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Disturbed mitochondrial dynamics in CD8+ TILs reinforce T cell exhaustion

Nature Immunology volume 21, pages 1540–1551 (2020)Cite this article

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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.

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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.

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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.

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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

Author notes

  1. These authors contributed equally: Yi-Ru Yu, Hana Imrichova.

Authors and Affiliations

  1. Department of Oncology, University of Lausanne, Lausanne, Switzerland

    Yi-Ru Yu, Haiping Wang, Tung Chao, Marcela Rincon-Restrepo, Fabien Franco, Raphael Genolet, Wan-Chen Cheng, Camilla Jandus, George Coukos, Nicola Vannini & Ping-Chih Ho

  2. Ludwig Institute for Cancer Research, University of Lausanne, Epalinges, Switzerland

    Yi-Ru Yu, Haiping Wang, Tung Chao, Marcela Rincon-Restrepo, Fabien Franco, Raphael Genolet, Wan-Chen Cheng, Camilla Jandus, George Coukos, Nicola Vannini & Ping-Chih Ho

  3. CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria

    Hana Imrichova & Christoph Bock

  4. Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC, USA

    Zhengtao Xiao & Jason W. Locasale

  5. Institute of Bioengineering, Institute of Materials Science and Engineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Min Gao & Li Tang

  6. Graduate Institute of Molecular and Comparative Pathobiology, School of Veterinary Medicine, National Taiwan University, Taipei, Taiwan

    Yi-Fan Jiang

  7. Department of Biomedicine, Laboratory Cancer Immunology, University Hospital and University of Basel, Basel, Switzerland

    Alfred Zippelius

  8. Medical Oncology, University Hospital Basel, Basel, Switzerland

    Alfred Zippelius

  9. Institute of Cellular and System Medicine, National Health Research Institute, Miaoli County, Taiwan

    Pu-Ste Liu

  10. Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria

    Christoph Bock

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  1. Yi-Ru Yu
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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.

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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.

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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.

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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.

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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).

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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.

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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.

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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.

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Yu, YR., Imrichova, H., Wang, H. et al. Disturbed mitochondrial dynamics in CD8+ TILs reinforce T cell exhaustion. Nat Immunol 21, 1540–1551 (2020). https://doi.org/10.1038/s41590-020-0793-3

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