Abstract

Repair of tissue damaged during inflammatory processes is key to the return of local homeostasis and restoration of epithelial integrity. Here we describe CD161+ regulatory T (Treg) cells as a distinct, highly suppressive population of Treg cells that mediate wound healing. These Treg cells were enriched in intestinal lamina propria, particularly in Crohn’s disease. CD161+ Treg cells had an all-trans retinoic acid (ATRA)-regulated gene signature, and CD161 expression on Treg cells was induced by ATRA, which directly regulated the CD161 gene. CD161 was co-stimulatory, and ligation with the T cell antigen receptor induced cytokines that accelerated the wound healing of intestinal epithelial cells. We identified a transcription-factor network, including BACH2, RORγt, FOSL2, AP-1 and RUNX1, that controlled expression of the wound-healing program, and found a CD161+ Treg cell signature in Crohn’s disease mucosa associated with reduced inflammation. These findings identify CD161+ Treg cells as a population involved in controlling the balance between inflammation and epithelial barrier healing in the gut.

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Data availability

The data generated for this study have been deposited at the Gene Expression Omnibus (GEO) under accession code GSE119375.

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Acknowledgements

The authors thank patients who contributed samples toward this study. This work was supported by the Wellcome Trust (grant 097261/Z/11/Z to B.A. and WT101159 to N.P.), the Crohn’s and Colitis Foundation of America (grant CCFA no. 3765 — CCFA genetics initiative to A.L.), British Heart Foundation (grant RG/13/12/30395 to G.L.), institutional start-up fund from Purdue University and National Heart, Lung, and Blood Institute (grant 5K22HL125593-02 to M.K.). Research was also supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at Guy’s and St Thomas’ National Health Service (NHS) Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. This research was supported (in part) by the Intramural Research Programs of the National Institute of Arthritis and Musculoskeletal and Skin Diseases, the National Institute of Diabetes and Digestive and Kidney Diseases and the National Heart, Lung, and Blood Institute of the National Institutes of Health. The authors thank J. O’Shea (National Institutes of Health) for his support and for providing access to ATAC-seq, the National Heart, Lung, and Blood Institute DNA Sequencing and Genomics Core for performing single-cell sequencing experiment and acknowledge the assistance of M. Arno (Genomics Centre, King’s College London) with gene expression microarray studies as well as S. Heck and R. Ellis (Biomedical Research Centre Flow Core Facility, King’s College London) for CyTOF data acquisition. In addition, the authors thank E. Mathé (Ohio State University) for critically reading the manuscript.

Author information

Author notes

  1. These authors contributed equally: Giovanna Lombardi, Behdad Afzali.

Affiliations

  1. MRC, Centre for Transplantation, King’s College London, London, UK

    • Giovanni A. M. Povoleri
    • , Estefania Nova-Lamperti
    • , Cristiano Scottà
    • , Giorgia Fanelli
    • , Pablo D. Becker
    • , Dominic Boardman
    • , Marco Romano
    • , Polychronis Pavlidis
    • , Reuben McGregor
    • , Eirini Pantazi
    • , Nick Powell
    • , Giovanna Lombardi
    •  & Behdad Afzali
  2. National Institute for Health Research Biomedical Research Centre at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London, London, UK

    • Giovanni A. M. Povoleri
    • , Estefania Nova-Lamperti
    • , Cristiano Scottà
    • , Giorgia Fanelli
    • , Pablo D. Becker
    • , Dominic Boardman
    • , Marco Romano
    • , Polychronis Pavlidis
    • , Reuben McGregor
    • , Eirini Pantazi
    • , Nick Powell
    •  & Giovanna Lombardi
  3. Bioinformatics and Computational Biology Core, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA

    • Yun-Ching Chen
    •  & Mehdi Pirooznia
  4. Comprehensive Cancer Centre, School of Cancer and Pharmaceutical Sciences, King’s College London, London, UK

    • Benedetta Costantini
    •  & Shahram Kordasti
  5. Immunoregulation Section, Kidney Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA

    • Daniel Chauss
    •  & Behdad Afzali
  6. Biodata Mining and Discovery Section, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA

    • Hong-Wei Sun
  7. Lymphocyte Cell Biology Section, Molecular Immunology and Inflammation Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, MD, USA

    • Han-Yu Shih
  8. Department of Infection, Immunity and Inflammation, NIHR Leicester Respiratory Biomedical Research Unit, University of Leicester, Leicester, UK

    • David J. Cousins
  9. Department of Medicine, Imperial College London, London, UK

    • Nichola Cooper
  10. Complement and Inflammation Research Section, National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA

    • Claudia Kemper
  11. Institute of Cellular Medicine, Newcastle University, Newcastle, UK

    • Arian Laurence
  12. Departments of Biochemistry and Computer Science, Purdue University, West Lafayette, IN, USA

    • Majid Kazemian
  13. National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA

    • Behdad Afzali

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Contributions

G.A.M.P. designed and performed experiments, analyzed data and wrote the manuscript. N.C., N.P. and P.P. provided patient samples and clinical and scientific input. S.K. designed the CyTOF panel, analyzed and interpreted data, provided scientific input and wrote the paper. M.K. analyzed and interpreted genomics data, provided scientific input and wrote the paper. G.L. provided scientific input, supervised the project and wrote the manuscript. B.A. conceptualized the study, supervised the project, analyzed data and wrote the manuscript. E.N.-L., C.S., G.F., Y.-C.C., P.B., D.B., B.C., M.R., R.M., E.P., D.Chauss, H.-W.S., H.-Y.S., D.Cousins, C.K., M.P. and A.L. performed experiments, analyzed data and/or provided scientific input.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Behdad Afzali.

Integrated supplementary information

  1. Supplementary Figure 1 CD161+ Treg cells are a discrete population of memory Treg cells.

    (a) Heat map showing signal intensity of selected markers for each node of the populations shown in Fig. 1c. (b–e) Clustering of populations of Tconv and Treg cells by single-cell RNA-seq (scRNAseq) showing the tSNE plot of 2,636 cells from n= 3 independent donors separated into six major (clusters 0–6) and two minor (clusters 7 and 8) clusters (b), feature plots showing expression of three Treg markers (IL2RA encoding CD25, IL7R encoding CD127 and FOXP3 encoding FOXP3), KLRB1 (encoding CD161) and two naive/memory markers (SELL encoding CD62L and CCR7 encoding CCR7) (c), a heat map showing the top 20 discriminatory genes per cluster with representative genes of each cluster indicated (d) and population clustering (e) based on expression of the transcripts shown in c. Clusters in b are color-coded and labeled according to expression of the markers shown in e. (f) Clustering of Tconv and Treg subpopulations using protein expression data sourced from CyTOF in Fig. 1a incorporating the same markers as in e and also including CD45RA and CD45RO. Clustering of subpopulations in f is similar to that from scRNAseq (e). (g,h) viSNE plots (g) and 2D minimum spanning tree (h) of CD4+ T cells clustered following staining with anti-CD4, anti-CD25, anti-CD127, anti-CD45RA and anti-CD161 for flow cytometry. Shown in g are heat maps for expression of the indicated markers, with an arrow indicating CD161 expression in Treg cells. Node size in h represents cell number and color CD161 median intensity. Grouped together are naive (circled in orange), memory (circled in yellow) and CD161+ (circled in purple) Treg cells, as well as populations of naive (circled in black), memory (circled in black) and CD161+ (circled in red) Tconv cells. The data in g and h are representative of n = 4 experiments. *P < 0.01, **P < 0.001, ***P < 0.0001 by one-way ANOVA.

  2. Supplementary Figure 2 CD161+ Treg cells are distinct from other CD161-expressing T cells.

    (a) Representative example of the FACS sorting strategy for naive (orange), memory (yellow) and CD161+ (purple) Treg cells. (b) Proportions of each Treg subpopulation in healthy human donor peripheral blood (cumulative data from n = 10 donors); bars show mean + s.e.m. (c) Representative (top) and cumulative (bottom) expression of TCR Vα24-Jα18 and Vα7.2 in total CD4+ T cells and CD161+ Treg cells. Both cell types show minimal expression of these invariant TCR chains. Data are from n = 3 independent experiments. (d) Contribution of the three highest (BV05, BV06 and BV07) and one of the lowest (BV19) TCRBV families to the overall TCR repertoire in naive, memory and CD161+ Treg cells as well as CD161+ and total Tconv cells. Data are from n = 3 independent experiments; bars show mean + s.e.m. (e) Average percentage of TCR sequences either unique or shared among naive, memory and CD161+ Treg cells, CD161+ and total Tconv cells (cumulative data from n = 3 experiments). *P < 0.01, **P < 0.001, ***P < 0.0001 by one-way ANOVA.

