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Two regulatory T cell populations in the visceral adipose tissue shape systemic metabolism

Nature Immunology volume 25, pages 496–511 (2024)Cite this article

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Abstract

Visceral adipose tissue (VAT) is an energy store and endocrine organ critical for metabolic homeostasis. Regulatory T (Treg) cells restrain inflammation to preserve VAT homeostasis and glucose tolerance. Here, we show that the VAT harbors two distinct Treg cell populations: prototypical serum stimulation 2-positive (ST2+) Treg cells that are enriched in males and a previously uncharacterized population of C–X–C motif chemokine receptor 3-positive (CXCR3+) Treg cells that are enriched in females. We show that the transcription factors GATA-binding protein 3 and peroxisome proliferator-activated receptor-γ, together with the cytokine interleukin-33, promote the differentiation of ST2+ VAT Treg cells but repress CXCR3+ Treg cells. Conversely, the differentiation of CXCR3+ Treg cells is mediated by the cytokine interferon-γ and the transcription factor T-bet, which also antagonize ST2+ Treg cells. Finally, we demonstrate that ST2+ Treg cells preserve glucose homeostasis, whereas CXCR3+ Treg cells restrain inflammation in lean VAT and prevent glucose intolerance under high-fat diet conditions. Overall, this study defines two molecularly and developmentally distinct VAT Treg cell types with unique context- and sex-specific functions.

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Fig. 1: Heterogeneity of VAT Treg cells.
Fig. 2: Developmental pathways of ST2+ and CXCR3+ VAT Treg cells.
Fig. 3: CXCR3+ VAT Treg cells are restrained by PPARγ, IL-33 and androgen.
Fig. 4: GATA3 regulates the differentiation of ST2+ VAT Treg cells.
Fig. 5: CXCR3+ VAT Treg cell differentiation requires T-bet.
Fig. 6: TH1 inflammation drives CXCR3+ VAT Treg cell differentiation.
Fig. 7: ST2+ and CXCR3+ VAT Treg cells have unique functions.
Fig. 8: ST2+ and CXCR3+ Treg cells interact with distinct cell types.

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Data availability

Sequencing data generated for this study have been deposited in the Gene Expression Omnibus (GEO) database with accession number GSE205427. All other data and materials are available upon request.

References

  1. Rosen, E. D. & Spiegelman, B. M. What we talk about when we talk about fat. Cell 156, 20–44 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Nawrocki, A. R. & Scherer, P. E. The delicate balance between fat and muscle: adipokines in metabolic disease and musculoskeletal inflammation. Curr. Opin. Pharmacol. 4, 281–289 (2004).

    Article  CAS  PubMed  Google Scholar 

  3. Ouchi, N., Parker, J. L., Lugus, J. J. & Walsh, K. Adipokines in inflammation and metabolic disease. Nat. Rev. Immunol. 11, 85–97 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Man, K., Kallies, A. & Vasanthakumar, A. Resident and migratory adipose immune cells control systemic metabolism and thermogenesis. Cell. Mol. Immunol. 19, 421–431 (2022).

    Article  CAS  PubMed  Google Scholar 

  5. 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  PubMed  PubMed Central  Google Scholar 

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

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  7. Cipolletta, D., Cohen, P., Spiegelman, B. M., Benoist, C. & Mathis, D. Appearance and disappearance of the mRNA signature characteristic of Treg cells in visceral adipose tissue: age, diet, and PPARγ effects. Proc. Natl Acad. Sci. USA 112, 482–487 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  8. Bapat, S. P. et al. Depletion of fat-resident Treg cells prevents age-associated insulin resistance. Nature 528, 137–141 (2015).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  9. Beppu, L. Y. et al. Tregs facilitate obesity and insulin resistance via a Blimp-1/IL-10 axis. JCI Insight https://doi.org/10.1172/jci.insight.140644 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Wu, D. et al. T reg-specific insulin receptor deletion prevents diet-induced and age-associated metabolic syndrome. J. Exp. Med. https://doi.org/10.1084/jem.20191542 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  11. 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  PubMed  Google Scholar 

  12. Vasanthakumar, A. et al. Sex-specific adipose tissue imprinting of regulatory T cells. Nature 579, 581–585 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  13. Molofsky, A. B. et al. Interleukin-33 and interferon-γ counter-regulate group 2 innate lymphoid cell activation during immune perturbation. Immunity 43, 161–174 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Han, J. M. et al. IL-33 reverses an obesity-induced deficit in visceral adipose tissue ST2+ T regulatory cells and ameliorates adipose tissue inflammation and insulin resistance. J. Immunol. 194, 4777–4783 (2015).

