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Dual roles of hexokinase 2 in shaping microglial function by gating glycolytic flux and mitochondrial activity

Nature Metabolism volume 4, pages 1756–1774 (2022)Cite this article

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Abstract

Microglia continuously survey the brain parenchyma and actively shift status following stimulation. These processes demand a unique bioenergetic programme; however, little is known about the metabolic determinants in microglia. By mining large datasets and generating transgenic tools, here we show that hexokinase 2 (HK2), the most active isozyme associated with mitochondrial membrane, is selectively expressed in microglia in the brain. Genetic ablation of HK2 reduced microglial glycolytic flux and energy production, suppressed microglial repopulation, and attenuated microglial surveillance and damage-triggered migration in male mice. HK2 elevation is prominent in immune-challenged or disease-associated microglia. In ischaemic stroke models, however, HK2 deletion promoted neuroinflammation and potentiated cerebral damages. The enhanced inflammatory responses after HK2 ablation in microglia are associated with aberrant mitochondrial function and reactive oxygen species accumulation. Our study demonstrates that HK2 gates both glycolytic flux and mitochondrial activity to shape microglial functions, changes of which contribute to metabolic abnormalities and maladaptive inflammation in brain diseases.

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Fig. 1: Restrictive expression of hexokinase 2 in microglia in the brain.
Fig. 2: Hexokinase 2 regulates microglial metabolic flux.
Fig. 3: HK2 is required for microglial motility and dynamics.
Fig. 4: Hexokinase 2 deficiency delays microglial repopulation.
Fig. 5: Prominent upregulation of hexokinase 2 in activated microglia.
Fig. 6: Hexokinase 2 ablation potentiates brain damages in a stroke model.
Fig. 7: HK2 ablation impairs mitochondrial function and boosts pro-inflammatory responses in immune-challenged microglia.
Fig. 8: The dual roles of hexokinase 2 in shaping microglial functions.

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Data availability

RNA-seq data of repopulating microglia are available from the GEO under accession GSE188642. The data that support the finding of this study are available from the corresponding authors upon request. Source data are provided with this paper.

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Acknowledgements

We greatly appreciate A. Gitler at Stanford University for comments and suggestions on the manuscript. We thank P. Shi at the Institute of Translational Medicine of Zhejiang University for help with microglia sorting, and M. -X. Guan at the Division of Medical Genetics and Genomics of Zhejiang University School of Medicine for help with Metabolic Extracellular Flux analysis, W. Wang at the Charles Institute of Dermatology of University College Dublin and L. Chen at the First Affiliated Hospital of Zhejiang University for generously providing hydrogel, C. Liu at the School of Brain Science and Brain Medicine of Zhejiang University for generously providing transgenic mice. We are thankful to S. Fang at the Core Facilities of the School of Brain Science and Brain Medicine of Zhejiang University, H. Wu at the Center of Cryo-Electron Microscopy of Zhejiang University, Y. Huang, Y. Li and J. Wang at the Core Facilities of Zhejiang University for technical assistance. We greatly appreciate X. Zhang and A. Ping at the Second Affiliated Hospital of Zhejiang University for help with human microglia sorting. This work was supported by grants from the National Key Research and Development Program of China (2017YFA0104200 to Z.G.), National Natural Science Foundation of China (32070974 to Z.G., 82090033 to Z.G. and 82101413 to Y.H.), major programme of the National Natural Science Foundation of China (82090033 to S.D.) and China Postdoctoral Science Foundation (2020M671693 to Y.H.).

Author information

Author notes

  1. These authors contributed equally: Yaling Hu, Kelei Cao, Fang Wang.

Authors and Affiliations

  1. Department of Neurobiology and Department of Neurology of Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Yaling Hu, Kelei Cao, Fang Wang, Weiying Wu, Weihao Mai, Liyao Qiu, Yuxiang Luo, Shumin Duan & Zhihua Gao

  2. Liangzhu Laboratory, Zhejiang University Medical Center, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, Hangzhou, China

    Yaling Hu, Kelei Cao, Fang Wang, Weiying Wu, Weihao Mai, Liyao Qiu, Yuxiang Luo, Shumin Duan & Zhihua Gao

  3. NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, China

    Yaling Hu, Kelei Cao, Fang Wang, Weiying Wu, Weihao Mai, Liyao Qiu, Yuxiang Luo, Binggui Sun, Shumin Duan & Zhihua Gao

