Assessing Susceptibility to Epilepsy in Three Rat Strains Using Brain

Article pubs.acs.org/jpr

Assessing Susceptibility to Epilepsy in Three Rat Strains Using Brain Metabolic Profiling Based on HRMAS NMR Spectroscopy and Chemometrics Florence Fauvelle,*,†,‡,Ψ,¶,§ Julien Boccard,# Fanny Cavarec,§,∥ Antoine Depaulis,§,∥,⊥ and Colin Deransart§,∥,⊥ †

IRBA, 91223 Bretigny sur Orgne, France Univ. Grenoble Alpes, IRMaGe MRI facility, F-38000 Grenoble, France Ψ CNRS, UIMS 3552, F-38000 Grenoble, France ¶ INSERM, US17, F-38000 Grenoble, France § INSERM U836, F-38042 Grenoble, France ∥ Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, F-38000 Grenoble, France ⊥ Centre Hospitalier Universitaire, F-38000 Grenoble, France # School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, CH-1211 Geneva, Switzerland ‡

S Supporting Information *

ABSTRACT: The possibility that a metabolomic approach can inform about the pathophysiology of a given form of epilepsy was addressed. Using chemometric analyses of HRMAS NMR data, we compared several brain structures in three rat strains with different susceptibilities to absence epilepsy: Genetic Absence Epilepsy Rats from Strasbourg (GAERS), Non Epileptic Control rats (NEC), and Wistar rats. Two ages were investigated: 14 days postnatal (P14) before the onset of seizures and 5 month old adults with fully developed seizures (Adults). The relative concentrations of 19 metabolites were assessed using 1H HRMAS NMR experiments. Univariate and multivariate analyses including multiblock models were used to identify the most discriminant metabolites. A straindependent evolution of glutamate, glutamine, scyllo-inositol, alanine, and glutathione was highlighted during cerebral maturation. In Adults, data from somatosensory and motor cortices allowed discrimination between GAERS and NEC rats with higher levels of scyllo-inositol, taurine, and phosphoethanolamine in NEC. This epileptic metabolic phenotype was in accordance with current pathophysiological hypothesis of absence epilepsy (i.e., seizure-generating and control networks) and putative resistance of NEC rats and was observed before seizure onset. This methodology could be very efficient in a clinical context. KEYWORDS: absence epilepsy, rat models, metabolomics, multivariate statistical analysis, multiblock model, consensus OPLS-DA

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INTRODUCTION There is a crucial need to “develop, for research purposes, new ways of classifying mental disorders based on dimensions of observable behavior and neurobiological measures” (http:// www.nimh.nih.gov). This requirement specially applies to neurological diseases that need a better medical management. Several global approaches have been developed in the field of “omics” that do not postulate any a priori physiopathological hypothesis.1,2 These approaches allow the sorting of data to focus on the most relevant information. For instance, metabolomics, which aim is to find specific metabolic profiles by using multivariate statistics, appears as a powerful mean for classification and prediction issues.3 In this study, we aimed to evaluate the metabolomic approach in the field of epilepsy. © 2015 American Chemical Society

Absence epilepsy (AE) is a childhood disorder characterized by the occurrence of spontaneous generalized spike-and-wave discharges (SWD) on the electroencephalographic recordings (EEG), concomitant with a behavioral arrest and loss of consciousness.4 In typical AE, seizure onset is generally observed in children around 5 or 7 (i.e. during brain maturation).4 However, very little is known about the pathophysiological processes that are involved during brain maturation and that lead to the occurrence of this form of epilepsy. To address these processes, animal models have been characterized,5 among which the Genetic Absence Epilepsy Received: December 19, 2014 Published: March 12, 2015 2177

DOI: 10.1021/pr501309b J. Proteome Res. 2015, 14, 2177−2189

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Journal of Proteome Research

Figure 1. A/Delineation of structures sampled in adult rats: somatosensory cortex (SSI), motor cortex (M1), ventrobasal thalamus (VB), striatum (St), hippocampus (H) and cerebellum (Cb). B and C/proton HRMAS NMR spectra of somatosensory cortex of GAERS rat, Adults (B) and P14 (C). Spectra are not normalized to the sample weight. D/Mean weight of rats ± SEM, top P14, bottom Adults. *p < 0.05. 1: Lac (Lactate), 2 Ala (alanine), 3 Ace (acetate), 4 NAA (N-acetylaspartate), 5 Glu+Gln (glutamate + glutamine), 6 GABA (gamma-amino-butyric acid), 7 Glu (glutamate), 8 Asp (aspartate), 9 Gsh (glutathione), 10 Pcr (phosphocreatine+creatine), 11 PE (phosphoethanolamine), 12 Cho (choline), 13 PC (phosphocholine), 14 GPC (glycerophosphocholine), 15 S-Ins (scyllo-inositol), 16 Tau (taurine), 17 M-Ins (myo-inositol), 18 Asc (ascorbate), 19 Hyp (hypotaurine), 20 Threo (threonine), 21 Ser (serine).

differences between GAERS and this strain.12 It is therefore critical to also compare data collected in GAERS with those collected in Wistar rats (i.e., the original strain from which both GAERS and NEC were derived). Indeed, the importance to use both strains was reported recently.13 High-Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HRMAS NMR) spectroscopy allows very rich metabolic profiles to be produced from intact inhomogeneous samples like cells, biopsies, and so forth.14,15 Thus, small samples like specific brain areas can be separately analyzed and do not need to be pooled like in previous studies involving GAERS rats.16,17 HRMAS NMR can therefore be an alternative method to liquid NMR18 for exploring the metabolome involved in the pathophysiology of neurological diseases. However, proton NMR spectra suffer from strong resonance overlapping, and additional contributions from lipids and macromolecules inherent to the HRMAS method can complicate the data processing step, specifically when binning of the complete NMR spectra is usedwhich is the most common method to extract variables for metabolomic studies.19 In in vivo NMR spectra, data processing is even trickier, and

