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Memory-like HCV-specific CD8+ T cells retain a molecular scar after cure of chronic HCV infection

Nature Immunology volume 22, pages 229–239 (2021)Cite this article

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Abstract

In chronic hepatitis C virus (HCV) infection, exhausted HCV-specific CD8+ T cells comprise memory-like and terminally exhausted subsets. However, little is known about the molecular profile and fate of these two subsets after the elimination of chronic antigen stimulation by direct-acting antiviral (DAA) therapy. Here, we report a progenitor–progeny relationship between memory-like and terminally exhausted HCV-specific CD8+ T cells via an intermediate subset. Single-cell transcriptomics implicated that memory-like cells are maintained and terminally exhausted cells are lost after DAA-mediated cure, resulting in a memory polarization of the overall HCV-specific CD8+ T cell response. However, an exhausted core signature of memory-like CD8+ T cells was still detectable, including, to a smaller extent, in HCV-specific CD8+ T cells targeting variant epitopes. These results identify a molecular signature of T cell exhaustion that is maintained as a chronic scar in HCV-specific CD8+ T cells even after the cessation of chronic antigen stimulation.

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Fig. 1: Emergence of TML/HCV subsets is associated with antigen recognition.
Fig. 2: TML/HCV and TTE/HCV cells are transcriptionally distinct.
Fig. 3: Progenitor–progeny relationship of TML/HCV and TTE/HCV cells.
Fig. 4: The molecular scar of chronicity after HCV cure.
Fig. 5: TML/HCV cells retain the chronic scar after HCV cure.
Fig. 6: The chronic scar is determined by antigen recognition.

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Data availability

The primary read files and expression count files for the scRNA-seq datasets reported in this paper are available to download from the Gene Expression Omnibus (GEO) under accession number GSE150305. The expression count files for the low-input RNA sequencing are available to download from GEO under accession number GSE150345. The raw data files for the low-input RNA sequencing are available from the European Genome-phenome Archive under accession number EGAD00001006259. The gene sets used for GSEA were: (1) exhaustion (chronic TCF-1−), memory-like (chronic TCF-1+) and memory signature (https://doi.org/10.1016/j.immuni.2016.07.021)19; (2) PD-1int CD8+ TILs signature (https://doi.org/10.1053/j.gastro.2018.08.030)29 and the TCF-1+ CD8+ TILs signature (https://doi.org/10.1016/j.immuni.2018.12.021)35. The remaining data supporting the findings of this study are available from the corresponding authors upon request.

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Acknowledgements

We thank all participants in the current study and H. Pircher, I. Schulien, K. Heim, V. Oberhardt and T. Gross for critical reading of the manuscript. The work presented here was supported by the CRC/TRR 179-Project (no. 01 to R.T., no. 02 to C.N.-H., no. 04 to T.B., no. 09 to R.B., no. 20 to M.H., no. 21 to B.B. and no. Z2 to R.E. of the German Research Foundation (DFG; TRR179 project no. 272983813). M.H. was supported by a Margarete von Wrangell fellowship. D.G. was supported by the Max Planck Society, the Behrens-Weise-Foundation and the DFG (no. GR4980/3-1). D.A.P. was supported by a Wellcome Trust Senior Investigator Award (no. 100326/Z/12/Z).

Author information

Author notes

  1. These authors contributed equally: Nina Hensel, Zuguang Gu, Sagar.

  2. These authors jointly supervised this work: Naveed Ishaque, Robert Thimme, Maike Hofmann.

Authors and Affiliations

  1. Department of Medicine II, University Hospital Freiburg, Freiburg, Germany

    Nina Hensel,  Sagar, Dominik Wieland, Janine Kemming, Oezlem Sogukpinar, Bertram Bengsch, Tobias Boettler, Christoph Neumann-Haefelin, Robert Thimme & Maike Hofmann

  2. Faculty of Medicine, University of Freiburg, Freiburg, Germany

    Nina Hensel, Dominik Wieland, Janine Kemming, Oezlem Sogukpinar, Florian Emmerich, Bertram Bengsch, Tobias Boettler, Christoph Neumann-Haefelin, Robert Thimme & Maike Hofmann

  3. Faculty of Biology, University of Freiburg, Freiburg, Germany

    Nina Hensel & Janine Kemming

  4. Heidelberg Center for Personalised Oncology (DKFZ-HIPO), German Cancer Research Center (DKFZ), Heidelberg, Germany

    Zuguang Gu

  5. Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany

    Sagar & Dominic Grün

  6. Charité – Universitätsmedizin Berlin and Berlin Institute of Health, Digital Health Center, Berlin, Germany

    Katharina Jechow, Roland Eils, Christian Conrad & Naveed Ishaque

  7. Division of Infection and Immunity, Cardiff University School of Medicine, Cardiff, UK

    Sian Llewellyn-Lacey, Emma Gostick & David A. Price

  8. Institute for Transfusion Medicine and Gene Therapy, University Medical Center, University of Freiburg, Freiburg, Germany

