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Transgelin 2 guards T cell lipid metabolism and antitumour function

Nature volume 635, pages 1010–1018 (2024)Cite this article

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Abstract

Mounting effective immunity against pathogens and tumours relies on the successful metabolic programming of T cells by extracellular fatty acids1,2,3. Fatty-acid-binding protein 5 (FABP5) has a key role in this process by coordinating the efficient import and trafficking of lipids that fuel mitochondrial respiration to sustain the bioenergetic requirements of protective CD8+ T cells4,5. However, the mechanisms that govern this immunometabolic axis remain unexplored. Here we report that the cytoskeletal organizer transgelin 2 (TAGLN2) is necessary for optimal fatty acid uptake, mitochondrial respiration and anticancer function in CD8+ T cells. TAGLN2 interacts with FABP5 to facilitate its cell surface localization and function in activated CD8+ T cells. Analyses of ovarian cancer specimens revealed that endoplasmic reticulum (ER) stress responses induced by the tumour microenvironment repress TAGLN2 in infiltrating CD8+ T cells, thereby enforcing their dysfunctional state. Restoring TAGLN2 expression in ER-stressed CD8+ T cells increased their lipid uptake, mitochondrial respiration and cytotoxic capacity. Accordingly, chimeric antigen receptor T cells overexpressing TAGLN2 bypassed the detrimental effects of tumour-induced ER stress and demonstrated therapeutic efficacy in mice with metastatic ovarian cancer. Our study establishes the role of cytoskeletal TAGLN2 in T cell lipid metabolism and highlights the potential to enhance cellular immunotherapy in solid malignancies by preserving the TAGLN2–FABP5 axis.

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Fig. 1: Defective lipid uptake and FABP5 surface localization in OC-infiltrating CD8+ T cells.
Fig. 2: TAGLN2 is required for FABP5-mediated lipid uptake in activated CD8+ T cells.
Fig. 3: XBP1s restrains TAGLN2 expression in ER-stressed CD8+ T cells.
Fig. 4: TAGLN2 overexpression rescues the bioenergetic defects of ER-stressed CD8+ T cells.
Fig. 5: Preserving TAGLN2 enhances the therapeutic effects of CER T cells in HGSOC.

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Data availability

The data that support the findings of this study are included in the main text and in the Supplementary Information. The following additional public datasets analysed in the study were used: the identification of potential FABP5-interacting protein partners was performed using the STRING database (v.11.5) for protein–protein interaction networks (all relevant interaction data can be accessed through the STRING database (available at https://string-db.org/) using FABP5 as a query); bulk RNA-seq for CD8+ TILs from a mouse autochthonous liver tumour model (GSE126974) and scRNA-seq of tumour-infiltrating T cells from patients with HGSOC (GSE184880 and GSE165897); and TAGLN2 transcript data in the indicated different CD8+ T cell populations from human peripheral blood mononuclear cells were downloaded from The Human Protein Atlas database (https://www.proteinatlas.org/ENSG00000158710-TAGLN2/immune+cell). Mouse scRNA-seq data generated in this study have been deposited under NCBI GEO accession number GSE248595. 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. Lim, S. A., Su, W., Chapman, N. M. & Chi, H. Lipid metabolism in T cell signaling and function. Nat. Chem. Biol. 18, 470–481 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Zhang, Y. et al. Enhancing CD8+ T cell fatty acid catabolism within a metabolically challenging tumor microenvironment increases the efficacy of melanoma immunotherapy. Cancer Cell 32, 377–391.e9 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Nava Lauson, C. B. et al. Linoleic acid potentiates CD8+ T cell metabolic fitness and antitumor immunity. Cell Metab. 35, 633–650.e639 (2023).

    Article  CAS  PubMed  Google Scholar 

  4. Pan, Y. et al. Survival of tissue-resident memory T cells requires exogenous lipid uptake and metabolism. Nature 543, 252–256 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  5. Lin, R. et al. Fatty acid oxidation controls CD8+ tissue-resident memory T-cell survival in gastric adenocarcinoma. Cancer Immunol. Res. 8, 479–492 (2020).

    Article  CAS  PubMed  Google Scholar 

  6. Reinfeld, B. I. et al. Cell-programmed nutrient partitioning in the tumour microenvironment. Nature 593, 282–288 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  7. Long, L. et al. CRISPR screens unveil signal hubs for nutrient licensing of T cell immunity. Nature 600, 308–313 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  8. Fox, C. J., Hammerman, P. S. & Thompson, C. B. Fuel feeds function: energy metabolism and the T-cell response. Nat. Rev. Immunol. 5, 844–852 (2005).

    Article  CAS  PubMed  Google Scholar 

  9. Byersdorfer, C. A. et al. Effector T cells require fatty acid metabolism during murine graft-versus-host disease. Blood 122, 3230–3237 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Pearce, E. L. et al. Enhancing CD8 T-cell memory by modulating fatty acid metabolism. Nature 460, 103–107 (2009).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  11. Jin, R. et al. Role of FABP5 in T cell lipid metabolism and function in the tumor microenvironment. Cancers 15, 657 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Liu, F. et al. Identification of FABP5 as an immunometabolic marker in human hepatocellular carcinoma. J. Immunother. Cancer 8, e000501 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Matulonis, U. A. et al. Ovarian cancer. Nat. Rev. Dis. Primers 2, 16061 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Matulonis, U. A. et al. Antitumor activity and safety of pembrolizumab in patients with advanced recurrent ovarian cancer: results from the phase II KEYNOTE-100 study. Ann. Oncol. 30, 1080–1087 (2019).

