Characterization of the SGOC metabolic network in cancer pathogenesis

 描述(申请人提供)：癌细胞调整其新陈代谢，以达到不受控制的增殖、存活和长期维持的要求。丝氨酸、甘氨酸和一碳(SGOC)代谢整合了氨基酸、葡萄糖和维生素的营养状态，并产生不同的输出，如生物合成、氧化还原状态的维持和甲基化反应的底物。我们最近发现，癌细胞将相对大量的葡萄糖流量转移到从头开始的丝氨酸代谢中，导致一种碳代谢的解除调控。在这里，我们将建立在我们的发现基础上，这些发现已经确定了一种碳代谢在癌症发病机制中的新角色。我们将进行一项综合的计算和实验分析，以调查癌症中涉及一种碳代谢的代谢网络中功能的多样性。在目标1中，我们将描述肿瘤组织中丝氨酸和一碳基因的表达网络。我们重建了编码丝氨酸代谢酶的基因网络。我们将把TCGA数据叠加到丝氨酸代谢网络上，以评估其在癌症中的表达。我们将通过比较正常组织、不同癌症组织、组织亚型和突变状态中网络成分的表达来分析该网络的癌症背景。在目标2中，我们将对癌细胞中的一种碳代谢进行通量分析和代谢组学研究。我们将利用我们实验室开发的代谢组学平台，并开发出一种分析13C丝氨酸通量的方法。我们将扩展这些方法，然后利用这些能力来测量不同癌细胞之间丝氨酸流量的多样性。我的目标3，我们将评估一碳代谢与抗代谢药物化疗的关系。我们将巩固我们的发现，并将它们与临床上可用的针对SGOC网络中的酶的药理学药物联系起来。接下来，我们将模拟用不同试剂处理的细胞的代谢组学反应。

 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.