BSL3 and BSL4 Agents: Proteomics, Glycomics, and Antigenicity

Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, and Robert G. Ulrich BSL3 and BSL4 Agents

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Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, and Robert G. Ulrich

BSL3 and BSL4 Agents Proteomics, Glycomics, and Antigenicity

The Editors Prof. Jiri Stulik Ministry of Defence Faculty of Military Health Service Trebesska 1575 500 01 Hradec Králové Czech Republic Prof. Rudolf Toman Slovak Academy of Sciences Institute of Virology Dubravska cesta 9 845 05 Bratislava 45 Slovak Republic Prof. Patrick Butaye Veterimary and Agrochemical Research Centre Department of Bacteriology and Immunology Groeselenberg 99 1180 Bruxelles Belgium Dr. Robert G. Ulrich Army Medical Research Inst. of Infectious Diseases 1425 Porter Street Frederick, MD 21702 USA Cover Section of a lung from a cynomolgus macaque exposed to aerosolized Yersinia pestis. The bacteria (red) are apparent throughout the lung, which is stained for fibrin (green). Cell nuclei are shown in blue. Courtesy of Derron Alves (staining by Christine Mech, microscopy by Gordon Ruthel).

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V

Contents Preface XIII List of Contributors XV 1

1.1 1.2 1.3 1.4 1.5

Introduction: Application of Proteomic Technologies for the Analysis of Microbial Infections 1 Jiri Stulik and Patrick Butaye Introduction 1 Search for New Factors of Virulence and Potential Diagnostic Markers 2 Search for New Vaccine Candidates 3 Analysis of Post-Translational Modifications of Bacterial Proteins and Protein–Protein Interactions 3 Conclusions 5 References 5

Part One 2 2.1 2.1.1 2.1.1.1 2.1.1.2 2.1.1.3 2.1.1.4 2.1.2 2.1.3 2.1.3.1 2.1.3.2 2.1.3.3 2.1.3.4 2.1.3.5

Basic Proteomic Methods 7

Separation of Proteins and Peptides 9 Ludovit Skultety Introduction 9 Gel-Based Separation 11 One-Dimensional Electrophoresis 11 Two-Dimensional Electrophoresis 11 Protein Staining and Image Analysis 13 2-DE Limitations 16 In Solution – “Gel Free” Proteomics 16 Column Chromatography 17 Size Exclusion Chromatography 18 Reversed-Phase Liquid Chromatography 18 Hydrophilic Interaction Liquid Chromatography Ion Exchanger Chromatography 20 Affinity Chromatography 21

20

VI

Contents

2.1.3.6 2.1.4 2.1.5

Multidimensional Chromatography 21 Liquid Phase IEF and Electrophoresis 23 Alternative Separation Technologies 23 Acknowledgment 24 References 24

3

Basic Mass Spectrometric Approaches 29 Lenka Hernychova and Martin Hubalek Introduction 29 Ionization 30 Matrix-Assisted Laser Desorption/Ionization 30 Electrospray Ionization 31 Mass Analyzers 32 Time of Flight 33 Reflectron TOF 34 Quadrupole and Ion Trap 35 Fourier Transformation Ion Cyclotron 35 Tandem Mass Analyzers 35 Ion Detection 36 Protein Identification 37 Combination of 2-DE and MS 37 Peptide Mass Fingerprinting 37 Peptide Sequencing (PMF) 38 Shotgun Proteomics 39 Conclusion 39 Acknowledgments 40 References 40

3.1 3.2 3.2.1 3.2.2 3.3 3.3.1 3.3.2 3.3.3 3.3.4 3.3.5 3.3.6 3.4 3.4.1 3.4.2 3.4.3 3.4.4 3.5

4 4.1 4.1.1 4.1.2 4.1.3 4.1.3.1 4.1.3.2 4.1.3.3 4.2 4.2.1 4.2.2 4.2.3 4.2.4 4.2.5

Quantitative Mass Spectrometric Approaches 43 Juraj Lenco and Vojteˇch Tambor Introduction 43 Gel-Based Quantitative Proteomic Methods 43 Shotgun Quantitative Proteomic Methods 44 Labeling Methods 44 Metabolic Incorporation of Stable Isotopes 45 Enzymatic Incorporation of Stable Isotopes 46 Chemical Incorporation of Stable Isotopes 46 iTRAQ Analysis of Bacterial Pathogens 47 Bacterial Cell Disruption and Protein Extraction 48 Determination of Protein Concentration 50 Protein Digestion 50 Peptide Labeling with iTRAQ Tags 51 Protocol for iTRAQ Analysis of Bacterial Proteins 51 References 52

Contents

5 5.1 5.2 5.3 5.3.1 5.3.1.1 5.3.1.2 5.3.1.3 5.3.2 5.3.3 5.4 5.4.1 5.4.1.1 5.4.1.2 5.4.1.3 5.4.2 5.4.3 5.4.4 5.5 5.6

6 6.1 6.2 6.2.1 6.2.1.1 6.2.1.2 6.3 6.4 6.4.1 6.4.1.1 6.4.1.2 6.4.2 6.4.2.1 6.4.2.2 6.4.2.3 6.4.2.4 6.5

BN-PAGE of Microbial Protein Complexes 55 Jiri Dresler and Jana Klimentova Introduction 55 Methods for Studying Protein–Protein Interactions 55 Blue Native Polyacrylamide Gel Electophoresis 56 Sample Preparation 57 Non-Denaturing Conditions 57 Selection of Detergent and Its Optimal Concentration 58 Membrane and Cytosolic Fraction Separation 58 1D BN-PAGE 59 2D BN/SDS-PAGE 59 Evaluation of BN-PAGE – Staining, MS, Western Blotting 60 Staining 60 Silver Staining 60 Fluorescent Staining 60 Coomassie Staining 60 Mass Spectrometry 61 Western Blotting 61 Other Methods of Visualization 61 BN/SDS-PAGE of ATP Synthase of Francisella tularensis 62 Conclusion 63 Acknowledgment 63 References 64 Analysis of Francisella tularensis Glycoproteins 67 Lucie Balonova and Lenka Hernychova Introduction to Post-Translational Modifications in Prokaryotes 67 Methodology 68 Analysis of Glycosylation 68 Glycoproteomics 68 Glycomics 69 Bioinformatics 70 Application of Glycoproteomic Approach Utilizing ProQ-Emerald and DIG Glycan Kits to Francisella tularensis (F. tularensis) 70 Bacterial Cultures and Sample Preparation 71 Preparation of Whole-Cell Lysates 71 Preparation of Membrane-Enriched Fractions 71 Analysis of Glycoproteins in Fractions Enriched in Membrane Proteins 71 Mini Two-Dimensional Gel Electrophoresis 71 Glycoprotein Detection Using DIG Glycan Differentiation Kit 72 Glycoprotein Detection Using Pro-Q Emerald 300 Glycoprotein Stain Kit 72 Glycoprotein Identification by Mass Spectrometry 72 Results 73

VII

VIII

Contents

6.5.1 6.5.2 6.6

Glycoprotein Detection Using DIG Glycan Differentiation Kit 73 Glycoprotein Detection Using Pro-Q Emerald 300 Glycoprotein Stain Kit 73 Conclusion 74 Acknowledgments 76 References 76

Part Two Identification of Proteins and Glycans from Microorganisms as Candidate Molecules for Use in Detection/Diagnosis, Therapy, and Prophylaxis 79 7

7.1 7.2 7.3 7.3.1 7.3.2 7.4

8 8.1 8.2 8.2.1 8.2.2 8.2.3 8.2.4 8.2.5 8.2.6 8.3 8.3.1 8.3.2 8.4

9 9.1 9.2 9.3 9.4

Comparative Proteome Analysis of Strains with Differential Virulence 81 Martin Hubalek and Ivona Pávková Introduction 81 Methods 82 Results 84 Whole-Cell Lysates 84 Membrane-Associated Proteins 86 Discussion 86 References 91 Analysis of Francisella tularensis Acetonitrile Extracts 95 Lenka Hernychova, Martin Hubalek, and Jana Udrzalova Introduction 95 Material and Methods 96 Materials 96 Microorganism 96 Preparation of Cell-Free Acetonitrile Extract 96 Enzymatic Digestion 97 MALDI-TOF MS 97 LC-MS/MS 98 Results 98 MALDI-TOF MS Analysis 98 LC-MS/MS Analysis 99 Conclusions 102 Acknowledgments 103 References 103 Analysis of Culture Filtrate Proteins of Francisella tularensis Klara Konecna, Martin Hubalek, and Lenka Hernychova Introduction 107 Materials and Methods 108 Results 109 Discussion 112

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Contents

Acknowledgments 113 References 113 10

10.1 10.2 10.2.1 10.2.2 10.2.3 10.3

11

11.1 11.2 11.2.1 11.2.2 11.2.3 11.3 11.3.1 11.3.2 11.3.3

12

12.1 12.2 12.2.1 12.2.2 12.2.3 12.2.4 12.3 12.3.1 12.3.2

Lipopolysaccharides of Coxiella burnetii: Chemical Composition and Structure, and Their Role in Diagnosis of Q Fever 115 Rudolf Toman and Pavol Vadovicˇ Introduction 115 Lipopolysaccharides of C. burnetii 116 Chemical Composition and Structure of LPS I 118 Chemical Composition and Structure of LPS II 120 The Role of LPS I in Diagnosis of Q Fever 120 Conclusion 121 Acknowledgments 121 References 121 Mimivirus Possesses Anonymous and Unique Gene Products Endowed for Antigenic Properties 125 Patricia Renesto and Didier Raoult Introduction 125 Material and Methods 126 Sample Preparation 126 2D-PAGE and Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry 126 Immunization and Western Blot 126 Results 127 Proteomic Analysis of Mimivirus Particle 127 Antigenic Properties of ORFan-Encoded Mimivirus Proteins 128 Concluding Remarks 128 References 128 Detection of Differentially Modified Pathogen Proteins by Western Blot after 2D Gel Electrophoresis and Identification by MALDI-TOF/TOF 131 Fred Fack, Julia Kessler, Patrick Pirrotte, Jacques Kremer, Dominique Revets, Wim Ammerlaan, and Claude P. Muller Introduction 131 Materials and Methods 132 Protein Sample Preparation and Fluorescence Labeling for DIGE Analysis 132 2D Electrophoresis and Gel Imaging 133 2D Western Blotting 133 Protein Identification by MALDI-TOF/TOF Analysis of In-Gel Tryptic Digests 134 Results 134 2D-DIGE Analysis of Measles Virus Infected THP1 Cells 134 Detection of Different Protein Forms by 2D Western Blot 135

IX

X

Contents

12.3.3 12.4

13

13.1 13.2 13.3 13.4

14 14.1 14.2 14.3

15

15.1 15.2 15.3 15.4 15.5 15.6 15.7 15.8 15.9

16 16.1 16.2 16.3

Monitoring Viral Replication and Cellular Response by Antibody Multiplexing 136 Discussion 137 References 138 Composition and Structure of Lipid A of the Intracellular Bacteria Piscirickettsia salmonis and Coxiella burnetii 139 Pavol Vadovicˇ, Robert Ihnatko, and Rudolf Toman Introduction 139 Composition and Structure of Lipid A of P. salmonis 140 Composition and Structure of Lipid A of C. burnetii 141 Conclusion 143 Acknowledgments 144 References 144 Proteins of Coxiella burnetii and Analysis of Their Function 145 Robert Ihnatko, Pavol Vadovicˇ, and Rudolf Toman Introduction 145 Proteins of C. burnetii and Their Functions 146 Conclusion and Perspectives 149 Note Added in Proof 149 Acknowledgments 150 References 150 Subtype and Toxin Variant Identification of Botulinum Neurotoxin Type A Using Proteomics Techniques 153 Suzanne R. Kalb, Jakub Baudys, Theresa J. Smith, James L. Pirkle, and John R. Barr Introduction 153 Botulinum Neurotoxins 153 Differentiation of Botulinum Neurotoxins 154 Amino Acid Sequence Identification Using Proteomics 155 Extraction of BoNT from Complex Matrices 157 Identification of BoNT Serotype with Proteomics 157 Identification of BoNT/A Subtype with Proteomics 159 Identification of BoNT/A1 Strain with Proteomics 161 Conclusions 163 Disclaimer 164 References 164 Protein Microarrays for Antigen Discovery 167 Mohan Natesan, Sarah Keasey, and Robert G. Ulrich Introduction 167 Microarray Assembly 168 Antibody Assays 170

Contents

16.4 16.5 16.6

Antigens and Proteomes of Viruses 171 Antigens and Proteomes of Pathogenic Bacteria Conclusions 176 Acknowledgment 177 References 177

17

MALDI-TOF Mass Spectrometry for Rapid Identification of Highly Pathogenic Microorganisms 179 Peter Lasch and Dieter Naumann Introduction 179 Microbial Identification by MALDI-TOF Mass Spectrometry 180 Basic Principles of MALDI-TOF Mass Spectrometry 180 Preparation of Microbial Samples for MALDI-TOF MS 181 Spectral Data Analysis: Preprocessing, Calibration, Peak Detection, and Data Visualization 182 Multivariate Classification Analysis – Pattern Recognition Methods 184 Identification of Taxon-Specific Biomarkers 186 Inactivation of Highly Pathogenic Microorganisms for MALDI-TOF Mass Spectrometry 187 Time and Concentration Dependence of TFA Inactivation 188 Centrifugation – Reduction of the Supernatant’s Cell Concentration 189 Sterile Filtration 189 Molecular and Structural Aspects of Spore Treatment by TFA 190 Identification of Important Bacterial Pathogenes Using MALDI-TOF MS 192 Bacillus anthracis 192 Burkholderia mallei/pseudomallei 196 Yersinia pestis 199 Concluding Remarks 207 Acknowledgments 208 References 208

17.1 17.2 17.2.1 17.2.2 17.2.3 17.2.4 17.2.5 17.3 17.3.1 17.3.2 17.3.3 17.3.4 17.4 17.4.1 17.4.2 17.4.3 17.5

Part Three 18

18.1 18.2 18.2.1 18.2.2 18.2.3

174

Analysis of Host–Pathogen Interactions 213

Quantitative Proteomic Profiling of the Interaction of Francisella tularensis LVS with Macrophages Using J774.2 Cell Line 215 Anetta Hartlova, Marek Link, Juraj Lenco, and Jiri Stulik Introduction 215 Material and Methods 216 Bacterial Strain, Cell Line, and Growth Conditions 216 Infection Assay 216 Preparation of Lysate and Isolation of Detergent-Insoluble Membrane Fractions 216

XI

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Contents

18.2.4 18.2.5 18.2.6 18.2.7 18.3 18.3.1 18.3.2 18.3.3

TRYPTIC Digestion and iTRAQ Labeling 217 Mass Spectrometric Analysis 218 Protein Identification and Database Search 218 Classification of iTRAQ Identified Proteins 218 Results 218 Characterization of DRM Isolation 218 Classification of 57 Identified Proteins of Both iTRAQ Labeling Significant Differentially Regulated Proteins 219 References 221

19

Proteome Analysis of Bacterial Protein Expression after Ingestion of Microbes by Macrophages 223 Martin Brychta and Ivona Pávková Introduction 223 Material and Methods 224 Bacterial Strains, Cell Lines, and Cultivation 224 Intracellular Growth of Francisella tularensis in Macrophages and Radiolabeling of Bacterial Proteins 224 Two-Dimensional Gel Electrophoresis (2-DE) and Autoradiography 225 Image Acquisition and Software Analysis 225 Protein Identification 225 Results 226 Discussion 228 Acknowledgment 230 References 230

19.1 19.2 19.2.1 19.2.2 19.2.3 19.2.4 19.2.5 19.3 19.4

Index

233

219

XIII

Preface The research initiatives and interests of COST B28 partners are organized in five working packages/groups (WGs):

• • • • •

WG1: Technology platform (including flow cytometry and microarrays) WG2: Antigenicity WG3: Proteomics and glycomics WG4: Genomics WG5: Microbiology (bacteriology, virology, mycology)

Research groups involved in WG2 and WG3 possess an extensive fundamental knowledge and expertise in the fields of proteomics, glycomics, and antigenicity of both bacteria and viruses. A mutual collaboration in these fields is of importance since the interconnection between the three research disciplines is obvious. The aim of this booklet is to summarize the present knowledge on proteomics, glycomics, and the structure/function of antigens in general, and to provide insights into the latest developments and applications of these scientific fields with regard to BSL3 and BSL4 agents. Contributions to the booklet illustrate a considerable broadness and variability of these applications on the agents investigated within the framework of this COST B28 action. The first part of this booklet deals with the general applications of proteomic and glycomic techniques in the analysis of microbial infections. These are then elaborated further, and for some techniques basic protocols are given. Many of them have also been demonstrated in a training school held within this COST Action B28 action organized by the main partners in these two working groups. The second part focuses on applications of these techniques in the identification and characterization of proteins and glycans from microorganisms and on the rapid and accurate detection and identification of them. Moreover, the diagnosis, therapy, and prophylaxis of the diseases caused by these microorganisms are discussed. This concerns mainly Francisella tularensis, the obligate intracellular gram-negative bacteria Coxiella burnetii and Piscirickettsia salmonis, mimivirus, and measels virus. Although the number of examples is limited, they still give a good insight into the respective fields. The third part is devoted to host–pathogen interactions, dealing with both prokaryote and eukaryote expressions of proteins.

XIV

Preface

This booklet has no ambitions to be a complete and final “manual” as it deals with an area in which new findings, techniques, and methodologies are being brought on nearly on a daily basis, thus making it almost impossible to give a real up to date overview. Nevertheless, it gives the reader an interesting insight into these new and rapidly developing fields. The information presented here may be helpful to students and researchers interested in proteomics, glycomics, and the structure/function of antigens and their further, so far unknown research fields and applications. For additional reading, we recommend references provided by the authors. More information on WG2 and WG3, and also on other WGs, can be found on the COST Action B28 website: http://www.cost-b28.be/. June 2011

Jiri Stulik Rudolf Toman Patrick Butaye Robert G. Ulrich

XV

List of Contributors Wim Ammerlaan Institute of Immunology Centre de Recherche Public de la Santé/National Public Health Institute 20A, rue Auguste Lumière 1950 Luxembourg Luxembourg Lucie Balonova Institute of Molecular Pathology Faculty of Military Health Science University of Defence Trebesska 1575 500 01 Hradec Králové Czech Republic John R. Barr Centers for Disease Control and Prevention National Center for Environmental Health Division of Laboratory Sciences 4770 Buford Hwy, N.E. Atlanta, GA 30341 USA

Jakub Baudys Centers for Disease Control and Prevention National Center for Environmental Health Division of Laboratory Sciences 4770 Buford Hwy, N.E. Atlanta, GA 30341 USA Martin Brychta Institute of Molecular Pathology Faculty of Military Health Sciences University of Defence Trebesska 1575 500 01 Hradec Králové Czech Republic and Charles University in Prague Faculty of Medicine in Hradec Králové Department of Medical Biology and Genetics 500 01 Hradec Králové Czech Republic

XVI

List of Contributors

Patrick Butaye Department of Bacteriology and Immunology Veterinary and Agrochemical Research Centre VAR-CODA-CERVA Groeselenberg 99 1180 Brussels Belgium and Ghent University Faculty of Veterinary Medicine Department of Pathology, Bacteriology and Poultry Diseases Salisburylaan 133 9820 Merelbeke Belgium Jiri Dresler Institute of Molecular Pathology University of Defence Faculty of Military Health Sciences Trebesska 1575 500 01 Hradec Králové Czech Republic Fred Fack Institute of Immunology Centre de Recherche Public de la Santé/National Public Health Institute 20A, rue Auguste Lumière 1950 Luxembourg Luxembourg Anetta Hartlova Centre of Advanced Studies Faculty of Military Health Sciences Trebesska 1575 500 01 Hradec Králové Czech Republic

Lenka Hernychova Institute of Molecular Pathology Faculty of Military Health Science University of Defence Trebesska 1575 500 01 Hradec Králové Czech Republic Martin Hubalek Institute of Molecular Pathology Faculty of Military Health Sciences University of Defence Trebesska 1575 500 01 Hradec Králové Czech Republic Robert Ihnatko Institute of Virology Slovak Academy of Sciences Laboratory for Diagnosis and Prevention of Rickettsial and Chlamydial Infections Dubravska cesta 9 845 05 Bratislava 45 Slovak Republic Suzanne R. Kalb Centers for Disease Control and Prevention National Center for Environmental Health Division of Laboratory Sciences 4770 Buford Hwy, N.E. Atlanta, GA 30341 USA Sarah Keasey United States Army Medical Research Institute of Infectious Diseases Department of Immunology 1425 Porter Street Frederick, MD 21702 USA

List of Contributors

Julia Kessler Institute of Immunology Centre de Recherche Public de la Santé/National Public Health Institute 20A, rue Auguste Lumière 1950 Luxembourg Luxembourg Jana Klimentova Institute of Molecular Pathology Faculty of Military Health Sciences University of Defence Trebesska 1575 500 01 Hradec Králové Czech Republic Klara Konecna Institute of Molecular Pathology Faculty of Military Health Sciences University of Defence Heyrovskeho 1203 500 05 Hradec Králové Czech Republic and Charles University in Prague Pharmaceutical Faculty in Hradec Kralove 500 05 Hradec Králové Czech Republic Jacques Kremer Institute of Immunology Centre de Recherche Public de la Santé/National Public Health Institute 20A, rue Auguste Lumière 1950 Luxembourg Luxembourg Peter Lasch Robert Koch-Institut Biomedical Spectroscopy (P25) Nordufer 20 13353 Berlin Germany

Juraj Lenco Centre of Advanced Studies Faculty of Military Health Sciences Trebesska 1575 500 01 Hradec Králové Czech Republic and Institute of Molecular Pathology FMHS UO Trebesska 1575 500 01 Hradec Králové Czech Republic Marek Link Centre of Advanced Studies Faculty of Military Health Sciences Trebesska 1575 500 01 Hradec Králové Czech Republic Claude P. Muller Institute of Immunology Centre de Recherche Public de la Santé/National Public Health Institute 20A, rue Auguste Lumière 1950 Luxembourg Luxembourg Mohan Natesan United States Army Medical Research Institute of Infectious Diseases Department of Immunology 1425 Porter Street Frederick, MD 21702 USA Dieter Naumann Robert Koch-Institut Biomedical Spectroscopy (P25) Nordufer 20 13353 Berlin Germany

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List of Contributors

Ivona Pávková Institute of Molecular Pathology Faculty of Military Health Sciences University of Defence Trebesska 1575 500 01 Hradec Králové Czech Republic

Dominique Revets Institute of Immunology Centre de Recherche Public de la Santé/National Public Health Institute 20A, rue Auguste Lumière 1950 Luxembourg Luxembourg

James L. Pirkle Centers for Disease Control and Prevention National Center for Environmental Health Division of Laboratory Sciences 4770 Buford Hwy, N.E. Atlanta, GA 30341 USA

Ludovit Skultety Institute of Virology Slovak Academy of Sciences Dubravska cesta 9 845 05 Bratislava Slovak Republic and Center for Molecular Medicine Vlarska 3-7 831 01 Bratislava Slovak Republic

Patrick Pirrotte Institute of Immunology Centre de Recherche Public de la Santé/National Public Health Institute 20A, rue Auguste Lumière 1950 Luxembourg Luxembourg Didier Raoult Unité des Rickettsies IRD-CNRS UMR 6236 Faculté de Médecine 27 Bd Jean Moulin 13385 Marseille France Patricia Renesto Unit of Virus Host Cell Interactions UMI 3265 Université Joseph Fourier-EMBL-CNRS 6, rue Jules Horowitz 38042 Grenoble France

Theresa J. Smith United States Army Medical Research Institute of Infectious Diseases Integrated Toxicology Ft. Detrick, MD 21702 USA Jiri Stulik Institute of Molecular Pathology FMHS UO Trebesska 1575 500 01 Hradec Králové Czech Republic and Centre of Advanced Studies Faculty of Military Health Sciences Trebesska 1575 500 01 Hradec Králové Czech Republic

List of Contributors

Vojteˇch Tambor Institute of Molecular Pathology FMHS UO Trebesska 1575 500 01 Hradec Králové Czech Republic Rudolf Toman Slovak Academy of Sciences Institute of Virology Laboratory for Diagnosis and Prevention of Rickettsial and Chlamydial Infections Dubravska cesta 9 845 05 Bratislava 45 Slovak Republic Jana Udrzalova Institute of Molecular Pathology Faculty of Military Health Sciences University of Defence Trebesska 1575 500 01 Hradec Králové Czech Republic

Robert G. Ulrich United States Army Medical Research Institute of Infectious Diseases Department of Immunology 1425 Porter Street Frederick, MD 21702 USA Pavol Vadovicˇ Institute of Virology Slovak Academy of Sciences Laboratory for Diagnosis and Prevention of Rickettsial and Chlamydial Infections Dubravska cesta 9 845 05 Bratislava 45 Slovak Republic

XIX

1

1 Introduction: Application of Proteomic Technologies for the Analysis of Microbial Infections Jiri Stulik and Patrick Butaye

1.1 Introduction

Proteomics belongs to the group of so-called “omics” technologies that are preferentially exploited for the global analysis of temporal or conditional alterations in gene expression, on either the gene or protein level. Proteomics is focused on proteins and provides information about their abundances, post-translational modifications, localization and mutual interactions. Current proteomic procedures combine efficient electrophoretic or chromatographic separation techniques with identification approaches based on mass spectrometry (MS) and computer technologies for bioinformatic analysis. It can be said that MS is now the major driving force in the field of proteomics and the quantitative shotgun tandem MS represents a large-scale high-throughput technology commonly employed for comparative identification and quantification protein studies [1]. The major benefit of the “omics” approaches is the ability to analyze a large number of genes or proteins simultaneously, enabling a more realistic view of a complex cell response to stimuli. The analysis of the global changes in protein profiling during the interactions between microbial pathogens and their hosts, so-called “infectomics”, is very attractive area within proteomics. It feeds the study of the fundaments of infections. It solves molecular mechanisms of microbial pathogenesis, helps in the efficient and rapid diagnosis of infectious disease and the development of novel prophylaxis and treatment strategies. Here, infectomics involves the study of proteins uniquely expressed or up-regulated in virulent clinical strains, proteins produced under stress conditions and, finally, proteins with immunostimulatory properties. Going deeper into infectomics one deals with structural studies of microbial proteins (to characterize potential post-translational modifications) and the analysis of protein–protein interactions, the so-called “interactome” [2]. In this publication we first give an overview on basic proteomic techniques. This introduces the reader into the more specialized studies performed on BSL3 and

BSL3 and BSL4 Agents: Proteomics, Glycomics, and Antigenicity, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.

2

1 Introduction: Application of Proteomic Technologies for the Analysis of Microbial Infections

BSL 4 agents. These organisms are difficult to handle and specialized laboratories are necessary to work with them. However, the safe preparation of materials for protein researchers can allow substantial progress in the understanding of these microorganisms.

1.2 Search for New Factors of Virulence and Potential Diagnostic Markers

Matching the protein patterns extracted from nonvirulent attenuated strains with their fully virulent counterparts is a common procedure to detect potential virulence factors and/or new diagnostic markers. This has been demonstrated in the comparative proteome study of exoproteins released by different enterotoxigenic Staphylococcus aureus strains. Using a gel-based approach different known enterotoxins and other possible virulence factors were revealed by comparing the protein profiles of two isolates, a food-derived strain with a prevalent enterotoxin gene cluster and a nonenterotoxigenic reference strain [3]. Another example of the gel-based approach is the protein profiling of two cystic fibrosis isolates of Pseudomonas aeruginosa strains associated with the initial and chronic infection process. These two genetically identical strains secrete several proteins uniquely expressed in the different stages of the disease [4]. Gel-based approaches utilizing preferentially two-dimensional gel electrophoresis (2-DE) for the study of enriched membrane proteins are typically exploited for the host–pathogen interactions. However, this method suffers from the loss of most of the hydrophobic membrane proteins due to their inefficient solubilization during isoelectric focusing, and therefore, the nonelectrophoretic approaches gradually replace gel-based techniques for such studies. An example of this is the study of the cell wall proteome of Listeria monocytogenes and Listeria innocua [5]. Here 30 proteins residing in the membrane were identified in both strains. Outer membrane vesicles (OMV) that are assumed to play an important role in cell to cell communication, for example, toxins or in the transfer of proteins and genetic materials between microbes represent potential virulence factors and proteins with immunostimulatory properties. The purification of sufficient OMV is, however, a serious problem [6]. Intracellular infectious microbes have to cope with the hostile intracellular milieu. Basically, their success for survival and multiplication depends on the coordinated reprogramming of their gene expression under the influence of stimuli, such as acid pH, iron depletion, oxidative stress, nitrogen radicals and heat. In-depth proteome analysis of the changing microbial protein profiles is very challenging since it informs us about protein-virulence factors that may serve as new targets for treatment. Unfortunately, progress in this area is still hampered by the inability of proper protein extraction methods. They lack efficacy through low gain and are too much contaminated with eukaryotic material. One way to avoid this could be the direct purification of bacteria-containing vacuoles and to identify the proteins uniquely expressed within this compartment [7].

1.4 Post-Translational Modifications of Bacterial Proteins and Protein–Protein Interactions

1.3 Search for New Vaccine Candidates

The application of proteomics for a search for vaccine candidates has been driven by the current need for a new type of safe and efficient subunit vaccines. The classical approach, called serological proteome (SERPA), is used for the identification of perspective proteins. This technology is based on the separation of complex protein mixtures by gel-based technology, mostly by 2-DE, followed by blotting of separated proteins to a membrane where the transferred proteins are probed by human or animal immune sera. The detected spots are then identified by MS after their excision from the matched preparative 2-DE gel. This approach is still routinely used but, instead of separating the whole-cell lysates, just the membrane proteins, secreted proteins or proteins located in outer membrane vesicles are being examined [8–10]. As already mentioned, 2-DE separation has strong limitations, and therefore, new nongel-based approaches are being examined. Protein microarray chips probed with sera from infected individuals overcome this problem, allowing protein solubilization and enabling the analysis of the whole proteome in an unbiased manner. This procedure was examined in the screening process for the identification of Francisella tularensis immunoreactive proteins. Probing a chip containing 1741 F. tularensis antigens with the sera from 46 patients diagnosed as tularemia patients led to the detection of 244 antigens exhibiting a high intensity signal [11]. Bioinformatics is an invaluable tool for the discovery of vaccine candidates because the human brain can hardly contain all information generated by the current high-throughput techniques. They allow us to focus on a significantly restricted number of proteins and to select those proteins that are likely to induce a good immune response. McMurry et al. [12] used a combination of different softwares for the detection of possible Mycobacterium tuberculosis immunodominant epitopes. First at the DNA level, open reading frames containing signal sequences featuring secreted proteins were detected, and then, the selected proteins were screened for matches to a list of MHC II binding motifs. Based on this selection, 17 peptides were synthesized and their immunostimulatory potential was verified by in vitro T-cell assays. Overall 15 epitopes gave acceptable results and are now candidates for the construction of new TB vaccine.

1.4 Analysis of Post-Translational Modifications of Bacterial Proteins and Protein– Protein Interactions

Post-translational modification adds an additional level of complexity to the current “omics” technologies. It is estimated that more than 100 different post-translational modifications exist in eukaryotic cells and the most widely distributed ones are glycosylation and phosphorylation. Highly sophisticated proteomic approaches are being developed to detect and quantify these modifications, but these procedures

3

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1 Introduction: Application of Proteomic Technologies for the Analysis of Microbial Infections

are still far from being a routine technology. Surprisingly, the recent data document that both types of modifications occur also in prokaryotes and their biological significance is starting to be unveiled. Regarding prokaryotes, both N- and O-glycosylations were detected and orthologs of the genes encoding members of both glycosylation pathways in eukaryotes were found in mucosal pathogens [13]. Glycosylation concerns mainly membrane and secreted bacterial proteins and most of them are known virulence factors. Hence, this modification might serve a specific function in the molecular mechanism of pathogenesis. This assumption was, for example, confirmed for the Toxoplasma gondii N-glycoproteins that are involved in several biological functions associated with the parasite’s intracellular development. Exposing cells to tunicamycin (which disrupts the glycosylation of newly synthesized proteins) then abrogated host cell invasion and parasite growth [14]. Unfortunately, the identification of glycoproteins is a highly intricate matter and encompasses laborious methods aimed at the detection of glycan modification(s). This and the methods of glycosidic linkage determination and identification of carbohydrate residues are still in their infancy in this area. The glycosylation of bacterial proteins and the reversible phosphorylation of serine, threonine and tyrosine residues were thought to be restricted to eukaryotic cells. However, in the last decade several prokaryotic tyrosine and serine/threonine kinases together with phosphatases have been described [15]. In contrast to eukaryotic kinases, the majority of these prokaryotic enzymes appear to be transmembrane proteins, and thus, it is anticipated that these kinases may function as prokaryotic receptors. Nevertheless, the ligands for these receptors and signaling pathways triggered by receptor occupation have to be unraveled. The extensive systematic prokaryotic phosphoproteome analyses require a good experience and skill in both phosphoprotein enrichment and in the following MS and bioinformatic analyses. Up to now, two indepth phosphorylation studies of a gram-positive bacterium Bacillus subtilis and a gram-negative bacterium Escherichia coli have been performed [16, 17]. They proved that a significantly lower number of prokaryotic proteins was phosphorylated in comparison to the mammalian or yeast proteins. Furthermore, phosphorylation on the serine and threonine residues prevailed and proteins were frequently multiphosphorylated but again their number was low in comparison to that found in eukaryotes. The kind of virulence factors either localized in the outer membrane or actively secreted is prone to direct interplay with the host cell biological system. A physical contact between microbes and target cells leads to significant alteration of the host cell proteomes on various levels, resulting in the modulation of cell signaling pathways, membrane ruffling, cytoskeleton rearrangement, endosomal traffficking, host cell protein modification and the induction of cell death programs. The combination of various fractionation procedures and gel- or nongel-based proteomic approaches enables us to study the changes in host proteomes on the level of individual subcellular compartments directly targeted by the microbe. Another approach is focused on the identification of interaction of the host and microbial virulence factors. However, despite the application of multiple tag and

References

pull-down procedures for the isolation of protein complexes, relatively few host proteins have been identified as the targets of microbial proteins until now. There is also a problem to distinguish nonspecific from specific binding partners of the isolated protein complexes. In this case, it appears that the recent developments in quantitative proteomics could help to solve this problem. One particular quantitative technique exploits metabolically incorporated isotopic labels into proteins. Using this approach, two cell cultures are differentially labeled. Then affinity purification isolates the bait from one population and a control from the other population. When the isolated complexes are analyzed, a mixed nonspecific interaction is characterized by an equal binding of the background proteins to the bait, while specific interactions with the bait result in differential ratios. This protocol was successfully used for the identification of the small G-protein Cdc42 as the host target of the SPI-1 effector, SopB/SigD in Salmonella [18].

1.5 Conclusions

The enormous progress in the proteomic methodologies will allow us to gradually describe the entire proteomes of pathogens. Likewise, it can be expected that new virulence factors will be identified and their biological role in the pathogenesis of diseases will be uncovered. These findings together with improvements in bioinformatics should then substantially speed up the design of new therapeutic and prophylactic agents. This COST Action B28 gathers excellence in these fields. In line with a microarray platform, a combination of genomics, proteomics, glycomics and antigenicity substantiated by a bioinformatics knowledge will allow us to make noticeable progress towards a better understanding of the agents under investigation and the diseases they cause. Of course, the input of bacteriologists and virologists dealing with these infections in the field and their efforts to understand their importance for humans, animals and environment cannot be omitted.

References 1 Chen, G., and Pramanik, B.N. (2008)

LC-MS for protein characterization: current capabilities and future trends. Expert Rev. Proteomics, 5, 435–444. 2 Charbonnier, S., Gallego, O., and Gavin, A.C. (2008) The social network of a cell: recent advances in interactome mapping. Biotechnol. Annu. Rev., 14, 1–28. 3 Pocsfalvi, G., Cacace, G., Cuccurullo, M., Serluca, G., Sorrentino, A., Schlosser, G., Blaiotta, G., and Malorni, A. (2008)

Proteomic analysis of exoproteins expressed by enterotoxigenic Staphylococcus aureus strains. Proteomics, 8, 2462–2476. 4 Hanna, S.L., Sherman, N.E., Kinter, M.T., and Goldberg, J.B. (2000) Comparison of proteins expressed by Pseudomonas aeruginosa strains representing initial and chronic isolates from a cystic fibrosis patient: an analysis by 2-D gel electrophoresis and capillary column liquid chromatography-tandem

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mass spectrometry. Microbiology, 146, 2495–2508. Calvo, E., Pucciarelli, M.G., Bierne, H., Cossart, P., Albar, J.P., and García-Del Portillo, P. (2005) Analysis of the Listeria cell wall proteome by two-dimensional nanoliquid chromatography coupled to mass spectrometry. Proteomics, 5, 433–443. Nally, J.E., Whitelegge, J.P., Aguilera, R., Pereira, M.M., Blanco, D.R., and Lovett, M.A. (2005) Purification and proteomic analysis of outer membrane vesicles from a clinical isolate of Leptospira interrogans serovar Copenhageni. Proteomics, 5, 144–152. Mattow, J., Siejak, F., Hagens, K., Becher, D., Albrecht, D., Krah, A., Schmidt, F., Jungblut, P.R., Kaufmann, S.H., and Schaible, U.E. (2006) Proteins unique to intraphagosomally grown Mycobacterium tuberculosis. Proteomics, 6, 2485–2494. Williams, J.N., Skipp, P.J., Humphries, H.E., Christodoulides, M., O’Connor, C.D., and Heckels J.E. (2007) Proteomic analysis of outer membranes and vesicles from wild-type serogroup B Neisseria meningitidis and a lipopolysaccharidedeficient mutant. Infect. Immun., 75, 1364–1372. Morsczeck, C., Prokhorova, T., Sigh, J., Pfeiffer, M., Bille-Nielsen, M., Petersen, J., Boysen, A., Kofoed, T., FrimodtMøller, N., Nyborg-Nielsen, P., and Schrotz-King, P. (2008) Streptococcus pneumoniae: proteomics of surface proteins for vaccine development. Clin. Microbiol. Infect., 14, 74–81. Chitlaru, T., Gat, O., Grosfeld, H., Inbar, I., Gozlan, Y., and Shafferman, A. (2007) Identification of in vivo-expressed immunogenic proteins by serological proteome analysis of the Bacillus anthracis secretome. Infect. Immun., 75, 2841–2852. Sundaresh, S., Randall, A., Unal, B., Petersen, J.M., Belisle, J.T., Hartley,

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M.G., Duffield, M., Titball, R.W., Davies, D.H., Felgner, P.L., and Baldi, P. (2007) From protein microarrays to diagnostic antigen discovery: a study of the pathogen Francisella tularensis. Bioinformatics, 23, 508–518. McMurry, J., Sbai, H., Gennaro, M.L., Carter, E.J., Martin, W., and De Groot, A.S. (2005) Analyzing Mycobacterium tuberculosis proteomes for candidate vaccine epitopes. Tuberculosis (Edinb), 85, 95–105. Hitchen, P.G., and Dell, A. (2006) Bacterial glycoproteomics. Microbiology, 152, 1575–1580. Fauquenoy, S., Morelle, W., Hovasse, A., Bednarczyk, A., Slomianny, C., Schaeffer, C., Van Dorsselaer, A., and Tomavo, S. (2008) Proteomics and glycomics analyses of N-glycosylated structures involved in Toxoplasma gondii–host cell interactions. Mol. Cell. Proteomics, 7, 891–910. Bakal, C.J., and Davies, J.E. (2000) No longer exclusive club: eukaryotic signalling domains in bacteria. Trends Cell. Biol., 10, 32–38. Macek, B., Mijakovic, I., Olsen, J.V., Gnad, F., Kumar, C., Jensen, P.R., and Mann, M. (2007) The serine/threonine/ tyrosine phosphoproteome of the model bacterium Bacillus subtilis. Mol. Cell. Proteomics, 6, 697–707. Macek, B., Gnad, F., Soufi, B., Kumar, C., Olsen, J.V., Mijakovic, I., and Mann, M. (2008) Phosphoproteome analysis of E. coli reveals evolutionary conservation of bacterial Ser/Thr/Tyr phosphorylation. Mol. Cell. Proteomics, 7, 299–307. Rogers, L.D., Kristensen, A.R., Boyle, E.C., Robinson, D.P., Ly, F.T., Finlay, B.B., and Foster, L.J. (2008) Identification of cognate host targets and specific ubiquitylation sites on the Salmonella SPI-1 effector SopB/SigD. J. Proteomics, 71, 97–108.

7

Part One Basic Proteomic Methods

9

2 Separation of Proteins and Peptides Ludovit Skultety

2.1 Introduction

Separating and analyzing each tiny plant on a meadow would be a ridiculously difficult task. Shrink these plants to submicroscopic size and you will begin to understand the challenge of proteomics to characterize every protein. Although new technologies have arrived on the scene, separation of one protein from another is usually the most laborious and limiting aspect of proteomics. Thus, reliable and effective methods of sample preparation and separation are the keys to the success of proteomic research. Biological materials usually do not contain just proteins of interest. They include also interfering substances, such as salts, small ionic molecules, ionic detergents, saccharides, lipids and other nonprotein components. Thus, the proteins of interest must be isolated by the appropriate step in the purification procedure, while the interfering substances are depleted or removed entirely. Their presence in the sample may result in difficulty in the further protein separation and also disturb the detection and identification of proteins in proteome studies. Depending on the type of sample, there are various ways to prepare a protein sample for further separation [1]. Basic methods include protein precipitation, dialysis, ultrafiltration and ultracentrifugation. In bulk protein purification, a common first step to isolate proteins is precipitation that may rely on different chemical principles. Precipitation can be obtained by ammonium sulfate, trichloroacetic acid (TCA), TCA in acetone, ethanol, acetone [2] or methylethylketone at acid pH in the case of hemeproteins [3]. Although many such methods have the advantage of concentrating and eliminating contamination, they also have the disadvantage of irreversible protein denaturation and protein insolubility. Dialysis is an old established procedure for reducing the salt concentration in samples. The separation is based on principles of diffusion that allows the low

BSL3 and BSL4 Agents: Proteomics, Glycomics, and Antigenicity, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.

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2 Separation of Proteins and Peptides

molecular weight contaminant removal from sample solutions. Using dialysis, the maximal interfering substances can be reduced. However, it may cause loss of the proteins in the sample, consume large volumes of interchanged buffer and require more time than other desalting techniques [4]. In the meanwhile, ultrafiltration and ultracentrifugation can remove high or low molecular weight interfering substances in a relatively short time by using selective permeable membranes [4–6] or centrifugal force [7], respectively. Although the removal of these interferences can concentrate the proteins, some of the interfering proteins with a molecular weight similar to the protein(s) of interest are also concentrated. This means that each purification method has its own set of advantages and disadvantages. Therefore, alternative or multistep methods might be necessary for protein concentration and/or desalting in order to get a high quality sample. The proteome of any organism is usually very complex and may consist of thousands protein species each with its unique chemical and physical properties. Because of the limited resolution of analytical separation techniques presently applied in protein profiling and expression analysis, only the most abundant proteins are usually identified by subsequent mass spectrometric (MS) analysis. Since the relative amounts of protein species present within any proteome of a living organism may differ by 7–10 orders of magnitude, the relatively less abundant proteins are usually masked by the more abundant ones, such as those needed for housekeeping or required structurally. This makes it difficult to relate the results of proteome profiling to the biology of the system. Low copy number regulatory proteins such as kinases, phosphatases or GTPases can be detected only after applying additional fractionation strategies to reduce sample complexity [8]. To gain a better understanding of the inner workings of any living organism, initial prefractionation methods must be employed in proteomics [9–12]. In order to find a suitable procedure that exhibits a satisfactory yield and purification factor (the number that represents how much “enrichment” you achieved or how well you removed all extraneous proteins), it is desirable to try more than three fractionation/separation methods. If possible all the procedures should have a purification factor of five or more and a yield of at least 30%. The time required to effect the procedure should be also considered. Nevertheless, the activity of the protein of interest may disappear because of, for example, irreversible adsorption of protein to the column materials, unstable conformation and/or proteolytic digestion. Thus, in order to slow down proteolysis, it is usually desirable to proceed as quickly as possible, adding a cocktail of protease inhibitors and keeping the protein mixture cooled during the purification. Based on the separation strategy, proteomic experiments can be divided into gel-based and gel-free approaches. Gel-based proteomics accomplish protein separation by one- or two-dimensional plate gel electrophoresis. Then, protein bands/ spots are usually analyzed by software and excised out. The “in gel” digested protein bands/spots are further fractionated and analyzed by MS. The gel-free proteomic experiment means “in solution” proteomics and is usually based on protein digestion and separation of resulting peptides by chromatographic or electrophoretic methods prior to MS analysis.

2.1 Introduction

2.1.1 Gel-Based Separation 2.1.1.1 One-Dimensional Electrophoresis Gel electrophoresis is a common laboratory technique that can be used both as preparative and analytical method. The principle of electrophoresis relies on the movement of a charged ion in an electric field. In practice, the proteins are usually denatured in a solution containing a detergent (typically sodium dodecyl sulfate; SDS) especially after prior reduction of disulfide bonds with mercaptoethanol or dithiothreitol. In these conditions, protein–protein interactions are prevented, proteins are unfolded and negatively charged SDS–protein complexes are formed. The amount of detergent bound is so large that any differences in native charge of the different proteins are swamped. The bigger the macromolecule, the more SDS is bound, so that all macromolecules treated with SDS have the same ratio of charge to mass. Thus, the force per unit mass in an electric field is the same, and all molecules should have the same velocity if there is no frictional drag. But electrophoresis with SDS is nearly always carried out in gels. The gels: (i) suppress connective currents produced by small temperature gradients and (ii) serve as molecular sieves. During SDS gel electrophoresis, the SDS–protein complex moves in an electric field toward the positive pole. Molecules that are small compared with the pores in the gel move readily through the gel, whereas molecules much larger than the pores are almost immobile. Intermediate-size molecules move through the gel with various degrees of facility. Polyacrylamide gels are the choice supporting media for electrophoresis because they are chemically inert and are readily formed by the polymerization of acrylamide. Moreover, their pore sizes can be controlled by choosing various concentrations of acrylamide and a cross-linking reagent (piperazine, methylenebisacrylamide) at the time of polymerization. The various SDS–polyacrylamide gel electrophoresis (SDS-PAGE) systems differ among other things in the buffers they use. The discontinuous Lämmli [13] system with Tris-glycine buffers is the most widely used. A stacking gel (Tris-glycine buffer pH 6.8; 3–4% acrylamide) is poured over a separation/running gel (Trisglycine buffer pH 8.8; 5–20% acrylamide). The longer the running gel the better the separation. The thinner the gel the nicer the bands. For proteins of a molecular weight (MW) of 10–60 kDa, separation gels containing 15% of acrylamide are well suited, gels with 10% acrylamide are used for proteins of MW of 30–120 kDa and 8% gels for proteins of MW of 50–200 kDa. To separate mixtures of small proteins and peptides of MW of 1.5–10.0 kDa, 18% gels with 7–8 M of urea can be used [14]. The alternative is a Tris-tricine system [15] that separate peptides of 1–100 kDa and does not require urea. Gradient gels (e.g., 8–15%) have a broader separation range and bands that are slightly better defined. 2.1.1.2 Two-Dimensional Electrophoresis It quickly became apparent that SDS-PAGE is not able to separate complex protein mixtures. Consequently, two-dimensional electrophoresis (2-DE) was introduced

11

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2 Separation of Proteins and Peptides Isoelectric focusing

–

–

OH–

OH

OH–

10 pl

+

H+

H+

H+

3

Inoubation SDS-PAGE

–

high MW

+ Figure 2.1 Schematic diagram of twodimensional electrophoresis. The technique sorts proteins according to two independent properties in two discrete steps: in the first dimension, isoelectric focusing separates

low proteins according to their isoelectric point (pI); in the second dimension, polyacrylamide gel electrophoresis in the presence of SDS separates proteins according to their MWs.

independently by O’Farrell and Klose in 1975. This technique sorts proteins according to two independent properties in two discrete steps: the first-dimension step (isoelectric focusing; IEF) separates proteins according to their isoelectric point (pI); the second-dimension step (SDS-PAGE) separates proteins according to their molecular weights (Figure 2.1). The result is reminiscent of a Dalmatian’s coat with spots corresponding usually to a single protein species in the sample. Thousands of different proteins and their isoforms can thus be separated, and information such as the protein pI, the apparent molecular weight and the amount of each protein can be obtained. However, for a long time 2-DE suffered a wallflower-like existence. It did deliver impressive pictures, but that was it. This was due to the fact that 2-DE was not able to reproduce consistently. Not only from laboratory to laboratory but also in the hands of the same experimenter. This sad state improved with the introduction of immobilized pH gradient (IPG) strips. The pH gradient fixed in the gel increased the reproducibility as well as the resolution of 2-DE by an order of magnitude [16]. Commonly used IPG strips are 18- to 24-cm long and available commercially in broad (pH 3–10) and narrow (e.g., pH 5.5–6.5, 7–11) pH ranges. The purchased

2.1 Introduction

strip is rehydrated overnight in a rehydration cassette. For rehydration solution, the Expasy home page [17] recommends 25 ml 8 M urea, 2% (w/v) CHAPS, 10 mM dithiothreitol (DTT) and 2% (v/v) IPG buffer of the relevant pH range together with a trace of bromophenol blue. The completely soaked strips are then transferred into the IEF chamber and covered with paraffin oil to prevent water from evaporating during the focusing. For loading, the sample could be either directly added to the rehydration solution or applied through a cup at the cathodic or anodic end during the focusing. The presence of a pH gradient inside the IPG strip is crucially important. Under the influence of an electric field, a protein as an amphoteric molecule moves to the position in the pH gradient where its net charge is zero. A protein with a negative net charge migrates toward the anode, becoming less negatively charged until it reaches zero net charge. A positively charged protein moves at the same time through the pH gradient until it also reaches its pI. If a protein diffuses away from its pI, it immediately gains charge and migrates back. This is the focusing effect of IEF, which concentrates proteins at their pIs and allows proteins to be separated on the basis of very small charge differences. After focusing, the proteins in the IPG strip have to be saturated with SDS. For that, the focused strips are first incubated for 10–15 min in an equilibration buffer [e.g., 40 mM Tris-HCl, pH 6.8, 6 M urea, 30% (v/v) glycerin, 2% (w/v) SDS] containing 2% (w/v) DTT (or dithioerythritol, β-mercaptoethanol). Afterward, the free SH groups are blocked with 2.5% (w/v) iodoacetamide in the incubating solvent for 5 min. Each strip is then transferred onto a SDS-PAGE gel and the electrophoresis is performed [18]. 2.1.1.3 Protein Staining and Image Analysis With the development of gel-based proteomics, an additional burden is placed on the methods used for protein detection. Besides the classical requirements, such as sensitivity, homogeneity from one protein to another, linearity throughout a wide dynamic range, speed, convenience and low cost, detection methods must now take into account another aspect, namely their compatibility with digestion and MS. This compatibility is evidenced by two different and complementary aspects. These are: (i) the absence of adducts and artefactual modifications of the peptides obtained after protease digestion of a protein detected and digested in gel and (ii) the quantitative yield of peptides recovered after digestion and analyzed by the MS instrument. While this quantitative yield is not very important per se, it is a crucial parameter as it strongly influences the signal to noise ratio of the mass spectrum and thus the number of peptides that can be detected from a given protein input, especially at low protein concentrations. This influences in turn the sequence coverage and thus the analysis provided by the MS instrument. Up to now, no protein detection method in the three families of methods that are of current use in proteomics perfectly matches these prerequisites. The most popular method is probably colloidal Coomassie blue staining [19]. Although this method is not very sensitive, it affords a very good compatibility with MS. It has also gained popularity in the early days of proteomics, when its sensitivity matched

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almost exactly the needs of protein amounts required for a decent analysis by MS. With improvement of the MS instruments, associated robotics and so on, this is no longer the case, and detection by colloidal Coomassie either requires unnecessary consumption of sample or results in significant numbers of potentially analyzable proteins that are undetected. Fluorescent detection methods offer an interesting alternative, and metal chelate-based methods [20, 21] have become increasingly popular. In their most recent versions [22], these methods offer an increased sensitivity allied with a very good compatibility with MS analysis. These methods are however not easy to use in small proteomics laboratories. Besides the expensive laser scanners or CCD cameras required for quantitative analysis, spot excision must be performed under UV light. It induces in turn collateral problems such as safety and photobleaching, both increasing when large numbers of spots are to be excised in comprehensive proteomics studies. Last but not least, silver staining still offers the maximal sensitivity and all the ancillary advantages associated with light absorption-based methods, such as easy visualization and quantitation (although the linear dynamic range of silver staining is not very good) and easy spot excision. However, in the proteomics context, the most important problem lies in its limited compatibility with MS. Although this aspect has been improved by optimization of the staining procedure [23, 24], destaining of the spots or bands after silver staining [25], or by development of silver–ammonia methods [26], the compatibility with MS remains far below that which can be achieved with fluorescent probes or colloidal Coomassie. This low compatibility has been attributed to the use of formaldehyde [27], which also induces artefactual formylations. [28]. It was therefore of great interest to have available a sensitive silver staining method totally formaldehyde-free. However, to date, the only formaldehyde-free silver staining methods use carbohydrazide [27] or reducing sugars in alkaline borate buffer [29] as developing agents. To take full advantage of the 2-DE separation, multiplex quantitative analysis of the component proteins of related but different protein samples might be performed. This technique is known as two-dimensional difference in-gel electrophoresis (2D-DIGE) which allows labeling protein mixtures with different fluorescent cyanine dyes, such as Cy2, Cy3 and Cy5 maleimides [30]. These CyDyes are structurally similar, but spectrally different (Cy2, λem = 520 nm; Cy3, λem = 580 nm; Cy5, λem = 670 nm). Compared to conventional 2-DE, 2D-DIGE has the major advantage that both the control (labeled with Cy3) and experimental (labeled with Cy5) sample are run in the same gel. These samples are then imaged separately but because they were run in the same gel, the images can be perfectly overlaid (Figure 2.2). This reduces the number of gels that must be run to make statistically valid comparisons and raises the confidence with which protein changes between samples can be detected and quantified. Use of a third dye (Cy2) permits an internal standard to be created by pooling an equal aliquot of all biological samples in the experiment. The internal standard is then run on every gel in the experiment. This means that every protein spot from all samples will be represented in the internal standard. This in turn allows more accurate quantification and spot statistics between gels.

2.1 Introduction

Image analysis Pooled internal standard labeled with Cy2 Cy2 Cy3 Protein extract 1 labeled with Cy3

Mix labeled extracts

Two-dimensional electrophoresis

Cy5

Protein extract 2 labeled with Cy5 Figure 2.2 Schematic diagram of the DIGE technology platform. Two different samples are derivatized with two different fluorophores, combined and then run on a single 2D gel. Proteins are detected using a dual laser scanning device or xenon arc-based instrument equipped with different excitation/ emission filters in order to generate two separate images. The images are then

matched by a computer-assisted overlay method, signals are normalized, and spots are quantified. Differences in protein expression are identified by evaluation of a pseudo-colored image and data spreadsheet. DIGE technology can maximally evaluate three different samples using Cy2-, Cy3- and Cy5-based chemistries.

In addition, the post-translational modifications of phosphoproteins and glycoproteins can be directly detected by different staining methods [31]. While the MS methods used to identify the protein(s) in a spot from a 2D gel are excellent, they have some serious disadvantages when it comes to determining if a protein is either modified or not. The presence of, or neutral loss of, unique mass unit “signatures” from a peptide fragment can be used as positive confirmation of the presence of a specific modification. Unfortunately it is almost impossible to determine with any degree of certainty that a protein in a spot is not modified. An alternative approach for determining whether a protein spot on a 2D gel is modified is to stain only the protein altered by individual types of post-translational modification with specific dyes. A new phosphoprotein-specific fluorescence dye called “Pro-Q™ Diamond” recently became available from Molecular Probes (Eugene, Ore., USA) and can be used to detect phosphorylated tyrosine, serine or threonine residues of proteins on SDS-PAGE and 2-DE [32]. Otherwise, Pro-Q™ Emerald 300 and Pro-Q™ Emerald 488 glycoprotein gel stains were recently developed for the detection of glycosylated proteins which relies upon the utilization of a fluorescent hydrazide. Thus, they provide an attractive alternative to the labeling with radioactive sugars that

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conjugated to glycoprotein by periodic acid Schiff’s mechanism [33]. Gels stained with both Pro-Q™ Diamond phosphoprotein stain and Pro-Q™ Emerald glycoprotein stain can also be post-stained with SYPRO® Ruby dye, which allows sequential detection of total protein profile in the same gel [33]. For immunochemical detection of specific proteins, determination of derivatization (phosphate group, sugar residues), exposure to enzyme substrate, binding of protein ligand or chemical microsequencing, it is better to transfer the separated proteins from the gel, onto a membrane. Nitrocellulose, polyvinylidene difluoride (PVDF), positively charged nylon or polybrene-coated glass fiber can be used [34]. The membrane in comparison to the gel is easily manipulable, and reactions or washing processes run faster, unhindered by diffusion problems. Furthermore, the adsorbed proteins on the membrane might be visualized by reversible stains [35, 36] before letting a blot react with antibodies, lectins, substrate or protein ligands. Evaluation of two visualized high-resolution 2-DE gels or blots by manual comparison is not an easy task. In large studies with patterns containing several hundreds or even thousands spots, it may be almost impossible to detect the appearance of a few new spots or the disappearance of single spot. Image collection hardware and image evaluation software are necessary to detect these differences as well as to obtain maximum information from the gel patterns. The systems that are commonly available commercially (Image Master 2D Platinum from GEH, Melanie from GeneBio, PDQuest from BioRad, Dymension from Syngene, Delta 2D from Decodon, Progenesis from Nonlinear Dynamic, etc.) seamlessly perform both classical and DIGE gel analysis. These systems provide powerful solutions from data acquisition to protein spot information. The spot detection and matching algorithms facilitate the extraction of statistically valid differences between groups of 2D gels, while requiring minimal user intervention. The applications integrate filtering, querying, reporting, statistical and graphing options so that you can easily view, compare, analyze and present the results. 2.1.1.4 2-DE Limitations As analytical 2-DE is remarkably well suited to studying protein expression in biological systems, it is used in most laboratories where proteomics are performed. However, this separation technique has some drawbacks that can severely limit the investigator’s ability to monitor protein expression on a truly global scale. Proteins that are notably difficult to separate using 2-DE are membrane, low copy number, large (>150 kDa) and highly basic proteins. Driven by the need to study these “difficult” proteins together with their less challenging counterparts, some alternative separation methods have to be applied. 2.1.2 In Solution – “Gel Free” Proteomics

However, methods that perform protein/peptide separations in the liquid phase generally have a lower ability to resolve complex mixtures and provide inferior

2.1 Introduction Table 2.1

Nomenclature for HPLC columns in cylindrical formats.

Description of columns

Diameter

Approximate typical flow rate (velocity 1–10 mm/s)

Open tubular liquid Nanobore Capillary Microbore Narrow (small) bore Normal bore Semipreparative Preparative

>25 μm i.d. 25 μm > i.d. > 100 μm 100 μm > i.d. > 1 mm 1 mm > i.d. > 2.1 mm 2.1 mm > i.d. > 4 mm 4 mm > i.d. > 5 mm 5 mm > i.d. > 10 mm i.d. < 10 mm

25 nl/min 25–4000 nl/min 0.4–200 μl/min 50–1000 μl/min 0.3–3.0 ml/min 1.0–10.0 ml/min 5.0–40 ml/min 20 ml/min

visualization of proteomes, as compared to 2-DE. Their advantages are analysis of “difficult” proteins, flexibility, relative speed, ease of automation of sample handling and direct connection to MS. Liquid phase separations are faster than their in-gel counterparts (two-dimensional separation can be achieved in just a few hours). Thus, it also shortens the time between sample preparation and protein identification. Liquid phase separations can be performed using chromatography, electrophoresis, or a combination of these methods. However, it is not necessary to limit protein separation only to these approaches. Any combination may be tried and can be especially useful for a particular biological system [37, 38]. Column chromatography is one of the most common methods of protein purification. Many types of available matrices used for column chromatography are usually packed in a column of different dimensions (Table 2.1) in the form of small beads. 2.1.3 Column Chromatography

It has often been stated that the column is the heart of the chromatograph. Without the correct choice of column and appropriate operating conditions, separation can be both frustrating and unrewarding. Nowadays, the increased need for highthroughput and high-sensitivity assays are the driving forces for new developments, including rectangular, square or other perimeter-shaped column (e.g., on chips) and, in particular, in particle designs such as perfusive packings, poroshell particles, inorganic/organic hybrids, monoliths and sub-2 μm nonporous particles. Advances are still being made in column technology with even smaller porous particles (1–2 μm in diameter), high-temperature (up to 200 °C) columns, nanocolumns with diameters under 100 μm and rapid-separation columns enabling highresolution separations in seconds. Some of the more common chromatographic columns used in proteomics include: (i) size exclusion chromatography (gel filtration), (ii) reverse phase liquid

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chromatography, (iii) hydrophobic interaction liquid chromatography, (iv) ion exchange chromatography and (v) affinity chromatography. 2.1.3.1 Size Exclusion Chromatography Size exclusion chromatography (SEC), also known as gel permeation chromatography or gel filtration chromatography, is generally considered the premier method for determining: (i) the molar mass averages of polydisperse macromolecules, (ii) separation of target protein in a mixture and (iii) salt removal or buffer exchange. The dissolved analyte is injected onto a column packed with porous, inert material (porous gels, other rigid inorganic packing particles) and is carried through the column by the solvent. Both molecular weight and shapes of proteins contribute to the degree of retention. Smaller molecules diffuse further into the pores of the beads and therefore move through the bed more slowly, while larger molecules enter less or not at all and thus move through the bed more quickly (Figure 2.3a). The process has been described as an inverse molecular sieving mechanism that depends on hydrodynamic volume of a dissolved molecule with respect to the average pore size of the column packing material [39]. Although it is widely used, low-pressure SEC is not suitable for protein purification for several reasons: (i) pouring and running the column is time-consuming, (ii) the resolution is poor, (iii) the sample volume is limited, (iv) the chromatography takes a long time because the flow rate is limited, and finally, (v) the sample is diluted by at least a factor of three. 2.1.3.2 Reversed-Phase Liquid Chromatography Reversed-phase liquid chromatography (RPLC) is the most frequently used method for peptide separation. However, it can also efficiently separate small, stable proteins (e.g., toxins). Peptides and/or proteins adsorb to the hydrophobic surface that consists of porous silica particles coated in general with n-alkyl chains (Figure 2.3b). For peptides, silica particles with pore dimensions of 100–300 Å are used. For proteins, the pore diameter should be 10 times larger. The n-alkyl chains are 2-, 4-, 8- or 18-C long, and their length unpredictably changes the separating properties of the RPLC. Two peptides that show two separate peaks on C18 columns may exhibit only one peak on C4 columns (or vice versa). The sample is almost exclusively eluted with a rising acetonitrile gradient. For separation of large or very hydrophobic proteins the mixture of 2-propanol with acetonitrile serve well. The quality of the separation depends not only on the beads but also on the steepness of the gradient and the temperature. The temperature is in play because peptides can maintain their secondary structure (α-helix, β-fold), which influences the adsorption (high temperature denatures secondary structures). Regarding the column length (typically 10–20 cm), the separation of peptides and smaller proteins improves with longer columns. In contrast, larger proteins should be separated on shorter columns, otherwise the yield becomes too small. However, RPLC is rarely used to separate larger proteins because they tend to denature under these conditions [34].

2.1 Introduction (a)

-

(b)

-

-

-Si-(C2-C18)

-

-

-

(C2-C18)-Si-

-Si-(C2-C18)

-

-

(C2-C18)-Si-

H2O

(c)

H2O

H2O

H2O H2O

H2O

H2 O H2 O

H2O H2O

H2O

H2O

H2O

H2O

(d)

(e)

Figure 2.3 Schematic diagram of chromato-

graphic separations. (a) Size exclussion chromatography. The columns separate proteins according to their size. The matrix consists of tiny porous beads. Protein molecules that are small enough to enter the holes in the beads are delayed and travel more slowly through the column. (b) Reverse phase chromatography. Peptides and/or proteins adsorb to the hydrophobic surface that consists of porous silica particles coated in general with n-alkyl chains (2-, 4-, 8-, or 18-C). The sample is normally eluted with an increasing acetonitrile gradient. (c) Hydrophilic interaction liquid chromatography. The mobile phase forms an aqueous layer on the surface of the polar stationary phase creating a liquid/liquid extraction system. The analyte is distributed between the

water reach stationary layer and the mobile phase with low water contents. Elution is obtained through increasing the hydrophilicity of the mobile phase by increasing the water content. (d) Ion exchange chromatography. The column is packed with small beads that carry positive or negative charges that retard proteins of the opposite charge. The association between a protein and the matrix depends on the pH and ionic strength of the solution passing down the column. (e) Affinity chromatography. The columns contain a matrix covalently coupled to a molecule that interacts specifically with the protein of interest (e.g., antibody, immobilized metal ions, lectins or an enzyme substrate, etc.). Protein that binds specifically to such a column can finally be released by a pH change or by concentrated salt solutions.

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2.1.3.3 Hydrophilic Interaction Liquid Chromatography The separation of peptides with many acidic and basic residues has always proved to be problematic with RPLC gradient elution. A rival technique for separating polar peptides is hydrophilic interaction LC (HILIC) that can be simply seen as a form of normal phase (NP) chromatography. However, the acronym HILIC was suggested to distinguish it from NP, as NP is typically performed with nonaqueous solvent buffers, while HILIC is performed with water-miscible solvents. In recent years, several stationary phases have emerged that are specifically developed for HILIC approaches. Popular phases include underivatized silica that contains functional groups such as siloxanes, silanols with (or without) a small quantity of metals or derivatized silica (various cation, anion or zwitterionic exchangers). Each of these materials display different retention characteristics and separation selectivities and require distinct buffer compositions for optimal results [40]. The typical mobile phase for HILIC includes acetonitrile (more than 70%) with a small amount of water [41]. It is commonly believed that in HILIC, the mobile phase forms a water rich layer on the surface of the polar stationary phase creating a liquid/liquid extraction system (Figure 2.3c). The analyte is distributed between the aqueous stationary layer and the mobile phase with low water content. More polar compounds will have a higher affinity (stronger polar interaction) for the stationary layer than less polar compounds. Elution is obtained through increasing the hydrophilicity of the mobile phase by increasing the water content. The final separation mechanism of elution, however, is most probably a superpositioning of partitioning and electrostatic interactions or hydrogen bonding to the stationary phase [42]. 2.1.3.4 Ion Exchanger Chromatography Ion exchanger chromatography (IEC) might be applied to both peptide and protein separation. It is based on the charge of the proteins that are separated via electrostatic interaction with a matrix (Figure 2.3d). Proteins with a high positive charge are bound to a column with a negative charge (cation exchanger) that often uses carboxymethyl groups (weak cation exchanger) or sulfopropyl groups (strong cation exchanger; SCX). The positive charge on the column (anion exchanger; AX) including the strong anion exchanger Q (quaternary resin) and the weak anion exchanger diethylaminoethane (DEAE) binds the negatively charged proteins [43]. The charged sample is loaded on the column which is equilibrated with a buffer of low ionic strength (e.g., 20 mM salt). Unbound proteins are washed out and the captured protein is eluted through increasing the salt concentration or changing the pH. The easiest way is to perform chromatography at a constant pH. The captured protein is then eluted through changes in ionic strength. Because the IEC column has a higher attraction for the charge of salts than for the charged protein, it will release the protein in favor of binding the salts. Proteins with weaker ionic interactions will elute at a lower salt concentration and thus different proteins will elute at different salt concentrations. Be aware that changes in pH alter the charge of a protein. Therefore, ensure that the pH of the system is adjusted and buffered accordingly.

