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。

基本信息

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
项目类别：
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

项目摘要

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动态和协调结合的关键组成部分。多种蛋白质释放 RNA 是整个基因表达调控的基础。在剪接、翻译等过程的时间尺度上可视化这些动态的技术能力或 mRNA 衰减，探测 RNA-蛋白质相互作用的转录组范围的方法仍然有限。微阵列到 RIP-/CLIP-seq – 提供静态、单时间点或平衡离线、实时快照。单分子方法可探测单个 RNA 的实时动态，具有分子精度，但是为弥补这一差距而开发的单分子方法在转录组规模上部署具有挑战性。测量大型合成 RNA 序列文库的蛋白质-RNA 平衡亲和力和解离率高达约 300 nt 虽然这些由于局部 RNA 序列和结构而突出了动力学多样性，但它们仍然如此。缺乏通过体内化学修饰探测全长转录物动态的能力，它们不直接测量结合率，重要的是，他们没有解决多个同时发生的蛋白质-RNA如何在这里，我们建议开发一种规避这些限制的技术，我们的方法重点关注 mRNA-蛋白质相互作用。蛋白质结合并释放数万个固定在零模式阵列上的单个 mRNA 波导（ZMW），在毫秒时间尺度上基于 ZMW 的平台提供了关键的吞吐量，多色荧光检测和信噪比指标是推动最先进技术发展的关键。在目标 1 中，我们将通过两个具体目标实现必要的技术突破。量化一种和两种蛋白质与表面固定化酵母菌的相互作用动力学的工作流程我们将在重现性和完整性方面验证该协议。在目标 2 中，我们将开发和优化转录组捕获和动力学数据的可重复性。方法还可以识别实验中的每个 mRNA，从而可以分配（多）蛋白质结合动力学我们将采用边合成边测序的方法，将酶促策略与稳健的方法进行对比。我们将通过比较 ZMW 中识别的序列来验证该方法。这些目标的综合结果将是一个原型。用于分析每个 mRNA 的（多）蛋白质相互作用动态的技术和概念验证该技术将补充静态转录组范围的方法，加深转录组的范围。可以在 RNA 生物学中提出和回答的机制问题。