Rats from Strasbourg (GAERS) is a well-accepted model of childhood AE.5,6 In GAERS, initial SWD are detected around 25 days postnatal in the somatosensory cortex, which has been identified as the site of seizure initiation in adult rats.7,8 Then, the number and duration of these discharges progressively increase with age for up to 4 months. This temporal profile of seizure development makes the GAERS a suitable model for studying changes that occur during brain maturation and that may underlie absence epileptogenesis. The GAERS model, unlike others, benefit from a control strain, the Non Epileptic Control rats (NEC), which derives from the same original Wistar colony and in which no seizure develop. The comparison between GAERS and NEC strains therefore offers the possibility to detect anomalies that may explain the development of AE.9 For example, when local cerebral metabolic rates for glucose were measured using the [14C]-2deoxyglucose method, an overall increase was recorded in adult GAERS compared to NEC.10 Because NEC rats were selected on the basis of absolutely no SWD following an EEG follow-up of several months,11 the possibility remains that genes of resistance may have been selected that could be reflected by the 2178


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Journal of Proteome Research automatic quantification methods20−23 have been proposed to both separate individual metabolite contributions and to overcome the macromolecular signal problem. These methods require the assignment of all resonances. They have been successfully applied to HRMAS NMR spectra.24−26 In a classical study of brain metabolism, metabolite levels are compared using univariate statistics. However, other statistical tools may be needed to identify the critical component involved in the pathology. Chemometric tools like multivariate statistical methods, including principal component analysis (PCA) and orthogonal projection to latent structure-discriminant analysis (OPLS-DA),27 can be of great help to address this issue. Furthermore, the use of quantified data instead of spectral bins allows to more easily go back to the pathophysiology. In this study, we aimed to compare the brain metabolic profiles of three rat strains with different susceptibility to absence epilepsy and to correlate these data to the pathophysiology. For this purpose, three rat strains (GAERS, NEC and Wistar), at two different ages, before (postnatal day 14 or P14) and after the onset of SWD (5 months-old animals thereafter termed “Adults”), were submitted to proton HRMAS analysis. Brain structures known to be involved in either SWD initiation and propagation (i.e., primary somatosensory cortex, ventrobasal thalamus and/or motor cortex) or their control (i.e., striatum28) and two structures thought to be uninvolved in these processes (i.e., hippocampus and/or cerebellum6) were specifically investigated. NMR spectra were quantified and submitted to multivariate statistics.

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structure, the hippocampus, as a neither generating nor control structure in pups, and the sampling of the cerebellum was only considered thereafter in Adults (Figure 1A). Thus, six structures were sampled in Adults and four in P14. The 10 P14 GAERS (22.83 ± 1.39 g) were obtained from 2 different litters (5 males out of 8 pups and 4 males out of 11 pups), the 7 P14 NEC (26.14 ± 0.55 g) were obtained from the same 1 litter (out of 10 pups), and the 9 P14 Wistar (30.49 ± 0.88 g) were obtained from 2 different litters (6 males out of 15 pups and 3 males out of 9 pups). P14 GAERS and NEC weighted significantly less (25% and 12%, respectively) than their P14 Wistar counterparts (Figure 1D). The 13 adult male GAERS (291.54 ± 13.62 g) were obtained from 4 different litters (2 of 5 animals and 2 of 2), the 13 adult male NEC (339.92 ± 14.13 g) from 3 different litters (2 of 5 and 1 of 3), and the 8 adult male Wistar (354.50 ± 4.88 g) from the same 1 litter. Adult GAERS and NEC weighted significantly less (18% and 4%, respectively) than their adult Wistar counterparts. These data confirm that GAERS rats are lighter than NEC and Wistar and that this difference tends to decrease with age in NEC but not in GAERS.12

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HRMAS NMR ANALYSIS

Sample Preparation

Thirty microliters of a cold 1 mM D2O solution of 3(trimetylsilyl) propionic-2,2,3,3-d4 acid (TSP) was added to 15 mg of frozen biopsy in a 50 μL zirconium rotor. Only samples from the right hemisphere were analyzed.

MATERIALS AND METHODS

Data Acquisition

Spectra were recorded on a Bruker Avance 400 spectrometer (Bruker Biospin, Wissembourg, France) at a proton frequency of 400.13 MHz, using a Carr−Purcell−Meiboom−Gill (CPMG) pulse sequence synchronized31 with the spinning rate of 4 kHz (interpulse delay 250 μs, total spin echo time 30 ms) and presaturation of residual water signal, for a total acquisition time of 16 min. Resonance assignment was performed as previously described.15,32

Animals

All animal procedures were run according to the French guidelines on the use of living animals in scientific investigations, with the approval of the “Grenoble-Institut des Neurosciences ethical committee” and with the French Ministry of Higher Education and Research agreement number 004 (01/04/2009). Both 14 day old male Wistar rat pups (P14) and 5 month old adult male Wistar Hannover rats were obtained from Charles River Laboratories (L’Arbresle, France), whereas GAERS and NEC of the same ages where bred at the animal facility at Grenoble Alpes University (Plateforme de Haute Technologie Animale, Grenoble). P14 (GAERS n = 10; NEC n = 7; Wistar n = 9) and adult rats (GAERS n = 13; NEC n = 13; Wistar n = 8) were killed by decapitation. Their brain was rapidly removed and transferred to a brain-matrix at 4 °C to be sliced in 2 mm thick coronal sections, which where rapidly dissected on ice, with delineation of brain structures according to the atlas of Paxinos and Watson.29 For both P14 and Adults, somatosensory cortex, ventrobasal thalamus, striatum, and hippocampus were dissected in both hemispheres, placed in cryotubes, snap frozen in liquid nitrogen, and then stored at −80 °C. The overall sample collection procedure for different structures was standardized to be performed in less than 8 and 13 min after decapitation in P14 and adult rats, respectively. We have shown in previous studies15,30 that 15 min acquisition time with a 15 mg biopsy was a good compromise for both sensitivity and reproducibility of NMR data. Because we initially started this study with P14, and due to the smaller size of the motor cortex at this age as compared with Adults, this structure was not sampled in rat pups. Furthermore, we initially sampled only one

