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Arabidopsis N-regulatory network dynamics: Integrating metabolome & transcriptome

拟南芥 N 调控网络动力学：整合代谢组

基本信息

批准号：
8309335
负责人：
Amy J Marshall Colon
金额：
$ 5.39万
依托单位：
NEW YORK UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-08-01 至 2013-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8309335
关键词：
Affect Amino Acids Arabidopsis Assimilations Bioinformatics Biology Computer Simulation Consumption Data Data Set Development Environment Enzymes Fertilizers Gene Expression Gene Expression Profile Gene Targeting Genes Genetic Genetic Transcription Genome Glutamine Goals Health Human Imagery Informatics Kinetics Light Linear Regressions Machine Learning Measurement Measures Medical Messenger RNA Metabolic Metabolic Pathway Methods Microarray Analysis Modeling Nitrates Nitrogen Output Pathway interactions Pharmacologic Substance Plant Roots Positioning Attribute Reaction Regulation Regulator Genes Research Research Personnel Resolution Seeds Series Signal Transduction Stable Isotope Labeling System Systems Biology T-DNA TF gene Techniques Testing Time Validation Visual Water Pollution base genome-wide ground water improved metabolomics molecular phenotype mutant network models nutrition response tool transcriptomics uptake validation studies

项目摘要

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.

所提出的研究的主要目标是使用系统生物学方法来集体分析和整合来自拟南芥中控制N-同化的N-调控网络的转录组、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。