High Resolution 1H NMR-based Metabolomics Indicates a Neurotransmitter Cycling Deficit in Cerebral Tissue from a Mouse Model of Batten Disease

The neuronal ceroid lipofuscinoses (NCLs) constitute a range of progressive neurological disorders primarily affecting children. Although six of the causative genes have been characterized, the underlying disease pathogenesis for this family of disorders is unknown. Using a metabolomics approach based on high resolution 1H NMR spectroscopy of the cortex, cerebellum, and remaining regions of the brain in conjunction with statistical pattern recognition, we report metabolic deficits associated with juvenile NCL in a Cln3 knock-out mouse model. Tissue from Cln3 null mutant mice aged 1–6 months was characterized by an increased glutamate concentration and a decrease in γ-amino butyric acid (GABA) concentration in aqueous extracts from the three regions of the brain. These changes are consistent with the reported altered expression of genes involved in glutamate metabolism in older mice and imply a change in neurotransmitter cycling between glutamate/glutamine and the production of GABA. Further variations in myo-inositol, creatine, and N-acetyl-aspartate were also identified. These metabolic changes were distinct from the normal aging/developmental process. Together, these changes represent the first documented pre-symptomatic symptoms of the Cln3 mouse at 1 month of age and demonstrate the versatility of 1H NMR spectroscopy as a tool for phenotyping mouse models of disease. The neuronal ceroid lipofuscinoses (NCLs) constitute a range of progressive neurological disorders primarily affecting children. Although six of the causative genes have been characterized, the underlying disease pathogenesis for this family of disorders is unknown. Using a metabolomics approach based on high resolution 1H NMR spectroscopy of the cortex, cerebellum, and remaining regions of the brain in conjunction with statistical pattern recognition, we report metabolic deficits associated with juvenile NCL in a Cln3 knock-out mouse model. Tissue from Cln3 null mutant mice aged 1–6 months was characterized by an increased glutamate concentration and a decrease in γ-amino butyric acid (GABA) concentration in aqueous extracts from the three regions of the brain. These changes are consistent with the reported altered expression of genes involved in glutamate metabolism in older mice and imply a change in neurotransmitter cycling between glutamate/glutamine and the production of GABA. Further variations in myo-inositol, creatine, and N-acetyl-aspartate were also identified. These metabolic changes were distinct from the normal aging/developmental process. Together, these changes represent the first documented pre-symptomatic symptoms of the Cln3 mouse at 1 month of age and demonstrate the versatility of 1H NMR spectroscopy as a tool for phenotyping mouse models of disease. Neuronal ceroid lipofuscinoses (NCLs) 3The abbreviations used are: NCLneuronal ceroid lipofuscinosisGABAγ-aminobutyric acidGADglutamic acid decarboxylaseNAAN-acetyl-aspartatePCAprincipal component analysisPLS-DAPLS-discriminant analysis. are a series of autosomal recessive diseases that collectively constitute the most common cause of childhood neurodegeneration with an incidence of 1 in 12,500 (1Goebel H.H. Sharp J.D. Brain Pathol. 1998; 8: 151-162Crossref PubMed Scopus (65) Google Scholar, 2Banerjee P. Dasgupta A. Siakotas A. Dawson G. Am. J. Med. Genet. 1992; 42: 549-554Crossref PubMed Scopus (24) Google Scholar). The disorders are typified by their progressive nature with symptoms including visual disturbances, psychomotor deterioration, mental impairment, worsening seizures, blindness, and, ultimately, premature death (3Gardiner R.M. Adv. Neurol. 2002; 89: 211-215PubMed Google Scholar, 4Hofmann S.L. Atashband A. Cho S.K. Das A.K. Gupta P. Lu J.Y. Curr. Mol. Med. 2002; 2: 423-437Crossref PubMed Scopus (32) Google Scholar, 5Mitchison H.M. Lim M.J. Cooper J.D. Brain Pathol. 2004; 14: 86-96Crossref PubMed Scopus (77) Google Scholar, 6Mitchison H.M. Mole S.E. Curr. Opin. Neurol. 2001; 14: 795-803Crossref PubMed Scopus (47) Google Scholar, 7Wisniewski K.E. Zhong N. Philippart M. Neurology. 