Investigating the Mechanistic Basis and Adaptive Significance of the Coordination of Plant Growth by External and Internal Cues

项目负责人：斯泰西·哈默（加州大学戴维斯分校）合作负责人：本·布莱克曼（弗吉尼亚大学）植物通过使它们的生长与昼夜周期同步，从而使叶子、茎和根的生长每天都有节奏，从而优化它们对当地资源的利用。这种协调是通过光信号、生物钟和激素信号网络之间复杂的相互作用来完成的。然而，人们对这些网络相互作用控制植物生长的方式知之甚少。该项目利用向日葵追踪太阳的强大能力来描述协调植物生长与日常环境波动的途径。首先，将确定控制太阳能跟踪的发展和环境因素。接下来，将对太阳跟踪茎不同部分的激素和基因转录水平进行高通量分析，从而确定控制这些生长节律的候选基因和途径。最后，基因组关联和连锁定位技术将利用普通向日葵及其野生近缘种丰富的自然变异，为太阳跟踪在植物适应环境中的作用提供重要信息。总之，这些研究将阐明各种信号网络之间的相互作用，这些信号网络可以优化植物生长与环境变化，并为提高植物性能的方法提供见解。植物的产量是通过茎和叶的日常生长模式来提高的，这种模式允许更有效的光合作用和更高的水分利用效率。虽然已经确定了许多调节植物生长的分子途径，但对它们如何相互协调以及如何与环境线索协调的理解仍然难以捉摸。向日葵的太阳追踪是解决这些基本问题的一个非常合适的特性，因为它提供了一个独特的切入点来确定内部和外部信号如何调节单个器官的生长。通过询问这种协调如何发生和演变的基本问题，这些研究将揭示面对全球气候变化和人口增长如何提高作物植物性能和保护植物多样性的重要见解。此外，该项目将产生对整个Compositae社区有用的大量资源。为了向公众提供对这些资源的访问，转录组和功能注释标记数据将存储在公共数据库中，包括NCBI Short Read Archive （http://www.ncbi.nlm.nih.gov/sra/）、Compositae Genome Project （http://compgenomics.ucdavis.edu/）、向日葵基因组资源联盟（http://www.sunflowergenome.org）和DRYAD （http://datadryad.org/）。种质资源将存入国家植物种质资源系统（http://www.ars-grin.gov/npgs/）。将开发一种学生众包方法，用于分析在自然和受控环境中生长的植物的延时视频。这个与iPlant合作开发的图像分析软件将通过iPlant phytoisque门户网站（https://pods.iplantcollaborative.org/wiki/display/ipg2p/PhytoBisque）免费提供。最后，将为参与这些研究的本科生、研究生和博士后提供基因组学、生态学和定量方法的跨学科培训。

PI: Stacey Harmer (University of California-Davis)Co-PI: Ben Blackman (University of Virginia)Plants optimize their use of local resources by synchronizing their growth with day/night cycles, resulting in daily rhythms in leaf, stem, and root growth. This coordination is accomplished through an intricate interplay between the light signaling, circadian clock, and hormone signaling networks. However, the manner in which these networks interact to control plant growth is poorly understood. This project exploits the robust ability of sunflower to track the sun to characterize pathways that coordinate plant growth with daily environmental fluctuations. First, developmental and environmental factors that control solar tracking will be defined. Next, high-throughput analysis of hormone and gene transcript levels in different portions of solar tracking stems will be carried out, allowing the identification of candidate genes and pathways controlling these growth rhythms. Finally, genome-enabled association and linkage mapping techniques will take advantage of the abundant natural variation present in common sunflower and its wild relatives to provide essential information about the role of solar tracking in plant adaptation to the environment. Together, these studies will elucidate the interactions between diverse signaling networks that optimize plant growth with environmental changes and provide insights into ways to improve plant performance.Plant yield is enhanced by daily growth patterns of stems and leaves that allow more efficient photosynthesis and higher water use efficiency. Although a number of molecular pathways that regulate plant growth have been identified, an understanding of how they are coordinated with each other and with environmental cues remains elusive. Solar tracking in sunflower is an extremely appropriate trait for addressing these basic questions since it provides a unique entry point to determine how internal and external cues regulate growth across a single organ. By asking fundamental questions about how this coordination occurs and evolves, these studies will reveal important insights into how to enhance crop plant performance and conserve plant diversity in the face of global climate change and an increasing human population. In addition, this project will generate extensive resources that will be useful to the entire Compositae community. To provide public access to these resources, transcriptome and functionally annotated marker data will be deposited in public databases including the NCBI Short Read Archive (http://www.ncbi.nlm.nih.gov/sra/), the Compositae Genome Project (http://compgenomics.ucdavis.edu/), the Sunflower Genome Resources Consortium (http://www.sunflowergenome.org), and DRYAD (http://datadryad.org/). Germplasm will be deposited with the National Plant Germplasm System (http://www.ars-grin.gov/npgs/). A student crowd-sourcing method will be developed for the analysis of time-lapse videos of plants grown in natural and controlled environments. This image analysis software developed with the iPlant Collaborative will be made freely available via the iPlant Phytobisque web portal (https://pods.iplantcollaborative.org/wiki/display/ipg2p/PhytoBisque). Finally, cross-disciplinary training in genomic, ecological, and quantitative approaches will be provided for the undergraduate and graduate students and post-doctoral fellows involved in these studies.