sAOP: linking chemical stressors to adverse outcomes pathway networks

Author:

Aguayo-Orozco Alejandro1,Audouze Karine2,Siggaard Troels1,Barouki Robert2,Brunak Søren1,Taboureau Olivier13

Affiliation:

1. Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

2. Environmental Toxicity, Therapeutic Targets, Cellular Signaling and Biomarkers (T3S) Unit, Université de Paris, INSERM UMR-S 1124, Paris, France

3. Université de Paris, INSERM U1133, Computational Modeling of Protein-Ligand Interactions group, CNRS UMR 8251, Unit of Functional and adaptive Biology, Paris, France

Abstract

Abstract Motivation Adverse outcome pathway (AOP) is a toxicological concept proposed to provide a mechanistic representation of biological perturbation over different layers of biological organization. Although AOPs are by definition chemical-agnostic, many chemical stressors can putatively interfere with one or several AOPs and such information would be relevant for regulatory decision-making. Results With the recent development of AOPs networks aiming to facilitate the identification of interactions among AOPs, we developed a stressor-AOP network (sAOP). Using the ‘cytotoxitiy burst’ (CTB) approach, we mapped bioactive compounds from the ToxCast data to a list of AOPs reported in AOP-Wiki database. With this analysis, a variety of relevant connections between chemicals and AOP components can be identified suggesting multiple effects not observed in the simplified ‘one-biological perturbation to one-adverse outcome’ model. The results may assist in the prioritization of chemicals to assess risk-based evaluations in the context of human health. Availability and implementation sAOP is available at http://saop.cpr.ku.dk Supplementary information Supplementary data are available at Bioinformatics online.

Funder

European Union’s Horizon 2020

Novo Nordisk Foundation

University of Paris Descartes-USPC

university of Paris Diderot

INSERM

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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