Crane Two-Component Signaling Systems, Part A

1. Auflage 2011
ISBN: 978-0-08-054871-5
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, Band Volume 422, 592 Seiten

Reihe: Methods in Enzymology

Multicellular organisms must be able to adapt to cellular events to accommodate prevailing conditions. Sensory-response circuits operate by making use of a phosphorylation control mechanism known as the 'two-component system.' Sections include: Computational Analyses of Sequences and Sequence Alignments Biochemical and Genetic Assays of Individual Components of Signaling Systems Physiological Assays and Readouts - Presents detailed protocols - Includes troubleshooting tips

Autoren/Hrsg.

Crane, Brian

Crane, Alexandrine

Inhaltsverzeichnis

1;Front Cover;1
2;Two-Component Signaling Systems, Part A;4
3;Copyright Page;5
4;Table of Contents;6
5;Contributors to Volume 422;10
6;Volumes in Series;14
7;Section I: Computational Analyses of Sequences and Sequence Alignments;38
7.1;Chapter 1: Comparative Genomic and Protein Sequence Analyses of a Complex System Controlling Bacterial Chemotaxis;40
7.1.1;Introduction;40
7.1.2;Bioinformatics Tools and Resources for Identifying and Analyzing Chemotaxis Components;43
7.1.3;Defining MCP Membrane Topology;46
7.1.4;Diversity of Input (Sensory) Domains in MCPs;48
7.1.5;HAMP Domain Identification;50
7.1.6;MCP Signaling Domain;51
7.1.7;MCP Pentapeptide Tether;51
7.1.8;The CheA Histidine Kinase: Domain Organization, Conservation, and Diversity;52
7.1.9;The CheY Response Regulator: Big Problems of the Small Protein;57
7.1.10;CheB and CheR;57
7.1.11;CheC and CheX;58
7.1.12;CheD;59
7.1.13;CheZ;61
7.1.14;CheW and CheV;63
7.1.15;References;64
7.2;Chapter 2: Two-Component Systems in Microbial Communities: Approaches and Resources for Generating and Analyzing Metagenomic Data Sets;69
7.2.1;Introduction;69
7.2.2;Generating Metagenomic Data;71
7.2.3;Assembly of Environmental Sequence Data;74
7.2.4;Gene Prediction in Environmental Sequence Data;75
7.2.5;Analysis of Two-Component System Genes in Environmental Sequence Data;76
7.2.6;Acknowledgments;81
7.2.7;References;81
7.3;Chapter 3: Identification of Sensory and Signal-Transducing Domains in Two-Component Signaling Systems;84
7.3.1;Introduction;84
7.3.2;Computational Tools for Domain Identification;86
7.3.3;Sequence Analysis of Histidine Kinases;89
7.3.4;Sequence Analysis of Response Regulators;97
7.3.5;Sequence Analysis of Prokaryotic Signal Transducers;101
7.3.6;Functional Annotation of Multidomain Proteins;104
7.3.7;References;106
7.4;Chapter 4: Features of Protein-Protein Interactions in Two-Component Signaling Deduced from Genomic Libraries;112
7.4.1;Introduction;112
7.4.2;Identifying Coupled Columns;115
7.4.3;Predicting Protein-Protein Interaction;129
7.4.4;Summary;135
7.4.5;Acknowledgments;135
7.4.6;References;135
7.5;Chapter 5: Sporulation Phosphorelay Proteins and Their Complexes: Crystallographic Characterization;139
7.5.1;Introduction;139
7.5.2;Methods;141
7.5.3;Insights from Structural Analysis;147
7.5.4;Conclusion;157
7.5.5;Acknowledgments;157
7.5.6;References;157
7.6;Chapter 6: Control Analysis of Bacterial Chemotaxis Signaling;160
7.6.1;Introduction;160
7.6.2;Basic Concepts in Dynamics and Mathematical Modeling;161
7.6.3;Robustness and Steady-State Sensitivity Analysis;164
7.6.4;Constructing and Interpreting a Bode Plot;166
7.6.5;Primer on Integral Feedback Control;169
7.6.6;Noise Filtering and the Kalman Filter;172
7.6.7;Future Perspectives and Further Information;175
7.6.8;References;176
7.7;Chapter 7: Classification of Response Regulators Based on Their Surface Properties;178
7.7.1;Introduction;178
7.7.2;Classification of the Receiver Domain of RRs Using Protein Interaction Surfaces;179
