khosla, 2001). Similarly, in plant secondary metabolism, numerous studies have revealed the existence of metabolic channeling and compartmentalization in secondary product formation (Jorgensen et al., 2005). Examples include the biosynthesis of isoprenoids (Leivar et al., 2005), alkaloids (Panicot et al., 2002), flavonoids (Burbulis and Winkel-Shirley, 1999; Winkel-Shirley, 1999), cyanogenic glucosides (Kristensen et al., 2005), and phenylpropanoids (Achnine et al., 2004; Winkel-Shirley, 1999), with the latter two products representing the best-documented cases. Cyanogenic glucosides, namely dhurrin, represent a class of secondary compounds produced by a number of plant species for protection against herbivores. In Sorghum bicolor, this membrane-bound, multifunctional enzyme system forms an efficient metabolon whereby only three enzymes are necessary to carry out the seven catalytic steps for production of dhurrin from L-tyrosine (Kahn et al., 1999). Channeling behavior in this pathway has long been suspected as early studies of the dhurrin metabolon were hindered by the low amount of pathway intermediates present in the enzyme reaction mixtures (Moller and Conn, 1979). Radioactive labeling experiments confirmed this hypothesis as dhurrin production was primarily derived from tyrosine, even in the presence of saturating intermediates (Moller and Conn, 1980). According to the proposed reaction mechanism, the first two enzymes of this system, CYP79A1 and CYP71E1, individually exhibit multifunctional qualities, each catalyzing four and two
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Gene Expression Tools for Metabolic Pathway Engineering
reactions, respectively. This behavior was evidenced both endogenously in S. bicolor and in recombinant E. coli clones (Bak et al., 1998; Halkier et al., 1995; Kahn et al., 1999). The channeling properties of this system were not fully appreciated until the S. bicolor three-enzyme pathway was introduced into and studied in Arabidopsis thaliana. In the engineered plant lines carrying only the first two enzymes for dhurrin biosynthesis, a stunted phenotype resulted from accumulation of the toxic intermediate, p-hydroxymandelonitrile. When the third enzyme of the dhurrin pathway was introduced into these transgenic plants, the normal growth phenotype was restored, dhurrin accumulated up to 4% by dry weight, and toxic byproducts were no longer detectable within the plant material (Kristensen et al., 2005). These dramatic results provide compelling evidence for the efficient coupling between the individual enzymes in dhurrin biosynthesis. Another large set of secondary molecules, phenylpropanoids, constitutes one of the best-studied metabolic systems in plants. Various membrane-bound multienzyme complexes, with similar basic architecture, can assemble to transform phenylalanine into several classes of valuable chemical compounds needed by the cell (Figure 11.3). Despite the existence of several branch points in the phenylpropanoid pathway, the first two enzymes, phenylalanine ammonia-lyase (PAL) and cinnamate 4-hydroxylase (C4H), are required for phenylalanine conversion to p-coumaric acid which can then be used to produce lignins, esters, flavonols, tannins, and anthocyanins. Early radioactive labeling experiments of cucumber microsomal fractions suggested channeling activity between the first two required enzymes, PAL and C4H. These studies demonstrated that the channeled intermediate, [14C]cinnamic acid, was a less efficient precursor than the upstream substrate, [3H]phenylalanine, in the production of p-coumaric acid (Czichi and Kindl, 1977). More recent experiments in transgenic tobacco plants have confirmed the loose enzymatic interaction between PAL and C4H through FRET analysis and have identified membrane association of these two enzymes through GFP fusions to PAL (Achnine et al., 2004). These results confirmed earlier cell centrifugation experiments where PAL, C4H, and chalcone synthase (CHS) activity was observed within the ER marker fraction (Wagner and Hrazdina, 1984). Similarly,
C4H 4CL
PAL1
Phenylalanine
CHS
F3’H CHI
DFR
F3H
LAR Proanthocyanidins LDOX BAN
Malonyl CoA
Anthocyanins
C4H PAL1
Phenylalanine
4CL
CHS
Malonyl CoA
F3’H CHI
FLS2 Quercetin (flavonol)
F3H FLS1
Kaempferol (flavonol)
Figure 11.3 Proposed model of phenylpropanoid metabolism in plants. Several ER membrane bound enzymes allow multienzyme complex assembly for metabolite transfer between sequential reaction steps. Key branch points exist resulting from a common set of intermediates between the specific phenylpropanoid pathways. (Adapted from Winkel, B.S., Annu. Rev. Plant Biol., 55: 85–107, 2004.)
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i mmunolocalization studies demonstrated that this membrane complex is tethered at the cytosolic side by several membrane bound protein anchors, including the second enzyme in the pathway, namely C4H, as well as one of the later enzymes in the production of flavonols and anthocyanins, F3’H (Hrazdina, 1992). The final evidence for direct interaction between several of the sequential pathway enzymes was confirmed through both yeast two-hybrid and coimmunoprecipitation studies, which demonstrated that the flavonoid enzymes assemble as a macromolecular complex with contacts between multiple proteins including CHS, chalcone isomerase (CHI), and dihydroflavonol 4-reductase (DFR) (Burbulis and Winkel-Shirley, 1999). The combination of these identified protein–protein interactions in addition to known membrane-bound proteins have led to widely accepted models for the multienzyme organization within the various phenylpropanoid pathways.
11.5 Advantages Conferred from Multifunctional Enzyme Systems There are at least six advantages associated with both the partial or complete assembly of a metabolic pathway into a multi-protein metabolon. First, one of the most significant and direct implications of metabolic channel formation is that sequential active sites are brought into close proximity, which serves both to decrease the intermediate transit time and to increase the catalytic efficiency that results from a net decrease in the Km value for channeled substrates. This is seen most strongly with the tryptophan synthase channel, where direct substrate channeling increases the reaction rates by one to two orders of magnitude over the uncomplexed enzymes (Hyde et al., 1988; Yanofsky, 1960). Second, metabolic channels offer cells a further opportunity to relieve the kinetic constraints within the dynamic intracellular environment. Despite the numerous and complex pathways inside the cytoplasm, the cell maintains its organization through thousands of enzymes that regulate these metabolic intermediates. Of the common metabolic reactions, about 80% of the cellular intermediates have only one function in the cell as they are passed between enzymes (Srere, 1987). Thus, channeling could specifically reduce the cellular levels of intermediates in these reaction networks, while keeping the effective local substrate concentrations high. Thirdly, cellular pathways oftentimes include toxic or unstable intermediates that require swift conversion to ensure organism survival. For instance, in the absence of channeling, dhurrin metabolism was observed to accumulate a toxic intermediate that caused a severe growth defect in A. thaliana (Kristensen et al., 2005). Fourth, channeling can physically prevent inhibitory compounds from accessing and deactivating the active site, and thus can increase the effective kcat of the multienzyme complex. For example, the AROM multifunctional enzyme coordinately protects each of the five active sites from proteolytic inactivation by the first substrate (Welch and Gaertner, 1980). A fifth advantage of multifunctional enzymes within cells is that direct channeling of a particular substrate can prevent metabolic cross talk between competing pathways. This phenomenon is evidenced in several of the metabolons described above, including the dhurrin metabolon, where the first intermediate, Z-p-hydroxyphenylacetaldoxime, can be converted to various glucosinates from a competing pathway in the absence of channeling; this phenotype disappears in the presence of the metabolon (Kholodenko et al., 1996). Finally, sixth dynamic multienzyme complexes, which can rapidly and reversibly assemble and disassemble, allow for differential control over the synthesis of secondary products in response to the intra- and extracellular environment. For instance, the PAL isozymes (part of the plant phenylpropanoid pathway, see above) are differentially expressed in response to stress and light, a phenomenon that likely reflects the highly adaptable nature of the phenylpropanoid enzyme complex and provides alternative branch points that allow for the production of an array of secondary compounds (Winkel-Shirley, 1999).
11.6 Engineering Multifunctional Enzymes Metabolic channels can be recast as multifunctional enzymes, and engineering of these enzyme systems has followed the trajectory of basic multifunctional enzymes in nature, with the tryptophan synthase metabolon (detailed above) standing out as an important example. In recent years, many groups have
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attempted to mimic this bifunctionality by engineering C-to-N-terminal fusions between two proteins which are normally not fused (Beaujean et al., 2000; Lindbladh et al., 1994; Shatalin et al., 1999; Tian and Dixon, 2006). These fusions are chimeric proteins with domains comprising two functional enzymes, usually joined by a short linker sequence. The use of fusion proteins is not a new technology (Gausing, 1977), but a recent development is the analysis of the kinetic properties of bifunctional fusion enzymes compared to their individual kinetic properties. For instance, Lindbladh and colleagues (1994) reported that a fusion of two yeast enzymes showed a two-fold decrease in each Km and a marked resistance to inhibition by an intermediate-scavenger when compared to the two free enzymes. In another work, a bifunctional fusion enzyme was found to produce a metabolic product (genistein) not observed in the case of free enzymes, demonstrating that the bifunctional enzyme decreased diversion of metabolic flux by acting on an otherwise transient intermediate (Tian and Dixon, 2006). A final example comes from Bulow and coworkers who engineered a bifunctional enzyme that contributed to the recycling of cofactors (Prachayasittikul et al., 2006). In that work, a chimeric enzyme that made use of the cofactors NAD and NADH was shown to have up to a two-fold higher recycling rate of cofactors than the respective free enzymes. Collectively, the analyses of these simple engineered fusion proteins demonstrate the advantages of bifunctional enzymes engineered by C-to-N terminal fusions. It is noteworthy, however, that expression of engineered fusion proteins can be problematic, especially in bacteria, as misfolding can arise from nonproductive interactions between the non-natural enzymes (Chang et al., 2005; Frydman et al., 1999; Netzer and Hartl, 1997). Furthermore, there is no guarantee that two translationally fused enzymes will retain their individual activities in the context of the fusion protein. Indeed, many fusion proteins result in one or both of the linked enzymes losing partial or complete functionality (our unpublished observations). Other approaches have been used to engineer bifunctional proteins. One technique, known as domain insertion, was employed by Betton and coworkers (Betton et al., 1997). The protein sequence of TEM-1 β-lactamase was inserted into that of the E. coli maltose binding protein at sites known to be permissive to amino acid insertions. The resulting protein exhibited both maltose binding activity and β-lactamase activity identical to the wild type (free) proteins. Also, maltose was shown to stabilize the active site of the β-lactamase domain. This observation served as evidence that fusion proteins need not always act independently of one another. In fact, many attempts have since been made to couple the functions of two proteins, thereby creating a protein “switch.” Progress toward the creation of engineered allosteric enzymes with switch-like behavior has been made using directed evolution and rational design. Domain insertion has been widely used to rationally engineer control of protein activity (Buskirk et al., 2004; Guntas et al., 2004; Skretas and Wood, 2005) and a review of domain insertion-based strategies for regulating protein function can be found in Ostermeier (2005). Rational protein engineering was also used by Skretas and Wood (2005) to create self-cleaving inteins that are inducible by small molecules. By inserting hormone-binding domains into an engineered intein domain, dose-dependent changes in intein cleavage activity were observed. Finally, computational methods have been instrumental in guiding the construction of bifunctional enzymes as evidenced by the pioneering studies of Hellinga and colleagues who redesigned ligand-binding proteins for new substrates in order to create novel biosensors (Looger et al., 2003; Marvin and Hellinga, 2001). At the same time, the use of directed evolution techniques, whereby combinatorial libraries of protein switches are screened via high-throughput assays, have given rise to potent bifunctional enzymes. For instance, small-molecule dependent inteins similar to those of engineered by Skretas and Wood were also isolated using directed evolution techniques (Buskirk et al., 2004). Another particularly noteworthy case comes from Ostermeier and coworkers who created a family of engineered allosteric enzymes (β-lactamases with activity regulated by the presence or absence of maltose) using a combination of domain insertion and circular permutation techniques (Guntas et al., 2005). By following a repeated algorithm of random domain insertion (inserting the β-lactamase domain randomly throughout the maltose-binding domain), screening, and random circular permutation of β-lactamase at favorable domain insertion points, they were able to create switches with up to 600 fold increases in catalytic activity in the presence of maltose
Engineering Multifunctional Enzyme Systems (a)
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(e)
(b)
(c)
(f )
(d)
Figure 11.4 (See color insert following page 13-20.) Influencing metabolic pathways via the addition of small molecule effectors. (a) Substrate and product molecule resulting from the presence or absence of (b) small molecule effectors that influence (c) active and (d) inactive enzymes within (e, f) metabolic pathways engineered for function that is coupled to ligand-binding events.
compared to activity in the absence of maltose. They also were able to alter the binding specificity of the switch, creating a sucrose-activated β-lactamase. Thus, using the molecular biological tools provided by experimental and computational rational design along with directed evolution, protein activity in several cases has been shown to be easily controlled in a ligand-dependent manner. While it remains to be explicitly demonstrated, the importance of controlling protein function in the context of metabolic pathway engineering cannot be overstated. Facile methods of diverting metabolic flux through a pathway (e.g., by adding a small molecule or changing temperature or pH) could lead to greatly increased metabolic control. Moreover, coupling the functions of multiple metabolic enzymes using multiple small molecule effectors could greatly influence the efficiency of a pathway (Figure 11.4). Thus, one can expect that multifunctional enzymes such as engineered protein switches will likely play a vital role in the future of metabolic engineering.
11.7 Engineering Metabolic Channels de Novo When thinking about the potential of metabolic channeling in a cellular engineering context, it is instructional to consider the experiments of Moller and colleagues who demonstrated that the S. bicolor dhurrin metabolon maintains its channeling behavior when this three enzyme system is introduced into A. thaliana (Kristensen et al., 2005; Moller and Conn, 1979, 1980). This fortuitous and remarkable result begs the question whether metabolic pathways, assembled from novel enzymes that have never coexisted in nature, can be intentionally engineered to exhibit channeling. Such a goal of building metabolic channels de novo will minimally require robust methods for in vivo assembly of enzymes either statically (covalently tethered enzymes) or dynamically (noncovalently tethered enzymes). Unfortunately, while many remarkable examples of assembling and engineering bifunctional enzymes
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have been demonstrated, as detailed above, the ability to move beyond two-protein systems is currently hampered by a lack of tools for assembling protein complexes comprised of more than two proteins. To this point, engineering of multienzyme systems has focused on techniques to genetically fuse or insert catalytic domains rather than the more challenging task of post-translational assembly of threedimensional enzyme structures in vivo, as is the case for naturally occurring metabolic machinery. However, translational fusion of more than two proteins is rather impractical since even the simple fusion of two proteins can pose significant challenges from an expression and folding standpoint (Marston, 1986; Netzer and Hartl, 1997). Fortunately, the molecular toolkit available to the metabolic engineer has expanded greatly over the last decade. Indeed, one can envision the use of protein interacting domains (IDs) as an enabling technology for the post-translational assembly of dynamic enzyme channels. Known for their high affinity interaction with one another, leucine zippers remain one of the best-studied examples of protein IDs. Several investigators have employed the characteristic homo- and heterodimerization between leucine zippers, such as c-Jun and c-Fos, to create artificial protein interactions in vivo (Pandya et al., 2004; Sellers and Struhl, 1989). It is also noteworthy that this artificial assembly of peptide complexes can have phenotypic consequences. For instance, by genetically fusing known IDs of certain proteins, specifically a single chain Fv (scFv) antibody fragment and its corresponding antigen, Mayer and colleagues were able to mandate specific protein assembly in vivo while excluding endogenous interactions (Fujiwara et al., 2002). Since interacting peptide domains of variable affinity are ubiquitous in nature, their continued discovery and pursuit will permit well-defined de novo metabolon creation for optimizing metabolic pathway kinetics. (a)
(b)
(c)
(d) Periplasm
Cytoplasm
NAD+ NADH ADP+Pi
ATP
Figure 11.5 (See color insert following page 13-20.) Proposed model for engineering bacterial multienzyme systems between sequential reactions. One approach to the engineering of dynamic metabolic channels is through the use of a library of generic interacting domains (a) to directionally tether specific recombinant enzymes to one another (b, c) in order to produce any metabolic product of interest. Efficiently directing cellular substrates into metabolons will lead to the creation of engineered metabolic machines in bacteria (d) that mimic, and even rival, those found in higher organisms such as plants.
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11.8 Concluding Remarks A longstanding and overarching goal of metabolic engineering is to assemble efficient metabolic pathways in robust production hosts. Since the toolkit for metabolic and biomolecular engineering has evolved to a state where protein structures can be readily optimized for activity (Endelman et al., 2004; Joo et al., 1999; Otey et al., 2006), solubility (Fisher et al., 2006; Pedelacq et al., 2002; Waldo et al., 1999), and a broad host of other features, it is time to consider enzyme function beyond the context of its substrate and start to take into account the spatial organization of each enzyme in relation to the pathway in which it participates. In the future, we envision an alphabet of IDs (or post-translational covalent linkages such as disulfide bond formation), such that enzymes from any host can be sequentially and posttranslationally organized into a high flux pathway for the production of a particular substrate of interest (Figure 11.5A through C). The ultimate goal stands to achieve fully competent bacterial factories where the substrate is transported into the cell, directly to an efficient assembly line of sequential enzymes, or metabolic channel, whereby the final product is pumped back into the media (Figure 11.5D). While the final achievement of this vision remains far in the distance, immediate opportunities exist for engineering multifunctional enzyme systems whereby we can fully realize the ancient maxim: “the whole is greater than the sum of the parts.”
References Achnine, L., Blancaflor, E.B., Rasmussen, S., and Dixon, R.A. 2004. Colocalization of L-phenylalanine ammonia-lyase and cinnamate 4-hydroxylase for metabolic channeling in phenylpropanoid biosynthesis. Plant Cell., 16: 3098–3109. Aevarsson, A., Seger, K., Turley, S., Sokatch, J.R., and Hol, W.G. 1999. Crystal structure of 2oxoisovalerate and dehydrogenase and the architecture of 2-oxo acid dehydrogenase multienzyme complexes. Nat. Struct. Biol., 6: 785–792. Anderson, K.S., Miles, E.W., and Johnson, K.A. 1991. Serine modulates substrate channeling in tryptophan synthase. A novel intersubunit triggering mechanism. J. Biol. Chem., 266: 8020–8033. Anderson, K.S., Kim, A.Y., Quillen, J.M., Sayers, E., Yang, X.J., and Miles, E.W. 1995. Kinetic characterization of channel impaired mutants of tryptophan synthase. J. Biol. Chem., 270: 29936–29944. Anderson, L.E., Gibbons, J.T., and Wu, X. 1996. Distribution of 10 enzymes of carbon metabolism in pea (Pisum sativum) chroloplasts. Int. J. Plant Sci., 157: 525–538. Arrio-Dupont, M., Foucault, G., Vacher, M., Devaux, P.F., and Cribier, S. 2000. Translational diffusion of globular proteins in the cytoplasm of cultured muscle cells. Biophys. J. 78: 901–907. Bak, S., Kahn, R.A., Nielsen, H.L., Moller, B.L., and Halkier, B.A. 1998. Cloning of three A-type cytochromes P450, CYP71E1, CYP98, and CYP99 from Sorghum bicolor (L.) Moench by a PCR approach and identification by expression in Escherichia coli of CYP71E1 as a multifunctional cytochrome P450 in the biosynthesis of the cyanogenic glucoside dhurrin. Plant Mol. Biol., 36: 393–405. Beaujean, A., Ducrocq-Assaf, C., Sangwan, R.S., Lilius, G., Bulow, L., and Sangwan-Norreel, B.S. 2000. Engineering direct fructose production in processed potato tubers by expressing a bifunctional alpha-amylase/glucose isomerase gene complex. Biotechnol. Bioeng. 70: 9–16. Beeckmans, S. and Kanarek, L. 1987. Enzyme-enzyme interactions as modulators of the metabolic flux through the citric acid cycle. Biochem. Soc. Symp., 54: 163–172. Betton, J.M., Jacob, J.P., Hofnung, M., and Broome-Smith, J.K. 1997. Creating a bifunctional protein by insertion of beta-lactamase into the maltodextrin-binding protein. Nat. Biotechnol., 15: 1276–1279. Bobik, T.A. 2006. Polyhedral organelles compartmenting bacterial metabolic processes. Appl. Microbiol. Biotechnol., 70: 517–525. Burbulis, I.E. and Winkel-Shirley, B. 1999. Interactions among enzymes of the Arabidopsis flavonoid biosynthetic pathway. Proc. Natl. Acad. Sci. USA, 96: 12929–12934.
