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:

Loss of MAT2A compromises methionine metabolism and represents a vulnerability in H3K27M mutant glioma by modulating the epigenome

Nature Cancer volume 3, pages 629–648 (2022)Cite this article

Subjects

An Author Correction to this article was published on 23 June 2022

This article has been updated

Abstract

Diffuse midline gliomas (DMGs) bearing driver mutations of histone 3 lysine 27 (H3K27M) are incurable brain tumors with unique epigenomes. Here, we generated a syngeneic H3K27M mouse model to study the amino acid metabolic dependencies of these tumors. H3K27M mutant cells were highly dependent on methionine. Interrogating the methionine cycle dependency through a short-interfering RNA screen identified the enzyme methionine adenosyltransferase 2A (MAT2A) as a critical vulnerability in these tumors. This vulnerability was not mediated through the canonical mechanism of MTAP deletion; instead, DMG cells have lower levels of MAT2A protein, which is mediated by negative feedback induced by the metabolite decarboxylated S-adenosyl methionine. Depletion of residual MAT2A induces global depletion of H3K36me3, a chromatin mark of transcriptional elongation perturbing oncogenic and developmental transcriptional programs. Moreover, methionine-restricted diets extended survival in multiple models of DMG in vivo. Collectively, our results suggest that MAT2A presents an exploitable therapeutic vulnerability in H3K27M gliomas.

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: Generation of a syngeneic mouse model of DIPG.
Fig. 2: Transcriptional profiling identifies alteration in methionine metabolism in DMG.
Fig. 3: H3K27M, PDGFRA and P53 R237H cooperate to alter metabolism.
Fig. 4: MAT2A expression is lower in DIPGs.
Fig. 5: Silencing MAT2a inhibits patient-derived DIPG lines.
Fig. 6: AMD1 Downregulates MAT2a.
Fig. 7: Silencing MAT2a alters the transcriptome and H3K36me3 deposition.
Fig. 8: MAT2A depletion or MR impedes DIPG growth in vivo.

Similar content being viewed by others

Data availability

All ChIP-Rx sequencing and RNA-sequencing data generated in this study have been deposited at NCBI Gene Expression Omnibus under accession codes GSE160006 and GSE160088. Source data for all figures has been provided as source data files.

Further information on research design is available in the Nature Research Reporting Summary linked to this article. The data that support the findings of this study are available from the corresponding author upon request. Source data are provided with this paper.

Change history

References

  1. Louis, D. N. et al. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro-oncology https://doi.org/10.1093/neuonc/noab106 (2021).

  2. Hoffman, L. M. et al. Clinical, radiologic, pathologic, and molecular characteristics of long-term survivors of diffuse intrinsic pontine glioma (DIPG): a collaborative report from the International and European Society for Pediatric Oncology DIPG registries. J. Clin. Oncol. 36, 1963–1972 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Warren, K. E. Diffuse intrinsic pontine glioma: poised for progress. Front. Oncol. 2, 205 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Sturm, D. et al. Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma. Cancer Cell 22, 425–437 (2012).

    Article  CAS  PubMed  Google Scholar 

  5. Ostrom, Q. T. et al. CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2008–2012. Neuro-oncology 17, iv1–iv62 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Jones, C. et al. Pediatric high-grade glioma: biologically and clinically in need of new thinking. Neuro-oncology 19, 153–161 (2017).

    CAS  PubMed  Google Scholar 

  7. Lewis, P. W. et al. Inhibition of PRC2 activity by a gain-of-function H3 mutation found in pediatric glioblastoma. Science 340, 857–861 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Nagaraja, S. et al. Transcriptional dependencies in diffuse intrinsic pontine glioma. Cancer Cell 31, 635–652 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Silveira, A. B. et al. H3.3 K27M depletion increases differentiation and extends latency of diffuse intrinsic pontine glioma growth in vivo. Acta neuropathologica https://doi.org/10.1007/s00401-019-01975-4 (2019).

  10. Vinci, M. et al. Functional diversity and cooperativity between subclonal populations of pediatric glioblastoma and diffuse intrinsic pontine glioma cells. Nat. Med. https://doi.org/10.1038/s41591-018-0086-7 (2018).

  11. Koncar, R. F. et al. Identification of novel RAS signaling therapeutic vulnerabilities in diffuse intrinsic pontine gliomas. Cancer Res. 79, 4026–4041 (2019).

    Article  CAS  PubMed  Google Scholar 

  12. Larson, J. D. et al. Histone H3.3 K27M accelerates spontaneous brainstem glioma and drives restricted changes in bivalent gene expression. Cancer Cell 35, 140–155 (2019).

    Article  CAS  PubMed  Google Scholar 

  13. Harutyunyan, A. S. et al. H3K27M induces defective chromatin spread of PRC2-mediated repressive H3K27me2/me3 and is essential for glioma tumorigenesis. Nat. Commun. 10, 1262 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Krug, B. et al. Pervasive H3K27 acetylation leads to ERV expression and a therapeutic vulnerability in H3K27M gliomas. Cancer Cell 35, 782–797 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Michealraj, K. A. et al. Metabolic regulation of the epigenome drives lethal infantile ependymoma. Cell https://doi.org/10.1016/j.cell.2020.04.047 (2020).

  16. Kaelin, W. G. Jr. & McKnight, S. L. Influence of metabolism on epigenetics and disease. Cell 153, 56–69 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Dawson, M. A. & Kouzarides, T. Cancer epigenetics: from mechanism to therapy. Cell 150, 12–27 (2012).

