1. MICROBIAL PROTEOMICS

2. METHODS OF Volume 49BIOCHEMICAL ANALYSIS 3. MICROBIAL PROTEOMICSFUNCTIONAL BIOLOGYOF WHOLE ORGANISMSIan Humphery-SmithBiosystems Informatics InstituteNewcastle-Upon-TyneUnited KingdomMichael HeckerInstitute for MicrobiologyErnst-Moritz-Arndt-UniversitatGreifswald, GermanyA JOHN WILEY & SONS, INC., PUBLICATION 4. Copyright # 2006 by John Wiley & Sons, Inc. All rights reservedPublished by John Wiley & Sons, Inc., Hoboken, New JerseyPublished simultaneously in CanadaNo part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or byany means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permittedunder Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permissionof the Publisher, or authorization through payment of the appropriate per-copy fee to the CopyrightClearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4470,or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to thePermissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax(201) 748-6008, or online at http://www.wiley.com/go/permission.Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts inpreparing this book, they make no representations or warranties with respect to the accuracy or completenessof the contents of this book and specifically disclaim any implied warranties of merchantability or fitnessfor a particular purpose. No warranty may be created or extended by sales representatives or written salesmaterials. The advice and strategies contained herein may not be suitable for you situation. You shouldconsult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss ofprofit or any other commercial damages, including but not limited to special, incidental, consequential, orother damages.For general information on our other products and services or for technical support, please contact our CustomerCare Department within the United States at (800) 762-2974, outside the United States at (317) 572-3993 orfax (317) 572-4002.Wiley also publishes its books in a variety of electronic formats. Some content that appears in print maynot be available in electronic format. For more information about Wiley products, visit our web site atwww.wiley.com.Library of Congress Cataloging-in-Publication Data:Humphery-Smith, Ian.Microbial proteomics / Ian Humphery-Smith, Michael Hecker.Includes bibliographical references and index.ISBN-13 978-0-471-69975-0 (cloth)ISBN-10 0-471-69975-6 (cloth)1. Microbial biotechnology. 2. Proteomics. I. Hecker, M. (Michael) II. Title.TP248.27.M53H86 2006660.6 02dc22 2005056954Printed in the United States of America10 9 8 7 6 5 4 3 2 1 5. CONTENTSPREFACE ixACKNOWLEDGMENTS xiCONTRIBUTORS xiiiPART I GENERAL PROTEOMICS OF MICROORGANISMS/MODELORGANISMS 11. Holistic Biology of Microorganisms: Genomics, Transcriptomics,and Proteomics 3Valerie Wasinger2. Strategies for Measuring Dynamics: The Temporal Componentof Proteomics 15Robert J. Beynon and Julie M. Pratt3. Quest for Complete Proteome Coverage 27C. David OConnor, Ian N. Clarke, and Paul Skipp4. Proteome of Mycoplasma pneumoniae 39Richard Herrmann and Thomas Ruppert5. Proteomics of Archaea 57Ricardo Cavicchioli, Amber Goodchild, and Mark RafteryPART II PROTEOMICS AND CELL PHYSIOLOGY 736. Elucidation of Mechanisms of Acid Stress in Listeriamonocytogenes by Proteomic Analysis 75Luu Phan-Thanh and Lothar Jansch7. Oxidation of Bacterial Proteome in Response to Starvation 89Thomas Nystrom8. Tale of Two Metal Reducers: Comparative Proteome Analysisof Geobacter sulferreducens PCA and Shewanella oneidensis MR-1 97Carol S. Giometti9. AMT Tag Approach to Proteomic Characterization of Deinococcusradiodurans and Shewanella oneidensis 113Mary S. Lipton, Margaret F. Romine, Matthew E. Monroe, Dwayne A. Elias, LjiljanaPasa-Tolic, Gordon A. Anderson, David J. Anderson, Jim Fredrickson, Kim K.Hixson, Christophe Masselon, Heather Mottaz, Nikola Tolic, and Richard D. Smithv 6. vi CONTENTSPART III PHYSIOLOGICAL PROTEOMICS OF INDUSTRIALBACTERIA 13510. Proteomics of Corynebacterium glutamicum: EssentialIndustrial Bacterium 137Andreas Burkovski11. Proteomics of Lactococcus lactis: Phenotypes for a Domestic Bacterium 149Mogens Kilstrup12. Proteomic Survey through Secretome of Bacillus subtilis 179Haike Antelmann, Jan Maarten van Dijl, Sierd Bron, and Michael HeckerPART IV PROTEOMICS OF PATHOGENIC MICROORGANISMS 20913. Analyzing Bacterial Pathogenesis at Level of Proteome 211Phillip Cash14. Unraveling Edwardsiella tarda Pathogenesis Using theProteomics Approach 237P. S. Srinivasa Rao, Yuen Peng Tan, Jun Zheng, and Ka Yin Leung15. Structural Proteomics and Computational Analysis of a Deadly Pathogen:Combating Mycobacterium tuberculosis from Multiple Fronts 245Michael Strong and Celia W. Goulding16. Proteomic Studies of Plant-Pathogenic Oomycetes and Fungi 271Catherine R. Bruce, Pieter van West, and Laura J. Grenville-Briggs17. Candida albicans Biology and Pathogenicity: Insights from Proteomics 285Aida Pitarch, Cesar Nombela, and Concha Gil18. Contributions of Proteomics to Diagnosis, Treatment, andPrevention of Candidiasis 331Aida Pitarch, Cesar Nombela, and Concha Gil19. Identification of Protein Candidates for Developing BacterialGhost Vaccines against Brucella 363Vito G. Delvecchio, Tim Alefantis, Rodolfo A. Ugalde, DiegoComerci, Maria Ines Marchesini, Akbar Khan, Werner Lubitz,and Cesar V. Mujer20. Genomics and Proteomics in Reverse Vaccines 379Guido GrandiPART V PROTEOME DATABASES, BIOINFORMATICS, ANDBIOCHEMICAL MODELING 39521. Databases and Resources for in silico Proteome Analysis 397Manuela Pruess, Paul Kersey, Tamara Kulikova, and Rolf Apweiler 7. 22. Interspecies and Intraspecies Comparison of MicrobialCONTENTS viiProteins: Learning about Gene Ancestry, Protein Function,and Species Life Style 415Bernard Labedan and Olivier Lespinet23. Cellular Kinetic Modeling of the Microbial Metabolism 437Igor I. Goryanin, Galina V. Lebedeva, Ekaterina A. Mogilevskaya,Eugeniy A. Metelkin, and Oleg V. DeminINDEX 489 8. PREFACEHere, we have attempted to regroup those authors best able to provide an overview ofmicrobial proteomics: whole-organism functional biology. Never has the scientificcommunity better been placed to assimilate and interpret whole-organism functionality, sothis text advances quite some distance down this rarely, if ever, trodden path in much detailand focus. This tome has been designed to allow sector specialists and novices to delvebeneath the surface of this fascinating field and start to meaningfully grapple with moreesoteric concepts, such as a definition of life itself and life processes in the simplest livingorganisms. Schrodinger, in his seminal work What Is Life (Cambridge University Press,1945), made significant in-roads by defining the properties of a necessary information-containingsystem, namely his aperiodic crystal. Not so long after, Alfred Hershey (19081997) and his assistant Martha Chase (19232003) at the Cold Spring Harbor Laboratoryshowed that deoxyribonucleic acid (DNA), and not proteins, of the phage virus containedthe phage genes, that is, the code of life. Armed with access to a critical mass ofexperimentally acquired data akin to that presented in this textbook, we are now wellplaced to go further and refine the shared characters of living systems and to definefeatures of life itself. Such definitions are capable of mathematically constraining ourunderstanding of such complex phenomena within limited parameter sets.As a consequence of our increased understanding over the last half century, we aregradually succeeding in placing these mathematical and observational constraints of whatexactly constitutes a living system. In the case of microbes, their relative simplicity hasmade them attractive tragets for extensive experimental manipulation directed toward bothdissecting their biology in a quest for improved disease prevention and treatment and animproved understanding of whole-organism functional biology. In this text, we shall seeexcellent examples of the reductionist approach directed at analyzing the multitude ofparts and more holistic modeling exercises. Future endeavors will face the more dauntingtask of better linking these information sets between different resolution scales in time andspace within living systems and the relations of the whole with the external environmentall in a concerted effort to survive and deal with a wide range of stress conditions.Following decades of study dedicated to microbial physiology, environmental ecology,and biochemistry, our ability to examine holistic behavior in the light of a detailedknowledge base as to the molecular constituents has only been possible for at most adecade, namely since the sequencing of the first bacterial genome by Fleishman et al. inJune 1995 in Science. By a strange coincidence, it was also June 1995, thatWasinger et al.first employed and defined the term proteome in the scientific literature in the journalElectrophoresis.Over the last decade we have seen genomic, transcriptomic, proteomic, metabolomic,and systems modeling applied to microbial systems. The academic and commercialstimulus has undoubtedly been the attraction of tractability, that is, for the first time everix 9. genomewide, gene-product-wide analyses could be contemplated and experimentallyimplemented under stringently controlled conditions, for example, chemostats.Although the experimental tractability of microbes may prove attractive, the realitymay be somewhat different whereby this knowledge base starts to demand further answersin an epigenic context in full cognizance of the high-dimensional complexity inherent inall living systems. The molecules and atoms involved in life processes must remain totallyoblivious of any distinction between the interior of a living cell and the far reaches of ouruniverse; that is, the same laws of the universe must apply no matter where a moleculefinds itself. The dilemma and challenge to a more accurate understanding of these livingsystems are major: How then is it that living cells undertake ordered molecular work?Much of the answer can be attributed to both temporal and spatial segregation andmaintenance of life processes by keeping reactants apart; that is, the greatest art of life isvery much stopping life from stopping.In reality, even when armed with increasingly complete experimental data sets, we areonly beginning to scratch the surface of those life forces that are transmitted from onegeneration to the next in living cells and organisms. Nonetheless, as shown in this text, wecan now accurately compute information linked to cellular half-lives (cellular doublingtimes), levels of ribonucleic acid (RNA) and protein expressions over time, RNA andprotein synthesis and breakdown rates, spatial and temporal segregation within cells,metabolite production, and systems modeling dealing with biochemical complexityincluding both positive and negative feedback loops that influence physiologicalthresholds. The latter, in turn, commit whole cells to a given biological activity. Froman initial starting point, the building blocks acquired from the previous generation, theprogeny must manage intracellular molecular concentrations and exploit its geneticmachinery