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控制植物中N-同化为氨基酸的调控机制。到目前为止，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来协调这一途径中的基因调控，并在全球范围内影响氨基酸的合成。为了实现这一目标，我们开发了一种创新的方法，将用于战略性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“激进主义”数据挖掘，即数学工具不仅用于数据分析，而且迭代地构建“实验空间”，有效地测试监管信号如何相互作用，从而实现模型构建和测试。这在数学上压缩了大量的排列(C、N、光等的影响)。转换成少量且可管理的可测试组合。我们将首先使用这种工具，组合设计和C：N矩阵，战略性地对一大系列输入变量进行采样(目标1)，并使用布尔逻辑和可视化方法逐步开发用于基因信号相互作用调节(包括剂量和动力学响应)的调节电路模型(目标2)。目标3将利用选择和优先处理的微阵列和代谢组分析来扩大对N同化调节电路的分析。我们将使用我们开发的新的生物信息学工具(PathExplore InteractClass)识别受多个信号共同调控的通路中的基因，该工具还能够与同源氨基酸水平相关联。将分析通路中共同调节的基因，以寻找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N同化的调控模型将利用我们用正向和反向遗传方法分离的假定的C：N传感成分的突变来测试(目标4)。这些目标的综合应该使我们能够模拟、预测和测试这一途径的调节扰动(S)如何被用来增强N同化，N同化是植物生长中的一个限制因素，影响农业、人类营养和健康。它们还为应用这些方法和工具来模拟生物和医疗系统中的其他调节电路提供了有价值的原则证明研究。

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.