    Article  CAS  PubMed  Google Scholar 

  18. Lu, C. & Thompson, C. B. Metabolic regulation of epigenetics. Cell Metab. 16, 9–17 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Noushmehr, H. et al. Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell 17, 510–522 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Vander Heiden, M. G. & DeBerardinis, R. J. Understanding the intersections between metabolism and cancer biology. Cell 168, 657–669 (2017).

    Article  PubMed Central  Google Scholar 

  21. Gaude, E. & Frezza, C. Tissue-specific and convergent metabolic transformation of cancer correlates with metastatic potential and patient survival. Nat. Commun. 7, 13041 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Filbin, M. G. et al. Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq. Science 360, 331–335 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Monje, M. et al. Hedgehog-responsive candidate cell of origin for diffuse intrinsic pontine glioma. PNAS 108, 4453–4458 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Mackay, A. et al. Integrated molecular meta-analysis of 1,000 pediatric high-grade and diffuse intrinsic pontine glioma. Cancer Cell 32, 520–537 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kondo, S. et al. Simultaneous on/off regulation of transgenes located on a mammalian chromosome with Cre-expressing adenovirus and a mutant loxP. Nucleic Acids Res. 31, e76 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Ahn, J. I. et al. Comprehensive transcriptome analysis of differentiation of embryonic stem cells into midbrain and hindbrain neurons. Dev. Biol. 265, 491–501 (2004).

    Article  CAS  PubMed  Google Scholar 

  27. 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 

  28. 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 

  29. 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 

  30. Mohammad, F. et al. EZH2 is a potential therapeutic target for H3K27M-mutant pediatric gliomas. Nat. Med. 23, 483–492 (2017).

    Article  CAS  PubMed  Google Scholar 

  31. Borrego, S. L. et al. Metabolic changes associated with methionine stress sensitivity in MDA-MB-468 breast cancer cells. Cancer Metab. 4, 9 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Chen, Z. et al. Mice deficient in methylenetetrahydrofolate reductase exhibit hyperhomocysteinemia and decreased methylation capacity, with neuropathology and aortic lipid deposition. Hum. Mol. Genet. 10, 433–443 (2001).

    Article  CAS  PubMed  Google Scholar 

  33. Pendleton, K. E. et al. The U6 snRNA m(6)A methyltransferase METTL16 regulates SAM synthetase intron retention. Cell 169, 824–835 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Ohnuma, M. et al. N1-aminopropylagmatine, a new polyamine produced as a key intermediate in polyamine biosynthesis of an extreme thermophile, Thermus thermophilus. J. Biol. Chem. 280, 30073–30082 (2005).

    Article  CAS  PubMed  Google Scholar 

  35. Regenass, U. et al. CGP 48664, a new S-adenosylmethionine decarboxylase inhibitor with broad spectrum antiproliferative and antitumor activity. Cancer Res. 54, 3210–3217 (1994).

    CAS  PubMed  Google Scholar 

  36. Orlando, D. A. et al. Quantitative ChIP-seq normalization reveals global modulation of the epigenome. Cell Rep. 9, 1163–1170 (2014).

    Article  CAS  PubMed  Google Scholar 

  37. Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Jessa, S. et al. Stalled developmental programs at the root of pediatric brain tumors. Nat. Genet. 51, 1702–1713 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Pathania, M. et al. H3.3(K27M) cooperates with Trp53 loss and PDGFRA gain in mouse embryonic neural progenitor cells to induce invasive high-grade gliomas. Cancer Cell 32, 684–700 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Schnütgen, F. et al. A directional strategy for monitoring Cre-mediated recombination at the cellular level in the mouse. Nat. Biotechnol. 21, 562–565 (2003).

    Article  PubMed  Google Scholar 

  41. Orentreich, N., Matias, J. R., DeFelice, A. & Zimmerman, J. A. Low methionine ingestion by rats extends life span. J. Nutr. 123, 269–274 (1993).

    CAS  PubMed  Google Scholar 

  42. Lee, B. C. et al. Methionine restriction extends lifespan of Drosophila melanogaster under conditions of low amino-acid status. Nat. Commun. 5, 3592 (2014).

    Article  PubMed  Google Scholar 

  43. Ser, Z. et al. Targeting one carbon metabolism with an antimetabolite disrupts pyrimidine homeostasis and induces nucleotide overflow. Cell Rep. 15, 2367–2376 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Schmidt, J. A. et al. Plasma concentrations and intakes of amino acids in male meat-eaters, fish-eaters, vegetarians and vegans: a cross-sectional analysis in the EPIC-Oxford cohort. Eur. J. Clin. Nutr. 70, 306–312 (2016).

    Article  CAS  PubMed  Google Scholar 

  45. Hu, J. & Cheung, N. K. Methionine depletion with recombinant methioninase: in vitro and in vivo efficacy against neuroblastoma and its synergism with chemotherapeutic drugs. Int. J. Cancer 124, 1700–1706 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. 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 

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

    Article  CAS  PubMed  Google Scholar 

  48. Quinlan, C. L. et al. Targeting S-adenosylmethionine biosynthesis with a novel allosteric inhibitor of Mat2A. Nat. Chem. Biol. 13, 785–792 (2017).

    Article  CAS  PubMed  Google Scholar 

  49. Strekalova, E. et al. S-adenosylmethionine biosynthesis is a targetable metabolic vulnerability of cancer stem cells. Breast Cancer Res. Treat. 175, 39–50 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Mahmood, N. et al. Methyl donor S-adenosylmethionine (SAM) supplementation attenuates breast cancer growth, invasion, and metastasis in vivo; therapeutic and chemopreventive applications. Oncotarget 9, 5169–5183 (2018).

    Article  PubMed  Google Scholar 

  51. Hashizume, R. et al. Pharmacologic inhibition of histone demethylation as a therapy for pediatric brainstem glioma. Nat. Med. 20, 1394–1396 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Bender, S. et al. Reduced H3K27me3 and DNA hypomethylation are major drivers of gene expression in K27M mutant pediatric high-grade gliomas. Cancer Cell 24, 660–672 (2013).

    Article  CAS  PubMed  Google Scholar 

  53. Zhang, D. et al. AMD1 is essential for ESC self-renewal and is translationally down-regulated on differentiation to neural precursor cells. Genes Dev. 26, 461–473 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Khan, A. et al. Dual targeting of polyamine synthesis and uptake in diffuse intrinsic pontine gliomas. Nat. Commun. 12, 971 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Guiraud, S. P. et al. High-throughput and simultaneous quantitative analysis of homocysteine-methionine cycle metabolites and co-factors in blood plasma and cerebrospinal fluid by isotope dilution LC-MS/MS. Anal. Bioanal. Chem. 409, 295–305 (2017).

    Article  CAS  PubMed  Google Scholar 

  56. Chantalat, S. et al. Histone H3 trimethylation at lysine 36 is associated with constitutive and facultative heterochromatin. Genome Res. 21, 1426–1437 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Chen, C. T., Gottlieb, D. I. & Cohen, B. A. Ultraconserved elements in the Olig2 promoter. PLoS ONE 3, e3946 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Grasso, C. S. et al. Functionally defined therapeutic targets in diffuse intrinsic pontine glioma. Nat. Med. 21, 555–559 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. 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  Google Scholar 

  62. Kim, S. Y. & Volsky, D. J. PAGE: parametric analysis of gene set enrichment. BMC Bioinf. 6, 144 (2005).

    Article  Google Scholar 

  63. Raudvere, U. et al. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 47, W191–W198 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Stovner, E. B. & Sætrom, P. epic2 efficiently finds diffuse domains in ChIP-seq data. Bioinformatics 35, 4392–4393 (2019).

    Article  CAS  PubMed  Google Scholar 

  66. Zang, C. et al. A clustering approach for identification of enriched domains from histone modification ChIP-seq data. Bioinformatics 25, 1952–1958 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Yu, G., Wang, L. G. & He, Q. Y. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).

    Article  CAS  PubMed  Google Scholar 

  68. Brennan, C. W. et al. The somatic genomic landscape of glioblastoma. Cell 155, 462–477 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).

    Article  PubMed  Google Scholar 

  70. Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Science Signal. 6, pl1 (2013).

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Institutes of Health grant award to S.A. (R01NS115831), Michael Mosier Defeat DIPG Foundation and V-Foundation (in honor of Connor’s Cure). S.C.M. is funded by a National Institutes of Health grant (R01NS116361), ALSF A award, The Pediatric Brain Tumor Foundation, Michael Mosier Defeat DIPG Foundation, Chad Tough Foundation, V Scholar Foundation, Cookies for Cancer Foundation, and ALSAC Foundation. M.E.H. was funded by the Joshua’s Wish Foundation. S.G.W. was funded by a National Institutes of Health grant (NIHS10OD023402). C.L.K. was funded by the Canadian Institutes of Health Research (CIHR) grant PJT-156086 and by a salary award from the Fonds de Recherche du Québec-Santé (FRQS).

Author information

Author notes

  1. Joint first authors: Brian J. Golbourn, Matthew E. Halbert.

Authors and Affiliations

  1. Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Brian J. Golbourn, Matthew E. Halbert, Katharine Halligan, Ann-Catherine J. Stanton, Abigail L. Locke, Stephanie M. Casillo, Apeng Chen, Michelle Wassell, Jennifer Perez, Esther P. Jane, Daniel R. David Premkumar, Robert F. Koncar, Lauren H. McCarl, Yue-Fang Chang, Taylor A. Gatesman, Andrea F. Cruz, Baoli Hu, Gary Kohanbash, Nduka M. Amankulor, Jeremy N. Rich, Robert M. Friedlander, Ian F. Pollack & Sameer Agnihotri

  2. John G. Rangos Sr. Research Center, Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA

    Brian J. Golbourn, Matthew E. Halbert, Ann-Catherine J. Stanton, Stephanie M. Casillo, Apeng Chen, Michelle Wassell, Jennifer Perez, Esther P. Jane, Daniel R. David Premkumar, Robert F. Koncar, Lauren H. McCarl, Taylor A. Gatesman, Andrea F. Cruz, Baoli Hu, Gary Kohanbash, Alberto Broniscer, Ian F. Pollack & Sameer Agnihotri

  3. Pediatrics, Division of Hematology-Oncology Program, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA

    Katharine Halligan & Alberto Broniscer

  4. Baylor College of Medicine, Texas Children’s Cancer and Hematology Centers, Dan L. Duncan Cancer Center, Houston, TX, USA

    Srinidhi Varadharajan, Yanhua Zhao & Stephen C. Mack

  5. Department of Human Genetics, McGill University, Montreal, Quebec, Canada

    Brian Krug, Nisha Kabir, Claudia L. Kleinman & Nada Jabado

  6. Department of Pediatrics, McGill University, The Research Institute of the McGill University Health Center, Montreal, Quebec, Canada

    Brian Krug & Nada Jabado

  7. Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, USA

    Nneka E. Mbah, Anthony Andren & Costas Lyssiotis

  8. Lady Davis Research Institute, Jewish General Hospital, Montreal, Quebec, Canada

    Nisha Kabir & Claudia L. Kleinman

  9. Department of Molecular, Cell, and Developmental Biology, University of California, Santa Cruz, CA, USA

    Lauren M. Sanders, Allison Cheney & Olena M. Vaske

  10. University of California Santa Cruz Genomics Institute, Santa Cruz, CA, USA

    Allison Cheney & Olena M. Vaske

  11. Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA, USA

    Steven J. Mullett & Stacy G. Wendell

  12. State Key Laboratory of Veterinary Etiological Biology, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, PR China

    Apeng Chen

  13. Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada

    Shideh Mirhadi & Michael F. Moran

  14. Department of Developmental Biology, University of Pittsburgh and Rangos Research Center Animal Imaging Core, Pittsburgh, PA, USA

    Yijen L. Wu

  15. Department of Pediatric Hematology and Oncology, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic

    Michal Zapotocky

  16. Department of Cell Biology, School of Basic Medical Sciences, Nanjing Medical University, Nanjing, China

    Xiuxing Wang

  17. Department of Pharmacy, UPMC Shadyside, Pittsburgh, PA, USA

    Alenoush Vartanian

  18. Department of Neurology, Adult Neurooncology Program, UPMC Hillman Cancer Center, Pittsburgh, PA, USA

    Frank Lieberman

  19. Department of Radiology, Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA

    Ashok Panigrahy

  20. Pediatric Neuro-Oncology Program, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA

    James Felker

  21. Department of Pediatric Hematology and Oncology, St Jude Children’s Research Hospital, Memphis, TN, USA

    Kelsey C. Bertrand

  22. Department of Developmental Neurobiology, St Jude Children’s Research Hospital, Memphis, TN, USA

    Stephen C. Mack

  23. Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA

    Sameer Agnihotri

Authors
  1. Brian J. Golbourn
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matthew E. Halbert
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Katharine Halligan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Srinidhi Varadharajan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Brian Krug
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nneka E. Mbah
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Nisha Kabir
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ann-Catherine J. Stanton
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Abigail L. Locke
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Stephanie M. Casillo
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Yanhua Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Lauren M. Sanders
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Allison Cheney
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Steven J. Mullett
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Apeng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Michelle Wassell
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Anthony Andren
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Jennifer Perez
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Esther P. Jane
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Daniel R. David Premkumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Robert F. Koncar
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Shideh Mirhadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Lauren H. McCarl
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Yue-Fang Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Yijen L. Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Taylor A. Gatesman
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Andrea F. Cruz
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Michal Zapotocky
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Baoli Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Gary Kohanbash
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Xiuxing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Alenoush Vartanian
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Michael F. Moran
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Frank Lieberman
    View author publications

    You can also search for this author in PubMed Google Scholar

  35. Nduka M. Amankulor
    View author publications

    You can also search for this author in PubMed Google Scholar

  36. Stacy G. Wendell
    View author publications

    You can also search for this author in PubMed Google Scholar

  37. Olena M. Vaske
    View author publications

    You can also search for this author in PubMed Google Scholar

  38. Ashok Panigrahy
    View author publications

    You can also search for this author in PubMed Google Scholar

  39. James Felker
    View author publications

    You can also search for this author in PubMed Google Scholar

  40. Kelsey C. Bertrand
    View author publications

    You can also search for this author in PubMed Google Scholar

  41. Claudia L. Kleinman
    View author publications

    You can also search for this author in PubMed Google Scholar

  42. Jeremy N. Rich
    View author publications

    You can also search for this author in PubMed Google Scholar

  43. Robert M. Friedlander
    View author publications

    You can also search for this author in PubMed Google Scholar

  44. Alberto Broniscer
    View author publications

    You can also search for this author in PubMed Google Scholar

  45. Costas Lyssiotis
    View author publications

    You can also search for this author in PubMed Google Scholar

  46. Nada Jabado
    View author publications

    You can also search for this author in PubMed Google Scholar

  47. Ian F. Pollack
    View author publications

    You can also search for this author in PubMed Google Scholar

  48. Stephen C. Mack
    View author publications

    You can also search for this author in PubMed Google Scholar

  49. Sameer Agnihotri
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conception and design was carried out by B.J.G., M.E.H., K.H., S.C.M. and S.A. Development of methodology was the responsibility of B.J.G., M.E.H., K.H., N.E.M., S.G.W., S.C.M. and S.A. Acquisition of data (such as providing animals and facilities) was carried out by B.J.G., M.E.H., K.H., S.V., B.K., N.E.M., N.K., A.-C.J.S., A.L.L., S.M.C., Y.Z., L.M.S., A. Cheney, S.J.M., A. Chen, M.W., A.A., R.F.K., S.M., Y.L.W., M.Z., S.G.W., O.M.V., S.C.M. and S.A.. Analysis and interpretation of data (such as statistical analysis, biostatistics, computational analysis) was conducted by B.J.G., M.E.H., K.H., S.V., B.K., N.E.M., N.K., A.-C.J.S., A.L.L., S.M.C., Y.Z., L.M.S., A. Cheney, S.J.M., A. Chen, M.W., R.F.K., S.M., Y.-F.C., Y.L.W., M.Z., B.H., G.K., X.W., A.V., M.F.M., F.L., N.M.A., S.G.W., O.M.V., A.P., J.F., K.C.B., C.L.K., J.N.R., R.M.F., A.B., C.L., N.J., I.F.P., S.C.M. and S.A. Writing, review and/or revision of manuscript was conducted by B.J.G., M.E.H., K.H., J.P., L.H.M., Y.-F.C., Y.L.W., T.A.G. A.F.C., A.V., A.P., J.F., K.C.B., C.L.K., J.N.R., R.M.F., A.B., C.L., N.J., I.F.P., S.C.M. and S.A. Administrative, technical, or material support (reporting or organizing data, constructing databases) was conducted by B.J.G., M.E.H., K.H., A.-C.J.S., A.L.L., S.M.C., A. Chen, M.W., J.P., E.P.J., D.R.D.P., S.M., L.H.M., M.Z., B.H., G.K., X.W., A.V., M.F.M., F.L., N.M.A., J.F., C.L.K., J.N.R., R.M.F., A.B., C.L., N.J., I.F.P., S.C.M. and S.A. Study supervision was performed by B.J.G., M.E.H., K.H., I.F.P., S.C.M. and S.A. S.A. was funded by National Institutes of Health grant award (R01NS115831), Michael Mosier Defeat DIPG Foundation and V-Foundation (in honor of Connor’s Cure). S.C.M. was funded by the Pediatric Brain Tumor Foundation, Michael Mosier Defeat DIPG Foundation, Chad Tough Foundation and Cookies for Cancer Foundation. C.L.K. is funded by the Canadian Institutes of Health Research grant PJT-156086 and by a salary award from the Fonds de Recherche du Québec-Santé salary award. S.G.W. and instrumentation is funded by a National Institutes of Health grant (NIHS10OD023402).

Corresponding authors

Correspondence to Stephen C. Mack or Sameer Agnihotri.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Cancer thanks Adam Green, Mei Kong 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 Generation of a Syngeneic Mouse Model of DIPG and RNA-seq profiling of NPC to H3WTMPP and H3K27MPP cells.

A. Western blot confirming epitope tagged transgene expression. B. Immunohistochemistry confirming transgene in negative in adjacent normal brain and cells are low for OLIG2 and positive for H3K27me3. Staining was confirmed in 3 independent mice. [AUs: Please include a scale bar]. C. Multidimensional scaling plot of RNA-seq data comparing control NPCs, H3WTPP and H3K27MPP cells. D-F. DESEQ2 Volcano plots comparing H3K27MPP cells to control NPCs (D), H3WTPP cells compared to control NPCs (E) and H3K27MPP cells to H3WTPP cells (F). Analysis was on RNA-sequencing performed on 3 biological replicates per condition. Statistical adjustments were made for multiple comparisons using iDEP.94 DESeq2 Statistical packages in R. Data displayed in blue or red represent genes with an FDR >0.05. G. Gene-set enrichment analysis (GSEA) using molecular terms, comparing H3WTPP and H3K27MPP from RNA-sequencing performed in biological triplicates for each condition.

Source data

Extended Data Fig. 2 SiRNA drop out screen reveals sensitivity to MAT2A and AMD1 loss.

A. Graphical representation of data in (Fig. 3B) plotted to percent difference in viability against -log10(adjusted P-value) generated from multiple T-tests unpaired, using Holm-Šídák method for multiple comparisons. (Left most graph comparing H3K27MPP vs H3WTPP, center H3K27MPP vs NSC, right H3WTPP vs NSC). siRNA in red Adjusted P-value<0.05 and percent loss of viability >25%. B. Cell count of NPC, H3WTPP, and H3K27MPP cells treated with 5 µM of MAT2A Inhibitor (AGI-24512) for 5 days. Experiments were performed in biological replicates (n=3). Statistical analysis performed as two-tailed, unpaired T-test. Data displayed as mean ± s.e.m. (NPC vs. NPC+MAT2Ai p=0.071), (H3WTPP vs. H3WTPP+MAT2Ai p=0.4486), and (H3K27MPP vs. H3K27MPP+MAT2Ai ***p<0.0001). C. Cell count of NPC, H3WTPP, and H3K27MPP cells treated with 5 µM of AMD1 inhibitor SAM426 for 5 days. Experiments were performed in biological replicates (n=3). Statistical analysis performed as two-tailed, unpaired T-test. Data displayed as mean ± s.e.m. (NPC vs. NPC+AMD1i p>0.9999), (H3WTPP vs. H3WTPP+AMD1i p=0.3248), and (H3K27MPP vs. H3K27MPP+AMD1i ***p<0.0001). D. Western blot comparing MAT2A and AMD1 expression in Histone H3 variant doxycycline inducible NPCs.

Source data

Extended Data Fig. 3 Effect of MAT2A inhibition in glioma lines.

A-C. Cell cycle analysis of control DMG cells compared to MAT2A knockdowns in (A) DIPG04, (B) BT-245 (C) DIPG13p. Experiments performed in biological triplicate. Statistical analysis as two-tailed, unpaired-T test. Data is displayed as mean± s.e.m. ((A) (DIPG04 NS No Dox vs. DIPG04 NS Dox %G2 *p=0.047), (DIPG04 MAT2A No Dox vs. Dox %G1 ****p=0.000004, %S ***p=0.000561, %G2 *p=0.014235). ((B) (BT-245 NS No Dox vs. Dox %S *p=0.041398), (BT-245 MAT2A No Dox vs. Dox %G1 ****p=0.000013, %S *p=0.017414, %G2 **p=0.004338). ((C) DIPG13p MAT2A No Dox vs. Dox %G1 ***p=0.000194, %S *p=0.011396). *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. p>0.05 not displayed. D. Summary of cell line features for MAT2A inhibitor viability response. E. Non-DMG/DIPG lines: Alamar blue viability response to varying doses of AG-24512. Experiments performed were in 5 biological replicates. Statistical analysis was performed using a one-way ANOVA followed by a Dunnett’s multiple comparison test. Data is displayed as mean± s.e.m. Adjusted P-values as follows: ((NHA) DMSO vs. 1 µM ****p<0.0001, DMSO vs. 10 µM *p=0.0107, DMSO vs. 100 µM ****p<0.0001), ((SF188) DMSO vs. 0.1 µM *p=0.0237, DMSO vs.10 µM *p=0.0297, DMSO vs. 100 µM ****p<0.0001), ((KNS42) DMSO vs. 1 µM *p=0.0269, DMSO vs. 10 µM **p=0.0086, DMSO vs. 100 µM ****p<0.0001), ((SJG2) DMSO vs. 1 µM ****p<0.0001, DMSO vs. 10 µM ****p<0.0001, DMSO vs. 100 µM ****p<0.0001). *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. p>0.05 not displayed. F. H3K27M mutant DIPG lines: Alamar blue viability in response to varying doses of AG-24512. Experiments performed were in 5 biological replicates. Statistical analysis was performed using a one-way ANOVA followed by a Dunnett’s multiple comparison test. Data is displayed as mean± s.e.m. Adjusted P-values as follows: ((DIPG04) DMSO 0.1 µM ***p=0.0004, DMSO vs. 10 µM ****p<0.0001, DMSO vs. 100 µM ****p<0.0001), ((BT-245) DMSO vs. 0.1, 1, 10, 100 µM ****p<0.0001), ((DIPG13p) DMSO vs. 10, 100 µM ****p<0.0001), ((NSC) DMSO vs. 100 µM ****p<0.0001). *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. p>0.05 not displayed.

Source data

Extended Data Fig. 4 MAT2A Overexpression reduces cell proliferation and increases EC50s to AG-24512.

A. Western blot of MAT2A-FLAG overexpression in H3K27MPP cells. B. Cell count of H3K27MPP cells comparing combinations of MAT2A over-expression, methionine deprivation, and MAT2A inhibitor. Experiments were performed in biological replicates (n=3). Data is displayed as mean± s.e.m. Statistical analysis performed as two-tailed, unpaired T-test. (H3K27MPP vs. H3K27MPP MAT2A OE *p=0.0285), (H3K27MPP+MR vs. H3K27MPP MAT2A OE+MR ***p=0.0004), and (H3K27MPP+MAT2Ai vs. H3K27MPP MAT2A OE+MAT2Ai **p=0.0013). *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. C. Quantification of SAM in H3K27MPP control and MAT2A over expressing (OE) cells compared with H3WTPP and NSC. Experiments were performed in biological replicates (n=4). Data is displayed as mean± s.e.m. Statistical analysis performed as two-tailed, unpaired T-test. (H3K27MPP vs. H3K27MPP MAT2A OE ****p<0.0001), (H3K27MPP vs. H3WTPP **p=0.0012), and (H3K27MPP vs. NPC **p=0.001). *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. D. Cell count of H3K27MPP cells comparing combinations of MAT2A Inhibitor (AGI-24512), AMD1 inhibitor (SAM426) and methionine deprivation conditions. Experiments were performed in biological replicates (n=3). Data is displayed as mean± s.e.m. Statistical analysis performed as two-tailed, unpaired T-test. (H3K27MPP+MR vs. H3K27MPP+MR+AMD1i **p=0.0016). E. Quantification of SAM in H3K27MPP cells comparing the combination of AMD1 inhibitor (SAM426) with methionine deprivation conditions. Experiments were performed in biological replicates (n=4) Data is displayed as mean± s.e.m. Statistical analysis performed as two-tailed, unpaired T-test. (H3K27MPP vs. AMD1i ***p=0.0003), (H3K27MPP vs. MR ***p=0.0002), and (H3K27MPP+MR vs. H3K27MPP+MR+AMD1i **p=0.0089). *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

Source data

Extended Data Fig. 5 The effects of dcSAM on MAT2A intron retention and protein stability.

A. Fold change in total MAT2A transcript levels relative to DMSO control in DIPG04 cells incubated for 6 hours in the following: 0.1 µM AGI-24512, 500 µM SAM and 500 µM dcSAM. Experiment was performed once, and samples were repeatedly measured in triplicate for each condition. B. Percent Intron 8 retention in total MAT2A transcript in DIPG04 cells incubated for 6 hours in the following: 0.1 µM AGI-24512, 500 µM SAM and 500 µM dcSAM. Experiment was performed once, n=3 technical replicates for each condition. C. Quantitative western blotting of MAT2A protein and actin in DIPG04 cells incubated for 48 hours in the following: 0.1 µM AGI-24512, 500 µM SAM and 500 µM dcSAM. D. Quantification of westerns from C) using Li-Cor fluorescent system. Biological replicate of n=3. Biological samples were measured 3 times and the average intensity was plotted for each biological replicate. Adjusted P-values as follows: (DMSO vs. AGI 10 µM ***p=0.0009), (DMSO vs. SAM 500 µM *p=0.0439), and (DMSO vs. dcSAM 500 µM *p=0.0189). *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. E. Fold change in total MAT2A transcript levels relative to DMSO control in NSC-PT2 cells incubated for 6 hours in the following: 0.1 µM AGI-24512, 500 µM SAM and 500 µM dcSAM. Experiment was performed once, and samples were repeatedly measured in triplicate for each condition. F. Percent intron 8 retention in total MAT2A transcript in NSC-PT2 cells incubated for 6 hours in the following: 0.1 µM AGI-24512, 500 µM SAM and 500 µM dcSAM. Experiment was performed once, n=3 technical replicates for each condition. G. Quantitative Western blotting of MAT2A protein and actin in NSC-PT2 cells incubated for 48 hours in the following: 0.1 µM AGI-24512, 500 µM SAM and 500 µM dcSAM. H. Quantification of westerns from G) using Li-Cor fluorescent system. Biological replicate of n=3. Biological samples were measured 3 times and the average intensity was plotted for each biological replicate. Adjusted P-values as follows: (DMSO vs. AGI 10 µM *p=0.033), (DMSO vs. SAM 500 µM *p=0.0153), and (DMSO vs. dcSAM 500 µM *p=0.0116). ((D, H) Statistical analysis performed as a repeated measures one-way ANOVA followed by Dunnett’s multiple comparisons test. Data displayed as mean ± s.e.m.).

Source data

Extended Data Fig. 6 Functional characterization of MAT2A knockdown in human DMG patients.

A. Quantitative Western Blotting of histone modifications (H3K4me3 and H3K36me3), and SDMA in BT-245 cells comparing MAT2A knockdown to control cells. B. Quantification of histone modifications (H3K4me3 and H3K36me3), SDMA, and MAT2A using Li-Cor fluorescent system for BT-245 cells, MAT2A knockdown vs control cells. Experiments performed in biological replicate of n=3, samples were repeatedly measured 3 times. Statistical analysis performed as two-tailed, unpaired T-test. Data displayed as mean ± s.e.m. (No Dox vs. Dox H3K4me3 **p=0.004557, H3K36me3 **p=0.003499, MAT2A ****p=0.00004, SDMA ***p=0.000578). *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. C. Quantitative Western Blotting of histone modifications (H3K4me3, H3K36me3), and SDMA in DIPG04 cells comparing MAT2A knockdown to control cells. D. Quantification of histone modifications (H3K4me3 and H3K36me3), SDMA, and MAT2A using Li-Cor fluorescent system for DIPG04 cells, MAT2A knockdown vs. control cells. Experiments performed in biological replicate of n=3, samples were repeatedly measured 3 times. Statistical analysis performed as two-tailed, unpaired T-test. Data displayed as mean ± s.e.m. (No Dox vs. Dox H3K4me3 ***p=0.000861, H3K36me3 ***p=0.000296, MAT2A **p=0.003325, SDMA **p=0.002049). *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

Source data

Extended Data Fig. 7 Silencing MAT2a alters the transcriptome and H3K36me3 deposition.

A. Volcano plot of differential gene expression comparing MAT2A knockdown to control DIPG04 cells. Canonical neuronal markers are highlighted in red (designating up-regulated genes) or blue (designating down-regulated genes). Statistical adjustments were made for multiple comparisons using iDEP.94 DESeq2 Statistical packages in R. B. Top 10 Gene sets, (Gene Ontology), derived from GSEA analysis of changes to the transcriptome in MAT2A knockdown. C. Heatmap of top 10 negative and positive enriching developmental cell signatures in MAT2A knockdown. D. Enrichment plots of selected developmental cell signatures from Extended Data Fig. 7C. E. Realtime PCR validation of selected canonical neurogenesis genes and markers of oligodendrocyte cells. Experiment was performed one time, n=3 technical triplicates for each gene. F. Heatmap of spike-in normalized H3K36me3 ChIP-Rx-seq reads centered at human genes in no doxycycline (top panel) vs. doxycycline (bottom panel) induction of MAT2A shRNA expression in DIPG04 cells. G. Bar plots of H3K36me3 ChIP-Rx-seq reads demonstrating alterations of H3K36me3 at different regions summarized across all human genes in DIPG04 cells. H. Venn diagram of overlapping genes between ChIP-Rx-seq and RNA-seq data. I. Plot of H3K36me3 (fold-change) vs. RNA seq (fold-change) with MAT2A KD, neurogenesis makers are indicated.

Source data

Extended Data Fig. 8 Leading edge analysis enriched developmental cell signatures in MAT2A KD.

A-C. Leading edge analysis of DIPG13pP Enrichment plots (Fig. 7F). D-F. Leading edge analysis of DIPG04 Enrichment plots (Extended Data Fig. 7D).

Extended Data Fig. 9 MAT2A knockdown decreases H3K36me3 globally in DMG cells.

A. Plot Showing a decrease in H3K36me3 signal with MAT2A KD in DIPG13p cells. Statistical analysis was performed on 1 Mb bins comparing a single DIPG13p sample with Mat2a KD vs control (no doxycycline) using a two-sided Wilcoxon test, **p=0.0013, the center line denotes the median and the lower and upper ends of the box denote the 25th and 75th percentiles respectively. The whiskers indicate the maximum and the minimum values of the data distribution. B. Plot Showing a decrease in H3K36me3 signal with MAT2A KD in DIPG04 cells. Statistical analysis was performed on 1 Mb bins comparing a single DIPG04 sample with Mat2a KD vs control (no doxycycline) using a two-sided Wilcoxon test, ****p<2.22e-16., the center line denotes the median and the lower and upper ends of the box denote the 25th and 75th percentiles respectively. The whiskers indicate the maximum and the minimum values of the data distribution. C, D. GSEA analysis using Cell Type Signature Datasets in DIPG13p (C) and DIPG04 (D) with genes which are significantly differential in both RNA-seq and ChIP datasets after MAT2A KD. E, F. GSEA analysis using GO Biological Processes Datasets in DIPG13p (E) and DIPG04 (F) with genes which are significantly differential in both RNA-seq and ChIP datasets after MAT2A KD.

Source data

Extended Data Fig. 10 Methionine Restriction induced viability defect and total H3K36me3 loss can be rescued with SAM repletion in DMG cells.

