Microbial systems biology : methods and protocols

Description

Dedication Preface Contents Contributors Chapter 1: Parallel Accelerator and Molecular Mass Spectrometry Measurement of Carbon-14-Labeled Analytes 1 Introduction 2 Experimental Design, Laboratory Requirements, and Materials 2.1 Deciding to Use PAMMS 2.2 Designing an Experiment 2.3 Laboratory Requirements 2.4 Labeled Analyte 2.5 Mobile Phase Preparation 3 Methods 3.1 Making PAMMS Measurements 3.2 Interpreting the Data References Chapter 2: Fast Sampling of the Cellular Metabolome 1 Introduction 1.1 Method Development 1.1.1 Methods for Rapid Sampling and Quenching 1.1.2 Fast Sampling Devices 1.1.3 Methods for Fast Quenching of Metabolism 1.1.4 Extraction of Metabolites from Quenched Cell Samples 1.1.5 Analytical Procedures 2 Materials 2.1 Cold Methanol Quenching Combined with Cold Centrifugation 2.2 Additional Materials for Cold Methanol Quenching Combined with Cold Filtration 2.3 Rapid Sampling of Culture Filtrate 2.4 Extraction 3 Methods 3.1 Rapid Sampling for Endometabolome Analysis: Cold Centrifugation Method 3.1.1 Preparation 3.1.2 Sampling 3.1.3 Extraction of the Cell Pellets 3.1.4 Further Sample Processing 3.2 Rapid Sampling for Endometabolome Analysis: Cold Filtration Method 3.2.1 Preparation 3.2.2 Sampling 3.2.3 Extraction of the Cell Cakes 3.2.4 Further Sample Processing 3.3 Rapid Sampling for Exometabolome Analysis 3.3.1 Preparation 3.3.2 Sampling 3.4 Differential Method 3.4.1 Preparation 3.4.2 Sampling 3.4.3 Extraction of the Quenched Total Broth and Filtrate Samples 3.4.4 Further Processing of the Total Broth and Filtrate Samples 3.4.5 Determination of the Intracellular Metabolite Levels for the Differential Method 3.5 Principles of Metabolite Quantification Using Isotope Dilution Mass Spectrometry 4 Notes References Chapter 3: Investigation of Protein-Lipid Interactions Using Native Mass Spectrometry 1 Introduction 2 Materials 2.1 Plasmid Construction, Protein Expression, and Purification 2.2 MS Experiments 2.3 Data Analysis Software 3 Methods 3.1 Soluble Protein Expression 3.2 Soluble Protein Purification 3.3 Membrane Protein Expression 3.4 Membrane Protein Purification 3.5 Ligand or Lipid Solution Preparation 3.6 Protein-Ligand/Lipid Titration Experiments Using Native MS 3.7 Native MS Data Analysis 3.8 Validation of MS Method by SPR and ITC Measurements 4 Notes References Chapter 4: Western Blot Processing Optimization: The Perfect Blot 1 Introduction 2 Materials 3 Methods 3.1 Sample Preparation 3.2 Preparation of Samples for Loading into Gels 3.3 Transferring the Protein from the Gel to the Membrane 3.4 Blocking 3.5 Incubation of Primary Antibodies 3.6 The Washing Membrane After Primary Antibody Incubation 3.7 The Secondary Antibody Incubation 3.8 Washing After Secondary Antibodies 3.9 Detection and Data Analysis 4 Notes References Chapter 5: FISHing on a Budget 1 Introduction 2 Materials 2.1 Equipment 2.2 Reagents 2.2.1 Cell Culture of Saccharomyces cerevisiae 2.2.2 Fixation, Spheroplasting, and Permeabilization 2.2.3 Washing, Hybridization, and Mounting 3 Methods 3.1 Choosing a FISH Method 3.2 Probe Design 3.3 Cell Culture, Fixation, and Spheroplasting 3.4 Hybridization, Washing 3.5 Mounting 3.6 Image Acquisition and Analysis 4 Notes References Chapter 6: NanoSIP: NanoSIMS Applications for Microbial Biology 1 Introduction 1.1 Recent Developments in NanoSIMS Systems Biology Research 1.2 Recent NanoSIMS Instrumentation Innovations 1.3 Moving Toward Standardized Methods 2 Materials 2.1 Sample Selection and Experimental Design 2.2 Incubations for Stable Isotope Tracing in Cultures and Microbial Communities 2.3 Options for Sample Preparation and Pre-analysis Characterization 2.4 High Spatial Resolution SIMS 2.5 Data Analysis 3 Methods 3.1 Sample Selection and Experimental Design 3.2 Isotopic Labeling of Cultures and Microbial Communities 3.3 Sample Preparation and Pre-analysis Characterization 3.3.1 Sample Flatness and Conductivity 3.3.2 Fixation 3.3.3 Embedding and Sectioning 3.3.4 Sample Mapping 3.4 NanoSIMS Analyses 3.4.1 NanoSIMS Tuning and Estimating Mass Resolving Power 3.4.2 Cs+ Analysis for Electronegative Elements and Isotope Ratios 3.4.3 NanoSIMS Trace Element Analysis 3.4.4 Standards and Controls 3.5 Data Processing and Image Analysis 3.5.1 Quantifying and Reporting Isotopic Data 3.5.2 Quantifying and Reporting Elemental Data 3.5.3 Measurement Precision 3.6 Combination with Synergistic Techniques 4 Future Directions References Chapter 7: Construction of Metatranscriptomic Libraries for 5′ End Sequencing of rRNAs for Microbiome Research 1 Introduction 2 Materials 2.1 Size Selection of the RNA (Optional, See Note 1) 2.2 RNA/RNA Ligation and Reverse Transcription 2.3 Nucleic Acid Purification and Quantification 2.4 PCR Amplification 3 Methods 3.1 RNA Size Selection (Optional) 3.2 Ligation of the M13-RNA Oligo 3.3 cDNA Synthesis Using P1-Tailed Primer 3.4 Amplification of the cDNA 4 Notes References Chapter 8: Computational Approaches for Designing Highly Specific and Efficient sgRNAs 1 Introduction 2 Possible Genetic Perturbations 2.1 Gene Knockout 2.2 Gene Knockin 2.3 Gene Expression Control 3 sgRNA Design Rules 3.1 Sequence Profile of the Target DNA and sgRNA 3.2 PAM and the Flanking Sequence 3.3 Location Targeted Within the Gene 3.4 Structural Accessibility 3.5 Microhomology Profile 3.6 Chromatin Characteristics and Epigenetic Features 3.7 Experimental Conditions 4 Role of Computational Tools in Designing sgRNAs 5 Computational Approaches for Designing sgRNA with High On-Target Efficiency 5.1 sgRNA Design Tools for Knockout and Knockin Experiments 5.2 Species-Specific Tools for Aiding in sgRNA Designing 5.3 Tools for Posterior Analysis of Data Obtained from CRISPR/Cas9-Based Experiments 5.4 CRISPR/Cas9 Genome Editing Databases 6 Notes References Chapter 9: Complex Network Analysis in Microbial Systems: Theory and Examples 1 Introduction 2 Tools 2.1 Pajek 2.2 NetworkX 2.3 Cytoscape 2.4 WGCNA 3 Biological Networks 3.1 Protein-Protein Interaction (PPI) Networks 3.2 Metabolic Networks 3.3 Gene Co-expression Networks 3.4 Sources of Datasets 4 Random Graph and Network Models 4.1 Erdös-Rényi (ER) Model 4.1.1 G(N,M) 4.1.2 G(N,p) 4.2 Barabsi-Albert (BA) Model 4.3 Configuration Model References Chapter 10: Prokaryotic Genome Annotation 1 Introduction 2 Materials 2.1 A High-Quality Genome 2.2 Computational Requirements 3 Methods 3.1 All-in-One Tools 3.2 Gene Prediction (Structural) 3.2.1 Protein Coding Sequences 3.2.2 Non-coding RNA 3.2.3 Refinement of Gene Calls 3.3 Functional Prediction (General) 3.4 Functional Prediction (Specific) 3.4.1 CAZy/dbCAN Protocol 3.4.2 antiSMASH 4 Notes References Chapter 11: Functional Annotation from Structural Homology 1 Introduction 1.1 Inferring Homology from Structural Similarity 1.2 The Many Tiers of Macromolecular Structural Homology 2 Materials 2.1 The Protein Databank (PDB) 2.2 BLASTp: Basic Local Alignment Search Tool Protein-Protein 2.3 FATCAT: Flexible Structure Alignment by Chaining Aligned Fragment Pairs Allowing Twists 2.4 PDBSum 2.5 UniProt: The Universal Protein Resource 2.6 PFAM: The Proteins Family Database 2.7 CATH: Class Architecture Topology Homologous Superfamily 2.8 SCOPe: Structural Classification of Proteins-Extended 2.9 SAS: Sequence Annotated by Structure 2.10 ConSurf 2.11 Chimera 2.12 CASTp: Computed Atlas of Surface Topography of Proteins 2.13 RASMOT 3D PRO: Recursive Automatic Search of MOTif in 3D Structures of PROteins 3 Methods 3.1 Identifying Homology Though Common Domains or Domain Architecture 3.2 Discovering Homology Through Domain Structure Similarity 3.3 Identifying Deeply Conserved 3D Motifs 3.4 Identifying Distant Homologs with Shared 3D Motifs 4 Conclusion 5 Notes References Chapter 12: Metabolic Modeling with MetaFlux 1 Introduction 2 Materials 3 Methods 3.1 Solving and Knockout Modes 3.2 Creating and Completing Models References Chapter 13: Application of the Metabolic Modeling Pipeline in KBase to Categorize Reactions, Predict Essential Genes, and Pred... 1 Introduction 1.1 Background Description of Metabolic Models 2 Materials 3 Methods 3.1 Model Reconstruction from Microbial Genomes 3.2 Gapfilling a Metabolic Model 3.3 Flux Balance Analysis and Media in KBase 3.4 Flux Variability Analysis 3.5 Predicting Essential Genes 3.6 Simulating Biolog Phenotype Profiles 3.7 Simulating Metabolite Biosynthesis 4 Conclusion 5 Notes References Chapter 14: Curating COBRA Models of Microbial Metabolism 1 Introduction 1.1 Constraint-Based Modeling 2 Materials 2.1 Annotated Genome 2.2 Software 2.2.1 Automated Metabolic Network Reconstruction and Model Development 2.2.2 Model Editors 2.2.3 Linear Programming Solver 2.2.4 Simulation Toolboxes 3 Model Development 3.1 Draft Network Reconstruction 3.2 Manual Curation of the Draft Reconstruction 3.2.1 Wrong Annotations 3.2.2 Generic Reactions 3.2.3 Non-enzymatic Reactions 3.2.4 Stoichiometry of Reactions 3.2.5 Reaction Directionality 3.2.6 Organism-Specific Reactions 3.2.7 Intracellular Transport Reactions 3.2.8 Gene-Protein-Reaction (GPR) Association Table 3.2.9 Non-growth Associated Energy Consumption 3.2.10 Growth-Associated Energy Consumption 3.2.11 Organism-Specific Biomass Composition 3.2.12 Nutritional Requirements 3.2.13 Extracellular Transport Reactions 3.3 Translating the Refined Reconstruction into a Mathematical Model 3.3.1 Mathematical Representation 3.3.2 Characteristics of the Medium 3.3.3 External and Internal Flux Constraints 3.3.4 System Constraints 3.3.5 Debugging the Network 3.3.6 MEMOTE 3.4 Applications of FBA Models 4 Notes References Chapter 15: A Beginner´s Guide to the COBRA Toolbox 1 Introduction 2 Materials 3 Methods 3.1 Initializing the Toolbox 3.2 Reading Models into MATLAB 3.3 Model Manipulation 3.3.1 Adding and Removing Metabolites 3.3.2 Adding, Removing, or Modifying Reactions Addition Using Reaction Formula Addition Using Separate Lists Method Adding Multiple Reactions Adding Non-metabolic Reactions Removing Reactions Modifying Reactions 3.3.3 Reordering/Cleaning a Model 3.4 Creating a Cobra Model 3.4.1 Setting/Changing the Objective Function 3.4.2 Writing Models in Different Format 3.5 System-Level COBRA Analyses 3.5.1 Flux Balance Analysis Print the Reaction Flux Values Examine the Activity of Individual Model Components 3.5.2 Flux Variability Analysis 3.5.3 Parsimonious Enzyme Usage FBA 3.5.4 Perturbation Analyses Single-Reaction Knockouts Single-Gene Knockouts Synthetic Lethal Mutations Robustness Analysis 4 Notes References Chapter 16: Rules of Engagement: A Guide to Developing Agent-Based Models 1 Introduction 1.1 Reaction-Diffusion Simulations Using Partial Differential Equations (PDEs) 1.2 Cellular Automata (CA) 1.3 Agent-Based Models (ABMs) 2 Materials 2.1 The Computer System and ABM Software 2.1.1 ABM Software Packages 2.1.2 Do It Yourself ABM Implementation 2.2 Biological and/or Biophysical Parameters 3 Methods 3.1 State of an Agent 3.2 Action of Individual Agents 3.3 Population Evolution in Time 3.4 Storage and Output of Information 4 Notes References Index