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:

Blocking methionine catabolism induces senescence and confers vulnerability to GSK3 inhibition in liver cancer

Nature Cancer volume 5, pages 131–146 (2024)Cite this article

Subjects

Abstract

Availability of the essential amino acid methionine affects cellular metabolism and growth, and dietary methionine restriction has been implicated as a cancer therapeutic strategy. Nevertheless, how liver cancer cells respond to methionine deprivation and underlying mechanisms remain unclear. Here we find that human liver cancer cells undergo irreversible cell cycle arrest upon methionine deprivation in vitro. Blocking methionine adenosyl transferase 2A (MAT2A)-dependent methionine catabolism induces cell cycle arrest and DNA damage in liver cancer cells, resulting in cellular senescence. A pharmacological screen further identified GSK3 inhibitors as senolytics that selectively kill MAT2A-inhibited senescent liver cancer cells. Importantly, combined treatment with MAT2A and GSK3 inhibitors therapeutically blunts liver tumor growth in vitro and in vivo across multiple models. Together, methionine catabolism is essential for liver tumor growth, and its inhibition can be exploited as an improved pro-senescence strategy for combination with senolytic agents to treat liver cancer.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Liver cancer cells undergo cell cycle arrest with DNA damage upon methionine deprivation.
Fig. 2: Blocking MAT2A induces senescence in liver cancer cells.
Fig. 3: MAT2A inhibition leads to cell cycle arrest and DNA damage in liver cancer cells.
Fig. 4: Identification of GSK3 inhibitors as senolytics in MAT2A-inhibited liver cancer cells.
Fig. 5: GSK3 inhibition promotes senolysis partially through apoptosis in TIS.
Fig. 6: Combined MAT2A and GSK3 inhibition therapeutically limits liver tumor growth.

Similar content being viewed by others

Data availability

Human liver cancer data were derived from the TCGA Research Network (http://cancergenome.nih.gov/), and were downloaded and analyzed at the Molecular Profiling Facility at the University of Pennsylvania as previously12. For single-cell RNA-seq data analysis, MAT2A transcript levels were compared between 1,497 non-malignant and 7,285 tumorigenic hepatocytes using published dataset under accession code GSE149614 (ref. 65). Expression and the percentage of cells in which gene expression was analyzed using the non-parametric Wilcoxon rank-sum test. Each point represents expression value of each single cell and the yellow dot represents the mean expression for each group. We further compared the mRNA levels of MAT2A in tumorigenic hepatocytes by integrating uninjured hepatocytes (4,800 cells) from a separate dataset under accession code GSE174748 (ref. 66). RNA-seq data that support the findings of this study have been deposited in the Gene Expression Omnibus (GEO) under accession code GSE184065, the analyzed data are deposited in figshare (https://doi.org/10.6084/m9.figshare.22215361), and raw image data files are deposited in figshare (10.6084/m9.figshare.24225508). The software and algorithms for data analyses used in this study are all well established from previous work and are referenced throughout the manuscript. Source data for Figs. 1a,c,i,j, 2a,g,j,l,n, 3c,g, 4c,d and 6b,d,f–h,j and Extended Data Figs. 1a,b,e,f, 2a–c, 4a–d,f,h, 5a,d–f,i,j, 6b, 7a, 8b,f,g and 9b–d,f,g have been provided as Source Data files. All other data supporting the findings of this study are available from the corresponding author upon reasonable requests. Source data are provided with this paper.

Code availability

No custom code was used in this study.

References

  1. Siegel, R. L., Miller, K. D., Fuchs, H. E. & Jemal, A. Cancer Statistics, 2021. CA Cancer J. Clin. 71, 7–33 (2021).

    Article  PubMed  Google Scholar 

  2. The Cancer Genome Atlas Research Network. Comprehensive and integrative genomic characterization of hepatocellular carcinoma. Cell 169, 1327–1341 (2017).

  3. Finn, R. S. et al. Atezolizumab plus bevacizumab in unresectable hepatocellular carcinoma. N. Engl. J. Med. 382, 1894–1905 (2020).