Metabolite Quantification

Quantification was performed as previously described20,26,30 with the jMRUI software package ((http://www.mrui.uab.es/ mrui/) using the “subtract-QUEST” procedure.22 The procedure involves a simulated metabolite database set including acetate (Ace), alanine (Ala), ascorbate (Asc), aspartate (Asp), choline (Cho), gamma-amino-butyric acid (GABA), glutamate (Glu), glutamine (Gln), glutathione (Gsh), glycerophosphocholine (GPC), glycine (Gly), lactate (Lac), myo-inositol (M-Ins), N-acetylaspartate (NAA), phosphocreatine and creatine (Pcr), phosphoethanolamine (PE), phosphocholine (PC), scyllo-inositol (S-Ins), taurine (Tau). Hypotaurine (Hyp), threonine (Threo), serine (Ser) were only detected in P14 and were then added to the database of young rats. The 16th first points of FID were used to estimate the nonparametric part of the signal (i.e., lipids and macromolecules). The amplitude of metabolites calculated by QUEST was normalized to the total spectrum signal, so only relative concentrations were produced. The Cramer Rao lower bounds (CRLB) determined by the jMRUI algorithm are estimates of the Standard Deviation of the fit for each metabolite. For most of the metabolites and for all quantifications, we obtained CRLB ≤ 5%, and for Ace, Asp, Gsh, CRLB ≤ 25%. For Hyp, Ser and Threo CRLB were >50% 2179


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Figure 2. Score scatter plot (A, C, E) relative to the first t[1] and second t[2] components of PCA analysis performed with NMR quantified data, with each spectrum loaded as an observation. Loading scatter plot (B, D, F) relative to the first p[1] and second p[2] components of the same models. Encircled: groups of metabolites that can be associated with the clusters of the related score plot. A/and B/: n = 304, 6 structures for adult rats and 4 for P14 rats (one observation Cb Adults excluded). The age is the dominant discriminating factor. C/and D/: all P14 n = 103. The structure is the dominant discriminating factor in P14. E/and F/: all Adults n = 201 (one excluded Cb) observations. The structure is the dominant discriminating factor in Adults. SSI: somatosensory cortex, VB ventrobasal thalamus, St striatum, H hippocampus, M1 motor cortex, Cb cerebellum.

first overview of the sample distribution. OPLS-DA27 were then run to distinguish groups of rats according to their age (Adults/ P14).33 OPLS-DA allows separating systematic variations in X variables that are unrelated to the class membership matrix (orthogonal) from linear variations in X that predict Y. A multiblock modeling approach was then applied to gain insight into the collected data by accounting for all cerebral structures to separate the three rat strains using a global model. Consensus orthogonal projection to latent structure-discriminant analysis (consensus OPLS-DA34) was carried out under the MATLAB 8 environment (The MathWorks, Natick, U.S.A.). All multivariate models were examined according to the following steps. First, the score plot was thoroughly inspected to assess the sample distribution in the model subspace and to detect potential clusters of observations. Exclusion of outliers was performed after examination of the residual error (i.e., the distance to the model). Second, the loadings, which express the covariance between X score vectors and the X matrix (i.e., the variables’ contributions to the latent variables (or components)) were analyzed to detect characteristic metabolic

so they were excluded from statistical analysis. Finally 19 metabolites were kept for both adult and pup rat statistics. A total of 305 spectra were acquired, divided as below (3 spectra are missing: 2 adult motor cortex and 1 P14 ventrobasal thalamus due to technical problems): (i) 202 spectra obtained from 6 structures (somatosensory cortex, motor cortex, ventrobasal thalamus, striatum, hippocampus, and cerebellum) of 34 adult rats (13 GAERS, 13 NEC, and 8 Wistar). (ii) 103 spectra obtained from 4 structures (somatosensory cortex, ventrobasal thalamus, striatum, hippocampus) of 26 P14 rats (10 GAERS, 7 NEC, and 9 Wistar). Statistics. In this study, we used quantified data as variables for multivariate statistics. The proposed strategy was first to start from a complete overview of all data and then to gradually reduce the scope of investigation, in order to highlight differences between the three strains in relation to their susceptibility to epilepsy. Quantified data were loaded in the SIMCA-P software version 13 (Umetrics, Umea, Sweden) and scaled to unit variance before analysis. PCA models were computed to gain a 2180


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performed using either adult or P14 animals mainly showed clusters related to brain areas. Third, the analysis of characteristic metabolic patterns (i.e., discriminant metabolites) was carried out using supervised consensus OPLS models accounting for the three strains. Such a strategy allowed evaluating the contribution of each brain structure to rat strain discrimination. Subtle Differences between Strains Despite AgeRelated Dominant Effect on Whole Data. We first performed PCA with all available data. Only one observation of adult cerebellum was discarded because of a critically high distance to the model (see Materials and Methods section). Figure 2A represents the score plot relative to the first two components of the PCA model summarizing 38.3% and 15.4% of the initial variability, respectively. As expected, the main clustering factor was age, with the first component t[1] separating adult rats (black circles) from pup rats (gray circles). This plot summarizes the high amplitude variations observed for several metabolites between pups and Adults, regardless of strains and brain structures. The loading plot (Figure 2B) shows that two groups of variables explain the separation between ages: (i) Tau, PE, PC, Asc, Cho, and Ala that are high in pups and low in Adults, whereas (ii) Lac, M-ins, GPC, Naa, Gly, Glu, Pcr, Gsh, Asp, and Gln are high in Adults and low in pups. In order to determine whether this age-related separation differs between the three strains, we applied OPLS-DA separately to each strain using ages as Y classes. All models were very robust with R2Y > 0.92 and Q2 > 0.91. The first predictive component separated P14 with negative scores from Adults with positive scores (not shown). A SUS-plot (Figure 3) was built to compare both models loadings: this plot allowed to distinguish metabolites that have a common evolution in the two statistical models (metabolites located on the diagonals) from metabolites that correspond to variables with a specific contribution for the corresponding statistical model (off diagonal). We observed that several metabolites identified in Figure 2B were located on the diagonals with high correlation values: PE, PC, Tau, Asc, and Cho are reliably associated with P14 from GAERS, NEC, and Wistar, indicating that they are at their highest level in all P14 relative to all Adults (Figure 3). On the other side of the diagonal M-Ins, GPC, Lac, and to a lesser extent, NAA, and Gly are reliably associated with Adults of the three strains, indicating higher level in Adults compared to P14. GABA and Ace, located near the center of the plot, are less affected by brain maturation (correlation close to zero). In addition, the SUS-plot revealed the unique feature of S-Ins, Gsh, Gln (in yellow Figure 3) and Glu, Ala (in red Figure 3) that are located outside the diagonals, indicating a different evolution from P14 to Adults between the three strains. Univariate statistics confirmed these effects (see Supporting Information). In order to avoid the strong age effect that dominate data variance and could mask more subtle differences, we further conducted separate analyses with either adult or P14 data. Separate PCA of P14 or Adults Shows the Structure Dominant Effect Whatever the Strain. In the PCA score plot of P14 samples (103 spectra), we observed a separation of the four analyzed structures along the first component t[1] while a strong scattering of observations was highlighted along the second component t[2] (Figure 2C). In particular, the somatosensory cortex was characterized by higher levels of

