The DOI matches its Crossref record; 49 of the 50 references it cites were found in a registry or an index.
The impact of microRNAs on protein output
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Other systemwide studies, including a separate analysis by Schwanhausser et al. (2011), also suggest that the differential regulation of translation may be limited to modest changes at a subset of genes”
From System wide analyses have underestimated protein abundances and the importance of transcription in mammals (2014) · cites this paper as Baek et al., 2008
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“We conclude that when poly(A)-tail length is uncoupled from TE, the translational repression often detected as a minor component of the overall repression33–35 arises from a mechanism different from the one that dominates pre-gastrulation.”
From Poly(A)-tail profiling reveals an embryonic switch in translational control (2014) · cites this paper as [33]
The cited source says what the claim attributes to it.
In the citing paper
“MicroRNA sites have also been validated in coding sequence (CDS) regions, albeit with typically weaker repressive activity (Baek et al., 2008;Grimson et al., 2007).”
From Scalable and deep profiling of mRNA targets for individual microRNAs with chimeric eCLIP (2022) · cites this paper as Baek et al., 2008
In the citing paper
“Global measurements indicate that miRNA targeting causes mostly mRNA destabilization, with translational repression comprising a detectable but minor component of the overall repression33–36.”
From Poly(A)-tail profiling reveals an embryonic switch in translational control (2014) · cites this paper as [33]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Lewis, B. P., Burge, C. B., & Bartel, D. P. (2005) · Cell · doi:10.1016/j.cell.2004.12.035 · Cited by 11,855
Self-citation<tt>BLAT</tt> —The <tt>BLAST</tt> -Like Alignment Tool
Kent, W. J. (2002) · Genome Research · doi:10.1101/gr.229202 · Cited by 8,480
Bolstad, B. M., Irizarry, R. A., Åstrand, M., & Speed, T. P. (2003) · Bioinformatics · doi:10.1093/bioinformatics/19.2.185 · Cited by 8,328
Ong, S.-E., Blagoev, B., Kratchmarova, I., Kristensen, D. B., Steen, H., Pandey, A., & Mann, M. (2002) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m200025-mcp200 · Cited by 5,574
Prediction of Mammalian MicroRNA Targets
Lewis, B. P., Shih, I.-H., Jones-Rhoades, M. W., Bartel, D. P., & Burge, C. B. (2003) · Cell · doi:10.1016/s0092-8674(03)01018-3 · Cited by 5,199
Self-citationMicroarray analysis shows that some microRNAs downregulate large numbers of target mRNAs
Lim, L. P., Lau, N. C., Garrett-Engele, P., Grimson, A., Schelter, J. M., Castle, J., Bartel, D. P., Linsley, P. S., & Johnson, J. M. (2005) · Nature · doi:10.1038/nature03315 · Cited by 4,755
Self-citationCombinatorial microRNA target predictions
Krek, A., Grün, D., Poy, M. N., Wolf, R., Rosenberg, L., Epstein, E. J., MacMenamin, P., da Piedade, I., Gunsalus, K. C., Stoffel, M., et al. (2005) · Nature Genetics · doi:10.1038/ng1536 · Cited by 4,603
miRBase: tools for microRNA genomics
Griffiths‐Jones, S., Saini, H. K., van Dongen, S., & Enright, A. J. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm952 · Cited by 4,432
Silencing of microRNAs in vivo with ‘antagomirs’
Krützfeldt, J., Rajewsky, N., Braich, R., Rajeev, K. G., Tuschl, T., Manoharan, M., & Stoffel, M. (2005) · Nature · doi:10.1038/nature04303 · Cited by 3,949
Cited by
Sorted by citations
Items 1,001–1,013 of 1,013
Noncoding RNAs regulate NF-κB signaling to modulate blood vessel inflammation
Cheng, H. S., Njock, M.-S., Khyzha, N., Dang, L. T., & Fish, J. E. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00422 · Cited by 69
Monteleone, M. C., Adrover, E., Pallarés, M. E., Antonelli, M. C., Frasch, A. C., & Brocco, M. A. (2014) · Epigenetics · doi:10.4161/epi.25925 · Cited by 64
Scalable and deep profiling of mRNA targets for individual microRNAs with chimeric eCLIP
Manakov, S. A., Shishkin, A. A., Yee, B. A., Shen, K. A., Cox, D. C., Park, S. S., Foster, H. M., Chapman, K. B., Yeo, G. W., & Van Nostrand, E. L. (2022) · doi:10.1101/2022.02.13.480296 · Cited by 43
Huang, Y., Chen, H.-C., Chiang, C.-W., Yeh, C.-T., Chen, S.-J., & Chou, C.-K. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks789 · Cited by 37
The mouth, the anus, and the blastopore—open questions about questionable openings
Hejnol, A., & Martindale, M. Q. (2009) · Animal Evolution · doi:10.1093/acprof:oso/9780199549429.003.0004 · Cited by 37
Twycross-Lewis, R., Kilduff, L. P., Wang, G., & Pitsiladis, Y. P. (2016) · Amino Acids · doi:10.1007/s00726-016-2237-9 · Cited by 36
Reciprocal regulation of microRNA and mRNA profiles in neuronal development and synapse formation
Manakov, S. A., Grant, S. G., & Enright, A. J. (2009) · BMC Genomics · doi:10.1186/1471-2164-10-419 · Cited by 34
Molotski, N., & Soen, Y. (2012) · RNA · doi:10.1261/rna.033142.112 · Cited by 26
MicroRNAs as Regulators of Phagocytosis
Gierlikowski, W., & Gierlikowska, B. (2022) · Cells · doi:10.3390/cells11091380 · Cited by 25
Genomic, phylogenetic, and cell biological insights into metazoan origins
Nichols, S. A., Dayel, M. J., & King, N. (2009) · Animal Evolution · doi:10.1093/acprof:oso/9780199549429.003.0003 · Cited by 24
Bjerke, G. A., & Yi, R. (2020) · RNA · doi:10.1261/rna.073635.119 · Cited by 22
Hippocampal MicroRNAs Respond to Administration of Antidepressant Fluoxetine in Adult Mice
Miao, N., Jin, J., Kim, S.-N., & Sun, T. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19030671 · Cited by 19
Swaroop (2009) · Annual Review of Genomics and Human Genetics · doi:10.1146/genom.2009.10.issue-1 · Cited by 7
Metadata
- Authors
- Baek, D., Villén, J., Shin, C., Camargo, F. D., Gygi, S. P., & Bartel, D. P.
- Affiliations
- Whitehead Institute for Biomedical Research (US)
- Harvard University (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Gygi, Steven P.
- Bartel, David P.
- Venue
- Nature
- Published
- 2008-07-30
- Volume
- 455
- Issue
- 7209
- Pages
- 64-71
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC2745094
- OpenAlex
- W1988163167
- Cited by
- 3,592
- References
- 50
- Citations by year
- 2026: 39 · 2025: 65 · 2024: 82 · 2023: 98 · 2022: 137 · 2021: 138 · 2020: 140 · 2019: 150 · 2018: 164 · 2017: 198 · 2016: 208 · 2015: 257 · 2014: 287 · 2013: 328 · 2012: 342
- FWCI
- 108.87
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- Damon Runyon Cancer Research Foundation
- National Human Genome Research Institute (R01HG003456)
- National Institute of General Medical Sciences (R01GM067031)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › MicroRNA in disease regulation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Plant Molecular Biology Research
- Keywords
- microRNA, protein production, protein synthesis inhibition, mRNA destabilization, 3' untranslated region, miR-223, neutrophils, gene expression regulation, SILAC
- MeSH terms
- Animals, Gene Expression Regulation, HeLa Cells, Humans, Isotope Labeling, Male, Neutrophils, Transfection, Protein Biosynthesis, Oligonucleotide Array Sequence Analysis, MicroRNAs, Proteomics, Mice
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
