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Identification of Absorption, Distribution, Metabolism, and Excretion (ADME) Genes Relevant to Steatosis Using a Systems Biology Approach
Citation:
Suarez, S., J. Lawson, AND H. El-Masri. Identification of Absorption, Distribution, Metabolism, and Excretion (ADME) Genes Relevant to Steatosis Using a Systems Biology Approach. Integrated Approaches to Testing and Assessment: Promises and Challenges of a More Flexible Approach to Toxicology Testing 4th Annual ASCCT Meeting, Durham, NC, October 01 - 02, 2015.
Impact/Purpose:
To be presented at the ASCCT Meeting in RTP, NC, Oct 1- 2, 2015.
Description:
Ensuring chemical safety and sustainability form a main priority of the U.S. Environmental Protection Agency. This entails efforts on multiple fronts to characterize the potential hazard posed by chemicals currently in use and those to be commercialized in the future. The use of an adverse outcome pathway (AOP) framework forms the basis of this strategy, along with exposure characterization. AOPs themselves are meant to be chemical agnostic, but specific chemical exposures and their effects can be informative for discovery and description of AOPs. In order to more comprehensively describe AOPs, we are collaborating on the construction of an absorption, distribution, metabolism, and excretion (ADME) module for the AOP knowledgebase repository. ADME parameters represent important links between exposure and AOP activation in the target tissue. We are specifically interested in identifying relevant genes related to ADME. Our overarching goal is create as comprehensive a list as possible, but to begin we are using Non-Alcoholic Fatty Liver Disease (NAFLD) and hepatic steatosis as a case study. To identify genes related to these conditions, we have utilized the publicly available toxicogenomics database, DrugMatrixTM. This database contains rodent chemical exposure data, along with differential gene expression data and corresponding associated pathology changes. We examined chemical exposures resulting in pathologically confirmed cases of steatosis, and from these exposures, utilized differential and co-expression analyses to identify gene changes resulting from the chemical exposure leading to steatosis. We then utilized pathway enrichment analysis to identify ADME related genes. Our desired product is a comprehensive database encompassing ADME related information, including genes and quantitative models connecting chemical exposure and their potential hazard by interaction with an AOP. The ultimate goal is to increase the speed and decrease the cost of hazard characterization of chemicals of unknown toxicity.