Novel mechanisms of atrazine endocrine disruption: an integrated approach reveals progesterone and glucocorticoid receptor targeting.

backgroundAtrazine is a widely used herbicide with reported endocrine-disrupting effects, yet the molecular targets and pathway-level mechanisms remain to be fully elucidated. We aimed to build an integrated, hypothesis-generating workflow to prioritize candidate atrazine targets by combining literature curation, network analysis, machine-learning–based prioritization, and molecular docking.

methodsCandidate endocrine-related genes were compiled from the Comparative Toxicogenomics Database and PubMed literature screening, yielding 78 genes. A protein–protein interaction (PPI) network was constructed and analyzed to identify a connected subnetwork of 26 genes. Gene Ontology (GO) and KEGG enrichment analyses were performed to characterize overrepresented biological processes and pathways. To prioritize candidate genes within the 26-gene subnetwork, we engineered composite network features and trained a weakly supervised model using three literature-labeled seed positives (ESR1, CYP19A1, and AR) versus the remaining genes as putative negatives, with internal cross-validation for exploratory assessment. Molecular docking was conducted for selected receptors to evaluate potential binding signals.

resultsNetwork topology and enrichment analyses highlighted endocrine-related and steroid hormone–associated biological functions within the atrazine-associated gene set. In exploratory internal cross-validation on the 26-gene subnetwork, an ensemble model showed high apparent discrimination. The prioritization step ranked NR3C1 and PGR among the top candidates for follow-up. Docking simulations suggested moderate binding affinities between atrazine and PGR (− 6.495 kcal/mol) and NR3C1 (− 6.248 kcal/mol), providing complementary in silico evidence consistent with these candidates and motivating investigation of progesterone- and glucocorticoid-related signaling.

conclusionsThis integrative in silico workflow supports a network-informed prioritization of potential atrazine endocrine targets and highlights NR3C1 and PGR as candidates warranting further investigation. Because the machine-learning component is trained on a very limited literature-labeled set and no external test set is available, the findings should be interpreted as hypothesis-generating and require validation in biological models (in vitro and/or in vivo).