Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Cancer suppresses mitochondrial chaperone activity in macrophages to drive immune evasion

Abstract

Contrary to tumor-infiltrating T cells with dysfunctional mitochondria, tumor-associated macrophages (TAMs) preserve their mitochondrial activity in the nutrient-limited tumor microenvironment (TME) to sustain immunosuppression. Here we identify TNF receptor-associated protein-1 (TRAP1), a mitochondrial HSP90 chaperone, as a metabolic checkpoint that restrains oxidative respiration and limits macrophage suppressive function. In the TME, TRAP1 is downregulated through TIM4–AMPK signaling, and its loss enhances immunoinhibitory activity, limits proinflammatory capacity and promotes tumor immune escape. Mechanistically, TRAP1 suppression augments electron transport chain activity and elevates the α-ketoglutarate/succinate ratio, remodeling mitochondrial homeostasis. The resulting accumulation of α-ketoglutarate further potentiates JMJD3-mediated histone demethylation, establishing transcriptional programs that reinforce an immunosuppressive state. Restoring TRAP1 by targeting TIM4 and JMJD3 reprograms TAMs, disrupts the immune-evasive TME and bolsters antitumor immunity. These findings establish TRAP1 as a critical regulator integrating metabolic and epigenetic control of suppressive TAM function and position the TRAP1 pathway as a promising target for cancer immunotherapy.

This is a preview of subscription content, access via your institution

Access options

Buy this article

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: TRAP1 expression negatively correlates with macrophage immunosuppression in human and mouse tumors.
Fig. 2: TRAP1 deficiency enhances immunosuppressive activity in TAMs and promotes tumor escape.
Fig. 3: TIM4–AMPKα signaling axis controls TRAP1 expression in TAMs.
Fig. 4: AMPKα signaling suppresses TRAP1 expression through HSF1 inactivation.
Fig. 5: TRAP1 deficiency enhances mitochondrial respiration and function in immunosuppressive macrophages.
Fig. 6: TRAP1 deficiency reprograms glutaminolysis and α-KG metabolism.
Fig. 7: TRAP1-mediated immunosuppressive effect hinges on JMJD3 histone demethylase function in tumor macrophages.
Fig. 8: Therapeutic inhibition of TIM4 and JMJD3 signaling curtails tumorigenesis.

Similar content being viewed by others

Data availability

RNA-seq and CUT&Tag data are available in the NCBI database under accession numbers GSE279754 and PRJNA1172334, respectively. Source data are provided with this paper.

References

  1. Bailis, W. et al. Distinct modes of mitochondrial metabolism uncouple T cell differentiation and function. Nature 571, 403–407 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Mills, E. L. et al. Succinate dehydrogenase supports metabolic repurposing of mitochondria to drive inflammatory macrophages. Cell 167, 457–470 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Leone, R. D. & Powell, J. D. Metabolism of immune cells in cancer. Nat. Rev. Cancer 20, 516–531 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Buck, M. D., Sowell, R. T., Kaech, S. M. & Pearce, E. L. Metabolic instruction of immunity. Cell 169, 570–586 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  6. Vardhana, S. A. et al. Impaired mitochondrial oxidative phosphorylation limits the self-renewal of T cells exposed to persistent antigen. Nat. Immunol. 21, 1022–1033 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Yu, Y.-R. et al. Disturbed mitochondrial dynamics in CD8+ TILs reinforce T cell exhaustion. Nat. Immunol. 21, 1540–1551 (2020).

    Article  PubMed  CAS  Google Scholar 

  8. 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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Geeraerts, X. et al. Macrophages are metabolically heterogeneous within the tumor microenvironment. Cell Rep. 37, 110171 (2021).

    Article  PubMed  CAS  Google Scholar 

  10. Raines, L. N. et al. PERK is a critical metabolic hub for immunosuppressive function in macrophages. Nat. Immunol. 23, 431–445 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Liu, P.-S. et al. α-Ketoglutarate orchestrates macrophage activation through metabolic and epigenetic reprogramming. Nat. Immunol. 18, 985–994 (2017).

    Article  PubMed  CAS  Google Scholar 

  12. Martínez-Reyes, I. & Chandel, N. S. Mitochondrial TCA cycle metabolites control physiology and disease. Nat. Commun. 11, 102 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Yoshida, S. et al. Molecular chaperone TRAP1 regulates a metabolic switch between mitochondrial respiration and aerobic glycolysis. Proc. Natl Acad. Sci. USA 110, E1604–E1612 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Matassa, D. S., Agliarulo, I., Avolio, R., Landriscina, M. & Esposito, F. TRAP1 regulation of cancer metabolism: dual role as oncogene or tumor suppressor. Genes 9, 195 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Xie, S. et al. The mitochondrial chaperone TRAP1 as a candidate target of oncotherapy. Front. Oncol. 10, 585047 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Miyanishi, M. et al. Identification of TIM4 as a phosphatidylserine receptor. Nature 450, 435–439 (2007).

    Article  PubMed  CAS  Google Scholar 

  17. Baghdadi, M. et al. TIM-4 glycoprotein-mediated degradation of dying tumor cells by autophagy leads to reduced antigen presentation and increased immune tolerance. Immunity 39, 1070–1081 (2013).

    Article  PubMed  CAS  Google Scholar 

  18. Kang, B. H. et al. Regulation of tumor cell mitochondrial homeostasis by an organelle-specific HSP90 chaperone network. Cell 131, 257–270 (2007).

    Article  PubMed  CAS  Google Scholar 

  19. Li, T. et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 77, e108–e110 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Li, B. et al. Comprehensive analyses of tumor immunity: implications for cancer immunotherapy. Genome Biol. 17, 174 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Huang, S. C.-C. et al. Cell-intrinsic lysosomal lipolysis is essential for alternative activation of macrophages. Nat. Immunol. 15, 846–855 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Abram, C. L., Roberge, G. L., Hu, Y. & Lowell, C. A. Comparative analysis of the efficiency and specificity of myeloid-Cre deleting strains using ROSA–EYFP reporter mice. J. Immunol. Methods 408, 89–100 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Chen, Y. et al. Tumor-associated macrophages: an accomplice in solid tumor progression. J. Biomed. Sci. 26, 78 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Huang, S. C.-C. et al. Metabolic reprogramming mediated by the mTORC2–IRF4 signaling axis is essential for macrophage alternative activation. Immunity 45, 817–830 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. O’Neill, L. A. J. & Hardie, D. G. Metabolism of inflammation limited by AMPK and pseudo-starvation. Nature 493, 346–355 (2013).

    Article  PubMed  Google Scholar 

  26. Herzig, S. & Shaw, R. J. AMPK: guardian of metabolism and mitochondrial homeostasis. Nat. Rev. Mol. Cell Biol. 19, 121–135 (2018).

    Article  PubMed  CAS  Google Scholar 

  27. Sag, D., Carling, D., Stout, R. D. & Suttles, J. Adenosine 5′-monophosphate-activated protein kinase promotes macrophage polarization to an anti-inflammatory functional phenotype. J. Immunol. 181, 8633–8641 (2008).

    Article  PubMed  CAS  Google Scholar 

  28. Mounier, R. et al. AMPKα1 regulates macrophage skewing at the time of resolution of inflammation during skeletal muscle regeneration. Cell Metab. 18, 251–264 (2013).

    Article  PubMed  CAS  Google Scholar 

  29. Trillo-Tinoco, J. et al. AMPKα-1 intrinsically regulates the function and differentiation of tumor myeloid-derived suppressor cells. Cancer Res. 79, 5034–5047 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Chow, A. et al. TIM-4+ cavity-resident macrophages impair anti-tumor CD8+ T cell immunity. Cancer Cell 39, 973–988 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Liu, W. et al. TIM-4 inhibits NLRP3 inflammasome via the LKB1/AMPKα pathway in macrophages. J. Immunol. 203, 990–1000 (2019).

    Article  PubMed  CAS  Google Scholar 

  32. Xia, H. et al. Autophagic adaptation to oxidative stress alters peritoneal residential macrophage survival and ovarian cancer metastasis. JCI Insight 5, e141115 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Budhu, S. et al. Targeting phosphatidylserine enhances the anti-tumor response to tumor-directed radiation therapy in a preclinical model of melanoma. Cell Rep. 34, 108620 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Dekker, F. A. & Rüdiger, S. G. D. The mitochondrial HSP90 TRAP1 and Alzheimer’s disease. Front. Mol. Biosci. 8, 697913 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Kmiecik, S. W. & Mayer, M. P. Molecular mechanisms of heat shock factor 1 regulation. Trends Biochem. Sci. 47, 218–234 (2022).

    Article  PubMed  CAS  Google Scholar 

  36. Dai, S. et al. Suppression of the HSF1-mediated proteotoxic stress response by the metabolic stress sensor AMPK. EMBO J. 34, 275–293 (2015).

    Article  PubMed  CAS  Google Scholar 

  37. Lavin, Y. et al. Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses. Cell 169, 750–765 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Zhang, Q. et al. Landscape and dynamics of single immune cells in hepatocellular carcinoma. Cell 179, 829–845 (2019).