    Article  CAS  PubMed  Google Scholar 

  16. Li, C. et al. TCR transgenic mice reveal stepwise, multi-site acquisition of the distinctive fat-Treg phenotype. Cell 174, 285–299 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Sidwell, T. et al. Attenuation of TCR-induced transcription by Bach2 controls regulatory T cell differentiation and homeostasis. Nat. Commun. 11, 252 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  18. Delacher, M. et al. Precursors for nonlymphoid-tissue Treg cells reside in secondary lymphoid organs and are programmed by the transcription factor BATF. Immunity 52, 295–312 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Vasanthakumar, A. & Kallies, A. The regulatory T cell: jack-of-all-trades. Trends Immunol. 36, 756–758 (2015).

    Article  CAS  PubMed  Google Scholar 

  20. Li, Y. et al. Insulin signaling establishes a developmental trajectory of adipose regulatory T cells. Nat. Immunol. 22, 1175–1185 (2021).

    Article  CAS  PubMed  Google Scholar 

  21. Mathis, D. Immunological goings-on in visceral adipose tissue. Cell Metab. 17, 851–859 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Wensveen, F. M. et al. NK cells link obesity-induced adipose stress to inflammation and insulin resistance. Nat. Immunol. 16, 376–385 (2015).

    Article  CAS  PubMed  Google Scholar 

  23. Wang, H. et al. Adipose group 1 innate lymphoid cells promote adipose tissue fibrosis and diabetes in obesity. Nat. Commun. 10, 3254 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  24. O’Sullivan, T. E. et al. Adipose-resident group 1 innate lymphoid cells promote obesity-associated insulin resistance. Immunity 45, 428–441 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Spallanzani, R. G. et al. Distinct immunocyte-promoting and adipocyte-generating stromal components coordinate adipose tissue immune and metabolic tenors. Sci. Immunol. https://doi.org/10.1126/sciimmunol.aaw3658 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Wohlfert, E. A. et al. GATA3 controls Foxp3+ regulatory T cell fate during inflammation in mice. J. Clin. Invest. 121, 4503–4515 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Kalekar, L. A. et al. Regulatory T cells in skin are uniquely poised to suppress profibrotic immune responses. Sci. Immunol. https://doi.org/10.1126/sciimmunol.aaw2910 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Hayakawa, M. et al. T-helper type 2 cell-specific expression of the ST2 gene is regulated by transcription factor GATA-3. Biochim. Biophys. Acta 1728, 53–64 (2005).

    Article  CAS  PubMed  Google Scholar 

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

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  31. Wei, G. et al. Genome-wide analyses of transcription factor GATA3-mediated gene regulation in distinct T cell types. Immunity 35, 299–311 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Koch, M. A. et al. The transcription factor T-bet controls regulatory T cell homeostasis and function during type 1 inflammation. Nat. Immunol. 10, 595–602 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Yu, F., Sharma, S., Edwards, J., Feigenbaum, L. & Zhu, J. Dynamic expression of transcription factors T-bet and GATA-3 by regulatory T cells maintains immunotolerance. Nat. Immunol. 16, 197–206 (2015).

    Article  CAS  PubMed  Google Scholar 

  34. Levine, A. G. et al. Stability and function of regulatory T cells expressing the transcription factor T-bet. Nature 546, 421–425 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  35. Tan, T. G., Mathis, D. & Benoist, C. Singular role for T-BET+CXCR3+ regulatory T cells in protection from autoimmune diabetes. Proc. Natl Acad. Sci. USA 113, 14103–14108 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  36. Hall, A. O. et al. The cytokines interleukin 27 and interferon-γ promote distinct Treg cell populations required to limit infection-induced pathology. Immunity 37, 511–523 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Stolarczyk, E. et al. Improved insulin sensitivity despite increased visceral adiposity in mice deficient for the immune cell transcription factor T-bet. Cell Metab. 17, 520–533 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Kim, J. M., Rasmussen, J. P. & Rudensky, A. Y. Regulatory T cells prevent catastrophic autoimmunity throughout the lifespan of mice. Nat. Immunol. 8, 191–197 (2007).

