• A Corrigendum to this article was published on 16 November 2017

Abstract

Regulatory T cells (Treg cells) perform two distinct functions: they maintain self-tolerance, and they support organ homeostasis by differentiating into specialized tissue Treg cells. We found that epigenetic modifications defined the molecular characteristics of tissue Treg cells. Tagmentation-based whole-genome bisulfite sequencing revealed more than 11,000 regions that were methylated differentially in pairwise comparisons of tissue Treg cell populations and lymphoid T cells. Similarities in the epigenetic landscape led to the identification of a common tissue Treg cell population that was present in many organs and was characterized by gain and loss of DNA methylation that included many gene sites associated with the TH2 subset of helper T cells, such as the gene encoding cytokine IL-33 receptor ST2, as well as the production of tissue-regenerative factors. Furthermore, the ST2-expressing population was dependent on the transcriptional regulator BATF and could be expanded by IL-33. Thus, tissue Treg cells integrate multiple waves of epigenetic reprogramming that define their tissue-restricted specialization.

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References

  1. 1.

    , & Regulatory T cells: mechanisms of differentiation and function. Annu. Rev. Immunol. 30, 531–564 (2012).

  2. 2.

    , , & FOXP3+ regulatory T cells in the human immune system. Nat. Rev. Immunol. 10, 490–500 (2010).

  3. 3.

    , & Tissue Tregs. Annu. Rev. Immunol. 34, 609–633 (2016).

  4. 4.

    et al. Lean, but not obese, fat is enriched for a unique population of regulatory T cells that affect metabolic parameters. Nat. Med. 15, 930–939 (2009).

  5. 5.

    et al. PPAR-γ is a major driver of the accumulation and phenotype of adipose tissue Treg cells. Nature 486, 549–553 (2012).

  6. 6.

    et al. The transcriptional regulators IRF4, BATF and IL-33 orchestrate development and maintenance of adipose tissue-resident regulatory T cells. Nat. Immunol. 16, 276–285 (2015).

  7. 7.

    et al. A special population of regulatory T cells potentiates muscle repair. Cell 155, 1282–1295 (2013).

  8. 8.

    et al. A distinct function of regulatory T cells in tissue protection. Cell 162, 1078–1089 (2015).

  9. 9.

    et al. Tissue-resident macrophage enhancer landscapes are shaped by the local microenvironment. Cell 159, 1312–1326 (2014).

  10. 10.

    et al. Environment drives selection and function of enhancers controlling tissue-specific macrophage identities. Cell 159, 1327–1340 (2014).

  11. 11.

    et al. Identification of regulatory networks in HSCs and their immediate progeny via integrated proteome, transcriptome, and DNA methylome analysis. Cell Stem Cell 15, 507–522 (2014).

  12. 12.

    et al. Epigenetic control of the foxp3 locus in regulatory T cells. PLoS Biol. 5, e38 (2007).

  13. 13.

    et al. T cell receptor stimulation-induced epigenetic changes and Foxp3 expression are independent and complementary events required for Treg cell development. Immunity 37, 785–799 (2012).

  14. 14.

    et al. Mucosal immunology. Individual intestinal symbionts induce a distinct population of RORγ+ regulatory T cells. Science 349, 993–997 (2015).

  15. 15.

    , , & GATA-3 function in innate and adaptive immunity. Immunity 41, 191–206 (2014).

  16. 16.

    et al. Global mapping of H3K4me3 and H3K27me3 reveals specificity and plasticity in lineage fate determination of differentiating CD4+ T cells. Immunity 30, 155–167 (2009).

  17. 17.

    , & Interleukin-33 in tissue homeostasis, injury, and inflammation. Immunity 42, 1005–1019 (2015).

  18. 18.

    & The regulation of IL-10 production by immune cells. Nat. Rev. Immunol. 10, 170–181 (2010).

  19. 19.

    et al. Role of DNA methylation in modulating transcription factor occupancy. Cell Rep. 12, 1184–1195 (2015).

  20. 20.

    et al. BATF-JUN is critical for IRF4-mediated transcription in T cells. Nature 490, 543–546 (2012).

  21. 21.

    et al. Amphiregulin, a TH2 cytokine enhancing resistance to nematodes. Science 314, 1746 (2006).

  22. 22.

    , & Regulatory T cell memory. Nat. Rev. Immunol. 16, 90–101 (2016).

  23. 23.

    et al. T cell receptor signal strength in Treg and iNKT cell development demonstrated by a novel fluorescent reporter mouse. J. Exp. Med. 208, 1279–1289 (2011).

  24. 24.

    , , & The aryl hydrocarbon receptor: multitasking in the immune system. Annu. Rev. Immunol. 32, 403–432 (2014).

  25. 25.

    , & GPR55 - a putative “type 3” cannabinoid receptor in inflammation. J. Basic Clin. Physiol. Pharmacol. 27, 297–302 (2016).

  26. 26.

    et al. The putative cannabinoid receptor GPR55 plays a role in mechanical hyperalgesia associated with inflammatory and neuropathic pain. Pain 139, 225–236 (2008).

  27. 27.

    et al. Antigen- and cytokine-driven accumulation of regulatory T cells in visceral adipose tissue of lean mice. Cell Metab. 21, 543–557 (2015).

  28. 28.

    Driving allotolerance: CAR-expressing Tregs for tolerance induction in organ and stem cell transplantation. J. Clin. Invest. 126, 1248–1250 (2016).

  29. 29.

    et al. The alarmin IL-33 promotes regulatory T-cell function in the intestine. Nature 513, 564–568 (2014).

  30. 30.

    et al. Treg-mediated immune tolerance and the risk of solid cancers: findings from EPIC-Heidelberg. J. Natl. Cancer Inst. 107, djv224 (2015).

  31. 31.

    , & Regulatory T cells prevent catastrophic autoimmunity throughout the lifespan of mice. Nat. Immunol. 8, 191–197 (2007).

  32. 32.

    et al. Regulatory T cell-derived interleukin-10 limits inflammation at environmental interfaces. Immunity 28, 546–558 (2008).

  33. 33.

    et al. Improved tagmentation-based whole-genome bisulfite sequencing for input DNA from less than 100 mammalian cells. Epigenomics 7, 47–56 (2015).

  34. 34.

    et al. Tagmentation-based whole-genome bisulfite sequencing. Nat. Protoc. 8, 2022–2032 (2013).

  35. 35.

    et al. Decoding the regulatory landscape of medulloblastoma using DNA methylation sequencing. Nature 510, 537–541 (2014).

  36. 36.

    & Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

  37. 37.

    , & BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biol. 13, R83 (2012).

  38. 38.

    & BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

  39. 39.

    , , , & circlize Implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).

  40. 40.

    et al. JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 44, D1, D110–D115 (2016).

  41. 41.

    , & Statistical mechanical modeling of genome-wide transcription factor occupancy data by MatrixREDUCE. Bioinformatics 22, e141–e149 (2006).

  42. 42.

    et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

  43. 43.

    et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

  44. 44.

    , & HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

  45. 45.

    , & Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

  46. 46.

    & Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

  47. 47.

    et al. Count-based differential expression analysis of RNA sequencing data using R and Bioconductor. Nat. Protoc. 8, 1765–1786 (2013).

  48. 48.

    , & Methylation plotter: a web tool for dynamic visualization of DNA methylation data. Source Code Biol. Med. 9, 11 (2014).

  49. 49.

    , & A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000 Res. 5, 2122 (2016).

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Acknowledgements

We thank A. Rudensky (Memorial Sloan-Kettering Cancer Center) for mice; F. Lyko for help with amplicon sequencing; Z. Gu for bioinformatics support; S. Schmitt, M. Wühl and F. Ilmberger for laboratory support; and the DKFZ core facilities Preclinical Research, Flow Cytometry and Genomics & Proteomics for technical support. Supported by the Helmholtz Association of German Research Centers (HZ-NG-505 to M.F.), the European Research Council (ERC-2015-CoG, #648145 REGiREG to M.F.), the German-Israeli Helmholtz Research School in Cancer Biology (M.D.) and the German Ministry of Research and Education (031L0076A and 01KU1216B to B.B.).

Author information

Author notes

    • Michael Delacher
    •  & Charles D Imbusch

    These authors contributed equally to this work.

Affiliations

  1. Immune Tolerance Research Group, German Cancer Research Center (DKFZ), Heidelberg, Germany.

    • Michael Delacher
    • , Ulrike Träger
    • , Ann-Cathrin Hofer
    • , Danny Kägebein
    •  & Markus Feuerer
  2. Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.

    • Charles D Imbusch
    • , Qi Wang
    • , Felix Frauhammer
    •  & Benedikt Brors
  3. Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), Heidelberg, Germany.

    • Dieter Weichenhan
    •  & Christoph Plass
  4. Division of Epigenetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.

    • Achim Breiling
  5. Genomics and Proteomics Core Facility, German Cancer Research Center (DKFZ), Heidelberg, Germany.

    • Agnes Hotz-Wagenblatt
  6. Research Group Genome Organization & Function, German Cancer Research Center (DKFZ), Heidelberg, Germany.

    • Jan-Philipp Mallm
  7. Heidelberg Center for Personalized Oncology (DKFZ-HIPO), German Cancer Research Center (DKFZ), Heidelberg, Germany.

    • Jan-Philipp Mallm
    •  & Katharina Bauer
  8. Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany.

    • Carl Herrmann
  9. Department of Molecular Medicine II, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.

    • Philipp A Lang
  10. National Center for Tumor Diseases (NCT), Heidelberg, Germany.

    • Benedikt Brors
  11. German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.

    • Benedikt Brors
  12. Institute of Immunology, Regensburg Center for Interventional Immunology (RCI), University Regensburg and University Hospital Regensburg, Regensburg, Germany.

    • Markus Feuerer

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Contributions

M.D., C.D.I., A.B., A.H.-W., Q.W., F.F., C.H., B.B. and M.F. analyzed data; M.D., D.W., C.P. and M.F. designed the study; M.D., D.W., P.A.L. and M.F. designed experiments; M.D., D.W., U.T., A.-C.H., D.K., J.-P.M. and K.B. performed the experiments; and M.D. and M.F. wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Markus Feuerer.

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https://doi.org/10.1038/ni.3799

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