A Protein Interactome-Based Framework Reveals the General Toxicity of Chemicals

Humans are exposed to various chemicals, and understanding their toxicity is essential for safeguarding their health. However, current toxicological methods often focus on specific diseases or chemicals, missing broader patterns of toxicity. Here, we propose a general framework that conceptualizes toxicity as arising from protein-interaction-mediated relationships between chemicals and diseases. Using the Comparative Toxicogenomics Database, we show that the network proximity between chemical targets and disease-associated proteins in the protein interactome predicts chemical-disease associations. We validate this finding through zebrafish experiments for acute toxicity and analyses of human exposome data sets for chronic chemical-disease correlation. We highlight the applications of our framework in predicting a previously unknown toxic effect of a fungicide, explaining drug cytotoxicity in a COVID drug screening, and uncovering the overlooked ″indirect-hit″ chemical-disease toxicity pattern. Our approach establishes a new paradigm for toxicity research, offering insights into the health risks posed by chemicals and addressing public health challenges.

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Publication Details

Journal
Environmental Science & Technology
Published
2025-11-04
DOI
https://doi.org/10.1021/acs.est.5c08341
Citations
1
Primary Topic
Health, Environment, Cognitive Aging
Type
article
Field-Weighted Citation Impact
1.20
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article

A Protein Interactome-Based Framework Reveals the General Toxicity of Chemicals

Réka Albert, Xiao Gan, Jun Xu, Bei Gao et al.
1 citations
Environmental Science & Technology
Health, Environment, Cognitive Aging
1.20
article

A Protein Interactome-Based Framework Reveals the General Toxicity of Chemicals

Réka Albert, Xiao Gan, Jun Xu, Bei Gao, Geng Li, Weiyi Yang
article en
1 citations

Abstract

Humans are exposed to various chemicals, and understanding their toxicity is essential for safeguarding their health. However, current toxicological methods often focus on specific diseases or chemicals, missing broader patterns of toxicity. Here, we propose a general framework that conceptualizes toxicity as arising from protein-interaction-mediated relationships between chemicals and diseases. Using the Comparative Toxicogenomics Database, we show that the network proximity between chemical targets and disease-associated proteins in the protein interactome predicts chemical-disease associations. We validate this finding through zebrafish experiments for acute toxicity and analyses of human exposome data sets for chronic chemical-disease correlation. We highlight the applications of our framework in predicting a previously unknown toxic effect of a fungicide, explaining drug cytotoxicity in a COVID drug screening, and uncovering the overlooked ″indirect-hit″ chemical-disease toxicity pattern. Our approach establishes a new paradigm for toxicity research, offering insights into the health risks posed by chemicals and addressing public health challenges.

Environmental Science & TechnologyVol. 59(45)
Pennsylvania State University (US), Nanjing University of Information Science and Technology (CN), Beijing Academy of Artificial Intelligence (CN)
Openalex Percentile: Top 17%
Health, Environment, Cognitive Aging
1.20
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