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Germinal center B cells selectively oxidize fatty acids for energy while conducting minimal glycolysis

Nature Immunology volume 21, pages 331–342 (2020)Cite this article

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Abstract

Germinal center B cells (GCBCs) are critical for generating long-lived humoral immunity. How GCBCs meet the energetic challenge of rapid proliferation is poorly understood. Dividing lymphocytes typically rely on aerobic glycolysis over oxidative phosphorylation for energy. Here we report that GCBCs are exceptional among proliferating B and T cells, as they actively oxidize fatty acids (FAs) and conduct minimal glycolysis. In vitro, GCBCs had a very low glycolytic extracellular acidification rate but consumed oxygen in response to FAs. [13C6]-glucose feeding revealed that GCBCs generate significantly less phosphorylated glucose and little lactate. Further, GCBCs did not metabolize glucose into tricarboxylic acid (TCA) cycle intermediates. Conversely, [13C16]-palmitic acid labeling demonstrated that GCBCs generate most of their acetyl-CoA and acetylcarnitine from FAs. FA oxidation was functionally important, as drug-mediated and genetic dampening of FA oxidation resulted in a selective reduction of GCBCs. Hence, GCBCs appear to uncouple rapid proliferation from aerobic glycolysis.

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Fig. 1: GCBCs perform OXPHOS but not aerobic glycolysis.
Fig. 2: GCBCs rely on FAO.
Fig. 3: GCBC uptake of glucose and FFA.
Fig. 4: GCBCs upregulate and rely on peroxisomal FAO for oxygen consumption.
Fig. 5: GCBCs are sensitive to dual mitochondrial and peroxisomal FAO in vitro and in vivo.
Fig. 6: 13C carbon tracing of glucose and palmitate in cultured B cells by LC–HRMS.
Fig. 7: Comparison of GCBCs and activated B cells for glycolysis-relevant gene expression.
Fig. 8: Competitive disadvantage of GCBCs after targeted CPT2 mRNA reduction.

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Data availability

The data that support the findings of this study are available from the corresponding author upon request. RNA-sequencing data have been deposited in the Gene Expression Omnibus with the accession code GSE128710.

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Acknowledgements

We thank R. Moreci and M. Carter for supporting experimental procedures, and D. Falkner and A. Yates for cell sorting. This work was funded by NIH grant no. R01 AI-46303 and NIH grant no. R01 AI-105018 to M.J.S.; and by grant no. SKF-015-039, grant no. SU2C-IRG-016-08 and start-up funds to G.M.D. through the Tumor Microenvironment Center at the University of Pittsburgh. T.H.W. was funded by the Deutsche Forschungsgemeinschaft (grant no. TRR130) and S.G.W. through grant no. S10OD023402. This work benefitted from ImageStreamX MARKII grant no. NIH 1S10OD019942-01.

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  1. These authors contributed equally: Florian J. Weisel, Mark J. Shlomchik.

Authors and Affiliations

  1. Department of Immunology, University of Pittsburgh, Pittsburgh, PA, USA

    Florian J. Weisel, Rebecca A. Elsner, Nikita Trivedi, Wei Luo, Daniel Wikenheiser, William F. Hawse, Shuchi Smita, Laura J. Conter, Stephen M. Joachim, Greg M. Delgoffe & Mark J. Shlomchik

  2. Health Sciences Metabolomics and Lipidomics Core, University of Pittsburgh, Pittsburgh, PA, USA

    Steven J. Mullett & Stacy G. Wendell

  3. Tumor Microenvironment Center, University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA

    Ashley V. Menk & Greg M. Delgoffe

  4. Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Maria Chikina & Shuchi Smita

  5. Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA, USA

    Stacy G. Wendell

  6. Division of Endocrinology and Metabolism, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA

    Michael J. Jurczak

  7. Center for Metabolism and Mitochondrial Medicine, University of Pittsburgh, Pittsburgh, PA, USA

    Michael J. Jurczak

  8. Division of Genetics, Department of Biology, Nikolaus-Fiebiger-Center for Molecular Medicine, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany

    Thomas H. Winkler

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Contributions

M.J.S., F.J.W. and G.M.D. designed the research. F.J.W., S.J.M., W.L., D.W., R.A.E., L.J.C., S.M.J., A.V.M., N.T., M.J.J., T.H.W. and G.M.D. performed experiments and analyzed data. W.F.H. and S.G.W. gave conceptual advice. M.C. and S.S. performed computational analysis. F.J.W. and M.J.S. wrote the manuscript.

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Extended data

Extended Data Fig. 1 Preparation of B cell populations in support of Figs. 1, 2, 4, 5, 6, 7 and 8.

Representative FACS plots are shown to determine frequency of indicated target populations (horizontal patterns). Splenocytes were enriched for indicated target population as described in the methods section. After each purification step (columns) cells were subjected to flow cytometric analysis with indicated surface markers to determine purity. Arrows indicate subsequent gating and numbers percent gated population. (RBC; red blood cell).

Extended Data Fig. 2 GCBC show high viability in culture in support of Figs. 1, 2, 4, 5, 6 and 7.

Indicated cell populations were bead purified and cultured either in RPMI media (a–c) or Seahorse XF Cell Mito Stress test media (d, e) for depicted times. Cell viability was assayed flow-cytometric staining for 7-AAD (a, d) at 120 min of culture and their viability was additionally determined at 0, 30 and 60 min utilizing Luna-FlTM automated counting with dual fluorescent microscope optics. Cells were exposed to acridine orange (AO) and propidium iodide (PI) simultaneously (b, c, e). Tabulated data are presented in (a) and (b) of 2 independent experiments depicted in red and blue. (b) shows representative images of the Lina-FlTM counter for data in (a). ns = not significant; *p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001; ****p ≤ 0.0001 by unpaired, two-tailed t-test.

Extended Data Fig. 3 GCBC maintain key transcriptional profile during in vitro culture in support of Figs. 1, 2, 4, 5 and 6.

Comparison of transcriptional profile of 259 most differentially regulated GC genes. Genes shown are most differentially regulated between freshly isolated GCBC and in vivo activated B cells (FDR < 0.01; FC > 4 log2) and therefore serve as GCBC identifier geneset. a, correlation of GCBC identifier genes of freshly isolated GCBC and GCBC cultured for 2 h (left; R2 = 0.96) which matches the conditions of seahorse experiments or GCBC cultured for 4 h with aCD40 (right; R2 = 0.86) which matches our 13C tracing studies. b, heatmap of expression levels of 267 GCBC identifier genes in freshly isolated (left column) and cultured GCBC under depicted conditions. R-correlation of freshly isolated GCBC to all culture conditions is depicted below each column and was computed using R “cor” function with “pearson” method.

Extended Data Fig. 4 GCBC only take up minimal amounts of glucose but physiological amounts of FFA in support of Fig. 3.

a, tabulated data of mean 2-NBDG fluorescence normalized to cell size (left) and representative flow histograms of 2-NBDG fluorescence (center left) and forward scatter (center right) of indicated cell populations pulsed in vitro for 30 min with 2-NBDG. Right shows an independent repeat of left. b, independent repeat of data presented in Fig. 3a. c, tabulated data of mean CD36 fluorescence normalized to cell size (left) and mean CD36 fluorescence of indicated cell populations. Shown are combined data of 2 independent experiments. d, independent repeat of data presented in Fig. 3b. e, tabulated data of mean BODIPY fluorescence normalized to cell size (left) and representative flow histograms of BODIPY fluorescence (middle) of indicated cell populations, pulsed in vitro for 30 min with BODIPY. Right panel shows an independent repeat of left panel. f, g, representative Amnis ImageStream images of cells presented in Fig. 3c, d, respectively. h, representative images of adaptive erode function for 100% (total cell, left) and 70% (intracellular, right). Fluorescence intensity is only calculated from areas colored in blue of the same cells shown in left and right panels. Bars represent mean + /- SEM; ns = not significant; *p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001; ****p ≤ 0.0001 by unpaired, two-tailed t-test.

Extended Data Fig. 5 Combined inhibition of mitochondrial and peroxisomal FAO with increased in vivo dosage of etomoxir and thioridazine in support of Fig. 5.

Absolute number of live splenic NP+ GCBC (a) and naïve NP- B cells (b) and MFI of 2-NBDG of GCBC after 30 min 2-NBDG in vitro pulse (c) of mice at d14 post NP-CGG immunization and in vivo treatment at d9, d11 and d13 with 22 mg/kg etomoxir and 11 mg/kg thioridazine or vehicle only as in Fig. 5c–e. Every dot represents and individual mouse and graphs are mean + /-SEM; ns = not significant; ****p < 0.0001 by unpaired, two-tailed t-test.

Extended Data Fig. 6 Independent repeat of 13C carbon tracing by LC-HRMS and Hexokinase-2 mRNA expression in support of Fig. 5.

(a–g) Bead purified naive, in vivo activated and GC B cells were stimulated with anti-CD40 in glucose and glutamine free RPMI media with the addition of 2 mg/ml [13C6]-glucose for 30 min or 4 h. Cells (a–e, g) and supernatants (f) were then subjected to LC-HRMS. Depicted is one representative experiment with n = 6 per sample. Each n represents a pool of 3 individual wells. (H) qRT-PCR for Hexokinase-2 expression from freshly isolated cell populations relative to RPS9. Bars are means + /-SEM. Normalization was performed as described in the methods section; ns = not significant; *p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001; ****p ≤ 0.0001 by unpaired, two-tailed t-test.

Extended Data Fig. 7 In vivo 13C tracing in support of Fig. 6.

LC-HRMS analysis of 13C3 lactate generated from 8 h continuous 13C6 glucose infusion using an “insulin clamp”. Mice received a primed (42.5 mU/kg)/continuous (4.5 mU/kg/min) infusion of insulin and a variable infusion of 20% glucose (50% 13C6-glucose: 50% 12C-glucose) to maintain euglycemia for 480 min. Mice were sacrificed and heart and liver were disrupted in liquid nitrogen. B cell populations were isolated as in Extended Data Fig. 1 and all samples were subjected to 13C3-lactate detection by liquid chromatography-high resolution mass spectrometry.

Extended Data Fig. 8 Absence of hypoxia-related gene signatures in the GCBC transcriptome in support of Fig. 7.

Depicted are quantile normalized expression values of HIF1alpha direct target genes that are involved in glycolysis (a) or not involved in glycolysis (b) and control genes that are known to be expressed or absent in GCBC (c). Significant down-regulation of hypoxia-related gene signatures in GCBC transcriptome compared to naïve B cells (left of d) and in vivo activated B cells (right of d). Data were obtained by RNA-sequencing of indicated cell populations as in Extended Data Fig. 1. Shown are averages of 3 independent RNA sequencing reactions per cell population with x-axis showing different cell populations as defined in the text (a–c). Genes are connected by lines for easier visualization and bars are means + /-SEM. Gene set enrichment plots illustrating differentially expressed genes in peak GCBCs compared to naive (left of d) and in vivo activated B cells (right of d; n = 3 per group) with respect to genes depicted in a and b. p-values were calculated using the rankSumTestWithCorrelation function in limma with t statistics.

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Weisel, F.J., Mullett, S.J., Elsner, R.A. et al. Germinal center B cells selectively oxidize fatty acids for energy while conducting minimal glycolysis. Nat Immunol 21, 331–342 (2020). https://doi.org/10.1038/s41590-020-0598-4

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