Succinate is a signaling metabolite sensed extracellularly by succinate receptor 1 (SUNCR1). The accumulation of succinate in macrophages is known to activate a pro-inflammatory program; however, the contribution of SUCNR1 to macrophage phenotype and function has remained unclear. Here we found that activation of SUCNR1 had a critical role in the anti-inflammatory responses in macrophages. Myeloid-specific deficiency in SUCNR1 promoted a local pro-inflammatory phenotype, disrupted glucose homeostasis in mice fed a normal chow diet, exacerbated the metabolic consequences of diet-induced obesity and impaired adipose-tissue browning in response to cold exposure. Activation of SUCNR1 promoted an anti-inflammatory phenotype in macrophages and boosted the response of these cells to type 2 cytokines, including interleukin-4. Succinate decreased the expression of inflammatory markers in adipose tissue from lean human subjects but not that from obese subjects, who had lower expression of SUCNR1 in adipose-tissue-resident macrophages. Our findings highlight the importance of succinate–SUCNR1 signaling in determining macrophage polarization and assign a role to succinate in limiting inflammation.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Data availability

Full scans for all immunoblots and source data for Figs. 18 are provided in the Supplementary Information. All other data are available from the corresponding authors upon request. All microarray data are in the GEO database with accession code GSE120121.

Additional information

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


  1. 1.

    Chouchani, E. T. et al. Ischaemic accumulation of succinate controls reperfusion injury through mitochondrial ROS. Nature 515, 431–435 (2014).

  2. 2.

    Rubic, T. et al. Triggering the succinate receptor GPR91 on dendritic cells enhances immunity. Nat. Immunol. 9, 1261–1269 (2008).

  3. 3.

    Tannahill, G. M. et al. Succinate is an inflammatory signal that induces IL-1β through HIF-1α. Nature 496, 238–242 (2013).

  4. 4.

    Toma, I. et al. Succinate receptor GPR91 provides a direct link between high glucose levels and renin release in murine and rabbit kidney. J. Clin. Invest. 118, 2526–2534 (2008).

  5. 5.

    Murphy, M. P. & O’Neill, L. A. J. Krebs cycle reimagined: the emerging roles of succinate and itaconate as signal transducers. Cell 174, 780–784 (2018).

  6. 6.

    Peti-Peterdi, J., Gevorgyan, H., Lam, L. & Riquier-Brison, A. Metabolic control of renin secretion. Pflugers Arch. 465, 53–58 (2013).

  7. 7.

    Hochachka, P. W. & Dressendorfer, R. H. Succinate accumulation in man during exercise. Eur. J. Appl. Physiol. Occup. Physiol. 35, 235–242 (1976).

  8. 8.

    Sadagopan, N. et al. Circulating succinate is elevated in rodent models of hypertension and metabolic disease. Am. J. Hypertens. 20, 1209–1215 (2007).

  9. 9.

    Aguiar, C. J. et al. Succinate causes pathological cardiomyocyte hypertrophy through GPR91 activation. Cell Commun. Signal. 12, 78 (2014).

  10. 10.

    van Diepen, J. A. et al. SUCNR1-mediated chemotaxis of macrophages aggravates obesity-induced inflammation and diabetes. Diabetologia 60, 1304–1313 (2017).

  11. 11.

    Serena, C. et al. Elevated circulating levels of succinate in human obesity are linked to specific gut microbiota. ISME J. 12, 1642–1657 (2018).

  12. 12.

    He, W. et al. Citric acid cycle intermediates as ligands for orphan G-protein-coupled receptors. Nature 429, 188–193 (2004).

  13. 13.

    Gilissen, J., Jouret, F., Pirotte, B. & Hanson, J. Insight into SUCNR1 (GPR91) structure and function. Pharmacol. Ther. 159, 56–65 (2016).

  14. 14.

    de Castro Fonseca, M., Aguiar, C. J., da Rocha Franco, J. A., Gingold, R. N. & Leite, M. F. GPR91: expanding the frontiers of Krebs cycle intermediates. Cell Commun. Signal. 14, 3 (2016).

  15. 15.

    Sapieha, P. et al. The succinate receptor GPR91 in neurons has a major role in retinal angiogenesis. Nat. Med. 14, 1067–1076 (2008).

  16. 16.

    Vargas, S. L., Toma, I., Kang, J. J., Meer, E. J. & Peti-Peterdi, J. Activation of the succinate receptor GPR91 in macula densa cells causes renin release. J. Am. Soc. Nephrol. 20, 1002–1011 (2009).

  17. 17.

    Li, Y. H., Woo, S. H., Choi, D. H. & Cho, E. H. Succinate causes alpha-SMA production through GPR91 activation in hepatic stellate cells. Biochem. Biophys. Res. Commun. 463, 853–858 (2015).

  18. 18.

    McCreath, K. J. et al. Targeted disruption of the SUCNR1 metabolic receptor leads to dichotomous effects on obesity. Diabetes 64, 1154–1167 (2015).

  19. 19.

    Hakak, Y. et al. The role of the GPR91 ligand succinate in hematopoiesis. J. Leukoc. Biol. 85, 837–843 (2009).

  20. 20.

    Ryan, D. G. & O’Neill, L. A. J. Krebs cycle rewired for macrophage and dendritic cell effector functions. FEBS Lett. 591, 2992–3006 (2017).

  21. 21.

    Saraiva, A. L. et al. Succinate receptor deficiency attenuates arthritis by reducing dendritic cell traffic and expansion of Th17 cells in the lymph nodes. FASEB J. 12, fj201800285 (2018).

  22. 22.

    Rubic-Schneider, T. et al. GPR91 deficiency exacerbates allergic contact dermatitis while reducing arthritic disease in mice. Allergy 72, 444–452 (2017).

  23. 23.

    Littlewood-Evans, A. et al. GPR91 senses extracellular succinate released from inflammatory macrophages and exacerbates rheumatoid arthritis. J. Exp. Med. 213, 1655–1662 (2016).

  24. 24.

    Peruzzotti-Jametti, L. et al. Macrophage-derived extracellular succinate licenses neural stem cells to suppress chronic neuroinflammation. Cell Stem Cell 22, 355–368.e13 (2018).

  25. 25.

    Lei, W. et al. Activation of intestinal tuft cell-expressed Sucnr1 triggers type 2 immunity in the mouse small intestine. Proc. Natl Acad. Sci. USA 115, 5552–5557 (2018).

  26. 26.

    Shan, B. et al. The metabolic ER stress sensor IRE1α suppresses alternative activation of macrophages and impairs energy expenditure in obesity. Nat. Immunol. 18, 519–529 (2017).

  27. 27.

    Manieri, E. & Sabio, G. Stress kinases in the modulation of metabolism and energy balance. J. Mol. Endocrinol. 55, R11–R22 (2015).

  28. 28.

    Arch, J. R., Hislop, D., Wang, S. J. & Speakman, J. R. Some mathematical and technical issues in the measurement and interpretation of open-circuit indirect calorimetry in small animals. Int. J. Obes. 30, 1322–1331 (2006).

  29. 29.

    Virtue, S. & Vidal-Puig, A. Assessment of brown adipose tissue function. Front. Physiol. 4, 128 (2013).

  30. 30.

    Mills, E. L. et al. Succinate dehydrogenase supports metabolic repurposing of mitochondria to drive inflammatory macrophages. Cell 167, 457–470 e413 (2016).

  31. 31.

    Hamilton, T. A., Zhao, C., Pavicic, P. G. Jr. & Datta, S. Myeloid colony-stimulating factors as regulators of macrophage polarization. Front. Immunol. 5, 554 (2014).

  32. 32.