A. Percent viable cell count of BT-245 cells grown in media with 10% of normal methionine levels relative to control (100%) and supplemented with escalating concentrations of SAM. Adjusted P-values as follows: (10%Met 0 µMSAM vs. 10%Met 5 µM SAM **p=0.0042), and (10%Met 0 µMSAM vs. 10%Met 50 µM SAM ****p<0.0001). B. Quantitative western blotting of MAT2A, H3K36me3, and histone H3 in BT-245 cells comparing 10% methionine media with 100% supplemented with 500 µM of SAM. C, D. Quantification of MAT2A and H3K36me3 using Li-Cor fluorescent system for BT-245 cells comparing. 10% methionine media with 100% supplemented with 500uM of SAM. Biological replicate of n=3, samples were repeatedly measured 3 times. ((C) (100%Met vs. 100%+SAM p=0.7178), (100%Met vs. 10%Met *p=0.0394), and (100%Met vs. 10%Met+SAM p=0.3989). ((D) (100%Met vs. 100%+SAM ****p<0.0001), (100%Met vs. 10%Met ****p<0.0001), and (100%Met vs. 10%Met+SAM *p=0.0171). E. Percent viable cell count of DIPG04 cells grown in media with 10% of normal methionine levels relative to control (100%) and supplemented with escalating concentrations of SAM. Adjusted P-values as follows: (10%Met 0 µMSAM vs. 10%Met 5 µM SAM *p=0.0141), and (10%Met 0 µMSAM vs. 10%Met 50 µM SAM ****p<0.0001). F. Quantitative western blotting of MAT2A, H3K36me3, and histone H3 in DIPG04 cells comparing 10% methionine media with 100% supplemented with 500 µM of SAM. G, H. Quantification of MAT2A and H3K36me3 using Li-Cor fluorescent system for DIPG04 cells comparing. 10% methionine media with 100% supplemented with 500uM of SAM. ***p<0.0001 Biological replicate of n=2, samples were repeatedly measured 3 times. ((G) (100%Met vs. 100%+SAM *p=0.0135), (100%Met vs. 10%Met *p=0.0479), and (100%Met vs. 10%Met+SAM p=0.7667). ((H) (100%Met vs. 100%+SAM p=0.7451), (100%Met vs. 10%Met ***p=0.0004), and (100%Met vs. 10%Met+SAM p=0.7897). I. Percent viable cell count of DIPG13P cells grown in media with 10% of normal methionine levels relative to control (100%) and supplemented with escalating concentrations of SAM. Adjusted P-values as follows: (10%Met 0 µMSAM vs. 10%Met 5 µM SAM p=0.1167), and (10%Met 0 µMSAM vs. 10%Met 50 µM SAM ****p<0.0001). J. Quantitative western blotting of MAT2A, H3K36me3 and histone H3 in DIPG13p cells comparing 10% methionine media with 100% supplemented with 500 µM of SAM. K, L. Quantification of MAT2A and H3K36me3 using Li-Cor fluorescent system for DIPG13p cells comparing 10% methionine media with 100% supplemented with 500 µM of SAM. ***p<0.0001 Biological replicate of n=3, samples were repeatedly measured 3 times. ((K) (100%Met vs. 100%+SAM ***p=0.0008), (100%Met vs. 10%Met **p=0.0015), and (100%Met vs. 10%Met+SAM *p=0.0242). ((L) (100%Met vs. 100%+SAM p=0.8428), (100%Met vs. 10%Met **p=0.0057), and (100%Met vs. 10%Met+SAM p=0.6469). ((A, E, I) Experiments performed were in biological triplicates. Statistical analysis performed as a one-way ANOVA followed by Šídák’s multiple comparisons test. Data displayed as mean ± s.e.m.). ((C-D, G-H,K-L) Statistical analysis performed as a two-tailed, unpaired T-test. Data displayed as mean ± s.e.m.).

Source data

Supplementary information

Reporting Summary

43018_2022_348_MOESM2_ESM.xlsx

Supplementary Table 1 Sheet1: RNA-seq: NPC versus H3WTPP versus H3K27MPP transcript changes. Sheet2: RNA-seq: MAT2A knockdown versus control transcript changes. Sheet3: H3K36me3 fold change: MAT2A KD versus control. Sheet4: RNA-seq transcript and H3K36me3 overlaps. Sheet5: Oligonucleotide sequences and product information.

Source data

Source Data Fig. 1

Unprocessed western blots.

Source Data Fig. 1

Statistical Source Data.

Source Data Fig. 2

Statistical Source Data.

Source Data Fig. 3

Unprocessed western blots.

Source Data Fig. 3

Statistical Source Data.

Source Data Fig. 4

Unprocessed western blots.

Source Data Fig. 4

Statistical Source Data.

Source Data Fig. 5

Unprocessed western blots.

Source Data Fig. 5

Statistical Source Data.

Source Data Fig. 6

Unprocessed western blots.

Source Data Fig. 6

Statistical Source Data.

Source Data Fig. 7

Unprocessed western blots.

Source Data Fig. 7

Statistical Source Data.

Source Data Fig. 8

Unprocessed western blots.

Source Data Fig. 8

Statistical Source Data.

Source Data Extended Data Fig. 1

Unprocessed western blots.

Source Data Extended Data Fig. 2

Unprocessed western blots.

Source Data Extended Data Fig. 2

Statistical Source Data.

Source Data Extended Data Fig. 3

Gating strategy for Extended Data.

Source Data Extended Data Fig. 3

Statistical Source Data.

Source Data Extended Data Fig. 4

Unprocessed western blots.

Source Data Extended Data Fig. 4

Statistical Source Data.

Source Data Extended Data Fig. 5

Unprocessed western blots.

Source Data Extended Data Fig. 5

Statistical Source Data.

Source Data Extended Data Fig. 6

Unprocessed western blots.

Source Data Extended Data Fig. 6

Statistical Source Data.

Source Data Extended Data Fig. 7

Statistical Source Data.

Source Data Extended Data Fig. 9

Statistical Source Data.

Source Data Extended Data Fig. 10

Unprocessed western blots.

Source Data Extended Data Fig. 10

Statistical Source Data.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Golbourn, B.J., Halbert, M.E., Halligan, K. 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). https://doi.org/10.1038/s43018-022-00348-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43018-022-00348-3

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