Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

c-Maf-dependent Treg cell control of intestinal TH17 cells and IgA establishes host–microbiota homeostasis

Nature Immunology volume 20, pages 471–481 (2019)Cite this article

Subjects

Abstract

Foxp3+ regulatory T cells (Treg cells) are crucial for the maintenance of immune homeostasis both in lymphoid tissues and in non-lymphoid tissues. Here we demonstrate that the ability of intestinal Treg cells to constrain microbiota-dependent interleukin (IL)-17–producing helper T cell (TH17 cell) and immunoglobulin A responses critically required expression of the transcription factor c-Maf. The terminal differentiation and function of several intestinal Treg cell populations, including RORγt+ Treg cells and follicular regulatory T cells, were c-Maf dependent. c-Maf controlled Treg cell–derived IL-10 production and prevented excessive signaling via the kinases PI(3)K (phosphatidylinositol-3-OH kinase) and Akt and the metabolic checkpoint kinase complex mTORC1 (mammalian target of rapamycin) and expression of inflammatory cytokines in intestinal Treg cells. c-Maf deficiency in Treg cells led to profound dysbiosis of the intestinal microbiota, which when transferred to germ-free mice was sufficient to induce exacerbated intestinal TH17 responses, even in a c-Maf-competent environment. Thus, c-Maf acts to preserve the identity and function of intestinal Treg cells, which is essential for the establishment of host–microbe symbiosis.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: c-Maf is expressed by effector Treg cells in vivo.
Fig. 2: c-Maf is required for Treg cell–derived IL-10 production.
Fig. 3: c-Maf-deficient Treg cells fail to control intestinal TH17 cells.
Fig. 4: Tissue-specific gene regulation by c-Maf in gut-associated Treg cells.
Fig. 5: Identification of direct c-Maf target gene candidates in gut-associated Treg cells.
Fig. 6: Elevated IL-17A and IL-22 protect mice with c-Maf-deficient Treg cells from DSS-induced colitis.
Fig. 7: c-Maf deficiency in Treg cells leads to dysbiosis of the gut microbiota.
Fig. 8: Altered microbiota drives intestinal TH17 cell responses and controls colitis susceptibility.

Similar content being viewed by others

Data availability

Sequencing data supporting the findings of this study have been deposited in the Sequence Read Archive (SRA) database under accession codes SRP123533 (ATAC-Seq) and PRJNA498200 (16S rRNA-Seq) and in the Gene Expression Omnibus (GEO) database under accession code GSE106396 (RNA-Seq).

References

  1. Bunker, J. J. & Bendelac, A. IgA responses to microbiota. Immunity 49, 211–224 (2018).

    Article  CAS  Google Scholar 

  2. Dubin, P. J. & Kolls, J. K. Th17 cytokines and mucosal immunity. Immunol. Rev. 226, 160–171 (2008).

    Article  CAS  Google Scholar 

  3. Sonnenberg, G. F., Fouser, L. A. & Artis, D. Border patrol: regulation of immunity, inflammation and tissue homeostasis at barrier surfaces by IL-22. Nat. Immunol. 12, 383–390 (2011).

    Article  CAS  Google Scholar 

  4. Ni, J., Wu, G. D., Albenberg, L. & Tomov, V. T. Gut microbiota and IBD: causation or correlation? Nat. Rev. Gastroenterol. Hepatol. 14, 573–584 (2017).

    PubMed  PubMed Central  Google Scholar 

  5. Gratz, I. K. & Campbell, D. J. Organ-specific and memory treg cells: specificity, development, function, and maintenance. Front. Immunol. 5, 333 (2014).

    Article  Google Scholar 

  6. Cretney, E. et al. The transcription factors Blimp-1 and IRF4 jointly control the differentiation and function of effector regulatory T cells. Nat. Immunol. 4, 304–311 (2011).

