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The long intergenic noncoding RNA landscape of human lymphocytes highlights the regulation of T cell differentiation by linc-MAF-4

Abstract

Long noncoding RNAs are emerging as important regulators of cellular functions, but little is known of their role in the human immune system. Here we investigated long intergenic noncoding RNAs (lincRNAs) in 13 subsets of T lymphocytes and B lymphocytes by next-generation sequencing–based RNA sequencing (RNA-seq analysis) and de novo transcriptome reconstruction. We identified over 500 previously unknown lincRNAs and described lincRNA signatures. Expression of linc-MAF-4, a chromatin-associated lincRNA specific to the TH1 subset of helper T cells, was inversely correlated with expression of MAF, a TH2-associated transcription factor. Downregulation of linc-MAF-4 skewed T cell differentiation toward the TH2 phenotype. We identified a long-distance interaction between the genomic regions of the gene encoding linc-MAF-4 and MAF, where linc-MAF-4 associated with the chromatin modifiers LSD1 and EZH2; this suggested that linc-MAF-4 regulated MAF transcription through the recruitment of chromatin modifiers. Our results demonstrate a key role for lincRNA in T lymphocyte differentiation.

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Figure 1: Identification of lincRNAs expressed in human lymphocyte subsets.
Figure 2: Definition of gene clusters in human lymphocytes.
Figure 3: LincRNA signatures of human lymphocyte subsets.
Figure 4: Gene-ontology semantic similarity matrix of 'protein-coding' genes proximal to lincRNA signatures.
Figure 5: Linc-MAF-4 contributes to TH1 differentiation.
Figure 6: Epigenetic characterization of the linc-MAF-4MAF genomic locus.

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Acknowledgements

We thank C. Cheroni for support in statistical analysis; M. Moro and M.C. Crosti for technical assistance with cell sorting; S. Biffo, D. Gabellini, P. Della Bona and A. Lanzavecchia for discussions and critical revision of the manuscript; B.J. Haas and A. Dobin for help with the integration of genome-guided Trinity with STAR aligner; the Istituto Nazionale Genetica Molecolare Bioinformatics Facility for support; and the Google Summer of Code Project for supporting C. Wheeler in the development of a plug-in used here for the open-source bioinformatics library BioRuby that adds support for the multiple-alignment format (https://github.com/csw/bioruby-maf). Supported by Il Consiglio Nazionale delle Ricerche–Il Ministero dell'Istuzione dell'Universita e della Ricerca (EPIGEN), Fondazione Cariplo (2013-0955), the Associazione Italiana per la Ricerca sul Cancro (IG2013-ID14596), the European Research Council (269022 to S.A.; 617978 to M.P.) and Fondazione Romeo ed Enrica Invernizzi.

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Contributions

V.R., A.A. and R.J.P.B. set up all the bioinformatics pipelines, performed the bioinformatics analyses and contributed to the preparation of the manuscript; G.R. and I.P. designed and performed the main experiments, analyzed the data and contributed to the preparation of the manuscript; S.C., P.G., E.P., E.S. and B.B. performed experiments and analyzed the data; M.M., R.D.F. and J.G. discussed results, provided advice and commented on the manuscript; S.A. and M.P. designed the study, supervised research and wrote the manuscript; and all authors discussed and interpreted the results.

Corresponding authors

Correspondence to Sergio Abrignani or Massimiliano Pagani.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Distribution and expression of lincRNAs in primary human lymphocytes subsets.

(a) Bar plots of expressed genes across a panel of 13 lymphocyte subsets. Average expression (± sdev) of at least four samples for each subset is reported

(b) Stacked bar plots of expressed genes percentages according to their biotype (protein coding, lincRNAs, pseudogenes, non-coding genes and other) across the analyzed human lymphocyte subsets

(c) Distribution of novel (striped) and previously annotated (black) lincRNAs in all human chromosomes

(d) Distribution of expressed novel (striped) and previously annotated (black) lincRNAs across the analyzed human lymphocyte subsets.

(e) Boxplots of gene expression values of lincRNA (blue) and protein coding genes (red) on either the whole dataset (global expression) or on a dataset filtered according to the specificity score (specific expression, Maximal JS score > 0.4)

(f) The density distribution of JS score for cell-specific receptor genes (black line) was fitted to a log-normal distribution (dotted red line). In order to derive a threshold for the cell-specificity score, we calculated the JS score value corresponding to one standard deviation away from the mean value of the fitted distribution (0.27). As a reference, the JS density distribution for the metabolic genes is reported (green line)

(g) Density distributions of maximal expression values of lincRNAs (blue area plot) and protein coding genes (red line), divided according to cellular specificity (maximal JS score < 0.4 or JS score > 0.4)

Source data

Supplementary Figure 2 Specificity of lincRNAs and protein-coding genes in primary human lymphocytes subsets.

