The Research Division of Drug Discovery and Safety of the University of Leiden (UL) participates to TransQST consortium. Bob van de Water lab team has mined the data of TG-GATEs database on primary human hepatocytes gene expression data and established WGCNA modules. These modules are available in an open source application tool called TXG-rMAP.
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 Primary Human Hepatocytes (PHH) dataset in TG-GATEs, including 158 different compounds. This dataset was the building block for an unsigned WGCNA analysis that clustered the gene set into 399 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.
Recently, a dedicated upload function allows to overlay a new set of gene expression data from PHH onto the built WGCNA network. New Eigengene Scores (EGs) for all the modules in the toxicogenomic network are calculated considering the newly uploaded data. Users can analyze dose- and time-response curves, compound correlation plots and gene ontology terms to derive mechanistic information from large transcriptomic datasets.
With the TXG-rMAP 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.
Find out more in the tool site: https://wgcna-lacdr-dds.nl/.
See below some screenshots of use cases:
Figure 1. TXG-rMAP at high dose of acetaminophen for 24 h displaying several activated modules (red), including module 144 (left). 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.