We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
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.
Microplastics and nanoplastics (MNPs) are widely distributed in human organs, including the kidney, brain, and liver, where they induce oxidative stress and inflammatory responses. However, the toxicological effects of MNPs on chronic kidney disease (CKD) remain largely unexplored. In this study, we employed an integrated approach combining bioinformatics, network toxicology, machine learning, and molecular docking to investigate the potential impacts of polyethylene terephthalate microplastics (PET-MPs) on CKD. A total of 191 CKD-related targets were obtained from the GEO database, while 875 potential targets of PET-MPs were identified using PubChem, ChEMBL, SwissTargetPrediction, and the SEA Search platform. Through weighted gene co-expression network analysis (WGCNA) and machine learning, seven core targets were ultimately identified. KEGG pathway enrichment and Gene Ontology (GO) analyses indicated that PET-MPs may exert effects by modulating immune- and inflammation-related pathways as well as energy metabolism. Molecular docking revealed high-affinity interactions between the PET-MP-derived small molecules and the core targets, suggesting direct interference with cellular activities that could influence CKD progression. This computational framework not only elucidates potential molecular mechanisms of MNP-induced nephrotoxicity but also identifies candidate targets for therapeutic intervention, aligning with the scope of machine-learning-driven drug molecule design.
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.
Multi-omics and genetic prioritization identify candidate molecular links between micro- and nanoplastics-associated signatures and chronic kidney disease
AI summary Read the abstract
Scientists used advanced computer modeling and lab tests to explore how tiny plastic particles (microplastics) found in our bodies might contribute to chronic kidney disease. They identified specific genes and biological pathways linking plastic exposure to kidney damage, and confirmed in lab-grown kidney cells that polystyrene microplastics can trigger changes linked to kidney scarring. While this is early-stage research that doesn't prove plastics directly cause kidney disease in people, it offers promising leads for understanding—and potentially preventing—environmentally-driven kidney damage.
The toxicological impact of PET-MPs exposure on atherosclerosis: insights from network toxicology, molecular docking, and machine learning
AI summary Read the abstract
Researchers used network toxicology, molecular docking, and machine learning to identify how PET microplastics may promote atherosclerosis, narrowing 28 candidate targets to seven key genes and predicting interactions with atherosclerosis-relevant pathways including inflammation and lipid metabolism.
Assessing the Impact of Long-Term Exposure to Polyethylene Terephthalate Microplastics (PET-MPs) on Bladder Cancer Based on Network Toxicology, Molecular Docking, and Molecular Dynamics Simulation
AI summary
This research conducted a life cycle assessment of Japanese plastic waste management practices, evaluating the environmental impacts of collection, sorting, and recycling or disposal pathways. The analysis identified environmental hotspots and compared the climate and pollution impacts of different waste management strategies.
Assessing the Impact of Long-Term Exposure to Polyethylene Terephthalate Microplastics (PET-MPs) on Bladder Cancer Based on Network Toxicology, Molecular Docking, and Molecular Dynamics Simulation
AI summary
This research evaluated bioplastics as alternatives to conventional petroleum-based plastics, assessing their biodegradation performance, mechanical properties, and environmental impacts. The study found that while bioplastics offer potential benefits, their degradation behavior varies significantly across different environmental conditions.
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.