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.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
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
Li, X. et al. Navigating metabolic pathways to enhance antitumour immunity and immunotherapy. Nat. Rev. Clin. Oncol. 16, 425–441 (2019).
Hanahan, D. & Coussens, L. M. Accessories to the crime: functions of cells recruited to the tumor microenvironment. Cancer Cell 21, 309–322 (2012).
Ho, P. C. et al. Phosphoenolpyruvate is a metabolic checkpoint of anti-tumor T cell responses. Cell 162, 1217–1228 (2015).
Chang, C. H. et al. Metabolic competition in the tumor microenvironment is a driver of cancer progression. Cell 162, 1229–1241 (2015).
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).
Pauken, K. E. et al. Epigenetic stability of exhausted T cells limits durability of reinvigoration by PD-1 blockade. Science 354, 1160–1165 (2016).
Sun, N., Youle, R. J. & Finkel, T. The mitochondrial basis of aging. Mol. Cell 61, 654–666 (2016).
Wiley, C. D. et al. Mitochondrial dysfunction induces senescence with a distinct secretory phenotype. Cell Metab. 23, 303–314 (2016).
Quiros, P. M., Mottis, A. & Auwerx, J. Mitonuclear communication in homeostasis and stress. Nat. Rev. Mol. Cell Biol. 17, 213–226 (2016).
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).
Buck, M. D. et al. Mitochondrial dynamics controls T cell fate through metabolic programming. Cell 166, 63–76 (2016).
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).
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).
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).
Baitsch, L. et al. Exhaustion of tumor-specific CD8+ T cells in metastases from melanoma patients. J. Clin. Invest. 121, 2350–2360 (2011).
Wherry, E. J. & Kurachi, M. Molecular and cellular insights into T cell exhaustion. Nat. Rev. Immunol. 15, 486–499 (2015).
Alfei, F. & Zehn, D. T cell exhaustion: an epigenetically imprinted phenotypic and functional makeover. Trends Mol. Med. 23, 769–771 (2017).
Utzschneider, D. T. et al. T cells maintain an exhausted phenotype after antigen withdrawal and population reexpansion. Nat. Immunol. 14, 603–610 (2013).
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).
Sen, D. R. et al. The epigenetic landscape of T cell exhaustion. Science 354, 1165–1169 (2016).
Philip, M. et al. Chromatin states define tumour-specific T cell dysfunction and reprogramming. Nature 545, 452–456 (2017).
Ghoneim, H. E. et al. De novo epigenetic programs inhibit PD-1 blockade-mediated T cell rejuvenation. Cell 170, 142–157.e19 (2017).
Mognol, G. P. et al. Exhaustion-associated regulatory regions in CD8+ tumor-infiltrating T cells. Proc. Natl Acad. Sci. USA 114, E2776–E2785 (2017).
Chen, Z. et al. TCF-1-centered transcriptional network drives an effector versus exhausted CD8 T cell-fate decision. Immunity 51, 840–855 (2019).
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).
Im, S. J. et al. Defining CD8+ T cells that provide the proliferative burst after PD-1 therapy. Nature 537, 417–421 (2016).
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).
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).
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).
Dankort, D. et al. Braf(V600E) cooperates with Pten loss to induce metastatic melanoma. Nat. Genet. 41, 544–552 (2009).
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).
Palikaras, K., Lionaki, E. & Tavernarakis, N. Mechanisms of mitophagy in cellular homeostasis, physiology and pathology. Nat. Cell Biol. 20, 1013–1022 (2018).
Zorova, L. D. et al. Mitochondrial membrane potential. Anal. Biochem. 552, 50–59 (2018).
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).
McWilliams, T. G. et al. mito-QC illuminates mitophagy and mitochondrial architecture in vivo. J. Cell Biol. 214, 333–345 (2016).
Xu, X. et al. Autophagy is essential for effector CD8+ T cell survival and memory formation. Nat. Immunol. 15, 1152–1161 (2014).
Song, M. et al. IRE1α–XBP1 controls T cell function in ovarian cancer by regulating mitochondrial activity. Nature 562, 423–428 (2018).
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).
Gattinoni, L. et al. Wnt signaling arrests effector T cell differentiation and generates CD8+ memory stem cells. Nat. Med. 15, 808–813 (2009).
Barili, V. et al. Targeting p53 and histone methyltransferases restores exhausted CD8+ T cells in HCV infection. Nat. Commun. 11, 604 (2020).
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).
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).
Deaton, A. M. & Bird, A. CpG islands and the regulation of transcription. Genes Dev. 25, 1010–1022 (2011).
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).
Li, H. et al. Dysfunctional CD8 T cells form a proliferative, dynamically regulated compartment within human melanoma. Cell 176, 775–789.e18 (2019).
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).
Xiao, Z., Dai, Z. & Locasale, J. W. Metabolic landscape of the tumor microenvironment at single cell resolution. Nat. Commun. 10, 3763 (2019).
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).
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).
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).
Shin, J. H. et al. PARIS (ZNF746) repression of PGC-1α contributes to neurodegeneration in Parkinson’s disease. Cell 144, 689–702 (2011).
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).
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).
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).
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).
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).
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).
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).
Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
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).
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).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Tarasov, A., Vilella, A. J., Cuppen, E., Nijman, I. J. & Prins, P. Sambamba: fast processing of NGS alignment formats. Bioinformatics 31, 2032–2034 (2015).
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).
Huang, W., Loganantharaj, R., Schroeder, B., Fargo, D. & Li, L. PAVIS: a tool for peak annotation and visualization. Bioinformatics 29, 3097–3099 (2013).
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).
Farlik, M. et al. Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics. Cell Rep. 10, 1386–1397 (2015).
Merkel, A. et al. gemBS: high throughput processing for DNA methylation data from bisulfite sequencing. Bioinformatics 35, 737–742 (2018).
Assenov, Y. et al. Comprehensive analysis of DNA methylation data with RnBeads. Nat. Methods 11, 1138–1140 (2014).
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).
Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).
Jerby-Arnon, L. et al. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell 175, 984–997.e24 (2018).
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).
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
Authors and Affiliations
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
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).
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).
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.
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.
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).
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.
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.
Supplementary information
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
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41590-020-0793-3
This article is cited by
-
Metabolic reprogramming in the tumor microenvironment of liver cancer
Journal of Hematology & Oncology (2024)
-
1-Pyrroline-5-carboxylate inhibit T cell glycolysis in prostate cancer microenvironment by SHP1/PKM2/LDHB axis
Cell Communication and Signaling (2024)
-
Bioorthogonal photocatalytic proximity labeling in primary living samples
Nature Communications (2024)
-
Metabolically enhanced CAR T cells efficiently clear solid tumors in mice
Nature Biotechnology (2024)
-
Take my breath away—mitochondrial dysfunction drives CD8+ T cell exhaustion
Genes & Immunity (2024)