  3. Supplementary Figure 3 Transcriptome and methylome of CD161+ Treg cells.

    (a) Principal-component analysis (PCA) 2D mapping of variance in expression of transcripts in freshly isolated naive, memory and CD161+ Treg cells (n = 3). (b–d) Scatterplots showing correlation between mean gene expression of freshly isolated Treg subpopulations (n = 3). (e,f) GSEA plots for core human Treg signature genes comparing freshly isolated naive to memory Treg cells (e) and to CD161+ Tregs cells (f) (n = 3 per group). NES, normalized enrichment score; empirical P and multiple-test adjusted q values from GSEA are shown. (g) Schematic representation of FOXP3, IL2RA and CTLA4 gene loci showing the target sequence and conserved CpGs assayed for methylation analysis in Fig. 2e. (h) Mean region methylation percentage of FOXP3 TSDR, IL2RA and CTLA4 loci from three male donors. Data are from n = 3 independent experiments; bars show mean + s.e.m. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001 by one-way ANOVA.

  4. Supplementary Figure 4 Regulation of CD161 by retinoic acid.

    (a) Heat map of leading edge (core enriched) genes from GSEA for ATRA-regulated genes comparing freshly isolated memory to CD161+ Treg cells (Fig. 3a). Data are from n = 3 independent experiments. (b) CCR9 surface protein expression on memory and CD161+ Treg cells; shown are representative flow cytometry plots (left) and cumulative data from n = 3 independent experiments (right). (c) CD161 expression on Treg cells co-cultured for 5 d with DCs with and without BMS493 at increasing concentrations. Shown are representative plots (left) and cumulative data (right) from n = 3 independent experiments. (d,e) Sequence logo for RARA DNA motif (d) and schematic representation of the KLRB1 and CCR9 gene loci (±5 kb) showing the predicted binding sites of RARA (e); the red arrowhead indicates the binding site for CCR9 selected for analysis in ChIP–qPCR. Bars show mean + s.e.m. throughout the figure. *P < 0.05 by paired t-test.

  5. Supplementary Figure 5 CD161+ Treg cells are highly regulatory.

    (a,b) Cumulative mean percentage suppressive function of freshly isolated naive, memory and CD161+ Treg cells from Fig. 4a (n = 4 experiments) (a) and representative IC50 value calculation (b; dashed arrows show the IC50 for CD161+ and memory Treg cells). (c) Cumulative mean percentage suppressive function of memory and CD161+ Treg cells after in vitro expansion for 2 weeks (n = 4 experiments). (d) Suppression of Tconv cell proliferation when in direct contact with Treg cells or when separated by a Transwell at a Treg:Tconv ratio of 1:1 (cumulative data from n = 4 independent experiments). (e) Suppressive function of Treg cells under neutral (null) conditions or in the presence of blocking antibodies directed against CD161, PDL1, TGFβRII or IL10R, all at a Treg:Tconv ratio of 1:2 (cumulative data from n = 3 independent experiments). (f) Suppressive function of CD161+ Treg cells under neutral (null) conditions or in the presence of TH1 and TH17 skewing conditions, all at a Treg:Tconv ratio of 1:2 (cumulative data from n = 3 independent experiments). (g) Expression of perforin, granzyme A and B in subpopulations of Treg cells, with NK cells as a positive control; shown are representative flow cytometry plots for each marker (left) and cumulative data from n = 3 independent experiments (right). Bar charts show mean + s.e.m. throughout the figure. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 by two-way ANOVA (a–e) and one-way ANOVA (g).