    Article  CAS  PubMed  Google Scholar 

  39. Caton, M. L., Smith-Raska, M. R. & Reizis, B. Notch–RBP-J signaling controls the homeostasis of CD8− dendritic cells in the spleen. J. Exp. Med. 204, 1653–1664 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Outtz, H. H., Tattersall, I. W., Kofler, N. M., Steinbach, N. & Kitajewski, J. Notch1 controls macrophage recruitment and Notch signaling is activated at sites of endothelial cell anastomosis during retinal angiogenesis in mice. Blood 118, 3436–3439 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Lo, C.-M. et al. Cholecystokinin knockout mice are resistant to high-fat diet-induced obesity. Gastroenterology 138, 1997–2005 (2010).

    Article  CAS  PubMed  Google Scholar 

  42. Lima, W. G., Martins-Santos, M. E. S. & Chaves, V. E. Uric acid as a modulator of glucose and lipid metabolism. Biochimie 116, 17–23 (2015).

    Article  CAS  PubMed  Google Scholar 

  43. Park, W. Y. et al. PEX13 is required for thermogenesis of white adipose tissue in cold-exposed mice. Biochim. Biophys. Acta Mol. Cell Biol. Lipids 1867, 159046 (2022).

    Article  CAS  PubMed  Google Scholar 

  44. Wang, Y. et al. METTL3 is essential for postnatal development of brown adipose tissue and energy expenditure in mice. Nat. Commun. 11, 1648 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Pierson, W. et al. Antiapoptotic Mcl-1 is critical for the survival and niche-filling capacity of Foxp3+ regulatory T cells. Nat. Immunol. 14, 959–965 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Hwang, E. S., Szabo, S. J., Schwartzberg, P. L. & Glimcher, L. H. T helper cell fate specified by kinase-mediated interaction of T-bet with GATA-3. Science 307, 430–433 (2005).

    Article  ADS  CAS  PubMed  Google Scholar 

  48. Groom, J. R. et al. CXCR3 chemokine receptor–ligand interactions in the lymph node optimize CD4+ T helper 1 cell differentiation. Immunity 37, 1091–1103 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Oboki, K. et al. IL-33 is a crucial amplifier of innate rather than acquired immunity. Proc. Natl Acad. Sci. USA 107, 18581–18586 (2010).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  50. Zhu, J. et al. The transcription factor T-bet is induced by multiple pathways and prevents an endogenous Th2 cell program during Th1 cell responses. Immunity 37, 660–673 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Oghumu, S. et al. Distinct populations of innate CD8+ T cells revealed in a CXCR3 reporter mouse. J. Immunol. 190, 2229–2240 (2013).

    Article  CAS  PubMed  Google Scholar 

  52. Shi, C. et al. Bone marrow mesenchymal stem and progenitor cells induce monocyte emigration in response to circulating toll-like receptor ligands. Immunity 34, 590–601 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Notini, A. J., Davey, R. A., McManus, J. F., Bate, K. L. & Zajac, J. D. Genomic actions of the androgen receptor are required for normal male sexual differentiation in a mouse model. J. Mol. Endocrinol. 35, 547–555 (2005).

    Article  CAS  PubMed  Google Scholar 

  54. Roediger, B. et al. Cutaneous immunosurveillance and regulation of inflammation by group 2 innate lymphoid cells. Nat. Immunol. 14, 564–573 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Anderson, K. G. et al. Intravascular staining for discrimination of vascular and tissue leukocytes. Nat. Protoc. 9, 209–222 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Liao, Y., Smyth, G. K. & Shi, W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 41, e108 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  CAS  PubMed  Google Scholar 

  58. 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  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  60. McCarthy, D. J. & Smyth, G. K. Testing significance relative to a fold-change threshold is a TREAT. Bioinformatics 25, 765–771 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Young, M. D., Wakefield, M. J., Smyth, G. K. & Oshlack, A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 11, R14 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  63. Lun, A. T. L. et al. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 20, 63 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. van den Brink, S. C. et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat. Methods 14, 935–936 (2017).

    Article  PubMed  Google Scholar 

  67. Soh, Y. Q. S. et al. Sequencing the mouse Y chromosome reveals convergent gene acquisition and amplification on both sex chromosomes. Cell 159, 800–813 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  69. Ji, Z. & Ji, H. TSCAN: pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res. 44, e117 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Bunis, D. G., Andrews, J., Fragiadakis, G. K., Burt, T. D. & Sirota, M. dittoSeq: universal user-friendly single-cell and bulk RNA sequencing visualization toolkit. Bioinformatics https://doi.org/10.1093/bioinformatics/btaa1011 (2020).