    Article  Google Scholar 

  7. Vasanthakumar, A. 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).

    Article  CAS  Google Scholar 

  8. Feuerer, M. 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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  11. Burzyn, D., Benoist, C. & Mathis, D. Regulatory T cells in nonlymphoid tissues. Nat. Immunol. 14, 1007–1013 (2013).

    Article  CAS  Google Scholar 

  12. Kühn, R., Löhler, J., Rennick, D., Rajewsky, K. & Muller, W. Interleukin-10-deficient mice develop chronic enterocolitis. Cell 75, 263–274 (1993).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  14. Sellon, R. K. et al. Resident enteric bacteria are necessary for development of spontaneous colitis and immune system activation in interleukin-10-deficient mice. Infect. Immun. 66, 5224–5231 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  Google Scholar 

  16. Ohnmacht, C. et al. The microbiota regulates type 2 immunity through RORγ+T cells. Science 349, 989–993 (2015).

    Article  CAS  Google Scholar 

  17. Kim, K. S. et al. Dietary antigens limit mucosal immunity by inducing regulatory T cells in the small intestine. Science 351, 858–863 (2016).

    Article  CAS  Google Scholar 

  18. Campbell, C. et al. Extrathymically generated regulatory T cells establish a niche for intestinal border-dwelling bacteria and affect physiologic metabolite balance. Immunity 48, 1245–1257.e9 (2018).

    Article  CAS  Google Scholar 

  19. Kawamoto, S. et al. Foxp3+ T cells regulate immunoglobulin A selection and facilitate diversification of bacterial species responsible for immune homeostasis. Immunity 41, 152–165 (2014).

    Article  CAS  Google Scholar 

  20. Xu, J. et al. c-Maf regulates IL-10 expression during Th17 polarization. J. Immunol. 182, 6226–6236 (2009).

    Article  CAS  Google Scholar 

  21. Neumann, C. et al. Role of Blimp-1 in programing Th effector cells into IL-10 producers. J. Exp. Med. 211, 1807–1819 (2014).

    Article  CAS  Google Scholar 

  22. Saraiva, M. et al. Interleukin-10 production by Th1 cells requires interleukin-12-induced STAT4 transcription factor and ERK MAP kinase activation by high antigen dose. Immunity 31, 209–219 (2009).

    Article  CAS  Google Scholar 

  23. Apetoh, L. et al. The aryl hydrocarbon receptor interacts with c-Maf to promote the differentiation of type 1 regulatory T cells induced by IL-27. Nat. Immunol. 11, 854–861 (2010).

    Article  CAS  Google Scholar 

  24. Gabryšová, L. et al. c-Maf controls immune responses by regulating disease-specific gene networks and repressing IL-2 in CD4+T cells. Nat. Immunol. 19, 497–507 (2018).

    Article  Google Scholar 

  25. Rutz, S. et al. Transcription factor c-Maf mediates the TGF-β-dependent suppression of IL-22 production in T(H)17 cells. Nat. Immunol. 12, 1238–1245 (2011).

    Article  CAS  Google Scholar 

  26. Xu, M. et al. c-MAF-dependent regulatory T cells mediate immunological tolerance to a gut pathobiont. Nature 554, 373–377 (2018).

    Article  CAS  Google Scholar 

  27. Wheaton, J. D., Yeh, C.-H. & Ciofani, M. Cutting edge: c-Maf is required for regulatory T cells to adopt RORγt+ and follicular phenotypes. J. Immunol. 199, 3931–3936 (2017).

    Article  CAS  Google Scholar 

  28. Pot, C. et al. Cutting edge: IL-27 induces the transcription factor c-Maf, cytokine IL-21, and the costimulatory receptor ICOS that coordinately act together to promote differentiation of IL-10-producing Tr1 cells. J. Immunol. 183, 797–801 (2009).

    Article  CAS  Google Scholar 

  29. Steward-Tharp, S. M. et al. A mouse model of HIES reveals pro- and anti-inflammatory functions of STAT3. Blood 123, 2978–2987 (2014).