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Buskirk, A.R., Ong, Y.C., Gartner, Z.J., and Liu, D.R. 2004. Directed evolution of ligand dependence: small-molecule-activated protein splicing. Proc. Natl. Acad. Sci. USA, 101: 10505–10510. Cane, D.E., Walsh, C.T., and Khosla, C. 1998. Harnessing the biosynthetic code: combinations, permutations, and mutations. Science, 282: 63–68. Chang, H.C., Kaiser, C.M., Hartl, F.U., and Barral, J.M. 2005. De novo folding of GFP fusion proteins: high efficiency in eukaryotes but not in bacteria. J. Mol. Biol., 353: 397–409. Charles, I.G., Keyte, J.W., Brammar, W.J., Smith, M., and Hawkins, A.R. 1986. The isolation and nucleotide sequence of the complex AROM locus of Aspergillus nidulans. Nucleic Acids Res., 14: 2201–2213. Cheung, C.W., Cohen, N.S., and Raijman, L. 1989. Channeling of urea cycle intermediates in situ in permeabilized hepatocytes. J. Biol. Chem., 264: 4038–4044. Cornish-Bowden, A. 1991a. How much effect on free metabolite concentrations does channelling have? J. Theor. Biol., 152: 39–40. Cornish-Bowden, A. 1991b. Failure of channelling to maintain low concentrations of metabolic intermediates. Eur. J. Biochem., 195: 103–108. Cornish-Bowden, A. and Cardenas, M.L. 1993. Channelling can affect concentrations of metabolic intermediates at constant net flux: artefact or reality? Eur. J. Biochem., 213: 87–92. Czichi, U. and Kindl, H. 1977. Phenylalanine ammonia-lyase and cinnamic acid hydrolase as assembled consecutive enzymes on microsomal membranes of cucumber cotyledons: Co-operation and subcellular distribution. Planta, 134: 133–143. Degenring, D., Rohl, M., and Uhrmacher, A.M. 2004. Discrete event, multi-level simulation of metabolite channeling. Biosystems, 75: 29–41. Douce, R., Bourguignon, J., Neuburger, M., and Rebeille, F. 2001. The glycine decarboxylase system: a fascinating complex. Trends Plant Sci., 6: 167–176. Eggers, D.K. and Valentine, J.S. 2001. Crowding and hydration effects on protein conformation: a study with sol-gel encapsulated proteins. J. Mol. Biol., 314: 911–922. Ellis, R.J. 2001. Macromolecular crowding: obvious but underappreciated. Trends Biochem. Sci., 26: 597–604. Ellis, R.J. and Minton, A.P. 2003. Cell biology: join the crowd. Nature, 425: 27–28. Elowitz, M.B., Surette, M.G., Wolf, P.E., Stock, J.B., and Leibler, S. 1999. Protein mobility in the cytoplasm of Escherichia coli. J. Bacteriol., 181: 197–203. Endelman, J.B., Silberg, J.J., Wang, Z.G., and Arnold, F.H. 2004. Site-directed protein recombination as a shortest-path problem. Protein Eng Des. Sel., 17: 589–594. Fields, S. and Song, O. 1989. A novel genetic system to detect protein-protein interactions. Nature, 340: 245–246. Fisher, A.C., Kim, W., and DeLisa, M.P. 2006. Genetic selection for protein solubility enabled by the folding quality control feature of the twin-arginine translocation pathway. Protein Sci., 15: 449–458. Frydman, J., Erdjument-Bromage, H., Tempst, P., and Hartl, F.U. 1999. Co-translational domain folding as the structural basis for the rapid de novo folding of firefly luciferase. Nat. Struct. Biol., 6: 697–705. Fujiwara, K., Poikonen, K., Aleman, L., Valtavaara, M., Saksela, K., and Mayer, B.J. 2002. A single-chain antibody/epitope system for functional analysis of protein-protein interactions. Biochemistry, 41: 12729–12738. Fulton, A.B. 1982. How crowded is the cytoplasm? Cell, 30: 345–347. Fushimi, K. and Verkman, A.S. 1991. Low viscosity in the aqueous domain of cell cytoplasm measured by picosecond polarization microfluorimetry. J. Cell Biol., 112: 719–725. Gantt, E. and Conti, S.F. 1969. Ultrastructure of blue-green algae. J. Bacteriol., 97: 1486–1493. Gausing, K. 1977. Regulation of ribosome production in Escherichia coli: synthesis and stability of ribosomal RNA and of ribosomal protein messenger RNA at different growth rates. J. Mol. Biol., 115: 335–354. Gontero, B., Cardenas, M.L., and Ricard, J. 1988. A functional five-enzyme complex of chloroplasts involved in the Calvin cycle. Eur. J. Biochem., 173: 437–443.
Engineering Multifunctional Enzyme Systems
11-13
Gontero, B., Mulliert, G., Rault, M., Giudici-Orticoni, M.T., and Ricard, J. 1993. Structural and functional properties of a multi-enzyme complex from spinach chloroplasts. 2. Modulation of the kinetic properties of enzymes in the aggregated state. Eur. J. Biochem., 217: 1075–1082. Graciet, E., Gans, P., Wedel, N., Lebreton, S., Camadro, J.M., and Gontero, B. 2003. The small protein CP12: a protein linker for supramolecular complex assembly. Biochemistry, 42: 8163–8170. Green, D. 1957. Studies in organized enzyme systems. The Harvey Lectures 52: 177–227. Guntas, G., Mitchell, S.F., and Ostermeier, M. 2004. A molecular switch created by in vitro recombination of nonhomologous genes. Chem. Biol., 11: 1483–1487. Guntas, G., Mansell, T.J., Kim, J.R., and Ostermeier, M. 2005. Directed evolution of protein switches and their application to the creation of ligand-binding proteins. Proc. Natl. Acad. Sci. USA, 102: 11224–11229. Halkier, B.A., Nielsen, H.L., Koch, B., and Moller, B.L. 1995. Purification and characterization of recombinant cytochrome P450TYR expressed at high levels in Escherichia coli. Arch. Biochem. Biophys., 322: 369–377. Hartl, F.U. and Hayer-Hartl, M. 2002. Molecular chaperones in the cytosol: from nascent chain to folded protein. Science, 295: 1852–1858. Hawkins, A.R. 1987. The complex Arom locus of Aspergillus nidulans. Evidence for multiple gene fusions and convergent evolution. Curr. Genet., 11: 491–498. Hawkins, A.R. and Smith, M. 1991. Domain structure and interaction within the pentafunctional arom polypeptide. Eur. J. Biochem., 196: 717–724. Hawkins, A.R., Moore, J.D., and Lamb, H.K. 1993. The molecular biology of the pentafunctional AROM protein. Biochem. Soc. Trans., 21: 181–186. Houben, K.F. and Dunn, M.F. 1990. Allosteric effects acting over a distance of 20–25 A in the Escherichia coli tryptophan synthase bienzyme complex increase ligand affinity and cause redistribution of covalent intermediates. Biochemistry, 29: 2421–2429. Hrazdina, G. (1992) Compartmentation in aromatic metabolism. In: Recent Advances in Phytochemistry. Vol. 26. Stafford, H. and Ibrahim, R. (Eds). New York: Plenum Press, 1–23. Hyde, C.C., Ahmed, S.A., Padlan, E.A., Miles, E.W., and Davies, D.R. 1988. Three-dimensional structure of the tryptophan synthase alpha 2 beta 2 multienzyme complex from Salmonella typhimurium. J. Biol. Chem., 263: 17857–17871. Ishikawa, M., Tsuchiya, D., Oyama, T., Tsunaka, Y., and Morikawa, K. 2004. Structural basis for channelling mechanism of a fatty acid beta-oxidation multienzyme complex. EMBO J., 23: 2745–2754. Joo, H., Lin, Z. and Arnold, F.H. 1999. Laboratory evolution of peroxide-mediated cytochrome P450 hydroxylation. Nature, 399: 670–673. Jorgensen, K., Rasmussen, A.V., Morant, M., Nielsen, A.H., Bjarnholt, N., Zagrobelny, M., Bak, S., and Moller, B.L. 2005. Metabolon formation and metabolic channeling in the biosynthesis of plant natural products. Curr. Opin. Plant Biol., 8: 280–291. Kahn, R.A., Fahrendorf, T., Halkier, B.A., and Moller, B.L. 1999. Substrate specificity of the cytochrome P450 enzymes CYP79A1 and CYP71E1 involved in the biosynthesis of the cyanogenic glucoside dhurrin in Sorghum bicolor (L.) Moench. Arch. Biochem. Biophys., 363: 9–18. Kao, H.P., Abney, J.R., and Verkman, A.S. 1993. Determinants of the translational mobility of a small solute in cell cytoplasm. J. Cell Biol., 120: 175–184. Kempner, E.S. and Miller, J.H. 1968. The molecular biology of Euglena gracilis. V. Enzyme localization. Exp. Cell Res., 51: 150–156. Kerfeld, C.A., Sawaya, M.R., Tanaka, S., Nguyen, C.V., Phillips, M., Beeby, M., and Yeates, T.O. 2005. Protein structures forming the shell of primitive bacterial organelles. Science, 309: 936–938. Kholodenko, B.N., Westerhoff, H.V., and Cascante, M. 1996. Effect of channelling on the concentration of bulk-phase intermediates as cytosolic proteins become more concentrated. Biochem. J., 313 (3): 921–926.
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Kristensen, C., Morant, M., Olsen, C.E., Ekstrom, C.T., Galbraith, D.W., Moller, B.L., and Bak, S. 2005. Metabolic engineering of dhurrin in transgenic Arabidopsis plants with marginal inadvertent effects on the metabolome and transcriptome. Proc. Natl. Acad. Sci. USA, 102: 1779–1784. Lamb, H.K., van den Hombergh, J.P., Newton, G.H., Moore, J.D., Roberts, C.F., and Hawkins, A.R. 1992. Differential flux through the quinate and shikimate pathways. Implications for the channelling hypothesis. Biochem. J., 284 (1): 181–187. Leivar, P., Gonzalez, V.M., Castel, S., Trelease, R.N., Lopez-Iglesias, C., Arro, M., Boronat, A., Campos, N., Ferrer, A., and Fernandez-Busquets, X. 2005. Subcellular localization of Arabidopsis 3-hydroxy-3methylglutaryl-coenzyme A reductase. Plant Physiol., 137: 57–69. Lindbladh, C., Rault, M., Hagglund, C., Small, W.C., Mosbach, K., Bulow, L., Evans, C., and Srere, P.A. 1994. Preparation and kinetic characterization of a fusion protein of yeast mitochondrial citrate synthase and malate dehydrogenase. Biochemistry, 33: 11692–11698. Looger, L.L., Dwyer, M.A., Smith, J.J., and Hellinga, H.W. 2003. Computational design of receptor and sensor proteins with novel functions. Nature, 423: 185–190. Luby-Phelps, K., Mujumdar, S., Mujumdar, R.B., Ernst, L.A., Galbraith, W., and Waggoner, A.S. 1993. A novel fluorescence ratiometric method confirms the low solvent viscosity of the cytoplasm. Biophys. J., 65: 236–242. Maher, A.D., Kuchel, P.W., Ortega, F., de Atauri, P., Centelles, J., and Cascante, M. 2003. Mathematical modelling of the urea cycle. A numerical investigation into substrate channelling. Eur. J. Biochem., 270: 3953–3961. Malaisse, W., Zhang, Y., and Sener, A. 2004. Enzyme-to-enzyme channeling in the early steps of glycolysis. Endocrine, 24: 105–109. Marston, F.A. 1986. The purification of eukaryotic polypeptides synthesized in Escherichia coli. Biochem. J., 240: 1–12. Marvin, J.S. and Hellinga, H.W. 2001. Conversion of a maltose receptor into a zinc biosensor by computational design. Proc. Natl. Acad. Sci. USA, 98: 4955–4960. Matchett, W.H. 1974. Indole channeling by tryptophan synthase of neurospora. J. Biol. Chem., 249: 4041–4049. Mendes, P., Kell, D.B., and Westerhoff, H.V. 1992. Channelling can decrease pool size. Eur. J. Biochem., 204: 257–266. Minton, A.P. 2001. The influence of macromolecular crowding and macromolecular confinement on biochemical reactions in physiological media. J. Biol. Chem., 276: 10577–10580. Moller, B.L. and Conn, E.E. 1979. The biosynthesis of cyanogenic glucosides in higher plants. N-Hydroxytyrosine as an intermediate in the biosynthesis of dhurrin by Sorghum bicolor (Linn) Moench. J. Biol. Chem., 254: 8575–8583. Moller, B.L. and Conn, E.E. 1980. The biosynthesis of cyanogenic glucosides in higher plants. Channeling of intermediates in dhurrin biosynthesis by a microsomal system from Sorghum bicolor (linn) Moench. J. Biol. Chem., 255: 3049–3056. Netzer, W.J. and Hartl, F.U. 1997. Recombination of protein domains facilitated by co-translational folding in eukaryotes. Nature, 388: 343–349. Ostermeier, M. 2005. Engineering allosteric protein switches by domain insertion. Protein Eng. Des. Sel., 18: 359–364. Otey, C.R., Landwehr, M., Endelman, J.B., Hiraga, K., Bloom, J.D., and Arnold, F.H. 2006. Structure-guided recombination creates an artificial family of cytochromes P450. PLoS Biol., 4: e112, 789–798. Ovadi, J. and Srere, P.A. 2000. Macromolecular compartmentation and channeling. Int. Rev. Cytol., 192: 255–280. Pandya, M.J., Cerasoli, E., Joseph, A., Stoneman, R.G., Waite, E., and Woolfson, D.N. 2004. Sequence and structural duality: designing peptides to adopt two stable conformations. J. Am. Chem. Soc., 126: 17016–17024.
Engineering Multifunctional Enzyme Systems
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Panicot, M., Minguet, E.G., Ferrando, A., Alcazar, R., Blazquez, M.A., Carbonell, J., Altabella, T., Koncz, C., and Tiburcio, A.F. 2002. A polyamine metabolon involving aminopropyl transferase complexes in Arabidopsis. Plant Cell, 14: 2539–2551. Pedelacq, J.D., Piltch, E., Liong, E.C., Berendzen, J., Kim, C.Y., Rho, B.S., Park, M.S., Terwilliger, T.C., and Waldo, G.S. 2002. Engineering soluble proteins for structural genomics. Nat. Biotechnol., 20: 927–932. Petersson, G. 1991. No convincing evidence is available for metabolic channeling between enzymes forming dynamic complexes. J. Theor. Biol., 152: 65–69. Pfeifer, B.A. and Khosla, C. 2001. Biosynthesis of polyketides in heterologous hosts. Microbiol. Mol. Biol. Rev., 65: 106–118. Prachayasittikul, V., Ljung, S., Isarankura-Na-Ayudhya, C., and Bulow, L. 2006. NAD(H) recycling activity of an engineered bifunctional enzyme galactose dehydrogenase/lactate dehydrogenase. Int. J. Biol. Sci., 2: 10–16. Rault, M., Giudici-Orticoni, M.T., Gontero, B., and Ricard, J. 1993. Structural and functional properties of a multi-enzyme complex from spinach chloroplasts. 1. Stoichiometry of the polypeptide chains. Eur. J. Biochem., 217: 1065–1073. Schlichting, I., Yang, X.J., Miles, E.W., Kim, A.Y., and Anderson, K.S. 1994. Structural and kinetic analysis of a channel-impaired mutant of tryptophan synthase. J. Biol. Chem., 269: 26591–26593. Schmidt, M., Hanna, J., Elsasser, S., and Finley, D. 2005. Proteasome-associated proteins: regulation of a proteolytic machine. Biol. Chem., 386: 725–737. Sellers, J.W. and Struhl, K. 1989. Changing fos oncoprotein to a jun-independent DNA binding protein with GCN4 dimerization specificity by swapping “leucine zippers”. Nature, 341: 74–76. Shatalin, K., Lebreton, S., Rault-Leonardon, M., Velot, C., and Srere, P.A. 1999. Electrostatic channeling of oxaloacetate in a fusion protein of porcine citrate synthase and porcine mitochondrial malate dehydrogenase. Biochemistry, 38: 881–889. Skretas, G. and Wood, D.W. 2005. Regulation of protein activity with small-molecule-controlled inteins. Protein Sci., 14: 523–532. Srere, P.A. 1987. Complexes of sequential metabolic enzymes. Ann. Rev. Biochem., 56: 89–124. Srere, P.A., Sumegi, B., and Sherry, A.D. 1987. Organizational aspects of the citric acid cycle. Biochem. Soc. Symp., 54: 173–178. Sullivan, D.T., MacIntyre, R., Fuda, N., Fiori, J., Barrilla, J., and Ramizel, L. 2003. Analysis of glycolytic enzyme co-localization in Drosophila flight muscle. J. Exp. Biol., 206: 2031–2038. Suss, K.H., Arkona, C., Manteuffel, R., and Adler, K. 1993. Calvin cycle multienzyme complexes are bound to chloroplast thylakoid membranes of higher plants in situ. Proc. Natl. Acad. Sci. USA, 90: 5514–5518. Tian, L. and Dixon, R.A. 2006. Engineering isoflavone metabolism with an artificial bifunctional enzyme. Planta, 224: 496–507. van den Berg, B., Ellis, R.J., and Dobson, C.M. 1999. Effects of macromolecular crowding on protein folding and aggregation. EMBO J., 18: 6927–6933. Wagner, G. and Hrazdina, G. 1984. Endoplasmic reticulum as a site of phenylpropanoid and flavonoid metabolism in Hippeastrum. Plant Physiol., 74: 901–906. Waldo, G.S., Standish, B.M., Berendzen, J., and Terwilliger, T.C. 1999. Rapid protein-folding assay using green fluorescent protein. Nat. Biotechnol., 17: 691–695. Welch, G.R. and Gaertner, F.H. 1980. Enzyme organization in the polyaromatic-biosynthetic pathway: the arom conjugate and other multienzyme systems. Curr. Top Cell Regul., 16: 113–162. Welch, G.R. and Easterby, J.S. 1994. Metabolic channeling versus free diffusion: transition-time analysis. Trends Biochem. Sci., 19: 193–197. Winkel, B.S. 2004. Metabolic channeling in plants. Annu. Rev. Plant Biol., 55: 85–107. Winkel-Shirley, B. 1999. Evidence for enzyme complexes in phenylpropanoid and flavonoid pathways. Physiol. Plant, 107: 142–149.
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Wu, X.M., Gutfreund, H., Lakatos, S., and Chock, P.B. 1991. Substrate channeling in glycolysis: a phantom phenomenon. Proc. Natl. Acad. Sci. USA, 88: 497–501. Yanofsky, C. 1960. The tryptophan synthetase system. Bacteriol. Rev., 24: 221–245. Zalokar, M. 1960. Cytochemistry of centrifuged hyphae of Neurospora. Exp. Cell Res., 19: 114–132. Zimmerman, S.B. and Trach, S.O. 1991. Estimation of macromolecule concentrations and excluded volume effects for the cytoplasm of Escherichia coli. J. Mol. Biol., 222: 599–620.
12 Practical Pathway Engineering— Demonstration in Integrating Tools 12.1 12.2 12.3 12.4
Introduction ����������������������������������������������������������������������������������� 12-1 Gene Discovery ����������������������������������������������������������������������������� 12-2 Protein Engineering ��������������������������������������������������������������������� 12-2 Metabolic Pathway Regulation ��������������������������������������������������� 12-3
Promoters • Regulation of Multiple Genes in Operons Expression Vectors
12.5 Pathway Optimization Using Functional Genomics............... 12-5
Sung Kuk Lee Ulsan National Institute of Science and Technology (UNIST)
Jay D. Keasling University of California
Comparative Genome Analysis • Transcriptome Analysis Proteome Analysis • Metabolome Analysis • Fluxome Analysis
12.6 Improvement of Cellular Properties...........................................12-9 Extension of the Substrate Range • Elimination of Substrate and Product Toxicity • Improvement of Global Regulatory Functions
12.7 Perspective ����������������������������������������������������������������������������������� 12-10 References ������������������������������������������������������������������������������������������������ 12-11
12.1 Introduction Pathway engineering is the use of recombinant DNA technology to modify existing, or to introduce entirely new, metabolic pathways and regulatory systems within cells in order to improve their capacity to overproduce a desired molecule [10,15,26,41,43]. By channeling metabolic pathways in organisms toward a desired metabolite through rational introduction, modification, and removal of genes, a wide range of valuable products can be produced. Unfortunately, many desired compounds are produced only in small amounts in their native or engineered hosts. In the past, the improvement of metabolic pathways has been done primarily by evolutionary breeding methods or repeated rounds of mutagenesis and selection of a desired phenotype [61,66]. Recent advances in recombinant DNA technology allow more rational approaches to be applied to metabolic pathway engineering. Productivity of the desired metabolite can be increased by carefully balancing the expression of the genes and metabolic flux, both within the metabolic pathway and between the pathway and the host’s native metabolism. Pathway engineering also involves improvement of the overall cellular physiology, extension of the host’s substrate range, and deletion or reduction of by-product formation [24]. In this chapter, we review the most important considerations for successful metabolic pathway engineering and illustrate the concepts with selected examples. 12-1
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Gene Expression Tools for Metabolic Pathway Engineering
12.2 Gene Discovery One of the most significant advances in the discovery of genes encoding a desired metabolic product has been the ability to sequence entire microbial genomes as well as the genomes of important plants and animals. The first whole genome sequence of the bacterium Haemophilus influenzae was reported in July 1995 [14]. To date, according to the National Center for Biotechnology Information (NCBI), 540 microbial genomes have been sequenced along with over 800 currently registered as being in progress. The genome sequencing efforts have greatly enhanced our ability to identify genes from different organisms that can work together in order to design and reconstruct metabolic pathways. Despite the ever-increasing number of sequenced genomes and specific genes encoding various enzymes, plant gene sequence information is still so insufficient that cloning of biosynthetic pathway genes for plant-derived natural products is an intensive and time-consuming area of research. One example is the morphine biosynthesis pathway, which consists of more than 15 enzymatic steps. Although all of the enzymes in the pathway have been characterized, the genes encoding only eight of the enzymes have been identified [45]. Because the genes encoding plant metabolic pathways are not located together in the genome, the entire genome must potentially be sequenced to identify all of the genes in the pathway and avoid the time-consuming process of identifying the genes one-by-one.
12.3 Protein Engineering Once the genes encoding the enzymes in a biosynthetic pathway have been identified in the natural producer, one or more of the enzymes may not be functional in the heterologous host [73]. Also, a number of protein properties such as the yield and kinetics, substrate specificity, reaction selectivity, thermal stability, etc. may need to be improved or altered in order to increase target compound production. There is a need to either find or redesign enzymes that will catalyze the production of the desired molecule with the desired kinetics in the heterologous host. Protein engineering is a widely accepted methodology to construct proteins with desired functions in the production host. There are two general strategies for protein engineering. The first is directed evolution [4,51,55], where an existing protein is subjected to random mutagenesis and the resulting mutants are screened for desired qualities. The advantage of directed evolution is that structural knowledge of the target protein is not required. However, a key requirement for the success of this strategy is the availability or the quality of a high-throughput screen (HTS). Since many metabolic products do not fluoresce or have a particular color, or do not provide a selective benefit to the host, HTSs are not available for improved production of the desired products [73]. Thus, laboratory evolution may not always be appropriate or possible for protein engineering. The other protein engineering strategy is rational design (structure-based computational method) based on a detailed knowledge of the structure and function of the protein to identify and make the desired changes to the amino acids sequence to affect the desired change in the protein structure and/or function [34,35,69]. When it works, this approach can be relatively inexpensive and more rapid than laboratory evolution, as screening or selection for the desired activity is not necessary. However, the drawback of this technique is that detailed structural knowledge of a protein is often unavailable, and even when it is available, it can be very difficult to predict the effects of various mutations. These strategies are not mutually exclusive. Indeed, an approach that combines the best of rational design and directed evolution will probably represent the most powerful approach for development of desired enzymes for a metabolic pathway. Moreover, the exponential growth in protein structure information and function as well as advancements in computer programs and HTSs will greatly expand the capabilities of protein engineering. Recently, a newly designed divergent evolution approach [72] has been
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Practical Pathway Engineering—Demonstration in Integrating Tools
proposed by Keasling and coworkers and does not require HTSs. The methodology uses a mathematical model to mimic the mechanisms of divergent molecular evolution of enzymes for creation of new, biosynthetic enzymes. Using this methodology, they constructed seven specific and active terpene synthases, each catalyzing the synthesis of one or a few very different products, from a single, promiscuous terpene synthase (Figure 12.1). When the redesigned enzymes were incorporated into a heterologous host optimized for production of high levels of the terpene precursor, the microbial host was capable of producing the desired final products. Although they focused on a promiscuous enzyme (which may be one of the easiest test cases), the study demonstrates the feasibility of exploiting the evolvability of an enzyme scaffold to design enzymes with more specificity and higher activity or to create new molecules that do not exist in nature. There is great promise that the use of multiple redesigned or new enzymes in novel or reconstructed metabolic pathways will enable the efficient production of natural and unnatural desired compounds.