  6. Supplementary Figure 6 CD161+ Treg cells produce multiple cytokines upon activation.

    (a) Differentially expressed genes (at least twofold change at P < 0.05) following 4 h of stimulation of memory and naive Treg cells with anti-CD3/CD28. (b) Volcano plot showing significant genes differentially expressed in CD161+ Treg cells after 4 h of activation with anti-CD3/CD28. Differentially expressed genes are shown in blue; red indicates significantly upregulated cytokine genes that are differentially expressed compared to memory Treg cells (see Fig. 5c); orange indicates other cytokine genes of interest upregulated in activated CD161+ Treg cells. The data in a are b are from three independent experiments. (c,d) Cell sorting of IL-17+ and IL-17 CD161+ Treg cells by surface IL-17 capture. Shown are representative flow cytometry plots with double staining for intracellular and extracellular (captured on the surface) IL-17 with (right) and without (left) fixation and permeabilization (c) and the sorting strategy using IL-17 surface capture and post-sorting purity for the IL-17+ and IL-17 fractions of CD161+ Treg cells (d). Shown in c and d are representative examples from n = 3 and n = 4 independent experiments, respectively.

  7. Supplementary Figure 7 Global analysis of Treg regulomes.

    (a) Open chromatin regions (OCRs) at prototypical Treg gene loci from Fig. 6a. (b) Genomic distribution of ATAC peaks (promoter, intragenic or intergenic regions) in the three groups shown in Fig. 6a. (c) P values (cumulative hypergeometric test P values calculated by Homer) for transcription factor (TF) motifs shown in each cluster in Fig. 6c. Data in ac are from n = 3 experiments. (d) Expression of key TFs participating in gene regulation in CD161+ Treg cells. Shown is the normalized signal intensity (mean + s.e.m.) from n = 3 microarrays for each Treg population. (e) Integrated TF network showing the contribution of each TF to DEGs and overlap between them (fold difference in expression of each TF is also indicated next to the TF). *P < 0.05, **P < 0.01, ***P < 0.001 (Partek analysis of microarrays).

  8. Supplementary Figure 8 CD161+ Treg cells accelerate wound healing.

    (a,b) GSEA plots for wound healing–associated genes (a) and wound healing–associated soluble mediators (b) comparing activated CD161+ to memory Treg cells. Data are from n = 3 independent experiments. NES, normalized enrichment score; empirical P and multiple-test-adjusted q values from GSEA are indicated. (c) Wound closure rate of Caco-2 cells cultured in the presence of either medium alone or medium supplemented with culture supernatants (snt) from activated memory or CD161+ Treg cells. Shown are cumulative data from n = 6 independent experiments. (d) Concentration (pg/ml) of stated cytokines in supernatants of CD161+ Treg cells transduced, or not, with control lentivirus or lentivirus encoding BACH2 (cumulative data from n = 6 experiments). Bar charts show mean + s.e.m.; *P < 0.05, **P < 0.01 by one-way ANOVA.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–8

  2. Reporting Summary

  3. Supplementary Table 1

    List of differentially expressed genes (DEGs)

  4. Supplementary Table 2

    Expression of killer cell lectin-like receptors (Klr) in wild-type mouse Treg cells

  5. Supplementary Table 3

    Open chromatin regions (OCRs) from ATAC-seq

  6. Supplementary Table 4

    Gene lists used for gene set enrichment analyses

  7. Supplementary Table 5

    Gene lists used for gene set enrichment analyses

  8. Supplementary Video 1

    Wound healing with medium alone. The video shows growth of Caco-2 cells cultured with medium alone. Time-lapse images were recorded from 0–120 h. Shown are representative videos from n = 3 independent experiments

  9. Supplementary Video 2

    Wound healing with memory Treg cell supernatants. The video shows growth of Caco-2 cells cultured with medium supplemented with culture supernatants (snt) of activated memory cells. Time-lapse images were recorded from 0–120 h. Shown are representative videos from n = 3 independent experiments

  10. Supplementary Video 3

    Wound healing with CD161+ Treg cell supernatants. The video shows growth of Caco-2 cells cultured with medium supplemented with culture supernatants (snt) of CD161+ Treg cells. Time-lapse images were recorded from 0–120 h. Shown are representative videos from n = 3 independent experiments

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https://doi.org/10.1038/s41590-018-0230-z