Arabidopsis 2010: Nitrogen Networks in Plants

拟南芥 2010：植物中的氮网络

• 批准号: 0519985
• 负责人: Gloria Coruzzi
• 金额: $ 260万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0519985&HistoricalAwards=false
• 关键词: Arabidopsis; 2010; Nitrogen; Networks; Plants

This project involves the determination of functional gene networks regulated by nitrogen status. Targets for functional analysis will be the key nodes controlling N-regulation of metabolic and developmental networks associated with growth and seed development- key agronomic traits. Regulatory hubs of these N-networks will be identified using "multinetwork" analysis in which "edges" connecting gene "nodes" are supported by multiple data/evidence including; metabolic pathways, protein:protein, protein:DNA, and microRNA:target datasets. Microarray data from leaves, roots, root cell-types and seeds of nitrogen-treated Arabidopsis will be analzed using this multinetwork to determine. Machine learning techniques will be used to predict mechanisms of N-regulation of networks and to identify putative regulatory nodes. Predictions of key regulatory nodes will be based on expression data, number, types, and weight of edges linked to a node, and will implicate transcription factors, signal transducers or genes encoding microRNAs as potential regulators. This analysis will be iterative, to refine predictions using kinetic microarray and growth data from wild-type and mutants in putative regulators. Specific aims of this project are: Aim 1. Integrate network responses to N-sources and interactions between nitrogen, carbon and light signaling. Aim 2. Integrate N-regulatory networks and development. Aim 3: Integrate omic-datasets and identify regulatory nodes in multinetworks. Aim 4. In vivo testing of N-network models and putative regulatory nodes. Long term, these systems-based models can be used in a predictive mode to target regulatory nodes that may be modified in transgenic plants to alter N-use efficiency for agronomic purposes. As such, the aims of this project are therefore directly in line with the 2010 goals to "Identify and analyze function of networks of genes; dissect nodes, and integrate computational modeling with experimentation". Broader Impacts: This project involves a number of collaborations with other plant genome groups including Arabidopsis small RNAs, Medicago and Lotus genome groups. The functional information on the components of these gene networks and regulatory nodes controlling them will be published and deposited in The Arabidopsis Information Resource Database (TAIR) using structured vocabularies to facilitate future bioinformatic analysis of the data. The expression data generated will also be made available to the community through public repositories such as ArrayExpress, and also on our N2010 website http://www.nyu.edu/fas/dept/biology/n2010/. The Arabidopsis transgenic/mutant lines generated in these studies will be deposited in the ABRC. The project will provide an excellent mechanism for training students and post-doctoral fellows in interdisciplinary research.

该项目涉及确定氮素状态调控的功能基因网络。功能分析的目标将是控制与生长和种子发育相关的代谢和发育网络的氮调节的关键节点-关键农艺性状。这些N-网络的调控中心将使用“多网络”分析来识别，其中连接基因“节点”的“边缘”由多个数据/证据支持，包括：代谢途径、蛋白质：蛋白质、蛋白质：DNA和microRNA：目标数据集。利用该网络对氮处理拟南芥的叶片、根、根细胞类型和种子的微阵列数据进行分析。机器学习技术将用于预测网络的N调节机制，并识别假定的调节节点。关键调控节点的预测将基于表达数据、数量、类型和连接到节点的边的权重，并将涉及转录因子、信号转导子或编码microRNA的基因作为潜在的调控因子。该分析将是迭代的，以使用动力学微阵列和来自推定调节子中的野生型和突变体的生长数据来改进预测。该项目的具体目标是：目标1。整合网络对氮源的响应以及氮、碳和光信号之间的相互作用。目标二。整合氮调节网络和发展。目标3：整合omic数据集并识别多网络中的监管节点。目标4。N-网络模型和推定调控节点的体内测试。从长远来看，这些基于系统的模型可以用于预测模式，以靶向调节节点，这些调节节点可以在转基因植物中被修饰以改变用于农艺目的的N利用效率。因此，该项目的目标直接符合2010年的目标，即“识别和分析基因网络的功能;解剖节点，并将计算建模与实验相结合”。更广泛的影响：该项目涉及与其他植物基因组组的一些合作，包括拟南芥小RNA，苜蓿和莲花基因组。这些基因网络的组成部分和控制它们的调控节点的功能信息将被发布并保存在拟南芥信息资源数据库（TAIR）中，使用结构化词汇表，以促进未来的数据生物信息学分析。生成的表达数据也将通过ArrayExpress等公共存储库以及我们的N2010网站http://www.nyu.edu/fas/dept/biology/n2010/提供给社区。在这些研究中产生的拟南芥转基因/突变株系将保藏在ABRC中。该项目将为培养跨学科研究的学生和博士后研究员提供一个极好的机制。