Disturbed mitochondrial dynamics in CD8+ TILs reinforce T cell exhaustion

Abstract

The metabolic challenges present in tumors attenuate the metabolic fitness and antitumor activity of tumor-infiltrating T lymphocytes (TILs). However, it remains unclear whether persistent metabolic insufficiency can imprint permanent T cell dysfunction. We found that TILs accumulated depolarized mitochondria as a result of decreased mitophagy activity and displayed functional, transcriptomic and epigenetic characteristics of terminally exhausted T cells. Mechanistically, reduced mitochondrial fitness in TILs was induced by the coordination of T cell receptor stimulation, microenvironmental stressors and PD-1 signaling. Enforced accumulation of depolarized mitochondria with pharmacological inhibitors induced epigenetic reprogramming toward terminal exhaustion, indicating that mitochondrial deregulation caused T cell exhaustion. Furthermore, supplementation with nicotinamide riboside enhanced T cell mitochondrial fitness and improved responsiveness to anti-PD-1 treatment. Together, our results reveal insights into how mitochondrial dynamics and quality orchestrate T cell antitumor responses and commitment to the exhaustion program.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Tumor-infiltrating CD8+ T cells display depolarized mitochondria.
Fig. 2: Impaired mitophagy results in accumulation of depolarized mitochondria in CD8+ TILs.
Fig. 3: CD8+ TILs accumulating depolarized mitochondria display characteristics of terminally exhausted T cells.
Fig. 4: Mitochondrial fitness in TILs orchestrates the epigenetic program.
Fig. 5: TCR and PD-1 signals contribute to the accumulation of damaged mitochondria in TILs.
Fig. 6: Coordination of TCR and metabolic stress drives mitochondrial dysfunction in CD8+ T cells.
Fig. 7: Accumulation of depolarized mitochondria reinforces phenotypic and epigenetic exhaustion programs.
Fig. 8: NR sustains mitochondrial fitness and antitumor responses in CD8+ T cells.

Data availability

The Smart-seq2, ATAC–seq and WGBS data are available in the Gene Expression Omnibus database under accession codes GSE144582 for ATAC-seq, GSE144583 for WGBS and GSE156506 for RNA-seq. The data analysis code is available at https://github.com/himrichova/CD8_TIL_exhaustion. Processed data are, furthermore, publicly available in the UCSC Genome Browser using the following link: http://genome-euro.ucsc.edu/s/himrichova/CD8_TIL_exhaustion_mm10. All the information and data are summarized and available at https://www.medical-epigenomics.org/papers/Yu2020/#home. Other relevant data are available from the corresponding author upon request. Source data are provided with this paper.

References

  1. 1.

    Li, X. et al. Navigating metabolic pathways to enhance antitumour immunity and immunotherapy. Nat. Rev. Clin. Oncol. 16, 425–441 (2019).

    CAS  PubMed  Google Scholar 

  2. 2.

    Hanahan, D. & Coussens, L. M. Accessories to the crime: functions of cells recruited to the tumor microenvironment. Cancer Cell 21, 309–322 (2012).

    CAS  PubMed  Google Scholar 

  3. 3.

    Ho, P. C. et al. Phosphoenolpyruvate is a metabolic checkpoint of anti-tumor T cell responses. Cell 162, 1217–1228 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Chang, C. H. et al. Metabolic competition in the tumor microenvironment is a driver of cancer progression. Cell 162, 1229–1241 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Rambold, A. S., Kostelecky, B., Elia, N. & Lippincott-Schwartz, J. Tubular network formation protects mitochondria from autophagosomal degradation during nutrient starvation. Proc. Natl Acad. Sci. USA 108, 10190–10195 (2011).

    CAS  PubMed  Google Scholar 

  6. 6.

    Pauken, K. E. et al. Epigenetic stability of exhausted T cells limits durability of reinvigoration by PD-1 blockade. Science 354, 1160–1165 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Sun, N., Youle, R. J. & Finkel, T. The mitochondrial basis of aging. Mol. Cell 61, 654–666 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Wiley, C. D. et al. Mitochondrial dysfunction induces senescence with a distinct secretory phenotype. Cell Metab. 23, 303–314 (2016).

    CAS  PubMed  Google Scholar 

  9. 9.

    Quiros, P. M., Mottis, A. & Auwerx, J. Mitonuclear communication in homeostasis and stress. Nat. Rev. Mol. Cell Biol. 17, 213–226 (2016).

    CAS  PubMed  Google Scholar 

  10. 10.

    van der Windt, G. J. W. et al. Mitochondrial respiratory capacity is a critical regulator of CD8+ T cell memory development. Immunity 36, 68–78 (2012).

    PubMed  Google Scholar 

  11. 11.

    Buck, M. D. et al. Mitochondrial dynamics controls T cell fate through metabolic programming. Cell 166, 63–76 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Bengsch, B. et al. Bioenergetic insufficiencies due to metabolic alterations regulated by the inhibitory receptor PD-1 are an early driver of CD8+ T cell exhaustion. Immunity 45, 358–373 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Scharping, N. E. et al. The tumor microenvironment represses T cell mitochondrial biogenesis to drive intratumoral T cell metabolic insufficiency and dysfunction. Immunity 45, 374–388 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Siska, P. J. et al. Mitochondrial dysregulation and glycolytic insufficiency functionally impair CD8 T cells infiltrating human renal cell carcinoma. JCI Insight 2, e93411 (2017).

    PubMed Central  Google Scholar 

  15. 15.

    Baitsch, L. et al. Exhaustion of tumor-specific CD8+ T cells in metastases from melanoma patients. J. Clin. Invest. 121, 2350–2360 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Alfei, F. & Zehn, D. T cell exhaustion: an epigenetically imprinted phenotypic and functional makeover. Trends Mol. Med. 23, 769–771 (2017).

    CAS  PubMed  Google Scholar 

  18. 18.

    Utzschneider, D. T. et al. T cells maintain an exhausted phenotype after antigen withdrawal and population reexpansion. Nat. Immunol. 14, 603–610 (2013).

    CAS  PubMed  Google Scholar 

  19. 19.

    Schietinger, A. et al. Tumor-specific T cell dysfunction is a dynamic antigen-driven differentiation program initiated early during tumorigenesis. Immunity 45, 389–401 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Sen, D. R. et al. The epigenetic landscape of T cell exhaustion. Science 354, 1165–1169 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Philip, M. et al. Chromatin states define tumour-specific T cell dysfunction and reprogramming. Nature 545, 452–456 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Ghoneim, H. E. et al. De novo epigenetic programs inhibit PD-1 blockade-mediated T cell rejuvenation. Cell 170, 142–157.e19 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Mognol, G. P. et al. Exhaustion-associated regulatory regions in CD8+ tumor-infiltrating T cells. Proc. Natl Acad. Sci. USA 114, E2776–E2785 (2017).

    CAS  PubMed  Google Scholar 

  24. 24.

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

    CAS  PubMed  Google Scholar 

  25. 25.

    Jadhav, R. R. et al. Epigenetic signature of PD-1+ TCF1+ CD8 T cells that act as resource cells during chronic viral infection and respond to PD-1 blockade. Proc. Natl Acad. Sci. USA 116, 14113–14118 (2019).

    CAS  PubMed  Google Scholar 

  26. 26.

    Im, S. J. et al. Defining CD8+ T cells that provide the proliferative burst after PD-1 therapy. Nature 537, 417–421 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    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  Google Scholar 

  28. 28.

    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.e10 (2019).

    CAS  PubMed  Google Scholar 

  29. 29.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Dankort, D. et al. Braf(V600E) cooperates with Pten loss to induce metastatic melanoma. Nat. Genet. 41, 544–552 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Pendergrass, W., Wolf, N. & Poot, M. Efficacy of MitoTracker Green and CMXrosamine to measure changes in mitochondrial membrane potentials in living cells and tissues. Cytom. A 61, 162–169 (2004).

