Prediabetes and Alzheimer’s disease both increase in prevalence with age. The former is a risk factor for the latter, but a mechanistic linkage between them remains elusive. We show that prediabetic serum hyperinsulinemia is reflected in the cerebrospinal fluid and that this chronically elevated insulin renders neurons resistant to insulin. This leads to abnormal electrophysiological activity and other defects. In addition, neuronal insulin resistance reduces hexokinase 2, thus impairing glycolysis. This hampers the ubiquitination and degradation of p35, favoring its cleavage to p25, which hyperactivates CDK5 and interferes with the GSK3β-induced degradation of β-catenin. CDK5 contributes to neuronal cell death while β-catenin enters the neuronal nucleus and re-activates the cell cycle machinery. Unable to successfully divide, the neuron instead enters a senescent-like state. These findings offer a direct connection between peripheral hyperinsulinemia, as found in prediabetes, age-related neurodegeneration and cognitive decline. The implications for neurodegenerative conditions such as Alzheimer’s disease are described.
Subscribe to Journal
Get full journal access for 1 year
only $18.75 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
The data that support the findings of this study are available from the corresponding authors upon reasonable request.
Global Report on Diabetes (World Health Organization, 2016).
Gjedde, A. & Marrett, S. Glycolysis in neurons, not astrocytes, delays oxidative metabolism of human visual cortex during sustained checkerboard stimulation in vivo. J. Cereb. Blood Flow Metab. 21, 1384–1392 (2001).
Nehlig, A., Wittendorp-Rechenmann, E. & Lam, C. D. Selective uptake of [14C]2-deoxyglucose by neurons and astrocytes: high-resolution microautoradiographic imaging by cellular 14C-trajectography combined with immunohistochemistry. J. Cereb. Blood Flow Metab. 24, 1004–1014 (2004).
Liu, C. C. et al. Neuronal LRP1 regulates glucose metabolism and insulin signaling in the brain. J. Neurosci. 35, 5851–5859 (2015).
Bingham, E. M. et al. The role of insulin in human brain glucose metabolism: an 18fluoro-deoxyglucose positron emission tomography study. Diabetes 51, 3384–3390 (2002).
Lutski, M., Weinstein, G., Goldbourt, U. & Tanne, D. Insulin resistance and future cognitive performance and cognitive decline in elderly patients with cardiovascular disease. J. Alzheimers Dis. 57, 633–643 (2017).
Kim, B. & Feldman, E. L. Insulin resistance as a key link for the increased risk of cognitive impairment in the metabolic syndrome. Exp. Mol. Med. 47, e149 (2015).
Young, S. E., Mainous, A. G. 3rd & Carnemolla, M. Hyperinsulinemia and cognitive decline in a middle-aged cohort. Diabetes Care 29, 2688–2693 (2006).
Heni, M., Kullmann, S., Preissl, H., Fritsche, A. & Haring, H. U. Impaired insulin action in the human brain: causes and metabolic consequences. Nat. Rev. Endocrinol. 11, 701–711 (2015).
Hirvonen, J. et al. Effects of insulin on brain glucose metabolism in impaired glucose tolerance. Diabetes 60, 443–447 (2011).
Kim, B., McLean, L. L., Philip, S. S. & Feldman, E. L. Hyperinsulinemia induces insulin resistance in dorsal root ganglion neurons. Endocrinology 152, 3638–3647 (2011).
Ott, A. et al. Diabetes mellitus and the risk of dementia: the Rotterdam study. Neurology 53, 1937–1942 (1999).
Shanik, M. H. et al. Insulin resistance and hyperinsulinemia: is hyperinsulinemia the cart or the horse? Diabetes Care 31, S262–S268 (2008).
van Houten, M., Posner, B. I., Kopriwa, B. M. & Brawer, J. R. Insulin-binding sites in the rat brain: in vivo localization to the circumventricular organs by quantitative radioautography. Endocrinology 105, 666–673 (1979).
Steffens, A. B., Scheurink, A. J., Porte, D. Jr. & Woods, S. C. Penetration of peripheral glucose and insulin into cerebrospinal fluid in rats. Am. J. Physiol. 255, R200–R204 (1988).
Begg, D. P. et al. Insulin detemir is transported from blood to cerebrospinal fluid and has prolonged central anorectic action relative to NPH insulin. Diabetes 64, 2457–2466 (2015).
Euser, S. M. et al. A prospective analysis of elevated fasting glucose levels and cognitive function in older people: results from PROSPER and the Rotterdam study. Diabetes 59, 1601–1607 (2010).
Hilfiker, S. et al. Synapsins as regulators of neurotransmitter release. Philos. Trans. R. Soc. Lond. B Biol. Sci. 354, 269–279 (1999).
Lalioti, V. et al. The atypical kinase Cdk5 is activated by insulin, regulates the association between GLUT4 and E-Syt1, and modulates glucose transport in 3T3-L1 adipocytes. Proc. Natl Acad. Sci. USA 106, 4249–4253 (2009).
Nohara, A., Okada, S., Ohshima, K., Pessin, J. E. & Mori, M. Cyclin-dependent kinase-5 is a key molecule in tumor necrosis factor-alpha-induced insulin resistance. J. Biol. Chem. 286, 33457–33465 (2011).
Sakamaki, J. et al. Role of the SIK2–p35–PJA2 complex in pancreatic beta-cell functional compensation. Nat. Cell Biol. 16, 234–244 (2014).
Fischer, A., Sananbenesi, F., Pang, P. T., Lu, B. & Tsai, L. H. Opposing roles of transient and prolonged expression of p25 in synaptic plasticity and hippocampus-dependent memory. Neuron 48, 825–838 (2005).
Chow, H. M. et al. CDK5 activator protein p25 preferentially binds and activates GSK3beta. Proc. Natl Acad. Sci. USA 111, E4887–E4895 (2014).
Scherf, M., Klingenhoff, A. & Werner, T. Highly specific localization of promoter regions in large genomic sequences by PromoterInspector: a novel context analysis approach. J. Mol. Biol. 297, 599–606 (2000).
Sans, C. L., Satterwhite, D. J., Stoltzman, C. A., Breen, K. T. & Ayer, D. E. MondoA-Mlx heterodimers are candidate sensors of cellular energy status: mitochondrial localization and direct regulation of glycolysis. Mol. Cell. Biol. 26, 4863–4871 (2006).
Palmer, A. K. et al. Cellular senescence in type 2 diabetes: a therapeutic opportunity. Diabetes 64, 2289–2298 (2015).
Liu, B., Xu, H., Paton, J. F. & Kasparov, S. Cell- and region-specific miR30-based gene knock-down with temporal control in the rat brain. BMC Mol. Biol. 11, 93 (2010).
Adams, P. D. & Enders, G. H. Wnt-signaling and senescence: a tug of war in early neoplasia? Cancer Biol. Ther. 7, 1706–1711 (2008).
Fraser, E. et al. Identification of the Axin and Frat binding region of glycogen synthase kinase-3. J. Biol. Chem. 277, 2176–2185 (2002).
Weber, J. D., Taylor, L. J., Roussel, M. F., Sherr, C. J. & Bar-Sagi, D. Nucleolar Arf sequesters Mdm2 and activates p53. Nat. Cell Biol. 1, 20–26 (1999).
Bernardi, R. et al. PML regulates p53 stability by sequestering Mdm2 to the nucleolus. Nat. Cell Biol. 6, 665–672 (2004).
Lovshin, J. A. & Drucker, D. J. Incretin-based therapies for type 2 diabetes mellitus. Nat. Rev. Endocrinol. 5, 262–269 (2009).
Chen, L. N. et al. Liraglutide ameliorates glycometabolism and insulin resistance through the upregulation of GLUT4 in diabetic KKAy mice. Int. J. Mol. Med. 32, 892–900 (2013).
Hamilton, A. & Holscher, C. Receptors for the incretin glucagon-like peptide-1 are expressed on neurons in the central nervous system. Neuroreport 20, 1161–1166 (2009).
Hawley, S. A., Gadalla, A. E., Olsen, G. S. & Hardie, D. G. The antidiabetic drug metformin activates the AMP-activated protein kinase cascade via an adenine nucleotide-independent mechanism. Diabetes 51, 2420–2425 (2002).
