Regulation of Amino Acid Biosynthesis Genes in Plants

描述（由申请人提供）：我们的长期目标是确定植物中控制N-同化为氨基酸的调节机制。迄今为止，我们的工作已经确定了参与这一途径的关键同工酶，表明它们的转录调控反映了同源氨基酸的水平，并发现了光，碳和氮信号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，以协调该途径中的基因调控，并全面影响氨基酸合成。为了实现这一点，这是无法实现的使用标准的单基因/遗传分析，我们已经开发出一种创新的方法，结合数学工具的战略实验设计，与模型构建，基因组学/生物信息学和分子遗传学。重要的是，这种方法利用了“主动”数据挖掘，其中数学工具不仅用于数据分析，而且用于迭代构建“实验空间”，有效地测试调控信号如何相互作用，以实现模型构建和测试。这在数学上压缩了大量的排列（C，N，光等的影响）。转化为少量可管理的可测试组合。我们将首先使用这样的工具，组合设计和C：N矩阵，策略性地采样一系列输入变量（目标1），并逐步开发调控电路的模型，用于使用布尔逻辑和可视化方法调控基因的信号相互作用（包括剂量和动力学反应）（目标2）。目标3将扩大使用微阵列和代谢组学分析的N-同化调控电路的分析选择和优先处理。将使用我们开发的新生物信息学工具（PathExplore + InteractClass）识别由多种信号共同调控的途径中的基因，该工具还可以与同源氨基酸水平相关。将分析途径中的共调节基因中潜在的顺式调节元件和相关的转录因子（如果已知），以生成调节回路的可测试模型。这些监管模式的N-同化将测试使用突变体在假定的C：N传感组件，我们已经分离出使用正向和反向遗传方法（目的4）。这些目标的综合应该使我们能够建模，预测和测试如何扰动的调节这一途径（S）可能被用来提高N-同化，植物生长的限制因素影响农业，人类营养和健康。它们还为这些方法和工具在生物和医学系统中模拟其他调节回路的应用提供了有价值的原理验证研究。

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.