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:

Aspartate signalling drives lung metastasis via alternative translation

Nature volume 638, pages 244–250 (2025)Cite this article

Subjects

Abstract

Lung metastases occur in up to 54% of patients with metastatic tumours1,2. Contributing factors to this high frequency include the physical properties of the pulmonary system and a less oxidative environment that may favour the survival of cancer cells3. Moreover, secreted factors from primary tumours alter immune cells and the extracellular matrix of the lung, creating a permissive pre-metastatic environment primed for the arriving cancer cells4,5. Nutrients are also primed during pre-metastatic niche formation6. Yet, whether and how nutrients available in organs in which tumours metastasize confer cancer cells with aggressive traits is mostly undefined. Here we found that pulmonary aspartate triggers a cellular signalling cascade in disseminated cancer cells, resulting in a translational programme that boosts aggressiveness of lung metastases. Specifically, we observe that patients and mice with breast cancer have high concentrations of aspartate in their lung interstitial fluid. This extracellular aspartate activates the ionotropic N-methyl-d-aspartate receptor in cancer cells, which promotes CREB-dependent expression of deoxyhypusine hydroxylase (DOHH). DOHH is essential for hypusination, a post-translational modification that is required for the activity of the non-classical translation initiation factor eIF5A. In turn, a translational programme with TGFβ signalling as a central hub promotes collagen synthesis in lung-disseminated breast cancer cells. We detected key proteins of this mechanism in lung metastases from patients with breast cancer. In summary, we found that aspartate, a classical biosynthesis metabolite, functions in the lung environment as an extracellular signalling molecule to promote aggressiveness of metastases.

Access through your institution
Buy or subscribe

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

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: Aspartate promotes the aggressiveness of lung metastases by inducing eIF5A hypusination.
Fig. 2: Aspartate triggers NMDA receptor activity.
Fig. 3: Aspartate-induced NMDA receptor activity regulates DOHH expression.
Fig. 4: Alternative translation induced by eIF5A hypusination results in collagen synthesis.
Fig. 5: Evidence for aspartate signalling in lung metastases of patients with breast cancer.

Similar content being viewed by others

Data availability

Mouse single-cell RNA-sequencing data and polysome and total RNA sequencing data generated as part of this study have been deposited in the Gene Expression Omnibus (GEO) under accession GSE236087. The publicly available microarray-based patient-metastasis dataset GSE1401882 can be downloaded from the GEO under accession GSE14018. All other data supporting the findings of this study are available within the Article and the supplementary information, and from the corresponding author on reasonable request. Source data are provided with this paper.

Code availability

No original software and/or algorithms were developed in the present study; however, code used for data analysis can be provided upon request. Any additional information required to reanalyse the data reported in this paper is available from the corresponding author upon request.

References

  1. Gerull, W. D., Puri, V. & Kozower, B. D. The epidemiology and biology of pulmonary metastases. J. Thorac. Dis. 13, 2585–2589 (2021).

    Article  PubMed  PubMed Central  MATH  Google Scholar 

  2. Mohammed, T.-L. H. et al. ACR appropriateness criteria screening for pulmonary metastases. J. Thorac. Imag. 26, W1–W3 (2011).

    Article  Google Scholar 

  3. Crist, S. B. et al. Unchecked oxidative stress in skeletal muscle prevents outgrowth of disseminated tumour cells. Nat. Cell Biol. 24, 538–553 (2022).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  4. Peinado, H. et al. Pre-metastatic niches: organ-specific homes for metastases. Nat. Rev. Cancer 17, 302–317 (2017).

    Article  CAS  PubMed  MATH  Google Scholar 

  5. Doglioni, G., Parik, S. & Fendt, S. M. Interactions in the (pre)metastatic niche support metastasis formation. Front. Oncol. 9, 219 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Altea-Manzano, P. et al. A palmitate-rich metastatic niche enables metastasis growth via p65 acetylation resulting in pro-metastatic NF-κB signaling. Nat. Cancer 4, 344–364 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Pelechano, V. & Alepuz, P. eIF5A facilitates translation termination globally and promotes the elongation of many non polyproline-specific tripeptide sequences. Nucleic Acids Res. 45, 7326–7338 (2017).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  8. Park, M. H., Nishimura, K., Zanelli, C. F. & Valentini, S. R. Functional significance of eIF5A and its hypusine modification in eukaryotes. Amino Acids 38, 491–500 (2010).

    Article  CAS  PubMed  Google Scholar 

  9. Elia, I. et al. Proline metabolism supports metastasis formation and could be inhibited to selectively target metastasizing cancer cells. Nat. Commun. 8, 15267 (2017).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  10. Elia, I. et al. Breast cancer cells rely on environmental pyruvate to shape the metastatic niche. Nature 568, 117–121 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  11. Buescher, J. M. et al. A roadmap for interpreting 13C metabolite labeling patterns from cells. Curr. Opin. Biotechnol. 34, 189–201 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Erreger, K. et al. Subunit-specific agonist activity at NR2A-, NR2B-, NR2C-, and NR2D-containing N-methyl-D-aspartate glutamate receptors. Mol. Pharmacol. 72, 907 (2007).

    Article  CAS  PubMed  Google Scholar 

  13. Song, X. et al. Mechanism of NMDA receptor channel block by MK-801 and memantine. Nature 556, 515–519 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  14. Rouillard, A. D. et al. The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database 2016, baw100 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  15. West, A. E. et al. Calcium regulation of neuronal gene expression. Proc. Natl Acad. Sci. USA 98, 11024–11031 (2001).

    Article  ADS  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  16. Xie, F. et al. Identification of a potent inhibitor of CREB-mediated gene transcription with efficacious in vivo anticancer activity. J. Med. Chem. 58, 5075–5087 (2015).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  17. Verrecchia, F. & Mauviel, A. Transforming growth factor-beta signaling through the Smad pathway: role in extracellular matrix gene expression and regulation. J. Invest. Dermatol. 118, 211–215 (2002).

    Article  CAS  PubMed  MATH  Google Scholar 

  18. Geukens, T. et al. Rapid autopsies to enhance metastatic research: the UPTIDER post-mortem tissue donation program. NPJ Breast Cancer 10, 31 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Vettore, L., Westbrook, R. L. & Tennant, D. A. New aspects of amino acid metabolism in cancer. Br. J. Cancer 122, 150–156 (2020).

    Article  CAS  PubMed  MATH  Google Scholar 

  20. North, W. G., Gao, G., Memoli, V. A., Pang, R. H. & Lynch, L. Breast cancer expresses functional NMDA receptors. Breast Cancer Res. Treat. 122, 307–314 (2010).

    Article  CAS  PubMed  Google Scholar 

  21. Li, L. & Hanahan, D. Hijacking the neuronal NMDAR signaling circuit to promote tumor growth and invasion. Cell 153, 86–100 (2013).

    Article  CAS  PubMed  Google Scholar 

  22. Zeng, Q. et al. Synaptic proximity enables NMDAR signalling to promote brain metastasis. Nature 573, 526–531 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  23. Krieg, S., Fernandes, S. I., Kolliopoulos, C., Liu, M. & Fendt, S.-M. Metabolic signaling in cancer metastasis. Cancer Discov. 14, 934–952 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Güth, R. et al. DHPS-dependent hypusination of eIF5A1/2 is necessary for TGFβ/fibronectin-induced breast cancer metastasis and associates with prognostically unfavorable genomic alterations in TP53. Biochem. Biophys. Res. Commun. 519, 838–845 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Karras, P., Black, J. R. M., McGranahan, N. & Marine, J. C. Decoding the interplay between genetic and non-genetic drivers of metastasis. Nature 629, 543–554 (2024).

    Article  ADS  CAS  PubMed  Google Scholar 

  26. Fendt, S.-M., Frezza, C. & Erez, A. Targeting metabolic plasticity and flexibility dynamics for cancer therapy. Cancer Discov. 10, 1797–1807 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Lee, L. J. et al. Cancer plasticity: the role of mRNA translation. Trends Cancer 7, 134–145 (2021).

    Article  CAS  PubMed  MATH  Google Scholar 

  28. van Gorsel, M., Elia, I. & Fendt, S. M. 13C tracer analysis and metabolomics in 3D cultured cancer cells. Methods Mol. Biol. 1862, 53–66 (2019).

    Article  PubMed  MATH  Google Scholar 

  29. Schmidt, E. K., Clavarino, G., Ceppi, M. & Pierre, P. SUnSET, a nonradioactive method to monitor protein synthesis. Nat. Methods 6, 275–277 (2009).