  4. Chinese Institute for Brain Research, Beijing, Beijing, China

    Woo-ping Ge

  5. Department of Neurobiology and Department of Anesthesiology, the Children’s Hospital, Zhejiang University School of Medicine and National Clinical Research Center for Child Health, Hangzhou, China

    Binggui Sun

  6. Department of Neurology of Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Ligen Shi, Junming Zhu, Jianmin Zhang & Zhiying Wu

  7. The Children’s Hospital, Zhejiang, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China

    Yicheng Xie

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Contributions

Z.G., S.D., Y.H., K.C. and F.W. conceived the project and designed experiments; Y.H., K.C., F.W. and L.Q. carried out experiments; Y.H., K.C., F.W., W.W., W.M. and Y.L. analysed the data; B.S., L.S., J.Z. and J.Z. provide technical assistance. Z.G., Y.H., K.C., F.W., and W.W. wrote the paper; Z.G., S.D., Y.H., K.C., F.W., W.W., Y.L., W.G., Z.W., Y.X. and S.D. reviewed and edited the paper; Z.G., S.D. and Y.H. provided funding acquisition.

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Correspondence to Shumin Duan or Zhihua Gao.

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Nature Metabolism thanks Marco Colonna and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Alfredo Giménez-Cassina, in collaboration with the Nature Metabolism team.

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Extended data

Extended Data Fig. 1 HK expression in human and mouse microglia.

(a) A correlation network graph of genes in metabolic pathways (1147 genes) expressed in different types of brain cells (data retrieved from GSE52564). Nodes represent transcripts, while edges represent the degree of correlation in expression. Heatmaps and line charts showing the expression profile of transcripts contained within 3 clusters corresponding to astrocytes, endothelial cells and microglia clusters are presented. (b) Principal component analysis (PCA) of the metabolic genes in glycolysis/gluconeogenesis in 7 cell types of the brain (data retrieved from GSE52564). (c) A comparison of metabolic gene expression in human and mouse microglia (data retrieved from GSE89960). (d-f) The expression of Hk1, Hk2 and Hk3 in mouse brain cells (data retrieved from GSE52564), n = 2 mice per group. MG, Microglia; ECs, Endothelial cells; OPC, Oligodendrocyte progenitor cells; Neu, Neurons; MO, Myelinating oligodendrocytes; NFO, Newly formed oligodendrocytes; Ast, Astrocytes. Data are presented as means ± SEM.

Source data

Extended Data Fig. 2 HK2 is selectively expressed in microglia.

(a) Representative micrographs (from 3 independent experiments) showing single-molecule fluorescence in situ hybridization (FISH) of Hk2 mRNA with different cellular markers, NeuN (neurons), Olig2 (oligodendrocytes) and GFAP (astrocytes) in the mouse brain. White arrowheads point to Hk2+ and Iba1+ microglia. Dotted boxes show regions of higher magnification.

Extended Data Fig. 3 Characterization of Hk2CreERT2-tdTomato mice.

(a) Flow cytometry analyses of tdT+ cells and CD11b+CD45low microglia in Hk2CreERT2-tdTomato mice. (b) Representative micrographs (from 3 independent experiments) showing the co-labeling of tdT+, Iba1+and Hk2+ in brain slices from Hk2CreERT2-tdTomato mice. Dotted boxes show regions of higher magnification. (c) FISH of Hk2 and tdT staining in brain slices from Hk2CreERT2-tdTomato mice and quantification. Dotted boxes show regions of higher magnification. n = 3 mice per group. (d) Representative micrographs (from 3 independent experiments) showing the three-dimension (3D) reconstruction of a tdT+ cell in brain slices from Hk2CreERT2-tdTomato mice using Imaris. (e) Representative micrographs (from 3 independent experiments) of tdT and different cellular markers, NeuN (neurons), Sox10 (oligodendrocytes) and GFAP (astrocytes) in the brains of Hk2CreERT2-tdTomato mice. (f) Representative micrographs (from 3 independent experiments) showing full co-localization of GFP, tdT and Iba1 in brain slices from Hk2CreERT2-tdTomato::Ai3fl/+ mice after tamoxifen induction. TAM, tamoxifen; D, day; tdT, tdTomato. Data are presented as means ± SEM.

Extended Data Fig. 4 HK2 is similarly expressed in microglia in different brain regions.

(a) Representative gating strategies for flow cytometry-based measurement of fluorescence intensity of microglial tdTomato in different brain regions. (b) Representative Western blot (from 2 independent experiments) analysis of HK2 protein in lysates from FACS-sorted microglia from adult cortex, midbrain and cerebellum and quantification (c). n = 2 from 16 mice per group. Data are presented as means ± SEM.

Source data

Extended Data Fig. 5 ECAR and OCR analyses of microglia after HK2 inhibition or deletion.

(a) ECAR measurement of primary mouse microglia (untreated or pretreated with 3-BrPA). (b) Quantification of basal ECAR and maximal ECAR from a. n = 7 or 4 independent cultures in Hk2fl/fl or Hk2-cKO groups, respectively. (c) OCR measurement of primary mouse microglia (untreated or pretreated with 3-BrPA). (d) Quantification of basal OCR and maximal OCR from c. n = 5 independent cultures per group. (e) A scheme of the standard Seahorse XF Mito-stress test. (f) OCR measurement of primary microglia from Hk2fl/fl (untreated or pretreated with 2-DG) and Hk2-cKO mouse brains in regular assay media containing pyruvate and glutamine. (g) Quantification of basal OCR and maximal OCR from f. n = 5 independent cultures per group. ECAR, extracellular acidification rate; OCR, oxygen consumption rate; OXPHOS, oxidative phosphorylation; TCA, tricarboxylic acid cycle; Glc, glucose; 3-BrPA, 3-Bromopyruvate; FCCP, Carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone; Olig, oligomycin; Antim, antimycin A; Rot, rotenone; 2-DG, 2-Deoxy-D-glucose; Data are presented as means ± SEM. Two-tailed Student’s t-tests for b, d. One-way ANOVA followed by Bonferroni’s post hoc tests for g, *p < 0.05, **p < 0.01 and ***p < 0.001.

Source data

Extended Data Fig. 6 Ablation of HK2 did not affect microglial density and morphology in the adult stage.

(a) A scheme of the experimental timeline for the analysis of microglial density and morphology from Hk2fl/fl and Hk2-cKO mice at 1 week after TAM administration. (b) Representative micrographs (from 3 independent experiments) showing the Iba1+ microglia and proliferative (BrdU+) microglia in adult Hk2fl/fl and Hk2-cKO mouse brains at 1 week after TAM administration. (c, d) Quantification of microglial number (c) and percentage of BrdU+ microglia (d) in the cortex from Hk2fl/fl and Hk2-cKO mice. n = 3 mice per group. (e) A scheme of the experimental timeline for analysis of microglial density and morphology from Hk2fl/fl and Hk2-cKO mice at 4 weeks after TAM administration. (f, g) Flow cytometry analysis and quantification of CD11b+ CD45low microglia in adult mice at 4 weeks after TAM administration. n = 3 mice per group. (h) Representative micrographs (from 3 independent experiments) showing 3D reconstruction of microglia from Hk2fl/fl and Hk2-cKO mice in the cortex using Imaris. (i) Scholl analysis of all reconstructed cells. n = 3 mice per group (total 36 cells). (j-l) Quantification of microglial morphology in the cortex from Hk2fl/fl and Hk2-cKO mice by Sholl analysis. n = 3 mice per group (total 36 cells). TAM, tamoxifen; D, day. Data are presented as means ± SEM. Two-tailed Student’s t-tests for c, d, f, g, j-l, *p < 0.05.

Source data

Extended Data Fig. 7 Ablation of HK2 did not affect Hk1 and Hk3 levels.

(a) The expression of Hk1, Hk2 and Hk3 in FACS-sorted microglia from Hk2fl/fl and Hk2-cKO mice at adult. n = 4 or 3 mice in Hk2fl/fl or Hk2-cKO groups, respectively. (b-d) The expression of Hk1, Hk2 and Hk3 in sorted microglia during repopulation in Hk2fl/fl and Hk2-cKO mice. n = 1481 or 264 cells for HomoMG, 162 or 264 cells for Precursor MG, 121 or 91 cells for G1/S MG, 200 or 108 cells for G2/M MG in Hk2fl/fl or Hk2-cKO groups, respectively. (e) Quantitative analyses of Hk1, Hk2, Hk3 and Cx3cr1 expression in the brain tissues from Hk2fl/fl and Hk2-cKO mice. n = 4 or 3 mice in Hk2fl/fl or Hk2-cKO groups, respectively. (f) CD11b+CD45low microglia and CD11b-CD45− non-microglial cells sorting from Hk2fl/fl and Hk2-cKO mice by flow cytometry. (g) Quantitative analyses of Hk2 and Cx3cr1 expression in CD11b−CD45− non-microglial cells and CD11b+CD45low microglia from Hk2fl/fl and Hk2-cKO mice. n = 3 mice for microglia and non-microglial cells or 4 mice for microglia in Hk2fl/fl or Hk2-cKO groups, respectively. Data are presented as means ± SEM. Two-tailed student t-tests for a, e, g. Wilcoxon rank sum test and Bonferroni correction for b-d, *p < 0.05, **p < 0.01 and ***p < 0.001.

Source data

Extended Data Fig. 8 A comparison of repopulated microglia in Hk2fl/fl and Hk2-cKO mice.

(a) Pseudotime trajectory was constructed using Monocle 2 by re-ordering microglia population and single cells on the trajectory. Each microglia on the minimum spanning tree (MST) were colored by pseudotime. (b) Pseudotime trajectory was constructed and each microglia on the MST were colored by annotated microglial cell types during repopulation in Hk2fl/fl and Hk2-cKO mice. (c) The number of cells in different subsets of microglia from Hk2fl/fl and Hk2-cKO mice. (d) t-SNE plots of Hk2, Cx3cr1, Cdkn1a and Slfn5 gene expression between Hk2fl/fl and Hk2-cKO mice. n = 1481 or 264 cells for HomoMG, 162 or 264 cells for Precursor MG, 121 or 91 cells for G1/S MG, 200 or 108 cells for G2/M MG in Hk2fl/fl or Hk2-cKO groups, respectively. The color key indicates the expression levels.

Extended Data Fig. 9 Analyses of microglial number, phagocytosis, motor behavior and inflammation in Hk2fl/fl and Hk2-cKO mice before and after stroke.

(a) Representative micrographs (from 3 independent experiments) showing the density of microglia in the peri-infarct zone from Hk2fl/fl and Hk2-cKO mice at 3 and 7 Dpi. Dashed lines and dashed boxes indicate the border of ischemic core and regions of higher magnification, respectively. Rotarod (b, c) and open field (d, e) tests of adult Hk2fl/fl and Hk2-cKO mice. n = 10 mice per group. (f) Representative micrographs (from 3 independent experiments) showing CD68+ labeling in Iba1+ microglia in the peri-infarct zone from Hk2fl/fl and Hk2-cKO mice under sham and ischemic conditions (3 Dpi) and quantification of microglial volume and CD68+ volume in microglia (g, h). Dashed boxes show regions of higher magnification of microglia. n = 30 cells from 3 mice per group. RT-qPCR analysis of Tnfα, Il-6 and Il-1β levels (i) and Ifitm3, Stat1 levels (j) in the infarcted tissues at 1 Dpi. n = 5 mice per group for sham-operated animals or 6 mice per group for ischemic animals. RPM, rotations per minute; Dpi, days post ischemia. Data are presented as means ± SEM. Two-tailed student t-tests for b-e. Two-way ANOVA followed by Bonferroni’s post hoc tests for g, h, *p < 0.05, **p < 0.01 and ***p < 0.001.

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Extended Data Fig. 10 Analyses of mitochondrial membrane potential and ROS levels in Hk2fl/fl and Hk2-deficient microglia upon LPS/ poly(I:C) stimulation.

(a-d) Analysis of microglial mitochondrial membrane potential and mitochondrial ROS after stimulation of LPS or poly(I:C) from Hk2fl/fl and Hk2-cKO mice in the presence or absence of Mito-TEPMO. n = 6 or 7 (a), 7 or 6 (b), 7 or 5 (c), 6 or 6(d) independent cultures for each treatment in Hk2fl/fl or Hk2-cKO groups, respectively. Data are presented as means ± SEM. Two-way ANOVA followed by Bonferroni’s post hoc tests for a-d, *p < 0.05, **p < 0.01 and ***p < 0.001.

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Supplementary information

Supplementary Information

Supplementary Tables 3–5 and authors statements

Reporting Summary

Supplementary Table 1

List of metabolic genes related to Fig. 1.

Supplementary Table 2

List of genes related to Fig. 5.

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Hu, Y., Cao, K., Wang, F. et al. Dual roles of hexokinase 2 in shaping microglial function by gating glycolytic flux and mitochondrial activity. Nat Metab 4, 1756–1774 (2022). https://doi.org/10.1038/s42255-022-00707-5

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