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Proteasome-guided haem signalling axis contributes to T cell exhaustion

Nature volume 653, pages 548–557 (2026) Cite this article

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An Author Correction to this article was published on 07 May 2026

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Abstract

The accumulation of depolarized mitochondria commits T cells to exhaustion1,2,3, yet the precise mechanism remains unclear. Here we find that exhausted CD8+ T cells increase proteasome activity owing to the accumulation of depolarized mitochondria, which drives the selective degradation of mitochondrial proteins and the release of regulatory haem through haemoprotein breakdown. In turn, increased regulatory haem disrupts BACH2-mediated transcriptional regulation, thereby exacerbating T cell exhaustion and compromising stemness-like properties. Inhibition of nuclear import of regulatory haem prevents BACH2 degradation and enhances the anti-tumour efficacy of antigen-specific T cells. We find that the therapeutic efficacy of human CD19+ chimeric antigen receptor (CAR)-T cells in patients with B cell acute lymphoblastic leukaemia negatively correlates with the proteasome gene signature in their CAR-T cells. Manufacturing CAR-T cells in the presence of bortezomib, an FDA-approved proteasome inhibitor, prevents T cell exhaustion and improves therapeutic efficacy. Our findings identify a proteasome-guided haem signalling axis, governed by mitochondrial integrity, as a regulator of CD8+ T cell exhaustion and propose innovative therapeutic strategies that exploit this pathway to optimize adoptive cellular immunotherapy.

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Fig. 1: Proteasome-mediated mitochondrial protein degradation.
The alternative text for this image may have been generated using AI.
Fig. 2: Haem exacerbates terminal differentiation of CD8+ T cells.
The alternative text for this image may have been generated using AI.
Fig. 3: Haem drives terminal exhaustion through the BACH2–BLIMP1 axis.
The alternative text for this image may have been generated using AI.
Fig. 4: Targeting PGRMC2-guided haem redistribution restores T cell anti-tumour responses.
The alternative text for this image may have been generated using AI.
Fig. 5: Modulation of proteasome activity enhances the therapeutic efficacy of CD19+ CAR-T cells.
The alternative text for this image may have been generated using AI.

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Data availability

All data generated and/or analysed in this study are publicly available. Bulk RNA-seq, ATAC-seq and scRNA-seq data from mouse samples have been deposited in the NCBI GEO database under accession numbers GSE312103, GSE312102 and GSE312100, respectively. scRNA-seq data from human patient-derived CAR-T cells and single-cell multi-omics sequencing data from healthy human donor CAR-T cells have been deposited in the GSA-Human database at the China National Center for Bioinformation/ Beijing Institute of Genomics, Chinese Academy of Sciences, under accession numbers HRA012311 and HRA014168, respectively. Source data are provided with this paper.

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Acknowledgements

Y.X. is supported by the National Key Research and Development Program of China (2021YFA1100700), CAMS Innovation Fund for Medical Sciences (2025-I2M-XHXX-139) and the National Natural Science Foundation of China (82570279, 82070170). P.-C. Ho is supported in part by the Swiss Science National Foundation (310030_215126, 310030L_208130, IZLCZ0_206083, CRSII5_205930) and Swiss Science National Foundation Consolidator Grant (TMCG-3_213736), the Cancer Research Institute (Lloyd J. Old STAR award), Helmut Horten Stiftung, Swiss Cancer Foundation and the Melanoma Research Alliance Established Investigator Award. Y.-M.C. is supported by the Ministry of Science and Technology in Taiwan. M.-C.L. is supported by National Taiwan University Hospital. C.S. is supported by the Swiss Cancer League (KLS-4836-08-2019) and the Geneva Cancer League (2106). S.-Y.C. is supported by AS-CDA-110-L09 and AS-GC-110-05. Z.X. is supported by the National Science Foundation of China (32100535). J.G. and S.C. is supported by the Swiss Science National Foundation (CRSII5_205930). M.A. is supported by a Swiss Cancer League grant (KLS-5383-08-2021-R) to A.Z. R.R. was supported by the UK Medical Research Council (MRC; grants MR/S024468/1 and MR/Y013301/1), BBSRC grant BB/X006344/1 and ERC Consolidator Award EP/X024709/1. C.W. is supported by the CAMS Innovation Fund for Medical Sciences (2023-I2M-2-010, 2024 RC310, 2024-I2M-TS-033) and Suzhou Municipal Key Laboratory (SZS2023005).

