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Ontology-based Semantic Mapping of Adverse Outcome Pathways
Citation:
Wang, Ronglin. Ontology-based Semantic Mapping of Adverse Outcome Pathways. ICBO 2019 : International Conference on Biomedical Ontology 2019, Buffalo, NY, July 29 - August 02, 2019.
Impact/Purpose:
Adverse outcome pathway has been proposed as a framework to organize toxicity information along the levels of biological hierarchy. Under the current paradigm of in vitro/in silico/short-term in vivo chemical screenings, most of the toxicity information are molecular phenotypes in nature. With the abundant and increasing data from public phenomics efforts available, ontology-based semantic mapping has the potential to help link these molecular phenotypes to those of greater regulatory relevance at the higher levels of biological organization. And OS-Mapping may also assist the evaluation and validation of current AOPs and their future development.
Description:
Most of the nearly 85,000 chemicals currently listed in the US TSCA (Toxic Substances Control Act) inventory are not characterized toxicologically. A paradigm shift has been well underway to move away from animal toxicity tests, and towards more resource-efficient in vitro, in silico, and short-term in vivo screenings. As such, there is a great need to link toxicity phenotypes at molecular level to those with greater regulatory relevance at higher levels of biological organization. The framework of adverse outcome pathway (AOP) was proposed to address this need (Ankley et al, 2010), and has been increasingly adopted in recent years to organize toxicity information along such a biological hierarchy. Many AOPs, each consisting of a molecular initiating event, several key events, and an adverse outcome, have been developed (https://aopwiki.org/). With abundant phenotypic data from ongoing public phenomics efforts and years of toxicity studies, ontology-based semantic mapping (OS-Mapping) offers a promising approach to bridge the gaps between molecular phenotypes and traditional endpoints provided by animal tests. To study the applications of OS-Mapping in evaluating existing AOPs and aiding their future development, over 1100 key events belonging to more than 200 AOPs were annotated by using entity-quality (EQ) statements. Also included in the study were toxicity responses previously annotated from more than 700 exposure studies of ten chemicals in six vertebrate species (Wang et al, 2019). Together, they were assembled into over 200 phenotypic profiles as queries, and compared semantically to more than 37 thousand phenotypic profiles organized by genes, diseases, and biological pathways (KEGG, Kyoto Encyclopedia of Genes and Genomes, https://www.genome.jp/kegg/; Reactome, https://reactome.org/) of human, mouse, and zebrafish. The Java application for semantic analysis was developed in-house based on OWLAPI (version 4.2.5; Horridge and Bechhofer, 2011), several publicly available reasoners, and the Semantic Measure Library (SML, version 0.9.4d; Harispe, 2014). The analyses proved to be insightful. For example, most of the key event pairs curated to be adjacent to each other had similarities, ranging between zero and one, less than 0.5. Some of the other key events, however, were found highly similar to one another. Many AOPs also shared high similarities, some of which were mapped to various mouse genes, KEGG/Reactome pathways, and human diseases. The findings like these will help to delineate the biology underlying these AOPs and provide some independent evidence for their robustness. Furthermore, semantic characterization of key events and AOPs will also provide an approach to construct AOP networks complementary to the current reliance on the manually defined key event relationships, and aid the future development of additional AOPs. Ankley, G.T., et al., 2010. Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment. Environ. Toxicol. Chem. 29 (3), 730–741. https://doi.org/10.1002/etc.34. Harispe, S., 2014. The semantic measures library and toolkit: fast computation of semantic similarity and relatedness using biomedical ontologies. Bioinformatics 30 (5), 740–742. https://doi.org/10.1093/bioinformatics/btt581. Horridge, M., Bechhofer, S., 2011. The OWL API: a java API for OWL ontologies. Semantic Web J. 2 (1), 11–21. https://doi.org/10.3233/SW-2011-0025. Wang, R-L., Edwards, S., Ives, C., 2019. Ontology-based semantic mapping of chemical toxicities. Toxicology, 412:89-100.
