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A computational framework for multi-scale data fusion in assessing the associations between micro- and nanoplastics and human hepatotoxicity
Summary
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.
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