Environmental Gene Regulatory Interaction Networks in Rice

主要合作伙伴：Endang Septiningsih（菲律宾国际水稻研究所）现实世界中的植物不断暴露于多种环境信号中，必须对自然界中发现的动态条件做出适当的反应。在一个人的生命周期中，环境信号可以波动，具有不同程度的可预测性，复杂的自然环境是常态。本项目将分析公开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，并在适应旱地和水田低地环境的四种不同地方品种中检查这些环境信号的基因组网络。这项研究将适应和扩大用于植物转录组数据的双聚类和网络推断的计算方法，包括使用来自多个基因型的数据。通过在不同的田间条件下进行转录组分析，研究自然季节环境下水稻整体基因表达模式。本研究提供了与环境信号相关的基因组网络及其在生态相关野外条件下的行为的系统确定。这项工作为更广泛地剖析植物对正常环境波动和导致植物胁迫的极端环境条件的反应奠定了基础，并开发了系统生物学方法来理解植物与环境的相互作用。这些结果将对了解植物对气候波动的反应，以及在农业环境中培育能够耐受温度和水分胁迫的品种特别有用。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聚类和网络。作为拓展工作的一部分，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系学生也将接受植物基因组学和系统生物学的培训。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举办一个水稻系统生物学培训讲习班来接受系统生物学方面的培训。

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.