We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Computational prediction of potential aggravating mechanisms of polyethylene terephthalate microplastics in diabetic foot ulcers: An integrated in silico approach combining network toxicology, bioinformatics, machine learning, and molecular dynamics simulations
Summary
Using computer modeling (not actual patient testing), researchers found clues that tiny plastic particles from PET plastic (commonly used in water bottles and food packaging) might make diabetic foot ulcers worse by interfering with genes that help repair cell damage and regulate blood sugar processing. While these findings are just a starting point—based on computer simulations rather than lab or human studies—they add to growing concerns that microplastics in our environment could impact chronic health conditions like diabetes complications, making this an area worth watching as research continues.
The increasing incidence of diabetic foot ulcer (DFU) and growing recognition of environmental pollutants have highlighted polyethylene terephthalate microplastics (PET-MP) as a potential metabolic disease trigger. However, the molecular mechanisms linking PET-MP to DFU remain unclear. This study employed integrated network toxicology and bioinformatics to decipher these mechanisms. PET-MP toxicity targets were screened using SwissTargetPrediction and ChEMBL, and DFU-related differentially expressed genes were obtained from GSE199939 and GSE134431. Functional analysis of overlapping genes included gene ontology, Kyoto encyclopedia of genes and genomes, gene set variation analysis, and protein-protein interaction network analysis. Machine learning models (least absolute shrinkage and selection operator, random forest, and support vector machine-recursive feature elimination) and SHapley Additive exPlanations analysis identified key genes, validated via nomogram, molecular dynamics simulation, and molecular docking. From 6723 DFU-related differentially expressed genes, 53 overlapping genes were identified. Functional analysis highlighted pathways including apoptosis, advanced glycation end product-receptor for advanced glycation end-product signaling, arachidonic acid metabolism, and nicotinamide adenine dinucleotide poly-ADP-ribosyltransferase activity. Machine learning and SHapley Additive exPlanations analysis identified PARP10 and PFKFB4 as key genes. Molecular docking revealed moderate binding affinities (Vina scores: -6.8 and -5.6). Molecular dynamics simulations confirmed conformational stability. PET-MP may exacerbate DFU by disrupting DNA damage repair, enhancing oxidative stress, and impairing glucose metabolism. These in silico findings identify PARP10 and PFKFB4 as potential candidate genes associated with PET-MP-related pathways in DFU, warranting further experimental validation.