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<p>Micro- and nanoplastics (MNPs) have been detected in human renal and urinary tissues and are increasingly recognized as potential environmental contributors to kidney injury and chronic kidney disease (CKD), but the molecular signatures connecting MNP-associated toxicogenomic signals with CKD remain poorly understood. In this study, we employed an integrative analytical framework combining toxicity prediction, toxicogenomic target analysis, transcriptomic analysis, machine learning, transcriptome-wide Mendelian randomization, single-nucleus RNA sequencing, and targeted in vitro expression validation to investigate candidate molecular links between MNP-associated toxicogenomic signatures and CKD. Among MNP monomers with available SMILES notations, PVC, PMMA, PP, and PS showed moderate predicted nephrotoxicity, whereas PE showed a borderline score and PET showed a lower predicted score. We identified 58 shared candidate genes between MNP-associated targets and CKD-related genes, which were mainly enriched in inflammatory and oxidative stress-related pathways. A machine learning model based on these features showed discriminative performance in training and external validation CKD datasets. Transcriptome-wide MR analyses prioritized several genes, including TGFB1, CTNNB1, PF4, and NR3C2, whose genetically predicted expression was associated with CKD risk, although colocalization evidence was limited. Single-nucleus analysis highlighted proximal tubular, endothelial, podocyte, and monocyte-lineage cells as CKD-relevant renal compartments. Exploratory molecular docking provided computational binding hypotheses between selected MNP monomeric units and candidate proteins. Targeted qRT-PCR validation further showed that PS-MP exposure increased the expression of profibrotic markers and altered the expression of selected candidate genes in proximal tubular epithelial cells. Overall, this study provides an integrative, systems-level characterization of candidate molecular links between MNP-associated toxicogenomic signatures and CKD. These findings highlight candidate genes, pathways, and CKD-relevant renal cell types, offering hypothesis-generating insights into environmentally related kidney injury and providing preliminary expression-level experimental support for selected prioritized genes.</p>

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Multi-omics and genetic prioritization identify candidate molecular links between micro- and nanoplastics-associated signatures and chronic kidney disease

  • 期刊论文

  • 发表日期：

    2026年7月23日

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  • Dianjie Zeng,
  • Ze Cheng,
  • Jiachen Liu,
  • Yinhuai Wang,
  • Fangzhi Chen

摘要

Micro- and nanoplastics (MNPs) have been detected in human renal and urinary tissues and are increasingly recognized as potential environmental contributors to kidney injury and chronic kidney disease (CKD), but the molecular signatures connecting MNP-associated toxicogenomic signals with CKD remain poorly understood. In this study, we employed an integrative analytical framework combining toxicity prediction, toxicogenomic target analysis, transcriptomic analysis, machine learning, transcriptome-wide Mendelian randomization, single-nucleus RNA sequencing, and targeted in vitro expression validation to investigate candidate molecular links between MNP-associated toxicogenomic signatures and CKD. Among MNP monomers with available SMILES notations, PVC, PMMA, PP, and PS showed moderate predicted nephrotoxicity, whereas PE showed a borderline score and PET showed a lower predicted score. We identified 58 shared candidate genes between MNP-associated targets and CKD-related genes, which were mainly enriched in inflammatory and oxidative stress-related pathways. A machine learning model based on these features showed discriminative performance in training and external validation CKD datasets. Transcriptome-wide MR analyses prioritized several genes, including TGFB1, CTNNB1, PF4, and NR3C2, whose genetically predicted expression was associated with CKD risk, although colocalization evidence was limited. Single-nucleus analysis highlighted proximal tubular, endothelial, podocyte, and monocyte-lineage cells as CKD-relevant renal compartments. Exploratory molecular docking provided computational binding hypotheses between selected MNP monomeric units and candidate proteins. Targeted qRT-PCR validation further showed that PS-MP exposure increased the expression of profibrotic markers and altered the expression of selected candidate genes in proximal tubular epithelial cells. Overall, this study provides an integrative, systems-level characterization of candidate molecular links between MNP-associated toxicogenomic signatures and CKD. These findings highlight candidate genes, pathways, and CKD-relevant renal cell types, offering hypothesis-generating insights into environmentally related kidney injury and providing preliminary expression-level experimental support for selected prioritized genes.