Abstract

Mice lacking functional presynaptic active zone protein Bassoon are characterized by an enlarged cerebral cortex and an altered cortical activation pattern. This morphological and functional phenotype is associated with defined metabolic distortions as detected by a metabonomic approach using high-field (14.1 T) high-resolution 1H-nuclear magnetic resonance spectroscopy (MRS) in conjunction with statistical pattern recognition. Within the cortex but not in the cerebellum, concentrations of N-acetyl aspartate, glutamine, and glutamate are significantly reduced, whereas the majority of all other detectable low molecular metabolites are unchanged. The reduction of the neuron-specific metabolite N-acetyl aspartate in the cortex coincides with a significant decrease in neuronal density in cortical layer V. Comparing the neuron with glia cell densities across the cortex reveals cortex layer–dependent alterations in the ratio between both cell types. Whereas the ratio shifts significantly toward neurons in the cortical input layers IV, the ratio is reversed in cortical layer V. Consequently, the previously observed altered neuronal activation pattern in the cortex is reflected not only in defined cytoarchitectural anomalies but also in metabolic disturbances in the glutamine–glutamate and N-acetyl aspartate metabolism.

Introduction

One frequently used approach to study the putative involvement of a particular protein in the control of a specific cellular mechanism is to modify the abundance and/or function of the respective protein and thereupon compare the efficacy of this cellular process in the appropriate genetically modified mouse. It turned out that the more complex the regulatory signal cascades for a cellular event are the more likely is the appearance of compensatory mechanisms that can adopt part of the function of the modified protein, and consequently, a phenotype of these genetically modified mice is, if at all, often only marginally different. Therefore, it becomes important to characterize these mice in a comprehensive way to recognize even small variations under resting conditions, which may become important only under specific stressful conditions. To characterize the actual status of a biological system at the molecular level in a qualitative and/or quantitative manner, 2 hypothesis-independent methods are employed, a proteomic approach (presence and relative abundance of proteins) and metabonomic approach defined as “the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification” (see Nicholson et al. 1999; Robertson 2005).

In an attempt to understand the function of the presynaptic localized cytomatrix protein Bassoon for control of the highly complex mechanism of transmitter release, a Bassoon-mutant mice was generated (Altrock et al. 2003). These mice are characterized in general by a reduction in the normal synaptic transmission; interestingly, the excitatory synapses were found to be ultrastructurally normal, although a subset was functionally inactive. At the organismal level, the disturbed synaptic function becomes manifest by the appearance of repeated rapidly generalizing seizures, which are thought to result from an imbalance of the excitatory and inhibitory neurotransmitter systems. Morphologically, an increased size of the brain caused primarily by a volume increase of the cerebral cortex and the hippocampus is described based on volumetric magnetic resonance imaging (MRI) measurements. The observed cortex enlargement is paralleled with a changed basal activation pattern in this structure, which in turn seems to correlate with certain structural alterations, as determined by manganese-enhanced MRI (ME-MRI) (Angenstein et al. 2007). This imaging technique takes advantage of the fact that the MRI contrast agent manganese accumulates in brain structures in an activity-dependent manner as well as depending on cell type composition and density (Roth and Garrick 2003; Takeda 2003; Aoki et al. 2004; Wadghiri et al. 2004). Although Bassoon is expressed widely throughout the brain (Richter et al. 1999), differences in ME-MRI were found in the cerebral cortex but not in the cerebellum (Angenstein et al. 2007).

To search 1) if the absence of a functional presynaptic localized Bassoon protein generally affects ongoing metabolic processes in the brain and 2) if the previously functional/structural alterations detected by ME-MRI, which were restricted to the cortex, correlate with specific metabolic perturbations in this structure, we applied a metabonomic approach to define a potential “metabolic phenotype” of Bassoon-mutant mice. The advantage of performing such a hypothesis-independent metabonomic approach arises from the fact that this method produces also information about seemingly unrelated biochemical pathways and, consequently, may become helpful to detect also pleiotropic effects of the studied gene product.

Materials and Methods

Animals

Bassoon-mutant mice were generated as described earlier (Altrock et al. 2003), backcrossed into C57/Bl6 mouse strain, and then crossed with SV129 strain yielding mice with a mixed C57/bl6/SV129 genetic background for mutants and wild types. Eight 2-month-old mutant mice and 8 wild-type littermate controls were used for the in vitro1H-NMR (1H-nuclear magnetic resonance) spectroscopy study and 4 BsnΔEx4/5-mutant mice and wild-type mice each were used for histological analysis. All animal experiments were carried out in Magdeburg and were approved by the animal care committee of the Land Sachsen-Anhalt (No. 2-705 IfN) in accordance with the regulations of the German Federal Law on Care and Use of Laboratory Animals and with the European Communities Council Directive (86/609/EC).

Manganese-Enhanced MRI

Mice were anesthetized with 1.0–1.5% isoflurane (in 70:30 N2O:O2; v/v) and fixed using a head holder with bite bars to reduce motion artifacts. MRI experiments were performed on a Bruker Biospec 47/20 scanner at 4.7 T (free bore of 20 cm) equipped with a BGA 12 (200 mT/m) gradient system. A 25-mm Litzcage small animal imaging system (DotyScientific Inc., Colombus, SC) was used for radio frequency excitation and signal reception. Two days before MRI measurement, animals were injected subcutaneously with an aqueous solution containing 1 μmol/g MnCl2 (e.g., 200 μl of a 100 mM MnCl2 solution for a 20 g mouse, which is equivalent to 125 mg/kg). The used concentration is a compromise between manganese toxicity and the fact that the relaxation rates, which define the signal intensity differences, are proportional to effective local concentration. In recent ME-MRI experiments, concentrations up to 175 mg/kg were used with only minor and temporary side effects (Silva et al. 2004). A 3D data set of T1-weighted images was obtained using a modified driven equilibrium Fourier transform pulse sequence with the following parameters: time repetition 21.2 ms, time echo 4.0 ms, flip angle 15°, field of view 30 × 30 × 20 mm, matrix 256 × 256 × 64 (yielding a nominal in-plane resolution of 117 × 117 μm and a nominal slice thickness of 312.5 μm), and 10 averages; the total scanning time was 93 min. Using this imaging pulse sequence, the typical signal to noise ratio in cortical areas was about 300:1 when manganese was administered to the mice and about 100:1 when no manganese was injected.

1H-NMR Spectroscopy

For high-field 1H-magnetic resonance spectroscopy, 2-month-old Bassoon-mutant and age-matched control mice were sacrificed by cervical dislocation and the brains were rapidly separated into cortex, cerebellum, and hippocampus. Tissue samples were weighted in the frozen state and homogenized at 0 °C. Metabolites were extracted with 2.5 ml of cold 5% perchloric acid and centrifuged for 15 min at 4000 × g to remove precipitated proteins and membrane components. The supernatants were adjusted to neutral pH with potassium hydroxide solutions. The precipitated potassium perchlorate was removed by centrifugation (10 min at 4000 × g), and the sample was lyophilized over night. The dried powder was dissolved in 600 μl deuterium oxide (D2O), and 1H-MR spectra were acquired on a Bruker AVANCE 600 NMR spectrometer with cryoplatform (Bruker Biospin GmbH, Karlsruhe, Germany) operating at 14.1 T (600 MHz proton resonance frequency). The spectrometer was fitted with a 5-mm CPTXI-1H-13C/15N/2H probehead with z gradients. The following parameters were used: 7.8 μs pulse length (90° pulse), 7200 Hz sweep width, digital resolution of 64 k data points, repetition time 10 s, and saturation of the residual water signal by a frequency-selective pulse before each free induction decay (FID). For each spectrum between 32 and 128, FIDs were accumulated depending on the wet weight of the investigated sample. The time-dependent signal was processed by applying a line broadening of 0.3 Hz prior to Fourier transformation. In each spectrum, 13 metabolites were quantified by signal integration: acetate, alanine, aspartate, choline, creatine, GABA (γ-amino butyric acid), glutamate, glutamine, (myo)inositol, lactate, N-acetyl aspartate, succinate, and taurine (see Fig. 2). Absolute metabolite concentrations, given in units of mmol/kg (wet weight), were measured by comparing the integral of an identified resonance signal with that of a known amount of sodium formiate (10 mM) added to the D2O. All concentration values are given as mean ± standard deviation.

Data Processing for Pattern Recognition

Spectra were integrated across the spectral region between 4.5 and 0.8 ppm over a series of 0.04 ppm integral regions and normalized to the total integral region as described in Griffin et al. 2004 and Pears et al. 2005. These data sets were thereupon imported into XLStat for analysis using the principal components analysis (PCA) package with the following parameters: PCA type: Pearson (n − 1), rotation: Varimax (Kaiser normalization), and number of factors: 4.

Tissue Preparation

Four wild-type and 4 Bassoon-mutant mice were deeply anesthetized with an intraperitoneal injection (80 μl) of a 1:1 mix of ketamin (50 mg/ml, Ratiopharm, Ulm, Germany) and Rompun (2%, Bayer, Leverkusen, Germany) and then intracardially perfused with approximately 20 ml of 0.1 M phosphate buffer saline (PBS) solution followed by 150 ml fixative consisting of 4% paraformaldehyde dissolved in 0.1 M phosphate buffer pH 7.4. The brains were removed from skull and immersed in the same fixative over night. After washing with PBS, the brains were cut horizontally on a Leica VT 1000S vibratome (Leica, Wetzlar, Germany) at section thickness of 50 μm. Parallel representative horizontal section series were done through the entire brain starting with the appearance of the dorsal hippocampus. Sections were collected in macrowells to be further processed free floating.

Immunocytochemistry

For morphological inspection, the sections were washed in 0.1 M PBS buffer (pH 7.4), then endogenous peroxidase activity was blocked with a mixture of MeOH:PBS (1:1 + 1 ml H2O2), and putative unspecific antibody binding was blocked with 5% bovine serum albumin diluted in PBS for 45 min. The primary antibodies, mouse anti-NeuN (Chemicon, Hampshire, UK) and rabbit anti-S100β (Swant, Bellinzona, Swizerland), were diluted 1:300 and 1:10 000 in PBS, respectively. The sections were incubated with a cocktail of anti-NeuN and anti-S100β for 2 days at +4 °C under gentle agitation. After several washes with PBS, the sections were sequentially incubated with secondary antibodies Alexa 488–conjugated goat anti-rabbit immunoglobulin G (IgG) and Alexa 568–conjugated goat anti-mouse IgG (both from Molecular Probes, Leiden, The Netherlands) and diluted at 1:200 in PBS for 2 h at room temperature. The unbound antibodies were removed with several washes of PBS, and the sections were mounted on slides and covered with Vectashield coverslips (Vector Laboratories Inc., Peterborough, UK).

Image Acquisition

For quantification of cortical cell density, we choose the auditory cortex of the right hemisphere because corresponding cortical areas could be easily identified within the horizontal sections of different animals by internal landmarks (i.e., the rostral tip of the ventral hippocampus). Multiple image stacks consisting of 13–20 individual focal planes each were acquired sequentially through the immunostained sections by a confocal laser scanning microscope (Leica TCS SP2 AOBS; Leica Microsystems, Mannheim, Germany) equipped with a Fluotar 25× oil-immersion objective with a numerical aperture of 0.75. Using a zoom factor of 1.2, a large field image was automatically scanned through all layers of the auditory cortex by acquiring multiple (usually 8 × 6) image stacks using the Arivis Browser (Arivis Multiple Image Tools GmbH, Rostock, Germany). Subsequently, the 2-channel multi-image stacks consisting of up to 1920 individual images [8 (x) × 6 (y) × 20 (z) × 2 channel] were automatically mounted and aligned into a large field single-image stack.

Quantitative Analysis of Immunocytochemical Data

For evaluation of the cortex thickness and determination of the neuronal and glia cell density, 3 individual focal planes of the mounted single-image stacks were selected and analyzed using morphometric analysis tools of the Arivis Browser. The entire cortical thickness from layer I to layer VI as well as the thickness of individual cortical layers were measured at 3 different rostrocaudal positions and averaged (see Table 2 and Fig. 5). For calculating the cell density, NeuN- and S100β-immunoreactive cell bodies (showing a nucleus) were counted within rectangles laid over the different cortical layers (supragranular layers: II/III, granular layer: IV, and infragranular layers: Va/Vb and VI) of the immunostained sections. Cells crossing the right and the bottom line of the rectangle were excluded from the calculation (forbidden lines), whereas cells crossing the left and the top line were counted. Cell densities were determined in 3 focal planes per animal of 4 wild-type and 4 mutant mice and presented as cells per mm2.

Results

The newly backcrossed BsnΔEx4/5 mice with a mixed C57/bl6/SV129 genetic background displayed a comparable cortex volume increase and redistribution of manganese within the cortical layers, confirming that the contribution of background genes to these structural/functional alterations is negligible. The cortex of the Bassoon-mutant mice appeared also more laminated in comparison with age-matched wild-type littermates, which results from a higher manganese accumulation within a superficial central and a deep cortical layer (Fig. 1 and Angenstein et al. 2007).

Figure 1.

Manganese accumulation within cortical layers differs between wild-type and Bassoon-mutant mice. Two days after subcutaneous application of 1 μmol/g MnCl2, ME-MRI depicts a laminar-dependent distribution of manganese within the cortex of Bassoon-mutant mice, which is not so pronounced in wild-type littermates (solid arrows, see also Angenstein et al. 2007). In contrast, manganese accumulation within the cerebellum was indistinguishable between control and BsnΔEx4/5 mice (open arrows).

Figure 1.

Manganese accumulation within cortical layers differs between wild-type and Bassoon-mutant mice. Two days after subcutaneous application of 1 μmol/g MnCl2, ME-MRI depicts a laminar-dependent distribution of manganese within the cortex of Bassoon-mutant mice, which is not so pronounced in wild-type littermates (solid arrows, see also Angenstein et al. 2007). In contrast, manganese accumulation within the cerebellum was indistinguishable between control and BsnΔEx4/5 mice (open arrows).

In contrast, the staining pattern of the cerebellum appears indistinguishable between wild-type and Bassoon-mutant mice (Fig. 1). Because manganese accumulates in the cerebellum in a layer-specific manner (Aoki et al. 2004), our ME-MRI data indicate that underlying processes that causes the observed manganese redistribution within cortical layers are not equally effective in the cerebellum.

Reduced concentration of N-acetyl aspartate, glutamine, and glutamate in the cortex of Bassoon-mutant mice but not in the cerebellum

The previously observed differences in the basal cortical activation pattern of Bassoon-mutant mice, and the fact that these mice suffer from recurrent epileptic seizures point to a disturbed balance of excitatory and inhibitory transmitter systems in this structure. To search if such a hypothesized imbalance in the efficacy of both transmitter systems causes specific metabolic perturbations, we performed a metabonomic approach using high-field (14.1 T) 1H-NMR spectroscopy.

This method enables the simultaneous quantification of 13 different low molecular weight metabolites and, consequently, describes the current metabolic status in the cortex, hippocampus, and cerebellum of wild-type mice and Bassoon-mutant mice at the time point of the preparation (Fig. 2). Using a perchloric acid–soluble extract, we found a significantly reduced concentration of N-acetyl aspartate and glutamine in the cortex and in the hippocampus of Bassoon-mutant mice and, in addition, a reduced concentration of glutamate in the cortex. The relative changes in the concentration of these metabolites were comparable in both structures, that is, the reduction of N-acetyl aspartate, glutamine, and glutamate was about 10.7%, 22.4%, and 5.7% in the cortex and 12.0%, 21.2%, and 5.7% in the hippocampus, respectively (Table 1). In contrast, in cerebellar tissue concentrations of these 3 metabolites were similar in wild-type and BsnΔEx4/5 mice. However, in the cerebellum, a small but significant increase in the concentration of lactate was observed, which was detectable neither in the cortex nor in the hippocampus.

Figure 2.

Spectroscopic determination of neurotransmitter metabolites in wild-type and Bassoon-mutant mice. Representative high-field and high-resolution 1H-NMR spectra of perchloric acid extracts of one wild-type mouse cortex is shown. Relevant resonance signals for the metabolites quantified in our study are labeled. The chemical shift is expressed in parts per million (ppm). NAA, N-acetyl aspartate; Glx, glutamine + glutamate.

Figure 2.

Spectroscopic determination of neurotransmitter metabolites in wild-type and Bassoon-mutant mice. Representative high-field and high-resolution 1H-NMR spectra of perchloric acid extracts of one wild-type mouse cortex is shown. Relevant resonance signals for the metabolites quantified in our study are labeled. The chemical shift is expressed in parts per million (ppm). NAA, N-acetyl aspartate; Glx, glutamine + glutamate.

Table 1

Metabolic composition of various brain areas of wild-type and Bassoon-mutant mice

Metabolite Cortex Hippocampus Cerebellum 
 Control (n = 8) Bsn (n = 8) P Control (n = 8) Bsn (n = 8) P Control (n = 8) Bsn (n = 8) P 
Taurine 11.12 ± 0.51 11.14 ± 0.52 0.932 10.98 ± 0.99 11.45 ± 1.08 0.380 9.30 ± 0.50 8.66 ± 0.73 0.059 
NAA 6.66 ± 0.42 5.95 ± 0.35 0.002 6.44 ± 0.46 5.67 ± 0.39 0.002 6.69 ± 0.51 6.51 ± 0.33 0.403 
Inositol 5.62 ± 0.38 5.42 ± 0.58 0.446 7.51 ± 0.56 7.66 ± 0.89 0.691 7.13 ± 0.88 7.33 ± 0.51 0.585 
GABA 1.79 ± 0.10 1.79 ± 0.15 0.959 1.57 ± 0.31 1.71 ± 0.28 0.370 1.32 ± 0.31 1.44 ± 0.22 0.370 
Choline 1.40 ± 0.13 1.45 ± 0.12 0.455 1.11 ± 0.28 1.13 ± 0.44 0.913 1.26 ± 0.17 1.16 ± 0.16 0.253 
Creatine 9.23 ± 0.62 9.73 ± 0.35 0.064 10.49 ± 0.80 10.43 ± 0.81 0.888 13.81 ± 0.57 13.27 ± 1.11 0.241 
Glutamine 3.40 ± 0.24 2.64 ± 0.55 0.003 2.58 ± 0.42 2.04 ± 0.32 0.011 3.59 ± 0.93 3.64 ± 0.87 0.912 
Glutamate 9.79 ± 0.51 9.23 ± 0.38 0.026 10.02 ± 0.84 9.45 ± 0.63 0.145 9.01 ± 0.70 8.69 ± 0.85 0.421 
Aspartate 2.53 ± 0.15 2.54 ± 0.28 0.926 2.18 ± 0.29 2.29 ± 0.57 0.618 2.68 ± 0.37 2.90 ± 0.44 0.297 
Succinate 0.55 ± 0.12 0.51 ± 0.15 0.566 0.87 ± 0.19 0.77 ± 0.27 0.390 0.81 ± 0.30 0.78 ± 0.26 0.844 
Acetate 0.97 ± 0.25 0.77 ± 0.23 0.123 3.57 ± 0.89 3.76 ± 0.38 0.584 2.76 ± 0.31 2.45 ± 0.44 0.127 
Alanine 0.57 ± 0.08 0.54 ± 0.07 0.394 0.84 ± 0.14 0.75 ± 0.14 0.214 0.52 ± 0.05 0.50 ± 0.09 0.542 
Lactate 7.44 ± 0.69 7.50 ± 0.72 0.879 9.48 ± 1.29 9.69 ± 0.97 0.716 9.24 ± 0.41 9.75 ± 0.50 0.043 
Metabolite Cortex Hippocampus Cerebellum 
 Control (n = 8) Bsn (n = 8) P Control (n = 8) Bsn (n = 8) P Control (n = 8) Bsn (n = 8) P 
Taurine 11.12 ± 0.51 11.14 ± 0.52 0.932 10.98 ± 0.99 11.45 ± 1.08 0.380 9.30 ± 0.50 8.66 ± 0.73 0.059 
NAA 6.66 ± 0.42 5.95 ± 0.35 0.002 6.44 ± 0.46 5.67 ± 0.39 0.002 6.69 ± 0.51 6.51 ± 0.33 0.403 
Inositol 5.62 ± 0.38 5.42 ± 0.58 0.446 7.51 ± 0.56 7.66 ± 0.89 0.691 7.13 ± 0.88 7.33 ± 0.51 0.585 
GABA 1.79 ± 0.10 1.79 ± 0.15 0.959 1.57 ± 0.31 1.71 ± 0.28 0.370 1.32 ± 0.31 1.44 ± 0.22 0.370 
Choline 1.40 ± 0.13 1.45 ± 0.12 0.455 1.11 ± 0.28 1.13 ± 0.44 0.913 1.26 ± 0.17 1.16 ± 0.16 0.253 
Creatine 9.23 ± 0.62 9.73 ± 0.35 0.064 10.49 ± 0.80 10.43 ± 0.81 0.888 13.81 ± 0.57 13.27 ± 1.11 0.241 
Glutamine 3.40 ± 0.24 2.64 ± 0.55 0.003 2.58 ± 0.42 2.04 ± 0.32 0.011 3.59 ± 0.93 3.64 ± 0.87 0.912 
Glutamate 9.79 ± 0.51 9.23 ± 0.38 0.026 10.02 ± 0.84 9.45 ± 0.63 0.145 9.01 ± 0.70 8.69 ± 0.85 0.421 
Aspartate 2.53 ± 0.15 2.54 ± 0.28 0.926 2.18 ± 0.29 2.29 ± 0.57 0.618 2.68 ± 0.37 2.90 ± 0.44 0.297 
Succinate 0.55 ± 0.12 0.51 ± 0.15 0.566 0.87 ± 0.19 0.77 ± 0.27 0.390 0.81 ± 0.30 0.78 ± 0.26 0.844 
Acetate 0.97 ± 0.25 0.77 ± 0.23 0.123 3.57 ± 0.89 3.76 ± 0.38 0.584 2.76 ± 0.31 2.45 ± 0.44 0.127 
Alanine 0.57 ± 0.08 0.54 ± 0.07 0.394 0.84 ± 0.14 0.75 ± 0.14 0.214 0.52 ± 0.05 0.50 ± 0.09 0.542 
Lactate 7.44 ± 0.69 7.50 ± 0.72 0.879 9.48 ± 1.29 9.69 ± 0.97 0.716 9.24 ± 0.41 9.75 ± 0.50 0.043 

Note: Summary of all quantified metabolites (expressed in mmol/kg) from various brain areas of wild-type (control) and Bassoon-mutant mice (Bsn). Significant differences (P < 0.05) between wild-type and Bassoon-mutant mice are highlighted by bold/italic numbers.

We further determined if based on the entire relevant 1H-NMR spectra, a separation of wild-type and Bassoon-mutant mice is possible. To this end, we used the measured 1H-NMR spectra between 0.8 and 4.50 ppm, that is, the region that includes all quantified metabolites, and performed a PCA. The overall comparison of the averaged spectra from wild-type and Bassoon-mutant mice revealed no clear alterations in the general pattern indicating that within the cortex, hippocampus, and cerebellum, the ratio between all detectable metabolites is not severely affected by the absence of a functional Bassoon protein (Fig. 3). To perform a PCA, data were integrated over a series of 0.04-ppm integral regions.

Figure 3.

Comparison of the averaged 1H-NMR spectra between Bassoon-mutant mice and wild-type age-matched littermates. To compare the entire metabolic composition within the 3 brain structures, each spectrum (see Fig. 2) was first normalized to the total integral region (total integral = 1.00), so that each individual integral represents its relative amount to the total amount of all low molecular weight metabolites contributing to the entire spectrum. Thereupon, the average of all 8 individual spectra of each group was formed and compared. For the following PCA, the spectra were integrated between 0.8 and 4.5 ppm over a series of 0.04-ppm integral regions. The differences between the resulting spectra (after normalization and integration) reveal only relative small variations in the entire metabolic composition in each investigated brain region. Region-specific changes are labeled. Note, because in this graphical presentation the amount of each metabolite is related to the amount of all measured metabolites, significant variations in low abundant molecules (e.g., glutamine) therefore appear only as small deviations in the graph. Lac, lactate; NAA, N-acetyl aspartate; Cre, creatine; Gln, glutamine).

Figure 3.

Comparison of the averaged 1H-NMR spectra between Bassoon-mutant mice and wild-type age-matched littermates. To compare the entire metabolic composition within the 3 brain structures, each spectrum (see Fig. 2) was first normalized to the total integral region (total integral = 1.00), so that each individual integral represents its relative amount to the total amount of all low molecular weight metabolites contributing to the entire spectrum. Thereupon, the average of all 8 individual spectra of each group was formed and compared. For the following PCA, the spectra were integrated between 0.8 and 4.5 ppm over a series of 0.04-ppm integral regions. The differences between the resulting spectra (after normalization and integration) reveal only relative small variations in the entire metabolic composition in each investigated brain region. Region-specific changes are labeled. Note, because in this graphical presentation the amount of each metabolite is related to the amount of all measured metabolites, significant variations in low abundant molecules (e.g., glutamine) therefore appear only as small deviations in the graph. Lac, lactate; NAA, N-acetyl aspartate; Cre, creatine; Gln, glutamine).

Calculating the difference between both integrated spectra revealed again no clear differences in the metabolic compositions of both mouse groups. However, this procedure confirmed and visualized defined variations in the relative amounts of N-acetyl aspartate, lactate, and creatine, which corresponded with the calculated differences in the absolute concentrations of these metabolites (Fig. 3 and Table 1).

The existing small changes in the relative and absolute concentrations of N-acetyl aspartate, lactate, and glutamine in the tissue of wild-type and BsnΔEx4/5 mice did not lead to a clear separation of the entire spectra by statistical pattern recognition. In contrast, PCA produced a clear separation of the entire high-resolution 1H-NMR spectra from cortical, hippocampal, and cerebellar tissues (Fig. 4) as already observed in another mouse study (Pears et al. 2005). This indicates that absence of a functional Bassoon protein does not affect the general metabolism but modifies specifically the glutamine–glutamate and neuronal N-acetyl aspartate metabolism. Furthermore, these perturbations in the glutamate–glutamine metabolism occur in the cortex but not in the cerebellum of Bassoon-mutant mice.

Figure 4.

Pattern recognition model of general metabolic differences across various brain structures of BsnΔEx4/5 mice and wild-type mice. Component score plot distinguishes clearly the metabolic profiles derived from all studied brain regions (squares—cortex, circles—hippocampus, and triangles—cerebellum). However, within each brain structure, there was no separation of the 1H-NMR spectra deriving from Bassoon-mutant and wild-type mice (full symbols—control mice and open symbols—BsnΔEx4/5 mice).

Figure 4.

Pattern recognition model of general metabolic differences across various brain structures of BsnΔEx4/5 mice and wild-type mice. Component score plot distinguishes clearly the metabolic profiles derived from all studied brain regions (squares—cortex, circles—hippocampus, and triangles—cerebellum). However, within each brain structure, there was no separation of the 1H-NMR spectra deriving from Bassoon-mutant and wild-type mice (full symbols—control mice and open symbols—BsnΔEx4/5 mice).

Neuron Density Is Reduced in Cortical Layer V of Bassoon-Mutant Mice

High-resolution solution-state 1H-NMR spectroscopy revealed a reduction in the concentration of the neuron-specific metabolite N-acetyl aspartate as well as glutamine and glutamate in the cortex of Bassoon-mutant mice. The reduced concentration of N-acetyl aspartate within the cortex in combination with the previously described observation of an enlarged cortex volume (Angenstein et al. 2007) imply a decreased neuron density within this structure. Furthermore, the redistribution of manganese into distinct layers of the cortex of Bassoon-mutant mice as seen by ME-MRI (Fig. 1) points to layer-specific morphological alterations because the manganese accumulation within cortical layers did not match with the ongoing neuronal activity. To test the hypothesis that within the cortex of Bassoon-mutant mice the neuron cell density is reduced probably in a layer-specific manner, we calculated neuron cell density by staining cortical sections with an antibody against NeuN, a neuronal-specific nuclear protein (Mullen et al. 1992). To get further information, if a hypothesized decrease in neuron densities is substituted with an increase in glia cell density or with an increased neuropil, we also stained the cortical sections with an antibody against S100β, a glia-specific protein (Fig. 5). Comparing neuron cell densities within all cortical layers of wild-type and BsnΔEx4/5 mice revealed altered neuron density in the cortex of the mutant mouse (Table 2). Changes were not uniform across the cortical layers but varied between individual layers; that is, a significant decrease of neuron density in cortical layer V and almost unchanged neuronal density in superficial cortical layers II/III. Accordingly, changes in glia cell density complemented the altered neuronal density in layers IV and V; that is, an increased glia cell density in layer V and a decreased glia cell density in layer IV. Such complementary changes in neuron and glia cell density are reflected by significant changes in the neuron to glia ratio in the cortical layers IV and V in Bassoon-mutant mice. Whereas in cortical layer IV of Bassoon-mutant mice the ratio of neurons to glia cells is shifted significantly toward neurons, this ratio is shifted clearly toward glia cells in cortical layer V.

Figure 5.

Analyzing the cortical cytoarchitecture of Bassoon-mutant and wild-type mice by immunocytochemistry. Horizontal sections were used to identify neurons by staining the neuronal nuclear protein NeuN (green) and glia cells by staining the glia-specific protein S100β. NeuN- and S100β-immunoreactive cell bodies (showing a nucleus) were counted within rectangles laid over the different cortical layers (supragranular layers: II/III, granular layer: IV, and infragranular layers: Va/Vb and VI) of the immunostained sections (see Materials and Methods).

Figure 5.

Analyzing the cortical cytoarchitecture of Bassoon-mutant and wild-type mice by immunocytochemistry. Horizontal sections were used to identify neurons by staining the neuronal nuclear protein NeuN (green) and glia cells by staining the glia-specific protein S100β. NeuN- and S100β-immunoreactive cell bodies (showing a nucleus) were counted within rectangles laid over the different cortical layers (supragranular layers: II/III, granular layer: IV, and infragranular layers: Va/Vb and VI) of the immunostained sections (see Materials and Methods).

Table 2

Neuron and glia cell density in the cortex of wild-type and Bassoon-mutant mice

  Layer II/III Layer IV Layer V Layer VI 
NeuN Control 1130 ± 107 1609 ± 207 925 ± 106 1201 ± 114 
 Bsn 1135 ± 55 (100.4%) 1829 ± 138 (113.7%) 677 ± 46 (73.2%) 1089 ± 73 (90.7%) 
S100 Control 144 ± 15 212 ± 18 151 ± 20 148 ± 24 
 Bsn 163 ± 2 (113.2%) 195 ± 24 (92.0%) 187 ± 31 (123.8%) 162 ± 31 (109.5%) 
NeuN/S100 Control 8.04 ± 1.75 7.65 ± 1.33 6.35 ± 1.89 8.37 ± 1.94 
 Bsn 6.97 ± 0.43 (86.7%) 9.44 ± 0.55 (123.4%) 3.69 ± 0.53 (58.1%) 7.22 ± 2.24 (86.3%) 
  Layer II/III Layer IV Layer V Layer VI 
NeuN Control 1130 ± 107 1609 ± 207 925 ± 106 1201 ± 114 
 Bsn 1135 ± 55 (100.4%) 1829 ± 138 (113.7%) 677 ± 46 (73.2%) 1089 ± 73 (90.7%) 
S100 Control 144 ± 15 212 ± 18 151 ± 20 148 ± 24 
 Bsn 163 ± 2 (113.2%) 195 ± 24 (92.0%) 187 ± 31 (123.8%) 162 ± 31 (109.5%) 
NeuN/S100 Control 8.04 ± 1.75 7.65 ± 1.33 6.35 ± 1.89 8.37 ± 1.94 
 Bsn 6.97 ± 0.43 (86.7%) 9.44 ± 0.55 (123.4%) 3.69 ± 0.53 (58.1%) 7.22 ± 2.24 (86.3%) 

Note: Measured neuron and glia cell density in different layers of the cortex. Neurons were visualized by staining of NeuN, a neuron-specific nuclear protein, and glia cells were stained with S100β (see Fig. 5) and expressed as stained cell per mm2. A significant difference (P < 0.05) in neuron cell density of Bassoon-mutant mice was found in layer V, and a significant alteration in the neuron to glia cell ratio was observed in cortical layer IV and V (P < 0.05, Mann–Whitney test; highlighted by bold/italic numbers). The measured width and cell densities correspond to the somatosensory cortex.

Altogether, immunohistochemical staining and analysis of the cortex of Bassoon-mutant mice confirmed a general reduction in the ratio between neuron to glia cell density in the cortex of Bassoon-mutant mice, except for the cortical input layer IV. Changes in neuron cell density were accompanied by opposite changes in glia cell densities, pointing to the presence of a similar total cell density in the cortex of BsnΔEx4/5 mice. Consequently, we conclude that the increase in cortex volume observed in Bassoon mutants is caused by an increased cell numbers rather than by an increase of individual cell volumes and/or increase in extracellular space.

Discussion

The principal findings of this study are as follows: 1) Absence of a functional Bassoon protein in mice causes specific perturbations in the glutamine/glutamate and N-acetyl aspartate metabolism in the cortex and hippocampus but not in the cerebellum as revealed by high-resolution (600 MHz) 1H-NMR spectroscopy. 2) Coinciding with the reduction of the neuron-specific metabolite, N-acetyl aspartate, in the cortex of Bassoon-mutant mice, the neuronal density is significantly reduced in cortical layer V, and the ratio of neuron to glia cells is reduced in all cortical layers except cortical layer IV. 3) The reduced neuronal density within the cortex is substituted by an increase in glia cell density. Consequently, the reason for an enlarged cortex volume in Bassoon-mutant mice is not an increased number of neurons but rather an increased number of glia cells. 4) Cortex layer–specific changes in cell density as well as changes in the neuron to glia cell ratio are not the cause for the observed alterations in manganese accumulation within the cortex of BsnΔEx4/5 mice as observed by ME-MRI.

Metabonomic Characterization of Bassoon-Mutant Mice

Bassoon-mutant mice are characterized by an enlarged brain size mainly caused by an enlarged cortex and hippocampus volume and an altered basal neuronal activation pattern in the cortex (Angenstein et al. 2007). To elucidate whether these structural and functional changes are linked with metabolic perturbations, we started a metabonomic approach. There are basically 2 possibilities to compare the metabolic composition using high-field 1H-NMR spectroscopy, first by measuring all integrals of identifiable resonances and thereupon calculating the absolute concentration of all appropriate metabolites or second by identifying differences in the pattern of the entire spectrum followed by a characterization of the spectrum regions, which causes the pattern mismatches. For screening purposes, the second approach is certainly preferable (Fiehn 2002; Griffin 2003); however, as our study indicates, relative small alterations in only 1 or 2 metabolites are hard to detect in this way. Consistent with a similar previous study (Pears et al. 2005), PCA enables a clear separation of spectra from different brain regions (Fig. 4) but no unambiguous separation of spectra from wild-type and Bassoon-mutant mice obtained from individual structures. This indicates that the metabolic composition in the various structures that defines the entire 1H-NMR spectrum is almost identical between both mouse groups. The most striking difference is the reduction of glutamine concentration in the cortex of Bassoon-mutant mice by 22.3%. Interestingly, the cortex volume of these mice is enlarged by about 24% (Angenstein et al. 2007), so that arithmetically the total absolute amount of glutamine in the cortex is nearly unchanged in BsnΔEx4/5 mice. The cause for the reduced glutamine concentration is unknown; theoretically, the observed volume increase could be simply due to tissue that lacks any glutamine, which is, however, not very likely. Furthermore, glutamine is an essential metabolite for the homeostasis of the glutamatergic and GABA-ergic transmitter systems (Bak et al. 2006). Whereas the concentration of GABA is not altered in Bassoon-mutant mice, we found a decrease in the concentration of glutamate. A consequential deficit in glutamatergic transmission would support previous findings that a subset of synapses is functionally inactive in Bassoon-mutant mice (Altrock et al. 2003). Reduced concentrations of glutamine/glutamate with a concomitant unchanged GABA concentration are no characteristic metabolic perturbations associated with recurrent epileptic seizures as detected in various animal models of epilepsy (Eloqayli et al. 2003; Shirayama et al. 2005; Melo et al. 2006). Consequently, the observed metabolic alterations in the cortex of BsnΔEx4/5 mice reflect rather the presence of an ongoing imbalance between the excitatory and inhibitory transmitter systems in the cortex than the effect of epileptic seizures. To what extend this imbalance triggers the epileptic activities has to be clarified in future studies.

In addition to changes in glutamine/glutamate concentrations, we detected a significantly reduced concentration of N-acetyl aspartate in the cortex and hippocampus of BsnΔEx4/5 mice. N-acetyl aspartate is considered a neuron-associated metabolite that may act as important cellular osmolyte, storage vehicle for aspartate and acetate, potential intercellular signaling molecule, precursor for myelin synthesis in glia cells, and a central part of a molecular water pump system (for a review, see Baslow 2003). Consequently, a reduced N-acetyl aspartate concentration could point to a reduced neuron density and/or to a reduced neuronal activity in the cortex. The latter conclusion arises from the hypothesis that the N-acetyl aspartate system is responsible for the elimination of metabolic water that is formed in large quantities especially during high neuronal activity (Baslow 2002). This would agree with a potential reduced amount of glutamatergic transmission in the cortex of Bassoon-mutant mice. However, previous mappings of neuronal activity within the cortex of these mice had indicated that the neuronal activity in the cortex is rather qualitatively than quantitatively different (Angenstein et al. 2007); therefore, the reduced neuronal density and/or reduced neuron to glia cell ratio appears the most likely interpretation of the 1H-NMR spectroscopy data.

Structural Alterations in the Cortex of Bassoon-Mutant Mice

The previous observation that the cortex of Bassoon-mutant mice is enlarged and concomitantly the concentration of the neuron-specific metabolite, N-acetyl aspartate, is reduced implies a reduced neuron density in this structure. An altered cellular architecture within the cortex of Bassoon-mutant mice was already hypothesized in our previous study that described a change in manganese accumulation within cortical layers of Bassoon-mutant mice (Angenstein et al. 2007). Manganese, an MRI contrast agent, enters cells in a complex manner, and correspondingly, observed changes in manganese uptake can indicate altered neuronal activity and/or changed neuronal and glia cell densities in these structures (Takeda 2003; Aoki et al. 2004; Wadghiri et al. 2004). Altered neuronal activities within the cortex of BsnΔEx4/5 mice as visualized by 14C-deoxyglucose incorporation and thallium autometallography did not correspond to the changed pattern of manganese incorporation in this structure; consequently, morphological changes were assumed as a reason for the observed altered manganese distribution (Angenstein et al. 2007).

In Bassoon-mutant mice, the main differences in neuronal activity were previously found especially in cortical layer IV, the thalamic input layer, which exhibited a reduced neuronal activity, and the superficial layers II/III that were characterized by a higher basal neuronal activity. Staining cortical section with NeuN, a neuronal nuclear protein, and S100β, a glia-specific protein, revealed a selective increased neuron to glia cell ratio within cortical layer IV, whereas this ratio was slightly reduced in the superficial layers II/III and significantly smaller in deep cortical layer V. Changes in neuronal densities appeared to be compensated by opposite changes in glia cell densities (Table 2); hence, the total cell density may not be different in Bassoon-mutant mice compared with wild-type littermates. Comparing previously observed cortical activity maps with the current morphological analysis demonstrates that the reduced activity within the cortical input layer IV in Bassoon-mutant mice is accompanied with an increased neuron to glia ratio. In all other cortical layers, the neuron to glia ratio is shifted toward glia cells. The most obvious shift appeared in cortical layer V in which the ratio between neurons and glia cells is almost halved, and a significant decrease in neuronal density is present. Because pyramidal cells of layer V drive subcortical structures (e.g., basal ganglia and colliculus) and influence the ongoing input to layer VI neurons, which in turn are connected again to the thalamic input layer IV (Douglas and Martin 2004), this morphological feature supports the previous assumption that cortical information processing is severely affected in Bassoon-mutant mice.

The comparison of the histological features with the observed manganese accumulation within the cortex of Bassoon-mutant mice points to no obvious matches. This indicates that, at least within the cortex, increases in the manganese accumulation within specific cortical layers, as observed by ME-MRI, are not equivalent with a specific morphological parameter, such as neuron, glia cell density, or neuron to glia ratio. Consequently, distinctive features revealed by ME-MRI may include in addition to structural (cell densities and neuron glia ratio) and functional (neuronal activation pattern and transport processes) alterations also molecular differences that control manganese accumulation within cells, such as Ca2+-channel composition or divalent metal ion transporter. Despite these potentially multifactorial reasons for an altered manganese accumulation in the brain, ME-MRI is a valuable tool to screen genetically modified mice for putative structural/functional alterations in various brain structures.

Conclusion

Absence of the functional presynaptic active zone protein Bassoon induces on cellular level impairments in synaptic transmission in a subset of synapses. At a systemic level, this specific cellular dysfunction causes clear morphological, metabolic, and functional reorganizations especially within the cortex. Based on the current and previous studies (Altrock et al. 2003; Angenstein et al. 2007), it appears that the cortex receives less input thus the neuronal activity is reduced in layer IV, concordant with a increased neuron to glia cell ratio in this layer. This is paralleled by an enhanced ongoing activity in superficial layers II/III. In these layers, the neuronal density is little affected and the neuron to glia cell ratio is only slightly reduced. Whether a potential reduced input from layer IV is responsible for the higher activation in superficial layers is unknown. In contrast, in deep cortical layers V/VI, both neuronal activity and the ratio between neurons and glia cells are reduced. Consequently, visualized cortical layer–specific changes in neuronal activity correlate well with alterations in the cytoarchitecture, especially in the neuron to glia cell ratio. These functional and structural anomalies relate also to the observed metabolic effect, that is, an altered glutamate/glutamine cycle and a reduced N-acetyl aspartate concentration. So far the metabolic characterization of the cortex does not account for layer-specific characteristic features, so that even closer matches between function (neuronal activity), structure (neuron, glia cell densities, and the corresponding ratio between them), and metabolism (composition and concentration of low molecular metabolites) become possible.

Such a comprehensive mapping of the cortex can visualize the impact of a single disturbed cellular control mechanism, in the case of the Bassoon-mutant mouse the regulation of transmitter-release probability, on a complex neuronal network and will, consequently, help to link observations and results made on a cellular level with findings on behavioral level.

Funding

Center of Advanced Imaging (BMBF-grant 01G00202 to FA and HGN); Deutsche Forschungs-gemeinschaft (SFB TR3 TPA7 to HGN); European Commission (SynScaff-511995) and a Max Planck Award of the Alexander von Humboldt Foundation and the Max Planck Society and the Fonds der chemischen Industrie 163 569 to (EDG); “Pakt für Forschung & Innovation” of the Leibniz Society PAKT-2007/GAMLS to (FA, EDG).

We thank Prof. Schinzer for the use of the NMR facilities of the Institute of Chemistry, Otto-von-Guericke University, Magdeburg, Germany and Bettina Kracht for providing us with the Bassoon-mutant and wild-type littermates. Conflict of Interest: None declared.

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