Arabidopsis 2010: Nitrogen Networks in Plants

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。在上一个周期中，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控网络和组件(转录因子和微RNA)，它们调节植物的新陈代谢和发育，以响应环境中感知到的氮信号。这些发现现在推动了新一轮高通量实验、建模和假说产生的系统生物学周期。该项目利用了在前一个周期中发现的新技术(深度转录组学)和新生物学，即在控制植物对环境的适应方面普遍存在的“RNA世界”。在这个项目中，将使用大规模实验来收集全基因组范围的量化证据，以证明基于RNA的网络模块在调节NResponse中的作用(目标1)。将使用两种互补的方法来测试miRNA-TF基序在介导根对N环境的适应中的作用。第一个是一个独特的实验装置(分根)，旨在识别和测试参与适应根生长的组件，以响应局部和系统的N信号(目标2)。第二部分阐述了这些基于RNA的网络基元和模块如何在微观进化时间内进化，以使种群能够适应自然环境中氮营养获取的变化(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、突变型和生态型的RNA数据(目标4)。总而言之，这个项目将提供一个“RNA工厂”的完整视图，它将i)生物技术(深度测序)中令人兴奋的新进展与ii)新生物学(smRNAs、mRNAs)相结合，以创建iii)可用于生成测试假说的网络中的新调控节点和边缘。在这个项目中开发的数据、方法和工具将适用于社区(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。所获得的结果还将使对调控成分的假说的推导和测试成为可能，这些调控成分可以被操纵以影响植物氮素利用效率的变化，这是一个影响能源利用和环境的问题。该项目的目标结合了拟南芥2010项目的四个目标：1.代谢生物学，特别是与能量捕获和利用有关的代谢生物学：确定响应N-代谢物信号的调控成分(氮利用效率)(目标1)。对环境的适应：确定网络组件(mir-TF)，这些组件利用独特的分根系统(目标2)调节根适应本地N环境的变化。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，以调节自然环境中根对氮的适应(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：改良植物氮素利用效率。这个项目申请的专利涉及提高氮素利用效率，这对能源和环境有影响。B.信息学工具的开发：在生态型内和跨生态型的下一代网络的背景下询问RNA世界并解释它，并将基因组与表型联系起来。c.为未知基因分配功能：网络分析将许多未知功能的基因与调节网络联系在一起，调节侧根发育对N环境的反应。D.系统生物学培训：博士后和学生将通过生物学家(Coruzzi，Crawford&Amp；Birnbaum)和数学/计算机科学家(Shasha&Amp；合作：除了上述生物学家、数学家和计算机科学家外，这个项目还涉及国际合作者(智利的Rodrigo Gutierrez)和高通量技术合作(McCombie，CSHL)。

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).