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.

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

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

Subjects

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.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

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

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.

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.

References

  1. Kojima, Y., Weissman, I. L. & Leeper, N. J. The role of efferocytosis in atherosclerosis. Circulation 135, 476–489 (2017).

    Article  CAS  Google Scholar 

  2. Elliott, M. R. & Ravichandran, K. S. The dynamics of apoptotic cell clearance. Dev. Cell 38, 147–160 (2016).

    Article  CAS  Google Scholar 

  3. Wang, Y. et al. Mitochondrial fission promotes the continued clearance of apoptotic cells by macrophages. Cell 171, 331–345 (2017).

    Article  CAS  Google Scholar 

  4. Yurdagul, A. Jr. et al. Macrophage metabolism of apoptotic cell-derived arginine promotes continual efferocytosis and resolution of Injury. Cell Metab. 31, 518–533 (2020).

    Article  CAS  Google Scholar 

  5. Bosurgi, L. et al. Macrophage function in tissue repair and remodeling requires IL-4 or IL-13 with apoptotic cells. Science 356, 1072–1076 (2017).

    Article  CAS  Google Scholar 

  6. Han, C. Z. & Ravichandran, K. S. Metabolic connections during apoptotic cell engulfment. Cell 147, 1442–1445 (2011).

    Article  CAS  Google Scholar 

  7. DeBerardinis, R. J. & Cheng, T. Q’s next: the diverse functions of glutamine in metabolism, cell biology and cancer. Oncogene 29, 313–324 (2010).

    Article  CAS  Google Scholar 

  8. O’Neill, L. A. & Pearce, E. J. Immunometabolism governs dendritic cell and macrophage function. J. Exp. Med. 213, 15–23 (2016).

    Article  Google Scholar 

  9. Jha, A. K. et al. Network integration of parallel metabolic and transcriptional data reveals metabolic modules that regulate macrophage polarization. Immunity 42, 419–430 (2015).

    Article  CAS  Google Scholar 

  10. Morioka, S. et al. Efferocytosis induces a novel SLC program to promote glucose uptake and lactate release. Nature 563, 714–718 (2018).

    Article  CAS  Google Scholar 

  11. Zhang, S. et al. Efferocytosis fuels requirements of fatty acid oxidation and the electron transport chain to polarize macrophages for tissue repair. Cell Metab. 29, 443–456 (2019).

    Article  CAS  Google Scholar 

  12. Dixon, S. J. et al. Ferroptosis: an iron-dependent form of nonapoptotic cell death. Cell 149, 1060–1072 (2012).

    Article  CAS  Google Scholar 

  13. Nicklin, P. et al. Bidirectional transport of amino acids regulates mTOR and autophagy. Cell 136, 521–534 (2009).

    Article  CAS  Google Scholar 

  14. Tabas, I. Macrophage death and defective inflammation resolution in atherosclerosis. Nat. Rev. Immunol. 10, 36–46 (2010).

    Article  CAS  Google Scholar 

  15. Grabner, R. et al. Lymphotoxin β receptor signaling promotes tertiary lymphoid organogenesis in the aorta adventitia of aged ApoE−/− mice. J. Exp. Med. 206, 233–248 (2009).

    Article  Google Scholar 

  16. Jais, A. et al. Heme oxygenase-1 drives metaflammation and insulin resistance in mouse and man. Cell 158, 25–40 (2014).

    Article  CAS  Google Scholar 

  17. Vats, D. et al. Oxidative metabolism and PGC-1β attenuate macrophage-mediated inflammation. Cell Metab. 4, 13–24 (2006).

    Article  CAS  Google Scholar 

  18. Perales-Clemente, E. et al. Restoration of electron transport without proton pumping in mammalian mitochondria. Proc. Natl Acad. Sci. USA 105, 18735–18739 (2008).

    Article  CAS  Google Scholar 

  19. Fernández-Ayala, D. J. et al. Expression of the Ciona intestinalis alternative oxidase (AOX) in Drosophila complements defects in mitochondrial oxidative phosphorylation. Cell Metab. 9, 449–460 (2009).

    Article  Google Scholar 

  20. Chandel, N. S. Evolution of mitochondria as signaling organelles. Cell Metab. 22, 204–206 (2015).

    Article  CAS  Google Scholar 

  21. Caron, E. & Hall, A. Identification of two distinct mechanisms of phagocytosis controlled by different Rho GTPases. Science 282, 1717–1721 (1998).

    Article  CAS  Google Scholar 

  22. A-Gonzalez, N. et al. Apoptotic cells promote their own clearance and immune tolerance through activation of the nuclear receptor LXR. Immunity 31, 245–258 (2009).

