The human hepatocyte TXG-MAPr: WGCNA transcriptomic modules to support mechanism-based risk assessment
Mechanism-based risk assessment is urged to advance and fully permeate into current safety assessment practices, possibly at early phases of drug safety testing. Toxicogenomics is a promising source of comprehensive and mechanisms-revealing data, but analysis tools to interpret mechanisms of toxicity and specific for the testing systems (e.g. hepatocytes) are lacking. In this study we present the TXG-MAPr webtool (available at https://txg-mapr.eu/WGCNA_PHH/TGGATEs_PHH/), an R-Shiny-based implementation of weighted gene co-expression networks (WGCNA) obtained from the Primary Human Hepatocytes (PHH) TG-GATEs dataset. Gene co-expression networks (modules) were annotated with functional information (pathway enrichment, transcription factor) to reveal their mechanistic interpretation. Several well-known stress response pathways were captured in the modules, are perturbed by specific stressors and show preserved in rat systems (rat primary hepatocytes and rat in vivo liver), highlighting stress responses that translate across species/testing systems. The TXG-MAPr tool was successfully applied to investigate the mechanism of toxicity of TG-GATEs compounds and using external datasets obtained from different hepatocyte cells and microarray platforms. Additionally, we suggest that module responses can be calculated from targeted RNA-seq data therefore imputing biological responses from a limited gene. By analyzing 50 different PHH donors responses to a common stressor, tunicamycin, we were able to suggest modules associated with donors traits, e.g. pre-existing disease state, therefore connected to donors variability. In conclusion, we demonstrated that gene co-expression analysis coupled to an interactive visualization environment, the TXG-MAPr, is a promising approach to achieve mechanistic relevant, cross-species and cross-platform evaluation of toxicogenomic data.