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Recent studies with a conditional mouse model of spinocerebellar ataxia type 1 (SCA1) suggest that neuronal dysfunction is reversible and neurodegeneration preventable with early interventions. Success of such interventions will depend on early detection of neuronal and glial abnormalities prior to cell loss and availability of objective methods to monitor progressive neurodegeneration. Cerebellar concentrations of N-acetylaspartate (NAA), myo-inositol and glutamate as measured by magnetic resonance spectroscopy (MRS) correlate with ataxia scores of patients with SCA1, indicating their potential as reliable biomarkers of neurodegeneration. Here we investigated if neurochemical levels are altered by early, pre-symptomatic disease and if they gauge disease progression in a mouse model of SCA1. Cerebellar neurochemical profiles of transgenic mice that overexpress the mutant human ataxin-1 (the SCA1[82Q] line) were measured longitudinally up to 1 year by MRS at 9.4 tesla and compared to those of transgenic mice that overexpress the normal human ataxin-1 (the SCA1[30Q] line) and wild-type controls. Multiple neurochemicals distinguished the SCA1[82Q] mice from controls with no overlap at all ages. Six neurochemicals were significantly different in SCA1[82Q] mice at 6 weeks, prior to major pathological and neurological changes. Alterations in NAA, myo-inositol and glutamate progressively worsened and were significantly correlated (p < 0.0001) with disease progression as assessed by histology (molecular layer thickness and an overall severity score). Therefore, the neurochemicals that correlate with clinical status in patients reflected progressive pathology in the mouse model. These data demonstrate that pre-symptomatic and progressive neurodegeneration in SCA1 can be non-invasively monitored using MRS.
Hereditary spinocerebellar ataxias (SCAs) are a clinically and genetically heterogeneous group of movement disorders characterized by cerebellar neurodegeneration (Klockgether and Dichgans, 1997). The first SCA for which the mutation was discovered (Orr et al., 1993), SCA1, is caused by dominant inheritance of an expanded CAG repeat that results in a polyglutamine expansion in ataxin-1 (Zoghbi and Orr, 1995). Pathologically, SCA1 is characterized by loss of cerebellar Purkinje cells (PCs) and neurodegeneration within the brainstem and spinocerebellar tracts causing ataxia, dysarthria and progressive bulbar dysfunction (Zoghbi and Orr, 1995). Neuronal and motor dysfunction is reversible in SCA1 by suppressing the expression of mutant ataxin-1 (Xia et al., 2004; Zu et al., 2004). However, successful implementation of such interventions will depend on early detection and monitoring of cerebellar abnormalities by objective means.
We demonstrated that cerebellar neurochemical levels (NAA, myo-inositol and glutamate) measured by proton magnetic resonance spectroscopy (1H MRS) distinguish patients with SCA1 from controls with very high specificity and sensitivity (Öz et al., 2010). In addition, the levels of these neurochemicals correlated with scores on a standardized ataxia rating scale (Schmitz-Hubsch et al., 2006), indicating their potential as robust biomarkers of progressive disease. Prior to their applicability in pre-clinical and clinical trials, the pathological correlates of these MRS biomarkers need to be delineated and their sensitivity to early and progressing pathology needs to be established. These goals can best be achieved in faithful mouse models of human neurodegenerative disease that allow MRS and histological assessment in the same brains. Therefore, here we studied a well-characterized mouse model of SCA1 that reproduces the neuropathological and behavioral features of the human disease (Burright et al., 1995; Clark et al., 1997).
The SCA1[82Q] mice overexpress the mutant human ataxin-1 protein with an 82 glutamine stretch under the control of a PC specific promoter (Burright et al., 1995). Ataxia develops as a result of neuronal dysfunction and atrophy, rather than cell loss, in these mice (Clark et al., 1997), making the model ideal for delineating the neurochemical correlates of PC dysfunction. Furthermore, expression of various neuronal genes are altered first (Lin et al., 2000), followed by pathological and subsequently neurological changes (Clark et al., 1997), as in many human neurodegenerative diseases. Hence this model is also ideal for testing the sensitivity of MRS biomarkers to pre-symptomatic abnormalities, but was never studied by non-invasive imaging before.
