Integrative network-based construction of ecologically relevant computational adverse outcome pathways for organic mercury-induced toxicity

Abstract

Adverse outcome pathways (AOPs) describe mechanisms of toxicity by connecting molecular events with outcomes at higher levels of biological organization. Computational AOPs (cAOPs), constructed using existing toxicological data, can accelerate the early stages of AOP development, which is often a time-consuming and resource-intensive process. In this study, an integrative network-based framework was developed to construct cAOPs, with a particular focus on elucidating the toxicity of organic mercury in fish. First, 124 organic mercury compounds, corresponding fish-specific toxicity endpoints, and proteins were curated from CTD and ECOTOX. Next, molecular docking investigations were performed to determine novel molecular interactions with 16 zebrafish protein orthologs. Subsequently, the toxicity endpoints, including the identified molecular interactions, were standardized and harmonized using data from AOP-Wiki, Gene Ontology, and MeSH. These endpoints were integrated with event relationship information from AOP-Wiki and published literature to construct an organic mercury-associated toxicity network comprising 197 nodes and 243 edges. Thereafter, node- and edge-level filtration criteria were designed based on AOP definitions and utilized to identify biologically relevant pathways within the toxicity network. Further, these pathways were ranked based on their novelty with respect to existing AOPs within AOP-Wiki. Finally, based on extensive evidence from published literature, four top-ranked novel pathways that describe binding with glutathione peroxidase or alterations in metallothionein levels leading to neurologic manifestations or dysbiosis in fish were proposed as cAOPs. Overall, this study presents an integrative network-based framework for constructing cAOPs applicable to diverse contaminants and species, supporting New Approach Methodologies for toxicological risk assessment.