Integrating Network Toxicology, Machine Learning, and Molecular Dynamics to Explore the Molecular Network of Triclosan-Induced Acute Myocardial Infarction

TL;DR

This study explores how triclosan causes heart damage by combining toxicology, machine learning, and simulations to identify PTGS2 as a key player.

Contribution

The integration of network toxicology, machine learning, and molecular dynamics to identify PTGS2 as a mediator of triclosan-induced heart injury.

Findings

37 candidate genes were identified and narrowed down to 8 core regulators, including PTGS2.

Molecular simulations confirmed stable, high-affinity binding of triclosan to PTGS2.

TCS-induced cardiomyocyte injury was reversed by a PTGS2 inhibitor, highlighting PTGS2 as a therapeutic target.

Abstract

Triclosan (TCS) exposure is linked to increased acute myocardial infarction (AMI) risk, but underlying mechanisms remain unclear. Here, we integrated network toxicology, machine learning, molecular simulations, and in vitro assays to delineate this pathway. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) identified 37 candidate genes, which were refined via machine learning to 8 core regulators (including PTGS2). Molecular docking and molecular dynamics (MD) simulations confirmed high-affinity, stable binding of TCS to PTGS2. In cardiomyocytes, TCS upregulated PTGS2 and the injury marker cTnI, an effect reversed by the PTGS2 inhibitor celecoxib. These findings establish PTGS2 as a critical mediator of TCS-induced cardiomyocyte injury, providing a potential therapeutic target for TCS-associated cardiovascular damage.

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