Related Experiment Video
Updated: Aug 6, 2026
Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro
Published on: January 31, 2022
Systematic comparison of temporal hepatotoxicant-induced gene network responses across 3 liver test systems
Tamara Y Danilyuk1, Marou Schouten1, Elsje J Burgers1
1Division of Cell Systems and Drug Safety, Leiden Academic Centre for Drug Research, Leiden University, 2333 CC Leiden, The Netherlands.
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 3 human liver in vitro models: primary human hepatocytes (PHH), hiPSC-derived hepatocyte-like cells (HLC), and HepG2 cells. Time-resolved RNA sequencing (0 to 48 h) 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, whereas 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 h 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.
Insights
Time-resolved transcriptomics captures dynamic cellular responses to drug-induced liver injury (DILI). Network analysis reveals conserved stress pathways and reproducible toxicological endpoints across models, improving chemical risk assessment.
Area of Science:
- Toxicology
- Genomics
- Hepatology
Background:
- Drug-induced liver injury (DILI) is a significant safety concern.
- Conventional toxicogenomic assessments lack temporal resolution for DILI.
- Understanding dynamic cellular responses is crucial for accurate DILI prediction.
Purpose of the Study:
- To characterize temporal and concentration-dependent transcriptomic responses to hepatotoxicants.
- To identify conserved and model-specific stress response pathways in vitro.
- To establish reproducible transcriptomic endpoints for DILI risk assessment.
Main Methods:
- Time-resolved RNA sequencing (0-48 hours) in three human liver models (PHH, HLC, HepG2).
- Likelihood ratio testing to identify time-responsive genes (TRGs).
- Integration of TRGs into TXG-MAPr co-expression modules for network analysis.
Main Results:
- A conserved core stress response (ER, oxidative, heat shock pathways) was observed across models.
- Distinct model-specific adaptive programs reflected cellular differences.
- Module-level transcriptomic points of departure (tPODs) were reproducible, especially for ER stress modules.
Conclusions:
- Time-resolved, network-based transcriptomics provides mechanistically grounded DILI endpoints.
- This approach enhances cross-system comparability and reproducibility.
- It offers a scalable framework for regulatory toxicology and chemical risk assessment.