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Acknowledgements

This work was funded by the National Health and Medical Research Council Ideas Grants and Investigator Grants to A.K., A.V. (GNT2012119) and J.R.G. (GNT2007812) and the Australia Research Council Discovery Grant to A.K. S.V.T. and D.M. acknowledge the University of Melbourne for providing a Melbourne Research Scholarship to support the conduct of this study. M.B. and T.E. were supported by the Deutsche Forschungsgemeinschaft (German Research Foundation, IRTG2168 272482170, SFB 1454 TP16 432325352). We would like to thank S. Turner (Monash University), D. Strugnell (Peter Doherty Institute) and N. Wang (Peter Doherty Institute) for mice and S. Wilcox, J. Bayliss and M. Köhne for technical help. We acknowledge the Melbourne Cytometry Platform for the provision of flow cytometry services. This work was supported by infrastructure and technical assistance from the Melbourne Mouse Metabolic Phenotyping Platform at the University of Melbourne.

Author information

Author notes

  1. Doaa Hamada

    Present address: Medical Microbiology and Immunology Department, Faculty of Medicine, Mansoura University, Mansoura, Egypt

  2. These authors contributed equally: Santiago Valle Torres, Kevin Man.

  3. These authors jointly supervised this work: Ajithkumar Vasanthakumar, Axel Kallies.

Authors and Affiliations

  1. Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia

    Santiago Valle Torres, Kevin Man, Teisha Mason, Carlson Tsui & Axel Kallies

  2. Department of Microbiology and Immunology, University of Melbourne, Melbourne, Victoria, Australia

    Santiago Valle Torres, Kevin Man, Tarek Elmzzahi, Darya Malko, Adelynn Tang, Teisha Mason, Carlson Tsui, Ajithkumar Vasanthakumar & Axel Kallies

  3. Immunogenomics and Neurodegeneration, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany

    Tarek Elmzzahi, Darya Malko, Matthias Becker, Mehrnoush Hadaddzadeh Shakiba, Doaa Hamada & Marc Beyer

  4. Olivia Newton-John Cancer Research Institute, Heidelberg, Victoria, Australia

    David Chisanga, Yang Liao, Adelynn Tang, Jian Wu, Wei Shi, Bhupinder Pal & Ajithkumar Vasanthakumar

  5. La Trobe University, Bundoora, Victoria, Australia

    David Chisanga, Yang Liao, Jian Wu, Wei Shi, Bhupinder Pal & Ajithkumar Vasanthakumar

  6. The Malaghan Institute of Medical Research, Wellington, New Zealand

    Melanie Prout & Graham Le Gros

  7. Department of Molecular and Biomedical Science, University of Adelaide, Adelaide, South Australia, Australia

    Caitlin A. Abbott & Shaun R. McColl

  8. Modular HPC and AI, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany

    Matthias Becker

  9. Department of Anatomy and Physiology, University of Melbourne, Melbourne, Victoria, Australia

    Vanessa Haynes & Matthew J. Watt

  10. Breast Cancer Risk and Prevention, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia

    Kara Britt

  11. Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia

    Kara Britt

  12. Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia

    Joanna R. Groom & Axel Kallies

  13. Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia

    Joanna R. Groom

  14. Platform for Single Cell Genomics and Epigenomics (PRECISE), German Center for Neurodegenerative Diseases (DZNE), University of Bonn, Bonn, Germany

    Marc Beyer

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  1. Santiago Valle Torres
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Contributions

A.K. and A.V. designed the project, interpreted results and wrote the manuscript. S.V.T. and K.M. performed most of the work, analyzed data, edited the manuscript and made figure panels. T.E. performed scRNAseq and analyzed the data, with assistance from M. Beyer. M. Becker, M.H.S. and D.H. J.W. and B.P. performed RNAseq with low cell input. D.C., Y.L. and W.S. analyzed all the bulk RNAseq and ATACseq datasets. A.T. performed flow cytometry, and D.M. performed HFD experiments. G.L.G. and M.P. analyzed multiple reporter mouse strains. J.R.G. provided reagents (mice). S.R.M. and C.A.A. analyzed the expression of various chemokine receptors. V.H. and M.J.W. performed metabolic experiments and interpreted the results. M.B. interpreted the scRNAseq data and edited the manuscript. T.M., C.T. and K.B. provided help with multiple experiments.

