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SDHA gain-of-function engages inflammatory mitochondrial retrograde signaling via KEAP1–Nrf2

Nature Immunology volume 20, pages 1311–1321 (2019)Cite this article

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Abstract

Whether screening the metabolic activity of immune cells facilitates discovery of molecular pathology remains unknown. Here we prospectively screened the extracellular acidification rate as a measure of glycolysis and the oxygen consumption rate as a measure of mitochondrial respiration in B cells from patients with primary antibody deficiency. The highest oxygen consumption rate values were detected in three study participants with persistent polyclonal B cell lymphocytosis (PPBL). Exome sequencing identified germline mutations in SDHA, which encodes succinate dehydrogenase subunit A, in all three patients with PPBL. SDHA gain-of-function led to an accumulation of fumarate in PPBL B cells, which engaged the KEAP1–Nrf2 system to drive the transcription of genes encoding inflammatory cytokines. In a single patient trial, blocking the activity of the cytokine interleukin-6 in vivo prevented systemic inflammation and ameliorated clinical disease. Overall, our study has identified pathological mitochondrial retrograde signaling as a disease modifier in primary antibody deficiency.

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Fig. 1: Metabolic B cell profile of HCs and patients with PAD.
Fig. 2: Activity of individual mitochondrial respiratory chain complexes in PPBL B cells.
Fig. 3: Metabolic characterization of PPBL B cells.
Fig. 4: Transcriptional profile of PPBL B cells.
Fig. 5: SDHA activity and inflammatory cytokine production.
Fig. 6: Fumarate accumulation, KEAP1–Nrf2 activity and IL-6 production.

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Data availability

Web links for publicly available datasets are provided at the appropriate places in the manuscript. Accession codes for metabolomics, RNA-seq and WES data have been added to the respective methods sections. No restrictions on data availability apply. Patient material may no longer be available. All figure panels, with the exception of Supplementary Fig. 5g, are associated with raw data.

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Acknowledgements

We thank the patients that participated in the study, the flow sorting team and the microscopy core facility of the Department of Biomedicine, University and University Hospital of Basel, the Blood Donor Center affiliated with the University Hospital of Basel, as well as G. Hoenger, U. Duthaler, C. Gasser, J. Hirsiger, C. Berger, T. Daikeler, F. Marquardsen and F. Baldin for technical support and/or help with the clinical management of patients. Funding was provided by Gebert Rüf Foundation (grant no. GRS-058/14 to C.H. and M.R.); Swiss National Science Foundation (SNSF) (grant nos. 31003A_172848 to C.H. and PP00P3_181038 to M.R.). This work was further supported by a grant from the Swiss National Supercomputing Center (project ID SM09).

Author information

Author notes

  1. These authors contributed equally: Anne-Valérie Burgener, Glenn R. Bantug, Mike Recher, Christoph Hess.

Authors and Affiliations

  1. Immunobiology Laboratory, Department of Biomedicine, University and University Hospital of Basel, Basel, Switzerland

    Anne-Valérie Burgener, Glenn R. Bantug, Jordan Loeliger, Gunhild Unterstab, Rebekah Steiner, Danielle Hunziker, Bojana Müller-Durovic, Jasmin Grählert, Raja Epple, Jonas Lötscher & Christoph Hess

  2. Cambridge Institute of Therapeutic Immunology & Infectious Disease, Department of Medicine, University of Cambridge, Cambridge, UK

    Glenn R. Bantug & Christoph Hess

  3. Immunodeficiency Laboratory, Department of Biomedicine, University and University Hospital of Basel, Basel, Switzerland

    Benedikt J. Meyer & Mike Recher

  4. Division of Dermatology and Dermatology Laboratory, Department of Biomedicine, University and University Hospital of Basel, Basel, Switzerland

    Rebecca Higgins, Adhideb Ghosh, Bettina Burger & Alexander Navarini

  5. Competence Center for Personalized Medicine University of Zürich/Eidgenössische Technische Hochschule, Zürich, Switzerland