    CAS  Google Scholar 

  32. 32.

    Palikaras, K., Lionaki, E. & Tavernarakis, N. Mechanisms of mitophagy in cellular homeostasis, physiology and pathology. Nat. Cell Biol. 20, 1013–1022 (2018).

    CAS  PubMed  Google Scholar 

  33. 33.

    Zorova, L. D. et al. Mitochondrial membrane potential. Anal. Biochem. 552, 50–59 (2018).

    CAS  PubMed  Google Scholar 

  34. 34.

    Narendra, D., Tanaka, A., Suen, D. F. & Youle, R. J. Parkin is recruited selectively to impaired mitochondria and promotes their autophagy. J. Cell Biol. 183, 795–803 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    McWilliams, T. G. et al. mito-QC illuminates mitophagy and mitochondrial architecture in vivo. J. Cell Biol. 214, 333–345 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Xu, X. et al. Autophagy is essential for effector CD8+ T cell survival and memory formation. Nat. Immunol. 15, 1152–1161 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Song, M. et al. IRE1α–XBP1 controls T cell function in ovarian cancer by regulating mitochondrial activity. Nature 562, 423–428 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Teague, R. M. et al. Interleukin-15 rescues tolerant CD8+ T cells for use in adoptive immunotherapy of established tumors. Nat. Med. 12, 335–341 (2006).

    CAS  PubMed  Google Scholar 

  39. 39.

    Gattinoni, L. et al. Wnt signaling arrests effector T cell differentiation and generates CD8+ memory stem cells. Nat. Med. 15, 808–813 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Barili, V. et al. Targeting p53 and histone methyltransferases restores exhausted CD8+ T cells in HCV infection. Nat. Commun. 11, 604 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Balkhi, M. Y., Wittmann, G., Xiong, F. & Junghans, R. P. YY1 upregulates checkpoint receptors and downregulates type I cytokines in exhausted, chronically stimulated human T cells. Iscience 2, 105–122 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Cao, Y. et al. ER stress-induced mediator C/EBP homologous protein thwarts effector T cell activity in tumors through T-bet repression. Nat. Commun. 10, 1280 (2019).

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Deaton, A. M. & Bird, A. CpG islands and the regulation of transcription. Genes Dev. 25, 1010–1022 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Enouz, S., Carrie, L., Merkler, D., Bevan, M. J. & Zehn, D. Autoreactive T cells bypass negative selection and respond to self-antigen stimulation during infection. J. Exp. Med. 209, 1769–1779 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Li, H. et al. Dysfunctional CD8 T cells form a proliferative, dynamically regulated compartment within human melanoma. Cell 176, 775–789.e18 (2019).

    CAS  PubMed  Google Scholar 

  46. 46.

    Patsoukis, N. et al. PD-1 alters T-cell metabolic reprogramming by inhibiting glycolysis and promoting lipolysis and fatty acid oxidation. Nat. Commun. 6, 6692 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Xiao, Z., Dai, Z. & Locasale, J. W. Metabolic landscape of the tumor microenvironment at single cell resolution. Nat. Commun. 10, 3763 (2019).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Larsen, G. A., Skjellegrind, H. K., Berg-Johnsen, J., Moe, M. C. & Vinje, M. L. Depolarization of mitochondria in isolated CA1 neurons during hypoxia, glucose deprivation and glutamate excitotoxicity. Brain Res. 1077, 153–160 (2006).

    CAS  PubMed  Google Scholar 

  49. 49.

    Jang, S. Y., Kang, H. T. & Hwang, E. S. Nicotinamide-induced mitophagy: event mediated by high NAD+/NADH ratio and SIRT1 protein activation. J. Biol. Chem. 287, 19304–19314 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Staron, M. M. et al. The transcription factor FoxO1 sustains expression of the inhibitory receptor PD-1 and survival of antiviral CD8+ T cells during chronic infection. Immunity 41, 802–814 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Shin, J. H. et al. PARIS (ZNF746) repression of PGC-1α contributes to neurodegeneration in Parkinson’s disease. Cell 144, 689–702 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Pan, H., Zhang, C., Wang, T., Chen, J. & Sun, S. K. In situ fabrication of intelligent photothermal indocyanine green-alginate hydrogel for localized tumor ablation. ACS Appl. Mater. Interfaces 11, 2782–2789 (2019).