    Article  CAS  PubMed  Google Scholar 

  4. Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    Article  CAS  PubMed  Google Scholar 

  5. DeBerardinis, R. J. & Chandel, N. S. We need to talk about the Warburg effect. Nat. Metab. 2, 127–129 (2020).

    Article  PubMed  Google Scholar 

  6. Faubert, B., Solmonson, A. & DeBerardinis, R. J. Metabolic reprogramming and cancer progression. Science 368, eaaw5473 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Li, F. & Simon, M. C. Cancer cells don’t live alone: metabolic communication within tumor microenvironments. Dev. Cell 54, 183–195 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Anderson, N. M. & Simon, M. C. The tumor microenvironment. Curr. Biol. 30, R921–R925 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Lyssiotis, C. A. & Kimmelman, A. C. Metabolic interactions in the tumor microenvironment. Trends Cell Biol. 27, 863–875 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Satriano, L., Lewinska, M., Rodrigues, P. M., Banales, J. M. & Andersen, J. B. Metabolic rearrangements in primary liver cancers: cause and consequences. Nat. Rev. Gastroenterol. Hepatol. 16, 748–766 (2019).

    Article  CAS  PubMed  Google Scholar 

  11. Missiaen, R. et al. GCN2 inhibition sensitizes arginine-deprived hepatocellular carcinoma cells to senolytic treatment. Cell Metab. 34, 1151–1167 e1157 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Li, F. et al. FBP1 loss disrupts liver metabolism and promotes tumorigenesis through a hepatic stellate cell senescence secretome. Nat. Cell Biol. 22, 728–739 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Wang, C. et al. Inducing and exploiting vulnerabilities for the treatment of liver cancer. Nature 574, 268–272 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Sanderson, S. M., Gao, X., Dai, Z. & Locasale, J. W. Methionine metabolism in health and cancer: a nexus of diet and precision medicine. Nat. Rev. Cancer 19, 625–637 (2019).

    Article  CAS  PubMed  Google Scholar 

  15. Ducker, G. S. & Rabinowitz, J. D. One-carbon metabolism in health and disease. Cell Metab. 25, 27–42 (2017).

    Article  CAS  PubMed  Google Scholar 

  16. Wang, Z. et al. Methionine is a metabolic dependency of tumor-initiating cells. Nat. Med. 25, 825–837 (2019).

    Article  CAS  PubMed  Google Scholar 

  17. Fang, L. et al. Methionine restriction promotes cGAS activation and chromatin untethering through demethylation to enhance antitumor immunity. Cancer Cell 41, 1118–1133 e1112 (2023).

    Article  CAS  PubMed  Google Scholar 

  18. Gao, X. et al. Dietary methionine influences therapy in mouse cancer models and alters human metabolism. Nature 572, 397–401 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Machado, M. V. et al. Mouse models of diet-induced nonalcoholic steatohepatitis reproduce the heterogeneity of the human disease. PLoS ONE 10, e0127991 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Itagaki, H., Shimizu, K., Morikawa, S., Ogawa, K. & Ezaki, T. Morphological and functional characterization of non-alcoholic fatty liver disease induced by a methionine-choline-deficient diet in C57BL/6 mice. Int. J. Clin. Exp. Pathol. 6, 2683–2696 (2013).

    PubMed  PubMed Central  Google Scholar 

  21. Ma, C. et al. NAFLD causes selective CD4+ T lymphocyte loss and promotes hepatocarcinogenesis. Nature 531, 253–257 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Campisi, J. Aging, cellular senescence, and cancer. Annu. Rev. Physiol. 75, 685–705 (2013).

    Article  CAS  PubMed  Google Scholar 

  23. Rodier, F. et al. Persistent DNA damage signalling triggers senescence-associated inflammatory cytokine secretion. Nat. Cell Biol. 11, 973–979 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Campisi, J. & d’Adda di Fagagna, F. Cellular senescence: when bad things happen to good cells. Nat. Rev. Mol. Cell Biol. 8, 729–740 (2007).

