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:

Inhibiting macrophage-derived lactate transport restores cGAS–STING signalling and enhances antitumour immunity in glioblastoma

Nature Cell Biology (2026)Cite this article

Subjects

Abstract

Glioblastoma (GBM) is a malignancy with a complex tumour microenvironment (TME) dominated by GBM stem cells (GSCs) and infiltrated by tumour-associated macrophages (TAMs) and exhibits aberrant metabolic pathways. Lactate is a critical glycolytic metabolite that promotes tumour progression; however, the mechanisms of lactate transport and lactylation in the TME of GBM remain elusive. Here we show that lactate is transported from TAMs to GSCs via MCT4–MCT1. TAMs provide lactate to GSCs, promoting GSC proliferation and inducing lactylation of the non-homologous end joining protein KU70 at lysine 317 (K317), which inhibits cGAS–STING signalling and remodels the immunosuppressive TME. Inhibition of lactate transport or targeting the lactylation of KU70, in combination with the immune checkpoint blockade, demonstrates additive therapeutic benefits in immunocompetent xenograft models. This study unveils TAM-derived lactate and lactylation as critical regulators in GSCs to enforce an immunosuppressive microenvironment, opening avenues for developing combinatorial therapy for GBM.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

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

Fig. 1: Lactate metabolism and transporter characteristics in GSCs and TAMs.
Fig. 2: Lactate transporting via MCT1–MCT4 between GSCs and TAMs supports GSC proliferation.
Fig. 3: TAM-derived lactate induced the lactylation of DNA damage repair KU70 K317 leads to GSC proliferation.
Fig. 4: Lactylation of KU70 K317 enhances the NHEJ function and maintains the stemness of GSC.
Fig. 5: Lactylation of KU70 K317 inhibits cGAS–STING signalling and maintains the immunosuppressive TME.
Fig. 6: Therapeutic efficacy of lactate transporter inhibitor combined with PD-1 antibody in GBM.

Similar content being viewed by others

Data availability

RNA-seq data that support the findings of this study have been deposited in the Gene Expression Omnibus (GEO) under accession code GSE266884 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE266884). Mass spectrometry data have been deposited in ProteomeXchange with the primary accession code PXD069969 (https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD069969) and PXD070007 (https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD070007). The human GBM data were derived from the TCGA Research Network (http://cancergenome.nih.gov/) and CCGA Research Network (https://www.cgga.org.cn/). All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.

References

  1. Louis, D. N., Perry, A. & Wesseling, P. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro. Oncol. 23, 1231–1251 (2021.

    PubMed  PubMed Central  Google Scholar 

  2. Miller, K. D. et al. Brain and other central nervous system tumor statistics, 2021. CA Cancer J. Clin. 71, 381–406 (2021).

    PubMed  Google Scholar 

  3. Ye, Z. et al. Targeting microglial metabolic rewiring synergizes with immune-checkpoint blockade therapy for glioblastoma. Cancer Discov. 13, 974–1001 (2023).

    PubMed  PubMed Central  Google Scholar 

  4. Ostrom, Q. T. et al. CBTRUS Statistical Report: primary brain and other central nervous system tumors diagnosed in the United States in 2015–2019. Neuro. Oncol. 24, v1–v95 (2022).

    PubMed  PubMed Central  Google Scholar 

  5. Tan, A. C., Ashley, D. M. & Lopez, G. Y. Management of glioblastoma: state of the art and future directions. CA Cancer J. Clin. 70, 299–312 (2020).

    PubMed  Google Scholar 

  6. Lathia, J. D., Mack, S. C., Mulkearns-Hubert, E. E., Valentim, C. L. & Rich, J. N. Cancer stem cells in glioblastoma. Genes Dev. 29, 1203–1217 (2015).

    PubMed  PubMed Central  Google Scholar 

  7. Prager, B. C., Xie, Q., Bao, S. & Rich, J. N. Cancer stem cells: the architects of the tumor ecosystem. Cell Stem Cell 24, 41–53 (2019).

    PubMed  PubMed Central  Google Scholar 

  8. Lan, X. et al. Fate mapping of human glioblastoma reveals an invariant stem cell hierarchy. Nature 549, 227–232 (2017).

    PubMed  PubMed Central  Google Scholar 

  9. Berg, T. J. & Marques, C. The irradiated brain microenvironment supports glioma stemness and survival via astrocyte-derived transglutaminase 2. Cancer Res. 81, 2101–2115 (2021).

    PubMed  Google Scholar 

  10. Dapash, M., Hou, D., Castro, B., Lee-Chang, C. & Lesniak, M. S. The interplay between glioblastoma and its microenvironment. Cells 10, 2257 (2021).

    PubMed  PubMed Central  Google Scholar 

  11. Hernandez, A. & Domenech, M. Glioblastoma: relationship between metabolism and immunosuppressive microenvironment. Cells 10, 3529 (2021).

    PubMed  PubMed Central  Google Scholar 

  12. Flint, T. R., Fearon, D. T. & Janowitz, T. Connecting the metabolic and immune responses to cancer. Trends Mol. Med. 23, 451–464 (2017).

    PubMed  Google Scholar 

  13. Vilbois, S., Xu, Y. & Ho, P. C. Metabolic interplay: tumor macrophages and regulatory T cells. Trends Cancer 10, 242–255 (2024).

