Y. Ito1,2, S. Takeda1,2, T. Nakajima3, A. Oyama2,3, H. Takeshita3, K. Miki1, Y. Takami3, Y. Takeya3,4, M. Shimamura5, H. Rakugi3, R. Morishita1
1. Department of Clinical Gene Therapy, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka, Japan; 2. Osaka Psychiatric Research Center, Osaka Psychiatric Medical Center, 3-16-21 Miyanosaka, Hirakata, Osaka, Japan; 3. Department of Geriatric and General Medicine, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka, Japan; 4. Department of Clinical Nursing, Division of Health Sciences, Graduate School of Medicine, Osaka University, 1-7 Yamadaoka, Suita, Osaka, Japan; 5. Department of Neurology, Department of Health Development and Medicine, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka, Japan
Corresponding Author: Shuko Takeda, MD, PhD and Ryuichi Morishita, MD, PhD, Department of Clinical Gene Therapy, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan, Tel: 81-6-6210-8351, Fax: 81-6-6210-8354, Email: firstname.lastname@example.org and email@example.com
J Prev Alz Dis 2023;
Published online June 22, 2023, http://dx.doi.org/10.14283/jpad.2023.85
BACKGROUND: Epidemiological evidence has demonstrated a clear association between diabetes mellitus and increased risk of Alzheimer’s disease (AD). Cerebral accumulation of phosphorylated tau aggregates, a cardinal neuropathological feature of AD, is associated with neurodegeneration and cognitive decline. Clinical and experimental studies indicate that diabetes mellitus affects the development of tau pathology; however, the underlying molecular mechanisms remain unknown.
OBJECTIVE: In the present study, we used a unique diabetic AD mouse model to investigate the changes in tau phosphorylation patterns occurring in the diabetic brain.
DESIGN: Tau-transgenic mice were fed a high-fat diet (n = 24) to model diabetes mellitus. These mice developed prominent obesity, severe insulin resistance, and mild hyperglycemia, which led to early-onset neurodegeneration and behavioral impairment associated with the accumulation of hyperphosphorylated tau aggregates.
RESULTS: Comprehensive phosphoproteomic analysis revealed a unique tau phosphorylation signature in the brains of mice with diabetic AD. Bioinformatic analysis of the phosphoproteomics data revealed putative tau-related kinases and cell signaling pathways involved in the interaction between diabetes mellitus and AD.
CONCLUSION: These findings offer potential novel targets that can be used to develop tau-based therapies and biomarkers for use in AD.
Key words: Alzheimer’s disease, dementia, diabetes mellitus, tau, post-translational modification.
Numerous epidemiological studies have reported that diabetes mellitus (DM) increases the risk of Alzheimer’s disease (AD) (1-6). Cerebrovascular damage induced by DM, which directly affects neurocognitive function, partially explains the reported pathological association. However, the findings of a large-scale cohort study implicate an link between DM and AD independently of vascular factors (6) raising the possibility that DM may directly contribute to the development of AD-related pathologies.
AD is a progressive neurodegenerative disease characterized by neuronal loss and cognitive decline (7, 8) and the two neuropathological hallmarks of AD are senile plaques comprising extracellular amyloid β (Aβ) deposits and neurofibrillary tangles (NFTs) indicating intracellular tau protein accumulation. Moreover, the severity of neuronal loss and cognitive decline correlates with the number of NFTs in AD (9), suggesting that tau is a direct contributor of neurodegeneration in these patients.
Till date, approximately 40 putative phosphorylation sites on tau have been shown to be involved in the pathogenesis of AD (10). Current evidence suggests that tau phosphorylation accelerates its own aggregation and impairs neuronal function (11), leading to NFT formation and neurodegeneration (12). Mouse models of AD have demonstrated that streptozotocin-induced DM increases the amount of phosphorylated tau (13) and that diet and genetically induced obesity induce tau phosphorylation in the brain (14). These findings raise the possibility that insulin resistance and glucose intolerance in peripheral organs may impact tau phosphorylation in the brain. However, the underlying mechanisms and key molecules remain unknown.
Majority of the previous studies on AD-related tau pathology have utilized conventional antibody-based approaches to identify and quantify tau phosphorylation. Although extensively validated anti-phosphorylated tau antibodies are commercially available for semiquantitative assessment using immunohistochemistry and immunoblotting (15), this approach can provide information on the target epitope alone and does not allow the mapping of phosphorylation sites based on the relative comparison among multiple phosphorylation sites. Each site has distinct pathophysiological roles, and the tau phosphorylation profile reflects the clinical severity of AD (16). A better understanding of the mechanisms underlying the effect of DM on tau pathology requires comprehensive quantitative analysis of phosphorylation of the entire tau protein.
In the present study, we used a mouse model of diabetic AD by feeding tau-transgenic (tau-tg) mice a high-fat diet (HFD) and investigated the effect of HFD-induced diabetic conditions on AD-related tau pathology by comparing these mice fed a normal chow diet (NCD). This model allowed the assessment of metabolic features and neurocognitive behavior as well as the biochemical measurement of cerebral tau accumulation. We investigated the detailed biochemical changes in tau and related proteins with the comprehensive phosphoproteomic analysis of the brain extracts of mice with HFD-induced diabetic AD, which allowed the mapping of tau phosphorylation sites. Finally, we used a bioinformatic approach based on the phosphoproteome results to predict several putative tau-related kinases and cell signaling pathways involved in the interaction between DM and AD.
PS19 tau-tg mice were purchased from Charles River Laboratories Japan (Kanagawa, Japan). These mice express the P301S mutant form of human tau under the direction of mouse prion protein promoter, and the expression of the mutant human tau protein is five-fold higher than that of the endogenous mouse tau protein (17). The genotypes were confirmed with the DNA analysis of tail samples. Female tau-tg and non-tg wild-type (WT) littermates were maintained in a temperature-controlled room (25°C ± 2°C) with a 12-hour light–dark cycle and were provided food and water ad libitum.
The animals were divided into two groups and fed a HFD (60% kcal fat; cat. no: HFD-60; Oriental Yeast, Tokyo, Japan) or NCD (12% kcal fat; cat. no: MF; Oriental Yeast) between the ages of 1.5 and 9 months (Fig. S1). For tissue collection, the mice were anesthetized with the intraperitoneal administration of 0.75 mg/kg medetomidine hydrochloride, 4.0 mg/kg midazolam, and 5.0 mg/kg butorphanol tartrate, followed by the intracardial perfusion of ice-cold phosphate-buffered saline (PBS). The right hemibrains were immediately frozen and stored at −80°C, and the left hemibrains were immersion-fixed in 4% paraformaldehyde for 24 h at 4°C.
All animal procedures were performed in Osaka University School of Medicine after the approval of the study protocol by the Animal Experiments Committee of Osaka University, Osaka, Japan (decision reference number 29-046-015). This study protocol was reviewed and approved by the Osaka University Living Modified Organisms (LMO) Research Safety Committee (approval number 04232). All animal experiments comply with the ARRIVE guidelines and were carried out in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals.
All mice were weighed once monthly. Blood glucose and plasma insulin levels were determined in fasting animals 16 h after feeding and in fed animals at random times. Blood glucose levels (mg/dL) were determined using the Glutest Neo alpha glucose oxidase method (Sanwa Kagaku Kenkyusho, Aichi, Japan), and plasma insulin levels were determined using an enzyme-linked immunosorbent assay kit (cat. no: M1104; Morinaga Institute of Biological Science, Inc., Kanagawa, Japan). Intraperitoneal glucose tolerance test (GTT) in 8-month-old mice was performed after an overnight fast, following the published guidelines (18). Briefly, d-glucose (2 g/kg body weight; cat. no: 045-31162; FUJIFILM Wako, Osaka, Japan) was intraperitoneally injected and blood samples from the tail vein were collected before and 15, 30, 60, and 120 min after the glucose injection. Abdominal fat pads were collected and weighted using a microbalance.
Preparation of brain extracts
Sequential protein extraction was performed using tissue dissected from right hemibrains. Briefly, the cortex and brainstem were homogenized in 5× PBS (w/v) (cat. no: 14249-95; Nacalai Tesque, Inc., Kyoto, Japan) supplemented with a protease inhibitor cocktail (PIC) (cat. no: 5871, Cell Signaling Technology, Inc., Tokyo, Japan). The homogenates were centrifuged at 10,000 g for 15 min at 4°C to extract the PBS-soluble fraction. The pellets were resuspended in the same volume of PBS containing 1% sarkosyl (cat. no: 20135-14, Nacalai Tesque, Inc., Kyoto, Japan) and PIC and incubated at 37°C for 30 min. The samples were centrifuged at 100,000 g for 30 min at 4°C to extract the sarkosyl-soluble fraction. The pellets were resuspended in 100 µL PBS containing PIC and sonicated (model: SONIFIER 250; Branson) for 2 min at room temperature. The suspensions were analyzed as sarkosyl-insoluble fractions. Protein concentrations were determined using a bicinchoninic acid assay kit (cat. no: T9300A; TaKaRa Bio, Shiga, Japan).
Samples were diluted in 2× Laemmli sample buffer (cat. no: 1610737, Bio-Rad, CA, USA) containing beta-mercaptoethanol (cat. no: 21438-82; Nacalai Tesque, Inc., Kyoto, Japan) and incubated at 95°C for 5 min. The samples were separated by gel electrophoresis in 5%–20% gradient polyacrylamide gels (cat. no: 2331830, ATTO, Tokyo, Japan) and transferred to polyvinylidene difluoride membranes (Trans-Blot Turbo Midi 0.2 µm PVDF tTransfer Packs; 1704157; Bio-Rad, CA, USA). The membranes were incubated with a blocking buffer (cat. no: WBAVDCH01; Merck Millipore, Darmstadt, Germany) for 90 min and probed with phosphorylated tau (p-Ser396; 1:2000; cat. no: 44-752G; Invitrogen, Tokyo, Japan), total tau (Tau5; 1:2000; cat. no: MAB361; Merck Millipore, Darmstadt, Germany), and β-actin (1:5000; cat. no: 4970; Cell Signaling Technology, Inc., Tokyo, Japan) antibodies overnight at 4°C. Next, the membranes were incubated with horseradish peroxidase-conjugated anti-mouse IgG (H+L) (1:2000; cat.no: W4021; Promega, WI, USA) or anti-rabbit IgG (H+L) (1:2000; cat. no: W4011; Promega, WI, USA) antibodies for 3 h at 4°C. Chemiluminescent signals were detected using the ECL western blotting detection reagent (cat. no: RPN2209; GE Healthcare Life Sciences, UK), and immunoreactivity was visualized using Image Quant LAS-4000 mini (GE Healthcare Life Sciences, UK). Band intensities were quantified using Image Quant TL 8.2 (GE Healthcare Life Sciences, UK).
Brains fixed in 4% paraformaldehyde were incubated in 30% sucrose in PBS for 2 days, followed by embedding in Tissue-Tek O.C.T. compound (Sakura Finetek Japan Co., Ltd., Tokyo, Japan). Next, 15-µm-thick coronal sections were prepared using a cryostat, and the sections were mounted on glass slides. For immunostaining, the sections were incubated in 0.3% H2O2 for 5 min, blocked in 5% bovine serum albumin in PBS at room temperature for 1 h, and immunostained using an anti-NeuN antibody (1:200; cat. no: MAB377; Merck Millipore, Darmstadt, Germany) in PBS supplemented with 1% bovine serum albumin at room temperature for 1 h. After washing them thrice with PBS, the sections were incubated with Alexa 594-conjugated goat anti-mouse IgG antibody (1:1000; cat. no: ab150116; Abcam, Cambridge, UK) and mounted using the Vectashield mounting medium (cat. no: H-1000; Vector Laboratories, CA, USA). Images were captured using a fluorescence microscope (BZ-9000; Keyence, Osaka, Japan) equipped with a digital camera, and the same settings were used for all images. The pixel intensities of fluorescent signals were analyzed and quantified using ImageJ (National Institutes of Health, Bethesda, MD, USA). Brain sections at bregma −7.0 mm were used to quantify NeuN-positive areas, and images of the brain stem were captured from all sections. In each image, the percentage of the area labeled with the anti-NeuN antibody, i.e., positive pixels, divided by the entire area, i.e., total pixels, was determined using ImageJ software.
Tail suspension test
The tail suspension test was conducted as previously described (19, 20). Briefly, the mice were secured to a flat metallic surface by the distal tail end and suspended in a visually isolated area. Their behavior was recorded for 30 sec using a digital camera. The severity of motor paralysis was rated by three observers blinded to the experimental groups. The severity of motor paralysis was determined based on the average of all scores (Supplemental Materials and Methods).
Habituation of exploratory activities was evaluated using the open-field test. The spontaneous motor activity of mice was monitored using an automated motion analysis system (SCANET MV-40; Melquest, Toyama, Japan). The activity score was calculated as the total number of beam interceptions per bank recorded in 0.1-sec bins over 10 min. The change in activity score was used to compare activity levels between the initial and final periods. Habituation was calculated as the activity scored during the final 2 min divided by the activity scored during the initial 2 min.
To assess circadian activity, the mice were individually housed with ad libitum food and water access for 4 days. The mice were maintained on a 12-hour light–dark cycle and supplied fresh food, water, and paper bedding at the beginning of the experiment. Circadian activity was monitored using the SCANET software for analyzing the data collected from 36 infrared beams/cage attached to each X and Y banks. Circadian activity was analyzed in 15-min bins using the SCANET software. The change in activity score was used to compare activity levels during the initial and final periods. Habituation was calculated as the activity score during the final dark phase divided by the activity score during the first dark phase (Supplemental Materials and Methods).
Data-independent acquisition-based phosphoproteomic analysis
Data-independent acquisition (DIA)-based phosphoproteomic analysis was performed according to a previously reported protocol (Kazusa Genome Technologies) (21). Liquid chromatography coupled to tandem mass spectrometry (MS) was performed using the UltiMate 3000 RSLC nanoLC system (Thermo Fisher Scientific). Peptides eluted from the column were analyzed using the Q Exactive HF-X system (Thermo Fisher Scientific) for both the data-dependent acquisition-based and DIA-based MS analyses.
MS data were searched against the dataset containing the UniProtKB/Swiss-Prot Mus musculus database and the additional sequence data on the isoforms of human tau with the P301S mutation and the mouse tau (Supplemental Materials and Methods). Proteome Discoverer v2.3 (Thermo Fisher Scientific) with Sequest HT was used for data-dependent acquisition-based MS data, and Scaffold DIA v3.0 (Proteome Software, Portland, OR, USA) was used for DIA-based MS data.
A chromatogram library was generated by searching the MS data in the library against the dataset containing the UniProtKB/Swiss-Prot Mus musculus database and the additional sequence data using the Scaffold DIA software. Peptide quantification was determined using the EncyclopeDIA algorithm (22) in the Scaffold DIA software.
In the present study, we used KeyMolnet, a comprehensive and stand-alone database of the relationships among human genes and proteins, small molecules, diseases, pathways, and drugs, developed by the Institute of Medicinal Molecular Design (23). Expert biologists regularly update the knowledgebase. The DIA-based phosphorylated proteome data were input into the KeyMolnet software (version 5.6 I; Institute of Medicinal Molecular Design, Tokyo, Japan), and corresponding molecules were identified as nodes on networks. The network search algorithm on KeyMolnet has been described elsewhere (24) (Supplemental Materials and Methods).
All data were expressed as means ± standard error of the mean. Comparison of the means among three or more groups was performed using one-way analysis of variance followed by the Tukey–Kramer multiple range test. Two-group comparisons were performed using Student’s or Welch’s t test, and the change in activity score was compared using the paired t test. All statistical analyses were performed using Statcel 4 (OMS Publishing, Tokorozawa, Japan). P values of <0.05 as well as scores >20 in the hypergeometric distribution were considered to indicate statistical significance.
HFD induces DM-related metabolic changes in the tau-tg mouse model of AD
We generated a diabetic AD mouse model (Fig. 1A) in an experimental paradigm where the tau-tg mice and the age- and sex-matched WT littermates were fed NCD or HFD between the ages of 1.5 and 9 months. Compared to the NCD-fed tau-tg and WT mice, the HFD-fed tau-tg and WT mice showed marked obesity (Fig. 1B), increased abdominal fat (Fig. 1C), mild fasting hyperglycemia (Fig. 1D), severe hyperinsulinemia (Fig. 1E), and glucose intolerance during the GTT (Fig. 1F, 1G). The severity of hyperglycemia, hyperinsulinemia, and glucose intolerance was comparable between the tau-tg and WT mice. However, the HFD-fed tau-tg mice showed a slight but statistically significant decrease in body weight at 9 months of age (Fig. 1B, 1C), consistent with reduced food intake due to motor dysfunction. Given the absence of weight loss in the NCD-fed tau-tg mice (Fig. 1B, 1C), the HFD-induced diabetic state was likely to have exacerbated the tau-related neurodegeneration AD.
*p < 0.05 and **p < 0.01 compared to NCD-fed mice, ##p < 0.01 compared to HFD-fed WT mice; analysis of variance followed by the Tukey–Kramer test; Abbreviations: AUC, area under the receiver operating characteristic curve; HFD, high-fat diet; NCD, normal chow diet; WT, wild-type
Early onset of behavioral abnormalities in HFD-fed tau-tg mice
The tau-tg mice used in the present study develop brain pathology associated with motor dysfunction and abnormal behavior due to neurodegeneration at 7–10 months of age (17). In the present study, we assessed motor dysfunction using the tail suspension test at 8 months of age. The HFD-fed tau-tg mice had significantly higher motor paralysis scores than the NCD-fed tau-tg mice (Fig. 2A). Motor impairment was associated with neurodegeneration based on the reduced number of NeuN-positive cells in the brain sections of the HFD-fed tau-tg mice compared to the NCD-fed tau-tg mice (Fig. 2B). These findings suggested that the diabetic conditions in the tau-tg mice induced tau-related neurodegeneration and early onset of motor impairment.
Genotype and diet had no apparent effect on total locomotor activity (Fig. 2C and 2D). However, the HFD tau-tg mice displayed anxiety-like behavior in the open-field cage, with no physiological time-dependent decrease in locomotor activity, in contrast to the other groups that showed normal acclimatization (Fig. 2E). Moreover, although all groups exhibited a physiological circadian rhythm in locomotor activity (Fig. 2F), the HFD-fed tau-tg mice did not display the normal reduction in locomotor activity over four consecutive days during the evaluation of the time-dependent change in dark-phase activity, which is an index of acclimatization (Fig. 2G).
*p < 0.05, Student’s t test. (B) Number of neurons was assessed by counting cells immunostained with the anti-NeuN antibody. Representative images of NeuN staining in brain stem sections of NCD-fed and HFD-fed tau-tg mice are shown (B, left panel). Scale bar, 100 μm. (B, right) Quantitative image analysis of NeuN-positive areas (n = 4/group). *p < 0.05, Student’s t test. (C, D) Open-field test at 8 months of age. Representative track paths (C), total locomotor activity (D), and change in the locomotor activity during the test (E) are shown (n = 5–8/group). NS, not significant; one-way analysis of variance was performed to determine the difference in total locomotor activity. *p < 0.05, paired t test comparing mean activity during the initial (0–2 min) and last (8–10 min) 2-min interval in each group. (F, G) Mean locomotor activity profiles over four consecutive days (F) and change in dark-phase locomotor activity (G) (n = 5–8/group). *p < 0.05, paired t test comparing mean dark-phase activities during the first and last day in each group. Abbreviations: HFD, high-fat diet; NCD, normal chow diet; NS, not significant; WT, wild-type
Accumulation of insoluble tau aggregates
Next, we sequentially extracted the soluble and insoluble tau species from the mouse brain tissue using previously reported methods (Fig. 3A) and performed western immunoblotting using the Tau5 antibody that recognizes total tau and the phospho-tau-specific pSer396 antibody (Fig. 3B, 3C). Only the amount of insoluble pSer396 tau was significantly increased by approximately 2.5-fold in the brains of the HFD-fed tau-tg mice compared to the NCD-fed tau-tg mice (Fig. 3C), with no significant differences observed in other groups. We confirmed the significant increase in insoluble pSer396 tau level in the cortex and brain stem as well (Fig. S2A and S2B). These results indicated that HFD-induced diabetic conditions could increase the accumulation of aggregated phosphorylated tau in the brains of tau-tg mice.
*p < 0.05, Student’s t test. Abbreviations: HFD, high-fat diet; NCD, normal chow diet; PBS, phosphate-buffered saline; PIC, protease inhibitor cocktail; Pt., pellet; Sup., supernatant
Phosphoproteomic analysis shows a unique tau phosphorylation profile
Figure 4A shows the schematic representation of the phosphoproteomic analysis employed in the present study. The phosphorylated peptides extracted from the brain homogenates of the HFD- and NCD-fed tau-tg mice were quantified using nanoscale liquid chromatography coupled to tandem MS, followed by the profiling of phosphorylated proteins using the Scaffold DIA search engine (Fig. 4A). We included information on the amino acid sequences of both the endogenous mouse and the mutated human tau (1N4R P301S mutation) to analyze the effect of DM on the phosphorylation profile of the endogenous mouse tau and that of the pathological human tau.
Of the 9064 distinct phosphorylated peptides, 652 were significantly altered (368 upregulated and 284 downregulated) in the brain extracts of the HFD-fed tau-tg mice compared to the NCD-fed tau-tg mice (Fig. 4B). We identified 73 phosphorylated tau peptides derived from either the human or the mouse tau (Table S1). Further analysis of the pathological human tau overexpressed in the tau-tg mice revealed 11 phosphorylation sites (Thr50, Thr52, Ser210, Thr212, Ser214, Thr217, Thr231, Ser238, Ser241, Ser289, and Thr386) that were significantly elevated in the HFD-fed tau-tg mice compared to the NCD-fed tau-tg mice (Fig. 4C and 4D, upper graph). Regarding the endogenous mouse tau, three phosphorylation sites (Ser50, Thr220, and Ser228 corresponding to Ser61, Thr231, and Ser238–Ala239 of human tau, respectively) were significantly elevated in the HFD-fed tau-tg mice compared to the NCD-fed tau-tg mice (Fig. 4D, lower graph). There were no sites with significantly reduced phosphorylation in the HFD-fed tau-tg mouse brain extracts. This unique pattern of tau phosphorylation implied that specific key molecules, such as kinases and phosphatases, linked DM to AD-related tau pathology.
n = 4–6/group. *p < 0.05, **p < 0.01, Welch’s t test. Abbreviations: DIA, data-independent acquisition; HFD, high-fat diet; LC–MS/MS, liquid chromatography coupled to tandem mass spectrometry; NCD, normal chow diet
Bioinformatic analysis predicts key molecules associated with diabetes-induced tau phosphorylation
We used proteins with statistically significant changes in phosphorylation profiles based on the phosphoproteomic analysis to identify key molecules linking DM with tau pathology in a network-based bioinformatics approach (Fig. 5). Functional classification revealed that cell signaling and synaptic pathways exhibited the most abundant alterations in the phosphorylation profiles observed in the HFD-fed tau-tg mice brain extracts (Fig. 5A), suggesting a role for these proteins in the association between DM and AD. In subsequent molecular network analysis to explore tau-related kinases modulated in DM (Fig. 5B), the phosphoproteomic analysis identified 20 kinases that are known to phosphorylate tau. Of these, six kinases (BR serine/threonine kinase 2, AKT serine/threonine kinase 3, Ca2+/calmodulin-dependent protein kinase II [CaMK2], protein kinase C gamma [PKCγ], TAO1, and tau tubulin kinase 1 [TTBK1]) exhibited significant alterations in their phosphorylation profiles. Figure 5C shows the top ten cell signaling pathways that were significantly regulated in the HFD-fed tau-tg mouse brain extracts based on the rank score, which reflected the number of altered proteins included in the specific pathway. Figure 5D shows the top ten tau-related phosphorylated peptides that were significantly regulated in the HFD-fed tau-tg mouse brain. These data suggested that multiple cell signaling pathways other than insulin signaling, such as the calpain and mitogen-activated protein kinase signaling pathways, were associated with diabetes-induced tau phosphorylation.
Scores > 20 were considered statistically significant. Abbreviations: HFD, high-fat diet; NCD, normal chow diet
Although studies clearly show that DM increases the risk of AD (4), the underlying mechanisms of this pathological interaction remain elusive (2). Understanding the mechanism by which DM predisposes to AD can potentially uncover novel targets for the treatment and prevention of AD. In the present study, we evaluated the behavioral phenotypes in a diabetic AD mouse model and performed comprehensive phosphoproteomic analysis to elucidate the key biochemical changes in the brain, including tau phosphorylation. Of note, the HFD-fed tau-tg mice used in the present study exhibited prominent obesity and severe insulin resistance with relatively mild hyperglycemia (Fig. 1), suggesting that this was a model of early-stage DM with hyperinsulinemic compensation of insulin resistance. Therefore, our findings suggest that early-stage DM could significantly affect the development of tau pathology, indicating the need to initiate DM management as early as possible to prevent AD.
In a previous study, we used another diabetic AD mouse model by crossing the APP23 mice with the ob/ob mice and demonstrated that DM accelerated cognitive impairment by increasing Aβ deposits in the cerebral vasculature (3). Although Aβ accumulates specifically in the brains of patients with AD and can be used as a disease-specific biomarker for diagnosis, tau pathology is a more direct contributor of neurodegeneration and cognitive impairment (25, 26). Studies on postmortem brain tissue specimens have also indicated that DM increases the risk of tau pathology in the brain (5, 6), although the mechanism underlying the DM-induced increase in cerebral tau accumulation remains unclear. Consistent with the in vitro and in vivo studies showing the significance of tau phosphorylation in its aggregation (25), the current study results indicate that HFD-induced diabetic conditions increase tau phosphorylation in a unique pattern (Fig. 4), leading to the accumulation of phosphorylated tau aggregates in the brain (Fig. 3).
In the present study, we reveal the comprehensive phosphorylation pattern of tau through the phosphoproteomic analysis of brain extracts obtained from the HFD-induced diabetic tau-tg mice. This approach does not require a priori knowledge of phosphorylation sites or antibodies, thereby overcoming the limitations of conventional antibody-based approaches. Most studies on tau phosphorylation profile have used well-known commercially available tau antibodies, such as AT8 (recognizing pSer202 and pThr205) and pSer396, that target phosphorylation sites in the middle or the C-terminal region of tau and contributes to microtubule binding and tau aggregation. The comprehensive phosphoproteomic analysis employed in the present study provided an unbiased map of diabetes-induced phosphorylation sites covering the entirety of the tau protein from the N- to the C-terminus, leading to the successful identification of previously unknown DM-related phosphorylation sites on the N-terminus of tau (Fig. 4C). Of the 11 phosphorylation sites that were significantly increased in the HFD-fed tau-tg mouse brain extracts, 7 sites in the middle or the C-terminal region of tau (Ser210, Thr212, Ser214, Thr217, Thr231, Ser238, Ser241, and Ser289) have been previously reported to be increased in the postmortem brain specimens of patients with AD (10, 16). Tau phosphorylation at Thr212 and Ser214 contributes to tau aggregation and NFT development (27). Recent evidence suggests that the detection of phosphorylated tau at Thr217 in the cerebrospinal fluid is a promising biomarker for early-stage AD (28). Although the pathophysiological roles of tau phosphorylation at Thr50 and Thr52 are unknown, the N-terminal region of tau mediates plasma membrane-associated protein interaction and axonal growth (29, 30). DM may affect these functions including the N-terminal region of tau by increasing its phosphorylation at Thr50 and Thr52 and leading to neuronal dysfunction and neurodegeneration. Further studies are warranted to elucidate the role of tau phosphorylation at these sites.
Our previous studies have shown that DM exacerbates AD by altering the insulin signaling pathway in the brain (2-4) in association with tau phosphorylation at Ser396 via glycogen synthase kinase 3β activation (31, 32). In the current study, we observed no significant changes in the total and phosphorylated levels of glycogen synthase kinase 3β (Fig. S3), suggesting that the insulin signaling may not be a major contributor to tau phosphorylation in HFD-fed tau-tg mice. Instead, the bioinformatic analysis of the phosphoproteomics results revealed that the calpain and mitogen-activated protein kinase signaling pathways were significantly altered in these mice (Fig. 5C, D).
Among the significantly altered phosphorylated proteins, we identified six tau-related kinases (BR serine/threonine kinase 2, AKT serine/threonine kinase 3, CaMK2, PKCγ, TAO1, and TTBK1) (Fig. 5B). AKT serine/threonine kinase and CaMK2 contribute to tau phosphorylation at Thr212 and Ser 214 (33,34), TTBK1 increases tau phosphorylation at Ser210 (35), PKCγ increases tau phosphorylation at Ser214 (36), and TAO1 phosphorylates tau at multiple sites (37). These findings suggest that HFD-induced diabetic conditions affect multiple signaling pathways and related kinases, increasing tau phosphorylation and leading to the progression of tau pathology. However, a major limitation of this analysis involves the use of whole brain extracts in the phosphoproteomics experiments, as there is no guarantee that the identified kinases were co-localized with tau in the same cells. The results of the bioinformatic analysis based on peptide phosphorylation in the mouse brain may not accurately represent the biological processes occurring in the human brain. These points should be taken into account when interpreting the results.
A variety of metabolic and vascular changes, including obesity, hyperglycemia, insulin resistance, hyperlipidemia, and vascular endothelial dysfunction, are known to occur in association with DM. In this study, we observed obesity, hyperinsulinemia, glucose intolerance, and mild hyperglycemia in the HFD-fed mice; however, those mice can concomitantly develop other pathological alterations, such as hyperlipidemia and cerebrovascular damage. Therefore, it is difficult to conclude that the observed effects on tau pathology and neuronal impairments were attributable solely to DM, and this point should be considered when interpreting the results of this study.
The mouse model used in this study is a tg-mouse that overexpresses a mutant form of tau (see Methods). This model may not accurately mimic the pathogenesis of AD, because human patients with the most common form of sporadic AD do not show mutations or overexpression of tau. Furthermore, cerebrovascular alterations, such as increases in vascular inflammation and blood–brain barrier permeability, have been reported in the same tau-tg mice (38). The dysfunction of the vascular system might have predisposed the tau-tg mice to HFD-induced metabolic changes. These points should be regarded as major limitations of the current study when considering the mechanistic effects of HFD on AD-related tau pathology.
In conclusion, HFD-induced diabetic conditions induce tau phosphorylation at specific phosphorylation sites and increases tau aggregation, leading to neurodegeneration and early-onset behavioral impairment. The unique phosphorylation pattern of tau and related kinases present signatures that can serve as novel targets in the development of tau-based therapies and biomarkers for AD.
Funding: The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.
Acknowledgments: This study was supported by Center for Medical Research and Education, Graduate School of Medicine, Osaka University. This work was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI grants, including a Grant-in-Aid for Young Scientists (A) (17H05080) and a Grant-in-Aid for Scientific Research (B) (21H02828) awarded to S.T., and a research grant from the Cell Science Research Foundation awarded to S.T.
Conflict of Interest Statement: The authors declare no conflicts of interest.
Ethical standards: All animal procedures were performed in Osaka University School of Medicine after the approval of the study protocol by the Animal Experiments Committee of Osaka University, Osaka, Japan (decision reference number 29-046-015). This study protocol was reviewed and approved by the Osaka University Living Modified Organisms (LMO) Research Safety Committee (approval number 04232). All animal experiments comply with the ARRIVE guidelines and were carried out in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals.
1. Livingston G, Sommerlad A, Orgeta V, et al. Dementia prevention, intervention, and care. Lancet 2017;390:2673-2734. https://doi.org:10.1016/S0140-6736(17)31363-6
2. Takeda S, Sato N, Rakugi H, Morishita R. Molecular mechanisms linking diabetes mellitus and Alzheimer disease: beta-amyloid peptide, insulin signaling, and neuronal function. Mol Biosyst 2011;7:1822-1827. https://doi.org/10.1039/c0mb00302f
3. Takeda S, Sato N, Uchio-Yamada K, et al. Diabetes-accelerated memory dysfunction via cerebrovascular inflammation and Abeta deposition in an Alzheimer mouse model with diabetes. Proc Natl Acad Sci U S A 2010;107:7036-7041. https://doi.org/10.1073/pnas.1000645107
4. Takeda S, Rakugi H, Morishita R. Roles of vascular risk factors in the pathogenesis of dementia. Hypertens Res 2020;43:162-167. https://doi.org/10.1038/s41440-019-0357-9
5. Ohara T, Doi Y, Ninomiya T, et al. Glucose tolerance status and risk of dementia in the community: the Hisayama study. Neurology 2011;77:1126-1134. https://doi.org/10.1212/WNL.0b013e31822f0435
6. Ott A, Stolk RP, van Harskamp F, Pols HA, Hofman A, Breteler MM. Diabetes mellitus and the risk of dementia: the Rotterdam Study. Neurology 1999;53:1937-1942. https://doi.org/10.1212/wnl.53.9.1937
7. Ballard C, Gauthier S, Corbett A, Brayne C, Aarsland D, Jones E. Alzheimer’s disease. Lancet 2011;377:1019-1031. https://doi.org/10.1016/S0140-6736(10)61349-9
8. Grundke-Iqbal I, Iqbal K, Quinlan M, Tung YC, Zaidi MS, Wisniewski HM. Microtubule-associated protein tau. A component of Alzheimer paired helical filaments. J Biol Chem 1986;261:6084-6089.
9. Gómez-Isla T, Hollister R, West H, et al. Neuronal loss correlates with but exceeds neurofibrillary tangles in Alzheimer’s disease. Ann Neurol 1997;41:17-24. https://doi.org/10.1002/ana.410410106
10. Martin L, Latypova X, Wilson CM, et al. Tau protein kinases: involvement in Alzheimer’s disease. Ageing Res Rev 2013;12:289-309. https://doi.org/10.1016/j.arr.2012.06.003
11. Alonso A, Zaidi T, Novak M, Grundke-Iqbal I, Iqbal K. Hyperphosphorylation induces self-assembly of tau into tangles of paired helical filaments/straight filaments. Proc Natl Acad Sci U S A 2001;98:6923-6928. https://doi.org/10.1073/pnas.121119298
12. Trojanowski JQ, Lee VM. Paired helical filament tau in Alzheimer’s disease. The kinase connection. Am J Pathol 1994;144:449-453.
13. Clodfelder-Miller BJ, Zmijewska AA, Johnson GV, Jope RS. Tau is hyperphosphorylated at multiple sites in mouse brain in vivo after streptozotocin-induced insulin deficiency. Diabetes 2006;55:3320-3325. https://doi.org/10.2337/db06-0485
14. Leboucher A, Laurent C, Fernandez-Gomez FJ, et al. Detrimental effects of diet-induced obesity on τ pathology are independent of insulin resistance in τ transgenic mice. Diabetes 2013;62:1681-1688. https://doi.org/10.2337/db12-0866
15. Nobuhara CK, DeVos SL, Commins C, et al. Tau antibody targeting pathological species blocks neuronal uptake and interneuron propagation of tau in vitro. Am J Pathol 2017;187:1399-1412. https://doi.org/10.1016/j.ajpath.2017.01.022
16. Wesseling H, Mair W, Kumar M, et al. Tau PTM profiles identify patient heterogeneity and stages of Alzheimer’s disease. Cell 2020;183:1699-1713.e13. https://doi.org/10.1016/j.cell.2020.10.029
17. Yoshiyama Y, Higuchi M, Zhang B, et al. Synapse loss and microglial activation precede tangles in a P301S tauopathy mouse model. Neuron 2007;53:337-351. https://doi.org/10.1016/j.neuron.2007.01.010
18. Ayala JE, Samuel VT, Morton GJ, et al. Standard operating procedures for describing and performing metabolic tests of glucose homeostasis in mice. Dis Model Mech 2010;3:525-534. https://doi.org/10.1242/dmm.006239
19. Guyenet SJ, Furrer SA, Damian VM, Baughan TD, La Spada AR, Garden GA. A simple composite phenotype scoring system for evaluating mouse models of cerebellar ataxia. J Vis Exp 2010;39:1787. https://doi.org/10.3791/1787
20. Lei P, Ayton S, Moon S, et al. Motor and cognitive deficits in aged tau knockout mice in two background strains. Mol Neurodegener 2014;9:29. https://doi.org/10.1186/1750-1326-9-29
21. Kawashima Y, Watanabe E, Umeyama T, et al. Optimization of data-independent acquisition mass spectrometry for deep and highly sensitive proteomic analysis. Int J Mol Sci 2019;20:5932. https://doi.org/10.3390/ijms20235932
22. Searle BC, Pino LK, Egertson JD, et al. Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry. Nat Commun 2018;9:5128. https://doi.org/10.1038/s41467-018-07454-w
23. Sato H, Ishida S, Toda K, et al. New approaches to mechanism analysis for drug discovery using DNA microarray data combined with KeyMolnet. Curr Drug Discov Technol 2005;2:89-98. https://doi.org/10.2174/1570163054064701
24. Satoh J, Tabunoki H, Arima K. Molecular network analysis suggests aberrant CREB-mediated gene regulation in the Alzheimer disease hippocampus. Dis Markers 2009;27:239-252. https://doi.org/10.3233/DMA-2009-0670
25. Takeda S. Tau propagation as a diagnostic and therapeutic target for dementia: potentials and unanswered questions. Front Neurosci 2019;13:1274. https://doi.org/10.3389/fnins.2019.01274
26. Takeda S, Wegmann S, Cho H, et al. Neuronal uptake and propagation of a rare phosphorylated high-molecular-weight tau derived from Alzheimer’s disease brain. Nat Commun 2015;6:8490. https://doi.org/10.1038/ncomms9490
27. Götz J, Chen F, van Dorpe J, Nitsch RM. Formation of neurofibrillary tangles in P301l tau transgenic mice induced by Abeta 42 fibrils. Science 2001;293:1491-1495. https://doi.org/10.1126/science.1062097
28. Barthélemy NR, Li Y, Joseph-Mathurin N, et al. A soluble phosphorylated tau signature links tau, amyloid and the evolution of stages of dominantly inherited Alzheimer’s disease. Nat Med 2020;26:398-407. https://doi.org/10.1038/s41591-020-0781-z
29. Brandt R, Léger J, Lee G. Interaction of tau with the neural plasma membrane mediated by tau’s amino-terminal projection domain. J Cell Biol 1995;131:1327-1340. https://doi.org/10.1083/jcb.131.5.1327
30. Brandt R, Trushina NI, Bakota L. Much more than a cytoskeletal protein: physiological and pathological functions of the non-microtubule binding region of tau. Front Neurol 2020;11:590059. https://doi.org/10.3389/fneur.2020.590059
31. Li T, Paudel HK. Glycogen synthase kinase 3beta phosphorylates Alzheimer’s disease-specific Ser396 of microtubule-associated protein tau by a sequential mechanism. Biochemistry 2006;45:3125-3133. https://doi.org/10.1021/bi051634r
32. Zhou XW, Winblad B, Guan Z, Pei JJ. Interactions between glycogen synthase kinase 3beta, protein kinase B, and protein phosphatase 2A in tau phosphorylation in mouse N2a neuroblastoma cells. J Alzheimers Dis 2009;17:929-937. https://doi.org/10.3233/JAD-2009-1113
33. Zheng-Fischhöfer Q, Biernat J, Mandelkow EM, Illenberger S, Godemann R, Mandelkow E. Sequential phosphorylation of Tau by glycogen synthase kinase-3beta and protein kinase A at Thr212 and Ser214 generates the Alzheimer-specific epitope of antibody AT100 and requires a paired-helical-filament-like conformation. Eur J Biochem 1998;252:542-552. https://doi.org/10.1046/j.1432-1327.1998.2520542.x
34. Yoshimura Y, Ichinose T, Yamauchi T. Phosphorylation of Tau protein to sites found in Alzheimer’s disease brain is catalyzed by Ca2+/calmodulin-dependent protein kinase II as demonstrated tandem mass spectrometry. Neurosci Lett 2003;353:185-188. https://doi.org/10.1016/j.neulet.2003.09.037
35. Xu J, Sato S, Okuyama S, et al. Tau-tubulin kinase 1 enhances prefibrillar tau aggregation and motor neuron degeneration in P301L FTDP-17 tau-mutant mice. FASEB J 2010;24:2904-2915. https://doi.org/10.1096/fj.09-150144
36. Taniguchi T, Kawamata T, Mukai H, et al. Phosphorylation of tau is regulated by PKN. J Biol Chem 2001;276:10025-10031. https://doi.org/10.1074/jbc.M007427200
37. Tavares IA, Touma D, Lynham S, et al. Prostate-derived sterile 20-like kinases (PSKs/TAOKs) phosphorylate Tau protein and are activated in tangle-bearing neurons in Alzheimer disease. J Biol Chem 2013;288:15418-15429. https://doi.org/10.1074/jbc.M112.448183
38. Propson NE, Roy ER, Litvinchuk A, Köhl J, Zheng H. Endothelial C3a receptor mediates vascular inflammation and blood-brain barrier permeability during aging. J Clin Invest 2021; 131:e140966. https://doi.org/10.1172/JCI140966
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