    Article  CAS  Google Scholar 

  30. Madan, R. et al. Nonredundant roles for B cell-derived IL-10 in immune counter-regulation. J. Immunol. 183, 2312–2320 (2009).

    Article  CAS  Google Scholar 

  31. Wende, H. et al. The transcription factor c-Maf controls touch receptor development and function. Science 335, 1373–1376 (2012).

    Article  CAS  Google Scholar 

  32. Linterman, M. A. et al. Foxp3+ follicular regulatory T cells control the germinal center response. Nat. Med. 17, 975–982 (2011).

    Article  CAS  Google Scholar 

  33. Okkenhaug, K. et al. Impaired B and T cell antigen receptor signaling in p110delta PI 3–kinase mutant mice. Science 297, 1031–1034 (2002).

    CAS  PubMed  Google Scholar 

  34. Ciofani, M. et al. A validated regulatory network for Th17 cell specification. Cell 151, 289–303 (2012).

    Article  CAS  Google Scholar 

  35. Chassaing, B., Aitken, J. D., Malleshappa, M. & Vijay-Kumar, M. Dextran sulfate sodium (DSS)-induced colitis in mice. Curr. Protoc. Immunol. 104, Unit 15.25.–15.25.14 (2014).

    PubMed  PubMed Central  Google Scholar 

  36. Ogawa, A., Andoh, A., Araki, Y., Bamba, T. & Fujiyama, Y. Neutralization of interleukin-17 aggravates dextran sulfate sodium-induced colitis in mice. Clin. Immunol. 110, 55–62 (2004).

    Article  CAS  Google Scholar 

  37. O’Connor, W. et al. A protective function for interleukin 17A in T cell mediated intestinal inflammation. Nat. Immunol. 10, 603–609 (2009).

    Article  Google Scholar 

  38. Sugimoto, K. et al. IL-22 ameliorates intestinal inflammation in a mouse model of ulcerative colitis. J. Clin. Invest. 118, 534–544 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Zindl, C. L. et al. IL-22-producing neutrophils contribute to antimicrobial defense and restitution of colonic epithelial integrity during colitis. Proc. Natl Acad. Sci. USA 110, 12768–12773 (2013).

    Article  CAS  Google Scholar 

  40. Roy, U. et al. Distinct microbial communities trigger colitis development upon intestinal barrier damage via innate or adaptive immune cells. Cell Rep. 21, 994–1008 (2017).

    Article  CAS  Google Scholar 

  41. Siewe, L. et al. Interleukin-10 derived from macrophages and/or neutrophils regulates the inflammatory response to LPS but not the response to CpG DNA. Eur. J. Immunol. 36, 3248–3255 (2006).

    Article  CAS  Google Scholar 

  42. Amsen, D. et al. Direct regulation of Gata3 expression determines the T helper differentiation potential of Notch. Immunity 27, 89–99 (2007).

    Article  CAS  Google Scholar 

  43. Cossarizza, A. et al. Guidelines for the use of flow cytometry and cell sorting in immunological studies. Eur. J. Immunol. 47, 1584–1797 (2017).

    Article  CAS  Google Scholar 

  44. Zimmermann, J. et al. High-resolution microbiota flow cytometry reveals dynamic colitis-associated changes in fecal bacterial composition. Eur. J. Immunol. 46, 1300–1303 (2016).

    Article  CAS  Google Scholar 

  45. Kau, A. L. et al. Functional characterization of IgA-targeted bacterial taxa from undernourished Malawian children that produce diet-dependent enteropathy. Sci. Transl. Med. 7, 276ra24–276ra24 (2015).

    Article  CAS  Google Scholar 

  46. Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl Acad. Sci. USA 108, 4516–4522 (2011).

    Article  CAS  Google Scholar 

  47. Gálvez, E. J. C., Iljazovic, A., Gronow, A., Flavell, R. & Strowig, T. Shaping of intestinal microbiota in Nlrp6- and Rag2-deficient mice depends on community structure. Cell Rep. 21, 3914–3926 (2017).

    Article  Google Scholar 

  48. Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).

    Article  CAS  Google Scholar 

  49. Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).

    Article  CAS  Google Scholar 

  50. Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).

    Article  CAS  Google Scholar 

  51. McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013).

    Article  CAS  Google Scholar 

  52. Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).

    Article  Google Scholar 

  53. Erben, U. et al. A guide to histomorphological evaluation of intestinal inflammation in mouse models. Int. J. Clin. Exp. Pathol. 7, 4557–4576 (2014).

    PubMed  PubMed Central  Google Scholar 

  54. Wu, T. D. & Nacu, S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 26, 873–881 (2010).

    Article  CAS  Google Scholar 

  55. Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

    Article  Google Scholar 

  56. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47–e47 (2015).

    Article  Google Scholar 

  57. Heinz, S. 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).

    Article  CAS  Google Scholar 

  58. Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    Article  CAS  Google Scholar 

  59. Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

    Article  CAS  Google Scholar 

  60. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This research was supported by the Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy—EXC 2167-390884018 (to A.S.), SFB650 (to A.S.) and CRC/TR 241 (to A.S. and C.R.), the German Federal Ministry of Education and Science (BMBF)—Project InfectControl 2020 Projekt DIAT FKZ: 03ZZ0827A (to A.S.), the state of Berlin and the ‘European Regional Development Fund’ ERDF 2014-2020, EFRE 1.8/11, Deutsches Rheuma-Forschungszentrum (to F.H., G.A.H, P.M. and M.F.M), the Helmholtz Association project grant VH-NG-933 (to T. Strowig), the National Health and Medical Research Council (NHMRC) project grants (1069075, 1106378 to A .Kallies), the Sylvia and Charles Viertel Foundation (fellowship to A. Kallies), a Walter and Eliza Institute Centenary Fellowship funded by the CSL (to W.S.), a fellowship by the Deutsche Forschungsgemeinschaft (to J.B.) and the Victorian State Government Operational Infrastructure Support and Australian Government NHMRC Independent Research Institute Infrastructure Support scheme. We thank the DRFZ Flow Cytometry Core Facility and the BCRT Flow Cytometry Lab for cell sorting. In addition, we thank Renee Gloury and Victoria von Götze for technical and experimental help.

Author information

Author notes

  1. These authors contributed equally: Sascha Rutz, Axel Kallies, Alexander Scheffold.

Authors and Affiliations

  1. Department of Cellular Immunology, Clinic for Rheumatology and Clinical Immunology, Charité—Universitätsmedizin Berlin, Berlin, Germany

    Christian Neumann

  2. German Rheumatism Research Center (DRFZ) Berlin, Leibniz Association, Berlin, Germany

    Christian Neumann, Alexander Beller, Frederik Heinrich, Christina Stehle, Gitta A. Heinz, Patrick Maschmeyer, Chiara Romagnani, Hyun-Dong Chang, Andrey Kruglov & Mir-Farzin Mashreghi

  3. Department of Microbiology and Immunology, University of Melbourne, The Peter Doherty Institute for Infection and Immunity, Melbourne, Australia

    Jonas Blume, Peggy P. Teh, Ajithkumar Vasanthakumar, Tom Sidwell & Axel Kallies

  4. Molecular Immunology Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia

    Jonas Blume, Peggy P. Teh, Ajithkumar Vasanthakumar, Tom Sidwell & Axel Kallies

  5. Research Group Microbial Immune Regulation, Helmholtz Centre for Infection Research, Braunschweig, Germany

    Urmi Roy, Eric J. C. Gálvez & Till Strowig

  6. Department of Medical Biology, University of Melbourne, Melbourne, Australia

    Yang Liao, Yifang Hu & Wei Shi

  7. Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia

    Yang Liao, Yifang Hu & Wei Shi

  8. Department of Cancer Immunology, Genentech, San Francisco, CA, USA

    Teresita L. Arenzana & Sascha Rutz

  9. Department of Bioinformatics and Computational Biology, Genentech, San Francisco, CA, USA

    Jason A. Hackney

  10. Department of Biochemical and Cellular Pharmacology, Genentech, San Francisco, CA, USA

    Celine Eidenschenk

  11. Sanquin Research, Department of Hematopoiesis and Landsteiner Laboratory, Amsterdam Infection and Immunity Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands

    Derk Amsen

  12. Medical Department I, Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany

    Chiara Romagnani

  13. Belozersky Institute of Physico-Chemical Biology and Biological Faculty, M.V. Lomonosov Moscow State University, Moscow, Russia

    Andrey Kruglov

  14. Institute of Immunology, Christian-Albrechts−Universität zu Kiel & Universitätsklinik Schleswig Holstein, Kiel, Germany

    Alexander Scheffold

Authors
  1. Christian Neumann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jonas Blume
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Urmi Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peggy P. Teh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ajithkumar Vasanthakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Alexander Beller
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yang Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Frederik Heinrich
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Teresita L. Arenzana
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jason A. Hackney
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Celine Eidenschenk
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Eric J. C. Gálvez
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Christina Stehle
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Gitta A. Heinz
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Patrick Maschmeyer
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Tom Sidwell
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Yifang Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Derk Amsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Chiara Romagnani
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Hyun-Dong Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Andrey Kruglov
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Mir-Farzin Mashreghi
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Wei Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Till Strowig
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Sascha Rutz
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Axel Kallies
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Alexander Scheffold
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.N. and J.B. designed and performed most experiments, analyzed the data, generated the figures and wrote the manuscript. U.R. and T. Strowig performed and supervised microbiota studies. P.P.T., Y.L., C.S., P.M. and T. Sidwell performed experiments. A.V. provided discussion and proofread the manuscript. A.B. and H.C. performed and supervised IgA studies. F.H., T.L.A., J.A.H., E.J.C.G., Y.H. and W.S. performed computational analyses of 16 S rRNA-seq, RNA-seq, ChIP-seq and ATAC-seq data. C.E. performed studies with V463 mut-STAT3 mice. G.A.H. and M.F.M. supervised sequencing experiments and provided reagents and equipment for their execution. D.A. and C.R. provided expertise and Notch1floxNotch2floxCd4Cre mice and Il10GFP reporter mice, respectively. A. Kruglov contributed to data interpretation and discussions and assisted in DSS colitis experiments. S.R., A. Kallies and A.S. conceived the project and wrote the manuscript.

Corresponding authors

Correspondence to Sascha Rutz, Axel Kallies or Alexander Scheffold.

Ethics declarations

Competing interests

T.L.A., J.A.H. and S.R. are full-time employees of Genentech and shareholders of Roche.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 Gating strategy.

Flow cytometric profiles illustrating the gating strategy for phenotypic analysis of (a) splenic or (b) colonic LP CD4+ TCRβ+ T cells. The presented profiles are representative of at least five independent stainings.

Supplementary Figure 2 Expression of eTreg cell and/or activation markers in c-Maf+ Treg cells versus that of c-Maf− Treg cells.

(a) Flow cytometric analysis shows expression of ICOS, CTLA4, TIGIT, CD69, Blimp-1 and CD25 in splenic c-Maf+ and c-Maf− Foxp3+ Treg cells. Histograms are representative of three independent experiments. (b–c) Flow cytometric profiles of c-Maf versus (b) ICOS and (c) TIGIT expression by Foxp3+ Treg cells from different organs. Contour plots are representative of at least three independent experiments. (d) Expression of c-Maf by MACS-sorted CD4+ CD25+ splenic Treg cells after in vitro stimulation for 72 hours with anti-CD3/CD28 and rIL-2 in the presence or absence of the Notch ligand Dll-4. Histograms (left) show c-Maf expression (gated on Foxp3+ cells). Graph on the right shows frequency of c-Maf+ cells among Foxp3+ Treg cells (pooled data from three independent experiments, mean ± SEM, **P<0.0015). (e) Expression of c-Maf by mLNs Foxp3+ Treg cells from Notch1/2fl/flCd4Cre (N1/2∆CD4) and wild-type SPF (Ctrl) mice. Histograms (left) show c-Maf expression. Graph on the right shows quantification of c-Maf gMFI (n = 4, mean ± SEM). All statistical differences were tested using an unpaired Student’s t-test (two-tailed).