    PubMed  Google Scholar 

  14. Xia, L. et al. The cancer metabolic reprogramming and immune response. Mol. Cancer 20, 28 (2021).

    PubMed  PubMed Central  Google Scholar 

  15. Khan, F. et al. Macrophages and microglia in glioblastoma: heterogeneity, plasticity, and therapy. J. Clin. Invest. 133, e163446 (2023).

    PubMed  PubMed Central  Google Scholar 

  16. Xuan, W., Lesniak, M. S., James, C. D., Heimberger, A. B. & Chen, P. Context-dependent glioblastoma-macrophage/microglia symbiosis and associated mechanisms. Trends Immunol. 42, 280–292 (2021).

    PubMed  PubMed Central  Google Scholar 

  17. Certo, M., Tsai, C. H., Pucino, V., Ho, P. C. & Mauro, C. Lactate modulation of immune responses in inflammatory versus tumour microenvironments. Nat. Rev. Immunol. 21, 151–161 (2021).

    PubMed  Google Scholar 

  18. Vander Heiden, M. G., Cantley, L. C. & Thompson, C. B. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324, 1029–1033 (2009).

    Google Scholar 

  19. Zhang, D. et al. Metabolic regulation of gene expression by histone lactylation. Nature 574, 575–580 (2019).

    PubMed  PubMed Central  Google Scholar 

  20. Li, X. et al. Lactate metabolism in human health and disease. Signal Transduct. Target Ther. 7, 305 (2022).

    PubMed  PubMed Central  Google Scholar 

  21. Fan, M. & Yang, K. Lactate promotes endothelial-to-mesenchymal transition via Snail1 lactylation after myocardial infarction. Sci. Adv. 9, eadc9465 (2023).

    PubMed  PubMed Central  Google Scholar 

  22. Li, Z. et al. Lactate shuttling links histone lactylation to adult hippocampal neurogenesis in mice. Dev. Cell 60, 1182–1198.e1188 (2025).

    PubMed  Google Scholar 

  23. Qian, Y. et al. MCT4-dependent lactate secretion suppresses antitumor immunity in LKB1-deficient lung adenocarcinoma. Cancer Cell 41, 1363–1380.e1367 (2023).

    PubMed  PubMed Central  Google Scholar 

  24. Colegio, O. R. et al. Functional polarization of tumour-associated macrophages by tumour- derived lactic acid. Nature 513, 559–563 (2014).

    PubMed  PubMed Central  Google Scholar 

  25. Abad, E., Graifer, D. & Lyakhovich, A. DNA damage response and resistance of cancer stem cells. Cancer Lett. 474, 106–117 (2020).

    PubMed  Google Scholar 

  26. Bao, S. et al. Glioma stem cells promote radioresistance by preferential activation of the DNA damage response. Nature 444, 756–760 (2006).

    PubMed  Google Scholar 

  27. Aleksandrov, R., Hristova, R., Stoynov, S. & Gospodinov, A. The chromatin response to double-strand DNA breaks and their repair. Cells 9, 1853 (2020).

    PubMed  PubMed Central  Google Scholar 

  28. Callen, E. et al. 53BP1 mediates productive and mutagenic DNA repair through distinct phosphoprotein interactions. Cell 153, 1266–1280 (2013).

    PubMed  PubMed Central  Google Scholar 

  29. Xu, G. et al. REV7 counteracts DNA double-strand break resection and affects PARP inhibition. Nature 521, 541–544 (2015).

    PubMed  PubMed Central  Google Scholar 

  30. Chen, Y. et al. Metabolic regulation of homologous recombination repair by MRE11 lactylation. Cell 187, 294–311.e221 (2024).

    PubMed  Google Scholar 

  31. Chen, H. et al. NBS1 lactylation is required for efficient DNA repair and chemotherapy resistance. Nature 631, 663–669 (2024).

    PubMed  PubMed Central  Google Scholar 

  32. Liu, X. et al. Activation of GPR81 by lactate drives tumour-induced cachexia. Nat. Metab. 6, 708–723 (2024).

    PubMed  PubMed Central  Google Scholar 

  33. Mack, S. C. et al. Chromatin landscapes reveal developmentally encoded transcriptional states that define human glioblastoma. J. Exp. Med. 216, 1071–1090 (2019).

    PubMed  PubMed Central  Google Scholar 

  34. Al Emam, A., Arbon, D., Jeeves, M. & Kysela, B. Ku70 N-terminal lysines acetylation/deacetylation is required for radiation-induced DNA-double strand breaks repair. Neoplasma 65, 708–719 (2018).

    PubMed  Google Scholar 

  35. Tubbs, A. & Nussenzweig, A. Endogenous DNA damage as a source of genomic instability in cancer. Cell 168, 644–656 (2017).

    PubMed  PubMed Central  Google Scholar 

  36. Tang, M. & Chen, G. SMYD2 inhibition-mediated hypomethylation of Ku70 contributes to impaired nonhomologous end joining repair and antitumor immunity. Sci. Adv. 9, eade6624 (2023).

    PubMed  PubMed Central  Google Scholar 

  37. Li, Z., Sun, C. & Qin, Z. Metabolic reprogramming of cancer-associated fibroblasts and its effect on cancer cell reprogramming. Theranostics 11, 8322–8336 (2021).

    PubMed  PubMed Central  Google Scholar 

  38. Wang, H., Franco, F. & Ho, P. C. Metabolic regulation of Treg in cancer: opportunities for immunotherapy. Trends Cancer 3, 583–592 (2017).

    PubMed  Google Scholar 

  39. Heuser, C., Renner, K., Kreutz, M. & Gattinoni, L. Targeting lactate metabolism for cancer immunotherapy—a matter of precision. Semin Cancer Biol. 88, 32–45 (2023).

