title: Integrating Publicly Available Data to Generate Computationally Predicted Adverse Outcome Pathways for Fatty Liver.
abstract: Newin vitrotesting strategies make it possible to design testing batteries for large numbers of environmental chemicals. Full utilization of the results requires knowledge of the underlying biological networks and the adverse outcome pathways (AOPs) that describe the route from early molecular perturbations to an adverse outcome. Curation of a formal AOP is a time-intensive process and a rate-limiting step to designing these test batteries. Here, we describe a method for integrating publicly available data in order to generate computationally predicted AOP (cpAOP) scaffolds, which can be leveraged by domain experts to shorten the time for formal AOP development. A network-based workflow was used to facilitate the integration of multiple data types to generate cpAOPs. Edges between graph entities were identified through direct experimental or literature information, or computationally inferred using frequent itemset mining. Data from the TG-GATEs and ToxCast programs were used to channel large-scale toxicogenomics information into a cpAOP network (cpAOPnet) of over 20 000 relationships describing connections between chemical treatments, phenotypes, and perturbed pathways as measured by differential gene expression and high-throughput screening targets. The resulting fatty liver cpAOPnet is available as a resource to the community. Subnetworks of cpAOPs for a reference chemical (carbon tetrachloride, CCl4) and outcome (fatty liver) were compared with published mechanistic descriptions. In both cases, the computational approaches approximated the manually curated AOPs. The cpAOPnet can be used for accelerating expert-curated AOP development and to identify pathway targets that lack genomic markers or high-throughput screening tests. It can also facilitate identification of key events for designing test batteries and for classification and grouping of chemicals for follow up testing.
authors.affiliation: *Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee; Integrated Systems Toxicology Division, National Health and Environmental Effects Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711; Current Affiliation: ILS/Contractor Supporting the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), Research Triangle Park, North Carolina.
authors.forename: Shannon M
authors.initials: SM
authors.lastname: Bell
authors.affiliation: Integrated Systems Toxicology Division, National Health and Environmental Effects Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711;
authors.forename: Michelle M
authors.initials: MM
authors.lastname: Angrish
authors.affiliation: Integrated Systems Toxicology Division, National Health and Environmental Effects Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711;
authors.forename: Charles E
authors.initials: CE
authors.lastname: Wood
authors.affiliation: Integrated Systems Toxicology Division, National Health and Environmental Effects Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711; edwards.stephen@epa.gov.
authors.forename: Stephen W
authors.initials: SW
authors.lastname: Edwards
mesh_terms: D000818:Animals; D035843:Biomedical Research / Q000379:methods*; D003198:Computer Simulation; D016208:Databases, Factual; D054750:Ecotoxicology / Q000379:methods*; D004785:Environmental Pollutants / Q000633:toxicity*; D005234:Fatty Liver / Q000139:chemically induced*; D057166:High-Throughput Screening Assays; D006801:Humans; D018570:Risk Assessment / Q000379:methods*
chemical_list: D004785:Environmental Pollutants
keywords: TG-GATEs; computationally predicted adverse outcome pathways; cpAOP; fatty liver; network integration; steatosis.