    Jaguin, M., Houlbert, N., Fardel, O. & Lecureur, V. Polarization profiles of human M-CSF-generated macrophages and comparison of M1-markers in classically activated macrophages from GM-CSF and M-CSF origin. Cell Immunol. 281, 51–61 (2013).

  33. 33.

    Liao, X. et al. Kruppel-like factor 4 regulates macrophage polarization. J. Clin. Invest. 121, 2736–2749 (2011).

  34. 34.

    Luan, B. et al. CREB pathway links PGE2 signaling with macrophage polarization. Proc. Natl Acad. Sci. USA 112, 15642–15647 (2015).

  35. 35.

    Avni, D., Ernst, O., Philosoph, A. & Zor, T. Role of CREB in modulation of TNFα and IL-10 expression in LPS-stimulated RAW264.7 macrophages. Mol. Immunol. 47, 1396–1403 (2010).

  36. 36.

    Boutens, L. & Stienstra, R. Adipose tissue macrophages: going off track during obesity. Diabetologia 59, 879–894 (2016).

  37. 37.

    Kwok, K. H., Lam, K. S. & Xu, A. Heterogeneity of white adipose tissue: molecular basis and clinical implications. Exp. Mol. Med. 48, e215 (2016).

  38. 38.

    Qiu, Y. et al. Eosinophils and type 2 cytokine signaling in macrophages orchestrate development of functional beige fat. Cell 157, 1292–1308 (2014).

  39. 39.

    Fabbiano, S. et al. Caloric restriction leads to browning of white adipose tissue through Type 2 immune signaling. Cell Metab. 24, 434–446 (2016).

  40. 40.

    Hui, X. et al. Adiponectin enhances cold-induced browning of subcutaneous adipose tissue via promoting M2 macrophage proliferation. Cell Metab. 22, 279–290 (2015).

  41. 41.

    Fujisaka, S. et al. Regulatory mechanisms for adipose tissue M1 and M2 macrophages in diet-induced obese mice. Diabetes 58, 2574–2582 (2009).

  42. 42.

    Odegaard, J. I. et al. Alternative M2 activation of Kupffer cells by PPARδ ameliorates obesity-induced insulin resistance. Cell Metab. 7, 496–507 (2008).

  43. 43.

    Mills, E. L., Kelly, B. & O’Neill, L. A. J. Mitochondria are the powerhouses of immunity. Nat. Immunol. 18, 488–498 (2017).

  44. 44.

    Trauelsen, M. et al. Receptor structure-based discovery of non-metabolite agonists for the succinate receptor GPR91. Mol. Metab. 6, 1585–1596 (2017).

  45. 45.

    Mauer, J. et al. Signaling by IL-6 promotes alternative activation of macrophages to limit endotoxemia and obesity-associated resistance to insulin. Nat. Immunol. 15, 423–430 (2014).

  46. 46.

    Reilly, S. M. & Saltiel, A. R. Countering inflammatory signals in obesity. Nat Immunol. 15, 410–411 (2014).

  47. 47.

    Pellegrinelli, V., Carobbio, S. & Vidal-Puig, A. Adipose tissue plasticity: how fat depots respond differently to pathophysiological cues. Diabetologia 59, 1075–1088 (2016).

  48. 48.

    Carobbio, S., Pellegrinelli, V. & Vidal-Puig, A. Adipose tissue function and expandability as determinants of lipotoxicity and the metabolic syndrome. Adv. Exp. Med. Biol. 960, 161–196 (2017).

  49. 49.

    Lumeng, C. N., DelProposto, J. B., Westcott, D. J. & Saltiel, A. R. Phenotypic switching of adipose tissue macrophages with obesity is generated by spatiotemporal differences in macrophage subtypes. Diabetes 57, 3239–3246 (2008).

  50. 50.

    Wellen, K. E. & Hotamisligil, G. S. Obesity-induced inflammatory changes in adipose tissue. J. Clin. Invest. 112, 1785–1788 (2003).

  51. 51.

