Mammalian microRNAs predominantly act to decrease target mRNA levels

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.

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.