patterns. The loading plot provides key points for interpreting the observation scores, because metabolites will be present at higher levels when measured in observations located in the same direction from the origin, compared to samples located in other areas, and at lower levels when positioned in the opposite direction. Additional procedures were applied to ensure the statistical validity of supervised discriminant analyses and extract relevant metabolic patterns. The optimal model size and prediction ability of OPLS-DA models was assessed using a 7-fold crossvalidation. Biomarker discovery was further carried out using SUS-plots, a graphical tool associated with OPLS able to summarize variable contributions (as correlation loadings, pcorr) from several pairwise OPLS-DA models.35 Finally, the consensus OPLS strategy was used to provide an additional level of interpretation by combining data from several data blocks (i.e., each brain area). Consensus OPLS-DA is a low-level data fusion strategy that performs a joint analysis of several data blocks using multiple kernel learning. It takes advantage of the OPLS ease of interpretation and needs only limited memory resources and computation time, even when analyzing highly multivariate data. Computationally, a weighted sum of association matrices is decomposed with the aid of a kernelized version of the OPLS algorithm to build a consensus subspace based on predictive and orthogonal latent variables. The consensus scores are common to all data tables, and block weights allow the influence of each table to be evaluated. Because model linearity is preserved by the use of a linear kernel, block loadings can easily be related between the different data tables. The optimal number of orthogonal components was chosen according to prediction ability using a 7-fold cross-validation. Finally, classical univariate statistics were also performed in parallel, in order to provide further supports to our results for those neurophysiologists and epileptologists that are not always familiar with chemometric approaches (see supplementary Methods).

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RESULTS

Metabolic Profiling

We analyzed by HRMAS NMR four brain structures in P14 and six in Adults for the three strains, resulting in 305 highly resolved spectra (Figure 1B and C). All resonances could be assigned, and we could quantify 19 metabolites with a good reliability in both groups. Some peaks barely detected were assigned to threonine, serine, and hypotaurine but were not included in statistical analysis due to their poor reliability. In adult rats, NAA, Lac, Pcr, Glu, Tau, M-Ins, and Gln had the highest amplitude in all structures. Because Lac is mainly due to post-mortem effects, and our values indicated a variability that did not exceed 6% of the mean, it suggested that our data were very reproducible. In P14, Tau had largely the highest amplitude. For multivariate analysis, each observation was only labeled with its animal number, age, brain area, and strain to avoid any preconceived etiopathological hypothesis related to either epileptogenesis or ictogenesis. Three steps of analysis were applied sequentially. In the first step, a global PCA of all data mainly showed an age-related clustering, and subtle differences between the three strains in this age clustering were revealed by separate OPLS-DA and SUS-plots. Second, separate PCA 2181


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Taken together, PCA models suggest that age and structure are the two factors that dominate the variance of our data set. However, these effects seem to be slightly different for the three strains. SUS-plot pointed out metabolites that differ between the three strains in their direction of evolution between P14 and adulthood. In order to get a better insight into these differences, we then compared the three strains using supervised multivariate analyses. Each Strain Has a Specific Metabolic Profile. Pup Rats. No clear strain-related clusters appeared on PCA analyses (Figure 2). We then performed consensus OPLS-DA to distinguish metabolic patterns related to specific rat strains, by accounting for all data structures in a single model. Unit variance scaling was applied to each data block before modeling. A model with three latent variables (two predictive and one orthogonal component) was obtained with high fit and prediction ability indices (R2Y = 0.89, Q2 = 0.71). The predictive score plot highlighted a clear partition of the three rat strains. Block weights of the predictive latent variables indicated the prominent importance of the somatosensory cortex to distinguish P14 GAERS from NEC and Wistar animals (Figure 4 right column), whereas hippocampus was the most discriminant structure to separate NEC from Wistar and GAERS individuals. We then analyzed loadings for each data block to evaluate the contribution of the variables (i.e., the measured compounds) to the predictive components related to class separation. In the somatosensory cortex, observations from GAERS animals were associated with higher Cho and Gln concentrations and NEC with lower Glu (Figure 4 bottom right). In the other structures, mainly high M-Ins in GAERS and low Glu in NEC were highlighted. Adult Rats. No clear strain-related clusters appeared on individual PCA models. Like with P14 rats, consensus OPLSDA was performed to integrate all data and assess the contribution of each structure to the partition of the three rat strains. Again, a model with three latent variables (two predictive and one orthogonal component) was obtained with high goodness of fit (R2Y = 0.92) and goodness of prediction values (Q2 = 0.72). The three strains were clearly separated on the score plot of the two predictive latent variables (tp1 vs tp2). Block weights clearly indicate that motor cortex and somatosensory cortex are the most important brain areas for the separation of NEC from Wistar and GAERS animals, whereas hippocampus is important for distinguishing the three strains. Striatum and ventrobasal thalamus have low weight on both predictive components, indicating a weaker strain discrimination ability, when compared to other cerebral structures (Figure 4 left column). The loadings revealed a marked impact of high S-Ins concentrations characteristic of NEC individuals in all structures. In somatosensory cortex (Figure 4 bottom left), three metabolites (i.e., S-Ins, Tau, and PE) had the highest contribution to NEC group; however, higher levels of Tau were also observed in striatum and hippocampus, and higher levels of PE were found in motor cortex, striatum, and ventrobasal thalamus of NEC. High Gln concentrations were highlighted in adult GAERS in all structures, although high M-Ins levels were limited to ventrobasal thalamus and cerebellum. Univariate statistics showed a similar trend (see Supporting Information).

Figure 3. SUS-plot, i.e plot of p(corr) values of two pairwise OPLSDA of P14-Adults comparisons (all strains mixed); top: GAERS versus NEC; bottom: GAERS versus Wistar. The blue diagonals show metabolites which levels share the same evolution between P14 and Adults for OPLS-DA models that are plotted. Metabolites on the bottom left side of diagonal are high in P14 (P14+), whereas metabolites on the top right side are high in Adults (Adults +). Metabolites in the yellow and red squares: S-Ins, Glu, Gln, and Ala have a specific evolution for the given OPLS-DA model.