    CAS  PubMed  Google Scholar 

  53. 53.

    Chao, Y. et al. Combined local immunostimulatory radioisotope therapy and systemic immune checkpoint blockade imparts potent antitumour responses. Nat. Biomed. Eng. 2, 611–621 (2018).

    CAS  PubMed  Google Scholar 

  54. 54.

    Hayashi, K., Sakamoto, W. & Yogo, T. Smart ferrofluid with quick gel transformation in tumors for MRI-guided local magnetic thermochemotherapy. Adv. Funct. Mater. 26, 1708–1718 (2016).

    CAS  Google Scholar 

  55. 55.

    Ho, P. C. et al. Immune-based antitumor effects of BRAF inhibitors rely on signaling by CD40L and IFNγ. Cancer Res. 74, 3205–3217 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Meeth, K., Wang, J. X., Micevic, G., Damsky, W. & Bosenberg, M. W. The YUMM lines: a series of congenic mouse melanoma cell lines with defined genetic alterations. Pigment Cell Melanoma Res. 29, 590–597 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Jiang, Y. F., Lin, H. L. & Fu, C. Y. 3D mitochondrial ultrastructure of Drosophila indirect flight muscle revealed by serial-section electron tomography. J. Vis. Exp. 19, 56567 (2017).

    Google Scholar 

  58. 58.

    Rooney, J. P. et al. In Methods in Molecular Biology (Methods and Protocols) Vol 1241 (eds. Palmeira, C. & Rolo, A.) 23–38 (Humana Press, 2015).

  59. 59.

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

    CAS  PubMed  Google Scholar 

  60. 60.

    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

  63. 63.

    Rendeiro, A. F. et al. Chromatin accessibility maps of chronic lymphocytic leukaemia identify subtype-specific epigenome signatures and transcription regulatory networks. Nat. Commun. 7, 11938 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Tarasov, A., Vilella, A. J., Cuppen, E., Nijman, I. J. & Prins, P. Sambamba: fast processing of NGS alignment formats. Bioinformatics 31, 2032–2034 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    PubMed  PubMed Central  Google Scholar 

  68. 68.

    Huang, W., Loganantharaj, R., Schroeder, B., Fargo, D. & Li, L. PAVIS: a tool for peak annotation and visualization. Bioinformatics 29, 3097–3099 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    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  Google Scholar 

  70. 70.

    Farlik, M. et al. Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics. Cell Rep. 10, 1386–1397 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Merkel, A. et al. gemBS: high throughput processing for DNA methylation data from bisulfite sequencing. Bioinformatics 35, 737–742 (2018).

    Google Scholar 

  72. 72.

    Assenov, Y. et al. Comprehensive analysis of DNA methylation data with RnBeads. Nat. Methods 11, 1138–1140 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Imrichova, H., Hulselmans, G., Atak, Z. K., Potier, D. & Aerts, S. i-cisTarget 2015 update: generalized cis-regulatory enrichment analysis in human, mouse and fly. Nucleic Acids Res. 43, W57–W64 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Jerby-Arnon, L. et al. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell 175, 984–997.e24 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Lun, A. T., Bach, K. & Marioni, J. C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17, 75 (2016).

    PubMed  Google Scholar 

Download references

Acknowledgements

We thank W.-L. Lo, S.C.-C. Huang and L.-F. Lu for insightful comments. We thank A. Nemc for preparing the WGBS libraries, V. Gernedl for preparing the ATAC–seq libraries and the Biomedical Sequencing Facility at CeMM for assistance with next-generation sequencing. We also thank M. Bosenberg (Yale University) for providing YUMM1.7 melanoma cells, T. Dawson (Johns Hopkins School of Medicine) for providing Park2flox/flox mice, I. Ganley (University of Dundee) for providing Mito-QC mice and R. Ahmed (Emory University) for providing the autophagy reporter. We thank the instrumental support from the EM facility of the biomedical sciences and the ASCEM in Academia Sinica. P.-C.H. was supported in part by SNSF project grants (31003A_163204 and 31003A_182470), the Swiss Institute for Experimental Cancer Research (ISREC 26075483) and a European Research Council (ERC) Starting Grant (802773-MitoGuide). N.V. was supported by the Kristian Gerhard Jebsen Foundation. An Austrian Science Fund (FWF) Special Research Programme grant, New Frontiers Group award of the Austrian Academy of Sciences and ERC Starting Grant (679146) was awarded to C.B. P.-S.L. was supported by the Ministry of Science and Technology grant (MOST-108-2320-B-400-025-MY3) and National Health Research Institute grant (NHRI-CS-108-PP-09). L.T. was supported in part by the Swiss National Science Foundation (project grant 315230_173243) and Swiss Cancer League (grant no. KFS-4600-08-2018). A.Z. was supported by Cancer League Switzerland (KFS-3394-02-2014) and an SNSF project grant (320030_162575). C.J. was supported by the Swiss Cancer League (KFS-3710-08-2015), an SNSF grant (PROOP3_179727) and the Ludwig Institute for Cancer Research.

Author information

Affiliations

Authors

Contributions

Y.-R.Y., N.V. and P.-C.H. designed the research. Y.-R.Y., H.W., T.C., W.-C.C. and M.R.-R. performed in vitro and in vivo experiments. H.I. and C.B. conducted epigenome analyses, and Z.X. and J.W.L. performed computational analysis of single-cell RNA-sequencing. F.F., Y.-F.J. and P.-S.L. performed electron microscopy analyses and confocal microscope analysis. M.G. and L.T. supported the production of NR-loaded alginate hydrogel. C.J. and A.Z. provided human samples. R.G. and G.C. performed human TIL TCR sequencing and analysis. Y.-R.Y., H.W., H.I. and N.V. analyzed the results. Y.-R.Y. and P.-C.H. wrote the manuscript.

Corresponding author

Correspondence to Ping-Chih Ho.

Ethics declarations

Competing interests

P.-C.H. is serving as a member of the scientific advisory board for Elixiron Immunotherapeutics and Acepodia and has received research grants from Elixiron Immunotherapeutics, Roche and Novartis. P.-C.H. received honoraria from Pfizer and Chugai.

Additional information

Peer review information L. A. Dempsey was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 Mitochondrial mass and membrane potential in CD8+ T cells isolated from Braf/Pten melanoma mouse model.

a, Mitochondrial mass and membrane potential of CD8+ T cells isolated from spleens and tumors of Braf/Pten mice seven weeks after tamoxifen administration were examined by MitoTracker Green (MG) and MitoTracker Deep Red (MDR), respectively. b-d, Relative fold changes of mitochondrial mass (b) and membrane potential (c), and the ratio of MDR to MG (d) in CD8+ T cells isolated from indicated tissues of Braf/Pten mice (n = 12 per group). e, Mitochondrial ROS in activated splenic and tumor-infiltrating CD8+ T cells were determined by MitoSOX (n = 12 per group). All data are mean ± s.e.m. and were analyzed by two-tailed, unpaired Student’s t-test. Data are cumulative results from at least three independent experiments. Each symbol represents one individual (b-e). Source data

Extended Data Fig. 2 Examination of mitochondrial phenotypes in CD8+ TILs.

a, Representative electron microscope images of mitochondrial ultrastructure (arrowhead) and autophagosome-like vesicles (arrow) in CD8+ TILs. Scale bar = 500 nm. b, MDR/MGlo populations of CD8+ T cells isolated from indicated tissues of Braf/Pten mice were determined using flow cytometry (n = 12 per group). c, MDR/MGhi (red) and MDR/MGlo (blue) populations were determined using flow cytometry, followed by the quantification of the percentage of MDR/MGlo populations in activated OT-I CD8+ T cells isolated from spleens and tumors of YUMM1.7-OVA melanoma-engrafted mice (Spleen, n = 13; Tumor, n = 10). d, Mitochondrial membrane potential was measured by flow cytometry analysis on MDR intensity with or without Oligomycin A (OA) treatments on sorted MDR/MGhi and MDR/MGlo CD8+ TILs. Quantifications represent the fold changes in MDR intensity after OA treatments on indicated CD8+ TILs (n = 11 per group). All data are mean ± s.e.m. and were analyzed by two-tailed, unpaired Student’s t-test. Data are cumulative results from at least three independent experiments. Each symbol or pair represents one individual (b-d). Source data

Extended Data Fig. 3 Mitochondrial fitness in TILs orchestrates DNA methylation patterns.

a, Heatmaps with normalized DNA methylation beta-values for CpG islands, promoters and genes that are significantly differentially methylated between MDR/MGhi and MDR/MGlo samples. Regions with multiple-testing adjusted p-values less than 0.05 and with a log2 of the quotient of mean DNA methylation levels larger than 0.5 were considered significant. b, Bar plots showing the absolute number of significantly methylated and demethylated regions in MDR/MGhi compared to MDR/MGlo population. c, Genome pie charts reflect the distribution of significantly differentially methylated CpG islands across the genome. The distribution calculated using the PAVIS tool. d, e, Pathways from the NCI-Nature 2016 Pathway database that are significantly enriched among genes assigned to hypomethylated regions in MDR/MGhi (d) or MDR/MGlo (e) with indicated P-value. Enrichment was calculated using the Enrichr tool where the combined score is calculated as c = log(p-value) * z, where c is the combined score, p-value is calculated by Fisher exact test, and z is a z-score for deviation from expected rank. f, Transcription factor (TF) binding motif analysis. The plot is showing expressed TFs whose binding motifs were enriched in significantly hypomethylated regions (CpG islands and promoters) in MDR/MGhi or MDR/MGlo CD8+ TILs. NES, Normalized Enrichment Score. Source data

Extended Data Fig. 4 MDR/MGlo populations of CD8+ T cells in spleens and dLNs.

a, MDR/MGlo populations in activated OT-I and P14 CD8+ T cells isolated from spleens and dLNs either on YUMM1.7-OVA side or YUMM1.7-gp33 side were determined using flow cytometry. Each line indicates paired activated CD8+ T cells from same tissue (Spleen and dLN from OVA side: n = 8 per group; dLN from gp33 side: n = 7 per group). b, MDR/MGlo populations in activated OT-I and OT-3 CD8+ T cells from spleens and dLNs of YUMM1.7-OVA melanoma-engrafted mice were determined using flow cytometry. Each line indicates paired activated CD8+ T cells from same tissue (Spleen, n = 10; dLN, n = 8). c, MDR/MGlo populations in activated WT and PD-1 KO CD8+ T cells from spleens and dLNs were determined using flow cytometry. Each line indicates paired activated CD8+ T cells from same tissue (Spleen, n = 7; dLN, n = 8). All data were analyzed by two-tailed, unpaired Student’s t-test. Source data

Extended Data Fig. 5 Identification of potential factors driving mitochondrial dysfunction in CD8+ T cells.

