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: 2243706
• 负责人: Anton Petrov
• 金额: $ 46.94万
• 依托单位: Georgia Tech Research Corporation
• 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=2243706&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.

基因组测序的进展已经揭示，哺乳动物基因组的大部分从DNA转录为RNA，但不翻译为蛋白质;这些被称为非编码RNA（ncRNA）。许多经典的ncRNA在生物学中发挥着重要作用，包括翻译（tRNA，rRNA），剪接（snRNA），转录后基因调控（miRNA）和许多其他生物学过程。虽然这些已知的ncRNA对所有生命都很重要，但它们可能只是ncRNA冰山的一角。事实上，我们预计有许多ncRNA类别仍然没有特征。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。与此同时，蛋白质研究已经确定数千种人类蛋白质与RNA结合。然而，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相互作用仍然是未知的。我们的目标是使用非常大规模的RNA-蛋白质结合测定结合计算分析来解决这两个问题，以发现新的ncRNA基因组。我们将确定与RBP强烈相互作用的特定RNA组，开发定义相互作用特异性的模型，以及从序列和结构数据预测相互作用的分类系统。我们还将创建一个可通过网络访问的研究结果数据库，允许任何人访问数据，并培训本科生，以增加科学领域的性别多样性。在过去的十年里，以RNA为中心的蛋白质组学方法如交联和免疫沉淀（CLIP）等以及相关方法使RNA-蛋白质相互作用的研究成为可能，这是前所未有的。这些努力极大地扩大了已鉴定的RBP的数量，目前UniProt将4，000种人类蛋白质（约占人类蛋白质组的20%）注释为“RNA结合”。然而，由于CLIP方法一次只能映射单个蛋白质，因此探索数千个带注释的RBP具有挑战性。因此，像ENCODE这样的联盟努力既耗时又昂贵，并且仅限于绘制人类蛋白质组中的一小部分RBP。因此，用目前的方法几乎不可能产生全面的RBP-ncRNA相互作用组。我们将使用一种新开发的高度多重化的方法，在一个实验中产生数百个RBP的转录组范围的测量。我们将结合联合收割机这与尖端的计算和进化策略，以发现和分类新类别的ncRNA基因组。我们的目标是全面发现和表征人类转录组中的新型ncRNA，并以使用现有方法不可能的方式评估其遗传学。为了实现这一目标，我们将开发新的实验方法和综合计算管道，通过结合已知的和新的RNA-蛋白质相互作用，系统地识别新的ncRNA类别，并揭示多价相互作用的簇。我们将确定保守的序列和结构基序，以及特定于新类别的进化模式，并开发计算系统，从这些数据中识别新类别的成员。该奖项是由NSF生物科学理事会四个部门共同赞助的创意实验室的结果。它将由分子和细胞生物科学部、环境生物学部和新兴前沿计划共同资助。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。