ARiBo pull-down for riboproteomic studies based on label-free quantitative mass spectrometry

Author:

Di Tomasso Geneviève,Miller Jenkins Lisa M.,Legault Pascale

Abstract

As part of their normal life cycle, most RNA molecules associate with several proteins that direct their fate and regulate their function. Here, we describe a novel method for identifying proteins that associate with a target RNA. The procedure is based on the ARiBo method for affinity purification of RNA, which was originally developed to quickly purify RNA with high yields and purity under native conditions. The ARiBo method was further optimized using in vitro transcribed RNA to capture RNA-associating proteins from cellular extracts with high yields and low background protein contamination. For these RNA pull-downs, stem–loops present in the immature forms of let-7 miRNAs (miRNA stem–loops) were used as the target RNAs. Label-free quantitative mass spectrometry analysis allowed for the reliable identification of proteins that are specific to the stem–loops present in the immature forms of two miRNAs, let-7a-1 and let-7g. Several proteins known to bind immature forms of these let-7 miRNAs were identified, but with an improved coverage compared to previous studies. In addition, several novel proteins were identified that better define the protein interactome of the let-7 miRNA stem–loops and further link let-7 biogenesis to important biological processes such as development and tumorigenesis. Thus, combining the ARiBo pull-down method with label-free quantitative mass spectrometry provides an effective proteomic approach for identification of proteins that associate with a target RNA.

Funder

Canadian Institutes of Health Research

Natural Sciences and Engineering Research Council of Canada

Parkinson Society of Canada

National Cancer Institute

Publisher

Cold Spring Harbor Laboratory

Subject

Molecular Biology

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3