We hypothesized that MRS biomarkers would be sensitive to early and progressing disease in this ataxia model, even prior to the development of major pathological changes and ataxia. Therefore, we compared the cerebellar neurochemical profiles of SCA1[82Q] mice obtained by 1H MRS at 9.4 tesla (T) longitudinally to those of two control groups: SCA1[30Q] transgenic mice that overexpress the normal human ataxin-1 (Burright et al., 1995) and wild-type (WT) mice (background strain FVB). We further assessed the cerebella of the mice by histology and evaluated correlations between neurochemical levels and quantitative pathology scores in the same brains.
Three groups of male (unless otherwise noted) mice were studied: 1. SCA1[82Q] transgenic mice (N=14) that overexpress the mutant human ataxin-1 with an 82 glutamine stretch; these animals belonged to the B05 strain described before (Burright et al., 1995). 2. WT controls (N=21, 2 females). Mice from the background strain (FVB) were used as the control group since the SCA1[82Q] mice were homozygous and did not have wild-type littermates. 3. SCA1[30Q] transgenic mice (N=8, 4 females) that overexpress the normal human ataxin-1; these belonged to the A02 strain described before (Burright et al., 1995). Four of these mice were homozygous and 4 heterozygous. This group represented a positive control.
MRS neurochemical profiles and histology data were obtained at ages of 6, 12, 24 weeks and 1 year. These time points were selected based on well-characterized pathological progression in these mice (Burright et al., 1995; Clark et al., 1997). To introduce inter-litter variability, we studied 4 ± 2 mice from each litter starting at 6 weeks of age and assigned each randomly to 6, 12, 24 week or 1 year follow-up. The animals were sacrificed for histological evaluation at their assigned time points within 24 hours of MR scanning. The study was designed this way in order to have a higher number of MRS measurements at the earlier time points where neurochemical alterations were expected to be more subtle than late stage disease. This design also provided 2–4 animals per group at each time point to compare the MRS and histology results in the same brains. Some of the WT mice were only studied by MRS: 4 WT mice were only scanned at 6 weeks, 3 WT mice only at 12 weeks and 4 others only at 12 and 24 weeks. To establish test-retest reproducibility of neurochemical concentrations we scanned 4 additional WT mice (16–18 weeks old) on 3 consecutive days.
All experiments were performed according to procedures approved by the University of Minnesota Institutional Animal Care and Use Committee. Animals were induced with 3–4% isoflurane and a 1:1 mixture of O2:N2O. Spontaneously breathing mice were fixed in a custom built mouse holder and maintained anesthetized with 1.5–2% isoflurane while monitoring body temperature and respiration rate (SA Instruments, Inc) to ensure unchanging physiological status. Body temperature was maintained at 36–37°C with a circulating bath of warm water and a heating fan that received feedback from the rectal temperature probe. The typical scanning time for each animal was approximately 50 min–1 h.