NAA, Asc, Glu, and Asp, whereas striatum presented higher level of GABA and PC (Figure 2D). All other metabolites were differently distributed in the plane. In Adults, a clear structure-based clustering appeared on the score plot of the PCA, whatever the strains. We observed a clear separation between all structures (Figure 2E), except for the somatosensory and motor cortices. Loadings (Figure 2F) revealed two groups of variables opposed along the first component t[1]: (i) Pcr and Lac were associated with cerebellum and (ii) PC, Cho, PE, Tau, Gsh, and GABA were associated with striatum. The second component mainly separated two groups of variables: NAA, Asc, Glu, Asp associated with cortical structures and GPC, Gly, and M-Ins associated with thalamic structure. Hippocampus data appeared in the center of the plot, with no specific trend. 2182


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Figure 4. Consensus OPLS-DA of P14 (right) and Adults (left). Top: scores relative to the two first predictive components tp1 and tp2. The separation between strains is clearly visible. Middle: weight of brain structures in the separation between GAERS, NEC, and Wistars. In Adults, M1 and SSI have the highest contribution to the first predictive component separating NEC and GAERS strains, whereas SSI has the highest weight in P14. Bottom: discriminating metabolites in somatosensory cortex. S-Ins, Tau, and PE are the metabolites the most strongly associated with the first predictive component separating NEC and GAERS strains in Adults.

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DISCUSSION Our study showed that the three strains of rats have specific metabolic profiles whatever their age. These strains could be discriminated according to (i) the evolution between P14 and Adults, in particular for five metabolites (S-Ins, Glu, Gln, Gsh, and Ala), and (ii) the weight of each brain structure with the motor and somatosensory cortices as the most discriminant factors for NEC/GAERS separation, along with the hippocampus for separation between the three strains. Our study also showed that Gln, Tau, S-ins, and PE at adulthood and Glu at P14 were the most important metabolites for discrimination between strains.

Tau) for most of the metabolites. This validates the robustness of our method, from the sampling of tissues to the quantification step with the jMRUI algorithm. Our approach produced a large amount of data, and because subtle differences between strains were expected, there was a need to progressively extract the most relevant information in the context of epilepsy, without any preconceived hypothesis. At each step of our analyses, the examination of loading plots, associated with score plots, provided a very useful tool for focusing on the most discriminant metabolites, which ultimately helps to go back to the biological issue. Furthermore, relevant hypotheses may be hardly deducible from the separate analysis of different cerebral structures, due to the natural heterogeneity and structure-specific characteristics of the brain. In that context, multiblock modeling34 constitutes an attractive approach to gather metabolomic multivariate data from several cerebral areas and offers a complete picture of metabolic events

Methodological Issues

Using HRMAS NMR, we were able to quantify up to 19 metabolites with a good reliability in very small brain samples, without any extraction procedure; the intragroup variability did not exceed 10% of the mean (6% for Lac, NAA, Pcr, M-Ins, 2183


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Glu, and Ala. The specific evolutions of S-Ins (not decreased) and Glu (increased) in NEC and of Gln (increased) in GAERS constituted the main patterns (supplementary Table 2). The role of each of these metabolites and their evolution profile from P14 to adulthood will be further discussed in the following text.

in the brain. Indeed, multiblock methods aim at building global models from data sets structured into coherent blocks of variables by combining common or specific information and assessing their underlying relationships. By using this global strategy, we highlighted the strong metabolic differences due to brain maturation, with a subtle difference between strains. We also showed strong metabolic differences between brain areas. Finally, our data suggest that motor cortex and somatosensory cortex have the highest weight in the discrimination between GAERS and NEC in Adults. This approach is very promising to analyze data recorded from several cerebral structures and provide meaningful biological information, at a local level but also in a more integrative perspective.36,37

Importance of Structures and Metabolites in Strain Discrimination

Our study also indicates that the separation between strains relies also on brain structures in which some metabolites appear to be key features. Which Structures Are the Most Discriminant? Consensus OPLS-DA analysis in pups showed the prominent role of the somatosensory cortex to distinguish GAERS from NEC and Wistar, whereas the hippocampus was the most discriminant structure to separate NEC from Wistar and GAERS. In Adults, the motor and somatosensory cortices were the most discriminant brain areas to separate NEC from Wistar and GAERS animals, whereas the hippocampus was important for distinguishing the three strains. These results are in line with the specific involvement of the somatosensory cortex as the primary generator of absence seizures in GAERS, as shown by electrophysiological recordings and fMRI data.7,8 In particular, the importance of this structure in rat pups suggests that early metabolic changes, before seizure onset, are likely to contribute to specific cellular wiring properties and electrogenic features. This is in line with recent data in our laboratory showing that immature SWD occur in GAERS pups in this structure soon after P14 (personal data). Although the adjacent motor cortex was not analyzed in rat pups, the discriminant feature of this region observed in Adults is likely due to its progressive involvement in the propagation network.45 Conversely, the early discriminant feature of the hippocampus already expressed in rat pups, before seizure onset, challenges the general view that this structure is only secondarily affected by the recurrence of seizures. It is in line with the hypothesis that changes in hippocampus could be associated with some behavioral comorbidities, such as psychotic-like and depressive disorders reported in GAERS46−48 or the weaker anxiety profile of NEC as compared to Wistar and GAERS.13 The metabolic profile of hippocampus could thus constitute an additional footprint specific of the behavioral comorbidities associated with this epileptic phenotype. Which Metabolites Are the Most Discriminant? Glutamate−Glutamine−GABA. In rat pups, Glu levels appeared greatly reduced in all structures in NEC as compared to GAERS and Wistars (supplementary Figure 1), whereas no difference was observed for GABA. In Adults, no difference was observed for GABA among the three different strains, whatever the structure, and Glu appeared slightly reduced only in the motor cortex and cerebellum of NEC as compared to GAERS (supplementary Table 1). These data suggest that NEC pups could have a lower brain excitability that may contribute to a resistance to the development of epileptic seizures. On the other hand, the similar levels of Glu and GABA in GAERS and Wistar is likely related to the propensity of both strains to display SWD, because occasional spontaneous SWD have been reported in Wistar rats.11,49−52 This is somewhat in disagreement with a previous study reporting similar contents of Glu and GABA in young GAERS and NEC,16 whereas higher levels of Glu and lower levels of GABA were observed in 5 month old GAERS.17 However, in this previous study, 30 day old rats were

Evolution of Metabolite Levels between P14 and Adults Is Different between Strains

Our study shows that the metabolic profile of the three different strains evolves differently between childhood and adulthood. In pups, 11 metabolites were already significantly different in at least one pairwise comparison and in at least one structure, while they had not yet developed SWD. This suggests that the differences observed at this age are not directly associated with seizure metabolism but with the phenotype of GAERS and NEC. In Adults, we found differences in 15 metabolites that were in general different from those found in pups (see supplementary Results and supplementary Table 1). Brain maturation is accompanied by strong metabolic modifications during the first 30 days after birth.38 Our PCA analysis clearly showed the important differences between the metabolic profiles of pups and Adults, regardless of the brain structures and the strains. These differences concern PE, PC, Tau, Cho, and Asc, which are higher in all structures in all strains in pups when compared to Adults, whereas GPC, M-Ins, Lac, NAA are higher in all structures in all strains in Adults, in agreement with previous reports.38−42 However, our study provides a more complete picture of neurochemical changes between the two ages for 19 metabolites including GPC, PC, and Cho. For these compounds, which are associated with membrane metabolism, we showed that GPC increased while PC and Cho decreased with brain maturation. When the amplitude of adult metabolites was expressed in percent of pup values, the increased GPC was by far the most important, at least for the somatosensory cortex, the hippocampus, and the ventrobasal thalamus. In the latter, the increase reached 350%. This increase appeared lower in NEC when compared to the two other strains. However, if we consider the sum of choline compounds, a significant general decrease was observed with brain maturation, in GAERS and NEC, but not in Wistar (besides hippocampus). These data suggest that the differences in membrane maturation among GAERS and NEC, as compared to the original Wistar strain, may contribute to their different phenotypes in cellular excitability and therefore to their opposite seizure susceptibility.43 Besides these global age-related differences, several studies have shown that each metabolite follows a specific evolution from birth to adulthood.38,44 In our study, clear differences were observed between pups and Adults and, for a given age, several differences between the three strains were also detected, suggesting that metabolic kinetics during brain maturation could constitute a footprint of epileptic phenotype. SUS-plot showed that evolution between pups and Adults was different between structures and strains in particular for S-Ins, Gsh, Gln, 2184


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the fact that Tau was suggested to decrease corticostriatal neurotransmission, an inhibitory effect more potent in adult than in juvenile rats. 69 The increase of Tau in the somatosensory cortex and striatum possibly relates to the involvement of the corticostriatal network that plays a role in the control of seizures by the basal ganglia system.28,70,71 This increase is thus likely to participate in an inhibitory effect on abnormal oscillatory activities, which may contribute to their nonepileptic phenotype. Tau has also been reported to prevent chemically induced limbic seizures,72 an effect that appears to beat least in this modelrather mediated through its neuroprotective role than through its direct influence on neuronal activity (increase in GABA receptor function). It is important to note that Tau was shown to differentiate stem cells and neural precursor into neurons rather than astrocytes.73 Because an increased expression of GFAP was found in adult GAERS,56 the possibility thus arises that differences may be somehow linked to the higher levels of Tau in NEC, which may reduce the number of astrocytes. Higher levels of Tau may also prevent NEC from developing seizures through an increase in dopamine levels within the nucleus accumbens.74 Indeed, we have shown that such an increase has antiepileptic effects in GAERS.75 Contrary to Tau, few data are found in the literature concerning PE. Interestingly, in their effort to understand the role of Tau in membrane stabilization and calcium fluxes in excitable tissues, Lehman et al. observed an increase of PE after Tau administration, and they suggested an interrelationship between PE and Tau extracellular levels.76 Concordantly, Tau could modulate myocardial contraction and calcium transport via an inhibition of conversion of phosphatidylethanolamine into phosphatidylcholine in the N-methyltransferase way.77 Similarly, a linear relationship between Tau content and the phosphatidylethanolamine/phosphatidylcholine ratio was reported in the developing brain.78 Whether this taurinemediated inhibition of N-methyltransferase could led to increased PE levels cannot be directly determined. Moreover, other authors have provided evidence in rat brain cytosol for the conversion of PE into PC via phosphoethanolamine methyltransferase.79 Whether Tau also inhibits this conversion is unknown. However, in our study, PE/PC ratio was significantly higher in NEC compared to GAERS, in both generating and control structures (data not shown). This increase could underlie a different membrane fluidity in the NEC somatosensory and motor cortices, and striatum as compared to the other strains.80 Whether this may prevent the occurrence of pathological neuronal hyperactivation and/or synchronization and epileptic seizure onset remains to be investigated. In regards to these arguments, we propose a general scheme that resumes all these features (Figure 5).

used, an age at which well-depicted spike-and-wave EEG patterns are already recorded in the somatosensory cortex (personal data). It is thus likely that brain metabolism related to absence epilepsy at this age is closer to Adults one. In addition, the larger volume of brain samples used in the previous studies would probably have involved additional structures and may have generated confounding data. Our data also allow us to shed some light on the evolution of specific metabolites from P14 to adulthood. For instance, Ala levels decreased in Wistar and NEC but not in GAERS (supplementary Table 2), suggesting that glycolysis remains important in adult GAERS, in line with Melo’s data suggesting an increased cerebral metabolism in GAERS.10,17 Similarly, Gln, which is selectively taken-up by astrocytes, is often considered as a marker of astrocytic metabolism.53,54 Its selective increase in GAERS (supplementary Figure 1) suggests that astrocytic metabolism is maintained at a higher level in this strain. Again, this is in agreement with data showing an increased number of astrocytes in GAERS,55,56 with increased mRNAs of the Glutamate−Aspartate transporter and Glial Fibrillary Acidic Protein (GFAP) in the cortex.57,58 Furthermore, M-Ins, which has been suggested as an astrocytic marker,59 was somehow increased in subcortical structures in P14 GAERS. It is also in line with pharmacological data showing that glial gap junction blockers in adult GAERS exert an antiepileptic action in vivo and limit the spread of synchronized activities in vitro.60 Scyllo-inositol. Our data showed that there is no decrease of S-Ins levels during brain maturation in NEC, unlike the two other strains. The striking difference in the amplitude of S-Ins levels in adult NEC compared to GAERS led us to hypothesize that this metabolite could be considered as a marker for “resistance” to epileptogenicity. Indeed, pretreatment with 5 mg/kg of S-Ins was reported to have anticonvulsant effects in pentylenetetrazole-induced seizures.61 However, the meaning of S-Ins variations in human and animal models remains debated: (i) high S-Ins levels have been suggested to be a marker of bad prognosis in chronic alcoholism62 and Alzheimer disease;63,64 (ii) S-Ins levels were decreased in frontal and parietal cortices in aged rats compared with young ones,65 whereas (iii) S-Ins has been claimed as a protective agent against Alzheimer disease.66,67 However, a long-term follow-up class two study in subjects with Alzheimer disease provided insufficient evidence to support or refute a benefit of S-Ins. Primary clinical efficacy outcomes were not significant.68 In a parallel study, we failed to demonstrate any clear antiepileptic effects of acute injections of S-Ins (5 mg/kg, i.p.) in GAERS (data not shown). It is probably related to the fact that such injections are not efficient to induce sufficient changes in the metabolism of already acquired epilepsy. Rather, the effects of S-Ins following chronic treatments in developing GAERS should be considered. Indeed, our data showing metabolic differences already present in P14 GAERS support the hypothesis that the epileptogenic process has already started at this age (personal data). Taurine−Phosphoethanolamine. Tau and PE were found to reach their highest value in the striatum in Adults but not in pups. In addition, in this structure, these two metabolites are significantly higher in adult NEC than in GAERS (supplementary Figure 1). As a result, the decrease of Tau and PE with age is less important in striatum of NEC than in the other strains. All these elements suggest that Tau and PE in the striatum could play an important role in NEC phenotype. Tau is also greater in the somatosensory cortex of NEC, in line with

From Metabolic Profiles to Epileptic Susceptibility

From an original Wistar strain, two strains were selected for their epileptic susceptibility, giving rise to the GAERS and NEC strains. Our data reveal subtle but clear differences in the metabolic profiles of these three strains. We hypothesize that these differences could be a marker for epileptic susceptibility. At 14 days, NEC pup rats are likely to already exhibit a lower excitability as suggested by lower glutamate levels. On the other hand, GAERS pup rats have higher glutamine levels in SSI, in accordance with a possible higher astrocyte number in this 2185


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very difficult to conclude only on the basis of univariate statistics. Nevertheless, we showed that the three strains of rats (GAERS, NEC, and Wistar) with different epileptic phenotypes have specific metabolic profiles whatever their age. Our method allowed us to discriminate the strains on the basis of (i) the evolution between pup and adult rats for five metabolites (scyllo-inositol, glutamate, glutamine alanine and glutathion) and (ii) the weight of each brain structure, with motor and somatosensory cortices as the most discriminant factors for NEC/GAERS separation, and hippocampus for separation between the three strains. Our study also showed that glutamine, taurine, scyllo-inositol, and phosphoethanolamine at adulthood, and glutamate at P14 allowed discrimination between strains. This approach allowed the focus on possible physiopathological hypotheses such as astrocyte dysfunction in GAERS. Furthermore, it also provided new original hypothesizes to be investigated concerning the putative involvement of scyllo-inositol, taurine, and phosphoethanolamine as specific determinants in the resistance to epilepsy of NEC rats. Our data showing changes in metabolic evolution between P14 and adulthood between the three strains should be further examined by looking at different time-points after birth. Such a rigorous longitudinal approach would confirm whether the metabolic changes observed in GAERS at P14, when no SWD discharges occur, could be considered as valuable predictive biomarkers for absence-epilepsy. In a clinical context, in vivo NMR spectroscopy would be an appropriate method for noninvasive diagnostic, although the variations of metabolite levels measured in our study were quite weak.

Figure 5. From metabolic profiles to seizure susceptibility. Diagram depicting the main striking metabolic differences between the three rat strains GAERS, NEC, and Wistar with different susceptibility for absence seizures, from P14 to adulthood.

structure. This is in line with a higher M-Ins level in subcortical structures. During brain maturation, the differences in glutamate level between the three strains are abolished while glutamine is even more increased in GAERS in all structures. On the other hand, scyllo-inositol remains elevated in NEC while it decreases in the other strains. Taurine and phosphoethanolamine levels decrease with brain maturation but less in NEC. We can suggest that at P14, the difference in susceptibility to epilepsy between the three strains relies mainly on a difference in excitability, whereas at adulthood, it would be exacerbated by both resistance of NEC and by impaired glutamate-glutamine shuttle in GAERS. Wistar rats are not so different from GAERS rats except for myo-inositol level at P14 and for glutamine level at adulthood, which are equivalent to NEC. These two features could explain the intermediate epileptic profile of Wistar rats: not epileptic but prone to exhibit sparse epileptic seizures.

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ASSOCIATED CONTENT

S Supporting Information *

Description of univariate statistics, supplementary methods and results (supplementary Figure 1, supplementary Table 1 and Table 2). This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: fl[email protected]. Tel.: +33 (0)4 56 52 06 00. Author Contributions

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The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

CONCLUSION Until now, a great number of preclinical and clinical studies on brain metabolism have highlighted several differences or similarities without clearly discriminating specific patterns. In the present metabolomic approach, the aim of which was to find specific metabolic profiles by using multivariate statistics, all metabolites and their specific weight were taken into account in the discrimination. This has found recently increasing applications for classification and prediction issues. Furthermore, another advantage of these methods might be to sort the data through a comprehensive approach, which would gradually refocus on the most relevant information. Among them, multiblock approach like consensus OPLS-DA seems particularly well suited for neuroscience studies. This strategy of data analysis was very powerful in our study despite the high number of significant variations of metabolite levels measured: 15 metabolites in at least one pairwise comparison in adult rats and 11 in pup rats. It would have been

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by French Service de Santé des Armées, INSERM and Agence Nationale pour la Recherche (ANR: program “Maladie Neurologique, Maladie Psychiatrique” Grant No. P008404 − GliEpi). F.C. received a Ph.D. fellowship from the Ministère Français de la Recherche. We are grateful to Séverine Maunoir-Regimbal and Tanguy Chabrol for their technical assistance in tissue sampling.