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Authors and Affiliations

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

    Yingxi Xu  (徐颖茜), Yu-Ming Chuang  (莊育銘), Tzu-Hsuan Chang  (張子軒), Jhan-Jie Peng  (彭湛傑), Josep Garnica, Pei-Chun Hsueh  (薛珮均), Mei-Chun Lin  (林玫君), Yi-Hao Wang  (王翊豪), Karina Lobo Hajdu, Anna Kohl, Santiago Carmona, Yi-Ru Yu  (余奕孺) & Ping-Chih Ho  (何秉智)

  2. Ludwig Institute for Cancer Research Lausanne branch, Lausanne, Switzerland

    Yingxi Xu  (徐颖茜), Yu-Ming Chuang  (莊育銘), Tzu-Hsuan Chang  (張子軒), Jhan-Jie Peng  (彭湛傑), Josep Garnica, Pei-Chun Hsueh  (薛珮均), Mei-Chun Lin  (林玫君), Yi-Hao Wang  (王翊豪), Karina Lobo Hajdu, Anna Kohl, Santiago Carmona, Yi-Ru Yu  (余奕孺) & Ping-Chih Ho  (何秉智)

  3. State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China

    Yingxi Xu  (徐颖茜), Yangtao Shangguan  (上官杨韬), Wenbing Liu  (刘文兵), Leling Xie  (谢乐灵), Min Wang  (王敏) & Jianxiang Wang  (王建祥)

  4. Tianjin Institutes of Health Science, Tianjin, China

    Yingxi Xu  (徐颖茜), Yangtao Shangguan  (上官杨韬), Wenbing Liu  (刘文兵), Leling Xie  (谢乐灵), Min Wang  (王敏) & Jianxiang Wang  (王建祥)

  5. Graduate Institute of Clinical Medical Sciences, Chang Gung University, Taoyuan, Taiwan

    Jhan-Jie Peng  (彭湛傑)

  6. Center for Molecular and Clinical Immunology, Chang Gung University, Taoyuan, Taiwan

    Jhan-Jie Peng  (彭湛傑)

  7. Swiss Institute of Bioinformatics, Lausanne, Switzerland

    Josep Garnica & Santiago Carmona

  8. Department of Otolaryngology, National Taiwan University Hospital, Taipei, Taiwan

    Mei-Chun Lin  (林玫君)

  9. Chemical Biology Mass Spectrometry Platform (CHEMBIOMS), Faculty of Sciences, University of Geneva, Geneva, Switzerland

    Yibo Wu  (伍一博)

  10. Department of Biomedical Sciences, University of Lausanne, Lausanne, Switzerland

    Yibo Wu  (伍一博)

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

    Maryam Akrami & Alfred Zippelius

  12. Department of Pathology and Immunology, Faculty of Medicine, University of Geneva, Geneva, Switzerland

    Chen Wang  (王宸) & Christoph Scheiermann

  13. Geneva Centre for Inflammation Research, Geneva, Switzerland

    Chen Wang  (王宸) & Christoph Scheiermann

  14. National Key Laboratory of Immunity and Inflammation, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou, China

    Chen Wang  (王宸)

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

    Alfred Zippelius

  16. Novogene Co. Ltd, Beijing, China

    Wei Qi  (齐炜)

  17. Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan

    Bugi Ratno Budiarto & Shih-Yu Chen  (陳世淯)

  18. Taiwan International Graduate Program in Molecular Medicine, National Yang Ming Chiao Tung University and Academia Sinica, Taipei, Taiwan

    Bugi Ratno Budiarto

  19. School of Basic Medical Sciences, Xi’an Jiaotong University, Xi’an, China

    Zhengtao Xiao  (肖正涛)

  20. Department of Pathology, University of Cambridge, Cambridge, UK

    Panagiota Vardaka & Rahul Roychoudhuri

  21. Laboratory of Lymphocyte Signaling and Development, Babraham Institute, Cambridge, UK

    Panagiota Vardaka & Rahul Roychoudhuri

  22. Department of Biomedical Engineering, Yale University, New Haven, CT, USA

    Zhiliang Bai  (白志亮) & Rong Fan  (樊荣)

  23. College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan

    Ping-Chih Ho  (何秉智)

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  1. Yingxi Xu  (徐颖茜)
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Contributions

Y.X., Y.-R.Y. and P.-C. Ho designed the study. Y.X., Y.S., L.X., J.-J.P., P.-C. Hsueh, T.-H.C., M.-C.L., Y.-H.W. and K.L.H. performed in vitro and in vivo experiments. Y.-M.C. and Y.S. conducted computational analyses of RNA-seq, ATAC-seq and proteomic data. A.K. initiated the haem measurement assay. Y.S., W.L., J.G., W.Q., B.R.B., S.-Y.C., Z.X., Z.B., R.F., and S.C. performed computational analyses of single-cell RNA-seq data. Y.W. carried out proteomic analyses. M.A. and A.Z. provided human T cell samples with dysfunctional mitochondria. P.V. and R.R. provided the IFNγ promoter-luciferase reporter cell line. C.W. and C.S. provided conceptual advice. J.W. supervised the CD19 CAR-T clinical trial. M.W. and J.W. provided single-cell RNA-seq data from patients treated with CD19 CAR-T cells. Y.X. analysed the data. Y.X. and P.-C. Ho wrote the manuscript.

Corresponding authors

Correspondence to Yingxi Xu  (徐颖茜), Jianxiang Wang  (王建祥) or Ping-Chih Ho  (何秉智).

Ethics declarations

Competing interests

P.-C. Ho is the co-founder of Pilatus Biosciences. A.Z. received consulting/advisor fees from Bristol-Myers Squibb, Merck Sharp & Dohme, Hoffmann–La Roche, NBE Therapeutics and Engimmune, and maintains further non-commercial research agreements with Hoffmann–La Roche, T3 Pharma, Bright Peak Therapeutics and AstraZeneca. R.R. holds or has held paid consultancies with Lyell Immunopharma, Achilles Therapeutics and Enhanc3D Genomics; and is a principal investigator of research projects funded by AstraZeneca and F-star Therapeutics on unrelated topics that do not constitute competing interests. The other authors declare no competing interests.

Peer review

Peer review information

Nature thanks Navdeep Chandel, Juan Cubillos-Ruiz, Christina Zielinski and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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

Extended data figures and tables

Extended Data Fig. 1 Terminally exhausted T cells engage proteasome-mediated degradation of mitochondrial proteins.

a,b, Enriched MSigDB gene sets in TTEx versus non-TEx melanoma TILs; proteasome-related genes are highlighted in red. c, Proteasome score of non-TEx, TPEx and TEx cells from the indicated datasets. d, Workflow for proteomic analysis of the indicated human T cell subsets. Created in BioRender Xu, Y. (2026), https://BioRender.com/68bs810. e, Fold change of proteasome subunits in TPEx, TTEx and Tmem relative to Tn, based on proteomics in (d). f, Schematic of mCherry-degron reporters. Created in BioRender Xu, Y. (2026), https://BioRender.com/9jlksue. g, Confocal images showing Cyto-mCherry-degron and Mito-mCherry-degron localization in CD8+ T cells. Scale bars, 10 μM. h, Difference in mCherry MFI between TCF1-eGFP+PD-1+TIM3− (TPEx) and TCF1-eGFP−PD-1+TIM3+ (TTEx) TILs expressing Mito-degron (left) or Cyto-degron (right) reporters (Mito-degron, n = 16; Cyto-degron, n = 15). i, j, KEGG pathway enrichment of upregulated (i) or downregulated (j) proteins in TTEx versus TE from MG132 treated cells after ubiquitinated-protein enrichment. Each symbol represents one individual. Data are means ± s.d. and were analysed by Wilcoxon rank-sum test (c) or two-tailed, unpaired Student’s t test (h). MSigDB, Molecular Signatures Database; TIL, tumour-infiltrating lymphocyte; non-TEx, non-exhausted T; TTEx, Terminal exhausted T; TPEx, Progenitor exhausted T; TEx, Exhausted T. Tn, naïve T; Tm, memory T, Teff, effector T, CHX, cycloheximide.