    Article  CAS  PubMed  Google Scholar 

  25. Li, Z. et al. Methionine metabolism in chronic liver diseases: an update on molecular mechanism and therapeutic implication. Signal Transduct. Target Ther. 5, 280 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Frau, M., Feo, F. & Pascale, R. M. Pleiotropic effects of methionine adenosyltransferases deregulation as determinants of liver cancer progression and prognosis. J. Hepatol. 59, 830–841 (2013).

    Article  CAS  PubMed  Google Scholar 

  27. Cai, J., Mao, Z., Hwang, J. J. & Lu, S. C. Differential expression of methionine adenosyltransferase genes influences the rate of growth of human hepatocellular carcinoma cells. Cancer Res. 58, 1444–1450 (1998).

    CAS  PubMed  Google Scholar 

  28. Martinez-Chantar, M. L. et al. l-methionine availability regulates expression of the methionine adenosyltransferase 2A gene in human hepatocarcinoma cells: role of S-adenosylmethionine. J. Biol. Chem. 278, 19885–19890 (2003).

    Article  CAS  PubMed  Google Scholar 

  29. Jeggo, P. A., Pearl, L. H. & Carr, A. M. DNA repair, genome stability and cancer: a historical perspective. Nat. Rev. Cancer 16, 35–42 (2016).

    Article  CAS  PubMed  Google Scholar 

  30. Lee, S. & Schmitt, C. A. The dynamic nature of senescence in cancer. Nat. Cell Biol. 21, 94–101 (2019).

    Article  CAS  PubMed  Google Scholar 

  31. Zhang, W. et al. Fluorinated N,N-dialkylaminostilbenes repress colon cancer by targeting methionine S-adenosyltransferase 2A. ACS Chem. Biol. 8, 796–803 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Li, J. T. et al. Dietary folate drives methionine metabolism to promote cancer development by stabilizing MAT IIA. Signal Transduct. Target Ther. 7, 192 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Schmitt, C. A., Wang, B. & Demaria, M. Senescence and cancer—role and therapeutic opportunities. Nat. Rev. Clin. Oncol. 19, 619–636 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Narita, M. et al. Rb-mediated heterochromatin formation and silencing of E2F target genes during cellular senescence. Cell 113, 703–716 (2003).

    Article  CAS  PubMed  Google Scholar 

  35. Chen, R. Z., Pettersson, U., Beard, C., Jackson-Grusby, L. & Jaenisch, R. DNA hypomethylation leads to elevated mutation rates. Nature 395, 89–93 (1998).

    Article  CAS  PubMed  Google Scholar 

  36. Sato, T., Issa, J. J. & Kropf, P. DNA hypomethylating drugs in cancer therapy. Cold Spring Harb. Perspect. Med. 7, a026948 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Christman, J. K. 5-Azacytidine and 5-aza-2′-deoxycytidine as inhibitors of DNA methylation: mechanistic studies and their implications for cancer therapy. Oncogene 21, 5483–5495 (2002).

    Article  CAS  PubMed  Google Scholar 

  38. Leite de Oliveira, R. & Bernards, R. Anti-cancer therapy: senescence is the new black. EMBO J. 37, e99386 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Kirkland, J. L. & Tchkonia, T. Cellular senescence: a translational perspective. EBioMedicine 21, 21–28 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Toso, A. et al. Enhancing chemotherapy efficacy in Pten-deficient prostate tumors by activating the senescence-associated antitumor immunity. Cell Rep. 9, 75–89 (2014).

    Article  CAS  PubMed  Google Scholar 

  41. Dorr, J. R. et al. Synthetic lethal metabolic targeting of cellular senescence in cancer therapy. Nature 501, 421–425 (2013).

    Article  PubMed  Google Scholar 

  42. Rizzieri, D. A. et al. An open-label phase 2 study of glycogen synthase kinase-3 inhibitor LY2090314 in patients with acute leukemia. Leuk. Lymphoma 57, 1800–1806 (2016).

    Article  CAS  PubMed  Google Scholar 

  43. Beurel, E., Grieco, S. F. & Jope, R. S. Glycogen synthase kinase-3 (GSK3): regulation, actions, and diseases. Pharmacol. Ther. 148, 114–131 (2015).

