Arabidopsis 2010: Nitrogen Networks in Plants

该项目涉及确定受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N调节的关键节点--关键农艺性状。这些N-网络的调控中枢将通过“多网络”分析来确定，其中连接基因“节点”的“边缘”得到多个数据/证据的支持，包括代谢途径、蛋白质：蛋白质、蛋白质：DNA和microRNA：目标数据集。来自氮素处理的拟南芥的叶、根、根细胞类型和种子的微阵列数据将使用这个多网络进行分析以确定。机器学习技术将被用来预测网络的N-调节机制，并识别假定的调节节点。对关键调控节点的预测将基于与节点相连的边的表达数据、数量、类型和权重，并将涉及转录因子、信号转导或编码microRNAs的基因作为潜在的调控因子。这项分析将是迭代的，以使用动力学微阵列和来自野生型和突变体的生长数据来完善预测。本项目的具体目标是：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2.整合N-监管网络和发展。目标3：整合基因组数据集，识别跨网络中的调控节点。目的4.N-网络模型和可能的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，以针对转基因植物中可能被修改的调控节点，以改变用于农学目的的氮素利用效率。因此，该项目的目标与2010年的目标直接一致，即“确定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、紫花苜蓿和莲花基因组组。关于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公布并存储在拟南芥信息资源数据库(TIR)中，使用结构化词汇表来促进未来对数据的生物信息学分析。生成的表情数据也将通过ArrayExpress等公共存储库和我们的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/突变株系将保存在ABRC中。该项目将为培训学生和博士后研究员进行跨学科研究提供一个极好的机制。

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.