a, GSEA of indicated signatures from the ranked list of genes differentially expressed in MDR/MGhi CD8+ TILs versus MDR/MGlo CD8+ TILs from YUMM1.7-OVA tumors. b, c, Hypoxia-related signaling in non-exhausted (non-Ex.), progenitor exhausted (Prog. Ex.), partially exhausted (Part. Ex.) and terminally exhausted (Term. Ex.) CD8+ T cells were examined by scRNA-seq analyses on melanoma samples collected from indicated references (b, non-Ex.: n = 253; Prog. Ex.: n = 42; Part. Ex.: n = 135; Term. Ex.: n = 135; c, non-Ex.: n = 441; Prog. Ex.: n = 63; Part. Ex.: n = 198; Term. Ex.: n = 198). d, e, The representative flow plots of mitophagy events (d) and MDR/MGlo population (e) in in vitro-activated OT-I Mito-QC and OT-I CD8+ T cells, respectively, cultured under indicated conditions. f, MDR/MGlo populations in in vitro-activated CD8+ T cells were determined by flow cytometry at indicated time points, followed by the quantifications of the percentage of MDR/MGlo population (n = 3 per group). g, MDR/MGlo populations in in vitro-activated CD8+ T cells treated with or without 50 μM MitoTempo was examined using flow cytometry (n = 3 per group). h, MDR/MGlo populations in in vitro-activated CD8+ T cells treated with indicated compounds were determined by flow cytometry. (O, oligomycin A; A, antimycin A) (n = 12 per group). i, j, MDR/MGlo populations in human in vitro-activated CD8+ T cells cultured either with indicated compounds (i) or under TCM/TCR/Hypoxia condition with or without the supplementation of 10 mM glucose (j) were determined by flow cytometry (n = 9 per group). Box plots display the data distribution though the quartiles with median in the centre and whiskers, which indicates 10 and 90 percentiles. Data are mean ± s.d. and were analyzed by two-tailed, unpaired Student’s t-test (f-j). Data are representative of two independent experiments with similar results (f and g) or are cumulative results from at least three independent experiments (h-j). Source data

Extended Data Fig. 6 Altered chromatin accessibility in in vitro-activated CD8+ T cells with depolarized mitochondria.

a, Distributions of differentially accessible peaks specific to MDR/MGhi or MDR/MGlo CD8+ T cells generated by oligomycin A/Mdivi-1 treatment. b, Bar graphs represent NCI-Nature 2016 pathways that are enriched among genes assigned to differentially accessible peaks in MDR/MGhi and MDR/MGlo CD8+ T cells generated by oligomycin A/Mdivi-1 treatment with indicated adjusted P-value. Enrichment was calculated using the Enrichr tool. c, Representative ATAC-seq tracks at Tcf7 and Lef1 loci from sorted MDR/MGhi or MDR/MGlo CD8+ T cells generated by oligomycin A/Mdivi-1 treatment. Source data

Extended Data Fig. 7 NR administration increases NAD level and improves anti-tumor immunity.

a, NAD concentrations in naïve and activated CD8+ T cells with indicated treatments were measured by colorimetric-based assay (naïve T cells: n = 3; control ACT: n = 4; NA ACT: n = 2; NAM ACT: n = 2; NR ACT: n = 3). b, Cellularity of indicated immune cells in blood collected from chow or NR diet-fed mice (n = 10 per group). c, Tumor growth of MC38-engrafted mice fed with indicated diet plus injection with PBS (Ctrl) or anti-PD-1/anti-CTLA-4 antibodies (ΙCBs) (diet with ctrl, n = 4; diet with ICBs, n = 7). Data are mean ± s.e.m. in a and mean ± s.d. in b, and all data were analyzed by two-tailed, unpaired Student’s t-test. Data are cumulative results from at least two independent experiments. Each symbol represents one individual. Source data

Supplementary information

Reporting Summary

Supplementary Table 1

Patient information.

Supplementary Table 2

Statistics table of WGBS data.

Supplementary Table 3

Significantly and differentially methylated regions in CpGs in promoters and genes.

Supplementary Table 4

Statistics table of in vivo ATAC–seq data.

Supplementary Table 5

Significantly and differentially accessible regions of in vivo ATAC–seq data.

Supplementary Table 6

Statistics table of in vitro ATAC–seq data.

Supplementary Table 7

Significantly and differentially accessible regions of in vitro ATAC–seq data.

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. 6

Statistical source data.

Source Data Fig. 7

Statistical source data.

Source Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

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.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yu, Y., Imrichova, H., Wang, H. et al. Disturbed mitochondrial dynamics in CD8+ TILs reinforce T cell exhaustion. Nat Immunol (2020). https://doi.org/10.1038/s41590-020-0793-3

Download citation

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing