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MCT2 mediates concentration-dependent inhibition of glutamine metabolism by MOG

Nature Chemical Biology volume 14, pages 1032–1042 (2018)Cite this article

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An Author Correction to this article was published on 23 October 2019

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Abstract

α-Ketoglutarate (αKG) is a key node in many important metabolic pathways. The αKG analog N-oxalylglycine (NOG) and its cell-permeable prodrug dimethyloxalylglycine (DMOG) are extensively used to inhibit αKG-dependent dioxygenases. However, whether NOG interference with other αKG-dependent processes contributes to its mode of action remains poorly understood. Here we show that, in aqueous solutions, DMOG is rapidly hydrolyzed, yielding methyloxalylglycine (MOG). MOG elicits cytotoxicity in a manner that depends on its transport by monocarboxylate transporter 2 (MCT2) and is associated with decreased glutamine-derived tricarboxylic acid–cycle flux, suppressed mitochondrial respiration and decreased ATP production. MCT2-facilitated entry of MOG into cells leads to sufficiently high concentrations of NOG to inhibit multiple enzymes in glutamine metabolism, including glutamate dehydrogenase. These findings reveal that MCT2 dictates the mode of action of NOG by determining its intracellular concentration and have important implications for the use of (D)MOG in studying αKG-dependent signaling and metabolism.

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Fig. 1: DMOG induces cytotoxicity that correlates with MCT2 expression and is not explained by differential inhibition of oxygen-sensitive dioxygenases.
Fig. 2: The methyl oxoacetate ester of DMOG is rapidly hydrolyzed to MOG in cell culture medium.
Fig. 3: MOG is sufficient to cause cytotoxicity in an MCT2-dependent manner.
Fig. 4: MOG inhibits glutamine catabolism in an MCT2-dependent manner.
Fig. 5: Evidence that inhibition of GDH-mediated glutamine carbon flux accounts for MOG-induced metabolic changes associated with cytotoxicity.
Fig. 6: NOG binds GDH and inhibits its enzymatic activity.

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The data that support the findings of this study are available from the corresponding author upon reasonable request. Accession codes and relevant web links can be found in the respective legends and methods sections.

Change history

  • 23 October 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Acknowledgements

We thank all members of the laboratory of D.A. for valuable discussions and input throughout this work, particularly J. Macpherson and N. Bevan for technical help. We are grateful to L. Cantley for advice during early stages of this work. We acknowledge S. O’Callaghan (Bio21 Institute, University of Melbourne) for the algorithm to correct for natural isotope abundance in metabolomics data. We thank J. Kleinjung for advice with statistical methods, M. Howell for advice and help with cell proliferation and viability measurements, J. Cerveira for help with flow cytometry measurements and A. Gould for comments on the manuscript. We are grateful to the staff at the Medical Research Council National Biomedical NMR Centre at the Francis Crick Institute, where NMR data were obtained. This work was funded by the MRC (MC_UP_1202/1) and by the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001033), the UK Medical Research Council (FC001033) and the Wellcome Trust (FC001033) to D.A.

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Author notes

  1. These authors contributed equally: Fiona Grimm, Aakriti Jain, Patrícia M. Nunes.

Authors and Affiliations

  1. Cancer Metabolism Laboratory, Francis Crick Institute, London, UK

    Louise Fets, Fiona Grimm, Aakriti Jain, Patrícia M. Nunes, Michalis Gounis, Ginevra Doglioni & Dimitrios Anastasiou

  2. Metabolomics Science Technology Platform, Francis Crick Institute, London, UK

    Paul C. Driscoll, Mariana Silva dos Santos & James I. MacRae

  3. Peptide Chemistry Science Technology Platform, Francis Crick Institute, London, UK

    George Papageorgiou & Nicola O’Reilly

  4. MRC–National Institute for Medical Research, London, UK

    Timothy J. Ragan

  5. Crick–GSK Biomedical LinkLabs, GSK Medicines Research Centre, Stevenage, UK

    Sebastien Campos & David House

  6. Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK

    Alan J. Wright

  7. Massachusetts General Hospital Cancer Center & Department of Medicine, Harvard Medical School, Boston, MA, USA

    Cyril H. Benes

  8. Department of Internal Medicine, Division of Hematology/Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Kevin D. Courtney

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Contributions

P.C.D. and T.J.R. performed NMR experiments; F.G. generated HIF1α-mutant cell lines and wrote scripts for metabolomics data analysis and visualization; A.J. and P.M.N. performed respiration experiments; M.G. assisted with the development of LC–MS analytical methods; G.D. generated and characterized cell lines; M.S.d.S. and J.I.M. assisted with and advised on metabolomics experiments; A.J.W. did flux modeling; G.P. and N.O’R. synthesized DM-[13C5]αKG; S.C. and D.H. synthesized MOG and advised on chemistry; C.H.B. contributed to the large-scale DMOG sensitivity screen; K.D.C. performed experiments and analyzed data; and L.F. and D.A. designed and performed experiments, analyzed data and wrote the manuscript. All authors reviewed and commented on the manuscript. Work by A.J. was supported by the MetaRNA Marie Skłodowska-Curie Innovative Training Nerwork (642738).

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Correspondence to Dimitrios Anastasiou.

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Supplementary information

Supplementary Text and Figures

Supplementary Tables 1 and 2, and Supplementary Figures 1–13

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Supplementary Note

Synthetic Procedures

Supplementary Dataset 1

Spearman’s rank correlation coefficients of gene transcripts with IC50DMOG, determined as described in Methods. Genes identified as positively or negatively correlating with sensitivity (as determined by a 5% FDR) are highlighted.

Supplementary Dataset 2

Spearman’s rank correlation coefficients of gene transcripts with IC50DMOG, using only the top quartile of SLC16A7-expressing cell lines (213 lines) used in Fig. 6e, f. Genes identified as positively or negatively correlating with sensitivity (as determined by a 5% FDR) are highlighted.

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Fets, L., Driscoll, P.C., Grimm, F. et al. MCT2 mediates concentration-dependent inhibition of glutamine metabolism by MOG. Nat Chem Biol 14, 1032–1042 (2018). https://doi.org/10.1038/s41589-018-0136-y

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