Corresponding authors

Correspondence to Ajithkumar Vasanthakumar or Axel Kallies.

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Nature Immunology thanks Dirk Brenner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: N. Bernard, in collaboration with the Nature Immunology team.

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Extended data

Extended Data Fig. 1 Heterogeneity of Treg cells in male and female VAT.

a, Gating strategy to identify and isolate CD4+ T cells in the VAT. b, Single cell RNA sequencing (scRNAseq) heatmap showing expression of signature transcripts for VAT Treg cell clusters 1-5 (yellow denotes upregulated genes). c, Heat map showing expression of selected genes in clusters 1, 2 and 3-6 (red - high, blue - low expression). d, Bar graph showing percentages of cell clusters in the Treg cell compartment in male and female mice identified by scRNAseq. e, Flow cytometric analysis of 25-week-old male and female Cxcr3CIBER mice showing ST2 and CXCR3 expression in VAT Treg cells. f, Volcano plot shows differential expression of genes between ST2+ and CXCR3+ VAT Treg cells (FDR 0.2). g, Graphs show frequencies of ST2+ (left) and CXCR3+ (right) VAT Treg cells in different adipose tissue depots of male (VAT – visceral adipose tissue, sAT – subcutaneous adipose tissue, pAT – perinephral adipose tissue) (n = 10 VAT, n = 6 sAT, n = 5 pAT) and female (n = 9 VAT, n = 8 sAT and pAT each). Symbols represent individual mice; horizontal lines indicate means. Data are representative (e) or pooled (g) from at least two independent experiments. Error bars indicate the standard deviation. Statistical analyses were performed using two-way ANOVA (g).

Extended Data Fig. 2 Impact of age and physiology on ST2+ and CXCR3+ VAT Treg cells.

a, VAT Treg cells from 7-week-old male and female mice were analysed. ST2 and CXCR3 expression in VAT Treg cells (left). Frequencies (centre) and numbers (right) of ST2+ and CXCR3+ cells (n = 10 mice of each sex). P (****) < 0.0001. b, Frequencies of VAT Treg cells from female (red) and male (blue) mice at different ages. Females n = 94; males n = 94. c, d Correlation between body weight (top) or VAT weight (bottom) and ST2+ (c) and CXCR3+ (d) VAT Treg cell numbers from 25–30-week-old mice. (c) Females n = 24 (top), 21 (bottom); males n = 38 (top), 33 (bottom). (d) Females n = 13 (top), 30 (bottom); males n = 15 (top), 32 (bottom). e, Graphs show frequencies (top) and numbers (bottom) of total VAT Treg cells from normal diet (ND) and high fat diet (HFD) fed females and males (n = 10 mice each sex) f, Flow cytometry histograms show CD73 expression in DN, CXCR3+ and ST2+ VAT Treg cells from male and female mice (left) and quantification (right). Females n = 9; males n = 10. g, Circulatory cells in male mice were stained with an intravascular (i.v.) CD45 antibody. ST2 and CXCR3 expression versus i.v. CD45 expression in VAT Treg cells (left). Frequencies of intravascularly labelled (i.v.+) and non-labelled (i.v.-) DN, CXCR3+ and ST2+ VAT Treg cells (right). h, Flow cytometry plots show CD73 co-expression with CD62L, CXCR3 and ST2 in donor VAT Treg cells adoptively transferred to Rag1-/- mice. i, Quantification of VAT Treg cells in male Ar-/- and wildtype control mice (n = 8 control, n = 10 Ar-/-). Quantification of VAT Treg cells in female Era-/- and wildtype control mice (n = 4 control, n = 7 Era-/-). Symbols represent individual mice (a, c-f, i, j) or mean of a minimum of 5 mice (b). Error bars indicate standard deviation; horizontal lines indicate means. Statistical analyses performed using an unpaired, two-tailed Student’s t-test (b, j), two-way ANOVA (a, e, f) or a linear regression model (c, d). Data pooled from at least two independent experiments (a - g, i, j); flow cytometry (FACS) plots representative (a, f, h).

