The Metabolic Pathway Engineering Handbook: Tools and Applications (v. 2)

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

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Contents

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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Introduction

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

xiii

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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xviii

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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Evolutionary Tools in Metabolic Engineering

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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Evolutionary Tools in Metabolic Engineering

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)

1-11

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.

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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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Improving Protein Functions by Directed Evolution

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.

2-6

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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Evolutionary Tools in Metabolic Engineering

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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Evolutionary Tools in Metabolic Engineering

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

Improving Protein Functions by Directed Evolution

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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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Evolutionary Tools in Metabolic Engineering

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

Improving Protein Functions by Directed Evolution

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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.

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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.

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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

Engineering DNA and RNA Regulatory Regions

3-3

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.

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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

Evolving Pathways and Genomes for the Production of Natural and Novel Compounds

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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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Evolutionary Tools in Metabolic Engineering

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 dou