    Article  CAS  PubMed  Google Scholar 

  15. Kandalaft, L. E., Dangaj Laniti, D. & Coukos, G. Immunobiology of high-grade serous ovarian cancer: lessons for clinical translation. Nat. Rev. Cancer 22, 640–656 (2022).

    Article  CAS  PubMed  Google Scholar 

  16. Song, M. et al. IRE1α–XBP1 controls T cell function in ovarian cancer by regulating mitochondrial activity. Nature 562, 423–428 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  17. Cao, Y. et al. ER stress-induced mediator C/EBP homologous protein thwarts effector T cell activity in tumors through T-bet repression. Nat. Commun. 10, 1280 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  18. Anadon, C. M. et al. Ovarian cancer immunogenicity is governed by a narrow subset of progenitor tissue-resident memory T cells. Cancer Cell 40, 545–557.e13 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Bowtell, D. D. The genesis and evolution of high-grade serous ovarian cancer. Nat. Rev. Cancer 10, 803–808 (2010).

    Article  CAS  PubMed  Google Scholar 

  20. Digre, A. & Lindskog, C. The Human Protein Atlas—spatial localization of the human proteome in health and disease. Protein Sci. 30, 218–233 (2021).

    Article  CAS  PubMed  Google Scholar 

  21. Conejo-Garcia, J. R. et al. Tumor-infiltrating dendritic cell precursors recruited by a β-defensin contribute to vasculogenesis under the influence of Vegf-A. Nat. Med. 10, 950–958 (2004).

    Article  CAS  PubMed  Google Scholar 

  22. Chae, C. S. et al. Tumor-derived lysophosphatidic acid blunts protective type-I interferon responses in ovarian cancer. Cancer Discov. 12, 1904–1921 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Cubillos-Ruiz, J. R. et al. Reprogramming tumor-associated dendritic cells in vivo using miRNA mimetics triggers protective immunity against ovarian cancer. Cancer Res. 72, 1683–1693 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Cubillos-Ruiz, J. R. et al. Polyethylenimine-based siRNA nanocomplexes reprogram tumor-associated dendritic cells via TLR5 to elicit therapeutic antitumor immunity. J. Clin. Invest. 119, 2231–2244 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Cubillos-Ruiz, J. R. et al. ER stress sensor XBP1 controls anti-tumor immunity by disrupting dendritic cell homeostasis. Cell 161, 1527–1538 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Scarlett, U. K. et al. Ovarian cancer progression is controlled by phenotypic changes in dendritic cells. J. Exp. Med. 209, 495–506 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Szklarczyk, D. et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 49, D605–D612 (2021).

    Article  CAS  PubMed  Google Scholar 

  28. Wan, C. H. et al. Panorama of ancient metazoan macromolecular complexes. Nature 525, 339–344 (2015).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  29. Yin, L. M., Ulloa, L. & Yang, Y. Q. Transgelin-2: biochemical and clinical implications in cancer and asthma. Trends Biochem. Sci. 44, 885–896 (2019).

    Article  CAS  PubMed  Google Scholar 

  30. Jo, S., Kim, H. R., Mun, Y. & Jun, C. D. Transgelin-2 in immunity: Its implication in cell therapy. J. Leukoc. Biol. 104, 903–910 (2018).

    Article  CAS  PubMed  Google Scholar 

  31. Ortega, F. J. et al. Cytoskeletal transgelin 2 contributes to gender-dependent adipose tissue expandability and immune function. FASEB J. 33, 9656–9671 (2019).

    Article  CAS  PubMed  Google Scholar 

  32. Na, B. R. et al. TAGLN2 regulates T cell activation by stabilizing the actin cytoskeleton at the immunological synapse. J. Cell Biol. 209, 143–162 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Armstrong, E. H., Goswami, D., Griffin, P. R., Noy, N. & Ortlund, E. A. Structural basis for ligand regulation of the fatty acid-binding protein 5, peroxisome proliferator-activated receptor β/δ (FABP5-PPARβ/δ) signaling pathway. J. Biol. Chem. 289, 14941–14954 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Furuhashi, M. & Hotamisligil, G. S. Fatty acid-binding proteins: role in metabolic diseases and potential as drug targets. Nat. Rev. Drug Discovery 7, 489–503 (2008).

    Article  CAS  PubMed  Google Scholar 

  35. Fung, H. Y., Teryek, M., Lemenze, A. D. & Bergsbaken, T. CD103 fate mapping reveals that intestinal CD103– tissue-resident memory T cells are the primary responders to secondary infection. Sci. Immunol. 7, eabl9925 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Giles, J. R. et al. Shared and distinct biological circuits in effector, memory and exhausted CD8+ T cells revealed by temporal single-cell transcriptomics and epigenetics. Nat. Immunol. 23, 1600–1613 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  38. Scott, A. C. et al. TOX is a critical regulator of tumour-specific T cell differentiation. Nature 571, 270–274 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Hetz, C., Zhang, K. & Kaufman, R. J. Mechanisms, regulation and functions of the unfolded protein response. Nat. Rev. Mol. Cell Biol. 21, 421–438 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Chen, X. & Cubillos-Ruiz, J. R. Endoplasmic reticulum stress signals in the tumour and its microenvironment. Nat. Rev. Cancer 21, 71–88 (2021).

    Article  CAS  PubMed  Google Scholar 

  41. Yoshida, H., Matsui, T., Yamamoto, A., Okada, T. & Mori, K. XBP1 mRNA is induced by ATF6 and spliced by IRE1 in response to ER stress to produce a highly active transcription factor. Cell 107, 881–891 (2001).

    Article  CAS  PubMed  Google Scholar 

  42. Logue, S. E. et al. Inhibition of IRE1 RNase activity modulates the tumor cell secretome and enhances response to chemotherapy. Nat. Commun. 9, 3267 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  43. Morita, S. et al. Targeting ABL-IRE1α signaling spares ER-stressed pancreatic β cells to reverse autoimmune diabetes. Cell Metab. 25, 1207 (2017).

    Article  CAS  PubMed  Google Scholar 

  44. Di Conza, G., Ho, P. C., Cubillos-Ruiz, J. R. & Huang, S. C. Control of immune cell function by the unfolded protein response. Nat. Rev. Immunol. 23, 546–562 (2023).

    Article  PubMed  Google Scholar 

  45. Kim, H. R. et al. An essential role for TAGLN2 in phagocytosis of lipopolysaccharide-activated macrophages. Sci. Rep. 7, 8731 (2017).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  46. Iyer, S. et al. Genetically defined syngeneic mouse models of ovarian cancer as tools for the discovery of combination immunotherapy. Cancer Discov. 11, 384–407 (2021).

    Article  CAS  PubMed  Google Scholar 

  47. Zhang, K. Y. et al. Longitudinal single-cell RNA-seq analysis reveals stress-promoted chemoresistance in metastatic ovarian cancer. Sci. Adv. 8, eabm1831 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Snaebjornsson, M. T., Janaki-Raman, S. & Schulze, A. Greasing the wheels of the cancer machine: the role of lipid metabolism in cancer. Cell Metab. 31, 62–76 (2020).

    Article  CAS  PubMed  Google Scholar 

  49. Shender, V. O. et al. Proteome–metabolome profiling of ovarian cancer ascites reveals novel components involved in intercellular communication. Mol. Cell. Proteomics 13, 3558–3571 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Perales-Puchalt, A. et al. Follicle-stimulating hormone receptor is expressed by most ovarian cancer subtypes and is a safe and effective immunotherapeutic target. Clin. Cancer Res. 23, 441–453 (2017).

    Article  CAS  PubMed  Google Scholar 

  51. Paffenholz, S. V. et al. Senescence induction dictates response to chemo- and immunotherapy in preclinical models of ovarian cancer. Proc. Natl Acad. Sci. USA 119, e2117754119 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Pramanik, J. et al. Genome-wide analyses reveal the IRE1a-XBP1 pathway promotes T helper cell differentiation by resolving secretory stress and accelerating proliferation. Genome Med. 10, 76 (2018).

  53. Bogdan, D. M. et al. FABP5 deletion in nociceptors augments endocannabinoid signaling and suppresses TRPV1 sensitization and inflammatory pain. Sci. Rep. 12, 9241 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  54. Lee, A. H., Scapa, E. F., Cohen, D. E. & Glimcher, L. H. Regulation of hepatic lipogenesis by the transcription factor XBP1. Science 320, 1492–1496 (2008).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  55. Iwawaki, T., Akai, R., Yamanaka, S. & Kohno, K. Function of IRE1 alpha in the placenta is essential for placental development and embryonic viability. Proc. Natl Acad. Sci. USA 106, 16657–16662 (2009).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  56. Chopra, S. et al. IRE1α–XBP1 signaling in leukocytes controls prostaglandin biosynthesis and pain. Science 365, eaau6499 (2019).

    Article  CAS  PubMed  Google Scholar 

  57. de Boer, J. et al. Transgenic mice with hematopoietic and lymphoid specific expression of Cre. Eur. J. Immunol. 33, 314–325 (2003).

    Article  PubMed  Google Scholar 

  58. Roby, K. F. et al. Development of a syngeneic mouse model for events related to ovarian cancer. Carcinogenesis 21, 585–591 (2000).

    Article  CAS  PubMed  Google Scholar 

  59. Ichikawa, T. et al. Chemical fixation creates nanoscale clusters on the cell surface by aggregating membrane proteins. Commun. Biol. 5, 487 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Bolte, S. & Cordelieres, F. P. A guided tour into subcellular colocalization analysis in light microscopy. J. Microsc. 224, 213–232 (2006).

    Article  MathSciNet  CAS  PubMed  Google Scholar 

  61. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Xu, J. et al. Single-cell RNA sequencing reveals the tissue architecture in human high-grade serous ovarian cancer. Clin. Cancer Res. 28, 3590–3602 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Zheng, L. et al. Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science 374, abe6474 (2021).

    Article  PubMed  Google Scholar 

  65. Andreatta, M. et al. Interpretation of T cell states from single-cell transcriptomics data using reference atlases. Nat. Commun. 12, 2965 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  66. Chu, Y. et al. Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance. Nat. Med. 29, 1550–1562 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Khan, O. et al. TOX transcriptionally and epigenetically programs CD8+ T cell exhaustion. Nature 571, 211–218 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We are grateful to all members of the Cubillos-Ruiz and Morales laboratories for their suggestions and critical review of this manuscript; staff at the Flow Cytometry Core Facility, the Proteomics and Metabolomics Core Facility, and the Genomics Resources Core Facility at Weill Cornell Medicine for their assistance with analyses; and members of the Mouse Genetics Core Facility at Weill Cornell Medicine and Memorial Sloan Kettering Cancer Center for generating Tagln2 floxed mice. This work was supported by the following institutions: NIH grants R01 NS114653, CA271619 and CA282072 (to J.R.C-R.), NIH R01 CA237154 (to M.K), and NIH R01 CA269382 (to D.Z.); Department of Defense grants W81XWH2010191, W81XWH-16-1-0438, W81XWH-22-OCRP-IIRA, W81XWH2110478 and W81XWH2110357 (to J.R.C-R.); The Mark Foundation for Cancer Research ASPIRE Award (J.R.C-R.); The AACR-Bristol Myers Squibb Immuno-Oncology Research Fellowship (S.-M.H.); The Cancer Research Institute–Irvington Institute Postdoctoral Fellowship (to C.-S.C. and C.S.); The Sigrid Jusélius Foundation (A.V.); The Cancer Foundation Finland (A.V.); NIH F31CA257631 (to A.E.); ERA PerMed JTC2020 PARIS/Academy of Finland project 344697l (to A.V.); Foundation for the Finnish Cancer Institute (K. Albin Johansson Cancer Research Fellowship for A.V); European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement number 101067835 (to M.M.F.); The Ovarian Cancer Research Alliance (to D.Z. and C.S.); National Cancer Center, Korea NCC-2310660-1 (to C.-S.C.); National Cancer Center, Korea NCC-19112605 (to M.M.S.); and National Research Foundation of Korea grants MSIT 2020R1C1C1010303 and RS-2023-00213292 (to M.S.). NIH grants S10RR027926 and S10OD032292 supported the Lipidomics Core Facility of Wayne State University.

Author information

Author notes

  1. Chang-Suk Chae

    Present address: Research Institute, National Cancer Center, Goyang, Republic of Korea

  2. Minkyung Song

    Present address: Departments of Integrative Biotechnology and of Biopharmaceutical Convergence, Sungkyunkwan University, Suwon, Republic of Korea

Authors and Affiliations

  1. Department of Obstetrics and Gynecology, Weill Cornell Medicine, New York, NY, USA

    Sung-Min Hwang, Deepika Awasthi, Tito A. Sandoval, Chang-Suk Chae, Yusibeska Ramos, Chen Tan, Camilla Salvagno, Alexander Emmanuelli, Evelyn Cantillo, Eloise Chapman-Davis, Kevin Holcomb, Diana K. Morales, Minkyung Song & Juan R. Cubillos-Ruiz

  2. Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA

    Sung-Min Hwang, Deepika Awasthi, Tito A. Sandoval, Chang-Suk Chae, Chen Tan, Camilla Salvagno, Alexander Emmanuelli, Evelyn Cantillo, Eloise Chapman-Davis, Kevin Holcomb, Minkyung Song & Juan R. Cubillos-Ruiz

  3. Cancer Biology and Genetics Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Jieun Jeong

  4. Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, Helsinki, Finland

    Matías Marin Falco & Anna Vähärautio

  5. Weill Cornell Graduate School of Medical Sciences, New York, NY, USA

    Alexander Emmanuelli, Ian T. McBain, Bikash Mishra, Lionel B. Ivashkiv & Juan R. Cubillos-Ruiz

  6. HSS Research Institute and David Z. Rosensweig Genomics Research Center, Hospital for Special Surgery, New York, NY, USA

    Bikash Mishra & Lionel B. Ivashkiv

  7. Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Dmitriy Zamarin

  8. Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA

    Xiaoqing Yu & Paulo C. Rodriguez

  9. Department of Integrated Immunobiology, Duke School of Medicine, Durham, NC, USA

    Jose R. Conejo-Garcia

  10. Duke Cancer Institute, Duke School of Medicine, Durham, NC, USA

    Jose R. Conejo-Garcia

  11. Department of Anesthesiology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA

    Martin Kaczocha

  12. Institute of Chemical Biology and Drug Discovery, Stony Brook University, Stony Brook, NY, USA

    Martin Kaczocha

  13. Stony Brook University Pain and Analgesia Research Center (SPARC), Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA

    Martin Kaczocha

  14. Foundation for the Finnish Cancer Institute, Helsinki, Finland

    Anna Vähärautio

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Contributions

S.-M.H. conceived, designed and performed most of the in vitro and in vivo experiments, analysed data, and wrote the manuscript draft. D.A. performed the Seahorse assays and other in vitro and in vivo experiments. J.J. conducted co-immunoprecipitation and LC–MS/MS analyses. T.A.S., C.-S.C., C.T., C.S., A.E., I.T.M. and B.M. assisted with in vitro and in vivo experiments. Y.R. performed confocal microscopy experiments. M.M.F. and X.Y. performed scRNA-seq analyses of samples from patients with HGSOC. M.S. designed the research, analysed data and performed in vivo scRNA-seq experiments. L.B.I., P.C.R, J.R.C.-G., M.K., D.K.M. and A.V. shared resources, analysed data and reviewed the manuscript. D.Z., E.C., E.C.-D. and K.H. provided clinical specimens. J.R.C.-R. conceived and designed the research, analysed and interpreted data, wrote the manuscript and directed the study.

Corresponding author

Correspondence to Juan R. Cubillos-Ruiz.

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Competing interests

J.R.C.-R. holds patents on the targeting of ER stress responses for the treatment of disease, as well as on the use of immune modulators for OC therapy. J.R.C.-R. is a scientific consultant for Moderna, Immagene, Autoimmunity Biologic Solutions, and Emerald Bioventures, and holds stock options in Vescor Therapeutics. D.Z. reports institutional grants from Merck, Genentech, AstraZeneca, Plexxikon, and Synthekine, and personal fees from AstraZeneca, Xencor, Memgen, Takeda, Synthekine, Immunos, Tessa Therapeutics, Miltenyi, and Calidi Biotherapeutics. D.Z. owns a patent on use of oncolytic Newcastle Disease Virus for cancer therapy. J.R.C.-G. has stock options in Compass Therapeutics, Anixa Biosciences, and Alloy Therapeutics; receives licensing fees from Anixa Biosciences for the patent of FSHCER T cells; receives honorarium from Alloy Therapeutics; has intellectual property with Compass Therapeutics and Anixa Biosciences; and is co-founder of Cellepus Therapeutics, a company that develops allogeneic γδ CAR T cells. All other authors declare no potential conflicts of interest.

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Peer review information

Nature thanks Eric Chevet, Tyler Curiel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Analysis of FABP5 expression in T cells.

a, FACS histograms and quantitative analysis of lipid uptake (BODIPY 500/510) by CD8+ T cells in peripheral blood of cancer-free women (n = 6), or from peripheral blood (n = 4) and matched solid tumours (n = 4) of OvCa patients. b,c, Human naïve CD8+ T cells from peripheral blood of cancer-free women were activated via CD3/CD28 stimulation for 32 h, followed by 16 h in the absence or presence of 50% PBS. b, Lipid uptake was assessed by FACS (n = 5). c, FABP5, CD36, and FABP4 expression was determined via qRT-PCR and data were normalized to ACTB (n = 5). d, Schemes illustrating the strategy to detect cell surface-localized or total intracellular FABP5 in activated CD8+ T cells. FACS histograms depict FABP5 staining, with grey peaks representing isotype controls. e,f, Histograms and quantitative analysis of cell surface (e) and total intracellular (f) FABP5 expression determined by FACS in gated CD8+CD44+ T cells of the indicated genotypes (n = 4 per genotype). g-j, PBMCs from cancer-free women were cultured in the absence or presence of 50% HGSOC ascites supernatants for 16 h. FACS histograms and quantitative analysis of cell surface and total intracellular FABP5 expression in the indicated cell types (n = 6 per condition). k-m, WT female mice (n = 9) were intraperitoneally injected with ID8-Defb29/Vegfa OvCa cells and euthanized at the indicated times after tumour implantation (n = 3 per group). Experimental scheme and readouts (k). Representative images of peritoneal lavage (l) and omentum (m) from each group. Data are presented as mean ± s.e.m. a, One-way ANOVA with Tukey’s multiple comparisons test. b,g-j, Two-tailed paired Student’s t-test. c, Two-tailed unpaired Student’s t-test. e,f, Two-tailed unpaired Student’s t-test. P < 0.05 is considered statistically significant and exact P-values are shown. The ‘n’ values represent biologically independent samples. gMFI, Geometric mean fluorescence intensity. The images in d and k were created using BioRender (https://biorender.com).

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Extended Data Fig. 2 Generation and characterization of mice selectively lacking TAGLN2 in T cells.

a, Description of the Talgln2 deletion strategy depicting floxed and deleted alleles. b,c, Deletion efficiency was analysed in activated CD4+ or CD8+ T cells from Tagln2fl/fl or Tagln2fl/flCd4Cre mice via qRT-PCR using a primer set that specifically detects the exon 3 region of Tagln2. Data were normalized to Actb (b). The intracellular levels of TAGLN2 were evaluated by FACS (c) (n = 3 per genotype). d, Histograms and quantitative analysis of TAGLN2 expression in the indicated cell types from the spleen and peripheral lymph nodes of Tagln2f/f or Tagln2f/f Cd4cre mice (n = 3 per genotype). e, FACS plots and quantitative analysis of double negative (CD4−CD8−), double positive (CD4+CD8+), or single positive (CD4+ or CD8+) thymocytes frequencies and absolute number in the thymus (n = 5 per genotype). f, FACS plots and quantitative analysis of CD3+CD4+ or CD3+CD8+ T cell frequencies and absolute number in the spleen and peripheral lymph nodes (n = 5 per genotype). g,h, Expression of CD44 and CD62L on CD3+CD4+ (d) and CD3+CD8+ (e) T cells in the spleen and peripheral lymph nodes (n = 5 per group). FACS plots and quantitative analysis of the indicated cell populations and absolute number. Naïve (CD62LhighCD44low), effector (CD62LlowCD44high) and Central memory (CD62LhighCD44high). i,j, WT or TAGLN2-deficient naïve CD8+ T cells were activated via CD3/CD28 stimulation for 24 h. Histograms and quantitative analysis of CD44 (i) and Ki-67 (j) expression are shown (n = 3 per genotype). Data are presented as mean ± s.e.m. b-j, Two-tailed unpaired Student’s t-test. P < 0.05 is considered statistically significant and exact P-values are shown. The ‘n’ values represent biologically independent samples. gMFI, Geometric mean fluorescence intensity.