7.7.3;Modeling and Subclassification of Receiver Domains of OmpR Subfamily RRs in B. subtilis and E. coli;181
7.7.4;Modeling and Subclassification of the Receiver Domain of RRs in V. vulnificus;195
7.7.5;References;205
8;Section II: Biochemical and Genetic Assays of Individual Components of Signaling Systems;208
8.1;Chapter 8: Purification and Assays of Rhodobacter capsulatus RegB-RegA Two-Component Signal Transduction System;210
8.1.1;Introduction;210
8.1.2;Expression and Purification of RegB;212
8.1.3;Expression and Purification of RegA;216
8.1.4;RegB Kinase and Phosphotransfer Assays;217
8.1.5;Acknowledgment;219
8.1.6;References;219
8.2;Chapter 9: Purification and Reconstitution of PYP-Phytochrome with Biliverdin and 4-Hydroxycinnamic Acid;221
8.2.1;Introduction;221
8.2.2;Vector Construct;222
8.2.3;Prepation of 4-Hydroxycinnamic Acid Anhydride and Biliverdin;222
8.2.4;Overexpression and Reconstitution of apo-Ppr with Chromophores;222
8.2.5;Purification of Ppr Reconstituted with Chromophores;223
8.2.6;Spectroscopic Measurements of holo-Ppr, Ppr-BV, and Ppr-pCA;224
8.2.7;In Vitro Autophosphorylation of Ppr;225
8.2.8;Acknowledgments;225
8.2.9;References;225
8.3;Chapter 10: Oxygen and Redox Sensing by Two-Component Systems That Regulate Behavioral Responses: Behavioral Assays and Structural Studies of Aer Using In Vivo Disulfide Cross-Linking;227
8.3.1;Introduction;227
8.3.2;Assays of Oxygen and Redox Sensing: General Considerations;231
8.3.3;Temporal Assay for Aerotaxis;234
8.3.4;Spatial-Gradient Capillary Assay for Aerotaxis;240
8.3.5;Using a Capillary to Determine the Preferred Partial Pressure of Oxygen for Bacteria;242
8.3.6;Spatial-Gradient Soft Agar Plate Assays for Aerotaxis;245
8.3.7;Spatial Assays for Redox Taxis;251
8.3.8;Temporal Assay for Redox Taxis;253
8.3.9;Disulfide Cross-Linking In Vivo to Elucidate the Structure of Aer;255
8.3.10;Site-Directed Mutagenesis for Cysteine Replacement;256
8.3.11;Disulfide Cross-Linking in the Cytosol Using Copper Phenanthroline;257
8.3.12;Differentiating Intra- from Interdimeric Disulfide Bonds;258
8.3.13;In Vivo Cross-Linking Using Bifunctional Sulfhydryl-Reactive Linkers;260
8.3.14;Determining the Boundaries of Transmembrane Segments in Receptors;261
8.3.15;Accessibility Studies in Membrane Vesicles;261
8.3.16;Acknowledgments;264
8.3.17;References;265
8.4;Chapter 11: Two-Component Signaling in the Virulence of Staphylococcus aureus: A Silkworm Larvae-Pathogenic Agent;270
8.4.1;Introduction;270
8.4.2;Silkworm Larvae Infection Model;271
8.4.3;Pathogenicity-Related Genes That Can Be Identified in the Silkworm Infection Assay;272
8.4.4;Identification of Genes Involved in the Killing of Silkworm Larvae by Bacteria;273
8.4.5;Identification of an SA0614 Response Regulator Mutant by Monitoring CPZ Sensitivity and Ability to Kill Silkworm Larvae;274
8.4.6;Silkworm Larvae Infection Assay;275
8.4.7;Measurement of the Number of Bacteria in Silkworm Hemolymph;276
8.4.8;Defect in Cell Wall Integrity of the SA0614 Response Regulator Mutant;278
8.4.9;Detergent and Lysozyme Sensitivity Test;278
8.4.10;Melanization-Inducing Activity of Bacterial Peptidoglycan;279
8.4.11;References;279
8.5;Chapter 12: TonB System, In Vivo Assays and Characterization;282
8.5.1;Introduction;282
8.5.2;Selection For and Against the tonB Gene;283
8.5.3;Precautions for Experiments Where TonB System Proteins Are Expressed from Plasmids;285
8.5.4;Phenotypic Assays for the TonB System;286
8.5.5;Mechanistically Informative Assays;292
8.5.6;Potentially Mechanistically Informative Assays;299
8.5.7;Acknowledgments;302
8.5.8;References;302
8.6;Chapter 13: Biochemical Characterization of Plant Ethylene Receptors Following Transgenic Expression in Yeast;307
8.6.1;Introduction;307
8.6.2;Transgenic Expression of Ethylene Receptors in Yeast;310
8.6.3;Histidine Kinase Activity;311
8.6.4;Isolation of Receptors for Use in Ethylene-Binding Assays;314
8.6.5;Ethylene-Binding Activity;317
8.6.6;Considerations When Working with Mercuric Perchlorate;320
8.6.7;Acknowledgments;322
8.6.8;References;322
8.7;Chapter 14: Structure of SixA, a Histidine Protein Phosphatase of the ArcB Histidine-Containing Phosphotransfer Domain in Escherichia Coli;325
8.7.1;Introduction;325
8.7.2;Overall Structure;327
8.7.3;Conservation of Catalytic Machinery and Active Site;330
8.7.4;Sequence Analysis of SixA Homologs;332
8.7.5;Eukaryotic Histidine Phosphatases;339
8.7.6;Acknowledgments;339
8.7.7;References;339
8.8;Chapter 15: Triggering and Monitoring Light-Sensing Reactions in Protein Crystals;342
8.8.1;Light-Regulated Histidine Kinases: A Brief Introduction;342
8.8.2;Microbial Rhodopsins;343
8.8.3;PAS/GAF/LOV Domains;344
8.8.4;Photoreceptors and Kinetic Crystallography: A Near Perfect Match;347
8.8.5;Kinetic Crystallography: Two Alternative Strategies;348
8.8.6;Trapping Intermediates for X-Ray Crystallography;350
8.8.7;Structural Interpretation of Kinetic Crystallography Results;352
8.8.8;Optical Properties of Protein Crystals;354
8.8.9;Mounting Crystals;355
8.8.10;Design of a Single Crystal Microspectrophotometer;357
8.8.11;Challenges of Recording UV-Visible Absorption Spectra in Crystals;359
8.8.12;Leaking Light Introduces Spectral and Kinetic Artifacts;361
8.8.13;Fluorescence Measurements;364
8.8.14;Microspectrophotometry: Summary and Warning;368
8.8.15;Light Activation of Photoreceptor Crystals;368
8.8.16;Aligning the Activating Light Beam and the Crystal Position;370
8.8.17;Summary and Outlook;371
8.8.18;References;371
8.9;Chapter 16: Synthesis of a Stable Analog of the Phosphorylated Form of CheY: Phosphono-CheY;375
8.9.1;Introduction;375
8.9.2;Protocols;382
8.9.3;Acknowledgments;387
8.9.4;References;387
8.10;Chapter 17: Application of Fluorescence Resonance Energy Transfer to Examine EnvZ/OmpR Interactions;389
8.10.1;Introduction;389
8.10.2;Overexpression of EnvZ and Preparation of Spheroplasts;391
8.10.3;Protein Purification and Fluorescent Labeling of OmpR;392
8.10.4;Fluorescence Resonance Energy Transfer;394
8.10.5;OmpR Has a Higher Affinity for EnvZ Than OmpR~P;394
8.10.6;Concluding Remarks;396
8.10.7;Acknowledgments;396
8.10.8;References;396
8.11;Chapter 18: Gene Promoter Scan Methodology for Identifying and Classifying Coregulated Promoters;398
8.11.1;Introduction;398
8.11.2;Challenge of Identifying Promoter Features Governing Gene Transcription;400
8.11.3;GPS Methodology as an Integrated Algorithm;402
8.11.4;Exploring Targets of Regulation of a Response Regulator Using GPS;405
8.11.5;Technical Specifications of GPS;410
8.11.6;Uncovering Promoter Profiles Regulated by Response Regulator PhoP Using GPS;415
8.11.7;Conclusions;417
8.11.8;Acknowledgments;418
8.11.9;References;419
8.12;Chapter 19: Targeting Two-Component Signal Transduction: A Novel Drug Discovery System;423
8.12.1;Introduction;423
8.12.2;Differential Growth Assay;424
8.12.3;High-Throughput Genetic System;428
8.12.4;Acknowledgments;431
8.12.5;References;431
8.13;Chapter 20: The Essential YycFG Two-Component System of Bacillus subtilis;433
8.13.1;Introduction;433
8.13.2;Construction of Conditional Mutants;434
8.13.3;Transposon Mutagenesis to Identify Regulatory Elements;437
8.13.4;Constructing In-Frame Deletions in the yyc Operon;440
8.13.5;Studying Interactions between the YycG Kinase and Its Regulatory Proteins;445
8.13.6;Subcellular Localization Studies;448
8.13.7;Concluding Remarks;452
8.13.8;Acknowledgments;452
8.13.9;References;452
9;Section III: Physiological Assays and Readouts;456
9.1;Chapter 21: Isolation and Characterization of Chemotaxis Mutants of the Lyme Disease Spirochete Borrelia burgdorferi Using Allelic Exchange Mutagenesis, Flow Cytometry, and Cell Tracking;458
9.1.1;Introduction;458
9.1.2;Borrelia burgdorferi Mutagenesis;459
9.1.3;Chemotaxis and Motility Analysis;460
9.1.4;Materials and Methods;461
9.1.5;Acknowledgments;472
9.1.6;References;473
9.2;Chapter 22: Phosphorylation Assays of Chemotaxis Two-Component System Proteins in Borrelia burgdorferi;475
9.2.1;Introduction;476
9.2.2;Regulation of CheY-P;476
9.2.3;Materials and Methods;479
9.2.4;Acknowledgments;483
9.2.5;References;483
9.3;Chapter 23: Regulation of Respiratory Genes by ResD-ResE Signal Transduction System in Bacillus subtilis;485
9.3.1;Introduction;485
9.3.2;Oxygen Limitation and ResDE-Dependent Transcription;486
9.3.3;Stimulatory Effect of NO on ResDE-Dependent Transcription;488
9.3.4;Phosphorylation Assay Using Full-Length ResE;490
9.3.5;In Vivo Effect of alpha-CTD Alanine Substitutions on ResDE-Dependent Transcription;492
9.3.6;In Vitro Effect of alpha-CTD Alanine Substitutions on ResDE-Dependent Transcription;495
9.3.7;Acknowledgments;498
9.3.8;References;499
9.4;Chapter 24: Detection and Measurement of Two-Component Systems That Control Dimorphism and Virulence in Fungi;502
9.4.1;Introduction;502
9.4.2;Experimental Approaches;505
9.4.3;Acknowledgment;522
9.4.4;References;522
9.5;Chapter 25: Using Two-Component Systems and Other Bacterial Regulatory Factors for the Fabrication of Synthetic Genetic Devices;525
9.5.1;Using Two-Component Signal Transduction Systems in Synthetic Biology Approaches;526
9.5.2;Using the NRI/NRII System to Build a Synthetic Genetic Clock;528
9.5.3;Fabrication of Synthetic Genetic Clock;530
9.5.4;Functions of Individual Clock Modules;533
9.5.5;Improved Procedures for Fabrication of Synthetic Genetic Modules and Integration of These Modules into Chromosomal Landing Pads;545
9.5.6;Fabricating Genetic Modules;548
9.5.7;References;549
10;Author Index;550
11;Subject Index;578

Leseproben

[1] Comparative Genomic and Protein Sequence Analyses of a Complex System Controlling Bacterial Chemotaxis
Kristin Wuichet; Roger P. Alexander; Igor B. Zhulin Abstract
Molecular machinery governing bacterial chemotaxis consists of the CheA–CheY two-component system, an array of specialized chemoreceptors, and several auxiliary proteins. It has been studied extensively in Escherichia coli and, to a significantly lesser extent, in several other microbial species. Emerging evidence suggests that homologous signal transduction pathways regulate not only chemotaxis, but several other cellular functions in various bacterial species. The availability of genome sequence data for hundreds of organisms enables productive study of this system using comparative genomics and protein sequence analysis. This chapter describes advances in genomics of the chemotaxis signal transduction system, provides information on relevant bioinformatics tools and resources, and outlines approaches toward developing a computational framework for predicting important biological functions from raw genomic data based on available experimental evidence. Introduction
Signal transduction systems link internal and external cues to appropriate cellular responses in all organisms. Prokaryotic signal transduction can be classified into three main families based on the domain organization and complexity: one-component systems, classical two-component systems anchored by class I histidine kinases, and multicomponent systems anchored by class II histidine kinases often referred to as chemotaxis systems (Bilwes et al., 1999; Dutta et al., 1999; Stock et al., 2000; Ulrich et al., 2005). As their name suggests, one-component systems consist of a single protein that is capable of both sensing a signal and directly affecting a cellular response, either through a single domain (such as a DNA-binding domain that senses a signal through its metal cofactor) or multiple domains (separate input and output domains) (Ulrich et al., 2005). As a consequence of their single protein nature and typical lack of transmembrane regions, one-component systems are predicted to primarily sense the internal cellular environment, while the division of input and output between two or more proteins and association of the sensor with the membrane in two-component systems allows them to detect both internal and external signals (Ulrich et al., 2005). The chemotaxis system centered around the class II histidine kinase CheA contains multiple proteins separating input and output, along with additional regulatory components that are not present in class I histidine kinase-containing two-component systems. There are many common input (sensing) modules among all three families of prokaryotic signal transduction; one-component systems and two-component systems also share common outputs (Ulrich et al., 2005), whereas two-component systems and chemotaxis systems share several common signaling modules (Dutta et al., 1999; Stock et al., 2000). The chemotaxis system is classically portrayed as a network of interacting proteins, which senses environmental stimuli to regulate motility. The system consists of two distinct pathways: an excitation pathway that has the downstream result of interacting with the motility organelle and an adaptation pathway that provides a mechanism for molecular memory (Baker et al., 2006; Wadhams and Armitage, 2004). The excitation pathway involves methyl-accepting chemotaxis proteins (MCPs) for sensing environmental signals that are transmitted to a scaffolding protein, CheW, and a histidine kinase, CheA, via a highly conserved cytoplasmic signaling module of the MCPs. The signals regulate the kinase activity of CheA and the phosphorylation state of its cognate response regulator CheY controls its affinity for the motor. Many chemotaxis systems have one or more phosphatases (CheC, CheX, and/or CheZ) involved in the excitation pathway that aid in dephosphorylating CheY (Szurmant and Ordal, 2004). Signal propagation through the MCPs is further controlled in most systems by an adaptation pathway that regulates their methylation state via the CheB methylesterase, a response regulator that is phosphorylated by CheA to stimulate the removal of methyl groups from the receptors, and the CheR methyltransferase that constitutively methylates specific glutamate residues of the receptors. Many chemotaxis systems have an additional adaptation protein, CheD, for the deamidation of particular amino acid side chains of many MCPs prior to their methylation, and in some of these systems CheD also interacts with CheC to increase its dephosphorylation activity (Chao et al., 2006; Kristich and Ordal, 2002). The final characterized chemotaxis protein is CheV, a fusion of CheW and a CheY-like receiver domain, which affects the signaling state of the MCP based on its phosphorylation state as controlled by the CheA kinase (Karatan et al., 2001; Pittman et al., 2001). In addition to component diversity between chemotaxis systems, there are also functional differences between their outputs. Historically, the focus of detailed molecular investigation is on the chemotaxis system that controls flagellar motility, but studies have demonstrated that