The Handbook of Metabolomics

Preface
Contents
Contributors
Chapter 1: Introduction to Metabolomics
1 Overview
2 Fluxes: Steady-State and Nonsteady-State Kinetics
References
Chapter 2: Considerations of Sample Preparation for Metabolomics Investigation
1 Introduction
2 Sample Integrity
3 Sample Processing
3.1 Sample Homogenization
3.2 Sample Extraction
3.2.1 NMR Analysis
Extraction of Polar Metabolites
pH Control
Extraction of Low-Polarity Metabolitesƒ
3.2.2 MS Analysis
3.3 Sample Cleanup
Glossary
Appendices
Protocol for Preparing Adherent Mammalian Cells for Metabolite Extraction
Trypsinization Method
Solvent-Quenching Method
Protocol for Preparing Blood Plasma for Metabolite Extraction
Protocol for Freeze Clamping
Protocol for TCA Extraction
Protocol for 60% Acetonitrile Extraction
Protocol for Chloroform/Methanol/Water Extraction
Protocol for Methanol Extraction of Lipids
References
Chapter 3: Clinical Aspects of Metabolomics
1 Introduction
2 Experimental Design and Protocols
2.1 In Vivo vs. In Vitro Studies
2.2 Oral vs. IV Agent Introduction
2.3 Stable Isotopes
2.4 Patient Accrual
2.4.1 Accrual to a Lung Cancer Metabolomics Studyƒ
2.4.2 Clinician´s Office
2.4.3 Criteria for Inclusion/Exclusion
2.4.4 Payments/Gift Cards
2.4.5 Considerations Within Study Design
2.4.6 Compliance Issues
2.5 Study Orders
2.6 Tissue Collection
2.6.1 Blood
2.6.2 Urine
3 Ethical and Legal Considerations
3.1 HIPAA Regulations
3.2 Institutional Review Board (IRB)
3.3 Amendments
3.4 Informed Consent
4 Statistics
4.1 Paired Samples or Unpaired Samples?
4.2 Power Analysis
4.3 Statistical Significance
5 Example: Metabolomics Lung Cancer Study
5.1 Study Design
5.2 Stratification
5.3 Equipment Needed for Tissue Collection
5.4 Preoperative Procedures
5.4.1 13C Glucose Infusion
5.5 Operating Theater
5.5.1 Methods
6 Conclusions
Resources
Glossary
Appendices
Appendix 1: Example Study Order
Appendix 2: Example IRB
Objectives
Background
Drug Information
Eligibility Criteria
Stratification Factors
Treatment Plan
Criteria for Removal from Protocol Treatment
Toxicities to be Monitored
Statistical Considerations
Discipline Review
Pathology
Registration Guidelines
Data Submission Schedule
Sample Collection
Ethical and Regulatory Considerations
Inclusion of Women and Minorities
Appendix 3: Example Consent Form
References
Chapter 4: Structural Mass Spectrometry for Metabolomics
1 Introduction to MS
1.1 What is MS? The MS Experiment
1.2 Why MS for Metabolomics? Perspective from Modern MS
2 Information Encoded by the Mass Spectrum
2.1 The Mass Spectrum
3 Types of Mass Spectrometers
3.1 MS-in-Space Designs
3.2 MS-in-Time Designs
3.3 Sensitivity
4 Chromatography-MS
5 Biomarkers vs. Pathway Approaches for MS
5.1 Biochemical Information Content
5.2 The Natural Abundance Problem in Enrichment Quantification
5.3 A Model Approach for Metabolomic MS Analysis
6 Concluding Remarks
References
Chapter 5: Metabolomic Applications of Inductively Coupled Plasma-Mass Spectrometry (ICP-MS)
1 Introduction
1.1 Inductively Coupled Plasma-Mass Spectrometry (ICP-MS)
2 ICP-MS Optimization
2.1 Sample Introductionƒ
2.2 Solution Analysis
2.3 Solids Analysis: Laser Ablation ICP-MS (LA-ICP-MS)
2.4 ICP-MS Accuracy and Precision
3 Mass Spectrometer Optimization
3.1 ICP-MS Interface
3.2 Ion Focusing: Standard Mode
3.3 Ion Separation: Quadrupole Mass Filter
3.4 Ion Detection
4 ICP-MS Applications in Metabolomics (``Metallomics´´)
4.1 Analytical Considerations
4.2 Applications in Metabolomics
5 Conclusions
6 Resources
6.1 Sources of Equipment
6.2 Standards
6.3 Useful URLS, etc.
Glossary
References
Chapter 6: Principles of NMR for Applications in Metabolomics
1 Introduction
1.1 Basic Principles of NMR
1.1.1 Chemical Shift
1.1.2 Peak Area
1.1.3 Scalar Coupling
1.1.4 Relaxation
1.2 Sensitivity
1.2.1 Cooled Probes and Other Circuitry
1.2.2 Microprobes and Automation
1.2.3 Digitization
1.2.4 Sample Sensitivity
1.2.5 Alternative Data Acquisition Techniques
1.2.6 Linearity/Dynamic Range
1.2.7 Lock Circuitry and Solvents
1.3 Data Processing
1.3.1 Zerofilling
1.3.2 Linear Prediction
1.3.3 Apodization
1.3.4 Resolution Enhancement/SNR Improvement
1.3.5 Area Determination
2 NMR Analysis of Metabolites
2.1 Information Contentƒ
2.1.1 1H NMR
2.1.2 13C NMR
2.1.3 15N NMR
2.1.4 31P NMR
2.2 Concentration Determination
3 Multidimensional NMR: Increasing Resolution and Information Access
3.1 2D NMR
3.1.1 NMR Experiments
3.1.2 Homonuclear 2D NMRƒ
Methods Based on Scalar Couplings
TOCSY
ROESY/NOESY
DOSY
3.1.3 Heteronuclear 2D NMRƒ
HSQC-TOCSY
HCCH-TOCSY
HMBC and Long-Range HSQC
3.1.4 2D Integration
3.1.5 3D NMR Experimentsƒ
4 Stable Isotopomer Analysis
4.1 Pathways
4.2 Effect of a Magnetic Stable Isotopeƒ
4.3 2D Detection
5 Conclusions
Glossary
References
Chapter 7: Novel NMR and MS Approaches to Metabolomics
1 Bioanalytical Metabolomics
2 Sample Preparation
2.1 Methods for Minimizing Technical Error
2.2 Protocol for Extracting High-Abundance Aqueous Metabolites
3 Resources for NMR- and MS-Based Metabolomics
3.1 Databases and Bioinformatics
3.2 Laboratory Information Management Systems
3.3 Software for NMR Data Analysis
4 Mass Spectrometry Methods for Identifying and Quantifying Metabolites
5 NMR Methods for Identifying and Quantifying Metabolites
5.1 Mathematical and Statistical Methods
5.2 Multidimensional NMR Methods
5.2.1 Protocol for Metabolite Identification
5.2.2 Metabolite Quantification Protocol
5.3 Selective NMR Methods
6 Future Prospects
Glossary
References
Chapter 8: Metabolic Flux Analysis
1 Introduction
2 Stoichiometric Metabolic Flux Analysis
2.1 Solution by Stoichiometric MFA
2.2 Overdetermined System
2.3 Underdetermined System
2.4 Limitations in Stoichiometric MFA Models
3 13C Metabolic Flux Analysis
3.1 Carbon Flux Network
3.1.1 Nomenclature
3.1.2 Parametrization of a Stoichiometric System
3.1.3 Representations of Isotopic Labeling Pattern
3.1.4 Carbon Atom Model
3.1.5 Carbon Isotopomer Model
3.1.6 Cumomer Model
3.2 Numerical Flux Estimation
3.2.1 Nonlinear Regression Model
3.2.2 Key Partial Derivatives
3.3 Designing 13C Tracer Experiments
3.4 Statistical Analysis
3.4.1 Monte Carlo Method
3.4.2 Nonlinear Regression Analysis
Glossary
References
Chapter 9: Introduction to Metabolic Control Analysis (MCA)
1 Introduction
1.1 Thermodynamics and Kinetics
1.2 A Simple Example: Linear pathway
2 Metabolic Control Analysis as a Tool to Identify Control Steps
3 Flux and Concentration Control Coefficients
4 Elasticity Coefficients
4.1 Connectivity Theorems
4.2 How Much Perturbation Can Be Tolerated Experimentally?
5 Conclusions
6 Resources and Further Reading
Glossary
References
Chapter 10: Application of Tracer-Based Metabolomics and Flux Analysis in Targeted Cancer Drug Design
1 Introduction
1.1 Differential Metabolic Adaptation of Cancer Cells Can Be New Complementary Targets in the Design of Rational Combinational Therapies in Chemotherapy
2 Metabolic Control Analysis as a Tool to Identify Control Steps and Potential Drug Targets
3 Metabolic and Fluxomic Profiling of Tumor Cells
4 Quantitative Metabolic Flux Analysis
5 Algorithms for Integrated Kinetic Model and Isotopomer Distribution Analysis in Metabolic Flux Analysis
6 Reactions Between Isotopomers
7 An Example: Colon Cancer Cell Line HT29, Transketolase Inhibition
8 The Perspective for Analysis of Bioactive Natural Effectors
References
Chapter 11: Noninvasive Fluxomics in Mammals by Nuclear Magnetic Resonance Spectroscopy
1 Introduction
2 Background: Fluxomics
2.1 Fluxomics and the Methods Used for Quantification of Flux
2.1.1 Stable Isotopic Labels and 13C NMR Spectroscopy
2.2 Metabolic Steady and Nonsteady State Conditions: Example of Hepatic Glucose Metabolism
2.3 Dynamic and Static 13C NMR Measurement of Flux at Steady State and Nonsteady State
2.3.1 In Vivo 13C NMR Studies in Intact Mammals and Perfused Organs
2.3.2 In Silico Mass Balance Models and Algorithm for Toxicodynamics and Pathodynamics
3 Examples of 13C NMR Studies
3.1 Dynamic Measurements
3.1.1 Steady State, Dynamic Measurements from In Vivo 13C NMR Spectral Time Course Datasets
3.1.2 Peak Area Determination from Dynamic In Vivo 13C NMR Spectra
Single Component Curve Fitting
3.2 Nonsteady State, Dynamic Measurements from In Vivo 13C NMR Spectral Time Course Data
3.3 Steady State, Static Measurement of Isotopomer Ratios and Compartmentation
3.3.1 Quantifying Tissue Compartmentation from 13C NMR Spectra
3.3.2 Quantifying Cellular Compartmentation from 13C NMR Spectra
3.4 Multiple Pathway Analysis for Fluxomics
4 In Vivo NMR Experimental Considerations
4.1 Animal Handling and NMR Hardware
4.2 Hardware
4.3 Statistical Analysis of Flux Data
5 Mathematical Models: Metabolic Flux Balance (MFA) and Metabolic Control Analysis (MCA)
5.1 Metabolic Flux Balance Analysis and Metabolic Control Analysis
5.2 Flux Balance Analysis
5.3 Metabolic Control Analysis
5.4 Example of an MCA NMR Experiment
5.5 Software for the Analysis of Metabolic Systems
6 Novel Technology for Fluxomics: Dynamic Nuclear Polarization
6.1 DNP Physics: The Solid State Effect and Hyperpolarization
6.2 Hardware and Pulse Sequences
6.3 Determination of Flux by DNP
7 Translation 13C NMR Fluxomics to Humans
8 Conclusions and Future Directions
Glossary
References
Chapter 12: Compositional Analysis of Phospholipids by Mass Spectrometry and Phosphorus-31 Nuclear Magnetic Resonance Spectroscopy
1 Introduction
1.1 General Classification of Lipids
1.2 Phospholipid Classification
1.2.1 Glycerophospholipids
1.2.2 Sphingophospholipids
2 Conventional Methods for Phospholipid Analysis
2.1 Thin Layer Chromatography (TLC)
2.2 High-Performance Liquid Chromatography (HPLC)
2.3 Separation of Molecular Species Within Each Phospholipid Class
2.3.1 Gas Chromatography
2.3.2 Reverse Phase HPLC
3 Mass Spectrometry for Phospholipid Analysis
3.1 Background
3.2 MALDI-TOF MS
3.2.1 Theory and Instrumentation
3.3 MALDI MS Applied to Phospholipid Analysis
3.3.1 In Vitro Analysis of Phospholipids in Crude Extracts
3.3.2 Imaging of Phospholipids in Tissues and Cells
4 31P NMR Analysis of Phospholipids
4.1 Background
4.2 Solvent Systems and Temperature Coefficients
5 Conclusions
Glossary
References
Chapter 13: The HumanCyc Pathway-Genome Database and Pathway Tools Software as Tools for Imaging and Analyzing Metabolomics Data
1 Pathway-Genome Databases and the Pathway Tools Software
2 Pathway-Genome Databases
3 The Pathway Tools Software
3.1 The Pathway-Genome Navigator
3.2 The PathoLogic Pathway Predictor
3.3 The Pathway-Genome Editors
3.4 The Pathway-Tools Ontology
3.5 The Ocelot Object Database
4 HumanCyc: The Human PGDB
5 Visualizing Systems Biology Measurements in Pathway Tools: The Cellular Overview Diagram and the Omics Viewer
5.1 Organization of the Cellular Overview Diagram
5.2 Querying the Overview
5.3 The Omics Viewer
6 Taking HumanCyc to Its Full Potential: The Pathway Tools API
7 Integrating Metabolomics and Gene Expression to Probe Metabolic Networks and Regulation
8 Conclusions
9 Important Resources
Glossary
References
Chapter 14: Metabolomics-Edited Transcriptomics Analysis (META)
1 Introduction
2 Illustration of META: Deciphering AMPK-Regulated Metabolic Networks
2.1 AMPK Regulation and Signaling Cascades
2.2 Hypothetical Investigation of AMPK-Related Metabolic Networks in Human Cancer Cells
2.2.1 Approach
2.2.2 Example Observation
Phenotypic Effect of AICAR
NMR Identification and Semi-quantification of Metabolites and 13C-Isotopomer Profiles
Quantification of Metabolite and 13C-Isotopomer Profiles by NMR and GC-MS
Confirmatory and Complementary Nature of NMR and MS Analysis
Metabolomics-Edited Transcriptomics Analysis (META) of AICAR Effect
META of Effect of AMPK-Knockdown in Cancer Cells
3 Deciphering Anticancer Selenite Action in Lung Cancer Cells
3.1 Anticancer Role of Selenium Agents
3.2 Metabolomics-Edited Transcriptomic Analysis of Anticancer Se Action in Human Lung Adenocarcinoma A549 Cells
3.2.1 Approach
3.2.2 Actual Observation
Phenotypic Effect of Selenite and SeM
NMR Identification and Semi-quantification of Metabolites and 13C-Isotopomer Profiles
Quantification of Metabolite and 13C-Isotopomer Profiles by 1H NMR, GC-MS, and HPLC
Metabolomics-Edited Transcriptomics Analysis (META) of Se Action in A549 Cells
4 Conclusions
Glossary
References
Index