    Florian Emmerich

  9. Systems Immunity Research Institute, Cardiff University School of Medicine, Cardiff, UK

    David A. Price

  10. Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg, Germany

    Bertram Bengsch & Dominic Grün

  11. Health Data Science Unit, Faculty of Medicine, University of Heidelberg, Heidelberg, Germany

    Roland Eils

  12. Department of Infectious Diseases, Molecular Virology, Heidelberg University, Heidelberg, Germany

    Ralf Bartenschlager

  13. Division Virus-Associated Carcinogenesis, German Cancer Research Center, Heidelberg, Germany

    Ralf Bartenschlager

  14. German Center for Infection Research (DZIF), partner site Heidelberg, Heidelberg, Germany

    Ralf Bartenschlager

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  1. Nina Hensel
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  2. Zuguang Gu
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  3. Sagar
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Contributions

N.H. performed and analyzed experiments with the help of D.W., K.J., J.K. and O.S. S.L.-L., E.G. and D.A.P. provided pMHC-I tetramers. F.E. performed four-digit HLA typing by next-generation sequencing. Z.G. and N.I. analyzed and interpreted low-input RNA-seq data. C.C., R.E., B.B., T.B., C.N.-H. and R.B. contributed to data interpretation. Sagar and D.G. performed scRNA-seq and analyzed the respective data. M.H. and R.T. designed the study, contributed to experimental design and planning, interpreted data and wrote the manuscript.

Corresponding authors

Correspondence to Robert Thimme or Maike Hofmann.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Experimental setup.

Gating strategy of flow cytometric-based analyses and sorting of HCV-specific CD8+ T cells for transcriptome analyses using low-input or single-cell RNAseq. Dead cells, CD14+ (monocytes) and CD19+ (B cells) cells were excluded. Naïve CD8+ T cells (CD45RA+CCR7+) were also excluded. HLA-A*02:01- and HLA-B*27:05-restricted HCV-specific CD8+ T cells were identified by peptide/MHCI tetramers and further analyzed.

Extended Data Fig. 2 TML/HCV and TTE/HCV cells exhibit distinct characteristics.

a, Volcano plot showing DEGs comparing low-input transcriptome data of TML/HCV and TTE/HCV subsets (targeting conserved epitopes) derived from 3 cHCV-infected patients (red: exemplary DEGs are specified). x-axis represents the log2FC; y-axis represents the –log10 adjusted p values (≤ 0.05). Dotted lines indicate filter criteria of log2FC of ± 1 and adj. p value of 0.05. b–d, Flow cytometric analysis including various T-cell memory and exhaustion/effector markers of TML/HCV and TTE/HCV subsets derived from 12 cHCV patients. Representative dot plots including control analyses of non-naïve and naïve bulk CD8+ T cells (b,d), t-SNE representation (c) and bar charts (d, median fluorescence intensity (MFI) normalized to naïve CD8 + T cells) are depicted. e,f, Representative overview of the TCR clonotype distribution (low-input transcriptome data) within TML/HCV and TTE/HCV subsets from one (out of 3) cHCV patient depicted in three-layer donut plots: the inner layer depicts singleton, doubleton and high-order clonotypes; the second layer displays the top percentiles of clonotypes from the higher-order clonotypes and outer layer displays individual abundances of most recurrent clonotypes (e). Clonal overlap was assessed (f; n = 3). Bar charts show the median with IQR. Significance was assessed by Mann-Whitney comparison tests (two-sided) for flow cytometric analysis and by Kruskal-Wallis test (one-sided) including Dunn’s multiple comparisons test for TCR clonotypes analysis. DEG analysis of the volcano plot (two-sided) was done by DESeq2. All p values are corrected by the Benjamini-Hochberg method.

Extended Data Fig. 3 Characterization of HCV-specific CD127lo, CD127int and CD127hi subsets.

a, mRNA expression level of TCF7, BCL2, CCR7 and TOX (single-cell RNA sequencing data; n = 784 cells from 6 cHCV) in cluster 1–3 are depicted by violin plots. b, Flow cytometric analysis including various T cell memory and exhaustion/effector markers of CD127lo, CD127int and CD127hi (representing cluster 1–3) HCV-specific CD8+ T cells derived from 12 cHCV-infected patients. Representative dot plots are depicted including control analyses of non-naïve and naïve bulk CD8+ T cells. Summary graphs display the median fluorescence intensity (MFI) of marker expression of HCV-specific CD8+ T cell subsets normalized to naïve CD8+ T cells. Significance was assessed by Kruskal-Wallis test (one-sided) including Dunn’s multiple comparisons test for flow cytometric analysis and Wilcoxon matched-pairs signed rank test (two-sided) for mRNA expression levels. The violin plots show the frequency with box plots depicting the median with IQR.

Extended Data Fig. 4 Phenotypic characteristics of HCV-specific CD8+ T cells after HCV cure.

