Literature DB >> 18668037

The impact of microRNAs on protein output.

Daehyun Baek1, Judit Villén, Chanseok Shin, Fernando D Camargo, Steven P Gygi, David P Bartel.   

Abstract

MicroRNAs are endogenous approximately 23-nucleotide RNAs that can pair to sites in the messenger RNAs of protein-coding genes to downregulate the expression from these messages. MicroRNAs are known to influence the evolution and stability of many mRNAs, but their global impact on protein output had not been examined. Here we use quantitative mass spectrometry to measure the response of thousands of proteins after introducing microRNAs into cultured cells and after deleting mir-223 in mouse neutrophils. The identities of the responsive proteins indicate that targeting is primarily through seed-matched sites located within favourable predicted contexts in 3' untranslated regions. Hundreds of genes were directly repressed, albeit each to a modest degree, by individual microRNAs. Although some targets were repressed without detectable changes in mRNA levels, those translationally repressed by more than a third also displayed detectable mRNA destabilization, and, for the more highly repressed targets, mRNA destabilization usually comprised the major component of repression. The impact of microRNAs on the proteome indicated that for most interactions microRNAs act as rheostats to make fine-scale adjustments to protein output.

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Year:  2008        PMID: 18668037      PMCID: PMC2745094          DOI: 10.1038/nature07242

Source DB:  PubMed          Journal:  Nature        ISSN: 0028-0836            Impact factor:   49.962


  48 in total

1.  BLAT--the BLAST-like alignment tool.

Authors:  W James Kent
Journal:  Genome Res       Date:  2002-04       Impact factor: 9.043

2.  Analysis of the mouse transcriptome based on functional annotation of 60,770 full-length cDNAs.

Authors:  Y Okazaki; M Furuno; T Kasukawa; J Adachi; H Bono; S Kondo; I Nikaido; N Osato; R Saito; H Suzuki; I Yamanaka; H Kiyosawa; K Yagi; Y Tomaru; Y Hasegawa; A Nogami; C Schönbach; T Gojobori; R Baldarelli; D P Hill; C Bult; D A Hume; J Quackenbush; L M Schriml; A Kanapin; H Matsuda; S Batalov; K W Beisel; J A Blake; D Bradt; V Brusic; C Chothia; L E Corbani; S Cousins; E Dalla; T A Dragani; C F Fletcher; A Forrest; K S Frazer; T Gaasterland; M Gariboldi; C Gissi; A Godzik; J Gough; S Grimmond; S Gustincich; N Hirokawa; I J Jackson; E D Jarvis; A Kanai; H Kawaji; Y Kawasawa; R M Kedzierski; B L King; A Konagaya; I V Kurochkin; Y Lee; B Lenhard; P A Lyons; D R Maglott; L Maltais; L Marchionni; L McKenzie; H Miki; T Nagashima; K Numata; T Okido; W J Pavan; G Pertea; G Pesole; N Petrovsky; R Pillai; J U Pontius; D Qi; S Ramachandran; T Ravasi; J C Reed; D J Reed; J Reid; B Z Ring; M Ringwald; A Sandelin; C Schneider; C A M Semple; M Setou; K Shimada; R Sultana; Y Takenaka; M S Taylor; R D Teasdale; M Tomita; R Verardo; L Wagner; C Wahlestedt; Y Wang; Y Watanabe; C Wells; L G Wilming; A Wynshaw-Boris; M Yanagisawa; I Yang; L Yang; Z Yuan; M Zavolan; Y Zhu; A Zimmer; P Carninci; N Hayatsu; T Hirozane-Kishikawa; H Konno; M Nakamura; N Sakazume; K Sato; T Shiraki; K Waki; J Kawai; K Aizawa; T Arakawa; S Fukuda; A Hara; W Hashizume; K Imotani; Y Ishii; M Itoh; I Kagawa; A Miyazaki; K Sakai; D Sasaki; K Shibata; A Shinagawa; A Yasunishi; M Yoshino; R Waterston; E S Lander; J Rogers; E Birney; Y Hayashizaki
Journal:  Nature       Date:  2002-12-05       Impact factor: 49.962

3.  A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.

Authors:  B M Bolstad; R A Irizarry; M Astrand; T P Speed
Journal:  Bioinformatics       Date:  2003-01-22       Impact factor: 6.937

4.  The role of site accessibility in microRNA target recognition.

Authors:  Michael Kertesz; Nicola Iovino; Ulrich Unnerstall; Ulrike Gaul; Eran Segal
Journal:  Nat Genet       Date:  2007-09-23       Impact factor: 38.330

5.  Determinants of targeting by endogenous and exogenous microRNAs and siRNAs.

Authors:  Cydney B Nielsen; Noam Shomron; Rickard Sandberg; Eran Hornstein; Jacob Kitzman; Christopher B Burge
Journal:  RNA       Date:  2007-09-13       Impact factor: 4.942

