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：
6724543
负责人：
Gloria CORUZZI
金额：
$ 40.27万
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 2008-02-29
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N-嵌入氨基酸的调节机制。迄今为止，我们的工作已经确定了该途径中涉及的关键同工酶，表明它们的转录调节反映了同源氨基酸的水平，并且发现了光，碳和氮信号传导调节基因表达的证据。现在，我们建议确定这些各种信号系统如何与该途径中基因的坐标调节以及全球影响氨基酸合成。为了实现这一目标，无法使用标准的单基因/遗传分析来实现这一目标，我们开发了一种创新的方法，将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结合在一起。重要的是，这种方法利用了“激进主义者”数据挖掘，其中数学工具不仅用于数据分析，而且还用于迭代地构建“实验空间”，从而有效地测试了监管信号的相互作用，以启用模型构建和测试。这种数学可以压缩大量置换量（C，N，光等的效果），以少量可管理的可测试组合。我们将首先使用此类工具，即组合设计＆C：N矩阵，从策略上采样了大量的输入变量（AIM 1），并逐步开发用于使用布尔逻辑和可视化方法的基因（包括剂量和动力学响应）的信号相互作用调节的调节模型（包括剂量和动力学响应）（AIM 2）。 AIM 3将使用微阵列和对优先处理的微阵列和代谢组分析来扩展N-综合调节回路的分析。通过我们开发的新生物信息学工具（PathExplore + Intercont ClassClass），将确定由多个信号共同调节的途径中的基因，这也使与同源氨基酸水平相关。将分析途径中的共同调节基因，以分析潜在的顺式调节元件和相关的转录因子（如已知），以生成可用于调节电路的可测试模型。这些N-概计的调节模型将使用假定的C：N感应组件中的突变体进行测试，我们使用正向和反向遗传方法进行了分离（AIM 4）。这些目的的综合应使我们能够建模，预测和测试该途径调节的扰动如何用于增强N-氮化，这是影响农业，人类营养和健康的植物生长的限制因素。他们还为这些方法和工具的应用提供了宝贵的原理研究，以模拟生物和医疗系统中其他监管电路。