Integrating Publicly Available Data to Generate Computationally Predicted Adverse Outcome Pathways for Fatty Liver

New in vitro testing 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 des...
Source: Toxicological Sciences - Category: Toxicology Authors: Tags: Computational Approach to Develop Fatty Liver AOPs Source Type: research