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Signaling by IL-6 promotes alternative activation of macrophages to limit endotoxemia and obesity-associated resistance to insulin

Nature Immunology volume 15, pages 423–430 (2014)Cite this article

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Abstract

Obesity and resistance to insulin are closely associated with the development of low-grade inflammation. Interleukin 6 (IL-6) is linked to obesity-associated inflammation; however, its role in this context remains controversial. Here we found that mice with an inactivated gene encoding the IL-6Rα chain of the receptor for IL-6 in myeloid cells (Il6raΔmyel mice) developed exaggerated deterioration of glucose homeostasis during diet-induced obesity, due to enhanced resistance to insulin. Tissues targeted by insulin showed increased inflammation and a shift in macrophage polarization. IL-6 induced expression of the receptor for IL-4 and augmented the response to IL-4 in macrophages in a cell-autonomous manner. Il6raΔmyel mice were resistant to IL-4-mediated alternative polarization of macrophages and exhibited enhanced susceptibility to lipopolysaccharide (LPS)-induced endotoxemia. Our results identify signaling via IL-6 as an important determinant of the alternative activation of macrophages and assign an unexpected homeostatic role to IL-6 in limiting inflammation.

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Figure 1: Signaling by IL-6 in myeloid cells regulates glucose homeostasis.
Figure 2: Il6raΔmyel mice fed a HFD have enhanced systemic inflammation.
Figure 3: Signaling by IL-6 promotes IL-4R expression in macrophages.
Figure 4: IL-6-induced STAT3 binds to distinct motifs in the Il4ra promoter.
Figure 5: Signaling by IL-6 in myeloid cells augments the IL-4 response.
Figure 6: Signaling by IL-6 in myeloid cells limits LPS-induced endotoxemia.

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Acknowledgements

We thank G. Schmall and T. Rayle for secretarial assistance, and B. Hampel and D. Kutyniok for technical assistance. Supported by the Deutsche Forschungsgemeinschaft (SFB 612 and SFB 670 to J.C.B.), the Leibniz Preis (BR 1492/7-1 to J.C.B.), the Cologne Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases (funded by the Deutsche Forschungsgemeinschaft within the Excellence Initiative by German Federal and State Governments), the US National Institutes of Health (DP1AR064158, HL076746 and DK094641 to A.C.) and the National Health and Medical Research Council of Australia (APP1041760, APP1042465 and SPRF APP1021168 to M.A.F.).

Author information

Author notes

  1. Jan Mauer and Bhagirath Chaurasia: These authors contributed equally to this study.

Authors and Affiliations

  1. Department of Mouse Genetics and Metabolism, Institute for Genetics, University of Cologne, Cologne, Germany

    Jan Mauer, Bhagirath Chaurasia, Julia Goldau, Merly C Vogt, Johan Ruud, Sebastian Theurich, A Christine Hausen, Joel Schmitz, F Thomas Wunderlich & Jens C Brüning

  2. Max Planck Institute for Neurological Research, Cologne, Germany

    Jan Mauer, Merly C Vogt, Johan Ruud, Sebastian Theurich, A Christine Hausen, Hella S Brönneke, F Thomas Wunderlich & Jens C Brüning

  3. Cologne Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases, Cologne, Germany

    Jan Mauer, Merly C Vogt, Johan Ruud, Sebastian Theurich, A Christine Hausen, Hella S Brönneke, F Thomas Wunderlich & Jens C Brüning

  4. Departments of Physiology and Medicine, Cardiovascular Research Institute, University of California, San Francisco, California, USA

    Khoa D Nguyen & Ajay Chawla

  5. Center for Endocrinology, Diabetes and Preventive Medicine, University Hospital Cologne, Cologne, Germany

    Joel Schmitz & Jens C Brüning

  6. Center for Molecular Medicine Cologne, Cologne, Germany

    Joel Schmitz & Jens C Brüning

  7. Cellular and Molecular Metabolism Laboratory, Baker IDI Heart and Diabetes Institute, Melbourne, Australia

    Emma Estevez, Tamara L Allen & Mark A Febbraio

  8. Max Planck Institute for Biology of Ageing, Cologne, Germany

    Andrea Mesaros & Linda Partridge

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Contributions

J.M., J.R., A.C., F.T.W. and J.C.B. designed the experiments; J.M., B.C., J.G., M.C.V., J.R. and K.D.N. did the experiments; S.T. helped with the flow cytometry; A.C.H. did the clamp operations; J.S. assisted during the ChIP analyses; H.S.B. did the indirect calorimetry; E.E., T.L.A., A.M., L.P. and M.A.F. supplied reagents; and J.M. and J.C.B. wrote the manuscript.

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Correspondence to Jens C Brüning.

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Integrated supplementary information

Supplementary Figure 1 Physiological characterization of Il6raΔmyel mice fed a NCD or HFD.

(a) Immunoblot of bone marrow-derived macrophages (BMDMs) generated from Il6rafl/fl mice (Ctrl) or Il6raΔmyel mice (Il6ra−/−) that were stimulated with IL-6 (50 ng/ml) for the indicated time points (Blot is representative of three independent experiments). (b) body composition (n=6), (c) fat pad weight (n=10), (d) serum leptin concentration (n=8), (e) oxygen (O2) consumption (n=6) and (f) daily caloric intake (n=8) of normal chow diet (NCD) or high fat diet (HFD) Il6rafl/fl and Il6raΔmyel mice. (Values are expressed as mean ±sem)

Supplementary Figure 2 Metabolic characterization of Il6raΔmyel mice fed a NCD or HFD.

NCD Il6rafl/fl or Il6raΔmyel mice were subjected to (a) glucose tolerance tests (GTT; n=12 vs 14) or (b) insulin tolerance tests (ITT; n=8 vs 14). (c) Blood glucose levels during euglycemic-hyperinsulinemic clamp analyses of HFD-fed Il6rafl/fl or Il6raΔmyel mice (n=8 vs 7). (d) Glucose infusion rate (GIR) during euglycemic-hyperinsulinemic clamp analyses (n=8 Il6rafl/fl; n=7 Il6raΔmyel; *p≤0.05; 2-Way-ANOVA with Bonferroni's post-test). (e) qRT-PCR analyses of livers from HFD Il6rafl/fl and Il6raΔmyel mice that were fasted for 16 hours (n=9 *p≤0.05; unpaired student's t-test; Data is expressed as % of Il6rafl/fl). (f) Triglyceride content in livers of HFD Il6rafl/fl and Il6raΔmyel mice that were fasted for 6 hours (n=11 vs 10; *p≤0.05; unpaired student's t-test). (g) Triglyceride concentration in serum of HFD Il6rafl/fl and Il6raΔmyel mice that were fasted for 6 hours (n=11 vs 10; p=0.08; unpaired student's t-test). (Values are expressed as mean ±sem)

Supplementary Figure 3 Gene-expression profiles in the WAT, BAT and liver of Il6raΔmyel mice fed a NCD or HFD.

(a) Immunohistochemical staining of MAC2-positive cells in WAT from HFD Il6rafl/fl or Il6raΔmyel mice and quantification of MAC2-positive crown-like structures (CLS) in WAT from HFD-fed Il6rafl/fl or Il6raΔmyel mice (n=6 per genotype; *p≤0.05; unpaired student's t-test; Data is expressed as % CLS of adipocytes). (b) qRT-PCR analyses of WAT from NCD and HFD Il6rafl/fl or Il6raΔmyel mice (n=8 vs 8 NCD; n=7 vs 7 HFD; *p≤0.05, **p≤0.01, ***p≤0.001; 2-Way-ANOVA with Bonferroni's post-test; Data is expressed as % of NCD Il6rafl/fl). (c) qRT-PCR analyses of BAT from NCD and HFD Il6rafl/fl or Il6raΔmyel mice (n=4 vs 5 NCD; n=9 vs 9 HFD; *p≤0.05, **p≤0.01 vs NCD; 2-Way-ANOVA with Bonferroni's post-test; Data is expressed as % of NCD Il6rafl/fl). (d) qRT-PCR analyses of liver from NCD and HFD Il6rafl/fl or Il6raΔmyel mice (n=8 vs 8 NCD; n=9 vs 9 HFD; *p≤0.05, **p≤0.01, ***p≤0.001; 2-Way-ANOVA with Bonferroni's post-test; Data is expressed as % of NCD Il6rafl/fl). (Values are expressed as mean ±sem)

Supplementary Figure 4 Gene-expression profiles in metabolic tissues of Il6−/− and LysM-CreTg/wt mice fed a HFD and macrophage-autonomous effects of IL-6.

qRT-PCR analyses of (a) WAT and (b) liver from HFD wildtype (WT) and IL-6 knockout (Il6−/−) mice (n=7 vs 7 *p≤0.05, **p≤0.01; unpaired student's t-test; Data is expressed as % of WT). qRT- PCR analyses of (c) WAT, (d) BAT and (e) liver from HFD-fed wildtype (WT) and heterozygous LysM-Cre (LysM-CreTg/wt) mice (n=5vs5; Data is expressed as % of WT). (f) Representative Gene ontology analyses of the 15 highest scoring canonical pathways containing gene sets that were differentially expressed between Il6rafl/fl (Ctrl) and Il6ra−/− bone marrow-derived macrophages (BMDMS) after stimulation with IL-6 (50 ng/ml; 4 hours; Threshold 0.05; Fisher's Exact t-test). (g) qRT-PCR analyses of in Ctrl BMDMS that were left untreated or stimulated with IL-6 (50 ng/ml; 4 hours; Representative data from three independent experiments, each in triplicates; ***p≤0.001; unpaired student's t-test; Data is expressed as % of NT). (h) Representative FACS plots of IL-4Rα expression in wild-type BMDMS after treatment with IL-6. (i) qRT-PCR analyses of Ctrl or Il6ra−/− BMDMS that were left untreated or stimulated with IL-6 (50 ng/ml; 12 hours) (Representative data from three independent experiments, each in duplicates; *p≤0.01 vs Ctrl; **p≤0.001 vs NT; 2-Way-ANOVA with Bonferroni's post-test; Data is expressed as % of NT Ctrl). (j) Immunoblot of wild-type BMDMS that were left untreated (NT) or stimulated with IL-10 (10 ng/ml; 30 min) in the absence (IgG) or presence of an IL-10-neutralizing antibody (αIL-10) (n=3). (k) qRT-PCR analyses of siRNA-transfected wild-type BMDMS that were left untreated (NT) or IL-6-stimulated (4h, 50ng/ml) (n=3 independent experiments each in triplicates; *p≤0.001; 2-Way-ANOVA with Bonferroni's post-test; Data is expressed as % of NT Ctrl siRNA). (Values are expressed as mean ±sem)

Supplementary Figure 5 STAT3-binding site prediction and ChIP analyses of IL-6-stimulated macophages.

(a) JASPAR prediction analysis of putative STAT3-binding sites in the Il4ra and Socs3 promoter (b) ChIP qRT-PCR showing occupancy of p-STAT3 over the Socs3 promoter (left panel) and over a non-open reading frame region (negative control IGX1A; right panel) in Il6rafl/fl BMDMs (Ctrl) and Il6ra−/− BMDMS stimulated with IL-6 (50ng/ml) for the indicated time points (n=3 vs 3 independent experiments; *p≤0.001 vs Ctrl **p≤0.001 vs NT; 2-Way-ANOVA with Bonferroni's post-test; Data is expressed as % of NT Ctrl). (c) qRT-PCR Cycle threshold (Ct) values obtained with the indicated primer sets on DNA samples from IgG ChIP (n=3 vs 3 independent experiments). (Values are expressed as mean ±sem)

Supplementary Figure 6 Effects of IL-6 and IL-4 on macrophages in vitro and in vivo.

(a) qRT-PCR analyses of bone marrow-derived macrophages (BMDMS) from Il6rafl/fl (Ctrl) or Il6raΔmyel (Il6ra−/−) mice that were left untreated or stimulated with IL-6 (50 ng/ml; 12 hours) (n=6; *p≤0.01 vs Ctrl **p≤0.001 vs NT; 2-Way-ANOVA with Bonferroni's post-test; Data is expressed as % of NT Ctrl). (b) Representative FACS plots of expression of CD206 and ARG1 in wild-type BMDMS. (c) qRT-PCR analyses of Ctrl BMDMS and Il6ra−/− BMDM that were left untreated or stimulated with IL-6 (50 ng/ml; 12 hours) and subsequently exposed to IL-4 (10 ng/ml) alone or IL-4 in combination with IL-6 for an additional 24 hours (n=6; *p≤0.05 vs Ctrl **p≤0.01 vs IL-4; 2-Way-ANOVA with Bonferroni's post-test; data is expressed as % of IL-4 Ctrl). (d) Gating strategy for FACS analysis of adipose tissue, blood, and peritoneum (e) Representative FACS plots of expression of CD206 in WAT, BAT, blood and peritoneal cavity of wild-type mice. (Values are expressed as mean ±sem)

Supplementary Figure 7 Effects of IL-4 treatment in Il6raΔmyel mice fed a HFD and proposed model.

(a) Glucose tolerance tests (GTT) of HFD Il6rafl/fl (left panel) and HFD Il6raΔmyel mice (right panel) before (basal) and after a 4-week treatment period with IL-4 (n=15 vs 18). (b) (left panel) Homeostatic model assessment of insulin resistance (HOMA-IR) indices of HFD Il6rafl/fl and HFD Il6raΔmyel mice before (basal) and after a 4-week treatment with IL-4 (basal n=8 vs 7; IL-4 n=15 vs 18; *p≤0.01; 2-Way-ANOVA with Bonferroni's post-test). (right panel) Percentual improvement of HOMA-IR indices upon IL-4 treatment (n=8vs7; *p≤0.05; unpaired student's t-test; Data is expressed as % of basal). (Values are expressed as mean ± sem). (c) Proposed model: Pro-inflammatory conditions such as obesity or endotoxemia lead to increased serum concentrations of free fatty acids (FFA), bacterial lipopolysaccharides (LPS) and, among other cytokines, interleukin 6 (IL-6). FFA and LPS on one hand stimulate toll-like receptor 4 (TLR4) to activate expression of pro-inflammatory mediators such as TNFα, IL1β, IL-12 and iNOS, which are associated with classical M1 macrophage activation. IL-6 on the other hand activates STAT3 to induce expression of the IL-4 receptor. The increased abundance of IL-4 receptors on the cell surface leads to enhanced sensitivity to IL-4, which is thought to mainly stem from eosinophils and CD4+ T-cells. Binding of IL-4 to its receptor activates anti-inflammatory STAT6, which is a central transcriptional activator of factors related to alternative M2 macrophage activation, such as MRC1, ARG1, Retnla/FIZZ1 and IL-10. IL-6- and IL-4-dependent signaling cascades then act synergistically to inhibit expression of M1-associated genes and to activate M2-associated genes, ultimately tilting the balance towards increased numbers of M2 macrophages. This shift in macrophage polarization by combined IL-6- and IL-4-action finally serves to limit inflammation, to retain insulin sensitivity and to restore homeostasis during sepsis.

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Mauer, J., Chaurasia, B., Goldau, 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). https://doi.org/10.1038/ni.2865

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