Integrated network toxicology, machine learning, molecular docking and experimental validation to elucidate mechanism of polyethylene terephthalate microplastics inducing periodontitis.

Growing evidence highlights the health risks of micro-plastics (MPs) exposure, with reported accumulations in enclosed anatomical sites such as the heart, placenta, and circulatory system. However, the role of MPs in periodontitis remains unexplored. This study employed bioinformatics, network toxicology, machine learning, molecular docking, and experimental approaches to explore the effects and underlying mechanisms of polyethylene terephthalate microplastics (PET-MPs) induced periodontitis. Multi-database screening, including GEO, ChEMBL, STITCH, GeneCards, OMIM, identified 23 candidate targets linked to PET-MPs exposure. Furthermore, we used STRING, Cytoscape software and machine learning approaches to identified 13 core targets. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and immune cell infiltration analyses revealed that PET-MPs influence immune-related pathways, such as C-type lectin receptor signaling, VEGF receptor signaling, and TNF signaling. Molecular docking revealed high-affinity binding interactions between PET-MPs and core targets, implicating direct interference with cellular processes. Experimental assays using gingival fibroblasts (GFs) exposed to PET-MPs showed dose-dependent cytotoxicity oxidative stress induction, and pro-inflammatory activation. PET-MPs upregulated inflammation-related markers including Caspase 3, KDR, PIM2, PTGS2, MTOR, MAPK14; while downregulating AKT1 and ALPL. These findings establish a mechanistic framework linking PET-MPs exposure to periodontitis progression via redox-inflammatory crosstalk, offering novel insight into the potential pathogenesis of microplastics on periodontitis.