Skip to main content

Thank you for visiting nature.com. 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.

  • Article
  • Published:

Microglial hexokinase 2 deficiency increases ATP generation through lipid metabolism leading to β-amyloid clearance

Nature Metabolism volume 4, pages 1287–1305 (2022)Cite this article

Subjects

An Author Correction to this article was published on 14 October 2022

This article has been updated

Abstract

Microglial cells consume adenosine triphosphate (ATP) during phagocytosis to clear neurotoxic β-amyloid in Alzheimer’s disease (AD). However, the contribution of energy metabolism to microglial function in AD remains unclear. Here, we demonstrate that hexokinase 2 (HK2) is elevated in microglia from an AD mouse model (5xFAD) and AD patients. Genetic deletion or pharmacological inhibition of HK2 significantly promotes microglial phagocytosis, lowers the amyloid plaque burden and attenuates cognitive impairment in male AD mice. Notably, the ATP level is dramatically increased in HK2-deficient or inactive microglia, which can be attributed to a marked upregulation in lipoprotein lipase (LPL) expression and subsequent increase in lipid metabolism. We further show that two downstream metabolites of HK2, glucose-6-phosphate and fructose-6-phosphate, can reverse HK2-deficiency-induced upregulation of LPL, thus supporting ATP production and microglial phagocytosis. Our findings uncover a crucial role for HK2 in phagocytosis through regulation of microglial energy metabolism, suggesting a potential therapeutic strategy for AD by targeting HK2.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Elevated expression of HK2 in Aβ plaque-associated-microglia in AD.
Fig. 2: Microglial HK2 deletion reduces Aβ plaque loads and attenuates cognition impairments in AD mice.
Fig. 3: HK2 inhibitors reduce Aβ plaque load and attenuate behavioural deficits in AD mice.
Fig. 4: HK2 inhibition or deletion promotes microglial Aβ phagocytosis and ATP production.
Fig. 5: HK2 inhibitions trigger fatty acid metabolism by upregulating LPL expression.
Fig. 6: Microglial HK2 deletion increases LPL expression, and LPL inactivation by its antagonist blocks the enhanced phagocytosis and ATP production induced by HK2 inhibitors.
Fig. 7: G-6-P and F-6-P, but not FBP, block LPL expression, phagocytosis and ATP production in microglia.

Similar content being viewed by others

Data availability

The SRA accession number for RNA-seq data reported in this paper is: PRJNA843185 (SAMN28745444–SAMN28745459); PRJNA613212 (SRR11347559–SRR11347568). Transcription levels of HK1, HK2 and HK3 in the prefrontal cortex of human brains at different ages are from public data in the GSE44772 dataset. Single-cell characteristics of microglia were analysed using the public data in the GSE127893 database. Source data are provided with this paper.

Change history

References

  1. Hardy, J. & Selkoe, D. J. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science 297, 353–356 (2002).

    Article  CAS  PubMed  Google Scholar 

  2. Holtzman, D. M., Goate, A., Kelly, J. & Sperling, R. Mapping the road forward in Alzheimer’s disease. Sci. Transl. Med. 3, 114ps148 (2011).

    Article  Google Scholar 

  3. Guo, T. et al. Molecular and cellular mechanisms underlying the pathogenesis of Alzheimer’s disease. Mol. Neurodegener. 15, 40 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Chen, Z. & Zhong, C. Decoding Alzheimer’s disease from perturbed cerebral glucose metabolism: implications for diagnostic and therapeutic strategies. Prog. Neurobiol. 108, 21–43 (2013).

    Article  CAS  PubMed  Google Scholar 

  5. Butterfield, D. A. & Halliwell, B. Oxidative stress, dysfunctional glucose metabolism and Alzheimer disease. Nat. Rev. Neurosci. 20, 148–160 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Wang, W., Zhao, F., Ma, X., Perry, G. & Zhu, X. Mitochondria dysfunction in the pathogenesis of Alzheimer’s disease: recent advances. Mol. Neurodegener. 15, 30 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Elder, G. J., Colloby, S. J., Firbank, M. J., McKeith, I. G. & Taylor, J. P. Consecutive sessions of transcranial direct current stimulation do not remediate visual hallucinations in Lewy body dementia: a randomised controlled trial. Alzheimers Res Ther. 11, 9 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Harris, J. J., Jolivet, R. & Attwell, D. Synaptic energy use and supply. Neuron 75, 762–777 (2012).

    Article  CAS  PubMed  Google Scholar 

  9. Camandola, S. & Mattson, M. P. Brain metabolism in health, aging, and neurodegeneration. EMBO J. 36, 1474–1492 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Suss, P. & Schlachetzki, J. C. M. Microglia in Alzheimer’s disease. Curr. Alzheimer Res. 17, 29–43 (2020).

    Article  CAS  PubMed  Google Scholar 

  11. Claes, C. et al. Plaque-associated human microglia accumulate lipid droplets in a chimeric model of Alzheimer’s disease. Mol. Neurodegener. 16, 50 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Clayton, K. et al. Plaque associated microglia hyper-secrete extracellular vesicles and accelerate tau propagation in a humanized APP mouse model. Mol. Neurodegener. 16, 18 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Engl, E. & Attwell, D. Non-signalling energy use in the brain. J. Physiol. 593, 3417–3429 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Bernhart, E. et al. Lysophosphatidic acid receptor activation affects the C13NJ microglia cell line proteome leading to alterations in glycolysis, motility, and cytoskeletal architecture. Proteomics 10, 141–158 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Kalsbeek, M. J., Mulder, L. & Yi, C. X. Microglia energy metabolism in metabolic disorder. Mol. Cell. Endocrinol. 438, 27–35 (2016).

    Article  CAS  PubMed  Google Scholar 

  16. Sathe, G. et al. Quantitative proteomic profiling of cerebrospinal fluid to identify candidate biomarkers for Alzheimer’s disease. Proteom. Clin. Appl 13, e1800105 (2019).

    Article  Google Scholar 

  17. Mor, F., Izak, M. & Cohen, I. R. Identification of aldolase as a target antigen in Alzheimer’s disease. J. Immunol. 175, 3439–3445 (2005).

    Article  CAS  PubMed  Google Scholar 

  18. Wilson, J. E. Isozymes of mammalian hexokinase: structure, subcellular localization and metabolic function. J. Exp. Biol. 206, 2049–2057 (2003).

    Article  CAS  PubMed  Google Scholar 

  19. Robey, R. B. & Hay, N. Mitochondrial hexokinases, novel mediators of the antiapoptotic effects of growth factors and Akt. Oncogene 25, 4683–4696 (2006).

    Article  CAS  PubMed  Google Scholar 

  20. Chow, H. M. et al. Age-related hyperinsulinemia leads to insulin resistance in neurons and cell-cycle-induced senescence. Nat. Neurosci. 22, 1806–1819 (2019).

    Article  CAS  PubMed  Google Scholar 

  21. Moon, J. S. et al. mTORC1-Induced HK1-dependent glycolysis regulates NLRP3 inflammasome activation. Cell Rep. 12, 102–115 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Li, Y. et al. Hexokinase 2-dependent hyperglycolysis driving microglial activation contributes to ischemic brain injury. J. Neurochem. 144, 186–200 (2018).

    Article  CAS  PubMed  Google Scholar 

  23. Baik, S. H. et al. A breakdown in metabolic reprogramming causes microglia dysfunction in Alzheimer’s disease. Cell Metab. 30, 493–507 e496 (2019).

    Article  CAS  PubMed  Google Scholar 

  24. Saraiva, L. M. et al. Amyloid-beta triggers the release of neuronal hexokinase 1 from mitochondria. PLoS ONE 5, e15230 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Gershon, T. R. et al. Hexokinase-2-mediated aerobic glycolysis is integral to cerebellar neurogenesis and pathogenesis of medulloblastoma. Cancer Metab. 1, 2 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Wyatt, E. et al. Regulation and cytoprotective role of hexokinase III. PLoS ONE 5, e13823 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer’s disease. Cell 153, 707–720 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Spangenberg, E. E. et al. Eliminating microglia in Alzheimer’s mice prevents neuronal loss without modulating amyloid-beta pathology. Brain 139, 1265–1281 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Song, G. et al. Inhibition of hexokinases holds potential as treatment strategy for rheumatoid arthritis. Arthritis Res Ther. 21, 87 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Oudard, S. et al. Phase II study of lonidamine and diazepam in the treatment of recurrent glioblastoma multiforme. J. Neurooncol. 63, 81–86 (2003).

    Article  PubMed  Google Scholar 

  31. Carapella, C. M. et al. The potential role of lonidamine (LND) in the treatment of malignant glioma. Phase II study. J. Neurooncol. 7, 103–108 (1989).

    Article  CAS  PubMed  Google Scholar 

  32. Gong, L., Wei, Y., Yu, X., Peng, J. & Leng, X. 3-Bromopyruvic acid, a hexokinase II inhibitor, is an effective antitumor agent on the hepatoma cells: in vitro and in vivo findings. Anticancer Agents Med. Chem. 14, 771–776 (2014).

