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Generation of computationally predicted Adverse Outcome Pathway networks through integration of publicly available in vivo, in vitro, phenotype, and biological pathway data.
Citation:
Oki, N., S. Bell, R. Wang, M. Nelms, AND S. Edwards. Generation of computationally predicted Adverse Outcome Pathway networks through integration of publicly available in vivo, in vitro, phenotype, and biological pathway data. SOT 2016 Annual Meeting, New Orleans, LA, March 13 - 17, 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 is becoming a widely used tool for organizing and summarizing the mechanistic information connecting molecular perturbations by environmental stressors with adverse ecological and human health outcomes. However, the conventional process for assembly of these AOPs is time and resource intensive, and has been a rate limiting step in their development. To accelerate the process, we developed computationally predicted AOPs (cpAOPs) by association mining of data from publicly available databases. A cpAOP network of ~21,000 associations was established among 105 phenotypes from TG-Gates rat liver data, 994 REACTOME pathways, 688 High-throughput (HT) assays from ToxCast, and 194 chemicals. A second network of 128,536 associations was generated by connecting 255 genes representing ToxCast biological targets to 4,980 diseases from the Comparative Toxicogenomics Database (CTD) using either HT screening activity from ToxCast for 286 chemicals or CTD gene expression changes in response to 2,330 chemicals. Both networks were separately evaluated through manual extraction of disease-specific cpAOPs and comparison with expert curation of the relevant literature. Both networks were then merged with ~130,000 publicly available phenotypes from PhenomicDB covering several model species to create a single resource for automated cpAOP extraction via the use of probability and weighting metrics. The resulting cpAOP list can be prioritized using a variety of metrics and ranked for further review by domain experts.