Literature DB >> 27982722

TargetLink, a new method for identifying the endogenous target set of a specific microRNA in intact living cells.

Yan Xu1, Yan Chen1, Daliang Li1, Qing Liu1, Zhenyu Xuan2, Wen-Hong Li1.   

Abstract

MicroRNAs are small non-coding RNAs acting as posttranscriptional repressors of gene expression. Identifying mRNA targets of a given miRNA remains an outstanding challenge in the field. We have developed a new experimental approach, TargetLink, that applied locked nucleic acid (LNA) as the affinity probe to enrich target genes of a specific microRNA in intact cells. TargetLink also consists a rigorous and systematic data analysis pipeline to identify target genes by comparing LNA-enriched sequences between experimental and control samples. Using miR-21 as a test microRNA, we identified 12 target genes of miR-21 in a human colorectal cancer cell by this approach. The majority of the identified targets interacted with miR-21 via imperfect seed pairing. Target validation confirmed that miR-21 repressed the expression of the identified targets. The cellular abundance of the identified miR-21 target transcripts varied over a wide range, with some targets expressed at a rather low level, confirming that both abundant and rare transcripts are susceptible to regulation by microRNAs, and that TargetLink is an efficient approach for identifying the target set of a specific microRNA in intact cells. C20orf111, one of the novel targets identified by TargetLink, was found to reside in the nuclear speckle and to be reliably repressed by miR-21 through the interaction at its coding sequence.

Entities:  

Keywords:  Colorectal cancer; LNA; locked nucleic acid; miR-21; microRNA; microRNA target; target identification; targetlink

Mesh:

Substances:

Year:  2016        PMID: 27982722      PMCID: PMC5324748          DOI: 10.1080/15476286.2016.1270006

Source DB:  PubMed          Journal:  RNA Biol        ISSN: 1547-6286            Impact factor:   4.652


  63 in total

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Authors:  Tsui-Ting Ho; Nanjiang Zhou; Jianguo Huang; Pratirodh Koirala; Min Xu; Roland Fung; Fangting Wu; Yin-Yuan Mo
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