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Extended Data Fig. 3 TAGLN2 is required for FABP5 surface expression and enables FABP5-dependent lipid uptake in activated CD8+ T cells.

a-c, WT or TAGLN2-deficient naïve CD8+ T cells were activated via CD3/CD28 stimulation for 24 h. Histograms and quantitative analysis of total FABP5 (a), surface FABP5 (b), and lipid uptake (c) by CD8+CD44+ T cells of the indicated genotypes are shown (n = 3 per genotype). d,e, WT naïve CD8+ T cells were activated via CD3/CD28 stimulation for 24 h and then Neon-electroporated with the indicated mRNAs, along with non-targeting (control) or Fabp5-specific siRNAs. Experimental scheme and readouts (d). Histograms and quantitative analysis of lipid uptake (BODIPY 500/510) by CD8+CD44+ T cells determined by FACS (n = 3 per condition) (e). f, WT, TAGLN2-deficient (Tagln2KO) or FABP5-deficient (Fabp5KO) naïve CD8+ T cells were activated via CD3/CD28 stimulation for 24 h and then Neon-electroporated with either control (Ctrl) or mouse Tagln2 mRNAs. After 48 h, lipidomic analyses were performed to evaluate the levels of diverse fatty acid species in CD8+ T cells. Quantification of the indicated fatty acids are shown (n = 3 per condition). Data are presented as mean ± s.e.m. a-c, Two-tailed unpaired Student’s t-test. e,f, One-way ANOVA with Tukey’s multiple comparisons test. P < 0.05 is considered statistically significant and exact P-values are shown. The ‘n’ values represent biologically independent samples. gMFI, Geometric mean fluorescence intensity. The images in d were created using BioRender (https://biorender.com).

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Extended Data Fig. 4 Status of TAGLN2 in OvCa-infiltrating CD8+ T cells.

a, Volcano plot of differentially expressed genes in dysfunctional tumour-specific CD8+ T cells (TCRTAG) compared to non-tumour-specific CD8+ T cells (TCROT1) from a mouse autochthonous liver tumor model (GSE126974). Selected differentially expressed genes with an adjusted P values < 0.05 and Log2 Fold Change > 1 or −1 are highlighted. (Two-sided Student’s t-test, P < 0.05, FC > 2). b, Relative expression dot plots of the indicated genes in different CD8+ T cell populations infiltrating human HGSOC tumors. The colour of each dot represents the average normalized expression from high (red) to low (blue). The size of each dot represents the percentage (pct) of positive cells for each gene. c, FACS analysis of TAGLN2 expression by CD8+ T cells present in peripheral blood of cancer-free women (n = 14), malignant ascites (n = 15), or peripheral blood (n = 8) of HGSOC patients. d, TAGLN2 transcripts in the indicated CD8+ T cell populations from human peripheral blood mononuclear cells (PBMC). The resulting transcript expression values calculated as normalized transcript per million (nTPM), resulting from the internal normalized pipeline (The Human Protein Atlas; proteinatlas.org). e, TAGLN2 expression was quantified in the indicated CD8+ T cell populations from malignant ascites (n = 15). f, Correlation analysis for IFNG, TNFA or GZMB versus TAGLN2 mRNA in CD8+ T cells from ascites. Data were normalized to ACTB (n = 15). g, Correlation of IFN-γ concentration versus levels of TAGLN2 in the indicated CD8+ T cell subsets present in the ascites (n = 14). h, Naïve peripheral CD8+ T cells from cancer-free women were activated via CD3/CD28 stimulation for 32 h and then incubated for 16 h with increasing amounts of HGSOC ascites supernatants (n = 5). Expression of TAGLN2, IFNG, and GZMB was assessed by qRT-PCR. Data were normalized to ACTB. i, FACS histograms and quantitative analysis of TAGLN2 expression in the indicated CD8+ T cells from peritoneal lavage of cancer-free female mice (n = 8; dotted lines) or mice bearing ID8-Defb29/Vegfa OvCa (n = 6; solid lines). Grey peaks denote isotype control staining. j,k, FACS analysis for CD44 (j) and Ki-67 (k) expression in the indicated T cells from the ascites of female mice bearing ID8-Defb29/Vegfa OvCa (n = 6 per group). l-n, Frequency of CD44+IFN-γ+ (l), CD44+TNF-α+ (m), and CD44+GZMB+ (n) cells within the indicated CD8+ T cells from the same mice described in (j) and (k). Data are presented as mean ± s.e.m. c,i-k, Two-tailed unpaired Student’s t-test. e,h, One-way ANOVA with Tukey’s multiple comparisons test. f,g, Spearman’s rank correlation test, Spearman coefficient (r) with exact P-value (two-tailed). l-n, Two-tailed paired Student’s t-test. Exact P-values are shown. The ‘n’ values represent biologically independent samples. gMFI, Geometric mean fluorescence intensity.

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Extended Data Fig. 5 Characterization of OvCa-associated T cells isolated from tumor-bearing mice devoid of TAGLN2 in T cells.

a-d, Female mice of the indicated genotypes bearing ID8-Defb29/Vegfa OvCa for 28 days. Peritoneal lavage, omentum, and spleen samples were harvested for immunophenotyping analyses. a, Expression of TAGLN2 in CD45+CD3+CD8+ T cells. b, Proportion of CD3+CD4+ and CD3+CD8+ T cells. c, Proportion of naïve, effector, and memory CD3+CD8+ T cell populations based on CD44 and CD62L expression. d, Proportion of PD-1+TOX+CD8+ T cells. FACS histograms and quantitative analyses are shown (n = 8 mice per group). Data are presented as mean ± s.e.m. a-d, Two-tailed unpaired Student’s t-test. P < 0.05 is considered statistically significant and exact P-values are shown. The ‘n’ values represent biologically independent samples. gMFI, Geometric mean fluorescence intensity.

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Extended Data Fig. 6 Putative transcription factor binding sites in the Tagln2 promoter.

a, ECR browser analysis of the mouse and human Tagln2 locus is shown. The mouse genomic sequence was used as the base sequence on the x-axis. Schematic representation of the genomic positions of exons (E1-5) and putative binding sites of NF-kB and unfolded protein response (UPR) transcription factors in the Tagln2 promoter regions, mainly in the CNS1 region. Asterisks denote UPR transcription factors. UTR, untranslated region. b, Schematic representation of ER stress sensors and their corresponding downstream transcription factors. c-e, Naïve CD8+ T cells isolated from Eif2ak3fl/fl or Eif2ak3fl/flVav1Cre mice (c), Atf6fl/fl or Atf6fl/flVav1Cre mice (d), or Xbp1fl/fl and Xbp1fl/flCd4Cre mice (e) were cultured under the indicated conditions. Expression of the Tagln2 transcript was determined by RT-qPCR, and data were normalized to endogenous levels of Actb in each sample (n = 3 per condition and genotype). f, Sequence of the mouse Tagln2 promoter from −646 to +138. XBP1s binding sites (Score > 10) are marked as BS1 and BS2. Location of ChIP-PCR primers (F/R) is indicated. g, Tagln2 promoter construct used for luciferase reporter assays. c-e, One-way ANOVA with Tukey’s multiple comparisons test. P < 0.05 is considered statistically significant and exact P-values are shown. The ‘n’ values represent biologically independent samples. The images in b were created using BioRender (https://biorender.com).

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Extended Data Fig. 7 Tagln2 overexpression in intratumoral CD4+ and CD8+ T cell subsets lacking XBP1s.

a, Scheme illustrating sample processing and experimental workflow. b, FACS sorting strategy for scRNA-sequencing. c, UMAP coloured by genotype classifications of Xbp1fl/fl (blue, n = 4,269 cells) and Xbp1fl/flCd4Cre (red, n = 3,325 cells). d, UMAP plot visualization of different T cell clusters colored by cell type. e, Heatmap showing the top 10 marker genes of the subclusters. f,g, Volcano plots showing differentially expressed genes (DEGs) with top 10 genes in CD4+ (f) or CD8+ (g) intratumoral T cell clusters from XBP1s-deficient compared to WT controls. Wilcoxon rank-sum test with Benjamini-Hochberg correction. (min_pct_expression > 10%, abs(avg_log2fc) > 0.2, -log10_pval_adj > 2). h, Enriched cellular pathways and functions in XBP1s-deficient CD8+ T cells at tumour sites. Z-scores greater than 2 indicate pathways and functions predicted to be significantly increased in XBP1s-deficient CD8+ T cells. i, Dot plot analysis showing the expression levels and distribution of seven major cytoskeletal genes (Tagln2, Wipf1, Wasf1, Wasf2, Hcls1, Was and Actr2) identified in each CD4+ and CD8+ T cell cluster in (d). j, Expression of previously reported RIDD target genes across the different CD3+ T cell clusters identified in (d). The images in a were created using BioRender (https://biorender.com).

Extended Data Fig. 8 Selective loss of XBP1s in T cells delays malignant progression and enhances TAGLN2, Ki-67, and CD44 expression in PPNM tumour-infiltrating CD8+ T cells.

a, Experimental scheme for mice of the indicated genotypes implanted with luciferase-expressing PPNM cancer cells. b,c, Assessment of peritoneal tumour burden over time in mice of the indicated genotypes (b) and quantification of bioluminescent signal for the same mice of the indicated genotype at different time points (n = 5 per genotype) (c). d, Overall survival curves for PPNM-bearing female mice of the indicated genotypes (n = 9 per genotype). e,f, Representative images of omentum (e) and solid tumours (f) from female mice of the indicated genotypes bearing PPNM-based HGSC for 40 days. Weight of omentum (e) and solid tumours (f) was determined in each group (Xbp1fl/fl, n = 5; Xbp1fl/flCd4 Cre, n = 8). g,h, Correlation of protein expression levels of TAGLN2 versus either Ki-67 or CD44 in the indicated intratumoral CD8+ T cell subsets in omentum (g) and solid tumor (h) from female mice of the indicated genotypes bearing PPNM-bearing HGSC for 40 days (Xbp1fl/fl, n = 5; Xbp1fl/flCd4 Cre, n = 8). Data are presented as mean ± s.e.m. c, One-way ANOVA with Tukey multiple comparisons test. d, Log-rank test for survival. e,f, Two-tailed unpaired Student’s t-test. g,h, Spearman’s rank correlation test, Spearman coefficient (r) with exact P-value (two-tailed). P < 0.05 is considered statistically significant and exact P-values are shown. The ‘n’ values represent biologically independent samples. gMFI, Geometric mean fluorescence intensity. The images in a were created using BioRender (https://biorender.com).

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Extended Data Fig. 9 Selective suppression of FABP5 surface expression in ER-stressed CD8+ T cells.

a-e, WT naïve CD8+ T cells were stimulated via CD3/CD28 for 24 h and then electroporated with the indicated mRNAs. T cells were maintained under CD3/CD28 stimulation for an additional 32 h and then treated with the ER stressor TM for 16 h. a, FACS histograms and quantitative analysis of TAGLN2; b, lipid uptake, total and surface expression of: c, FABP5, d, FABP4, and e, CD36 in CD8+CD44+ T cells determined by FACS (n = 5 per condition). f-j, Pan B cells isolated from Tagln2fl/fl or Tagln2fl/flVav1Cre mice were cultured under the indicated conditions (f). FACS histograms and quantitative analysis of TAGLN2 (g), surface FABP5 (h), total FABP5 (i) and lipid uptake (j) in CD19+ B cells. (n = 3 per condition). k-o, γδ T cells isolated from Tagln2fl/fl or Tagln2fl/flVav1Cre mice were cultured under the indicated conditions (k). FACS histograms and quantitative analysis of TAGLN2 (l), surface FABP5 (m), total FABP5 (n) and lipid uptake (o) in CD3+γδ+ T cells. (n = 3 per condition). Data are presented as mean ± s.e.m. a-e,g-j,l-o, One-way ANOVA with Tukey multiple comparisons test. P < 0.05 is considered statistically significant and exact P-values are shown. The ‘n’ values represent biologically independent samples. gMFI, Geometric mean fluorescence intensity. The images in f and k were created using BioRender (https://biorender.com).

