The Mechanism of Bisphenol S‐Induced Atherosclerosis Elucidated Based on Network Toxicology, Molecular Docking, and Machine Learning

The increasing prevalence of environmental pollutants has raised public concern about their potential role in diseases such as atherosclerosis (AS). Existing studies suggest that chemicals, including bisphenol S (BPS), may adversely affect cardiovascular health, but the specific mechanisms remain unclear. This study aims to elucidate the effects of BPS on AS and the underlying mechanisms. Through an extensive search of databases such as ChEMBL, STITCH, SwissTargetPrediction, SuperPred, SEA, and GEO, we identified 34 potential targets related to BPS-induced AS. A target network was constructed using the STRING platform and Cytoscape software. GO and KEGG functional enrichment analysis using the DAVID database revealed that BPS may promote the occurrence of AS by interfering with critical biological processes such as glutathione metabolism, nitrogen metabolism, and tyrosine metabolism. This was followed by the selection of 4 core targets-aminopeptidase n (ANPEP), alcohol dehydrogenase 5 (ADH5), lysosomal pro-x carboxypeptidase (PRCP), and microsomal glutathione s-transferase 1 (MGST1)-using five machine learning methods. These core targets play a pivotal role in BPS-induced AS. Furthermore, molecular docking confirmed the tight binding between BPS and these core targets. In conclusion, this study provides a theoretical framework for understanding the molecular mechanisms of BPS-induced AS and contributes scientific evidence for the development of prevention and treatment strategies for cardiovascular diseases triggered by BPS exposure.

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Publication Details

Journal
Journal of Applied Toxicology
Published
2025-02-20
DOI
https://doi.org/10.1002/jat.4768
Citations
23
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
34.60

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article

The Mechanism of Bisphenol S‐Induced Atherosclerosis Elucidated Based on Network Toxicology, Molecular Docking, and Machine Learning

Xuan He, Bing Guo
23 citations
Journal of Applied Toxicology
Computational Drug Discovery Methods
34.60
article

The Mechanism of Bisphenol S‐Induced Atherosclerosis Elucidated Based on Network Toxicology, Molecular Docking, and Machine Learning

Xuan He, Bing Guo
article en
23 citations

Abstract

The increasing prevalence of environmental pollutants has raised public concern about their potential role in diseases such as atherosclerosis (AS). Existing studies suggest that chemicals, including bisphenol S (BPS), may adversely affect cardiovascular health, but the specific mechanisms remain unclear. This study aims to elucidate the effects of BPS on AS and the underlying mechanisms. Through an extensive search of databases such as ChEMBL, STITCH, SwissTargetPrediction, SuperPred, SEA, and GEO, we identified 34 potential targets related to BPS-induced AS. A target network was constructed using the STRING platform and Cytoscape software. GO and KEGG functional enrichment analysis using the DAVID database revealed that BPS may promote the occurrence of AS by interfering with critical biological processes such as glutathione metabolism, nitrogen metabolism, and tyrosine metabolism. This was followed by the selection of 4 core targets-aminopeptidase n (ANPEP), alcohol dehydrogenase 5 (ADH5), lysosomal pro-x carboxypeptidase (PRCP), and microsomal glutathione s-transferase 1 (MGST1)-using five machine learning methods. These core targets play a pivotal role in BPS-induced AS. Furthermore, molecular docking confirmed the tight binding between BPS and these core targets. In conclusion, this study provides a theoretical framework for understanding the molecular mechanisms of BPS-induced AS and contributes scientific evidence for the development of prevention and treatment strategies for cardiovascular diseases triggered by BPS exposure.

Journal of Applied ToxicologyVol. 45(6)
Hunan University of Traditional Chinese Medicine (CN), Changsha Medical University (CN)
Hunan University, Hunan University of Chinese Medicine
Openalex Percentile: Top 0%
Computational Drug Discovery Methods
34.60
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