ArticleToxicological sciences : an official journal of the Society of Toxicology2024
A network-based transcriptomic landscape of HepG2 cells uncovering causal gene-cytotoxicity interactions underlying drug-induced liver injury.
Article in Toxicological sciences : an official journal of the Society of Toxicology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 9 citations in OpenAlex.
- Systematic comparison of temporal hepatotoxicant-induced gene network responses across 3 liver test systems.Toxicological sciences : an official journal of the Society of Toxicology · 2026Article
- Advancing transcriptomics-based mechanistic assessment of nephrotoxicity in vitro using the human RPTEC/TERT1 TXG-MAPr gene co-expression network.Toxicological sciences : an official journal of the Society of Toxicology · 2026Article
- A human liver organoid platform for hepatotoxicity assessment: evaluation using reference compounds.Frontiers in toxicology · 2026Article
- Comprehensive analysis of high-throughput transcriptomics to distinguish drug-induced liver injury (DILI) phenotypes.Archives of toxicology · 2025Review
- Integrating Transcriptomic and Targeted New Approach Methodologies into a Tiered Framework for Chemical Bioactivity Screening.Environmental health perspectives · 2025Article
- Computational modelling identifies primary mediators of crosstalk between DNA damage and oxidative stress responses.PLoS computational biology · 2025Article
- Progress in toxicogenomics to protect human health.Nature reviews. Genetics · 2025Review
- Collaborative SAR Modeling and Prospective In Vitro Validation of Oxidative Stress Activation in Human HepG2 Cells.Journal of chemical information and modeling · 2023Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
Drug-induced liver injury (DILI) remains the main reason for drug development attritions largely due to poor mechanistic understanding. Toxicogenomic to interrogate the mechanism of DILI has been broadly performed. Gene coregulation network-based transcriptome analysis is a bioinformatics approach that potentially contributes to improve mechanistic interpretation of toxicogenomic data. Here we performed an extensive concentration time course response-toxicogenomic study in the HepG2 cell line exposed to 20 DILI compounds, 7 reference compounds for stress response pathways, and 10 agonists for cytokines and growth factor receptors. We performed whole transcriptome targeted RNA sequencing to more than 500 conditions and applied weighted gene coregulated network analysis to the transcriptomics data followed by the identification of gene coregulated networks (modules) that were strongly modulated upon the exposure of DILI compounds. Preservation analysis on the module responses of HepG2 and PHH demonstrated highly preserved adaptive stress response gene coregulated networks. We correlated gene coregulated networks with cell death onset and causal relationships of 67 critical target genes of these modules with the onset of cell death was evaluated using RNA interference screening. We identified GTPBP2, HSPA1B, IRF1, SIRT1, and TSC22D3 as essential modulators of DILI compound-induced cell death. These genes were also induced by DILI compounds in PHH. Altogether, we demonstrate the application of large transcriptome datasets combined with network-based analysis and biological validation to uncover the candidate determinants of DILI.
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Registered trials
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