Gene dosage determines the behavioural phenotype in the 5xFAD mouse model of Alzheimer’s disease: analysis of the contribution of brain insulin signalling and gut microbiota

doi:10.21203/rs.3.rs-2087511/v1

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Gene dosage determines the behavioural phenotype in the 5xFAD mouse model of Alzheimer’s disease: analysis of the contribution of brain insulin signalling and gut microbiota

https://doi.org/10.21203/rs.3.rs-2087511/v1

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Background: There are many hypotheses about the neuropathological origin of sporadic Alzheimer’s disease (AD). The amyloid cascade hypothesis remains the most widely accepted by the scientific community, although the role of amyloid-β (Aβ) as the sole cause of neuropathological features of the non-inherited form of AD is questionable.

Methods: The 5xFAD mouse model shows amyloid plaques formation and cognitive impairment from 4 months of age. However, no attention is paid to the effect of genetic load, heterozygous (HTZ) versus homozygous (HZ) condition, on the behaviour, histology, metabolic physiology, and composition of gut microbiota. We studied the phenotype of HTZ and HZ mice of both sexes at 4-6 months (early disease onset) and 10-12 months of age (late disease onset).

Results: We described that from 4-6 months old, both HTZ and HZ mice exhibited hippocampal impairment caused by accumulation of Aβ₄₀ and Aβ₄₂, increased neuroinflammatory markers and altered PI3K-Akt pathway. However, only HZ mice showed cognitive impairment in the Y-maze test and the Morris water maze. In addition, dysregulation of insulin-linked metabolic pathways was detected at the peripheral level between HTZ and HZ mice. This striking difference between behavioural, immunopathological and physiological phenotypes extends to the microbiota in terms of Firmicutes/Bacteroidetes ratio, being most pronounced in HZ mice at 10-12 months old.

Conclusions: Taken together, our results suggest that Aβ aggregation may not be the sole cause of worsening cognitive impairment, but that other factors such as PI3K-Akt, insulin-linked metabolic pathways, and microbiota composition could be influencing disease progression, allowing for alternative disease-modifying interventions.

Akt pathway

cognitive impairment

insulin

hippocampus

microbiota

neuroinflammation

spatial working memory

There are many hypotheses about the neuropathological origin of Alzheimer’s disease (AD), but they are increasingly debated because they do not fully explain the etiopathogenesis of this destructive disease. One of them focuses on the accumulation of insoluble amyloid-β (Aβ) peptide (senile plaques) and hyperphosphorylated tau protein (neurofibrillary tangles) in the central nervous system as causative factors of the clinical symptoms that occur in patients with AD (Glenner & Wong, 1984). In the brain, astrocytes and neurons produced soluble Aβ form, which is cleaved from a transmembrane protein called amyloid precursor protein (APP) by catalysis of β- and α-secretase (Qiu et al., 1995; Shoji et al., 1992). Different mutations in genes involved in APP processing pathways, such as APP (Goate et al., 1991), presinilin-1 (PSEN1) and -2 (PSEN2) (Levy-Lahad et al., 1995; Rogaev et al., 1995; Sherrington et al., 1995), have been reported to accelerate the overexpressing and deposition of Aβ peptides, mostly the 42 amino acid isoform (Bekris et al., 2010; Hsiao et al., 1996; Tanzi & Bertram, 2005). Many of these multiple mutations are detected in the majority of inherited cases of early-onset familial AD, which accounts for 1-5% of all cases (Liu & Li, 2019). Therefore, the “amyloid cascade hypothesis” (Bartholomew et al., 2002; Hardy & Selkoe, 2002) remains the most widely accepted by the scientific community. Although the role of Aβ as a cause of the neuropathological hallmarks of AD remains an open question (Liu & Li, 2019), other hypotheses such as astrocyte dysfunction and neuroinflammation (Bronzuoli et al., 2019), insulin resistance (Ott et al., 1999) or alterations of the gut-brain microbiota axis (Bairamian et al., 2022) are being considered.

Interestingly, the hippocampus is the most damaged region in patients with AD. Several post mortem studies on the human brain have described that amyloid plaques are deposited in the hippocampal formation, especially in the cornu ammonis 1 (CA1), subiculum and the entorhinal cortex (H. Braak & Braak, 1991; Heiko Braak & Braak, 1991; Furcila et al., 2018). But not only the presence of Aβ deposits could predict impairment cognitive functions and memory loss in AD subjects. It is well known that hippocampal formation is involved in memory processes (Bird & Burgess, 2008) and that this region is particularly vulnerable to damage induced by, among others, abnormal inflammatory responses or oxidative stress. In this regard, several lines of evidence show that the integrated coordination of neuronal responses through the PI3K (phosphoinositide 3 kinase)/Akt pathway in the brain has a significant functional impact on key events that are negatively affected in AD (Long et al., 2021; O’Neill, 2013). Central insulin resistance is a typical feature associated with premature ageing and manifests in neurological diseases such as AD in its early stages (Milstein & Ferris, 2021). In this regard, the PI3K-Akt pathway mediates insulin signalling in the brain, where defective insulin signalling is a risk factor for developing AD (Gabbouj et al., 2019). In addition, the PIK3/Akt pathway is crucial in many biological processes such as cell proliferation, growth, migration, and survival (Xu et al., 2020) and many reports have supported that this pathway is inhibited by Aβ oligomers promoting neuronal death and ultimately dementia (Choi et al., 2012, 2013). In addition, the activation of this pathway can also be induced by lipopolysaccharides (LPS) that are released by bacteria located mainly in the gut (Sánchez-Alegría et al., 2018; Saponaro et al., 2012). LPS are components of the bacterial wall that can cross both the intestinal barrier and the blood-brain barrier, activating natural immunity pathways in the brain and causing the appearance of reactive microglia and astrocytes, which triggers neuroinflammation (Banks, 2008; Braniste et al., 2014; Logsdon et al., 2018). In fact, interest in the microbiota, especially the microbiota within the gut, is growing today because it may greatly influence physiological function and even behaviour, playing an important role in the development of AD (Bairamian et al., 2022; Cryan & Dinan, 2012; Kowalski & Mulak, 2019).

Accordingly, 5xFAD mice are widely used as animal models of AD that co-overexpress human APP with three familial Alzheimer’s disease (FAD) mutations and human PSEN1 with two FAD mutations accelerating amyloid plaque formation (Oakley et al., 2006). Many reports have described that 5xFAD mice exhibited cognitive alterations, more specifically, impairment in hippocampus-dependent memory (Cho et al., 2014; Girard et al., 2014; Kimura & Ohno, 2009; Oakley et al., 2006), but these reports did not pay attention to the effect of genetic load on histology, physiology, and gut microbiota composition to better understand the development of the disease. This is especially important because gene-environment interactions could accelerate the course of the disease, as revealed by experiments analysing the impact of alcohol consumption on cognitive impairment and amyloid deposition in 3-TG transgenic mice (Ledesma et al., 2021). The genetic load of AD-associated genes may be a crucial factor in understanding non-genetic influences on the development of AD. Therefore, in this study, a heterozygous (HTZ) and homozygous (HZ) transgenic 5xFAD mouse model was used to test whether only the Aβ load presented in the brain leads to impaired cognition or whether other factors together exacerbate the disease progression.

2.1. Animals and Ethics Statement

All experiments were performed in non-transgenic (no-tg) female and male mice and in heterozygous (HTZ) and homozygous 5xFAD transgenic female and male mice that co-express and co-inherit FAD mutant forms of human APP and PSEN1 (Oakley et al., 2006; Ohno et al., 2006). Two life stages of mice were used: mature adult mice with 4-6 months old (considered early disease onset); and middle-aged mice with 10-12 months old (considered late disease onset). Mice were housed in groups of 3-4 on a 12 h light/dark cycle (lights on at 07:00 hours), with water and food provided ad libitum. Experiments were conducted between 09:00 hours and 15:00 hours. Animal were housed in groups of 3-4 per cage except during the saccharin and sucrose consumption test, for which mice were housed individually.

All experiments were performed in compliance with the ARRIVE guidelines (Kilkenny et al., 2010) and in accordance with the European Communities Council Directives 2010/63/EU, Regulation (EC) nº 86/609/ECC (24 November 1986) and Spanish National and Regional Guidelines for Animal Experimentation (Real Decreto 53/2013). The experimental protocols were approved by The Local Ethical Committee for Animal Research of the University of Malaga (CTS-8221, July 2016). Accordingly, every effort was made to minimize animal suffering and reduce the number of animals used.

2.2. Quantitative genotyping

Genotyping was performed by PCR analysis of ear DNA to detect the human APP gene. The primers were: common Forward: ACCCCCATGTCAGAGTTCCT, wild-type Reverse: TATACAACCTTGGGGGATGG, mutant Reverse: CGGGCCTCTTCGCTATTAC (Invitrogen) were used to amplify the Tg transgene (APPSwFlLon, PSEN1*M146L*L286V)6799Vas inserted on mouse chromosome 3 in 5xFAD transgenic mice. This assay is able to distinguish between hemizygotes and homozygotes. The JAX protocol #31769 (https://www.jax.org/Protocol?stockNumber=006554&protocolID=31769) was followed. PCR was performed using a Thermocycler PCR instrument (Eppendorf) under standard conditions. The PCR product was visualised on a 2% agarose gel.

