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Differentiation of exhausted CD8+ T cells after termination of chronic antigen stimulation stops short of achieving functional T cell memory

Abstract

T cell exhaustion is associated with failure to clear chronic infections and malignant cells. Defining the molecular mechanisms of T cell exhaustion and reinvigoration is essential to improving immunotherapeutic modalities. Here we confirmed pervasive phenotypic, functional and transcriptional differences between memory and exhausted antigen-specific CD8+ T cells in human hepatitis C virus (HCV) infection before and after treatment. After viral cure, phenotypic changes in clonally stable exhausted T cell populations suggested differentiation toward a memory-like profile. However, functionally, the cells showed little improvement, and critical transcriptional regulators remained in the exhaustion state. Notably, T cells from chronic HCV infection that were exposed to antigen for less time because of viral escape mutations were functionally and transcriptionally more similar to memory T cells from spontaneously resolved HCV infection. Thus, the duration of T cell stimulation impacts exhaustion recovery, with antigen removal after long-term exhaustion being insufficient for the development of functional T cell memory.

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Fig. 1: Study design, patients and virus-specific CD8+ T cell responses.
Fig. 2: HCV-specific TEX cells have a characteristic phenotype that is significantly changed after DAA therapy and antigen removal.
Fig. 3: The phenotypic change of TEX cells to TMEM cells after HCV cure is incomplete.
Fig. 4: Functional analysis reveals that TESC cells, but not TEX cells after viral cure, display functional properties similar to those of TMEM cells from HCV natural resolvers.
Fig. 5: The TEX cell phenotypic and functional profile shows limited evolution over time after HCV cure.
Fig. 6: Transcriptional analysis confirms broad changes in TEX cells after antigen removal, but also identifies exhaustion scars in the transcriptional landscape.

Data availability

HCV epitope sequences are deposited in the NCBI Sequence Read Archive under accession numbers SRR11811484SRR11811504. RNA-seq and TCR sequencing data from this study will be made publicly available through the NCBI Gene Expression Omnibus and/or NCBI database of Genotypes and Phenotypes. The remaining data supporting the findings of this study are available from the corresponding authors upon request.

References

  1. 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).

    CAS  PubMed  Article  Google Scholar 

  2. Wherry, E. J. & Kurachi, M. Molecular and cellular insights into T cell exhaustion. Nat. Rev. Immunol. 15, 486–499 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. Wherry, E. J., Blattman, J. N., Murali-Krishna, K., van der Most, R. & Ahmed, R. Viral persistence alters CD8 T cell immunodominance and tissue distribution and results in distinct stages of functional impairment. J. Virol. 77, 4911–4927 (2003).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. Kasprowicz, V. et al. High level of PD-1 expression on hepatitis C virus (HCV)-specific CD8+ and CD4+ T cells during acute HCV infection, irrespective of clinical outcome. J. Virol. 82, 3154–3160 (2008).

    CAS  PubMed  Article  Google Scholar 

  5. Bengsch, B. et al. Coexpression of PD-1, 2B4, CD160 and KLRG1 on exhausted HCV-specific CD8+ T cells is linked to antigen recognition and T cell differentiation. PLoS Pathog. 6, e1000947 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  6. Blackburn, S. D. et al. Coregulation of CD8+ T cell exhaustion by multiple inhibitory receptors during chronic viral infection. Nat. Immunol. 10, 29–37 (2009).

    CAS  PubMed  Article  Google Scholar 

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

    CAS  PubMed  Article  Google Scholar 

  8. Angelosanto, J. M., Blackburn, S. D., Crawford, A. & Wherry, E. J. Progressive loss of memory T cell potential and commitment to exhaustion during chronic viral infection. J. Virol. 86, 8161–8170 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. Shin, H., Blackburn, S. D., Blattman, J. N. & Wherry, E. J. Viral antigen and extensive division maintain virus-specific CD8 T cells during chronic infection. J. Exp. Med. 204, 941–949 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. Wherry, E. J., Barber, D. L., Kaech, S. M., Blattman, J. N. & Ahmed, R. Antigen-independent memory CD8 T cells do not develop during chronic viral infection. Proc. Natl Acad. Sci. USA 101, 16004–16009 (2004).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. Gallimore, A. et al. Induction and exhaustion of lymphocytic choriomeningitis virus–specific cytotoxic T lymphocytes visualized using soluble tetrameric major histocompatibility complex class I–peptide complexes. J. Exp. Med. 187, 1383–1393 (1998).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. Wei, S. C., Duffy, C. R. & Allison, J. P. Fundamental mechanisms of immune checkpoint blockade therapy. Cancer Discov. 8, 1069–1086 (2018).

