Mammalian microRNAs predominantly act to decrease target mRNA levels - PubMed (original) (raw)
Mammalian microRNAs predominantly act to decrease target mRNA levels
Huili Guo et al. Nature. 2010.
Abstract
MicroRNAs (miRNAs) are endogenous approximately 22-nucleotide RNAs that mediate important gene-regulatory events by pairing to the mRNAs of protein-coding genes to direct their repression. Repression of these regulatory targets leads to decreased translational efficiency and/or decreased mRNA levels, but the relative contributions of these two outcomes have been largely unknown, particularly for endogenous targets expressed at low-to-moderate levels. Here, we use ribosome profiling to measure the overall effects on protein production and compare these to simultaneously measured effects on mRNA levels. For both ectopic and endogenous miRNA regulatory interactions, lowered mRNA levels account for most (>/=84%) of the decreased protein production. These results show that changes in mRNA levels closely reflect the impact of miRNAs on gene expression and indicate that destabilization of target mRNAs is the predominant reason for reduced protein output.
Figures
Figure 1. Ribosome profiling in human cells captured features of translation
a, Schematic diagram of ribosome profiling. Sequencing reproducibility and evidence for mapping to the correct mRNA isoforms are illustrated (Supplementary Fig. 1a, b). b, RPF density near the ends of ORFs, combining data from all quantified genes. Plotted are RPF 5′ termini, as reads per million reads mapping to genes (rpM). Illustrated below the graph are the inferred ribosome positions corresponding to peak RPF densities, at which the start codon was in the P site (left) and the stop codon was in the A site (right). The offset between the 5′ terminus of an RPF and the first nucleotide in the human ribosome A site was typically 15 nucleotides (nt). c, Density of RPFs and mRNA-Seq tags near the ends of ORFs in HeLa cells. RPF density is plotted as in panel b, except positions are shifted +15 nucleotides to reflect the position of the first nucleotide in the ribosome A site. Composite data are shown for ≥600-nucleotide ORFs that passed our threshold for quantification (≥100 RPFs and ≥100 mRNA-Seq tags). d, Fraction of RPFs and mRNA-Seq tags mapping to each of the three codon nucleotides in panel c.
Figure 2. MicroRNAs downregulated gene expression mostly through mRNA destabilization, with a small effect on translational efficiency
a, Cumulative distributions of mRNA-Seq changes (left) and RPF changes (right) after introducing miR-155. Plotted are distributions for the genes with ≥1 miR-155 3′UTR site (blue), the subset of these genes detected in the pSILAC experiment (proteomics-detected, red), the subset of the proteomics-detected genes with proteins responding with log2-fold change ≤ −0.3 (proteomics-supported, green), and the control genes, which lacked miR-155 sites throughout their mRNAs (no site, black). The number of genes in each category is indicated in parentheses. b, Cumulative distributions of mRNA-Seq changes (left) and RPF changes (right) after introducing miR-1. Otherwise, as in panel a. c, Cumulative distributions of mRNA-Seq changes (left) and RPF changes (right) after deleting mir-223. Otherwise, as in panel a, with proteomics-supported genes referring to genes with proteins that responded with log2-fold change ≥ 0.3 in the SILAC experiment. d, Cumulative distributions of translational efficiency changes for the polyadenylated mRNA that remained after introducing miR-155. For each gene, the translational efficiency change was calculated by normalizing the RPF change by the mRNA-Seq change. For each distribution, the mean log2-fold change (± standard error) is shown (inset). e, Cumulative distributions of translational efficiency changes for the polyadenylated mRNA that remained after introducing miR-1. Otherwise, as in panel d. f, Cumulative distributions of translational efficiency changes for the polyadenylated mRNA that remained after deleting mir-223. Otherwise, as in panel d.
Figure 3. Ribosome changes from miRNA targeting corresponded to mRNA changes
a, Correspondence between ribosome (RPF) and mRNA (mRNA-Seq) changes after introducing miR-155, plotting data for the 707 quantified genes with at least one miR-155 3′UTR site (blue circles). Proteomics-detected targets and proteomics-supported targets are highlighted (pink diamonds and green crosses, respectively). Expected standard deviations (error bars) were calculated based on the number of reads obtained per gene and assuming random counting statistics. The R2 derived from Pearson's correlation of all data is indicated. b, Correspondence between ribosome and mRNA changes after introducing miR-155, plotting data for 707 genes randomly selected from the 3186 quantified genes lacking a miR-155 site anywhere in the mRNA. Otherwise, as in panel a. c and d, As in panels a and b, but plotting results for the miR-1 experiment. e and f, As in panels a and b, but plotting results for the miR-223 experiment.
Figure 4. Ribosome and mRNA changes were uniform along the length of the ORFs
a, Ribosome and mRNA changes along the length of ORFs after introducing miR-155. mRNA segments of quantified genes were binned based on their distance from the first nucleotide of the start codon, with the boundaries of the segments chosen such that each bin contained the same number of nucleotides (Supplementary Fig. 8b). Binning was done separately for mRNAs with no miR-155 site and proteomics-supported miR-155 targets. Fold changes in RPFs and mRNA-Seq tags mapping to each bin were then plotted with respect to the median distance of the central nucleotide of each segment from the first nucleotide of the start codon. Changes in RPFs and mRNA-Seq tags for mRNAs with no site (grey and black, respectively) and for proteomic-supported targets (light and dark green, respectively) are shown. Only bins with read contribution from ≥20 genes are shown (see Supplementary Fig. 8b). The ANCOVA test for systematic change across the ORF length was performed by first calculating the differences between RPF changes and mRNA-Seq changes for each group of genes, fitting lines through these changes in translational efficiency, then testing for a difference between the resulting slopes. b, As in panel a, but plotting results for the miR-1 experiment. c, As in panel a, but plotting results for the miR-223 experiment.
Comment in
- Small RNAs: Targeting transcripts for destruction.
Swami M. Swami M. Nat Rev Genet. 2010 Oct;11(10):672. doi: 10.1038/nrg2870. Epub 2010 Aug 24. Nat Rev Genet. 2010. PMID: 20733592 No abstract available.
Similar articles
- Translational repression stabilizes messenger RNA of autophagy-related genes.
Khambu B, Uesugi M, Kawazoe Y. Khambu B, et al. Genes Cells. 2011 Aug;16(8):857-67. doi: 10.1111/j.1365-2443.2011.01532.x. Genes Cells. 2011. PMID: 21790910 - A novel class of microRNA-recognition elements that function only within open reading frames.
Zhang K, Zhang X, Cai Z, Zhou J, Cao R, Zhao Y, Chen Z, Wang D, Ruan W, Zhao Q, Liu G, Xue Y, Qin Y, Zhou B, Wu L, Nilsen T, Zhou Y, Fu XD. Zhang K, et al. Nat Struct Mol Biol. 2018 Nov;25(11):1019-1027. doi: 10.1038/s41594-018-0136-3. Epub 2018 Oct 8. Nat Struct Mol Biol. 2018. PMID: 30297778 Free PMC article. - MicroRNA targeting specificity in mammals: determinants beyond seed pairing.
Grimson A, Farh KK, Johnston WK, Garrett-Engele P, Lim LP, Bartel DP. Grimson A, et al. Mol Cell. 2007 Jul 6;27(1):91-105. doi: 10.1016/j.molcel.2007.06.017. Mol Cell. 2007. PMID: 17612493 Free PMC article. - Mechanisms of regulation of mature miRNAs.
Towler BP, Jones CI, Newbury SF. Towler BP, et al. Biochem Soc Trans. 2015 Dec;43(6):1208-14. doi: 10.1042/BST20150157. Biochem Soc Trans. 2015. PMID: 26614662 Review. - When the message goes awry: disease-producing mutations that influence mRNA content and performance.
Mendell JT, Dietz HC. Mendell JT, et al. Cell. 2001 Nov 16;107(4):411-4. doi: 10.1016/s0092-8674(01)00583-9. Cell. 2001. PMID: 11719181 Review.
Cited by
- Recent advances in the development and clinical application of miRNAs in infectious diseases.
Nunes S, Bastos R, Marinho AI, Vieira R, Benício I, de Noronha MA, Lírio S, Brodskyn C, Tavares NM. Nunes S, et al. Noncoding RNA Res. 2024 Sep 2;10:41-54. doi: 10.1016/j.ncrna.2024.09.005. eCollection 2025 Feb. Noncoding RNA Res. 2024. PMID: 39296638 Free PMC article. Review. - CRISPR-based dissection of miRNA binding sites using isogenic cell lines is hampered by pervasive noise.
Prajapat MK, Vidigal JA. Prajapat MK, et al. bioRxiv [Preprint]. 2024 Sep 3:2024.09.03.611048. doi: 10.1101/2024.09.03.611048. bioRxiv. 2024. PMID: 39282279 Free PMC article. Preprint. - MicroRNAs in Hepatocellular Carcinoma Pathogenesis: Insights into Mechanisms and Therapeutic Opportunities.
Mahboobnia K, Beveridge DJ, Yeoh GC, Kabir TD, Leedman PJ. Mahboobnia K, et al. Int J Mol Sci. 2024 Aug 29;25(17):9393. doi: 10.3390/ijms25179393. Int J Mol Sci. 2024. PMID: 39273339 Free PMC article. Review. - miR‑155 promotes an inflammatory response in HaCaT cells via the IRF2BP2/KLF2/NF‑κB pathway in psoriasis.
Chen L, Liu C, Xiang X, Qiu W, Guo K. Chen L, et al. Int J Mol Med. 2024 Nov;54(5):91. doi: 10.3892/ijmm.2024.5415. Epub 2024 Sep 2. Int J Mol Med. 2024. PMID: 39219281 Free PMC article. - A new perspective on microRNA-guided gene regulation specificity, and its potential generalization to transcription factors and RNA-binding proteins.
Seitz H. Seitz H. Nucleic Acids Res. 2024 Sep 9;52(16):9360-9368. doi: 10.1093/nar/gkae694. Nucleic Acids Res. 2024. PMID: 39149906 Free PMC article. Review.
References
- Hutvagner G, Zamore PD. A microRNA in a multiple-turnover RNAi enzyme complex. Science. 2002;297:2056–2060. - PubMed
- Liu J, et al. Argonaute2 is the catalytic engine of mammalian RNAi. Science. 2004;305:1437–1441. - PubMed
- Jones-Rhoades MW, Bartel DP, Bartel B. MicroRNAs and their regulatory roles in plants. Annu Rev Plant Biol. 2006;57:19–53. - PubMed
Publication types
MeSH terms
Substances
Grants and funding
- F32 GM080853/GM/NIGMS NIH HHS/United States
- R01 GM067031-08/GM/NIGMS NIH HHS/United States
- HHMI/Howard Hughes Medical Institute/United States
- R01 GM067031-06/GM/NIGMS NIH HHS/United States
- GM080853/GM/NIGMS NIH HHS/United States
- R01 GM067031/GM/NIGMS NIH HHS/United States
- R01 GM067031-07/GM/NIGMS NIH HHS/United States
LinkOut - more resources
Full Text Sources
Other Literature Sources