Linking Gene Regulation to Metabolism

描述（由申请人提供）：许多健康问题是由中枢代谢失调引起的，或与中枢代谢失调有关。例如，侵略性的癌细胞将碳从有氧呼吸转移到厌氧能量生产和生物质。从酵母到人类，碳命运的三个主要调节因子是保守的：蛋白激酶A (PKA)， AMP活化蛋白激酶（AMPK）和雷帕霉素靶蛋白（TOR）。该项目的长期目标是了解哺乳动物细胞中碳命运的责任如何在这三种主要调节因子及其下游转录因子（tf）之间分配。在本应用中，我们将研究AMPK和PKA在酿酒酵母模型中的碳命运调控。相当多的dna结合tf作为末端执行器AMPK和PKA是已知的。每个TF在几个途径中调节编码酶的基因，每个途径由几个TF调节。在任何给定的途径中，一些基因由一个已知的TF调节，一些由几个TF调节，而一些则没有。这一建议的结果将是了解AMPK和PKA如何通过协调这些末端效应tf的活动来调节碳命运的重要一步。目的1阐明AMPK和PKA对基因表达和碳命运的影响。为了实现这一目标，我们将在限制生长的葡萄糖供应（AMPK活跃而PKA不活跃）或过量葡萄糖供应（PKA活跃而AMPK不活跃）下培养酵母，监测碳命运，并进行基因表达谱分析。我们还将对突变株进行这些实验，在突变株中，我们可以控制AMPK和PKA的激活水平，而不依赖于葡萄糖的可用性。目的2量化每种效应因子TF在介导AMPK和PKA对基因表达和碳命运的影响中的作用。为了实现这一目标，我们将进行与aim 1类似的实验，使用可以独立控制AMPK和PKA激活的突变体，并删除12个下游tf中的一个。目的3建立PKA和AMPK通过效应tf与代谢结果联系的定量模型。为此，我们将构建AMPK和PKA下游基因调控的定量模型。我们还将使用通量平衡分析来估计代谢通量，并构建代谢通量和碳命运的酶基因表达模型。综上所述，这两个模型将定量预测基因表达和碳将如何受到调控系统干预的影响。最后，我们将通过使用单个酶被删除或对tf被删除的菌株来测试这些预测。

DESCRIPTION (provided by applicant): Many health problems result from, or are dependent on, disregulation of central metabolism. For example, aggressive cancer cells shunt carbon away from aerobic respiration toward anaerobic energy production and biomass. The three master regulators of carbon fate are conserved from yeast to man: Protein Kinase A (PKA), AMP Activated Protein Kinase (AMPK), and Target of Rapamycin (TOR). The long term goal of this project is to understand how the responsibility for carbon fate is divided among these three master regulators and the transcription factors (TFs) downstream of them in mammalian cells. In this application, we will study the regulation of carbon fate by AMPK and PKA in the model yeast Saccharomyces cerevisiae. Quite a few of the DNA-binding TFs that serve as end effectors AMPK and PKA are known. Each TF regulates genes encoding enzymes in several pathways and each pathway is regulated by several TFs. Within any given pathway, some genes are regulated by a single known TF, some by several, and some by none. The outcome of this proposal will be a significant step toward understanding how AMPK and PKA regulate carbon fate by coordinating the activities of these end effector TFs. Aim 1 Elucidate the influence of AMPK and PKA on gene expression and carbon fate. To achieve this aim, we will grow yeast with growth-limiting glucose supplies (in which AMPK is active and PKA is not) or excess glucose supplies (in which PKA is active and AMPK is not), monitor carbon fate, and carry out gene expression profiling. We will also carry out these experiments with mutant strains in which we can control the activation level of the AMPK and PKA independently of glucose availability. Aim 2 Quantify the role of each effector TF in mediating the influence of AMPK and PKA on gene expression and on carbon fate. To achieve this aim, we will carry out experiments like those of Aim 1 using mutants in which we can control AMPK and PKA activation independently and one of 12 downstream TFs has been deleted. Aim 3 Build a quantitative model linking PKA and AMPK to metabolic outcomes via effector TFs. To achieve this aim, we will construct a quantitative model of gene regulation downstream of AMPK and PKA. We will also estimate metabolic fluxes using flux balance analysis and construct a model of enzyme gene expression on metabolic fluxes and hence carbon fate. Taken together, these two models will make quantitative predictions about how both gene expression and carbon would be affected by interventions in the regulatory system. Finally, we will test these predictions by using strains in which individual enzymes have been deleted or pairs of TFs have been deleted.