All experiments were performed on a 9.4 T/31 cm magnet (Magnex Scientific, Abingdon, UK) interfaced to a Varian INOVA console (Varian, Inc., Palo Alto, CA, USA) and equipped with a 15 cm gradient coil insert (450 mT/m, 200 μs) and strong second order shim coils (Resonance Research, Inc., Billerica, MA). A quadrature surface RF coil with two geometrically decoupled single-turn coils (10 mm diameter) was used as the MR transceiver. Following positioning of the mouse in the magnet, coronal and sagittal multi-slice images were obtained with a rapid acquisition with relaxation enhancement (RARE) sequence (Hennig et al., 1986) (repetition time TR = 4 s, echo train length = 8, echo time TE = 60 ms, slice thickness = 1 mm, 7 slices) to select a 5–7 μL volume-of-interest (VOI) centered on the midline in the cerebellum. The VOI was placed consistently during longitudinal studies by using anatomical landmarks and its size reduced in SCA1[82Q] mice with age to cover the same cerebellar region despite cerebellar atrophy. All first- and second-order shims were adjusted using FASTMAP with echo-planar readout (Gruetter and Tkáč, 2000). Localized 1H MR spectra were acquired with a short-echo localization by adiabatic selective refocusing (LASER) sequence (TE = 15 ms, TR = 5 s, 256 averages) (Garwood and DelaBarre, 2001). Water was suppressed using variable power RF pulses with optimized relaxation delays (VAPOR) (Tkáč et al., 1999). Spectra were acquired and saved as single scans, which were individually frequency and phase corrected. Scans that showed evidence for motion were excluded and the remaining scans summed. Unsuppressed water spectra were acquired from the same VOI as a metabolite quantification reference. In addition, unsuppressed water spectra were acquired in a subset of animals from each group at a series of TE values (TE = 5–400 ms; TR = 30 s for full relaxation) to evaluate cerebrospinal fluid (CSF) contribution to the VOI (Ernst et al., 1993). No CSF contribution was found by fitting the integrals of these water spectra with a biexponential decay function and hence no correction for CSF was necessary.
The contribution of individual metabolites to the spectra was quantified using an automated deconvolution program (LCModel) (Provencher, 1993) as described previously (Tkáč et al., 2004; Marjanska et al., 2005; Öz et al., 2005). The following metabolites were included in the basis set: alanine (Ala), aspartate (Asp), ascorbate/vitamin C (Asc), glycerophosphocholine (GPC), phosphocholine (PCho), creatine (Cr), phosphocreatine (PCr), γ-aminobutyric acid (GABA), glucose (Glc), glutamine (Gln), glutamate (Glu), glutathione (GSH), glycine (Gly), myo-inositol (myo-Ins), lactate (Lac), N-acetylaspartate (NAA), N-acetylaspartylglutamate (NAAG), phosphoethanolamine (PE), scyllo-inositol (scyllo-Ins), taurine (Tau), and macromolecules (MM). The model metabolite spectra were generated using density matrix simulations (Henry et al., 2006) with the MATLAB software (MathWorks, Natick, MA) based on previously reported chemical shifts and coupling constants (Govindaraju et al., 2000; Tkáč, 2008). The MM spectra were experimentally obtained from the mouse brain using an inversion recovery technique. Absolute metabolite concentrations were obtained relative to an unsuppressed water spectrum acquired from the same VOI. Metabolites quantified with Cramér-Rao lower bounds (CRLB, estimated error of quantification) > 50% were classified as not detected and metabolites quantified with CRLB ≤ 50% in at least 90% of the spectra were included in the neurochemical profile. Only 3 metabolites (Ala, Gly and scyllo-Ins) were excluded from final analysis based on these strict criteria. If the correlation between two metabolites was consistently high (correlation coefficient < − 0.5), their sum was reported rather than the individual values. Strong negative correlation was found in two cases, so that Cr + PCr and GPC + PCho (from here on will be referred to as tCr for total creatine and tCho for total choline), were included in the neurochemical profile. Therefore 15 statistically independent concentrations were evaluated.
Following the collection of the last MR spectra, brains were harvested and processed for histology. Mice were deeply anesthetized with sodium pentobarbital (100 mg/kg i.p.) and sacrificed by transcardial perfusion with pH 7.4 phosphate buffered saline followed by 10% formalin. Brains were removed and postfixed in 10% formalin. Histology was performed in a blind fashion (regarding the genotype and age of the animals) on paraffin-embedded sections using hematoxylin-and-eosin (H & E), Luxol-fast-blue-PAS and immunohistochemistry for glial fibrillary acidic protein (GFAP) and calbindin. Luxol-fast-blue demonstrated no changes in myelin, even in the most severely affected mice, and GFAP was not a reliable quantitative measure of gliosis in this model due to animal-to-animal variation of staining intensity. Therefore, to seek associations between the MRS and histology measures, we used the molecular layer (ML) thickness at the primary fissure (Zu et al., 2004) and semi-quantitative measures (severity scale) based primarily on H & E and in some cases calbindin staining. Much of the pathology in these models is related to shrinkage of PCs rather than neuronal loss (Clark et al., 1997), therefore the measurement of the thickness of the ML, which contains the dendritic trees of the PCs, quantifies these atrophic changes better than neuronal counting. Similarly, hypertrophy of Bergmann glia is more typical than hyperplasia. Therefore, a severity score that includes all aspects of pathology was the most effective method to quantify pathology. The following severity scale was used: 0, no pathological changes; 0.5, near-normal but ML somewhat thinner; 1, mild changes including heterotopic PCs, vacuoles in PCs, thinning of ML, largely confined to the posterior lobules; 2, similar to 1 but more widespread; heterotopic PCs more numerous and often higher in the ML; 3, widespread ML thinning, numerous heterotopic PCs involving anterior lobules nearly as frequently as posterior, mild PC loss, primarily in the posterior lobules; 4, severe disorganization of cerebellar cortex with generalized severe atrophy of ML, frequent heterotopic PCs and PC loss.