12.4 Metabolic Pathway Regulation Recently, successful reconstruction of metabolic pathways in microorganisms to produce valuable natural products such as terpenoids [37,54,72,74], polyketides [47] and nonribosomal peptides [70] in metabolically engineered Escherichia coli or Saccharomyces cerevisiae has been reported. In each of
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7 100 14 15 16 17 18 80 I562T 13 14 15 16 17 18 A336C Retention time (min) Retention time (min) 60 T445C Another γ-humulene synthase 2 E-β-farnesene/Z,E-α-farnesene synthase S484C Optimized α-ylangene production 40 4 F312Q 2 I562L 100 5 100 7 20 5 6 M339A 1 M565L 80 M447F 80 0 12 13 14 15 16 17 18 60 60 Rentention time (min) 40 40 A336S wild-type M339N 4 3 20 S484C 1 3 20 S484C 1 7 I562V 0 M565I 0 12 13 14 15 16 17 18 12 13 14 15 16 17 18 Retention time (min) Retention time (min) sibirene synthase α-longipinene synthase 4 3 100 100 80 80 60 60 40 40 3 20 5 7 20 5 6 0 0 12 13 14 15 16 17 18 12 13 14 15 16 17 18 Retention time (min) Retention time (min) longifolene synthase γ-humulene synthase 2
Figure 12.1 Divergent evolution of novel sesquiterpene synthases from γ-humulene synthases. Chromatograms show the GC-MS analysis for sesquiterpene production from both wild-type (center) and variants of γ-humulene synthases. All γ-humulene synthase variants were designed based on the systematic remodeling and constructed by site-directed saturation mutagenesis and site-directed mutagenesis. (From Yoshikuni, Y., T. E. Ferrin, and J. D. Keasling, Nature 440, 1078–1082, 2006. With permission.)
12-4
Gene Expression Tools for Metabolic Pathway Engineering
these cases, multiple genes encoding the enzymes of the complicated biosynthetic pathways needed to be introduced into the heterologous host, and their expression needed to be coordinated to minimize metabolic burden and the accumulation of toxic intermediates that would decrease final product yields. Coordinating the expression of all of these genes requires the use of a combination of gene expression control elements: promoters and ribosome binding sites (RBSs) of the appropriate strength, mRNA stability elements, riboregulators, etc.
12.4.1 Promoters One of the first considerations in expressing the genes of a biosynthetic pathway is the promoter choice for varying the level of gene expression in individual cells of the culture. The ability to make subtle changes in the expression of enzymes that catalyze the synthesis of desired molecules is important for balancing and optimizing metabolic pathways. Although there are very good expression systems for high-level production of recombinant proteins, simultaneous overexpression of several enzymes from strong promoters may not improve existing pathways but instead stress the organisms by increasing the metabolic burden [23,38]. In most cases, it is sufficient to express the genes encoding a metabolic pathway at relatively low levels, such that only catalytic amounts of the enzymes in the pathway are produced. The ability to fine-tune production of the desired enzymes allows one to balance the metabolic pathway and requires the use of inducible promoters [30,31]. Although regulatable promoters (propionate-inducible [32], salicylate-inducible [71], and tetracycline-inducible [58] promoters) are available, most widely used, inducible promoters (e.g., lac- and PBAD-type promoters) do not provide a wide range of promoter strengths in a continuous manner. Recently, several laboratories have created artificial, constitutive promoters that cover a wide range of gene expression levels [2,22,39,60]. These promoters are of great value for steady-state expression of genes because they have a single expression level without the need for an inducer. On the other hand, the use of regulatable promoters may be better for tuning metabolic pathways and expressing toxic proteins that should be tightly repressed until a certain time point or density of the culture is reached. When tuning the expression of multiple genes encoding the enzymes of a metabolic pathway, it is often desirable to place the genes under the control of independent, inducible promoters. This allows one to alter the expression level of one or more genes independently of other genes in the same metabolic pathway or in competing metabolic pathways [29]. However, some pairs of promoters suffer from cross-talk—an inducer of one promoter affects expression from another promoter—making it difficult to simultaneously and independently control the expression of multiple genes. Lee et al. [29] found that cross-talk between two of the most useful promoters in E. coli (the arabinose-inducible araBAD and the lactose-inducible lac promoters) prevents them from being used simultaneously in the same cell over wide ranges of expression levels. They engineered an araBAD promoter expression system that is more compatible with the IPTG-inducible lac promoter system by the directed evolution of AraC, which allows the promoters to be independently regulated in a cell.
12.4.2 Regulation of Multiple Genes in Operons The synthesis of natural or unnatural products in microorganisms usually involves the introduction of a large number of genes encoding the enzymes of a metabolic pathway. Since the number of inducible promoters available is limited, it is necessary to place several genes under control of a single promoter (Figure 12.2), much like native operons in prokaryotes [37]. In operons, balanced expression of multiple genes can be controlled by altering post-transcriptional processes such as mRNA stability and translation initiation. A good example was reported by Pfleger et al. [48] who described a method for tuning the expression of multiple genes within operons by generating libraries of tunable intergenic regions (TIGRs) (Figure 12.3). Balancing expression of three genes in an operon that encodes a heterologous mevalonate
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Figure 12.2 Production of amorphadiene via the DXP or mevalonate isoprenoid pathways and depiction of the synthetic operons used in this study. Black triangles represent the PLAC promoter. (From Martin, V. J. J., D. J. Pitera, S. T. Withers, J. D. Newman, and J. D. Keasling, Nature Biotechnology 21, 796–802, 2003. With permission.)
biosynthetic pathway resulted in a seven-fold increase in mevalonate production [48]. In addition, the use of RBSs of different strengths allow control of the yield of proteins from a given coding region [11].
12.4.3 Expression Vectors Once the genes encoding a metabolic pathway have been placed under control of an optimal expression system, they can be integrated via a homologous recombination process into the bacterial genome in order to guarantee genetic stability and expression. Alternatively, low-copy plasmids are excellent for expression of heterologous genes when extreme stability and low metabolic burden imposed on the cells are desired [23]. Due to the metabolic burden associated with high plasmid copy number, multi-copy plasmids may not be appropriate for some metabolic engineering applications, even when the objective is to increase the intracellular concentration of an enzyme in order to improve flux through an existing pathway.
12.5 Pathway Optimization Using Functional Genomics The introduction of a heterologous metabolic pathway can have deleterious effects on the host due to the metabolic burden of the introduced pathway, toxicity of the product or metabolic intermediates, improper folding, or location of the enzymes in the metabolic pathway, etc. To maximize product titers/ yields, it is important to alleviate, to the extent possible, the negative impacts of the heterologous pathway on the host. Because cellular metabolism is complex, it is often difficult to determine how a heterologous pathway is deleterious to the host and then use directed pathway engineering to alleviate the negative interactions. In the past, such deleterious effects of the introduced or native, up-regulated pathways were alleviated by random mutagenesis of the host and selection for improved producers. However effective, little information about the reasons for the improvements are gleaned from such efforts and therefore little can be learned for future directed, pathway engineering projects.
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Figure 12.3 Optimization of mevalonate production in E. coli using tunable intergenic regions (TIGRs). (a) Biosynthetic pathway of heterologous artemisinin production. (b) Reactions and metabolic intermediates in the top half of the mevalonate-based isopentenyl pyrophosphate biosynthetic pathway (shown in less detail in the dashed box of (a)). AtoB (acetoacetyl-CoA thiolase), HMGS (HMG-CoA synthase) and tHMGR (truncated HMG-CoA reductases). (c) Structure of the two intergenic regions that resulted from the TIGR approach. Dashed boxes represent RBSs. (From Pfleger, B. F., D. J. Pitera, C. D Smolke, and J. D. Keasling, Nature Biotechnology 24, 1027–1032, 2006. With permission.)
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The recently developed, functional genomics techniques that have been employed to examine the expression of all genes and the production (or consumption) of proteins and metabolites can be exploited to better diagnose the problems created by the introduction of the heterologous metabolic pathway. The techniques allow a better understanding of the impact of the heterologous pathway on the host by mapping the cellular effects of genetic modifications at the level of DNA sequence (comparative genomic analysis), mRNA (transcriptome analysis), proteins (proteome analysis), metabolites (metabolome analysis), and fluxes (flux analysis) between strains with the desired phenotype and corresponding wild-type strains. In pathway optimization, therefore, several of these different techniques should be used in parallel to obtain a global understanding of the host and the introduced pathway and to identify and engineer targets to improve production [7] (Figure 12.4).
12.5.1 Comparative Genome Analysis Analysis of the genomes of two or more related organisms that have different functions, products, or product titers can be useful for identifying genes responsible for the differences among the organisms and for creating modified organisms with the desired traits. A good example of comparative genomic analysis was reported by Ohnishi et al. [42] who identified mutations beneficial for increased production of L-lysine in Corynebacterium glutamicum. Sixteen genes known to be involved in production Genome
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Figure 12.4 Analytical techniques employed in functional genomics. Genes and factors conferring desired phenotype can be identified through comparison of DNA sequence, transcript profiles, proteome analysis, metabolite profiling, or flux analyses of strains with the desired phenotype with corresponding reference strains.
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of L-lysine were selected and sequenced. As a result, point mutations were identified in five genes, and introduction of three of these mutations into the wild-type strain increased L-lysine productivity two-fold. Because they compared only a limited fraction of the genome (only those genes directly responsible for production of L-lysine), it is likely that several mutations in other genes were present and contributed to the high product titers. Although it might have been too expensive or impractical at the time to sequence the entire genome to find all mutations, continued decreases in the cost and time to sequence whole genomes is making genome-wide sequence comparisons less costly and more routine, enabling one to determine all mutations in an evolved host.
12.5.2 Transcriptome Analysis The transcriptome is the collection of all mRNA molecules present in one or a population of cells. Unlike the genome, which is roughly fixed for a given cell, the transcriptome can vary dramatically with external environmental conditions. DNA arrays are the most commonly used tool for genome-wide transcription analysis, and the most direct application of DNA arrays to metabolic engineering is the identification of discriminatory genes characteristic of desired physiological states, such as those contributing to high productivity [62]. Mutations cannot directly be identified by genome-wide transcription analysis, but the transcriptome analysis allows mapping of all changes at the mRNA level, which should be diagnostic of a particular genetic or regulatory change. There are some examples of how this technique has been used in the field of metabolic engineering. Recently, the technique was applied to ethanol tolerant E. coli strains [16]. The transcript profile of the ethanol-tolerant strain indicated increased glycine metabolism, a loss of function of a regulatory protein, and increased metabolism of serine and pyruvate. Similar work in a mutant strain of S. cerevisiae capable of importing galactose at a rate three-fold higher than wild-type strains found that PGM2, which encodes the major isoenzyme of phosphoglucomutase, was slightly up-regulated relative to that in wild-type strains [6,44]. By overexpressing PGM2 the galactose uptake rate could be increased by 70% compared to that of the reference strain [6,44]. One disadvantage of the application of transcript profiling to metabolic engineering is that mRNA levels are not always proportional to the expression levels of the proteins they encode. Thus, it is necessary to profile biomolecules that are more indicative of the physiology of the cell, namely the proteome, metabolome, and fluxome.
12.5.3 Proteome Analysis The proteome of a given cell is the collection of proteins produced by it. Proteome analysis, the large-scale analysis of all, or most, proteins in an organism [46], potentially allows one to understand gene regulation better than transcriptome analysis, but it is one step away from the direct effects of genetic changes. The changes in the protein levels in different mutants or under different environmental conditions can be determined and used to identify target enzymes/proteins for further manipulation [20]. Most generally, the complex protein mixture is separated using either two-dimensional gel electrophoresis or liquid chromatograph prior to identification and quantification of individual proteins using mass spectrometry [1,17,59]. Lee and coworkers examined variations in protein profiles of E. coli in response to the overproduction of human leptin, a serine-rich (11.6% of total amino acids) protein, using two-dimensional gel electrophoresis. Based on the information gleaned from the proteomics analysis, they coexpressed and cycK, encoding cysteine synthase, with the leptin gene and successfully enhanced leptin productivity two-fold and the host cell growth rate [19]. Using a quantitative shotgun proteomics technique, Lee et al. [28] examined the proteomic changes in E. coli engineered to express seven to nine genes for the degradation of cis-1,2-dichloroethylene (cis-DCE). They found that the metabolic engineering that leads to enhanced aerobic degragation of cis-DCE and reduced toxicity from cis-DCE epoxide resulted in enhanced synthesis of glutathione coupled with stress response to reactive oxygen species and repression of enzymes involved in fatty acid synthesis, gluconeogenesis, and the tricarboxylic acid cycle [28].
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12.5.4 Metabolome Analysis The metabolome represents the collection of all metabolites in an organism and are the end products of gene expression [8,9]. Metabolome analysis covers the quantification of intracellular and extracellular metabolite concentrations in the response of living systems to physiological stimuli or genetic modification. Analysis of the metabolome might aid inverse metabolic engineering by giving insight into metabolic function of mutated genes by comparison with a reference strain. Since the metabolism of an organism directly impacts synthesis of macromolecules and thus the physiology of the cells, metabolomics should yield a more complete picture of the impact of pathway engineering on the cell. With the aid of sophisticated nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), capillary electrophoresis-mass spectrometry (CE-MS), and liquid chromatography-mass spectrometry (LC-MS), high-throughput quantitative analysis of metabolites has become possible [33]. Its use for diagnosing changes to the host cell has yet to be demonstrated.
12.5.5 Fluxome Analysis The amount of material that flows through a cell’s metabolic pathways is perhaps the largest determining factor in the growth and physiology of the host and in the productivity of the desired metabolite. Determination of as many fluxes as possible is the goal of fluxome analysis, and it is the result of this analysis that can potentially be the most insightful for improving metabolic pathways. By determining how the cell’s metabolic fluxes change in response to environmental or genetic perturbation, one can begin to determine the flexibility of the network to genetic changes that might improve product titers [5,25]. Metabolic flux analysis can be performed in several ways: under determined, exactly determined, and over determined [63]. All methods rely on knowing the underlying metabolic network, which can be gotten more easily lately with the advances in genome sequencing and annotation. Under determined analysis of metabolic fluxes typically relies on measuring the inputs (substrate uptake rates) and outputs (biomass composition and growth rate, synthesis rates of extracellular products, etc.), posing an objective function, and using an optimization routine to calculate the intracellular metabolic fluxes [52,67,68]. Exactly determined methods typically rely on the same types of information but require the determination of one or more fluxes or elimination of unknowns to make the system soluble. Over determined methods typically use stable isotopes (e.g., 13C) to trace the isotope transition from substrate to product (typically, but not exclusively, proteogenic amino acids) [21,56,64,65]. In addition to its other benefits, isotopomer analysis allows one to determine the error in specific metabolic fluxes. The major challenge in fluxome analysis is that flux data rarely reveal a direct engineering target, primarily because fluxes are controlled at multiple levels (e.g., transcription, translation, mRNA degradation, protein activation/inactivation, and allosteric control of enzymes) and because changes in the expression of a particular enzyme can have dramatic impacts on the metabolic network and in unpredictable ways [40]. Thus, metabolic flux analysis is most often used to document changes in metabolic fluxes after a particular genetic or environmental change has been effected. The challenge going forward will be to integrate the data from the various omics methods into a coherent representation of the cell to allow one to predict what genes should be changed to effect the desired impact on the productivity of the desired metabolite [57].
12.6 Improvement of Cellular Properties Yeast, bacterial, animal, and plant cells can be engineered into excellent and environmentally friendly “factories” for commercially interesting chemicals. For cost savings and high production, it is important to improve the biological cell factories by manipulating enzymatic, transport and regulatory functions that are not directly related to the engineered metabolic pathways. These cell properties include extension of the substrate range, elimination of substrate and product toxicity, and improvement of global
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regulatory functions. The introduction of recombinant DNA technology has allowed a more directed intervention into the genetics of the production hosts.
12.6.1 Extension of the Substrate Range To reduce the high cost of industrial biosynthetic production of useful compounds, it is desirable to increase the use of inexpensive and widely available substrates. Sugars (from sugarcane and sugar beets) and starches (from corn and root crops) have been used as a potential substrate for ethanol fermentation by microbial processes. Recently, attention shifted from starch to cellulose and hemicellulose (from wood and plants), because it is the most abundant source of carbohydrates in biomass for the production of bioenergy and biomaterials [53]. The major fermentable sugars derived from cellulose and hemicellulose hydrolysis are glucose and xylose [75]. Unlike glucose, xylose cannot be fermented by S. cerevisiae, the traditional ethanol producer. Through metabolic engineering, S. cerevisiae has been engineered to ferment xylose to ethanol [61]. In another example, lactose is abundant in milk and a major constituent of whey, a by-product of cheese production. Some attempts to use whey as a substrate for biotech processes have been made by expressing either lactose lactose permease plus β-galactosidase from Klyveromyces [12] or a secreted β-galactosidase from Aspergillus [27] in order to convert lactose to fermentable sugars, galactose and glucose.
12.6.2 Elimination of Substrate and Product Toxicity In many biological production processes, high concentrations of substrate or product are inhibitory or toxic to the enzymatic or cellular catalyst [18,36]. Production stops when the product concentration reaches an inhibitory level. The same problem is true for toxic substrates. The general solution for such inhibitory phenomena is to maintain the concentrations of the inhibitors as low as possible by altering and improving process conditions. The product concentration can be kept low inside the cell by transporting the final products out of the cell (by overexpressing native or engineered transporters) or by extracting the product from the medium. In the case of toxic intermediates, balancing production and consumption of the intermediate without significantly slowing product formation is essential [49,50].
12.6.3 Improvement of Global Regulatory Functions Reprogramming the cell for improved product formation or tolerance to a particular product or environment can be an arduous task, typically involving the manipulation of several to tens to hundreds of genes. Using the typical, single-gene modification methods such a task can be daunting, if not impossible. To enable multigene modifications to a host cell, an alternative approach for reprogramming gene transcription by altering global transcriptional regulators was proposed by Stephanopoulos and coworkers [3]. This cellular engineering approach, termed global transcription machinery engineering (gTME), generates diversity at the transcriptional level by altering key proteins that regulate the transcription of many genes simultaneously. Using this technique, Stephanopoulos and coworkers improved ethanol tolerance and production by S. cerevisiae [3].
12.7 Perspective Metabolic pathway engineering has been used to improve the production of existing metabolites and enable the production of new metabolites. It is now a rapidly growing area with great potential to impact industrial biocatalysis. Rapid advances in recombinant DNA technology, functional genomics, analytical technologies, the design of artificial biological systems and the understanding of their natural counterparts, known as synthetic biology [13], will extend the applications of pathway
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engineering. In the future, it will be a powerful tool to synthesize structurally diverse and complex chemicals and useful compounds from abundant renewable biomass [53]. Thus, metabolic pathway engineering will have a significant impact on society in terms of the production of fuels, chemicals, materials and novel drugs.
References 1. Aebersold, R. and M. Mann. 2003. Mass spectrometry-based proteomics. Nature 422:198–207. 2. Alper, H., C. Fischer, E. Nevoigt, and G. Stephanopoulos. 2005. Tuning genetic control through promoter engineering. Proceedings of the National Academy of Sciences of the United States of America 102: 12678–12683. 3. Alper, H., J. Moxley, E. Nevoigt, G. R. Fink, and G. Stephanopoulos. 2006. Engineering yeast transcription machinery for improved ethanol tolerance and production. Science 314:1565–1568. 4. Arnold, F. H. 2001. Combinatorial and computational challenges for biocatalyst design. Nature 409:253–257. 5. Becker, J., C. Klopprogge, O. Zelder, E. Heinzle, and C. Wittmann. 2005. Amplified expression of fructose 1,6-bisphosphatase in Corynebacterium glutamicum increases in vivo flux through the pentose phosphate pathway and lysine production on different carbon sources. Applied and Environmental Microbiology 71:8587–8596. 6. Bro, C., S. Knudsen, B. Regenberg, L. Olsson, and J. Nielsen. 2005. Improvement of galactose uptake in Saccharomyces cerevisiae through overexpression of phosphoglucomutase: Example of transcript analysis as a tool in inverse metabolic engineering. Applied and Environmental Microbiology 71:6465–6472. 7. Bro, C. and J. Nielsen. 2004. Impact of ‘ome’ analyses on inverse metabolic engineering. Metabolic Engineering 6:204–211. 8. Buchholz, A., J. Hurlebaus, C. Wandrey, and R. Takors. 2002. Metabolomics: quantification of intracellular metabolite dynamics. Biomolecular Engineering 19:5–15. 9. Burja, A. M., S. Dhamwichukorn, and P. C. Wright. 2003. Cyanobacterial postgenomic research and systems biology. Trends in Biotechnology 21:504–511. 10. Chotani, G., T. Dodge, A. Hsu, M. Kumar, R. LaDuca, D. Trimbur, W. Weyler, and K. Sanford. 2000. The commercial production of chemicals using pathway engineering. Biochimica Et Biophysica Acta-Protein Structure and Molecular Enzymology 1543:434–455. 11. Desmit, M. H. and J. Vanduin. 1994. Translational initiation on structured messengers—another role for the Shine-Dalgarno interaction. Journal of Molecular Biology 235:173–184. 12. Domingues, L., J. A. Teixeira, and N. Lima. 1999. Construction of a flocculent Saccharomyces cerevisiae fermenting lactose. Applied Microbiology and Biotechnology 51:621–626. 13. Drubin, D. A., J. C. Way, and P. A. Silver. 2007. Designing biological systems. Genes & Development 21:242–254. 14. Fleischmann, R. D., M. D. Adams, O. White, R. A. Clayton, E. F. Kirkness, A. R. Kerlavage, C. J. Bult, J. F. Tomb, B. A. Dougherty, J. M. Merrick, K. Mckenney, G. Sutton, W. Fitzhugh, C. Fields, J. D. Gocayne, J. Scott, R. Shirley, L. I. Liu, A. Glodek, J. M. Kelley, J. F. Weidman, C. A. Phillips, T. Spriggs, E. Hedblom, M. D. Cotton, T. R. Utterback, M. C. Hanna, D. T. Nguyen, D. M. Saudek, R. C. Brandon, L. D. Fine, J. L. Fritchman, J. L. Fuhrmann, N. S. M. Geoghagen, C. L. Gnehm, L. A. Mcdonald, K. V. Small, C. M. Fraser, H. O. Smith, and J. C. Venter. 1995. Whole-genome random sequencing and assembly of Haemophilus-Influenzae Rd. Science 269:496–512. 15. Flores, N., J. Xiao, A. Berry, F. Bolivar, and F. Valle. 1996. Pathway engineering for the production of aromatic compounds in Escherichia coli. Nature Biotechnology 14:620–623. 16. Gonzalez, R., H. Tao, J. E. Purvis, S. W. York, K. T. Shanmugam, and L. O. Ingram. 2003. Gene arraybased identification of changes that contribute to ethanol tolerance in ethanologenic Escherichia coli: Comparison of KO11 (Parent) to LY01 (resistant mutant). Biotechnology Progress 19:612–623.