2.1 Introduction

2.1.3.5 Affinity Chromatography Selective separation of a specific protein or group of proteins can be achieved using affinity chromatography (AC) which is based on the ability of a biologically active molecule to bind specifically and reversibly to a complementary molecule. The binding sites of the immobilized substances must be sterically accessible after their coupling to the solid support and should not be deformed by immobilization. In the case of specific proteins, an affinant is attached to the active surface of the column packing material or column surface. The sample is injected onto the column and the protein(s) of interest is captured by the affinant [9]. Compounds that do not possess a complementary binding site for the bound ligand will either pass directly through the column or be eluted by a subsequent washing step (Figure 2.3e). The bound protein(s) is then recovered by washing the column with a competitive substrate or a solution that disrupts the interaction between protein and affinant (e.g., denaturants). While the use of antibodies directed to a specific protein remains the most popular affinity-based separation method, many other affinity techniques for isolating a specific class of proteins or peptides have been developed. These methods include immobilized metal ion affinity chromatography (IMAC) containing nickel or copper ions, to capture histidine-containing peptides [44, 45] or gallium [46] and alternatively zirconium [47] to isolate phosphopeptides. In addition, affinity methods have been developed to isolate peptides containing specific types of residues such as cysteine, tryptophan or methionine [48]. There are, as well, a variety of different lectins that have been used to selectively isolate glycoproteins based on the composition of the carbohydrate side chain [49]. 2.1.3.6 Multidimensional Chromatography Since the resolving power of a single chromatographic step is very limited and because of use of different types of columns and solvent systems naturally leads to different protein or peptide separation, efforts have been made to develop multidimensional approaches. This concept was described first by Giddings (1984) [65] as an orthogonal system of two or more coupled separations based on different retention mechanisms that effectively create another opportunity for resolving analytes. Two main issues that must be considered at the beginning are the way columns are coupled, online or offline, and whether to perform separations at the peptide or protein level. Thus, the investigator may chosen to perform the proteolytic digestion of the whole protein sample and separate the peptides using 2D LC (“shotgun”, online approach), or to separate intact proteins on the first column, followed by a proteolytic digestion (offline approach). The main advantage of online multidimensional LC techniques is the possibility of full automation of the complete comprehensive 2D-LC process without handling or transfer of samples. However, this shotgun approach results in very complex samples consisting of several hundred thousand peptides with concentrations varying by 10 or more orders of magnitude. Other shortcomings are the loss of post-translational modifications and the difficulty of proper assignment. Separating the intact proteins of a complex sample prior to digestion has the advantage of: (i) reduced sample complexity, (ii) more efficient separations, (iii) higher flexibility with respect to

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column dimensions and mobile phase selection and (iv) more straightforward method development and troubleshooting. Thus, the protein identification, posttranslational modification assessments and the dynamic range are improved by several orders of magnitude. A variety of multidimensional combinations has been reported [50, 51]. These methods may use a wide variety of chromatographic techniques and number of dimensions, but practical considerations of interfacing the method with MS limit the utility of many combinations. For large-scale MS characterization of proteins, reversed-phase nanoLC is usually the last step and it is most often combined with capillary electrophoresis, IEF, IEC, RPLC or AC. While the latter approach is mainly applied to more specific functional proteomics, on line 2D LC using a SCX column in series with a RP column (e.g., with the multidimensional protein identification technology; MudPIT) is performed for analysis of comprehensive protein expression profiles of the whole proteome. The tryptic peptides are eluted from the SCX column by incremental increases of salt in the first dimension. In the second dimension these peptides are first trapped on a RP enrichment column and finally separated on an analytical RP column [52, 53] (Figure 2.4).

Trap charged peptide on SCX column Trap uncharged petides on RP trap column Complex peptide mixture

Wash RP gradient

Process data

Proteases Higher salt concentration Salt injection Total lysate Release peptides from SCX column Database search Trap peptides on RP trap Figure 2.4 Schematic diagram of the MudPIT approach. The protein mixture is divided in aliquots, which are each digested by a protease. After protease inactivation, the aliquots are pooled and acidified. The peptide mixture is then loaded on a strong cation exchanger (SCX) column, followed by a reverse phase column (RP) coupled by a nanoelectrospray device to a tandem mass

spectrometer. The SCX column is eluted by incremental increases of salt. This transfers a population of peptides in the RP column, where they bind. Using a reverse phase gradient, the separated peptides are eluted into a tandem mass spectrometer. Peptide fragmentation data is then obtained to identify the peptides and hence the proteins from which they are derived.

2.1 Introduction

2.1.4 Liquid Phase IEF and Electrophoresis

Analytical 2-DE combined with MS is a powerful procedure that allows the high resolution of proteins and their rapid identification. However, analytical 2-DE procedures are rarely capable of supplying sufficient amounts of low abundance proteins for further characterization by MS without first recovery of proteins from several gels; liquid phase IEF and electrophoresis might be the methods of choice. The Rotofor cell (Bio-Rad) has been developed for preparative scale IEF in the liquid phase. This technique has the unique ability to enrich low-abundance proteins up to 500-fold at their respective isoelectric points [54, 55]. The individual proteins are then isolated on the basis of their size differences in liquid phase continuous SDS-PAGE [56, 57]. The Prep cell (Bio-Rad) has been designed for that purpose. Two-dimensional preparative liquid phase electrophoresis (2DLPE) allows high protein loads (up to 1 g) and large volumes (up to 55 ml), thus yielding sufficient amounts of low abundance proteins for further characterization by MS [58]. 2.1.5 Alternative Separation Technologies

A novel approach, similar to 2-DE, is under development at Lynx Therapeutics (Hayward, Calif., USA). As in 2-DE systems, the Lynx Protein Profiler uses separation by charge, combined with separation by mass. However, the gels are replaced by flat plates that contain multiple channels. In the first dimension, electrophoresis in an cross-linked polymer sieving solution is performed for separation based upon mass. In this system, proteins are not complexed with SDS but are fluorescently prelabeled for detection. The separation range is 6000– 200 000 MW. After the first-dimension separation, the separated proteins are electrically driven orthogonally into 100 parallel channels, which are coated with covalently attached buffers. Applying an electric field to these channels generates a stable pH gradient within which proteins are resolved based on their isoelectric points [59]. An alternative approach for complex mixture analysis has been developed also by Ciphergen Biosystems, Inc. (Fremont, Calif., USA). This technology, marketed as the Protein-Chip system, selectively captures the proteins of interest using an aluminum strip with eight domains that carry different affinity adsorbents. The adsorbents might be nonspecific, such as cationic, anionic, hydrophobic or hydrophilic materials, or highly specific, such as antibodies or receptors. Proteins are adsorbed from a complex mixture, unbound material and interferences are washed away, and bound species are analyzed by MS systems [60, 61]. In addition, microscale lab-on-a-chip devices have been developed for performing chemical reactions and separations [62–64]. Several versions of this technology have been commercialized, but all rely on similar principles of microfluidics [34]. Reagents and chemicals are transported electrically using electro-osmotic flow or,

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in some cases, by hydrodynamic flow using pressure or vacuum. Analytes are separated by electromigration using electrophoresis and electroosmosis.

Acknowledgment

The author thanks Dr. Graham Palmer from Rice University (Houston, Tex., USA) for language revision and critical reading of the manuscript.

References 1 Zellner, M., Winkler, W., Hayden, H.,

2

3

4

5

6

7

8

9

et al. (2005) Quantitative validation of different protein precipitation methods in proteome analysis of blood platelets. Electrophoresis, 26, 2481–2490. Jiang, L., He, L., and Fountoulakis, M. (2004) Comparison of protein precipitation methods for sample preparation prior to proteomic analysis. J. Chromatogr. A, 1023, 317–320. Teale, F.W.J. (1959) Cleavage of the heme-protein link by acid methylethylketone. Biochim. Biophys. Acta, 35, 543550. Manza, L.L., Stamer, S.L., Ham, A.J.L., et al. (2005) Sample preparation and digestion for proteomic analyses using spin filters. Proteomics, 5, 1742–1745. Tirumalai, R.S., Chan, K.C., Prieto, D.A., et al. (2003) Characterization of the low molecular weight human serum proteome. Mol. Cell. Proteomics, 2, 1096–1103. Haper, R.G., Workman, S.R., Schuetzner, S., et al. (2004) Low-molecular-weight human serum proteome using ultrafiltration, isoelectric focusing, and mass spectrometry. Electrophoresis, 25, 1299–1306. Lebowitz, J., Lewis, M.S., and Schuck, P. (2002) Modern analytical ultracentrifugation in protein science: a tutorial review. Protein Sci., 11, 2067–2079. Stasyk, T., and Huber, L.A. (2004) Zooming in: fractionation strategies in proteomics. Proteomics, 4, 3704–3716. Lee, W.C., and Lee, K.H. (2004) Applications of affinity chromatography

10

11

12

13

14

15

16

in proteomics. Anal. Biochem., 324, 1–10. Lescuyer, P., Hochstrasser, D.F., and Sanchez, J.C. (2004) Comprehensive proteome analysis by chromatographic protein prefractionation. Electrophoresis, 25, 1125–1135. Görg, A., Boguth, G., Kopf, A., et al. (2002) Sample prefractionation with Sephadex isoelectric focusing prior to narrow pH range two-dimensional gels. Proteomics, 2, 1652–1657. Righetti, P.G., Castagna, A., Antonioli, P., et al. (2005) Prefractionation techniques in proteome analysis: the mining tools of the third millennium. Electrophoresis, 26, 297–319. Lämmli, U.K. (1970) Cleavage of structural proteins during assembly of the head of bacteriophage T4. Nature, 227, 680–685. Hashimoto, F., Horigome, T., Kanbayashi, M., et al. (1983) An improved method for separation of low-molecularweight polypeptides by electrophoresis in sodium dodecyl sulfate–polyacrylamide gel. Anal. Biochem., 129, 192–199. Patton, W.F., Chung-Welch, N., Lopez, M.F., et al. (1991) Tris-tricine and Tris-borate buffer systems provide better estimates of human mesothelial cell intermediate filament protein molecular weights than the standard Tris-glycine system. Anal. Biochem., 197, 25–33. Corbett, J.M., Dunn, M.J., Posch, A., et al. (1994) Positional reproducibility of protein spots in two-dimensional polyacrylamide gel electrophoresis using immobilised pH gradient isoelectric

References

17

18

19

20

21

22

23

24

25

focusing in the first dimension: an interlaboratory comparison. Electrophoresis, 15, 1205–1211. Bjellqvist, B., Sanchez, J.C.H., Pasquali, C.H., et al. (1993) Micropreparative two-dimensional electrophoresis allowing the separation of samples containing milligram amounts of proteins. Electrophoresis, 14, 1375–1378. Rabilloud, T. (2002) Two-dimensional gel electrophoresis in proteomics: old, old fashioned, but it still climbs up the mountains. Proteomics, 2, 3–10. Neuhoff, V., Arold, N., Taube, D., et al. (1988) Improved staining of proteins in polyacrylamide gels including isoelectric focusing gels with clear background at nanogram sensitivity using Coomassie Brilliant Blue G-250 and R-250. Electrophoresis, 9, 255–262. Berggren, K., Chernokalskaya, E., Steinberg, T.H., et al. (2000) Backgroundfree, high sensitivity staining of proteins in one- and two-dimensional sodium dodecyl sulfate-polyacrylamide gels using a luminescent ruthenium complex. Electrophoresis, 21, 2509–2252. Rabilloud, T., Strub, J.M., Luche, S., et al. (2001) A comparison between Sypro Ruby and ruthenium II tris (bathophenanthroline disulfonate) as fluorescent stain for protein detection in gels. Proteomics, 1, 699–704. Lamanda, A., Zahn, A., Roder, D., et al. (2004) Improved ruthenium II tris (bathophenantroline disulfonate) staining and destaining protocol for a better signal-to-background ratio and improved baseline resolution. Proteomics, 4, 599–608. Shevchenko, A., Wilm, M., Vorm, O., et al. (1996) Mass spectrometric sequencing of proteins silver-stained polyacrylamide gels. Anal. Chem., 68, 850–858. Mortz, E., Krogh, T.N., Vorum, H., et al. (2001) Improved silver staining protocols for high sensitivity protein identification using matrix-assisted laser desorption/ ionization-time of flight analysis. Proteomics, 1, 1359–1363. Gharahdaghi, F., Weinberg, C.R., Meagher, D.A., et al. (1999) Mass spectrometric identification of proteins

26

27

28

29

30

31

32

33

34

35

from silver-stained polyacrylamide gel: a method for the removal of silver ions to enhance sensitivity. Electrophoresis, 20, 601–605. Chevallet, M., Diemer, H., Luche, S., et al. (2006) Improved mass spectrometry compatibility is afforded by ammoniacal silver staining. Proteomics, 6, 2350–2354. Richert, S., Luche, S., Chevallet, M., et al. (2004) About the mechanism of interference of silver staining with peptide mass spectrometry. Proteomics, 4, 909–916. Osés-Prieto, J.A., Zhang, X., and Burlingame, A.L. (2007) Formation of epsilon-formyllysine on silverstained proteins: implications for assignment of isobaric dimethylation sites by tandem mass spectrometry. Mol. Cell. Proteomics, 6, 181–192. Chevallet, M., Luche, S., Diemer, H., et al. (2008) Sweet silver: a formaldehydefree silver staining using aldoses as developing agents, with enhanced compatibility with mass spectrometry. Proteomics, 8, 4853–4861. Alban, A., David, S.O., Bjorkesten, L., et al. (2003) A novel experimental design for comparative two-dimensional gel analysis, two-dimensional difference gel electrophoresis incorporating a pooled internal standard. Proteomics, 3, 36–44. Sriyam, S., Sinchaikul, S., Tantipaiboonwong, P., et al. (2007) Enhanced detectability in proteome studies. J. Chromatogr. B, 849, 91–104. Steinberg, T.H., Agnew, B.J., Gee, K.R., et al. (2003) Global quantitative phosphoprotein analysis using Multiplexed Proteomics technology. Proteomics, 3, 1128–1144. Wu, J., Lenchik, N.J., Pabst, M.J., et al. (2005) Functional characterization of two-dimensional gel-separated proteins using sequential staining. Electrophoresis, 26, 225–237. Rehm, H. (2006) Protein Biochemistry and Proteomics, Academic Press, London, p. 236. Antharavally, B.S., Carterb, B., Bella, P.A., et al. (2004) A high-affinity reversible protein stain for Western blots. Anal. Biochem., 329, 276–280.

25

26

2 Separation of Proteins and Peptides 36 Berggren, K., Steinberg, T.H., Lauber,

37

38

39

40

41

42

43

44

45

46

W.M., et al. (1999) A luminescent ruthenium complex for ultrasensitive detection of proteins immobilized on Membrane Supports. Anal. Biochem., 276, 129–143. Nilsson, C.L., and Davidsson, P. (2000) New separation tools for comprehensive studies of protein expression by mass spectrometry. Mass Spectrom. Rev., 19, 390–397. Issaq, H.J., Conrads, T.P., Janini, G.M., et al. (2002) Methods for fractionation, separation and profiling of proteins and peptides. Electrophoresis, 23, 3048–3061. Striegel, A. (2008) Size-exclusion chromatography, smaller, faster, multi-detection, and multi-dimensions. Anal. Bioanal. Chem., 390, 303–305. Boersema, P.J., Mohammed, S., and Heck, A.J.R. (2008) Hydrophilic interaction liquid chromatography (HILIC) in proteomics. Anal. Bioanal. Chem., 391, 151–159. Novakova, L., Solichova, D., Pavloviova, S., et al. (2008) Hydrophilic interaction liquid chromatography method for the determination of ascorbic acid. J. Sep. Sci., 31, 1634–1644. Hemstrom, P., and Irgum, K. (2006) Hydrophilic interaction chromatography. J. Sep. Sci., 29, 1784–1821. Choudhary, G., and Horvath, C. (1996) Ion-exchange chromatography. Methods Enzymol., 270, 47–82. Wang, S., Zhang, X., and Regnier, F.E. (2002) Quantitative proteomics strategy involving the selection of peptides containing both cysteine and histidine from tryptic digests of cell lysates. J. Chromatogr. A, 949, 153–162. Tishchenko, G., Dybal, J., Mészárosová, K., et al. (2002) Purification of the specific immunoglobulin G1 by immobilized metal ion affinity chromatography using nickel complexes of chelating porous and nonporous polymeric sorbents based on poly(methacrylic esters) effect of polymer structure. J. Chromatogr. A, 954, 115–126. Posewitz, M.C., and Tempst, P. (1999) Immobilized gallium(III) affinity chromatography of phosphopeptides. Anal. Chem., 71, 2883–2892.

47 Feng, S., Ye, M., Zhou, H., et al. (2007)

48

49

50

51

52

53

54

55

56

57

Immobilized zirconium ion affinity chromatography for specific enrichment of phosphopeptides in phosphoproteome analysis. Mol. Cell. Proteomics, 6, 1656–1665. Zhang, H., Yan, W., and Aebersold, R. (2004) Chemical probes and tandem mass spectrometry, a strategy for the quantitative analysis of proteomes and subproteomes. Curr. Opin. Chem. Biol., 8, 66–75. Madera, M., Mechref, Y., and Novotny, M.V. (2005) Combining lectin microcolumns with high-resolution separation techniques for enrichment of glycoproteins and glycopeptides. Anal. Chem., 77, 4081–4090. Motoyama, A., and Yates, J.R. (2008) Multidimensional LC separations in shotgun proteomics. Anal. Chem., 80, 7187–7193. Nagele, E., Vollmer, M., Horth, P., et al. (2004) 2D-LC/MS techniques for the identification of proteins in highly complex mixture. Expert Rev. Proteomics, 1, 37–46. Wolters, D.A., Washburn, M.P., and Yates, J.R. (2001) An automated multidimensional protein identification technology for shotgun proteomics. Anal. Chem., 73, 5683–5690. Wang, H., and Hanash, S. (2003) Multi-dimensional liquid phase based separations in proteomics. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci., 787, 11–18. Dolnik, V. (2008) Capillary electrophoresis of proteins 2005–2007. Electrophoresis, 29, 143–156. Hey, J., Posch, A., Cohen, A., et al. (2008) Fractionation of complex protein mixtures by liquid-phase isoelectric focusing. Methods Mol. Biol., 424, 225–239. D’Amici, G.M., Timperio, A.M., and Zolla, L. (2008) Coupling of native liquid phase isoelectrofocusing and blue native polyacrylamide gel electrophoresis: a potent tool for native membrane multiprotein complex separation. J. Proteome Res., 7, 1326–1340. Davidsson, P., Westman, A., Puchades, M., et al. (1999) Characterization of

References

58

59 60

61

proteins from human cerebrospinal fluid by a combination of preparative two-dimensional liquid-phase electrophoresis and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Anal. Chem., 71, 642–647. Yuana, X., and Desiderioa, D.M. (2005) Proteomics analysis of human cerebrospinal fluid. J. Chromatogr. B, 815, 179–189. Wehr, T. (2001) Separation technology in proteomics. LCGC, 19, 702–711. Chapman, K. (2002) The ProteinChip Biomarker System from Ciphergen Biosystems, a novel proteomics platform for rapid biomarker discovery and validation. Biochem. Soc. Trans., 30, 82–87. Vorderwülbecke, S., Cleverley, S., Weinberger, S.R., et al. (2005) Protein

62

63

64

65

quantification by the SELDI-TOFMS−based ProteinChip System. Nat. Methods, 2, 393–395. Mouradian, S. (2002) Lab-on-a-chip, applications in proteomics. Curr. Opin. Chem. Biol., 6, 51–56. Peng, Y., Pallandre, A., Tran, N.T., et al. (2008) Recent innovations in protein separation on microchips by electrophoretic methods. Electrophoresis, 29, 157–178. Le Nel, A., Minc, N., Smadja, C., et al. (2008) Controlled proteolysis of normal and pathological prion protein in a microfluidic chip. Lab Chip, 8, 294–301. Giddings, J.C. (1984) Twodimensional separations: concept and promise. Anal. Chem., 56, 1258–1270.

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3 Basic Mass Spectrometric Approaches Lenka Hernychova and Martin Hubalek

3.1 Introduction

Proteomics in principle is technology dealing with mixtures of proteins at a given time and condition and the aim is the characterization of the protein quality and quantity. Such studies typically pose challenges owing to the high degree of complexity of cellular proteomes that necessitates highly sensitive analytical techniques. Mass spectrometry has increasingly become the method of choice for the analysis of complex protein samples. Mass spectrometric measurements are carried out in the gas phase on ionized analytes. By definition, a mass spectrometer consists of an ion source, a mass analyzer that measures the mass to charge ratio (m/z) of the ionized analytes and a detector that registers the number of ions at each m/z value (Figure 3.1). One of the biggest recent discoveries in the methodology of biomolecular analysis was certainly the invention of soft ionization techniques prior to mass spectrometry analysis. Two techniques very different in principle were developed: (i) matrix-assisted laser desorption/ionization (MALDI) by Koichi Tanaka [1] and Karas with Hilenkampf [2–4] and (ii) electrospray ionization (ESI) by John Bennett Fenn [5, 6]. Since the invention of these new ionization techniques, mass spectrometry of biomolecules recorded an unprecedented boom in improvements of sensitivity, resolution, robustness and comprehensibility and established itself as one of the most potent and reliable bioanalytical techniques. The Nobel price for chemistry in 2002 was not surprisingly awarded for these efforts.

BSL3 and BSL4 Agents: Proteomics, Glycomics, and Antigenicity, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.

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3 Basic Mass Spectrometric Approaches Ion source

Mass analyzer

Detector

Figure 3.1 Simplified schematic of a mass spectrometer.

MALDI spot

+

Pulsed laser

+ [M+H]

+20 kV

+

Ground Variable Grid Grid

Figure 3.2 Matrix-assisted laser desorption ionization depicted with matrix in yellow and analyte in gray.

3.2 Ionization 3.2.1 Matrix-Assisted Laser Desorption/Ionization

MALDI is a soft ionization technique used in mass spectrometry that deals well with thermolabile, nonvolatile organic compounds, especially those of high molecular mass (e.g., polymers, other macromolecules). Moreover this ionization is used successfully in biochemical areas for the analysis of biopolymers (e.g., proteins, peptides, glycoproteins, oligosaccharides, oligonucleotides). It is similar in character to electrospray ionization in relative softness and the ions produced, although MALDI causes many fewer multiply charged ions. Nevertheless MALDI ionization is relatively straightforward to use and reasonably tolerant to buffers and other additives and it is usually coupled to a time of flight (TOF) mass analyzer. The ionization is triggered by a laser beam (e.g., a nitrogen laser, Nd:YAG, Er:YAG, CO2). The laser is fired at the crystals on the MALDI spot (Figure 3.2) when the matrix absorbs the laser energy and it is thought that primarily the matrix is ionized by this event. The matrix is then thought to transfer part of its charge to the analyte molecules (e.g., mix of peptides), thus ionizing the molecules while

3.2 Ionization

still protecting them from the disruptive energy of the laser. Ions observed after this process consist of a neutral molecule [M] and an added or removed ion. Together, they form a quasimolecular ion, for example [M+H]+ in the case of an added proton, [M+Na]+ in the case of an added sodium ion, or [M-H]− in the case of a removed proton. MALDI is capable of creating singly charged ions, but multiply charged ions ([M+nH]n+) can also be created. Note that these are all evenelectron species. Ion signals of radical cations can be observed, for example, in the case of matrix molecules and other stable molecules. The matrix consists of crystallized small organic molecules, usually acidic, of which the three most commonly used are 3,5-dimethoxy-4-hydroxycinnamic acid (sinapinic acid; SA), α-cyano-4-hydroxycinnamic acid (alpha-cyano; CHCA) and 2,5-dihydroxybenzoic acid (DHB). A solution of one of these molecules is made, often in a mixture of highly purified water and an organic solvent [e.g., acetonitrile (ACN) or ethanol]. Trifluoroacetic acid (TFA) may also be added. For example a matrix-solution would be 20 mg/ml sinapinic acid in ACN:water:TFA (50 : 50 : 0.5). 3.2.2 Electrospray Ionization

ESI is the primary ion source used in liquid chromatography–mass spectrometry because it is a liquid–gas interface capable of coupling liquid chromatography with mass spectrometry. In ESI (Figure 3.3), the sample is dissolved in a polar, volatile solvent and pumped through a narrow, stainless steel capillary. A high voltage of 3 or 4 kV is applied to the tip of the capillary. The lens voltages are optimized individually for each sample. A flow rate is between 1 μl/min and 1 ml/min. The sample emerging from the tip is dispersed into an aerosol of highly charged droplets. The aerosol is at least partially produced by a process involving the formation of a Taylor cone. An uncharged carrier gas such as nitrogen is sometimes used to help nebulize the liquid and assists to direct the spray emerging from the capillary tip towards the mass analyzer. The charged droplets diminish in size by solvent evaporation, assisted by a warm flow of nitrogen know as the drying gas which passes across the front of the ionization source. The analyte molecules are forced closer together, repel each other and break up the droplets. This process is called Coulombic fission because it is driven by repulsive Coulombic forces between charged molecules. The process repeats until the analyte is free of solvent and is a lone ion. These ions enter through the orifice into an intermediate vacuum region and from there through a small aperture into the analyzer of mass spectrometer, which is held under high vacuum. In electrospray processes, the ions observed may be quasimolecular ions the same as described in MALDI ionization (see above). For large macromolecules, there can be many charge states, occurring with different frequencies; the charge can be as great as [M+20H]20+, for example. All these are even-electron ion species: electrons (alone) are not added or removed, unlike in some other ionizations. The

31

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3 Basic Mass Spectrometric Approaches Coulombic fission Capillary +

+ – –

+

– +

+

–

+ +

+

+

–

+

+

+

+

+

+

–

–

–

+ –

++++ ++++ ++++

+ –

+

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+ + +

+

+

+

+

–

+ –

+ ++ + ++ +

–

+ ++ + + ++ +

+

+

+++ ++++ +++

+ –

+ ++ + + ++ +

Taylor cone

–

+ + ++++ ++++ ++++

+ +

++++ ++++ ++++

+++ ++++ +++

+ + + + + +

Electrons

Oxidation

+

+

Diminishing size of charged droplets by solvent evaporation

+

–

Source of high tension

Electrons

Reduction

Figure 3.3 The mechanism of electrospray ionization.

formation of ions in electrospray is somewhat homologous to acid–base reactions. Redox reactions do occur and a circuit with measurable current flow exists, but atomic and molecular ions are the primary carriers of charge in the solution and gas phases. There are many variations on the basic electrospray technique that generally offer better sensitivity [7]. Two important versions are microspray (μ-spray) and nanospray (n-spray) [8]. The primary difference is in the reduced flow rate of the analyte containing liquid (μl/min and nl/min, respectively); this allows the application of micro- or nanoscale chromatography that yield much wanted sensitivity in proteomic experiments. This change causes adjustments in setup such as the reduced internal diameter of the tubing or lack of nebulization gas.

3.3 Mass Analyzers

The mass analyzer is central to the mass spectrometry instrument (Figure 3.1). In the context of proteomics, its key parameters are sensitivity, resolution, mass accuracy and ability to generate information-rich ion mass spectra from peptides (MS/MS spectra). There are four basic types of mass analyzers currently used in proteomics research that differ in principal applied to measure a mass of the analyte (Figure 3.4). These are time of flight (TOF), quadrupole (Q); ion trap (IT)

3.3 Mass Analyzers (a)

(d)

Pulsed laser

Reflector time-of-flight (TOF)

Q1

q2

33

TOF

Quadrupole time-of-flight Sample plate

TOF Reflector

(b)

Reflector

Time-of-flight time-of-flight (TOF-TOF)

(e) TOF1

Ion trap

TOF2

Collision cell

(c) Triple quadrupole or linear ion trap

Q1

q2

Q3

(f) Fourier transform ion cyclotron resonance mass spectrometer (FT-MS)

Q1

Super conducting magnet

Figure 3.4 Mass analyzers and their instrumental configuration used in proteome research.

This figure was kindly provided by Aebersold R. et al. [9].

and Fourier transform ion cyclotron analyzers (FT ICR). They are very different in design and performance, each with its own strength and weakness. These analyzers can stand alone or, in some cases, be put together to take advantage of the strengths of each. 3.3.1 Time of Flight

In TOF analyzers, the mass to charge ratio is deduced from its flight time through a tube of specified length that is under vacuum. The performance of the TOF analyzers has greatly improved, in particular in terms of resolution and mass accuracy. TOF mass analyzers are basis for analytical platforms operated with both ESI and MALDI ionizations. TOF is a method of mass spectrometry in which ions are accelerated by an electric field of known strength [10]. This acceleration results in an ion having the same kinetic energy as any other ion that has the same charge. The velocity of the ion depends on the mass to charge ratio. The time that the particle subsequently needs for to reach a detector at a known distance is measured. From this time and the known experimental parameters the mass to charge ratio of the ion can be calculated. The resolution in MALDI-TOF can be improved with delayed extraction. This refers to a prolonged onset of the extraction potential by a defined short time after the ionization event [11]. A TOF analyzer can work at least in two different modes: linear and reflectron. The linear mode has a higher ion transmission, thus making it possible to measure larger molecular ions such as intact proteins. The ions in this mode enter the flight tube and travel directly to the detector without additional focusing by reflectron. This, however, causes a loss of resolution as compare to reflectron mode. The result of this type of measurement is a linear mass spectrum (Figure 3.5).

34

3 Basic Mass Spectrometric Approaches 5816

100

1.0E+4

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70 60 50 40 30

1828 2086

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15273 15400.2

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25001.0

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Figure 3.5 Mass spectrum of Coxiella burnetii strain RSA 439 (phase I) acetonitrile extract

measured on MALDI-TOF mass spectrometer in linear positive mode.

1030.5552

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842.5131 1550.8820 980.5414 1337.7818 1743.9587 1090.5320 2127.2062 2945.6239 1196.6668 1885.0298 2212.1331 2639.2293 1163.6821 1424.8441 815.3812 1907.0844 2161.1719 2626.2539 2928.2491 763.4511 1211.6884 1500.6995 2388.3261 1716.9937

0 699.0

1259.2

1819.4

2379.6

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3500.0

Mass (m/z)

Figure 3.6 Mass spectrum of peptides from human protein, alpha crystallin B chain (accession number P02511). The protein was digested by trypsin and measured on MALDITOF mass spectrometer in reflectron positive mode.

3.3.2 Reflectron TOF

The kinetic energy distribution in the direction of ion flight can be corrected by using a reflectron [12]. The reflectron setting of a TOF analyzer uses an electrostatic field to reflect the ion beam towards the detector (Figure 3.4a). The more energetic ions penetrate deeper into the reflectron and take a slightly longer path to the detector. Less energetic ions of the same charge and mass penetrate only a short distance into the reflectron and take a shorter path to the detector. The detector is placed at the focal point, so the ions of different energies focused by the reflectron strike the detector at the same time The result of this type of measurement is a reflectron mass spectrum (see Figure 3.6).

3.3 Mass Analyzers

3.3.3 Quadrupole and Ion Trap

These types of mass analyzer despite performing differently are based on the same physical principle. Both quadrupoles and ion traps (Figure 3.4c,e) constrain ions to circular orbits under the influence of combined direct current (DC) and radiofrequency (RF) potentials. For any combination of DC and RF fields, only ions of a selected m/z follow stable orbits. The fundamental difference is that ions in a 3D QIT have virtually no component of linear translation, whereas in a linear quadrupole, made up of four parallel rods, ions travel along spiral paths, emerging to be detected or diverted into a further mass analyzer. The basic mode of measurement of both analyzers is the scanning through the possible DC and RF field combinations; IT can on top trap the ion inside of the analyzer, making the analyzer able to perform fragmentation of the selected ion. A few years ago a hybrid between the two analyzers emerged. The so-called linear ion trap (LIT) adds the component of linear translation into the LIT, thus enhancing the performance, especially in terms of capacity. This feature expands the dynamic range and the overall sensitivity and LITs have been replacing classical quadrupole trapping devices. 3.3.4 Fourier Transformation Ion Cyclotron

At a fixed magnetic field, all ions of the same m/z value have the same cyclotron frequency and move together in a coherent ion packet (Figure 3.4f). This cyclotron motion of ions is responsible for the induction of an electric current in coils surrounding the cell, m/z being determined from the cyclotron frequency that is calculated by Fourier transformation. The development of a robust instrument of such type with an external source represented a breakthrough in terms of resolving power and mass accuracy. 3.3.5 Tandem Mass Analyzers

Tandem mass spectrometry is an effective method to obtain information that leads to determination of the amino acid sequence of the peptide. The selected peptide ion is exposed to a condition inducing fragmentation and the resulting spectrum represents the amino acid composition. Based on physical characteristics, only ion trap and FT ICR analyzers are able to gain tandem mass spectra without the need of a second mass analyzer due to their trapping functionality. The ion selection, fragmentation and mass analysis take place in one space where the events are separated into specified time periods. The events can also be separated in space when more than one analyzer is coupled together. The different geometry follows the advantages of each analyzer and forms an instrument of various possibilities. The popular triple quadrupole (Q-Q-Q; Figure 3.4c) was developed a long time ago

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3 Basic Mass Spectrometric Approaches

and still has very potent usage thanks to various modes of measurement. It can be set to follow, for example, only subsets of the peptides that contain specific functional groups, such as phosphorylation or glycosylation in so-called precursor ion or neutral loss scans. In a typical experiment, the precursor or neutral loss scan detects the components of interest and then triggers a conventional MS/MS (product ion scan) to identify the amino acid sequence and localize the modification. Triple quadrupole derived instruments are also capable of detection of specific transitions between the precursor and one fragment of a given peptide, so-called multiple reaction monitoring (MRM) [13]. The selectivity resulting from two stages of analyzer combined with the high duty cycle results in quantitative analyses of unmatched sensitivity. A recent upgrade to the geometry where the last quadrupole has been replaced by a linear ion trap, forming Q-Q-LIT, strengthens the performance and enlarges the capabilities. A TOF analyzer can be mounted inside mass spectrometers as a single unit or combined in tandem either with a second TOF analyzer to form a TOF-TOF instrument (Figure 3.4b) or following quadrupole ion geometry to form a quadrupole time of flight (QqTOF) instrument (Figure 3.4d). The later two combinations produce tandem mass spectrometry data that are of high mass accuracy. The result of this type of measurement is a MS/MS mass spectrum of the selected peptide with a definite m/z mass (see Figure 3.7). The comparison of the instrument performance is potentially a controversial subject, because specification depends on the type of application, the sample analyzed and the experimental setup. The y-series of fragments (see Figure 3.8) is indicated by vertical bars, with the difference corresponding to a given amino acid residual mass. 3.3.6 Ion Detection

In an instrument that produces and transmits a continuous ion beam, the ions arriving at a detector represent an electrical current. This current can be amplified K

100

T

V

D

E

G G

E

I

A

V

IN

'

%

0

0

100 200 300 400 500 600 700 800 900 1000 1100 1200 1300

m/z

Figure 3.7 Illustrative mass spectrum of peptide fragments as measured on QqTOF mass spectrometer. NIVAIEGGEDVTK, doubly charged ion, [M+2H]2+ = 672.8545, was fragmented by collision induced dissociation.

3.4 Protein Identification x3 y3 z3

R1 O H2N

C H

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H

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H+

R4 N

C

H

H

COOH

a3 b3 c3

Figure 3.8 The nomenclature for fragment ions in a MS/MS spectrum of peptides.

and recorded (e.g., as a function of m/z in a conventional scan, or at selected m/z values in a multiple ion monitoring experiment). The first stage of amplification is usually achieved with an electron multiplier.

3.4 Protein Identification

No method or instrument exists that is capable of identifying and quantifying the components of a complex protein sample in a simple, single-step operation. Out of a bewildering multitude of techniques and instruments, two main tracks can be identified. The first is a combination of 2-DE and MS. The second track combines limited protein purification with techniques of automated MS/MS measurement, so-called shotgun proteomics. 3.4.1 Combination of 2-DE and MS

In the first track the proteins are separated by 2-DE (see Chapter 1). Selected spots are excised, digested and analyzed by MS. The proteins can be identified either by peptide mass fingerprinting (PMF) or by peptide sequencing. 3.4.2 Peptide Mass Fingerprinting

PMF is an analytical technique for protein identification [14–18]. A necessary condition of this method is the known genome of the analyzed organism. The unknown protein of interest is first experimentally cleaved into smaller peptides, whose absolute masses can be accurately measured with a mass spectrometer [19]. These masses are then in silico compared to either a database containing known protein or the gene sequences. This is achieved by using computer programs that translate the known genome of the organism into proteins, then theoretically cut the proteins into peptides and calculate the absolute masses of the peptides from

37

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3 Basic Mass Spectrometric Approaches

each protein. The software then compares the masses of the peptides of the unknown protein to the theoretical peptide masses of each protein encoded in the genome. The results are statistically analyzed to find the best match. The presence of multiple proteins in the sample can significantly complicate the analysis and potentially compromise the results. Thus the method has gained a great popularity when applied to the identification of proteins separated by two-dimensional electrophoresis. In this case the protein mixture is purified into distinct spots containing usually only one or a few proteins. 3.4.3 Peptide Sequencing (PMF)

An alternative means of protein identification not only relies on the masses of the peptides resulting from the protein digest but also utilizes tandem mass spectrometry to select a specific peptide ion in the first part of the instrument (or in the first moment of the analysis) and then the condition of the fragmentation induces the peptide ion to break into fragments. For example, during collision-induced dissociation, peptides collide with a gas within the mass spectrometer. The resulting fragment ions occurring as a result of peptide bond fragmentation (called, e.g., b-ions and y-ions) have mass differences corresponding to the residual masses of the respective amino acids. The accepted nomenclature for fragment ions was first proposed by Roepstorff and Fohlman [20] and subsequently modified by Johnson et al. [21] (see Figure 3.8). The types of fragment ions observed in a MS/MS spectrum depend on many factors, including primary sequence, the amount of internal energy, the means of energy transfer, the charge state and so on. The important feature is the fact that the tandem mass spectrum contains partial information about the amino acid sequence of the peptide. There is more than one strategy to use this experimental spectral information for peptide/protein identification [22, 23]. The strategies can be classified into three categories. 1)

Database searching, where peptide sequences are identified by correlating acquired fragment ion spectra with theoretical spectra predicted for each peptide contained in a protein sequence database. This approach follows the same principle as searching with peptide mass fingerprinting differing only in the data loaded for search.

2)

De novo sequencing is the second approach, where peptide sequences are explicitly read directly from fragment ion spectra.

3)

The third approach merges both previous possibilities and usually starts with extraction of short sequence tags of 3–5 residues, followed by error tolerant database search [24].

The database searching approach is nowadays the most popular method for largescale proteomic data and is implemented in many popular search engines such as Sequest, MASCOT or Phenyx. However, the other strategies provide attractive

3.5 Conclusion

alternatives in specific situations. The last approach has been implemented recently into search engine called ProteinPilot and is gaining popularity also for large-scale proteomic datasets. Peptide mass fingerprinting by MALDI-TOF and peptide sequencing by ESI-MS/ MS have become highly efficient at the identification of gel-separated proteins. Gel separation, however, leads typically to identification of the most abundant proteins. Incremental improvements in 2-DE technology have alleviated, but not eliminated, these and other shortcomings of the 2-DE/MS approach. 3.4.4 Shotgun Proteomics

The possibility of getting specific peptide information that can be linked to protein sequences without the need of having a purified protein led to the development of an approach called shotgun proteomics [25–27]. In this approach, the whole protein mixture in complex sample is digested; the resulting peptides are separated by electrophoretic or chromatographic principles and analyzed by tandem mass spectrometry. A number of technical issues had to be addressed before the method could be used both for the identification of protein mixtures and for quantitative proteomic experiments. First, single-dimension peptide chromatography does not provide sufficient peak capacity to separate complex peptide mixtures. Second, mass spectrometers are inherently poor quantitative devices. Third, the amount of data collected by the method is huge and its analysis daunting. Substantial progress has been achieved in each of these areas, resulting in the emergence of increasingly robust and productive platforms. To provide more peak capacity, various combinations of protein and peptide separation schemes have been explored. Twodimensional or three-dimensional chromatographic separation of peptide mixtures generated by tryptic digestion of protein sample is probably the most popular at present. Several studies suggest that, in principle, these methods are capable of detecting proteins of very low abundance, although considerable effort is required and a sufficient amount of starting protein sample must be available.

3.5 Conclusion

The primary technique employed for the identification of peptides and proteins from biological sources is mass spectrometry. The instrumentation of mass spectrometry has seen incredible growth over the past 25 years and its sensitivity has increased approximately fivefold every three years [28]. The recent development of a novel mass spectrometer (Orbitrap) and new dissociation methods such as electron transfer dissociation (ETD) has made possible the new means of proteomic application. Although bottom-up proteomics (analysis of enzymatically produced peptides) remains the workhorse for proteomic analysis, middle-down and topdown strategies (analysis of longer peptides and intact proteins, respectively)

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should allow more complete characterization of protein isoforms and posttranslational modifications.

Acknowledgments

This work was financially supported by the Ministry of Education, Youth and Sport, Czech Republic (ME08105), and the Ministry of Defence, Czech Republic (FVZ0000604).

References 1 Tanaka, K., Waki, H., Ido, Y., Akita, S.,

2

3

4

5

6

7

8

Yoshida, Y., and Yoshida, T. (1988) Protein and polymer analyses up to m/z 100 000 by laser ionization time of flight mass spectrometry. Rapid Commun. Mass. Spectrom., 2 (20), 151–153. Karas, M., Bachman, D., Bahr, U., and Hillenkamp, F. (1987) Matrix-assisted ultraviolet laser desorption of non-volatile compounds. Int. J. Mass Spectrom. Ion Process., 78, 53–68. Karas, M., and Hillenkamp, F. (1988) Laser desorption ionization of proteins with molecular masses exceeding 10 000 daltons. Anal. Chem., 60 (20), 2299–2301. Hillenkamp, F., Karas, M., Beavis, R.C., and Chait, B.T. (1991) Matrix-assisted laser desorption/ionization mass spectrometry of biopolymers. Anal. Chem., 63 (24), 1193A–1203A. Fenn, J.B., Mann, M., Meng, C.K., Wong, S.F., and Whitehouse, C.M. (1989) Electrospray ionization for mass spectrometry of large biomolecules. Science, 246, 64–71. Fenn, J.B., Mann, M., Meng, C.K., Wong, S.F., and Whitehouse, C.M. (1990) Electrospray ionization-principles and practice. Mass. Spectrom. Rev., 9 (1), 37–70. Grace, J.M., and Marijnissen, J.C.M. (1994) A review of liquid atomization by electrical means. J. Aerosol Sci., 25 (6), 1005–1019. Wilm, M., and Mann, M. (1996) Analytical properties of the nanoelectrospray ion source. Anal. Chem., 68 (1), 1–8.

9 Aebersold, R., and Mann, M. (2003) Mass

10

11

12

13

14

15

16

spectrometry-based proteomics. Nature, 422 (6928), 198–207. Stephens, W.E. (1946) A pulsed mass spectrometer with time dispersion. Phys. Rev., 69, 691. Brown, R.S., and Lennon, J.J. (1995) Mass resolution improvement by incorporation of pulsed ion extraction in a matrix-assisted laser desorption/ionization linear time-of-flight mass spectrometer. Anal. Chem., 67, 1998. Mamyrin, B.A., Karataev, V.I., Shmikk, D.V., and Zagulin, V.A. (1973) The mass-reflectron, a new nonmagnetic time-of-flight mass spectrometer with high resolution. Sov. Phys. JETP, 37, 45. Hiller, S., Krock, B., Cembella, A., and Luckas, B.J. (2007) Rapid detection of cyanobacterial toxins in precursor ion mode by liquid chromatography tandem mass spectrometry. Mass Spectrom., 42, 1238–1250. Pappin, D.J., Hojrup, P., and Bleasby, A.J. (1993) Rapid identification of proteins by peptide-mass fingerprinting. Curr. Biol., 3 (6), 327–332. Henzel, W.J., Billeci, T.M., Stults, J.T., Wong, S.C., Grimley, C., and Watanabe, C. (1993) Identifying proteins from two-dimensional gels by molecular mass searching of peptide fragments in protein sequence databases. Proc. Natl. Acad. Sci. U.S.A., 90 (11), 5011–5015. Mann, M., Højrup, P., and Roepstorff, P. (1993) Use of mass spectrometric

References

17

18

19

20

21

22

molecular weight information to identify proteins in sequence databases. Biol. Mass Spectrom., 22 (6), 338–345. James, P., Quadroni, M., Carafoli, E., and Gonnet, G. (1993) Protein identification by mass profile fingerprinting. Biochem. Biophys. Res. Commun., 195 (1), 58–64. Yates, J.R., Speicher, S., Griffin, P.R., and Hunkapiller, T. (1993) Peptide mass maps: a highly informative approach to protein identification. Anal. Biochem., 214 (2), 397–408. Clauser, K.R., Baker, P., and Burlingame, A.L. (1999) Role of accurate mass measurement (+/− 10 ppm) in protein identification strategies employing MS or MS/MS and database searching. Anal. Chem., 71 (14), 2871–2882. Roepstorff, P., and Fohlman, J. (1984) Proposal for a common nomenclature for sequence ions in mass spectra of peptides. Biomed. Mass Spectrom., 11 (11), 601. Johnson, R.S., Martin, S.A., Biemann, K., Stults, J.T., and Watson, J.T. (1987) Novel fragmentation process of peptides by collision-induced decomposition in a tandem mass spectrometer: differentiation of leucine and isoleucine. Anal. Chem., 59 (21), 2621–2625. Nesvizhskii, A.I., Vitek, O., and Aebersold, R. (2007) Analysis and validation of proteomic data generated by

23

24

25

26

27

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tandem mass spectrometry. Nat. Methods, 4 (10), 787–797. Domon, B., and Aebersold, R. (2006) Mass spectrometry and protein analysis. Science, 312 (5771), 212–217. Shilov, I.V., Seymour, S.L., Patel, A.A., Loboda, A., Tang, W.H., Keating, S.P., Hunter, C.L., Nuwaysir, L.M., and Schaeffer, D.A. (2007) The Paragon Algorithm, a next generation search engine that uses sequence temperature values and feature probabilities to identify peptides from tandem mass spectra. Mol. Cell. Proteomics, 6 (9), 1638–1655. Hu, L., Ye, M., Jiang, X., Feng, S., and Zou, H. (2007) Advances in hyphenated analytical techniques for shotgun proteome and peptidome analysis–a review. Anal. Chim. Acta, 598 (2), 193–204. Fournier, M.L., Gilmore, J.M., MartinBrown, S.A., and Washburn, M.P. (2007) Multidimensional separations-based shotgun proteomics. Chem. Rev., 107 (8), 3654–3686. Nesvizhskii, A.I. (2007) Protein identification by tandem mass spectrometry and sequence database searching. Methods Mol. Biol., 367, 87–119. Zhou, M., and Veenstra, T. (2008) Mass spectrometry: m/z 1983–2008. Biotechniques, 44 (5), 667–668, 670.

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4 Quantitative Mass Spectrometric Approaches Juraj Lenco and Vojteˇch Tambor

4.1 Introduction

Along with an expanding interest in proteomics in recent years, there has been a strong desire to develop methods permitting not only protein identification but also quantitation. Indeed, the quantitation of proteins in biological samples prepared upon different physiological states has become maybe the most important task in proteomics. Despite the progress, in mass spectrometry in particular, there is still no ideal method for protein quantitation because even the latest methods suffer from certain drawbacks. Currently, there are two main streams in quantitative proteomic: (i) the classical gel-based approach relies on protein staining in gel matrices, (ii) the second one, known as shotgun proteomics, employs mass spectrometry as a major tool for acquiring quantitative information. The latter utilizes mainly incorporation of stable isotopes into proteins or peptides, which are subsequently analyzed by mass spectrometry. However, one can take also advantage of label-free methods for shotgun proteomics. 4.1.1 Gel-Based Quantitative Proteomic Methods

In the gel-based approach, proteins are separated either by 1D or 2D polyacrylamide gel electrophoresis (PAGE) into protein bands or protein spots, followed by their visualization. The intensity of the staining is consequently used for quantitation; however digital images must be acquired first. Specialized software tools (e.g., Melanie, PDQuest, Delta2D, Progenesis) are used in order to compare the intensities of protein bands or protein spots among the gels [1]. At present, there is a wide range of protein staining methods, differing in several aspects, for example, sensitivity, mode of detection, and compatibility with subsequent mass spectrometric (MS) analysis [2]. Conventional staining protocols include Coomassie™ Blue G-250 and R-250 dyes, or a color reaction based on silver ion reduction of ionic to metallic silver onto the protein surface. Increasingly popular fluorescent dyes (e.g., Sypro™ Ruby, Deep Purple™ formerly known as BSL3 and BSL4 Agents: Proteomics, Glycomics, and Antigenicity, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.

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4 Quantitative Mass Spectrometric Approaches

Lightning Fast) offer ameliorated sensitivity and linearity for quantification, compared to classic staining agents. The most important aspect in quantitative analysis is, however, the linearity of the dynamic range. From this point of view, fluorescent dyes seem to be the most appropriate type of staining in the case of quantitative studies. Also, when considering the main drawback of 2D electrophoresis (i.e., the low gel-to-gel reproducibility), differential gel electrophoresis (DIGE) may be a method of choice for such kind of analyses. DIGE is based on covalent preelectrophoresis staining of protein samples with structurally similar Cy2, Cy3 or Cy5 fluorescent dyes. After labeling, proteins are mixed and resolved on a single gel. Since each dye has a specific excitation and emission wavelength, three different images are therefore acquired, which are subsequently easily comparable [3]. It must be stressed that the gel-based approach, more specifically 2D-PAGE, is currently the only method able to distinguish and quantify protein isoforms resulting from post-translation modifications. However, 2D-PAGE is a time- and labordemanding method and suffers from poor resolution of proteins with extreme physicochemical properties. 4.1.2 Shotgun Quantitative Proteomic Methods

The most significant difference, as compared to the gel-based approach, lies in the nature of the sample to be analyzed. Whereas in the gel-based approach the separation occurs at protein level and peptides resulting from a single protein are analyzed by mass spectrometry, in the shotgun experiments a vast mixture of peptides resulted from a digest of a complex protein sample must be separated, followed by mass spectrometric analysis. This brings specific requirements on analytical platforms, especially high-performance liquid chromatography (HPLC) or LC and tandem mass spectrometry (MSMS), resulting in a combination of various protein and peptide separation schemes and in the development of robust and productive mass spectrometric platforms. Moreover, the progress in the field of shotgun proteomics achieved during the last 10 years allowed the addition of a quantitative dimension to data acquired via LC-MS/MS instruments. Due to the wide range of physicochemical properties of peptides, their mass spectrometric analysis is not inherently quantitative. Hence, for accurate quantification it is necessary to compare individual peptides among experiments. This can be carried out in two different ways: labeled and label-free methods. In this brief review we focus on labeling methods only. 4.1.3 Labeling Methods

Most of the currently employed methods utilize the differential incorporation of stable isotopes into proteins or peptides. The most used stable isotopes are 13C, 15 N, 18O and 2D. Since they have physicochemical properties identical to their

4.1 Introduction

native counterparts 12C, 14N, 16O 1H, the peptides labeled with them also behave identically during chromatographic separation, but owing to a specific mass difference in their m/z, they can be simply recognized by a mass spectrometer. The stable isotopes may be introduced into the analyte in three different ways. 4.1.3.1 Metabolic Incorporation of Stable Isotopes Generally, in order to avoid accidental errors caused during sample preparation prior to mass spectrometric analysis, the labeled samples to be compared are recommended to be mixed as soon as possible in the proteomic workflow. From this point of view, the metabolic labeling during cell growth is the earliest possible point for introducing stable isotopes. The first work involving metabolic labeling of proteins for shotgun quantitative analysis employed 15N-enriched cell culture media. One pool of cells was grown in a medium containing natural isotope 14N, whereas the second pool was grown in a 15N-enriched medium [4]. However, an approach named stable isotope labeling with amino acids in cell culture (SILAC), which used only certain aminoacids, became much more popular. Although [1H3]-Leu and its heavy counterpart [2D3]-Leu were used in the work which introduced SILAC for the first time [5], the most commonly used isotope-coded amino acids nowadays are light and heavy lysine and arginine: [12C6]-Lys/[13C6]-L-Lys, [12C6]-Arg/[13C6]-Arg. These amino acids ensure that all tryptic peptides carry at least one specific isotope tag [6]. Besides the aforementioned versions of arginine, which both carry the natural isotope 14N, even a third channel [13C6, 15N4]-Arg may be added, permitting quantification of three different samples in one single run. As mentioned above, the main advantage of the metabolic approach lies in the level at which the samples can be mixed together. Therefore, it is possible to mix the differentially treated samples just after finishing the in vitro experiment, even without determining the protein concentration. As a result, all sources of possible quantification errors introduced during sample preparation are expelled. However, it is obvious that this approach cannot be routinely applied to in vivo experiments. One should also take into account a specific requirement linked to SILAC. Prior to the SILAC experiment, the amino acids with naturally light isotopes must be replaced by heavy amino acids in one pool of cells. This can be achieved in at least six division times in the appropriate medium. In the case of metabolic labeling in general, quantitative information is acquired from the MS spectra. As a consequence, it brings about a twofold increase (and if three channels are used even a threefold increase) in spectral complexity. Recently, a quantitative ISIS method has been introduced (isobaric SILAC with immonium ion splitting) employing metabolic labeling, which in contrast to SILAC enables quantitation after peptide fragmentation in MSMS mode, and thus does not increase the spectral complexity [7]. The method is based on isobaric isotope-coded pairs of amino acids readily producing immonium ions: leucine, isoleucine and valine. In MS mode the labeled peptides occur as the same peak, while under fragmentation in MSMS mode, light and heavy immonium ions are produced, differing in 1 amu.

45

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4 Quantitative Mass Spectrometric Approaches

4.1.3.2 Enzymatic Incorporation of Stable Isotopes Although several studies of metabolic labeling at the level of whole higher multicellular organisms have been reported, for example, Drosophila melanogaster [8], generally post-biosynthesis labeling is a more practical way for incorporating stable isotopes in such cases, especially when the samples are of human origin. The next point in the shotgun proteomics workflow suitable for labeling is the enzymatic digestion of proteins into peptides, since certain proteases, for example, trypsin, Glu-C and Lys-C, catalyze the exchange of two oxygen atoms at the C-termini of the peptides by two oxygen atoms coming from solvent water during the reaction [9]. When two protein samples to be compared are digested in H216O and H218O separately, the resulting peptides differ in 4 amu, which is sufficient to recognize peptide pairs properly by mass spectrometry [10, 11]. The most important stumbling block of this approach remains the variable incorporation of either one or two 18O atoms into the peptides. In addition, isotope envelopes of the higher m/z pairs may overlap, hence specialized software is needed in order to distinguish the isotopic contribution of the 16O-labeled peptide to the 18O-labeled peptide peak. 4.1.3.3 Chemical Incorporation of Stable Isotopes The most common way for introducing the stable isotopes is a chemical reaction. In practice, amino groups at N-termini and lysine side chains followed by thiol groups of cysteines are primarily used for this purpose. The isotope-coded affinity tags (ICAT) method represents the first chemical method ever [12]. It is based on labeling cysteine residues with tags carrying a thiol reactive part, a mass-coded part containing either eight 1H or eight 2D, followed by biotin. After ICAT labeling, the proteins are digested and peptides carrying the tags are purified on avidin resins. Thus, only peptides containing cysteine are subsequently analyzed, resulting in a significant decrease in complexity of the analyzed sample, while maintaining a high proteome coverage. This method suffers from slight shifts in retention times of light peptides in comparison to their heavy counterparts because of the high number of deuterium atoms. Therefore, second generation ICAT uses 12C and 13C instead of hydrogen isotopes [13]. Moreover, the new version allows cleavage of the biotin moiety from the tags after affinity purification, resulting in a lower mass of analyzed peptides. In contrast to the thiol groups, at least one amino group is present in each proteolytic peptide. The wide range of currently available methods aimed at labeling via amino groups thus represent an appropriate alternative for those who consider analysis of only cysteine-containing peptides unsatisfactory. Mostly, the amino group is targeted by N-hydroxysuccinimide chemistry; for example, global internal standard technology (GIST) is based on labeling mediated by Nacetoxysuccinimide [14], and isotope-coded protein label (ICPL), offering three channels, is based on labeling mediated by N-nicotinoyloxysuccinimide [15]. In most chemical techniques, the quantitative information is acquired in MS mode by comparison of heavy and light labeled peptides. As mentioned in the paragraph addressing metabolic labeling, this approach brings a higher spectral

4.2 iTRAQ Analysis of Bacterial Pathogens

complexity and permits only quantification of a limited number of samples. These drawbacks have been overcome by isobaric techniques in which relative quantification is acquired from MS/MS spectra. The isobaric tags consist of a reactive part mediating the chemical reaction, a reporter group and a balancer group. The sum of molecular weight of reporter and balancer group is constant in all the tags. Labeled peptides are initially detected as a single peak in MS spectrum. However, under fragmentation in MS/MS mode the peptides release the reporter group, producing specific signals that provide quantitative information. The first isobaric method ever was tandem mass tags (TMT) [16]. It, however, has not gained widespread application yet. In contrast, isobaric tag for relative and absolute quantitation (iTRAQ) after commercialization became very popular [17]. iTRAQ is based on labeling mediated by methylpiperazine acetic acid Nhydroxysuccinimide ester. It provides up to four different channels for quantification, and the latest version provides even eight channels [18]. Under fragmentation the tags release reporter groups detectable as peaks 114.1, 115.1, 116.1 and 117.1. This however means that a tandem mass spectrometer covering also the lower segment of the MS/MS spectra is absolutely essential for the iTRAQ analysis. Even though the reporter segment of the MS/MS spectrum was proved to be peak free, fragments not originated from iTRAQ could be detected in a limited number of spectra. Therefore, another essential requirement on tandem mass spectrometer is a resolution high enough to be able to resolve the iTRAQ reporter peaks from incidental ballast. Though most of the methods have been developed for peptide labeling, in general, the reaction may be performed at protein level as well, as shown by ICPL [15] and iTRAQ [19]. This may be advantageous, since after labeling the protein samples may be mixed and processed as one sample. Moreover, when 1D electrophoresis or isoelectric focusing is involved in the shotgun proteomics experiments, it is possible to detect protein cleavage and posttranslational modification respectively. However, one should keep in mind which amino acid is targeted by the labeling, because altered amino acids may no longer be recognized by the protease. Although chemical labeling represents the most frequently used approach for isotope coding, challenging issues typical for all chemical reactions still remain: the efficiency of the reaction and the side reactions (Table 4.1, Figure 4.1).

4.2 iTRAQ Analysis of Bacterial Pathogens

On the basis of our own experience, in this part we would like to emphasize practical aspects of the iTRAQ analysis of protein samples originating from bacterial pathogens. Though all the steps described in the protocol provided with the iTRAQ kit by the manufacturer are comprehensible and correct, they cannot cover all the challenging issues that may emerge during the workflow. The sample preparation for an iTRAQ shotgun proteomic experiment consists of several essential steps: bacterial cell disruption and protein extraction,

47

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4 Quantitative Mass Spectrometric Approaches

Table 4.1 Summary of the mentioned methods for incorporating stable isotopes into proteins/peptides.

SILAC

ISIS

16

O/18O

ICAT cICAT

GIST

ICPL

TMT

iTRAQ

Labeling

Metabolic Metabolic Enzymatic Chemical Chemical

Chemical

Chemical

Chemical

Level of labeling

Proteins

Proteins

Peptides

Proteins

Peptides

Proteins/ peptides

Peptides

Proteins/ peptides

Targeted amino acids

R, K, (L)

I, L, V

C-termini

C

N-termini, N-termini, N-termini, N-termini, K K K K

Increase in spectral complexity

Yes

No

Yes

Yes

Yes

Yes

No

No

Decrease of sample complexity

No

No

No

Yes

No

No

No

No

Number of channels

2/3

2

2

2

2

2/3

2/6

4/8

MS/MS

MS

MS

MS

MS

MS/MS

MS/MS

Mode for MS quantification

determination of protein concentration, protein digestion and peptide labeling with iTRAQ tags. It must be stressed that all the aforementioned steps must be adjusted in order to avoid possible incompatibilities that might significantly impair the whole experiment. Moreover, in the case of dangerous virulent bacterial strains, special requirements on cell lysis and sample handling must be employed in order to avoid potential contamination of the working environment. 4.2.1 Bacterial Cell Disruption and Protein Extraction

These two steps are mostly done simultaneously, because the composition of the lysis buffer is designed to both dissolve and denature proteins. The preferred lysis buffer is 8 M urea since this agent can be simply separated from proteins. Yet, one should keep in mind that samples dissolved in urea must not be heated over 37 °C since elevated temperatures cause urea to modify proteins by carbamylation. This brings about the incomplete labeling by amino groups targeting iTRAQ tags. Detergents should be avoided in lysis buffer because they may decrease the trypsin activity or the separation efficiency of HPLC. In addition, they are generally hard to remove. Nevertheless, cleavable detergents have emerged on the market recently (e.g., RapiGest from Waters [20], PPS Silent Surfactant from Protein Discovery [21] and ProteasMAX from Promega) which improve proteolytic digestion and can

4.2 iTRAQ Analysis of Bacterial Pathogens (a)

(b)

Metabolic labeling

(c)

49

Enzymatic labeling

Chemical labeling on protein level

(d) Chemical labeling on peptide level

Digest

Labeling

Digest

Digest

Labeling Digest

Tandem mass spectrometry Figure 4.1 Schematic representation of four

different methods utilized for incorporation of stable isotopes into proteins or peptides. (a) Proteins are labeled metabolically using isotope-coded aminoacids. Application of coded lysine or arginine ensures that each correctly cleaved peptide carries an isotope tag. In case of others aminoacid the number of isotope tags in peptides is not defined. (b) Trypsin digestion in H218O leads to formation of tryptic peptides with two 18O atoms at the C-terminus. The protein C-terminus is labeled as well even if this site

does not undergo the enzymatic cleavage. (c) Mostly, chemical labeling at protein level targets free amino groups, at the side chain of lysine and at the N-terminus in particular. This brings about the formation of larger tryptic peptides, as trypsin does not recognize the modified lysine anymore. In addition, not each peptide carries coding tags. (d) Chemical labeling on peptide level ensures that each peptide is labeled, however the samples to be compared are mixed together at the last possible step of the workflow.

be removed prior to HPLC separation by incubation under acidic pH or are directly degraded during the trypsin digestion. The main stumbling block of using these agents is their price. As far as bacterial cells are concerned, the best method for bacterial disruption in the authors’ hands seems to be the French pressure cell. The equipment is

50

4 Quantitative Mass Spectrometric Approaches

however quite expensive and thus it is not common in microbiological laboratories. Freeze–thaw lysis in liquid nitrogen or sonication represent good alternatives. For gram-positive bacteria, enzymatic lysis using lysozyme may be also employed. In the case of dangerous virulent bacteria the cells are recommended to be disrupted by detergent lysis either in SDS buffer or in the presence of cleavable detergents, followed by inactivation of residual bacteria by incubation at 95 °C. In general, the best way to protect the samples against native proteolysis is by supplementing the lysis buffer with protease inhibitors. However, it must be stressed that, without their removal before digestion, these might also decrease the activity of trypsin. Keeping the samples on ice until digestion and the denaturing environment itself are often sufficient to protect the samples from native proteolysis. 4.2.2 Determination of Protein Concentration

Nowadays, there is a wide range of colorimetric as well as fluorescent methods for protein determination. From the view of susceptibility to chemicals used in lysis buffer, the bicinchoninic acid assay (BCA) seems to be superior over other techniques (limit 5% SDS or 3 M urea) [22]. Thus, it is not necessary to perform protein precipitation before BCA assay, which results in more accurate results and time savings. 4.2.3 Protein Digestion

Trypsin is the most used protease to digest proteins into peptides. Prior to digestion, agents interfering with the enzymatic activity must be removed, or at least diluted to a non-interfering concentration (1–2 M urea, 0.05% SDS). For instance, SDS may be removed using gel filtration devices (e.g., Extracti-Gel D detergent removing gel from Pierce, Detergent-OUT kit from Millipore) or by lowering the SDS amount under its critical micelle concentration (0.173–0.230%) followed by ultrafiltration using a 5 kDa cutoff membrane. Although only nonprotein-bound SDS is removed, the residual concentration should not impair trypsin activity. Many protocols encourage investigators to precipitate the proteins. Unfortunately, this method brings problems with poor dissolution of the protein precipitate. In contrast to SDS, urea is easily removable by ultrafiltration or dialysis. As the trypsin digestion is followed by iTRAQ peptide labeling, the digestion buffer must not contain chemicals carrying free amino groups. Thus, instead of commonly used Tris-HCl or ammonium bicarbonate buffers, we strongly encourage investigators to perform the trypsin digestion in triethylammonium bicarbonate (TEAB) solution that ensures both a mild basic pH and compatibility with iTRAQ labeling. Prior to adding trypsin to the samples, proteins should be incubated with reducing agent in order to disrupt disulfide bonds. Dithiothreitol (DTT) or tris(2-

4.2 iTRAQ Analysis of Bacterial Pathogens

carboxyethyl)phosphine hydrochloride (TCEP) may be used for this purpose. Subsequently, free thiol groups are blocked irreversibly by iodoacetamide (IAM), or reversibly by methyl methanethiosulfonate (MMTS). 4.2.4 Peptide Labeling with iTRAQ Tags

After digestion, the peptides are ready for labeling. The drawback of N-hydroxysuccinimide chemistry is a low stability in aqueous environments. The reaction is carried out in presence of ethanol or propanol with just a moderate content of water. Providing that the digestion was performed in a large volume of aqueous buffer, the water content must be reduced in a Speed-Vac. The iTRAQ tags are allowed to react with the peptides for 60 min. Afterwards, the remaining tags are quenched by adding excess water and additionally incubating for 60 min. 4.2.5 Protocol for iTRAQ Analysis of Bacterial Proteins

The protocol described here was optimized and successfully used for the gramnegative bacterium Francisella tularensis; it can be used for other microbes, even microbes classified as BSL 3 and BSL 4 pathogens. 1)

After spindown, resuspend the bacterial cells in the lysis buffer composed of 0.1–0.3% SDS in Milli-Q water. Note: Use as low a concentration of SDS as possible, because the higher the concentration is, the more demanding is its removal.

2)

Immerse the sample tubes in boiling water bath for 5 min. Note: In the case of pathogenic bacteria, prolong this step in order to ensure the inactivation of nondisrupted cells.

3)

Spindown the samples at 14 000 g.

4)

Determine the protein concentration in the supernatant using the BCA kit (Sigma Aldrich).

5)

Dilute the samples containing 200 μg of protein 10–20 times with Milli-Q water. Note: The final concentration of SDS must be sharply under the level of the critical micelle concentration.

6)

Concentrate the samples using spin filters Amicon MWCO 3000 (Millipore) to 50 μl. This step should lead to a dramatic reduction in SDS concentration.

7) Determine the protein concentration in the concentrate using the BCA kit again. 8)

Dilute the samples containing 100 μg once with 1 M triethylammonium bicarbonate buffer pH 8.5 (Sigma Aldrich).

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4 Quantitative Mass Spectrometric Approaches

9)

Reduce the disulfide bonds with TCEP at 5 mM for 60 min at 60 °C.

10) Block free thiol groups with MMTS at 10 mM at room temperature for 10 min. 11)

Digest the proteins overnight at 37 °C using trypsin (Promega) in 1 : 50 ratio.

12) Dry the peptides in a vacuum centrifuge and redissolve them in 30 μl of 50 mM triethylammonium bicarbonate buffer, pH 8.5. 13) Modify the peptides using iTRAQ labels dissolved in 70 μl of absolute ethanol (Applied Biosystems). 14)

Stop the reaction by adding 300 μl of water. After 60 min of incubation, mix the samples in 1 : 1 : 1 : 1 ratio.

15)

Dry the mixture in a vacuum centrifuge.

16)

Reconstitute the peptides in 100 μl of mobile phase A for strong cation exchange (SCX) chromatography (50 mM KH2PO4, pH 3.0 in water). For elution of peptides use 50 mM KH2PO4, pH 3.5, 250 mM KCl. SCX chromatography allows the removal of residual SDS prior to reversed-phase HPLC and can be also used to fractionate the bacterial digest into several peptidecontaining fractions.