    Article  CAS  PubMed  Google Scholar 

  30. Delaunay, S. et al. Elp3 links tRNA modification to IRES-dependent translation of LEF1 to sustain metastasis in breast cancer. J. Exp. Med. 213, 2503–2523 (2016).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  31. Liang, S. et al. Polysome-profiling in small tissue samples. Nucleic Acids Res. 46, e3 (2018).

    Article  PubMed  MATH  Google Scholar 

  32. 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).

    Article  PubMed  PubMed Central  MATH  Google Scholar 

  33. Stephens, M. False discovery rates: a new deal. Biostatistics 18, 275–294 (2017).

    MathSciNet  PubMed  MATH  Google Scholar 

  34. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  35. Korotkevich, G. et al. Fast gene set enrichment analysis. Preprint at bioRxiv https://doi.org/10.1101/060012 (2021).

  36. Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  38. Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  39. Christen, S. et al. Breast cancer-derived lung metastases show increased pyruvate carboxylase-dependent anaplerosis. Cell Rep. 17, 837–848 (2016).

    Article  CAS  PubMed  Google Scholar 

  40. Rinaldi, G. et al. In vivo evidence for serine biosynthesis-defined sensitivity of lung metastasis, but not of primary breast tumors, to mTORC1 inhibition. Mol. Cell 81, 386–397 e387 (2021).

    Article  CAS  PubMed  MATH  Google Scholar 

  41. Parik, S. et al. GBM tumors are heterogeneous in their fatty acid metabolism and modulating fatty acid metabolism sensitizes cancer cells derived from recurring GBM tumors to temozolomide. Front. Oncol. 12, 988872 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 e3529 (2021).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  43. Young, M. D. & Behjati, S. SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. Gigascience 9, giaa151 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Janssen, P. et al. The effect of background noise and its removal on the analysis of single-cell expression data. Genome Biol. 24, 140 (2023).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  45. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  46. McInnes, M., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at arXiv https://doi.org/10.48550/arXiv.1802.03426 (2018).

  47. Blondel V. D., Guillaume J.-L., Lambiotte R. & Lefebvre E. Fast unfolding of communities in large networks. J. Stat. Mech. https://doi.org/10.1088/1742-5468/2008/10/P10008 (2008).

  48. Germain, P.-L., Lun, A., Garcia Meixide, C., Macnair, W. & Robinson, M. D. Doublet identification in single-cell sequencing data using scDblFinder. F1000Research 10, 979 (2022).

    Article  PubMed Central  Google Scholar 

  49. Denis, J. F. et al. Activation of Smad2 but not Smad3 is required to mediate TGF-β signaling during axolotl limb regeneration. Development 143, 3481–3490 (2016).

    Article  CAS  PubMed  MATH  Google Scholar 

  50. Vennin, C. et al. Transient tissue priming via ROCK inhibition uncouples pancreatic cancer progression, sensitivity to chemotherapy, and metastasis. Sci. Transl. Med. 9, eaai8504 (2017).

    Article  PubMed  PubMed Central  MATH  Google Scholar 

  51. Fridlender, Z. G. et al. Polarization of tumor-associated neutrophil phenotype by TGF-beta: “N1” versus “N2” TAN. Cancer Cell 16, 183–194 (2009).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  52. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  MATH  Google Scholar 

Download references

Acknowledgements

The authors thank J. Lamote and S. Vinckier for providing advice and expertise for flow cytometry and for microscopy experiments; N. Hersmus and N. Peredo for digital PCR support and for imaging video annotations; all physicians from the Multidisciplinary Breast Center Leuven for their clinical contribution to the UPTIDER cohorts; and the whole UPTIDER team and all the patients who agreed to donate tissues postmortem. We acknowledge the members of the Laboratory of Cellular Metabolism and Metabolic Regulation (VIB-KU Leuven) for performing blinded analysis of several H&E and picrosirius red staining experiments shown. We apologize to all colleagues whose work we could not cite owing to space restrictions. The figures present in Extended Data Fig. 1c (agreement number: YZ27DAESEJ), Extended Data Fig. 1h (agreement number: LJ27DAFBLZ) and Fig. 5g (agreement number: OF27DLNP8H) were created in BioRender.com. The work conducted in the Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. was supported by the “Ministerium für Kultur und Wissenschaft des Landes Nordrhein-Westfalen” and “Der Regierende Bürgermeister von Berlin, Senatskanzlei Wissenschaft und Forschung”, and was further supported by the Bundesministerium für Bildung und Forschung, BMBF. G.D. and J.F.-G. received funding from Research Foundation Flanders (FWO) PhD (11E9520N) and senior postdoctoral fellowships (12V9620N). G.D. has previously received funding from Kom op tegen Kanker and J.F.-G. previously received an FWO junior fellowship. S.I. received funding from EMBO postdoctoral fellow (ALTF 13-2022) and from Research Foundation Flanders (FWO) junior postdoctoral fellowship (1266225N). P.A.-M. was supported by a Marie Sklodowska-Curie Actions individual fellowship (MSCA-IF-2018-839896) and has received funding from European Union (ERC-StG-101116912) and Beug Foundation. X.-Z.L. received sequential funding from EMBO postdoctoral fellowship (ALTF 401-2022), the Gilead Sciences Research Scholars Program in Solid Tumors and Research Foundation Flanders (FWO) junior postdoctoral fellowship (1231025 N). Y.L. received a Chinese Scholarship Council fellowship. M.R. has been an FWO and Stichting tegen Kanker fellow. G.F. is recipient of a post-doctoral fellowship sponsored by the KOOR from the University Hospitals Leuven. The UPTIDER programme is supported by a grant from the University Hospitals from Leuven (KOOR 2021), as well as a C1 grant (14/21/114) from KU Leuven. P.C. acknowledge the Belgian Foundation for Cancer Research (2020-068). G.B. acknowledges funding from Research Council KU Leuven (C14/19/099 and AKUL/19/34) and from the Research Foundation Flanders (G094522N) and coordinates an FWO research network (WOG) on ‘Ca2+ signalling in health, disease and therapy’ (CaSign; W001422N). J.-C.M. received funding from Stichting Tegen Kanker (#2022-178) and FWO (G045824N and G0C5320N). H.F.A. together with S.-M.F. has received funding from the King Baudouin Foundation and H.F.A. was a Stichting tegen Kanker fellow. S.-M.F. acknowledges funding from FWO Project G011724N, Beug Foundation, Fonds Baillet Latour, Francqui Stichting, Foundation ARC, KU Leuven, Stichting tegen Kanker and Interuniversity BOF (iBOF) programme.

Author information

Author notes

  1. Patricia Altea-Manzano

    Present address: Laboratory of Metabolic Regulation and Signaling in Cancer, Andalusian Molecular Biology and Regenerative Medicine Centre (CABIMER)–University of Seville–CSIC–University Pablo de Olavide, Seville, Spain

  2. H. Furkan Alkan

    Present address: Children’s Research Institute and the Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, USA

  3. These authors contributed equally: Juan Fernández-García, Sebastian Igelmann, Patricia Altea-Manzano

Authors and Affiliations

  1. Laboratory of Cellular Metabolism and Metabolic Regulation, VIB Center for Cancer Biology, VIB, Leuven, Belgium

    Ginevra Doglioni, Juan Fernández-García, Sebastian Igelmann, Patricia Altea-Manzano, Xiao-Zheng Liu, Yawen Liu, Tine Tricot, Sarah El Kharraz, Annalisa Scopelliti, Matteo Rossi, Ines Vermeire, Dorien Broekaert, Ana Margarida Ferreira Campos, H. Furkan Alkan, Mélanie Planque & Sarah-Maria Fendt

  2. Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium

    Ginevra Doglioni, Juan Fernández-García, Sebastian Igelmann, Patricia Altea-Manzano, Xiao-Zheng Liu, Yawen Liu, Tine Tricot, Sarah El Kharraz, Annalisa Scopelliti, Matteo Rossi, Ines Vermeire, Dorien Broekaert, Ana Margarida Ferreira Campos, H. Furkan Alkan, Mélanie Planque & Sarah-Maria Fendt

  3. Laboratory of Cancer Signaling, GIGA-Institute, University of Liège, Liège, Belgium

    Arnaud Blomme, Ning An, Marine Leclercq & Pierre Close

  4. Laboratory of Molecular and Cellular Signaling, Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium

    Rita La Rovere & Geert Bultynck

  5. Department of Gastroenterology, Affiliated Hospital of Jiangsu University, Jiangsu University, Zhenjiang, China

    Yawen Liu

  6. Laboratory of Intravital Microscopy and Dynamics of Tumor Progression, VIB Center for Cancer Biology, VIB, Leuven, Belgium

