The Research Division of Drug Discovery and Safety of the University of Leiden (UL) participates to TransQST consortium. Bob van de Water’s lab team has mined data of the TG-GATEs database on primary human hepatocytes, rat in vivo liver and kidney gene expression data and established WGCNA modules. These modules are available in an open source application tool called TXG-MAPr.
Toxicogenomic data in safety testing represent a critical source to uncover underlying mechanisms of drug-induced toxicities. Co-regulated gene network approaches can organize high dimensional toxicogenomic data, while not being biased by know biology. Leiden’s team applied weighted gene co-expression network analysis (WGCNA) on the publicly available datasets in TG-GATEs on different toxicogenomic model systems, including primary human hepatocytes (PHH), rat in vivo liver and rat in vivo kidney. The three datasets were independently processed with an unsigned WGCNA analysis that clustered the gene sets into functional modules, representing the bridge between individual gene variations and emergent global properties. The modules serve as a dynamic visualization of the transcriptome under experimental conditions (compounds, concentrations and time points). They developed a user friendly tool using the R Shiny package for visualization of the toxicogenomic network and analyzing the mechanism of toxicities, called the TXG-MAPr.
In the TXG-MAPr, users can analyze dose- and time-response curves, compound correlation plots and functional annotation of the WGCNA modules to derive mechanistic information of the toxicity. In collaboration with UKHD and IMIM, they included the prediction of transcription factor activities, as well as physical interactions between downstream proteins encoded by the transcriptome, which might be useful in analyzing the perturbations triggered by exposure with toxic compounds. Users can investigate module perturbation of the TG-GATEs compounds by looking at the module eigengene scores (EGs). In addition, a dedicated upload function allows to overlay a new set of gene expression data onto the built WGCNA network for the 3 model systems. New EGs for all modules in the toxicogenomic network are calculated considering the newly uploaded data, which can help users in understanding the mechanisms of adverse drug reactions.
With the TXG-MAPr tool, Leiden’s team presents a promising and innovative tool that contributes to the mechanistic understanding of potential adverse drug reactions that can be used during early drug development. This live demo of this tool was presented to the Early Review experts panel and they cheered the potential of the tool for the research community.
See below some screenshots of use cases:
Figure 1. TXG-MAPr (left) at high dose of acetaminophen for 24 h displaying several activated modules (red circles), including module 144 (right). This module contains NRF2 target genes, which are activated upon oxidative stress induced by acetaminophen.
Figure 2. Comparison between high dose cyclosporine A with medium dose tunicamycin treatment showed high compound correlation (Pearson R = 0.84), indicating overlap in module activation.