Uncovering Novel Signaling Interactions in Plant Metabolic Networks

揭示植物代谢网络中新的信号传导相互作用

• 批准号: 0951170
• 负责人: Eve Wurtele
• 金额: $ 92万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-04-01 至 2015-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0951170&HistoricalAwards=false
• 关键词: Uncovering; Novel; Signaling; Interactions; Plant

The factors that regulate composition in plants provide a key to utilization of crops for production of improved and novel constituents. Starch, an easily accessible and energy-cheap storage of reduced carbon, is formed via photosynthesis- that is, using solar power. However, little is known about the molecular mechanisms of this process. This research focuses on expanding the understanding of the molecular processes, or network that controls the amount of starch in plants.The objective of this research is to identify new genes and mechanisms that impact starch content in Arabidopsis leaves. The hypothesis is that an as-yet-unidentified gene set interacts with a novel signaling mechanism that affects starch biosynthesis. The aim is to broaden the starch regulatory network by identifying functions for a group of previously uncharacterized proteins that impact this network. The specific aims of this project are:Aim 1. Analyze genes that are candidates in the starch network. By examining the starch content and other constituents in mutants for genes provides an approach to determine the role of each gene. Aim 2. Refine and expand the starch transcriptional network. Data from experiments of Aim 1 will be analyzed by statistical methods and a novel combination of network analysis and genetic algorithms in the context of the known starch network. These studies will reveal how selected perturbations in the starch network effect the accumulation of starch and the health of the plant, and will generate experimentally-testable hypotheses about which genes participate in the network. Aim 3. Identify proteins that interact with known or candidate proteins of the starch network. Much regulation of the composition in a living organism is achieved by formation of complexes among specific proteins. This can activate or deactivate particular processes. Computational methods will be used to predict from a protein?s sequence and structure what other proteins it might bind to. By using experimental methods, we will test these predictions. These data will expand and elaborate on potential interactions within the starch metabolic and regulatory network. Taken together, these studies will define novel factors that control starch accumulation in plants. This integrated research platform will also provide a prototype of how to study plant composition. In the long term, these results will help improve crops for food, industrial products, and fuels.Broader impactsThis project includes a comprehensive plan for hands-on high school and undergraduate research activities in bioinformatics and molecular genetics of plants, for integrating research and outreach activities among the postdoctoral researcher and graduate students, and for postdoctoral mentoring. The multidisciplinary approaches in the research will strengthen the experience, broadening the understanding of young scientists. It will also create a new puzzle-like module on biological networks within the MetaBlast game. MetaBlast is a 3D interactive cellular world (http://metablast.org) populated with organelles and proteins. It can be run in an immersive environment or on a laptop computer, thus it can be used in classrooms and at home. The MetaBlast team includes faculty, teachers, and students in biology, art, computer science, music, and game design. The biological network module will be designed to illustrate the complexities and layers of biological networks. The mission for the student player will be to engineer interrelated parts of the (simplified) starch metabolic and regulatory network such that they function to regulate starch metabolism. The unit will be implemented in two undergraduate biology courses. Educational activities will strongly consider the underprivileged and underrepresented in science. This effort will include the integration of students from five programs at Iowa State University targeting underrepresented undergraduate and high school students.

调节植物组成的因子为利用作物生产改良的和新的成分提供了关键。淀粉是一种容易获得且能源廉价的碳还原储存方法，它是通过光合作用形成的，也就是说，使用太阳能。然而，人们对这一过程的分子机制知之甚少。本研究的重点是扩大对控制植物淀粉含量的分子过程或网络的理解。本研究的目的是确定影响拟南芥叶片淀粉含量的新基因和机制。这一假说是一个尚未确定的基因组与一种新的信号机制相互作用，影响淀粉的生物合成。其目的是通过确定一组以前未表征的影响淀粉调节网络的蛋白质的功能来拓宽淀粉调节网络。该项目的具体目标是：目标1。分析淀粉网络中的候选基因。通过检测突变体中淀粉含量和其他成分的基因提供了一种方法来确定每个基因的作用。目标2.完善和扩大淀粉转录网络。 目标1的实验数据将通过统计方法和网络分析与遗传算法的新组合在已知的淀粉网络的背景下进行分析。这些研究将揭示淀粉网络中的选定扰动如何影响淀粉的积累和植物的健康，并将产生关于哪些基因参与网络的实验可检验的假设。目标3。识别与淀粉网络的已知或候选蛋白质相互作用的蛋白质。生物体中组成的许多调节是通过特定蛋白质之间形成复合物来实现的。 这可以激活或停用特定进程。计算方法将被用来预测从蛋白质？它的序列和结构可能与其他蛋白质结合。我们将通过实验方法来检验这些预测。 这些数据将扩展和阐述淀粉代谢和调控网络内的潜在相互作用。 总之，这些研究将确定控制植物淀粉积累的新因素。这个综合研究平台还将提供如何研究植物成分的原型。 从长远来看，这些结果将有助于改善粮食作物，工业产品和燃料。更广泛的影响这个项目包括一个全面的计划，在生物信息学和植物分子遗传学的实践高中和本科生的研究活动，整合研究和博士后研究人员和研究生之间的推广活动，并为博士后指导。研究中的多学科方法将加强经验，扩大年轻科学家的理解。它还将在MetaBlast游戏中创建一个关于生物网络的新的益智模块。MetaBlast是一个3D交互式细胞世界（metablast.org），充满了细胞器和蛋白质。它可以在沉浸式环境或笔记本电脑上运行，因此可以在教室和家中使用。MetaBlast团队包括生物学，艺术，计算机科学，音乐和游戏设计方面的教师，教师和学生。生物网络模块将被设计来说明生物网络的复杂性和层次。 学生玩家的使命是设计（简化的）淀粉代谢和调节网络的相互关联的部分，以便它们能够调节淀粉代谢。 该单元将在两个本科生物课程中实施。教育活动将大力考虑科学领域的弱势群体和代表性不足的群体。这一努力将包括整合来自爱荷华州州立大学五个项目的学生，目标是代表性不足的本科生和高中生。