Characterization of the SGOC metabolic network in cancer pathogenesis



 DESCRIPTION (provided by applicant): Cancer cells adapt their metabolism to achieve the requirements of uncontrolled proliferation, survival, and long-term maintenance. Serine, glycine, and one carbon (SGOC) metabolism integrates nutritional status from amino acids, glucose and vitamins, and generates diverse outputs, such as the biosynthesis, the maintenance of redox status and the substrates for methylation reactions. We have recently found that cancer cells divert a relatively large amount of flux from glucose into de novo serine metabolism leading to the deregulation of one carbon metabolism. Here we will build upon findings we have made that have identified newfound roles for one carbon metabolism in cancer pathogenesis. We will carry out an integrated computational and experimental analysis to investigate the diversity of functions within the metabolic network involving one carbon metabolism in cancer. In aim 1, we will characterize the serine and one carbon gene expression network in tumor tissues. We reconstruct the network of genes that encode enzymes that metabolize serine. We will overlay TCGA data onto the serine metabolic network to assess its expression in cancer. We will analyze the cancer context of the network by comparing expression of network components across normal tissues, different cancer tissues, histological subtypes, and mutational statuses. In aim 2, we will carry out a flux analysis and metabolomics of one carbon metabolism in cancer cells. We will capitalize on a metabolomics platform our lab has developed and we have developed a method for 13C serine flux analysis. We will expand on these methods and then exploit these capabilities to measure the diversity of serine flux across different cancer cells. I aim 3, we will evaluate the relationship between one carbon metabolism and anti-metabolite chemotherapy. We will consolidate our findings and relate them in the context of clinically available pharmacological agents that target enzymes in the SGOC network. We will next model the metabolomics response of cells treated with different agents.