We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
A computational framework for multi-scale data fusion in assessing the associations between micro- and nanoplastics and human hepatotoxicity
AI summary Read the abstract
Researchers developed a computational toxicology framework integrating multi-source data and network analysis to map associations between micro- and nanoplastics and hepatotoxicity, identifying key molecular pathways through which MNPs may damage the liver, offering a scalable alternative to traditional in vivo testing.
Micro- and nanoplastics (MNPs) are increasingly recognized as novel environmental pollutants given their potential risks to ecosystems and human health. The liver, a vital organ for maintaining systemic homeostasis, exhibits heightened susceptibility to MNPs. While conventional studies have revealed the liver's vulnerability to MNPs, a multi-scale research paradigm for efficiently delineating the associations between MNPs and hepatotoxicity remains lacking. Therefore, our study developed a computational toxicology framework integrating multi-source heterogeneous data and network-based strategies. By transforming compound-induced gene response signatures and hepatotoxicity-associated pathway perturbations data into a link score matrix and comprehensive hepatotoxicity scores, this framework systematically assessed associations between 11 MNPs and human hepatotoxicity. This scoring system was used to categorize MNPs into distinct hepatotoxicity association levels, facilitating a multi-level analysis from genes and pathways to toxicity endpoints and overall toxicity. It revealed that polystyrene (PS), polytetrafluoroethylene-polymethyl methacrylate (PTFE-PMMA), polypropylene (PP), polyhydroxyalkanoates (PHA), polyhydroxybutyrate (PHB), polymethyl methacrylate (PMMA), and polyethylene (PE) exhibited strong associations with hepatotoxicity, while polyvinyl chloride (PVC), polyvinyl acetate (PEVA), polyamide (PA), and polylactic acid (PLA) showed weaker associations. Furthermore, the four endpoints of oxidative stress, hepatic fibrosis, metabolism, and inflammation were identified to be closely related to MNPs. Enrichment analysis of transcriptomic data from PS- and PE-treated rat liver tissues further highlighted the involvement of pathways such as "Non-alcoholic fatty liver disease" in MNP-induced hepatotoxicity. In conclusion, the computational framework proposed in this study enhances our insight into MNP-triggered hepatotoxicity and provides a new perspective for assessing the toxicity risks of pollutants.
More Papers Like This
The hepatotoxicity assessment of micro/nanoplastics: A preliminary study to apply the adverse outcome pathways
AI summary Read the abstract
Researchers reviewed the literature on how micro- and nanoplastics cause liver damage and organized the findings into an Adverse Outcome Pathway framework. They found that plastic particles can trigger oxidative stress, inflammation, and metabolic disruption in the liver, potentially leading to dysfunction. The study provides a structured way to understand the chain of events from plastic particle exposure to liver harm, highlighting potential health risks for humans.
Environmental PET-microplastic exposure and risk of non-alcoholic fatty liver disease: An integrated computational toxicology and multi-omics study
AI summary Read the abstract
Researchers used computational toxicology and machine learning to identify six key genes linking PET microplastic exposure to non-alcoholic fatty liver disease (NAFLD), with the model achieving high diagnostic accuracy and molecular docking suggesting that PET-derived chemicals may directly bind to proteins controlling liver fat metabolism.
Hepatotoxic Mechanisms of Micro- and Nanoplastics in Animal Models: A Scoping Review with Human Health Implications
AI summary Read the abstract
This scoping review examines hepatotoxic mechanisms of micro- and nanoplastics in animal models, identifying oxidative stress, inflammation, lipid peroxidation, and epigenetic alterations as the primary pathways through which plastic particles damage liver tissue.
Adverse outcome pathway networks of microplastic ecotoxicity to aquatic organisms: A critical review
AI summary Read the abstract
Researchers used automated text-mining combined with multi-level ecotoxicological review to construct adverse outcome pathway networks for microplastic toxicity in aquatic organisms. They mapped how microplastics cause harm from initial tissue contact through molecular disturbances to higher-level biological effects in gills, gut, liver, gonads, and brain. The study found strong evidence for early-stage toxic mechanisms but identified critical knowledge gaps in understanding downstream biological consequences.
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.
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.