References 1 Berth, M., Moser, F.M., Kolbe, M., and

2

3

4

5

Bernhardt, J. (2007) The state of the art in the analysis of two-dimensional gel electrophoresis images. Appl. Microbiol. Biotechnol., 76, 1223–1243. Miller, I., Crawford, J., and Gianazza, E. (2006) Protein stains for proteomic applications: which, when, why? Proteomics, 6, 5385–5408. Unlu, M., Morgan, M.E., and Minden, J.S. (1997) Difference gel electrophoresis: a single gel method for detecting changes in protein extracts. Electrophoresis, 18, 2071–2077. Oda, Y., Huang, K., Cross, F.R., Cowburn, D., and Chait, B.T. (1999) Accurate quantitation of protein expression and site-specific phosphorylation. Proc. Natl. Acad. Sci. U.S.A., 96, 6591–6596. Ong, S.E., Blagoev, B., Kratchmarova, I., Kristensen, D.B., Steen, H., Pandey, A., and Mann, M. (2002) Stable isotope labeling by amino acids in cell culture,

6

7

8

9

SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics, 1, 376–386. Blagoev, B., Ong, S.E., Kratchmarova, I., and Mann, M. (2004) Temporal analysis of phosphotyrosine-dependent signaling networks by quantitative proteomics. Nat. Biotechnol., 22, 1139–1145. Colzani, M., Schutz, F., Potts, A., Waridel, P., and Quadroni, M. (2008) Relative protein quantification by isobaric SILAC with immonium ion splitting (ISIS). Mol. Cell. Proteomics, 7, 927–937. Krijgsveld, J., Ketting, R.F., Mahmoudi, T., Johansen, J., Artal-Sanz, M., Verrijzer, C.P., Plasterk, R.H., and Heck, A.J. (2003) Metabolic labeling of C. elegans and D. melanogaster for quantitative proteomics. Nat. Biotechnol., 21, 927–931. Schnolzer, M., Jedrzejewski, P., and Lehmann, W.D. (1996) Protease-catalyzed incorporation of 18O into peptide fragments and its application for protein sequencing by electrospray and matrix-

References

10

11

12

13

14

15

16

assisted laser desorption/ionization mass spectrometry. Electrophoresis, 17, 945–953. Mirgorodskaya, O.A., Kozmin, Y.P., Titov, M.I., Korner, R., Sonksen, C.P., and Roepstorff, P. (2000) Quantitation of peptides and proteins by matrix-assisted laser desorption/ionization mass spectrometry using (18)O-labeled internal standards. Rapid Commun. Mass Spectrom., 14, 1226–1232. Heller, M., Mattou, H., Menzel, C., and Yao, X. (2003) Trypsin catalyzed 16O-to-18O exchange for comparative proteomics: tandem mass spectrometry comparison using MALDI-TOF, ESI-QTOF, and ESI-ion trap mass spectrometers. J. Am. Soc. Mass Spectrom., 14, 704–718. Gygi, S.P., Rist, B., Gerber, S.A., Turecek, F., Gelb, M.H., and Aebersold, R. (1999) Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat. Biotechnol., 17, 994–999. Yi, E.C., Li, X.J., Cooke, K., Lee, H., Raught, B., Page, A., Aneliunas, V., Hieter, P., Goodlett, D.R., and Aebersold, R. (2005) Increased quantitative proteome coverage with (13)C/(12)C-based, acid-cleavable isotope-coded affinity tag reagent and modified data acquisition scheme. Proteomics, 5, 380–387. Chakraborty, A., and Regnier, F.E. (2002) Global internal standard technology for comparative proteomics. J. Chromatogr. A, 949, 173–184. Schmidt, A., Kellermann, J., and Lottspeich, F. (2005) A novel strategy for quantitative proteomics using isotopecoded protein labels. Proteomics, 5, 4–15. Thompson, A., Schafer, J., Kuhn, K., Kienle, S., Schwarz, J., Schmidt, G., Neumann, T., Johnstone, R., Mohammed, A.K., and Hamon, C. (2003) Tandem mass tags: a novel quantification

17

18

19

20

21

22

strategy for comparative analysis of complex protein mixtures by MS/MS. Anal. Chem., 75, 1895–1904. Ross, P.L., Huang, Y.N., Marchese, J.N., Williamson, B., Parker, K., Hattan, S., Khainovski, N., Pillai, S., Dey, S., Daniels, S., Purkayastha, S., Juhasz, P., Martin, S., Bartlet-Jones, M., He, F., Jacobson, A., and Pappin, D.J. (2004) Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol. Cell. Proteomics, 3, 1154–1169. Pierce, A., Unwin, R.D., Evans, C.A., Griffiths, S., Carney, L., Zhang, L., Jaworska, E., Lee, C.F., Blinco, D., Okoniewski, M.J., Miller, C.J., Bitton, D.A., Spooncer, E., and Whetton, A.D. (2007) Eight-channel iTRAQ enables comparison of the activity of 6 leukaemogenic tyrosine kinases. Mol. Cell. Proteomics, 7: 853–863. Wiese, S., Reidegeld, K.A., Meyer, H.E., and Warscheid, B. (2007) Protein labeling by iTRAQ: a new tool for quantitative mass spectrometry in proteome research. Proteomics, 7, 340–350. Ross, A.R., Lee, P.J., Smith, D.L., Langridge, J.I., Whetton, A.D., and Gaskell, S.J. (2002) Identification of proteins from two-dimensional polyacrylamide gels using a novel acid-labile surfactant. Proteomics, 2, 928–936. Norris, J.L., Porter, N.A., and Caprioli, R.M. (2003) Mass spectrometry of intracellular and membrane proteins using cleavable detergents. Anal. Chem., 75, 6642–6647. Brown, R.E., Jarvis, K.L., and Hyland, K.J. (1989) Protein measurement using bicinchoninic acid: elimination of interfering substances. Anal. Biochem., 180, 136–139.

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5 BN-PAGE of Microbial Protein Complexes Jiri Dresler and Jana Klimentova

5.1 Introduction

The genomic era has brought many challenging data for proteomics. The availability of completed genomes of many organisms enables the investigation of protein expression levels, structures and modifications. However, the analysis of protein structures alone is not sufficient for the complex knowledge about them. Natural compartmentization facilitates proteome analysis of cells, cell organelles and organelle subfractions and the protein complexes are the basis for the next level of compartmentization. Since many complex cellular processes (e.g., metabolic, developmental, regulatory pathways) are accomplished by sophisticated multiprotein machines, the investigation of protein interactions becomes far more important. This integrative view could lead to deeper insight into their physiological as well as pathologic function and cell behavior. Thus the new comprehensive study of the complexome, that is, all the proteins involved in different protein complexes, has arisen.

5.2 Methods for Studying Protein–Protein Interactions

The first widely used method to study protein–protein interactions was the twohybrid yeast system, discovered in 1989; and it became the workhorse for the analysis of protein–protein interactions in vivo [1, 2]. However, this method is limited by its accuracy and labor-intensive nature [1]. Another possibility for large-scale protein interaction analysis are protein chips [3], but technical problems (e.g., denaturation, substrate biocompatibility) must be surmounted in order to scale-up this method for high-throughput analysis. To date, these technologies have generated large interaction networks for bacteria [4], yeast [5], fruit fly [6] and nematodes [7].

BSL3 and BSL4 Agents: Proteomics, Glycomics, and Antigenicity, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.

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5 BN-PAGE of Microbial Protein Complexes

Other methods to study protein–protein interactions are represented by tandem affinity purification (TAP) [8] and immunoprecipitation [9]. These complementary methods are frequently used to confirm interactions previously detected by other approaches. However, all these methods are laborious and, in addition, they suffer from false positive and/or negative results. On the contrary, blue native polyacrylamide gel electrophoresis (BN-PAGE), the method described here, is a relatively simple and sensitive method to study protein–protein interactions.

5.3 Blue Native Polyacrylamide Gel Electophoresis

BN-PAGE is a special electrophoretic approach developed by Schägger and von Jagow [10] originally used for the analysis of mitochondrial protein complexes. This method can be applied to protein complexes in the molecular weight range of 10–10 000 kDa [11]. However, a modified method using agarose instead of polyacrylamide was introduced in order to study protein complexes of approximate molecular weight greater than 1200 kDa [12]. This approach has been used in combination with other techniques to determine the oligomeric state, stoichiometry, enzymatic activity and molecular structure of various multiprotein complexes [13–15]. Since many samples can be separated in one electrophoretic run, direct comparison of protein complexes readily allows the identification of differences in their composition and stoichiometry. The conventional 2D IEF/SDS PAGE system is able to separate hundreds to thousands of proteins according to their pI and molecular weight [16]. However, hydrophobic (e.g., membrane) proteins are hardly detectable or are underrepresented by this approach [17]. This could be one of the reasons that hindered deeper analysis of the membrane proteome despite its importance for living cells [18]. BN-PAGE presents an alternative strategy to separate membrane proteins with high resolution while maintaining their enzymatic function [19]. This method is based on several common principles, including the following statements: non-denaturing conditions must be kept throughout the sample preparation and electrophoresis, mild non-ionic detergents are used for the solubilization and an anionic dye Coomassie brilliant blue G-250 (CBB G-250; Figure 5.1) is used during electrophoresis. This dye binds to protein surfaces, preferentially to aromatic residues and arginine, and the binding of a large number of negatively charged dye molecules to protein complexes facilitates their migration in BNPAGE. Moreover, the tendency of protein aggregation is thus decreased considerably [11]. After the separation of native complexes by BN-PAGE in the first dimension, these complexes can be denatured (broken down to individual subunits) and separated according to their size in the second dimension using “classical” SDS PAGE. In this approach proteins belonging to the same complex are aligned vertically in the second SDS dimension [20].

5.3 Blue Native Polyacrylamide Gel Electophoresis

O

O– O

H

S

O

N+

N

N

O S O

O– Na+

Figure 5.1 Coomassie brilliant blue G-250.

In practical proteomics the goal is to identify all proteins present in a defined functional state and to characterize their qualitative and quantitative changes in response to various environmental changes. Regarding this, BN-PAGE could be one of the candidates to standardize the first step during the sequential highresolution fractionation of the proteome [21]. 5.3.1 Sample Preparation

For BN-PAGE, protein complexes have to be extracted from the lipid phase retaining their native state. The aim is to: (i) solubilize the complexes (which is of high importance especially in membrane protein complexes) and (ii) retain their native state and keep it intact during electrophoresis [22]. Long-term storage of native protein samples is not recommended because of the danger of protein aggregation and complex dissociation. 5.3.1.1 Non-Denaturing Conditions As stated before, native conditions must be preserved during the whole sample preparation and electrophoresis. This includes: neutral pH, low salt concentration, no reducing and denaturing agent (SDS, urea), all manipulations at 4 °C, no heating and ideally no freezing, because repeated freezing and thawing of the sample may cause the formation of artificial aggregates and/or break-up of existing protein complexes, leading to false results [18].

57

58

5 BN-PAGE of Microbial Protein Complexes H

H

O O

H

O

O

H O H H

H

O

O O

O

H

O

H

O

H O

O O

O

O

H

H

O

H

O

O

O

O

O O

H

Digitonin

H O

O

O

H

H O

O

H

O

H

O

H

H

H

O

O

O O

O H

H

O O

H

H

O

H

O O H

DDM

Figure 5.2 Digitonin and DDM.

5.3.1.2 Selection of Detergent and Its Optimal Concentration In BN-PAGE only mild non-ionic detergents are used, because anionic, cationic and zwitterionic detergents typically disrupt the protein–protein interactions. Moreover, ionic detergents are more sensitive to pH, ionic strength and can interfere with charge-based analytical methods. In contrast, non-ionic detergents are often mild and less effective in disrupting protein aggregation [23]. For BN-PAGE, n-dodecyl-β-D-maltosid (DDM), digitonin, Triton X-100 and Brij96 have proven to be useful [23, 24]. Among them DDM and digitonin (Figure 5.2) are the most frequently used [25] and are good starting points for the optimization of solubilization strategy. However, correct detergent type and its concentration are determined strictly on the basis of experiments in order to maintain protein–protein interaction on the one hand and to ensure sufficient solubilization on the other hand. There is no general rule of detergent type and concentration selection optimal for every sample [26]. If the aim of the investigation is to study super complexes (of higher molecular mass), it is advisable to use milder detergent like digitonin or a lower concentration of DDM that preserve intermolecular interactions. A higher concentration or stronger detergent may be useful, when focused on particular complexes of lower molecular mass. Nevertheless, determining the right detergent concentration for the solubilization of protein sample of interest requires carrying out a dilution series with different detergent content [26]. 5.3.1.3 Membrane and Cytosolic Fraction Separation In bacterial protein samples it appeared to be useful to subfractionate membranes and cytosole first prior to solubilization because of the very different nature of these two compartments [27, 28]. After bacterial cell lysis (e.g., mechanically by high pressure in a French pressure cell), the membranes are pelleted by centrifugation at high speed (ca. 100 000 g) and consequently solubilized as described. The cytosolic fraction (the supernatant after centrifugation) can further be desalted by dialysis or on a desalting column [29] or used readily for BN-PAGE.

5.3 Blue Native Polyacrylamide Gel Electophoresis

5.3.2 1D BN-PAGE

Prior to BN-PAGE, the protein sample must be supplemented with CBB G-250 to a final concentration of ca. 0.2% (w/v) [22]. The gel composition differs from the classical Laemli system. For 1D BN-PAGE gradient gels are usually prepared in gel buffers based on Bis-Tris and amino caproic acid, whereas the electrophoresis buffers contain Bis-Tris and Tricine at pH 7.0 [30]. The cathode buffer is further supplemented by 0.02% CBB G-250. For this reason, 1D native gels immediately after the electrophoretic run are constantly blue. For the purposes of Western blotting or silver staining it is recommended to exchange the blue cathode buffer for a transparent one (without CBB G-250) after about the half-time of the electrophoretic run [31]. It is of high importance to conduct BN-PAGE at 4 °C to prevent protein complex denaturation caused by overheating. After the first dimension BN-PAGE the protein complexes are separated as bands and can be further studied by various methods. The complexes can be visualized by a variety of techniques, excised and analyzed by MS regarding that each band contains an entire complex. Their enzymatic activity can be studied. They can be transferred to a membrane and detected by immunoblotting [32]. One of the approaches is also in-gel denaturation to individual subunits and their 2D separation. 5.3.3 2D BN/SDS-PAGE

SDS electrophoresis in the second dimension is usually employed as a following method for resolving the protein complex composition and also gives molecular mass information for these subunits. Theoretically, all the proteins released from one complex should be positioned in a straight vertical line on a 2D gel [18]. Typically, the line of interest in the native gel is excised and denaturation is provided by its incubation in SDS solution containing a reducing agent (e.g., βmercaptoethanol or dithiothreitol). Alternatively, equilibration of the gel strip can also comprise alkylation (by iodoacetamide or N,N′-dimethylacrylamide). The equilibrated strip is then directly transferred onto the top of a standard SDS gel in a horizontal position [30] and surrounded by focusing gel or agarose. An alternative approach is to cut out single bands from the 1D stripe, equilibrate them individually as described and arrange them within wells of focusing gel in the same direction as they ran in the first dimension. This improves resolution, sharpens the resulting bands and reduces smearing in the second dimension [33].

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5.4 Evaluation of BN-PAGE – Staining, MS, Western Blotting

The separating gels can be used for different applications, typically staining and subsequent MS analysis or gel-blot analysis. Moreover, these gels can be used for “in gel” activity assay, for example, measuring the activity of mitochondrial enzyme complexes. 5.4.1 Staining 5.4.1.1 Silver Staining Silver staining is considered to be extremely sensitive in comparison with other techniques. The most frequently used techniques involve fast silver staining according to Schevchenko [34], permanganate [35] and ammoniac silver staining [36]. The disadvantages of silver staining are that it is time-consuming and it immobilizes proteins in the gel due to cross linking. Such “fixation” of proteins disables protein complexes from transferring to a gel-blot. The subsequent MS analysis of silver stained proteins is feasible only when they are fast silver stained and destained immediately after excision of spots/bands. However, native fast silver staining is less sensitive than ammoniac silver staining [32]. 5.4.1.2 Fluorescent Staining In BN-PAGE, labeling of proteins for high sensitivity can also be achieved before loading on 1D BN-PAGE. Fluorescent dyes CyDye (Cy2, Cy3, Cy5) bind to the protein complexes via lysine side groups. Visualization is then possible in the second dimension [37]. This approach provides the possibility to compare the protein complex composition of two or more different samples. Samples of interest are labeled after solubilization (each one by a different dye), before 1D BNPAGE they are mixed and in 1D and subsequent 2D they run together. The different protein composition is then visualized by scanning these gels under the wavelengths specific for the dyes used. However, utilization of this kind of visualization is limited to 2D because the blue color in BN-PAGE quenches fluorescence emission. During the denaturing step between BN-PAGE and SDS-PAGE, Coomassie is replaced by SDS and the blue dye is shifted to the mobility front. Therefore the visualization of proteins by CyDye labeling is favorable only on the level of protein subunits [26]. A similar problem is encountered with Sypro staining. The putative principle is the binding of a ruthenium-containing dye to SDS fixed to separated protein. Sufficient staining is thus achieved only in 2D or in 1D native gels after their long equilibration in SDS solution. 5.4.1.3 Coomassie Staining In spite of the high sensitivity of silver staining and the wide dynamic range of various fluorescent detection methods, CBB G-250 staining [38] is still the most

5.4 Evaluation of BN-PAGE – Staining, MS, Western Blotting

widely used protein detection technique for proteins separated by polyacrylamide gel electrophoresis. The advantages of this approach are low costs, naked eye visibility, mass spectrometry compatibility and the possible adaptations for fast or highly sensitive staining [32]. 5.4.2 Mass Spectrometry

Protein identification by MS can be carried out well after both the first and second dimensions [27, 37, 39]. The treatment of native 1D gels and SDS 2D gels differs. In the case of 1D native gels, the excised bands have to be denatured (usually by high concentration of urea), reduced and alkylated prior to trypsin digestion and extraction [40]. In 2D SDS gels the treatment depends on whether they were alkylated after 1D or not. The digestion of proteins and following extraction of peptide mixture is then identical. After BN-PAGE, the peptide mixtures are usually analyzed by online HPLC-MS. In online electrospray (ESI) analysis, HPLC separation before MS or MS/MS analysis improves both the quantity and quality of identified proteins. Offline ESI or matrix-assisted laser desorption ionization (MALDI) analysis is less suitable due to the fact that one band usually contains more than one protein subunit. After the second dimension SDS-PAGE, the analysis by offline ESI or MALDI is much easier and does not differ from the MS analysis of “classical” gels. 5.4.3 Western Blotting

Identification of proteins after BN- or BN/SDS-PAGE can be also accomplished by gel blot and subsequent antibody detection. However, this method has some further aspects in the non-denaturing mode that have to be taken into account. If the native gel is to be transferred onto the blot, exchange of cathode buffer after one half of the run of the electrophoresis is advisable. Coomassie blocks the hydrophobic binding sites on gel blot and affects antigen detection in a negative way [18]. Moreover, many antibodies are not able to recognize the antigen in the native structure of the protein complex. That is the reason why some antibodies are utilizable well only after SDS-PAGE, where proteins are denatured. 5.4.4 Other Methods of Visualization

Two other suitable methods of protein visualization are zinc-reverse [41] and copper-reverse [42] staining. In addition, the expression and concomitant assembly of the protein subunits of protein complexes can be studied by radiolabeling methods. Following the chronological sequence of the assembly/disassembly of synthesized complex subunits, 35S-methionine is often used [43]. Another possible approach to visualize protein complexes with enzymatic activity uses assays

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delivering colored products. Nevertheless, this method does not enable identification of one specific enzyme and therefore the use of antibody detection or mass spectrometry is inevitable for their unambiguous identification [37].

5.5 BN/SDS-PAGE of ATP Synthase of Francisella tularensis

The principle of BN- and BN/SDS-PAGE is demonstrated here on a membrane protein complex, the ATP synthase of bacterium F. tularensis. This F1F0-ATP synthase is a well described multiprotein complex, which is part of the respiratory chain and performs ATP synthesis/hydrolysis connected with the transport of protons across the membrane. The complex consists of two discrete sectors (subcomplexes), designated as F0 and F1. The F0 subcomplex is membrane-embedded and provides the pathway for the passage of protons through the membrane, down the electrochemical gradient. The F1 subcomplex is membrane-extrinsic and contains catalytic sites for ATP synthesis. In bacteria F1 consists of five subunits: alpha, beta, gamma, delta and epsilon in a stoichiometry α3β3γδε; F0 consists of subunits A, B and C in a stoichiometry AB2C9–12 [37]. The ATP synthase complex and its fragments were detected in bands on BNPAGE and in corresponding spots on SDS-PAGE. In Figure 5.3, on BN-PAGE, F1F0-ATP synthase represents one of the most intensive bands, in which all of the subunits were detected by MS analysis except for one – the delta subunit. On the second dimension gel, a vertical set of spots corresponding to this band were identified as subunits alpha, beta, gamma and B; the remaining subunits delta, epsilon, A and C were not detected again. Non-detection of the mentioned subunits could result from their low molecular mass (as in the subunit C – 10 kDa – which probably ran out of the 2D gel) and/or their low molecular ratio (as in the subunits A, delta and epsilon, that are only one molecule per supercomplex). The second band on 1D gel and also the second allied set of spots on 2D represented the released F1 subcomplex. This subcomplex seems to be further decomposed into individual subunits. In the third band depicted in Figure 5.3 the subunits alpha and beta were detected. These form together a circle where three subunits alpha alternate regularly with three subunits beta. This arrangement may lead to higher stability of their interaction in comparison with the interaction of other subunits. In lower molecular weights subunits alpha and beta were found as well, but these bands corresponded to individual subunits from the broken complexes (their position on the gel corresponded well to their respective MWs). The protein complex breakdown probably resulted from instability due to the detergent type and/or concentration. This example demonstrated that it is possible to analyze membrane protein complexes such as F1F0-ATP synthase under described conditions. However, the absence of the delta subunit on 1D and several others on 2D as well as the appearance of fragments on positions with lower MW than the entire complex shows also weak points of this method. The researcher must be cautious about reproduc-

Acknowledgment

BN-PAGE and BN/SDS-PAGE of membrane proteins of F. tularensis. Sample preparation: bacteria were cultivated in liquid medium, broken in French pressure cell, membranes were pelleted by centrifugation at

Figure 5.3

100 000 g and solubilized by 1% (w/v) digitonin. 1D is a 4–15% native gel, 2D is 12% SDS gel. The proteins were visualized by Coomassie brilliant blue G-250.

tion of his results and rather repeat the same procedure under different conditions (especially using several types and concentrations of detergents).

5.6 Conclusion

The presented method of BN-PAGE as well as BN/SDS-PAGE provides a simple complementary method for the investigation of protein–protein interactions. This method is especially suitable for the analysis of membrane protein complexes. The feasibility of this approach was demonstrated by the analysis of F1F0-ATP synthase, a typical membrane embedded supercomplex. This technology applies not only to multiprotein complexes involved in energy metabolism but also to protein complexes essential for other cellular functions.

Acknowledgment

This study was financially supported by Ministry of Education, Youth and Sports, Czech Republic (OC151).

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References 1 Enright, A.J., et al. (1999) Protein

2

3

4

5

6

7

8

9

10

11

12

interaction maps for complete genomes based on gene fusion events. Nature, 402 (6757), 86–90. Legrain, P., and Selig, L. (2000) Genome-wide protein interaction maps using two-hybrid systems. FEBS Lett., 480 (1), 32–36. MacBeath, G., and Schreiber, S.L. (2000) Printing proteins as microarrays for high-throughput function determination. Science, 289 (5485), 1760–1763. Rain, J.C., et al. (2001) The proteinprotein interaction map of Helicobacter pylori. Nature, 409 (6817), 211–215. Uetz, P., and Hughes, R.E. (2000) Systematic and large-scale two-hybrid screens. Curr. Opin. Microbiol., 3 (3), 303–308. Giot, L., et al. (2003) A protein interaction map of Drosophila melanogaster. Science, 302 (5651), 1727–1736. Li, S., et al. (2004) A map of the interactome network of the metazoan C. elegans. Science, 303 (5657), 540–543. Puig, O., et al. (2001) The tandem affinity purification (TAP) method: a general procedure of protein complex purification. Methods, 24 (3), 218–229. Ho, Y., et al. (2002) Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature, 415 (6868), 180–183. Schagger, H., and Von Jagow, G. (1991) Blue native eletrophoresis for isolation of membrane protein complexes in emzymatically active form. Anal. Biochem., 199 (2), 223–231. Schagger, H., Cramer, W.A., and von Jagow, G. (1994) Analysis of molecular masses and oligomeric states of protein complexes by blue native electrophoresis and isolation of membrane protein complexes by two-dimensional native electrophoresis. Anal. Biochem., 217 (2), 220–230. Henderson, N.S., et al. (2000) Separation of intact pyruvate dehydrogenase complex

13

14

15

16

17

18

19

20

21

using blue native agarose gel electrophoresis. Electrophoresis, 21 (14), 2925–2931. Poetsch, A., et al. (2000) Dye removal, catalytic activity and 2D crystallization of chloroplast H(+)-ATP synthase purified by blue native electrophoresis. Biochim. Biophys. Acta, 1466 (1–2), 339–349. Dudkina, N.V., et al. (2005) Structure of dimeric ATP synthase from mitochondria: an angular association of monomers induces the strong curvature of the inner membrane. FEBS Lett., 579 (25), 5769–5772. Sunderhaus, S., et al. (2006) Carbonic anhydrase subunits form a matrixexposed domain attached to the membrane arm of mitochondrial complex I in plants. J. Biol. Chem., 281 (10), 6482–6488. Righetti, P.G. (2005) Electrophoresis: the march of pennies, the march of dimes. J. Chromatogr. A, 1079 (1–2), 24–40. Santoni, V., Molloy, M., and Rabilloud, T. (2000) Membrane proteins and proteomics: un amour impossible? Electrophoresis, 21 (6), 1054–1070. Reisinger, V., and Eichacker, L.A. (2006) Analysis of membrane protein complexes by blue native PAGE. Proteomics, 6 (Suppl. 2), 6–15. Jung, C., Higgins, C.M., and Xu, Z. (2000) Measuring the quantity and activity of mitochondrial electron transport chain complexes in tissues of central nervous system using blue native polyacrylamide gel electrophoresis. Anal. Biochem., 286 (2), 214–223. Krause, F. (2006) Detection and analysis of protein-protein interactions in organellar and prokaryotic proteomes by native gel electrophoresis: (Membrane) protein complexes and supercomplexes. Electrophoresis, 27 (13), 2759–2781. Aivaliotis, M., Karas, M., and Tsiotis, G. (2006) High throughput two-dimensional blue-native electrophoresis: a tool for functional proteomics of cytoplasmatic protein complexes from Chlorobium

References

22

23

24

25

26

27

28

29

30

31

tepidum. Photosynth. Res., 88 (2), 143–157. Wittig, I., Braun, H.P., and Schagger, H. (2006) Blue native PAGE. Nat. Protoc., 1 (1), 418–428. Eubel, H., Jansch, L., and Braun, H.P. (2003) New insights into the respiratory chain of plant mitochondria. Supercomplexes and a unique composition of complex II. Plant. Physiol., 133 (1), 274–286. Kashino, Y. (2003) Separation methods in the analysis of protein membrane complexes. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci., 797 (1–2), 191–216. Schagger, H. (2002) Respiratory chain supercomplexes of mitochondria and bacteria. Biochim. Biophys. Acta, 1555 (1–3), 154–159. Reisinger, V., and Eichacker, L.A. (2007) How to analyze protein complexes by 2D blue native SDS-PAGE. Proteomics, 7 (Suppl. 1), 6–16. Lasserre, J.P., et al. (2006) A complexomic study of Escherichia coli using twodimensional blue native/SDS polyacrylamide gel electrophoresis. Electrophoresis, 27 (16), 3306–3321. Farhoud, M.H., et al. (2005) Protein complexes in the archaeon Methanothermobacter thermautotrophicus analyzed by blue native/SDS-PAGE and mass spectrometry. Mol. Cell. Proteomics, 4 (11), 1653–1663. Camacho-Carvajal, M.M., et al. (2004) Two-dimensional Blue native/SDS gel electrophoresis of multi-protein complexes from whole cellular lysates: a proteomics approach. Mol. Cell. Proteomics, 3 (2), 176–182. Swamy, M., et al. (2006) Blue native polyacrylamide gel electrophoresis (BN-PAGE) for the identification and analysis of multiprotein complexes. Sci. STKE, 2006 (345), l4. Wittig, I., et al. (2006) Supercomplexes and subcomplexes of mitochondrial oxidative phosphorylation. Biochim. Biophys. Acta, 1757 (9–10), 1066–1072.

32 Gonzalez, L., et al. (2006) 2-D native-

33

34

35

36

37

38

39

40

41

PAGE/SDS-PAGE visualization of an oligomer’s subunits: application to the analysis of IgG. Electrophoresis, 27 (10), 2016–2023. D’Amici, G.M., Timperio, A.M., and Zolla, L. (2008) Coupling of native liquid phase isoelectrofocusing and blue native polyacrylamide gel electrophoresis: a potent tool for native membrane multiprotein complex separation. J. Proteome Res., 7 (3), 1326–1340. Shevchenko, A., et al. (1996) Mass spectrometric sequencing of proteins silver-stained polyacrylamide gels. Anal. Chem., 68 (5), 850–858. Ansorge, W. (1985) Fast and sensitive detection of protein and DNA bands by treatment with potassium permanganate. J. Biochem. Biophys. Methods, 11 (1), 13–20. Oakley, B.R., Kirsch, D.R., and Morris, N.R. (1980) A simplified ultrasensitive silver stain for detecting proteins in polyacrylamide gels. Anal. Biochem., 105 (2), 361–363. Granvogl, B., Reisinger, V., and Eichacker, L.A. (2006) Mapping the proteome of thylakoid membranes by de novo sequencing of intermembrane peptide domains. Proteomics, 6 (12), 3681–3695. Neuhoff, V., et al. (1988) Improved staining of proteins in polyacrylamide gels including isoelectric focusing gels with clear background at nanogram sensitivity using Coomassie Brilliant Blue G-250 and R-250. Electrophoresis, 9 (6), 255–262. Novakova, Z., et al. (2006) Separation of nuclear protein complexes by blue native polyacrylamide gel electrophoresis. Electrophoresis, 27 (7), 1277–1287. Fandino, A.S., et al. (2005) LC-nanosprayMS/MS analysis of hydrophobic proteins from membrane protein complexes isolated by blue-native electrophoresis. J. Mass. Spectrom., 40 (9), 1223–1231. Fernandez-Patron, C., et al. (1998) Understanding the mechanism of the zinc-ion stains of biomacromolecules in

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66

5 BN-PAGE of Microbial Protein Complexes electrophoresis gels: generalization of the reverse-staining technique. Electrophoresis, 19 (14), 2398–2406. 42 Lee, C., Levin, A., and Branton, D. (1987) Copper staining: a five-minute protein stain for sodium dodecyl sulfate-

polyacrylamide gels. Anal. Biochem., 166 (2), 308–312. 43 Rokka, A., et al. (2005) Synthesis and assembly of thylakoid protein complexes: multiple assembly steps of photosystem II. Biochem. J., 388 (Pt 1), 159–168.

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6 Analysis of Francisella tularensis Glycoproteins Lucie Balonova and Lenka Hernychova

6.1 Introduction to Post-Translational Modifications in Prokaryotes

Post-translational modifications (PTMs) of proteins play crucial roles in the assembly, degradation, structure, and function of expressed genes. PTMs of bacterial pathogenic proteins strongly influence the nature of interaction with the host cell system. However, little is known about the character and function of such modifications in intracellular bacteria. Glycosylation, together with phosphorylation, represent the most common posttranslational modifications of proteins. Over 50% of today’s known proteins, as well as 80% of membrane proteins, are estimated to be modified with glycans [1]. The commonly accepted theory limited the ability of organisms to glycosylate their proteins to eukaryotes. This false conclusion originated from the fact that the most frequently studied prokaryotes, such as Escherichia coli, Bacillus subtilis, and Salmonella species were identified as nonglycosylated [2–4]. However, advances in analytical technologies and genome sequencing enabled the discovery of glycosylation in prokaryotes, such as bacteria. The existence of prokaryotic glycoproteins is no longer considered to be novel and is well documented, as rapid progress has been made in the past few years [5–7]. The first report on the occurrence of glycosylation in prokaryotes already appeared in 1976 by the discovery of surface-layer glycoproteins in the gram-negative Halobacterium salinarum [8]. Despite this breakthrough, limited interest in prokaryotic glycoprotein research may have been initially caused by the fact that archeal surface-layer glycoproteins originate from nonpathogenic organisms with no medical relevance. However, intensive medical research has demonstrated that some of the prokaryotes synthesizing glycoproteins are important pathogens, including a species of Mycobacterium, Campylobacter, Streptococcus, and Neisseria. Current enhanced interest in glycoprotein discovery in bacteria can be explained by a proven correlation between the presence of glycosylation and bacterial pathogenicity [9–14]. To date, a noticeable number of membrane-associated, surfaceassociated, exoenzymes, and even secreted glycoproteins from diverse bacterial species have been characterized. BSL3 and BSL4 Agents: Proteomics, Glycomics, and Antigenicity, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.

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6.2 Methodology 6.2.1 Analysis of Glycosylation

A significant breakthrough in the analysis of glycosylation has been achieved by combining chromatographic or electrophoretic separations with mass spectrometric approaches [15–17]. Over the past decade, mass spectrometry has emerged as a powerful tool for the analysis of large biomolecules, including glycoproteins. Both matrix-assisted laser desorption/ionization mass spectrometry and electrospray ionization mass spectrometry have become popular methods in the analysis of glycoproteins [18]. 6.2.1.1 Glycoproteomics General glycoproteomic approaches first involve: (i) the specific detection of putative glycoproteins or (ii) the isolation or enrichment of a glycoprotein pool.

1)

In order to determine the presence of glycosylation, the use of gel electrophoresis followed by carbohydrate-specific staining remains a highly desirable method of choice. The technique utilizes the ability of gel-based separation to resolve protein entities into separate spots that can be further analyzed individually. Currently, many carbohydrate-specific detection kits that are based on a reaction between the glycan moiety of separated glycoproteins and a fluorophore, a chromophore or a tag, are commercially available [19]. As an example, the Pro-Q Emerald glycoprotein stain (available from Molecular Probes) reacts with periodate-oxidized carbohydrate groups, creating a bright green fluorescent signal on glycoproteins. Another alternative glycosylationdetecting system is an immunoblotting-like approach utilizing a variety of lectins. Lectins comprise a group of proteins that enable not only the isolation of glycoproteins, but they also provide insight into the structure of glycan moieties due to the unique specificities toward various oligosaccharide patterns. For instance, the DIG glycan differentiation kit, available from Roche Diagnostics, utilizes five different lectins: Galanthus nivalis agglutinin, Sambucus nigra agglutinin, Maackia amurensis agglutinin, Peanut agglutinin, and Datura stramonium agglutinin. Each lectin has a different specificity towards the glycan moiety of a glycoprotein.

2)

The isolation of low-abundant glycoproteins from the highly abundant remainder of the nonglycosylated proteome is an essential step in the analysis of glycoproteins [20]. To date, several chromatographic separation techniques have been developed for this purpose. Among them, lectin affinity chromatography is best employed. The most frequently used lectins, Con A and wheat germ agglutinin, possess broad specificities towards the majority of eukaryotic glycoproteins [21, 22]. As bacterial glycosylations can be unique, they may not be recognized by either the above-mentioned lectins, which

6.2 Methodology

may possess different recognizable glycan patterns. The use of lectins, moreover, suffers from the limitation of trapping O-glycostructures. Therefore, formerly developed chemical isolation strategies, allowing the capture and isolation of the entire glycoprotein pool, regardless of the glycan structure, are desirable. Some of those strategies have been recently summarized by Bond and Kohler [23]. For instance, the hydrazide-derivatized magnetic beads have been introduced for glycoprotein enrichment. This two-step method utilizes the oxidation of cis-diol groups of carbohydrates to aldehydes with periodate described by Bobbitt [24] followed by the reaction of aldehydes with immobilized hydrazide groups to form covalent hydrazon bonds, reviewed by O’Shannessy [25]. The use of an aminophenylboronic acid (APBA) also appears to be useful for selective trapping glycoproteins. APBA covalently binds galactose, and mannose-equipped glycans containing cis-diol groups to form boronic diesters that are stable under alkaline conditions. In contrast to the hydrazide chemistry, this method enables the effective elution of the entire glycoprotein from magnetic beads under acidic conditions due to the reversible nature of the diesters [26–29]. On-gel/blot detected (i) or on-column isolated (ii) putative glycoproteins are then subjected to in-gel or in-solution proteolytic digestion, respectively. The resulting (glyco)peptides are subsequently either analyzed by mass spectrometry or, if analyzing a complex mixture, separated using nano-high-performance liquid chromatography followed by mass spectrometry as a means for the identification of putative glycoproteins. The use of mass spectrometry as an effective tool in glycoproteomics has been described in comprehensive reviews by Mechref and Novotny [18], Harvey [30], and Hitchen and Dell [31]. 6.2.1.2 Glycomics The structures of the oligosaccharide moieties of glycosylated proteins are investigated after their enzymatic or chemical release by glycomics [18]. Peptide-Nglycosidase F (PNGase F), the most commonly used enzyme to release the glycans, liberates intact N-glycans from eukaryotic peptide backbones by cleavage of the amidic bond between the terminal N-acetylglucosamine of a sugar moiety and an asparagine of a protein. As the carbohydrate–peptide linkages in bacteria are often different from those in eukaryotes, PNGase F may not be useful for this purpose. Similarly, other endoglycosidases, such as PNGase A and endoglycosidase H, may not be applicable. As a universal glycan-releasing method in the analysis of bacterial glycosylations that enables the removal of both N- and O-glycosylations [32], chemical cleavage may be generally useful. Among these chemical approaches, β-elimination has been successfully performed in eukaryotes as well as in bacteria [33]. However, the carbohydrate chains containing the unusual monosaccharide as a linker to protein moiety may not always be affected by β-elimination, as demonstrated in Campylobacter jejuni [34]. Once the glycans are released, their subsequent characterization such as sequencing and linkage analysis can be addressed [31].

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The complex mixture of released glycans has to be separated from the deglycosylated and nonglycosylated protein/peptide residues. For this purpose, various chromatographic techniques including hydrophilic interaction chromatography [35], reversed-phase liquid chromatography, size-exclusion chromatography, and HPLC on graphitized carbon [36] are widely used. Glycans are then identified by mass spectrometry. For their structural analysis, matrix-assisted laser desorption/ iozination (MALDI) has been proven useful [32]. The most widely used matrix in the analysis of neutral glycans is 2,5-dihydroxybenzoic acid (DHB). This matrix, however, is not ideal for acidic glycans as the detection limit is poor compared to neutral glycans. Also fragmentation can occur with losses of acidic groups. Hence, for the analysis of acidic glycans, 2,4,6-trihydroxyacetophenone (THAP) is a widely used matrix [37]. Measurements should be performed in both positive- and negative-ion mode when analyzing native glycans, with respect to their neutral or acidic nature (i.e., the presence of sialic acids). The other mass spectrometric technique that has the potential to be utilized in the analysis of native glycans is ESI/MS [38–40]. As a first pioneering work on that field, Duffin et al. [38] detected sialylated glycans in the negative ion mode while asialylated glycans were detected in the positive ion mode. By using tandem MS, the structures of studied oligosaccharides were investigated.

6.3 Bioinformatics

In the glycoproteomic approach, analogous to protein identification, putative glycoproteins are identified by searching the peptide masses obtained by mass spectrometry against sequence databases. In the glycomic approach, the determination of glycan structures from acquired MS spectra is achieved through the GlycoMod Tool, an ExPASy software tool of the Swiss Institute of Bioinformatics (http:// expasy.org/tools/glycomod/). This software can predict all possible oligosaccharide structures that occur on proteins from their experimentally obtained masses. The monosaccharide composition of glycans, however, should be confirmed by either exoglycosidase sequencing [41] or tandem mass spectrometry (MALDI-PSD and MALDI-CID). New software that enables us to predict the structure of glycans from the MS/MS data is available as SimGlycan (http://www.premierbiosoft.com/ glycan/index.html).

6.4 Application of Glycoproteomic Approach Utilizing ProQ-Emerald and DIG Glycan Kits to Francisella tularensis (F. tularensis)

The present study is focused on the comparative glycoproteomic analysis of three F. tularensis bacterial strains: live vaccine strain (LVS), strain FSC200, and a highly virulent strain, SCHU S4. The outermost surface of bacteria and both extra- and

6.4 ProQ-Emerald and DIG Glycan Kits to Francisella tularensis (F. tularensis)

intra-cellular membranes are considered to be glycosylated rather than cellular proteins. Moreover, the membrane-localized proteins primarily mediate many fundamental biological cellular processes including host–parasite interactions, invasiveness, cell signaling, or induction of the immune response of infected organisms. Therefore, our study is focused on the analysis of fractions enriched in membrane proteins. 6.4.1 Bacterial Cultures and Sample Preparation

Three subspecies of F. tularensis were analyzed. F. tularensis strains LVS and FSC200 were acquired from the Francisella strain collection (Sweden) and F. tularensis strain SCHU S4 was acquired from the Collection of Animal Pathogenic Microorganisms, Veterinary Research Institute (Brno, Czech Republic). Highly virulent SCHU S4 strain bacteria were grown, harvested, and lysed within a BioSafety level 3 containment facility. All strains were cultured in Chamberlain medium (12 h, 36.6 °C, 200 rpm) until the late logarithmic growth phase of bacteria. Bacterial cultures were harvested by centrifugation and pellets were washed three times with PBS, pH 7.5. 6.4.1.1 Preparation of Whole-Cell Lysates The bacteria of strain SCHU S4 were lysed by freeze–thawing repeatedly in liquid nitrogen vapors and cell debris along with undisrupted microbes were removed by centrifugation. In case of LVS and FSC200 strains, bacteria were lysed using a FrenchPress (16 000 psi). Undisrupted microbes were eliminated by centrifugation. 6.4.1.2 Preparation of Membrane-Enriched Fractions Fractions enriched in membrane proteins were prepared by sodium carbonate extraction according to method described by Molloy et al. [42]. Briefly, the supernatant was diluted with ice-cold 0.1 M sodium carbonate, pH 11. The resulting solution was gently stirred at 4 °C for 1 h. Carbonate-treated membranes were collected by ultracentrifugation at 46 000 rpm for 1 h at 4 °C. The supernatant was discarded and the membrane pellet was resuspended in ice-cold 50 mM Tris/HCl, pH 8.0, to remove contaminants, and then collected by centrifugation at 46 000 rpm for 30 min. The final membrane protein-containing pellet was solubilized in rehydration buffer for 2D electrophoresis. 6.4.2 Analysis of Glycoproteins in Fractions Enriched in Membrane Proteins 6.4.2.1 Mini Two-Dimensional Gel Electrophoresis For solubilization of sparingly soluble membrane proteins a rehydration buffer was used containing 7 M urea, 2 M thiourea, 1% (w/v) ASB-14, 4% (w/v) CHAPS, 1% (w/v) DTT, 1% Ampholytes pH 3–10 (Bio-Rad; Hercules, Calif., USA), and

71

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6 Analysis of Francisella tularensis Glycoproteins

0.5% Pharmalytes pH 8–10.5 (Amersham Biosciences; Uppsula, Sweden). Typically, proteins differing in their pI values were separated using polyacrylamide gel strips with an immobilized pH gradient pH 3–10 (GE Healthcare; Uppsala, Sweden) in the first dimension. Following IEF, the IPG strips were equilibrated in equilibration buffer containing 2% SDS, 50 mM Tris/HCl, pH 8.8, 6 M urea, 30% glycerol, and 1% DTT. This was immediately followed by a second equilibration of strip in the same solution containing 4% iodoacetamide in the place of DTT. In the second dimension, the IPG strips were embedded onto 12% homogeneous polyacrylamide gels, thus allowing protein separation according to their molecular weight. 6.4.2.2 Glycoprotein Detection Using DIG Glycan Differentiation Kit The separated proteins were transferred onto a BioTrace NT (Gelman Sciences) 0.45 μm nitrocellulose membrane and stained with the Ponceau S solution to visualize the success of protein transfer. Several lectins, Sambuccus nigra agglutinin (SNA), Peanut agglutinin (PNA), Datura stramonium agglutinin (DSA), and Maackia amurensis agglutinin (MAA), were utilized in this study for the detection of glycoproteins. Transferrin, asialofetuin, and fetuin were used as positive controls for SNA, PNA, and DSA and MAA lectins, respectively. As a negative control, recombinant FTT Igl C protein from E. coli was used. To avoid nonspecific binding, the nitrocellulose membrane was incubated in blocking solution overnight. After washing, the membrane was incubated in the digoxigenin-labeled lectin solution for 1 h. Unbound lectins were removed by washing. The lectin-retained membrane glycoproteins were then incubated with anti-digoxigenin-labeled alkaline phosphatase for 1 h. Following repeated washes, a staining solution containing the substrate NBT/BCIP was used to visualize the presence of glycoproteins. 6.4.2.3 Glycoprotein Detection Using Pro-Q Emerald 300 Glycoprotein Stain Kit In-gel separated proteins were oxidized for 30 min. After washing to remove residual periodate, the gels were incubated in Pro-Q Emerald 300 staining buffer for 2 h and consequently washed. Stained gels were visualized using a CCD camera Image station 2000R (Estman Kodak). After detection of glycoproteins, gels were stained with SYPRO Ruby protein gel stain. 6.4.2.4 Glycoprotein Identification by Mass Spectrometry Protein spots corresponding to in-gel (Pro-Q Emerald staining) or on-blot (DIG Glycan kit) detected putative glycoproteins were excised from the representative gels and were subjected to in-gel tryptic digestion. The mass spectra were recorded in reflectron mode on a 4800 MALDI-TOF/TOF mass spectrometer (Applied Biosystems) with CHCA as the matrix. Acquired data were processed using GPS Explorer™ Software ver. 3.6 (Applied Biosystems). Database searching was performed using the same software platform against the F. tularensis genome databases.

6.5 Results

6.5 Results 6.5.1 Glycoprotein Detection Using DIG Glycan Differentiation Kit

Three subspecies of F. tularensis were analyzed: the highly virulent subspecies tularensis strain SCHU S4, a less virulent subspecies holarctica strain LVS, and FSC200. We found differences in the detection of membrane-localized glycoproteins among the strains. Representative lectin-specific putative glycoproteins identified in F. tularensis strain LVS are summarized in Table 6.1. Thus, the use of lectins with different selectivities provided some insight into the type of carbohydrate residues likely to be present in the glycan moiety (Figure 6.1). 6.5.2 Glycoprotein Detection Using Pro-Q Emerald 300 Glycoprotein Stain Kit

The presence of glycoproteins was detected by carbohydrate-specific fluorescent staining. In this study, two subspecies of F. tularensis were analyzed: the less Table 6.1

Putative glycoproteins found in F. tularensis strain LVS.

Identified putative glycoprotein

Gene name

Lectin specificity

Molecular weight (kDa)

pI

Intracellular growth locus, subunit B

FTL0112

SNA

57.8

4.76

H(+)-transporting two-sector ATPase

FTH1734

SNA

55.4

4.94

Glycerophosphodiester phosphodiesterase

FTH1463

SNA, DSA, PNA, MAA

38.9

5.39

Ribose-phosphate pyrophosphokinase

FTL0949

SNA, DSA, PNA, MAA

34.8

5.68

Recombinase A protein

FTH1750

PNA, MAA

38.7

5.97

Fatty acid/phospholipid synthesis protein lsX

FTH1117

PNA

37.7

9.17

Membrane protease subunit HflC

FTH0887

PNA

34.5

9.35

Succinate dehydrogenase iron–sulfur protein

FTL1785

PNA

26.4

8.42

50S ribosomal protein L5

FTL0248

PNA

19.9

9.75

Outer membrane associated protein

FTL1328

MAA

41.3

5.59

73

74

6 Analysis of Francisella tularensis Glycoproteins

FTL0112

FTL1734 FTL0949

FTL1463

FTL0112 FTL0949

LVS Figure 6.1 SNA-specific putative glycoproteins of F. tularensis strain LVS.

FTL1096

FTL0112 FTL0325 FTL0073 FTT0583

Sypro Ruby

LVS

Figure 6.2 Representative mini 2-D gel of membrane proteins isolated from F. tularensis strain LVS with detected glycosylation.

virulent subspecies holarctica strain LVS (Figure 6.2) and the highly virulent subspecies F. tularensis strain SCHU S4 (Figure 6.3). We found differences in the detection of putative glycoproteins between the strains. Tables 6.2 and 6.3 summarize the identified putative glycoproteins.

6.6 Conclusion

Studying bacterial glycoproteins has gained importance due to the recently revealed role of these proteins in the host–pathogen interactions. Innovative proteomic and

6.6 Conclusion

FTT1103

FTT0831

Sypro Ruby

FTT0583

SCHU

Figure 6.3 Representative mini 2-D gels of membrane proteins isolated from F. tularensis

strain SCHU with detected glycoproteins.

Table 6.2

Putative glycoproteins found in F. tularensis strain LVS.

Identified putative glycoprotein

Gene name

MW (kDa)

pI

Conserved hypotetical lipoprotein OMPA family protein Intracellular growth locus, subunit B Outer membrane associated protein FopA Hypothetical membrane protein

FTL1096 FTL0325 FTL0112 FTT0583 FTL0073

38.7 46.8 58.9 41.3 27.6

5.23 6.27 4.69 5.13 4.89

Table 6.3

Putative glycoproteins found in F. tularensis strain SCHU.

Identified putative glycoprotein

Gene name

MW (kDa)

pI

Conserved hypotetical lipoprotein OMPA family protein Outer membrane associated protein FopA

FTT1103 FTT0831 FTT0583

38.7 46.8 41.3

5.23 6.27 5.13

glycomic technologies, combining separation methodology such as twodimensional gel electrophoresis and lectin affinity or carbohydrate-specific detection with mass spectrometric analysis, have provided sufficient capabilities for the identification and characterization of putative glycosylated proteins in the gramnegative pathogenic bacterium F. tularensis. In our study, the presence of glycoproteins in F. tularensis was investigated using two glycoproteomic approaches. First, a variety of lectins to detect a presence of

75

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6 Analysis of Francisella tularensis Glycoproteins

glycosylations were used. Second, the presence of glycoproteins was detected by carbohydrate-specific fluorescent staining. Thus far, the analysis of F. tularensis proteins has revealed differences in presence of membrane-associated glycoproteins among three analyzed bacterial subspecies (subsp. holarctica, strains LVS and FSC200; subsp. tularensis, strain SCHU). Further studies are planned to characterize the glycan composition and the structural elucidation of the detected proposed glycoproteins.

Acknowledgments

This work was financially supported by the Ministry of Education, Youth and Sport, Czech Republic (ME08105 and MSMT 0021627502), and the Ministry of Defence, Czech Republic (FVZ0000604).

References 1 Apweiler, R., Hermjakob, H., and

2

3

4

5

6

7

8

Sharon, N. (1999) On the frequency of protein glycosylation, as deduced from analysis of the SWISS-PROT database. Biochim. Biophys. Acta, 1473 (1), 4–8. Messner, P. (2004) Prokaryotic glycoproteins: unexplored but important. J. Bacteriol., 186 (9), 2517–2519. Eichler, J. (2003) Facing extremes: archaeal surface-layer (glyco)proteins. Microbiology, 149 (Pt 12), 3347–3351. Messner, P., and Schäffer, C. (2001) Prokaryotic glycoproteins, in Progress in the Chemistry of Organic Natural Products (ed. W. Herz, H. Falk, G.W. Kirby, R.E. Moore, and C. Tamm), Springer-Verlag, Vienna, pp. 51–124. Virji, M. (1997) Post-translational modifications of meningococcal pili. Identification of common substituents: glycans and alpha-glycerophosphate – a review. Gene, 192 (1), 141–147. Parge, H.E., Forest, K.T., Hickey, M.J., et al. (1995) Structure of the fibreforming protein pilin at 2.6 A resolution. Nature, 378 (6552), 32–38. Castric, P. (1995) pilO, a gene required for glycosylation of Pseudomonas aeruginosa 1244 pilin. Microbiology, 141 (Pt 5), 1247–1254. Mescher, M.F., and Strominger, J.L. (1976) Purification and characterization

9

10

11

12

13

of a prokaryotic glucoprotein from the cell envelope of Halobacterium salinarium. J. Biol. Chem., 251 (7), 2005–2014. Power, P.M., Roddam, L.F., Rutter, K., et al. (2003) Genetic characterization of pilin glycosylation and phase variation in Neisseria meningitidis. Mol. Microbiol., 49 (3), 833–847. Schmidt, M.A., Riley, L.W., and Benz, I. (2003) Sweet new world: glycoproteins in bacterial pathogens. Trends Microbiol., 11 (12), 554–561. Dobos, K.M., Khoo, K.H., Swiderek, K.M., Brennan, P.J., and Belisle, J.T. (1996) Definition of the full extent of glycosylation of the 45-kilodalton glycoprotein of Mycobacterium tuberculosis. J. Bacteriol., 178 (9), 2498–2506. Benz, I., and Schmidt, M.A. (2002) Never say never again: protein glycosylation in pathogenic bacteria. Mol. Microbiol., 45 (2), 267–276. Kuo, C., Takahashi, N., Swanson, A.F., Ozeki, Y., and Hakomori, S. (1996) An N-linked high-mannose type oligosaccharide, expressed at the major outer membrane protein of Chlamydia trachomatis, mediates attachment and infectivity of the microorganism to HeLa cells. J. Clin. Invest., 98 (12), 2813–2818.

References 14 Aas, F.E., Vik, A., Vedde, J., Koomey, M.,

15

16

17

18

19

20

21

22

23

and Egge-Jacobsen, W. (2007) Neisseria gonorrhoeae O-linked pilin glycosylation: functional analyses define both the biosynthetic pathway and glycan structure. Mol. Microbiol., 65 (3), 607–624. Madera, M., Mechref, Y., Klouckova, I., and Novotny, M.V. (2007) Highsensitivity profiling of glycoproteins from human blood serum through multiplelectin affinity chromatography and liquid chromatography/tandem mass spectrometry. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci., 845 (1), 121–137. Claverol, S., Burlet-Schiltz, O., Gairin, J.E., and Monsarrat, B. (2003) Characterization of protein variants and post-translational modifications: ESI-MSn analyses of intact proteins eluted from polyacrylamide gels. Mol. Cell. Proteomics, 2 (8), 483–493. Novotny, M.V., and Mechref, Y. (2005) New hyphenated methodologies in high-sensitivity glycoprotein analysis. J. Sep. Sci., 28 (15), 1956–1968. Mechref, Y., and Novotny, M.V. (2002) Structural investigations of glycoconjugates at high sensitivity. Chem. Rev., 102 (2), 321–369. Steinberg, T.H., Pretty On Top, K., Berggren, K.N., et al. (2001) Rapid and simple single nanogram detection of glycoproteins in polyacrylamide gels and on electroblots. Proteomics, 1 (7), 841–855. Burlingame, A.L. (1996) Characterization of protein glycosylation by mass spectrometry. Curr. Opin. Biotechnol., 7 (1), 4–10. Mechref, Y., Madera, M., and Novotny, M.V. (2008) Glycoprotein enrichment through lectin affinity techniques. Methods Mol. Biol., 424, 373–396. Madera, M., Mechref, Y., and Novotny, M.V. (2005) Combining lectin microcolumns with high-resolution separation techniques for enrichment of glycoproteins and glycopeptides. Anal. Chem., 77 (13), 4081–4090. Bond, M.R., and Kohler, J.J. (2007) Chemical methods for glycoprotein discovery. Curr. Opin. Chem. Biol., 11 (1), 52–58.

24 Bobbitt, J.M. (1956) Periodate oxidation

25

26

27

28

29

30

31

32

33

34

of carbohydrates. Adv. Carbohydr. Chem., 48 (11), 1–41. O’Shannessy, D.J. (1990) Hydrazidoderivatized supports in affinity chromatography. J. Chromatogr., 510, 13–21. Sparbier, K., Wenzel, T., and Kostrzewa, M. (2006) Exploring the binding profiles of ConA, boronic acid and WGA by MALDI-TOF/TOF MS and magnetic particles. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci., 840 (1), 29–36. Rawn, J.D., and Lienhard, G.E. (1974) The binding of boronic acids to chymotrypsin. Biochemistry, 13 (15), 3124–3130. Sparbier, K., Koch, S., Kessler, I., Wenzel, T., and Kostrzewa, M. (2005) Selective isolation of glycoproteins and glycopeptides for MALDI-TOF MS detection supported by magnetic particles. J. Biomol. Tech., 16 (4), 407–413. Lee, J.H., Kim, Y., Ha, M.Y., Lee, E.K., and Choo, J. (2005) Immobilization of aminophenylboronic acid on magnetic beads for the direct determination of glycoproteins by matrix assisted laser desorption ionization mass spectrometry. J. Am. Soc. Mass. Spectrom., 16 (9), 1456–1460. Harvey, D.J. (2001) Identification of protein-bound carbohydrates by mass spectrometry. Proteomics, 1 (2), 311–328. Hitchen, P.G., and Dell, A. (2006) Bacterial glycoproteomics. Microbiology, 152 (Pt 6), 1575–1580. Dwek, R.A., Edge, C.J., Harvey, D.J., Wormald, M.R., and Parekh, R.B. (1993) Analysis of glycoprotein-associated oligosaccharides. Annu. Rev. Biochem., 62, 65–100. Liu, X., McNally, D.J., Nothaft, H., et al. (2006) Mass spectrometry-based glycomics strategy for exploring N-linked glycosylation in eukaryotes and bacteria. Anal. Chem., 78 (17), 6081–6087. Young, N.M., Brisson, J.R., Kelly, J., et al. (2002) Structure of the N-linked glycan present on multiple glycoproteins in the gram-negative bacterium, Campylobacter jejuni. J. Biol. Chem., 277 (45), 42530–42539.

77

78

6 Analysis of Francisella tularensis Glycoproteins 35 Boersema, P.J., Mohammed, S., and

39 Weiskopf, A.S., Vouros, P., and Harvey,

Heck, A.J. (2008) Hydrophilic interaction liquid chromatography (HILIC) in proteomics. Anal. Bioanal. Chem., 391 (1), 151–159. 36 Ninonuevo, M., An, H., Yin, H., et al. (2005) Nanoliquid chromatography-mass spectrometry of oligosaccharides employing graphitized carbon chromatography on microchip with a high-accuracy mass analyzer. Electrophoresis, 26 (19), 3641–3649. 37 Papac, D.I., Wong, A., and Jones, A.J. (1996) Analysis of acidic oligosaccharides and glycopeptides by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Anal. Chem., 68 (18), 3215–3223. 38 Duffin, K.L., Welply, J.K., Huang, E., and Henion, J.D. (1992) Characterization of N-linked oligosaccharides by electrospray and tandem mass spectrometry. Anal. Chem., 64 (13), 1440–1448.

D.J. (1997) Characterization of oligosaccharide composition and structure by quadrupole ion trap mass spectrometry. Rapid Commun. Mass Spectrom., 11 (14), 1493–1504. 40 Viseux, N., de Hoffmann, E., and Domon, B. (1998) Structural assignment of permethylated oligosaccharide subunits using sequential tandem mass spectrometry. Anal. Chem., 70 (23), 4951–4959. 41 Kuster, B., Naven, T.J., and Harvey, D.J. (1996) Rapid approach for sequencing neutral oligosaccharides by exoglycosidase digestion and matrixassisted laser desorption/ionization time-of-flight mass spectrometry. J. Mass Spectrom., 31 (10), 1131–1140. 42 Molloy, M.P., Herbert, B.R., Slade, M.B., et al. (2000) Proteomic analysis of the Escherichia coli outer membrane. Eur. J. Biochem., 267 (10), 2871–2881.

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Part Two Identification of Proteins and Glycans from Microorganisms as Candidate Molecules for Use in Detection/Diagnosis, Therapy, and Prophylaxis

81

7 Comparative Proteome Analysis of Strains with Differential Virulence Martin Hubalek and Ivona Pávková

7.1 Introduction

In comparative or quantitative proteomics the expression of proteins and their expression levels from different biological states are compared to understand various biological processes such as normal and diseased cells or tissues. It is widely used to find new diagnosis markers and to discover novel molecular targets of drugs. The number of methodologies suitable for comparative proteomics can be divided into two major categories: gel-based and gel-free methods [1, 2]. In gel-based methods, the proteins are separated by means of gel techniques, namely two-dimensional gel electrophoresis (2-DE). Commercially available software programs (e.g., Melanie, ImageMaster, PDQ) enable us to carry out an automated quantitative comparison of protein spots on 2-DE images. Today, the differential staining of gels with fluorescent dyes (difference gel electrophoresis; DIGE) is preferred to highlight differences in the spot pattern, as the protein labeling prior to running 2-DE and the addition of an internal standard eliminate gel to gel variations and reproducibility problems [3, 4]. Owing to its high resolution and the ability to separate proteins with distinct post-translational modifications, 2-DE remains a valuable method for comparative proteomic analyses. However, in spite of these advantages, it has a number of limitations and disadvantages, like the inability to detect low-abundant proteins, or proteins with extreme pI values and high molecular weight, or to separate hydrophobic membrane proteins with more than three transmembrane domains [1, 5]. To overcome the limitations of conventional 2-DE technology, gel-free proteomic approaches have been developed over the past years. For comparative proteomics, shotgun peptide sequencing, also referred to as shotgun proteomics, has been combined with stable isotope labeling techniques. Chemical labeling techniques with isotope-coded affinity tags (ICAT) and isobaric tags for relative and absolute quantitation (iTRAQ) and the metabolic method called stable isotope labeling by amino acids in cell culture (SILAC) are the most frequently used gel-free methods [6, 7].

BSL3 and BSL4 Agents: Proteomics, Glycomics, and Antigenicity, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.

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7 Comparative Proteome Analysis of Strains with Differential Virulence

According to the study of Kubota et al. [1], the coverage of the whole proteome analyzed by 2-DE looks similar to that obtained using shotgun sequencing. However, the ratio of overlap of identified proteins was relatively small (35% for 2-DE; 42% for ICAT analysis). Comparative proteomics have become a very popular tool in discovering proteins in relation to the pathogenicity of bacteria by comparing the protein patterns of non- or less-virulent strains and their pathogenic counterparts. The virulence factors in a number of pathogenic bacteria such as Neisseria meningitidis, Yersinia pestis, Mycobacterium tuberculosis, Pseudomonas aeruginosa, Chlamydia and Staphylococcus aureus have been elucidated. Most of the bacterial comparative studies are performed by 2-DE combined with MS. There are only limited studies based on the shotgun sequencing [8]. In the case of Francisella tularensis we attempted to map and compare proteins across the available subspecies and isolates. First, the comparative proteomic analysis of whole-cell lysates isolated from three subspecies of F. tularensis (subspp. tularensis, holarctica, mediaasiatica) using the 2-DE approach was performed in order to identify proteins that differ among these three individual subspecies [9]. As the known virulence factors are largely membrane proteins and membraneassociated proteins that are in general lower-abundant and hydrophobic compared to the cytosolic proteins, fractions enriched for membrane-associated proteins from F. tularensis subsp. tularensis and holarctica strains of different virulence were prepared and compared [10]. Both studies led to the identification of a number of differentially expressed proteins across the selected strains.

7.2 Methods

Both studies applied the same strategy of comparison. The protein samples were separated by isoelectric focusing in the first dimension using 18 cm IPG strips with a pH gradient of either pH 3–10 or pH 6–11. In the second dimension, a 9–16% gradient SDS-PAGE was used. Proteins were visualized by sensitive ammoniacal silver staining for comparative analysis or by Coomassie G-250 for MS identifications. The gels were scanned by a CCD camera and the data were analyzed by the Melanie III package. Statistical analysis was performed. Relative spot volumes (% vol) were used for spot quantitations. Normalized data for the matched spots were analyzed by Student’s t-test. Spots with a P value ≤ 0.01 were accepted as significantly different. When a spot was present in all images, only those groups showing relative spot volume differences more than twofold were accepted. For the identification, protein spots visualized by Coomassie staining were excised, destained, and digested with trypsin. Protein identification by peptide mass fingerprinting was performed on a MALDI-TOF mass spectrometer VoyagerDE STR (Perseptive Biosystems; Framingham, Mass., USA). The translated F. tularensis open reading frames were identified using the MS-FIT algorithm of the

7.2 Methods

Protein-Prospector program. A mass accuracy of 100 ppm was applied. A successful match required a minimum of four peptides, a MOWSE score > 1000, and MALDI-TOF coverage > 25%. For proteins not identified by MALDI-TOF-MS, LC-nanoESI-MS/MS was performed on CapLC QTOF Ultima API (Waters; Manchester, UK). Data were processed using the ProteinLynx Global Server 2.1. A 100% probability of match and a minimum two unique peptides matching fragmentation spectra were required for successful protein identification. Several online available databases or programs were used for the further characterization of selected proteins: B-PSORT v. 2.0.4. (www.psort.org) for the prediction of protein localization in bacteria, LipoP 1.0 server (http://www.cbs.dtu.dk/ services/LipoP) for the prediction of lipoproteins, Interproscan (http://www.ebi. ac.uk/InterProScan), Pfam database (http://www.sanger.ac.uk/Software/Pfam), protein–protein BLAST and PSI-BLAST programs in the nr database at NCBI to search for protein homologs in other bacterial genomes (http://www.ncbi. nlm.nih.gov/BLAST). The difference between the study of whole-cell lysates and the membraneassociated protein fraction lays mainly in the sample preparation prior to IEF. In both cases the cells were disintegrated using repeated cycles of freeze–thawing in liquid nitrogen. The whole-cell lysate samples were precipitated overnight in 20% TCA in acetone (−18 °C) containing 0.2% dithiothreitol, then solubilized in IEF buffer containing 9 M urea, 4% w/v CHAPS, and 70 mM DTT. The membrane fraction was obtained by ultracentrifugation at 115 000 g for 1 h at 4 °C after treatment with ice-cold sodium carbonate (pH 11). The pellet was solubilized in rehydration buffer containing 7 M urea, 2 M thiourea, 1% (w/v) ASB-14, 1% Triton-X100, 40 mM Tris, 2 mM triphosphine. The difference in sample preparation reflects the hydrophobic property of membrane proteins that form membrane vesicles after cell lysis. The treatment with sodium carbonate was used to convert closed membrane vesicles into open sheets, concomitantly stripping loosely attached peripheral proteins [11]. The membrane proteins were then recovered by ultracentrifugation and resolved in a rehydration buffer containing a different ratio of urea and thiourea and also a detergent cocktail to help proteins enter the gel strips for isoelectric focusing. The second difference between the studies was in the scope of the selected strains: 13 different isolates of Francisella tularensis representing three F. tularensis subspp. (tularensis, mediaasiatica, holarctica) were used for comparative proteome analysis (see Table 7.1). Four strains were analyzed in a membrane-associated proteins study: the highly virulent F. tularensis subsp. tularensis strain SCHU S4, the virulent subsp. holarctica strain 130, and the two avirulent subsp. holarctica strains 2062 and LVS.

83

84

7 Comparative Proteome Analysis of Strains with Differential Virulence Table 7.1 Francisella tularensis strains included in comparative proteome analysis of whole-cell

lysates. FSC no.a)

Subspecies

Strain information

Alternative strain designation

13 33 41 199 237 35 74 200 247 257 147 148 149

tularensis tularensis tularensis tularensis tularensis holarctica holarctica holarctica holarctica holarctica mediaasiatica mediaasiatica mediaasiatica

Isolated by Eigelsbach Squirrel, CDC standard, Georgia, USA Tick, British Columbia, Canada,1935 Mite, Slovakia, 1988 Human ulcer, Ohio, 1941 Beaver, Hamilton, Montana, 1976 Hare, Sweden, 1974 Human, Ljusdal, Sweden, 1998 Human, Vosges, France, 1993 Tick, Moscow area, Russia, 1949 Miday gerbil, Kazakhstan, 1965 Ticks, Central Asia, 1982 Hare, Central Asia, 1965

FAM standard SnMF Vavenby SE-221-38 Schu-S4 B423 A SVA T7 Rem nr 3001 SVA T20 503/840 543 240 120

a)

Francisella strain collection number at Swedish defense research agency.