    Max Nobis & Colinda L. G. J. Scheele

  7. Laboratory of Intravital Microscopy and Dynamics of Tumor Progression, Department of Oncology, KU Leuven, Leuven, Belgium

    Max Nobis & Colinda L. G. J. Scheele

  8. Intravital Imaging Expertise Center, VIB Center for Cancer Biology, VIB, Leuven, Belgium

    Max Nobis

  9. Laboratory for Molecular Cancer Biology, VIB Center for Cancer Biology, VIB, Leuven, Belgium

    Panagiotis Karras & Jean-Christophe Marine

  10. Laboratory for Molecular Cancer Biology, Department of Oncology, KU Leuven, Leuven, Belgium

    Panagiotis Karras & Jean-Christophe Marine

  11. Leibniz Institut für Analytische Wissenschaften-ISAS-e.V., Dortmund, Germany

    Yu-Heng Hsieh, Fiorella A. Solari & Albert Sickmann

  12. Department of Dermatology, University Hospital Essen and German Cancer Consortium, Essen, Germany

    Luiza Martins Nascentes Melo, Gabrielle Allies & Alpaslan Tasdogan

  13. Department of Gynaecology and Obstetrics, UZ Leuven, Leuven, Belgium

    Patrick Neven & Karen Van Baelen

  14. Laboratory for Translational Breast Cancer Research, Department of Oncology, KU Leuven, Leuven, Belgium

    Marion Maetens, Karen Van Baelen & Christine Desmedt

  15. Spatial Metabolomics Expertise Center, VIB Center for Cancer Biology, VIB, Leuven, Belgium

    Mélanie Planque

  16. Department of Pathology, UZ Leuven, Leuven, Belgium

    Giuseppe Floris

  17. Department of Imaging and Pathology, Laboratory of Translational Cell And Tissue Research, KU Leuven, Leuven, Belgium

    Giuseppe Floris

  18. Medizinische Fakultät, Medizinische Proteom-Center (MPC), Ruhr-Universität Bochum, Bochum, Germany

    Albert Sickmann

  19. WELBIO Department, WEL Research Institute, Wavre, Belgium

    Pierre Close

Authors
  1. Ginevra Doglioni
    View author publications

    Search author on:PubMed Google Scholar

  2. Juan Fernández-García
    View author publications

    Search author on:PubMed Google Scholar

  3. Sebastian Igelmann
    View author publications

    Search author on:PubMed Google Scholar

  4. Patricia Altea-Manzano
    View author publications

    Search author on:PubMed Google Scholar

  5. Arnaud Blomme
    View author publications

    Search author on:PubMed Google Scholar

  6. Rita La Rovere
    View author publications

    Search author on:PubMed Google Scholar

  7. Xiao-Zheng Liu
    View author publications

    Search author on:PubMed Google Scholar

  8. Yawen Liu
    View author publications

    Search author on:PubMed Google Scholar

  9. Tine Tricot
    View author publications

    Search author on:PubMed Google Scholar

  10. Max Nobis
    View author publications

    Search author on:PubMed Google Scholar

  11. Ning An
    View author publications

    Search author on:PubMed Google Scholar

  12. Marine Leclercq
    View author publications

    Search author on:PubMed Google Scholar

  13. Sarah El Kharraz
    View author publications

    Search author on:PubMed Google Scholar

  14. Panagiotis Karras
    View author publications

    Search author on:PubMed Google Scholar

  15. Yu-Heng Hsieh
    View author publications

    Search author on:PubMed Google Scholar

  16. Fiorella A. Solari
    View author publications

    Search author on:PubMed Google Scholar

  17. Luiza Martins Nascentes Melo
    View author publications

    Search author on:PubMed Google Scholar

  18. Gabrielle Allies
    View author publications

    Search author on:PubMed Google Scholar

  19. Annalisa Scopelliti
    View author publications

    Search author on:PubMed Google Scholar

  20. Matteo Rossi
    View author publications

    Search author on:PubMed Google Scholar

  21. Ines Vermeire
    View author publications

    Search author on:PubMed Google Scholar

  22. Dorien Broekaert
    View author publications

    Search author on:PubMed Google Scholar

  23. Ana Margarida Ferreira Campos
    View author publications

    Search author on:PubMed Google Scholar

  24. Patrick Neven
    View author publications

    Search author on:PubMed Google Scholar

  25. Marion Maetens
    View author publications

    Search author on:PubMed Google Scholar

  26. Karen Van Baelen
    View author publications

    Search author on:PubMed Google Scholar

  27. H. Furkan Alkan
    View author publications

    Search author on:PubMed Google Scholar

  28. Mélanie Planque
    View author publications

    Search author on:PubMed Google Scholar

  29. Giuseppe Floris
    View author publications

    Search author on:PubMed Google Scholar

  30. Albert Sickmann
    View author publications

    Search author on:PubMed Google Scholar

  31. Alpaslan Tasdogan
    View author publications

    Search author on:PubMed Google Scholar

  32. Jean-Christophe Marine
    View author publications

    Search author on:PubMed Google Scholar

  33. Colinda L. G. J. Scheele
    View author publications

    Search author on:PubMed Google Scholar

  34. Christine Desmedt
    View author publications

    Search author on:PubMed Google Scholar

  35. Geert Bultynck
    View author publications

    Search author on:PubMed Google Scholar

  36. Pierre Close
    View author publications

    Search author on:PubMed Google Scholar

  37. Sarah-Maria Fendt
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Concept and design: G.D. and S.-M.F. Development of methodology: G.D., S.I., P.A.-M., A.B., R.L.R., M.N., T.T. and P.K. Acquisition of data (including providing animals, acquiring and managing patient samples and providing facilities): G.D., S.I., P.A.-M., A.B., Y.L., M.N., N.A., P.K., Y.-H.H., F.A.S., L.M.N.M., G.A., A. Scopelliti, M.R., I.V., A.M.F.C., P.N., M.M., K.V.B., M.P., G.F., A. Sickmann, J.-C.M., C.L.G.J.S., C.D. and P.C.. Analysis and interpretation of data (including statistical analysis, biostatistics and computational analysis): G.D., J.F.-G., S.I., P.A.-M., R.L.R., M.N., M.L., M.P., A.M.F.C., A.T., G.B. and S.-M.F. Writing, review and/or revision of the manuscript: G.D., T.T. and S.-M.F. Administrative, technical or material support (reporting or organizing data and constructing databases): G.D., J.F.-G., S.E.K., X.-Z.L., T.T., I.V. and D.B. Funding acquisition: G.D., H.F.A. and S.-M.F. Study supervision: S.-M.F.

Corresponding author

Correspondence to Sarah-Maria Fendt.

Ethics declarations

Competing interests

S.-M.F. has received funding from BlackBelt Therapeutics, Auron Therapeutics, Gilead and Alesta Therapeutics, is on the advisory board of Alesta Therapeutics and has consulted for Fund+ and Droia Ventures. The other authors declare no competing interests.

Peer review

Peer review information

Nature thanks the 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 figures and tables

Extended Data Fig. 1 Aggressive lung metastases display increased translation independent of classical regulators or stress stimuli.

a. Total lung metastatic area 17 days after i.v. injections with 4T1 cells in mice pre-treated with tumor secreted factors (TSFs, n = 10) or control medium (n = 14). Data are presented as mean ± sd. Unpaired two-tailed t-test with Welch correction. Representative images are depicted in b. b. Representative H&E stainings from a., two individual lung lobes from one mouse each were selected with automatic tissue detection algorithm in Zen Software and pasted in a white background, scale bars = 1 mm. c. Schematic representation of the pre-metastatic niche formation and lung metastasis model. TSFs, Tumor secreted factors; i.v., intravenous injection. Created with BioRender.com. d. Percentage of cancer cells present in the lung 16 days after i.v. injection with CD90.1+ 4T1 cells in mice pre-treated with TSFs or control medium, measured by flow cytometry (FACS, n = 3 mice) and scRNA-seq (pool of n = 3 mice). FACS data are presented as mean ± s.d. Unpaired two-tailed t-test with Welch correction. e. GSEA normalized enrichment scores (NES) for the top 15 upregulated gene sets found on cancer cells based on scRNA-seq comparing 4T1 lung metastases from mice pre-treated with control medium or TSFs. Dot colors and areas indicate FDR-adjusted P-values and gene-set sizes, respectively. Gene sets related to translation are highlighted in bold. P-values based on fgsea’s adaptive multilevel splitting Monte Carlo approach, subject to FDR adjustment using the Benjamini-Hochberg (BH) approach. f. Volcano plots based on single-cell differential expression analysis comparing cancer cells in 4T1 lung metastases from mice pre-treated with control medium or TSFs. All genes in the Hallmark PI3K-AKT-MTOR signaling gene set (left) and either of the Hallmark MYC targets (V1/V2) gene sets (right) are highlighted in blue. Log2 Fold Changes (FC) and negative log10-transformed P-values are indicated in the x and y axes respectively. The horizontal dashed lines represent (from bottom to top) raw, BH-adjusted and Bonferroni adjusted P-values of 0.05, while the vertical ones represent absolute fold-changes of 1.5. P-values based on Seurat’s Wilcoxon rank-sum test implementation. g. ATF4 and phosphorylated eIF2α levels in 4T1 lung metastases in mice pre-treated with TSFs (n = 3) or control medium (n = 3), and in cultured 4T1 cells treated with or without tunicamycin induced ER-stress. h. Schematic representation of eIF5A hypusination pathway. Enzymes are indicated in italics. Solid lines represent single reactions. dH = deoxyhypusine; H = hypusine. Created with BioRender.com. i. Hypusine levels in 4T1 (left) or EMT6.5 (right) cells silenced for Dhps or scramble shRNA. Quantification of hypusine signal normalized over total eIF5A signal is indicated on top of each lane. j. Box and whisker plots of aspartate concentrations in control- or primary tumor (PT)-conditioned medium and lung interstitial fluid of mice pre-treated with TSFs (n = 23) or control medium (n = 22). Box hinges indicate the 1st/3rd quartiles of the corresponding data, while box mid-lines represent medians, and whiskers span the range of the data. Individual data points are indicated by the white dots. One-way ANOVA (P < 0.0001) with Tukey’s multiple-comparison tests. k. Box and whisker plots of aspartate concentrations in the blood plasma and lung interstitial fluid of healthy mice or mice injected (m.f.p.) with 4T07, 4T1 or EMT6.5 breast cancer cells, 17 days after injection (plasma, n = 13; healthy, n = 10; 4T07, n = 11; 4T1, n = 10; EMT6.5, n = 7). Box hinges indicate the 1st/3rd quartiles of the corresponding data, while box mid-lines represent medians, and whiskers span the range of the data. Individual data points are indicated by the white dots. One-way ANOVA (P < 0.0001) with Tukey’s multiple-comparison tests; P < 0.0001 between plasma and all lung interstitial fluid conditions. l. Percentage of cancer cells present in the liver 14 days after intrasplenic (i.s.) injections with CD90.1+ 4T1 cells in mice pre-treated with TSFs (n = 8) or control medium (n = 8). Data are presented as mean ± sd. Unpaired two-tailed t-test with Welch correction. I.s. injection of 4T1 cancer cells did not yield a change in liver metastases in TSF-treated mice compared to control mice. m. Box and whisker plots of aspartate concentrations in the liver and bone interstitial fluid and brain cerebrospinal fluid of mice pre-treated with TSFs or control medium (liver, n = 10 control, n = 10 TSFs; bone, n = 10 control, n = 10 TSFs; brain, n = 6 control, n = 7 TSFs). Box hinges indicate the 1st/3rd quartiles of the corresponding data, while box mid-lines represent medians, and whiskers span the range of the data. Individual data points are indicated by the white dots. Two-way ANOVA with Šídák’s multiple-comparison tests. No statistically significant changes are observed upon TSF treatment for any of the organs. n. Box and whisker plots of aspartate concentrations in the blood plasma and lung interstitial fluid of mice pre-treated with daily injections of aspartate (20 mM, i.p., 10 days, n = 8) or PBS (n = 8). Box hinges indicate the 1st/3rd quartiles of the corresponding data, while box mid-lines represent medians, and whiskers span the range of the data. Individual data points are indicated by the white dots. Two-way ANOVA with Tukey’s multiple-comparison tests; P < 0.0001 between all plasma and all lung interstitial fluid conditions, no statistically significant changes are observed upon aspartate treatment in blood plasma. o. Ratio of aspartate concentrations in the lung interstitial fluid of mice pre-treated with daily injections of aspartate (20 mM, i.p., 10 days) vs PBS. Days indicate the time of interstitial fluid collection after the last injection of aspartate or PBS. Data are presented as mean ± sd. Unpaired two-tailed t-test with Welch correction at each time point. p. Average metabolite concentrations in the lung interstitial fluid of mice pre-treated with daily injections of aspartate (20 mM, i.p., 10 days, n ≥ 8) or PBS (n ≥ 8). * indicates P = 0.00174. All other metabolites show no statistically significant changes. Unpaired two-tailed t-test with Welch correction between PBS-treated and aspartate-treated mice. q. Hypusine and Dohh detected in lung metastases 10 days after i.v. injections with 4T1 cancer cells, in mice pre-treated with daily injections of aspartate (20 mM, i.p., 10 days) or PBS, assessed by multiplex immunohistochemistry. Left: representative images from n = 5 4T1 PBS, n = 4 4T1 aspartate independent mice are shown. White = EpCAM; red = Hypusine; green = Dohh; blue = DAPI nuclear staining. Scale bars = 100 μm. Scale bars zoom-in = 5 μm. The corresponding H&E staining is represented on the right of the panel. Right: quantification of lung metastasis cells positive for Dohh or Hypusine. Box hinges indicate the 1st/3rd quartiles of the corresponding data, while box mid-lines represent medians, and whiskers span the range of the data. Individual data points are indicated by the white dots. Unpaired two-tailed t-test with Welch correction. r. Percentage of cancer cells present in the lung 14 days after i.v. injections with CD90.1+ EMT6.5 cells silenced for Dhps or scramble shRNA in mice pre-treated with daily injections of aspartate (20 mM, i.p., 10 days) or PBS. N = 10 shScr PBS, n = 9 shScr aspartate, n = 10 shDhps aspartate. Data are presented as mean ± sd. One-way ANOVA (P = 0.0029) with Tukey’s multiple-comparison tests.

Source Data

Extended Data Fig. 2 Pulmonary aspartate increases lung metastasis aggressiveness via NMDA receptor activity.

