Pathogenomics: Genome Analysis of Pathogenic Microbes

Pathogenomics Edited by Jrg Hacker and Ulrich Dobrindt

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Pathogenomics Genome Analysis of Pathogenic Microbes Edited by Jrg Hacker and Ulrich Dobrindt

Series Editors Prof. Dr. Dr. h. c. mult. Jrg Hacker Universitt Wrzburg Institut fr Molekulare Infektionsbiologie Rntgenring 11 97070 Wrzburg Germany

Dr. Ulrich Dobrindt Universitt Wrzburg Institut fr Molekulare Infektionsbiologie Rntgenring 11 97070 Wrzburg Germany

&

All books published by Wiley-VCH are carefully produced. Nevertheless, authors, editors, and publisher do not warrant the information contained in these books, including this book, to be free of errors. Readers are advised to keep in mind that statements, data, illustrations, procedural details or other items may inadvertently be inaccurate. Library of Congress Card No.: applied for British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library. Bibliographic information published by Die Deutsche Bibliothek Die Deutsche Bibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data is available in the Internet at .  2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim All rights reserved (including those of translation into other languages). No part of this book may be reproduced in any form – nor transmitted or translated into machine language without written permission from the publishers. Registered names, trademarks, etc. used in this book, even when not specifically marked as such, are not to be considered unprotected by law. Printed in the Federal Republic of Germany. Printed on acid-free paper. Typesetting Khn & Weyh, Satz und Medien, Freiburg Printing betz-druck GmbH, Darmstadt Bookbinding J. Schffer GmbH i. G., Grnstadt

ISBN-13: ISBN-10:

978-3-527-31265-8 3-527-31265-X

V

Foreword The determination of the genome sequences of many prokaryotic and eukaryotic organisms, together with the high-throughput techniques (transcriptomics, proteomics, metabolomics, interactomics, etc.) and the powerful tools of bioinformatics, have opened up to us all new perspectives for the deeper understanding of the basic mechanisms of life. Because of the small size of bacterial genomes and the collinearity of their genetic information, prokaryotes are particularly suited to genome-based in-depth analysis of the essential processes which allow these microorganisms to survive and replicate in many different environments. Some of them can even multiply in those parts of the human body which are normally well protected by highly developed antimicrobial defense mechanisms. This latter property is the most outstanding evolutionary achievement of microbial pathogens that are capable of causing infectious diseases in humans. The present monograph summarizes the state of genome-based research on some of the most important bacterial (and to a lesser extent viral and fungal) human pathogens today. The term pathogenomics has been coined for this new branch of microbiology. Not surprisingly, the major focus of pathogenomics was first on those human bacterial pathogens that (a) can cause major epidemics, especially in developing countries (e.g., Shigella spp. or Vibrio cholerae); (b) represent major health problems in almost all human societies in the form of food contaminants and/or agents of nosocomial infections (e.g., pathogenic Enterobacteriaceae, staphylococci, and streptococci); and (c), due to their frequent occurrence in humans (e.g., Helicobacter pylori and Mycobacterium tuberculosis) represent lifethreatening problems not only for many people in developing countries, but also for immune-compromised and elderly persons in all the industrialized countries. These microorganisms therefore also represent the major objects described in this book. A primary goal of pathogenomics is to use the new experimental tools in combination to unravel those genes – and gene arrangements – of pathogens that are essential for causing disease, and thereby to shed new light on the evolution, interbacterial gene transfer, and distribution of these virulence genes among bacterial populations. This area of pathogenomics, which is already highly advanced, makes up the largest part of this monograph.

VI

Foreword

The other, certainly not less important goal is to study the functional significance of the pathogen-specific genes in the infection process. In the years to come, the enormous amount of genetic information that has piled up in the last 10 years from the sequencing of the genomes of most of the important bacterial pathogens (and their closely related nonpathogenic environmental counterparts) will need to be functionally analyzed. It is anticipated that this functional pathogenomics will finally unravel the mechanisms of differential and coordinated regulation of the virulence genes, the structural, molecular, and physiological functions of their gene products, which will lead to a more comprehensive view on the pathogenic microorganisms. Some aspects of this most interesting future line of microbial research that can be expected to become the mainstream in future pathogenomics are already addressed in some of the chapters of this book. Infections are the outcome of the encounter between the microbial pathogen and its host. Describing microbial infections in molecular terms therefore requires among other things a profound understanding of the host cell responses. The availability today of the genome sequences of man and some of the major model hosts in which microbial infections can be experimentally studied (the mouse in particular, but also alternative hosts such as amebae, the nematode Caenorhabditis elegans, Drosophila melanogaster, or the slime mold Dictyostelium) together with the new genetic and bioinformatics tools open up new avenues towards uncovering at least some of the basic host cell responses. Whilst cellular microbiology has already delivered an enormous set of valuable host-response information at the cellular level, the new in vivo imaging techniques, siRNA technology, and the routine genetic manipulations of some of the abovementioned model hosts may now allow such molecular infection studies to be performed in real hosts. This monograph devotes several chapters to this important aspect of pathogenomics. Science and, especially, the public and political worlds expect pathogenomics to provide novel ideas and strategies to combat infectious diseases through the rational design of better diagnostic tools and novel anti-infectives and vaccines. Indeed, some promising new developments deriving from pathogenomics are already visible and are in part outlined in some chapters of this monograph. These new approaches will most undoubtedly help in fighting the most dangerous infectious agents which still claim the highest death toll in mankind. However, scientists should be modest in their promises relating to the possibilities that arise from this exciting new field of science, since one major lesson which the attentive and critical reader of this monograph will quickly learn is the enormous genetic flexibility and adaptation potential of pathogenic microorganisms – and that means they will keep us busy for decades to come despite all the obvious successes of pathogenomics. The book thus offers a representative view of the present state of the art in the new research area of pathogenomics. It is a valuable and reasonably comprehensive source of information for scientists and advanced students who wish to become acquainted with this most exciting field of modern microbiology. Wrzburg, October 2005

Werner Goebel

VII

Contents Foreword Preface

V XIX

List of Contributors Color Plates

XXI

XXVII

I

Methods

1

Bioinformatics: Data Mining Among Genome Sequences Susanne Kneitz and Thomas Dandekar

1.1

Systematic Genome Analysis of Pathogens as a Basis for Pharmacogenomic Strategies 3 Direct Sequence Annotation Tools for Functional Genomics 4 Identification of Protein Function 4 Obtaining Protein Information from a Domain Server 5 Pathway Analysis 6 Network Analysis 7 Adaptation in Time and to Stimuli 8 Experimental Design for Microarray Analysis 8 Data Analysis 9 Pathogen-Specific Challenges 13 Pathogen Adaptation Potential 14 The Fight Against Resistance 14 Drug Design and Antibiotics 15 Annotation Platforms Suitable for Pathogenomics 15 Conclusions 17

1.2 1.3 1.4 1.5 1.6 1.7 1.7.1 1.7.2 1.8 1.9 1.10 1.11 1.12 1.13

1 3

VIII

Contents

2

Transcriptome Analysis: Towards a Comprehensive Understanding of Global Transcription Activity 21 Ben Sidders and Neil Stoker

2.1 2.2 2.2.1 2.2.2 2.3 2.3.1 2.3.2 2.3.3 2.4 2.4.1 2.4.1.1 2.4.1.2 2.4.2 2.4.3 2.4.4 2.4.5 2.5 2.5.1 2.5.2 2.5.3 2.5.3.1 2.5.3.2 2.5.3.3 2.5.6 2.5.7 2.5.8 2.6

Introduction 21 Development of Transcriptomics 21 From Genomics to Functional Genomics 21 From Gene to Whole Genome 22 Introducing the Microarray 23 What Is a Microarray? 23 The Affymetrix Gene Chip 23 The Spotted Microarray 24 Microarray Methods 25 Experimental Design 25 Type of Experiment 25 Replicates 28 RNA Extraction 28 Labeling/Reverse Transcription 30 Hybridization 31 Scanning 31 Data Normalization and Analysis 32 Image Quantification 32 Data Processing 33 Data Analysis 34 Detection of Differential Expression 34 Pattern Recognition 35 Graphical Representations 35 Microarray Analysis Tools 36 Microarray Follow-Up 36 Data Storage and Reanalysis 37 Transcriptomics: Where We Are Now and What’s to Come

3

Physiological Proteomics of Bacillus subtilis and Staphylococcus aureus: Towards a Comprehensive Understanding of Cell Physiology and Pathogenicity Michael Hecker and Susanne Engelmann

3.1 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2

43

Introduction 43 Proteomics of Bacillus subtilis: The Gram-positive Model Organism 46 The Vegetative Proteome 46 Proteomes of Nongrowing Cells: Proteomic Signatures of Stress/Starvation Stimuli 47 Physiological Proteomics of Staphylococcus aureus 53 The Postgenome Era of S. aureus 53 Proteomes of Growing and Nongrowing Cells 56

37

Contents

3.3.3 3.4

Extracellular Proteins and Pathogenicity Networks 62 Outlook: Second Generation Proteomics and New Fields in S. aureus Physiology and Infection Biology 65

4

Impact of Genome Sequences on Mutational Analysis of Fungal and Bacterial Pathogens 69 Vladimir Pelicic and Xavier Nassif 69

4.1 4.2 4.2.1 4.2.2 4.3 4.3.1 4.3.2 4.3.3 4.3.3.1 4.3.3.2 4.3.3.3 4.3.3.4 4.4

The Long Road from Sequence to Function 69 Classical Genetics Still at the Forefront in the Postgenome Era 70 Reverse Genetics 70 Transposon Mutagenesis 71 Genome-Scale Mutational Analyses 72 Saccharomyces cerevisiae 73 Bacterial Workhorses: E. coli and Bacillus subtilis Bacterial Pathogens 75 Mycoplasma Species 76 Pseudomonas aeruginosa 76 Staphylococcus aureus 77 Neisseria meningitidis 77 Conclusion 79

II

Genomics of Pathogenic Bacteria

5

Pathogenomics of Escherichia coli and Shigella Species Ulrich Dobrindt and Jrg Hacker

5.1 5.2 5.3 5.3.1 5.3.2 5.3.3

Introduction 85 Comparative Genomics of Shigella 86 Comparative Genomics of Escherichia coli 92 Comparison of Complete Genome Sequences 92 Comparative Genomics Using DNA Arrays 94 Mobile Genetic Elements and Evolution of Pathogenic E. coli 95 Genomic Islands/Pathogenicity Islands 95 Plasmids and Bacteriophages 99 Genetic Diversity Among Extraintestinal Pathogenic E. coli 100 Conclusions 101

5.3.4 5.3.5 5.3.6 5.4

75

83 85

IX

X

Contents

6

Pathogenomics of Salmonella Species 109 Helene Andrews-Polymenis and Andreas J. Bumler

6.1 6.2 6.3 6.4

Introduction 109 Salmonella Signature Genes 109 Subspecies I Signature Genes 112 Host Restriction 115

7

Pathogenomics of Enterococcus faecalis Janet M. Manson and Michael S. Gilmore

7.1 7.2 7.3 7.3.1 7.3.2 7.3.3 7.3.4 7.3.4.1 7.3.4.2 7.3.5 7.4

Introduction 125 Enterococcal Pathogenesis 125 Genome Sequence of E. faecalis 126 Mobile Elements, Acquired DNA, and Antimicrobial Resistance 127 Environmental Adaptation and Stress Response 131 Survival In Vivo 133 Potential Virulence Factors 134 Hemolysins, Proteases, and other Enzymes 134 Cell-Wall-Associated Virulence Factors 136 Pathogenicity Island of E. faecalis 138 Conclusions and Future Perspectives 140

8

Genomics of Streptococci 149 Joseph J. Ferretti and W. Michael McShan

8.1 8.2 8.2.1 8.2.1.1 8.2.1.1.1 8.2.1.1.2 8.2.2.1 8.2.2.2 8.2.2.3 8.2.2 8.2.2.1 8.2.3 8.2.3.1 8.2.3.2

Introduction 149 Bacterial Genomes 152 Pyogenic Group 152 Streptococcus pyogenes 152 Virulence Factors 153 Horizontal Gene Transfer 154 Streptococcus agalactiae 155 Group C (GCS) and Group G Streptococci 156 Streptococcus uberis 157 Bovis Group 157 Streptococcus bovis and Streptococcus suis 157 Mitis Group 158 Streptococcus pneumoniae 158 Streptococcus mitis, Streptococcus sanguis, and Streptococcus gordonii 159 Anginosus and Salivarius Group 159 Streptococcus salivarius 159 Streptococcus thermophilus 160 Mutans Group 160 Streptococcus mutans and Streptococcus sobrinus 160

8.2.4 8.2.4.1 8.2.4.2 8.2.5 8.2.5.1

125

Contents

8.2.6 8.2.7 8.3 8.3.1 8.3.2 8.3.2.1 8.3.2.2 8.3.2.3 8.3.3

Other Organisms: Enterococcus faecalis 161 Comparative Genomics 161 Streptococcal Genomic Bacteriophages 162 Prophages and Streptococcal Genomes 162 GAS Genome Prophages 163 Prophages and Virulence Factors 163 Prophage Attachment Sites and Host Biology 165 Prophage Diversity 166 Prophages Associated with other Streptococcal Species

9

Pathogenic Staphylococci: Lessons from Comparative Genomics Knut Ohlsen, Martin Eckart, Christian Httinger, and Wilma Ziebuhr

9.1 9.2 9.2.1 9.2.2 9.2.2.1 9.2.2.2 9.2.2.3 9.2.3 9.2.3.1 9.2.3.2 9.2.3.3 9.2.3.4 9.3 9.3.1 9.3.2 9.3.3 9.3.4 9.3.5 9.3.6 9.4

Introduction 175 Comparative Genomics of S. aureus 176 Overall Genome Structure 177 Core Genome 178 Metabolism 178 Information Pathways 180 Virulence Factors 181 Accessory Genome 184 Pathogenicity Islands 185 Staphylococcal Cassette Chromosome 190 Bacteriophages 192 Plasmids 194 Staphylococcus epidermidis 195 Genomic Islands 197 Phage SPb and other Bacillus Genes 197 Virulence Factors 197 Staphylococcal Cassette Chromosome 198 Adherence and Biofilm Formation 199 Insertion Sequences 201 Concluding Remarks 202

10

Pathogenomics: Insights into Tuberculosis and Related Mycobacterial Diseases 211 Alexander S. Pym, Stephen V. Gordon, and Roland Brosch

10.1 10.2 10.3 10.4 10.5 10.5.1 10.5.2 10.6

Introduction 211 Molecular Basis of Pathogenicity 212 Evolution of the M. tuberculosis Complex 216 Some Metabolic Insight from the Genome Sequences Other Major Mycobacterial Human Pathogens 222 Mycobacterium leprae 222 Mycobacterium ulcerans 223 Concluding Remarks 224

166

220

175

XI

XII

Contents

11

Genomes of Pathogenic Neisseria Species 231 Christoph Schoen, Heike Claus, Ulrich Vogel, and Matthias Frosch

11.1 11.2 11.2.1 11.2.2 11.2.2.1

Introduction 231 Genomes of Pathogenic Neisseria Species 232 The Flexible Genome Pool 233 Repetitive DNA Sequence Elements Govern Neisserial Biology 236 DNA Uptake Sequences, Horizontal Gene Transfer, and Antigenic Diversity 236 Simple Sequence Repeats and Phase Variation 237 Insertion Sequences and the Regulation of Gene Expression 238 Genome-Wide Mutational Analyses 239 Comparative Genomics 240 Novel Virulence Factors of Meningococci Identified by Genomic Approaches 244 Future Perspectives 249

11.2.2.2 11.2.2.3 11.2.3 11.2.4 11.2.5 11.3 12

Genomics of Pathogenic Clostridia and Bacilli 257 Armin Ehrenreich, Gerhard Gottschalk, and Holger Brggemann

12.1 12.1.1 12.1.2 12.1.3 12.1.4 12.1.5 12.1.6 12.2 12.2.1 12.2.2

Genomics of Pathogenic Clostridia spp. 257 Introduction 257 C. perfringens 258 C. tetani 260 C. botulinum 262 C. difficile 263 Conclusions and Perspectives 264 Genomics of Pathogenic Bacilli 265 Introduction 265 Pathogenic Properties of Bacilli not Belonging to the B. cereus Group 266 Pathogenicity of B. cereus 266 Pathogenicity of B. anthracis 268 Course of Anthrax 268 Virulence Factors of B. anthracis 269 Genome of B. anthracis 270 Chromosomal Genes 270 Genes Located on Plasmids pXO1 and pXO2 271 Regulation of Virulence Genes 272 Molecular Diversity in B. anthracis Genomes 272 Genome of a Highly Virulent B. cereus Strain Resembling B. anthracis in Pathogenesis 273 Comparison of B. cereus Group Genomes: How Did Pathogenicity Evolve? 273

12.2.3 12.2.4 12.2.4.1 12.2.4.2 12.2.5 12.2.5.1 12.2.5.2 12.2.5.3 12.2.5.4 12.2.5.5 12.2.6

Contents

13

The Genomes of Pathogenic Bartonella Species 281 Carolin Frank, Eva Berglund, and Siv G. E. Andersson 281

13.1 13.1.1 13.1.2 13.2 13.2.1 13.2.2 13.3 13.4 13.5 13.5.1 13.5.2 13.5.3 13.5.4 13.6 13.6.1 13.6.2 13.6.3 13.7 13.8

Introduction 281 Bartonella in a Phylogenetic Context 281 Hosts and Vectors for Bartonella Species 282 Bartonella Species and Pathogenicity 284 Infection of Reservoir and Incidental Host 284 Bartonella Species as Human Pathogens 285 The Bartonella Genomes 286 Genomic Islands and Phages 286 Genomic Islands and Phages in Bartonella Species 287 The B. henselae Prophage 288 B. henselae Genomic Islands and Islets 288 B. quintana Harbors Remnants of the B. henselae Islands 290 Role of Phages and Islands in the Evolution of Bartonella 290 The Chromosome II-Like Segment in Bartonella 291 Type IV Secretion Systems in Bartonella Species 292 The virB-D4 Operon 293 The trw Operon 293 B. quintana’s Evolution into a Human Pathogen 294 Conclusions and Future Perspectives 295

14

Pathogenomics of Gastric and Enterohepatic Helicobacter Species Sebastian Suerbaum, Sandra Schwarz, and Christine Josenhans

14.1 14.2 14.2.1 14.2.1.1 14.2.1.2 14.2.1.3 14.2.1.4 14.2.1.5 14.2.1.6 14.3 14.3.1 14.3.2 14.4

Introduction 301 Helicobacter pylori 302 Key Features of the H. pylori Genome Related to Pathogenesis Colonization Factors: Urease and Motility 302 Phase Variation 302 The H. pylori Outer Membrane Protein Family 303 Intraspecies Variation of H. pylori Genomes 303 The cag Pathogenicity Island 304 Nucleotide Sequence Variation in H. pylori 305 Helicobacter hepaticus 305 The HHGI1 Genomic Island 306 Other Putative H. hepaticus Virulence Factors 307 Genome Comparisons of Gastric and Enterohepatic Helicobacter Species with Related Bacteria 307 Outlook 308

14.5

301

302

XIII

XIV

Contents

15

Genomics of the Opportunistic Pathogen Legionella pneumophila Christel Cazalet and Carmen Buchrieser

The Genus Legionella: Epidemiology, Life Cycle, and Pathogenesis 315 15.2 Genomics of Legionella pneumophila 316 15.3 Specific Features of the Legionella Genomes 318 15.3.1 Eukaryotic-like Proteins in Legionella pneumophila: Modulation of Host Functions? 318 15.3.2 Secretion Machineries of L. pneumophila: Central to Its Life and to Pathogenesis 324 15.3.2.1 Type IV Secretion Systems in Legionella 325 15.3.2.1.1 The dot/icm Type IVB Secretion System 325 15.3.2.1.2 The lvh Type IVA Secretion System 326 15.3.2.2 A Putative Type I Secretion System in Legionella 326 15.3.2.3 A Type II Secretion System in Legionella 327 15.3.2.4 Secretion Across the Cytoplasmic Membrane 327 15.3.2.5 A Putative Type V Secretion System (Autotransporter) Specific to Strain Paris 328 15.3.3 Comparative Genomics: Diversity of the Species L. pneumophila 15.3.3.1 Genomic (Pathogenicity) Islands in the L. pneumophila Genomes 329 15.3.3.2 Plasmids and Genetic Diversity of L. pneumophila 331 15.4 Conclusions 333

315

15.1

329

16

Genomics of Listeria monocytogenes Michael Kuhn and Werner Goebel

16.1 16.2 16.3

Introduction: From Pregenomics to Postgenomics 339 Listeria monocytogenes: A Facultative Intracellular Pathogen 339 Listeria monocytogenes Genetics in the Pregenomic Era: Identification and Characterization of Important Virulence Factors 340 Internalins and the Invasion of Nonprofessional Phagocytic Cells 340 Listeriolysin O and Two Listerial Phospholipases Allow Escape from the Phagocytic Vacuole 342 Intracellular Motility and Cell-to-Cell Spread: The Surface Protein ActA 343 PrfA and the Regulation of Virulence Gene Expression 344 Genome Sequence of L. monocytogenes and Its Comparison with the Closely Related L. innocua 346 Genomic Approaches to Studying the other Members of the Genus Listeria 348 Evolutionary Aspects 349

16.3.1 16.3.2 16.3.3 16.3.4 16.4 16.5 16.6

339

Contents

16.7.5 16.8 16.9 16.10

Identification of Listerial Virulence Factors in the Postgenomic Era 351 Internalins and Other Surface Proteins 351 Growth in the Host Cell Cytoplasm 352 Resistance to Bile 353 Two-component Systems and the Regulation of Virulence Gene Expression 354 Vitamin B12 Biosynthesis and Anaerobic Use of Ethanolamine Proteomics 355 Transcriptomics 356 Conclusions 358

III

Genomics of Pathogens and Their Hosts: Applications

17

Genomics of Viruses 369 Esteban Domingo, Alejandro Brun, Jos Ignacio Nuez, Juan Cristina, Carlos Briones, and Cristina Escarms

17.1 17.2 17.3 17.4

Introduction: Wide Scope of Virogenomics 369 Retrieving Information 371 Applications of Data Banks to Virology 374 Beyond Reference Strains: Towards a Second-Generation Virogenomics? 379 Virogenomics Through Microarrays 382

16.7 16.7.1 16.7.2 16.7.3 16.7.4

17.5

367

18

Genomics of Pathogenic Fungi 389 Gerwald A. Khler, Alan Kuo, George Newport, and Nina Agabian

18.1 18.2 18.2.1 18.2.2 18.2.3 18.3 18.3.1 18.3.2 18.3.3 18.3.4 18.3.5 18.4 18.4.1 18.4.2 18.5 18.6

Introduction 389 Genomics of Primary Fungal Pathogens 390 Histoplasma 390 Coccidioides 396 Blastomyces and Paracoccidioides 396 Genomics of Opportunistic Fungal Pathogens 397 Aspergillus 397 Cryptococcus 398 Pneumocystis 401 Microsporidia 402 Candida 403 The Tool Box for Functional Genomics 406 Expression Analysis 407 Transformation and Mutagenesis 407 Fungal Virulence – From the Genomic Point of View 410 Conclusion 411

354

XV

XVI

Contents

19

Genomics of Pathogenic Parasites 417 Gabriele Pradel and Thomas James Templeton

19.1 19.2 19.3

Exploring the Genomes of Pathogenic Protozoans 418 The Shaping of the Proteomes of the Pathogenic Protists 420 Role of Horizontal Gene Transfer in Protozoan Genome Plasticity 421 The Apicomplexa 422 Plasmodium, the Malaria Parasite 423 Cryptosporidium 427 Toxoplasma 428 The Pathogenic Kinetoplastids 429 Trypanosoma 430 Leishmania 431 The Pathogenic Diplomanad Giardia and the Parabasalid Trichomonas 431 Postgenomic Strategies and the Search for Cure 433 Gene Expression Analysis 434 Proteomics 434 Drug and Vaccine Development 435 Vector Genetics 436

19.4 19.4.1 19.4.2 19.4.3 19.5 19.5.1 19.5.2 19.6 19.7 19.7.1 19.7.2 19.7.3 19.7.4 20

Model Host Systems: Tools for Comprehensive Analysis of Host–Pathogen Interactions 445 Michael Steinert and Gernot Glckner

20.1 20.2 20.3 20.4 20.5 20.6 20.7 20.8 20.9

Introduction 445 Host–Pathogen Interactions 446 Arabidopsis thaliana: A Plant as a Model for Human Disease 447 Dictyostelium discoideum: Perspectives from a Social Amoeba 448 Caenorhabditis elegans: Answers from a Worm 449 Drosophila melanogaster: A Fruitful Model 450 Danio rerio: Fishing for Knowledge 451 Mus musculus: Of Mice and Men 451 Clean Models and Dirty Reality 453

21

Expression Analysis of Human Genes During Infection Erwin Bohn and Ingo B. Autenrieth

21.1 21.2

Introduction 457 Comparison of Gene Expression Profiles of Macrophages and Dendritic Cells In Vitro Upon Infection with Different Pathogens 458 Septicemia 460 Gene Expression in Epithelial Cells Modulated by Bacteria 461 Helicobacter pylori 462

21.3 21.4 21.4.1

457

Contents

21.4.2 21.4.3 21.4.4 21.5 21.6 21.7

Yersinia enterocolitica 464 Pseudomonas aeruginosa 466 Bartonella henselae 468 Common Signatures 469 Genetic Polymorphisms and Mutations Affect Gene Expression: Impact on Infection Susceptibility and Infection Course 471 Concluding Remarks 473

22

Pathogenomics: Application and New Diagnostic Tools Sren Schubert and Jrgen Heesemann

22.1 22.2

Introduction: “In Our Hands” 481 Microbiological Diagnostics of Bacterial Pathogens: Aims, Tasks, and Current Limitations 482 The Pregenomic Era: Conventional and Molecular Methods in Microbiological Diagnostics 483 Conventional Culture-Based Methods in Microbiological Diagnostics 483 Molecular Microbiological Diagnostic Methods 484 Typing of Bacterial Isolates Using 16S-rRNA 484 Fluorescence In Situ Hybridization 484 PCR Methods for Microbial Diagnostics 485 The Postgenomic Era: Use of DNA Microarrays in the Diagnosis of Infectious Diseases in Humans and Animals 487 DNA Arrays: Platforms, Techniques and Targets 487 Detection and Typing of Microbial Pathogens 488 Pathoarrays 489 16S-/23S-rDNA Arrays 494 Detection of Antibiotic Resistance in Microbial Pathogens Using Microarray Technology 494 Microarray Technology in Bacteria: Further Areas of Applications 495 Gene Expression Microarrays and Host–Pathogen Interaction 495 DNA Microarray Technology in Food Technology 496 DNA Microarray Technology in Environmental Microbiology 496 Pathogenomic Tools (Microarrays) in the Diagnosis of Microbiologic Agents as Bioweapons 497 Current Limitations on the Use of DNA Microarrays in Diagnostics in Medical Microbiological Laboratories 498 Final Remarks 498

22.3 22.3.1 22.3.2 22.3.2.1 22.3.2.2 22.3.2.3 22.4 22.4.1 22.4.2 22.4.3 22.4.4 22.4.5 22.5 22.5.1 22.5.2 22.5.3 22.5.4 22.6 22.7

481

XVII

XVIII

Contents

23

The Search for New Antibiotics 505 Harald Labischinski, Christoph Freiberg, and Heike Brtz-Oesterhelt

23.1 23.2 23.2.1 23.2.2 23.2.3 23.3

The Need for Novel Antibiotics 505 Where Will the New Antibiotics Come From? 507 The Past 507 The Present 509 Future Directions 511 Contributions of Genomic Technologies to Antibacterial Research 513 Target Identification and Validation 513 Target Prioritization 517 Genetic Tools for Drug Screening and Mode-of-Action Determination 517 Genome-Wide Expression Profiling for Mode-of-Action Characterization 520 Outlook for Genomic Technologies for Antibiotic Drug Discovery Alternative Approaches in Antibacterial Drug Discovery 521 Targeting the Resistance Mechanism 522 Extremely Narrow-Spectrum Drugs 523 Phage Therapies and other Bacteriolytic Approaches 524 Strategies for Reducing Virulence and/or Influencing Pathogenesis 525

23.3.1 23.3.2 23.3.3 23.3.4 23.3.5 23.4 23.4.1 23.4.2 23.4.3 23.4.4

24

Reverse Vaccinology: Revolutionizing the Approach to Vaccine Design Laura Serino, Mariagrazia Pizza, and Rino Rappuoli 533

24.1 24.2 24.3 24.4 24.5 24.6 24.7 24.8

Impact of Genomics on Vaccine Design 533 MenB Vaccine Approach by Reverse Vaccinology 535 Following the MenB Experience: Other Pathogens 538 Functional Genomics 539 Gene Expression In Vivo: IVET and STM 540 Transcriptome Analysis and Comparative Genomics 541 Proteomics and Vaccine Design 546 Conclusions 546 Index

555

521

533

XIX

Preface In the year 1995, the first full genome sequence of a free-living-organism, the bacterium Haemophilus influenzae strain Rd, was published. This publication, which appeared in the journal Science, represented the starting point for a new field in molecular biology called genomics. Today, 10 years later, complete genome sequences of almost all the major pathogenic microbes have been determined. As a consequence, a new discipline has arisen, which has been named “pathogenomics.” As the name implies, pathogenomics is the analysis at the genomic level of the processes involved in bacterial pathogenesis caused by the interaction of pathogenic microbes and their hosts. The present volume is the first handbook to be entirely devoted to the newly established discipline of pathogenomics. We are very grateful to our colleagues for their input, especially those associated with the German Pathogenomics competence network, established by the German Federal Ministry of Science and Education to analyze pathogenic microbes at the genomic level. Werner Goebel in particular – the network’s speaker, who contributed the Foreword to this book’s preface – has influenced the entire discipline with his spirit and his vision. Our thanks are due to the staff at Wiley-VCH, most notably Andrea Pillmann, who encouraged us to put this book together. We are also very grateful to all our authors for their contributions to this important project. Wrzburg, September 2005

Ulrich Dobrindt, Jrg Hacker

XXI

List of Contributors Nina Agabian University of California San Francisco Department of Cell and Tissue Biology 521 Parnassus, Box 0640 San Francisco, CA 94143-0640 USA Siv Andersson Uppsala University Evolutionary Biology Center Department of Molecular Evolution Norbyvgen 18C 752 36 Uppsala University Sweden Helene Andrews-Polymenis Department of Medical Microbiology and Immunology College of Medicine Texas A&M University HSC 407 Reynolds Medical Building College Station, TX 77843-1114 USA Ingo B. Autenrieth Universittsklinikum Tbingen Institut fr Medizinische Mikrobiologie und Hygiene Elfriede-Aulhorn-Str. 6 72076 Tbingen Germany

Andreas Bumler Department of Medical Microbiology and Immunology School of Medicine University of California at Davis One Shields Ave. Davis, CA 95616-8645 USA Eva Berglund Uppsala University Evolutionary Biology Center Department of Molecular Evolution Norbyvgen 18C 752 36 Uppsala University Sweden Erwin Bohn Universittsklinikum Tbingen Institut fr Medizinische Mikrobiologie und Hygiene Elfriede-Aulhorn-Str. 6 72076 Tbingen Germany Carlos Briones Centro de Astrobiologa (CSIC-INTA) Carretera de Ajalvir, Km. 4 Torrejn de Ardoz 28850 Madrid Spain

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List of Contributors

Roland Brosch Institut Pasteur Unit de Gntique Molculaire Bactrienne 28 Rue du Dr. Roux 75724 Paris Cedex 15 France Heike Brtz-Oesterhelt Bayer Healthcare AG Pharma Research Center Anti-Infective Research Gebude 405 42096 Wuppertal Germany Holger Brggemann Institut Pasteur Unit de Gnomique des Microorganismes Pathognes (GMP) 28, Rue du Dr Roux 75724 Paris, Cedex 15 France Alejandro Brun CISA-INIA Valdeolmos 28130 Madrid Spain Carmen Buchrieser Laboratoire de Gnomique des Microorganismes Pathognes Institut Pasteur 25 Rue du Dr. Roux 75724 Paris, Cedex 15 France Christel Cazalet Laboratoire de Gnomique des Microorganismes Pathognes Institut Pasteur 25 Rue du Dr. Roux 75724 Paris, Cedex 15 France

Heike Claus Institut fr Hygiene und Mikrobiologie Universitt Wrzburg Josef-Schneider-Str. 2 97080 Wrzburg Germany Juan Cristina Centro de Biologa Molecular “Severo Ochoa” (CSIC-UAM) Universidad Autnoma de Madrid Cantoblanco 28049 Madrid Spain Thomas Dandekar University of Wrzburg Chair of Bioinformatics Biocenter Am Hubland 97074 Wrzburg Germany Ulrich Dobrindt Institut fr Molekulare Infektionsbiologie Universitt Wrzburg R ntgenring 11 97070 Wrzburg Germany Esteban Domingo Centro de Biologa Molecular “Severo Ochoa” (CSIC-UAM) Universidad Autnoma de Madrid Cantoblanco 28049 Madrid Spain Martin Eckart Institut fr Molekulare Infektionsbiologie R ntgenring 11 97070 Wrzburg Germany

List of Contributors

Armin Ehrenreich G ttingen Genomics Laboratory Georg-August-Universitt G ttingen Grisebachstraße 8 37077 G ttingen Germany

Matthias Frosch Institut fr Hygiene und Mikrobiologie Universitt Wrzburg Josef-Schneider-Str. 2 97080 Wrzburg Germany

Susanne Engelmann Ernst-Moritz-Arndt-Universitt Institut fr Mikrobiologie Friedrich-Ludwig-Jahn-Straße 15 17487 Greifswald Germany

Michael S. Gilmore The Schepens Eye Research Institute and Harvard Medical School Department of Ophthalmology 20 Staniford St. Boston, MA 02114 USA

Cristina Escarms Centro de Biologa Molecular “Severo Ochoa” (CSIC-UAM) Universidad Autnoma de Madrid Cantoblanco 28049 Madrid Spain Joseph J. Ferretti Department of Microbiology and Immunology University of Oklahoma Health Sciences Center Oklahoma City, OK 73104 USA Carolin Frank Uppsala University Evolutionary Biology Center Department of Molecular Evolution Norbyvgen 18C 752 36 Uppsala University Sweden Christoph Freiberg Bayer Healthcare AG Pharma Research Center Anti-Infective Research Gebude 405 42096 Wuppertal Germany

Gernot Glckner Leibnitz-Institut fr Altersforschung Fritz-Lipmann-Institut Beutenbergstr. 11 07745 Jena Germany Werner Goebel University of Wrzburg Theodor-Boveri-Institute for Biological Sciences Chair of Microbiology Am Hubland 97074 Wrzburg Germany Stephen V. Gordon TB Research Group Veterinary Laboratories Agency Weybridge Woodham Lane, New Haw Addlestone, Surrey KT15 3NB United Kingdom

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Gerhard Gottschalk G ttingen Genomics Laboratory Georg-August-Universitt G ttingen Grisebachstraße 8 37077 G ttingen Germany Jrg Hacker Institut fr Molekulare Infektionsbiologie Universitt Wrzburg R ntgenring 11 97070 Wrzburg Germany Michael Hecker Ernst-Moritz-Arndt-Universitt Institut fr Mikrobiologie Friedrich-Ludwig-Jahn-Straße 15 17487 Greifswald Germany Jrgen Heesemann Max-von-Pettenkofer-Institut fr Hygiene und Medizinische Mikrobiologie Universitt Mnchen Pettenkoferstraße 9a 80336 Mnchen Germany Christian Httinger Institut fr Molekulare Infektionsbiologie R ntgenring 11 97070 Wrzburg Germany Christine Josenhans Hannover Medical School Institute for Medical Microbiology OE5210 Carl-Neuberg-Straße 1 30625 Hannover Germany

Susanne Kneitz IZKF, Labor fr MicroarrayAnwendungen Institut fr Virologie und Immunbiologie 97078 Wrzburg Germany Gerwald A. Khler University of California San Francisco Department of Cell and Tissue Biology 521 Parnassus, Box 0640 San Francisco, CA 94143-0640 USA Michael Kuhn University of Wrzburg Competence Network PathoGenoMik Biocenter Am Hubland 97074 Wrzburg Germany Alan Kuo University of California San Francisco Department of Cell and Tissue Biology 521 Parnassus, Box 0640 San Francisco, CA 94143-0640 USA Harald Labischinski Combinature Biopharm AG Robert-R ssle-Str. 10, Gebude 80 13125 Berlin Germany Janet M. Manson The Schepens Eye Research Institute and Harvard Medical School Department of Ophthalmology 20 Staniford St. Boston, MA 02114 USA

List of Contributors

W. Michael McShan Department of Pharmaceutical Sciences P.O. Box 26901, CPB 307 University of Oklahoma Health Sciences Center Oklahoma City, OK 73190 USA Xavier Nassif INSERM U570 Facult Ren Descartes Paris 5 Site Necker-Enfants Malades 156 rue de Vaugirard 75730 Paris Cedex 15 France

Mariagrazia Pizza Chiron Vaccines Via Fiorentina, 1 53100 Siena Italy Gabriele Pradel University of Wrzburg Research Center for Infections Diseases R ntgenring 11 97070 Wrzburg Germany

George Newport University of California San Francisco Department of Cell and Tissue Biology 521 Parnassus, Box 0640 San Francisco, CA 94143-0640 USA

Alexander S. Pym Unit for Clinical and Biomedical TB Research South African MRC 491 Ridge Road PO Box 70380 Overport, 4067 Durban South Africa

Jos Ignacio Nuez CISA-INIA Valdeolmos 28130 Madrid Spain

Rino Rappuoli Chiron Vaccines Via Fiorentina, 1 53100 Siena Italy

Knut Ohlsen Institut fr Molekulare Infektionsbiologie R ntgenring 11 97070 Wrzburg Germany

Christoph Schoen Institut fr Hygiene und Mikrobiologie Universitt Wrzburg Josef-Schneider-Str. 2 97080 Wrzburg Germany

Vladimir Pelicic INSERM U570 Facult Ren Descartes Paris 5 Site Necker-Enfants Malades 156 rue de Vaugirard 75730 Paris Cedex 15 France

Sren Schubert Max von Pettenkofer-Institut fr Hygiene und Medizinische Mikrobiologie Universitt Mnchen Pettenkoferstr. 9a 80336 Mnchen Germany

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List of Contributors

Sandra Schwarz Institute for Medical Microbiology OE5210 Hannover Medical School Carl-Neuberg-Straße 1 30625 Hannover Germany

Sebastian Suerbaum Hannover Medical School Institute for Medical Microbiology OE5210 Carl-Neuberg-Straße 1 30625 Hannover Germany

Laura Serino Chiron Vaccines Via Fiorentina, 1 53100 Siena Italy

Thomas J. Templeton Weill Medical College of Cornell University Department of Microbiology and Immunology 1300 York Avenue, Box 62 New York, NY 10021 USA

Ben Sidders The Royal Veterinary College Pathology and Infectious Diseases Royal College Street London, NW1 0TU United Kingdom Michael Steinert Universitt Wrzburg Institut fr Molekulare Infektionsbiologie R ntgenring 11 97070 Wrzburg Germany Neil Stoker The Royal Veterinary College Pathology and Infectious Diseases Royal College Street London, NW1 0TU United Kingdom

Ulrich Vogel Institut fr Hygiene und Mikrobiologie Universitt Wrzburg Josef-Schneider-Str. 2 97080 Wrzburg Germany Wilma Ziebuhr Institut fr Molekulare Infektionsbiologie R ntgenring 11 97070 Wrzburg Germany

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Color Plates

Fig. 3.2 Protein pattern of heat-shocked B. subtilis cells. During exposure to heat shock (37–to 48 C), B. subtilis cells were pulselabeled with 35S-l-methionine. After the separation of crude protein extract by 2-D gels, the proteins were visualized by silver staining and the protein synthesis rate was

determined by phosphoimaging. The two images were overlaid with the aid of the Delta-2D software package (dual-channel imaging technique [11]; Decodon, Greifswald, Germany). The red-labeled proteins form the heat stress stimulon. (This figure also appears on page 48.)

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Fig. 3.4 The phosphate starvation stimulon of B. subtilis consists of both general stress proteins also induced by phosphate starvation (rB regulon, rB-dependent proteins marked in blue) and starvation-specific proteins (PhoR regulon, marked in red). The stress/starvation induction profile of both groups and the functions of the proteins

are indicated. C, control; h, heat stress; e, ethanol stress; s, NaCl stress; a, pH 5.5; o, oxidative stress (H2O2); p, puromycin; g, glucose starvation; O2, oxygen starvation; ph, phosphate starvation; aa, amino acids starvation. (This figure also appears on page 50.)

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Fig. 3.5 Multicolor imaging of expression patterns under different growth conditions in B. subtilis. Delta-2D software (Decodon) was used to visualize complex protein expression patterns on the 2-D gel image in the standard pH range 4–7. The color code is presented in the top left corner. Proteins only induced by single stresses are colored red (H, heat), light-blue (O, oxidative stress), and orange

(E, ethanol), respectively. Proteins induced by oxidative stress as well as heat stress are colored yellow, proteins induced by ethanol stress and heat stress are colored dark-blue, proteins induced by oxidative and ethanol stress can be recognized as purple spots, and, finally, proteins induced by all three stimuli are displayed in green. (This figure also appears on page 51.)

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3 Fig. 3.6 Dynamics of protein synthesis profiles of growing and glucose-starved cells of B. subtilis. A Individual dual-channel 2-D patterns of protein synthesis and accumulation recorded during the different phases of the growth curve are assembled into a “life movie.” B Growth curve (optical density at 500 nm) and 35S-l-methionine incorporation (million cpm per 60 lg protein). C Patterns of selected examples representing different

branches of cellular physiology. Sample points correspond to the following growth phases depicted are shown in the growth curve: 1, 2, exponential growth; 3–7, glucose starvation; 8, 9, recovery of growth after readdition of glucose. The bar graphs on the left display normalized relative synthesis rates of the individual proteins at the different time points. (This figure also appears on page 52.)

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3 Fig. 3.7 Proteomic signatures of B. subtilis of different physiological stress/starved conditions. Comparisons of the protein profile of both exponentially growing and stressed B. subtilis cells reveal signature-like changes that are specific to certain stress stimuli (e.g., induction of the catalase KatA by oxidative

stress). The individual sections of the 2-D gels display typical parts of the proteomic signatures of oxidative or heat stress, the stringent response or limitation of glucose or phosphate. (This figure also appears on page 54.)

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3 Fig. 3.9 Assignment of proteins identified in S. aureus COL to biochemical pathways and other essential cellular components. Proteins that have not been identified in the 2-D gel images thus far are colored green. A Purine and pyrimidine metabolism, B glycolysis, pentose phosphate shunt, and citric acid

Fig. 3.10 Protein pattern of cells of S. aureus COL grown under aerobic (green) and anaerobic (red) conditions in synthetic medium. Cells were pulse-labeled (5 min) with 35S-lmethionine under aerobic conditions and 30 min after imposition of anaerobic growth conditions. Radioactively labeled proteins were visualized by the phosphoimaging

cycle, C oxidative stress resistance, D ATPase components, E proteolysis, F components of the translational machinery, G amino acid metabolism, H fatty acid synthesis and metabolism of cell wall components, and I biotin metabolism. (This figure also appears on pages 58/59.)

technique. Proteins whose synthesis was increased after shifting to anaerobic growth conditions are shown in red (e.g., enzymes involved in glycolysis and fermentation) and those whose synthesis was decreased are shown in green (e.g., enzymes involved in the TCA cycle). (This figure also appears on page 60.)

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Fig. 3.12 The extracellular proteome of S. aureus RN6390 at low (green image) and high cell densities (red image). 250 lg proteins of the supernatant of cells grown in TSB medium at an optical density OD540 = 1 and 5 were separated by 2-D gels and stained with Sypro

Ruby. Extracellular proteins present in increased amounts at high cell densities are labeled red and those proteins only present at low cell densities are labeled green. (This figure also appears on page 63.)

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Fig. 3.13 A Growth of S. aureus RN6390 in TSB-Medium. The sampling is indicated by an arrow and a letter in the respective growth curve. B, C Virulence factors of S. aureus RN6390 whose amount depends on the growth phase. The amount of the respective proteins at OD540 = 1 (green) of cells grown in TSB medium was compared with the amount of these proteins at higher optical densities

(red). B Virulence factors only present at low cell densities. C Virulence factors only present at high cell densities. In addition, the amount of the respective proteins in the wild type strain was compared to the amount of these proteins in various regulatory mutants (agr, sarA, sigB) known to be impaired in virulence. Proteins were stained with Sypro Ruby. (This figure also appears on page 64.)

Fig. 5.2 Comparison of the genetic organization of GEI IINissle 1917 and the pheV-associated PAI of E. coli strain CFT073 (A) demonstrating the loss of the a-hemolysin-encoding determinant (hly) and of large parts of the P fimbrial operon (pap) in strain Nissle 1917. The DNA sequences comparison of the two islands is

visualized using Artemis and ACT [104]. " Identical regions of the two islands are highlighted in red. Functionally related DNA regions are indicated by different colors as shown. (B) Enlarged section of GEI IINissle1917 comprising the partially deleted P fimbrial determinant. (This figure also appears on page 98.)

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Fig. 8.1 GAS Genome alignments. The seven available GAS genomes were compared using the software package Mauve, a method that identifies conserved genomic regions, rearrangements and inversions in conserved regions, and the exact sequence breakpoints of such rearrangements across multiple genomes [11]. Corresponding regions share the

same color and are connected by lines. Collinear regions are positioned above the central axis (relative to M1) while inverted regions are drawn below the axis. Light gray regions were too divergent in at least one genome to be meaningfully aligned. (This figure also appears on page 153.)

Fig. 8.2 GAS vs S. uberis alignment. The corresponding regions in the M1 and S. uberis genomes identified by Mauve analysis are shown. Several large regions of collinearity are evident between the two species. Corresponding regions share the same color and are connected by lines. (This figure also appears on page 157.)

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Fig. 9.1 Structure of type I, II, III, and IV staphylococcal cassette chromosome mec (SCCmec) elements (adapted from [70]). SCCmec is characterized by two common gene complexes, the ccr (cassette chromosome recombinase) gene complex (gray) and the mec gene complex (blue). Integrated IS431, Tn554, and plasmid pT181 are also indicated. Gene symbols: cad, cadmium

resistance; ccr, cassette chromosome recombinase; ermA, erythromycin resistance; hsdR, host specificity determinant; kdp, two-component system regulating potassium transport; mec, penicillin binding protein 2a, methicillin resistance; mer, mercury resistance; tetK, tetracycline resistance. (This figure also appears on page 191.)

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Fig. 9.2 Pairwise comparison of the Staphylococcus epidermidis ATCC 12228 and Staphylococcus epidermidis RP62A genomes displayed using the Artemis Comparison Tool (ACT; http:// www.sanger.ac.uk/Software/ACT). The red bars represent homologous matches between the genomes and the blue bars indicate a homologous, but inverted chromosomal region.

Genomic islands, IS elements, phages, SCC cassettes, and genes involved in adherence and biofilm formation are marked as colored boxes. Gene symbols: aae, autolysin/adhesin; aap, accumulation-associated protein; atlE, autolysin; bhp, Bap homologous protein; ica, intercellular adhesin. (This figure also appears on page 196.)

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Fig. 11.1 Multiple alignment of the four neisserial genomes so far fully sequenced using the program Mauve [100]. Locally collinear blocks of DNA are depicted in the same colors and connected via correspondingly colored lines. Equally colored blocks on different sides of black lines corresponding to

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the respective genome sequence indicate chromosomal inversions. The chromosomal locations of potential prophages [30], putative genetic islands [13], and some putative virulence genes are given for strain MC58. (This figure also appears on page 235.)

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Fig. 11.3 Comparison of the cps locus of pathogenic and apathogenic Neisseria species. Regions with identical functions are depicted in the same colors. The following sequence data were used to compare the cps loci: NmB B1940: assembled from the GenBank sequences L09188, L09189, M57677, M95053, and Z13995; NmB MC58: AE002098; NmA Z2491: AL157959; NmC FAM18: NC_003221, homologous genes according to BLASTX [75] searches against the annotated neisserial genomes; Ng FA1090: AE004969; Nl DSM4691 and

Ns LMG5290: H. Claus and U. Vogel, unpublished data. The gene assignment of the meningococcal sequences was done following the annotation of the MC58 genome. Ng, N. gonorrhoeae; Nl, N. lactamica; Nm, N. meningitidis; Ns, N. sicca; DSM, German type culture collection (Deutsche Sammlung von Mikroorganismen), Braunschweig, Germany; LMG, Belgian collection of microorganisms (Laboratorium voor Microbiologie, University of Gent), Gent, Belgium. (This figure also appears on page 243.)

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Fig. 15.2 Alignment of the complete genome sequences of L. pneumophila strains Paris, Lens, and Philadelphia 1, using the ACT (Artemis comparison tool) software (http:// www.sanger.ac.uk/Software/ACT/). Color code: Red blocks represent homologous sequences; blue blocks, homologous

sequences, but inverted. 1, Disruption of the synteny by a 260-kbp inversion in strain Lens; 2, cluster encoding several efflux pumps missing in strain Lens; 3, lvh type IV secretion system; 4, 65-kbp pathogenicity island of strain Philadelphia 1. (This figure also appears on page 329)

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Fig. 16.1 Circular genome maps of L. monocytogenes EGD-e and L. innocua CLIP 11262, showing the position and orientation of genes. From the outside: Circles 1 and 2, L. innocua and L. monocytogenes genes on the + and – strands, respectively. Color coding: green, L. innocua genes; red, L. monocytogenes genes; black, genes specific to L. monocytogenes or L. innocua, respectively; orange, rRNA

operons; purple, prophages. Circle 3, G+C content of L. monocytogenes (< 32.5% G+C in light yellow, 32.5–43.5% G+C in yellow, and > 43.5% G+C in dark yellow). The scale in megabasepairs is indicated on the outside of the genome circles, with the origin of replication at position 0. Reprinted with permission from Ref. [2]. (This figure also appears on page 347.)

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Fig. 16.2 The virulence gene cluster locus in the six species of the genus Listeria. The cluster is flanked by the housekeeping genes (black boxes) prs, vclB (lmo0209), and ldh in all six species. Genes controlled by PrfA are shown as boxes with black arrowheads. vclA (lmo0208) is present in all species except L. grayi. vclP is present in L. welshimeri, L. seeligeri, and L. ivanovii. vclZ (lmo0207) is present

in L. monocytogenes and L. innocua. vclY and vclX are inverted in L. seeligeri. Species-specific genes (medium gray) not under PrfA control include vclJ, vclF1, vclG1, vclG2, vclF2 of L. grayi, and vclC of L. seeligeri. Homologous genes are represented by boxes of the same color. Reprinted with permission from Refs. [73, 75]. (This figure also appears on page 350.)

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Fig. 19.2 Domain architectures for select surface proteins from the pathogenic protozoa, demonstrating the almost exclusive nonoverlap in the catalogs of extracellular proteins. Red stars indicate proteins composed of domain(s) invented in a lineage- or cladespecific fashion (e.g., a molecule found in

Plasmodium but not in the apicomplexan Cryptosporidium or in kinetoplastids). The remaining domains originated either via vertical inheritance (followed by gene loss in widespread lineages) or by lateral transfer from a bacterial or metazoan source. (This figure also appears on page 425.)

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1 Bioinformatics: Data Mining Among Genome Sequences Susanne Kneitz and Thomas Dandekar

1.1 Systematic Genome Analysis of Pathogens as a Basis for Pharmacogenomic Strategies

Rather than identifying individual resistance events and fighting them using a classical pharmacological strategy, systematic genome analysis of the pathogen presents a more ambitious but very powerful pharmacogenomic strategy by which to identify new treatments against the pathogen. The basis for the systematic analysis of genomes is solid experimental data, in particular relating to pathogenicity factors. Current methods allow large-scale collection of data on a genomic scale involving the complete genome sequence (e.g., by DNA capillary sequencers), an overview of the transcriptome (e.g., by EST, expressed sequence tag, sequencing or SAGE, serial analysis of gene expression data), and insights into the proteome (e.g., large-scale two-dimensional gel analysis coupled with mass spectroscopy). The available genome sequences of various pathogens provide a wide range of novel targets for drug design which can be identified by means of microarray analysis. For example, a recent paper describes the application of functional genomics tools such as microarrays and proteomics for development of new drugs that are not only active against drug-resistant Mycobacterium tuberculosis but also can shorten the course of M. tuberculosis therapy [1]. On a note of caution, the quality of large-scale data is often not as good as for single observations. Examples are uncertainties in contig assembly, repetitive sequences, and gene prediction (DNA data), representational bias, and missing out of low-copy messengers (transcriptome data). Proteome data have particular problems, e.g., membrane proteins and highly charged proteins are not well resolved. Multiple gel spots may indicate modifications of the same protein. In addition, certain protein modifications (glycosylation, phosphorylation etc.) are not easily detected. Bioinformatic tools are nevertheless the key to systematic analysis of these data with the aim of fighting development of resistance in the best way possible and of devising strategies against the pathogen on a rational, pharmacogenomic basis. They exploit a number of approaches such as the analysis of gene expression, pathway modeling, and detailed, database-guided biochemical analysis of the var-

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ious steps leading to survival of the pathogen. Sequence information about involved genes, ESTs, or proteins is essential for starting the genome analysis. Iterative sequence alignments [2] offer a much improved way of detecting sequence similarities by aligning iteratively all newly identified related candidate sequences and using position-specific scoring matrices to search for families of related proteins in different genomes. Specific motifs in proteins are recognized by tools such as PROSITE [3]. Detailed analysis of gene expression by software such as Bioconductor and R (software package) [4] allows prediction about activated or repressed genes, gene clusters, groups, and pathways in pathogens. There follows a detailed analysis of all involved pathogen genes (and, if data are available, interacting host genes), enzymes, and pathways by the application of methods such as pathway alignment and elementary mode analysis [5].

1.2 Direct Sequence Annotation Tools for Functional Genomics

With the advent of large-scale sequencing techniques, many sequenced pathogenic genomes are now available. This allows comparative genomics to be carried out on a wide scale, but also requires also the representation of data on individual genomes in a suitable format. Analyzing novel sequences from a large sequencing effort or a complete genome involves a number of different tasks. The first is the identification of transcripts (including splicing in eukaryotic genomes) and the determination of reading frames using programs such as Genescan, Orpheus, Genepredict and Prophet [5]. The next step is the analysis of mRNA, including identification of regulatory elements. A helpful tool we have developed for this is the RNA analyzer [6], which identifies individual regulatory elements in RNA sequences using a decision tree and individual subprograms that execute sequence and secondary structure searches for various elements. It exploits fast folding routines from the Vienna package [7]. Similarly, the program UTRscan [8] identifies a number of further regulatory elements in the mRNA. Pathogenomics should also profit from insights in new regulatory features such as riboswitches, metabolite-mediated translational repression by RNA structures forming aptamers [9]. Detection software is available for these structures as well [10].

1.3 Identification of Protein Function

The next task is to determine the complete repertoire of protein functions. Note that one should first establish all molecular functions of a protein. This means taking stock of all the different domains contained in an individual protein using tools such SMART (annotation of functional domains) [11] and COG (clusters of orthologous genes, i.e., genes with similar function) [12]. As another example of a

1.4 Obtaining Protein Information from a Domain Server

tool, the AnDOM (“annotation of domains”) server allows the analysis of structural domains in given protein sequences in order to identify parts of the sequence that are homologous to a known three-dimensional structure [13]. It utilizes position-specific scoring matrices (PSSMs) made from a large alignment of homologous sequences to individual structural domains (the parts of a protein folding as independent folding units with a specific function such as catalysis or cofactor binding) of known experimental structure (using PDB [protein data bank] as a reference database). Comparing of a query sequence to the stored PSSMs allows rapid identification of any structural domains homologous to a known structure domain according to SCOP, structural classification of proteins [14] (http://scop.mrc-lmb.cam.ac.uk/scop/).

1.4 Obtaining Protein Information from a Domain Server

As an example of information thus readily available from a protein sequence, consider the output of AnDOM [13]. It shows the regions homologous to known three-dimensional structural domains which are hidden in a given sequence. For instance, plasmoredoxin from Plasmodium falciparum [15] can be annotated using its sequence by employing the AnDom server. Genbank entry AAF87222 contains the plasmoredoxin [thioredoxin-like redox-active protein (Plasmodium falciparum)] sequence. The server is located at http://andom.bioapps.biozentrum.uniwuerzburg.de/cgi-bin/start_impala.cgi. A green bar shows that the main central part of the structure is homologous to the domain in crystal structure 1o8x from the SCOP family c.47.1.10., which is the tryparedoxin I from Crithidia fasciculata. Its residues 22–141 structurally match residues 39–157 of the plasmoredoxin sequence, further confirming its predicted and experimentally confirmed role in redox metabolism [15]. Thus, color coding reflects the predicted individual three-dimensional structures of the domains according to homology: green represents those parts of the structure where there is a mixture of a-helix part plus a b-strand part situated in this domain (SCOP class 3), blue represents folding class a/b (SCOP class 4, helix follows strand follows helix and so on, often in catalytic domains), and violet would correspond to a multidomain protein (SCOP class 5). The positions of the main structural domains are shown graphically at the top; next, the output displays the similarities to all protein structural domains found, including the names of the domains, significance, and references to sequence comparisons. Finally, the detailed domain alignments are given by the server according to their homology to known structures. In this way, the first structural clues to potential drug targets of a pathogen become immediately available. Several other structure predictors are also available [16]. Protein interactions are important because they better reveal protein partners within the infectious organism or the host and with each other. Conservation of gene order [17] and gene fusion events as well as the combined presence or

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absence of clusters of genes in prokaryotes are good predictors in prokaryotes of direct interactions at the protein level of the encoded gene products. Gene context conservation is easily detected with the STRING software [18]. A huge database of more then 100 genomes is used in STRING to determine whether there is conservation of gene neighborhood or a gene fusion event in genomes related to the organism and the protein gene used as the query. For predictions of protein interactions in eukaryotic pathogens such as trypanosomes or plasmodia, gene context is less reliable; however, recent versions of the STRING software include predictions based on text mining, two hybrid assays, coexpression, and homology to allow helpful predictions about physical association, or at least functional association, of proteins in a protein cluster in eukaryotes as well. Functional association allows predictions about substrate specificity (e.g., a tryptophan pathway metabolite) by looking at the cluster with which the gene of interest is associated (in this example this would be tryptophan-metabolizing enzymes). In this way even proteins of unknown function can be put into a functional context and their substrate predicted, if the exact biochemical function of at least one protein of the cluster predicted by STRING to be associated is known. These predictions are complementary to sequence analysis methods where homology allows prediction of protein function, but homology is less clear in relation to the exact substrate specificity. Recognizing all the hidden features in a pathogenic genome is a continuous process, as new software, input from additional experimental data, and the exponential growth of databases allow new insights; for example, five years after the original annotation of a pathogenic genome as exemplified by Mycoplasma pneumoniae, about a third of its annotation can be substantially be improved by these new data and techniques [5, 19]. Automated data and software platforms such as GenDB [20], Pedant [21], and Magpie [22] for annotation together with integrated databases [23] increase annotation speed and allow systematic comparison of genomes. The combination of these and similar tools allows rapid annotation of genomes and rapid identification of pathogen-specific features such as host interaction factors and toxins.