    Adhideb Ghosh

  6. Department of Pharmacology and Toxicology, University of Lausanne, Lausanne, Switzerland

    Olivier Bignucolo

  7. Center for Cancer and Cell Biology, Van Andel Institute, Grand Rapids, MI, USA

    Eric H. Ma & Russell G. Jones

  8. Goodman Cancer Research Centre, McGill University, Montreal, Quebec, Canada

    Eric H. Ma & Russell G. Jones

  9. Department of Physiology, McGill University, Montreal, Quebec, Canada

    Eric H. Ma & Russell G. Jones

  10. Epigenomics Group, D-BSSE, Eidgenössische Technische Hochschule, Basel, Switzerland

    Marco Geigges

  11. Immunobiology Laboratory, entro Nacional de Investigaciones Cardiovasculares Carlos III, Madrid, Spain

    Michel Enamorado, Sarai Martínez-Cano & David Sancho

  12. Mucosal Immunology Section, Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Washington DC, USA

    Michel Enamorado

  13. Bioinformatics Facility, Department of Biomedicine, University and University Hospital of Basel, Basel, Switzerland

    Robert Ivanek

  14. Proteomics Core Facility, Biozentrum, University of Basel, Basel, Switzerland

    Alexander Schmidt

  15. Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, UK

    Sarah Dimeloe

  16. Electron Microscopy Core Facility, Biozentrum, University of Basel, Basel, Switzerland

    Ursula Sauder

  17. Division of Hematology and Oncology, Claraspital, Basel, Switzerland

    Monika Ebnöther

  18. Division Medical Immunology, Laboratory Medicine, University Hospital Basel, Basel, Switzerland

    Ingmar Heijnen

  19. Division of Hematology, Cantonal Hospital of Aarau, Aargau, Switzerland

    Nathan Cantoni

  20. Division of Internal Medicine and Rheumatology, Hospital St. Anna, Luzern, Switzerland

    Rolf Brücker

  21. Division of Infectious Diseases, Children’s Hospital of St. Gallen, St. Gallen, Switzerland

    Christian R. Kahlert

Authors
  1. Anne-Valérie Burgener
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  2. Glenn R. Bantug
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Contributions

A.V.B. performed and analyzed most of the experiments and helped write the report. G.R.B. performed experiments, conceived, coordinated and supervised the revision experiments and helped write the report. R.H. and A.G. performed and analyzed the WES studies. A.N. analyzed and interpreted the WES studies and helped write the report. O.B. performed the molecular dynamics studies and helped write the report. E.H.M. and R.G.J. performed, analyzed and interpreted metabolic tracing studies and helped write the report. B.B., B.M., B.M.D., D.H., G.H., J.G., J.Löliger, J.Lötscher, R.E., R.S., S.D. and U.S. planned, performed and interpreted various experiments. M.Enamorado, S.M.C. and D.S. assessed mitochondrial super-complex formation. M.G. and R.I. performed bioinformatics analyses. A.S. planned, performed and analyzed proteomic experiments and helped write the report. I.H. helped to identify PAD patients and performed immunologic analyses. C.R.K., M.Ebnöther, N.C. and R.B. helped with recruiting PAD patients and coordinated clinical characterization and analyses. M.R. organized and supervised the PAD patient cohort, contributed to conceiving the study, supervised and advised experiments, analyzed and interpreted data and helped write the report. C.H. conceived the study, supervised and coordinated the research and wrote the report.

Corresponding author

Correspondence to Christoph Hess.

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Competing interests

The authors declare no competing interests.

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Peer Review Information Ioana Visan was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Integrated supplementary information

Supplementary Figure 1 Oxygen consumption rates are increased in PPBL B cells

(a) Age distribution of basal OCR and basal ECAR in primary B cells from HCs (n = 15) and PAD patients (n = 14). (b) Basal OCR in primary B cells from female vs. male and CMV– vs. CMV+ HCs (n = 13) and PAD patients (n = 14). (c) Representative cell surface phenotype of B cells (CD19+ lymphocytes) from HCs and PPBL patients stained with CD24, CD38, TACI, and CD27 antibodies. HCs (n=2) and PPBL patients (n=2) were independently tested once. (d) CFSE dilution histogram of sorted CD19+ IgD+CD27–; IgD+CD27+ and PPBL B cells stimulated with IL-21 and CD40L or CPG for 5 days (upper panel) and summarized in bar diagram (lower graph). Representative of 3 independent experiments. (e) Gating strategy for sorting MZ-like B cells from healthy controls. CD19+ cells were discriminated based on SSC/FSC area (upper left) and doublets were gated out (upper right). MZ-like B cells are IgD+CD27+ cells (lower left). Sorted MZ-like B cells after FACS sorting (lower right). (f) Relative distribution of CD4+ and CD8+ T cell subsets from HCs (n=3) and a PPBL patients (n=3). Representative of 3 independent experiments. (g) Representative histograms of CFSE dilution assays of anti-CD3/anti-CD28 activated CD4+ and CD8+ T cells from a HC and a PPBL patient. Representative of 3 independent experiments. (h) T cell reactivity against CMV peptides (pp65 and IE-1) between HCs (n=2) and PPBL patient T cells (n=2) were assessed by IFN-gamma ELISpot. The result is positive if the geometric mean is > 10 (red line) (i) Representative mitochondrial perturbation profiles of primary T cells from HCs and PPBL patients. Summary graphs present basal OCR (left) and ECAR (middle) of primary T cells from HCs (n = 14) and PPBL patients (n = 3). Representative mitotracker red (MTR) and mitotracker deep red (MTDR) staining of primary B cells from HCs (n = 3) and PPBL patients (n = 3) (right). Representative of 3 independent experiments. Pooled data are presented as mean ± SEM. Statistical significance was assessed by two-sided unpaired t-test (b,h). * p < 0.05, ** p < 0.01, ns, not significant.

Supplementary Figure 2 Complex II is hyper-functional in PPBL B cells

(a) Representative transmission electron microscopy images of a primary B cell from a HC and a PPBL patient (left). White scale bar, 2000 nm. Black scale bar, 1000 nm. Summary graphs of cell area; mitochondrial area; mitochondria count per cell, and mitochondrial aspect ratio (length/width) from HCs (n = 3) and PPBL patients (n = 3) (right). 45 B cells were analyzed per individual in 3 independent experiments. (b) Representative immunoblot analysis of primary B cells from a HC and a PPBL patient. Blots were probed for Complex I (CI) subunit NDUFB8, Complex II (CII) subunit SDHB, Complex III (CIII) subunit UQCRC2, Complex IV (CIV) subunit COX II, Complex V (CV) ATP synthase subunit alpha, as well as actin (upper). Bar graph depicts quantification of the abundance of each complex subunit relative to actin (lower). Experiment was performed once. (c) BN–PAGE immunoblot analysis of enriched mitochondria from B-LCLs of HCs (n = 3) and PPBL patients (n= 3), probed for CI (NDUFA9), CII (SDHA), CIII (UQCRC1), CIV (subunit I) and Tom20 (left). Summary bar graph (right). Representative of 4 independent experiments. (d) Representative mitotracker deep red (MTDR) and mitotracker red (MTR) staining of primary B cells from HCs (n = 3) and PPBL patients (n = 3). Bar graph summarizes the geometric mean fluorescence intensity (gMFI) of MTDR and MTR of both populations. Pooled data are represented as mean ± SEM. Statistical significance was assessed by two-sided Mann-Whitney test (a), two-sided unpaired t-test (c,d). * p < 0.05, ** p < 0.01, ns, not significant.

Supplementary Figure 3 PPBL B cells have an inflammatory transcriptional profile

(a) Heatmap visualization of relative transcript abundance of genes involved in TCA, glycolysis and glutamine metabolism, in primary MZ-like B cells from HCs (n = 4) and primary B cells form PPBL patients (n = 3). (b) Heatmap visualization or relative transcript abundance of genes involved in respiratory complexes I, III, IV and V in primary MZ-like B cells from HCs (n = 4) and primary B cells form PPBL patients (n = 3).

Supplementary Figure 4 SDH drives an inflammatory phenotype in PPBL B cells

(a) Production of IL-6 by MZ-like B-LCLs from HCs (n = 3), quantified in cell culture supernatants of cells activated for 48 hours with IL-21 plus CD40L, in presence or absence of monomethyl fumarate. (b) Production of IL-6 by Patients B-LCLs (n = 3), quantified in cell culture supernatants of cells cultured for 24 hours in presence or absence of UK-5099 (1, 10 μM), BPTES (1, 10 μM) or dichloroacetate (DCA; 0.1, 0.5 mM). (c) OCR and ECAR measurements of primary B cells from healthy controls (n=16) and primary B cells from Patient #15 (PPBL patient). Basal values are shown. (d) Number of hydrogen bonds formed over time between SDHB and residues 43–46 of SDHA, wild type (wt vs. A45T). Red segments highlight the trajectory extract shown in Supplementary Video V1. Corresponding frequency distributions of the counted hydrogen bonds are shown in the insets. Experiment was repeated independently 12 times with similar results. (e) Frequency distributions of the residues from the N-terminal SDHA (left) and SDHB (right) involved in inter-domain hydrogen bonds in simulations with SDHA wt and SDHA-A45T. (f) Time fraction of simulated contacts between Ala/Thr45 and adjacent SDHA residues (Arg458, Phe459, Asp511, Arg512 and Met514), depicted as a percentage of the SDHA–SDHB interaction time in wt and A45T. Experiment was repeated independently 12 times with similar results. (g) Visualization of Sanger sequencing of B-LCL derived cDNA of Patient 8, carrying the A45T mutation (left). Asterisk indicates changes of nucleotide G to A at position 133. Compound SDHA plus SDHB activity (enzyme complex derived from B-LCLs) from Patient 8 relative to HCs (n = 3) (right). Compound SDHA–SDHB activity was independently tested 4 times in Patient 8 and in the control subject. Pooled data are represented as mean ± SEM. Statistical significance assessed by two-sided paired t-test (a), two-sided unpaired t-test (g), one-way ANOVA, adjustments were made for multiple comparisons with BH procedure (f). * p < 0.05, ** p < 0.01, ns, not significant.

Supplementary Figure 5 Fumarate drives Nrf2-dependent transcription of IL-6

(a) Comparison of genes that were differentially expressed between non-treated and fumarate-treated monocytes (y-axis) and those differentially expressed between primary MZ-like B cells from HCs (n = 4) and primary B cells from PPBL patients (n = 3) (x-axis). (b) Representative immunoblot probing for histone H3 methylation among primary B cells of a HC vs. PPBL patient. The bar graph summarizes data from HCs (n = 3) and PPBL patients (n = 3). (c) Representative imaging flow cytometry data showing nuclear abundance of Nrf2 in primary PPBL B cells in presence/absence of the Nrf2 inhibitor, K67. Black scale bar, 7 μm. Histograms depict similarity dilate scores of NRF2 nuclear localization in presence or absence of K67. Experiment was performed once. (d) mRNA abundance of canonical NRF2 regulated genes in PPBL B cell lines (n = 3) and cell lines derived from marginal zone-like B cells from HCs (n=3) (left). Abundance of GSH and GSSG from primary B cells (right) (e) Quantification of IL-6 production by MZ-like B-LCLs from HCs (n = 3) of cells incubated with monomethyl fumarate for 48 hours in presence vs. absence of the Nrf2 inhibitor, ML-385. (f) mRNA abundance of NQO1 and IL6 from PPBL (n=3) and healthy MZ (n=3) B-LCLs activated with IL-21 plus CD40L in the presence or absence of the NRF2 activator, CDDO (1 μM). (g) Effect of Nrf2 inhibition using K67 on IL-6 production, and expression of activation markers, in primary naïve and memory B cells from healthy donors activated with CpG (2.5 μg/ml) for 24 hours (n = 5 healthy donors). (h) Schematic summary of the clinical trial performed in Patient 8. (i) Graphical summary of the model proposed from this study. Pooled data are shown as mean ± SEM. Correlation in (a) was assessed using Pearson’s r, statistical significance was assessed by two-sided unpaired t-test (b,e,d [right]), two-way ANOVA (d [left]).

Supplementary Information

Supplementary Information

Supplementary Figs. 1–5 and Tables 1–4.

Reporting Summary

Supplementary Video 1: Enhanced SDHA/SDHB interactions in the A45T mutant

The video shows the trajectory extracts highlighted in red in Supplementary Fig. 4d, highlighting the interaction between SDHB (green) and SDHA (blue), wild type (left) and A45T (right). The residues from the N-ter of SDHA that interact transiently with SDHB and the corresponding residue of SDHB, are depicted as spheres. The carbon, oxygen and nitrogen atoms are colored black, red and blue. The experiment was repeated independently 12 times with similar results.

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Burgener, AV., Bantug, G.R., Meyer, B.J. et al. SDHA gain-of-function engages inflammatory mitochondrial retrograde signaling via KEAP1–Nrf2. Nat Immunol 20, 1311–1321 (2019). https://doi.org/10.1038/s41590-019-0482-2

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