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The impact of microRNAs on protein output

Author

Listed:
  • Daehyun Baek

    (Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cambridge, Massachusetts 02142, USA
    Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA)

  • Judit Villén

    (240 Longwood Avenue, Harvard Medical School, Boston, Massachusetts 02115, USA)

  • Chanseok Shin

    (Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cambridge, Massachusetts 02142, USA
    Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA)

  • Fernando D. Camargo

    (Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cambridge, Massachusetts 02142, USA)

  • Steven P. Gygi

    (240 Longwood Avenue, Harvard Medical School, Boston, Massachusetts 02115, USA)

  • David P. Bartel

    (Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cambridge, Massachusetts 02142, USA
    Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA)

Abstract

MicroRNAs are endogenous ∼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.

Suggested Citation

  • Daehyun Baek & Judit Villén & Chanseok Shin & Fernando D. Camargo & Steven P. Gygi & David P. Bartel, 2008. "The impact of microRNAs on protein output," Nature, Nature, vol. 455(7209), pages 64-71, September.
    • Handle: RePEc:nat:nature:v:455:y:2008:i:7209:d:10.1038_nature07242
    DOI: 10.1038/nature07242
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    Citations

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    Cited by:

    1. Wenliang Zhu & Xuan Kan, 2014. "Neural Network Cascade Optimizes MicroRNA Biomarker Selection for Nasopharyngeal Cancer Prognosis," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-7, October.
    2. Yuheng Lu & Christina S Leslie, 2016. "Learning to Predict miRNA-mRNA Interactions from AGO CLIP Sequencing and CLASH Data," PLOS Computational Biology, Public Library of Science, vol. 12(7), pages 1-18, July.
    3. Zhdanov, Vladimir P., 2010. "ncRNA-mediated bistability in the synthesis of hundreds of distinct mRNAs and proteins," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(4), pages 887-890.
    4. Adam A Margolin & Shao-En Ong & Monica Schenone & Robert Gould & Stuart L Schreiber & Steven A Carr & Todd R Golub, 2009. "Empirical Bayes Analysis of Quantitative Proteomics Experiments," PLOS ONE, Public Library of Science, vol. 4(10), pages 1-15, October.
    5. Chikako Ragan & Michael Zuker & Mark A Ragan, 2011. "Quantitative Prediction of miRNA-mRNA Interaction Based on Equilibrium Concentrations," PLOS Computational Biology, Public Library of Science, vol. 7(2), pages 1-11, February.
    6. Youjia Hua & Shiwei Duan & Andrea E Murmann & Niels Larsen & Jørgen Kjems & Anders H Lund & Marcus E Peter, 2011. "miRConnect: Identifying Effector Genes of miRNAs and miRNA Families in Cancer Cells," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-16, October.
    7. Ronak Lakhia & Harini Ramalingam & Chun-Mien Chang & Patricia Cobo-Stark & Laurence Biggers & Andrea Flaten & Jesus Alvarez & Tania Valencia & Darren P. Wallace & Edmund C. Lee & Vishal Patel, 2022. "PKD1 and PKD2 mRNA cis-inhibition drives polycystic kidney disease progression," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    8. Yasemin Oztemur & Tufan Bekmez & Alp Aydos & Isik G Yulug & Betul Bozkurt & Bala Gur Dedeoglu, 2015. "A Ranking-Based Meta-Analysis Reveals Let-7 Family as a Meta-Signature for Grade Classification in Breast Cancer," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-16, May.
    9. Parawee Lekprasert & Michael Mayhew & Uwe Ohler, 2011. "Assessing the Utility of Thermodynamic Features for microRNA Target Prediction under Relaxed Seed and No Conservation Requirements," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-13, June.
    10. Ray M Marín & Jiří Vaníček, 2012. "Optimal Use of Conservation and Accessibility Filters in MicroRNA Target Prediction," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-11, February.
    11. Wenshu Zeng & Lu Yue & Kim S. W. Lam & Wenxin Zhang & Wai-Kin So & Erin H. Y. Tse & Tom H. Cheung, 2022. "CPEB1 directs muscle stem cell activation by reprogramming the translational landscape," Nature Communications, Nature, vol. 13(1), pages 1-19, December.

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