Advances in Microbial Physiology

1. Auflage 2005
ISBN: 978-0-08-046050-5
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, Band Volume 50, 346 Seiten

Reihe: Advances in Microbial Physiology

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.

Autoren/Hrsg.

Inhaltsverzeichnis

1;Cover;1
2;Microbial Physiology;4
3;Contents;6
4;Contributors to Volume 50;10
5;Metabolic Genomics;12
5.1;Abbreviations;12
5.2;Introduction;13
5.3;Metabolomics and Metabolic Flux Analysis;13
5.3.1;Metabolic Flux Analysis;13
5.3.2;Metabolomics;14
5.3.3;Influence of Genomics on MFA;15
5.4;Functional Genomics Approaches;16
5.4.1;Genome Sequence Annotation for Charting Metabolic Pathways;17
5.4.2;Mutational and Phenotypic Analysis;19
5.4.3;Proteomics;21
5.5;Microarrays and Transcriptome Profiling;21
5.5.1;Overview of Transcriptome Analysis;22
5.5.1.1;Microarray Platforms;22
5.5.1.2;Technical Considerations for Microarray Experiment Design;22
5.5.1.3;Replication and Reproducibility;23
5.5.1.4;Normalization and Statistical Analysis of Microarray Data;25
5.5.1.5;Cluster Analysis of Microarray Data;26
5.5.1.6;From Snapshot to Motion Picture;29
5.5.2;Global Repression of Biosynthetic Pathways on Rich Growth Media;30
5.5.3;Expression Profiling of Acetate-grown E. coli;32
5.5.4;Whole-cell Perspectives of Growth on Glucose;34
5.5.4.1;Transcriptome Comparisons of Aerobic vs. Anaerobic Growth on Glucose;34
5.5.4.2;Steady-state Growth and Steady-state Gene Expression;35
5.5.4.3;Acetate Excretion and Induction of the Glyoxylate Shunt;38
5.5.5;Integration of Transcriptome and Metabolic Flux Analysis;38
5.6;Concluding Remarks;42
5.7;Acknowledgements;43
5.8;References;43
6;How Escherichia coli and Saccharomyces cerevisiae Build Fe/S Proteins;52
6.1;Abbreviations;54
6.2;Introduction;54
6.3;Identification of isc and suf Genes;57
6.4;Genetic Regulation: Oxidative Stress, Iron Limitation and other Shocks;60
6.4.1;Regulation of the Expression of the isc Locus: Use of IscR, a Dedicated Regulator;60
6.4.2;Regulation of the suf Operon: Use of Global Cellular Regulators;61
6.4.3;Regulation of the suf Genes in Synechocystis: A Third Combination?;63
6.5;Sulfur Donors: the Cysteine Desulfurases;63
6.5.1;The E. coli Cysteine Desulfurases;63
6.5.1.1;General Features;63
6.5.1.2;The Cysteine Desulfurase IscS;65
6.5.1.2.1;Physiological Analysis;65
6.5.1.2.2;Biochemical and Structural Analyses;66
6.5.1.2.3;Functional Analysis;66
6.5.1.3;The Cysteine Desulfurase SufS;67
6.5.1.3.1;Physiological Analysis;67
6.5.1.3.2;Biochemical and Structural Analyses;67
6.5.1.4;The Cysteine Desulfurase CSD;68
6.5.2;The S. cerevisiae Cysteine Desulfurase Nfs1;69
6.6;Sulfur Acceptors: IscU And SufE;70
6.6.1;The IscU Family;70
6.6.1.1;IscS/IscU Interaction;70
6.6.1.2;Structural Analysis of IscU;71
6.6.2;The SufE Family;72
6.6.2.1;SufS/SufE Interaction;72
6.6.2.2;Structural Analysis of SufE;73
6.6.3;The CsdE Family;74
6.7;Iron Sources;75
6.7.1;Relationships between the ISC System and Frataxin;75
6.7.2;Relationships between the SUF System and Siderophores;77
6.8;Scaffolds;78
6.8.1;The IscU/ISU Type;78
6.8.1.1;Physiological Role;78
6.8.1.2;Biophysical and Structural Analyses of IscU;78
6.8.1.3;IscU Transfers Fe/S cluster to Apo-proteins;79
6.8.2;The IscA/SufA/ISA Type;80
6.8.2.1;Physiological Role;80
6.8.2.2;Biophysical and Structural Analyses of IscA;80
6.8.2.3;Biophysical Analysis of SufA;82
6.8.2.4;IscA/SufA Transfers Fe/S Cluster to Apo-proteins;82
6.9;The ATP Hydrolyzing Components;84
6.9.1;A Chaperone/Co-chaperone in the ISC System;84
6.9.1.1;Phenotypic Analysis;84
6.9.1.2;Are HscA/HscB True Chaperones?;85
6.9.1.3;What Is the Substrate of the Chaperones?;86
6.9.1.3.1;Identification of IscU/Isu1 as Substrates of the Chaperone System;86
6.9.1.3.2;Determinants of the Interaction Between IscU/ISU1 and Chaperones;87
6.9.1.3.3;How Do the Chaperone/Co-chaperone Function in vitro?;88
6.9.1.4;The Role in vivo of the Chaperones;89
6.9.2;An ABC in the SUF System;90
6.9.2.1;Phenotypic Analysis;90
6.9.2.2;Is SufBCD a True ABC Transporter?;91
6.9.2.3;SufBCD Interacts with SufS/SufE;92
6.10;Ferredoxins and Ferredoxin Reductases;92
6.10.1;Yah1 and Arh1 of S. cerevisiae;92
6.10.2;Bacterial Ferredoxins and Their Reductases;93
6.10.2.1;Physiological Role;93
6.10.2.2;Biochemical Analysis;94
6.10.2.3;Structural Analysis;95
6.11;What About Repair?;95
6.12;Conclusion and Prospects;96
6.13;Acknowledgements;100
6.14;References;100
7;Function, Attachment and Synthesis of Lipoic Acid in Escherichia coli;114
7.1;Abbreviations;115
7.2;Introduction;115
7.3;Lipoic Acid-dependent Enzymes;116
7.3.1;PDH;116
7.3.2;2-OGDH;118
7.3.3;Glycine Cleavage System;118
7.3.4;Structures of Lipoylated Proteins;120
7.4;Protein Lipoylation Pathways;125
7.4.1;Lipoate-Protein Ligase (LplA);128
7.4.2;Octanoyl-ACP:Protein N-Octanoyltransferase (LipB);129
7.5;Biosynthesis of Lipoic Acid;133
7.5.1;Overview;133
7.5.2;Lipoic Acid Synthesis Proceeds by an Unexpected and Extraordinary Pathway;137
7.6;Conclusions and Future Directions;143
7.7;Acknowledgements;145
7.8;References;146
8;Microbial Dimethylsulfoxide and Trimethylamine-N-Oxide Respiration;158
8.1;Abbreviations;160
8.2;Introduction;160
8.2.1;Microbial DMSO and TMAO Respiration;160
8.2.2;Occurrence of DMSO and Other Sulfoxides in the Natural Environment;161
8.2.3;Occurrence of TMAO in Natural Environments;162
8.3;Organisation of the DMSO and TMAO respiratory chains;163
8.3.1;The E. coli DMSO Respiratory Chain, DmsABC;164
8.3.2;The E. coli TMAO Respiratory Chain, TorCA;165
8.3.3;The Rhodobacter DMSO Respiratory Chain, DorCA;166
8.3.4;The Shewanella oneidensis DMSO Respiratory Chain;167
8.4;Molecular Properties of the Catalytic subunits of DMSO and TMAO reductases;169
8.4.1;The Molybdenum Cofactor;169
8.4.2;Structure and Catalysis in DMSO and TMAO Reductases;170
8.4.3;Substrate Specificity of DMSO and TMAO Reductases;173
8.5;Expression and Assembly of DMSO and TMAO Reductases;176
8.5.1;Protein Transport and Enzyme Localisation;176
8.5.2;Molybdenum Cofactor Synthesis;178
8.5.3;Cofactor Insertion and Enzyme Assembly: The Role of Chaperones;179
8.6;Genetic Organisation of Operons Encoding DMSO and TMAO Reductases and Regulation of Gene Expression;182
8.6.1;The DMSO Reductase Operons of E. coli;182
8.6.2;TMAO Reductase Operons of E. coli and Shewanella;186
8.6.3;DMSO Reductase Operons of Rhodobacter spp.;188
8.7;Concluding Remarks;192
8.8;Acknowledgements;194
8.9;References;194
9;Energy Metabolism and Its Compartmentation in Trypanosoma brucei;210
9.1;Abbreviations;211
9.2;Introduction;211
9.3;Peculiar Organelles in Energy Metabolism;213
9.4;Energy Metabolism of Long Slender Bloodstream form T. brucei;214
9.4.1;Pathways in Energy Metabolism;214
9.4.2;Respiratory Chain and Oxidative Phosphorylation;218
9.4.3;Flux Control;219
9.5;Energy Metabolism of Procyclic Form T. brucei;220
9.5.1;Transition to Procyclic Metabolism;220
9.5.2;Pathways in Energy Metabolism;222
9.5.2.1;Partial Oxidation of Pyruvate Instead of Krebs Cycle Activity;222
9.5.2.2;Other Functions for Parts of the Krebs Cycle;223
9.5.3;Respiratory Chain and Oxidative Phosphorylation;225
9.5.4;Redox and ATP Balance in Glycosome and Mitochondrion;228
9.6;Concluding Remarks;229
9.6.1;Perspectives for Drug Design;229
9.6.2;Function and Origin of Glycosomal Localization of Glycolysis;230
9.7;Acknowledgements;231
9.8;References;231
10;The First Cell;238
10.1;Introduction;239
10.1.1;The Startup;241
10.1.2;The Academy of the Origin of Life;243
10.2;Pre-biotic Chemiosmosis;246
10.2.1;Surfaces versus Vesicles;248
10.3;The Second Important Conclusion from the Miller-Urey Experiment;248
10.4;Carbon in Biologically Useful Oxidation States;251
10.5;The Next Step was the Generation of Biologically Important Small Organic Molecules;253
10.6;Formation of Cell Membrane;254
10.7;Uphill Energy Conversion and Ability to Drive Reactions;259
10.8;The First Nucleic Acids;260
10.9;How to make RNA Inside a Vesicle;263
10.10;Pre-Protein Polypeptides;265
10.11;Free Radicals and Ultraviolet Flux;266
10.12;Conclusions;266
10.13;Acknowledgements;267
10.14;References;267
11;Author Index;272
12;Subject Index;300
13;Please refer to Colour Plate Section at the back of the book;308

Leseproben

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...