Extended Data Fig. 3 GATA3 controls ST2+ VAT Treg cell differentiation.

a, FACS plots show ST2 and GATA3 expression in VAT Treg cells of male and female mice. b, GATA3 expression in CD4+ Foxp3−and Foxp3+ cells of Gata3fl/flFoxp3Cre and control mice. c, Graphs show frequencies of total (left) and activated (right) splenic Treg cells of Gata3fl/flFoxp3Cre (left n = 4 females, n = 6 males; right n = 8 females, n = 13 males) and control (left n = 8 females, n = 11 males; right n = 8 females, n = 7 males) mice. d, Treg cell frequencies and numbers in small intestine lamina propria (n = 13 control, n = 6 Gata3fl/flFoxp3Cre) and lung (n = 10 per genotype). e, Numbers of ST2+ and CXCR3+ VAT Treg cells in Gata3fl/flFoxp3Cre (n = 6 each sex) and control (n = 8 females, n = 7 males) mice. f, Frequencies (control n = 12 each sex; Gata3fl/flFoxp3Cre n = 10 females, n = 11 males) and numbers (control n = 12 females, n = 11 males; Gata3fl/flFoxp3Cre n = 10 each sex) of VAT Treg cells. g, Numbers of TNF+ (control n = 9 females, n = 15 males; Gata3fl/flFoxp3Cre n = 4 females, n = 12 males), IL-5+ (control n = 8 females, n = 10 males; Gata3fl/flFoxp3Cre n = 5 females, n = 7 males) and IFN-γ+ (control n = 8 females, n = 10 male; Gata3fl/flFoxp3Cre n = 5 females, n = 7 males) cells. h, Body (control n = 13 females, n = 15 males; Gata3fl/flFoxp3Cre n = 11 females, n = 10 males) and VAT (control n = 8 females, n = 14 males; Gata3fl/flFoxp3Cre n = 10 females, n = 15 males) weights of male and female Gata3fl/flFoxp3Cre and control mice. i, Body composition of male and female Gata3fl/flFoxp3Cre and control mice (control n = 4 females, n = 5 male; Gata3fl/flFoxp3Cre n = 5 females, n = 4 males). Data representative (a, b) or pooled (c-i) from at least two independent experiments. Symbols represent individual mice, error bars indicate standard deviation, horizontal lines are means. Statistical analyses performed using two-way ANOVA or unpaired, two-tailed Student’s t-test (d).

Extended Data Fig. 4 GATA3 and T-bet control the differentiation of VAT Treg cells.

a, b, Volcano plots from bulk RNAseq analysis showing differential expression of genes in female (a) and male (b) Gata3fl/flFoxp3Cre and control mice. c, In silico foot printing analysis of open chromatin regions in ST2+ VAT Treg cells show binding sites for the transcription factors BATF, BACH2, GATA3 and PPARγ. d, ATAC-seq tracks from ST2+ and CXCR3+ VAT Treg cells showing differential chromatin accessibility in the Pparg locus. Accessible region in ST2+ Treg cells show putative GATA3 binding site. e, Bar graph showing scRNAseq UMAP cluster frequency of CD4 T cells from normal diet (ND) and high fat diet (HFD) fed male and female mice. f, FACS plots showing expression of CXCR3 and Foxp3 in splenic CD4+ T cells from Tbx21fl/flFoxp3Cre (n = 6 females, n = 7 males) and control mice (n = 9 females, n = 14 males). g, Frequencies of Treg cells (Foxp3+) among splenic CD4+TCRβ+ cells in Tbx21fl/flFoxp3Cre and control mice. h, Proportions (left) and numbers (right) of VAT Treg cells in Tbx21fl/flFoxp3Cre and control mice (control n = 6 females, n = 14 males; Gata3fl/flFoxp3Cre n = 9 females, n = 10 males). i, j, Volcano plots from bulk VAT Treg cell RNAseq analysis showing differential expression of genes in female (i) and male (j) Tbx21fl/flFoxp3Cre and control mice. Symbols represent individual mice; horizontal lines indicate means. Data are representative (f) or pooled (g, h) from at least two independent experiments. Error bars indicate the standard deviation. Statistical analyses were performed using two-way ANOVA.