    Medina-Gomez, G. et al. PPAR gamma 2 prevents lipotoxicity by controlling adipose tissue expandability and peripheral lipid metabolism. PLoS Genet. 3, e64 (2007).

  52. 52.

    Ceperuelo-Mallafre, V. et al. Adipose tissue glycogen accumulation is associated with obesity-linked inflammation in humans. Mol. Metab. 5, 5–18 (2016).

  53. 53.

    Serena, C. et al. Obesity and Type 2 diabetes alters the immune properties of human adipose derived stem cells. Stem Cells 34, 2559–2573 (2016).

  54. 54.

    Maymo-Masip, E. et al. The rise of soluble TWEAK levels in severely obese subjects after bariatric surgery may affect adipocyte-cytokine production induced by TNFα. J. Clin. Endocrinol. Metab. 98, E1323–E1333 (2013).

  55. 55.

    Gilmour, J. S. et al. Local amplification of glucocorticoids by 11 beta-hydroxysteroid dehydrogenase type 1 promotes macrophage phagocytosis of apoptotic leukocytes. J. Immunol. 176, 7605–7611 (2006).

  56. 56.

    Zhang, X., Goncalves, R. & Mosser, D. M. The isolation and characterization of murine macrophages. Curr. Protoc. Immunol. Ch. 14, Unit 11 (2008).

  57. 57.

    Trouplin, V. et al. Bone marrow-derived macrophage production. J. Vis. Exp. 81, e50966 (2013).

  58. 58.

    Gonzalez-Roca, E. et al. Accurate expression profiling of very small cell populations. PLoS ONE 5, e14418 (2010).

  59. 59.

    Gautier, L., Cope, L., Bolstad, B. M. & Irizarry, R. A. affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 307–315 (2004).

  60. 60.

    Heber, S. & Sick, B. Quality assessment of affymetrix genechip data. OMICS 10, 358–368 (2006).

  61. 61.

    Irizarry, R. A. et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249–264 (2003).

  62. 62.

    Eklund, A. C. & Szallasi, Z. Correction of technical bias in clinical microarray data improves concordance with known biological information. Genome Biol. 9, R26 (2008).

  63. 63.

    Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

  64. 64.

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

  65. 65.

    Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 4, 1184–1191 (2009).

  66. 66.

    Ostuni, R. et al. Latent enhancers activated by stimulation in differentiated cells. Cell 152, 157–171 (2013).

  67. 67.

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

  68. 68.

    Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

  69. 69.

    Bailey, T. L. et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009).

Download references


This study was supported by grants from the Spanish Ministry of Science, Innovation and Universities (PI14/00228 and PI17/01503 to J.V., SAF2015-65019-R to S.F.-V., SAF2014-56819-R and SAF2015-71878-REDT to A.C., BFU2016-78951-R to G.M.-M., PI15/00143 to C.S. and PI15/01562 to A.M., BFU2015-70454-REDT and BFU2017-90578-REDT to S.F.-V. and G.M.-M.) co-financed by the European Regional Development Fund (ERDF). The Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM) (CB07708/0012) is an initiative of the Instituto de Salud Carlos III. N.K. is recipient of a predoctoral fellowship from MINECO, Spain (FPI, BES-2016-077745). C.S. acknowledges support from the ‘Ramón y Cajal’ program from MINECO (RYC2013-13186) and S.F.-V. the Miguel Servet tenure-track program (CP10/00438 and CPII16/00008) from the Fondo de Investigación Sanitaria, co-financed by the ERDF. We want to particularly acknowledge the patients and the BioBank IISPV (PT17/0015/0029) integrated in the Spanish National Biobanks Network for its collaboration. We also thank K. McCreath and A. Cervera for kindly providing the C57BL/6 Sucnr1fl/fl mice and for their helpful comments on the manuscript. Finally, we thank IRB Barcelona Functional Genomics Core Facility for Microarray processing and IRB Barcelona Biostatistics and Bioinformatics facility.

