Skip to main content

Thank you for visiting 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.

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


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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

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.

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.


  1. 1.

    Picard, C. et al. International union of immunological societies: 2017 primary immunodeficiency diseases committee report on inborn errors of immunity. J. Clin. Immunol. 38, 96–128 (2018).

    Article  Google Scholar 

  2. 2.

    Durandy, A., Kracker, S. & Fischer, A. Primary antibody deficiencies. Nat. Rev. Immunol. 13, 519–533 (2013).

    CAS  Article  Google Scholar 

  3. 3.

    Minegishi, Y. et al. An essential role for BLNK in human B cell development. Science 286, 1954–1957 (1999).

    CAS  Article  Google Scholar 

  4. 4.

    Silva, P. et al. Autosomal recessive agammaglobulinemia due to defect in mu heavy chain caused by a novel mutation in the IGHM gene. Gene Immun. 18, 197–199 (2017).

    CAS  Article  Google Scholar 

  5. 5.

    van Zelm, M. C. et al. Human CD19 and CD40L deficiencies impair antibody selection and differentially affect somatic hypermutation. J. Allergy Clin. Immunol. 134, 135–144 (2014).

    Article  Google Scholar 

  6. 6.

    Fischer, A. et al. Autoimmune and inflammatory manifestations occur frequently in patients with primary immunodeficiencies. J. Allergy Clin. Immunol. 140, 1388–1393, e1388 (2017).

    CAS  Article  Google Scholar 

  7. 7.

    Bantug, G. R., Galluzzi, L., Kroemer, G. & Hess, C. The spectrum of T cell metabolism in health and disease. Nat. Rev. Immunol. 18, 19–34 (2018).

    CAS  Article  Google Scholar 

  8. 8.

    Dimeloe, S., Hess, C., Burgener, A. V. & Grählert, J. T-cell metabolism governing activation, proliferation and differentiation; a modular view. Immunology 150, 35–44 (2017).

    CAS  Article  Google Scholar 

  9. 9.

    Hess, C. & Kemper, C. Complement-mediated regulation of metabolism and basic cellular processes. Immunity 45, 240–254 (2016).

    CAS  Article  Google Scholar 

  10. 10.

    Boothby, M. & Rickert, R. C. Metabolic regulation of the immune humoral response. Immunity 46, 743–755 (2017).

    CAS  Article  Google Scholar 

  11. 11.

    Carr, E. L. et al. Glutamine uptake and metabolism are coordinately regulated by ERK/MAPK during T lymphocyte activation. J. Immunol. 185, 1037–1044 (2010).

    CAS  Article  Google Scholar 

  12. 12.

    Jellusova, J. et al. Gsk3 is a metabolic checkpoint regulator in B cells. Nat. Immunol 18, 303–312 (2017).

    CAS  Article  Google Scholar 

  13. 13.

    Jiang, S., Yan, W., Wang, S. E. & Baltimore, D. Let-7 suppresses B cell activation through restricting the availability of necessary nutrients. Cell Metab. 27, 393–403, e394 (2018).

    CAS  Article  Google Scholar 

  14. 14.

    Mehta, M. M., Weinberg, S. E. & Chandel, N. S. Mitochondrial control of immunity: beyond ATP. Nat. Rev. Immunol. 17, 608–620 (2017).

    CAS  Article  Google Scholar 

  15. 15.

    Tan, H. et al. Integrative proteomics and phosphoproteomics profiling reveals dynamic signaling networks and bioenergetics pathways underlying T cell activation. Immunity 46, 488–503 (2017).

    CAS  Article  Google Scholar 

  16. 16.

    Ogura, M. et al. Mitochondrial reactive oxygen species suppress humoral immune response through reduction of CD19 expression in B cells in mice. Eur. J. Immunol. 47, 406–418 (2017).

    CAS  Article  Google Scholar 

  17. 17.

    Sena, L. A. et al. Mitochondria are required for antigen-specific T cell activation through reactive oxygen species signaling. Immunity 38, 225–236 (2013).

    CAS  Article  Google Scholar 

  18. 18.

    Bantug, G. R. et al. Mitochondria-endoplasmic reticulum contact sites function as immunometabolic hubs that orchestrate the rapid recall response of memory CD8+ T cells. Immunity 48, 542–555, e6 (2018).

    CAS  Article  Google Scholar 

  19. 19.

    Gubser, P. M. et al. Rapid effector function of memory CD8+ T cells requires an immediate-early glycolytic switch. Nat. Immunol. 14, 1064–1072 (2013).

    CAS  Article  Google Scholar 

  20. 20.

    Xiao, M. et al. Inhibition of α-KG-dependent histone and DNA demethylases by fumarate and succinate that are accumulated in mutations of FH and SDH tumor suppressors. Genes Devel. 26, 1326–1338 (2012).

    CAS  Article  Google Scholar 

  21. 21.

    Arts, R. J. W. et al. Glutaminolysis and fumarate accumulation integrate immunometabolic and epigenetic programs in trained immunity. Cell Metab. 24, 807–819 (2016).

    CAS  Article  Google Scholar 

  22. 22.

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

    CAS  Article  Google Scholar 

  23. 23.

    Alderson, N. L. et al. S-(2-Succinyl)cysteine: a novel chemical modification of tissue proteins by a krebs cycle intermediate. Arch. Biochem. Biophys. 450, 1–8 (2006).

    CAS  Article  Google Scholar 

  24. 24.

    Carulli, G. et al. Modifications in B-Lymphocyte number and phenotype in the course of pregnancy in a woman with persistent polyclonal B-Cell lymphocytosis: a flow cytometric study. J. Clin. Exp. Hematopathol. 55, 77–82 (2015).

    Article  Google Scholar 

  25. 25.

    Salcedo, I., Campos-Caro, A., Sampalo, A., Reales, E. & Brieva, J. A. Persistent polyclonal B lymphocytosis: an expansion of cells showing IgVH gene mutations and phenotypic features of normal lymphocytes from the CD27+ marginal zone B-cell compartment. Br. J. Haematol. 116, 662–666 (2002).

    CAS  Article  Google Scholar 

  26. 26.

    Salabei, J. K., Gibb, A. A. & Hill, B. G. Comprehensive measurement of respiratory activity in permeabilized cells using extracellular flux analysis. Nat. Protoc. 9, 421–438 (2014).

    CAS  Article  Google Scholar 

  27. 27.

    Cerutti, A., Cols, M. & Puga, I. Marginal zone B cells: virtues of innate-like antibody-producing lymphocytes. Nat. Rev. Immunol. 13, 118–132 (2013).

    CAS  Article  Google Scholar 

  28. 28.

    Seifert, M. et al. Functional capacities of human IgM memory B cells in early inflammatory responses and secondary germinal center reactions. Proc. Natl Acad. Sci. USA 112, E546–E555 (2015).

    CAS  Article  Google Scholar 

  29. 29.

    Blatnik, M., Thorpe, S. R. & Baynes, J. W. Succination of proteins by fumarate: mechanism of inactivation of glyceraldehyde-3-phosphate dehydrogenase in diabetes. Ann. NY Acad. Sci. 1126, 272–275 (2008).

    CAS  Article  Google Scholar 

  30. 30.

    Kornberg, M. D. et al. Dimethyl fumarate targets GAPDH and aerobic glycolysis to modulate immunity. Science 17, eaan4665 (2018).

    Google Scholar 

  31. 31.

    Adam, J. et al. Renal cyst formation in Fh1-deficient mice is independent of the Hif/Phd pathway: roles for fumarate in KEAP1 succination and Nrf2 signaling. Cancer Cell 20, 524–537 (2011).

    CAS  Article  Google Scholar 

  32. 32.

    Itoh, K. et al. Keap1 represses nuclear activation of antioxidant responsive elements by Nrf2 through binding to the amino-terminal Neh2 domain. Genes Dev. 13, 76–86 (1999).

    CAS  Article  Google Scholar 

  33. 33.

    Kobayashi, A. et al. Oxidative and electrophilic stresses activate Nrf2 through inhibition of ubiquitination activity of Keap1. Mol. Cell Biol. 26, 221–229 (2006).

    CAS  Article  Google Scholar 

  34. 34.

    Kinch, L., Grishin, N. V. & Brugarolas, J. Succination of Keap1 and activation of Nrf2-dependent antioxidant pathways in FH-deficient papillary renal cell carcinoma type 2. Cancer Cell 20, 418–420 (2011).

    CAS  Article  Google Scholar 

  35. 35.

    Taguchi, K. et al. Keap1 degradation by autophagy for the maintenance of redox homeostasis. Proc. Natl Acad. Sci. USA 109, 13561–13566 (2012).

    CAS  Article  Google Scholar 

  36. 36.

    Shanmugam, G., Narasimhan, M., Tamowski, S., Darley-Usmar, V. & Rajasekaran, N. S. Constitutive activation of Nrf2 induces a stable reductive state in the mouse myocardium. Redox Biol. 12, 937–945 (2017).

    CAS  Article  Google Scholar 

  37. 37.

    Ma, Q. Role of nrf2 in oxidative stress and toxicity. Annu. Rev. Pharmacol. Toxicol. 53, 401–426 (2013).

    CAS  Article  Google Scholar 

  38. 38.

    Gerstgrasser, A. et al. Cell-specific activation of the Nrf2 antioxidant pathway increases mucosal inflammation in acute but not in chronic colitis. J. Crohns Colitis 11, 485–499 (2017).

    PubMed  Google Scholar 

  39. 39.

    Zhang, X., Chen, X., Song, H., Chen, H. Z. & Rovin, B. H. Activation of the Nrf2/antioxidant response pathway increases IL-8 expression. Eur. J. Immunol. 35, 3258–3267 (2005).

    CAS  Article  Google Scholar 

  40. 40.

    Sporn, M. B. & Liby, K. T. NRF2 and cancer: the good, the bad and the importance of context. Nat. Rev. Cancer 12, 564–571 (2012).

    CAS  Article  Google Scholar 

  41. 41.

    Wruck, C. J. et al. Nrf2 induces interleukin-6 (IL-6) expression via an antioxidant response element within the IL-6 promoter. J. Biol. Chem. 286, 4493–4499 (2011).

    CAS  Article  Google Scholar 

  42. 42.

    Descatoire, M. et al. Identification of a human splenic marginal zone B cell precursor with NOTCH2-dependent differentiation properties. J Exp Med 211, 987–1000 (2014).

    CAS  Article  Google Scholar 

  43. 43.

    Hammad, H. et al. Transitional B cells commit to marginal zone B cell fate by Taok3-mediated surface expression of ADAM10. Nat. Immunol. 18, 313–320 (2017).

    CAS  Article  Google Scholar 

  44. 44.

    Moriyama, Y. et al. Delta-like 1 is essential for the maintenance of marginal zone B cells in normal mice but not in autoimmune mice. Int. Immunol. 20, 763–773 (2008).

    CAS  Article  Google Scholar 

  45. 45.

    Zhang, P., Zhao, Y. & Sun, X. H. Notch-regulated periphery B cell differentiation involves suppression of E protein function. J. Immunol. 191, 726–736 (2013).

    CAS  Article  Google Scholar 

  46. 46.

    Wakabayashi, N. et al. Regulation of notch1 signaling by nrf2: implications for tissue regeneration. Sci. Signal 3, ra52 (2010).

    Article  Google Scholar 

  47. 47.

    Chen, Q. et al. Serum metabolite biomarkers discriminate healthy smokers from COPD smokers. PloS ONE 10, e0143937 (2015).

    Article  Google Scholar 

  48. 48.

    Hoffmann, R. F. et al. Prolonged cigarette smoke exposure alters mitochondrial structure and function in airway epithelial cells. Respir. Res. 14, 97 (2013).

    Article  Google Scholar 

  49. 49.

    Muller, T. & Hengstermann, A. Nrf2: friend and foe in preventing cigarette smoking-dependent lung disease. Chem. Res. Toxicol. 25, 1805–1824 (2012).

    Article  Google Scholar 

  50. 50.

    Chaney, J. L. & Clark, P. L. Roles for synonymous codon usage in protein biogenesis. Annu. Rev. Biophys. 44, 143–166 (2015).

    CAS  Article  Google Scholar 

  51. 51.

    Wittig, I., Braun, H.-P. & Schägger, H. Blue native PAGE. Nat. Protoc. 1, 418–428 (2006).

    CAS  Article  Google Scholar 

  52. 52.

    McGuirk, S. et al. PGC-1α supports glutamine metabolism in breast cancer. Cancer Metab. 1, 22 (2013).

    Article  Google Scholar 

  53. 53.

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  Article  Google Scholar 

  54. 54.

    McKenna, A. et al. The genome analysis toolkit: a mapreduce framework for analyzing next-generation dna sequencing data. Genome Res. 20, 1297–1303 (2010).

    CAS  Article  Google Scholar 

  55. 55.

    Agarwal, V., Bell, G. W., Nam, J.-W. & Bartel, D. P. Predicting effective microRNA target sites in mammalian mRNAs. eLife 4, 101 (2015).

    Article  Google Scholar 

  56. 56.

    Reese, M. G., Eeckman, F. H., Kulp, D. & Haussler, D. Improved splice site detection in genie. J. Comput. Biol. 4, 311–323 (1997).

    CAS  Article  Google Scholar 

  57. 57.

    Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).

    CAS  Article  Google Scholar 

  58. 58.

    Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).

    CAS  Article  Google Scholar 

  59. 59.

    Saunders, C. T. et al. Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics 28, 1811–1817 (2012).

    CAS  Article  Google Scholar 

  60. 60.

    Inaoka, D. K. et al. Structural insights into the molecular design of flutolanil derivatives targeted for fumarate respiration of parasite mitochondria. Int. J. Mol. Sci. 16, 15287–15308 (2015).

    CAS  Article  Google Scholar 

  61. 61.

    Biasini, M. et al. SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res. 42, W252–W258 (2014).

    CAS  Article  Google Scholar 

  62. 62.

    Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J. Mol. Graphics 14, 33–38, 27–28 (1996).

    Article  Google Scholar 

  63. 63.

    Pettersen, E. F. et al. UCSF Chimera–a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).

    CAS  Article  Google Scholar 

  64. 64.

    Huo, X. et al. Preliminary molecular characterization and crystallization of mitochondrial respiratory complex II from porcine heart. FEBS J. 274, 1524–1529 (2007).

    CAS  Article  Google Scholar 

  65. 65.

    Vanommeslaeghe, K. et al. CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J. Comput. Chem. 31, 671–690 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Bussi, G. Hamiltonian replica exchange in GROMACS: a flexible implementation. Mol. Phys. 112, 379–384 (2014).

    CAS  Article  Google Scholar 

  67. 67.

    Parrinello, M. & Rahman, A. Polymorphic transitions in single crystals: A new molecular dynamics method. J. Appl. Phys. 52, 7182–7190 (1981).

    CAS  Article  Google Scholar 

  68. 68.

    Hoover, W. Canonical dynamics: equilibrium phase-space distributions. Phys. Rev. A Gen. Phys. 31, 1695–1697 (1985).

    CAS  Article  Google Scholar 

  69. 69.

    Hess, B. P-LINCS: a parallel linear constraint solver for molecular simulation. J. Chem. Theory Comput. 4, 116–122 (2008).

    CAS  Article  Google Scholar 

  70. 70.

    Zeileis, A. & Grothendieck, G. zoo: S3Infrastructure for regular and irregular time series. J. Stat. Software 14, 1–27 (2005).

    Article  Google Scholar 

Download references


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




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.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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.

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

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.

Supplementary Dataset: Immunoblots

Unmodified blots.

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation

Further reading


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