Extended Data Fig. 5 Distinct cytokine profiles of female and male VAT.

a, FACS plots show TCRβ versus IL-5 or IL-13 expression in CD45+ VAT cells of female (top) and male (bottom) C57BL/6 mice. b, Il4AmCyan (n = 5 each sex) and Il5TdTom (n = 7 females, n = 5 males) expression in female (top) and male (bottom) VAT CD45+ VAT cells. Quantification on right. c, d, FACS plots show TCRβ versus IFN-γ or TNF expression in VAT CD45+ cells (c) and Foxp3 versus IFN-γ or TNF expression in VAT CD4+ cells of C57BL/6 female (d, top) and male (d, bottom) mice. Frequencies of IFN-γ+ (n = 8 females, n = 10 males) or TNF+ (n = 5 females, n = 9 males) among CD4+Foxp3−cells and amount (mean fluorescence index, MFI) of IFN-γ (n = 9 females, n = 10 males) or TNF (n = 8 females, n = 14 males). e, FACS plots show TCRβ versus Ifng expression in CD45+ VAT cells of female (top) and male (bottom) IfngeYFP mice. f, Frequencies of Ifng+ among CD45+ cells (left, n = 9 females, n = 11 males) and amount (MFI) in VAT TCRβ+ and TCRβ- cells (right, n = 5 females, n = 6 males) of IfngeYFP mice. g, Numbers of ST2+ (control n = 6 each sex; Ifng-/- n = 4 females, n = 9 males) and CXCR3+ (control n = 7 females, n = 6 males; Ifng-/- n = 7 females, n = 9 males) VAT Treg cells in female and male Ifng-/- mice. h, FACS plots show expression CD4 and Foxp3 in T cells from spleen (top) and VAT (bottom) in control (PBS) and single-dose diphtheria toxin (DT) treated Foxp3DTR mice. i, Numbers of TCRβ+ cells in control (PBS) and Foxp3DTR mice 10-days-post DT administration. j, Proportions of CD62L+, CXCR3+ and ST2+ VAT Treg cells in female control (PBS) or Foxp3DTR mice 10-days-post DT administration. Symbols represent individual mice, horizontal lines mean error bars standard deviation. Data pooled from at least two independent experiments (b, d, f, g, i, j); FACS plots representative (a - e, h). Unpaired, two-tailed Student’s t-test (b, d, f) or two-way ANOVA (g, i, j).

Extended Data Fig. 6 ST2+ and CXCR3+ VAT Treg cells have distinct functions.

a, Graphs show numbers of CD8 (control n = 22 females, n = 32 males; Ppargfl/flFoxp3Cre n = 17 each sex; Tbx21fl/flFoxp3Cre n = 11 each sex) and CD4 T cells (control n = 22 females, n = 32 males; Ppargfl/flFoxp3Cre n = 17 each sex; Tbx21fl/flFoxp3Cre n = 8 each sex) and TNF (control n = 21 females, n = 26 males; Ppargfl/flFoxp3Cre n = 9 females, n = 5 males; Tbx21fl/flFoxp3Cre n = 4 females, n = 10 males) or IFNγ (control n = 21 females, n = 28 males; Ppargfl/flFoxp3Cre n = 9 females, n = 5 males; Tbx21fl/flFoxp3Cre n = 8 females, n = 12 males) producing TCRβ+ cells in the VAT of male and female Tbx21fl/flFoxp3Cre, Ppargfl/flFoxp3Cre and control mice. b, VAT Treg cell quantification in male and female Tbx21fl/flFoxp3Cre, Ppargfl/flFoxp3Cre and control mice on HFD (control n = 10 females, n = 10 males; Tbx21fl/flFoxp3Cre n = 9 females, n = 8 males; Ppargfl/flFoxp3Cre n = 9 females, n = 14 males). c, d, Insulin tolerance was assessed in Ppargfl/flFoxp3Cre mice (c) and Tbx21fl/flFoxp3Cre mice (d) under conditions of normal diet (ND) and high fat-diet (HFD) feeding. ND on top and HFD bottom, females on left, males on right. Symbols represent individual mice (a, b) or mean of 3−5 mice (c, d). Data are pooled from 2−3 independent experiments. Error bars indicate the standard deviation (a, b) or s.e.m. (c, d). Statistical analyses were performed using two-way ANOVA (a, b) or unpaired, two-tailed Student’s t-test (c, d).

Extended Data Fig. 7 ST2+ and CXCR3+ Treg cells interact with distinct cell types.

a, FACS histogram overlays showing expression of GzmB, CTLA-4, Areg, CD25, IL-10 and CD39 in ST2+ and CXCR3+ VAT Treg cells. b, Graph showing frequency of IL-10-GFP expressing ST2+ VAT Treg cells from male (n = 16) and female (n = 9) mice. c, Dot plot shows reciprocal interaction between ST2+ and CXCR3+ VAT Treg cells and the VAT microenvironment. d, FACS gating scheme used to identify different myeloid cells in the VAT. e, Graph shows Ccl2-RFP mean fluorescence index (MFI) in VAT dendritic cells, macrophage subsets and neutrophils from male (n = 8) and female (n = 10) mice. f, FACS plots show frequency of Ccl2-RFP+ cells in male and female VAT resident and inflammatory macrophages. Quantification on the right. Symbols represent individual mice; horizontal lines indicate means. Data are pooled from at least two independent experiments (b, e, f); FACS plots are representative (a, d, f). Error bars indicate the standard deviation. Statistical analyses were performed using two-way ANOVA (e, f) or unpaired, two-tailed Student’s t-test (b).

Extended Data Fig. 8 ST2 and CXCR3 + VAT Treg cells regulate distinct inflammatory pathways.

a, b, Distribution of dendritic cell and macrophage populations in the VAT of male Tbx21fl/flFoxp3Cre (n = 5), Ppargfl/flFoxp3Cre (n = 8) and control mice under normal diet (ND, n = 4) and high fat diet (HFD, n = 5) conditions. (a) Macrophage frequencies (left) and numbers on right (b) Dendritic cell frequencies (left) and numbers on right. c, Representative flow cytometry plot showing expression of IL-5 by ST2+ Treg cells in male mice. d-f, (d) Representative FACS plots showing Cxcl9 and Cxcl10 reporter expression in male and female REX3 reporter mice. (e) Frequencies (left) and numbers (right) of Cxcl9+ cells in the VAT CD45+ compartment of male (n = 4) and female (n = 6) REX3 mice. (f) Frequencies (left) and numbers (right) of Cxcl10+ cells in the VAT CD45+ compartment of male (n = 4) and female (n = 6) REX3 mice. g, Gating strategy for macrophage and dendritic cells in the VAT. h, Graph showing frequency of Cxcl10 (left) and Cxcl9 (right) expressing macrophages and dendritic cells in male (n = 4) and female (n = 6) REX3 mice. Symbols represent individual mice; horizontal lines indicate means. Data are pooled from at least two independent experiments (a, b, e, f, h); FACS plots are representative (c, d, g). Error bars indicate the standard deviation. Statistical analyses were performed using two-way ANOVA (a, b, h) or unpaired, two-tailed Student’s t-test (e, f).

Extended Data Fig. 9 Altered VAT transcriptional profiles after Treg-cell-specific deletion of T-bet and PPARγ.

a-e, Volcano plots shows genes differentially expressed between total VAT of female vs male control mice (a), female vs male Ppargfl/flFoxp3Cre (b), female vs male Tbx21fl/flFoxp3Cre (c), female Tbx21fl/flFoxp3Cre vs control (d) and male Tbx21fl/flFoxp3Cre vs control (e) mice. f, KEGG (top) and GO (bottom) pathway enrichment analysis for differentially expressed genes in female VAT of Tbx21fl/flFoxp3Cre vs control mice. Positive values refer to pathways upregulated (red), negative values refer to pathways downregulated (blue) in the VAT. g, Flow cytometry plots showing glucose uptake measured by 2-NBDG staining in ST2+ and CXCR3+ male and female VAT Treg cells. Quantification on the right (n = 3 females, n = 6 males). h, i, Volcano plots showing genes differentially expressed between total VAT of female Ppargfl/flFoxp3Cre vs control (i) and male Ppargfl/flFoxp3Cre vs control (j) mice. Each dot in volcano plot represents a gene; genes highlighted in red are up-regulated and genes highlighted in blue are down-regulated.

Supplementary information

Reporting Summary

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Supplementary Table 1

scRNAseq cluster marker genes.

Supplementary Table 2

ST2 and CXCR3 signatures.

Supplementary Table 3

GATA3 versus WT Treg RNAseq DE genes.

Supplementary Table 4

scRNAseq: ND versus HFD (all clusters).

Supplementary Table 5

T-bet versus WT Treg RNAseq DE genes.

Supplementary Table 6

Adipose tissue DE genes.

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Torres, S.V., Man, K., Elmzzahi, T. et al. Two regulatory T cell populations in the visceral adipose tissue shape systemic metabolism. Nat Immunol 25, 496–511 (2024). https://doi.org/10.1038/s41590-024-01753-9

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