Identifying metabolic adaptations characteristic of cardiotoxicity using paired transcriptomics and metabolomics data integrated with a computational model of heart metabolism.

Improvements in the diagnosis and treatment of cancer have revealed long-term side effects of chemotherapeutics, particularly cardiotoxicity. Here, we present paired transcriptomics and metabolomics data characterizing in vitro cardiotoxicity to three compounds: 5-fluorouracil, acetaminophen, and doxorubicin. Standard gene enrichment and metabolomics approaches identify some commonly affected pathways and metabolites but are not able to readily identify metabolic adaptations in response to cardiotoxicity. The paired data was integrated with a genome-scale metabolic network reconstruction of the heart to identify shifted metabolic functions, unique metabolic reactions, and changes in flux in metabolic reactions in response to these compounds. Using this approach, we confirm previously seen changes in the p53 pathway by doxorubicin and RNA synthesis by 5-fluorouracil, we find evidence for an increase in phospholipid metabolism in response to acetaminophen, and we see a shift in central carbon metabolism suggesting an increase in metabolic demand after treatment with doxorubicin and 5-fluorouracil.

Validation of a multicellular tumor microenvironment system for modeling patient tumor biology and drug response.

Lung cancer rates are rising globally and non-small cell lung cancer (NSCLC) has a five year survival rate of only 24%. Unfortunately, the development of drugs to treat cancer is severely hampered by the inefficiency of translating pre-clinical studies into clinical benefit. Thus, we sought to apply a tumor microenvironment system (TMES) to NSCLC. Using microvascular endothelial cells, lung cancer derived fibroblasts, and NSCLC tumor cells in the presence of in vivo tumor-derived hemodynamic flow and transport, we demonstrate that the TMES generates an in-vivo like biological state and predicts drug response to EGFR inhibitors. Transcriptomic and proteomic profiling indicate that the TMES recapitulates the in vivo and patient molecular biological state providing a mechanistic rationale for the predictive nature of the TMES. This work further validates the TMES for modeling patient tumor biology and drug response indicating utility of the TMES as a predictive tool for drug discovery and development and potential for use as a system for patient avatars.

Predicting changes in renal metabolism after compound exposure with a genome-scale metabolic model.

The kidneys are metabolically active organs with importance in several physiological tasks such as the secretion of soluble wastes into the urine and synthesizing glucose and oxidizing fatty acids for energy in fasting (non-fed) conditions. Once damaged, the metabolic capability of the kidneys becomes altered. Here, we define metabolic tasks in a computational modeling framework to capture kidney function in an update to the iRno network reconstruction of rat metabolism using literature-based evidence. To demonstrate the utility of iRno for predicting kidney function, we exposed primary rat renal proximal tubule epithelial cells to four compounds with varying levels of nephrotoxicity (acetaminophen, gentamicin, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for six and twenty-four hours, and collected transcriptomics and metabolomics data to measure the metabolic effects of compound exposure. For the transcriptomics data, we observed changes in fatty acid metabolism and amino acid metabolism, as well as changes in existing markers of kidney function such as Clu (clusterin). The iRno metabolic network reconstruction was used to predict alterations in these same pathways after integrating transcriptomics data and was able to distinguish between select compound-specific effects on the proximal tubule epithelial cells. Genome-scale metabolic network reconstructions with coupled omics data can be used to predict changes in metabolism as a step towards identifying novel metabolic biomarkers of kidney function and dysfunction.