Regulation of Amino Acid Biosynthesis Genes in Plants

植物氨基酸生物合成基因的调控

基本信息

批准号：
7025051
负责人：
Gloria CORUZZI
金额：
$ 43.83万
依托单位：
NEW YORK UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
1983
资助国家：
美国
起止时间：
1983-12-01 至 2008-08-31
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): Our long-term goal is to define the regulatory mechanisms controlling N-assimilation into amino acids in plants. Our work to date has identified key isoenzymes involved in this pathway, shown that their transcriptional regulation reflects levels of cognate amino acids, and uncovered evidence that light, carbon and nitrogen signaling modulate gene expression. We now propose to determine how these various signaling systems interact to coordinate regulation of genes in this pathway and globally affect amino acid synthesis. To accomplish this, which cannot be achieved using standard single-gene/genetic analysis, we have developed an innovative approach that combines math tools for strategic experimental design, with model building, genomics/bioinformatics and molecular genetics. Importantly, this approach exploits "activist" data mining, in which math tools are used not simply for data analysis, but to iteratively construct "experimental spaces" that efficiently test how regulatory signals interact, to enable model building and testing. This mathematically compresses an enormous number of permutations (effects of C, N, light, etc.) into a small and manageable number of testable combinations. We will first use such tools, Combinatorial Design & C:N Matrix, to strategically sample a large series of input variables (Aim 1), and stepwise develop models of regulatory circuits for signal interactions regulation of genes (including dose and kinetic responses) using Boolean logic and visualization methods (Aim 2). Aim 3 will expand the analysis of the N-assimilation regulatory circuit using microarray and metabolome analysis of selected and prioritized treatments. Genes in pathways co-regulated by multiple signals will be identified using new bioinformatic tools we have developed (PathExplore + InteractClass), which also enable correlation with levels of cognate amino acids. Co-regulated genes in pathways will be analyzed for potential cis-regulatory elements and associated transcription factors (where known), to generate testable models for regulatory circuits. These regulatory models of N-assimilation will be tested using mutants in putative C:N sensing components we have isolated using forward and reverse genetic approaches (Aim 4). The synthesis of these aims should allow us to model, predict, and test how perturbations of the regulation of this pathway(s) may be used to enhance N-assimilation, a limiting factor in plant growth affecting agriculture, human nutrition, and health. They also provide a valuable proof-of-principle study for the application of these approaches and tools to model other regulatory circuits in biological and medical systems.

描述（由申请人提供）：我们的长期目标是定义控制植物中氮同化为氨基酸的调控机制。迄今为止，我们的工作已经确定了参与该途径的关键同工酶，表明它们的转录调节反映了同源氨基酸的水平，并发现了光、碳和氮信号传导调节基因表达的证据。我们现在建议确定这些不同的信号系统如何相互作用以协调该途径中基因的调节并全局影响氨基酸合成。为了实现使用标准单基因/遗传分析无法实现的这一目标，我们开发了一种创新方法，将用于战略实验设计的数学工具与模型构建、基因组学/生物信息学和分子遗传学相结合。重要的是，这种方法利用了“积极分子”数据挖掘，其中数学工具不仅用于数据分析，还用于迭代构建“实验空间”，有效测试监管信号如何相互作用，从而实现模型构建和测试。这在数学上将大量排列（C、N、光等的影响）压缩为少量且可管理的可测试组合。我们将首先使用组合设计和 C:N 矩阵等工具对大量输入变量进行策略性采样（目标 1），并逐步开发用于基因信号相互作用调节（包括剂量和动力学反应）的调节电路模型布尔逻辑和可视化方法（目标 2）。目标 3 将使用选定和优先治疗的微阵列和代谢组分析来扩展 N 同化调节回路的分析。将使用我们开发的新生物信息学工具（PathExplore + InteractClass）来识别由多个信号共同调节的通路中的基因，该工具还可以与同源氨基酸水平相关。将分析途径中的共同调控基因的潜在顺式调控元件和相关转录因子（如果已知），以生成可测试的调控回路模型。这些 N 同化调节模型将使用我们使用正向和反向遗传方法分离的假定 C:N 传感组件中的突变体进行测试（目标 4）。这些目标的综合应该使我们能够建模、预测和测试如何利用该途径的调节扰动来增强氮同化，氮同化是影响农业、人类营养和健康的植物生长的限制因素。他们还为应用这些方法和工具来模拟生物和医学系统中的其他调节回路提供了有价值的原理验证研究。