ToxMet: a web tool for toxicogenomic data analysis using genome-scale metabolic modeling

Chemical toxicity assessment commonly includes in vivo rat exposure experiments, with transcriptomic measurements collected at various exposure times and chemical doses. The mechanisms underlying chemical-induced toxicity are then inferred by analyzing changes in gene expression. Recently, genome-scale metabolic models (GSMs), which represent the metabolic network of a cell/organism and contain metabolites, reactions, genes, and the relationship between the genes and reactions, have been used to provide a systems-level understanding of gene expression. However, most of the algorithms that integrate gene expression with GSMs require familiarity with MATLAB or Python programming, making them less accessible for users without computational experience. Here, we introduce ToxMet (https://toxmet.bhsai.org), an open-access, user-friendly web application that provides tabular and graph-based network views to visualize the latest rat GSM (iRno v4.2) and predicts chemical-induced metabolic perturbations in rat tissues by integrating toxicogenomic measurements with the rat GSM. ToxMet uses two well-validated computational algorithms, TIMBR and Pheflux, to predict metabolic perturbations and provides the prediction results as interactive and downloadable tables, scatter plots, and network visualizations. As such, the web tool can process a maximum of 10 conditions for a single job, and the results can be used for dose-response studies to monitor organ metabolism at the subsystem level. We evaluated ToxMet's ability to predict toxicity mechanisms by applying it to publicly available toxicogenomic data for two exemplar toxicants: gentamicin and thioacetamide, which are known to induce kidney and liver injury, respectively. ToxMet predicted known toxicity mechanisms for both chemicals, thus demonstrating its ability to provide novel insights into the metabolic mechanisms of chemical-induced toxicity and aid in the discovery of biomarkers and therapeutics using gene expression data.