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ABBREVIATIONS AE, (Absence epilepsy); Cb, (cerebellum); CRLB, (Cramer Rao Lower Bounds); EEG, (electroencephalographic recordings); GAERS, (Genetic Absence Epilepsy Rats from 2186


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(13) Marques-Carneiro, J. E.; Faure, J. B.; Cosquer, B.; Koning, E.; Ferrandon, A.; de Vasconcelos, A. P.; Cassel, J. C.; Nehlig, A. Anxiety and locomotion in Genetic Absence Epilepsy Rats from Strasbourg (GAERS): inclusion of Wistar rats as a second control. Epilepsia 2014, 55 (9), 1460−1468. (14) Beckonert, O.; Coen, M.; Keun, H. C.; Wang, Y.; Ebbels, T. M.; Holmes, E.; Lindon, J. C.; Nicholson, J. K. High-resolution magicangle-spinning NMR spectroscopy for metabolic profiling of intact tissues. Nat. Protoc. 2010, 5 (6), 1019−1032. (15) Rabeson, H.; Fauvelle, F.; Testylier, G.; Foquin, A.; Carpentier, P.; Dorandeu, F.; van Ormondt, D.; Graveron-Demilly, D. Quantitation with QUEST of brain HRMAS-NMR signals: application to metabolic disorders in experimental epileptic seizures. Magn. Reson. Med. 2008, 59 (6), 1266−1273. (16) Melo, T. M.; Sonnewald, U.; Bastholm, I. A.; Nehlig, A. Astrocytes may play a role in the etiology of absence epilepsy: a comparison between immature GAERS not yet expressing seizures and adults. Neurobiol. Dis. 2007, 28 (2), 227−235. (17) Melo, T. M.; Sonnewald, U.; Touret, M.; Nehlig, A. Cortical glutamate metabolism is enhanced in a genetic model of absence epilepsy. J. Cereb. Blood Flow Metab. 2006, 26 (12), 1496−1506. (18) Holmes, E.; Tsang, T. M.; Tabrizi, S. J. The application of NMR-based metabonomics in neurological disorders. NeuroRx 2006, 3 (3), 358−72. (19) Feng, J.; Isern, N. G.; Burton, S. D.; Hu, J. Z. Studies of Secondary Melanoma on C57BL/6J Mouse Liver Using 1H NMR Metabolomics. Metabolites 2013, 3 (4), 1011−1035. (20) Graveron-Demilly, D. Quantification in magnetic resonance spectroscopy based on semi-parametric approaches. MAGMA 2013, 27 (2), 113−30. (21) Provencher, S. W. Automatic quantitation of localized in vivo 1H spectra with LCModel. NMR Biomed. 2001, 14 (4), 260−264. (22) Ratiney, H.; Sdika, M.; Coenradie, Y.; Cavassila, S.; van Ormondt, D.; Graveron-Demilly, D. Time-domain semi-parametric estimation based on a metabolite basis set. NMR Biomed. 2005, 18 (1), 1−13. (23) Wilson, M.; Reynolds, G.; Kauppinen, R. A.; Arvanitis, T. N.; Peet, A. C. A constrained least-squares approach to the automated quantitation of in vivo (1)H magnetic resonance spectroscopy data. Magn. Reson. Med. 2010, 65 (1), 1−12. (24) Reynolds, G.; Wilson, M.; Peet, A.; Arvanitis, T. N. An algorithm for the automated quantitation of metabolites in in vitro NMR signals. Magn. Reson. Med. 2006, 56 (6), 1211−1219. (25) Opstad, K. S.; Bell, B. A.; Griffiths, J. R.; Howe, F. A. Toward accurate quantification of metabolites, lipids, and macromolecules in HRMAS spectra of human brain tumor biopsies using LCModel. Magn. Reson. Med. 2008, 60 (5), 1237−1242. (26) Fauvelle, F.; Carpentier, P.; Dorandeu, F.; Foquin, A.; Testylier, G. Prediction of neuroprotective treatment efficiency using a HRMAS NMR-based statistical model of refractory status epilepticus on mouse: a metabolomic approach supported by histology. J. Proteome Res. 2012, 11 (7), 3782−3795. (27) Bylesjo, M.; Sjodin, A.; Eriksson, D.; Antti, H.; Moritz, T.; Jansson, S.; Trygg, J. MASQOT-GUI: spot quality assessment for the two-channel microarray platform. Bioinformatics 2006, 22 (20), 2554− 2555. (28) Deransart, C.; Depaulis, A. The control of seizures by the basal ganglia? A review of experimental data. Epileptic Disord. 2002, 4 (Suppl 3), S61−S72. (29) Paxinos, G.; Watson, C. The rat brain stereotaxic coordinates.; Academic Press: New York, 2007. (30) Fauvelle, F.; Dorandeu, F.; Carpentier, P.; Foquin, A.; Rabeson, H.; Graveron-Demilly, D.; Arvers, P.; Testylier, G. Changes in mouse brain metabolism following a convulsive dose of soman: a proton HRMAS NMR study. Toxicology 2009, 267 (1−3), 99−111. (31) Wieruszeski, J. M.; Montagne, G.; Chessari, G.; RousselotPailley, P.; Lippens, G. Rotor synchronization of radiofrequency and gradient pulses in high-resolution magic angle spinning NMR. J. Magn. Reson. 2001, 152 (1), 95−102.

Strasbourg); GFAP, (Glial Fibrillary Acidic Protein); H, (Hippocampus); HRMAS (High-Resolution Magic Angle Spinning),; jMRUI, (java based version of the Magnetic Resonance User Interface; M1, (motor cortex); NEC, (Non Epileptic Controls); NMR, (Nuclear Magnetic Resonance); OPLS-DA, (orthogonal projection to latent structure-discriminant analysis); P14, (14 days postnatal); PCA, (principal component analysis); SSI, (somatosensory cortex); St, (striatum); SUS-plot, (shared and unique structure plot); SW, (spike-and-wave discharge); VB, (ventrobasal thalamus); Metabolites: Ace, (acetate); Ala, (alanine); Asc, (ascorbate); Asp, (aspartate); Cho, (choline); GABA, (gamma-aminobutyric acid); Gln, (glutamine); Glu, (glutamate); Gly, (glycine); GPC, (glycerophosphocholine); Gsh, (glutathione); Hyp, (hypotaurine); Lac, (lactate); M-Ins, (myo-inositol); NAA, (N-acetylaspartate); PC, (phosphocholine); Pcr, (phosphocreatine and creatine); PE, (phosphoethanolamine); S-Ins, (scyllo-inositol); Tau, (taurine); Threo, (threonine); Ser, (serine)

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