6.  Isolation of microRNA targets by miRNP immunopurification.

Authors:  George Easow; Aurelio A Teleman; Stephen M Cohen
Journal:  RNA       Date:  2007-06-25       Impact factor: 4.942

7.  MicroRNA targeting specificity in mammals: determinants beyond seed pairing.

Authors:  Andrew Grimson; Kyle Kai-How Farh; Wendy K Johnston; Philip Garrett-Engele; Lee P Lim; David P Bartel
Journal:  Mol Cell       Date:  2007-07-06       Impact factor: 17.970

8.  Potent effect of target structure on microRNA function.

Authors:  Dang Long; Rosalind Lee; Peter Williams; Chi Yu Chan; Victor Ambros; Ye Ding
Journal:  Nat Struct Mol Biol       Date:  2007-04-01       Impact factor: 15.369

9.  Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics.

Authors:  Shao-En Ong; Blagoy Blagoev; Irina Kratchmarova; Dan Bach Kristensen; Hanno Steen; Akhilesh Pandey; Matthias Mann
Journal:  Mol Cell Proteomics       Date:  2002-05       Impact factor: 5.911

10.  Identification of human microRNA targets from isolated argonaute protein complexes.

Authors:  Michaela Beitzinger; Lasse Peters; Jia Yun Zhu; Elisabeth Kremmer; Gunter Meister
Journal:  RNA Biol       Date:  2007-06-28       Impact factor: 4.652

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  1776 in total

Review 1.  MicroRNAs, wild-type and mutant p53: more questions than answers.

Authors:  Matthew Jones; Ashish Lal
Journal:  RNA Biol       Date:  2012-06-01       Impact factor: 4.652

2.  MicroRNAs are differentially abundant during Aedes albopictus diapause maintenance but not diapause induction.

Authors:  Z A Batz; A C Goff; P A Armbruster
Journal:  Insect Mol Biol       Date:  2017-08-04       Impact factor: 3.585

3.  The decapping activator HPat a novel factor co-purifying with GW182 from Drosophila cells.

Authors:  Elisabeth Jäger; Silke Dorner
Journal:  RNA Biol       Date:  2010-05-14       Impact factor: 4.652

Review 4.  MicroRNAs in renal development.

Authors:  Jacqueline Ho; Jordan A Kreidberg
Journal:  Pediatr Nephrol       Date:  2012-06-02       Impact factor: 3.714

5.  DNA methylation and histone H3-K9 modifications contribute to MUC17 expression.

Authors:  Sho Kitamoto; Norishige Yamada; Seiya Yokoyama; Izumi Houjou; Michiyo Higashi; Masamichi Goto; Surinder K Batra; Suguru Yonezawa
Journal:  Glycobiology       Date:  2010-10-06       Impact factor: 4.313

6.  Genome-wide analysis reveals methyl-CpG-binding protein 2-dependent regulation of microRNAs in a mouse model of Rett syndrome.

Authors:  Hao Wu; Jifang Tao; Pauline J Chen; Atif Shahab; Weihong Ge; Ronald P Hart; Xiaoan Ruan; Yijun Ruan; Yi E Sun
Journal:  Proc Natl Acad Sci U S A       Date:  2010-10-04       Impact factor: 11.205

Review 7.  MicroRNAs in mucosal inflammation.

Authors:  Viola Neudecker; Xiaoyi Yuan; Jessica L Bowser; Holger K Eltzschig
Journal:  J Mol Med (Berl)       Date:  2017-07-20       Impact factor: 4.599

8.  Circular RNA Vav3 sponges gga-miR-375 to promote epithelial-mesenchymal transition.

Authors:  Xinheng Zhang; Yiming Yan; Wencheng Lin; Aijun Li; Huanmin Zhang; Xiaoya Lei; Zhenkai Dai; Xinjian Li; Hongxin Li; Weiguo Chen; Feng Chen; Jingyun Ma; Qingmei Xie
Journal:  RNA Biol       Date:  2019-01-15       Impact factor: 4.652

Review 9.  MicroRNAs in normal and psoriatic skin.

Authors:  Jing Xia; Weixiong Zhang
Journal:  Physiol Genomics       Date:  2013-12-10       Impact factor: 3.107

10.  Clusterin is a gene-specific target of microRNA-21 in head and neck squamous cell carcinoma.

Authors:  Wojciech Mydlarz; Mamoru Uemura; Sun Ahn; Patrick Hennessey; Steven Chang; Semra Demokan; Wenyue Sun; Chunbo Shao; Justin Bishop; Julie Krosting; Elizabeth Mambo; William Westra; Patrick Ha; David Sidransky; Joseph Califano
Journal:  Clin Cancer Res       Date:  2013-12-10       Impact factor: 12.531

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