    PubMed  Article  Google Scholar 

  14. Hegde, P. S. & Chen, D. S. Top 10 challenges in cancer immunotherapy. Immunity 52, 17–35 (2020).

    CAS  PubMed  Article  Google Scholar 

  15. Hamid, O. et al. Five-year survival outcomes for patients with advanced melanoma treated with pembrolizumab in KEYNOTE-001. Ann. Oncol. 30, 582–588 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. Seder, R. A., Darrah, P. A. & Roederer, M. T cell quality in memory and protection: implications for vaccine design. Nat. Rev. Immunol. 8, 247–258 (2008).

    CAS  PubMed  Article  Google Scholar 

  17. Hoogeveen, R. C. & Boonstra, A. Checkpoint inhibitors and therapeutic vaccines for the treatment of chronic HBV infection. Front Immunol. 11, 401 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. Fisicaro, P. et al. Pathogenetic mechanisms of T cell dysfunction in chronic HBV infection and related therapeutic approaches. Front Immunol. 11, 849 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. Wherry, E. J. T cell exhaustion. Nat. Immunol. 12, 492–499 (2011).

    CAS  PubMed  Article  Google Scholar 

  20. Day, C. L. et al. PD-1 expression on HIV-specific T cells is associated with T cell exhaustion and disease progression. Nature 443, 350–354 (2006).

    CAS  PubMed  Article  Google Scholar 

  21. Schuch, A. et al. Phenotypic and functional differences of HBV core-specific versus HBV polymerase-specific CD8+ T cells in chronically HBV-infected patients with low viral load. Gut 68, 905–915 (2019).

  22. Hoogeveen, R.C. et al. Phenotype and function of HBV-specific T cells is determined by the targeted epitope in addition to the stage of infection. Gut 68, 893–904 (2018).

  23. Kasprowicz, V. et al. Hepatitis C virus (HCV) sequence variation induces an HCV-specific T cell phenotype analogous to spontaneous resolution. J. Virol. 84, 1656–1663 (2010).

    CAS  PubMed  Article  Google Scholar 

  24. Lauer, G. M. & Walker, B. D. Hepatitis C virus infection. N. Engl. J. Med. 345, 41–52 (2001).

    CAS  PubMed  Article  Google Scholar 

  25. Wolski, D. et al. Early transcriptional divergence marks virus-specific primary human CD8+ T cells in chronic versus acute infection. Immunity 47, 648–663 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. Holmes, J. A., Rutledge, S. M. & Chung, R. T. Direct-acting antiviral treatment for hepatitis C. Lancet 393, 1392–1394 (2019).

    PubMed  Article  Google Scholar 

  27. Wieland, D. et al. TCF1+ hepatitis C virus-specific CD8+ T cells are maintained after cessation of chronic antigen stimulation. Nat. Commun. 8, 15050 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  28. Martin, B. et al. Restoration of HCV-specific CD8+ T cell function by interferon-free therapy. J. Hepatol. 61, 538–543 (2014).

    CAS  PubMed  Article  Google Scholar 

  29. Alfei, F. et al. TOX reinforces the phenotype and longevity of exhausted T cells in chronic viral infection. Nature 571, 265–269 (2019).

    CAS  PubMed  Article  Google Scholar 

  30. Hensel, N. et al. Memory-like HCV-specific CD8+ T cells retain a molecular scar after cure of chronic HCV infection. Nat. Immunol. 22, 229–239 (2021).

    CAS  PubMed  Article  Google Scholar 

  31. Holmes, J. A. et al. Dynamic changes in innate immune responses during direct-acting antiviral therapy for HCV infection. J. Viral Hepat. 26, 362–372 (2019).

    CAS  PubMed  Article  Google Scholar 

  32. Kuntzen, T. et al. Viral sequence evolution in acute hepatitis C virus infection. J. Virol. 81, 11658–11668 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. Cox, A. L. et al. Cellular immune selection with hepatitis C virus persistence in humans. J. Exp. Med. 201, 1741–1752 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. Timm, J. et al. CD8 epitope escape and reversion in acute HCV infection. J. Exp. Med. 200, 1593–1604 (2004).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. Gruener, N. H. et al. Sustained dysfunction of antiviral CD8+ T lymphocytes after infection with hepatitis C virus. J. Virol. 75, 5550–5558 (2001).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. Araki, K. et al. Translation is actively regulated during the differentiation of CD8+ effector T cells. Nat. Immunol. 18, 1046–1057 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. Khan, O. et al. TOX transcriptionally and epigenetically programs CD8+ T cell exhaustion. Nature 571, 211–218 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. Yao, C. et al. Single-cell RNA-seq reveals TOX as a key regulator of CD8+ T cell persistence in chronic infection. Nat. Immunol. 20, 890–901 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. 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).