1.5 Pathway Analysis

At the next level, the pathway perspective, individual enzyme activities encoding protein reading frames are assembled and combined into specific pathways. A concise and integrated way of comparing pathway results from different genomes is comparative pathway alignment. Species-specific differences in terms of the presence or absence of individual enzymes are identified by insertions or gaps in this comparative table [5]. In general, potential target structures for medical intervention (e.g., for drug design of an inhibitor against a biosynthetic enzyme in the pathogen of interest) should not occur in the human patient (or at least in the human the target homology protein should have clear structural and affinity differences). The occurrence of the target in several pathogens will make the target

1.6 Network Analysis

structure more interesting from an application point of view (e.g. cell wall synthesis enzymes in gram-positive bacteria – with a potential to lead to alternative drugs to penicillin-like substances).

1.6 Network Analysis

Finally, the different pathways are parts of cellular networks. Here the challenge is to define individual pathways in such a network in a clear and mathematical way. This is necessary both for a concise description of the network capabilities and for prediction of the effects of inhibition of a particular enzyme: in many instances alternative routes through the network still allow most of the metabolites to be produced. An enzyme inhibitor which can be compensated by the metabolic network is in most cases compatible with life in prokaryotic cells. Genes which encode such nonessential enzymes are themselves nonessential and lead to a nonlethal phenotype. The nonessential enzymes for a given metabolic network can easily be identified by calculation of the elementary modes. These are nondecomposable (“elementary”) sets of enzymes. Each set can sustain a steady state for all internal metabolites used by this set of enzymes as substrates and products. The external metabolites used by each enzyme set need not fulfil this equilibrium condition. Using this mathematical requirement, the software METATOOL [24] calculates all elementary modes for a given metabolic network. Testing conditions are included in this program so that the stable flux modes calculated are elementary, i.e., nondecomposable in subsets which fulfil the steady state condition for internal metabolites. This helps to answer the above questions: any observed network flux state is always a linear combination of the elementary modes. Inhibition of a given enzyme will thus inhibit exactly those elementary modes in which this enzyme occurs [25]. Recent research from our laboratory shows that this perspective of enzymes directing metabolite flows can also be turned around: metabolites also shape the way in which pathways evolve. This is strikingly shown by the observation that metabolite networks tend to be driven in structure and enzyme substrate specificities by the most frequently represented metabolites of this network. This helps and partly explains the observed widespread recruitment of enzymes to new pathways, allowing pathogens to rapidly change and adapt to hostile environments including xenobiotics [26] and antibiotics. In this way connections and regulatory networks may be sketched, in particular induction of antibiotic resistance and pathways involved in signal transmission and activation of pathogenicity factors. For instance, subtle genome variations within the structure of a 150-kbp pathogenicity island change Enterococcus strains in terms of virulence and most known auxiliary traits that enhance virulence of the organism [27]. Genome-based approaches also identify new targets associated with disease which are not apparent in the commensal behavior of harmless enterococci. Methicillin-resistant Staphylococcus aureus (MRSA) can be identified by typing the Spa (“Staphylococcus aureus encoded protein A”) gene. Efficient soft-

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ware for this is available, allowing rapid determination of the dynamics of resistant clones in a hospital setting [28]. After networks have been identified, specific proteins can be singled out as the most promising targets for drug design. A number of bioinformatic strategies such as homology modeling, pharmacophores, and virtual ligand screening are at our disposal today. New work targeted against resistance development also exploits new approaches to differentiating between target structures and related structures in the human host, e.g., rapid identification of structural domains (e.g., using ref. [13]) on a genome scale with subsequent targeting of parasite specific protein domains.

1.7 Adaptation in Time and to Stimuli

Pathogens survive in a harsh environment. There is a constant race between host defense and parasite escape. In addition, their life cycle involves often abrupt changes of habitat (e.g. bloodborne, airborne, waterborne, intracellular, in a macrophage, in the gut). Quick adaptation is critical, and rapid changes can be monitored using gene expression analysis. Microarray analysis has now become the method of choice for rapid capture of genome-wide adaptation (including changes within minutes). The essentials are summarized below. In this technology the probe, consisting of oligonucleotides, either cDNA or DNA sequences are spotted on a glass support, or oligonucleotides are synthesized directly on the array using photolithographic masks. RNA samples are fluorescently labeled and hybridized to the arrayed probes. The signal intensities of the hybridized spots are used to estimate relative RNA concentration. Subsequent longer time scales are then reflected in the proteome. 1.7.1 Experimental Design for Microarray Analysis

The large-scale screening of thousands of genes at the same time by microarrays would be impossible without bioinformatics. Data analysis is becoming more and more challenging, with increasing complexity of experimental design and biological systems. Large-scale screening of genes requires careful experimental design, as there are some facts for which bioinformatics cannot compensate. The final goal needs to be taken into account. For example, a diagnostic tool monitoring a severe infection or even sepsis will certainly need much higher confidence levels and thus higher sample numbers than a random screen for possibly interesting genes. The exploration of a single pathway on a DNA chip (e.g., pentose phosphate cycle in the human pathogen Listeria monocytogenes when switching from extracellular to intracellular survival) with a limited number of genes may make global normalization impossible; a sufficient number of independent sequences from house keeping genes and external controls need to be spotted on the array

1.7 Adaptation in Time and to Stimuli

for signal intensities to be adjusted. In dual labeling experiments, if more than two samples are to be compared, it is useful to run each sample against some internal standard so as to be able to compare relative signal intensities. This internal standard might be some control RNA closely related to the test samples or a commercially available reference RNA. To introduce as little variation as possible, a sufficient amount of reference RNA for all arrays planned in one study should be prepared, pooled, and aliquoted at the beginning of a study. To avoid labeling specific false positive signals, it is essential to include dye switch experiments if two probes are being compared on the same slide. Pooling of several probes may cancel out real differences and at the same time increase the false positive rate due to dominating outliers. Finally, different hybridization protocols will give significantly differing results, and therefore protocols, including possible RNA amplification, must not be changed during the experiment. 1.7.2 Data Analysis

The data analysis and experimental design need to be adapted to each other. Since careful analysis of microarray data yields an enormous amount of information about rapid pathogen adaptations to environmental changes, we will summarize important steps in this analysis. This is still a constantly evolving field and is being continually improved; furthermore, no single method that will serve all purposes can be described. Figure 1.1 gives a brief outline of a standard scheme for the analysis of your data. Image acquisition is usually by a camera or a scanner and a frame grabber (ADC, analog to digital converter). Although images are regarded as raw data, different acquisition equipment and settings can have a significant effect on the data that are produced. Several methods are available for image quantification. Widely used freely available tools are Spotfinder by TIGR (http://www.tigr.org/ software/tm4/spotfinder. html) and ScanAlyze from Stanford (http://www. microarrays.org/software.html). Note that TIGR (The Institute of Genome Research) also offers a valuable and constantly updated resource for microbial genomes, including almost all pathogen genomes sequenced to date if made publicly available. When a system-integrated database is used, signal intensities can be automatically assigned to the corresponding gene/EST. If no database is available, this must be done manually, e.g., using the MSAccess database, which is available on most computers. Usually spot location can be used as a link between data and description tables. After quantification, for the comparison of arrays, it is necessary to normalize signal intensities in order to reduce system variations as far as possible. Most normalization methods are based on the assumption that the data are normally distributed, so it is necessary to log-transform data prior to normalization. Widely used normalization methods are variance stabilization (vsn) [29] or locally weighted regression scatter plot smoothing (lowess) [30]. Different normalization methods and most of the following data analysis procedures are implemented in a

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Fig. 1.1 Overview of microarray data analysis. Typical steps in gene array analysis are shown. Normalization, gene reduction, clustering, and detailed database analysis are necessary before a well-founded biological interpretation of the data is possible. vsn, variance stabilization; lowess, locally weighted regression scatter plot smoothing.

1.7 Adaptation in Time and to Stimuli

software package called bioconductor (http://www.bioconductor.org/, [31]) built on the R environment for statistical computing (http://www.R-project.org/, [32]). After normalization, if there are only two samples to compare, abundancies may be compared by a simple fold change or statistical tests such as Student’s t-test. Yet, since there is usually only a small number of replicates there is a high risk of underestimating variance and thus obtaining artificially low p-values. Random permutation of data and comparing the amount of genes that exceed a certain threshold can help to estimate the false positive rate. When there are more than two experimental conditions, the signals resulting from a gene in the different test samples should be compared using relative signal intensities. Cluster analysis is then used to group the genes according to their behavior in the experiments. Some cluster algorithms lose power due to noisy or nonsignificant signals. Since genes which do not change during an experiment will not provide much information for differentiation, genes having a higher variance can be selected or – if the number of classes of genes is known – p-values can be used for gene selection. Unsupervised learning is used if classes or their labels are not known a priori. Easy-to-use array data analysis programs for class discovery on a Windows platform are provided by, for instance, Stanford and TIGR. Unsupervised cluster algorithms like hierarchical clustering, SOMs [33], K-means, or PCA, principal component analysis [34] are implemented in these tools. All of these algorithms are also implemented in bioconductors. The most commonly used cluster methods in microarray data analysis are hierarchical clustering and K-mean clustering. Hierarchical clustering and the graphic representation of the trees gives a good overview of distinct groups within a dataset, but the interpretation of larger groups may be complicated. K-mean clustering requires the number of clusters (k) to be given. Each sample will be randomly assigned to one cluster. The distance between the center of each cluster (centroid) and each sample is calculated and the samples are assigned iteratively to the nearest centroid. Each sample is assigned to exactly one centroid. To identify the optimal choice for the value of k it is advisable to test several k. K-means clustering is a fast algorithm, suitable for large datasets, but can be affected by outliers. With a dataset with known class labels, supervised learning methods are superior to unsupervised methods. Based on a training set, supervised methods try to construct a classifier, which can be used to identify the nature of an unknown case. Different algorithms are used for the analysis of microarray data. Decision trees described by Leo Breiman [35] construct a classifier based on hierarchically arranged separation rules. They produce models that can be quite easily interpreted by humans. However, to obtain a reasonable result by decision trees, it is imperative to reduce the number of genes first. Neural networks are statistical models used for pattern recognition and classification. The network is composed of a large number of interconnected nodes. Neural networks learn by example data. They cannot be programmed to perform a specific task. The network finds out how to solve the problem by itself; its operation can be unpredictable and thus hard to interpret. Support vector machines

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(SVM) are classifiers which try to linearly separate data vectors of different classes by so-called hyperplanes in a high dimensional space [36] (http://www.csie.ntu. edu.tw/~cjlin/libsvm/). Like neural networks they do not generate gene lists; however, in most expression profiling studies the goal is identification of differentially expressed genes. To identify discriminative genes, subsequent statistical methods or classification using the cluster number as class label may be used. If two genes are found in the same cluster, this reveals similarities of expression profiles, but not the reasons for these similarities. Statistical significance does not always mean biological relevance. Coregulated genes may be controlled by the same regulatory pathway or share a specific promoter, but apparent coregulation may also be biological noise. Tools for direct sequence annotation and pathway analysis as discussed above can help to interpret and thus promote improved understanding of the biological function of the genes. Finally, it should be borne in mind that expression analysis can only measure RNA abundances, but gene expression and protein abundance are regulated at many steps. These steps include transcriptional control, alternative splicing, transport and location control, and mRNA degradation control. All these control mechanisms can cause discrepancies between gene expression on the mRNA level and the protein level in response to perturbations. In addition, alternative splicing can Tab. 1.1 Tools to analyze pathogen gene expression changes using microarrays.

The R-project for statistical computing

http://www.r-project.org/

Bioconductor home page

http://www.bioconductor.org/

Useful bioconductor packages Normalization

vsn, marray, limma

MA plot

limma, marray

Principal component analysis

stats

K-means clustering

stats

Hierarchical clustering

cluster

Self-organizing maps

SOM

Regression trees

rpart

Support vector machines

Vienna package e1071

Partitioning around medoids

pamr

Free Affymetrix annotation tool NetAffx

http://www.affymetrix.com/analysis/index.affx

Stanford tools

http://www.microarrays.org/software.html

TIGR tools

http://www.tigr.org/software/

1.8 Pathogen-Specific Challenges

produce different forms of a protein from the same gene, and the direct transfer of results may be ambiguous or problematic. To cope with all these different technical challenges in analysis of gene expression arrays, useful tools for array analysis are summarized in Table 1.1.

1.8 Pathogen-Specific Challenges

Several software tools are geared to specific pathogen challenges. Thus, prediction of antigenicity for a given protein sequence can be calculated. This yields information about whether a protein stretch from a pathogen protein will create a strong immune response in the host. Several empirical scales and algorithms to calculate this are available; well known ones are the Kyte and Doolittle and Hopps and Woods scales [37, 38]. A server for such predictions can be found at http:// occawlonline.pearsoned.com/bookbind/pubbooks/bc_mcampbell_genomics_1/ medialib/activities/kd/kyte-doolittle.htm These predictions do not really take account of structural information, so combination with the AnDOM server (or similar tools) is useful to identify the best patches to raise a strong immune response from a peptide in pathogen research efforts. Another modern tool, the BcePred server (http://www.imtech.res.in/ raghava/bcepred/) [39] uses physicochemical properties to predict continuous B-cell epitopes. More sophisticated methods of homology modeling and refinement will be needed in addition for vaccine development [40]. Further important information about a pathogen can be deduced from the complete genome sequence in addition to its open reading frames. Thus, detection of pathogenicity islands is possible by genome comparisons to related nonpathogenic strains: specific structures in the genome containing clusters of genes responsible for pathogen interaction with the host, toxin production, and resistance, etc. These clusters occur often during the evolutionary change from a nonpathogenic to a pathogenic life style and often involve the acquisition of new genes transferred by a larger recombination event, e.g., from phage insertions into the genome. Such an event can often be detected by monitoring the nucleotide composition over the whole genome: insertion regions from phages or other major insertion events often show up by their AT and GC content, which is different from that of their flanking regions and the rest of the genome. Rapid identification of pathogen-specific features is possible by differential genome analysis comparing a range of related genomes to the pathogen genome and extensive sequence comparisons to identify orthologous gene functions. The above two methods – identification of pathogen islands and differential genome analysis – readily identify groups of virulence factors in the pathogen in question, e.g., for Haemophilus influenzae and Helicobacter pylori [41], or, as a clinically even more important example, in hemorrhagic and nonpathogenic Escherichia coli [42].

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A further approach uses clusters of orthologous genes (COGs) [12]. Several pathogenicity factors can directly be identified using sequence analysis and identifying their belonging to a cluster of orthologous genes which encodes a virulence factor. Other more advanced methods of sequence analysis add to this repertoire to search for pathogen-specific genes or protein functions (see also above), e.g., predicting pathogen-specific gene expression by looking at common transcription factor binding sites in the target genes using the TESS server (http://www.cbil. upenn.edu/tess/).

1.9 Pathogen Adaptation Potential

Another important question concerns pathogen adaptation potential. In particular, the redundancy in enzymatic pathways allows pathogens to escape antibiotic attack or inhibition of enzyme activities. To calculate metabolic capabilities in this respect, elementary mode analysis (see above) [5] is useful: the elementary modes are the basic metabolic paths in the metabolic network. Scrutinizing the list of calculated modes for a specific pathogen reveals which metabolites require which enzymes for their synthesis. Consideration of subnetworks makes this analysis easier [43]. In some cases the metabolic web of a pathogen allows the compounds required for growth to be produced by alternative routes. However, some key enzymes can be singled out which either are central for many paths or are unique in their capabilities and no detour around their blockage is possible. Similarly, the capabilities for metabolic adaptation to a wide range of environmental conditions can be estimated using elementary mode analysis. Another way to look at enzyme flexibility in pathogens is a more comprehensive evolutionary perspective. Here all our data [26, 44, 45] indicate that widespread recruitment to new pathways allows pathogens to cope within surprisingly few generations with new pathways to fight new man-made antibiotics.

1.10 The Fight Against Resistance

Good new approaches are available to prevent resistance. In particular, calculated enzyme flexibility is very seldom so high that a simultaneous attack on several pathways does not have a good chance of success [44, 45]. The evolutionary flexibility of pathogens can be estimated by considering genetic mechanisms for immune escape and new mutations available: Are there specific antigen-shifting mechanisms? What about recombination, resistance plasmids, genome instability, transposons? – to name just some of the more common mechanisms. Interestingly, this list shows that meticulous genome annotation (see above) is in fact

1.12 Annotation Platforms Suitable for Pathogenomics

quite important in determining and assessing pathogen flexibility in this genetic respect. Another important consideration takes account of the typical resistance mechanisms against antibiotics. Fortunately, similar mechanisms for this are used in many pathogens: resistance is often achieved by (a) inactivation of the attacking antibiotic, (b) modification of the antibiotic target within the bacterial cell, (c) increased transport of antibiotic out of the cell, or (d) prevention of antibiotic uptake. Combined strategies, fighting with both a standard antibiotic and resensitization by blocking such typical resistance mechanisms against antibiotics, are thus quite promising and a bioinformatic prediction about the capabilities in regard to these four mechanisms can be made for the pathogen in question.

1.11 Drug Design and Antibiotics

Drug design follows the basic principle that the target enzyme to be blocked by an antibiotic should not occur in the human patient, but should occur in all pathogens one desires to limit with the antibiotic. Furthermore, it must be essential for the growth or, even better, for the survival of these pathogenic organisms. A further improvement (see above) is the use of drug combinations and, in addition, an estimate as to the number of mutations required for the development of resistance. In particular for viral pathogens such as HIV, the load in the bloodstream is so high that single-point mutations are selected in very short times, with rapid antibiotic failure if a single mutation of this kind suffices for resistance [46].

1.12 Annotation Platforms Suitable for Pathogenomics

Another area of bioinformatics research is to achieve easy comparison of different direct and higher annotation tools for specific genomes and sequences in pathogenomics. Good, powerful genome annotation platforms are already available, such as the noncommercial GenDB [20] and MAGPIE [22], or the commercial ones from PEDANT [21] and BioScout (Lion Biosciences AG, Heidelberg). This should also be seen in the context of similar ongoing activities exploring the advantages of XML (extended markup language) in bioinformatics [47] in different laboratories: There are new XML schemes for bioinformatics [48], EMBL data [49], and a protein markup language [50]. An XML broker exists for integration of microarray data [51] and integrated systems for biological pathways [23]. Strong new integrated data platforms for proteomics [52], XML-based remote procedure calls [53], and an SQL-(sequence query language)based server for online integration of life science data [54] have recently become available. A number of useful integrated public and user-friendly tools for general genome and pathway analysis readily available on the world wide web are summa-

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1 Bioinformatics: Data Mining Among Genome Sequences Tab. 1.2 Useful links and databases for genome and pathway analysis.

Links and general overview of databases Computational Molecular Biology at NIH

http://molbio.info.nih.gov/molbio/db.html

Polish Academy of Sciences

http://www.ibb.waw.pl/biodat/05-02.html

Sanger Institute

http://www.sanger.ac.uk/Info/Links/databases.shtml

Washington University, St. Louis

http://library.wustl.edu/subjects/life/genet. html

NCBI databases

http://www.ncbi.nlm.nih.gov/

Sequence annotation Sequence annotation for nucleotides, protein sequence, and whole genome sequence

GenBank

Gene annotation Gene-oriented clusters of transcript sequences

UniGene

Catalog of human genes and genetic disorders, with links to literature references, sequence records, maps, and related databases

OMIM (Online Mendelian Inheritance in Man)

Map, sequence, expression, structure, function, citation, homology information and related web sites for genes

Entrez Gene

Sanger Institute gene annotation Transcript/translation information, location, SNPs, orthologue prediction, disease matches, related web sites for genes

http://www.ensembl.org/

Stanford software and tools Unification tool which dynamically collects and compiles data from many scientific databases (batch searches possible)

http://genome-www5.stanford.edu/cgi-bin/ source/sourceSearch

KEGG pathway database

http://www.genome.jp/kegg/

SwissProt protein sequence database

http://us.expasy.org/sprot/

References

rized in Table 1.2 as a first primer for any further intended analyses. The wellknown National Center for Biotechnology Information (NCBI) is creating public databases and software tools for the dissemination of biomedical information. The databases are built from sequences submitted by individual laboratories and by data exchange with the international nucleotide sequence databases, and also from the other two major nucleotide databases, the European Molecular Biology Laboratory (EMBL) and the DNA Database of Japan (DDBJ). In addition to GenBank, further databases for annotation are supported and made available to the scientific community. A number of software programs, in particular different implementations [2] of BLAST and iterative Blast (Psi-BLAST), are available. Very useful also are the tutorials and the notes on the different parameters and qualifiers the programs accept. For example, the Entrez browser system offered here (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=Protein&itool=toolbar) also retrieves, in addition to journal articles, protein and nucleotide sequences from a specific pathogen of interest.

1.13 Conclusions

We show here that many predictions about pathogens are possible on the basis of sequence and other data by applying bioinformatic tools, including analysis of gene expression data and software designed to reveal pathogen-specific features. However, bioinformatics provides only a valuable start as it rapidly identifies potential new targets (including subtle structural differences), new leads, or further cellular processes to consider. Subsequent further development and validation of leads and targets is done in close collaboration with experimental researchers. References 1 Zhang, Y., and L. M. Amzel. 2002.

Tuberculosis drug targets. Curr. Drug Targets. 3(2):131–154. 2 Altschul, S. F, T. L. Madden, and D. J. Lipman. 1997. Gapped BLAST and PSIBLAST, a new generation of protein database search programs. Nucleic Acids Res. 25(17):3389–3402. 3 Hulo, N., C. J. Sigrist, and A. Bairoch. 2004. Recent improvements to the PROSITE database. Nucleic Acids Res. 32 Database issue:D134–137. 4 Huber, W., and R. Gentleman. 2004. Matchprobes: a Bioconductor package for the sequence-matching of microar-

ray probe elements. Bioinformatics. 20(10):1651–1652. 5 Dandekar, T., and R. Sauerborn. 2002. Comparative genome analysis and pathway reconstruction. Pharmacogenomics. 3(2):245–256. 6 Bengert, B., and T. Dandekar. 2003. A software tool-box for analysis of regulatory RNA elements. Nucleic Acids Res. 31(13):3441–3445. 7 Hofacker, W., P. Fontana, and P. Schuster. 1994. Fast folding and comparison of RNA secondary structures (The Vienna RNA Package). Monatshefte fr Chemie (Chemical Monthly). 125:167– 188.

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18

1 Bioinformatics: Data Mining Among Genome Sequences 8 Pesole, G., and S. Liuni. 1999. Internet

resources for the functional analysis of 5¢ and 3¢ untranslated regions of eukaryotic mRNA. Trends Genet. 15:378. 9 Barrick, J. E., K. A. Corbino, and R. R. Breaker. 2004, New RNA motifs suggest an expanded scope for riboswitches in bacterial genetic control. Proc. Natl. Acad. Sci. USA. 101(17):6421–6426. 10 Bengert, P., and T. Dandekar. 2004. Riboswitch finder – a tool for identification of riboswitch RNAs. Nucleic Acids Res. 31:3441–3445. 11 Letunic, I., R. R. Copley, and P. Bork. 2004. SMART 4.0: towards genomic data integration. Nucleic Acids Res. 32 Database issue:D142–144. 12 Tatusov, R. L., N. D. Fedorova, and D. A. Natale. 2003. The COG database: an updated version includes eukaryotes. BMC Bioinformatics. 4(1):41. 13 Schmidt S., P. Bork, and T. Dandekar. 2002. A versatile structural domain server using profile weight matrices. J. Chem. Inf. Comput. Sci. 42(2):405–407. 14 Lo Conte, L., B. Alley, and C. Chothia. 2000. SCOP: A structural classification of proteins database. Nucleic Acids Res. 28(1):257–259. 15 Becker, K., S. M. Kanzok, and S. Rahlfs. 2003. Plasmoredoxin, a novel redoxactive protein unique for malaria parasites. Eur. J. Biochem. 270:1057–1064 16 Lesk, A. M. 2002. Introduction to bioinformatics. Oxford University Press, Oxford 17 Dandekar, T., B. Snel, and P. Bork. 1998. Conservation of gene order: a fingerprint of proteins that physically interact. Trends Biochem. Sci. 23(9):324– 328. 18 Von Mering, C., M. Huynen, and B. Snel. 2003. STRING: a database of predicted functional associations between proteins. Nucleic Acids Res. 31(1):258–261. 19 Dandekar, T., M. A. Huynen, and P. Bork. 2000. Re-annotating the Mycoplasma pneumoniae genome sequence: adding value, function and reading frames. Nucleic Acids Res. 28(17):3278– 3288. 20 Meyer, F., A. Goesmann, and A. Puhler. 2003. GenDB–an open source genome

21

22

23

24

25

26

27

28

29

30

31

annotation system for prokaryote genomes. Nucleic Acids Res. 31(8):2187– 2195. Frishman, D., M. Mokrejs, and H. W. Mewes. 2003. The PEDANT genome database. Nucleic Acids Res. 31(1):207– 211. Gaasterland, T., and C. W. Sensen. 1996. MAGPIE: automated genome interpretation. Trends Genet. 12(2):76–78. Krishnamurthy, L., J. Nadeau, and W. Xu. 2003. Pathways database system: an integrated system for biological pathways. Bioinformatics. 19(8):930–937. Pfeiffer, T., I. Sanchez-Valdenebro, and S. Schuster. 1999. METATOOL: For studying metabolic networks. Bioinformatics. 15(3):251–257. Schuster, S., D. Fell and T. Dandekar. 2000. A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks. Nature Biotechnol. 18(3):326–332. Schmidt, S., S. Sunyaev, and T. Dandekar. 2003. Metabolites: a helping hand for pathway evolution? Trends in Biochem. Sci. 28(6):336–341. Shankar, N., A. S. Baghdayan, and M. S. Gilmore. 2002. Modulation of virulence within a pathogenicity island in vancomycin-resistant Enterococcus faecalis. Nature. 417(6890):746–750. Harmsen, D., H. Claus, and U. Vogel. 2003. Typing of methicillin-resistant Staphylococcus aureus in a university hospital setting by using novel software for Spa repeat determination and database management. J Clin. Microbiol. 41(12):5442–5448. Huber, W., A. von Heydebreck, and M. Vingron. 2002. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics. 18 Suppl 1:S96–S104. Cleveland, W. S. 1979. Robust locally weighted regression and smoothing scatterplots. J. Am. Statist. Assoc. 74:829–836. Gentleman, R. C., V. J. Carey, and J. Zhang. 2004. Bioconductor: open software development for computational biology and bioinformatics. Genome

References Biol. 5(10):R10. http://genomebiology. com/2004/5/10/R80. 32 Ihaka, R., and R. Gentleman. 1996. R: A language for data analysis and graphics. J. Comput. Graph. Statist. 5(3):299–314. 33 Tamayo, P., D. Slonim, and T. R. Golub. 1999. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc. Natl. Acad. Sci. U S A. 16;96(6):2907–2912. 34 Eisen, M. B., P. T. Spellman, and D. Botstein. 1998. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. U S A. 8;95(25):14863–14868. 35 Breiman, L., J. Friedman, and C. J. Stone. 1984. Classification and regression trees. Wadsworth, Belmont, Calif. 36 Chang, C. C., and C. J. Lin. LIBSVM – a library for support vector machines, http://www.csie.ntu.edu.tw/~cjlin/ libsvm/ 37 Kyte, J., and Doolittle, R. 1982. A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 157:105–132. 38 Womble DD. 2000. GCG: The Wisconsin Package of sequence analysis programs. Methods Mol. Biol. 132:3-22. 39 Saha, S., and G. P. S. Raghava. 2004. BcePred: prediction of continuous B-cell epitopes in antigenic sequences using physico-chemical properties. In: International Conference on Artificial Immune Systems 2004. G. Nicosia and J. Timis, eds. Springer ICARIS, LNCS 3239:197–204 40 Sung, M. H., and R. Simon. 2004. Candidate epitope identification using peptide property models: application to cancer immunotherapy. Methods. Dec 34(4):460–467. 41 Huynen, M., T. Dandekar, and P. Bork. 1998. Differential genome analysis applied to the species-specific features of Helicobacter pylori. FEBS Lett. 426(1):1–5. 42 Schuster, S., T. Pfeiffer, F. Moldenhauer, I. Koch and T. Dandekar (2002). Exploring the pathway structure of metabolism: decomposition into subnetworks and application to Mycoplasma pneumoniae. Bioinformatics. 18:351–361.

43 Perna, N. T., G. Plunkett III, and F. R.

Blattner. 2001. Genome sequence of enterohemorrhagic Escherichia coli O157:H7. Nature. 409 (6819):529–533. 44 Ziebuhr, W., K. Xiao, and T. Dandekar. 2004. Pharmacogenomic strategies against resistance development in microbial infections. Pharmacogenomics. 5:361–379. 45 Dandekar, T. and S. Schmidt. 2004. Metabolites and pathway flexibility. In Silico Biol. 5: 1–13 46 Iwasa, Y., F. Michor and M.A. Nowak. 2003. Evolutionary dynamics of escape from biomedical intervention. Proc. R. Soc. Lond. B Biol. Sci. 270(1533):2573– 2578. 47 Achard, F., G. Vaysseix and E. Barillot. 2001. XML, bioinformatics and data integration. Bioinformatics. 17(2):115– 125. 48 Bruhn, R. E., and P. J. Burton. 2003. Designing XML schemas for bioinformatics. Biotechniques. 34(6):1200–1206. 49 Wang, L., J. J. Riethoven and A. Robinson. 2002. XEMBL: distributing EMBL data in XML format. Bioinformatics. 18(8):1147–1148. 50 Hanisch, D., R. Zimmer, and T. Lengauer. 2002. ProML – the protein markup language for specification of protein sequences, structures and families. In Silico Biol. 2:313–324. 51 Tjandra, D., S. Wong, and L. Esserman. 2003. An XML message broker framework for exchange and integration of microarray data. Bioinformatics. 19(14):1844–1845. 52 Taylor, F., N. W. Paton, and S. G. Oliver. 2003. A systematic approach to modeling, capturing, and disseminating proteomics experimental data. Nature Biotechnol. 21(3):247–254 53 Riva, A., and I. S. Kohane. 2002. Accessing genomic data through XML-based remote procedure calls. Proceedings of the 2002 AMIA Annual Symposium, pp 662–666. 54 Freier, R., M. Hofestdt, and A. Stephanik. BioDataServer: 2002. An SQL-based service for the online integration of life science data. In Silico Biol. 2:37–57.

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2 Transcriptome Analysis: Towards a Comprehensive Understanding of Global Transcription Activity Ben Sidders and Neil Stoker

2.1 Introduction

In the past few years the field of transcriptomics has grown rapidly. This chapter explores the development of the biology and technologies that gave rise to this new field, and its importance. We will focus in particular on the microarray and the issues important to its use and correct interpretation. Using published examples we explore the many varied uses to which the microarray has been put. The examples show how the data being generated are increasing our understanding of pathogenic bacteria and the hosts that they infect. We hope that this chapter will act primarily as an introduction to the complex field of transcriptomics. Directions to relevant reviews are provided wherever we feel they will be of benefit to the reader.

2.2 Development of Transcriptomics 2.2.1 From Genomics to Functional Genomics

The data from genome sequencing projects has led to a wealth of information about many organisms. For example, there are now approaching 200 complete microbial genome sequences, with an additional 500 or so microbial projects ongoing. In particular, most of the major bacterial pathogens have had their genomes sequenced. For more details see the websites of TIGR [1], NCBI [2], the GOLD database [3], or other chapters within this volume. The information contained within these genomic sequences can tell us large amounts about an organism’s capabilities so long as we are able to identify and annotate the majority of the genes within the genome [4, 5]. However, the properties (genes) that make pathogens unique are often organism- or species-specific. Therefore, unless functional evidence exists, the genetic sequence alone does not

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fully unravel the molecular mechanisms by which a microbe causes disease. In order to understand what physiological, metabolic, and pathological mechanisms a particular pathogen employs within its host, we have to know how the genome is used to produce and regulate the proteome of the cell. To fulfil this need, the field of functional genomics emerged and is continuing to evolve. Functional genomics includes technologies that allow us to study the transcriptome and the regulation of transcription (the production of messenger RNA). This is important in a biological context because transcription is the first regulated step in protein production. How transcription is regulated governs microbial physiology and pathogenic mechanisms. Also within the umbrella of functional genomics are technologies that aim to study directly the protein content (proteome) of the cell – proteomics (see Chapter 3 and also Ref. [6]). 2.2.2 From Gene to Whole Genome

Thirty years ago the Southern blot hybridization revolutionized gene analysis, allowing single genes to be studied within complex genomes [7]. Soon techniques such as reverse-transcription polymerase chain reaction (RT-PCR) and versions of the Northern blot followed. These allowed quantification as well as detection of RNA molecules but remained limited to single genes. This limitation was reduced with the development of macroarrays using nylon membranes to support many genes at once. However, the introduction of the microarray made it possible to study the expression of every single gene within a genome at once [8, 9]. The first technological advance that was key to the development of the microarray was the use of glass as a solid substrate for the DNA probes. This allowed many more probes to be accurately held on one surface and in a much smaller area. The second was the development of printing methods that increased the resolution and accuracy of printing onto a glass surface. The genome-wide study of the complete (whole organism) mRNA expression profile (the transcriptome) is known as transcriptomics. Although transcriptomics – the comparison of a control and an experimental sample’s expression profile – is the primary application for microarrays, they can be used for many purposes including the analysis of genome structure and as a strain-screening tool. The conclusions being drawn from pathogen microarray studies are revolutionizing microbiology. Our understanding of microbial pathogenesis is adapting to the view that bacterial activities are the product of a whole organic system rather than the activity of a single gene or regulon. Host responses to microbial infection are also being characterized using host genome microarrays. Studying both the bacterial and host expression profiles during infection will provide the most complete understanding of host–pathogen interactions.

2.3 Introducing the Microarray

2.3 Introducing the Microarray 2.3.1 What Is a Microarray?

Microarrays are densely packed arrays of DNA probes which are fixed to a solid substrate in a predetermined matrix on a small surface area. The arrays are then amenable to hybridization with RNA or DNA nucleotide samples isolated from experimental cultures. The nucleotides are labeled before hybridization and afterwards the fluorescent intensity from each probe spot can be measured. When compared to a control, the fluorescent intensity from each spot can be taken to indicate the relative level of that nucleotide present in the sample. Because the arrays contain probes for all of the open reading frames in a genome, this is achieved on a genome-wide scale with each array used. There are two commonly used forms of microarray, the spotted array and the Affymetrix gene chip, which differ in their method of construction and use. The choice of one array type over another depends largely on cost, application, and commercial availability as well as the need to design and construct specialized arrays within the laboratory. The fundamental differences between the synthesis methods and their use and analysis mean they often show varying degrees of agreement with one another and the expression levels that they report are usually not amenable to direct comparison [10]. 2.3.2 The Affymetrix Gene Chip

Affymetrix uses a method that directly synthesizes an oligonucleotide sequence on a silicon slide/chip using a technique known as light-directed combinatorial chemical synthesis [11]. Affymetrix gene chips are more commonly used for eukaryotic research as they can achieve higher spot densities, allowing the incorporation of more probes to study the many more genes and splice variants that occur. Microbial researchers do not require as many genes or splice variants on an array so can use the more easily obtainable spotted arrays. The latest human oligonucleotide genechip by Affymetrix can incorporate up to 47 000 probes [12], whereas the Mycobacterium tuberculosis whole-genome spotted array contains just 4410 probes [13]. However, it is worth noting that Affymetrix have just released a new Escherichia coli gene chip that contains 10 000 probes covering four common environmental and laboratory strains on the same array, thereby allowing microbiologists to benefit from the higher density arrays. In this chapter we will focus on spotted microarrays (PCR or long-oligo) as they are the most common platform for the analysis of microbial expression profiles. For more information on Affymetrix Gene Chip technology and their applications visit the company website [12] or see Ref. [11].

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2.3.3 The Spotted Microarray

Spotted arrays most commonly use coated (or “activated”) glass as the solid substrate, and are generally the size and shape of, if not an actual, microscope slide. Using glass as the substrate is beneficial for two reasons: (1) it allows the formation of concentrated spots, thereby increasing the array density, and (2) glass provides a flat surface for grid formation and for the confocal microscope to focus on. The coating chemistry can vary [14], but the common feature of the activated glass is that it allows cross-linking between the positively charged surface and the negatively charged nucleotide probes. The spotted array uses either PCR amplicons or presynthesized oligonucleotides of unique fragments from each of the target genes as probes. The PCR product or oligonucleotide probes are then spotted onto the array surface by an automated robot that uses specially designed pins to pick up the sample from a well and deposit it (spot it) onto the array surface. The probes are spotted in a prearranged grid formation; this allows the analysis software to link each spot with the gene that it represents. PCR probes are generated in a 96-well or higher format using primers specifically designed to amplify a unique region of the ORF (open reading frame). Primer design is most commonly undertaken using software packages such as PrimeArray [15, 16], Primer 3 [16], or GenomePRIDE [17] which automatically develop primers for all coding sequences in a genome. The PCR amplicon probes are generally between 200 and 2000 base pairs long. The PCR products must then be purified, and it is often sensible to test a subset to ensure primer and probe specificity. Long-oligonucleotide spotted arrays were developed as an alternative and beneficial method for the generation of probes, removing the need for time-consuming and error-prone PCR. The probes are single-stranded oligonucleotides synthesized by an external company, so their use makes them inherently faster and cheaper. Also, because they are single-stranded to begin with, they are more sensitive to hybridization with their complementary ORF cDNA (see below). The probes are melting-temperature-normalized and are normally 50–70 nucleotides long. The oligonucleotides are specifically designed to locations in each ORF that increase specificity and reduce cross-reactivity with other probes. As mentioned, the oligonucleotides used for these arrays are purchased in a ready-to-use state (see http:// www.operon.com). The user can choose or design specific probes, or alternatively it is possible to purchase a predesigned set of probes that cover every ORF from one of many genomes. Operon currently sells complete genome array ready sets of oligonucleotide probes for many bacterial pathogens including E. coli, M. tuberculosis, Bacillus anthracis, Neisseria meningitides, and Listeria monocytogenes. It is also possible to buy a set of approximately 35 000 probes that match the human genome, allowing host studies with this method too. However, this system is relatively new, and it is currently unknown whether there are problems with the storage of single-stranded nucleotides that are inherently more susceptible to degradation than double-stranded probes generated by PCR.

2.4 Microarray Methods

2.4 Microarray Methods 2.4.1 Experimental Design

Producing useful data from microarrays depends heavily on the manner in which the study was performed so that sources of error are reduced. There are two fundamental sources of error that occur during a microarray experiment: random errors and systematic errors. Random errors are controlled for by obtaining an adequate number of repeats to minimize extraneous influencing factors. Systematic error, or bias, is reduced as far as possible by using an appropriate experimental design (although further reduction is usually necessary using statistical means). An introduction to the key points of microarray experimental design is provided here (for a more detailed appraisal see Ref. [18]).

2.4.1.1 Type of Experiment Microarrays can be used for many types of experiments. There are experiments that use two samples of DNA (DNA–DNA arrays) and can be used to analyze genome structure to identify strain variation [19] and also to resequence genes [20], etc. Then there are the experiments that analyze transcriptional changes using RNA, for which a system of nomenclature has been proposed. This divides microarray RNA experiments into type 1 and 2 experiments [21]: . A type 1 experiment uses differentially labeled control and experimental RNA populations and makes a direct comparison between the two on one array (RNA–RNA arrays). Type 1 experiments include those that aim to identify genes up- or downregulated under experimental conditions such as during a host infection compared to in vitro culture [22] (Fig. 2.1). . A type 2 experiment makes indirect comparisons between two RNA samples. In this type of experiment each RNA sample (experimental and control) is hybridized to a separate array along with a reference gDNA sample (RNA–DNA arrays). Type 2 experiments include more recent array studies that analyze expression profiles, and more complex studies such as those that follow a population over a time course and those that make multiple comparisons to a reference sample [23] (Fig. 2.2). Note that genomic DNA (gDNA) should be extracted from a cell culture in the stationary phase, as partially replicated chromosomes will affect the gene copy number.

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Fig. 2.1 A type 1 (RNA–RNA) experiment makes direct comparisons between two RNA samples (an experimental and a control) on one array. The fold change in gene expression between experimental and control popula-

tions is calculated by dividing the fluorescence reading from the experimental population (Cy5, in this case) by the reading from the control (Cy3). This ratio is then transformed to its logarithm to the base 2.

2.4 Microarray Methods

Fig. 2.2 A type 2 (RNA–DNA) experiment makes indirect comparisons between two RNA samples (an experimental and a control). Each RNA sample is hybridized to a separate array along with a genomic DNA (gDNA) standard. The fold changes in gene expression between experimental and control

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populations are a ratio of ratios, calculated by: (1) dividing the florescence reading of each RNA sample by that of the gDNA standard, and then by (2) dividing the experimental by the control. This ratio is then transformed to its logarithm to the base 2.

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2.4.1.2 Replicates As with all biological experiments it is important to have a sufficient number of replicates to insure statistical validity and to remove the effect of any external, random influences. There are two types of replicates to consider when using microarrays: biological and technical. Biological replicates are repetitions of the same experiment but performed with different RNA samples. Technical replicates are simply multiple arrays performed with the same RNA sample. Technical replicates can be performed at different levels – for example, labeling of the cDNA, duplicating spots on the array, and rescanning arrays. It has been suggested that microarray experiments should contain at least three biological replicates to increase reliability [24]; however, other groups have suggested that far more replicates are needed to increase the sensitivity of a microarray experiment so that genes with low-fold changes in expression are easily detected [25]. It is also clear that biological replicates are more important than technical replicates, should there be limited resources. There is therefore a hierarchy of replicates (Fig. 2.3), with biological replicates at the top, and the method of analysis must reflect this (see below). Inherent variations (or systematic errors) occur within any microarray experiment, some of which can be minimized during the experimental procedure. The most common of these is the dye bias, which occurs because the size and shape of the Cy labels influences their rate of incorporation. Cy3 is much smaller and more easily incorporated into cDNA than Cy5. The effect of this is usually adjusted for during data normalization, although some groups prefer to carry out dye swap experiments when performing type 1 studies [26, 27]. In type 2 experiments (RNA–gDNA) the same dyes must be consistently used to label the DNA and RNA populations so that gDNA can act as a constant denominator. This allows raw data from any array produced in this way to be compared almost directly, and is reportedly more reproducible than an RNA–RNA array [28]. 2.4.2 RNA Extraction

Rapid RNA extraction is a vital step in a microarray experiment if it aims to assess gene expression levels in a meaningful manner. The RNA sample tested must reflect transcription levels as they were naturally and not as a result of culture manipulation or RNA degradation. The first step is therefore usually immediate immersion of the culture in a “stop solution” such as guanidinium thiocyanate that halts all biological activity including transcription and enzyme-mediated degradation of RNA. The cell must then be lysed and the contents disrupted, the exact method depending on the bacterial species being used. It is quite common now for commercial kits to be used for the RNA extraction, but essentially the RNA is purified by precipitation and resuspended in RNase-free water. Many laboratories have their own favored method of extracting nucleotide samples from bacterial cultures, and the protocols will vary depending on the species under study.

Fig. 2.3 The hierarchy of replicates shows how complex a microarray experiment can become. It also highlights the multiple layers at which replicates are needed to ensure a microarray experiment remains valid and is not influenced by internal or external forces which are out of the scientist’s control.

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Because mRNA has a short half-life [29], speed of isolation through to analysis is critical. It is possible to store RNA for short periods at –80 C without any significant degradation. Before the RNA is used for further analysis it is essential to DNAse-treat the sample. This is to ensure the preparation is pure, as RNA extraction protocols often isolate genomic DNA at the same time, which would give a false positive signal in an array experiment. In some cases researchers have removed ribosomal RNA (rRNA), but we find this inconsistent and unnecessary for bacteria. As an added benefit, many arrays contain probes against rRNA so that it can be used as a positive control, or even as a normalization factor. 2.4.3 Labeling/Reverse Transcription

After isolation and purification, total RNA is reverse-transcribed to cDNA, at which point it is labeled in a process known as “direct labeling” [30]. cDNA is a much more convenient and stable molecule to work with than mRNA. This is because RNA has a very short half life and is very easily degraded by ubiquitous RNases. Approximately 2–10 lg of bacterial RNA are used from each sample in a microarray experiment. During the reverse transcription, labels are incorporated into the cDNA. These are usually dCTP molecules labeled with the fluorescent markers Cy3 or Cy5, which fluoresce at different wavelengths. In a type 1 experiment the control and experimental RNA populations must be labeled differently, whereas in a type 2 experiment both the control and experimental RNA populations are labeled using the same fluorescent dye whilst the gDNA standard is labeled using the other. Because microbial mRNAs are not polyadenylated, this feature cannot be targeted with primers during the reverse transcription. Therefore, the reverse transcription of a bacterial total mRNA population requires another approach. Using random primers was the first method developed, but, as Talaat et al. [31] have shown, these are often insensitive, and their nonspecificity results in some mRNAs not being transcribed at all. This can be avoided if genome-directed primers are used. These are designed by an algorithm that has been written to calculate the minimum number of primers needed to amplify every ORF in a genome. This ensures that the primers are highly sensitive to all mRNAs in a cell. Using this algorithm, it has been shown that a set of just 37 oligonucleotide primers is enough to amplify every ORF (approximately 4000) in the M. tuberculosis genome, and that they were far more specific when it came to selectively amplifying mycobacterial, rather than mammalian, RNA [31]. If gDNA is being used, as in a type 2 experiment, it is labeled using random or genome-directed primers and the DNA polymerase I Klenow fragment. The reverse transcription process used to convert the mRNA population into cDNA prior to hybridization is not 100% efficient [32]. The 3¢ end of a gene is often over-represented as occasionally reverse transcriptase only synthesizes a short sequence before falling off. A direct-labeling approach where the mRNA population is labeled without the need for a reverse transcription or amplification

2.4 Microarray Methods

step has been developed [33]. Although not widely used, this method promises to reduce bias and increase the accuracy of microarray results. There are also methods of RNA labeling that improve the efficiency of the labeling reaction and are designed for small (submicrogram) yields of RNA; this is known as “cold labeling” [34]. This is necessary, for example, when extracting bacterial RNA from in vivo experiments because bacteria in the host are then usually far more diffuse than when grown in vitro. This protocol first reverse transcribes the mRNA to cDNA using unlabeled nucleotides. The cDNA is then randomly primed and labeled using the Klenow fragment of DNA polymerase, which has a much higher rate of label incorporation than does reverse transcriptase [35]. This method therefore allows either smaller amounts of RNA to be used (reportedly as little as 0.35 lg of RNA) or, in principle, lower concentrations of Cy dyes, which would make microarray experiments much more cost-effective. 2.4.4 Hybridization

The two cDNA (or cDNA and gDNA) samples are purified from the labeling reaction and combined so that both are competitively hybridized to the array at the same time. Before hybridization, the cDNA/DNA samples must be denatured to single-stranded molecules with a brief incubation at 95 C and added to the hybridization mixture (a salt and detergent solution) which provides a highly stringent environment for the binding reactions to occur in. The slide must also be prehybridized before use to prevent nonspecific binding of nucleotides. During the hybridization reaction the samples are hybridized to the array underneath a slightly raised cover slip. The entire array is then incubated within a sealed chamber, which is submersed in a water bath, for 16–20 h. The incubation temperature is set so that it is high enough to induce stringent binding conditions, the exact temperature depending on the bacterial species and the G/C content of the genome. After hybridization the microarray must be stringently washed to remove any unbound or nonspecifically bound cDNA/DNA. 2.4.5 Scanning

Following hybridization, the levels of fluorescence from each spot for both Cy3 and Cy5 are determined using a confocal scanner. A false color image is produced from a prescan of the array; the image represents the level of fluorescent intensity using a color gradient and is solely for easy observational analysis, allowing researchers to check on the internal controls. If the negative controls are negative, the positive controls are positive, and the background is not too imposing, a gain at which the array will be scanned can be set. The gain refers to the laser power used by the confocal microscope to excite the fluorophores without inducing saturation, and is expressed as the ratio of output (intensity) to input (laser power). It is important that the slide be scanned at a gain just below the threshold where

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any of the spots become saturated, otherwise these will be of no use. Conversely, if the signal intensity for any probe of the spots is too low, they may be confused with the background and be unreliable. A crude observational analysis of the false color image is enough to ascertain what gain to use. Often, the array is scanned a second time at the maximum gain allowed to clarify the reading from spots with very low intensities. The array is scanned under two wavelengths (or channels), 635 nm for Cy5 and 532 nm for Cy3, and for each dye the computer produces a separate monochromatic image (as a TIFF – tagged information file format – file) that reflects the fluorescent intensities on a black and white gradient. Once the slide has been scanned, the monochromatic TIFF images for each dye can be exported into a piece of image analysis software that performs the quantification of the fluorescence intensity.

2.5 Data Normalization and Analysis 2.5.1 Image Quantification

TIFF images of both channels (Cy3 and Cy5) from the microarray are imported into, and processed by, a piece of image analysis software such as Imagene (http://www.biodiscovery.com/imagene.asp) or BlueFuse (http://www.cambridgebluegnome.com/products/bluefuseformicroarrays/index.htm). The image analysis software programs perform the laborious task of measuring the fluorescence intensities of many thousands of spots at once, which they do by superimposing a malleable grid over the array surface, from which it detects and quantifies the median intensity of the pixels within each spot as well as within a local background area. The median is used because it is not strongly influenced by outliers, and is therefore a robust estimate of the mean. The software links the spot coordinates to an internal database that contains details of all of the genes represented by the spots and produces a tab-delimited or XML results file. Included in this file are the spot coordinates, gene names and identifiers, and the mode and median of the fluorescence readings as well as information about the background reading, spot size, and many other pieces of supplementary information. Usually a separate file for each of the Cy dyes is produced, meaning that an array experiment will produce many large files that contain huge amounts of data. However, the format of this data file means it can easily be imported into any data analysis software package for processing.

2.5 Data Normalization and Analysis

2.5.2 Data Processing

As mentioned, systematic errors (or bias) are reduced as far as possible by using an appropriate experimental design; however, further reduction is usually necessary using statistical processes. First, all dubiously low measurements – normally those lower than the background plus two standard deviations – must be either excluded or set at a base level above the background. Secondly, the data must be normalized, which is an extremely important step in ensuring the validity of the array readings. Normalization is a process that transforms microarray fluorescence intensities to account for systematic errors. These can be divided into “within array” and “between array” normalizations. Systematic errors usually accounted for include, for example, the differences in dye incorporation efficiencies (Cy3 is much smaller and more easily incorporated into cDNA than Cy5), the quantity of RNA hybridized to the array, spot size, probe length, spacial effects etc. Normalization can be done in several ways, and both statistical methods and strategies based on internal controls have been developed. Less commonly used are those based on internal controls and invariant genes. This is because many genes previously thought to be invariant have been shown actually to vary quite significantly, and also because data from a type 1 experiment still suffers from a dye bias. Type 2 experiments using gDNA are a promising method of overcoming these limitations. A nonnormalized data set will exhibit a bias towards spots with a strong fluorescent intensity, whereas a normalized data set should center on a log ratio (see below) of zero and be independent of spot intensity. Most often used for withinarray normalizations like this are statistical, globally applied methods. One statistical method of transformation commonly used to calibrate microarray data is LOWESS (locally weighted scatterplot smoothing [36]), which smooths scatterplots of log ratios in a weighted, least-squares fashion to remove intensity-dependent bias. Another is MAD (median absolute deviation), which provides a robust estimate of standard deviation. For further information on microarray normalization methods and the theory underlying them, many reviews exist, but Refs. [37, 38] are suggested as excellent starting points for a basic discussion, or Ref. [39] for more in-depth coverage. Once the data from technical and biological replicates are compiled and the microarray data is ready for analysis, the fold difference in mRNA levels is calculated. This fold change in expression between the two samples hybridized to the array is represented using a ratio of the fluorescence intensities from the two signals. A problem with the ratio generated by microarray analysis is that it is not symmetrical; a gene induced two-fold is given a ratio of 2 whereas a gene repressed two-fold is given a ratio of –0.5. Therefore, to make ratios from both up- and downregulated genes symmetrical, the data is transformed by its logarithm to the base 2 (log2). This normalizes the ratios to the value of 1, meaning that a two-fold increase is represented as a ratio (log2) of 1, a two-fold decrease is represented as a ratio (log2) of –1, and a gene with an unchanged expression level is given a ratio (log2) of 0.

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2.5.3 Data Analysis 2.5.3.1

Detection of Differential Expression

On a basic level, many scientific studies report the results of their microarray experiment as a gene list. These lists contain all of the genes significantly up- or downregulated during the experimental conditions under study. A gene whose expression changes more than two-fold, either up or down, was often thought to be significantly differentially expressed. However, this is a completely arbitrary cutoff point, and a measure of statistical significance must be used. Simple statistical analysis using t-tests and analysis of variance (ANOVA) can be used, but they must take account of (a) the experimental structure and (b) multiple testing. The experimental structure may be regarded as hierarchical with biological replicates at the top and technical repeats below, as discussed above. The structure is an important consideration because t-tests, for example, assume that all data points are independent, i.e., that the replicates were performed at the same level. Moderated t-tests are better than standard t-tests as they account for the fact that outliers (very high or low data points) will influence a standard t-test and produce artificially high significance levels, increasing the number of false positives (see below). Even when a set of data has been assessed for its significance, we have to bear in mind that all statistical inferences have a probability of being incorrect. There are two important types of error inherent in statistical analysis: false positives and false negatives. The false negative rate is a reflection of the statistical power of the test being performed and can be reduced simply by increasing sample replicates. The false positive rate is governed by the significance level being tested for (i.e., a P-value less than 0.05); however, it is influenced by the number of comparisons or tests being performed – known as multiple testing. Multiple’ testing refers to the fact that a microarray experiment will perform thousands of multiple comparisons (one per gene) simultaneously. Using a standard significance cutoff of P < 0.05 would fail to account for the effect of this many comparisons and would automatically lead to 50 genes being classed as “significant” per 1000 (5%) purely by chance. The most stringent method of compensating for this, known as the Bonferroni correction [40], can be applied in two ways. The first is to adjust the P-value obtained from each test by multiplying it by the number of genes being tested and then only accept its significance if P is still below 0.05. The second is to manipulate your original false positive rate (say 0.05), dividing it by the number of tests (say, 1000), which provides a new significance cutoff point of 0.00005. This is extremely conservative and will normally result in very few significant genes. The false discovery rate (FDR) is an alternative solution to the multiple testing problem in that it provides an estimate of the amount of false discoveries without being as conservative as the Bonferroni correction [41, 42]. The FDR returns an expected percentage of false predictions within the data set, defined as the ratio of the number of false predictions over the total number of predictions.

2.5 Data Normalization and Analysis

The situation can become extremely complex and biologists should collaborate with statisticians in order to analyze microarray data accurately. Ideally each should be aware of the other’s expertise throughout a microarray experiment, from experimental design and execution through to analysis and interpretation. Reference [38] provides an excellent starting point for the finer details of data analysis.

2.5.3.2 Pattern Recognition If more complex questions are to be asked of a microarray experiment than merely identifying which genes are up- or downregulated, more complex procedures need to be performed with the data. For example, researchers often use cluster analysis to group genes with similar expression profiles. A cluster algorithm iterates over an array data set and acts as a grouping tool, essentially assigning two genes with similar expression profiles to the same cluster. There are many types of cluster algorithm including hierarchical, k-means, principal component analysis (PCA), self-organizing maps (SOMs), etc. In this way clustering can help in the assignment of functionality to unknown genes, divulge groups of genes controlled by the same promoter, and generally provide a more holistic integration of microarray data. However, as with all statistical analysis, clustering can never be perfect, primarily because it requires user input to determine cluster cutoff values. For more detailed descriptions of clustering and its uses, see Ref. [43].

2.5.3.3 Graphical Representations Although they are not particularly informative in terms of hard data, graphical representations of microarray data are quite useful. The distribution and spread of the array data is often a convenient method to visualize the changes occurring in the two samples studied. For this purpose there are two commonly used scatterplots. A log R/G plot shows the log transformed intensity of one channel plotted against the other. This is a very straightforward and intuitive plot, but the high correlation between the two channel intensities can make visualizing smaller outliers harder. A better way to view the distribution of microarray data is to use an MA plot where the average spot intensity (A) is plotted along the x axis and the log2 ratio (M) is plotted on the y axis [44]. This makes it easier to see those genes differentially expressed. The MA plot is also used to visualize the relationship between spot intensity and the level of expression before and after normalization procedures like LOWESS to assess their effect and accuracy. The results of clustering are often displayed as a tree, which leads to a colored representation of the intensity over a time course (for example) so that the relationship between genes’ expression profiles are easy to see. Array data can be presented in numerous other ways, too, most of which help us to wade through the mass of data generated by the experiment.

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2.5.6 Microarray Analysis Tools

Many software packages have been designed to automate microarray data normalization and analysis. A commonly used program is Genespring (http://www. silicongenetics.com) which provides a fairly automated analysis process and allows the user to view and manipulate many microarray experiments at once. Another attractive benefit is that Genespring has the ability to produce many userfriendly graphical representations of the data. However, this software is costly, whereas there are many open-source alternatives freely available, such as the TM4 suite [45] from The Institute of Genomic Research (TIGR, www.tigr.org/software). TM4 contains four applications: TIGR Spotfinder, Microarray Data Analysis System (MIDAS), Microarray Data Manager (MADAM), and Multiexperiment Viewer (MeV). Also included is a Minimal Information About a Microarray Experiment (MIAME, see Section 2.5.8)-compliant database. These tools cover all aspects of image analysis through to data storage and we recommend them as a good alternative to commercially available software. Alternatively, there are many programs or routines that have been designed by researchers and statisticians that act as pure statistical tools to crunch the raw data from a microarray experiment. These tools, unlike Genespring, put all aspects of analysis into the researchers hands, allowing a far more flexible analysis that is tailored to the experiment. Examples of these programs and routines include SMA (Statistics for Microarray Analysis, http://www.stat.berkeley.edu/ users/terry/zarray/Software/smacode.html), Bioconductor (http://www.bioconductor.org/), LIMMA (Linear Models for Microarray Data, http://bioinf.wehi. edu.au/limma/), and YASMA (Yet Another Statistical Microarray Analysis tool, http://people.cryst.bbk.ac.uk/wernisch/yasma.html). These are add-on packages that work in the “R” statistical environment (http://www.r-project.org/), so, although they are useful for data analysis, knowledge of the R language is required in order to exploit these tools fully. 2.5.7 Microarray Follow-Up

Microarray data often depend on the conditions in which the bacteria were studied, the method of RNA isolation, the statistical analysis, and so on. As a result, there is a lot of uncertainty about using microarray data as concrete evidence. Because of this, it is common for researchers to use microarrays as an exploratory tool that generate leads which can be followed up individually with simpler, more developed and robust (but lower-throughput) processes such as RTq-PCR or Northern blots. RTq-PCR (quantitative or real-time PCR) is frequently used to validate at least some of the results from microarrays.

2.6 Transcriptomics: Where We Are Now and What’s to Come

2.5.8 Data Storage and Reanalysis

Microarrays provide a vast quantity of data, and interpretation of these data is a complex subject matter in its own right. Often the results of experiments such as those above are open to question. The reinterpretation of microarray data will be key in the future as we learn the best ways to handle it. Reinterpretation is not possible, however, if the original data values for the array are not released upon publication of the results. Currently there are no universal guidelines from journals as to what is required in terms of data when a piece of research is submitted for publication, although it is common (but not compulsory) for the complete data set to be published online for access by other researchers. The format of the data is extremely varied and this is a major hurdle to their reinterpretation. Several groups have been set up which aim to standardize what is required of microarray data when it is published. The foremost of these is MIAME (Minimum Information About a Microarray Experiment, http://www.mged.org/), which aims to facilitate the access to, and usability of, microarray data. The standardization of microarray data, and the format in which they are presented, will facilitate the reanalysis of data and allow other researchers to use the data for meta-analyses etc. Centralized databases that are capable of compiling and storing raw microarray data from many sources, the conclusions drawn from the data, and the experimental conditions under which they were derived are slowly being developed. An increase in the use of these databases, perhaps made mandatory, is required if they are to be of real use to researchers in the future. In the meantime, however, simple guidelines for microarray experiments have been proposed [46]: . Data should be in tab-delimited and easily accessible format, such as a text (.txt) file and not as a pdf (.pdf) file. This would allow them to be easily transported into databases and further analysis software. . The data should include the definitive accession number for each gene as well as (or opposed to) common gene names, which are often inconsistent and regularly changing. . Data that are not included in the analysis (e.g., values for spots lower than the background) should be identified and not merely left out. If followed, these guidelines would make the reuse of microarray data much easier.

2.6 Transcriptomics: Where We Are Now and What’s to Come

The study of the transcriptome is rapidly advancing the biological knowledge of pathogenic organisms. Microarray technology allows us to identify, in a holistic manner, all of the components in an organism that work together to mediate their pathogenic activities [47, 48]. In particular, many new virulence factors have been

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identified without having to revert to the laborious techniques of the past [22]. The true importance of transcriptomics and microarrays is probably best shown by the diverse range of areas in addition to pathogen transcriptomics to which microarrays are contributing significantly, including (among many others) operon identification [49], gene essentiality studies [50], genotyping [19], detection of virulent isolates [51], elucidation of the host response to infection [52], and the effect of virulent organisms on host cells [53]. Transcriptomics is used as an indication of cellular activity at the protein level. In prokaryotes there is thought to be a high (but not an absolute) correlation between transcription and translation. Although there will always be exceptions to this, studies have been performed that assessed the relationship between transcriptional activities and the resulting level of biological activity at the protein level [54]. An obvious drawback becoming apparent to this kind of study is that the current technology available to analyze the proteome of a cell is far behind that used to analyze the transcriptome. For example, Scherl et al. [54] were able to identify proteins corresponding to just 23% of the Staphylococcus aureus predicted ORFs despite using a myriad of technologies to increase their detection rate. To fully unravel the story of a host and its pathogen, much more work is needed in exploring the integration of transcriptomic data with that derived from proteomic studies.

Acknowledgements

We would like to thank both Mike Withers and Yi Zhang for advice and guidance during the preparation of the data analysis sections of this chapter. B. S. is funded by a Royal Veterinary College scholarship.

References 1 http://www.tigr.org. The Institute for 2

3 4

5

Genome Research (TIGR). http://www.ncbi.nlm.nih.gov/genomes/ Complete.html. NCBI’s Genome database. http://www.genomesonline.org. GOLD database. Fickett, J. W. 1996. Finding genes by computer: the state of the art. Trends Genet. 12:316–320. Bocs, S., A. Danchin, and C. Medigue. 2002. Re-annotation of genome microbial coding-sequences: finding new genes and inaccurately annotated genes. BMC Bioinformatics. 3:5.

6 Lim, M., and K. Elenitoba-Johnson.

2004. Proteomics in pathology research. Lab. Invest. 84:1227–1244. 7 Southern, E. M. 1975. Detection of specific sequences among DNA fragments separated by gel electrophoresis. J. Mol. Biol. 98:503–517. 8 Schena, M., D. Shalon, R. W. Davis, and P. O. Brown. 1995. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science. 270:467-470. 9 Shalon, D., S. J. Smith, and P. O. Brown. 1996. A DNA microarray system for analyzing complex DNA samples

References using two-color fluorescent probe hybridization. Genome Res. 6:639–645. 10 Tong, X., J. W. Campbell, G. Balazsi, K. A. Kay, B. L. Wanner, S. Y. Gerdes, and Z. N. Oltvai. 2004. Genome-scale identification of conditionally essential genes in E. coli by DNA microarrays. Biochem. Biophys. Res. Commun. 322:347–354. 11 Lipshutz, R. J., S. P. Fodor, T. R. Gingeras, and D. J. Lockhart. 1999. High density synthetic oligonucleotide arrays. Nat. Genet. 21:20–24. 12 http://www.affymetrix.com/index.affx. Affymetrix High Density Oligonucleotide Microarray web site. 13 http://bugs.sghms.ac.uk/. Mycobacterium tuberculosis Microarray project. 14 Beier, M., and J. Hoheisel. 1999. Versatile derivatisation of solid support media for covalent bonding on DNA-microchips. Nucl. Acids Res. 27:1970–1977. 15 Raddatz, G., M. Dehio, T. F. Meyer, and C. Dehio. 2001. PrimeArray: genomescale primer design for DNA-microarray construction. Bioinformatics. 17:98–99. 16 Rozen, S., and H. Skaletsky. 2000. Primer3 on the WWW for general users and for biologist programmers. Methods Mol Biol. 132:365–386. 17 Haas, S. A., M. Hild, A. P. H. Wright, T. Hain, D. Talibi, and M. Vingron. 2003. Genome-scale design of PCR primers and long oligomers for DNA microarrays. Nucl. Acids Res. 31:5576– 5581. 18 Churchill, G. A. 2002. Fundamentals of experimental design for cDNA microarrays. Nat. Genet. 32 Suppl:490–495. 19 Behr, M. A., M. A. Wilson, W. P. Gill, H. Salamon, G. K. Schoolnik, S. Rane, and P. M. Small. 1999. Comparative genomics of BCG vaccines by whole-genome DNA microarray. Science. 284:1520– 1523. 20 Grimm, V., S. Ezaki, M. Susa, C. Knabbe, R. D. Schmid, and T. T. Bachmann. 2004. Use of DNA microarrays for rapid genotyping of TEM betalactamases that confer resistance. J. Clin. Microbiol. 42:3766–3774. 21 Yang, Y. H., and T. Speed. 2002. Design issues for cDNA microarray experiments. Nat. Rev. Genet. 3:579–588.

22 Orihuela, C. J., J. N. Radin, J. E. Sublett,

G. Gao, D. Kaushal, and E. I. Tuomanen. 2004. Microarray analysis of pneumococcal gene expression during invasive disease. Infect. Immun. 72:5582– 5596. 23 Stintzi, A. 2003. Gene expression profile of Campylobacter jejuni in response to growth temperature variation. J. Bacteriol. 185:2009–2016. 24 Lee, M. L., F. C. Kuo, G. A. Whitmore, and J. Sklar. 2000. Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations. Proc. Natl. Acad. Sci. U. S. A. 97:9834–9839. 25 Zien, A., J. Fluck, R. Zimmer, and T. Lengauer. 2003. Microarrays: how many do you need? J. Comput. Biol. 10:653–667. 26 Rosenzweig, B. A., P. S. Pine, O. E. Domon, S. M. Morris, J. J. Chen, and F. D. Sistare. 2004. Dye bias correction in dual-labeled cDNA microarray gene expression measurements. Environ. Health Perspect.. 112:480–487. 27 Dobbin, K., J. H. Shih, and R. Simon. 2003. Statistical design of reverse dye microarrays. Bioinformatics. 19:803– 810. 28 Talaat, A. M., S. T. Howard, W. Hale IV, R. Lyons, H. Garner, and S. A. Johnston. 2002. Genomic DNA standards for gene expression profiling in Mycobacterium tuberculosis. Nucleic Acids Res. 30:e104. 29 Bernstein, J. A., A. B. Khodursky, P. H. Lin, S. Lin-Chao, and S. N. Cohen. 2002. Global analysis of mRNA decay and abundance in Escherichia coli at single-gene resolution using two-color fluorescent DNA microarrays. Proc. Natl. Acad. Sci. U. S. A. 99:9697–9702. 30 DeRisi, J., L. Penland, P. O. Brown, M. L. Bittner, P. S. Meltzer, M. Ray, Y. Chen, Y. A. Su, and J. M. Trent. 1996. Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nat. Genet. 14:457–460. 31 Talaat, A. M., P. Hunter, and S. A. Johnston. 2000. Genome-directed primers for selective labeling of bacterial tran-

39

40

2 Transcriptome Analysis: Towards a Comprehensive Understanding of Global Transcription Activity scripts for DNA microarray analysis. Nat. Biotechnol. 18:679–682. 32 Baugh, L. R., A. A. Hill, E. L. Brown, and C. P. Hunter. 2001. Quantitative analysis of mRNA amplification by in vitro transcription. Nucleic Acids Res. 29:e29. 33 Gupta, V., A. Cherkassky, P. Chatis, R. Joseph, A. L. Johnson, J. Broadbent, T. Erickson, and J. DiMeo. 2003. Directly labeled mRNA produces highly precise and unbiased differential gene expression data. Nucl. Acids Res. 31:e13. 34 Eriksson, S., S. Lucchini, A. Thompson, M. Rhen, and J. C. Hinton. 2003. Unravelling the biology of macrophage infection by gene expression profiling of intracellular Salmonella enterica. Mol. Microbiol. 47:103–118. 35 Thompson, L. J., D. S. Merrell, B. A. Neilan, H. Mitchell, A. Lee, and S. Falkow. 2003. Gene expression profiling of Helicobacter pylori reveals a growth-phase-dependent switch in virulence gene expression. Infect. Immun. 71:2643–255. 36 Berger, J., S. Hautaniemi, A.-K. Jarvinen, H. Edgren, S. Mitra, and J. Astola. 2004. Optimized LOWESS normalization parameter selection for DNA microarray data. BMC Bioinformatics. 5:194. 37 Quackenbush, J. 2002. Microarray data normalization and transformation. Nat. Genet. 32 Suppl:496–501. 38 Leung, Y. F., and D. Cavalieri. 2003. Fundamentals of cDNA microarray data analysis. Trends Genet. 19:649–659. 39 Nadon, R., and J. Shoemaker. 2002. Statistical issues with microarrays: processing and analysis. Trends Genet. 18:265– 271. 40 Hochberg, Y. 1988. A sharper Bonferroni procedure for multiple tests of significance. Biometrika. 75:800–803. 41 Benjamini, Y., and Y. Hochberg. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B (Stat. Methodol.). 57:289–300. 42 Reiner, A., D. Yekutieli, and Y. Benjamini. 2003. Identifying differentially expressed genes using false discovery

rate controlling procedures. Bioinformatics. 19:368–375. 43 Quackenbush, J. 2001. Computational analysis of microarray data. Nat. Rev. Genet. 2:418–427. 44 Dudoit, S., Y. H. Yang, C. M, and T. Speed. 2002. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Stat. Sin. 12:111–140. 45 Saeed, A. I., V. Sharov, J. White, J. Li, W. Liang, N. Bhagabati, J. Braisted, M. Klapa, T. Currier, M. Thiagarajan, A. Sturn, M. Snuffin, A. Rezantsev, D. Popov, A. Ryltsov, E. Kostukovich, I. Borisovsky, Z. Liu, A. Vinsavich, V. Trush, and J. Quackenbush. 2003. TM4: a free, open-source system for microarray data management and analysis. Biotechniques. 34:374–378. 46 Kendall, S. L., S. C. Rison, F. Movahedzadeh, R. Frita, and N. G. Stoker. 2004. What do microarrays really tell us about M. tuberculosis? Trends Microbiol. 12:537–544. 47 Maurer, L. M., E. Yohannes, S. S. Bondurant, M. Radmacher, and J. L. Slonczewski. 2005. pH regulates genes for flagellar motility, catabolism, and oxidative stress in Escherichia coli K-12. J. Bacteriol. 187:304–319. 48 Stewart, G. R., L. Wernisch, R. Stabler, J. A. Mangan, J. Hinds, K. G. Laing, D. B. Young, and P. D. Butcher. 2002. Dissection of the heat-shock response in Mycobacterium tuberculosis using mutants and microarrays. Microbiology. 148:3129–3138. 49 Tjaden, B., D. R. Haynor, S. Stolyar, C. Rosenow, and E. Kolker. 2002. Identifying operons and untranslated regions of transcripts using Escherichia coli RNA expression analysis. Bioinformatics. 18 Suppl 1:S337–S344. 50 Sassetti, C. M., D. H. Boyd, and E. J. Rubin. 2003. Genes required for mycobacterial growth defined by high density mutagenesis. Mol. Microbiol. 48:77–84. 51 Wu, C. F., J. J. Valdes, W. E. Bentley, and J. W. Sekowski. 2003. DNA microarray for discrimination between pathogenic 0157:H7 EDL933 and non-pathogenic Escherichia coli strains. Biosens. Bioelectron. 19:1–8.

References 52 Nau, G. J., J. F. Richmond, A. Schle-

singer, E. G. Jennings, E. S. Lander, and R. A. Young. 2002. Human macrophage activation programs induced by bacterial pathogens. Proc. Natl. Acad. Sci. U. S. A. 99:1503–1508. 53 Yuan, J.-P., T. Li, H.-B. Chen, Z.-H. Li, G.-Z. Yang, B.-Y. Hu, X.-D. Shi, S.-Q. Tong, Y.-X. Li, and X.-K. Guo. 2004. Analysis of gene expression profile in gastric cancer cells stimulated

with Helicobacter pylori isogenic strains. J. Med. Microbiol. 53:965–974. 54 Scherl, A., P. Francois, M. Bento, J. M. Deshusses, Y. Charbonnier, V. Converset, A. Huyghe, N. Walter, C. Hoogland, and R. D. Appel. 2005. Correlation of proteomic and transcriptomic profiles of Staphylococcus aureus during the post-exponential phase of growth. J. Microbiol. Methods 60:247–257.

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3 Physiological Proteomics of Bacillus subtilis and Staphylococcus aureus: Towards a Comprehensive Understanding of Cell Physiology and Pathogenicity Michael Hecker and Susanne Engelmann

3.1 Introduction

The sequencing of the first complete genome sequence of a living organism in 1995 was a turning point in biology [1], the “blueprint of life” of Haemophilus influenzae followed by that of many other organisms, bacteria, lower and higher eukaryotes included, was decoded. This genome sequence, however, does not provide life itself because it only tells us what may happen, and not what really happens, in the cell. Functional genomic approaches such as mRNA profiling and proteomics are required to bring the “virtual life of the genes to the real life of the proteins.” Within the ensemble of functional genomics, covering from genome sequencing to bioinformatics and systems biology, proteomics will retain its crucial and privileged position because it deals – as no other discipline does – directly with the players of life, the proteins. The proteomics of today has profited greatly from genome sequencing, because protein identification by mass spectrometry (MS) techniques needs the genome sequence. The goal of recent proteomic projects is to visualize the entire proteome, which can only be achieved by a combination of gel-based and non-gelbased techniques. For physiological studies the highly sensitive two-dimensional polyacrylamide gel electrophoresis (2-D PAGE) [2] is still the state of the art. The majority of cellular proteins can be visualized within the main proteomic window pI 4–7. However, many proteins are not located within this main window. Additional subproteomic fractions such as alkaline and acidic proteins, cell-wall-bound or extracellular proteins have to be analyzed separately on the way towards the entire proteome. Many proteins, however, escape detection by 2-D gel-based proteomics, among them the intrinsic membrane proteins, the most prominent proteome subgroup that needs the establishment of gel-free procedures. For the future, simple and feasible quantitative non-gel-based proteomic approaches that are mainly based upon a combination of multidimensional chromatography of peptides and MS/MS procedures are urgently required to make membrane protein and other subproteomic fractions available for high-throughput comparative and quantitative physiological proteomic analysis. At present, however, physiolog-

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ical studies still have to rely mainly on gel-based proteomics as an extremely valuable tool in microbial physiology that can, in combination with various visualization and quantitation software packages, very rapidly provide comparative and quantitative data for multisample comparisons [3].

3.2 Proteomics of Bacillus subtilis: The Gram-positive Model Organism 3.2.1 The Vegetative Proteome

Bacillus subtilis, the model organism for gram-positive bacteria, is one of the most advanced bacteria in the context of gel-based physiological proteomics (see Refs. [3, 4] for reviews). In contrast to the genome, which is relatively stable, the proteome is highly flexible even in bacterial systems. From a physiological point of view, two major proteome classes can be distinguished: proteomes of growing cells, which have mainly housekeeping functions, and proteomes of nongrowing cells, which have adaptive functions against those stress or starvation stimuli that forced the growing cell population into a nongrowing state. According to our DNA array studies, cells growing in a minimal medium that does not contain amino acids, vitamins, or purine/pyrimidine bases express about 2500 genes. Of these 2500 genes, 1550 proteins should be visible in the standard window of pI 4– 7. Of the 1550 vegetative proteins, almost 50% have been identified by gel-based proteomics [5] (unpublished results). It is interesting to note that most of the metabolic routes predicted for a B. subtilis cell are covered within this main window. Many enzymes of these metabolic pathways directly visualized on the vegetative proteome have never been seen before (Fig. 3.1, see page 44–45). A list of the 400 most abundant vegetative proteins is available, including 88 proteins of still unknown function [5]. This vegetative proteome, now ready for physiological application, offers the chance to analyze the regulation of entire metabolic pathways. Even for the main catabolic pathways such as glycolysis or the tricarboxylic acid (TCA) cycle, the proteomic view of these pathways provides new information on the regulation mechanisms [5–7].

3 Fig. 3.1 Reference 2-D map of cytosolic proteins of Bacillus subtilis exponentially growing in minimal medium. Protein spots are labeled with protein names according to the SubtiList database. Protein extracts were separated on 18-cm immobilized pH gradient (IPG) strips and 2-D gels were stained with colloidal Coomassie brilliant blue. Detailed information on each individual protein identified is available at http://microbio2.biologie.uni-greifswald.de:8880/sub2d.htm.

3.2 Proteomics of Bacillus subtilis: The Gram-positive Model Organism

3.2.2 Proteomes of Nongrowing Cells: Proteomic Signatures of Stress/Starvation Stimuli

The transition from a growing to a nongrowing state is accompanied by a dramatic reprogramming of the protein synthesis profile. Vegetative proteins no longer required at high concentrations are no longer synthesized in nongrowing cells, and in some cases are even degraded in a specific manner [8, 9]. Other proteins with specific and nonspecific adaptive functions are strongly induced on a time-dependent scale. On the basis of proteomic studies, three main groups of stress/starvation proteins have been defined [10]. The specific stress proteins induced by a single stress stimulus only protect the cell against this stress stimulus by neutralizing the stress factor, by adaptation to its presence, or by repair of damages caused by stress. Starvation-specific proteins, on the other hand, are produced to overcome the starvation by uptake of the limiting substrate with a very high affinity, by searching for alternate substrates not used in the presence of the preferred one (catabolite repression), or by chemotactic moving to new substrates. In addition to these stress- or starvation-specific proteins, general stress proteins are also induced, in this case not by a specific but by a wide range of unrelated stress or starvation stimuli. These general stress/starvation proteins may provide the nongrowing cell with a multiple stress resistance machine irrespective of what kind of stress or starvation stimuli induced the nongrowing situation [4, 10]. The single stimulons can be visualized by the dual channel imaging technique [11], which is an excellent tool by which to identify all proteins induced or repressed by the growth-restricting stimuli. Two digitized images of 2-D gels have to be generated and combined in alternate additive dual-color channels. The first one (densogram), showing accumulated proteins visualized by various staining techniques, is false-colored green. The second image (autoradiograph), showing the proteins newly synthesized and thereby radioactively labeled during a 5-min 35 pulse with S-l-methionine is false-colored red. When the two images are combined, proteins accumulated and synthesized in growing cells are colored yellow. After the imposition of a stress or starvation stimulus, however, proteins not previously accumulated in the cell but newly synthesized are colored red, because these red proteins have already been radioactively labeled, but are not yet stainable. Looking for the red-colored proteins is a simple approach to finding all those proteins induced by a single stimulus, thereby defining the entire set of proteins induced by one stimulus, called a stimulon [4]. Heat stress, for instance, induces more than 100 red-labeled proteins (Fig. 3.2) [11]. These proteins may be identified to gain an overview of how the cell is protected against heat stress. Proteins repressed by the stimulus can also be visualized by this powerful technique. Green-colored proteins no longer being synthesized (no longer red) but still present in the cell are the candidates repressed by the stimulus. The next step in analyzing the adaptation network is to dissect the stimulons into their individual regulation groups, the regulons. A regulon is a group of genes distributed on the bacterial chromosome but regulated by one global regulator (activator, repressor, alternative r factor, etc.). To compare the proteome/tran-

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Fig. 3.2 Protein pattern of heat-shocked B. subtilis cells. During exposure to heat shock (37 to 48 C), B. subtilis cells were pulselabeled with 35S-l-methionine. After the separation of crude protein extract by 2-D gels, the proteins were visualized by silver staining and the protein synthesis rate was

determined by phosphoimaging. The two images were overlaid with the aid of the Delta-2D software package (dual-channel imaging technique [11]; Decodon, Greifswald, Germany). The red-labeled proteins form the heat stress stimulon. (This figure also appears with the color plates.)

scriptome profile of a mutant with the wild type under inducing conditions is the state of the art for defining the regulon structure (see Refs. [4, 12] for reviews). Furthermore, the allocation of as yet unknown proteins to stimulons or regulons that have an already known function is a simple, but convincing approach to a first prediction of their function [12]. For instance, unknown proteins induced by oxidative stress and belonging to the PerR regulon will probably be involved in protection against oxidative stress. This dissection of stimulons into individual regulons characterized by a typical induction pattern of the members of the regulon is shown in Fig. 3.3 for the heat stress stimulon and in Fig. 3.4 for the phosphate starvation stimulon as examples (see Ref. [4] for review). All proteomic and transcriptomic data can be assembled into an adaptational network consisting of a

3.2 Proteomics of Bacillus subtilis: The Gram-positive Model Organism

great number of stress/starvation stimulons and regulons. This network provides comprehensive information on the functional genomics and physiology of the stress/starvation response of bacteria. An essential feature of this network is the interplay between the individual regulation groups, because the individual stress/ starvation regulons do not exist independently of each other, but are tightly connected, forming the adaptational network. “Color coding” is a new software tool developed by Decodon GmbH that is highly convenient for visualizing complex protein expression patterns [3]. Proteins induced by more than one stimulus can be visualized by a specific color code. The reason for the induction by various stimuli is in many cases that the corresponding gene is controlled by more than one regulator. For instance, the green-colored YvyD (Fig. 3.5) induced by heat, ethB H anol, and oxidative stress is controlled both by r and by r [13]. By applying this technique, already known and probably new overlapping regulons within the adaptational network can be visualized.

Fig. 3.3 The heat stress stimulon of B. subtilis consists of proteins of the HrcA regulon, the CtsR regulon, and the rB regulon. The induction profiles of representative proteins of the respective regulons under various stress conditions are shown: C, control; H, heat; E, ethanol; S, salt; G, glucose starvation; Pm, puromycin; Ox, oxidative stress (H2O2).

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Fig. 3.4 The phosphate starvation stimulon of B. subtilis consists of both general stress proteins also induced by phosphate starvation (rB regulon, rB-dependent proteins marked in blue) and starvation-specific proteins (PhoR regulon, marked in red). The stress/starvation induction profile of both groups and the functions of the proteins

are indicated. c, control; h, heat stress; e, ethanol stress; s, NaCl stress; a, pH 5.5; o, oxidative stress (H2O2); p, puromycin; g, glucose starvation; O2, oxygen starvation; ph, phosphate starvation; aa, amino acids starvation. (This figure also appears with the color plates.)

The pictures shown so far have given a snapshot of a single moment in the life of a B. subtilis cell. This life, however, is not static but highly dynamic, with sequential gene expression programs in a time-dependent manner as an essential feature. If one assembles such proteomic pictures through time, growth and developmental processes can be followed at the molecular level as in a “life movie,” as shown in Fig. 3.6 for growing cells that entered the stationary phase because of glucose starvation. Using the same color code as already described, synthesized (red) and accumulated (green) proteins were followed along the growth curve. There is a total reprogramming of the protein synthesis pattern when cells enter the stationary growth phase. Many vegetative proteins no longer required in nongrowing cells and no longer synthesized change their color from yellow to green. Many proteins on the other hand, not yet accumulated and stain-

3.2 Proteomics of Bacillus subtilis: The Gram-positive Model Organism

Fig. 3.5 Multicolor imaging of expression patterns under different growth conditions in B. subtilis. Delta-2D software (Decodon) was used to visualize complex protein expression patterns on the 2-D gel image in the standard pH range 4–7. The color code is presented in the top left corner. Proteins only induced by single stresses are colored red (H, heat), light-blue (O, oxidative stress), and orange

(E, ethanol), respectively. Proteins induced by oxidative stress as well as heat stress are colored yellow, proteins induced by ethanol stress and heat stress are colored dark-blue, proteins induced by oxidative and ethanol stress can be recognized as purple spots, and, finally, proteins induced by all three stimuli are displayed in green. (This figure also appears with the color plates.)

able, but strongly induced by glucose starvation, are colored red, forming the glucose starvation stimulon. Collecting all these proteome data combined with basic physiological knowledge, one can gain a comprehensive picture what is happening in the cell, understanding cell physiology as a entity. The proteomic signatures of stress or starvation stimuli [14] can be used as diagnostic tools for prediction of the physiological state of cells grown in a bioreactor or in a biofilm, etc. This is valid not only for growing cells (proteome signatures indicate the availability of nutrients for instance), but also for nongrowing cells. On the basis of the proteomic signatures, one can predict whether nongrowing cells have suffered from heat or oxidative stress or from glucose or phosphate starvation (Fig. 3.7, see page 54). During the stationary phase of B. licheniformis cells there is a strong proteomic signature of oxidative stress (KatA, AhpC etc.), followed by a signature of protein stress, indicated by the induction of ClpC [15]. Proteomic signatures can also be used to predict the action mechanism of unknown drugs. Nitrofurantoin, for instance, an antibiotic that has been in use for a long time, induces almost the same signature as diamide does, strongly suggesting

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3.3 Physiological Proteomics of Staphylococcus aureus 3 Fig. 3.6 Dynamics of protein synthesis profiles of growing and glucose-starved cells of B. subtilis. A Individual dual-channel 2-D patterns of protein synthesis and accumulation recorded during the different phases of the growth curve are assembled into a “life movie.” B Growth curve (optical density at 500 nm) and 35S-l-methionine incorporation (million cpm per 60 lg protein). C Patterns of selected examples representing different

branches of cellular physiology. Sample points correspond to the following growth phases depicted are shown in the growth curve: 1, 2, exponential growth; 3–7, glucose starvation; 8, 9, recovery of growth after readdition of glucose. The bar graphs on the left display normalized relative synthesis rates of the individual proteins at the different time points. (This figure also appears with the color plates.)

that nitrofurantoin induces oxidative stress in the cell [16]. In collaboration with the Bayer company, we have established a comprehensive antibiotics proteomic signature library. By using this library combined with the stress/starvation signature library, it is possible to predict the molecular action mechanisms of unknown drugs. To give two examples: phenyl-thiazolylurea-sulfonamides showed a proteomic signature of stringent control induced by amino acid starvation [17], while substance Bay 50-2369 showed the same signature as chloramphenicol does, strongly suggesting that the peptidyl transferase is the target protein for the substance [18–20].

3.3 Physiological Proteomics of Staphylococcus aureus 3.3.1 The Postgenome Era of S. aureus

With the long experience in Bacillus proteomics, this expertise has been transferred to a closely related pathogenic gram-positive bacterium, Staphylococcus aureus. S. aureus is a human pathogen of increasing importance, mainly as a result of the spread of antibiotic resistance. The pathogenicity of this species is very complex and involves the strongly regulated production of cell-wall-associated and extracellular proteins forming a changing set of virulence factors. Due to the great variety of these proteins, S. aureus causes a broad spectrum of infectious diseases ranging from superficial abscesses to endocarditis, osteomyelitis, and toxic shock syndrome [21]. Methicillin-resistant S. aureus (MRSA) strains are currently predominant and dangerous nosocomial pathogens, since infections caused by these strains have become difficult to treat. Vancomycin has become the drug of choice for treating MRSA infections. However, the emergence of vancomycin-resistant MRSA strains is leading to urgent demands for alternative anti-MRSA therapies and the development of totally new approaches to antibacterial drug research. This is the reason why the specialists are looking forward to the postgenome era

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3.3 Physiological Proteomics of Staphylococcus aureus 3 Fig. 3.7 Proteomic signatures of B. subtilis of different physiological stress/starved conditions. Comparisons of the protein profile of both exponentially growing and stressed B. subtilis cells reveal signature-like changes that are specific to certain stress stimuli (e.g., induction of the catalase KatA by oxidative

stress). The individual sections of the 2-D gels display typical parts of the proteomic signatures of oxidative or heat stress, the stringent response or limitation of glucose or phosphate. (This figure also appears with the color plates.)

opening great chances to bring new and urgently required antibacterial drugs on the market (see Refs. [22–24] for review). The postgenomic era of S. aureus began in 2001 with the publication of the genome sequence of two reference strains [25]. The S. aureus genome codes for about 2600–2700 proteins, among them about 1000 genes coding for proteins with still unknown functions. The genome sequence allowed the prediction of more than 60 gene regulators with helix–turn–helix motifs (among them are five SarA and three Fur homologues), 17 two-component systems, and two alternative r factors [25]. The complete genome sequence of seven and partial sequence information of another two S. aureus strains are now available in the databases. Preliminary genome data show that the various strains encode a different set of virulence factors or superantigens mostly located on pathogenicity islands or plasmids, which may be the reason for the broad spectrum of infectious caused by the different strains. The KEGG (www.genome.jp/kegg/pathway/map/) database has provided the first, still preliminary information on metabolic pathways encoded in the genome of S. aureus, from the carbon core catabolism including fermentation to the amino acid metabolism and other pathways. The genome sequence information is required for functional genomic approaches; this is valid not only for DNA chip technologies, but also for highthroughput protein identification via mass spectrometry techniques. Proteomics can now be used to bring the genome sequence to the cell physiology of S. aureus, relying on the panorama view of proteomics providing an increasingly complete picture of the cell physiology of growing and nongrowing cells, including a comprehensive and new understanding of its pathogenicity. There is a great deal of information in the published literature about the regulation, structure, and function of various virulence factors, but only limited information about basic cellular physiology. It has become increasingly accepted that this basic cell physiology determines not only growth and survival, but pathogenicity as well. For this reason, much more knowledge about cell physiology is required to understand pathogenicity, which is probably a central point for the better and more successful combating of multiresistant strains and, thus, of various kinds of diseases. Functional genomics opens up the opportunity for a new and comprehensive understanding of the cell physiology and infection biology of the parasite.

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3.3.2 Proteomes of Growing and Nongrowing Cells

What is valid for B. subtilis is also true for S. aureus: only a part of the genome is active under definite life circumstances, and the proteomes of growing cells will be different from the proteomes of nongrowing cells. First of all a vegetative proteome map has to be established for S. aureus as the basis for physiological studies. A “theoretical proteome map” simply derived from the genome sequence shows two major peaks, a neutral–acid peak and a more alkaline one. Whereas in the alkaline region most of the ribosomal proteins and membrane proteins should be expected from their pI values, the majority of vegetative proteins such as metabolic enzymes are located in the main proteomic window pI 4–7. This main window provides a good starting point for physiological studies. On the way towards the entire proteome, however, we must not ignore that fact that not only alkaline proteins, but also cell-surface-bound or even extracellular proteins have to be considered in addition to the main fractions of cytosolic proteins. Analyzing the majority of intrinsic membrane proteins requires the establishment of gel-free proteomes (see chapter 3.1). Neutral/weakly acid and alkaline proteins of S. aureus strains COL and 8325 from midexponential phase cells growing in tryptone soy broth at 37 C were analyzed by Cordwell et al. [26]. From the 347 protein spots, 266 proteins were identified, corresponding to approximately 12% of the proteome. Recently we provided a proteome map of growing cells of S. aureus COL with 460 entries [27] (Kohler et al., in press) (Fig. 3.8). Our data on the gel-based proteomics of S. aureus are being integrated into a comprehensive proteome database, Staph-2D (http://microbio2. biologie.uni-greifswald.de:8880/sub2d.htm). In a recent study a combined proteomic and transcriptomic approach was used to analyze the gene expression pattern in postexponential cells of S. aureus N315. Five hundred ninety-one proteins, corresponding to 23% of the proteome, were identified by gel-based and gel-free proteomics [28]. Despite the fact that the main metabolic pathways can be derived from the genome sequence, only very limited information about its regulation is available. Many metabolic enzymes have been identified on the proteome map of growing cells; most of the metabolic pathways have been covered by the proteomic approach [27] (Kohler et al., in press), offering the chance to analyze the regulation of entire metabolic routes in the postgenome era (see Fig. 3.9). As in B. subtilis, the regulation of core carbon catabolism is a good model for such studies, because almost all glycolytic or TCA cycle enzymes belonging to the most abundant vegetative proteins have been visualized by proteomics. Glycolysis is activated by glucose excess or by a shift from aerobic to anaerobic conditions that also triggers repression of the TCA cycle and strong induction of fermentation/overflow metabolisms (Fig. 3.10). The pyruvate formate lyase (Pfl) is one of the most abundant enzymes induced by the anaerobic shift. The arginine deiminase pathway providing additional ATP molecules with energy limitation is also activated under anaerobic conditions, but only in the absence of glucose [27]. In addition to meta-

3.3 Physiological Proteomics of Staphylococcus aureus

Fig. 3.8 Reference 2-D map of cytosolic proteins of S. aureus COL in a pI range of 4–7. Protein extracts of cells grown in TSB medium at the exponential and stationary growth phases and under anaerobic conditions were mixed and separated on 2-D gels. Protein spots are labeled with protein names according to the S. aureus N315 database. Protein extracts were stained with colloidal Coomassie brilliant blue.

bolic enzymes, a global regulator, SrrA, known to be involved in anaerobic gene regulation was also induced by the anaerobic shift. These data represent an example of the opportunities to gain information about the regulation of cell physiology by systematic application of physiological proteomics. From a physiological point of view, proteins produced in response to growthrestricting stimuli in their environment are of crucial significance for survival in nature, because stress and starvation are the rule and not the exception in natural ecosystems. The proteomes of nongrowing cells are probably more heterogeneous than proteomes of growing cells, because most of the stress/starvation stimuli induce a great number of stress/starvation proteins organized in an adaptational gene expression network of nongrowing cells. Following a similar approach as for B. subtilis, proteins newly induced or repressed by environmental stimuli can be allocated to stress or starvation stimulons that can be dissected into single regulons analyzing mutants in global regulators by a combined proteomic and transcriptomic approach. Color coding and related techniques can be used to define overlapping areas within the adaptational network.

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3.3 Physiological Proteomics of Staphylococcus aureus

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3 Fig. 3.9 Assignment of proteins identified in S. aureus COL to biochemical pathways and other essential cellular components. Proteins that have not been identified in the 2-D gel images thus far are colored green. A Purine and pyrimidine metabolism, B glycolysis, pentose phosphate shunt, and citric acid

Fig. 3.10 Protein pattern of cells of S. aureus COL grown under aerobic (green) and anaerobic (red) conditions in synthetic medium. Cells were pulse-labeled (5 min) with 35S-lmethionine under aerobic conditions and 30 min after imposition of anaerobic growth conditions. Radioactively labeled proteins were visualized by the phosphoimaging

cycle, C oxidative stress resistance, D ATPase components, E proteolysis, F components of the translational machinery, G amino acid metabolism, H fatty acid synthesis and metabolism of cell wall components, and I biotin metabolism. (This figure also appears with the color plates.)

technique. Proteins whose synthesis was increased after shifting to anaerobic growth conditions are shown in red (e.g., enzymes involved in glycolysis and fermentation) and those whose synthesis was decreased are shown in green (e.g., enzymes involved in the TCA cycle). (This figure also appears with the color plates.)

Only a few data are available that demonstrate the power of proteomics for analyzing the stress or starvation responses. Preliminary proteome data have been published on the heat stress response. A temperature shift from 37 C to 48 C induced the production of at least eight proteins, among them GroEL and GroES [29]. Our unpublished data showed mainly the heat induction of the HrcA and CtsR regulon, represented by induction of the GroEL/S- and DnaK machinery as well as of the Clp proteins. In contrast to B. subtilis, the groES/L and dnaK operon are regulated by the two heat shock regulators, HrcA and CtsR. They act together synergistically to maintain low base levels of expression of these operons in the B absence of stress [30]. Surprisingly, the members of the r -dependent response, strongly heat-inducible in B. subtilis, are not induced in heat-stressed cells of S. aureus grown in synthetic medium.

3.3 Physiological Proteomics of Staphylococcus aureus

Hydrogen peroxide treatment induced only a few proteins in S. aureus. Some protein spots that seemed to be newly induced were found as the result of a protein shift to a more acidic position, caused by oxidation of cysteine residues to sulfonic acid (Fig. 3.11) [31]. The glyceraldehyde-3-phosphate dehydrogenase behaved in this way, being inactivated by this irreversible oxidative damage. In parallel with the inactivation of the enzyme there was a drop in the ATP level followed by a cessation of growth. After 40 min adaptation the enzyme “shifted back,” and in parallel the ATP level increased again, followed by resumption of growth. This “enzyme reactivation” was due to a newly synthesized Gap protein. Repair of fully oxidized sulfhydryl groups is probably impossible from an energetic point of view.

Fig. 3.11 Sectors of 2-D gels covering the region where the Gap protein is located. Protein extracts of S. aureus COL before (control) and 5 min after addition of 100 mM H2O2 were separated on 2-D gels. Note the shift of Gap to a more acidic position (Gapox) under

oxidative stress conditions. Both spots (Gapred and Gapox) were analyzed by tandem hybrid mass spectrometry. A modified peptide was found in Gapox. The sequencing of this peptide indicated the oxidation of a cysteinyl residue (Cys-151) to sulfonic acid.

Oxacillin as a cell-wall-active antibiotic induced a proteomic signature that indicated oxidative damage and protein stress [32]. However, the data are still preliminary and do not allow a complete picture as yet. Triton X-100 as another surfaceB active substance induced members of the r and SarA regulons [26]. It is interesting to note that strains sensitive and those resistant to methicillin showed a slightly different proteomic signature in response to Triton X-100 [26]. Proteomic starvation signatures are not yet available, probably because of the difficulty of designing growth media that trigger a well-defined stationary phase caused by phosphate, glucose, or nitrogen starvation. Stationary phase cells showed significant de-repression of the TCA cycle enzymes and repression of the glycolytic enzymes compared to cells grown on glucose excess. Because S. aureus has the ability to invade different tissues, oxygen could be one of the most crucial growth-limiting stimuli forcing adaptational processes against oxygen limitation and starvation. S. aureus can grow under low-oxygen conditions by fermentation or nitrate respiration. A two-component system, SrrAB, which has a strong similarity to the ResDE two-component system of B. subtilis [33], is probably the main

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global regulator of the aerobic–anaerobic shift of metabolism. Throup et al. [34] provided the first proteomic data indicating that SrrAB (SrhSR) controls the upregulation of fermentation enzymes (lactate dehydrogenase, alcohol dehydrogenase) as well as the downregulation of aerobic TCA cycle enzymes (succinyl-CoA synthetase, aconitase, fumarase). A mutant in srrAB is characterized by a severe growth defect under anaerobic conditions [34]. There are only a few proteomic data available on general stress/starvation B responses (see Refs. [10, 35] for review). The r -dependent response seems to be totally different from the general stress response of B. subtilis, in relation not only B to the signal transduction pathway of environmental stimuli to r , but also the B genes controlled by r [36, 37] (Pan-Farr et al., submitted). Recent data suggest B that the r regulon does not belong to the heat stress stimulon and only a minority of genes are involved in the development of a nonspecific multiple stress resistance, the main feature of the general stress response in Bacillus species. Proteomic data in combination with more complete transcriptomic data allowed this surprising conclusion [36, 37] (Pan-Farr et al., submitted). These transcriptomic B results support recent data that r -dependent proteins in S. aureus are directly involved in the virulence in a catheter infection model (Lorenz et al., submitted). Proteomic information on other general stress or starvation responses such as the RelA-dependent stringent control or the probably related CodY regulon is still lacking. 3.3.3 Extracellular Proteins and Pathogenicity Networks

For the purposes of defining the structure of stimulons and regulons, transcriptomics provides a more complete picture than does proteomics, because – as already mentioned – many proteins still escape detection by gel-based proteomics. In many areas of application, however, proteomics can not be replaced by transcriptomics, because proteomics can also visualize events that never have been seen before by transcriptomics. Protein secretion is one such field of application that is crucial for Staphylococcus biology because of most of the virulence factors having signal sequences belong to the secretome. Using the dual channel technique it is possible to follow the protein secretion kinetics along the growth curve (Fig. 3.12). There are some proteins (e.g., SsaA, IsaA, Aly, Spa, SceD, LytM, and Aur) that are synthesized and secreted during exponential growth, but most are secreted only during the transient or even the stationary phase of growth [38, 39]. SarA, RNAIII, SaeR, ArlR, and others are key regulators involved in the expression of the majority of virulence genes whose products are required for host cell adhesion, for tissue invasion, or for tissue damage. The expression of these virulence proteins is regulated in a coordinated way, ensuring the expression of cellsurface-associated proteins during growth in a first step, followed by the expression of extracellular virulence proteins after settlement in the host in a second step [39–41]. Proteomics is again a highly sophisticated strategy by which to define the entire set of extracellular proteins controlled by these global virulence regula-

3.3 Physiological Proteomics of Staphylococcus aureus

Fig. 3.12 The extracellular proteome of S. aureus RN6390 at low (green image) and high cell densities (red image). 250 lg proteins of the supernatant of cells grown in TSB medium at an optical density OD540 = 1 and 5 were separated by 2-D gels and stained with Sypro

Ruby. Extracellular proteins present in increased amounts at high cell densities are labeled red and those proteins only present at low cell densities are labeled green. (This figure also appears with the color plates.)

tors. The differential proteomics display can also be used to visualize the entire set of extracellular proteins in the wild type compared to mutants. On the basis of a comparison between the secretomes of a wild type and its corresponding mutant strains, many extracellular proteins have been allocated to the individual regulons, showing both already known and also new members of the regulons [38, 39]. Figure 3.13 gives an overview of the structure of a few selected virulence regulons. The definite function of still unknown members of the virulence regulon, however, needs to be verified in specific follow-up studies analyzing the expression and role of the protein in infection model systems. The proteomic approach is also a useful tool for analyzing the extracellular protein pattern of different clinical isolates which may help to correlate the individual diseases with the gene expression and protein secretion pattern [42]. Recent studies indicate that this pathogenicity network is not confined to the interactions between SarA, agr, SaeR, B ArlR, or r . Many more global regulators seem to be encoded in the genome sequence [41]. A combined application of transcriptomics and proteomics of the

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Fig. 3.13 A Growth of S. aureus RN6390 in TSB-Medium. The sampling is indicated by an arrow and a letter in the respective growth curve. B, C Virulence factors of S. aureus RN6390 whose amount depends on the growth phase. The amount of the respective proteins at OD540 = 1 (green) of cells grown in TSB medium was compared with the amount of these proteins at higher optical densities

(red). B Virulence factors only present at low cell densities. C Virulence factors only present at high cell densities. In addition, the amount of the respective proteins in the wild type strain was compared to the amount of these proteins in various regulatory mutants (agr, sarA, sigB) known to be impaired in virulence. Proteins were stained with Sypro Ruby. (This figure also appears with the color plates.)

wild type and mutants in global regulators – transcriptomics to visualize the gene expression pattern and proteomics to visualize the protein secretion profile – might be the state-of-the-art approach to defining the expression of virulence genes, which is a very complex gene expression network. This network consists of many overlapping regulons expressed in a time-controlled manner to ensure the optimal pattern and level of the individual virulence factors at the different locations in the host. Infection models in combination with DNA array studies must be used to visualize the gene expression pattern in the host cell and in the para-

3.4 Outlook: Second Generation Proteomics and New Fields in S. aureus Physiology …

site, aiming at a more comprehensive picture of what is happening on both sides during the infection process.

3.4 Outlook: Second Generation Proteomics and New Fields in S. aureus Physiology and Infection Biology

The panorama view of proteomics visualizes cellular events never seen before. Not just a few interesting proteins, but almost all proteins of the cell can be followed by proteomics. The challenge now is not to get lost in the mass of data and not to remain on the surface of the problem, but to leave the first, descriptive level which is absolutely required for a comprehensive and mechanistic understanding of the phenomena to be studied. The panorama view of proteomics, however, allows selection of the most interesting phenomena that deserve more detailed study. To understand life processes, more detailed first-level proteomics (protein expression profiling) must be combined with second-level proteomics in combination with biochemistry and molecular genetics. This second-level proteomics allows crucial questions to be addressed, such as analyzing: . The fate of each individual protein (protein targeting/protein secretion) . The protein interaction network (interactome) . Post-translational modifications such as protein phosphorylation at the proteomic scale . Protein aging or protein damage at the proteomic scale Finally, it also . Allows researchers to follow the stability or proteolysis rate of each individual protein separated by 2-D PAGE These proteomic studies of the second generation require gel-based and non-gelbased proteomics relying on multidimensional chromatography followed by highly sophisticated MS/MS techniques. This proteomics of the second generation will in the future open new and fascinating fields of S. aureus physiology not yet addressed in a systematic way, an essential part and great challenge for S. aureus cell physiology in the postgenome era. The combination of first- and secondlevel proteomics with comparative genomics, structural genomics, transcriptomics, metabolomics, and bioinformatics will open the way towards a comprehensive understanding of life, ending up in a systems biology approach to S. aureus in the near future.

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Acknowledgments

We are very grateful to all coworkers and students for their excellent data on the proteomics of Bacillus and Staphylococcus species. The support of Jrg Bernhardt and Stephan Fuchs in preparing the figures is acknowledged. We also thank Uwe Vlker, Jrg Hacker, Wilma Ziebuhr, Knut Ohlsen, Christof von Eiff, and Fritz Gtz for longstanding and fruitful collaboration, and Decodon GmbH (Greifswald) for providing Delta-2D software. This work was supported by grants from the Bundesministerium fr Bildung und Forschung (031U107A/-207A; 031U213B), the Deutsche Forschungsgemeinschaft (GK212/3-00; HE1887/7-1; HE1887/7-2; HE1887/6-5; HE1887/6-6), the European Union (Bacell factory QLK3-CT-1999-00413; Bacell network GLG2-CT-1999-01455) and the Fonds der Chemischen Industrie to M. H.

References 1 Fleischmann, R. D., M. D. Adams,

O. White, R. A. Clayton, E. F. Kirkness, A. R. Kerlavage, C. J. Bult, J. F. Tomb, B. A. Dougherty, J. M. Merrick, et al. 1995. Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science 269:496–512. 2 O’Farrell, P. H. 1975. High resolution two-dimensional electrophoresis of proteins. J. Biol. Chem. 250:4007–4021. 3 Hecker, M. and U. Vlker. 2004. Towards a comprehensive understanding of Bacillus subtilis cell physiology by physiological proteomics. Proteomics 4:3727–3750. 4 Hecker, M. 2003. A proteomic view of cell physiology of Bacillus subtilis – bringing the genome sequence to life. Adv. Biochem. Eng. Biotechnol. 83:57– 92. 5 Eymann, C., A. Dreisbach, D. Albrecht, J. Bernhardt, D. Becher, S. Gentner, L. T. Tam, K. Bttner, G. Buurman, C. Scharf, S. Venz, U. Vlker, and M. Hecker. 2004. A comprehensive proteome map of growing Bacillus subtilis cells. Proteomics 4:2849–2876. 6 Ludwig, H., G. Homuth, M. Schmalisch, F. M. Dyka, M. Hecker, and J. Stlke. 2001. Transcription of glycolytic genes and operons in Bacillus subtilis: evidence for the presence of multiple

levels of control of the gapA operon. Mol. Microbiol. 41:409–422. 7 Tobisch, S., D. Zhlke, J. Bernhardt, J. Stlke, and M. Hecker. 1999. Role of CcpA in regulation of the central pathways of carbon catabolism in Bacillus subtilis. J. Bacteriol. 181:6996–7004. 8 Bernhardt, J., J. Weibezahn, C. Scharf, and M. Hecker. 2003. Bacillus subtilis during feast and famine: visualization of the overall regulation of protein synthesis during glucose starvation by proteome analysis. Genome Res. 13:224– 237. 9 Kock, H., U. Gerth, and M. Hecker. 2004. MurAA, catalysing the first committed step in peptidoglycan biosynthesis, is a target of Clp-dependent proteolysis in Bacillus subtilis. Mol. Microbiol. 51:1087–1102. 10 Hecker, M. and U. Vlker. 2001. General stress response of Bacillus subtilis and other bacteria. Adv. Microb. Physiol. 44:35–91. 11 Bernhardt, J., K. Bttner, C. Scharf, and M. Hecker. 1999. Dual channel imaging of two-dimensional electropherograms in Bacillus subtilis. Electrophoresis 20:2225–2240. 12 Hecker, M. and S. Engelmann. 2000. Proteomics, DNA arrays and the analysis of still unknown regulons and

References unknown proteins of Bacillus subtilis and pathogenic gram-positive bacteria. Int. J. Med. Microbiol. 290:123–134. 13 Drzewiecki, K., C. Eymann, G. Mittenhuber, and M. Hecker. 1998. The yvyD gene of Bacillus subtilis is under dual control of sigmaB and sigmaH. J. Bacteriol. 180:6674–6680. 14 VanBogelen, R. A. 2003. Probing the molecular physiology of the microbial organism, Escherichia coli using proteomics. Adv. Biochem. Eng. Biotechnol. 83:27–55. 15 Voigt, B., T. Schweder, D. Becher, A. Ehrenreich, G. Gottschalk, J. Feesche, K. H. Maurer, and M. Hecker. 2004. A proteomic view of cell physiology of Bacillus licheniformis. Proteomics 4:1465–1490. 16 Leichert, L. I., C. Scharf, and M. Hecker. 2003. Global characterization of disulfide stress in Bacillus subtilis. J. Bacteriol. 185:1967–1975. 17 Beyer, D., H. P. Kroll, R. Endermann, G. Schiffer, S. Siegel, M. Bauser, J. Pohlmann, M. Brands, K. Ziegelbauer, D. Haebich, C. Eymann, and H. BrtzOesterhelt. 2004. New class of bacterial phenylalanyl-tRNA synthetase inhibitors with high potency and broad-spectrum activity. Antimicrob. Agents Chemother. 48:525–532. 18 Bandow, J. E., H. Brtz, L. I. Leichert, H. Labischinski, and M. Hecker. 2003. Proteomic approach to understanding antibiotic action. Antimicrob. Agents Chemother. 47:948–955. 19 Freiberg, C., H. Brtz-Oesterhelt, and H. Labischinski. 2004. The impact of transcriptome and proteome analyses on antibiotic drug discovery. Curr. Opin. Microbiol. 7:451–459. 20 Sender, U., J. Bandow, S. Engelmann, U. Lindequist, and M. Hecker. 2004. Proteomic signatures for daunomycin and adriamycin in Bacillus subtilis. Pharmazie 59:65–70. 21 Crossley, K. B. and Archer L. A. 1997. The staphylococci in human disease. Churchill Livingstone, New York. 22 Chan, P. F., R. Macarron, D. J. Payne, M. Zalacain, and D. J. Holmes. 2002. Novel antibacterials: a genomic

approach to drug discovery. Curr. Drug Targets Infect. Disord. 2:291–308. 23 Blondelle, S. E. and R. A. Houghten. 1996. Novel antimicrobial compounds identified using synthetic combinatorial library technology. Trends Biotechnol. 14:60–65. 24 Rachakonda, S. and L. Cartee. 2004. Challenges in antimicrobial drug discovery and the potential of nucleoside antibiotics. Curr. Med. Chem. 11:775– 793. 25 Kuroda, M., T. Ohta, I. Uchiyama, T. Baba, H. Yuzawa, I. Kobayashi, L. Cui, A. Oguchi, K. Aoki, Y. Nagai, J. Lian, T. Ito, M. Kanamori, H. Matsumaru, A. Maruyama, H. Murakami, A. Hosoyama, Y. Mizutani-Ui, N. K. Takahashi, T. Sawano, R. Inoue, C. Kaito, K. Sekimizu, H. Hirakawa, S. Kuhara, S. Goto, J. Yabuzaki, M. Kanehisa, A. Yamashita, K. Oshima, K. Furuya, C. Yoshino, T. Shiba, M. Hattori, N. Ogasawara, H. Hayashi, and K. Hiramatsu. 2001. Whole genome sequencing of meticillin-resistant Staphylococcus aureus. Lancet 357:1225–1240. 26 Cordwell, S. J., M. R. Larsen, R. T. Cole, and B. J. Walsh. 2002. Comparative proteomics of Staphylococcus aureus and the response of methicillin-resistant and methicillin-sensitive strains to Triton X100. Microbiology 148:2765–2781. 27 Kohler, C., C. von Eiff, G. Peters, R. A. Proctor, M. Hecker, and S. Engelmann. 2003. Physiological characterization of a heme-deficient mutant of Staphylococcus aureus by a proteomic approach. J. Bacteriol. 185:6928–6937. 28 Scherl, A., P. Francois, M. Bento, J. M. Deshusses, Y. Charbonnier, V. Converset, A. Huyghe, N. Walter, C. Hoogland, R. D. Appel, J. C. Sanchez, C. G. Zimmermann-Ivol, G. L. Corthals, D. F. Hochstrasser, and J. Schrenzel. 2005. Correlation of proteomic and transcriptomic profiles of Staphylococcus aureus during the post-exponential phase of growth. J. Microbiol. Methods 60:247– 257. 29 Ohta, T., K. Honda, M. Kuroda, K. Saito, and H. Hayashi. 1993. Molecular characterization of the gene operon of heat shock proteins HSP60 and HSP10 in

67

68

3 Physiological Proteomics of Bacillus subtilis and Staphylococcus aureus … methicillin-resistant Staphylococcus aureus. Biochem. Biophys. Res. Commun. 193:730–737. 30 Chastanet, A., J. Fert, and T. Msadek. 2003. Comparative genomics reveal novel heat shock regulatory mechanisms in Staphylococcus aureus and other Gram-positive bacteria. Mol. Microbiol. 47:1061–1073. 31 Weber, H., S. Engelmann, D. Becher, and M. Hecker. 2004. Oxidative stress triggers thiol oxidation in the glyceraldehyde-3-phosphate dehydrogenase of Staphylococcus aureus. Mol. Microbiol. 52:133–140. 32 Singh, V. K., R. K. Jayaswal, and B. J. Wilkinson. 2001. Cell wall-active antibiotic induced proteins of Staphylococcus aureus identified using a proteomic approach. FEMS Microbiol. Lett. 199:79–84. 33 Nakano, M. M., P. Zuber, P. Glaser, A. Danchin, and F. M. Hulett. 1996. Two-component regulatory proteins ResD-ResE are required for transcriptional activation of fnr upon oxygen limitation in Bacillus subtilis. J. Bacteriol. 178:3796–3802. 34 Throup, J. P., F. Zappacosta, R. D. Lunsford, R. S. Annan, S. A. Carr, J. T. Lonsdale, A. P. Bryant, D. McDevitt, M. Rosenberg, and M. K. Burnham. 2001. The srhSR gene pair from Staphylococcus aureus: genomic and proteomic approaches to the identification and characterization of gene function. Biochemistry 40:10392–10401. 35 Hecker, M. , S. Engelmann, and S. J. Cordwell. 2003. Proteomics of Staphylococcus aureus – current state and future challenges. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 787:179–195.

36 Bischoff, M., P. Dunman, J. Kormanec,

37

38

39

40

41

42

D. Macapagal, E. Murphy, W. Mounts, B. Berger-Bchi, and S. Projan. 2004. Microarray-based analysis of the Staphylococcus aureus sigmaB regulon. J. Bacteriol. 186:4085–4099. Gertz, S., S. Engelmann, R. Schmid, A. K. Ziebandt, K. Tischer, C. Scharf, J. Hacker, and M. Hecker. 2000. Characterization of the sigma(B) regulon in Staphylococcus aureus. J. Bacteriol. 182:6983–6991. Ziebandt, A. K., H. Weber, J. Rudolph, R. Schmid, D. Hper, S. Engelmann, and M. Hecker. 2001. Extracellular proteins of Staphylococcus aureus and the role of SarA and sigma B. Proteomics 1:480–493. Ziebandt, A. K., D. Becher, K. Ohlsen, J. Hacker, M. Hecker, and S. Engelmann. 2004. The influence of agr and sigma(B) in growth phase dependent regulation of virulence factors in Staphylococcus aureus. Proteomics 4:3034– 3047. Dunman, P. M., E. Murphy, S. Haney, D. Palacios, G. Tucker-Kellogg, S. Wu, E. L. Brown, R. J. Zagursky, D. Shlaes, and S. J. Projan. 2001. Transcription profiling-based identification of Staphylococcus aureus genes regulated by the agr and/or sarA loci. J. Bacteriol. 183:7341–7353. Novick, R. P. 2003. Autoinduction and signal transduction in the regulation of staphylococcal virulence. Mol. Microbiol. 48:1429–1449. Vytvytska, O., E. Nagy, M. Bluggel, H. E. Meyer, R. Kurzbauer, L. A. Huber, and C. S. Klade. 2002. Identification of vaccine candidate antigens of Staphylococcus aureus by serological proteome analysis. Proteomics 2:580–590.