    Article  CAS  PubMed  Google Scholar 

  44. Milanovic, M. et al. Senescence-associated reprogramming promotes cancer stemness. Nature 553, 96–100 (2018).

    Article  CAS  PubMed  Google Scholar 

  45. Grassilli, E. et al. Inhibition of GSK3B bypass drug resistance of p53-null colon carcinomas by enabling necroptosis in response to chemotherapy. Clin. Cancer Res. 19, 3820–3831 (2013).

    Article  CAS  PubMed  Google Scholar 

  46. Yang, J. et al. GSK-3β promotes cell survival by modulating Bif-1-dependent autophagy and cell death. J. Cell Sci. 123, 861–870 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Galluzzi, L. et al. Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018. Cell Death Differ. 25, 486–541 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Cabon, L. et al. BID regulates AIF-mediated caspase-independent necroptosis by promoting BAX activation. Cell Death Differ. 19, 245–256 (2012).

    Article  CAS  PubMed  Google Scholar 

  49. DeBerardinis, R. J. & Chandel, N. S. Fundamentals of cancer metabolism. Sci. Adv. 2, e1600200 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  50. DeBerardinis, R. J., Lum, J. J., Hatzivassiliou, G. & Thompson, C. B. The biology of cancer: metabolic reprogramming fuels cell growth and proliferation. Cell Metab. 7, 11–20 (2008).

    Article  CAS  PubMed  Google Scholar 

  51. Marjon, K. et al. MTAP deletions in cancer create vulnerability to targeting of the MAT2A/PRMT5/RIOK1 axis. Cell Rep. 15, 574–587 (2016).

    Article  CAS  PubMed  Google Scholar 

  52. Kryukov, G. V. et al. MTAP deletion confers enhanced dependency on the PRMT5 arginine methyltransferase in cancer cells. Science 351, 1214–1218 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Kalev, P. et al. MAT2A inhibition blocks the growth of MTAP-deleted cancer cells by reducing PRMT5-dependent mRNA splicing and inducing DNA damage. Cancer Cell 39, 209–224 e211 (2021).

    Article  CAS  PubMed  Google Scholar 

  54. Mavrakis, K. J. et al. Disordered methionine metabolism in MTAP/CDKN2A-deleted cancers leads to dependence on PRMT5. Science 351, 1208–1213 (2016).

    Article  CAS  PubMed  Google Scholar 

  55. Golbourn, B. J. et al. Loss of MAT2A compromises methionine metabolism and represents a vulnerability in H3K27M mutant glioma by modulating the epigenome. Nat. Cancer 3, 629–648 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Chen, H. A. et al. Senescence rewires microenvironment sensing to facilitate anti-tumor immunity. Cancer Discov. 13, 432–453 (2023).

  57. Marin, I. et al. Cellular senescence is immunogenic and promotes anti-tumor immunity. Cancer Discov. (2022).

  58. Wang, L. et al. High-throughput functional genetic and compound screens identify targets for senescence induction in cancer. Cell Rep. 21, 773–783 (2017).

    Article  CAS  PubMed  Google Scholar 

  59. Wang, L. et al. cFLIP suppression and DR5 activation sensitize senescent cancer cells to senolysis. Nat. Cancer 3, 1284–1299 (2022).

    Article  CAS  PubMed  Google Scholar 

  60. Thorne, C. A. et al. GSK-3 modulates cellular responses to a broad spectrum of kinase inhibitors. Nat. Chem. Biol. 11, 58–63 (2015).

    Article  CAS  PubMed  Google Scholar 

  61. Agrawal, S. et al. El-MAVEN: a fast, robust, and user-friendly mass spectrometry data processing engine for metabolomics. Methods Mol. Biol. 1978, 301–321 (2019).

