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Integrative network-based construction of ecologically relevant computational adverse outcome pathways for organic mercury-induced toxicity
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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, associated fish toxicity endpoints, and proteins were curated from the Comparative Toxicogenomics Database and ECOTOX database, followed by molecular docking to identify novel interactions with 16 zebrafish protein orthologs. Subsequently, toxicity endpoints and identified molecular interactions were standardized and harmonized using established ontologies and AOP-Wiki. These data 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, which was subsequently filtered using AOP definition-based criteria to identify biologically relevant pathways. Further, these pathways were ranked based on their novelty with respect to existing AOPs in AOP-Wiki, resulting in the proposal of four novel cAOPs describing glutathione peroxidase binding or altered metallothionein levels leading to neurological manifestations or dysbiosis in fish. 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.
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Metadata
- Authors
- Madgaonkar, S. R., Chivukula, N., Garisetti, V., Marigoudar, S. R., Sharma, K. V., & Samal, A.
- Affiliations
- Homi Bhabha National Institute (IN)
- Institute of Mathematical Sciences (IN)
- Government of India (IN)
- Corresponding author
- Samal, Areejit
- ORCID
- Venue
- Environmental Toxicology and Chemistry
- Publisher
- Oxford University Press (OUP)
- Published
- 2026-01-28
- Volume
- 45
- Issue
- 3
- Pages
- 731-747
- ISSN
- 0730-7268 (ISSN-L)
- 1552-8618
- OpenAlex
- W7125951084
- Cited by
- 0
- References
- 110
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Environmental Toxicology and Chemistry (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Funders
- Department of Atomic Energy
- The Institute of Mathematical Sciences
- Marine Ecotoxicology and Ecological Risk Assessment
- (MEERA) Programme (MoES/OSMART/EFC/2021)
- Topics
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Mercury impact and mitigation studies
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Environmental Toxicology and Ecotoxicology
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- adverse outcome pathways, organic mercury, fish toxicity, zebrafish, molecular docking, glutathione peroxidase, metallothionein, neurotoxicity, dysbiosis, AOP-Wiki, new approach methodologies, toxicological risk assessment, Comparative Toxicogenomics Database
- MeSH terms
- Adverse Outcome Pathways, Animals, Mercury, Organomercury Compounds, Water Pollutants, Chemical, Zebrafish, Mercury Compounds, Molecular Docking Simulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
