Amyloid-β plaque formation and reactive gliosis are required for induction of cognitive deficits in App knock-in mouse models of Alzheimer’s disease

The Morris water maze (MWM) is one of the most commonly used paradigms for assessing hippocampal-dependent spatial learning and memory in mouse models of AD [5, 15]. In this task, mice are required to use extra-maze cues to learn the location of a platform submerged below the water surface. During the initial training session, when the platform can be seen (“visible training”), the motor and visual capacities of the mice were assessed (Fig. 1a). Subsequently, the mice were trained to acquire the spatial location of a platform when the platform was not visible (“hidden training”) (Fig. 1a). One day after the sixth session of hidden training, a probe test (Probe test 1) was conducted without a platform to determine whether mice had learned the location of the platform using extra-maze cues (Fig. 1a). These mice were also subjected to a second probe test (Probe test 2) 7 days after the seventh session of hidden training (Fig. 1a).

To assess motor and visual capabilities, mice were compared across 4-day visible training sessions, during which the platform was indicated by a black cubic landmark (Fig. 1a). App^{NL–G–F/NL–G–F}, App^NL/NL and WT mice performed equally well in these sessions: latency (Fig. 1b; F[2, 41] = 0.12, p = 0.890) and distance (Fig. 1c; F[2, 41] = 0.07, p = 0.929) to reach the platform, as well as swimming speed (Fig. 1d; F[2, 41] = 0.59, p = 0.561), were similar among genotypes. As the training progressed, all genotypes reached the visible platform in the minimum amount of time via the shortest path, as reflected by significant reductions in latency (Fig. 1b; F[1.3, 54] = 45.75, p < 0.001) and distance (Fig. 1c; F[1.5, 60.9] = 39.80, p < 0.001) across the training. Swimming speed over the course of training was significantly increased in all genotypes (Fig. 1d; F[3, 123] = 26.73, p < 0.001). These results indicate that swimming and visual abilities were comparable among App^{NL–G–F/NL–G–F}, App^NL/NL, and WT mice at 24 months of age.

Following the visual training sessions, mice were trained to find a submerged platform during 7-day hidden training sessions (Fig. 1a). App^{NL–G–F/NL–G–F} mice spent more time (Fig. 1e; F[2, 41] = 7.88, p = 0.001, post hoc, WT vs. App^{NL–G–F/NL–G–F}, p < 0.001) and travelled a longer distance (Fig. 1f; F[2, 41] = 15.50, p < 0.001, post hoc, WT vs. App^{NL–G–F/NL–G–F}, p < 0.001) to reach the platform than WT mice across the training. Moreover, relative to WT mice, App^{NL–G–F/NL–G–F} mice exhibited lower path efficiency, as determined by the ratio of the actual distance to the ideal path that the mice could have taken to reach the platform (Fig. 1g; F[2, 41] = 18.66, p < 0.001, post hoc, WT vs. App^{NL–G–F/NL–G–F}, p < 0.001) [16, 17], indicating that spatial accuracy was affected in these mice. Swimming speed did not differ between App^{NL–G–F/NL–G–F} and WT mice (Fig. 1h; F[2, 41] = 0.36, p = 0.700), suggesting that difference in latency was not due to a difference in the ability to swim. Our data also revealed that App^{NL–G–F/NL–G–F} mice could still learn the location of the hidden platform, as these mice exhibited decreases in latency (Fig. 1e; WT, F[3.6, 58] = 4.61, p = 0.004; App^{NL–G–F/NL–G–F}, F[3.4, 51.3] = 2.62, p = 0.054) and distance (Fig. 1f; WT, F[3.2, 51.0] = 7.44, p < 0.001; App^{NL–G–F/NL–G–F}, F[3.8, 57.4] = 2.93, p = 0.030) as the training progressed. Swimming speed over the course of training was significantly decreased in WT mice (Fig. 1h; WT, F[6, 96] = 5.45, p < 0.001), while it remained constant in App^{NL–G–F/NL–G–F} mice throughout the sessions (App^{NL–G–F/NL–G–F}, F[2.9, 43.9] = 2.18, p = 0.106). Taken together, these results suggest that 24-month-old App^{NL–G–F/NL–G–F} mice exhibited a significant decline in the ability to learn the spatial location of a submerged platform.

In Probe test 1 (Fig. 1a), App^{NL–G–F/NL–G–F} mice spent less time in the target quadrant than WT mice (Fig. 2a; F[2, 41] = 4.23, p = 0.021, post hoc, WT vs. App^{NL–G–F/NL–G–F}, p = 0.025), although they still exhibited a preference for the target quadrant over non-target quadrants (Additional file 1: Fig. S1a; WT, t(16) = − 4.45, p < 0.001; App^{NL–G–F/NL–G–F}, t(15) = − 2.14, p = 0.049). In addition, the percentages of time spent in the target quadrant were significantly greater than the chance level for both WT and App^{NL–G–F/NL–G–F} mice (Fig. 2a; WT, t(32) = 4.45, p < 0.001; App^{NL–G–F/NL–G–F}, t(30) = 2.14, p = 0.040). During Probe test 1, although differences in the number of platform crossings between WT and App^{NL–G–F/NL–G–F} mice did not reach statistical significance (Fig. 2b; F[2, 41] = 2.95, p = 0.064), App^{NL–G–F/NL–G–F} mice exhibited significantly longer proximity to the platform than WT mice (Fig. 2c; F[2, 41] = 5.61, p = 0.007, post hoc, WT vs. App^{NL–G–F/NL–G–F}, p = 0.005); this parameter is believed to provide a more sensitive evaluation of spatial memory performance [18, 19]. The total distance travelled (Fig. 2d; F[2, 41] = 2.68, p = 0.081) and swimming speed (Fig. 2e; F[2, 41] = 2.49, p = 0.095) in Probe test 1 did not differ between WT and App^{NL–G–F/NL–G–F} mice, suggesting that the two genotypes had similar motor capability and motivation to search for the platform. Together, these results suggest that App^{NL–G–F/NL–G–F} mice had reduced spatial memory 1 day after the last training session, but still exhibited a spatial bias toward the former location of platform.

In Probe test 2 (Fig. 1a), the percentage of time spent in the target quadrant was significantly lower in App^{NL–G–F/NL–G–F} mice than in WT mice (Fig. 2f; F[2, 41] = 9.03, p < 0.001, post hoc, WT vs. App^{NL–G–F/NL–G–F}, p < 0.001). In addition, App^{NL–G–F/NL–G–F} mice did not exhibit a spatial bias toward the target quadrant over non-target quadrants (Additional file 1: Fig. S1b; WT, t(16) = − 3.30, p < 0.001; App^{NL–G–F/NL–G–F}, t(15) = 1.70, p = 0.110). Moreover, unlike WT mice (Fig. 2f; WT, t(32) = 3.30, p = 0.002), the percentage of time spent in the target quadrant by App^{NL–G–F/NL–G–F} mice was comparable to the chance level (Fig. 2f; App^{NL–G–F/NL–G–F}, t(30) = − 1.70, p = 0.099). Furthermore, although the number of platform crossings during the test did not differ between WT and App^{NL–G–F/NL–G–F} mice (Fig. 2g; F[2, 41] = 1.81, p = 0.176), the proximity to the platform was significantly higher in App^{NL–G–F/NL–G–F} mice (Fig. 2h; F[2, 41] = 8.94, p < 0.001, post hoc, WT vs. App^{NL–G–F/NL–G–F}, p < 0.001).

Total distance travelled (Fig. 2i; F[2, 41] = 3.55, p = 0.038, post hoc, WT vs. App^{NL–G–F/NL–G–F}, p = 0.102) and swimming speed (Fig. 2j; F[2, 41] = 3.13, p = 0.054) were comparable between App^{NL–G–F/NL–G–F} and WT mice, providing further confirmation that motor capability and motivation did not differ between genotypes. These results suggest that App^{NL–G–F/NL–G–F} mice could not retain the acquired spatial memory 7 days after the last training session.

Taken together, our findings indicate App^{NL–G–F/NL–G–F} mice exhibit definitive deficits in spatial learning and memory at the age of 24 months.

App^NL/NL mice exhibited motor and visual capabilities comparable to those of WT mice during the visible training sessions, as evidenced by the lack of a between-genotype difference in latency (Fig. 1b), distance (Fig. 1c) and swimming speed (Fig. 1d).

During the hidden training sessions, App^NL/NL mice performed as well as WT mice: latency (Fig. 1e; post hoc, WT vs. App^NL/NL, p = 0.362) and distance (Fig. 1f; post hoc, WT vs. App^NL/NL, p = 0.450) did not differ between WT and App^NL/NL mice. In addition, path efficiency was similar between App^NL/NL and WT mice across the training (Fig. 1g; post hoc, WT vs. App^NL/NL, p = 0.999), indicating that both genotypes reached the hidden platform with comparable spatial accuracy. Swimming speed in App^NL/NL mice was also comparable to that in WT mice and significantly decreased throughout the sessions (Fig. 1h; App^NL/NL, F[6, 60] = 5.40, p < 0.001). Taken together, these results suggest that spatial learning ability in App^NL/NL mice was equivalent of that in WT mice at 24 months of age.

In Probe test 1, time spent in the target quadrant did not significantly differ between WT and App^NL/NL mice (Fig. 2a; post hoc, WT vs. App^NL/NL, p = 0.955). App^NL/NL spent a significantly higher percentage of time in the target quadrant than would be predicted by chance (App^NL/NL, t(20) = 2.80, p = 0.011), and exhibited a preference toward the target quadrant over non-target quadrants (Additional file 1: Fig. S1a; App^NL/NL, t(10) = − 2.80, p = 0.019). Neither the number of platform crossings (Fig. 2b) nor proximity to the platform (Fig. 2c; post hoc, WT vs. App^NL/NL, p = 0.554) differed between WT and App^NL/NL mice. The total distance travelled (Fig. 2d) and swimming speed (Fig. 2e) during the test did not differ between WT and App^NL/NL mice. Taken together, these results suggest that spatial memory at 1 day after the last training session was equivalent in App^NL/NL and WT mice at 24 months of age.

In Probe test 2, the percentage of time spent in the target quadrant did not differ significantly between WT and App^NL/NL mice (Fig. 2f; post hoc, WT vs. App^NL/NL, p = 0.907). App^NL/NL mice still spent a significantly higher percentage of time in the target quadrant than predicted by chance (Fig. 2f; App^NL/NL, t(20) = 5.93, p < 0.001), and exhibited a spatial bias toward this quadrant over other quadrants (Additional file 1: Fig. S1b; App^NL/NL, t(10) = − 5.94, p < 0.001). The number of platform crossings (Fig. 2g) and proximity to the platform (Fig. 2h; post hoc, WT vs. App^NL/NL, p = 0.293) were similar between WT and App^NL/NL mice, as reflected by total distance travelled (Fig. 2i; post hoc, WT vs. App^NL/NL, p = 0.844) and swimming speed (Fig. 2j) during the test, indicating that motor capability and motivation were comparable between these genotypes. Taken together, these results suggest that App^NL/NL mice had normal memory function even 7 days after the last training session. Taken together, these results suggest that App^NL/NL mice do not exhibit any alterations in spatial learning and memory, even at the age of 24 months.

To determine whether cognitive deficits are correlated with Aβ-related brain pathology, we immunostained coronal brain sections from App^{NL–G–F/NL–G–F}, App^NL/NL and WT mice with the anti-Aβ antibody 82E1, anti-Iba1 antibody as a microglial marker, and anti-GFAP antibody as an astrocytic marker. Intense Iba1 immunoreactivity were clustered around regions of Aβ immunoreactivity both in the cortex (Fig. 3b) and hippocampus (Fig. 3d) of 24-month-old App^{NL–G–F/NL–G–F} mice, and high-magnification images in the cortex confirmed accumulation of microglia around the Aβ plaques (Fig. 3b, lower panels). App^{NL–G–F/NL–G–F} mice also exhibited intense GFAP immunoreactivity in the cortex (Fig. 3c) and hippocampus (Fig. 3e), suggestive of astrogliosis. Higher-magnification images revealed that many reactive astrocytes were present around the Aβ plaques (Fig. 3c, lower panels). Taken together, these findings indicate that App^{NL–G–F/NL–G–F} mice developed extensive microgliosis and astrocytosis associated with the aggressive Aβ plaque formation in their brains.

By sharp contrast, despite extensive analysis, we did not detect any Aβ-related pathology in the brains of App^NL/NL mice at 24 months of age. No Aβ deposits were detected in the cortex (Fig. 3b and c) or hippocampus (Fig. 3d and e), and Iba1 and GFAP immunoreactivities were similar between WT and App^NL/NL mice in both regions (Fig. 3b and c for cortex; Fig. 3d and e for hippocampus), indicating the absence of reactive gliosis in the brains of App^NL/NL mice. Taken together, these results suggest that App^NL/NL mice developed neither Aβ deposition nor neuroinflammation in the brain, even at 24 months of age.

The original lines of App-KI (App^{NL–G–F/NL–G–F} and App^NL/NL) mice on a C57BL/6J genetic background [10] were obtained from RIKEN Center for Brain Science (Wako, Japan) and maintained at the Institute for Animal Experimentation in National Center for Geriatrics and Gerontology as described previously [14]. After weaning, all mice were housed socially in same-sex groups and only male mice were used for the experiments with mixed genotypes. All handling and experimental procedures were performed in accordance with the Guidelines for the Care of Laboratory Animals of National Center for Geriatrics and Gerontology (Obu, Japan).

All experiments were performed in a white circular pool (1.0 m in diameter; O’hara & Co., Tokyo, Japan) with a light intensity on the center of the pool of approximately 80 lx. Water temperature was maintained at 24 ± 1 °C and was made opaque using nontoxic white paint during hidden training sessions and probe tests.

First, 24-month-old mice (n = 11–17/genotype) were individually handled for 3 days before starting the experiment to acclimate them to the introduction and removal of the pool. After the habituation period, mice were subjected to 4-day visible training sessions (four trials per day, with an intertrial interval of approximately 15 min) (Fig. 1a), in which a platform (10 cm in diameter) was made visible by attaching a black cubic landmark. The aim of the visible training was to exclude mice with motor, visual or motivational impairments. If the mouse found the platform within a 2-min time limit, the mouse remained there for 20 s. If not, the mouse was gently guided towards the platform before staying on it. After the 20-s period on the platform, mice were placed in the cage heated by a heating pad to dry and then transported back to their home cage. The location of the platform and the start position were changed randomly in each trial. For each trial, latency to reach the platform (s), swimming distance (cm) and swimming speed (cm/s) were automatically measured using TimeMWM software (O’hara & Co., Tokyo, Japan).

Following the visible training sessions, the mice were subjected to 7-day hidden training sessions (four trials per day, with an intertrial interval of approximately 20 min) (Fig. 1a), in which the platform was placed 0.8–1.0 cm below the water surface. Four distinct objects of different geometry were used around the pool as spatial cues. At the end of the trial, either when the mouse had found the platform or when a 60-s time limit had elapsed, mice were allowed to rest on the platform for 15 s. Then, mice were placed in the cage heated by a heating pad to dry before returning to their home cage. The start position was changed randomly to avoid track memorization, while the location of the platform was fixed throughout the experiment. Latency (s) and distance travelled (cm) to reach the platform and swimming speed (cm/s) were also measured by the software. To quantify the efficiency of the strategy pursued in reaching the platform, path efficiency was calculated by dividing the distance between the first and last locations by the total distance travelled (Path efficiency = Distance between the starting point and the final point (the location of the platform)/Total distance travelled).

To confirm that this spatial task was acquired based on navigation by distal cues, two probe tests were conducted as following: the first probe test at one day after the sixth session (Probe test 1) and the second probe test at 7 days after the seventh session (Probe test 2) of the hidden training (Fig. 1a). In these tests, the platform was removed from the pool and mice were allowed to search the platform for 60 s. Time spent in each quadrant (TQ; target quadrant, OQ; opposite quadrant; RQ; right quadrant, LQ; left quadrant) (s), number of crossings over the platform location and average proximity to the platform (cm) were measured by the software. Total distance travelled (cm) and swimming speed (cm/s) were also measured to rule out the involvement of motor function and motivation to search the platform as confounding factors.

After the behavioral experiments, these mice were administered intraperitoneally with the combined agent with medetomidine (0.3 mg/kg), midazolam (4 mg/kg) and butorphanol (5 mg/kg). The whole brain tissues were collected and subjected to immunohistochemistry and other experiments.

Immunohistochemical staining was performed in male mice of the 3 genotypes (n = 4/genotype) to examine the degree of Aβ amyloidosis and reactive gliosis in cortical and hippocampal regions (Fig. 3a). During maintenance of anesthesia with the same cocktail combination (medetomidine (0.3 mg/kg) + midazolam (4 mg/kg) + butorphanol (5 mg/kg)), mice were then perfused intracardially with ice-cold saline followed by 4% paraformaldehyde (PFA) in 0.1 M phosphate buffer (PB). The whole brains were collected and immersed in the same fixative solution at 4 °C overnight. For cryoprotection, the fixed brains were transferred into 20% and then 30% sucrose in 0.1 M PB at 4 °C until the tissues sank. Brains were sliced coronally into 25-μm free-floating sections using a cryostat (Leica CM3050; Leica Microsystems, Wetzlar, Germany). For antigen retrieval, sections were incubated with 70% formic acid at 5 min. After wash with PBS containing 0.1% Triton X-100 (PBS-T), the sections were blocked in a buffer containing 5% normal goat serum, 0.5% bovine serum albumin (BSA) and 0.3% Triton X-100 in PBS for 1 h, and then incubated overnight at 4 °C with primary antibodies in a dilution buffer containing 3% normal goat serum, 0.5% BSA and 0.3% Triton X-100 in PBS. The following primary antibodies were used: mouse anti-Aβ 82E1 (1:200; 10323; IBL, Gunma, Japan), rabbit anti-Iba1 (1:500; 019-19741; Wako, Osaka, Japan) and rabbit anti-GFAP (1:500; ROI003; SHIMA Laboratories Co., Ltd., Tokyo, Japan). After three washes with PBS-T were given, the sections were then incubated for 2–3 h with the following secondary antibodies in the dilution buffer; Alexa Fluor 594 goat anti-mouse IgG (1:500; ab150116; Abcam) and Alexa Fluor 488 goat anti-rabbit IgG (1:500; ab150077; Abcam). After three washes with PBS-T, the sections were incubated with DAPI (2 μg/ml) at 5 min and mounted in Aqua-Poly/Mount (Polysciences Inc., Warrington, USA). Immunofluorescence images of the sections were captured using a florescence microscope (BZ-9000; Keyence, Osaka, Japan) or a confocal laser-scanning microscope (LSM 780; Carl Zeiss, Oberkochen, Germany).

As previously described [14], statistical differences between genotypes against behavioral parameters with one dependent variable were determined by repeated-measures analysis of variance (ANOVA). When necessary, Greenhouse–Geisser estimates of sphericity were used to correct for degrees of freedom. Bonferroni post hoc comparisons were used to evaluate group differences. For the comparisons of multiple means with genotypes as one independent variable, one-way ANOVA followed by the Tukey’s post hoc tests was used. One-sample t-test was used to compare performance on the probe test of the MWM task against chance level (25%). Differences of the percentage of time spent between target and non-target quadrants during probe tests were evaluated using paired t-test. Data are presented as mean ± SEM. All alpha levels were set at 0.05.

We thank Sachie Chikamatsu and Kimi Takei for technical assistance and Drs. Nobuyuki Kimura and Tetsuya Kimura for the comments on the data. Schematics of mouse brain slice are reproduced by curtesy of Database Center for Life Science (© 2016 DBCLS TogoTV).

The authors declare that they have no competing interests.

The datasets used and/or analyzed during the current study are available from the corresponding authors on reasonable request.

Not applicable.

All animal experimental procedures were performed according to the NIH Guide for the Care and Use of Laboratory Animals and other national regulations and policies with the approval of the Animal Care and Use Committee at National Center for Geriatrics and Gerontology, Japan (Approval number: 30-1).

This study was supported by JSPS KAKENHI Grant No. JP18K15381 (to Y.S.), the Research Funding for Longevity Science from National Center for Geriatrics and Gerontology, Japan, Grant No. 28-26, Takeda Science Foundation (JP) and DAIKO FOUNDATION (JP) (to K.M.I.).

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The datasets used and/or analyzed during the current study are available from the corresponding authors on reasonable request.