Source data

Extended Data Fig. 2 Mitochondrial depolarization induces CBL-B–dependent mitochondrial protein degradation.

a, PCA of proteomes from the indicated groups. b, Heatmap of proteasome subunits differentially expressed between MDR/MGhigh and MDR/MGlow T cells. c, Pathway enrichment of proteins upregulated in MDR/MGlow vs MDR/MGhigh P14 TILs, based on GO Biological Process gene sets. d, Relative degradation of Cyto-degron or Mito-degron in MDR/MGlow vs MDR/MGhigh (n = 3; except cyto-degron at 2 h, n = 2). e, Schematic of regulatory haem quantification. Created in BioRender Xu, Y. (2026), https://BioRender.com/ryadgvd. f, Relative RH concentration in MDR/MGhigh and MDR/MGlow subsets from hypoxia tumour-conditioned stimulation (n = 4). g, RH concentration in MDR/MGhigh and MDR/MGlow after metabolic perturbation with indicated concentration of MG132 treatment (MDR/MGhigh, 0 μM, n = 3; 0.1 μM, n = 8; 0.2 μM, n = 8; 0.3 μM, n = 12; 0.4 μM, n = 6; MDR/MGlow, 0 μM, n = 3; 0.1 μM, n = 6; 0.2 μM, n = 6; 0.3 μM, n = 6; 0.4 μM, n = 3). h, Regulatory haem in MDR/MGhigh and MDR/MGlow subsets from human CD8+ T cells expressing an NY-ESO-1-specific TCR following repeated antigen stimulation (n = 4). i, Regulatory haem in MDR/MGhigh and MDR/MGlow TILs isolated from YUMM1.7-gp33 melanomas (three mice pooled per sample; MDR/MGhigh, n = 10; MDR/MGlow, n = 9). j, PCA of proteomes from the indicated groups. k, Cyto-degron-mCherry MFI differences in CBLB-deficient MDR/MGlow human T cells following cycloheximide treatment (n = 13). l. Regulatory haem concentrations in CBLB-deficient MDR/MGhigh human T cells under hypoxic tumour-conditioned stimulation (n = 5). Data are representative of two independent experiments with similar results (d) or pooled from two (g, h), three (i) or four (f, k, l) independent experiments. Each symbol represents one individual. Data are means ± s.d. and were analysed by Wilcoxon signed-rank test (f,l), simple linear regression (d, g), paired two-tailed Student’s t-test (h), or unpaired two-tailed Student’s t-test (i,k). PCA, Principal component analysis; TIL, tumour-infiltrating lymphocyte; RH, regulatory haem; MDR, MitoTracker Deep Red; MG, MitoTracker Green; TPEx, Progenitor exhausted T; TIEx, Intermediate exhausted T; TTEx, Terminal exhausted T.

