Arabidopsis N-regulatory network dynamics: Integrating metabolome & transcriptome

描述(申请人提供)：拟议的研究的主要目标是使用系统生物学的方法来集体分析和整合来自控制拟南芥N同化的N调控网络的转录组、代谢组和流组组的依赖时间的数据。这种综合的方法将使我们能够在系统范围的水平上动态模拟N信号通过N调节网络的传播流，并识别参与该调节的转录级联。这一目标将通过四个目标来实现：1.创建高分辨率的动态转录组数据集，通过对经过一段时间的硝酸盐处理的拟南芥的根和芽进行微阵列分析，生成一个依赖于时间的氮素调控网络。2.使用标记为N15的稳定同位素在一段时间内量化N同化网络中对N信号的响应的代谢物水平和代谢通量。3.利用滞后相关、线性回归和机器学习(状态空间分析)等一系列分析技术，整合转录组、代谢组和流动组数据，建立了氮吸收/同化控制的随时间变化的动态网络模型。4.通过T-DNA突变体和可诱导表达系统产生的假设测试模型，对调控网络预测进行功能验证。正在测试的压倒一切的假设是，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)依次激活控制合成用于氮素储存的ASN的基序，抑制控制氮素吸收/同化的基序。这项拟议的研究将使我能够通过整合全基因组转录数据和代谢组数据来确定对这些无机和有机N信号做出反应的调控基因，这些信号调控N吸收和同化途径中的基因。这些目标的综合应可用于建模、预测和测试如何利用“系统”的扰动来提高氮素利用效率，从而影响能源使用(化肥/生物燃料)、环境中的硝酸盐污染和人类营养。 公共卫生相关性：本提案中描述的系统方法的长期目标是对影响无机氮吸收/同化为氨基酸的基因调控网络进行建模和预测性操作，以提高氮的利用效率。这将减少能源消耗，减少硝酸盐对地下水的污染(健康和环境)，并提高种子产量，对人类健康(营养)和生物燃料(能源)产生影响。此外，系统生物学方法和将在该项目中开发的工具可以应用于任何可获得基因组数据的物种，这将使研究人员能够对具有潜在医疗和制药应用的生物学中的广泛调控电路进行建模和操作。

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.