    CAS  PubMed  Article  Google Scholar 

  40. Henn, M. R. et al. Whole-genome deep sequencing of HIV-1 reveals the impact of early minor variants upon immune recognition during acute infection. PLoS Pathog. 8, e1002529 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. Tully, D. C. et al. Differences in the selection bottleneck between modes of sexual transmission influence the genetic composition of the HIV-1 founder virus. PLoS Pathog. 12, e1005619 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  42. Kotecha, N., Krutzik, P. O. & Irish, J. M. Web-based analysis and publication of flow cytometry experiments. Curr. Protoc. Cytom. 10, 10.17 (2010).

    Google Scholar 

  43. Metsalu, T. & Vilo, J. ClustVis: a web tool for visualizing clustering of multivariate data using principal-component analysis and heatmap. Nucleic Acids Res. 43, W566–W570 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

    CAS  Article  PubMed  Google Scholar 

  45. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  PubMed  Article  Google Scholar 

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

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

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  48. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B. 57, 289–300 (1995).

    Google Scholar 

  49. Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

    PubMed  Article  Google Scholar 

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. Mootha, V. K. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).

    CAS  PubMed  Article  Google Scholar 

  52. Khan, A. et al. JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework. Nucleic Acids Res. 46, D1284 (2018).

    PubMed  Article  Google Scholar 

  53. The ENCODE Project Consortium. A user’s guide to the encyclopedia of DNA elements (ENCODE). PLoS Biol. 9, e1001046 (2011).

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Acknowledgements

We are grateful to the patients participating in the clinical trial. Important contributions were made by the The Harvard Stem Cell Institute-Center for Regenerative Medicine (HSCI-CRM) Flow Cytometry Core Facility at Massachusetts General Hospital (MGH), the NIH Tetramer Core Facility and the University of Oklahoma Medical Center HLA Typing Facility (W. Hildebrand). We also thank all members of the laboratory of G.M.L. for insightful comments, critical reading of the manuscript and advice on figure design. This work was supported by NIH grants U19 AI086230 (to G.M.L., W.N.H., R.T.C., N.H., T.M.A. and A.Y.K.), U01 AI131314 (to G.M.L., L.L.L.-X. and A.Y.K.) and R01 DA046277 (to G.M.L. and A.Y.K.) and fellowships from the German Research Foundation DFG (to H.K.D. and L.M.B.). AbbVie sponsored the clinical trial (NCT02476617).

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Authors and Affiliations

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Contributions

P.T., D.W. and G.M.L. conceived and designed the experiments. P.T., D.W., J.A.-J., R.C.H., M.D., H.K.D., L.M.B., D.C.T., D.J.B., A.T.-C., M.R., D.K., N.A., A.C., J.A.C., L.M.-R. and T.E. performed and analyzed experiments. D.W., S.S., D.L. and D.R.S. analyzed RNA-seq data. R.T.C., A.Y.K. and G.M.L. designed the clinical trial and patient selection. J.B., J.G., L.L.L.-X and J.A. contributed to the clinical cohort recruitment and clinical database management. G.M.L., N.H. and W.N.H. supervised RNA-seq experiments and data analysis. T.M.A. designed and supervised the viral sequencing study. P.T., S.S., D.R.S. and G.M.L. drafted the manuscript with the help of all other authors.

Corresponding authors

Correspondence to Pierre Tonnerre or Georg M. Lauer.

Ethics declarations

Competing interests

AbbVie sponsored the clinical trial (NCT02476617) and provided input in the trial design and clinical and biological sample collection schedule. W.N.H. is an employee of Merck and Company and holds equity in Tango Therapeutics and Arsenal Biosciences. All other authors declare no competing interests.

Additional information

Peer review information Nature Immunology thanks Hazem Ghoneim, Barbara Rehermann and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Zoltan Fehervari was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Detection and frequency of HCV-specific CD8+ T cells.

a, Screening strategy to detect HCV-specific CD8+ T cells by flow cytometry using pools of Class I MHC multimers labeled with PE or APC. b, Positive detection was followed by individual multimer staining to identify and/or distinguish multiple HCV-specific CD8+ T cell responses. c, Representative flow cytometry dot plots of HCV-specific CD8+ T cells before and after magnetic bead enrichment. d, HCV-specific-multimer positive CD8+ T cell frequencies, pre- and post-DAA treatment or after spontaneous resolution of the infection. Statistical testing by Wilcoxon tests (paired, nonparametric, two-sided for TEX and TESC pre- and post-DAA) or Mann-Whitney tests (unpaired, nonparametric, two-sided when compared to Resolvers), *P < 0.05, **P < 0.01, ***P < 0.001. e, HCV-specific CD8+ T cell responses (recognized epitopes and associated HLA class I restrictions) detected in a cohort of patients with spontaneously resolved HCV infection.

