Skip to main content

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

  • Article
  • Published:

Dapl1 controls NFATc2 activation to regulate CD8+ T cell exhaustion and responses in chronic infection and cancer

Nature Cell Biology volume 24, pages 1165–1176 (2022)Cite this article

Subjects

Abstract

CD8+ T cells are central mediators of immune responses against infections and cancer. Here we identified Dapl1 as a crucial regulator of CD8+ T cell responses to cancer and infections. Dapl1 deficiency promotes the expansion of tumour-infiltrating effector memory-like CD8+ T cells and prevents their functional exhaustion, coupled with increased antitumour immunity and improved efficacy of adoptive T cell therapy. Dapl1 controls activation of NFATc2, a transcription factor required for the effector function of CD8+ T cells. Although NFATc2 mediates induction of the immune checkpoint receptor Tim3, competent NFATc2 activation prevents functional exhaustion of CD8+ T cells. Interestingly, exhausted CD8+ T cells display attenuated NFATc2 activation due to Tim3-mediated feedback inhibition; Dapl1 deletion rescues NFATc2 activation and thereby prevents dysfunction of exhausted CD8+ T cells in chronic infection and cancer. These findings establish Dapl1 as a crucial regulator of CD8+ T cell immunity and a potential target for cancer immunotherapy.

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

Access options

Buy this article

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

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

Fig. 1: Dapl1 deficiency promotes antitumour immunity and improves ACT efficacy.
Fig. 2: Dapl1 deficiency promotes Tim3 expression and improves responses to ICR blockade.
Fig. 3: Dapl1 regulates the functionality of TEX cells.
Fig. 4: Dapl1 deletion reprogrammes CD8+ TIL cells.
Fig. 5: Dapl1 is a specific regulator of NFATc2 activation.
Fig. 6: NFATc2 mediates the effector function and Tim3 hyperexpression of Dapl1-deficient CD8+ T cells.
Fig. 7: Dapl1-mediated NFATc2 inhibition contributes to the regulation of Havcr2 expression and CD8+ T cell functions.
Fig. 8: TEX cells display suppressed NFATc2 activation, which can be rescued by Dapl1 deletion or Tim3 blockade.

Similar content being viewed by others

Data availability

The scRNAseq data that support the findings of this study have been deposited in the Gene Expression Omnibus under the accession code GSE175689. Previously published scRNAseq data that were re-analysed here are available under the accession code GSE139829. Previously published RNAseq data that were re-analysed here are available under the accession code GSE126777. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding authors on reasonable request.

Code availability

All the code will be available from the corresponding authors on reasonable request, including but not limited to the following: scRNAseq analysis and bulk RNAseq analysis.

References

  1. Zhang, N. & Bevan, M. J. CD8+ T cells: foot soldiers of the immune system. Immunity 35, 161–168 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Durgeau, A., Virk, Y., Corgnac, S. & Mami-Chouaib, F. Recent advances in targeting CD8 T-cell immunity for more effective cancer immunotherapy. Front. Immunol. 9, 14 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Hashimoto, M. et al. CD8 T cell exhaustion in chronic infection and cancer: opportunities for interventions. Annu. Rev. Med. 69, 301–318 (2018).

    Article  CAS  PubMed  Google Scholar 

  4. McLane, L. M., Abdel-Hakeem, M. S. & Wherry, E. J. CD8 T cell exhaustion during chronic viral infection and cancer. Annu. Rev. Immunol. 37, 457–495 (2019).

    Article  CAS  PubMed  Google Scholar 

  5. van der Leun, A. M., Thommen, D. S. & Schumacher, T. N. CD8+ T cell states in human cancer: insights from single-cell analysis. Nat. Rev. Cancer 20, 218–232 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Pardoll, D. M. The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer 12, 252–264 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Zou, W., Wolchok, J. D. & Chen, L. PD-L1 (B7-H1) and PD-1 pathway blockade for cancer therapy: mechanisms, response biomarkers, and combinations. Sci. Transl. Med. 8, 328rv324 (2016).

    Article  CAS  Google Scholar 

  8. Grywalska, E., Pasiarski, M., Gozdz, S. & Rolinski, J. Immune-checkpoint inhibitors for combating T-cell dysfunction in cancer. OncoTargets Ther. 11, 6505–6524 (2018).

    Article  CAS  Google Scholar 

  9. Miller, B. C. et al. Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade. Nat. Immunol. 20, 326–336 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Li, H. et al. Dysfunctional CD8 T cells form a proliferative, dynamically regulated compartment within human melanoma. Cell 176, 775–789 (2019).

    Article  CAS  PubMed  Google Scholar 

  11. Kallies, A., Zehn, D. & Utzschneider, D. T. Precursor exhausted T cells: key to successful immunotherapy? Nat. Rev. Immunol. 20, 128–136 (2020).

    Article  CAS  PubMed  Google Scholar 

  12. Siddiqui, I. et al. Intratumoral Tcf1+PD-1+CD8+ T cells with stem-like properties promote tumor control in response to vaccination and checkpoint blockade immunotherapy. Immunity 50, 195–211 (2019).

    Article  CAS  PubMed  Google Scholar 

  13. Kurtulus, S. et al. Checkpoint blockade immunotherapy induces dynamic changes in PD-1−CD8+ tumor-infiltrating T cells. Immunity 50, 181–194 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Escobar, G., Mangani, D. & Anderson, A. C. T cell factor 1: a master regulator of the T cell response in disease. Sci. Immunol. 5, eabb9726 (2020).

  15. Utzschneider, D. T. et al. T cell factor 1-expressing memory-like CD8+ T cells sustain the immune response to chronic viral infections. Immunity 45, 415–427 (2016).

    Article  CAS  PubMed  Google Scholar 

  16. Beltra, J. C. et al. Developmental relationships of four exhausted CD8+ T cell subsets reveals underlying transcriptional and epigenetic landscape control mechanisms. Immunity 52, 825–841 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Man, K. et al. Transcription factor IRF4 promotes CD8+ T cell exhaustion and limits the development of memory-like T cells during chronic infection. Immunity 47, 1129–1141 (2017).

    Article  CAS  PubMed  Google Scholar 

  18. Martinez, G. J. et al. The transcription factor NFAT promotes exhaustion of activated CD8+ T cells. Immunity 42, 265–278 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Seo, W., Jerin, C. & Nishikawa, H. Transcriptional regulatory network for the establishment of CD8+ T cell exhaustion. Exp. Mol. Med. 53, 202–209 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Wherry, E. J. et al. Molecular signature of CD8+ T cell exhaustion during chronic viral infection. Immunity 27, 670–684 (2007).

    Article  CAS  PubMed  Google Scholar 

  21. Raczkowski, F. et al. The transcription factor interferon regulatory factor 4 is required for the generation of protective effector CD8+ T cells. Proc. Natl Acad. Sci. USA 110, 15019–15024 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Klein-Hessling, S. et al. NFATc1 controls the cytotoxicity of CD8+ T cells. Nat. Commun. 8, 511 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Pearce, E. L. et al. Control of effector CD8+ T cell function by the transcription factor eomesodermin. Science 302, 1041–1043 (2003).

    Article  CAS  PubMed  Google Scholar 

  24. Ma, X. et al. DAPL1, a susceptibility locus for age-related macular degeneration, acts as a novel suppressor of cell proliferation in the retinal pigment epithelium. Hum. Mol. Genet. 26, 1612–1621 (2017).

    Article  CAS  PubMed  Google Scholar 

  25. Zhou, X. et al. The deubiquitinase Otub1 controls the activation of CD8+ T cells and NK cells by regulating IL-15-mediated priming. Nat. Immunol. 20, 879–889 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Khan, S. H. & Badovinac, V. P. Listeria monocytogenes: a model pathogen to study antigen-specific memory CD8 T cell responses. Semin. Immunopathol. 37, 301–310 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Qiu, Z., Khairallah, C. & Sheridan, B. S. Listeria monocytogenes: a model pathogen continues to refine our knowledge of the CD8 T cell response. Pathogens 7, 55 (2018).

    Article  CAS  PubMed Central  Google Scholar 

  28. Overwijk, W. W. et al. Tumor regression and autoimmunity after reversal of a functionally tolerant state of self-reactive CD8+ T cells. J. Exp. Med. 198, 569–580 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Wu, T. et al. The TCF1–Bcl6 axis counteracts type I interferon to repress exhaustion and maintain T cell stemness. Sci. Immunol. 1, eaai8593 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Chen, Z. et al. TCF-1-centered transcriptional network drives an effector versus exhausted CD8 T cell-fate decision. Immunity 51, 840–855 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Hudson, W. H. et al. Proliferating transitory T cells with an effector-like transcriptional signature emerge from PD-1+ stem-like CD8+ T cells during chronic infection. Immunity 51, 1043–1058 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Zander, R. et al. CD4+ T cell help is required for the formation of a cytolytic CD8+ T cell subset that protects against chronic infection and cancer. Immunity 51, 1028–1042 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Walunas, T. L., Bruce, D. S., Dustin, L., Loh, D. Y. & Bluestone, J. A. Ly-6C is a marker of memory CD8+ T cells. J Immunol 155, 1873–1883 (1995).

    CAS  PubMed  Google Scholar 

  34. Cerwenka, A., Carter, L. L., Reome, J. B., Swain, S. L. & Dutton, R. W. In vivo persistence of CD8 polarized T cell subsets producing type 1 or type 2 cytokines. J. Immunol. 161, 97–105 (1998).

    CAS  PubMed  Google Scholar 

  35. Curtsinger, J. M., Lins, D. C. & Mescher, M. F. CD8+ memory T cells (CD44high, Ly-6C+) are more sensitive than naive cells to (CD44low, Ly-6C−) to TCR/CD8 signaling in response to antigen. J. Immunol. 160, 3236–3243 (1998).

    CAS  PubMed  Google Scholar 

  36. Muller, M. R. & Rao, A. NFAT, immunity and cancer: a transcription factor comes of age. Nat. Rev. Immunol. 10, 645–656 (2010).

    Article  PubMed  CAS  Google Scholar 

  37. Vaeth, M. & Feske, S. NFAT control of immune function: new frontiers for an abiding trooper. F1000Res. 7, 260 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Faget, D. V., Lucena, P. I., Robbs, B. K. & Viola, J. P. NFAT1 C-terminal domains are necessary but not sufficient for inducing cell death. PLoS ONE 7, e47868 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Loh, C., Carew, J. A., Kim, J., Hogan, P. G. & Rao, A. T-cell receptor stimulation elicits an early phase of activation and a later phase of deactivation of the transcription factor NFAT1. Mol. Cell. Biol. 16, 3945–3954 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Liu, Z. et al. The kinase LRRK2 is a regulator of the transcription factor NFAT that modulates the severity of inflammatory bowel disease. Nat. Immunol. 12, 1063–1070 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Hogan, P. G. Calcium-NFAT transcriptional signalling in T cell activation and T cell exhaustion. Cell Calcium 63, 66–69 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Carmona, S. J., Siddiqui, I., Bilous, M., Held, W. & Gfeller, D. Deciphering the transcriptomic landscape of tumor-infiltrating CD8 lymphocytes in B16 melanoma tumors with single-cell RNA-seq. Oncoimmunology 9, 1737369 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Wang, Y. et al. The transcription factor TCF1 preserves the effector function of exhausted CD8 T cells during chronic viral infection. Front. Immunol. 10, 169 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Sakuishi, K. et al. Targeting Tim-3 and PD-1 pathways to reverse T cell exhaustion and restore anti-tumor immunity. J. Exp. Med. 207, 2187–2194 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Reiley, W. W. et al. Regulation of T cell development by the deubiquitinating enzyme CYLD. Nat. Immunol. 7, 411–417 (2006).

    Article  CAS  PubMed  Google Scholar 

  46. Reiley, W. W. et al. Deubiquitinating enzyme CYLD negatively regulates the ubiquitin-dependent kinase Tak1 and prevents abnormal T cell responses. J. Exp. Med. 204, 1475–1485 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Podtschaske, M. et al. Digital NFATc2 activation per cell transforms graded T cell receptor activation into an all-or-none IL-2 expression. PLoS ONE 2, e935 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Ahmed, R., Salmi, A., Butler, L. D., Chiller, J. M. & Oldstone, M. B. Selection of genetic variants of lymphocytic choriomeningitis virus in spleens of persistently infected mice. Role in suppression of cytotoxic T lymphocyte response and viral persistence. J. Exp. Med. 160, 521–540 (1984).

    Article  CAS  PubMed  Google Scholar 

  49. Nelson, J. D., Denisenko, O. & Bomsztyk, K. Protocol for the fast chromatin immunoprecipitation (ChIP) method. Nat. Protoc. 1, 179–185 (2006).

    Article  CAS  PubMed  Google Scholar 

  50. Xiao, G., Harhaj, E. W. & Sun, S. C. NF-κB-inducing kinase regulates the processing of NF-κB2 p100. Mol. Cell 7, 401–409 (2001).

    Article  CAS  PubMed  Google Scholar 

  51. Zhao, M. et al. Rapid in vitro generation of bona fide exhausted CD8+ T cells is accompanied by Tcf7 promotor methylation. PLoS Pathog. 16, e1008555 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Dunsford, L. S., Thoirs, R. H., Rathbone, E. & Patakas, A. in Immuno-Oncology (ed. Tan, S.-L.) 89–101 (Humana New York, 2020).

Download references

Acknowledgements

We thank J. P. B. Viola for NFAT expression vectors, E. J. Wherry for LCMV clone 13 and the Mutant Mouse Resource & Research Centers for Nfatc2-KO mice. We thank the Genetically Engineered Mouse Facility as well as the flow cytometry, sequencing and microarray, and animal facilities of the shared resources at The MD Anderson Cancer Center, supported by the NIH/NCI Cancer Center Support Grant (CCSG) P30CA016672. The Advanced Technology Genomics Core (ATGC) is supported by grant no. CA016672 (ATGC) and NIH grant no. 1S10OD024977-01. This study was supported by a grant from the National Institutes of Health (grant no. AI64639 to S.-C.S.). T.G. was a visiting student supported by a scholarship from the China Scholarship Council (grant no. 201906380080).

Author information

Author notes

  1. Xiaofei Zhou

    Present address: Flagship Labs 91, Inc., Cambridge, MA, USA

  2. Jiseong Kim

    Present address: Bristol Myers Squibb, Seattle, WA, USA

  3. Yanchuan Li

    Present address: Department of Pediatrics, Texas Children’s Cancer Center, Baylor College of Medicine, Houston, TX, USA

  4. Chun-Jung Ko

    Present address: Graduate Institute of Immunology, College of Medicine, National Taiwan University, Taipei, Taiwan

  5. Xiaoping Xie

    Present address: AbbVie, South San Francisco, CA, USA

  6. Tianxiao Gao

    Present address: Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China

  7. Xuhong Cheng

    Present address: Memorial Hermann–Texas Medical Center, Houston, TX, USA

Authors and Affiliations

  1. Department of Immunology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Lele Zhu, Xiaofei Zhou, Meidi Gu, Jiseong Kim, Yanchuan Li, Chun-Jung Ko, Xiaoping Xie, Tianxiao Gao, Xuhong Cheng & Shao-Cong Sun

Authors
  1. Lele Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaofei Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Meidi Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiseong Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yanchuan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chun-Jung Ko
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiaoping Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Tianxiao Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xuhong Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Shao-Cong Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

L.Z. designed and performed the research, prepared the figures and wrote the manuscript. X.Z. performed scRNAseq data analysis, made significant contributions to data interpretation and organization, and provided critical reagents, scientific advice and expertise. M.G. provided essential mouse models, scientific advice and contributed to the experiments. J.K. designed and performed the research and made significant contributions to the initial characterization of Dapl1 function. Y.L., C.-J.K., X.X., T.G. and X.C. contributed to the performance of the experiments. S.-C.S. supervised the work and wrote the manuscript.

Corresponding authors

Correspondence to Lele Zhu or Shao-Cong Sun.

Ethics declarations

Competing interests

Although they participated in this research while employed in the University of Texas MD Anderson Cancer Center, X.Z. is presently an employee of Flagship Labs 91, Inc, J.K. is presently an employee of Bristol Myers Squibb and X.X. is presently an employee of AbbVie. All competing interests are unrelated to the current study. The other authors declare no competing interests.

Peer review

Peer review information

Nature Cell Biology thanks Ping-Chih Ho, Axel Kallies and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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

Extended data

Extended Data Fig. 1 Characterization of Dapl1 expression and Dapl1-KO mice.

a, b, qRT-PCR (a) and immunoblot (b) analysis of Dapl1 expression in CD4 and CD8 T cells, B cells, bone marrow DCs (BMDC), and bone marrow derived macrophages (BMDM). c, Dapl1 knockout strategy using CRISPR–Cas9, indicating introduction of a stop codon (taa) and an EcoRI restriction site (gaattc) to the 3’ boundary of exon 3. d, Genotyping PCR using tail DNA from wild-type (WT,+/+), Dapl1 KO (–/–), and heterozygous (+/–) mice. The reverse PCR primer binds to the mutated region to distinguish WT and KO alleles. e,f, RT-PCR (e) and immunoblot (f) analysis of Dapl1 expression using WT or Dapl1 KO (KO) CD8 T cells. g-i, Flow cytometry analysis of the frequency and absolute cell numbers of CD4 and CD8 T cells (g), naive (CD44loCD62Lhi) and memory (CD44hiCD62Llo) CD4 T cells (h), and naive (CD44lo) and memory (CD44hi) CD8 T cells (i) in the spleen of wild-type (WT) or Dapl1 KO mice. Data are presented as a representative plot (left) and summary graphs (right). n = 5 per genotype. j,k, ELISA analysis of secreted IL-2 and IFN-γ (j) and qRT-PCR analysis of the indicated mRNAs (k) in wild-type and Dapl1 KO naive CD8 or CD4 T cells, stimulated with anti-CD3 and anti-CD28 for 48 h (j) or for the indicated time points (k). n = 6 per genotype. l, Immunoblot analysis of Dapl1 in human CD8 T cells transduced with a control shRNA (Ctrl) or three different Dapl1 shRNAs. m, n, qRT-PCR analysis of IFNG and IL-2 mRNAs (m) and flow cytometry analysis of intracellular IFN-γ and IL-2 proteins (n) in human CD8 T cells transduced with control or Dapl1 shRNA (#3) and stimulated with anti-CD3 plus anti-CD28 for the indicated time points (m) or 48 h (n). Data are pooled from two independent experiments. Summary data are shown as the mean±s.d. with P values determined using a two-tailed unpaired Student’s t-test (a, g-k, m, n). ns, not significant.

Source data

Extended Data Fig. 2 Dapl1 deficiency promotes antitumour responses and Tim3 expression in CD8, but not CD4, T cells.

a, Flow cytometry analysis of the frequency of IFN-γ-producing CD4 T cells in the tumour (upper) and draining lymph nodes (dLN, lower) of wild-type (WT) or Dapl1 KO (KO) mice injected subcutaneously with B16-OVA for 20 days. n = 5 per genotype. b,c, Flow cytometry analysis of the frequency and absolute cell number of CD4 and CD8 T cells in draining lymph nodes and tumours (TILs) of MC38 colon cancer cell-implanted wild-type or Dapl1 KO mice. n = 5 per genotype. d-g, Schematic of experimental design (d), flow cytometry analysis of the frequency of donor Pmel1 CD8 T cells (CD45.2+) and host CD8 T cells (CD45.1+) (e) and donor (CD45.2+) IFN-γ-, IL-2- or TNF-a-producing Pmel1 CD8 T cells (f,g) in the tumour of B16F10-implanted B6.SJL mice (CD45.1+) adoptively transferred with in vitro activated wild-type or Dapl1 KO Pmel1 (CD45.2+) CD8 T cells (collected on day 5, 10 and 15 after Pmel1 cells transfer). n = 5 per genotype. h, Flow cytometry analysis of Tim3, PD1, CTLA4 or Lag3 expression levels in CD8 TILs of wild-type (WT) or Dapl1 KO mice implanted with B16-OVA for 20 days. n = 5 per genotype. i,j, Flow cytometry analysis of the frequency of PD1+, Tim3+, CTLA4+ or Lag3+ CD8 (i) or CD4 TILs (j) in wild-type or Dapl1 KO mice implanted with MC38 colon cancer cells for 24 days. n = 5 per genotype. k-m, Immunoblot analysis of Dapl1 expression in Dapl1 KO Pmel1 CD8 T cells transduced with either an empty vector or HA-Dapl1 expression vector (k), schematic of experimental design for adoptive transfer of the transduced Dapl1 KO Pmel1 cells (CD45.2+) into B16F10-implanted B6.SJL mice (CD45.1+) (l), and flow cytometry analysis of the frequency of IFN-γ- and TNF-a-producing CD8 T cells in the tumour (m). n = 5 per genotype. n,o, Flow cytometry analysis of the frequency and absolute cell numbers of CD8 TILs in day-24 B16-OVA-implanted wild-type and Dapl1 KO mice injected i.p. with anti-PD1 (100 mg/mouse) or an IgG isotype control (n) or anti-Tim3 (200 mg/mouse) or an IgG isotype control (o) on days 7, 9, and 11, n = 5 (n) or 4 (o) per genotype. Summary data are shown as the mean ± s.d. with P values determined using a two-tailed unpaired Student’s t-test (a-c, e, g-j, m-o). ns, not significant.

Source data

Extended Data Fig. 3 Kinetic analysis of Dapl1 function in regulating anti-LCMV CD8 T cell responses.

a, Flow cytometry analysis of the frequency of H2Db/GP33−41 Tetramer+ CD8 T cells in the spleen of day 7, 14, 21, and 30 LCMV clone 13-infected wild-type (WT) and Dapl1 KO mice. Data are presented as a representative FACS plot (left) and summary graph (right). n = 5 per genotype. b,c, Flow cytometry analysis of the frequency of Tim3+, PD1+, Lag3+, or TIGIT+ CD8 T cells in the spleen of day 7, 14, 21, and 30 LCMV clone 13-infected wild-type and Dapl1 KO mice. Data are presented as a representative FACS plot (b) and summary graphs (c). n = 5 per genotype. d,e, Flow cytometry analysis of the frequency of TCF1+Tim3–, TCF1+Tim3+ and TCF1–Tim3+ subsets in gated CD8+ GP33+ PD1+ T cells (d) and CX3CR1+ cytotoxic effector CD8 cells in gated CD8+GP33+ T cells (e) in the spleen of day 7, 14, 21, and 30 LCMV clone 13-infected wild-type and Dapl1 KO mice. Data are presented as a representative FACS plot (left) and summary graphs (right). n = 5 per genotype. Data are pooled from two independent experiments. Summary data are shown as the mean±s.d. with P values determined using a two-tailed unpaired Student’s t-test (a, c-e). ns, not significant.

Source data

Extended Data Fig. 4 Characterization of in vitro generated exhausted CD8 T cells.

a, b, Flow cytometry analysis of the indicated ICRs, TOX, T-bet, and TCF1 (a) or effector-related molecules (b) in wild-type OT1 CD8 T cells cultured in vitro under mock or OVA257-264-stimulated effector or exhaustion conditions. c-g, Immunoblot analysis of Dapl1 knockdown (c) and flow cytometry analysis of the expression of PD1+ (d), Tim3+ (e), TOX (f), and TCF1 (g) in human CD8 T cells transduced with a control (Ctrl) shRNA or a Dapl1−specific shRNA and cultured under unexhaustion (cultured with 50 U/ml hIL-2) and exhaustion (three round of stimulation with anti-CD3/anti-CD28 Dynabeads) conditions. Data are representative of three independent experiments.

Source data

Extended Data Fig. 5 Dapl1 has a cell-intrinsic function in regulating CD8 T cell functions.

a, Schematic of experimental design. b-g Flow cytometry analysis of the frequency of splenic (Spl) or TIL CD45.1+CD8+ or CD45.2+CD8+ T cells (b), Granzyme B-, IFN-γ-, TNF-a-producing or Ki67+ CD8 TILs (c), PD1+,Tim3+, Lag3+ or TIGIT+ CD8 TILs (d), TCF1+Tim3–, TCF1+Tim3+ and TCF1–Tim3+ subsets in gated CD8+PD1+ TILs (e), Granzyme B-, IFN-γ-, TNF-a-producing cells in gated PD1+Tim3+ CD8 TILs (f), or Ki67+ CD8 + cells in gated PD1+Tim3+ CD8+ TILs (g) of B16-OVA tumour-bearing Rag1 KO recipient mice adoptively transferred with a mixture of BM cells derived from B6.SJL mice (CD45.1+) and Dapl1 KO (CD45.2+) or wild-type mice (CD45.2+). Data are presented as representative FACS plots and summary graphs. n = 5 chimeric mice. h-l, Flow cytometry analysis of the frequency of CD45.1+CD8+ or CD45.2+CD8+ T cells (h), H2Db/GP33-41 Tetramer+ CD8+ T cells (i), IL-2-, IFN-γ-, Granzyme B-, TNF-a-, or Ki67+-producing CD8 T cells (j), PD1+, Tim3+, Lag3+, TIGIT+, PD1+Tim3+ CD8 T cells or TCF1+Tim3-, TCF1+Tim3+, TCF1−Tim3+ subsets (k), and IFN-γ-, IL-2-, TNF-a- or Granzyme B-producing CD8 T cells (l), gated on PD1+Tim3+ CD8+T cells in the spleen of Rag1 KO recipient mice adoptively transferred with a mixture of BM cells derived from B6.SJL mice (CD45.1+) and Dapl1 KO (CD45.2+) or wild-type (CD45.2+) mice, infected i.v. with LCMV clone 13 for 30 days. Data are presented as representative FACS plots and summary graphs. n = 5 chimeric mice. Summary data are shown as the mean±s.d. with P values determined using a two-tailed unpaired Student’s t-test (b-l). ns, not significant.

Source data

Extended Data Fig. 6 Dapl1 has a cell-intrinsic role in regulating CD8 T cell responses to LM-OVA infection and cancer.

a, Schematic of experimental design for generating mixed bone marrow chimeric mice and LM-OVA infection. b,c, Flow cytometry analysis of the frequency of IFN-γ- or Granzyme B-producing CD8 effector T cells (b) and TCF1+ CD8+, TCF1- CD8+ cells (c) in the spleen of Rag1 KO recipient mice adoptively transferred (for 6 week) with a mixture of BM cells derived from B6.SJL mice (CD45.1+) and Dapl1 KO (CD45.2+) or wild-type (WT, CD45.2+) mice, infected i.v. with LM-OVA for 7 days. Data are presented as representative FACS plots and summary graphs. n = 5 chimeric mice. d-k, Schematic of experimental design (d) and flow cytometry analysis of the frequency of wild-type (CD45.1+CD45.2+) and Dapl1 KO (CD45.2+) CD8 T cells (e), Ki67+ CD8 T cells (f), effector CD8 T cells (g), Tim3+ or PD1+ CD8 T cells (h), TCF1+Tim3–, TCF1+Tim3+ and TCF1–Tim3+ subsets in gated PD1+ CD8 TILs (i), Ki67+ cells in gated PD1+Tim3+ CD8 T cells (j), and Granzyme B-, IFN-γ-, or TNF-a-producing cells in gated PD1+Tim3+ CD8 T cells (k) in the spleen (Spl) or the tumour (TIL) of B16F10 tumour-bearing B6.SJL mice (CD45.1+) adoptively transferred with a mixture (1:1) of wild-type (CD45.1+CD45.2+) and Dapl1 KO (CD45.2+) Pmel1 CD8 T cells. Data are presented as representative FACS plots and summary graphs. n = 5 recipient mice. Summary data are shown as the mean ± s.d. with P values determined using a two-tailed unpaired Student’s t-test (b, c, e-k). ns, not significant.

Source data

Extended Data Fig. 7 Dapl1 regulates progenitor-like CD8 T cell proliferation and conversion to effector-like CD8 T cells.

a, tSNE plot based on unsupervised clustering analysis of scRNAseq data from human uveal melanoma dataset GSE139829 showing 6 clusters of CD8 TILs. b,c, Expression profile of the indicated genes in each cluster of CD8 TILs (b) and Violin plots showing predominant expression of Tcf7 and Dapl1 in the cluster 1 progenitor population (c). d, qRT-PCR analysis of Dapl1 expression in PD1– Tim3– progenitor (TPro) or PD1+Tim3+ CD8 TEX cells isolated from LCMV clone 13−infected wild-type or Dapl1 KO mice. n = 6 per genotype. e-j, Schematic of experimental design (e) and flow cytometry analysis of the frequency of TCF1hi and TCF1+Tim3−progenitor exhausted CD8 T cells (f,g), CXCR3+, Ly6c+ or CCR2+ effector CD8 T cells in gated PD1lowTCF1low CD8 T cells (h), apoptotic cells in gated PD1–Tim3−CD8 T cells (i), and Ki67+ proliferative cells in gated TCF1+Tim3- CD8 T cells (j) derived from the tumour of B16F10 LCMVmini tumour-bearing B6.SJL mice on day 3 and day 10 following adoptive transfer with antigen-specific progenitor CD8 T cells sorted from the spleen of LCMV clone 13-infected wild-type and Dapl1 KO mice. Data are shown as representative plots and summary graphs. n = 5 per genotype. Summary data are shown as the mean±s.d. with P values determined using a two-tailed unpaired Student’s t-test (d, f-j). ns, not significant.

Source data

Extended Data Fig. 8 NFATc2 is regulated by Dapl1 and mediates the hyper-responses of Dapl1-KO CD8 T cells.

a-c, Immunoblot analysis of the indicated phosphorylated (p-) and total proteins in whole-cell lysates (a) or in cytoplasmic (CE) and nuclear (NE) extracts (b, c) of wild-type (WT) or Dapl1 KO (KO) CD8 (a, b) or CD4 (c) naïve T cells stimulated with anti-CD3 plus anti-CD28 for the indicated time points. d, Flow cytometry analysis of nuclear NFATc2 in human CD8 cells transduced with a non-silencing control shRNA (Ctrl shRNA) or Dapl1−specific shRNA, either not treated (NT) or stimulated for 30 min with ionomycin (Iono). Data are presented as representative FACS plots (upper) and a summary graph (lower), n = 5 biologically independent repeats. e,f, CoIP analysis of Dapl1 interaction with NFATc2 (e) or with NFATc1 (f) in 293 T cells transfected with (+) HA-Dapl1 along with (+) wild-type NFATc2 or a constitutive active NFATc2 (CA-NFATc2) (e), wild-type NFATc1 or a constitutive active NFATc1 (CA-NFATc1) (f). g, Flow cytometry analysis of intracellular IFN-γ in naive CD8 T cells from the indicated genotypes, stimulated for 48 h with anti-CD3 and anti-CD28. Data are presented as representative FACS plots (upper) and summary graphs (lower). n = 5 per genotype. h, Flow cytometry analysis of IFN-γ- or granzyme B-producing CD8 T cells in the tumour of the indicated mouse strains injected subcutaneously with 2 × 105 B16-OVA cells for 16 days. i, ICS and flow cytometry analysis of GP33−41 antigen-specific CD8 T cells producing the indicated cytokines in the spleen of LCMV clone 13−infected (for 30 days) wild-type (WT), Dapl1 KO, NFATc2 KO and Dapl1-NFATc2 double KO (dKO) mice. The splenocytes were restimulated in vitro for 6 h with GP33-41 peptide in the presence of monensin before ICS. Summary data are shown as the mean±s.d. with P values determined using a two-tailed unpaired Student’s t-test (d,g). Western blots are representative of three independent experiments (a-c, e,f).

Source data

Extended Data Fig. 9 Attenuated activation of NFATc2 and NFATc1 in exhausted CD8 T cells.

a, b Flow cytometry analysis of sorted PD1+Tim3+ exhausted (TEX) and PD1–Tim3– progenitor (TPro) CD8 TILs (a) and the expression of TCF1 (b) in PD1+Tim3+ exhausted (TEX) and PD1–Tim3– progenitor (TPro) CD8 cells derived from day 20 tumour of B16-OVA tumour-bearing wild-type (WT) or Dapl1 KO (KO) mice. c-e, Flow cytometry analysis of nuclear NFATc2 (c) and NFATc1 (d,e) in wild-type and Dapl1 KO TPro CD8 TILs (c), wild-type TEX and TPro CD8 TILs (d), or wild-type and Dapl1 KO TEX CD8 TILs (e), either untreated (NT) or ionomycin-stimulated (Iono, 30 min). n = 5 per genotype. f, g, Flow cytometry analysis of sorted PD1+Tim3+ exhausted (TEX) and PD1–Tim3– progenitor (TPro) CD8 T cells (f) and the expression of TCF1 (g) in PD1+Tim3+ exhausted (TEX) and PD1–Tim3– progenitor (TPro) CD8 cells from LCMV clone 13-infected wild-type or Dapl1 KO mice. h-j, Flow cytometry analysis of nuclear NFATc2 (h) and NFATc1(i,j) in wild-type and Dapl1 KO progenitor (TPro) CD8 T cells (h), wild-type TEX and TPro cells (i), and wild-type and Dapl1 KO TEX cells (j) from the spleen of LCMV clone 13-infected mice, either not treated (NT) or stimulated with ionomycin (Iono) for 30 min. n = 5 per genotype. k, l, Gating strategies for sorting PD1+Tim3+ exhausted (TEX) and PD1–Tim3– progenitor (TPro) Pmel1 CD8 TILs from the tumour (k) of B16F10-implanted B6.SJL mice (CD45.1+) adoptively transferred with Dapl1 KO Pmel1 CD8 T cells (CD45.2+) transduced with either an empty vector or HA-Dapl1 expression vector, and flow cytometry analysis of nuclear NFATc1 in untreated (NT) or ionomycin-stimulated (Iono, 30 min) PD1+Tim3+ exhausted (TEX) and PD1–Tim3– progenitor (TPro) Pmel1 CD8 TILs (l). n = 5 per genotype. m, Flow cytometry analysis of nuclear NFATc2 in in vitro generated exhausted human CD8 T cells transduced with a non-silencing control shRNA (Ctrl shRNA) or a Dapl1-specific shRNA, either not treated (NT) or stimulated for 30 min with ionomycin. n = 5 biologically independent repeats. n-p, Flow cytometry analysis of nuclear NFATc2 (n) and nuclear NFATc1 (o,p) in PD1+Tim3+ CD8 T cells in the tumour of B16-OVA-bearing wild-type or Dapl1 KO mice injected with anti-Tim3 or IgG isotype control on days 7, 9, and 11. n = 5 per genotype. Summary data are shown as the mean±s.d. with P values determined using a two-tailed unpaired Student’s t-test (c-e, h-j, l, m, o). ns, not significant.

Source data

Extended Data Fig. 10 The gating strategy.

Live immune cell populations were gated on the FSC-A and SSC-A. Single cells were gated basing on FSC-A and FAS-H. The subpopulations of the indicated immune cell were gated basing on specific markers as indicated in the individual panels.

Supplementary information

Reporting Summary

Peer Review File

Supplementary Table

Supplementary Table 1. Primers for qPCR, genotyping and ChIP assays.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 5

Unprocessed western blots.

Source Data Fig. 6

Statistical source data.

Source Data Fig. 7

Statistical source data.

Source Data Fig. 7

Unprocessed western blots.

Source Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 1

Unprocessed western blots.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 2

Unprocessed western blots.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Unprocessed western blots.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 8

Unprocessed western blots.

Source Data Extended Data Fig. 9

Statistical source data.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, L., Zhou, X., Gu, M. et al. Dapl1 controls NFATc2 activation to regulate CD8+ T cell exhaustion and responses in chronic infection and cancer. Nat Cell Biol 24, 1165–1176 (2022). https://doi.org/10.1038/s41556-022-00942-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41556-022-00942-8

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer