The human hepatocyte TXG-MAPr: gene co-expression network modules to support mechanism-based risk assessment

Giulia Callegaro,Wouter Den Hollander,Emre Guney,Laura Furlong,Bob Van De Water,Jennifer Mollon,Jeffrey J Sutherland,Panuwat Trairatphisan,Marije Niemeijer,James L Stevens,Solène Grosdidier,Julio Saez-Rodriguez,Yue W Webster,Janet Piñero Gonzalez,Steven J Kunnen

doi:10.1007/s00204-021-03141-w

Abstract

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.

Highlights

Mechanism-based risk assessment is the gold standard for safety assessment of drugs and chemicals, but is resourceand time-intensive (Lanzoni et al 2019)
We applied weighted gene co-expression network analysis (WGCNA) to the TG-GATEs primary human hepatocytes (PHH) dataset (Igarashi et al 2015), which includes exposure data for 158 compounds at various time points and concentration levels to identify the predominant networks of co-expressed genes (Fig. 1a exemplifies the process for a subset of co-expressed genes)
By performing WGCNA, the final data matrix was reduced to 941 columns and 398 rows which corresponds to a 97.7% reduction in dimensionality of gene expression

Summary

Introduction

Mechanism-based risk assessment is the gold standard for safety assessment of drugs and chemicals, but is resourceand time-intensive (Lanzoni et al 2019). Hepatotoxicity and hepatocarcinogenesis are major concerns for environmental exposures (Colombo et al 2019; Yorita Christensen et al 2013) Both non-genotoxic and genotoxic carcinogens often produce hepatoxicity prior to the emergence of liver tumors in longer term preclinical studies (Karin and Dhar 2016). In both situations, hepatotoxicity can be regarded as a multistep, multicellular disease process, where an initial molecular stress is followed by a series of cellular key events that couple the initial stress to an apical endpoint observable as a pathology, e.g. hepatotoxicity or liver tumors. In the absence of a mechanism that links cellular (stress) events to a pathology, risk assessment is typically based on the apical endpoint which can take months or years to develop

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