    Article  CAS  Google Scholar 

  23. Yvan-Charvet, L. et al. ABCA1 and ABCG1 protect against oxidative stress-induced macrophage apoptosis during efferocytosis. Circ. Res. 106, 1861–1869 (2010).

    Article  CAS  Google Scholar 

  24. Viaud, M. et al. Lysosomal cholesterol hydrolysis couples efferocytosis to anti-inflammatory oxysterol production. Circ. Res. 122, 1369–1384 (2018).

    Article  CAS  Google Scholar 

  25. Tavakoli, S. et al. Characterization of macrophage polarization states using combined measurement of 2-deoxyglucose and glutamine accumulation: implications for imaging of atherosclerosis. Arterioscler Thromb. Vasc. Biol. 37, 1840–1848 (2017).

    Article  CAS  Google Scholar 

  26. Johnson, M. O. et al. Distinct regulation of TH17 and TH1 cell differentiation by glutaminase-dependent metabolism. Cell 175, 1780–1795 (2018).

    Article  CAS  Google Scholar 

  27. Papathanassiu, A. E. et al. BCAT1 controls metabolic reprogramming in activated human macrophages and is associated with inflammatory diseases. Nat. Commun. 8, 16040 (2017).

    Article  CAS  Google Scholar 

  28. O’Neill, L. A. & Artyomov, M. N. Itaconate: the poster child of metabolic reprogramming in macrophage function. Nat. Rev. Immunol. 19, 273–281 (2019).

    Article  Google Scholar 

  29. Liu, P. S. et al. α-Ketoglutarate orchestrates macrophage activation through metabolic and epigenetic reprogramming. Nat. Immunol. 18, 985–994 (2017).

    Article  CAS  Google Scholar 

  30. Scialò, F., Fernández-Ayala, D. J. & Sanz, A. Role of mitochondrial reverse electron transport in ROS signaling: potential roles in health and disease. Front. Physiol. 8, 428 (2017).

    Article  Google Scholar 

  31. Murphy, M. P. How mitochondria produce reactive oxygen species. Biochem. J. 417, 1–13 (2009).

    Article  CAS  Google Scholar 

  32. Lapuente-Brun, E. et al. Supercomplex assembly determines electron flux in the mitochondrial electron transport chain. Science 340, 1567–1570 (2013).

    Article  CAS  Google Scholar 

  33. Guarás, A. et al. The CoQH2/CoQ ratio serves as a sensor of respiratory chain efficiency. Cell Rep. 15, 197–209 (2016).

    Article  Google Scholar 

  34. Stipanuk, M.H and Caudill, M.A. Biochemical, Physiological and Molecular Aspects of Human Nutrition 3rd edn (Elsevier/Saunders, 2013).

  35. Jin, H. et al. Integrative multiomics analysis of human atherosclerosis reveals a serum response factor-driven network associated with intraplaque hemorrhage. Clin. Transl. Med 11, e458 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Gautier, E. L. et al. Gene-expression profiles and transcriptional regulatory pathways that underlie the identity and diversity of mouse tissue macrophages. Nat. Immunol. 13, 1118–1128 (2012).

    Article  CAS  Google Scholar 

  37. Devos, M. et al. Keratinocyte expression of A20/TNFAIP3 controls skin inflammation associated with atopic dermatitis and psoriasis. J. Invest. Dermatol. 139, 135–145 (2019).

    Article  CAS  Google Scholar 

  38. Aidoud, N. et al. A combination of lipidomics, MS imaging, and PET scan imaging reveals differences in cerebral activity in rat pups according to the lipid quality of infant formulas. FASEB J. 32, 4776–4790 (2018).

    Article  CAS  Google Scholar 

Download references

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

Authors
  1. Johanna Merlin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stoyan Ivanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Adélie Dumont
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alexey Sergushichev
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Julie Gall
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Marion Stunault
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Marion Ayrault
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Nathalie Vaillant
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Alexia Castiglione
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Amanda Swain
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Francois Orange
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Alexandre Gallerand
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Thierry Berton
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Jean-Charles Martin
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Stefania Carobbio
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Justine Masson
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Inna Gaisler-Salomon
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Pierre Maechler
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Stephen Rayport
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Judith C. Sluimer
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Erik A. L. Biessen
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Rodolphe R. Guinamard
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Emmanuel L. Gautier
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Edward B. Thorp
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Maxim N. Artyomov
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Laurent Yvan-Charvet
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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.

Supplementary information

Source data

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s42255-021-00471-y

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing