Collaborative Research: Ideas Lab: Discovery of Novel Functional RNA Classes by Computational Integration of Massively-Parallel RBP Binding and Structure Data

合作研究：创意实验室：通过大规模并行 RBP 结合和结构数据的计算集成发现新的功能性 RNA 类别

• 批准号: 2243704
• 负责人: Elena Rivas
• 金额: $ 93.26万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-15 至 2028-02-29
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2243704&HistoricalAwards=false
• 关键词: Collaborative; Research; Ideas; Lab; Discovery

Advances in genome sequencing have revealed that large parts of mammalian genomes are transcribed from DNA to RNA but are not translated into protein; these are referred to as non-coding RNAs (ncRNAs). Many classical ncRNAs play fundamental roles in biology including translation (tRNAs, rRNAs), splicing (snRNAs), post-transcriptional gene regulation (miRNAs), and many other biological processes. While these known ncRNAs are important for all life, they may be just the tip of the ncRNA iceberg. In fact, we expect that there are many ncRNA classes that remain uncharacterized. These are referred to as the ‘dark matter’ of the genome because we don’t know what biological roles they may play. In parallel, protein studies have determined that thousands of human proteins bind to RNA. Yet it remains unknown how many of these RNA binding proteins (RBPs) interact with ncRNAs, and which specific ncRNAs they might interact with. Our goal is to tackle both problems using very large-scale RNA-protein binding assays combined with computational analysis to uncover new classes of ncRNAs en masse. We will identify specific groups of RNAs that interact strongly with RBPs, develop models that define interaction specificity, and classification systems to predict interactions from sequence and structural data. We will also create a web-accessible database of our findings, allowing anyone to access the data, and train undergraduates with the goal of increasing gender diversity in science. We expect to reveal the biological functions of novel ncRNA classes, which will lay the foundation for biotechnology development.Over the past decade, global RNA-centric proteomics methods like crosslinking and immunoprecipitation (CLIP) and related approaches have enabled unprecedented exploration of RNA-protein interactions. These efforts have vastly expanded the number of identified RBPs, with 4,000 human proteins (~20% of the human proteome) currently annotated as “RNA-binding” by UniProt. However, because CLIP approaches can only map a single protein at a time, it is challenging to explore the thousands of annotated RBPs. As a result, consortium efforts like ENCODE are time-consuming and expensive, and have been limited to mapping a fraction of the RBPs in the human proteome. Thus, the creation of a comprehensive RBP-ncRNA interactome is near impossible with current approaches. We will use a newly developed, highly multiplexed approach to generate transcriptome-wide measurements across hundreds of RBPs in a single experiment. We will combine this with cutting edge computational and evolutionary strategies to uncover and classify novel classes of ncRNAs en masse. Our goal is to comprehensively discover and characterize novel classes of ncRNAs in the human transcriptome and assess their phylogeny in a way that is impossible using existing methods. To achieve this goal, we will develop novel experimental methods and integrative computational pipelines that will systematically identify novel classes of ncRNAs by combining both known and novel RNA-protein interactions and uncover clusters of multivalent interactions. We will identify conserved sequence and structural motifs, and evolutionary patterns specific to the novel classes, and develop computational systems to recognize members of the novel classes from these data.This award was the result of an Ideas Lab that was co-sponsored by the four divisions in the NSF Directorate of Biological Sciences. It will be co-funded by the Division of Molecular and Cellular Biosciences, the Division of Environmental Biology, and the Emerging Frontiers program.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

基因组测序的进展表明，大部分哺乳动物基因组从DNA转录为RNA，但未转化为蛋白质。这些被称为非编码RNA（NCRNA）。许多经典的NCRNA在生物学中扮演着基本角色，包括翻译（TRNA，RRNA），剪接（SNRNA），转录后基因调节（miRNA）和许多其他生物学过程。尽管这些已知的NCRNA对所有生命都很重要，但它们可能只是NCRNA冰山的尖端。实际上，我们预计会有许多NCRNA类都没有表征。这些被称为基因组的“暗物质”，因为我们不知道它们可能扮演什么生物学角色。同时，蛋白质研究确定了成千上万的人蛋白与RNA结合。然而，尚不清楚这些RNA结合蛋白（RBP）中有多少与NCRNA相互作用，以及它们可能与哪些特定的NCRNA相互作用。我们的目标是使用非常大规模的RNA-蛋白结合测定法与计算分析相结合，以发现新的NCRNA insse，解决这两个问题。我们将确定特定的RNA组与RBP强烈相互作用，开发定义相互作用特异性的模型以及分类系统，以预测序列和结构数据的相互作用。我们还将创建一个可访问我们发现的网络访问数据库，使任何人访问数据，并培训本科生，以增加科学的性别多样性。我们希望揭示新的NCRNA类别的生物学功能，这将奠定生物技术开发的基础。在过去的十年中，全球以RNA为中心的蛋白质组学方法（如交联和免疫沉淀（剪辑）和相关方法）已促进了对RNA蛋白相互作用的前所未有的探索。这些努力大大扩大了已鉴定的RBP的数量，目前由Uniprot注释为“ RNA结合”的4,000种人类蛋白质（约20％的人蛋白质）。但是，由于夹子方法一次只能一次绘制单个蛋白质，因此探索数千个带注释的RBP的挑战是挑战。结果，诸如编码之类的财团工作是耗时且昂贵的，并且仅限于绘制人类蛋白质中RBP的一部分。这是，我们将将其与最先进的计算和进化策略相结合，以揭示和分类新的Ncrnas insse。我们的目标是全面地发现和表征人类转录组中新型的NCRNA类，并以使用现有方法不可能的方式评估其系统发育。为了实现这一目标，我们将开发新颖的实验方法和集成的计算管道，这些方法将通过结合已知和新型RNA - 蛋白质相互作用和发现多价相互作用的簇来系统地识别NCRNA的新型NCRNA。我们将确定保守的序列和结构基序，以及新型类别特有的进化模式，并开发计算系统以识别这些数据中新型类别的成员。这是由NSF生物学科学局中的四个部门共同赞助的Ideas Ideas Lab的结果。它将由分子和细胞生物科学，环境生物学划分以及新兴领域计划的分区共同资助。该奖项反映了NSF的法定任务，并认为值得通过基金会的智力优点和更广泛的影响审查标准通过评估来进行评估。