Transcriptome-wide, single-molecule dynamics of RNA-protein interaction.

RNA-蛋白质相互作用的转录组范围内的单分子动力学。

• 批准号: 10042693
• 负责人: Sean E O'Leary
• 金额: $ 22.42万
• 依托单位: UNIVERSITY OF CALIFORNIA RIVERSIDE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10042693
• 关键词: Active Sites; Adopted; Affinity; Binding; Binding Proteins; Biological; Biological Assay; Biology; Cell physiology; Cells; Chemicals; Complement; Complementary DNA; Computer software; Data; Data Analyses; Detection; Development; Disease; Dissociation; Enzymes; Equilibrium; Evaluation; Event; Fluorescence; Gene Expression; Genetic Transcription; Goals; Health; Homeostasis; Image; Immobilization; In Situ; Individual; Kinetics; Label; Length; Libraries; Link; Maps; Measures; Messenger RNA; Methods; Modification; Molecular; Noise; Outcome; Performance; Poly(A) Tail; Population; Process; Proteins; Protocols documentation; RNA; RNA Decay; RNA Probes; RNA Sequences; RNA Splicing; RNA-Directed DNA Polymerase; RNA-Protein Interaction; Reaction; Regulation; Reproducibility; Resolution; Reverse Transcriptase Polymerase Chain Reaction; Saccharomyces cerevisiae; Sampling; Signal Transduction; Structure; Surface; Surveys; Techniques; Technology; Testing; Time; Transcript; Translations; Work; Yeasts; base; blind; crosslinking and immunoprecipitation sequencing; experience; experimental study; in vivo; insight; mRNA Decay; millisecond; novel; prototype; single molecule; software development; success; transcriptome; transcriptome sequencing

RNA-protein interactions are a critical component of cellular function. Dynamic and coordinated binding and release of RNA by multiple proteins underpins regulation throughout gene expression. However, our technological capacity to visualize these dynamics on the timescales of processes such as splicing, translation, or mRNA decay, remains limited. Transcriptome-wide methods that probe RNA-protein interactions – from microarrays to RIP-/CLIP-seq – provide static, single-timepoint, or equilibrium snapshots. Conversely, real-time single-molecule methods probe real-time dynamics on individual RNAs with exquisite molecular precision, but are challenging to deploy at transcriptome scale. Single-molecule methods developed to bridge this gap have measured protein-RNA equilibrium affinities and dissociation rates on large libraries of synthetic RNA sequences up to ~300 nt. While these have highlighted kinetic diversity due to local RNA sequence and structure, they still lack the ability to probe dynamics on full-length transcripts with in vivo chemical modifications, they do not directly measure binding rates, and, importantly they have not addressed how multiple simultaneous protein-RNA interactions coordinate. Here we propose development of a technology that circumvents these limitations, focusing on mRNA-protein interactions. Our approach leverages direct observation of fluorescently-labeled proteins binding and releasing tens of thousands of single mRNAs immobilized across an array of zero-mode waveguides (ZMWs), on millisecond timescales. The ZMW-based platform offers the critical throughput, multicolor fluorescence detection, and signal-to-noise metrics needed to advance the state of the art. The key requisite technological breakthroughs will be made through two specific aims. In Aim 1, we will develop a workflow to quantify the interaction dynamics of one and two proteins with a surface-immobilized Saccharomyces cerevisiae transcriptome. We will validate this protocol in terms of reproducibility and completeness of transcriptome capture, and the reproducibility of the kinetic data. In Aim 2 we will develop and optimize an approach to also identify each mRNA in the experiment, allowing (multi)protein-binding dynamics to be assigned to RNA identity. We will adopt a sequencing-by-synthesis approach, contrasting enzymatic strategies to robustly read out RNA sequence in place. We will validate this approach by comparing the in-ZMW identified sequences with bulk RNA-seq data for the mRNA population. The combined outcome of these Aims will be a prototype technology and proof-of-concept for profiling (multi)protein interaction dynamics on each mRNA in the transcriptome. This technology will complement static transcriptome-wide approaches, deepening the range of mechanistic questions that can be asked and answered across RNA biology.

RNA-蛋白质相互作用是细胞功能的关键组成部分。动态和协调的绑定， 多种蛋白质释放RNA支持整个基因表达的调节。但我们的 技术能力，以可视化这些动态的时间尺度的过程，如拼接，翻译， 或mRNA衰变的能力仍然有限。探索RNA-蛋白质相互作用的全转录组方法-从 微阵列到RIP-/CLIP-seq -提供静态、单时间点或平衡快照。相反，实时 单分子方法以精确的分子精度探测单个RNA的实时动态， 很难在转录组规模上部署。为弥合这一差距而开发的单分子方法已经 在合成RNA序列的大型文库上测量的蛋白质-RNA平衡亲和力和解离速率 高达~300 nt。虽然这些研究强调了由于局部RNA序列和结构而导致的动力学多样性，但它们仍然 缺乏在具有体内化学修饰的全长转录物上探测动力学的能力， 测量结合率，而且，重要的是，他们还没有解决如何多个同时蛋白质-RNA 互动协调。在这里，我们建议开发一种技术来规避这些限制， 专注于mRNA-蛋白质相互作用。我们的方法利用直接观察荧光标记的 蛋白质结合并释放固定在零模式阵列上的数万个单个mRNA， 波导（ZMW），在毫秒时间尺度上。基于ZMW的平台提供关键的吞吐量， 荧光检测和信噪比指标，以推进最先进的技术水平。 将通过两个具体目标实现必要的技术突破。在目标1中，我们将开发一个 量化一种和两种蛋白质与表面固定化酵母菌相互作用动力学的工作流程 酿酒酵母转录组。我们将在以下方面对本方案进行验证： 转录组捕获和动力学数据的再现性。在目标2中，我们将开发和优化 一种方法，也确定每个mRNA在实验中，允许（多）蛋白质结合动力学分配 RNA身份我们将采用合成测序方法，对比酶促策略， 读取RNA序列。我们将通过比较在ZMW中鉴定的序列来验证这种方法 与mRNA群体的批量RNA-seq数据。这些目标的综合结果将是一个原型， 技术和概念验证，用于分析（多）蛋白质相互作用动力学在每个mRNA中， 转录组这项技术将补充静态转录组范围的方法，加深范围， 可以在RNA生物学中提出和回答的机械问题。