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

PI：Brian D. Gregory（宾夕法尼亚大学）Co-PI：Eric Lyons 和 Mark Beilstein（亚利桑那大学） 长非编码 RNA (lncRNA) 是一类新兴分子，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，它们不属于其他明确定义的小沉默 RNA 产生类别，例如小干扰 RNA (siRNA) 和 microRNA (miRNA)。尽管lncRNA在发育、表观遗传修饰和胁迫反应中很重要，但它们的结构、蛋白质相互作用和功能仍有很多需要了解，特别是在模型植物和作物物种中。该项目将结合基因组学、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、可通过网络访问的基因组分析工具和数据管理系统将为lncRNA的植物基因表达调控提供新的见解，并为专注于改进众多作物和遗传模型植物的研究提供重要的新发现和资源。在推广和培训方面，该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，该项目还将开发一门题为“RNA生物学应用概念”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在生物系统中作用的各个方面。 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主导的研究项目中使用的协作研究技术。该课程将在宾夕法尼亚大学和亚利桑那大学同时授课，双向音频/视频会议和讲座主题在每个地点交替授课。所有项目成果都将通过项目网站 (https://genomevolution.org/wiki/index.php/EPIC-CoGe_Tutorial)、iPlant Collaborative 和长期存储库（例如 GenBank 和 Short Read Archive (SRA)）供更广泛的研究界轻松获取。 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定义一个 lncRNA 子集，它们对于正常发育和应激反应中的正确基因调控非常重要。 具体来说，该项目将重点鉴定和功能表征那些在遗传模型（Eutrema salsugineum和拟南芥）和作物物种（亚麻荠、白菜、玉米和高粱）中具有（1）核、（2）高度结构化、（3）胁迫响应性、（4）蛋白质结合和（5）进化保守性的lncRNA，重点关注它们在胁迫适应中的作用。最后，该项目将扩展 EPIC-CoGe，这是一个所有物种植物表观基因组数据的中央存储库，具有先进的数据集成、可视化和分析工具，以允许集成功能基因组数据，从而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.