Arabidopsis 2010: Nitrogen Networks in Plants

点击翻译按钮获取中文摘要

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).