Flow cytometric analysis including various T cell memory and exhaustion/effector markers of HCV-specific CD8+ T cells before (n = 12) and after (n = 7) HCV cure and in rHCV (n = 5). a, t-SNE representation with scaled expression (color code: red: high; blue: low) is depicted. b, Representative dot plots (including control analyses of non-naïve and naïve bulk CD8+ T cells) and bar charts summarizing manually gated flow cytometric data (median fluorescence intensity (MFI) normalized to naïve CD8+ T cells) are depicted. c, Serum viral loads (HCV RNA), the frequency and cell counts [/ml] of TML/HCV subsets from patients before (n = 12) and after HCV cure (n = 7). Bar charts show the median with IQR. Significance was assessed by Kruskal-Wallis test (one-sided) including Dunn’s multiple comparisons test (b), Mann-Whitney comparison test (two-sided) (c; right) and Wilcoxon matched-pairs signed rank test (two-sided) (c, left).

Extended Data Fig. 5 Phenotypic characteristics of the TML/HCV subset.

Flow cytometric analysis including various T cell memory and exhaustion/effector markers of TML/HCV cells before (n = 12) and after (n = 7) HCV cure and in rHCV (n = 5). t-SNE representation with scaled expression depicted as color code (red: high; blue: low) a, representative dot plots (including control analyses of non-naïve and naïve bulk CD8+ T cells) and bar charts summarizing manually gated flow cytometric data (b; median fluorescence intensity (MFI) normalized to naïve CD8+ T cells) are depicted. Bar charts show the median with IQR. Significance was assessed by Kruskal-Wallis test (one-sided) including Dunn’s multiple comparisons test.

Extended Data Fig. 6 Characteristics of TML/HCV cells after HCV cure.

a, Flow cytometric analysis of TOX1, PD-1, Bcl-2 and CCR7 in TML/HCV subsets at the end of DAA therapy (EOT) (n = 6) and at long-term (>6 months) follow-up (FU) after HCV cure (n = 7) is depicted as bar chart (median fluorescence intensity (MFI) normalized to naïve CD8+ T cells). b, Principle component analysis (PCA) of the transcriptomes of TML/HCV subsets before and after HCV cure and in rHCV; and of TTE/HCV cells (cHCV) and TM cells (rHCV) (cHCV, cured HCV, rHCV: n = 3 each). (c) WGCNA of low input transcriptome data was performed. Numbers of genes in each defined module are depicted in the bar chart. Bar charts show the median with IQR. Significance was assessed by Mann-Whitney comparison test (two-sided).

Extended Data Fig. 7 Phenotypic characteristics of CD127hi and CD127int subsets before and after HCV cure.

Flow cytometric analysis including various T cell memory and exhaustion/effector markers of CD127int and CD127hi (representing cluster 1 and 2) HCV-specific CD8 + T cells from patients before (n = 12) and after (n = 7) HCV cure. Representative dot plots (including control analyses of non-naïve and naïve bulk CD8+ T cells), bar charts (a; median fluorescence intensity (MFI) normalized to naïve CD8+ T cells) and t-SNE representation b,c, are depicted. Scaled expression is depicted as color code (red: high; blue: low). Bar charts show the median with IQR. Significance was assessed by Kruskal-Wallis test (one-sided) including Dunn’s multiple comparisons test.

Extended Data Fig. 8 Phenotypic characteristics of HCV-specific CD8+ T cells targeting conserved versus variant epitopes.

Flow cytometric analysis including various T cell memory and exhaustion/effector markers of HCV-specific CD8+ T cells in rHCV (n = 5), cHCV and cured HCV targeting either conserved epitopes (AG; cHCV: n = 12, cured HCV: n = 7) or variant epitopes (ESC; cHCV: n = 8, cured HCV: n = 6). t-SNE representation with scaled expression depicted as color code (red: high; blue: low) a, representative dot plots (including control analyses of non-naïve and naïve bulk CD8+ T cells) and bar charts summarizing manually gated flow cytometric data (b; median fluorescence intensity (MFI) normalized to naïve CD8+ T cells) are depicted. Bar charts show the median with IQR. Significance was assessed by Kruskal-Wallis test (one-sided).

Extended Data Fig. 9 Phenotypic characteristics of TML/HCV cells targeting conserved versus variant epitopes.

Flow cytometric analysis including various T cell memory and exhaustion/effector markers of TML/HCV cells in rHCV (n = 5), cHCV and cured HCV targeting either conserved epitopes (AG; cHCV: n = 12, cured HCV: n = 7) or variant epitopes (ESC; cHCV: n = 8, cured HCV: n = 6). t-SNE representation with scaled expression depicted as color code (red: high; blue: low) a, representative dot plots (including control analyses of non-naïve and naïve bulk CD8+ T cells) and bar charts (b; median fluorescence intensity (MFI) normalized to naïve CD8+ T cells) are depicted. Bar charts show the median with IQR. Significance was assessed by Kruskal-Wallis test (one-sided) including Dunn’s multiple comparisons test.

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Hensel, N., Gu, Z., Sagar 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). https://doi.org/10.1038/s41590-020-00817-w

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