Abstract
Glucocorticoids represent the mainstay of therapy for a broad spectrum of immune-mediated inflammatory diseases. However, the molecular mechanisms underlying their anti-inflammatory mode of action have remained incompletely understood1. Here we show that the anti-inflammatory properties of glucocorticoids involve reprogramming of the mitochondrial metabolism of macrophages, resulting in increased and sustained production of the anti-inflammatory metabolite itaconate and consequent inhibition of the inflammatory response. The glucocorticoid receptor interacts with parts of the pyruvate dehydrogenase complex whereby glucocorticoids provoke an increase in activity and enable an accelerated and paradoxical flux of the tricarboxylic acid (TCA) cycle in otherwise pro-inflammatory macrophages. This glucocorticoid-mediated rewiring of mitochondrial metabolism potentiates TCA-cycle-dependent production of itaconate throughout the inflammatory response, thereby interfering with the production of pro-inflammatory cytokines. By contrast, artificial blocking of the TCA cycle or genetic deficiency in aconitate decarboxylase 1, the rate-limiting enzyme of itaconate synthesis, interferes with the anti-inflammatory effects of glucocorticoids and, accordingly, abrogates their beneficial effects during a diverse range of preclinical models of immune-mediated inflammatory diseases. Our findings provide important insights into the anti-inflammatory properties of glucocorticoids and have substantial implications for the design of new classes of anti-inflammatory drugs.
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Data availability
The data supporting the findings of this study are available on reasonable request from the corresponding author or J.-P.A. The data are not publicly available as they comprise information that could compromise research participant privacy, although source data are provided with this paper. Bulk RNA-seq datasets generated in this study are available at the GEO under accession numbers GSE250273 and GSE250274. The following databases were used in this study: the Broad Institute sgRNA design tool (https://portals.broadinstitute.org/gppx/crispick/public), Ensembl mouse reference genome GRCm38 (http://www.ensembl.org/Mus_musculus/Info/Index), mzcloud and mzVault (https://www.mzcloud.org/) and ChemSpider (http://www.chemspider.com/). Source data are provided with this paper.
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Acknowledgements
We thank W. Baum who helped in generating the K/BxN serum; A. Klej, R. Weinkam and R. Mancuso for technical assistance; and A. Papathanassiu (Ergon Pharmaceuticals) for providing the ACOD1 inhibitor ERG344. This work was supported by the Deutsche Forschungsgemeinschaft (DFG, CRC1181-A03/A01/B08 to G.K., G. Schett and S.F.; FOR2886 PANDORA, B01/A03/B05/Z01, to G.K., G. Schett, S.U. and A.K.; CRC1149, 251293561, C02 and CRC 1506, 450627322, C05 to J.T.; Tu220/25-1, 505870049 and Major Research Instrumentation grant 441730715), the Emerging Field Initiative (EFI) of the Friedrich-Alexander University Erlangen-Nürnberg (FAU) (EFI_Verbund_Med_05_MIRACLE to G.K., U. Sonnewald and J.H.), the Interdisziplinäres Zentrum für Klinische Forschung (IZKF) of the Friedrich-Alexander University Erlangen-Nürnberg (IZKF J91 to J.-P.A.), the Bundesministerium für Bildung und Forschung (BMBF) (MASCARA to G.K. and G.S and MelAutim to G.K.), the Christian Doppler Laboratory Arginine Metabolism in Rheumatoid Arthritis and Multiple Sclerosis (to G. Schabbauer, S.B., M.K. and M.H.), the FWF Sonderforschungsbereich SFB F83 (to G.S.) and the EU (Horizon 2020 ERC-2014-StG 640087, SOS and Horizon 2020 ERC-2020-CoG 101001866, INSPIRE to G.K.; Horizon Europe ERC-2021-StG 101039438, NEXUS, to S.U.; and Horizon 2020 ERC-2018-SyG nanoSCOPE and RTCure to G.S.). J.-P.A. received additional financial support from the Canadian Institutes for Health Research (CIHR, 201711MFE-395641-294537) and Fonds de recherche du Québec—Santé (FRQS, 259908). M.H. was awarded a DOC fellowship by the Austrian Academy of Sciences. S.U. was supported by the Hightech Agenda Bavaria.
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J.-P.A., M.Z., M.F. and U. Stifel designed the study, performed experiments, interpreted results and wrote the manuscript. B.K., R.V.T., C.G., G.E., C. Stoll, O.B.B., M.P.M., C. Scholtysek, M.B., K.P.-Z., M.S.J.M., M.D., M.K. and M.H. performed experiments, collected data and interpreted results. S.U., D.M., U. Sonnewald, D.S., A.K., S.F. and J.H. provided expertise, essential material and input and wrote the manuscript. D.C. and F.H. performed bioinformatics analysis and interpreted the data. S.B., G. Schabbauer, A.G., E.L. and G. Schett wrote the manuscript and provided valuable input. J.T. and G.K. designed the study and experiments and wrote the manuscript. All of the authors read and commented on the manuscript.
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Extended data figures and tables
Extended Data Fig. 1 Glucocorticoid-mediated control of transcription and metabolism.
(a) Heatmap illustrating the predicted activity of indicated transcription factors (TF) from the bulk mRNA sequencing data of bone marrow-derived macrophages (BMDMs) treated with vehicle (Ctrl), dexamethasone (GC, 100 nM) and LPS (100 ng/ml) for 4 or 24 h. (b) Pathway analysis derived from the bulk mRNA sequencing data illustrating pathways enriched in BMDMs treated with a combination of GC and LPS in comparison to BMDMs treated with LPS only. (c) Quantification of indicated parameters derived from a mito stress test (n = 4) and a glycolysis stress test (n = 8) of BMDMs treated with Ctrl, GC and LPS for 24 h. Oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were measured. (d) Gating strategy of the flow cytometry analysis of BMDMs treated with Ctrl, GC and LPS for 6 or 24 h and stained with MitoTracker Green and Mitotracker Red (corresponding to data in Fig. 1d and Fig. 2b). Data are presented as mean + SEM. One-sided Hypergeometric test with Bonferroni’s correction (b); one-way ANOVA with Tukey’s multiple comparison test (c).
Extended Data Fig. 2 Regulation of TCA cycle metabolism by glucocorticoids.
(a) Mass spectrometry-based analysis of U-13C glucose carbon tracing in bone marrow-derived macrophages (BMDMs) treated with vehicle (Ctrl), dexamethasone (GC, 100 nM) and LPS (100 ng/ml). Relative U-13C exchange rate (RelER) for the indicated metabolites and the number of exchanged carbon atoms (M0 to M6). (b) Extracellular acidification rate (ECAR) of a glycolysis stress test, with quantified glycolysis and glycolytic capacity of fetal liver-derived macrophages from Nls+/+ and Nlsmut mice treated with vehicle, GC and LPS for 24 h (n = 8). (c) Immunofluorescence imaging of BMDMs treated with Ctrl, GC and LPS for 24 h and stained with MitoTracker Red and an antibody directed against the glucocorticoid receptor. (d) Immunofluorescence microscopy of PDH-X protein in BMDMs upon stimulation with Ctrl, GC and LPS for 6 h. Fluorescence signal rendering for PDH-X and MitoTracker is represented in the lower right panel, with quantification in Fig. 2g. (e) Western blot analysis of pyruvate dehydrogenase (PDH) protein levels in BMDMs treated with Ctrl, GC, 100 nM and LPS for 6, 12 and 24 h. Data are presented as mean + SEM. One-way ANOVA with Tukey’s multiple comparison test (b).
Extended Data Fig. 3 Parallel regulation of metabolism and inflammation by glucocorticoids.
(a) ELISA-based quantification of the indicated cytokines in the supernatants human monocytes-derived macrophages from healthy donors under normoxia and hypoxia (1% O2) and treated as indicated with vehicle (Ctrl), dexamethasone (GC, 100 nM) and LPS and IFN-γ (100 ng/ml and 20 ng/ml, respectively) (n = 12). (b) Quantification of indicated parameters derived from a mito stress test and a glycolysis stress test of bone marrow-derived macrophages (BMDMs) treated with Ctrl, GC and LPS in the presence of a vehicle or roxadustat (RXD) (10 µM) for 24 h (n = 8). Oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were measured. (c) ELISA-based quantification of the indicated cytokines in the supernatants of BMDMs treated with Ctrl, GC and LPS in the presence of a vehicle or UK5099 (10 µM) as indicated (n = 6). Data are presented as mean + SEM. One-way ANOVA with Tukey’s multiple comparison test (a-c).
Extended Data Fig. 4 Itaconate as mediator of the action of glucocorticoids.
(a,b) ELISA-based quantification of the indicated cytokines in the supernatants of bone marrow-derived macrophages (BMDMs) from Acod1+/+ and Acod1−/− mice treated as indicated with vehicle (Ctrl), dexamethasone (GC, 100 nM), LPS (100 ng/ml) and (a) dimethyl itaconate (DMI; 62.5 µM) or (b) 4-octyl itaconate (4OI; 62.5 µM) (n = 7 for a, n = 6 for b). (c) Heatmap illustrating the predicted activity of indicated transcription factors (TF) derived from bulk mRNA sequencing data of BMDMs from Acod1+/+ and Acod1−/− mice treated with Ctrl, GC and LPS for 24 h. (d) ELISA-based quantification of the indicated cytokines in the supernatants of human monocytes-derived macrophages from healthy donors treated as indicated with Ctrl, GC and LPS and IFN-γ (20 ng/ml) in the presence of a vehicle or the ACOD1 inhibitor ERG344 (500 µM) (n = 12). (e) ELISA-based quantification of the indicated cytokines in the supernatants of human THP-1 monocyte-derived sgRen and sgAcod1 macrophages treated as indicated with Ctrl, GC and LPS and IFN-γ (n = 6). Similar results were observed with two other sgRNAs targeting Acod1. (f) Western blot analysis of NRF2 protein level in Nfe2l2+/+ and Nfe2l2−/− BMDMs. Six hours prior to collection, cells were treated with 2 µM MG-132 to promote NRF2 stabilization. (g) Western blot analysis of NRF2 protein level in BMDMs treated with Ctrl, GC and LPS for 24 h. Six hours prior to collection, cells were treated with 2 µM MG-132 to promote NRF2 stabilization. (h) ELISA-based quantification of the indicated cytokines in the supernatants of BMDMs from Nfe2l2+/+ and Nfe2l2−/− mice treated as indicated with Ctrl, GC and LPS (n = 6). Data are presented as mean + SEM. One-way ANOVA with Tukey’s multiple comparison test (a-b, d-e and h).
Extended Data Fig. 5 Murine models of immune-mediated diseases.
(a) Timeline of the treatment protocol used for the mouse model of LPS-induced lung injury. (b-c) Gating strategy and representative plots of the flow cytometry analysis of cells isolated from the bronchoalveolar lavage fluid (BALF) of Acod1+/+ and Acod1−/− mice during LPS-induced lung injury (corresponding to data in Fig. 5a). (d) Representative H&E stainings of lung sections of Acod1+/+ and Acod1−/− mice during LPS-induced lung injury (scale bars = 100 µm). (e) Timeline of the used treatment protocol for the mouse model of K/BxN serum transfer arthritis. (f) Timeline of the treatment protocol used for the mouse model of ovalbumin (OVA)-induced allergic airway inflammation. (g) Gating strategy of the flow cytometry analysis of cells derived from the BALF of mice during ovalbumin-induced allergic airway inflammation (corresponding to data in Fig. 5i).
Extended Data Fig. 6 Metabolic rewiring promotes anti-inflammatory effects of glucocorticoids.
(a) Graphical summary of the results presented in the manuscript.
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Auger, JP., Zimmermann, M., Faas, M. et al. Metabolic rewiring promotes anti-inflammatory effects of glucocorticoids. Nature (2024). https://doi.org/10.1038/s41586-024-07282-7
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DOI: https://doi.org/10.1038/s41586-024-07282-7
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