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Non-canonical glutamine transamination sustains efferocytosis by coupling redox buffering to oxidative phosphorylation

Nature Metabolism volume 3, pages 1313–1326 (2021)Cite this article

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Abstract

Macrophages rely on tightly integrated metabolic rewiring to clear dying neighboring cells by efferocytosis during homeostasis and disease. Here we reveal that glutaminase-1-mediated glutaminolysis is critical to promote apoptotic cell clearance by macrophages during homeostasis in mice. In addition, impaired macrophage glutaminolysis exacerbates atherosclerosis, a condition during which, efficient apoptotic cell debris clearance is critical to limit disease progression. Glutaminase-1 expression strongly correlates with atherosclerotic plaque necrosis in patients with cardiovascular diseases. High-throughput transcriptional and metabolic profiling reveals that macrophage efferocytic capacity relies on a non-canonical transaminase pathway, independent from the traditional requirement of glutamate dehydrogenase to fuel ɑ-ketoglutarate-dependent immunometabolism. This pathway is necessary to meet the unique requirements of efferocytosis for cellular detoxification and high-energy cytoskeletal rearrangements. Thus, we uncover a role for non-canonical glutamine metabolism for efficient clearance of dying cells and maintenance of tissue homeostasis during health and disease in mouse and humans.

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Fig. 1: Macrophage Gls1 deletion impairs efferocytosis in vitro and in vivo.
Fig. 2: Myeloid-Gls1 deletion impairs efferocytosis in the pathological process of atherosclerosis.
Fig. 3: Glutamine metabolism fuels mitochondrial OxPHOS to support efferocytosis.
Fig. 4: Non-canonical transaminase pathway allows glutaminolysis to fuel OxPHOS and support efferocytosis.
Fig. 5: Glutamine metabolism supports the high-energy requirement of cytoskeletal rearrangement and corpse engulfment.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request. Topological analyses are available at https://artyomovlab.wustl.edu/shiny/gam/ and at biotest.hematometabolism.science upon request. Raw and processed sequencing data are deposited on the Gene Expression Omnibus under accession code GSE183176. Source data are provided with this paper.

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Acknowledgements

We thank B. Caraveo for computional development of DreamBio, a new topological tool for Integrated Network Analysis. N. Grandchamp provided Lenti-ONE AOX vector through GEG Tech. We thank S. Fernandez for the noninvasive study of atheroma plaques by ultrasound echography as part of the European Center for Research in Imaging (Cerimed), F. Labret for assistance with flow cytometry, V. Corcelle for assistance in animal facilities and M. Irondelle for assistance with confocal microscopy. This work was supported by grants from the Fondation de France, ANR and the European Research Council consolidator program (ERC2016COG724838) to L.Y.-C. CCMA electron microscopy equipment was funded by the Région Sud - Provence-Alpes-Côte d’Azur, the Conseil Départemental des Alpes Maritimes and the GIS-IBiSA.

Author information

Author notes

  1. These authors contributed equally: J. Merlin, S. Ivanov, A. Dumont.

Authors and Affiliations

  1. Institut National de la Santé et de la Recherche Médicale (Inserm) U1065, Université Côte d’Azur, Centre Méditerranéen de Médecine Moléculaire (C3M), Centre National de la Recherche Scientifique (CNRS) (R.G.), Atip-Avenir, Fédération Hospitalo-Universitaire (FHU) Oncoage, Nice, France

    Johanna Merlin, Stoyan Ivanov, Adélie Dumont, Julie Gall, Marion Stunault, Marion Ayrault, Nathalie Vaillant, Alexia Castiglione, Alexandre Gallerand, Rodolphe R. Guinamard & Laurent Yvan-Charvet

  2. Computer Technologies Department, ITMO University, Saint Petersburg, Russia

    Alexey Sergushichev

  3. Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA

    Amanda Swain & Maxim N. Artyomov

  4. Université Côte d’Azur, Centre Commun de Microscopie Appliquée (CCMA), Nice, France

    Francois Orange

  5. Centre de Recherche Cardiovasculaire et Nutritionnelle (C2VN), INSERM, Institut National de la Recherche Agricole (INRA), BioMet, Aix-Marseille University, Marseille, France

    Thierry Berton & Jean-Charles Martin

  6. Department of Cell Physiology and Metabolism, University of Geneva Medical Centre, Geneva, Switzerland

    Stefania Carobbio & Pierre Maechler

  7. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK

    Stefania Carobbio

  8. Metabolic Research Laboratories, Addenbrooke’s Treatment Centre, Institute of Metabolic Science, Addenbrooke’s Hospital, University of Cambridge, Cambridge, UK