    Article  CAS  PubMed  Google Scholar 

  62. Reiberger, T. et al. An orthotopic mouse model of hepatocellular carcinoma with underlying liver cirrhosis. Nat. Protoc. 10, 1264–1274 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. GSEA Home. Broad Institute http://software.broadinstitute.org/gsea/index.jsp

  64. 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  CAS  PubMed  PubMed Central  Google Scholar 

  65. Lu, Y. et al. A single-cell atlas of the multicellular ecosystem of primary and metastatic hepatocellular carcinoma. Nat. Commun. 13, 4594 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Filliol, A. et al. Opposing roles of hepatic stellate cell subpopulations in hepatocarcinogenesis. Nature 610, 356–365 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the members of the Simon and Li laboratories for their helpful discussions and insights on the manuscript. We are grateful to J. Tobias for help with processing the human TCGA data, and to H. Ji and L. Hu for the critical reading of the manuscript. This work was supported by the National Key R&D Program of China (2022YFA1103900 to F.L.), the National Natural Science Foundation of China (82273223 to F.L., 32270798 to P.L.), and Shanghai Pujiang Program (21PJ1400800 to F.L.), and the National Institutes of Health grants P01CA104838 and R35CA197602 to M.C.S.

Author information

Author notes

  1. Nicole M. Anderson

    Present address: Department of Cell and Molecular Biology, University of Mississippi Medical Center, Jackson, MS, USA

  2. These authors contributed equally: Fuming Li, Pingyu Liu.

Authors and Affiliations

  1. Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, China

    Fuming Li, Wen Mi, Liucheng Li, Guanlin Wang, Jinyang Li & Lingzhi Zhu

  2. Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Fuming Li, Nicole M. Anderson, Nicholas P. Lesner, Michelle Burrows, Jacqueline Plesset, Ariana Majer, Brian Keith & M. Celeste Simon

  3. Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China

    Pingyu Liu

  4. Department of Cancer Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Brian Keith

  5. Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    M. Celeste Simon

Authors
  1. Fuming Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pingyu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wen Mi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liucheng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nicole M. Anderson
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nicholas P. Lesner
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Michelle Burrows
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jacqueline Plesset
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ariana Majer
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Guanlin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Jinyang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Lingzhi Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Brian Keith
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. M. Celeste Simon
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

F.L. and M.C.S. conceived the project and designed the experiments, B.K. and M.C.S. supervised the overall study. F.L., P.L., W.M., L. L., N.M.A., N.P.L., M.B., J.P. and A.M. performed the experiments. M.B. provided help with animal husbandry and technical assistance in the mouse experiments. G.W., J.L. and L.Z. performed single cell and regular RNA-seq data analysis. F.L. and P.L. analyzed the data and wrote the manuscript. F.L. and M.C.S. revised the manuscript. All authors revised and approved the manuscript.

Corresponding authors

Correspondence to Fuming Li or M. Celeste Simon.

Ethics declarations

Competing interests

The authors declare no potential conflicts of interest.

Peer review

Peer review information

Nature Cancer thanks Jason Locasale and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 HCC cells undergo senescence upon methionine starvation.

a, Cell numbers of HepG2, PLC/PRF/5 and SNU449 cells with or without 72-hour methionine (Met) deprivation. b, Cell death quantification (Annexin V+%) of indicated HCC cells with or without 72-hour methionine (Met) deprivation. c, Western blot analysis of Huh7 cells with or without 72-hour methionine (Met) deprivation. Actin was used as a loading control. d, Representative crystal violet staining of control and methionine-deprived Huh7 and Hep3B cells replated in methionine-containing medium for 3-week clonogenicity assays. e, Quantification of cells with >2 53BP1 foci in control and methionine-deprived Huh7 and Hep3B cells. To quantify, 5 random fields containing at least 30 cells were counted and averaged. n = 5 fields for each group. f, Quantification of HepG2, PLC/PRF5 and SNU449 cell numbers when cultured in medium with or without homocysteine (Hcy, 100 µM). Data presented as mean ± s.e.m. of three independent experiments; statistical significance was determined by a two-tailed Student’s t−test (a,b,e,f). Experiments were repeated three times independently, with similar results (c,d).