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4 Impact of Genome Sequences on Mutational Analysis of Fungal and Bacterial Pathogens Vladimir Pelicic and Xavier Nassif

4.1 The Long Road from Sequence to Function

Less than ten years after the publication of the first complete nucleotide sequence of a free-living organism, that of Haemophilus influenzae Rd [1], more than 220 complete genomes have been sequenced and made available to the public (see Genomes OnLine Database at http://www.genomesonline.org), which has radically changed our way of thinking and doing biology. This apparently endless expansion, with several hundred additional genome projects currently underway (http://www.genomesonline.org), is rapidly leading to the identification of the entire repertoires of genes for almost all the known pathogenic microbes affecting mankind. This sequencing frenzy is boosted by the common belief that genome sequences will be the key in a future, hopefully at hand, to the rational design of novel antimicrobial therapies which could have a fundamental impact on health care in this century [2]. This is expected to be the consequence of a better understanding of a wide range of bacterial and fungal biological processes, including the mechanisms of bacterial and fungal pathogenesis, which will result from the identification of the genes that contribute to these processes and the functional characterization of the corresponding proteins. However, this hope relies on our ability to convert rough sequence data into polished biological information, which promises to be a fantastic challenge since, more often than not, bioinformatic analysis of the sequence actually provides incomplete information, if any, as to gene function. Indeed, in most of the sequenced genomes, approximately 40% of the open reading frames (ORFs) could not be assigned any potential function, either because they are unique to the studied microbe or because they are similar to other genes whose function is also unknown. Moreover, the majority of the functions that could be predicted rely mainly, if not entirely, on circumstantial evidence which, no matter how plausible, needs to be experimentally validated – or, for that matter, invalidated. Studying, amongst other things, gene or protein expression, protein localization, or protein–protein interactions may generate information relevant to the mechanisms of bacterial and fungal pathogenesis [3]. However, the resulting data

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– e.g., a list of genes transcriptionally active during infection – always need to be subsequently tested by mutagenesis, since the role of a gene in a defined virulence trait becomes actual only after it has been demonstrated by mutational analysis. Therefore, the most direct and efficient path to gene function remains to directly define the phenotypic alterations resulting from gene mutation, which can be done on a gene or a genome scale, as discussed below.

4.2 Classical Genetics Still at the Forefront in the Postgenome Era

Classically, the most popular and successful approaches for identifying microbial virulence determinants have been directed and random mutagenesis [3]. Despite their apparent lack of “hype” compared to the genome-scale functional approaches presented later, these methods have all but disappeared with the advent of the genomic era. On the contrary, both approaches have been greatly facilitated by the availability of complete genomic sequences and will undoubtedly remain important tools for determining gene function. 4.2.1 Reverse Genetics

In reverse genetics, the functional allele encoding a protein suspected of playing a role in the infectious process is replaced by a disrupted or deleted copy by exploiting the homologous recombination properties of the cell. The virulence of the resulting mutants is then compared to the virulence of the wild-type strain using suitable in vitro or in vivo models of infection [3]. If the mutant is significantly less virulent than the wild-type strain, the mutated gene is deemed to play a role in infection. In the pregenome era, the identification of potential virulence factors by bioinformatic methods was hindered by the scarcity of available sequence data. Instead, labor-intensive but elegant techniques were often required to identify a handful of candidate genes, whatever the pathogen studied. Well-characterized virulence factors from a particular pathogenic microbe were often used to design degenerate primers in order to assess the presence of a related sequence by polymerase chain reaction (PCR) in the species under scrutiny, as for example in the case of the identification of the urease genes in Mycobacterium tuberculosis [4]. Alternatively, subtractive hybridization was used to identify the regions of difference between a pathogenic strain and a nonpathogenic relative, such as M. bovis and the vaccine strain M. bovis BCG [5], or two relatives with different pathogenic properties, such as Neisseria meningitidis and N. gonorrhoeae [6], which often contained genes that play an active role in the pathogenic process. Now that hundreds of genomes are or will be shortly available, most of the above techniques have become obsolete and have been advantageously replaced by a personal computer, which increasingly tends to become one of the researcher’s best friends. Indeed, all of the genetic differences between any two sequenced strains or the genes

4.2 Classical Genetics Still at the Forefront in the Postgenome Era

homologous to characterized virulence factors can be identified in silico in a matter of minutes. Moreover, it is possible to identify genes presenting defined subtle sequence characteristics which could not be readily identified by classical methods. For example, Hood et al. screened the genome sequence of H. influenzae for tandemly iterated nucleotides associated with putative ORFs [7], because the loss or gain of nucleotide repeats is known to mediate phase variation of surfaceexposed factors that are often involved in pathogenicity. One of the identified proteins, a predicted glycosyltransferase, whose phase-variable expression was associated with the phenotypic switching of a lipopolysaccharide epitope, was thus found to be necessary for virulence in vivo. In another elegant study, Coutte et al. screened the genome of Bordetella pertussis for ORFs potentially encoding proteases [8], with the idea that one of them might be involved in the maturation of B. pertussis major adhesin, the filamentous hemagglutinin (FHA). The identified candidates were all mutagenized and analyzed for possibly impaired FHA maturation. This confirmed that SphB1, a subtilisin-like protein, specifically cleaves FHA and is therefore the first example of a specialized maturation protease serving in a secretion pathway of a gram-negative bacterium. In conclusion, the ongoing accumulation of fantastic amounts of biological information as a result of bioinformatic analysis of complete genomes and studies using high-throughput analysis methods to monitor gene expression and protein expression on a genome scale, ensures that directed mutagenesis will remain a favorite in the researcher’s toolbox for assessing gene function on small-scale basis – up to a dozen genes, for example. 4.2.2 Transposon Mutagenesis

Most of our current understanding of the mechanisms of microbial pathogenesis comes from studies where scores of mutants, most often randomly generated by transposon mutagenesis, were monitored in suitable models of infection in search of those presenting reduced virulence [3]. For example, the individual screening of 9516 transposon mutants of Salmonella typhimurium in a macrophage culture assay led to the identification of 115 candidates with a diminished capacity for intracellular survival, of which 83 were also less virulent in vivo [9]. For example, the loss of virulence of one of the mutants, which was mutated in the phoP gene, was subsequently shown to be the result of its reduced resistance to defensins, an important microbicidal mechanism of the phagocyte [10]. The main advantages of this approach over directed mutagenesis are that (a) no a priori assumptions (which are often misleading) have to be made about the identity of the genes important for virulence, and (b) large number of mutants are generated and analyzed, which provides plenty of functional information, at a much faster pace. This throughput can even be improved by the use of transposons engineered to contain unique DNA tags, a procedure known as signature-tagged mutagenesis [11], which allowed large number of mutants to be screened simultaneously in vivo for pathogenic microbes as diverse as Staphylococcus aureus, Legionella pneu-

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mophila, Aspergillus fumigatus, or M. tuberculosis [12]. This was previously unfeasible due to the excessively high numbers of animals that would have been necessary and therefore represents an important improvement. For example, 100 infant mice were sufficient to screen 9600 mutants of the intestinal pathogen Vibrio cholerae for defects in the colonization of the small intestine [13]. This led to the identification of 251 attenuated mutants that contained mutations in known colonization factors, as well as in genes whose role in colonization was not previously appreciated. Another study performed in M. tuberculosis led to the identification of mutants presenting impaired replication in mouse lungs that harbored several transposon insertions in a cluster of genes [14]. Interestingly, these genes were subsequently shown to be involved in the synthesis and transport of a complex cell-wall-associated lipid, which is solely required for growth in the lungs. Recently, even more processive screening methods were described, such as TraSH (transposon site hybridization), where the mutants within a pool are identified by hybridizing RNA probes corresponding to the chromosomal sequences immediately adjacent to each transposon insertion site, to a microarray containing DNA fragments specific for every predicted ORF [15]. TraSH was used to monitor simultaneously the behavior of as many as 100 000 transposon mutants of M. tuberculosis in a mouse model of infection [16], which led to the identification of 194 genes important for mycobacterial growth in vivo. In general, this approach has contributed immensely to our understanding of the mechanisms of microbial pathogenesis, and its potential has been further increased by the availability of entire genomes, even though this is less apparent than for reverse genetics. For example, it is now much easier to identify the interrupted genes in most of the selected mutants, which was a laborious and impractical task in the pregenome era. Moreover, it is possible to estimate, following a Poisson distribution, how saturating a library of mutants might be, i.e., how many genes are likely to have been mutated and analyzed, which helps to give an idea of the exhaustiveness of the functional analyses that are performed.

4.3 Genome-Scale Mutational Analyses

The fundamental limitation of the above mutational approaches is that, no matter how large in scale they are, they can never be actually exhaustive since the collections of mutants are undefined and always incomplete. By identifying all the genes that constitute an organism, genome sequences provided the missing information for the construction of a comprehensive collection of mutants, which would be suitable for actually assessing gene function on a global scale. For this reason, although the creation of a comprehensive arrayed collection of mutants remains a daunting challenge, the potential of such collections for the identification of all the genes involved in a defined biological process and, ultimately, the exact contribution of each gene to microbial life [17] have prompted several pro-

4.3 Genome-Scale Mutational Analyses

jects for the construction of these toolboxes based on approaches initially developed in the model organism Saccharomyces cerevisiae. 4.3.1 Saccharomyces cerevisiae

The first microorganism to be domesticated by humans, one of the first to be sequenced, S. cerevisiae (baker’s yeast) remains a forerunner in the postgenomic era. It was used to demonstrate the feasibility of constructing archived genomewide collections of defined mutants. The two mutagenesis methods described above, random and directed mutagenesis, were both used to construct genomewide collection of yeast mutants. In the first approach, Ross-MacDonald et al. used a multipurpose mini-Tn3 derivative to randomly mutagenize, within Escherichia coli, a library of S. cerevisiae genomic DNA [18]. In addition to the selectable markers for yeast and bacteria, the mini-transposon also contained a promoterless lacZ reporter gene and a hemagglutinin epitope tag, allowing thus analysis of gene expression and protein localization. In a first step, E. coli strains containing mutant plasmids, i.e., harboring transposon insertions, were selected and stored individually in 96-well plates. In a second step, 92 544 plasmids were prepared from these strains, digested, and transformed in a diploid yeast strain where the interrupted fragments integrated at their corresponding genomic loci by homologous recombination. To enrich in yeast transformants containing transposon insertions within ORFs, 11 232 strains containing lacZ fusions expressed during vegetative growth were selected for further analysis and arrayed in a 96-well format. The precise location of the mini-transposon in 6358 strains was determined by sequencing the corresponding plasmid-borne insertion alleles, which indicated that insertions affected 1917 different ORFs (31% of yeast’s 6200 ORFs), a large number of which were previously nonannotated. In the second approach, systematic deletion of every S. cerevisiae ORF was started via targeted mutagenesis [19], an effort underpinned by yeast’s highly efficient homologous recombination. In brief, short regions of homology (45 bp) immediately upstream and downstream each of the 6200 ORFs were placed, together with unique DNA barcodes (allowing the simultaneous screening of large number of mutants similarly to signaturetagged mutagenesis), at both ends of a suitable selectable marker through a twostep PCR methodology. A consortium of yeast laboratories in Europe and North America then used the corresponding PCR products to transform the yeast in a 96-well format and to create start-to-stop codon gene deletion mutants, which were individually arrayed and centralized. In a preliminary report, deletion alleles were constructed for 2026 ORFs [19]. To date, the Saccharomyces Genome Deletion Project consortium has deleted 96% of all annotated ORFs, including nearly all ORFs larger than 100 codons (http://sequence-www.stanford.edu/group/yeast/ yeast_deletion_project/). Deletion alleles were used to generate, when possible, four different strains: haploids of both mating types, and both heterozygous and homozygous diploids, which helped demonstrate that 18.7% of yeast’s genes are

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essential for viability since deletion alleles could not be recovered in haploid strains. The value of these toolboxes for gene function identification, which is now undisputed, was first validated in the above original studies. The simultaneous assay of 558 homozygous deletion strains – a subset of the gene deletion mutants collection – for growth in rich and minimal media demonstrated that reliable quantitative fitness data can be simultaneously obtained under various conditions for large number of mutants [19], as was later abundantly documented. Similarly, 7680 transposon insertion haploid strains were scored, using phenotypic macroarrays, for 20 different phenotypes, which confirmed or provided novel functional information for 407 genes [18]. In addition, protein localization was analyzed in 1340 diploid strains carrying in-frame hemagglutinin epitope tag insertions [18], illustrating the extreme versatility of this approach. Globally, these mutant collections, which can be easily obtained by any researcher, have encouraged an unparalleled blossoming of genome-wide studies that have generated a plethora of functional information, a comprehensive review of which is clearly beyond the scope of this article. However, a mere 5 years of use led to significant achievements, since more than 100 experimental conditions were assayed and more than 5000 phenotypic traits assigned to yeast genes [20]. Interestingly, as predicted, some data may actually have important therapeutic implications. For example, a study of the effects of as many as 78 commercially available compounds on pools of 3503 heterozygous deletion strains [21] provided, among other things, a clue to the cholesterol-lowering side-effect of molsidomine, a potent vasodilator used for the past 20 years to treat angina. It was found that a metabolite of molsidomine inhibits lanosterol synthase, an enzyme essential for sterol synthesis that may therefore be a safe target for the development of new cholesterol-lowering drugs. The production of a global map of gene function – an utterly fantastic goal – is therefore becoming increasingly feasible in yeast, and has been scheduled by some for the beginning of 2007 [22]. Both genome-wide mutagenesis strategies have advantages and drawbacks [17]. Random mutagenesis and sequencing of the transposon insertion sites can rapidly lead to the construction of a large collection of mutants, which can immediately be used to generate a wealth of functional information. Moreover, it is relatively cost-effective and is therefore suitable for single laboratories’ efforts. However, although near-saturation mutagenesis of the genome can be obtained in extremely large libraries, transposon mutagenesis generates a lot of redundancy, some genes being mutated multiple times due to their size and to characteristics inherent in the transposon itself, and exhaustiveness cannot be attained. On the other hand, systematic targeted mutagenesis of every gene is restricted to organisms with a high rate of recombination; it is laborious and expensive, and is therefore best accomplished by a consortium of laboratories. However, it can result in a collection of mutants that is actually comprehensive.

4.3 Genome-Scale Mutational Analyses

4.3.2 Bacterial Workhorses: E. coli and Bacillus subtilis

Due to their undisputed position as model organisms in the bacterial world, the construction of genome-wide collections of mutants by systematic targeted mutagenesis of every gene has been attempted only in E. coli and Bacillus subtilis [23, 24], with the intention of creating flexible genomic resources freely available to all researchers. In E. coli, two different projects are currently underway in K12 strains MG1655 and W3110, both relying on high-throughput targeted mutagenesis methods. In the American project (strain MG1655), PCR amplification fragments of every ORF are mutated individually by in vitro transposition of Tn5-based mobile elements [24]. In the Japanese project (strain W3110), deletion alleles are created using a PCR-based strategy similar to the one used in the Saccharomyces Genome Deletion Project described above (http://ecoli.aist-nara.ac.jp/). In both cases, the mutant alleles are integrated into the E. coli genome by homologous recombination, which has been enhanced by the production of the bacteriophage k red recombination system that is encoded by the red genes present on a resident plasmid [25]. This plasmid can be subsequently cured by growth at high temperature. In the American project, of which an overview has been recently published [24], 1976 of the 4288 originally annotated E. coli ORFs (46%) have been successfully mutated in the first pass, whereas no progress report is available for the Japanese project (http://ecoli.aist-nara.ac.jp/). In B. subtilis, the global mutagenesis of the genome involved a consortium of laboratories and relied on the cloning of PCR-amplified fragments internal to each gene within a nonreplicating plasmid, which integrated in the corresponding genes via single crossover recombination after transformation [23]. In addition, the plasmid harbored a promoterless lacZ gene, allowing the generation of transcriptional fusions with the interrupted genes and an inducible promoter for the expression of the genes situated downstream of the target gene in multicistronic organizations, thus minimizing polar effects. However, because the main goal in this project was to estimate the minimum gene set required to sustain bacterial life, mutagenesis was unfortunately not comprehensive. It was not attempted on 1144 of the 4101 annotated B. subtilis ORFs (27.8%) either because they were studied previously for essentiality, or because they could be predicted with confidence to be either essential or dispensable. Finally, although the value of these toolboxes for gene function identification seems obvious in light of the S. cerevisiae example, functional studies using them are still to be reported. 4.3.3 Bacterial Pathogens

In all the other microorganisms where genome-wide libraries of mutants have been created, mobile elements have been used, due to their cost-effectiveness and facility of utilization, to generate large libraries of insertion mutants in which the transposon insertions have been sequenced on a systematic basis. Moreover, since

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these resources are readily usable, they have often been immediately used to generate substantial functional information.

4.3.3.1 Mycoplasma Species Unsurprisingly, the first attempt was in the sequenced bacterial pathogens with the smallest genomic contents, Mycoplasma genitalium and M. pneumoniae [26]. The scope of this study was to define the genes that cannot be mutated and are therefore essential for cellular life. In brief, pools of mutants obtained after electroporation of a plasmid containing the composite transposon Tn4001 from S. aureus were propagated in culture under selective pressure and genomic DNA was isolated. A laborious method was used to identify the junctions between the transposons and the chromosome. The transposon insertion sites, amplified by inverse PCR using primers specific for the transposon, were cloned and the recombinant plasmids were subsequently sequenced. The sequencing of 2209 clones defined 685 and 669 distinct insertion sites that occurred in 140 and 179 different genes of M. genitalium and M. pneumoniae, respectively. Unfortunately, in this study, the transposon mutants were analyzed within mixed populations, thus leaving no resource that could be subsequently used for functional studies. In contrast, arrayed libraries of defined transposon mutants have been constructed more recently for several pathogens.

4.3.3.2 Pseudomonas aeruginosa For Pseudomonas aeruginosa, the leading cause of death in patients with cystic fibrosis, two large-scale libraries of mutants were generated in two different projects using multipurpose Tn5 derivatives, which were introduced into the genome by conjugation. The mini-transposons harbored promoterless phoA and lacZ [27] or Photorhabdus luminescens luxCDABE reporter genes (http://pseudomutant. pseudomonas.com/) capable of generating translational fusions if appropriately inserted in a target gene. In both cases, the identification of the transposon insertion sites, which clearly represents the limiting step in this approach where large number of mutants are to be analyzed, relied on less laborious methods than in the previous effort. Briefly, efficient PCR techniques, either inverse PCR (http:// pseudomutant.pseudomonas.com/) or a two-stage semidegenerate PCR [27], were used to amplify the transposon insertion sites and the PCR products were directly sequenced. In the first project [27], where 42 240 mutants were arrayed, 36 154 of the transposon insertions sites (80%) were successfully mapped, of which 30 100 were unique. This yielded insertions in 4892 different ORFs, representing 87.8% of the annotated coding sequences in the P. aeruginosa genome. Since it was estimated that 300–400 of the 678 ORFs in which transposon insertions were not recovered are likely to be essential, the mutational coverage was probably around 94%, which is a major achievement. In the second project (http://pseudomutant. pseudomonas.com/), although the coverage was more modest, 2519 insertion sites were mapped, yielding insertion in 1284 different ORFs. Interestingly, in

4.3 Genome-Scale Mutational Analyses

1253 mutants, the mini-transposon generated active translational fusions with the P. luminescens lux genes. Importantly, the potential of the larger collection of mutants in producing essentially complete lists of candidate genes implicated in various biological processes has been validated by the results of two simple screens aimed at phenotypes with well-understood genetic bases [27], these being twitching motility and prototrophic growth. In both cases, all the genes expected from earlier studies and several previously undescribed candidates were identified.

4.3.3.3 Staphylococcus aureus In S. aureus, a derivative of the eukaryotic mariner transposon, delivered on a thermosensitive plasmid, was used to generate an archived library of 10 325 clones, of which 9540 were transposon insertion mutants [28]. Transposon insertion sites were amplified by inverse PCR and directly sequenced. Unfortunately, since mutagenesis was performed in the Newman strain, whose genome sequence is not known, the sequencing results could not be fully exploited. Nevertheless, using available S. aureus genome sequences, it was possible to map 8450 transposon insertions sites and to establish that 6917 of these were within coding sequences. Insertions occurred in at least 1812 different ORFs, which probably represent some 67% of the coding sequences in the Newman strain genome. However, this study illustrates perfectly how these resources may lead to a comprehensive understanding of the mechanisms of pathogenesis by leading to the identification of all the determinants of virulence. Indeed, 1736 mutants with insertions in different ORFs were screened in triplicate in the nematode Caenorhabditis elegans as model host in order to identify S. aureus virulence genes, which identified 71 genes important for pathogenicity. Among the 30 genes of unknown function, some were also important virulence factors in a murine model of infection.

4.3.3.4 Neisseria meningitidis As in the S. aureus project, a mini-transposon derived from the eukaryotic Himar1 mariner transposon was used to generate an ordered library of 4548 mutants in N. meningitidis [29], a human pathogen which is one of the leading causes of fatal sepsis and meningitis worldwide. However, transposition was performed in vitro on N. meningitidis chromosomal DNA, which was subsequently reintroduced into the bacterium by natural transformation where it integrated in the chromosome via allelic exchange. Transposon insertion sites were amplified by ligation-mediated PCR from the chromosomal DNAs of each mutant, which were prepared and stored in 96-well plates, and directly sequenced. As in the previous case, mutagenesis was performed in a strain 8013 whose genome sequence is not known but is currently being finished (our unpublished data), which temporarily hinders the exploitation of the sequencing results. Nevertheless, using the available N. meningitidis genome sequences it was possible to tentatively map 3221 transposon inser-

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tions out of the 3881 that could be sequenced, and to determine that insertions occurred in at least 940 different ORFs, or approximately 45% of the 2100 expected N. meningitidis coding sequences (our unpublished data). Since PCRamplified DNA is a suitable target for in vitro mariner transposition as well [30], the missing ORFs are currently being systematically amplified by PCR and subjected to targeted mutagenesis to generate the missing mutants. This hybrid mutagenesis strategy, unlike the previous examples, is expected to result in the creation of a comprehensive collection of mutants. Another difference to the previous examples is that this toolbox has been optimized for functional studies by using mini-transposons that were engineered to contain unique identifying DNA barcodes, as in signature-tagged mutagenesis or in the Saccharomyces Genome Deletion Project, which allows up to 48 mutants to be screened simultaneously [29]. The 4548 mutants have already been assayed in three experimental conditions [29, 31, 32], which assigned phenotypic traits to dozens of meningococcal genes, many of which were previously undescribed. For example, the simultaneous assay of pools of mutants for fitness in human serum identified 18 genes required for resistance to complement-mediated lysis including almost all the genes expected from earlier studies [29]. Although this key virulence attribute of N. meningitidis was well characterized, four previously undescribed candidates were identified. However, the identification of the genes required for type IV pilus (Tfp) formation in N. meningitidis, which play a critical role in its pathogenic lifestyle by facilitating bacterial attachment to human cells, is probably the best illustration of the use of this resource for the identification of all the genes involved in a defined biological process [31]. Mutants affected as to piliation were identified by their impaired ability to form aggregates, another phenotype mediated by these organelles. Fifteen genes, of which only seven were previously characterized in Neisseria species, were found to be essential for Tfp biogenesis, a number similar to the number of genes essential for Tfp biogenesis in other well-studied bacteria. Importantly, this study also pinpointed another advantage of genome-wide collections of mutants, and that is the possibility of adding to the results generated by direct phenotypic screens by complementary reverse genetics analysis of all the other genes of interest that were not detected. The mutants in the corresponding genes, which may have attracted interest on the basis of particular sequence homologies or data available in the literature, can be readily retrieved in the library and analyzed immediately in the same phenotypic screens. For example, a gene that was homologous to a known Tfp biogenesis gene (pilZ) from P. aeruginosa [33] was, surprisingly, found to be dispensable for fiber biogenesis in N. meningitidis. This negative result is interesting since it suggested that there were some subtle differences between otherwise extremely conserved Tfp biogenesis machineries in two different piliated bacterial species.

References

4.4 Conclusion

The availability of entire genome sequences has further increased the popularity and predominance of mutational analysis for determining gene function and blazed a trail for the creation of comprehensive collections of mutants, which was previously unfeasible. The extremely profound impact of this tool in baker’s yeast, where it helped to narrow the gap between sequence and function and may rapidly lead to the production of a global functional map, has prompted similar projects in many pathogenic microorganisms, despite the numerous inherent difficulties. Although it may be unrealistic to foresee the production of a global map of gene function in any pathogen, the widespread use of these libraries for exquisite in vivo or in vitro phenotypic analysis, which is still in its infancy in pathogenic microorganisms, is expected to expedite dramatically the unraveling of bacterial and fungal pathogenicity in the next few years and, hopefully, the design of novel antimicrobial therapies.

References 1 Fleischmann, R. D., M. D. Adams,

2

3

4

5

O. White, R. A. Clayton, E. F. Kirkness, A. R. Kerlavage, C. J. Bult, J. F. Tomb, B. A. Dougherty, J. M. Merrick, et al. 1995. Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science 269:496–512. Sander, C. 2000. Genomic medicine and the future of health care. Science 287:1977–1978. Hensel, M., and D. W. Holden. 1996. Molecular genetic approaches for the study of virulence in both pathogenic bacteria and fungi. Microbiology 142:1049–1058. Reyrat, J.–M., F. X. Berthet, and B. Gicquel. 1995. The urease locus of Mycobacterium tuberculosis and its utilization for the demonstration of allelic exchange in Mycobacterium bovis bacillus CalmetteGurin. Proc Natl Acad Sci USA 92:8768–8772. Mahairas, G. G., P. J. Sabo, M. J. Hickey, D. C. Singh, and C. K. Stover. 1996. Molecular analysis of genetic differences between Mycobacterium bovis BCG and virulent M. bovis. J. Bacteriol. 178:1274–1282.

6 Tinsley, C. R., and X. Nassif. 1996. Anal-

7

8

9

10

ysis of the genetic differences between Neisseria meningitidis and Neisseria gonorrhoeae: two closely related bacteria expressing two different pathogenicities. Proc Natl Acad Sci USA 93:11109– 11114. Hood, D. W., M. E. Deadman, M. P. Jennings, M. Bisercic, R. D. Fleischmann, J. C. Venter, and E. R. Moxon. 1996. DNA repeats identify novel virulence genes in Haemophilus influenzae. Proc Natl Acad Sci USA 93:11121–11125. Coutte, L., R. Antoine, H. Drobecq, C. Locht, and F. Jacob–Dubuisson. 2001. Subtilisin-like autotransporter serves as maturation protease in a bacterial secretion pathway. EMBO J 20:5040–5048. Fields, P. I., R. V. Swanson, C. G. Haidaris, and F. Heffron. 1986. Mutants of Salmonella typhimurium that cannot survive within the macrophage are avirulent. Proc Natl Acad Sci USA 83:5189– 5193. Fields, P. I., E. A. Groisman, and F. Heffron. 1989. A Salmonella locus that controls resistance to microbicidal proteins

79

80

4 Impact of Genome Sequences on Mutational Analysis of Fungal and Bacterial Pathogens from phagocytic cells. Science 243:1059–1062. 11 Hensel, M., J. E. Shea, C. Gleeson, M. D. Jones, E. Dalton, and D. W. Holden. 1995. Simultaneous identification of bacterial virulence genes by negative selection. Science 269:400–403. 12 Mecsas, J. 2002. Use of signature-tagged mutagenesis in pathogenesis studies. Curr Opin Microbiol 5:33–37. 13 Merrell, D. S., D. L. Hava, and A. Camilli. 2002. Identification of novel factors involved in colonization and acid tolerance of Vibrio cholerae. Mol Microbiol 43:1471–1491. 14 Cox, J. S., B. Chen, M. McNeill, and W. R. Jacobs Jr. 1999. Complex lipid determines tissue-specific replication of Mycobacterium tuberculosis in mice. Nature 402:79–83. 15 Sassetti, C. M., D. H. Boyd, and E. J. Rubin. 2001. Comprehensive identification of conditionally essential genes in mycobacteria. Proc Natl Acad Sci USA 98:12712–12717. 16 Sassetti, C. M., and E. J. Rubin. 2003. Genetic requirements for mycobacterial survival during infection. Proc Natl Acad Sci USA 100:12989–12994. 17 Coelho, P. S. R., A. Kumar, and M. Snyder. 2000. Genome-wide mutant collections: toolboxes for functional genomics. Curr Opin Microbiol 3:309–315. 18 Ross-Macdonald, P., P. S. Coelho, T. Roemer, S. Agarwal, A. Kumar, R. Jansen, K. H. Cheung, A. Sheehan, D. Symoniatis, L. Umansky, et al. 1999. Large-scale analysis of the yeast genome by transposon tagging and gene disruption. Nature 402:413–418. 19 Winzeler, E. A., D. D. Shoemaker, A. Astromoff, H. Liang, K. Anderson, B. Andre, R. Bangham, R. Benito, J. D. Boeke, H. Bussey, et al. 1999. Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285:901–906. 20 Scherens, B., and A. Goffeau. 2004. The uses of genome-wide yeast mutant collections. Genome Biol 5:229. 21 Lum, P. Y., C. D. Armour, S. B. Stepaniants, G. Cavet, M. K. Wolf, J. S. Butler, J. C. Hinshaw, P. Garnier, G. D. Prestwich, A. Leonardson, et al. 2004. Dis-

22

23

24

25

26

27

28

29

30

covering modes of action for therapeutic compounds using a genome-wide screen of yeast heterozygotes. Cell 116:121–137. Hughes, T. R., M. D. Robinson, N. Mitsakakis, and M. Johnston. 2004. The promise of functional genomics: completing the encyclopedia of a cell. Curr Opin Microbiol 7:546–554. Kobayashi, K., S. D. Ehrlich, A. Albertini, G. Amati, K. K. Andersen, M. Arnaud, K. Asai, S. Ashikaga, S. Aymerich, P. Bessieres, et al. 2003. Essential Bacillus subtilis genes. Proc Natl Acad Sci USA 100:4678–4683. Kang, Y., T. Durfee, J. D. Glasner, Y. Qiu, D. Frisch, K. M. Winterberg, and F. R. Blattner. 2004. Systematic mutagenesis of the Escherichia coli genome. J Bacteriol 186:4921–4930. Datsenko, K. A., and B. L. Wanner. 2000. One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. Proc Natl Acad Sci USA 97:6640–6645. Hutchison, C. A., S. N. Peterson, S. R. Gill, R. T. Cline, O. White, C. M. Fraser, H. O. Smith, and J. C. Venter. 1999. Global transposon mutagenesis and a minimal Mycoplasma genome. Science 286:2165–2169. Jacobs, M. A., A. Alwood, I. Thaipisuttikul, D. Spencer, E. Haugen, S. Ernst, O. Will, R. Kaul, C. Raymond, R. Levy, et al. 2003. Comprehensive transposon mutant library of Pseudomonas aeruginosa. Proc Natl Acad Sci USA 100:14339–14344. Bae, T., A. K. Banger, A. Wallace, E. M. Glass, F. Aslund, O. Schneewind, and D. M. Missiakas. 2004. Staphylococcus aureus virulence genes identified by bursa aurealis mutagenesis and nematode killing. Proc Natl Acad Sci USA 101:12312–12317. Geoffroy, M. C., S. Floquet, A. Mtais, X. Nassif, and V. Pelicic. 2003. Largescale analysis of the meningococcus genome by gene disruption: resistance to complement-mediated lysis. Genome Res 13:391–398. Pelicic, V., S. Morelle, D. Lampe, and X. Nassif. 2000. Mutagenesis of Neisseria meningitidis by in vitro transposi-

References tion of Himar1 mariner. J Bacteriol 182:5391–5398. 31 Carbonnelle, E., S. Helaine, L. Prouvensier, X. Nassif, and V. Pelicic. 2005. Type IV pilus biogenesis in Neissseria meningitidis: PilW is involved in a step occurring after pilus assembly, essential for fiber stability and function. Mol Microbiol 55:54–64. 32 Helaine, S., E. Carbonnelle, L. Prouvensier, J. L. Beretti, X. Nassif, and

V. Pelicic. 2005. PilX, a pilus-associated protein essential for bacterial aggregation, is a key to pilus-facilitated attachment of Neisseria meningitidis to human cells. Mol Microbiol 55:65–77. 33 Alm, R. A., A. J. Bodero, P. D. Free, and J. S. Mattick. 1996. Identification of a novel gene, pilZ, essential for type 4 fimbrial biogenesis in Pseudomonas aeruginosa. J Bacteriol 178:46–53.

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5 Pathogenomics of Escherichia coli and Shigella Species Ulrich Dobrindt and Jrg Hacker

5.1 Introduction

The enterobacterial “species” Escherichia coli and Shigella can be grouped into a phylogenetic cluster which diverged from other members of the c-subdivision of gram-negative purple bacteria around the time of the emergence of mammalian organisms [1]. Shigella and E. coli are closely related, and it is becoming more and more clear that both could be considered as members of one species as their distinction is only based on the pathogenic character of the bacteria. Their chromosomal organization shares more than 90% homology according to DNA–DNA reassociation experiments [2]. E. coli and Shigella populations are clonal. E. coli isolates can be grouped into particular clones that have evolved under competition as distinct genetic types. They can be classified according to their pathotype and their host [3–7]. These clones arose in parallel by both the loss and the ordered gain of genetic information. The clones are maintained during adaptation to their respective niches, and because of horizontal gene transfer, their further evolution is constantly in progress, thus resulting in very dynamic and diverse genome structures. In the case of E. coli K-12 strain MG1655, about 18% of the genome has been estimated to represent horizontally acquired sequences [8]. The stepwise acquisition of “foreign DNA” from distantly related organisms as well as the loss of DNA regions (genome reduction) resulted in different metabolic and pathogenic features that distinguish the different genera, strains, and pathotypes. Arising from a nonpathogenic E. coli ancestor, the loss of the ompT and cadA gene in combination with the acquisition of two pathogenicity islands and one virulence plasmid led to the evolution of pathogenic Shigella [9]. The parallel gain and loss of mobile genetic elements, such as bacteriophages, plasmids, and the LEE (“locus of enterocyte effacement”) pathogenicity island, in different lineages of pathogenic E. coli enabled the evolution of separate clones which belong to different E. coli pathotypes [6]. Accordingly, there is growing evidence that the genome of E. coli and Shigella can be considered as being composed of a conserved core of genes providing the backbone of genetic information required for essential cellular processes.

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5 Pathogenomics of Escherichia coli and Shigella Species

In addition, a flexible gene pool exists which is not common for all strains and consists of an individual assortment of strain-specific genetic information which may provide additional properties enabling these strains to adapt to special environmental conditions. Therefore, differences in genome size reflect the size variation of the flexible gene pool and are mainly due to the acquisition and loss of genomic DNA. A surprisingly large proportion of the flexible gene pool consists of uncharacterized unknown open reading frames (ORFs) without any obvious function. Another major constituent of the flexible gene pool is the group of accessory genetic elements, e.g., plasmids, transposons, insertion sequence elements, prophages, nonfunctional fragments thereof, and genomic islands. They can either be integrated into the chromosome or replicate independently as extrachromosomal elements. Several types of these elements can be laterally transferred and are present in probably all of the major bacterial phylogenetic groups, thus contributing to the inter- and intraspecies variability in genome content [9–11].

5.2 Comparative Genomics of Shigella

In contrast to E. coli, Shigella flexneri is pathogenic only for humans. S. flexneri can invade the colon epithelium and cause intense acute inflammatory response. Its ability to enter the host cell cytosol, where it can replicate and spread into neighboring cells, is unusual among enterobacteria. Mainly responsible for the invasive character is a large virulence plasmid that contains the genes required for invasion. Additionally, chromosomal genes located within pathogenicity islands contribute directly to the pathogenic process and to survival during infection [12, 13]. Consequently, genome comparison of S. flexneri and the phylogenetically indistinguishable E. coli should enable identification of the basis of the specific clinical manifestation of S. flexneri infection. The genomes of two S. flexneri serotype 2a strains, 2457T and 301, have been completely sequenced and some of their characteristics are compiled in Table 5.1. Full genome sequence determination of one S. dysenteriae and one S. boydii strain is in progress (http://www.sanger.ac.uk/Projects/Microbes/) (see also Table 5.2). The S. flexneri chromosome exhibits the conserved backbone and flexible island mosaic structure typical of E. coli/Shigella. The genome sequences are very similar, comprising a common colinear chromosomal backbone of 3.9 million base pairs (Mbp) which is also shared with E. coli K-12 strain MG1655 and the O157:H7 strain EDL933. Nevertheless, more than 1400 single nucleotide exchanges have been observed which are scattered all over the S. flexneri genome. Strain 301 carries an unusual set of three spacer tRNAs in the rrnH operon [14, 15]. The strain 2457T genome lacks 357 E. coli genes and contains a large fraction of accessory and (formerly) mobile genetic elements, i.e., 284 intact or nonfunctional insertion sequence (IS) elements that mediated several genomic rearrangements, as well as cryptic prophages. The number of IS-related DNA stretches (n= 314) is even higher in the genome of strain 301 [14, 15], thus also increasing the genome size of strain 301 relative to that of strain 2457T.

5.2 Comparative Genomics of Shigella

87

Accordingly, S. flexneri genomes carry more IS elements than those of any other enteric bacteria. Colinearity of the chromosomal backbone in both genomes is disturbed due to apparently IS-element-mediated chromosomal rearrangements relative to each other and to E. coli K-12. There are at least 15 larger rearrangements between the two Shigella genomes, and nearly all inversions are bordered by insertion sequences. Large rearrangements exist, e.g., around the origin of replication. Around the replication terminus a large inversion occurred in both Shigella strains, followed by reinversion of most of the rearranged DNA region, so that two smaller stretches of inverted sequences indicate the beginning and the end points of the initial event. This implies that the major driving force behind these rearrangements within the S. flexneri genome is recombination between related IS elements. Tab. 5.1 Characteristics of publicly available complete E. coli and S. flexneri

genome sequences. Strain

Pathotype

Size (bp)

G+C content (%)

No. of predicted ORFs

No. of ISs or IS-like elements

No. of pro- Plasmids phages or prophagelike elements

S. flexneri 2457T

2a

4 599 354

50.9

4084

284

12

4 (pINV[15] 2457T, pSf2, pSf4, pSf-R27)

S. flexneri 301

2a

4 607 203

50.9

4434

314

ND

pCP301

[14]

E. coli MG1655

K-12

4 639 221

50.8

4294

42

10

–

[30]

E. coli CFT073

UPEC 5 231 428 (O6:K2:H1)

50.5

5533

ND

5

–

[34]

E. coli EDL933

EHEC 5 528 445 (O157:H7)

50.5

5361

ND

13

pO157

[33]

E. coli Sakai

EHEC 5 594 477 (O157:H7)

50.5

5361

137

24

pO157, pOSAK1

[32]

ORFs, open reading frames; IS, insertion sequence; ND, not determined;

Reference

88

5 Pathogenomics of Escherichia coli and Shigella Species

Tab. 5.2 Ongoing complete E. coli and Shigella genome sequencing projects

(as at April 2005). Organism

Strain

Disease/ pathotype

Genome Status size (Mbp)

Remarks

Institutions involved

E. coli 536 O6:K15:H31

ExPEC

4.9

Sequence annotation

University of Wrzburg; Gttingen Genomics Laboratory, Germany

E. coli O6:K5:H1

DSM6601

non4.9 pathogenic

In progress Gap closure

E. coli O18:K1:H7

RS218

ExPEC

5.2

In progress ~ 200 contigs Genome Center, Univer> 1 kbp sity of Wisconsin, USA

E. coli O42

O42

EAEC

5.355

Finished

4 contigs > 1 kbp

Sanger Centre, UK

E. coli O127:H6

E2348/69

EPEC

5.067

In progress 8 contigs > 1 kbp

Sanger Centre, UK

S. dysenteriae M131

Dysentery

4.8

Finishing/ 330 contigs gap closure > 1 kbp

Sanger Centre, UK

S. sonnei

Dysentery

5.255

Finishing/ 19 contigs gap closure > 1 kbp

Sanger Centre, UK

53G

Finished

Gesellschaft fr Biotechnologische Forschung (GBF), Braunschweig; Medical Hannover; University of Wrzburg; Gttingen Genomics Laboratory, Germany

Acquisition of new traits by HGT (horizontal gene transfer) as well as a complementary loss-of-function mechanism have been proposed [16, 17] to adapt to specialized niches and to increase virulence. Acquisition of the virulence plasmid enabled S. flexneri to enter the intracellular environment in human intestinal epithelial cells. In this new niche, genes required in the intestinal lumen may be deleterious or are no longer beneficial and may accumulate mutations without a selective force to maintain them. Lysine decarboxylase (CadA) produces cadaverine, which inhibits the escape of S. flexneri from the vacuole into the cell cytosol [18, 19]. As replication and spread of S. flexneri depends on access to the cytosol, biosynthesis of cadaverine attenuates virulence. The cadA and cadC genes are deleted as well as the surface protease OmpT encoding gene, which determines the intercellular spreading ability of Shigella [9]. Lack of surface structures, e.g., flagella and curli fimbriae, in S. flexneri may provide the advantage of fewer antigens that could be recognized by the host immune system. In strain 2457T, 14 genes of flagellar biosynthesis are nonfunctional due to frameshift and point

5.2 Comparative Genomics of Shigella

mutations as well as to IS1-mediated truncation [15]. Pseudogenes result from disruption of coding sequences by point mutations, single nucleotide indels, and insertion of IS elements. IS elements cause disruption and truncation of genes as well as larger deletions and insertions. IS elements are probably the major cause of the S. flexneri genome dynamics. Many other phenotypic characteristics of S. flexneri relative to E. coli (e.g., the loss of utilization of lactose, maltose, and xylose) can be assigned to pseudogenes in Shigella which are still functional in E. coli. With respect to the presence of large numbers of pseudogenes, S. flexneri resembles another enteric pathogen restricted to humans, and that is Salmonella enterica serovar Typhi. The accumulation of pseudogenes is considered to be one of the main reasons why both evolved from the rest of their species to become human restricted pathogens [20]. S. flexneri harbors a large virulence plasmid, pINV, that contains all of the genes (ipa, mxi-spa, virG/lscA, virF) required to express the invasive phenotype. This plasmid is also present in enteroinvasive E. coli strains. Sequence comparison of the four available virulence plasmid sequences of S. flexneri, pINV-2457T, pWR100, pWR501, and pCP301 [14, 15, 21, 22] showed that they are essentially identical. Size differences are mainly due to the presence of IS elements. Interestingly, about 50% of all ORFs located on the invasion plasmid are related to putative IS elements indicative of IS-mediated acquisition of gene blocks of various origins. However, the analysis of non-IS-related coding sequences of different pINV variants also indicated that positive selection is also a major driving force involved in the evolution of pINV variants [23]. In contrast to strain 301, strain 2457T also contains two small multicopy plasmids as well as another 165-kbp plasmid with high homology to the S. enterica serovar Typhi R27-like plasmid [15]. The latter was considered to be limited to Salmonella implicated in the acquisition and accumulation of antibiotic resistance. However, sequence similarity between pR27 and the large virulence plasmid of Yersinia pestis, pMT1, has been observed, indicative of a common origin. Comparative genome analysis also provides a powerful means of linking pathogenic processes with specific acquired genes and their encoded products. In addition to the 3.9-Mbp backbone shared with E. coli, several regions unique to Shigella can be found scattered over the chromosome. Sixty-four such Shigella islands (SIs) greater than 1000 base pairs can be detected and many of them exhibit typical features of genomic islands or prophages. Shigella prophages play an important role in serotype conversion, which is an important virulence trait of S. flexneri. Four different temperate bacteriophages involved in O antigen modification have been characterized so far. Compared to E. coli, most prophages found in S. flexneri differ in their DNA sequence, indicating that they were acquired after the separation of the two bacterial lineages [14, 15, 24, 25].

89

PAI I536

PAI V536 PAI IJ96 PAI IIJ96

O6:K15:H31

O6:K15:H31

O6:K15:H31

O6:K15:H31

–

O4:K :H5

–

O4:K :H5

O6:K2:H1

O6:K2:H1

E. coli 536 (UPEC)

E. coli 536 (UPEC)

E. coli 536 (UPEC)

E. coli 536 (UPEC)

E. coli J96 (UPEC)

E. coli J96 (UPEC)

E. coli CFT073 (UPEC)

E. coli CFT073 (UPEC)

PAI IIAL862 kps PAI

K1

O18:K1:H7

O78

E. coli AL862 (SEPEC)

E. coli EV36 (K12/K1 hybrid E. coli)

E. coli C5 (UPEC)

E. coli 222 (APEC)

LEE

E. coli RW1374 (STEC)

O103:H2

LEE

E. coli E2348/69 (EPEC) O127:H6

Vat-PAI

PAI IC5

PAI IAL862

E. coli AL862 (SEPEC)

PAI IICFT073

PAI ICFT073

PAI III536

PAI II536

Designation

Organism

Tab. 5.3 Well-characterized pathogenicity islands of E. coli and Shigella.

58

a-Hemolysin, P fimbriae (Pap)

Type III secretion, invasion, parts of the she PAI (Sh. flexneri 2a)

Type III secretion, invasion

> 80

35

22

~ 100

a-Hemolysin, P fimbriae (Prs), cytotoxic necrotizing factor 1 (CNF1), heat-resistant hemagglutinin Autotransporter vacuolating toxin

n.d.

61

61

K1 capsule

afa8 adhesin

afa8 adhesin

71

110

a-Hemolysin, P fimbriae (Prs), cytotoxic necrotizing factor 1 (CNF1)

P fimbriae (Pap), iron acquisition

> 170

76.8

S fimbriae (SfaI), iro siderophore system, hemoglobin protease

a-Hemolysin, P fimbriae (Pap)

102

a-Hemolysin, P fimbriae (Prf), put. adhesin

79.6

75.8

a-Hemolysin, putative adhesins

K15 capsule

Size (kbp)

Encoded traits

pheV

selC

thrW

leuX

pheV

pheV

pheU

pheU

pheV

pheU

pheV

pheV

thrW

leuX

selC

[90]

[89]

[88]

[87]

[86]

[56]

[56]

[34, 85]

[34, 54]

[53]

[53]

[84]

[43]

[43]

[43]

Chromosomal Referinsertion site ence

90

5 Pathogenomics of Escherichia coli and Shigella Species

LEE

LEE (OI 148) LEE LEE

E. coli (EPEC and EHEC) O111:H8 O111:H– O26:H11 O26:H–

E. coli EDL933 (EHEC) O157:H7

E. coli RDEC-1 (REPEC) O15:H–

O15:H–

Locus of proteolysis activity (LPA)

O91:H

O78:H11

E. coli (EHEC)

E. coli 10407(ETEC)

SHI-1 (she) SHI-2 Shigella resistance locus (SRL) Shi-O

Shigella flexneri

S. flexneri

S. flexneri

S. flexneri

Genes involved in serotype conversion

Ferric dicitrate transport, antibiotic resistances

Aerobactin synthesis, colicin V immunity

Enterotoxin (Set), protease (Pic)

Yersiniabactin synthesis, transport

Invasion

Serine protease (EspI), vitamin B12 receptor (BtuB), adhesin

Diffuse adherence adhesin

Autotransporter/enterotoxin

Type III secretion, invasion

Type III secretion, invasion, put. adhesin, enterotoxin

Type III secretion, invasion, put. adhesin

Type III secretion, invasion

Type III secretion, invasion

Encoded traits

11

66

23–30

46.6

31–43

46

33

> 11

thrW

serX

selCv

pheV

asnTv

selC

selC

pheV

ssrA

pheV

~ 85 15.2

pheU

pheU

selC

pheU

[103]

[102]

[27, 44]

[101]

[98–100]

[97]

[96]

[95]

[94]

[93]

[93]

[92]

[33]

[91]

Chromosomal Referinsertion site ence

59.5

?

43

?

Size (kbp)

DR, direct repeat; APEC, avian pathogenic E. coli; UPEC, uropathogenic E. coli; SEPEC, sepsis-causing E. coli; EPEC, enteropathogenic E. coli; EHEC, enterohemorrhagic E. coli; ETEC, enterotoxigenic E. coli; REPEC, rabbit enteropathogenic E. coli

HPI (PAI IV536)

pathogenic E. coli

TPAI-1

EPEC Afa-PAI

–

O55:H

E. coli 135/12 (EPEC)

EspC-PAI

E. coli E2348/69 (EPEC) O127:H7

–

LEE

E. coli 84/110-1 (REPEC) O103:H2

E. coli 83/39 (REPEC)

Designation

Organism

Tab. 5.3 Continued.

5.2 Comparative Genomics of Shigella 91

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5 Pathogenomics of Escherichia coli and Shigella Species

The genomic island content of the two S. flexneri strains is similar, but their organization and chromosomal insertion site may differ. Shigella pathogenicity islands which have already been analyzed in detail are shown in Table 5.3. Several islands may be involved in niche-specific processes or virulence: the Pic protease/ mucinase and ShET1 enterotoxin on the she pathogenicity island (PAI) [26], or the aerobactin siderophore gene cluster on the SHI-2 PAI inserted at selC [27] or on SHI-3 inserted at pheU in S. boydii [28]. Eight other smaller islands contain the sit genes coding for another siderophore system, possible specific adhesins similar to long polar fimbriae (Lpf) or the Saf protein of S. enterica serovar Typhimurium. The most notable island type carries genes related to the type III secreted effector protein IpaH that may support escape of Shigella from macrophage vacuoles. They are secreted by the plasmid-encoded type three secretion system. Five ipaH copies are localized on the virulence plasmid of strain 2457T and seven more are present on the chromosome. Three of them are nonfunctional. In strain 301 four complete and three incomplete ipaH copies have been found on the chromosome as well as five copies on the virulence plasmid pCP301. In both S. flexneri strains, incomplete ipaH copies result from disruption by IS elements or by frameshift mutations [14, 15]. The comparative genome analysis of S. flexneri and E. coli [14, 15] confirms the previous finding that the two are closely related and may belong to the same genus [2, 29]. Shigella evolved from multiple E. coli strains in correlation with the appearance of man much later than, e.g., E. coli O157:H7 and K-12, which diverged from a common ancestor about 4.5 million years ago [6]. To meet the demand of its unique pathogenic lifestyle, the chromosomally determined phenotypic properties result from convergent evolution during niche adaptation, mostly due to gene acquisition or loss of function, some from negative selection pressure.

5.3 Comparative Genomics of Escherichia coli 5.3.1 Comparison of Complete Genome Sequences

The chromosome of Escherichia coli K-12 is the best studied microbial genome. Accordingly, that of the E. coli K-12 strain MG1655 was the first E. coli genome which has been completely sequenced [30]. Earlier results already revealed an unexpected level of structural and genetic diversity among genomes of different E. coli strains also mirrored by genome size variation within the species E. coli between 4.6 and 5.5 Mbp [31]. The genomes of four E. coli strains – the nonpathogenic K-12 strain MG1655, two strains of enterohemorrhagic E. coli O157:H7 (EDL933 and Sakai) and the uropathogenic E. coli O6:K2:H1 strain CFT073 – have been completely sequenced [30, 32–34] (see Table 5.1). The availability of these complete genome sequences

5.3 Comparative Genomics of Escherichia coli

allows detailed comparison of the genetic and structural genome variability not only of different E. coli strains, but also of different pathotypes. A genomic comparison of strains CFT073, EDL933, and MG1655 revealed that only 39.2% (2996 genes) of their combined set of proteins are common to all three strains [34], underlining the astonishing diversity among E. coli isolates. The two E. coli O157:H7 genomes are extremely similar. However, comparison of either O157: H7 sequence to E. coli K-12 reveals that an extraordinary amount of gene loss and gain has occurred since these strains last shared a common ancestor about 4.5 million years ago. The 5.5-Mbp O157:H7 genome is nearly 1 Mbp larger than that of E. coli K-12. While roughly 4.1 Mbp of the chromosome was very similar between O157:H7 and K-12, this conserved “backbone” was interrupted by hundreds of “islands” and “islets” of sequences specific to one strain or the other. 0.53 Mbp of E. coli MG1655-specific sequences are absent from the EDL933 genome, which itself contains over 1.4 Mbp of DNA without a counterpart in the K-12 genome. These sequences are clustered in 177 regions ranging from 50 kbp to nearly 90 kbp in length and comprise more than 1000 putative ORFs, including some that have been previously associated with O157:H7 virulence, as well as many new candidate pathogenicity factors, such as iron utilization and host cell adherence-associated genes. Surprisingly, two large islands were discovered, each containing nearly identical genes encoding urease in a strain typically characterized by its lack of urease activity in clinical assays. However, this gene cluster could be expressed in an E. coli K-12 background, indicating that regulation of these determinants which may contribute to the acid tolerance of enterohemorrhagic E. coli (EHEC) is different in various E. coli backgrounds [35]. A large fraction of the genomic differences can be accounted for by the activity of mobile genetic elements. Nearly 40% of the O157-specific elements are found in one of at least 18 cryptic prophage or the one intact bacteriophage (933W) which also contains the most characteristic virulence genes of O157:H7, i.e., those coding for the Shiga toxins [33]. Whole-genome structure comparison of several O157 strains not only demonstrated that the O157-specific sequences are highly conserved among the strains, but also showed that an unexpectedly high genomic diversity exists. Prophages especially exhibit extensive structural and positional diversity, suggesting that variation of prophages is one of the most important factors in generating genome diversity among O157 strains [36, 37]. The 5.2-Mbp genome sequence of uropathogenic E. coli (UPEC) strain CFT073 [34] supported these views, and comparison with an O157:H7 and a K-12 genome revealed 2996 genes of the core chromosome common to all three strains. However, the comparison also reveals 1303 Mbp of DNA only present in the UPEC strain but absent in both of the other strains. E. coli K-12 and O157:H7 are more closely related to each other than either is to this extraintestinal pathogenic E. coli (ExPEC) strain. Although the island contents differ, many of the same chromosomal sites serve as chromosomal insertion sites of these strain-specific elements. According to the genome sequence, there are a number of previously unknown toxins and adhesins that may contribute to pathogenesis in the human urogenital tract. There are ongoing projects to sequence the full genome of other ExPEC and

93

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intestinal pathogenic E. coli (IPEC) strains (see Table 5.2), and their comparison with existing sequences is expected as well to reveal genes specific for extraintestinal or intestinal pathogens, but not found in commensal strains, as to identify genes responsible for distinct aspects of their diseases. The majority of strain-/ pathotype-specific regions are found at limited positions in the individual chromosomes, suggesting that the strain-specific elements accumulated over time by repeated horizontal gene transfer, frequently with successive transfers of different elements into the locus of the core chromosome. The fate of horizontally transferred sequences depends on their cost or benefit to the bacteria. The fact that so many different horizontally acquired sequences exist in islands differentiating these closely related E. coli strains suggests that many of them are temporary residents of the genome or provide an advantage specific to the individual lifestyle of particular strains. 5.3.2 Comparative Genomics Using DNA Arrays

The genomes of several different pathogenic and nonpathogenic E. coli as well as those of Shigella isolates have been compared in different studies by DNA–DNA hybridization using DNA arrays. Comparative genomic hybridization using a K-12-specific array allows detection of chromosomal regions that have been replaced by acquisition and chromosomal insertion of horizontally acquired DNA and gene loss. The K-12-specific genome content of different E. coli isolates has been compared to assess the incidence of gene transfer and gene loss for E. coli genome evolution: a total of 67 events, including 37 additions and 30 deletions, were required to account for the distribution of all genes present in the chromosome of K-12 strain MG1655 [38]. The genome content of 26 different ExPEC and IPEC strains was compared using a K-12-specific array in combination with an array comprising many probes specific for virulence-associated genes of ExPEC and IPEC. The conserved chromosomal E. coli backbone was assessed to comprise about 3100 genes and the majority of variable ORFs of the K-12 genome were functionally grouped as hypothetical, unclassified, or as prophages and cell envelope genes. Analysis of the genome content using the so-called “E. coli pathoarray” revealed that many virulence-associated ORFs of ExPEC are also widespread among nonpathogenic, commensal isolates [39]. Another more recent study of 22 ExPEC, IPEC, and Shigella strains confirmed the range of order of the common backbone of the E. coli genome as estimated by Dobrindt and coworkers [39] to contain about 2800 ORFs. The mosaic distribution of absent regions indicated that the genomes of pathogenic strains were highly diversified because of insertions and deletions [40]. An array based on the E. coli K-12 genome has also been used to determine the common E. coli core gene pool in O26 verotoxigenic E. coli strains [41].

5.3 Comparative Genomics of Escherichia coli

5.3.3 Mobile Genetic Elements and Evolution of Pathogenic E. coli

Mobilizable plasmids and bacteriophages represent key elements to enable bacteria to exchange genetic information by conjugation and transduction. During recent years, genomic research revealed that pathogenicity islands (PAIs) have also played a major role for the transformation of avirulent into virulent bacteria. Only limited knowledge exists about the origin of PAIs, but it has been speculated that they may derive from integrating plasmids or phages that have lost the genes required for replication and self-transfer in exchange for a more stable association and inheritance with the host chromosome [11]. Evidence to support this model was in the findings that PAIs often encode phage-like integration systems, as has already been discussed in detail. Additional analysis of PAI-specific DNA sequences revealed the presence of regions with similarity to phage- and/or plasmid-related genes or sequences [42, 43], which is another indication that PAIs may have evolved from other mobile genetic elements. A relationship between PAIs and plasmids is also supported by the fact that island-encoded properties have been detected on plasmids in related species or a different isolate of the same species. Examples are aerobactin-encoding genes that are often part of plasmids that additionally encode colicin ColV. As the aer genes of Shigella island SHI-2 are associated with regions of sequence homology to ColV-specific genes, parts of SHI-2 may have evolved from an integrating plasmid [27, 44]. Alternatively, some PAIs may have been acquired by mechanisms related to conjugation. An increasing number of elements (termed “conjugative transposons,” “constins,” or “conjugative genomic islands”) have been discovered in gram-negative bacteria including E. coli that are normally integrated in the chromosome, but can excise in a precise manner to be subsequently transferred to recipient cells by conjugation [45]. In gram-negative bacteria, some of these elements exhibit several features reminiscent of PAIs, including a site-specific recombinase and lack of autonomous replication. In contrast to PAIs, they carry genes required for mating-pair formation and conjugative DNA metabolism which are related to plasmid-encoded conjugation systems [46]. 5.3.4 Genomic Islands/Pathogenicity Islands

Many virulence-associated genes of pathogenic E. coli are frequently located on (formerly) mobile genetic elements such as bacteriophages, plasmids, and genomic islands. A variety of pathogenic E. coli isolates have been analyzed with respect to the presence and spread of virulence-associated genes. The presence of distinct genomic islands (GEIs) encoding virulence determinants, designated pathogenicity islands (PAIs), has been shown for a broad variety of bacteria including various strains of the different E. coli pathotypes [10, 11, 47]. Studies on their prevalence and phylogenetic relation in, e.g., ExPEC have been undertaken recently [48–52]. As a result, ExPEC virulence factors exhibit distinct patterns of

95

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phylogenetic distribution. This provides evidence of both vertical and horizontal transmission of the corresponding virulence-associated genes as well as of hostspecific associations and strong associations among different virulence-associated genes. The GEIs that have been identified so far carry a variety of virulence factors that represent the entire spectrum of bacterial virulence-associated genes, including adhesins, toxins, iron uptake systems, secretion systems, and strategies to avoid the host defense mechanisms. E. coli PAIs that have been analyzed in detail are listed in Table 5.3. GEIs represent characteristic features of E. coli and Shigella and seem to play a important role for evolution of different variants or pathotypes. A considerable fraction of the entire genome represents PAIs with significant structural and functional diversity. There are hardly any pathogenic enterobacterial species with only one PAI per strain, and the number of multiple PAIs detected in individual isolates is constantly increasing [43, 53–56]. This is corroborated by the complete genome sequences: in pathogenic E. coli strains, up to 13 PAI-like genetic entities have been identified in one strain [32–34]. Eight chromosomal regions in S. flexneri 2a exhibit some features of PAIs [14, 15]. The growing sequence information about PAIs of different gram-negative species demonstrates that horizontal gene transfer and homologous recombination play a pivotal role in the evolution of PAIs and enterobacteria. Comparison of PAIs of E. coli reveals that identical or almost identical PAIs can be detected in different enterobacterial species, pathotypes, or strains. Typical examples are the so-called “high pathogenicity island” (HPI), initially described in pathogenic yersiniae, and the “locus of enterocyte effacement” (LEE). However, many PAIs have a mosaic-like, modular structure, and although many of them superficially resemble each other with respect to the presence and/or genetic linkage of certain virulence determinants, great variability exists with regard to PAI composition, structural organization, and chromosomal localization even among strains of the same patho- or serotype [43, 51, 55, 57]. Recently, even the LEE island in E. coli, which is generally considered to be a clonal unit inside a clonal host and thus expected to evolve as a single unit, has been shown to exhibit a mosaic-like composition [58–60]. Many PAI regions exhibit notable homology to fragments of mobile genetic elements such as bacteriophages and enterobacterial virulence plasmids. Furthermore, multiple copies of accessory DNA elements or fragments thereof that are present on islands in one genome facilitate homologous recombination within one or between different islands or horizontally acquired DNA elements, thus leading to rearrangements, deletions, and acquisition of “foreign” DNA. Comparison of the overall genome structure and genetic organization of GEIs of nonpathogenic probiotic E. coli strain Nissle 1917 (O6:K5:H1) with PAIs of UPEC O6 strains CFT073 and 536 showed that the islands of nonpathogenic E. coli strain Nissle 1917 resemble more closely those of UPEC strain CFT073 than those of UPEC strain 536 [61] (see also Fig. 5.1). Although the overall genetic structure of the investigated GEIs is very similar, important differences exist which are responsible for the nonpathogenic nature of strain Nissle 1917, whereas strain CFT073 is uropathogenic. The pheV-associated PAI of strain CFT073 carries

5.3 Comparative Genomics of Escherichia coli

the complete hly and pap gene clusters coding for the important virulence factors a-hemolysin and P fimbriae, respectively. These determinants, together with other putative hypothetical ORFs, represent a 30-kbp region which is presumably of crucial importance for this strain’s virulence properties. Interestingly, only a fragmented pap operon is present in a similar DNA context on GEI IINissle 1917 associated with a transposon-like element. Comparing the “left and right” halves of GEI IINissle1917 and the pheV-associated island of strain CFT073, it is clearly visible that there are many inversions and rearrangements in the left half of GEI IINissle1917, whereas the right half is rather colinear with the corresponding counterpart in strain CFT073 (Fig. 5.2). During evolution of GEI IINissle 1917 from a pheV-associated CFT073-like island, the intact pap gene cluster has probably been disrupted and partially deleted due to insertion of IS1 and IS10 elements and consecutive

Fig. 5.1 Comparison of the overall genome structure of three E. coli O6 strains. The chromosomal localization and the presence of virulence-associated genes on genomic islands/pathogenicity islands (gray bars) as well as of other virulence-associated gene clusters (white bars) of two uropathogenic E. coli O6 strains, CFT073 and 536, and of one nonpathogenic probiotic E. coli O6 strain, Nissle 1917, is indicated. The chromosomal maps are based on the E. coli K-12 strain MG1655 chromosome. fim, type 1 fimbrial determinant; mch/mcm, microcin determinants; foc, F1C fimbrial determinant;

iro, salmochelin determinant; HPI, high pathogenicity island; wb*O6, O6 LPS side chain biosynthesis determinant; sat, secreted autotransporter toxin determinant; iuc, aerobactin determinant; iha, IrgA homologue (Iha) adhesin determinant; kps, capsule determinant; chu, E. coli hemin uptake determinant; cva, colicin V export-related gene; wa*, LPS core biosynthesis determinant; pap, P fimbrial determinant; prf, P-related fimbriae determinant; pix, pilus involved in E. coli X2194 adhesion determinant; hly, a-hemolysin determinant; PAI, pathogenicity island.

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5.3 Comparative Genomics of Escherichia coli 3 Fig. 5.2 Comparison of the genetic organization of GEI IINissle 1917 and the pheV-associated PAI of E. coli strain CFT073 (A) demonstrating the loss of the a-hemolysin-encoding determinant (hly) and of large parts of the P fimbrial operon (pap) in strain Nissle 1917. The DNA sequences comparison of the two islands is visualized using Artemis and ACT [104].

Identical regions of the two islands are highlighted in red. Functionally related DNA regions are indicated by different colors as shown. (B) Enlarged section of GEI IINissle1917 comprising the partially deleted P fimbrial determinant. (This figure also appears with the color plates.)

recombination events. These events were important for the evolution of an ancestor of strain Nissle 1917 as they were responsible for inactivation of the P-fimbrial operon (and probably for the loss of the a-hemolysin-encoding determinant as well), reducing the hypothetical virulence capacity of the corresponding strain. According to the available data on genome content and organization of the E. coli O6 strains Nissle 1917, CFT073, and 536, it is tempting to speculate on the evolution of the nonpathogenic, probiotic character of the first strain and of uropathogenicity of the latter two isolates. Strain Nissle 1917 is characterized by a specific combination of traits which enable successful survival and colonization of the human intestine. In addition, this strain does not express important virulence factors of uropathogenic E. coli due to gene loss by deletion and point mutations [61, 62]. Meanwhile, raw genome data of nonpathogenic probiotic E. coli strain Nissle 1917 support these results and confirm that this strain’s 5.1-Mbp genome is more similar to that of UPEC strain CFT073 than to the other completely sequenced E. coli strains [63]. An interesting aspect of PAI evolution may also be duplication of entire islands. The genome of the EHEC O157:H7 strain EDL933 contains two PAI-like structures (the so-called O-islands #43 and #48) inserted next to the serW and serX tRNA-encoding genes, which comprise 106 genes and include those for tellurite resistance and urease production [33]. The sepsis strain AL862 carries two 61-kbp PAIs which are located at pheV and pheU and which contain the afimbrial adhesin variant 8 (afa8) gene cluster. Whether they are real duplicates will have to be confirmed by sequence determination of both islands [56]. 5.3.5 Plasmids and Bacteriophages

Whereas substantial knowledge has been acquired on the genetic structure and diversity of GEIs in E. coli, relatively few data exist on comparative genomics of E. coli plasmids and bacteriophages. The complete genome sequences of four E. coli strains indicate that these strains differ considerably in number of prophage-related sequences [30, 32–34]. It is well known that genes coding for different adhesins, resistance genes, and the heat-labile and the heat-stable enterotoxins of ETEC strains are localized on plasmids [64–67]. F17 fimbrial adhesin determinants have been discovered together with genes coding for the cytotoxic necrotizing factor 2 on virulence plasmids of necrotoxigenic E. coli strains [68]. Many

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EPEC strains contain large EPEC adherence factor (EAF) plasmids which carry the bfp locus coding for bundle forming pilus and the per genes whose gene products are important for the regulation of LEE-encoded gene expression [69]. The EAST1 toxin is frequently encoded on EAF plasmids [70]. The presence of the socalled pO157 plasmid is characteristic of the majority of EHEC strains. This plasmid carries the ehx genes coding for an enterohemolysin as well as for a catalase– peroxidase (katP) and potential adherence factors [105]. Several other plasmids ranging in size from 2 kbp to 87 kbp have been described in O157:H7 strains, too. Their composition and importance for virulence of these strains is unclear. Most of the EAEC strains carry large plasmids which share a high degree of homology [71]. The aggregative adherence fimbriae 1-encoding determinant has been detected on a 60-MDa plasmid which may also harbor the EAST1 toxin gene [70]. EIEC strains share a large pInv plasmid (140 MDa) with S. flexneri. This plasmid carries the invasion-related genes which encode a type III secretion apparatus (mxi, spa) as well as secreted proteins (ipa) involved in the invasion phenotype and a toxin designated Shigella enterotoxin 2 [72]. Other genes providing intestinal E. coli strains with advantageous properties, e.g., resistance to antibiotics, expression of colicins and siderophores, are plasmid-encoded as well. Colicin-encoding plasmids can also harbor siderophore- or other virulence-associated determinants [73–76]. The fact that gene blocks located on GEIs can also be present on plasmids indicates that a similar heterogeneity and diversity may exist among closely related plasmids as among functionally related islands in E. coli. The different types of shigatoxins (Stx), the major virulence factor of enterohemorrhagic E. coli (EHEC) strains, are usually encoded on temperate bacteriophages [77]. Another virulence-associated gene cluster frequently encoded on bacteriophages in E. coli encodes the cytolethal distending toxin. Whereas the cdtIII determinant of ExPEC strain 1404 is plasmid-encoded [78], similar genes in EHEC strain 493/89 may have been acquired by bacteriophage transduction [79]. 5.3.6 Genetic Diversity Among Extraintestinal Pathogenic E. coli

In recent years, knowledge about virulence traits and evolution of ExPEC has accumulated. ExPEC strains cause frequent infections in man and animals. The corresponding isolates are distinct from normal commensal and intestinal pathogenic E. coli isolates in that they typically derive from different phylogenetic groups and lack distinctive virulence factors characteristic of extraintestinal types of infection. Instead, they exhibit considerable genome diversity and possess a broad range of virulence factors including toxins, siderophores, adhesins, lipopolysaccharides, polysaccharide capsules, proteases, and invasins which are frequently encoded on islands and other mobile DNA elements [80]. Suppression subtractive hybridization analysis of avian ExPEC isolates further underlined the marked genome plasticity among human and avian ExPEC isolates. Furthermore, it turned out that even among strains causing the same disease, different alternative virulence factors can be involved in a “mix and match” combinatorial fashion

5.4 Conclusions

at each step of the infection [81, 82]. It has been shown that a considerable fraction of genetic information of ExPEC, which has so far been considered as virulenceassociated, is also present in many commensal, probiotic E. coli isolates [61, 83]. Thus, many of these features can be considered rather as contributing to fitness (e.g., iron uptake systems, bacteriocins, proteases, fimbriae, and other adhesins), thereby generally increasing adaptability, competitiveness, and the ability to colonize the human body efficiently, than as typical virulence factors directly involved in infection. Whether a commensal E. coli will develop into a pathogen depends not only on the acquisition of fitness-conferring genetic information enabling successful colonization of the host, but also requires the presence of functional genes directly contributing to pathogenesis. This highlights the thin line between “virulence” and “fitness” or “colonization” factors and questions the definition of several “ExPEC virulence factors.”

5.4 Conclusions

Comparative genomics analysis of E. coli and S. flexneri revealed that horizontal gene transfer, gene loss, and IS element-mediated chromosomal rearrangements play important roles in the evolution of these pathogens. Strain differences are not exclusively restricted to large islands, but also occur in a considerable number of smaller gene clusters coding for integrative functions such as nutrient utilization or surface modification. There is no doubt that the large PAIs are of major importance in conferring a virulent phenotype; however, the smaller clusters are of additional benefit in the course of an infection. Comparison of the available complete genome sequences revealed the genetic diversity underlying the phenotypic diversity of this species. Gene acquisition and loss is extensive, providing different lineages with distinct metabolic, pathogenic, and other capabilities. No clear distinction can be drawn between pathogenic and commensal E. coli strains, particularly in regard to extraintestinal disease. As colonizing sites outside the gut are unlikely to provide any selective advantage in terms of transmissibility, it is clear that any so-called “extraintestinal virulence factors” are likely to have evolved to enhance survival in the gut and/or transmission between hosts, and therefore will be shared with at least some commensal strains. In addition, genomic islands are seldom fixed, as first thought, and bear the potential for ongoing rearrangements, deletions, and insertions. Accessory DNA elements especially, such as IS elements and transposons, play a pivotal role during genome plasticity and evolution in Shigella and E. coli. It is becoming more and more evident that genome evolution in these bacteria cannot be described by a simple “backbone and flexible gene pool” model, but also by “palimpsest” where parts of the genome have seen repeated insertions and deletions. There is still considerable interest in sequencing additional E. coli and other Shigella genomes. The existing data raise important questions about the levels and mechanisms generating and maintaining genome variability within and between

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E. coli and Shigella populations that will require substantial genome-scale sequence information to resolve. Furthermore, knowledge of all proteins expressed in a given bacterial pathogen will provide the entire repertoire of surface proteins that are potential vaccine candidates. Genome comparison between closely related pathogenic and nonpathogenic variants will reveal factors involved in pathogenicity, and furthermore it is anticipated that factors that may direct host specificity of, e.g., human and animal ExPEC isolates could be identified by pathogenomics.

Acknowledgments

Our work relating to this topic was supported by the Deutsche Forschungsgemeinschaft (Sonderforschungsbereich 479), by the “Bayerische Forschungsstiftung”, the Bundesministerium fr Bildung und Forschung (BMBF) competence network “Pathogenomics”, and by the European Union (COLIRISK). We thank G. Gottschalk (Gttingen) and C. Buchrieser (Paris) for fruitful collaboration.

References 1 Woese, C. R. 1987. Bacterial evolution. 2

3

4

5

6

Microbiol Rev. 51:221–271. Pupo, G. M., R. Lan, and P. R. Reeves. 2000. Multiple independent origins of Shigella clones of Escherichia coli and convergent evolution of many of their characteristics. Proc Natl Acad Sci U S A. 97:10567–10572. Achtman, M., and G. Pluschke. 1986. Clonal analysis of descent and virulence among selected Escherichia coli. Annu Rev Microbiol. 40:185–210. Maslow, J. N., T. S. Whittam, C. F. Gilks, R. A. Wilson, M. E. Mulligan, K. S. Adams, and R. D. Arbeit. 1995. Clonal relationships among bloodstream isolates of Escherichia coli. Infect Immun. 63:2409–2417. Boyd, E. F., and D. L. Hartl. 1998. Chromosomal regions specific to pathogenic isolates of Escherichia coli have a phylogenetically clustered distribution. J Bacteriol. 180:1159–1165. Reid, S. D., C. J. Herbelin, A. C. Bumbaugh, R. K. Selander, and T. S. Whittam. 2000. Parallel evolution of viru-

7

8

9

10

11

12

lence in pathogenic Escherichia coli. Nature. 406:64–67. Wang, F. S., T. S. Whittam, and R. K. Selander. 1997. Evolutionary genetics of the isocitrate dehydrogenase gene (icd) in Escherichia coli and Salmonella enterica. J Bacteriol. 179:6551–6559. Lawrence, J. G., and H. Ochman. 1998. Molecular archaeology of the Escherichia coli genome. Proc Natl Acad Sci U S A. 95:9413–9417. Ochman, H., J. G. Lawrence, and E. A. Groisman. 2000. Lateral gene transfer and the nature of bacterial innovation. Nature. 405:299–304. Hacker, J., and J. B. Kaper eds. 2002. Pathogenicity islands and the evolution of pathogenic microbes. Springer, Berlin Heidelberg New York. Dobrindt, U., B. Hochhut, U. Hentschel, and J. Hacker. 2004. Genomic islands in pathogenic and environmental microorganisms. Nat Rev Microbiol. 2:414–424. Jennison, A. V., and N. K. Verma. 2004. Shigella flexneri infection: pathogenesis and vaccine development. FEMS Microbiol Rev. 28:43–58.

References 13 Cossart, P., and P. J. Sansonetti. 2004.

14

15

16

17

18

19

20

Bacterial invasion: the paradigms of enteroinvasive pathogens. Science. 304:242–248. Jin, Q., Z. Yuan, J. Xu, Y. Wang, Y. Shen, W. Lu, J. Wang, H. Liu, J. Yang, F. Yang, X. Zhang, J. Zhang, G. Yang, H. Wu, D. Qu, J. Dong, L. Sun, Y. Xue, A. Zhao, Y. Gao, J. Zhu, B. Kan, K. Ding, S. Chen, H. Cheng, Z. Yao, B. He, R. Chen, D. Ma, B. Qiang, Y. Wen, Y. Hou, and J. Yu. 2002. Genome sequence of Shigella flexneri 2a: insights into pathogenicity through comparison with genomes of Escherichia coli K12 and O157. Nucleic Acids Res. 30:4432–4441. Wei, J., M. B. Goldberg, V. Burland, M. M. Venkatesan, W. Deng, G. Fournier, G. F. Mayhew, G. Plunkett, 3rd, D. J. Rose, A. Darling, B. Mau, N. T. Perna, S. M. Payne, L. J. Runyen– Janecky, S. Zhou, D. C. Schwartz, and F. R. Blattner. 2003. Complete genome sequence and comparative genomics of Shigella flexneri serotype 2a strain 2457T. Infect Immun. 71:2775–2786. Ochman, H., and N. A. Moran. 2001. Genes lost and genes found: evolution of bacterial pathogenesis and symbiosis. Science. 292:1096–1099. Sokurenko, E. V., D. L. Hasty, and D. E. Dykhuizen. 1999. Pathoadaptive mutations: gene loss and variation in bacterial pathogens. Trends Microbiol. 7:191– 195. Maurelli, A. T., R. E. Fernandez, C. A. Bloch, C. K. Rode, and A. Fasano. 1998. “Black holes” and bacterial pathogenicity: a large genomic deletion that enhances the virulence of Shigella spp. and enteroinvasive Escherichia coli. Proc Natl Acad Sci U S A. 95:3943–3948. Fernandez, I. M., M. Silva, R. Schuch, W. A. Walker, A. M. Siber, A. T. Maurelli, and B. A. McCormick. 2001. Cadaverine prevents the escape of Shigella flexneri from the phagolysosome: a connection between bacterial dissemination and neutrophil transepithelial signaling. J Infect Dis. 184:743–753. Parkhill, J., G. Dougan, K. D. James, N. R. Thomson, D. Pickard, J. Wain, C. Churcher, K. L. Mungall, S. D. Bentley, M. T. Holden, M. Sebaihia, S. Baker,

D. Basham, K. Brooks, T. Chillingworth, P. Connerton, A. Cronin, P. Davis, R. M. Davies, L. Dowd, N. White, J. Farrar, T. Feltwell, N. Hamlin, A. Haque, T. T. Hien, S. Holroyd, K. Jagels, A. Krogh, T. S. Larsen, S. Leather, S. Moule, P. O’Gaora, C. Parry, M. Quail, K. Rutherford, M. Simmonds, J. Skelton, K. Stevens, S. Whitehead, and B. G. Barrell. 2001. Complete genome sequence of a multiple drug resistant Salmonella enterica serovar Typhi CT18. Nature. 413:848–852. 21 Venkatesan, M. M., M. B. Goldberg, D. J. Rose, E. J. Grotbeck, V. Burland, and F. R. Blattner. 2001. Complete DNA sequence and analysis of the large virulence plasmid of Shigella flexneri. Infect Immun. 69:3271–3285. 22 Buchrieser, C., P. Glaser, C. Rusniok, H. Nedjari, H. D’Hauteville, F. Kunst, P. Sansonetti, and C. Parsot. 2000. The virulence plasmid pWR100 and the repertoire of proteins secreted by the type III secretion apparatus of Shigella flexneri. Mol Microbiol. 38:760–771. 23 Lan, R., G. Stevenson, and P. R. Reeves. 2003. Comparison of two major forms of the Shigella virulence plasmid pINV: positive selection is a major force driving the divergence. Infect Immun. 71:6298–6306. 24 Brssow, H., C. Canchaya, and W. D. Hardt. 2004. Phages and the evolution of bacterial pathogens: from genomic rearrangements to lysogenic conversion. Microbiol Mol Biol Rev. 68:560–602. 25 Allison, G. E., and N. K. Verma. 2000. Serotype-converting bacteriophages and O-antigen modification in Shigella flexneri. Trends Microbiol. 8:17–23. 26 Al-Hasani, K., I. R. Henderson, H. Sakellaris, K. Rajakumar, T. Grant, J. P. Nataro, R. Robins-Browne, and B. Adler. 2000. The sigA gene which is borne on the she pathogenicity island of Shigella flexneri 2a encodes an exported cytopathic protease involved in intestinal fluid accumulation. Infect Immun. 68:2457–2463. 27 Vokes, S. A., S. A. Reeves, A. G. Torres, and S. M. Payne. 1999. The aerobactin iron transport system genes in Shigella

103

104

5 Pathogenomics of Escherichia coli and Shigella Species flexneri are present within a pathogenicity island. Mol Microbiol. 33:63–73. 28 Purdy, G. E., and S. M. Payne. 2001. The SHI-3 iron transport island of Shigella boydii 0-1392 carries the genes for aerobactin synthesis and transport. J Bacteriol. 183:4176-4182. 29 Lan, R., and P. R. Reeves. 2002. Escherichia coli in disguise: molecular origins of Shigella. Microbes Infect. 4:1125– 1132. 30 Blattner, F. R., G. Plunkett, 3rd, C. A. Bloch, N. T. Perna, V. Burland, M. Riley, J. Collado-Vides, J. D. Glasner, C. K. Rode, G. F. Mayhew, J. Gregor, N. W. Davis, H. A. Kirkpatrick, M. A. Goeden, D. J. Rose, B. Mau, and Y. Shao. 1997. The complete genome sequence of Escherichia coli K-12. Science. 277:1453– 1474. 31 Bergthorsson, U., and H. Ochman. 1998. Distribution of chromosome length variation in natural isolates of Escherichia coli. Mol Biol Evol. 15:6–16. 32 Hayashi, T., K. Makino, M. Ohnishi, K. Kurokawa, K. Ishii, K. Yokoyama, C. G. Han, E. Ohtsubo, K. Nakayama, T. Murata, M. Tanaka, T. Tobe, T. Iida, H. Takami, T. Honda, C. Sasakawa, N. Ogasawara, T. Yasunaga, S. Kuhara, T. Shiba, M. Hattori, and H. Shinagawa. 2001. Complete genome sequence of enterohemorrhagic Escherichia coli O157:H7 and genomic comparison with a laboratory strain K-12. DNA Res. 8:11–22. 33 Perna, N. T., G. Plunkett, 3rd, V. Burland, B. Mau, J. D. Glasner, D. J. Rose, G. F. Mayhew, P. S. Evans, J. Gregor, H. A. Kirkpatrick, G. Posfai, J. Hackett, S. Klink, A. Boutin, Y. Shao, L. Miller, E. J. Grotbeck, N. W. Davis, A. Lim, E. T. Dimalanta, K. D. Potamousis, J. Apodaca, T. S. Anantharaman, J. Lin, G. Yen, D. C. Schwartz, R. A. Welch, and F. R. Blattner. 2001. Genome sequence of enterohaemorrhagic Escherichia coli O157:H7. Nature. 409:529–533. 34 Welch, R. A., V. Burland, G. Plunkett, 3rd, P. Redford, P. Roesch, D. Rasko, E. L. Buckles, S. R. Liou, A. Boutin, J. Hackett, D. Stroud, G. F. Mayhew, D. J. Rose, S. Zhou, D. C. Schwartz, N. T. Perna, H. L. Mobley, M. S. Don-

35

36

37

38

39

40

41

42

43

nenberg, and F. R. Blattner. 2002. Extensive mosaic structure revealed by the complete genome sequence of uropathogenic Escherichia coli. Proc Natl Acad Sci U S A. 99:17020–17024. Heimer, S. R., R. A. Welch, N. T. Perna, G. Posfai, P. S. Evans, J. B. Kaper, F. R. Blattner, and H. L. Mobley. 2002. Urease of enterohemorrhagic Escherichia coli: evidence for regulation by fur and a trans—acting factor. Infect Immun. 70:1027–1031. Ohnishi, M., K. Kurokawa, and T. Hayashi. 2001. Diversification of Escherichia coli genomes: are bacteriophages the major contributors? Trends Microbiol. 9:481–485. Ohnishi, M., J. Terajima, K. Kurokawa, K. Nakayama, T. Murata, K. Tamura, Y. Ogura, H. Watanabe, and T. Hayashi. 2002. Genomic diversity of enterohemorrhagic Escherichia coli O157 revealed by whole genome PCR scanning. Proc Natl Acad Sci U S A. 99:17043–17048. Ochman, H., and I. B. Jones. 2000. Evolutionary dynamics of full genome content in Escherichia coli. EMBO J. 19:6637–6643. Dobrindt, U., F. Agerer, K. Michaelis, A. Janka, C. Buchrieser, M. Samuelson, C. Svanborg, G. Gottschalk, H. Karch, and J. Hacker. 2003. Analysis of genome plasticity in pathogenic and commensal Escherichia coli isolates by use of DNA arrays. J Bacteriol. 185:1831–1840. Fukiya, S., H. Mizoguchi, T. Tobe, and H. Mori. 2004. Extensive genomic diversity in pathogenic Escherichia coli and Shigella strains revealed by comparative genomic hybridization microarray. J Bacteriol. 186:3911–3921. Anjum, M. F., S. Lucchini, A. Thompson, J. C. Hinton, and M. J. Woodward. 2003. Comparative genomic indexing reveals the phylogenomics of Escherichia coli pathogens. Infect Immun. 71:4674– 4683. Cheetham, B. F., and M. E. Katz. 1995. A role for bacteriophages in the evolution and transfer of bacterial virulence determinants. Mol Microbiol. 18:201– 208. Dobrindt, U., G. Blum-Oehler, G. Nagy, G. Schneider, A. Johann, G. Gottschalk,

References

44

45

46

47

48

49

50

51

and J. Hacker. 2002. Genetic structure and distribution of four pathogenicity islands (PAI I(536) to PAI IV(536)) of uropathogenic Escherichia coli strain 536. Infect Immun. 70:6365–6372. Moss, J. E., T. J. Cardozo, A. Zychlinsky, and E. A. Groisman. 1999. The selCassociated SHI-2 pathogenicity island of Shigella flexneri. Mol Microbiol. 33:74– 83. Burrus, V., G. Pavlovic, B. Decaris, and G. Guedon. 2002. Conjugative transposons: the tip of the iceberg. Mol Microbiol. 46:601–610. Schubert, S., S. Dufke, J. Sorsa, and J. Heesemann. 2004. A novel integrative and conjugative element (ICE) of Escherichia coli: the putative progenitor of the Yersinia high-pathogenicity island. Mol Microbiol. 51:837–848. Hacker, J., and E. Carniel. 2001. Ecological fitness, genomic islands and bacterial pathogenicity. A Darwinian view of the evolution of microbes. EMBO Rep. 2:376–381. Bingen-Bidois, M., O. Clermont, S. Bonacorsi, M. Terki, N. Brahimi, C. Loukil, D. Barraud, and E. Bingen. 2002. Phylogenetic analysis and prevalence of urosepsis strains of Escherichia coli bearing pathogenicity island-like domains. Infect Immun. 70:3216–3226. Bonacorsi, S., O. Clermont, V. Houdouin, C. Cordevant, N. Brahimi, A. Marecat, C. Tinsley, X. Nassif, M. Lange, and E. Bingen. 2003. Molecular analysis and experimental virulence of French and North American Escherichia coli neonatal meningitis isolates: identification of a new virulent clone. J Infect Dis. 187:1895–1906. Johnson, J. R., P. Delavari, M. Kuskowski, and A. L. Stell. 2001. Phylogenetic distribution of extraintestinal virulence–associated traits in Escherichia coli. J Infect Dis. 183:78–88. Johnson, J. R., T. T. O’Bryan, M. Kuskowski, and J. N. Maslow. 2001. Ongoing horizontal and vertical transmission of virulence genes and papA alleles among Escherichia coli blood isolates from patients with diverse-source bacteremia. Infect Immun. 69:5363– 5374.

52 Johnson, J. R., A. L. Stell, P. Delavari,

53

54

55

56

57

58

59

A. C. Murray, M. Kuskowski, and W. Gaastra. 2001. Phylogenetic and pathotypic similarities between Escherichia coli isolates from urinary tract infections in dogs and extraintestinal infections in humans. J Infect Dis. 183:897–906. Swenson, D. L., N. O. Bukanov, D. E. Berg, and R. A. Welch. 1996. Two pathogenicity islands in uropathogenic Escherichia coli J96: cosmid cloning and sample sequencing. Infect Immun. 64:3736–3743. Kao, J. S., D. M. Stucker, J. W. Warren, and H. L. Mobley. 1997. Pathogenicity island sequences of pyelonephritogenic Escherichia coli CFT073 are associated with virulent uropathogenic strains. Infect Immun. 65:2812–2820. Guyer, D. M., J. S. Kao, and H. L. Mobley. 1998. Genomic analysis of a pathogenicity island in uropathogenic Escherichia coli CFT073: distribution of homologous sequences among isolates from patients with pyelonephritis, cystitis, and catheter-associated bacteriuria and from fecal samples. Infect Immun. 66:4411–4417. Lalioui, L., and C. Le Bouguenec. 2001. afa–8 Gene cluster is carried by a pathogenicity island inserted into the tRNA(Phe) of human and bovine pathogenic Escherichia coli isolates. Infect Immun. 69:937–948. Redford, P., and R. A. Welch. 2002. Extraintestinal Escherichia coli as a model system for the study of pathogenicity islands. Curr Top Microbiol Immunol. 264:15–30. Castillo, A., L. E. Eguiarte, and V. Souza. 2005. A genomic population genetics analysis of the pathogenic enterocyte effacement island in Escherichia coli: the search for the unit of selection. Proc Natl Acad Sci U S A. 102:1542–1547. Rumer, L., J. Jores, P. Kirsch, Y. Cavignac, K. Zehmke, and L. H. Wieler. 2003. Dissemination of pheU- and pheV-located genomic islands among enteropathogenic (EPEC) and enterohemorrhagic (EHEC) E. coli and their possible role in the horizontal transfer of the locus of enterocyte effacement

105

106

5 Pathogenomics of Escherichia coli and Shigella Species

60

61

62

63

64

65

66

67

(LEE). Int J Med Microbiol. 292:463– 475. Jores, J., L. Rumer, and L. H. Wieler. 2004. Impact of the locus of enterocyte effacement pathogenicity island on the evolution of pathogenic Escherichia coli. Int J Med Microbiol. 294:103–113. Grozdanov, L., C. Raasch, J. Schulze, U. Sonnenborn, G. Gottschalk, J. Hacker, and U. Dobrindt. 2004. Analysis of the genome structure of the nonpathogenic probiotic Escherichia coli strain Nissle 1917. J Bacteriol. 186:5432–5441. Grozdanov, L., U. Zhringer, G. BlumOehler, L. Brade, A. Henne, Y. A. Knirel, U. Schombel, J. Schulze, U. Sonnenborn, G. Gottschalk, J. Hacker, E. T. Rietschel, and U. Dobrindt. 2002. A single nucleotide exchange in the wzy gene is responsible for the semirough O6 lipopolysaccharide phenotype and serum sensitivity of Escherichia coli strain Nissle 1917. J Bacteriol. 184:5912–5925. Sun, J., F. Gunzer, A. M. Westendorf, J. Buer, M. Scharfe, M. Jarek, F. Gssling, H. Blcker, and A. P. Zeng. 2005. Genomic peculiarity of coding sequences and metabolic potential of probiotic Escherichia coli strain Nissle 1917 inferred from raw genome data. J Biotechnol. 117:147–161. Bertin, A. 1998. Phenotypic expression of K88 adhesion alone or simultaneously with K99 and/or F41 adhesins in the bovine enterotoxigenic Escherichia coli strain B41. Vet Microbiol. 59:283–294. Gomez-Duarte, O. G., A. Ruiz-Tagle, D. C. Gomez, G. I. Viboud, K. G. Jarvis, J. B. Kaper, and J. A. Giron. 1999. Identification of lngA, the structural gene of longus type IV pilus of enterotoxigenic Escherichia coli. Microbiology. 145[Pt 7]:1809–1816. Mazaitis, A. J., R. Maas, and W. K. Maas. 1981. Structure of a naturally occurring plasmid with genes for enterotoxin production and drug resistance. J Bacteriol. 145:97–105. Mainil, J. G., G. Daube, E. Jacquemin, P. Pohl, and A. Kaeckenbeeck. 1998. Virulence plasmids of enterotoxigenic

68

69

70

71

72

73

74

75

76

Escherichia coli isolates from piglets. Vet Microbiol. 62:291–301. Mainil, J. G., J. Gerardin, and E. Jacquemin. 2000. Identification of the F17 fimbrial subunit- and adhesin-encoding (f17A and f17G) gene variants in necrotoxigenic Escherichia coli from cattle, pigs and humans. Vet Microbiol. 73:327–335. Donnenberg, M. S., J. A. Giron, J. P. Nataro, and J. B. Kaper. 1992. A plasmid-encoded type IV fimbrial gene of enteropathogenic Escherichia coli associated with localized adherence. Mol Microbiol. 6:3427–3437. Nataro, J. P., and J. B. Kaper. 1998. Diarrheagenic Escherichia coli. Clin Microbiol Rev. 11:142–201. Vial, P. A., R. Robins-Browne, H. Lior, V. Prado, J. B. Kaper, J. P. Nataro, D. Maneval, A. Elsayed, and M. M. Levine. 1988. Characterization of enteroadherent-aggregative Escherichia coli, a putative agent of diarrheal disease. J Infect Dis. 158:70–79. Nataro, J. P., J. Seriwatana, A. Fasano, D. R. Maneval, L. D. Guers, F. Noriega, F. Dubovsky, M. M. Levine, and J. G. Morris, Jr. 1995. Identification and cloning of a novel plasmid-encoded enterotoxin of enteroinvasive Escherichia coli and Shigella strains. Infect Immun. 63:4721–4728. Martinez, J. L., M. Herrero, and V. de Lorenzo. 1994. The organization of intercistronic regions of the aerobactin operon of pColV-K30 may account for the differential expression of the iucABCD iutA genes. J Mol Biol. 238:288–293. Otto, B. R., S. J. van Dooren, J. H. Nuijens, J. Luirink, and B. Oudega. 1998. Characterization of a hemoglobin protease secreted by the pathogenic Escherichia coli strain EB1. J Exp Med. 188:1091–1103. Gophna, U., A. Parket, J. Hacker, and E. Z. Ron. 2003. A novel ColV plasmid encoding type IV pili. Microbiology. 149:177–184. Gomez–Lus, R. 1998. Evolution of bacterial resistance to antibiotics during the last three decades. Int Microbiol. 1:279– 284.

References 77 Herold, S., H. Karch, and H. Schmidt.

78

79

80

81

82

83

84

85

2004. Shiga toxin-encoding bacteriophages – genomes in motion. Int J Med Microbiol. 294:115–121. Peres, S. Y., O. Marches, F. Daigle, J. P. Nougayrede, F. Herault, C. Tasca, J. De Rycke, and E. Oswald. 1997. A new cytolethal distending toxin (CDT) from Escherichia coli producing CNF2 blocks HeLa cell division in G2/M phase. Mol Microbiol. 24:1095–1107. Janka, A., M. Bielaszewska, U. Dobrindt, L. Greune, M. A. Schmidt, and H. Karch. 2003. Cytolethal distending toxin gene cluster in enterohemorrhagic Escherichia coli O157:H- and O157:H7: characterization and evolutionary considerations. Infect Immun. 71:3634– 3638. Johnson, J. R., and T. A. Russo. 2002. Extraintestinal pathogenic Escherichia coli: “the other bad E coli.” J Lab Clin Med. 139:155–162. Mokady, D., U. Gophna, and E. Z. Ron. 2005. Extensive gene diversity in septicemic Escherichia coli strains. J Clin Microbiol. 43:66–73. Schouler, C., F. Koffmann, C. Amory, S. Leroy-Setrin, and M. Moulin-Schouleur. 2004. Genomic subtraction for the identification of putative new virulence factors of an avian pathogenic Escherichia coli strain of O2 serogroup. Microbiology. 150:2973–2984. Hejnova, J., U. Dobrindt, R. Nemcova, C. Rusniok, A. Bomba, L. Frangeul, J. Hacker, P. Glaser, P. Sebo, and C. Buchrieser. 2005. Characterization of the flexible genome complement of the commensal Escherichia coli strain A0 34/86 (O83:K24:H31). Microbiology. 151:385–398. Schneider, G., U. Dobrindt, H. Brggemann, G. Nagy, B. Janke, G. Blum-Oehler, C. Buchrieser, G. Gottschalk, L. Emdy, and J. Hacker. 2004. The pathogenicity island-associated K15 capsule determinant exhibits a novel genetic structure and correlates with virulence in uropathogenic Escherichia coli strain 536. Infect Immun. 72:5993– 6001. Rasko, D. A., J. A. Phillips, X. Li, and H. L. Mobley. 2001. Identification of

DNA sequences from a second pathogenicity island of uropathogenic Escherichia coli CFT073: probes specific for uropathogenic populations. J Infect Dis. 184:1041–1049. 86 Cieslewicz, M., and E. Vimr. 1997. Reduced polysialic acid capsule expression in Escherichia coli K1 mutants with chromosomal defects in kpsF. Mol Microbiol. 26:237–249. 87 Houdouin, V., S. Bonacorsi, N. Brahimi, O. Clermont, X. Nassif, and E. Bingen. 2002. A uropathogenicity island contributes to the pathogenicity of Escherichia coli strains that cause neonatal meningitis. Infect Immun. 70:5865–5869. 88 Parreira, V. R., and C. L. Gyles. 2003. A novel pathogenicity island integrated adjacent to the thrW tRNA gene of avian pathogenic Escherichia coli encodes a vacuolating autotransporter toxin. Infect Immun. 71:5087–5096. 89 Elliott, S. J., L. A. Wainwright, T. K. McDaniel, K. G. Jarvis, Y. K. Deng, L. C. Lai, B. P. McNamara, M. S. Donnenberg, and J. B. Kaper. 1998. The complete sequence of the locus of enterocyte effacement (LEE) from enteropathogenic Escherichia coli E2348/69. Mol Microbiol. 28:1–4. 90 Jores, J., L. Rumer, S. Kiessling, J. B. Kaper, and L. H. Wieler. 2001. A novel locus of enterocyte effacement (LEE) pathogenicity island inserted at pheV in bovine Shiga toxin-producing Escherichia coli strain O103:H2. FEMS Microbiol Lett. 204:75–79. 91 Sperandio, V., J. B. Kaper, M. R. Bortolini, B. C. Neves, R. Keller, and L. R. Trabulsi. 1998. Characterization of the locus of enterocyte effacement (LEE) in different enteropathogenic Escherichia coli (EPEC) and Shiga-toxin producing Escherichia coli (STEC) serotypes. FEMS Microbiol Lett. 164:133–139. 92 Zhu, C., T. S. Agin, S. J. Elliott, L. A. Johnson, T. E. Thate, J. B. Kaper, and E. C. Boedeker. 2001. Complete nucleotide sequence and analysis of the locus of enterocyte Effacement from rabbit diarrheagenic Escherichia coli RDEC–1. Infect Immun. 69:2107–2115. 93 Tauschek, M., R. A. Strugnell, and R. M. Robins-Browne. 2002. Characterization

107

108

5 Pathogenomics of Escherichia coli and Shigella Species

94

95

96

97

98

99

and evidence of mobilization of the LEE pathogenicity island of rabbit-specific strains of enteropathogenic Escherichia coli. Mol Microbiol. 44:1533–1550. Mellies, J. L., F. Navarro-Garcia, I. Okeke, J. Frederickson, J. P. Nataro, and J. B. Kaper. 2001. espC Pathogenicity island of enteropathogenic Escherichia coli encodes an enterotoxin. Infect Immun. 69:315–324. Keller, R., J. G. Ordonez, R. R. de Oliveira, L. R. Trabulsi, T. J. Baldwin, and S. Knutton. 2002. Afa, a diffuse adherence fibrillar adhesin associated with enteropathogenic Escherichia coli. Infect Immun. 70:2681–2689. Schmidt, H., W. L. Zhang, U. Hemmrich, S. Jelacic, W. Brunder, P. I. Tarr, U. Dobrindt, J. Hacker, and H. Karch. 2001. Identification and characterization of a novel genomic island integrated at selC in locus of enterocyte effacement-negative, Shiga toxin-producing Escherichia coli. Infect Immun. 69:6863– 6873. Fleckenstein, J. M., D. J. Kopecko, R. L. Warren, and E. A. Elsinghorst. 1996. Molecular characterization of the tia invasion locus from enterotoxigenic Escherichia coli. Infect Immun. 64:2256– 2265. Bach, S., C. Buchrieser, M. Prentice, A. Guiyoule, T. Msadek, and E. Carniel. 1999. The high-pathogenicity island of Yersinia enterocolitica Ye8081 undergoes low-frequency deletion but not precise excision, suggesting recent stabilization in the genome. Infect Immun. 67:5091– 5099. Karch, H., S. Schubert, D. Zhang, W. Zhang, H. Schmidt, T. Olschlager, and J. Hacker. 1999. A genomic island,

termed high-pathogenicity island, is present in certain non-O157 Shiga toxin-producing Escherichia coli clonal lineages. Infect Immun. 67:5994–6001. 100 Schubert, S., A. Rakin, H. Karch, E. Carniel, and J. Heesemann. 1998. Prevalence of the “high-pathogenicity island” of Yersinia species among Escherichia coli strains that are pathogenic to humans. Infect Immun. 66:480–485. 101 Rajakumar, K., C. Sasakawa, and B. Adler. 1997. Use of a novel approach, termed island probing, identifies the Shigella flexneri she pathogenicity island which encodes a homolog of the immunoglobulin A protease-like family of proteins. Infect Immun. 65:4606–4614. 102 Luck, S. N., S. A. Turner, K. Rajakumar, H. Sakellaris, and B. Adler. 2001. Ferric dicitrate transport system (Fec) of Shigella flexneri 2a YSH6000 is encoded on a novel pathogenicity island carrying multiple antibiotic resistance genes. Infect Immun. 69:6012–6021. 103 Adhikari, P., G. Allison, B. Whittle, and N. K. Verma. 1999. Serotype 1a O-antigen modification: molecular characterization of the genes involved and their novel organization in the Shigella flexneri chromosome. J Bacteriol. 181:4711– 4718. 104 Rutherford, K., J. Parkhill, J. Crook, T. Horsnell, P. Rice, M. A. Rajandream, and B. Barrell. 2000. Artemis: sequence visualization and annotation. Bioinformatics. 16:944–945. 105 Brunder, W., Schmidt, H., Karch, H. (1996) KatP, a novel catalyse-peroxidase encoded by the Large plasmid of enterohaemorrhagic Escherichia coli O157:H7. Microbiology 142:3305–3315.

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6 Pathogenomics of Salmonella Species Helene Andrews-Polymenis and Andreas J. Bumler

6.1 Introduction

The genus Salmonella contains a group of closely related pathogens that can be differentiated by serology into 2463 serotypes [1]. The genus contains two species, Salmonella bongori (containing 20 different serotypes) and Salmonella enterica (containing 2443 different serotypes). S. enterica is further subdivided into six subspecies: enterica (subspecies I), salamae (subspecies II), arizonae (subspecies IIIa), diarizonae (subspecies IIIb), houtenae (subspecies IV), and indica (subspecies VI) [1]. Unlike members of the closely related genus Escherichia, which contains both pathogenic and commensal organisms, all members of the genus Salmonella are considered pathogenic for humans. However, Salmonella serotypes vary with regard to their natural host range and the disease manifestations associated with infection of different host species. Genomic comparison of Salmonella serotypes with closely related commensal organisms has revealed genetic differences that are important for their pathogenic lifestyle. Identification of genomic differences between Salmonella serotypes is beginning to provide new insights into the genetic basis of host adaptation and the pathogenesis of certain diseases. This chapter will review recent progress in understanding Salmonella pathogenesis and evolution as a result of whole genome sequencing.

6.2 Salmonella Signature Genes

What genetic features set Salmonella serotypes apart from closely related organisms such as E. coli? Comparison of bacterial genomes shows that Salmonella-specific DNA regions encode many of the biological properties that distinguish Salmonella serotypes from other members of the family Enterobacteriaceae. An alignment of the chromosomes of S. enterica serotype Typhimurium and E. coli K-12 provided the first evidence that their collinear genetic maps are interrupted by

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large DNA segments that are present in only one of these organisms [2, 3]. Highresolution global analysis of these genetic differences was initiated with the completion of the S. enterica serotype Typhimurium LT2 genome sequence [4]. McClelland and coworkers used genomic subtraction to identify 935 genes present in S. enterica serotype Typhimurium (LT2), but absent from E. coli (K-12 and O157:H7), and from sample sequences of Klebsiella pneumoniae (MGH 78578) and Yersinia pestis (CO92) [5, 6]. Of these 935 genes, 224 were never scored as absent when genomic DNA of 22 Salmonella serotypes was hybridized to an LT2 microarray containing 4483 of the 4596 annotated S. Typhimurium ORFs [5]. Furthermore, 56 of these 224 genes were reliably identified as being present in all 22 Salmonella serotypes by microarray and were designated Salmonella “signature” genes. In a similar study, Falkow and coworkers hybridized genomic DNA from 18 different Salmonella serotypes to a S. enterica serotype Typhimurium strain LT2 microarray containing 4169 ORFs. This work revealed a list of 2244 genes shared by all members of the genus [7]. Some 1931 of these genes are also present in the genome of E. coli K-12, thus leaving a list of some 313 Salmonella signature genes. For Salmonella signature genes whose functions are known it seems clear that their presence explains many of the biological features that distinguish Salmonella serotypes from closely related enterobacteria such as E. coli. One important biochemical property for differentiating Salmonella serotypes from E. coli or Shigella serotypes is their ability to produce hydrogen sulfide. This biochemical reaction is part of a complex pathway (involving 88 genes) that allows Salmonella serotypes to utilize 1,2-propanediol and ethanolamine as carbon sources under anaerobic conditions [8]. Genes required for degradation of ethanolamine are encoded by the eutSPQTDMNEJGHABCLK operon whose expression is controlled by the adjacent eutR regulatory gene [9, 10]. Genes required for degradation of 1,2-propanediol are encoded by a gene cluster consisting of porR, pduF, and the pduABCDEGHJKLMNOPQSTUVWX operon [11]. The degradation of 1,2-propanediol and ethanolamine furthermore requires the cofactor cobalamin (vitamin B12), whose biosynthesis involves genes encoded by the cobCD operon, cysG, and the cobI operon (cbiABCDETFGHJKLMNQOPUST) [12]. Cobalamin biosynthesis occurs only anaerobically in Salmonella serotypes [13, 14]. The only known electron acceptor supporting anaerobic growth of Salmonella serotypes with 1,2-propanediol or ethanolamine as carbon source is tetrathionate [8]. The genes involved in using tetrathionate as a terminal respiratory electron acceptor are encoded by the divergently described operons ttrSR and ttrBCA [15]. During anaerobic growth on 1,2-propanediol or ethanolamine, Salmonella serotypes reduce tetrathionate to thiosulfate, which is then further reduced to hydrogen sulfide by enzymes encoded by the asrABC operon [16] and phsABC operon [17]. Although the cbiABCDETFGHJKLMNQOPUST operon, the cobCD operon, the porR pduF pduABCDEGHJKLMNOPQSTUVWX gene cluster, the ttrSR ttrBCA gene cluster, the asrABC operon, and the phsABC operon are located at different map positions on the S. Typhimurium genome, each operon contains Salmonella signature genes identified by microarray analysis (Fig. 6.1) [7], while only the eut operon and cysG are also present in E. coli. Despite the fact that these operons act in concert

6.2 Salmonella Signature Genes

during 1,2-propanediol and ethanolamine respiration, their map position and phylogenetic distribution suggests that each was either independently acquired by horizontal gene transfer in the genus Salmonella or lost by deletion from E. coli during divergence of their lineages. 1,2-Propanediol is produced by the fermentation of the common plant sugar rhamnose [18], suggesting that its utilization may contribute to intestinal colonization of Salmonella serotypes.

Fig. 6.1 Location on the S. enterica serotype Typhimurium LT2 chromosome of Salmonella signature genes involved in 1,2-propanediol respiration and epithelial invasion. The global transcriptional regulator CsrA controls expression of genes required for both phenotypic pathways.

A second characteristic that differentiates Salmonella serotypes from E. coli is the ability to cause invasive enteric infections resulting in an inflammatory diarrhea [19]. The genes important for invasion of the intestinal epithelium and the elicitation of an inflammatory response in the intestine are among the Salmonella signature genes identified by microarray analysis [5, 7]. Invasion of intestinal epithelial cells is mediated by a type III secretion system (T3SS-1) encoded by 31 genes located on Salmonella pathogenicity island 1 (SPI1) (Fig. 6.1) [20]. With the exception of the avrA gene [21], T3SS1 genes located on SPI1 are present in all Salmonella serotypes but absent from the genomes of related enterobacteria [22]. The T3SS-1 translocates effector proteins into the host cell cytosol. Seven T3SS-1 translocated effector proteins are encoded by genes located outside SPI1, including sopA [23], sopB (sigD) (located on SPI5) [24], sopD [25], sopE [26], sopE2 [27], slrP [28], and sspH1 [29]. The genes sipA, sopB, sopD, and sopE2 are present in all phylogenetic lineages of the genus Salmonella [21] and are required for bacterial invasion [30]. The different map positions of SPI1, SPI5, sopA, sopD, and sopE2 imply that individual components of the invasion machinery were acquired independently during the divergence of the genus Salmonella from the E. coli lineage by horizontal gene transfer. The above considerations suggest that some 97 Salmonella signature genes determine two important characteristics that distinguish the genus from closely related enterobacteria, namely the ability to invade and cause inflammation in the intestinal mucosa and the ability to use 1,2-propanediol anaerobically as carbon

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source (Fig. 6.1). Interestingly, transcriptional profiling using a S. Typhimurium LT2 microarray shows that both groups of genes are coordinately regulated by the CsrA protein [31]. That is, a csrA mutant expresses the pdu operon, the eut operon, the cob operon, the phs operon, the sopE2 gene, the sopA gene, the SPI5 invasion genes, and the SPI1 invasion genes at markedly reduced levels compared to the S. enterica serotype Typhimurium wild type. CsrA may thus be a global regulator of a large subset of Salmonella signature genes involved in the intestinal phase of infection. Several additional Salmonella signature genes may be critical for interaction of Salmonella serotypes with their vertebrate hosts. Some of these Salmonella signature genes are organized in operons involved in the biosynthesis of fimbrial adhesions, including bcfABCDEFG and sthABCDE [7, 32]. Other Salmonella signature genes involved in host pathogen interaction encode functions allowing bacteria to evade killing by macrophages, a property important for bacterial survival in intestinal tissue upon T3SS-1-mediated penetration of the epithelium. These genes include the magnesium transporters mgtBC encoded by SPI3 [33] and genes encoding a second type III secretion system (T3SS-2) encoded by SPI2 [34]. However, while microarray analysis shows that some SPI2 genes (i.e., sseB, sscA, sscB, ssaNQ, and yscR) seem to be conserved in S. bongori [7], large parts of SPI2 appear to be absent or highly divergent in S. bongori by hybridization analysis [35, 36]. SPI2 is, however, highly conserved among serotypes of S. enterica. In summary, as many as 121 Salmonella signature genes may encode functions that allow Salmonella serotypes to adhere, utilize carbon sources, invade, and subsequently survive in tissue during intestinal colonization of their vertebrate hosts.

6.3 Subspecies I Signature Genes

While the genomic features that differentiate Salmonella serotypes and their closest relative E. coli nicely fit the differences in metabolism and pathogenicity that have been historically described in the literature, the relationship between genetic and phenotypic differences within the species S. enterica are less clearly defined. The species S. enterica contains six subspecies, and all of these are capable of causing disease in mammals [1]. However, the overwhelming majority of cases of disease in mammals and birds are the result of infection with S. enterica subspecies I; the remaining subspecies are commonly isolated from cold-blooded animals [1, 37]. These findings suggest that genetic factors exclusive to S. enterica subspecies I may influence the epidemiological success of this subspecies among mammals and birds [38–40]. Understanding the relationship between the genetics and pathogenesis of S. enterica subspecies I is complicated by the fact that it contains 1454 serotypes [1], and there is considerable genetic diversity and diversity in disease syndromes and host range within this group [41]. Nevertheless, the completion of four genomes of S. enterica subspecies I serotypes [4, 42–44], with 12 more underway [45], and microarray analysis [5, 7, 46] have provided a list of genes

6.3 Subspecies I Signature Genes

exclusive to subspecies I, and there is preliminary information about the function of several of these genes. Understanding the emergence of S. enterica subspecies I as the dominant subspecies infecting mammals and birds has been the subject of recent genomic analysis using microarrays [5]. This study revealed that 216 genes were gained by S. enterica subspecies I after its divergence from other subspecies. Of these genes, 128, or 59%, are currently unnamed, which illustrates that our understanding of factors that contribute to the epidemiological success of Salmonella serotypes is still in its infancy. Microarray analysis further revealed that 74 of these genes were present or possibly present across all S. enterica subspecies I strains tested and can thus be considered subspecies I signature genes. Subspecies I signature genes with known function or sequence homology can be grouped into three categories, as follows. One group of subspecies I signature genes encode products that are located in the bacterial outer membrane. These include envF, sinI, yfeN, STM0280, STM2816, STM2423, and STM3026, whose products are predicted to be located in the outer membrane but whose function is poorly characterized [5, 47]. Furthermore, S. enterica subspecies I serotypes have gained three fimbrial gene clusters, including stfACDEFG, safABCD, and stcABCD [32, 48]. The saf operon encodes Salmonella atypical fimbria and is located on SPI6 [48], a DNA region that also contains the putative regulatory gene sinR [49]. The saf operon is present in the vast majority of S. enterica subspecies I isolates (195 of 198 tested), and deletion of safA or the sinR region does not result in virulence defects in genetically susceptible mice (BALB/c) [48, 49]. The effect of deleting the saf operon on long-term intestinal persistence has not been studied. In addition to fimbrial gene sequences, S. enterica subspecies I serotypes gained a nonfimbrial adhesin, termed ShdA [50]. The shdA gene encodes an outer membrane autotransporter protein that binds fibronectin and type I collagen on the bacterial surface [51, 52] and contributes to long-term intestinal persistence of S. enterica serotype Typhimurium in genetically resistant mice (CBA) [53]. The shdA gene is located on the CS54 genetic island, adjacent to another subspecies I signature gene termed ratB [53]. Although its mode of action is not known, the product of the ratB gene has recently also been shown to be critical for intestinal persistence in a murine model of infection [53]. Based on mathematical models that combine epidemiology with population biology, persistent intestinal carriage is predicted to be a factor that will enhance the ability of a pathogen to circulate in populations of mammals and birds [50]. Acquisition of intestinal colonization factors such as shdA and ratB by S. enterica subspecies I may have contributed to the epidemiological success of this group of pathogens within populations of warm-blooded vertebrates. A second group of subspecies I signature genes encode products that affect the properties of the bacterial cell surface. Microarray analysis suggests that the O-antigen biosynthesis (rfb) gene cluster of S. enterica subspecies I contains the subspecies I signature genes rfbP (wbaP), rfbK, rfbU, rfbI, rfbC, and rfbM (manC) [5]. The O-antigen comprises the portion of the bacterial lipopolysaccharide (LPS) that is exposed on the surface of the bacterium and is composed of repeating oli-

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gosaccharide units (O-repeat). The rfbP gene encodes an enzyme with two functions: a galactosyltransferase function necessary for the first step of O-antigen synthesis, and a flippase function necessary for flipping the O-antigen subunit on undecaprenyl pyrophosphate from the cytoplasmic to the periplasmic face of the cytoplasmic membrane [54]. The product of the rfbK gene is important for completion of outer core synthesis and subsequent attachment of the O-antigen [55]. The rfbU gene encodes a mannosyl transferase that is necessary for the incorporation of GDPmannose into the oligosaccharide backbone of the O-antigen repeat unit [56]. The rfbC gene product is involved in production of an activated precursor (dTDP-l-rhamnose) necessary for the incorporation of rhamnose moieties into the oligosaccharide backbone of the O-antigen [57]. The rfbI gene encodes an enzyme involved in adding a dideoxyhexose branch to the oligosaccharide backbone of the O-antigen [57]. Finally, the product of the rfbM gene is required to form GDPmannose, the activated precursor necessary for the incorporation of mannose residues into the oligosaccharide backbone of the O-antigen [58]. Comparative sequence analysis of rfbM from Salmonella serotypes belonging to subspecies I, II, and VI shows that although this gene contains considerable nucleotide polymorphisms, it is not a subspecies I signature gene [59]. Thus, the list of 74 subspecies I signature genes identified by microarray analysis [5] may be further reduced as more information becomes available for individual genes. Mutational analysis shows that subspecies I signature genes involved in O-antigen biosynthesis are required for host–pathogen interaction, because a S. enterica serotype Typhimurium rfbK mutant is rough and shows a reduced ability to colonize the intestine of 3-week-old chicks [55]. However, comparison of a bacterial strain expressing intact LPS with its isogenic derivative that is defective for O-antigen biosynthesis provides little insight into the selective advantage conferred by subspecies I signature genes located in the rfb gene cluster, because the difference between S. enterica subspecies I serotypes and serotypes belonging to other S. enterica subspecies is not the presence or absence of O-antigen, but the presence of O-antigen biosynthesis gene clusters that differ genetically. The biological significance of genetic differences between O-antigen biosynthesis gene clusters of S. enterica subspecies I and other S. enterica subspecies is not obvious, because most of the O-antigens used to distinguish Salmonella serotypes serologically occur in two or more subspecies. Since most O-antigens expressed by S. enterica subspecies I serotypes can also be detected in serotypes that do not belong to this subspecies, it is not clear which selective forces are responsible for the presence of subspecies I signature genes in the rfb gene cluster. A third group of subspecies I signature genes encode those involved in transport and utilization of nutrients. These include subspecies I signature genes STM3251–STM3256 encoding components of a putative sugar phosphotransferase transport system. Additional subspecies I signature genes of this group are located in the phn operon, which confers the ability to utilize phosphonate as a sole phosphorus source by mediating phosphonate transport and breakdown [60]. The phn operon is absent from E. coli but present in K. pneumoniae, and mutations in the S. enterica serotype Typhimurium phn operon do not seem to affect viru-

6.4 Host Restriction

lence in mice [60]. The selective advantage conferred upon S. enterica subspecies I serotypes by possessing the ability to utilize the above nutrients remains to be identified.

6.4 Host Restriction

The 1454 different serotypes that can currently be distinguished within S. enterica subspecies I [1] contain a few specialists that appear to be better adapted to their respective host species than other members of the genus Salmonella. These “specialists” commonly cause a systemic infection in their preferred host species but are isolated infrequently from other host species. These features are perhaps best exemplified by S. enterica serotype Typhi, the cause of typhoid fever, and S. enterica serotype Paratyphi A, a cause of paratyphoid fever in humans. Both serotypes persist in the human population by person-to-person transmission through contaminated food or water but are rarely isolated from nonhuman vertebrates. Furthermore, S. enterica serotypes Typhi and Paratyphi A do not cause disease in nonprimate vertebrates or even in lower primate species. Although there has been substantial interest in identifying the molecular basis for this host restriction, the bacterial genes responsible have long eluded identification. Recent genome sequence analysis shows that the genomes of the generalist S. enterica serotype Typhimurium strain LT2 and the two sequenced S. enterica serotype Typhi isolates (CT18 and TY2) have similar sizes (4857 kbp, 4809 kbp, and 4792 kbp respectively), while the S. enterica serotype Paratyphi A genome is somewhat smaller (4585 kbp) [4, 42–44]. Comparison of these genomes and microarray analysis have allowed direct comparison between more generalist and more specialist genomes, and have provided first insights into the genetic changes accompanying the evolution of hostrestricted serotypes [4, 5, 7, 42–44, 46, 61]. The genetic differences between the host generalist and the host specialist can be classified into four categories: (1) genes present in the generalist genome that are absent from the specialist genome, (2) pseudogenes, (3) genes present in the specialist genome that are absent from the generalist genome, and (4) rearrangements of gene order in the genome itself. Genomic comparison of 18 different Salmonella serotypes using a S. enterica serotype Typhimurium strain LT2-based microarray revealed a cluster of genes belonging to the first category: those absent from strictly human-adapted serotypes (Typhi and Paratyphi A) but present in other serotypes of S. enterica subspecies I [7]. While 99.5% of genes present on the S. enterica serotype Typhimurium microarray had homologues in the genomes of other S. enterica serotype Typhimurium isolates (SARA1, SARA6), only between 89% and 94% had homologues in the genomes of S. enterica serotypes Typhi and Paratyphi A isolates. The majority of the genes absent from S. enterica serotypes Typhi and Paratyphi A isolates were also absent from genomes of other generalist serotypes. However, cluster analysis identified a group of 53 genes whose absence was a shared genetic feature of genomes of strictly human-adapted S. enterica subspecies I serotypes. The

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6 Pathogenomics of Salmonella Species Table 6.1 Genome degradation affecting fimbrial and nonfimbrial adherence determinants

in typhoidal Salmonella serotypes compared to S. enterica serotype Typhimurium. Genetic determinant

Determinant (pseudogene) present in the genome of S. enterica serotype Typhimurium strain LT2

Paratyphi A strain SARB42

Typhi strain CT18 Typhi strain Ty2

shdA

+

+ (shdA)

+ (shdA)

+ (shdA)

misL

+

+

+ (misL)

+ (misL)

sivH

+

+ (sivH)

+ (sivH)

+ (sivH)

ratB

+

+ (ratB)

+ (ratB)

+ (ratB)

csg (agf)

+

+ (csgF)

+

+

fim

+

+

+ (fimI)

+

tcf

–

+

+

+

sef

–

+

+ (sefAD)

+ (sefAD)

saf

+

+ (safD)

+

+

bcf

+

+ (bcfF)

+ (bcfC)

+ (bcfC)

sta

–

–

+

+

stb

+

+

+

+ (stbC)

stc

+

+

+

+ (stcC)

std

+

+

+

+

ste

–

+

+ (steA)

+ (steA)

stg

–

–

+ (stgC)

+ (stgC)

sth

+ (sthC)

+

+ (sthCE)

+ (sthCE)

stk

–

+

–

–

lpf

+

–

–

–

pef

+

–

–

–

stf

+

+ (stfCF)

–

–

sti

+

–

–

–

stj

+

–

–

–

pil

–

–

+

+

+ Determinant is present in a genome; – determinant is absent in a genome

6.4 Host Restriction

majority of these genes are either of unknown function or they encode putative intestinal colonization factors, including ratB and the fimbrial genes lpfCDE, stbD, stfF, and stiB [7]. Although ratB and stbD were scored as being absent in the microarray analysis, sequence analysis shows that these genes are present in the genomes of S. enterica serotypes Typhi and Paratyphi A (where ratB was identified as a pseudogene) [42–44]. Furthermore, sequence analysis shows that stfF is present in the genome of S. enterica serotype Paratyphi A [44]. Pseudogene formation comprises a second category of genetic differences between specialists and generalists that lead to loss of function mutations. Complete genome sequencing reveals that the generalist S. enterica serotype Typhimurium has far fewer pseudogenes than the host-restricted serotypes S. enterica serotypes Typhi and Paratyphi A. There are approximately 210 pseudogenes in the genome of S. enterica serotype Typhi (strains CT18 and Ty2) [42, 43] and 173 pseudogenes in the genome of S. enterica serotype Paratyphi A strain SARB42 [44] compared to only 39 pseudogenes in the genome of S. enterica serotype Typhimurium strain LT2 [4]. Of the 173 pseudogenes present in S. enterica serotype Paratyphi A, 166 have orthologues but only 28 are also pseudogenes in S. enterica serotype Typhi [44]. One group of pseudogenes present in strictly human-adapted serotypes is formed by putative intestinal colonization factors, including autotransporter genes (misL, shdA, and sivH), fimbrial biosynthesis genes (fimI, csgF, sefAD, safD, bcfCF, stbC, stcC, steA, stfCF, stgC, and sthCE) and the intestinal colonization factor ratB (Table 6.1) [4, 32, 42–44]. These data show that genome degradation through deletion of genes or through pseudogene formation resulted in loss of putative adherence determinants in the genomes of strictly human-adapted serotypes. As a result, the genomes of S. enterica serotypes Typhi and Paratyphi A contain between 7 and 10 intact determinants, compared to 16 present in the genome of S. enterica serotype Typhimurium (Table 6.1). The only adherence determinants that are both present and intact in all three genomes of human-adapted serotypes are the fimbrial operons stdABCDE and tcfABCD. The extent of the genome degradation in the host specialist serotypes may correlate with increased specialization of these pathogens to one particular niche (i.e., host species) or, put another way, with the loss of ability of these serotypes to use multiple niches. Genome degradation may thus in part explain the host restriction to humans exhibited by S. enterica serotypes Typhi and Paratyphi A. The majority of S. enterica subspecies I serotypes (i.e., nontyphoidal Salmonella serotypes) cause infections in humans that remain localized to the intestine and mesenteric lymph node and result in diarrhea. In contrast, S. enterica serotypes Typhi and Paratyphi A (i.e., typhoidal Salmonella serotypes) cause systemic infections in humans with diarrhea being an insignificant symptom [19, 62]. It is generally assumed that acquiring the ability to cause enteric fever (i.e., typhoid fever or paratyphoid fever) involved gain of function (i.e., acquisition of genes) by typhoidal Salmonella serotypes. Genomic analysis has thus focused on identifying a third category of genes, namely those that are present in the specialist genome but absent in the more generalist isolates. Phylogenetic analysis by multilocus enzyme electrophoresis shows that the ability to cause enteric fever evolved inde-

117

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6 Pathogenomics of Salmonella Species

pendently in four lineages within S. enterica subspecies I, represented by S. enterica serotypes Typhi, Paratyphi A, Paratyphi B, and Paratyphi C [63]. Recent analysis of S. enterica subspecies I serotypes using a S. enterica serotype Typhimurium LT2 microarray supplemented with open reading frames from S. enterica serotype Typhi CT18 did not identify any genes that are conserved among typhoidal serotypes but absent from all nontyphoidal serotypes [46]. The absence of signature genes for typhoidal Salmonella serotypes may reflect the fact that the ability to cause enteric fever developed in four lineages independently, perhaps by acquisition of different genetic material in each lineage, which would suggest that serotypes of each lineage cause enteric fever by somewhat different mechanisms. It may thus be more revealing to identify gene acquisition events that accompanied the formation of an individual typhoidal Salmonella serotype by comparing its genome with that of nontyphoidal serotypes. The genome of S. enterica serotype Typhi CT18 reveals 601 genes in 82 blocks that are unique compared to S. enterica serotype Typhimurium LT2 [42]. Insertions unique to S. enterica serotype Typhi include four prophages (phages 10, 15, 18, 46), three pathogenicity islands (SPI7, SPI8, SPI10), four chaperone-usher fimbrial systems (staABACDEFG, tcfABCD, steABCDEF, stgABCDEF), a homologue of an E. coli hemolysin gene (STY1498), homologues of the Campylobacter toxin gene cdtB (STY1886), homologues of the Bordetella pertussis toxin genes ptxA and ptxB (STY1890 and STY1891), and a putative polysaccharide acetyltransferase gene (STY2629) [32, 42]. Unique genetic material in the S. enterica serotype Paratyphi A SARB42 genome includes three prophages, (SPA-1, SPA2554-2600, SPA-3-P2) and 12 other insertions containing two or more genes including the stkABCDEFG fimbrial operon [44]. However, most unique insertions identified in the genomes of typhoidal serotypes by comparison with S. enterica serotype Typhimurium LT2 are also present in genomes of at least some nontyphoidal Salmonella serotypes [46]. These data suggest that the ability to cause enteric fever did not result from a single horizontal transfer event but may have required acquisition of a unique combination of new insertions in each lineage of typhoidal serotypes, including the incorporation of different genetic material into the genomes of S. enterica serotypes Typhi and Paratyphi A. The final category of genomic difference between host generalist and host specialist serotypes of S. enterica subspecies I are large-scale genomic rearrangements. S. enterica serotype Paratyphi A (SARB42), for example, generally has conservation of orthologous gene order with respect to S. enterica serotype Typhimurium LT2. However, a recombination event between the rrnH and rrnG operons has inverted a large portion of the S. enterica serotype Paratyphi A genome with respect to the S. enterica serotype Typhimurium LT2 genome [44, 64]. A similar situation is apparent for S. enterica serotype Typhi TY2 and CT18, where overall the gene order is similar to that of S. enterica serotype Typhimurium, but genomic rearrangements have occurred around the rrn genes [42, 65]. Genome rearrangements due to homologous recombination between the rrn operons, resulting in translocations and inversions, are also observed in the genome of S. enterica serotype Gallinarum, a host-restricted pathogen consisting of two biotypes, Galli-

6.4 Host Restriction

narum and Pullorum, that are strictly fowl-adapted [66, 67]. In contrast, the gene order in the S. enterica subspecies I genomes of host generalists is conserved with respect to that of S. enterica serotype Typhimurium LT2 [66]. It is currently unknown why large genomic rearrangements are exclusively found in the genomes of host-restricted serotypes of S. enterica serotype subspecies I. The above examples illustrate that the formation of host-restricted serotypes within S. enterica subspecies I was a complex process that involved numerous genetic changes whose significance has not been established, making it difficult to identifying the key events driving this evolutionary process. One strategy to gain further insight into the evolution of host-restricted pathogens has been the analysis of strains in which adaptation to one niche is incomplete, providing the opportunity to obtain a snapshot of genetic changes that can be observed when a host-restricted lineage is beginning to branch from that of its generalist relatives. A possible example of a pathogen with incomplete host restriction is the pigeonassociated variant of S. enterica serotype Typhimurium. While the majority of S. enterica serotype Typhimurium isolates (including the sequenced LT2 strain) are host generalists, one clone represented by the phage types DT2 and DT99 causes a systemic infection in pigeons but is rarely isolated from other host species [68]. Host restriction in the pigeon-associated variant of S. enterica serotype Typhimurium is incomplete since it is still capable of causing disease in other host species, although virulence seems to be somewhat reduced compared to that of generalist isolates [69–71]. While genomic rearrangements are absent from the genomes of S. enterica serotype Typhimurium generalists and isolates of the phage type DT99, the genome of some S. enterica serotype Typhimurium DT2 isolates carry genomic inversions between rrn genes [72]. Thus, the development of genomic rearrangements is still incomplete within the pigeon-associated variant of S. enterica serotype Typhimurium, suggesting that genomic rearrangements are a consequence rather than a cause of host restriction. Analysis of DT2 and DT99 isolates using a LT2 microarray identified no genes that were present in the genomes of S. enterica serotype Typhimurium generalists but absent from the specialist genome of the pigeon-associated variant, suggesting that genome degradation by loss of discrete genes is not a major driving force for the evolution of host restriction [70]. Whether pseudogene formation or acquisition of genetic material by horizontal transfer has contributed to formation of the pigeon-associated variant of S. enterica serotype Typhimurium remains to be investigated.

Acknowledgments

We would like to thank M. Raffatellu for reading the manuscript. Work in A.B.’s laboratory is supported by USDA/NRICGP grant #2002-35204-12247 and Public Health Service grants #AI40124 and #AI44170. H.A. is supported by Public Health Service grant #AI52250.

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References 1 Brenner, F.W., R.G. Villar, F.J. Angulo,

R. Tauxe, and B. Swaminathan. 2000. Salmonella nomenclature. J. Clin. Microbiol. 38: 2465–2467. 2 Riley, M. and A. Anilionis. 1976. Evolution of the bacterial genome. Annu. Rev. Microbiol. 32: 519–560. 3 Riley, M. and S. Krawiec. 1987. Genome organization. In: Escherichia coli and Salmonella typhimurium: cellular and molecular biology, (ed) F.C. Neidhardt. American Society for Microbiology, Washington, DC, pp 967–981. 4 McClelland, M., K.E. Sanderson, J. Spieth, S.W. Clifton, P. Latreille, L. Courtney, S. Porwollik, J. Ali, M. Dante, F. Du, S. Hou, D. Layman, S. Leonard, C. Nguyen, K. Scott, A. Holmes, N. Grewal, E. Mulvaney, E. Ryan, H. Sun, L. Florea, W. Miller, T. Stoneking, M. Nhan, R. Waterston, and R.K. Wilson. 2001. Complete genome sequence of Salmonella enterica serovar Typhimurium LT2. Nature 413: 852–856. 5 Porwollik, S., R.M. Wong, and M. McClelland. 2002. Evolutionary genomics of Salmonella: gene acquisitions revealed by microarray analysis. Proc. Natl. Acad. Sci. U. S. A. 99: 8956– 8961. 6 McClelland, M., L. Florea, K. Sanderson, S.W. Clifton, J. Parkhill, C. Churcher, G. Dougan, R.K. Wilson, and W. Miller. 2000. Comparison of the Escherichia coli K-12 genome with sampled genomes of a Klebsiella pneumoniae and three Salmonella enterica serovars, Typhimurium, Typhi and Paratyphi. Nucleic Acids Res. 28: 4974–4986. 7 Chan, K., S. Baker, C.C. Kim, C.S. Detweiler, G. Dougan, and S. Falkow. 2003. Genomic comparison of Salmonella enterica serovars and Salmonella bongori by use of an S. enterica serovar typhimurium DNA microarray. J. Bacteriol. 185: 553–563. 8 Price-Carter, M., J. Tingey, T.A. Bobik, and J.R. Roth. 2001. The alternative electron acceptor tetrathionate supports B12-dependent anaerobic growth of Sal-

monella enterica serovar typhimurium on ethanolamine or 1,2-propanediol. J. Bacteriol. 183: 2463–2475. 9 Stojiljkovic, I., A.J. Bumler, and F. Heffron. 1995. Ethanolamine utilization in Salmonella typhimurium: nucleotide sequence, protein expression, and mutational analysis of the cchA cchB eutE eutJ eutG eutH gene cluster. J. Bacteriol. 177: 1357–1366. 10 Kofoid, E., C. Rappleye, I. Stojiljkovic, and J. Roth. 1999. The 17-gene ethanolamine (eut) operon of Salmonella typhimurium encodes five homologues of carboxysome shell proteins. J. Bacteriol. 181: 5317–5329. 11 Bobik, T.A., G.D. Havemann, R.J. Busch, D.S. Williams, and H.C. Aldrich. 1999. The propanediol utilization (pdu) operon of Salmonella enterica serovar Typhimurium LT2 includes genes necessary for formation of polyhedral organelles involved in coenzyme B(12)-dependent 1,2-propanediol degradation. J. Bacteriol. 181: 5967–5975. 12 Roth, J.R., J.G. Lawrence, M. Rubenfield, S. Kieffer-Higgins, and G.M. Church. 1993. Characterization of the cobalamin (vitamin B12) biosynthetic genes of Salmonella typhimurium. J. Bacteriol. 175: 3303–3316. 13 Jeter, R.M. 1990. Cobalamin-dependent 1,2-propanediol utilization by Salmonella typhimurium. J. Gen Microbiol. 136(Pt 5): 887–896. 14 Roof, D.M. and J.R. Roth. 1988. Ethanolamine utilization in Salmonella typhimurium. J. Bacteriol. 170: 3855–3863. 15 Hensel, M., A.P. Hinsley, T. Nikolaus, G. Sawers, and B.C. Berks. 1999. The genetic basis of tetrathionate respiration in Salmonella typhimurium. Mol. Microbiol. 32: 275–287. 16 Huang, C.J. and E.L. Barrett. 1991. Sequence analysis and expression of the Salmonella typhimurium asr operon encoding production of hydrogen sulfide from sulfite. J. Bacteriol. 173: 1544– 1553. 17 Heinzinger, N.K., S.Y. Fujimoto, M.A. Clark, M.S. Moreno, and E.L. Barrett.

References

18

19

20

21

22

23

24

1995. Sequence analysis of the phs operon in Salmonella typhimurium and the contribution of thiosulfate reduction to anaerobic energy metabolism. J. Bacteriol. 177: 2813–2820. Obradors, N., J. Badia, L. Baldoma, and J. Aguilar. 1988. Anaerobic metabolism of the L-rhamnose fermentation product 1,2-propanediol in Salmonella typhimurium. J. Bacteriol. 170: 2159–2162. Zhang, S., R.A. Kingsley, R.L. Santos, H. Andrews-Polymenis, M. Raffatellu, J. Figueiredo, J. Nunes, R.M. Tsolis, L.G. Adams, and A.J. Bumler. 2003. Molecular pathogenesis of Salmonella enterica serotype typhimurium-induced diarrhea. Infect. Immun. 71: 1–12. Mills, D.M., V. Bajaj, and C.A. Lee. 1995. A 40 kb chromosomal fragment encoding Salmonella typhimurium invasion genes is absent from the corresponding region of the Escherichia coli K-12 chromosome. Mol. Microbiol. 15: 749–759. Mirold, S., K. Ehrbar, A. Weissmuller, R. Prager, H. Tschape, H. Russmann, and W.D. Hardt. 2001. Salmonella host cell invasion emerged by acquisition of a mosaic of separate genetic elements, including Salmonella pathogenicity island 1 (SPI1), SPI5, and sopE2. J. Bacteriol. 183: 2348–2358. Li, J., H. Ochman, E.A. Groisman, E.F. Boyd, F. Solomon, K. Nelson, and R.K. Selander. 1995. Relationship between evolutionary rate and cellular location among the Inv/Spa invasion proteins of Salmonella enterica. Proc. Natl. Acad. Sci. U. S. A. 92: 7252–7256. Wood, M.W., M.A. Jones, P.R. Watson, A.M. Siber, B.A. McCormick, S. Hedges, R. Rosqvist, T.S. Wallis, and E.E. Galyov. 2000. The secreted effector protein of Salmonella dublin, SopA, is translocated into eukaryotic cells and influences the induction of enteritis. Cell Microbiol. 2: 293–303. Galyov, E.E., M.W. Wood, R. Rosqvist, P.B. Mullan, P.R. Watson, S. Hedges, and T.S. Wallis. 1997. A secreted effector protein of Salmonella dublin is translocated into eukaryotic cells and mediates inflammation and fluid secretion in

infected ileal mucosa. Mol. Microbiol. 25: 903–912. 25 Jones, M.A., M.W. Wood, P.B. Mullan, P.R. Watson, T.S. Wallis, and E.E. Galyov. 1998. Secreted effector proteins of Salmonella dublin act in concert to induce enteritis. Infect. Immun. 66: 5799–5804. 26 Wood, M.W., R. Rosqvist, P.B. Mullan, M.H. Edwards, and E.E. Galyov. 1996. SopE, a secreted protein of Salmonella dublin, is translocated into the target eukaryotic cell via a sip-dependent mechanism and promotes bacterial entry. Mol. Microbiol. 22: 327–338. 27 Stender, S., A. Friebel, S. Linder, M. Rohde, S. Mirold, and W.D. Hardt. 2000. Identification of SopE2 from Salmonella typhimurium, a conserved guanine nucleotide exchange factor for Cdc42 of the host cell. Mol. Microbiol. 36: 1206–1221. 28 Tsolis, R.M., S.M. Townsend, E.A. Miao, S.I. Miller, T.A. Ficht, L.G. Adams, and A.J. Bumler. 1999. Identification of a putative Salmonella enterica serotype typhimurium host range factor with homology to IpaH and YopM by signature-tagged mutagenesis. Infect. Immun. 67: 6385–6393. 29 Miao, E.A., C.A. Scherer, R.M. Tsolis, R.A. Kingsley, L.G. Adams, A.J. Bumler, and S.I. Miller. 1999. Salmonella typhimurium leucine-rich repeat proteins are targeted to the SPI1 and SPI2 type III secretion systems. Mol. Microbiol. 34: 850–864. 30 Raffatellu, M., R.P. Wilson, D. Chessa, H. Andrews-Polymenis, Q.T. Tran, S. Lawhon, S. Khare, L.G. Adams, and A.J. Bumler. 2005. SipA, SopA, SopB, SopD and SopE2 contribute to Salmonella enterica serotype Typhimurium invasion of epithelial cells. Infect. Immun. 73: 146–154. 31 Lawhon, S.D., J.G. Frye, M. Suyemoto, S. Porwollik, M. McClelland, and C. Altier. 2003. Global regulation by CsrA in Salmonella typhimurium. Mol. Microbiol. 48: 1633–1645. 32 Townsend, S.M., N.E. Kramer, R. Edwards, S. Baker, N. Hamlin, M. Simmonds, K. Stevens, S. Maloy, J. Parkhill, G. Dougan, and A.J. Bum-

121

122

6 Pathogenomics of Salmonella Species ler. 2001. Salmonella enterica serovar Typhi possesses a unique repertoire of fimbrial gene sequences. Infect. Immun. 69: 2894–2901. 33 Blanc-Potard, A.B., F. Solomon, J. Kayser, and E.A. Groisman. 1999. The SPI-3 pathogenicity island of Salmonella enterica. J. Bacteriol. 181: 998–1004. 34 Ochman, H., F.C. Soncini, F. Solomon, and E.A. Groisman. 1996. Identification of a pathogenicity island for Salmonella survival in host cells. Proc. Natl. Acad. Sci. USA. 93: 7800–7804. 35 Ochman, H. and E.A. Groisman. 1996. Distribution of pathogenicity islands in Salmonella spp. Infect. Immun. 64: 5410–5412. 36 Hensel, M., J.E. Shea, A.J. Bumler, C. Gleeson, F. Blattner, and D.W. Holden. 1997. Analysis of the boundaries of Salmonella pathogenicity island 2 and the corresponding chromosomal region of Escherichia coli K-12. J. Bacteriol. 179: 1105–1111. 37 Aleksic, S., F. Heinzerling, and J. Bockemhl. 1996. Human infection caused by salmonellae of subspecies II to VI in Germany, 1977–1992. Zentralbl. Bakteriol. 283: 391–398. 38 Bumler, A.J. 1997. The record of horizontal gene transfer in Salmonella. Trends Microbiol. 5: 318-322. 39 Bumler, A.J., R.M. Tsolis, T.A. Ficht, and L.G. Adams. 1998. Evolution of host adaptation in Salmonella enterica. Infect. Immun. 66: 4579–4587. 40 Kingsley, R.A. and A.J. Bumler. 2002. Pathogenicity islands and host adaptation of Salmonella serovars. Curr. Top. Microbiol. Immunol. 264: 67–87. 41 Boyd, E.F., F.-S. Wang, P. Beltran, S.A. Plock, K. Nelson, and R.K. Selander. 1993. Salmonella reference collection B (SARB): strains of 37 serovars of subspecies I. J. Gen. Microbiol. 139: 1125– 1132. 42 Parkhill, J., G. Dougan, K.D. James, N.R. Thomson, D. Pickard, J. Wain, C. Churcher, K.L. Mungall, S.D. Bentley, M.T. Holden, M. Sebaihia, S. Baker, D. Basham, K. Brooks, T. Chillingworth, P. Connerton, A. Cronin, P. Davis, R.M. Davies, L. Dowd, N. White, J. Farrar, T. Feltwell, N. Hamlin, A. Haque, T.T.

Hien, S. Holroyd, K. Jagels, A. Krogh, T.S. Larsen, S. Leather, S. Moule, P. O’Gaora, C. Parry, M. Quail, K. Rutherford, M. Simmonds, J. Skelton, K. Stevens, S. Whitehead, and B.G. Barrell. 2001. Complete genome sequence of a multiple drug resistant Salmonella enterica serovar Typhi CT18. Nature 413: 848–852. 43 Deng, W., S.R. Liou, G. Plunkett, 3rd, G.F. Mayhew, D.J. Rose, V. Burland, V. Kodoyianni, D.C. Schwartz, and F.R. Blattner. 2003. Comparative genomics of Salmonella enterica serovar Typhi strains Ty2 and CT18. J. Bacteriol. 185: 2330–2337. 44 McClelland, M., K.E. Sanderson, S.W. Clifton, P. Latreille, S. Porwollik, A. Sabo, R. Meyer, T. Bieri, P. Ozersky, M. McLellan, C.R. Harkins, C. Wang, C. Nguyen, A. Berghoff, G. Elliott, S. Kohlberg, C. Strong, F. Du, J. Carter, C. Kremizki, D. Layman, S. Leonard, H. Sun, L. Fulton, W. Nash, T. Miner, P. Minx, K. Delehaunty, C. Fronick, V. Magrini, M. Nhan, W. Warren, L. Florea, J. Spieth, and R.K. Wilson. 2004. Comparison of genome degradation in Paratyphi A and Typhi, humanrestricted serovars of Salmonella enterica that cause typhoid. Nat. Genet. 36: 1268–1274. 45 Porwollik, S. and M. McClelland. 2003. Lateral gene transfer in Salmonella. Microbes Infect. 5: 977–989. 46 Porwollik, S., E.F. Boyd, C. Choy, P. Cheng, L. Florea, E. Proctor, and M. McClelland. 2004. Characterization of Salmonella enterica subspecies I genovars by use of microarrays. J. Bacteriol. 186: 5883–5898. 47 Gunn, J.S., C.M. Alpuche-Aranda, W.P. Loomis, W.J. Belden, and S.I. Miller. 1995. Characterization of the Salmonella typhimurium pagC/pagD chromosomal region. J. Bacteriol. 177: 5040– 5047. 48 Folkesson, A., A. Advani, S. Sukupolvi, J.D. Pfeifer, S. Normark, and S. Lofdahl. 1999. Multiple insertions of fimbrial operons correlate with the evolution of Salmonella serovars responsible for human disease. Mol. Microbiol. 33: 612–622.

References 49 Groisman, E.A., M.H. Saier, and

H. Ochman. 1992. Horizontal transfer of a phosphatase gene as evidence for mosaic structure of the Salmonella genome. EMBO J. 11: 1309–1316. 50 Kingsley, R.A., K. van Amsterdam, N. Kramer, and A.J. Bumler. 2000. The shdA gene is restricted to serotypes of Salmonella enterica subspecies I and contributes to efficient and prolonged fecal shedding. Infect. Immun. 68: 2720–2727. 51 Kingsley, R.A., R.L. Santos, A.M. Keestra, L.G. Adams, and A.J. Bumler. 2002. Salmonella enterica serotype Typhimurium ShdA is an outer membrane fibronectin-binding protein that is expressed in the intestine. Mol. Microbiol. 43: 895–905. 52 Kingsley, R.A., A.M. Keestra, M.R. de Zoete, and A.J. Bumler. 2004. The ShdA adhesin binds to the cationic cradle of the fibronectin 13FnIII repeat module: evidence for molecular mimicry of heparin binding. Mol. Microbiol. 53: 345–355. 53 Kingsley, R.A., A.D. Humphries, E.H. Weening, M.R. De Zoete, S. Winter, A. Papaconstantinopoulou, G. Dougan, and A.J. Bumler. 2003. Molecular and phenotypic analysis of the CS54 island of Salmonella enterica serotype typhimurium: identification of intestinal colonization and persistence determinants. Infect. Immun. 71: 629–640. 54 Wang, L., D. Liu, and P.R. Reeves. 1996. C-terminal half of Salmonella enterica WbaP (RfbP) is the galactosyl-1-phosphate transferase domain catalyzing the first step of O-antigen synthesis. J. Bacteriol. 178: 2598–2604. 55 Turner, A.K., M.A. Lovell, S.D. Hulme, L. Zhang-Barber, and P.A. Barrow. 1998. Identification of Salmonella typhimurium genes required for colonization of the chicken alimentary tract and for virulence in newly hatched chicks. Infect. Immun. 66: 2099–2106. 56 Xiang, S.H., M. Hobbs, and P.R. Reeves. 1994. Molecular analysis of the rfb gene cluster of a group D2 Salmonella enterica strain: evidence for its origin from an insertion sequence-mediated recombination event between group

E and D1 strains. J. Bacteriol. 176: 4357–4365. 57 Xiang, S.H., A.M. Haase, and P.R. Reeves. 1993. Variation of the rfb gene clusters in Salmonella enterica. J. Bacteriol. 175: 4877–4884. 58 Thomsen, L.E., M.S. Chadfield, J. Bispham, T.S. Wallis, J.E. Olsen, and H. Ingmer. 2003. Reduced amounts of LPS affect both stress tolerance and virulence of Salmonella enterica serovar Dublin. FEMS Microbiol Lett. 228: 225– 231. 59 Jensen, S.O. and P.R. Reeves. 2001. Molecular evolution of the GDP-mannose pathway genes (manB and manC) in Salmonella enterica. Microbiology 147: 599–610. 60 Jiang, W., W.W. Metcalf, K.S. Lee, and B.L. Wanner. 1995. Molecular cloning, mapping, and regulation of Pho regulon genes for phosphonate breakdown by the phosphonatase pathway of Salmonella typhimurium LT2. J. Bacteriol. 177: 6411–6421. 61 Boyd, E.F., S. Porwollik, F. Blackmer, and M. McClelland. 2003. Differences in gene content among Salmonella enterica serovar typhi isolates. J. Clin. Microbiol. 41: 3823–3828. 62 Santos, R.L., S. Zhang, R.M. Tsolis, R.A. Kingsley, L.G. Adams, and A.J. Bumler. 2001. Animal models of Salmonella infections: enteritis vs. typhoid fever. Microb. Infect. 3: 237–247. 63 Selander, R.K., P. Beltran, N.H. Smith, R. Helmuth, F.A. Rubin, D.J. Kopecko, K. Ferris, B.D. Tall, A. Cravioto, and J.M. Musser. 1990. Evolutionary genetic relationships of clones of Salmonella serovars that cause human typhoid and other enteric fevers. Infect. Immun. 58: 2262–2275. 64 Liu, S.L. and K.E. Sanderson. 1995. The chromosome of Salmonella paratyphi A is inverted by recombination between rrnH and rrnG. J. Bacteriol. 177: 6585– 6592. 65 Liu, S.L. and K.E. Sanderson. 1995. Rearrangements in the genome of the bacterium Salmonella typhi. Proc. Natl. Acad. Sci. U. S. A. 92: 1018–1022. 66 Sanderson, K.E. and S.L. Liu. 1998. Chromosomal rearrangements in

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67

68

69

70

enteric bacteria. Electrophoresis 19: 569–72. Liu, G.R., A. Rahn, W.Q. Liu, K.E. Sanderson, R.N. Johnston, and S.L. Liu. 2002. The evolving genome of Salmonella enterica serovar Pullorum. J. Bacteriol. 184: 2626–2633. Rabsch, W., H.L. Andrews, R.A. Kingsley, R. Prager, H. Tschape, L.G. Adams, and A.J. Bumler. 2002. Salmonella enterica serotype Typhimurium and its host-adapted variants. Infect. Immun. 70: 2249–2255. Pasmans, F., F. Van Immerseel, K. Hermans, M. Heyndrickx, J.M. Collard, R. Ducatelle, and F. Haesebrouck. 2004. Assessment of virulence of pigeon isolates of Salmonella enterica subsp. enterica serovar typhimurium variant copenhagen for humans. J. Clin. Microbiol. 42: 2000–2002. Andrews-Polymenis, H.L., W. Rabsch, S. Porwollik, M. McClelland, C. Rosetti,

L.G. Adams, and A.J. Bumler. 2004. Host restriction of Salmonella enterica serotype Typhimurium pigeon isolates does not correlate with loss of discrete genes. J. Bacteriol. 186: 2619–2628. 71 Pasmans, F., F. Van Immerseel, M. Heyndrickx, A. Martel, C. Godard, C. Wildemauwe, R. Ducatelle, and F. Haesebrouck. 2003. Host adaptation of pigeon isolates of Salmonella enterica subsp. enterica serovar Typhimurium variant Copenhagen phage type 99 is associated with enhanced macrophage cytotoxicity. Infect. Immun. 71: 6068– 6074. 72 Helm, R.A., S. Porwollik, A.E. Stanley, S. Maloy, M. McClelland, W. Rabsch, and A. Eisenstark. 2004. Pigeon-associated strains of Salmonella enterica serovar Typhimurium phage type DT2 have genomic rearrangements at rRNA operons. Infect. Immun. 72: 7338–7341.

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7 Pathogenomics of Enterococcus faecalis Janet M. Manson and Michael S. Gilmore

7.1 Introduction

In the last two decades, enterococci have emerged as an important cause of nosocomial infection, and with the appearance of vancomycin resistance, some strains are often resistant to most currently approved antimicrobial agents. The robust nature of this bacterium coupled with the rapid spread of antimicrobial resistance in this organism has led to increased interest in the pathogenesis of enterococcal infection. In this chapter we review how the genome of E. faecalis relates to the pathogenesis of enterococcal infection. We discuss the role of mobile DNA in the evolution and acquisition of virulence traits, and investigate the genetic properties of E. faecalis that contribute to its ability to adapt and survive in different environments. Finally we look at genes that could contribute to virulence in this organism and the role of the E. faecalis pathogenicity island in the bacteria/host relationship.

7.2 Enterococcal Pathogenesis

Enterococcus faecalis is a gram-positive coccus, has a low G+C content (37.5%), and is a facultative anaerobe that grows optimally at 35 C (with a growth range of 10– 45 C). E. faecalis forms part of the normal host microflora of both the human and animal intestinal tracts and can also be isolated from insects such as cockroaches, flies, and beetles [1]. In humans typical concentrations of enterococci in stool are 8 up to 10 CFU per gram [2], and because of this they are also abundant in wastewater, sewage, on vegetation, and in other environment areas where contamination has occurred. Although E. faecalis is a normal member of the human gastrointestinal tract, problems occur when this organism gains access to sites it does not normally colonize. Since 1986, enterococci have consistently ranked among the most common pathogens isolated from nosocomial infections [3]. Enterococci are associated with a variety of clinical infections, the most common being urinary tract infections.

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Urinary tract infections caused by enterococci are generally acquired in a longterm health care facility, and because of the bacteria’s increased exposure to antimicrobial agents in such environments, are also more likely to be resistant to some antimicrobial agents. In the 2000 SENTRY surveillance program enterococci were the second ranked cause of nosocomial urinary tract infections, accounting for 12.6% of all infections [4]. The second most frequent infections caused by enterococci are intra-abdominal abscesses and postsurgical wound infections, and these are followed in frequency by bloodstream infections (bacteremia) and bacterial endocarditis. According to National Nosocomial Infections Surveillance (NNIS) data from January 1992 to July 1998, enterococci ranked first as the causative agent in surgical site infection and third in bloodstream infections among patients in intensive care units [5]. At a much lower frequency, enterococci can be involved in central nervous system and neonatal infections, and very rarely can be involved in osteomyelitis, respiratory tract infections, and cellulitis. E. faecalis is the most prevalent enterococcal species associated with human infections. Data from the SENTRY surveillance program (1997–1999) showed that during this time period, E. faecalis accounted for 57.2–76.8% of enterococcal nosocomial infections, while Enterococcus faecium made up 4.6% to 20% [6]. To cause disease, bacterial pathogens need to be able to adapt to the physiological conditions found within the host. A potential reason for the emergence of E. faecalis as a causative agent of nosocomial infection is the robust nature of this organism. E. faecalis has an intrinsic ability to grow in hypotonic, hypertonic, acidic, or alkaline conditions and to withstand detergents, oxidative stress, and desiccation. To discover more about the mechanisms this bacteria uses both to survive and to cause infection in the clinical setting, the genome of the first reported clinical vancomycin-resistant E. faecalis isolate in the United States, E. faecalis V583 [7], was sequenced [8].

7.3 Genome Sequence of E. faecalis

The genome of E. faecalis V583 is 3.2 Mbp and contains three plasmids, pTEF1, pTEF2, and pTEF3, which are 66.3 kbp, 57.6 kbp, and 17.9 kbp, respectively. The total genome encodes 3337 predicted open reading frames (ORFs), of which onequarter comprise mobile elements and acquired DNA, including a 150-kbp pathogenicity island [9]. As E. faecalis V583 is the only E. faecalis isolate sequenced to date, it is uncertain whether these characteristics are also true of other isolates from this bacterial genus. The genome sizes of other E. faecalis strains have previously been mapped and were estimated to range from 2825 kbp to 3250 kbp [10, 11]. Strain-specific variability in gene content and genomic organization may account for the differences in pathogenicity in some bacterial isolates. Comparison of E. coli K-12 with E. coli OH157:H7 found 1500 ORFs specific to OH157:H7 [12], while comparative differences in three Listeria monocytogenes strains noted between 51 and 97 strain-specific genes [13].

7.3 Genome Sequence of E. faecalis

Encoded on the pathogenicity island are genes for cytolysin, enterococcal surface protein (Esp), and other putative virulence or adaptation genes, and this region of DNA has an atypical G+C content (32.2%). In E. faecalis V583 (EF0479– EF0628), the previously described pathogenicity island contains a 17-kbp deletion (ORFs EF0047–EF0057), an IS256-like insertion in cylB, and an insertion of an IS905-like element. In addition, a 2.8-kbp region possessing the features of a group II intron (EF0012) is absent. 1) As previously noted, 85% of the E. faecalis V583 ORFs share their greatest homology with other low G+C gram-positive bacteria; however, there is no largescale synteny to any other genome in this group, which is probably due to the large amount of mobile DNA elements present in the E. faecalis genome [8]. A feature E. faecalis does share with this group is a strong transcriptional skew, with 90% of the ORFs in E. faecalis aligned to the direction of replication. 7.3.1 Mobile Elements, Acquired DNA, and Antimicrobial Resistance

A total of 47 insertion sequences are present in E. faecalis V583; only 38 of these are considered functional, and of these 26 are present in the chromosome, 10 of them belonging to the IS256 family. There are two main clusters of IS elements, one of which is associated with the pathogenicity island (G+C content 32.2%), while the other is associated with a separate region of atypical nucleotide composition (G+C content 31%). The disparate G+C content of these regions combined with the presence of mobile DNA elements suggests origination by horizontal gene transfer. From EF1855 to EF1874, six either full or partial IS elements are present. Also present in this region are panB, panC, and panD, (EF1860–EF1858), which encode three of the four steps of the pantothenate biosynthesis pathway. Homology comparisons show a greater relationship of these enzymes to counterparts in clostridia, rather than other lactic acid bacteria. There are three homologues present in the rest of the genome of the remaining enzyme in this pathway, one of which is encoded on the pathogenicity island. Also in this atypical nucleotide region are homologues of the Streptococcus pneumoniae VncRS twocomponent signal transduction system (EF1863–EF1864) and the Vex secretion ABC transporter (EF1867–EF1869). Previously mutations in the VncRS locus in S. pneumoniae had been linked to vancomycin tolerance, and it was hypothesized that VncR functioned as a repressor of autolytic functions [14]; however, a second study suggested that VncRS is involved in the regulation of a proximal ABC transporter, Vex [15]. More recent work has cast further doubt on the role of the VncRS two-component system, and its function is at present undetermined [16]. Homologues of the Vex transport system are also present in both E. faecium and Streptococcus agalactiae, and in both of these organisms the genes have greater than 88% 1) All genes referred to in this chapter as being

present on the pathogenicity island are annotated according to the gene locus

described in E. faecalis MMH594, which contains the full length island [9] (GenBank accession number AF454824).

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identity at the nucleotide level to E. faecalis. A homologue of the VncS sensor kinase is present in both E. faecalis and S. agalactiae, again sharing greater than 88% identity at the nucleotide level, but is absent from E. faecium. This degree of similarity suggests a common source for these genes in these three organisms (Table 7.1).

Table 7.1 Mobile and/or exogenously acquired DNA in the E. faecalis V583 genome.

Locus

Description

EF0131–EF0166

Possible integrated plasmid, contains genes of plasmid, phage, and conjugative plasmid origin

EF0479–EF0628

E. faecalis pathogenicity island

EF1855–EF1874

Area of atypical nucleotide composition containing 6 full/partial IS elements, panB, panC, panD, and VncRS/Vex homologues

EF2512–EF2545

Possible integrated plasmid

EF2284–EF2334

Area of atypical nucleotide composition with some similarities to Tn1549, and containing the vanB operon.

EF0302–EF0355

Possible integrated prophage (PHAGE01)

EF1275–EF1293

Possible integrated prophage (PHAGE02)

EF1416–EF1489

Possible integrated prophage (PHAGE03)

EF1987–EF2043

Possible integrated prophage (PHAGE04); contains a putative ferrochetalase, a cold shock protein, and homologues of PblB and PblA.

EF2084–EF2145

Possible integrated prophage (PHAGE05)

EF2798–EF2856

Possible integrated prophage (PHAGE06); contains a homologue of PblB

EF2936–EF2955

Possible integrated prophage (PHAGE07)

pTEF1

66.3-kb plasmid

pTEF2

57.6-kb plasmid

pTEF3

17.9-kb plasmid

As well as the large number of insertion elements, the E. faecalis V583 genome contains seven regions that are derived from probable phage integration. Homology searches of all seven phage regions show their best matches to phages from other lactic acid bacteria. Interestingly, the fourth phage region contains the hemH gene (EF1989, a putative ferrochelatase), a cold shock protein, CspC (EF1991), and homologues of both PblB (EF2001) and PblA (EF2003). A second protein with sequence identity to PblB is also present in the sixth phage region

7.3 Genome Sequence of E. faecalis

(EF2811). In Streptococcus mitis, PblA and PblB resemble phage capsid and tail fiber proteins and are present on an inducible phage, SM1. In S. mitis these proteins have been linked to platelet adherence, as disruption of either of these genes resulted in reduced platelet binding [17]; however, their role in E. faecalis virulence is unknown. Phage integration events in Streptococcus pyogenes are responsible for the major gaps in the alignments of different M serotypes [18], and the presence of seven integrated prophages in the E. faecalis V583 genome suggests an important role for phage integration in genome plasticity. The E. faecalis V583 chromosome also contains three regions that are possibly integrated plasmids. Present in two of these regions are genes encoding aggregation substance (EF0485, EF0149). One of the integrated plasmid regions is located within the pathogenicity island (EF0485–EF0506) and is very similar to pTEF1, but the aggregation substance is more closely related to that from pTEF2. The second region (EF0131–EF0166), has genes of plasmid, phage, and conjugative transposon origin, and is flanked by a phage integrase (EF0166). The third plasmid region (EF2512–EF2545) is very similar to this, has no aggregation substance gene, but does contain cell-wall surface anchor protein (EF2525) and a putative sortase (EF2524). The presence of three integrated plasmids points to a potentially important role for plasmids in the genome evolution of E. faecalis. In Staphylococcus epidermidis, it has been noted that that IS256 occurs preferentially in nosocomial isolates [19], and this IS element has also been linked to phenotypic variation in biofilm formation [20, 21]. It could be interesting to assess the frequency of IS256 in E. faecalis commensal strains relative to clinical isolates, to see whether this trend is also seen in enterococci. It has been speculated that the presence of multiple copies of IS256 could play a role in genomic flexibility and development of virulence, as inactivation of genes by IS elements allows the development of novel phenotypes [19]. Insertion element movement and integration allows rapid evolution of a bacterial genome. Another example of this is the genome comparisons of Yersinia pestis and Yersinia pseudotuberculosis, which illustrated that differences in virulence are linked to insertion sequence integration and homologous recombination [22]. Inactivation of certain genes and pathways in Y. pestis appear to have played a substantial role in the development of virulence in this organism [22]. Conjugative plasmids in E. faecalis have long been studied as one of the ways in which E. faecalis disseminates antimicrobial resistance genes and other genetic information rapidly. E. faecalis V583 contains three plasmids, pTEF1, pTEF2 (similar to the pheromone response plasmids pAD1 [23] and pAM373 [24]), and pTEF3, which belongs to the pAMb1 family of broad host range plasmids. PTEF2 shares regions of similarity with pCF10 [25], with identical prgA, prgB, and prgC genes (EFB0010–EFB0012), but lacks prgQ and has a novel pheromone inhibitor (EFB0005.1). Only one insertion element, IS256 (EFB0052), is present in this plasmid. The smallest plasmid, pTEF3, contains a prgZ-like pheromone receptor, similar to pTEF2. This prgZ homologue (EFC0001) neighbors several insertion sequences (EFC0002, EFC0004, EFC0007).

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pTEF1 shares extensive sequence homology with the well-characterized enterococcal pheromone response plasmid pAD1 [23]. pTEF1 has both an identical pheromone inhibitor (iAD1) and aggregation substance (Asa1); however, there is a 31kbp inversion falling between the sex pheromone inhibitor and traE1. This plasmid contains a total of eight insertion sequences, five of which are IS1216. The insertion sequences are clustered in two regions at either end of the 31-kbp inversion, one of which (EFA0006–EFA0016) contains an erythromycin resistance gene (EFA0007), a putative multidrug resistance protein (EFA0010), and a putative drug resistance transporter (EFA0014). The second area (EFA0056–EFA0063) contains a 6-aminoglycoside N-acetyltransferase (EFA0060) conferring high-level gentamicin resistance. In addition to the antimicrobial resistance genes, encoded on the V583 chromosome are four putative drug resistance transporters of the EmrB/QacA family (EF0420, EF0785, EF1370, EF1814), three major facilitator type drug resistance transporters (EF1078, EF1943, EF2068), three putative streptomycin resistance genes (EF1076, EF2300, EF2861), and genes predicted to encode daunorubicin resistance (EF1032), tunicamycin resistance (EF1055), and tellurite resistance (EF2698). Vancomycin resistance in E. faecalis V583 is due to the presence of the vanB operon [26] (EF2293–EF2299); however, this is not present on the conjugative element Tn1549 [27]. Instead, the operon is located on a previously unknown mobile element sharing some similarities with Tn1549, but also containing multiple insertions, deletions, and rearrangements. This mobile element has an atypical nucleotide content, suggesting acquisition via horizontal gene transfer. From the E. faecalis genome sequence, homologues for most of the competence proteins of S. pneumoniae can be found, but several are absent. E. faecalis V583 contains four proteins that have identity to the Bacillus subtilis comG operon (EF2044–EF2046, EF1986), but is missing comG4, comG5, and comG7. Interestingly, a prophage is present between comG3 and comG6, suggesting that these genes may in fact be present in other strains of E. faecalis that do not contain the integrated phage. The genome sequences of other lactic acid bacteria such as L. monocytogenes [28], Lactococcus lactis [29], and S. pyogenes [30], also reveal the presence of homologues for most of the genes involved in competence. Due to the discovery of a homologue of the B. subtilis competence regulatory protein MecA in L. monocytogenes, the ability of L. monocytogenes to take up DNA was tested, but competence was not detected in the various strains screened [31]. A protein with identity to MecA is also present in E. faecalis (EF2677). In L. monocytogenes, it was hypothesized that these competence-related proteins may be involved in other functions, and that perhaps these bacteria have developed similar complex regulatory mechanisms in response to certain signals in their environmental niches [31]. It is interesting to speculate on the possibility of natural transformation in E. faecalis, especially due to the large amount of mobile DNA present in its genome, and to suggest that perhaps under certain environmental conditions (e.g., the acidic pH of the gut, or the alkaline pH of the bile duct) this organism could be capable of up taking foreign DNA.

7.3 Genome Sequence of E. faecalis

7.3.2 Environmental Adaptation and Stress Response

Environmental adaptation traits and stress resistance mechanisms have been linked to virulence, as bacterial survival in the host is often reliant on these factors. Disruption of genes encoding such traits often leads to attenuation of the organism in an animal model [32–35]. As mentioned previously, E. faecalis is a very robust organism able to survive in many harsh environments. E. faecalis V583 contains a V-type and F1F0-type ATPase responsible for regulating the intracellular pH and proton motive force. Cation transport ATPases can also contribute to pH + homeostasis and, interestingly, a homologue of a K -ATPase is present on the pathogenicity island. A further mechanism for pH homeostasis is the arginine deiminase pathway, which can raise the pH of the environment due to the release + of ammonia and its reaction with H [36]. The E. faecalis V583 genome shows the presence of all the putative members of this pathway, and has two putative ornithine cadamoyltransferases (EF0105, EF0732), and ornithine cyclodeaminases (EF0118, EF0616), and contains four putative carbamate kinases (EF0106, EF0386, EF0735, EF2575). The genome also contains an MscL-like protein (EF3152), thought to act as a electromechanical switch involved in sensing the state of lipid bilayers [37]. In addition, the genome contains five cold shock proteins and four universal stress proteins. The presence of 14 predicted metal ion P-type ATPases may account for the resistance of this organism to metals and ensure cation homeostasis [38]. Present in the E. faecalis V583 genome are a number of proteins involved in heat shock response, including homologues of heat shock proteins DnaK (EF1308) and GroEL (EF2633), gsp66 and gsp67, respectively [39]. The genome also contains a homologue of CtsR (EF3283) from B. subtilis which has been shown to control molecular chaperone gene expression [40]. Unlike Staphylococcus aureus, S. pneumoniae, S. pyogenes, and L. lactis, the groR operon does not show the presence of CtsR binding sites, and this suggests that in E. faecalis the groE and dnaK operons are primarily controlled by HrcA [39]. CtsR recognition sites have been noted upstream from the clpB (EF2355), clpP (EF0771), and clpE (EF0706) genes [40]. The Clp ATP-dependent proteases have been shown in B. subtilis to play essential roles in stress survival [41]. Recent data from L. monocytogenes suggests that in that organism, clpB is required for virulence and has a role in thermotolerance, but is not involved in other stress responses [42]. The role of these proteins has not been investigated in E. faecalis. Also present is an HtrA (DegP) homologue (EF3027), part of the widely distributed family of serine proteases. In Streptococcus mutans, htrA mutants have a reduced ability to withstand high temperature, and are also more sensitive to low pH and H2O2 [43]. In S. pyogenes the virulence of a degP knockout was reduced in a mouse model, and was sensitive to both temperature and oxidative stress [32]. It was hypothesized that this enzyme is responsible for degrading misfolded or aggregated proteins [32]. More recent work has suggested that DegP influences the expression of at least two virulence factors in S. pyogenes [44]. In S. pneumo-

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niae, HtrA is involved in growth at high temperatures, resistance to oxidative stress, and the ability to undergo genetic transformation [45]. Gls24 is a general stress protein found in E. faecalis (EF0079), and has an adjacent homologue, GlsB (EF0080), which is 71% identical at the amino acid level [46]. A further two homologues of this protein are located on the E. faecalis pathogenicity island (EF0055 and EF0117), both having 55% identity to Gls24 and 54% identity to each other. Gls24 has been shown to be induced at the onset of starvation and also when exposed to bile salt and CdCl2 stress [46]. It has been observed that expression of the gls24 gene is increased when E. faecalis is grown in serum (15-fold increase) and urine (nine-fold increase) [47]. Recently disruption of gls24 has been shown to affect both virulence and stress response; however, disruption of glsB increased bile salt sensitivity, but had no effect on virulence [48]. The actual function of Gls24 and GlsB is unknown at present. Other genes present which may have a role in bile salt resistance include SagA [49], (EF0394), a putative ABC transporter (EF0675-EF0674) which has homology to the BilE bile exclusion system in L. monocytogenes [50], and a putative bile salt hydrolase which is present on the pathogenicity island. E. faecalis has a respiratory pathway that in the absence of hematin or fumarate produces substantial amounts of superoxide [51]. Superoxide anion has a destructive effect on a wide variety of cells and tissues, in addition to biological compounds such as lipids, proteins, and nucleic acids, and because of this, E. faecalis has a number of strategies to combat oxidative stress. Firstly, absent from the E. faecalis genome sequence are superoxide-sensitive enzymes associated with the tricarboxylic acid (TCA) cycle. The E. faecalis V583 genome sequence also shows the presence of several enzymes and gene products that contribute to oxidative stress resistance, such as NADH peroxidase (npr) (EF1211), NADH:peroxiredoxin oxidoreductase (EF2738), alkyl hydroperoxidase resistance protein (aphCF), NADH oxidase (nox) (EF1586), and manganese superoxide dismutase (EF0463), which catalyzes the reduction of superoxide to oxygen and hydrogen peroxide. In addition to these genes, V583 contains a cydABCD operon (EF2061-EF2058), ohr (organic hydroperoxidase resistance protein) (EF0453) [52], and two Dps family proteins (EF3233, EF0606), which have been implicated in the protection of DNA from oxidative stress. A second ohr/OsmC protein (EF3201) may also aid in resistance to organic hydroperoxides. The genome contains an apoenzyme of catalase (katA) (EF1597), which can produce catalase upon the addition of heme [53]. – Extracellular O2 generated by E. faecalis has been shown to damage colonic epithelial cell DNA both in vitro and in vivo [54], and electron spin resonance data sug– gests that nutrient conditions in the rat intestine favor the production of O2 [55]. Previously a link has been suggested between the invasiveness of E. faecalis isolates and their rates of superoxide production [56]. Despite its ability to adapt to many different environmental stresses, E. faecalis possesses a moderate number of regulatory genes, including only three alternative sigma factors, 17 two-component systems, and one orphan response regulator. Three response regulator systems are present on mobile elements, the previously mentioned VncRS homologue (EF1863–EF1864), VanRSB (involved in regulation

7.3 Genome Sequence of E. faecalis

of the van operon) (EF2298–EF2299), and KpdDE, which is found on the pathogenicity island. Two-component signal transduction systems allow bacteria to monitor their environment and respond to a wide array of stresses [57], and recent work has investigated the role of these two-component systems in stress response in E. faecalis V583 [58]. Generation of insertional mutations in 17 of the 18 response regulators demonstrated one response regulator that is essential for cell viability (vicR) (EF1193) [58]. Mutation in the CroRS two-component system has previously been linked to the intrinsic resistance of E. faecalis to cephalosporins [59], and insertional mutation of this locus increased susceptibility to bacitracin, cefotaxime, cefuroxime, and vancomycin, but not ampicillin [58], and has also been associated with defects in growth and cell morphology [60]. These data suggest a role for CroRS in the regulation of cell wall integrity in E. faecalis. The role of two-component systems in E. faecalis JH2-2 has also been examined, and in this genetic background four systems were induced by environmental stresses [60]. In E. faecalis OG1RF, an EtaRS (EF1050–EF1051) mutant was found to be more acidsensitive, and was attenuated in a mouse peritonitis model [61]. S B No r or r -like sigma factors have been found in the genome of E. faecalis, which raises the question of how this bacterium regulates gene expression during stress responses. A regulatory protein HypR (EF2958) has recently been characterized that is involved in oxidative stress response, and disruption reduced survival of E. faecalis in an in vivo–in vitro macrophage infection model [62]. Quorum sensing is an important mechanism used by many prokaryotes to adapt to different environments encountered during infection. E. faecalis has a homologue of LuxS, which is required for the biosynthesis of the type 2 autoinducer (AI-2). AI-2 has been shown to be detected by a sensory protein in a cell-densitydependent manner, and is thought to be involved in the regulation of a wide range of bacterial physiological functions [63]. In S. pyogenes, LuxS activity has been shown to play a role in the expression of virulence factors associated with epithelial cell internalization [64]. The role of LuxS in E. faecalis is at present unknown. Adding to its ability to survive in diverse environments, E. faecalis contains 35 probable PTS-type sugar transporters and encodes pathways for the utilization of 15 different sugars. This is comparable to L. monocytogenes, of which 4% of the genome encodes PTS and ABC transporters [28]. As mentioned previously, the TCA cycle is absent in E. faecalis and energy is derived via glycolysis or the pentose phosphate pathway. E. faecalis has a catabolite repression pathway to actively regulate energy metabolism while growing on easily fermentable sugars such as glucose [51]. The ability of E. faecalis to respond to rapidly changing environments and stresses probably contributes to its survival through all stages of infection. 7.3.3 Survival In Vivo

Originally thought to be an adhesin [65], the E. faecalis endocarditis antigen EfaA has since been found to be present in all E. faecalis isolates [66], and is the third gene of the efaCBA operon (EF2074-EF2076). A homologue, ScaCBA from Strepto-

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coccus gordonii, has been shown to be a Mn ABC transporter [67] and is regulated by a DtxR family metalloregulator, ScaR [68]. EfaA expression has recently been 2+ shown to be Mn -dependent and regulated by a DtxR family protein, EfaR 2+ (EF1005), suggesting that in E. faecalis this protein is also involved in Mn transport [69]. Analysis of the V583 genome shows an additional ten putative EfaRbinding sites, demonstrating that manganese availability is potentially an important factor in infection and gene regulation in vivo [69]. In correlation with this, the abundance of mRNA of efaA has been shown to increase 2195-fold in stationary phase when grown in urine [47], and disruption of efaA has been shown to result in reduced virulence in a mouse peritonitis model [66]. A homologue of the 2+ putative EfaCBA Mn transporter is also present on the pathogenicity island. A further protein potentially involved in in vivo survival is EF0467, a member of 2+ the MgtC family, which has been implicated in survival and growth in low Mg concentrations. EF0467 has 37% identity to MgtC from Salmonella typhimurium, which has been shown to be required for survival inside macrophages and growth 2+ in low Mg medium. 7.3.4 Potential Virulence Factors 7.3.4.1

Hemolysins, Proteases, and other Enzymes

The genome sequence of E. faecalis V583 shows the presence of three putative hemolysins (EF0700, EF0982, EF1685). EF0700 has its closest identity to L64811 (57.2%) from L. lactis, while EF0982 is most similar to Lm01366 from L. monocytogenes. The third putative hemolysin, EF1685, shares 46% identity to hemolysin III from Bacillus cereus [70]. E. faecalis V583 is nonhemolytic on blood agar; however, it is possible that expression of these genes could be induced in in vivo conditions. It is unknown at present whether these genes do indeed encode functional hemolysins. A putative exfoliative toxin A (EF0645) is likewise present in the chromosome. This protein shares 32% identity to Staphylococcus hyicus exfoliative toxin A and again its function in E. faecalis is unknown. Also encoded on the chromosome are two putative xanthan lyases, EF0818 and EF3023, which share 36% identity at the protein level. EF0818 and EF3023 have 31–28% identity to the xanthan lyase from Bacillus sp. G11 [71], and 29–27% identity to hyaluronate lyase from S. pneumoniae [72]. Both these enzymes are involved in breaking glycosidic bonds in polysaccharides, although they have different substrate specificities. Hyaluronidases are an important pathogenic bacterial spreading factor, and cleave hyaluronan, which is a constituent of the extracellular matrix of connective tissues [73]. The substrate specificity of these two enzymes in E. faecalis is uncertain. GelE (EF1818) encodes a secreted zinc metalloprotease which has been shown to cleave a number of substrates including casein, hemoglobin, collagen, fibrin, and gelatin, as well as degrading pheromone and inhibitor peptides [74, 75]. GelE has been hypothesized to clear the bacterial cell wall of misfolded proteins, and

7.3 Genome Sequence of E. faecalis

disruption of this gene has been shown to increase the bacterial chain length [76]. The presence of GelE is essential for the degradation of polymerized fibrin, which has important implications in pathogenesis, and the linkage of GelE to decreased chain length may suggest a role in increasing dissemination of bacteria in highdensity environments [76]. A gelE knockout has shown reduced virulence in models of mouse peritonitis [77], rabbit endophthalmitis [78], and in a Caenorhabditis elegans virulence model [79]. Located downstream from GelE is an ORF called sprE (EF1817), coding for a serine protease, and the presence of functional GelE has been implicated in SprE maturation [80]. SprE encodes a 26-kDa protein that shares homology with the S. aureus V8 protease, and has also been shown to contribute to pathogenesis in several infection models including C. elegans, mouse peritonitis, and a rabbit endophthalmitis model [78, 79, 81]. Deletion of the serine protease, but not gelatinase has been shown to cause attenuation of bacterial infection in the Arabidopsis thaliana root pathogenicity model with the SprE mutant forming poor bacterial communities [82]. The expression of both gelE and sprE is dependent on the fsr genes that encode a two-component signal transduction system [81, 83]. The fsr locus (EF1822–EF1820) encodes three proteins, FsrA, FsrB, and FsrC, which share sequence similarity with AgrA, AgrB, and AgrC, respectively, of S. aureus. This locus has been identified as a two-component signal transduction system that is dependent upon a secreted peptide lactone, meaning that regulation is controlled in a cell-density-dependent manner [83–85]. Accumulation of the peptide, encoded at the C terminus of the FsrB protein, is thought to be sensed by the FsrC histidine kinase, and leads to activation of the FsrA response regulator [85]. Hancock and Perego recently showed a role for fsr and GelE in biofilm formation [86]. Interestingly, although gelatinase and serine protease were found to contribute to virulence in a rabbit endophthalmitis model, an fsrB mutant showed much greater attenuation, suggesting the effect of this gene on the expression of additional traits that contribute to virulence [78]. E. faecalis V583 also contains a second membrane-associated zinc metalloprotease (EF2380), Eep. This protein has been shown to play a role in the production of the sex pheromone cAD1, and is thought to be involved in processing the pheromone precursor structure, or in regulating the expression or secretion [87]. The final secreted virulence factor in E. faecalis is cytolysin, which is a structurally novel toxin. As previously mentioned, this gene locus is present on the pathogenicity island; however, most of this gene locus is absent in E. faecalis V583 due to a 17-kbp deletion. In V583, the genes cylR1, cylR2, clyLL, cylLS, and cylM are present, but the genes cylB, cylA, and cylI, which are essential for mature cytolysin production and self-immunity, are either absent or nonfunctional. Cytolysin is comprised of two nonidentical peptide subunits, both of which are required for lytic activity [88], and is capable of lysing erythrocytes, polymorphonuclear leukocytes, and macrophages [89], as well as a broad range of gram-positive bacteria. Recently cytolysin expression has been shown to be induced in response to target cells present in the environment through a quorum-sensing autoinduction mech-

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anism [90]. In virtually all infection models tested, cytolysin has been shown to contribute to pathogenesis [91–94].