Author information

Author notes

  1. These authors contributed equally: Noelia Keiran, Victoria Ceperuelo-Mallafré.

  2. These authors jointly supervised this work: Joan Vendrell, Sonia Fernández-Veledo.


  1. Unitat de Recerca, Hospital Universitari de Tarragona Joan XXIII, Institut d´Investigació Sanitària Pere Virgili, Tarragona, Spain

    • Noelia Keiran
    • , Victoria Ceperuelo-Mallafré
    • , Enrique Calvo
    • , Maria Isabel Hernández-Alvarez
    • , Miriam Ejarque
    • , Catalina Núñez-Roa
    • , Elsa Maymó-Masip
    • , M. Mar Rodríguez
    • , Ana Megia
    • , Carolina Serena
    • , Joan Vendrell
    •  & Sonia Fernández-Veledo
  2. CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, Madrid, Spain

    • Noelia Keiran
    • , Victoria Ceperuelo-Mallafré
    • , Enrique Calvo
    • , Maria Isabel Hernández-Alvarez
    • , Miriam Ejarque
    • , Catalina Núñez-Roa
    • , Elsa Maymó-Masip
    • , M. Mar Rodríguez
    • , Ana Megia
    • , Antonio Zorzano
    • , Carolina Serena
    • , Joan Vendrell
    •  & Sonia Fernández-Veledo
  3. Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain

    • Maria Isabel Hernández-Alvarez
    •  & Antonio Zorzano
  4. Departamento de Ciencias Básicas de la Salud, Área de Bioquímica y Biología Molecular, Universidad Rey Juan Carlos, Madrid, Spain

    • Daniel Horrillo
    •  & Gema Medina-Gómez
  5. General and Digestive Surgery Service, Hospital St. Pau i Sta Tecla, Institut d´Investigació Sanitària Pere Virgili, Tarragona, Spain

    • Rosa Fradera
  6. Instituto de Investigaciones Biomédicas “Alberto Sols” CSIC-UAM, Madrid, Spain

    • Juan Vladimir de la Rosa
    •  & Antonio Castrillo
  7. Unidad de Biomedicina (Unidad Asociada al CSIC), Instituto Universitario de Investigaciones Biomédicas y Sanitaria (IUBIS), Universidad de Las Palmas de Gran Canaria, Las Palmas, Spain

    • Juan Vladimir de la Rosa
    •  & Antonio Castrillo
  8. General and Digestive Surgery Service, Hospital Universitari de Tarragona Joan XXIII, Institut d´Investigació Sanitària Pere Virgili, Tarragona, Spain

    • Rosa Jorba
  9. Departament de Bioquímica i Biomedicina Molecular, Facultat de Biología, Barcelona, Spain

    • Antonio Zorzano
  10. Universitat Rovira i Virgili, Tarragona, Spain

    • Joan Vendrell


  1. Search for Noelia Keiran in:

  2. Search for Victoria Ceperuelo-Mallafré in:

  3. Search for Enrique Calvo in:

  4. Search for Maria Isabel Hernández-Alvarez in:

  5. Search for Miriam Ejarque in:

  6. Search for Catalina Núñez-Roa in:

  7. Search for Daniel Horrillo in:

  8. Search for Elsa Maymó-Masip in:

  9. Search for M. Mar Rodríguez in:

  10. Search for Rosa Fradera in:

  11. Search for Juan Vladimir de la Rosa in:

  12. Search for Rosa Jorba in:

  13. Search for Ana Megia in:

  14. Search for Antonio Zorzano in:

  15. Search for Gema Medina-Gómez in:

  16. Search for Carolina Serena in:

  17. Search for Antonio Castrillo in:

  18. Search for Joan Vendrell in:

  19. Search for Sonia Fernández-Veledo in:


J.V. and S.F.-V. conceived, designed and supervised the research project. N.K., V.C.-M. and E.C. participated in the conception and design of the study, sample collection, experiment planning and conduction. C.S. analyzed human metabolic data. M.E. performed flow cytometry data acquisition and analysis of data. M.I.H.-A. carried out transcriptome analysis. J.V.d.l.R. analyzed ChIP–seq genomic data. R.F. and R.J. participated in the human sample recruitment. D.H. provided technical assistance and analysis of data of the metabolic phenotype study in mice. C.N.-R., E.M.-M. and M.M.R. performed cell culture and technical animal procedures assistance. A.Z., A.C., A.M., G.M.-G. and C.S. provided scientific discussion and revised the manuscript. S.F.-V. and J.V. are the guarantors of this work.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Joan Vendrell or Sonia Fernández-Veledo.

Integrated supplementary information

  1. Supplementary Figure 1 Generation of myeloid-specific deletion of Sucnr1 in mice.

    a, Schematics showing from top to bottom, (1) the structure of the targeting vector including the loxP sites (red arrowheads) on either side of exon 2 of the Sucnr1 locus; (2) the locus after Flp-mediated recombination of the FRT sites (blue arrowheads); (3) the locus after Cre-mediated deletion of the loxP sites. Primers used for genotyping are shown by small arrows (Flp2 and flp3). Yellow boxes represent exons. b, Representative image of systematically mice genotyping; 300 bp band for wild-type (WT) allele; 450 bp band for loxP-containing (fl/fl) allele (left); and 700 bp band for the presence of Cre recombinase gene in mice (right).

  2. Supplementary Figure 2 LysM-Cre Sucnr1fl/fl mice fed a NCD develop an inflammatory profile that precedes insulin resistance.

    a, Fat weight of scWAT and vWAT from LysM-Cre Sucnr1fl/fl mice and age-matched control (Sucnr1fl/fl) littermates fed a NCD (n = 4 mice). b, Oxygen consumption (VO2), carbon dioxide (VCO2) production and energy expenditure (EE), for 3 d (n = 3). c, Food intake as in a from two independent experiments (n = 10 mice). d, GTT (left) and ITT (right) from top to bottom at 7 weeks (n = 6 mice for GTT and n = 5 for ITT), 12 weeks (n = 5 mice) and 15 weeks (n = 5 for Sucnr1fl/fl and n = 7 or 6 for GTT and ITT, respectively, for LysM-Cre Sucnr1fl/fl mice) of age. e, qPCR analysis of selected pro-inflammatory markers in scWAT and vWAT and liver from mice as in a (n = 4 mice). f, CD11b+ cells from total scWAT and vWAT SVF of mice as in a, expressed in percentages (n = 6 for Sucnr1fl/fl or 3 for LysM-Cre Sucnr1fl/fl biologically independent samples). g,h, Flow cytometry analyses of CD11b+/Ly6G+ population from SVF as in f (g, n = 3) biologically independent samples, positive cells are quantified and presented relative to the Sucnr1fl/fl group set as 1 and of CD11b+/CD11c+ and CD11b+/CD206+ population in SVF of scWAT as in f (h). Amounts of CD11c+, CD206+ or CD11c+CD206+ population are quantified and presented as relative percentage over the total number of cells analyzed (n = 3 biologically independent samples). All data are shown as mean ± s.e.m.; *P < 0.05; **P < 0.01; ***P < 0.001 (two-tailed unpaired t-test in bar graphs and two-way ANOVA in GTT and ITT curves).

