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
AI summary Read the abstract
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.
More Papers Like This
Network toxicology and bioinformatics analysis reveal the molecular mechanisms of polyethylene terephthalate microplastics in exacerbating diabetic nephropathy
AI summary Read the abstract
This computational study used bioinformatics to explore how polyethylene terephthalate (PET) microplastics might worsen diabetic kidney disease. The analysis identified key genes and inflammatory pathways that are affected by both PET microplastics and kidney damage in diabetes. The findings suggest that microplastic exposure could accelerate kidney problems in people who already have diabetes, though lab and clinical studies are needed to confirm this.
Toxicological Effects of Polyethylene Terephthalate Microplastics on Chronic Kidney Disease: An Integrative Bioinformatics, Network Toxicology, and Machine Learning Study.
AI summary Read the abstract
This study used computer-based methods, including gene databases and molecular docking, to examine how PET plastic particles might affect chronic kidney disease. The analysis identified seven genes that both PET microplastics and kidney disease may affect, often related to immune, inflammation, and energy processes. No animals or human volunteers were tested directly.
Elucidating the Mechanism of Polyethylene Terephthalate Micro / Nanoplastics Inducing Gestational Diabetes Mellitus through Network Toxicology and Molecular Docking Analysis
AI summary Read the abstract
Researchers used computer modeling to investigate how tiny plastic particles shed from PET water bottles and packaging may contribute to gestational diabetes, identifying three key regulatory proteins (STAT1, PIK3R1, PTPN11) that PET microplastics appear to disrupt. The findings suggest these particles could interfere with insulin signaling during pregnancy, pointing to a potential environmental driver of a condition that affects millions of expectant mothers.
Exploring the Potential Mechanism of Polyethylene Terephthalate Associated Cardiotoxicity through Network Toxicology and Molecular Docking
AI summary Read the abstract
Researchers used computational approaches including network toxicology, molecular docking, and molecular dynamics simulations to explore how polyethylene terephthalate microplastics may affect cardiovascular function. The study identified potential molecular pathways through which PET exposure could contribute to cardiotoxicity. The findings provide a theoretical framework for understanding how plastic contaminants might interact with heart-related biological targets.
Integrated network toxicology, machine learning, molecular docking and experimental validation to elucidate mechanism of polyethylene terephthalate microplastics inducing periodontitis
AI summary Read the abstract
Researchers combined computational biology, machine learning, and laboratory experiments to explore how polyethylene terephthalate microplastics might contribute to periodontitis, a common gum disease. They identified key molecular targets and signaling pathways through which microplastics could promote gum tissue inflammation. The study provides the first evidence linking microplastic exposure to the biological mechanisms underlying periodontal disease.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.