    PubMed  Google Scholar 

  40. Reinfeld, B. I. & Madden, M. Z. Cell-programmed nutrient partitioning in the tumour microenvironment. Nature 593, 282–288 (2021).

    PubMed  PubMed Central  Google Scholar 

  41. Felmlee, M. A., Jones, R. S., Rodriguez-Cruz, V., Follman, K. E. & Morris, M. E. Monocarboxylate transporters (SLC16): function, regulation, and role in health and disease. Pharm. Rev. 72, 466–485 (2020).

    PubMed  PubMed Central  Google Scholar 

  42. Park, S. J. et al. An overview of MCT1 and MCT4 in GBM: small molecule transporters with large implications. Am. J. Cancer Res 8, 1967–1976 (2018).

    PubMed  PubMed Central  Google Scholar 

  43. Floch, R. L. et al. CD147 subunit of lactate/H+ symporters MCT1 and hypoxia-inducible MCT4 is critical for energetics and growth of glycolytic tumors. Proc. Natl Acad. Sci. USA 108, 16663–16668 (2011).

    PubMed  PubMed Central  Google Scholar 

  44. Sattler, B. & Kranz, M. Preclinical incorporation dosimetry of [18F]FACH-a novel 18F-labeled MCT1/MCT4 lactate transporter inhibitor for imaging cancer metabolism with PET. Molecules 25, 2024 (2020).

    PubMed  PubMed Central  Google Scholar 

  45. Li, G. et al. Glycometabolic reprogramming-induced XRCC1 lactylation confers therapeutic resistance in ALDH1A3-overexpressing glioblastoma. Cell Metab. 36, 1696–1710.e1610 (2024).

    PubMed  Google Scholar 

  46. Quinet, A., Tirman, S., Cybulla, E., Meroni, A. & Vindigni, A. To skip or not to skip: choosing repriming to tolerate DNA damage. Mol. Cell 81, 649–658 (2021).

    PubMed  PubMed Central  Google Scholar 

  47. Weinstock, D. M., Richardson, C. A., Elliott, B. & Jasin, M. Modeling oncogenic translocations: distinct roles for double-strand break repair pathways in translocation formation in mammalian cells. DNA Repair (Amst.) 5, 1065–1074 (2006).

    PubMed  Google Scholar 

  48. Zhao, B., Rothenberg, E. & Ramsden, D. A. The molecular basis and disease relevance of non-homologous DNA end joining. Nat. Rev. Mol. Cell Biol. 21, 765–781 (2020).

    PubMed  PubMed Central  Google Scholar 

  49. Woodbine, L., Gennery, A. R. & Jeggo, P. A. The clinical impact of deficiency in DNA non-homologous end-joining. DNA Repair (Amst.) 16, 84–96 (2014).

    PubMed  Google Scholar 

  50. Kaneda, M. M. et al. PI3Kgamma is a molecular switch that controls immune suppression. Nature 539, 437–442 (2016).

    PubMed  PubMed Central  Google Scholar 

  51. Qiao, T. et al. Inhibition of LDH-A by oxamate enhances the efficacy of anti-PD-1 treatment in an NSCLC humanized mouse model. Front Oncol. 11, 632364 (2021).

    PubMed  PubMed Central  Google Scholar 

  52. Renner, K. et al. Restricting glycolysis preserves T cell effector functions and augments checkpoint therapy. Cell Rep. 29, 135–150.e139 (2019).

    PubMed  Google Scholar 

  53. Vander Linden, C. et al. Therapy-induced DNA methylation inactivates MCT1 and renders tumor cells vulnerable to MCT4 inhibition. Cell Rep. 35, 109202 (2021).

    Google Scholar 

  54. Beloueche-Babari, M. & Casals Galobart, T. Monocarboxylate transporter 1 blockade with AZD3965 inhibits lipid biosynthesis and increases tumour immune cell infiltration. Br. J. Cancer 122, 895–903 (2020).

    PubMed  PubMed Central  Google Scholar 

  55. Flavahan, W. A. et al. Brain tumor initiating cells adapt to restricted nutrition through preferential glucose uptake. Nat. Neurosci. 16, 1373–1382 (2013).

    PubMed  PubMed Central  Google Scholar 

  56. Hu, Y. & Smyth, G. K. ELDA: extreme limiting dilution analysis for comparing depleted and enriched populations in stem cell and other assays. J. Immunol. Methods 347, 70–78 (2009).

    PubMed  Google Scholar 

  57. Li, B. et al. Microbiota depletion impairs thermogenesis of brown adipose tissue and browning of white adipose tissue. Cell Rep. 26, 2720–2737.e2725 (2019).

    PubMed  Google Scholar 

  58. Zhao, F., Kim, W. & Gao, H. ASTE1 promotes shieldin-complex-mediated DNA repair by attenuating end resection. Nat. Cell Biol. 23, 894–904 (2021).

    PubMed  Google Scholar 

  59. Ying, W., Cheruku, P. S., Bazer, F. W., Safe, S. H. & Zhou, B. Investigation of macrophage polarization using bone marrow derived macrophages. J. Vis. Exp. 23, 50323 (2013).

    Google Scholar 

  60. Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).

    PubMed  PubMed Central  Google Scholar 

  61. Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res 4, 1521 (2015).

    PubMed  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  63. McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337.e324 (2019).