  3. Supplementary Figure 3 The inflammatory profile associated with LysM-Cre Sucnr1fl/fl mice fed a HFD is accompanied by an increase in fat-depot mass and intracellular accumulation of succinate.

    a–k, Male LysM-Cre Sucnr1fl/fl mice and age-matched Sucnr1fl/fl littermates were fed a HFD. a, Representative images of mouse size and scWAT depot content. b, Organ weight (n = 5 mice). c,d, Intracellular succinate levels in scWAT and vWAT (c, n = 4 mice except n = 5 for Sucnr1fl/fl scWAT) adipocytes (AD) (d, n = 4 for Sucnr1fl/fl and n = 3 or 7 for LysM-Cre Sucnr1fl/fl scWAT and vWAT, respectively) and ATMs (n = 6 or 7 for Sucnr1fl/fl scWAT and vWAT respectively and n = 3 for LysM-Cre Sucnr1fl/fl from biologically independent samples). e, Hematoxylin and eosin (H&E) staining of scWAT sections, representative images from two independent experiments (n = 4 mice). Scale bars 200 μm. f, Food intake from two independent experiments (n = 9 mice). g, VO2 consumption, VCO2 production for 4 d (n = 4 mice). h, Analysis of energy expenditure (EE) versus body weight of ANCOVA (n = 4 mice). i, GTT and ITT at 11 (left) or 15 weeks of age (right) (n = 5 mice except n = 7 for GTT at 15 weeks). j, VO2 consumption, VCO2 production and EE for a period of 24 h, before differences in body weight after 7 weeks on HFD (n = 5 mice). k, Flow cytometry analyses of CD11b+CD11c+ and CD11b+CD206+ population in SVF of scWAT. Amounts of CD11c+, CD206+ or CD11c+CD206+ are quantified and presented as relative percentage over the total number of cells analyzed, (n = 2 biologically independent samples). All data are shown as mean ± s.e.m.; *P < 0.05; **P < 0.01; ***P < 0.001 (two-tailed unpaired t-test in bar graphs and two-way ANOVA in GTT and ITT curves).

  4. Supplementary Figure 4 SUCNR1 is expressed predominantly in anti-inflammatory macrophages, and intra- and extracellular accumulation of succinate is associated with a pro-inflammatory phenotype.

    a, Intracellular succinate levels in peritoneal macrophages from Sucnr1fl/fl mice without stimulation (ctrl) and stimulated with LPS or recombinant human IL-4 for 6 h (n = 3 biologically independent samples). (b) Succinate secretion in 24 h conditioned medium (left) (n = 4 biologically independent samples) and expression of SUCNR1 (right) (n = 5 biologically independent samples) in THP1 cells without stimulation (ctrl) and stimulated with LPS or recombinant human IL-4 for 6 h. c, qPCR analysis of expression of typical pro-inflammatory (left) and anti-inflammatory (right) markers from hPBMCs macrophages (BMI 22.87 ± 1.51) obtained by treatment with GM-CSF or M-CSF for 7 d (n = 4 biologically independent samples). d, qPCR analysis of expression of Sucnr1 in BMDMs unstimulated (ctrl) or stimulated with GM-CSF or M-CSF for 7 d (n = 3 biologically independent samples). e,f, Intracellular succinate levels in BMDMs stimulated with M-CSF for 7 d from Sucnr1fl/fl mice without treatment (ctrl) or stimulated with succinate (Succ) or dimethyl succinate (DS) for 1 h (g) and from Sucnr1fl/fl versuss LysM-Cre Sucnr1fl/fl mice (f) (n = 3 biologically independent samples). Values are expressed as mean ± s.e.m.; *P < 0.05; **P < 0.01; ***P < 0.001 (two-tailed unpaired t-test).

