Microbial Functional Genomics, the first comprehensive treatment of this subject, provides a much–needed synthesis of genome–wide studies on gene networks and functions, as well as the use of genomic data and technology in addressing a host of biological problems. Topics covered include:
- Genomics: introduction, history, and current challenges and scope
- Microbial diversity and evolution from a genomics perspective
- Computational methods for genome annotation and functional prediction of genes
- DNA microarray technology and its application to gene expression data analysis, mutation analysis, and microbial detection
- Mutagenesis as a genomic tool for studying gene function
- The functional genomics of model organisms, bacterial pathogens, and environmentally significant microorganisms
- The impact of genomics on antimicrobial drug discovery and toxicology
Microbial Functional Genomics represents a timely summary of the principles, approaches, and applications at the forefront of this exciting and rapidly progressing field.
1. Genomics: Toward a Genome–level Understanding of the Structure, Functions, and Evolution of Biological Systems (Jizhong Zhou, Dorothea K. Thompson, and James M. Tiedje).
1.2 Definitions and Classifications.
1.2.1 Classification based on system attributes.
1.2.2 Classification based on relationships to other scientific disciplines.
1.2.3 Classification based on types of organisms studied.
1.3 Historical Perspective of Genomics.
1.4 Challenges of Studying Functional Genomics.
1.4.1 Defining gene function.
1.4.2 Indentifying and characterizing the molecular machines of life.
1.4.3 Delineating gene regulatory networks.
1.4.4 System–level understanding of biological systems beyond individual cells.
1.4.5 Computational challenges.
1.4.6 Multidisciplinary collaborations.
1.5 Scope and General Approaches.
1.5.1 Structural Genomics.
1.6 Importance of Microbial Functional Genomics to the Study of Eukaryotes.
2. Microbial Diversity and Genomics (Konstantinos Konstantinidis and James M. Tiedje).
2.2 Biochemical Diversity.
2.3 Genetic Diversity.
2.3.1 The unseen majority.
2.3.2 How many prokaryotic species are there?
2.4 The Challenge of Describing Prokaryotic Diversity.
2.4.1 Methods to study microbial diversity.
2.4.2 Limitations of culture–independent methods.
2.4.3 Interesting findings from culture–independent approaches.
2.5 Diversity of Microbial Genomes and Whole–Genome Sequencing.
2.5.1 Genomic diversity within species.
2.5.2 Genome structure and its relation to the ecological niche.
2.5.3 General trends in genome functional content.
2.5.4 Biases in the collection of sequenced species: a limit to understanding.
3. Computational Genome Annotation (Ying Xu).
3.2 Prediction of Protein–Coding Genes.
3.2.1 Evaluation of coding potential.
3.2.2 Identification of translation start.
3.2.3 Ab initio gene prediction through information fusion.
3.2.4 Gene identification through comparative analysis.
3.2.5 Interpretation of gene prediction.
3.3 Prediction of RNA–Coding Genes.
3.4 Identification of Promoters.
3.4.1 Promoter prediction through feature recognition.
3.5 Operon Identification.
3.6 Functional Categories of Genes.
3.7 Characterization of Other Features in a Genome.
3.8 Genome–Scale Gene Mapping.
3.9 Existing Genome Annotation Systems.
4. Microbial Evolution from a Genomics Perspective (Jizhong Zhou and Dorothea K. Thompson).
4.2 Identification of Orthologous Genes.
4.3 Genome Perspectives on Molecular Clock.
4.3.1 Historical overview.
4.3.2 Current genomic view on molecular evolutionary clock.
4.3.3 Timing genome divergence.
4.4 Genome Perspectives on Horizontal Gene Transfer.
4.4.1 Historical overview of horizontal gene transfer.
4.4.2 Identification of HGT.
4.4.3 Mechanisms underlying HGT.
4.4.4 Types of genes subjected to HGT.
4.4.5 Classification and scope of HGT.
4.4.6 Evolutionary impact of HGT.
4.5 Genomic Perspectives on Gene Duplication, Gene Loss, and Other Evolutionary Processes.
4.5.1 Gene and genome duplication.
4.5.2 Genomic perspectives on gene loss.
4.5.3 Genomic perspectives on other evolutionary processes.
4.6 Universal Tree of Life.
4.6.1 Establishment of a universal tree of life.
4.6.2 Challenges and current view of the universal tree.
4.6.3 Genome–based phylogenetic analysis.
4.7 Minimal Genomes.
4.8 Genomic Insights into Lifestyle Evolution.
4.9 Genome Perspective of Mitochondrial Evolution.
5. Computational Methods for Functional Prediction of Genes (Ying Xu).
5.2 Methods for Gene Function Inference.
5.2.1 Gene functions at different levels.
5.2.2 Searching for clues to gene function.
5.3 From Gene Sequence to Function.
5.3.1 Hierarchies of protein families.
5.3.2 Searching family trees.
5.3.3 Orthologous vs. paralogous genes.
5.3.4 Genes with multiple domains.
5.4 Identification of Sequence Motifs.
5.5 Structure–Based Function Prediction.
5.5.1 Protein fold recognition through protein threading.
5.5.2 From structure to function.
5.5.3 Disordered vs. ordered regions in proteins.
5.6 Nonhomologous Approaches to Functional Inference.
5.7 Functional Inference at a Systems Level.
6. DNA Microarray Technology (Jizhong Zhou and Dorothea K. Thompson).
6.2 Types of Microarrays and Advantages.
6.2.1 Concepts, principles, and history.
6.2.2 Microarray types and their advantages.
6.3 Microarray Fabrication.
6.3.1 Microarray fabrication substrates and modification.
6.3.2 Arraying technology.
6.3.3 Critical issues for microarray fabrication.
6.4 Microarray Hybridization and Detection.
6.4.1 Probe design, target preparation, and quality.
6.4.4 Critical issues in hybridization and detection.
6.5 Microarray Image Processing.
6.5.1 Data acquisition.
6.5.2 Assessment of spot quality and reliability, and background subtraction.
6.6 Using Microarrays to Monitor Gene Expression.
6.6.1 General approaches to revealing differences in gene expression.
6.6.2 Specificity, sensitivity, reproducibility, and quantitation of microarray–based detection for monitoring gene expression.
6.6.3 Microarray experimental design for monitoring gene expression.
7. Microarray Gene Expression Data Analysis (Ying Xu).
7.2 Normalization of Microarray Gene Expression Data.
7.2.1 Sources of systematic errors.
7.2.2 Experimental design to minimize systematic variations.
7.2.3 Selection of reference points for data normalization.
7.2.4 Normalization methods.
7.3 Data Analysis.
7.3.1 Data transformation.
7.3.2 Principle component analysis.
7.4 Identification of Differentially Expressed Genes.
7.5 Identification of Coexpressed Genes.
7.5.1 Basics of gene expression data clustering.
7.5.2 Clustering of gene expression data.
7.5.3 Cluster identification from noisy background.
7.5.4 EXCAVATOR: a software for gene expression data analysis.
7.5.5 Discovering subtypes through data clustering.
7.6 Applications of Gene Expression Data Analysis for Pathway Inference.
7.6.1 Data–constrained pathway construction.
8. Mutagenesis as a Genomic Tool for Studying Gene Function (Alexander S. Beliaev).
8.2 Transposon Mutagenesis.
8.2.1 Overview of transposition in bacteria.
8.2.2 Transposons as tools for mutagenesis.
8.2.3 Transposon–based approaches for identification of essential genes.
8.2.4 Signature–tagged mutagenesis for studying bacterial pathogenicity.
8.3 Targeted Mutagenesis Through Allelic Exchange.
8.3.1 Suicide vector systems for allelic exchange.
8.3.2 Strategies commonly utilized for targeted mutagenesis by allelic exchange.
8.3.3 Application of allele exchange approach in functional genomic studies for sequenced microorganisms.
8.4 Gene Silencing Using Antisense mRNA Molecules.
8.4.1 Antisense RNA regulation in vivo.
8.4.2 Antisense approach to large–scale functional genomic studies.
9. Mass Spectrometry (Nathan VerBerkmoes, Joshua Sharp, and Robert Hettich).
9.2 Fundamentals of Mass Spectrometry.
9.2.1 Basic components of any mass spectrometer.
9.2.2 Ionization methods.
9.2.3 Mass analyzers.
9.2.4 Coupling separation methods with mass spectrometry.
9.2.5 Ion structural characterization.
9.3 Fundamentals of Protein and Peptide Mass Spectrometry.
9.3.1 Protein measurements.
9.3.2 Peptide measurements.
9.4 Mass Spectrometry for Protein and Proteome Characterization.
9.4.1 Overview of mass spectrometry approaches for protein studies.
9.4.2 Bottom–up mass spectrometry proteomics.
9.4.3 Top–down mass spectrometry proteomics.
9.4.4 Relating mass spectrometry proteomic data to biological information.
10. Identification of Protein Ligand Interactions (Timothy Palzkill).
10.2 High–Throughput Cloning of Open Reading Frames.
10.2.1 Bacteriophage l att recombination–based cloning.
10.2.2 Topoisomerase–based cloning.
10.2.3 In vivo recombination–based cloning in yeast.
10.2.4 Advantages and disadvantages of recombinational cloning systems.
10.3 Yeast Two–Hybrid Selection System.
10.3.1 Analysis of genome–wide protein protein interactions in yeast.
10.3.2 Genome–wide yeast two–hybrid analysis of other organisms.
10.4 Use of Phage Display to Detect Protein Ligand Interactions.
10.4.1 Display of proteins on M13 filamentous phage.
10.4.2 Display of proteins on the T7 bacteriophage.
10.4.3 Combining yeast two–hybrid and phage display data.
10.5 Detecting Interactions with Protein Fragment Complementation Assays.
10.5.2 Protein fragment complementation using dihydrofolate reductase.
10.5.3 Monitoring protein interactions by intracistronic b–galactosidase complementation.
10.6 Use of Mass Spectrometry for Protein Protein Interaction Mapping.
10.6.2 Identification of substrates for E. coli GroEL.
10.6.3 Identification of protein complexes in Saccharomyces cerevisiae.
10.7 Protein Assays for Protein Expression Profiling and Interactions.
10.7.1 Antibody arrays for protein expression profiling.
10.7.2 Functional analysis using peptide, protein, and small–molecule arrays.
10.8 Surface Plasmon Resonance Biosensor Analysis.
10.8.1 Measuring interactions of biomolecules with SPR.
10.8.2 Integration of SPR biosensors with mass spectrometry.
11. The Functional Genomics of Model Organisms: Addressing Old Questions from a New Perspective (Dorothea K. Thompson and Jizhong Zhou).
11.2 Escherichia coli: A Model Eubacterium.
11.2.1 E. coli genome.
11.2.2 E. coli transcriptomics.
11.2.3 E. coli proteomics.
11.2.4 Modeling E. coli metabolism: in silico metabolomics.
11.3 Bacillus subtilis: A Paradigm for Gram–Positive Bacteria.
11.3.1 B. subtilis genome.
11.3.2 B. subtilis transcriptomics.
11.3.3 B. subtilis proteomics.
11.4 Saccharomyces cerevisiae: A Model for Higher Eukaryotes.
11.4.1 Yeast genome.
11.4.2 Yeast transcriptomics.
11.4.3 Yeast proteomics.
11.4.4 Yeast interactome: mapping protein protein interactions.
11.5 Comparative Genomics of Model Eukaryotic Organisms.
12. Functional Genomic Analysis of Bacterial Pathogens and Environmentally Significant Microorganisms (Dorothea K. Thompson and Jizhong Zhou).
12.2 Advancing Knowledge of Bacterial Pathogenesis through Genome Sequence and Function Annotation.
12.2.1 Predicting virulence genes from sequence homology.
12.2.2 Repeated DNA elements indicate potential virulence factors.
12.2.3 Evolution of bacterial pathogens: gene acquisition and loss.
12.3 Comparative Genomics: Clues to Bacterial Pathogenicity.
12.3.1 The genomics of Mycobacterium tuberculosis: virulence gene identification and genome plasticity.
12.3.2 Microarray–based comparative genomics of Helicobacter pylori.
12.3.3 Comparative analysis of the Borrelia burgdorferi and Treponema pallidum genomes.
12.3.4 Sequence comparison of pathogenic and nonpathogenic species of Listeria.
12.3.5 Comparative genomics of Chlamydia pneumoniae and Chlamydia trachomatis: two closely related obligate intracellular pathogens.
12.4 Discovery of Novel Infection–Related Genes Using Signature–Tagged Mutagenesis.
12.4.1 Vibrio cholerae genes critical for colonization.
12.4.2 Virulence genes of Staphylococcus aureus infection.
12.4.3 Escherichia coli K1: identification of invasion genes.
12.4.4 Diverse genes implicated in Streptococcus pneumoniae virulence.
12.5 Application of Microarrays to Delineating Gene Function and Interaction.
12.5.1 Exploring the transcriptome of bacterial pathogens.
12.5.2 Elucidating the molecular intricacies of host pathogen interactions.
12.5.3 Identification of antimicrobial drug targets.
12.6 The Proteomics of Bacterial Pathogenesis.
12.6.1 Comparative proteomics.
12.6.2 Defining the proteome of individual bacterial pathogens.
12.6.3 Proteomic approach to host pathogen interactions.
12.7 Genome Sequence and Functional Analysis of Environmentally Important Microorganisms.
12.7.1 Dissimilatory metal ion–reducing bacterium Shewanella oneidensis.
12.7.2 Extreme radiation–resistant bacterium Deinococcus radiodurans.
12.7.3 Hyperthermophilic archaeon Pyrococcus furiosus.
13. The Impact of Genomics on Antimicrobial Drug Discovery and Toxicology (Dorothea K. Thompson and Jizhong Zhou).
13.2 Antibacterial Drug Discovery: A Historical Perspective.
13.3 Challenges of New Drug Discovery.
13.3.1 Resistance to antimicrobial agents and the need for new antibiotic discovery.
13.3.2 Desirable properties of antimicrobial targets.
13.4 Microbial Genomics and Drug Target Selection.
13.4.1 Mining genomes for antimicrobial drug targets..
13.4.2 Comparative genomics: assessing target spectrum and selectivity.
13.4.3 Genetic strategies: verifying the essentiality or expression of gene targets.
13.4.4 Microarray analysis: establishing functionality for novel drug targets.
13.5 Determining Therapeutic Utility: Drug Target Screening and Validation.
13.5.1 Target–based drug screening.
13.5.2 Microarrays and drug target validation.
13.6 Genomics and Toxicology: The Emergence of Toxicogenomics.
13.6.1 Microarrays in mechanistic toxicology.
13.6.2 Microarrays in predictive toxicology.
14. Application of Microarray–based Genomic Technology to Mutation Analysis and Microbial Detection (Jizhong Zhou and Dorothea K. Thompson).
14.2 Oligonucleotide Microarrays for Mutation Analysis.
14.2.1 Microarray–based hybridization assay with allele–specific oligonucleotides.
14.2.2 Microarray–based single–base extension for genotyping.
14.2.3 Microarray–based ligation detection reaction for genotyping.
14.3 Microarrays for Microbial Detection in Natural Environments.
14.3.1 Limitations of conventional molecular methods for microbial detection.
14.3.2 Advantages and challenges of microbial detection in natural environments.
14.3.3 Functional gene arrays.
14.3.4 Phylogenetic oligonucleotide arrays.
14.3.5 Community genome arrays.
14.3.6 Whole–genome open reading frame arrays for revealing genome differences and relatedness.
14.3.7 Other types of microarrays for microbial detection and characterization.
15. Future Perspectives: Genomics Beyond Single Cells (James M. Tiedje and Jizhong Zhou).
15.2 The Informational Base of Microbial Biology: Genome Sequences.
15.2.1 Determination of the Genetic Content of Both Cultured and Uncultured Microorganisms.
15.2.2 Community Genomics or Metagenomics.
15.3 Gene Functions and Regulatory Networks.
15.4 Ecology and Evolution.
15.5 System–level Understanding of Microbial Community Dynamics.