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Extended Data Fig. 10 Induction of ER stress responses and progressive TAGLN2 loss in adoptively transferred CER T cells.

a, FSH-CER retroviral construct. b, Expression of the follicle-stimulating hormone receptor (FSHR) by PPNM cancer cells determined by immunoblot analysis. β-actin was used as loading control. c,d, CD45.1+CER T cells were isolated from the indicated tumour sites seven days after adoptive transfer into WT female mice developing PPNM HGSC. Experimental scheme (c). Xbp1s, Sec61a1, ERdj4, and Tagln2 expression in pre- or post-infusion CER T cells was determined via qRT-PCR. Data were normalized to Actb (n = 12) (d). e-h, FACS histograms and quantitative analysis of TAGLN2 and surface FABP5 expression in the indicated T cells isolated from peritoneal lavage (e) or omentum (f) at different days after adoptive transfer into WT female mice developing PPNM HGSC (D3: n = 6 per group, D7 and D14: n = 7 per group). Dark blue bar graph represents the expression of TAGLN2 and surface FABP5 in pre-infusion CER T cells (n = 1). FACS plots and quantitative analysis of CD44+IFN-γ+ (g) and CD44+GZMB+ (h) frequencies in the indicated CER T cells isolated from the same mice described in e and f (n = 6-7 per group). i,j, CD8+GFP+ sorted CER T cells were stimulated with recombinant chorionic gonadotropin alpha (CGα) in the absence or presence of TM (i). Xbp1s, Sec61a1, and Tagln2 expression was determined via qRT-PCR. Data were normalized to Actb (n = 3 per condition) (j). k, Experimental scheme for CER T cell killing assays. T cells were washed to remove TM and then cocultured with PPNM cancer cells at a 1:1 ratio. l, FACS plots and quantitative analysis of cancer cell death assessed by Annexin V and PI staining 18 h after incubation with mock-transduced CD8+ T cells (n = 3-4 per condition). Data are presented as mean ± s.e.m. d, Two-tailed paired Student’s t-test. e-h, One-way ANOVA with Tukey’s multiple comparisons test. j, Two-tailed unpaired Student’s t-test. P < 0.05 is considered statistically significant and exact P-values are shown. The ‘n’ values represent biologically independent samples. gMFI, Geometric mean fluorescence intensity. The images in a, c, i and k were created using BioRender (https://biorender.com).

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Extended Data Fig. 11 Therapeutic effects of CER-Tagln2 T cells in mice with OvCa.

a, FACS-based assessment of transduction efficiency using GFP expression as a marker. b, CER or CER-Tagln2 T cells were incubated in the presence or absence of TM for 16 h and TAGLN2 expression was then measured by FACS. c, Expression of the FSHR determined by immunoblot analysis in which GAPDH was used as loading control. d-g, Adoptive transfer of the indicated T cells into MP tumour-bearing female mice. d, Experimental scheme. e, Representative bioluminescence images of MP tumours over time; and f, quantification of peritoneal tumour burden in the indicated groups (n = 8 mice per group). g, Overall survival curves for the same mice described in e and f. The initial number of mice allocated to each experimental group was n = 8, but only mice that developed OvCa were included in the survival analysis. h-j, WT female mice were challenged i.p. with PPNM cancer cells. After 8 days, mice were treated with either CER or CER-Tagln2 T cells alone or in combination with isotype control or anti–PD-1 antibodies as described in the Methods (n = 10 mice per group). h, Experimental scheme and redouts. i, Representative bioluminescence images of MP tumours over time. j, Overall survival rates for the same mice described in i. The initial number of mice allocated to each experimental group was n = 10, but only mice that developed OvCa were included in the survival analysis shown. k, Proposed model: ER stress responses induced by the TME disable a cytoskeletal-metabolic axis that maintains competent T cell anti-tumour function. TAGLN2 mediates optimal FABP5 surface localization to ensure robust T cell lipid uptake, fatty acid oxidation (FAO), mitochondrial respiration, and effector function. ER stress-driven IRE1α-XBP1s activation silences TAGLN2 in the OvCa TME. OvCa-reactive CER T cells overexpressing TAGLN2 bypass the detrimental effects of ER stress in the TME and show enhanced functionality, persistence, and anti-tumor activity. Data are presented as mean ± s.e.m. f, One-way ANOVA with Tukey’s multiple comparisons test. g,j, Log-rank test for survival. P < 0.05 is considered statistically significant and exact P-values are shown. The ‘n’ values represent biologically independent samples. gMFI, Geometric mean fluorescence intensity. The images in d, h and k were created using BioRender (https://biorender.com).

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Hwang, SM., Awasthi, D., Jeong, J. et al. Transgelin 2 guards T cell lipid metabolism and antitumour function. Nature 635, 1010–1018 (2024). https://doi.org/10.1038/s41586-024-08071-y

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