7.3 Results 7.3.1 Whole-Cell Lysates

The silver-stained patterns of proteins resolved along the nonlinear pH 3–10 gradients yielded about 1800 distinct spots; the basic protein patterns then encompassed approximately 500 spots that were partially overlapped with a wide pH range protein spectrum. We observed evident differences in the protein spectra of individual F. tularensis subspecies that comprised both qualitative changes (mostly due to a presence of charge variants) and quantitative changes (Table 7.2, Figure 7.1). However, there was almost no variation observed within subspecies. This was reported also by Broekhuijsen et al. [12] on the genomic level. The detected alterations in protein expression among subspecies were classified as groups of spots specifically detected in individual F. tularensis subspecies and groups of spots common for subspp. tularensis and mediaasiatica (TM), subspp. tularensis and holarctica (TH), and subspp. mediaasiatica and holarctica (MH). In total, we identified 27 protein spots either specifically present or at significantly higher abundance in subspecies tularensis strains, 22 spots in subspecies mediaasiatica strains, and 26 spots in subspecies holarctica strains. The presence of these groups of spots on 2-DE maps of F. tularensis lysates enables unambiguous discrimination of individual F. tularensis subspecies. Furthermore, 27 identified proteins occurred specifically or were at higher abundance in group TM, 19 proteins characterized group

7.3 Results Table 7.2 Summary of the whole-cell lysate comparative study of F. tularensis subspp. Groups of spots characteristic for the subspp. tularensis, mediaasiatica and holarctica (T, M, H), or common to two subspp. (TM, HM, TH) are presented. The first column lists the number of detected spots, the second column contains the number of unique protein sequences as translated from the genome. The number of spots containing the same proteins found in different position due to a different charge (charge variants) and of spots that are more abundant in given group (differentially present) are listed in the following columns. The number of unique characteristic proteins (specifically present) is listed in last column.

Subspp.

Number of spots

Number of ORFs

Charge variants

Differentially present

Uniquely present

T M H TM HM TH Total

27 22 26 27 19 9 130

21 20 24 22 15 10 81

17 12 15 15 17 9 43

7 4 3 4 0 0 16

3 6 5 8 2 1 22

kDa WCL A

kDa WCL B 1769 1369

69 54

1539

1377

35 22

75 1241

0316 0373 0896

5.5

5.8

1674

16 11

14 6.6

pI

Figure 7.1 Representative silver-stained 2-D

map of the whole cell lysate proteins from F. tularensis subsp. tularensis strain SCHU S4: separated on (WCL A) wide pH gradient 3–10

0075

1157

21

1357

4.8 5.1

0607 1181 1229

0611

32

0435

1794

1241

48

0142 0336

6.0

6.7

8.4

8.8

0327

9.2 9.6 pI

and (WCL B) basic pH gradient 6–11. The proteins are labeled by their FTT numbers (corresponding to the FTT numbers in the first column in Table 7.4).

MH and nine proteins fell into group TH encompassing spots missing or diminished in mediaasiatica strains. For a summary of protein spot comparison see Table 7.2. Each row in Table 7.2 contains detailed information about specific spots for each group. To keep the article concise only the protein spots that were found characteristic for subspecies tularensis are listed in Table 7.4.

85

86

7 Comparative Proteome Analysis of Strains with Differential Virulence

7.3.2 Membrane-Associated Proteins

For all the analyzed strains, approximately 880 distinct spots could be observed after separation on pH 3–10 nonlinear gradients and subsequent silver-staining. The basic protein separation (pH 6–11) produced approximately 480 spots, partially overlapping the wide pH range protein spectrum. The most pronounced and most interesting differences were found in the comparison between the most virulent F. tularensis subsp. tularensis strain SCHU S4 and the three other subsp. holarctica strains where both quantitative and qualitative differences in expression of specific proteins could be detected (Table 7.3). In summary, six proteins were observed uniquely for the SCHU S4 strain and three identified proteins were significantly more abundant in the SCHU S4 strain. Several proteins were also found to have different mass and charge variants on the 2-DE gel images. Among these, five proteins showed different charge and mass variants and seven additional proteins showed only an acidic or basic shift in the SCHU S4 strain compared to the subsp. holarctica strains. The expression of nine identified proteins was significantly lower in the SCHU S4 strain and the spots for six other proteins could be found only in subsp. holarctica strains (Figure 7.2).

7.4 Discussion

F. tularensis subspecies exhibit marked differences in their virulence, but they display a very close phylogenetic relationship and genomic similarity [13]. In both presented studies the aim was to find proteins that differ between the subtypes and possibly relate the findings to the virulence. The proteins specifically expressed in tularensis subspecies strains are the most noticeable as they could significantly contribute to enhanced virulence of these strains (Tables 7.4 and 7.5). With the analysis methods used in our studies, we were able to identify 27 protein spots specifically presented in whole-cell lysates and 21 protein spots specifically preTable 7.3 Differential membrane-associated proteins of F. tularensis subsp.

tularensis SCHU S4 in comparison with the subsp. holarctica strains 130, 2062 and LVS. Category

Number of differential proteins

Specifically present in SCHU S4 At higher abundance in SCHU S4 Charge and mass variants Charge variants At lower abundance in SCHU S4 Not found in SCHU S4

6 3 5 7 9 6

7.4 Discussion MP A

75

1484

MP B

69 54

48

1483

0380

0380

1103 1676

32

1591

35 0209

21

1666

1157

0373

16 11 kDa

1260

3

1346,1701

1651 0903

1359,1714

0245 0365 1357,1712

22

14 kDa

1043 0018

0503

pI

Figure 7.2 Representative silver-stained 2-D

map of the fraction enriched in membraneassociated proteins from F. tularensis subsp. tularensis strain SCHU S4: separated on wide pH gradient 3–10 (MP A) and basic pH

6

pI

11

10

gradient 6–11 (MP B). The proteins are labeled by their FTT numbers (corresponding to the FTT numbers in the first column in Table 7.5).

sented in fractions enriched for membrane-associated proteins isolated from subsp. tularensis strains. Three proteins from whole-cell lysates and six proteins from fractions enriched in membrane proteins composed a group of subsp. tularensis which specifically present spots whose counterparts in less virulent subspecies were not detected (labeled as SP in Tables 7.4 and 7.5). These proteins might be responsible for the graduated virulence of subsp. tularensis strains and might provide novel targets for vaccine development and antigens that could be used for subspecies-specific diagnosis. 4-Hydroxy-3-methylbut-2-en-1-yl diphosphate synthase is involved in a nonmevalonate terpenoid biosynthesis pathway [14]. From this point of view it is interesting to note that in vivo infection with F. tularensis leads to a significant increase in levels of Vgamma9 Vdelta2 cells within 7–18 days after the onset of disease. Powerful stimuli of these cells are nonpeptidic pyrophosphorylated molecules [15]. The FTT1157 type IV pili lipoprotein homologous to PilP proteins is believed to function as chaperone in the assembly of the outer membrane secretin, a component necessary for type IV pili biogenesis [16]. These proteins are also known to be directly linked to virulence, for example, in Pseudomonas aeruginosa, Neisseria gonorrhoeae, and other pathogens [17]. In F. tularensis the mutation of pilin building block proteins was shown to impair the ability of a subsp. holarctica strain to disseminate the infection to the spleen in mice [18]. The function and significance of the 3-hydroxyisobutyrate dehydrogenase homolog (FTT1666) is uncertain, although the unexpected signature common for serine/threonine and tyrosine protein kinases could indicate a role in a signaling pathway for this

87

88

7 Comparative Proteome Analysis of Strains with Differential Virulence

Differential proteins of subsp. tularensis from whole-cell lysate study as compared to proteins in subspp. holarctica and mediaasiatica. This table contains identification data of protein spots listed in row 1 of Table 7.2.

Table 7.4

FTTa)

Protein nameb)

MW/pIc)

pH (IPG)d)

Profilee)

0435

Carbon/nitrogen hydrolase family protein

32/5.6

3–10

SP

0607

4-Hydroxy-3-methylbut-2-en-1-yl diphosphate synthase

43/8.6

6–11

SP

1157

Type IV pili lipoprotein

20/9.0

6–11

SP

1769

ClpB protein

69/5.3

3–10

DP

1369

Transketolase

65/5.6; 64/5.7

3–10

DP, DP

0611

Beta-lactamase

32/7.4

6–11

DP

1229

Thymidylate synthase

31/8.4

6–11

DP

0327

50S ribosomal protein L23

11/9.2

6–11

DP

0142

50S ribosomal protein L10

17/8.8

6–11

DP

1539

Putative uncharacterized protein

54/5.1; 54/5.2

3–10

CV, CV

1377

3-Oxoacyl-[acyl-carrier-protein] synthase II

52/5.6

3–10

CV

1354, 1712

Intracellular growth locus, subunit C

22/5.7; 22/5.9

3–10

CV, CV

0316

Ribosome recycling factor

21/5.1

3–10

CV

0373

Nucleoside diphosphate kinase

17/5.7

3–10

CV

0896

Phosphoribosylaminoimidazole carboxylase,catalytic subunit

16/5.7

3–10

CV

1794

Heat shock protein

15/5.5; 14/5.3

3–10

CV, CV

1241

Serine hydroxymethyltransferase

48/6.2; 48/6.5; 48/6.7

3–10 6–11

CV, CV, CV

1181

Gamma-glutamyltranspeptidase

40/8.6

6–11

CV

0075

Succinate dehydrogenase putative iron sulfur subunit

30/8.6

6–11

CV

1674

6,7-Dimethyl-8-ribityllumazine synthase (Riboflavin synthase)

16/8.0

6–11

CV

0336

50 S ribosomal protein L24

12/9.1

6–11

CV

a) Loci correspond to locus tag designations for predicted coding sequences in the SCHU S4 genome sequence. b) Name of the protein. c) Theoretical molecular weight (kDa) and pI. d) pH range of IPG strip where protein was identified; if there are two strips enclosed in record, protein was found as overlapping on both 2DE maps. e) Differential profile of variable protein spots; SP – proteins specifically present in subspecies or in combination of subspecies and not detected elsewhere, CV – charge variant of protein identified in different subspecies at different position on gel, DP – differentially present spots detected at higher abundance than in other subspecies or combination of subspecies (all spots belong to the category of significantly different at P < 0.01 and minimally twofold different in value of normalized volume).

7.4 Discussion

89

Differential membrane-associated proteins of F. tularensis subsp. tularensis in comparison with the subsp. holarctica strains.

Table 7.5

FTTa)

Protein nameb)

MW/pIc)

pH (IPG)d)

Profilee)

1260

Hypothetical lipoprotein

16 042/5.79

3–10

SP

1666

3-Hydroxyisobutyrate dehydrogenase

33 478/6.59

3–10

SP

0018

Secretion protein

40 039/9.18

6–11

SP

1651

Conserved hypothetical protein

23 098/9.35

6–11

SP

0903

Hypothetical protein

19 351/9.40

6–11

SP

1157

Type IV pili lipoprotein

23 007/9.58

6–11

SP

1346, 1701

Hypothetical protein

14 503/8.41

3–10

DP

0380

NAD-specific glutamate dehydrogenase

49 108/6.49

3–10 6–11

DP

1043

FKBP-type peptidyl-prolyl cis-trans isomerase family protein

29 327/8.94

6–11

DP

1103

Conserved hypothetical lipoprotein

38 720/5.23

3–10

CV

1676

Hypothetical membrane protein

37 469/6.56

3–10

CV

1591

Lipoprotein

41 624/4.58

3–10

CV

0365

Phenol hydroxylase

27 712/5.63

3–10

CV

1484

Pyruvate dehydrogenase, E2 component

67 252/4.77

3–10

CV

1483

Dihydrolipoamide dehydrogenase

50 485/5.62

3–10

CV

0209

Periplasmic solute binding family protein

33 766/5.46

3–10

CV

0245

Universal stress protein

30 187/5.52

3–10

CV

1354, 1712

Intracellular growth locus, subunit C, IglC

22 433/5.94

3–10

CV

0503

Succinyl-CoA synthetase, subunit α

30 095/6.10

3–10

CV

0373

Nucleoside diphosphate kinase

15 527/5.94

3–10

CV

1359, 1714

Intracellular growth locus, subunit A, IglA

22 419/8.84

6–11

CV

a) Loci correspond to locus tag designations for predicted coding sequences in the SCHU S4 genome sequence. b) Name of the protein. c) Theoretical molecular weight (kDa) and pI. d) pH range of IPG strip where protein was identified; if there are two strips enclosed in record, protein was found as overlapping on both 2DE maps. e) Differential profile of variable protein spots; SP – proteins specifically present in subspecies or in combination of subspecies and not detected elsewhere; CV – charge variant of protein identified in different subspecies at different position on gel; DP – differentially present spots detected at higher abundance than in other subspecies or combination of subspecies (all spots belong to the category of significantly different at P < 0.01 and minimally twofold different in value of normalized volume).

90

7 Comparative Proteome Analysis of Strains with Differential Virulence

protein [19]. The protein encoded by FTT1651 was found to be a homolog of a group of putative heme-binding proteins and could be therefore involved in iron uptake. The secretion protein FTT0018 is homologous to the membrane fusion proteins that function as components in efflux pumps that might export substances which are toxic to the bacterium. Other subsp. tularensis-specific protein spots were found at a significantly higher amount in comparison to their counterparts detected in mediaasiatica and holarctica strains (labeled as DP in Tables 7.4 and 7.5). The beta-lactamase precursor exhibits a broad spectrum of hydrolytic activity, recognizing cephalosporins, penicillins, monobactams, and carbapenems as substrates [20], like thymidylate synthase which is necessary for intracellular growth and survival of Salmonella typhimurium in vitro in both professional phagocytes and epithelial cells [21]. The ClpB protein was found to be essential for intramacrophage growth of F. tularensis novicida [22]. A predicted lipoprotein is encoded by the two identical genes FTT1346 and FTT1701 localized in the 33.9-kb duplicated Francisella pathogenicity island [23]. The FKBP-type peptidyl-prolyl cis-trans isomerase family protein FTT1043 has a homology to a macrophage infectivity potentiator which is a virulence factor for several pathogens, including Legionella pneumophila [24]. Another 17 whole-cell lysate protein spots and 12 protein spots from fractions enriched in membrane proteins had their mass and/or charge variant counterparts in less virulent subspecies and therefore represented tularensis specific protein species (labeled as CV in Tables 7.4 and 7.5). Such heterogeneity can result from amino acid substitutions or differential post-translational protein modifications. These proteins can also contribute to graded virulence, as the existence of proteins differing in electrophoretic mobility was previously reported in comparative proteomic analyses of virulent and attenuated Mycobacterium tuberculosis strains [25] and invasive and cytotoxic strains of Pseudomonas aeruginosa [26]. Several F. tularensis proteins are related to virulence factors in other pathogens: serine hydroxymethyltransferase is upregulated during an integral response to signals eliciting curli formation. Curli are fimbrial structures expressed by Escherichia coli and Salmonella enteritidis that specifically interact with matrix proteins such as fibronectin and laminin. A simultaneous binding of fibrinolytic proteins and matrix proteins to fimbriae could provide these pathogens with both adhesive and invasive properties [27, 28]. Gamma-glutamyltranspeptidase was recently shown to play a significant role in H. pylori-mediated apoptosis [29, 30]. Nucleoside diphosphate kinase belongs to ATP-utilizing enzymes that convert external ATP, presumably effluxed from macrophages, to various adenine nucleotides, which then activate purinergic receptors such as P2Z, leading to enhanced macrophage cell death [31]. Mutation of beta subunit of riboflavin synthase abrogates fatal pleuropneumonia in swine induced by Actinobacillus pleuropneumoniae [32]. Protection against oxygen radicals mediated by peroxidase/catalase is required by intracellular pathogen Legionella pneumonia for its multiplication inside pulmonary macrophages. The lipoprotein FTT1103 has a domain common for the disulfide oxidoreductase DsbA family, which includes known virulence factors in several gram-negative bacteria, for example, Pseudomonas aeruginosa [33], Salmo-

References

nella enterica [34], Shigella flexneri [35]. The lipoprotein FTT1591 is similar to the VacJ lipoproteins, essential for virulence in Shigella flexneri [36]. The E2 component of pyruvate dehydrogenase was found to play an important role in the intracellular growth of the intracytosolic pathogen Listeria monocytogenes [37]; and the flavoprotein dihydrolipoamide dehydrogenase is known to be essential for the survival of Streptococcus pneumoniae in a host organism [38]. The two proteins IglA and IglC, which are encoded in the duplicated Francisella pathogenicity island, also differed [23]. Both proteins are required for intramacrophage microbial multiplication, which is a prerequisite for the induction of programmed host cell death. In conclusion, the methods used in the presented comparative proteome analysis enabled us to detect several differentially expressed proteins in whole-cell lysates and fractions enriched for membrane-associated proteins from the highly virulent F. tularensis subsp. tularensis strains and the less virulent subsp. holarctica and/or subsp. mediaasiatica strains. Most of the differentially expressed membraneassociated proteins were not detected in analyses of whole-cell lysates. This underlines the importance of sample prefractionation to visualize less abundant proteins. Moreover many virulence factors known from other species belong to the class of membrane-associated proteins. This facilitates the necessity to apply several methods to unravel biological processes such as pathogenesis. Proteomics has the advantage to look at the status of many proteins at once, but the methodology is still limited. Even in experiments that utilize similar principles, such as the analysis of whole-cell lysate and membrane-associated proteins by 2-DE, the results are complementary. By the number of identified proteins and the ratio of membrane proteins with more than one transmembrane domain, it is obvious that what has been seen is still just a part of the whole proteome. Possibly, an application of the shotgun approach with the involvement of an appropriate quantitation strategy such as stable isotope labeling can add unseen proteins on the list of interest.

References 1 Kubota, K., Kosaka, T., and Ichikawa, K.

5 Braun, R.J., Kinkl, N., Beer, M., and

(2005) Combination of two-dimensional electrophoresis and shotgun peptide sequencing in comparative proteomics. J. Chromatogr., 815, 3–9. 2 Graves, P.R., and Haystead, A.J. (2002) Molecular Biologist’s guide to proteomics. Microbiol. Mol. Biol. Rev., 66, 39–63. 3 Honoré, B., Ostergaard, M., and Vorum, H. (2004) Functional genomics studied by proteomics. BioAssays, 26, 901–915. 4 Görg, A., Weiss, W., and Dunn, M.J. (2004) Current two-dimensional electrophoresis technology for proteomics. Proteomics, 4, 3665–3685.

Ueffing, M. (2007) Two-dimensional electrophoresis of membrane proteins. Anal. Bioanal. Chem., 389, 10333–11045. 6 Kuhn, K., Prinz, T., Schäfer, J., Baumann, C., Schärfke, M., Kienle, S., Schwarz, J., Steiner, S., and Hamon, C. (2005) Protein sequence tags: a novel solution for comparative proteomics. Proteomics, 5, 2364–2368. 7 Fenselau, C. (2007) A review of quantitative methods for proteomic studies. J. Chromatogr. B, 855, 14–20. 8 Wu, H.-J., Wang, A.H.-J., and Jennings, M.P. (2008) Discovery of virulence factors of pathogenic bacteria. Curr. Opin. Chem. Biol., 12, 93–101.

91

92

7 Comparative Proteome Analysis of Strains with Differential Virulence 9 Hubálek, M., Hernychová, L., Brychta,

10

11

12

13

14

15

M., Lencˇ o, J., Zechovská, J., and Stulik, J. (2004) Comparative proteome analysis of cellular proteins extracted form highly virulent Francisella tularensis subsp. tularensis and less virulent F. tularensis subsp. holarctica and F. tularensis subsp. mediaasiatica. Proteomics, 10, 3048–3060. Pavkova, I., Reichelova, M., Larsson, P., Hubalek, M., Vackova, J., Forsberg, A., and Stulik, J. (2006) Comparative proteome analysis of fractions enriched for membrane-associated proteins from Francisella tularensis subsp. tularensis and F. tularensis subsp. holarctica strains. J. Proteome Res., 5, 3125–3134. Fujiki, Y., Hubbard, A.L., Fowler, S., and Lazarow, P.B. (1982) Isolation of intracellular membranes by means of sodium carbonate treatment: application to endoplasmic reticulum. J. Cell Biol., 93, 97–102. Broekhuijsen, M., Larsson, P., Johansson, A., Bystrom, M., Eriksson, U., Larsson, E., Prior, R.G., Sjöstedt, A., Titball, R.W., and Forsman, M. (2003) Genome-wide DNA microarray analysis of Francisella tularensis strains demonstrates extensive genetic conservation within the species but identifies regions that are unique to the highly virulent F. tularensis subsp. tularensis. J. Clin. Microbiol., 41, 2924–2931. Sandström, G., Sjöstedt, A., Forsman, M., Pavlovich, N.V., and Mishankin, B.N. (1992) Characterization and classification of strains of Francisella tularensis isolated in the central Asian focus of the Soviet Union and in Japan. J. Clin. Microbiol., 30, 172–175. Hintz, M., Reichenberg, A., Altincicek, B., Bahr, U., Gschwind, R.M., Kollas, A.K., Beck, E., Wiesner, J., Eberl, M., and Jomaa, H. (2001) Identification of (E)-4-hydroxy-3-methyl-but-2-enyl pyrophosphate as a major activator for human gammadelta T cells in Escherichia coli. FEBS Lett., 509, 317–322. Poquet, Y., Kroca, M., Halary, F., Stenmark, S., Peyrat, M.A., Bonneville, M., Fournié, J.J., and Sjöstedt, A. (1998) Expansion of Vgamma9 Vdelta2 T cells is triggered by Francisella tularensis-derived

16

17

18

19

20

21

22

23

phosphoantigens in tularemia but not after tularemia vaccination. Infect. Immun., 66, 2107–2114. Drake, S.L., Sandstedt, S.A., and Koomey, M. (1997) PilP, a pilus biogenesis lipoprotein in Neisseria gonorrhoeae affects expression of PilQ as a high-molecularmass multimer. Mol. Microbiol., 23, 657–668. Soto, G.E., and Hultgren, S.J. (1999) Bacterial adhesions: common themes and variations in architecture and assembly. J. Bacteriol., 181, 1059–1071. Forslund, A.-L., Kuoppa, K., Svensson, K., Salomonsson, E., Johansson, A., Byström, M., Oyston, P.C., Michell, S.L., Titball, R.W., Noppa, L., Frithz-Lindsten, E., Forsman, M., and Forsberg, A. (2006) Direct repeat-mediate deletion of a type IV pilin gene results in major virulence attenuation of Francisella tularensis. Mol. Microbiol., 59, 1818–1830. Wilson, M., McNab, R., and Henderson, B. (2002) Bacterial Disease Mechanisms. An Introduction to Cellular Microbiology, Cambridge University Press, Cambridge, UK. Rasmussen, B.A., Bush, K., Keeney, D., Yang, Y., Hare, R., O′Gara, C., and Medeiros, A.A. (1996) Characterization of IMI-1 beta-lactamase, a class A carbapenem-hydrolyzing enzyme from Enterobacter cloacae. Antimicrob. Agents Chemother., 40, 2080–2086. Kok, M., Bühlmann, E., and Pechere, J.C. (2001) Salmonella typhimurium thyA mutants fail to grow intracellularly in vitro and are attenuated in mice. Microbiology, 147, 727–733. Gray, C.G., Cowley, S.C., Cheung, K.K., and Nano, F.E. (2002) The identification of five genetic loci of Francisella novicida associated with intracellular growth. FEMS Microbiol. Lett., 24, 53–56. Larsson, P., Oyston, P.C., Chain, P., Chu, M.C., Duffield, M., Fuxelius, H.H., Garcia, E., Halltorp, G., Johansson, D., Isherwood, K.E., Karp, P.D., Larsson, E., Liu, Y., Michell, S., Prior, J., Prior, R., Malfatti, S., Sjöstedt, S., Svensson, K., Thompson, N., Vergez, L., Wagg, J.K., Wren, B.W., Lindler, L.E., Andersson, S.G., Forsman, M., and Titball, R.W. (2005) The complete genome sequence of

References

24

25

26

27

28

29

30

Francisella tularensis, the causative agent of tularemia. Nat. Genet., 37, 153–159. Cianciotto, P., and Fields, B.S. (1992) Legionella penumophila mip gene potentiates intracellular infection of protozoa and human macrophages. Proc. Natl. Acad. Sci. U.S.A., 89, 5188–5191. Mattow, J., Schaible, U.E., Schmidt, F., Hagens, K., Siejak, F., Brestrich, G., Haeselbarth, G., Muller, E.C., Jungblut, P.R., and Kaufmann, S.H. (2003) Comparative proteome analysis of culture supernatant proteins from virulent Mycobacterium tuberculosis H37Rv and attenuated M. bovis BCG Copenhagen. Electrophoresis, 24, 3405–3420. Nouwens, A.S., Willcox, M.D.P., Walsh, B.J., and Cordwell, S.J. (2002) Proteomic comparison of membrane and extracellular proteins from invasive (PAO1) and cytotoxic (6206) strains of Pseudomonas aeruginosa. Proteomics, 1, 1325–1346. Chirwa, N.T., and Herrington, M.B. (2003) CsgD, a regulator of curli and cellulose synthesis, also regulates serine hydroxymethyltransferase synthesis in Escherichia coli K-12. Microbiology, 149, 525–535. Sjobring, U., Pohl, G., and Olsen, A. (1994) Plasminogen, absorbed by Escherichia coli expressing curli or by Salmonella enteritidis expressing thin aggregative fimbriae, can be activated by simultaneously captured tissue-type plasminogen activator (t-PA). Mol. Microbiol., 14, 443–452. Busiello, I., Acquaviva, R., Di Popolo, A., Blanchard, T.G., Ricci, V., Romano, M., and Zarrilli, R. (2004) Helicobacter pylori gamma-glutamyltranspeptidase upregulates COX-2 and EGF-related peptide expression in human gastric cells. Cell. Microbiol., 6, 255–267. Shibayama, K., Kamachi, K., Nagata, N., Yagi, T., Nada, T., Doi, Y., Shibata, N., Yokoyama, K., Yamane, K., Kato, H., Iinuma, Y., and Arakawa, Y. (2003) A novel apoptosis-inducing protein from Helicobacter pylori. Mol. Microbiol., 47, 443–451.

31 Zaborina, O., Dhiman, N., Ling Chen,

32

33

34

35

36

37

38

M., Kostal, J., Holder, I.A., and Chakrabarty, A.M. (2000) Secreted products of a nonmucoid Pseudomonas aeruginosa strain induce two modes of macrophage killing: external-ATPdependent, P2Z-receptor-mediated necrosis and ATP-independent, caspase-mediated apoptosis. Microbiology, 146, 2521–2530. Fuller, T.E., Thacker, B.J., and Mulks, M.H. (1996) A riboflavin auxotroph of Actinobacillus pleuropneumoniae is attenuated in swine. Infect. Immun., 64, 4659–4664. Ha, U.H., Wang, Y., and Jin, S. (2003) DsbA of Pseudomonas aeruginosa is essential for multiple virulence factors. Infect. Immun., 71, 1590–1595. Miki, T., Okada, N., and Danbara, H. (2004) Two periplasmic disulfide oxidoreductases, DsbA and SrgA, Target outer membrane protein SpiA, a component of the Salmonella pathogenicity island 2 type III secretion system. J. Biol. Chem., 279, 34631–34642. Yu, J. (1998) Inactivation of DsbA, but not DsbC and DsbD, affects the intracellular survival and virulence of Shigella flexneri. Infect. Immun., 66, 3909–3917. Suzuki, T., Murai, T., Fukuda, I., Tobe, T., Yoshikawa, M., and Sasakawa, C. (1994) Identification and characterization of a chromosomal virulence gene, vacJ, required for intercellular spreading of Shigella flexneri. Mol. Microbiol., 11, 31–41. O’Riordan, M., Moors, M.A., and Portnoy, D.A. (2003) Listeria intracellular growth and virulence require host-derived lipoic acid. Science, 17, 462–464. Smith, A.W., Roche, H., Trombe, M.C., Briles, D.E., and Hakansson, A. (2002) Characterization of the dihydrolipoamide dehydrogenase form Streptococcus pneumoniae and its role in pneumococcal infection. Mol. Microbiol., 44, 431–448.

93

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8 Analysis of Francisella tularensis Acetonitrile Extracts Lenka Hernychova, Martin Hubalek, and Jana Udrzalova

8.1 Introduction

Accurate and rapid detection, identification and typing of clinically relevant bacteria is very important for efficient infection disease therapy [1–4]. Moreover, the permanent threat of bioterrorism acts worldwide still represents a major security problem. National and international organizations have created rules concerning public health emergencies. Fast and easy detection and identification of biological warfare (BW) agents is extremely important. For this purpose various mass spectrometric approaches became very popular in the last several years. Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) of intact bacterial cells provides characteristic and reproducible mass spectral fingerprints containing unique biomarker profiles [5–7] that might be exploited to improve the identification and typing of gram-negative and gram-positive bacterial strains. In order to characterize these proteins in more detail electrospray ionization tandem mass spectrometry (ESI-MS/MS) has been employed [8–13]. Many different strategies for analyzing intact bacterial cells have been published [6, 8, 14]. Most of them are based on bacterial mass fingerprints obtained by linear mode MALDI-TOF MS accompanied by different biostatistical analyses [6, 15–20] or phylogenetic grouping [21]. For security reasons a cell-free acetonitrile (ACN) extraction procedure for MS analysis of BW agents has been developed [5, 7, 15, 22]. Rapid screening and characterization of bacteria has been the main objective of countless numbers of various software products and databases. They may employ an improved data analysis [23] that generates a database of microorganisms containing biomarker masses derived from ribosomal protein sequences and N-terminal Met losses [23]. Our investigations have focused on both detection and reliable identification and typing of specific F. tularensis proteins. In order to obtain unique protein peaks, we prepared cell-free ACN extracts from the bacteria and the samples were

BSL3 and BSL4 Agents: Proteomics, Glycomics, and Antigenicity, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.

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8 Analysis of Francisella tularensis Acetonitrile Extracts

subjected to MALDI-TOF MS analyses. Fingerprints of the linear MS spectra and the m/z values of characteristic peaks were used for the initiation of an integrated database as an approach for the tentative recognition of F. tularensis strains/ isolates. The specific proteins were identified in the tryptic digest of the cell-free ACN extracts by MS/MS techniques.

8.2 Material and Methods 8.2.1 Materials

Sinapinic acid (SA), 2, 5-dihydroxybenzoic acid (DHB), Protein calibration mix2, and Peptide calibration mix1 were purchased from LaserBio Labs (SophiaAntipolis, France). Trifluoroacetic acid (TFA), formic acid (FA), trichloracetic acid (TCA), and Tris-HCl were obtained from Sigma-Aldrich (St. Louis, Mo., USA). Trypsin was purchased from Promega (Madison, USA) and Microcon centrifugal filter device YM-10 was obtained from Millipore (Bedford, Mass., USA). Acetonitrile (LiChrosolv quality), water (LiChrosolv quality), thiomersal, and dithiotreithol (DTT) were furnished by Merck (Darmstadt, Germany). Guanidine-HCl and EDTA were purchased from Janssen (Geel, Belgium). Sodium 2-iodoacetate and hydrogen ammonium carbonate (NH4HCO3) were obtained from Fluka (Buchs, Switzerland). 8.2.2 Microorganism

Francisella tularensis subsp. holarctica live vaccine strain (LVS) was acquired from the Francisella strain collection (Sweden). F. tularensis subsp. tularensis strain Schu S4 was acquired from the Collection of Animal Pathogenic Microorganisms, Veterinary Research Institute (Brno, Czech Republic). Highly virulent Schu S4 strain bacteria were grown, harvested and lysed within a BioSafety level 3 containment facility while bacteria of the less virulent LVS strain were prepared in a BioSafety level 2 laboratory by the same methodology as the Schu S4 strain. All strains were cultured in Chamberlain medium [24] under standard conditions until the late logarithmic growth phase of bacteria. 8.2.3 Preparation of Cell-Free Acetonitrile Extract

Bacterial cultures were harvested by centrifugation (5 000 rpm, 20 min, 4 °C) and pellets were washed two times with PBS, pH 7.5. The cellular material (5 mg) was resuspended in 1.5 ml of solution containing 70% acetonitrile, 0.5% TFA, and water. After vigorous extraction by vortexing (5 min), the cells were sedimented by

8.2 Material and Methods

centrifugation (12 000 rpm, 15 min, 10 °C) and the supernatant, ACN extract, was stored at −20 °C before MS analysis. 8.2.4 Enzymatic Digestion

The ACN extracts of F. tularensis strains LVS and Schu S4 were separately reduced, alkylated, and digested on a Microcon centrifugal device (YM-10, MWCO 10000; Millipore) as follows. The concentrated cell-free extract (5 μl) was diluted by denaturing freshly prepared buffer (300 μl) containing 6 M guanidine-HCl, 100 mM Tris-HCl, and 5 mM EDTA. The volume of the mixture was reduced on a Microcon filter (12 000 rpm, 4 °C, 1 h) to approximately 50 μl. DTT (100 μl of 100 mM) in 100 mM ammonium hydrogen carbonate was added, mixed, and incubated for 1 h at 56 °C. The volume was reduced again to approximately 50 μl and alkylated with iodoacetamide (100 μl of 300 mM) in the same solvent for 30 min at 25 °C in the dark. The sample was concentrated and washed twice with 200 μl of cleavage buffer (50 mM ammonium hydrogen carbonate, 5% ACN). Sequence grade trypsin (0.2 μg) in cleavage buffer (100 μl) was added and the proteins were digested in a thermomixer at 37 °C overnight. The peptides were recovered by spinning at 12 000 rpm for 1 h at 15 °C into a clean vial followed by washing the filter unit with 50 μl of 0.1% FA containing 30% ACN and 50 μl of 60% ACN. The peptides were vacuum-dried and reconstituted in 30 μl of sample buffer containing 0.1% FA and 2% ACN. 8.2.5 MALDI-TOF MS

A concentrated ACN extract (1 μl) was spotted on MALDI target sample plate with hydrophobic surface (2 × 96 well positions) and allowed to air-dry at room temperature. Matrix solution (1 μl) containing SA (10 mg/ml) in aqueous 30% ACN with 0.5% TFA was dropped onto each sample spot. The sample plate was inserted into the MALDI-TOF instrument after solvent evaporation. Mass spectra were acquired using a Voyager DE STR MALDI-TOF MS (Applied Biosystems; Framingham, Mass., USA) equipped with a delayed extraction and UV nitrogen laser (337 nm, 3 ns pulse width). Analyses were performed in a linear positive ion mode at accelerating voltage 25 kV, 93% grid voltage, 0.15% guide wire, extraction delay time 320 ns, and low mass gate 1000 m/z. The mass range was set from 1000 to 25 000 m/z. The instrument was calibrated before each analysis with a mixture of both Protein calibration mix1 and Protein calibration mix2. The mass accuracy for each standard was within 500 ppm of the corresponding average molecular weight. Each mass spectrum was obtained by averaging 200 laser shots. The Data Explorer program (Matrix Science) was used to view and process data files from the instrument. Processing parameters were as follows: advanced baseline correction 32, 0.5, 0.1; noise filter/smooth, Gaussian smooth at filter width 15.

97

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8 Analysis of Francisella tularensis Acetonitrile Extracts

8.2.6 LC-MS/MS

Protein identification in the acetonitrile extracts was performed on a tandem mass spectrometer coupled to a nanoscale HPLC. The extracted peptide mixture was separated on a CapLC system (Waters; USA) using precolumn concentration (Atlantis dC18, 5 μm NanoEase Trap column; Waters) and gradient elution on analytical nano-column Atlantis dC18 75 μm × 150 mm, 3 μm NanoEase at a flow rate of 250 nl/min. The gradient of buffer B (80% acetonitrile, 0.1% formic acid) consisted of a linear increase from 5 to 35% in 60 min. The column was connected to PicoTip emitters (New Objective; USA) mounted into the nanospray of a Qtof Ultima API (Waters; UK). Data acquisition was performed in a datadependent manner for the time of the separation collecting up to five MS/MS events at the same time. Data were processed by Peptideauto script of MassLynx 4.0 that provided background subtraction (polynomial order 99 and 10% below curve), smoothing (Savitzky Golay, twice, over three channels), and centroiding (top, 80%, minimal peak width at half height 4). The resulting pkl file was searched against bacterial genomes in the NCBI nr database using Phenyx 2.5. (GeneBio; Geneva, Switzerland) with the following criteria in two rounds. Round 1: fixed carbamidomethylation of Cys, variable Met oxidation, tryptic fragments with one miscleavage, parent mass tolerance 50 ppm, peptide score threshold ≥5, peptide P value ≥ 1 e–6. Round 2: fixed carbamidomethylation of Cys, variable Met oxidation, varriable Pyroglutamate of Gln, variable deamidation of Gln and Asn, tryptic fragments with two miscleavages, halved cleaved fragments allowed, parent mass tolerance 80 ppm, peptide score threshold ≥5, peptide P value ≥ 1 e–5.

8.3 Results 8.3.1 MALDI-TOF MS Analysis

The linear MALDI-TOF mass spectra of the ACN cell-free extracts from quintuple preparations were acquired in positive ion mode. The mass spectral data were normalized, baseline-corrected, and de-noised. The extraction procedures were optimized with respect to the signal to noise ratios, reproducibility, and the number of proteins extracted. Figures 8.1 and 8.2 show the representative mass spectra of F. tularensis strain LVS ACN extract and F. tularensis strain Schu ACN extract, respectively. The linear MALDI-TOF mass spectra of bacterial ACN extracts gave homogeneous and reproducible spectral profile. Moreover the different spectral profile with unique peak values characterized by their m/z was measured for each F. tularensis strain.

% Intensity

8.3 Results

100 90 80 70 60 50 40 30 1102 20 1033 1246 10 0 999.0

Voyager Spec #1=>AdvBC(32,0.5,0.1)=>SM15[BP = 9474.3, 7345] 9475

99

7345.4

8132 7464

5144

7386 6740 7546

10241 17239

3046

10293 4738 6142 7073 8188 9679 11029 5799.4

15990

10599.8

15400.2

20200.6

0 25001.0

Mass (m/z)

Figure 8.1 The mass spectrum of F. tularensis strain LVS acetonitrile extract measured in

positive linear mode MALDI-TOF mass spectrometer.

Voyager Spec #1=>BC=>SM15=>AdvBC(32,0.5,0.1)[BP = 7384.6, 18429] 7385

100

1.8E+4

90 8131

% Intensity

80 70 3251 3511

60 50 40 30 20

3335 2672 3903 1936 1409 2483

5607

10 0 999.0

10241

5772

5799.4

7259

10986 8338

12835 17187

11028 10599.8

15400.2

20200.6

0 25001.0

Mass (m/z)

The mass spectrum of F. tularensis strain Schu acetonitrile extract measured in positive linear mode MALDI-TOF mass spectrometer.

Figure 8.2

8.3.2 LC-MS/MS Analysis

In addition, bacterial ACN extracts were analyzed by a LC-MS/MS approach for detailed protein characterization. This shotgun procedure was chosen as more informative for the identification and typing of bacteria. The proteins of the ACN extract of the bacterium were cleaved with trypsin and the resulting peptides were separated by reverse-phase chromatography and analyzed by MS/MS. The experiment was repeated either with four (LVS strain) or five (Schu S4 strain) freshly prepared ACN extracts of the strains. We chose the following criteria for successful identification: protein has to be repeatedly identified in all analyzed samples, total z-score for protein has to be higher than 10, more than two identified unique peptides. Based on the MS/MS data and the highest z-score, ten representative proteins of F. tularensis strain LVS (Table 8.1) and ten representative proteins of

100

8 Analysis of Francisella tularensis Acetonitrile Extracts Table 8.1 List of selected F. tularensis strain LVS proteins identified by LC-MS/MS approach.

The proteins in the table are described by their accession number, name, mass, and number of identified unique peptides in each measurement. Accession number

Name of protein

Mass (Da)

Number of peptides

89255458 89256252 89255967 89256014 89256686 89256983 89257193 89256792 89257048 89257020

Outer membrane protein Histone-like protein HU form B Hypothetical protein FTL_0569 Hypothetical protein FTL_0617 Peptidyl-prolyl cis-trans isomerase Chaperone protein, groEL Heat shock protein Peroxidase/catalase Succinate dehydrogenase iron-sulfur protein Elongation factor Tu (EF-Tu)

19 346 9 494 19 654 16 809 10 241 57 403 16 740 81 227 26 566 43 391

6, 10, 4, 11 9, 10 7, 8 6, 10, 7, 5 10, 9, 12, 14 9, 8, 8, 7 7, 4, 6, 9 6, 7, 8, 7 12, 14, 17, 11 10, 8, 9, 8 14, 13, 13, 20

Table 8.2 List of selected F. tularensis strain Schu S4 proteins identified by LC-MS/MS

approach. The proteins in the table are described by their accession number, name, mass, and number of identified unique peptides in each measurement. Accession number

Name of protein

Mass (Da)

Number of peptides

56708414 56708483 56708764 56708500 56707529 56708524 56708705 56707839 56707643 56707307

Intracellular growth locus, subunit B Hypothetical protein FTT1441 ClpB protein NAD-dependent epimerase Glutamate dehydrogenase Pyruvate dehydrogenase, E1 component Chaperone protein, groEL Peroxidase/catalase Succinyl-CoA synthetase subunit beta Elongation factor Tu (EF-Tu)

58 869 18 507 95 929 36 378 49 108 100 228 57 429 82 501 41 542 43 405

11, 10, 17, 11, 12 11, 12, 6, 8, 6 9, 10, 11, 12, 12 4, 5, 4, 3, 3 12, 14, 11, 9, 9 5, 7, 12, 10, 5 6, 6, 5, 6, 6 15, 21, 20, 16, 19 7, 9, 10, 7, 8 22, 25, 25, 24, 19

F. tularensis strain Schu S4 (Table 8.2) were chosen from list of all proteins identified by LC-MS/MS approach. Selected proteins showed in Tables 8.1 and 8.2 represent most likely the highly expressed F. tularensis proteins presented in acetonitrile extracts. These proteins can be divided into four groups according to their localization and function: (i) membrane proteins, (ii) proteins involved in carbohydrate metabolism and posttranslation modification, (iii) proteins with oxidoreductase activity, and (iv) DNAbinding, elongation factor, and heat shock proteins. It is well known that the membrane proteins (MPs) associated with disease progression show promise as detection tools in public health and biodefense and

8.3 Results

can guide drug and vaccine designers in their quest to disrupt the ability of the microbe to infect. Due to the important role of MPs in cell adhesion, invasion, and intracellular survival of pathogens in the host, they are potential biomarkers or drug targets. Consequently, it is highly probable that the identified MPs may serve for reliable detection, identification, and typing of the bacterium. The outer membrane protein (OMP; F. tularensis protein from Table 8.1 with accession number 89255458) contains the OmpH domain. This type of protein forms in P. multocida a transmembrane porin that plays a key role as a molecular sieve, allowing the diffusion of small hydrophilic solutes through the outer membrane, and it also acts as a receptor for bacteriophages and bacteriocins. Antibodies raised against porins such as OmpH provide strong protection against P. multocida [25]. Moreover, OmpH and Skp in E. coli form a complex that acts as a periplasmic chaperone for newly synthesized OMPs and protects them from aggregation during passage through the bacterial periplasm [26, 27]. The second group forms proteins which are involved in carbohydrate metabolism and post-translation modification (F. tularensis proteins from Table 8.2 with accession numbers 56708500 and 56707643). NAD-dependent epimerase belongs to a family of proteins which utilize NAD as a cofactor. The proteins in this family use nucleotide–sugar substrates for a variety of chemical reactions [28]. One of the best studied proteins in this family of NAD-dependent epimerases/hydratases is UDP-galactose 4-epimerase which catalyzes the conversion of UDP-galactose to UDP-glucose during galactose metabolism [29]. Succinyl-CoA synthetase share a catalytic mechanism, which involves phosphorylation by ATP (or GTP) of a specific histidine residue in the active site [30]. The primary structure of this protein from E. coli has been described [31]. The third group contains proteins with oxidoreductase activity (F. tularensis proteins from Tables 8.1 and 8.2 with accession numbers 56707529, 56708524, 89256014, 56708483, 89257048). Glutamate dehydrogenase is an enzyme which reductively aminates 2-oxoglutarate to glutamate in many bacteria [32]. This enzyme plays a role in glutamate synthesis when E. coli bacterial cells are under energy restriction [33]. The pyruvate dehydrogenase complex catalyzes the overall conversion of pyruvate to acetyl-CoA and CO2. It contains multiple copies of three enzymatic components: pyruvate dehydrogenase (E1), dihydrolipoamide acetyltransferase (E2), and lipoamide dehydrogenase (E3) [34]. The pyruvate dehydrogenase component (E1) catalyzes the reductive acetylation of the E2-bound lipoyl groups. The enzyme shows high specificity for its oxo acid substrate and for the folded lipoylated lipoyl domain [35]. The gene organization of pyruvate dehydrogenase complexes was studied in various gram-negative bacteria [36]. Four genes that encode the protein subunits comprising the succinate dehydrogenase enzyme complex were characterized either in the rickettsia C. burnetii [37] or in B. subtilis [38]. Succinate dehydrogenases from bacteria and archaea using menaquinone as an electron acceptor contain two heme-B groups in the membrane-anchoring protein or proteins, located close to opposite sides of the membrane [39]. The remaining group of identified proteins presents DNA-binding, elongation factor, and heat shock proteins. Bacteria synthesize a set of small, usually basic

101

102

8 Analysis of Francisella tularensis Acetonitrile Extracts

proteins of about 90 residues that bind DNA and are known as histone-like proteins [40]. HU-type proteins were found as the DNA-binding proteins HU (F. tularensis protein from Table 8.1 with accession number 89256252) in a variety of eubacteria, cyanobacteria, and archaebacteria, and are also encoded in the chloroplast genome of some algae [41]. The exact function of these proteins is not known with certainty but they are capable of wrapping DNA and stabilizing it from denaturation under the extreme environmental conditions. DNA-binding proteins are primarily located in the cytoplasm but it appears that these proteins could also be present at the surface of bacterial cells, as the cells need to be competent for DNA uptake from the external milieu [42]. Elongation factor Tu (F. tularensis proteins with accession numbers 89257020 and 56707307) was identified in this study. Other proteome analyses confirmed that elongation factor Tu (EF-Tu) is associated with the cytoplasmic membranes of gram-positive bacteria and the outer membranes of gram-negative bacteria [43, 44]. Moreover, Barel M. et al. [45] described the interaction between F. tularensis elongation factor and the surface nucleolin of monocyte-like THP-I cells and demonstrated that bacterial ligand EF-Tu plays an important role in F. tularensis adhesion and the entry process and may therefore facilitate the invasion of host tissues [45]. The heat shock proteins groEL and ClpB (F. tularensis proteins from Tables 8.1 and 8.2 with accession numbers 89256983, 56708705, 56708764) belong among abundant bacterial proteins. The protein groEL is a “helper” molecule that is referred to as a molecular chaperone, a subfamily of which is the chaperonins. Their functions are well understood at present. The groES protein binds to the groEL protein and they prevent misfolding and promote the refolding of proteins and the proper assembly of unfolded polypeptides generated under stress conditions. It was found that the 60-kDa protein groEL is the immunodominant antigen in several bacteria (Legionella ssp., Ch. pneumoniae, S. typhi, Y. enterocolitica, F. tularensis, etc.); it plays a role in protection from oxygen radicals within infected macrophages and has the potential for inducing the immune response against unrelated bacterial species. Both proteins groEL and groES are located in the bacterial cytoplasm but can appear in the periplasmic space [46] or on the surface of bacteria [47]. The next protein ClpB embodies chaperone activity too. The E. coli ClpB protein is a member of the highly conserved Hsp100/Clp protein family. The combined action of ClpB and the DnaK, DnaJ, and GrpE chaperones leads to the activation of DNA replication [48]. Further, the ClpB protein involves the noncovalent folding, assembly, and/ or disassembly of other polypeptides or RNA molecules, including any transport and oligomerization processes they may undergo, and the refolding and reassembly of protein and RNA molecules denatured by stress.

8.4 Conclusions

Two separate but complementary methods were used for the determination of bacterial proteins in acetonitrile extracts. The first method was focused on the

References

direct and rapid analysis of both acetonitrile extracts by MALDI-TOF MS followed by identification against the appropriate microbial databases (MicrobeLynx, ProteinProfiler, Saramis). The second procedure used LC/MS/MS analysis (CapLC, Q-TOF) of trypsin-digested acetonitrile extracts for the identification of bacterial proteins. Both MALDI-TOF MS and LC MS/MS were demonstrated as tools for the rapid screening of F. tularensis strains and the identification of proteins that were extracted with ACN. The MALDI-TOF MS approach, based on the recognition of specific spectral profiles, has recently been examined for typing F. tularensis strains LVS and Schu S4. It was suggested that this approach could be applied not only for the detection of bacterial pathogen but also for its typing. The proteomic data on F. tularensis presented in this work have brought additional knowledge to this relatively new and scarcely investigated field. It has been clear from the very beginning that the spectral profiling of F. tularensis and its organic solvent extracts by MALDI-TOF MS is not sufficient for the precise identification and typing of bacterial agents. In high-throughput proteomics, there are several distinct analytical platforms for the discovery and characterization of specific proteins. We selected a shotgun strategy based on the LC-MS/MS technique. This methodology may confirm the identity of the F. tularensis pathogen according to its specific proteins or peptides. The proteins presented in Tables 8.1 and 8.2 were repeatedly identified upon their consistently appearing peptides in five and more MS/MS measurements. These peptides are most probably “proteotypic” peptides that are most likely to be confidently observed in any MS-based proteomic method. This knowledge could be utilized for the creation of a F. tularensis proteotypic peptide database that might serve for the simple identification of this bacterium by MS/MS techniques in future operating in a multiple reaction monitoring (MRM) scanning mode [49]. In conclusion, we have shown that proteomic approaches involving MALDI-TOF MS and LC-MS/MS are suitable for the detection, identification, and typing of F. tularensis. Moreover, they constitute an important resource for building up homeland security with prospects of improving the public health system in the way to deal better with the infectious disease in general, no matter whether the agent is delivered deliberately by humans or by nature.

Acknowledgments

This work was financially supported by the Ministry of Education, Youth and Sport, Czech Republic (ME08105), and the Ministry of Defence, Czech Republic (FVZ0000604).

References 1 Claydon, M.A., Davey, S.N., Edward-

Jones, V., and Gordon, D.B. (1996)

J. Microbiol. Methods, 14, 1584–1586.

103

104

8 Analysis of Francisella tularensis Acetonitrile Extracts 2 Holland, R.D., Wilkes, J.G., Rafii, F.,

3

4

5

6

7

8 9

10 11 12 13 14

15

16 17

18

19

Sutherland, J.B., Persons, C.C., Voorhees, K.J., and Lay, J.O. (1996) Rapid Commun. Mass Spectrom., 10, 1227–1232. Krishnamurthy, T., Ross, P.L., and Rajamani, U. (1996) Rapid Commun. Mass Spectrom., 10, 883–888. Krishnamurthy, T., and Ross, P.L. (1996) Rapid Commun. Mass Spectrom., 10, 1992–1996. Hathout, Y., Demirev, P.A., Ho, Y.-P., Bundy, J.L., Ryzhov, V., Sapp, L., Stutler, J., Jackman, J., and Fenselau, C. (1999) Appl. Environ. Microbiol., 65, 4313–4319. Bright, J.J., Claydon, M.A., Soufian, M., and Gordon, D.B. (2002) J. Microbiol. Methods, 48, 127–138. Shaw, E.I., Moura, H., Woolfitt, A.R., Ospina, M., Thompson, H.A., and Barr, J.R. (2004) Anal. Chem., 76, 4017–4022. Goodacre, R., Heald, J.K., and Kell, D.B. (1999) FEMS Microbiol. Lett., 176, 17–24. Stapels, M.D., Cho, J.C., Giovannoni, S.J., and Barofsky, D.F. (2004) J. Biomol. Tech., 15, 191–198. Demirev, P.A., Ramirez, J., and Fenselau, C. (2001) Anal. Chem., 73, 5725–5731. Lo, A.A., Hu, A., and Ho, Y.P. (2006) J. Mass Spectrom., 41, 1049–1060. Garza, S., and Moini, M. (2006) Anal. Chem., 78, 7309–7316. Marrie, T.J., and Raoult, D. (1997) Int. J. Antimicrob. Agents, 8, 145–161. Demirev, P.A., Feldman, A.B., Kowalski, P., and Lin, J.S. (2005) Anal. Chem., 77, 7455–7461. Demirev, P.A., Ho, Y.P., Ryzhov, V., and Fenselau, C. (1999) Anal. Chem., 71, 2732–2738. Arnold, R.J., and Reilly, J.P. (1998) Rapid Commun. Mass Spectrom., 12, 630–636. Keys, C.J., Dare, D.J., Sutton, H., Wells, G., Lunt, M., McKenna, T., McDowall, M., and Shah, H.N. (2004) Infect. Genet. Evol., 4, 221–242. Hettick, J.M., Kashon, M.L., Slaven, J.E., Ma, Y., Simpson, J.P., Siegel, P.D., Mazurek, G.N., and Weissman, D.N. (2006) Proteomics, 6, 6416–6425. Jarman, K.H., Cebula, S.T., Saenz, A.J., Petersen, C.E., Valentine, N.B., Kingsley, M.T., and Wahl, K.L. (2000) Anal. Chem., 72, 1217–1223.

20 Pineda, F.J., Lin, J.S., Fenselau, C., and

21 22 23

24 25 26 27 28

29

30

31 32

33 34

35

36

37

38

39

Demirev, P.A. (2000) Anal. Chem., 72, 3739–3744. Stackebrandt, E., Pauker, O., and Erhard, M. (2005) Curr. Microbiol., 50, 71–77. Fenselau, C., and Demirev, P.A. (2001) Mass Spectrom. Rev., 20, 157–171. Pineda, F.J., Antoine, M.D., Demirev, P.A., Feldman, A.B., Jackman, J., Longenecker, M., and Lin, J.S. (2003) Anal. Chem., 75, 3817–3822. Chamberlain, R.E. (1965) Appl. Microbiol., 13, 232–235. Lee, J., Kim, Y.B., and Kwon, M. (2007) J. Microbiol., 45, 179–184. Walton, T.A., and Sousa, M.C. (2004) Mol. Cell., 15, 367–374. Thome, B.M., and Müller, M. (1991) Mol. Microbiol., 5, 2815–2821. Thoden, J.B., Hegeman, A.D., Wesenberg, G., Chapeau, M.C., Frey, P.A., and Holden, H.M. (1997) Biochemistry, 36, 6294–6304. Thoden, J.B., Wohlers, T.M., FridovichKeil, J.L., and Holden, H.M. (2001) J. Biol. Chem., 276, 15131–15136. Fraser, M.E., Joyce, M.A., Ryan, D.G., and Wolodko, W.T. (2002) Biochemistry, 41, 537–546. Buck, D., Spencer, M.E., and Guest, J.R. (1985) Biochemistry, 24, 6245–6252. Tempest, D.W., Meers, J.L., and Brown, C.M. (1970) Biochem. J., 117, 405–407. Wang, H., Wang, F., and Wei, D. (2009) Appl. Microbiol. Biotechnol., 82, 249–259. Ogata, H., La Scola, B., Audic, S., Renesto, P., Blanc, G., Robert, C., Fournier, P.E., Claverie, J.M., and Raoult, D. (2006) PLoS Genet., 2, e76. Graham, L.D., Packman, L.C., and Perham, R.N. (1989) Biochemistry, 28, 1574–1581. de Kok, A., Hengeveld, A.F., Martin, A., and Westphala, A.H. (1998) Biochim. Biophys. Acta, 1385, 353–366. Heinzen, R.A., Mo, Y.-Y., Robertson, S.J., and Mallavia, L.P. (1995) Gene, 155, 27–34. Phillips, M.K., Hederstedt, L., Hasnain, S., Rutberg, L., and Guest, J.R. (1987) J. Bacteriol., 169, 864–873. Schirawski, J., and Unden, G. (1998) Eur. J. Biochem., 257, 210–215.

References 40 Drlica, K., and Rouviere-Yaniv, J. (1987) 41 42 43

44

Microbiol. Rev., 51, 301–319. Wang, S.L., and Liu, X.Q. (1991) Proc. Natl. Acad. Sci. U.S.A., 88, 10783–10787. Dubnau, D. (1999) Annu. Rev. Microbiol., 53, 217–244. Severin, A., Nickbarg, E., Wooters, J., Quazi, S.A., Matsuka, Y.V., Murphy, E., Moutsatsos, I.K., Zagursky, R.J., and Olmsted, S.B. (2007) J. Bacteriol., 189, 1514–1522. Kolberg, J., Hammerschmidt, S., Frank, R., Jonák, J., Sanderová, H., and Aase, A. (2008) FEMS Immunol. Med. Microbiol., 53, 222–230.

45 Barel, M., Hovanessian, A.G., Meibom,

46 47

48 49

K., Briand, J.P., Dupuis, M., and Charbit, A. (2008) BMC Microbiol., 8, 145. Lee, J.E., and Ahn, T.I. (2000) Res. Microbiol., 151, 605–618. Garduno, R.A., Garduno, E., and Hoffman, P.S. (1998) Infect. Immun., 66, 4602–4610. Igor Konieczny, I., and Liberek, K. (2002) J. Biol. Chem., 277, 18483–18488. Hiller, S., Krock, B., Cembella, A., and Luckas, B. (2007) J. Mass Spectrom., 42, 1238–1250.

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9 Analysis of Culture Filtrate Proteins of Francisella tularensis Klara Konecna, Martin Hubalek, and Lenka Hernychova

9.1 Introduction

Secreted proteins and secretion pathways are essential for bacterial life and the pathogenesis of infections opens up possibilities of screening novel drugs for antimicrobial therapy, compounds specifically able to inhibit the secretory pathways of pathogens, target molecules for novel diagnostic approaches and immunoprotective secreted proteins for the construction of subcellular vaccines. In bacteria target molecules for secretion must be exported across the cell envelope and for the process of protein secretion, specific molecular machineries are necessary. The secreted proteins act outside of the bacterial cell, either bound to the bacterial cell surface, or freely in the extracellular milieu (host cell); and they are often implicated in the pathogenesis of infections and are involved in virulence. The cell wall of gram-negative bacteria is a double membrane complex structure, enclosing the periplasmic space. These bacteria contain numerous apparently independent systems for exporting proteins across or insertion into the inner cytoplasmic membrane and the outer lipopolysaccharide membrane. Eight translocation systems using different mechanisms and different sources of energy to catalyze the export of proteins across or insertion into the outer membranes are known in gram-negative bacteria [1]. Analysis of the fully sequenced genome of the virulent Francisella tularensis (F. tularensis) strain SCHU S4 has revealed the presence of genes encoding ATPbinding cassette systems [2], a TolC ortholog [3] and type IV pili components [4]. F. tularensis homologs to proteins encoded by the IAHP cluster (the gene cluster encoding the components of a type VI secretion system) were found within the Francisella pathogenicity island [5]. Type IV pili (Tfp) structures are complex adhesins participating in important host cell interactions; they mediate a surface motility and have been shown to be involved in biofilm formation and cell signaling [6]. Several F. tularensis genes required for Tfp expression are homologous to four genes required for type II secretion system (T2SS). The possibility that

BSL3 and BSL4 Agents: Proteomics, Glycomics, and Antigenicity, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.

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9 Analysis of Culture Filtrate Proteins of Francisella tularensis

the Tfp clusters of Francisella is served in T2SS cannot be excluded. Hager’s group published the characterization of seven F. tularensis subsp. novicida proteins secreted via the type IV pilus secretion system [7]. The identification of some culture filtrate proteins isolated from another two subspecies of F. tularensis (subsp. tularensis, subsp. holarctica) was reported in a recently published study by Lee et al. [8]. In this study proteomic tools such as high-resolution 2-DE and MS/MS analysis were used for the analysis of F. tularensis culture filtrate proteins.

9.2 Materials and Methods

F. tularensis subsp. holarctica strain FSC 200 (kindly provided by Dr. Ake Forsberg, FOI Swedish Defense Research Agency, Umea, Sweden) was cultivated in chemically defined Chamberlain medium. Overnight cultures were diluted to OD600 0.10 and bacteria were grown under standard conditions till OD600 0.75. To avoid cell lysis and to maximize the protein harvest from the culture filtrate, the late log exponential growth phase was chosen. The eventual lysis was excluded after repeated determination of lactate dehydrogenase in culture filtrates (data not shown). Bacterial cells were removed by centrifugation (7300 rpm, 15 min, 24 °C) and the supernatants were vacuum filtered through a 0.2-μm pore membrane (Millipore Corporation; Bedford, Mass., USA). The filtrates were concentrated by a Centricon Plus-20 tubes membrane (Millipore). The concentrated samples were dialyzed against 40 mM Tris/HCl (pH 7.34), vacuum concentrated on an Eppendorf Vacufuge Concentrator 5301 (Eppendorf), and protein concentrations were determined by a modified bicinchoninic acid assay (Sigma-Aldrich). The culture filtrate proteins (CFPs) were purified by a ReadyPrep 2-D cleanup kit (Bio-Rad) and then solubilized in rehydration buffer for 2D electrophoresis [7 M urea, 2 M thiourea, 1% ASB14, 4% (w/v) CHAPS, 1% (w/v) DTT, 1% Ampholytes pH 3–10, 0.5% Pharmalytes pH 8–10.5]. Proteins were separated by 2D gel electrophoresis using IEF in the pH gradient 3–10 in the first dimension, followed by SDS-PAGE gradient gel electrophoresis (9–16%). Analytical 2-DE gels (for image analysis, visualized by sensitive ammoniacal silver staining) and preparative 2-DE gels (for subsequent MS analysis, visualized by Coomassie G-250) were prepared. Three silver stained gels from three independent experiments were prepared. The 2-DE gel maps were scanned by a CCD camera (Image Station 2000R, Eastman Kodak) and the data were analyzed by ImageMaster 2D Platinum 6.0 software (Uppsala, Sweden). Protein spots were excised from preparative gels, in-gel digested by trypsin and subsequently analyzed and identified by MS (for more details, see [9]). The identified open reading frame (ORF) sequences were sorted into functional categories based on the COG program [10]. The sequences were also analyzed for prediction of signal peptide presence by the program SignalP [11], localization with algorithms in PSORTb v.2.0 [12] and for the prediction of putative nonclassical secretions by the SecretomeP program [13].

9.3 Results

9.3 Results

Image analysis of culture filtrate protein profiles revealed an average of 240 protein spots on 2-DE silver staining maps (see Figure 9.1). Protein candidates for secretion were marked out from the group of identified proteins on the basis of prediction: signal peptide, extracellular or outer membrane localization and the presence of non-classically secreted proteins. The identified proteins with results from bioinformatic analysis together with the presence of immunoreactive activity are shown in Table 9.1. The presence of a signal peptide and localization in the outer bacterial membrane were predicted namely in two proteins: peroxidase/catalase FTL_1504 and pyruvate/2-oxoglutarate dehydrogenase complex FTL_1248. Other proteins (chaperone protein dnaK FTL_1191, superoxide dismutase (Fe) FTL_1791) had a predicted signal peptide and the SecretomeP score was calculated higher than 0.5 (indicative of secretion). As regards induction of antibody response, some of the identified culture proteins were previously presented, such as immunoreactive antigens [14, 15], namely: chaperone protein dnaK, FTL_ 1191, peroxidase/catalase FTL_1504, isocitrate dehyedrogenase FTL_ 0588, chaperone protein groEL FTL_1714, succinyl-CoA synthetase beta chain FTL_1553 and glyceraldehyde-3phosphate dehydrogenase FTL_1146 (see Table 9.1).

MW (kDa)

67

3 1 2 45 70 -

8

9

60 11

50 -

10 12

30 -

13

14

15

20 -

16

17

10 4.7

5.1

5.5

5.8

6.1

pI

Figure 9.1 The representative profile of culture filtrate proteins of F. tularensis strain FSC200.

Silver stained 9–16% gel, pH 3–10 gradient IPG strip. Black arrows denote identified protein spots.

109

ORF number

FTL_1191

FTL_1504

FTL_0588

FTL_1714

FTL_1490

1

2,3,4,5

6,7

8

9

2,3-bisphosphoglycerateindependent phosphoglycerate mutase

Chaperone protein groEL

Isocitrate dehydrogenase

Peroxidase/catalase

Chaperone protein dnaK

Protein name

57.6/5.83

57.4/4.96

83.5/6.51

82.5/5.37

69.3/4.88

Theoretical MW (kDa)/pI

No

No

No

Yes

No

SignalP

Psort

P

OM unknown C

C

Antibody response +

+ + +

−

0.073 860

0.106 307

0.101 530

0.940 072

0.705 830

SecretomeP

G/Carbohydrate transport and metabolism

O/Posttranslational modification, protein turnover, chaperones

C/Energy production and conversion

P/Inorganic ion transport and metabolism

O/Posttranslational modification, protein turnover, chaperones

COG

Identified culture filtrate proteins of F. tularensis strain FSC200; P – periplasmic, OM – outer membrane, C – cytoplasmic, CM – cytoplasmic membrane.

Spot number

Table 9.1

110

9 Analysis of Culture Filtrate Proteins of Francisella tularensis

FTL_1248

FTL_1553

FTL_1146

FTL_1461

FTL_1780

FTL_1791

FTL_0617

10

11

12

13

14

15

16,17

Conserved hypothetical protein

Superoxide dismutase (Fe)

Triosephosphate isomerase

Purine nucleoside phosphorylase

Glyceraldehyde-3phosphate dehydrogenase

Succinyl-CoA synthetase beta chain

Pyruvate/2-oxoglutarate dehydrogenase complex

Protein name

18.5/5.34

21.9/5.39

27.7/4.98

26.9/5.87

21.9/5.39

41.5/5.24

49.5/5.70

Theoretical MW (kDa)/pI

No

No

No

No

No

No

Yes

SignalP

Psort

CM, P C C

Unknown Unknown Unknown C

Antibody response − + +

− − − −

0.239 914

0.893 256

0.069 773

0.162 984

0.300 729

0.053 738

0.066 402

SecretomeP

P/Inorganic ion transport and metabolism

P/Inorganic ion transport and metabolism

G/Carbohydrate transport and metabolism

F/Nucleotide transport and metabolism

G/Carbohydrate transport and metabolism

C/Energy production and conversion

C/Energy production and conversion

COG

SignalP – Prediction of signal peptide; Psort – Prediciton of cellular localization; SecretomeP – Prediction of non-classically secreted proteins (a score above 0.5 is considered indicative of secretion, signal peptide is impaired in score); COG – Prediction of functional category.

ORF number

Spot number

9.3 Results 111

112

9 Analysis of Culture Filtrate Proteins of Francisella tularensis

9.4 Discussion

One of the key roles in the survival ability of microbes within a hostile niche involves secreted proteins which are able to modulate the host cell responses. The extracellular presence of proteins can be induced by active secretion, including the formation of vesicles or by other mechanisms, for example, leakage during bacterial division or lysis/autolysis. This study introduces a proteomic procedure to enable the detection and identification of bacterial culture filtrate proteins, among which could be found candidates for real secreted proteins. The selected secreted proteins can help with the explanation of F. tularensis virulence. Protein candidates for secretion can be marked out from the group of extracelullarly localized proteins on the basis of several factors, such as prediction of signal peptide (secretion via sec dependent pathways), prediction of extracellular or outer membrane localization, or on the basis of homology with well characterized secreted proteins in other bacteria. However, it is difficult to distinguish between true secreted proteins and the proteins released from the cell by other means. Based on the bioinformatic analysis, some proteins from the group of 12 identified Francisella proteins could be designated as the most significant candidates for secretion, namely: chaperone protein dnaK FTL_1191, peroxidase/catalase FTL_1504 and superoxide dismutase (Fe) FTL_1791. Identified peroxidase/catalase together with superoxide dismutase (Fe) are involved in scavenging/detoxification of host-derived reactive oxygen species (ROS). This defense bacterial mechanism seems to be especially critical for intracellular replication and survival in a hostile niche inside host cells. Peroxidase/catalase enzyme is also released in the extracellular space in another bacteria, such as Mycobacterium tuberculosis [16], Helicobacter pylori [17] or Bacillus sp. 13 [18]. Despite the absence of a predicted signal peptide, superoxide dismutase was also found localized extracellularly in some bacterial pathogens [13]. The chaperone protein dnaK belongs to the heat shock proteins. Chaperone proteins are in general cytoplasmic proteins, but recent studies suggest that they can be also membrane associated or secreted [17, 19] in other bacteria. Similar to superoxide dismutase, this protein was predicted to be a non-classically secreted protein. It is interesting to note that 6 out of 12 identified proteins induced a strong antibody response in naturally infected individuals and/or experimentally infected mice. The identification of immunoreactive proteins may also provide candidates for use in a subunit vaccine with protective potential. Some identified proteins were predicted to have a cytoplasmic localization. This fact raises the question as to why these proteins with a predicted cytoplasmic localization leak into cultivation medium and why other abundant cytoplasmic proteins stay inside the bacterial cell (a comparative analysis with whole-cell lysate was done – data not shown). This and other questions will be under further scientific interest.

References

Acknowledgments

This work was financially supported by the Ministry of Defence, Czech Republic (FVZ0000604), and NATO, collaboration linkage grant LST. CLG. 979934.

References 1 Baier, M.H., Jr. (2006) Protein secretion

2

3

4

5

6

7

and membrane insertion systems in gram-negative bacteria. J. Membr. Biol., 214 (2), 75–90. Atkins, S.H., Dassa, E., Walker, N.J., Griffin, K.F., Harland, D.N., Taylor, R.R., Duffield, M.L., and Titball, R.W. (2006) The identification and evaluation of ATP binding cassette systems in the intracellular bacterium Francisella tularensis. Res. Microbiol., 157 (6), 593. Gil, H., Platz, G.J., Forestal, C.A., Monfett, M., Bakshi, C.S., Sellati, T.J., Furie, M.B., Benach, J.L., and Thanassi, D.G. (2006) Deletion of TolC orthologs in Francisella tularensis identifies roles in multidrug resistance and virulence. Proc. Natl. Acad. Sci. U.S.A., 103 (34), 12897. Forslund, A.L., Kuoppa, K., Svensson, K., Salomonsson, E., Johansson, A., Byström, M., Oyston, P.C.F., Michell, S.L., Titball, R.W., Noppa, L., Frithz-Lindsten, E., Forsman, M., and Forsberg, A. (2006) Direct repeat-mediated deletion of a type IV pilin gene results in major virulence attenuation of Francisella tularensis. Mol. Microbiol., 59 (6), 1818. de Bruin, O.M., Ludu, J.S., and Nano, F.E. (2007) The Francisella Pathogenicity Island Protein IglA localizes to the bacterial cytoplasm and is needed for intracellular growth. BMC Microbiol., 7, 1. Sauvonnet, N., Vignon, G., Pugsley, A.P., and Gounon, P. (2000) Pilus formation and protein secretion by the same machinery in Escherichia coli. EMBO J., 19 (10), 2221. Hager, A.J., Bolton, D.L., Pelletier, M.R., Brittnacher, M.J., Gallagher, L.A., Kaul, R., Skerrett, S.J., Miller, S.I., and Guina, T. (2006) Type IV pili-mediated secretion modulates Francisella virulence. Mol. Microbiol., 62 (1), 227.

8 Lee, B.Y., Horwitz, M.A., and Clements,

9

10

11

12

13

14

D.L. (2006) Identification, recombinant expression, immunolocalization in macrophages, and T-cell responsiveness of the major extracellular proteins of Francisella tularensis. Infect. Immun., 74 (7), 4002. Pavkova, I., Reichelova, M., Larsson, P., Hubalek, M., Vackova, J., Forsberg, A., and Stulik, J. (2006) Comparative proteome analysis of fractions enriched for membrane-associated proteins from Francisella tularensis subsp. tularensis and F. tularensis subsp. holarctica strains. J. Proteome Res., 5 (11), 3125. Tatusov, R.L., Fedorova, N.D., Jackson, J.D., Jacobs, A.R., Kiryutin, B., Koonin, E.V., Krylov, D.M., Mazumder, R., Mekhedov, S.L., Nikolskaya, A.N., Rao, B.S., Smirnov, S., Sverdlov, A.V., Vasudevan, S., Wolf, Y.I., Yin, J.J., and Natale, D.A. (2003) The COG database: an updated version includes eukaryotes. BMC Bioinformatics, 4, 41. Bendtsen, J.D., Nielsen, H., von Heijne, G., and Brunak, S. (2004) Improved prediction of signal peptides: SignalP 3.0. J. Mol. Biol., 340 (4), 783. Gardy, J.L., Laird, M.R., Chen, F., Rey, S., Walsh, C.J., Ester, M., and Brinkman, F.S. (2005) PSORT v.2.0: expanded prediction of bacterial protein subcellular localization and insights gained from comparative proteome analysis. Bioinformatics, 21 (5), 617. Bendtsen, J.D., Kiemer, L., Fausbøll, A., and Brunak, S. (2005) Non-classical protein secretion in bacteria. BMC Microbiol., 5, 58. Havlasova, J., Hernychova, L., Brychta, M., Hubalek, M., Lenco, J., Larsson, P., Lundqvist, M., Forsman, M., Krocova, Z., Stulik, J., and Macela, A. (2005)

113

114

9 Analysis of Culture Filtrate Proteins of Francisella tularensis Proteomic analysis of anti-Francisella tularensis LVS antibody response in murine model of tularemia. Proteomics, 5 (8), 2090. 15 Janovska, S., Pavkova, I., Hubalek, M., Lenco, J., Macela, A., and Stulik, J. (2007) Identification of immunoreactive antigens in membrane proteins enriched fraction from Francisella tularensis LVS. Immunol. Lett., 108 (2), 151. 16 Raynaud, C., Etienne, G., Peyron, P., Lanéelle, M.A., and Daffé, M. (1998) Extracellular enzyme activities potentially involved in the pathogenicity of Mycobacterium tuberculosis. Microbiology, 144 (Pt 2), 577. 17 Vanet, A., and Labigne, A. (1998) Evidence for specific secretion rather than autolysis in the release of some Helicobacter pylori proteins. Infect. Immun., 66 (3), 1023.

18 Ogawa, J., Sulistyaningdyah, W.T., Li,

Q.S., Tanaka, H., Xie, S.X., Kano, K., Ikeda, T., and Shimizu, S. (2004) Two extracellular proteins with alkaline peroxidase activity, a novel cytochrome c and a catalase-peroxidase, from Bacillus sp. No.13. Biochim. Biophys. Acta, 1699 (1–2), 65. 19 Fossati, G., Izzo, G., Rizzi, E., Gancia, E., Modena, D., Moras, M.L., Niccolai, N., Giannozzi, E., Spiga, O., Bono, L., Marone, P., Leone, E., Mangili, F., Harding, S., Errington, N., Walters, C., Henderson, B., Roberts, M.M., Coates, A.R., Casetta, B., and Mascagni, P. (2003) Mycobacterium tuberculosis chaperonin 10 is secreted in the macrophage phagosome: is secretion due to dissociation and adoption of a partially helical structure at the membrane? J. Bacteriol., 185 (14), 4256.

115

10 Lipopolysaccharides of Coxiella burnetii: Chemical Composition and Structure, and Their Role in Diagnosis of Q Fever Rudolf Toman and Pavol Vadovicˇ

10.1 Introduction

Coxiella burnetii is a highly infectious gram-negative bacterium that causes Q fever, a zoonotic disease, capable of being transmitted from animals to humans [1]. A single organism may initiate infection. Despite the fact that C. burnetii is unable to grow or replicate outside host cells, there is a spore-like form of the bacterium that is extremely resistant to heat, pressure, desiccation, and many standard antiseptic compounds; this allows the microbe to persist in the environment for long periods under harsh conditions. This persistence, coupled with a primary mode of transmission by inhalation of infected aerosols, allows for the development of acute infection following only indirect exposure to an infected source [1]. C. burnetii fulfils all requirements for a potential biological weapon: it consistently causes disability; it can be manufactured on a large scale; it remains stable under production, storage, and transportation conditions; it can be efficiently disseminated; and consequently, it remains viable for years [2]. An easy aerosol dissemination, environmental persistence, and high infectivity [1, 2] make the bacterium a serious threat for military personnel and civilians. The potential of C. burnetii as a biological warfare threat is directly related to its infectivity. It has been estimated that 50 kg of dried, powdered bacterium would produce casualties a rate equal to that of similar amounts of anthrax or tularemia organisms [2, 3]. In humans, the most common acute form of Q fever is characterized as a flue-like illness or atypical pneumonia, or less frequently as granulomatous hepatitis, with a significant incidence of neurologic complications [4]. Persistent infections in humans can lead to a chronic form of Q fever, which may be associated with a fatal endocarditis [4]. Q fever was diagnosed in various animals. In livestock, it is associated with pneumonia and reproductive disorders with abortion, stillbirth, placentitis, endometritis, and infertility [5]. The main route of infection is inhalation of contaminated aerosol or dust containing bacteria shed by infected animals with milk, feces, placenta, or

BSL3 and BSL4 Agents: Proteomics, Glycomics, and Antigenicity, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.

116

10 Lipopolysaccharides of Coxiella burnetii

vaginal secretions [5, 6]. Oral trasmission seems less common, but the consumption of contaminated raw milk and dairy products represents a potential source of human infection [5]. As several clinical symptoms of Q fever are similar to commonly occurring infections, an unambiguous diagnosis of the disease is quite difficult [4, 6]. In addition, most laboratories cannot work with this pathogen as the biological protection requirements are very high in this case (biosafety level 3 conditions). Various serological methods are currently used for a rapid and sensitive diagnosis of the disease [4, 7] but ambiguous results have been obtained in several cases. Thus, when bacterial antigens are used in the serological diagnosis, crossreactions have been observed with structurally related antigens of other bacterial species. For example, investigations have shown that C. burnetii cross-reacts with Chlamydia, Legionella, and Bartonella species [8]. Indirect immunofluorescence assay was proposed [8] as a reference serological method and applies purified C. burnetii cells as an antigen which are being propagated in embryonated hen eggs, tissue culture, or animals. Other serological methods include microagglutination, enzyme-linked immunoadsorbent assay (ELISA), complement fixation test, and Western blot. Recently, ELISA has been shown to be the most sensitive and the easiest to perform and its utility for epidemiological screening and diagnosis of acute and chronic forms of Q fever has been confirmed [7, 9]. Polymerase chain reaction is very useful, especially in those cases in which serological tests brought ambiguous results [6]. Its wider application is limited at present by higher costs as compared to other methods used in the field. Various mass spectrometric approaches have been used recently for rapid detection and identification of C. burnetii. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) of the intact bacterial cells and their acetonitrile extracts together with electrospray ionization mass spectrometry (ESI-MS) of the latter provided characteristic and reproducible mass spectral fingerprints containing unique biomarker profiles and proteins that can be exploited for rapid detection, identification, and typing of the bacterium [10–13].

10.2 Lipopolysaccharides of C. burnetii

Like other gram-negative bacteria, C. burnetii expresses at its surface various amphophilic macromolecules among which LPSs and proteins are of particular biological, immunological and medical significance [14]. LPSs of all gram-negative bacteria share the common structural features. They consist of an O-polysaccharide chain, a core oligosaccharide region, and a lipid component, termed lipid A [15]. This type of LPS is the classical one, as found in wild-type (S-form) strains of the gram-negative bacteria. The two saccharide regions, the O-chain and core oligosaccharide, may be distinguished by their genetic determination, biosynthesis, chemical structure, and biological properties [16, 17]. Defects in the gene clusters

10.2 Lipopolysaccharides of C. burnetii

responsible for the synthesis of the O-chain (rfb and rfc loci) lead to an LPS containing a highly truncated O-chain or an LPS consisting of lipid A and the core region only. Such mutants are called rough (R) mutants due to the rough appearance of their colony morphology [17]. Their major feature is a noticeably reduced virulence in comparison with their parent S-form strain due to increased susceptibility to complement-mediated serum killing. Nevertheless, in the last decade many (R) LPSs have been found in pathogenic wild-type bacteria, including those colonizing especially the mucosal surfaces of the respiratory and urogenital tracts, such as Neisseria meningitis, N. gonorrhoeae, Haemophilus influenzae, Bordetella pertussis, or Chlamydiaceae. At present, it is not known with certainty whether these bacteria are not able to biosynthetize (S) LPSs or whether the genes encoding synthesis of O-specific chains are suppressed or deleted by regulatory mechanisms induced by environmental cues [18]. However, the occurrence of such (R) type LPSs in the pathogenic wild-type bacteria indicates that the O-chain cannot be considered as the only major factor of pathogenicity in the respective bacterium and that it is dispensable without deleterious effects for it. C. burnetii undergoes a virulent (phase I) to avirulent (phase II) variation upon serial laboratory passages in yolk sacs of embryonated hen eggs [14]. This phase variation is accompanied by modifications in both composition and structure of the LPS macromolecule [14, 19, 20]. In phase I, C. burnetii biosynthesizes (S) LPS I with O-specific chain whereas in phase II, it synthesizes (R) LPS II [19]. This phase variation was assumed [21] to resemble in many aspects the well known smooth to rough variation found with many gram-negative bacteria. It was proposed [22] that the LPS I reduces gradually its O-polysaccharide chain during the phase variation, and in the phase II, a deep (R) LPS II is present in the outer membrane of the C. burnetii cell. When mapping the O antigen-encoding region in several phase II antigenic variants of the Nine Mile strain of C. burnetii, a large group of LPS biosynthetic genes has been deleted [23, 24]. However, other phase II isolates contained no apparent deletions [24]. As there is no molecular evidence of large deletions in the same regions deleted in Nine Mile-derived isolates, these isolates might undergo an LPS drift similar to that observed by Ftacek et al. [20], where mixtures of at least three LPS populations were present in the C. burnetii strain Priscilla, depending on passage history. The LPS molecules extracted from any (S) strain are heterogeneous in size. They include at least some (R) LPS and in some cases variously truncated LPS molecules. Thus, it can be assumed that each strain/isolate has a cell population that expresses multiple LPS structures. The similar phenomenon was also observed [20] in the LPSs isolated during serial passage of C. burnetii in embryonated hen eggs. No noticeable shortening of the O-polysaccharide chains was observed as anticipated previously [22] but a redistribution of the existing LPS populations took place due to an increasing prevalence of those cells in the whole cell population that express LPS molecules with truncated O-chains and those being of (R) type. At present, the molecular mechanisms influencing the LPS modifications during the C. burnetii phase variation remain unclear.

117

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10 Lipopolysaccharides of Coxiella burnetii

10.2.1 Chemical Composition and Structure of LPS I

The LPS I plays an important role in the interaction of C. burnetii with a host, its pathogenicity and immunogenicity; it is capable of inducing antibody response, and it is considered to be a protective immunogen [25, 26]. Despite these facts, knowledge on its primary structure has been insufficient thus far. It was found [21, 27, 28] that the LPS I contains, in addition to the sugar residues found in the LPS II and some frequently occuring sugars, two unusual sugar units in its O-polysaccharide chain, namely virenose (Vir, 6-deoxy-3-C-methylgulose) and dihydrohydroxystreptose [Strep, 3-C-(hydroxymethyl)lyxose]. Both sugars have not been found in other LPSs and are considered unique biomarkers of C. burnetii. The enantiomeric forms and ring conformations of both saccharides were established from the optical rotation and NMR data [28]. Vir was found to be the d-gulo enantiomer with the 4C1 ring conformation and Strep was shown to be the l-lyxo enantiomer also with the 4C1 conformation. A tentative structural analysis of LPS I showed [29] that Strep was present in the parent LPS I in a furanose form. Therefore, it was suggested [28] that a furanose to pyranose tautomerization took place in the course of the isolation procedure. The methylation-linkage analyses of two polysaccharide fractions of the LPS I performed recently [30] revealed the presence of terminal Vir, Strep, and mannose (Man), 4-substituted Vir, 4-substituted Man, and 2,3- and 3,4-disubstituted d-glycero-d-manno-heptose (Hep). Among the amino sugars, 4-substituted glucosamine (GlcN) was detected. The methylation data demonstrated the pyranose form of Vir, Man, Hep, and GlcN, and the furanose form of Strep. From previous studies [31], it could be anticipated that two terminal Man, 2,3- and 3,4-disubstituted Hep were from the core region of LPS I, and thus other terminal and substituted sugars should be located in the O-polysaccharide chain. In previous works [21, 28, 29], it was suggested that Vir and Strep are located almost exclusively in terminal positions. However, these recent findings show that this is probably true only for Strep, as Vir is also (1→4)linked. Similarly, Man is present in terminal position but about 23% of it is also involved in 1→4 linkages (Figure 10.1). Progress in a more detailed characterization of the O-specific chain in the LPS I is hampered by the presence of several O-chain populations differing in size, shape, and chemical composition. Therefore, only a tentative structural arrangement of sugar residues in the LPS I can be given as shown in Figure 10.2. The lipid A portion of an LPS is linked to the core oligosaccharide mostly via 3-deoxy-d-manno-oct-2-ulosonic acid (Kdo) and serves as the hydrophobic anchor of LPS in the outer membrane [32]. Previous reports have suggested [32] that lipid A, as the principal endotoxic component of LPS, plays a major role in the pathogenesis of bacterial infections and is an important contributor to massive inflammation, sepsis, and septic shock, leading to fatalities in gram-negative bacteria infections. It also promotes the activation of the innate immune system via induction of inflammatory cytokines released by human cells. Lipid A usually contains a diglucosamine backbone substituted with both ester- and amide-linked fatty acyl

10.2 Lipopolysaccharides of C. burnetii

Terminal position

Linkage position

4 Vir 1

Vir 1 Man 1

4 Man 1

Strep 1

4 GlcN 1

Figure 10.1 Schematic presentation of sugar linkages found in the O-chain of the LPS I of C. burnetii in virulent phase I. Vir – virenose (6-deoxy-3-C-methyl-d-gulose); Strep – dihydrohydroxystreptose [3-C-(hydroxymethyl)-l-lyxose]; Man – d-mannose; GlcN – d-glucosamine.

O-chain Vir, Strep, Man, Glc, GlcN

Core region outer inner Man

Man, Hep

Hep

Kdo

Lipid A

Tentative structural arrangement of the sugar residues in the LPS I of C. burnetii in virulent phase I. Vir – virenose (6-deoxy-3-C-methyl-d-gulose); Strep – dihydrohydroxystreptose [3-C-(hydroxymethyl)-l-

Figure 10.2

lyxose]; Man – d-mannose; Glc – d-glucose; GlcN – d-glucosamine; Hep – d-glycero-dmanno-heptose; Kdo – 3-deoxy-d-manno-oct-2ulosonic acid. The underlined sugars are prevalent in the O-chain.

side-chains and may carry phosphate groups at O-1 and O-4′. In addition, other substituents or sugar constituents, such as 4-amino-4-deoxy-l-arabinose, have been found. Structural studies of various bacterial LPSs have confirmed that lipid A represents the most conserved region of an LPS [33]. Although the biochemical synthesis of lipid A is a highly conserved process, investigations of the lipid A structures of various bacteria and even the isolates of the same bacterium including C. burnetii show an impressive amount of diversity [32–34]. These differences can be attributed to the action of latent enzymes that modify the canonical lipid A molecule. Variation of the lipid A domain of LPS serves as one strategy utilized [32] by gram-negative bacteria to promote survival by providing resistance to components of the innate immune system and helping to evade recognition by Toll-like receptor 4. Our detailed investigations on structure and function of lipid A isolated from the LPS of C. burnetii are presented in this booklet elsewhere.

119

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10 Lipopolysaccharides of Coxiella burnetii

α-D-Man p 1 α-D-Man p-(1

2)-α-D,D-Hep p-(1

4 3) -α-D,D-Hep p-(1

5)-α-Kdo-(2 4

2 α-Kdo-(4

2) -α-Kdo

Figure 10.3 Structural features of the lipid A proximal region of the LPS II of C. burnetii in

avirulent phase II. Man p – d-mannopyranose; D,D,-Hep p – d-glycero-d-manno-heptopyranose; Kdo – 3-deoxy-d-manno-oct-2-ulosonic acid.

10.2.2 Chemical Composition and Structure of LPS II

Both compositional and structural studies of the LPS II produced a lot of controversy in the past. This subject has been reviewed [19] by Toman recently. Based on the combined results of methylation-linkage analysis, fast atom bombardment-, ESI-, and MALDI-MS [31, 35], a structural model for the LPS II could be proposed. The monosaccharides Man, D,D-Hep, and Kdo were found to be the constituent sugars of the lipid A proximal region in a molar ratio 2 : 2 : 3 with the linkages depicted in Figure 10.3. Most recently, we performed initial studies [36] on the chemical composition and structure of an LPS isolated from the C. burnetii strain Nine Mile, variant “Crazy” (Cr) that was together with the phase II variant investigated [23] in a considerable detail by the methods of molecular biology. In contrast to the LPS I and II, the LPS Cr gave only one band at about 14 kDa on sodium dodecyl sulfate polyacrylamide gel electrophoresis. Sugar analysis revealed the presence of Man, Glc, D,D-Hep, Strep, and GlcN in a molar ratio 3.1 : 0.1 : 1.0 : 1.5 : 1.2, respectively. No Vir was found. MALDI-MS analyzes of lipid A indicated chemical structure similar to that found in the LPS I and II [34–36]. The truncated LPS II structure was shown [23] to be a result of large chromosomal DNA deletions in the phase II cells of strain Nine Mile. The deleted region is characterized by a high number of genes that are predicted to function in LPS or lipooligosaccharide biosynthesis, as well as several that were predicted to function in general carbohydrate and sulfur metabolisms. Deletions in the variant Cr were larger extending on both ends beyond the phase II deletion junctions. However, both chemical composition and structure of the LPS Cr are more complex than those of the LPS II. The reason for this discrepancy remains unknown. 10.2.3 The Role of LPS I in Diagnosis of Q Fever

Phase variation of C. burnetii has a direct impact on the serological diagnosis of Q fever. During acute Q fever, C. burnetii induces antibodies against phase II (protein antigens), while in the later stages of the disease, and especially in its

References

chronic form often manifested as endocarditis, the high titers of antibodies are directed against phase I (LPS I antigen) [4]. The immunoreactive proteins involved in a highly specific and reliable diagnosing acute Q fever have not been established with certainty thus far. Further, there has been achieved some progress in the elucidation of interaction of phase I antibodies with the LPS I antigen. A remarkable decrease in the serological activity of the O-polysaccharide antigen was observed when Vir and Strep were selectively removed from its chain [30]. At present, however, it is not known with certainty whether the immunoreaction proceeds only with both sugars in terminal positions or also with those Vir residues located in the O-polysaccharide backbone. The unique C. burnetii biomarkers Vir and Strep could be used in future for rapid, sensitive and unambiguous detection of the virulent form of C. burnetii (Q fever) and differentiation of the individual isolates/variants. Thus, a monoclonal antibody (mAb, IgG class) has been generated that is highly specific for the presence of Vir in C. burnetii LPS [37]. The immunoblot and ELISA studies have confirmed that only Vir containing C. burnetii cells and LPSs were recognized by the mAb. The intensity of the signals differed depending on the isolate/variant tested. In immunoblot, the most reactive region was about 14–36 kDa. Variability in the immunoreactivity of isolates/variants studied was seen in both distribution of the individual bands and their intensities.

10.3 Conclusion

There is a need to better characterize the relatively poorly understood LPS I as the elucidation of mechanisms of induction and development of various biological activities of the LPS I and its functions at the molecular level requires a detailed knowledge of its chemical composition and structure. This problem appears to be of utmost importance in connection with the future studies on mechanisms of pathogenesis and immunity of Q fever, its early and reliable diagnosis, and effective prophylaxis against the disease.

Acknowledgments

This work was supported in part by grant 2/0127/09 of the Scientific Grant Agency of the Ministry of Education of the Slovak Republic and the Slovak Academy of Sciences.

References 1 Williams, J.C. (1991) Infectivity,

virulence, and pathogenicity of Coxiella

burnetii for various hosts, in Q Fever: The Biology of Coxiella burnetii

121

122

10 Lipopolysaccharides of Coxiella burnetii

2

3

4 5

6 7

8

9

10

11

12

13

(eds J.C. Williams and H.A. Thompson), CRC Press, Boca Raton, FL, pp. 22–71. Madariaga, M.G., Rezai, K., Trenholme, G.M., et al. (2003) Q fever: a biological weapon in your backyard. Lancet Infect. Dis., 3, 709–721. World Health Organization (1970) Health Aspects of Chemical and Biological Weapons: Report of A WHO Group of Consultants, WHO, Geneva, Switzerland. Maurin, M., and Raoult, D. (1999) Q fever. Clin. Microbiol. Rev., 12, 518–553. Arricau-Bouvery, N., and Rodolakis, A. (2005) Is Q fever an emerging or re-emerging zoonis? Vet. Res., 36, 327–349. Choi, E. (2002) Tularemia and Q fever. Med. Clin. North Am., 86, 393–416. Slaba, K., Skultety, L., and Toman, R. (2005) Efficiency of various serological techniques for diagnosing Coxiella burnetii infection. Acta Virol., 49, 123–127. Fournier, P.E., Marrie, T.J., and Raoult, D. (1998) Diagnosis of Q fever. J. Clin. Microbiol., 36, 1823–1834. Uhaa, I.J., Fishbein, D.B., Olson, J.G., et al. (1994) Evaluation of specificiy of indirect enzyme-linked immunosorbent assay for diagnosis of human Q fever. J. Clin. Microbiol., 32, 1560–1565. Shaw, E.I., Moura, H., Woolfitt, A.R., et al. (2004) Identification of biomarkers of whole Coxiella burnetii phase I by MALDI-TOF mass spectrometry. Anal. Chem., 76, 4017–4022. Skultety, L., Hernychova, L., Bereghazyova, E., et al. (2007) Detection of specific spectral markers of Coxiella burnetii isolates by MALDI-TOF mass spectrometry. Acta Virol., 51, 55–58. Pierce, C.Y., Barr, J.R., Woolfitt, A.R., et al. (2007) Strain and phase identification of the U. S. category B agent Coxiella burnetii by matrix assisted laser desorption/ionization time-of-flight mass spectrometry and multivariate pattern recognition. Anal. Chim. Acta, 583, 23–31. Hernychova, L., Toman, R., Ciampor, F., et al. (2008) Detection and identification of Coxiella burnetii based on the mass spectrometric analyses of the extracted proteins. Anal. Chem., 80, 7097–7104.

14 Williams, J.C., and Waag, D.M. (1991)

15

16

17

18

19

20

21

22

23

Antigens, virulence factors, and biological response modifiers of Coxiella burnetii: strategies for vaccine development, in Q Fever: The Biology of Coxiella burnetii (eds J.C. Williams and H.A. Thompson), CRC Press, Boca Raton, FL, pp. 175–222. Mayer, H., Tharanathan, R.N., and Weckesser, J. (1985) Analysis of lipopolysaccharides of gram-negative bacteria. Methods Microbiol., 18, 157–207. Keenleyside, W.J., and Whitfield, C. (1999) Genetics and biosynthesis of lipopolysaccharide O-antigens, in Endotoxin in Health and Disease (eds H. Brade, S.M. Opal, S.N. Vogel, and D.C. Morrison), Marcel Dekker, New York, pp. 331–358. Heinrichs, D.E., Whitfield, C., and Valvano, M.A. (1999) Biosynthesis and genetics of lipopolysaccharide core, in Endotoxin in Health and Disease (eds H. Brade, S.M. Opal, S.N. Vogel, and D.C. Morrison), Marcel Dekker, New York, pp. 305–330. Preston, A., Mandrell, R.E., Gibson, B.W., et al. (1996) The lipooligosaccharides of pathogenic gram-negative bacteria. Crit. Rev. Microbiol., 22, 139–180. Toman, R. (1999) Lipopolysaccharides from virulent and low-virulent phases of Coxiella burnetii, in Rickettsiae and Rickettsial Diseases at the Turn of the Third Millenium (eds D. Raoult and P. Brouqui), Elsevier, Paris, pp. 84–91. Ftacek, P., Skultety, L., and Toman, R. (2000) Phase variation of Coxiella burnetii strain Priscilla: influence of this phenomenon on biochemical features of its lipopolysaccharide. J. Endotoxin Res., 6, 369–376. Mayer, H., Radziejewska-Lebrecht, J., and Schramek, S. (1988) Chemical and immunochemical studies on lipopolysaccharides of Coxiella burnetii phase I and phase II. Adv. Exp. Med. Biol., 228, 577–591. Diaz, Q.M., and Lukacova, M. (1998) Immunological consequences of Coxiella burnetii phase variation. Acta Virol., 42, 181–185. Hoover, T.A., Culp, D.W., Vodkin, M.H., et al. (2002) Chromosomal DNA deletions

References

24

25

26

27

28

29

30

explain phenotypic characteristics of two antigenic variants, phase II and RSA 514 (Crazy), of the Coxiella burnetii Nine Mile strain. Infect. Immun., 70, 6726–6733. Denison, A.M., Massung, R.F., and Thompson, H.A. (2007) Analysis of the O-antigen biosynthesis regions of phase II isolates of C. burnetii. FEMS Microbiol. Lett., 267, 102–107. Toman, R. (1996) Lipopolysaccharides of Coxiella burnetii, in Rickettsiae and Rickettsial Diseases. Proc. Vth Int. Symp (eds J. Kazár and R. Toman), VEDA, Bratislava, pp. 379–388. Gajdošová, E., Kovácˇová, E., Toman, R., et al. (1994) Immunogenicity of Coxiella burnetii whole cells and their outer membrane components. Acta Virol., 38, 339–344. Skultety, L., Toman, R., and Patoprsty, V. (1998) A comparative study of lipopolysaccharides from two Coxiella burnetii strains considered to be associated with acute and chronic Q fever. Carbohydr. Polym., 35, 189–194. Toman, R., Skultety, L., Ftacek, P., et al. (1998) NMR study of virenose and dihydrohydroxystreptose isolated from Coxiella burnetii phase I lipopolysaccharide. Carbohydr. Res., 306, 291–296. Toman, R. (1991) Basic structural features of a lipopolysaccharide from the Coxiella burnetii strain Nine Mile in the virulent phase I. Acta Virol., 35, 224. Vadovic, P., Slaba, K., Fodorova, M., et al. (2005) Structural and functional characterization of the glycan antigens involved in immunobiology of Q fever. Ann. N.Y. Acad. Sci., 1063, 149–153.

31 Toman, R., and Skultety, L. (1996)

32

33

34

35

36

37

Structural study on a lipopolysaccharide from Coxiella burnetii strain Nine Mile in avirulent phase II. Carbohydr. Res., 283, 175–185. Alexander, C., and Rietschel, E.T. (2001) Bacterial lipopolysaccharides and innate immunity. J. Endotoxin Res., 7, 167–202. Zähringer, U., Lindner, B., and Rietschel, E.T. (1999) Chemical structure of lipid A: recent advances in structural analysis of biologically active molecules, in Endotoxin in Health and Disease (eds H. Brade, S.M. Opal, S.N. Vogel, and D.C. Morrison), Marcel Dekker, New York, pp. 93–114. Toman, R., Garidel, P., Andra, J., et al. (2004) Physicochemical characterization of the endotoxins from Coxiella burnetii strain Priscilla in relation to their bioactivities. BMC Biochem., 5. http:// www.biomedcentral.com/1471-2191/5/1 (accessed 11 January 2011). Toman, R., Hussein, A., Slabá, K., et al. (2003) Further structural characteristics of the lipopolysaccharide from Coxiella burnetii strain Nine Mile in low virulent phase II. Acta Virol., 47, 129–130. Vadovic, P., Fuleova, A., Ihnatko, R., et al. (2009) Structural studies of lipid A from a lipopolysaccharide of the Coxiella burnetii isolate RSA 514 (Crazy). Clin. Microbiol. Infect., 15, 198–199. Palkovicova, K., Ihnatko, R., Vadovic, P., et al. (2009) A monoclonal antibody specific for a unique biomarker, virenose, in a lipopolysaccharide of Coxiella burnetii. Clin. Microbiol. Infect., 15, 183–184.

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11 Mimivirus Possesses Anonymous and Unique Gene Products Endowed for Antigenic Properties Patricia Renesto and Didier Raoult

11.1 Introduction

Acantamoeba polyphaga mimivirus (Mimivirus) is the largest known virus isolated so far [1]. Recent clinical evidence raised the possibility that Mimivirus might be a human pathogen causing pneumonia [2–4], as suspected when it was first isolated in a cooling tower following an outbreak of pneumonia [1]. Sequencing of its 1.2 Mb genome [5] revealed highly specific characteristics accounting for its classification as the first member of the new Mimiviridae family [6]. Among atypical features are the presence of key translation enzymes, a full complement of DNA repair pathway components, and the presence of three different topoisomerases (types IA, IB, II). Only 23% of the predicted coding genes exhibit a convincing homology to proteins of known function and 39% of them do not exhibit a clear (E < 10−5) sequence database match [5]. Such coding regions without sequence similarity to other genes in databases were considered as orphan open reading frames (ORFs) and termed ORFans [7]. As their number increased with each sequenced genome, the status of these species-specific putative genes became matter of controversy, with opinions ranging from considering them pieces of junk DNA to seeing them as encoding normally expressed functional proteins [8]. Mass spectrometry-based analysis has recently emerged as a technique of choice to identify more comprehensively the set of viral proteins associated to viral particles [9–11]. By using such an approach we investigated the presence of ORFanencoded proteins within Acanthamoeba polyphaga mimivirus particles. In a second step, solubilized proteins separated by two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) were analyzed by Western blot, thus demonstrating the antigenicity of such ORFan-encoded proteins.

BSL3 and BSL4 Agents: Proteomics, Glycomics, and Antigenicity, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.

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11.2 Material and Methods 11.2.1 Sample Preparation

Mimivirus grown in Acanthameba polyphaga strain Linc AP-1 (in 75-cm2 cell culture flasks with peptone yeast extract glucose medium) was purified through a sucrose gradient (25%) and washed twice with phosphate buffer saline (PBS) in the presence of protease inhibitors (Complete, Roche). The resulting pellet was solubilized in Tris-HCl 40 mM, pH 7.5, supplemented with SDS 2% (w/v) and DTT 60 mM, followed by 5 min heating at 95 °C. The unsoluble fraction was removed by centrifugation (12 000 g, 4 °C, 10 min) and soluble proteins were precipitated using a PlusOne 2-D clean-up kit (Amersham) to remove SDS. The final pellet was resuspended in solubilization buffer [7 M urea, 2 M thiourea, 4% (w/v) CHAPS] and stored at −80 °C until isoelectric focusing (IEF) was performed. 11.2.2 2D-PAGE and Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry

Mimivirus proteins were resolved on 2D-PAGE as described [12], using Immobiline™ DryStrips in the range pH 4–7 or pH 6–11 (Amersham). Following migration, gels were stained by a method compatible with mass spectrometry [13] and proteins of interest as well as those mainly expressed were excised from the gel. Tryptic peptides obtained following in-gel digestion were extracted from the gel and analyzed on a matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) Bruker Ultraflex mass spectrometer (Bruker Daltonics, Bremen, Germany). The corresponding mass lists were used to identify the proteins, using Mascot software. Searches were performed against all available sequences in public databases, including those for eukaryotes (www.matrixscience.com). For accuracy, the spectra of at least two separate samples of each protein were analyzed and compared. Overall, a successful identification was reached for more than 70% of the spots analyzed. 11.2.3 Immunization and Western Blot

Following transfer onto nitrocellulose membranes, Mimivirus proteins were incubated for 1.5 h with the serum of a BALB/c mouse (1 : 6400) immunized by three intraperitoneal injections of 5 μg of purified viral particles resuspended in CpG as immune adjuvant. Horseradish peroxidase-conjugated goat antimouse antibodies were used as secondary antibodies (1 : 1000, Amersham). Detection was done by chemiluminescence (ECL, Amersham).

11.3 Results

11.3 Results 11.3.1 Proteomic Analysis of Mimivirus Particle

Completion of 2D-PAGE coupled to MALDI-TOF MS allowed us to identify 42 Mimivirus proteins. The diversity of functions represented in the viral particle was quite large, with bona fide “structural” proteins amounting for a small fraction of the number. Based on both the area and intensity of spots corresponding to ORFan gene products observed on 2D-PAGE gels we concluded that such proteins are expressed and contribute to the making of the Mimivirus particle (Figure 11.1). While some proteins appeared hydrolyzed (e.g., that encoded by Mimi_L442), a good correlation between observed and theoretical pI and MW values was observed. Analysis of 2D-PAGE gels also evidenced that most of the Mimivirus proteins were not resolved into single spots but rather as a train of spots. This results from the

111.0 93.0

L425

111.0 93.0

R135

53.5

R135

53.5 L724

L724 36.1 29.5

36.1 29.5 L442

21.3

ORF

b) Silver staining on 2D-PAGE

Protein function

L829

L725

pI3

10

pI3

a) Negative staining

21.3

L725

10

c) Western blot Mass (kDa)

pI

Number of isoforms

MIMI_R135

Choline dehydrogenase or related protein

76948

7.55

4

MIMI_L425

Capsid protein D13L

51377

5.14

4

MIMI_L442

Unknown Orfan

139334

5.88

11

MIMI_L724

Unknown Orfan

24034

7.86

2

MIMI_L725

Unknown Orfan

26538

6.91

5

MIMI_L829

Unknown

49229

6.07

8

Figure 11.1 Proteomic analysis of Acanta-

moeba polyphaga mimivirus particles. Purified viral particles shown by electron microscopy (a) were solubilized and proteins separated by 2D-PAGE (b). Spots were then cut from the gel and subjected to MALDI-TOF MS analysis

for identification. Alternatively, gels were transferred to nitrocellulose membrane and probed with serum from a mouse immunized with the viral particles (c). Predominantly expressed as well as immunoreactive spots identified by MALDI-TOF MS are indicated.

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existence of several isoforms in part due to protein glycosylation [12]. Of note is that, as previously observed [12], no eukaryotic proteins were identified in our samples, confirming their purity and demonstrating that eukaryotic proteins are not encapsidated within Mimivirus particles. 11.3.2 Antigenic Properties of ORFan-Encoded Mimivirus Proteins

Immunoblots performed on samples resolved by 2D-PAGE provided evidence of the antigenicity of Mimivirus proteins (Figure 11.1). Mimivirus proteins, including those encoded by ORFan genes, were indeed specifically recognized with the serum from a mouse immunized with the viral particles. The antigenicity of ORFan-encoded proteins, and most specifically of that encoded by MIMI_L724, was also observed when the membrane was probed with the serum of a patient infected with Mimivirus [2–4]. 11.3.3 Concluding Remarks

Altogether, these results contribute to the ongoing debate on the evolutionary origin of the gene content of large DNA viruses such as Mimivirus and suggest that ORFans may indeed correspond to bona fide viral proteins, the functions and origins of which remain to be discovered.

References 1 La Scola, B., Audic, S., Robert, C.,

2

3

4

5

Jungang, L., de Lamballerie, X., Drancourt, M., Birtles, R., Claverie, J.-M., and Raoult, D. (2003) A giant virus in amoebae. Science, 299, 2033. Berger, P., Papazian, L., Drancourt, M., La Scola, B., Auffray, J.P., and Raoult, D. (2006) Ameba-associated microorganisms and diagnosis of nosocomial pneumonia. Emerg. Infect. Dis., 12, 248–255. La Scola, B., Marrie, T.J., Auffray, J.P., and Raoult, D. (2005) Mimivirus in pneumonia patients. Emerg. Infect. Dis., 11, 449–452. Raoult, D., Renesto, P., and Brouqui, P. (2006) Laboratory infection of a technician by giant Mimivirus. Ann. Int. Med., 144, 702–703. Raoult, D., Audic, S., Robert, C., Abergel, C., Renesto, P., Ogata, H., La Scola, B., Suzan, M., and Claverie, J.-M. (2004) The

6

7

8

9

10

1.2-megabase genome sequence of Mimivirus. Science, 306, 1344–1350. Suzan-Monti, M., La Scola, B., and Raoult, D. (2006) Genomic and evolutionary aspects of Mimivirus. Virus Res., 117, 145–155. Fischer, D., and Eisenberg, D. (1999) Finding families for genomic ORFans. (1999) Bioinformatics, 15 May, 759–762. Siew, N., Saini, H.K., and Fischer, D. (2005) A putative novel alpha/beta hydrolase ORFan family in Bacillus. FEBS Lett., 579, 3175–3182. Kattenhorn, L.M., Mills, R., Wagner, M., Lomsadze, A., Makeev, V., Borodovsky, M., Ploegh, H.L., and Kessler, B.M. (2004) Identification of proteins associated with murine cytomegalovirus virions. J. Virol., 78, 11187–11197. O’Connor, C.M., and Kedes, D.H. (2006) Mass spectrometric analyses of purified

References rhesus monkey rhadinovirus reveal 33 virion-associated proteins. J. Virol., 80, 1574–1583. 11 Zachertowska, A., Brewer, D., and Evans, D.H. (2005) Characterization of the major capsid proteins of myxoma virus particles using MALDI-TOF mass spectrometry. J Virol. Methods, 132, 1–12. 12 Renesto, P., Abergel, C., Decloquement, P., Moinier, D., Azza, S., Ogata, H.,

Fourquet, P., Gorvel, J.P., and Claverie, J.-M. (2006) Mimivirus giant particles incorporate a large fraction of anonymous and unique gene products. J. Virol., 80, 11678–11685. 13 Shevchenko, A., Wilm, M., Vorm, O., and Mann, M. (1996) Mass spectrometric sequencing of proteins silver-stained polyacrylamide gels. Anal. Chem., 68, 850–858.

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12 Detection of Differentially Modified Pathogen Proteins by Western Blot after 2D Gel Electrophoresis and Identification by MALDI-TOF/TOF Fred Fack, Julia Kessler, Patrick Pirrotte, Jacques Kremer, Dominique Revets, Wim Ammerlaan, and Claude P. Muller

12.1 Introduction

Viruses have developed complex strategies to exploit host cell mechanisms for efficient replication [1]. Post-translational modifications, such as glycosylation of viral surface proteins, phosphorylation, sumoylation, methylation, acetylation, and so on, of both viral and cellular proteins have been shown to play important roles in virus–host interactions. Post-translational modifications of viral and cellular proteins may also modulate virus virulence and attenuation. They are, for example, essential for the induction of type I interferon, a first line of defense against invading viruses and the induction of adaptive immunity. After viral infection transcription factors, such as interferon regulatory factor 3 (IRF-3), NF-kB, and ATF-2/c-JUN, are activated and induce the expression of beta IFN (IFN-beta) and other antiviral genes [2]. In this process C-terminal serine and threonine residues of IRF3 monomers constitutively expressed in the cytoplasma become hyperphosphorylated. This results in a conformational change necessary for dimerization and translocation of IRF3 to the nucleus where it binds to IRF DNA consensus sites and modulates the transcription of immune response genes such as interferon alpha and beta [2]. This signaling pathway can be disturbed by viral strategies to overcome the host defense. For instance, VP35 of the Ebola virus can interfere with the activation of IRF3 in vitro [3]. These modifications of amino acid side chains affect the isoelectric point (pI) of a protein, reflected in two-dimensional protein gels by distinct spots without a mass shift. Measles virus (MV) can induce host cell responses associated with different levels of virulence. To evaluate both the antiviral response to viral infection and the viral replication appropriate protein markers were chosen. The IFN-inducible MxA proteins are highly conserved, large GTPases with homology to dynamin and have been found in all vertebrate species examined so far [4]. A perinuclear aggregation of MxA and viral proteins and the possible antiviral effect of this sequestration of viral components has been shown for Bunyaviruses [5]. MxA is a good candidate to monitor the induction of a primary interferon regulated antiviral BSL3 and BSL4 Agents: Proteomics, Glycomics, and Antigenicity, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.

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response, and MV-NP can serve as an indicator for viral protein synthesis and to a certain extent for viral replication. To compare protein patterns of cells, the 2D-DIGE technology is the method of choice. Protein extracts of infected and uninfected cells are labeled typically with Cy3 and Cy5 dyes to separate them in a same gel and to generate fully superimposable images with a fluorescence scanner [6]. An internal standard containing a mix of the different protein extracts is labeled with a third color, generally Cy2 dye, allowing for relative quantification for comparing and matching different gels. Current equipment and image analysis software resolve thousands of protein spots in 18 or 24 cm 2D gels. However, several hundred micrograms of protein are generally required. We have evaluated small 2D gels and Western blotting for 2D-DIGE analysis of low amounts of biological samples. The detection of protein isoforms by 2D Western blots using flurorescence-labeled secondary antibodies has a number of advantages and can be combined with 2D-DIGE imaging. The availability of secondary antibodies as fluorescent conjugates for several different species has made the simultaneous detection of three specific proteins in a single blotting experiment possible. This multiplexing capacity provides a robust and straightforward detection system of various forms of different target proteins in a single Western blot experiment, and it is particularly attractive when protein quantities are limiting (i.e., <10 μg) and when several protein expression levels and post-translational modification should be investigated in relation to a stably expressed control protein. In this report we describe the detection of up-regulated measles virus and host cell proteins with a minigel-based 2D-DIGE approach and their identification by MALDI-TOF/TOF mass spectrometer. The results were confirmed by combining a fluorescence Western blot (ECL Plex) with Cy-dye-labeled protein extracts. The multiplexed antigen detection was used to monitor on a same blot the induction of an antiviral cell response protein and the synthesis of viral proteins.

12.2 Materials and Methods 12.2.1 Protein Sample Preparation and Fluorescence Labeling for DIGE Analysis

THP1-cells infected with measles virus vaccine strain (Schwarz, MOI = 1) were grown in suspension to a density of 106 cells/ml. Cells were harvested after 24 and 34 h post-infection when >80% expressed the viral hemagglutinine protein at the cell surface and cell mortality was <10%. A mock infected culture was prepared as a control. After two washing steps in TBS pH 7.5, total protein was extracted with a DIGE compatible lysis buffer (7 M urea, 2 M thiourea, 30 mM tris-HCl, pH 8.5, 2% ASB14 ). Following acetone precipitation the proteins were quantified (2D quant, GE Heahlthcare) and used either directly for isoelectric focusing (IEF) or

12.2 Materials and Methods

after minimal labeling with cyanodyes (GE Healthcare). Protein extracts from the mock- and virus-infected cells were differentially labeled with Cy3 and Cy5, respectively. A mixture of proteins from both extracts was labeled with Cy2, a third color that served as a control standard. For one of the three biological replicates the Cy3 and Cy5 dyes were swapped, to exclude dye-specific artefacts due to preferential labeling. 12.2.2 2D Electrophoresis and Gel Imaging

Aliquots of Cy2, Cy3, and Cy5 labeled proteins were mixed and separated by IEF on 7 cm IPG strips (GE Healthcare) with pH ranges of pH 3–11 and pH 4–7, respectively. After IEF the strips were equilibrated for 15 min in DTT and iodoacetamide buffers, before they were transferred onto a gradient ZOOM-IPG gel (Invitrogen) for the second dimension separation in MES buffer. Electrophoresis (Novex tanks) was continued for 30 min at 150 V/250 mA until the bromphenol blue had migrated out of the gel. For fluorescence scanning, gels were briefly washed in cold water prior to staining, or fixed for 1 h in a 50% : 10% methanol/ acetic acid solution followed by a washing step in cold water. The gels were scanned on a Typhoon 9400 flat bed fluorescence scanner (GE Healthcare) with a lateral resolution of 100 μm, using the excitation and emission wavelengths recommended for the three dyes, (i.e., Cy2, 491/509 nm; Cy3, 553/569 nm; Cy5, 645/664 nm). The three scanned images of the same gel were analyzed with the differential in gel analysis (DIA) module of the DeCyder 2D imaging software ver. 5.1 (GE Healthcare) to detect differentially expressed proteins. Gels of three biological replicates were analyzed in the biological variation analysis (BVA) module of the same software. 12.2.3 2D Western Blotting

2D gels (8 × 10 cm) were transferred to low-fluorescence PVDF membranes (GE Healthcare) using a semi-dry blotter (PHASE, Germany) at a current of 0.8 mA/ cm2 and 7–9 V. Following the transfer the membranes were marked on the protein side by pencil, washed in water for 10 min, prior to drying (1 h at 37 °C or overnight at room temperature) and either scanned or used for hybridization. The membranes were blocked for 120 min in a washing buffer (TBS, 0.3% Tween 20) containing 2% milk. After three washes the primary antibody (dilution 1 : 2000 or 1 : 4000) was incubated for 1.5 h with the membranes. After three washes of 10 min the secondary antibody was added for 1 h. Following three final washes of 10 min each, the membrane was rinsed in TBS without Tween 20 and dried as described above. The dry membranes with pre-labeled proteins and/or fluorescence-labeled secondary antibodies were scanned with the protein side facing the scanner and covered with a clean glass plate to press the membrane during the scanning

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12 Detection of Differentially Modified Pathogen Proteins by Western Blot

process (background reduction). Image acquisition and treatment was as for gel imaging, excepted for roughly 30% reduced PMT – voltage settings compared to the gel scans. Primary antibodies included a mouse monoclonal (BNP49) against a sequential epitope at the N-terminus of MV-nucleoprotein (MV-NP), a commercial antiMxa (polyclonal chicken, BAClab, Switzerland), and an antibeta actin (polyclonal, rabbit, ABCam) at dilutions of 1 : 4000 and 1 : 2000, respectively. Fluorescence-labeled second-step antibodies were used as Cy3-conjugate anti mouse IgG (GE Healthcare, 1 : 5000 dilution), Cy5 antirabbit IgG conjugate (GE Healthcare, 1 : 5000 dilution) and AF488 antichicken IgG conjugate (Invitrogen, 1 : 5000 dilution). 12.2.4 Protein Identification by MALDI-TOF/TOF Analysis of In-Gel Tryptic Digests

Protein spots were visualized for picking on a transilluminator (Safe Imager, Invitrogen) using a post electrophoresis stain (Sypro Ruby, Invitrogen). Protein spots from preparative gels were picked manually using 200 μl tips (Rainin, Belgium). Gel plugs were transferred in V-shaped 96-well propylene plates and washed in alternating bathes of ultrapure water and acetonitrile (Biosolve, the Netherlands) to remove fluorescence stain. Following reduction with DTT and alkylation with iodoacetamide the gel plugs were washed and dried prior to rehydration on ice with 2 μl of 12.5 ng/μl proteomics grade trypsin (Roche). After 30 min on ice the gel plugs were covered with 50 mM ammonium bicarbonate for overnight incubation at 37 °C. The in-gel digested tryptic peptides were extracted from the gel in two extraction steps, acidified and dried prior to resuspension and spotting on a steel target with alpha-cyano-4-hydroxycinnamic acid matrix (Bruker). The tryptic digest was subsequently analyzed on a MALDI TOF/TOF instrument (Ultraflex I, Bruker). Proteins of interest were identified by peptide mass fingerprint analysis (PMF) and by MS/MS analysis of selected peptides using MASCOT software (Matrix Science).

12.3 Results 12.3.1 2D-DIGE Analysis of Measles Virus Infected THP1 Cells

The 2D-DIGE analysis (DIA) of MV- and mock-infected THP1 cells (Figure 12.1) detected 600–680 protein spots. When MV-infected cells (Figure 12.1, right panel) were compared to mock infected cells (Figure 12.1, middle panel) a number of proteins were differentially expressed. Some of these proteins were strongly upregulated after measles infection, visible as red spots on the overlay image (Figure 12.1, left panel). In the biological triplicates an average of 639 spots were matched

12.3 Results TO Cy 3

TV Cy 5

2

1

pH 4

1

1

pH 7

2D DIGE analysis of mock- and measles virus-infected THP1 monocyte proteins. Total protein extracts of mockinfected (TO, Cy3 label; middle panel) and measles virus-infected (TV, Cy5 label; right panel) cells were separated on a small 2D gel (pH 4–7) together with an experimental

Figure 12.1

2

standard (Cy2 label, not shown). The region of interest (left panel, white rectangle) is shown on an overlay of the images of the mock and virus infected samples. Arrows indicate the location of spot groups 1 and 2 identified by mass spectrometry as MV-NP and MxA, respectively.

and analyzed, 12.3% were down- and 2.5% were up-regulated by at least twice the standard deviation. The most significantly up-regulated protein spots were identified by their peptide mass fingerprint (PMF) and MS/MS sequencing of selected peptides. The most abundant spots of group 1 visible in the Cy5 image (Figure 12.1) and in the overlay image, were identified as MV-NP (MW 58.2 kDa, pI 5.11). While the two most abundant spots of group 1 were always detected, the intensity of the other spots of this group was more variable. The Cy3 image shows a weak protein spot in uninfected cells, indicating that a cellular protein co-migrates with the MV-NP. Three spots corresponding to the MV-NP protein were identified. In addition a host cell protein, the myxovirus resistance protein (MxA, MW 75.8 kDa, pI 5.6) is up-regulated 1.8-fold in response to the viral infection (Figure 12.1, spot group 2). Constitutive levels of MxA were very low, but detectable in the Cy3 image (Figure 12.1, middle panel, arrow 2). 12.3.2 Detection of Different Protein Forms by 2D Western Blot

To detect with a high resolution the different nucleoprotein isoforms and to confirm their identity, a 2D gel was blotted onto a PVDF membrane and developed with moAb BNP49 and a peroxydase conjugate. This conventional staining allowed to detect NP, but not to match these signals with the DIGE fluorescence image. To overcome these limitations a Cy3-conjugated secondary antibody was used to generate complementary blot images on a fluorescence scanner. In these experiments non-infected cell extract was labeled with Cy2 instead of Cy3 (in contrast

135

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12 Detection of Differentially Modified Pathogen Proteins by Western Blot TO Cy 2

TV Cy 5

Cy 3

Figure 12.2 ECL-Plex identification of measles was blotted on a PVDF membrane and

virus nucleoprotein (MV-NP) with different post-translational modifications in a total protein extract. A 2D gel with pre-labeled mock (Cy2, left panel) and measles virus infected (Cy5, middle panel) cellular proteins

hybridized with a mouse antiMV-NP moAb BNP49 detected with a Cy3-labeled antimouse secondary antibody (right panel). The position of the MV-NP spot with the highest pI is indicated on the three images.

to the initial color attribution used in the DIGE experiment). After transfer of the Cy2- and Cy5-labeled proteins from the gel on the PVDF membrane the blot was incubated with the MV-NP specific antibody BNP49, stained with a Cy3-labeled conjugate, washed, and dried before scanning. The fluorescence images corresponding to the pre-labeled proteins (Figure 12.2, left and middle) and to the fluorescence associated with the specific moAb (Figure 12.2, right panel) were analyzed with the DeCyder software. The strongest spot recognized by the antibody corresponded to a highly expressed protein in the infected sample, with only a weak background stain in the uninfected sample (Cy2). Only three spots were detected in the DIGE experiment, whereas the antibody detected a fourth spot with a lower pI. 12.3.3 Monitoring Viral Replication and Cellular Response by Antibody Multiplexing

We evaluated antibody multiplexing to detect and localize on a same analytical blot three different proteins: MV-NP, virus-induced MxA and β-actin, the product of a constitutively expressed house-keeping gene. The overlay of the fluorescence images (Figure 12.3) shows the three proteins detected by ECL Plex technology. For MxA, an additional more acidic and less abundant isoform was detected by Western blotting, which was not detected in the images of the Cy-labeled proteins. The third fluorescent label was used to detect beta-actin. The pattern and localization of the spots corresponds to the one observed in the Cy3 and Cy5 images of the DIGE experiment (Figures 12.1 and 12.2). The β-actin detection can serve as an internal blotting and hybridization control and as a molecular weight and pI reference on the 2D image. We have successfully re-probed a membrane with a second specific antibody six months after the first hybridization and detected in a same scanning session the fluorescence generated at different hybridization rounds.

12.4 Discussion

2 1 3

Figure 12.3 Co-detection of a viral (MV-NP), an induced host cell protein (MxA) and a constitutively expressed host cell protein by antibody multiplexing. A blot with measles virus infected cellular proteins was probed with three different primary antibodies from three different species. Bound primary antibodies are detected by species-specific

differentially labeled conjugates (Cy3, Cy5, AF488). The overlay of the three dye-specific fluorescence scans shows: MV-NP (1) detected with a Cy3 conjugate (green), MxA (2) detected by an AF488 antichicken conjugate (blue) and beta-actin (3) by a Cy5 antirabbit conjugate (red).

12.4 Discussion

We used small 2D gels for differential proteome analysis with 2D-DIGE technology. Although the size of these gels limits the lateral resolution of spots, we found that a pH range of three units on a 7 cm strip typically resolved 600–700 spots and could be used for differential in gel analysis and biological variation analysis. These gels were very convenient for blotting, hybridization and 2D Western blot imaging on a DIGE-compatible fluorescence scanner. This approach has been used to investigate protein samples containing only several micrograms of total protein, which is roughly ten times less protein extract than recommended for a large 2DDIGE gel. The detection of protein isoforms in the low nanogram range illustrates the good detection sensitivity and the broad dynamic range of the fluorescence scans ensures that the protein is not only localized but that different spots of a protein can also be quantified. The availability of fluorescent dyes for protein labeling and of fluorescently prelabeled secondary antibodies allows thus to exploit the multiplexing capacity of the fluorescence imaging to detect simultaneously up to three specific antibodies, or proteins or a combination of both. The co-localization of pre-labeled proteins with specific antibodies bound to their targets and spots detected by fluorescent secondary antibodies is facilitated by the image analysis with DIGE compatible image analysis software. Thus the Cy3-labeled antibody detection of MV-NP consolidates the DIGE analysis, where three spots appeared up-regulated after infection, and detects an

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12 Detection of Differentially Modified Pathogen Proteins by Western Blot

additional fourth differentially modified nucleoprotein spot. The pI differences and the observed molecular weight of the protein spots suggest that different levels of phosphorylation [7] could account for the observed spot pattern. MALDI-TOF/ TOF analysis of the peptides extracted from a 2D-gel protein spot allowed for reliable protein identification with sequence coverage around 30% but did not allow the detection of phosporylation sites from the peptides of these mass fingerprint spectra. Taken together, the high resolution of spots, the sensitivity of detection and the software supporting spot matching and analysis have allowed us to identify specific protein isoforms of MxA and MV-NP, corresponding to post-translational modifications or truncation analogs. Also if no pre-labeled proteins are used, spot detection and matching of images observed with different labels during a same scan session is very efficient for unambiguous identification of protein spots. Furthermore the re-probing of a same protein sample on a blot is possible after months of archiving and can be of interest when new questions or controls require complementary Western blotting. The increasing number of mono-specific polyclonal antibodies against selected protein targets and the availability of sensitive and stable conjugates with different fluorescence tags for multiplexing will further facilitate the use of these techniques in the future. This approach can be applied to the identification of strong antigenic pathogen proteins recognized by antibodies, including polyclonal antibodies or clinical sera, and should contribute to the validation of protein markers for diagnostic testing with protein array technologies.

References 1 Kash, J.C., Goodman, A.G., Korth, M.J.,

5 Kochs, G., Janzen, C., Hohenberg, H., and

and Katze, M.G. (2006) Hijacking of the host-cell response and translational control during influenza virus infection. Virus Res., 119 (1), 111–120. 2 tenOever, B.R., Servant, M.J., Grandvaux, N., Lin, R., and Hiscott, J. (2002) Recognition of the measles virus nucleocapsid as a mechanism of IRF-3 activation. J. Virol., 76 (8), 3659–3669. 3 Basler, C.F., Mikulasova, A., MartinezSobrido, L., Paragas, J., Muhlberger, E., Bray, M., Klenk, H.D., Palese, P., and Garcia-Sastre, A. (2003) The Ebola virus VP35 protein inhibits activation of interferon regulatory factor 3. J. Virol., 77 (14), 7945–7956. 4 Martens, S., and Howard, J. (2006) The interferon-inducible GTPases. Annu. Rev. Cell Dev. Biol., 22, 559–589.

Haller, O. (2002) Antivirally active MxA protein sequesters La Crosse virus nucleocapsid protein into perinuclear complexes. Proc. Natl. Acad. Sci. U.S.A., 99 (5), 3153–3158. 6 Unlu, M., Morgan, M.E., and Minden, J.S. (1997) Difference gel electrophoresis: a single gel method for detecting changes in protein extracts. Electrophoresis, 18 (11), 2071–2077. 7 Gombart, A.F., Hirano, A., and Wong, T.C. (1995) Nucleoprotein phosphorylated on both serine and threonine is preferentially assembled into the nucleocapsids of measles virus. Virus Res., 37 (1), 63–73.

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13 Composition and Structure of Lipid A of the Intracellular Bacteria Piscirickettsia salmonis and Coxiella burnetii Pavol Vadovicˇ, Robert Ihnatko, and Rudolf Toman

13.1 Introduction

Lipopolysaccharides (LPSs) are heat-stable amphophilic molecules composed of a predominantly lipophilic region, lipid A, and a covalently linked hydrophilic polyor oligosaccharide portion. LPSs represent an essential component of the outer membrane of various gram-negative bacteria [1]. In 1985, it was shown that a synthetic lipid A exhibited similar biological properties to E. coli lipid A (Figure 13.1), thus proving that lipid A is part of the molecule responsible for its endotoxic activity [2, 3]. Structurally, lipid A is typically composed of a β-d-GlcN-(1-6)-α-d-GlcN disaccharide carrying two phosphoryl groups at positions O-1 and O-4′. Both phosphates can be further substituted [1] with groups such as ethanolamine, ethanolamine phosphate, ethanolamine diphosphate, GlcN, Ara4N, and d-arabinofuranose. To this structure, there are attached up to four acyl chains by ester or amide linkages. These chains can then in turn be substituted by further fatty acids to provide LPS molecules with up to seven acyl substituents, which vary quite considerably among species in the nature, number, length, order, and saturation. These acyl chains can be attached to the lipid A either symmetrically (3 + 3, e.g., in Neisseria meningitidis) or asymmetrically (4 + 2, e.g., in E. coli). Unsaturated fatty acids are rarely seen in lipid A, but they have been reported [2] in the Rhodobacter sphaeroides and R. capsulatus LPSs and in Enterobacteriaceae grown at low temperature. It appears that the major contributing factors to endotoxicity are the number and length of the acyl chains present and the phosphorylation state of the disaccharide backbone. For example, structures with only one phosphate at either O-1 or O-4′ appear in most assays to be <1000-fold less active than the E. coli lipid A (Figure 13.1) as it was found in the naturally occurring monophosphorylated Bacteroides fragilis lipid A [2, 4, 5]. However, phosphate by itself does not appear to be essential, because substitution with a phosphono-oxyethyl group does not alter the activity of the compound, suggesting that only correctly placed negative charges can restore activity [2, 6]. The GlcN monosaccharide preparations phosphorylated and acylated in various positions lack activity in general, suggesting BSL3 and BSL4 Agents: Proteomics, Glycomics, and Antigenicity, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.

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13 Lipid A of the Intracellular Bacteria Piscirickettsia salmonis and Coxiella burnetii

O

-

4'

P

HO

6

O

OH 6'

O

5'

O O

3'

4

O

GlcN II

5

HO 1'

3

O

2'

O 1

O O

O

O

O

OH

NH O

OH

OH

14

14

OH

O

O

14

P

2

NH

O

O

GlcN I

14

12 14

Figure 13.1 Chemical structure of the E. coli lipid A.

that the disaccharide backbone is also required for optimum recognition by the humoral/cellular receptors [7]. Lipid A with 2,3-diamino-2,3-dideoxy-d-glucose (GlcN3N) replacing GlcN has a similar activity to those lipids A having GlcN, as seen in Campylobacter jejuni [1]. Naturally, much work has concentrated on the role of the nature, number, and length of the acyl chains attached to lipid A. For example, structures similar to that of the E. coli lipid A with two phosphates, but with seven or five fatty acids are less active by a factor of approximately 100 [2, 8]. From these studies, it appears that it is the diphosphorylated E. coli-like hexaacyl lipid A (Figure 13.1) containing two β-(1-6)-linked GlcN or GlcN3N residues which is optimally recognized by mammalian receptors to express the full spectrum of endotoxic activities [2]. In our studies we focused on the elucidation of structural features of lipids A of P. salmonis and C. burnetii, strains Priscilla and Nine Mile in virulent phase I and avirulent phase II (NM I, NM II). The former is a representative of lipid A with a strong endotoxic effect, while the latter exhibits low endotoxic activity. 13.2 Composition and Structure of Lipid A of P. salmonis

The lipid A isolated from an LPS of P. salmonis by mild acid hydrolysis was investigated for its composition and structure using chemical analysis, gas chromatography–mass spectrometry (GC-MS), and electrospray ionization (ESI)MS combined with the tandem MS (MS/MS) [9]. The study revealed a moderate

13.3 Composition and Structure of Lipid A of C. burnetii

compositional and structural heterogeneity of lipid A with respect to the content of phosphate groups and Ara4N residues, and with regard to the degree of acylation (Figure 13.2). It appears that at least two molecular species are present in lipid A. The major species represents the hexaacyl lipid A, consisting of a β-(1→6)linked GlcN disaccharide backbone carrying two phosphate groups (one at the glycosidic hydroxyl group of the reducing GlcN I, the other at the O-4′ position of the nonreducing GlcN II). The primary fatty acids consist of three 3hydroxytetradecanoic C14:0(3-OH) and one 3-hydroxyhexadecanoic C16:0(3-OH) acids. The latter is amide-linked to GlcN I and one of the C14:0(3-OH) is amidelinked to GlcN II. Two secondary fatty acids are represented by C14:0(3-OH) and are equally distributed between the O-2′ and O-3′ positions. The phosphate group at O-4′ carries a nonstoichiometric substituent Ara4N. The minor lipid A species (Figure 13.2) contains exclusively C14:0(3-OH) with asymmetric distribution (4 + 2) at GlcN II and GlcN I, respectively [9].

13.3 Composition and Structure of Lipid A of C. burnetii

Chemical investigations of the lipid A from C. burnetii strain Priscilla were accomplished [10] using GC-MS and matrix assisted laser desorption/ionization-MS.

4'

R

O

P HO

6

O

OH 6'

O

5'

O O

3'

4

O

GlcN II

5

HO 1'

O

2'

3

O 1

P

2

NH

O

O

GlcN I

O O

O

O

O

OH

NH O

-

OH

O OH

O

OH

O

OH

X

14

14

14 14 14

X = C14 or C16 Figure 13.2 Structural features of the P. salmonis lipid A. X is length of the acyl fatty acid

chain. X = C16 or C14 for the major or minor molecular species, respectively; R = H or nonstoichiometric Ara4N.

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13 Lipid A of the Intracellular Bacteria Piscirickettsia salmonis and Coxiella burnetii

These analyses revealed a considerable microheterogeneity of the two major tetraacylated molecular species (Figure 13.3). They share the classical backbone of a diphosphorylated GlcN disaccharide, in which both GlcN I and GlcN II carry amide-linked iso or normal C16:0(3-OH). One of the species has ester-linked nC16:0 at both GlcNs while the other has ester-linked anteiso C15:0 instead of nC16:0 on the O-3′ position of GlcN II. Other less abundant molecular species are closely related to them and differ one from another only by a mass difference of ±14 (CH2), depending on the fatty acid bound [10]. Investigations of the lipids A from C. burnetii strains NM I and NM II were performed with ESI followed by MS/MS [11]. It was reported that the structures of the lipids A of NM I and NM II (Figure 13.4) were quite different from that described for the lipid A from C. burnetii strain Priscilla by Toman et al. [10]. The study revealed the presence of a tetraacyl β-(1→6)-linked GlcN disaccharide carrying only one phosphate group at the O-4′ position of GlcN II. Both amide linked fatty acids on GlcN I and GlcN II were C16:0(3-OH). From the ester linked fatty acids, one C15:0 is linked to the O-3 position of GlcN I and one C15:0(3-OH) is

O

-

P OH

6

O

OH 6'

O

4'

5'

O

4

O

HO

GlcN II 3'

O

1'

2'

5

O

O

3

2

NH O

O

GlcN I

O

O

NH

1

P

O

OH

OH

O

OH

OH

X

16

16*

16*

* = iC16 or nC16 X = nC16 or aC15 Figure 13.3 Chemical structures of two major molecular species present in the lipid A from

C. burnetii strain Priscilla. X is length of the acyl fatty acid chain. * = iC16 or nC16; X = nC16 or aC15.

13.4 Conclusion OH 6'

O O

-

P OH

6

O

4'

5'

O

4

O

HO

1'

3'

O

5

HO

GlcN II

O

3

GlcN I

2'

2

NH

O

O

1

OH

NH O

O

OH O OH

X

X

16 16

X = C14-C16 Figure 13.4 Tentative structure of the lipid A from LPS of C. burnetii strain NM I and NM II. X is length of the acyloxyacyl or acyl fatty acid chain (C14−C16).

linked to the hydroxyl group of the amide linked C16:0(3-OH) on GlcN II. However, the length of the ester linked fatty acids can vary from C14 to C16, which contributes to a considerable heterogeneity of the investigated lipid A [11]. In comparison with the structures of the lipids A investigated thus far, the proposed structure in Figure 13.4 is quite unusual and requires further verification.

