Citation:

Bell, S., M. Angrish, C. Wood, AND S. Edwards. Integrating publicly-available data to generate computationally-predicted adverse outcome pathways for hepatic steatosis. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 150(2):510-520, (2016).

Impact/Purpose:

Adverse Outcome Pathways can expand and enhance the use of ToxCast and other in vitro toxicity information by providing a mechanistic link to adverse outcomes of regulatory concern, but the expert-driven development of AOPs is labor-intensive and time-consuming. This work is intended to complement that process by creating a broad array of computationally-predicted AOPs (cpAOPs) by data mining of publicly available data. This increases the coverage of AOPs and provides minimal information in many cases where nothing is available otherwise. It also provides a starting point for expert-driven development of AOPs and thereby potentially accelerating that process.

Description:

The adverse outcome pathway (AOP) framework provides a way of organizing knowledge related to the key biological events that result in a particular health outcome. For the majority of environmental chemicals, the availability of curated pathways characterizing potential toxicity is limited. Methods are needed to assimilate large amounts of available molecular data and quickly generate putative AOPs for further testing and use in hazard assessment. A graph-based workflow was used to facilitate the integration of multiple data types to generate computationally-predicted (cp) AOPs. 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 measured by differential gene expression and high-throughput screening targets. Sub-networks of cpAOPs for a reference chemical (carbon tetrachloride, CCl4) and outcome (hepatic steatosis) were extracted using the network topology. Comparison of the cpAOP subnetworks to published mechanistic descriptions for both CCl4 toxicity and hepatic steatosis demonstrate that computational approaches can be used to replicate manually curated AOPs and identify pathway targets that lack genomic markers or high-throughput screening tests. The cpAOPnet can be used for accelerating expert-curated AOP development. It can also facilitate identification of key events for designing test batteries and for classification and grouping of chemicals for follow up testing.

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