to respond to the cells needs in the face of environmental stress, shock of manydescriptions, and on-going need to survive. This balance between constituents andmolecular abundance intracellularly must then grapple with an astounding 9.6 trilliondistinct possibilities for proteinprotein interactions, as facilitated by the diversity of 5-mer binding sites expressed on the surface of the proteome of Escherichia coli(Humphery-Smith and Gestel, unpublished data).We wish our readers a stimulating encounter with this fascinating discipline ofmicrobial proteomics: whole-organism functional biology.IAN HUMPHERY-SMITH and MICHAEL HECKERx PREFACE 10. ACKNOWLEDGMENTSWe wish to thank most sincerely Lisa Shipley and Carol Mattacola for their assistance withthe collation of chapters and the implementation of editorial changes to submittedmanuscripts. Elsewhere, the editors wish to thank all authors for their contributions andparticularly their collective patience while we awaited completion of all chapters.Withoutthese efforts, this text would never have achieved the necessary level of overall disciplinecoverage so as to do justice to our chosen field.xi 11. CONTRIBUTORSTim Alefantis, Vital Probes, Mayfield, Pennsylvania, Identification of Protein Candi-datesfor Developing Bacterial Ghost Vaccines against Brucella.David J. Anderson, Biological Sciences Division, Pacific Northwest National Labora-tory,Richland, Washington, AMT Tag Approach to Proteomic Characterization ofDeinococcus radiodurans and Shewanella oneidensis.Gordon A. Anderson, Biological Sciences Division, Pacific Northwest NationalLaboratory, Richland, Washington, AMT Tag Approach to Proteomic Characterizationof Deinococcus radiodurans and Shewanella oneidensis.Haike Antelman, Institut fu r Mikrobiologie, Ernst-Moritz-Amdt-Universitat, Greifs-wald,Germany, A Proteomic Survey through the Secretome of Bacillus subtilis.Rolf Apweiler, European Bioinformatics Institute, Hinxton, Cambridge, United King-dom,Databases and Resources for in silico Proteome Analysis.Robert Benyon, Protein Function Group, Faculty of Veterinary Science, University ofLiverpool, Liverpool, United Kingdom, Strategies for Measuring Dynamics: TheTemporal Component of Proteomics.Sierd Bron, Department of Genetics, Groningen Biomolecular Sciences and Biotechnol-ogyInstitute, Haren, The Netherlands, A Proteomic Survey through the Secretome ofBacillus subtilis.Catherine R. Bruce, Aberdeen Oomycete Group, College of Life Sciences andMedicine, University of Aberdeen, Aberdeen, Scotland, Proteomic Studies of Plant-Pathogenic Oomycetes and Fungi.Andreas Burkovski, Lehrstuhl fu r Mikrobiologie, Friedrich-Alexander-UniversitatErlangen-Nurnberg, Erlangen, Germany, Protemics of Corynebacterium glutamicum:Essential Industrial Bacterium.Phillip Cash, Department of Medical Microbiology, University of Aberdeen, Aberdeen,Scotland, Analyzing Bacterial Pathogenesis at the Level of the Proteome.Ricardo Cavicchioli, School of Biotechnology and Biomolecular Sciences,University of New South Wales, Sydney, New South Wales, Australia, Proteomicsof Archaea.Ian N. Clarke, Molecular Microbiology Group, University Medical School, South-amptonGeneral Hospital, Southampton, United Kingdom, Quest for CompleteProteome Coverage.xiii 12. xiv CONTRIBUTORSDiego Comerci, Instituto de Investigaciones Biotecnologicas, Universidad Nacional deGeneral San Martin, Buenos Aires, Argentina, Identification of Protein Candidates forDeveloping Bacterial Ghost Vaccines against Brucella.Vito G. DelVecchio, Vital Probes, Mayfield, Pennsylvania, Identification of ProteinCandidates for Developing Bacterial Ghost Vaccines against Brucella.Oleg V. Demin, A. N. Belozersky Institute of Physico-Chemical Biology, MoscowState University, Moscow, Russia, Cellular Modeling of Biochemical Processes inMicroorganisms.Dwayne A. Elias, Biological Sciences Division, Pacific Northwest National Laboratory,Richland, Washington, AMT Tag Approach to Proteomic Characterization ofDeinococcus radiodurans and Shewanella oneidensis.Jim Frederickson, Environmental Molecular Sciences Laboratory, Pacific NorthwestNational Laboratory, Richland, Washington, AMT Tag Approach to ProteomicCharacterization of Deinococcus radiodurans and Shewanella oneidensis.Concha Gil, Department of Microbiology II, Faculty of Pharmacy, ComplutenseUniversity of Madrid, Madrid, Spain, Candida albicans Biology and Pathogenicity:Insights from Proteomics; Contributions of Proeteomics to Diagnosis, Treatment, andPrevention of Candidiasis.Carol S. Giometti, Argonne National Laboratory, Argonne, Illinois, A Tale of Two MetalReducers: Comparative Proteome Analysis of Geobacter sulferreducens PCA andShewanella oneidensis MR-1.Amber Goodchild, Johnson & Johnson Research, Eveleigh, New SouthWales, Australia,Proteomics of Archaea.Igor Goryanin, Edinburgh Centre for Bioinformatics, University of Edinburgh,Edinburgh, Scotland, Cellular Modeling of Biochemical Processes in Microorganisms.Celia W. Goulding, UCLA-DOE Institute of Genomics and Proteomics, Los Angeles,California, Structural Proteomics and Computational Analysis of a Deadly Pathogen:Combating Mycobacterium tuberculosis from Multiple Fronts.Guido Grandi, Chiron Vaccines Research, Sienna, Italy, Genomics and Proteomics inReverse Vaccines.Laura J. Grenville-Briggs, Aberdeen Oomycete Group, College of Life Sciences andMedicine, University of Aberdeen, Aberdeen, Scotland, Proteomic Studies of Plant-Pathogenic Oomycetes and Fungi.Michael Hecker, Institut fur Mikrobiologie, Ernst-Moritz-Amdt-Universitat, Greifswald,Germany, A Proteomic Survey through the Secretome of Bacillus subtilis.Richard Herrmann, ZMBH Universitat Heidelberg, Heidelberg, Germany, Proteome ofMycoplasma pneumoniae.Kim K. Hixson, Biological Sciences Division, Pacific Northwest National Laboratory,Richland, Washington, AMT Tag Approach to Proteomic Characterization ofDeinococcus radiodurans and Shewanella oneidensis. 13. CONTRIBUTORS xvLothar Jansch, Deutsche Forschung Gesellschaft, Biologisches Zentrum, Braunschweig,Germany, Elucidation of the Mechanisms of Acid Stress in Listeria monocytogenes byProteomic Analysis.Paul Kersey, European Bioinformatics Institute, Hinxton, Cambridge, United Kingdom,Databases and Resources for in silico Proteome Analysis.Akbar Khan, Chemical and Biological Defense Directorate, Defense Threat ReductionAgency, Alexandria, Virginia, Identification of Protein Candidates for DevelopingBacterial Ghost Vaccines against Brucella.Mogens Kilstrup, Microbial Physiology and Genetics Group, Bio-Centrum-DTU,Technical University of Denmark, Lyngby, Denmark, Proteomics of Lactococcuslactis: Phenotypes for a Domestic Bacterium.Tamara Kulikova, European Bioinformatics Institute, Hinxton, Cambridge, UnitedKingdom, Databases and Resources for in silico Proteome Analysis.Bernard Labedan, E volution Moleculaire et Bioinformatique des Genomes, Institut deGenetique et Microbiologie, Universite Paris-Sud, Orsay, France, Interspecies andIntraspecies Comparison of Microbial Proteins: Learning about Gene Ancestry,Protein Function, and Species Life Style.Galina V. Lebedeva, A. N. Belozersky Institute of Physico-Chemical Biology, MoscowState University, Moscow, Russia, Cellular Modeling of Biochemical Processes inMicroorganisms.Olivier Lespinet, E volution Moleculaire et Bioinformatique des Genomes, Institut deGenetique et Microbiologie, Universite Paris-Sud, Orsay, France, Interspecies andIntraspecies Comparisons of Microbial Proteins: Learning about Gene Ancestry,Protein Function, and Species Life Style.Ka Yin Leung, Department of Biological Sciences, Faculty of Science, NationalUniversity of Singapore, Unraveling Edwardsiella tarda Pathogenesis Using theProteomics Approach.Mary S. Lipton, Biological Sciences Division, Pacific Northwest National Laboratory,Richland, Washington, AMT Tag Approach to Proteomic Characterization ofDeinococcus radiodurans and Shewanella oneidensis.Werner Lubitz, Biotech Innovation Research Development and Consulting GmbH & CoKEG, Wien, Austria, Identification of Protein Candidates for Developing BacterialGhost Vaccines against Brucella.Maria Ines Marchesini, Instituto de Investigaciones Biotecnologicas, UniversidadNacional de General San Martin, Buenos Aires, Argentina, Identification of ProteinCandidates for Developing Bacterial Ghost Vaccines against Brucella.Christophe Masselon, Biological Sciences Division, Pacific Northwest NationalLaboratory, Richland, Washington, AMT Tag Approach to Proteomic Characterizationof Deinococcus radiodurans and Shewanella oneidensis. 14. xvi CONTRIBUTORSEugeniy A. Metelkin, A. N. Belozersky Institute of Physico-Chemical Biology, MoscowState University, Moscow, Russia, Cellular Modeling of Biochemical Processes inMicroorganisms.Matthew E. Monroe, Biological Sciences Division, Pacific Northwest NationalLaboratory, Richland, Washington, AMT Tag Approach to Proteomic Characterizationof Deinococcus radiodurans and Shewanella oneidensis.Heather Mottaz, Environmental Molecular Sciences Laboratory, Pacific NorthwestNational Laboratory, Richland, Washington, AMT Tag Approach to ProteomicCharacterization of Deinococcus radiodurans and Shewanella oneidensis.Cesar Nombela, Department of Microbiology II, Faculty of Pharmacy, ComplutenseUniversity of Madrid, Madrid, Spain, Candida albicans Biology and Pathogenicity:Insights from Proteomics; Contributions of Proteomics to Diagnosis, Treatment, andPrevention of Candidiasis.Thomas Nystrom, Department of Cell Molecular Biology, Goteborg University,Goteborg, Sweden, Oxidation of the Bacterial Proteome in Response to Starvation.C. David OConnor, Centre for Proteomic Research and School of Biological Sciences,University of Southampton, Southampton, United Kingdom, Quest for CompleteProteome Coverage.Ljiljana Pasa-Tolic, Biological Sciences Division, Pacific Northwest National Labora-tory,Richland, Washington, AMT Tag Approach to Proteomic Characterization ofDeinococcus radiodurans and Shewanella oneidensis.Luan Phan-Thanh, Unite Pathologie Infectieuse et Immunologie, Institut National de laRecherche Agronomique, Nouzilly, France, Elucidation of the Mechanisms of AcidStress in Listeria monocytogenes by Proteomic Analysis.Aida Pitarch, Department of Microbiology II, Faculty of Pharmacy, ComplutenseUniversity of Madrid, Madrid, Spain, Candida albicans Biology and Pathogenicity:Insights from Proteomics; Contributions of Proteomics to Diagnosis, Treatment, andPrevention of Candidiasis.Julie M. Pratt, Protein Function Group, Faculty of Veterinary Science, University ofLiverpool, Liverpool, United Kingdom, Strategies for Measuring Dynamics: TheTemporal Component of Proteomics.Manuela Pruess, European Bioinformatics Institute, Hinxton, Cambridge, UnitedKingdom, Databases and Resources for in silico Proteome Analysis.Mark Raftery, Bioanalytical Mass Spectrometry Facility, University of New SouthWales, Sydney, New South Wales, Australia, Proteomics of Archaea.P. S. Srinivasa Rao, Department of Biological Sciences, Faculty of Science, NationalUniversity of Singapore, Unraveling Edwardsiella tarda Pathogenesis Using theProteomics Approach.Margaret F. Romine, Environmental Molecular Sciences Laboratory, Pacific NorthwestNational Laboratory, Richland, Washington, AMT Tag Approach to ProteomicCharacterization of Deinococcus radiodurans and Shewanella oneidensis. 15. CONTRIBUTORS xviiThomas Ruppert, ZMBH Universitat Heidelberg, Heidelberg, Germany, Proteome ofMycoplasma pneumoniae.Paul Skipp, Centre for Proteomic Research and School of Biological Sciences,University of Southampton, Southampton, United Kingdom, Quest for CompleteProteome Coverage.Richard D. Smith, Biological Sciences Division, Pacific Northwest National Laboratory,Richland, Washington, AMT Tag Approach to Proteomic Characterization ofDeinococcus radiodurans and Shewanella oneidensis.Michael Strong, UCLA-DOE Institute of Genomics and Proteomics, Los Angeles,California, Structural Proteomics and Computational Analysis of a Deadly Pathogen:Combating Myobacterium tuberculosis from Multiple Fronts.Yuen Peng Tan, Department of Biological Sciences, Faculty of Science, NationalUniversity of Singapore, Unraveling Edwardsiella tarda Pathogenesis Using theProteomics Approach.Nikola Tolic, Biological Sciences Division, Pacific Northwest National Laboratory,Richland, Washington, The AMT Tag Approach to Proteomic Characterization ofDeinococcus radiodurans and Shewanella oneidensis.Rodolfo A. Ugalde, Instituto de Investigaciones Biotecnologicas, Universidad Nacionalde General San Martin, Buenos Aires, Argentina, Identification of Protein Candidatesfor Developing Bacterial Ghost Vaccines against Brucella.Jan Maarten van Dijl, Department of Molecular Bacteriology, University of Groningen,Groningen, The Netherlands, A Proteomic Survey through the Secretome of Bacillussubtilis.Pieter van West, Aberdeen Oomycete Group, College of Life Sciences and Medicine,University of Aberdeen, Aberdeen, Scotland, Proteomic Studies of Plant-PathogenicOomycetes and Fungi.Valerie Wasinger, Bioanalytical Mass Spectrometry Facility, University of New SouthWales, Sydney, Australia, Holistic Biology of Microorganisms: Genomics, Transcrip-tomics,and Proteomics.Jun Zheng, Department of Biological Sciences, Faculty of Science, National Universityof Singapore, Unraveling Edwardsiella tarda Pathogenesis Using the ProteomicsApproach.Ekaterina A. Zobova, A. N. Belozersky Institute of Physico-Chemical Biology, MoscowState University, Moscow, Russia, Cellular Modeling of Biochemical Processes inMicroorganisms. 16. PART IGENERAL PROTEOMICSOF MICROORGANISMS/MODELORGANISMS 17. CHAPTER 1Holistic Biology of Microorganisms:Genomics, Transcriptomics,and ProteomicsVALERIE WASINGERUniversity of New South Wales, Sydney, Australia1.1 INTRODUCTIONAn organisms phenotype is determined by the environment and its genetic content, and assuch understanding the relationship between genome, transcriptome, and proteome is one ofthe fundamental goals of biology. Over the last decade quantitative biological analysis hasbeen performed at the genomic, transcribed, and translated level. This has occurred throughlarge-scale DNA sequencing, genomewide genetic analysis, DNA and protein chips, andtwo-dimensional gel electrophoresis (2DE) or multidimensional liquid chromatographyintegrated with mass spectrometry (LC-MS) for the fast and highly sensitive analysis ofproteins. The rapid speed of progress in genomics has been impressive and unrivaled inproteomics, with more than 160 organisms having been sequenced, including the humangenome. Despite this achievement, the information provided by any given genome hasgiven little insight into the workings of a cell. A better understanding of the cellularmachinery, biology, and disease processes will ultimately result frommining these genomesfor their cognate proteinsproteomics. This chapter aims to highlight the contributionproteomics has made to our collective knowledge of whole-organism microbiology.The expansion of the field of proteomics from the display of large numbers of proteinsusing 2DE to the incorporation of new technologies and a global perspective of proteinexpression, requisite to genomic sciences, has refined proteomics into a robust scientificfield. A focus on quantitative measurement of proteins, patterns of changes in proteinexpression, and protein interactions in the context of a whole cell has given the field ofproteomics a new level of maturity previously not seen.1.2 PROTEOME IN PERSPECTIVEUnlike genome sequencing, there has been no completion of a proteome, while aproteomic endpoint remains ill defined. Despite this, the study of the total proteinMicrobial Proteomics: Functional Biology of Whole Organisms, Edited by Ian Humphery-Smithand Michael Hecker. Copyright # 2006 John Wiley & Sons, Inc.3 18. 4 HOLISTIC BIOLOGY OF MICROORGANISMScomplement of a genome is feasible [13]. What remains the biggest challenge to theidentification and analysis of complex biological samples is the collective variability of allproteins physicochemical properties and a dependence on in vivo and ex vivo parameters.These parameters contribute to proteome complexity and are phenotypically manifested asa variation of relative protein abundance; modifications or truncations, for exampleenzymatic cleavage; altered molecular or protein interactions; complex formation orbreakdown; and presence or absence of proteins. Proteome complexity can be explainedfrom three diverse perspectives: evolutionary complexity, internal complexity, and samplecomplexity (addressed later).1.2.1 Evolutionary ComplexityComplexity is often attributed to the number of base pairs in a sequence that give rise tofunctional genes [4]. We readily admit that as we move up the evolutionary ladder, themost complex organism of all is our own kind. Nonetheless, our genetic likeness to plants(A. thaliana) and worms (C. elegans) is striking [5]. However, annotation of a gene withina DNA sequence is not an indication that a gene is expressed or able to serve a usefulmetabolic or structural purpose. The one geneone protein tenet has long been rejected[6, 7] because alternate gene-splicing and posttranslational modifications can result inmultiple active forms of a protein. Gene numbers from theoretical proteomes of fullysequenced organisms are compared in Figure 1.1 and are also available from http://www.ebi.ac.uk/. This figure demonstrates evolutionary complexity is not governed by genecontent, nor is sequence information sufficient to describe the individuality of an organism[8]. Complexity is more significantly a reflection of protein regulation [9, 10]. As we moveup the evolutionary hierarchy there is a distinct increase in gene product modification: forMycoplasma genitalium this has been quantified at a rate of 1.2 times more proteins than4035302520151050Escherichia coli K12Saccaromyces cerevisiaeMycoplasma genitaliumArabidopsis thalianaFugu rubripesHomo sapiensGene number x 100Organism in order of increasing evolutionary complexityFigure 1.1 Predicted proteome of fully sequenced organisms collated from nonredundantproteome sets from SwissProt and TREMBL entries (http://www.ebi.ac.uk/). 19. AMALGAMATION OF TOOLS FOR A PROTEOMIC TOOL BOX 5genes [1]; for Escherichia coli it is 1.3 proteins per gene; for yeast, 3 proteins per gene;and for human, as many as 10 proteins per gene [8]. This has been estimated fromboth two-dimensional (2D) sodium dodecyl sulfate polyacrylamide gel electrophoresis(SDS-PAGE) and tandem MS (MS/MS) work.1.2.2 Internal ComplexityInternal complexity is a property of the network of interacting protein, transcript, andgenes within a cell. As life is not just an assembly of these individual components, thestudy of gene function has been explored in terms of the relatively static localized gene andprotein interaction networks and the massively parallel study of global networksembracing the higher order and collective behavior of genes and proteins as a proteome-scalenetwork [11]. The study of the sum of all interacting parts in a biological system iscalled systems biology and addresses the network of interactions of biochemical/signalingpathways and their modulation, the influence of spatial and temporal differences in thesenetworks, and how this knowledge can be applied to providing therapeutic targets indisease processes [12]. The recent interest in large-scale identification of functionallylinked proteins can be affiliated to the development of high-throughput experimentationand computational procedures and in extensive database curation of this information.Computational approaches for finding gene and protein interactions complement andextend experimental approaches such as synthetic lethal and suppressor screens, yeasttwo-hybrid experiments, and high-throughput MS interaction assays [13]. Computationalmethods based on sequence do not assume knowledge about protein function and cantherefore be used to assign function to uncharacterized proteins linked to networkpathways of known function. Computational methods for studying protein associationsinclude (i) phylogenetic profiling, based on the co-occurrence of proteins in differentgenomes [14]; (ii) domain fusion or Rosetta stone sequences [15, 16] where fuseddomains of a protein in one organism are used to predict interaction of these domainsseparated through evolution in another organism; and (iii) gene clustering in one organismwhere the genes have been separated by evolution in another organism [17].The very nature of proteomics embodies all of these complexities. As a result, diverseapproaches have and are being used to overcome some of the issues associated with thesecomplexities (e.g., large protein numbers), thereby creating unique data sets. Furthermore,comparing multiple technologies then has the advantage of highlighting differences inmeasurements, thereby extending the proteomic coverage, but only if these approachescan be unified meaningfully.1.3 AMALGAMATION OF TOOLS FOR A PROTEOMIC TOOL BOXThe field of proteomics has always been heavily reliant on protein characterizationtechnologies. A powerful repertoire of tools has been implemented for the separation andidentification of thousands of proteins simultaneously. In 1995, this consisted of Edmansequencing amino acid analysis, peptide mass fingerprinting, ladder sequencing, andexpression of cloned inserts, to name a few, and these were approached in an hierarchicalmanner for protein identification. Success using these tools was contingent on thesensitivity of instruments for accurate mass determination [1] and available proteindatabases for protein identification. There was little scope for characterization of 20. 