2.3. Behavioural testing

General considerations for all behavioural assessments. Three days before the first behavioural test, the animals were habituated to handling by the experimenter in the testing room, to get adapted to the experimental conditions. On each testing day, mice were allowed to adapt in a behaviour room for at least 30 min. The illumination of the arena or maze was 35-40 lux. The mazes were cleaned with 30% ethanol between each session. All tests were recorded and assessed using video tracking software (Ethovision XT 9.0, Noldus, Wageningen, The Netherlands). The estrous cycle of the females was not controlled, as they were randomly cycling.

2.3.1. Neurological screening

Neurological screening was performed using a battery of neurological tests as previously described (Zambrana-Infantes et al., 2018). Briefly, sensorimotor orientation, coordinated limb movements and neurological function were tested. The neurological testing was rated on the following scale: 0= absent, 1= weak or 2= strong presented as a percentage of occurrences for each genotype (Björklund et al., 1980; Marshall & Teitelbaum, 1974).

2.3.2. Saccharin and Sucrose preference tests

To analyse whether our mouse model of AD shows anhedonia, we performed both saccharin preference test (SachPT) and sucrose preference test (SPT) because the caloric content of the sucrose solution may influence the assessment (Scheggi et al., 2018). Mice were trained to consume either saccharin or sucrose for 24 hours in the two days prior to the start of the test. For the preference test, animals were given a free choice between two pre-weighed bottles, one containing tap water and one containing 0.05% saccharin or sucrose (Sigma-Aldrich). The position of the bottles was balanced among animals to avoid possible effects of side preferences on drinking behaviour. Animal consumption was measured by weighing the bottles. SachPT and SPT were calculated according to the following formula: (sweet solution intake) / (sweet solution intake + water intake) × 100 (Moreno-Fernández et al., 2020; Strekalova et al., 2004). The acceptance criterion for preference was ≤ 65%, below this value anhedonia was considered (Strekalova et al., 2004).All mice were weighed during the 4 days of the test.

2.3.3. Open field (OF)

Exploratory activity and anxiety-like behaviour were assessed in OF as previously described (Rosell-Valle et al., 2021; Santin et al., 2009). The maze (50 x 50 cm) was divided into two zones: central (8 cm x 8 cm) and peripheral areas with 38-cm-high Plexiglas walls. Each animal was placed in the centre of the apparatus and its behaviour was recorded for 5 min. The locomotion activity (distance travelled; cm) and the time spent in the centre zone were analysed.

2.3.4. Elevated plus maze (EPM)

Anxiety-like behaviour was evaluated using the elevated plus maze (Malleret et al., 1999; Santin et al., 2009). The maze consisted of two open arms (30 x 5 cm), two closed arms (30 x 5 x 15 cm), and a central connecting platform (5x 5 cm). The maze was raised to a height of 38.5 cm above the floor. The mouse was placed in the centre of the EPM and its behaviour was recorded for 5 min. The following behaviour was assessed: locomotion (distance travelled; cm), the time spent in the open arm, and the number of arm entries.

2.3.5. Y-maze test (YMT)

Working memory was evaluated by spontaneous alternation behaviour (SAB) (Hughes, 2004). Each mouse was initially placed in one arm of the maze (40 x 4.5 x 14 cm, made of grey Plexiglas), and allowed to explore the maze for 8 min. The following behaviours were assessed: locomotion (distanced travelled; cm), alternation sequence, and number of arm entries (when the hind limbs had completely entered the arm). A correct trial was defined as entry into the exit arm without entering other arms. The percentage of SAB for each animal was calculated: [(Number of correct trials)/(Total arm entries – 2)] × 100 (Maurice et al., 1994; Rosell-Valle et al., 2021).

2.3.5. Morris water maze (MWM)

The Morris water maze was used to study learning and memory (Morris et al., 1982). The maze consisted of a circular pool (120-cm diameter and X height) filled with nontoxic opaque water at 23 ± 2ºC temperature and virtually subdivided into four equal quadrants (Q1-Q4). The escape platform (10-cm diameter) was raised 0.5 cm above the water surface for visual learning. The hidden platform was located 0.5 cm below the water surface for acquisition and reversal spatial learning. To aid the mice orient themselves with respect to the pool, the test room was decorated with eye-catching visual cues.

The following behavioural learning procedure was evaluated: in habituation, training mice received 2 trials during one day and were allowed to explore the pool freely for 60 s. The pool was virtually subdivided into distinct zones: peripheral (20 cm from the walls), intermediate (20 cm to 50 cm from the walls) and centre (50 cm from the walls) to evaluate the amount of time each animal spent in the respective zone (Garthe et al., 2009). In visual learning, mice received 4 trials over two days (30-min intertribal intervals). The trial ended when the mouse climbed onto the visible escape platform, or after a maximum trial duration of the 60s. If a mouse failed to find the escape platform during a trial, it was placed on the platform for 10s. The location of the platform and the starting point of animals were changed on each trial. In spatial acquisition learning, animals received 6 trials over four days (with 30-min intertribal intervals). The hidden platform was located in quadrant 1 (Q1) throughout the training. The starting point of mice was changed on each trial. The maximum duration of the trial was 60s unless the mouse reached the hidden platform. Twenty-four hours after the last training session of acquisition, a memory retention test (probe) was performed. The mice received a trial without an escape platform and the animals were allowed to swim freely 60s. In reversal spatial learning, the hidden escape platform was placed in a new position (opposite to its placement in acquisition learning, quadrant 3 (Q3)) and mice received 6 trials over one day (30-min intertribal intervals). Finally, second memory retention test was performed twenty-four hours after the last training session.

We examined the following learning parameters: escape latency, path length (total distance navigated), swimming speed, cumulative distance to the platform as a measure of spatial learning ability (Gallagher et al., 1993) and time spent in the quadrants.

2.4. Tissue and Histological procedures

2.4.1. Brain Protein Extract

All mice were sacrificed at 6 and 12 months of age. Prior to sacrifice, the animals were anaesthetized with 50 mg/kg of pentobarbital and then blood was drawn directly from the right atrium. The animals were then transcardially perfused with 0.1 M phosphate-buffered saline (PBS). The feces were then collected from the colon and immediately frozen at -80ºC for microbiota analysis. Brain samples were quickly removed and bisected down the midline; one hemi-brain was used for histological and immunohistochemical procedures and the other hemi-brain was kept on dry ice to be stored at -80º for biochemical analysis.

2.4.2. Immunohistochemistry

The hemi-brains were fixed in 0.1M PBS containing 4% paraformaldehyde (PFA) for 48 hours and cryopreserved in 30% sucrose in 0.1M PBS solution for 5 days at 4ºC. The cerebral hemispheres were cut 50 µm thick in the coronal plane on a microtome and sections from HTZ and HZ 5xFAD mice and No-tg mice were performed as previously described (Castilla-Ortega et al., 2011; Medina-Vera et al., 2020; Rosell-Valle et al., 2021). Serial sections were blocked with 5% donkey serum, 0.5% Triton X-100 in 0.1M PBS for 45 minutes at room temperature (RT). For analysis of amyloid-β plaques, rabbit anti-Aβ (1:500, Abcam), rabbit anti-Amyloid β_1-40 (Aβ₄₀; 1:500, ThermoFisher) and rabbit anti-Aβ_1-42 (Aβ₄₂; 1:500, ThermoFisher) were used. For neuroinflammatory analysis, rabbit anti-glial fibrillary acidic protein (GFAP; 1:1000; Dako Cytomation Glostrup, Danmark) and rabbit anti-Iba1 (1:500, Abcam) were used. The primary antibodies were incubated overnight at RT. After rinsing, the sections were incubated with a biotinylated goat anti-rabbit secondary antibody (1:800, GE Healthcare) for 2h at RT. All antibodies were diluted in PBS, 0.5% Triton X-100 and 2.5% donkey serum. Peroxidase-conjugated ExtraAvidin method and diaminobenzidine as the chromogen was used to visualize the reaction product.

The number of labelled Aβ plaques/GFAP and Iba1 positive cells was quantified in the hippocampus (CA1, CA3 and dentate gyrus (DG)). Images were acquired with a DP70 digital Camera (Olympus Iberia, S.A.U.) connected to an Olympus BX41 microscope. ImageJ software was used for immunostaining quantification (C. A. Schneider et al., 2012). The images were binarized to 16-bit black and a fixed intensity threshold was applied for each immunostaining. Eight mice per group and three sections per mouse at three different levels of the hippocampus and subiculum were used.

2.5. Western Blot Analysis

Frozen hemi-brain samples (17 mg per sample) were dissected and homogenised in 1mL of cold RIPA lysis buffer (50 mM Tris-HCl pH 7.4, 150 mM NaCl, 0.5% NaDOC, 1 mM EDTA, 1% Triton, 0.1% SDS, 1 mM Na3VO4, 1 mM NaF) supplemented with a protease cocktail (Hoffmann-La Roche). The suspension was incubated for 2 hours at 4 °C, followed by centrifugation at 12,000 rpm for 15 minutes at 4 °C. The supernatant was transferred to a new clean centrifuge tube and Bradford colorimetric method was used to determine the total protein concentration. Protein extracts were diluted 1:1 in the loading buffer (DTT 2X) and heated for 5 minutes at 99°C before being subjected to electrophoresis.

Protein expression, including IRS1, PI3K p85, Akt, GSK-3β, mTOR, PTEN, PP2Cα, was analysed by western blotting. Tissue protein (10-15μg) was subjected to electrophoresis on 4-12% Criterion XT Precast Bis-Tris gels (Bio-Rad, Hercules, CA, EE.UU.) for 30 minutes at 80V and 2 hours at 150V. Proteins were transferred onto a 0.2 µm nitrocellulose membrane (Bio-Rad, Hercules, CA, EE.UU.) for 1hour at 80V using wet transfer equipment. The membrane was washed twice for 5 min in TBST (10 mM Tris–HCl, 150 mM NaCl, 0.1% Tween 20, pH 7.6) and blocked with 5% BSA-TBST for one hour at room temperature on a shaker platform. Subsequently, the membrane was incubated with the respective primary antibodies overnight at 4°C diluted in 2% BSA-TBST (see Supplementary Table S1 for additional information on primary antibodies). The following day, the membrane was washed three times for 5 min with TBST. An appropriate HRP conjugated rabbit/mouse secondary antibody (Promega, Madison, WI, EE.UU.) was diluted 1:10000 in 2% BSA-TST and incubated with the membrane for 1h shaking at room temperature. Finally, the membrane was washed as above and exposed to chemiluminescent reagent using the Western Blotting Luminol Reagent kit (Santa Cruz Biotechnology, Santa Cruz, CA, USA) for 1 min. The membrane-bound protein was then visualized by chemiluminescence using the Chemi-Doc TM MP Imaging System (Bio-Rad, Barcelona, Spain). After detecting phosphorylation proteins, particular antibodies were removed from the membrane by incubating with stripping buffer (2% SDS, 62.5 mM Tris HCl (pH 6.8), 0.8% β-mercaptoethanol) for 30 min at 50 ͦC. Membranes were thoroughly cleaned in ultrapure water before being pre-incubated with the corresponding total antibody. The bands were quantified by densitometry analysis using ImageJ software (C. A. Schneider et al., 2012). Normalization was performed using a reference protein of the same membrane, the γ-Adaptin. The results are presented as the ratio of total protein expression to γ-Adaptin, and the ratio of phosphorylated protein expression to total protein expression (Bass et al., 2017).

2.6. Bio-Plex Multiplex Assay

Plasma levels of insulin, gastric inhibitory polypeptide (GIP), glucagon-like peptide-1 (GLP-1), leptin, ghrelin, glucagon, resistin and plasminogen activator inhibitor-1 (PAI-1) were measured using a multiplex immunoassay system with commercial kits: Bio-Plex Pro™ mouse diabetes 8-plex immunoassay (Bio-Rad, Hercules, CA, USA, cat. number: #171F7001M). Plates were run on a Bio-Plex MAGPIX™ Multiplex Reader with Bio-Plex Manager™ MP Software (Luminex, Austin, TX, USA). Hormone concentrations were expressed in pg/mL, and detection limits were 68.29 (insulin), 4.31 (GIP), 0.59 (GLP-1), 5.07 (leptin), 0.64 (ghrelin), 0.50 (glucagon), 184.89 (resistin) and 2.98 (PAI-1) pg/mL.

2.7. Gut Microbiota analysis

Analysis of gut microbiota was performed as previously described (Segovia-Rodríguez et al., 2022). Colon stool samples at 4-6 months and 10-12 months of age (180–200 mg) were used for DNA extraction. The QIAamp® DNA Stool Mini Kit (Qiagen France S.A.S.) was used according to the manufacturer’s instructions. DNA concentration and purity were determined by absorbance at 260 nm (A260) and A260/A280 ratio, respectively, using a NanoDrop spectrophotometer (NanoDrop TM One Spectrophotometer, Thermo Fisher Scientific Inc., Spain). DNA samples were sent to StarSEQ® GmbH (Mainz, Germany) for the analysis of The V4–V5 hypervariable regions of the bacterial 16 S rRNA gene, amplified from the isolated DNA using the primer combination 515F–909 R. The Illumina MiSeq System was used to sequence DNA products of this PCR amplification and the 16 S metagenomics analysis was performed using the Illumina App. For the quantitative analysis of the relative abundance of bacteria of each fecal microbiome sample, a proprietary algorithm from Illumina® was used to provide a species-level classification for paired-end reads. The taxonomy database is an Illumina-curated version of the Greengenes database (database/13_5). Only a phylum analysis focusing on both Bacteroidetes and Firmicutes phyla was considered for the present study.

2.8. Statistical analyses

The data obtained from neurological screening were analysed by a nonparametric Kruskal-Wallis test. P values less than 0.05 were considered significant. For behavioural tests, western blot analysis and immunostaining quantification, the significance of differences within and between groups was evaluated by a two-way analysis of variance (ANOVA), factors: “Age” and “Genotype”, followed by Tukey post-hoc test for multiple comparisons. For MWM, thigmotaxis behaviour and learning parameters were analysed separately by repeated measures two-way ANOVA (“genotype” x “trial session” [day]) and two-way ANOVA (“genotype” x “training day”). Alternatively, for comparisons between two groups, the Student’s t-test was also used. A p-value of 0.05 was considered statistically significant. Pearson’s correlation coefficient was used to discriminate the potential influence of hyperdynamic locomotion on cognitive deterioration in YMT (Miedel et al., 2017).

Microbiota analysis was assessed after sequencing. Paired-end sequences of each sample were exported to FASTQ format. Bioinformatic processing of the sequences was then performed in the VirtualBox of the QIIME2 Core 2019.4 distribution based on the Ubuntu Linux operating system. For subsequent data analysis, R software (version 3.6.0) was used together with the Bioconductor “bio format" packages. A paired-end demultiplexed sequencing protocol was used to import the sequences and the dada2 denoise-paired command for quality filtering, denoising and merging of paired-end reads. Core metrics were obtained for alpha and beta diversities. Alpha diversity measures species richness in a given community, while Beta diversity analyses the different compositions, in terms of abundance of different taxa, among different samples. Alpha rarefaction analysis was performed with a sample depth of 25,000 per sample. Sequences were grouped in operational taxonomic units (OTUs) with 97% similarity. Taxonomic assignment used a classification from the SILVA for 16S database release 132. The obtained sequences were filtered and assigned to at least one family. Finally, a specific analysis was performed for each taxon or group according to the results obtained. As detailed above, for the present study only the phylum taxon was considered for analysis. Other microbiota descriptions are being analysed for further independent study.

Clustering and ordination methods as Principal Component Analysis (PCA) were applied to the behavioural variables, neuroinflammation, Akt pathway, insulin-linked metabolic pathway, and Firmicutes/Bacteroidetes ratio to reduce them to a smaller set of dimensions underlying genotype (Rosell-Valle et al., 2021). PCA was followed by varimax orthogonal rotation, which ensures that the extracted factors are independent of each other. The resulting factors with eigenvalues >1 was selected, and the contribution of each variable to a factor (factor loading) was considered significant when it was greater than 0.5 in absolute value. Subsequently, factor scores were calculated for each animal using the regression method, and the results were analysed using a two-way ANOVA followed by Tukey post-hoc test.

All data are expressed as mean ± standard error of the mean (SEM). Statistical analyses were conducted in GraphPad Prism, version 8 (GraphPad Software, Inc., La Jolla, CA) and R software for microbiota analysis.

3.1 HZ 5xFAD mice showed severe neurological deficits

We used a battery of neurological procedures to assess cognitive impairment of somatosensory and sensorimotor reflexes in two age ranges in the 5xFAD mouse model. At 4-6 months of age, although not significantly so, slight impairment in somesthesis (H_(3,N=17)= 4.934, P > 0.05), corneal (H_(3,N=17)= 0.277, P > 0.05), auditory (H_(3,N=17)= 0.277, P > 0.05), righting (H_(3,N=17)= 1.429, P > 0.05), inclined plane test (H_(3,N=17)= 1.429, P > 0.05) and extension reflexes (H_(3,N=17)= 3.048, P > 0.05) was observed in HZ mice (Table 1). However, at older age, HZ 5xFAD mice showed high neurological deficits in head shaking (H_(3,N=34)= 8.067, P < 0.05) and corneal reflexes (H_(3,N=34)= 8.642, P < 0.05), vibrissae (H_(3,N=34)= 6.274, P < 0.05), auditory (H_(3,N=34)= 7.052, P < 0.05), righting (H_(3,N=34)= 10.39, P<0.05) and extension reflexes (H_(3,N=34)= 18.30, P < 0.0001) compared to no-tg group (Table 1). Nevertheless, HTZ mice showed preservation of neurological functions at any age.

3.2 HTZ and HZ 5xFAD mice showed a different taste profile to a preference for a palatable sweet solution

Saccharin and sucrose preference tests were used as an indicator of anhedonia, based on the natural preference of rodent for sweet taste. Sucrose preference is also indicative of motivation for caloric foods. Two-way ANOVA showed genotype (F_(2,42)= 5.437, P < 0.001) and age (F_(1,42)= 7.222, P < 0.01) effects on saccharin preference since saccharin intake was significantly higher in HZ at 10-12 months than that observed in no-tg mice (Tukey’s tests: P < 0.05, Figure 1A). In contrast, a genotype x age interaction was observed (Two-way ANOVA: F_(2,41)= 3.591, P < 0.05) in sucrose preference. Post-hoc analysis revealed that both HTZ and HZ at 4-6 and 10-12 months of age significantly decreased sucrose intake compared to non-transgenic mice, suggesting anhedonia (Tukey test: P < 0.05, Figure 1B). Thus, transgenic 5xFAD mice displayed a preference below the anhedonic threshold set at 65% (one-simple test: P < 0.05). These contradictory data on saccharin versus sucrose preference could be related to the different motivation for food (lower food intake and negative energy balance) shown by 5XFAD mice (López-Gambero et al., 2021).

Regarding animal weight during preference tests, two-way ANOVA revealed a genotype effect from 4-6 months old (SacPT: 4-6 months: F_(2,72)= 20.08, P < 0.0001; 10-12 months: F_(2,100)= 10.15, P < 0.0001; SPT: 4-6 months: F_(2,80)= 7.217, P < 0.01; 10-12 months: F_(2,100)= 13.62, P < 0.0001; see supplementary Figure S1A and S1B). The HZ group had a significantly lower weight than the non-tg group maintained for 4 days of preference testing (Tukey’s tests: P < 0.05).

3.3 HTZ and HZ 5xFAD mice showed an abnormal emotional response in OF and EPM

OF and EPM tests were conducted to assess emotional status by monitoring anxiety-like behaviours (Malleret et al., 1999). Two-way ANOVA revealed an effect of genotype (F_(2,45)= 6.167, P < 0.01) and age (F_(1,45)= 5.527, P < 0.05) on time spent in the centre of the OF (Figure 1C). At 10-12 months of age, HZ mice showed less time spent in the centre compared to non-tg mice, and a similar trend was observed at 4-6 months old, although it was not significant. No differences in locomotion were observed between genotypes (Figure 1D).

In the EPM, less time spent in the open arms has been associated with anxiety-like behaviour (Malleret et al., 1999). Surprisingly, both HTZ and HZ groups showed a significant increase in the time spent in the open arms (one-way ANOVA: F_(2,29)= 7.490, P<0.01; Tukey’s tests: P < 0.05, Figure 1E) from 10-12 months of age, being present at earlier times in the 4-6 months old HZ mice. These paradoxical results could be related to the sensorial deficits/cognitive impairment exhibited by the HZ mice, which are unable to assess the risks of the exposed arms (narrow arm width, high illumination, and high height). Therefore, abnormal vibrissae perception in 5xFAD could explain this behavioural response, as described previously (Flanigan et al., 2014). Following this rationale, we cannot interpret this increase in time in the exposed arms as anxiolytic behaviour. No differences in locomotion were observed between genotypes (Figure 1F).

3.4 HZ 5xFAD mice showed impaired spatial working memory

We performed a Y-maze test to assess spatial working memory (Hughes, 2004). Statistical analysis showed that the HZ group had impaired working memory at 10-12 months of age (two-way ANOVA: genotype effect: F_(2,43)= 6.801, P < 0.01). Only HZ mice differed from the HTZ and non-transgenic littermates showing a significant reduction in the percentage of SAB (Tukey’s tests: P < 0.05, Figure 1G). No significant differences in locomotion and number of arm entries were observed between genotypes (Figure 1H and supplementary Figure S1C). In addition, there was no correlation between SAB and number of arm entries and locomotion (Pearson’s comparisons: P > 0.05, see supplementary Figure S1D), suggesting that the cognitive impairment observed only in the HZ group was not produced by locomotor or exploratory activities.

(A) Saccharin preference test. (B) Sucrose preference test. Dashed lines represent the criterion for anhedonia ≤ 65%. (C) Time spent in the centre and (D) locomotion in the open filed. (E) Time spent in the open arms and (F) locomotion in the elevated plus maze. (G) Spontaneous alternation behaviour and (H) locomotion in the Y-maze test. All data represent the mean ± SEM. Tukey's Multiple Comparisons Test: difference transgenic vs non-transgenic mice. (*) P < 0.05; (**) P < 0.01, (****) P < 0.0001.

3.5 HZ 5xFAD mice showed impaired cognitive flexibility in the Morris water maze

Spatial reference learning and memory were assessed using the Morris water maze test paradigm at both ages. In habituation training, we found no significant differences between genotype on path length and swimming speed at either age (one-way ANOVA: P > 0.05, see supplementary Figure S2A-B), although 4–6 month old 5xFAD transgenic mice showed a slight decrease in swimming speed (one-way ANOVA: P > 0.05, see supplementary Figure S2C-D).

In the visual learning, surprisingly 4-6 month old HTZ mice showed a longer escape latency compared to HZ and non-tg mice on the first day of training (two-way ANOVA: genotype effect: F_(2,128)= 12.20, P < 0.0001; trial session effect: F_(7,128)= 2.664, P < 0.05; Tukey’s test: P ≤ 0.05, Figure 2A), although on the second day of training they learned the platform location as efficiently as possible compared to non-tg mice. Likewise, the 4-6 month old HTZ mice showed a higher cumulative distance to visible escape latency on the first day of training compared to the non-tg group (two-way ANOVA: genotype x trial session interaction: F_(2,32)= 4.040, P < 0.05; Tukey’s test: P ≤ 0.05, see supplementary Figure S2E). No significant differences were found between genotypes on path length in the peripheral zone (two-way ANOVA: P > 0.05, see Supplementary Figure S2F). However, the 10-12 month old HZ mice showed longer escape latency compared to non-tg mice (two-way ANOVA: genotype effect: F_(2,176)= 39.03, P < 0.0001; trial session effect: F_(7,176)= 8.324, P < 0.0001; Tukey’s test: P ≤ 0.05, Figure 2B) and cumulative distance (two-way ANOVA: genotype effect: F_(2,44)= 5.773, P < 0.01; trial session effect: F_(1,44)= 5.509, P < 0.05; Tukey’s test: P ≤ 0.05 (see supplementary Figure S2G) compared to non-tg mice. In addition, we found that swimming strategies differed by genotype and age. For example, 10-12 month old HZ mice had a clear ‘wall-hugging’ or thigmotaxis (time spent in the peripheral zone) behaviour during visual learning compared to HTZ and non-tg mice, which showed a direct search pattern to reach the platform (two-way ANOVA: genotype effect: F_(2,46)= 31.40, P < 0.0001; day effect: F_(2,46)= 12.80, P < 0.0001; Tukey’s test: P ≤ 0.05, see Supplementary Figure S2H). For navigation of all mice, a positive correlation was found between time spent in the peripheral zone and escape latency (r²= 0.76, P < 0.01; i.e., much time in the peripheral zone and much escape latency, see supplementary Figure S2I).

During acquisition spatial learning, all animals successfully learned the position of the hidden escape platform in target quadrant 1 (Q1) at both 4-6 months and 10-12 months of age, although HTZ mice showed a worse learning curve at earlier age that had a longer escape latency (two-way ANOVA: genotype effect: F_(2,64)= 34.43, P < 0.0001, Tukey’s test: P≤ 0.05, Figure 2C) and cumulative distance to reach the hidden platform (two-way ANOVA: genotype effect: F_(2,67)= 27.10, P < 0.0001, Tukey’s test: P ≤ 0.05, Figure 2D) than non-tg mice. However, the 10–12-month-old HTZ mice were efficient at reaching the goal, so they did not show noticeable memory impairment. For the HZ condition, 10-12 month-old mice showed increased escape latency and cumulative distance compared to non-tg mice (two-way ANOVA: escape latency: genotype effect: F_(2,88)= 13.24, P < 0.0001; trial session effect: F_(3,88)= 5.643, P < 0.01; cumulative distance: genotype effect: F_(2,88)= 10.80, P < 0.0001; trial session effect: F_(3,88)= 4.474, P < 0.001; Turkey test: P ≤ 0.05, Figure 2E and F). In addition, the 10-12 month-old HZ group continued to show thigmotaxis behaviour (two-way ANOVA; genotype effect: F_(2,92)= 5.275, P < 0.001; training day effect: F_(3,92)= 2.806, P < 0.05; Tukey’s test: P ≤ 0.05, see supplementary Figure S2J).

Interestingly, we observed no significant differences between the 5xFAD transgenic groups and non-tg mice in the first memory retention test at any age (two-way ANOVA: P > 0.05, Figure 2G and H). All mice in two age ranges spent more time in the target Q1 (two-way ANOVA: 4-6 months: quadrant effect: F_(3,64)= 36.66, P < 0.0001, 10-12 months: quadrant effect: F_(3,88)= 87.70, P < 0.0001, Figure 2G and H). Supplementary Figure S2K shows the path travelled in the memory retention test 1 of the two age ranges. Nevertheless, during reversal spatial learning, 4-6 month-old HZ mice took significantly longer to reach the location of the new platform in Q3 (one-way ANOVA: P > 0.05; Figure 2I), and this difference was more noticeable at 10-12 months of age (one-way ANOVA: F_(2,23)= 4.191, P < 0.05; Figure 2J). The second memory retention test was performed 24 hours after reversal training. The 4-6 month-old HZ mice continued to remember the location of the previous platform in Q1, persisting much longer than non-tg mice (two-way ANOVA: 4-6 months: quadrant effect: F_(3,64)= 4.786, P < 0.01; 10-12 months: genotype effect: F_(2,88)= 6.956, P < 0.01; quadrant effect: F_(3,88)= 36.76, P < 0.0001; Tukey’s test: P ≤ 0.05, Figure 2K and L). Supplementary Figure S2L shows the path travelled in memory retention test 1 of the two age ranges. Finally, we further assessed the memory extinction deficit. At 4-6 months, all genotypes showed a memory extinction in which they spent less time in Q1 after 72 hours of the last acquisition learning (two-way ANOVA: time effect: F_(2,48)= 5.578, P < 0.01; Figure 2M). However, the impairment of memory extinction was significant at 10-12 months of age. Statistical analysis revealed that HZ mice showed a higher preference for the first goal and perseverative navigation even after 72 hours, indicating a lack of cognitive flexibility in this age range to develop adequate new spatial learning (two-way ANOVA: genotype effect: F_(2,66)= 5.364, P < 0.01, time effect: F _(2,66)= 3.662, P < 0.05; Tukey’s test: P ≤ 0.05, Figure 2N).

Escape latency in visual learning at 4-6 months (A) and 10-12 months of age (B). Escape latency (C) and cumulative distance (D) in the acquisition spatial learning at 4-6 months of age. Escape latency (E) and the cumulative distance (F) in the acquisition spatial learning at 10-12 months of age. Time spent in the quadrants in the memory retention test at (G) 4-6 months and (H) 10-12 months of age. Escape latency in the reversal spatial learning at 4-6 months (I) and 10-12 months (J) of age. Time spent in the quadrants in the memory retention test at 4-6 months (K) and 10-12 months of age (L).

3.6 Hippocampal dysfunction in HTZ and HZ 5xFAD mice

To determine whether the genetic loading led a differential pattern of Aβ production and deposition, we quantified Aβ₄₀, Aβ₄₂ and total Aβ in the hippocampal area DG, CA1, and CA3 of 5xFAD transgenic mice by immunohistochemistry. In both HTZ and HZ 5xFAD mice, Aβ deposition was significantly visible in the whole hippocampal area from 4-6 months of age (two-way ANOVA: Aβ₄₀: ‘genotype x area’ interaction: F_(6,44) = 4.268, P < 0.01, Figure 3A left and B; Aβ₄₂: ‘genotype x area’ interaction: F_(6,44) = 6.577, P < 0.001, Figure 3C left and D; Total Aβ: ‘genotype x area’ interaction: F_(6,44) = 15.34, P < 0.01, Figure 3E left and F). As predicted, Aβ accumulation was increased in older 5xFAD transgenic mice (two-way ANOVA: Aβ₄₀: ‘genotype x area’ interaction: F_(6,40) = 19.61, P < 0.001, Figure 3A right and B; Aβ₄₂: ‘genotype x area’ interaction: F_(6,40) = 12.75, P < 0.001, Figure 3C right and D; Total Aβ: ‘genotype x area’ interaction: F_(6,40) = 27.75, P < 0.001, Figure 3E right and F), being more prominent in HZ 5xFAD mice especially in the aggregation of Aβ₄₂ and total Aβ (Tukey’s test: P ≤ 0.05).

Regarding the neuroinflammatory response, measured as glial (astrocytes and microglia) reactivity, from 4-6 month old HZ mice showed a greater increase in GFAP densitometry in the whole hippocampal area than non-tg and HTZ (two-way ANOVA: genotype effect: F_(2,27) = 45.38, P < 0.001, Tukey’s test: P ≤ 0.05, Figure 3G left and H), having these differences maintained at 10-12 months of age (Figure 3G right and H). At 10-12 months of age, HTZ mice showed an increase in GFAP densitometry (two-way ANOVA: genotype effect: F_(2,33) = 119.2, P < 0.001, Tukey’s test: P ≤ 0.05, Figure 3G right and H). However, from 4-6 months of age, both HTZ and HZ mice showed increased Iba1 densitometry in the whole hippocampal area compared to non-tg (two-way ANOVA: genotype effect: F_(2,27) = 54.47, P < 0.001, Tukey’s test: P ≤ 0.05, Figure 3I left and J). At 10-12 months of age, we observed similar values (two-way ANOVA: genotype effect: F_(2,45) = 64.94, P < 0.001, Tukey’s test: P ≤ 0.05, Figure 3I right and J).

Quantification of (A) β-amyloid 1-40 (Aβ₄₀), (C) β-amyloid 1-42 (Aβ₄₂), and (E) total β-amyloid (Aβ) in the hippocampus of the three experimental groups at 4-6 months (left) and 10-12 months of age (right). Images correspond to representative immunostaining of (B) Aβ₄₀, (D) Aβ₄₂, and (F) total of Aβ. Quantification of (G) GFAP and (I) Iba1 expression in the hippocampus of the three groups at 4-6 months (left) and 10-12 months of age (right). Representative immunostaining of (H) GFAP and (J) Iba1 in the hippocampus of each group was performed in the dentate gyrus (DG), CA1, CA3 and whole hippocampus (Total). Histograms represent the mean ± S.E.M. (n=6). Tukey’s Multiple Comparisons Test: difference between transgenic vs non-transgenic mice: (*) P < 0.05, (**) P < 0.01, (***) P < 0.001 and (****) P < 0.0001; difference between HTZ and HZ condition: ($) P < 0.05, ($$) P < 0.01, ($$$) P < 0.001 and ($$$$) P < 0.0001.

On the other hand, the IRS1-PI3K-Akt pathway, which is primarily activated by insulin, is an important hub of physiological responses fundamental for healthy aging. To this end, we aimed to evaluate the activation and inhibition of its substrates in the hippocampus of 5xFAD transgenic mice at 4-6 and 10-12 months of age. Figure 4 displays the western blot analysis of the insulin-PI3K-Akt signalling pathway. We first analysed the phosphorylation of IRS1, which is the initial input of the Akt pathway. Serine 612 phosphorylation was enhanced in the HZ group, suggesting a clear inhibition of the IRS1 at 10-12 months of age since phosphorylation at this serine residue is linked to inactivation of IRS1 signalling toward PI3K (De Fea & Roth, 1997) (two-way ANOVA: interaction: F_(2,27)= 8.016, P < 0.01; Figure 4A) when compared to the HTZ and no-tg groups. This is most often seen when the insulin receptor is hypo-active because of a lack of insulin activation. This fact is seen only in the HZ older group, being significantly higher compared to the younger group (Tukey test: P ≤ 0.01, Figure 4A). Subsequently, we analysed the active phosphorylation state of PI3K (p-PI3K) and Akt (p-Akt). The activating phosphorylation of Tyrosine 607 of the p85 PI3K’s subunit decreased significantly in both the HTZ and the HZ groups at 4-6 months of age (two-way ANOVA: interaction: F_(2,28)= 6.271, P < 0.01; Figure 4B), being re-established in both groups at 10-12 months-old mice (Tukey test: P ≤ 0.05). However, the levels of total PI3K protein remained unchanged (Tukey test: ns, Figure 4B). Showing a similar profile, the activating phosphorylation of Serine 473 of Akt also presented a significant decrease in 4-6-month-old HZ mice (two-way ANOVA: interaction: F_(2,28)= 6.457, P < 0.01; Figure 4C) and been also re-established at 10-12 months of age in the HZ (Tukey test: P ≤ 0.01). The total amount of Akt total is simultaneously decreased in 4–6-month-old HZ mice (Tukey test: P ≤ 0.05, Figure 4C). According to increased activity of Akt at 10-12 months of age, the phosphorylation of its substrate GSK-3β (p-GSK3β) at Serine 9 was also raised in the HZ group (two-way ANOVA: interaction: F_(2,27)= 4.140, P < 0.01; Figure 4D), thus inhibiting the kinase GSK-3β. When compared to the younger group, the phosphorylation state of Serine 2448 of the mTOR protein, a downstream substrate of Akt, was significantly higher in the HZ at 10-12 months of age (two-way ANOVA: age effect: F_(2,28)= 4.998, P < 0.05; Figure 4E). By last, PTEN (two-way ANOVA: interaction: F_(2,28)= 13.48, P < 0.0001; Figure 4F) and PP2A (two-way ANOVA: interaction: F_(2,28)= 15.92, P < 0.0001; Figure 4F), two Akt phosphatases, were also analysed revealing a substantial reduction in total protein at 4-6 months old, with levels entirely reversed at 12 months in both HTZ and HZ groups (Tukey test: P ≤ 0.01).

Bar charts represent the expression of (A) Insulin receptor substrate 1 (IRS-1) phosphorylation at serine 612, and amount of total IRS-1. (B) p85 regulatory domain of the phosphatidylinositol 3 kinase (p85-PI3K) phosphorylation at tyrosine 607, and quantity of total p85-PI3K. (C) Protein Kinase B (Akt) phosphorylation on serine 473, and amount of total Akt. (D) Glycogen synthase kinase 3β (GSK-3β) phosphorylation at serine 9, and amount of total GSK-3β. (E) mammalian Target of Rapamycin (mTOR) phosphorylation at serine 2448, and amount of total mTOR; (F) amount of total PTEN and PP2C. The blots shown are a representation of all bands at (G) 4-6-months of age, and (H) 10-12 months of age (See Supplementary Figures S3 and S4 for additional information). The corresponding expression of γ-Adaptin is shown as a loading control per lane. All samples were obtained at the same time and processed in parallel. Histograms represent the mean ± SEM (n=6). Bonferroni tests were performed: difference between transgenic vs non-transgenic mice: (*) P < 0.05, (**) P < 0.01, (***) and P < 0.001; difference between HTZ and HZ condition: ($) P < 0.05, ($$) P < 0.01, ($$$) P < 0.001.

A correlation test was performed to assess whether hippocampal dysfunction was directly associated with cognitive impairment in 5xFAD transgenic mice (Table 2). Accumulation of Aβ (Aβ₄₀, Aβ₄₂, and total Aβ) was positively correlated to SachPT, time spent in open arms in EPM, and time spent to reach the platform in the phases of visual, acquisition and reversal spatial learning in MWM (P < 0.05). In contrast, SP, OPF and YMT were negatively related to Aβ accumulation (P < 0.05). As for neuroinflammatory markers, GFAP and Iba1 were positively related to SachPT and EPM, and negatively related to SPT (P < 0.05). We found correlations between pGSK3β and pmTOR expression and SachPT, SPT, and EPM (P < 0.05), which were significantly elevated in 10-12-month-old HZ mice. No correlations were found between MWM and neuroinflammatory markers and the PI3K-Akt signalling pathway, although only PTEN was negatively associated with acquisition and reversal spatial learning (P < 0.05), which was significantly elevated in 10-12-month-old HTZ and HZ mice.

3.7 Dysregulation of neuropeptide networks modulating insulin pathways in HTZ and HZ 5xFAD mice

Next, we evaluated insulin-linked metabolic pathways by monitoring plasma levels of peptides involved in modulating insulin release and signalling. Disruption of these neuropeptide networks could eventually contribute to the cognitive and behavioural disorders associated with AD (Cunnane et al., 2020; Robbins et al., 2020). Figure 5 shows the premature dysfunction of plasma hormone levels in 5xFAD transgenic mice, which is most notable at 4-6 months of age.

Insulin regulates metabolism by increasing cell permeability to monosaccharides, amino acids, and fatty acids. Two-way ANOVA revealed significant effects of genotype (F_(2,75)= 7.136, P < 0.01) and age (F_(1,75)= 4.946, P < 0.05). The 4–6-month-old HZ mice had low circulating plasma insulin levels compared to non-tg (Tukey’s test: P < 0.05, Figure 5A). These results were related to a reduction in the plasma level of GIP, which is a potent stimulator of insulin secretion. Statistical analysis showed that both HTZ and HZ mice showed low basal plasma levels of GIP compared to non-tg at 4-6 months of age (two-way ANOVA: age effect: F_(1,75)= 18.42, P < 0.0001, Tukey’s test: P < 0.05, Figure 5B). Another peptide involved in the insulin-linked metabolic pathways is GLP-1, which has potent stimulatory effects on glucose-dependent insulin release. Two-way ANOVA revealed significant effects of genotype (F_(2,75)= 3.777, P < 0.05) and age (F_(1,75)= 10.37, P < 0.01). Post hoc comparison tests showed a significant decrease in basal plasma levels of GLP-1 in HZ mice, which was more marked at older age (Tukey’s test: P < 0.05, Figure 5C).

Leptin and ghrelin are two hormones implicated in the regulation of food intake and energy balance. Because the leptin system seems to be dependent on sexual dimorphism-dependent regulation (Quevedo et al., 1998; Roca et al., 1999), we analysed results by sex. We did not observe a dysregulation of plasma leptin levels between 5xFAD transgenic and no-transgenic mice (two-way ANOVA: P > 0.05, Figure 5D), whereas for plasma ghrelin levels there was a significant effect of age (two-way ANOVA: F_(1,75)= 68.89, P < 0.0001). HZ mice showed lower circulating levels than HTZ mice at 4-6 months of age (Tukey’s test: P < 0.05, Figure 5E). Regarding circulating plasma glucagon levels, no significant differences were found between genotypes at any age (two-way ANOVA: P > 0.05, Figure 5F). Resistin is a peptide hormone produced by mature rodent adipocytes that regulates insulin sensitivity in both skeletal muscle and hepatic tissue (Steppan et al., 2001; Way et al., 2001) and appears to be associated with sexual dimorphism-dependent regulation (Gui et al., 2004). A ‘genotype x age x sex’ interaction was observed (two-way ANOVA: F_(6,69)= 3.076, P < 0.01) in circulating plasma resistin levels. The post hoc analysis demonstrated a lower level of resistin in male and female HTZ mice than in non-tg and HZ mice at 4-6 months of age (Tukey’s test: P < 0.05, Figure 5G). PAI-1 is a serine protease inhibitor that regulates fibrinolysis, hence decreasing blood clot clearance. Two-way ANOVA demonstrated a significant effect of genotype (F_(2,75)= 4.676, P < 0.05). Post hoc comparison analysis showed an increased PAI-1 levels in the HTZ group compared to non-tg mice (Tukey’s test: P ≤ 0.05, Figure 5H).

Plasma levels of (A) insulin, (B) gastric inhibitory polypeptide (GIP), (C) glucagon-like peptide-1 (GLP-1), (D) leptin, (E) ghrelin, (F) glucagon, (G) resistin and (F) plasminogen activator inhibitor-1 (PAI-1). All data represent the mean ± SEM. Tukey’s Multiple Comparisons Test: difference between transgenic vs non-transgenic mice: (*) P < 0.05, (**) P < 0.01, (***) and P < 0.001; difference between HTZ and HZ condition: ($) P < 0.05.

To determine a relationship between cognitive impairment and the main changes found in plasma insulin-linked metabolic pathways, we performed a correlation test (Table 3). No correlations were found between insulin and GIP, and behaviour (P > 0.05). GLP-1 was positively related to spontaneous alternation behaviour in YMT, indicating low GLP-1 levels were effectively lower with decreased SAB. Whereas plasma levels of PAI-1 were negatively related to SAB in Y-maze test. Ghrelin was positively related to the visual learning phase in MWM (P < 0.05). Plasma levels of resistin were positively related to SPT and the reversal spatial learning phase in MWM, whereas they were negatively associated with SachPT (P < 0.05).

3.8 Decreased Firmicutes/Bacteroidetes ratio in 10–12-month-old HZ mice

The relative abundance of two main phyla of the gut microbiota composition and the Firmicutes/Bacteroidetes ratio were analysed. The relative abundance of Firmicutes phylum tended to decrease with age in the three genotypes (two-way ANOVA: age effect: F_(1,62)= 98.67, P < 0.0001, Tukey’s test: P ≤ 0.05, Figure 6A), while the abundance of Bacteroidetes phylum increased, being more prominent in middle-aged non-tg and HZ mice than in mature adult mice (two-way ANOVA: age effect: F_(1,62)= 9.591, P < 0.001, Tukey’s test: P ≤ 0.05, Figure 6B). Therefore, as a result, the Firmicutes/Bacteroidetes ratio was significantly reduced reaching the lowest values in HZ and non-tg groups at 10-12 months of age (two-way ANOVA: age effect: F_(1,62)= 31.11, P < 0.0001, Tukey’s test: P ≤ 0.05, Figure 6C) compared to those of 4-6 months of age. This trend was less pronounced in HTZ mice. Notably, no significant differences were found between the three genotypes in the two life stages analysed.

3.9 Principal Components Analysis revealed that factors other than Aβ accumulation increase the risk of developing AD

The relationship between cognitive impairment and pathophysiological alteration described above in the 5xFAD transgenic animal model at both 4-6 months and 10-12 months of age was subjected to PCA. The main results of the principal component analysis are given in Table 4. As a result, three components jointly explained 68.674% of the variance that allowed describing the different profiles of three genotypes (Figure 6B). The three dimensions and the Euclidean distances of each animal are shown in Figure 6C. The first component explained 29.975% of the total variance and was associated with Aβ accumulation since the results of the Aβ₄₀, Aβ₄₂ and total Aβ had high factor loading. Time spent in the open arms of EPM and neuroinflammation markers (GFAP and Iba1) loaded positively on this factor. The second component that explained 24.080% of the total variance was loaded by age and was therefore considered as the aging component. Insulin-linked metabolic pathways (resistin and ghrelin) and Firmicutes/Bacteroidetes ratio were negatively loaded, and the insulin-PI3K-Akt signalling pathway (pGSK3, pmTOR and PTEN) was positively loaded on this component. The third component, which explained 14.619%, represented memory impairment, as it was characterised by the phases of visual and acquisition learning in the MWM, the classic test for learning and memory. The insulin-PI3K-Akt signalling pathway (pIRS1) was also loaded positively on this component. Factor scores were then calculated and compared between groups. Two-way ANOVA revealed differences in the Aβ accumulation component (genotype effect: F_(2,36)= 125.1, P < 0.0001) showing that this increase led to a changed in emotional-like behaviour and neuroinflammation in HTZ and HZ mice compared to non-tg mice (Tukey’s test: P < 0.05, Figure 6D). In addition, there was an age effect (two-way ANOVA: F_(2,36)= 370.4, P < 0.0001) indicating that HZ mice performed worse on the aging component than HTZ and non-tg mice (Tukey’s test: P ≤ 0.05, Figure 6E). For the memory impairment component, two-way ANOVA revealed a genotype effect (F_(2,36)= 3.177, P < 0.05) showing differences between HTZ and HZ mice, which were more pronounced at 10-12 months of age (Tukey’s test: P ≤ 0.05, Figure 6F).

(A) Firmicutes and (B) Bacteroidetes phyla. (C) The Firmicutes/ Bacteroidetes ratio. (D) Exploratory principal component analysis (PCA). Three components (factors) together explained 68.674% of the variance associated with Aβ, aging, and memory impairment. (E) Diagram of the three components in rotated space. (F-H) PCA factorial scores. All data represent the mean ± SEM. Tukey’s Multiple Comparisons Test: difference between transgenic vs non-transgenic mice: (*) P < 0.05, (**) P < 0.01, (***) P < 0.001 and (****) P < 0.0001; difference between HTZ and HZ condition: ($) P < 0.05, ($$) P < 0.01.

Amyloid-β plaques and hyperphosphorylated tau protein are the two most common neuropathological findings in AD (Hardy & Selkoe, 2002). However, there are other pathological features associated with AD, including inflammatory activation of microglia and astrocytes, synaptic failure, and impairment in insulin-PIK3-Akt signalling in the brain (Serrano-Pozo et al., 2011; Talbot et al., 2012a). Multiple studies using transgenic animal models, such as the 5xFAD model, have focused on homozygous conditions to assess neurological deficiencies. However, few of these have paid attention to the genetic load and age-dependent neurodegenerative status of the disease. Here, using heterozygous and homozygous conditions, we demonstrate that HZ have earlier and more severe cognitive dysfunction, despite having very similar alterations at the neuropathological level. In summary, the findings were as follows: 1) HZ animals showed more severe cognitive deficits that HTZ animals: 2) Both HTZ and HZ mice showed hippocampal dysfunction from early ages, involving increased Aβ accumulation and neuroinflammatory response, in addition to altered insulin-PI3K-Akt signalling compared to age-matched non-tg mice; 3) HZ mice showed premature dysfunction of plasma insulin-linked metabolic pathways, although a decline was observed at 10-12 months of age, irrespective of genotypes; 4) The reduction of the Firmicutes/Bacteroidetes ratio is associated with ageing, being more pronounced in the HZ group. Therefore, apart from Aβ accumulation, these abnormalities underlie neurodegeneration and worsening cognitive impairment. It is remarkable to show that certain AD-associated abnormalities, unrelated to the main genetic cause of AD (i.e., Aβ accumulation), correspond to metabolic dysfunctions that are easily modified by lifestyles (i.e., diet, infections, exercise, etc.). Thus, HTZ animals could serve as a relevant model to study gene-environment interactions, leading to sporadic or accelerated AD.

In this study, we first determined the behavioural phenotype of HTZ and HZ 5xFAD transgenic mice at two lifespans: 4-6 months and 10-12 months of age. It has been described that AD patients often complain of anhedonia (Naudin et al., 2015; Reichman & Coyne, 1995), which can be broadly defined as a loss of interest or capacity to experience pleasure, and is a core symptom in mood disorders such as depression (Anisman & Zacharko, 1990), as well as has been associated with increased cortical Aβ load (Babulal et al., 2016). In rodents, intake or preference for sucrose/saccharin solutions is the most commonly adopted hedonic measure (Scheggi et al., 2018; Strekalova et al., 2004). Our data show from 4-6 months of age both HTZ and HZ mice had reduced their sucrose intake below 65%, which is the criterion for anhedonia (Scheggi et al., 2018). It is in agreement with results previously described in other AD mouse models, such as 3xTg-AD, which exhibited changes in depression-related behaviour and showed significantly lower sucrose preference (Romano et al., 2014). Although, other studies described that 5xFAD mice have intact reward behaviour showing no variation in sucrose preference at early ages (Heckmann et al., 2019; Tang et al., 2016), as we observed in SachPT that saccharin consumption did not show a comparable change. These data are in agreement with previous evidence that sucrose and non-nutritive saccharin solutions produce different taste sensations in rodents (Nissenbaum & Sclafani, 1987; Sako et al., 1994), and as previously described 5xFAD mice show different motivation for food (López-Gambero et al., 2021). Hence, alternative methods, i.e., submissive behaviour (Strekalova et al., 2004), or reduced sexual activity (Grønli et al., 2005) should be proposed to better assess and confirm the manifestation of anhedonia in the 5xFAD model.

Given that some metabolic regulatory hormones are involved in the control of food intake, energy expenditure, and body weight (Friedman & Halaas, 1998; Schwartz et al., 2000), it seems likely that some metabolic alteration underlies this difference in sucrose and saccharin consumption shown by HTZ and HZ 5xFAD mice. Recently, our group have demonstrated that both 6-month-old HTZ and HZ mice exhibit low body weight, food intake, and energy expenditure, which are associated with metabolic dysfunctions in the hypothalamus (López-Gambero et al., 2021). As revealed in this study, both HTZ and HZ mice show decreased hypothalamic signaling of insulin and leptin, as well as reduced circulating levels, dysregulation in key metabolic sensors and neuropeptides controlling energy balance (López-Gambero et al., 2021). In line with these findings, our results show an alteration of insulin-related metabolic networks in HZT and HZ mice, being more noteworthy at 4-6 months of age. However, at 10-12 months of age circulating levels of insulin-linked metabolic hormones were reduced independently of the genotypes that were associated with ageing, as shown by PCA.

There is increasing evidence that AD is related to alterations in brain energy metabolism (Cunnane et al., 2020). As reported, human post-mortem (Rivera et al., 2005; Talbot et al., 2012b) and clinical (de la Monte, 2009; Kim & Feldman, 2015) analyses have revealed brain insulin deficiency and insulin resistance in AD. In the brain, insulin and the PIK3-Akt protein pathway play an important role in neuronal growth and synapse formation (Gabbouj et al., 2019). Changes in the PI3K-Akt pathway in AD brain arise predominantly as reduced phosphorylation or total levels of insulin-PI3K-Akt signalling cascade components. We found decreased levels of PI3K and Akt at 4-6 months of age. However, when compared to 10-12 months of age, the protein expression is much higher. This increase could be due to the increased release of lipid products by immune cells that act as second messengers to trigger protein kinases, including Akt and GSK3β. In addition, previous research has found that Aβ oligomers inhibit the Akt pathway, leading to neuronal death (Steen et al., 2005). Furthermore, post-mortem AD brain samples revealed lower levels of PI3K p85 subunit, as well as decreased Akt phosphorylation (Moloney et al., 2010; Steen et al., 2005). We found a decrease at 4-6 months of age, and this seems to be due to the inhibition by increased Aβ, and/or a decrease in plasma insulin. This plasma insulin will bring less insulin to the brain and thus a decrease in insulin-PI3K-Akt pathway signalling. In our previous work (Medina-Vera et al., 2020) and as confirmed in this study, there is a high number of Aβ accumulation in the hippocampus at 12 months of age.

In addition to neurons, the PI3K-Akt signalling pathway mediates the function of other cell types, such as microglia and astrocytes (Spagnuolo et al., 2015). Inflammatory markers such as tumour necrosis factor-alpha (TNFα) and other cytokines (IL1β, IL6) are elevated in the brain and blood of both AD and diabetic patients (Perry et al., 2010; Sjöholm & Nyström, 2006). TNFα is an important factor in peripheral insulin resistance as it stimulates c-Jun kinase (JNK), which results in insulin receptor substrate 1 (IRS1) inhibitory serine phosphorylation leading to blockage of insulin signalling (Gregor & Hotamisligil, 2011). We have found elevated phosphorylation of serine residues in IRS1 at 10-12 months of age. Pre-clinical studies and post-mortem examination of human hippocampal tissue indicate an association between elevated inhibitory serine phosphorylation of IRS1 and Aβ plaques, which were negatively related to working memory and episodic memory (Bomfim et al., 2012; Talbot et al., 2012a). Consistent with these studies, we also found a link between the PI3K-Akt signalling pathway and memory impairment, as confirmed by PCA.

Accordingly, with the presence of Aβ plaques in both the hippocampal and cortical areas at early ages, HZ 5xFAD mice show significant impairments in hippocampal-dependent memory formation at 4-6 months of age (Cho et al., 2014; Girard et al., 2014; Kimura & Ohno, 2009; Oakley et al., 2006) and increase markedly with age (Creighton et al., 2019; Griñán-Ferré et al., 2016) using various tests. In addition, 8-month-old 5xFAD mice develop increased exploratory behaviour in the EPM, which correlates with Aβ accumulation in the brain (Jawhar et al., 2012; Peters et al., 2013; F. Schneider et al., 2014). It has been suggested that this increased exploratory behaviour reflects disinhibitory tendencies, similar to those seen in AD patients (Jawhar et al., 2012). Our data show an impairment of spatial working memory only in HZ mice from 4-6 months of age, with a greater detriment of cognitive functions in middle age. Although in our previous report, we described that both 12-month-old HTZ and HZ mice exhibited memory impairment analysed by the novel object recognition (NOR) test (Medina-Vera et al., 2020), there are several substantial differences between NOR and MWM in terms of the learning and memory process involved. The NOR is an efficient method for assessing episodic memory in mice that evaluates the capability of animals to spontaneously discriminate between familiar and novel objects after the prescribed retention interval (Akkerman et al., 2012; Antunes & Biala, 2012; Dere et al., 2005), whereas MWM is a more complex task that evaluates visual learning memory (Morris et al., 1982). Thus, unlike the NOR, which measures cognitive capability, the MWM is a paradigm that tests cognitive efficiency and flexibility. In this regard, 10-12 month old HZ mice showed slower learning during the acquisition of spatial learning and impaired cognitive flexibility compared to HTZ and non-tg mice. Hence, these findings suggest that the severity of cognitive impairment is more pronounced in middle-aged HZ mice than in HTZ, indicating that factors other than Aβ accumulation from early ages may underlie disease progression such as alteration of the neuropeptide network involved in the regulation of insulin signalling. Although a few published studies have evaluated the effect of genetic load on cognitive function in the 5xFAD model, behavioural data remain limited.

Because microglia cells and astrocytes have been linked to their activation by different Aβ species, the inflammatory response has gained special attention in the context of AD. The development of neuroinflammation in the brain in the pre-symptomatic phase of AD could underlie synaptic dysfunctions and cognitive impairments when cognitive symptoms are still minor (Bairamian et al., 2022). Here, both HTZ and HZ groups showed high expression of microglia and astrocytes from 4-6 months old, associated with increased Aβ accumulation in the hippocampus, as also previously described (Boza-Serrano et al., 2018; Oakley et al., 2006), although was more pronounced in HZ mice. Therefore, this difference in terms of neuroinflammation in the early stage of the disease may be related to cognitive impairment occurring in HZ, which were more affected by disease progression and aging. It has been described that activation of microglia and astrocytes contribute to the infiltration of blood cells into the CNS, especially when the BBB suffers biochemical or mechanical damage modifying its permeability. Consequently, this adverse event results in the exchange of circulating substances through the BBB between the CNS and the blood, which causes loss of homeostatic functions leading to neuronal dysfunction and damage (Bairamian et al., 2022). In this regard, several reports support the idea that BBB permeability is bidirectional and that a major component affecting barrier disruption is the gut microbiota (Bairamian et al., 2022), whereby alterations along the brain-gut-microbiota axis may contribute significantly to the pathogenesis of AD (Bairamian et al., 2022; Kowalski & Mulak, 2019). Regarding microbiota analysis, our results show that Firmicutes were significantly depleted, while Bacteroidetes were upregulated in 10-12-month-old HZ 5xFAD mice compared to age-matched heterozygous condition. In a recent study, the authors have described that old 5xFAD mice showed an age-dependent alteration of the Firmicutes/Bacteroidetes ratio associated with peripheral inflammation, particularly originating in the gut, which in turn exacerbates AD progression (Shukla et al., 2021). Vogt and colleague showed a decrease in Firmicutes and an increase in Bacteroidetes in faecal samples from AD participants compared to healthy participants, suggesting that microbial alterations in the gut may influence AD pathology (Vogt et al., 2017). The involvement of microbial populations belonging to the phyla Firmicutes and Bacteroidetes in the pathogenesis of AD remains a challenge, although they have been linked to insulin resistance, increased Aβ deposition and LPS, which are capable of trigging systemic inflammation and the release of pro-inflammatory cytokines (Bairamian et al., 2022; Kowalski & Mulak, 2019). We are currently analysing the different taxonomical levels in the three genotypes to explore the relationship between different population of microbiota in the 5xFAD mouse that might contribute to the behavioural / neurochemical phenotype, in order to compare with the gut microbiota observed in AD patients to better understand the developing or maintenance of the disease.

Overall, our result provides an in-depth characterization of behavioural phenotype, cellular marker, PI3K-Akt signalling, and circulating metabolic hormones in the plasma of HTZ and HZ 5xFAD mice over two lifespans. Genetic load determines a greater severity of the cognitive impairment and certain metabolic alterations in HZ versus HTZ. Our data illustrate that to not only Aβ burden presented in the brain, especially in the hippocampus leads to impaired cognition and in the 5xFAD mouse model. The Aβ accumulation in combination with increased inflammatory response and dysfunction of PI3K-Akt and insulin-linked metabolic pathways in the early stages may contribute disease worsening. In addition, we described variation in the Firmicutes/Bacteroidetes ratio that was more pronounced in HZ mice. Therefore, this study offers new insights underlying this neurodegenerative disease.

Aβ: amyloid-β; AD: Alzheimer’s disease; APP: amyloid precursor protein; CA: cornu ammonis; DG: dentate gyrus; EPM: elevated plus maze; FAD: familial Alzheimer’s disease; GFAP: anti-glial fibrillary acidic protein; GIP: gastric inhibitory polypeptide; GLP-1: glucagon-like peptide-1; HTZ: heterozygous; HZ: homozygous; LPS: lipopolysaccharides; MWM: Morris water maze; OF: open field; OTUs: operational taxonomic units; Q: quadrant; PAI-1: plasminogen activator inhibitor-1; PBS: phosphate-buffered saline; PCA: principal component analysis; PFA: paraformaldehyde; PSEN1: presinilin-1; PSEN2: presinilin-1; PI3K: phosphoinositide 3 kinase; RT: room temperature; SAB: spontaneous alternation behaviour; SachPT: saccharin preference test; SEM: standard error of the mean; SPT: sucrose preference test; YMT: Y-maze test.

Acknowledgements

Not applicable

Authors’ contribution

FRdF, SJL and CRV were responsible for the study concept and design. CRV, ENZI, DMV, AJLP, and JAN performed animal experiments and histology. CRV, DMV, and AGJ contributed to data collection and/or interpretation and performed the statistical analyses reviewed by JS, FJP, and EB. CRV, DMV, and FRdF wrote the manuscript that was reviewed by JS and SJL for important intellectual content. All authors critically reviewed the content and approved the final version for publication.

Funding statement

This research was funded by the European Regional Development Funds-European Union (ERDF-EU) and Fatzheimer project EULAC-HEALTH H2020, grant number EU-LACH16/T010131; Ministerio de Economía, Industria y Competitividad, Gobierno de España, grant number RTC-2016-4983-1; EU-ERDF and Instituto de Salud Carlos III (ISCIII), grant numbers PI19/01577 and PI19/00343; Consejería de Transformación Económica, Industria, Conocimiento y Universidades, Junta de Andalucía, grant number P18-TP-5194. D.M-V. (FI20/00227) holds a “PFIS” predoctoral contract from the National System of Health, EU-ERDF-Instituto de Salud Carlos III. A.J.L.-G. (IFI18/00042) holds an “iPFIS” predoctoral contract from the National System of Health, EU-ERDF-Instituto de Salud Carlos III.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its supplementary information files. Raw data are available upon reasonable request from the corresponding author.

Ethical approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing financial interests.

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Table 1 to 4 is available in the Supplementary Files section.

table1.png
Neurological screening at non-transgenic (No-tg), heterozygous (HTZ) and homozygous (HZ) transgenic 5xFAD mice.
table2.png
Correlations between cognitive impairment and hippocampal dysfunction in 5xFAD mice.
table3.png
Correlations between cognitive impairment and insulin-linked metabolic pathways in plasma of 5xFAD mice.
table4.png
Rotated (Varimax-Kaiser normalization) matrix of extracted components with eigenvalues greater than 1.
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Supplementary Table 1. Primary antibodies used for protein expression by Western blotting.
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Supplementary Figure 1. Behavioural tests in heterozygous and homozygous 5xFAD mouse model. Animal weight during Saccharin Preference Test (A) and Sucrose Preference Test (B) at 4-6, and 10-12 months old. Spatial working memory was assessed in Y-maze test (YMT). No significant differences were observed in the number of arm entries between genotypes (C). There was no correlation between spontaneous alternation behaviour and the number of arm entries and locomotion (D) (Pearson’s comparisons: P > 0.05). All data represent the mean ± SEM. Tukey’s Multiple Comparisons Test: the difference between transgenic vs non-transgenic mice: (*) P < 0.05.
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Supplementary Figure 2. Spatial learning in heterozygous and homozygous 5xFAD mouse model. In habituation training, no significant differences were found between genotypes on path length (A-B) and swimming speed (C-D) at any age. In visual learning, HTZ mice at 4-6 month old showed a higher cumulative distance to the visible escape latency on the first training day than the no-tg group (E). No significant differences were found between genotypes on path length in the peripheral zone (F). At 1012 month old, HZ mice showed a higher cumulative distance compared to non-tg mice (G). At 10-12 month old HZ mice showed thigmotaxic (time spent in the peripheral zone) behaviour during the visual learning compared to HTZ and no-tg mice that showed a direct search pattern to reach the platform (H). For navigation of all mice, a positive correlation between the time spent in the peripheral zone and the escape latency was found (I). During the acquisition spatial learning, 10-12 month old HZ group continued to display a thigmotaxis behaviour (J). Memory retention test 1 path (K) and test 2 path (L) of the two age ranges. All data represent the mean ± SEM. Tukey’s Multiple Comparisons Test: the difference between transgenic vs non-transgenic mice: () P < 0.05, () P < 0.01, () P < 0.001; difference between HTZ and HZ condition: ($) P < 0.05.
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Supplementary Figure 3. Whole membranes from Gel 1 and 2 (unedited blots). Two membranes were used for immunoblottings of 4-6 months-age samples. Red Ponceau Staining and individual membranes are shown below. All bands were quantified for histograms charts and statistical analysis. Proteins were normalized with its respective γ-Adaptin.
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Supplementary Figure 4. Whole membranes from Gel 1 and 2 (unedited blots). Two membranes were used for immunoblottings of 10-12 months-age samples. Red Ponceau Staining and individual membranes are shown below. All bands were quantified for histograms charts and statistical analysis. Proteins were normalized with its respective γ-Adaptin.

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Gene dosage determines the behavioural phenotype in the 5xFAD mouse model of Alzheimer’s disease: analysis of the contribution of brain insulin signalling and gut microbiota

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