Environmental Gene Regulatory Interaction Networks in Rice

水稻环境基因调控相互作用网络

• 批准号: 1126971
• 负责人: Michael Purugganan
• 金额: $ 362.89万
• 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: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1126971&HistoricalAwards=false
• 关键词: Environmental; Gene; Regulatory; Interaction; Networks

PI: Michael Purugganan [New York University (NYU)]CoPI: Richard Bonneau (NYU)Key Collaborator: Endang Septiningsih (International Rice Research Institute, the Philippines)Plants in the real world are continuously exposed to multiple environmental signals and must respond appropriately to the dynamic conditions found in nature. Environmental signals can fluctuate during an individual's life cycle with varying degrees of predictability, and complex natural environments are the norm. This project will analyze public data and undertake time-series microarray experiments to infer Environmental Gene Regulatory Interaction Networks (EGRINs) in the rice leaf associated with fluctuating environmental signals. The research focus will be on temperature and water availability, and examine genomic networks for these environmental signals in four different landraces adapted to rainfed upland versus paddy field lowland environments. This research will adapt and scale-up computational methods for biclustering and network inference for use with plant transcriptome data, including the use of data from multiple genotypes. Rice global gene expression patterns in natural seasonal environments will be studied by carrying out transcriptome analyses in different agricultural field conditions. This study provides a systematic determination of genomic networks associated with environmental signals and their behavior in ecologically relevant field conditions. This work lays the foundation for a broader dissection of plant responses to both normal environmental fluctuations as well as extreme environmental conditions that lead to plant stress, and develop systems biological approaches to understanding plant-environmental interactions. These results will be especially useful in understanding plant response to climate fluctuations, as well as develop cultivars that can tolerate temperature and water stress in agricultural environments.This project will develop software packages for gene network inference in plants, and provide inferred gene biclusters and networks to the plant science community through Gramene (www.gramene.org). As part of the outreach efforts, graduate-level science journalism students from the NYU Science, Health and Environment Reporting Program will also be trained in plant genomics and systems biology. Finally, plant biologists from both the US and the International Rice Research Institute (IRRI) will also be trained in systems biology by developing a Rice Systems Biology training workshop to be held at IRRI.

主要研究者：Michael Purugganan [纽约大学（NYU）]CoPI：Richard Bonneau（NYU）主要合作者：Endang Septiningsih（菲律宾国际水稻研究所）植物在真实的世界中不断暴露于多种环境信号，必须对自然界中发现的动态条件做出适当的反应。环境信号在个体的生命周期中可能会波动，具有不同程度的可预测性，复杂的自然环境是常态。该项目将分析公共数据，并进行时间序列微阵列实验，以推断水稻叶片中与波动环境信号相关的环境基因调控相互作用网络（EGRINs）。研究的重点将是温度和水的可用性，并检查这些环境信号的基因组网络在四个不同的地方品种适应干旱的高地与稻田低地环境。这项研究将调整和扩大用于植物转录组数据的双聚类和网络推理的计算方法，包括使用来自多个基因型的数据。通过在不同农田条件下进行转录组分析，研究水稻在自然季节环境中的整体基因表达模式。这项研究提供了一个系统的确定与环境信号及其行为在生态相关的领域条件下的基因组网络。这项工作奠定了基础，更广泛的解剖植物对正常的环境波动以及极端环境条件下，导致植物胁迫的反应，并开发系统生物学方法来理解植物与环境的相互作用。这些结果将特别有助于理解植物对气候波动的反应，以及培育能够耐受农业环境中温度和水分胁迫的品种。该项目将开发用于植物基因网络推断的软件包，并通过Gramene（www.gramene.org）向植物科学界提供推断的基因双簇和网络。 作为推广工作的一部分，来自纽约大学科学、健康和环境报告项目的研究生水平的科学新闻专业学生也将接受植物基因组学和系统生物学方面的培训。最后，来自美国和国际水稻研究所（IRRI）的植物生物学家也将通过在IRRI举办的水稻系统生物学培训讲习班接受系统生物学培训。