Extended Data Fig. 2 HCV-epitope sequences and identification of functional escape mutations.

a, Sequences of select HCV epitopes across patients included in this study. Escape mutations are written in red and T cell status, whether the cells have full recognition of the virus (TEX) or are partially (TP-ESC) or fully escaped (TF-ESC) is indicated as determined through functional assays of the recognition of the variant epitopes compared to wild-types by intracellular cytokine detection of IFNγ by flow-cytometry (b).

Extended Data Fig. 3 Phenotypical landscape of TEX and TESC, pre- and post-DAA, as compared to TMEM.

a, Flow cytometry gating strategy and representative flow cytometry dot plots. b, Dot plot histograms displaying the expression levels of the 37 proteins analyzed by flow cytometry across TEX and TF-ESC, pre- and post-DAA therapy, and in resolver TMEM. Statistical testing by Mann-Whitney tests when comparing TEX versus TF-ESC or TMEM (unpaired, nonparametric, two-sided), or by Wilcoxon tests (paired, nonparametric, two-sided) when comparing paired samples pre- versus post-DAA. A schematic representation of the comparison rules and statistical tests used are presented in Extended Data Fig. 3c. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. c, Schematic representation of the comparison rules and statistical tests used to compare expression levels across the different T cell populations of interest. d, Principal component analysis of TEX and TF-ESC, pre- and post-DAA therapy, as well as resolver TMEM, based on the expression levels of CD38, HLA-DR, PD-1, CD39, TIGIT, CCR7, CD45RA, Integrin-Beta-7 and CD62L, and as presented also in Fig. 3e by t-SNE analysis. e, Principal component analysis based on the expression levels of the 37 proteins analyzed by flow-cytometry and expressed by TEX, TP-ESC, TF-ESC and TFLU, pre- and post-DAA therapy, as well as by resolver TMEM, with respective contribution and direction (arrows) of each of the 37 different proteins throughout PC1 and PC2 dimensions.

Extended Data Fig. 4 Phenotypical and functional changes in TEX over a long-term period post-DAA cure.

a, Representative flow cytometry dot plots showing the expression and co-expression patterns of HLA-DR and CD38 (upper panels) as well as PD-1 and CD39 (lower panels) by bulk CD8+ T cells (grey dots) or HCV-specific CD8+ T cells (colored dots), pre- and overtime post- DAA therapy or after spontaneous resolution. b, Representative flow cytometry plots showing the expression and co-expression patterns of CD69 and CD107a (upper panels) as well as IFNγ and TNFα cytokines (lower panels) by HCV-specific CD8+ T cells following ex vivo stimulation with or without cognate antigens, pre- and overtime post- DAA therapy or after spontaneous resolution.

Extended Data Fig. 5 Transcriptional landscape of TEX and TESC, pre- and post-DAA, and as compared to TMEM.

a, Heatmap showing all genes that were differentially expressed between pre-DAA TEX and post-DAA TEX. b, Top 20 recovered genes after DAA treatment which are similar to resolver T cells. c, Heatmap showing the 176 significantly DEGs between pre- and post-DAA that are shared by TEX and TESC, as described in Fig. 6b. d, Heatmap showing the top 20 unrecovered genes after DAA treatment which were significantly different between post-DAA TEX and TMEM cells.

Extended Data Fig. 6 Expression patterns of key transcription factors and co-factors.

a, Expression (log2 counts) of EOMES, LMCD1, SETD7 and CTH in TEX and TESC pre- and post-DAA (paired samples n = 6) as well as in resolver TMEM cells (n = 8). Box plots show the median (vertical bar), 25th and 75th percentiles (lower and upper bounds of the box, respectively) and 1.5 times the interquartile range (or minimum/maximum values if they fall within that range; end of whiskers). Statistical testing by moderated t-test (two-sided, unadjusted). b, Linear regression analysis to model the relationship in gene count expression of TOX and the other transcription factors and co-factors identified in Fig. 6d, by the different populations of HCV-specific CD8+ T cells, pre- and post-DAA therapy or after spontaneous resolution. Error bands represents the 95% confidence level interval. Pearson correlation coefficient R and significance p (two-sided) values are reported from the linear regression analysis performed with R software. c, Gene count expression of TOX, ETV1, NKX3-1, SETD7, CTH, EOMES and LMCD1, by HCV-specific CD8+ T cells from a validation cohort of additional individual with TEX post-DAA (n = 9), as compared to resolver TMEM cells (n = 8), and following batch effect correction. Statistical testing by Mann-Whitney tests (two-sided), *P < 0.05, **P < 0.01, ***P < 0.001.

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Tonnerre, P., Wolski, D., Subudhi, S. et al. Differentiation of exhausted CD8+ T cells after termination of chronic antigen stimulation stops short of achieving functional T cell memory. Nat Immunol 22, 1030–1041 (2021). https://doi.org/10.1038/s41590-021-00982-6

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