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. 2014 Mar 20;53(6):1031-1043.
doi: 10.1016/j.molcel.2014.02.013. Epub 2014 Mar 13.

Global analyses of the effect of different cellular contexts on microRNA targeting

Jin-Wu Nam #  1   2   3   4 , Olivia S Rissland #  1   2   3 , David Koppstein  1   2   3 , Cei Abreu-Goodger  5 , Calvin H Jan  1   2   3 , Vikram Agarwal  1   2   6 , Muhammed A Yildirim  1   2   3 , Antony Rodriguez  7 , David P Bartel  1   2   3
Affiliations

Global analyses of the effect of different cellular contexts on microRNA targeting

Jin-Wu Nam et al. Mol Cell. .

Abstract

MicroRNA (miRNA) regulation clearly impacts animal development, but the extent to which development-with its resulting diversity of cellular contexts-impacts miRNA regulation is unclear. Here, we compared cohorts of genes repressed by the same miRNAs in different cell lines and tissues and found that target repertoires were largely unaffected, with secondary effects explaining most of the differential responses detected. Outliers resulting from differential direct targeting were often attributable to alternative 3' UTR isoform usage that modulated the presence of miRNA sites. More inclusive examination of alternative 3' UTR isoforms revealed that they influence ∼10% of predicted targets when comparing any two cell types. Indeed, considering alternative 3' UTR isoform usage improved prediction of targeting efficacy significantly beyond the improvements observed when considering constitutive isoform usage. Thus, although miRNA targeting is remarkably consistent in different cell types, considering the 3' UTR landscape helps predict targeting efficacy and explain differential regulation that is observed.

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Figures

Figure 1
Figure 1. Most miRNA-target interactions are unaffected by cell type
(A–F) Pairwise comparisons of mRNA changes after transfecting the same miRNA into different cell lines. Shown are changes for genes with at least one 7mer 3'UTR site for the indicated miRNA, plotting the results for genes expressed in both cell lines. The region corresponding to a log2 change > −0.3 is shaded (grey); n, number of genes outside the grey region. Genes significantly differentially repressed are highlighted (blue) and tallied (number in parentheses). In some cases, not all of the differentially repressed genes fit within the plots. (G–L) As in A–F, but for control genes. For the miR-124 transfections, mRNA changes are plotted for genes with miR-155 sites (excluding any that contained sites to both miRNAs), and vice versa.
Figure 2
Figure 2. The 3'UTR landscape affects miRNA targeting
(A) Different AIRs for miR-124 sites the LRCC1 gene in different cell types. Shown is the RefSeq annotation track of LRCC1 (dark blue), with the associated 3P tags from the three cell lines assayed (above) and the corresponding AIRs (below). (B, C) Extent to which APA affects miRNA site inclusion. Shown are the number and percentage of sites for which AIRs for miR-124 (B) or miR-155 (C) change by at least 0.3 in each pairwise cell-type comparison. The arrows point to the cell line with the higher AIR, and the width is proportional to the number of sites with differential AIR. (D–G) Relationship between AIR and miRNA-mediated repression. For each site type [8mer (D), 7mer-m8 (E), 7mer-A1 (F) and a representative pair of control sites (G)], predicted targets were binned by their AIR. For each bin, the mean fold-change mediated by either miR-124 or miR-155 for each transfection of the various cell lines (HEK293, HeLa and Huh7) is plotted. The red line is the least-squares best fit to the data (Pearson r2, F-test)..
Figure 3
Figure 3. The weighted context+ model improves target prediction
(A) Calculation of wContext+ scores. For each site, the context+ score, calculated using the TargetScan linear regression model, is weighted by a cell-type–specific AIR. For genes with multiple sites, the scores for each individual site are added to yield the total wContext+ score. (B) Improved performance of the wContext+ model. Plotted are r2 values calculated from the correlation (Pearson r) between score and observed change in the indicated transfection dataset. For the previous model (context+), three different 3'UTR annotations were used: the RefSeq annotation (dark blue); the longest isoform, as determined by 3P-seq (light blue); the major isoform, as determined by 3P-seq (purple).
Figure 4
Figure 4. Differential miRNA-mediated repression is often due to alternative 3'UTR isoform usage
(A) Genes with differential AIRs are enriched in genes that are differentially repressed. As in Figure 1D, but highlighting genes with significantly different repression that also have wContext+ score differences ≥ 0.03 (orange). (B) Higher AIR of CHURC1 miR-155 sites in HeLa compared to HEK293 cells. Otherwise, as in Figure 2A. (C) Greater miR-155 repression of CHURC1 in HeLa cells. Plotted are the wContext+ and expression change for CHURC1 in HeLa (pink) and HEK293 (blue) cells. (D, E) As in (B, C), except for ATAD2B, a gene with higher AIR and greater miR-155 repression in HEK293 cells. (F) As in (A), except comparing changes mediated by miR-124 in HeLa and HEK293 cells. (G) As in (C), except for ANTXR2, a gene with higher AIR and greater miR-124 repression in HeLa cells. (H) As in (A), except comparing changes mediated by miR-124 in HEK293 and HeLa cells. (I) As in (C), except for CLDN1, a gene with higher AIR and greater miR-124 repression in HeLa cells (J) Direct measurements of miR-155-mediated repression of 3'UTR segments from nine genes initially classified as differentially regulated despite having similar AIRs. Renilla luciferase reporters followed by 3'UTR segments (with either wild-type or mutated miR-155 sites) from the indicated genes were transfected into either HeLa or HEK293 cells in the presence of the cognate (miR-155) or a non-cognate (miR-1) miRNA. Five genes were repressed more in HeLa cells in the genome-wide analyses (highlighted in pink), and four were repressed more in HEK293 cells (highlighted in blue). Plotted are the normalized repression values, with error bars representing the third largest and third smallest values. Significance was calculated with the Mann-Whitney U-test (*, P < 0.05, **, P < 0.01, *** P < 0.001).
Figure 5
Figure 5. Alternative 3'UTR isoform usage affects targeting by endogenous miRNAs
(A) Relationship between AIR and endogenous repression by miR-22. As in Figure 2D–G, but comparing mRNA changes in mouse tissues (muscle, heart, liver, kidney, white adipose tissue (WAT) and lung) with and without miR-22. (B) Improved performance of the wContext+ model for predicting endogenous miR-22 targeting in mice. Otherwise, as in Figure 3B. (C) Improved performance of the wContext+ model for predicting endogenous miR-430 targeting in zebrafish embryos. As in Figure 3B, except analyzing predicted miR-430 targets in wild-type embryos and embryos that lack miR-430 (MZ-Dicer) at 9 hours post fertilization (hpf).
Figure 6
Figure 6. Considering isoform ratios improves the model of miRNA targeting in non-cognate cell types
(A) The performance of non-cell-type–specific wContext+ models for exogenous miRNAs. A comparison of performance of the original context+ model (dark blue), the cell-type–specific wContext+ model (pink) and the wContext+ model based on 3P-seq from other cell types (grey; error bars, standard deviation). Otherwise, as in Figure 3B. (B) As in (A), but for endogenous targeting by murine miR-22. (C) Non-cell-type–specific wContext+ model improves prediction of endogenous targeting mediated by miR-223 in neutrophils and miR-155 in B and Th1 cells. Otherwise, as in (A).
Figure 7
Figure 7. Repression by miR-22 shapes the 3'UTR landscape
(A–E) Influence of miR-22 targeting on 3'UTR isoform usage. Weighted 3'UTR lengths were determined using 3P-seq data from heart (A), liver (B), muscle (C), kidney (D) and WAT (E). Plotted are the cumulative distributions of the differences in lengths (subtracting that of the wild-type tissue from that of the miR-22Δ tissue) for genes with control sites in the variable region (grey) and those with miR-22 sites in the variable region (red). Significance was determined using the Kolmogorov-Smirnov test.
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