Network toxicology and bioinformatics analysis reveal the molecular mechanisms of polyethylene terephthalate microplastics in exacerbating diabetic nephropathy
Abstract The escalating prevalence of diabetic nephropathy (DN) has raised concerns about environmental pollutants, particularly polyethylene terephthalate microplastics (PET-MP), as potential contributors to metabolic diseases. However, the molecular mechanisms linking PET-MP exposure to DN remain...
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Nature Portfolio
2025-06-01
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| author | Shengnan Zeng Hui Guo |
| author_facet | Shengnan Zeng Hui Guo |
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| description | Abstract The escalating prevalence of diabetic nephropathy (DN) has raised concerns about environmental pollutants, particularly polyethylene terephthalate microplastics (PET-MP), as potential contributors to metabolic diseases. However, the molecular mechanisms linking PET-MP exposure to DN remain unclear. This study integrates network toxicology and bioinformatics to explore PET-MP-induced nephrotoxicity in DN. PET-MP-related toxicity targets were identified using SwissTargetPrediction and SuperPred. DN-associated differentially expressed genes (DEGs) were derived from the GSE96804 dataset. Overlapping genes were analyzed via enrichment analyses (GO, KEGG), Gene Set Variation Analysis (GSVA), and protein-protein interaction (PPI) networks. Immune cell infiltration was assessed with CIBERSORT. Key genes were identified using machine learning models (LASSO, RF, SVM-RFE) and validated by a nomogram and molecular docking. Among 10,124 DN-related DEGs, 64 overlapped with PET-MP targets. These genes were enriched in pathways like VEGF signaling, PI3K activity, and oxidative stress responses. GSVA revealed significant dysregulation in 2,258 pathways, including inflammation, immune response, and ROS metabolism. Immune infiltration analysis showed reduced CD8 + T cells, monocytes, and neutrophils in DN, alongside increased Tregs and M2 macrophages. Machine learning models identified CASP3 and GRB2 as key feature genes, validated by robust cross-validation and two independent DN datasets. Molecular docking indicated favorable binding affinities of PET to CASP3 (Vina score: -5.3) and GRB2 (Vina score: -5.2), suggesting disruptions in apoptosis and signal transduction pathways. PET-MP may exacerbate DN by disrupting critical molecular and cellular pathways, compromising the regulation of apoptosis, immune responses, and cellular homeostasis. CASP3 and GRB2 emerge as central mediators, providing mechanistic insights into PET-MP-driven nephrotoxicity. This study underscores the role of environmental microplastics in metabolic disorders and highlights potential therapeutic targets for DN. |
| format | Article |
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| spelling | doaj-art-48a9ee33ec0b4dfc88cf76cfe56c77302025-08-20T02:31:04ZengNature PortfolioScientific Reports2045-23222025-06-0115111410.1038/s41598-025-03285-0Network toxicology and bioinformatics analysis reveal the molecular mechanisms of polyethylene terephthalate microplastics in exacerbating diabetic nephropathyShengnan Zeng0Hui Guo1Department of Pediatric Nephrology, West China Second Hospital, Sichuan UniversityDepartment of Pediatric Nephrology, West China Second Hospital, Sichuan UniversityAbstract The escalating prevalence of diabetic nephropathy (DN) has raised concerns about environmental pollutants, particularly polyethylene terephthalate microplastics (PET-MP), as potential contributors to metabolic diseases. However, the molecular mechanisms linking PET-MP exposure to DN remain unclear. This study integrates network toxicology and bioinformatics to explore PET-MP-induced nephrotoxicity in DN. PET-MP-related toxicity targets were identified using SwissTargetPrediction and SuperPred. DN-associated differentially expressed genes (DEGs) were derived from the GSE96804 dataset. Overlapping genes were analyzed via enrichment analyses (GO, KEGG), Gene Set Variation Analysis (GSVA), and protein-protein interaction (PPI) networks. Immune cell infiltration was assessed with CIBERSORT. Key genes were identified using machine learning models (LASSO, RF, SVM-RFE) and validated by a nomogram and molecular docking. Among 10,124 DN-related DEGs, 64 overlapped with PET-MP targets. These genes were enriched in pathways like VEGF signaling, PI3K activity, and oxidative stress responses. GSVA revealed significant dysregulation in 2,258 pathways, including inflammation, immune response, and ROS metabolism. Immune infiltration analysis showed reduced CD8 + T cells, monocytes, and neutrophils in DN, alongside increased Tregs and M2 macrophages. Machine learning models identified CASP3 and GRB2 as key feature genes, validated by robust cross-validation and two independent DN datasets. Molecular docking indicated favorable binding affinities of PET to CASP3 (Vina score: -5.3) and GRB2 (Vina score: -5.2), suggesting disruptions in apoptosis and signal transduction pathways. PET-MP may exacerbate DN by disrupting critical molecular and cellular pathways, compromising the regulation of apoptosis, immune responses, and cellular homeostasis. CASP3 and GRB2 emerge as central mediators, providing mechanistic insights into PET-MP-driven nephrotoxicity. This study underscores the role of environmental microplastics in metabolic disorders and highlights potential therapeutic targets for DN.https://doi.org/10.1038/s41598-025-03285-0Polyethylene terephthalate microplasticsDiabetic nephropathyToxicologyMolecular pathwaysMachine learningMolecular Docking |
| spellingShingle | Shengnan Zeng Hui Guo Network toxicology and bioinformatics analysis reveal the molecular mechanisms of polyethylene terephthalate microplastics in exacerbating diabetic nephropathy Scientific Reports Polyethylene terephthalate microplastics Diabetic nephropathy Toxicology Molecular pathways Machine learning Molecular Docking |
| title | Network toxicology and bioinformatics analysis reveal the molecular mechanisms of polyethylene terephthalate microplastics in exacerbating diabetic nephropathy |
| title_full | Network toxicology and bioinformatics analysis reveal the molecular mechanisms of polyethylene terephthalate microplastics in exacerbating diabetic nephropathy |
| title_fullStr | Network toxicology and bioinformatics analysis reveal the molecular mechanisms of polyethylene terephthalate microplastics in exacerbating diabetic nephropathy |
| title_full_unstemmed | Network toxicology and bioinformatics analysis reveal the molecular mechanisms of polyethylene terephthalate microplastics in exacerbating diabetic nephropathy |
| title_short | Network toxicology and bioinformatics analysis reveal the molecular mechanisms of polyethylene terephthalate microplastics in exacerbating diabetic nephropathy |
| title_sort | network toxicology and bioinformatics analysis reveal the molecular mechanisms of polyethylene terephthalate microplastics in exacerbating diabetic nephropathy |
| topic | Polyethylene terephthalate microplastics Diabetic nephropathy Toxicology Molecular pathways Machine learning Molecular Docking |
| url | https://doi.org/10.1038/s41598-025-03285-0 |
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