A high-throughput method to computationally develop candidate adverse outcome pathways in humans: a proof of concept with insecticides and Parkinson Disease
A high-throughput method to computationally develop candidate adverse outcome pathways in humans: a proof of concept with insecticides and Parkinson Disease
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Dübendorf, CH · Author affiliation
Department of Environmental Toxicology, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, SwitzerlandLocation evidence
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Original abstract
Abstract Adverse outcome pathways (AOPs) describe stressor non-specific sequences of events between a first molecular trigger (molecular initiating event, MIE), causally linked key events (KEs), and an adverse outcome (AO). AOPs are intended to aid in chemical toxicity testing as a new approach methodology. However, commonly used AOP development methods depend on manual curation, which is labor intensive. As a result, there are still relatively few AOPs and a huge number of toxicity mechanisms and possible adverse outcomes remain undescribed. Therefore, systematic and high-throughput approaches to predict new AOPs are needed. Here, we developed and implemented a data integration-based framework to generate new candidate AOPs using insecticides and Parkinson Disease as a proof of concept. We integrated and statistically linked disconnected databases (e.g., Comparative Toxicogenomics Database, Human Protein Atlas, and Gene Ontology) to form MIE – KE (cell level) – KE (tissue level) – AO candidate AOPs. Through this systematic process, we generated 562,117 candidate AOPs, which we then scored using a weight of evidence (WoE) approach and prioritized 12,756 AOPs with a WoE >0.5. Through random sampling of 100 prioritized AOPs, we found 70% had external literature supporting their biological plausibility, and only 15% represented identifiably implausible associations. The prioritized AOPs describe varied mechanisms of toxicity related to e.g., MAPK, PTEN, and FGFR signaling pathways, with “increases phosphorylation of MAPK1 ” as the most frequent MIE. Our AOP generating approach yields consistently structured AOPs and can complement existing and emerging development methods to expand AOP coverage across different stressors and outcomes.