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<p>Per- and polyfluoroalkyl substances (PFAS) are persistent and ubiquitous environmental contaminants associated with diverse adverse health outcomes. Despite prior extensive molecular analyses, mechanistic understanding of PFAS hepatotoxicity remains incomplete. In recent years, human liver spheroids have emerged as a useful tool in this space for investigating human-relevant responses to PFAS exposure across longer-term exposure periods. Here, we reprocessed publicly available PFAS RNA-sequencing data from human liver spheroids using the R-ODAF pipeline to generate a high-confidence set of differentially expressed genes (DEGs) and applied an integrated multimodal platform combining cheminformatic target prediction and transcription factor (TF) activity inference within a liver-specific prior knowledge network. While conventional pathway enrichment recapitulated known PFAS effects on SREBF- and PPAR-driven lipid metabolism, it provided limited insight for compounds with fewer DEGs, such as PFBS. Multimodal integration revealed compound-specific regulatory programs, with PFOS and PFOA exhibiting a time- and dose-dependent suppression of a conserved TF module comprising SREBF, CEBP, and HNF family members. Cheminformatic protein target predictions for the various PFAS compounds highlighted several common targets, including MAP3K5 and PLCG1, as well as several compound-specific target predictions. Incorporating these predictions with network biology analyses elucidated upstream cascades differentiating PFOA- versus PFOS-mediated regulation of lipid metabolism, with the PFOA-specific inhibition of PTPN7 being a discriminating feature, highlighting potential molecular bases for compound-specific toxicological profiles. Collectively, our findings demonstrate that integrating structural, transcriptomic, and network-biology approaches enhances mechanistic resolution beyond traditional enrichment analyses, providing testable hypotheses for PFAS-induced perturbations and guiding experimental validation of key targets. </p>

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Multimodal cheminformatic and network biology analysis enables deeper mechanistic understanding of PFAS-induced hepatotoxicity

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月26日

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  • Matthew Mason,
  • Sara Masarone,
  • Thomas Clelford,
  • Ella Atlas,
  • Matthew J. Meier,
  • Andrea Rowan-Carroll,
  • Lavinia-Lorena Pruteanu,
  • Panuwat Trairatphisan,
  • Friedemann Schmidt,
  • Jordan Lane,
  • Layla Hosseini-Gerami

摘要

Per- and polyfluoroalkyl substances (PFAS) are persistent and ubiquitous environmental contaminants associated with diverse adverse health outcomes. Despite prior extensive molecular analyses, mechanistic understanding of PFAS hepatotoxicity remains incomplete. In recent years, human liver spheroids have emerged as a useful tool in this space for investigating human-relevant responses to PFAS exposure across longer-term exposure periods. Here, we reprocessed publicly available PFAS RNA-sequencing data from human liver spheroids using the R-ODAF pipeline to generate a high-confidence set of differentially expressed genes (DEGs) and applied an integrated multimodal platform combining cheminformatic target prediction and transcription factor (TF) activity inference within a liver-specific prior knowledge network. While conventional pathway enrichment recapitulated known PFAS effects on SREBF- and PPAR-driven lipid metabolism, it provided limited insight for compounds with fewer DEGs, such as PFBS. Multimodal integration revealed compound-specific regulatory programs, with PFOS and PFOA exhibiting a time- and dose-dependent suppression of a conserved TF module comprising SREBF, CEBP, and HNF family members. Cheminformatic protein target predictions for the various PFAS compounds highlighted several common targets, including MAP3K5 and PLCG1, as well as several compound-specific target predictions. Incorporating these predictions with network biology analyses elucidated upstream cascades differentiating PFOA- versus PFOS-mediated regulation of lipid metabolism, with the PFOA-specific inhibition of PTPN7 being a discriminating feature, highlighting potential molecular bases for compound-specific toxicological profiles. Collectively, our findings demonstrate that integrating structural, transcriptomic, and network-biology approaches enhances mechanistic resolution beyond traditional enrichment analyses, providing testable hypotheses for PFAS-induced perturbations and guiding experimental validation of key targets.