a. Hypusine levels in 4T1 (top) and MCF10A HRASV12 (bottom) spheroids grown in lung-like medium (LLM) supplemented with or without aspartate. A representative image of n = 3 independent experiments is shown. Quantification of hypusine signal normalized over total eIF5A signal is indicated on top of each lane. b. Total spheroid areas for 4T1 (left) and MCF10A HRASV12 (right) cells grown in LLM supplemented with or without aspartate (n = 14 4T1 no aspartate, n = 13 MCF10A HRASV12 no aspartate, n = 12 4T1 aspartate, n = 8 MCF10A HRASV12 aspartate). Data are presented as mean ± s.d. (n indicates independent samples). Unpaired two-tailed t-test with Welch correction. Representative images are shown on the right. Scale bar = 250 μm. c. Total spheroid areas for HUH7 (left) and B16F10 (right) cells grown in LLM supplemented with or without aspartate. N = 11 HUH7 no aspartate, n = 9 B16F10 no aspartate, n = 10 HUH7 aspartate, n = 9 B16F10 aspartate. Data are presented as mean ± s.d. (n indicates independent samples). Unpaired two-tailed t-test with Welch correction. Representative images are shown on the right. Scale bar = 250 μm. d. Hypusine levels in HUH7 (left) and B16F10 (right) spheroids grown in LLM supplemented with or without aspartate. A representative image of n = 3 independent experiments is shown. Quantification of hypusine signal normalized over total eIF5A signal is indicated on top of each lane. e. Top: SUnSET assay showing puromycin incorporation in 4T1 and MCF10A HRASV12 spheroids grown in LLM supplemented with or without aspartate. Spheroids were treated with cycloheximide (CHX, 100 nM) or vehicle, followed by puromycin (10 μg/ml). Bottom: Ponceau red staining showing equal protein loading. f. Left: GSEA normalized enrichment scores (NES) for the top 15 upregulated gene sets based on total RNA sequencing data for 4T1 spheroids grown in LLM supplemented with aspartate vs no aspartate. Dot colors and areas indicate FDR-adjusted P-values and gene-set sizes, respectively. Gene sets related to translation are highlighted in bold. Right: Volcano plot based on differential expression analysis of total RNA sequencing data for 4T1 spheroids grown in LLM supplemented with or without aspartate. Genes in Reactome’s translation gene set are highlighted in blue. Log2 Fold Changes (FC) and negative log10-transformed P-values are indicated in the x and y axes respectively. Both axes were further subject to inverse hyperbolic sine transformation for improved visualization. The horizontal dashed lines represent (from bottom to top) raw and BH-adjusted P-values of 0.05, while the vertical ones represent absolute fold-changes of 1.5. P-values based on fgsea’s adaptive multilevel splitting Monte Carlo approach, subject to FDR adjustment using the Benjamini-Hochberg (BH) approach. g. Hypusine levels in 4T1 (top) and MCF10A HRASV12 (bottom) spheroids silenced for eIf5a1, eIf5a2, or scramble shRNA, grown in LLM supplemented with or without aspartate. A representative image of n = 3 independent experiments is shown. Quantification of hypusine signal normalized over actin signal is indicated on top of each lane. h. Total spheroid areas for 4T1 cells silenced for eIf5a1 (n = 8 4T1 no aspartate, n = 8 4T1 aspartate), eIf5a2 (n = 10 4T1 no aspartate, n = 10 4T1 aspartate) or scramble shRNA (n = 9 4T1 no aspartate, n =  10 4T1 aspartate), grown in LLM supplemented with or without aspartate. Data are presented as mean ± s.d. (n indicates independent samples). Two-way ANOVA with Tukey’s multiple-comparison tests. i. Total spheroid areas for MCF10A HRASV12 cells silenced for eIF5a1 (n = 12 MCF10A HRASV12 no aspartate, n = 11 MCF10A HRASV12 aspartate), eIF5a2 (n = 11 MCF10A HRASV12 no aspartate, n = 10 MCF10A HRASV12 aspartate) or scramble shRNA (n = 10 MCF10A HRASV12 no aspartate, n = 11 MCF10A HRASV12 aspartate), grown in LLM supplemented with or without aspartate. Data are presented as mean ± s.d. (n indicates independent samples). Two-way ANOVA with Tukey’s multiple-comparison tests. j. 13C4-labeled fractions for various metabolites downstream of aspartate (included) in 4T1 spheroids (n = 3) grown in LLM supplemented with 13C4-aspartate. Data are presented as mean ± s.d. A representative graph of n = 3 experiments is shown. k. Average fractions of total carbon corresponding to 13C isotopes in different metabolites, in 4T1 spheroids (n = 3) grown in LLM supplemented with 13C4-aspartate. l. Histograms of CD44 surface expression in 4T1 spheroids dissociated with Trypsin (n = 3) or Accutase (n = 3). Data are normalized to mode (y axis is scaled so that the maximum of each curve is at 100) and further smoothed for display purposes. m. Intracellular vs cell surface levels of 13C-glutamate in 4T1 spheroids silenced for Grin2d (n = 3) or scramble shRNA (n = 3), grown in LLM supplemented with 13C5-glutamate. Data are presented as mean ± s.d. (n indicates independent samples). Two-way ANOVA with Tukey’s multiple-comparison tests. n. Total spheroid areas for 4T1 cells grown in LLM supplemented with or without L- or D-aspartate. N = 15 no aspartate, n = 16 L-aspartate, n = 13 D-aspartate. Data are presented as mean ± s.d. (n indicates independent samples). One-way ANOVA (P < 0.001) with Tukey’s multiple-comparison tests. Representative images are shown on the bottom. Scale bar = 250 μm. o. Hypusine levels in 4T1 spheroids grown in LLM supplemented with or without L- or D-aspartate. A representative image of n = 3 experiments is shown. Quantification of hypusine signal normalized over eIF5A signal is indicated on top of each lane. p. Hypusine levels in 4T1 spheroids grown in LLM supplemented with or without aspartate or glutamate. A representative image of n = 3 experiments is shown. Quantification of hypusine signal normalized over eIF5A signal is indicated on top of each lane. q. Grin2d detected in lung metastases 14 days after i.v. injections with 4T1 cancer cells, in mice pre-treated with daily injections of aspartate (20 mM, i.p., 10 days) or PBS, assessed by multiplex immunohistochemistry. Representative images from n = 5 4T1 shScr PBS, n = 5 4T1 shScr aspartate, n = 5 4T1 shGrin2d PBS, n = 5 4T1 shGrin2d aspartate independent mice are shown. Green = membrane marker ATP1A1; Red = Grin2d; Blue = DAPI nuclear staining. Scale bars = 25 μm. Arrows indicate membrane colocalization of Grin2d and ATP1A1. This staining is part of a multiplex staining and only relevant stains are shown. r. Relative fractions of Grin2b and Grin2d copies quantified by droplet digital PCR in 4T1 lung metastases, n = 3. Data are presented as mean ± s.d. (n indicates independent samples). Unpaired two-tailed t-test with Welch correction. s. Average ionomycin-normalized calcium response traces in 4T1 cells overexpressing Grin2b. The shaded ribbon represents standard error of the mean at each time point, while the grey rectangles indicate sequential addition of aspartate, glutamate and ionomycin at the times indicated by the arrows. A representative experiment (n = 38 cells) out of 2 independent experiments is shown. Grin2b overexpression stimulated a calcium response following glutamate but not aspartate addition in 4T1 cells. This shows that the NMDA receptor subunit expression determines the agonist to which breast cancer cells respond. t. Ionomycin-normalized calcium response upon sequential addition of aspartate and glutamate (n = 79 cells). Unpaired two-tailed t-test with Welch correction.

Source Data

Extended Data Fig. 3 Aspartate-induced NMDA receptor activity promotes CREB phosphorylation.

a. Hypusine levels in 4T1 (left) and MCF10A HRASV12 (right) spheroids silenced for Grin2d or scramble shRNA, grown in lung-like medium (LLM) supplemented with or without aspartate. A representative image of n = 3 experiments is shown. Quantification of hypusine signal normalized over total eIF5A signal is indicated on top of each lane. b. Hypusine levels in 4T1 lung metastases silenced for Grin2d (n = 3) or scramble shRNA (n = 3) in mice pre-treated with tumor secreted factors (TSFs). Quantification of hypusine signal normalized over eIF5A signal is indicated on top of each lane. c. Total spheroid areas for 4T1 (left) and MCF10A HRASV12 (right) cells silenced for Grin2d (n = 6 4T1 no aspartate, n = 6 MCF10A HRASV12 no aspartate, n = 6 4T1 aspartate, n = 6 MCF10A HRASV12 aspartate) or scramble shRNA (n = 6 4T1 no aspartate, n = 6 MCF10A HRASV12 no aspartate, n = 4 4T1 aspartate, n = 6 MCF10A HRASV12 aspartate), grown in LLM supplemented with or without aspartate. Data are presented as mean ± s.d. (n indicates independent samples). Two-way ANOVA with Tukey’s multiple-comparison tests. Representative images are shown on the right. Scale bar = 250 μm. Data from the scramble group are the same as those in the scramble group from Fig. 2a. d. Percentage of cancer cells present in the lung 16 days after i.v. injections with CD90.1+ 4T1 cells silenced for Grin2d (with two different shRNA sequences) or scramble shRNA in mice pre-treated with TSFs or control medium (n = 5 4T1 shScr control medium, n = 5 4T1 shScr TSFs, n = 5 4T1 shGrin2d#1 control medium, n = 5 4T1 shGrin2d#1 TSFs, n = 5 4T1 shGrin2d#2 control medium, n = 5 4T1 shGrin2d#2 TSFs). Data are presented as mean ± sd. Two-way ANOVA with Šídák’s multiple-comparison tests. e. Total spheroid areas for 4T1 (left) and MCF10A HRASV12 (right) cells grown in LLM supplemented with or without aspartate and treated with or without the NMDA receptor inhibitor MK-801 for 3 days. N = 8 4T1 no aspartate, n = 6 4T1 aspartate, n = 6 4T1 no aspartate with MK-801, n = 7 4T1 aspartate with MK-801, n = 6 MCF10A HRASV12 no aspartate, n = 7 MCF10A HRASV12 aspartate, n = 6 MCF10A HRASV12 no aspartate with MK-801, n = 6 MCF10A HRASV12 aspartate with MK-801. Data are presented as mean ± s.d. (n indicates independent samples). Representative images are shown on the right. Scale bar = 250 μm. Two-way ANOVA with Tukey’s multiple-comparison tests. f. Hypusine levels in 4T1 (left) and MCF10A HRASV12 (right) spheroids grown in LLM supplemented with or without aspartate and treated with or without the NMDA receptor inhibitor MK-801 for 3 days. A representative image of n = 3 independent experiments is shown. Quantification of hypusine signal normalized over eIF5A signal is indicated on top of each lane. g. Relative mRNA expression of Dohh and Dhps in 4T1 and MCF10A HRASV12 spheroids grown in LLM supplemented with or without aspartate. Data for each gene and cell line are normalized relative to the average of the respective control (no aspartate) condition. Bars represent averages, and single dots individual replicates. Error bars represent ± s.d. (n = 3 independent replicates). Three-way ANOVA with Tukey’s multiple-comparison tests. h. Dohh and Dhps mRNA expression levels based on scRNA-seq data for cancer cells in 4T1 lung metastases from mice pre-treated with TSFs (n = 139 cells) or control medium (n = 29 cells). Data are normalized as counts per 100k reads (CP100K). Crossbars represent mean ± s.e.m. i. Relative mRNA expression of Dohh in 4T1 spheroids silenced for Grin2d or scramble shRNA, grown in LLM supplemented with or without aspartate. Data are normalized relative to the average of the respective control (scramble no aspartate) condition. Bars represent averages, and single dots individual replicates. Error bars represent ± s.d. (n = 4 independent replicates). Two-way ANOVA with Šídák’s multiple-comparison tests. j. Hypusine levels in 4T1 spheroids overexpressing Dohh or an empty vector, grown in LLM supplemented with or without aspartate. A representative image of n = 3 experiments is shown. Quantification of hypusine signal normalized over total eIF5A signal is indicated on top of each lane. k. Total spheroid areas for 4T1 cells overexpressing Dohh (n = 9) or an empty vector (n = 9), grown in LLM supplemented without aspartate for 3 days. Data are presented as mean ± s.d. (n indicates independent samples). Unpaired two-tailed t-test with Welch correction. l. Venn diagram depicting the intersection between transcription factors downstream of NMDA receptor and predicted to bind to Dohh or Dhps. Prediction was based on the JASPAR Predicted Transcription Factor Targets from the Harmonizome database (maayanlab.cloud/Harmonizome/dataset). m. Phosphorylated CREB levels in 4T1 (left) and MCF10A HRASV12 (right) spheroids silenced for Grin2d or scramble shRNA, grown in LLM supplemented with or without aspartate. A representative image of n = 3 experiments is shown. Quantification of phosphorylated CREB signal normalized over total CREB signal is indicated on top of each lane. n. Left: representative H&E staining for 4T1 lung metastases represented in Fig. 3a, Fig. 4a,c, Extended Data Fig. 3n. Middle: phosphorylated CREB detected in lung metastases 14 days after i.v. injection with 4T1 cancer cells, in mice pre-treated with daily injections of aspartate (20 mM, i.p., 10 days) or PBS, assessed by multiplex immunohistochemistry. Representative images from n = 5 4T1 shScr PBS, n = 5 4T1 shScr aspartate, n = 5 4T1 shGrin2d PBS, n = 5 4T1 shGrin2d aspartate independent mice are shown. Blue = DAPI nuclear staining; magenta = pCREB. Scale bars = 25 μm. Right: ratio of pCREB intensity normalized on 4T1 shScr PBS. Box hinges indicate the 1st/3rd quartiles of the corresponding data, while box mid-lines represent medians, and whiskers span the range of the data. Individual data points are indicated by the white dots. Two-way ANOVA with Tukey’s multiple-comparison tests. This staining is part of a multiplex staining and only relevant stains are shown. Stainings for H&E and pCREB orginate from consecutive cuts. The same metastatic regions are also used in Fig. 3a and Fig. 4a,c. o. Relative mRNA expression of Dohh (left) and Dhps (right) in 4T1 and MCF10A HRASV12 spheroids grown in LLM supplemented with aspartate and treated with or without the CREB inhibitor Compound3i. Data for each gene are normalized relative to the average of the respective control (no CREB inhibitor) condition. Bars represent averages, and single dots individual replicates. Error bars represent ± s.d. (n = 3 independent replicates). Two-way ANOVA with Šídák’s multiple-comparison tests. p. Phosphorylated CREB (left) and hypusine (right) levels in 4T1 and MCF10A HRASV12 spheroids grown in LLM supplemented with aspartate and treated with or without the CREB inhibitor Compound3i. A representative image of n = 3 experiments is shown. Quantification of phosphorylated CREB signal normalized over total CREB signal or hypusine signal normalized over total eIF5A signal is indicated on top of each lane. q. Total spheroid areas for 4T1 (left) and MCF10A HRASV12 (right) cells grown in LLM supplemented with aspartate and treated with or without the CREB inhibitor Compound3i. N = 12 4T1 aspartate, n = 8 4T1 aspartate with CREB inhibitor, n = 12 MCF10A HRASV12 aspartate, n = 12 MCF10A HRASV12 aspartate with CREB inhibitor. Data are presented as mean ± s.d. (n indicates independent samples). Unpaired two-tailed t-test with Welch correction. Representative images are shown on the right. Scale bar = 250 μm.

Source Data

Extended Data Fig. 4 eIF5A hypusination results in TGFβ-mediated collagen synthesis.