    Article  PubMed  CAS  Google Scholar 

  39. Argüello, R. J. et al. SCENITH: a flow cytometry-based method to functionally profile energy metabolism with single-cell resolution. Cell Metab. 32, 1063–1075 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Kühlbrandt, W. Structure and function of mitochondrial membrane protein complexes. BMC Biol. 13, 89 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Jha, A. K. et al. Network integration of parallel metabolic and transcriptional data reveals metabolic modules that regulate macrophage polarization. Immunity 42, 419–430 (2015).

    Article  PubMed  CAS  Google Scholar 

  42. Joshi, A. et al. The mitochondrial HSP90 paralog TRAP1 forms an OXPHOS-regulated tetramer and is involved in mitochondrial metabolic homeostasis. BMC Biol. 18, 10 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Choi, J., Stradmann-Bellinghausen, B., Yakubov, E., Savaskan, N. E. & Régnier-Vigouroux, A. Glioblastoma cells induce differential glutamatergic gene expressions in human tumor-associated microglia/macrophages and monocyte-derived macrophages. Cancer Biol. Ther. 16, 1205–1213 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Sciacovelli, M. et al. The mitochondrial chaperone TRAP1 promotes neoplastic growth by inhibiting succinate dehydrogenase. Cell Metab. 17, 988–999 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Robinson, M. M. et al. Novel mechanism of inhibition of rat kidney-type glutaminase by bis-2-(5-phenylacetamido-1,2,4-thiadiazol-2-yl)ethyl sulfide (BPTES). Biochem. J. 406, 407–414 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Satoh, T. et al. The JMJD3–IRF4 axis regulates M2 macrophage polarization and host responses against helminth infection. Nat. Immunol. 11, 936–944 (2010).

    Article  PubMed  CAS  Google Scholar 

  47. Egan, B. et al. An alternative approach to ChIP–seq normalization enables detection of genome-wide changes in histone H3 lysine 27 trimethylation upon EZH2 inhibition. PLoS ONE 11, e0166438 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Liao, Y. et al. Inhibition of EZH2 transactivation function sensitizes solid tumors to genotoxic stress. Proc. Natl Acad. Sci. USA 119, e2105898119 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Kruidenier, L. et al. A selective jumonji H3K27 demethylase inhibitor modulates the proinflammatory macrophage response. Nature 488, 404–408 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Mehner, C. et al. Tumor cell-produced matrix metalloproteinase 9 (MMP-9) drives malignant progression and metastasis of basal-like triple negative breast cancer. Oncotarget 5, 2736–2749 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Pickup, M., Novitskiy, S. & Moses, H. L. The roles of TGFβ in the tumour microenvironment. Nat. Rev. Cancer 13, 788–799 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Coffelt, S. B. et al. IL-17-producing γδ T cells and neutrophils conspire to promote breast cancer metastasis. Nature 522, 345–348 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Lampropoulou, V. et al. Itaconate links inhibition of succinate dehydrogenase with macrophage metabolic remodeling and regulation of inflammation. Cell Metab. 24, 158–166 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Oh, M.-H. et al. Targeting glutamine metabolism enhances tumor-specific immunity by modulating suppressive myeloid cells. J. Clin. Invest. 130, 3865–3884 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Leone, R. D. et al. Glutamine blockade induces divergent metabolic programs to overcome tumor immune evasion. Science 366, 1013–1021 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Pan, W. et al. The DNA methylcytosine dioxygenase TET2 sustains immunosuppressive function of tumor-infiltrating myeloid cells to promote melanoma progression. Immunity 47, 284–297 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Yang, K. & Chi, H. AMPK helps T cells survive nutrient starvation. Immunity 42, 4–6 (2015).

    Article  PubMed  Google Scholar 

  58. Choudhury, A. et al. Inhibition of HSP90 and activation of HSF1 diminish macrophage NLRP3 inflammasome activity in alcohol-associated liver injury. Alcohol. Clin. Exp. Res. 44, 1300–1311 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Takii, R. et al. Heat shock transcription factor 1 inhibits expression of IL-6 through activating transcription factor 3. J. Immunol. 184, 1041–1048 (2009).

    Article  PubMed  Google Scholar 

  60. Birge, R. B. et al. Phosphatidylserine is a global immunosuppressive signal in efferocytosis, infectious disease, and cancer. Cell Death Differ. 23, 962–978 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Di Conza, G. et al. Tumor-induced reshuffling of lipid composition on the endoplasmic reticulum membrane sustains macrophage survival and pro-tumorigenic activity. Nat. Immunol. 22, 1403–1415 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to thank A. Chow and T. Merghoub for providing Timd4−/− mice. We thank H. Fujioka from the Cleveland Center for Membrane and Structural Biology for expert technical assistance. H.Z. was supported by the VeloSano Catalyst Pilot Funds (RES600351) and the Ohio State University Professional Development Award. J.P. was supported by the National Research Foundation of Korea (NRF-2021R1A6A3A14039520). L.N.R. was supported by the Immunology T32 Training Program (AI089474). Y.-J.C. was supported by National Science and Technology Council, Taiwan (114-2917-I-564-005). H.-Y.C. was supported by the Pelotonia Scholars Program. A.Y.H. is supported by the St. Baldrick’s Foundation, Alex’s Lemonade Stand Foundation for Childhood Cancer, Children’s Cancer Research Fund, the I’m Not Done Yet Foundation, Sam Day Foundation, Hyundai Hope-on-Wheels Program, MIB Agents and the Theresia G. and Stuart F. Kline Family Foundation. P.-C.H. is supported, in part, by the European Research Council Staring Grant (802773-MitoGuide), the Helmut Horten Stifung and Melanoma Research Alliance Established Investigator Award. C.-W.J.L. is funded by an NIH National Cancer Institute K22 award (K22CA241290) and startup funds from the Department of Microbial Infection and Immunity and Pelotonia Institute of Immuno-Oncology at the Ohio State University. S.C.-C.H. is supported by the American Cancer Society Research Scholar Grant (RSG-22-135-01-IBCD), OSUCCC Center for Cancer Metabolism Seed Grant Award (AWD-120006), PIIO-Priority Research Voucher Program Award, Melanoma Research Foundation Career Development Award, Andrew McDonough B+ Foundation Grant Award, Cancer Research Institute CLIP Investigator Award and funds from the Department of Microbial Infection and Immunity and Pelotonia Institute of Immuno-Oncology at the Ohio State University.

Author information

Authors and Affiliations

Authors

Contributions

H.Z. and S.C.-C.H. conceived the study. H.Z., J.P., Y.-J.C., L.L., L.N.R., M.H., C.-C.L., H.-Y.C, W.C. and Y.O. performed the experiments. A.Y.H., L.Z. and Z.L. provided key experimental resources. H.Z., J.P., M.H., Y.-J. C., L.L., C.-C.L., P.-C.H., C.-W.J.L. and S.C.-C.H. analyzed the data. Y.W. and C.-W.J.L. performed bioinformatics analysis. H.Z. and S.C.-C.H. wrote the manuscript.

Corresponding author

Correspondence to Stanley Ching-Cheng Huang.

Ethics declarations

Competing interests

P.-C.H. is a member of scientific advisory for Elixiron Immunotherapeutics and a cofounder of Pilatus Biosciences. The other authors declare no competing interests.

Peer review

Peer review information

Nature Immunology thanks Dmitry Gabrilovich, Evanna Mills and the other anonymous reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: L. A. Dempsey, in collaboration with the rest of the Nature Immunology team.

Additional information

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

Extended data

Extended Data Fig. 1 TRAP1 expression negatively correlates with immunosuppression in human and murine tumors.

(a) Representative gating strategy used for flow cytometry analysis of immune cells. (b) Heatmap showing TRAP1 expression in macrophages, monocytes, neutrophils (Neu), DC, NK, CD8+ T and CD4+ T cells isolated from the brain, liver, peritoneal cavity (PEC), lymph nodes (LN), spleen (SP), blood, and bone marrow (BM) tissues of naïve wild-type mice (n = 5 mice per group). Data are representative of three independent experiments. (c) Frequencies of TRAP1+ macrophages (Mac), monocytes (Mono), neutrophils (Neu), DC, NK, CD8+ T and CD4+ T cells in tumors from YUMM1.7 tumor-bearing mice on day 16 (n = 5 mice per group). Data are representative of two independent experiments. (d) Immunoblot analysis for TRAP1, VDAC1/2, prohibitin 1 (PHB1), GAPDH, and α-Tubulin in the mitochondrial and cytosolic fractions of naïve wild-type macrophages. Data are representative of two independent experiments. (e) Single-cell RNA-seq dataset (GSE146771) analysis of CD86 and CD163 expression in TRAP1hi and TRAP1lo macrophages from patients with colon cancer. (f) Pearson correlation of TRAP1 expression with genes encoding molecules associated with immune response in patients with skin cutaneous melanoma from the TCGA. (g) Representative flow cytometry plots (left) and quantitative analysis (right) of TRAP1+ macrophages in the spleen and tumor from B16-F10 tumor-bearing mice on day 16 (n = 5 mice per group). Data are representative of two independent experiments. (h, i) Quantitative analysis of TRAP1+ macrophages (h) and CD206+ macrophages (i) in the peritoneum, spleen, and tumor of YUMM1.7 tumor-bearing mice on day 16 (n = 5 mice per group). Data are representative of two independent experiments. (j) Expression of indicated genes in BMDMs treated without (Ctrl) or with YUMM1.7 TCM for 24 h, assessed by RT-qPCR analysis (n = 4). Data are representative of four independent experiments. All data are mean ± s.e.m. and were analyzed using a two-tailed, paired Student’s t-test (g), unpaired t-test (e), or a one-way ANOVA with Sidak’s multiple comparison test (h, i).

Source data

Extended Data Fig. 2 TRAP1 deficiency enhances immunosuppressive capacity of TAMs.

(a) Expression of CD206 and CD301 by BMDMs co-cultured with either YUMM1.7 (top), B16-F10 (middle), or LLC (bottom) tumor cells for 72 h (n = 4). Data are representative of three independent experiments. (b) Expression of indicated genes in Trap1+/+ or Trap1−/− BMDMs treated with YUMM1.7 TCM for 24 h, assessed by RNA-seq (n = 3 in Trap1+/+ group; n = 4 in Trap1−/− group). (c) Expression of pro-inflammatory genes in TRAP1 wild-type or knockout BMDMs treated with YUMM1.7 TCM for 24 h, assessed by RT-qPCR analysis (n = 4, normalized to TCM-treated wild-type macrophages). (d) TRAP1 wild-type or null BMDMs were transduced with either retrovirus overexpressing a control reporter gene (EV) or a reporter gene plus the Trap1 sequence (Trap1O/E), and co-cultured with B16-F10 cells for 72 h. Expression of CD206 and CD301 was determined by flow cytometry analysis (n = 3). (e, f) Representative histogram (left) and quantitative plots (right) of CD206 (e) and ARG1 (f) in TRAP1 wild-type and knockout BMDMs transduced with EV or Trap1O/E, treated with YUMM1.7 TCM for 24 h (n = 3). Data are representative of two independent experiments. All data are mean ± s.e.m. and were analyzed using a two-tailed, unpaired Student’s t-test (c) or a one-way ANOVA with Sidak’s multiple comparison test (a, d-f).

Source data

Extended Data Fig. 3 TRAP1 deletion in macrophages advances immune evasion.

(a) Schematic of the experimental design for YUMM1.7 tumor inoculation. Trap1+/+ x Rag1−/− and Trap1−/− x Rag1−/− mice were administered i.p. with anti-NK1.1 antibody followed by subcutaneous injection 5 × 105 YUMM1.7 cells. (b-f) Tumor growth (b), tumor weight (c), representative tumor image (d), and frequency of CD206+ TAMs (e) and ARG1+ TAMs (f) from Trap1+/+ x Rag1−/− and Trap1−/− x Rag1−/− tumor-bearing mice on day 16 (n = 4 mice per group). Data are representative of two independent experiments. (g-o) Wild-type mice were injected subcutaneously with B16-F10 cells together with either Trap1+/+ or Trap1−/− BMDMs, or Trap1−/−BMDMs overexpressing TRAP1 (Trap1O/E). Tumor growth (g) and tumor weight (h) from mice given the indicated treatment. Absolute number of TAMs (i), frequencies of CD206+ TAMs (j) and ARG1+ TAMs (k), absolute number of TILs (l), frequencies of IFNγ+ CD8 (m), IFNγ+ CD4 (n) and FoxP3+ CD4 (o) T cells (n = 5 mice per group). Data are representative of two independent experiments. (p) Frequencies of TAMs, TILs, total DCs, DC1s, DC2s, neutrophils, monocytes, NKs in Trap1wt and Trap1cKO tumor-bearing mice on day 16 (n = 5 mice per group). Data are representative of two independent experiments. (q, r) Tumor growth (q) and tumor weight (r) in Trap1wt and Trap1cKO mice subcutaneously injected with 5 × 105 Lewis lung carcinoma (LLC) cells. Tumors were harvested on day 16 post-tumor transplantation (n = 4 mice per group). Each symbol represents one individual. Data are representative of two independent experiments. (s) Proliferation of CTV-labeled CD8+ T cells activated with anti-CD3 and anti-CD28, and co-cultured with Trap1+/+ or Trap1−/− BMDMs treated with YUMM1.7 TCM or LPS+IFNγ (M1) at a 1:10 ratio for 72 h (n = 4). Data are representative of two independent experiments. All data are mean ± s.e.m. and were analyzed using a two-tailed, unpaired Student’s t-test (c, e, f, p, r), a one-way ANOVA with Sidak’s multiple comparison test (h-o, s) or a two-way ANOVA with Tukey’s multiple comparison test (b, g, q).

Source data

Extended Data Fig. 4 TIM4-AMPKα signaling axis regulates TRAP1 expression in TAMs.

(a) Expression of p-AMPKα+ macrophages in the peritoneum, spleen, and tumor of YUMM1.7 tumor-bearing mice on day 16 (n = 5). Data are representative of two independent experiments. (b) Immunoblot of p-AMPKα (short and long exposure), AMPKα and β-Actin in BMDMs treated with or without YUMM1.7 TCM for 24 h. Data are representative of two independent experiments. (c-f) Expression of p-AMPKα (c), TRAP1 (d), CD206 (e) and ARG1 (f) in BMDMs treated with TCM in the presence or absence of compound c (CC) for 24 h (n = 3). Data are representative of two independent experiments. (g) Indicated gene expression in BMDMs treated with TCM in the presence or absence of CC for 24 h, assessed by RT-qPCR (n = 4). Data are representative of three independent experiments. (h-i) Expression of pro-inflammatory genes in Prkaa1sh or Lucsh transduced BMDMs treated with TCM (h) or in wild-type BMDMs treated with TCM in the presence or absence of CC (i) for 24 h, assessed by RT-qPCR (n = 4). Data are representative of three independent experiments. (j, k) Expression of CD206 (j) and ARG1 (k) in Trap1+/+ or Trap1−/− BMDMs treated with TCM in the presence or absence of CC for 24 h (n = 3). Data are representative of two independent experiments. (l) Quantitation of TIM4+ macrophages (left Y-axis) and CD206 expression (right Y-axis) in the peritoneum, spleen, and tumor of YUMM1.7 tumor-bearing mice on day 16 (n = 5). Data are representative of two independent experiments. (m-o) BMDMs co-cultured with YUMM1.7 cells for 24 h in the presence or absence (IgG2b) of αTIM4, and expression of p-AMPKα (m), TRAP1 (n) and CD206 (o) was determined by flow cytometry (n = 3). Data are representative of two independent experiments. (p) Expression of pro-inflammatory genes in Timd4+/+ and Timd4−/− macrophages treated with TCM for 24 h, assessed by RT-qPCR (n = 4). Data are representative of two independent experiments. All data are mean ± s.e.m. and were analyzed using a two-tailed, unpaired Student’s t-test (g-i, p) or one-way ANOVA with Sidak’s multiple comparison test (a, c-f, j-o).

Source data

Extended Data Fig. 5 TCM-derived PS activates TIM4-AMPKα signaling to suppress TRAP1 expression.

(a) Phosphatidylserine (PS) levels in YUMM1.7 TCM were measured by ELISA (n = 4). Data are representative of three independent experiments. (b-e) BMDMs were cultured with either YUMM1.7 TCM or apoptotic YUMM1.7 cells (AC) for 24 h in the presence of IgG2b or αTIM4, and the expression levels of TIM4 (b), p-AMPKα (c), TRAP1 (d), and ARG1 (e) were analyzed by flow cytometry (n = 4). Data are representative of three independent experiments. (f-i) BMDMs were co-cultured with either PS or TCM for 24 h, and expression levels of TIM4 (f), p-AMPKα (g), TRAP1 (h) and ARG1 (i) were determined by flow cytometry (n = 4). Data are representative of three independent experiments. All data are mean ± s.e.m. and were analyzed using a two-tailed, unpaired Student’s t-test (a) or a one-way ANOVA with Sidak’s multiple comparison test (b-i).

Source data

Extended Data Fig. 6 Hypoxia enhances TIM4-AMPKα signaling pathway.

(a-e) BMDMs were treated with TCM for 24 h under either normoxic or hypoxic conditions. Expression levels of TIM4 (a), p-AMPKα (b), TRAP1 (c), CD206 (d) and ARG1 (e) were assessed by flow cytometry (n = 4). Data are representative of two independent experiments. All data are mean ± s.e.m. and were analyzed using one-way ANOVA with Sidak’s multiple comparison test (a-e).

Source data

Extended Data Fig. 7 TRAP1 deficiency reprograms glutamine and α-KG metabolism in IL-4-stimulated macrophages.

(a) GSEA of glutamine metabolism in Trap1+/+ or Trap1−/− BMDMs stimulated with IL-4 (n = 2). GOBP, Gene Ontology Biological Process. HPO, Human Pathway Ontology. (b, c) Quantification of glutamine consumption (n = 6) (b) and intracellular α-KG (n = 5) (c) in IL-4-stimulated Trap1+/+ or Trap1−/−BMDMs. Data from three independent experiments. (d, e) Activity of SDH (d; n = 3) and αKGDH (e; n = 3) in TAMs from Trap1wt and Trap1cKO tumor-bearing mice. AU, arbitrary units. Data from two independent experiments. (f-h) SDH activity (n = 3) (f), intracellular succinate (n = 4) (g) and α-KG/succinate ratio (n = 4) (h) of IL-4-stimulated Trap1+/+ or Trap1−/− BMDMs. Data are representative of two independent experiments. (i) Schematic of SDH-mediated succinate oxidation and GLS-mediated glutaminolysis. (j) Basal OCR of Trap1+/+ or Trap1−/− BMDMs treated with IL-4 in the presence or absence of DMM or BPTES (n = 6). Data are representative of two independent experiments. (k) α-KG levels in TCM-treated Trap1+/+ or Trap1−/− BMDMs with or without DMM for 24 h (n = 3). Data are representative of two independent experiments. (l, m) Expression of CD206 and CD301 (l), and PD-L2 and RELMα (m) in Trap1+/+ or Trap1−/− BMDMs stimulated with IL-4 in the presence or absence of DMM, measured by flow cytometry (n = 4). Data are representative of three independent experiments. (n) Gene expression in Trap1+/+ or Trap1−/− BMDMs stimulated with IL-4 in the presence or absence of BPTES for 6 h, assessed by RT-qPCR (n = 4). Data are representative of two independent experiments. (o) α-KG levels in TCM-treated Trap1+/+ or Trap1−/− BMDMs with or without diethyl succinate (Suc) for 24 h (n = 3). Data are representative of two independent experiments. (p) Gene expression in Trap1+/+ or Trap1−/−BMDMs stimulated with IL-4 under conditions with or without Suc for 6 h, assessed by RT-qPCR (n = 4). Data are representative of two independent experiments. All data are mean ± s.e.m. and were analyzed using a two-tailed, unpaired Student’s t-test (b-h) or one-way ANOVA with Sidak’s multiple comparison test (j-p).

Source data

Extended Data Fig. 8 TIM4-AMPKα signaling regulates mitochondrial metabolism.

(a) Basal OCR of BMDMs treated with YUMM1.7 TCM in the presence or absence of CC (n = 6). Data are representative of two independent experiments. (b) Basal OCR of Prkaa1sh or Lucsh transduced BMDMs treated with YUMM1.7 TCM (n = 6). Data are representative of two independent experiments. (c-f) BMDMs were transduced with Prkaa1sh or Lucsh, and treated with YUMM1.7 TCM for 24 h. SDH (Complex II) activity (c), levels of intracellular succinate (d), α-KG (e), and α-KG/succinate ratio (f) were measured (n = 4). Data are representative of two independent experiments. (g) BMDMs were treated with αTIM4 or IgG2b, and cultured with YUMM1.7 TCM for 24 h. Basal OCR was measured by Seahorse Flux Analyzer (n = 6). Data are representative of two independent experiments. (h) Intracellular α-KG levels of YUMM1.7 TCM-treated BMDMs in the presence of αTIM4 or IgG2b (n = 4). Data are representative of two independent experiments. (i) Expression of indicated genes in BMDMs treated with YUMM1.7 TCM for 24 h in the presence of αTIM4 or IgG2b, assessed by RT-qPCR (n= 4, normalized to naïve macrophages). Data are representative of two independent experiments. (j) Expression of indicated genes in TIM4 wild-type (Timd4+/+) or knockout (Timd4−/−) BMDMs treated with YUMM1.7 TCM for 24 h, assessed by RT-qPCR analysis (n = 4, normalized to naive wild-type macrophages). Data are representative of two independent experiments. All data are mean ± s.e.m. and were analyzed using a two-tailed, unpaired Student’s t-test (i-j) or a one-way ANOVA with Sidak’s multiple comparison test (a-h).

Source data

Extended Data Fig. 9 Histone demethylase JMJD3 is essential for TRAP1-mediated immunosuppression.

(a) Immunoblot analysis of JMJD3, H3K27me3, H3 and β-Actin in YUMM1.7 TCM-treated BMDMs with αTIM4 or IgG2b; representative of two independent experiments. (b) Jmjd3 expression in Trap1+/+ and Trap1−/− naïve BMDMs (n = 5); representative of two independent experiments. (c) CUT&Tag analysis of H3K27me3 enrichment in TCM-treated Trap1+/+ and Trap1−/− BMDMs. To account for the globally decreased H3K27me3 in the absence of TRAP1, Trap1−/− track was normalized to the H3K27me3/H3 ratio from Fig. 7a, which shows the relative H3K27me3/H3 ratios are 0.817 and 0.475 for Trap1+/+ and Trap1−/−, respectively. Key genes shown include Arg1, Mrc1, Pparg, and Irf4. (d) Jmjd3 expression in Jmjd3wt and Jmjd3cKO naïve BMDMs (n = 4); representative of three independent experiments. (e) Expression of CD206 and CD301 in Jmjd3wt and Jmjd3cKO BMDMs co-cultured with YUMM1.7 cells for 72 h (n = 4); representative of three independent experiments. (f) Pro-inflammatory genes in TCM-treated Jmjd3wt and Jmjd3cKO BMDMs by RNA-seq (n = 3). (g, h) CD206 (g) and ARG1 (h) in TCM-treated Trap1+/+ and Trap1−/− BMDMs with or without GSK-J4 (n = 3); representative of two independent experiments. (i) Basal OCR of TAMs from Jmjd3wt and Jmjd3cKO tumor-bearing mice (n = 6); representative of three independent experiments. (j) ETC genes in TCM-treated Jmjd3wt and Jmjd3cKO BMDMs by RNA-seq (n = 3). (k) Frequencies of TAMs, TILs, neutrophils, monocytes, NKs, total DCs, DC1s, and DC2s in Jmjd3wtand Jmjd3cKO tumor-bearing mice on day 16 (n = 5); representative of two independent experiments. (l, m) Tumor growth (l) and weight (m) in Jmjd3wt and Jmjd3cKO mice subcutaneously injected with LLC cells (n = 4); representative of two independent experiments. (n) IFNγ and GZMB production in CD8+ T cells activated with anti-CD3/CD28, and co-cultured with TAMs from Jmjd3wt or Jmjd3cKO tumor-bearing mice at a 1:10 ratio for 72 h (n = 4); representative of two independent experiments. All data are mean ± s.e.m. and were analyzed using a two-tailed, unpaired Student’s t-test (b, d, k, m), one-way ANOVA with Sidak’s multiple comparison test (e, g, h, n) or two-way ANOVA with Tukey’s multiple comparison test (l).

Source data

Extended Data Fig. 10 Restoring TRAP1 expression in TAMs promotes anti-tumor immunity.

(a) Schematic of the experimental design for treatments. (b-f) Frequencies of TAMs (b), TILs (c), MDSCs (d), DCs (e) and NK cells (f) from YUMM1.7 tumor-bearing mice treated with IgG2b control, GSK-J4, αTIM4 or αTIM4 + GSK-J4 on day 16 (n = 5 mice per group). Data are representative of two independent experiments. Each symbol represents one individual. (g-k) Tumor growth (g), tumor weight (h), and frequencies of CD206+ TAMs (i), ARG1+ TAMs (j), and TRAP1+ TAMs (k) in Trap1wt or Trap1cKO YUMM1.7 tumor-bearing mice treated with either IgG2b or αTIM4. Drug administration time points are indicated by arrows (n = 3 mice for Trap1wt + IgG2b and Trap1wt + αTIM4 group; n = 4 mice for Trap1cKO + IgG2b and Trap1cKO + αTIM4 group). Data are representative of two independent experiments. Each symbol represents one individual. (l) Schematic illustrating how TIM4-AMPK signaling regulates mitochondrial TRAP1 activity, supporting mitochondrial metabolism and facilitating α-KG-JMJD3 epigenetic modifications that promote immunosuppressive activity in TAMs. Figure created with BioRender. All data are mean ± s.e.m. and were analyzed using one-way ANOVA with Sidak’s multiple comparison test (b-f, h-k) or a two-way ANOVA with Tukey’s multiple comparison test (g).

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–4.

Reporting Summary

Peer Review File

Supplementary Data 1

Statistical source data for Supplementary Fig. 1.

Supplementary Data 2

Statistical source data for Supplementary Fig. 2.

Supplementary Data 3

Statistical source data for Supplementary Fig. 3.

Supplementary Data 4

Statistical source data for Supplementary Fig. 4.

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.

Source Data Extended Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 9

Statistical source data.

Source Data Extended Data Fig. 10

Statistical source data.

Source Data Figs. 3–5 and 7 and Extended Data Figs. 1, 4 and 9

Unprocessed immunoblots.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, H., Park, J., Wang, Y. et al. Cancer suppresses mitochondrial chaperone activity in macrophages to drive immune evasion. Nat Immunol 26, 2185–2200 (2025). https://doi.org/10.1038/s41590-025-02324-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41590-025-02324-2

Search

Quick links

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