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RNA 结构范围内的功能相互作用发现与多重 RNA 基序文库。

RNA structure-wide discovery of functional interactions with multiplexed RNA motif library.

机构信息

Department of Life Science Frontiers, Center for iPS Cell Research and Application, Kyoto University, 53 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan.

Twist Bioscience, 681 Gateway Blvd South, San Francisco, CA, 94080, USA.

出版信息

Nat Commun. 2020 Dec 8;11(1):6275. doi: 10.1038/s41467-020-19699-5.

Abstract

Biochemical assays and computational analyses have discovered RNA structures throughout various transcripts. However, the roles of these structures are mostly unknown. Here we develop folded RNA element profiling with structure library (FOREST), a multiplexed affinity assay system to identify functional interactions from transcriptome-wide RNA structure datasets. We generate an RNA structure library by extracting validated or predicted RNA motifs from gene-annotated RNA regions. The RNA structure library with an affinity enrichment assay allows for the comprehensive identification of target-binding RNA sequences and structures in a high-throughput manner. As a proof-of-concept, FOREST discovers multiple RNA-protein interaction networks with quantitative scores, including translational regulatory elements that function in living cells. Moreover, FOREST reveals different binding landscapes of RNA G-quadruplex (rG4) structures-binding proteins and discovers rG4 structures in the terminal loops of precursor microRNAs. Overall, FOREST serves as a versatile platform to investigate RNA structure-function relationships on a large scale.

摘要

生物化学分析和计算分析已经在各种转录本中发现了 RNA 结构。然而,这些结构的作用大多是未知的。在这里,我们开发了折叠 RNA 元件分析与结构文库(FOREST),这是一种多重亲和测定系统,可从全转录组 RNA 结构数据集中识别功能相互作用。我们通过从基因注释的 RNA 区域中提取经过验证或预测的 RNA 基序来生成 RNA 结构文库。具有亲和性富集测定的 RNA 结构文库允许以高通量的方式全面识别靶标结合的 RNA 序列和结构。作为概念验证,FOREST 发现了具有定量分数的多个 RNA-蛋白质相互作用网络,包括在活细胞中起作用的翻译调节元件。此外,FOREST 揭示了 RNA 四链体(rG4)结构结合蛋白的不同结合景观,并在前体 microRNA 的末端环中发现了 rG4 结构。总的来说,FOREST 是一个通用的平台,可以大规模研究 RNA 结构-功能关系。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa75/7723054/bf4388d13823/41467_2020_19699_Fig1_HTML.jpg

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