MicroRNA in vivo precipitation identifies miR-151-3p as a computational unpredictable miRNA to target Stat3 and inhibits innate IL-6 production
MicroRNAs (miRNAs) function as important regulators in the immune response and inflammation. Several approaches have been reported to computationally predict miRNAs and their potential targets. However, there are still many miRNA–target interactions that are unpredictable by using the current comput...
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Published in | Cellular & molecular immunology Vol. 15; no. 2; pp. 99 - 110 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
London
Nature Publishing Group UK
01.02.2018
Nature Publishing Group |
Subjects | |
Online Access | Get full text |
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Summary: | MicroRNAs (miRNAs) function as important regulators in the immune response and inflammation. Several approaches have been reported to computationally predict miRNAs and their potential targets. However, there are still many miRNA–target interactions that are unpredictable by using the current computational algorithms. We established a miRNA
in vivo
precipitation method (miRIP) to identify unpredictable miRNAs with definite targets in these cells. Because Stat3 is a well-known transcription factor involved in innate immunity and inflammation, we utilized the miRIP method to identify miRNAs that bind
Stat3
mRNA in macrophages. Among the captured miRNAs, miR-151-3p was confirmed to interact with
Stat3
mRNA 3′-UTR and downregulate the Stat3 protein levels. LPS stimulation decreased miR-151-3p expression, thereby increasing IL-6 production. Therefore, we found that miR-151-3p inhibited LPS-induced IL-6 production by targeting
Stat3
. These data further confirmed miRIP as an efficient method to identify unpredictable miRNAs and explore miRNAs-mediated regulation in innate immunity and inflammation. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 These authors contributed equally to this work. |
ISSN: | 1672-7681 2042-0226 |
DOI: | 10.1038/cmi.2017.82 |