    Stefania Carobbio

  9. Inserm UMR-S1270, Institut du Fer à Moulin, Sorbonne Université, Paris, France

    Justine Masson

  10. Department of Molecular Therapeutics, NYS Psychiatric Institute, New York, NY, USA

    Justine Masson, Inna Gaisler-Salomon & Stephen Rayport

  11. Department of Psychiatry, Columbia University, New York, NY, USA

    Inna Gaisler-Salomon & Stephen Rayport

  12. SPC-IBBR, University of Haifa, Haifa, Israel

    Inna Gaisler-Salomon

  13. Department of Pathology, Cardiovascular Research Institute Maastricht, Maastricht University Medical Center, Maastricht, the Netherlands

    Judith C. Sluimer & Erik A. L. Biessen

  14. Institute for Molecular Cardiovascular Research, RWTH Klinikum Aachen, Aachen, Germany

    Erik A. L. Biessen

  15. Sorbonne Université, INSERM, UMR_S 1166 ICAN, Paris, France

    Emmanuel L. Gautier

  16. Department of Pathology, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA

    Edward B. Thorp

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  1. Johanna Merlin
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Contributions

L.Y.-C. conceived the project, designed the experiments and wrote the manuscript. J.M., S.I. and A.D. performed most of the molecular, histological and in vivo experiments. A.S., J.G., M.S., M.A., N.V., A.C., A.S. and A.G. helped with the experimental design and assisted with the data analysis. F.O., T.B. and J.C.M. provided access to platform facilities and assisted with the data acquisition and analysis. S.C., J.M., I.G.S., P.M. and S.R. provided transgenic mice and intellectual discussion. J.C.S. and E.A.L.B. aided in the design and analysis of human studies. R.R. and E.L.G., E.B.T., M.N.A. provided scientific advice and helped with the experimental design. L.Y.-C. also designed and supervised the study and obtained funding. All of the authors read, edited and approved the manuscript.

Corresponding author

Correspondence to Laurent Yvan-Charvet.

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The authors declare no competing interests.

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Peer review information Nature Metabolism thanks Ping-Chih Ho-and the other, anonymous, reviewers for their contribution to the peer review of this work. Primary handling editor: George Caputa

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

Extended data

Extended Data Fig. 1 GLS1 is dispensable for macrophage homeostasis but supports macrophage effector functions.

(a) Comparative analysis of Gls1 mRNA expression in different macrophage populations from a publicly available dataset (Immgen). Red indicates up- and blue down-regulated expression (b) Macrophage population gating strategy (left) and numbers (right) measured by flow cytometry in multiple mouse tissues (bone marrow, peritoneal cavity, spleen, liver and brain). Both sexes were analyzed. (c) [3H]-Thymidine incorporation (left) and apoptosis percentage (right) in Gls1fl/fl control or Mac∆Gls1 macrophages at steady state or after an overnight stimulation with IL-4. (d) Phospho-S6 (left) and c-myc (right) expression measured by flow cytometry in these cells. (e) RNAseq of control or Mac∆Gls1 cell sorted PCMs at steady state or after IL-4 stimulation. (f) RNAseq analysis with focus on alternatively activated genes in control or Mac∆Gls1 PCMs stimulated overnight or not with IL-4. (g) CD206 and CD301 expression by flow cytometry in Cre+ control or Mac∆Gls1 mice injected i.p. with PBS or IL-4−complex. (h) mRNA expression of key efferocytic receptors such as MertK, Timd4 or Gas6 or immunoregulatory cytokines such as Tgfβ or Il10 (left panel) or IL-10 secretion (right panel) in reparative Gls1fl/fl control and MacΔGls1 PCMs. (i) Phagocytosis assays of heat-killed, fluorescently pre-labeled E.coli (calculated after quenching the extracellular probe by trypan blue) in these cells. All values are mean ± SEM and are representative of at least one independent experiment (n = 4 to 8 independent animals for b-d, n = 3 to 5 for g-i). P values were determined by a two-tailed Student’s t-test. Source data are provided as a Source Data file (b-d, g-i).

Source data

Extended Data Fig. 2 Atherosclerosis development relies on GLS1-dependent glutaminolyis.

