We integrated network toxicology, machine learning, and molecular docking to elucidate molecular mechanisms underlying the link between ATBC exposure and Alzheimer's disease (AD) pathogenesis.
Methods
Potential action targets of ATBC were screened from ChEMBL, TargetNet, and SwissTarget Prediction databases; disease-associated targets were derived from differential expression analysis of GEO datasets. Overlapping candidates underwent protein-protein interaction network construction (STRING) and subsequent Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Machine learning employing SHAP prioritized pivotal targets, while molecular docking and dynamics simulations validated binding affinities.
Results
We identified 68 shared targets, of which five were designated as critical (CCKBR, RAF1, GABRG2, STS, RAPGEF3). GO enrichment revealed that ATBC compromises neuronal function and synaptic plasticity by perturbing glial cell differentiation, synaptic transmission, benzodiazepine receptor activity, and serine/threonine kinase activity. KEGG analysis implicated neuroactive ligand-receptor interactions, calcium, FoxO, and PI3K-Akt signaling pathways. Molecular simulations confirmed stable compound-target binding.
Conclusions
This integrative computational approach elucidates mechanisms underlying plasticizer-associated neurotoxicity in AD, establishing a framework for investigating neurological impacts of environmental contaminants.
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