13.4 Conclusion

From the studies presented we conclude that the major P. salmonis lipid A species represents the hexaacyl form, resembling the classical lipid A found in the Enterobacteriaceae family. This fact might be one of the reasons for the high endotoxic potency of the P. salmonis bacterium. On the contrary, the structures of the investigated lipids A from two C. burnetii strains (NM I, NM II) and Priscilla show a considerable heterogeneity due to their fatty acid variations at the O-3 and O-3′ positions of the GlcN disaccharide. They differ noticeably from those of a typical enterobacterial lipid A with high endotoxicity, and in fact their endotoxin activity

143

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13 Lipid A of the Intracellular Bacteria Piscirickettsia salmonis and Coxiella burnetii

was considerably lower than that found with the E. coli lipid A [10]. It appears that the endotoxicity of LPS molecules is determined primarily by the number, nature, and arrangement of acyl chains and phosphate groups on the lipid A part of the molecule. Molecules displaying an acylation pattern similar to that of E. coli and Salmonella spp. are the ones optimally recognized by human monocytes.

Acknowledgments

This work was supported in part by grant 2/0127/09 of the Scientific Grant Agency of the Ministry of Education of the Slovak Republic and the Slovak Academy of Sciences.

References 1 Alexander, C., and Rietschel, E.T. (2001)

2

3

4

5

6

7

Bacterial lipopolysaccharides and innate immunity. J. Endotoxin Res., 7, 167–202. Erridge, C., Bennett-Guerrero, E., and Poxton, I.R. (2002) Structure and function of lipopolysaccharides. Microbes Infect., 4, 837–851. Galanos, C., Luderitz, O., Rietschel, E.T., et al. (1985) Synthetic and natural E. coli free lipid A express identical endotoxic activities. Eur. J. Biochem., 148, 1–5. Takada, H., and Kotani, S. (1992) Bacterial endotoxic lipopolysaccharides, in Molecular Biochemistry and Cellular Biology, vol. I (eds D.C. Morrison and J.L. Ryan), CRC Press, Boca Raton, FL, pp. 107–134. Rietschel, E.T., Brade, H., Brade, L., et al. (1987) Lipid A, the endotoxic centre of bacterial lipopolysaccharides: relation of chemical structure to biological activity. Prog. Clin. Biol. Res., 231, 25–53. Ulmer, A.J., Heine, H., Feist, W., et al. (1992) Biological activity of synthetic phosphono-oxyethyl analogues of lipid A and lipid A partial structures. Infect. Immun., 60, 3309–3314. Aschauer, I., Grob, A., Hildebrandt, J., et al. (1990) Highly purified lipid X is

8

9

10

11

devoid of immunostimulatory activity. Isolation and characterisation of immunostimulating contaminants in a batch of synthetic lipid X. J. Biol. Chem., 265, 9159–9164. Rietschel, E.T., Kirikae, T., Schade, F.U., et al. (1993) The chemical structure of bacterial endotoxin in relation to bioactivity. Immunobiology, 187, 169–190. Vadovic, P., Fodorova, M., and Toman, R. (2007) Structural features of lipid A of Piscirickettsia salmonis, the etiological agent of the salmonid rickettsial septicemia. Acta Virol., 51, 249–259. Toman, R., Garidel, P., Andra, J., et al. (2004) Physicochemical characterization of the endotoxins from Coxiella burnetii strain Priscilla in relation to their bioactivities. BMC Biochem., http:// www.biomedcentral.com/1471-2191/5/1 (accessed 11 January 2011). Zamboni, D.S., Campos, M.A., Torrecilhas, A.C.T., et al. (2004) Stimulation of toll-like receptor 2 by Coxiella burnetii is required for macrophage production of proinflammatory cytokines and resistance to infection. J. Biol. Chem., 279, 54405–54415.

145

14 Proteins of Coxiella burnetii and Analysis of Their Function Robert Ihnatko, Pavol Vadovicˇ , and Rudolf Toman

14.1 Introduction

In recent decades a rapid growth in the field of proteomics has provided new data in the studies of human bacterial pathogens. As one of the most important parts of living systems, proteins are functional molecules that are usually localized within the specialized subcellular compartments. They are often organized into multiprotein complexes that function as molecular drivers of metabolic and regulatory processes. The world of proteins is very complex and the entire proteome is a highly dynamic entity that can be changed in response to the intracellular and extracellular environment. Due to alternative transcription initiation and/or alternative splicing, the expression of a single gene might produce several transcripts. Moreover, proteins might be further post-translationaly modified. With such a high level of complexity, large-scale protein analysis (proteomics) becomes a necessary tool. Proteomics attempts to describe all the proteins expressed by a cell at any one time. Since it can also provide information on post-translational events, proteomics is an ideal complementary approach to traditional genome-based methods for investigating gene expression and cellular metabolism. The relative simplicity of bacteria, combined with the extensive genomic data that are now available also for the C. burnetii bacterium [1] makes the study of C. burnetii proteomes a fruitful area of research. Thus, it is reasonable to expect that the combined data from proteomics and genomics will provide us with a comprehensive insight into the metabolism, pathogenity, and virulence of this bacterium in the near future. Although the C. burnetii proteome is relatively simple, it still consists of more than 2000 protein species with different chemical and physical properties [1]. Using a gel-based approach that addresses the separation of proteins by one- or two-dimensional gel electrophoresis and/or a gel-free approach that is usually based on protein digestion and separation of resulting peptides by chromatographic methods, only the most abundant proteins are usually identified by subsequent mass spectrometric (MS) techniques. So far, the identification of only 197 and 172 proteins of C. burnetii in virulent phase I and low-virulent phase II has BSL3 and BSL4 Agents: Proteomics, Glycomics, and Antigenicity, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.

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14 Proteins of Coxiella burnetii and Analysis of Their Function

been published [2, 3] respectively. Thus, only a small percentage of the entire proteome of C. burnetii has been unraveled. Since the magnitude of protein species abundance within a bacterial cell may differ by 7–10 orders of magnitude, the relatively low-abundance proteins are most probably masked by high-abundance ones, for example, by the housekeeping and structural proteins. This makes it difficult to relate the results of proteome profiling to the biology of the system. In order to gain a better understanding of the inner workings of a bacterium, sample treatment methods probably need to be involved in this research, such as initial prefractionation methods such as protein and peptide affinity purification, protein prefractionation by chromatography, zoom gels of narrow pH ranges for isoelectric focusing (IEF), preparative protein IEF, fractional centrifugation, and so on. Even the detection limit of current mass spectrometers plays an important role. Despite a rapid development of mass spectrometry, it is still not possible to detect unambiguously the low copy number proteins in the cells. This short review attempts to summarize current knowledge on some abundant proteins in the C. burnetii proteome and to provide an integrated view on their possible function in the development and survival of the pathogen in a host.

14.2 Proteins of C. burnetii and Their Functions

It is well known that C. burnetii undergoes phase variation after repeated passages in an immunologically incompetent host such as an embryonated hen egg that leads to the development of a low-virulent form of the bacterium. The phase variation has a major impact on the structural, functional, and biological properties of the bacterium [4, 5]. Thus, initial studies were focused [2, 3] on the identification of proteins present in both virulent phase I and low-virulent phase II bacterium. Using peptide mass fingerprinting and tandem mass spectrometry (MS/MS) methods, about 200 products of the C. burnetii open reading frames were identified in whole-cell lysates of both phases of the bacterium and the function of each identified protein was predicted by an extensive bioinformatic analysis. In this paper, we focus on those proteins that play an important role in the life cycle of C. burnetii. However, there are some limitations in this respect as only the small proteome portions in phase I and II bacterium have been unraveled until now. Thus, one cannot exclude the presence or absence of several proteins of interest in both bacterial forms, which in turn prevents establishing with more certainty their function in the development and survival of the bacterium. It was found earlier [6–8] that a lipopolysaccharide (LPS I) located in the outer membrane of phase I C. burnetii cells (Figure 14.1) contains, in contrast with an LPS II from phase II cells, two unusual sugars (virenose, dihydrohydroxystreptose). Both sugars are not found in other bacterial LPSs and can be considered as unique biomarkers of the bacterium. The candidate genes for their synthesis in phase I cells were found in the deleted region of the genome of C. burnetii in phase

14.2 Proteins of C. burnetii and Their Functions

Lipopolysaccharide (LPS I) Porin Outer membrane

Peptidoglycan

OmpA

Periplasmic space Inner membrane

Membrane proteins Figure 14.1 A simplified scheme of the membrane of Coxiella burnetii in virulent phase I.

OmpA, outer membrane protein A.

II and their products [NDP-hexose 3-C-methyltransferase TylCIII (CBU 0691), methyltransferase of the FkbM family (CBU 0683)] were confirmed only in the proteome of phase I cells [2]. The Tol proteins are located in the cell envelope of gram-negative bacterium and maintain the integrity of its outer membrane. They were also suggested to play a role in the integration of some outer membrane components like porins or LPSs [9–11]. The Tol-Pal protein complex probably links peptidoglycan to the outer membrane and thus efficiently stabilizes the cell envelope integrity [12]. A similar function of the Tol-Pal system might be expected in C. burnetii as the precursor of TolB protein was also found in both phases of the bacterium [2, 3]. The process of peptidoglycan turnover control is strictly regulated by a complex of autolytic enzymes with a specificity either for the carbohydrate or peptide linkages of peptidoglycan. One member of this major class of autolysins, N-acetylmuramoyl-lalanine amidase (CBU 0379) was identified [3] in phase II of C. burnetii. In bacterial resistance, the proteins able to pump out the influx of most toxins are of great importance. The protein pumps belonging to the resistance– nodulation–division family together with the outer membrane channels are highly effective in this process [13]. As an example, one can mention the AcrB pump of Escherichia coli that forms a complex with AcrA and the outer membrane channel TolC. This complex eliminates some antibiotics, detergents, and various disinfectants from the inside of the bacterium [14–17]. The AcrB/AcrD/AcrF family transporter (CBU 0804) was also found [2] in the proteome of C. burnetii in phase I. A precise regulation of each physiological process is essential for a proper function and survival of living systems. A myriad of different proteins with a regulation function has been identified in living systems till now. So far identified [2, 3] in both phases of C. burnetii are: a PhoH family protein (CBU 0568) possessing an

147

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14 Proteins of Coxiella burnetii and Analysis of Their Function

ATP-binding activity and being expressed upon phosphate starvation [18, 19], a carbon storage regulator homolog 2 (CsrA-2, CBU 1050), a putative universal stress protein A (CBU 1916), a DnaK suppressor protein (DksA, CBU 1969), a membrane sensor protein (CBU 0697), and a pleiotropic regulatory protein (CBU 0696). The homologs of CsrA were found in many prokaryotic cells where they usually act on translation of target genes or stabilization of the target mRNA transcripts. CsrA is an RNA-binding protein that can prevent translation of the target mRNA by binding to a specific site of mRNA or blocking ribosome binding and facilitating mRNA decay. In contrast, CsrA has also been found [20] to act as a positive regulator by increasing and stabilizing the translation of certain mRNA. DksA is a transcription factor that binds to RNA polymerase and potentiates the control of rRNA promoters by increasing dependence of the promoter on initiating nucleoside triphosphate and guanosine 5′-diphosphate 3′-diphosphate (ppGpp) [21–23]. Furthermore, DksA and ppGpp regulate together promoters of genes for amino acid biosynthesis and bacterial virulence factors [22, 24, 25]. As the C. burnetii proliferation is executed within the harsh environmental conditions of phagolysosome, the bacterium expresses proteins with a capacity to protect it from the proteolytic attack. Several proteins with such a function have been identified [2, 3] in proteomic studies thus far. An AhpC/Tsa family protein (CBU 1706), superoxide dismutase (SodB, CBU 1708), a rhodanese domain protein (CBU 0065), and a bacterioferritin comigratory protein (CBU 0963) were found [2, 3] in both phases of C. burnetii. A chlorohydrolase family protein (CBU 0521) and a rhodanese-like domain protein (CBU 0943) have been detected [3] in phase II bacterium so far. It is generally known that SodB and catalase (CBU 0281) are highly effective in protection against reactive oxygen radicals (Figure 14.2). The protective function of SodB against hyperoxides has been well documented in C. burnetii [26–28] and its presence confirmed in the proteomes of both phases of the bacterium [2, 3]. The AhpC/Tsa family protein and the bacterioferritin comigratory protein belong to peroxiredoxins that are highly effective antioxidants [29] which catalyze the reduction of hydrogen peroxide, organic hyperoxides, and peroxonitrite that are often present within the phagolysosomal environment. In addition, the stringent starvation protein A homolog (CBU 1747), a highly conserved transcription

I.

O2−

O2

SodB-M

II.

(n+1)+

O2− + 2H+ SodB-Mn+

SodB-M

n+

H2O2 SodB-M(n+1)+

Figure 14.2 The superoxide dismutase (SodB) catalyzes the dismutation of superoxide and

the process can be written with the half-reactions I and II, where M represents Cu (n = 1), Fe (n = 2), Mn (n = 2) or Ni (n = 2).

Note Added in Proof

factor among gram-negative bacteria that is essential for acid tolerance [30, 31], and the PhoH family protein were identified [2, 3] in both phases of C. burnetii. In phase II cells, two proteins (CBU 1983 and CBU 1916) were found [3] that belong to the universal stress protein A superfamily (UspA), the expression of which is usually upregulated by a wide variety of stress conditions, that is, nutrient starvation or exposure to oxidants [32]. Although there are some insights into the regulation of the E. coli uspA gene in the literature, the exact roles of the Usp proteins are still not clear. Their biochemical function appears to be linked to both resistance to DNA-damaging agents and to respiratory uncouplers [32]. In C. burnetii, the proteins involved in pathogen entry into host cells are of great importance. Among these, the enhanced entry proteins B and C (CBU 1137, CBU 1136) and Mip precursor (CBU 0630) have been identified [2, 3] in both phases of the bacterium. In a closely related Legionella pneumophila, the peptidyl-prolyl cistrans isomerase Mip was shown [33] to be essential for early establishment and initiation of intracellular infection. In contrast, the enhC gene mutation causes significantly reduced entry into host cells when compared to the wild type of this bacterium [34]. It has been shown recently [35] that C. burnetii express a type IV secretion system protein, the function of which is similar to that of the components of the L. pneumophila Dot/Icm system. The Dot/Icm-related secretion system has been suggested [35] to play an important role in the formation of a specialized vacuole that supports replication of C. burnetii. The DotB protein (CBU 1645) as a secretion system IV component with an ATPase activity has been found [3] in the phase II C. burnetii cells thus far.

14.3 Conclusion and Perspectives

In the past decade, remarkable progress has been observed in the field of proteomics with the introduction of new MS techniques that have brought a considerable improvement in the detection and analysis of proteins or the components of protein complexes, mainly due to their speed and sensitivity. The data presented indicate an immense potential for getting a significantly deeper insight into the functional interaction of C. burnetii proteins and their roles in metabolism, pathogenesis, and virulence. However, we are only at the beginning of understanding the biological role of C. burnetii proteins. The major portion of knowledge about the C. burnetii proteome remains still incomplete and unclear. Thus, there are still many challenges that remain for us to explore.

Note Added in Proof

The most recent information on the identified proteins and their suggested functions from virulent phase I and avirulent phase II C. burnetii can be found in Skultety, L., Hajduch, M., Flores-Ramirez, G., et al. (2011) Proteomic comparison

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of virulent phase I and avirulent phase II of Coxiella burnetii, the causative agent of Q fever. J. Prot., doi:10.1016/j.jprot.2011.05.017.

Acknowledgments

This work was supported in part by grant 2/0127/09 of the Scientific Grant Agency of the Ministry of Education of the Slovak Republic and the Slovak Academy of Sciences.

References 1 Seshadri, R., Paulsen, I.T., Eisen, J.A.,

2

3

4

5

6

7

et al. (2003) Complete genome sequence of the Q-fever pathogen Coxiella burnetii. Proc. Natl. Acad. Sci. U.S.A., 100, 5455–5460. Skultety, L., Hernychova, L., Toman, R., et al. (2005) Coxiella burnetii whole cell lysate protein identification by mass spectrometry and tandem mass spectrometry. Ann. N.Y. Acad. Sci., 1063, 115–122. Samoilis, G., Psaroulaki, A., Vougas, K., et al. (2007) Analysis of whole cell lysate from the intracellular bacterium Coxiella burnetii using two gel-based protein separation techniques. J. Proteome Res., 6, 3032–3041. Williams, J.C., Hoover, T.A., Waag, D.M., et al. (1990) Antigenic structure of Coxiella burnetii. A comparison of lipopolysaccharide and protein antigens as vaccines against Q fever. Ann. N.Y. Acad. Sci., 590, 370–380. Ftacek, P., Skultety, L., and Toman, R. (2000) Phase variation of Coxiella burnetii strain Priscilla: influence of this phenomenon on biochemical features of its lipopolysaccharide. J. Endotoxin Res., 6, 369–376. Toman, R., and Skultety, L. (1996) Structural study on a lipopolysaccharide from Coxiella burnetii strain Nine Mile in avirulent phase II. Carbohydr. Res., 283, 175–185. Toman, R., Skultety, L., Ftacek, P., et al. (1998) NMR study of virenose and dihydrohydroxystreptose isolated from Coxiella burnetii phase I lipopolysaccharide. Carbohydr. Res., 306, 291–296.

8 Skultety, L., Toman, R., and Patoprsty, V.

9

10

11

12

13

14

15

(1998) A comparative study of lipopolysaccharides from two Coxiella burnetii strains considered to be associated with acute and chronic Q fever. Carbohydr. Polymers, 35, 189–194. Ray, M.C., Germon, P., Vianney, A., et al. (2000) Identification by genetic suppression of Escherichia coli TolB residues important for TolB-Pal interaction. J. Bacteriol., 182, 821–824. Rigal, A., Bouveret, E., Lloubes, R., et al. (1997) The TolB protein interacts with the porins of Escherichia coli. J. Bacteriol., 179, 7274–7279. Lazzaroni, J.C., Germon, P., Ray, M.C., et al. (1999) The Tol proteins of Escherichia coli and their involvement in the uptake of biomolecules and outer membrane stability. FEMS Microbiol. Lett., 177, 191–197. Cascales, E., Bernadac, A., Gavioli, M., et al. (2002) Pal lipoprotein of Escherichia coli plays a major role in outer membrane integrity. J. Bacteriol., 184, 754–759. Nikaido, H. (2001) Preventing drug access to targets: cell surface permeability barriers and active efflux in bacteria. Semin. Cell Dev. Biol., 12, 215–223. Tsukagoshi, N., and Aono, R. (2000) Entry into and release of solvents by Escherichia coli in an organic-aqueous two-liquid-phase system and substrate specificity of the AcrAB-TolC solventextruding pump. J. Bacteriol., 182, 4803–4810. Nikaido, H. (1998) Multiple antibiotic resistance and efflux. Curr. Opin. Microbiol., 1, 516–523.

References 16 Poole, K. (2001) Multidrug resistance

17

18

19

20

21

22

23

24

25

in gram-negative bacteria. Curr. Opin. Microbiol., 4, 500–508. Yu, E.W., Aires, J.R., and Nikaido, H. (2003) AcrB multidrug efflux pump of Escherichia coli: composite substratebinding cavity of exceptional flexibility generates its extremely wide substrate specificity. J. Bacteriol., 185, 5657–5664. Kim, S.K., Makino, K., Amemura, M., et al. (1993) Molecular analysis of the phoH gene, belonging to the phosphate regulon in Escherichia coli. J. Bacteriol., 175, 1316–1324. Kazakov, A.E., Vassieva, O., Gelfand, M.S., et al. (2003) Bioinformatics classification and functional analysis of PhoH homologs. In Silico. Biol., 3, 3–15. Lucchetti-Miganeh, C., Burrowes, E., Baysse, C., et al. (2008) The posttranscriptional regulator CsrA plays a central role in the adaptation of bacterial pathogens to different stages of infection in animal hosts. Microbiology, 154, 16–29. Rutherford, S.T., Lemke, J.J., Vrentas, C.E., et al. (2006) Effects of DksA, GreA, and GreB on transcription initiation: insights into the mechanisms of factors that bind in the secondary channel of RNA polymerase. J. Mol. Biol., 366, 1243–1257. Paul, B.J., Berkmen, M.B., and Gourse, R.L. (2005) DksA potentiates direct activation of amino acid promoters by ppGpp. Proc. Natl. Acad. Sci. U.S.A., 102, 7823–7828. Paul, B.J., Barker, M.M., Ross, W., et al. (2004) DksA: a critical component of the transcription initiation machinery that potentiates the regulation of rRNA promoters by ppGpp and the initiating NTP. Cell, 118, 311–322. Nakanishi, N., Abe, H., Ogura, Y., et al. (2006) ppGpp with DksA controls gene expression in the locus of enterocyte effacement (LEE) pathogenicity island of enterohaemorrhagic Escherichia coli through activation of two virulence regulatory genes. Mol. Microbiol., 61, 194–205. Sharma, A.K., and Payne, S.M. (2006) Induction of expression of hfq by DksA is

26

27

28

29

30

31

32

33

34

35

essential for Shigella flexneri virulence. Mol. Microbiol., 62, 469–479. Akporiaye, E.T., and Baca, O.G. (1983) Superoxide anion production and superoxide dismutase and catalase activities in Coxiella burnetii. J. Bacteriol., 154, 520–523. Heinzen, R.A., Frazier, M.E., and Mallavia, L.P. (1992) Coxiella burnetii superoxide dismutase gene: cloning, sequencing, and expression in Escherichia coli. Infect. Immun., 60, 3814–3823. Niviere, V., and Fontecave, M. (2004) Discovery of superoxide reductase: a historical perspective. J. Biol. Inorg. Chem., 9, 119–123. Poole, L.B. (2005) Bacterial defenses against oxidants: mechanistic features of cysteine-based peroxidases and their flavoprotein reductases. Arch. Biochem. Biophys., 433, 240–254. Hansen, A.M., Gu, Y., Li, M., et al. (2005) Structural basis for the function of stringent starvation protein a as a transcription factor. J. Biol. Chem., 280, 17380–17391. Hansen, A.M., Qiu, Y., Yeh, N., et al. (2005) SspA is required for acid resistance in stationary phase by downregulation of H-NS in Escherichia coli. Mol. Microbiol., 56, 719–734. Kvint, K., Nachin, L., Diez, A., et al. (2003) The bacterial universal stress protein: function and regulation. Curr. Opin. Microbiol., 6, 140–145. Helbig, J.H., Konig, B., Knospe, H., et al. (2003) The PPIase active site of Legionella pneumophila Mip protein is involved in the infection of eukaryotic host cells. Biol. Chem., 384, 125–137. Cirillo, S.L.G., Lum, J., and Cirillo, J.D. (2000) Identification of novel loci involved in entry by Legionella pneumophila. Microbiology, 146, 1345–1359. Zamboni, D.S., McGrath, S., Rabinovitch, M., et al. (2003) Coxiella burnetii express type IV secretion system proteins that function similarly to components of the Legionella pneumophila Dot/Icm system. Mol. Microbiol., 49, 965–976.

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15 Subtype and Toxin Variant Identification of Botulinum Neurotoxin Type A Using Proteomics Techniques Suzanne R. Kalb, Jakub Baudys, Theresa J. Smith, James L. Pirkle, and John R. Barr

15.1 Introduction

Botulinum neurotoxins (BoNTs) are the most deadly substances known. They are protein neurotoxins produced by Clostridium botulinum (a BSL2 or BSL3 agent depending on the quantity of toxin produced) as well as other members of the Clostridium genus. Intoxication with BoNT causes the disease known as botulism, which, if not immediately fatal, may require prolonged hospitalization. BoNT is highly toxic, and therefore has significant potential for use as a bioterrorism agent. The BoNT proteins are extremely diverse with seven known toxin serotypes, which can be further differentiated into at least 26 toxin subtypes. Within the subtypes, toxin sequence may vary by as little as 1.6% at the amino acid level to produce toxin variants. Differentiation of BoNT at the various levels is important in identification of the correct type of antitoxin treatment for the patient and can assist with epidemiological or forensics studies. Our laboratory has developed mass spectrometry-based methods to study the proteomics, or amino acid sequence, of BoNT, to differentiate it at all of the above-mentioned levels. Although differentiation of BoNT at the serotype level is easier due to the greater number of amino acid variations (which can range from 37 to 70% of the total protein), differentiation at and below the subtype level is more challenging due to the large sequence similarity (98.4% or greater) between the samples at these levels. Nonetheless, we have been successful in our attempts to differentiate BoNT with both minor and substantial variations through utilization of proteomic techniques to differentiate the serotype, subtype, and even toxin variants within subtypes of these highly potent neurotoxins.

15.2 Botulinum Neurotoxins

Botulinum neurotoxins (BoNTs) are potent neurotoxins which cause the disease known as botulism. BoNT is produced by some species of the genus Clostridium, BSL3 and BSL4 Agents: Proteomics, Glycomics, and Antigenicity, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.

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in particular Clostridium botulinum, C. butyricum, C. baratii, and C. argentinese. BoNT producing organisms are currently classified as either BSL2 or BSL3 agents, depending on the quantity of toxin produced [1]. Ounce for ounce, BoNT is one of the most lethal substances known with an estimated LD50 in humans of approximately 70 μg for the average weight human when consumed orally [2]. This extreme toxicity has led, in part, to its current CDC designation as a category A agent for bioterrorism, making it one of the most likely agents for bioterrorism [3]. In vivo, BoNT causes botulism and acts as a potent neurotoxin through inhibition of nerve impulses. BoNT is a highly specific protease, cleaving proteins necessary for nerve transmission, leading to flaccid paralysis. Paralysis of the diaphragm can cause respiratory distress, necessitating prolonged hospitalization and ventilator support. If left untreated, botulism may be fatal. The current approved treatment of botulism involves administration of therapeutic immunoglobulin product and is most effective when administered within 24 h of exposure [4]; therefore rapid determination of exposure to BoNT is an important public health goal.

15.3 Differentiation of Botulinum Neurotoxins

BoNT currently can be classified into seven serotypes, A-G, which are defined by their response to polyclonal antisera [5]. Amino acid identity among the different serotypes ranges from approximately 30–40% in most cases, but can be as high as 63% in some cases. The approximately 150 000 Da proteins are composed of three functional domains – a receptor-binding domain, translocation domain, and an enzymatic domain. The neurotoxins bind to ectoacceptors on target neuronal cells via a two-part mechanism involving interactions with gangliosides and specific proteins that participate in docking and uptake of acetylcholine [6]. The proteins are taken up into endosomes and, following a drop in pH, the translocation domain undergoes a conformational change which forms a pore that allows the enzymatic domain to escape to the cytoplasm [7], where it cleaves specific SNARE proteins that are critical for the docking and release of acetylcholine vesicles. Each serotype has a slightly different enzymatic target. Although all serotypes of BoNT target neuronal proteins, BoNT/A, /C, and /E cleave synaptosome-associated protein (SNAP-25) whereas BoNT/B, /D, /F, and /G cleave vesicle-associated membrane protein 2 (VAMP-2, also called synaptobrevin 2), as seen in Figure 15.1. BoNT/A cleaves SNAP-25 between Q196 and R197 [8–11], BoNT/C cleaves SNAP-25 at the adjacent residue between R197 and A198 [12, 13], and BoNT/E cleaves SNAP 25 between R180 and I181 [9–11]. VAMP-2 is cleaved by BoNT/B, /D, /F, and /G at Q75 [14], K58 [10, 15], Q57 [15], and A61 [16, 17] respectively. Among the serotypes only one, BoNT/C, cleaves more than one protein target. In addition to cleaving SNAP-25, BoNT C also cleaves syntaxin between K253 and A254 [18, 19]. Because therapeutic antisera are serotype-specific, it is important to differentiate the serotype of BoNT in order to guide clinical treatment.

15.4 Amino Acid Sequence Identification Using Proteomics

Figure 15.1 Schematic of the SNARE complex proteins necessary for nerve transmission via acetylcholine release. The sites where proteins are cleaved by individual BoNT serotypes are depicted.

Most of the BoNT serotypes exhibit genetic differences which lead to amino acid variance within the serotype, and this variance is defined as a subtype. Historically, subtypes were defined by cultural/biochemical differences of the strains housing the BoNTs [20], functional differences of the toxins [21], or differential binding by monoclonal antibodies [22–24]. More recently, subtypes have been defined on the basis of phylogenetic groupings or sequence variance of 1.6% [25] to 2.6% [24]. An amino acid variance of less than 1.6% is currently defined as a toxin variant. Some BoNTs may vary by a single amino acid mutation, for an amino acid difference of 0.08%. While amino acid variance can be less than 2% among some BoNT/B and /E subtypes, it can be as much as 27% among the BoNT/F subtypes. While serotype differences have profound effects on antisera treatment, subtype differences are more subtle and may result in some, but not total, loss of antisera effectiveness. Subtype identification can be helpful in epidemiological and forensic investigations, where identity and source of toxin is important.

15.4 Amino Acid Sequence Identification Using Proteomics

The term proteomics refers to the study of proteins, in particular, the study of the structure and function of proteins. The amino acid sequence of a protein constitutes the primary structure of a protein, and knowledge of the amino acid sequence is often the basis of proteomics studies. Identification of the amino acid sequence traditionally has been accomplished through Edman sequencing, but more recently, mass spectrometry has proven to be a reliable technique for amino acid

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15 Subtype and Toxin Variant Identification of Botulinum Neurotoxin Type A

Figure 15.2 Depiction of a four-amino-acid peptide with creation of b and y ions.

a)

b)

Figure 15.3 Histogram representation of the mass spectra of peptide QWERT depicting b

ions (a) and y ions (b).

sequence identification [26]. In most cases, a protein is enzymatically digested into peptide components prior to mass spectrometry. The resulting peptides are separated with HPLC and introduced into a mass spectrometer, which measures the molecular weight of each peptide. Each peptide ion is then fragmented inside the mass spectrometer, and the molecular weights of the varied fragment ions are measured to produce an amino acid sequence of that peptide. Inside the mass spectrometer, peptide ions fragment along the peptide backbone when collided with gas molecules. Peptides often fragment at the amide bond of the peptide, as depicted in Figure 15.2, as this bond is the most labile. If the charge is retained on the N-terminal portion of the peptide ion, the ion is referred to as a b ion [27, 28]. If the charge is retained on the C-terminal portion, the ion is a y ion [27, 28]. Peptides have the potential to fragment at any labile bond, and amino acid sequence can be determined by measuring the mass difference between b or y ions as depicted in Figure 15.3. This technique is referred to as

15.6 Identification of BoNT Serotype with Proteomics

ladder sequencing, and can be used to determine the amino acid sequence of a peptide. Often, mass spectrometry analysis is accompanied by database searching to elucidate the amino acid sequence of peptides [29]. The mass spectrometer collects information on the intact molecular weight of the peptide and the fragmentation pattern of the peptide. Because the peptide can fragment in many places and produce both b and y ions resulting in a complicated mass spectrum, it is helpful to first calculate the potential b and y ions from a list of peptides and then search data against that list of possibilities. The list of peptides is generated through a theoretical enzymatic digest of a protein. Distinct proteins will produce discrete lists of peptides, and by searching the mass spectral data against the different possibilities, a protein can be identified. This technique can allow for differentiation of very similar proteins, such as those which differ by a single amino acid, provided that mass spectral data is obtained for the substituted amino acid.

15.5 Extraction of BoNT from Complex Matrices

Typically, BoNT exists within a complex matrix such as a food or clinical sample or a culture supernatant. Such matrices contain numerous endogenous proteins which could interfere with BoNT detection and differentiaton. Additionally, such matrices often contain an abundance of proteins in comparison to the low level of desired protein, the toxin. Therefore, the selective extraction and concentration of low levels of BoNT from a complex matrix is essential to BoNT detection. The use of antibodies which are serotype-selective is an efficient way of selectively extracting the toxin from cultures, clinical and food samples. In this process, antibodies are bound and cross-linked to magnetic beads and then added to the culture, serum or a stool extract. If BoNT is present, it binds to the antibody-coated beads. The beads are then removed from the sample and washed to remove materials that were nonspecifically bound. In order to increase specificity and decrease nonspecific binding, we use high-affinity monoclonal antibodies from yeast displays which are known to bind specific epitopes on the BoNT. Wherever possible, two antibodies for each serotype that yielded the most sensitivity and selectivity were used for BoNT extraction to maximize BoNT extraction.

15.6 Identification of BoNT Serotype with Proteomics

BoNT/A and /B are approximately 38% identical at the amino acid sequence level. With such a large difference in sequence, an enzymatic digest on these toxins should produce two very different sets of resultant peptides. These two sets of peptides can be analyzed by MS/MS for sequence verification. It should be possible to match each set of data against its proper protein of origin by searching

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Figure 15.4 The amino acid sequence of BoNT/A. Residues in bold and underlined differ

between BoNT/A and BoNT/B.

the resulting data against a database which contains amino acid sequences of both proteins. Using BoNT/A and BoNT/B as an example, this process was followed to distinguish BoNT at the serotype level. Both neurotoxins were digested with trypsin, resulting in a series of peptides with either lysine or arginine at the C-terminus. The peptides from both digests were then separated by HPLC and introduced into an LTQ-FT mass spectrometer capable of producing data with very high mass resolution and accuracy. The mass spectrometer first measured the mass of each peptide and then fragmented each peptide, producing a fragment mass spectrum (MS/MS) for most of the tryptic peptides detected from the neurotoxins. The data were then analyzed by searching against a standard protein database which contained the amino acid sequences of many known proteins of Clostridia, including BoNT/A and BoNT/B. Figure 15.4 is the amino acid sequence of BoNT/A. Residues which differ between BoNT/A and /B are marked in bold and underlined. It is apparent by looking at the number of residues which are modified that BoNT/B is quite dissimilar from BoNT/A, and would therefore produce a very different set of tryptic peptides in comparison to BoNT/A. Following tryptic digestion and mass spectral analysis, we had MS/MS evidence for the presence of about 73% of the BoNT/A protein and about 67% for BoNT/B. These protein coverages included many of the residues which are mutated between BoNT/A and /B. One such example is

15.7 Identification of BoNT/A Subtype with Proteomics a)

964.5

Abundance

100

y8

y6

718.4

y4 y2

289.1

504.3 y3

y5

y7

617.4

403.2

81 7 . 5

0 200 300 400 500 600 700 800 900 1000 1100 1200 m/z b)

888.4

100 Abundance 0

y10

y8

y7 y6

787.5

1102.5 y9

1001.5

673.4 400

600

800 m/z 1000

y11

1201.7 y12 1348.7 1200

1400

1600

Figure 15.5 MS/MS spectra of the tryptic peptides WIFVTITNNR from BoNT/A (a) and WFFVTITNNLNNAK from BoNT/B (b). Many fragment ions which differ between the two peptides are marked.

depicted in Figure 15.5. Figure 15.5a is the MS/MS spectrum of the tryptic peptide WIFVTITNNR from the digest of BoNT/A. Figure 15.5b is the MS/MS spectrum of a similar tryptic peptide (WFFVTITNNLNNAK) from the digest of BoNT/B. Although these peptides are somewhat similar, their intact molecular masses are different, and they have different fragmentation patterns. Because the tryptic peptide of the sequence WFFVTITNNLNNAK is found only in the amino acid sequence of BoNT/B, we can therefore assign the identity of this neurotoxin as BoNT/B. Similarly, we can assign the identity of the other neurotoxin as BoNT/A. We obtained MS/MS data similar to those shown in Figure 15.5 for many other peptides in these neurotoxins; specifically, data on 77 peptides in the digest of BoNT/A which differ from BoNT/B and 71 peptides in the digest of BoNT/B which differ from BoNT/A. Through this process, we were able to correctly identify BoNT/A and /B, demonstrating our ability to use a proteomic technique to distinguish the serotype of BoNT.

15.7 Identification of BoNT/A Subtype with Proteomics

Because the amino acid sequences of the BoNT/A subtypes differ by as little as 4% and as much as 16%, it is theoretically possible to use this same proteomic technique to identify the subtype of BoNT/A. BoNT/A1 shows approximately 90%

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Figure 15.6 The amino acid sequence of BoNT/A1. Residues in bold and underlined differ

between BoNT/A1 and BoNT/A2.

identity to BoNT/A2. Figure 15.6 is the amino acid sequence of BoNT/A1. Residues underlined and in bold differ between /A1 and /A2, and there are 131 amino acid differences between these two closely related proteins. As with BoNT/A and /B, BoNT/A1 and /A2 were digested with trypsin to produce a series of peptides, and MS/MS data were acquired for many of the peptides. The data were then analyzed by searching against a standard Clostridium protein database which contained the amino acid sequences of both BoNT/A1 and /A2. Both proteins were correctly identified as either BoNT/A1 or /A2, despite the fact that their proteins are 90% identical by primary sequence. MS/MS evidence exists for 65–70% of the total protein amino acid sequence in each case, or 76 of the 131 altered amino acids. As an example, Figure 15.7a is a MS/MS spectrum of the peptide SFGHEVLNLTR from the tryptic digest of BoNT/A1 and Figure 15.7b is SFGHDVLNLTR from BoNT/A2. Both peptides are very similar; however, they differ in the fifth residue. Even though a mutation from a glutamic acid to an aspartic acid is a conserved mutation; nonetheless, this mutation results in a measurable mass difference of the intact peptide and a different fragmentation pattern. Because these peptides are so similar, many ions are the same, such as b ions 1 through 4 and y ions 1 through 6. However, b ions 5 through 11 and y ions 7 through 11 differ by 14 mass units in these spectra, due to the location of the

15.8 Identification of BoNT/A1 Strain with Proteomics a)

b)

Figure 15.7 MS/MS spectra of the peptides SFGHEVLNLTR (a) and SFGHDVLNLTR (b) from, respectively, the tryptic digests of BoNT/A1 and BoNT/A2.

glutamic to aspartic acid mutation in the residue in position 5. The tryptic peptide of the sequence SFGHEVLNLTR is found only in the amino acid sequence of BoNT/A1, so we can therefore assign the identity of this neurotoxin as BoNT/A1. In the same fashion, we can assign the identity of the other neurotoxin as BoNT/ A2. We obtained MS/MS data similar to those shown in Figure 15.7 for many other peptides in these neurotoxins; specifically, data on 33 peptides in the digest of BoNT/A1 which define the neurotoxin as /A1 and 32 peptides in the digest of BoNT/A2. Although this technique did not identify all of the amino acid mutations between BoNT/A1 and /A2, the identification of those 76 amino acid mutations was more than sufficient to identify the protein as BoNT/A1 or /A2, allowing us to use a proteomic technique to differentiate the BoNT/A1 subtype from BoNT/A2.

15.8 Identification of BoNT/A1 Strain with Proteomics

The amino acid sequences of neurotoxins of known strains within the highly conserved BoNT/A1 subtype differ by as few as one amino acid or as many as five. Because we were successful with the use of proteomic techniques to properly identify both the serotype and subtype of BoNT, we explored the possibility of using this technique to differentiate closely related BoNT/A1 toxin variants. For this work, we analyzed toxin preparations from two closely related C. botulinum

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15 Subtype and Toxin Variant Identification of Botulinum Neurotoxin Type A

Figure 15.8 Amino acid sequence of the BoNT/A1, Hall strain. Residues in bold and

underlined differ between /A1 Hall and /A1(B), CDC 1744.

strains. BoNT/A1 Hall and BoNT A1(B) CDC 1744 differ by only two amino acids, or less than 0.15%. Figure 15.8 is the amino acid sequence of BoNT/A1, Hall strain. Residues in bold and underlined differ between the /A1 Hall strain and the /A1(B), CDC 1744 strain. There are only two residues which differ between these strains; 1P and 26A in /A1 Hall strain become 1Q and 26V in the /A1(B), CDC 1744 strain. Following a tryptic digest of these strains of neurotoxins, we obtained MS/MS evidence for approximately 67–70% of each protein. Fortunately, we also obtained MS/MS evidence for the mutation at residue 26. Figure 15.9a is a MS/MS spectrum of the peptide IPNAGQMQPVK from the tryptic digest of BoNT/A1 Hall and Figure 15.9b is IPNVGQMQPVK from BoNT/A1(B), CDC 1744. Both peptides are similar, but differ in the fourth residue. The mutation between an A and a V results in both a mass difference of 28 Da for the intact peptide and a different fragmentation pattern seen in the MS/MS spectra. Many ions are the same, such as b ions 1 through 3 and y ions 1 through 7. The remainder of the b and y ions differ in these spectra by 28 mass units because of the A to V mutation. These MS/MS data demonstrate that this method can be used to assign the identity of these neurotoxins as BoNT/A1 Hall or /A1(B), CDC 1744. Furthermore, the second known mutation, 1P to Q, was uncovered through the use of an additional enzymatic digest using Asp-N, which cleaves at the N-terminus of aspartic acids.

15.9 Conclusions a)

535.5 b10+2

Abundance

100

0

y5 602.4

b3 343.22 b4 396.3 200

300

400

500

b)

y7 y9 y 787.4 858.48 972.5

600 700 m/z

800

900 1000 1100

549.3 b10+2

Abundance

100

y7 787.3 b3 343.2 b4 424.2 0

y5 602.4

y9 y8 1000.5 886.3

200 300 400 500 600 700 800 900 1000 1100 1200 m/z

Figure 15.9 MS/MS spectra of the peptides IPNAGQMQPVK (a) and IPNVGQMQPVK (b) from, respectively, the tryptic digests of the BoNT/A1 Hall strain and the BoNT/A1(B), CDC 1744 strain.

Digesting these neurotoxins produced peptides PFVNKQFNYK and QFVNKQFNYK from BoNT/A1 Hall and /A1(B), CDC 1744 respectively. Figure 15.10 are the MS/MS spectra from those peptides, and it is apparent that these spectra are related, yet different due to the amino acid mutation in the first position and the molecular weight of the intact peptide. Using a technique of combined enzymatic digests, we obtained 76–79% sequence coverage on these proteins and were able to identify 100% of the differences between these two strains. This work demonstrates the use of a proteomic approach to differentiate two BoNT/A1 toxin variants showing highly conserved amino acid sequence.

15.9 Conclusions

Diagnosis, treatment, and prevention of disease are the primary goals of public health. Establishing the presence of botulinum neurotoxins in clinical or food samples is important for correct diagnosis of botulism and identification of potential exposures. Rapid identification of botulinum serotype is critical for timely administration of appropriate antisera. Additional information on toxin subtype or variant can be important factors when tracing the source of an outbreak. Therefore, identifying the serotype of BoNT to confirm a diagnosis of botulism or identifying the subtype or strain of BoNT to aid in preventing additional cases of botulism is critical. Because BoNTs produced by BSL2 or BSL3 level agents are highly toxic, BoNT identification is of special concern to the public health

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15 Subtype and Toxin Variant Identification of Botulinum Neurotoxin Type A a) 634.8 y10+2

Abundance

100

0

y4 571.5 y3 424.4

y5 699.4

y7 941.5

y6 827.5

y8 1040.6

b9 1138.5

200 300 400 500 600 700 800 900 1000 1100 1200 1300 m/z

b) 658.2 y10+2

100

Abundance

164

y5 y4 699.4 571.5 y3 424.4

y7 941.5 y6 827.5

y8 1040.6

b9 1169.5

0 200 300 400 500 600 700 800 900 1000 1100 1200 1300 m/z Figure 15.10 MS/MS spectra of the peptides PFVNKQFNYK (a) and QFVNKQFNYK (b) from, respectively, the tryptic digests of the BoNT/A1 Hall strain and the BoNT/A1(B), CDC 1744 strain.

community in this climate of increased concern about bioterrorism. Proteomic techniques involving the use of mass spectrometry can rapidly identify the primary sequence of the neurotoxin and differentiate the neurotoxin and are therefore significant to public health agendas.

Disclaimer

The opinions, interpretations, and recommendations are those of the authors and are not necessarily those of the Centers for Disease Control and Prevention or the US Army.

References 1 U.S. Department of Health. (2007)

Biosafety in Microbiological and Biomedical Laboratories (BMBL), 5th edn, U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, US Government Printing Office, Washington, D.C.

2 Herrero, B.A., Ecklung, A.E., Street, C.S.,

Ford, D.F., and King, J.K. (1967) Exp. Mol. Pathol., 6, 84–95. 3 Arnon, S.S., Schechter, R., Ingelsby, T.V., Henderson, D.A., Bartlett, J.G., Ascher, M.S., Eitzen, E., Fine, A.D., Hauer, J., Layton, M., Lillibridge, S., Osterholm,

References

4

5

6

7

8

9

10

11

12

13

14

15

M.T., O’Toole, R., Parker, G., Perl, T.M., Swerdlow, P.K., and Tonat, K. (2001) JAMA, 285 (8), 1059–1070. Centers for Disease Control and Prevention (1998) Botulism in the United States: 1899–1996. Handbook for Epidemiologists, Clinicians and Laboratory Workers, Centers for Disease Control and Prevention, Atlanta, GA. Leuchs, J. (1910) Article for information of the toxin and antitoxin of Bacillus botulinum. Z. Hyg. Infektionskr., 76, 55–84. Poulain, B., Tauc, L., Maisey, E.A., Wadsworth, J.D., Mohan, P.M., and Dolly, J.O. (1988) Proc. Natl. Acad. Sci. U.S.A., 85 (11), 4090–4094. Montal, M.S., Blewitt, R., Tomich, J.M., and Montal, M. (1992) FEBS Lett., 313 (1), 12–18. Binz, T.J., Blasi, S., Yamasaki, A., Baumeister, E., Link, T.C., Sudhof, R., Jahn, R., and Niemann, H. (1994) J. Biol. Chem., 269, 1617–1620. Blasi, J., Chapman, E.R., Line, E., Binz, T., Yamasaki, S., De Canilli, P., Sudhof, T.C., Niemann, H., and Jahn, R. (1993) Nature, 365 (6442), 160–163. Schiavo, G., Rossetto, O., Catsicas, S., Polverino De Laureto, P., Dasgupta, B.R., Benfenati, F., and Montecucco, C. (1993) J. Biol. Chem., 268, 23784–23787. Schiavo, G., Santucci, A., Dasgupta, B.R., Mehta, P.P., Jontes, J., Benfenati, F., Wilson, M.C., and Montecucco, C. (1993) FEBS Lett, 335, 99–103. Foran, P., Lawrence, G.W., Shone, C.C., Foster, K.A., and Dolly, J.O. (1996) Biochemistry, 35, 2630–2636. Williamson, L.C., Halpern, J.L., Montecucco, C., Brown, J.E., and Neale, E.A. (1996) J. Biol. Chem., 271, 7694–7699. Schiavo, G., Benfenati, F., Poulain, B., Rossetto, O., Polverino De Laurento, P., Dasgupta, B.R., and Montecucco, C. (1992) Nature, 359, 832–835. Yamasaki, S., Baumeister, A., Binz, T., Blasi, J., Link, E., Cornille, F., Roques, B., Fykse, E.M., Sudhof, T.C., Jahn, R., and Niemann, H. (1994) J. Biol. Chem., 269, 12764–12772.

16 Schiavo, G., Malizio, C., Trimble, W.S.,

17

18

19

20 21

22

23 24

25

26

27 28 29

Polverino De Laureto, P., Milan, G., Sugiyama, H., Johnson, E.A., and Montecucco, C. (1994) J. Biol. Chem., 269, 20213–20216. Yamasaki, S., Binz, T., Hayashi, T., Szabo, E., Yamasaki, N., Eklund, M., Jahn, R., and Niemann, H. (1994) Biochem. Biophys. Res. Commun., 200, 829–835. Blasi, J., Chapman, E.R., Yamasaki, S., Binz, T., Niemann, H., and Jahn, R. (1993) EMBO J., 12, 4821–4828. Schiavo, G., Shone, C.C., Bennett, M.K., Scheller, R.H., and Montecucco, C. (1995) J. Biol. Chem., 270, 10566–10570. Wagner, E., Meyer, K.F., and Dozier, C.C. (1925) J. Bacteriol., 10 (4), 321–412. Kozaki, S., Kamata, Y., Nishiki, T., Kakinuma, H., Maruyama, H., Takahashi, H., Karasawa, T., Yamakawa, K., and Nakamura, S. (1998) Infect. Immun., 66 (10), 4811–4816. Gibson, A.M., Modi, N.K., Roberts, T.A., Hambleton, P., and Melling, J. (1988) J. Appl. Bacteriol., 64, 285–291. Kozaki, S., Nakaue, S., and Kamata, Y. (1995) Microbiol. Immunol., 39, 767–774. Smith, T.J., Lou, J., Geren, I., Forsyth, C.M., Tsai, R., LaPorte, S.L., Tepp, W.H., Bradshaw, M., Johnson, E.A., Smith, L.A., and Marks, J.D. (2005) Infect. Immun., 73, 5450–5457. Hill, K.K., Smith, T.J., Helma, C.H., Ticknor, L.O., Foley, B.T., Svensson, R.T., Brown, J.L., Johnson, E.A., Smith, L.A., Okinaka, R.T., Jackson, P.J., and Marks, J.D. (2007) J. Bacteriol., 189 (3), 818–832. Hunt, D.F., Yates, J.R., III., Shabanowitz, J., Winston, S., and Hauer, C.R. (1986) Proc. Natl. Acad. Sci. U.S.A., 83, 6233–6237. Roepstorff, P., and Fohlman, J. (1984) Biomed. Mass Spectrom., 11, 601. Biemann, K. (1988) Biomed. Mass Spectrom., 1988, 99. Eng, J.K., McCormack, A.L., and Yates, J.R., III. (1994) J. Am. Soc. Mass. Spec., 5 (11), 976–989.

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16 Protein Microarrays for Antigen Discovery Mohan Natesan, Sarah Keasey, and Robert G. Ulrich

16.1 Introduction

Protein microarrays based on pathogen proteomes are sensitive tools for identifying antigens displayed by infectious agents to host antibody responses. The study of complex proteomes consisting of products from all open reading frames of a targeted pathogen is made possible by using annotated genomic sequences as a framework for high-throughput gene cloning and expression. Proteomes of many pathogens have been examined by this approach, from small viruses to bacteria comprised of over 5000 proteins. Potential applications include discovery of new vaccine candidates and diagnostic markers. Reagent consumption is one advantage, as only minute amounts of array probes are required and a typical assay uses just microliters of sample. Because recombinant proteins are utilized in assays instead of whole pathogens, many studies can be performed outside of the BSL3/4 laboratories. High-content microarrays accelerate identification of antibodies interacting with complex viral and bacterial proteomes, while focused microarrays consisting of small panels of proteins can increase assay throughput. Detection of pathogen-specific, host antibody responses continues to be the most commonly used diagnostic test, especially for samples with negligible amounts of amplifiable genomic material. For some virus infections the diagnostic rates of samples from acute infection are higher for serological assays combined with either nucleic acid or antigen detection [1], while in other cases serological assays are better than nucleic acid-based assays for discriminating primary versus secondary immune responses [2]. Diagnostic markers may also be potential vaccines. For example, the Yersinia pestis proteins CaF1 and LcrV serve as both diagnostic biomarkers of plague and effective vaccines [3]. Similarly, the protective antigen (PA) of Bacillus anthracis is both a biomarker of anthrax and a vaccine [4]. However, as many research years were invested to identify and validate these antigens, more efficient approaches are needed to meet the emergence of new biothreats and the continuously changing nature of clinical infections. The complexity of proteomes for most pathogens presents the biggest challenge. Bacterial proteomes may contain 5000 or more individual proteins for strain isolates, BSL3 and BSL4 Agents: Proteomics, Glycomics, and Antigenicity, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.

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increasing up to 20 000 distinct proteins by inclusion of all related strains (the “metaproteome”). The predicted proteomes for many pathogenic viruses vary from 8 to 10 proteins (Ebola, dengue viruses, etc.) to >250 (pox viruses) open reading frames (ORFs) for individual genomes, while the inclusion of genetic variants substantially increases the diversity of the metaproteome. Although there have been many attempts to establish empirical rules for predicting immune responses to polypeptides, there currently is no substitute for experimental evidence. Protein microarrays were developed as a high-throughput method to capture and analyze antibodies resulting from the host response to infection. Robotic microarray spotters rapidly produce planar substrates with many thousands of discrete probe surfaces. The measurement of antibody binding by microarray is possible over a wide dynamic range, with lower limits of nanograms/milliliter based on resolution of protein spots and detection signals. In contrast to ELISA and other assay formats, microarrays require only microliters of serum for analysis, thereby lowering consumption of clinical samples and increasing assay sensitivity. Microarray assays can be semi-automated, require only small quantities of protein probes, and are especially advantageous for high-throughput analysis. Many variations of protein microarrays have been reported, from rapid PCR-to-product approaches with no protein purification [5] to in situ synthesis of proteins on the microarray surface [6]. We use an approach that involves full characterization of gene clones and proteins products prior to assembly of the microarray.

16.2 Microarray Assembly

Annotated genomic sequences for most pathogens are publically available as a source for predicted proteomes. However, we must first note that reconciliations between the theoretical proteome and experimentally confirmed gene products may not available for many sequenced genomes [7, 8]. Cloning of protein-encoding DNA, sequencing and other commonly used molecular biology methods will not be addressed here. The method chosen for production of recombinant proteins should be similar to the host environment of the pathogen to ensure greatest fidelity of folding and post-translational modifications (Figure 16.1). Generally this will mean E. coli expression for bacterial products and either insect cells (baculovirus) or mammalian cells for viral proteins. Alternatively, in vitro translation provides an easily standardized method for more high-throughput applications. Following purification, the inclusion of glycerol in buffers is essential to prevent denaturation during protein spotting, though spotted microarrays can be stored (−20 °C) for short periods of time before use with little impact on quality. A very small amount of protein from concentrated samples (100–200 μg/ml) is deposited on planar surfaces, utilizing the same technology adopted for printing DNA microarrays. The most commonly used pin spotting method is very rapid and relies on capillary forces to release spots on contact with the surface, resulting in 60–600 μm diameter spots, depending on buffer composition and pin diameter. The slower

16.2 Microarray Assembly a)

Yersinia pestis

Vaccinia virus

Genomic + plasmid DNA is olated

ORFs cloned into Gateway Entry plasmid

b)

Transformation of ORF clones into bacmid-containing E. coli vector

ORF clones shuttled to pDEST vector

GST-tagged ORF clone expanded in E. coli

Transfection into Sf9 cells Eukaryotic protein expression

In vitro transcription and translation of individual Y. pestis ORF clones Bacterial (gram -) protein expression

GST-based affinity purification

Array spotting

Pathogen proteome microarrays. (a) General scheme for microarray production, showing vaccinia virus and Yersinia pestis as examples. (b) Confocal laser scanner image of proteins spotted in duplicate onto

Figure 16.1

microarray slides, visualized using a rabbit anti-GST antibody bound to Cy5-labeled anti-rabbit antibody. Vaccinia virus (left); Yersinia pestis (right).

but more accurate dip-pen lithography (DPN) uses atomic force microscopy microcantilevers to deposit spots in the range of 1–60 μm diameter (Nano eNabler, Bioforce Nanosciences, Ames, Ind., USA). Inkjet printers use piezoelectric elements to transfer the protein solution in the form of droplets to the target surface, printing spots with diameters of 80–150 mm. Although inkjet printers are very fast, they tend to have maintenance problems due to the more complicated delivery system. Alternatively, continuous flow microspotters use microfluidic channel networks to circulate dilute or concentrated protein samples over a spot defined by a template, achieving uniform and maximum protein adsorption [9]. Although chemically activated surfaces can be used, we find that nitrocellulosecoated slides (Gentel Biosciences; Whatman) provide the most consistent results for spotting a variety of proteins. Quality control measures should be considered. Unlike oligonucleotides, the highly variable physical properties of proteins impact

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the overall quality of high-content microarrays. Native folding and functionality of all proteins after spotting and during use are difficult to assess, but may not be as critical for polyclonal antibody recognition compared to other applications. Although it is not practical to measure function of all of arrayed proteins, a spot check of enzyme activity or protein–protein interactions for control proteins is beneficial. Further, many proteins fail to express or to form stable products under standard conditions, with success rates of 60–80% for purified products very common. A variety of reasons contribute to these failures, often requiring several independent strategies to increase the recovery rate. For many proteins, the addition of a soluble fusion partner, for example, glutathione-S-transferase (GST) or maltose binding protein (MBP), minimizes aggregation and aids in stable folding. The use of fusion tags linked to the targeted antigens helps in recovery of stably folded products, and can be used as a general marker for quality control. For example, purification schemes employing GST fusions will favor proteins with stable structures and will increase the yield of folded proteins because unfolded glutathione-S-transferase (GST) will not bind to the immobilized glutathione (the GST ligand) used for affinity purification. Before committing printed microarrays to experiments, protein spot densities of representative slides should be measured, for example, by using an anti-GST antibody compared to a dilution series of known quantities of protein also printed on each slide. Intra-slide and intra-lot variability in spot intensity and morphology, the number of missing spots and the presence of control spots should also be evaluated.

16.3 Antibody Assays

Serum IgG and IgM binding to arrayed proteins are independently probed on the same surface by fluorescently labeled secondary antibodies and detected by a confocal laser scanner (such as Genepix, Molecular Devices, Sunnyvale, Calif., USA). The much lower levels of IgA that may be present in a sample require a separate assay for detection. An accurate and rapid screening of thousands of proteins for antibody interactions is possible. Sera that may contain viable virus particles can be γ-irradiated with little harm to antibodies, while filtration removes suspected bacteria. Optimal dilutions of sera must be established to obtain minimal saturation of fluorescent signals while maintaining significant binding events. We find that for most applications, dilutions from 1 : 50–200 are usually sufficient for human sera. Microarray slides are pre-incubated (1 h, 22 °C) with a blocking buffer (1% BSA, 0.1% Tween-20 in PBS). Antibody samples are diluted in probe buffer (1× PBS, 5 mM MgCl2, 0.05% Triton X-100, 1% glycerol, 1% BSA), based on individual titer to optimize the signal above background. Diluted sera are overlaid (120–300 μl) on the slides, covered with glass cover slips, and incubated (1 h, 22 °C) in a humid incubator to prevent sample evaporation. Following incubation, cover slips are removed and the slides are washed with probe buffer. Although manual slide processing is not difficult, we also use a semi-automated hybridization work-

16.4 Antigens and Proteomes of Viruses

station (such as a Tecan HS 400 Pro, Tecan Group Ltd, Switzerland) for all steps through washing of the arrays for increased assay precision. Antibody binding is detected by simultaneous incubation with a dilution of fluorescently labeled (Alexa-647 or Alexa-532) goat antihuman IgG and goat antihuman IgM (Invitrogen, Carlsbad, Calif., USA). Our experience indicates that simultaneous detection of IgG and IgM using probe labels with minimal spectral overlap is accurate and the most efficient method to use. The slides are washed three times following incubation with the secondary antibody and allowed to air dry completely before analysis. Images of microarray surfaces are scanned and data captured using Genepix Pro 6.0 software (Molecular Devices). ProtoArray Prospector (Invitrogen/Life Technologies, Calif., USA) is a comprehensive software ensemble that we use for data analysis to evaluate statistical deviation of antibody binding signals from negative controls and the mean signal. Chebyshev’s Inequality (CI) P value and Z score can be used data comparison across multiple arrays. The Immune Response Biomarker Profiling (IRBP) Toolbox of Protoarray Prospector facilitates comparisons among groups of microarray experiments derived from different categories of samples, for example, disease and control. Fluorescent signals are normalized across the experiments and an M statistics algorithm is applied to identify signal increases in one group compared to another. For each protein spotted, the algorithm counts a number of assays in one group that have a higher signal than the highest signal in another group, then calculates how many assays have a signal bigger than the second largest signal in another group, third largest, and so on. These calculations establish cutoff values defining “hits” and P values that represent the probability that there is no signal increase in one group compared to another. As a result, the proteins of interest are those with the lowest P values that exhibit the biggest difference in number of hits between the groups of comparison. It is also important to visually inspect images of arrays to confirm positive scores, absence of dust particles, stray signals or other potential artifacts. MultiExperiment Viewer (MEV) software developed by TIGR (www.mev.tm4.org) is used to visualize the fluorescent signals across multiple arrays on heat maps. In addition, the MEV program offers a variety of tools including hierarchical clustering and principal component analysis to explore structures in complex datasets. Such structures may confirm the original hypothesis (e.g., difference in antibody response will separate disease and control groups in different clusters) and may illuminate relationships between proteins.

16.4 Antigens and Proteomes of Viruses

Our work with poxviruses is an example of a moderately complex proteome examined by microarray [10]. Of the seven known orthopox species, variola virus causes the most severe disease (smallpox) and various forms of the attenuated vaccinia virus are used for vaccination. To construct an orthopoxvirus proteome microarray,

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Infection Replication

Complement regulatory protein ~100 extracellular proteins

IMV

EEV IMV envelope proteins

EEV envelope proteins

~250 intracellular proteins Cell fusion protein

Summary of antigens EEV envelope proteins IMV envelope proteins DNA binding core protein IV membrane scaffold protein core proteins A-type inclusion protein, fragment Schlafen nucleoside triphosphate phosphohydrolase ribonucleoside reductase

Figure 16.2 The antibody response to poxvirus is directed towards proteomes originating

from intracellular and extracellular protein pools.

open reading frames (ORFs) from monkeypox Zaire-1979_005 (DQ011155; 202 genes), WRAIR7-61 (AY603973; 178 genes), and vaccinia Copenhagen (M35027; 273 genes) were PCR-amplified, cloned, and expressed in insect cells using Gateway baculovirus expression (Invitrogen). Poxviruses replicate in the cytoplasm of host cells from genomes encoding 150–300 proteins (Figure 16.2), while the mature virion is comprised of approximately 100 proteins. Gateway recombination cloning (pDEST20, Invitrogen) was employed to facilitate high-throughput production of proteins from all ORF clones. All DNA clones were sequence-verified through the entire length of their inserts. Baculovirus-based expression was used to produce the recombinant viral proteins as GST-tagged fusions to ensure high yield of properly folded proteins with post-translational modifications that are similar to those encountered in the human host. Isolated bacmid DNA was transfected into Sf9 insect cells to assemble competent virus particles, which were amplified to a high titer by successive rounds of insect cell infection and used to infect insect cell cultures for protein production. Using glutathione-agarose in 96-well plates, the GST-tagged proteins were affinity purified from cell lysates to 90% or greater homogeneity in a single step, as determined by SDS-polyacrylamide gel electrophoresis (PAGE). Purified proteins were analyzed by Western blot assay for correct size and abundance before inclusion in the microarray. The final protein microarray included 92–95% (166–192 proteins) of proteomes from monkeypox viruses and 92% coverage (250 proteins) of the vaccinia virus proteome. Virus particles can also be spotted and included in the assay, and this can be used to compare relative antibody titers to virus versus individual component proteins. For the orthopoxvirus microarray, recombinant pathogen and control proteins were printed onto glass slides coated with a thin layer of nitrocellulose [11, 12]. A standard assay for measuring antibody interactions with the vaccinia proteome microarray was first developed with a pool of therapeutic human sera col-

16.4 Antigens and Proteomes of Viruses

lected from vaccinia-immune individuals (VIg) and these data were compared to results obtained from individuals vaccinated against smallpox. The assay consisted of incubating a dilution of serum on the microarray surface, washes and finally detection with a fluorescently labeled anti-Ig antibody. Images were captured by scanning with a confocal laser (GenePix 4000B; Molecular Devices, Calif., USA) and analyzed (ProtoArray Prospector v3.1). Positive binding events were determined by Z scores [13] and Chebyshev’s Inequality P value (CI-P). Incubating the microarray with VIg identified nine proteins that consistently bound antibody, while antibody interactions with all other proteins were insignificant, requiring no further treatment to suppress nonspecific signals. These antigens were also recognized by antibodies from individual subjects following primary smallpox vaccination and were diverse in function, consisting of regulatory, surface, core, and secreted proteins (Figure 16.2). The validity of the statistical methods used was also confirmed by using an unbiased clustering analysis of the normalized data. Identical vaccinia proteins clustered into proteins recognized by primary and secondary vaccinated subjects based solely on signal intensities. The vaccinia proteins C3L and I1L were newly reported as antibody-recognized antigens. The nine antigenic proteins we identified did not bind antibody from nonvaccinated sera, confirming the specificity of these antibody–antigen interactions. However, O2L and H7R were reactive with antibodies from both VIg and nonvaccinated control sera, suggesting that these were cross-reactive or nonspecific interactions. Our results indicated that only a small subset of proteins present within the complex orthopox proteome was essential for immunity. We also examined the relationship between antibody binding to the arrayed proteins and protective immunity, noting that three antigens (A33R, C3L, I1L) correlated with titers of antibody that significantly neutralized infection in cell culture. In addition, the human response to individual vaccinia proteins was variable: sera from more than half of the vaccinated individuals contained IgG that recognized >4 vaccinia proteins, while the remaining samples recognized 1–3 proteins. Because of the potential for individual variation in immunity, it is essential to examine an expanded panel of unique serum samples to fully capture the antibody response represented within the targeted population. Further, antibodies from most individuals recognized a greater number of viral proteins after a boost vaccination compared to a single vaccination, suggesting that repeated infection may expand the total number of proteins recognized by IgG. The results from smallpox vaccinations identified key antigens associated with human immunity to infection, and defined the minimal number of antigens required for assay development. Whereas a panel of nine proteins will lead to comprehensive coverage of antibody responses to the vaccinia proteome, a single protein will not provide sufficient data to accurately predict a vaccine response. Smallpox vaccination is also effective for monkeypox due to a high degree of similarity between the infecting viruses. Using the expanded Orthopoxvirus protein microarray, we observed that serum IgG from cynomolgus macaques recovering from monkeypox recognized at least 23 separate proteins within the orthopox proteome, while only 14 of these proteins were recognized by IgG from vaccinated

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humans. We also detected specific IgM responses prior to onset of symptoms of monkeypox in nonhuman primates [14], an observation that may be common to other viral infections. By examining antibodies from ZAIRE-infected macaques we observed extensive cross-reactivity between the proteomes of ZAIRE and WRAIR strains, but could not establish any basis for clade-specific immunity. Of the 25 IgG binding proteins identified in this study, 80% were previously identified in virus particles by mass spectrometry methods [15, 16]. While no direct correlation with protein abundance and antigen recognition was found, nine of the 15 most abundant proteins (based on mol%) in IMV were targets of significant antibody binding. Many serological assays based on intact viruses cannot distinguish between closely related species [17, 18]. Our work with monkeypoxvirus demonstrated that it is possible to use microarrays of recombinant viral proteins to distinguish antibody responses to closely related viruses by including multiple probes in the analysis. The inclusion of protein panels to increase specificity of antibody measurements was further supported by studies of dengue virus infection [19]. Dengue is a mosquito-borne infection caused by four distinct serotypes of dengue virus, each comprised of ten proteins in the mature virion. As the most prevalent mosquito-borne viral disease in humans, dengue is a major urban public health problem worldwide. Several worldwide outbreaks of infection with dengue virus occurred in rapid succession over the last few years. In Central America and Mexico, traditional areas of low fatality rates, the number of deaths increased 142% in 2007 (Pan-American Health Organization data). Although several commonly used serological assays readily detect infection, cross-reactivity between strains of dengue virus is extensive. Proteins from isolates of all four dengue virus strains were cloned, baculovirus-expressed and printed in a microarray. Naïve macaques were challenged with wild-type viruses of each serotype and convalescent serum antibodies were examined. Analysis of antibody binding to a subset of dengue virus elements consisting of envelope, membrane, NS1 and NS3, or capsid proteins was sufficient to determine the identity of the infecting strain. We also compared responses of rhesus macaques vaccinated with tetravalent vaccines comprised of inactivated or live-attenuated viruses. We detected temporal increases in antibodies against envelope proteins in response to either combination vaccine, while vaccination with the live attenuated strains resulted in an additional antibody response to capsid proteins. This detailed understanding of the host response to individual components of the viral proteome is important for the overall disease control strategy to identify immune responses to specific strains in circulation and to assess vaccine efficacy in relationship to these strains.

