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Informatics approaches in the Biological Characterization of Adverse Outcome Pathways
Citation:
Pittman, M., C. LaLone, Dan Villeneuve, AND S. Edwards. Informatics approaches in the Biological Characterization of Adverse Outcome Pathways. GEMS Fall Meeting, RTP, NC, November 09, 2016.
Impact/Purpose:
The purpose of this submission is for clearance of presentation abstract for the Genetics and Environmental Mutagenesis Yearly Fall Meeting 2016
Description:
Adverse Outcome Pathways (AOPs) are a conceptual framework to characterize toxicity pathways by a series of mechanistic steps from a molecular initiating event to population outcomes. This framework helps to direct risk assessment research, for example by aiding in computational prioritization of chemicals, genes, and tissues relevant to an adverse health outcome. We have designed and implemented a computational workflow to access a wealth of public data relating genes, chemicals, diseases, pathways, and species, to provide a biological context for putative AOPs. We selected three AOP case studies: ER/Aromatase Antagonism Leading to Reproductive Dysfunction, AHR1 Activation Leading to Cardiotoxicity, and AChE Inhibition Leading to Acute Mortality, and deduced a taxonomic range of applicability for each AOP. We developed computational tools to automatically access and analyze the pathway activity of AOP-relevant protein orthologs, finding broad similarity among vertebrate species for the ER/Aromatase and AHR1 AOPs, and similarity extending to invertebrate animal species for AChE inhibition. Additionally, we used public gene expression data to find groups of highly co-expressed genes, and compared those groups across organisms. To interpret these findings at a higher level of biological organization, we created the AOPdb, a relational database that mines results from sources including NCBI, KEGG, Reactome, CTD, and OMIM. This multi-source database connects genes, pathways, and chemicals relevant to an AOP, filling annotation gaps faced by methods that rely on data from a single data source. We demonstrate how the AOPdb aids in hypothesis testing through biological characterization of a given AOP using guided queries.Disclaimer: The views expressed in this abstract are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.
URLs/Downloads:
MPITTMAN_GEMS_FINAL.PDF (PDF, NA pp, 1419.437 KB, about PDF)PITTMAN_GEMS_TECHREVIEW_MDN.PDF (PDF, NA pp, 1569.177 KB, about PDF)
PITTMAN_GEMS_ABSTRACTDRAFT_OCT17_MNMP.DOCX