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 mechanisms-revealing data, but interpretative analysis tools 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 network analysis (WGCNA) obtained from the Primary Human Hepatocytes (PHH) TG-GATEs dataset. The 398 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, were perturbed by specific stressors and showed preservation in rat systems (rat primary hepatocytes and rat in vivo liver), with the exception of DNA damage and oxidative stress responses. A subset of 87 well-annotated and preserved modules was used to evaluate mechanisms of toxicity of endoplasmic reticulum (ER) stress and oxidative stress inducers, including cyclosporine A, tunicamycin and acetaminophen. In addition, module responses can be calculated from external datasets obtained with different hepatocyte cells and platforms, including targeted RNA-seq data, therefore, imputing biological responses from a limited gene set. As another application, donors’ sensitivity towards tunicamycin was investigated with the TXG-MAPr, identifying higher basal level of intrinsic immune response in donors with pre-existing liver pathology. 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.

Adaptive stress response pathways play a key role in the switch between adaptation and adversity, and are important in druginduced liver injury. Previously, we have established an HepG2 fluorescent protein reporter platform to monitor adaptive stress response activation following drug treatment. HepG2 cells are often used in high-throughput primary toxicity screening, but metabolizing capacity in these cells is low and repeated dose toxicity testing inherently difficult. Here, we applied our bacterial artificial chromosome-based GFP reporter cell lines representing Nrf2 activation (Srxn1-GFP and NQO1-GFP), unfolded protein response (BiP-GFP and Chop-GFP), and DNA damage response (p21-GFP and Btg2-GFP) as long-term differentiated 3D liver-like spheroid cultures. All HepG2 GFP reporter lines differentiated into 3D spheroids similar to wild-type HepG2 cells. We systematically optimized the automated imaging and quantification of GFP reporter activity in individual spheroids using high-throughput confocal microscopy with a reference set of DILI compounds that activate these three stress response pathways at the transcriptional level in primary human hepatocytes. A panel of 33 compounds with established DILI liability was further tested in these six 3D GFP reporters in single 48 h treatment or 6 day daily repeated treatment. Strongest stress response activation was observed after 6-day repeated treatment, with the BiP and Srxn1-GFP reporters being most responsive and identified particular severe-DILI-onset compounds. Compounds that showed no GFP reporter activation in two-dimensional (2D) monolayer demonstrated GFP reporter stress response activation in 3D spheroids. Our data indicate that the application of BAC-GFP HepG2 cellular stress reporters in differentiated 3D spheroids is a promising strategy for mechanism-based identification of compounds with liability for DILI.

The transcription factor NRF2, governed by its repressor KEAP1, protects cells against oxidative stress. There is interest in modelling the NRF2 response to improve the prediction of clinical toxicities such as drug-induced liver injury (DILI). However, very little is known about the makeup of the NRF2 transcriptional network and its response to chemical perturbation in primary human hepatocytes (PHH), which are often used as a translational model for investigating DILI. Here, microarray analysis identified 108 transcripts (including several putative novel NRF2-regulated genes) that were both downregulated by siRNA targeting NRF2 and upregulated by siRNA targeting KEAP1 in PHH. Applying weighted gene co-expression network analysis (WGCNA) to transcriptomic data from the Open TG-GATES toxicogenomics repository (representing PHH exposed to 158 compounds) revealed four co-expressed gene sets or ‘modules’ enriched for these and other NRF2-associated genes. By classifying the 158 TG-GATES compounds based on published evidence, and employing the four modules as network perturbation metrics, we found that the activation of NRF2 is a very good indicator of the intrinsic biochemical reactivity of a compound (i.e. its propensity to cause direct chemical stress), with relatively high sensitivity, specificity, accuracy and positive/negative predictive values. We also found that NRF2 activation has lower sensitivity for the prediction of clinical DILI risk, although relatively high specificity and positive predictive values indicate that false positive detection rates are likely to be low in this setting. Underpinned by our comprehensive analysis, activation of the NRF2 network is one of several mechanism-based components that can be incorporated into holistic systems toxicology models to improve mechanistic understanding and preclinical prediction of DILI in man. Electronic supplementary material The online version of this article (10.1007/s00204-018-2354-1) contains supplementary material, which is available to authorized users.