Source data

Extended Data Fig. 2 MAT2A as a potential oncogene in HCC.

a, Quantification of DAB intensity of MAT2A IHC staining of liver sections from MYCOE;Trp53KO (n = 7 sections from 4 mice for AL, n = 12 sections from 5 mice for tumour) orthotopic model and DEN (n = 7 sections from 3 mice for AL, n = 16 sections from 4 mice for tumour) model. b, The frequency and normalized UMI of MAT2A transcripts in 7,285 pro-tumorigenic hepatocytes as compared to 1497 non-malignant hepatocytes (left) or 4800 uninjured hepatocytes from healthy livers (Right) using public single cell RNA-seq data. c, GSEA plots of indicated gene sets based on MAT2A transcript levels in 374 HCC patient samples from TCGA dataset. MAT2A-high: top 50%, n = 187; MAT2A-low: bottom 50%, n = 187. Data presented as mean ± s.e.m.; statistical significance was determined by a two-tailed Student’s t-test (a), one-side non-parametric Wilcoxon rank-sum test (b) or the Kolmogorov Smirnov (K-S) test (c).

Source data

Extended Data Fig. 3 Genetic MAT2A inhibition induces DNA damage and promotes SASP gene expression.

a, Representative immunofluorescence images of Huh7-sgR26, Huh7-sgMAT2A-1 and Huh7-sgMAT2A-2 cells stained with 53BP1 (red), γH2AX (red) and DAPI (blue). Scale bar: 50 µm. b, Quantification of cells with >2 53BP1 foci in Huh7-sgR26 (n = 5 200x fields), Huh7-sgMAT2A-1 (n = 5 200x fields) and Huh7-sgMAT2A-2 (n = 5 200x fields) cells. To quantify, 5 random 200x fields containing at least 30 cells were counted and averaged. c, qRT-PCR analysis of indicated gene expression in Huh7-sgR26, Huh7-sgMAT2A-1 and Huh7-sgMAT2A-2 cells. Data are presented as the mean ± s.e.m of three independent experiments; statistical significance was determined by a two-tailed Student’s t-test (b,c). Experiments were repeated three times independently, with similar results (a).

Source data

Extended Data Fig. 4 MAT2A inhibition by FIDAS-5 induces senescence in liver cancer cells.

a, Cell viabilities of Huh7 and Hep3B cells treated with indicated doses of FIDAS-5 for 72 hours. b, Huh7 and Hep3B cell growth curves in the presence of 1 µM FIDAS-5. c, Relative SAM and SAH abundance determined by LC-MS analysis of control (n = 4) and FIDAS-5 (n = 4) treated Huh7 cells for 72 hours. d, Cell death quantification (Annexin V+%) of Huh7 and Hep3B cells with or without 72-hour FIDAS-5 exposure (Huh7: 5 µM; Hep3B:1 µM). e, Representative immunofluorescence images of vehicle control and FIDAS-5-induced senescent (FIS) Huh7 cells stained with 53BP1 (red), γH2AX (red) and DAPI (blue). Scale bar: 50 µm. f, Quantification of cells with >2 53BP1 foci in control (n = 5 200x fields) and FIS (n = 5 200x fields) Huh7 cells. To quantify, 5 random fields containing at least 30 cells were counted and averaged. g, Representative crystal violet staining of control and FIS Huh7 cells replated in growth medium for 3-week clonogenicity assays. h, qRT-PCR analysis of indicated gene expression in control and FIS Huh7 cells. i, j, Representative SA-β-gal staining (i) and quantification (j) of vehicle control (Veh) and FIDAS-5 treated TOV21G cells. To quantify in (j), 5 random 100x fields were counted and averaged. n = 5 200x fields for each group. Scale bar: 100 µm. k, Mouse body weight curves over time in vehicle control (Veh, n = 7 female BAB/c mice) and FIDAS-5 (n = 5 female BAB/c mice) treatment groups. Data presented as mean ± s.e.m. of three independent experiments (a,b,d,f,h,j,k) or mean ± s.e.m.(c); statistical significance was determined by a two-tailed Student’s t-test (a-d,f,h,j,k). Experiments were repeated three times independently, with similar results (e,g,i).

