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。动态和协调的结合以及 在整个基因表达中，多种蛋白质的RNA释放。但是，我们的 在拼接，翻译，诸如拼接，翻译， 或mRNA衰变，仍然有限。探测RNA - 蛋白质相互作用的全转录组方法 - 从 微阵列rip-/clip-seq - 提供静态，单个时间点或同等快照。相反，实时 单分子方法对单个RNA进行独特的分子精度探测实时动力学，但 面临在转录组量表部署的挑战。为弥合此差距而开发的单分子方法具有 测量的蛋白质RNA等效亲和力和合成RNA序列的大库中的解离速率 最多〜300 nt。尽管这些突出了由于局部RNA序列和结构而引起的动力学多样性，但它们仍然 缺乏在体内化学修饰的全长转录本上探测动态的能力，它们不会直接 测量结合速率，重要的是，它们尚未解决多个简单蛋白-RNA的方式 交互坐标。在这里，我们建议开发一种规避这些限制的技术， 专注于mRNA-蛋白质相互作用。我们的方法利用荧光标记的直接观察 蛋白质结合并释放数以万计的单个mRNA，固定在零模式的阵列中 波导（ZMW），在毫秒时标。基于ZMW的平台提供了关键的吞吐量， 多色荧光检测以及推进最新状态所需的信噪比指标。钥匙 必要的技术突破将通过两个具体的目标进行。在AIM 1中，我们将开发一个 量化一个和两种蛋白质与表面毫米固定糖果的相互作用动力学的工作流程 酿酒酵母转录组。我们将根据可重复性和完整性来验证该协议 转录组捕获以及动力学数据的可重复性。在AIM 2中，我们将开发并优化 在实验中还识别每个mRNA的方法，允许分配（多）蛋白质结合动力学 到RNA身份。我们将采用一种逐个测序方法，将酶促策略与之相反 读出RNA序列到位。我们将通过比较IN-ZMW确定的序列来验证这种方法 带有MRNA种群的大量RNA-Seq数据。这些目标的综合结果将是原型 在每个mRNA上进行分析（多）蛋白质相互作用动力学的技术和概念验证 转录组。这项技术将补充整个静态转录组方法，加深 可以在RNA生物学上询问和回答的机械问题。