Late-long-term potentiation magnitude, but not Aβ levels and amyloid pathology, is associated with behavioral performance in a rat knock-in model of Alzheimer disease

Abstract

Cleavage of Amyloid precursor protein by β- and γ-secretases lead to Aβ formation. The widely accepted pathogenic model states that these mutations cause AD via an increase in Aβ formation and accumulation of Aβ in Amyloid plaques. APP mutations cause early onset familial forms of Alzheimer's disease (FAD) in humans. We generated App-Swedish (App^s ) knock-in rats, which carry a pathogenic APP mutation in the endogenous rat App gene. This mutation increases β-secretase processing of APP leading to both augmented Aβ production and facilitation of glutamate release in App^s/s rats, via a β-secretase and APP-dependent glutamate release mechanism. Here, we studied 11 to 14-month-old male and female App^s/s rats. To determine whether the Swedish App mutation leads to behavioral deficits, App^s/s knock-in rats were subjected to behavioral analysis using the IntelliCage platform, an automated behavioral testing system. This system allows behavioral assessment in socially housed animals reflecting a more natural, less stress-inducing environment and eliminates experimenter error and bias while increasing precision of measurements. Surprisingly, a spatial discrimination and flexibility task that can reveal deficits in higher order brain function showed that App^s/s females, but not App^s/s male rats, performed significantly worse than same sex controls. Moreover, female control rats performed significantly better than control and App^s/s male rats. The Swedish mutation causes a significant increase in Aβ production in 14-month-old animals of both sexes. Yet, male and female App^s/s rats showed no evidence of AD-related amyloid pathology. Finally, App^s/s rats did not show signs of significant neuroinflammation. Given that the APP Swedish mutation causes alterations in glutamate release, we analyzed Long-term potentiation (LTP), a long-lasting form of synaptic plasticity that is a cellular basis for learning and memory. Strikingly, LTP was significantly increased in App^s/s control females compared to both App^s/s sexes and control males. In conclusion, this study shows that behavioral performances are sex and App-genotype dependent. In addition, they are associated with LTP values and not Aβ or AD-related pathology. These data, and the failures of anti-Aβ therapies in humans, suggest that alternative pathways, such as those leading to LTP dysfunction, should be targeted for disease-modifying AD therapy.

Keywords: Alzheimer’s disease; amyloid precursor protein (APP); amyloid-β; cognition; knock-in rat; long term potentiation (LTP); synaptic plasticity; β-secretase.

FIGURE 1

Place learning analysis of App^h/h and App^s/s rats. (A) Schematic of operant corner, with “correct” corner in green and incorrect in yellow. (B) Area under the “correct” learning curve analysis for individual rats stratified by each day of the drinking session. Data are represented as mean ± SEM and were analyzed by two–way RM ANOVA followed by post-hoc Sidak’s multiple comparisons test when ANOVA showed significant differences (App^h/h rats: day factor, F_{(1, 35)} = 14.23, P = 0.0006; sex factor, F_{(1, 35)} = 0.5230, P = 0.4744; day × sex interaction, F_{(1, 35)} = 0.5370, P = 0.4686; post-hoc Sidak’s multiple comparisons test, females day 1 vs. day 2 P = 0.0806, males day 1 vs. day 2 P = 0.0054. App^s/s rats: day factor, F_{(1, 19)} = 74.29, P < 0.0001; sex factor, F_{(1, 19)} = 1.784, P = 0.1974; day × sex interaction, F_{(1, 19)} = 0.3865, P = 0.5415; post-hoc Sidak’s multiple comparisons test, females day 1 vs. day 2 P < 0.0001, males day 1 vs. day 2 P < 0.0001). (C) Area under the “correct” learning curve analysis for individual rats stratified by sex and genotype. Data are represented as mean ± SEM and were analyzed by two–way ANOVA followed by post-hoc Sidak’s multiple comparisons test when ANOVA showed significant differences. Correct day 1: sex factor, F_{(1, 54)} = 1.358, P = 0.2490; genotype factor, F_{(1, 54)} = 0.0246, P = 0.8760; sex × genotype interaction, F_{(1, 54)} = 0.0012, P = 0.9719. Correct day 2: sex factor, F_{(1, 54)} = 1.515, P = 0.2238; genotype factor, F_{(1, 54)} = 1.998, P = 0.0960; sex × genotype interaction, F_{(1, 54)} = 0.9083, P = 0.3448). We tested 18 female App^h/h, 19 male App^h/h, 10 female App^s/s, and 11 male App^s/s. No animals met the exclusion criteria of <25 visits per drinking session, and none died during the behavioral experiments. ^**P < 0.01, and ^****P < 0.0001.

FIGURE 2

Place learning with corner switch analysis of App^h/h and App^s/s rats. (A) Schematic of corner switching program on top left, with a 45 min between each corner switch. For rats assigned to corner 1, bottom left, green indicates “correct” corner which then alternates to an adjacent corner every 45 min (arrows), with yellow indicating “incorrect” corners and red indicating “incorrect, previously correct” corner. (B) Area under the “correct” learning curve analysis for individual rats stratified by each day of the drinking session. Data are represented as mean ± SEM and were analyzed by two–way RM ANOVA followed by post-hoc Sidak’s multiple comparisons test when ANOVA showed significant differences (App^h/h rats: day factor, F_{(1, 35)} = 3.540, P = 0.0683; sex factor, F_{(1, 35)} = 0.05113, P = 0.8224; day × sex interaction, F_{(1, 35)} = 1.041, P = 0.4525. App^s/s rats: day factor F_{(1, 19)} = 0.3123, P = 0.5828; sex factor, F_{(1, 19)} = 0.1210, P = 0.5059; day × sex interaction, F_{(1, 19)} = 1.900, P = 0.1840). (C) Area under the “correct” learning curve analysis for individual rats stratified by sex and genotype. Data are represented as mean ± SEM and were analyzed by two–way ANOVA followed by post-hoc Sidak’s multiple comparisons test when ANOVA showed significant differences. Correct day 1: sex factor F_{(1, 54)} = 0.6217, P = 0.4339; genotype factor, F_{(1, 54)} = 2.078, P = 0.0812; sex × genotype interaction, F_{(1, 54)} = 0.01476, P = 0.9037. Correct day 2: sex factor, F_{(1, 54)} = 1.406, P = 0.2409; genotype factor, F_{(1, 54)} = 2.330, P = 0.1328; sex × genotype interaction, F_{(1, 54)} = 0.6338, P = 0.4294. We tested 18 female App^h/h, 19 male App^h/h, 10 female App^s/s, and 11 male App^s/s. No animals met the exclusion criteria of <25 visits per drinking session, and none died during the behavioral experiments.

FIGURE 3

Behavioral sequencing analysis of App^h/h and App^s/s rats. (A) Schematic of behavioral sequencing program on top left, for a rat assigned to corner 2 or 4. Green indicates “correct” corner which then alternates to an adjacent corner after a nosepoke, with yellow indicating “incorrect” corners and red indicating “opposite i.e., incorrect, previously correct” corner. (B) Area under the “correct learning curve analyses for individual rats stratified by each day of the drinking session. Data are represented as mean ± SEM and were analyzed by two–way RM ANOVA followed by post-hoc Sidak’s multiple comparisons test when ANOVA showed significant differences (App^h/h rats: day factor, F_{(1.870, 65.45)} = 118.6, P < 0.0001; sex factor, F_{(1, 35)} = 27.08, P < 0.0001; day × sex interaction, F_{(2, 70)} = 6.697, P = 0.0022; post-hoc Sidak’s multiple comparisons test, females day 1 vs. day 2 P < 0.0001; day 1 vs. day3 P < 0.0001; day 2 vs. day 3 P = 0.0359, males day 1 vs. day 2 P = 0.0006; day 1 vs. day3 P < 0.0001; day 2 vs. day 3 P < 0.0001. App^s/s rats: day factor, F_{(1.525, 28.98)} = 65.85, P < 0.0001; sex factor, F_{(1, 19)} = 0.09169, P = 7653; day × sex interaction, F_{(2, 38)} = 0.1354, P = 0.8738; post-hoc Sidak’s multiple comparisons test, females day 1 vs. day 2 P < 0.0001; day 1 vs. day3 P < 0.0001; day 2 vs. day 3 P < 0.0001, males day 1 vs. day 2 P = 0.0016; day 1 vs. day3 P = 0.0012; day 2 vs. day 3 P = 0.0450). *p < 0.05, ^**p < 0.01, ^***p < 0.001, ^****p < 0.0001. (C) Area under the “correct” learning curve analysis for individual rats stratified by sex and genotype. Data are represented as mean ± SEM and were analyzed by two–way ANOVA followed by post-hoc Sidak’s multiple comparisons test when ANOVA showed significant differences. Correct day 1: sex factor, F_{(1, 54)} = 12.30, P = 0.0009; genotype factor, F_{(1, 54)} = 6.694, P = 0.0124; sex × genotype interaction, F_{(1, 54)} = 11.39, P = 0.0014; post-hoc Sidak’s multiple comparisons test, female App^h/h vs. female App^s/s P = 0.0008; female App^h/h vs. male App^h/h P < 0.0001; female App^h/h vs. male App^s/s P = 0.004; female App^s/s. vs. male App^h/h P = 0.9882; female App^s/s. vs. male App^s/s P > 0.9999; male App^h/h vs. male App^s/s P = 0.9939. Correct day 2: sex factor, F_{(1, 54)} = 0.7694, P = 0.3843; genotype factor, F_{(1, 54)} = 17.69, P < 0.0001; sex × genotype interaction, F_{(1, 54)} = 44.50, P < 0.0001; post-hoc Sidak’s multiple comparisons test, female App^h/h vs. female App^s/s P < 0.0001; female App^h/h vs. male App^h/h P < 0.0001; female App^h/h vs. male App^s/s P = 0.0038; female App^s/s. vs. male App^h/h P = 0.1334; female App^s/s. vs. male App^s/s P = 0.0038; male App^h/h vs. male App^s/s P = 0.3970. Correct day 3: sex factor, F_{(1, 54)} = 2.865, P = 0.0963; genotype factor, F_{(1, 54)} = 0.1859, P = 0.681; sex × genotype interaction, F_{(1, 54)} = 0.9552, P = 0.3327. We tested 18 female App^h/h, 19 male App^h/h, 10 female App^s/s, and 11 male App^s/s. No animals met the exclusion criteria of <25 visits per drinking session, and none died during the behavioral experiments.

FIGURE 4

Increased processing by β-secretase and decreased processing by α-secretase of Swedish APP. (A) Western blot (WB) of brain lysates from App^s/s and App^h/h rats with an anti-GAPDH antibody (left) and the anti C-terminal APP antibody Y188 (right). A longer exposure was used to visualize the two C-Terminal APP fragments βCTF and αCTF (bottom). The samples analyze contain equal amounts of brain lysates from: App^h/h females n = 4, App^s/s females n = 5, App^h/h males n = 5, App^s/s males n = 4. (B) Levels of sAPPα were determined by ELISA (App^h/h females n = 4, App^s/s females n = 5, App^h/h males n = 5, App^s/s males n = 4). Data are represented as mean ± SEM and were analyzed by two–way ANOVA followed by post-hoc Sidak’s multiple comparisons test when ANOVA showed significant differences (sex factor, F_{(1, 14)} = 0.05753, P = 0.8139; genotype factor, F_{(1, 14)} = 69.01, P < 0.0001; sex × genotype interaction, F_{(1, 14)} = 1.037, P = 0.3259; post-hoc Sidak’s multiple comparisons test, female App^h/h vs. female App^s/s P < 0.0001; female App^h/h vs. male App^h/h P = 0.9953; female App^h/h vs. male App^s/s P = 0.0005; female App^s/s. vs. male App^h/h P < 0.0001; female App^s/s. vs. male App^s/s P = 0.9478; male App^h/h vs. male App^s/s P = 0.0009). (C) Levels of sAPPβSw were determined by ELISA (App^h/h females n = 4, App^s/s females n = 5, App^h/h males n = 5, App^s/s males n = 4). Control samples have no signal because the ELISA does not recognize wild type sAPPβ. Data are represented as mean ± SEM and were analyzed by two–way ANOVA followed by post-hoc Sidak’s multiple comparisons test when ANOVA showed significant differences (sex factor, F_{(1, 14)} = 0.6506, P = 0.4334; genotype factor, F_{(1, 14)} = 3347, P < 0.0001; sex × genotype interaction, F_{(1, 14)} = 0.6531, P = 0.4325; post-hoc Sidak’s multiple comparisons test, female App^h/h vs. female App^s/s P < 0.0001; female App^h/h vs. male App^h/h P > 0.9999; female App^h/h vs. male App^s/s P < 0.0001; female App^s/s. vs. male App^h/h P < 0.0001; female App^s/s. vs. male App^s/s P = 0.8520; male App^h/h vs. male App^s/s P < 0.0001).

FIGURE 5

Analysis of Aβ species and amyloid pathology in App^h/h and App^s/s rats brains. (A) ELISA levels of Aβ40, Aβ42 and Aβ43 in App^h/h females n = 4, App^s/s females n = 5, App^h/h males n = 5, App^s/s males n = 4. Ratio of Aβ42/Aβ40 and Aβ43/Aβ40 are also presented. Data are represented as mean ± SEM and were analyzed by two–way ANOVA followed by post-hoc Sidak’s multiple comparisons test when ANOVA showed significant differences (Aβ40: sex factor, F_{(1, 14)} = 0.7569, P = 0.3990; genotype factor, F_{(1, 14)} = 440.9, P < 0.0001; sex × genotype interaction, F_{(1, 14)} = 0.1036, P = 0.7523; post-hoc Sidak’s multiple comparisons test, female App^h/h vs. female App^s/s P < 0.0001; female App^h/h vs. male App^h/h P = 0.9993; female App^h/h vs. male App^s/s P < 0.0001; female App^s/s. vs. male App^h/h P < 0.0001; female App^s/s. vs. male App^s/s P = 0.9593; male App^h/h vs. male App^s/s P < 0.0001. Aβ42: sex factor, F_{(1, 14)} = 1.379, P = 0.2598; genotype factor, F_{(1, 14)} = 224.3, P < 0.0001; sex × genotype interaction, F_{(1, 14)} = 0.4704, P = 0.5040; post-hoc Sidak’s multiple comparisons test, female App^h/h vs. female App^s/s P < 0.0001; female App^h/h vs. male App^h/h P = 0.9997; female App^h/h vs. male App^s/s P < 0.0001; female App^s/s. vs. male App^h/h P < 0.0001; female App^s/s. vs. male App^s/s P = 0.7560; male App^h/h vs. male App^s/s P < 0.0001. Aβ43: sex factor, F_{(1, 14)} = 1.877, P = 0.1923; genotype factor, F_{(1, 14)} = 0.006110, P = 0.9388; sex × genotype interaction, F_{(1, 14)} = 0.2458, P = 0.6277. Aβ42/Aβ40: sex factor, F_{(1, 14)} = 0.2542, P = 0.6220; genotype factor, F_{(1, 14)} = 1.009, P = 0.3322; sex × genotype interaction, F_{(1, 14)} = 0.06414, P = 0.8037. Aβ40/Aβ43: sex factor, F_{(1, 14)} = 0.1730, P = 0.6838; genotype factor, F_{(1, 14)} = 83.80, P < 0.0001; sex × genotype interaction, F_{(1, 14)} = 0.03059, P = 0.8637; post-hoc Sidak’s multiple comparisons test, female App^h/h vs. female App^s/s P = 0.0001; female App^h/h vs. male App^h/h P > 0.9999; female App^h/h vs. male App^s/s P < 0.0001; female App^s/s. vs. male App^h/h P < 0.0001; female App^s/s. vs. male App^s/s P = 0.9990; male App^h/h vs. male App^s/s P < 0.0001). (B) Quantitation of oligomeric Aβ detected by dot-blots using the oligomer-specific antibody A11. Both the s100 and p100 brain fractions were tested. Before immunoblot analysis, membranes were stained with Ponceau red. Quantitative analysis of A11 blot was normalized to the Ponceau red quantitative analysis. We analyzed: App^h/h females n = 4, App^s/s females n = 5, App^h/h males n = 5, App^s/s males n = 4. However, the s100 fraction of one App^s/s male sample, which gave a red Ponceau signal but not an A11 signal, was excluded. Data are represented as mean ± SEM and were analyzed by two–way ANOVA followed by post-hoc Sidak’s multiple comparisons test when ANOVA showed significant differences (oligomeric Aβ in s100: sex factor, F_{(1, 13)} = 0.4499, P = 0.5141; genotype factor, F_{(1, 13)} = 2.6523, P = 0.1273; sex × genotype interaction, F_{(1, 13)} = 2.738, P = 0.1591. Oligomeric Aβ in p100: sex factor, F_{(1, 14)} = 0.4088, P = 0.5329; genotype factor, F_{(1, 14)} = 2.907, P = 0.1103; sex × genotype interaction, F_{(1, 14)} = 0.4088, P = 0.5329). The dot blot images (WB with A11 and Red Ponceau staining) are shown on the right. (C) Histopathological analysis of 14-month-old App^h/h and App^s/s rats (App^h/h, 5 male and 4 female rats, and App^s/s, 5 male and 5 female). The left panels show representative images of the anterior hippocampus and overlaying somatosensory cortex of App^h/h and App^s/s rat brains. Illustrates of, from the top to bottom, neurons (NeuN) and Amyloidβ (6E10+4G8). The scale bar is equivalent to 500 microns. The right panels show high-magnification picture of the hippocampal CA1 subregion for the staining depicted in the left panels. The scale bar is equivalent to 50 microns.

FIGURE 6

Analysis of tau pathology and phosphorylation in App^h/h and App^s/s rats brains. (A) Histopathological analysis, using the anti-phospho-Tau antibody AT8 staining, of 14-month-old App^h/h and App^s/s rats (App^h/h, 5 male and 4 female rats, and App^s/s, 5 male and 5 female). The left panels show representative images of the anterior hippocampus and overlaying somatosensory cortex of App^h/h and App^s/s rat brains. The scale bar is equivalent to 500 microns. The right panels show high-magnification picture of the hippocampal CA1 subregion for the staining depicted in the left panels. The scale bar is equivalent to 50 microns. (B) WB of brain lysates from App^s/s and App^h/h rats with DA9, CP13 and PHF1, which recognize: total Tau, tau phosphorylated on S²⁰², and tau phosphorylated on S^396–404, respectively. The samples analyze are the same used in Figure 4A.

FIGURE 7

Neuroinflammation analysis in App^h/h and App^s/s rats. (A) Histopathological analysis, using the astrocytes (GFAP) and microglia (Iba1) markers, of 14-month-old App^h/h and App^s/s rats (App^h/h, 5 male and 4 female rats, and App^s/s, 5 male and 5 female). The left panels show representative images of the anterior hippocampus and overlaying somatosensory cortex of App^h/h and App^s/s rat brains. The scale bar is equivalent to 500 microns. The right panels show high-magnification picture of the hippocampal CA1 subregion for the staining depicted in the left panels. The scale bar is equivalent to 50 microns. (B) Levels of IFN-δ, IL-1β, IL-4, IL-5, IL-6, CXCL1, IL-10, IL-13, and TNF-α in the CNS of 14 months old App^h/h and App^s/s rats (App^h/h, 5 male and 4 female rats, and App^s/s, 4 male and 5 female) were measured by ELISA. Data are represented as mean ± SEM and were analyzed by two–way ANOVA followed by post-hoc Sidak’s multiple comparisons test when ANOVA showed significant differences (IFN-γ: sex factor, F_{(1, 14)} = 1.541, P = 0.2348; genotype factor, F_{(1, 14)} = 0.6209, P = 0.4438; sex × genotype interaction, F_{(1, 14)} = 2.295, P = 0.1520. IL-10: sex factor, F_{(1, 14)} = 0.7531, P = 0.4001; genotype factor, F_{(1, 14)} = 0.7504, P = 0.4010; sex × genotype interaction, F_{(1, 14)} = 0.5898, P = 0.4553. IL-13: sex factor, F_{(1, 14)} = 0.5895, P = 0.4554; genotype factor, F_{(1, 14)} = 1.128, P = 0.3062; sex × genotype interaction, F_{(1, 14)} = 0.5577, P = 0.4675. IL-1β: sex factor, F_{(1, 14)} = 3.074, P = 0.1014; genotype factor, F_{(1, 14)} = 3.743, P = 0.0735; sex × genotype interaction, F_{(1, 14)} = 0.3192, P = 0.5810. IL-4: sex factor, F_{(1, 14)} = 1.239, P = 0.2844; genotype factor, F_{(1, 14)} = 0.4730, P = 0.5029; sex × genotype interaction, F_{(1, 14)} = 1.550, P = 0.2336. IL-5: sex factor, F_{(1, 14)} = 0.001888, P = 0.9660; genotype factor, F_{(1, 14)} = 0.4417, P = 0.5171; sex × genotype interaction, F_{(1, 14)} = 0.4502, P = 0.5131. IL-6: sex factor, F_{(1, 14)} = 0.7154, P = 0.4119; genotype factor, F_{(1, 14)} = 0.6159, P = 0.4457; sex × genotype interaction, F_{(1, 14)} = 0.8091, P = 0.3836. CXCL1: sex factor, F_{(1, 14)} = 1.453, P = 0.2481; genotype factor, F_{(1, 14)} = 0.0001067, P = 0.9919; sex × genotype interaction, F_{(1, 14)} = 0.4418, P = 0.5150. TNF-α: sex factor, F_{(1, 14)} = 0.00004924, P = 0.9945; genotype factor, F_{(1, 14)} = 2.434, P = 0.1411; sex × genotype interaction, F_{(1, 14)} = 0.7000, P = 0.4160).

FIGURE 8

I/O responses analysis in App^h/h and App^s/s rats. (A) Representative traces of fEPSP in response to increasing stimulus from -5 to -80 μA. (B) I-O curve generated from the slope fEPSP versus stimulus strength (2-way ANOVA summary, F_{(3, 38)} = 1.710; p = 0.1813). (C) I-O curve generated from FV amplitude versus stimulus strength (2-way ANOVA summary, F_{(3, 38)} = 1.262, p = 0.3010). (D) I-O curve generated from the slope fEPSP versus FV amplitude. Data are represented as mean ± SEM. Data were analyzed by two-way ANOVA for repeated measures followed by post-hoc Tukey’s multiple comparisons test when ANOVA showed statistically significant differences. n = number of animals; n′ = number of slices.

FIGURE 9

Analysis of PPR in App^h/h and App^s/s rats. (A) In App^s/s rats, PPR were decreased at 500, 200, 80, and 40 ms IPI compared to control male rats and at 80, 40, and 20 ms IPI compared to control female rats (2-way ANOVA summary, F_{(3, 38)} = 4.573, p = 0.0079; post-hoc Tukey’s multiple comparison test: at 500 ms IPI App^s/s female vs App^h/h male p = 0.0061**, App^s/s female vs App^s/s male p = 0.9991, App^s/s female vs App^h/h female p = 0.0798, App^h/h female vs App^s/s male p = 0.4504, App^h/h male vs App^s/s male p = 0.1825, App^h/h male vs App^h/h female p = 0.3847; at 200 ms IPI App^s/s female vs App^h/h male p = 0.0055**, App^s/s female vs App^s/s male p = 0.9982, App^s/s female vs App^h/h female p = 0.0840, App^h/h female vs App^s/s male p = 0.6325, App^h/h male vs App^s/s male p = 0.2409, App^h/h male vs App^h/h female p = 0.0852; at 80 ms IPI App^s/s female vs App^h/h male p = 0.0137*, App^s/s female vs App^h/h female p = 0.0364*, App^s/s female vs App^s/s male p = 0.9683, App^h/h female vs App^s/s male p = 0.2707, App^h/h male vs App^s/s male p = 0.1872, App^h/h male vs App^h/h female p = 0.9982; at 40 ms IPI App^s/s female vs App^h/h male p = 0.0203*, App^s/s female vs App^h/h female p = 0.0317*, App^s/s female vs App^s/s male p = 0.8634, App^h/h female vs App^s/s male p = 0.2400, App^h/h male vs App^s/s male p = 0.2340, App^h/h male vs App^h/h female p = 0.9971; at 20 ms IPI App^s/s female vs App^h/h male p = 0.1407, App^s/s female vs App^h/h female p = 0.0345*, App^s/s female vs App^s/s male p = 0.2703, App^h/h female vs App^s/s male p = 0.4372, App^h/h male vs App^s/s male p = 0.8996, App^h/h male vs App^h/h female p = 0.8708). (B) Representative traces of fEPSP evoked at 80 ms IPI are shown. Dotted lines represent the response after the first stimulation and solid lines represent the second responses. Data are represented as mean ± SEM. Data were analyzed by two-way ANOVA for repeated measures followed by post-hoc Tukey’s multiple comparisons test when ANOVA showed statistically significant differences. n = number of animals; n′ = number of slices.

FIGURE 10

LTP analysis in App^h/h and App^s/s rats. LTP recording in both genders of App^s/s are weaker than controls. Additionally, in male App^h/h is weaker compared to female App^h/h (2-way ANOVA summary: female App^h/h vs. female App^s/s F_{(1, 17)} = 36.82, p < 0.0001****; female App^h/h vs. male App^s/s F_{(1, 17)} = 25.48, p < 0.0001****; female App^h/h vs. male App^h/h F_{(1, 21)} = 11.54, p = 0.0027**; female App^s/s vs. male App^s/s F_{(1, 14)} = 1.266, p = 0.2795; male App^h/h vs. male App^s/s F_{(1, 18)} = 5.355, p = 0.0327*; male App^h/h vs. female App^s/s F_{(1, 18)} = 12.02, p = 0.0028**). Each genotype/gender were compared separately. Data are represented as mean ± SEM. Data were analyzed by two-way ANOVA (Column factor). n = number of animals; n′ = number of slices.

FIGURE 11

Swedish mutation impaired all phases of LTP. (A) Plot of fEPSP slope change in 11–20 m (short term potentiation, STP). The average traces of the baseline (dotted line) and STP (solid line) are shown on top. (ANOVA summary; sex factor, F_{(1, 35)} = 4.820, p = 0.0349; genotype factor, F_{(1, 35)} = 48.38 p < 0.0001; sex × genotype interaction, F_{(1, 35)} = 6.638, p = 0.0144). (B) Plot of fEPSP slope change in 51–60 m (early LTP, E-LTP). The average traces of the baseline (dotted line) and E-LTP (solid line) are shown on top. (ANOVA summary, sex factor, F_{(1, 35)} = 2.850, p = 0.1002; genotype factor, F_{(1, 35)} = 35.18 p < 0.0001; sex × genotype interaction, F_{(1, 35)} = 9.448, p = 0.0041). (C) Plot of fEPSP slope changes in 111–120 m (late-LTP, L-LTP). The average traces of the baseline (dotted line) and L-LTP (solid line) are shown on top. (ANOVA summary, sex factor, F_{(1, 35)} = 1.134, p = 0.2942; genotype factor, F_{(1, 35)} = 18.46 p = 0.0001; sex × genotype interaction, F_{(1, 35)} = 10.23, p = 0.0029). Data are represented as mean ± SEM. Data were analyzed by one-way ANOVA for repeated measures followed by post-hoc Tukey’s multiple comparisons test when ANOVA showed statistically significant differences.