Supplementary Figure 3 Phenotypic characterization of Maf∆Treg mice.

(a) Expression of Il10:GFP by Foxp3+ and Foxp3− CD4+ TCRβ+ T cells isolated from indicated organs of naïve Il10GFP reporter mice as measured by flow cytometry. Representative flow cytometric profiles of Foxp3 versus Il10:GFP expression by splenic and colonic CD4+ TCRβ+ T cells are shown on top. The graph below shows quantification (n = 4, mean ± SEM, *P<0.05, ***P<0.001). (b) Flow cytometric profiles of c-Maf versus Foxp3 expression by CD4+ TCR+ T cells from different organs of Foxp3Cre (Ctrl) and Maffl/flFoxp3Cre (Maf∆Treg) mice. The presented profiles are representative of at least three independent stainings. (c) Frequency of Foxp3+ Treg cells among CD4+ TCRβ+ T cells from indicated organs of Maf∆Treg and control mice (n = 5, mean ± SEM). (d) Enumeration of total cells isolated from different organs of Maf∆Treg and control mice (n = 5, mean ± SEM, *P<0.05, **P<0.01). (e) Flow cytometric analysis shows expression of ICOS, TIGIT, CD69, CD25 in splenic CD62L− Foxp3+ Treg cells of Maf∆Treg and control mice. Histograms are representative of three independent experiments. (f) Flow cytometric analysis of Blimp-1 expression in colonic Foxp3+ Treg cells from Maf∆Treg and control mice. On top, representative contour plots displaying Blimp-1 versus c-Maf expression are shown. Blimp-1-deficient Foxp3+ Treg cells from Prdm1fl/flCd4Cre (Prdm1∆CD4) mice served as a negative control for the Blimp-1 staining. Below, Blimp-1 expression is shown by representative histograms (left) and Blimp-1 gMFI is quantified (right, n = 3, mean ± SEM). (g) Flow cytometric analysis of IRF4 expression in colonic Foxp3+ Treg cells from Maf∆Treg and control mice. On the left, a representative contour plot displaying IRF4 versus c-Maf expression is shown. On the right, IRF4 expression in colonic Foxp3+ Treg cells is shown by representative histograms and IRF4 gMFI is quantified (n = 3, mean ± SEM). Foxp3− conventional T cells (Tcon) from control mice served as a negative control for the IRF4 staining. (h) Flow cytometric analysis of AhR expression in colonic Foxp3+ Treg cells from Maf∆Treg and control mice. On the left, representative contour plots displaying AhR versus c-Maf expression are shown. AhR-deficient Foxp3+ Treg cells from Ahr-/- mice served as a negative control for the AhR staining. On the right, AhR expression is shown by representative histograms and AhR gMFI is quantified (n = 3, mean ± SEM, **P = 0.0097). All statistical differences were tested using an unpaired Student’s t-test (two-tailed).

Supplementary Figure 4 Rorc∆Treg mice have normal intestinal TH17 cell responses.

