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Critical role of IRF1 and BATF in forming chromatin landscape during type 1 regulatory cell differentiation

Nature Immunology volume 18, pages 412–421 (2017)Cite this article

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Abstract

Type 1 regulatory T cells (Tr1 cells) are induced by interleukin-27 (IL-27) and have critical roles in the control of autoimmunity and resolution of inflammation. We found that the transcription factors IRF1 and BATF were induced early on after treatment with IL-27 and were required for the differentiation and function of Tr1 cells in vitro and in vivo. Epigenetic and transcriptional analyses revealed that both transcription factors influenced chromatin accessibility and expression of the genes required for Tr1 cell function. IRF1 and BATF deficiencies uniquely altered the chromatin landscape, suggesting that these factors serve a pioneering function during Tr1 cell differentiation.

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Figure 1: IRF1 and BATF are required for Tr1 differentiation in vitro.
Figure 2: The effects of IRF1 and BATF deficiency on Tr1 function.
Figure 3: IRF1 affects induction and resolution of autoimmune disease.
Figure 4: IRF1 and BATF control generation of IL-10+ Tr1 cells in vivo.
Figure 5: IRF1 and BATF bind to the Il10 locus.
Figure 6: IRF1 and BATF control the Il10 locus.
Figure 7: BATF- and IRF1-deficiences uniquely contribute to Tr1 chromatin and transcriptional landscapes.

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Acknowledgements

We thank D. Kozoriz (Evergrande Center) for cell sorting, F. Quintana (Harvard Medical School) for providing AhR mutant mice, the New York University Langone Medical Center genomics core (A. Heguy and P. Zappile) for help with sequencing, and the Genome Technology Center (at the Laura and Isaac Perlmutter Cancer Center supported by the Cancer Center Support Grant P30CA016087) for help with sequencing. We are most thankful to M. Collins for advice and editing of the manuscript. This work was supported by the US national Institutes of Health (R01 NS 030843, R01 NS 045937, P01 AI073748, P01 NS076410, P01 AI039671 and 5P01 AI056299-10 to V.K.K.; P01NS076410 to D.L. and M.P.; 5T32AI100853 to M.P.; 1R01-DK103358-01 to E.R.M., R.B. and D.L.; and 1R01-GM112192-01 to R.B.), the National Multiple Sclerosis Society (K.K.), Marie Skłodowska-Curie Actions (of the European Commission / Research Executive Agency), grant agreement number 302915, Marie Curie International Outgoing Fellowship (K.K.), the National Science Foundation (IOS-1126971 to R.B.), the DBT-Wellcome Trust (IA/I/12/1/500524 to A.A.), the Klarman Cell Observatory at the Broad Institute (A.R.), the Howard Hughes Medical Institute (A.R.) and the National Cancer Institute (Koch Institute Support (P30-CA14051 to A.R.).

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Authors and Affiliations

  1. Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, Massachusetts, USA

    Katarzyna Karwacz, Asaf Madi & Vijay K Kuchroo

  2. Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, New York, USA

    Emily R Miraldi, Aaron Watters, Nicholas Carriero & Richard Bonneau

  3. Skirball Institute for Biomolecular Medicine, New York University School of Medicine, New York, New York, USA

    Emily R Miraldi, Maria Pokrovskii & Dan Littman

  4. Department of Electrical Engineering and Computer Science and Center for Computational Biology, University of California, Berkeley, Berkeley, California, USA.,

    Nir Yosef

  5. Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts, USA

    Ivo Wortman & Aviv Regev

  6. Department of Biology, New York University, New York, New York, USA

    Xi Chen & Richard Bonneau

  7. Translational Health Science and Technology Institute (an autonomous institute of Department of Biotechnology, Center for Human Microbial Ecology, Govt. of India), NCR Biotech Science Cluster, Faridabad, India

    Amit Awasthi

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  1. Katarzyna Karwacz
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Contributions

K.K. planned, performed and analyzed all of the biological experiments and wrote the manuscript. E.R.M. designed and actuated computational analyses for RNA-seq/ATAC-seq, and contributed to the interpretation of the results and writing of the manuscript. M.P. prepared ATAC-seq libraries, coordinated sequencing with the NYUMC genomics core, and contributed to interpretation of the results and general discussion. A.M., N.Y., X.C., A.W. and N.C. contributed to computational analysis. I.W. prepared RNA-seq libraries and coordinated sequencing. A.A. provided reagents and advice. A.R. developed and undertook transcriptional analysis of IL-27-induced Tr1 cells. R.B. contributed to computational analysis, interpretation of the results and writing of the manuscript. D.L. advised and provided insight into the analysis of ATAC-seq and RNA-seq analysis. V.K.K. supervised the study and wrote the manuscript.

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Correspondence to Vijay K Kuchroo.

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Integrated supplementary information

Supplementary Figure 1 IRF1 and BATF affect T cell gene expression in vitro and in vivo

(a) Quantification of IL-10 and IFN-γ expression in WT, Irf1-/- and Batf-/- naïve cells differentiated in Tr1 conditions for 72 hrs. Dots represent individual mice. (b) FoxP3 expression (mRNA) in WT and Irf1−/− cells treated with TGF-β1 for 72 hrs. (c) Il10 expression in TH0 CD4+ T cells transduced with a retroviral vector expressing IRF1 and/or BATF. (d) Heatmap showing differential gene expression in cells isolated from WT, Irf1−/− and Batf−/− mice immunized with MOG35-55 and restimulated in vitro in the presence of MOG35-55 and IL-23. (e) IL-17A expression measured by FACS in WT and Irf1−/− cells differentiated in vitro in Th17 polarizing conditions (IL-1β+IL-6+IL-23). (f) Quantification of the frequency of IL-17A+ cells shown in (e). (g) Comparison of gene expression in CD4+ T cells differentiated in vitro with IL-27 and CD4+ T cells isolated from mice immunized with MOG35-55 and later restimulated in the presence of IL-27. Shown are fold changes between cells treated with IL-27 and cells treated in the absence of IL-27. Data are pooled from 3 independent experiments (a, b, c), representative of three independent experiments (e, f), pooled from 2 independent experiments (d, g). NS, not significant (P > 0.05); *P < 0.05, **P < 0.001 and ***P < 0.0001 (unpaired t-test, error bars represent mean ±s.e.m.).

Supplementary Figure 2 IRF1 and IRF4 play opposite roles during Tr1 and Th17 differentiation

(a) FACS analysis of IL-10 expression in WT and Irf4−/− cells differentiated in Th0 and Tr1 conditions for 72 hours. (b) Il17a expression (mRNA) in CD4+ T cells differentiated in Th17 polarizing conditions (IL-1β+IL-6+IL-23) with either IRF1 (right bar) or a GFP control (middle bar) retrovirally transduced (c) IL-17A luciferase reporter activity in 293T cells expressing IRF1, IRF4 or both. (d) Il10 expression (mRNA) in CD4+ T cells differentiated in Tr1 polarizing conditions with either IRF4 (right bar) or a GFP control (middle bar) retrovirally transduced. (e) IL-10 luciferase reporter (proximal promoter, HSS+2.98 and CNS-9 fragments) activity in 293T cells expressing IRF1, IRF4 or both. (f) IRF1 binding to sites within Il17a, Il23r and Rorc in WT, Irf1−/− and Irf4−/− CD4+ T cells differentiated in Th17 polarization conditions (IL-1β+IL-6+IL-23). Relative mean enrichment fold (±s.e.m.) in reference to a control antibody is presented. Data are representative of 3 independent experiments (a, c, e), 2 independent experiments (b, d) or pooled from 2 independent experiments (f).

Supplementary Figure 3 Differential accessibility analysis (ATAC-seq)

(a) Number of differentially accessible peaks detected using DESeq2, for many comparisons of interest (log2|FC|>1, FDR=10%). (b) PCA of all ATAC-seq samples, using all ATAC-seq peaks in the dataset (180,478 peaks, log10-transformed intensities).

Supplementary Figure 4 BATF- and IRF1-deficiencies alter Tr1 gene expression patterns

(a) PCA of RNA-seq gene expression data for Irf1−/−, Batf−/− and control Tr1 cells at 72h (15,715 genes, log2-noramlized relative to mean). The PCA scores plot, with samples plotted as functions of gene expression (black (WT), red (Irf1−/−), blue (Batf−/−) balls), is super-imposed on the loadings plot (showing the top 50 genes whose patterns contribute most to the dominant gene expression trends in the dataset). (b) The number of differentially expressed genes, comparing KO to control (FDR=10%, log2|fold-change|>1). (c) The most dramatically KO-dependent genes (FDR=1%, log2|fold-change|>2, for either Batf−/− or Irf1−/−, relative to control).

Supplementary Figure 5 Tr1 IRF1-dependent Network (Zoom-in)

RNA-seq and ATAC-seq datasets were integrated to generate putative transcriptional regulatory networks for Irf1−/− Tr1 cells. Nodes represent differentially expressed transcription factors and target genes, colored according to relative gene expression (log2(Irf1−/−/Control)) at 72h; red and blue indicate high and low relative expression, respectively. Edges are drawn between differentially expressed TFs and putative gene targets if that TF’s motif was enriched in ATAC-seq peaks cis to genes that were increased or decreased in response to Irf1−/− (Praw<10-3, hypergeometric CDF). Edge colors (blue - inhibitory, red - activating) are based on correlation between TF and putative target gene expression. “ON” indicates genes de-repressed in the knockout cells, while “OFF” indicates genes repressed in the knockout cells.

Supplementary Figure 6 Tr1 BATF-dependent Network (Zoom-in)

RNA-seq and ATAC-seq datasets were integrated to generate putative transcriptional regulatory networks for Batf−/− Tr1 cells. Nodes represent differentially expressed transcription factors and target genes, colored according to relative gene expression (log2(Batf−/−/Control)) at 72h; red and blue indicate high and low relative expression, respectively. Edges are drawn between differentially expressed TFs and putative gene targets if that TF’s motif was enriched in ATAC-seq peaks cis to genes that were increased or decreased in response to Batf−/− (Praw<10-3, hypergeometric CDF). Edge colors (blue - inhibitory, red - activating) are based on correlation between TF and putative target gene expression. “ON” indicates genes de-repressed in the knockout cells, while “OFF” indicates genes repressed in the knockout cells.

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Karwacz, K., Miraldi, E., Pokrovskii, M. et al. Critical role of IRF1 and BATF in forming chromatin landscape during type 1 regulatory cell differentiation. Nat Immunol 18, 412–421 (2017). https://doi.org/10.1038/ni.3683

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