Multimodal cheminformatic and network biology analysis enables deeper mechanistic understanding of PFAS-induced hepatotoxicity
6 affiliations
- 1Ignota Labs Ltd, St. John’s Innovation Centre, Cambridge, UK
- 2Healthy Environments and Consumer Safety Branch (HECSB), Environmental Health, Science and Research Bureau, Health Canada, Ottawa, Canada
- 3College of Medicine and Health Sciences, Center for Biotechnology, Khalifa University, Abu Dhabi, United Arab Emirates
- 4Department of Genomics, MEDFUTURE Institute for Biomedical Research, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
- 5Department of Chemistry and Biology, North University Center, Technical University of Cluj-Napoca, Baia Mare, Romania
- 6Digital Toxicology, Sanofi, Frankfurt, Germany
Abstract
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.
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12859-026-06566-1.
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BibTeX
@article{mason2026multimodal,
title = {{Multimodal cheminformatic and network biology analysis enables deeper mechanistic understanding of PFAS-induced hepatotoxicity}},
author = {Matthew Mason and Sara Masarone and Thomas Clelford and Ella Atlas and Matthew J. Meier and Andrea Rowan-Carroll and Lavinia-Lorena Pruteanu and Panuwat Trairatphisan and Friedemann Schmidt and Jordan Lane and Layla Hosseini-Gerami},
journal = {BMC Bioinformatics},
year = {2026},
month = jul,
doi = {10.1186/s12859-026-06566-1},
url = {https://doi.org/10.1186/s12859-026-06566-1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Multimodal cheminformatic and network biology analysis enables deeper mechanistic understanding of PFAS-induced hepatotoxicity
AU - Matthew Mason
AU - Sara Masarone
AU - Thomas Clelford
AU - Ella Atlas
AU - Matthew J. Meier
AU - Andrea Rowan-Carroll
AU - Lavinia-Lorena Pruteanu
AU - Panuwat Trairatphisan
AU - Friedemann Schmidt
AU - Jordan Lane
AU - Layla Hosseini-Gerami
PY - 2026
DA - 2026/07/01
JO - BMC Bioinformatics
DO - 10.1186/s12859-026-06566-1
UR - https://doi.org/10.1186/s12859-026-06566-1
AB - 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. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s12859-026-06566-1.
SN - 1471-2105
ER -