Data from the different mouse groups were compared using two-way repeated measures ANOVA, with fixed effects for experimental group, week of scan, and their interaction, and a random effect for the repeated measurements on the same mouse. Analyses were done using all available data (not just mice with data that were complete according to the design), in SAS using PROC MIXED, assuming approximately normally distributed outcomes. Model contrasts of means were used to test group comparisons at each week; p-values were corrected for these multiple comparisons (3 groups across 4 time points resulting in 12 comparisons) using a step-down approach (Holm, 1979) for each metabolite. We used the Holm method because it does not assume that the tests are independent of each other. Additional p-value correction across the multiple metabolites was not carried out. Linear regression analyses were performed to evaluate the relationship between neurochemical levels and histological severity scores and ML thickness measurements.
High quality MR spectra (good signal-to-noise ratio, resolution, water/lipid/artifact suppression) were obtained from the cerebella of both control and ataxia mice across their life span (Fig. 1). Consistent placement of the VOI centered in the cerebellar midline and acquisition of data from the same tissue volume over time facilitated the reproducibility of the spectral quality and pattern. In the SCA1[82Q] mice, where cerebellar atrophy was apparent over time (Fig. 1), this was accomplished by reducing the VOI size relative to the control animals starting at 12 weeks. Test-retest reproducibility of cerebellar neurochemical concentrations was evaluated in 4 WT mice scanned on 3 consecutive days. The average coefficient of variation for 6 metabolites (NAA, tCr, tCho, Glu, myo-Ins, Tau) was ≤ 5%, for 5 metabolites (Asc, GABA, Glc, Gln, GSH) ≤ 10% and in the remaining 4 metabolites (Asp, Lac, NAAG, PE) ≤ 25%.
As a result of the excellent test-retest reproducibility, progressive neurochemical alterations in SCA1[82Q] mice over time were discernible even in individual animals, as demonstrated by the NAA, Glu and myo-Ins resonances in Fig. 1.
The neurochemical profiles of the two control groups (WT and SCA1[30Q] mice) were very similar, except for lower Glc levels at 6 weeks (p < 0.05) and lower Tau levels (p < 0.05) at all ages in the cerebella of the SCA1[30Q] mice (Fig. 2). On the other hand, highly significant neurochemical alterations (Table 1) were detected in the cerebella of the SCA1[82Q] mice relative to both control groups starting at the earliest age studied. At 6 weeks, NAA and Tau were significantly lower and Gln, tCr, myo-Ins and Glc significantly higher in the cerebella of SCA1[82Q] mice than controls (Table 1). The concentrations of Asc and Lac were also different in the SCA1[82Q] cerebella than one or both control groups but with higher p-values (0.02–0.04) (Fig. 2), and as such are interpreted as trends since p-values were not corrected across the multiple metabolites.