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17. Gygi, S. P., G. L. Corthals, Y. Zhang, Y. Rochon, and R. Aebersold. 2000. Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology. Proceedings of the National Academy of Sciences of the United States of America 97:9390–9395. 18. Hack, C. J., J. M. Woodley, M. D. Lilly, and J. M. Liddell. 2000. Design of a control system for biotransformation of toxic substrates: toluene hydroxylation by Pseudomonas putida UV4. Enzyme and Microbial Technology 26:530–536. 19. Han, M. J., K. J. Jeong, J. S. Yoo, and S. Y. Lee. 2003. Engineering Escherichia coli for increased productivity of serine-rich proteins based on proteome profiling. Applied and Environmental Microbiology 69:5772–5781. 20. Han, M. J. and S. Y. Lee. 2003. Proteome profiling and its use in metabolic and, cellular engineering. Proteomics 3:2317–2324. 21. Hellerstein, M. K. and E. Murphy. 2004. Stable isotope-mass spectrometric measurements of molecular fluxes in vivo: Emerging applications in drug development. Current Opinion in Molecular Therapeutics 6:249–264. 22. Jensen, P. R. and K. Hammer. 1998. Artificial promoters for metabolic optimization. Biotechnology and Bioengineering 58:191–195. 23. Jones, K. L. K., S.-W., and J. D. Keasling. 2000. Low-copy plasmids can perform as well as or better than high-copy plasmids for metabolic engineering of bacteria. Metabolic Engineering 2:328–338. 24. Kern, A., E. Tilley, I. S. Hunter, M. Legisa, and A. Glieder. 2007. Engineering primary metabolic pathways of industrial micro-organisms. Journal of Biotechnology 129:6–29. 25. Kiefer, P., E. Heinzle, O. Zelder, and C. Wittmann. 2004. Comparative metabolic flux analysis of lysine-producing Corynebacterium glutamicum cultured on glucose, or fructose. Applied and Environmental Microbiology 70:229–239. 26. Kleerebezem, M. and J. Hugenholtz. 2003. Metabolic pathway engineering in lactic acid bacteria. Current Opinion in Biotechnology 14:232–237. 27. Kumar, V., S. Ramakrishnan, T. T. Teeri, J. K. Knowles, and B. S. Hartley. 1992. Saccharomyces cerevisiae cells secreting an Aspergillus niger beta-galactosidase grow on whey permeate. Biotechnology (NY) 10:82–85. 28. Lee, J., L. Cao, S. Y. Ow, M. E. Barrios-Llerena, W. Chen, T. K. Wood, and P. C. Wright. 2006. Proteome changes after metabolic engineering to enhance aerobic mineralization of cis-1,2-dichloroethylene. Journal of Proteome Research 5:1388–1397. 29. Lee, S. K., H. H. Chou, B. F. Pfleger, J. D. Newman, Y. Yoshikuni, and J. D. Keasling. 2007. Directed evolution of AraC for improved compatibility of arabinose and lactos-inducible promoters. Applied and Environmental Microbiology 73: 5711–5715. 30. Lee, S. K. and J. D. Keasling. 2006. Effect of glucose or glycerol as the sole carbon source on gene expression from the Salmonella prpBCDE promoter in Escherichia coli. Biotechnology Progress 22:1547–1551. 31. Lee, S. K. and J. D. Keasling. 2005. A propionate-inducible expression system for enteric bacteria. Applied and Environmental Microbiology 71:6856–6862. 32. Lee, S. K. and J. D. Keasling. 2006. Propionate-regulated high-yield protein production in Escherichia coli. Biotechnology and Bioengineering 93:912–918. 33. Lee, S. Y., D. Y. Lee, and T. Y. Kim. 2005. Systems biotechnology for strain improvement. Trends in Biotechnology 23:349–358. 34. Li, Q. S., U. Schwaneberg, M. Fischer, J. Schmitt, J. Pleiss, S. Lutz-Wahl, and R. D. Schmid. 2001. Rational evolution of a medium chain-specific cytochrome P-450 BM-3 variant. Biochimica Et Biophysica Acta-Protein Structure and Molecular Enzymology 1545:114–121. 35. Looger, L. L., M. A. Dwyer, J. J. Smith, and H. W. Hellinga. 2003. Computational design of receptor and sensor proteins with novel functions. Nature 423:185–190. 36. Marshall, C. T. and J. M. Woodley. 1995. Process synthesis for multistep microbial conversions. Bio-Technology 13:1072–1078.
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37. Martin, V. J. J., D. J. Pitera, S. T. Withers, J. D. Newman, and J. D. Keasling. 2003. Engineering a mevalonate pathway in Escherichia coli for production of terpenoids. Nature Biotechnology 21:796–802. 38. Mattanovich, D., B. Gasser, H. Hohenblum, and M. Sauer. 2004. Stress in recombinant protein producing yeasts. Journal of Biotechnology 113:121–135. 39. Mijakovic, I., D. Petranovic, and P. R. Jensen. 2005. Tunable promoters in systems biology. Current Opinion in Biotechnology 16:329–335. 40. Nielsen, J. 2001. Metabolic engineering. Applied Microbiology and Biotechnology 55:263–283. 41. Nielsen, J. 1998. The role of metabolic engineering in the production of secondary metabolites. Current Opinion in Microbiology 1:330–336. 42. Ohnishi, J., S. Mitsuhashi, M. Hayashi, S. Ando, H. Yokoi, K. Ochiai, and M. Ikeda. 2002. A novel methodology employing Corynebacterium glutamicum genome information to generate a new L-lysine-producing mutant. Applied Microbiology and Biotechnology 58:217–223. 43. Ostergaard, S., L. Olsson, and J. Nielsen. 2000. Metabolic engineering of Saccharomyces cerevisiae. Microbiology and Molecular Biology Reviews 64:34–50. 44. Ostergaard, S., C. Roca, B. Ronnow, J. Nielsen, and L. Olsson. 2000. Physiological studies in aerobic batch cultivations of Saccharomyces cerevisiae strains harboring the MEL1 gene. Biotechnology and Bioengineering 68:252–259. 45. Page, J. E. 2005. Silencing nature’s narcotics: metabolic engineering of the opium poppy. Trends in Biotechnology 23:331–333. 46. Pandey, A. and M. Mann. 2000. Proteomics to study genes and genomes. Nature 405:837–846. 47. Pfeifer, B. A., C. C. C. Wang, C. T. Walsh, and C. Khosla. 2003. Biosynthesis of yersiniabactin, a complex polyketide-nonribosomal peptide, using Escherichia coli as a heterologous host. Applied and Environmental Microbiology 69:6698–6702. 48. Pfleger, B. F., D. J. Pitera, C. D Smolke, and J. D. Keasling. 2006. Combinatorial engineering of intergenic regions in operons tunes expression of multiple genes. Nature Biotechnology 24:1027–1032. 49. Pfleger, B. F., D. J. Pitera, J. D. Newman, V. J. J. Martin, and J. D. Keasling. 2007. Microbial sensors for small molecules: Development of a mevalonate biosensor. Metabolic Engineering 9:30–38. 50. Pitera, D. J., C. J. Paddon, J. D. Newman, and J. D. Keasling. 2007. Balancing a heterologous mevalonate pathway for improved isoprenoid production in Escherichia coli. Metabolic Engineering 9:193–207. 51. Powell, K. A., S. W. Ramer, S. B. del Cardayre, W. P. C. Stemmer, M. B. Tobin, P. F. Longchamp, and G. W. Huisman. 2001. Directed evolution and biocatalysis. Angewandte Chemie-International Edition 40:3948–3959. 52. Pramanik, J. and J. D. Keasling. 1997. Stoichiometric model of Escherichia coli metabolism: Incorporation of growth-dependent biomass composition and mechanistic engergy requirements. Biotechnology and Bioengineering 50:398–421. 53. Ragauskas, A. J., C. K. Williams, B. H. Davison, G. Britovsek, J. Cairney, C. A. Eckert, W. J. Frederick, J. P. Hallett, D. J. Leak, C. L. Liotta, J. R. Mielenz, R. Murphy, R. Templer, and T. Tschaplinski. 2006. The path forward for biofuels and biomaterials. Science 311:484–489. 54. Ro, D. K., E. M. Paradise, M. Ouellet, K. J. Fisher, K. L. Newman, J. M. Ndungu, K. A. Ho, R. A. Eachus, T. S. Ham, J. Kirby, M. C. Y. Chang, S. T. Withers, Y. Shiba, R. Sarpong, and J. D. Keasling. 2006. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. Nature 440:940–943. 55. Rohlin, L., M. K. Oh, and J. C. Liao. 2001. Microbial pathway engineering for industrial processes: evolution, combinatorial biosynthesis and rational design. Current Opinion in Microbiology 4:330–335. 56. Sauer, U. 2004. High-throughput phenomics: experimental methods for mapping fluxomes. Current Opinion in Biotechnology 15:58–63.
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57. Sauer, U. 2006. Metabolic networks in motion: C-13-based flux analysis. Molecular Systems Biology 2: 1774. 58. Skerra, A. 1994. Use of the tetracycline promoter for the tightly regulated production of a murine antibody fragment in Escherichia coli. Gene 151:131–135. 59. Smolka, M. B., H. L. Zhou, S. Purkayastha, and R. Aebersold. 2001. Optimization of the isotopecoded affinity tag-labeling procedure for quantitative proteome analysis. Analytical Biochemistry 297:25–31. 60. Solem, C. and P. R. Jensen. 2002. Modulation of gene expression made easy. Applied and Environmental Microbiology 68:2397–2403. 61. Sonderegger, M. and U. Sauer. 2003. Evolutionary engineering of Saccharomyces cerevisiae for anaerobic growth on xylose. Applied and Environmental Microbiology 69:1990–1998. 62. Stafford, D. E. and G. Stephanopoulos. 2001. Metabolic engineering as an integrating platform for strain development. Current Opinion in Microbiology 4:336–340. 63. Stephanopoulos, G. N., A. A. Aristidou, and J. Nielsen. 1998. Metabolic Engineering Principles and Methodologies. Academic Press, San Diego. 64. Tang, Y., F. Pingitore, A. Mukhopadhyay, R. Phan, T. C. Hazen, and J. D. Keasling. 2007. Pathway confirmation and flux analysis of central metabolic pathways in Desulfovibrio vulgaris Hildenborough using gas chromatography-mass spectrometry and Fourier transform-ion cyclotron resonance mass spectrometry. Journal of Bacteriology 189:940–949. 65. Tang, Y. J. J., J. S. Hwang, D. E. Wemmer, and J. D. Keasling. 2007. Shewanella oneidensis MR-1 fluxome under various oxygen conditions. Applied and Environmental Microbiology 73:718–729. 66. van Maris, A. J. A., A. A. Winkler, D. Porro, J. P. van Dijken, and J. T. Pronk. 2004. Homofermentative lactate production cannot sustain anaerobic growth of engineered Saccharomyces cerevisiae: Possible consequence of energy-dependent lactate export. Applied and Environmental Microbiology 70:2898–2905. 67. Varma, A., B. W. Boesch, and B. O. Palsson. 1993. Biochemical production capabilities of Escherichia coli. Biotechnology and Bioengineering 42:59–73. 68. Varma, A., B. W. Boesch, and B. O. Palsson. 1993. Stoichimetric interpretation of Escherichia coli glucose catabolism under various oxygenation rates. Applied and Environmental Microbiology 59:2465–2473. 69. Voigt, C. A., S. L. Mayo, F. H. Arnold, and Z. G. Wang. 2001. Computational method to reduce the search space for directed protein evolution. Proceedings of the National Academy of Sciences of the United States of America 98:3778–3783. 70. Watanabe, K., K. Hotta, A. P. Praseuth, K. Koketsu, A. Migita, C. N. Boddy, C. C. C. Wang, H. Oguri, and H. Oikawa. 2006. Total biosynthesis of antitumor nonribosomal peptides in Escherichia coli. Nature Chemical Biology 2:423–428. 71. Yen, K. M. 1991. Construction of cloning cartridges for development of expression vectors in Gramnegative bacteria. Journal of Bacteriology 173:5328–5335. 72. Yoshikuni, Y., T. E. Ferrin, and J. D. Keasling. 2006. Designed divergent evolution of enzyme function. Nature 440:1078–1082. 73. Yoshikuni, Y. and J. D. Keasling. 2007. Pathway engineering by designed divergent evolution. Current Opinion in Chemical Biology 11:233–239. 74. Yoshikuni, Y., V. J. J. Martin, T. E. Ferrin, and J. D. Keasling. 2006. Engineering cotton (+)-delta-cadinene synthase to an altered function: Germacrene D-4-ol synthase. Chemistry & Biology 13:91–98. 75. Yu, Z. and H. Zhang. 2003. Ethanol fermentation of acid-hydrolyzed cellulosic pyrolysate with Saccharomyces cerevisiae. Bioresource Technology 93:199–204.
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Jay D. Keasling University of California
13. Genome-Wide Technologies: DNA Microarrays, Phenotypic Microarrays, and Proteomics Seh Hee Jang, Mee-Jung Han, Sang Yup Lee, Jong Hyun Choi, and Xiao Xia Xia......................................................................................................................... 13-1 Introduction • DNA Microarray • Phenotypic Microarray • Proteomics • Combined Genome-Wide Analysis • Conclusions and Future Prospects
14. Monitoring and Measuring the Metabolome Maria Rowena N. Monton and Tomoyoshi Soga................................................................................................................... 14-1 Introduction • Mass Spectrometry • Nuclear Magnetic Resonance Spectroscopy
T
he richness and versatility of biological systems make them ideally suited to solve some of the world’s most significant challenges, such as converting cheap, renewable resources into energy-rich molecules; producing high-quality, inexpensive drugs to fight disease; and remediating polluted sites. Over the years, significant strides have been made in engineering microorganisms to produce fuels, bulk chemicals, and valuable drugs from inexpensive starting materials; to detect and degrade nerve agents as well as less toxic organic pollutants; and to accumulate metals and reduce radionuclides. The components needed to engineer the chemistry inside a microbial cell III-1
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are significantly different from those commonly used to overproduce pharmaceutical proteins. Besides gene expression tools to control metabolism and mathematical models to assess metabolic flux, there is a general need for functional genomics tools to assess the impact of metabolic pathways on the host. Engineering metabolic chemistry generally begins with the introduction of one or more genes encoding enzymes that transform readily available, intracellular intermediates into a desired chemical. Unlike production of pharmaceutical proteins in which the genes are highly expressed to maximize production of the target protein, the genes encoding the transformational enzymes do not need to be highly expressed; rather the enzymes need to be produced in catalytic amounts only sufficient to adequately transform the metabolic intermediates into the desired products at a sufficient rate. Expression of the desired genes at too high a level will rob the cell of metabolites (nucleotides for the excess mRNA, amino acids for the excess protein, etc.) that might be otherwise used to produce the desired molecule of interest [11]. In addition to decreasing final product titers, overexpression of the genes in a metabolic pathway many elicit a number of stress responses in the host cell that will decrease cell growth and further decrease product titers. Furthermore, because intermediates of a foreign metabolic pathway can be toxic to a heterologous host [5], which can result in decreased production of the desired final compound, it is essential that the relative levels of the enzymes be coordinated in such a way that no intermediate in the pathway accumulates to toxic levels. The ability to assess the impact of metabolic pathways on cellular metabolism is essential to optimize cells for production of the desired product. In analogy to the creation of computer programs, the development of software has been made possible only through the development and use of debugging software to quickly identify and correct problems with code [2,8]. The development of similar tools for biological debugging would reduce development times for building and optimizing engineered cells. For the development of microbial chemical factories, functional genomics can serve in the role of debugging routines [1,9,10], because the introduction of a metabolic pathway often elicits the equivalent of a “bug” in the cell’s natural program, which will be indicated by various stress responses (e.g., stringent response, unfolded protein response, etc.) [3,6,7]. These stresses are reflected in mRNA and proteins expressed at that time and are thus detectable using DNA arrays or proteomics [3,6,7]. Overexpression of a metabolic pathway might rob the cell of central metabolic intermediates resulting in decreased cell growth, a change in the profiles of intracellular and extracellular metabolites, which would be reflected in the metabolite profile, and a change in metabolic fluxes, which would be reflected in the flux profile. Imbalances in a metabolic pathway will result in an accumulation of potentially toxic metabolites that inhibit growth [5]. Information from one or more of these techniques can be used to then modify expression of genes in the metabolic pathway or in the host to improve titers and/or productivity of the final product. Because of the complexity and amount of information that must be collected and correlated, computational bioinformatics methods to manage and analyze the data are essential. An example of the use of functional genomics to assess the toxicity associated with a heterologous metabolic pathway and correct the problems associated with the pathway was recently demonstrated by Kizer and coworkers [4]. They analyzed the toxicity associated with a heterologous mevalonate-based isopentenyl pyrophosphate biosynthetic pathway that had been engineered into Escherichia coli so that the strain would produce large quantities of isoprenoids. Although the engineered E. coli produced high levels of isoprenoids, further pathway optimization lead to an imbalance in carbon flux and the accumulation of the pathway intermediate 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA), which proved to be cytotoxic to E. coli. Using both DNA microarray analysis and metabolite profiling, Kizer and coworkers studied E. coli strains inhibited by the intracellular accumulation of HMG-CoA. They showed that HMG-CoA inhibits fatty acid biosynthesis in the microbial host leading to a generalized membrane stress. The cytotoxic effects of HMG-CoA accumulation were counteracted by the addition of palmitic acid (16:0) and, to a lesser extent, oleic acid (cis-∆9-18:1) in the growth medium. This work demonstrates the utility of using transcriptomic and metabolomic methods to optimize synthetic biological systems. We anticipate this type of work will become commonplace as the technologies become easier to use and more affordable.
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In Chapters 13 and 14, we describe several emerging technologies and their potential to impact metabolic engineering. Jang and coworkers describe the use of DNA microarrays, phenotypic microarrays, and proteomics techniques for assessing stress responses in the cell. Monton and Soga describe the use of metabolomics to assess changes in cellular metabolism. And Alm and Arkin describe the use of bioinformatics to compile and analyze the large datasets that arise from these techniques. The integration of these debugging technologies will certainly make metabolic engineering more predictable, easier, and faster.
References 1. Bro, C. and J. Nielsen. 2004. Impact of ‘ome’ analyses on inverse metabolic engineering. Metab. Eng., 6:204–11. 2. Campbell, R. V. D. 1952. Presented at the ACM Annual Conference/Annual Meeting, Pittsburgh, Pennsylvania. 3. Gill, R. T., J. J. Valdes, and W. E. Bentley. 2000. A comparative study of global stress gene regulation in response to overexpression of recombinant proteins in Escherichia coli. Metab. Eng., 2:178–89. 4. Kizer, L., D. J. Pitera, B. Pfleger, and J. D. Keasling. 2008. Functional genomics for pathway optimization: application to isoprenoid production. Appl. Environ. Microbiol., 74: 3229–41. 5. Martin, V. J. J., D. J. Pitera, S. T. Withers, J. D. Newman, and J. D. Keasling. 2003. Engineering the mevalonate pathway in Escherichia coli for production of terpenoids. Nat. Biotechnol., 21:796–802. 6. Oh, M. K. and J. C. Liao. 2000. DNA microarray detection of metabolic responses to protein overproduction in Escherichia coli. Metab. Eng., 2:201–9. 7. Oh, M. K. and J. C. Liao. 2000. Gene expression profiling by DNA microarrays and metabolic fluxes in Escherichia coli. Biotechnol. Prog., 16:278–86. 8. Orden, A. 1952. Presented at the ACM Annual Conference/Annual Meeting, Pittsburgh, Pennsylvania. 9. Park, J. H., K. H. Lee, T. Y. Kim, and S. Y. Lee. 2007. Metabolic engineering of Escherichia coli for the production of L-valine based on transcriptome analysis and in silico gene knockout simulation. Proc. Natl. Acad. Sci. USA, 104:7797–802. 10. Park, S. J., S. Y. Lee, J. Cho, T. Y. Kim, J. W. Lee, J. H. Park, and M. J. Han. 2005. Global physiological understanding and metabolic engineering of microorganisms based on omics studies. Appl. Microbiol. Biotechnol., 68:567–79. 11. Pfleger, B. F., D. J. Pitera, J. D. Newman, V. J. Martin, and J. D. Keasling. 2007. Microbial sensors for small molecules: development of a mevalonate biosensor. Metab. Eng., 9:30–8.
13 Genome-Wide Technologies: DNA Microarrays, Phenotypic Microarrays, and Proteomics Seh Hee Jang Korea Advanced Institute of Science and Technology
Mee-Jung Han Korea Advanced Institute of Science and Technology
Sang Yup Lee Korea Advanced Institute of Science and Technology
Jong Hyun Choi Korea Advanced Institute of Science and Technology
Xiao Xia Xia Korea Advanced Institute of Science and Technology
13.1 Introduction ����������������������������������������������������������������������������������� 13-1 13.2 DNA Microarray ��������������������������������������������������������������������������� 13-2 Fundamentals of DNA Microarray • Applications of DNA Microarray • Reliability and Reproducibility Issues
13.3 Phenotypic Microarray ����������������������������������������������������������������13-6 Fundamentals of Phenotypic Microarray • Applications of Phenotypic Microarray
13.4 Proteomics �������������������������������������������������������������������������������������13-8 Fundamentals of Proteomics • Protein Mapping • Quantitative Protein Profiling
13.5 Combined Genome-Wide Analysis.......................................... 13-13 13.6 Conclusions and Future Prospects............................................ 13-15 Acknowledgments ���������������������������������������������������������������������������������� 13-16 References ������������������������������������������������������������������������������������������������ 13-16
13.1 Introduction Since the completion of the first genome sequencing of a microorganism [1], hundreds of genomes have been sequenced and archived in National Center for Biotechnology Information (NCBI) and many databases. The organisms are a complex system and their genomes are immense, and thus powerful technologies are being developed to meet the demands in analysis of thousands of genes and their products and functions. Compared to traditional methodologies which were typically based on one target in one experiment, recently developed omics technologies, including transcriptomics, proteomics, metabolomics, and physiomics are allowing us to generate large amounts of these data. Accessibility to these omics data is providing a foundation for in-depth understanding of living organisms. Genome-wide technologies were based on the two-dimensional experimental methods which were initially developed by O’Farrell et al. [2] for proteomics, and phenotypic and DNA microarrays by Bochner et al. [3] and Fodor et al. [4], respectively. Thanks to these methodologies, experiments are no longer limited to one by one type, and can be performed for hundreds to tens of thousands of targets and conditions simultaneously. The effort to miniaturize the experimental scales without the loss of validity and reproducibility 13-1
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Ribosome
Protein
Membrane
Polymerase Protein folding & modification
DNA mRNA Transcription
Translation
Metabolites
DNA microarray
Proteomics
Phenotypic microarray
Figure 13.1 Genome-wide technologies. DNA microarray is used for transcriptome profiling which allows for the simultaneous monitoring of relative mRNA abundance in multiple samples. Proteome profiling is conducted by two-dimensional gel electrophoresis and mass spectrometry. Phenotype microarrays is used for simultaneous testing of a large number of cellular phenotypes.
is also remarkable, and has contributed in widespread use of these technologies. Nowadays, these technologies have successfully been applied to biological research, and are providing new information on global cellular physiology and regulations of the cells (Figure 13.1). Together with computational analyses, these high-throughput omics technologies gave birth of systems biology [5]. In recent years, systems biotechnology [6], which allows development of improved strains and bioprocesses by taking systemslevel analytical approaches, also appeared. In this chapter, we review the advances in DNA microarray, phenotypic microarray and proteomics with their applications. Also, we describe the importance of combined analysis of these omics data within a systems biology framework for successful metabolic engineering.
13.2 DNA Microarray In the past decade, DNA microarray has attracted great interests among biological researchers as it enables to monitor the mRNA expression levels on a genome-wide scale on a single slide, and to obtain new information on the gene interactions and regulations simultaneously. The microarray was originally used as a screening method in the 1980s when complete genome sequences were not available. Bacterial cosmid libraries were used to select colonies forming hybridization with probe DNAs. In the 1990s, DNA microarray and mRNA expression profiling started to be applied to in depth study of the organisms whose genome sequences were completely determined. Using classical methods to assay gene expression, scientists were able to examine a relatively small number of genes at a time. Although those traditional methods are still important to study the biological systems, they do not offer a complete picture, which is essential to uncover new biological phenomena at a whole cell level. The opposite feature of DNA microarray made it increasingly popular in modern bioscience and biotechnology. The primary objective of using DNA microarray is for the functional genomic studies of living organisms [7,8]. The entire set of open reading frames (ORFs) of the organism can be arrayed on a slide and their mRNA expression profiles obtained in a single experiment. Because of these characteristics, DNA microarray technology allows the comparative analysis of gene expression levels in different cell types and genotypes, and/or varying environmental conditions. Additionally, computational analysis of microarray data allows the classification of genes according to their mRNA expression patterns, providing information on the regulatory mechanisms in the cell.
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13.2.1 Fundamentals of DNA Microarray The basic concept of DNA microarray is the precise positioning of DNA probes at high density on a solid support to make it a DNA/RNA detector. The DNA microarrays can be categorized depending on the solid support used (glass or filter), the type of DNA immobilized on the array (cDNA, oligonucleotide, or genomic fragment), and the manufacturing method (inkjet printing, spotting, mask-based in situ synthesis). Currently, two main types of DNA microarrays used are spotted glass slide (or filter) arrays, which are relatively simple to prepare, and in situ synthesized oligonucleotide arrays (e.g., Affymetrix GeneChip), which require special equipments [9–11] (Figure 13.2). In the case of spotted microarray, the PCR-amplified cDNAs or presynthesized oligonucleotides can be deposited onto polylysine or aminosilane coated glass slide [12]. A high-speed robot is used to spot cDNA or oligonucleotides on the slide. The slides are then hybridized with cDNAs labeled separately with two different fluorescent dyes (e.g., Cy3 and Cy5) during the reverse-transcription of mRNAs from two different types of cells or the cells under two different conditions. The relative intensities of the two fluorescent dyes in a spot represent the relative mRNA expression levels of the genes under two conditions being compared. Although the fold changes from cDNA or oligonucleotide microarrays are not identical, the general trends of changes are similar to each other when the same genes are compared using the two microarrays. One advantage of the oligonucleotide microarray is that homologies among the thousands to tens of thousands of genes can be minimized during the probe design. Because of this reason, the oligonucleotide microarray is useful in monitoring global gene expression levels and to detect mutations and single nucleotide polymorphisms. The cDNA microarrays, however, have potential cross-hybridization problems due to sequence homologies among the genes. However, for the targeted gene expression profiling, it is still an important tool. The comparative nature of gene expression measurements with spotted arrays generates a problem in comparing data obtained from different experiments and other research groups. This problem can be alleviated by using universal references of the microarray community [13]. DNA oligonucleotides can also be synthesized in situ on the DNA chip with photo-labeled protecting groups and photolithographic masks [9–11]. The in situ synthesized oligonucleotide microarray contains much larger number of oligomers as the array manufacturing process adopts a semiconductor
(a)
(b) Sample RNA Reference RNA Light
cDNA or oligonucleotide Printing
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Figure 13.2 Two main types of DNA microarrays; spotted arrays (a), in situ synthesized oligonucleotide arrays (b). (a) High-speed robot is used to spot cDNA or oligonucleotide onto polylysine or aminosilane coated slide, and this slide is hybridized with fluorescent dye labeled cDNA. (b) DNA chip can be synthesized with photo-labeled protecting groups and photolithographic masks, which is similar to micro-fabrication process.
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micro-fabrication process. Commercially available arrays are manufactured at a density of over 1.3 million unique features per array (http://www.affymetrix.com/technology/manufacturing/index.affx). Another advantage of this type of microarray is that the shape and size of the spots are uniform, free of any irregularities caused by mechanical depositing. However, there is a probe design issue in that the probes directly synthesized on substrates contain a number of nucleotide chains that are different from the design sequences [14]. It is assumed in the data analysis of the microarray that the mRNA composition in the sample is proportional to the total mRNA extracted and to the fluorescence intensity data. The first step of the data analysis is data preprocessing to improve the confidence of the results, not to improve data quality. For this purpose, spot filtering and normalization are performed. While spot filtering flags doubtful or uninformative spots, data normalization identifies and removes the effects of systematic variation in the measured fluorescence intensities, other than differential expression. Due to the complicated process of manufacturing and hybridizing microarrays, a certain amount of systematic variation exists in the data. Therefore, normalization is a very important step for the analysis of all microarray data as it can prevent systematic bias. For the total intensity normalization, a normalization factor can be calculated from either the total integrated intensity (in one color hybridization) or the total average fold difference of the Cy3 and Cy5 channels (in two color hybridization). The normalization factor then can be used in adjusting the scale or fold change for every spot on the chip. The normalization factor can also be calculated from housekeeping genes to adjust experimental variability in the samples since housekeeping genes are assumed to have rather constant profiles throughout the experiments. It should be noted that the normalization can lead to over-enhancement of the information in the arrays [15]. To extract useful information from expression profiles, computational tools that cluster and display data are necessary. There are many ways to analyze gene expression data, such as hierarchical clustering [16] and self-organizing map (SOM) clustering [17], which have been widely used to display the data. Hierarchical clustering is relatively simple and the results are easily visualized. The distances between the genes are calculated for all of the genes based on their expression patterns. The genes having close expression patterns are merged to generate a cluster. Then the distances between these small clusters are calculated to produce a new cluster and the process is repeated again until only one cluster is left. The advantage of this clustering is that it forms a hierarchy of clusters enabling small groups of coexpressed genes to be identified. The SOM clustering assigns genes to a series of groups on the basis of expression pattern similarities. Random vectors are constructed for each group and a gene is assigned to the closest vector. With the clustering algorithms, distinct patterns of gene expression are identified and genes are grouped on the basis of the similarity of their expression profiles, for example, TCA/glyoxylate cycle-related genes, amino acid synthesis/degradation-related genes, and nucleotide synthesis/degradation-related genes. It is notable that the expression patterns of a group of genes with similar functions were found to be coregulated by temporal analysis [18]. Thus, expression profiling with DNA microarrays can reveal putative co-functional families of genes. Unfortunately, the clustering methods have one crucial disadvantage in that they are not robust to missing values. These algorithms can only function when the data sets are complete. Therefore, missing value estimation is required to use these clustering methods. Alter et al. [19] suggested singular value decomposition (SVD) method for genome-wide data processing and modeling. The SVD provides a mathematical framework for data processing and modeling in which biological meaning can be found. Troyankskaya et al. [20] was able to successfully apply SVD for missing value estimation. Principal component analysis (PCA) is a useful analytic method for identifying a subset of genes that are responsible for the observed transcriptional differences and the distinct pattern underlying the differences [21]. The PCA can visualize multidimensional data by projecting them onto a lower dimensional space. Therefore, transcriptional fingerprints underlying phenotypic variation can be easily visualized. In addition, the evaluation of the principal components can suggest the underlying factors responsible for the phenotypic variations.
Genome-Wide Technologies
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13.2.2 Applications of DNA Microarray DNA microarrays have been used for gene expression profiling in a variety of organisms, including bacteria, yeast, plants, and tissues/cell lines [15]. The primary goals of these experiments were the identification of new genes involved in a pathway of interest, monitoring the differentially expressed genes under the conditions of comparison, identifying the gene regulatory circuits and the genes involved in by examining those showing similar expression patterns. Since DNA microarray permits a genomewide survey in a single assay, it can be considered as a hypothesis generating rather than hypothesis driven experimental technique. Therefore, the expression profiles generated from microarray experiments can be used as a starting point to identify candidate genes for further studies. In addition to providing a broad survey of gene expression levels, transcriptional profiling is also able to reveal the genes showing particular expression patterns, which can be used to generate a hypothesis on the gene function to be validated by further experiments. DNA microarrays have also been successfully used for monitoring transcript levels [22], single nucleotide polymorphism [23], or genomic variations among the different strains [24]. Additionally, DNA microarrays have been applied to investigate physiological changes in tissue/cell lines [25–30]. The patterns produced by mRNA expression profiling can also be used to study the properties of various cellular pathways including regulatory mechanisms [31,32]. Since the mRNA expression profiles obtained by DNA microarray experiments suggest the transcriptional response of a cell to a particular stimulus, the pattern of gene expression can be regarded as the fingerprint of a cell for a particular stimulus. Golub et al. [33] successfully classified the tumors by their expression profiles. van’t Veer et al. [34] showed that gene expression profiles of 117 primary breast tumors could be used to predict the clinical outcome of node-negative breast cancer patients. The DNA microarray is also an efficient tool for new drug discovery, pharmacogenomics, which utilizes global gene expression databases of molecular cell responses to drug exposure [35]. Traditional drug discovery has been performed by identifying a target molecule (typically protein) in a biological pathway followed by developing an inhibitory compound against the target. However, large-scale systematic approaches to drug discovery are now possible by comparing expression of thousands of genes between normal and diseased states, and identifying multiple potential drug targets which allow for the selection of new promising therapeutics for further testing [36]. Also, predictions on the chemosensitivity of cells can be made by analyzing its microarray profile [37]. Furthermore, drug target validation can be performed with the help of a database of deletion mutants [38]. From the metabolic engineering point of view, the DNA microarray proved to be an efficient tool for suggesting novel targets to be engineered for strain improvement and bioprocess development [39,40]. By comparing transcriptome profiles between different strains or between the samples obtained from different conditions, potential target genes or regulatory mechanisms can be identified to engineer the local metabolic pathways for improving the performance of microorganisms. Some of the examples are described below. DNA microarray was used to identify an additional glucose PTS (ptcBAC) in Lactococcus lactis [41]. This additional glucose PTS gene and the glk and ptnABCD genes were disrupted to completely remove the glucose metabolism in the strain. After introducing the lactose metabolic genes, the deletion strain could only ferment the galactose moiety of lactose. Therefore, the resulting strain could be used for the in situ production of glucose. Transcriptome analysis was also used to examine metabolic changes in recombinant Escherichia coli cells producing an insulin-like growth factor I (IGF-I) fusion protein in high cell density culture [40]. By comparatively analyzing the transcriptome data before and after induction, two down-regulated genes after induction were selected for amplification. This resulted in the increase of IGF-I production by several fold. An inverse metabolic engineering strategy using DNA microarrays was applied on Saccharomyces cerevisiae strains [42]. By comparing the mutant strain generated by chemical mutagenesis and its parental strain (metabolically engineered xylose-utilizing S. cerevisiae strain), a number of genes involved in
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the xylose metabolism were found to have altered expression in the mutant strain. One of the genes with an altered expression, encoding a transcriptional regulator, was subsequently manipulated in the reference strain. This resulted in the manipulated strain to show similar physiological characteristics with the mutant strain. Another inverse metabolic engineering example is the identification of the factors contributing to ethanol tolerance [43]. The transcriptome profile of a strain with increased ethanol tolerance was compared with that of its parental strain under several different growth conditions. Several genes and factors influencing ethanol tolerance were identified: an increase in glycine metabolism, loss of function of a regulatory protein and increased metabolism of serine and pyruvate. DNA microarrays were also used for selecting marker genes for monitoring of fermentation process. By performing a detailed gene expression analysis on the Bacillus subtilis fed-batch fermentation processes with different ratio of casamino acids and ammonia, a few genes were identified for fermentation monitoring, for example, acoA and glnA as an indicator for glucose and nitrogen limitation, respectively [44]. Another application of DNA microarray is the integration of transcriptome data during the simulation of stoichiometric metabolic models to obtain improved flux predictions. The key idea here is that the regulatory information can be obtained from transcriptome data which give additional constraints on the metabolic fluxes in the model. Akesson et al. [45] combined gene expression data with the in silico model of S. cerevisiae to achieve improved predictions of its metabolic behavior. So far, the on-off type regulatory mechanisms (i.e., setting the flux to zero if the corresponding gene is not expressed) have been implemented during the simulation of the genome-scale metabolic model, but it is expected that better algorithms that are capable of incorporating more complicated regulatory mechanisms will be developed in the future.
13.2.3 Reliability and Reproducibility Issues The DNA microarray has proven to be a powerful technique for high throughput comprehensive analysis of thousand to tens of thousands of genes in parallel. However, the nature of microarray has its own requirements in terms of the amount of RNA needed, data acquisition, and normalization techniques. These requirements together with the differences in types and composition of probes, deposition technologies, and labeling and hybridization protocols, prevent direct and reliable comparison of microarray data. To address this issue, Minimal Information About a Microarray Experiment (MIAME), a set of common guidelines for designing and communicating microarray experiments, has been proposed by the Microarray Gene Expression Data Society (http://www.mged.org/miame). DNA microarrays focus on identifying genes differentially expressed under different experimental conditions and classifying the expression patterns by grouping genes. However, the expression levels of different genes cannot be compared to each other because of the differences in hybridization efficiencies of genes having different length, DNA sequence, and mRNA stabilities. One way to solve this problem is the use of reference containing genomic DNA [46] or labeled oligonucleotides of known abundance and complementary to every probe in the array [47]. The lack of linearity and proportionality of mRNA/ cDNA concentration to signal intensity at high concentrations is another reason to make microarray only semi-quantitative. To estimate changes in mRNA abundance, Northern hybridization and RT-PCR can be used as complementary experimental techniques [48,49]. It should be emphasized that an accurate measurement of absolute transcript levels by microarrays is not reliable yet. Although the ratios can be estimated reasonably well, it is necessary to increase the sensitivity and accuracy of the technology to extract more valuable information from the expression data.
13.3 Phenotypic Microarray The genomics, transcriptomics and proteomics technologies allow us to perform global analyses of the important macromolecules in cells. Thanks to these technologies, the information flow from
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DNA to RNA then to protein can be depicted. However, the information flow does not end with proteins in the cell. One more important information category is metabolome, which is not within the scope of this chapter. Readers can refer to several excellent review articles [50–52]. The information flow can proceed further down to determine cellular phenotypes. Like other technologies, traditional phenotype studies could only examine a few components at a time. Although many excellent methods have been developed to understand the phenotypes of an organism, it has been almost impossible to survey all possible components with different combinations. Therefore, a novel technique was needed to give a global view of cellular properties by detecting phenotypic properties of living organisms. The primary goal of the phenotype microarray is to give quantitative measurement of thousands of cellular phenotypes all at once [53].
13.3.1 Fundamentals of Phenotypic Microarray Biolog (Hayward, CA) has developed a high throughput phenotypic microarray (PM) to assay a thousand metabolic and chemical sensitivity phenotypes in a single run within 2 days [53,54]. In PM, the bacterial cells are inoculated into 20 96-well plates; ten plates to assay utilization of various substrates and remaining ten plates to examine sensitivities to chemicals. Because each well contains tetrazolium dye, cell growth can be detected by measuring the reduction of the tetrazolium dye (blue color at reduced state). One of the key ideas of PM is that cell respiration is coupled to a large number and a wide range of cellular phenotypes. Cells must uptake nutrients to survive and then catabolize and/or convert them to produce essential molecular components. By polymerization, these components turn into macromolecules to create cellular components and structures. Therefore, during the growth, an actual physical flow of electrons exists in the cell. The electron flow starts from the carbon source to NADH, then to the electron transport chain of the cell, finally, to the tetrazolium dye [53]. Because the dye turns blue at reduced state via irreversible reaction, cell growth can be monitored indirectly. The intensity of the color, which is proportional to cell growth, can be monitored during the incubation and analyzed to yield quantitative results. For the analysis of the results, the kinetic data from color formation is plotted against time for each well. Two different colors are used to be assigned to each plot; red for the reference and green for the target strain, which is similar to DNA microarray analysis. Comparison of the data between the reference and target strains is achieved by overlaying these plots. Depending on the growth states of the reference and the target strains, the overlaid plots have three different colors: yellow, red, and green, which are again similar to the results of DNA microarray experiments. One obvious example of using the PM is the comparison of phenotypes between the mutant and wild-type strains. During the comparison of the overlaying data, red and green color can be assigned to wild-type and mutant strains, respectively. When equivalent growth is observed for both strains, the overlaid plot will show yellow. However, if either the wild-type or the mutant shows better growth, then the plot will be either red or green, respectively.
13.3.2 Applications of Phenotypic Microarray During the initial studies with PM [55], 39 American Type Culture Collection (ATCC) reference taxa and 45 gram-negative isolates from water samples were successfully identified within 4–24 h. Bochner [53] reported that PM can be used to assay nearly 700 phenotypes of E. coli. Additionally, PM could be used to directly assay the effects of genetic changes in the cell [54]. To explore the extent of phenotypic variations among closely related bacterial strains, the PM was used to analyze a diverse collection of E. coli O157:H7 isolates and other Gram-negative enteric pathogens [56]. The function of gene can be studied using a mutant or the knock-out strain by monitoring the change of cell phenotypes. In general, these mutants or knock-out strains are expected to show one or more altered phenotypic properties when the mutated or deleted gene has a role under the certain conditions. Zhou et al. [57] showed the phenotypic changes in E. coli with a large set of well-defined deletion
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mutants. During this study, several new phenotypes were identified by PM. Ulrich et al. [58] revealed that cell density-responsive mechanism in Burkholderia thailandensis both positively and negatively affects the metabolism of numerous substrates by PM experiments. The PM has also been applied to revealing the profiles of carbon source utilization in Hypocrea jecorina [59], drug discovery in yeast [60], phenotypic profiling of Staphylococcus aureus mutant strains [61], and global physiological analysis of E. coli under limited carbon and energy source [62]. Recently, a large scale spotted cell microarray was developed by Narayanaswamy et al. [63]. They applied this microarray to define the responses of yeast cells to a mating pheromone. They have successfully created cell chips having 4,848 yeast deletion mutant strains onto coated glass slides using a microarray robot, and identified the genes contributing to certain cellular characteristics. As mentioned above, PM is based on the detection of bacterial growth and respiration. Therefore, it has a limitation for detecting phenotypic changes, such as morphological alterations and motility changes. Nonetheless, PM helps not only in studying microbial diversity, but also in bridging the gap between the phenotypic knowledge and the genome sequences. Particularly, from the metabolic engineering point of view, PM can be used in conjunction with metabolomics to compensate each other. The metabolome represents the entire collection of intracellular and extracellular metabolites under a particular condition. However, there are far fewer metabolites than the number of genes in a cell. For example, S. cerevisiae which contains approximately 6,000 genes has only 560 low-molecular metabolites [64]. Thus, the metabolome represents a combined information originating from the metabolism, potentially giving further insight into the function of genes [65]. However, due to the nature of combined information, it is difficult to relate changes in metabolite concentration to specific genetic changes. Since the PM provides direct observation of phenotype caused by genetic change, combined use of metabolomics and PM will be able to alleviate the drawbacks of integrative information and to give information on new targets for metabolic engineering.
13.4 Proteomics Proteomics is any large-scale protein-based systematic analysis of the proteome or a defined subproteome from a cell, tissue, or entire organism, usually by biochemical methods. The term “proteome” was first introduced in the mid-1990s by Wilkins and Williams to indicate the entire “PROTEin” complement expressed by a genOME” of a cell, tissue, or entire organism [66]. In contrast to conventional biochemical studies that focus on a single protein or simple macromolecular complexes, proteomics takes a broader, more comprehensive and systematic approach to the investigation of biological systems. Proteome analysis provides information about changes in protein levels, changes in protein synthesis, degradation rates or post-translational modifications and protein interactions. Furthermore, proteomics provides the capacity to discover many novel targets that may be even more important than the already known proteins and genes under a given condition. This leads to the development of superior production systems, correlates metabolic pathways and molecular mechanisms for cell survival and production of specific bioproducts, and enables a systems view of the organism under study. Since it is not constrained by prior knowledge, proteomics is again a discovery-based rather than a hypothesisdriven approach.
13.4.1 Fundamentals of Proteomics Two-dimensional gel electrophoresis (2-DE) coupled with mass spectrometry (MS) has been widely used for proteome analysis. 2-DE resolves complex protein mixtures first by isoelectric point and then by size in a gel matrix (Figure 13.3). It has dominated proteome profile analysis for more than 30 years [2]. However, the early 2-D gel studies were extremely limited in their analytical scope, since efficient technologies to identify and further characterize the separated proteins did not exist at that time. With the rapid advances in various technologies, including the availability of large-scale
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(b)
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OmpF
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pI
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3
pI
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Figure 13.3 Two representative subproteomes of E. coli: (a) Whole cellular proteins and (b) secreted proteins into culture medium. Protein samples were separated on a pH 3–10 IPG strip followed by 12 (w/v) SDS-PAGE. The gels were stained with a general protein stain, e.g., silver.
genome sequences and protein databases, the development of database search engines capable of exploiting these databases, and the introduction of high-sensitivity, easy-to-use MS techniques in the early 1990s, it became possible to identify and examine the proteins resolved by 2-DE, and to study proteins at large-scale. Recently, in order to reduce complexity and increase sensitivity in detecting low abundance proteins, proteomics researchers have become increasingly aware of gel-independent technologies combined with subcellular fractionation by n-dimensional chromatographies. The technological and methodological advances in proteome research with gel and gel-independent based approaches, and predictive proteomics including 2-DE, MS, and computational tools were excellently reviewed by Han and Lee [67]. Proteome studies has been focused on mainly three categories: (i) identification of protein components of a sample of interest without quantitative analysis (protein mapping); (ii) quantitative comparison of protein levels in two or more samples (quantitative protein profiling); and (iii) analysis of protein interactions including binary interactions and isolation of macromolecular complexes (protein interaction). Here, the first two categories more relevant to metabolic engineering are reviewed.
13.4.2 Protein Mapping Prior to quantitative comparison, the first task of proteome analyses involves the detection and identification of as many protein components as possible in a sample, making an establishment of a reference map of 2-D gel. The goal is to define the total protein complement of a cell, or a subproteome in subcellular organelles such as liver, lung, or kidney, and in cellular compartment such as mitochondria, ribosomes, or membranes. Recently, the subcellular proteome techniques have become popular due to the additional benefits of reduced sample complexity, ability to identify additional unique proteins, localizing newly discovered proteins to specific organelles, and in some cases, allowing functional validation [68]. Twenty-nine proteome maps, including 18 derived from human, six derived from mouse, and the other five from species such as E. coli (several different narrow pH range of 2-D gel maps), Arabidopsis thaliana, Dictyostelium discoideum, S. aureus, and S. cerevisiae, are available in SWISS-2DPAGE database (http://kr.expasy.org/ch2d/). The E. coli 3.5–10 SWISS-2DPAGE map shows 40% of the E. coli
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proteome [69], among which 231 proteins have been identified by techniques such as gel comparison, microsequencing, N-terminal sequencing and amino acid composition analysis. The use of narrow range pH gradients (pH 4–5, 4.5–5.5, 5–6, 5.5–6.7, 6–9 and 6–11) was shown to potentially display proteins existing at low levels (up to a few protein molecules per cell), resulting in the discrimination of >70% of the entire E. coli proteome [70]. The number of displayed proteins was higher than that identified by gel-independent based approaches, but not all of the proteins could be identified. Recently, E. coli subcellular proteomics based on 2-DE can be used to assign various proteins to the cytosol, periplasm, inner membrane or outer membrane by biochemical fractionation; this method was used to assemble the largest proteome database to date [71]. Analysis of 2,160 spots revealed 575 unique ORFs, including 151 hypothetical ORFs, 76 proteins of completely unknown functions, and 222 proteins without localization assignments in the Swiss-Prot Database. Several recent studies have focused on the analysis of extracellular proteins from bacteria used in the bioprocess industry such as E. coli W3110 [72], Mannheimia succiniciproducens [73], Bacillus sp. [74], and pathogens such as Helicobacter pylori [75], Edwardsiella tarda [76], S. aureus [77] and Streptococcus pneumonia [78]. The secretory proteome of the pathogenic and nonpathogenic strains may provide information on physiology applicable in the study of both pathogens and strains used for production of recombinant proteins. For example, extracellular proteins can be used as fusion partner to secret a target protein into culture medium for readily protein purification in downstream processes. As shown in Figure 13.3, one of highly abundant proteins among extracellular proteins in E. coli BL21 (DE3), OmpF, was successfully used for secretory production of recombinant human β-endorphin into culture medium [77]. However, the utility of 2-DE approach is limited by the ability to reliably monitor proteins that are present in very low abundance in a sample, as well as proteins that are very hydrophobic or that are very acidic or basic [80]. Low copy number proteins might represent key regulatory molecules within cells or signaling molecules in tissues and organs, driving the development of advanced proteomic methodologies or technologies to detect low abundance of proteins; for instance sequential extractions with increasingly stronger solubilization solutions, subcellular fractionation, selective removal of the most abundant protein components, preparative isoelectric focusing separations, and chromatographic fractionation of sample mixtures [81]. Hydrophobic proteins that are present in membranes might have key roles in communicating extracellular information to the inside of cells. Molloy et al. [82] introduced a new isolation method of sequential extractions with increasing concentrations of sodium carbonate in analyzing E. coli outer membrane proteins (OMPs). This led to the successful identification of 21 out of 26 of the predicted integral OMPs. The largest database of E. coli membrane proteins constructed to date is that reported by Fountoulakis and Gasser [83] who identified 394 different gene products using the same method described by Molloy et al. [82]. Notably, these studies demonstrate that membrane proteins, which are commonly absent from 2-D gel maps, are amenable to 2-DE separation using specific techniques. From proteome profiling of these OMPs, high abundant proteins, such as E. coli OmpA [84] and FadL [85], and Pseudomonas aeruginosa OprF [86] have been widely used as an anchoring motif of cell surface display in microorganisms. In pathogenic organisms, membrane proteins are a target that prevents transfer of regulatory signaling or infect toxic products into host cells. The preferred analytical method for protein composition is multidimensional chromatography coupled with tandem MS. Gevaert et al. [87] identified 800 E. coli proteins from sorted methioninecontaining peptides using the combined technologies consisting of combined fractional diagonal chromatography (COFRADIC), LC-MS/MS and MALDI-TOF-MS. More than 1,100 E. coli proteins (about a quarter of those encoded in the E. coli genome) were identified by high performance liquid chromatography (HPLC)-MS/MS analysis [88]. Perhaps the most popular of these techniques to date is the multidimensional protein identification technology, often referred to as MudPIT [89]. In this method, mixtures of trypsin-digested peptides are loaded onto a biphasic microcapillary column containing a strong cation exchange resin upstream of a reverse-phase resin directly coupled to a MS/MS. Peptides
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are displaced from the strong cation exchange resin using a salt step gradient, and subsequently bind to the reverse-phase resin. Elution from the reverse-phase resin is accomplished using an acetonitrile gradient, and the peptides are analyzed online by MS/MS. Repeated rounds of step and gradient elutions can result in analysis and identification of a large number of peptides in a single run. Thus, the gel-independent approaches have clear superiority over 2-DE methods in detection and identification of multiple protein components in a proteome. However, because of the complexity of any given proteome and the separation limits of 1-D or 2-D LC, it is still required to reduce the complexity prior to protein separation and characterization.
13.4.3 Quantitative Protein Profiling The most common type of proteomic studies is quantitative protein profile comparison of the samples obtained from two or more experimental conditions such as normal versus diseased cells or tissues, or responses to environmental stimuli or stresses. Up- and down-regulation of specific proteins in response to a number of chemical and physical stresses, such as heat, oxidative agents and hyperosmotic shock, can be monitored. These responses are thought to act as protective mechanisms leading to the elimination of stress agent and/or repair of cellular damage. The cellular responses, as reflected by the proteome, can differ widely according to the stress imposed. Thus, comparative proteome profiling under various genotypic and environmental conditions can reveal new regulatory circuits and the relative abundance of proteins at the system-wide level. Most of protein profiling data have been currently obtained using 2-DE. 2-DE is still a core technology for quantitative comparisons of proteins from two or more closely related experimental samples. Recently, to overcome shortcomings in conventional 2-DE method, 2-D difference gel electrophoresis (DIGE) introduced by Ünlü et al. [90] was used in various applications in quantitative proteomics including the analysis of bacteria [91], yeast [92], mouse brain [93], cat visual cortex [94], human breast cancer cells [95], and human colon cancer [96]. The basis of the technique is the use of two or three massand charge-matched N-hydroxy succinimidyl ester derivatives of the fluorescent cyanine dyes Cy2, Cy3 and Cy5 that possess distinct excitation and emission spectra. Protein samples for comparative analysis are labeled with different fluorescent dyes, mixed together, co-separated, and visualized on a single 2-D gel using excitation and emission wavelengths for image acquisition that are specific for each fluorescent tag. This technique avoids the complications of gel-to-gel variation seen in conventional 2-D gel experiments, reduces the number of gels that need to be run, and enables detection of quantitative differences in incompletely resolved spots. These fluorescent dyes also have a wide dynamic range and are more sensitive than most other detection methods. However, similar to conventional 2-DE, even the large 2-D DIGE gel can typically resolve only about 1,500 to 2,000 spots or less and detect only a small portion of complex proteomes. As an alternative method to 2-DE, multiple gel-independent approaches were developed for largescale and throughput quantitative comparison. In these methods, two samples may be labeled with stable isotopes prior to sample separation, either by metabolic incorporation or through chemical derivatization. In this way, proteins derived from the different samples (e.g., normal versus abnormal or untreated versus treated samples) can be directly separated, identified, and quantified using nLC-MS/ MS. An attractive method for quantitative comparison of two proteomes is the isotope-coded affinity tag (ICAT) method [97,98]. The ICAT reagent has a protein-reactive group, a biotin tag and an ethylene glycol linker connecting the two functional groups, which can be synthesized with hydrogen (light ICAT) or deuterium (heavy ICAT). For comparison, one sample is reacted with the light reagent and the second sample is reacted with the heavy reagent under identical labeling conditions. After trypsin digestion, the extremely complex tryptic peptide mixture is simplified by affinity purification of the cysteinecontaining derivatized peptides on an avidin affinity resin. The eluted peptides are then analyzed using LC-MS/MS or 2-D LC-MS/MS. The ratios of MS signals from the light and heavy ICAT-labeled forms of the same peptide are compared to determine the relative abundance of the parent protein in the
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respective samples, and MS/MS is used to identify the proteins. An alternative isotopic labeling method includes: 16O or 18O incorporation from H216O or H218O, respectively, at the carboxyl terminus of peptides during proteolytic cleavage by trypsin [99,100]; and stable-isotope metabolic protein labeling in which cells can be cultured in isotopically defined media [101,102]. Recently, a multiplexed protein quantification strategy that provides relative and absolute measurements of proteins in complex mixtures was developed by Ross et al. [103]. The multiplex strategy simultaneously determines the relative levels of proteins at multiple states (e.g. several experimental controls or time-course studies) up to four samples in parallel. A multiplexed set of isobaric reagents that yield amine-derivatized peptides (iTRAQ reagents; Applied Biosystems, CA) was used for labeling at the N-termini and lysine side chains of peptides in a digest mixture. The derivatized peptides are indistinguishable in MS, but exhibit intense low-mass MS/MS signature ions that support quantification. Absolute quantification of targeted proteins can also be achieved using synthetic peptides tagged with one of the members of the multiplex reagent set. Thus, these advances in proteomics technologies led to the generation of unprecedentedly large amounts of proteome data, which are used for global examination of cellular metabolism in fundamental as well as applied researches. Many proteomic studies revealed changes in proteome profiles in response to various stresses, such as changes in pH, oxygen, cell density, temperature, organic solvents, or nutrient starvation [67]. These studies resulted in the identification of various stress-induced proteins. Proteomic analysis has also been used to directly monitor cellular changes occurring during the production of heterologous proteins in microorganisms and develop efficient strains for the enhanced production of bioproducts and biodegradable polymers. Initial effort was made in “trial-and-error” type approaches, in which various genetic modifications are repeatedly tried until a desired objective is achieved. However, since bioproducts are formed by coordinated enzyme reactions acting through the metabolic pathways, it is essential to understand the metabolism and regulation that occur during cell growth and product formation. Recently, these investigations have been streamlined with the use of new high-throughput analytical, molecular biological, and mathematical tools, all of which have been combined to facilitate development of “custom-made” production systems in microorganisms. In this important context, proteome analysis enables estimation of whole protein (enzyme) expression levels, facilitating the construction and validation of metabolic pathways that researchers can use to elucidate which molecules supply the energy and building blocks or precursors (e.g., amino acids and other metabolic intermediates) necessary for cell function and product formation. For example, Han et al. [104] reported that protein profiling of recombinant E. coli during the overproduction of human leptin was used to identify a target gene, leading to the development of a successful metabolic engineering strategy for achieving the increased productivity of leptin and other serine-rich proteins by coexpression of the cysK gene. More recently, the physiological changes of recombinant E. coli during secretory production of a recombinant humanized antibody fragment were monitored by 2-DE [105]. Twenty-five protein spots were differentially expressed in the control and production fermentations at 72 h, while 19 other protein spots were present only in the control or production fermentation at this time. The synthesis of the stress protein, phage shock protein A (PspA), was strongly correlated with the synthesis of a recombinant product. Coexpression of the pspA gene with a recombinant antibody fragment in E. coli significantly improved the yield of the secreted biopharmaceutical. Thus, it appears that proteomics can be effectively used to identify candidates for successful metabolic engineering toward enhanced bioproducts formation. In addition, proteomic studies for analyzing the composition of inclusion bodies (IBs) have been carried out in order to improve the quality (or uniformity) of the desired product, and the downstream process of recombinant proteins such as protein purification and refolding. Indeed, proteomic studies led to the enhanced production of recombinant proteins including IBs and secretory proteins, and improved industrial processes. For example, two small heat shock proteins (sHsps), IbpA and IbpB, were first identified by the conventional biochemical technique as the major proteins associated with the IBs of recombinant proteins produced in E. coli [106]. IbpA and IbpB were recently found
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to facilitate the production of recombinant proteins in E. coli and play important roles in protecting recombinant proteins from degradation by cytoplasmic proteases [107]. Amplification of the ibpA and/ or ibpB genes enhanced production of recombinant proteins as IBs, whereas ibpAB gene knock-out enhanced the secretory production of recombinant proteins as soluble forms. More recently, LeThanh et al. [108] reported similar results with Han et al. [107] that α-glucosidase production was enhanced at elevated IbpA and IbpB levels, and reduced in ibpAB negative mutant strain in a temperature-dependent manner. Also, it was revealed that IbpA and IbpB prevent IBs of α-glucosidase from degradation in a temperature-dependent manner. These findings suggest that manipulation of ibpAB gene expression may prove to be a valuable new technique for fine-tuning the production of recombinant proteins in E. coli. In addition, these results demonstrate the effectiveness of employing proteome profiling in the development of production strains suitable for industrial applications. The use of sHsps has recently been extended to significantly enhance the performance of 2-DE [109]. Proteolytic degradation is one of the critical problems in 2-DE. Loss of protein spots in 2-D gels due to residual protease activity is commonly observed when using immobilized pH gradient gels for isoelectric focusing. Three sHsps, IbpA and IbpB from E. coli and Hsp26 from S. cerevisiae, were found to be able to protect proteins in vitro from proteolytic degradation. Addition of sHsps during 2-DE of human serum or whole cell extracts of bacteria (E. coli, M. succiniciproducens), plant A. thaliana, and human kidney cells allowed detection of up to 50% more protein spots than those obtained with currently available protease inhibitors. This may change the way proteome profiling is carried out by generally enabling the detection of many more protein spots that could not be seen previously. Taken together, these findings and other reports continue to emphasize the fact that proteomics is likely to become increasingly important not only in pure biological research but also in various biotechnological applications.
13.5 Combined Genome-Wide Analysis Genome-wide technologies allow large-scale analysis of biological systems at transcript, protein, metabolite, and phenotype levels, leading to a wealth of information that is useful in discovery-based science. Ideally, information obtained from each of these technologies must be integrated to establish a deeper understanding of the relationship between genotype and any particular phenotype. Several successful examples of engineering of strains to enhance the production of recombinant proteins or metabolites from the understanding of cellular physiology and metabolism using combined approaches of transcriptome, proteome, metabolome, or fluxome have been reported (Table 13.1). High cell density cultivation (HCDC) is often practiced in industry because it can increase the productivity of desired products with several other advantages of reduced reactor volume and reduced wastes. Cell density is an important factor affecting microbial physiology due to the availability of various nutrients and possible cell to cell signaling processes. Cells during HCDC often experience different kinds of undesirable conditions, including the fluctuating concentrations of medium components such as glucose and oxygen, or accumulation of byproducts such as acetic acid and lactic acid, which are not Table 13.1 Representative Examples for Enhanced Production of a Target through the Combined GenomeWide Analysis Technologies DNA microarrays and 2-DE DNA microarrays, 2-DE, and/ or in silico simulation DNA microarrays and metabolic flux analysis DNA microarrays and metabolic flux analysis
Target L-threonine α-hemolysin Lovastatin L-lysine
Description Comparative analysis of a parent E. coli strain and amino acid overproducing its mutant strain Comparative analysis of a parent E. coli strain and active α-hemolysin overproducing its mutant strain Comparative analysis of wild-type A. terreus and its lovastatin overproducing strain Comparative analysis of L-lysine producing C. glutamicum
Reference [110] [111] [112] [113]
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beneficial for cell growth. These unfavorable conditions have been known to induce stress responses in microbial cells, change the cellular protein composition, and consequently, lead to the decrease of specific productivity as cell density increases. To find the specific reasons for this phenomenon, Yoon et al. [39] reported the results of a combined transcriptome and proteome analyses during the HCDC of E. coli. It was found that the expression of most of amino acid biosynthesis genes was down-regulated as cell density increased. This finding will be important to carefully examine amino acid biosynthetic pathways and the reactions within as some of them may be rate controlling steps in the production of recombinant proteins. Another integrated analysis of transcriptome and proteome profiles was carried out for E. coli W3110 and its L-threonine-overproducing mutant strain [110]. This study found that the expression of genes involved in glyoxylate shunt, the tricarboxylic acid (TCA) cycle and amino acid biosynthesis were significantly up-regulated whereas ribosomal protein genes were down-regulated. In addition, mutation in the thrA and ilvA genes was suggested to be important for the overproduction of L-threonine. This combined analysis provided valuable information regarding the regulatory mechanism of L-threonine production and the physiological changes in the mutant strain. Recently, a combined analysis of proteome, transcriptome, metabolome, and/or in silico simulation of genome-scale metabolic network was used to engineer an E. coli strain [111]. The E. coli mutant strain obtained by random mutagenesis, which secretes four-fold more active α-hemolysin (HlyA) than its parent strain, was characterized using both high-density microarray for mRNA profiling and a proteomic strategy for protein expression. The relative mRNA and protein expression levels of tRNA-synthetases including AsnS, Asps, LysS, PheT, and TrpS were decreased in the mutant compared to the parent. This combined examination of the mRNA and protein expression profiles showed that downregulation of the tRNA-synthetases in the mutant slowed the general translation rate, and more specifically slowed the rate of HlyA synthesis. Improved secretion of α-hemolysin at low synthesis rate is attributable to a balance between translation and secretion. The use of rare codons in hlyA gene has been shown to reduce its rate of translation, because the number of available aminoacyl tRNAs is limited. A variant of the hlyA gene by altering five bases but encoding the same amino acid sequence was designed using a mathematical model of prokaryotic translation. In this way, the rate of translation could be artificially slowed down, leading to further improved secretory production of α-hemolysin. A combined analysis of transcriptome and metabolome was reported to develop an Aspergillus strain overproducing lovastatin, a cholesterol-lowering drug [112]. Improved lovastatin production was initiated by generating a library of strains by expressing the genes thought to be involved in lovastatin synthesis or known to broadly affect secondary metabolite production in the parental strain. These strains were characterized by metabolome and transcriptome profiling, followed by a statistical association analysis to extract potential key parameters affecting the production of lovastatin and (C)-geodin. Using this approach, target genes were identified and manipulated to improve lovastatin production by greater than 50%. In another study, Krömer et al. [113] performed combined transcriptome, metabolome, and fluxome analysis of L-lysine producing Corynebacterium glutamicum at different stages of batch culture. A decrease in glucose uptake rate resulted in the shift of cellular activities from growth to L-lysine production, redirecting the metabolic fluxes from the TCA cycle toward anaplerotic carboxylation and lysine biosynthesis. During this shift, the intracellular metabolite pools exhibited transient dynamics, including an increase of L-lysine up to 40 mM before its excretion to the medium. The expression levels of most genes involved in L-lysine biosynthesis remained constant whereas the metabolic fluxes showed marked changes, suggesting that metabolic fluxes are strongly regulated at the metabolic level. These are some of the good examples of integrating omics studies and computational analysis toward the development of improved strains. As recently reviewed by Lee et al. [6], this systems approach towards strain development will become more and more popular as it allows identification of new targets for metabolic engineering in more systematic manner (Figure 13.4).
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•Genome sequencing •ORF annotation and prediction
•Protein level •Enzyme activity
Genomics
Proteomics
Metabolic engineering
Transcriptomics • Transcription level • Regulation
Rational design • Reconstruction of metabolic pathway
Figure 13.4 Metabolic engineering with combined omics analyses. Since each omics technology has different perspective of the cell, to understand whole cellular physiology and mechanism, integrated usage of omics technologies is demanded to generate valuable information which can be used for efficient strain development.
13.6 Conclusions and Future Prospects Genome-wide technologies are becoming not only popular but also important research and screening tools for studying differentially expressed genes and proteins and final phenotypes. Their ability to monitor hundreds to tens of thousands of biological characteristics simultaneously is unsurpassed. However, some limitations still exist in current technologies and have become more apparent. Although the existence and trend of the changes in biological characteristics can be reliably detected in their appropriate sensitivity range, accurate measurements of absolute expression levels and the reliable detection of low abundance genes, proteins, or phenotypes are still difficult. Due to these limitations, tremendous volumes of data generated from genome-wide technologies contain large number of false positives and/or false negatives. For this reason, more highly sensitive and accurate instruments will need to be continuously developed. Many cutting edge biological and biotechnological studies are currently driven by the highthroughput acquisition and examination of omics data supported by systems biological and bioinformatic analyses [6]. Because each technology alone is not good enough to understand cellular physiology and regulatory mechanisms as a whole, combined analysis will become increasingly important. This
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type of integrated analysis will lead to an understanding of cellular physiology and metabolism at the systems level, and will pave the way toward more efficient metabolic engineering.
Acknowledgments Our work described in this chapter was supported by the Korean Systems Biology Research Grant (M10309020000-03B5002-00000) of the Ministry of Science and Technology. Further supports by LG Chem Chair Professorship, IBM SUR program, Microsoft, KOSEF through the Center for Ultramicrochemical Process Systems, and by Brain Korea 21 project are appreciated.
References 1. Fleischmann, R.D. et al. Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science, 269, 496, 1995. 2. O’Farrell, P.H. High resolution two-dimensional electrophoresis of proteins. J. Biol. Chem., 250, 4007, 1975. 3. Bochner, B.R. Sleuthing out bacterial identities. Nature, 339, 157, 1989. 4. Fodor, S.A. et al. Multiplexed biochemical assays with biological chips. Nature, 364, 555, 1993. 5. Kitano, H. Systems biology: a brief overview. Science, 295, 1662, 2002. 6. Lee, S.Y., Lee, D.Y., and Kim, T.Y. Systems biotechnology for strain improvement. Trends Biotechnol. 23, 349, 2005. 7. Brown, P.O. and Botstein, D. Exploring the new world of the genome with DNA microarrays. Nat. Genet., 21, 33, 1999. 8. Lockhart, D.J. and Winzeler, E.A. Genomics, gene expression and DNA arrays. Nature, 405, 827, 2000. 9. Singh-Gasson, S. et al. Maskless fabrication of light-directed oligonucleotide microarrays using a digital micromirror array. Nat. Biotechnol., 17, 974, 1999. 10. Lipshutz, R.J. et al. High density synthetic oligonucleotide arrays. Nat. Genet., 21, 20, 1999. 11. Hughes, T. R. et al. Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer. Nat. Biotechnol., 19, 342, 2001. 12. Schena M. Microarrays: biotechnology’s discovery platform for functional genomics. Trends Biotechnol., 16, 301, 1998. 13. Foy, C.A., and Anderson, M.T. The development of microarray standards. Anal. Bioanal. Chem. 381, 87, 2005. 14. Draghici, S. et al. Reliability and reproducibility issues in DNA microarray measurements. Trends Genetics, 22, 101, 2006. 15. Andrew, J.H. et al. Options available-from start to finish-for obtaining data from DNA microarrays II. Nat. Genet. Suppl., 32, 481, 2002. 16. Eisen, M.B. et al. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA, 95, 14863, 1988. 17. Tamayo, P. et al. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc. Natl. Acad. Sci. USA, 96, 2907, 1999. 18. DeRisi, J.L., Iyer, V.R., and Brown, P.O. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science, 278, 680, 1997. 19. Alter, O., Brown, P., and Botstein, D. Singular value decomposition for genome-wide expression data processing and modeling. Proc. Natl. Acad. Sci. USA, 97, 10101, 2000. 20. Troyankskaya, O. et al. Missing value estimation methods for DNA microarrays, Bioinformatics, 17, 520, 2001. 21. Raychaudhuri, S., Stuart, J.M., and Altman, R.B. Principal components analysis to summarize microarray experiments: application to sporulation time series. Pac. Symp. Biocomput., 455, 2000.
Genome-Wide Technologies
13-17
22. Dharmadi, Y. and Gonzalez, R. DNA microarrays: experimental issues, data analysis, and application to bacterial systems. Biotechnol. Prog., 20, 1309, 2004. 23. Syvanen, A.C. Toward genome-wide SNP genotyping. Nat. Genet. Suppl., S5, 2005. 24. Edwards-Ingram, L.C. et al. Comparative genomic hybridization provides new insights into the molecular taxonomy of the Saccharomyces sensu stricto complex. Genome Res., 14, 1043, 2004. 25. Karaca, G. et al. Herpesvirus of turkeys: microarray analysis of host gene responses to infection. Virology, 318, 102, 2004. 26. Fu, M. et al. Egr-1 target genes in human endothelial cells identified by microarray analysis. Gene, 315, 33, 2003. 27. Sandler, N.G. et al. Global gene expression profiles during acute pathogen-induced pulmonary inflammation reveal divergent roles for Th1 and Th2 responses in tissue repair. J. Immunol., 171, 3655, 2003. 28. Miki, R. et al. Delineating developmental and metabolic pathways in vivo by expression profiling using the RIKEN set of 18,816 full-length enriched mouse cDNA arrays. Proc. Natl. Acad. Sci. USA, 98, 2199, 2001. 29. Ahrendt, S.A. et al. Rapid p53 sequence analysis in primary lung cancer using an oligonucleotide probe array. Proc. Natl. Acad. Sci. USA, 96, 7382, 1999. 30. Lock, C. et al. Gene-microarray analysis of multiple sclerosis lesions yields new targets validated in autoimmune encephalomyelitis. Nature Med., 8, 500, 2002. 31. Tummala, S.B. et al. Transcriptional analysis of product concentration driven changes in cellular programs of recombinant Clostridium acetobutylicum strains. Biotechnol. Bioeng., 84, 842, 2003. 32. Roth, F.P. et al. Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation. Nat. Biotechnol., 16, 939, 1998. 33. Golub, T.R. et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286, 531, 1999. 34. van’t Veer, L.J. et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature, 415, 530, 2002. 35. Weinstein, J.N. Pharmacogenomics-teaching old drugs new tricks. N. Engl. J. Med., 343, 1408, 2000. 36. Scherf, U. et al. A gene expression database for the molecular pharmacology of cancer. Nat. Genet., 24, 236, 2000. 37. Staunton, J.E. et al. Chemosensitivity prediction by transcriptional profiling. Proc. Natl. Acad. Sci. USA, 98, 10787, 2001. 38. Lennon, G.G. High-throughput gene expression analysis for drug discovery. Drug Discov. Today, 5, 59, 2000. 39. Yoon, S.H. et al. Combined transcriptome and proteome analysis of Escherichia coli during high cell density culture. Biotechnol. Bioeng., 81, 753, 2003. 40. Choi, J.H. et al. Enhanced production of insulin-like growth factor I fusion protein in Escherichia coli by the coexpression of the down–regulated genes identified by transcriptome profiling. Appl. Environ. Microbiol., 69, 4737, 2003. 41. Pool, W.A. et al. Natural sweetening of food products by engineering Lactococcus lactis for glucose production. Metab. Eng., 8, 456, 2006. 42. Wahlbom, F. et al. Molecular analysis of a Saccharomyces cerevisiae mutant with improved ability to utilise xylose shows enhanced expression of proteins involved in transport, initial xylose metabolism and the pentose phosphate pathway. Appl. Environ. Microbiol., 69, 740, 2003. 43. Gonzalez, R. Gene array-based identification of changes that contribute to ethanol tolerance in ethanologenic Escherichia coli: comparison of KO11 (parent) to LY01 (resistant mutant). Biotechnol. Prog., 19, 612, 2003. 44. Jurgen, B. et al. Global expression profiling of Bacillus subtilis cells during industrial-close fed-batch fermentations with different nitrogen sources. Biotechnol. Bioeng., 92, 277, 2005.
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45. Akesson, M., Forster, J., and Nielsen, J. Integration of gene expression data into genome-scale metabolic models. Metab. Eng. 6, 285, 2004. 46. Wei, Y. et al. High-density microarray-mediated gene expression profiling of Escherichia coli. J. Bacteriol., 183, 545, 2001. 47. Dudley, A.M. et al. Measuring absolute expression with microarrays with a calibrated reference sample and an extended signal intensity range. Proc. Natl. Acad. Sci. USA, 99, 7554, 2002. 48. Helmann, J.D. et al. Global transcriptional response of Bacillus subtilis to heat shock. J. Bacteriol., 183, 7318, 2001. 49. Khil, P.P. and Camerini-Otero, R.D. Over 1000 genes are involved in the DNA damage response of Escherichia coli. Mol. Microbiol., 44, 89, 2002. 50. Glassbrook, N., Beecher, C., and Ryals, J. Metabolic profiling on the right path. Nat. Biotechnol., 18, 1142, 2000. 51. Dunn, W.B., Bailey, N.J., and Johnson, H.E. Measuring the metabolome: current analytical technologies Analyst, 130, 606, 2005. 52. Last, R.L., Jones, A.D., and Shachar-Hill, Y. Towards the plant metabolome and beyond. Nat. Rev. Mol. Cell Biol., 8, 167, 2007. 53. Bochner, B.R., Gadzinski, P., and Panomitros, E. Phenotype microarrays for high-throughput phenotypic testing and assay for gene function. Genome Res., 11, 1246, 2001. 54. Bochner, B.R. New technologies to assess genotype-phenotype relationships. Nature Rev. Genetics, 4, 309, 2003. 55. Klingler, J.M. et al. Evaluation of the biolog automated microbial identification system. Appl. Environ. Microbiol., 58, 2089, 1992. 56. Mukherjee, A. et al. Exploring genotypic and phenotypic diversity of microbes using microarray approaches. Toxicol. Mech. Methods, 16, 121, 2006. 57. Zhou, L. et al. Phenotypic microarray analysis of Escherichia coli K-12 mutants with deletions of all two-component systems. J. Bacteriol., 185, 4956, 2003. 58. Ulrich, R.L. et al. Mutational analysis and biochemical characterization of the Burkholderia thailandensis DW503 quorum-sensing network. J. Bacteriol., 186, 4350, 2004. 59. Druzhinina, I. et al. Global carbon utilization profiles of wild type, mutant and transformant strains of Hypocrea jecorina. Appl. Environ. Microbiol., 72, 2126, 2006. 60. Outeiro, T.F. and Giorgini, F. Yeast as a drug discovery platform in huntington’s and parkinson’s diseases. Biotechnol. J., 1, 1, 2006. 61. von Eiff, C. et al. Phenotype microarray profiling of Staphylococcus aureus menD and hemB mutants with the small-colony-variant phenotype. J. Bacteriol., 188, 687, 2006. 62. Ihssen, J. and Egli, T. Global physiological analysis of carbon- and energy-limited growing Escherichia coli confirms a high degree of catabolic flexibility and preparedness for mixed substrate utilization. Appl. Environ. Microbiol., 7, 1568, 2005. 63. Narayanaswamy, R. et al. Systematic profiling of cellular phenotypes with spotted cell microarrays reveals mating-pheromone response genes. Genome Biol., 7, R6, 2006. 64. Bro, C. and Nielsen, J. Impact of “ome” analyses on inverse metabolic engineering. Metab. Eng., 6, 204, 2004. 65. Förster, J., Gombert, A.K., and Nielsen, J. A functional genomics approach using metabolomics and in silico pathway analysis. Biotechnol. Bioeng., 79, 703, 2002. 66. Wilkins, M.R. et al. Progress with proteome projects: why all proteins expressed by a genome should be identified and how to do it. Biotechnol. Genet. Eng. Rev., 13, 19, 1996. 67. Han, M.J. and Lee, S.Y. The Escherichia coli proteome: past, present, and future prospects. Microbiol. Mol. Biol. Rev., 70, 362, 2006. 68. Taylor, S.W., Fahy, E., and Ghosh, S.S. Global organellar proteomics. Trends Biotechnol., 21, 82, 2003. 69. Tonella, L. et al. ‘98 Escherichia coli SWISS-2DPAGE database update. Electrophoresis, 19, 1960, 1998.
Genome-Wide Technologies
13-19
70. Tonella, L. et al. New perspectives in the Escherichia coli proteome investigation. Proteomics, 1, 409, 2001. 71. Lopez-Campistrous, A. et al. Localization, annotation and comparison of the Escherichia coli K-12 proteome under two states of growth. Mol. Cell. Proteomics, 4, 1205, 2005. 72. Nandakumar, M.P., Cheung, A., and Marten, M.R. Proteomic analysis of extracellular proteins from Escherichia coli W3110. J. Proteome Res., 5, 1155, 2006. 73. Lee, J.W. et al. The proteome of Mannheimia succiniciproducens, a capnophilic rumen bacterium. Proteomics, 6, 3550, 2006. 74. Tjalsma, H. et al. Signal peptide-dependent protein transport in Bacillus subtilis: a genome-based survey of the secretome. Microbiol. Mol. Biol. Rev., 64, 515, 2000. 75. Bumann, D. et al. Proteome analysis of secreted proteins of the gastric pathogen Helicobacter pylori. Infect. Immun., 70, 3396, 2002. 76. Tan, Y.P. et al. Comparative proteomic analysis of extracellular proteins of Edwardsiella tarda. Infect. Immun., 70, 6475, 2002. 77. Ziebandt, A.K. et al. Extracellular proteins of Staphylococcus aureus and the role of SarA and sigma B. Proteomics, 1, 480, 2001. 78. Len, A.C. et al. Cellular and extracellular proteome analysis of Streptococcus mutans grown in a chemostat. Proteomics, 3, 627, 2003. 79. Jeong, K.J. and Lee, S.Y. Excretion of human beta-endorphin into culture medium by using outer membrane protein F as a fusion partner in recombinant Escherichia coli. Appl. Environ. Microbiol., 68, 4979, 2002. 80. Harry, J.L. et al. Proteomics: capacity versus utility. Electrophoresis, 21, 1071, 2000. 81. Zuo, X., Lee, K., and Speicher, D.W. Electrophoretic prefractionation for comprehensive analysis of proteomes In Proteome Analysis: Interpreting the Genome, Speicher, D.W., Ed. Elsevier Science, New York, 2004, 93. 82. Molloy, M.P. et al. Proteomic analysis of the Escherichia coli outer membrane. Eur. J. Bioche., 267, 2871, 2000. 83. Fountoulakis, M. and Gasser, R. Proteomic analysis of the cell envelope fraction of Escherichia coli. Amino Acids, 24, 19, 2003. 84. Lee, S.Y., Choi, J.H., and Xu, Z. Microbial cell-surface display. Trends Biotechnol., 21, 45, 2003. 85. Lee, S.H. et al. Display of bacterial lipase on the Escherichia coli cell surface by using FadL as an anchoring motif and use of the enzyme in enantioselective biocatalysis. Appl. Environ. Microbiol., 70, 5074, 2004. 86. Lee, S.H. et al. Display of lipase on the cell surface of Escherichia coli using OprF as an anchor and its application to enantioselective resolution in organic solvent. Biotechnol. Bioeng., 90, 223, 2005. 87. Gevaert, K. et al. Chromatographic isolation of methionine-containing peptides for gel-free proteome analysis: identification of more than 800 Escherichia coli proteins. Mol. Cell. Proteomics, 1, 896, 2002. 88. Corbin, R.W. et al. Toward a protein profile of Escherichia coli: comparison to its transcription profile. Proc. Natl. Acad. Sci. USA, 100, 9232, 2003. 89. Washburn, M.P., Wolters, D., and Yates, J.R. III. Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat. Biotechnol., 19, 242, 2001. 90. Ünlü, M., Morgan, M.E., and Minden, J.S. Difference gel electrophoresis: a single gel method for detecting changes in protein extracts. Electrophoresis, 18, 2071, 1997. 91. Yan, J.X. et al. Fluorescence two-dimensional difference gel electrophoresis and mass spectrometry based proteomic analysis of Escherichia coli. Proteomics, 2, 1682, 2002. 92. Hu, Y. et al. Proteome analysis of Saccharomyces cerevisiae under metal stress by two-dimensional differential gel electrophoresis. Electrophoresis, 24, 1458, 2003. 93. Skynner, H.A. et al. Alterations of stress related proteins in genetically altered mice revealed by two-dimensional differential in-gel electrophoresis analysis. Proteomics, 2, 1018, 2002.
13-20
Application of Emerging Technologies to Metabolic Engineering
94. Van den, B.G. et al. Fluorescent two-dimensional difference gel electrophoresis and mass spectrometry identify age-related protein expression differences for the primary visual cortex of kitten and adult cat, J. Neurochem., 85, 193, 2003. 95. Gharbi, S. et al. Evaluation of two-dimensional differential gel electrophoresis for proteomic expression analysis of a model breast cancer cell system. Mol. Cell. Proteomics., 1, 91, 2002. 96. Friedman, D.B. et al. Proteome analysis of human colon cancer by two-dimensional difference gel electrophoresis and mass spectrometry. Proteomics, 4, 793, 2004. 97. Gygi, S.P. et al. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat. Biotechnol., 17, 994, 1999. 98. Zhou, H. et al. Quantitative proteome analysis by solid-phase isotope tagging and mass spectrometry. Nat. Biotechnol., 20, 512, 2002. 99. Mirgorodskaya, O.A. et al. Quantitation of peptides and proteins by matrix-assisted laser desorption/ionization mass spectrometry using 18O-labeled internal standards. Rapid Commun. Mass Spectrom., 14, 1226, 2000. 100. Yao, X. et al. Proteolytic 18O labeling for comparative proteomics: model studies with two serotypes of adenovirus. Anal. Chem., 73, 2836, 2001. 101. Oda, Y. et al. Accurate quantitation of protein expression and site-specific phosphorylation. Proc. Natl. Acad. Sci. USA, 96, 6591, 1999. 102. Washburn, M.P. et al. Analysis of quantitative proteomic data generated via multidimensional protein identification technology. Anal. Chem., 74, 1650, 2002. 103. Ross, P.L. et al. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol. Cell. Proteomics, 3, 1154, 2004. 104. Han, M.J. et al. Engineering Escherichia coli for increased productivity of serine-rich proteins based on proteome profiling. Appl. Environ. Microbiol., 69, 5772, 2003. 105. Aldor, I.S. et al. Proteomic profiling of recombinant Escherichia coli in high-cell-density fermentations for improved production of an antibody fragment biopharmaceutical. Appl. Environ. Microbiol., 71, 1717, 2005. 106. Allen, S.P. et al. Two novel heat shock genes encoding proteins produced in response to heterologous protein expression in Escherichia coli. J. Bacteriol., 174, 6938, 1992. 107. Han, M.J. et al. Roles and applications of small heat shock proteins in the production of recombinant proteins in Escherichia coli. Biotechnol. Bioeng., 88, 426, 2004. 108. LeThanh, H., Neubauer, P., and Hoffmann, F. The small heat-shock proteins IbpA and IbpB reduce the stress load of recombinant Escherichia coli and delay degradation of inclusion bodies. Microb. Cell Fact. 4, 6, 2005. 109. Han, M.J., Lee, J.W., and Lee, S.Y. Enhanced proteome profiling by inhibiting proteolysis with small heat shock proteins. J. Proteome Res., 4, 2429, 2005. 110. Lee, J.H. et al. Global analyses of transcriptomes and proteomes of a parent strain and an L-threonineoverproducing mutant strain. J. Bacteriol., 185, 5442, 2003. 111. Lee, P.S. and Lee, K.H. Engineering HlyA hypersecretion in Escherichia coli based on proteomic and microarray analyses. Biotechnol. Bioeng., 89, 195, 2005. 112. Askenazi, M. et al. Integrating transcriptional and metabolite profiles to direct the engineering of lovastatin-producing fungal strains. Nat. Biotechnol., 21, 150, 2003. 113. Krömer, J.O. et al. In-depth profiling of lysine-producing Corynebacterium glutamicum by combined analysis of the transcriptome, metabolome, and fluxome. J. Bacteriol., 186, 1769, 2004.
14 Monitoring and Measuring the Metabolome Maria Rowena N. Monton Keio University
Tomoyoshi Soga Keio University
14.1 Introduction ����������������������������������������������������������������������������������� 14-1 14.2 Mass Spectrometry ����������������������������������������������������������������������� 14-2 Direct Injection Mass Spectrometry (DIMS) • Gas Chromatography– Mass Spectrometry • Liquid Chromatography–Mass Spectrometry Capillary Electrophoresis–Mass Spectrometry
14.3 Nuclear Magnetic Resonance Spectroscopy..............................14-8 References �������������������������������������������������������������������������������������������������� 14-9
14.1 Introduction The “metabolome,” the complete set of small (less than 1000 Da) molecules (metabolites) present in cells in a particular physiological or developmental state,1 closely reflects cellular activities at a functional level.2 Its complexities and dynamism pose significant challenges from the analytical viewpoint. Metabolomics, the qualitative and quantitative analysis of the metabolome, is an extremely demanding science. Whereas DNA, RNA, and proteins are largely coherent chemically since they are formed from a limited pool of monomers (four nucleotide bases or 20 amino acids) strung together, metabolites are much more diverse chemically and physically because of the number of possible atomic combina tions, 3 as well as spatial orientation. Metabolites can range from ionic inorganic species to hydrophilic carbohydrates, volatile alcohols and ketones, amino and nonamino organic acids, hydrophobic lipids and complex natural products.4 Their dynamic concentration range is very wide, spanning over an estimated nine orders of magnitude (from pmol to mmol). 3 Moreover, metabolites participate in a complex network of reactions, which are subject to rapid enzymatic turnover, thus requiring extreme care in sampling.5 Such intricacies render analysis of the complete metabolome practically impossible. Metabolomic approaches, depending on the intended area of application, study subsets of the metabolome, requiring technologies that are robust, rapid, comprehensive, sensitive, and accurate. These approaches include:2–7 • Metabolite/metabolic profiling—qualitative and quantitative analysis of metabolites common to a specific pathway or chemical class. • Targeted analysis—quantitative analysis of one or a group of selected metabolites. • Metabolic fingerprinting—comprehensive analysis of samples for the purpose of classification based on observed metabolite pattern. • Metabolic footprinting—global analysis of intracellular metabolites secreted in to the spent growth medium. The current arsenal of analytical technologies for metabolomics includes mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy, and chromatography (gas chromatography (GC), 14-1
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Application of Emerging Technologies to Metabolic Engineering
liquid chromatography (LC)), and electrophoresis (capillary electrophoresis (CE))-based separation platforms. To a limited extent, molecular spectroscopic methods (UV, Raman, IR) are also used. Some stand-alone techniques are employed, but it is generally regarded that strategic combinations of highperformance separation techniques with sophisticated detection systems constitute the best methods. Upstream separation boosts detector performance, and confidence in metabolite identification is reinforced by the use of two (or more) parameters in tandem (e.g., retention time/migration time plus accurate molecular mass). These combinations may be performed off-line as distinct techniques carried out in succession, or on-line as integrated steps comprising a single analytical procedure. The off-line mode permits independent optimization of analytical conditions and circumvents technical difficulties in coupling different systems. However, the on-line mode is used more extensively because of higher throughput and minimal sample loss due to handling.