  5. Supplementary Figure 5 SUCNR1 expression is dependent on IL-4 signaling through KLF4.

    a, Distribution of PU.1, H3K4me1, Stat6 and H3K27Ac tag densities in the vicinity of the Sucnr1 promoter in WT BMDMs untreated or treated for 2 h with IL-4. Data were obtained from GEO public database with accession number GSE38377 (left). Motif analysis obtained from IL-4 stimulation for 2 h, showing ChIP–seq dataset GSE72964, in which Stat6 peak was found in the promoter region, using web based MEME suite software (right). A motif logo representation (lower logo) as the best known motif matched, based on predictive P value was obtained using TOMTOM from MEME software suit (upper logo represents consensus sequence from JASPAR database). b,c, qPCR analysis in THP1 cells transfected with siRNAs against STAT6, KLF4 or ctrl and stimulated with human IL-4 for 6 h (b, n = 3 except for IL1RN n = 4 biologically independent samples) and in BMDMs stimulated with M-CSF followed by IL-4 stimulation for 6 h from Sucnr1fl/fl and Stat6−/− mice (c, n = 3 biologically independent samples). d, qPCR analysis from BMDMs as in c from Sucnr1fl/fl mice transfected as in b, before IL-4 treatment (n = 3 biologically independent samples). Results are presented relative to their respective basal state as 1 (without IL-4) for bd. e,f, Representative images of immunoblot analysis and densitometric analysis in arbitrary units in BMDMs from Stat6−/− mice (e, n = 2 independent experiments) and in BMDMs stimulated with M-CSF from Sucnr1fl/fl mice (d, n = 3 independent experiments). Uncropped blots are provided in source data. Values are expressed as mean ± s.e.m.; *P < 0.05; **P < 0.01; ***P < 0.001 (two-tailed unpaired t-test).

  6. Supplementary Figure 6 Sucnr1 deficiency alters the transcriptional signature of ATMs in a depot-specific manner.

    a,b, Heat map with GSEA normalized enrichment scores of gene expression profile for microarray of ATMs from LysM-Cre Sucnr1fl/fl and Sucnr1fl/fl mice from scWAT and vWAT in the pathway of interferon gamma, interferon alpha and inflammatory response (a) and TNF response and epithelial mesenchymal transition (b) gene sets from Hallmark. Data are from n = 4 biologically independent samples.

  7. Supplementary Figure 7 GSEA analysis of the genes regulated differentially in scWAT versus vWAT.

    a,b, Microarrays analysis of scWAT and vWAT ATMs from LysM-Cre Sucnr1fl/fl and Sucnr1fl/fl mice. a, Log2(FC) in scWAT (x-axis) versus vWAT (y-axis) ATMs: top left quadrants (circles) are genes expressed higher in LysM-Cre Sucnr1fl/fl than in Sucnr1fl/fl for vWAT ATMs and lower in LysM-Cre Sucnr1fl/fl than in Sucnr1fl/fl for scWAT ATMs. Linear models with empirical Bayes statistic (Limma) were used for differential expression. Top right quadrants (diamonds) are genes expressed higher in LysM-Cre Sucnr1fl/fl than in Sucnr1fl/fl for both vWAT and scWAT ATMs. Bottom left quadrants (squares) are genes expressed lower in LysM-Cre Sucnr1fl/fl than in Sucnr1fl/fl for both vWAT and scWAT ATMs. Bottom right quadrants (triangles) are genes expressed lower in LysM-Cre Sucnr1fl/fl than in Sucnr fl/fl for vWAT and higher in LysM-Cre Sucnr1fl/fl than in Sucnr1fl/fl for scWAT ATMs. Non-yellow points correspond to genes with an |(log2(FC)| > log(2) and adjusted P < 0.05 in at least one of the two comparisons: orange and pink points distinguish between differentially expressed genes that go in the same direction (orange) for vWAT and scWAT ATMs and differentially expressed genes that go in opposite (pink) directions (signatures used in b for enrichment analysis), respectively. Differentially expressed genes for only scWAT ATMs and for only vWAT ATMs are represented in light and dark blue, respectively. Total number of genes in each of the groups is provided in the adjacent colored table. b, Hallmark gene set enrichment analysis for ATMs as in a using differentially expressed gene lists ‘scWAT LysM up & vWAT down’ (violet-triangle in a, shown in positive) and ‘scWAT LysM down & vWAT up’ (pink-circle in a, shown in negative), (hypergeometric test, Benjamini–Hochberg-adjusted P values, filtered by gene sets with P < 0.05). Data are from n = 4 biologically independent samples.

Supplementary information

Source data

About this article

Publication history




Issue Date