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The impact of microRNAs on protein output

Nature volume 455, pages 64–71 (2008)Cite this article

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.

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Figure 1: The impact of transfected miRNAs on protein output.
Figure 2: The proteomic impact of deleting mir-223 in mouse neutrophils.
Figure 3: Correspondence between computational target predictions and observed protein changes.
Figure 4: Comparison of protein and mRNA changes accompanying miR-223 loss.

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Primary accessions

Gene Expression Omnibus

Data deposits

Array data and small RNA sequencing data were deposited in the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE12075.

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Acknowledgements

We thank S.-J. Hong, T. Brummelkamp and S. Stehling-Sun for discussions, C. Bakalarski for writing and implementing the Vista algorithm for automated protein quantification, W. Johnston for technical assistance, and P. Wisniewski for cell sorting. This was supported by a Damon Runyon postdoctoral fellowship (C.S.) and grants from the NIH (D.P.B and S.P.G). D.P.B. is an investigator of the Howard Hughes Medical Institute.

Author Contributions The order of listing of the first three authors is arbitrary. C.S. and F.C. performed the experimental work with cells and animals. J.V. performed the mass spectrometry and associated computational analysis. D.B. performed the computational analysis of targeting. All authors contributed to the design of the study and preparation of the manuscript.

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Author notes

  1. Daehyun Baek, Judit Villén and Chanseok Shin: These authors contributed equally to this work.

Authors and Affiliations

  1. Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cambridge, Massachusetts 02142, USA ,

    Daehyun Baek, Chanseok Shin, Fernando D. Camargo & David P. Bartel

  2. Howard Hughes Medical Institute and Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA,

    Daehyun Baek, Chanseok Shin & David P. Bartel

  3. Department of Cell Biology, 240 Longwood Avenue, Harvard Medical School, Boston, Massachusetts 02115, USA,

    Judit Villén & Steven P. Gygi

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  1. Daehyun Baek
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Correspondence to Steven P. Gygi or David P. Bartel.

Supplementary information

Supplementary Information 1

The file contains Supplementary Discussion; Supplementary Tables 1-2; Supplementary Figures 1-8 with Legends. Supplementary Discussion discusses potential caveats of ectopically introduced miRNAs in investigating miRNA targeting and corresponding advantages of examining miRNA-deficient cells. Supplementary Table 1 lists 3'UTR motifs overrepresented in messages of proteins most upregulated in miR-223-/Y neutrophils. Supplementary Table 2 lists the top 20 miRNAs co-expressed with miR-223 in wild-type neutrophils cultured in vitro. Supplementary Figure 1 shows the schematic of our quantitative proteomics approach using SILAC in experiments transfecting miR-1, 124, and 181 into HeLa cells.Supplementary Figure 2 illustrates the quantification of peptides from two proteins that respond the loss of miR-223 and two proteins that do not. Supplementary Figure 3 illustrates the reproducibility of the quantification when comparing technical replicates and when comparing different peptides representing the same protein. Supplementary Figure 4 shows additional characterization of the neutrophils differentiated in vitro. Supplementary Figure 5 shows the response of proteins predicted to be miR-223 targets after deleting miR-223. Supplementary Figure 6 illustrates the protein and mRNA changes for the additional cohorts of genes lacking 3'UTR sites. Supplementary Figure 7 shows comparative immunoblots illustrating the consequences of losing miR-223 in neutrophils differentiated in vitro and those isolated from the mouse. Supplementary Figure 8 shows the protein repression of genes predicted to be miR-124, miR-1, and miR-181 targets from experiments transfecting those miRNAs into HeLa cells. (PDF 9035 kb)

nature07242-s2.xls

This file contains Supplementary Data 1. This Excel file lists the number of uniquely quantified peptides, the number of independent measurements, the median log2-fold-change in quantified protein (with 25th and 75th percentiles), and the log2-fold-change in mRNA (with error) for each human message in our dataset after transfecting miR-124. Also listed is the number of miR-124 sites in the UTRs and ORF of each message. (XLS 4712 kb)

nature07242-s3.xls

This file contains Supplementary Data 2. This Excel file lists the number of uniquely quantified peptides, the number of independent measurements, the median log2-fold-change in quantified protein (with 25th and 75th percentiles), and the log2-fold-change in mRNA (with error) for each human message in our dataset after transfecting miR-1. Also listed is the number of miR-1 sites in the UTRs and ORF of each message. (XLS 4717 kb)

nature07242-s4.xls

This file contains Supplementary Data 3. This Excel file lists the number of uniquely quantified peptides, the number of independent measurements, the median log2-fold-change in quantified protein (with 25th and 75th percentiles), and the log2-fold-change in mRNA (with error) for each human message in our dataset after transfecting miR-181. Also listed is the number of miR-181 sites in the UTRs and ORF of each message. (XLS 4712 kb)

nature07242-s5.xls

This file contains Supplementary Data 4. This Excel file lists the number of uniquely quantified peptides, the number of independent measurements, the median log2-fold-change in quantified protein (with 25th and 75th percentiles), and the mean log2-fold-change in mRNA (with s.e.m.) for each mouse message in our dataset, comparing neutrophils with and without miR-223. Also reported are the profiling data for sorted progenitors and sorted neutrophils, and the number of miR-223 sites in the UTRs and ORF of each message. (XLS 5882 kb)

nature07242-s6.zip

This file contains Supplementary Data 5a. This fasta file lists the complete 5' UTR, ORF, and 3' UTR sequences of our human reference cDNAs. (ZIP 18837 kb)

nature07242-s7.zip

This file contains Supplementary Data 5b. This fasta file lists the complete 5' UTR, ORF, and 3' UTR sequences of our mouse reference cDNAs. (ZIP 18057 kb)

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Baek, D., Villén, J., Shin, C. et al. The impact of microRNAs on protein output. Nature 455, 64–71 (2008). https://doi.org/10.1038/nature07242

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