16.5 Antigens and Proteomes of Pathogenic Bacteria

Bacterial proteomes present the greatest challenge based on size alone. For the case of gram-negative bacteria, a microarrayed panel of proteins from a reference

16.5 Antigens and Proteomes of Pathogenic Bacteria

proteome was used to identify antibody-response patterns that were unique to each of eight separate pathogens [14]. The chromosome of Y. pestis CO92 encodes approximately 3885 proteins, while an additional 181 are episomally expressed by pCD1, pMT1, and pPCP1. For comparison, the proteome of Y. pestis KIM contains 4202 individual proteins, 87% in common with CO92, and the closely related enteric pathogen Y. pseudotuberculosis contains approximately 4038 proteins (chromosome plus plasmids). The microarray was printed with over 75% of the 4066 ORFs present within the Y. pestis genome. In a manner similar to the vaccinia microarray, the Y. pestis proteins were produced as full-length polypeptides fused to GST as an affinity isolation tag. An in vitro translation method, based on E. coli lysates, was used to express the proteins in a gram-negative background. The ORF clones were fully sequenced to confirm quality and identity before use, and expressed proteins were characterized by SDS gels and Western blots (probed with anti-GST antibody). Additional proteins were included in the bacterial microarray (Figure 16.3) to aid orientation of a grid for signal measurement, as negative and positive controls, and to provide an IgG standard curve. Different approaches for studying the antibody repertoire against plague were first compared. Up to 40% of the Y. pestis proteins recognized by antibodies from infected rhesus monkeys were also observed in rabbit antibodies produced in response to total protein extracts from Y. pestis, indicating that it was not necessary for the animal model to fully reproduce the human infectious disease. In addition, there was a high degree of similarity between antibody responses to extracted

a)

b)

Alexa647 anti-mouse Ab 1

BSA gradient Anti-GST Ab gradient Calmodulin gradient GST gradient Biotin Ab gradient 2

Figure 16.3 Validation of the Yersinia pestis

protein microarray with protein–protein interaction and rabbit antisera against proteome extracts. (a) Binding of antiproteome Ig. Microarray was incubated with rabbit hyperimmune sera against the whole Y. pestis proteome (diluted 1 : 1000) and

Anti-biotin Ab V5 control gradient Human IgG gradient Y. pestis antigenic protein 1. y1349 2. y2882 bound Ig was detected with an Alexa 647-labeled goat anti-rabbit antibody and a laser confocal scanner. (b) Enlarged image of two subgrids from the microarray illustrating control proteins (colored boxes) and representative antibody binding to arrayed Y. pestis proteins (gray boxes).

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proteomes and intact bacteria. While antibodies directed against only a few antigens on the surface of the bacterium are anticipated to result in neutralization of the pathogen, many proteins are potentially available for recognition by antibodies following release or degradation of bacteria and presentation to the immune system. Yet, proteins recognized by antibodies directed against Y. pestis appear to constitute only a small percentage of the total bacterial proteome. Both categories of antigens are candidate biomarkers of infection. Convalescence sera from nonhuman primates that survived an otherwise lethal aerosol challenge with Y. pestis CO92 (plague) or Bacillus anthracis Ames spores (anthrax) were examined. A small subset of Y. pestis proteins (<30) were recognized by antibodies from plague survivors, while none of these were detected with antibody from anthrax survivors or nonchallenged controls, indicating a high degree of assay specificity. We were also able to confirm antibody responses to CaF1 and LcrV, both components of a recombinant plague vaccine presently in clinical trials, and identify antibody recognition of previously unreported antigens correlating with protective immunity. In addition, we examined cross-reactivities of antibodies produced against proteomes extracted from Burkholderia mallei, B. cepecia, B. pseudomallei, Pseudomonas aeruginosa, Salmonella typhimurium, Shigella flexneri, and Escherichia coli by using the Y. pestis microarray. Because many proteins are highly conserved among this group of gram-negative pathogens, antibody responses should detect these similarities. Indeed, patterns of antibody recognition were identified that reflected the orthologous relationships among proteomes of these bacteria. Rabbit antibodies recognized Y. pestis proteins that clustered into three general categories: (i) proteins recognized in all gram-negative species examined (cross-reactive proteins), (ii) combinations of proteins unique to one pathogen (fingerprint), and (iii) proteins unique to only one pathogen (signature). Importantly, we identified new candidates for antibody biomarkers of bacterial infections and patterns of cross-reactivity that may be useful diagnostic tools or potential vaccines. Antibodies from nonhuman primates that recovered from a potentially lethal aerosol challenge with Y. pestis recognized several proteins in common with antibodies produced against the extracted bacterial proteome, supporting the importance of these proteins as potential disease biomarkers. Additional unique biomarkers of disease were identified in sera obtained from vaccinated or acutely infected nonhuman primates just before overt sepsis. Detecting antibody responses during asymptomatic infections or prior to clinical disease is a much under explored area that may benefit from the increased sensitivity provided by assays employing protein microarrays.

16.6 Conclusions

Antibodies are primary biomarkers of infection that are important for diagnosis, evaluating vaccine efficacy, and discovery of new vaccines. Protein microarrays are sensitive tools for measuring these complex antigen–antibody interactions.

References

Genomic sequences are the starting point, and a dedicated system for protein production, characterization, and array printing is required. The use of purified, well characterized proteins to assemble the microarray will insure the highest quality and most reproducible results.

Acknowledgment

The opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the United States Army, or the United States Government.

References 1 Huhtamo, E., Hasu, E., Uzcategui, N.Y.,

2

3

4

5

Erra, E., Nikkari, S., Kantele, A., Vapalahti, O., and Piiparinen, H. (2009) Early diagnosis of dengue in travelers: comparison of a novel real-time RT-PCR, NS1 antigen detection and serology. J. Clin. Virol., 47, 49–53. Domingo, C., de Ory, F., Sanz, J.C., Reyes, N., Gascón, J., Wichmann, O., Puente, S., Schunk, M., López-Vélez, R., Ruiz, J., and Tenorio, A. (2009) Molecular and serologic markers of acute dengue infection in naive and flavivirusvaccinated travelers. Diagn. Microbiol. Infect. Dis., 65, 42–48. Titball, R.W., and Williamson, E.D. (2004) Yersinia pestis (plague) vaccines. Expert Opin. Biol. Ther., 4, 965–973. Tournier, J.N., Ulrich, R.G., QuesnelHellmann, A., Mohamadzadeh, M., and Stiles, B.G. (2009) Anthrax, toxins and vaccines: a 125-year journey targeting Bacillus anthracis. Expert Rev. Anti Infect. Ther., 7, 219–236. Felgner, P.L., Kayala, M.A., Vigil, A., Burk, C., Nakajima-Sasaki, R., Pablo, J., Molina, D.M., Hirst, S., Chew, J.S., Wang, D., Tan, G., Duffield, M., Yang, R., Neel, J., Chantratita, N., Bancroft, G., Lertmemongkolchai, G., Davies, D.H., Baldi, P., Peacock, S., and Titball, R. (2009) A Burkholderia pseudomallei protein microarray reveals serodiagnostic and cross-reactive antigens. Proc. Natl. Acad. Sci. U.S.A., 106, 13499–13504.

6 Ramachandran, N., Raphael, J.V.,

7

8

9

10

11

Hainsworth, E., Demirkan, G., Fuentes, M.G., Rolfs, A., Hu, Y., and LaBaer, J. (2008) Next-generation high-density self-assembling functional protein arrays. Nat. Methods, 5, 535–538. Kalume, D., Peri, S., Reddy, R., Zhong, J., Okulate, M., Kumar, N., and Pandey, A. (2005) Genome annotation of Anopheles gambiae using mass spectrometry-derived data. BMC Genomics, 6, 128. Ansong, C., Purvine, S.O., Adkins, J.N., Lipton, M.S., and Smith, R.D. (2008) Proteogenomics: needs and roles to be filled by proteomics in genome annotation. Brief. Funct. Genomics Proteomics, 7, 50–62. Natarajan, S., Katsamba, P.S., Miles, A., Eckman, J., Papalia, G.A., Rich, R.L., Gale, B.K., and Myszka, D.G. (2008) Continuous-flow microfluidic printing of proteins for array-based applications including surface plasmon resonance imaging. Anal. Biochem., 373, 141–146. Schmid, K., Keasey, S.L., Pittman, P., Emerson, G.L., Meegan, J., Tikhonov, A.P., Chen, G., Schweitzer, B., and Ulrich, R.G. (2008) Analysis of the human immune response to vaccinia by use of a novel protein microarray suggests that antibodies recognize less than 10% of the total viral proteome. Proteomics Clin. Appl., 2, 1528–1538. Zhu, H., Klemic, J.F., Chang, S., Bertone, P., Casamayor, A., Klemic, K.G., Smith,

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12

13

14

15

16

D., Gerstein, M., Reed, M.A., and Snyder, M. (2000) Analysis of yeast protein kinases using protein chips. Nat. Genet., 26, 283–289. Schweitzer, B., Predki, P., and Snyder, M. (2003) Microarrays to characterize protein interactions on a whole-proteome scale. Proteomics, 3, 2190–2199. Cheadle, C., Vawter, M.P., Freed, W.J., and Becker, K.G. (2003) Analysis of microarray data using Z score transformation. J. Mol. Diagn., 5, 73–81. Keasey, S.L., Schmid, K.E., Lee, M.S., Meegan, J., Tomas, P., Minto, M., Tikhonov, A.P., Schweitzer, B., and Ulrich, R.G. (2009) Extensive antibody cross-reactivity among infectious gram-negative bacteria revealed by proteome microarray analysis. Mol. Cell. Proteomics, 8, 924–935. Chung, C.S., Chen, C.H., Ho, M.Y., Huang, C.Y., Liao, C.L., and Chang, W. (2006) Vaccinia virus proteome: identification of proteins in vaccinia virus intracellular mature virion particles. J. Virol., 80, 2127–2140. Resch, W., Hixson, K.K., Moore, R.J., Lipton, M.S., and Moss, B. (2007) Protein

composition of the vaccinia virus mature virion. Virology, 358, 233–247. 17 Beltramello, M., Williams, K.L., Simmons, C.P., Macagno, A., Simonelli, L., Quyen, Q., Nguyen Than, H., Sukupolvi-Petty, S., Navarro-Sanchez, E., Young, P.R., de Silva, A.M., Rey, F.A., Varani, L., Whitehead, S.S., Diamond, M.S., Harris, E., Lanzavecchia, A., and Sallusto, F. (2010) The human immune response to dengue virus is dominated by highly cross-reactive antibodies endowed with neutralizing and enhancing activity. Cell Host Microbe, 8, 271–283. 18 Kuniholm, M.H., Wolfe, N.D., Huang, C.Y.H., Mpoudi-Ngole, E., Tamoufe, U., Burke, D.S., and Gubler, D.J. (2006) Seroprevalence and distribution of flaviviridae, togaviridae, and bunyaviridae arboviral infections in rural Cameroonian adults. Am. J. Trop. Med. Hyg., 74, 1078–1083. 19 Fernandez, S., Cisney, E.D., Tikhonov, A.P., Schweitzer, B., Putnak, R., Simmons, M., and Ulrich, R.G. (2010) Antibody recognition of the dengue virus proteome and implications for development of vaccines. Clin. Vaccine Immunol., 18, 523–532.

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17 MALDI-TOF Mass Spectrometry for Rapid Identification of Highly Pathogenic Microorganisms Peter Lasch and Dieter Naumann

17.1 Introduction

Rapid, reproducible and cost effective identification of microorganisms by spectroscopic techniques such as IR [1], Raman [2, 3], pyrolysis mass spectrometry [4] or matrix-assisted laser desorption/ionization – time of flight (MALDI-TOF) mass spectrometry (MS) [5–8] represents an increasing area of interdisciplinary research. At the forefront of these technological advances, MALDI-TOF MS has evolved from a niche research activity into a practical application with the potential to revolutionize the way microorganisms are identified [9–11]. The method relies on the reproducible detection of microbial protein mass patterns obtained from whole cells, cell lysates, or crude bacterial extracts [12]. Microbial MALDI-TOF mass spectra can be regarded as snapshots of the protein composition of individual strains. The protein biomarkers are typically high-abundance proteins with housekeeping functions, such as ribosomal or nucleic acid-binding proteins [10, 13, 14]. As these proteins are highly conserved and consistently expressed under nearly all growth conditions they can be regarded as sensitive, specific and robust biomarkers of the organism under study. For microbial identification the mass spectra can be analyzed in two different ways. With the fingerprint method unknown bacteria are identified by matching their mass spectra against validated microbial reference spectra [7, 15]. With this approach an extensive knowledge of biomarker identities is not necessarily required. Alternative MS methods for microbial identification rely on proteomic information by searching protein databases [16], or on a comparison of the experimental mass spectra with protein masses predicted from microbial genomes [16, 17]. All these techniques have in common that a multitude of bacterial mass peaks is analyzed which allows accurate typing of microorganisms at the genus, species and sometimes even at the subspecies level. The MALDI-TOF MS technique has a number of advantages over traditional microbiology, or nucleic acid technologies-based methods. First, sample preparation is comparably simple and can be carried out within minutes. Second, the

BSL3 and BSL4 Agents: Proteomics, Glycomics, and Antigenicity, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.

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technique does not require costly taxon-specific consumables. The whole procedure can be standardized allowing automation and high-throughput screening. Microbiologists thus identified MALDI-TOF MS as a potentially viable physical method to complement established microbial identification methods. The MS technique was employed in numerous studies in clinical, food and environmental microbiology, taxonomical research, and was also considered as a rapid identification method in scenarios when bacterial agents are intentionally released as method of terrorist attack. Having these aspects in mind, our research group at the Robert Koch-Institut (RKI) initiated in the year 2003 a project that aimed at establishing a spectroscopybased methodology for the specific and sensitive detection of highly pathogenic microorganisms. This project was carried out in close cooperation with the Center for Biological Security (ZBS) at the RKI, which is the central institution for issues of biological security in Germany. The main obstacle we had to overcome in this project was to define experimental conditions which comply with legal regulations for working with biosafety level 3 (BSL-3) microorganisms. In particular, the development of a simple, rapid and reliable microbial inactivation procedure that is compatible with the MALDI-TOF MS-based identification technique was regarded as an indispensable requirement for systematic studies with BSL-3 microorganisms such as Bacillus anthracis, Burkholderia mallei/pseudomallei, Yersinia pestis and others. In this chapter we discuss important details of such an inactivation protocol for BSL-3 microorganisms and we present key aspects of our studies to characterize pathogenic microorganisms of the genera Bacillus, Burkholderia and Yersinia.

17.2 Microbial Identification by MALDI-TOF Mass Spectrometry 17.2.1 Basic Principles of MALDI-TOF Mass Spectrometry

Matrix-assisted laser desorption/ionization is a so-called soft ionization technique which was developed in the late 1980s [18]. The technique allows to ionize relatively large nonvolatile organic compounds and biomolecules such as proteins, nucleic acids or lipids. The key aspect of MALDI is cocrystallization of analyte macromolecules in a suitable matrix of small organic molecules, such as α-cyano4-hydroxycinnamic acid (HCCA), 3,5-dimethoxy-4-hydroxycinnamic acid (sinapinic acid), or 2,5-dihydroxybenzoic acid (DHB). These matrix compounds show strong optical absorptions in the UV range, so that they effectively absorb appropriate laser light. When an analyte/matrix cocrystal is illuminated by intense short laser pulses matrix molecules are ablated causing vaporization (desorption) and ionization of small quantities of the embedded analyte. Analyte ions observed after this process are mostly singly charged, but multiply charged ions can also be created. In practice, the formation of analyte ions is achieved in a vacuum.

17.2 Microbial Identification by MALDI-TOF Mass Spectrometry

By far the most common mass analyzer of modern MALDI instruments is a time of flight (TOF) mass spectrometer. In an axial TOF analyzer, the ionized analyte molecules are accelerated by a constant electric field which is applied between a conductive sample support and a nearby electrode. The potential energy Epot of a charged particle in an electric field is directly proportional to the particle’s charge state z, the elementary charge e and the potential difference U: E pot = zeU

(17.1)

When accelerated ions enter the field-free flight tube, the potential energy has been converted into kinetic energy Ekin. Using the known relation between the kinetic energy, velocity v and the ion mass m given by: E kin =

m 2 v 2

(17.2)

one can easily derive the following formula: m 2eU = 2 z v

(17.3)

s v= , t

(17.4)

with:

where s is the length of the flight tube and t is the time of flight. Furthermore, as the term 2eU/s2 = c is constant in a given experimental setup, Equations 17.3 and 17.4 can be transformed as follows: m = ct 2. z

(17.5)

According to Equation 17.5 the mass to charge ratio m/z of an ion is directly related to its time of flight (TOF). In TOF instruments the flight time t can be thus measured to separate analyte ions according to their m/z. 17.2.2 Preparation of Microbial Samples for MALDI-TOF MS

Depending on the sample preparation technique, the MALDI-TOF MS-based methodology for identification of microorganisms requires between 104 and 107 bacterial cells. These cell numbers can be easily obtained by cell cultivating on solid media [19]. It is generally accepted that cultivation time (growth phase), growth temperature, the composition of the culture medium, the presence/ absence of oxygen, or CO2 and a number of other factors [20] have a potential influence on the protein expression of the microorganisms under investigation. Furthermore, sample preparation and instrument specific factors also may alter the bacterial mass spectral fingerprints. It is our experience and those of others, that although these factors do not significantly hamper identification at the species

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level, the distinction at the subspecies level may become problematic if cultivation conditions, sample preparation and measurement parameters are not rigorously standardized [21]. Different investigators use various ways to cultivate microbial strains or isolates belonging to different genera. Furthermore, depending on the requirements for sample uniformity a variety of different sample preparation procedures have been suggested, all of them having their pros and cons. The most common and most simple way of microorganisms preparation that works for a large number of different species is the so-called direct transfer method. Here, the microbial material is directly transferred from a microbial culture onto a MALDI-TOF MS target. After a very short drying step, the material can be overlayed by the matrix solution, usually HCCA or DHB. The direct transfer procedure is, however, recommended only for nonpathogenic bacteria since it does not ensure inactivation of pathogenic microorganisms [22]. Another sample preparation method suitable for MALDI-TOF MS of microorganisms in routine clinical microbiology is the ethanol/formic acid extraction procedure. This protocol, developed by Bruker Daltonics, is considered the second standard protocol in Bruker’s workflow for characterizing microorganisms (BioTyper workflow). The protocol involves a number of short incubation and centrifugation steps using ethanol, formic acid and acetonitrile for inactivation, cell wall disruption and protein extraction. As survival of spore forming microrganisms cannot be excluded, the procedure is not recommended for BSL-3 microorganisms, particularly for spore-forming Bacillus species. Another detriment of the ethanol/formic acid extraction procedure is the possible occurrence of mild oxidation of microbial proteins [22]. The TFA inactivation protocol is a sample preparation approach which was developed at RKI for reliable inactivation of highly pathogenic microorganisms, including spore-formers of the genus Bacillus. The TFA protocol was specifically developed to meet the strict legal requirements for working with BSL-3 microorganisms. Because of its relevance for MALDI-TOF MS characterization of BSL-3 microorganisms, the protocol is detailed in a separate section (see Section 17.3). 17.2.3 Spectral Data Analysis: Preprocessing, Calibration, Peak Detection, and Data Visualization

Spectral data analysis is a challenging task of the MALDI-TOF MS-based workflow for microbial identification. Considering that a MALDI-TOF mass spectrum is a complex signal which usually consists of hundreds of peaks and certain level of noise, adequate spectral preprocessing is required prior to peak detection and classification analysis. Preprocessing: The purpose of spectral data preprocessing is to improve the accuracy and robustness of subsequent mass peak detection and multivariate pattern

17.2 Microbial Identification by MALDI-TOF Mass Spectrometry

analysis. We found that a specifically designed preprocessing workflow with routine tests for spectral quality, smoothing, baseline correction and intensity normalization is a key step for reliable classification analysis of microorganisms. The quality of microbial mass spectra is assessed visually immediately after data acquisition with regard to the following criteria: first and foremost, the signal to noise ratio (SNR) and the presence of a sufficient number of mass peaks is evaluated. Further quality criteria are a relatively flat shape of the spectral baseline and the absence of interfering, or confounding, mass peaks from plasticizers and other synthetic polymer additives. Outliers, that is, spectra failing to meet one or more of the quality requirements are not accepted for multivariate classification analysis and are thus routinely removed. The remaining spectra are subsequently denoised by applying a Savitzky–Golay smoothing filter with 21 smoothing points. The next pre-processing routine, baseline correction, aims at flattening the spectral baseline. For this, mass spectra are divided into a defined number of intervals, usually 160, for a typical m/z range from 2000 to 20 000. A baseline is obtained by determining the minimum intensity values in each interval. These points are connected by a shape-preserving cubic interpolation function. Baseline correction is carried out by subtracting the interpolation curve from the spectra. Vector normalization as the final step of spectral pre-processing, enables then the comparison of mass spectral intensities, which is essential to produce so-called gel views (see below). The result of vector normalization is a spectrum in which the sum of squared intensities over all m/z values equals a constant value (in our case 1000). Peak Detection: Automatic and reproducible peak detection from microbial mass spectra is an important prerequisite for further spectral analysis. Ideally, a peak detection routine should be robust, rapid and should not require human intervention. For convenience of analysis we thus developed own algorithms of peak detection which could be specifically adapted and optimized for MS-based classification analysis. One of the key features of this routine was a sigmoid intensity threshold function introduced to model the m/z dependence of the analytical sensitivity of the MALDI-TOF MS technique. The threshold function defines at which intensity an experimental mass signal will be recognized as a mass peak by the automatic peak detection routine. In our implementation, the threshold at low m/z values was usually considerably higher compared to thresholds in the high mass region. Another feature of the peak detection routine allows to define the number of resulting peaks per spectrum. This feature turned out to be particularly useful for subsequent pattern matching using cluster analysis or neural network analysis. Calibration: In measurements with TOF instruments calibration is usually performed by external calibrants. These calibrants are typically contained in sets of standard peptides/proteins of known masses which are used to correct the constant theoretical term c = 2eU/s2 of Equation 17.5 by introducing linear or

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quadratic calibration models. The models are helpful to improve the mass accuracy and to minimize the influence of instrument- and sample-specific factors with an impact on the flight time t such as alterations of the local electric field, changes of the sample topography, target flatness and other factors [23]. The mass accuracy which can be achieved by external calibration using linear TOF instruments is at the order of 600 ppm (0.06%; ppm: parts per million). The mass accuracy of the measurements can be further improved with the help of ribosomal biomarker proteins as internal calibrants, [24]. It must be stressed however, that the internal calibration method fails if internal calibration peaks are absent or if the peak assignment is incorrect [23]. Preconditions for internal calibration are thus the presence of taxonomic information of the strains under investigation and the presence of biomarker peaks of known molecular masses. The mass accuracy which can be achieved using internal calibration is usually better than 250 ppm. Data Visualization: When dealing with large spectral databases which sometimes comprise hundreds of individual spectra, data inspection and assessment cannot be done by manual screening of the individual spectra alone. In these instances simulated gel views for visual analysis of the data set are popular means. In gel view representations, spectral peak intensities are converted to gray scales and plotted as a function of the m/z values. Gel views are usually generated from preprocessed and internally calibrated mass spectra. Bar Code Mass Spectra: Bar code mass spectra derived from peak tables proved to be suitable input vectors for classification analysis based on microbial MALDI-TOF mass spectra [25]. In our studies we have used these spectra as well; the algorithm to generate bar code spectra can be summarized as follows: a calibrated and preprocessed mass spectrum is firstly subdivided into non-equidistant intervals. The size of these intervals (Δxi, i is a positive integer {1, 2, 3, . . . }) at a m/z position xi is given in relative units (ppm). The relation between Δxi and xi is constant (Δxi/xi = constant, typically 800 ppm). Furthermore, the center of the next interval xi+1 can be obtained according to the equation xi + Δxi = xi+1. According to these relations, the size of the intervals scales linearly with the m/z position. When a mass peak is identified by the automatic peak detection routine in a given interval ranging from [(xi − Δxi/2) − (xi + Δxi/2)], a y-value of one is assigned. The y-value will be zero if no mass peak is present in this interval. The resulting bar code spectrum represents the input vector for cluster and ANN analysis. In this way the effective data point spacing is considerably increased which significantly lowers the number of spectral features and thus reduces the dimensionality of the classification problem. 17.2.4 Multivariate Classification Analysis – Pattern Recognition Methods

A large variety of computational methods for multivariate classification analysis of microbial mass spectra has been suggested. Since a comprehensive discussion

17.2 Microbial Identification by MALDI-TOF Mass Spectrometry

of these methods is beyond the scope of this chapter, we will restrict ourselves on hierarchical cluster analysis as an unsupervised pattern analysis method and neural network analysis for supervised classification. Unsupervised Hierarchical (Agglomerative) Cluster Analysis: Because of its simplicity and ease of interpretation unsupervised hierarchical cluster analysis (UHCA) enjoys great popularity for analysis of microbial mass spectra. The main idea of UHCA is to organize patterns (spectra) into meaningful or useful groups using some type of similarity measure. The technique belongs to the data-driven (unsupervised) classification techniques which are particularly useful for extracting information from unclassified patterns, or during an exploratory phase of pattern recognition [26]. In the first phase of UHCA, a distance, or similarity matrix is obtained which is symmetric along its diagonal and contains a complete set of distances between all pairs of spectra. The matrix has the dimension n × n, with n being the number of spectral patterns. Spectral distance can be obtained in different ways depending on how the similarity of two patterns is calculated. Popular distance measures are Euclidean distances, including the city block distance (Manhattan block distance), Mahalanobis distance or correlation coefficients. The second step in UHCA, the clustering process, can be illustrated as follows: first, the two most similar spectra are determined and merged to form a new cluster. Subsequently, the spectral distances between the new cluster and the remaining spectra are evaluated according to a predefined linkage method. This process is repeated n–1 times until all objects are combined into one cluster. The fusion sequence can be represented as a dendrogram, a tree-like structure which gives a graphical illustration of the similarity of mass spectral fingerprints. We and others have extensively used hierarchical clustering to produce dendrograms which give useful information on the relatedness of microbial strains and isolates [27–29]. Multilayer Perceptron Artificial Neural Network (MLP-ANN) Analysis: Multilayer perceptron–artificial neural networks (MLP-ANNs) are computational models which are popular means to model complex relationships between inputs and outputs and to find patterns [26]. In supervised learning the MLP class of neural networks requires a set of training samples which are used to infer a classifier to predict a correct output value (class assignment). To avoid overfitting and to assess the robustness of ANN class assignment, the general strategy of ANN analysis routinely includes procedures of training, internal validation and (external) testing, ideally under blinded conditions. Training and internal validation requires spectra of known class assignments; the model performance is determined on the basis of errors between the obtained outputs and desired target values of both, the training and the internal validation subsets. At the training stage ANN performance can be optimized by modifying the way of pre-processing, adding or eliminating spectral features, or changing the network’s architecture. When training is finished the classifier can be challenged by an external validation (test) subset.

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17 MALDI-TOF Mass Spectrometry for Rapid Identification of Highly Pathogenic Microorganisms

One of the constraints which has limited the broad application of ANNs for species identification in microbiology is the large number of categories, that is, species to be identified. Furthermore, as training and validation of ANNs necessitates relatively high sample numbers, ANN model development for clinical microbiology tasks oftentimes demands a lengthy and tedious training process. A popular approach to reduce time and efforts when designing neural network models for multiclass classification problems is the use of modular, or hierarchical ANN classification schemes [30, 31, 32]. Here, small and flexible networks specifically designed for specialized classification tasks are combined to build up large modular ANN systems. Instead of teaching and optimizing monolithic classifiers with large output vectors, individual subnets can be trained and validated independently. The hierarchy of classifiers can be modified at a later stage to add more classes or to perform more specialized classification tasks. The primary advantage of modular ANN models lies in the fact, that individual ANNs can be specifically optimized to identify only a few (down to two) classes [31]. 17.2.5 Identification of Taxon-Specific Biomarkers

Experiences from our work and those of others suggest that efficient strategies for identification of taxon-specific biomarkers involve measurements of a representative number of strains per taxonomic unit. Ideally, a biomarker is identified from a training set, which may contain only a limited number of samples. In such cases biomarker candidates should be subsequently statistically validated on the basis of independent sample sets. In our studies we used however an alternative approach. Systematic searches for taxon-specific biomarkers involve a series of independent (unpaired) t-tests which are known for a long time in statistical hypothesis testing. In the t-test series the null hypotheses H0 are verified which assume that the means of two normally distributed populations are equal. For hypothesis testing a given microbial database of preprocessed mass spectra is usually sub-divided into two classes, for example, into “species A” and “species B”. Then, the discriminative power of each spectral feature, which is typically an intensity at a given m/z value, can be determined by independent (unpaired) t-tests. Each t-test provides a P value which is a statistical measure of the distinctness between the mass spectral features of species A and B obtained at a given m/z value. A P value of one confirms the null hypothesis H0 of equal class means while small P values cast doubt on this hypothesis. The results, the P values of the t-tests, can be plotted in a logarithmic scale as a function of the m/z values. These plots are easy to interpret: the smaller the P value, the higher the discriminative potential of the spectral feature under investigation. It is an advantage of the proteomic strategy that the validity of the biomarker ions identified at a certain statistical confidence level can be additionally verified. A popular strategy of confirming microbial biomarkers involves the application of tandem mass spectrometries in combination with separation techniques for de novo sequencing. For example, in one of our studies we have used LC-MALDI

17.3 Inactivation of Highly Pathogenic Microorganisms for MALDI-TOF Mass Spectrometry

tandem MS (MS/MS) to reveal the amino acid sequence of a species-specific biomarker of Y. pestis which was initially detected in gel views and which could be statistically verified on the basis of the t-tests [33] (for details see Section 17.4.3). Once the amino acid sequence of a biomarker of interest is available, systematic comparisons with protein sequence databases (or the analysis of microbial genome data) allow an accurate assignment of the biomarker’s molecular identity. This information can be further used to achieve a better understanding of the classification problem in general. For example, a comparison with sequence data from strains and species which were not available in the experimental studies provides indispensable information on the specificity of the biomarker under investigation. Furthermore, although MALDI MS using linear TOF analyzers typically provides a limited mass accuracy only, biomarker ions can oftentimes be tentatively identified solely on the basis of their m/z values [14, 16, 34]. This strategy has been employed numerous times by us and others; it requires complete bacterial genome sequences and the knowledge of the species identity. Since most of the microbial biomarkers identified by MALDI-TOF MS are relatively small (<15 kDa), the tentative identification benefits from the relatively small number of potential matches in the protein, or genomic sequence databases. When applying such a strategy one must however be aware of possible post-translational modifications of microbial proteins. It has been shown numerous times that modifications like cleavage of N-terminal methionine, methylation, acetylation, fragmentation, and amino acid side chain derivatization may constitute serious challenges for the identification of biomarkers [35]. Although identification of some of the processed proteins will be possible in some instances, accurate molecular assignment of microbial biomarkers generally requires the utilization of alternative analytical techniques (tandem MS, LC-MS, etc.). Thus, mass spectrometry data obtained by linear MALDI-TOF instruments provide some evidence regarding the protein identity from whole cells, microbial cell lysates, or crude cellular extracts, but cannot conclusively proof the identity of the biomarker in question.

17.3 Inactivation of Highly Pathogenic Microorganisms for MALDI-TOF Mass Spectrometry

In Section 17.2 we outlined that a combination of MALDI-TOF MS and computeraided pattern recognition is well suited for sensitive detection, differentiation and identification of microorganisms in routine clinical microbiology. This technique has the potential to displace traditional biochemical tests for pathogen identification in clinical laboratories [11, 36]. During the time of preparation of this article (2010) at least two commercial products have entered the market: The BioTyper solution from Bruker Daltonics [9] and AXIMA@SARAMIS (Shimadzu in cooperation with Anagnostec GmbH). Both commercial products can be used to routinely identify microorganisms. With the direct transfer and the ethanol/formic acid extraction sample preparation procedures [19, 22] two simple and rapid

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sample preparation procedures are available that allow reproducible detection of microbial protein patterns. The identification of highly pathogenic BSL-3 microorganisms such as B. anthracis, Burk. mallei/pseudomallei, F. tularensis, Y. pestis and others by MALDI-TOF MS is, however, hampered by the fact that both procedures cannot ensure complete inactivation of microorganisms. Especially, the inactivation of microbial spores represents a great challenge because of their greater resistance to chemical disinfectants, heat and radiation [37]. In collaborations with partners from Anagnostec GmbH and the Bundeswehr Research Institute for Protective Technologies and NBC Protection (WIS) in Munster, Germany, we initiated in 2003 a program to develop a MS compatible inactivation protocol for highly pathogenic microorganisms. Because the results of these efforts have been published elsewhere [38] we will in the following present only selected aspects of this work. Inactivation of microorganisms can be achieved by a large number of physical or chemical methods. Physical methods include heat inactivation (autoclavation), radiation (UV, γ-irradiation), inactivation by pressure or mechanical methods (milling). Popular methods of chemical inactivation rely on treatment by substances like formaldehyde, oxidizing agents (e.g., peracetic acid, PAA), or strong acids. Among these methods, inactivation by concentrated trifluoroacetic acid (TFA) matched most closely the following criteria: 1)

The disinfection procedure should reliably sterilize vegetative cells and bacterial endospores at very high concentrations. For example, with an estimated concentration of 1011–1012 CFU/g the Daschle letter contained extraordinarily high numbers of B. anthracis spores [39, 40].

2)

The inactivation method should preserve the structural integrity of the microbial biomolecules on which the MS analysis is based. The inactivation procedure should thus completely prevent germination but avoid at the same time destruction of the structural integrity of microbial proteins.

3) The method should be inexpensive, simple and rapid. On site inactivation should be possible. 17.3.1 Time and Concentration Dependence of TFA Inactivation

Optimization of TFA concentration and contact time for effective microbial inactivation was carried out using spore suspensions of B. cereus ATCC 10987, the most resistant strain of the RKI strain collection. In these experiments the inactivation process was stopped after 5, 15 or 30 min by a neutralizer. Logarithmic reduction factors were determined for each time point and each TFA concentration. The maximal spore killing effect was detected in the first five minutes of TFA treatment. At a TFA concentration of 80% and an incubation time of 30 min the inactivation data suggested a reasonable tradeoff between spore killing efficiency and experimental efforts that would be still acceptable in the laboratory routine. Tests

17.3 Inactivation of Highly Pathogenic Microorganisms for MALDI-TOF Mass Spectrometry

with vegetative cells of the genera Bacillus, Burkholderia, Escherichia and Yersinia showed complete inactivation at these conditions. The treatment of microorganisms with 80% TFA for 30 min reliably inactivated vegetative cells. The procedure was also effective for solutions containing bacterial endospores at concentrations below 107 CFU/ml. For sterilization of higher concentrated spore solutions we thus tested a combination of the TFA method with supplementary methods which have the potential to further reduce the number of surviving cells. 17.3.2 Centrifugation – Reduction of the Supernatant’s Cell Concentration

Decanting the supernatant after centrifugation is known as a simple and effective method to reduce cell counts. The centrifugation method was tested by applying a g-force of 16 000 g for 20 min. For solutions with initial spore concentrations of 108–1010 CFU/ml the logarithmic reduction factors varied between 2.3 (B. anthracis) and 5.2 (B. subtilis) [38]. 17.3.3 Sterile Filtration

Subsequent to centrifugation the supernatant can be carefully aspirated and transferred for filtering into Millipore’s Ultrafree® MC filter tubes of a pore size of 0.22 μm. The tubes contain a Durapore® polyvinylidene fluoride (PVDF) membrane which turned out to be resistant to highly concentrated TFA. Tests of several batches of TFA pretreated Ultrafree® MC filters demonstrated structurally intact filter membranes with a preserved ability to retain spores. MALDI-TOF MS control measurements of filtrates of a 80 : 20 TFA/water mixture demonstrated furthermore the absence of spectral contaminations from the filter tubes or the PVDF filter material. By combining TFA treatment with centrifugation and sterile filtration we suggested a disinfection protocol which will be in the following referred to as the TFA inactivation protocol. The protocol involves the following steps: 1) 2) 3)

Treatment of viable cells or bacterial endospores by TFA, 80% for 5 min. Adequate mixing and shaking is mandatory. Centrifugation for 20 min at 16 000 g. Sterile filtration of the supernatant using acid resistant spore filters.

The TFA inactivation protocol was successfully validated in 67 independent tests using spore suspensions of 14 bacterial strains, including two strains of B. anthracis, of concentrations up to 2.9 × 1010 CFU/ml [38]. The schematic workflow of microbial treatment prior to MALDI-TOF MS is given by Figure 17.1. Once highly pathogenic microorganisms are sterilized in a BSL-3 containment laboratory, the TFA extracts can be passed out to a standard laboratory for MALDI-TOF MS characterization.

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Biosafety laboratory 20 µl sample solution + 80 µl TFA, gentle shaking for 5 min

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Figure 17.1 Schematic overview on the procedures for MALDI-TOF MS compatible inactiva-

tion of vegetative cells and spores.

17.3.4 Molecular and Structural Aspects of Spore Treatment by TFA

To get more insights into the molecular mechanism of inactivation, TFA-treated and untreated spore preparations were studied using electron microscopy (EM). As the reference, or control, sample preparation method, a two hour treatment of spores by 10% paraformaldehyde (FA) and 0.05% glutaraldehyde (GA) was employed [41]. The different morphologies of TFA-treated and reference spores of B. atrophaeus are presented by the electron micrographs in Figure 17.2. Micrographs in Figure 17.2b, d display sectioned spores of B. atrophaeus DSM 2277 after FA/GA control treatment; the TFA-induced changes from the same spore batch are depicted in Figure 17.2a, c. Apparently, treatment by 80% TFA causes efficient extraction of core materials. Furthermore, substantial morphology changes of the cortex and the inner coat are observed. These changes are accompanied by a sig-

17.3 Inactivation of Highly Pathogenic Microorganisms for MALDI-TOF Mass Spectrometry a)

b)

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Figure 17.2 Electron micrographs of sectioned spores of B. atrophaeus DSM 2277. (a, c) Spores after 30 min treatment with trifluoroacetic acid (TFA). (b, d) Spore morphology after control treatment by a

formaldehyde/glutaraldehyde mixture (FA/ GA). Note the extraction of the spore’s core by TFA. ic – Inner coat, oc – outer coat, cx – cortex, co – core, m – core membrane.

nificant increase of the diameter of TFA treated spores. The almost complete extraction of the spore’s core, cortex and inner coat structures (cf. Figure 17.2a,c) suggests that the mechanism of TFA inactivation involves the destruction of permeability barriers and effective extraction of acid-soluble spore components. With the development and verification of the TFA inactivation protocol we could demonstrate that microorganisms can be reliably inactivated without compromising the sensitivity and the performance of the MALDI-TOF MS identification technique. The protocol could be in the following applied for inactivation of hundreds of strains from the genera Bacillus, Burkholderia and Yersinia and to establish databases of MALDI-TOF mass spectra. The results of these studies and a discussion thereof will be presented next.

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17.4 Identification of Important Bacterial Pathogenes Using MALDI-TOF MS 17.4.1 Bacillus anthracis

B. anthracis has been defined as a possible agent in biological warfare and bioterrorism. Its applicability as a biowarfare agent became apparent by an accidental release from a Soviet military facility in Sverdlovsk [42, 43]. Also the intentional release of B. anthracis spores in the United States in autumn 2001 causing 18 confirmed cases of anthrax [39, 40] demonstrated that B. anthracis may become a serious threat from terrorist groups [43]. The analysis of potential biothreat agents, such as “white powders” of anthrax (hoax) letters, represents one field of activity at the Center for Biological Security (ZBS) at the RKI. The need for a rapid, independent and reliable technique suitable to complement traditional microbiological and PCR-based methods prompted us to test and verify the newly developed MS compatible inactivation technique with preparations of B. anthracis and related species. In cooperation with Dr. Beyer from the University Hohenheim (Germany) we thus characterized 374 strains from Bacillus and related genera, among them 102 strains of B. anthracis and 121 strains of its closest relative, B. cereus. The results of this study were published [44] and a summary of these data is given next. Figure 17.3 shows two MALDI-TOF mass spectra of B. anthracis and B. cereus. The spectra were produced by averaging all available mass spectra of these species. Although the peak intensity profiles are strikingly different, a detailed analysis revealed a relatively large number of shared spectral peaks and the presence of only a few species-specific mass signals. A potential candidate for an anthraxspecific peak was identified at m/z 5413 (see arrows in Figure 17.3a,b). Another potential biomarker of B. anthracis was found at m/z 6679 whereas biomarkers for B. cereus were identified at m/z 6695 and 6711. Previously, these markers have been assigned to species-specific small acid-soluble proteins (SASPs) [45–47]. The spectral features that allow species discrimination and identification of B. anthracis are shown in the simulated gel view of Figure 17.4 for B. cereus group members in the diagnostically important region between m/z 5000 and 10 000. This gel view representation demonstrates the existence of B. cereus group-specific biomarkers at m/z 5171, 5886 and 7368. It shows also B. anthracis-specific peaks at m/z 5413, 6492 and 6679. The latter peak is due to a SASP and is of particular interest, because the peak was found in only two out of 121 strains of B. cereus while the vast majority of B. cereus strains exhibits intense SASP peaks at m/z 6695 or 6711. Note that the latter signals were determined also in some strains of B. thuringiensis and B. mycoides. Some of the B. cereus group-specific biomarkers, for example, at m/z 4334, 5171 and 5886, could be tentatively assigned to ribosomal subunit proteins. For example, a search in the UniProtKB/Swiss-Prot database revealed that the 50S ribosomal proteins L36, L34 and L33-2 of B. anthracis have a molecular mass

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17.4 Identification of Important Bacterial Pathogenes Using MALDI-TOF MS 193

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B. weihenstephanensis. B. cereus group-specific mass peaks are found at m/z 5171, 5886, and 7368. Anthrax-specific biomarkers could be determined at m/z 5413 and 6679.

Figure 17.4 Gel view of MALDI spectra from B. cereus group members, B. anthracis, B. cereus, B. thuringiensis, B. mycoides, B. pseudomycoides, and

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17.4 Identification of Important Bacterial Pathogenes Using MALDI-TOF MS

([M+H]+) of 4334.3, 5171.1 and 5886.8 Da, respectively. The assignments to small ribosomal subunit proteins are supported by BLAST searches. For example, the program NCBI BLASTP 2.2.17 revealed for the 50S ribosomal protein L34 of B. anthracis (SwissProt entry Q81JG9) 25 database entries of 100% amino acid sequence identity. All of these entries originated from strains of B. cereus group members such as B. anthracis, B. cereus, B. thuringiensis and B. weihenstephanensis. Vice versa, none of the organisms with less than 100% coverage belonged to the B. cereus group. Spore Biomarkers: A closer inspection of the mass spectral profiles of B. anthracis and other members of the B. cereus group revealed further interesting details. For example, a large number of spectra from this group exhibited prominent mass peaks at m/z 6679, 6695, 6711 and 6835. These signals are well known in the literature and have been identified as spore biomarkers [45–47]. As noted earlier, the spore markers arise from small acid soluble proteins (SASP), a group of proteins present in large amounts in the core region of Bacillus endospores. SASPs play a key role in the protection of the spore’s DNA from UV light [48, 49] and serve upon germination by their degradation as a source for amino acids [50]. Because the amino acid sequences of selected SASPs are species-specific, SASP peaks have been suggested as MS biomarkers for rapid differentiation and identification of spore preparations from B. anthracis and B. cereus [45–47, 51, 52]. In agreement with the literature, we found a B. anthracis-specific SASP spore marker at m/z 6679. This signal can be predicted from the sspB gene of B. anthracis which codes for the α/β-type SASP with a nominal molecular weight of 6810 Da As suggested by Demirev et al. and Castanha et al. [46, 53], SASPs may undergo posttranslational modification in which a methionine with a mass of 131 is cleaved giving the experimental mass of m/z 6679. Furthermore, mass spectral profiles of B. cereus strains displayed two other spore marker peaks, either at m/z 6695 or at 6711 (see Figure 17.4). According to genome sequences of B. cereus (sasP-2 gene) these mass signals have been associated with α/β-type SASPs with masses of 6826 and 6842 Da (N-terminal Met cleavage) [46, 51]. The presence of species-specific SASP peaks correlates well with a number of other mass signals. For example, a species-invariant mass peak at m/z 6835 is found in all spectra of Bacillus strains exhibiting one of the SASP peaks at m/z 6679, 6695 and 6711. This invariant signal is due to the presence of a second SASP encoded by the sasP-1 gene with a predicted m/z of 6835 (m/z 6966, Metcleavage) [46]. The experimental findings of the Bacillus study strongly suggest intense spore marker peaks in mass spectra of growing cell cultures. This fact was surprising to us but was in the following interpreted as a result of the specifically increased sensitivity of the TFA sample preparation and the MALDI-TOF MS detection technique towards the SASP marker substance. For example, its has been described earlier that acid treatment effectively extracts SASPs from bacterial endospores [50, 54]. Second, as SASPs are easily ionizable it is likely that SASPs are preferentially detected by MALDI-TOF MS. Finally, Setlow reported substantial amounts

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17 MALDI-TOF Mass Spectrometry for Rapid Identification of Highly Pathogenic Microorganisms Table 17.1 Confusion matrix showing ANN classification results of independent test data.

ANN prediction (i) B. anthracis

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of SASPs, between 8 and 20% of the total spore protein content, being present in dormant endospores [50]. Obviously, even small amounts of spores in a large surplus of vegetative cells may produce relatively intense SASP signals. Multivariate Classification Analysis by ANNs: To identify important species and groups of species within the genus Bacillus, supervised ANN classification analysis was carried out. Bar code MALDI spectra of three classes served as ANN input patterns: B. anthracis [class (i)], B. cereus group members except B. anthracis [class (ii)] and non-B. cereus group members [class (iii)]. As outlined in Section 17.2.4.2 an MLP-ANN was trained and validated with labeled mass spectra combined in a training and internal validation subset. After optimization the ANN model could be challenged by independent (external) test data. The classification results achieved with this ANN are given by Table 17.1. The data suggest that B. anthracis can be identified with 100% accuracy as no false positive or false negative classifications are observed. The corresponding accuracy of classification for class (ii) and (iii) equaled 96%. These high values demonstrate that MALDI-TOF MS of Bacillus can be successfully applied to unambiguously identify strains of B. anthracis (i), or to classify Bacilli as members (ii), or nonmembers of the B. cereus group (iii). In addition, as the database of MS spectra contains a significant number of spectra with SASP spore biomarkers, the ANN classifier can be used to identify MS fingerprints of vegetative cells, spores or mixtures of both. The latter aspect is crucial because most of the published MALDI-TOF MS work of B. anthracis is limited to spores. 17.4.2 Burkholderia mallei/pseudomallei

Members of the genus Burkholderia are known as rod-shaped gram-negative and obligately aerobic bacteria. Some of them, like B. cepacia, are important human pathogens and can cause pulmonary infections in people with cystic fibrosis (CF).

17.4 Identification of Important Bacterial Pathogenes Using MALDI-TOF MS

Two other closely related members of the genus Burkholderia, Burkholderia mallei and Burkholderia pseudomallei, are considered potential biological warfare agents. B. mallei is responsible for glanders, a disease that occurs mostly in horses and related animals. Transmission to humans takes place primarily via occupational exposure of workers who handle infected animals. B pseudomallei is the causative agent of melioidosis, a disease affecting humans and animals like goats, sheep, and horses. Both pathogens have the potential to produce fatal disease in humans and are thus listed as category B biothreat agents by the Centers for Disease Control. B. mallei and B. pseudomallei are closely related with the nonpathogenic species Burkholderia thailandensis and Burkholderia oklahomensis [55, 56]. It is furthermore established that from a strictly taxonomic point of view B. mallei can be considered a subclone of B. pseudomallei [57, 58]. Having these aspects in mind we systematically investigated strains of the genus Burkholderia using a combination of MALDI TOF MS and chemometrics. The goals of these efforts were twofold: Firstly, to assess the efficacy of differentiation of the highly pathogenic species B. pseudomallei and B. mallei from other Burkholderia species including nonpathogenic and clinically relevant Burkholderia species. The second goal was the detection and molecular identification of taxon-specific biomarkers. For this purpose, a selection of MALDI-TOF MS data of an earlier clinical study on pathogens responsible for pulmonary infections in cystic fibrosis patients [59] was complemented by mass spectra of B. mallei, B. pseudomallei and B. thailandensis. In this way, a MALDI-TOF mass spectral database from 172 Burkholderia strains originating from 17 species of this genus was compiled. Although the Burkholderia database is currently being expanded by rare strains or type strains of newly described species (B. oklahomensis [56]), the large number of strains and species already included in the analysis suggests that B. mallei and B. pseudomallei can be identified using the MALDI-TOF MS fingerprinting approach. For sample preparation and inactivation, the TFA inactivation procedure [38] was employed. Taxon-Specific Biomarkers: The analysis of the Burkholderia data base started with a visual inspection. Pseudo gel views (see Section 17.2.3.4) are popular means which provide an informative overview on the complex mass patterns and allow to assess the spectral reproducibility, the quality of calibration and to visually identify biomarker candidates. The gel view produced from mass spectra of strains and species of the genus Burkholderia is given by Figure 17.5. It shows our current (autumn 2010) Burkholderia data base consisting of 452 spectra from 172 strains of 16 Burkholderia species in the mass range of m/z 4200–9800. Mass spectra of Burkholderia cepacia complex (Bcc) organisms, a group which currently consists of 17 validly described species [60, 61] are given in the upper and central parts of Figure 17.5 at lines 1–397 and 406–407 (Burkholderia lata), respectively (altogether 399 spectra). Furthermore, spectra of Burkholderia gladioli, a species that neither belongs to Bcc, nor to the B. mallei/pseudomallei/thailandensis complex are shown at lines 398–405 (2 strains, 8 spectra). The lower part of the gel view with the lines 408–452 (45 spectra) has been produced from mass spectra of the highly pathogenic species of B. mallei (19 spectra, 9 strains) and B. pseudomallei (20 spectra, 12 strains) and from spectra of nonpathogenic B. thailandensis

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Figure 17.5 Pseudo gel view of 452 mass spectra from 171 strains of the genus Burkholderia in the mass range of m/z 4200–9800. The illustration shows

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17 MALDI-TOF Mass Spectrometry for Rapid Identification of Highly Pathogenic Microorganisms

17.4 Identification of Important Bacterial Pathogenes Using MALDI-TOF MS

isolates (6 spectra, 2 strains). The analysis of the gel view of Figure 17.5 demonstrates the high degree of spectral reproducibility in general and suggests the presence of genus and species-specific biomarkers. For example, genus biomarkers of Burkholderia can be identified at m/z 4411, 5196 and 7171 which could be assigned to the 50S L36, 50S L34, and 50S L35 ribosomal subunit proteins, respectively. Our experimental findings confirm the results of a clinical study conducted at an earlier time [59] which is important because no highly pathogenic strains of Burkholderia were characterized in this clinical study. The analysis of the gel view of Figure 17.5 suggests furthermore the presence of Bcc-specific mass signals. For example, peaks at m/z 4124 (doubly charged 30S L21 ribosomal subunit protein), 4804 (doubly charged [M+2H]2+ ion of HU-alpha), 7313 (singly charged [M+H]+ 50S L29 ribosomal subunit), 8249 (30S L21 ribosomal subunit) and 9606 (HU-alpha) were typically obtained in mass spectra of the Bcc species, but not in those of the B. mallei/pseudomallei/thailandensis complex. In these species, corresponding biomarker peaks were consistently found at m/z 4117, 4812, 7311, 8233 and 9622. Peak assignments were carried using a strategy that combined a plausible interpretation of experimental results, literature data and systematic sequence comparisons with protein, or genomic sequence databases by BLAST searches (see Section 17.2.5). Unsupervised Hierarchical Cluster Analysis: Cluster analysis of the Burkholderia database was carried out on the basis of bar code spectra obtained from peak tables with 30 entries per mass spectrum using the spectral information contained in the region of m/z 2000–12 000. D values were employed as measures for interspectral distances and Ward’s algorithm was applied as the clustering method. The dendrogram demonstrates the presence of three main clusters (data not shown): cluster I contains mass spectra of isolates from B. contaminans and clinical isolates identified as members of the former taxon K, that is, of B. contaminans or B. lata (see [59] for details). The second cluster, cluster II is formed by spectra of the following Bcc species: B. pyrrocina, B. stabilis, B. cepacia, B. cenocepacia, B. vietnamensis, B. anthina, B. lata, B. ambifaria, B. multivorans and B. dolosa. With a few exceptions, mass spectra of these species constitute well separated speciesspecific subclusters. Furthemore, we found mass spectra of B. gladioli, which is not a member of the Bcc, also localized in cluster II. Spectra of the highly pathogenic species B. mallei and B. pseudomallei and the phylogentically similar species B. thailandensis constitute cluster III (see Figure 17.6). It was interesting to note that spectra of these species formed species subclusters within cluster III indicating distinct mass spectral patterns of isolates of the highly pathogenic species and of B. thailandensis. 17.4.3 Yersinia pestis

Yersinia are gram-negative, rod-shaped facultative anaerobes and some of them, Y. enterocolitica, Y. pseudotuberculosis and Y. pestis, are of clinical relevance. Y. pestis,

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17 MALDI-TOF Mass Spectrometry for Rapid Identification of Highly Pathogenic Microorganisms B. thailandensis E 125 B. thailandensis DSM 13276 B. thailandensis E 125 B. thailandensis DSM 133276 B. thailandensis DSM 133276 B. thailandensis E 125 B. pseudomallei 7431 B. pseudomallei 10274 B. pseudomallei 3463 B. pseudomallei CCUG 15648 B. pseudomallei CCUG 15648 B. pseudomallei CCUG 15648 B. pseudomallei CCUG 15649 B. pseudomallei CCUG 13790 B. pseudomallei CCUG 15651 B. pseudomallei CCUG 15651 B. pseudomallei CCUG 15651 B. pseudomallei CCUG 15649 B. pseudomallei CCUG 15649 B. pseudomallei CCUG 13790 B. pseudomallei CCUG 13790 B. mallei Zagreb B. mallei Zagreb B. mallei Bogur B. mallei ATCC 23344 B. mallei ATCC 23344/Uni Calgary; CDN B. mallei ATCC 23344/BgVV B. mallei ATCC 23344/Uni Calgary; CDN B. mallei Bogur B. mallei ATCC 23344/BgVV B. mallei ATCC 23344 B. mallei Bogur B. mallei ATCC 23344 B. pseudomallei 8708 B. pseudomallei 4845 B. pseudomallei ATCC 23343 B. pseudomallei 3462 B. mallei Zagreb B. mallei 3708 B. mallei 10230 B. pseudomallei 11642 B. mallei 10247 B. mallei 10245 B. mallei 10248 B. mallei 10229

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analysis of MALDI-TOF mass spectra from Burkholderia strains. The dendrogram shows subcluster III with 45 spectra obtained from strains of B. mallei, B. pseudomallei, and

0 B. thailandensis. Spectra from 13 other Burkholderia species were grouped in clusters I and II, respectively (dendrogram not shown, also see text for details).

the causative agent of plague is known to be endemic in some parts of the world. It infects primarily rodents and is transmitted to humans by flea vectors. The highly infectious disease is now rare, but in three former waves of pandemic plague killed hundreds of million people. The three major clinical forms of an infection with Y. pestis are known as the bubonic, septicemic and pneumonic form. The last form is extraordinarily contagious and – if untreated – associated with fatality rates close to 100% [62]. Intentional release of Y. pestis via aerosols would preferentially

17.4 Identification of Important Bacterial Pathogenes Using MALDI-TOF MS

cause a primary pneumonic plague outbreak in the exposed population. Plague is thus considered to be one of the most serious bioterrorism threats [62]. Our efforts within the scope of the Yersinia study [33] were focused on the detection of taxon-specific biomarkers for the genus Yersinia and the clinically important species of Yersinia, with particular emphasis on Y. pestis-specific markers. Furthermore, to test the MALDI-TOF MS workflow for BSL-3 agents, the study involved also tests by multivariate classification techniques such as UHCA and modular ANNs. The acquired MALDI-TOF mass spectral database comprised records from altogether 146 Yersinia strains originating from all 13 currently known species of this genus. For systematic purposes, the Yersinia database was complemented by spectra from 35 strains of other Enterobacteriaceae family members. Again. microbial samples were prepared under standardized conditions according to the TFA inactivation protocol [38] (see also Chapter 3). Our data suggest some level of conformity between the known phylogeny of Yersinia and the results of MALDI-TOF MS. This could be demonstrated by UHCA using bar coded mass spectra in the range of m/z 3500–10 500 (see Figure 17.7). The dendrogram given by Figure 17.7 shows that spectra of the Yersinia database fall into three main clusters: the first cluster (I) contains two well separated subclusters which contain spectra of Y. enterocolitica (Ia) and Y. enterocolitica-like species (Ib) (for a definition of these species, see [63]). Cluster (II) is formed by spectra of Enterobacteriaceae family members other than Yersinia, that is, strains of the genera Citrobacter, Edwardsiella, Enterobacter, Escherichia, Hafnia, Klebsiella, Proteus, Salmonella and Serratia. The third cluster (III) is formed by two subclusters. One of them, subcluster IIIa is constituted by spectra of Y. pseudotuberculosis, Y. pestis and Y. similis. The second subcluster (IIIb) contains only spectra of Y. ruckeri. The latter aspect is interesting, since the taxonomic status of Y. ruckerii is still a subject of controversial discussions; according to some authors Y. ruckerii is perhaps not even a Yersinia species [63, 64]. The finding of a close proteomic relationship between Y. enterocolitica of subcluster Ia and Y. enterocolitica-like species (subcluster Ib) is supported by molecular genetic analyses, such as multilocus sequence typing (MLST) [64]. A MLST tree derived from concatenated sequences of four housekeeping gene loci (glnA, gyrB, recA, Y-HSp60) demonstrated a close genetic relatedness of Y. enterocolitica and Y. enterocolitica-like species. In the same study, strains of these species were found to be distinct from Y. pseudotuberculosis which clustered tightly with Y. pestis. The latter results are supported by other studies and correlate also with our MS data. For example, Sprague and colleagues found Y. pestis, Y. pseudotuberculosis and Y. similis tightly clustered in phylogenetic trees obtained by 16S rRNA gene sequences analysis [65]. The same conclusion can be drawn from the dendrogram of Figure 17.6 (see cluster IIIa) which illustrates an unusually high degree of similarity of the MALDI TOF mass spectra of Y. pestis, Y. pseudotuberculosis and Y. similis. While the phylogenetic relationship of Y. similis represents an interesting, but only minor aspect of our research, the close relatedness of Y. pestis and Y. pseudotuberculosis is in view of the clinical consequences of paramount importance. In

201

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17 MALDI-TOF Mass Spectrometry for Rapid Identification of Highly Pathogenic Microorganisms

Mass range: m/z 3,500 - 10,500 distance matrix: logical cluster method: Ward´s algorithm 1000 ppm, bar code spectra with 30 peaks

Ia Y. enterocolitica

I

Y. enterocolitica-like species: (except Y. ruckeri)

Y. aldovae Y. aleksiciae Y. bercovieri Y. frederiksenii Y. intermedia Y. kristensenii Y. mollaretii Y. rohdei

Ib

Enterobacteriaceae (excl. Yersinia spp.):

II

*

Citrobacter spp. Edwardsiella spp. Enterobacter spp. Escherichia spp. Hafnia spp. Klebsiella spp. Proteus spp. Salmonella spp. Serratia spp. Shigella spp.

&

IIIa

Y. pseudotuberculosis Y. pestis Y. similis

III

IIIb

Y. ruckeri

Heterogeneity 800

700

600

500

400

300

200

100

0

17.4 Identification of Important Bacterial Pathogenes Using MALDI-TOF MS Figure 17.7 Hierarchical cluster analysis of mass spectra from 182 strains of the Enterobacteriaceae family (11 genera, 38 species). The dendrogram illustrates three main clusters: cluster I is formed by spectra of Y. enterocolitica and Y. enterocolitica-like species. Enterobacteriaceae family members

other than Yersinia form cluster II while cluster III is composed of MS data from strains of Y. pseudotuberculosis, Y. pestis, Y. similis, and Y. ruckerii.

fact, we found that the latter two species were indistinguishable on the basis of plain UHCA which is obviously a consequence of a relatively small interspecies variance compared with the intra-species variance and repeatability or reproducibility errors. Obviously, existing systematic differences such as the Y. pestis-specific signal at m/z 3065 (see below) or the Y. pseudotuberculosis-specific signal at m/z 6474 are too small to be of much consequence when using unsupervised pattern analysis methods like UHCA. ANN Classification Analysis: Classification analysis of mass spectra from Yersinia and related genera was carried out using hierarchically (modular) organized ANN classifiers (see Section 17.2.4). The modular ANN employed in this study strictly aimed at identification of Y. pestis and thus consisted of three distinct ANNs: a so-called top level ANN which allowed differentiation between mass spectra of Yersinia strains and (i) strains of other Enterobacteriaceae. If a toplevel output neuron for the class Yersinia was activated the spectra were analyzed by a second ANN, called sublevel 1. This ANN was specifically optimized for differentiation into three further classes, namely (ii) Y. enterocolitica-like, (iii) Y. enterocolitica and Y. pestis/pseudotuberculosis. Discrimination of the strains of the latter category into the classes (iv) Y. pseudotuberculosis and (v) Y. pestis was then achieved by a third ANN (sublevel 2). A schematic overview on the hierarchy of the modular neural network along with the predefined classes is given by Figure 17.8. The accuracy of ANN top level classification was 100% in all three spectral subsets, that is, the training, internal and external validation subset. Perfect classification was obtained also by the sublevel 1 ANN suggesting that strains of Y. enterocolitica, Y. enterocolitica-like species, and Y. pestis/pseudotuberculosis could be unambiguously differentiated. Furthermore, our efforts to discriminate between strains of Y. pseudotuberculosis and Y. pestis revealed in all data sets for Y. pseudotuberculosis an accuracy of 100%. Upon internal validation of Y. pestis, 12 of 13 spectra were correctly identified whereas external (independent) validation was accurate in nine of nine cases [33]. Identification of Taxon-Specific Biomarkers: An overview of taxon-specific biomarkers identified in the course of the Yersinia study along with biomarker assignments is listed in Table 17.2. Biomarker identification and assignment was carried out using the strategies outlined in Section 17.2.5: statistical methods such as systematic t-tests were employed for biomarker discovery while the molecular assignment

203

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17 MALDI-TOF Mass Spectrometry for Rapid Identification of Highly Pathogenic Microorganisms

ANN toplevel (i)

(ii-v)

Enterobacteriaceae (other than Yersinia)

Yersinia

ANN, sublevel 1 (ii)

(iii)

(iv,v)

Y. enterocolitica-like Y. enterocolitica species

Y. pseudotub./pestis

ANN, sublevel 2

(iv) Y. pseudotuberculosis Figure 17.8 Identification of strains from

Y. pestis using MALDI-TOF MS and modular artificial neural networks (ANN). Classification is achieved by a hierarchy of three ANNs: a toplevel ANN which differentiates between mass spectra of Yersinia strains and strains of

(v) Y. pestis

other genera of the Enterobacteriaceae family. Two sublevel ANNs allow classification of strains from the genus Yersinia with the classes Y. enterocolitica-like species, and the species Y. enterocolitica, Y. pseudotuberculosis and Y. pestis.

was based on a plausible interpretation of experimental data, protein sequence analyses (BLAST) and literature data. Examples of statistical tests for biomarker identification (t-tests) are given by Figure 17.9 (note the inverse logarithmic scaling). Figure 17.9a shows discriminative mass biomarkers that allow differentiation between MALDI-TOF mass spectra of Yersinia (class a) and Enterobacteriaceae other than Yersinia (class b). The three most discriminating biomarkers at m/z 6241, 4350 and 6046 were all found in mass spectra of strains from Yersinia. Using the same strategy, species-specific mass peaks for Y. enterocolitica (vs other Yersinia species) were identified at m/z 9608, 3632 and 9238 (see Figure 17.9b). Furthermore, univariate t-tests turned out to be useful to identify marker peaks for Y. pestis at m/z 3065 and Y. pseudotuberculosis at m/z 6474, respectively (see Figure 17.9c). Family-Specific Peaks: MALDI-TOF mass spectra of strains from the Enterobacteriaceae family suggest the existence of family-specific biomarkers at m/z 4185 and 8370. These peaks could be reproducibly detected in the mass spectra of almost all bacterial strains characterized in this study [33]. Using protein database, chemometrics and published assignments [10, 13, 35], both mass peaks were related to the 30S ribosomal protein S21, either as the single [M+H]+ or double charged [M+2H]2+ ion.

17.4 Identification of Important Bacterial Pathogenes Using MALDI-TOF MS Table 17.2

Selected biomarkers found at the family, genus, and species level.

Observed average mass (m/z)

Tentative protein identity

Predicted mass (m/z)

Enterobacteriaceae (family level) 4185 [M+2H]2+; 30S ribosomal protein S21 8370 [M+H]+; 30S ribosomal protein S21

4185.3a) 8369.6a)

Yersinia (genus level) 4350 [M+H]+; 50S ribosomal protein L36 1 6046 [M+H]+; 50S ribosomal protein L32 6241 [M+H]+, 50S ribosomal protein L33

4350.3 6045.8a) 6241.4a),b)

Y. enterocolitica (species level) 3632 [M+2H]2+; 50S ribosomal protein L29 4805 [M+2H]2+; DNA-binding protein HU-alpha 7262 [M+H]+; 50S ribosomal protein L29 7318 [M+H]+; putative Yop proteins translocation protein E 9238 [M+H]+; DNA-binding protein HU-beta 9608 [M+H]+; DNA-binding protein HU-alpha

3631.7 4804.5 7262.3 7318.4a) 9238.4 9607.9

Y. pseudotuberculosis and Y. pestis (complex) 6637 [M+2H]2+; ribosomal subunit interface protein 7274 [M+H]+; 50S ribosomal protein L29 9268 [M+H]+; DNA-binding protein HU-beta 9659 [M+H]+; 30S ribosomal protein S20

6636.9a) 7274.4 9268.5 9659.2a)

Y. pseudotuberculosis (species level) 6474 Unassignedc)

–

Y. pestis (species level) 3065 [M+H]+; plasminogen activator Pla (fragment) NSGDSVSIGGDAAGISNKNYTVTAGLQYRF

3064.3

a) Listed masses without N-terminal Met. b) Methylated. c) Not present in Y. pestis, but found also in many strains of Y. enterocolitica. The protein identity was determined using a protein database search engine (www.uniprot.org/) which allows to access the UniProtKB/Swiss-Prot database.

Genus-Specific Peaks: All of the analyzed Yersinia species displayed a genusspecific peak at m/z 6241. This specifically identifying mass signal could be assigned to the 50S L33 ribosomal subunit protein which is confirmed by an assignment recently published (Y. rohdei) [66]. The 50S L33 protein is posttranslationally modified by N-terminal Met cleavage and methylation. Both modifications of the 50S L33 subunit were found also in the 50S L33 subunit of other species, for example, E. coli or S. enterica [10, 35]. The second genus-specific signal at m/z 6046 was tentatively assigned to the 50S ribosomal protein L32. Again, the N-terminal methionine residue is cleaved. Sequence comparisons (BLAST) of the 50S L32 subunit of Y. pestis gave perfect

205

17 MALDI-TOF Mass Spectrometry for Rapid Identification of Highly Pathogenic Microorganisms 200

1

a)

class a: Yersinia spp. class b: Enterobact. (excl. Yers.)

175 150

P

a

125

2

100

1. - m/z 6241 a 2. - m/z 4350 3. - m/z 6046 a 4. - m/z 4365 b 5. - m/z 5382 b

3 4

75

5

50 25 0 3000

4000

5000

6000

7000

1

b)

8000

m/z

10000

a

1. - m/z 4805 2. - m/z 9608 a 3. - m/z 3632 a 4. - m/z 9238 a 5. - m/z 7188 b

P

200 150

9000

class a: Y. enterocolitica class b: Y. spp. (excl. Y. enterocol.)

250

100

3

5

2 4

50 0 3000

4000

5000

6000

c)

7000

1

40

8000

2 30

9000

m/z

10000

class a: Y. pestis class b: Y. pseudotuberculosis 1. - m/z 6474 b 2. - m/z 3065 a 3. - m/z 7445 a

P

206

3 20

10

0 3000

4000

5000

6000

Figure 17.9 Identification of taxon-specific

biomarker proteins by systematic statistical analyses (independent t-tests). The univariate t-tests provided P values which allowed to rate biomarkers. Small P values cast doubt on the null hypothesis (equal class means). (a) Identification of biomarkers suitable to differentiate between mass spectra from strains of Yersinia (class a) and spectra from

7000

8000

9000

m/z

10000

other Enterobacteriaceae family members (class b). (b) Identification of Y. enterocolitica -specific mass signals (class a) and peaks typical for spectra of Yersinia species other than Y. enterocolitica (class b). (c) Mass signals that allow differentiation between Y. pestis (class a) and Y. pseudotuberculosis (class b).

17.5 Concluding Remarks

sequence identity to strains of the clinically relevant Yersinia species and strains of Y. ruckeri, Y. intermedia and Y. fredericksenii. Database entries for other Yersinia species could not be found. For Enterobacteriaceae other than Yersinia the BLAST routine revealed modified amino acid sequences for the 50S L32 protein. Species-Specific Peaks of Y. pestis: The 16S rRNA of Y. pestis and its closest phylogenetic relative Y. pseudotuberculosis are identical [67]. The unusually high degree of genetic relatedness between strains of these species was subject of intense research and some authors even stated that Y. pestis is a clone which only recently (1500–20 000 years ago) has evolved from Y. pseudotuberculosis [68]. In consequence both species share an exceptionally high number of identical proteins. For example, a systematic comparison of all ribosomal subunit proteins present in the UniProtKB/SwissProt database demonstrated 100% sequence identity. In the light of these considerations it was somewhat unexpected to detect a unique Y. pestis-specific biomarker (m/z 3065) allowing unambiguous species identification. As the protein databases did not contain entries for a peptide of this mass, the analysis of tandem MALDI MS data helped to reveal the molecular identity of the ion at m/z 3065 as a fragment of one of the virulence factors of Y. pestis, the plasminogen activation factor (Pla). Pla is known as a surface protease which plays a key role in primary pneumonic plague infections by promoting the invasion of Y. pestis from subcutaneous sites of inoculation into the lymphatic system [69]. As many virulence factors, Pla is plasmid-encoded. This aspect in mind we carefully re-examined our database for a peak at 3065 m/z in spectra of strains that lack the Pla-encoding plasmid [33]. Interestingly, no such peaks could be detected in spectra of such strains, a fact which additionally supported our assignment.

17.5 Concluding Remarks

Identification of microorganisms specifically of vegetative cells and spores by matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) mass spectrometry is an emerging new technology. The technique provides specific biomarker profiles which can be employed for bacterial identification at the genus, species, and sometimes at the subspecies level holding the potential to serve as a rapid identification technique in clinical or food microbiology and also for sensitive detection of biosafety level (BSL)-3 microorganisms. With our studies we introduced a new MALDI-TOF MS compatible methodology for inactivation of microorganisms which is based on sample treatment by trifluoroacetic acid (TFA). The TFA inactivation protocol is simple and rapid and assures reliable inactivation of vegetative cells and spores of BSL-3 level microorganisms. The TFA method can be applied to reproducibly collect mass spectra from a large variety of microorganisms. We presented selected data from

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17 MALDI-TOF Mass Spectrometry for Rapid Identification of Highly Pathogenic Microorganisms

MALDI-TOF MS studies of the genera Bacillus, Burkholderia and Yersinia which demonstrate the great potentials of MALDI-TOF MS as a rapid, reliable and objective identification technique for highly pathogenic microorganisms not only for scientific research purposes but also for routine analyses.

Acknowledgments

The authors wish to thank Wolfgang Beyer (Universität Hohenheim, Germany) for providing Bacillus strains, Michal Drevinek (National Institute for NBC Protection, Milin, Czech Republic) for TFA extracts of strains of the genera Yersinia and Burkholderia, and Alejandro Miñán and Alejandra Bosch (CINDEFI, La Plata, Argentina) for clinical strains of Burkholderia and related genera. The authors are grateful to Herbert Nattermann, Daniela Jacob and Roland Grunow (ZBS2, RKI Berlin, Germany), Ralf Dieckmann (BfR, Berlin, Germany), Thomas Maier, Markus Kostrzewa (Bruker Daltonics, Leipzig, Germany) and Heiko Russmann (WIS, Munster, Germany) for fruitful discussions and support. Furthermore, the excellent technical assistance of Maren Stämmler, Silke Becker and Petra Lochau (RKI, Berlin, Germany) is acknowledged.

References 1 Naumann, D., Helm, D., and

2

3

4

5

Labischinski, H. (1991) Microbiological characterizations by FT-IR spectroscopy. Nature, 351 (6321), 81–82. Puppels, G.J., de Mul, F.F.M., Otto, C., Greve, J., Robert-Nicoud, M., Arndt-Jovin, D.J., and Jovin, T.M. (1990) Studying single living cells and chromosomes by confocal Raman microspectroscopy. Nature, 347, 301–303. Williams, A.C., and Edwards, H.G.M. (1994) Fourier transform Raman spectroscopy of bacterial cell walls. J. Raman Spectrosc., 25, 673–677. Goodacre, R. (1994) Characterisation and quantification of microbial systems using pyrolysis mass spectrometry: introducing neural networks to analytical pyrolysis. Microbiol. Eur., 2 (2), 16–22. Claydon, M.A., Davey, S.N., EdwardsJones, V., and Gordon, D.B. (1996) The rapid identification of intact microorganisms using mass spectrometry. Nat. Biotechnol., 14 (11), 1584–1586.

6 Fenselau, C. (1994) Mass Spectrometry for

the Characterization of Microorganisms; ACS Symposium Series, vol. 541, American Chemical Society, Washington, DC. 7 Holland, R.D., Wilkes, J.G., Rafii, F., Sutherland, J.B., Persons, C.C., Voorhees, K.J., and Lay, J.O., Jr. (1996) Rapid identification of intact whole bacteria based on spectral patterns using matrix-assisted laser desorption/ ionization with time-of-flight mass spectrometry. Rapid Commun. Mass. Spectrom., 10 (10), 1227–1232. 8 Krishnamurthy, T., and Ross, P.L. (1996) Rapid identification of bacteria by direct matrix-assisted laser desorption/ ionization mass spectrometric analysis of whole cells. Rapid Commun. Mass. Spectrom., 10 (15), 1992–1996. 9 Maier, T., Klepel, S., Renner, U., and Kostrzewa, M. (2006) Fast and reliable MALDI-TOF MS–based microorganism identification. Nat. Methods, 3 (application note).

References 10 Dieckmann, R., Helmuth, R., Erhard, M.,

11

12

13

14

15

16

17

18

and Malorny, B. (2008) Rapid classification and identification of salmonellae at the species and subspecies levels by whole-cell matrix-assisted laser desorption ionization-time of flight mass spectrometry. Appl. Environ. Microbiol., 74 (24), 7767–7778. Seng, P., Drancourt, M., Gouriet, F., La Scola, B., Fournier, P.E., Rolain, J.M., and Raoult, D. (2009) Ongoing revolution in bacteriology: routine identification of bacteria by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Clin. Infect. Dis., 49 (4), 543–551. Lay, J.O., Jr. (2001) MALDI-TOF mass spectrometry of bacteria. Mass Spectrom. Rev., 20 (4), 172–194. Ryzhov, V. (2001) Fenselau, characterization of the protein subset desorbed by MALDI from whole bacterial cells. C. Anal. Chem., 73 (4), 746–750. Pineda, F.J., Antoine, M.D., Demirev, P.A., Feldman, A.B., Jackman, J., Longenecker, M., and Lin, J.S. (2003) Microorganisms identification by matrix-assisted laser/ desorption ionization mass spectrometry and model derived ribosomal protein biomarkers. Anal. Chem., 75 (15), 3817–3822. Arnold, R.J., and Reilly, J.P. (1998) Fingerprint matching of E. coli strains with matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry of whole cells using a modified correlation approach. Rapid Commun. Mass. Spectrom., 12 (10), 630–636. Demirev, P.A., Ho, Y.P., Ryzhov, V., and Fenselau, C. (1999) Microorganism identification by mass spectrometry and protein database searches. Anal. Chem., 71 (14), 2732–2738. Warscheid, B., and Fenselau, C. (2003) Characterization of Bacillus spore species and their mixtures using postsource decay with a curved-field reflectron. Anal. Chem., 75, 5618–5627. Karas, M., and Hillenkamp, F. (1988) Laser desorption ionization of proteins with molecular masses exceeding 10 000 daltons. Anal. Chem., 60 (20), 2299–2301.

19 Freiwald, A., and Sauer, S. (2009)

20

21

22

23

24

25

26

27

Phylogenetic classification and identification of bacteria by mass spectrometry. Nat. Protoc., 4 (5), 732–742. Valentine, N., Wunschel, S., Wunschel, D., Petersen, C., and Wahl, K. (2005) Effect of culture conditions on microorganism identification by matrix-assisted laser desorption ionization mass spectrometry. Appl. Environ. Microbiol., 71 (1), 58–64. Sauer, S., Freiwald, A., Maier, T., Kube, M., Reinhardt, R., Kostrzewa, M., and Geider, K. (2008) Classification and identification of bacteria by mass spectrometry and computational analysis. PLoS ONE, J3 (7), e2843. MALDI BioTyper 2.0 User Manual. (2008) Revision 2, Bruker Daltonic GmbH. Wolski, W.E., Lalowski, M., Jungblut, P., and Reinert, K. (2005) Calibration of mass spectrometric peptide mass fingerprint data without specific external or internal calibrants. BMC Bioinform., 6, 203. Chamrad, D.C., Koerting, G., Gobom, J., Thiele, H., Klose, J., Meyer, H.E., and Blueggel, M. (2003) Interpretation of mass spectrometry data for highthroughput proteomics. Anal. Bioanal. Chem., 376 (7), 1014–1022. Williamson, Y.M., Moura, H., Woolfitt, A.R., Pirkle, J.L., Barr, J.R., Carvalho Mda, G., Ades, E.P., Carlone, G.M., and Sampson, J.S. (2008) Differentiation of Streptococcus pneumoniae conjunctivitis outbreak isolates by matrix-assisted laser desorption ionization-time of flight mass spectrometry. Appl. Environ. Microbiol., 74 (19), 5891–5897. Marques de Sa, J.P. (2001) Pattern Recognition: Concepts, Methods and Applications, Springer-Verlag, Berlin, Heidelberg, New York. Anderson, D., Brookhouse, I., and Ralphson, K. (1999) Numerical identification and classification of microorganisms using MALDI-TOF. Proceedings of the 47th Annual Conference of the American Society for Mass Spectrometry, pp. 411–412.

209

210

17 MALDI-TOF Mass Spectrometry for Rapid Identification of Highly Pathogenic Microorganisms 28 Conway, G.C., Smole, S.C., Sarracino,

29

30

31

32

33

34

35

D.A., Arbeit, R.D., and Leopold, P.E. (2001) Phyloproteomics: species identification of Enterobacteriaceae using matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry. J. Mol. Microbiol. Biotechnol., 3 (1), 103–112. Stackebrandt, E., Päuker, O., and Erhard, M. (2005) Grouping myxococci (Corallococcus) strains by matrix-assisted laser desorption ionization time-of-flight (MALDI TOF) mass spectrometry: comparison with gene sequence phylogenies. Curr. Microbiol., 50 (2), 71–77. Gasser, A., and Kamel, M. (1998) Modular network classifiers: a comparative study. J. Intell. Robot. Syst., 21, 117–129. Udelhoven, T., Naumann, D., and Schmitt, J. (2000) Development of a hierarchical classification system with artificial neural networks and FT-IR spectra for the identification of bacteria. Appl. Spectrosc., 54 (10), 1471–1479. Schmitt, J., and Udelhoven, T. (2000) Use of artificial neural networks in biomedical analysis, in Infrared and Raman Spectroscopy of Biological Materials, (eds H.U. Gremlich and B. Yan), Marcel Dekker, New York, pp. 379–419. Lasch, P., Drevinek, M., Nattermann, H., Stämmler, M., Dieckmann, R., Schwecke, T., and Naumann, D. (2010) Characterization of Yersinia using MALDI-TOF mass spectrometry and chemometrics. Anal. Chem., 82 (20), 8464–8475. Demirev, P.A., Lin, Y.S., Pineda, F.J., and Fenselau, C. (2001) Bioinformatics and mass spectrometry for microorganism identification: proteome-wide posttranslational modifications and database search algorithms for characterization of intact H. pylori. Anal. Chem., 73, 4566–4573. Arnold, R.J., and Reilly, J.P. (1999) Observation of Escherichia coli ribosomal proteins and their posttranslational modifications by mass spectrometry. Anal. Biochem., 269 (1), 105–112.

36 Mukhopadhyay, R. (2009) MS makes

37

38

39

40

41

42

43

44

45

46

identification of clinical pathogens a cinch. Anal. Chem., 81 (23), 9537. Setlow, P. (2006) Spores of Bacillus subtilis: their resistance to and killing by radiation, heat and chemicals. J. Appl. Microbiol., 101 (3), 514–525. Lasch, P., Nattermann, H., Erhard, M., Stämmler, M., Grunow, N., Bannert, R., Appel, B., and Naumann, D. (2008) MALDI-TOF mass spectrometry compatible inactivation method for highly pathogenic microbial cells and spores. Anal. Chem., 80 (6), 2026–2034. Kennedy, H. (2001) (Oct 31: 2001)Daschle Letter Bombshell Billions of Anthrax Spores. New York Daily News, p. 5. Bartlett, J.G., Inglesby, T.V., Jr., and Borio, L. (2002) Management of anthrax. Clin. Infect. Dis., 35, 851–858. Gelderblom, H.R., Bannert, N., and Pauli, G. (2007) Arguments pro disinfection in diagnostic electron microscopy: a response to Madeley and Biel. J. Infect., 54 (3), 307–308. Abramova, F.A., Grinberg, L.M., Yampolskaya, O.V., and Walker, D.H. (1993) Pathology of inhalational anthrax in 42 cases from the Sverdlovsk outbreak of 1979. Proc. Natl. Acad. Sci. U.S.A., 90 (6), 2291–2294. Edwards, K.A., Clancy, H.A., and Baeumner, A.J. (2006) Bacillus anthracis: toxicology, epidemiology and current rapid-detection methods. Anal. Bioanal. Chem., 384 (1), 73–84. Lasch, P., Beyer, W., Nattermann, H., Stämmler, M., Siegbrecht, E., and Naumann, D. (2009) Rapid identification of Bacillus anthracis using MALDI-TOF mass spectrometry and artificial neural networks. Appl. Environ. Microbiol., 75 (22), 7229–7242. Hathout, Y., Demirev, P.A., Ho, Y.P., Bundy, J.L., Ryzhov, V., Sapp, L., Stutler, J., Jackman, J., and Fenselau, C. (1999) Identification of Bacillus spores by matrix-assisted laser desorption ionization-mass spectrometry. Appl. Environ. Microbiol., 65 (10), 4313–4319. Castanha, E.R., Fox, A., and Fox, K.F. (2006) Rapid discrimination of Bacillus anthracis from other members of the B. cereus group by mass and sequence of

References

47

48

49

50

51

52

53

54

55

56

“intact” small acid soluble proteins (SASPs) using mass spectrometry. J. Microbiol. Methods, 67 (2), 230–240. Elhanany, E., Barak, R., Fisher, M., Kobiler, D., and Altboum, Z. (2001) Detection of specific Bacillus anthracis spore biomarkers by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Rapid Commun. Mass Spectrom., 15 (22), 2110–2116. Setlow, P. (2001) Resistance of spores of Bacillus species to ultraviolet light. Environ. Mol. Mutagen., 38 (2–3), 97–104. Setlow, P. (2007) I will survive: DNA protection in bacterial spores. Trends Microbiol., 15 (4), 172–180. Setlow, P. (1988) Small, acid-soluble spore proteins of Bacillus species: structure, synthesis, genetics, function, and degradation. Annu. Rev. Microbiol., 42, 319–338. Castanha, E.R., Vestal, M., Hattan, S., Fox, A., Fox, K.F., and Dickinson, D. (2007) Bacillus cereus strains fall into two clusters (one closely and one more distantly related) to Bacillus anthracis according to amino acid substitutions in small acid-soluble proteins as determined by tandem mass spectrometry. Mol. Cell Probes., 21 (3), 190–201. Hathout, Y., Setlow, B., CabreraMartinez, R.M., Fenselau, C., and Setlow, P. (2003) Small, acid-soluble proteins as biomarkers in mass spectrometry analysis of Bacillus spores. Appl. Environ. Microbiol., 69 (2), 1100–1107. Demirev, P.A., Ramirez, J., and Fenselau, C. (2001) Tandem mass spectrometry of intact proteins for characterization of biomarkers from Bacillus cereus T spores. Anal. Chem., 73 (23), 5725–5731. Whiteaker, J.R., Warscheid, B., Pribil, P., Hathout, Y., and Fenselau, C. (2004) Complete sequences of small acid-soluble proteins from Bacillus globigii. J. Mass Spectrom., 39 (10), 1113–1121. Brett, P.J., DeShazer, D., and Woods, D.E. (1998) Burkholderia thailandensis sp. nov., a Burkholderia pseudomalleilike species. Int. J. Syst. Bacteriol., 48 (1), 317–320. Glass, M.B., Steigerwalt, A.G., Jordan, J.G., Wilkins, P.P., and Gee, J.E. (2006) Burkholderia oklahomensis sp. nov., a

57

58

59

60

61

62

Burkholderia pseudomallei-like species formerly known as the Oklahoma strain of Pseudomonas pseudomallei. Int. J. Syst. Evol. Microbiol., 56 (9), 2171–2176. Godoy, D., Randle, G., Simpson, A.J., Aanensen, D.M., Pitt, T.L., Kinoshita, R., and Spratt, B.G. (2003) Multilocus sequence typing and evolutionary relationships among the causative agents of melioidosis and glanders, Burkholderia pseudomallei and Burkholderia mallei. J Clin. Microbiol., 41 (5), 2068–2079. Wattiau, P., Van Hessche, M., Neubauer, H., Zachariah, R., Wernery, U., and Imberechts, H. (2007) Identification of Burkholderia pseudomallei and related bacteria by multiple-locus sequence typing-derived PCR and real-time PCR. J. Clin. Microbiol., 45 (3), 1045–1048. Miñán, A., Bosch, A., Lasch, P., Stämmler, M., Serra, D.O., Degrossi, J., Gatti, B., Vay, C., D’aquino, M., Yantorno, O., and Naumann, P. (2009) Rapid identification of Burkholderia cepacia Complex species including strains of the novel Taxon K, recovered from cystic fibrosis patients by intact cell mass spectrometry (ICMS). Analyst, 134 (6), 1138–1148. Vanlaere, E., Lipuma, J.J., Baldwin, A., Henry, D., De Brandt, E., Mahenthiralingam, E., Speert, D., Dowson, C., and Vandamme, P. (2008) Burkholderia latens sp. nov., Burkholderia diffusa sp. nov., Burkholderia arboris sp. nov., Burkholderia seminalis sp. nov. and Burkholderia metallica sp. nov., novel species within the Burkholderia cepacia complex. Int. J. Syst. Evol. Microbiol., 58 (7), 1580–1590. Vanlaere, E., Baldwin, A., Gevers, D., Henry, D., De Brandt, E., LiPuma, J.J., Mahenthiralingam, E., Speert, D.P., Dowson, C., and Vandamme, P. (2009) Taxon K, a complex within the Burkholderia cepacia complex, comprises at least two novel species, Burkholderia contaminans sp. nov. and Burkholderia lata sp. nov. Int. J. Syst. Evol. Microbiol., 59 (1), 102–111. Inglesby, T.V., Dennis, D.T., Henderson, D.A., Bartlett, J.G., Ascher, M.S., Eitzen,

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17 MALDI-TOF Mass Spectrometry for Rapid Identification of Highly Pathogenic Microorganisms E., Fine, A.D., Friedlander, A.M., Hauer, J., Koerner, J.F., Layton, M., McDade, J., Osterholm, M.T., O’Toole, T., Parker, G., Perl, T.M., Russell, P.K., Schoch-Spana, M., and Tonat, K. (2000) Plague as a biological weapon: medical and public health management. Working Group on Civilian Biodefense. JAMA, 283 (17), 2281–2290. 63 Sulakvelidze, A. (2000) Yersiniae other than Y. enterocolitica, Y. pseudotuberculosis, and Y. pestis: the ignored species. Microbes. Infect., 2 (5), 497–513. 64 Kotetishvili, M., Kreger, A., Wauters, G., Morris, J.G., Jr., Sulakvelidze, A., and Stine, O.C. (2005) Multilocus sequence typing for studying genetic relationships among Yersinia species. J. Clin. Microbiol., 43 (6), 2674–2684. 65 Sprague, L.D., Scholz, H.C., Amann, S., Busse, H.J., and Neubauer, H. (2008) Yersinia similis sp. nov. Int. J. Syst. Evol. Microbiol., 58 (Pt 4), 952–958.

66 Wynne, C., Fenselau, C., Demirev, P.A.,

and Edwards, N. (2009) Top-down identification of protein biomarkers in bacteria with unsequenced genomes. Anal. Chem., 81 (23), 9633–9642. 67 Trebesius, K., Harmsen, D., Rakin, A., Schmelz, J., and Heesemann, J. (1998) Development of rRNA-targeted PCR and in situ hybridization with fluorescently labelled oligonucleotides for detection of Yersinia species. J. Clin. Microbiol., 36 (9), 2557–2564. 68 Achtman, M., Zurth, K., Morelli, G., Torrea, G., Guiyoule, A., and Carniel, E. (1999) Yersinia pestis, the cause of plague, is a recently emerged clone of Yersinia pseudotuberculosis. Proc. Natl. Acad. Sci. U.S.A., 96 (24), 14043–14048. 69 Lathem, W.W., Price, P.A., Miller, V.L., and Goldman, W.E. (2007) A plasminogen-activating protease specifically controls the development of primary pneumonic plague. Science, 315 (5811), 509–513.

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Part Three Analysis of Host–Pathogen Interactions

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18 Quantitative Proteomic Profiling of the Interaction of Francisella tularensis LVS with Macrophages Using J774.2 Cell Line Anetta Hartlova, Marek Link, Juraj Lenco, and Jiri Stulik

18.1 Introduction

Over the past decade, it has become apparent that the plasma membrane is not a homogeneous lipid matrix but is organized into specialized membrane microdomains enriched in cholesterol, sphingolipids, and certain proteins [1]. Of particular interest is the accumulation of the specific set of proteins including glycophosphatidylinositol-anchored proteins (GPI proteins) and different signaling molecules in the membrane microdomains commonly called lipid rafts, as a reaction to the changing cell environment [2, 3]. It is proposed that small diffusible rafts upon certain stimuli such as ligand binding coalesce into a larger platform stabilized by protein–protein and lipid–protein interactions [4]. These coalesced platforms are of functional importance in the regulation of various biological processes such as membrane trafficking, signal transduction, or pathogen internalization within the plasma membrane [5–7]. It has been shown that a growing number of various pathogens exploit the membrane rafts as an infectious strategy to enter the host cells or survive inside host cells by avoiding fusion of its phagosome with lysozome [8]. Therefore, pathogen intervention may serve as a good example for examining the physiological significance of rafts. Simultaneously, learning more about membrane rafts may give an insight into the molecular mechanisms of the pathological state. Despite the importance of rafts in cellular signaling, direct characterization of a membrane raft in a native state is still difficult. Thus, the predominant evidence for the functional organization of membranes has been derived from studies of membrane fragments that are insoluble in cold non-ionic detergents at low temperatures, resulting in a detergent-resistant membrane (DRMs) [9]. To characterize the protein profile of so-called DRMs, a mass spectrometry-based approach represents a powerful tool enabling a large-scale characterization of raft protein components [10]. Nevertheless, it should not be forgotten that DRMs do not accurately reflect the protein and lipid composition of a native raft. Hence, other complementary microscopic and/or biophysical techniques should be used [11]. In this study, we have performed an iTRAQ workflow to identify and quantify proteins associated with DRMs of macrophages during the early stage of FranBSL3 and BSL4 Agents: Proteomics, Glycomics, and Antigenicity, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.

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cisella tularensis infection, using the macrophage-like cell line J774.2 and F. tularensis live vaccine strain (LVS) as a model. The isobaric tags for relative and absolute quantitation (iTRAQ) technology is a shotgun-based technique which enables the identification and relative quantification of proteins in up to eight different biological samples in a single experiment; and it is even suitable for the identification of low-abundance proteins [12].

18.2 Material and Methods 18.2.1 Bacterial Strain, Cell Line, and Growth Conditions

Francisella tularensis subsp. holarctica live vaccine strain (LVS; ATCC29684, American Type Culture Collection, Rockville, Md, USA) used in this study was grown on a modified Thayer–Martin plate at 37 °C and under 5% CO2. Mouse macrophage-like cell line J774.2 (European Collection of Cell Cultures) was maintained in Dulbecco’s modified essential medium (DMEM) supplemented with 10% v/v heat-inactivated fetal bovine serum. Cells were grown at 37 °C and under 5% CO2. 18.2.2 Infection Assay

J774.2 infection was performed at a multiplicity of infection (moi) of 500 centrifuging bacteria dispersed in complete medium onto prechilled macrophages at 400 g for 10 min at 4 °C. Cells were then rapidly warmed to 37 °C for 2 min in a water bath to trigger phagocytosis and further incubated for a total of 15 min together at 37 °C and under 5% CO2 [13]. Afterwards, cells were extensively washed with PBS. After the washing step, cells were detached from the culture plates with a cell scraper, transferred to falcon tubes, and centrifuged. 18.2.3 Preparation of Lysate and Isolation of Detergent-Insoluble Membrane Fractions

Cell pellets of both control and infected cells were resuspended of a total volume of 2 ml lysis buffer (25 mM Tris, pH 7.5, 150 mM NaCl, 10 mM β-glycerophosphate, 5 mM EDTA, 0.5% Triton X-100, 1 mM Na3VO4, 1 mM NaF, and EDTA free protease inhibitor cocktail from Roche) and lysed on ice by dounce homogenization (15 strokes, tight pestle). Both lysates were mixed with an equal volume of 80% ice-cold sucrose in MES buffer [25 mM 2-(4-morpholino)ethane sulfonic acid, pH 6.5, 150 mM NaCl, 5 mM EDTA]. Then 2 ml of the 40% sucrose-cell lysate was transferred in 5 ml Ultra-Clear centrifuge tubes (Beckman) on ice. On top of each of these were layered 2 ml of ice-cold 30% sucrose in MES buffer, followed by 1 ml

18.2 Material and Methods

of ice-cold 5% sucrose in MES buffer. Samples were centrifuged at 200 000 g for 17 h at 4 °C in a Beckman Max ultracentrifuge using swinging rotor MLS-50. After centrifugation, 10 × 500 μl fractions were collected on ice from each tube and numbered 1 to 10, starting from the upper fractions of the gradient. Distribution of raft (flotillin-1) and nonraft marker (CD71) was analyzed by Western blotting. Fractions 2 to 5, representing the low-density Triton-insoluble (DRM-containing) portion, were pooled and kept on ice. Pooled fractions 2–5 (2.0 ml) were each mixed with 3.0 ml of ice-cold MES buffer and recentrifuged at 200 000 g for 2 h at 4 °C. Supernatants were discarded and the DRM pellets were dissolved in 0.2% RapiGest. The protein concentration was determined by microBCA assay (Pierce). Then the pH of the samples was adjusted with 0.5 M TEAB (triethylammonium bicarbonate) to pH 8.5. 18.2.4 TRYPTIC Digestion and iTRAQ Labeling

Further, 5 μg of protein from each sample was reduced, alkylated, digested with trypsin, and labeled according to the manufacturer’s iTRAQ reagents protocol from Applied Biosystems with slight modifications. Briefly, after full sample dissolution, 2 μl of reducing reagent were added, and the sample was incubated for 1 h at 60 °C. Cysteines were blocked by the addition of 1 μl of cysteine blocking reagent followed by room temperature incubation for 10 min. The sample was then digested overnight with trypsin (1 μg/50 μg protein sample) at 37 °C. After digestion, peptides of each sample were labeled with 114 (control J774.2) and 116 (infectious J774.2) separately. The labeling peptides were then pooled and dried in a vacuum concentrator prior to mass spectrometric analysis (Figure 18.1).

Figure 18.1 Scheme of the iTRAQ workflow.

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18.2.5 Mass Spectrometric Analysis

Each dried iTRAQ-labeled peptide fraction was re-dissolved in 20 μl of 0.1% trifluoracetic acid and 5% acetonitrile and then 8 μl of sample was injected into UltiMate nanoLC system using a C18 reversed phase column (PepMap, 75 μm × 150 mm, 3 μm, 100 Å, 25 nl/min) with precolumn desalting (PepMap100, 300 μm × 1 mm, 5 μm, 100 Å, 25 μl/min). Peptides were eluted using a linear gradient 5–50% buffer B (80% acetonitrile with 0.1% trifluoracetic acid) in 70 min. Fractionation of the eluent was performed on a Probot micro fraction collector starting at 16 min. Mass spectrometry was performed using a MALDI TOF/TOF 4800 analyzer (Applied Biosystems MDS Sciex). 18.2.6 Protein Identification and Database Search

Protein identification and quantification for iTRAQ experiments was carried out using the ProteinPilot software ver. 2.0 (Applied Biosystems MDS Sciex). The search was performed against an IPI mouse database (ver. 3.26). The Paragon algorithm in ProteinPilot software was used as the default search program with one missed cleavage of trypsin as a digest agent and cysteine modification of methyl methanethiosulfonate [14]. Only proteins identified with at least 95% confidence, or a ProtScore of 1.3, were reported. 18.2.7 Classification of iTRAQ Identified Proteins

Proteins identified by iTRAQ analysis were classified according to their biological and molecular functions, by inputting their protein identification numbers into the Protein Analysis Through Evolutionary Relationships (PANTHER) classification system [15].

18.3 Results 18.3.1 Characterization of DRM Isolation

Immunoblot analysis of the distribution of a raft (flotillin-1) and nonraft marker (CD71) across the sucrose gradient was performed to characterize the isolation of DRMs. As observed in Figure 18.1, a raft protein (flotillin-1) is present in the lower fractions of the gradient [2–4], which corresponds to the detergent insoluble fractions. In contrast, CD71 (transferring receptor) as a nonraft marker was only detectable in Triton X-100 soluble fractions (fractions 8–10) but not in the DRM-

18.3 Results

Figure 18.2 Immunoblot (IB) of raft marker (flotillin-1) and nonraft marker (CD71) in DRMs.

containing fractions [2–4]. Thus, membrane raft fractions represented by DRMs were well separated from a nonraft plasma membrane (Figure 18.2). 18.3.2 Classification of 57 Identified Proteins of Both iTRAQ Labeling

Data analysis on the 57 proteins identified by iTRAQ was performed using the PANTHER classification system to class each protein according to its respective molecular functions. All proteins identified by iTRAQ were found to represent a total number of eight molecular functions (Figure 18.3). The top two groups were unsurprisingly cytoskeletal proteins and signaling molecules. The identification of the cytoskeletal proteins, including actin and actin-related proteins, provide evidence that the actin skeleton plays an important role in the control of membrane domain organization [16]. The second group is comprised of proteins known to be involved in a range of signal transduction pathways, such as heterotrimeric and small G proteins and Src family tyrosine kinases. This result supports the tenet of the raft hypothesis [2]. 18.3.3 Significant Differentially Regulated Proteins

As shown in Table 18.1, four proteins were significantly down-regulated in the DRMs of infectious macrophages compare to the DRMs of noninfectious cells based on iTRAQ reporter mass ratios. The accuracy of each protein ratio is given by a calculated “error factor (EF)” in the software and a P value to assess whether the protein is significantly differentially expressed. For the selection of differentially expressed proteins we considered the following situation: the proteins must have a P value <0.05 and EF <2. The actual value for the average protein ratio is expected to be found between (reported average ratio) × (error factor) and (reported average ratio)/(error factor) for 95% of the time [14].

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Figure 18.3 Classification of the total 57 identified proteins shared between two iTRAQ labels

with the PANTHER classification system according to their different molecular functions.

Table 18.1 Significantly down-regulated proteins.

Accession number

Name of protein

Protein coverage (%; extracted from ProteinPilot ver. 2.0)

Linear ratio of average infectious : noninfectious DRMs of macrophages

P51863 Q8BVE3 P50518 P62814

V-ATPase V-ATPase V-ATPase V-ATPase

25 29 37 28

0.60 0.67 0.64 0.65

d subunit H subunit E subunit B subunit

The results demonstrate iTRAQ reagent-based quantitative proteomic analysis yielding quantitative information about the protein abundance changes over time to characterize cellular processes with respect to the host – pathogen interaction. Temporal iTRAQ analysis of detergent-resistant membranes from macrophages identified 57 proteins. Interestingly, many of the identified proteins (such as G proteins and Src family tyrosine kinases) were involved in signaling, which supports the theory of the crucial role of membrane microdomains in organizing signaling platforms [17]. However, the most striking finding was that only four proteins were significantly down-regulated during interaction with the intracellular bacterium Francisella tularensis LVS. The most likely reason is that the examined system did not induce profound changes in the protein composition of macrophage. Even though the percentage of macrophages internalizing bacteria was determined using fluores-

References

cent microscopy and the colony-forming units (CFU) method to give optimal interaction before proteomic analysis. Significantly down-regulated proteins belong to the vacuolar ATPase (V-ATPase) subunit family. V-ATPase is a heteromultimeric enzyme composed of a peripheral catalytic V1 complex (components A–H) attached to an integral membrane V0 (a, c, c′, c″, d) proton pore complex. Vacuolar ATPase is responsible for acidifying a variety of intracellular compartments in eukaryotic cells [18]. Besides, V-ATPase can be also found in the plasma membrane in some specialized cells [19] as well as in macrophages [20] and neutrophils [21]. In the study, three subunits (B, E, H) of V1 complex were found, as well as the d subunit of the Vo complex. The presence of all V-type H+ translocating ATPase subunits was decreased within macrophage DRMs during Francisella tularensis LVS interaction. The observation suggested the association of V-ATPase with DRMs and the interference of V-ATPase activity as a response to the microenvironment, such as bacterial invasion. The results can be explained in two ways. First, V-ATPase is supposed to be a component of the plasma membrane and may play a significant role in the control of the cytoplasmic pH. Nevertheless, the precise role of plasmalemmal V-ATPase in the regulation of intracellular pH in monocytes is still unclear [22]. Another explanation is that there is a low rate of colocalization of V-ATPase with the Francisella containing vacuole (FCV) during the early stage of infection.

References 1 Simons, K., and Ikonen, E. (1997)

2

3

4

5

6

7

Functional rafts in cell membrane. Nature, 387, 569–572. Simons, K., and Toomre, D. (2000) Lipid rafts and signal transduction. Nat. Rev. Mol. Cell Biol., 1, 31–39. Chatterjee, S., and Mayor, S. (2001) The GPI-anchor and protein sorting. Cell. Mol. Life Sci., 58, 1969–1987. Pike, J.L. (2006) Rafts defined: a report on the Keystone symposium on lipid rafts and cell function. J. Lipid Res., 47, 1597–1598. Ikonen, E. (2001) Roles of lipid rafts in membrane transport. Curr. Opin. Cell Biol., 13, 470–477. Dykstra, M., et al. (2003) Location is everything: lipid rafts and immune cell signaling. Annu. Rev. Immunol., 21, 457–481. Manes, S., del Real, G., and Martinez, A.C. (2003) Pathogens: raft hijackers. Nat. Rev. Immunol., 3, 557–568.

8 Alonso, A., and Portillo, F.G. (2004)

9

10

11

12

13

Hijacking of eukaryotic functions by intracellular bacterial pathogens. Int. Microbiol., 7, 181–191. Brown, D.A., and Rose, J.K. (1992) Sorting of GPI-anchored proteins to glycolipid-enriched membrane subdomains during transport to the apical cell surface. Cell, 357, 533–544. Foster, L.J., de Hoog, C.L., and Mann, M. (2003) Unbiased quantitative proteomics of lipid rafts reveals high specificity for signaling factors. Proc. Natl. Acad. Sci. U.S.A., 100, 5813–5818. Gaus, K., et al. (2003) Visualizing lipid structure and raft domains in living cells with two-photon microscopy. Proc. Natl. Acad. Sci. U.S.A., 100, 15554–15559. Aggarwal, K., Choe, L.H., and Lee, K.H. (2006) Shotgun proteomics using the iTRAQ Isobaric tags. Brief Funct. Genomic Proteomic, 5, 112–120. Checroun, C., Wehrly, T.D., Fischer, E.R., Hayes, S.F., and Celli, J. (2006)

221

222

18 Quantitative Proteomic Profiling of the Interaction of Francisella tularensis

14

15

16

17

Autophagy-mediated reentry of Francisella tularensis into the endocytic compartment after cytoplasmic replication. Proc. Natl. Acad. Sci. U.S.A., 103, 14578–14583. Shilov, I.V., Seymour, S.L., Patel, A.A., Loboda, A., Tang, W.H., Keating, S.P., Hunter, C.L., Nuwaysir, L.M., and Schaeffer, D. (2007) The Paragon Algorithm: a next generation search engine that uses sequence temperature values and feature probabilities to identify peptides from tandem mass spectra. Mol. Cell. Proteomics, 6, 1638–1655. Thomas, P.D., Campbell, M.J., Kejariwal, A., Mi, H., Karlak, B., Daverman, R., Diemer, K., Muruganujan, A., and Narechania, A. (2003) PANTHER: a library of protein families and subfamilies indexed by function. Genome Res., 13, 2129–2141. Chichili, G.R., and Rodgers, W. (2007) Clustering of membrane raft proteins by the actin cytoskeleton. J. Biochem. Chem., 282, 36682–36691. Janes, P.V., Ley, S.C., and Magee, A.I. (1999) Aggregation of lipid rafts accompanies signaling via the T cell

18

19

20

21

22

antigen receptor. J. Cell Biol., 147, 447–461. Forgac, M. (2007) Vacuolar ATPases: rotary proton pumps in physiology and pathophysiology. Nat. Rev. Mol. Cell Biol., 8, 917–929. Frattini, A., Orchard, P.J., Sobacchi, C., Giliani, S., Abinun, M., et al. (2000) Defects in TCIRG1 subunit of the vacuolar proton pump are responsible for a subset of human autosomal recessive osteopetrosis. Nat. Genet., 25, 343–346. Swallow, C.J., Grinstein, S., and Rotstein, O.D. (1990) A vacuolar type H(+)-ATPase regulates cytoplasmic pH in murine macrophages. J. Biol. Chem., 5, 7645–7654. Nanda, A., Brumell, J.H., Nordstrom, T., Kjeldsen, L., Sengelov, H., et al. (1996) Activation of proton pumping in human neutrophils occurs by exocytosis of vesicles bearing vacuolar-type H+ATPases. J. Biol. Chem., 271, 15963–15970. Heming, A.T., and Bidani, A. (2003) Intracellular pH regulation in U937 human monocytes: roles of V-ATPase and Na+/H+ exchange. Immunobiology, 207, 141–148.

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19 Proteome Analysis of Bacterial Protein Expression after Ingestion of Microbes by Macrophages Martin Brychta and Ivona Pávková

19.1 Introduction

The intracellular pathogens have the unique capacity to sense the host cell environment and to respond to it by altering their gene expression and protein synthesis. Thus the best way to characterize a pathogen-relevant proteome requires direct contact with the host for true physiologic consequences. Such proteome analysis may provide useful information about the mechanisms of bacterial adaptation to their intracellular environment and for the identification of potential virulence factors, analysis of antimicrobial substances, or targets for vaccine design [1, 2]. Francisella tularensis, the causative agent of zoonotic disease tularemia, is a typical intracellular pathogen with a high predilection for growth in macrophages even though it is able to survive and multiply in many other cell types. Inside the macrophage, Francisella tularensis escapes from the phagosome within 2–4 h and starts to replicate at 6 h in the cytoplasm of the host cell. F. tularensis LVS-infected J774 cells are killed by apoptosis within 24–48 h [3]. Even though the life cycle of F. tularensis has been described completely, the genes involved in all its stages have yet to be elucidated. In addition, virulence factors responsible for its high infectivity also remain unknown [4]. One of the few known virulence factors (MglA) regulates the transcription of a number of genes necessary for intramacrophage growth and genes located within the Francisella pathogenicity island (FPI) [5–7]. FPI genes are required for intramacrophage growth and virulence, and appear to encode a putative type VI secretion system. However, the function of these proteins individually remains still unknown [8]. One of the most studied FPI proteins is the intracellular growth locus C protein (IglC). Several additional genes were found to be involved in intracellular growth: alanine racemase, the ClpB heat-shock protease and glutamine phosphoribosylpyrophosphate aminotransferase [9]. The exact role of surface components, including LPS, pili and capsule, in F. tularensis virulence still remains to be elucidated.

BSL3 and BSL4 Agents: Proteomics, Glycomics, and Antigenicity, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.

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19 Proteome Analysis of Bacterial Protein Expression after Ingestion of Microbes by Macrophages

In this study, we present a detailed proteome analysis of F. tularensis subsp. holarctica live vaccine strain (LVS) grown inside the macrophage-like mouse cell line J774.2 using the pulse-labeling of bacterial proteins in various infectious periods.

19.2 Material and Methods 19.2.1 Bacterial Strains, Cell Lines, and Cultivation

Francisella tularensis LVS FSC 155 was kindly provided by Dr. Ake Forsberg from FOI, Umea, Sweden. Before each experiment, the stock culture was grown on McLeod agar plates supplemented with bovine hemoglobin and IsoVitalex (Beton Dickinson, Cockeysville, Md, USA), inoculated into chemically defined Chamberlain’s medium and grown overnight with mild shaking at 37 °C. The overnight culture was diluted with fresh medium (OD600 0.10–0.15) and bacteria were grown to OD600 0.5–0.6 (exponential growth phase). The mouse macrophage line J774.2 was grown in cell medium containing Dulbecco’s modified Eagle medium (DMEM; GIBCO BRL, Grand Island, N.Y., USA) with 10% (v/v) fetal calf serum (FCS). 19.2.2 Intracellular Growth of Francisella tularensis in Macrophages and Radiolabeling of Bacterial Proteins

Harvested bacteria from 2.0 ml culture were resuspended into 0.5 ml DMEM medium. Then 0.5 ml of DMEM containing 1010 bacterial cells was added to a tissue culture dish containing 2 × 107 J774.2 cells [multiplicity of infection (MOI) = 500]. After 3.5 h incubation at 37 °C and 5% CO2, the DMEM medium was changed to methionine and cysteine-free DMEM medium. After 30 min incubation, cycloheximide (200 μg/ml, Sigma) was added in order to inhibit the synthesis of eukaryotic proteins, and bacterial proteins were pulse-labeled with TRANS 35S label, a metabolic labeling reagent containing >70% 35S-methionine and 15% 35S-cysteine (220 μCi/ml, MP Biomedicals, Irvine, Calif., USA) for 2 h. The cells were washed in ice-cold DMEM medium, benzonase was added and cells were lysed with 0.1% sodium deoxycholate in PBS. Bacteria were pelleted by centrifugation, washed in PBS and resuspended in 50 μl LPA buffer (28 mM Tris-HCl, 22 mM Tris-Base, 0.3% sodium dodecylsulfate) with protease inhibitors (Complete Mini, protease inhibitor cocktail tablets, Roche, Mannhein, Germany). The tubes were immersed in boiling water for 3 min and immediately cooled on ice. The sample was treated with ReadyPrep 2-D Cleanup Kit (BioRad; Hercules, Calif., USA) and resuspended in 2-DE buffer consisting of 6 M urea, 2 M thiourea, 4% CHAPS, 40 mM Tris-base, 0.5% bromophenol blue, 1.2% De Streak. Radio-

19.2 Material and Methods

activity was determined using a LS 6000LL liquid scintillation system (Beckman; Fullerton, Calif., USA). For control samples, harvested bacteria from 2.0 ml culture were resuspended into 5 ml DMEM medium and handled in the same way as described previously with the exclusion of J774.2 cells. 19.2.3 Two-Dimensional Gel Electrophoresis (2-DE) and Autoradiography

A protein sample containing 2 × 106 cpm was diluted to a total volume of 350 μl 2-DE buffer. Two gels of different pH ranges were prepared for each sample. The protein mixture was loaded on 18 cm IPG strips with pH gradient 3–10 and 6–11. In the second dimension, an SDS-polyacrylamide gel electrophoresis (PAGE) gradient 9–16% was cast, the resolved IPG strip was placed on top, and electrophoresis was performed. The gels were fixed with 2,5-diphenyloxazole in DMSO, dried out and exposed to KODAK BioMax Film for 5 days at −80 °C. 19.2.4 Image Acquisition and Software Analysis

To confirm reproducibility, at least four independent samples were prepared for both control (bacteria grown in DMEM medium) and experimental group (bacteria grown intracellularly). Each sample was resolved on a gel with pH range 3–10 and gel with 6–11. Film images were digitalized using a CCD camera image station 2000R (Eastman Kodak, Rochester, N.Y., USA). The gels were subjected to image analysis using the Image Master 2D Platinum 6.0 software (GE Healthcare, Amersham Biosciences AB, Uppsala, Sweden) and the three most reproducible films for each group were selected for the comparative analysis. Thus, a matchset consisting of six images, three for experimental and three for control groups, was created. Only spots observed on all three gels of a replicated group were analyzed. Relative spot volumes (% vol), were used for spot quantitations. Normalized data for the matched spots were analyzed by Student’s t-test. Spots with a P value ≤ 0.05 showing relative spot volume differences more than twofold were accepted. 19.2.5 Protein Identification

For the identification by MS analysis, protein spots on 2-DE gels were visualized by Coomassie G-250 (Invitrogen). The protein spots of interest were excised, in-gel digested with trypsin (0.5 μg/ml, Promega) and analyzed by MS (for the generation of PMF) and in TOF/TOF mode (for fragmentation analysis of the ten most intense peptides) on a 4800 Plus MALDI TOF/TOF analyzer (Applied Biosystems, Framingham, Mass., USA). The mass spectra were recorded in the positive reflectron mode. Data were evaluated using GPS Explorer ver. 3.6 and searched against the F. tularensis subsp. holarctica LVS genome.

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19 Proteome Analysis of Bacterial Protein Expression after Ingestion of Microbes by Macrophages

19.3 Results

In order to examine the influence of the defined host cell milieu on bacterial gene expression, the virulent strain of F. tularensis LVS was cultivated inside the macrophage-like mouse cell line J774.2 for 6 h. At this time point, most of the bacteria are free in the cytoplasm and start to replicate [3]. As the pathogen proteome has usually been overwhelmed by the host proteome due to size differences of the proteomes and organisms and the ratio of pathogen to host [10], we used metabolic radiolabeling of bacterial proteins synthesized in an exact time interval by inhibited synthesis of eukaryotic proteins using cycloheximide. The intracellular immunofluorescence assay revealed that 95% cells were infected with an average 4.71 bacteria at 6 h post-infection (p.i.) using MOI 500 (data not shown). The computer-assisted image analysis of 2-DE autoradiography protein patterns revealed 42 proteins exhibiting statistically significant up- or down-regulation only in bacteria grown inside the macrophages. Protein spots with quantitative changes (vol%) encoding 31 different gene products were detected on 3–10 pH patterns: 18 of them were up-regulated and 13 down-regulated (see Figure 19.1). Eleven

Figure 19.1 Representative 2D autoradiographic pattern of F. tularensis LVS grown inside the

host macrophage (3–10 pH gradient). Full arrow – up-regulated protein; broken arrow – downregulated protein.

19.3 Results

Figure 19.2 Representative 2D autoradiographic pattern of F. tularensis LVS grown inside the host macrophage (6–11 pH gradient). Full arrow – up-regulated protein; broken arrow – downregulated protein.

proteins with quantitatively changed spots were found on 6–11 pH patterns: 9 up-regulated, 2 down-regulated (see Figure 19.2). Only proteins successfully identified by mass spectrometry are mentioned. The differentially expressed proteins were grouped into one of the clusters of orthogonal groups of proteins according to the CoGnitor algorithms (http:// www.ncbi.nlm.nih.gov/COG/; see Table 19.1) [11]. The largest number of proteins was classified as proteins involved in translation, ribosomal structure and biogenesis (11 proteins), followed by the group of proteins essential for post-translational modifications, protein turnover and chaperones (seven proteins) and by the group of proteins participating in lipid transport and metabolism (five proteins). Three proteins are of unknown functions or were not classified in any COG group. Finally, the groups containing proteins involved in amino acid transport and metabolism, transcription, energy production and conversion, cell wall/membrane/ envelope biogenesis, replication, recombination and repair, inorganic ion transport and metabolism, coenzyme transport and metabolism, signal transduction mechanism and carbohydrate transport contained from one to two proteins.

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19 Proteome Analysis of Bacterial Protein Expression after Ingestion of Microbes by Macrophages Table 19.1 Classification of proteins differentially expressed in F. tularensis LVS grown inside

host macrophages compared to F. tularensis LVS grown in culture medium into one of the clusters of orthogonal groups (COGs) of proteins according to the CoGnitor algorithms. COG code

COG function group name

Number of different proteins (up-regulated/down-regulated)

J

Translation, ribosomal structure, and biogenesis

10/1

O

Posttranslational modification, protein turnover, chaperones

5/2

I

Lipid transport and metabolism

5/0

K

Transcription

2/0

M

Cell wall/membrane/envelope biogenesis

0/2

P

Inorganic ion transport and metabolism

0/2

C

Energy production and conversion

1/1

E

Amino acid transport and metabolism

1/1

H

Coenzyme transport and metabolism

1/1

Q

Secondary metabolites biosynthesis, transport, and catabolism

1/1

L

Replication, recombination, and repair

1/0

U

Intracellular trafficking, secretions, and vesicular transport

1/0

T

Signal transduction mechanism

0/1

S

Function unknown

0/1

Any category

1/1

19.4 Discussion

Host–pathogen interactions reflect the balance between hostile cell defenses and pathogen virulence mechanisms. Understanding the nature of these interactions provides insights into metabolic processes and critical regulatory events of the host cell as well as into the mechanisms of pathogenesis by infectious microorganisms. Upon interaction with the host cell, significant expression alterations occur within the pathogen. Several techniques have been established to characterize the global phenotypic response by intracellular bacterial pathogens during the course of infection, including the two-dimensional gel electrophoresis [1, 2, 10]. Even though the proteome of the intracellular pathogenic bacterium F. tularensis has been investigated in a number of host-free models [12–14], there is currently

19.4 Discussion

limited data concerning the modulation of F. tularensis gene expression during the course of microbe–host cell interaction [15, 16]. In the present study, we examined the phenotypic response of F. tularensis LVS during growth inside mouse macrophage-cell line J774.2 by two-dimensional gel electrophoresis. For this purpose, the bacterial proteins were pulse-labeled with TRANS 35S label, separated on 2-DE and detected autoradiographically and the obtained 2D images were compared with those from bacteria grown in cell medium. This model has previously been used by Igor Golovliov [15]; however, only four proteins were found to be induced during intracellular growth of F. tularensis. Several modifications of the model and the marked progress in mass spectrometry techniques enabled us to detect and identify dramatically higher number (42 proteins) of differently expressed proteins. Such alterations in protein synthesis might be due to the F. tularensis response to various stimuli of the host cell and might enable the bacterium to adapt and to replicate within the intracellular niche. According to the COG algorithm, the most pronounced shift to up-regulation represented the group of proteins involved into the translation processes and ribosomal functions, corroborating the elevated protein synthesis during the intracellular life cycle of F. tularensis. Translational proteins are in general synthesized at high levels during intracellular infection [10] and this finding indicates the increased proteosynthesis of F. tularensis in response to the intracellular environment. Another group of up-regulated protein includes the molecular chaperones and proteins involved in protein turnover. Molecular chaperones are essential for ensuring the correct folding, stabilization and translation of proteins under all conditions. Increased heat shock protein synthesis in response to intracellular environment has also been demonstrated in a number of other intracellular bacteria like S. typhimurium, L. pneumophila, or M.tuberculosis [17–20], reflecting the adaptation of pathogens to the intracellular environment [21]. Five other up-regulated proteins participate in the fatty acid biosynthesis process, indicating the increased production of fatty acids in bacteria in response to the intracellular niche. The remaining up- or down-regulated proteins are very heterogeneous in light of their function as they belong to different COG groups. The down-regulated protein of unknown function is encoded by a gene of FPI and exhibits homology with a component of the putative type VI secretion system [22]. Two other proteins could not be classified into any COG group due to the absence of any known conserved domain or homology to proteins in organisms that have been genome sequenced to date. Thus their function and real role in bacterial pathogenesis has to be elucidated in further functional studies. For this purpose the construction of a gene knockout mutant for the selected protein is underway. In conclusion, the metabolic labeling of proteins and their separation by 2-DE is a powerful tool for examining the global response in bacterial protein synthesis upon exposure to the intracellular environment of the host cell. The proteins whose synthesis was shown to be different in intracellularly grown F. tularensis might contribute to the adaptation and survival of the bacteria inside the host cell.

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Identifying the exact role of selected proteins might provide deeper insights into the molecular mechanisms of pathogenesis and virulence and will be helpful in identifying novel targets for drug or vaccine development.

Acknowledgment

This work was supported by the Czech Science Foundation (GACR 310/06/P266).

References 1 Zhang, C.G., Chromy, B.A., et al. (2005)

2

3

4

5

6

7

8

9

Host–pathogen interactions: a proteomic view. Expert Rev. Proteomics, 2 (2), 187–202. Coiras, M., Camafeita, E., et al. (2008) Application of proteomics technology for analyzing the interactions between host cells and intracellular infectious agents. Proteomics, 8 (4), 852–873. Sjostedt, A. (2006) Intracellular survival mechanisms of Francisella tularensis, a stealth pathogen. Microbes Infect., 8 (2), 561–567. Sjostedt, A. (2007) Tularemia: history, epidemiology, pathogen physiology, and clinical manifestations. Ann. N Y Acad. Sci., 1105, 1–29. Baron, G.S., and Nano, F.E. (1998) MglA and MglB are required for the intramacrophage growth of Francisella novicida. Mol. Microbiol., 29 (1), 247–259. Lauriano, C.M., Barker, J.R., et al. (2004) MglA regulates transcription of virulence factors necessary for Francisella tularensis intraamoebae and intramacrophage survival. Proc. Natl. Acad. Sci. U.S.A., 101 (12), 4246–4249. Bonquist, L., Lindgren, H., et al. (2008) MglA and Igl proteins contribute to the modulation of Francisella tularensis live vaccine strain-containing phagosomes in murine macrophages. Infect. Immun., 76 (8), 3502–3510. Nano, F.E., and Schmerk, C. (2007) The Francisella pathogenicity island. Ann. NY Acad. Sci., 1105, 122–137. Gray, C.G., Cowley, S.C., et al. (2002) The identification of five genetic loci of Francisella novicida associated with

10

11

12

13

14

15

16

intracellular growth. FEMS Microbiol. Lett., 215 (1), 53–56. Kwaik, Y.A., and Harb, O.S. (1999) Phenotypic modulation by intracellular bacterial pathogens. Electrophoresis, 20 (11), 2248–2258. Tatusov, R.L., Natale, D.A., et al. (2001) The COG database: new developments in phylogenetic classification of proteins from complete genomes. Nucleic Acids Res., 29 (1), 22–28. Hubalek, M., Hernychova, L., et al. (2004) Comparative proteome analysis of cellular proteins extracted from highly virulent Francisella tularensis ssp. tularensis and less virulent F. tularensis ssp. holarctica and F. tularensis ssp. mediaasiatica. Proteomics, 4 (10), 3048–3060. Pavkova, I., Hubalek, M., et al. (2005) Francisella tularensis live vaccine strain: proteomic analysis of membrane proteins enriched fraction. Proteomics, 5 (9), 2460–2467. Pavkova, I., Reichelova, M., et al. (2006) Comparative proteome analysis of fractions enriched for membraneassociated proteins from Francisella tularensis subsp. tularensis and F. tularensis subsp. holarctica strains. J. Proteome Res., 5 (11), 3125–3134. Golovliov, I., Ericsson, M., et al. (1997) Identification of proteins of Francisella tularensis induced during growth in macrophages and cloning of the gene encoding a prominently induced 23-kilodalton protein. Infect. Immun., 65 (6), 2183–2189. Twine, S.M., Mykytczuk, N.C., et al. (2006) In vivo proteomic analysis of the

References intracellular bacterial pathogen, Francisella tularensis, isolated from mouse spleen. Biochem. Biophys. Res. Commun., 345 (4), 1621–1633. 17 Buchmeier, N.A., and Heffron, F. (1990) Induction of Salmonella stress proteins upon infection of macrophages. Science, 248 (4956), 730–732. 18 Abshire, K.Z., and Neidhardt, F.C. (1993) Analysis of proteins synthesized by Salmonella typhimurium during growth within a host macrophage. J. Bacteriol., 175 (12), 3734–3743. 19 Abu Kwaik, Y., Eisenstein, B.I., et al. (1993) Phenotypic modulation by Legionella pneumophila upon infection

of macrophages. Infect. Immun., 61 (4), 1320–1329. 20 Lee, B.Y., and Horwitz, M.A. (1995) Identification of macrophage and stress-induced proteins of Mycobacterium tuberculosis. J. Clin. Invest., 96 (1), 245–249. 21 Stewart, G.R., and Young, D.B. (2004) Heat-shock proteins and the hostpathogen interaction during bacterial infection. Curr. Opin. Immunol., 16 (4), 506–510. 22 Barker, J.R., and Klose, K.E. (2007) Molecular and genetic basis of pathogenesis in Francisella tularensis. Ann. N. Y. Acad. Sci., 1105, 138–159.

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Index a acetonitrile (ACN) 31, 95ff. affinity chromatography (AC) 21 amino acid sequence 155, 157f., 160, 162 anthrax 176 antibody assay 170f. antibody response 110ff. antigen 167ff., 171, 174 artificial neural network (ANN) 185f., 196f., 204f. ATP-synthase 62

b Bacillus anthracis 176, 188, 192ff. bacterioferritin comigratory protein 148 bar code mass spectra 184 bioinformatics 3, 70 bioinformatic analysis 109, 112 biological warfare (BW) 95, 192 biomarker 176 biosafety level 3 (BSL3) microorganism 180, 182, 188f., 207 bioterrorism 95, 163, 192 blue native polyacrylamide gel electrophoresis (BN-PAGE) 56ff. – 1D BN-PAGE 59 – 2D BN-PAGE 59 – sample preparation 57f. – staining 60 BN/SDS-PAGE 62f. botulinum neurotoxin (BoNT) 153ff. – amino acid sequence 155ff., 160, 162 – differentiation 154f. – extraction from complex matrices 157 – domains 154 – identification of BoNT/A subtype 159ff. – identification of BoNT/A1 strains 161ff.

– identification of serotype 157, 163 – serotypes 154 botulism 154, 163 Burkholderia mallei 196ff. Burkholderia pseudomallei 197ff.

c calibration 183 catalase 148 cell disruption 48 cell-free acetonitrile extract 96 centrifugation 189 chaperone 229 Clostridium botulinum 153ff. cluster of orthogonal groups (COG) 227ff. column chromatography 17ff. Coomassie brilliant blue G-250 56f., 82 Coomassie staining 60, 82 Coulombic fission 31f. Coxiella burnetii 115ff., 139 ff., 145ff. – function of proteins 145ff. – lipopolysaccharides 115ff. – membrane 147 – proteins 145ff. culture filtrate protein 107ff. cytosolic fraction 58

d data visualization 184 database searching 38, 157, 218 detergent 58 detergent-resistant membrane (DRM) 218 diagnostic marker 2 dip-pen lithography (DPN) 169 direct current (DC) 35 direct transfer method 182 DNA microarray 168

215,

BSL3 and BSL4 Agents: Proteomics, Glycomics, and Antigenicity, First Edition. Edited by Jiri Stulik, Rudolf Toman, Patrick Butaye, Robert G. Ulrich. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA.

234

Index

e electron transfer dissociation (ETD) 39 electrospray ionization (ESI) 29, 31ff., 61 electrospray ionization mass spectrometry (ESI-MS) 39, 95, 116 endotoxic activity 139f. enzymatic digestion 97 enzyme-linked immunosorbent assay (ELISA) 116, 168 error factor (EF) 219 extraction 97f.

f family-specific peak 204f. fluorescence 136 fluorescent staining 60 Fourier transform ion cyclotron analyzer (FT ICR) 33, 35 Francisella pathogenicity island (FPI) 223 Francisella tularensis 62f., 83ff., 95ff., 107ff., 188, 215ff., 223

g gene expression 223, 226 gene product 125ff. genus-specific peak 205 global internal standard technology (GIST) 46 glutathione-S-transferase (GST) 170, 172 glycan 79ff. glycomics 69 glycophosphatidylinositol-anchored protein (GPI protein) 215 glycoprotein 67ff., 71ff. – detection 72f. glycoproteomics 68, 70 glycosylation 4, 15, 67ff. – analysis 68ff. gram-negative bacteria 116, 147, 149, 174f., 199

h heat shock protein 100ff. high-performance liquid chromatography (HPLC) 44, 98 high-throughput technology 1, 3 host–pathogen interactions 213ff., 228f. HPLC column 17 hydrophilic interaction liquid chromatography (HILIC) 20

i image acquisition 225 image analysis 13ff. immobilized metal ion affinity chromatography (IMAC) 21

immobilized pH gradient (IPG) 12f. immunization 126 inactivation 187ff. incorporation 45f. infectomics 1 interactome 1 intracellular pathogen 223 ion detection 36 ion exchanger chromatography (IEC) 20 ion trap (IT) 32, 35 ionization 30ff. isobaric tag for relative and absolute quantitation (iTRAQ) 47, 50f., 81, 215ff., 219 – protocol for analysis of bacterial proteins 51f. isoelectric focusing (IEF) 12f., 132, 146 isotope-coded affinity tags (ICAT) 46, 81 isotope-coded protein label (ICPL) 46

l labeling method 44 LC-MS/MS 98ff., 103 linear ion trap (LIT) 35 lipid A 119, 139ff. – composition 140f. – structure 140ff. lipopolysaccharide (LPS) 115ff., 139f. – chemical composition 115ff. – structure 1115ff. lipopolysaccharide I (LPS I) 118f., 121, 146 – chemical composition 118f. – structure 118f. lipopolysaccharide II (LPS II) 120f. – chemical composition 120f. – structure 120f. lipoprotein 91 liquid phase electrophoresis 23 liquid phase IEF 23 liquid phase separation 17 live vaccine strain (LVS) 216, 224 lysate 216

m macrophage 215ff., 223ff. MALDI-TOF MS 97f., 103, 126f., 179ff. – basic principles 180f. – identification of bacterial pathogens 192ff. – inactivation of microorganisms 187 – preparation of microbial samples 181f. maltose binding protein (MBP) 170 mass analyzer 32ff. mass spectrometer 30, 156 mass spectrometry (MS) 1, 10, 14, 29ff., 61, 72, 95, 108, 155f., 218

Index – quantitative approaches 43ff. mass spectrum 33f. matrix 157 matrix protein 90 matrix-assisted laser desorption/ionization (MALDI) 29ff. matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) 33, 39, 95ff., 126, 131ff. measles virus 131f., 134f. measles virus nucleoprotein (MV-NP) 136f. membrane 16 membrane fraction 58, 71 membrane protein (MP) 100f. membrane-associated protein 86, 89 microarray assembly 168ff. microbial identification 180ff. microbial infection 1ff. microfluidics 23 microorganism 96 Mimivirus 125ff. – proteomic analysis 127 mini 2-D gel electrophoresis 71, 75 monkeypoxvirus 174 multidimensional chromatography 21 multilocus sequence typing (MLST) 201 multiplayer perceptron artificial neural network (MLP-ANN) analysis 185f. multiple reaction monitoring (MRM) 36 multivariate classification analysis 184f., 196

n neurotoxin 154, 158 normal phase (NP) chromatography

peptide separation 9ff., 18 periplasm 101 phosphorylation 4, 15, 67 Piscirickettsia salmonis 139ff. plague 200 polyacrylamide gel electrophoresis (PAGE) 43, 126 post-translational modification (PTM) 3ff., 15, 67, 90, 168 prefractionation method 10 preprocessing 182f. protective antigen (PA) 167 protein 9ff., 19, 49, 79ff., 88, 100, 110f. protein classification 228 protein concentration 50 protein digestion 50 protein expression 84, 223 protein extraction 48 protein identification 37, 98, 134, 225 protein microarray 167ff. protein–protein interaction 1, 3ff., 55f. protein spot 90 protein staining 13ff. proteome 167, 171, 174, 226 proteome analysis 81ff., 91 proteomics 1ff., 81ff., 91, 145, 149, 153ff., 159ff., 215ff. – application 1ff. – basic methods 7ff. – definition 1 – gel-based quantitative methods 43f. – gel-free proteomics 16f. – procedures 1 – shotgun proteomics 44

20

q o O-chain 116ff. one-dimensional electrophoresis open reading frame (ORF) 108, 128, 167, 172 outer membrane protein (OMP) outer membrane vesicles (OMV) oxidoreductase 100f.

11 110f. , 125,

Q fever 115ff. – diagnosis 120f. quadrupole (Q) 32, 35

r 101 2

p pathogen 47, 167ff., 179ff. pathogen protein 131ff. pathogenic microorganism 174ff., 179ff. pattern recognition method 184f. peak detection 183 peptide 9ff., 19, 49, 156, 162 peptide labeling 51 peptide mass fingerprinting (PMF) 37f., 135, 146

radiofrequency (RF) 35 radiolabeling 224 reactive oxygen species (ROS) 112 reflectron TOF 34 reversed-phase liquid chromatography (RPLC) 18f.

s sample preparation 9, 71, 132 SDS-polyacrylamide gel electrophoresis (SDS-PAGE) 11f., 56, 172 secondary antibody 170f. secreted protein 107, 112 semi-dry Western blot 71

235

236

Index separation 9ff., 58 – alternative separation technologies 23 – gel-based separation 11 serological proteome analysis (SERPA) 3 shotgun proteomics 39, 44, 81 signal to noise ratio (SNR) 183 silver staining 60, 84, 87 size exclusion chromatography (SEC) 18f. small acid soluble proteins (SASP) 195f. smallpox 171 SNARE-protein 154f. software analysis 225 species-specific peak 207 spectral data analysis 182 spore biomarker 195f. spore treatment 190f. stable isotope 45f., 49 stable isotope labeling with amino acids in cell culture (SILAC) 45, 81 staining 81f., 86 sterile filtration 189 superoxide dismutase 148

t tandem affinity purification (TAP) 56 tandem mass analyzer 35 tandem mass tags (TMT) 47 taxon-specific biomarker 186, 197, 203, 206 TFA inacitivation 188, 190ff., 197, 207 time of flight (TOF) 30, 32ff., 181 Tol protein 147 toxin variant 153ff.

translation 168 tryptic digestion 217 two-dimensional difference in-gel electrophoresis (2D-DIGE) 14ff., 132, 134, 137 two-dimensional electrophoresis (2-DE) 11ff., 81f., 108f., 131ff., 133, 225ff. two-dimensional Western blotting 133ff. type II secretion (T2S) 107f. type IV pili (TFp) 87, 107f. typing 95

u unsupervised hierarchical cluster analysis (UHCA) 185, 199ff.

v vaccination 171 vaccine 3 vaccinia-immune individual (VIg) 173 vacuolar ATPase (V-ATPase) 221 virulence 2 virulence factor 4f., 90 virus 171ff. visualization 61

w Western blotting 61, 126, 131ff. whole-cell lysate 71, 83ff., 87

y Yersinia pestis 175f., 188, 199ff., 206