Source data

Extended Data Fig. 5 Characterization of early FIDAS-5 treated HCC cells.

a, Relative numbers of Huh7 cells cultured in growth medium with indicated ROS scavengers for 72 hours. b, Western blot analysis of indicated histone methylation marks in Huh7 cells treated with indicated concentration of FIDAS-5 for indicated time. H3 was used as loading control. c, d, Representative immunofluorescence images (c) and statistical analysis (d) of Veh and treatment-induced senescent Huh7 cells stained with H3K9me3 antibody (green). To quantify in d, 10 random fields containing at least 80 cells were counted and averaged for the percentage of H3K9me3+ nuclei. n = 10 200x fields for each group. Scale bar: 50 µm. e, Relative gene expression by qRT-PCR analysis of FIDAS-5 and vehicle treated Huh7 and Hep3B cells for 48 hours. f, Quantification of cells with >2 53BP1 foci in FIDAS5-treated Huh7, Hep3B cells for indicated time. To quantify, 5 random fields containing at least 30 cells were counted and averaged. n = 5 200x fields for each group. g, Western blot analysis of p21 and GAPDH from Huh7-sgR26, Huh7-sgCDKN1A-1, Huh7-sgCDKN1A-2 and Huh7-sgCDKN1A-3 cells. h, i, Representative SA-β-gal staining (h) and quantification (i) of Huh7 cells and FIDAS-5 treated Huh7-sgR26, Huh7-sgCDKN1A-1 and Huh7-sgCDKN1A-2 cells. Scale bar: 100 µm. j, Relative cell numbers of FIDAS-5 treated Huh7-sgR26, Huh7-sgP21-1 and Huh7-sgP21-2 cells. Data presented as mean ± s.e.m. of three independent experiments; statistical significance was determined by a two-tailed Student’s t-test (a,e,f,i,j). Experiments were repeated three times independently, with similar results (b,c,g,h).

Source data

Extended Data Fig. 6 DNA damage in early decitabine-treated HCC cells.

a, Representative immunofluorescence images of vehicle control and 48-hour decitabine-treated Huh7 and Hep3B cells stained with 53BP1 (red), γH2AX (red) and DAPI (blue). The arrows indicate cytoplasmic DNA. Scale bar: 10 µm. b, Quantification of cells with >2 53BP1 foci or >2 γH2AX foci in decitabine-treated Huh7 and Hep3B cells for 48 h. To quantify, 5 random fields containing at least 30 cells were counted and averaged. n = 5 200x fields for each group. c, Representative crystal violet staining of control and decitabine-induced senescent Huh7 cells replated in growth medium for 3-week clonogenicity assays. Data presented as mean ± s.e.m. of three independent experiments; statistical significance was determined by a two-tailed Student’s t-test (b). Experiments were repeated three times independently, with similar results (a,c).

Source data

Extended Data Fig. 7 GSK3 inhibition induces cell death in TIS liver cancer cells.

a, Relative cell numbers of Huh7 cells treated with different doses of BIO and CHIR99021 for 72 hours. b, Representative crystal violet staining of control and FIS Huh7 and Hep3B cells treated with indicated doses of CHIR98014 for 6 days. c, Representative crystal violet staining of vehicle control and TIS Huh7 cells treated with indicated doses of LY2090314 for 6 days. d, Representative crystal violet staining of Huh7-sgR26, Huh7-sgMAT2A-1 and Huh7-sgMAT2A-7 cells treated with 0.5 µM LY2090314 for 6 days. e, Representative crystal violet staining of Huh6 cells treated with indicated drug combinations for 6 days. Data presented as mean ± s.e.m. of three independent experiments; statistical significance was determined by a two-tailed Student’s t-test (a). Experiments were repeated three times independently, with similar results (b-e).