chemotaxis systems are also involved in regulating type IV pili-based motility (Bhaya et al., 2001; Sun et al., 2000; Whitchurch et al., 2004). Even more recently, chemotaxis systems were implicated in controlling diverse cellular functions, such as intracellular levels of cyclic di-GMP, transcription, and other functions (Berleman and Bauer, 2005; D’Argenio et al., 2002; Hickman et al., 2005; Kirby and Zusman, 2003). Many organisms have multiple chemotaxis systems that can have both overlapping and/or unrelated functional outputs (Berleman and Bauer, 2005; Guvener et al., 2006; Kirby and Zusman, 2003; Martin et al., 2001; Wuichet and Zhulin, 2003). Beyond the functional diversity of the system outputs, there can be significant mechanistic diversity within these functional classes. For example, the signaling and adaptation mechanisms in Escherichia coli and Bacillus subtilis differ markedly. In E. coli, positive stimuli inhibit CheA activity, whereas in B. subtilis the opposite is true. In E. coli, MCP demethylation increases in response to negative stimuli only, whereas in B. subtilis, it occurs in response to both positive and negative stimuli (Szurmant and Ordal, 2004). The diversity found among chemotaxis systems cannot be efficiently addressed by experimental means alone, nor can the questions about the function and origin of this system. Initial genomic studies have already identified the core set of chemotaxis proteins as CheA, CheW, CheY, and MCP, which are present in all chemotaxis systems (Zhulin, 2001), unlike the sporadic distributions of CheC, CheD, and CheZ (Kirby et al., 2001; Szurmant and Ordal, 2004; Terry et al., 2006) and the occasional absence of CheB and CheR (Terry et al., 2006; Zhulin, 2001). Diversity within the CheA domain organization was also reported (Acuna et al., 1995; Bhaya et al., 2001; Whitchurch et al., 2004), as well as the broad repertoire of MCP sensor domains (Aravind and Ponting, 1997; Shu et al., 2003; Taylor and Zhulin, 1999; Ulrich and Zhulin, 2005; Zhulin, 2001; Zhulin et al., 2003) and their evolutionary trends (Wuichet and Zhulin, 2003), and the length variability of the MCP signaling module (Alexander and Zhulin, 2007; LeMoual and Koshland, 1996). Motivating factors to further study the chemotaxis system using comparative genomic methods are the wealth of genomic data available for prokaryotes, the large evolutionary distances between prokaryotes that have this system, and the propensity for its components to be encoded in gene clusters. The extensive molecular and biochemical characterizations of the system and its components and the availability of three-dimensional structures for most of the components provide most valuable information for comparison and validation of findings obtained through computational analysis. Although this chapter focuses on the chemotaxis system, the methodology of this research is applicable to all signal transduction systems, prokaryotic and eukaryotic, with the caveats that certain thresholds (e.g., sequence conservation) must be altered to suit the evolutionary rate of a given protein or domain and that some techniques (e.g., gene neighborhood analysis) are best applied to prokaryotic systems. Bioinformatics Tools and Resources for Identifying and Analyzing Chemotaxis Components
Many tools and databases are available to aid comparative genomic analyses. The SMART (Letunic et al., 2006) and Pfam (Finn et al., 2006) databases are primary sources for Hidden Markov Models (HMMs) that can identify conserved domains and domain combinations within protein sequences. Each model captures the key sequence features of a specific domain, based on the multiple alignments...