• biomarkers;
  • GABA ;
  • glutamate;
  • metabolism;
  • N-acetylaspartate;
  • transgenic rats


  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

We investigated metabolite levels during the progression of pathology in McGill-R-Thy1-APP rats, a transgenic animal model of Alzheimer's disease, and in healthy age-matched controls. Rats were subjected to in vivo 1H magnetic resonance spectroscopy (MRS) of the dorsal hippocampus at age 3, 9 and 12 months and of frontal cortex at 9 and 12 months. At 3 months, a stage in which only Aβ oligomers are present, lower glutamate, myo-inositol and total choline content were apparent in McGill-R-Thy1-APP rats. At age 9 months, lower levels of glutamate, GABA, N-acetylaspartate and total choline and elevated myo-inositol and taurine were found in dorsal hippocampus, whereas lower levels of glutamate, GABA, glutamine and N-acetylaspartate were found in frontal cortex. At age 12 months, only the taurine level was significantly different in dorsal hippocampus, whereas taurine, myo-inositol, N-acetylaspartate and total creatine levels were significantly higher in frontal cortex. McGill-R-Thy1-APP rats did not show the same changes in metabolite levels with age as displayed in the controls, and overall, prominent and complex metabolite differences were evident in this transgenic rat model of Alzheimer's disease. The findings also demonstrate that in vivo 1H MRS is a powerful tool to investigate disease-related metabolite changes in the brain.

Abbreviations used

Alzheimer's disease


amyloid precursor protein

amyloid beta


cerebral metabolic rate of glucose


Cramer-Rao lower bounds


dorsal hippocampus


frontal cortex


glutamine synthetase




magnetic resonance imaging


magnetic resonance spectroscopy




point resolved spectroscopy




rapid acquisition with refocused echoes




total choline


total creatine


echo time


repetition time


volume of interest

Alzheimer's disease (AD) is a progressive neurodegenerative disease and the most common cause of dementia in the elderly. It is characterized by accumulation of extracellular plaques containing aggregated amyloid β (Aβ) peptides and intracellular neurofibrillary tangles composed of hyperphosphorylated tau proteins. In addition, regional loss of neurons and synapses, progressive cognitive decline and regional hypometabolism occurs (Mosconi 2005; Serrano-Pozo et al. 2011). Emerging evidence also suggest that, intraneuronal Aβ oligomers may contribute substantially to AD disease progression (Haass and Selkoe 2007).

There is no definite biomarker for the diagnosis of AD, which motivates the search for neuroimaging markers that may facilitate early detection of the disease. Using 1H magnetic resonance spectroscopy (MRS), the regional concentration of low-molecular-weight metabolites can be measured non-invasively and provides insight into neurochemical processes of normal and pathological conditions in vivo. Performing 1H MRS of patients with AD has revealed a consistent pattern of decreased levels of N-acetylaspartate (NAA) or NAA/total creatine (tCr) and increased myo-inositol (mIns) or mIns/tCr (Kantarci et al. 2003; Shiino et al. 2012). NAA is synthesised in neurons (Wiame et al. 2010) and the level decreases with neuronal loss or reversible neuronal or mitochondrial dysfunction (Gasparovic et al. 2001; Narayanan et al. 2001). This has enabled the widespread use of NAA as a marker of neuronal density, health and function (Moffett et al. 2007). In contrast, mIns is commonly considered to be a glial marker, and its increased content in humans and animal models is associated with elevated immunoreactivity of GFAP (Bitsch et al. 1999; Chen et al. 2009; Yang et al. 2011), which is rapidly synthesised during astrogliosis (Eng et al. 2000). Furthermore, less consistent alterations of glutamate or glutamate/tCr (Fayed et al. 2011; Rupsingh et al. 2011), total choline (tCho) or tCho/tCr (Jessen et al. 2000; Kantarci et al. 2003) and tCr (Jessen et al. 2009) have been reported in AD.

The study of transgenic animals is instrumental to achieve a better understanding of early aspects of the disease. A range of transgenic mouse models of AD have been investigated with in vivo 1H MRS, but to our knowledge, no longitudinal monitoring of metabolite content has been performed in vivo in a transgenic rat model of AD. This study was thus carried out to non-invasively investigate cerebral metabolite levels in a transgenic rat model of AD prior to and after the appearance of Aβ plaques, and to longitudinally characterize metabolite concentrations as pathology progressed. We employed the McGill-R-Thy1-APP model, in which rats express the human amyloid precursor protein (APP) carrying the double Swedish mutation and the Indiana mutation driven by the thymocyte antigen promoter (Thy 1.2). The latter restricts the expression of the transgene to neurons. Homozygous rats develop intraneuronal Aβ oligomers within 1 week after birth, and display cognitive symptoms within 3 months. Extracellular amyloid plaques occur in the hippocampal formation at age 6 months, appear in cortical areas around age 13 months and spread to large parts of the cerebrum within 20 months (Leon et al. 2010). Here, we analysed a volume comprising the dorsal hippocampus and subiculum at ages 3, 9 and 12 months, whereas an additional volume from the frontal cortex was analysed at ages 9 and 12 months.


  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References


Seventeen McGill-R-Thy1-APP rats (10 females and 7 males) were scanned at age 3 months, whereas 13 (4 female and 9 male) were scanned at age 9 and 12 months. McGill-R-Thy1-APP rats are originally generated using Wistar rats, and express the 751 isoform of the human APP carrying the Swedish and Indiana mutations under transcriptional control of the murine Thy1.2 promoter (Leon et al. 2010). Eleven Wistar rats (6 female and 5 male) were scanned at age 3 months, nine were scanned at age 9 months (6 female, 3 male), and eight were scanned at 12 months of age (6 female, 2 male) (HanTac:WH/Wistar Hannover GALAS rats from Taconic, Ejby, Denmark). These rats were used as controls as non-transgenic littermates were not available. The same McGill-R-Thy1-APP rats were scanned at ages 9 and 12 months, whereas the rats used in the 3 months group were born later and thus scanned at a later time point. The control rats were the same throughout the study, with the exception of the later addition of two extra male rats at age 3 months. One male control rat died between age 9 and 12 months. The mix of repeated and independent measures was accounted for in the statistical analysis.

The transgenic rats used in the experiment were homozygotes and bred at the animal facilities of the Norwegian University of Science and Technology from two original pairs obtained from McGill University (Leon et al. 2010). Throughout the study period, animals had free access to water and food and were kept under standard laboratory conditions (room temperature 19–22°C, 50–60% humidity, 12-h light/dark cycle). The experimental procedures were approved by the Norwegian Animal Research Authority, and the animals were treated in compliance with the European Convention (ETS 123 of 1986).


Briefly, DNA was isolated from two ear samples from each rat using the High Pure PCR Template Preparation Kit from Roche (Cat. No. 11 796 828 001; Basel, Switzerland). A quantitative PCR (qPCR, also called real-time PCR) was performed using a StepOnePlus real-time PCR system (Life Technologies Ltd, Paisley, UK). For the qPCR reaction RT2 qPCR Primer Assays from Qiagen (Hilden, Germany) were used: GAPDH from rat (Cat. No. PPR06557A) was used as the normalization gene, and APP from human (Cat. No. PPH05947A) was used to detect the transgene. FastStart Universal SYBR Green Master (Rox) from Roche (Cat. No. 04913850001) was used as a hot start reaction mix. After the qPCR ΔΔCT values were calculated for all samples. A known homozygous sample was used as reference.

In vivo magnetic resonance imaging and spectroscopy protocols

1H MRS and MRI were performed using a 7 T Bruker BioSpec 70/20 magnet (Bruker BioSpin, Ettlingen, Germany) with water-cooled BGA-12 (400 mT/m) gradients. A 72-mm volume resonator was used for RF transmission and an actively decoupled quadrature rat head surface coil (20-mm) was used for RF reception. The animals were anaesthetized with 2–4% isoflurane (Baxter, Deerfield, IL, USA) in 30% O2 and 70% N2 during scanning. The rats lay in prone position on a bed heated by circulating water (Bruker BioSpin, Ettlingen, Germany). The head of every animal was fixed in the same position with inbuilt tooth bar and nose mask. Temperature and respiration were monitored using an MR compatible small animal monitoring and gating system (Small Animal Instruments Inc, Stony Brook, NY, USA), with temperature maintained at 36°C (rectal thermometer) and respiration frequency at approximately 60–70 breaths per minute.

Coronal images of the brain were obtained using a rapid acquisition with refocused echoes (RARE) sequence with the following parameters; RARE factor 8, TR = 3500 ms, TE = 10 ms, effective TE = 40 ms and number of averages = 6. A total of 30 slices were acquired with field of view = 30 × 30.5 mm and a matrix size of 192 × 224. Slice thickness was 0.6 mm and distance between slices was 0.6 mm. Total MRI scan time was 9 min and 48 s.

For the 1H MRS acquisitions a single voxel point resolved spectroscopy (PRESS) sequence (bandwidth = 4 kHz, 4096 complex points, TR = 5000 msec, TE = 12 msec, number of averages = 512 for the 3 months old rats and 400 for rats at age 9 and 12 months) was used in combination with outer volume suppression and VAPOR water suppression. Volumes of interest (VOIs) were the dorsal hippocampus and the frontal cortex. The size of the VOIs (20–24 μL) was adjusted to the anatomical structure of selected brain regions, and care was taken to place the VOI as identically as possible in each rat and to avoid ventricles (Fig. 1). First- and second-order shim values were optimized using FASTMAP (Gruetter 1993), and eddy current compensation and static magnetic field drift correction were applied during the acquisition. One scan without water suppression was acquired for each VOI for quantification purposes. In addition, T2 water relaxation was measured in both VOIs of controls and McGill-R-Thy1-APP rats (total = 8 and 6 respectively) using a PRESS sequence without water suppression and with varying echo times (TE = 12, 20, 40, 60, 80, 100, 120, 144, 288, 350, 500, 1000 and 1500 ms). Mono-exponential fitting of the T2 curves provided a correction factor for metabolite quantification. Total acquisition time for the spectra was typically 30 (ns = 400) or 40 (ns = 512) min for each VOI.


Figure 1. The centred positions of the VOIs illustrated in coronal images obtained in a typical control rat brain at age 9 months together with spectra from representative control and McGill-R-Thy1-APP rats at age 3, 9 and 12 months. (a) Dorsal hippocampus, in which the VOI was always placed in the same hemisphere and contained both grey matter mainly from dorsal parts of hippocampus and subiculum with minor contributions from retrosplenial, visual and parietal cortices in addition to some contributions from white matter such as cingulum, corpus callosum and the dorsal hippocampal commissure. (b) Frontal cortex. VOI sizes: 20–24 μL. Line broadening 3 Hz. For more details see Methods.

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Metabolite concentrations were obtained using LCModel (Stephen Provencher Inc., Oakville, Ontario, Canada) (Provencher 1993). The unsuppressed water signal recorded from each VOI was used as an internal reference for water scaling in LCModel, assuming the concentration of water to be 43.3 M in grey matter (tissue contains approximately 80% water) and 35.88 M in white matter (tissue contains approximately 65% water) (Tkac et al. 2003; De Souza and Dobbing 1971). The frontal cortex VOI consisted exclusively of grey matter, whereas the dorsal hippocampus VOI consisted of both grey and white matter (80/20%). T2 relaxation of water was similar in both VOIs in control and McGill-R-Thy1-APP rats at all ages investigated, thus a mean value was calculated (46.7 ± 2.3 ms), and the same correction factor was applied to both groups for both brain areas. T1 or T2 for the metabolites were not measured or corrected for, as precise determination of the relaxation values would be a time-consuming process compromising the experiment. Literature values were not used as these are scarce for rat brain investigated at 7 T. However, the long TR and short TE applied should yield negligible relaxation effects on metabolite concentrations.

Statistical analysis

Metabolite values that had Cramer-Rao lower bounds (CRLBs) ≥ 20% were excluded from further analysis. CRLBs (or % SD) are estimates of the uncertainty of the concentrations as calculated by LCModel and are expressed in percent of the estimated concentrations. Moreover, spectra with a signal to noise ratio ≤ 4 (S/N; calculated automatically by LCModel) were removed from the data set (Jansen et al. 2006). This led to exclusion of one McGill-R-Thy1-APP rat at age 3 months, one McGill-R-Thy1-APP rat in both VOIs at 9 months and 3 McGill-R-Thy1-APP rats in the dorsal hippocampus at age 12 months.

Using IBM SPSS Statistics version 19 (Armonk, NY, USA) software, results were analysed using mixed linear model with the fixed effect variable defined by genotype and age, and rat ID as random effect. Post hoc linear contrasts were used to compare the average concentration of each metabolite both between McGill-R-Thy1-APP and control rats and across age for each genotype. Differences were considered significant when < 0.05. The data from dorsal hippocampus and frontal cortex were analysed separately.


  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

LCModel provided modelled spectra with an average S/N ranging from 9 to 12 after the application of the above mentioned exclusion criteria (for a typical spectrum and the corresponding LCModel fit, see Fig. 2). The average linewidth at half height ranged from 0.033 to 0.039 ppm, well below the 0.1 ppm linewidth or less considered essential for in vivo 1H MRS spectra (Forster et al. 2012). The following metabolites could be reliably quantified with LCModel: glutamate, GABA, glutamine, NAA, mIns, taurine, tCr (creatine + phosphocreatine), and tCho [mainly glycerophosphocholine (GPC) and phosphocholine (PCh) with minor contributions from free choline and acetylcholine (Klein 2000)]. The majority of the metabolites had average CRLBs lower than 10%, with the exception of GABA (CRLB 12–15%) and glutamine (CRLB 8–12%). The estimated concentrations in control rats were for the most part in good agreement with those reported for the same rat brain regions by others (Kim et al. 2011, 2012).


Figure 2. A typical spectrum from the dorsal hippocampus of a 3 months old control rat shown with (a) the LCModel fit; (b) Peak assignments. Abbreviations: mIns: myo-inositol, tCr: total creatine, tCho: total choline, Gln: glutamine, Glu: glutamate, NAA: N-acetylaspartate, MM: macromolecules. Localization sequence; PRESS, TE = 12 ms, TR = 5 s, NS = 512, VOI = 24 μL (line broadening 3 Hz).

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Differences in metabolite levels between McGill-R-Thy1-APP rats and controls were found in dorsal hippocampus and frontal cortex at all ages investigated. At the pre-plaque stage at age 3 months, lower concentrations of glutamate (−14%, < 0.001), mIns (−10%, = 0.037) and tCho (−17%, = 0.002) were evident in dorsal hippocampus compared with controls (Fig. 3). More widespread differences in metabolite levels were found in the same brain area at age 9 months, and several metabolite levels changed between age 3 and 9 months. In McGill-R-Thy1-APP rats, there was an increase in the level of glutamate (9%, = 0.028), mIns (16%, = 0.002), tCr (10%, = 0.010) and tCho (20%, = 0.001). In controls, the levels of GABA, NAA and tCho increased (16, 7 and 14% with p values 0.016, 0.027 and 0.001 respectively). Despite the rise in glutamate content, the level was still significantly lower than in controls (−10%, = 0.005). GABA, NAA and tCho levels were also lower than in controls (−11, −8 and −12%, respectively, with the p-values 0.049, 0.035 and 0.021), whereas taurine and mIns levels were higher in McGill-R-Thy1-APP rats at this age (21 and 14% with p-values 0.003 and 0.015 respectively). Between ages 9 and 12, metabolite levels in McGill-R-Thy1-APP rats remained stable, whereas there was a decrease in the levels of glutamate (−8%, = 0.035) and tCho (−6%, = 0.044) in controls. At 12 months of age, the taurine concentration was higher in McGill-R-Thy1-APP rats (16%, = 0.030) compared with controls, but no other significant differences were found between the groups.


Figure 3. Concentrations (mM) of metabolites in the dorsal hippocampus of control (white circles) and McGill-R-Thy1-APP (AD, black triangles) rats at age 3, 9 and 12 months obtained with in vivo 1H MRS (for details see Methods section). Values are averages ± SEMs. Statistical analysis was performed using mixed linear model and post hoc linear contrasts. Significant differences between controls and McGill-R-Thy1-APP rats at each age are indicated with *< 0.05 and **< 0.01, whereas dotted lines indicate significant (< 0.05) change with age within the group. The only significant difference between 3 and 12 months old rats was found for total choline in McGill-R-Thy1-APP rats (= 0.002), which was higher at 12 months. In addition, an increase in mIns was close to significance in McGill-R-Thy1-APP rats (= 0.057).

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In the frontal cortex at age 9 months, lower glutamate (−14%, ≤ 0.001), glutamine (−13%, = 0.013), GABA (−10%, = 0.046) and NAA (−7%, = 0.018) were apparent in the McGill-R-Thy1-APP group compared with controls (Fig. 4). From age 9 to 12 months, the concentrations of glutamine and tCr increased significantly in McGill-R-Thy1-APP rats (9 and 4% with p-values 0.007 and 0.017 respectively), while glutamate, glutamine, taurine, NAA and tCr levels decreased significantly in the control group (−12, −14, −21, −10 and −8% with p-values 0.001, 0.025, < 0.001, < 0.001 and < 0.001 respectively). As a consequence, increased levels of taurine (25%, < 0.001), mIns (18%, = 0.014), NAA (7%, = 0.016) and tCr (10%, = 0.003) levels were apparent in McGill-R-Thy1-APP rats at age 12 months compared with controls.


Figure 4. Concentrations (mM) of metabolites in the frontal cortex of control (white circles) and McGill-R-Thy1-APP (AD, black triangles) rats at age 9 and 12 months obtained with in vivo 1H MRS (for details see Methods section). Values are averages ± SEMs. Statistical analysis was performed using mixed linear model and post hoc linear contrasts. Significant differences between controls and McGill-R-Thy1-APP rats at each age are indicated with *< 0.05 and **< 0.01, whereas dotted lines indicate significant (< 0.05) change with age within the group.

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In general, metabolite levels in the McGill-R-Thy1-APP rats did not follow the variations observed in controls in this study. No decreases in metabolite levels occurred with age in McGill-R-Thy1-APP rats.


  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

We explored metabolite levels in the dorsal hippocampus and frontal cortex of the McGill-R-Thy1-APP rat model of AD during the progression of Aβ pathology from early adult age up to 12 months. Prominent differences were evident in McGill-R-Thy1-APP rats compared with age-matched controls already at age 3 months, and the level of several metabolites changed with age in both control and McGill-R-Thy1-APP rats.

Changes observed in healthy animals

Longitudinal changes in brain metabolite content have been analysed in developing rat brain (Florian et al. 1996; Tkac et al. 2003). However, few have investigated this in adult rats with time. We report variations in metabolite levels in healthy Wistar rats from early adulthood (3 months) to middle age (12 months). In dorsal hippocampus, the level of GABA, NAA, and tCho increased between age 3 and 9 months and the level of glutamate and tCho decreased between 9 and 12 months. In the frontal cortex, the level of glutamate, glutamine, NAA, taurine and tCr decreased. Thus, we show that hippocampus and frontal cortex may have fluctuating metabolite levels with time, as previously demonstrated using brain extracts (Zhang et al. 2009; Paban et al. 2010).

Glutamate, GABA and NAA

A decrease in glutamate, the major excitatory neurotransmitter in the brain, may reflect neuronal loss, metabolic deficits in biochemical pathways that generate or utilize glutamate or a combination of these scenarios. As the level of NAA was unaltered in the McGill-R-Thy1-APP rats compared with controls at age 3 months, the observed decrease in glutamate level at this age cannot be attributed to loss of glutamatergic neurons or a general overall dysfunction of neuronal mitochondria. Decreased hippocampal glutamate has previously been reported in patients with AD (Rupsingh et al. 2011) and in APP-PS1 mice (Marjanska et al. 2005; Oberg et al. 2008). Glutamate is also present in GABAergic neurons and astrocytes (Ottersen et al. 1992), but as glutamine and GABA levels were unaltered at this age, the decrease in glutamate indicates selective dyshomeostasis of glutamate pools in glutamatergic neurons. This decrease precedes accumulation of Aβ plaques, and coincides with impaired memory and learning ability in this AD model (Leon et al. 2010). The decrease in glutamate content could also be caused by decreased hippocampal glucose metabolism, in line with the reduced CMRglc reported in patients with AD and mild cognitive impairment (Mosconi et al. 2005; Li et al. 2008).

The concentrations of glutamate, GABA and NAA were all lower in dorsal hippocampus and frontal cortex compared with controls at age 9 months. The decrease in NAA levels is consistent with numerous reports of decreased NAA in various brain regions in AD patients (Block et al. 2002; Wang et al. 2009; Fayed et al. 2011; Foy et al. 2011; Rupsingh et al. 2011). Post mortem studies of brain tissue from AD patients also indicate that cortical GABA concentration is decreased compared with controls (see (Lanctot et al. 2004) for a review). Although GABA levels in McGill-R-Thy1-APP rats remained stable during the study period, the difference in GABA levels at age 9 months in both brain areas might be of pathophysiological importance as GABA is the major inhibitory neurotransmitter of the brain and regulates neuronal excitability. This is also in accordance with a recent study that demonstrated a lower GABA concentration in the dorsal hippocampus of 5xFAD mice at age 40 and 44 weeks (Mlynarik et al. 2012). Thus, both glutamatergic and GABAergic neurons were affected in both brain areas analysed in McGill-R-Thy1-APP rats at 9 months of age. It is not possible to resolve whether the lower levels of glutamate and GABA in both brain areas are because of loss of neurons, reduced CMRglc or decreased neuronal mitochondrial function, but as the levels of NAA did not decrease with age in McGill-R-Thy1-APP rats, it is unlikely that neurodegeneration occurred during the study period. Neurodegeneration has not yet been assessed in detail in the McGill-R-Thy1-APP model, but presence of cholinergic and glutamatergic but not GABAergic dystrophic neurites surrounding sites of plaque deposition has been demonstrated in 20 months old rats (Leon et al. 2010). The differences in glutamate, GABA and NAA mentioned above disappeared at 12 months, primarily because of changes of the levels in the control rats. This could suggest that metabolite changes observed very early in AD also to a certain extent appear in healthy animals with age. Similar fluctuations have previously been demonstrated in AD mice (von Kienlin et al. 2005; Oberg et al. 2008; Forster et al. 2012). In hippocampus/thalamus of TASTPM mice, glutamate levels were lower than in controls at all ages except 13 months (Forster et al. 2012). In the dorsal hippocampus of 5xFAD mice, the glutamate level was lower at age 36 weeks, but the difference was no longer observed at ages 40 and 44 weeks (Mlynarik et al. 2012). The validity of our results at age 12 months in dorsal hippocampus is supported by the fact that the same differences or lack thereof were obtained when comparing the groups using ratios of metabolite levels in relation to tCr (results not shown). It should, however, be mentioned that it cannot be excluded that potential influences of gender differences in metabolite concentrations might have influenced the results in this study.

Myo-inositol, glutamine and taurine

In vivo quantification of glutamine in humans with 1H MRS is difficult because of overlap with the glutamate resonances at low magnetic field strengths, but both post mortem glutamine synthetase (GS) protein level (Le Prince et al. 1995) and activity (Hensley et al. 1995) have been shown to be lower in AD tissue compared with controls. GS is localized in astrocytes (Norenberg 1979; Norenberg and Martinez-Hernandez 1979) and catalyses glutamine synthesis from glutamate. Our results demonstrate that glutamine metabolism in dorsal hippocampus remained unaltered throughout the study period, and further show that amyloid pathology has an effect on astrocytes in the frontal cortex in the McGill-R-Thy1-APP model of AD because glutamine levels were lower than in controls at 9 months. Moreover, the concentration increased in frontal cortex of McGill-R-Thy1-APP rats from 9 to 12 months, and thus developed in the opposite direction of controls with age.

The lower mIns level in dorsal hippocampus at age 3 months is difficult to interpret, but lower mIns/tCr was also found in the hippocampus of APP/PS1 mice at age 2.5 months (Oberg et al. 2008) but increased by age 3 months (Chen et al. 2009). Others, however, found an increase in later stages after the appearance of amyloid plaques (Marjanska et al. 2005). As suggested by Mlynarik et al. (2012), this could mean that an increase in mIns is only detected when the pathology is severe, and that the early pre-plaque increase in mIns reported by Chen et al. (2009) might indicate that they have a better animal model or could be a result of subtle effects of transgenes during embryogenesis. Our results support that hippocampal mIns increased after the appearance of plaques, and the level was also elevated in frontal cortex around the time of cortical plaque appearance in the McGill-R-Thy1-APP rat model. This could be indicative of reactive astrocytosis or gliosis, which occurs in human AD brain (Gylys et al. 2004) and in animal models of AD (Olabarria et al. 2010), particularly in proximity to Aβ deposits (Mandybur and Chuirazzi 1990; Porchet et al. 2003). Others have related increased mIns to activated microglia associated with plaques (Marjanska et al. 2005), which is also a prominent feature in the AD brain. Indeed, amyloid plaques in McGill-R-Thy1-APP rats are surrounded by activated microglia (Leon et al. 2010), indicating inflammation, but occurrence of gliosis has not yet been investigated. In agreement with findings in the hippocampus plus thalamus in TASTPM mice, the difference in mIns levels disappeared around the age of 12 months (Forster et al. 2012).

Although the concurrent higher levels of mIns and glutamine in frontal cortex of McGill-R-Thy1-APP rats at age 12 months could both occur as a result of gliosis, they might also represent separate processes. The protein level and immunoreactivity of GS increases in reactive astrocytes (Petito et al. 1992; Hammer et al. 2008), but this is not necessarily accompanied by increased glutamine concentration (Melo et al. 2005). Both mIns and taurine are major osmolytes in the brain (Thurston et al. 1989; Pasantes-Morales and Schousboe 1997), and the concurrent higher levels of both metabolites in dorsal hippocampus and frontal cortex could thus well indicate changes in cell volume regulation after the appearance of plaques. Taurine has also been suggested to have several other physiological roles in the brain, such as neuromodulation (Muramatsu et al. 1978) and neuroprotection, and it should be noted that taurine protects neurons against both Aβ- and glutamate receptor agonist-induced toxicity (Paula-Lima et al. 2005; Wu et al. 2005). The higher level of taurine in McGill-R-Thy1-APP rats could be related to any of these suggested roles. Elevated taurine/tCr has previously been demonstrated in hippocampus and cingulate cortex of APP Tg2576 mice at age 19–20 months (Dedeoglu et al. 2004; Marjanska et al. 2005), whereas dorsal hippocampal taurine has been shown to remain unaltered between age 36 and 44 weeks in 5xFAD mice (Mlynarik et al. 2012).

Total choline and creatine

Differences in tCho levels were found in the dorsal hippocampus at age 3 and 9 months. Alterations in membrane synthesis and degradation can produce changes in the tCho peak, as GPC and PCh are breakdown products, and PCh is a precursor for phosphatidylcholine, a major component of cell membranes. Previous studies on tCho levels in AD are conflicting (Mlynarik et al. 2012) and have shown tCho to be either increased (Pfefferbaum et al. 1999; Griffith et al. 2008; Forster et al. 2012), decreased (Jessen et al. 2000) or unaltered (Block et al. 2002; Wang et al. 2009). The tCr level reflects high-energy phosphate metabolism, and we found that it changed with age in both dorsal hippocampus and frontal cortex and also differed between McGill-R-Thy1-APP rats and controls in frontal cortex. Several others have also demonstrated that tCr differed with age (Pfefferbaum et al. 1999) and between AD patients and controls (Huang et al. 2001; Foy et al. 2011) and between transgenic AD mice and controls (Forster et al. 2012). Not only does this indicate changes in energy metabolism in AD but it also has implications for the use of tCr as an internal reference for quantification.

Concluding remarks

The results obtained in this study demonstrate complex and prominent metabolite alterations during the progression of AD pathology, but also with age in healthy control rats. Changes in metabolite levels were present already at the pre-plaque stage of 3 months, and in general, the McGill-R-Thy1-APP rats did not follow the variations in metabolite levels as displayed in the controls. Metabolite levels were different in the two brain areas investigated and with age, demonstrating the importance of the appropriate choice of brain area in relation to timing. Overall, the findings demonstrate that in vivo 1H MRS is a powerful tool to investigate disease-related metabolic changes in the brain.


  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

The authors thank Ingrid Heggland for genotyping, Kersti Tambet and Anne Haukvik for participation in practical aspects of the animal experiment, Øyvind Salvesen for statistical advice and Øystein Risa and Marte Thuen for setting up the experiments on the MR instrument. The authors have no conflicts of interests to report. We thank the Norwegian Health Association (Dementia) and the Department of Neuroscience DMF/NTNU for financial support.


  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
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