Advances in Microbial Physiology (eBook)

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2005 | 1. Auflage
346 Seiten
Elsevier Science (Verlag)
978-0-08-046050-5 (ISBN)

Infos

Advances in Microbial Physiology is one of the most successful and prestigious series from Academic Press, an imprint of Elsevier. It publishes topical and important reviews, interpreting physiology to include all material that contributes to our understanding of how microorganisms and their component parts work.

First published in 1967, it is now in its 50th volume. The Editors have always striven to interpret microbial physiology in the broadest context and have never restricted the contents to traditional views of whole cell physiology. Now edited by Professor Robert Poole, University of Sheffield, Advances in Microbial Physiology continues to be an influential and very well reviewed series.

* In 2004, the Institute for Scientific Information released figures showing that the series had an Impact Factor of 8.947, with a half-life of 6.3 years, placing it 5th in the highly competitive category of Microbiology.

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Advances in Microbial Physiology is one of the most successful and prestigious series from Academic Press, an imprint of Elsevier. It publishes topical and important reviews, interpreting physiology to include all material that contributes to our understanding of how microorganisms and their component parts work.First published in 1967, it is now in its 50th volume. The Editors have always striven to interpret microbial physiology in the broadest context and have never restricted the contents to "e;traditional? views of whole cell physiology. Now edited by Professor Robert Poole, University of Sheffield, Advances in Microbial Physiology continues to be an influential and very well reviewed series. In 2004, the Institute for Scientific Information released figures showing that the series had an Impact Factor of 8.947, with a half-life of 6.3 years, placing it 5th in the highly competitive category of Microbiology.

