Letter | Published:

SHMT2 drives glioma cell survival in ischaemia but imposes a dependence on glycine clearance

Nature volume 520, pages 363367 (16 April 2015) | Download Citation

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Abstract

Cancer cells adapt their metabolic processes to support rapid proliferation, but less is known about how cancer cells alter metabolism to promote cell survival in a poorly vascularized tumour microenvironment1,2,3. Here we identify a key role for serine and glycine metabolism in the survival of brain cancer cells within the ischaemic zones of gliomas. In human glioblastoma multiforme, mitochondrial serine hydroxymethyltransferase (SHMT2) and glycine decarboxylase (GLDC) are highly expressed in the pseudopalisading cells that surround necrotic foci. We find that SHMT2 activity limits that of pyruvate kinase (PKM2) and reduces oxygen consumption, eliciting a metabolic state that confers a profound survival advantage to cells in poorly vascularized tumour regions. GLDC inhibition impairs cells with high SHMT2 levels as the excess glycine not metabolized by GLDC can be converted to the toxic molecules aminoacetone and methylglyoxal. Thus, SHMT2 is required for cancer cells to adapt to the tumour environment, but also renders these cells sensitive to glycine cleavage system inhibition.

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Change history

  • 15 April 2015

    A minor change was made to the legend of Extended Data Fig. 5 to clarify the publisher on a figure credit.

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Acknowledgements

We thank members of the Sabatini laboratory for assistance and feedback, in particular Y. Shaul, T. Wang, S. Wang and O. Yilmaz. Authors would like to thank J. Taylor for GBM sample collection, and T. DiCesare for illustrations. This work was supported by a Basic Research Fellowship from the American Brain Tumor Association to D.K.; MIT School of Science Fellowship in Cancer Research and National Institutes of Health (NIH) T32GM007287 to B.P.F., fellowships from the Jane Coffin Childs Memorial Fund and Leukemia and Lymphoma Society to K.B.; a grant from the NIH (K99 CA168940) to R.P.; an American Cancer Society fellowship and an American Brain Tumor Association Discovery Grant to Y.C.; a fellowship from the US National Institute of Aging to W.W.C.; NIH (K08-NS087118) to S.H.R.; support from NIH (R01CA168653, 5P30CA14051), the Smith Family Foundation, the Burroughs Wellcome Fund, the Damon Runyon Cancer Research Foundation, and the Stern family to M.G.V.H.; DOD CDMRP Discovery Award, grants from the David H. Koch Institute for Integrative Cancer Research at MIT, The Alexander and Margaret Stewart Trust Fund, and NIH (CA103866, CA129105, and AI07389) to D.M.S.; D.M.S. is an investigator of the Howard Hughes Medical Institute.

Author information

Affiliations

  1. Whitehead Institute for Biomedical Research, Nine Cambridge Center, Cambridge, Massachusetts 02142, USA

    • Dohoon Kim
    • , Kivanc Birsoy
    • , Elizaveta Freinkman
    • , Richard L. Possemato
    • , Yakov Chudnovsky
    • , Michael E. Pacold
    • , Walter W. Chen
    • , Jason R. Cantor
    • , Manjae Kwon
    • , Seong Woo Kang
    •  & David M. Sabatini
  2. Howard Hughes Medical Institute and Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

    • Dohoon Kim
    • , Kivanc Birsoy
    • , Elizaveta Freinkman
    • , Richard L. Possemato
    • , Yakov Chudnovsky
    • , Michael E. Pacold
    • , Walter W. Chen
    • , Jason R. Cantor
    • , Seong Woo Kang
    •  & David M. Sabatini
  3. The David H. Koch Institute for Integrative Cancer Research at MIT, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA

    • Dohoon Kim
    • , Brian P. Fiske
    • , Kivanc Birsoy
    • , Elizaveta Freinkman
    • , Richard L. Possemato
    • , Yakov Chudnovsky
    • , Michael E. Pacold
    • , Walter W. Chen
    • , Jason R. Cantor
    • , Dan Y. Gui
    • , Seong Woo Kang
    • , Matthew G. Vander Heiden
    •  & David M. Sabatini
  4. Department of Biology, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA

    • Dohoon Kim
    • , Brian P. Fiske
    • , Kivanc Birsoy
    • , Elizaveta Freinkman
    • , Richard L. Possemato
    • , Yakov Chudnovsky
    • , Michael E. Pacold
    • , Walter W. Chen
    • , Jason R. Cantor
    • , Dan Y. Gui
    • , Manjae Kwon
    • , Seong Woo Kang
    • , Matthew G. Vander Heiden
    •  & David M. Sabatini
  5. Broad Institute of Harvard and MIT, Seven Cambridge Center, Cambridge, Massachusetts 02142, USA

    • Dohoon Kim
    • , Brian P. Fiske
    • , Kivanc Birsoy
    • , Elizaveta Freinkman
    • , Richard L. Possemato
    • , Yakov Chudnovsky
    • , Michael E. Pacold
    • , Walter W. Chen
    • , Jason R. Cantor
    • , Dan Y. Gui
    • , Seong Woo Kang
    • , Matthew G. Vander Heiden
    •  & David M. Sabatini
  6. Human Metabolome Technologies, Inc., Tsuruoka 997-0052, Japan

    • Kenjiro Kami
  7. Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA

    • Michael E. Pacold
    • , Shakti H. Ramkissoon
    • , Keith L. Ligon
    •  & Matthew G. Vander Heiden
  8. Human Metabolome Technologies America, Inc., Boston, Massachusetts 02134, USA

    • Laura M. Shelton
  9. Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts 02115, USA

    • Shakti H. Ramkissoon
    •  & Keith L. Ligon
  10. Department of Pathology, Boston Children’s Hospital, Boston, Massachusetts 02115, USA

    • Shakti H. Ramkissoon
    •  & Keith L. Ligon
  11. Department of Pathology, NYU Langone Medical Center and Medical School, New York, New York 10016, USA

    • Matija Snuderl

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Contributions

D.K. and D.M.S. conceived the study and designed most of the experiments. D.K. performed most of the experiments (cell viability and proliferation, western blotting, immunohistochemistry, xenografts) with assistance from K.B., R.L.P., Y.C., W.W.C., S.K. and M.K.; B.P.F. and M.G.V.H. designed, carried out and analysed pyruvate kinase activity and LC–MS based experiments with input and assistance from E.F., M.E.P. and D.Y.G.; E.F., D.K. and J.R.C. designed and carried out LC–MS-based derivatization experiments measuring aminoacetone levels. K.K. and L.M.S. conducted and analysed CE–MS metabolite profiling. M.S. provided GBM sections and conducted analyses and imaging of IHC. S.H.R. and K.L.L. assisted with neurosphere-forming cell characterizations. D.K. and D.M.S. wrote and all authors edited the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to David M. Sabatini.

Extended data

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    This file contains a Supplementary Figure showing uncropped blots with size marker indications.

Excel files

  1. 1.

    Supplementary Table 1

    A list of genes whose loss causes toxicity in the developing brain, and the associated disorder, targeted organs, and other relevant information.

  2. 2.

    Supplementary Table 2

    This table contains data from oncomine-based gene expression studies comparing glioma vs. normal brain, and list of metabolic genes that are highly overexpressed in gliomas in each study.

  3. 3.

    Supplementary Table 3

    This table contains a data summary for gene expression omnibus microarray-based studies comparing expression of select genes in neural stem cells versus differentiated controls.

  4. 4.

    Supplementary Table 4

    A list of individual shRNAs used and abundance scores for each shRNA under each pool condition.

  5. 5.

    Supplementary Table 5

    This table contains Data for all metabolites in LN229 cells expressing shGFP or shSHMT2_1 as measured using CE-MS based, quantitative metabolite profiling.

  6. 6.

    Supplementary Table 6

    A list of metabolites identified through LC-MS based, untargeted discovery using Progenesis software, in positive mode.

  7. 7.

    Supplementary Table 7

    A list of metabolites identified through LC-MS based, untargeted discovery using Progenesis software, in negative mode.

  8. 8.

    Supplementary Table 8

    This table contains methods and data for 13-C labelled species in the labelling rate analyses shown in Figure 4e-g and Extended Figure 5c-d.

  9. 9.

    Supplementary Table 9

    This table contains plots for 13-C labelled species abundance over time for the labelling rate analyses shown in Figure 4e-g and Extended Figure 5c-d.

About this article

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DOI

https://doi.org/10.1038/nature14363

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