(a) Metabolic pathway (left) and RNAseq analysis (right) of Apoe−/− versus WT mouse aortas (6 weeks on WD) performed with Phantasus software (GSE10000). Red indicates up- and blue down-regulated expression. (b) Oil red O staining (left) or 14C glutamine accumulation after i.v. injection (right) in descending aortas extracted from female WT and Apoe−/− mice maintained on a WD for 6 weeks. (c) 14C glutamine incorporation in aortas obtained from female Apoe−/− and Apoe−/− Mac∆Gls1 mice fed for 6 weeks on WD. (d) Cholesterol (left) and triglyceride (right) content in plasma of Apoe−/− and Apoe−/− Mac∆Gls1 mice. (e) Representative sections (left) and quantification (right) of aortic plaques from Apoe−/− or Apoe−/− HSC∆Gls1 mice (12 weeks WD) stained for Oil Red O and Hematoxylin Eosin. Scale bar: 200 μm. (f) Representative sections (top) and quantification (bottom) of aortic plaques from Apoe−/− or Apoe−/− Mac∆Gls1 mice (12 weeks WD) stained for CD68 and Ki67. (g) Gating strategy (left) and quantification (right) of PCM efferocytic index in Apoe−/− and Apoe−/− MacΔGls1 mice 1-hour after labeled ACs i.p. injection. Both sexes were analyzed. All values are mean ± SEM and are representative of at least one independent experiment (n = 4 independent animals for c, n = 7 to 9 for e-g). P values were determined by a two-tailed Student’s t-test. Source data are provided as a Source Data file (c-g).

Source data

Extended Data Fig. 3 Glutaminolysis supports mitochondrial OXPHOS and ATP generation to promote efficient efferocytosis.

(a) Publicly available gene expression datasets analysis of macrophages ingesting apoptotic cells (GSE98169). Red indicates up- and blue down-regulated expression. (b) Gls1 mRNA expression and (c) Glutamine/Glutamate ratio during efferocytosis in control BMDMs in a time course experiment. (d) Quantification of leucine and valine levels and (e) ornithine, arginine, proline and putrescine levels in control or Mac∆Gls1 BMDMs at steady state or after IL-4 stimulation. (f) Efferocytic index in control or Mac∆Gls1 BMDMs in basal conditions or stimulated overnight with IL-4 and + /− Gabapentin or Bcat inhibitor (left panel) or treated with IL-4 and ornithine (right panel). (g) OCR measurements in reparative control and MacΔGls1 PCMs or (h) in resting and reparative control or Mac∆Gls1 BMDMs. (i) ECAR measured by Seahorse in these BMDMs. (j) Mitochondrial complex I (i.e, NADH-ubiquinone oxidoreductase) (left panel) and complex II (i.e, succinate dehydrogenase (SDH)) (right panel) activities in reparative MacΔGls1 BMDMs. (k) OCR measurements after one (45 min) and two (1-hour rest + 45 min) incubations with ACs in control or Mac∆Gls1 BMDMs (resolving condition) (left panel) or after a second incubation with ACs in control or Mac∆Gls1 BMDMs following a standard ‘Seahorse’ procedure (right panel). (l) OCR measurements after empty or Gls1 lentivirus overexpression in resting control macrophages or reparative control and Mac∆Gls1 BMDMs. (m) ATP production measurements in these cells. (n) Mitochondrial ROS quantification using Mitosox probe in resting and reparative control or Mac∆Gls1 BMDMs and (o) in control and Mac∆Gls1 BMDMs after empty or Gls1 lentivirus overexpression. (p) ATP production measurements in reparative control or Mac∆Gls1 BMDMs after lentiviral vector-mediated overexpression of empty or mitochondrial alternative oxidase (AOX). (q) Efferocytic index in resolving control or Mac∆Gls1 BMDMs + /− 3NPA or antimicyn A between the two rounds of efferocytosis. (r) Efferocytic index after one round efferocytosis (left) or after two round efferocytosis (right) in control or Risp−/− macrophages. All values are mean ± SEM and are representative of at least one independent experiment (n = 2 to 3 independent animals for b-e, n = 3 to 9 for f-r). P values were determined by ordinary two-tailed Student’s t-test (d, e, g, j-m, o, p, r) or one-way ANOVA with Tukey post hoc test for multiple comparisons (f, h, i, n, q). Source data are provided as a Source Data file (d-r).

Source data

Extended Data Fig. 4 Gls1 deficiency metabolically reprograms macrophages.

(a) CoMBI-T profiling analysis from RNAseq data of reparative IL-4 treated Gls1-deficient or sufficient PCMs. (b) KEGG mapping with DreamBio from the same RNAseq data. Red indicates up- and green down-regulated expression.

Extended Data Fig. 5 Gls1 deficiency limits non-canonical transaminase metabolism to modulate OXPHOS and redox balance.