14.2 Mass Spectrometry MS is the most important tool for probing the metabolome, unmatched in terms of sensitivity, and with near universality of application. It can provide molecular weight accuracy on the order of ± 0.01% and structural information as well.8 A number of ionization methods are available; however, electron ionization (EI), chemical ionization (CI), electrospray ionization (ESI) and matrix-assisted laser desorption/ ionization (MALDI) are the most common in metabolomics. The first two are used almost exclusively with GC–MS, while the last two, which are compatible with nonvolatile, thermolabile compounds, have wider applications. ESI and MALDI (Table 14.1) belong to the so-called “soft” ionization techniques, which cause little or no fragmentation of the molecular ion.8 They can be operated in both positive and negative ion modes, thus potentially expanding coverage. Several designs of mass analyzers are available to match specific needs. A comparison of four commonly used ones (quadrupole, ion trap, time-of-flight (TOF), Fourier transform-ion cyclotron resonance (FT-ICR)) in terms of performance is shown in Table 14.2.8 Some hybrid (i.e., combination of basic types, e.g., quadrupole—TOF) mass spectrometers have also been developed, enabling very high mass accuracy, sensitivity, resolution, and scan speed. When necessary to structurally characterize an unknown metabolite, or to provide unequivocal identification by direct interpretation of its Table 14.1 Comparison of ESI and MALDI Sensitivity Tolerance for complex mixtures Tolerance for salts Integration with upstream separation
ESI
MALDI
High Low Low Adaptable
High High Medium Essentially off-line
Table 14.2 Performance of Commonly Employed Mass Analyzers Mass accuracy Mass range Resolution Relative cost MS/MS capability
Quadrupole
Ion Trap
TOF
FT-ICR
Medium Medium Low Low Yesa
Medium Medium Low Low Yes
High Very high High Medium Yesb
Very high High Very high High Yes
With multiple quadrupoles. With quadrupole-TOF or reflectron-TOF instrument.
a
b
Monitoring and Measuring the Metabolome
14-3
mass spectrum or by comparison with putative fragments, tandem MS (MS/MS) may be used, and fragmentation may be accomplished via collision-induced dissociation (CID).
14.2.1 Direct Injection Mass Spectrometry (DIMS) In DIMS, samples, frequently without derivatization, are directly introduced in to the mass spectrometer by infusion using a syringe pump or an automated flow injection system (in ESI), or by deposition onto the target plate (in MALDI). The total run time per sample can be as short as 30s9 enabling hundreds of samples to be processed per day. Hence, DIMS is particularly suitable for highthroughput screening. It has been widely exploited in the areas of microbial and plant metabolomics for chemotaxonomy,10–12 and in clinical diagnostics for targeted analyses of key metabolites related to metabolic disorders.13,14 With ESI or MALDI as ion sources, the presence of molecular ions simplifies the interpretation of mass spectra, and the use of high-mass accuracy, high-mass resolution instruments (e.g., FT–ICR–MS) permits assignment of empirical formulae to peaks. Structural isomers, however, cannot be mass-resolved and a prior separation step is necessary. For quantitation, DIMS should be used with caution because of matrix effects, i.e., changes in MS response of a chemical species due to the presence of other species in the mixture. Such effects can be minimized and the accuracy of results improved by executing a clean-up step prior to MS analysis15 or by incorporating an internal standard.16
14.2.2 Gas Chromatography–Mass Spectrometry GC and MS are inherently compatible techniques—they both require the sample to be in the gaseous phase—hence, GC–MS technology matured early and is considered as a standard tool for the analysis of volatile compounds and those that can be derivatized to yield volatile, thermostable products. In this hyphenation of techniques, GC confers high resolution while MS provides high selectivity and sensitivity. Efficiencies on the order of 105 plates enabling identification of hundreds of compounds in the pmol level, in a single chromatographic run, can be obtained. Volatile metabolites can be collected by headspace solid-phase microextraction (SPME),17 headspace sorbent extraction,18 or solvent extraction,19 then injected directly to the gas chromatograph. Comprehensive studies, however, generally require a preceding derivatization step since many metabolites contain polar functional groups that are thermally labile at the temperatures required for their separation, or are not volatile at all.20 An overview of the derivatization methods used in metabolomics is presented in Table 14.3. GC columns can either be of packed or open tubular (capillary) type. Because of their superior resolution, however, majority of GC columns today are of the latter. Fast, efficient separations can be obtained using fused silica capillaries (0.1–0.7 mm in internal diameter) as short as 10 m, but longer capillaries are required for highly complex mixtures. The stationary phase can be coated onto the wall of the capillary as a liquid film (wall-coated open tubular (WCOT)), a porous polymer (polymer layer open tubular (PLOT)), or adsorbed onto a support material lined on the wall (support-coated open tubular (SCOT)). WCOT columns are broadly used because they afford higher efficiencies and better stability. The film can vary in thickness from 0.1 to 3 mm, with thicker films required for low-boiling compounds. Polysiloxane-based columns (e.g., DB-1, DB-5, VF-1ms) work well for nonpolar compounds and those of intermediate polarities, while polyethylene glycol (PEG)-based ones (e.g., DB-WAX) are better for highly polar compounds. The low flows associated with capillary columns permit their direct insertion into the MS ion source, frequently EI. Eluted compounds can be identified by comparing their retention indices (RI) against those of known standards analyzed under similar conditions, or by their mass spectra since EI induces
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Application of Emerging Technologies to Metabolic Engineering
Table 14.3 Derivatization Methods in GC–MS Applied for Metabolomics Type of Reaction Silylation
Alkylation/ esterification
Type of Reagent
Typical Reaction Conditions
Classes of Metabolites Derivatized
TMS, HMDS, TBS, Reaction in pyridine under Sugars and derivatives, amino MTBSTFA, BSTFA, anhydrous conditions and acids, organic acids, MDBSTFA, QSM, MSHFBA, heating terpenoids, fatty acids, TSIM, MSTFA, TMCS, flavonoids, amides, and TMSDEA, BSA, etc. phytohormones Amino acids and derivatives, Chloroformates Reaction in pyridine in mono- and polycarboxylic aqueous solution, room temperature, with or without acids, keto acids, hydroxyl acids, fatty acids, aliphatic a second reactant and alicylic amines, and amine-alcohols Diazomethane Nonpolar solvent in the absence of water Acidic esterification
H2SO4, Na2SO4, methanol/ ethanol
Source: Villas-Bôas, S.G. et al. Mass Spectrom. Rev., 24, 613, 2005. With permission.
fragmentation of the molecular ions. GC–MS has been used extensively for both metabolite profiling and targeted analyses.17–22 Multidimensional separations operating on orthogonal mechanisms provide greater resolving power for extremely complex mixtures than one-dimensional systems, though this comes at the expense of longer analysis times. A significant development in GC in the last decade is comprehensive twodimensional (2D) GC (GC × GC). In such configuration, the sample is first separated in a long, nonpolar column, then small fractions of the effluent are trapped and focused using a modulator, and sequentially released into a second short, polar (or shape-selective) column for further separation. Separation in the second column is extremely fast (1–10 s), thus requiring a rapid detection system. Among currently available MS detectors, only TOF instruments are compatible because of their high-scan speed capabilities (more than 50 spectra per second).23 Because the peak capacity (i.e., the number of peaks that can be potentially resolved in a column) is significantly increased (equal to the product of the peak capacities of the individual columns), the number of components that can be separated is also increased, thus GC × GC is ideal for very complex samples. In addition, components coeluting in the first column can possibly be resolved in the second, because the latter is driven by an independent separation principle. GC × GC data are presented in a 2D space (example shown in Figure 14.1), from which a chemical map of compound properties can be derived, making it particularly attractive for sample fingerprinting.24 It is broadly used for petrochemical and environmental analyses and is now increasingly deployed for metabolomic approaches, principally in plants.25,26
14.2.3 Liquid Chromatography–Mass Spectrometry To date, LC is the dominant liquid-phase separation technique. It can simultaneously accommodate analytes with widely varying properties, such as ionic/neutral, hydrophilic/hydrophobic, and acid/base, and it is this ruggedness which has contributed to its ease of use and utility.27 Similar to the trend in GC, the downscaling of the chromatographic column has been a major step toward improved separation performance, higher sensitivity, lower sample volume requirement and lower solvent consumption. LC, as it is practised today, is largely in the micro (0.5–1 mm) or capillary (0.1–0.5 mm) scale. The low flow rates in these small-internal diameter columns, in combination with the introduction of ESI
14-5
Monitoring and Measuring the Metabolome (a)
(b) 3.5
3.5 3.0
4 1
2.0 D2 Retention time (s)
3.0
3
2.5
1.0
12.50 13.75 15.00 16.25 17.50 18.75 20.00
(c)
(d)
3.5
3.5
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Figure 14.1 Expanded windows showing a possible fingerprint region for GC × GC space analysis of the headspace volatiles from (a) Agrostis stolonifera; (b) Pennisetum clandestinum; (c) Trifolium repens and (d) Eucalyptus leucoxycolon. (Reprinted from Perera, R.M.M., Marriott, P.J., and Galbally, I.E. Analyst, 127, 2002, 1601. With permission of The Royal Society of Chemistry.)
and its variants (microESI, nanoESI), which permit direct analysis of compounds in the aqueous or hydroorganic eluate, have made LC-MS routine. Several separation modes (e.g., ion-exhange, size-exclusion, affinity) are available, but the most common by far is reversed-phase LC (RPLC), in which the sample components are eluted in the order of increasing hydrophobicity. The mobile phases used are MS-compatible, which accounts for the popularity of RPLC-MS couplings. High separation efficiencies can be obtained, and can be further improved when operated in the gradient elution mode (i.e., the organic component of the mobile phase is changed stepwise or continuously), enabling resolution of components that coelute otherwise. ESI, the usual interface, is susceptible to ion suppression effects; hence, good resolution is necessary for reliable peak assignment and quantitation. RPLC-MS has been used successfully in many metabolomic applications, including metabolite profiling in plants28,29 and microorganisms,30 and targeted analysis in the clinical setting.31 In most cases, the use of MS/MS expanded the capability for definitive metabolite identification. RPLC columns are typically packed with spherical silica particles (3–5 µm) whose surfaces have been modified by alkyl chains varying in length between C 4 and C18 to promote analyte retention. Chromatographic efficiency can be increased by decreasing the size of these particles or using a longer column, but pressure limits of most commercial instruments (10,000 psi) have largely precluded this. Recent innovations in pump systems, however, enabled operations at elevated pressures, and led to the development of ultrahigh-pressure or ultra performance LC (UPLC). Particles smaller than 2 µm can
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be packed in long capillaries using packing pressures as high as 60,000 psi and running pressures at optimum flow rates on the order of 20,000 psi. These columns can generate as many as 300,000 plates, and analysis times can be significantly shortened.32 A comparison of results using conventional LC–MS and UPLC–MS under similar analytical conditions is shown in Figure 14.2. UPLC–MS is used increasingly in metabolomic applications33–35 where sample complexity requires a technology of high resolving power. An option to improve efficiency is to use continuous media, such as monoliths, instead of particlepacked columns. A monolithic column is made of a single piece of a porous solid with small-sized skeletons and relatively large through-pores.36 Its high permeability allows enhanced mass transport and high flow rates at reasonable column back pressures for efficient (100,000 plates), fast operations. Interest in monolithic columns has picked up considerably in recent years for proteomic37 and genomic38 studies, and the same interest can be expected in metabolomic research. For highly polar compounds that are not retained well, and therefore not separated, on RPLC columns, hydrophilic interaction chromatography (HILIC)39 may be used. HILIC is similar to the traditional normal phase LC (NPLC) in that the components of a sample elute in the order of increasing hydrophilicity. However, in lieu of highly hydrophobic solvents (e.g. hexane) used in NPLC, HILIC uses low-aqueous, high-organic (ca. 80%) mobile phases, thus eliminating the problem associated with the insolubility of polar analytes in the nonpolar mobile phase. Moreover, such mobile phases are compatible with MS, and coupling can be implemented with ease. The superiority of HILIC over traditional RPLC has been demonstrated in profiling oligosaccharides and sugar nucleotides,40 and some highly polar urinary constitutents.41
14.2.4 Capillary Electrophoresis–Mass Spectrometry CE is a group of separation techniques that can be used for both small and large molecules. It is ideal for charged or chargeable species, but its utility can be expanded to include even neutrals when they are incorporated into charged carriers. References to CE generally mean its most popular form, capillary zone electrophoresis (CZE). However, several other modes are available, including micellar electrokinetic chromatography (MEKC), capillary gel electrophoresis (CGE), capillary sieving electrophoresis (a)
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Figure 14.2 3D maps for (a) HPLC–MS and (b) UPLC–MS of white male mouse urine from AM collection mouse showing retention time, m/z and intensity. (Reprinted with permission from Wilson, I.D. J. Proteome Res., 4, 2005, 591. Copyright 2005 American Chemical Society.)
Monitoring and Measuring the Metabolome
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(CSE), capillary isotachophoresis (CITP), and capillary isoelectric focusing (CIEF). CSE is an important tool in DNA sequencing, while CIEF is a mainstay in many proteomic laboratories. Compared to GC and LC, CE is still an emerging technology whose development began in the early 1980s. It can be regarded as the capillary analog of traditional slab gel electrophoresis in the same way that column chromatography is to planar chromatography. Using mostly aqueous solutions, analytes can be separated according to their mass-to-charge ratios inside narrow-bore capillaries with internal diameters that are typically less than 100 µm for efficient dissipation of Joule heat. The use of fused silica capillaries enables application of high electric fields, resulting in short analysis times. Separation efficiencies in CE are generally higher than in LC, which can be attributed in part to the plug-like profile of electroosmotic flow (EOF), the bulk flow of solution inside the capillary; hence, analyte zone dispersion is minimal. Under normal conditions (i.e., anode at the inlet and cathode at the outlet), the magnitude of the EOF is higher than the electrophoretic mobilities of most analytes, so cations, neutrals, and anions will migrate, in the given order, in the same direction and be analyzed simultaneously. In addition, CE can be automated easily and running costs are low. Despite its many advantages, widespread acceptance of CE in the research community has been slow in coming, and this can be traced back to the capillary itself. To maintain performance, sample loading is limited to ca. 1% (a few nl at most compared to µl-range for LC)) of the total capillary volume. While this translates to minimal sample requirement, it also means that the concentration sensitivity is low. This drawback becomes aggravated since most commercial CE instruments are fitted with absorbance-related detectors, thus the available path length for detection is short. The concentration limits of detection in CE (µM level) are acknowledged to be worse by two orders of magnitude compared to chromatographic systems, but the use of sample enrichment techniques and more sensitive detectors have helped close the gap. The resolving power of CE has been demonstrated for diverse classes of analytes, ranging from small inorganic anions to large proteins, and even whole organisms. Compared to the more established GC and LC methods, few CE applications in metabolomics have been reported, many of them with oncapillary detectors, such as UV or laser-induced fluorescence (LIF). However, with the availability of commercial interfaces for combining CE with MS, CE–MS-based techniques have grown steadily, particularly for polar metabolites that are not separated well by chromatographic means. The most robust approach for CE–MS coupling is via a sheath liquid interface, in which a hydroorganic solution (sheath liquid) flows between a grounded stainless steel capillary and a coaxial separation capillary. It serves to complete the CE electrical circuit and helps provide a stable electrospray. CE–MS can be configured in a number of ways, with a given configuration optimized for the separation and detection of a group of compounds. (1) Amino acids, amines, nucleosides, and other positively chargeable compounds are separated as their cations using a highly acidic separation solution in a bare, fused silica capillary, and then detected by ESI–MS as their protonated molecular ions, [M + H] + .42 (2) The metabolites for key pathways for cellular energy production such as glycolysis, tricarboxylic acid (TCA) and pentose phosphate cycles, and other negatively chargeable compounds are separated as their anions under alkaline conditions. A capillary coated with a cationic polymer is employed to reverse the EOF (from cathode to anode) so that it is in the same direction as the electrophoretic migration of the anions, and analysis time can be shortened. The separated anions are subsequently detected by MS as their deprotonated molecular ions, [M-H]−.43 (3) Multivalent anions such as nucleotides and CoA derivatives are separated on a noncharged polymer-coated capillary, under slightly alkaline conditions and a pressure-assist system to counteract the EOF and deliver a constant flow of solution toward the mass spectrometer where they are detected as their deprotonated molecular ions. These metabolites can not be analyzed together with other anions under (2) because of significant adsorption on the positively charged wall.44 Schematic diagrams of these CE–MS conditions are shown in Figure 14.3. High separation efficiencies could be obtained in each of them, even permitting separation of isomers (Figure 14.4). By combining data from these conditions, thousands of peaks can be separated, identified and quantified, and the strategy can be used for obtaining global metabolite profiles. Its usefulness has
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Application of Emerging Technologies to Metabolic Engineering (a)
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Figure 14.3 Schematic diagrams of the various CE–MS configurations. (a) for cationic metabolites; (b) for anionic metabolites, and (c) for nucleotides and CoA compounds. (Reprinted with permission from Soga, T. et al. J. Proteome Res., 2, 2003, 488. Copyright 2003 American Chemical Society.)
been previously demonstrated for investigating how changes in metabolites are implicated in Bacillus subtilis sporulation,45 for determination of the major metabolites in rice leaves,46 and for biomarker discovery.47
14.3 Nuclear Magnetic Resonance Spectroscopy NMR spectroscopy is a nondestructive analytical method. It can use one of several high-abundance isotopes with nonzero nuclear spin (e.g., 1H, 13C, 14N, 19F, 31P), thus it is applicable to most biologically important molecules. For metabolomics, however, the preferred nucleus is 1H. Biofluids, cells, and even intact tissues can be analyzed directly without extensive sample preparation steps. Thus, NMR is an attractive platform for high-throughput fingerprinting and profiling studies. Its major application is in the clinical setting, where it is used to track changes in metabolite levels in response to stress (e.g., a disease or a drug), setting off a related field of study termed metabonomics.48 Unlike MS which cannot detect compounds that are not effectively ionized in the ionization source and is susceptible to matrix effects, NMR is compound-independent and can uniformly detect all compounds having NMR-measurable nuclei.49 Its principal limitations, however, are its poor sensitivity and high sample requirement (high µl). NMR has been prefaced with LC to enhance its resolving capability for complex mixtures (e.g., components with spectral overlaps). The coupling is best operated using the stopped-flow mode, in which the chromatographic run is briefly stopped once the peak of interest reaches the NMR probe and is resumed after the spectrum has been recorded. Though chromatographic performance is compromised to some extent, sensitivity gain can be achieved because the time window for NMR data acquisition is wide enough. A strategy that exploits the combination of two powerful detectors is LC–NMR–MS. It allows simultaneous acquisition of NMR and MS data in a single LC run, and enables comprehensive analysis of a complex matrix through real-time comparison and complementation of NMR and MS data.49 The detectors may be combined to the LC system serially, i.e., the LC eluent is first analyzed by NMR and then directed to the mass spectrometer for second analysis. Alternatively, the LC eluent may be split for
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Figure 14.4 CE-MS selected ion electropherograms for a standard mixture of 20 metabolites of glycolysis and the TCA cycle. (Reprinted with permission from Soga, T. et al. J. Proteome Res., 2, 2003, 488. Copyright 2002 American Chemical Society.)
parallel detection by NMR and MS. LC–NMR–MS has been demonstrated to be a particularly useful for resolving ambiguities that cannot be handled by LC–MS or LC–NMR alone. It is not yet used for routine analysis, however, several applications for analysis of drug metabolites50 and for natural product screening 51,52 have already been reported.
References 1. Oliver, S.G. et al. Systematic functional analysis of the yeast genome. Trends Biotechnol., 16, 373, 1998. 2. Goodacre, R. et al. Metabolomics by numbers: acquiring and understanding global metabolite data. Trends Biotechnol., 22, 245, 2004. 3. Dunn, W.B. and Ellis, D.I. Metabolomics: current analytical platforms and methodologies. Trends Anal. Chem., 24, 285, 2005. 4. Villas-Bôas, S.G. et al. Mass spectrometry in metabolome analysis. Mass Spectrom. Rev., 24, 613, 2005. 5. Birkemeyer, C. et al. Metabolome analysis: the potential of in vivo labeling with stable isotopes for metabolite profiling. Trends Biotechnol., 23, 28, 2005. 6. Nielsen, J. and Oliver, S. The next wave in metabolome analysis. Trends Biotechnol., 23, 544, 2005.
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7. Dunn, W.B., Bailey, N.J.C., and Johnson, H.E. Measuring the metabolome: current analytical technologies. Analyst, 130, 606, 2005. 8. Suizdak, G. Mass Spectrometry for Biotechnology. Academic Press, San Diego, 1996, xi. 9. Goodacre, R. et al. Chemometric discrimination of unfractionated plant extracts analyzed by electrospray mass spectrometry. Phytochemistry, 62, 2003, 859. 10. Kaderbhai, N.N. et al. Functional genomics via metabolic footprinting: monitoring metabolite section by Escherichia coli tryptophan metabolism mutants using FT-IR and direct injection electrospray mass spectrometry. Comp. Func. Genom., 4, 376, 2003. 11. Smedsgaard, J. and Frisvad, J.C. Using direct electrospray mass spectrometry in taxonomy and secondary metabolite profiling of crude fungal extracts. J. Microbiol. Methods, 24, 1996, 5. 12. Dunn, W.B., Overy, S., and Quick, W.P. Evaluation of automated electrospray-TOF mass spectrometryfor metabolic fingerprinting of the plant metabolome. Metabolomics, 1, 2005, 137. 13. Rashed, M.S. et al. Diagnosis of inborn errors of metabolism from blood spots by acylcarnitines and amino acids profiling using automated electrospray tandem mass spectrometry. Pediatr. Res., 38, 1995, 324. 14. Bonafe, L. Evaluation of urinary acylglycines by electrospray tandem mass spectrometry in mitochondrial energy metabolism defects and organic acidurias. Mol. Genet. Metab., 69, 2000, 302. 15. Liu, C. et al. On-line microdialysis sample cleanup for electrospray ionization mass spectrometry of nucleic acid samples. Anal. Chem., 68, 1996, 3295. 16. Wittmann, C. and Heinzle, E. MALDI-TOF MS for quantification of substrates and products i