Our objective is to provide a centralized, publicly available resource with comprehensive, well-annotated data and analysis tools that informs design and interpretation of environmental health studies and promotes novel insights into the etiologies of environmentally influenced diseases. Most human diseases involve interactions between genetic and environmental factors; however, the basis of these complex interactions are not well understood and limit improvements in toxicity prediction, risk assessment, research prioritization and therapeutic interventions. We developed the Comparative Toxicogenomics Database (CTD; http://ctdbase.org) to enhance understanding about environment-disease connections by providing manually curated data describing chemical-gene interactions and chemical- and gene-disease relationships from the peer-reviewed literature and integrating these data with select external data sets (e.g., pathways and biological process data) and novel data analysis tools. We propose to develop and implement a new module of manually curated data describing cross-species chemical-phenotype information and analytical capabilities that will incorporate these data into the broader biological context of CTD. These additions will significantly increase the impact of CTD and specifically aim to advance: a) understanding of environmental disease progression via pre-disease phenotypes, b) identification of potential biomarkers of exposure, c) the capacity to conduct and interpret studies across species and experimental systems, and d) development of biological networks that associate chemicals, genes, phenotypes, and diseases. This project will leverage our interdisciplinary team's expertise in toxicology, software development, curation, bioinformatics and statistics, as well as the flexible infrastructure and demonstrated value of CTD to facilitate understanding of critical environmental health issues in direct alignment with emerging research priorities.

Public Health Relevance

Despite the thousands of chemicals used in commerce, our understanding of their effects on human health is not well understood. The Comparative Toxicogenomics Database (CTD) is a unique, publicly available resource that provides information about chemical-gene-disease relationships (or networks). This project will introduce curated chemical-phenotype data and new analysis tools with the goal of providing additional insights into how chemical exposures influence human diseases.

National Institute of Health (NIH)
National Institute of Environmental Health Sciences (NIEHS)
Research Project (R01)
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Special Emphasis Panel (ZRG1)
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Balshaw, David M
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North Carolina State University Raleigh
Earth Sciences/Resources
United States
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Davis, Allan Peter; Wiegers, Thomas C; Wiegers, Jolene et al. (2018) Chemical-Induced Phenotypes at CTD Help Inform the Predisease State and Construct Adverse Outcome Pathways. Toxicol Sci 165:145-156
Grondin, Cynthia J; Davis, Allan Peter; Wiegers, Thomas C et al. (2018) Accessing an Expanded Exposure Science Module at the Comparative Toxicogenomics Database. Environ Health Perspect 126:014501
Davis, Allan Peter; Grondin, Cynthia J; Johnson, Robin J et al. (2018) The Comparative Toxicogenomics Database: update 2019. Nucleic Acids Res :
Planchart, Antonio; Green, Adrian; Hoyo, Cathrine et al. (2018) Heavy Metal Exposure and Metabolic Syndrome: Evidence from Human and Model System Studies. Curr Environ Health Rep 5:110-124
Davis, Allan Peter; Grondin, Cynthia J; Johnson, Robin J et al. (2017) The Comparative Toxicogenomics Database: update 2017. Nucleic Acids Res 45:D972-D978
Zhang, Guozhu; Roell, Kyle R; Truong, Lisa et al. (2017) A data-driven weighting scheme for multivariate phenotypic endpoints recapitulates zebrafish developmental cascades. Toxicol Appl Pharmacol 314:109-117
Mattingly, Carolyn J; Boyles, Rebecca; Lawler, Cindy P et al. (2016) Laying a Community-Based Foundation for Data-Driven Semantic Standards in Environmental Health Sciences. Environ Health Perspect 124:1136-40
Reif, David M; Truong, Lisa; Mandrell, David et al. (2016) High-throughput characterization of chemical-associated embryonic behavioral changes predicts teratogenic outcomes. Arch Toxicol 90:1459-70
Davis, Allan Peter; Wiegers, Thomas C; King, Benjamin L et al. (2016) Generating Gene Ontology-Disease Inferences to Explore Mechanisms of Human Disease at the Comparative Toxicogenomics Database. PLoS One 11:e0155530
Zhang, Guozhu; Marvel, Skylar; Truong, Lisa et al. (2016) Aggregate entropy scoring for quantifying activity across endpoints with irregular correlation structure. Reprod Toxicol 62:92-9

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