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PD-L1–PD-1 interactions limit effector regulatory T cell populations at homeostasis and during infection

Nature Immunology volume 23, pages 743–756 (2022)Cite this article

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Abstract

Phenotypic and transcriptional profiling of regulatory T (Treg) cells at homeostasis reveals that T cell receptor activation promotes Treg cells with an effector phenotype (eTreg) characterized by the production of interleukin-10 and expression of the inhibitory receptor PD-1. At homeostasis, blockade of the PD-1 pathway results in enhanced eTreg cell activity, whereas during infection with Toxoplasma gondii, early interferon-γ upregulates myeloid cell expression of PD-L1 associated with reduced Treg cell populations. In infected mice, blockade of PD-L1, complete deletion of PD-1 or lineage-specific deletion of PD-1 in Treg cells prevents loss of eTreg cells. These interventions resulted in a reduced ratio of pathogen-specific effector T cells: eTreg cells and increased levels of interleukin-10 that mitigated the development of immunopathology, but which could compromise parasite control. Thus, eTreg cell expression of PD-1 acts as a sensor to rapidly tune the pool of eTreg cells at homeostasis and during inflammatory processes.

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Fig. 1: Treg cell heterogeneity at homeostasis and eTreg cell association with PD-1.
Fig. 2: PD-1 signaling restrains eTreg cell populations at homeostasis.
Fig. 3: TCR signals are necessary to maintain eTreg cell populations at homeostasis.
Fig. 4: Infection-induced IFN-γ promotes upregulation of myeloid PD-L1 via STAT1, which limits PD-1+ eTreg cells.
Fig. 5: PD-L1 blockade ameliorates the crash of PD-1+ eTreg cells during the acute phase of infection.
Fig. 6: Impact of PD-L1 blockade on effector T cell responses during infection.
Fig. 7: Treg cell-specific deletion of PD-1 enhances eTreg cell populations at homeostasis and prevents Treg cell depletion during infection.
Fig. 8: Treg cell-specific deletion of PD-1 results in reduction of parasite-specific TH1 cells and a systemic increase in parasite burden.

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Data availability

RNA-seq datasets discussed in this publication have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus and are accessible through accession no. GSE186350. The remaining data that support the findings of this study are available on request from the corresponding author. Source data are provided with this paper.

References

  1. Josefowicz, S. Z., Lu, L.-F. & Rudensky, A. Y. Regulatory T cells: mechanisms of differentiation and function. Annu. Rev. Immunol. 30, 531–564 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Sakaguchi, S. et al. Regulatory T cells and human disease. Annu. Rev. Immunol. 38, 541–566 (2020).

    Article  CAS  PubMed  Google Scholar 

  3. Kieback, E. et al. Thymus-derived regulatory T cells are positively selected on natural self-antigen through cognate interactions of high functional avidity. Immunity 44, 1114–1126 (2016).

    Article  CAS  PubMed  Google Scholar 

  4. Levine, A. G., Arvey, A., Jin, W. & Rudensky, A. Y. Continuous requirement for the TCR in regulatory T cell function. Nat. Immunol. 15, 1070–1078 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Lenschow, D. J. et al. B7/CD28 costimulation is essential for the homeostasis of the CD4+CD25+ immunoregulatory T cells that control autoimmune diabetes. Immunity 12, 431–440 (2000).

    Article  PubMed  Google Scholar 

  6. Bluestone, J. A. & Tang, Q. How do CD4+CD25+ regulatory T cells control autoimmunity? Curr. Opin. Immunol. 17, 638–642 (2005).

    Article  CAS  PubMed  Google Scholar 

  7. Chinen, T. et al. An essential role for IL-2 receptor in regulatory T cell function. Nat. Immunol. 17, 1322–1333 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Duhen, T., Duhen, R., Lanzavecchia, A., Sallusto, F. & Campbell, D. J. Functionally distinct subsets of human FOXP3+ Treg cells that phenotypically mirror effector TH cells. Blood 119, 4430–4440 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Zheng, Y. et al. Regulatory T-cell suppressor program co-opts transcription factor IRF4 to control TH2 responses. Nature 458, 351–356 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Chaudhry, A. et al. CD4+ regulatory T cells control TH17 responses in a Stat3-dependent manner. Science 326, 986–991 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 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 

  14. Hall, A. O. H. 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  CAS  Google Scholar 

  15. Sprouse, M. L. et al. Cutting edge: low-affinity tcrs support regulatory T cell function in autoimmunity. J. Immunol. 200, 909–914 (2018).

    Article  CAS  PubMed  Google Scholar 

  16. Wyss, L. et al. Affinity for self antigen selects Treg cells with distinct functional properties. Nat. Immunol. 17, 1093–1101 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Kumagai, S. et al. The PD-1 expression balance between effector and regulatory T cells predicts the clinical efficacy of PD-1 blockade therapies. Nat. Immunol. 21, 1346–1358 (2020).

    Article  CAS  PubMed  Google Scholar 

  18. Kamada, T. et al. PD-1+ regulatory T cells amplified by PD-1 blockade promote hyperprogression of cancer. Proc. Natl Acad. Sci. USA 116, 201822001 (2019).

    Article  CAS  Google Scholar 

  19. Odorizzi, P. M., Pauken, K. E., Paley, M. A., Sharpe, A. & Wherry, E. J. Genetic absence of PD-1 promotes accumulation of terminally differentiated exhausted CD8+ T cells. J. Exp. Med. 212, 1125–1137 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hui, E. et al. T cell costimulatory receptor CD28 is a primary target for PD-1–mediated inhibition. Science. 4, eaaf1292 (2017).

    CAS  Google Scholar 

  21. Fife, B. T. et al. Interactions between PD-1 and PD-L1 promote tolerance by blocking the TCR-induced stop signal. Nat. Immunol. 10, 1185–1192 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Chen, X. et al. PD-1 regulates extrathymic regulatory T-cell differentiation. Eur. J. Immunol. 44, 2603–2616 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Ellestad, K. K., Thangavelu, G., Ewen, C. L., Boon, L. & Anderson, C. C. PD-1 is not required for natural or peripherally induced regulatory T cells: severe autoimmunity despite normal production of regulatory T cells. Eur. J. Immunol. 44, 3560–3572 (2014).

    Article  CAS  PubMed  Google Scholar 

  24. Belkaid, Y., Piccirillo, C. A., Mendez, S., Shevach, E. M. & Sacks, D. L. CD4+CD25+ regulatory T cells control Leishmania major persistence and immunity. Nature 420, 502–507 (2002).

    Article  CAS  PubMed  Google Scholar 

  25. Maizels, R. M. & Smith, K. A. Regulatory T Cells in Infection. Advances in Immunology Vol. 112 (Elsevier, 2011).

  26. Benson, A. et al. Microbial infection-induced expansion of effector T cells overcomes the suppressive effects of regulatory T cells via an IL-2 deprivation mechanism. J. Immunol. 188, 800–810 (2012).

    Article  CAS  PubMed  Google Scholar 

  27. Oldenhove, G. et al. Decrease of Foxp3+ Treg cell number and acquisition of effector cell phenotype during lethal infection. Immunity 31, 772–786 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Moran, A. E. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Thornton, A. M. et al. Helios+ and Helios− Treg subpopulations are phenotypically and functionally distinct and express dissimilar TCR repertoires. Eur. J. Immunol. 49, 398–412 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kornete, M., Mason, E., Istomine, R. & Piccirillo, C. A. KLRG1 expression identifies short-lived Foxp3(+) Treg effector cells with functional plasticity in islets of NOD mice. Autoimmunity 50, 1–9 (2017).

    Article  CAS  Google Scholar 

  31. Smigiel, K. S. et al. CCR7 provides localized access to IL-2 and defines homeostatically distinct regulatory T cell subsets. J. Exp. Med. 211, 121–136 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. vanLoosdregt, J. et al. Canonical Wnt signaling negatively modulates regulatory T cell function. Immunity 39, 298–310 (2013).

    Article  CAS  Google Scholar 

  33. Garcia-Diaz, A. et al. Interferon receptor signaling pathways regulating PD-L1 and PD-L2 expression. Cell Rep. 19, 1189–1201 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Gazzinelli, R. T. et al. Parasite-induced IL-12 stimulates early IFN-γ synthesis and resistance during acute infection with Toxoplasma gondii. J. Immunol. 153, 2533–2543 (1994).

    CAS  PubMed  Google Scholar 

  35. Chikuma, S. et al. PD-1-mediated suppression of IL-2 production induces CD8+ T cell anergy in vivo. J. Immunol. 182, 6682–6689 (2009).

    Article  CAS  PubMed  Google Scholar 

  36. Tan, C. L. et al. PD-1 restraint of regulatory T cell suppressive activity is critical for immune tolerance. J. Exp. Med. https://doi.org/10.1084/jem.20182232 (2020).

  37. Francisco, L. M. et al. PD-L1 regulates the development, maintenance, and function of induced regulatory T cells. J. Exp. Med. 206, 3015–3029 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Huang, A. C. et al. A single dose of neoadjuvant PD-1 blockade predicts clinical outcomes in resectable melanoma. Nat. Med. 25, 454–461 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Bally, A. P. R., Austin, J. W. & Boss, J. M. Genetic and epigenetic regulation of PD-1 expression. J. Immunol. 196, 2431–2437 (2016).

    Article  CAS  PubMed  Google Scholar 

  40. Chemnitz, J. M., Parry, R. V., Nichols, K. E., June, C. H. & Riley, J. L. SHP-1 and SHP-2 associate with immunoreceptor tyrosine-based switch motif of programmed death 1 upon primary human T Cell stimulation, but only receptor ligation prevents T cell activation. J. Immunol. 173, 945–954 (2004).

    Article  CAS  PubMed  Google Scholar 

  41. Parry, R. V. et al. CTLA-4 and PD-1 receptors inhibit T-cell activation by distinct mechanisms CTLA-4 and PD-1 receptors inhibit T-cell activation by distinct mechanisms. Mol. Cell. Biol. 25, 9543–9553 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Darrasse-Jèze, G. et al. Feedback control of regulatory T cell homeostasis by dendritic cells in vivo. J. Exp. Med. 206, 1853–1862 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Wohn, C. et al. Absence of MHC class II on cDC1 dendritic cells triggers fatal autoimmunity to a cross-presented self-antigen. Sci. Immunol. https://doi.org/10.1126/sciimmunol.aba1896 (2020).

  44. Mcberry, C. et al. PD-1 modulates steady-state and infection-induced IL-10 production in vivo. Eur. J. Immunol. 44, 469–479 (2014).

    Article  CAS  PubMed  Google Scholar 

  45. Belkaid, Y. et al. The role of interleukin (IL)-10 in the persistence of Leishmania major in the skin after healing and the therapeutic potential of anti-IL-10 receptor antibody for sterile cure. J. Exp. Med. 194, 1497–1506 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Bhadra, R., Gigley, J. P., Weiss, L. M. & Khan, I. A. Control of Toxoplasma reactivation by rescue of dysfunctional CD8+ T-cell response via PD-1-PDL-1 blockade. Proc. Natl Acad. Sci. USA 108, 9196–9201 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Pack, A. D., Collins, M. H., Rosenberg, C. S. & Tarleton, R. L. Highly competent, non-exhausted CD8+ T cells continue to tightly control pathogen load throughout chronic Trypanosoma cruzi infection. PLoS Pathog. 14, 1–20 (2018).

    Article  CAS  Google Scholar 

  48. Xu, D. et al. A potential new pathway for PD-L1 costimulation of the CD8- T cell response to Listeria monocytogenes infection. PLoS ONE https://doi.org/10.1371/journal.pone.0056539 (2013).

  49. Kauffman, K. D. et al. PD-1 blockade exacerbates Mycobacterium tuberculosis infection in rhesus macaques. Sci. Immunol. 6, 139–148 (2021).

    Article  CAS  Google Scholar 

  50. Ogishi, M. et al. Inherited PD-1 deficiency underlies tuberculosis and autoimmunity in a child. Nat. Med. 27, 1646–1654 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Holt, M. P., Punkosdy, G. A., Glass, D. D. & Shevach, E. M. TCR signaling and CD28/CTLA-4 signaling cooperatively modulate T regulatory cell homeostasis. J. Immunol. 198, 1503–1511 (2017).

    Article  CAS  PubMed  Google Scholar 

  52. Paterson, A. M. et al. Deletion of CTLA-4 on regulatory T cells during adulthood leads to resistance to autoimmunity. J. Exp. Med. 212, 1603–1621 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Wang, S. et al. Infection-induced intestinal dysbiosis is mediated by macrophage activation and nitrate production. MBio 10, 1–13 (2019).

    Article  Google Scholar 

  54. Klover, P. J. et al. Loss of STAT1 from mouse mammary epithelium results in an increased neu-induced tumor burden. Neoplasia 12, 899–905 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Md Sakib Hossain, D. et al. Dinaciclib induces immunogenic cell death and enhances anti-PD1-mediated tumor suppression. J. Clin. Invest. 128, 644–654 (2018).

    Article  Google Scholar 

  56. Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

    Article  CAS  PubMed  Google Scholar 

  57. 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  CAS  Google Scholar 

  58. Galili, T., O’Callaghan, A., Sidi, J. & Sievert, C. Heatmaply: an R package for creating interactive cluster heatmaps for online publishing. Bioinformatics 34, 1600–1602 (2018).

    Article  CAS  PubMed  Google Scholar 

  59. Samusik, N., Good, Z., Spitzer, M. H., Davis, K. L. & Nolan, G. P. Automated mapping of phenotype space with single-cell data. Nat. Methods 13, 493–496 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This project was supported by the National Institute of Allergy and Infectious Diseases R01 AI125563 and R01 AI41158 grants, awarded to C.A.H; in addition to P01 AI039671 and P01 AI056299 grants, awarded to A.H.S. J.A.P. was supported by a training grant, T32-CA-009140. We thank W. Pear for mentorship and guidance during the formation of this project. We also thank L. ‘Enzo’ Burton for patience and encouragement.

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Authors and Affiliations

  1. Department of Pathobiology, University of Pennsylvania, Philadelphia, PA, USA

    Joseph A. Perry, Lindsey Shallberg, Joseph T. Clark, Jodi A. Gullicksrud, Jonathan H. DeLong, Bonnie B. Douglas, Zachary Lanzar, Keenan O’Dea, Christoph Konradt, Jeongho Park, Daniel Grubaugh, Arielle Glatman Zaretsky, Igor E. Brodsky, David A. Christian & Christopher A. Hunter

  2. Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Andrew P. Hart

  3. Purdue University College of Veterinary Medicine, West Lafayette, IN, USA

    Christoph Konradt

  4. Department of Immunology, Harvard Medical School, Boston, MA, USA

    Juhi R. Kuchroo & Arlene H. Sharpe

  5. Merck & Co., Kenilworth, NJ, USA

    Rene de Waal Malefyt

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Contributions

J.A.P. conceptualized the project, designed/executed all experiments and performed data analysis, produced figures and wrote the paper. L.S. performed RNA-seq, including the subsequent transcriptomic analysis and wrote the results of RNA-seq data. J.T.C., J.A.G., J.H.D., B.B.D., A.P.H., Z.L., K.O., C.K. and J.P. participated in the experiments and conceptual feedback regarding experimental design, data analysis and manuscript editing. J.R.K., R.W.M. and A.H.P. provided conceptual feedback and generated and supplied animal models (PD-1 KO and Foxp3cre × PD-1flox mice). D.G. and I.E.B. assisted with manuscript revision and contributed the L. monocytgenes experiment. A.G.Z. provided data, which helped shape the initial project hypothesis in addition to providing counsel regarding the data. D.A.C. directly supervised experimental execution and interpretation of data and C.A.H. supervised the project in its entirety. Every author evaluated and approved this manuscript. The data presented in this manuscript were reviewed in raw form by the authors and the appropriate statistical tests were applied. The figures are accurate representations of the data and there are no manipulations of images except for general resizing for publishing. The journal policies of materials, data sharing, ethical animal use and conflicts of interest have been adhered to. We are confident that the conclusions presented here are based on accurate interpretations of the data collected for this study. Our colleagues listed as co-authors have contributed to and have earned the author designation for this manuscript.

Corresponding author

Correspondence to Christopher A. Hunter.

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Competing interests

At the time of the study, R.W.M. was employed by Merck & Co Inc., Palo Alto and has declared no financial interest in Merck & Co Inc. The remaining authors declare no competing interests.

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Nature Immunology thanks Jeffrey Bluestone, Hiroyoshi Nishikawa and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Jamie D. K. Wilson, in collaboration with the Nature Immunology team.

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Extended data

Extended Data Fig. 1 Treg cell heterogeneity at homeostasis and Treg cell expression of PD-1.

(a) Splenocytes from naïve 8 week-old male C57BL/6 mice were analyzed via high-parameter flow cytometry to identify CD4+ T cells and subset them into Foxp3+ and Foxp3− subsets, depicted is the gating strategy to identify Treg and Tconv CD4+ T cells. (b) Qualitative analysis of bulk CD3+, CD4+ T cells was conducted to produce a 2-dimensional UMAP representation using dimensional reduction algorithms (excluding CD4, Foxp3 and PD-1 expression as calculated factors). (c-d) Regions of CD4+ T cells expressing Foxp3 and or PD-1 were identified via median heatmap of expression of the generated UMAP plot. (e) The initial distribution UMAP was then qualitatively assessed using median heatmap distribution trends amongst the bulk CD4+ T cell pool of Treg cell associated proteins: Helios, GITR, CD25, PD-L1, and CTLA-4, in addition to proteins associated with effector function in Tregs (f) KLRG1, CD73, and ICOS. (g) Histogram comparisons were then made and quantified between Foxp3+ and Foxp3− subsets for the inhibitory proteins CTLA-4 and PD-L1 (n = 5/group, 2 way ANOVA with Tukey multiple comparisons test, *** = p = 0.0002, **** = p < 0.0001, 6 experimental replicates). (h) Proteins associated with activation (CD69, CD11a, CD44, ICOS, and CD127) were also compared and quantified (n = 5/group, 2 way ANOVA with Tukey multiple comparisons test, **** = p < 0.0001, 6 experimental replicates). All data presented are means + /- SEM and show individual data points.

Source data

Extended Data Fig. 2 Qualitative X-shift identification of Treg heterogeneity in the PD-1hi cluster of Treg cells.

(a) Splenocytes from naïve 8 week-old male C57BL/6 mice were analyzed via high-parameter flow cytometry to identify CD4+ T cells and were then grouped into Foxp3+ and Foxp3−subsets. (b) CD4+ Foxp3+ T cells were then subset into PD-1−ve, PD-1low, and PD-1hi groups using a PD-1 KO host as a negative stain comparative control. (c) UMAP qualitative analysis was generated specifically on CD4+ Foxp3+ T cells (Treg cells), excluding CD4, PD-1, and Foxp3 as variables in the calculation. (D) Depiction of PD-1 expression as a median heatmap amongst the Treg cell UMAP. (e) The Treg cell UMAP was then reanalyzed via the X-shift algorithm (excluding CD4, PD-1, and Foxp3 from the calculation) to potentially identify Treg subsets as clusters within the UMAP, with each X-shift identified subset depicted as a separate color. (f) Within the same UMAP, the PD-1−ve, PD-1low, and PD-1hi groups are portrayed as black, blue, and red respectively, to compare the location of these subsets to the locations of the X-shift identified Treg subsets. (g) Graphed MFI of fluorescence of stained proteins on these cells identified in the UMAP X-shift analysis to qualitatively compare different trends amongst the Treg cell clusters at homeostasis. (h) UMAP qualitative analysis on splenocyte-derived Treg cells from naïve C57BL/6 mice following stimulation and cytokine staining. (i) Heatmaps of median expression of IL-10 and PD-1 within the UMAP generated in H. (j) Re-analysis via X-shift algorithm to identify unique clusters within the cytokine-stain UMAP, indicated by separate colors in the plot. (k) Overlay of PD-1−, PD-1low, and PD-1hi subsets within the cytokine-stain UMAP.

Source data

Extended Data Fig. 3 Constitutive PD-L1 expression at homeostasis and anti-PD-L1 blocking antibody detection.

(a) Splenocytes from 8 week-old male C57BL/6 mice were qualitatively analyzed for PD-L1 expression compared to an FMO (fluorescence minus one) via flow cytometry across multiple leukocyte populations: Treg cells (CD3+, CD4+, Foxp3+), B cells (CD3−, B220+, CD19+), cDC1s (CD3−, B220−, CD19−, NK1.1−, Ly6G−, CD64−, CD11c+, MHC-II+, XCR1+), cDC2s (CD3−, B220−, CD19−, NK1.1−, Ly6G−, CD64−, CD11c+, MHC-II+, SIRPα+), and macrophages (CD3−, B220−, CD19−, NK1.1−, Ly6G−, CD64+, CD11b+, MHC-II+, Ly6Clow). (b) Groups of 9 week-old male C57BL/6 mice were treated with an IP injection of isotype (Rat IgG2b) (n = 4) or anti-PD-L1 blocking antibody (n = 5) for 72 hours. Splenocytes from these groups were then harvested and stained with an anti-Rat-IgG2b FITC antibody to determine if the PD-L1 blocking antibody was opsonizing the previously identified PD-L1+ subsets (Tregs, B cells, cDC1s, cDC2s, and Macrophages). The anti-PD-L1 blocking antibody was readily detected while subsets from the isotype treated animals had minimal anti-Rat-IgG2b staining (2-way ANOVA with Sidak’s multiple comparisons test,* = p = 0.0222, ** = p = 0.0044, *** = p = 0.0004, **** = p < 0.0001, 3 experimental replicates). (C) Example gating strategy using splenocytes from a naïve C57BL/6 host, for the populations identified in (A), starting with singlet cells, and refining down to B cells, neutrophils, monocytes, macrophages, cDC1s, and cDC2s. All data presented are means + /- SEM and show individual data points.

Source data

Extended Data Fig. 4 Anti-PD-L1 blockade results in increased eTreg cell activation and proliferation in naïve hosts.

(a-c) 9 week-old male Nur77GFP reporter mice were treated with a single dose of isotype or anti-PD-L1 blocking antibody for 72 hours. Splenocytes were then harvested and assessed via high-parameter flow cytometry. Treg cell data was then concatenated between the isotype and anti-PD-L1 treated groups, and the subsequent qualitative interpretation was conducted via UMAP analysis (excluding Foxp3, PD-1, PD-L1, and CD4 as calculation factors). (a) Side-by-side pseudo-color density plot comparison of Treg cells from isotype and anti-PD-L1 treated hosts depicting regional shifts within the same UMAP calculation. (b) Heatmap expression analysis across the total combined UMAP data from both groups, depicting median heat maps of TCR activation associated proteins Nur77, CD11a, and Ki67, with overlapping enrichment of activated Treg cells in anti-PD-L1 treated hosts. (C) Additional heatmap analysis of Treg cell associated CD25, inhibitory receptors PD-1 and CTLA-4, and KLRG1, with an enrichment of overlap between PD-1, CTLA-4, and KLRG1 expression in context of PD-L1 blockade. (d-F) 9 week-old male C57BL/6 mice were also treated with a single dose of isotype (n = 4) or anti-PD-L1 blocking antibody (n = 5) for 72 hours, and their splenocytes were also isolated and analyzed via high-parameter flow cytometry. (d) Flow plot data of splenic Treg cells from isotype and anti-PD-L1 treated hosts comparing changes to the PD-1+ CTLA-4hi subset following PD-L1 blockade (two-tailed unpaired student’s t-test, * = p = 0.0394, 4 experimental replicates). (e) Treg cells from isotype and PD-L1 blockade treated hosts, gated on activated (CD11ahi) cells in cell cycle (Ki67+), indicating an increase in PD-1+ Treg cells in cell cycle following treatment (2-way ANOVA with Fisher’s LSD individual comparisons test, * = p = 0.032, ** = p = 0.0037, 4 experimental replicates). (f) Gating strategy utilized for flow cytometry sorting to isolate cTreg cells (CD25+ PD-1−) vs eTreg cells (CD25− PD-1+). (g) Flow cytometry data of Treg, CD4+ Tconv, and CD8+ T cells for the expression of Ki67 following 96 hours of tacrolimus (FK506) treatment (n = 5/group two-tailed unpaired student’s t-test, **** = p < 0.0001, 2 experimental replicates). All data presented are means + /- SEM and show individual data points.

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Extended Data Fig. 5 The development of homeostatic eTregs is not dependent on PD-1, and eTregs are limited by PD-1.

(a-f) Splenocytes from naïve 8 week-old female C57BL/6 mice or total PD-1−/− mice were isolated and analyzed via high-parameter flow cytometry. (a) Pre-gated CD4+ T cells gated on Foxp3+ events (Treg cells) depicting an enrichment of Treg cells at homeostasis in PD-1−/− age matched hosts (n = 3/group two-tailed unpaired student’s t-test, ** = p = 0.0037, 4 experimental replicates). (b) Comparative flow plots of Treg cells between C57BL/6 and PD-1−/− hosts with gating on activated Treg cells in cell cycle (CD11ahi Ki67+), demonstrating an increase in Tregs cells undergoing proliferation at homeostasis in PD-1−/− hosts (n = 3/group two-tailed unpaired student’s t-test, ** = p = 0.0044, 4 experimental replicates). (c) Treg cell staining of ICOS and CTLA-4, depicting the proportion and number of eTreg-associated (ICOS+ CTLA-4hi) Treg cells is increased in PD-1−/− mice (n = 3/group two-tailed unpaired student’s t-test, ** = p = 0.0022, 4 experimental replicates), while (d) demonstrates this enhancement is specific to the eTreg compartment (BCL-2low, CD25low), as the non-eTreg compartment (BCL-2hi, CD25hi) is consistent in number when compared to C57BL/6 mice (n = 5/group, 2-way ANOVA with Sidak’s multiple comparisons test, *** = p = 0.0001, 3 experimental replicates). Splenocytes from isotype and anti-PD-L1 treated groups were also stimulated and stained for IL-10 and analyzed via flow cytometry. (e) Flow plots of Treg cells from C57BL/6 and PD-1−/− hosts gated on CD11ahi IL-10+ events, depicting an increase in the proportion and number of IL-10+ Treg cells in PD-1-/- hosts (n = 3/group two-tailed unpaired student’s t-test, ** = p = 0.0011, 3 experimental replicates). (f) Splenic cDC2 subsets were identified via flow cytometry (CD3−, B220−, CD19−, NK1.1−, Ly6G−, CD64−, CD11c+, MHC-II+, SIRPα+), and gated on CD80+ events based on an FMO (n = 4/group, two-tailed unpaired student’s t test, * = p = 0.0122, 2 experimental replicates). All data presented are means + /- SEM and show individual data points.

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Extended Data Fig. 6 IFN-γ mediated changes to myeloid PD-L1 expression.

Cohorts of 8 week-old male C57BL/6 mice (n = 10/group) were treated with an isotype antibody or IFNγ blocking antibody and half of each group (n = 5) were infected with 20 cysts of ME49 intraperitoneally (IP). Splenocytes and Peritoneal exudate cells (PEC) were isolated 72 hours later and analyzed via high-parameter flow cytometry. (a) Comparative histograms evaluating 72 hour timepoint changes in the MFI of PD-L1 expression amongst splenocytes between experimental groups within leukocyte subsets: neutrophils (CD3−, B220−, CD19−, NK1.1−, Ly6G+, Ly6C+, CD11b+), cDC1s (CD3−, B220−, CD19−, NK1.1−, Ly6G−, CD64−, CD11c+, MHC-II+, XCR1+), cDC2s (CD3−, B220−, CD19−, NK1.1−, Ly6G−, CD64−, CD11c+, MHC-II+, SIRPα+), monocytes (CD3−, B220−, CD19−, NK1.1−, Ly6G−, CD64+, CD11b+, MHC-II+, Ly6C+), macrophages (CD3−, B220−, CD19−, NK1.1−, Ly6G−, CD64+, CD11b+, MHC−II+, Ly6C−) and Treg cells (B220−, CD19−, Ly6G−, NK1.1−, CD3+, CD4+, Foxp3+) (n = 5/group, 2-way ANOVA with Tukey’s multiple comparisons test, * = p = 0.0239, ** = p < 0.01, **** = p < 0.0001, 2 experimental replicates). (b) Cohorts of 8 week-old female STAT1flox mice without any cre expressing alleles (n = 5), or STAT1flox mice crossed onto either the CD11ccre (n = 4) or LysMcre (n = 5) background were infected with 20 cysts of ME49 IP. Splenocytes and PEC were harvested on day 7 of infection and analyzed via flow-cytometry. (b) Histogram comparisons of PD-L1 MFI changes in splenic monocytes and macrophages following conditional deletion of STAT1 (2-way ANOVA with Tukey’s multiple comparisons test, * = p = 0.0475, **** = p < 0.0001, 2 experimental replicates). All data presented are means + /- SEM and show individual data points.

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Extended Data Fig. 7 Impact of PD-L1 blockade across tissues and infection.

(a-d) Cohorts of 8 week-old male C57BL/6 mice were treated with an isotype antibody or PD-L1 blocking antibody 24 hours prior to infection with 20 cysts of ME49 IP. The antibody treatments were repeated every 72 hours throughout the course of infection until the mice were killed on day 10 and PEC, spleen, and liver were harvested and analyzed via high-parameter flow cytometry. (a-b) Flow plots of bulk CD4+ T cells with subsequent gates on the Foxp3+ T cells (Treg cells) for liver (A) and PEC (B), demonstrating the drop in Treg cells from homeostatic levels during infection, and the maintenance of Tregs during infection with PD-L1 blocking antibody treatment (n = 5/group two-tailed unpaired student’s t-test, * = p = 0.0153, ** = p = 0.0088, 4 experimental replicates). (c-d) Flow plots of Treg cells from treatment groups showing enrichment of PD-1hi Treg compartment as a consequence of PD-L1 blockade treatment during infection in liver (C), and PEC (D) (n = 5/group two-tailed unpaired student’s t-test, ** = p = 0.0013, **** = p < 0.0001, 4 experimental replicates). (e) Cohorts of 8 week-old male C57BL/6 mice were treated with an isotype antibody (n = 5 uninfected, n = 5 infected) or PD-L1 blocking antibody (n = 4) 24 hours prior to intravenous infection with 104 pfu of L. monocytogenes cysts of ME49 IP. The antibody treatments were repeated every 72 hours until splenocytes were harvested and analyzed via high-parameter flow cytometry on day 6 of infection. Depicted are flow plots of splenocyte-derived bulk CD4+ T cells gated on Foxp3+ T cells (Treg cells), (1-way ANOVA with Tukey’s multiple comparisons test, Isotype naïve vs Isotype infected: ** = p = 0.0033, Isotype infected vs anti-PD-L1 infected: ** = p = 0.0055, 1 experimental replicate). (f) Splenocyte-derived flow plots of cDC2s from cohorts of 8 week-old male C57BL/6 mice at day 10 of infection with T. gondii (20 cysts ME49 IP), that had been treated with isotype (n = 5), anti-PD-L1 (n = 5), with the inclusion of an additional cohort treated with a combination of blocking anti-IL-10r/anti-PD-L1 antibodies (n = 5) depicting exvivo changes in the proportion of CD80+ CD86+ cells (n = 5/group, 1-way ANOVA with Tukey’s multiple comparisons test, * = p = 0.0117, ** = p = 0.0056, **** = p < 0.0001, 2 experimental replicates). All data presented are means + /- SEM and show individual data points.

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Extended Data Fig. 8 During infection, PD-1−/− mice maintain an increased eTreg pool with diminished parasite-specific responses.

(a-g) 8 week-old female C57BL/6 and PD-1−/− mice were IP infected with 20 cysts of T. gondii and splenocytes were harvested and analyzed via flow cytometry at day 10 of infection. (a) Plots of CD4+ T cells from C57BL/6 (n = 3) and PD-1−/− (n = 5) mice with gating on Foxp3+ events (Treg cells) depicting a preservation of Treg cells in PD-1−/− hosts during infection (two-tailed unpaired student’s t-test, * = p = 0.0158, 3 experimental replicates). (b) Treg cell staining of BCL-2 and CD25, demonstrating an eTreg specific increase (BCL-2low, CD25low), as the non-eTreg compartment (BCL-2hi, CD25hi) is consistent in number when comparing C57BL/6 (n = 3) and PD-1−/− (n = 5) mice (2-way ANOVA with Sidak’s multiple comparisons test, ** = p = 0.0020, 3 experimental replicates). (c) Plots depicting and increase in the proportion and number of eTreg-associated (ICOS+ CTLA-4hi) Treg cells when comparing C57BL/6 (n = 3) to PD-1−/− (n = 5) mice (two-tailed unpaired student’s t-test, * = p = 0.0177, 3 experimental replicates). Splenocytes from C67BL/6 and PD-1−/− treated groups were stimulated and then stained for IL-10 and analyzed via flow cytometry, (d) plots of Treg cells from 8 week-old female C57BL/6 (n = 3) and PD-1−/− (n = 5) mice gated on CD11ahi IL-10+ events, depicting an increase in the proportion and number of IL-10+ Treg cells in PD-1-/- hosts (two-tailed unpaired student’s t-test, * = p = 0.0181, 3 experimental replicates). (e) Splenic cDC2 subsets were identified via flow cytometry (CD3−, B220−, CD19−, NK1.1−, Ly6G−, CD64−, CD11c+, MHC-II+, SIRPα+), and gated on CD80+ events based on an FMO, comparing the proportion of CD80+ cDC2 events between C57BL/6 (n = 5) and PD-1−/− (n = 5) mice (two-tailed unpaired student’s t test, ** = p = 0.0017, 2 experimental replicates). (f-g) Splenocytes from infected hosts were tetramer stained using the toxoplasma specific AS15 peptide, and the number of CD11ahi parasite-specific CD4+ T cells was compared between C57BL/6 (n = 3) and PD-1−/− (n = 4) (two-tailed unpaired student’s t-test, * = p = 0.0108), (g) while the phenotype of the parasite specific CD4+ T cells (CD11ahi Tetramer+) was evaluated for the expression of KLRG1 and T-bet, resulting in a loss of observed Tbet+ KLRG1+ parasite specific T cells in PD-1−/− hosts (two-tailed unpaired student’s t-test, * = p = 0.0012, 3 experimental replicates). (h) Parasite burden was assessed via qPCR from tissue samples of lungs, liver, and heart at day 10 of infection, resulting in no significant differences in parasite burden (n = 5/group, 2-way ANOVA with Sidak’s multiple comparisons test, 3 experimental replicates). All data presented are means + /− SEM and show individual data points.

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Extended Data Fig. 9 Primary T. gondii infection depletes eTreg cell populations in both C57BL/6 and hemizygous Foxp3cre x PD-1wt/flox mice, while eTreg cells in homozygous Foxp3cre x PD-1flox/flox hosts are spared.

(a-e) 8 week-old male C57BL/6 (n = 5), Foxp3cre x PD-1wt/flox (n = 6), and Foxp3cre x PD-1flox/flox (n = 5) mice were IP infected with 20 cysts of T. gondii (ME49 strain), at day 10 of infection splenocytes were harvested from each group and analyzed via high parameter flow cytometry. (a) Flow plots of bulk CD4+ T cells from each infected group and were gated on Foxp3+ events (Treg cells), depicting similar Treg depletion in C57BL/6 and hemizygous (PD-1wt/flox) groups, with increased Treg preservation in the homozygous (PD-1flox/flox) hosts (1-way ANOVA with Tukey’s multiple comparisons test * = p = 0.0227, ** = p = 0.0050, 2 experimental replicates). (b) Flow plots of splenic Treg cells depicting an increase in eTreg-associated ICOS+ CTLA-4hi cells in the Foxp3cre x PD-1flox/flox group, but not the C57BL/6 or Foxp3cre x PD-1wt/flox cohorts (1-way ANOVA with Tukey’s multiple comparisons test, ** = p < 0.01, 2 experimental replicates). (c) Flow plots depicting enhancement to the eTreg associated BCL-2low CD25low compartment in Foxp3cre x PD-1flox/flox mice only, while the non-eTreg compartment (BCL-2hi, CD25hi) was consistent in number across all three groups (2-way ANOVA with Tukey’s multiple comparisons test *** = p = 0.0002, **** = p < 0.0001, 2 experimental replicates). Splenocytes from all three groups were also stimulated and stained for IL-10. (d) Flow plots of Treg cells and their expression of IL-10 vs CD11a. There is no significant change between C57BL/6 and hemizygous groups, however homozygous mice have a significant increase in the number and proportion of IL-10+ Treg cells (1-way ANOVA with Tukey’s multiple comparisons test, ** = p = 0.0011, *** = p = 0.0004). Splenocytes were permeabilized exvivo and stained for the downstream TCR-activation protein Nur77 and analyzed via flow cytometry. (e) Treg cell plots from the three respective groups depicting no significant differences in Nur77+ Treg cells between C57BL/6 and hemizygous groups, while Treg cells from homozygous hosts (Foxp3cre x PD-1flox/flox) have an increased proportion and number of Nur77+ Treg cells compared to the other two groups during infection (1-way ANOVA with Tukey’s multiple comparisons test *** = p < 0.001). All data presented are means + /- SEM and show individual data points.

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Supplementary Table 1 Reagent information.

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Perry, J.A., Shallberg, L., Clark, J.T. et al. PD-L1–PD-1 interactions limit effector regulatory T cell populations at homeostasis and during infection. Nat Immunol 23, 743–756 (2022). https://doi.org/10.1038/s41590-022-01170-w

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