Arabidopsis 2010: Nitrogen Networks in Plants

拟南芥 2010：植物中的氮网络

• 批准号: 0929338
• 负责人: Gloria Coruzzi
• 金额: $ 279.66万
• 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: 2009
• 资助国家: 美国
• 起止时间: 2009-07-15 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0929338&HistoricalAwards=false
• 关键词: Arabidopsis; 2010; Nitrogen; Networks; Plants

This project exploits and develops systems biology approaches to identify the regulatory networks mediating plant adaptation to nitrogen (N) nutrient changes in the environment. In the previous cycle, transcriptome datasets analyzed and integrated in the context of an Arabidopsis multinetwork identified the first regulatory networks and components (transcription factors and micro RNAs) that regulate plant metabolism and development in response to sensing nitrogen signals in their environment. These discoveries now fuel a new round of the systems biology cycle of high throughput experimentation, modeling, and hypothesis generation. This project exploits new technology (deep-transcriptomics) and new biology discovered during the previous cycle, namely the prevalence of the "RNA world" in controlling plant adaptation to the environment. In this project, large-scale experimentation will be used to gather genome-wide quantitative evidence for the role of RNA-based network modules in mediating Nresponses (Aim 1). Validation of the role of miRNA-TF motifs in mediating root adaptation to Nenvironments will be tested using two complementary approaches. The first is a unique experimental set up (split-root) designed to identify and test components involved in adapting root growth in response to local vs. systemic N-signals (Aim 2). The second addresses how these RNA-based network motifs and modules evolve across micro-evolutionary time to enable populations to adapt to changes in N-nutrient acquisition in natural environments (Aim 3). To integrate the exhaustive data, models and pipeline analysis tools will be developed to encompass RNA data from wild-type, mutants and ecotypes (Aim 4). In total, this project will provide a complete view of the "RNA plant" that will merge exciting new advancements in i) biotechnology (deep sequencing) with ii) new biology (smRNAs, mRNAs) to create iii) new regulatory nodes and edges in networks that can be used to generate hypotheses for testing. The data, approaches and tools developed in this project will be applicable to (and made available for) the community to enable a systems approach to answer a wide range of problems in plant biology. The results obtained will also enable the derivation and testing of hypotheses for regulatory components that can be manipulated to effect changes in N-use efficiency in plants, an issue that affects energy-use and the environment.The aims of this project integrate across the four goals of the Arabidopsis 2010 project:1. Metabolic biology, particularly relevant to energy capture and use: Identify regulatory components responding to N-metabolite signals (N-use efficiency) (Aim 1).2. Adaptation to the environment: Identify network components (mir-TFs) that mediate root adaptation to changes in local N-environments using a unique split-root system (Aim 2).3. Multi-scale analysis of genome evolution and genetic systems: Examine how N-regulatory networks evolve in ecotypes to mediate root adaptation to N in natural environments (Aim 3).4. Development of resources (including informatic tools) for genome-wide experimental approaches to determine gene function: Develop informatic pipelines for analysis and integration of the RNA-plant into next-generation networks and for comparative ecotype analysis (Aim 4).Broader impacts of researchA. Applications to Agriculture: Modification N-use efficiency in plants. Patents filed from this project relate to improving nitrogen use efficiency, which has implications for energy and the environment.B. Development of informatic tools: To interrogate the RNA-world and interpret it in the context of next-generation networks within and across ecotypes, and to associate genomes with phenotypes.C. Assign function to unknown genes: Network analysis associated many genes of unknown function with regulatory networks mediating response of lateral root development to the N-environment.D. Training in Systems Biology: Postdocs and students will be trained in Systems Biology by comentorship between biologists (Coruzzi, Crawford & Birnbaum) and Math/Computer scientists (Shasha & Tranchina) from The Courant Institute of Math & Computer Science.E. Collaborations: In addition to the biologists, mathematician, and Computer Scientist listed above, this project also involves international collaborators (Rodrigo Gutierrez, Chile) and high-throughput technology collaborations (McCombie, CSHL).

该项目利用和开发系统生物学方法来确定调节植物适应环境中氮（N）营养变化的调节网络。在前一个周期中，在拟南芥多网络的背景下分析和整合的转录组数据集确定了第一个调节网络和组件（转录因子和微RNA），它们调节植物代谢和发育，以响应其环境中的氮信号。这些发现现在推动了新一轮的高通量实验，建模和假设生成的系统生物学循环。该项目利用了上一周期发现的新技术（深度转录组学）和新生物学，即“RNA世界”在控制植物适应环境方面的普遍性。在这个项目中，大规模的实验将被用来收集全基因组的定量证据的作用，RNA为基础的网络模块在介导Nresponses（目标1）。验证miRNA-TF基序在介导根适应N环境中的作用将使用两种互补的方法进行测试。第一个是一个独特的实验设置（分裂根），旨在识别和测试参与适应根生长的组件，以响应本地与系统的N-信号（目标2）。第二个目标是这些基于RNA的网络基序和模块如何在微进化时间内进化，以使种群能够适应自然环境中N-营养素获取的变化（目标3）。为了整合详尽的数据，将开发模型和管道分析工具，以涵盖来自野生型、突变体和生态型的RNA数据（目标4）。总的来说，这个项目将提供一个完整的“RNA植物”的视图，它将合并i）生物技术（深度测序）与ii）新生物学（smRNAs，mRNAs）的令人兴奋的新进展，以创建iii）网络中的新监管节点和边缘，可用于生成测试假设。本项目中开发的数据、方法和工具将适用于（并提供给）社区，以使系统方法能够回答植物生物学中的广泛问题。所获得的结果也将使推导和测试的调节组件，可以被操纵，以影响植物中的N-利用效率的变化，一个问题，影响能源的使用和environment.The本项目的目标集成在拟南芥2010年项目的四个目标：1。代谢生物学，特别是与能量捕获和用途：识别响应N-代谢物信号的调节组分（N-利用效率）（目标1）。适应环境：识别网络组件（mir-TF），其使用独特的分裂根系统来介导根适应局部N环境中的变化（目标2）。基因组进化和遗传系统的多尺度分析：研究氮调节网络如何在生态型中进化以介导根系对自然环境中氮的适应（目标3）。为确定基因功能的全基因组实验方法开发资源（包括信息学工具）：开发信息学管道，用于分析RNA植物并将其整合到下一代网络中，以及用于比较生态型分析（目标4）。农业应用：改良植物的氮利用效率。该项目申请的专利涉及提高氮的利用效率，这对能源和环境有影响。开发信息工具：询问RNA世界，并在下一代网络的环境中解释它，并将基因组与表型联系起来。将功能分配给未知基因：网络分析将许多功能未知的基因与调节侧根发育对N环境的反应的调节网络联系起来。系统生物学培训：博士后和学生将通过生物学家（Coruzzi，Crawford Birnbaum）和数学/计算机科学家（Shasha Tranchina）之间的comentorship进行系统生物学培训。合作：除了上面列出的生物学家，数学家和计算机科学家外，该项目还涉及国际合作者（Rodrigo Gutierrez，智利）和高通量技术合作（McCombie，CSHL）。