Identifying multi-scale translational safety biomarkers using a network-based systems approach

Callegaro Giulia, Schimming Johannes P, Piñero González Janet, Kunnen Steven J., Wijaya Lukas S., Trairatphisan Panuwat, van den Berk Linda, Beetsma Kim, Furlong Laura I., Sutherland Jeffrey J., Mollon Jennifer, Stevens James L., van de Water Bob
DOI: 10.1016/j.isci.2023.106094
PMID: 36895646
Keyword: bioinformatics · gene network · transcriptomics


Animal testing is the current standard for drug and chemicals safety assessment, but hazards translation to human is uncertain. Human in vitro models can address the species translation but might not replicate in vivo complexity. Herein, we propose a network-based method addressing these translational multiscale problems that derives in vivo liver injury biomarkers applicable to in vitro human early safety screening. We applied weighted correlation network analysis (WGCNA) to a large rat liver transcriptomic dataset to obtain co-regulated gene clusters (modules). We identified modules statistically associated with liver pathologies, including a module enriched for ATF4-regulated genes as associated with the occurrence of hepatocellular single-cell necrosis, and as preserved in human liver in vitro models. Within the module, we identified TRIB3 and MTHFD2 as a novel candidate stress biomarkers, and developed and used BAC-eGFPHepG2 reporters in a compound screening, identifying compounds showing ATF4-dependent stress response and potential early safety signals.