Data from: 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 four toxicants (acetaminophen, carbon tetrachloride,
2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for six hours.
TIMBR predictions were compared with paired fresh and spent media
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. Here, we identified changes in the TCA
metabolites citrate and alpha-ketoglutarate along with changes in
carbohydrate metabolism and interruptions in ATP production and the TCA
Cycle. 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.