    PubMed  PubMed Central  Google Scholar 

  64. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    PubMed  PubMed Central  Google Scholar 

  65. Korsunsky, I., Millard, N. & Fan, J. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    PubMed  PubMed Central  Google Scholar 

  66. Aibar, S. & Gonzalez-Blas, C. B. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).

    PubMed  PubMed Central  Google Scholar 

  67. Ravi, V. M. et al. Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma. Cancer Cell 40, 639–655.e613 (2022).

    PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant nos. 82525047 to X.W and 82573312 to Q.Z).

Author information

Author notes

  1. These authors contributed equally: Daqi Li, Gaoyuan Cui, Kailin Yang, Chenfei Lu, Yuhan Jiang, Le Zhang.

Authors and Affiliations

  1. National Health Commission Key Laboratory of Antibody Techniques, Department of Cell Biology, Jiangsu Provincial Key Laboratory of Human Functional Genomics, School of Basic Medical Sciences, Nanjing Medical University, Nanjing, China

    Daqi Li, Gaoyuan Cui, Chenfei Lu, Yuhan Jiang, Danling Gu, Jiancheng Gao, Qiankun Lin, Hang Yu, Fan Lin, Qian Zhang & Xiuxing Wang

  2. Institute for Brain Tumours, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China

    Daqi Li, Gaoyuan Cui, Chenfei Lu, Yuhan Jiang, Danling Gu, Jiancheng Gao, Qiankun Lin, Hang Yu, Yun Chen, Qianghu Wang, Guangfu Jin, Chaojun Li, Xu Qian, Qian Zhang & Xiuxing Wang

  3. Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA

    Daqi Li, Qiulian Wu & Jeremy N. Rich

  4. Department of Radiation Oncology, Holden Comprehensive Cancer Center, Iowa Neuroscience Institute, University of Iowa, Iowa City, IA, USA

    Kailin Yang

  5. Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China

    Chenfei Lu, Zhumei Shi, Yongping You, Junxia Zhang & Xiuxing Wang

  6. Analysis Center, Nanjing Medical University, Nanjing, China

    Le Zhang

  7. Department of Neurology and Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA

    Deobrat Dixit

  8. Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA

    Zhe Zhu

  9. Department of Medicine, Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, USA

    Ryan C. Gimple

  10. The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi People’s Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, China

    Junfei Shao

  11. Department of Clinical Pharmacology, School of Pharmacy, Nanjing Medical University, Nanjing, China

    Qigang Zhou & Xiuxing Wang

  12. Department of Neurosurgery of Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Chong Liu

  13. Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain–Machine Integration, State Key Laboratory of Brain–Machine Intelligence, Zhejiang University, Hangzhou, China

    Chong Liu

  14. Department of Neurosurgery, The First Affiliated Hospital of Sun Yat-sen University, Guangdong Provincial Key Laboratory of Brain Function and Disease, Guangdong Translational Medicine Innovation Platform, Guangzhou, China

    Nu Zhang

  15. Department of Nutrition and Food Hygiene, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China

    Xu Qian

  16. Jiangsu Cancer Hospital, Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China

    Xiuxing Wang

Authors
  1. Daqi Li
    View author publications

    Search author on:PubMed Google Scholar

  2. Gaoyuan Cui
    View author publications

    Search author on:PubMed Google Scholar

  3. Kailin Yang
    View author publications

    Search author on:PubMed Google Scholar

  4. Chenfei Lu
    View author publications

    Search author on:PubMed Google Scholar

  5. Yuhan Jiang
    View author publications

    Search author on:PubMed Google Scholar

  6. Le Zhang
    View author publications

    Search author on:PubMed Google Scholar

  7. Qiulian Wu
    View author publications

    Search author on:PubMed Google Scholar

  8. Deobrat Dixit
    View author publications

    Search author on:PubMed Google Scholar

  9. Zhe Zhu
    View author publications

    Search author on:PubMed Google Scholar

  10. Ryan C. Gimple
    View author publications

    Search author on:PubMed Google Scholar

  11. Danling Gu
    View author publications

    Search author on:PubMed Google Scholar

  12. Jiancheng Gao
    View author publications

    Search author on:PubMed Google Scholar

  13. Qiankun Lin
    View author publications

    Search author on:PubMed Google Scholar

  14. Hang Yu
    View author publications

    Search author on:PubMed Google Scholar

  15. Zhumei Shi
    View author publications

    Search author on:PubMed Google Scholar

  16. Yun Chen
    View author publications

    Search author on:PubMed Google Scholar

  17. Qianghu Wang
    View author publications

    Search author on:PubMed Google Scholar

  18. Guangfu Jin
    View author publications

    Search author on:PubMed Google Scholar

  19. Fan Lin
    View author publications

    Search author on:PubMed Google Scholar

  20. Junfei Shao
    View author publications

    Search author on:PubMed Google Scholar

  21. Qigang Zhou
    View author publications

    Search author on:PubMed Google Scholar

  22. Chong Liu
    View author publications

    Search author on:PubMed Google Scholar

  23. Chaojun Li
    View author publications

    Search author on:PubMed Google Scholar

  24. Yongping You
    View author publications

    Search author on:PubMed Google Scholar

  25. Nu Zhang
    View author publications

    Search author on:PubMed Google Scholar

  26. Junxia Zhang
    View author publications

    Search author on:PubMed Google Scholar

  27. Xu Qian
    View author publications

    Search author on:PubMed Google Scholar

  28. Qian Zhang
    View author publications

    Search author on:PubMed Google Scholar

  29. Jeremy N. Rich
    View author publications

    Search author on:PubMed Google Scholar

  30. Xiuxing Wang
    View author publications

    Search author on:PubMed Google Scholar

Contributions

X.W., D.L., J.N.R., Q.Z. and X.Q. conceived the project and designed the studies. D.L., G.C., C.F.L., Y.J. and L.Z. conducted the experiments. Q.Z. and D.L. wrote the paper. D.G., J.G., Q.L. and H.Y. performed the data analysis and bioinformatics analysis under the supervision of Q.W. and Z.S. Z.Z., J.Z., G.J., Y.C., Q.G.Z., Q.W., F.L., J.S., C. Liu, C. Li, Y.Y. and N.Z. commented on the study. X.W., K.Y., R.C.G. and D.D. revised the manuscript and supervised the work.

