Systematic comparison of temporal hepatotoxicant-induced gene network responses across three liver test systems.

Authors

Tamara Y Danilyuk, Marou Schouten, Elsje J Burgers, Barira Islam, Joost B Beltman, Peter Bouwman, Giulia Callegaro, Bob van de Water

Published in

Toxicological sciences : an official journal of the Society of Toxicology. Jul 24, 2026. Epub Jul 24, 2026.

Abstract

Drug-induced liver injury (DILI) arises from dynamic and time-dependent cellular stress responses that remain insufficiently captured by conventional single-timepoint toxicogenomic assessments. We systematically characterized temporal and concentration-dependent transcriptomic responses to the clinically relevant hepatotoxicants ketoconazole, diclofenac and nitrofurantoin across three human liver in vitro models: primary human hepatocytes (PHH), hiPSC-derived hepatocyte-like cells (HLC) and HepG2 cells. Time-resolved RNA sequencing (0-48 hours) combined with likelihood ratio testing identified time-responsive genes (TRGs), which were subsequently integrated into TXG-MAPr gene co-expression modules to enable mechanistic interpretation at the network level. Across all models and compounds, a conserved core stress response was observed, characterized by activation of ER stress (ATF4), oxidative stress (NRF2) and heat shock (HSF1) pathways, while distinct model-specific adaptive programs reflected differences in metabolic competence and differentiation status. Mapping TRGs onto co-expression networks revealed coordinated temporal activation patterns and highlighted both shared and system-specific transcriptional programs. Concentration-response analysis at 24 hours demonstrated that module-level transcriptomic points of departure (tPODs) were highly reproducible across models for a subset of functionally annotated networks, particularly ER stress modules associated with hepatocellular injury in vivo. Notably, these modules showed substantial gene-level concordance across systems, supporting their biological robustness and translational relevance. These findings establish that time-resolved, network-based transcriptomics provides mechanistically grounded, reproducible and quantitative endpoints that enhance cross-system comparability and offer a scalable framework for regulatory toxicology and next-generation chemical risk assessment.

PMID:
42498667
Bibliographic data and abstract were imported from PubMed on 25 Jul 2026.

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