7.3.4.2 Cell-Wall-Associated Virulence Factors In addition to hemolysins and proteases, E. faecalis contains a number of cell-wallassociated proteins that could potentially be involved in pathogenesis. The ability to adhere to host tissues is a critical step in the onset of most microbial infections. Recent examination of the E. faecalis genome has identified the presence of 41 putative cell-wall-anchored proteins, and 17 of these were predicted to contain structural characteristics of the MSCRAMM (microbial surface component recognizing adhesive matrix molecules) type [95]. In addition to this, the E. faecalis genome contains 134 predicted surface-exposed proteins, of which 65 contain repeat regions and may have a role in antigenic variation via a slippage mechanism [8]. The only characterized adhesion from E. faecalis is Ace (EF1099), a 674-aminoacid protein that has similarities with the central region of the A domain of the collagen-binding protein Cna of S. aureus [96]. Ace is thought to be involved in E. faecalis collagen binding, as mutation of Ace showed reduction in 46 C growthelicited binding to immobilized collagen type I, collagen type IV, and mouse laminin [97, 98], and Ace-specific antibody has been shown to inhibit this binding in wild-type E. faecalis [96, 97]. Present in the V583 genome is a putative internalin protein family protein (EF2686), which has 29% identity to InlA from L. monocytogenes and contains a premature stop codon. The internalin family of proteins is characterized by the presence of leucine-rich repeats. In L. monocytogenes the receptor for InlA is E-cadherin, and this protein is required for cell invasion [99, 100]. The interaction of internalin A with E-cadherin on enterocytes is an early critical step for the onset of listeriosis in vivo [101]. In some strains of Listeria, this protein is truncated, but clinical strains express a full-length protein more frequently [102]. Disruption of a homologue of the internalin family of proteins in group A Streptococcus has been shown to result in reduced virulence and increased susceptibility to phagocytosis [103]. Interestingly, EF2686 in E. faecalis was found to be induced 162-fold in stationary phase when cultured in urine compared to culture in 2YT broth, showing an induction of gene expression in an infection-type environment [47]. Aggregation substance (EF0149, EF0485, EFA0047, EFB0011) is expressed on the cell surface of E. faecalis, allowing close contact for conjugation. This protein has been implicated as a virulence trait, due to the fact that cells expressing it have increased adherence and internalization into phagocytes [104, 105], renal cells [106], and epithelia [107–110]. Expression of aggregation substance has been shown to be inducible by human serum in the absence of plasmid-free Enterococcus [106]. Analysis of Asc10, the aggregation substance encoded on pCF10, showed that a domain of this protein was required for aggregation, HT-29 internalization, and maximum levels of lipoteichoic acid binding [107]. Aggregation substance has been shown to be involved in the survival of enterococci within poly-

7.3 Genome Sequence of E. faecalis

morphonuclear leukocytes [104], but does not enhance the urinary tract colonization ability of E. faecalis [111]. The E. faecalis genome encodes a protein (EF1249) which shares 44% identity to FbpA from L. monocytogenes. In L. monocytogenes, this protein has been shown to bind to human fibronectin, and a fbpA mutant was found to have a reduced ability to colonize and survive in a listeriosis mouse model [112]. Inactivation of fbpA also resulted in reduction of protein levels of two other Listeria virulence factors, LLO and InlB, suggesting that FbpA also acts to modulate expression of these two proteins [112]. The function of EF1249 in E. faecalis is at present unknown. Enterococcal surface protein, Esp [113], is found on the pathogenicity island in MMH594, but is absent from V583 due to the previously mentioned 17-kbp deletion in the pathogenicity island of this isolate. The gene, esp, encodes a large cellwall-associated protein and contains a variable number of highly conserved 82amino-acid repeats [113]. Within these repeats is a 13-amino-acid stretch that is identical to a sequence found in the repeats of the Rib and Ca proteins from S. agalactiae [113]. Studies using a mouse model of urinary tract infections have shown that this protein enhances colonization and persistence in urinary bladders [114]. The Esp protein has recently been linked to enhanced biofilm formation, and its contribution was most pronounced in the presence of glucose [115]; however, biofilm production has been shown to occur in the absence of this protein, showing that it is not essential for this process [86, 116]. GelE has also been implicated in biofilm formation [86]. Disruption of GelE or the response regulator required for GelE production in V583 affected biofilm formation, while complementation of GelE restored the wild-type biofilm phenotype [86]. The same study showed that of the 17 two-component regulatory systems present in E. faecalis V583, only the fsr locus was involved in biofilm production. Biofilm production in E. faecalis has also been linked to the protein BopD (EF0954). This protein has homology to a sugar-binding transcriptional regulator, and has 50% similarity to CcpA (EF1741). The actual role of this regulatory gene is unknown, but the association of enhanced biofilm formation in the presence of glucose and the possible involvement of a sugar-binding transcriptional regulator suggest a linkage to increased biofilm production in E. faecalis in the presence of specific carbohydrates. Contributing to immune cell evasion is the E. faecalis variable capsular polysaccharide (EF2495–EF2485). Expression of capsular polysaccharide from the cpsC-K genes has been shown to contribute to host immune evasion in E. faecalis, with mutants being more susceptible to phagocytic killing [117]. Recent work suggests a limited number of E. faecalis capsule serotypes, 60% of the isolates tested being placed into four serotypes [118]. Also present in E. faecalis V583 is a common cell wall polysaccharide, Epa (enterococcal polysaccharide antigen). An epa knockout has been shown to be unable to translocate across a monolayer of polarized human enterocyte-like T84 cells, and complementation was shown to restore its translocation ability [119]. In the same model, 9 out of 15 E. faecalis isolates were able to translocate, showing strain-specific differences in this phenotype [119].

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Other genes that may have a role in immune evasion include homologues of the dlt operon (EF2749–EF2746), responsible for D-alanine incorporation of teichoic acid polymers in cell wall. Formation of D-alanyl-lipoteichoic acid has been shown to be required for host cell adhesion in L. monocytogenes [120]. D-Alanine incorporation into lipoteichoic acid reduces the negative charge, and therefore electrostatic interactions with cationic peptides are altered. The E. faecalis V583 genome also contains a protein, EF0031, which shares identity with MprF. This protein was first described in S. aureus [121], and confers resistance to human defensins. MprF has been demonstrated as modifying phosphatidylglycerol with L-lysine, therefore reducing the negative charge of the membrane [121]. Disruption of the mprF gene in S. aureus led to a 13-fold higher sensitivity to defensin from human neutrophils; however, the action of this protein in E. faecalis has not yet been investigated [121]. 7.3.5 Pathogenicity Island of E. faecalis

The E. faecalis pathogenicity island is 153 kbp and encodes 129 predicted ORFs. Present on this element are genes encoding for virulence factors, transposases, transcriptional regulators, and other proteins potentially involved in adaptation to diverse environments. Spread across the pathogenicity island are 11 transposases and insertion elements, with homology to ORFs from other low G+C gram-positive organisms such as E. faecium, S. aureus, Listeria innocua, L. monocytogenes, S. pneumoniae, and S. epidermidis. In addition, the 5¢ region of the pathogenicity island has extensive nucleotide sequence identity to the pheromone-responsive plasmids pAM373, pCF10, and pAD1 (EF0005-EF0026), suggesting integration of a pheromone-response plasmid. The only transfer-related genes present, however, comprise a TraG-like protein (EF0011) and a region with 87% identity at the nucleotide level to the second transfer origin (oriT) identified in pAD1. Within the pathogenicity island are the virulence traits, Esp, cytolysin, and aggregation substance. In addition are a number of proteins that may have potential roles in virulence and/or environmental adaptation in E. faecalis. One of these traits is a putative bile salt hydrolase (cbh) (EF0040), which could add a new ecological niche to this E. faecalis strain, perhaps allowing growth in bile-rich environments such as the bile ducts, and therefore potentially giving this strain a competitive advantage. EF0040 has 67% similarity to the Bsh from L. monocytogenes, which is a bile salt hydrolase [122]. In L. monocytogenes, Bsh is a virulence factor and is regulated by the virulence regulator PrfA [122]. In vivo, deletion of this gene results in decreased fecal carriage in guinea pigs, and reduced liver colonization and virulence was noted in a mouse intravenous model [122]. The contribution of cbh to the pathogenesis of enterococcal infections has not yet been investigated. In terms of adaptation to different environments, in addition to the bile salt hydrolase, the pathogenicity island contains a phosphotransferase system (PTS) (EF0078–EF0081) and adjacent xylose metabolism genes (EF0082–EF0083), potentially allowing growth on xylose. Other metabolism genes include a ketopantoate

7.3 Genome Sequence of E. faecalis

reductase (EF0037), an ornithine cyclodeaminase (EF0124), a polysaccharide deacetylase (EF0108), and a glycosyl hydrolase (EF0077). These pathways may aid survival in nutrient limiting conditions, or in diverse environments. Additional mechanisms to cope with nutrient limiting conditions include a second putative manganese transporter with homology to EfaCBA, and a putative iron transporter (EF0095, EF0096), both of which may aid in survival in vivo. As previously mentioned, two Gls24-like proteins are present on the pathogenicity island (EF0117 and EF0055). The Gls24 protein has been shown to be induced under certain stress conditions [46], and disruption of gls24 affects virulence in a mouse model [48]. The exact function of Gls24 or its homologues is unknown. Also potentially involved in stress response is a Dps-family surface protein (EF0119), with closest homology to Lactobacillus rhamnosus. Some members of this protein family have been shown to bind nonspecifically to DNA under starved conditions, protecting it from cleavage by reactive oxygen species [123]. In addition a DNA-J homologue is present (EF0028), and a putative DNA-damage-inducible protein (EF0032). Potentially these three proteins may contribute to DNA repair and protection, and may have an important role in bacterial survival in harsh environmental conditions. Encoded on the pathogenicity island is a putative potassium ABC transporter and a neighboring two-component regulatory system (EF0087–EF0091) with homology to the kdp operon from E. coli [124], but with closer identity to putative homologues in L. innocua. This operon may be involved in stress response to osmotic shock, allowing transport of potassium ions. Other potential genes involved in virulence or environmental adaptation include a putative N-actylmannosamine-6-phosphate epimerase (EF0066), which is part of the N-acetylmannosamine utilization pathway, often found in oropharyngeal pathogens [125]. In addition, a large cell-wall-associated protein is present (EF0109), with a predicted size of 207 kDa. This large protein may have a role in colonization or virulence through adhesion or immune evasion, but its role is at present unknown. Two genes (EF0120 and EF0122) present on the pathogenicity island have homology to IbrB, and IbrA, respectively, from E. coli [126]. In E. coli these proteins are responsible for activation of the otherwise silent phage-derived eib genes, which bind immunoglobin [126]. Interestingly, also on the pathogenicity island is a protein EF0052, with 49% similarity to the nisin-resistance protein from L. lactis subsp. lactis biovar diacetylactis DRC3, which has been demonstrated to be involved in resistance to nisin in this strain [127, 128]. In a study of S. agalactiae virulence using signature-tagged mutagenesis, two attenuated mutants were identified with insertions in an NisR homologue, although the function of this gene has not been characterized [129]. As nisin is a cationic peptide, it is possible that this protein may confer cross-resistance to other cationic peptides such as defensins, or bacteriocins produced by other gut bacteria.

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7.4 Conclusions and Future Perspectives

E. faecalis is a leading cause of nosocomial infection and this is probably associated with its ability both to survive in the hospital environment and also to overcome the harsh conditions associated with colonization and infection. In addition, over a quarter of the genome is mobile and acquired DNA, suggesting that this organism has the ability to rapidly acquire genetic information and evolve to adapt to its environment. The expeditious acquisition and dissemination of antimicrobial resistance in this organism has been previously demonstrated. Although several traits that contribute to virulence have been characterized in E. faecalis, little is known about the function of many of the potential virulence genes encoded both on the chromosome and in the pathogenicity island. The bacterial/host relationship is very complex, and the mechanisms that enterococci use to change from commensal organism to the etiologic agent of disease are not yet fully understood. However, with information from the genome sequence it is now possible to look closer at gene loci that may have important roles in this interaction.

References 1 Martin, J. D. and J. O. Mundt. 1972.

2

3

4

5

Enterococci in insects. Appl. Microbiol. 24:575–580. Rice, E. W., J. W. Messer, C. H. Johnson and D. J. Reasoner. 1995. Occurrence of high-level aminoglycoside resistance in environmental isolates of enterococci. Appl. Environ. Microbiol. 61:374–376. Schaberg, D. R., D. H. Culver and R. P. Gaynes. 1991. Major trends in the microbial etiology of nosocomial infection. Am. J. Med. 91:72S–75S. Gordon, K. A. and R. N. Jones. 2003. Susceptibility patterns of orally administered antimicrobials among urinary tract infection pathogens from hospitalized patients in North America: comparison report to Europe and Latin America. Results from the SENTRY Antimicrobial Surveillance Program (2000). Diagn. Microbiol. Infect. Dis. 45:295–301. National Nosocomial Infections Surveillance System. 1999. National Nosocomial Infections Surveillance (NNIS) System report, data summary from January 1990–May 1999, issued June 1999. Am. J. Infect. Control 27:520–532.

6 Low, D. E., N. Keller, A. Barth and R. N.

7

8

9

10

Jones. 2001. Clinical prevalence, antimicrobial susceptibility, and geographic resistance patterns of enterococci: results from the SENTRY Antimicrobial Surveillance Program, 1997–1999. Clin. Infect. Dis. 32:133–145. Sahm, D. F., J. Kissinger, M. S. Gilmore, P. R. Murray, R. Mulder, J. Solliday and B. Clarke. 1989. In vitro susceptibility studies of vancomycin-resistant Enterococcus faecalis. Antimicrob. Agents Chemother. 33:1588–1591. Paulsen, I. T., L. Banerjei, G. S. Myers, K. E. Nelson, R. Seshadri, T. D. Read, D. E. Fouts, J. A. Eisen, S. R. Gill, J. F. Heidelberg et al. 2003. Role of mobile DNA in the evolution of vancomycinresistant Enterococcus faecalis. Science 299:2071–2074. Shankar, N., A. S. Baghdayan and M. S. Gilmore. 2002. Modulation of virulence within a pathogenicity island in vancomycin-resistant Enterococcus faecalis. Nature 417:746–750. Singh, K. V. and B. E. Murray. 1994. Revised estimates of enterococcal chro-

References mosomal sizes. DNA Cell. Biol. 13:1145–1146. 11 Oana, K., Y. Okimura, Y. Kawakami, N. Hayashida, M. Shimosaka, M. Okazaki, T. Hayashi and M. Ohnishi. 2002. Physical and genetic map of Enterococcus faecium ATCC19434 and demonstration of intra- and interspecific genomic diversity in enterococci. FEMS Microbiol. Lett. 207:133–139. 12 Hayashi, T., K. Makino, M. Ohnishi, K. Kurokawa, K. Ishii, K. Yokoyama, C. G. Han, E. Ohtsubo, K. Nakayama, T. Murata et al. 2001. Complete genome sequence of enterohemorrhagic Escherichia coli O157:H7 and genomic comparison with a laboratory strain K-12. DNA Res. 8:11–22. 13 Nelson, K. E., D. E. Fouts, E. F. Mongodin, J. Ravel, R. T. DeBoy, J. F. Kolonay, D. A. Rasko, S. V. Angiuoli, S. R. Gill, I. T. Paulsen et al. 2004. Whole genome comparisons of serotype 4b and 1/2a strains of the food-borne pathogen Listeria monocytogenes reveal new insights into the core genome components of this species. Nucleic Acids Res. 32:2386–2395. 14 Novak, R., E. Charpentier, J. S. Braun and E. Tuomanen. 2000. Signal transduction by a death signal peptide: uncovering the mechanism of bacterial killing by penicillin. Mol. Cell. 5:49–57. 15 Robertson, G. T., J. Zhao, B. V. Desai, W. H. Coleman, T. I. Nicas, R. Gilmour, L. Grinius, D. A. Morrison and M. E. Winkler. 2002. Vancomycin tolerance induced by erythromycin but not by loss of vncRS, vex3, or pep27 function in Streptococcus pneumoniae. J. Bacteriol. 184:6987–7000. 16 Haas, W., J. Sublett, D. Kaushal and E. I. Tuomanen. 2004. Revising the role of the pneumococcal vex-vncRS locus in vancomycin tolerance. J. Bacteriol. 186:8463–8471. 17 Bensing, B. A., I. R. Siboo and P. M. Sullam. 2001. Proteins PblA and PblB of Streptococcus mitis, which promote binding to human platelets, are encoded within a lysogenic bacteriophage. Infect. Immun. 69:6186–6192. 18 Smoot, J. C., K. D. Barbian, J. J. Van Gompel, L. M. Smoot, M. S. Chaussee,

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20

21

22

23

24

G. L. Sylva, D. E. Sturdevant, S. M. Ricklefs, S. F. Porcella, L. D. Parkins et al. 2002. Genome sequence and comparative microarray analysis of serotype M18 group A Streptococcus strains associated with acute rheumatic fever outbreaks. Proc. Natl. Acad. Sci. U. S. A. 99:4668– 4673. Kozitskaya, S., S. H. Cho, K. Dietrich, R. Marre, K. Naber and W. Ziebuhr. 2004. The bacterial insertion sequence element IS256 occurs preferentially in nosocomial Staphylococcus epidermidis isolates: association with biofilm formation and resistance to aminoglycosides. Infect. Immun. 72:1210–1215. Kiem, S., W. S. Oh, K. R. Peck, N. Y. Lee, J. Y. Lee, J. H. Song, E. S. Hwang, E. C. Kim, C. Y. Cha and K. W. Choe. 2004. Phase variation of biofilm formation in Staphylococcus aureus by IS256 insertion and its impact on the capacity adhering to polyurethane surface. J. Korean Med. Sci. 19:779–782. Conlon, K. M., H. Humphreys and J. P. O’Gara. 2004. Inactivations of rsbU and sarA by IS256 represent novel mechanisms of biofilm phenotypic variation in Staphylococcus epidermidis. J. Bacteriol. 186:6208–6219. Chain, P. S., E. Carniel, F. W. Larimer, J. Lamerdin, P. O. Stoutland, W. M. Regala, A. M. Georgescu, L. M. Vergez, M. L. Land, V. L. Motin et al. 2004. Insights into the evolution of Yersinia pestis through whole-genome comparison with Yersinia pseudotuberculosis. Proc. Natl. Acad. Sci. U. S. A. 101:13826–13831. Francia, M. V., W. Haas, R. Wirth, E. Samberger, A. Muscholl-Silberhorn, M. S. Gilmore, Y. Ike, K. E. Weaver, F. Y. An and D. B. Clewell. 2001. Completion of the nucleotide sequence of the Enterococcus faecalis conjugative virulence plasmid pAD1 and identification of a second transfer origin. Plasmid 46:117– 127. De Boever, E. H., D. B. Clewell and C. M. Fraser. 2000. Enterococcus faecalis conjugative plasmid pAM373: complete nucleotide sequence and genetic analyses of sex pheromone response. Mol. Microbiol. 37:1327–1341.

141

142

7 Pathogenomics of Enterococcus faecalis 25 Dunny, G. M. and B. A. Leonard. 1997.

34 Halsey, T. A., A. Vazquez-Torres, D. J.

Cell-cell communication in gram-positive bacteria. Annu. Rev. Microbiol. 51:527–564. 26 Evers, S., D. F. Sahm and P. Courvalin. 1993. The vanB gene of vancomycinresistant Enterococcus faecalis V583 is structurally related to genes encoding D-Ala:D-Ala ligases and glycopeptideresistance proteins VanA and VanC. Gene 124:143–144. 27 Garnier, F., S. Taourit, P. Glaser, P. Courvalin and M. Galimand. 2000. Characterization of transposon Tn1549, conferring VanB-type resistance in Enterococcus spp. Microbiology 146:1481–1489. 28 Glaser, P., L. Frangeul, C. Buchrieser, C. Rusniok, A. Amend, F. Baquero, P. Berche, H. Bloecker, P. Brandt, T. Chakraborty et al. 2001. Comparative genomics of Listeria species. Science 294:849–852. 29 Bolotin, A., P. Wincker, S. Mauger, O. Jaillon, K. Malarme, J. Weissenbach, S. D. Ehrlich and A. Sorokin. 2001. The complete genome sequence of the lactic acid bacterium Lactococcus lactis ssp. lactis IL1403. Genome Res. 11:731–753. 30 Ferretti, J. J., W. M. McShan, D. Ajdic, D. J. Savic, G. Savic, K. Lyon, C. Primeaux, S. Sezate, A. N. Suvorov, S. Kenton et al. 2001. Complete genome sequence of an M1 strain of Streptococcus pyogenes. Proc. Natl. Acad. Sci. U. S. A. 98:4658–4663. 31 Borezee, E., T. Msadek, L. Durant and P. Berche. 2000. Identification in Listeria monocytogenes of MecA, a homologue of the Bacillus subtilis competence regulatory protein. J. Bacteriol. 182:5931–5934. 32 Jones, C. H., T. C. Bolken, K. F. Jones, G. O. Zeller and D. E. Hruby. 2001. Conserved DegP protease in gram-positive bacteria is essential for thermal and oxidative tolerance and full virulence in Streptococcus pyogenes. Infect. Immun. 69:5538–5545. 33 Brenot, A., K. Y. King, B. Janowiak, O. Griffith and M. G. Caparon. 2004. Contribution of glutathione peroxidase to the virulence of Streptococcus pyogenes. Infect. Immun. 72:408–413.

Gravdahl, F. C. Fang and S. J. Libby. 2004. The ferritin-like Dps protein is required for Salmonella enterica serovar Typhimurium oxidative stress resistance and virulence. Infect. Immun. 72:1155– 1158. 35 Brown, J. S., S. M. Gilliland, S. Basavanna and D. W. Holden. 2004. phgABC, a three-gene operon required for growth of Streptococcus pneumoniae in hyperosmotic medium and in vivo. Infect. Immun. 72:4579–4588. 36 Casiano-Colon, A. and R. E. Marquis. 1988. Role of the arginine deiminase system in protecting oral bacteria and an enzymatic basis for acid tolerance. Appl. Environ. Microbiol. 54:1318–1324. 37 Sukharev, S. I., P. Blount, B. Martinac and C. Kung. 1997. Mechanosensitive channels of Escherichia coli: the MscL gene, protein, and activities. Annu. Rev. Physiol. 59:633–657. 38 Agranoff, D. D. and S. Krishna. 1998. Metal ion homeostasis and intracellular parasitism. Mol. Microbiol. 28:403–412. 39 Laport, M. S., J. A. Lemos, C. Bastos Md Mdo, R. A. Burne and M. Giambiagi-De Marval. 2004. Transcriptional analysis of the groE and dnaK heat-shock operons of Enterococcus faecalis. Res. Microbiol. 155:252–258. 40 Derre, I., G. Rapoport and T. Msadek. 1999. CtsR, a novel regulator of stress and heat shock response, controls clp and molecular chaperone gene expression in gram-positive bacteria. Mol. Microbiol. 31:117–131. 41 Krger, E., E. Witt, S. Ohlmeier, R. Hanschke and M. Hecker. 2000. The clp proteases of Bacillus subtilis are directly involved in degradation of misfolded proteins. J. Bacteriol. 182:3259– 3265. 42 Chastanet, A., I. Derre, S. Nair and T. Msadek. 2004. clpB, a novel member of the Listeria monocytogenes CtsR regulon, is involved in virulence but not in general stress tolerance. J. Bacteriol. 186:1165–1174. 43 Diaz-Torres, M. L. and R. R. Russell. 2001. HtrA protease and processing of extracellular proteins of Streptococcus

References mutans. FEMS Microbiol. Lett. 204:23– 28. 44 Lyon, W. R. and M. G. Caparon. 2004. Role for serine protease HtrA (DegP) of Streptococcus pyogenes in the biogenesis of virulence factors SpeB and the hemolysin streptolysin S. Infect. Immun. 72:1618–1625. 45 Ibrahim, Y. M., A. R. Kerr, J. McCluskey and T. J. Mitchell. 2004. Role of HtrA in the virulence and competence of Streptococcus pneumoniae. Infect. Immun. 72:3584–3591. 46 Giard, J. C., A. Rinc, H. Capiaux, Y. Auffray and A. Hartke. 2000. Inactivation of the stress- and starvation-inducible gls24 operon has a pleiotrophic effect on cell morphology, stress sensitivity, and gene expression in Enterococcus faecalis. J. Bacteriol. 182:4512–4520. 47 Shepard, B. D. and M. S. Gilmore. 2002. Differential expression of virulencerelated genes in Enterococcus faecalis in response to biological cues in serum and urine. Infect. Immun. 70:4344– 4352. 48 Teng, F., E. C. Nannini and B. E. Murray. 2005. Importance of gls24 in virulence and stress response of Enterococcus faecalis and use of the Gls24 protein as a possible immunotherapy target. J. Infect. Dis. 191:472–480. 49 Breton, Y. L., A. Maze, A. Hartke, S. Lemarinier, Y. Auffray and A. Rinc. 2002. Isolation and characterization of bile salts-sensitive mutants of Enterococcus faecalis. Curr. Microbiol. 45:434–439. 50 Sleator, R. D., H. H. Wemekamp-Kamhuis, C. G. M. Gahan, T. Abee and C. Hill. 2005. A PrfA-regulated bile extrusion system (BilE) is a novel virulence factor in Listeria monocytogenes. Mol. Microbiol. 55:1183–1195. 51 Huycke, M. M. 2002. Physiology of enterococci. In: The Enterococci: Pathogenesis, Molecular Biology, and Antibiotic Resistance. M. S. Gilmore, D. B. Clewell, P. Courvalin, G. M. Dunny, B. E. Murray, and L. B. Rice, editors. ASM Press, Washington, DC, 133–175. 52 Rinc, A., J. C. Giard, V. Pichereau, S. Flahaut and Y. Auffray. 2001. Identification and characterization of gsp65, an organic hydroperoxide resistance (ohr)

gene encoding a general stress protein in Enterococcus faecalis. J. Bacteriol. 183:1482–1488. 53 Frankenberg, L., M. Brugna and L. Hederstedt. 2002. Enterococcus faecalis heme-dependent catalase. J. Bacteriol. 184:6351–6356. 54 Huycke, M. M., V. Abrams and D. R. Moore. 2002. Enterococcus faecalis produces extracellular superoxide and hydrogen peroxide that damages colonic epithelial cell DNA. Carcinogenesis 23:529–536. 55 Huycke, M. M., D. Moore, W. Joyce, P. Wise, L. Shepard, Y. Kotake and M. S. Gilmore. 2001. Extracellular superoxide production by Enterococcus faecalis requires demethylmenaquinone and is attenuated by functional terminal quinol oxidases. Mol. Microbiol. 42:729– 740. 56 Huycke, M. M., W. Joyce and M. F. Wack. 1996. Augmented production of extracellular superoxide by blood isolates of Enterococcus faecalis. J. Infect. Dis. 173:743–746. 57 Stock, A. M., V. L. Robinson and P. N. Goudreau. 2000. Two-component signal transduction. Annu. Rev. Biochem. 69:183–215. 58 Hancock, L. E. and M. Perego. 2004. Systematic inactivation and phenotypic characterization of two-component signal transduction systems of Enterococcus faecalis V583. J. Bacteriol. 186:7951– 7958. 59 Comenge, Y., R. Quintiliani, Jr., L. Li, L. Dubost, J. P. Brouard, J. E. Hugonnet and M. Arthur. 2003. The CroRS twocomponent regulatory system is required for intrinsic beta-lactam resistance in Enterococcus faecalis. J. Bacteriol. 185:7184–7192. 60 Le Breton, Y., G. Boel, A. Benachour, H. Prevost, Y. Auffray and A. Rinc. 2003. Molecular characterization of Enterococcus faecalis two-component signal transduction pathways related to environmental stresses. Environ. Microbiol. 5:329–337. 61 Teng, F., L. Wang, K. V. Singh, B. E. Murray and G. M. Weinstock. 2002. Involvement of PhoP-PhoS homologs in

143

144

7 Pathogenomics of Enterococcus faecalis Enterococcus faecalis virulence. Infect. Immun. 70:1991–1996. 62 Verneuil, N., M. Sanguinetti, Y. Le Breton, B. Posteraro, G. Fadda, Y. Auffray, A. Hartke and J. C. Giard. 2004. Effects of the Enterococcus faecalis hypR gene encoding a new transcriptional regulator on oxidative stress response and intracellular survival within macrophages. Infect. Immun. 72:4424–4431. 63 Schauder, S. and B. L. Bassler. 2001. The language of bacteria. Genes Dev. 15:1468–1480. 64 Marouni, M. J. and S. Sela. 2003. The luxS gene of Streptococcus pyogenes regulates expression of genes that affect internalization by epithelial cells. Infect. Immun. 71:5633–5639. 65 Lowe, A. M., P. A. Lambert and A. W. Smith. 1995. Cloning of an Enterococcus faecalis endocarditis antigen: homology with adhesins from some oral streptococci. Infect. Immun. 63:703–706. 66 Singh, K. V., T. M. Coque, G. M. Weinstock and B. E. Murray. 1998. In vivo testing of an Enterococcus faecalis efaA mutant and use of efaA homologs for species identification. FEMS Immunol. Med. Microbiol. 21:323–331. 67 Kolenbrander, P. E., R. N. Andersen, R. A. Baker and H. F. Jenkinson. 1998. The adhesion-associated sca operon in Streptococcus gordonii encodes an inducible high-affinity ABC transporter for 2+ Mn uptake. J. Bacteriol. 180:290–295. 68 Jakubovics, N. S., A. W. Smith and H. F. Jenkinson. 2000. Expression of the viru2+ lence-related Sca (Mn ) permease in Streptococcus gordonii is regulated by a diphtheria toxin metallorepressor-like protein ScaR. Mol. Microbiol. 38:140– 153. 69 Low, Y. L., N. S. Jakubovics, J. C. Flatman, H. F. Jenkinson and A. W. Smith. 2003. Manganese-dependent regulation of the endocarditis-associated virulence factor EfaA of Enterococcus faecalis. J. Med. Microbiol. 52:113–119. 70 Baida, G. E. and N. P. Kuzmin. 1995. Cloning and primary structure of a new hemolysin gene from Bacillus cereus. Biochim. Biophys. Acta. 1264:151–154. 71 Hashimoto, W., H. Nankai, B. Mikami and K. Murata. 2003. Crystal structure

72

73

74

75

76

77

78

79

80

of Bacillus sp. GL1 xanthan lyase, which acts on the side chains of xanthan. J. Biol. Chem. 278:7663–7673. Ponnuraj, K. and M. J. Jedrzejas. 2000. Mechanism of hyaluronan binding and degradation: structure of Streptococcus pneumoniae hyaluronate lyase in complex with hyaluronic acid disaccharide at 1.7 A resolution. J. Mol. Biol. 299:885–895. Kreil, G. 1995. Hyaluronidases – a group of neglected enzymes. Protein Sci. 4:1666–1669. Makinen, P. L., D. B. Clewell, F. An and K. K. Makinen. 1989. Purification and substrate specificity of a strongly hydrophobic extracellular metalloendopeptidase (“gelatinase”) from Streptococcus faecalis (strain 0G1–10). J. Biol. Chem. 264:3325–34. Bleiweis, A. S. and L. N. Zimmerman. 1964. Properties of proteinase from Streptococcus faecalis Var. liquefaciens. J. Bacteriol. 88:653–659. Waters, C. M., M. H. Antiporta, B. E. Murray and G. M. Dunny. 2003. Role of the Enterococcus faecalis GelE protease in determination of cellular chain length, supernatant pheromone levels, and degradation of fibrin and misfolded surface proteins. J. Bacteriol. 185:3613–3623. Singh, K. V., X. Qin, G. M. Weinstock and B. E. Murray. 1998. Generation and testing of mutants of Enterococcus faecalis in a mouse peritonitis model. J. Infect. Dis. 178:1416–1420. Engelbert, M., E. Mylonakis, F. M. Ausubel, S. B. Calderwood and M. S. Gilmore. 2004. Contribution of gelatinase, serine protease, and fsr to the pathogenesis of Enterococcus faecalis endophthalmitis. Infect. Immun. 72:3628–3633. Sifri, C. D., E. Mylonakis, K. V. Singh, X. Qin, D. A. Garsin, B. E. Murray, F. M. Ausubel and S. B. Calderwood. 2002. Virulence effect of Enterococcus faecalis protease genes and the quorum-sensing locus fsr in Caenorhabditis elegans and mice. Infect. Immun. 70:5647–5650. Kawalec, M., J. Potempa, J. L. Moon, J. Travis and B. E. Murray. 2005. Molecular diversity of a putative virulence factor: purification and characterization of

References isoforms of an extracellular serine glutamyl endopeptidase of Enterococcus faecalis with different enzymatic activities. J. Bacteriol. 187:266–275. 81 Qin, X., K. V. Singh, G. M. Weinstock and B. E. Murray. 2000. Effects of Enterococcus faecalis fsr genes on production of gelatinase and a serine protease and virulence. Infect. Immun. 68:2579– 2586. 82 Jha, A. K., H. P. Bais and J. M. Vivanco. 2005. Enterococcus faecalis mammalian virulence-related factors exhibit potent pathogenicity in the Arabidopsis thaliana plant model. Infect. Immun. 73:464– 475. 83 Qin, X., K. V. Singh, G. M. Weinstock and B. E. Murray. 2001. Characterization of fsr, a regulator controlling expression of gelatinase and serine protease in Enterococcus faecalis OG1RF. J. Bacteriol. 183:3372–3382. 84 Nakayama, J., Y. Cao, T. Horii, S. Sakuda, A. D. Akkermans, W. M. de Vos and H. Nagasawa. 2001. Gelatinase biosynthesis-activating pheromone: a peptide lactone that mediates a quorum sensing in Enterococcus faecalis. Mol. Microbiol. 41:145–154. 85 Gilmore, M. S., P. S. Coburn, S. R. Nallapareddy and B. E. Murray. 2002. Enterococcal virulence. In: The Enterococci: Pathogenesis, Molecular Biology, and Antibiotic Resistance. M. S. Gilmore, D. B. Clewell, P. Courvalin, G. M. Dunny, B. E. Murray, and L. B. Rice, editors. ASM Press, Washington, DC, 301–354. 86 Hancock, L. E. and M. Perego. 2004. The Enterococcus faecalis fsr two-component system controls biofilm development through production of gelatinase. J. Bacteriol. 186:5629–5639. 87 An, F. Y., M. C. Sulavik and D. B. Clewell. 1999. Identification and characterization of a determinant (eep) on the Enterococcus faecalis chromosome that is involved in production of the peptide sex pheromone cAD1. J. Bacteriol. 181:5915–5921. 88 Gilmore, M. S., R. A. Segarra, M. C. Booth, C. P. Bogie, L. R. Hall and D. B. Clewell. 1994. Genetic structure of the Enterococcus faecalis plasmid pAD1-

encoded cytolytic toxin system and its relationship to lantibiotic determinants. J. Bacteriol. 176:7335–7344. 89 Miyazaki, S., A. Ohno, I. Kobayashi, T. Uji, K. Yamaguchi and S. Goto. 1993. Cytotoxic effect of hemolytic culture supernatant from Enterococcus faecalis on mouse polymorphonuclear neutrophils and macrophages. Microbiol. Immunol. 37:265–270. 90 Coburn, P. S., C. M. Pillar, B. D. Jett, W. Haas and M. S. Gilmore. 2004. Enterococcus faecalis senses target cells and in response expresses cytolysin. Science 306:2270–2272. 91 Ike, Y., H. Hashimoto and D. B. Clewell. 1984. Hemolysin of Streptococcus faecalis subspecies zymogenes contributes to virulence in mice. Infect. Immun. 45:528–530. 92 Chow, J. W., L. A. Thal, M. B. Perri, J. A. Vazquez, S. M. Donabedian, D. B. Clewell and M. J. Zervos. 1993. Plasmidassociated hemolysin and aggregation substance production contribute to virulence in experimental enterococcal endocarditis. Antimicrob. Agents Chemother. 37:2474–2477. 93 Jett, B. D., H. G. Jensen, R. E. Nordquist and M. S. Gilmore. 1992. Contribution of the pAD1-encoded cytolysin to the severity of experimental Enterococcus faecalis endophthalmitis. Infect. Immun. 60:2445–2452. 94 Garsin, D. A., C. D. Sifri, E. Mylonakis, X. Qin, K. V. Singh, B. E. Murray, S. B. Calderwood and F. M. Ausubel. 2001. A simple model host for identifying Gram-positive virulence factors. Proc. Natl. Acad. Sci. U. S. A. 98:10892– 10897. 95 Sillanpaa, J., Y. Xu, S. R. Nallapareddy, B. E. Murray and M. Hook. 2004. A family of putative MSCRAMMs from Enterococcus faecalis. Microbiology 150:2069– 2078. 96 Rich, R. L., B. Kreikemeyer, R. T. Owens, S. LaBrenz, S. V. Narayana, G. M. Weinstock, B. E. Murray and M. Hook. 1999. Ace is a collagen-binding MSCRAMM from Enterococcus faecalis. J. Biol. Chem. 274:26939– 26945.

145

146

7 Pathogenomics of Enterococcus faecalis 97 Nallapareddy, S. R., X. Qin, G. M. Wein-

98

99

100

101

102

103

104

stock, M. Hook and B. E. Murray. 2000. Enterococcus faecalis adhesin, ace, mediates attachment to extracellular matrix proteins collagen type IV and laminin as well as collagen type I. Infect. Immun. 68:5218–5224. Nallapareddy, S. R., K. V. Singh, R. W. Duh, G. M. Weinstock and B. E. Murray. 2000. Diversity of ace, a gene encoding a microbial surface component recognizing adhesive matrix molecules, from different strains of Enterococcus faecalis and evidence for production of ace during human infections. Infect. Immun. 68:5210–5217. Gaillard, J. L., P. Berche, C. Frehel, E. Gouin and P. Cossart. 1991. Entry of Listeria monocytogenes into cells is mediated by internalin, a repeat protein reminiscent of surface antigens from gram-positive cocci. Cell 65:1127–1141. Mengaud, J., H. Ohayon, P. Gounon, R. M. Mege and P. Cossart. 1996. E-cadherin is the receptor for internalin, a surface protein required for entry of L. monocytogenes into epithelial cells. Cell 84:923–932. Lecuit, M., S. Vandormael-Pournin, J. Lefort, M. Huerre, P. Gounon, C. Dupuy, C. Babinet and P. Cossart. 2001. A transgenic model for listeriosis: role of internalin in crossing the intestinal barrier. Science 292:1722–1725. Jacquet, C., M. Doumith, J. I. Gordon, P. M. Martin, P. Cossart and M. Lecuit. 2004. A molecular marker for evaluating the pathogenic potential of foodborne Listeria monocytogenes. J. Infect. Dis. 189:2094–2100. Reid, S. D., A. G. Montgomery, J. M. Voyich, F. R. DeLeo, B. Lei, R. M. Ireland, N. M. Green, M. Liu, S. Lukomski and J. M. Musser. 2003. Characterization of an extracellular virulence factor made by group A Streptococcus with homology to the Listeria monocytogenes internalin family of proteins. Infect. Immun. 71:7043–7052. Rakita, R. M., N. N. Vanek, K. JacquesPalaz, M. Mee, M. M. Mariscalco, G. M. Dunny, M. Snuggs, W. B. Van Winkle and S. I. Simon. 1999. Enterococcus faecalis bearing aggregation substance is

105

106

107

108

109

110

111

resistant to killing by human neutrophils despite phagocytosis and neutrophil activation. Infect. Immun. 67:6067–6075. Vanek, N. N., S. I. Simon, K. JacquesPalaz, M. M. Mariscalco, G. M. Dunny and R. M. Rakita. 1999. Enterococcus faecalis aggregation substance promotes opsonin-independent binding to human neutrophils via a complement receptor type 3-mediated mechanism. FEMS Immunol. Med. Microbiol. 26:49–60. Kreft, B., R. Marre, U. Schramm and R. Wirth. 1992. Aggregation substance of Enterococcus faecalis mediates adhesion to cultured renal tubular cells. Infect. Immun. 60:25–30. Waters, C. M., H. Hirt, J. K. McCormick, P. M. Schlievert, C. L. Wells and G. M. Dunny. 2004. An amino-terminal domain of Enterococcus faecalis aggregation substance is required for aggregation, bacterial internalization by epithelial cells and binding to lipoteichoic acid. Mol. Microbiol. 52:1159–1171. Wells, C. L., E. A. Moore, J. A. Hoag, H. Hirt, G. M. Dunny and S. L. Erlandsen. 2000. Inducible expression of Enterococcus faecalis aggregation substance surface protein facilitates bacterial internalization by cultured enterocytes. Infect. Immun. 68:7190–7194. Isenmann, R., M. Schwarz, E. Rozdzinski, R. Marre and H. G. Beger. 2000. Aggregation substance promotes colonic mucosal invasion of Enterococcus faecalis in an ex vivo model. J. Surg. Res. 89:132–138. Olmsted, S. B., G. M. Dunny, S. L. Erlandsen and C. L. Wells. 1994. A plasmid-encoded surface protein on Enterococcus faecalis augments its internalization by cultured intestinal epithelial cells. J. Infect. Dis. 170:1549–1556. Johnson, J. R., C. Clabots, H. Hirt, C. Waters and G. Dunny. 2004. Enterococcal aggregation substance and binding substance are not major contributors to urinary tract colonization by Enterococcus faecalis in a mouse model of ascending unobstructed urinary tract infection. Infect. Immun. 72:2445– 2448.

References 112 Dramsi, S., F. Bourdichon, D. Cabanes,

121 Peschel, A., R. W. Jack, M. Otto,

M. Lecuit, H. Fsihi and P. Cossart. 2004. FbpA, a novel multifunctional Listeria monocytogenes virulence factor. Mol. Microbiol. 53:639–649. 113 Shankar, V., A. S. Baghdayan, M. M. Huycke, G. Lindahl and M. S. Gilmore. 1999. Infection-derived Enterococcus faecalis strains are enriched in esp, a gene encoding a novel surface protein. Infect. Immun. 67:193–200. 114 Shankar, N., C. V. Lockatell, A. S. Baghdayan, C. Drachenberg, M. S. Gilmore and D. E. Johnson. 2001. Role of Enterococcus faecalis surface protein Esp in the pathogenesis of ascending urinary tract infection. Infect. Immun. 69:4366– 4372. 115 Tendolkar, P. M., A. S. Baghdayan, M. S. Gilmore and N. Shankar. 2004. Enterococcal surface protein, Esp, enhances biofilm formation by Enterococcus faecalis. Infect. Immun. 72:6032–6039. 116 Kristich, C. J., Y. H. Li, D. G. Cvitkovitch and G. M. Dunny. 2004. Esp-independent biofilm formation by Enterococcus faecalis. J. Bacteriol. 186:154–163. 117 Hancock, L. E. and M. S. Gilmore. 2002. The capsular polysaccharide of Enterococcus faecalis and its relationship to other polysaccharides in the cell wall. Proc. Natl. Acad. Sci. U. S. A. 99:1574– 1579. 118 Hufnagel, M., L. E. Hancock, S. Koch, C. Theilacker, M. S. Gilmore and J. Huebner. 2004. Serological and genetic diversity of capsular polysaccharides in Enterococcus faecalis. J. Clin. Microbiol. 42:2548–2557. 119 Zeng, J., F. Teng, G. M. Weinstock and B. E. Murray. 2004. Translocation of Enterococcus faecalis strains across a monolayer of polarized human enterocyte-like T84 cells. J. Clin. Microbiol. 42:1149–1154. 120 Abachin, E., C. Poyart, E. Pellegrini, E. Milohanic, F. Fiedler, P. Berche and P. Trieu-Cuot. 2002. Formation of D-alanyl-lipoteichoic acid is required for adhesion and virulence of Listeria monocytogenes. Mol. Microbiol. 43:1–14.

L. V. Collins, P. Staubitz, G. Nicholson, H. Kalbacher, W. F. Nieuwenhuizen, G. Jung, A. Tarkowski et al. 2001. Staphylococcus aureus resistance to human defensins and evasion of neutrophil killing via the novel virulence factor MprF is based on modification of membrane lipids with 1-lysine. J. Exp. Med. 193:1067–1076. 122 Dussurget, O., D. Cabanes, P. Dehoux, M. Lecuit, C. Buchrieser, P. Glaser and P. Cossart. 2002. Listeria monocytogenes bile salt hydrolase is a PrfA-regulated virulence factor involved in the intestinal and hepatic phases of listeriosis. Mol. Microbiol. 45:1095–1106. 123 Martinez, A. and R. Kolter. 1997. Protection of DNA during oxidative stress by the nonspecific DNA-binding protein Dps. J. Bacteriol. 179:5188–5194. 124 Altendorf, K., and W. Epstein. 1996. The Kdp-ATPase of Escherichia coli. In: Biomembranes. A. G. Lee, editor, vol. 5. JAI Press, Greenwich, Conn., 403–420. 125 Vimr, E. and C. Lichtensteiger. 2002. To sialylate, or not to sialylate: that is the question. Trends Microbiol. 10:254–257. 126 Sandt, C. H., J. E. Hopper and C. W. Hill. 2002. Activation of prophage eib genes for immunoglobulin-binding proteins by genes from the IbrAB genetic island of Escherichia coli ECOR-9. J. Bacteriol. 184:3640–3648. 127 Froseth, B. R., R. E. Herman and L. L. McKay. 1988. Cloning of nisin resistance determinant and replication origin on 7.6-kilobase EcoRI fragment of pNP40 from Streptococcus lactis subsp. diacetylactis DRC3. Appl. Environ. Microbiol. 54:2136–2139. 128 Froseth, B. R. and L. L. McKay. 1991. Molecular characterization of the nisin resistance region of Lactococcus lactis subsp. lactis biovar diacetylactis DRC3. Appl. Environ. Microbiol. 57:804–811. 129 Jones, A. L., K. M. Knoll and C. E. Rubens. 2000. Identification of Streptococcus agalactiae virulence genes in the neonatal rat sepsis model using signature-tagged mutagenesis. Mol. Microbiol. 37:1444–1455.

147

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8 Genomics of Streptococci Joseph J. Ferretti and W. Michael McShan

Research directed against a specific medical problem has resulted in contributions to fundamental biological knowledge. The most dramatic example of this is the discovery that deoxyribonucleic acid (DNA) is the substance that transmits genetic information. This initial discovery of the phenomenon known as “the transformation of pneumococcal types” led to the identification of the transforming substance as DNA. Maclyn McCarty, 1911–2005 The Transforming Principle: Discovering that Genes are made of DNA

8.1 Introduction

The genus Streptococcus is made up of a group of low-percentage G+C gram-positive bacteria that includes nearly forty species. The individual species are pathogens or commensals of either humans or animals, such as cows, horses, and pigs. Although a species of streptococci may preferentially colonize a specific host, a number of species can infect alternate hosts when the opportunity arises. These organisms are found mainly on the mucosal surfaces of the upper respiratory tract or oral cavity, and may also be found in the intestinal tract or on the skin. Because of the great medical, veterinary, and industrial interest in these organisms, they were early targets for genome sequencing. In the last several years, the genome sequences of 14 different strains of streptococci have been completed, with another 14 in various stages of completion (Tables 8.1, 8.2).

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8 Genomics of Streptococci [a]

Tab. 8.1 Complete sequences

Organism

Size

GenBank

RefSeq

Center

Streptococcus agalactiae 2603V/R

2.16

AE009948

NC_004116

TIGR

Streptococcus agalactiae NEM316

2.21

AL732656

NC_004368

Institut Pasteur

Streptococcus mutans UA159

2.03

AE014133

NC_004350

University of Oklahoma

Streptococcus pneumoniae R6

2.04

AE007317

NC_003098

Eli Lilly Company

Streptococcus pneumoniae TIGR4

2.16

AE005672

NC_003028

TIGR

Streptococcus pyogenes SF370

1.85

AE004092

NC_002737

University of Oklahoma

Streptococcus pyogenes MGAS10394

1.90

CP000003

NC_006086

NIH-RML

Streptococcus pyogenes MGAS315

1.90

AE014074

NC_004070

NIH-RML

Streptococcus pyogenes MGAS8232

1.90

AE009949

NC_003485

NIH-RML

Streptococcus pyogenes SSI-1

1.89

BA000034

NC_004606

Osaka University

Streptococcus thermophilus CNRZ1066

1.80

CP000024

NC_006449

INRA-FR

Streptococcus thermophilus LMG 18311

1.80

CP000023

NC_006448

Catholic University of Louvain

NIH-RML, National Institutes of Heatlh – Rocky Mountain Laboratories; INRA-FR, Institut National de la Recherche Agronomique, France. a http://www.ncbi.nlm.nih.gov/genomes/lproks.cgi

8.1 Introduction Tab. 8.2 Incomplete sequences

Organism

Web site

Streptococcus agalactiae A909

http://www.tigr.org/tdb/mdb/mdbinprogress.html

Streptococcus equi

http://www.sanger.ac.uk/Projects/S_equi/

Streptococcus gordonii str. Challis

http://www.tigr.org/tdb/mdb/mdbinprogress.html

Streptococcus mitis NCTC 12261

http://www.tigr.org/tdb/mdb/mdbinprogress.html

Streptococcus pneumoniae 23F

http://www.sanger.ac.uk/Projects/S_pneumoniae/

Streptococcus pneumoniae 670-6B P

http://www.tigr.org/tdb/mdb/mdbinprogress.html

Streptoccus pneumoniae 23F

http://www.sanger.ac.uk/Projects/S_pneumoniae/

Streptococcus pyogenes Manfredo

http://www.sanger.ac.uk/Projects/S_pyogenes/

Streptococcus pyogenes NZ131 (M49)

http://www.genome.ou.edu/

Streptococcus sanguis strain SK36

http://www.sanguis.mic.vcu.edu/

Streptococcus sobrinus 6715

http://www.tigr.org/tdb/mdb/mdbinprogress.html

Streptococcus suis 89/1591

http://genome.jgi-psf.org/microbial/index.html

Streptococcus suis P1/7

http://www.sanger.ac.uk/Projects/S_suis/

Streptococcus thermophilus LMD-9

http://genome.jgi-psf.org/microbial/index.html

Streptococcus uberis 0140J

http://www.sanger.ac.uk/Projects/S_uberis/

Streptococcus zooepidemicus

http://www.sanger.ac.uk/Projects/S_zooepidemicus/

Comparative microbial genomics had its beginning with the evaluation of differences and similarities in distantly related species, including an analysis of the minimal gene set essential to sustaining a functional microbe in accord with its environmental niche [1]. However, with the completion of many new genomes, inter- and intraspecies comparisons of genomes became possible. Such is the case with the streptococci, including the genome sequences of 11 different species, with multiple strains of several species. This chapter will provide an overview of the present state of genome biology of each of the species, with a following section on bacteriophages unique to the streptococci. These organisms were serologically differentiated from each other on the basis of the major carbohydrate found in the cell wall by Rebecca Lancefield [2], and hence the organisms were designated into serological groups. More recently, Kawamura et al. [3] divided the genus Streptococcus into six major groups based on their 16S rRNA sequences: i.e., the pyogenic, mitis, bovis, salivarius, anginosus, and mutans groups. Excellent recent reviews of the biology, genetics, and host response to all the streptococcal species can be found in Fischetti et al.’s Gram-Positive Pathogens [4].

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8.2 Bacterial Genomes 8.2.1 Pyogenic Group 8.2.1.1

Streptococcus pyogenes

Streptococcus pyogenes, the group A streptococcus (GAS), is a strict human pathogen responsible for a wide variety of diseases, ranging from acute infections such as pharyngitis, scarlet fever, and erysipelas to severe invasive infections such as necrotizing fasciitis (flesh-eating disease) and streptococcal toxic shock syndrome, as well as the sequelae of rheumatic fever and acute glomerulonephritis. This organism has been divided into over 150 distinct M types based on serological differences found in a surface protein known as the M protein (specified by the emm gene). The genome sequences of seven different strains of GAS have been completed, including M1 [5], M3 (two strains) [6, 7], M5 (www.sanger.ac.uk/Projects/ S_pyogenes/), M6 [8], M18 [9], and M49 (McShan, personal communication) strains (Table 8.1). The average G+C content ranged from 38.4% to 38.7 % and the genome size from 1.84 Mbp to 1.90 Mbp. A prominent feature of the genomes is the presence of mobile genetic elements; i.e., bacteriophage genomes, insertion sequences (IS), and other transposase-containing elements. Bacteriophages are ubiquitous in GAS genomes [10], which contain between two and six different integrated bacteriophage genomes, accounting for up to 12% of the GAS genome (M6). The presence of these phage genomes accounts for the major differences in gene content and size observed between GAS strains, with most core and functional categories of genes being highly conserved. The genomes of the six different M-type strains for which complete sequences are available all contained six rRNA operons. A characteristic of these genomes is that most genes (approx. 75%) are located on the leading rather than the lagging strand of replication. Another feature is that gene order and chromosome organization of the various GAS genomes is generally conserved. Nevertheless, variation here also occurs, as evidenced by large chromosome inversions found in M3 and M5 strains. Nakagawa and colleagues [7] showed that large chromosome rearrangements were found in 65% of M3 strains analyzed in Japan after 1990, but only 25% of strains analyzed before 1985. These recombinations occurred across the replication axis and between homologous bacterial chromosome and prophage sequences, resulting in gross changes in gene order (Fig. 8.1). The alignment of six different GAS genomes is also shown in Fig. 8.1, where it can be seen for the most part that gene order and orientation are highly preserved. Note that significant inversions are present in both the M3 (SSI-1) and M5 genomes. Genetic diversity has been described in GAS strains as ascertained by single nucleotide polymorphism analysis, whole genome polymerase chain reaction (PCR) scanning, and DNA–DNA microarray analysis [12]. Allelic variation and polymorphisms are particularly common among the GAS virulence genes [13], as

8.2 Bacterial Genomes

Fig. 8.1 GAS Genome alignments. The seven available GAS genomes were compared using the software package Mauve, a method that identifies conserved genomic regions, rearrangements and inversions in conserved regions, and the exact sequence breakpoints of such rearrangements across multiple genomes [11]. Corresponding regions share the same color

and are connected by lines. Collinear regions are positioned above the central axis (relative to M1) while inverted regions are drawn below the axis. Light gray regions were too divergent in at least one genome to be meaningfully aligned. (This figure also appears with the color plates.)

noted for emm, sic [14], scl [15], ska [16], speA [17], and speB [18]. This is especially the case with surface antigen genes such as emm, which has undergone significant gene duplication, recombination, and point mutations to form 158 different emm genes and the M family of genes [19]. These kinds of changes result in great genetic diversity of the GAS and represent a common survival strategy of microbial pathogens to avoid detection or to escape a host’s immune system.

8.2.1.1.1 Virulence Factors A comprehensive review of GAS virulence factors, including cell-associated and extracellular proteins as well as genes that regulate their expression, has been compiled by Hynes [20]. The genes for these virulence factors are either chromosomally located or associated with a bacteriophage. Well-known and well-characterized virulence factor genes found on the bacterial chromosome include those for M protein, hyaluronic acid capsule, C5a peptidase, M-like proteins, fibronectin binding proteins, cysteine proteinase, streptolysin O, streptolysin S, streptokinase, CAMP factor, and hyaluronidases. Phage-associated GAS virulence factors (discussed below) include superantigens (pyrogenic exotoxins), DNases (streptodornases), and phospholipases.

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More than 40 putative virulence factors were originally identified in the M1 genome, and since that time additional newly described virulence factors have been identified in the recently sequenced genomes: e.g., phospholipase A2 (Sla) [21], EndoS (endoglycosidase activity on IgG) [22], SpyA (ADP-ribosyltransferase) [23], Lbp (laminin binding protein [24], IdeS, immunoglobulin G degrading enzyme [25], and R6 surface protein [8]. Since approximately one-third of the genes in each genome remain to be characterized in terms of function, additional virulence factors will most certainly be identified in the future. Approximately 50% of GAS strains obtained in population-based surveillance studies in the United States have the ability to opacify sera. These strains are said + to be OF and contain the gene for serum opacity factor (sof) [26]. McShan and colleagues (personal communication) have recently sequenced an M49 strain, the first complete sequence of a GAS strain known to contain sof. The M49 genome contains only two complete bacteriophage genomes and, as in other strains, each phage has genes located near the insertion site which specify superantigens SpeH and MF3.

8.2.1.1.2 Horizontal Gene Transfer Horizontal gene transfer mediated by bacteriophages is perhaps the most important phenomenon occurring in the GAS, where they have a major impact on pathogenicity as well as bacterial genome diversity and evolution. These bacteriophages have been shown to contain genes termed “morons,” because they add more DNA to the phage genome [27]. Virtually all of these bacteriophage-associated genes specify proteins with signal peptides essential for secretion into the extracellular environment where they may enhance the survival of the bacterial cell in that particular environment. Phage-associated GAS virulence factors, many of which were unknown prior to genome sequencing, include SpeA, SpeC, SpeG, SpeH, SpeI, SpeJ, SpeK, SpeL, SpeM, MF2. MF3, MF4, Sla, Ssa, Sda, Sdn, as well as HylP. Most of these factors are superantigens, also known as streptococcal pyrogenic exotoxins and erythrogenic toxins, which are potent activators of T cells that release cytokines and cause toxic shock (see Ref. [28]]). All of the streptodornases identified to date, with the exception of MF (SpeF or DNaseB), are phage encoded [29]. Proteomic analysis showed that these proteins shared a mosaic pattern, suggestive of multiple recombination events between phage virulence genes occurring during phage excision and integration. As in many of the chromosomal genes, considerable allelic variation exists in the associated virulence genes [13]. Taken together, these facts emphasize the important contribution phages make via horizontal gene transfer to the generation of new strains with increased virulence. The primary mechanism of horizontal gene transfer among the GAS has been by bacteriophages, though it is clear that conjugative transfer of plasmids containing antibiotic resistance genes occurs [30]. Natural transformation of GAS has never been observed; however, Hanski and colleagues [31] discovered a streptococcal invasion locus (sil) that appears to be involved with DNA transfer in vitro. The

8.2 Bacterial Genomes

sil locus is contained on one of two notable complex transposable elements that have been recently identified in GAS. The IS1562 transposon was found associated with many strains causing necrotizing fasciitis, and inactivation of this transposable element caused decreased lethality in a mouse model. Identified in an M14 strain, the transposon contains at least five genes (silA-E) that are highly homologous to the quorum-sensing competence regulons of Streptococcus pneumoniae. silA and silB encode a putative two-component system whereas silD and silE encode two putative ABC transporters. silC is a small open reading frame (ORF) of unknown function preceded by a combox promoter [31]. The authors speculate that the sil locus may have been obtained by horizontal gene transfer since it has a lower G+C content (32%) than the 38.5% found in the M1 genome. Among sequenced GAS genomes, the sil locus is absent from the M1, M3, and M5 genomes, but is found in the M18 and M49 genomes. IS1562 has been suggested to be responsible for facilitating recombination events in GAS [32] and is also found in genome strains MGAS8232 and NZ131 (unpublished results). The genome of the M6 strain MGAS10394 contains a genetic element (MGAS10394.4) that confers macrolide resistance (mefA) [8]. This element is very closely related to Tn1207.3 [33], having a portion of another IS element incorporated into the distal end. MGAS10394 also has a gene encoding a novel surface-exposed protein that elicits an antibody response detectable in convalescent sera [8]. Thus, horizontal gene transfer, as well as an active recombination system, contributes to overall genome plasticity and GAS genetic diversity. Genome decay by the loss of genes and gene function is thought to counterbalance the acquisition of new sequences by horizontal gene transfer [34, 35], and both of these processes contribute to GAS evolution.

8.2.2.1 Streptococcus agalactiae Streptococcus agalactiae, a group B streptococcus (GBS), is a human pathogen responsible for neonatal sepsis and meningitis, as well as for severe invasive disease in adults. Additionally, GBS are also animal pathogens responsible for bovine mastitis. Nine serotypes of GBS have been identified based on the differences in polysaccharide capsule [36]. The sequences of GBS serotypes III and V genomes [37, 38] have been completed, and the genome of a serotype Ia strain is in progress (Table 8.2). The two complete genomes were approximately 2.2 Mbp in size with a G+C content of 35.6%. Although no complete bacteriophages were found in any of the sequenced genomes, bacteriophages have long been used in various GBS typing systems [39] and epidemiological studies [40–42]. The most prominent observation in the two genomes was a clustering of virulence factors in regions or islands encoding at least five contiguous genes and of sizes up to 81 kbp. The majority of putative virulence factors were found in these islands, 14 islands in the type II strain and 15 in the type V strain, as well as mobile genetic element sequences such as phage and insertion sequences, transposons, and plasmids. Although the role of these sequences in gene acquisition is

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not clear because of extensive rearrangements among the islands, many of the virulence genes had G+C contents differing from that of the entire genome [38]. As in the GAS, there is a strong association of genetic transfer elements and virulence factors contributing to the common theme that pathogenicity islands play an important role in increased virulence and genetic diversity. Both of the sequenced GBS strains contained a larger number of two-component regulatory systems than other streptococcal species, e.g., 17–21 in GBS vs 14 in S. pneumoniae, and 13 in both GAS and S. mutans. A number of other differences were also observed, including differences in metabolic pathways and transport systems, as well as a larger number of transcriptional regulators. These observations suggest that the GBS have a wider range of life style opportunities than other gram-positive streptococci, which allows them to survive in response to variations in the external environment. A central question relates to the differences which exist in GBS strains that are preferentially associated with humans or animals. Spellerberg and colleagues [43] identified a putative composite transposon that contains a gene encoding the surface protein Lmb (mediates binding of GBS to human laminin) and ScpB (C5a peptidase), both factors which have been identified as important GBS virulence factors. All GBS strains of human origin, as well as group C and G strains isolated from humans, contained this putative composite transposon. However, the majority of GBS strains from animal origin contained little or none of the scpB or lmb sequences.

8.2.2.2 Group C (GCS) and Group G Streptococci The streptococci of groups C and G are found in humans and animals, where they may cause serious diseases similar to those described for GAS disease (see Ref. [44] for a comprehensive review). These organisms include Streptococcus equi subsp. equi, Streptococcus dysgalactiae subsp. equisimilis, and Streptococcus equi subsp. zooepidemicus. These three species are “large” colony forming streptococci and resemble the GAS in many biochemical and clinical characteristics. Other, typically small colony forming streptococcal species can also carry the group C antigen, including Streptococcus dysgalactiae subsp. dysgalactiae and Streptococcus milleri [45]. Streptococcus canis is a member of the group G streptococci. The genome sequences of S. equi and S. zooepidemicus are in progress (Table 8.2). GCS, in contrast to the other pathogenic streptococci, are remarkable in that they are commonly associated with both human and animal disease. S. equi is the causative agent of strangles, an acute upper respiratory infection of horses. S. equisimilis is the most common member of the GCS associated with human disease, causing pharyngitis, bacteremia, skin infections, and puerperal sepsis. Like GAS, GCS often carry genes encoding superantigens [46–48]. For example, evidence from the in-progress S. equi genome reveals that two streptococcal superantigen genes (speL and speM) are present; remarkably, these superantigens are also found in a subpopulation of GAS isolates (15% and 5%, respectively) [48]. The carriage of these genes by separate streptococcal species suggests that these genes are avail-

8.2 Bacterial Genomes

Fig. 8.2 GAS vs S. uberis alignment. The corresponding regions in the M1 and S. uberis genomes identified by Mauve analysis are shown. Several large regions of collinearity are evident between the two species. Corresponding regions share the same color and are connected by lines. (This figure also appears with the color plates.)

able for horizontal transfer, perhaps via a mobile element that enables gene transfer across phylogenetic borders.

8.2.2.3 Streptococcus uberis The species Streptococcus uberis is heterogeneous with respect to Lancefield typing and is another important animal pathogen, which causes bovine mastitis. The genome sequence of this organism has just been completed (J. Leigh and P. Ward, personal communication) (Table 8.2). S. uberis 0140J has a genome size of 1.85 Mbp and a G+C content of 36.6%. Several proposed virulence-associated genes have been characterized in S. uberis, including the capsule, neutrophil toxin, M-like protein, R-like protein, plasminogen activator PauA, and the CAMP factor [49–51]. Two substantial tracts of bacteriophage-associated DNA are present, as are paralogues of several superantigens found in GAS. The alignment of the S. uberis and GAS M1 genomes is shown in Fig. 8.2, where several rearrangements are apparent, but with substantial homology in the overall genome order between the two genomes, suggesting a close evolutionary relatedness between the two organisms. As with several of the other streptococci that infect animals of agricultural importance, this genetic relatedness with GAS may provide a pool of genes that could be horizontally transferred from the animal to the human pathogen in situations where humans and animals are in close contact, such as on farms or in food processing facilities. 8.2.2 Bovis Group 8.2.2.1

Streptococcus bovis and Streptococcus suis

Both Streptococcus bovis and Streptococcus suis are classified as group D streptococci. S. suis is a pathogen of both pigs and humans, causing meningitis where

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pigs and humans are in close contact. The genome sequence of S. suis is in progress (Table 8.2). A number of virulence factors have been identified in virulent isolates of S. suis, including suilysin (a cholesterol binding cytolysin), MRP (muramidase-released protein), and EF (extracellular protein) [52]. Bacteriophages have been isolated from both S. bovis and S. suis [53–56]; however, any role that these elements play in the dissemination of virulence genes or horizontal transfer is unknown. 8.2.3 Mitis Group 8.2.3.1

Streptococcus pneumoniae

The most notable member of the mitis group is Streptococcus pneumoniae, which is found primarily in the nasopharynx. It is responsible for a great deal of human disease, including acute bacterial pneumonia and invasive disease. It contains no Lancefield group-specific antigens and is subdivided into more than 90 groups based on polysaccharide capsule type. Two genomes of S. pneumoniae have been completely sequenced, and a third is in progress (Tables 8.1, 8.2). Additionally, a sequence diversity study of 14 different strains with important phenotypic and pathogenetic differences is in progress (http://genome.microbio.uab.edu/strep/ info/). The two S. pneumoniae sequenced strains have a genome size and G+C content of 2.03 Mbp and 40% respectively for the type 2 capsule R6 strain, [57] and 2.16 Mbp and 39.7% for the type 4 capsule TIGR4 strain [58]. With an almost 10% difference in gene content between the two strains, there are strain-specific multiple gene clusters present in each genome. Several of these clusters contain genes encoding cell surface proteins known to interact with host cells and which appear to have sequence variability. S. pneumoniae has been appropriately termed a “paradigm for recombination-mediated genetic plasticity” [59], because of its natural competence and high transformability. The S. pneumoniae genome contains numerous IS, BOX, and RUP repetitive elements which account for up to 5% of the genome and represent hotspots for genetic recombination. More than 250 duplicated genes are present in the genome as well as nearly 400 genes with iterative motifs that can result in phase variation. Additionally, codon preference profiles of almost 500 genes in S. pneumoniae showed a lower G+C content (33.4%) than in most of the genome (39.7%), suggesting that they were obtained by horizontal gene transfer [60]. Some of these genes may have come from other organisms since Tettelin et al. identified at least 40 genes with significant homology to genes in gram-negative bacteria [58]. Evidence of intragenic recombinations creating mosaic structure is apparent in several genes, including the penicillin binding proteins, which contained segments identical to genes of other streptococcal species [61]. Genome heterogeneity, mosaic genes, and plasticity are features not only of S. pneumoniae, but are also found in other members of the mitis group of streptococci [62].

8.2 Bacterial Genomes

8.2.3.2 Streptococcus mitis, Streptococcus sanguis, and Streptococcus gordonii Further members of the mitis group of streptococci, but which also possess the Lancefield group H antigen, are Streptococcus mitis, Streptococcus sanguis, and Streptococcus gordonii. These species predominate in the oral cavity, and the sequencing of each of the genomes is in progress (Table 8.2). While generally considered human commensals, these organisms may play important roles in dental or medical disease. One series of studies found that S. mitis released superantigen-like molecules that might promote the oral inflammatory response [63]. Subsequently, at least one outbreak of streptococcal toxic shock syndrome was reported with S. mitis as the causative agent, validating the earlier observations [64]. Pneumolysin, a major virulence factor in S. pneumoniae, is also present in S. mitis [65], suggesting that horizontal transfer occurs between these species. S. mitis is an occasional cause of infective endocarditis, and bacterial binding of platelets is an important mechanism in the pathogenesis of this disease. Platelet binding by S. mitis is mediated by the surface proteins PblA and PblB, encoded by a lysogenic bacteriophage [66, 67]. Thus, it is possible that S. mitis shares a number of genetic characteristics with the better known pathogenic streptococci, and the analysis of the genome of this organism may reveal even further insights into potential virulence mechanisms. No similar association between a temperate phage and the transmission of virulence traits has been seen yet in S. sanguis or S. gordonii. 8.2.4 Anginosus and Salivarius Group 8.2.4.1

Streptococcus salivarius

A group K Streptococcus, Streptococcus salivarius is a primary colonizer of the oral mucosa and is a permanent member of the oral flora [68]. The anginosus (milleri) group of streptococcal species are commensal organisms found in the oral cavity and pharynx that are frequent agents of human disease. Members of the group can differ in Lancefield classification while being related at the genetic level. Streptococcus intermedius (group F, C, or not typeable) has been found associated with abscesses of the brain and liver, while Streptococcus anginosus (group G or C) and Streptococcus constellatus subsp. constellatus (group F mostly) are isolated from infections in a wider range of anatomical sites [69]. In 1999, a new member of the anginosus group, S. constellatus subsp. pharynges, was isolated from cases of pharyngitis and was serologically a member of Lancefield group C [70]. The toxin intermedilysin is produced by S. intermedius, distinguishing it from the other members of this group who all lack this gene. Further, intermedilysin production level in isolates from brain and liver abscesses is higher than in strains from normal habitats, such as dental plaque, or from peripheral infection sites, suggesting that this protein is a key factor in inducing or maintaining deep-seated infections [71]. The potential genetic relationships to other pathogenic streptococci of this group of species is poorly characterized and should be clarified by future genome analysis.

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8.2.4.2 Streptococcus thermophilus Streptococcus thermophilus is a thermophilic lactic acid bacterium used for the manufacture of dairy products. On the basis of phylogenetic sequence data of the RNase P RNA gene, rnpB, it is part of the salivarius group [72]. The complete sequence of two different strains of S. thermophilus has been reported (Table 8.1) [73]. Both strains have a genome size of 1.8 Mbp with 42 regions of sequence differences and a remarkably high level (10%) of gene decay. Horizontal gene transfer is evident in the genomes with the presence of numerous phages, transposons, and integrons. One region identified as a hotspot of IS sequences contains a mosaic of fragments with high identity to gene regions from Lactobacillus bulgaricus and L. lactis, organisms commonly found in milk. Interestingly, many of the genes involved in carbohydrate metabolism are decayed or missing. However a specific symporter for lactose (the main carbohydrate of milk) is present in S. thermophilus, but absent in other streptococci. The significant differences in gene content from other streptococci most likely have occurred by horizontal gene transfer and gene decay as the organism evolved into its unique ecological niche. 8.2.5 Mutans Group 8.2.5.1

Streptococcus mutans and Streptococcus sobrinus

The two species Streptococcus mutans and Streptococcus sobrinus are found in the oral cavity, colonize exclusively the tooth surface of humans and some animals, and are responsible for dental caries. The genome of a serotype c strain of S. mutans has been sequenced and has a genome size of 2 Mbp and a G+C content of 36.8% [74]. Although considered an oral pathogen, it lacks virtually all virulence functions of other pathogenic streptococci. Its primary virulence mechanisms include glucosyltransferase enzymes capable of synthesizing adherent glucans, which bind to the smooth surfaces of teeth, glucan binding proteins, and its acidogenic potential as a lactic acid producer. Additionally, it has the ability to metabolize a wide variety of carbohydrates and has pathways for the synthesis of all of its required amino acids. Approximately 15% of the S. mutans genome is devoted to genes for various transport systems, most of which are devoted to the carbohydrates it uses. It also possesses genes for proteases, peptidases, and other exoenzymes for action on various food substrates present in the oral cavity. Horizontal gene transfer occurs via transformation in S. mutans as it has a fully active competence system. However, no bacteriophages have been identified in its genome. The alignment of the genomes of S. mutans and S. mitis indicates a lesser degree of homology than with organisms in the same species.

8.2 Bacterial Genomes

8.2.6 Other Organisms: Enterococcus faecalis

Originally known as Streptococcus faecalis, Enterococcus faecalis is the organism most commonly associated with the group D streptococci; however, DNA analyses showed that enterococci are biologically, serologically, and genetically different from the streptococci [75]. This organism is reviewed in Chapter 7 of the present volume. 8.2.7 Comparative Genomics

As a group of organisms, the streptococci have both common themes and differences which emphasize their individuality and genetic diversity. The availability of several genome sequences within a given species illustrates that significant differences occur even in the same serotype. Horizontal gene transfer and recombination, common to all the streptococci, has contributed greatly to the generation of strategies for the survival of an organism in its ecological niche. These strategies include altering or deleting surface structure genes to evade the host immune system, gaining new genes such as surface proteins with particular binding affinities, gaining or deleting metabolic pathways for suitable nutrient acquisition, and addition of new virulence genes such as exoenzymes and toxins. Prime examples are the bacteriophage transfer of superantigen genes to new GAS hosts and the highly efficient transformation system of S. pneumoniae, generating mosaic structures, genomic rearrangements, and genetic diversity. In both cases new clones with increased virulence properties are created which have the ability to eventually disseminate a new wave of disease. The ecological niche and the environment of an organism is reflected in the genetic makeup of pathways and other fitness traits essential for its existence. Organisms such as the GAS in the pyogenic group live in a nutrient-rich environment [76] and cannot survive without substrates available from host tissues. Their metabolic pathways mirror this need with virtually no pathways present for the synthesis of amino acids, but at the same time possessing several ABC transporters for amino acid uptake systems, as well as additional transporter systems for the uptake of dipeptides and oligopeptides. In contrast, organisms such as the mitis group of streptococci contain additional pathways essential for an environment not so rich in nutrients where different kinds of substrates are present. These include additional and different uptake and transport systems as well as different regulatory controls. One member of the mitis group, S. pneumoniae, has a high proportion of sugar transporter genes, a feature that allows it to compete for sugars with other respiratory tract bacteria. Gene expression profiling under various environmental conditions will be an important avenue for future investigation. Evolutionary changes in genomes can be illustrated by aligning orthologous regions of sequences. Large-scale changes such as rearrangements may result in

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regions of one genome being inverted or reordered relative to another. Figure 8.1 illustrates how closely related organisms of the same species are generally closely aligned, with a high conservation of gene order and orientation. Inversions and rearrangements in these genomes is equally obvious, indicating more recent changes. Such a change led Nakagawa et al. to suggest that clonal expansion of the rearranged M3 strains may be related to recent increases in rheumatic fever and severe invasive infections in Japan [7]. The lack of alignment of genomes from different streptococcal species illustrates that larger changes have occurred with little concordance of gene block sequence and order, and suggests that these genomes are constantly undergoing rearrangements (not shown). The genomes of the streptococci are proving to be powerful tools in the understanding of how these organisms gain new virulence genes as well as discovering new approaches to combating their pathogenic potential. Postgenomic approaches such as microarrays, proteomics, and structure–function studies will fill in many of the gaps remaining in our understanding of these organisms, particularly in identifying ORFs with unknown function, new putative virulence genes, and regulatory networks controlling gene expression. Clearly, important work remains to be completed for rational drug development aimed at newly identified target genes as well as the identification of new antigens for vaccines.

8.3 Streptococcal Genomic Bacteriophages 8.3.1 Prophages and Streptococcal Genomes

The single most striking feature of all of the sequenced GAS genomes is the number of endogenous phage genomes found in each strain, ranging from two in M49 strain NZ131 to seven or eight in M6 strain MGAS10394, depending upon whether element MGAS10394.4 is considered a real prophage genome or a transposon. A comprehensive review of the bacteriophages of group A streptococci can be found in the book Gram Positive Pathogens [77]. While temperate phages exist that target S. pneumoniae and S. agalactiae [78–80], it is remarkable that none were found in the genome strains, providing a dramatic contrast to S. pyogenes. For example, the genome of GBS NEM316 harbors 14 pathogenicity islands containing elements (especially integrases or transposases) related to bacteriophages or IS elements, but no complete or partial phage genomes [37]. Likewise, while a number of IS elements were found in the S. pneumoniae genomes, no prophage genome or significant remnant was present in either. Thus, it appears that the high frequency of phage carriage in the GAS genomes is not stochastic, but reflects a strong selective pressure for possessing the virulence genes carried by these GAS phages, and demonstrates how intimately these phages are linked to the biology of GAS.

8.3 Streptococcal Genomic Bacteriophages

8.3.2 GAS Genome Prophages 8.3.2.1

Prophages and Virulence Factors

The number and type of phages found in each GAS genome varies, as do the associated virulence genes. The insertion sites for the various phage genomes in the common GAS genome backbone is shown in Fig. 8.3, with several having the same insertion site. Although phage genomes are found throughout the bacterial chromosome, there is a prevalence of insertion near the terminus region on the lagging strand. Virulence genes are found associated with multiple attachment sites, and it is evident that frequent genetic rearrangements have occurred that have shuffled these virulence genes to different phage backgrounds. The phage-associated virulence genes, while dispensable for phage replication and maturation, confer novel phenotypes upon the host streptococcus that increase its fitness. A considerable range of virulence genes are carried by the genomes of GAS prophages, including superantigens and streptodornases. A charac-

Fig. 8.3 Bacteriophage locations on the GAS backbone. The locations of the genome prophages on the S. pyogenes genome are shown on a generalized GAS DNA backbone based upon the M1 genome; prophages that share the same attachment site are boxed together.

Toxin genes that are associated with a particular prophage are indicated with the phage name. The rRNA operons are indicated as white blocks, while the cluster of virulence genes flanking emm are hatched. The origin of replication is indicated (oriC).

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teristic of these genes is an atypically low G+C content relative to the rest of the genome, a feature shared with other genes of microbial pathogens transferred by horizontal gene transfer. For example, most of the pyrogenic exotoxins have a G+C content of about 30% whereas that of the genome is about 38.5%. While these atypical percentages of the virulence genes are suggestive of origination from an unknown ancestor, the actual genetic source for any of these ORFs is unknown and may also have originated within the species by gene duplication and descent. However, cryptic and sometimes expressed coding regions often are found in regions flanking the attachment sites in the genomes of temperate bacteriophages, even from those infecting nonpathogenic bacteria [81–83]. It seems possible that the origin of some virulence factors may have occurred by a process

Fig. 8.4 Phylogram of the S. pyogenes-identified genome prophages and the related S. thermophilus temperate phages. The phylogenetic tree analysis of the genome phages shows probable groups with related evolutionary histories. The prophages of GAS are

indicated in black and the S. thermophilus phages in red. Multiple sequence alignment of the prophage genomes was done using ClustalX [86]. The genomes of S. thermophilus have been previously reported [85, 87–90].

8.3 Streptococcal Genomic Bacteriophages

of selection for function from preexisting phage genes. The relationships seen between some prophages of S. pyogenes and S. thermophilus (Fig. 8.4) raises the possibility that prototoxin genes may have arisen in a separate genera and reached GAS by horizontal transfer [84, 85]. Many of the genome prophages contain mutations or deletions that prevent the phage from entering the lytic cycle, thus fixing the prophage DNA in the streptococcal genome. The inactivation of a prophage while maintaining the associated virulence gene may be a frequent and evolutionarily favored event, preserving the benefit to the streptococcus conferred by the toxin while eliminating the danger of prophage induction and subsequent lysis. Frequent examples can be found in the GAS genome prophages where either a genetic defect (inactivation of the portal protein in phage SF370.2) or large genome deletion (MGAS10394.7) has fixed the associated toxin gene as a permanent element of the host. Homologous recombination with another phage genome could rescue the disabled prophage or recombine to generate a novel, functional phage. If the forces that lead to prophage decay are constant over time, then inactivated prophages may be long-term residents of their host, and conversely, the fully inducible phages may have been recently acquired. The number of inducible prophage genomes in the M3 MGAS315 strain may reflect this strain’s status as a recently emerged clone [6]. The emergence of this highly virulent strain has been proposed to have resulted from the sequential addition of prophages (MGAS315.5, acquired in the 1920s; MGAS315.2, acquired around 1940; and MGAS315.4, acquired around 1980) to generate the current M3 clone [91]. The inducible status of these phages suggests that the timeframe for maintenance of intact prophages in GAS may be measured in decades. The M6 MGAS10394 strain has, by contrast, a large number of phage remnants, suggesting that the toxins associated with these prophages have been long-established host features.

8.3.2.2 Prophage Attachment Sites and Host Biology Prophage integration occurs by a Campbell-type homologous exchange between a common sequence shared between the phage and host chromosomes (attP and attB, respectively). These duplications are of varying lengths, ranging from a few bases to nearly 100 bp and often represent coding regions of the host genome [92, 93]. In the S. pyogenes genome prophages, the identifiable duplications range from 12 bp in phage MGAS10394.1 to around 96 bp in T12 and the phages that share its attachment site [94]. In most bacterial species, the majority of duplications occur at the 3¢ end of some host target gene, and integration either leaves the target gene intact (via the duplication) or, in at least one case, provides an alternative carboxy terminus for the specified protein [92, 95]. In the S. pyogenes prophages, integration and duplication occurs frequently at either the 5¢ and 3¢ end of genes. Integration into the 5¢ end of a gene or its predicted promoter is an unusual strategy, potentially altering host gene function. Genes that are targeted for 5¢ integration in GAS include methyl-directed mismatch repair protein mutL, a dipeptidase gene, recombination protein recX, proB, a HAD-like hydrolase, and a conserved

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hypothetical protein. Other prophages appear to target the promoter region immediately upstream of the actual ORF, including the promoter regions of yesN, c-glutamyl kinase, and an iron-dependent repressor. Similarly, the transposon MGAS10394.4 with some prophage features separates the comE operon proteins 2 and 3, probably creating a polar mutation that silences protein 3. In all of these cases, the insertion of a prophage at the 5¢ end of a gene has the potential for altering host gene expression by introducing a polar mutation or an alternative promoter, and such transcription-altering prophages may be an important class of genetic regulatory elements in S. pyogenes in modifying the biology of their hosts.

8.3.2.3 Prophage Diversity Pregenomic studies predicted that considerable diversity existed in GAS phages, with phages having a variety of toxin genes integrating into the bacterial chromosome at multiple sites [94, 96–98]. The GAS genome sequences have confirmed these predictions, and further, have revealed that virulence genes have often been reassorted with functional phage modules for integration and regulation or maturation and packaging to increase diversity. As shown in Fig. 8.3, the results of phage diversification have placed some virulence genes (such as speA) at multiple integration sites on the GAS chromosome. Thus, while natural selection has favored the preservation of overall phage gene order, the modules for integration, regulation, replication, DNA packaging, structural proteins, host lysis, and virulence have been often exchanged with alleles from other phages to generate the diversity that may well account for the sudden appearance of new strains of GAS. 8.3.3 Prophages Associated with other Streptococcal Species

Temperate bacteriophages are associated with a number of the other streptococcal species and may well form part of a common pool of phage genetic material that is available for horizontal transfer of virulence and other genes. For example, S. mitis is an occasional cause of infective endocarditis, and bacterial binding of platelets is an important mechanism in the pathogenesis of this disease. Platelet binding by S. mitis is mediated by the surface proteins PblA and PblB, encoded by a lysogenic bacteriophage [66, 67]. Thus, it may well be that S. mitis shares a number of genetic characteristics with the better known pathogenic streptococci, and the analysis of the genome of this organism may reveal even further insights into potential virulence mechanisms. No similar association between a temperate phage and the transmission of virulence traits has been seen yet in S. sanguis or S. gordonii. Bacteriophages have been isolated from both S. bovis and S. suis [53– 56]; however, any role that these elements play in the dissemination of virulence genes or horizontal transfer is unknown.

References

Note added in proof Since the preparation of this chapter, two additional genomes of Group A Streptococci have been published; i.e., Sumby P, Porcella SF, Madrigal AG, Barbian KD, Virtaneva K, Ricklefs SM, Sturdevant DE, Graham MR, Vuopio-Varkila J, Hoe NP, Musser JM. Evolutionary Origin and Emergence of a Highly Successful Clone of Serotype M1 Group A Streptococcus Involved Multiple Horizontal Gene Transfer Events, J. Infect. Dis. (2005) 192:771–782; and Nicole M. Green, Stephen B. Beres, Edward A. Graviss, James E. Allison, Allison J. McGeer, Jaana Vuopio-Varkila, Rance B. LeFebvre, and James M. Musser, Genetic Diversity among Type emm28 Group A Streptococcus Strains Causing Invasive Infections and Pharyngitis, J. Clin. Microbiol. (2005) Vol. 43:4083–4091. And six additional Group B Streptococcus genomes – in one publication: Herv Tettelin, Vega Masignani, Michael J. Cieslewicz, Claudio Donati, Duccio Medini, Naomi L. Ward, Samuel V. Angiuoli, Jonathan Crabtree, Amanda L. Jones, A. Scott Durkin, Robert T. DeBoy, Tanja M. Davidsen, Marirosa Mora, Maria Scarselli, Immaculada Margarit y Ros, Jeremy D. Peterson, Christopher R. Hauser, Jaideep P. Sundaram, William C. Nelson, Ramana Madupu, Lauren M. Brinkac, Robert J. Dodson, Mary J. Rosovitz, Steven A. Sullivan, Sean C. Daugherty, Daniel H. Haft, Jeremy Selengut, Michelle L. Gwinn, Liwei Zhou, Nikhat Zafar, Hoda Khouri, Diana Radune, George Dimitrov, Kisha Watkins, Kevin J. B. O’Connor, Shannon Smith, Teresa R. Utterback, Owen White, Craig E. Rubens, Guido Grandi, Lawrence C. Madoff, Dennis L. Kasper, John L. Telford, Michael R. Wessels, Rino Rappuoli, and Claire M. Fraser. Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: Implications for the microbial “pangenome”. PNAS (2005) 102: 13950–13955.

References 1 Koonin, E. V. 2003. Comparative geno-

4 Fischetti, V. A., R. P. Novick, J. J. Fer-

mics, minimal gene–sets and the last universal common ancestor. Nat Rev Microbiol 1: 127–136. 2 Lancefield, R. C. 1928. The antigenic complex of Streptococcus haemolyticus. III. Chemical and immunological properties of the species-specific substance. J Exp Med 47: 481–491. 3 Kawamura, Y., X. G. Hou, F. Sultana, H. Miura, and T. Ezaki. 1995. Determination of 16S rRNA sequences of Streptococcus mitis and Streptococcus gordonii and phylogenetic relationships among members of the genus Streptococcus. Int J Syst Bacteriol 45: 406–408.

retti, D. A. Portnoy, and J. I. Rood, editors. 2005. Gram–Positive Pathogens. ASM Press, Washington, DC 5 Ferretti, J. J., W. M. McShan, D. Ajdic, D. J. Savic, G. Savic, K. Lyon, C. Primeaux, S. Sezate, A. N. Suvorov, S. Kenton, H. Lai, S. Lin, Y. Qian, H. G. Jia, F. Z. Najar, Q. Ren, H. Zhu, L. Song, J. White, X. Yuan, S. W. Clifton, B. A. Roe, and R. McLaughlin. 2001. Complete genome sequence of an M1 strain of Streptococcus pyogenes. Proc Natl Acad Sci USA 98: 4658–4663. 6 Beres, S. B., G. L. Sylva, K. D. Barbian, B. Lei, J. S. Hoff, N. D. Mammarella,

167

168

8 Genomics of Streptococci M. Y. Liu, J. C. Smoot, S. F. Porcella, L. D. Parkins, D. S. Campbell, T. M. Smith, J. K. McCormick, D. Y. Leung, P. M. Schlievert, and J. M. Musser. 2002. Genome sequence of a serotype M3 strain of group A Streptococcus: phage-encoded toxins, the high-virulence phenotype, and clone emergence. Proc Natl Acad Sci U S A 99: 10078– 10083. 7 Nakagawa, I., K. Kurokawa, A. Yamashita, M. Nakata, Y. Tomiyasu, N. Okahashi, S. Kawabata, K. Yamazaki, T. Shiba, T. Yasunaga, H. Hayashi, M. Hattori, and S. Hamada. 2003. Genome sequence of an M3 strain of Streptococcus pyogenes reveals a large-scale genomic rearrangement in invasive strains and new insights into phage evolution. Genome Res 13: 1042–1055. 8 Banks, D. J., S. F. Porcella, K. D. Barbian, S. B. Beres, L. E. Philips, J. M. Voyich, F. R. DeLeo, J. M. Martin, G. A. Somerville, and J. M. Musser. 2004. Progress toward characterization of the group A Streptococcus metagenome: complete genome sequence of a macrolide-resistant serotype M6 strain. J Infect Dis 190: 727–738. 9 Smoot, J. C., K. D. Barbian, J. J. Van Gompel, L. M. Smoot, M. S. Chaussee, G. L. Sylva, D. E. Sturdevant, S. M. Ricklefs, S. F. Porcella, L. D. Parkins, S. B. Beres, D. S. Campbell, T. M. Smith, Q. Zhang, V. Kapur, J. A. Daly, L. G. Veasy, and J. M. Musser. 2002. Genome sequence and comparative microarray analysis of serotype M18 group A Streptococcus strains associated with acute rheumatic fever outbreaks. Proc Natl Acad Sci U S A 99: 4668–4673. 10 Hynes, W. L., L. Hancock, and J. J. Ferretti. 1995. Analysis of a second bacteriophage hyaluronidase gene from Streptococcus pyogenes: evidence for a third hyaluronidase involved in extracellular enzymatic activity. Infect Immun 63: 3015–3020. 11 Darling, A. C., B. Mau, F. R. Blattner, and N. T. Perna. 2004. Mauve: multiple alignment of conserved genomic sequence with rearrangements. Genome Res 14: 1394–1403.

12 Beres, S. B., G. L. Sylva, D. E. Sturde-

13

14

15

16

17

18

19

vant, C. N. Granville, M. Liu, S. M. Ricklefs, A. R. Whitney, L. D. Parkins, N. P. Hoe, G. J. Adams, D. E. Low, F. R. DeLeo, A. McGeer, and J. M. Musser. 2004. Genome-wide molecular dissection of serotype M3 group A Streptococcus strains causing two epidemics of invasive infections. Proc Natl Acad Sci U S A 101: 11833–11838. Reid, S. D., N. P. Hoe, L. M. Smoot, and J. M. Musser. 2001. Group A Streptococcus: allelic variation, population genetics, and host–pathogen interactions. J Clin Invest 107: 393–399. Stockbauer, K. E., D. Grigsby, X. Pan, Y. X. Fu, L. M. Mejia, A. Cravioto, and J. M. Musser. 1998. Hypervariability generated by natural selection in an extracellular complement-inhibiting protein of serotype M1 strains of group A Streptococcus. Proc Natl Acad Sci U S A 95: 3128–3133. Lukomski, S., K. Nakashima, I. Abdi, V. J. Cipriano, B. J. Shelvin, E. A. Graviss, and J. M. Musser. 2001. Identification and characterization of a second extracellular collagen-like protein made by group A Streptococcus: control of production at the level of translation. Infect Immun 69: 1729–1738. Huang, T. T., H. Malke, and J. J. Ferretti. 1989. Heterogeneity of the streptokinase gene in group A streptococci. Infect Immun 57: 502–506. Musser, J. M., A. R. Hauser, M. H. Kim, P. M. Schlievert, K. Nelson, and R. K. Selander. 1991. Streptococcus pyogenes causing toxic-shock-like syndrome and other invasive diseases: clonal diversity and pyrogenic exotoxin expression. Proc Natl Acad Sci USA 88: 2668–2672. Stockbauer, K. E., L. Magoun, M. Liu, E. H. Burns, Jr., S. Gubba, S. Renish, X. Pan, S. C. Bodary, E. Baker, J. Coburn, J. M. Leong, and J. M. Musser. 1999. A natural variant of the cysteine protease virulence factor of group A Streptococcus with an arginine–glycine–aspartic acid (RGD) motif preferentially binds human integrins avb3 and aIIbb3. Proc Natl Acad Sci U S A 96: 242–247. McGregor, K. F., B. G. Spratt, A. Kalia, A. Bennett, N. Bilek, B. Beall, and D. E.

References Bessen. 2004. Multilocus sequence typing of Streptococcus pyogenes representing most known emm types and distinctions among subpopulation genetic structures. J Bacteriol 186: 4285–4294. 20 Hynes, W. 2004. Virulence factors of the group A streptococci and genes that regulate their expression. Front Biosci 9: 3399–3433. 21 Nagiec, M. J., B. Lei, S. K. Parker, M. L. Vasil, M. Matsumoto, R. M. Ireland, S. B. Beres, N. P. Hoe, and J. M. Musser. 2004. Analysis of a novel prophageencoded group A Streptococcus extracellular phospholipase A(2). J Biol Chem 279: 45909–45918. 22 Collin, M., and A. Olsen. 2001. EndoS, a novel secreted protein from Streptococcus pyogenes with endoglycosidase activity on human IgG. EMBO J 20: 3046– 3055. 23 Coye, L. H., and C. M. Collins. 2004. Identification of SpyA, a novel ADPribosyltransferase of Streptococcus pyogenes. Mol Microbiol 54: 89–98. 24 Terao, Y., S. Kawabata, E. Kunitomo, I. Nakagawa, and S. Hamada. 2002. Novel laminin-binding protein of Streptococcus pyogenes, Lbp, is involved in adhesion to epithelial cells. Infect Immun 70: 993–997. 25 von Pawel-Rammingen, U., B. P. Johansson, and L. Bjorck. 2002. IdeS, a novel streptococcal cysteine proteinase with unique specificity for immunoglobulin G. EMBO J 21: 1607–1615. 26 Beall, B., G. Gherardi, M. Lovgren, R. R. Facklam, B. A. Forwick, and G. J. Tyrrell. 2000. emm and sof gene sequence variation in relation to serological typing of opacity-factor-positive group A streptococci. Microbiology 146( Pt 5): 1195– 1209. 27 Hendrix, R. W., J. G. Lawrence, G. F. Hatfull, and S. Casjens. 2000. The origins and ongoing evolution of viruses. Trends Microbiol 8: 504–508. 28 McCornick, J. K., Peterson, M. L., and Schlievert, A. M. 2005. Toxins and Superantigens of Group A Streptococci. In: Gram–Positive Pathogens. Fischetti, V. A., R. P. Novick, J. J. Ferretti, D. A. Portnoy, and J. I. Rood, editors. ASM Press, Washington, DC

29 Aziz, R. K., S. A. Ismail, H. W. Park,

and M. Kotb. 2004. Post-proteomic identification of a novel phage-encoded streptodornase, Sda1, in invasive M1T1 Streptococcus pyogenes. Mol Microbiol 54: 184–197. 30 Malke, H. 1979. Conjugal transfer of plasmids determining resistance to macrolides, lincosomides and streptogramin-B type antibiotics among group A, B, D and H streptococci. FEMS Microbiol Lett 5: 335–338. 31 Hidalgo–Grass, C., M. Ravins, M. DanGoor, J. Jaffe, A. E. Moses, and E. Hanski. 2002. A locus of group A Streptococcus involved in invasive disease and DNA transfer. Mol Microbiol 46: 87–99. 32 Berge, A., M. Rasmussen, and L. Bjorck. 1998. Identification of an insertion sequence located in a region encoding virulence factors of Streptococcus pyogenes. Infect Immun 66: 3449–3453. 33 Santagati, M., F. Iannelli, C. Cascone, F. Campanile, M. R. Oggioni, S. Stefani, and G. Pozzi. 2003. The novel conjugative transposon tn1207.3 carries the macrolide efflux gene mef(A) in Streptococcus pyogenes. Microb Drug Resist 9: 243–247. 34 Hacker, J., U. Hentschel, and U. Dobrindt. 2003. Prokaryotic chromosomes and disease. Science 301: 790– 793. 35 Wren, B. W. 2000. Microbial genome analysis: insights into virulence, host adaptation and evolution. Nat Rev Genet 1: 30–39. 36 Chaffin, D. O., S. B. Beres, H. H. Yim, and C. E. Rubens. 2000. The serotype of type Ia and III group B streptococci is determined by the polymerase gene within the polycistronic capsule operon. J Bacteriol 182: 4466–4477. 37 Glaser, P., C. Rusniok, C. Buchrieser, F. Chevalier, L. Frangeul, T. Msadek, M. Zouine, E. Couve, L. Lalioui, C. Poyart, P. Trieu–Cuot, and F. Kunst. 2002. Genome sequence of Streptococcus agalactiae, a pathogen causing invasive neonatal disease. Mol Microbiol 45: 1499–1513. 38 Tettelin, H., V. Masignani, M. J. Cieslewicz, J. A. Eisen, S. Peterson, M. R.

169

170

8 Genomics of Streptococci

39

40

41

42

43

44

45

Wessels, I. T. Paulsen, K. E. Nelson, I. Margarit, T. D. Read, L. C. Madoff, A. M. Wolf, M. J. Beanan, L. M. Brinkac, S. C. Daugherty, R. T. DeBoy, A. S. Durkin, J. F. Kolonay, R. Madupu, M. R. Lewis, D. Radune, N. B. Fedorova, D. Scanlan, H. Khouri, S. Mulligan, H. A. Carty, R. T. Cline, S. E. Van Aken, J. Gill, M. Scarselli, M. Mora, E. T. Iacobini, C. Brettoni, G. Galli, M. Mariani, F. Vegni, D. Maione, D. Rinaudo, R. Rappuoli, J. L. Telford, D. L. Kasper, G. Grandi, and C. M. Fraser. 2002. Complete genome sequence and comparative genomic analysis of an emerging human pathogen, serotype V Streptococcus agalactiae. Proc Natl Acad Sci U S A 99: 12391–12396. Haug, R. H., R. Gudding, and G. Bakken. 1981. Serotyping and bacteriophage typing of human and bovine group-B streptococci. J Med Microbiol 14: 479–482. Russell, H., N. L. Norcross, and D. E. Kahn. 1969. Isolation and characterization of Streptococcus agalactiae bacteriophage. J Gen Virol 5: 315–317. Stringer, J. 1980. The development of a phage-typing system for group-B streptococci. J Med Microbiol 13: 133–143. Boyer, K., L. Vogel, S. Gotoff, C. Gadzala, J. Stringer, and W. Maxted. 1980. Nosocomial transmission of bacteriophage type 7/11/12 group B streptococci in a special care nursery. Am J Dis Child 134: 964–966. Franken, C., G. Haase, C. Brandt, J. Weber-Heynemann, S. Martin, C. Lammler, A. Podbielski, R. Lutticken, and B. Spellerberg. 2001. Horizontal gene transfer and host specificity of beta-haemolytic streptococci: the role of a putative composite transposon containing scpB and lmb. Mol Microbiol 41: 925–935. Malke. H. 2005. Genetics and Pathogenicity Factors of Group C and G Streptococci. In: Gram-Positive Pathogens. Fischetti, V. A., R. P. Novick, J. J. Ferretti, D. A. Portnoy, and J. I. Rood, editors. ASM Press, Washington, DC Carmeli, Y., and K. L. Ruoff. 1995. Report of cases of and taxonomic considerations for large-colony-forming

46

47

48

49

50

51

52

53

Lancefield group C streptococcal bacteremia. J Clin Microbiol 33: 2114–2117. Sachse, S., P. Seidel, D. Gerlach, E. Gunther, J. Rodel, E. Straube, and K. H. Schmidt. 2002. Superantigen-like gene(s) in human pathogenic Streptococcus dysgalactiae, subsp equisimilis: genomic localisation of the gene encoding streptococcal pyrogenic exotoxin G (speG(dys)). FEMS Immunol Med Microbiol 34: 159–167. Igwe, E. I., P. L. Shewmaker, R. R. Facklam, M. M. Farley, C. van Beneden, and B. Beall. 2003. Identification of superantigen genes speM, ssa, and smeZ in invasive strains of beta-hemolytic group C and G streptococci recovered from humans. FEMS Microbiol Lett 229: 259–264. Proft, T., P. D. Webb, V. Handley, and J. D. Fraser. 2003. Two novel superantigens found in both group A and group C Streptococcus. Infect Immun 71: 1361– 1369. Oliver, S. P., R. A. Almeida, and L. F. Calvinho. 1998. Virulence factors of Streptococcus uberis isolated from cows with mastitis. Zentralbl Veterinarmed B 45: 461–471. Gase, K., J. J. Ferretti, C. Primeaux, and W. M. McShan. 1999. Identification, cloning, and expression of the CAMP factor gene (cfa) of group A streptococci. Infect Immun 67: 4725–4731. Rosey, E. L., R. A. Lincoln, P. N. Ward, R. J. Yancey, Jr., and J. A. Leigh. 1999. PauA: a novel plasminogen activator from Streptococcus uberis. FEMS Microbiol Lett 178: 27–33. Staats, J. J., B. L. Plattner, G. C. Stewart, and M. M. Changappa. 1999. Presence of the Streptococcus suis suilysin gene and expression of MRP and EF correlates with high virulence in Streptococcus suis type 2 isolates. Vet Microbiol 70: 201–211. Harel, J., G. Martinez, A. Nassar, H. Dezfulian, S. J. Labrie, R. Brousseau, S. Moineau, and M. Gottschalk. 2003. Identification of an inducible bacteriophage in a virulent strain of Streptococcus suis serotype 2. Infect Immun 71: 6104–6108.

References 54 Tarakanov, B. V. 1996. [Biology of lyso-

genic strains of Streptococcus bovis and virulent mutants of their temperate phages]. Mikrobiologiia 65: 656–662. In Russian. 55 Klieve, A. V., G. L. Heck, M. A. Prance, and Q. Shu. 1999. Genetic homogeneity and phage susceptibility of ruminal strains of Streptococcus bovis isolated in Australia. Lett Appl Microbiol 29: 108– 112. 56 Styriak, I., P. Pristas, and P. Javorsky. 1998. Lack of surface receptors not restriction-modification system determines F4 phage resistance in Streptococcus bovis II/1. Folia Microbiol (Praha) 43: 35–38. 57 Hoskins, J., W. E. Alborn, Jr., J. Arnold, L. C. Blaszczak, S. Burgett, B. S. DeHoff, S. T. Estrem, L. Fritz, D. J. Fu, W. Fuller, C. Geringer, R. Gilmour, J. S. Glass, H. Khoja, A. R. Kraft, R. E. Lagace, D. J. LeBlanc, L. N. Lee, E. J. Lefkowitz, J. Lu, P. Matsushima, S. M. McAhren, M. McHenney, K. McLeaster, C. W. Mundy, T. I. Nicas, F. H. Norris, M. O’Gara, R. B. Peery, G. T. Robertson, P. Rockey, P. M. Sun, M. E. Winkler, Y. Yang, M. Young–Bellido, G. Zhao, C. A. Zook, R. H. Baltz, S. R. Jaskunas, P. R. Rosteck, Jr., P. L. Skatrud, and J. I. Glass. 2001. Genome of the bacterium Streptococcus pneumoniae strain R6. J Bacteriol 183: 5709–5717. 58 Tettelin, H., K. E. Nelson, I. T. Paulsen, J. A. Eisen, T. D. Read, S. Peterson, J. Heidelberg, R. T. DeBoy, D. H. Haft, R. J. Dodson, A. S. Durkin, M. Gwinn, J. F. Kolonay, W. C. Nelson, J. D. Peterson, L. A. Umayam, O. White, S. L. Salzberg, M. R. Lewis, D. Radune, E. Holtzapple, H. Khouri, A. M. Wolf, T. R. Utterback, C. L. Hansen, L. A. McDonald, T. V. Feldblyum, S. Angiuoli, T. Dickinson, E. K. Hickey, I. E. Holt, B. J. Loftus, F. Yang, H. O. Smith, J. C. Venter, B. A. Dougherty, D. A. Morrison, S. K. Hollingshead, and C. M. Fraser. 2001. Complete genome sequence of a virulent isolate of Streptococcus pneumoniae. Science 293: 498–506. 59 Claverys, J. P., M. Prudhomme, I. Mortier-Barriere, and B. Martin. 2000. Adaptation to the environment: Streptococcus

pneumoniae, a paradigm for recombination-mediated genetic plasticity? Mol Microbiol 35: 251–259. 60 Martin–Galiano, A. J., J. M. Wells, and A. G. de la Campa. 2004. Relationship between codon biased genes, microarray expression values and physiological characteristics of Streptococcus pneumoniae. Microbiology 150: 2313–2325. 61 Dowson, C. G., T. J. Coffey, and B. G. Spratt. 1994. Origin and molecular epidemiology of penicillin-binding-proteinmediated resistance to beta-lactam antibiotics. Trends Microbiol 2: 361–366. 62 Hakenbeck, R., N. Balmelle, B. Weber, C. Gardes, W. Keck, and A. de Saizieu. 2001. Mosaic genes and mosaic chromosomes: intra- and interspecies genomic variation of Streptococcus pneumoniae. Infect Immun 69: 2477–2486. 63 Matsushita, K., W. Fujimaki, H. Kato, T. Uchiyama, H. Igarashi, H. Ohkuni, S. Nagaoka, M. Kawagoe, S. Kotani, and H. Takada. 1995. Immunopathological activities of extracellular products of Streptococcus mitis, particularly a superantigenic fraction. Infect Immun 63: 785–793. 64 Lu, H. Z., X. H. Weng, B. Zhu, H. Li, Y. K. Yin, Y. X. Zhang, D. W. Haas, and Y. W. Tang. 2003. Major outbreak of toxic shock-like syndrome caused by Streptococcus mitis. J Clin Microbiol 41: 3051–3055. 65 Neeleman, C., C. H. Klaassen, D. M. Klomberg, H. A. de Valk, and J. W. Mouton. 2004. Pneumolysin is a key factor in misidentification of macrolideresistant Streptococcus pneumoniae and is a putative virulence factor of S. mitis and other streptococci. J Clin Microbiol 42: 4355–4357. 66 Siboo, I. R., B. A. Bensing, and P. M. Sullam. 2003. Genomic organization and molecular characterization of SM1, a temperate bacteriophage of Streptococcus mitis. J Bacteriol 185: 6968–6975. 67 Bensing, B. A., I. R. Siboo, and P. M. Sullam. 2001. Proteins PblA and PblB of Streptococcus mitis, which promote binding to human platelets, are encoded within a lysogenic bacteriophage. Infect Immun 69: 6186–6192.

171

172

8 Genomics of Streptococci 68 Whiley, R. A., and D. Beighton. 1998.

Current classification of the oral streptococci. Oral Microbiol Immunol 13: 195– 216. 69 Whiley, R. A., D. Beighton, T. G. Winstanley, H. Y. Fraser, and J. M. Hardie. 1992. Streptococcus intermedius, Streptococcus constellatus, and Streptococcus anginosus (the Streptococcus milleri group): association with different body sites and clinical infections. J Clin Microbiol 30: 243–244. 70 Whiley, R. A., L. M. Hall, J. M. Hardie, and D. Beighton. 1999. A study of small-colony, beta-haemolytic, Lancefield group C streptococci within the anginosus group: description of Streptococcus constellatus subsp. pharyngis subsp. nov., associated with the human throat and pharyngitis. Int J Syst Bacteriol 49 Pt 4: 1443–1449. 71 Nagamune, H., R. A. Whiley, T. Goto, Y. Inai, T. Maeda, J. M. Hardie, and H. Kourai. 2000. Distribution of the intermedilysin gene among the anginosus group streptococci and correlation between intermedilysin production and deep-seated infection with Streptococcus intermedius. J Clin Microbiol 38: 220– 226. 72 Tapp, J., M. Thollesson, and B. Herrmann. 2003. Phylogenetic relationships and genotyping of the genus Streptococcus by sequence determination of the RNase P RNA gene, rnpB. Int J Syst Evol Microbiol 53: 1861–1871. 73 Bolotin, A., B. Quinquis, P. Renault, A. Sorokin, S. D. Ehrlich, S. Kulakauskas, A. Lapidus, E. Goltsman, M. Mazur, G. D. Pusch, M. Fonstein, R. Overbeek, N. Kyprides, B. Purnelle, D. Prozzi, K. Ngui, D. Masuy, F. Hancy, S. Burteau, M. Boutry, J. Delcour, A. Goffeau, and P. Hols. 2004. Complete sequence and comparative genome analysis of the dairy bacterium Streptococcus thermophilus. Nat Biotechnol 22: 1554–1558. 74 Ajdic, D., W. McShan, R. McLaughlin, G. Savic, J. Chang, M. Carson, C. Primeaux, R. Tian, S. Kenton, H. Jia, S. Lin, Y. Qian, S. Li, H. Zhu, F. Najar, H. Lai, J. White, B. Roe, and J. Ferretti. 2002. Genome sequence of Streptococcus mutans UA159, a cariogenic dental

75

76

77

78

79

80

81

82

83

pathogen. Proc Natl Acad Sci U S A 99: 14434–14439. Schleifer, K. H., and R. Kilpper-Balz. 1984. Transfer of Streptococcus faecalis and Streptococcus faecium to the genus Enterococcus nom. rev. as Enterococcus faecalis comb. nov. and Enterococcus faecium comb. nov. Int J Syst Bacteriol 34: 31–34. Steiner, K., and H. Malke. 2000. Life in protein-rich environments: the relA-independent response of Streptococcus pyogenes to amino acid starvation. Mol Microbiol 38: 1004–1016. McShan, W. M. 2005. The Bacteriophages of Group A Streptococci. In: Gram-Positive Pathogens. Fischetti, V. A., R. P. Novick, J. J. Ferretti, D. A. Portnoy, and J. I. Rood (eds.). ASM Press, Washington, DC Pritchard, D. G., S. Dong, J. R. Baker, and J. A. Engler. 2004. The bifunctional peptidoglycan lysin of Streptococcus agalactiae bacteriophage B30. Microbiology 150: 2079–2087. Obregon, V., P. Garcia, R. Lopez, and J. L. Garcia. 2003. VO1, a temperate bacteriophage of the type 19A multiresistant epidemic 8249 strain of Streptococcus pneumoniae: analysis of variability of lytic and putative C5 methyltransferase genes. Microb Drug Resist 9: 7–15. Obregon, V., J. L. Garcia, E. Garcia, R. Lopez, and P. Garcia. 2003. Genome organization and molecular analysis of the temperate bacteriophage MM1 of Streptococcus pneumoniae. J Bacteriol 185: 2362–2368. Boyce, J. D., B. E. Davidson, and A. J. Hillier. 1995. Identification of prophage genes expressed in lysogens of the Lactococcus lactis bacteriophage BK5-T. Appl Environ Microbiol 61: 4099–4104. Ventura, M., C. Canchaya, M. Kleerebezem, W. M. de Vos, R. J. Siezen, and H. Brussow. 2003. The prophage sequences of Lactobacillus plantarum strain WCFS1. Virology 316: 245–255. Ventura, M., S. Foley, A. Bruttin, S. C. Chennoufi, C. Canchaya, and H. Brussow. 2002. Transcription mapping as a tool in phage genomics: the case of the temperate Streptococcus thermophilus phage Sfi21. Virology 296: 62–76.

References 84 Canchaya, C., F. Desiere, W. McShan,

J. Ferretti, J. Parkhill, and H. Brussow. 2002. Genome analysis of an inducible prophage and prophage remnants integrated in the Streptococcus pyogenes strain SF370. Virology 302: 245–258. 85 Desier