4.8 Article

Distributed biotin-streptavidin transcription roadblocks for mapping cotranscriptional RNA folding

期刊

NUCLEIC ACIDS RESEARCH
卷 45, 期 12, 页码 -

出版社

OXFORD UNIV PRESS
DOI: 10.1093/nar/gkx233

关键词

-

资金

  1. National Institute of General Medical Sciences of the National Institutes of Health [1DP2GM110838, GM67153]
  2. Searle Funds at The Chicago Community Trust

向作者/读者索取更多资源

RNA folding during transcription directs an order of folding that can determine RNA structure and function. However, the experimental study of co-transcriptional RNA folding has been limited by the lack of easily approachable methods that can interrogate nascent RNA structure at nucleotide resolution. To address this, we previously developed cotranscriptional selective 2'-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq) to simultaneously probe all intermediate RNA transcripts during transcription by stalling elongation complexes at catalytically dead EcoRI(E111Q) roadblocks. While effective, the distribution of elongation complexes using EcoRI(E111Q) requires laborious PCR using many different oligonucleotides for each sequence analyzed. Here, we improve the broad applicability of cotranscriptional SHAPE-Seq by developing a sequence-independent biotin-streptavidin (SAv) roadblocking strategy that simplifies the preparation of roadblocking DNA templates. We first determine the properties of biotin-SAv roadblocks. We then show that randomly distributed biotin-SAv roadblocks can be used in cotranscriptional SHAPE-Seq experiments to identify the same RNA structural transitions related to a riboswitch decision-making process that we previously identified using EcoRI(E111Q). Lastly, we find that EcoRI(E111Q) maps nascent RNA structure to specific transcript lengths more precisely than biotin-SAv and propose guidelines to leverage the complementary strengths of each transcription roadblock in cotranscriptional SHAPE-Seq.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据