Arabidopsis N-regulatory network dynamics: Integrating metabolome & transcriptome

描述（由申请人提供）：拟议研究的主要目标是使用系统生物学方法来共同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、代谢组和通量组组件的时间依赖性数据。这种综合的方法将使我们能够动态地模拟N-信号传播的流量，通过N-调节网络在一个系统范围内的水平，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实现：1。创建高分辨率的动态转录组数据集，以产生一个时间依赖性的氮调节网络，通过对拟南芥根和芽进行微阵列分析与硝酸盐处理的时间过程。2.使用稳定同位素标记的N15在一段时间内对响应于N信号的N同化网络中的代谢物水平和代谢通量进行定量。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），创建用于控制N-吸收/同化的时间依赖性动态网络模型。4.通过测试模型产生的假设与T-DNA突变体和诱导表达系统的调控网络预测的功能验证。正在测试的最重要的假设是，无机氮信号（硝酸盐）激活图案参与调节硝酸盐的吸收，还原和同化成有机氮（Glu/Gln），用于生物合成反应。有机N产物（Glu/Gln）反过来激活控制合成用于N储存的Asn的基序，并抑制控制N吸收/同化的基序。拟议的研究将使我能够确定这些无机和有机的N-信号，调节基因的N-吸收和同化途径，通过整合全基因组转录组数据与代谢组数据的调控基因。这些目标的综合应允许建模，预测和测试如何扰动的“系统”可能被用来提高氮的使用效率，影响能源使用（化肥/生物燃料），硝酸盐污染的环境和人类营养。 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（健康与环境），提高种子产量，并对人类健康（营养）和生物燃料（能源）产生影响。此外，系统生物学的方法和工具，将在这个项目中开发，可适用于任何物种的基因组数据是可用的，这将使研究人员能够模拟和操纵生物学的监管电路与潜在的医疗和制药应用的广泛范围。

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.