Unveiling the impact of bisphenol a exposure on gene expression and immune response in diabetic nephropathy through integrative toxicogenomics and molecular dynamics approaches.

Background: Bisphenol A (BPA) exposure has been implicated in various health issues, including diabetic nephropathy (DN). Understanding the molecular mechanisms underlying BPA-induced DN is crucial for developing effective therapeutic strategies.

Methods: We analyzed the dataset GSE96804 to identify differentially expressed genes (DEGs) associated with DN. We intersected these with BPA-related toxicity targets from the Comparative Toxicogenomics Database. Enrichment analyses were performed to elucidate the functional implications of these genes, and machine learning models (LASSO, RF, SVM-RFE) were employed to identify key feature genes. Immune cell infiltration and immune-related pathways were analyzed using the ssGSEA algorithm, and molecular docking simulations were conducted to explore interactions between BPA and key proteins.

Results: Our findings revealed significant enrichment of BPA-related DEGs in critical pathways such as the AGE-RAGE signaling pathway, diabetic cardiomyopathy, and the renin-angiotensin system. Machine learning identified four key genes (ALB, CTSD, GAS6, VDAC1) consistently associated with BPA exposure and DN. Immune cell infiltration analysis indicated altered immune microenvironments in DN, with significant correlations between feature gene expression and immune cell types. Molecular docking and dynamics simulations demonstrated stable interactions between BPA and ALB, highlighting specific amino acid residues critical for binding.

Conclusion: This study underscores the significant impact of BPA on gene expression and immune responses in DN. The identified key genes and their interactions with BPA provide valuable insights into the pathophysiological mechanisms of BPA-induced DN, paving the way for future research and potential therapeutic interventions.