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A Novel Vector-Based Method for Exclusive Overexpression of Star-Form MicroRNAs

Overview of attention for article published in PLOS ONE, July 2012
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Title
A Novel Vector-Based Method for Exclusive Overexpression of Star-Form MicroRNAs
Published in
PLOS ONE, July 2012
DOI 10.1371/journal.pone.0041504
Pubmed ID
Authors

Bo Qu, Xiao Han, Yuanjia Tang, Nan Shen

Abstract

The roles of microRNAs (miRNAs) as important regulators of gene expression have been studied intensively. Although most of these investigations have involved the highly expressed form of the two mature miRNA species, increasing evidence points to essential roles for star-form microRNAs (miRNA*), which are usually expressed at much lower levels. Owing to the nature of miRNA biogenesis, it is challenging to use plasmids containing miRNA coding sequences for gain-of-function experiments concerning the roles of microRNA* species. Synthetic microRNA mimics could introduce specific miRNA* species into cells, but this transient overexpression system has many shortcomings. Here, we report that specific miRNA* species can be overexpressed by introducing artificially designed stem-loop sequences into short hairpin RNA (shRNA) overexpression vectors. By our prototypic plasmid, designed to overexpress hsa-miR-146b-3p, we successfully expressed high levels of hsa-miR-146b-3p without detectable change of hsa-miR-146b-5p. Functional analysis involving luciferase reporter assays showed that, like natural miRNAs, the overexpressed hsa-miR-146b-3p inhibited target gene expression by 3'UTR seed pairing. Our demonstration that this method could overexpress two other miRNAs suggests that the approach should be broadly applicable. Our novel strategy opens the way for exclusively stable overexpression of miRNA* species and analyzing their unique functions both in vitro and in vivo.

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Geographical breakdown

Country Count As %
France 1 2%
Unknown 41 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 29%
Researcher 10 24%
Professor 4 10%
Student > Doctoral Student 3 7%
Student > Master 3 7%
Other 4 10%
Unknown 6 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 40%
Biochemistry, Genetics and Molecular Biology 10 24%
Immunology and Microbiology 2 5%
Medicine and Dentistry 2 5%
Chemistry 2 5%
Other 2 5%
Unknown 7 17%