Inhalt

Cover 1
Microbial Physiology 4
Contents 6
Contributors to Volume 50 10
Metabolic Genomics 12
Abbreviations 12
Introduction 13
Metabolomics and Metabolic Flux Analysis 13
Metabolic Flux Analysis 13
Metabolomics 14
Influence of Genomics on MFA 15
Functional Genomics Approaches 16
Genome Sequence Annotation for Charting Metabolic Pathways 17
Mutational and Phenotypic Analysis 19
Proteomics 21
Microarrays and Transcriptome Profiling 21
Overview of Transcriptome Analysis 22
Microarray Platforms 22
Technical Considerations for Microarray Experiment Design 22
Replication and Reproducibility 23
Normalization and Statistical Analysis of Microarray Data 25
Cluster Analysis of Microarray Data 26
From Snapshot to Motion Picture 29
Global Repression of Biosynthetic Pathways on Rich Growth Media 30
Expression Profiling of Acetate-grown E. coli 32
Whole-cell Perspectives of Growth on Glucose 34
Transcriptome Comparisons of Aerobic vs. Anaerobic Growth on Glucose 34
Steady-state Growth and Steady-state Gene Expression 35
Acetate Excretion and Induction of the Glyoxylate Shunt 38
Integration of Transcriptome and Metabolic Flux Analysis 38
Concluding Remarks 42
Acknowledgements 43
References 43
How Escherichia coli and Saccharomyces cerevisiae Build Fe/S Proteins 52
Abbreviations 54
Introduction 54
Identification of isc and suf Genes 57
Genetic Regulation: Oxidative Stress, Iron Limitation and other Shocks 60
Regulation of the Expression of the isc Locus: Use of IscR, a Dedicated Regulator 60
Regulation of the suf Operon: Use of Global Cellular Regulators 61
Regulation of the suf Genes in Synechocystis: A Third Combination? 63
Sulfur Donors: the Cysteine Desulfurases 63
The E. coli Cysteine Desulfurases 63
General Features 63
The Cysteine Desulfurase IscS 65
Physiological Analysis 65
Biochemical and Structural Analyses 66
Functional Analysis 66
The Cysteine Desulfurase SufS 67
Physiological Analysis 67
Biochemical and Structural Analyses 67
The Cysteine Desulfurase CSD 68
The S. cerevisiae Cysteine Desulfurase Nfs1 69
Sulfur Acceptors: IscU And SufE 70
The IscU Family 70
IscS/IscU Interaction 70
Structural Analysis of IscU 71
The SufE Family 72
SufS/SufE Interaction 72
Structural Analysis of SufE 73
The CsdE Family 74
Iron Sources 75
Relationships between the ISC System and Frataxin 75
Relationships between the SUF System and Siderophores 77
Scaffolds 78
The IscU/ISU Type 78
Physiological Role 78
Biophysical and Structural Analyses of IscU 78
IscU Transfers Fe/S cluster to Apo-proteins 79
The IscA/SufA/ISA Type 80
Physiological Role 80
Biophysical and Structural Analyses of IscA 80
Biophysical Analysis of SufA 82
IscA/SufA Transfers Fe/S Cluster to Apo-proteins 82
The ATP Hydrolyzing Components 84
A Chaperone/Co-chaperone in the ISC System 84
Phenotypic Analysis 84
Are HscA/HscB True Chaperones? 85
What Is the Substrate of the Chaperones? 86
Identification of IscU/Isu1 as Substrates of the Chaperone System 86
Determinants of the Interaction Between IscU/ISU1 and Chaperones 87
How Do the Chaperone/Co-chaperone Function in vitro? 88
The Role in vivo of the Chaperones 89
An ABC in the SUF System 90
Phenotypic Analysis 90
Is SufBCD a True ABC Transporter? 91
SufBCD Interacts with SufS/SufE 92
Ferredoxins and Ferredoxin Reductases 92
Yah1 and Arh1 of S. cerevisiae 92
Bacterial Ferredoxins and Their Reductases 93
Physiological Role 93
Biochemical Analysis 94
Structural Analysis 95
What About Repair? 95
Conclusion and Prospects 96
Acknowledgements 100
References 100
Function, Attachment and Synthesis of Lipoic Acid in Escherichia coli 114
Abbreviations 115
Introduction 115
Lipoic Acid-dependent Enzymes 116
PDH 116
2-OGDH 118
Glycine Cleavage System 118
Structures of Lipoylated Proteins 120
Protein Lipoylation Pathways 125
Lipoate-Protein Ligase (LplA) 128
Octanoyl-ACP:Protein N-Octanoyltransferase (LipB) 129
Biosynthesis of Lipoic Acid 133
Overview 133
Lipoic Acid Synthesis Proceeds by an Unexpected and Extraordinary Pathway 137
Conclusions and Future Directions 143
Acknowledgements 145
References 146
Microbial Dimethylsulfoxide and Trimethylamine-N-Oxide Respiration 158
Abbreviations 160
Introduction 160
Microbial DMSO and TMAO Respiration 160
Occurrence of DMSO and Other Sulfoxides in the Natural Environment 161
Occurrence of TMAO in Natural Environments 162
Organisation of the DMSO and TMAO respiratory chains 163
The E. coli DMSO Respiratory Chain, DmsABC 164
The E. coli TMAO Respiratory Chain, TorCA 165
The Rhodobacter DMSO Respiratory Chain, DorCA 166
The Shewanella oneidensis DMSO Respiratory Chain 167
Molecular Properties of the Catalytic subunits of DMSO and TMAO reductases 169
The Molybdenum Cofactor 169
Structure and Catalysis in DMSO and TMAO Reductases 170
Substrate Specificity of DMSO and TMAO Reductases 173
Expression and Assembly of DMSO and TMAO Reductases 176
Protein Transport and Enzyme Localisation 176
Molybdenum Cofactor Synthesis 178
Cofactor Insertion and Enzyme Assembly: The Role of Chaperones 179
Genetic Organisation of Operons Encoding DMSO and TMAO Reductases and Regulation of Gene Expression 182
The DMSO Reductase Operons of E. coli 182
TMAO Reductase Operons of E. coli and Shewanella 186
DMSO Reductase Operons of Rhodobacter spp. 188
Concluding Remarks 192
Acknowledgements 194
References 194
Energy Metabolism and Its Compartmentation in Trypanosoma brucei 210
Abbreviations 211
Introduction 211
Peculiar Organelles in Energy Metabolism 213
Energy Metabolism of Long Slender Bloodstream form T. brucei 214
Pathways in Energy Metabolism 214
Respiratory Chain and Oxidative Phosphorylation 218
Flux Control 219
Energy Metabolism of Procyclic Form T. brucei 220
Transition to Procyclic Metabolism 220
Pathways in Energy Metabolism 222
Partial Oxidation of Pyruvate Instead of Krebs Cycle Activity 222
Other Functions for Parts of the Krebs Cycle 223
Respiratory Chain and Oxidative Phosphorylation 225
Redox and ATP Balance in Glycosome and Mitochondrion 228
Concluding Remarks 229
Perspectives for Drug Design 229
Function and Origin of Glycosomal Localization of Glycolysis 230
Acknowledgements 231
References 231
The First Cell 238
Introduction 239
The Startup 241
The Academy of the Origin of Life 243
Pre-biotic Chemiosmosis 246
Surfaces versus Vesicles 248
The Second Important Conclusion from the Miller-Urey Experiment 248
Carbon in Biologically Useful Oxidation States 251
The Next Step was the Generation of Biologically Important Small Organic Molecules 253
Formation of Cell Membrane 254
Uphill Energy Conversion and Ability to Drive Reactions 259
The First Nucleic Acids 260
How to make RNA Inside a Vesicle 263
Pre-Protein Polypeptides 265
Free Radicals and Ultraviolet Flux 266
Conclusions 266
Acknowledgements 267
References 267
Author Index 272
Subject Index 300
Please refer to Colour Plate Section at the back of the book 308