Corresponding authors

Correspondence to Junxia Zhang, Xu Qian, Qian Zhang, Jeremy N. Rich or Xiuxing Wang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Cell Biology thanks Zhimin Lu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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 Lactate metabolism and transporter characteristics in GSCs and TAMs.

a, Dot plot of analyze HALLMARK_GLYCOLYSIS pathway in different cell types using scRNA-seq data. b, c, UMAP dimensional reduction of the basis of the expression pattern of lactate transmembrane transport in cell-type clusters. d, Heatmap of expression of 6 genes in lactate transmembrane transport pathway from TCGA-GBM Agilent-4502A database. e, Dot plot of analyze 6 genes in lactate transmembrane transport pathway using scRNA-seq data (HRA004899, GSE182109 and GSE256490). f, UMAP plot showing the expression pattern of SLC16A1 and SLC16A3 using single-cell RNA-seq data (GSE182109 and GSE256490).

Extended Data Fig. 2 Lactate metabolism and transporter characteristics in GSCs and TAMs.

a-d, Quantitation of BSG and SLC16A1 (a), SOX2 and SLC16A1 (b), AIF1 and SLC16A3 (c), CD163 and SLC16A3 (d) positive cells with analysis of scRNA-seq data. e, Gene expression surface plots illustrate the spatial overlap of SLC16A1 and PROM1, SLC16A3 and CD163 gene expression. f, Kaplan-Meier survival analysis of lactate transmembrane transport signature by the median of different glioma datasets: TCGA GBM LGG and CGGA GBM datasets. g, Kaplan-Meier survival analysis of SLC16A1 combined with SLC16A3 by the median of TCGA GBM LGG and CGGA GBM datasets. h, Representative images of hematoxylin and eosin staining of mouse brains collected on day 40 after transplantation of GL261 in control and Slc16a3-conditionally knockout groups. i, Representative images of hematoxylin and eosin staining of mouse brains collected on day 30 after transplantation of mouse glioma stem cell in control and Slc16a3-conditionally knockout groups. j, L-lactate concentration in GL261-GFP isolated from tumour tissue in control and Slc16a3-conditionally knockout groups (n = 3 biologically independent mice). k, L-lactate concentration in mouse MC9 GSC or TAM isolated from tumour tissue in control and Slc16a3-conditionally knockout groups (n = 3 biologically independent mice). Data are presented as mean ± SD. Statistical significance was determined by a two-tailed log-rank test (f, g) or two-tailed Student’s t-test (j, k).

Source data

Extended Data Fig. 3 Lactate transporting between GSCs and TAMs via MCT1–MCT4 supports GSC proliferation.

a, Cell viability detections after different concentrations (0-10 mM) of pyruvate-stimulated GSCs. Results were measured using the CellTiter-Glo assay (n = 6). b, Cell viability detections after different concentrations (0-10 mM) of acetate-stimulated GSCs. Results were measured using the CellTiter-Glo assay (n = 6). c, Cell viability detections after different concentrations (0-10 mM) of β-hydroxybutyrate-stimulated GSCs. Results were measured using the CellTiter-Glo assay (n = 6). d, Cell viability detections of GSCs after being stimulated with the supernatant of TAMs transduced with shCONT, shMCT4-1, shMCT4-2 and rescued by MCT4 (n = 6). e, Representative images of neurospheres of 3028 and MES28 after being stimulated with the supernatant of TAMs transduced with shCONT, shMCT4-1, shMCT4-2 and rescued by MCT4 (n = 6). Scale bar, 100 mm. f, Immunoblot analysis of MCT4 level in TAM transduced with shCONT, shMCT4-1, shMCT4-2 and rescued by MCT4. g, Immunoblot analysis of MCT1 level in 2 GSCs and 2 compared DGCs. h, Fractional abundance of M + 3 lactate in MES28 GSC and MES28 DGC when stimulated with 10 mM 13C3-labelled lactate (n = 3). i, Cell viability detections of DGCs individually or treated with the supernatant of TAMs transduced with control shRNA, shMCT4, or shMCT4 rescued with lactate (n = 6). j, Immunoblot analysis of MCT1 level in 3028 and MES28 GSCs transduced with shCONT, shMCT1-1, shMCT1-2 and rescued by MCT1. k, Oxygen consumption rates of MES28 GSC co-cultured with supernatant of TAM (n = 4). l, Oxygen consumption rates of MES28 GSC expressing non-targeting or MCT1-targeting shRNAs (n = 4). Data are presented as mean ± SD (h, k, l), box plots show the median (centre), the 25th and 75th percentiles (bounds of box), and whiskers indicate the minimum and maximum values (a-d, i). Statistical significance was determined by a two-tailed Student’s t-test.