Source data

Extended Data Fig. 8 GSK3 maintains viability of TIS liver cancer cells.

a, Western blot analysis of GSK3A, GSK3B and GAPDH in lysates from control and FIS Huh7 cells with or without 48-hour LY2090314 treatment at indicated doses. b, Normalized TOP Flash and FOP Flash luciferase activity in control and FIS Huh7 cells. c, Subcellular fractionation and western blot analysis of indicated proteins in vehicle and FIS Huh7 cells. d, Kaplan–Meier overall survival plots stratified by GSK3A or GSK3B mRNA levels from HCC TCGA database. e, Western blot analysis of GSK3A, GSK3B and GAPDH in lysates from Huh7 cells expressing indicated sgRNAs. f, Cell growth curves of Huh7 cells expressing indicated sgRNAs. g, Cell death quantification (Annexin V+%) of Huh7 cells expressing indicated sgRNAs. h, Western blot analysis of GSK3A, GSK3B and GAPDH from Huh7 cells expressing indicated shRNAs. i, Representative crystal violet staining of Huh7-shCtrl, Huh7-shGSK3A/B-1 and Huh7-shGSK3A/B-2 cells with or without 0.5 µM LY2090314 treatment for 6 days. j, Flow cytometry plots of Annexin-V/PI apoptosis assay in Huh7 cells treated with indicated doses of LY2090314 for 72 hours. k, Western blot analysis of GSK3A, GSK3B and GAPDH from FIS Huh7 cells expressing indicated siRNAs. Data presented as mean ± s.e.m. of three independent experiments; statistical significance was determined by a two-tailed Student’s t-test (b,f,g) or log-rank Mantel–Cox test (d). Experiments were repeated three times independently, with similar results (a,c,e,h-k).

Source data

Extended Data Fig. 9 Combined MAT2A and GSK3 inhibition limits liver tumor growth.

a, Representative crystal violet staining of Huh7 cells treated with indicated doses of drug combination for 12 days. b-d, Quantification of end point Huh7 and Hep3B xenograft tumor weight (b), volume (c) and mouse body weight (d) from vehicle control (Veh), FIDAS-5, LY2090314 and combined FIDAS-5/LY2090314 treatment groups. Huh7: n = 6 female BAB/c mice for each group. Hep3B: n = 5 female NSG mice for Veh, n = 6 female NSG mice for FIDAS-5, n = 6 female NSG mice for LY2090314, n = 7 female NSG mice for combination group. e, f, Representative IHC images (e) and quantification (f) of PCNA and γH2AX staining in Hep3B xenograft tumor sections from indicated treatment groups. To quantify, 3–5 representative images with a ×200 field of view (FOV) were used for quantification using Image J and averaged for each animal. n = 6 mice for each group. Scale bar: 100 µm. g, Body weight change of vehicle control (n = 3 male mice) and combined FIDAS-5/LY2090314 (n = 3 male mice)-treated C57BL/6 J mice. h, Representative HE staining of liver sections from vehicle and combined FIDAS-5/LY2090314-treated wild type mice. Scale bar: 100 µm. Data presented as mean ± s.e.m. (b-g); statistical significance was determined by a two-tailed Student’s t-test (b-f) Experiments were repeated three times independently, with similar results (a).

Source data

Supplementary information

Supplementary Information

Supplementary Fig. 1. Representative SAM and SAH chromatogram peaks from liquid chromatography–mass spectrometry.

Reporting Summary

Supplementary Tables 1–3

Supplementary Tables 1–3.

Supplementary Table 4

Supplementary Table 4.

Source data

Source Data

Unprocessed western blots.

Source Data Fig. 1

Statistical source data.

Source Data Fig. 1

IF images.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 2

IF images.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 3

IF images.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 4

IF images.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 5

IF images.

Source Data Fig. 6

Statistical source data.

Source Data Fig. 6

IF images.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 1

IF images.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 3

IF images.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 4

IF images.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 5

IF images.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 6

IF images.

Source Data Extended Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 7

IF images.

Source Data Extended Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 8

IF images.

Source Data Extended Data Fig. 9

Statistical source data.

Source Data Extended Data Fig. 9

IF images.

Rights and permissions

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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, F., Liu, P., Mi, W. et al. Blocking methionine catabolism induces senescence and confers vulnerability to GSK3 inhibition in liver cancer. Nat Cancer 5, 131–146 (2024). https://doi.org/10.1038/s43018-023-00671-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43018-023-00671-3

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