Structural, Functional, and Evolutionary Analysis of Long Non-coding RNAs in Control of Stress Response and the Epigenome in Diverse Plant Species

长链非编码rna （Long non-coding RNAs, lncRNAs）是一类新兴的分子，因其在各种生物过程中的作用而受到关注。lncrna的定义是它们不编码蛋白质，因此不是mrna。此外，它们不适合其他定义明确的小沉默rna产生类别，如小干扰rna （siRNA）和微rna （miRNAs）。尽管lncrna在发育、表观遗传修饰和应激反应中发挥着重要作用，但它们的结构、蛋白质相互作用和功能，特别是在模型和作物植物物种中，仍有很多需要了解的地方。本项目将结合基因组、进化和生物信息学方法来解决这一重大差距。预计该项目开发的数据、可访问的基因组分析工具和数据管理系统将为lncrna调控植物基因表达提供新的见解，并为许多作物和遗传模式植物的改良研究提供重要的新发现和资源。在外联和培训方面，本项目将为学生和博士后提供RNA生物学、计算科学和进化生物学的跨学科研究培训。此外，该项目将开发一门名为“RNA生物学中的应用概念”的跨学科课程，该课程将利用大规模计算和数据集来了解RNA在生物系统中的各个方面的作用。这门基于项目的课程将教授RNA生物学的基础知识，下一代测序技术，分布式和高性能计算，数据密集型科学，以及将在学生驱动的研究项目中使用的合作研究技术。该课程将在宾夕法尼亚大学和亚利桑那大学同时授课，采用双向音频/视频会议和讲座主题，在每个地点轮流授课。所有的项目成果都将通过项目网站（https://genomevolution.org/wiki/index.php/EPIC-CoGe_Tutorial）、iPlant协作以及GenBank和Short Read Archive （SRA）等长期存储库方便地提供给更广泛的研究界。该项目具有独特的定位，可以深入了解lncrna的结构和功能，以及它们与基因组中特定表观基因组调控修饰的相互作用。该项目的具体目标是确定在正常发育和应激反应中对适当基因调控很重要的lncrna子集。具体而言，该项目将专注于识别和功能表征这些lncrna(1)核，(2)高结构，(3)应激响应，(4)蛋白结合，(5)进化保守的遗传模型（Eutrema salsugineum和拟南芥）和作物物种（Camelina sativa, Brassica rapa， Zea mays和Sorghum bicolor），重点关注它们在逆境适应中的作用。最后，该项目将扩展EPIC-CoGe，一个涵盖所有物种的植物表观基因组数据的中央存储库，通过先进的数据集成、可视化和分析工具，允许功能基因组数据的集成，为全基因组表观基因组相互作用提供新的见解。

PI: Brian D. Gregory (University of Pennsylvania)Co-PIs: Eric Lyons and Mark Beilstein (University of Arizona) Long non-coding RNAs (lncRNAs) are an emerging class of molecules gaining attention for their roles in various biological processes. lncRNAs are defined by the fact that they do not code for proteins and are therefore not mRNAs. In addition, they do not fit into other well-defined small silencing RNA-producing categories such as small interfering RNAs (siRNA) and microRNAs (miRNAs). Despite the importance of lncRNAs in development, epigenetic modification, and stress responses, there is still much to be learned about their structure, protein interactions, and functions, especially in model and crop plant species. This project will address this significant gap using a combination of genomic, evolutionary, and bioinformatics approaches. It is anticipated that the data, web-accessible genome analytical tools, and data management systems developed by the project will provide novel insights into plant gene expression regulation by lncRNAs, and provide important new findings and resources for studies focused on the improvement of numerous crop and genetic model plants. With regard to outreach and training, this project will provide interdisciplinary research training in RNA biology, computational science and evolutionary biology for students and postdoctoral associates. In addition, the project will develop an interdisciplinary course entitled "Applied Concepts in RNA Biology" that will leverage large-scale computing and datasets to understand various aspects of the role of RNA in biological systems. This project-based course will teach the fundamentals of RNA biology, next-generation sequencing techniques, distributed and high performance computing, data-intensive science, and collaborative research techniques that will be used in student-driven research projects. The course will be taught simultaneously at the University of Pennsylvania and the University of Arizona, with two-way audio/video conferencing and lecture topics alternatively taught at each site. All project outcomes will be made readily accessible to the broader research community through a project website (https://genomevolution.org/wiki/index.php/EPIC-CoGe_Tutorial), the iPlant Collaborative and long-term repositories such as GenBank and the Short Read Archive (SRA). This project is uniquely positioned to provide insights into the structure and function of lncRNAs, and their interaction with specific epigenomic regulatory modifications in the genome. The specific goals of the project are to define a subset of lncRNAs that are important for proper gene regulation in both normal development and stress response. Specifically, the project will focus on identifying and functionally characterizing those lncRNAs that are (1) nuclear, (2) highly structured, (3) stress responsive, (4) protein bound, and (5) evolutionary conserved in genetic models (Eutrema salsugineum and Arabidopsis thaliana) and in crop species (Camelina sativa, Brassica rapa, Zea mays, and Sorghum bicolor), focusing on their roles in stress adaptation. Finally, the project will expand EPIC-CoGe, a central repository for plant epigenomics data across all species, with advanced data integration, visualization, and analysis tools to allow for the integration of functional genomics data to provide new insight into genome-wide epigenomic interactions.