Salani, B. et al. Metformin impairs glucose consumption and survival in Calu-1 cells by direct inhibition of hexokinase-II. Sci. Rep. 3, 2070 (2013).
Templeman, N. M. et al. Reduced circulating insulin enhances insulin sensitivity in old mice and extends lifespan. Cell Rep. 20, 451–463 (2017).
Perez-Leighton, C. E., Boland, K., Billington, C. J. & Kotz, C. M. High and low activity rats: elevated intrinsic physical activity drives resistance to diet-induced obesity in non-bred rats. Obesity 21, 353–360 (2013).
Hayflick, L. The limited in vitro lifetime of human diploid cell strains. Exp. Cell Res. 37, 614–636 (1965).
Lee, B. Y. et al. Senescence-associated beta-galactosidase is lysosomal beta-galactosidase. Aging Cell 5, 187–195 (2006).
Musi, N. et al. Tau protein aggregation is associated with cellular senescence in the brain. Aging Cell 17, e12840 (2018).
Cho, J. H. & Johnson, G. V. Glycogen synthase kinase 3beta phosphorylates tau at both primed and unprimed sites. Differential impact on microtubule binding. J. Biol. Chem. 278, 187–193 (2003).
Yang, Y. & Herrup, K. Loss of neuronal cell cycle control in ataxia-telangiectasia: a unified disease mechanism. J. Neurosci. 25, 2522–2529 (2005).
Sapieha, P. & Mallette, F. A. Cellular senescence in postmitotic cells: beyond growth arrest. Trends Cell Biol. 28, 595–607 (2018).
Campisi, J. Aging, cellular senescence, and cancer. Annu. Rev. Physiol. 75, 685–705 (2013).
Liu, L. & Duff, K. A technique for serial collection of cerebrospinal fluid from the cisterna magna in mouse. J. Vis. Exp. https://doi.org/10.3791/960 (2008).
Chow, H.-C. et al. ATM is activated by ATP depletion and modulates mitochondrial function through NRF1. J. Cell Biol. 218, 909–928 (2019).
Kim, J. Y., Grunke, S. D., Levites, Y., Golde, T. E. & Jankowsky, J. L. Intracerebroventricular viral injection of the neonatal mouse brain for persistent and widespread neuronal transduction. J. Vis. Exp. https://doi.org/10.3791/51863 (2014).
Christensen, D. Z., Thomsen, M. S. & Mikkelsen, J. D. Reduced basal and novelty-induced levels of activity-regulated cytoskeleton associated protein (Arc) and c-Fos mRNA in the cerebral cortex and hippocampus of APPswe/PS1DeltaE9 transgenic mice. Neurochem. Int. 63, 54–60 (2013).
Mayer, J. et al. Reduced adolescent-age spatial learning ability associated with elevated juvenile-age superoxide levels in complex I mouse mutants. PLoS One 10, e0123863 (2015).
Seibenhener, M. L. & Wooten, M. C. Use of the open field maze to measure locomotor and anxiety-like behavior in mice. J. Vis. Exp. https://doi.org/10.3791/52434 (2015).
Li, J. et al. Nuclear accumulation of HDAC4 in ATM deficiency promotes neurodegeneration in ataxia telangiectasia. Nat. Med. 18, 783–790 (2012).
Can, A. et al. The mouse forced swim test. J. Vis. Exp. https://doi.org/10.3791/3638 (2012).
Wang, J. H. et al. Gain and fidelity of transmission patterns at cortical excitatory unitary synapses improve spike encoding. J. Cell Sci. 121, 2951–2960 (2008).
Zucker, R. S. & Regehr, W. G. Short-term synaptic plasticity. Annu. Rev. Physiol. 64, 355–405 (2002).
Wang, J. H. & Kelly, P. Calcium-calmodulin signalling pathway up-regulates glutamatergic synaptic function in non-pyramidal, fast spiking rat hippocampal CA1 neurons. J. Physiol. 533, 407–422 (2001).
Wang, J. H. & Zhang, M. J. Differential modulation of glutamatergic and cholinergic synapses by calcineurin in hippocampal CA1 fast-spiking interneurons. Brain Res. 1004, 125–135 (2004).
Wei, J., Zhang, M., Zhu, Y. & Wang, J. H. Ca(2+)-calmodulin signalling pathway up-regulates GABA synaptic transmission through cytoskeleton-mediated mechanisms. Neuroscience 127, 637–647 (2004).
Wang, J. H. & Stelzer, A. Shared calcium signaling pathways in the induction of long-term potentiation and synaptic disinhibition in CA1 pyramidal cell dendrites. J. Neurophysiol. 75, 1687–1702 (1996).
Porras, O. H., Loaiza, A. & Barros, L. F. Glutamate mediates acute glucose transport inhibition in hippocampal neurons. J. Neurosci. 24, 9669–9673 (2004).
Kozakov, D. et al. The ClusPro web server for protein-protein docking. Nat. Protoc. 12, 255–278 (2017).
Yan, Y., Zhang, D., Zhou, P., Li, B. & Huang, S. Y. HDOCK: a web server for protein–protein and protein–DNA/RNA docking based on a hybrid strategy. Nucleic Acids Res. 45, W365–W373 (2017).
Esteve, P. et al. Elevated levels of secreted-frizzled-related-protein 1 contribute to alzheimer’s disease pathogenesis. Nat. Neurosci. 22, 1258–1268 (2019).
The authors would like to thank members of the Herrup Laboratory, R. P. Hart, M. Plummer and Q. Cai of Rutgers University, and B. K. Yee of the Hong Kong Polytechnic University for helpful advice and comments. They acknowledge the Biosciences Central Research Facility and Animal and Plant Care Facility of the Hong Kong University of Science and Technology for technical assistance. This work was supported, in part, by grants from the Innovation and Technology Commission (ITCPD/17-9), and grants from the following: the Hong Kong Research Grants Council-General Research Fund (RGC-GRF) (GRF660813, GRF16101315 and HKUST12/CRF/13G) to K.H.; three RGC-GRF grants (GRF16100219, GRF16100718 and GRF16103317), an Alzheimer’s Association Research Fellowship (AARF-17-531566) and a HKUST Institute for Advanced Study Junior Fellowship to H.-M.C.; and the National Natural Science Foundation of China (grant: 91849205) and Fundamental Research Funds for Central Universities of China-Xiamen University (grant: 20720150062 and 20720160075) to J.Z. A.C. held a RGC Hong Kong PhD fellowship during the time the experiments were being done.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Integrated supplementary information
Supplementary Figure 1 Associations between peripheral and neuronal insulin resistance, neuron morphology and animal behavior.
A) Body weight of young (3-months) and aged (22-24-month) C57BL/6J mice (n=20, ***P=0.00004, two-tailed unpaired t-test). (B) Mice categorized into four groups based on their age and HOMA-IR index: Young (3-month) versus aged (22-24-month); and insulin-sensitive (HOMA-IR<2) (N) versus insulin-resistant (HOMA≥2) (IR) (n=10, ***P=0.00001, two-tailed unpaired t-test). (C) Representative Western blots of different proteins in all animals (n=40, repeated 4 times). Quantification is shown in Fig. 1e. (D) Representative images of immunohistochemistry staining in frontal cortices of aged normal (N) and insulin resistant (IR) animals (n=10). (E) Representative Western blots with lysates of DIV14 cortical neurons or HT22 cells treated with insulin as indicated. Quantification is shown (n=5, ***P=0.0001, **P=0.001, ns=non-significant; one-way ANOVA). (F) Quantification of percentage alternations in a Y-maze paradigm (n=6, *P=0.023, #P=0.034, ns=non-significant; one-way ANOVA) and (G) their correlation with corresponding HOMA-IR index (n=24, r=-0.6419, P=0.0007) by two-sided Spearman’s rank analysis. (H) Quantification of immobilized time (Sec) in a forced swim test (n=6, ns=non-significant; one-way ANOVA); (I) latency time to fall in a rotarod test (n=6, ns=non-significant; one-way ANOVA); (J) total distance travelled, (K) total number of rearing and (L) zone preferences in an open-field test paradigm (n=6, ***P=0.007, ns=non-significant; one-way ANOVA). (M-O) Quantification of (M) Bassoon-, (N) Homer1- and (O) Synapsin I-positive puncta number per 100µm length of MAP2-positive neurite from immunocytochemistry assays of primary cortical neurons treated with insulin for various time courses (n=19, ***P=0.001, **P=0.002, *P=0.01, #P=0.05, ns=non-significant; One-way ANOVA). (P-R) Quantification of (P) primary, (Q) secondary and (R) tertiary MAP2-positive neurites signals of immunocytochemistry assays of primary cortical neurons treated with insulin for various time courses (n=30, ***P=0.001, **P=0.008, #P<0.05, ns=non-significant, One-way ANOVA). Unless otherwise specified, all in vitro insulin treatments were performed at 100nM for different time courses as indicated. Full scan of blots can be found in Supplementary Fig. 10. Values represent the mean ± SEM.
Representative images of RNA-FISH assay for Arc and Fos mRNA in frontal cortex of all groups of mice with and without exposure to a stimulating novel environment prior to sacrifice. Quantification of Fos- and Arc-positive neurons in the forebrain is shown. Gapdh mRNA was used as a staining control (n=10, ***P=0.0001, **P=0.001, *P=0.01; two-tailed unpaired t-test). All labeled values represent mean ± SEM.
(A-B) Quantification of relative intensities of SIK2, p35/p25 and PJA2 of Western blotting signals shown in (A) Fig. 2g (n=4, ***P=0.0001, **P=0.0006, *P=0.009, #P=0.05, ##P=0.04, ns=non-significant; one-way ANOVA) and (B) Fig. 2h (n=4, ***P=0.0001, **P=0.002, *P=0.005, #P=0.01, ##P=0.04, ns=non-significant; one-way ANOVA). (C) Representative western blots of HK2, SIK2, PJA2 and p35+p25 in lysates harvested from DIV14 cortical neurons or HT22 cells treated with insulin for different time course. Quantification is shown on the right (n=5, *** P=0.0001; **P=0.001, ##P=0.008, *P=0.021, #P=0.035, one-way ANOVA). (D) Calpain activity in neurons treated with vehicle or insulin for 48 hours (n=18, ns=non-significant, two-tailed unpaired t-test). (E) Western blots for mCherry signals in HT22 cell pre-expressed mCherry-p35-WT or mCherry-p35-SA treated with insulin for various time courses (n=4). (F) Western blots of MondoA and MLX in HT22 cells treated with vehicle or insulin for 48 hours (n=4). Graphs to the right show quantification of relative band intensities (n=4, P=ns, two-tailed unpaired t-test). Unless otherwise specified, all in vitro insulin treatments were performed at 100nM for different time courses as indicated. Full scan of blots can be found in Supplementary Fig. 11. Values represent the mean ± SEM.
Supplementary Figure 4 Chronic insulin exposure or direct knockdown of HK2 induces neuronal senescence, cognitive and memory impairment.
(A) Representative immunostaining images for p16INK4A, HMGB1 and MAP2 in DIV14 neurons treated with insulin for different time courses. The appearance of nuclear p16INK4A signals and the loss of nuclear HMGB signals are well-established markers of senescence. Quantification of the p16INK4A+ and HMGB+ neurons is shown on the right (n=15, ***P=0.0001; **P=0.001, P=0.01, ns=non-significant, one-way ANOVA). (B) qPCR for Cdkn2a and Cdkn1a gene expression in neuronal cultures subjected to different period of chronic insulin treatment (n=10, ***P=0.001, **P=0.009, *P=0.032, one-way ANOVA). (C) Representative image of SA-β-gal in cerebellum of young insulin resistant mice (n=10). (D) Representative immunostaining images for p16INK4A, MAP2 and SA-β-gal in frontal cortex and hippocampal regions of young insulin resistant mice. Region with extensive neurite loss is highlighted in the dash boxes (n=10). (E) qPCR for Cdkn2a and Cdkn1a gene expression levels in frontal cortex and hippocampus tissues of young and aged mice (repeated independently for four times; n=10 each group, *** P=0.0005; **P=0.001, *P=0.02, #P=0.03; one-way ANOVA). (F-G) Representative images of (F) EGFP fluorescence signals and SA-β-gal histochemistry signals in sections harvested from mice stereotaxically injected AAV9 carrying EGFP scrambled or HK2 shRNA shuttles into hippocampal CA2 region. Quantification of SA-β-gal- and GFP-double positive hippocampal neurons Fig. 5e (Scrambled vs HK2 shRNA: n=11 vs n=10, ***P=0.0001, two-tailed unpaired t-test). (G) qPCR for Cdkn2a and Cdkn1a gene expression in hippocampal is shown (n=10, **P=0.009; *P<0.034, two-tailed unpaired t-test). (H-J) Escape latency during the (H) training phase and (I) probe trial of Morris water maze (MWM) test (Scrambled vs HK2 shRNA: n=11 vs n=9, *P=0.037; two-tailed unpaired t-test). (J) Percentage time spent in target quadrant (TQ) during the probe trial of MWM (Scrambled vs HK2 shRNA: n=11 vs n=9, #P=0.045; two-tailed unpaired t-test). (K-L) Correlation between (K) GFP-only (n=10, r=-0.7852, P=0.0071) or (L) SA-β-gal-and-GFP-double positive (n=10, r=-0.8654, P=0.0012) neurons in hippocampus of individual animal received HK2-shRNA with corresponding percentage time in TQ by two-tailed Spearman’s rank analyses. (M-N) SA-β-gal staining in (M) frontal cortex and (N) hippocampus, followed by immunohistochemistry for p16INK4A, p21 and MAP2 in the same hippocampal brain sections. Region with extensive neurite loss is highlighted in white boxes. Age and insulin resistance status are indicated (n=10, repeated independently for three times). (O) Quantification the percent SA-β-gal and p16INK4A double-positive cells per section (n=10, ***P=0.0001, P=0.001; one-way ANOVA). (P-T) Correlations of the abundance of SA-β-gal-positive neurons in cortex with mouse behavioral performance in (P) Morris water maze (n=24, r=-0.8069, P=0.0001); (Q) Y-maze maze (n=24, r=-0.5717, P=0.0035); (R) rotarod (n=24, r=0.3477, ns=non-significant); (S) forced swim (n=24, r=0.094, ns=non-significant) and (T) open field test (n=24, r=0.189, ns=non-significant) paradigms by two-sided Spearman’s rank analysis. Unless otherwise specified, all in vitro insulin treatments were performed at 100nM. Values represent the mean ± SEM.
(A) Neurons were treated with insulin and simultaneously with roscovitine, CDK5 siRNA or an expression vector overexpressing GFP-CDK5. The percentage of SA-β-gal or cleaved caspase-3 positive signals were calculated (n=9, ***P=0.001, one-way ANOVA). (B) Representative SA-β-gal staining and immunohistochemistry images in the frontal cortex region of normal and insulin resistant aged animals. Proportions of different cellular populations in young IR as well as Aged IR animals are shown on the right (n=10, ***P=0.001, one-way ANOVA). (C) Representative Western blots of Axin immunoprecipitates from cortical lysates of CK-p25 transgenic mice and littermates. Blots were probed with the antibodies as shown (n=9, experiments were repeated independently for 3 times). (D) Western blots of immunoprecipitates by GSK3β (Left) or Axin (Right) antibody in lysates harvested from insulin-treated DIV14 neurons. Blots were probed with the antibodies as shown (n=4, experiments were repeated independently for 3 times). (E) Predicted molecular models of Axin-binding domain of GSK3β (aa265-420) V267G/E268R (PDB: 5AIR_A) with p25 (PDB:IUNL_D) and GSK3β-binding domain of Axin (PDB:4NM5_B) showing possible modes of interaction. Energy released in predicted interactions of GSK3β decoy is shown in the table at the bottom of the panel. (F-G) Representative images of (F) SA-β-gal coupled with MAP2 immunostaining in brain sections harvested from P6-7 pups which had injected with AAV carrying an empty GFP-vector or GFP-tagged stabilized β-catenin (S33Y) for 5 days. Quantification of SA-β-gal+ in different cell population is shown (n=6, ***P=0.0001, ns=non-significant, one-way ANOVA). (G) qPCR for Cdkn2a and Cdkn1a mRNA levels in frontal cortex tissues of these animals (n=9, **P=0.003, ##P=0.001; two-tailed unpaired t-test). Unless otherwise specified, all in vitro insulin treatments were performed at 100nM for 72 hours. Full scan of blots can be found in Supplementary Fig. 11. Values represent the mean ± SEM.
Supplementary Figure 6 Liraglutide but not metformin sustains HK2 activity and prevents insulin-induced senescence.
(A-B) Fluorescence of NDAH was used as indicator to measure the kinase of hexokinase. Dose-dependent effects of (A) liraglutide or (B) metformin co-treatment with insulin on total hexokinase activities in DIV14 primary cortical neurons were shown. All measurements were normalized by protein quantity (n=8, ***P=0.0001, **P=0.001, ##P=0.004, *P=0.009, #P=0.01; one-way ANOVA). (C-D) Dose-dependent effect of (C) liraglutide or (D) metformin co-treatment with insulin on cell survival in DIV14 primary cortical neurons (n=8, ns=non-significant, one-way ANOVA). (E-F) Electrophysiological traces of sEPSC and sIPSC assessed by whole cell patch electrodes in DIV21 cortical neuron cultures treated with insulin alone or co-treated with 10nM liraglutide as shown in Fig. 8g, h. Panels show the cumulative amplitude during the 1-3 minutes of monitoring (Vehicle vs insulin vs insulin + liraglutide: n=18 vs n=20 vs n=18; *P<0.05, ns=non-significant, two-sided Kolmogorov-Smirnov test). (G) Schematic diagram showing DIV14 primary cortical neurons were treated with 100 nM insulin alone or 10 nM Liraglutide alone or both for various time courses as indicated for panels H-M. (H-J) Quantification of (H) Bassoon-; (I) Homer1- and (J) Synapsin I-puncta per 100 µm MAP2+ neurite in neurons subjected to different treatment courses of insulin and liraglutide as indicated (n=15, ***P=0.001, *P=0.033, ns=non-significant, one-way ANOVA). (K-M) Quantification of (K) primary; (L) secondary and (M) tertiary neurites of neuron subjected to different courses of insulin and liraglutide (n=10, ***P=0.0003, **P=0.005, *P=0.04, ns=non-significant, one-way ANOVA). Unless otherwise specified, all in vitro insulin treatments were performed at 100nM for various time courses indicated. Values represent the mean ± SEM.
Supplementary Figure 7 Schematic diagram showing the regulation of p35 protein dynamics and relationship with neuronal function.
(A) Under normal physiological conditions, hexokinase-2 (HK2) catalyzes the formation of glucose-6-phosphate (G6P) form glucose. G6P then activates the MondoA-MLX complex leading to the enhanced transcription of SIK2. As a target of SIK2, p35 is then phosphorylated at Ser91, which favors its ubiquitination by PJA2 (an E3 ubiquitin protein ligase) and subsequent proteasome-mediated degradation. In this situation, the production and degradation of p35 is balanced and the amount of p35 that escapes ubiquitination serves as the dominant activator of CDK5. (B) When glucose metabolism is impaired during conditions such as insulin resistance, HK2 activity is reduced. This lowers the levels of G6P and impairs the MondoA-MLX complex-mediated transcription of SIK2. With less SIK2, p35 remains unphosphorylated and is no longer a favorable substrate for PJA2. This impairs the degradation of p35 by the proteasome, hence resulting in its accumulation. When p35 accumulates, even baseline activities of calpain are sufficient to cleave a portion of the p35 protein into p25. p25, which is not readily degraded, aberrantly activates CDK5, promotes tau hyper-phosphorylation as well as the activation of BACE1. At the same time, p25 also binds to GSK3β blocking the Axin binding site, which leads to β-catenin accumulation and nuclear-translocation, where it acts as a proto-oncogene and triggers neuronal cell-cycle re-entry. This unscheduled neuronal cell cycle fails to be completed and the resulting cellular stress induces senescence in the cycling neuron. Together, these downstream outcomes add to the impaired neuronal physiology and function, and eventually lead to neurodegeneration.
About this article
Cite this article
Chow, H., Shi, M., Cheng, A. et al. Age-related hyperinsulinemia leads to insulin resistance in neurons and cell-cycle-induced senescence. Nat Neurosci 22, 1806–1819 (2019) doi:10.1038/s41593-019-0505-1