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Defining a Computational Framework for the Assessment of Taxonomic Applicability
Citation:
Mortensen, H., M. Pittman, C. LaLone, AND S. Edwards. Defining a Computational Framework for the Assessment of Taxonomic Applicability. SOT, Baltimore, MD, March 12 - 16, 2017.
Impact/Purpose:
The purpose of this submission is to receive clearance for an Abstract to be submitted to a scientific society.
Description:
The Adverse Outcome Pathway (AOP) framework describes the effects of environmental stressors across multiple scales of biological organization and function. This includes an evaluation of the potential for each key event to occur across a broad range of species in order to determine the taxonomic applicability of each AOP. Computational tools are needed to facilitate this process. Recently, we developed a tool that uses sequence homology to evaluate the applicability of molecular initiating events across species (Lalone et al., Toxicol. Sci., 2016). To extend our ability to make computational predictions at higher levels of biological organization, we have created the AOPdb. This database links molecular targets identified associated with key events in the AOPwiki to publically available data (e.g. gene-protein, pathway, species orthology, ontology, chemical, disease) including ToxCast assay information. The AOPdb combines different data types in order to characterize the impacts of chemicals to human health and the environment and serves as a decision support tool for case study development in the area of taxonomic applicability. As a proof of concept, the AOPdb allows identification of relevant molecular targets, biological pathways, and chemical and disease associations across species for four AOPs from the AOP-Wiki (https://aopwiki.org): Estrogen receptor antagonism leading to reproductive dysfunction (Aop:30); Aromatase inhibition leading to reproductive dysfunction (Aop:25); AHR1 activation leading to developmental abnormalities and embryolethality (Aop:21); Acetylcholinesterase inhibition leading to acute mortality (Aop:16). Computational methods that define the “functional neighborhood” for each AOP from the MIE and KE genes identifies the relevant biology associated with each AOP. With these integrated data, we outline a framework for the computational assessment of taxonomic relevance for AOPs. The views expressed in this poster are those of the authors and may not reflect U.S. EPA policy.
URLs/Downloads:
TSA MORTENSEN_QAPP_8_3_16_SIGNED RDO TJH2016 08 03 A.PDF (PDF, NA pp, 123.395 KB, about PDF)HM_TAXAPPLIC_SOT_2017_FINALFINAL.PPTX
MORTENSEN_SOT_2017_ABSTRACT_FINAL.DOCX