Abstract
It is generally thought that under basal conditions, neurons produce ATP mainly through mitochondrial oxidative phosphorylation (OXPHOS), and glycolytic activity only predominates when neurons are activated and need to meet higher energy demands. However, it remains unknown whether there are differences in glucose metabolism between neuronal somata and axon terminals. Here, we demonstrated that neuronal somata perform higher levels of aerobic glycolysis and lower levels of OXPHOS than terminals, both during basal and activated states. We found that the glycolytic enzyme pyruvate kinase 2 (PKM2) is localized predominantly in the somata rather than in the terminals. Deletion of Pkm2 in mice results in a switch from aerobic glycolysis to OXPHOS in neuronal somata, leading to oxidative damage and progressive loss of dopaminergic neurons. Our findings update the conventional view that neurons uniformly use OXPHOS under basal conditions and highlight the important role of somatic aerobic glycolysis in maintaining antioxidant capacity.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
The original mass spectra for TMT experiments have been deposited in the public proteomics repository ProteomeXchange under accession number PXD035391. UniProt FASTA databases (https://www.uniprot.org/taxonomy/10090) were used to search mass spectrometry spectra lists. This paper does not report the original code. The data analyzed for this study are available at https://data.mendeley.com/datasets/x844x4ky8x/2. Source data are provided with this paper. Any additional information required to reanalyze the data reported in this paper is available from the corresponding author.
Code availability
This paper does not report original code.
References
Harris, J. J., Jolivet, R. & Attwell, D. Synaptic energy use and supply. Neuron 75, 762–777 (2012).
Watts, M. E., Pocock, R. & Claudianos, C. Brain energy and oxygen metabolism: emerging role in normal function and disease. Front. Mol. Neurosci. 11, 216 (2018).
Bélanger, M., Allaman, I. & Magistretti, P. J. Brain energy metabolism: focus on astrocyte–neuron metabolic cooperation. Cell Metab. 14, 724–738 (2011).
Magistretti, P. J. & Allaman, I. A cellular perspective on brain energy metabolism and functional imaging. Neuron 86, 883–901 (2015).
Yellen, G. Fueling thought: management of glycolysis and oxidative phosphorylation in neuronal metabolism. J. Cell Biol. 217, 2235–2246 (2018).
Dienel, G. A. Brain glucose metabolism: integration of energetics with function. Physiol. Rev. 99, 949–1045 (2019).
Cheng, X. T., Huang, N. & Sheng, Z. H. Programming axonal mitochondrial maintenance and bioenergetics in neurodegeneration and regeneration. Neuron 110, 1899–1923 (2022).
Zhao, R. Z., Jiang, S., Zhang, L. & Yu, Z. B. Mitochondrial electron transport chain, ROS generation and uncoupling. Int. J. Mol. Med. 44, 3–15 (2019).
Butterfield, D. A. & Halliwell, B. Oxidative stress, dysfunctional glucose metabolism and Alzheimer disease. Nat. Rev. Neurosci. 20, 148–160 (2019).
Dionísio, P. A., Amaral, J. D. & Rodrigues, C. M. P. Oxidative stress and regulated cell death in Parkinson’s disease. Ageing Res. Rev. 67, 101263 (2021).
Herrero-Mendez, A. et al. The bioenergetic and antioxidant status of neurons is controlled by continuous degradation of a key glycolytic enzyme by APC/C-Cdh1. Nat. Cell Biol. 11, 747–752 (2009).
Rodriguez-Rodriguez, P., Fernandez, E. & Bolaños, J. P. Underestimation of the pentose-phosphate pathway in intact primary neurons as revealed by metabolic flux analysis. J. Cereb. Blood Flow Metab. 33, 1843–1845 (2013).
Li, X. et al. Mitochondria-translocated PGK1 functions as a protein kinase to coordinate glycolysis and the TCA cycle in tumorigenesis. Mol. Cell 61, 705–719 (2016).
Rodic, S. & Vincent, M. D. Reactive oxygen species (ROS) are a key determinant of cancer’s metabolic phenotype. Int. J. Cancer 142, 440–448 (2018).
Hsu, P. P. & Sabatini, D. M. Cancer cell metabolism: Warburg and beyond. Cell 134, 703–707 (2008).
Vander Heiden, M. G., Cantley, L. C. & Thompson, C. B. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324, 1029–1033 (2009).
Nelson, D. L., Lehninger, A. L. & Cox, M. M. Lehninger Principles of Biochemistry (Macmillan, 2008).
Craig, A. M., Jareb, M. & Banker, G. Neuronal polarity. Curr. Opin. Neurobiol. 2, 602–606 (1992).
Namba, T. et al. Extracellular and intracellular signaling for neuronal polarity. Physiol. Rev. 95, 995–1024 (2015).
Zhang, J. et al. Measuring energy metabolism in cultured cells, including human pluripotent stem cells and differentiated cells. Nat. Protoc. 7, 1068–1085 (2012).
Lobas, M. A. et al. A genetically encoded single-wavelength sensor for imaging cytosolic and cell surface ATP. Nat. Commun. 10, 711 (2019).
Beier, K. T. et al. Rabies screen reveals GPe control of cocaine-triggered plasticity. Nature 549, 345–350 (2017).
Baeza-Lehnert, F. et al. Non-canonical control of neuronal energy status by the Na+ pump. Cell Metab. 29, 668–680 (2019).
Jang, S. et al. Glycolytic enzymes localize to synapses under energy stress to support synaptic function. Neuron 90, 278–291 (2016).
Díaz-García, C. M. et al. Neuronal stimulation triggers neuronal glycolysis and not lactate uptake. Cell Metab. 26, 361–374 (2017).
Ashrafi, G., Wu, Z., Farrell, R. J. & Ryan, T. A. GLUT4 mobilization supports energetic demands of active synapses. Neuron 93, 606–615 (2017).
Koenig, J. B. et al. Glycolytic inhibitor 2-deoxyglucose prevents cortical hyperexcitability after traumatic brain injury. JCI Insight 5, e126506 (2019).
Rangaraju, V., Calloway, N. & Ryan, T. A. Activity-driven local ATP synthesis is required for synaptic function. Cell 156, 825–835 (2014).
San Martín, A. et al. A genetically encoded FRET lactate sensor and its use to detect the Warburg effect in single cancer cells. PLoS ONE 8, e57712 (2013).
Mächler, P. et al. In vivo evidence for a lactate gradient from astrocytes to neurons. Cell Metab. 23, 94–102 (2016).
Olenchock, B. A. & Vander Heiden, M. G. Pyruvate as a pivot point for oncogene-induced senescence. Cell 153, 1429–1430 (2013).
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).
Christofk, H. R. et al. The M2 splice isoform of pyruvate kinase is important for cancer metabolism and tumour growth. Nature 452, 230–233 (2008).
Israelsen, W. J. & Vander Heiden, M. G. Pyruvate kinase: function, regulation and role in cancer. Semin. Cell Dev. Biol. 43, 43–51 (2015).
David, C. J., Chen, M., Assanah, M., Canoll, P. & Manley, J. L. HnRNP proteins controlled by c-Myc deregulate pyruvate kinase mRNA splicing in cancer. Nature 463, 364–368 (2010).
Dayton, T. L., Jacks, T. & Vander Heiden, M. G. PKM2, cancer metabolism, and the road ahead. EMBO Rep. 17, 1721–1730 (2016).
Luo, W. et al. Pyruvate kinase M2 is a PHD3-stimulated coactivator for hypoxia-inducible factor 1. Cell 145, 732–744 (2011).
Kim, J. W., Tchernyshyov, I., Semenza, G. L. & Dang, C. V. HIF-1-mediated expression of pyruvate dehydrogenase kinase: a metabolic switch required for cellular adaptation to hypoxia. Cell Metab. 3, 177–185 (2006).
Giasson, B. I. et al. Oxidative damage linked to neurodegeneration by selective α-synuclein nitration in synucleinopathy lesions. Science 290, 985–989 (2000).
Nguyen, H. N. et al. LRRK2 mutant iPSC-derived DA neurons demonstrate increased susceptibility to oxidative stress. Cell Stem Cell 8, 267–280 (2011).
Burbulla, L. F. et al. Dopamine oxidation mediates mitochondrial and lysosomal dysfunction in Parkinson’s disease. Science 357, 1255–1261 (2017).
Michel, P. P., Hirsch, E. C. & Hunot, S. Understanding dopaminergic cell death pathways in Parkinson disease. Neuron 90, 675–691 (2016).
Bloem, B. R., Okun, M. S. & Klein, C. Parkinson’s disease. Lancet 397, 2284–2303 (2021).
González-Rodríguez, P. et al. Disruption of mitochondrial complex I induces progressive parkinsonism. Nature 599, 650–656 (2021).
Graham, L. C. et al. Proteomic profiling of neuronal mitochondria reveals modulators of synaptic architecture. Mol. Neurodegener. 12, 77 (2017).
Board, M., Humm, S. & Newsholme, E. A. Maximum activities of key enzymes of glycolysis, glutaminolysis, pentose phosphate pathway and tricarboxylic acid cycle in normal, neoplastic and suppressed cells. Biochem. J. 265, 503–509 (1990).
Anastasiou, D. et al. Inhibition of pyruvate kinase M2 by reactive oxygen species contributes to cellular antioxidant responses. Science 334, 1278–1283 (2011).
Attwell, D. & Laughlin, S. B. An energy budget for signaling in the grey matter of the brain. J. Cereb. Blood Flow Metab. 21, 1133–1145 (2001).
Turrigiano, G. G. & Nelson, S. B. Homeostatic plasticity in the developing nervous system. Nat. Rev. Neurosci. 5, 97–107 (2004).
Greenlund, L. J., Deckwerth, T. L. & Johnson, E. M. Jr Superoxide dismutase delays neuronal apoptosis: a role for reactive oxygen species in programmed neuronal death. Neuron 14, 303–315 (1995).
Bossy-Wetzel, E. et al. Crosstalk between nitric oxide and zinc pathways to neuronal cell death involving mitochondrial dysfunction and p38-activated K+ channels. Neuron 41, 351–365 (2004).
Arnold, P. K. et al. A non-canonical tricarboxylic acid cycle underlies cellular identity. Nature 603, 477–481 (2022).
Allaman, I., Bélanger, M. & Magistretti, P. J. Methylglyoxal, the dark side of glycolysis. Front. Neurosci. 9, 23 (2015).
Zheng, X. et al. Metabolic reprogramming during neuronal differentiation from aerobic glycolysis to neuronal oxidative phosphorylation. eLife 5, e13374 (2016).
Cruz, E., Bessières, B., Magistretti, P. & Alberini, C. M. Differential role of neuronal glucose and PFKFB3 in memory formation during development. Glia 70, 2207–2231 (2022).
Gui, D. Y., Lewis, C. A. & Vander Heiden, M. G. Allosteric regulation of PKM2 allows cellular adaptation to different physiological states. Sci. Signal. 6, pe7 (2013).
Peters, S. J. Regulation of PDH activity and isoform expression: diet and exercise. Biochem. Soc. Trans. 31, 1274–1280 (2003).
Wiśniewski, J. R., Gizak, A. & Rakus, D. Integrating proteomics and enzyme kinetics reveals tissue-specific types of the glycolytic and gluconeogenic pathways. J. Proteome Res. 14, 3263–3273 (2015).
Mishra, D. & Banerjee, D. Lactate dehydrogenases as metabolic links between tumor and stroma in the tumor microenvironment. Cancers 11, 750 (2019).
Rodríguez-Saavedra, C. et al. Moonlighting proteins: the case of the hexokinases. Front. Mol. Biosci. 8, 701975 (2021).
Lerchundi, R., Huang, N. & Rose, C. R. Quantitative imaging of changes in astrocytic and neuronal adenosine triphosphate using two different variants of ATeam. Front. Cell. Neurosci. 14, 80 (2020).
Brewer, G. J. & Torricelli, J. R. Isolation and culture of adult neurons and neurospheres. Nat. Protoc. 2, 1490–1498 (2007).
Gray, E. G. & Whittaker, V. P. The isolation of nerve endings from brain: an electron-microscopic study of cell fragments derived by homogenization and centrifugation. J. Anat. 96, 79–88 (1962).
Choi, S. W., Gerencser, A. A. & Nicholls, D. G. Bioenergetic analysis of isolated cerebrocortical nerve terminals on a microgram scale: spare respiratory capacity and stochastic mitochondrial failure. J. Neurochem. 109, 1179–1191 (2009).
Wittig, I., Braun, H. P. & Schägger, H. Blue native PAGE. Nat. Protoc. 1, 418–428 (2006).
Pnevmatikakis, E. A. & Giovannucci, A. NoRMCorre: an online algorithm for piecewise rigid motion correction of calcium imaging data. J. Neurosci. Methods 291, 83–94 (2017).
Soltanian-Zadeh, S., Sahingur, K., Blau, S., Gong, Y. & Farsiu, S. Fast and robust active neuron segmentation in two-photon calcium imaging using spatiotemporal deep learning. Proc. Natl Acad. Sci. USA 116, 8554–8563 (2019).
Kerlin, A. M., Andermann, M. L., Berezovskii, V. K. & Reid, R. C. Broadly tuned response properties of diverse inhibitory neuron subtypes in mouse visual cortex. Neuron 67, 858–871 (2010).
Chen, T. W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).
Pinto, L. & Dan, Y. Cell-type-specific activity in prefrontal cortex during goal-directed behavior. Neuron 87, 437–450 (2015).
Mende, M. et al. Sensory-derived glutamate regulates presynaptic inhibitory terminals in mouse spinal cord. Neuron 90, 1189–1202 (2016).
Liang, B. et al. Distinct and dynamic ON and OFF neural ensembles in the prefrontal cortex code social exploration. Neuron 100, 700–714 (2018).
Acknowledgements
We thank H. Zhang, Q. Zhu, L. Gong and Y. Hu for experimental instrument management from the Experiment Center for Science and Technology, Nanjing University of Chinese Medicine. We thank the Department of Analytical and Testing Centre, Nanjing Medical University, for experimental instrument management. We thank PKU-Nanjing Joint Institute of Translational Medicine, Brain Observatory (http://www.raygenitm.com/), for providing the miniature two-photon microscopy platform. We would also like to thank the Nanjing Brain Observatory for assistance in miniaturized two-photon surgery, data collection and data analysis. We thank Lu Ming Biotech for providing AFADESI spatial-resolved metabolomics detection and bioinformatics analysis. We thank Biotree Biomedical Technology for providing TMT-based quantitative proteomics analysis. This work was supported by funds from the National Key R&D Program of China (2021ZD0202900-1 to G.H.) and the National Natural Science Foundation of China (81991523 to G.H. and 82073823 to Y.W.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Author information
Authors and Affiliations
Contributions
G.H. and Y.W. conceived the study and designed the experiments. Y.W., Q.M., Qian Zhang, S.M., M. Li and M.M. contributed to data collection and analysis. X. Xu and Yu Fan contributed to electrophysiology. K.W. contributed to viral injection. X.Z. contributed to genotyping. Qingyu Zhang and Y. Fang contributed to Seahorse experiments. X. Xia performed proteomics processing and analysis. J.D., Yi Fan and M. Lu discussed and commented on the manuscript. G.H. and Y.W. obtained funding. Y.W. and Q.M. wrote the paper. G.H. supervised this research and edited the paper.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Neuroscience thanks Luis Barros, Juan Bolaños and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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 Identification of isolated neuronal somata and synaptosomes.
a, Typical electron microscopy image of synaptosomes, containing mitochondria (m) and synaptic vesicles (sv). b, Quantitative analysis of the supernatant glutamate (Glu, p < 0.0001) and γ-aminobutyric acid (GABA, p = 0.0144) after the synaptosomes were treated with 30 mM KCl for 5 min (n = 6 biological replicates). c, AO/PI staining of isolated neuronal somata. Left, bright field of isolated neuronal somata. Right, green arrows indicate living somata; orange arrows indicate apoptotic somata; red arrows indicate necrotic somata. d, Statistical analysis of apoptotic and necrotic neuronal somata (n = 6 biological replicates). e, Capillary western blot analysis of synaptic markers (VAMP and synaptophysin (SYP)), nuclear component (Histone), and glial cell markers (GFAP and Iba1) in the whole brain homogenates (H), somata (S) or nerve terminals (T) (n = 3 independent experiments). Samples were normalised to the total protein indicated by blue circles. Scale bars, 200 nm (a) and 200 μm (c). Data are presented as mean ± SEM and *p < 0.05 and ***p < 0.001. Two-way ANOVA followed with Sidak’s multiple comparisons test (b).
Extended Data Fig. 2 Different glucose metabolism between the somata and nerve terminals in resting neurons.
a, The iATPSnFR fluorescence was measured at 0.03, 0.1, 0.3, 1 and 10 mM ATP in an ex vivo brain slice (n = 37 from 3 mice). b, Photograph of the miniature two-photon imaging system. The cranial window embedded with a dosing hose in the red circle. The miniature two-photon microscope in the cyan ellipse. The micro-syringe pump in the magenta rectangle. c-e. Quantification and comparison of calcium signal properties in the SSp: frequency, p < 0.0001 (c), peak amplitude (d), and peak duration, p < 0.0001 (e) after injected with 100 μM D-2-Amino-5-phosphonovaleric acid (APV) and 50 μM 6-cyano-7-Nitroquinoxaline-2,3-dione (CNQX) (n = 172 neurons from 3 mice). f-h, Oligomycin A and 2-DG were applied to compare OXPHOS and glycolytic contributions to the ATP level between the somata and nerve terminals in resting neurons. 100 μM APV and 50 μM CNQX were injected into the SSp at 0 min. 20 μM Oligomycin A was injected into the SSp at 5 min and 10 mM 2-DG was injected into the SSp at 30 min until the end of imaging at 60 min. Fluorescence image of the SSp under a 1030-nm channel to locate nerve terminals (red) and 920-nm channel to record the iATPSnFR fluorescence in somata (cyan) on the ipsilateral side of the virus injection site and nerve terminals (magenta) on the contralateral side at 0 min, 30 min, and 60 min (f). Time-lapse interactive confidence lines of the iATPSnFR fluorescence with a 99% confidence interval (g). The OXPHOS index (OXPHOS/(OXPHOS + glycolysis)) in somata (n = 73 from 3 mice) and nerve terminals (n = 82 from 3 mice), p < 0.0001 (h). For the boxplots, the centre line indicates the median, the box limits indicate the first and third quartiles, and the whiskers indicate the data range. For interactive confidence interval line plots, the centre for the error bands indicates the mean. Scale bars, 50 μm (f) and 10 min (g). Data are presented as mean ± SEM and *p < 0.05 and ***p < 0.001. Student’s two-tailed unpaired t-test (c-e and h).
Extended Data Fig. 3 Different glucose metabolism between the somata and nerve terminals in firing neurons.
a, Left, representative images show a patched cell at 4× magnification (top) and 40× magnification (bottom) differential interference contrast microscopy in a mouse SSp brain slice. Right, whole-cell recordings showing continuous firing of the pyramidal neuron in the SSp following 100 pA current injection (n = 15 independent experiments). b, The somatic iATPSnFR fluorescence of neurons in the SSp brain slice was measured before and after a 5 s injection of 100 pA current. The brain slice was treated separately with aCSF, aCSF containing 10 μM Oligomycin A (p = 0.0042), or glucose-free aCSF containing 10 μM Oligomycin A and 3.5 mM 2-DG (p < 0.0001), n = 15 from 3 mice. c, The nerve terminal calcium signals in the SSp brain slice evoked by a 30 s burst of 10 Hz field stimulation. Synaptophysin-mCherry was expressed to locate nerve terminals. The horizontal line represents the duration of the 30 s field stimulation (n = 28 from 3 mice). d, The terminal iATPSnFR fluorescence of neurons in the SSp brain slice was measured before and after a 30 s burst of 10 Hz field stimulation. The brain slice was treated separately with aCSF, aCSF containing 10 μM Oligomycin A (p < 0.0001), or glucose-free aCSF containing 10 μM Oligomycin A and 3.5 mM 2-DG (p < 0.0001), n = 55 from 3 mice. e, The OXPHOS index (OXPHOS/(OXPHOS + glycolysis)) in somata (n = 15 from 3 mice) and nerve terminals (n = 55 from 3 mice), p < 0.0001. For the boxplots, the centre line indicates the median, the box limits indicate the first and third quartiles, and the whiskers indicate the data range. Data are presented as mean ± SEM and **p < 0.01 and ***p < 0.001. Student’s two-tailed unpaired t-test (b, d and e).
Extended Data Fig. 4 Ex vivo somata perform higher aerobic glycolysis and lower OXPHOS compared to the terminals.
a-f, Oligomycin A and 2-DG were applied to compare OXPHOS and glycolytic contributions to the ATP level between the somata and nerve terminals. The ex vivo brain slices were treated with 10 μM Oligomycin A at 1.5 min and 3.5 mM 2-DG at 9 min until the end of imaging at 18 min. Fluorescence image of the SSp brain slices expressed with synaptophysin-mCherry to locate nerve terminals (red) and iATPSnFR to record the ATP level in somata (cyan) on the ipsilateral side of the virus injection site and nerve terminals (magenta) on the contralateral side at 0 min, 9 min, and 18 min (a). Time-lapse interactive confidence lines of the iATPSnFR fluorescence in the SSp with a 99% confidence interval (b). The OXPHOS index (OXPHOS/(OXPHOS + glycolysis)) in somata (n = 21 from 3 mice) and nerve terminals (n = 17 from 3 mice), p < 0.0001 (c). Fluorescence image of the hippocampus brain slices expressed with iATPSnFR to record the ATP level in the somata of granule neurons (cyan) and pyramidal neurons (magenta) at 0 min, 9 min, and 18 min (d). Time-lapse interactive confidence lines of the iATPSnFR fluorescence in the hippocampus with a 99% confidence interval (e). The OXPHOS index (OXPHOS/(OXPHOS + glycolysis)) in the somata of granule neurons (n = 42 from 3 mice) and pyramidal neurons (n = 37 from 3 mice), p < 0.0001 (f). For the boxplots, the centre line indicates the median, the box limits indicate the first and third quartiles, and the whiskers indicate the data range. For interactive confidence interval line plots, the centre for the error bands indicates the mean. Scale bars, 50 μm (a and d) and 3 min (b and e). ***p < 0.001. Student’s two-tailed unpaired t-test (c and f).
Extended Data Fig. 5 The lactate level in the somata was lower than that in the terminals.
a, The ratio between mTFP and Venus fluorescence (at 430 nm excitation) was measured at 0.001, 0.01, 0.1, 1 and 10 mM lactate in an ex vivo brain slice (n = 28 from 3 mice). b, Images of mTFP (cyan) and Venus (magenta) fluorescence and the corresponding mTFP/Venus ratio (colour-coded, scale on the right) in the SSp brain slices. Synaptophysin-mCherry was expressed to locate nerve terminals (red). c, Quantification of the mTFP/Venus ratio in the somata (n = 58 from 3 mice) and nerve terminals (n = 88 from 3 mice), p < 0.0001. For the boxplots, the centre line indicates the median, the box limits indicate the first and third quartiles, and the whiskers indicate the data range. Scale bars, 50 μm. Data are presented as mean ± SEM and ***p < 0.001. Student’s two-tailed unpaired t-test (c).
Extended Data Fig. 6 PKM2 express higher in isolated neuronal somata than synaptosomes.
a, Capillary western blot analysis of PKM1 and PKM2 in primary neurons expressed with 3×flag-tagged PKM1 or PKM2. The samples are normalised to the total protein indicated by blue circles (n = 3 independent experiments). b, Capillary western blot analysis of PKM1 and PKM2 in the somata (S) and nerve terminals (T). The samples are normalised to the total protein indicated by blue circles. c, Statistical analysis of protein band intensity normalised to the total protein in somata and nerve terminals (n = 3 biological replicates). d, Capillary western blot analysis PKM2 in the cytoplasm and nucleus of isolated neuronal somata. The samples are normalised to the total protein indicated by blue circles. e, Statistical analysis of protein band intensity normalised to the total protein in the cytoplasm and nucleus (n = 3 biological replicates, p < 0.0001). f, The PKM2 dimer and tetramer formation in isolated neuronal somata was analysed by blue native page. g, Statistical analysis of the PKM2 dimer and tetramer band intensity (n = 3 biological replicates, p = 0.0011). Data are presented as mean ± SEM and **p < 0.01 and ***p < 0.001. Two-way ANOVA followed with Sidak’s multiple comparisons test (c) or Student’s two-tailed unpaired t-test (e and g).
Extended Data Fig. 7 PKM2 expression is higher in the neuronal somata than that in the terminals.
a-h, Distribution of PKM1 (yellow) and PKM2 (red) in the somata and nerve terminals in the substantia nigra (a), locus ceruleus (c), diagonal band nucleus (e) and cerebellum (g) of 2-month-old C57BL/6 J mice. The somata were labelled with the tyrosine hydroxylase (TH) (a and c), choline acetyltransferase (CHAT) (e) and calbindin (CAL) (g) antibody (green). The terminals were labelled with the synaptophysin (SYP) antibody (blue). Nuclei were labelled with DAPI (cyan). Quantification of PKM1 and PKM2 (p < 0.0001) fluorescence intensity in the somata (n = 259) and nerve terminals (n = 339) in the substantia nigra (b). Quantification of PKM1 (p < 0.0001) and PKM2 (p < 0.0001) fluorescence intensity in the somata (n = 176) and nerve terminals (n = 213) in the locus ceruleus (d). Quantification of PKM1 and PKM2 (p < 0.0001) fluorescence intensity in the somata (n = 119) and nerve terminals (n = 162) in the diagonal band nucleus (f). Quantification of PKM1 and PKM2 (p < 0.0001) fluorescence intensity in the somata (148) and nerve terminals (207) in the cerebellum (h). For the boxplots, the centre line indicates the median, the box limits indicate the first and third quartiles, and the whiskers indicate the data range. Scale bars, 20 μm. ***p < 0.001. Two-way ANOVA followed with Sidak’s multiple comparisons test (b, d, f and h).
Extended Data Fig. 8 Efficiency of PKM2 deletion in the SSp and SNc.
a, Upper, Schematic diagram of injection site (modified from Allen Mouse Brain Atlas, online version 1, 2008). Middle, an image showing expression of AAV-hSyn-Cre in NSE-positive neurons. Lower, quantification showing Cre-expressing NSE-positive cells comprise 82% of the total NSE-positive neurons in the SSp (n = 8 mice). b-g, RNAscope analysis of pkm1 (f) and pkm2 (b) mRNA expression or PKM2 immunofluorescence signals (d) in the somata of cortical neurons labelled with the neuron-specific enolase (NSE) antibody in the SSp of Pkm2flox/flox mice injected with AAV-hSyn-Cre unilaterally (right) for one month. The number of pkm2 mRNA spots per soma in the cortical neurons of the SSp with (n = 212 from 3 mice) or without (n = 239 from 3 mice) injection of AAV-hSyn-Cre, p < 0.0001 (c). Quantification of somatic PKM2 fluorescence intensity in the cortical neurons of the SSp with (n = 220 from 3 mice) or without (193 from 3 mice) injection of AAV-hSyn-Cre, p < 0.0001 (e). The number of pkm1 mRNA spots per soma in the cortical neurons of the SSp with (n = 205 from 3 mice) or without (n = 197 from 3 mice) injection of AAV-hSyn-Cre (g). h, Upper, Schematic diagram of injection site (modified from Allen Mouse Brain Atlas, online version 1, 2008). Middle, an image showing expression of AAV-TH-Cre in TH-positive neurons. Lower, quantification showing Cre-expressing TH-positive cells comprise 75% of the total TH-positive neurons in the SNc (n = 8 mice). i-n, RNAscope analysis of pkm1 (m) and pkm2 (i) mRNA expression or PKM2 immunofluorescence signals (k) in the somata of dopaminergic neurons labelled with the tyrosine hydroxylase (TH) antibody in the SNc of Pkm2flox/flox mice injected with AAV-TH-Cre unilaterally (right) for one month. The number of pkm2 mRNA spots per soma in the dopaminergic neurons of the SNc with (n = 218 from 3 mice) or without (n = 205 from 3 mice) injection of AAV-TH-Cre, p < 0.0001 (j). Quantification of somatic PKM2 fluorescence intensity in the dopaminergic neurons of the SNc with (n = 193 from 3 mice) or without (n = 224 from 3 mice) injection of AAV-TH-Cre, p < 0.0001 (l). The number of pkm1 mRNA spots per soma in the dopaminergic neurons of the SNc with (n = 185 from 3 mice) or without (n = 211 from 3 mice) injection of AAV-TH-Cre (n). For the boxplots, the centre line indicates the median, the box limits indicate the first and third quartiles, and the whiskers indicate the data range. Scale bars, 20 μm. Data are presented as mean ± SEM and ***p < 0.001. Student’s two-tailed unpaired t-test (c, e, g, j, l and n).
Extended Data Fig. 9 PKM2 deletion results in the switch from aerobic glycolysis to oxidative phosphorylation.
a-f, Oligomycin A and 2-DG were applied to compare OXPHOS and glycolytic contributions to the ATP level after PKM2 deletion. The ex vivo brain slices were treated with 10 μM Oligomycin A at 1.5 min and 3.5 mM 2-DG at 9 min until the end of imaging at 18 min. Fluorescence image of the SSp brain slices expressed with iATPSnFR to record the ATP level at 0 min, 9 min, and 18 min (a). Time-lapse interactive confidence lines of the iATPSnFR fluorescence in the SSp with a 99% confidence interval (b). The somatic OXPHOS index (OXPHOS/(OXPHOS + glycolysis)) with (n = 26 somata from 3 mice) or without (n = 23 somata from 3 mice) injection of AAV-hSyn-Cre in the SSp, p < 0.0001 (c). Fluorescence image of the SNc brain slices expressed with iATPSnFR to record the ATP level at 0 min, 9 min, and 18 min (d). Time-lapse interactive confidence lines of the iATPSnFR fluorescence in the SNc with a 99% confidence interval (e). The somatic OXPHOS index (OXPHOS/(OXPHOS + glycolysis)) with (n = 22 somata from 3 mice) or without (n = 20 somata from 3 mice) injection of AAV-TH-Cre in the SNc, p < 0.0001 (f). For the boxplots, the centre line indicates the median, the box limits indicate the first and third quartiles, and the whiskers indicate the data range. For interactive confidence interval line plots, the centre for the error bands indicates the mean. Scale bars, 50 μm (a and d) and 3 min (b and e). ***p < 0.001. Student’s two-tailed unpaired t-test (c and f).
Extended Data Fig. 10 PKM2 deletion leads to oxidative damage to cortical neurons.
a-d, 4-HNE (a) and γ-H2AX (c) signals (magenta) in cortical neurons labelled with the neuron-specific enolase (NSE) antibody (cyan) in the SSp of Pkm2flox/flox mice injected with AAV-hSyn-Cre unilaterally (right) for one month. Quantification of 4-HNE fluorescence intensity in cortical neurons of the SSp with (n = 179 from 3 mice) or without (n = 157 from 3 mice) injection of AAV-hSyn-Cre, p < 0.0001 (b). Quantification of γ-H2AX fluorescence intensity in cortical neurons of the SSp with (n = 186 from 3 mice) or without (n = 161 from 3 mice) injection of AAV-hSyn-Cre, p < 0.0001 (d). For the boxplots, the centre line indicates the median, the box limits indicate the first and third quartiles, and the whiskers indicate the data range. Scale bars, 20 μm. ***p < 0.001. Student’s two-tailed unpaired t-test (b and d).
Supplementary information
Supplementary Video 1
Left, time-lapse imaging of the SSp under a 920-nm channel to record ATP levels (cyan) and a 1,030-nm channel to record calcium activity (magenta) in an awake mouse. Right, ATP and calcium traces from five somata of the SSp.
Supplementary Video 2
Left, time-lapse imaging of the SSp under a 920-nm channel to record ATP levels in somata and nerve terminals with 20 μM oligomycin A injected into the SSp at 5 min and 10 mM and 2-DG injected into the SSp at 30 min until the end of imaging at 60 min. Right, ATP traces from three somata and terminals of the SSp.
Supplementary Video 3
Left, time-lapse imaging of the SSp under a 920-nm channel to record ATP levels in the somata of Pkm2fl/fl mice with or without injection of AAV-hSyn-Cre in the SSp. Oligomycin A (20 μM) was injected into the SSp at 5 min and 10 mM, and 2-DG was injected into the SSp at 30 min until the end of imaging at 60 min. Right, ATP traces from three somata of Pkm2fl/fl mice with or without injection of AAV-hSyn-Cre in the SSp.
Source data
Source Data Fig. 1
Statistical source data.
Source Data Fig. 2
Statistical source data.
Source Data Fig. 3
Statistical source data.
Source Data Fig. 4
Statistical source data.
Source Data Fig. 5
Statistical source data.
Source Data Extended Data Fig. 1
Statistical source data.
Source Data Extended Data Fig. 1
Unprocessed western blots.
Source Data Extended Data Fig. 2
Statistical source data.
Source Data Extended Data Fig. 3
Statistical source data.
Source Data Extended Data Fig. 4
Statistical source data.
Source Data Extended Data Fig. 5
Statistical source data.
Source Data Extended Data Fig. 6
Statistical source data.
Source Data Extended Data Fig. 6
Unprocessed western blots.
Source Data Extended Data Fig. 7
Statistical source data.
Source Data Extended Data Fig. 8
Statistical source data.
Source Data Extended Data Fig. 9
Statistical source data.
Source Data Extended Data Fig. 10
Statistical source data.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) 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.
About this article
Cite this article
Wei, Y., Miao, Q., Zhang, Q. et al. Aerobic glycolysis is the predominant means of glucose metabolism in neuronal somata, which protects against oxidative damage. Nat Neurosci 26, 2081–2089 (2023). https://doi.org/10.1038/s41593-023-01476-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41593-023-01476-4
This article is cited by
-
ECHDC2 inhibits the proliferation of gastric cancer cells by binding with NEDD4 to degrade MCCC2 and reduce aerobic glycolysis
Molecular Medicine (2024)
-
Neuronal LAMP2A-mediated reduction of adenylyl cyclases induces acute neurodegenerative responses and neuroinflammation after ischemic stroke
Cell Death & Differentiation (2024)
-
Weak neuronal glycolysis sustains cognition and organismal fitness
Nature Metabolism (2024)
-
A neuronal subcompartment view of ATP production
Nature Reviews Neuroscience (2024)
-
Neuronal hypoglycolysis sustains body health
Nature Metabolism (2024)