Genome-Scale Characterization of Toxicity-Induced Metabolic Alterations in Primary Hepatocytes

Context-specific Genome-scale Metabolic Network Reconstructions (GENREs) provide a means to understand cellular metabolism at a deeper level of physiological detail. Here, we use transcriptomics data from chemically exposed rat hepatocytes to constrain a GENRE of rat hepatocyte metabolism and predict biomarkers of liver toxicity using the Transcriptionally Inferred Metabolic Biomarker Response (TIMBR) algorithm. We profiled alterations in cellular hepatocyte metabolism following in vitro exposure to three toxicants (acetaminophen, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for six hours. TIMBR predictions were compared with paired metabolomics data from the same exposure conditions. Agreement between computational model predictions and experimental data led to the identification of specific metabolites and thus metabolic pathways associated with toxicant exposure; where predictions and experimental data disagreed, we identified testable hypotheses to reconcile differences between the model predictions and experimental data. The presented pipeline for using paired transcriptomics and metabolomics data provides a framework for interrogating multiple omics datasets to generate mechanistic insight of metabolic changes associated with toxicological responses. Overall design: Examination of RNA from compound-treated primary rat hepatocytes for 5 conditions (including the control) with 3-4 replicates for each condition.