6 HOLISTIC BIOLOGY OF MICROORGANISMSSampleDNA ProteinDisplay/arrayRNA ChipPrefractionationMetabolitesAffinity-based analysis 1D Chromatography 1DE2D ChromatographyDetection2DENon-MS-based analysis MS-based analysis LabelDatabasesSeparation technologyQuantitationIdentification &characterizationAutomationBioinformaticsFigure 1.2 Schematic of integration of technologies for proteomics. The unification of DNA,mRNA, and metabolite information is essential for a complete understanding of biologicalfunction [58].unknown proteins on a large scale. Today, the proteomic tool box has assimilatedgenomic, metabolomic, transcriptomic, and proteomic tools, with MS technologies stillproviding the backbone for much of the proteomic analysis. A representation of anintegrated proteomic tool box is given in Figure 1.2.One of the major technological challenges facing protein analysis is the dynamic rangeof expression of proteins within massively complex samples [18]. This difference can be asgreat as 12 orders of magnitude, and this is currently beyond the range of detection capablefor our existing technologies. Additionally, a few highly abundant proteins making up alarge percentage of total proteins can mask the detection of lower abundance andbiologically significant proteins [19]. A third technological challenge, alluded to earlier, issample complexity. The expression of a myriad of different protein species with similarphysicochemical properties is compounded by the numerous modifications that can takeplace posttranscriptionally. Over 300 different modifications have been described forproteins [20, 21].These on-going challenges have been addressed in a number of ways and havebenefited agriculture, industry, and biomedicine. Two-dimensional gel electrophoresis forproteomic applications is still widely accepted as the technology capable of delivering the 21. AMALGAMATION OF TOOLS FOR A PROTEOMIC TOOL BOX 7greatest separation power for highly complex samples. The expansion of pH separationusing a series of overlapping narrow-range gels to assemble a contiguous collage ofprotein expression and allow for a more meticulous analysis of proteomes was establishedin 1997 for O. anthropi [22] and M. genitalium. Close to two-thirds of M. genitalium openreading frames (ORFs) were shown to be expressed using this approach [2]. This approachis now commonly used and bacterial examples include Spiroplasma melliferum [23],Mycobacterium tuberculosis [24], Corynebacterium glutamicum [25], and Streptococcusmutans [26].Proteomics is also providing practical applications through the identification ofimmunogenic proteins as potential vaccine targets. Many hypotheses generated in silico bygenomics have been validated through functional studies of the transcriptome andproteome and have led to the identification of essential genes. Using 2DE [27] and DNAmicroarrays [28] for differential expression analysis has confirmed that the resistance ofM. tuberculosis to the anti-tuberculosis cell envelope drug isoniazid was the result ofoverexpression of components of the fatty acid synthase system, in particular AcpM and acarrier protein synthase KasA. A compilation of M. tuberculosis [29] and Mycobacteriumbovis [30] induced immunoresponses in splenocytes using 2D liquid-phase electrophoresishas also identified multiple antigens associated with posttranslationally modified proteins.In 1999, Jungblut et al. [31] compared the proteomes of two nonvirulent vaccine strains ofM. bovis BCG with two virulent strains of M. tuberculosis to identify protein candidatesfor potential vaccine development as well as for diagnostic and therapeutic purposes. Over30 differences were identified among the strains, including three cell envelope proteins,some antigenic proteins, and novel unannotated proteins. This work, as well as work fromothers, has contributed to a better understanding of the pathogenic and physiologicalmechanism available to this organism and also contributed to the development of newvaccine candidates. Mycobacterium bovis BCG (live attenuated) has been the only widelyused vaccine available against tuberculosis until recently, with new tuberculosis vaccinetrials begun in 2004.Two-dimensional gel electrophoresis is a powerful protein separation technique incombination with MS, yet it does not always deliver the appropriate sensitivity for thediscovery of low-level proteins [32], proteins with extreme isoelectric point (pI) or mass[11], or membrane-associated proteins because of solubility issues [33]. For these reasons,the development of alternative approaches to 2DE, such as multidimensionalchromatography coupled to MS, has been vital to the success of Bifidobacteriumproteomics. Bifidobacterium is a gram-positive prokaryote that naturally colonizes thehuman gut exerting health-promoting effects. Clinical studies have claimed thatbifidobacterial probiotics promote gastrointestinal tract homeostasis and health becauseof antidiarrheal, immunomodulating, and possibly anticarcinogenic properties. Proteo-micshas contributed to the comprehensive understanding of the physiologicalmechanisms underlying these properties [34]. A predominant portion of Bifidobacteriuminfantis proteome consists of enzymes of the glycolytic and pentose-phosphate pathways,enzymes of anaerobic metabolism, transcriptional factors, shock proteins, ribosomalproteins, and proteases. The high level of these proteins during the exponential phase ofgrowth underlines their central role in cell survival, replication, and energy metabolism,giving an indication of the basal functions, which are essential for the vitality of thebifidobacteria biological system.A unification of genomics, transcriptomics, and proteomics will provide acomprehensive knowledge base of gene function and a powerful reference of protein 22. 8 HOLISTIC BIOLOGY OF MICROORGANISMSproperties. There are, however, significant challenges that are being addressed but mayremain unresolved until technological advances can overcome them. The goal ofdeciphering the entire protein complement of an organism and making heuristicrelationships is formidable and often limited by cell dynamics. Understanding biologicalevents such as modification, complexity, environmental input, and technologicalconstraints of detection thresholds and bioinformatics will be required to overcome thechallenges for a complete proteome study [1, 35].1.4 RELATIONSHIP BETWEEN GENOME, TRANSCRIPTOME,AND PROTEOMEAll life is linked by a common genetic scaffold constrained to 4 nucleotides and 20 aminoacids and as such phenotypic differences require far more investigation than was firstanticipated. Genomics presents only one level of functional information. Both qualitativeand quantitative unique levels of information are also given by the transcriptome andproteome as well as the interactome and metabolome.1.4.1 Transcriptome and ProteomicsTranscriptome analysis involves messenger ribonucleic acid (mRNA), the relayer ofinformation for protein synthesis. Several methods, including serial analysis of geneexpression (SAGE), oligonucleotide and complementary deoxyribonucleic acid (cDNA)microarrays, and large-scale sequencing of expressed tags, are available to measure geneexpression at the mRNA level globally and quantitatively. Measurements of proteinexpression and transcript do not always correlate. This is due to protein abundance alsobeing influenced by protein stability, translation rate, modulation of transcript abundanceby other proteins, posttranslational modifications, and half life; therefore, mRNA cannotalways be a predictor of protein abundance [36].The absolute range of transcript abundance is largely unknown in microorganisms;however, a significant amount of work has been done in yeast. A study by Futcher et al.[37] has revealed that for each mRNA there are approximately 4000 molecules ofcognate protein produced and a small number of these proteins make up at least 50% ofall cellular protein in yeast. For these abundant proteins, several statistical methodshave shown a correlation between mRNA and protein abundances [3]. A studyinvolving the use of SAGE [38] for mRNA measurements and 2DE for proteinmeasurements [39] has also shown a close correlation between mRNA and proteinlevels for high-copy-number proteins but relatively poor correlation for proteinstranscribed at 10 or less copies per cell. Seventy-five percent of genes are transcribed atone or fewer copies per cell [38] with some transcripts per cell as low as 0.001 per cellgeneration [40]. This has also been confirmed recently using immunodetection of high-affinityepitope-tagged ORFs known as tandem affinity purification (TAP) [41] forwhich 80% of the Saccharomyces cerevisiae proteome was analyzed [3]. Transcriptnumbers, protein molecules, and the positive correlation between mRNA and proteinabundance for some proteins are shown in Figure 1.3, as is a comparison of the range ofproteomic coverage for the techniques of TAP, multidimensional protein identificationtechnology (MudPIT), and 2DE [3]. 23. RELATIONSHIP BETWEEN GENOME, TRANSCRIPTOME, AND PROTEOME 9Figure 1.3 Analysis of protein expression in yeast showing (a) correlation between proteinabundance and transcripts: Top panel shows the relationship between steady-state mRNA andprotein levels in yeast as determined by microarray analysis. Middle panel shows ORFs sorted intodiscrete mRNA levels and means plotted against mean protein abundance. Lower panel shows onlya comparison of protein and mRNA levels for some essential soluble proteins. (b) Absolute range ofexpression possible using TAP/Western blot, LC-MS using a multidimensional chromatographyapproach, and 2DE [3]. (See color insert.)1.4.2 Metabolome and ProteomicsThere is a nonlinear relationship between the presence of a metabolite and a gene, asmany genes may be involved in the synthesis or degradation of one metabolite. Anexample of metabolomics is provided by a well studied microbe, C. glutamicum,important in metabolic engineering as its production of glutamine and lysine is usedextensively in the food industry [25]. This organism has been comprehensively studiedin terms of genome [42, 43], transcriptome [44, 45], proteome [4648], and metabolome[45]. However, until recently integration of these studies had not occurred. An in-depthstudy of glycolysis, pentose-phosphate pathway, tricarboxylic acid (TCA) cycle, andlysine synthesis by Kromer et al. [45] of this organism has revealed a dynamicrelationship between transcriptome, metabolome, and fluxome (changes in metabolites).It was found that growth continued despite depletion of essential threonine andmethionine from growth media (achieved by scavenging intracellular stores) with anincrease in total soluble proteins, indicating protein synthesis was still active.Additionally, genes active in translation such as ribosomal proteins and other proteinsynthesis machinery were significantly expressed. A maximal flux for most enzymescorrelated with maximal gene expression, and this could be affected by down regulationat the transcriptional level of enzymes. A conclusion of this work is that it is essential tomeasure gene, transcript, protein, and metabolite differences to gain insight intobiological systems. 24. 1.5 WHAT HAVE MODEL ORGANISMS CONTRIBUTED TO OURUNDERSTANDING OF BIOLOGICAL SYSTEMS?Organisms that are representative of more complex systems are amenable to experimentalstudy and are associated with extensive accumulated information from many sources andcan be defined as model organisms [49]. In the last 30 years, fundamental researchinvolving model organisms has profoundly advanced our understanding of biologicalsystems. As a result of considerable effort around the globe, organism-specific databaseshave been created as knowledge of an organism as well as comparative information ofrelevance to numerous research directions and perspectives becomes available. Manymetabolic and developmental pathways are conserved in nature, irrespective ofclassification level. This feature invites the use of model organisms to minimize theeffort required to understand complex biological systems. Table 1.1 summarizes some ofthe advances that three model organisms have afforded us and how this information hasbeen applied to other organisms.Life in its most minimal form is still biologically complex. The smallest bacterialgenome capable of independent survival is that of M. genitalium (517 genes). Interest inthis bacterium has focused on derivation of the minimal gene set or what is essentiallyrequired to make a cell alive. By selectively switching off each gene in turn, Hutchisonet al. [50] were able to determine an essential requirement of 260350 M. genitaliumgenes, 100 of which had unknown function. This has since been revised to 250 bycomparison to other bacterial genomes [51].On a similar line of thought, the simplest eukaryotic genome, S. cerevisiae, plays animportant role as a model organism for understanding more complex genomes such as ourown. The best example of the value of yeast as a model involves the study of some human-disease-causing genes and their orthologues in yeast, such as hereditary nonpolyposiscolon cancer (MSH2 and MLH1), neurofibromatosis type 1 (IRA2), ataxia telangiectasia(TEL1), and Werners syndrome (SGS1) [32]. In humans, genetic inheritance of theseTABLE 1.1 Summary of Three Model Organisms and How They Have InfluencedProteomicsReason for CelebratedOrganism Model Status Findings Applications ReferencesM. genitalium Smallest self-replicatingbacterium; determinecore genes that drivelifeMinimal gene set of300, 427 abundantlyexpressed proteinsDerive a syntheticminimal cell forgenetic or biochemicalmanipulation, uses innanotechnology,therapeutic vectordelivery of DNA[2, 50, 52]S. cerevisiae Simplest eukaryoticmodel for more complexorganisms; best-studiedeukaryotic systemDescription of proteininteraction map,first chromosomeever sequencedUnknown functionprediction in otherorganisms[15]; [16]; [53];[54]; [55]E. coli Biochemical, molecular,and metabolically bestcharacterized system2D SDS-PAGEdevelopedusing E. coliStandard proteinseparation technique inproteomics; virtual cellbased on accumulatedE. coli knowledge andapplied across species[56]; [57]10 HOLISTIC BIOLOGY OF MICROORGANISMS 25. genes results in disease. Initial insight as to the function of these genes was obtainedbecause of their sequence homology to yeast genes and genes of other organisms.Escherichia coli is the epitome of bacterial model organisms as it is one of the best-studiedmicrobes in terms of genome sequence, metabolic and regulatory networks,proteome, and mutant phenotype studies. Both the pathogenic (O157:H7) andnonpathogenic (K12) strains have been sequenced and compared and databases existenabling comparison of metabolic and genomic information (Ecocyc: http://ecocyc.org/;Kegg: http://www.genome.ad.jp/kegg/kegg2.html).There are many more microbial model organisms, and some of their contributions toproteomics and science in general are discussed by contributing authors in this book.1.6 CONCLUSIONThere is an amazing lack of cross communication between the fields of genomics,transcriptomics, and proteomics. This dilemma can be attributed to the dynamic and open-endednature of the proteome. It is compounded by the evolutionary, internal, and samplecomplexities of studied organisms and the rapid generation of volumes of data fromdiverse groups. However, it is apparent that the best understood organisms have hadinformation contributed from all fields. This has most easily occurred for the microbes andwill also occur with higher organisms as the inertia to propel research forward and unifyknowledge is reached. This will result in a holistic understanding that will be beneficial forall biological systems.ACKNOWLEDGMENTSI would like to acknowledge support in part from the Australian Government Systemic InfrastructureInitiative grant.REFERENCES1. Wasinger, V. C., Cordwell, S. J., Cerpa-Poljak, A., Yan, J. X., Gooley, A. A., Wilkins, M. R.,Duncan, M.W., Harris, R.,Williams, K. L., and Humphery-Smith, I., Progress with gene-productmapping of the Mollicutes: Mycoplasma genitalium, Electrophoresis 1995, 16, 10901094.2. Wasinger, V. C., Pollack, J. D., and Humphery-Smith, I., The proteome of Mycoplasmagenitalium; CHAPS soluble component, Eur. J. Biochem. 2000, 267, 15711582.3. Ghaemmaghami, S., Huh, W-K., Bower, K., Howson, R., Belle, A., Dephoure, N., OShea, E.,and Weissman, J., Global analysis of protein expression in yeast, Nat. Biotechnol. 2003, 425,737741.4. Adami, C., Ofria, C., and Collier, T. C., Evolution of biological complexity, PNAS 2000, 97(9),44634468.5. Southan, C., Has the yo-yo stopped? An assessment of human protein-coding gene number,Proteomics 2004, 4(6), 17121726.6. Jaenisch, R., and Bird, A., Epigenetic regulation of gene expression: How the genome integratesintrinsic and environmental signals, Nat. Genet. 2003, 33, s245254.REFERENCES 11 26. 12 HOLISTIC BIOLOGY OF MICROORGANISMS7. Strohman, R., Epigenesis: The missing beat in biotechnology? Biotechnology (NY) 1994, 12(2),156164.8. Kellner, R., Proteomics. Concepts and perspectives, Frenius J. Anal. Chem. 2000, 366, 517524.9. Harrison, P. M., Kumar, A., Lang, N., Snyder, M., and Gerstein, M., A question of size: Theeukaryotic proteome and the problems in defining it, Nucleic Acids Res. 2002, 30(5), 10831090.10. Graveley, B. R., Alternative splicing: Increasing diversity in the proteomic world, Trends Genet.2001, 17(2), 100107.11. Huang, S., Back to the biology in systems biology: What can we learn from biomolecularnetworks? Brief Funct. Genomic Proteomic 2004, 2(4), 279297.12. Zhu, H., Huang, S., and Dhar, P., The next step in systems biology: Simulating the temporospatialdynamics of molecular network, Bioessays 2004, 26(1), 6872.13. Date, S. V., and Marcotte, E. M., Discovery of uncharacterized cellular systems by genome-wideanalysis of functional linkages, Nat. Biotechnol. 2003, 21(9), 10551062.14. Pellegrini, M., Marcotte, E. M., Thompson, M. J., Eisenberg, D., and Yeates, T. O., Assigningprotein functions by comparative genome analysis: Protein phylogenetic profiles, Proc. Natl.Acad. Sci. USA 1999, 96(8), 42854288.15. Marcotte, E. M., Pellegrini, M., Thompson, M. J., Yeates, T. O., and Eisenberg, D., A combinedalgorithm for genome-wide prediction of protein function, Nature 1999, 402(6757), 8386.16. Enright, A. J., Iliopoulos, I., Kyrpides, N. C., and Ouzounis, C. A., Protein interaction maps forcomplete genomes based on gene fusion events, Nature 1999, 402(6757), 8690.17. Dandekar, T., Snel, B., Huynen, M., and Bork, P., Conservation of gene order: A fingerprint ofproteins that physically interact, Trends Biochem. Sci. 1998, 23(9), 324328.18. Corthals, G. L., Wasinger, V. C., Hochstrasser D. F., and Sanchez, J-C., The dynamic rangeof protein expression: A challenge for proteomic research, Electrophoresis 2000, 21,11041115.19. Oda, Y., Nagasu, T., and Chait, B. T., Enrichment analysis of phosphorylated proteins as a toolfor probing the phosphoproteome, Nat. Biotechnol. 2001, 19, 379382.20. Krishna, R. G., and Wold, F., Post-translational modification of proteins, Adv. Enzymol. Relat.Areas Mol. Biol. 1993, 67, 265298.21. James, P., Mass spectrometry and the proteome, in P. James (Ed.), Proteome Research: MassSpectrometry, Springer, 2001, p. 6.22. Wasinger, V. C., Bjellqvist, B., and Humphery-Smith, I., Proteomic contigs of Ochrobactrumanthropi, application of extensive pH gradients, Electrophoresis 1997, 18, 13731383.23. Cordwell, S. J., Basseal, D. J., Bjellqvist, B., Shaw, D. C., and Humphery-Smith, I., Character-isationof basic proteins from Spiroplasma melliferum using novel immobilised pH gradients,Electrophoresis 1997, 18(8), 13931398.24. Urquhart, B. L., Cordwell, S. J, and Humphery-Smith, I., Comparison of predicted and observedproperties of proteins encoded in the genome of Mycobacterium tuberculosis H37Rv, Biochem.Biophys. Res. Commun. 1998, 253(1), 7079.25. Schaffer, S., Weil, B., Nguyen, V. D., Dongmann, G., Gunther, K., Nickolaus, M., Hermann, T.,and Bott, M., A high-resolution reference map for cytoplasmic and membrane-associatedproteins of Corynebacterium glutamicum, Electrophoresis 2001, 22(20), 44044422.26. Len, A. C., Cordwell, S. J., Harty, D.W., and Jacques, N. A., Cellular and extracellular proteomeanalysis of Streptococcus mutans grown in a chemostat, Proteomics 2003, 3(5), 627646.27. Mdluli, K., Slayden, R. A., Zhu, Y., Ramaswamy, S., Pan, X., Mead, D., Crane, D. D., Musser, J.M., and Barry, C. E. 3rd., Inhibition of a Mycobacterium tuberculosis beta-ketoacyl ACPsynthase by isoniazid, Science 1998, 280(5369), 16071610. 27. REFERENCES 1328. Wilson, M., DeRisi, J., Kristensen, H. H., Imboden, P., Rane, S., Brown, P. O., and Schoolnik,G. K., Exploring drug-induced alterations in gene expression in Mycobacterium tuberculosisby microarray hybridization, Proc. Natl. Acad. Sci. USA, 1999, 96(22), 1283312838.29. Covert, B. A., Spencer, J. S., Orme, I. M., and Belisle, J. T., The application of proteomicsin defining the T cell antigens of Mycobacterium tuberculosis, Proteomics 2001, 1(4), 574586.30. Gulle, H., Fray, L. M., Gormley, E. P., Murray, A., and Moriarty, K. M., Responses of bovine Tcells to fractionated lysate and culture filtrate proteins of Mycobacterium bovis BCG, Vet.Immunol. Immunopathol. 1995, 48(12), 183190.31. Jungblut, P. R., Schaible, U. E., Mollenkopf, H. J., Zimny-Arndt, U., Raupach, B., Mattow, J.,Halada, P., Lamer, S., Hagens, K., and Kaufmann, S. H., Comparative proteome analysis ofMycobacterium tuberculosis and Mycobacterium bovis BCG strains: Towards functionalgenomics of microbial pathogens, Mol. Microbiol. 1999, 33(6), 11031117.32. Botstein, D., Chervitz S. A., and Cherry J. M., Yeast as a model organism, Science 1997,277(5330), 12591260.33. Anderson, N. L., Polanski, M., Pieper, R., Gatlin, T., Tirumalai, R. S., Conrads, T. P., Veenstra,T. D., Adkins, J. N., Pounds, J. G., Fagan, R., and Lobley, A., The human plasma proteome: Anonredundant list developed by combination of four separate sources, Mol. Cell Proteomics2004, 3(4), 311326.34. Vitali, B., Wasinger, V., Brigidi, P., and Guilhaus, M., A proteomic view of Bifidobacteriuminfantis generated by multi-dimensional chromatography coupled with tandem mass spectro-metry,Proteomics, in press.35. Wilkins, M. R., Sanchez, J. C., Gooley, A. A., Appel, R. D., Humphery-Smith, I., Hochstrasser,D. F., and Williams, K. L., Progress with proteome projects: Why all proteins expressed by agenome should be identified and how to do it, Biotechnol. Genet. Eng. Rev. 1996, 13, 1950.36. Hatzimanikatis, V., and Lee, K. H., Dynamical analysis of gene networks requires both mRNAand protein expression information, Metab. Eng. 1999, 1(4), 275281.37. Futcher, B., Latter, G. I., Monardo, P., McLaughlin, C. S., and Garrels, J. I., A sampling of theyeast proteome, Mol. Cell. Biol. 1999, 19(11), 73577368.38. Velculescu, V. E., Zhang, L, Vogelstein, B., and Kinzler, K. W., Serial analysis of geneexpression, Science 1995, 270(5235), 484487.39. Gygi, S. P., Corthals, G. L., Zhang, Y., Rochon, Y., and Aebersold, R., Evaluation of two-dimensionalgel electrophoresis-based proteome analysis technology, Proc. Natl. Acad. Sci. USA2000, 97, 93909395.40. Holland, M. J., Transcript abundance in yeast varies over six orders of magnitude, J. Biol. Chem.2002, 277(17), 1436314366.41. Rigaut, G., Shevchenko, A., Rutz, B., Wilm, M., Mann, M., and Seraphin, B., A generic proteinpurification method for protein complex characterization and proteome exploration, Nat.Biotechnol. 1999, 17(10), 10301032.42. Ikeda, M., and Nakagawa, S., The Corynebacterium glutamicum genome: Features and impactson biotechnological processes, Appl. Microbiol. Biotechnol. 2003, 62(23), 99109.43. Kalinowski, J., Bathe, B., Bartels, D., Bischoff, N., Bott, M., Burkovski, A., Dusch, N., Eggeling,L., Eikmanns, B. J., Gaigalat, L., Goesmann, A., Hartmann, M., Huthmacher, K., Kramer, R.,Linke, B., McHardy, A. C., Meyer, F., Mockel, B., Pfefferle,W., Puhler, A., Rey, D. A., Ruckert,C., Rupp, O., Sahm, H., Wendisch, V. F., Wiegrabe, I., and Tauch, A., The completeCorynebacterium glutamicum ATCC 13032 genome sequence and its impact on the productionof L-aspartate-derived amino acids and vitamins, J. Biotechnol. 2003, 104(13), 525.44. Hayashi, M., Mizoguchi, H., Shiraishi, N., Obayashi, M., Nakagawa, S., Imai, J., Watanabe, S.,Ota, T., and Ikeda, M., Transcriptome analysis of acetate metabolism in Corynebacterium 28. 14 HOLISTIC BIOLOGY OF MICROORGANISMSglutamicum using a newly developed metabolic array, Biosci. Biotechnol. Biochem. 2002, 66(6),13371344.45. Krmer, J. O., Sorgenfrei, O., Klopprogge, K., Heinzle, E., andWittmann, C., In-depth profiling oflysine-producing Corynebacterium glutamicum by combined analysis of the transcriptome,metabolome, and fluxome, J. Bacteriol. 2004, 186(6), 17691784.46. Hermann, T., Pfefferle,W., Baumann, C., Busker, E., Schaffer, S., Bott, M., Sahm, H., Dusch, N.,Kalinowski, J., Puhler, A., Bendt, A. K., Kramer, R., and Burkovski, A., Proteome analysis ofCorynebacterium glutamicum, Electrophoresis, 2001, 22(9), 17121723.47. Bendt, A. K., Burkovski, A., Schaffer, S., Bott, M., and Farwick, M., and Hermann, T., Towardsa phosphoproteome map of Corynebacterium glutamicum, Proteomics 2003, 3(8), 16371646.48. Hermann, T., Finkemeier, M., Pfefferle, W., Wersch, G., Kramer, R., and Burkovski, A., Two-dimensionalelectrophoretic analysis of Corynebacterium glutamicum membrane fraction andsurface proteins. Electrophoresis 2000, 21(3), 654659.49. Barr, M. M., Super models, Physiol Genomics 2003, 13(1), 1524.50. Hutchison, C. A., Peterson, S. N., Gill, S. R., Cline, R. T., White, O., Fraser, C. M., Smith, H. O.,and Venter, J. C., Global transposon mutagenesis and a minimal Mycoplasma genome. Science1999, 286(5447), 20892090.51. Koonin, E. V., How many genes can make a cell: the minimal-gene-set concept, Annu. Rev.Genomics Hum. Genet. 2000, 1, 99116.52. Zimmer, C., Genomics. Tinker, tailor: Can Venter stitch together a genome from scratch?Science 2003, 299(5609), 10061007.53. Schwikowski, B., Uetz P, and Fields, S., A network of protein-protein interactions in yeast, Nat.Biotechnol. 2000, 18(12), 12571261.54. Mewes, H. W., Hani, J., Pfeiffer, F., and Frishman, D., MIPS: A database for protein sequencesand complete genomes, Nucleic Acids Res. 1998, 26(1), 3337.55. Goffeau, A., Four years of post-genomic life with 6,000 yeast genes, FEBS Lett. 2000, 480(1),3741.56. OFarrell, P. H., High resolution two-dimensional electrophoresis of proteins, J. Biol. Chem.1975, 250(10), 40074021.57. Sundararaj, S., Guo, A., Habibi-Nazhad, B., Rouani, M., Stothard, P., Ellison, M., and Wishart,D. S., The CyberCell Database (CCDB): A comprehensive, self-updating, relational database tocoordinate and facilitate in silico modeling of Escherichia coli, Nucleic Acids Res. 2004, 32,D293295.58. Wasinger, V. C., and Corthals, G. L., Proteomic tools for biomedicine, J. Chromatogr. B. 2002,2002(771), 3348. 29. CHAPTER 2Strategies for Measuring Dynamics: TheTemporal Component of ProteomicsROBERT J. BEYNON and JULIE M. PRATTUniversity of Liverpool, Liverpool, England2.1 INTRODUCTIONThe explosion of interest in proteomics reflects the ability, realizable for the first time, tomeasure a significant proportion of proteins that are expressed in a specific cell or cellularsubsystem and also to compare two proteomes and assess the relative expression of specificproteins under two different conditions. Many such studies sit formally in the subdomain ofcomparative proteomics, with the express goal of discerning proteins that are differentiallyabundant, usually in a pairwise comparison. Unfortunately, differential abundance hassometimes been taken as synonymous with differential expression or, worse, differentialtranscription. The latter assumption, that the only determinant of protein concentration in acell is the rate of input into the protein pool (i.e., synthesis) is certainly not warranted andbelies the roles that output from the pool (protein export or intracellular protein degradation)and posttranslational modifications can have on the concentration of a protein. More andmore studies are now attempting to measure the levels (usually relative rather than absolute)of messenger ribonucleic acid (mRNA) and protein in the same experiment: the formerusually by microarray experiment, the latter by two-dimensional gel electrophoresis (2DGE)quantitiative imaging or by stable isotope labeling. A recurrent theme in such studies isthe weak correlation between the relative abundances of mRNA and protein (Gygi et al.,1999, 2000; Chen et al., 2002; Greenbaum et al., 2003; Griffin et al., 2002). However,there was no reason, a priori, why this correlation should ever have been expected to behigh. The intracellular concentration of a protein is under the control of two opposingprocesses: protein synthesis and protein degradation. The former is the outcome of theconcentration of cognate mRNA, the rate of initiation of translation, and the overallribosomal activity. The latter is a function of the activity of the degradative process and theinherent susceptibility of the protein to the degradative processes. Rather than attempting tocorrelate the amount of protein to the amount of mRNA in the cell, we should thereforedetermine the relationship between the mRNA concentration and the rate of synthesis ofthat protein. Conversely, while measurements of the activity of the proteolytic machineryin the cell can give some indication of the overall degradative capacity, the intrinsicMicrobial Proteomics: Functional Biology of Whole Organisms, Edited by Ian Humphery-Smithand Michael Hecker. Copyright # 2006 John WileySons, Inc.15 30. 16 STRATEGIES FOR MEASURING DYNAMICS: THE TEMPORAL COMPONENT OF PROTEOMICSsusceptibility of each protein varies, and thus, determination of the rate of degradationis required. This intrinsic susceptibility to the degradative apparatus can be modulatedby ligands and posttranslational events such as phosphorylation, and therefore, while thetranscriptome is directly linked to the rate of synthesis of a protein, the rate of degradationmay in turn be linked more directly to the cellular manifestation of the metabolome.It follows that a complete understanding of proteome dynamics requires that wedeconvolute the single statistic defining the changing amount of a protein in the cell intothe measurement of the rate of synthesis and the rate of degradation of each protein. Forexample, a protein can decline in amount in the cell through accelerated breakdown(a catabolic response) or by a diminution of the rate of synthesis (an anabolic response).Only the latter might be reflected in corresponding changes in the cognate transcript. Inthis chapter, we discuss some strategies for measurement of protein turnover in microbialsystems. Although radiolabeling has been used with some success, predominantly withsingle proteins, we will restrict this discussion to stable isotopes, as these lend themselvesto sensitive and high-throughput mass spectrometric analyses that underpin mostproteomics studies (Beynon and Pratt, 2005). Radiolabeling has rarely been applied ona global, proteomewide scale (for a notable exception, see Grunenefelder et al., 2001).2.2 MECHANISTIC PERSPECTIVE OF PROTEIN TURNOVERThe amount of protein in a cell is controlled by rates of input and of output. Even forintracellular, nonsecretory proteins, protein degradation ensures that for most proteins thepool is subject to turnover and there is continued synthesis of new protein molecules,matched in the steady state by an equal rate of replacement of protein molecules.In the simplest of systems, protein synthesis can be considered to be a first-orderprocess, in which, for a fixed mRNA concentration and a fixed ribosomal activity, the rateof synthesis is constant measurable in molecules over time and is insensitive to the cellularconcentration of the protein. Of course, feedback mechanisms operate to regulate the rateof synthesis through transcriptional control. However, the process can still be consideredto be zero order. By contrast, it is generally held that, to a reasonable approximation,protein degradation is a first-order process. The rate of degradation of proteins in a pool isexpressed as a fractional rate constant, for example 0.1per hour, which is equivalent to theloss of 10% of the pool each hour. The flux through the degradative process, in moleculesper unit time, is then expressed by size of protein pool multiplied by the fractional rate ofdegradation. This term now has the same dimensionality as protein synthesis, and the threeterms are related such that P ks=kd. It follows that measuring two of these threeparameters permits calculation of the third. Since the amount of protein in the cell (P) isoften the most readily determined, two common experimental strategies measure the rateof synthesis or the rate of degradation of the protein.A first-order model of protein degradation brings with it a series of assumptions andconsequences.First, a first-order process is intrinsically stochastic, and thus, a newly synthesizedprotein molecule has an equal chance of being degraded as a molecule synthesized some timeago. Thus, a protein does not age unless it undergoes irreversible covalent or noncovalentchanges that change the intrinsic susceptibility to the degradative process. Second, we need todiscriminate, at least conceptually, between the commitment phase of degradation and thecompletion phase. The commitment step, the point at which a protein molecule becomesdestined for degradation, is the primary arbiter of clearance rate and is followed by a more rapid 31. MEASUREMENT OF PROTEIN TURNOVER IN MICROBIAL SYSTEMS 17completion phase, in which the committed protein molecule is proteolyzed, ultimately toamino acids. It is clear that the first stage should be rate limiting and the second proteolyticphase should occur at a much higher rate than the commitment process. Otherwise, the cellwould potentially accumulate substantial quantities of partially degraded proteolytic productsthat could retain partial functionality and interfere with normal cellular process, possibly asseen in senescence. At the sametime, the proteolytic apparatus cannot be permitted to act in anuncontrolled fashion. Restrictions on the activity of the degradative machinery include(a) compartmentalization of the apparatus in vacuoles or tightly regulated macromolecularcomplexes, (b) the inherent resistance of most native protein structures to proteolysis, and(c) the requirement for a nonproteolytic commitment step.2.3 MEASUREMENT OF PROTEIN TURNOVER IN MICROBIAL SYSTEMSIn all experimental designs to measure rates of protein turnover, the overall principles arethe same but divided into two types of experiments. Proteins are metabolically labeled inan intact cell with either a stable or an unstable (radioactive) isotopically labeled precursor.Over time, the processes of protein metabolism cause a change in the abundance of a labelin the protein pool. This can be due to either the incorporation of label, measuring the rateof synthesis of the protein, or the loss of label from prelabeled proteins, which is a measureof degradation. In both instances, there are complications that can compromise theexperiments. First, during synthesis, some labeled proteins will be degraded and returnlabel to the precursor pool. This results in an artifactual persistence of label in the proteinpool if a single pulse label is administered. Alternatively, in a degradation study,reutilization of label can again manifest itself as an apparently lower rate of degradation.Fortunately, and particularly in the context of microbial systems, it is possible to controlfor these effects by appropriate design of labeling protocols.Incorporation experiments can be used to generate partially labelled proteins, and theextent of labeling is then assessed to define the rate of synthesis. Alternatively, the proteinpool can be fully prelabeled, followed by a light chase (the equivalent of a cold chasein radiolabeling experiments) and the loss of label from a protein can be determined as ameasure of protein degradation. Inasmuch as it is possible to discriminate between theheavy and light pools by mass spectrometry, it can be argued that the two processes areformally equivalent. The decision as to which approach to take is more likely to be driven byother experimental considerations, such as the choice of precursor, the availability of anauxotroph for the particular amino acid, and the ability to deplete the precursor pools.In proteomics, the parameter that is measured is that of the exact mass of a protein or apeptide. It follows that the incorporation of a stable-isotope-labeled amino acid, even if there isonly one instance of that amino acid in the peptide, will lead to an increase in mass, and with amass spectrometer of the appropriate performance (which means any instrument marketedtoday for proteomics), it ought to be possible to discern the stable isotope labeled from theunlabeled counterpart. It follows that the rate of incorporation or loss of a stable-isotope-labeledamino acid can be monitored by mass spectrometry. Given the exquisite sensitivity andselectivity of mass spectrometry, it should therefore be entirely feasible to use stable isotopelabeling to monitor the dynamics of a proteome and, under appropriate conditions, be able tomeasure the rate of synthesis or degradation of any individual protein in that proteome. Theonly notable caveat here is of course low molecular weight proteins, wherein the likelihood ofoccurrence of individually labeled peptides decreases with reduction in mass. 32. 18 STRATEGIES FOR MEASURING DYNAMICS: THE TEMPORAL COMPONENT OF PROTEOMICSThe use of stable isotopes does, however, introduce a technical complexity.Radioisotopically labeled amino acids can be obtained at very high specific radioactivity,such that a trace level of incorporation can lead to a readily measurable signal, assessed,for example, by scintillation counting or fluorography. By contrast, the mass spectrometersused in proteomics are not capable of measuring trace levels of enrichment of stableisotopes. Typically, a minimum of 5% incorporation might be considered to be accuratelymeasurable, and thus, there must be significant metabolic replacement (turnover) of aprotein in order that the rate of turnover can be assessed. This requires an experimentaldesign that can ensure that the labeling achieves this extent of incorporation.There is one final consideration. In synthesis experiments, the protein of interest mustnot be allowed to become fully labeled, as a fully labeled protein is no longer informativewith respect to its turnover. Fully labeled proteins are of greater value in comparativeproteomics in what have become known as SILAC (stable isotope labeling in cell culture)experiments but are not appropriate for turnover studies. In fact, an importantconsideration in SILAC studies is to ensure that a protein pool has experienced sufficienthalf-lives, or the cells sufficient doubling times, so that complete labeling is assured.2.4 STRATEGIES FOR MEASUREMENT OF PROTEOME DYNAMICS2.4.1 Growth ConditionsStable isotope incorporation into microbes requires the availability of media thatsustain appropriate growth of the organism while being amenable to the facilesubstitution of the chosen precursor with labeled precursor. Commonly studied bacteria(e.g., Escherichia coli, Salmonella typhimurium, Klebsiella pneumoniae, Pseudomonasaeruginosa) (Nieweg and Bremer, 1997; Todar 2004) or bakers yeast (Saccharomycescerevisiae) can grow on fully defined media in which the sole carbon source can beglucose, glycerol, or other appropriate sugar and the sole nitrogen source can be, forexample, NH4Cl. This allows the use of the less costly, universal labels 13C and 15N in theform of 13C-glycerol or 13C-glucose or 15NH4Cl. Many yeasts can be grown with 15NH4Cl orglutamate as sole nitrogen source (Candida utilis, Candida krusei, Pichia fermentans, andKluyveromyces fragilis; Large, 1986), allowing these precursors to be used to label cells with15N. However, microbial growth under these conditions may be rather slow and thereforemay not be appropriate for all studies. For studies in which the goal is to identify the effect ofchanging carbon source, stress (e.g., pH, osmotic stress, oxidative stress), the effect ofantibiotics, or the effect of mutations on protein turnover within various pathways, areasonable growth rate is more practicable. Growth rates can be substantially increased byincluding an excess of all the amino acids in the defined medium, for example, growth ofcoagulase-negative staphylococci (Hussain et al., 1991) and Listeria monocytogenes(Klarsfeld et al., 1994). The inclusion of amino acids extends investigations to additionalorganisms that are unable to synthesize many of the amino acids de novo (Staphylococcusaureus, Staphylococcus epidermidis) and also bacteria that require carbon- and nitrogen-containingsupplements for growth. For example, Lactobacillus requires purines,pyrimidines, vitamins, and several amino acids in order to grow. Haemophilus influenzaeis a fastidious bacterium which requires haemin and nicotinamide adenine dinucleotide(NAD) for growth. Valine, arginine, and cysteine are required for growth and enterotoxinproduction in S. aureus (Onoue and Mori, 1997). In all the above cases, it is possible to 33. STRATEGIES FOR MEASUREMENT OF PROTEOME DYNAMICS 19generate (often commercially available) a defined medium which may be supplementedwith 13C-, 15N-, or 2H/13C-labeled amino acids. Because the cells are unable to synthesizethese amino acids de novo, they are incorporated into proteins with high efficiency, andthe relative isotope abundance (RIA) of the incorporated amino acid will be the same asthat for the precursor pool. Indeed, given such auxotrophy, the opportunity exists forcomplete labeling of the proteins using a precursor RIA of unity. The ability to includethe labeled amino acid of choice in media permits the achievement of fully labelledcells after about seven generations, facilitating turnover studies of the type described byPratt et al. (2002) for S. cerevisiae.Many studies, however, are directed toward the changes that are occurring in geneexpression when pathogens encounter the environment within the host, and in these casesconsiderable ingenuity is necessary to design a labeling strategy that will allow evaluationof changes in protein dynamics. Most pathogenic bacteria of animals, which have adaptedthemselves to growth in animal tissues, require complex media for their growth. Blood,serum, and tissue extracts are frequently added to culture media for the cultivationof pathogens. Prior dialysis to remove free amino acids will greatly increase theincorporation of the labeled amino acid introduced to the growth medium. However, theRIA is unlikely to be unity under these conditions, as proteins in the growth medium willbe proteolyzed and contribute unlabeled amino acids to the precursor pool, diminishing theRIA. For pathogens that enter host cells, there will be access to all the precursors availablewithin the specific cellular compartment targeted by the pathogen. If it is possible to growhost cells in tissue culture, these cells can be prelabeled with the chosen heavy-isotopeprecursor prior to infection to enhance the achievable levels of labeling. A particularlyelegant solution to this problem was adopted in a study of the proteome of the malarialparasite, Plasmodium falciparum, during the cell cycle (Nirmalan et al., 2004).Plasmodium falciparum infects erythrocytes and obtains its amino acids for growth byuptake from the plasma by proteolysis of a- and b-hemoglobin and by de novo biosynthesis.However, human a- and b-hemoglobin lack isoleucine and P. falciparum is unable tosynthesize this amino acid de novo. The parasite sustained growth in custom-made RPMI(Roswell Park Memorial Institute) medium, containing erythrocytes but lacking isoleucineother than 13C615N-isoleucine and elicited over 90% labeling after 60 h. This method wouldbe readily applicable to the determination of protein turnover in the malarial parasite.However, there are many organisms and specific experiments that would require morecomplex media and in these cases a more radical approach is required.2.4.2 Culture ConditionsWhen measuring protein turnover rates in microbial organisms, the effect of changes in theconcentration of nutrients, pH, and cell density on the growth rate of the culture should beconsidered. Usually cells are introduced into fresh medium from a stationary-phase,overnight culture. These cells will take a while to emerge from the lag phase, then willgrow at their fastest rate in the log phase until nutrients become scarce and they will againenter the stationary phase. Thus the medium changes throughout growth in theconcentration of nutrients, waste products, and protons (pH). Changes in pH can beminimized by including a buffer system at the optimal pH for growth, but there will beconsiderable fluctuations in nutrient availability. Further, changes in cell density (e.g.,cellcell interaction, quorum sensing) will certainly have an effect on gene expression andmay affect protein turnover. For proteins that are rapidly turned over, the impact will be 34. 20 STRATEGIES FOR MEASURING DYNAMICS: THE TEMPORAL COMPONENT OF PROTEOMICSminimal as sampling can be completed during the log phase of growth, but for long-livedproteins, studies in batch culture will reflect only an average turnover rate. To overcomethis problem, turnover can be studied by growth in a chemostat in which cell density andnutrient supply are kept constant.2.4.3 Choice of PrecursorWe suggest that the most appropriate precursor is a stable-isotope-labeled amino acid. Therehave been some studies of metabolic labeling using, for example, [13C] glucose as sole carbonsource (Cargile et al., 2004), but this approach brings specific problems. First, this approachseems to have been predominantly driven by the need to generate protein labeled for nuclearmagnetic resonance (NMR) studies, and as such the goal is complete replacement of all 12Catoms with the 13C counterpart. However, in metabolic labeling for turnover, the one thing thatcannot be allowed to occur is that a protein should become fully labelled within the timewindow of exposure to label. Under this circumstance, it is no longer possible to determine therate of synthesis of the protein, as it maintains the same degree of labeling irrespective ofthe time of exposure to the precursor pool. Of course, if the goal is to label the protein com-pletelyin order to conduct a chase experiment, in which the loss of label is monitored (a directmeasurement of degradation), then it is desirable to start with a fully labeled protein pool.Accurate measurement of turnover rate requires that we measure the rate ofincorporation of precursor into protein. To achieve this, we must determine the abundanceof the stable isotope in the target protein or proteolytic peptides derived from a protein.However, the extent of incorporation still cannot be accessed unless we also know theextent of enrichment by the stable isotope of the precursor pool. This is defined as theprecursor RIA. Stable-isotope-labeled amino acids can be purchased with RIA in excessof 98%, and thus it might be argued that since this is effectively unity, there are noexperimental concerns. However, a number of factors conspire to reduce the precursor RIAto a lower, unknown value. First, preexisting biomass has the potential to dilute theprecursor with unlabeled amino acid, both from the amino acid pool and the protein pool.This is less of a problem in microbial systems than in, for example, animal systemsbecause a newly seeded culture contributes rather small quantities of biomass to the finalexpanded cell mass. Second, many microbial cells are fully competent in the biosynthesisde novo of all amino acids. Endogenous biosynthesis from unlabeled precursors willtherefore also reduce the precursor RIA. However, microbes are often parsimonious intheir investment in biosynthesis, and biosynthetic pathways for amino acids are usuallytightly controlled. Provision of adequate concentrations of a specific amino acid in themedium will often repress endogenous biosynthesis and thus eliminate this dilution effect.If possible, use of a strain that is auxotrophic for the particular amino acid will eliminatethis as a source of problem. An additional complication occurs when labeled amino acidsare metabolized in such a way that stable isotope flows from one amino acid to another.This does not automatically invalidate turnover rate calculations but serves to complicatethe mass offset of the stable-isotope-labeled variant of the protein and may hinder manualor automated interpretation of spectra. Ideally, there should be no metabolic flow oflabeled atom centers either to or from the precursor amino acid. One modification that maybe more informative is the loss of a single deuteron from an amino acid that is labeled atthe a-carbon atom. This deuteron, although chemically stable, is metabolically labile andis lost during the reversible reaction of transamination, for example (Fig. 2.1). Thus, theamino acid that is incorporated into protein has a labeled mass of one less than the aminoacid added to the medium. This is not a particularly serious complication, although one 35. STRATEGIES FOR MEASUREMENT OF PROTEOME DYNAMICS 219 Da 18 Da 9 Da 18 Da100%0100%0100%0100%0100%0100%0100%1561.961544.971528.83 1536.85 1553.911562.081553.041545.111527.01 1536.951562.011545.031552.961526.931536.921552.98 1562.041526.93 1545.041536.961552.951562.011545.031526.921523.061561.981552.941526.891535.95 1546.001552.921526.891536.87 1566.920 m/z1520 1530 1540 1550 1560 1570100%0100%0100%0100%0100%0100%0100%1439.991407.79 1424.841440.091431.061408.89 1448.901440.041431.001407.861431.011407.84 1415.971440.061450.011430.991410.05 1419.061440.041430.981410.08 1441.02 1451.011430.961407.801415.89 1424.83 1441.970 m/z1410 1420 1430 1440 1450100%0100%0100%0100%0100%0100%0100%2148.282138.212125.072149.392148.402130.29 2138.822148.352130.252125.23 2138.44 2154.312130.25 2148.352138.302126.12 2144.23 2154.312148.342130.222127.102138.332145.202126.27 2138.33 2148.202155.222130.182130.182126.22 2138.27 2145.19 2155.240 m/z2130 2140 2150 2160t0 h2 h4 h6 h10 h24 h51 hFigure 2.1 Determination of protein turnover in S. Scerevisiae. Saccharomyces cerevisiae wasgrown in glucose-limited steady-state culture in a chemostat in the presence of a minimal mediumcontaining [2H10]leucine. After seven doubling times, the proteins were virtually fully labeled withleucine. The medium feedstock was then changed to one containing unlabeled leucine, and at thesame time, a large bolus of unlabeled leucine, equilvalent to at least a 20-fold excess over that presentin the medium, was added. Because the cells were glucose limited, the addition of leucine had noeffect on the growth rate. Subsequently, the culture was sampled, and proteins were separated by gelelectrophoresis and analyzed by MALDI-TOF mass spectrometry. The loss of heavy label fromleucine containing peptides is the result of a composite process, reflecting loss from the vessel, andintracellular degradation. Knowledge of the former permits calculation of the latter.should be aware that this is a likely reaction for most amino acids and can serve to confirmrapid equilibration with the intracellular amino acid pool.With microbial systems, the manipulation of the growth conditions should be able toachieve the goal of setting the precursor pool RIA to unity. Under these circumstances,there is no stochastic decision about incorporation of the labeled amino acid, and everyinstance of the amino acid in the protein is labeled (heavy H). Thus, a population ofprotein molecules pool shifts from a mass of M to M nO, where n is the number ofinstances of the amino acid in the protein and O is the mass offset attributable to the heavyamino acid. In turn, in a typical proteomics experiment, every peptide will also be fullylabeled and, if the peptide contains at least one instance of the amino acid, will show thecorresponding mass shift.If the precursor RIA is significantly less than unity, additional complications arise interms of patterns of labeling. To illustrate this, consider the situation where an amino acidhas an RIA of 0.5. As the ribosomal apparatus encounters a codon for this amino acid,there is a 50% chance of incorporation of a light or a heavy variantwe can excludeisotope effects at this level. Moreover, the process is stochastic, so different proteinmolecules will have distinct distributions of the light and heavy amino acids. The labelingpattern follows a binomial distribution. For example, if we consider the situation where atryptic peptide within a protein sequence contains a single instance of the amino acid, the 36. 22 STRATEGIES FOR MEASURING DYNAMICS: THE TEMPORAL COMPONENT OF PROTEOMICSoptions are simple; the peptide can either contain none or a single instance of the labeledamino acid. If the protein has two instances of the amino acid, then the three products,theoretically distinguishable by mass, are LL, [LH, HL], and HH in a ratio of 1 : 2 : 1.Extending to a third instance of the amino acid, the labeling patterns yield fourdistinguishable products, LLL, [LLH, LHL, HLL], [LHH, HLH, HHL], and HHH in theratio 1 : 3 : 3 : 1. This progression will be recognized as a binomial series. The massseparation is readily discernible, and indeed, analysis of the distribution between thedifferent species can yield valuable information about the precursor RIA. However, the neteffect is to complicate the labeling pattern, and this situation can and should be avoided.Such problems could also be reduced for tryptic peptides by incorporating stable-isotope-labeled lysine or arginine or both into proteins, as most tryptic peptides would thenonly contain a single instance of the label. If the labeled lysine and arginine showed thesame mass offset, the analytical problem is even simpler, as the separation between heavyand light species is the same irrespective of whether the C-terminal amino acid is lysine orarginine. However, we would also make a case for a different mass separation of lysine-andarginine-terminated peptides, as the additional information obtained from knowledgeof the C-terminal amino acid can bring about a significant reduction in search space, sincean additional goal is identification of the protein (Pratt et al., 2002; Beynon, 2003).2.5 EXPERIMENTAL STRATEGYThere are two fundamentally different approaches to the measurement of protein turnoverby stable isotope labeling. Both require that the protein or a peptide derived therefrom beresolved from other molecules in order that the ratio of labeled to unlabeled variants maybe determined. We will not dwell on the postlabeling methodologies, as these are routineto proteomics. For example, a protein might be isolated as a spot on a 2D gel or as animmunoprecipitate before being analyzed by matrix-assisted laser desorption ionizationtime-of-flight (MALDI-TOF) mass spectrometry. Alternatively, a protein or proteinmixture can be proteolyzed and the peptides selectively enriched and/or resolved by one ortwo dimensions of chromatography prior to mass spectrometry. The objective is tomeasure the ratio of labeled peptide to its unlabeled cognate.In synthesis experiments, the measured parameter is the rate of incorporation of labeledamino acid into a protein. Cells are exposed to the labeled precursor, and at time pointssubsequently, the proteins of interest are isolated and proteolyzed and the heavy and lightvariants are analyzed by mass spectrometry. If the cells are undergoing a transition fromexponential to stationary phase or if the precursor is being depleted in the medium (such ascould happen in batch culture), then the rate of synthesis or the precursor RIA could bechanging through the labeling experiment. It is only by frequent sampling that this can bedetected.In degradation studies, a protein is prelabeled and the loss of label is then monitored. Thisis, of course, formally equivalent to the synthesis experiments described above, in which theroles