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The effect of cellular context on miR-155-mediated gene regulation in four major immune cell types

Nature Immunology volume 19, pages 1137–1145 (2018)Cite this article

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Abstract

Numerous microRNAs and their target mRNAs are coexpressed across diverse cell types. However, it is unknown whether they are regulated in a manner independent of or dependent on cellular context. Here, we explored transcriptome-wide targeting and gene regulation by miR-155, whose activation-induced expression plays important roles in innate and adaptive immunity. Through mapping of miR-155 targets through differential iCLIP, mRNA quantification with RNA-seq, and 3′ untranslated region (UTR)-usage analysis with poly(A)-seq in macrophages, dendritic cells, and T and B lymphocytes either sufficient or deficient in activated miR-155, we identified numerous targets differentially bound by miR-155. Whereas alternative cleavage and polyadenylation (ApA) contributed to differential miR-155 binding to some transcripts, in most cases, identical 3′-UTR isoforms were differentially regulated across cell types, thus suggesting ApA-independent and cellular-context-dependent miR-155-mediated gene regulation. Our study provides comprehensive maps of miR-155 regulatory networks and offers a valuable resource for dissecting context-dependent and context-independent miRNA-mediated gene regulation in key immune cell types.

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Fig. 1: miR-155-mediated Ago binding occurs at distinct sites in four immune cell types.
Fig. 2: miR-155 represses distinct sets of genes in four immune cell types.
Fig. 3: Context-specific miR-155 targeting leads to differences in gene regulation between cell types.
Fig. 4: Verification of cell-type-dependent miR-155-mediated repression.
Fig. 5: Poly(A)-seq captures change in 3′-UTR-isoform usage during CD4+ T cell activation.
Fig. 6: The role of alternative polyadenylation in cellular-context-dependent regulation of gene expression by miR-155.
Fig. 7: Top iCLIP target sites of other miRNAs induce significant gene repression.

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Data availability

Data from this study have been deposited in the GEO database under accession code GSE116561. Individual dataset accession codes are as follows. RNA-seq dataset (GSE116348); Differential iCLIP dataset (GSE116466); and poly(A)-seq dataset (GSE116468).

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Acknowledgements

We thank A. Chaudhry and other laboratory members for experimental assistance and discussions. This work was supported by National Institutes of Health grants Al034206 (A.Y.R.), HG007893 (A.Y.R. and C.S.L.), CA164190 (C.S.L.), and P30 CA008748, as well as the Hilton-Ludwig Cancer Prevention Initiative funded by the Conrad N. Hilton Foundation and Ludwig Cancer Research. J.-P. H. was supported by an Irvington Fellowship of the Cancer Research Institute. A.Y.R. is supported as an Investigator with the Howard Hughes Medical Institute. We thank J. Chaudhuri (Memorial Sloan Kettering Cancer Center) for providing antibodies.

Author information

Author notes

  1. Yuheng Lu

    Present address: Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA

  2. Gabriel B. Loeb

    Present address: Department of Medicine, University of California San Francisco, San Francisco, CA, USA

  3. These authors contributed equally: Jing-Ping Hsin, Yuheng Lu, Gabriel B. Loeb.

Authors and Affiliations

  1. Howard Hughes Medical Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Jing-Ping Hsin, Gabriel B. Loeb & Alexander Y. Rudensky

  2. Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Jing-Ping Hsin, Gabriel B. Loeb & Alexander Y. Rudensky

  3. Ludwig Center at Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Jing-Ping Hsin, Gabriel B. Loeb & Alexander Y. Rudensky

  4. Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Yuheng Lu & Christina S. Leslie

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Contributions

J.-P.H., G.B.L., and A.Y.R. designed the study. J.-P.H. carried out all experiments. Y.L. and C.S.L. designed analytical tools and performed data analyses. J.-P.H., Y.L., and A.Y.R. wrote the manuscript; all authors edited and approved the final manuscript.

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Correspondence to Christina S. Leslie or Alexander Y. Rudensky.

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Integrated supplementary information

Supplementary Figure 1 Overview of the iCLIP libraries.

(a) miR-155 expression was induced upon activation in B cells, dendritic cells, macrophages, and CD4+ T cells. Error bars represent the standard error estimated from three independent samples (n = 3). (b) Densogram showed RNA-Ago2 complex under digestion of RNase I of different concentrations. UV cross-linked RNA-Ago2 was immunoprecipitated from dendritic cells, and RNAs were labeled with P32isotope at 5’ ends. (c) Macrophage microRNA expression levels in wild type and miR-155 KO cells as measured by iCLIP. Top 25 microRNAs with highest expression in wild type were highlighted; miR-155-5p and miR-139-3p, the most down-regulated and most up-regulated miRNAs in miR-155 KO cells, were labeled. (d) Reproducibility between iCLIP libraries of the same cell type and genotype. Pearson correlation coefficients for the log-transformed iCLIP read counts (log2(read count + 1)) within peaks were computed. In each pairwise comparison, peaks with < 5 reads are excluded. (e) The proportions of iCLIP reads mapped to different genomic categories in each library. Analyses of data from four independent iCLIP experiments are shown

Supplementary Figure 2 Expression and regulation of miR-155-target mRNAs.

(a) Pairwise comparison of WT mRNA expression levels for co-expressed genes (wild-type FPKM > 1 in both cells and difference < 16-fold) containing miR-155 dependent sites. In each comparison, the distributions of RNA-Seq FPKM values for common and cell-type specific target genes were shown separately. (b) Correlation between the FDR of miR-155 dependent iCLIP sites and the regulation of corresponding mRNAs in four cell types. Spearman correlation coefficients and P-values were computed separately for CDS (grey) and 3’UTR (black) sites. Data are representative of three independent RNA-Seq experiments

Supplementary Figure 3 Noncanonical miR-155-target sites and regulation.

(a) Number of miR-155 target sites per cell, including non-canonical sites. (b) Seed type composition of miR-155 dependent sites in co-expressed genes, including non-canonical sites. (c) Averaged profiles of normalized iCLIP read coverages around both canonical and non-canonical miR-155 sites in both wild-type (solid lines) and miR-155 deficient libraries (dotted lines) for all four cell types. Non-canonical miR-155 sites are represented with lighter colors. (d) Examples of non-canonical miR-155 target sites identified by differential iCLIP in all four cell types and in 3’UTRs containing no canonical miR-155 target sites, showing the miRNA-mRNA duplexes predicted by chimiRic and the mRNA-level regulation (log2 fold-changes from RNA-Seq) of corresponding genes. (e) Regulation of mRNA expression by non-canonical miR-155 sites in 3’UTR. The distributions of expression fold changes were shown for multiple sets of target genes with only non-canonical 3’UTR miR-155 sites selected by different FDR cutoffs, along with the genes containing canonical 3’UTR miR-155 seed matches and the genes containing canonical 3’UTR miR-155 dependent iCLIP sites for comparison. Data are representatives of independent iCLIP (n = 4) and RNA-Seq (n = 3) experiments

Supplementary Figure 4 Summary of quantification of protein levels of miR-155 targets.

(a) Quantification of protein levels of miR-155 targets. Representative western blot analyses of indicated protein expression in B cells (b), dendritic cells (c), and macrophages (d). Repression was determined based on normalized protein levels in wild type vs. miR-155 KO cells. Error bar displays standard error from at least three biologically independent samples; P-value was measured by two-sided t-test. * P < 0.05, ** P < 0.01

Supplementary Figure 5 Summary of miR-155-dependent sites identified from differential iCLIP.

(a) The number of miR-155 dependent iCLIP sites per gene (including the ones within introns and ncRNAs) in each cell type. (b) Top five genes ranked by the normalized abundance of WT iCLIP reads within miR-155 dependent sites in each cell type. Data are representative of four independent iCLIP experiments

Supplementary Figure 6 Usage of 3′-UTR isoforms across four cell types characterized by poly(A)-seq.

(a) The changes in 3’UTR isoform usage for 3’UTRs with two major isoforms at 24 h after CD4+ T cell activation. Highlighted were 3’UTRs with significant usage changes. Data are representative of three independent PolyA-Seq experiments. (b) Correlation between the abundances of RNA-Seq (FPKM) and PolyA-Seq (FPM) for genes with a single 3’UTR isoform in wild-type cells. Spearman correlation coefficients were also shown. (c) Length distribution of all 3’UTR isoforms characterized by PolyA-Seq. (d) Correlation between miR-155 mediated repression per isoform and distance from miR-155 binding sites to the polyA ends. Spearman correlation coefficient and P-value were displayed for each cell type. Analyses of data from independent RNA-Seq (n = 3) and PolyA-Seq (n = 4) experiments are shown

Supplementary Figure 7 Effect of differential ApA usage on context-specific miR-155 regulation.

(a) The effect of ApA isoform usage on the regulation of miR-155 sites in multi-isoform 3’UTRs. In each individual cell type, the usage of 3’UTR isoforms containing miR-155 site is plotted against the gene-level regulation (RNA-Seq log2(ko/wt)). Spearman correlation coefficient and P-value were displayed for each cell. (b) Venn diagrams show the number of miR-155 target genes affected by differential ApA usage in each pairwise comparison. Analyses of data from independent RNA-Seq (n = 3) and PolyA-Seq (n = 4) experiments are shown

Supplementary Figure 8 Summary of iCLIP peaks.

Proportion of top iCLIP peaks (ranked by normalized read counts in wild-type libraries) containing miR-155 seed matches that correspond to differentially bound miR-155 sites. Data are representative of four independent iCLIP experiments

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8, Supplementary Tables 2, 5, 9 and 10, and Supplementary Note

Reporting Summary

Supplementary Table 1

Number of unique iCLIP reads mapped to all high-confidence mature mouse miRNAs from miRBase in each iCLIP library

Supplementary Table 3

Summary of all significant (FDR < 2.5%) miR-155 dependent sites in mRNAs across four immune cells. The information includes the genomic coordinates and annotations of miR-155 seed matches, log2 fold changes and P-values fitted by negative binomial GLM, numbers of unique reads within corresponding iCLIP peaks from all libraries, and the type of miR-155 seed matches

Supplementary Table 4

Summary of differential mRNA expression (only considering RNA-Seq alignments in CDS exons) between wild type and miR-155 KO genotypes in all four immune cells. For each gene, the log2 fold changes and P-values fitted by negative binomial GLM are shown, along with the average FPKM levels in wild type and miR-155 KO libraries

Supplementary Table 6

Summary of all significant (FDR < 2.5%) miR-155 dependent sites in introns and non-coding RNAs across four immune cells, in the same format as Supplementary Table 3

Supplementary Table 7

Summary of ApA usage at three time points during CD4+ T cell activation (0h, 24h and 48h after activation) captured by PolyA-Seq. The information includes the genomic coordinates and annotations of PolyA-Seq peaks, along with numbers of reads within peaks from all libraries

Supplementary Table 8

Summary of ApA usage at 48 h after immune activation across four immune cells captured by PolyA-Seq, in the same format as Supplementary Table 7

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Hsin, JP., Lu, Y., Loeb, G.B. et al. The effect of cellular context on miR-155-mediated gene regulation in four major immune cell types. Nat Immunol 19, 1137–1145 (2018). https://doi.org/10.1038/s41590-018-0208-x

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