(a) Flow cytometric analysis of c-Maf expression in different Treg cell subsets from PPs. Histograms are representative of three independent experiments. (b) Frequency of TFR cells (PD1+CXCR5+ cells among Foxp3+ Treg cells) in PPs of Maf∆Treg and control mice. Representative contour plots of PD1 versus CXCR5 expression by Foxp3+ Treg cells are shown on the left. The graph on the right shows quantification (n = 4, mean ± SEM, ***P < 0.0001). (c) Flow cytometric profiles of RORγt versus Foxp3:YFP expression by colonic Foxp3+ Treg cells of mosaic Foxp3Cre/+ or Maffl/flFoxp3Cre/+ female mice. The presented profiles are representative of at least two independent stainings. (d) RORγt (n = 4, mean ± SEM, *P = 0.0462) versus T-bet (n = 5, mean ± SEM, *P = 0.0212) expression by colonic Foxp3− conventional T cells of Maf∆Treg and control mice as measured by flow cytometry. Representative flow cytometric profiles are shown on top. Graphs below show quantification. (e) Contour plots show RORγt and c-Maf expression of Foxp3+ and Foxp3− CD4+TCRβ+ T cells from mLNs of Maf∆Treg and control mice as measured by flow cytometry. The presented plots are representative of at least three independent stainings. (f) Frequencies of IL-17A+ (*P = 0.0092) and IFN-γ+ cells among colonic Foxp3+ Treg cells of Foxp3Cre (Ctrl, n = 4, mean ± SEM) and Rorcfl/flFoxp3Cre (Rorc∆Treg, n = 3, mean ± SEM) mice after ex vivo PMA/ionomycin restimulation as measured by flow cytometry. (g) Frequency of IL-17A+ cells among colonic Foxp3− conventional T cells of Rorc∆Treg (n = 3, mean ± SEM) and control (n = 4, mean ± SEM) mice after ex vivo PMA/ionomycin restimulation as measured by flow cytometry. All statistical differences were tested using an unpaired Student’s t-test (two-tailed).

Supplementary Figure 5 Tissue-specific gene regulation by c-Maf in gut-associated Treg cells.

(a) Foxp3+ Treg cells were sorted from spleen of Maf∆Treg and control mice and subjected to RNA sequencing. MA plot showing comparison of gene expression between c-Maf-deficient and control Treg cells. Genes up-regulated in c-Maf-deficient Treg cells are highlighted in red; genes down-regulated are highlighted in blue (FDR < 0.1). DE analysis was performed using limma. P values were adjusted to control the global false discovery rate (FDR) across all comparisons with the ‘global’ option of the limma package. Data represent the combined analysis of three biologically independent samples. (b) Venn diagram displaying the overlap between genes differentially expressed (DE) between c-Maf-deficient and control Treg cells from LP, PPs and spleens. (c) Heatmap showing relative expression (z-score) of genes that were differentially expressed (FDR < 0.1) between c-Maf-deficient and control Treg cells from LP (n = 2), PPs (n = 3) and spleens (n = 3). DE analysis was performed using limma. P values were adjusted to control the global FDR across all comparisons with the ‘global’ option of the limma package. (d) Gene set enrichment plots showing significantly enriched (FDR < 0.1) hallmark gene sets in c-Maf-deficient versus control LP Treg cells. Data represent the combined analysis of two biologically independent samples. (e) MA plot showing comparison of gene expression between LP (n = 2) and splenic Treg cells (n = 3) from control mice. Genes up-regulated in LP Treg cells are highlighted in red; genes down-regulated are highlighted in blue (FDR < 0.05). DE analysis was performed using limma. P values were adjusted to control the global FDR across all comparisons with the ‘global’ option of the limma package. (f) Sorted c-Maf-deficient and control LP Treg cells (n = 3) were subjected to ‘Assay for Transposase-Accessible Chromatin’ sequencing (ATAC-seq). MA plot displaying regions of accessible chromatin. Regions over-represented in c-Maf-deficient Treg cells are highlighted in red; regions under-represented are highlighted in blue. Note that multiple peaks can be associated with individual genes. (g) Representative c-Maf chromatin immunoprecipitation sequencing (ChIP-seq) and ATAC sequencing tracks. Arrows indicate c-Maf occupancy in open chromatin regions of Treg cell gene loci. Data represent the combined analysis of three biologically independent samples. c-Maf ChIP-seq was published by Ciofani et al.34

Supplementary Figure 6 Il10∆Treg mice are susceptible to DSS-induced colitis.

Foxp3Cre (Ctrl) and Il10fl/flFoxp3Cre (Il10∆Treg) mice were orally challenged with 1.5% DSS (w/v) for 5 days. Colitis severity was assessed 3 days later (day 8). (a) Colon length (n = 4, mean ± SEM) and (b) colitis scores are shown (n = 4, mean ± SEM). *P = 0.03 as calculated by unpaired Student’s t-test (two-tailed). Data are representative of three independent experiments with four to five mice per genotype.

Supplementary Figure 7 c-Maf is essential for Treg cell–mediated control of intestinal IgA responses.

Analysis of faecal microbiota diversity of co-housed littermate Maf∆Treg (n = 10, mean ± SEM) and control mice (n = 11, mean ± SEM) using 16 S rRNA gene sequencing. (b) Quantification of IgA in serum of Maf∆Treg and control mice as measured by ELISA (n = 4, mean ± SEM, *P = 0.0129). (c) Frequency of IgA+ B cells in PP of naïve Maf∆Treg and control mice. Representative contour plots of IgA versus B220 expression by CD45+ cells are shown on the left. The graph on the right shows quantification (n = 4, mean ± SEM). (d) Quantification of IgA+ cells in the colonic LP of Maf∆Treg and control mice as measured by immunohistochemistry (n = 4, mean ± SEM, *P = 0.0045). (e) Differential representation of bacterial families between the IgA+ and IgA− fractions of co-housed littermate Maf∆Treg and control mice shown in form of an IgA coating index (see Methods section). For a given taxon, the value of the IgA coating index can range from a maximum of 1.0 (taxon detected exclusively in the IgA+ fraction) to a minimum of −1.0 (present only in the IgA− fraction). n = 4, *P = 0.0286 as calculated by Mann–Whitney U test (two-tailed). Whiskers represent the minimum and maximum of data. (f) Quantification of IgA in faeces of Il10∆Treg and control mice as measured by ELISA (n = 4, mean ± SEM). All statistical differences were tested using an unpaired Student’s t-test (two-tailed) unless stated otherwise.

Supplementary Figure 8 Phenotypic analysis of intestinal Foxp3+ Treg cells in colonized germ-free mice.

(a) Experimental design for colonization of germ-free (GF) mice with microbiota (Mb) from either Maf∆Treg or control mice. (b–d) GF + Ctrl Mb (n = 6, mean ± SEM) and GF + Maf∆Treg Mb (n = 8, mean ± SEM) mice were orally challenged with 1.5% DSS (w/v) for 5 days. Colonic Foxp3+ Treg cells were analyzed 2 days later. Graphs show pooled data of two independent experiments. (b) Frequency of Foxp3+ Treg cells among colonic CD4+ TCRβ+ T cells. (c) Frequency of c-Maf+ and RORγt+ cells among colonic Foxp3+ Treg cells. (d) Frequency of IL-10+ cells among colonic Foxp3+ Treg cells after ex vivo PMA/ionomycin restimulation. (e) Frequency of TFR cells (PD1+ CXCR5+) among Foxp3+ cells from PPs of GF + Ctrl Mb (n = 4, mean ± SEM) and GF + Maf∆Treg Mb (n = 3, mean ± SEM) mice after 2 weeks of co-housing with either Maf∆Treg or control mice. (f) Quantification of IgA in faeces of GF + Ctrl Mb (n = 4, mean ± SEM) and GF + Maf∆Treg Mb (n = 3, mean ± SEM) mice after 2 weeks of co-housing with either Maf∆Treg or control mice as measured by ELISA. (e–f) Data are representative of two independent experiments.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8, Supplementary Tables 1–5

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Neumann, C., Blume, J., Roy, U. et al. c-Maf-dependent Treg cell control of intestinal TH17 cells and IgA establishes host–microbiota homeostasis. Nat Immunol 20, 471–481 (2019). https://doi.org/10.1038/s41590-019-0316-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41590-019-0316-2

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing