Genomic Approaches for Cross-Species Extrapolation in Toxicology

by Benson; William H.

• ISBN13: 9781420043341
• ISBN10: 142004334X
• Edition: 1st
• Format: Hardcover
• Copyright: 2006-12-13
• Publisher: CRC Press

Summary

The latest tools for investigating stress response in organisms, genomic technologies provide great insight into how different organisms respond to environmental conditions. However, their usefulness needs to be tested, verified, and codified. Genomic Approaches for Cross-Species Extrapolation in Toxicology provides a balanced discussion drawn from the experience of thirty-five scientists and professionals from diverse fields including environmental toxicology and chemistry, biomedical toxicology, molecular biology, genetics, physiology, bioinformatics, computer science, and statistics.The book introduces genomic, transcriptomic, proteomic, and metabolomic technologies. It describes the advantages and challenges associated with these approaches compared to traditional methodologies, particularly from the perspective of cross-species extrapolation within human and environmental toxicology, and explores solutions that will facilitate the incorporation of these technologies into predictive toxicology. The book goes on to identify and prioritize species of animals that can serve as surrogates for environmental and human health in comparative toxicogenomic studies. The chapter authors elucidate similarities and differences among species, relate stressor-mediated responses to adverse outcomes, and extend this science into innovative approaches to risk assessment and regulatory decision-making.

Table of Contents

List of Figures	xiii
List of Tables	xv
The Editors	xvii
Contributors	xix
Preface	xxi
Acknowledgments	xxiii
Chapter 1 "Omics" Approaches in the Context of Environmental Toxicology	1
Jon C. Cook, Nancy D. Denslow, Taisen Iguchi, Elwood A. Linney, Ann Miracle, Joseph R. Shaw, Mark R. Viant, and Timothy R. Zacharewski
1.1 Introduction	1
1.2 Overview of Omics Technologies	1
1.3 Discovery-Driven versus Hypothesis-Driven Research: A Need for Balance	4
1.4 Advantages, Challenges, and Solutions of Omics Technologies	6
1.4.1 Advantages of Genomics Approaches	6
1.4.2 Challenges of Genomics Approaches	8
1.4.3 Solutions Offered for Genomics Approaches	9
1.4.4 Validation of Genomics	9
1.4.5 Potential of Genomics Approaches for Ecotoxicology	10
1.4.6 Transcriptomics	10
1.4.6.1 Emerging Transcriptomics Resources	14
1.4.7 Proteomics	14
1.4.8 Metabolomics — Molecular Phenotype and Metabolic Trajectories	16
1.4.9 Experimental Considerations for Metabolomics	18
1.4.10 Annotation of Cellular Metabolome	20
1.5 Pathway Mapping — The Future of Omics Technologies	21
1.6 Example of Cross-Species Extrapolation Using Transcriptomics	21
1.7 Recommendations	25
1.8 Future	25
References	26
Chapter 2 Selection of Surrogate Animal Species for Comparative Toxicogenomics	33
Nancy D. Denslow, John K. Colbourne, David Dix, Jonathan H. Freedman, Caren C. Helbing, Sean Kennedy, and Phillip L. Williams
2.1 Introduction	33
2.2 Brief Review on Studies Using Comparative Genomics	34
2.3 Selection Criteria for Surrogate Species	36
2.3.1 Toxicologic Information	37
2.3.1.1 Weighting Selection Criteria Based on Three Research Needs	39
2.3.1.2 Genome Information	40
2.4 Selection of Surrogate Species	40
2.5 Discussion	46
2.5.1 Mammalian Models	47
2.5.1.1 Core Biological Studies and Human Health	47
2.5.1.2 Ecotoxicology and Risk Assessment	48
2.5.2 Aquatic Models for Human and Ecological Health	49
2.5.2.1 Core Biology and Human Health Models	49
2.5.2.2 Ecotoxicology and Risk Assessment	50
2.5.3 Amphibian Models	52
2.5.3.1 Core Biology and Human Health Models	52
2.5.3.2 Ecotoxicology and Risk Assessment	53
2.5.4 Ciona	55
2.5.5 Avian Models	56
2.5.5.1 Core Biological Studies and Human Health	57
2.5.5.2 Ecotoxicology and Risk Assessment	58
2.5.6 Nematode Models	59
2.5.7 A Community-Based Approach for Promoting Daphnia as a Model for Ecotoxicogenomics	60
2.5.7.1 The Daphnia Genomics Consortium (DGC)	61
2.5.7.2 Community Resources	62
2.5.7.3 The Daphnia Genome Project	63
2.6 Conclusions	64
References	65
Appendix A	70
Appendix B	71
Appendix C	73
Chapter 3 Species Differences in Response to Toxic Substances: Shared Pathways of Toxicity — Value and Limitations of Omics Technologies to Elucidate Mechanism or Mode of Action	77
David Eaton, Evan Gallagher, Mike Hooper, Dan Schlenk, Patricia Schmeider, and Claudia Thompson
3.1 What Omics Approaches Would Be of Greatest Value in Predictive Toxicology That Utilizes Biologically Relevant Effects in Organisms or the Environment?	78
3.2 How Can Omics Be Utilized to Understand Mechanism and Mode of Action?	84
3.2.1 Discriminate between Defense/Adaptive Mechanisms from Direct "Toxic Response" and Secondary Downstream Events Responsible for Pathology	85
3.2.2 Integrate Omics with "Traditional" or Alternative Animal Models	86
3.3 How Do We Integrate Responses across Gene Expression, Proteomics, and Metabolomics and Apply This to Make a Science-Based Statement about Health of an Organism?	87
3.4 How Does Development of Omics Technologies Affect the Interspecies Extrapolation Process?	88
3.4.1 Effects Assessment in Field Studies	89
3.4.2 Susceptibility Assessment	90
3.5 What Are Key Limitations and Considerations in Using Omics Technologies to Inform Mechanisms of Cross-Species Differences in Response to Xenobiotics?	92
3.5.1 Time of Sample Collection	92
3.5.2 Duration of Exposure	93
3.5.3 Dose–Response Considerations	93
3.5.4 Target Tissues	93
3.5.5 Age, Gender	94
3.5.6 Nutrition	95
3.5.7 Conservation of Responses across Species	95
3.5.8 Validation	95
3.5.9 Kinetics, Identification of Rate-Limiting Steps	96
3.5.10 In Vitro versus In Vivo Studies: Correlations	97
3.6 Conclusions	97
3.7 Recommendations	98
References	100
Chapter 4 Bioinformatic Approaches and Computational Models for Data Integration and Cross-Species Extrapolation in the Postgenomic Era	103
Kenneth S. Ramos, Renae L. Malek, John Quakenbush, Ilya Shmulevich, Joshua Stuart, and Michael Waters
4.1 Introduction	103
4.2 Mechanistic versus Classification Studies	106
4.3 Computational Methods for Orthologue Identification	108
4.3.1 Available Orthology Resources	109
4.3.2 All-against-All Pair-Wise Sequence Analysis	110
4.3.3 Reciprocity and Transitivity	110
4.3.4 Phylogenetically Based Approaches	110
4.3.5 Future Directions	111
4.4 Interpreting Expression Data across Species	112
4.4.1 Motivation	112
4.4.2 Identification of Core Processes	112
4.4.3 Using Core Processes to Interpret Gene Expression Studies	114
4.5 Integrating Data across Domains	115
4.5.1 Analysis of Multiple Domains' Omics Data	116
4.5.2 Development of a Knowledge-Based Science of Toxicology	117
4.5.3 Toxicogenomics Databases and Standards for Exchange of Data	117
4.5.4 Systems Toxicology and Toxicogenomics Knowledge Bases	120
4.5.4.1 The Chemical Effects in Biological Systems (CEBS) Knowledge Base	121
4.5.5 Comparative Toxicogenomics	122
4.5.6 Optimizing Collection of Data and Development of Knowledge	122
4.6 Supervised and Unsupervised Analysis for Toxicogenomics	124
4.7 Networks	126
4.7.1 What Class of Models Should We Choose?	127
4.7.2 How Do We Represent Networks?	128
4.7.3 To What Extent Do Such Models Represent Reality?	129
4.7.4 Do We Have the "Right" Types of Data to Infer These Models?	130
4.7.5 Biological Systems Are Nonlinear Dynamical Systems	130
4.7.6 What Do We Hope to Learn from These Models'?	133
4.8 The Problem of Validation	134
4.9 Predictive Toxicology	136
4.10 Educating the Community	138
4.11 Recommendations for Advancing the Field	141
4.12 Concluding Remarks	141
References	142
Chapter 5 The Extension of Molecular and Computational Information to Risk Assessment and Regulatory Decision Making	151
James S. Bus, Richard A. Canady, Tracy K. Collier, J. William Owens, Svril D. Pettit, Nathaniel L. Scholz, and Anita C. Street
5.1 Introduction	151
5.1.1 Scope of the Chapter	152
5.2 Overview of Human and Ecological Risk Assessment	154
5.2.1 Human Health Risk Assessment	155
5.2.2 Ecological Assessment	157
5.2.3 Differences in Statutory Requirements	160
5.2.3.1 Human Health Risk Assessment	160
5.2.3.2 Ecological Risk Assessment	160
5.3 Potential of Omics to Improve Risk Assessment	162
5.3.1 Reducing Uncertainty in Human Health Risk Assessment	162
5.3.1.1 Omics Approaches to Addressing Cross-Species Uncertainties	162
5.3.1.2 Screening	162
5.3.1.3 Impact of Genomics Technology on Reducing Uncertainty in Chemical-Specific Risk Assessments	163
5.3.1.4 Use of Genomics Methods for Refining Operational Principles of Risk Assessment	164
5.3.2 Reducing Uncertainty in Ecological Risk Assessment	166
5.4 The Path Forward?	168
5.4.1 Extrapolations and Inferences from Omics Data	168
5.4.2 Groundtruthing and Validation	168
5.4.2.1 Conceptualizing Omics in the Regulatory Risk Framework	168
5.4.2.2 Implementation Issues for Omics	170
5.4.3 Institutional Limitations	172
5.4.3.1 Risk Assessment and Management Infrastructure Limitations	172
5.4.3.2 Phenotypic Anchoring and TSCA Liability/Safe Harbor	173
5.4.3.3 Data Standards across Regulatory Agencies	174
5.4.3.4 Privacy Act	175
5.5 Conclusions and Recommendations	176
Acknowledgments	178
References	178
Index	181