Arabidopsis N-regulatory network dynamics: Integrating metabolome & transcriptome

描述（由申请人提供）：拟制研究的主要目标是使用系统生物学方法集体分析和整合来自拟南芥中控制N同化的N-调节网络的转录组，代谢组和通量组成分的时间依赖性数据。这种综合方法将使我们能够在全系统水平上动态模拟n -信号通过n -调节网络传播的流动，并确定参与这种调节的转录级联。这一目标将通过四个目标来实现：1。通过对硝酸盐处理过的拟南芥根和芽进行微阵列分析，建立高分辨率动态转录组数据集，生成时间依赖性氮调控网络。2. 利用稳定同位素标记的N15在一段时间内定量分析n同化网络中对n信号的代谢水平和代谢通量。3. 整合转录组、代谢组和通量组数据，使用一系列分析技术，包括滞后相关、线性回归和机器学习（状态空间分析），创建一个时间依赖的动态网络模型，用于控制N-吸收/同化。4. 通过T-DNA突变体和诱导表达系统测试模型生成假设来验证调节网络预测的功能。最重要的假设是，无机氮信号（硝酸盐）激活了参与调节硝酸盐吸收、还原和同化为有机氮（Glu/Gln）的基序，用于生物合成反应。有机氮产物（Glu/Gln）反过来激活控制氮储存合成的Asn基序，并抑制控制氮吸收/同化的基序。拟议的研究将使我能够通过整合全基因组转录组学数据和代谢组学数据，确定对这些无机和有机n信号做出反应的调节基因，这些信号调节n摄取和同化途径中的基因。这些目标的综合应该允许建模、预测和测试如何利用“系统”的扰动来提高氮的使用效率，从而影响能源使用（肥料/生物燃料）、环境的硝酸盐污染和人类营养。

DESCRIPTION (provided by applicant): The primary goal of the proposed research is to use a systems biology approach to collectively analyze and integrate time-dependent data from the transcriptome, metabolome, and fluxome components of the N- regulatory network controlling N-assimilation in Arabidopsis. This integrative approach will allow us to dynamically model the flow of N-signal propagation through the N-regulatory network on a systems-wide level and identify the transcriptional cascade involved in this regulation. This goal will be achieved through four aims: 1. Creation of high-resolution dynamic transcriptome datasets to generate a time-dependent nitrogen regulatory network, by performing microarray analysis on Arabidopsis roots and shoots treated with nitrate over a time course. 2. Quantification of metabolite levels and metabolic flux in the N-assimilatory network in response to N-signal, using stable isotope labeled N15 over a time course. 3. Integration of transcriptome, metabolome, and fluxome data to create a time-dependent dynamic network model for the control of N- uptake/assimilation, using a series of analytical techniques including lag correlation, linear regression, and machine learning (state space analysis). 4. Functional validation of regulatory network predictions by testing model generated hypotheses with T-DNA mutants and inducible expression systems. The overriding hypothesis being tested is that inorganic-N signals (nitrate) activate motifs involved in regulating nitrate uptake, reduction and assimilation into organic-N (Glu/Gln), used for biosynthetic reactions. The organic-N products (Glu/Gln) in turn activate motifs controlling Asn synthesized for N-storage, and repress ones controlling N- uptake/assimilation. The proposed research will allow me to identify the regulatory genes responding to these inorganic and organic N-signals that regulate genes in the N-uptake and assimilation pathways by integrating genome wide transcriptomic data with metabolomic data. The synthesis of these aims should allow for modeling, predicting and testing how perturbations of the "system" may be used to enhance N-use efficiency, which impacts energy-use (fertilizers/biofuels), nitrate contamination of the environment and human nutrition. PUBLIC HEALTH RELEVANCE: The long-term goal of the systems approach described in this proposal is to model and predictively manipulate gene regulatory networks affecting uptake/assimilation of inorganic nitrogen into amino acids to improve nitrogen-use-efficiency. This would decrease energy consumption, reduce ground water contamination by nitrates (Health and Environment) and improve seed yield, with implications for human health (Nutrition) and biofuels (Energy). Moreover, the systems biology approach and the tools that will be developed in this project can be applied to any species for which genome data is available, which will enable researchers to model and manipulate a broad spectrum of regulatory circuits in biology with potential medical and pharmaceutical applications.