(a) ATP production measured by Seahorse in control or Mac∆Gls1 BMDMs at steady state or after overnight IL-4 stimulation + /− EGCG, AOA or NAC. (b) KDM6 activity assay (left) and Tet2 activity assay (right) in resting and reparative control or Mac∆Gls1 BMDMs. (c) OCR quantification control or Mac∆Gls1 BMDMs stimulated overnight with IL-4 in presence or absence of αKetoglutarate. (d) GLUD1 protein expression assessed by Western blotting in control or HSC∆Glud1 PCMs. (e) Effect of Gls1 deficiency on PCA (pyroglutamate), a glutathione precursor, as a read out of cellular GSH replenishment. (f) Efferocytic index in resolving control or Mac∆Gls1 BMDMs + /− AOA treatment between the two rounds of efferocytosis. (g) Efferocytic index in control or Mac∆Gls1 BMDMs stimulated overnight or not with IL-4 and transfected with scramble or Got1/Got2 siRNA. (h) BMDMs analyzed by liquid chromatography-mass spectrometry. (i) ROS quantification using flow cytometry in resting and reparative control or Mac∆Gls1 BMDMs. (j) OCR quantification and ATP production measured by Seahorse. (k) NAD(P)H levels and (l) efferocytic index determined by flow cytometry in control or Mac∆Gls1 BMDMs in basal conditions or overnight IL-4 stimulation + /− GSH. All values are mean ± SEM and are representative of at least one independent experiment (n = 3 to 9 independent animals for a, n = 3 for b, e, g, k, n = 3 to 8 for c, f, h, i, l, n = 4 to 12 for j). P values were determined by ordinary two-tailed Student’s t-test (b, e, g-i) or one-way ANOVA with Tukey post hoc test for multiple comparisons (a, f, j-l). Each statistical bar color-coded represents an independent one-way ANOVA test against IL-4 conditions. Source data are provided as a Source Data file (a-c, e-l).

Source data

Extended Data Fig. 6 Glutaminolysis is essential for post-transcriptional regulation of actin dynamics and efferocytosis.

(a) Schematic representation of efferocytosis steps and RNAseq analysis of “find-me”, “eat-me” and “tolerate-me” signals in reparative IL-4 treated control and MacΔGls1 PCMs. (b) Schematic representation of actin polymerization and depolarization (left panel) and RNAseq analysis with focus on F-actin dynamic regulators in reparative control or Mac∆Gls1 PCMs (right panel). (c) Dbl expression quantified by flow cytometry in control or IL-4-treated macrophages that were incubated ± ACs for 45 min. (d) RNAseq analysis with focus on GTP-converting ezymes. (e) qPCR quantification (left) and efferocytic index (right) in Gls1fl/fl control and Mac∆Gls1 BMDMs stimulated overnight with IL-4 after Nme1/6 or Sucgl1 lentivirus overexpression. (f) Cdc42 and (g) Rac1 activity assays in control or Mac∆Gls1 BMDMs in basal condition or stimulated overnight with IL-4 + /− AOA. (h) Efferocytic index in reparative control or Mac∆Gls1 BMDMs or (i) in resolving control or Mac∆Gls1 BMDMs + /− NSC23766 (RAC1 inhibitor), ML141 (CDC42 inhibitor) or MDIVI-1 (DRP1-mediated mitochondrial fission inhibitor). All values are mean ± SEM and are representative of at least one independent experiment (n = 2 to 3 independent animals for c-f, n = 5 to 9 for g, n = 3 to 6 for h, i). P values were determined by ordinary one-way ANOVA with Tukey post hoc test for multiple comparisons (c, e-i). Each statistical bar color-coded represents an independent one-way ANOVA test. Source data are provided as a Source Data file (c, e-i).

Source data

Extended Data Fig. 7 Graphical abstract illustrating how non-canonical glutamine transamination sustains efferocytosis in atherosclerotic plaques.

Glutaminase (GLS) 1 is upregulated upon reparative (IL-4 stimulation) or resolving (continued clearance of apoptotic cells) to convert glutamine to glutamate. Non-canonical glutamine transamination then acts through the aspartate-arginino-succinate (AAS) shunt to sustain redox buffering (NADPH generation to efficiently supply reduced glutathione GSSG) and fuel energy (i.e, ATP) production through efficient oxidative phosphorylation for cytoskeletal rearrangements.

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Merlin, J., Ivanov, S., Dumont, A. et al. Non-canonical glutamine transamination sustains efferocytosis by coupling redox buffering to oxidative phosphorylation. Nat Metab 3, 1313–1326 (2021). https://doi.org/10.1038/s42255-021-00471-y

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