Source data

Extended Data Fig. 3 Haem released due to the mitochondrial protein degradation instructs terminal differentiation.

a, Protein abundance of BACH2, BLIMP1, PI3K and mTOR in MDR/MGhigh and MDR/MGlow subsets treated with Oligomycin A, Antimycin A, and Mdivi-1 along with indicated concentration of MG132 (n = 3, except BACH2, 0.2 μM, n = 2; 0.4 μM, n = 2). b, Relative RH concentration in TCF1-eGFP+PD-1+TIM3− (TPEx), TCF1-eGFP−PD-1+TIM3+ (TIEx) and TCF1-eGFP−PD-1+TIM3+ (TTEx) P14 TILs (n = 5). c, RH concentration in indicated human T cell subsets (Tn, n = 6; Teff, n = 10; TPEx, n = 8; TTEx, n = 7; Tmem, n = 8). d, Relative RH concentration in TIF and paired serum from tumour-bearing mice (n = 6). e, Relative RH concentration in repetitively stimulated CD8+ T cells treated with indicated haemin concentration (n = 8). f, Cell expansion of repetitively stimulated CD8+ T cells treated with indicated haemin concentrations (n = 3). g-l, Representative flow plots of CTV (g), Nurr77-GFP (i) and CD44 (k) fluorescence intensity and the quantitative results of CTV (h), Nurr77-GFP (j) and CD44 (l) in repetitively stimulated CD8+ T cells treated with haemin (n = 3). m, PCA of bulk RNA-seq profiles from the indicated groups (n = 5). n, GO Biological Process gene set enrichment analysis of bulk RNA-seq from haemin-treated versus control cells. o, PCA of bulk ATAC-seq profiles from the indicated groups (n = 4). p-r, Representative flow plots of PD-1 (p), TCF-1 (q) and cytokine production (r) in expanded P14 cells from LCMV Armstrong-rechallenge. Data are representative of two (b, c) or three (f, h, j, l) independent experiments with similar results or are the cumulative results from three (a, d, e) independent experiments. Each symbol represents one individual. Data are means ± s.d. and were analysed by Wilcoxon signed-rank test (d), simple linear regression (a), or One-way ANOVA (b, c, e, f, h, j, l). TIF, tumour interstitial fluid; TPEx, Progenitor exhausted T; TIEx, Intermediate exhausted T; TTEx, Terminal exhausted T.

Source data

Extended Data Fig. 4 Haem drives terminal differentiation of CD8+ T cells via the BACH2-BLIMP1 axis.

a-d, Representative flow plots (a, c) and quantitative results (b, d) of BACH2 and BLIMP1 in MDR/MGlow and MDR/MGhigh subsets under treatment with Oligomycin A, Antimycin A, and Mdivi-1 (BACH2, n = 4; BLIMP1, n = 3). e-h, BACH2 (e, f) and BLIMP1 (g, h) expression in TPEx and TTEx P14 TILs (BACH2, n = 20; BLIMP1, n = 19). i, Regulatory haem concentration in normal FBS and haem-depleted FBS (n = 3). j, Luminescence of the indicated Jurkat reporter cells cultured in normal or haem-depleted FBS under PMA and Ionomycin stimulation (n = 3). k, Western blot plots of HA-tagged BACH2 and β-actin in the indicated Jurkat reporter cells cultured in normal or haem-depleted FBS. HA-tagged BACH2 and β-actin were run on the same gel as loading controls. For gel source data, see Supplementary Fig. 1. l, Representative flow plots show BLIMP1 in Bach2WT or Bach2MUT overexpressed- Bach2CKO P14 cells treated with or without haemin under repetitive stimulation. m,n, Representative flow plots of TCF-1+PD-1+TIM3− cells (TPEx), TCF-1+PD-1+TIM3+ cells (TIEx), and TCF-1−PD-1+TIM3+ cells (TTEx) in tumours (m) and TCF-1+TOX− cells (TTSM) and TCF-1+TOX+ cells (TPEx) in dLN (n). o, Representative flow plots of BLIMP1 in Bach2WT or Bach2MUT P14 cells in tumours and dLNs. p, Representative flow plots of MDR and MG staining in Bach2WT or Bach2MUT P14 TILs. q, Average expression of subset markers across P14 TIL clusters and ProjecTILs reference datasets used for annotation. Data are representative of two (j, k) or three (i) independent experiments with similar results or are the cumulative results from three (b, d, f, h) independent experiments. Each symbol represents one individual. Data are means ± s.d. and were analysed by two-tailed, unpaired student’s t test (b, d, f, h, i) or two-way ANOVA (j). TIL, tumour-infiltrating lymphocyte; dLN, draining lymph node; CKO, conditional knockout; TPEx, Progenitor exhausted T; TIEx, Intermediate exhausted T; TTEx, Terminal exhausted T; TTSM, tumour specific memory T.