Textauszug

Metabolic Genomics

Dong-Eun Chang; Tyrrell Conway    Advanced Center for Genome Technology, The University of Oklahoma, Norman, OK 73019, USA

Publisher Summary

The increasing availability of complete genome sequences and advances in analytical techniques for functional genomics make it possible to study microbial metabolism from a global perspective. Prior to the genomics era, metabolic studies were, of necessity, focused on individual or small numbers of genes or enzymes involved in particular metabolic pathways and hence were limited in providing useful information for analyzing global regulatory networks governing bacterial metabolism. High-throughput analysis of all messenger RNAs (transcriptome), proteins (proteome), and metabolites (metabolome) provides microbiologists with a new set of tools with which to investigate bacterial metabolism and integrate this knowledge at all levels of cellular processes. The chapter discusses traditional strategies for studies of metabolism, including flux analysis, which is one of the earliest global perspectives of microbial physiology. It considers the tools of functional genomics and in particular transcriptome analysis and continues to broaden an understanding of metabolic networks.

ABBREVIATIONS

MFA Metabolic flux analysis

TCA cycle Tricarboxylic acid cycle

PCA Principal component analysis

SVM Support vector machines

PEP Phosphoenolpyruvate

1 INTRODUCTION

The increasing availability of complete genome sequences and advances in analytical techniques for functional genomics make it possible to study microbial metabolism from a global perspective. Prior to the genomics era, metabolic studies were, of necessity, focused on individual or small numbers of genes or enzymes involved in particular metabolic pathways and hence were limited in providing useful information for analyzing global regulatory networks governing bacterial metabolism. High-throughput analysis of all messenger RNAs (transcriptome), proteins (proteome), and metabolites (metabolome) provides microbiologists with a new set of tools with which to investigate bacterial metabolism and integrate this knowledge at all levels of cellular processes.

In this chapter, we begin with a brief description of traditional strategies for studies of metabolism, including flux analysis, which is one of the earliest global perspectives of microbial physiology. We then consider how the tools of functional genomics, and in particular transcriptome analysis, have broadened and will continue to broaden our understanding of metabolic networks.

2 METABOLOMICS AND METABOLIC FLUX ANALYSIS

Although whole-cell level analytical tools for functional genomics have been developed only recently, the global concept of metabolic flux control is not new; the first effort to analyze metabolism from this perspective is attributed to Heinrich Kacser, who published the metabolic control theory over 30 years ago (Kacser and Burns, 1973). Combined with the advances in molecular biological techniques that allow precise modification of specific enzymatic reactions in metabolic pathways, the concept of metabolic flux control and analysis has brought a paradigm shift in bacterial physiology from studies of individual enzymatic reactions to the interactions of biochemical reactions in cellular networks (Stephanopoulos, 1999). In this section, we briefly describe metabolic flux analysis (MFA), recent developments in metabolomics, and then review a few examples to show how MFA, metabolomics, and genomics have influenced each other.

2.1 Metabolic Flux Analysis

Mathematical models that provide a complete dynamic description of metabolism must incorporate enzyme kinetics and regulation, parameters that are difficult to obtain for an entire cell system. On the other hand, MFA provides a means to estimate non-measurable in vivo reaction rates based on the flux balancing of easily measured parameters in stoichiometric reaction models (Varma and Palsson, 1994a; Holms, 1996). MFA requires only two kinds of metabolic information to calculate intracellular fluxes; the first is information about the metabolic stoichiometry of all chemical reactions in the biological system, and the second is the measurable fluxes, such as substrate uptake, growth rate, end product formation, or CO2 evolution (Varma and Palsson, 1994a). MFA is based on an assumption that metabolic fluxes are in a quasi-steady state, i.e., intracellular metabolite pools do not change over the experimental time (Varma and Palsson, 1994a).

MFA is most frequently focused on the carbon fluxes through the central metabolic pathways that provide precursor metabolites, energy, and reducing power to fulfill the requirements for biomass synthesis, maintenance energy, and the excretion of metabolic end products. Carbon fluxes in Escherichia coli growing in glucose minimal medium were calculated and a model was formulated which successfully predicted the behavior of E. coli, i.e., the time profiles of cell density, and concentration of substrate (glucose) and acidic end products (acetate, ethanol, and formate), in various culture conditions (Holms, 1986; Varma et al., 1993; Varma and Palsson, 1994b). MFA has also been developed for industrially important strains that produce valuable metabolites, including amino acids-producing Corynebacterium and nucleic acid- and riboflavin-producing Bacillus subtilis (Vallino and Stephanopoulos, 1993; Sauer et al., 1997). These studies identified critical metabolic steps that could be manipulated to optimize production. The availability of annotated genome sequences of an increasing number of organisms has led to integrated genome-scale computational (in silico) models of regulatory and metabolic networks of E. coli and Saccharomyces cerevisiae, as well as Haemophilus influenzae (Schilling et al., 2002; Famili et al., 2003; Covert et al., 2004). The construction of in silico models of genome-scaled metabolic networks and their verification by phenotyping and transcriptome analysis is an important step forward.

2.2 Metabolomics

The quasi-steady state assumption for MFA makes it difficult to calculate the flux distribution in bacterial cells during growth transitions or when growing in complex media. Moreover, MFA is not always able to predict flux, as in the case of parallel (redundant) pathways, metabolic cycles, or reaction steps that can operate in either direction in vivo (Wiechert, 2001). Recognizing the limitations of MFA, it is clear that additional information derived from measurement of intracellular metabolite pools is required for more robust MFA and model verification. While existing technology has not made it possible to measure all intracellular metabolites, significant progress has been made.

Ferenci and colleagues were the first to use the term “metabolome” to describe the pool of metabolites in the cell (Tweeddale et al., 1998). They measured the extent of change in the metabolite pool of E. coli cells at different growth rates and in an rpoS mutant (defective induction of general stress survival genes) by using two-dimensional thin-layer chromatography of 14C-glucose-derived metabolites (Tweeddale et al., 1998). Changes in the metabolome of slow-growing cells were consistent with strict physiological control of metabolism. That is, the response of glucose-limited E. coli cells was to induce a number of catabolic pathways as a means for scavenging alternative carbon sources. Analysis of the global regulatory mutant allowed the authors to distinguish those changes that are under RpoS control.

In the early 1980s, a technique based on 13C labeling to measure intracellular metabolite pools was developed (Wiechert, 2001). 13C MFA makes use of the isotopomer concept, that is, the distribution of labeling patterns for a particular metabolite that can be measured by high-resolution nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry (MS), liquid chromatography (LC)–MS, or gas chromatography (GC)–MS (Wiechert, 2001). The labeling pattern data are fed into a software package designed to model fluxes. There are a number of excellent papers describing the use of isotopomers for MFA (Schmidt et al., 1999; Dauner et al., 2001; Fischer and Sauer, 2003; Kromer et al., 2004; Wahl et al., 2004). In the following section, a few examples of studies that illustrate the potential of metabolomics for the research of microbial metabolism are discussed.

2.3 Influence of Genomics on MFA

Sauer and colleagues used GC–MS to examine the redistribution of flux in response to blockage of central metabolic pathways (Fischer and Sauer, 2003). In E. coli, lesions in the entry point to glycolysis, and to a lesser extent the tricarboxylic acid (TCA) cycle, increased flux through the Entner–Doudoroff pathway as an alternative to glycolysis, somehow bypassing the pentose phosphate pathway. Shimizu and his colleagues also used MFA based on [U-13C] glucose labeling and...

Produkt-Details

• Erscheint lt. Verlag: 21.10.2005
• Mitarbeit: Herausgeber (Serie): Robert K. Poole
• Sprache: englisch
• Themenwelt: Sachbuch/Ratgeber
• Themenwelt: Studium ► 1. Studienabschnitt (Vorklinik) ► Physiologie
• Themenwelt: Naturwissenschaften ► Biologie ► Biochemie
• Themenwelt: Naturwissenschaften ► Biologie ► Mikrobiologie / Immunologie
• Themenwelt: Naturwissenschaften ► Biologie ► Zoologie
• Themenwelt: Naturwissenschaften ► Physik / Astronomie ► Angewandte Physik
• Themenwelt: Technik
• ISBN-10: 0-08-046050-X / 008046050X
• ISBN-13: 978-0-08-046050-5 / 9780080460505