a. GSEA normalized enrichment scores (NES) for the top 15 gene sets commonly upregulated in translation based on changes in the ratio of Polysomal to Subpolysomal RNA levels, between 4T1 spheroids silenced for scramble shRNA grown in lung-like medium (LLM) supplemented with aspartate and 4T1 spheroids silenced for (i) Dhps (orange symbols) or (ii) Grin2d (purple symbols) grown in LLM supplemented with aspartate, or (iii) 4T1 spheroids silenced for scramble shRNA grown in LLM without aspartate (green symbols). White dots represent the average NES over all three comparisons. Gene sets related to TGFβ signaling are highlighted in bold. b. Word cloud highlighting the top 100 most frequently found terms among enriched gene sets, based on GSEA results analogous to those used to generate Extended Data Fig. 4a, but considering all three comparisons simultaneously (see Methods). c. Phosphorylated SMAD3 levels in 4T1 spheroids silenced for Grin2d or scramble shRNA (left), HUH7 (middle) and B16F10 (right) spheroids, grown in LLM supplemented with or without aspartate. A representative image of n = 3 experiments is shown. Quantification of phosphorylated SMAD3 signal normalized over total SMAD3 signal is indicated on top of each lane. d. Phosphorylated SMAD3 levels in 4T1 (left) and MCF10A HRASV12 (right) spheroids grown in LLM supplemented with aspartate and treated with or without a TGFβ inhibitor. A representative image of n = 3 experiments is shown. Quantification of phosphorylated SMAD3 signal normalized over total SMAD3 signal is indicated on top of each lane. e. Total spheroid areas for 4T1 (left) and MCF10A HRASV12 (right) cells grown in LLM supplemented with or without aspartate and treated with or without the TGFβ inhibitor for 5 (4T1) or 3 days (MCF10A HRASV12). N = 6 4T1 no aspartate, n = 11 4T1 no aspartate with TGFβ inhibitor, n = 11 4T1 aspartate, n = 9 aspartate with TGFβ inhibitor; n = 7 MCF10A HRASV12 no aspartate, n = 6 MCF10A HRASV12 aspartate, n = 9 MCF10A HRASV12 no aspartate with TGFβ inhibitor, n = 9 MCF10A HRASV12 aspartate with TGFβ inhibitor. Data are presented as mean ± s.d. (n indicates independent samples). Two-way ANOVA with Tukey’s multiple-comparison tests. f. GSEA normalized enrichment scores (NES) for gene sets containing either of the terms COLLAGEN, MATRISOME, ECM, and EXTRACELLULAR_MATRIX found on cancer cells based on scRNA-seq comparing 4T1 lung metastases from mice pre-treated with control medium or TSFs. Dot colors and areas indicate FDR-adjusted P-values and gene-set sized, respectively. P-values based on fgsea’s adaptive multilevel splitting Monte Carlo approach, subject to FDR adjustment using the Benjamini-Hochberg (BH) approach. g. Relative mRNA expression of Col1a1 in 4T1 (top) and MCF10A HRASV12 (bottom) spheroids grown in LLM supplemented with or without aspartate. Data for each gene and cell line are normalized relative to the average of the respective control (no aspartate) condition. Bars represent averages, and single dots individual replicates. Error bars represent ± s.d. (n = 3 independent replicates). Unpaired two-tailed t-test with Welch correction. h. Box and whisker plots of relative abundance of collagen I in 4T1 (left) and MCF10A HRASV12 (right) spheroids grown in LLM supplemented with or without aspartate, measured by immunofluorescence. N = 10 4T1 no aspartate, n = 6 4T1 aspartate, and n = 5 MCF10A HRASV12 no aspartate and n = 9 MCF10A HRASV12 aspartate. The total fluorescence intensity was normalized over the number of DAPI-stained nuclei. Relative fluorescence intensities per cell are depicted, normalized to the mean intensity for the control condition. Box hinges indicate the 1st/3rd quartiles of the corresponding data, while box mid-lines represent medians, and whiskers span the range of the data. Individual data points are indicated by the white dots. Unpaired two-tailed t-test with Welch correction. Representative three-dimensional representation are depicted on the right. Blue, DAPI-stained nuclei; red, collagen I. i. Box and whisker plots of relative abundance of collagen I in 4T1 spheroids silenced for Dhps (top) or Grin2d (bottom) or scramble shRNA, grown in LLM supplemented with or without aspartate, measured by immunofluorescence. Top: n = 15 no aspartate scramble, n = 11 aspartate scramble, n = 5 no aspartate shDhps, n = 6 aspartate shDhps. Bottom: n = 6 no aspartate scramble, n = 6 aspartate scramble, n = 7 no aspartate shGrin2d, n = 6 aspartate shGrin2d. The total fluorescence intensity was normalized over the number of DAPI-stained nuclei. Relative fluorescence intensities per cell are depicted, normalized to the mean intensity for the control condition. Box hinges indicate the 1st/3rd quartiles of the corresponding data, while box mid-lines represent medians, and whiskers span the range of the data. Individual data points are indicated by the white dots. Two-way ANOVA with Tukey’s multiple-comparison tests. Representative three-dimensional representation are depicted in Extended Data Fig. 4j. j. Box and whisker plots of relative abundance of collagen I in MCF10A HRASV12 spheroids silenced for DHPS, GRIN2D or scramble shRNA, grown in LLM supplemented with or without aspartate, measured by immunofluorescence. N = 6 no aspartate scramble, n = 7 aspartate scramble, n = 8 no aspartate shDHPS, n = 6 aspartate shDHPS, n = 8 no aspartate shGRIN2D, n = 6 aspartate shGRIN2D. The total fluorescence intensity was normalized over the number of DAPI-stained nuclei. Relative fluorescence intensities per cell are depicted, normalized to the mean intensity for the control condition. Box hinges indicate the 1st/3rd quartiles of the corresponding data, while box mid-lines represent medians, and whiskers span the range of the data. Individual data points are indicated by the white dots. Two-way ANOVA with Tukey’s multiple-comparison tests. Representative three-dimensional representation are depicted on the right and include samples shown in Extended Data Fig. 4i. Blue, DAPI-stained nuclei; red, collagen I. k. Box and whisker plots of relative abundance of collagen I in 4T1 (left) and MCF10A HRASV12 (right) spheroids grown in LLM supplemented with or without aspartate and treated with or without the TGFβ inhibitor, measured by immunofluorescence. N = 5 4T1 no aspartate, n = 4 4T1 aspartate, n = 6 4T1 no aspartate with TGFβ inhibitor, n = 5 4T1 no aspartate with TGFβ inhibitor, and n = 5 MCF10A HRASV12 no aspartate, n = 4 MCF10A HRASV12 aspartate, n = 5 MCF10A HRASV12 no aspartate with TGFβ inhibitor, n = 6 MCF10A HRASV12 aspartate with TGFβ inhibitor. The total fluorescence intensity was normalized over the number of DAPI-stained nuclei. Relative fluorescence intensities per cell are depicted, normalized to the mean intensity for the control condition. Box hinges indicate the 1st/3rd quartiles of the corresponding data, while box mid-lines represent medians, and whiskers span the range of the data. Individual data points are indicated by the white dots. Two-way ANOVA with Šídák’s multiple-comparison tests. Representative three-dimensional representation are depicted on the right. Blue, DAPI-stained nuclei; red, collagen I. l. Relative spheroid areas for 4T1 cells silenced for Grin2d, Dhps or scramble shRNA grown in LLM with 1.5% Matrigel, supplemented with or without aspartate and with or without 1.5% Collagen-I. N = 3 no aspartate scramble, n = 3 aspartate scramble, n = 4 no aspartate scramble with Collagen-I, n = 5 aspartate scramble with Collagen-I; n = 3 no aspartate shDhps, n = 3 aspartate shDhps, n = 5 no aspartate shDhps with Collagen-I, n = 4 aspartate shDhps with Collagen-I; n = 3 no aspartate shGrin2d, n = 4 aspartate shGrin2d, n = 7 no aspartate shGrin2d with Collagen-I, n = 7 aspartate shGrin2d with Collagen-I. Data are presented as mean ± s.d, normalized to the mean spheroid areas for the respective no aspartate conditions (n indicates independent samples). Two-way ANOVA with Tukey’s multiple-comparison tests. m. Relative spheroid areas for 4T1 cells grown in LLM with 1.5% Matrigel, supplemented with or without aspartate and with or without 1.5% Collagen-I and treated with or without the TGFβ inhibitor. N = 4 no aspartate, n = 4 aspartate, n = 4 no aspartate with Collagen-I, n = 4 aspartate with Collagen-I; n = 4 no aspartate with TGFβ inhibitor, n = 4 aspartate with TGFβ inhibitor, n = 5 no aspartate with TGFβ inhibitor and Collagen-I, n = 4 aspartate with TGFβ inhibitor and Collagen-I. Data are presented as mean ± s.d, normalized to the mean spheroid area for the respective no aspartate conditions (n indicates independent samples). Two-way ANOVA with Tukey’s multiple-comparison tests. n. Top: Quantification of linearized collagen based on Picrosirius Red staining and polarized light microscopy detected in EMT6.5 lung metastases silenced for Dhps or scramble shRNA in mice pre-treated with daily injections of aspartate (20 mM, i.p., 10 days) or PBS. Significant collagen red/yellow/green increase (*) 0.0023/0.1758/0.0042 EMT6.5 aspartate scramble, n = 5 PBS and n = 5 aspartate. Significant collagen red/yellow/green decrease (**) < 0.0001/ < 0.0001/ < 0.0001 (EMT6.5 shDhps), n = 5 PBS scramble, n = 5 aspartate scramble and n = 5 aspartate shDhps. Data are normalized by metastasis area (megapixel) and expressed as mean ± SEM. Two-way ANOVA with Šídák’s multiple-comparison tests. Bottom: representative images of linearized collagen based on Picrosirius Red staining and polarized light microscopy detected in EMT6.5 lung metastases. Red color mostly indicates thick collagen I fibers and green color mostly indicates thin collagen III fibers. A representative image of n = 5 EMT6.5 PBS scramble, n = 5 EMT6.5 aspartate scramble, n = 5 EMT6.5 aspartate shDhps lung metastases is shown. Scale bars = 20 μm.

Source Data

Extended Data Fig. 5 Expression of NMDA receptor subunits and staining of proteins indicative of aspartate signaling in patients with breast cancer.

a. RMA-normalized mRNA expression levels of NMDA-receptor-related genes in breast cancer-derived lung metastases vs breast cancer-derived bone/brain/liver metastases, based on patient biopsies (GSE14018). N = 16 lung metastases and n = 20 non-lung metastases samples (n = 8 bone metastases, n = 7 brain metastases, n = 5 liver metastases). Box hinges indicate the 1st/3rd quartiles of the corresponding data, while box mid-lines represent medians, and whiskers span the range of the data excluding outliers (data points more than 1.5 x IQR away from the 1st/3rd quartiles, where IQR = inter-quartile range), with the latter indicated in red. Individual data points are indicated by the white dots. Statistics show P-values based on differential expression analysis with limma. b. Top: DAPI and PanCK (left), GRIN2D and membrane marker (right) detected in metastatic and adjacent lungs from the UPTIDER breast cancer patient 2026 shown in Fig. 5e, assessed by multiplex immunohistochemistry. Blue = DAPI nuclear staining, red = GRIN2D, green = membrane marker ATP1A1 (scale bars = 100 μm). Membrane marker has been added to identify surface localization of GRIN2D, the same exact representative image was used in Fig. 5e. Bottom: representative image of H&E staining of the whole lung from patient 2026, scale bar = 2 mm. H&E and multiplex immunohistochemistry stainings originate from non-consecutive cuts. c. Quantification of GRIN2D, Hypusine, DOHH, nuclear pCREB, nuclear pSMAD3, COL1A1 and COL6A1 intensities in tissues derived from the UPTIDER breast cancer patients. For GRIN2D, Hypusine and DOHH, fluorescence intensity values per single cell are depicted, across 5 independent regions. For pCREB and pSMAD3, fluorescence intensity values per nucleus are depicted, across 5 independent regions. For COL1A1 and COL6A1, mean fluorescence intensities per unit area are depicted, across 5 independent regions. Two-way ANOVA, with P values for the tissue type (metastatic vs adjacent) factor shown above the graphs. All single comparisons per patients were significant based on unpaired two-tailed t-test with Welch correction, except for COL1A1 and COL6A1, for which single comparisons per patients were significant in 7 out of 10 measurements based on unpaired two-tailed t-test with Welch correction. Representative images are shown in Fig. 5e and Extended Data Fig. 6. d. Representative images of linearized collagen based on Picrosirius Red staining and polarized light microscopy detected in metastatic and adjacent lungs from the UPTIDER breast cancer patient 2026, quantified in Fig. 5f. Red color mostly indicates thick collagen I fibers and green color mostly indicates thin collagen III fibers. A representative image of n = 5 patients is shown. Scale bars = 1 mm.

Source Data

Extended Data Fig. 6 Representative images of IHC performed in breast cancer patients.

Left: GRIN2D, Hypusine, DOHH, phosphorylated CREB, COL1A1, COL6A1, phosphorylated SMAD3 and PanCK detected in metastatic and adjacent lungs from the UPTIDER breast cancer patients (n = 7), assessed by multiplex immunohistochemistry. Blue = DAPI nuclear staining, green = membrane marker ATP1A1; red = Hypusine, yellow = DOHH; magenta = phosphorylated CREB; cyan = COL1A1; green = COL6A1; yellow = phosphorylated SMAD3; white = PanCK. Scale bars = 50 μm. Membrane marker ATP1A1 has been added to identify surface localization of GRIN2D. Quantification for all patients, n = 7 (for a total of 5 regions per patient) is shown on Extended Data Fig. 5c. Right: Representative images of H&E stainings of the whole lungs for each patient, scale bars = 2 mm. H&E and multiplex immunohistochemistry stainings originate from non-consecutive cuts.

Extended Data Fig. 7 Protein and mRNA expression of genetically modified breast cancer cells and method validation.

a. Relative mRNA expression levels of Dhps in 4T1 spheroids silenced for Dhps or scramble shRNA. Data are normalized relative to the average of the control (shRNA against scramble sequence) condition. Bars represent averages, and single dots individual replicates. Error bars represent ± s.d (n = 3 independent replicates). Unpaired two-tailed t-test with Welch correction. b. Relative mRNA expression levels of Grin2d in 4T1 spheroids silenced for Grin2d or scramble shRNA. Data are normalized relative to the average of the control (shRNA against scramble sequence) condition. Bars represent averages, and single dots individual replicates (n = 3 independent replicates). Unpaired two-tailed t-test with Welch correction. c. Relative mRNA expression levels of DHPS in MCF10A HRASV12 spheroids silenced for DHPS or scramble shRNA. Data are normalized relative to the average of the control (shRNA against scramble sequence) condition. Bars represent averages, and single dots individual replicates (n = 3 independent replicates). Unpaired two-tailed t-test with Welch correction. d. Relative mRNA expression levels of GRIN2D in MCF10A HRASV12 spheroids silenced for GRIN2D or scramble shRNA. Data are normalized relative to the average of the control (shRNA against scramble sequence) condition. Bars represent averages, and single dots individual replicates (n = 3 independent replicates). Unpaired two-tailed t-test with Welch correction. e. Hypusine levels in MCF10A HRASV12 cells silenced for DHPS or scramble shRNA. Quantification of hypusine signal normalized over total eIF5A signal is indicated on top of each lane. f. Relative mRNA expression levels of Dhps in EMT6.5 cells silenced for Dhps or scramble shRNA. Data are normalized relative to the average of the control (shRNA against scramble sequence) condition. Bars represent averages, and single dots individual replicates (n = 3 independent replicates). Unpaired two-tailed t-test with Welch correction. g. Relative mRNA expression levels of Grin2d in EMT6.5 cells silenced for Grin2d or scramble shRNA. Data are normalized relative to the average of the control (shRNA against scramble sequence) condition. Bars represent averages, and single dots individual replicates (n = 3 independent replicates). Unpaired two-tailed t-test with Welch correction. h. Relative mRNA expression levels of Dohh in 4T1 cells expressing an overexpression vector for Dohh or an empty vector. Data are normalized relative to the average of the control (empty vector) condition. Bars represent averages, and single dots individual replicates (n = 3 independent replicates). OE = overexpression. Unpaired two-tailed t-test with Welch correction. i. Relative mRNA expression levels of Grin2d in 4T1 cells expressing an overexpression vector for Grin2d or an empty vector. Data are normalized relative to the average of the control (empty vector) condition. Bars represent averages, and single dots individual replicates (n = 3 independent replicates). Unpaired two-tailed t-test with Welch correction. j. Relative mRNA expression levels of Grin2b in 4T1 cells expressing an overexpression vector for Grin2b or an empty vector. Data are normalized relative to the average of the control (empty vector) condition. Bars represent averages, and single dots individual replicates (n = 3 independent replicates). Unpaired two-tailed t-test with Welch correction. k. Relative mRNA expression levels of eIf5a1 and eIf5a2 in 4T1 cells silenced for eIf5a1 or eIf5a2 or scramble shRNA. Data are normalized relative to the average of the control (shRNA against scramble sequence) condition. Bars represent averages, and single dots individual replicates. Error bars represent ± s.d (n = 3 independent replicates). Two-way ANOVA with Šídák’s multiple-comparison tests. l. Relative mRNA expression levels of eIF5A1 and eIF5A2 in MCF10A HRASV12 cells silenced for eIF5A1 or eIF5A2 or scramble shRNA. Data are normalized relative to the average of the control (shRNA against scramble sequence) condition. Bars represent averages, and single dots individual replicates. Error bars represent ± s.d (n = 3 independent replicates). Two-way ANOVA with Šídák’s multiple-comparison tests. m. Dhps levels in 4T1, EMT6.5 and MCF10A HRASV12 cells silenced for DHPS or scramble shRNA. Quantification of Dhps signal normalized over actin signal is indicated on top of each lane. n. Left: Grin2d levels in 4T1 cells expressing an overexpression vector for Grin2d or an empty vector. Quantification of Grin2d signal normalized over actin signal is indicated on top of each lane. Right: Grin2b detected in 4T1 cells expressing an overexpression vector for Grin2b or an empty vector. Quantification of Grin2b signal normalized over actin signal is indicated on top of each lane. o. Relative mRNA expression levels of Grin2d in 4T1 cells or spheroids cultured as a 2D monolayer in RPMI (left) or as 3D spheroids in lung-like medium (right) with or without aspartate. Data are normalized relative to the average of the control (2D no aspartate) condition. Bars represent averages, and single dots individual replicates (n = 4 independent replicates). One-way ANOVA (P < 0.0001) with Tukey’s multiple-comparison tests. p. Average ionomycin-normalized calcium response traces in 4T1 cells overexpressing Grin2d (left) or Grin2b (right). The shaded ribbons represent standard errors of the mean at each time point in each sample, while the grey rectangles indicate sequential additions of aspartate (left) or glutamate (right) plus ionomycin at the times indicated by the arrow. A representative experiment (n = 14 cells for Grin2d OE and n = 15 cells Grin2b OE) out of 2 independent experiments is shown.

Supplementary information

Supplementary Figure 1

Raw images of the western blot experiments

Reporting Summary

Supplementary Figure 2

Gating strategy for CD90.1-expressing 4T1 cells from lung of female BALB/c mice (6–8 weeks old)

Supplementary Tables

Supplementary Table 1: Information related to LLM formulation. Supplementary Table 2: Method information related to lentiviral sequences for knockdown or overexpression cell lines generation. Supplementary Table 3: Method information related to primer sequences. Supplementary Table 4: Clinicopathological information of patients from protocol S57123 (UZ Leuven). Supplementary Table 5: Clinicopathological information of patients from the UPTIDER programme (KU/UZ Leuven).

Supplementary Video 1

Live intracellular calcium imaging from traces shown in Fig. 2d (top).

Supplementary Video 2

Live intracellular calcium imaging from traces shown in Fig. 2d (bottom).

Supplementary Video 3

Live intracellular calcium imaging from traces shown in Extended Data Fig. 2s.

Supplementary Video 4

Live intracellular calcium imaging from traces shown in Extended Data Fig. 7p (left).

Supplementary Video 5

Live intracellular calcium imaging from traces shown in Extended Data Fig. 7p (right).

Source data

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

Doglioni, G., Fernández-García, J., Igelmann, S. et al. Aspartate signalling drives lung metastasis via alternative translation. Nature 638, 244–250 (2025). https://doi.org/10.1038/s41586-024-08335-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-024-08335-7

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