    Article  CAS  PubMed  Google Scholar 

  33. Mansour, M. A., Ibrahim, W. M., Salama, M. M. & Salama, A. F. Dual inhibition of glycolysis and autophagy as a therapeutic strategy in the treatment of Ehrlich ascites carcinoma. J. Biochem. Mol. Toxicol. 34, e22498 (2020).

    Article  CAS  PubMed  Google Scholar 

  34. Landel, V. et al. Temporal gene profiling of the 5XFAD transgenic mouse model highlights the importance of microglial activation in Alzheimer’s disease. Mol. Neurodegener. 9, 33 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Guo, L. et al. Inhibition of mitochondrial complex II by the anticancer agent lonidamine. J. Biol. Chem. 291, 42–57 (2016).

    Article  CAS  PubMed  Google Scholar 

  36. Gao, M.-L. et al. Functional microglia derived from human pluripotent stem cells empower retinal organ. Sci. China Life Sci. 65, 1057–1071 (2022).

    Article  CAS  PubMed  Google Scholar 

  37. Ebert, D., Haller, R. G. & Walton, M. E. Energy contribution of octanoate to intact rat brain metabolism measured by 13C nuclear magnetic resonance spectroscopy. J. Neurosci. 23, 5928–5935 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Sun, X. L. & Weckwerth, W. COVAIN: a toolbox for uni- and multivariate statistics, time-series and correlation network analysis and inverse estimation of the differential Jacobian from metabolomics covariance data. Metabolomics 8, S81–S93 (2012).

    Article  Google Scholar 

  39. Wilson, J. L. et al. Inverse data-driven modeling and multiomics analysis reveals Phgdh as a metabolic checkpoint of macrophage polarization and proliferation. Cell Rep. 30, 1542–1552 e1547 (2020).

    Article  CAS  PubMed  Google Scholar 

  40. Nilsson-Ehle, P., Egelrud, T., Belfrage, P., Olivecrona, T. & Borgstrom, B. Positional specificity of purified milk lipoprotein lipase. J. Biol. Chem. 248, 6734–6737 (1973).

    Article  CAS  PubMed  Google Scholar 

  41. Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290 e1217 (2017).

    Article  CAS  PubMed  Google Scholar 

  42. Sala Frigerio, C. et al. The major risk factors for Alzheimer’s disease: age, sex, and genes modulate the microglia response to abeta plaques. Cell Rep. 27, 1293–1306 e1296 (2019).

    Article  CAS  PubMed  Google Scholar 

  43. Lookene, A., Skottova, N. & Olivecrona, G. Interactions of lipoprotein lipase with the active-site inhibitor tetrahydrolipstatin (Orlistat). Eur. J. Biochem. 222, 395–403 (1994).

    Article  CAS  PubMed  Google Scholar 

  44. Kano, S. & Doi, M. NO-1886 (ibrolipim), a lipoprotein lipase-promoting agent, accelerates the expression of UCP3 messenger RNA and ameliorates obesity in ovariectomized rats. Metabolism 55, 151–158 (2006).

    Article  CAS  PubMed  Google Scholar 

  45. Nishimura, M. et al. Effects of NO-1886 (Ibrolipim), a lipoprotein lipase-promoting agent, on gene induction of cytochrome P450s, carboxylesterases, and sulfotransferases in primary cultures of human hepatocytes. Drug Metab. Pharmacokinet. 19, 422–429 (2004).

    Article  CAS  PubMed  Google Scholar 

  46. Coleman, D. L. & Eicher, E. M. Fat (fat) and tubby (tub): two autosomal recessive mutations causing obesity syndromes in the mouse. J. Hered. 81, 424–427 (1990).

    Article  CAS  PubMed  Google Scholar 

  47. Ziboh, V. A., Dreize, M. A. & Hsia, S. L. Inhibition of lipid synthesis and glucose-6-phosphate dehydrogenase in rat skin by dehydroepiandrosterone. J. Lipid Res. 11, 346–354 (1970).

    Article  CAS  PubMed  Google Scholar 

  48. Bessoule, J. J., Lessire, R., Rigoulet, M., Guerin, B. & Cassagne, C. Fatty acid synthesis in mitochondria from Saccharomyces cerevisiae. FEBS Lett. 214, 158–162 (1987).

    Article  CAS  PubMed  Google Scholar 

  49. Liao, F. F. & Xu, H. Insulin signaling in sporadic Alzheimer’s disease. Sci. Signal 2, pe36 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Bernier, L. P. et al. Microglial metabolic flexibility supports immune surveillance of the brain parenchyma. Nat. Commun. 11, 1559 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Parkhurst, C. N. & Gan, W. B. Microglia dynamics and function in the CNS. Curr. Opin. Neurobiol. 20, 595–600 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Kettenmann, H., Hanisch, U. K., Noda, M. & Verkhratsky, A. Physiology of microglia. Physiol. Rev. 91, 461–553 (2011).

    Article  CAS  PubMed  Google Scholar 

  53. Lawson, L. J., Perry, V. H. & Gordon, S. Turnover of resident microglia in the normal adult mouse brain. Neuroscience 48, 405–415 (1992).

    Article  CAS  PubMed  Google Scholar 

  54. van Furth, R. & Cohn, Z. A. The origin and kinetics of mononuclear phagocytes. J. Exp. Med. 128, 415–435 (1968).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Parkhurst, C. N. et al. Microglia promote learning-dependent synapse formation through brain-derived neurotrophic factor. Cell 155, 1596–1609 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Zhang, Y. et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929–11947 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Aldana, B. I. Microglia-specific metabolic changes in neurodegeneration. J. Mol. Biol. 431, 1830–1842 (2019).

    Article  CAS  PubMed  Google Scholar 

  58. Li, F., Faustino, J., Woo, M. S., Derugin, N. & Vexler, Z. S. Lack of the scavenger receptor CD36 alters microglial phenotypes after neonatal stroke. J. Neurochem. 135, 445–452 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Spriet, L. L. New insights into the interaction of carbohydrate and fat metabolism during exercise. Sports Med 44, S87–S96 (2014).

    Article  PubMed  Google Scholar 

  60. Wang, H. & Eckel, R. H. Lipoprotein lipase in the brain and nervous system. Annu Rev. Nutr. 32, 147–160 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Gao, Y. et al. Disruption of lipid uptake in astroglia exacerbates diet-induced obesity. Diabetes 66, 2555–2563 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Nishitsuji, K., Hosono, T., Uchimura, K. & Michikawa, M. Lipoprotein lipase is a novel amyloid beta (Abeta)-binding protein that promotes glycosaminoglycan-dependent cellular uptake of Abeta in astrocytes. J. Biol. Chem. 286, 6393–6401 (2011).

    Article  CAS  PubMed  Google Scholar 

  63. Scacchi, R. et al. The H+ allele of the lipoprotein lipase (LPL) HindIII intronic polymorphism and the risk for sporadic late-onset Alzheimer’s disease. Neurosci. Lett. 367, 177–180 (2004).

    Article  CAS  PubMed  Google Scholar 

  64. Wang, S. S. et al. Myelin injury in the central nervous system and Alzheimer’s disease. Brain Res. Bull. 140, 162–168 (2018).

    Article  CAS  PubMed  Google Scholar 

  65. Bruce, K. D. et al. Lipoprotein lipase is a feature of alternatively-activated microglia and may facilitate lipid uptake in the CNS during demyelination. Front Mol. Neurosci. 11, 57 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Westerman, B. A. et al. GFAP-Cre-mediated transgenic activation of Bmi1 results in pituitary tumors. PLoS ONE 7, e35943 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Nature Science Foundation of China (grant nos. 91849205, 81925010 and U1905207 to J.Z.; 81801337 and 82071520 to L.L.; 92149303 to H.X. and J.Z.; 92049202 to H.X.), the National Key Research and Development Program of China (grant no. 2021YFA1101402 to J.Z.), the Fundamental Research Funds for the Central Universities (grant nos. 20720190118 and 20720180049 to J.Z.; 20720190075 to L.L.), Fujian Province Nature Science Foundation (grant no. 2019J05006 to L.L.) and Xiamen Youth Innovation Fund (grant no. 3502Z20206031 to L.L.).

Author information

Authors and Affiliations

  1. Fujian Provincial Key Laboratory of Neurodegenerative Disease and Aging Research, Institute of Neuroscience, School of Medicine, Xiamen University, Xiamen, China

    Lige Leng, Ziqi Yuan, Xiao Su, Han Wang, Jin Xue, Kai Zhuang, Zhenlei Chen, Hui Lin, Wenting Xie, Huifang Li, Huaxi Xu & Jie Zhang

  2. The Brain Science Center, Beijing Institute of Basic Medical Sciences, Beijing, China

    Ruiyuan Pan & Zengqiang Yuan

  3. Department of Pathology, Case Western Reserve University, Cleveland, OH, USA

    Ju Gao & Xinglong Wang

  4. Department of Anesthesiology, First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China

    Zhenyi Chen & Jie Zhang

  5. Department of Neurobiology, School of Basic Medicine, Fourth Military Medcial University, Xi’an, Shaanxi, China

    Keke Ren, Wenting Wang & Shengxi Wu

  6. Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology & Visual Sciences Key Lab, Beijing, China

    Xiao Zhang & Zi-Bing Jin

  7. School of Life Sciences, The Chinese University of Hong Kong, Sha Tin, Hong Kong

    Hei-Man Chow

  8. Institute of Neuroscience, Fujian Medical University, Fuzhou, China

    Jie Zhang

Authors
  1. Lige Leng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ziqi Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ruiyuan Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiao Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Han Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jin Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kai Zhuang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ju Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Zhenlei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Hui Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Wenting Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Huifang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Zhenyi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Keke Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Xiao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Wenting Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Zi-Bing Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Shengxi Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Xinglong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Zengqiang Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Huaxi Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Hei-Man Chow
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Jie Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

L.L. and J.Z. conceptualized the study. L.L., Z.Y., H.Lin, H.Li. and W.X. prepared and maintained AD mice. L.L., Z.Y., K.Z. and Z.C. designed and performed morphological analysis and biochemical assays. L.L., Z.Y and Z.C. performed behaviour tests. K.R., W.W., X.S. and J.X. performed electrophysiology experiments. H.W. prepared the primary microglia, astrocyte and neuron cultures. X.Z. and Z.-B.J. performed human microglia differentiation. J.G. and X.W. performed and analysed staining on AD patients’ samples. R.P. repeated the drug-treated AD experiments. L.L. and J.Z. wrote the manuscript. H.X., Z.Y., H.-M.C., R.P., Z.C., S.W. and X.W. discussed and edited the manuscript. J.Z. supervised the project. All authors reviewed and gave final approval to the manuscript.

Corresponding authors

Correspondence to Lige Leng or Jie Zhang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Metabolism thanks Inhee Mook-Jung 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.

Additional information

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

Extended data

Extended Data Fig. 1 The expressions of HK1/2/3 in various mouse brain regions and various kinds of neuronal cells.

(a)Western blot analysis of HK1, HK2 and HK3 protein levels in various brain regions of mice. Immunoblots were probed with antibodies against the indicated proteins. Actin served as a loading control. (b) Western blot analysis of HK2 protein levels in skeletal muscle, liver, cortex and hippocampus of wildtype mice. Immunoblots were probed with antibodies against the indicated proteins. Actin served as a loading control. (c, d) Western blot analysis of HK1 and HK3 protein levels in primary microglia treated with Aβ. Immunoblots were probed with antibodies against the indicated proteins. Quantification of proteins levels are showed in (d), n = 4 independent experiments. Actin served as a loading control. (e) Schematic of the major enzymes and corresponding genes of glucose metabolism in primary microglia. (f) Heatmap showing the change in mRNA levels of the indicated glycolysis genes in microglia treated with oligo-Aβ(100 nM, 24 h), as measured via qPCR analysis (n = 4 per group). (g) PLX3397 or control vehicle was given 6-month-old 5xFAD mice at 290 mg/kg for 21 days, the brains were sectioned and stained with HK2 (red) and Iba1 (green). Scale bar: 5 μm. Representative confocal images are shown. (h) Immunofluorescence of HK2 (green) and Iba1 (red) in cortex from 4, 6, 8, 10-month-old 5×FAD mice. Scale bar: 20 μm. Representative confocal images are shown. (i-k) Representative immunofluorescent staining of HK2 (red) and Aβ (green) in the cortex, the hippocampal DG and CA1 areas from 6-month-old 5×FAD mice (i), and CA1 area from indicated aged 5×FAD mice (j). Quantifications of percentage of HK2+ Aβ+/ HK2+ are shown in (k), n = 4 slices from 3 mice. (l) 3D reconstruction after double immunofluorescent staining of HK2 (red) with Aβ (green) in the indicated aged 5×FAD mice. (m) Immunofluorescence of HK2 (green) and CD68 (red) in hippocampus from 4, 6, 8, 10-month-old 5×FAD mice. Scale bar: 20 μm. Representative confocal images are shown in m. n = 3 slices from 3 mice. Data represent mean ± SEM, n.s.: not significant. When compared with control group, *p < 0.05, **p < 0.01, ***p < 0.001, one-way ANOVA with Tukey’s post hoc analysis.

Source data

Extended Data Fig. 2 Lonidamine and 3-BP significantly attenuate amyloid plaque load in AD mice, and increase the protein levels in autophagy pathway without inducing obviously apoptosis, related to Figure 3.

(a) HK activity assay from hippocampus of 6-month-old 5×FAD mice treated with HK2 inhibitors (Lonidamine and 3-BP) and control by intraperitoneal (IP) injection for 7 days. (b) Large scale immunohistochemistry staining of Ab from 6-month-old 5×FAD mice treated with HK2 inhibitors (Lonidamine and 3-BP) and control by intraventricular (ICV) injection. Representative images of brain section (Bregma position) are shown on panel (b), Scale bar: 1mm, n=3 slices from 3 mice. (c) Immunofluorescence of Thioflavine T (blue), Ab (green) and Iba1(red) in cortex from 6-month-old 5×FAD mice and 5×FAD-CcKO mice. Representative images are shown on panel (c), Scale bar: 20μm. n=3 slices from 3 mice. (d) Immunofluorescence of Thioflavine T (blue), Ab (green) and Iba1(red) in cortex from 6-month-old 5×FAD mice, 5×FAD+Lonidamine mice and 5×FAD+3-BP mice. Representative images are shown on panel (d), Scale bar: 20μm. n=3 slices from 3 mice. (e) Double immunofluorescent staining of Ab (Red) and ATG9A (Green) in cortical and hippocampal regions from 6-month-old 5×FAD mice, 5×FAD+Lonidamine mice and 5×FAD+3-BP mice. Representative images are shown on panel (e). Scale bar: 100μm. n=3 slices from 3 mice. (f) Immunofluorescent staining of Ab (green) and C-caspase-3 (red) in hippocampus from the above mice, representative images are presented, n=3 slices from 3 mice. Scale bar:100μm. (g, h) 6-month-old 5xFAD mice and the aged-control littermate WT mice were treated with 3-BP or control by intraperitoneal (IP) injection. The protein levels of Caspase 3, ATG7 and LC3II/LCI in hippocampus were measured by western blotting. Immunoblots were probed with antibodies against the indicated proteins. Quantification of proteins levels are showed in (h), n=3 mice. Actin served as a loading control. Data represent mean±SEM, n.s.: not significant. When compared with control group, *p < 0.05, **p < 0.01, ***p < 0.001, one-way ANOVA with Tukey’s post hoc analysis.

Source data

Extended Data Fig. 3 Lonidamine and 3-BP attenuate neuroinflammation in AD mice and cultured microglia treated with Aβ.

(a) Six-month-old 5xFAD mice and the aged-control littermate WT mice were treated with 3-BP or control by intraperitoneal (IP) injection. The protein level of IL-1β in hippocampus and cortex were measured by western blotting. Immunoblots were probed with antibodies against the indicated proteins. Quantification of proteins levels are showed in (b), n = 3-4 mice. Actin served as a loading control. (d) Immunofluorescence staining of Iba1 in the above mice brain. Quantitation of microglia body diameter and microglia processes total length are showed in (d), n = 8 slices from 3 mice. (e) Real-time PCR in the cortex and hippocampus of 9-month-old 5×FAD mice after giving HK2 inhibition, n = 4 mice. (f) Real-time PCR in the primary microglia after giving HK2 inhibition, n = 4 mice. Data represent mean ± SEM. When compared with control group, *p < 0.05, **p < 0.01, ***p < 0.001. When compared with 5×FAD group, #p < 0.05, ##p < 0.01, ###p < 0.001, one-way ANOVA with Tukey’s post hoc analysis.

Source data

Extended Data Fig. 4 Lonidamine and 3-BP stimulate the migration of microglia and BV2 cells.

(a) Primary microglial cells were plated onto transwell chamber inserts. Following 24 h incubation with solvent, cells were treated with Lonidamine (48 h, 300 μM) or 3-BP (48 h, 30 μM) in the present or absent of Aβ (24 h, 100 nM) as indicated in the figure. The cells were then stained with hematoxylin and eosin, and counted under Nikon inverted microscope. Scale bar, 20 µm. (b) Quantitation of the number of migrated cells, n = 3 independent experiments. (c) BV2 were plated onto transwell chamber inserts. Following 24 h incubation with solvent, cells were treated with Lonidamine (48 h, 300 μM) or 3-BP (48 h, 30 μM) in the present or absent of Aβ (24 h, 100 nM) as indicated in the figure. The cells were then stained with hematoxylin and eosin, and counted under Nikon inverted microscope. Scale bar, 20 µm. (d) Quantitation of the number of migrated BV2 cells, n = 3 independent experiments. (e) f/f or cKO primary microglia in the present or absent of Aβ (24 h, 100 nM) were plated onto transwell chamber inserts. Following 24 h incubation, the cells were then stained with hematoxylin and eosin, and counted under Nikon inverted microscope. Scale bar, 20 µm. (f) Quantitation of the number of migrated primary microglia, n = 3 independent experiments. Data represent mean ± SEM, n.s.: not significant. When compared with control group, *p < 0.05, **p < 0.01, ***p < 0.001. When compared with Aβ group, #p < 0.05, ##p < 0.01, ###p < 0.001, one-way ANOVA with Tukey’s post hoc analysis.

Source data

Extended Data Fig. 5 The volcano plots of DEGs of microglia treated with Aβ or 3-BP.

(a) RNA-Seq based transcriptome analysis of microglia treated with Aβ or lonidamine. Heatmap of differentially expressed genes (DEGs) are shown. (b-d) The DEGs were sub-grouped into three comparations, and the analyses of gene ontology of biological functions for these DEGs were presented: (b) Aβ vs control; (c) lonidamine vs control; (d) Aβ+ Lonidamine vs Aβ. The lipid metabolism pathway was significantly enriched (red). The volcano plots of DEGs are shown in e-j. The analysis was sub-grouped to three comparations: (e) Control vs Aβ; (f) Control vs 3-BP; (g) Aβ vs Aβ + 3-BP; (h) Control vs Aβ; (i) Control vs Lonidamine; (j) Aβ vs Ab+ Lonidamine. Red plots represent upregulated genes (p < 0.05). Green plots represent downregulated genes (p < 0.05). Blue points represent no significant differentially expressed genes (p > 0.05), one-way ANOVA with Tukey’s post hoc analysis.

Extended Data Fig. 6 Targeted metabolomics analysis of primary microglia treated with Aβ(100 nM)±3-BP(30 μM).

(a) Differentially expressed metabolites were identified from primary microglia treated with Aβ(100 nM)±3-BP(30 μM) and related metabolic networks were draw. (b) To allow for inverse modelling of biochemical regulation from metabolomics covariance data, a metabolic reconstruction and pathway reduction from available genome sequences is performed (RECON). The biological variance of the independent biological replicates per cell type is visible, which is further exploited first for the calculation of the Covariance matrix COV and subsequently for the Jacobian matrix JAC using the stochastic Lyapunov matrix Equation (1). (c) The levels of lactate were measured in microglia treated with 3-BP for 48 h in the present of Aβ, n = 4 independent experiments. Data represent mean ± SEM, not significant: fold change (Aβ + 3-BP/Aβ) at.0.8~1.2; significant increase: fold change (Aβ + 3-BP /Aβ) >1.2; significant decrease: fold change (Aβ + 3-BP /Aβ) <0.8, one-way ANOVA with Tukey’s post hoc analysis. Data represent mean ± SEM, *p < 0.05, **p < 0.01, ***p < 0.001, one-way ANOVA with Tukey’s post hoc analysis.

Source data

Extended Data Fig. 7 Single cell characteristics of microglia with high or low expression of hk2.

(a) UMAP projections of the GSE127893 database coloured according to the expression of HK2. (b)Violin plots for the expression of HK2 of the GSE127893 database. (c-g) Heatmap showing clustering analysis of microglia cells, featuring the gene expression in lipid metabolism classified according to the expression of HK2: lipid metabolism (c); homoeostatic genes (d); lysosomal function (e); stage 1 DAM genes (f) and stage 2 DAM genes (g).

Extended Data Fig. 8 HK2 inhibition increases LPL expression in microglia of AD mice brain, and LPL inhibitor blocks the beneficial effects of 3-BP/Lonidamine on microglia.

(a-c) Western blot and Real-time PCR analysis of LPL expression in cortex samples from 9-month-old 5×FAD mice and aged control WT mice treated with HK2 inhibitors or control vehicle. Immunoblots were probed with antibodies against the indicated proteins. Quantification of protein levels are showed in (b), n = 3 mice. Quantification of Lpl mRNA levels were showed in (c), n = 6 mice. (d, e) Immunofluorescence staining of LPL (red) with Iba1(green) or HK2(green) in cortex from 9-month-old 5×FAD mice treated with HK2 inhibitors. Representative confocal images are shown on panel (e), Scale bar:20 μm and 5 μm. Quantitation of LPL fluorescence intensity are showed in (e), n = 6 slices from 3 mice. Data represent mean ± SEM. When compared with control group, *p < 0.05, **p < 0.01, ***p < 0.001. When compared with Aβ group, #p < 0.05, ##p < 0.01, ###p < 0.001, one-way ANOVA with Tukey’s post hoc analysis.

Source data

Extended Data Fig. 9 The inhibitions of PFKFB3, PKM2, and PDH did not promote phagocytosis and ATP production in microglia.

(a) Schematic diagram of inhibitors target enzymes in glycolysis pathway. (b) The ATP production of microglia treated with these inhibitors were measured, n = 3 independent experiments. (c) The ATP production of microglia treated with PFK158 (PFK inhibitor) in different dose and time quantum, n = 3 independent experiments. (d) RT-PCR was performed to test the effect of siRNA-induced mRNA Knockdown of PFKFB3 in primary microglia, n = 3 independent experiments. (e) The ATP production of microglia treated with PFK158 (PFK siRNA). n = 3 independent experiments. Data represent mean ± SEM. When compared with control group, *p < 0.05, **p < 0.01, ***p < 0.001. When compared with Aβ group, #p < 0.05, ##p < 0.01, ###p < 0.001, one-way ANOVA with Tukey’s post hoc analysis.

Source data

Extended Data Fig. 10 HK2 inhibitors decreased NAPDH production, and NAPDH blocked the phagocytosis, ATP production, and LPL expression induced by HK2 inhibition.

NADP+ (a), NADPH (b), and ratio of total NADP+/NADPH (c) in primary microglia after treated with HK2 inhibitors with or without Aβ were measured, n = 3 independent experiments. (d) The flow cytometry of FAM-microspheres uptake experiments was performed to test the phagocytosis of primary microglia treated by HK2 inhibitors (48 h) with or without NADPH (24 h). Values were normalized to the vehicle control, n = 3 independent experiments. (e) The ATP production of microglia treated by HK2 inhibitors (48 h) with or without NADPH (24 h), n = 3 independent experiments. (f) The protein level of LPL in primary microglia were measured by western blotting. Immunoblots were probed with antibodies against the indicated proteins. Quantification of proteins levels are showed in (g), n = 4 independent experiments. Actin served as a loading control. (h) Real-time PCR was performed to test the level of LPL in primary microglia after treating NAPDH, n = 3 independent experiments. (i) LPL-mediated transactivation in BV2 cells treated with NADPH, n = 3 independent experiments. Data represent mean ± SEM. When compared with control group or compared with the specific group indicated, *p < 0.05, **p < 0.01, ***p < 0.001. When compared with Aβ group, #p < 0.05, ##p < 0.01, ###p < 0.001, one-way ANOVA with Tukey’s post hoc analysis.

Source data

Supplementary information

Supplementary Information

Primer sequence list, Supplementary datasheet legends and Figs. 1–5.

Reporting Summary

Supplementary Data 1

Differential expressed gene (DEG) lists in microglia-treated groups: Aβ versus control, 3-BP versus control and Aβ + 3-BP versus Aβ.

Supplementary Data 2

Differential expressed gene (DEG) lists in microglia-treated groups: Aβ versus control, lonidamine versus control and Aβ + lonidamine versus Aβ.

Supplementary Data 3

Fraction contribution in microglia-treated groups in 13C metabolic flux.

Supplementary Data 4

Targeted metabolomics analysis of primary microglia treated with Aβ ± 3-BP.

Source data

Source Data Fig. 1

Unprocessed western blot gels.

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Unprocessed western blot gels.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Unprocessed western blot gels.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Unprocessed western blot gels.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 6

Unprocessed western blot gels.

Source Data Fig. 6

Statistical source data.

Source Data Fig. 7

Unprocessed western blot gels.

Source Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 1

Unprocessed western blot gels.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Unprocessed western blot gels.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Unprocessed western blot gels.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 8

Unprocessed western blot gels.

Source Data Extended Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 9

Statistical source data.

Source Data Extended Data Fig. 10

Unprocessed western blot gels.

Source Data Extended Data Fig. 10

Statistical source data.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Leng, L., Yuan, Z., Pan, R. et al. Microglial hexokinase 2 deficiency increases ATP generation through lipid metabolism leading to β-amyloid clearance. Nat Metab 4, 1287–1305 (2022). https://doi.org/10.1038/s42255-022-00643-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s42255-022-00643-4

This article is cited by

Search

Advanced search

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