Source data

Extended Data Fig. 4 TAMs-derived lactate induced the lactylation of DNA damage repair protein KU70 K317 leads to GSC proliferation.

a, Cell viability detections after different concentrations (0-10 mM) of lactate stimulated NSCs (n = 6). b, Fractional abundance of M + 3 lactate in GSCs and NSCs when stimulated with 10 mM 13C3-labelled lactate (n = 3). c, Gene Set Enrichment Analysis (GSEA) scores calculated for RNA sequencing data using Gene Ontology (GO) gene sets showing the correlation between GSCs and NSCs with the Cellular response to CAMP signaling pathway. d, Immunoblot analysis of the pan-lactylation level of 3028 (left) and MES28 (right) after transduction with control shRNA and shGPR81 by stimulating with 1 mM or 10 mM lactate. e, Heatmaps showing lactylated proteins involved in non-homologous end joining, DNA replication, and base excision repair pathways in GSCs. f, KU70 mRNA levels were assayed by qRT-PCR in GSCs with KU70 knockdown using two independent shRNAs. A shCONT was used as the control (n = 3). g, Immunoblot analysis of GSCs γ-H2AX level with the stimulation of 10 mM lactate combined transduced with shCONT, shFEN1, shFEN1 rescued with FEN1-WT or FEN1-K201R. h, Immunoblot analysis of GSCs γ-H2AX level with the stimulation of 10 mM lactate combined transduced with shCONT, shLIG1, or shLIG1 rescued with LIG1-WT or LIG1-K226R. i, Cell viability detections of GSCs under the stimulation of 10 mM lactate combined transduced with shCONT, shFEN1, shFEN1 rescued with FEN1-WT or FEN1-K201R. Results were measured using the CellTiter-Glo assay (n = 6). j, Cell viability detections of GSCs under the stimulation of 10 mM lactate combined transduced with shCONT, shLIG1, or shLIG1 rescued with LIG1-WT or LIG1-K226R. Results were measured using the CellTiter-Glo assay (n = 6). k, Immunoblot analysis of IQGAP3 and IQGAP3 lactylation levels combined transduced with shCONT, shIQGAP3, or shIQGAP3 rescued with IQGAP3-WT or IQGAP3-K981R by stimulating with 10 mM lactate. l, Cell viability detections of GSCs under the stimulation of 10 mM lactate combined transduced with shCONT, shIQGAP3, or shIQGAP3 rescued with IQGAP3-WT or IQGAP3-K981R. Results were measured using the CellTiter-Glo assay (n = 6). Data are presented as mean ± SD (a, b, f), box plots show the median (centre), the 25th and 75th percentiles (bounds of box), and whiskers indicate the minimum and maximum values (i, j, l). Statistical significance was determined by a two-tailed Student’s t-test.

Source data

Extended Data Fig. 5 TAMs-derived lactate induced the lactylation of DNA damage repair protein KU70 K317 leads to GSCs proliferation.

a, Quantification of lactylation percentage for KU70 K317 by intensity values of LC/MS. b, Sequence alignment of KU70 in various species. *Lactylation site. c, The synthetic KU70 peptides spanning lactylation K317 and non-modified peptide. d, Dot-blot results validated the efficiency of the KU70 K317lac specific antibodies when detecting modified peptides. e, Immunoblot results showed the KU70 K317lac specific antibodies when detecting the lysate of Hela cell line. f, Coomassie blue staining shown the purified His-KU70 WT and His-KU70 K317R. g, Purified His-KU70 WT or K317R were incubated with or without P300 for 30 min. Lac-CoA was added. Immunoblot analysis were performed. h, Purified His-KU70 WT or K317R were incubated with or without P300 for 30 min. Ac-CoA was added. Immunoblot analysis were performed. i, KU70 K317 lactylation-specific antibody was validated by experiments of the purified His-KU70 WT or K317R incubated with or without the modified peptide (Peptide A). P300 and Lac-CoA were added. j, KU70 K317 lactylation and KU70 K317 acetylation-specific antibody were used to detect the modification level after P300 knockdown in GSCs. k, Cell viability detections of 3028 and MES28 GSCs after deletion of P300 under the stimulation of lactate (n = 6). l, Representative images of neurospheres of 3028 (top) and MES28 (bottom) under the stimulation of 10 mM lactate combined transduced with shCONT, shP300-1 and shP300-2 (n = 6). Scale bar, 100 mm. m, Purified His-KU70 WT or K317R were incubated with or without CBP for 30 min. Lac-CoA (top) or Ac-CoA (bottom) was added. Immunoblot analysis were performed. n, Purified His-KU70 WT or K317R were incubated with or without KAT2B for 30 min. Lac-CoA (top) or Ac-CoA (bottom) was added. Immunoblot analysis were performed. o, KU70 K317 lactylation and KU70 K317 acetylation-specific antibody were used to detect the modification level after CBP knockdown in GSCs. p, KU70 K317 lactylation and KU70 K317 acetylation-specific antibody were used to detect the modification level after KAT2B knockdown in GSCs. The experiments were performed three times independently (d-j, m-p). Box plots show the median (centre), the 25th and 75th percentiles (bounds of box), and whiskers indicate the minimum and maximum values. Statistical significance was determined by a two-tailed Student’s t-test.

Source data

Extended Data Fig. 6 Lactylation of KU70 K317 enhances the NHEJ function and maintains stemness of GSC.

a, KU70 K317 lactylation and acetylation level detection after stimulated with different concentration of lactate. b, Representative pictures of comet assay under the stimulation of lactate combined transduced with shCONT, shKU70 and rescued using KU70 WT and KU70 K317R in GSCs. Scale bars, 100μm. c, Apoptosis assay under the stimulation of lactate combined transduced with shCONT, shKU70 and rescued using KU70 WT and KU70 K317R in GSCs. d, Immunoblot analysis under the stimulation of lactate combined transduced with shCONT, shKU70, or shKU70 rescued with KU70 WT or KU70 K317R. e, Senescence-associated β-galactosidase assay under the stimulation of lactate combined transduced with shCONT, shKU70 and rescued using KU70 WT and KU70 K317R in GSCs. The experiments were performed three times independently (a, b, d).

Source data

Extended Data Fig. 7 Lactylation of KU70 K317 enhances the NHEJ function and maintains stemness of GSC.

a, b, Two independent shRNAs targeting MCT1 decreased the self-renewal of GSCs compared with shCONT, as measured by sphere number quantification (n = 6) (a) and the extreme limiting dilution assays (b) in 3028 and MES28. c, d, Sphere number quantification (n = 6) (c) and the extreme limiting dilution assays (d) performed in 3028 and MES28 with the deletion of KU70 and rescued by KU70 WT and KU70 K317R. e, Cell viability of six patient-derived GSCs (MES28, 3028, 2907, GSC23, RKI and 3264) after 48 h treatment of vehicle control (DMSO) and various concentrations of NHEJ inhibitor NU7441 combined with the stimulation of lactate (n = 6). f, GST pull-down assay performed to detect the interaction between KU80 and KU70 WT or KU70 K317R in vitro. g, Immunoblot results showing the interaction between KU70 and KU80 with anti-KU70 antibody in 3028 and MES28 GSCs with or without P300-Flag (1287-1663AA) and the 10 mM lactate stimulated. h, Immunoblot analysis of GSCs γ-H2AX level and the interaction level between KU70 and KU80 under the stimulation of 10 mM lactate combined transduced with shCONT, shKU70 and rescued by KU70 (WT) or KU70 (E330A) or KU70 (K317R) or KU70 (K317R E330A) after KU70 knockdown. i, Effects of shCONT, shKU70 and rescued by KU70 (WT) or KU70 (E330A) or KU70 (K317R) or KU70 (K317R E330A) after KU70 knockdown on the efficiency of NHEJ in MES28 GSC with the stimulation of lactate (n = 3). j, Immunoblot analysis after KU70 K317-Peptide blockade in GSCs. The experiments were performed three times independently (f, g, j). Data are presented as mean ± SD (e, i), box plots show the median (centre), the 25th and 75th percentiles (bounds of box), and whiskers indicate the minimum and maximum values (a, c). Statistical significance was determined by a two-tailed Student’s t-test (a, c, i) and a likelihood ratio test (LRT) comparing the single-hit model to alternative models (two-sided). P-values were computed based on the χ² distribution of the likelihood ratio statistic (b, d).

Source data

Extended Data Fig. 8 Lactylation of KU70 K317 inhibits cGAS-STING signaling and maintains the immunosuppressive TME.

a, Gene Set Enrichment Analysis (GSEA) scores calculated for RNA sequencing data using Gene Ontology (GO) gene sets showing MCT1 expression correlates with a transcriptional signature of Interferon Alpha production signaling (left) and Interferon Beta production signaling (right) in MES28 GSC. b, GSEA scores calculated for RNA sequencing using GO gene sets showing KU70 expression correlates with type I interferon production signaling in MES28 GSC. c, GSEA scores calculated for RNA sequencing using GO gene sets showing KU70 K317lac level correlates with type I interferon production signaling in MES28 GSC. d, e, GSEA using GO gene sets showing MCT1 (d) and KU70 (e) expression correlate with immune response related signals in TCGA GBM dataset. f, Immunoblot analysis of stemness marker level of 3028 (left) and MES28 (right) after transduced with control shRNA, shcGAS and shMCT1 by stimulating with lactate. g, Diagram of the NHEJ and HR reporter assay. Effects of Mct1 knockdown on the efficiency of NHEJ and HR in mouse glioma cell line GL261 under the stimulation of lactate (n = 3). h, ELISA experiments detecting the levels of type I interferon (IFNα and IFNβ) while GL261 expressing shCont and shMct1 (n = 3). i, The gating strategy of GSCs in flow cytometric analysis. j, Flow cytometry plots and quantification of Sox2+ Prom1+ GSCs in GFP+ cells as indicated (n = 3 biologically independent mice). Data are presented as mean ± SD. Statistical significance was determined by a two-tailed Student’s t-test.

Source data

Extended Data Fig. 9 Lactylation of KU70 K317 inhibits cGAS-STING signaling and maintains the immunosuppressive TME.

a, Flow cytometry percentage of macrophage, microglia, MDSC, Dendritic cell, CD8 + T cell, NK cell and CD4 + T cell from CD45+ cells and Treg from CD4 + T cells in Lyz2-Cre with Slc16a3wt/wt and Lyz2-Cre with Slc16a3fl/fl C57BL/6 J mice groups bearing 5 × 104 mouse glioma stem cell MC9 (n = 3 biologically independent mice). b, In vivo bioluminescent imaging of tumour growth was performed in immunocompetent mice bearing 5 × 104 mouse glioma stem cell MC9 and 5 × 104 BMDMs using KU70 K317-Peptide or KU70 K317R-Peptide on days 8, 15, 35. c, Representative images of hematoxylin and eosin staining of mouse brains collected on day 25 after transplantation of MC9 and BMDMs treated with KU70 K317-Peptide or KU70 K317R-Peptide. Scale bar, 2 mm. Each image is representative of at least three similar experiments. d, Kaplan-Meier survival curves of immunocompetent mice after being treated with KU70 K317-Peptide or KU70 K317R-Peptide (n = 10). e, Representative images showing immunohistochemically stained with KU70 K317lac in as indicated brain sections of the indicated mice on day 25 after treated with KU70 K317-Peptide or KU70 K317R-Peptide. Scale bar, 50 μm. f, Flow cytometry analysis of CD8 + T cells and granzyme B + CD8 + T cells in BMDMs and treated MC9-derived tumours at day 25 after injection (n = 3 biologically independent mice). g, Flow cytometry analysis of exhaustion markers in CD8 + T cells in BMDMs and treated MC9-derived tumours at day 25 after injection (n = 3 biologically independent mice). h, Expression of PD-1 on CD8 + T cells after the cytotoxic experiments with or without the syrosingopine treatment. i, Kaplan-Meier survival analysis of lactate transmembrane transport signature combined with PD-1 expression level by the median of TCGA GBM LGG and CGGA GBM datasets. j, After co-cultured with the supernatant of PBMC-derived macrophages, cytotoxicity experiments of 3028 and MES28 GSCs with activated CD8 + T cells. The ratios between CD8 + T cells and GSCs are indicated (n = 3). k, ELISA experiments detecting the levels of IFNγ in the supernatant of co-cultured system of activated T cells and GSCs (n = 3). Data are presented as mean ± SD. Statistical significance was determined by a two-tailed Student’s t-test (a, f, g, j, k) or two-tailed log-rank test (d, i).

Source data

Extended Data Fig. 10 Inhibition of lactate transporters synergizes with immune checkpoint inhibitor to prolong survival in glioblastoma orthotopic xenograft models.

a, Flow cytometry analysis of CD8 + T cells and granzyme B + CD8 + T cells in BMDMs and treated GL261-derived tumours at day 25 after injection (n = 3 biologically independent mice). b, Immunoblot results show tumour-promoting macrophage markers and apoptosis markers after MCT4 konckdown in TAMs (left) and BMDMs (right). c, Immunoblot analysis showing the Ku70 and cgas level in GL261 after Ku70 and cgas knockdown. d, In vivo bioluminescent imaging of tumour growth was performed in C57BL/6 J mice bearing 5 × 104 BMDMs and 5 × 104 GL261 transduced with shCont, shKu70, shcgas or combined with shKu70 and shcgas on days 8, 15, 35. e, Kaplan-Meier survival curves of immunocompetent mice bearing GL261 and BMDMs transduced with shCont, shKu70, shcgas or combined with shKu70 and shcgas derived intracranial tumours (n = 5). f, Representative images of hematoxylin and eosin staining of mouse brains collected on day 25 after transplantation of BMDMs and Gl261 transduced with shCont, shKu70, shcgas or combined with shKu70 and shcgas. Scale bar, 2 mm. Each image is representative of at least three similar experiments. g, In vivo bioluminescent imaging of tumour growth was performed in immunocompetent mice bearing GL261 and BMDMs using combination of the KU70 K317-Peptide and PD-1 antibody on days 8, 15, 35. h, Representative images of hematoxylin and eosin staining of mouse brains collected on day 25 after transplantation of GL261 and BMDMs treated with KU70 K317-Peptide and PD-1 antibody. Scale bar, 2 mm. Each image is representative of at least three similar experiments. i, Kaplan-Meier survival curves of immunocompetent mice after being treated with KU70 K317-Peptide and PD-1 antibody (n = 5). j, Schematic diagram illustrating that targeting lactate transporter MCT1 and MCT4 or KU70 K317 lactylation combined with anti-PD-1 remodels the immune suppressive microenvironment. k, Schematic diagram illustrating that TAMs-derived lactate remodels glioblastoma immune microenvironment through regulation of NHEJ repair by KU70 K317 lactylation. Data are presented as mean ± SD. Statistical significance was determined by a two-tailed Student’s t-test (a) or two-tailed log-rank test (e, i). Panels j and l created with BioRender.com.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2 and associated legends and Flow cytometry gating strategies.

Reporting Summary

Supplementary Data 1

Source data for Supplementary Figs. 1 and 2.

Supplementary Data 2

Unprocessed western blots for Supplementary Figs. 1 and 2.

Supplementary Table 1

Genotyping of conditional knockout mice.

Supplementary Table 2

DNA oligos used in this study.

Supplementary Table 3

Exact P value in Figs. 1–6, Extended Data Figs. 1–10 and Supplementary Figs. 1 and 2.

Source data

Source Data Figs. 1–6

Statistical source data for Figs. 1–6.

Source Data Figs. 3–5 and Extended Data Figs. 3–8 and 10

Unprocessed western blots for Figs. 1–6 and Extended Data Figs. 1–10.

Source Data Extended Data Figs. 2–5 and 7–10

Statistical source data for Extended Data Figs. 1–10.

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

Li, D., Cui, G., Yang, K. et al. Inhibiting macrophage-derived lactate transport restores cGAS–STING signalling and enhances antitumour immunity in glioblastoma. Nat Cell Biol (2026). https://doi.org/10.1038/s41556-025-01839-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • DOI: https://doi.org/10.1038/s41556-025-01839-y

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer