Quantitative Metabolomic Analysis of the Rat Hippocampus: Effects of Age and of the Development of Alzheimer’s Disease-Like Pathology

Abstract

Background:

Alzheimer’s disease (AD) is the most common type of dementia in the elderly. Incomplete knowledge about the pathogenesis of this disease determines the absence of medications for the treatment of AD today. Animal models can provide the necessary knowledge to understand the mechanisms of biochemical processes occurring in the body in health and disease.

Objective:

To identify the most promising metabolomic predictors and biomarkers reflecting metabolic disorders in the development of AD signs.

Methods:

High resolution ¹H NMR spectroscopy was used for quantitative metabolomic profiling of the hippocampus of OXYS rats, an animal model of sporadic AD, which demonstrates key characteristics of this disease. Animals were examined during several key periods: 20 days group corresponds to the “preclinical” period preceding the development of AD signs, during their manifestation (3 months), and active progression (18 months). Wistar rats of the same age were used as control.

Results:

Ranges of variation and mean concentrations were established for 59 brain metabolites. The main metabolic patterns during aging, which are involved in energy metabolism pathways and metabolic shifts of neurotransmitters, have been established. Of particular note is the significant increase of scyllo-inositol and decrease of hypotaurine in the hippocampus of OXYS rats as compared to Wistars for all studied age groups.

Conclusions:

We suggest that the accumulation of scyllo-inositol and the reduction of hypotaurine in the brain, even at an early age, can be considered as predictors and potential biomarkers of the development of AD signs in OXYS rats and, probably, in humans.

Keywords

Aging Alzheimer’s disease hippocampal metabolome hypotaurine nuclear magnetic resonance spectroscopy OXYS rats scyllo-inositol

INTRODUCTION

Alzheimer’s Disease International considers Alzheimer’s disease (AD) and its most common (∼95%) sporadic form, which is becoming the main cause of senile dementia, the main social problem of the 21st century. The incidence of AD is growing against the background of an increase in life expectancy. Incomplete knowledge of the pathogenesis leads to the fact that there are no effective methods for preventing and slowing down the progression of AD, despite significant investments in their development. For more than 20 years, the amyloid hypothesis was the most popular for research into the pathogenesis of AD. This hypothesis states that the cause of AD development is the accumulation of soluble amyloid-β (Aβ) in the form of oligomers and amyloid plaques. This initiates a pathogenic cascade that leads to the accumulation of hyperphosphorylated tau protein in neurofibrillary tangles, to mitochondrial dysfunction, loss of synapses, etc., and, eventually, a loss of cognitive function [1 –3]. Recent studies performed using modern diagnostic methods have confirmed that the preclinical period of sporadic AD can last for decades [4 –6]. But it still not clear at what point of life this disease starts and what contributes to its development. Moreover, it has been shown that in a third of patients with a clinical AD diagnosis, despite the loss of neurons, there is no accumulation of Aβ and increased formation of tau-protein tangles, while in many people who survived into the eighth and ninth decades of life with minimal cognitive decline, autopsy shows significant accumulation but minimal loss of neurons [7]. These results confirmed that AD is a heterogeneous disease, the pathophysiological mechanisms of which are not completely clear and go beyond the accepted dogmas. Accordingly, deciphering the pathogenesis of sporadic AD is a prerequisite for the development of new therapeutic and prophylactic approaches.

New opportunities to better understand the pathophysiology of AD and thus identify potential biomarkers are provided by the technological innovations of various omics. A recent analysis of 1,543 transcriptomes from five brain regions of AD patients revealed a significant molecular heterogeneity of the disease [8]. The study of “molecular portraits” of patients with AD, aligned by the average values of clinical and pathological signs, the presence of tau-protein and Aβ in biopsy, etc., revealed five molecular subtypes corresponding to different combinations of multiple metabolic pathways disorders. This indicates differences in the mechanisms underlying the starting of the pathological process. It should be emphasized that this study, like similar brain studies [9, 10], was performed with the use of postmortem samples of patients with severe signs of the disease. In humans, it is impossible to study the early preclinical stages of AD and the mechanisms of transition from body changes characteristic of “healthy” aging to a pathological process, since the manifestation of the disease occurs later than the formation of neurodegenerative changes and the development of underlying phenomena at the molecular level. Analytical approaches used in metabolomic studies make it possible to study biochemical changes occurring during pathological processes, including those that may be involved in the progression of AD. Metabolomics is a powerful tool in analytical chemistry that detects changes in the metabolome, a pool of metabolites that is an accurate biochemical profile of an organism in normal or pathological conditions [9 –19]. The use of metabolomic methods of analysis can help to identify biomarkers that can be used for the early diagnosis of AD [18 , 20–24], and can also contribute to the search for new therapeutic targets, and to monitor the therapeutic response and disease progression. The previous metabolomic studies demonstrated a significant effect of AD progression on metabolites and associated metabolic pathways, including: energy metabolism; fatty acid metabolism; abnormal lipid metabolism; altered amino acids metabolism (e.g., arginine, glutamate); and several others [25]. The serum of patients with AD has been extensively studied to identify potential markers of the disease [11 , 26–29]. Research on different transgenic animal models (models of an early familial form of AD or models established by the injection of Aβ) also showed a potential relationship between AD progression and metabolic pathways of amino acids, lipids, vitamins, nucleotide-related bases, neurotransmitters, energy metabolism, and oxidative stress [15 , 31]. Nevertheless, the alteration of the hippocampal metabolome in the dynamics of the development of AD signs in a model of the sporadic form of the disease, especially at the preclinical stage, has not been explored.

The aim of this study was a comprehensive study of hippocampal metabolism at all stages (including asymptomatic) of the AD development in the senescence-accelerated OXYS rats, which are a unique model of the sporadic form of AD [32 –34]. Hippocampal neurodegeneration in OXYS rats is associated with increased levels of phosphor-tau and altered learning ability at an early stage (3 months); synaptic insufficiency, destructive changes in neurons, behavioral disorders, and cognitive decline during progression against the background of an increase in the level of amyloid-β protein precursor (AβPP), increased accumulation of Aβ, and the formation of amyloid plaques in the brain. We used high resolution ¹H NMR spectroscopy to compare the age-related changes in metabolomic profiles of the hippocampus of OXYS and Wistar (control) rats.

MATERIALS AND METHODS

Chemicals

All reagents were purchased from Sigma-Aldrich (St. Louis, MO, USA) with the exception of the following: phosphate-buffered saline was purchased from Biolot (Moscow, Russia), D2O 99.9% and sodium 4,4-dimethyl-4-silapentane-1-sulfonate (DSS) were purchased from Cambridge Isotope Laboratories Inc. (Tewkesbury, MA, USA). For preparation of aqueous solutions water deionized to a quality of 18.2 MΩ using an ultrapure water system (SG water, Munich, Germany) was used.

Animals

The OXYS rat strain was developed at the Institute of Cytology and Genetics (ICG), SB RAS (Novosibirsk, Russia), from a Wistar stock by selection for susceptibility to the cataractogenic effect of a galactose-rich diet and brother-sister mating of highly susceptible rats. In the first five generations, the development of cataracts was caused by overconsumption of galactose, and later there was a selection for early spontaneous cataracts, genetically associated with the latter in animals that inherited a complex of signs of premature aging. At present, we have the 120th generation of OXYS rats, which at a young age develop retinopathy similar to age-related macular degeneration in humans, osteoporosis, arterial hypertension, accelerated involution of the thymus, sarcopenia, neurodegenerative changes in the brain with features characteristic of AD in addition to cataracts. The genetic model of senescence - accelerated OXYS rats was described in more detail earlier in the work [33]. The OXYS rat strain is characterized by accelerated aging and spontaneous development of all the key features of AD. The study was performed using males of three age groups: age 20 days, which corresponds to the “preclinical” period without the development of AD signs; 3 months - with manifestations of signs, and 18 months with the active progression of AD [33 –35]. Age-matched Wistar rats were used as control. Groups of 20-day-old and 3-month-old OXYS rats consisted of five samples, the remaining groups consisted of six animals. All animals were provided by the Center for Genetic Resources of Laboratory Animals of the Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences. Standard laboratory conditions (light cycle 12 h light/12 h dark, temperature 22±2°C, relative humidity 60%) were used to keep the animals. All animals had ad libitum access to water and standard feed for rodents (PK-120-1, OOO Laboratorsnab, Moscow, Russia). The keeping of animals (including appropriate facilities, qualified personnel, necessary documentation), and all experiments with animals were carried out in accordance with the regulation on the ethics of using animals in research supported by the Russian Science Foundation (https://www.rscf.ru/), as well as with the European Union Directive 2010/63/EU and approved by the Commission on Bioethics at the Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences (No. 34 of 15 June 2016).

Hippocampal tissue collection

Carbon dioxide was used to euthanize the animals, and then they were decapitated. The skull was opened, the brain was removed, and the hippocampus was carefully isolated from the cerebral hemispheres. All manipulations with the brain were performed on ice. Hippocampal samples were placed in a tube and immediately frozen in liquid nitrogen. Prior to analysis, all collected samples were stored at –70°C. The obtained biomaterial was further used for the extraction of water-soluble metabolites.

Metabolite extraction and NMR spectroscopy

The extraction of water-soluble metabolites from the rat brain was performed by the sample preparation protocol earlier evaluated in our laboratory [36]. Briefly, frozen brain tissue was weighed, homogenized in cold (–20°C) water-methanol-chloroform mixture in a ratio 1:2:2 (v/v; 1600μl of solvent mixture per 150 mg of wet tissue), vortexed for 30 s, kept on ice for 10 min, and incubated at –20°C for 30 min. The mixtures were centrifuged at 12,000 rpm, 4°C for 30 min to pellet proteins. The top hydrophilic fraction was collected and dried on a vacuum evaporator. A detailed description of the sample preparation protocol is published in [36].

The metabolomic composition of the obtained extracts was determined by the method of nuclear magnetic resonance using an AVANCE III HD 700 MHz NMR spectrometer (Bruker BioSpin, Ettlingen, Germany) at the Center of Collective Use “Mass spectrometric investigations” SB RAS. The dissolution of dry extracts was carried out in 600μL deuterated phosphate buffer (0.01 M, pH = 7.4) containing DSS at the concentration of 6μM as an internal standard. The samples were thoroughly mixed on a vortex, then placed on a shaker for 30 min. The sample was transferred from the tube to an NMR ampule. The ¹H spectra were recorded using the 5 mm TXI 1H-13 C/15 N/D ZGR probehead. The spectra were acquired using a single pulse zgpr sequence (detection pulse was 90 degrees) with the water signal suppression (saturation of the water signal with low power (20μW) continuous RF during the delay between repetitions), 14 ppm spectral width, 5 s relaxation delay, 6.7 s acquisition time. For each sample, the total spectrum of the sample was obtained by the sum of the 64 accumulated spectra. The temperature of the sample during the recording of the spectrum was maintained at 25°C. Other parameters of the spectra recording were the same as described in the works [36 –38].

Identification and quantification of metabolites

MestReNova V.12 (Mestrelab Research S.L.) software was used to manually correct the phase and baseline of the collected spectra, as well as signal processing and integration. The chemical shift and the absolute concentration of metabolites were determined relatively DSS. The concentration of metabolites in the samples was calculated by the integration of the peak area of the metabolite relatively to the DSS signal (chemical shift 0.00 ppm, concentration of 6μM); a detailed description of the concentration determination is published in the work [36]. For each metabolite, the normalization per gram of wet brain tissue was performed. The assignment of the metabolite resonances was carried out by comparing the obtained data with the data in Human Metabolome Database [39, 40], or by adding reference compounds whenever needed, and also based on our own experience in the metabolomic profiling [36–38 , 41–43]. We compared the resonances of individual substances with the resonances observed in the spectrum of the sample, which is actually a superposition of the spectra of individual substances. The concentrations of the metabolite were determined by signals which do not overlap in the spectrum with signals from other metabolites. Supplementary Table 1 with resonances for each metabolite that were used to determine the metabolite concentrations is presented in the Supplementary Material.

Analysis of metabolite concentrations

The normalized metabolite concentration data was used to demonstrate the general metabolomic differences. To analyze the contributions of all metabolites into metabolic fingerprints we apply the following methods: principal component analysis (PCA) and sparse partial least squares discriminant analysis (sPLS-DA). To determine the meaningful patterns of metabolite concentration changes the Metabolite Set Enrichment Analysis (MSEA) and Metabolite Pathway Analysis (MetPA) were applied. These analyzes were performed on the MetaboAnalyst 5.0 web-platform [44, 45]. The Statistical Analysis (one factor) module was used to construct the PCA scores and Volcano plots. The MSEA plots were constructed with the Enrichment Analysis module.

Statistical analysis

The obtained data of metabolite concentration were analyzed by one-way and two-way analysis of variance (ANOVA) using GraphPad Prism 9.3.1 (San Diego, CA, USA). The Shapiro–Wilk test was used to check the correspondence of distributions to the normality conditions. The Levene’s test was used to assess the homogeneity of dispersions. Post hoc multiple comparisons test was used with ANOVA analysis of variance. The genotype (Wistar, OXYS) and age of the animals (20 days, 3 months, and 18 months) were considered as independent factors in two-way ANOVA. The results were considered statistically significant at p < 0.05.

RESULTS

Quantitative metabolomic profiling of hippocampus

NMR spectroscopy was applied to perform the quantitative metabolomic profiling of rat hippocampus. A typical ¹H NMR spectrum of the extract from rat hippocampus is shown in Fig. 1.

Fig. 1

Typical ¹ H NMR spectrum of protein free extract of hippocampus tissue from 18-month-old OXYS rat with metabolite annotation. The area from 4.6 to 5.8 ppm does not contain signals except the water signal, and it is omitted in the figure.

A list of identified and quantified metabolites with their chemical shifts and multiplicities is presented in Supplementary Table 1. Identified metabolites belong to different classes of compounds: amino acids, organic acids, antioxidants, osmolytes, glycosides, purine and pyrimidine derivatives. We identified 59 different metabolites in extracts from the hippocampus (see Supplementary Table 1). The determined concentrations of metabolites are in the range from 1 nmol/g to 14μmol/g. The most abundant metabolites (the concentration above 2 micromoles/g) in the brain tissue are the following compounds: N-acetylaspartate (NAA), aspartate, lactate, creatine, glutamate, glutamine, taurine, gamma-aminobutyric acid (GABA), and myo-inositol. Supplementary Tables 2 and 3 summarize the ranges and the mean values of metabolite concentrations (nmol/g) in the rat hippocampus for all studied groups, and also indicate the metabolites with statistically significant interstrain differences.

Using normalized (autoscaled: mean-centered and divided by the standard deviation of each variable) concentration data, we evaluated the differences in the metabolomic profiles of studied groups by principal component analysis. The PCA score plots for concentration of aqueous metabolites extracted from the hippocampus of rats are shown in Fig. 2.

Fig. 2

PCA score plots for concentration of aqueous metabolites extracted from hippocampus of Wistar and OXYS rat strains.

Figure 2 shows the difference in sample sets depending on the age of the animals. According to the first principal component (PC1), the metabolomic profile of the hippocampus of 20-day-old rats of both strains differs significantly from the metabolomic profiles of 3- and 18-month-old animals. Supplementary Figure 1 shows the results of sparse partial least squares discriminant analysis of the same data. There is a better visualization of a difference between groups. It can be seen that, according to the second component, there is also a division between groups of 3- and 18-month-old animals.

Further, to determine the interstrain differences, PCA score plots were constructed (Fig. 3) for pairwise comparison of the metabolomic profiles of both strains of rats of each of the age groups. The metabolomic profile of the hippocampus of OXYS rats differs from that of coeval Wistar rats, which can be more clearly illustrated using partial least squares discriminant analysis (PLS-DA), see Supplementary Figure 2). The data are clearly separated along the first principal component with 36.2%, 17.6%, and 22.1% of the total variance for 20-day-old, 3-month-old, and 18-month-old animals, respectively. The same data set was used for the determination the metabolites with the highest interstrain difference in concentrations. To this end, we constructed Volcano plots (Fig. 3), demonstrating statistically significant differences for a large set of metabolomic data (p < 0.05, fold change (FC) > 1.3). The obtained Volcano plots show that for all ages, two compounds display the highest and statistically significant interstrain difference: the increase in the concentration of scyllo-inositol, and the decrease in hypotaurine.

Fig. 3

PCA score plots and Volcano plots of brain metabolites of Wistar and OXYS rat at the age of 20 days (A), 3 months (B), and 18 months (C). In Volcano plots, the x-axis displays the fold change (FC), the horizontal line depicts a cut-off of FDR-adjusted p-value = 0.05; metabolites with fold change threshold (OXYS versus Wistar) of 1.3 are highlighted.

Using the MSEA and MetPA methods, we identified metabolic pathways with the significantly different activity between OXYS and Wistar rats of the same age. Figure 4 shows an overview of enriched metabolite sets, which are presented by a dot plot with p < 0.05. At this significance value for 20-day-old rats, 19 metabolic pathways involving 40 of the 59 metabolites detected are significant. At the age of 3 months, only four metabolic pathways involving 11 metabolites are significant, while at the age of 18 months 19 pathways involving 38 metabolites are significant. As can be seen from the figure, for all three animal ages, the most significant changes are observed in the metabolism of taurine and hypotaurine. Comparing the early stage with the late stage, it can be stated that only ten of the nineteen significant metabolic pathways are the same for these ages, and the remaining nine are different.

Fig. 4

Enriched metabolite sets (p-value < 0.05). Comparison of the metabolic pathways for OXYS and Wistar rats of the same age: at the age of 20 days (A), 3 months (B), and 18 months (C).

Age-related changes of metabolomic profile

To explore how age contributes to metabolic shifts in aging and AD, we grouped metabolites into up-regulated and down-regulated categories between ages 20 days and 3 months and 3 to 18 months of age, as presented in Venn diagrams (Fig. 5A-D).

Fig. 5

Metabolic shift during aging in the hippocampus of Wistar (A) and OXYS (B) rats, similarities and difference between rat strains from 20 days to 3 months (C) and from 3 to 18 months (D). Enriched metabolite sets (p-value < 0.05) for common metabolites for OXYS and Wistar rats from 20 days to 3 months (E), from 3 to 18 months (F) and specific metabolites only for Wistar rats (G) and OXYS rats (G) from 3 to 18 months. For Enrichment Analysis module, we analyzed a group containing at least 10 metabolites.

We found a similar metabolic shift from 20 days to 3 months in both Wistar and OXYS rats: 17 metabolites were increased (valine, isoleucine, lactate, GABA, acetate, glutamine, choline, glycerophosphocholine, myo-inositol, scyllo-inositol, glycine, glycerol, guanosine, inosine, nicotinamide, hypoxanthine, and adenosine) and 18 metabolites were decreased (isobutyrate, succinate, acetylcarnitine, phosphoethanolamine, taurine, ascorbate, GSH, tyrosine, histidine, AMP, NAD⁺, NADH, alpha-aminobutyric acid (AABA), hypotaurine, allantoin, cytidine, UDP/UTP, and GMP). According to the MSEA analysis, these metabolites are involved in following pathways: the purine metabolism; aminoacyl-tRNA biosynthesis; pyrimidine metabolism; valine, leucine and isoleucine biosynthesis; taurine and hypotaurine metabolism; alanine, aspartate and glutamate metabolism; glyoxylate and dicarboxylate metabolism; glycerophospholipid metabolism; butanoate metabolism; and nicotinate and nicotinamide metabolism (p < 0.05), of which the categories of valine, leucine, and isoleucine biosynthesis and taurine and hypotaurine metabolism were the most enriched (Fig. 5). Only Wistar rats showed a decrease in the concentration of 8 metabolites (pyruvate, asparagine, phosphocholine, fumarate, phenylalanine, formate, pantothenic acid, and 3-hydroxyisovalerate), while only OXYS rats showed an increase in N-acetylaspartate, glutamate, creatine, and carnosine, and the level of ATP decreases at the age of 3 months.

From the age of 3 to 18 months, we also found similar changes in the concentrations of metabolites in rats of both strains: 11 metabolites decreased (leucine, phosphocholine, hypotaurine, cytidine, aspartate, uridine, valine, isoleucine, GABA, choline, and glycine) and the level of glycerophosphocholine increased. These metabolites are involved in: aminoacyl-tRNA biosynthesis; valine, leucine and isoleucine biosynthesis; glycerophospholipid metabolism; valine, leucine and isoleucine degradation; pantothenate and CoA biosynthesis; alanine, aspartate and glutamate metabolism; glycine, serine and threonine metabolism; pyrimidine metabolism; phosphonate and phosphinate metabolism (p < 0.05).

The concentration of NADH, threonine, ADP, and ATP were higher, and the concentration of pyruvate, tyrosine, glycerol, inosine, hypoxanthine, and adenosine were lower in 18-month-old Wistar rats compared to 3-month-old rats. These metabolites were involved in: purine metabolism; glycine, serine and threonine metabolism; phenylalanine, tyrosine and tryptophan biosynthesis; tyrosine metabolism; aminoacyl-tRNA biosynthesis and valine, leucine, and isoleucine biosynthesis (p < 0.05). It should be also noted that some of these metabolites (NADH, ADP, ATP, and pyruvate) actively participate in the cellular energy generation.

Age-dependent alterations in OXYS rats are characterized of upregulation of acetylcarnitine and carnosine and downregulation of succinate, taurine, serine, inosinate (IMP), NAA, glutamate, glutamine, and creatine in hippocampus. Enrichment analysis showed that these metabolites are involved in alanine, aspartate and glutamate metabolism, D-glutamine and D-glutamate metabolism, nitrogen metabolism, arginine biosynthesis, butanoate metabolism, histidine metabolism, glyoxylate and dicarboxylate metabolism, arginine and proline metabolism, aminoacyl-tRNA biosynthesis, taurine and hypotaurine metabolism and purine metabolism (p < 0.05).

In general, assessing the age-related changes in metabolites in the Wistar rat hippocampus, we observed that the concentrations of pyruvate, phosphocholine, tyrosine, hypotaurine and cytidine decreased and the level of glycerophosphocholine increased throughout the studied age periods. Ten metabolites were discordant: the concentrations of valine, isoleucine, GABA, choline, glycine, glycerol, inosine, hypoxanthine and adenosine increased at the age of 3 months and then decreased by 18 months of age; the level of NADH showed the opposite trend.

For OXYS rats, the concentrations of succinate, taurine, hypotaurine, and cytidine decreased and the levels of glycerophosphocholine and carnosine increased throughout the studied age periods. Also ten metabolites were discordant: the concentrations of valine, isoleucine, GABA, NAA, glutamate, glutamine, creatine, choline and glycine were upregulated at 3 months of age and then decreased by 18 months; the acetylcarnitine level showed the opposite trend.

Thus, we found that the aging for both strains of rats is accompanied by changes in the concentrations of following metabolites in the hippocampus: the increase in glycerophosphocholine, which is an important precursor of the neurotransmitter acetylcholine, and the decrease in cytidine and physiological antioxidant hypotaurine. The levels of valine, isoleucine, GABA, choline, and glycine were discordant.

Changes of metabolomic profile associated with the development of AD

Next, to identify the metabolic changes associated with the development of AD signs, we compared hippocampal metabolic patterns of age-matched rats of both strains. We found the most significant differences in the number of metabolites with different concentrations in Wistar and OXYS rats in the hippocampus of 20-day-old animals. The concentrations of isobutyrate, alanine, NAA, phosphoethanolamine, phosphocholine, uridine, tyrosine, histidine, phenylalanine, AMP, formate, NAD⁺, pantothenic acid, 3-hydroxyisovalerate, hypotaurine, and allantoin were downregulated, and the level of scyllo-inositol was upregulated in OXYS rats as compared to age-matched control. The levels of hypotaurine and glycine were lower, and scyllo-inositol level was higher in OXYS rats as compared to Wistar rats at the age of 3 months. At the age of 18 months the concentrations of hypotaurine, glycine, succinate, threonine, ADP, ATP, NADH, and carnosine in OXYS rats were significantly lower, and the levels of scyllo-inositol and GABA were higher than in the Wistar rats. Thus, at all stages of the AD signs development, including asymptomatic one, a reduced level of hypotaurine and increased level of scyllo-inositol in the hippocampus of OXYS rat were observed. Supplementary Table 4 shows the results of two-way ANOVA analysis for the hippocampus metabolomic profiles of OXYS rats relatively to Wistar rats for different ages, and Fig. 6 demonstrates the age-dependent variations of 12 selected metabolites in the hippocampus of Wistar and OXYS rats.

Fig. 6

Alterations of concentrations of some hippocampus metabolites for Wistar and OXYS rats during aging and AD development. Data presented as mean±SD. Significant differences: *p < 0.05 between rat strains; ^#p < 0.05 between different ages.

Investigation of energy metabolism, redox state of glutathione, and ratio NAD⁺/NADH in the hippocampus of OXYS and Wistar rats at different ages

To evaluate the parameters of energy metabolism in the hippocampus of rats of different ages, we compared the concentrations of metabolites that characterize energy metabolism, such as ATP and ATP degradation products (ADP, AMP, IMP, inosine, and hypoxanthine), described above, and also evaluated the adenylate energy charge values (AEC) (Fig. 7). To assess the state of energy metabolism, it is important not only the absolute amount of ATP in the cell, but also the ratio of the components of the adenylate system, since ATP, ADP, and AMP are powerful regulators of metabolic processes. As one can see, AEC depends on the genotype of the animals (F1,34 = 12.4, p < 0.002) and age (F2,34 = 26.9, p < 0.0001). Factors “genotype” and “age” interact (F1,34 = 10.4, p < 0.0004): AEC for Wistar rats increases with age, and for OXYS rats it increases from the age of 20 days to 3 months, and then remains at the same level at the age of 18 months. At the same time, at the age of 20 days it was significantly higher, and at 18 months significantly lower than that for Wistars (Fig. 7A).

Fig. 7

Indexes during aging and AD development. Adenylate energy charge values (A), ratio lactate to pyruvate (B), ratio NAD⁺ to NADH (C), glutathione/oxidized glutathione ratio (GSH/GSSG) (D), excitation-inhibition index (E/I ratio) (E), sum of glutamate–glutamine (GLX) (F), index of glutamatergic processing (G) and excitotoxic index (H). The data are presented as mean±SD. Significant differences: *p < 0.05 between rat strains; ^#p < 0.05 between different ages.

We also investigated the lactate/pyruvate ratio in the rat hippocampus as markers of the ischemic hypoxia [46]. This parameter depends on the age of the animals (F1,34 = 27.757, p < 0.0001) and factors “genotype” and “age” interact (F1,34 = 9.032, p < 0.001). The lactate/pyruvate ratio progressively increases with age in Wistar rats (p < 0.05), and from 20 days to 3 months in OXYS rats (p < 0.05). In OXYS rats, the lactate/pyruvate ratio is increased at the age of 20 days and reduced at the age of 18 months as compared to Wistar rats. It should be noted that such changes in this ratio are primarily associated with the increased lactate levels from 20 days to 3 months in both rat strains.

Ratio NAD⁺ to NADH plays a crucial role in regulating the intracellular redox state, especially in the mitochondria and nucleus. Both metabolites decreased significantly from 20 days to 3 months in rats, and their ratio decreases with age in the hippocampus of both rat strains–the differences between 20 days and 3 months were significant. Additionally, a significant influence of the genotype (F2,34 = 6.27, p < 0.019) was shown by two-way ANOVA analysis: the ratio NAD⁺ to NADH was lower in OXYS rats as compared to Wistar rats. However, the significant difference between strains was detected only for the age of 20 days (p < 0.05).

The ratio of reduced glutathione to oxidized glutathione (GSH/GSSG) is widely used for the evaluation of oxidative stress status in biological systems. We did not observe a significant difference in GSH/GSSG ratios between the rat strains. However, we detected a significant decrease of this ratio in the hippocampus of both rat strains from the age of 20 days to 3 months (p < 0.001).

Evaluation of the E/I, GLX, glutamatergic processing, and excitotoxic indexes in the hippocampus of OXYS and Wistar rats at the different ages

At the next step we analyzed neurophysiology parameters, such as: the glutamate/GABA ratio (excitation-inhibition index (E/I)); the sum of glutamate and glutamine (GLX index); the glutamine-glutamate ratio (index of glutamatergic processing) and the relationship between glutamate-glycine-GABA (excitotoxic index) as described in [47].

The E/I index represents the relationship between the excitatory and inhibitory synaptic inputs appropriating to neuronal events. Two-way ANOVA showed that there is a significant effect of the genotype (F1,34 = 9.391, p < 0.01) and age (F2,34 = 4.729, p < 0.05) on the E/I index. The E/I index was lower in OXYS rats than in Wistars for all three ages. For both rat strains, the E/I index significantly decreases from 20 days of age to 3 months, and then increase to 18 months (Fig. 7E). These changes are primarily associated with alterations of GABA levels with age in both rat strains (Fig. 6).

The GLX index is a sum of glutamate and its precursor, glutamine. It may reflect the increased or decreased glutamatergic neurotransmission. According to ANOVA analysis, there was a significant impact of age (F2,34 = 10.531, p < 0.01) but not genotype on the GLX index in the rat hippocampus. GLX significant increased at the age of 3 months in both rats strains (p < 0.05). OXYS rats showed a significant decrease in GLX at the age of 18 months (p < 0.05). We did not observe significant differences between the strains (Fig. 7F).

Index of glutamatergic processing (glutamine-glutamate ratio) depends on age (F2,34 = 61.87, p < 0.001), but not genotype. In Wistar rats, the index of glutamatergic processing significantly increases with age (p < 0.05). For OXYS rats, this parameter increases at the age of 3 months only. Significant differences between strains were observed at the ages of 3 and 18 months: the index of glutamatergic processing was higher at the age of 3 months and lower at the age of 18 months in OXYS rats as compared to Wistar rats (p < 0.05) (Fig. 7G). An increased glutamine/glutamate ratio has been proposed to be associated with the decreased glial function or the dysfunction of the glia–neuron communication [48]. It is worth noting that the greatest influence on these parameters, the GLX index and the glutamatergic processing index, is caused by the change in the level of glutamine with age (Supplementary Tables 2 and 3) .

Two-way ANOVA shows that there is a significant effect of the genotype (F1,34 = 35.154, p < 0.0001) and age (F2,34 = 30.716, p < 0.0001) on the excitotoxic index. The excitotoxic index was lower in OXYS rats than in Wistar rats for all ages (p < 0.05). The excitotoxic index increases at the age of 3 months due to age-related changes in the level of glycine (Fig. 6) and then does not change in both rat strains (Fig. 7H).

DISCUSSION

In this study, we compared the age-related changes in the metabolomics of hippocampus of Wistar rats and accelerated senescence OXYS rats that spontaneously develop all key sings of AD and identified both similarities and differences between the strains. As we showed earlier, OXYS rats develop the manifestation of behavioral alterations and learning and memory deficits at the same time with the hyperphosphorylation of the tau protein in the hippocampus and cortex, impaired long-term potentiation and first signs of neurodegeneration at about 3-5 months. With age, neurodegenerative changes in the brain of OXYS rats become amplified against the background of overproduction of AβPP, accumulation of Aβ, and by the age of 16–18 months reach the well-pronounced stages of the AD-like pathology [33].

We showed that the aging of both rat strains is associated with the increase in the hippocampal concentration of glycerophosphocholine and the decrease in cytidine and physiological antioxidant hypotaurine. We also identified three metabolites (pyruvate, phosphocholine, and tyrosine) which decrease with age in the hippocampus of Wistar rats only. At the same time, we detected the decrease of hippocampal succinate and taurine and the increase of carnosine during the development of the AD-like pathology in OXYS rats. In our opinion, the progressive decrease of the hypotaurine levels with age is of particular interest. Hypotaurine is an important sulfur-containing and nonpeptidic amino acid, is an antioxidant and a precursor of taurine. Hypotaurine reacts efficiently and directly with ROS, including hydroxyl radicals, superoxide anions and singlet oxygen [49], and may be involved in the age-related changes in the cognitive GABAergic system in the metabolism of taurine and hypotaurine [50]. It was shown that the hypotaurine level elevates in response to hypoxia to exert the antioxidant protection upon reperfusion [51, 52]. Hypotaurine level is associated with the aging of C. elegans: hypotaurine promotes the longevity and stress tolerance via the stress response factors DAF-16/FOXO and SKN-1/NRF2 and the regulation of the insulin/IGF-1 signaling pathway [53]. A little is known about the alteration of hypotaurine and its anti-aging effect in the brain of mammals. In our study, we showed that the level of hypotaurine in the rat hippocampus decreases with age. Moreover, this decrease intensifies with the development of signs of AD development: the concentration of hypotaurine in OXYS rats was lower than that in Wistar rats for all ages. Thus, hypotaurine may be considered as one of potential targets for geroprotective effects and AD therapy.

Of even greater interest, in our opinion, are the results of the study of an endogenous isomer of myo-inositol, scyllo-inositol, which was elevated in OXYS rats in all age groups. Earlier, it was shown that the concentration of scyllo-inositol in the brain of older human adults (age 74±3 years) is higher than that in young adults (age 21±1 years), that may be associated with the normal aging [54, 55]. In the brain, scyllo-inositol, like other inositols, acts as an osmoregulator in astrocytes. The increase in brain levels of inositols (scyllo-inositol and myo-inositol) with age appears to reflect changes in astroglial cell metabolism, a chronic inflammatory state, and oxidative stress observed with aging [56, 57]. In our study, we observed an increase in the age of scyllo-inositol and myo-inositol in both rat strains (Supplementary Tables 2 and 3) associated with an increase in the density of astrocytes in the rat hippocampus [58]. Scyllo-inositol is known to be able to stabilize soluble Aβ oligomers and prevent the fibril formation from Aβ oligomers, and it has been clinically tested as a promising therapy for AD [57 , 60]. Noticeably, the in vivo measurements of scyllo-inositol in the brain of patients with mild AD revealed an increase compared to the cognitively healthy older adults. It has been hypothesized that this effect can be attributed to the adaptive response to the accumulation of Aβ in patients with AD [61]. However, in OXYS rats, the increased accumulation of scyllo-inositol occurs earlier than the increased accumulation of Aβ and the development of neurodegenerative processes [62]. Therefore, the increase of scyllo-inositol might be considered as a predictor and a potential biomarker for the development of AD signs in rats OXYS and, probably, in humans.

It is important to note that the largest number of metabolites with the different levels in the rat hippocampus we identified at the age of 20 days. Moreover, the overwhelming majority of metabolites had the reduced levels, and only scyllo-inositol level was increased. These metabolites are mostly amino acids involved in phenylalanine, tyrosine, and tryptophan biosynthesis, aminoacyl-tRNA biosynthesis, alanine, aspartate and glutamate metabolism, and glycerophospholipid metabolism. These changes most likely relate to a delay in the completion of the postnatal brain maturation in OXYS rats as shown earlier [63]. This hypothesis is also supported by the results obtained for the energy metabolism: AEC and the ratio of lactate to pyruvate were higher and the ratio of oxidized to reduced NAD (NAD⁺/NADH) was lower in 20-day-old OXYS rats as compared Wistar rats. The NAD⁺/NADH redox couple is known to be a regulator of cellular energy metabolism, that is, of glycolysis and mitochondrial oxidative phosphorylation [64]. The decrease in the NAD⁺/NADH ratio, the reduced cellular NAD⁺ level, and the increased NADH have been observed during aging [65]. The reduced NAD⁺/NADH ratio observed by us in the OXYS rat, we believe, is associated primarily with mitochondrial dysfunction, the first signs of which were detected as early as at the age of 20 days [66].

Cellular energy status is often associated with the adenylate energy charge index, a key metabolic modulator [67]. AEC is used for the estimation of the energy status and metabolic stress in biological cells. Earlier, a significant AEC increase was detected in the brain of 2-week-old OXYS rats, and a tendency to increase at the age of 20 days [68]. At the same time, at the age of 20 days OXYS rats already show some indications of the mitochondrial dysfunction, including the decreased activity of the respiratory complexes. Signs of AD develop in OXYS rats simultaneously with the increasing dysfunction of mitochondria and the dramatic decrease in the number of mitochondria in the hippocampus and cortex [66, 69], which leads to a significantly lower AEC value in old OXYS rats at a late stage of AD development and indicates a decrease in energy metabolism.

Changes in the levels of a large number of metabolites in the hippocampus of both Wistar and OXYS rats occur from the age of 20 days to 3 months. These metabolites are mostly amino acids involved in metabolism of purine and pyrimidine, amino acids biosynthesis, neurotransmitter metabolism, and phospholipid metabolism. The observed changes are primarily associated with the growth of rats, and although at the age of 20 days the postnatal formation of the brain is completed in rodents, they only reach the sexual maturity by 45 days. At the same time, at the age of 3 months, the period of manifestation of AD signs in OXYS rats, only three metabolites had interstrain differences, which may be associated with the enhanced compensatory processes. By the age of 18 months, we also found a large number of metabolites, the changes of which were the same for both strains. One can suggest that age and AD development have common patterns of metabolic shifts. It is important to note that the level of N-acetylaspartate did not change significantly with age in hippocampus of Wistar rats and changed in OXYS rats; moreover, NAA level was lower in OXYS rats as compared Wistar rats at the age of 20 days. NAA is one of the most abundant brain metabolites and is highly concentrated in neurons [70]; the brain level of NAA correlates with the cognitive function [71]. Patients with AD consistently show a 15–20% reduction in the N-acetylaspartate level in posterior cingulate gyrus gray matter [71]. A reduction of the NAA level with age was reported for non-transgenic C57BL/6 mice and 3xTg-AD mice [31] and 5xFAD transgenic AD mice [16]. In our study, we found a decrease in the NAA level in the hippocampus of OXYS rats, but only until they showed AD signs; at other ages, a significant effect of the strain was not found. Thus, NAA can be considered as one of the possible markers of preclinical stage of AD.

In the CNS, many amino acids act as neurotransmitters or are precursors for the neurotransmitter synthesis. We found that a number of such metabolites were discordant (glycine, GABA, glutamate, glutamine, NAA, valine, choline). Our previous study show, that there is a decline of glutamate/GABA system with age in the hippocampus of Wistar rats. But in OXYS rats, there are no significant changes or compensatory enhancements in this system inside the hippocampus during the development of neurodegenerative processes, which are typical for AD [72]. The analysis of neurophysiology parameters shows that the index of glutamatergic processing (glutamine-glutamate ratio) and the GLX index increase to the age of 3 months in both rat strains; this reflects the glutamatergic activation with age. E/I index (relationship between the excitatory and inhibitory synaptic inputs) and excitotoxic index in OXYS rats is lower than in Wistar rats; this points out the predominance of the inhibition processes in the hippocampus of OXYS rats. This assumption is also confirmed by behavioral data; one of the first manifestations of accelerated aging of the OXYS brain is the formation of a passive type of behavior [34]. The metabolites responsible for E/I, GLX, glutamatergic processing, and excitotoxic indexes (glutamate, glutamine, GABA, and glycine) distinguish normal aging from AD and therefore point to metabolic features characteristic of AD development.

Thus, we can conclude that the comparison of age-related changes in the hippocampal metabolomes of Wistar and OXYS rats reveals both similarities and differences. The observed differences may contribute to the development of AD signs in OXYS rats, and serve as biomarkers of AD. In particular, the accumulation of scyllo-inositol and the reduction of hypotaurine in the brain even at an early age make possible to consider these metabolites as predictors and potential biomarkers for the development of AD signs in OXYS rats and, possibly, in humans.

Footnotes

ACKNOWLEDGMENTS

We thank the Ministry of Science and Higher Education of the RF for the access to scientific equipment. The animals were kindly provided by the Breeding Experimental Animal Laboratory of the ICG SB RAS (Novosibirsk, Russia).

FUNDING

This research was funded by Russian Science Foundation grant number 22-24-20035, and Novosibirsk Region, agreement No p-37.

CONFLICT OF INTEREST

The authors have no conflict of interest to report.

DATA AVAILABILITY

Raw NMR spectra, description of specimens and samples and metabolite concentrations are available in the Animal Metabolite Database repository, Experiment IDs 169 and 267, at https://amdb.online/amdb/experiments/169/ and (Accessed on June 11, 2023). All obtained data are available from the corresponding author upon request.

The supplementary material is available in the electronic version of this article: .

References

Karran

, De Strooper

(2022) The amyloid hypothesis in Alzheimer disease: New insights from new therapeutics. Nat Rev Drug Discov 21, 306–318.

Breijyeh

, Karaman

(2020) Comprehensive review on Alzheimer’s disease: Causes and treatment. Molecules 25, 5789.

Kurkinen

, Fułek

, Beszłej

, Kurpas

, Leszek

(2023) The amyloid cascade hypothesis in Alzheimer’s disease:Should we change our thinking? Biomolecules 13, 453.

López-Sanz

, Serrano

, Maestú

(2018) The role ofmagnetoencephalography in the early stages of Alzheimer’s disease. Front Neurosci 12, 572.

Ackley

, Hayes-Larson

, Brenowitz

, Swinnerton

, Mungas

, Fletcher

, Singh

, Whitmer

, DeCarli

, Maria Glymour

(2021) Amyloid-PET imaging offers small improvements in predictions of future cognitive trajectories. Neuroimage Clin 31, 102713.

Bilgel

, An

, Walker

, Moghekar

, Ashton

, Kac

, Karikari

, Blennow

, Zetterberg

, Jedynak

, Thambisetty

, Ferrucci

, Resnick

(2023) Longitudinal changes in Alzheimer’s-related plasmabiomarkers and brain amyloid. Alzheimers Dement 19, 4335–4345.

Femminella

, Thayanandan

, Calsolaro

, Komici

, Rengo

, Corbi

, Ferrara

(2018) Imaging and molecular mechanisms of Alzheimer’s disease: A review. Int J Mol Sci 19, 3702.

Neff

, Wang

, Vatansever

, Guo

, Ming

, Wang

, Horgusluoglu-Moloch

, Song

, Li

, Castranio

, TCW

, Ho

, Goate

, Fossati

, Noggle

, Gandy

, Ehrlich

, Katsel

, Schadt

, Cai

, Brennand

, Haroutunian

, Zhang

(2021) Molecular subtyping of Alzheimer’s disease using RNA sequencing data reveals novel mechanisms and targets. Sci Adv 7, eabb5398.

Huo

, Yu

, Yang

, Zhu

, Bennett

, Zhao

(2020) Brain and blood metabolome for Alzheimer’s dementia: Findings from a targeted metabolomics analysis. Neurobiol Aging 86, 123–133.

10.

Gonzalez-Riano

, Garcia

, Barbas

(2016) Metabo-lomics studies in brain tissue: A review. J Pharm Biomed Anal 130, 141–168.

11.

Varma

, Oommen

, Varma

, Casanova

, An

, Andrews

, O’Brien

, Pletnikova

, Troncoso

, Toledo

, Baillie

, Arnold

, Kastenmueller

, Nho

, Doraiswamy

, Saykin

, Kaddurah-Daouk

, Legido-Quigley

, Thambisetty

(2018) Brain and blood metabolite signatures of pathology and progression in Alzheimer disease: A targeted metabolomics study. PLOS Med 15, e1002482.

12.

Gomase

, Changbhale

, Patil

, Kale

(2008) Metabolomics. Curr Drug Metab 9, 89–98.

13.

Wilkins

, Trushina

(2018) Application of metabolomics in Alzheimer’s disease. Front Neurol 8, 719.

14.

Ardanaz

, Ramírez

, Solas

(2022) Brain metabolicalterations in Alzheimer’s disease. Int J Mol Sci 23, 3785.

15.

Stojanovic

, Taktek

, Khieu

, Huang

, Jiang

, Rennie

, Chakravarthy

, Costain

, Cuperlovic-Culf

(2021) NMR analysis of the correlation of metabolic changes in blood and cerebrospinal fluid in Alzheimer model male and female mice. PLoS One 16, e0250568.

16.

Füzesi

, Muti

, Berker

, Li

, Sun

, Habbel

, Nowak

, Xie

, Cheng

, Zhang

(2022) High resolution magic angle spinning proton NMR study of Alzheimer’s disease with mouse models. Metabolites 12, 253.

17.

Teruya

, Chen

Y-J

, Kondoh

, Fukuji

, Yanagida

(2021) Whole-blood metabolomics of dementia patients reveal classes of disease-linked metabolites. Proc Natl Acad Sci U S A 118, e2022857118.

18.

Lista

, González-Domínguez

, López-Ortiz

, González-Domínguez

, Menéndez

, Martín-Hernández

, Lucia

, Emanuele

, Centonze

, Imbimbo

, Triaca

, Lionetto

, Simmaco

, Cuperlovic-Culf

, Mill

, Li

, Mapstone

, Santos-Lozano

, Nisticò

(2023) Integrative metabolomics science in Alzheimer’s disease: Relevanceand future perspectives. Ageing Res Rev 89, 101987.

19.

Saoi

, Britz-McKibbin

(2021) New advances in tissue metabolomics: A review. Metabolites 11, 672.

20.

Zhao

, Chen

, Iqbal

, Liu

, Zhang

, Xiao

, Jin

, Yao

, Shen

(2021) Targeted metabolomics study of early pathological features in hippocampus of triple transgenic Alzheimer’s disease male mice. J Neurosci Res 99, 927–946.

21.

Kim

, Shim

, Kim

, Lee

, Chung

, Kowall

, Ryu

, Lee

(2019) Metabolomic analysis identifies alterations of amino acid metabolome signatures in the postmortem brain of Alzheimer’s disease. Exp Neurobiol 28, 376–389.

22.

Horgusluoglu

, Neff

, Song

, Wang

, Arnold

, Krumsiek

, Galindo-Prieto

, Ming

, Nho

, Kastenmüller

, Han

, Baillie

, Zeng

, Andrews

, Cheng

, Hao

, Goate

, Bennett

, Saykin

, Kaddurah-Daouk

, Zhang

(2022) Integrative metabolomics-genomics approach reveals key metabolic pathways and regulators of Alzheimer’s disease. Alzheimers Dement 18, 1260–1278.

23.

Cuperlovic-Culf

, Badhwar

(2020) Recent advances from metabolomics and lipidomics application in Alzheimer’s disease inspiring drug discovery. Expert Opin Drug Discov 15, 319–331.

24.

Qiu

, Cai

, Yao

, Lin

, Xie

, Tang

, Zhang

(2023) Small molecule metabolites: Discovery of biomarkers and therapeutic targets. Signal Transduct Target Ther 8, 132.

25.

Yin

, Harms

, Hankemeier

, Kindt

, de Lange

ECM

(2023) Status of metabolomic measurement for insights in Alzheimer’s disease progression—what is missing? Int J Mol Sci 24, 4960.

26.

Nho

, Kueider-Paisley

, MahmoudianDehkordi

, Arnold

, Risacher

, Louie

, Blach

, Baillie

, Han

, Kastenmüller

, Jia

, Xie

, Ahmad

, Hankemeier

, Duijn

, Trojanowski

, Shaw

, Weiner

, Doraiswamy

, Saykin

, Kaddurah-Daouk

(2019) Alteredbile acid profile in mild cognitive impairment and Alzheimer’sdisease: Relationship to neuroimaging and CSF biomarkers. Alzheimers Dement 15, 232–244.

27.

Zhang

, Hu

, Wang

, Liao

, Zhang

, Kiburg

, Shang

, Bulloch

, Huang

, Zhang

, Tang

, Hu

, Yu

, Yang

, He

, Zhu

(2022) Plasma metabolomic profiles of dementia: A prospective study of 110,655 participants in the UK Biobank. BMC Med 20, 252.

28.

Weng

, Muti

, Zhong

, Kivisäkk

, Hyman

, Arnold

, Cheng

(2022) A nuclear magnetic resonance spectroscopy method in characterization of blood metabolomics for Alzheimer’s disease. Metabolites 12, 181.

29.

Nielsen

, Maltesen

, Havelund

, Færgeman

, Gotfredsen

, Vestergård

, Kristensen

, Pedersen

(2021) Characterising Alzheimer’s disease through integrative NMR- andLC-MS-based metabolomics. Metab Open 12, 100125.

30.

, Zheng

, Xia

, Zhao

, Hu

, Bai

, Yan

, Gao

(2017) Early effect of amyloid β-peptide on hippocampal and serum metabolism in rats studied by an integrated method of NMR-based metabolomics and ANOVA-simultaneous component analysis. Biomed Res Int 2017, 3262495.

31.

Dong

, Brewer

(2019) Global metabolic shifts in age and Alzheimer’s disease mouse brains pivot at NAD+/NADH redox sites. J Alzheimers Dis 71, 119–140.

32.

Kolosova

, Kozhevnikova

, Muraleva

, Rudnitskaya

, Rumyantseva

, Stefanova

, Telegina

, Tyumentsev

, Fursova

(2022) SkQ1 as a tool for controlling accelerated senescence program: Experiments with OXYS rats. Biochem 87, 1552–1562.

33.

Kolosova

, Stefanova

, Korbolina

, Fursova

, Kozhevnikova

(2014) Senescence-accelerated OXYS rats: A genetic model of premature aging and age-related diseases. Adv Gerontol 4, 294–298.

34.

Stefanova

, Kozhevnikova

, Vitovtov

, Maksimova

, Logvinov

, Rudnitskaya

, Korbolina

, Muraleva

, Kolosova

(2014) Senescence-accelerated OXYS rats: A model of age-related cognitive decline with relevance to abnormalities in Alzheimer disease. Cell Cycle 13, 898–909.

35.

Rudnitskaya

, Burnyasheva

, Kozlova

, Peunov

, Kolosova

, Stefanova

(2022) Changes in glial support of the hippocampus during the development of an Alzheimer’s disease-like pathology and their correction by mitochondria-targeted antioxidant SkQ1. Int J Mol Sci 23, 1134.

36.

Glinskikh

, Snytnikova

, Zelentsova

, Borisova

, Tsentalovich

, Akulov

(2021) The effect of blood contained in the samples on the metabolomic profile of mouse brain tissue: A study by NMR spectroscopy. Molecules 26, 3096.

37.

Yanshole

, Snytnikova

, Kiryutin

, Yanshole

, Sagdeev

, Tsentalovich

(2014) Metabolomics of the rat lens: A combined LC-MS and NMR study. Exp Eye Res 125, 71–78.

38.

Snytnikova

, Tsentalovich

, Sagdeev

, Kolosova

, Kozhevnikova

(2022) Quantitative metabolomic analysis of changes in the rat blood serum during autophagy modulation: A focus on accelerated senescence. Int J Mol Sci 23, 12720.

39.

Wishart

, Guo

, Oler

, Wang

, Anjum

, Peters

, Dizon

, Sayeeda

, Tian

, Lee

, Berjanskii

, Mah

, Yamamoto

, Jovel

, Torres-Calzada

, Hiebert-Giesbrecht

, Lui

, Varshavi

, Allen

, Arndt

, Khetarpal

, Sivakumaran

, Harford

, Sanford

, Yee

, Cao

, Budinski

, Liigand

, Zhang

, Zheng

, Mandal

, Karu

, Dambrova

, Schiöth

, Greiner

, Gautam

(2022) HMDB 5.0: The Human Metabolome Database for 2022. Nucleic Acids Res 50, D622–D631.

40.

Human Metabolome Database, https://hmdb.ca, Last updated January, 2022, Accessed on May 12, 2023.

41.

Snytnikova

, Yanshole

, Iskakov

, Yanshole

, Chernykh

, Stepakov

, Novoselov

, Tsentalovich

(2017) Quantitative metabolomic analysis of the human cornea and aqueous humor. Metabolomics 13, 152.

42.

Snytnikova

, Khlichkina

, Yanshole

, Iskakov

, Egorova

, Stepakov

, Novoselov

, Tsentalovich

(2017) Metabolomics of the human aqueous humor. Metabolomics 13, 5.

43.

Yanshole

, Yanshole

, Snytnikova

, Tsentalovich

(2019) Quantitative metabolomic analysis of changes in the lens and aqueous humor under development of age-related nuclear cataract. Metabolomics 15, 29.

44.

Pang

, Chong

, Zhou

, de Lima Morais

, Chang

, Barrette

, Gauthier

, Jacques

P-É

, Li

, Xia

(2021) MetaboAnalyst 5.0:Narrowing the gap between raw spectra and functional insights. Nucleic Acids Res 49, W388–W396.

45.

MetaboAnalyst 5.0, https://metaboanalyst.ca/docs/About.xhtml, Last updated on June 18, 2023, Accessed on June 18, 2023.

46.

Lazaridis

, Andrews

(2014) Brain tissue oxygenation, lactate-pyruvate ratio, and cerebrovascular pressure reactivity monitoring in severe traumatic brain injury: Systematic review and viewpoint. Neurocrit Care 21, 345–355.

47.

Estrada-Rojo

, Carmona-Aparicio

, Arriaga-Avila

, Coballase-Urrutia

, Pérez-Arredondo

, Guevara-Guzmán

, Cárdenas-Rodríguez

, Navarro

(2021) Effects oftime-of-day on the concentration of defined excitatory andinhibitory amino acids in the cerebrospinal fluid of rats: Amicrodialysis study. Amino Acids 53, 1597–1607.

48.

Franco-Pérez

, Montes

, Sánchez-Hernández

, Ballesteros-Zebadúa

(2020) Whole-brain irradiationdifferentially modifies neurotransmitters levels and receptors inthe hypothalamus and the prefrontal cortex.. Radiat Oncol 15, 269.

49.

Baseggio Conrado

, D’Angelantonio

, D’Erme

, Pecci

, Fontana

(2017) The interaction of hypotaurine and other sulfinates with reactive oxygen and nitrogen species: A survey of reaction mechanisms. Adv Exp Med Biol 975 Pt 1, 573–583.

50.

, Kohno

, Georgiev

, Ikoma

, Ishii

, Petrenko

, Baba

(2008) Taurine activates glycine and γ-aminobutyric acid A receptors in rat substantia gelatinosa neurons. Neuroreport 19, 333–337.

51.

Sakuragawa

, Hishiki

, Ueno

, Ikeda

, Soga

, Yachie-Kinoshita

, Kajimura

, Suematsu

(2010) Hypotaurine is an energy-saving hepatoprotective compound against ischemia-reperfusion injury of the rat liver. J Clin Biochem Nutr 46, 126–134.

52.

Liao

W-T

, Liu

, Zhou

S-M

, Xu

, Gao

Y-Q

, Liu

W-Y

(2019) UHPLC-QTOFMS-based metabolomic analysis of the hippocampus in hypoxia preconditioned mouse. Front Physiol 9, 1950.

53.

Wan

Q-L

, Fu

, Meng

, Luo

, Dai

, Yang

, Wang

, Zhou

(2020) Hypotaurine promotes longevity and stress tolerance via the stress response factors DAF-16/FOXO and SKN-1/NRF2 in Caenorhabditis elegans. Food Funct 11, 347–357.

54.

Deelchand

, Eberly

, McCarten

, Hemmy

, Auerbach

, Marjańska

(2023) Scyllo-inositol: Transverse relaxation timeconstant at 3 T and concentration changes associated with aging andalcohol use. NMR Biomed 36, e4929.

55.

Kaiser

, Schuff

, Cashdollar

, Weiner

(2005) Hypotaurine promotes longevity and stress tolerance via the stress response factors DAF-16/FOXO and SKN-1/NRF2 in Caenorhabditis elegans.. NMR Biomed 18, 51–55

56.

Lind

, Boraxbekk

C-J

, Petersen

, Paulson

, Siebner

, Marsman

(2020) Regional myo-inositol, creatine, and choline levels are higher at older age and scale negatively with visuospatial working memory: A cross-sectional proton MR spectroscopy study at 7 Tesla on normal cognitive ageing. J Neurosci 40, 8149–8159.

57.

López-Gambero

, Sanjuan

, Serrano-Castro

, Suárez

, Rodríguez de Fonseca

(2020) The biomedical uses of inositols:A nutraceutical approach to metabolic dysfunction in aging andneurodegenerative diseases. Biomedicines 8, 295.

58.

Rudnitskaya

, Kozlova

, Burnyasheva

, Tarasova

, Pankova

, Starostina

, Stefanova

, Kolosova

(2020) Features of postnatal hippocampal development in a rat model of sporadic Alzheimer’s disease. Front Neurosci 14, 533.

59.

, Thomason

, McLaurin

(2012) scyllo-inositol, preclinical, and clinical data for Alzheimer’s disease. Adv Pharmacol 64, 177–212.

60.

Jin

, Selkoe

(2015) Systematic analysis of time-dependent neural effects of soluble amyloid β oligomers in culture and in vivo: Prevention by scyllo-inositol. Neurobiol Dis 82, 152–163.

61.

Griffith

, den Hollander

, Stewart

, Evanochko

, Buchthal

, Harrell

, Zamrini

, Brockington

, Marson

(2007) Elevated brainscyllo-inositol concentrations in patients with Alzheimer’s disease. NMR Biomed 20, 709–716.

62.

Stefanova

, Muraleva

, Korbolina

, Kiseleva

, Maksimova

, Kolosova

(2015) Amyloid accumulation is a late event in sporadic Alzheimer’s disease-like pathology in nontransgenic rats. Oncotarget 6, 1396–1413.

63.

Kozlova

, Rudnitskaya

, Burnyasheva

, Stefanova

, Peunov

, Kolosova

(2022) Delayed formation of neonatal reflexes and of locomotor skills is associated with poor maternal behavior in OXYS rats prone to Alzheimer’s disease-like pathology. Biomedicines 10, 2910.

64.

Xiao

, Wang

R-S

, Handy

, Loscalzo

(2018) NAD(H) and NADP(H) redox couples and cellular energy metabolism. Antioxid Redox Signal 28, 251–272.

65.

Yoshino

, Baur

, Imai

(2018) NAD+Intermediates: The biology and therapeutic potential of NMN and NR. Cell Metab 27, 513–528.

66.

Tyumentsev

, Stefanova

, Muraleva

, Rumyantseva

, Kiseleva

, Vavilin

, Kolosova

(2018) Mitochondrial dysfunction as a predictor and driver of Alzheimer’s disease-like pathology in OXYS rats. J Alzheimers Dis 63, 1075–1088.

67.

Atkinson

, Walton

(1967) Adenosine triphosphate conservation in metabolic regulation. J Biol Chem 242, 3239–3241.

68.

Sergeeva

, Bagryanskaya

, Korbolina

, Kolosova

(2006) Development of behavioural dysfunctions in accelerated-senescence OXYS rats is associated with early postnatal alterations in brain phosphate metabolism. Exp Gerontol 41, 141–150.

69.

Stefanova

, Ershov

, Maksimova

, Muraleva

, Tyumentsev

, Kolosova

(2019) The rat prefrontal-cortex transcriptome: Effects of aging and sporadic Alzheimer’s disease-like pathology. J Gerontol A Biol Sci Med Sci 74, 33–43.

70.

Moffett

, Arun

, Ariyannur

, Namboodiri

AMA

(2013) N-Acetylaspartate reductions in brain injury: Impact on post-injury neuroenergetics, lipid synthesis, and protein acetylation. Front Neuroenergetics 5, 11.

71.

Moffett

, Ross

, Arun

, Madhavarao

, Namboodiri

(2007) N-Acetylaspartate in the CNS: From neurodiagnostics to neurobiology. Prog Neurobiol 81, 89–131.

72.

Burnyasheva

, Stefanova

, Kolosova

, Telegina

(2023) The hippocampal glutamate/GABA system is affected by aging but not by an Alzheimer’s disease-like pathology in rats. Research Square, doi: 10.21203/rs.3.rs-2676636/v1.

Quantitative Metabolomic Analysis of the Rat Hippocampus: Effects of Age and of the Development of Alzheimer’s Disease-Like Pathology

Abstract

Background:

Objective:

Methods:

Results:

Conclusions:

Keywords

INTRODUCTION

MATERIALS AND METHODS

Chemicals

Animals

Hippocampal tissue collection

Metabolite extraction and NMR spectroscopy

Identification and quantification of metabolites

Analysis of metabolite concentrations

Statistical analysis

RESULTS

Quantitative metabolomic profiling of hippocampus

Age-related changes of metabolomic profile

Changes of metabolomic profile associated with the development of AD

Investigation of energy metabolism, redox state of glutathione, and ratio NAD+/NADH in the hippocampus of OXYS and Wistar rats at different ages

Evaluation of the E/I, GLX, glutamatergic processing, and excitotoxic indexes in the hippocampus of OXYS and Wistar rats at the different ages

DISCUSSION

Footnotes

ACKNOWLEDGMENTS

FUNDING

CONFLICT OF INTEREST

DATA AVAILABILITY

References

Investigation of energy metabolism, redox state of glutathione, and ratio NAD⁺/NADH in the hippocampus of OXYS and Wistar rats at different ages