Source data

Extended Data Fig. 5 Blocking nuclear translocation of haem sustains CD8+ T cell effector functions.

a, b, Representative flow plots of IFNγ and TNF production by Pgrmc2+/+ and Pgrmc2CKO P14 T cells in tumours (a) and dLN (b). c, The quantification of IFNγ+TNF+ production in a, b (n = 11). d-g, Representative flow plots (d, f) and quantitative results (e, g) of BACH2 and BLIMP1 expression in Pgrmc2+/+ and Pgrmc2CKO P14 TILs (n = 11). h, Tumour growth of YUMM1.7-gp33 melanoma-engrafted mice treated with Pgrmc2+/+ (left) or Pgrmc2CKO (right) P14 T cells (n = 6). Black dotted lines indicate a tumour volume of 500 mm3. i, Tumour growth of YUMM1.7-HER2 melanoma-engrafted mice treated with Pgrmc2+/+ (left) or Pgrmc2CKO (right) HER2 CAR T-cells (n = 14). Black dotted lines indicate a tumour volume of 500 mm3. j, k, Representative flow plots (j) and quantitative results (k) of MDR and MG staining in co-transferred Pgrmc2+/+ and Pgrmc2CKO P14 TILs (n = 8). Data are pooled from two (c, e, g-i, k) independent experiments. Each symbol represents one individual. Data are means ± s.d. and were analysed by two-way ANOVA (c), Wilcoxon signed-rank test (e, g) or two-tailed, paired Student’s t-test (k). CAR, chimeric antigen receptor; TIL, tumour-infiltrating lymphocyte; dLN, draining lymph node; CKO, conditional knockout.

Source data

Extended Data Fig. 6 Modulation of proteasome activity enhances therapeutic efficacy of CD19 CAR T-cells.

a, Workflow for sorting of CAR-T cells at Day 9 and Day 21 after anti-CD19 CAR-T infusion. Created in BioRender Xu, Y. (2026), https://BioRender.com/m0gieu5. b, Gating strategy for CAR T-cell sorting. c, Marker genes expression across CAR T clusters. d, Module scores for memory (upper left), cytotoxicity (upper right), proliferation (bottom left), and exhaustion (bottom right) across CAR T clusters. e, Pseudotime trajectory inferred for the indicated clusters. f, PD-1 (left), TIM3 (middle) or CD39 (right) expression after low-dose Bortezomib pretreatment followed by repeated Nalm6 rechallenge (n = 5). g-k, Single cell Multi-omics analysis of CAR T-cells pretreated with Bortezomib (pre_Bort) or vehicle (pre_Ctrl) and after Nalm6 co-culture (co-cultured_Bort): marker gene expression (g), module scores (h), different gene expression (i, j) and differential chromatin accessibility at promoters (k). Data are pooled from five (f) independent experiments. Each symbol represents one individual. Data are means ± s.d. and were analysed by two-tailed, paired student’s t test (f), Wilcoxon rank-sum test (d, h). Ctrl, Control; Bort, Bortezomib; CAR, chimeric antigen receptor.

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Supplementary information

Supplementary Fig. 1 (download PDF )

Uncropped immunoblots from the Extended Data Figures. a, Immunoblots for Extended Data Fig. 4k of HA–BACH2 and β-actin in Jurkat cells.

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Supplementary Tables 1–9.

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Xu, Y., Shangguan, Y., Chuang, YM. et al. Proteasome-guided haem signalling axis contributes to T cell exhaustion. Nature 653, 548–557 (2026). https://doi.org/10.1038/s41586-026-10250-y

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