Ovariectomy-induced hormone deprivation aggravates Aβ_1-42 deposition in the basolateral amygdala and cholinergic fiber loss in the cortex but not cognitive behavioral symptoms in a triple transgenic mouse model of Alzheimer’s disease

Abstract

Alzheimer’s disease is the most common type of dementia, being highly prevalent in elderly women. The advanced progression may be due to decreased hormone synthesis during post-menopause as estradiol and progesterone both have neuroprotective potentials. We aimed to confirm that female hormone depletion aggravates the progression of dementia in a triple transgenic mouse model of Alzheimer’s disease (3xTg-AD). As pathological hallmarks are known to appear in 6-month-old animals, we expected to see disease-like changes in the 4-month-old 3xTg-AD mice only after hormone depletion. Three-month-old female 3xTg-AD mice were compared with their age-matched controls. As a menopause model, ovaries were removed (OVX or Sham surgery). After 1-month recovery, the body composition of the animals was measured by an MRI scan. The cognitive and anxiety parameters were evaluated by different behavioral tests, modeling different aspects (Y-maze, Morris water maze, open-field, social discrimination, elevated plus maze, light–dark box, fox odor, operant conditioning, and conditioned fear test). At the end of the experiment, uterus was collected, amyloid-β accumulation, and the cholinergic system in the brain was examined by immunohistochemistry. The uterus weight decreased, and the body weight increased significantly in the OVX animals. The MRI data showed that the body weight change can be due to fat accumulation. Moreover, OVX increased anxiety in control, but decreased in 3xTg-AD animals, the later genotype being more anxious by default based on the anxiety z-score. In general, 3xTg-AD mice moved less. In relation to cognition, neither the 3xTg-AD genotype nor OVX surgery impaired learning and memory in general. Despite no progression of dementia-like behavior after OVX, at the histological level, OVX aggravated the amyloid-β plaque deposition in the basolateral amygdala and induced early cholinergic neuronal fiber loss in the somatosensory cortex of the transgenic animals. We confirmed that OVX induced menopausal symptoms. Removal of the sexual steroids aggravated the appearance of AD-related alterations in the brain without significantly affecting the behavior. Thus, the OVX in young, 3-month-old 3xTg-AD mice might be a suitable model for testing the effect of new treatment options on structural changes; however, to reveal any beneficial effect on behavior, a later time point might be needed.

1 Introduction

Alzheimer’s disorder (AD) is the most common type of dementia, which is among the top 10 leading causes of death in the world (1, 2). It is characterized by disturbances of memory, attention, and sleep (1, 3). The patients often have difficulties in their daily life due to their impaired behavioral abilities (4). Morphologically, amyloid plaques [formed by amyloid-β 1-42 (Aβ_1-42)] and hyperphosphorylated tau aggregates appear in the hippocampus, cortex, and amygdala, brain areas that are critical in cognitive and emotional function (5, 6).

Plenty of risk factors have been identified regarding AD. These can be lifestyle related, like diet, physical activity, and environmental conditions, or medical factors, like obesity and cardiovascular conditions (1). However, the three major risk factors are age, gender, and genetical mutations (7–9). It is well known that the incidence of AD is increasing with age, but it is also important to note that women represent 70% of the patients (10). The increasing female prevalence among elderly can be due to hormonal change during menopause (11, 12). Namely, the low levels of sex steroids, like 17β-estradiol (E2) and progesterone (PG), may have an important role in the pathomechanism (13). Indeed, both E2 and PG play a pivotal role in neuroprotection, thereby improving cognitive function, memory, attention, synaptic plasticity, spine density, and dendrite formation (14–17). The loss of the ovarian hormones can affect these functions, and also increase the appearance of amyloidogenic markers, aggravating the progression of AD (18–20). Beside the natural decrease in ovarian steroids during menopause, the surgical removal of the gland in younger generation may also have detrimental effect on their cognitive capabilities (21, 22). It is estimated that, in USA, 100,000 cases of dementia may be attributable annually to bilateral oophorectomy (23). This later state can be modeled by ovariectomy (OVX) in animals (24, 25).

AD can also be characterized by genetical mutations, leading to family accumulations. Research has identified five main “AD genes”: apolipoprotein E (ApoE) ϵ4 allele, amyloid precursor protein (APP), presenilin-1 (PSEN1), presenilin-2 (PSEN2), and microtubule-associated protein tau (MAPT). These genes may contribute to the formation of amyloid plaques, leading to memory loss and behavioral changes (8, 26–33), as well as to different tauopathies such as AD (34, 35). Genetic animal models were generated based on these human mutations. The triple transgenic mouse (3xTg-AD), bearing the humanoid mutation of APP, PSEN1, and tau, is widely used and well characterized (36–38). This mouse strain develops AD-like structural (amyloid plaques and hyperphosphorylated tau) and behavioral (progressive cognitive decline) symptoms.

The most relevant and affected neurocircuit in AD patients is the cholinergic system (39, 40), most of all the basal forebrain cholinergic (BFC) neurons (41, 42), being the main therapeutic target (43). The cholinergic neurons from the medial septum (MS), nucleus basalis magnocellularis (NBM), and substantia innominata complex are highly affected in AD, and also express E2 receptors (44–48), proving the importance of sexual steroids in the pathophysiology of the disease. OVX may decrease, while E2 treatment normalizes the number of cholinergic neurons in the BFC, as well as the length and branching of these neurons (49–51). In the 3xTg-AD mouse model, a cholinergic decline was also discovered, showing the loss of ChAT immunoreactive neurons in the MS and in the vertical limb of the diagonal band of Broca (52, 53).

Based on the important role of sexual steroids in neuronal health and their role in mental diseases, we aimed to investigate the aggravating effect of hormone deprivation induced by bilateral OVX on AD-related somatic, behavioral, and histological changes in the 3xTg-AD mice. The lack of E2 and PG may anticipate difficulties in cognitive function and anxiety-related behavior, perturbs somatic characteristics (like body weight or body fat ratio), and assumes morphological changes on amyloid deposition and in the cholinergic system. To test this hypothesis, the following concepts were used: (I) As OVX is often accompanied by body weight increase (54), and uterus weight decrease (55, 56), we were concentrating on these somatic parameters mainly to confirm the effectiveness of the OVX surgery. (II) The major symptom of dementia is cognitive disability; therefore, we used behavioral tests measuring (57) (i) short-term memory [Y-maze, often used in AD testing (58); based on spontaneous exploration of the mice]; (ii) social discrimination (SD); (iii) spatial memory [Morris water maze (MWM) as the gold standard in AD research (59, 60); also known as avoidance-based complex association]; (iv) reward-based simple association [operant conditioning (OC)]; and (v) punishment-based simple association [conditioned fear test (CFT)]. (III) As anxiety is often comorbid with AD (61, 62), and is a core symptom during menopause, or after OVX (63), we tested these symptoms by (i) elevated plus maze (EPM), as a gold standard in anxiety research (64), showing changes during the menstrual cycle (65); (ii) light–dark box (LD) test, which utilizes the fear from open, light spaces, similarly to EPM; and (iii) fox odor test (FOT), measuring the innate fear from a predator odor. (IV) At the structural level, we were concentrating on Aβ accumulation as well as cholinergic cell and fiber loss.

2 Materials and methods

2.1 Mouse strains

Three-month-old 3xTg-AD [B6;129-Tg(APPSwe,tauP301L)1Lfa Psen1tm1Mpm/Mmjax] mice and their control strains (C57BL6/J) were used (66). This age corresponds to young adult humans without hormonal disturbances. The 3xTg-AD animals were homozygotes for three AD-related human-based genetic mutations: PSEN1, APPSwe, and tauP30IL (36–38). We maintained the colony by breeding homozygous mice to each other. Only females were used in this experiment. All animals were bred and housed at the Institute of Experimental Medicine, Budapest, Hungary. The mice were maintained under reversed light–dark cycle (lights off at 8:00 a.m., lights on at 8:00 p.m.) and provided with standard mice chow [without estrogen-free dietary restrictions (67)] and water ad libitum. The animal rooms have a temperature of 22 ± 2°C and a relative humidity of 55 ± 10%. All tests were approved by the local committee of animal health and care (PE/EA/918-7/2019) and performed according to the European Communities Council Directive recommendations for the care and use of laboratory animals (2010/63/EU).

2.2 Experimental design

Mice were ovariectomized (OVX) or Sham operated without removing the ovaries (Sham), under ketamine–xylazine anesthesia (dose: 125 mg/kg ketamine and 25 mg/kg xylazine dissolved in 0.9% saline, administered in 10 ml/kg concentration intraperitoneally). During surgery, the animals were divided into the following four groups: (1) Control-Sham (n = 8), (2) Control-OVX (n = 9), (3) 3xTg-AD-Sham (n = 7), and (4) 3xTg-AD-OVX (n = 12) (Figure 1A; the unequal animal numbers are due to surgical-related loss). Two series were conducted; each contained all four groups. After 1 month, a magnetic resonance imaging (MRI) measurement was performed. During this period, the ovarian hormones were supposed to disappear [maximal luteinizing hormone levels can be detected at this point (68)] and enough time has passed for the development of supposed behavioral changes. Then, the following behavioral test battery was used: Y-maze, SD, EPM, LD, FOT, MWM, OC, and CFT, with at least 24-h rest between the different tests (Figure 1B). The order of the tests was chosen from the milder stressors (5–10 min single test) to more burdensome ones (through restricted diet in OC till foot shock in CFT). All behavioral tests were performed during the first half of the active (dark) cycle (between 9:00 a.m. and 2:00 p.m.). At the end of the experiments, animals were sacrificed, and brains were dissected and post-fixed in 4% PFA for 24 h, dehydrated in 30% sucrose solution for 24 h, and then 30-µm-thick slices were made with a freezing microtome (Leica SM2010 R). Uterus dissection and weighting were also performed to validate the success of the OVX. Due to technical reasons (e.g., missing video recording and loss of brain slide during staining) in some experiments, data from one to two animals are missing.

Figure 1

2.3 Magnetic resonance imaging measurements

Body composition (body weight, fat, lean, free water, and total water) measurements were performed with a body composition analyzer for live small animals (EchoMRI™-700, EchoMRI LLC, Houston, TX), as described by the manufacturer. The animals were put in a restrainer and placed in the MRI machine for approximately 1 min (Figure 2A). The measurement was done in duplicate consecutively, without a time gap, and averaged. The body fat and lean weight were expressed as percentage of the body weight, and hydration ratio was calculated as the following:

2.4 Behavioral tests

2.4.1 Cognitive behavioral tests

2.4.1.1 Y-maze test

The test was performed in a Y-shaped apparatus, with 3 arms (A, B, and C), with 30 × 7 × 20 cm dimensions, and in a 15–20 lux environment (Figure 3A) (57). Mice were placed in arm A and were allowed to explore the maze freely for 10 min. Before the entry of each animal, the maze was cleaned with 70% ethanol. Locomotion was calculated based on the total number of entries, while the spontaneous alternation reflects short-term memory and was calculated as the percentage (%) of “correct” alternation/total alterations. “Correct” alternation means entry into all three arms on consecutive choices (i.e., ABC, BCA, or CAB). Parameters were measured manually by an experimenter blind to the treatment groups.

2.4.1.2 Social discrimination test

The test was performed in a 40 × 40 × 15 cm apparatus under red light (69). The experiment consisted of four phases, each lasting 5 min (Figure 3D). Firstly, the mice were placed in the box for acclimatization [open-field phase (OF)]. Secondly, two metal cages were placed into the box and fear from objects as well as side preference was evaluated. The goal was to habituate the animals to the container (object habituation). Then, a stimulus mouse [C57BL6, 25- to 30-day-old male, test naïve, sexually immature (70)] was placed under one of the metal cages (sociability phase). In the last 5 min, another stimulus mouse was placed under the other metal cage, and the position of the two cages was swapped [social discrimination (SD)]. The mice were left to explore freely the two animals. In the OF, the distance moved, and the time spent in the central or peripheral zone was analyzed automatically by EthoVision XT (Noldus IT, Wageningen, The Netherlands, version 15). Other parts of the test were analyzed using Solomon Coder (Solomon Coder, Hungary; https://solomoncoder.com/) by an experimenter blind to the treatment groups. The time and frequency sniffing the left or right container were evaluated. The sociability index (third phase) was calculated as:

The discrimination index (DI, fourth phase) was calculated as:

2.4.1.3 Morris water maze test

A plastic circular pool (90 cm in diameter and 40 cm in height) was filled with tap water (24 ± 2°C), made opaque by white wall paint (Figure 4A) (38). A platform (6 cm in diameter) was placed 1 cm above the water for learning day 1, then moved 1 cm lower than the level of the water for days 2–5. The apparatus was divided into four quadrants and the platform was installed in the middle of one quadrant. Mice were released into the water from different points across trials (Figure 4A, marks 1–4) and were allowed to swim freely for 60 s to find the platform. If the mice could not find the platform during the 1 min, then it was guided there and left on the platform for 10 s. The learning phase (days 1–5) consisted of four trials with 30-min intertrial interval (ITI) when the animals were dried by a towel and returned to their home cages. On day 6 (probe day), the platform was removed from the water and the mice had 60 s to search for it. Latency to reach the platform during the learning phase was recorded manually, while during the probe test, time spent in different zones was calculated by EthoVision XT 15.

2.4.1.4 Operant conditioning test

The test was performed in an automated operant chamber (Med Associates, St. Albans, VT, USA) with two nose holes (Figure 5A) (57). As a reward, 45 mg of food pellets (Bio-Serv Dustless Precision Rodent Pellet, Bilaney Consultants GmbH, Germany) was used (71). Animals were placed inside a test chamber for 30 min to freely explore the environment. A nose poke into one of the nose holes was immediately associated with a reward followed by a 25-s-long time out with the chamber light switched on (time-out period), while the other nose hole was not baited (incorrect). During the time-out period, responses were not rewarded, but were registered. The test was divided into two phases: habituation (days 1–2) and learning (days 3–7), and data only from the learning phase is shown (Figures 5B, C, days 1–5). Reward preference (RP) (ratio of responses on the rewarded nose hole) was calculated. Number of rewarded responses and time-out reward hole nose pokes were also recorded.

2.4.1.5 Conditioned fear test

The mouse was placed into a Plexiglas chamber (25 × 25 × 30 cm) with an electrical grid floor (Coulbourn Instruments) that delivered the foot shocks (SuperTech Instruments). For 2.5 min, the animals were left in the boxes for habituation [baseline (BL)]. Then, at pseudorandom intervals (60–90 s), a 30-s-long conditioned stimulus (CS: 80 dB pure tone at 7 kHz) was played and co-terminated with an unconditioned stimulus (foot shock: 0.7 mA, 1 s long, seven times in total), for a total of 11 min (Figure 5D). The following day, the experiment was repeated, except that the animals did not receive foot shocks at the end of the CSs (72). The chambers were cleaned with soap water and water after every trial. The experiment was conducted in bright light (700 lux). Time spent in immobility was measured automatically by Ethovision XT 15 on the second day. Time spent in immobility was calculated for the BL (mean for 10 s) as well as for CSs (mean for 7 CS per 10 s).

2.4.2 Anxiety-related behavioral tests

2.4.2.1 Elevated plus maze test

A plus-shaped device was used, which comprised two opposite open arms and two enclosed arms (30 × 7 × 30 cm) (Figure 6A) (73). The mice were placed in the center of the apparatus facing the open arm and were allowed to explore the maze for 5 min. Before the entry of each animal, the maze was cleaned with 70% ethanol. The time spent and number of entries into the different arms as well as the distance moved (cm) were quantified with EthoVision XT 15. The open arm preference (OP) (74) was calculated as:

2.4.2.2 Light–dark box test

LD was performed in a 40 × 20 × 25 cm box, which had two compartments: a light (white colored) compartment that is open from above and a dark (black colored) compartment that is closed from every side (Figure 6B). A small gate (5 × 5 cm) between the two compartments, where the animal can freely pass, was present. The mice were placed in the light part of the box and were allowed to explore the environment for 10 min. The duration of time spent in each compartment, the total number of entries, and latency to dark compartments were measured by Solomon Coder.

2.4.2.3 Fox odor avoidance test

Exposure to fox-derived synthetic predator odor, 2-methyl-2-thriazoline (2MT, #M83406, Sigma Aldrich), was performed in a separate experimental room under a fume hood. A transparent Plexiglas arena (43 × 27 × 19 cm) was used (Figure 7A). During the test, a 2MT solution-soaked filter paper (40 μl in 1 ml of distilled water, 50 μl/animal) was placed in a plastic 50-ml conical tube cap in one corner of the box (75). A 7 × 11 cm “odor zone” around the odor source was defined. The opposite part (25%) of the box was appointed as “avoidance zone”. During the test, the animal was placed in the avoidance zone and left to freely explore the arena for 10 min. Time spent in the odor zone and the distance moved (cm) was measured with EthoVision XT 15. Different anxiety-related behaviors, like the time spent freezing, grooming, and sniffing as well as the exploratory behaviors like time spent rearing and exploring was analyzed manually by Solomon Coder by an experimenter blind to the treatment groups.

2.5 Histological evaluations

2.5.1 Hematoxylin–eosin staining for uterus morphology

After weighing, uteruses were fixed in 4% PFA for 24 h, then dehydrated with 30% sucrose solution. Thirty-micrometer slices were made with a freezing microtome (Leica SM2010 R). Hematoxylin–eosin (HE) staining was performed on the slices to see morphological changes in the epithelium layer thickness, lumen size, and the integrity of the endometrial glands (Figure 8A). Samples were imaged with a Nikon Eclipse E1 R (Nikon, Tokyo, Japan) microscope at 4× magnification.

2.5.2 Amyloid-β_1-42 and choline acetyltransferase immunohistochemistry

For Aβ_1-42 and ChAT staining, peroxidase-based immunohistochemistry with nickel-diaminobenzidine tetrahydrochloride (Ni-DAB) visualization was undertaken (17). Firstly, only for the Aβ staining, a 10-min concentrated formic acid (Sigma-Aldrich, #F0507) exploration was implemented. Secondly, endogen peroxidase was blocked by a 3% peroxide (H₂O₂) solution. After blocking, slices were incubated 72 h with the primary antibody recognizing Aβ (Rabbit, 1:500, Invitrogen, #71-5800) or ChAT (Goat, 1:1,000, Millipore, #AB144P). After 72 h, brain slices were incubated with a biotinylated secondary antibody (biotinylated anti-rabbit, 1:200, Jackson ImmunoResearch, #111-065-003 or biotinylated anti-goat 1:200, Jackson ImmunoResearch, #705-065-147) at room temperature (RT), for 2 h. An avidin–biotin kit (VECTASTAIN Elite ABC-Peroxidase Kits, PK-6100, Vector Laboratories) was used for 2 h, RT, to detect biotinylated molecules. Then, the visualization was performed with a Ni-DAB and glucose oxidase mixture. Samples were imaged with a Nikon Eclipse E1 R (Nikon, Tokyo, Japan) microscope at 4× magnification.

In case of Aβ plaques, the integrated optical density (IOD) was measured by ImageJ/Fiji in the basolateral amygdala (BLA), the somatosensory and motor cortex (CTX) between Bregma 0.50 mm and −1.20 mm, and the CA1 region of the hippocampus (CA1-HC) between Bregma −1.19 mm and −2.69 mm (Figure 9A). In other brain areas of 5-month-old 3xTg-AD mice, no amyloid deposition was found. After ChAT staining, the number of ChAT-positive cells was counted in the NBM, a brain region containing cholinergic cell bodies, and highly affected in AD (Figure 10A) (76, 77).

2.5.3 Acetylcholinesterase histochemistry

To label cholinergic fibers in the somatosensory cortex (SSC), the target area of the NBM neurons (78), an AChE histochemistry was performed (17). Slices were selected from the coordinates: Bregma +0.50 mm to −1.06 mm (Figure 10A). Free-floating brain slices were incubated in a mixture of sodium acetate buffer (0.1 M; pH 6) acetylthiocholine iodide (0.05%), sodium citrate (0.1 M), copper sulfate (0.03 M), and potassium ferricyanide (5 mM). This was followed by ammonium sulfide (1%) and then silver nitrate (1%) incubation (17, 79). Analysis was performed with ImageJ/Fiji software. Samples were imaged with a Nikon Eclipse E1 R (Nikon, Tokyo, Japan) microscope at 10× magnification. IOD was measured between layer IV and V of the SSC (Figure 10A).

2.6 Z-score calculations

Integrated z-score was calculated for four major parameters: somatic, cognitive, anxiety, and locomotion, as proposed by Guilloux et al. (80), and previously presented in (73, 81). For each parameter, a normalized value (studentization) was calculated according to the following equation:

and the included parameters were adjusted to have the same directionality. Somatic z-score was calculated from the averages of body weight change, fat/BW percentage, and uterus weight (×−1) z-scores. Cognitive z-score was calculated from alteration in the Y-maze; the area under the curve (AUC) of the latencies to platform during learning days 1–5 in MWM (×−1), and latency to platform on the probe day (×−1) in MWM; average freezing during baseline and conditioned stimuli in CFT; and the AUC of the reward preference learning days 3–7 in operant conditioning. Anxiety z-score was averaged from the z-scores of open arm duration (×0.5) and open arm preference (×0.5) in EPM; time spent in light compartment in LD; time spent freezing (×−1) and percentage of time spent in the odor zone in FOT; and percentage of time spent with freezing in CFT day 2 (×−1). Locomotion z-score was calculated from the parameters that reflected mobility in the given experiment [distance moved in EPM (×0.5), OF and fox odor tests; total number of entries in the Y-maze, EPM (×0.5), and LD], then averaged for each animal. Somatic, cognitive, anxiety, and locomotor z-scores were averaged for every group and statistically tested. If multiple parameters indicating the same meaning within an experiment were included in averaged z-score calculations (e.g., distance moved and closed arm entries on EPM in the locomotion z-score), then they were multiplied by ×0.5 in order to avoid unwanted weighting of the specific test.

2.7 Statistical analysis

GraphPad Prism (version 6.0) was used for statistical analyses. Two-way ANOVA (MRI, Y-maze, OF, Sociability, SD, EPM, LD box, FOT, and histology; on factors genotype and OVX) or repeated-measures ANOVA (MWM, body weight change, OC, and CFT; additional factor: time) was used to compare the groups, followed by Tukey HSD or Sidak post-hoc test. For comparison of two groups, Student’s t-test was used (Aβ staining). All data are presented as mean ± SEM and p< 0.05 was considered as a statistically significant difference.

3 Results

3.1 Changes in body composition measured with MRI

Regarding body weight changes, a difference was found between the two genotypes [F_(3,59) = 12.59, p< 0.0001], the 3xTg-AD mice being heavier than the controls. However, OVX surgery itself increased the body weight during a 40-day period [F_(1,19) = 16.35, p = 0.0007] without any influence of the genotype (Figure 2B). This increased body weight can be explained by the increased body fat ratio, where a genotype effect was also detectable with more fat in 3xTg-AD animals [F_(1,32) = 10.01, p = 0.0034] (Figure 2C). OVX was able to increase the fat accumulation in both genotypes [F_(1,32) = 38.38, p< 0.0001] (Figure 2C). Simultaneously, lean body weight ratio decreased [genotype: F_(1,32) = 11.97, p = 0.0016, OVX: F_(1,32) = 47.45, p< 0.0001] (Figure 2D). A significant negative correlation between body fat and lean ratio was also detected (r = −0.9870, p< 0.0001) (Figure 2E). The hydration ratio (((total water − free water)/lean)*100) of all animals was in the normal range (80 ± 5%), without any effect of genotype or OVX (Figure 2F).

Figure 2

3.2 Behavioral tests

3.2.1 Cognitive behavioral tests

3.2.1.1 Y-maze test

There was no difference between the groups in the main parameter of short-term memory, the alternation (Figure 3B). The 3xTg-AD mice moved significantly less compared to control animals [F_(1,31) = 19.52, p = 0.0001] without any effect or influence of OVX (Figure 3C).

Figure 3

3.2.1.2 Social discrimination (SD) test

We confirmed the reduced locomotion of 3xTg-AD mice during the first 5 min OF phase [F_(1,32) = 13.80, p = 0.0008], without OVX effect (Figure 3E). Neither the genotype, nor the OVX influenced the number of entries or time spent in the centrum, not even if we corrected it with locomotion (Table 1).

During the object habituation phase, none of the mice preferred any side; thus, the next phases did not require any correction (Figure 3F). OVX did not significantly affect the number of object approaches as well [F_(1,32) = 3.05, p = 0.1298; Table 1].

In the sociability phase, every mouse showed more interest to the stimulus mouse-containing cage [repeated-measures ANOVA: F_(1,32) = 33.81, p< 0.0001; single-sample t-test against 50%; Control-Sham: t₍₇₎ = 4.27, p = 0.0037; Control-OVX: t₍₉₎ = 5.49, p = 0.0004; 3xTg-AD-Sham: t₍₆₎ = 3.50, p = 0.0128; 3xTg-AD-OVX: t₍₉₎ = 3.90, p = 0.0036] without significant difference between groups (Figure 3G). There was a tendency for OVX animals to approach the social container a fewer number of times [F_(1,32) = 3.89, p = 0.0881; Table 1].

In the social discrimination phase, an increased interest towards the new mouse was detected in all groups [F_(1,62) = 7.75, p = 0.0071], suggesting that—in general—the test animals preferred the new stimulus mice, as expected (Figure 3H). However, when we checked the groups one by one, only the Control-OVX group seemed to have intact memory with a tendency in the 3xTg-AD-OVX group [single-sample t-test against 0; Control-Sham: t₍₇₎ = 1.17, p = 0.2806; Control-OVX: t₍₉₎ = 2.48, p = 0.0348; 3xTg-AD-Sham: t₍₆₎ = 0.53, p = 0.6179; 3xTg-AD-OVX: t₍₉₎ = 2.03, p = 0.0732]. A larger number of animals are probably needed for this test to work properly. Nevertheless, there was no overall difference between groups.

3.2.1.3 Morris water maze test

The latencies to reach the platform showed a significant improvement in time during the learning phase, independently from genotypes or surgery [F_(4,132) = 43.42, p< 0.0001] (Figure 4B). At the end of the 5th day, the animals were able to find the platform within 20 s (o average: 17.01 ± 1.21 s), suggesting that all groups learned the task. A significant interaction between OVX and time was detected [F_(4,128) = 4.31, p = 0.0026]; the OVX groups started to learn the task a bit later, as day 2 was not significantly different from day 1 in contrast to Sham-operated groups. Moreover, during days 4 and 5, the OVX groups differed significantly from the Sham-operated ones [F_(1,32) = 6.09, p = 0.0191], suggesting a flatter learning curve. Additionally, the fluctuation observable in the 3xTg-AD-OVX group suggests random choice, thus, not appropriate learning of this group. The genotype also showed a tendency for time dependence [F_(4,128) = 2.18, p = 0.0744], with a subtle learning impairment of the 3xTg-AD mice. All the animals remembered the place of the platform as during the probe test they spent more than 25% of the time in the platform quadrant [single-sample t-test against 25; Control-Sham: t₍₇₎ = 2.14, p = 0.0701; Control-OVX: t₍₉₎ = 5.50, p = 0.0004; 3xTg-AD-Sham: t₍₆₎ = 6.12, p = 0.0009; 3xTg-AD-OVX: t₍₉₎ = 3.02, p = 0.0130]. No difference was found between groups in the probe day in the latency to reach the platform or time spent in the quadrant, where the platform was during the probe day (Figure 4C).

Figure 4

3.2.1.4 Operant conditioning test

In reward preference, an improvement during time was detected [F_(4,128) = 9.73, p< 0.0001] without any influence of the genotype or surgical removal of the ovaries (Figure 5B). In the number of rewarded responses, a similar time effect was seen [F_(4,128) = 16.10, p< 0.001] (Figure 5C), with a tendency for genotype × OVX interaction [F_(1,32) = 53.49, p = 0.0707]. There was a tendency for 3xTg-AD-Sham-operated animals to respond fewer times than Control-Sham-operated ones (p = 0.0794), while 3xTg-AD animals after OXV responded significantly more than the Sham-operated ones (p = 0.0407).

Figure 5

3.2.1.5 Conditioned fear test

We expressed the time spent in immobile posture during different phases as the percentage of the time period to get comparable values [i.e., the 150-s BL period is hardly comparable to the 30-s CS periods or random breaks (Br)] (Figure 5E). When we were concentrating on CS-induced changes, there was a significant interaction between the genotype and OVX [repeated-measures ANOVA on the seven CS: F_(1,31) = 4.72, p = 0.0375]; the OXV increased freezing in control, but decreased in 3xTg-AD mice. The same effect was also seen in the cumulative time spent in freezing (in 1-min bins) (Figure 5F); not surprisingly this time, the interaction was significant between all three (genotype, OVX, and time) factors [F_(10,320) = 53.26, p = 0.0005]. The time spent with inactivity during the initial context-dependent phase (BL, 150 s) (Figure 5G) and during the seven CS (conditioned phase, Figure 5H) was also calculated. Using repeated-measures ANOVA on context and cue-induced freezing, the CS, as a cue, significantly elevated the immobility time [F_(1,32) = 8.16, p = 0.0075]. There was a tendency again for genotype and OVX interaction [F_(1,32) = 4.03, p = 0.0531]. This was due to the significant interaction during tone-dependent freezing [F_(1,32) = 5.53, p = 0.0251] (Figure 5H) as no difference was detectable in the context-dependent phase [F_(1,32) = 3.12, p = 0.0871] (Figure 5G).

3.2.2 Anxiety-related behavioral tests

3.2.2.1 Elevated plus maze test

There was a significant interaction between genotype and OVX in the time spent in open arms [F_(1,32) = 7.774, p = 0.0088], and in open arm preference [F_(1,32) = 4.484, p = 0.0421] (Figures 6C, D), but no difference was detected in the time spent in the closed arm [time %; Control-Sham: 239.52 ± 9.99, Control-OVX: 250.09 ± 10.68, 3xTg-AD-Sham: 237.62 ± 14.09, 3xTg-AD-OVX: 260.13 ± 7.06; genotype: F_(1,32) = 0.1554, p = 0.6961; OVX: F_(1,32) = 2.569, p = 0.1188]. More specifically, Control-OVX animals spent less time in the open arm compared to the Control-Sham group (p = 0.0192), whose effect was not detectable in 3xTg-AD mice. In the locomotion parameters, similar to distance moved and the number of entries into the closed arms, no significant differences were detected between the groups (Figures 6E, F).

Figure 6

3.2.2.2 Light–dark box test

No differences were seen in the anxiety-related parameters like time spent in the light compartment (Figure 6G). However, in the locomotor activity represented by the number of entries to the dark compartment, a genotype effect was detected [F_(1,30) = 9.80, p = 0.0039] (Figure 6H). 3xTg-AD animals moved significantly less than the controls.

3.2.2.3 Fox odor test

A tendency for decreased time spent in the odor zone was seen in the 3xTg-AD animals compared to the control groups [F_(1,27) = 3.51, p = 0.0719] (Figure 7B). Accordingly, the 3xTg-AD animals spent more time freezing [F_(1,31) = 25.33, p< 0.0001] (Figure 7C) and reared [F_(1,31) = 7.15, p = 0.0118] (Figure 7E) and vertically explored the environment [F_(1,31) = 22.48; p< 0.0001] (Figure 7D) less than controls. These may suggest that 3xTg-AD animals were more anxious in the presence of a predator odor. A tendency of genotype difference was also seen in the locomotor activity, expressed by the distance moved [F_(1,31) = 3.46, p = 0.0723] (Figure 7F). Other parameters (like grooming and sniffing) were not different between groups and thereby not shown. The OVX surgery had no significant effect on the parameters examined during FOT.

Figure 7

3.3 Z-scores

The somatic z-score showed a significant interaction between genotype and surgery groups [F_(1,32) = 41.35, p < 0.0001] (Table 2). Animals who underwent OVX surgery had a higher somatic z-score [F_(1,32) = 12.92, p = 0.0010], whose effect was more pronounced in Control than in 3xTg-AD mice. In cognitive z-score, no significant differences were detected between the groups. Anxiety z-score showed an interaction between genotype and surgery groups [F_(1,32) = 23.26, p < 0.0001]. Namely, OVX increased anxiety in Control, but decreased in 3xTg-AD animals. Indeed, in general, 3xTg-AD animals had a lower anxiety z-score, meaning more anxious behavior [F_(1,32) = 17.61, p = 0.0002]. The locomotor differences were also supported by its z-score. Namely, the 3xTg-AD animals had a lower z-score number, meaning, in general, they moved less [F_(1,32) = 19.64, p = 0.0001]. A significant interaction between genotype and surgery groups was also detected [F_(1,32) = 27.45, p < 0.0001]. More specifically, OVX reduced locomotion in Control, but not that much in 3xTg-AD mice, which was moving less even before that.

3.4 Histological evaluations

3.4.1 Uterus

The representative pictures with HE staining showed increased epithelium thickness, deteriorated endometrial glands, and a substantial difference between the size of the uterus and lumen (Figure 8A). Both in the control and 3xTg-AD groups, the normalized weight of the uterus was significantly lower after OVX compared to the Sham group [F_(1,31) = 121.80, p< 0.0001] (Figure 8B).

Figure 8

3.4.2 Amyloid-β accumulation in different brain areas

Amyloid-β plaques were only quantified in 3xTg-AD mice, because no deposition was detected in the Control-Sham or Control-OVX groups (see Supplementary Figure 1). In the BLA, the 3xTg-AD-OVX mice had significantly more plaques than their Sham-operated mates [t₍₉₎ = 2.72, p = 0.0236] (Figures 9B, C). In the CTX and CA1-HC, no significant effect of OVX was found (Figures 9D–G).

Figure 9

3.4.3 Morphological changes in the cholinergic system

ChAT-positive cells were counted in the NBM region. We found no difference in the number of the cells between 3xTg-AD and control animal, neither in Sham-operated nor in OVX groups (Figure 10C). However, the AChE fiber density was significantly decreased in 3xTg-AD animals [F_(1,22) =29.49, p< 0.0001], with a significant interaction between genotype and OVX [F_(1,22) = 11.61, p = 0.0025]. In 3xTg-AD mice, OVX surgery exacerbated the fiber loss compared to the Sham group (p = 0.0147) (Figures 10B, D).

Figure 10

4 Discussion

In contrast to our hypothesis, OVX did not aggravate the appearance of AD-related symptoms in the cognitive behavioral tests, but in morphological examinations, signs of neurodegeneration were visible (see amyloid deposition in the BLA, and cholinergic fiber density in the SSC). Table 3 contains the summary of the changes.

We confirmed that our model worked, as OVX induced the expected increase in body weight with fat accumulation as well as decrease in uterus weight and lean body percentage. The lack of sexual steroids can cause an increased risk for obesity, since E2 and PG also mediate glucose and lipid metabolism, and also affects adipocyte physiology (54, 82, 83). Indeed, in human studies, an increased visceral fat mass can be seen on women after menopause (84, 85). This is supported in mice by our MRI findings, where the body fat ratio of the OVX groups increased. Importantly, obesity is a prominent risk factor for AD: increasing Aβ plaques, adipokines, and cytokines, and effecting insulin homeostasis [reviewed in (86–88)]. Thus, this might be associated with how OVX might aggravate the development of AD-like symptoms. In support, 3xTg-AD animals per se were fatter and greasier, suggesting—together with the OC data—some metabolic disturbances, which require further studies. In contrast, after OVX, the weight of the uterus decreased, which can be explained by the estrogen deficit. Indeed, E2 has a proliferative effect on the uterus; hence, its lack causes hypotrophy (55, 89, 90). In future studies, luteinizing hormone measurements can help better understand the effect of OVX on the hypothalamic–hypophyseal–gonadal axis and their role in the development of AD (91–93). The MRI data also showed a decreased body lean ratio in the OVX groups, which may be the prodrome of a most common problem in menopausal patients, osteoporosis. Indeed, female sex hormone depletion was linked closely to low bone mineral density (94). Estrogen receptors can be also found in the bone, mediating protection of the bone structure, by inhibiting osteoclast activity and stimulating development of long bones and pubic epiphyses (94–96). The OVX-induced somatic changes presented in the literature were also supported by the somatic z-score, calculated from the body weight change, body fat ratio, and uterus weight. Interestingly, the OVX-induced changes were smaller in 3xTg-AD mice (see genotype × OVX interaction in somatic z-score).

As the major symptom of dementia is the cognitive decline, we evaluated five different memory tests to have a comprehensive picture. They measure different modalities [spontaneous exploration (Y-maze), social stimulus, simple association-based reward (OC) or punishment (CFT), or even complex association based on spatial memory (MWM)]. The cumulative effect (z-score) was very similar to the single tests, with overall ineffectiveness of the genetic deletion in the 3xTg-AD animals as well as the OVX. According to the literature, 3xTg-AD animals develop memory loss after 6 months (36, 38). Hence, for our animals that were between 4 and 5 months old, the results are not unexpected. However, we could not support our hypothesis, as the OVX did not aggravate the cognitive decline (no OVX effect was detected whatsoever). Even the tendencies for learning impairment in MWM were detected separately for OVX and AD without any interaction. The only genotype × OVX interaction in cognition was seen during the CS-induced freezing in CFT, when OVX aggravated the symptoms in Control, but decreased in 3xTg-AD animals. Although we used CFT as an associative learning and memory test (97), its result strongly depends on the animal’s anxiety state (98). Indeed, these CFT results were very similar to the anxiety z-score data. The intact memory can also be explained by the lack of Aβ deposition in the hippocampus and cortical areas (99, 100). We might assume that more time is needed for the development of the symptoms; therefore, investigating memory deficit would be informative with older animals only even after OVX (101).

Anxiety is a core symptom of postmenopausal women (102), as well as might be comorbid with AD (103). However, anxiety symptoms remain largely unexplored, despite the significant impact on quality of life, if not diagnosed and treated (102). As anxiety is associated with both AD and OVX (23), we assumed that both interventions will increase its level in mice, with a possible synergistic effect. However, we found a significant anxiogenic effect of OVX in EPM only, the most frequently used anxiety test (64, 104). On the contrary, an AD effect was visible in the FOT test measuring innate fear and anxiety-related behavior (75). We found that 3xTg-AD animals spend more time freezing, which suggests that these animals were more frightened (75, 105). Also, 3xTg-AD animals spend less time exploring and rearing, which might reflect anxiety, too (see immobility in CFT) (106). Nevertheless, these findings may be related to the increased Aβ deposition in the BLA (Figure 9), as this region is responsible for formation of fear-related responses and can be linked to anxious behavior (105, 107–109). The increased overall anxiety z-score of 3xTg-AD animals coincides with the increased anxiety in human AD patients (110, 111).

Moreover, the locomotor activity shown by the different behavioral tests (distance moved in EPM, OF, and FOT; total number of entries in the Y-maze; and number of entries to closed arms or dark compartment in EPM and LD) and the locomotion z-score calculated from these parameters showed a difference between the two genotypes with lower levels in 3xTg-AD animals. In line with previous results, this decreased locomotor activity may reflect anxious behavior. However, we cannot close out a moderate motoric disabilities as well (112, 113). The decrease in movement can be related to the presence of Aβ in the motoric and somatosensory cortex (Figure 9) (114). Nevertheless, in line with an anxious phenotype, OVX also decreased locomotion, which was mainly detectable in controls. In support, volcano mice presented a scalloped pattern of daily activity during the estrous cycle and OVX reduced the total movement (115). Moreover, in estrogen receptor knockout mice (on C57BL6 background), E2 injection to OVX animals increased total activity and amplitude (116). The smaller effects in the AD model might be due to the already low levels, which cannot be easily decreased further.

Despite subtle behavioral changes, morphological changes were more equivocal. Namely, Aβ plaques, one of the most characteristic morphological changes of AD (99, 117), appeared only in 3xTg-AD animals; however, we could detect their presence already around 5 months. Although we expected that OXV alone will lead to the appearance of pathological hallmarks in control animals, in humans, OVX induced behavioral and morphological changes only in the elderly or those having genetic mutations [e.g., ApoE-4 genotype (118–120)]. In line with this, OVX was able to increase the number of amyloid plaques in the 3xTg-AD animals, further increasing the translational values of our model. However, we detected changes in the BLA, but not in the HC and CTX. We have to note that in much older animals, OVX-induced Aβ formation was found also in the CTX and HC (121–123). Thus, BLA might be a sensitive area, where changes occur earlier than in other parts of the brain. It is known that stress, i.e., glucocorticoids, increases excitability of BLA, while E2 decreases it (124). Thus, in our hands, repeated testing, as a stressor, as well as E2 decline due to OVX, might have promoted the stress sensitivity of BLA (125, 126). In support of the E2 effect, the replacement of the hormone after OVX can decrease the number and density of Aβ plaques in rodents (25, 100, 121). This is also in line with human studies, where OXV patients were treated with hormone replacement therapy, resulting in no difference in Aβ deposition (120). These differences (namely, age, genetic predisposition, and hormone replacement) might be the cause of the controversy in the literature on OVX-induced amyloidosis in the brain reported to be missing by some (120, 127) or increased by others (13, 128–130). However, Palm et al., also using 3xTg-AD mice, showed no difference after E2 treatment in Aβ deposition (123), while Carroll et al. (121) used PG to reduce the p-Tau accumulation in the CA1 region of the hippocampus, subiculum, and frontal cortex.

The novelty of our study is that we included more behavioral tests and examined the cholinergic system as well. The importance of the cholinergic system in AD is outstanding, being the target of almost all the drugs in the market (131, 132). Thus, we decided to examine the cell numbers in the NBM (133), and their projections to the SSC (79). In the ChAT-positive cell numbers, no difference was found in 5-month-old mice, probably because of their young age. However, AChE-positive fiber degeneration was detected in 3xTg-AD mice and even aggravated after OVX, suggesting that axonal and dendritic degenerations start earlier than behavioral decline (114, 134).

Our study has certain limitations. First, we used standard diet, and phytoestrogens might have influenced the outcome. Next, we did not monitor the cycle, and the cyclic changes might increase variability in Sham-operated groups. Furthermore, to keep the number of used animals as low as possible, we used repeated testing, which might influence each other’s results. For some tests, more animals/group might have been required to see statistically significant differences.

All in all, we confirmed that OVX induced menopausal symptoms and removal of the sexual steroids aggravated the appearance of AD-related alterations in the brain without significantly influencing behavior. Thus, the OVX in young, 3-month-old 3xTg-AD mice might be a suitable model for testing the effect of new treatment options at the structural level, which can speed up testing (it is not necessary to wait 6–12 months for the animals to age). However, to reveal any beneficial effect on behavior, a later time point might be needed.

Funding

This study was supported by the National Research Development and Innovation Office of Hungary (grant numbers K141934, K138763, and K120311) as well as by the Thematic Excellence Program 2021 Health Sub-programme of the Ministry for Innovation and Technology in Hungary, within the framework of the TKP2021-EGA-16 project of the Pécs of University. The agencies had no further role in study design, and in the collection, analysis, or interpretation of the data.

Acknowledgments

We thank all the core facilities of our institute for their supportive help: the Behavioral Studies Unit for help with behavioral testing (Dr. Kornél Demeter), the Nikon Microscopy Center for help with microscopy (Dr. László Barna and Dr. Pál Vági), the Virus Technology Unit and the Medical Gene Technology Unit for help with mouse lines, and the Metabolic Phenotyping Unit (Dr Csaba Fekete) for the help by the MRI measurement.

Publisher’s note

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References

• 1

  Alzheimer’s Association. 2020 alzheimer’s disease facts and figures. Alzheimer’s Dement (2020) 16:391–460. doi: 10.1002/alz.12068

  • CrossRef
  • Google Scholar

• 2

  WHO. The top 10 causes of death (2020). Available at: https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death.

  • Google Scholar

• 3

  LyketsosCGCarrilloMCRyanJMKhachaturianASTrzepaczPAmatniekJet al. Neuropsychiatric symptoms in alzheimer’s disease. Alzheimers Dement (2011) 7:532–9. doi: 10.1016/j.jalz.2011.05.2410

  • CrossRef
  • Google Scholar

• 4

  ScheltensPBlennowKBretelerMMBde StrooperBFrisoniGBSallowaySet al. Alzheimer’s disease. Lancet (London England) (2016) 388:505–17. doi: 10.1016/S0140-6736(15)01124-1

  • CrossRef
  • Google Scholar

• 5

  LaFerlaFMOddoS. Alzheimer’s disease: Abeta, tau and synaptic dysfunction. Trends Mol Med (2005) 11:170–6. doi: 10.1016/j.molmed.2005.02.009

  • CrossRef
  • Google Scholar

• 6

  QuerfurthHWLaFerlaFM. Alzheimer’s disease. N Engl J Med (2010) 362:329–44. doi: 10.1056/NEJMra0909142

  • CrossRef
  • Google Scholar

• 7

  NebelRAAggarwalNTBarnesLLGallagherAGoldsteinJMKantarciKet al. Understanding the impact of sex and gender in alzheimer’s disease: A call to action. Alzheimers Dement (2018) 14:1171–83. doi: 10.1016/j.jalz.2018.04.008

  • CrossRef
  • Google Scholar

• 8

  CacaceRSleegersKVan BroeckhovenC. Molecular genetics of early-onset alzheimer’s disease revisited. Alzheimers Dement (2016) 12:733–48. doi: 10.1016/j.jalz.2016.01.012

  • CrossRef
  • Google Scholar

• 9

  RettbergJRDangHHodisHNHendersonVWSt. JohnJAMackWJet al. Identifying postmenopausal women at risk for cognitive decline within a healthy cohort using a panel of clinical metabolic indicators: potential for detecting an at-alzheimer’s risk metabolic phenotype. Neurobiol Aging (2016) 40:155–63. doi: 10.1016/j.neurobiolaging.2016.01.011

  • CrossRef
  • Google Scholar

• 10

  FisherDWBennettDADongH. Sexual dimorphism in predisposition to alzheimer’s disease. Neurobiol Aging (2018) 70:308–24. doi: 10.1016/j.neurobiolaging.2018.04.004

  • CrossRef
  • Google Scholar

• 11

  PikeCJ. Sex and the development of alzheimer’s disease. J Neurosci Res (2017) 95:671–80. doi: 10.1002/jnr.23827

  • CrossRef
  • Google Scholar

• 12

  RoofRLHallED. Gender differences in acute CNS trauma and stroke: neuroprotective effects of estrogen and progesterone. J Neurotrauma (2000) 17:367–88. doi: 10.1089/neu.2000.17.367

  • CrossRef
  • Google Scholar

• 13

  YaoJIrwinRChenSHamiltonRCadenasEBrintonRD. Ovarian hormone loss induces bioenergetic deficits and mitochondrial β-amyloid. Neurobiol Aging (2012) 33:1507–21. doi: 10.1016/j.neurobiolaging.2011.03.001

  • CrossRef
  • Google Scholar

• 14

  CersosimoMGBenarrochEE. Estrogen actions in the nervous system: Complexity and clinical implications. Neurology (2015) 85:263–73. doi: 10.1212/WNL.0000000000001776

  • CrossRef
  • Google Scholar

• 15

  KramárEABabayanAHGallCMLynchG. Estrogen promotes learning-related plasticity by modifying the synaptic cytoskeleton. Neuroscience (2013) 239:3–16. doi: 10.1016/j.neuroscience.2012.10.038

  • CrossRef
  • Google Scholar

• 16

  SellersKJErliFRavalPWatsonIAChenDSrivastavaDP. Rapid modulation of synaptogenesis and spinogenesis by 17β-estradiol in primary cortical neurons. Front Cell Neurosci (2015) 9:137. doi: 10.3389/fncel.2015.00137

  • CrossRef
  • Google Scholar

• 17

  KwakowskyAPotapovKKimSPeppercornKTateWPAbrahamIM. Treatment of beta amyloid 1-42 (Abeta(1-42))-induced basal forebrain cholinergic damage by a non-classical estrogen signaling activator in vivo. Sci Rep (2016) 6:21101. doi: 10.1038/srep21101

  • CrossRef
  • Google Scholar

• 18

  HaywardGCBaranowskiBJMarkoDMMacPhersonREK. Examining the effects of ovarian hormone loss and diet-induced obesity on alzheimer’s disease markers of amyloid-β production and degradation. J Neurophysiol (2021) 125:1068–78. doi: 10.1152/jn.00489.2020

  • CrossRef
  • Google Scholar

• 19

  QinYAnDXuWQiXWangXChenLet al. Estradiol replacement at the critical period protects hippocampal neural stem cells to improve cognition in APP/PS1 mice. Front Aging Neurosci (2020) 12:240. doi: 10.3389/fnagi.2020.00240

  • CrossRef
  • Google Scholar

• 20

  MosconiLBertiVQuinnCMcHughPPetrongoloGVarsavskyIet al. Sex differences in Alzheimer risk: Brain imaging of endocrine vs chronologic aging. Neurology (2017) 89:1382–90. doi: 10.1212/WNL.0000000000004425

  • CrossRef
  • Google Scholar

• 21

  GeorgakisMKBeskou-KontouTTheodoridisISkalkidouAPetridouET. Surgical menopause in association with cognitive function and risk of dementia: A systematic review and meta-analysis. Psychoneuroendocrinology (2019) 106:9–19. doi: 10.1016/J.PSYNEUEN.2019.03.013

  • CrossRef
  • Google Scholar

• 22

  RoccaWABowerJHMaraganoreDMAhlskogJEGrossardtBRDe AndradeMet al. Increased risk of cognitive impairment or dementia in women who underwent oophorectomy before menopause. Neurology (2007) 69:1074–83. doi: 10.1212/01.WNL.0000276984.19542.E6

  • CrossRef
  • Google Scholar

• 23

  ParkerWHJacobyVShoupeDRoccaW. Effect of bilateral oophorectomy on women’s long-term health. Womens Health (Lond Engl) (2009) 5:565–76. doi: 10.2217/WHE.09.42

  • CrossRef
  • Google Scholar

• 24

  BrintonRD. Minireview: Translational animal models of human menopause: Challenges and emerging opportunities. Endocrinology (2012) 153:3571–8. doi: 10.1210/EN.2012-1340

  • CrossRef
  • Google Scholar

• 25

  KaraFBelloyMEVonckenRSarwariZGarimaYAnckaertsCet al. Long-term ovarian hormone deprivation alters functional connectivity, brain neurochemical profile and white matter integrity in the Tg2576 amyloid mouse model of alzheimer’s disease. Neurobiol Aging (2021) 102:139–50. doi: 10.1016/J.NEUROBIOLAGING.2021.02.011

  • CrossRef
  • Google Scholar

• 26

  CorderEHSaundersAMStrittmatterWJSchmechelDEGaskellPCSmallGWet al. Gene dose of apolipoprotein e type 4 allele and the risk of alzheimer’s disease in late onset families. Science (1993) 261:921–3. doi: 10.1126/science.8346443

  • CrossRef
  • Google Scholar

• 27

  HoriYHashimotoTNomotoHHymanBTIwatsuboT. Role of apolipoprotein e in β-amyloidogenesis: Isoform-specific effects on protofibril to fibril conversion of aβ in vitro and brain aβ deposition in vivo. J Biol Chem (2015) 290:15163–74. doi: 10.1074/jbc.M114.622209

  • CrossRef
  • Google Scholar

• 28

  HashimotoTSerrano-PozoAHoriYAdamsKWTakedaSBanerjiAOet al. Especially apolipoprotein E4, increases the oligomerization of amyloid β peptide. J Neurosci (2012) 32:15181–92. doi: 10.1523/JNEUROSCI.1542-12.2012

  • CrossRef
  • Google Scholar

• 29

  LiuC-CZhaoNFuYWangNLinaresCTsaiC-Wet al. ApoE4 accelerates early seeding of amyloid pathology. Neuron (2017) 96:1024–1032.e3. doi: 10.1016/j.neuron.2017.11.013

  • CrossRef
  • Google Scholar

• 30

  CastellanoJMKimJStewartFRJiangHDeMattosRBPattersonBWet al. Human apoE isoforms differentially regulate brain amyloid-β peptide clearance. Sci Transl Med (2011) 3:89ra57. doi: 10.1126/scitranslmed.3002156

  • CrossRef
  • Google Scholar

• 31

  DuanYYeTQuZChenYMirandaAZhouXet al. Brain-wide Cas9-mediated cleavage of a gene causing familial alzheimer’s disease alleviates amyloid-related pathologies in mice. Nat BioMed Eng (2021) 6 (2):168–180. doi: 10.1038/s41551-021-00759-0

  • CrossRef
  • Google Scholar

• 32

  LanoiseléeH-MNicolasGWallonDRovelet-LecruxALacourMRousseauSet al. APP, PSEN1, and PSEN2 mutations in early-onset Alzheimer disease: A genetic screening study of familial and sporadic cases. PloS Med (2017) 14:e1002270. doi: 10.1371/journal.pmed.1002270

  • CrossRef
  • Google Scholar

• 33

  JonssonTAtwalJKSteinbergSSnaedalJJonssonPVBjornssonSet al. A mutation in APP protects against alzheimer’s disease and age-related cognitive decline. Nature (2012) 488:96–9. doi: 10.1038/nature11283

  • CrossRef
  • Google Scholar

• 34

  SergeantNDavidJPGoedertMJakesRVermerschPBuéeLet al. Two-dimensional characterization of paired helical filament-tau from alzheimer’s disease: demonstration of an additional 74-kDa component and age-related biochemical modifications. J Neurochem (1997) 69:834–44. doi: 10.1046/j.1471-4159.1997.69020834.x

  • CrossRef
  • Google Scholar

• 35

  GoedertMSpillantiniMGCairnsNJCrowtherRA. Tau proteins of alzheimer paired helical filaments: Abnormal phosphorylation of all six brain isoforms. Neuron (1992) 8:159–68. doi: 10.1016/0896-6273(92)90117-V

  • CrossRef
  • Google Scholar

• 36

  OddoSCaccamoAShepherdJDMurphyMPGoldeTEKayedRet al. Triple-transgenic model of alzheimer’s disease with plaques and tangles: intracellular abeta and synaptic dysfunction. Neuron (2003) 39:409–21. doi: 10.1016/s0896-6273(03)00434-3

  • CrossRef
  • Google Scholar

• 37

  OddoSCaccamoAKitazawaMTsengBPLaFerlaFM. Amyloid deposition precedes tangle formation in a triple transgenic model of alzheimer’s disease. Neurobiol Aging (2003) 24:1063–70. doi: 10.1016/j.neurobiolaging.2003.08.012

  • CrossRef
  • Google Scholar

• 38

  BelfioreRRodinAFerreiraEVelazquezRBrancaCCaccamoAet al. Temporal and regional progression of alzheimer’s disease-like pathology in 3xTg-AD mice. Aging Cell (2019) 18:e12873. doi: 10.1111/acel.12873

  • CrossRef
  • Google Scholar

• 39

  BallingerECAnanthMTalmageDARoleLW. Basal forebrain cholinergic circuits and signaling in cognition and cognitive decline. Neuron (2016) 91:1199–218. doi: 10.1016/j.neuron.2016.09.006

  • CrossRef
  • Google Scholar

• 40

  ZáborszkyLGombkotoPVarsanyiPGielowMRPoeGRoleLWet al. Specific basal forebrain-cortical cholinergic circuits coordinate cognitive operations. J Neurosci (2018) 38:9446–58. doi: 10.1523/JNEUROSCI.1676-18.2018

  • CrossRef
  • Google Scholar

• 41

  WhitehousePJPriceDLStrubleRGClarkAWCoyleJTDeLongMR. Alzheimer’s disease and senile dementia: loss of neurons in the basal forebrain. Science (1982) 215:1237–9. doi: 10.1126/SCIENCE.7058341

  • CrossRef
  • Google Scholar

• 42

  SchmitzTWNathan SprengR. Basal forebrain degeneration precedes and predicts the cortical spread of alzheimer’s pathology. Nat Commun 2016 71 (2016) 7:1–13. doi: 10.1038/ncomms13249

  • CrossRef
  • Google Scholar

• 43

  DesmaraisJEGauthierS. Clinical use of cholinergic drugs in Alzheimer disease. Nat Rev Neurol 2010 68 (2010) 6:418–20. doi: 10.1038/nrneurol.2010.105

  • CrossRef
  • Google Scholar

• 44

  KalesnykasGRoschierUPuoliväliJWangJMiettinenR. The effect of aging on the subcellular distribution of estrogen receptor-alpha in the cholinergic neurons of transgenic and wild-type mice. Eur J Neurosci (2005) 21:1437–42. doi: 10.1111/J.1460-9568.2005.03953.X

  • CrossRef
  • Google Scholar

• 45

  KimSHBaradZCheongRYÁbrahámIM. Sex differences in rapid nonclassical action of 17β-oestradiol on intracellular signalling and oestrogen receptor α expression in basal forebrain cholinergic neurones in mouse. J Neuroendocrinol (2020) 32:e12830. doi: 10.1111/JNE.12830

  • CrossRef
  • Google Scholar

• 46

  SarchielliEGuarnieriGIdrizajESqueccoRMelloTComeglioPet al. The G protein-coupled oestrogen receptor, GPER1, mediates direct anti-inflammatory effects of oestrogens in human cholinergic neurones from the nucleus basalis of meynert. J Neuroendocrinol (2020) 32:e12837. doi: 10.1111/jne.12837

  • CrossRef
  • Google Scholar

• 47

  MitraSWHoskinEYudkovitzJPearLWilkinsonHAHayashiSet al. Immunolocalization of estrogen receptor β in the mouse brain: Comparison with estrogen receptor α. Endocrinology (2003) 144:2055–67. doi: 10.1210/EN.2002-221069

  • CrossRef
  • Google Scholar

• 48

  MerchenthalerILaneMVNumanSDellovadeTL. Distribution of estrogen receptor alpha and beta in the mouse central nervous system: in vivo autoradiographic and immunocytochemical analyses. J Comp Neurol (2004) 473:270–91. doi: 10.1002/CNE.20128

  • CrossRef
  • Google Scholar

• 49

  PingSETrieuJWlodekMEBarrettGL. Effects of estrogen on basal forebrain cholinergic neurons and spatial learning. J Neurosci Res (2008) 86:1588–98. doi: 10.1002/JNR.21609

  • CrossRef
  • Google Scholar

• 50

  SaenzCDominguezRDe LacalleS. Estrogen contributes to structural recovery after a lesion. Neurosci Lett (2006) 392:198–201. doi: 10.1016/J.NEULET.2005.09.023

  • CrossRef
  • Google Scholar

• 51

  ÁbrahámIMKoszegiZTolod-KempESzegoÉM. Action of estrogen on survival of basal forebrain cholinergic neurons: Promoting amelioration. Psychoneuroendocrinology (2009) 34 (Suppl 1):S104–12. doi: 10.1016/j.psyneuen.2009.05.024

  • CrossRef
  • Google Scholar

• 52

  PerezSEHeBMuhammadNOhKJFahnestockMIkonomovicMDet al. Cholinotrophic basal forebrain system alterations in 3xTg-AD transgenic mice. Neurobiol Dis (2011) 41:338–52. doi: 10.1016/J.NBD.2010.10.002

  • CrossRef
  • Google Scholar

• 53

  ZhuDMontagneAZhaoZ. Alzheimer’s pathogenic mechanisms and underlying sex difference. Cell Mol Life Sci (2021) 78:4907–20. doi: 10.1007/s00018-021-03830-w

  • CrossRef
  • Google Scholar

• 54

  Mauvais-JarvisF. Estrogen and androgen receptors: regulators of fuel homeostasis and emerging targets for diabetes and obesity. Trends Endocrinol Metab (2011) 22:24–33. doi: 10.1016/J.TEM.2010.10.002

  • CrossRef
  • Google Scholar

• 55

  CookePSBuchananDLYoungPSetiawanTBrodyJKorachKSet al. Stromal estrogen receptors mediate mitogenic effects of estradiol on uterine epithelium. Proc Natl Acad Sci U.S.A. (1997) 94:6535–40. doi: 10.1073/PNAS.94.12.6535

  • CrossRef
  • Google Scholar

• 56

  Quispe CallaNEVicetti MiguelRDAcevesKMHuangHHowittBCherpesTL. Ovariectomized mice and postmenopausal women exhibit analogous loss of genital epithelial integrity. Tissue Barriers (2021) 9(2):1865760. doi: 10.1080/21688370.2020.1865760

  • CrossRef
  • Google Scholar

• 57

  FazekasCLBalázsfiDHorváthHRBaloghZAliczkiMPuhovaAet al. Consequences of VGluT3 deficiency on learning and memory in mice. Physiol Behav (2019) 212:112688. doi: 10.1016/j.physbeh.2019.112688

  • CrossRef
  • Google Scholar

• 58

  PrieurEJadavjiN. Assessing spatial working memory using the spontaneous alternation y-maze test in aged Male mice. Bio-protocol (2019) 9(3):e3162. doi: 10.21769/BIOPROTOC.3162

  • CrossRef
  • Google Scholar

• 59

  BrandeisRBrandysYYehudaS. The use of the morris water maze in the study of memory and learning. Int J Neurosci (1989) 48:29–69. doi: 10.3109/00207458909002151

  • CrossRef
  • Google Scholar

• 60

  VorheesCVWilliamsMT. Morris water maze: procedures for assessing spatial and related forms of learning and memory. Nat Protoc (2006) 1:848–58. doi: 10.1038/NPROT.2006.116

  • CrossRef
  • Google Scholar

• 61

  RobertoNPortellaMJMarquiéMAlegretMHernándezIMauleónAet al. Neuropsychiatric profile as a predictor of cognitive decline in mild cognitive impairment. Front Aging Neurosci (2021) 13:718949. doi: 10.3389/FNAGI.2021.718949

  • CrossRef
  • Google Scholar

• 62

  JohanssonMStomrudELindbergOWestmanEJohanssonPMvan WestenDet al. Apathy and anxiety are early markers of alzheimer’s disease. Neurobiol Aging (2020) 85:74–82. doi: 10.1016/J.NEUROBIOLAGING.2019.10.008

  • CrossRef
  • Google Scholar

• 63

  RoccaWAGrossardtBRGedaYEGostoutBSBowerJHMaraganoreDMet al. Long-term risk of depressive and anxiety symptoms after early bilateral oophorectomy. Menopause (2018) 25:1275–85. doi: 10.1097/GME.0000000000001229

  • CrossRef
  • Google Scholar

• 64

  RodgersRJDalviA. Anxiety, defence and the elevated plus-maze. Neurosci Biobehav Rev (1997) 21:801–10. doi: 10.1016/S0149-7634(96)00058-9

  • CrossRef
  • Google Scholar

• 65

  ByrnesEMBridgesRS. Reproductive experience alters anxiety-like behavior in the female rat. Horm Behav (2006) 50:70–6. doi: 10.1016/J.YHBEH.2006.01.006

  • CrossRef
  • Google Scholar

• 66

  PairojanaTPhasukSSureshPHuangSPPakaprotNChompoopongSet al. Age and gender differences for the behavioral phenotypes of 3xTg alzheimer’s disease mice. Brain Res (2021) 1762:147437. doi: 10.1016/J.BRAINRES.2021.147437

  • CrossRef
  • Google Scholar

• 67

  ThigpenJESetchellKDRSaundersHEHasemanJKGrantMGForsytheDB. Selecting the appropriate rodent diet for endocrine disruptor research and testing studies. ILAR J (2004) 45:401–16. doi: 10.1093/ILAR.45.4.401/2/ILAR-45-4-401IGF7.GIF

  • CrossRef
  • Google Scholar

• 68

  GeeDMFlurkeyKFinchCE. Aging and the regulation of luteinizing hormone in C57BL/6J mice: impaired elevations after ovariectomy and spontaneous elevations at advanced ages. Biol Reprod (1983) 28:598–607. doi: 10.1095/BIOLREPROD28.3.598

  • CrossRef
  • Google Scholar

• 69

  ChavesTTörökBFazekasCLCorreiaPSiposEVárkonyiDet al. Median raphe region GABAergic neurons contribute to social interest in mouse. Life Sci (2022) 289:120223. doi: 10.1016/J.LFS.2021.120223

  • CrossRef
  • Google Scholar

• 70

  EngelmannMHädickeJNoackJ. Testing declarative memory in laboratory rats and mice using the nonconditioned social discrimination procedure. Nat Protoc (2011) 6:1152–62. doi: 10.1038/NPROT.2011.353

  • CrossRef
  • Google Scholar

• 71

  AliczkiMFodorABaloghZHallerJ. Behavior DZ-h and, 2014 undefined. the effects of lactation on impulsive behavior in vasopressin-deficient brattleboro rats. Available at: https://www.sciencedirect.com/science/article/pii/S0018506X14001597 (Accessed June 16, 2022).

  • Google Scholar

• 72

  BruzsikBBiroLZelenaDSiposESzebikHSarosdiKRet al. Somatostatin neurons of the bed nucleus of stria terminalis enhance associative fear memory consolidation in mice. J Neurosci (2021) 41:1982. doi: 10.1523/JNEUROSCI.1944-20.2020

  • CrossRef
  • Google Scholar

• 73

  BalázsfiDFodorATörökBFerencziSKovácsKJHallerJet al. Enhanced innate fear and altered stress axis regulation in VGluT3 knockout mice. Stress (2018) 21:151–61. doi: 10.1080/10253890.2017.1423053

  • CrossRef
  • Google Scholar

• 74

  GállZFarkasSAlbertÁFerenczEVanceaSUrkonMet al. Effects of chronic cannabidiol treatment in the rat chronic unpredictable mild stress model of depression. Biomolecules (2020) 10(5):801. doi: 10.3390/biom10050801

  • CrossRef
  • Google Scholar

• 75

  BruzsikBBiroLSarosdiKRZelenaDSiposESzebikHet al. Neurochemically distinct populations of the bed nucleus of stria terminalis modulate innate fear response to weak threat evoked by predator odor stimuli. Neurobiol Stress (2021) 15:100415. doi: 10.1016/J.YNSTR.2021.100415

  • CrossRef
  • Google Scholar

• 76

  Hanna Al-ShaikhFSDuaraRCrookJELesserERSchaeverbekeJHinkleKMet al. Selective vulnerability of the nucleus basalis of meynert among neuropathologic subtypes of Alzheimer disease. JAMA Neurol (2020) 77:225–33. doi: 10.1001/JAMANEUROL.2019.3606

  • CrossRef
  • Google Scholar

• 77

  GeorgeAAVieiraJMXavier-JacksonCGeeMTCirritoJRBimonte-NelsonHAet al. Implications of oligomeric amyloid-beta (oAβ 42) signaling through α7β2-nicotinic acetylcholine receptors (nAChRs) on basal forebrain cholinergic neuronal intrinsic excitability and cognitive decline. J Neurosci (2021) 41:555–75. doi: 10.1523/JNEUROSCI.0876-20.2020

  • CrossRef
  • Google Scholar

• 78

  LehmannJNagyJIAtmadjaSFibigerHC. The nucleus basalis magnocellularis: the origin of a cholinergic projection to the neocortex of the rat. Neuroscience (1980) 5:1161–74. doi: 10.1016/0306-4522(80)90195-5

  • CrossRef
  • Google Scholar

• 79

  KoszegiZSzegoÉMCheongRYTolod-KempEÁbrahámIM. Postlesion estradiol treatment increases cortical cholinergic innervations via estrogen receptor-α dependent nonclassical estrogen signaling in vivo. Endocrinology (2011) 152:3471–82. doi: 10.1210/en.2011-1017

  • CrossRef
  • Google Scholar

• 80

  GuillouxJPSeneyMEdgarNSibilleE. Integrated behavioral z-scoring increases the sensitivity and reliability of behavioral phenotyping in mice: relevance to emotionality and sex. J Neurosci Methods (2011) 197:21–31. doi: 10.1016/J.JNEUMETH.2011.01.019

  • CrossRef
  • Google Scholar

• 81

  FazekasCLSiposEKlaricTTörökBBellardieMErjaveGNet al. Searching for glycomic biomarkers for predicting resilience and vulnerability in a rat model of posttraumatic stress disorder. Stress (2020) 23:715–31. doi: 10.1080/10253890.2020.1795121

  • CrossRef
  • Google Scholar

• 82

  HeinePATaylorJAIwamotoGALubahnDBCookePS. Increased adipose tissue in male and female estrogen receptor-alpha knockout mice. Proc Natl Acad Sci U.S.A. (2000) 97:12729–34. doi: 10.1073/PNAS.97.23.12729

  • CrossRef
  • Google Scholar

• 83

  BeckP. Effect of progestins on glucose and lipid metabolism. Ann N Y Acad Sci (1977) 286:434–45. doi: 10.1111/J.1749-6632.1977.TB29435.X

  • CrossRef
  • Google Scholar

• 84

  AbildgaardJPlougTAl-SaoudiEWagnerTThomsenCEwertsenCet al. Changes in abdominal subcutaneous adipose tissue phenotype following menopause is associated with increased visceral fat mass. Sci Rep (2021) 11(1):14750. doi: 10.1038/S41598-021-94189-2

  • CrossRef
  • Google Scholar

• 85

  AmbikairajahAWalshETabatabaei-JafariHCherbuinN. Fat mass changes during menopause: a metaanalysis. Am J Obstet Gynecol (2019) 221:393–409.e50. doi: 10.1016/J.AJOG.2019.04.023

  • CrossRef
  • Google Scholar

• 86

  PiconePDi CarloMNuzzoD. Obesity and alzheimer’s disease: Molecular bases. Eur J Neurosci (2020) 52:3944–50. doi: 10.1111/EJN.14758

  • CrossRef
  • Google Scholar

• 87

  KangSLeeYHLeeJE. Metabolism-centric overview of the pathogenesis of alzheimer’s disease. Yonsei Med J (2017) 58:479–88. doi: 10.3349/YMJ.2017.58.3.479

  • CrossRef
  • Google Scholar

• 88

  Medina-ContrerasJVillalobos-MolinaRZarain-HerzbergABalderas-VillalobosJ. Ovariectomized rodents as a menopausal metabolic syndrome model. a minireview. Mol Cell Biochem (2020) 475:261–76. doi: 10.1007/S11010-020-03879-4

  • CrossRef
  • Google Scholar

• 89

  AldermanMHTaylorHS. Molecular mechanisms of estrogen action in female genital tract development. Differentiation (2021) 118:34–40. doi: 10.1016/J.DIFF.2021.01.002

  • CrossRef
  • Google Scholar

• 90

  LevinER. Membrane estrogen receptors signal to determine transcription factor function. Steroids (2018) 132:1–4. doi: 10.1016/j.steroids.2017.10.014

  • CrossRef
  • Google Scholar

• 91

  KwakowskyAHerbisonAEÁbrahámIM. The role of cAMP response element-binding protein in estrogen negative feedback control of gonadotropin-releasing hormone neurons. J Neurosci (2012) 32:11309–17. doi: 10.1523/JNEUROSCI.1333-12.2012

  • CrossRef
  • Google Scholar

• 92

  CheongRYCzieselskyKPorteousRHerbisonAE. Expression of ESR1 in glutamatergic and GABAergic neurons is essential for normal puberty onset, estrogen feedback, and fertility in female mice. J Neurosci (2015) 35:14533. doi: 10.1523/JNEUROSCI.1776-15.2015

  • CrossRef
  • Google Scholar

• 93

  YipSHLiuXHesslerSCheongIPorteousRHerbisonAE. Indirect suppression of pulsatile LH secretion by CRH neurons in the female mouse. Endocrinology (2021) 162(3):bqaa237. doi: 10.1210/ENDOCR/BQAA237

  • CrossRef
  • Google Scholar

• 94

  FarkasSSzabóAHegyiAETörökBFazekasCLErnsztDet al. Estradiol and estrogen-like alternative therapies in use: The importance of the selective and non-classical actions. Biomedicines (2022) 10(4):861. doi: 10.3390/BIOMEDICINES10040861

  • CrossRef
  • Google Scholar

• 95

  LiedertANemitzCHaffner-LuntzerMSchickFJakobFIgnatiusA. Effects of estrogen receptor and wnt signaling activation on mechanically induced bone formation in a mouse model of postmenopausal bone loss. Int J Mol Sci (2020) 21:1–12. doi: 10.3390/IJMS21218301

  • CrossRef
  • Google Scholar

• 96

  WeiZGeFCheYWuSDongXSongD. Metabolomics coupled with pathway analysis provides insights into sarco-osteoporosis metabolic alterations and estrogen therapeutic effects in mice. Biomolecules (2021) 12(1):41. doi: 10.3390/BIOM12010041

  • CrossRef
  • Google Scholar

• 97

  WebsterSJBachstetterADNelsonPTSchmittFAVan EldikLJ. Using mice to model alzheimer’s dementia: an overview of the clinical disease and the preclinical behavioral changes in 10 mouse models. Front Genet (2014) 5:88. doi: 10.3389/FGENE.2014.00088

  • CrossRef
  • Google Scholar

• 98

  CamposACFogaçaMVAguiarDCGuimarãesFS. Animal models of anxiety disorders and stress. Rev Bras Psiquiatr (2013) 35(Suppl 2):S101–11. doi: 10.1590/1516-4446-2013-1139

  • CrossRef
  • Google Scholar

• 99

  FurcilaDDefelipeJAlonso-NanclaresL. A study of amyloid-β and phosphotau in plaques and neurons in the hippocampus of alzheimer’s disease patients. J Alzheimers Dis (2018) 64:417–35. doi: 10.3233/JAD-180173

  • CrossRef
  • Google Scholar

• 100

  BehnamedinJameie SPirastehANaseriASadatJameie MFarhadiMFahanikBabaee Jet al. β-amyloid formation, memory, and learning decline following long-term ovariectomy and its inhibition by systemic administration of apigenin and β-estradiol. Basic Clin Neurosci (2021) 12(3):383–94. doi: 10.32598/BCN.2021.2634.1

  • CrossRef
  • Google Scholar

• 101

  StoverKRCampbellMAVan WinssenCMBrownRE. Early detection of cognitive deficits in the 3xTg-AD mouse model of alzheimer’s disease. Behav Brain Res (2015) 289:29–38. doi: 10.1016/J.BBR.2015.04.012

  • CrossRef
  • Google Scholar

• 102

  SiegelAMMathewsSB. Diagnosis and treatment of anxiety in the aging woman. Curr Psychiatry Rep (2015) 17(12):93. doi: 10.1007/S11920-015-0636-3

  • CrossRef
  • Google Scholar

• 103

  BottoRCallaiNCermelliACausaranoLRaineroI. Anxiety and depression in alzheimer’s disease: a systematic review of pathogenetic mechanisms and relation to cognitive decline. Neurol Sci (2022) 43(7):4107–24. doi: 10.1007/S10072-022-06068-X

  • CrossRef
  • Google Scholar

• 104

  ArrantAESchramm-SapytaNLKuhnCM. Use of the light/dark test for anxiety in adult and adolescent male rats. Behav Brain Res (2013) 256:119–27. doi: 10.1016/J.BBR.2013.05.035

  • CrossRef
  • Google Scholar

• 105

  AdhikariALernerTNFinkelsteinJPakSJenningsJHDavidsonTJet al. Basomedial amygdala mediates top-down control of anxiety and fear. Nature (2015) 527:179–85. doi: 10.1038/NATURE15698

  • CrossRef
  • Google Scholar

• 106

  SturmanOGermainPLBohacekJ. Exploratory rearing: a context- and stress-sensitive behavior recorded in the open-field test. Stress (2018) 21:443–52. doi: 10.1080/10253890.2018.1438405

  • CrossRef
  • Google Scholar

• 107

  JanakPHTyeKM. From circuits to behaviour in the amygdala. Nature (2015) 517:284–92. doi: 10.1038/NATURE14188

  • CrossRef
  • Google Scholar

• 108

  SahP. Fear, anxiety, and the amygdala. Neuron (2017) 96:1–2. doi: 10.1016/J.NEURON.2017.09.013

  • CrossRef
  • Google Scholar

• 109

  EtkinAPraterKESchatzbergAFMenonVGreiciusMD. Disrupted amygdalar subregion functional connectivity and evidence of a compensatory network in generalized anxiety disorder. Arch Gen Psychiatry (2009) 66:1361–72. doi: 10.1001/ARCHGENPSYCHIATRY.2009.104

  • CrossRef
  • Google Scholar

• 110

  PatelPMasurkarAV. The relationship of anxiety with alzheimer’s disease: A narrative review. Curr Alzheimer Res (2021) 18:359–71. doi: 10.2174/1567205018666210823095603

  • CrossRef
  • Google Scholar

• 111

  ZhaoQFTanLWangHFJiangTTanMSTanLet al. The prevalence of neuropsychiatric symptoms in alzheimer’s disease: Systematic review and meta-analysis. J Affect Disord (2016) 190:264–71. doi: 10.1016/J.JAD.2015.09.069

  • CrossRef
  • Google Scholar

• 112

  LeeG. Impaired cognitive function is associated with motor function and activities of daily living in mild to moderate alzheimer’s dementia. Curr Alzheimer Res (2020) 17:680–6. doi: 10.2174/1567205017666200818193916

  • CrossRef
  • Google Scholar

• 113

  AlbersMWGilmoreGCKayeJMurphyCWingfieldABennettDAet al. At The interface of sensory and motor dysfunctions and alzheimer’s disease. Alzheimers Dement (2015) 11:70–98. doi: 10.1016/J.JALZ.2014.04.514

  • CrossRef
  • Google Scholar

• 114

  JawharSTrawickaAJenneckensCBayerTAWirthsO. Motor deficits, neuron loss, and reduced anxiety coinciding with axonal degeneration and intraneuronal aβ aggregation in the 5XFAD mouse model of alzheimer’s disease. Neurobiol Aging (2012) 33:196.e29–196.e40. doi: 10.1016/J.NEUROBIOLAGING.2010.05.027

  • CrossRef
  • Google Scholar

• 115

  Juárez-TapiaCMiranda-AnayaM. Ovariectomy influences the circadian rhythm of locomotor activity and the photic phase shifts in the volcano mouse. Physiol Behav (2017) 182:77–85. doi: 10.1016/J.PHYSBEH.2017.10.002

  • CrossRef
  • Google Scholar

• 116

  RoystonSEYasuiNKondilisAGLordSVKatzenellenbogenJAMahoneyMM. ESR1 and ESR2 differentially regulate daily and circadian activity rhythms in female mice. Endocrinology (2014) 155:2613–23. doi: 10.1210/EN.2014-1101

  • CrossRef
  • Google Scholar

• 117

  KazimSFSeoJHBianchiRLarsonCSSharmaAWongRKSet al. Neuronal network excitability in alzheimer’s disease: The puzzle of similar versus divergent roles of amyloid β and tau. eNeuro (2021) 8(2):ENEURO.0418-20. doi: 10.1523/ENEURO.0418-20.2020

  • CrossRef
  • Google Scholar

• 118

  BoveRSecorEChibnikLBBarnesLLSchneiderJABennettDAet al. Age at surgical menopause influences cognitive decline and Alzheimer pathology in older women. Neurology (2014) 82:222–9. doi: 10.1212/WNL.0000000000000033

  • CrossRef
  • Google Scholar

• 119

  MosconiLBertiVDykeJSchelbaumEJettSLoughlinLet al. Menopause impacts human brain structure, connectivity, energy metabolism, and amyloid-beta deposition. Sci Rep 2021 111 (2021) 11:1–16. doi: 10.1038/s41598-021-90084-y

  • CrossRef
  • Google Scholar

• 120

  ZeydanBTosakulwongNSchwarzCGSenjemMLGunterJLReidRIet al. Association of bilateral salpingo-oophorectomy before menopause onset with medial temporal lobe neurodegeneration. JAMA Neurol (2019) 76:95–100. doi: 10.1001/JAMANEUROL.2018.3057

  • CrossRef
  • Google Scholar

• 121

  CarrollJCRosarioERChangLStanczykFZOddoSLaFerlaFMet al. Progesterone and estrogen regulate Alzheimer-like neuropathology in female 3xTg-AD mice. J Neurosci (2007) 27:13357. doi: 10.1523/JNEUROSCI.2718-07.2007

  • CrossRef
  • Google Scholar

• 122

  ZhaoLYaoJMaoZChenSWangYBrintonRD. 17β-estradiol regulates insulin-degrading enzyme expression via an ERβ/PI3-K pathway in hippocampus: relevance to alzheimer’s prevention. Neurobiol Aging (2011) 32:1949–63. doi: 10.1016/J.NEUROBIOLAGING.2009.12.010

  • CrossRef
  • Google Scholar

• 123

  PalmRChangJBlairJGarcia-MesaYLeeHGCastellaniRJet al. Down-regulation of serum gonadotropins but not estrogen replacement improves cognition in aged-ovariectomized 3xTg AD female mice. J Neurochem (2014) 130:115. doi: 10.1111/JNC.12706

  • CrossRef
  • Google Scholar

• 124

  RodriguesSMSapolskyRM. Disruption of fear memory through dual-hormone gene therapy. Biol Psychiatry (2009) 65:441–4. doi: 10.1016/J.BIOPSYCH.2008.09.003

  • CrossRef
  • Google Scholar

• 125

  ShanskyRMHamoCHofPRLouWMcEwenBSMorrisonJH. Estrogen promotes stress sensitivity in a prefrontal cortex-amygdala pathway. Cereb Cortex (2010) 20:2560–7. doi: 10.1093/CERCOR/BHQ003

  • CrossRef
  • Google Scholar

• 126

  TianZWangYZhangNyanGYFengBbingLSet al. Estrogen receptor GPR30 exerts anxiolytic effects by maintaining the balance between GABAergic and glutamatergic transmission in the basolateral amygdala of ovariectomized mice after stress. Psychoneuroendocrinology (2013) 38:2218–33. doi: 10.1016/J.PSYNEUEN.2013.04.011

  • CrossRef
  • Google Scholar

• 127

  GreenPSBalesKPaulSBuG. Estrogen therapy fails to alter amyloid deposition in the PDAPP model of alzheimer’s disease. Endocrinology (2005) 146:2774–81. doi: 10.1210/EN.2004-1433

  • CrossRef
  • Google Scholar

• 128

  DingFYaoJZhaoLMaoZChenSBrintonRD. Ovariectomy induces a shift in fuel availability and metabolism in the hippocampus of the female transgenic model of familial alzheimer’s. PloS One (2013) 8(3):e59825. doi: 10.1371/JOURNAL.PONE.0059825

  • CrossRef
  • Google Scholar

• 129

  Levin-AllerhandJALominskaCEWangJSmithJD. 17Alpha-estradiol and 17beta-estradiol treatments are effective in lowering cerebral amyloid-beta levels in AbetaPPSWE transgenic mice. J Alzheimers Dis (2002) 4:449–57. doi: 10.3233/JAD-2002-4601

  • CrossRef
  • Google Scholar

• 130

  YaoQFengMYangBLongZLuoSLuoMet al. Effects of ovarian hormone loss on neuritic plaques and autophagic flux in the brains of adult female APP/PS1 double-transgenic mice. Acta Biochim Biophys Sin (Shanghai) (2018) 50:447–55. doi: 10.1093/ABBS/GMY032

  • CrossRef
  • Google Scholar

• 131

  HampelHMesulamM-MCuelloACFarlowMRGiacobiniEGrossbergGTet al. The cholinergic system in the pathophysiology and treatment of alzheimer’s disease. Brain (2018) 141:1917–33. doi: 10.1093/brain/awy132

  • CrossRef
  • Google Scholar

• 132

  ChenZRHuangJBYangSLHongFF. Role of cholinergic signaling in alzheimer’s disease. Molecules (2022) 27(6):1816. doi: 10.3390/MOLECULES27061816

  • CrossRef
  • Google Scholar

• 133

  GibbsRB. Effects of ageing and long-term hormone replacement on cholinergic neurones in the medial septum and nucleus basalis magnocellularis of ovariectomized rats. J Neuroendocrinol (2003) 15:477–85. doi: 10.1046/J.1365-2826.2003.01012.X

  • CrossRef
  • Google Scholar

• 134

  OakleyHColeSLLoganSMausEShaoPCraftJet al. Intraneuronal beta-amyloid aggregates, neurodegeneration, and neuron loss in transgenic mice with five familial alzheimer’s disease mutations: potential factors in amyloid plaque formation. J Neurosci (2006) 26:10129–40. doi: 10.1523/JNEUROSCI.1202-06.2006

  • CrossRef
  • Google Scholar