In the histological evaluation, the WT animals received a severity score of 0 at all ages, while the SCA1[30Q] mice were scored 0 and 0.5 except for one mouse that was scored 1 (at 6 weeks) and another that was scored 2 (at 24 weeks, see below). At 6 weeks, the cerebella of the SCA1[82Q] mice showed minor pathological alterations, especially in the area covered by the MRS voxel containing the primary fissure. These alterations included vacuoles in PC, heterotopic PCs and slight thinning of the molecular layer (Fig. 3). The SCA1[82Q] mice show no signs of ataxia at this age (Clark et al., 1997), therefore the neurochemical alterations detected by MRS at 6 weeks were due to pre-symptomatic pathology.
The time courses of metabolite levels revealed two distinct trends (Fig. 4, Table 1). The levels of NAA, Glu and myo-Ins were progressively more different than the control values as the SCA1[82Q] mice aged and disease progressed (Fig. 4, left). On the other hand, levels of tCr, Gln and Tau were altered in the SCA1[82Q] mice at 6 weeks, but converged with the levels of one or both control groups as the animals aged (Fig. 4, right).
The largest longitudinal changes in NAA, Glu and myo-Ins levels in the cerebella of the SCA1[82Q] mice occurred between 6 and 12 weeks (Fig. 4). Glu levels were the same in all groups at 6 weeks, but became significantly lower in SCA1[82Q] mice than controls at 12 weeks (Table 1) and decreased further at 24 weeks. The NAA and myo-Ins levels, on the other hand, seemed to level off starting at 12 weeks. The pathological alterations in the SCA1[82Q] mice continued to progress until 24 weeks with the mice receiving a score of 2 at 6 weeks, 3 at 12 weeks and 4 at 24 and 52 weeks. Therefore, the major alterations in NAA and myo-Ins levels preceded the pathological progression to the severe disease stage. When data from all perfused mouse brains were combined, robust correlations between the NAA, myo-Ins and Glu levels and the pathology measures were apparent (Fig. 5). Note, however, that NAA and myo-Ins levels that correspond to severity scores 3 and 4 were very similar, demonstrating their leveling off at severe disease. At a severity score of 3 there is little PC loss in the area covered by the MRS voxel, while cell loss is prominent at a severity score of 4. Therefore, the NAA and myo-Ins changes in this neurodegeneration model were marking neuronal dysfunction and dendritic atrophy and not cell loss.
Tau and GSH were also positively correlated with the molecular layer thickness and negatively correlated with the severity score (not shown). Although these correlations were less robust than those of NAA, myo-Ins and Glu vs. the histology measures, they were still significant (p < 0.001 for Tau and p < 0.01 for GSH vs. both histology measures). The GSH correlations were particularly interesting since no group differences were observed for this metabolite across different ages, but a significant decrease with age was observed for all groups, which likely was the reason for the observed correlation with the histology measures.
Since the neurochemical alterations in the SCA1[82Q] mice relative to controls were numerous at each age and highly statistically significant, plotting two altered metabolites at each age against each other separated the ataxia mice from controls with no overlap (Fig. 6). Furthermore, the progression of disease in individual mice could be monitored in such plots. For example, the mouse marked with an arrow in Fig. 6 was an obvious outlier in the SCA1[30Q] group at 12 weeks and at 24 weeks its neurochemical levels were closer to the SCA1[82Q] mice than any of the control mice. This mouse was in fact the only control animal that received a severity score of 2 when sacrificed at 24 weeks and interestingly had the lowest NAA and highest myo-Ins values in its group at 6 weeks (not shown), but otherwise was no different than other group members.
We have demonstrated in a mouse model of SCA1 that neurochemical levels measurable by 1H MRS can be used to monitor early and progressive neurodegeneration. By utilizing a 9.4 T scanner, together with state-of-the-art MR data acquisition and quantification methods, we were able to noninvasively and reproducibly measure the concentrations of 15 neurochemicals in a small brain region (Fig. 2) and monitor the onset and progression of neurochemical abnormalities in individual mice (Figs. 1 and and6).6). A subset of the neurochemicals which correlated with ataxia scores of patients in our previous study (Öz et al., 2010) also displayed robust correlations with pathology scores in mice.
The neurochemical alterations detected in SCA1[82Q] mice were primarily due to the expansion of the polyglutamine repeat in ataxin-1, rather than the overexpression of the human protein, based on the similar neurochemical profiles of the WT and SCA1[30Q] mice. Taurine was the only metabolite that was different between these two groups at all ages. Decreased Tau may be an indicator of the slight thinning of the molecular layer in the SCA1[30Q] cerebella (severity score 0.5) because the somata, dendrites, and dendritic spines of PCs contain the highest Tau levels in the cerebellar cortex (Ottersen, 1988). This would also be consistent with the correlation of Tau levels with the histology scores (not shown). The SCA1[30Q] group also showed that mere overexpression of human ataxin-1 may lead to further pathology in some mice based on the 2 outliers that received severity scores of 1 and 2. Interestingly, high levels of normal human ataxin-1 also result in neurodegeneration in a Drosophila model of SCA1 (Fernandez-Funez et al., 2000).
Most of the neurochemical alterations observed in the SCA1[82Q] mice in a longitudinal fashion were the same as those we detected in a cross-sectional study of patients with SCA1. Namely, NAA and Glu were lower and myo-Ins higher than controls in all 3 brain regions studied in patients: the cerebellar vermis, cerebellar hemispheres and the brainstem (pons) (Öz et al., 2010). Note that in two of these regions, levels of total NAA (tNAA, NAA + NAAG) were reported due to the cross-correlation of NAA and NAAG. The time courses of tNAA and its correlation to histology measures in the current study were identical to those of NAA alone, justifying the comparison of the mouse and patient findings. In addition to altered NAA, Glu and myo-Ins, higher Gln and tCr levels than controls were also observed in the cerebella of patients. These alterations were only detected at the earliest time point in mice and the patients were studied at the early-to-moderate disease stage. Therefore it will be interesting to determine the natural history of these changes in patients and to determine if Gln and tCr will decrease at later disease stages in patients as well. Although complicated by the slow progression of neurodegenerative diseases, longitudinal studies in patients are critical prior to utilization of the technique as an outcome measure in clinical trials. Another metabolite altered early in the SCA1[82Q] mice was Tau. However, brain Tau levels in rodents are much higher than humans, despite the similarity of the rest of the neurochemical profiles, therefore Tau may not be an ideal biomarker that can be utilized in parallel in mouse models and humans.
Remarkably the 3 neurochemicals, NAA, myo-Ins and Glu, that correlated strongly with disease progression as assessed by histology were the same ones that correlated most robustly with the ataxia scores in patients (Öz et al., 2010). Clearly, these associations do not establish causality, however they demonstrate that these neurochemicals gauge disease reliably.
Two sets of MRS biomarkers were identified in the SCA1[82Q] mice: those that correlated with disease progression (Figs. 4 and and5)5) and those that anticipated disease progression (Fig. 4). Among the first group, NAA is localized exclusively in neurons and is indicative of both neuronal cell number and viability (Clark, 1998; Dautry et al., 2000; Demougeot et al., 2001). In this study, alterations in NAA clearly mark neuronal dysfunction and dendritic atrophy, and not cell loss, because NAA levels did not change after severity score 3 when most cell loss occurred in the selected VOI. Mitochondrial NAA synthesis is correlated with oxygen consumption and ATP production in isolated rat brain mitochondria suggesting that decreased NAA levels may reflect decreased mitochondrial energy production (Bates et al., 1996). Asp, the precursor of NAA, tended to be higher in SCA1[82Q] mice (Fig. 2) with uncorrected p-values < 0.03 at 6 weeks, which would be consistent with decreased mitochondrial NAA synthesis, but needs further investigation.
Increased myo-Ins levels were another marker of early and progressive pathology. Myo-Ins is primarily localized to glial cells based on culture work (Brand et al., 1993). Therefore increased myo-Ins levels have typically been attributed to gliotic activity in neurological disorders (Pouwels et al., 1998; Kantarci et al., 2004; Öz et al., 2005; Vrenken et al., 2005). Gliosis was not a prominent feature in the SCA1[82Q] mice, even when examined by GFAP immunostaining. Therefore the progressive increase in myo-Ins levels may reflect the glial component that was relatively increased in the selected VOI because of shrinkage of the non-glial elements of the molecular layer. Alternatively, the increased myo-Ins levels may be related to one of its multiple functions in cells, such as serving as an osmolyte (Fisher et al., 2002).
The third marker of disease progression, Glu, was altered in the SCA1[82Q] cerebella after NAA and myo-Ins at 12 weeks (Fig. 4). Glu is primarily localized to neurons, with the highest levels present in glutamatergic neurons (Storm-Mathisen et al., 1992). While the Glu decrease in SCA1[82Q] mice may be surprising since the mutant gene expression and pathology is restricted to the GABAergic PCs, which have low levels of Glu (Storm-Mathisen et al., 1992), secondary affects on associated cells are likely. Namely, PCs receive excitatory input from glutamatergic parallel and climbing fibers in the ML which would be disrupted by PC dendritic pathology. In fact, the expression of 5 genes that code for regulators of glutamate signaling pathways in PCs are altered in the cerebella of SCA1[82Q] mice at 5 and 12 weeks of age (Serra et al., 2004).
The early increase in Gln, a metabolite that anticipates disease progression, indicates an imbalance between the Glu and Gln pools which may also be due to a disruption of the Glu-Gln cycle between the presynaptic granule cells/climbing fibers and Bergman glia in the ML. For example, glutamate uptake into Purkinje cells appears reduced due to the downregulation of EAAT4 (Lin et al., 2000; Serra et al., 2004), a Purkinje cell specific glutamate transporter. As a result, excess Glu may be taken up by Bergman glia and converted to Gln. The parallel reduction of Glu and Gln in the SCA1[82Q] mice with age (Fig. 4) on the other hand likely corresponds to the progressive dendritic atrophy of PCs with a decrease in synaptic contacts by parallel and climbing fibers.
Total creatine was also higher in the SCA1[82Q] mice than controls only at 6 weeks. Our data indicated that the higher tCr levels in the SCA1[82Q] mice were due to increased PCr, which serves as an energy buffer in the cell. Increased levels of PCr early on in the disease may indicate decreased energy utilization, possibly due to compromised cerebellar development in the SCA1[82Q] mice. Namely, expression of mutant ataxin-1 is most detrimental during the first postnatal months of these mice since it destabilizes RORα, a transcription factor critical for cerebellar development (Serra et al., 2006). Therefore, increased energy reserves at 6 weeks of age are consistent with reduced energy utilization during cerebellar development in these mice. Two other metabolites related to energy metabolism, lactate and glucose, may provide additional insight. Both were higher in the SCA1[82Q] cerebella relative to WT mice, possibly due to reduced oxidative metabolism and decreased glucose utilization. However, the interpretation of glucose and lactate findings is complicated by the fact that isoflurane increases brain lactate (Valette et al., 2007) and blood and brain glucose levels (Kofke et al., 1987). Therefore, the changes we have seen may be indicative of a different response of the SCA1[82Q] mice to isoflurane than the controls. Taken together, the NAA, tCr, Glc and Lac data strongly suggest alterations in energy metabolism in the cerebella of the SCA1[82Q] mice relative to controls, regarding both energy production and utilization.
In conclusion, these parallel MRS and histology data demonstrate that neurochemical levels measured by MRS can accurately gauge progressive neuronal dysfunction. They can therefore be utilized to noninvasively and reliably monitor disease progression and potentially response to therapy in pre-clinical and clinical trials.
We thank the staff of the Center for MR Research for maintaining and supporting the NMR system, Bob Ehlenfeldt and Orion Rainwater for maintaining the mouse colonies and LuAnn Anderson for expert technical help with histology. This work was supported by the National Institutes of Health grants R21 NS060253 (G. Ö.) and R01 NS022920 (H.T.O.). The Center for MR Research is supported by National Center for Research Resources (NCRR) biotechnology research resource grant P41RR008079, Neuroscience Center Core Blueprint Award P30NS057091 and the Keck Foundation.