(a) Silhouette scores (y-axis) are reported as a function of K (x-axis), the number of clusters used to partition the gene expression dataset of lincRNA genes. The average Silhouette value was calculated by taking the average of each clusters's average Si. In the graph Si data are reported for lincRNAs genes, for which the highest Si value (implying better clustering of the data) is 15

(b) Specificity of lincRNAs and protein coding genes (FPKM >1) by K-Means clustering across 13 human lymphocyte populations. Colour intensity represents the Z-score log2-normalized raw FPKM counts estimated by Cufflinks

Source data

Supplementary Figure 3 LincRNA signatures in a differentiation time course.

CD4+ naïve, TH1, TH2 and TH17 signature lincRNAs trends in CD4+ naïve T cells differentiated in TH0 conditions. RNA was collected at different time points during CD4+ naïve T cells differentiation and RNA-seq experiments were performed. Thin lines represent the trends of each signature lincRNA. Bold lines represent the average trend of all signature lincRNAs for each subset. Data are represented as a log2 normalized ratio between each time point and the relative time 0.

Source data

Supplementary Figure 4 Regulation of MAF transcription by linc-MAF-4.

(a) Expression levels (FPKM) of linc-MAF-4 and its neighboring protein coding genes DYNLRB2 and CDYL2 in CD4+ T cell subsets

(b) Expression of TBX21 an GATA3 in activated CD4+ naïve T cells differentiated in TH1 or TH2 polarizing conditions assessed at different time points by RT-qPCR (average of four independent experiments ± SEM)

(c) Expression of linc-MAF-4 and MAF assessed at different time points by RT-qPCR in activated CD4+ naïve T cells differentiated in TH1, TH2 and TH0 polarizing conditions. Bar plot of the percentage of c-Maf positive cells determined by intracellular staining at different time points is also shown (average of four independent experiments ± SEM)

(d) CD4+ naïve T cells differentiated in TH17 polarizing conditions according to Kleinewietfeld et al. (Nature 2013; 496, 518). Upper panels: intracellular staining of IL-17 and CCR6 protein expression at day 8 of differentiation (data are representative of four independent experiments) Lower panels: linc-MAF-4, MAF, RORC and IL17 transcript levels assessed at different time points by RT-qPCR (average of four independent experiments ± SEM)

(e) Test of linc-MAF-4 siRNAs in CD4+ naïve T cells. Four siRNA sequences were transfected independently in activated CD4+ naïve T cells and linc-MAF-4, MAF, GATA3 and IL4 transcript levels were assessed by RT-qPCR at day 3 post-transfection and activation (average of five independent experiments ± SEM)

(f) Intracellular staining of c-Maf and GATA-3 in naive CD4+ T cells stimulated with anti-CD3 and anti-CD28 and transfected with a control siRNA or linc-MAF-4 siRNA assessed at day 4 post-transfection and activation. Data are representative of five independent experiments

Source data

Supplementary Figure 5 Chromosome-conformation capture on in vitro–differentiated CD4+ TH1 cells.

(a) 2.5% agarose gel of the experimental triplicate used for 3C followed by BAC controls amplified with different primers that span the region between linc-MAF-4 and MAF

(b) Sequencing results with pertaining electropherograms and BLAST alignments for M1-L7 and M1-L12 amplicons

(c) Validation of anti-LSD1 and EZH2 antibodies used in RIP assay. LSD1 and EZH2 immunoprecipitates specifically retrieve HOTAIR RNA in HeLa cells as shown by Tsai et al. Science 329, 689 (2010). RNU2.1 and a region upstream the TSS of linc-MAF-4 were used as negative controls

(d) ChIP-qPCR analysis of EZH2 and H3K27me3 at MYOD1 locus, of H3K27me3 at a control region within the chromatin loop and of LSD1 at beta-actin locus in activated CD4+ naïve T cells transfected with linc-MAF-4 siRNA (black) or ctrl siRNA (white) (average of at least three independent experiments ± SEM)

Source data

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5, Supplementary Tables 1 and 3, and Supplementary Note (PDF 4639 kb)

Supplementary Table 2

GSEA gene lists CD4+ TH1 and TH2 specific genes (XLSX 14 kb)

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Ranzani, V., Rossetti, G., Panzeri, I. et al. The long intergenic noncoding RNA landscape of human lymphocytes highlights the regulation of T cell differentiation by linc-MAF-4. Nat Immunol 16, 318–325 (2015). https://doi.org/10.1038/ni.3093

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