2001; 57: 576-581Crossref PubMed Scopus (97) Google Scholar). Furthermore, all share the histopathological finding of accumulation of autofluorescent lipopigment in lysosomes, similar to the pigment lipofuscin found in normal aging brains (8Sekhon S.S. Maxwell D.S. J. Neurocytol. 1974; 3: 59-72Crossref PubMed Scopus (56) Google Scholar, 9Mann D.M. Yates P.O. Stamp J.E. J. Neurol. Sci. 1978; 37: 83-93Abstract Full Text PDF PubMed Scopus (122) Google Scholar), and ceroid, found in pathological conditions (10Wolfe L.S. Ivy G. Wiktop C.J. Chem. Scr. 1987; 27: 79-84Google Scholar). The mechanism underlying this aberrant accumulation and how it correlates with neurodegeneration is unclear. Indeed, one of the paradoxes of the NCLs is that the deposition of lipopigment does not apparently lead to disease in non-neuronal cell types. neuronal ceroid lipofuscinosis γ-aminobutyric acid glutamic acid decarboxylase N-acetyl-aspartate principal component analysis PLS-discriminant analysis. NCLs have traditionally been divided into subtypes based upon age of onset and clinical course, although the identification of several of the underlying gene defects has enabled a more definitive genetic classification. The following Cln genes are responsible for each subtype: Cln1, infantile NCL (Santavuori-Haltia); Cln2, late infantile NCL (Jansky-Bielschowsky); Cln3, juvenile NCL (Batten disease); and Cln4, adult NCL (Kufs disease). There are also four variant late infantile forms: Cln5, Finnish variant; Cln6, Costa Rican variant; Cln7, Turkish Variant; and Cln8, Turkish variant and Northern epilepsy (11Mole S.E. Gardiner M. Int. J. Neurol. 1991; 25–26: 52-59PubMed Google Scholar, 12Mole S.E. Brain Pathol. 2004; 14: 70-76Crossref PubMed Scopus (87) Google Scholar). Even though characterization of these genes has accelerated research into the disease pathology, the precise events downstream of these mutations and how they result in the similar clinical manifestations of NCLs, albeit on different time scales, remain elusive. The generation of genetically accurate mouse models has made this species the principal platform for investigating the NCLs, offering numerous models encompassing a variety of NCL subtypes (5Mitchison H.M. Lim M.J. Cooper J.D. Brain Pathol. 2004; 14: 86-96Crossref PubMed Scopus (77) Google Scholar, 13Cooper J.D. Curr. Opin. Neurol. 2003; 16: 121-128Crossref PubMed Scopus (87) Google Scholar). In this present study we have applied a metabolomics approach to define biochemical abnormalities associated with a mouse model of juvenile NCL or Batten disease (14Mitchison H.M. Bernard D.J. Greene N.D. Cooper J.D. Junaid M.A. Pullarkat R.K. de Vos N. Breuning M.H. Owens J.W. Mobley W.C. Gardiner R.M. Lake B.D. Taschner P.E. Nussbaum R.L. Neurobiol. Dis. 1999; 6: 321-334Crossref PubMed Scopus (169) Google Scholar), which is caused by mutations in the Cln3 gene. Metabolic profiles derived from 1H NMR spectroscopic analysis of biofluids and tissue extracts, in conjunction with multivariate statistics, have previously been demonstrated to be highly discriminatory for a number of neurological diseases, including a variant late infantile NCL (Cln8) (15Griffin J.L. Muller D. Woograsingh R. Jowatt V. Hindmarsh A. Nicholson J.K. Martin J.E. Physiol. Genomics. 2002; 11: 195-203Crossref PubMed Scopus (44) Google Scholar). Using this approach, we have identified a number of metabolic deficits associated with this mouse model of juvenile NCL. In particular, changes in the concentration of glutamate, glutamine, and γ-aminobutyric acid (GABA) were detected that are indicative of progressive perturbations between glutamate and glutamine cycling and the conversion of glutamate to GABA and that may explain some of the neurological deficits associated with this disease. Tissues—Wild type and Cln3 knock-out mice on the same 129S6/Sv background were used in this study (14Mitchison H.M. Bernard D.J. Greene N.D. Cooper J.D. Junaid M.A. Pullarkat R.K. de Vos N. Breuning M.H. Owens J.W. Mobley W.C. Gardiner R.M. Lake B.D. Taschner P.E. Nussbaum R.L. Neurobiol. Dis. 1999; 6: 321-334Crossref PubMed Scopus (169) Google Scholar). All procedures were carried out in accordance with National Institutes of Health guidelines and the University of Rochester Animal Care and Use Committee Guidelines. All animals were housed under identical conditions. Tissue was taken rapidly (typically within 30 s) from animals killed by cervical dislocation. Specifically, tissue was taken from a stable mouse colony consisting of mice aged 1, 2, 3, and 6 months (n = 5 for all ages). The cortex, cerebellum, and remaining cerebral tissue were snap-frozen using liquid nitrogen and stored at –80 °C. Preparation of Tissue Extracts—Frozen tissue samples (30–100 mg) were pulverized using a Polytron (2× 30-s bursts) (Kinematic) in 6% (w/v) perchloric acid (1 ml) (Aldrich). Samples were centrifuged, and the supernatants were neutralized to pH 7.0 with KOH (5 m). Following lyophilization, dried extracts were reconstituted in 250 μl of D2O (Goss Scientific Instruments) buffered in 240 mm sodium phosphate, pH 7.0, containing 0.25 mm sodium (3-trimethylsilyl)-2,2,3,3-tetradeuteriopropionate (TSP) (Cambridge Isotope Laboratories, Inc.). Extracts were pipetted into a 96-well plate for NMR spectroscopy. Solution 1H NMR Spectroscopy—Samples were analyzed using a 400 MHz DRX Bruker Avance spectrometer with a triple axis inverse flow probe. Spectra were acquired over 128 scans using a conventional presaturation pulse sequence for solvent suppression based on the start of the nuclear Overhauser effect spectroscopy (NOESY) pulse sequence (relaxation delay = 1.3 s; t1 = 3 μs; mixing time = 150 ms; spectral width = 12 ppm; time domain = 32,000 data points; solvent presaturation was applied during the relaxation delay and mixing time) at 25 °C. All spectra were processed using one-dimensional NMR manager software (Advanced Chemistry Development Inc., Toronto, Canada). Spectra were multiplied by an exponential weighting function and Fourier-transformed from the time to frequency domain. Spectra were phased, baseline-corrected, and referenced to the sodium (3-trimethylsilyl)-2,2,3,3-tetradeuteriopropionate singlet at δ0 ppm. Pattern Recognition—Spectra were integrated between 0.2 and 9.96 ppm over a series of 0.04 ppm integral regions, using an integration macro written within the one-dimensional NMR manager software package. To account for dilution or bulk mass differences between samples, individual integral regions were normalized to the total integral region following exclusion of the water resonance. Individual integrals were thereby standardized to the total integral of all low molecular weight metabolites (16Spraul M. Neidig P. Klauck U. Kessler P. Holmes E. Nicholson J.K. Sweatman B.C. Salman S.R. Farrant R.D. Rahr E. Beddell C.R. Lindon J.C. J. Pharm. Biomed. Anal. 1994; 12: 1215-1225Crossref PubMed Scopus (104) Google Scholar, 17Holmes E. Nicholls A.W. Lindon J.C. Ramos S. Spraul M. Neidig P. Connor S.C. Connelly J. Damment S.J. Haselden J. Nicholson J.K. NMR Biomed. 1998; 11: 235-244Crossref PubMed Scopus (236) Google Scholar). Data sets were imported into the SIMCA package (Umetrics, Umeå, Sweden) and pre-processed using Pareto scaling by weighting each integral region or variable by (1/Sk)½, where Sk represents the standard deviation of the variable. This increased the representation of lower concentration metabolites in the resultant data models while minimizing the from Data were analyzed using principal component analysis to by and PLS-discriminant analysis within the SIMCA package E. N. S. to and Data using and Scholar). is a that the in a data into a number of principal which are with each and account for of the total of the principal component is a of the that a of the within a data is are to the by the principal with similar t1 is the of a associated with principal component 1 to define a a approach was is a of used to or more type and by for that are to In this approach the are to the between and be used to that be three or more principal E. N. S. to and Data using and Scholar). of is used to the between data spectral data and age that the variable be from E. N. S. to and Data using and Scholar). each model the and the variable on were in conjunction with the to which metabolites most to or a in the the between the and the component are a of the to which a variable the or was using the and of which between and 1 E. N. S. to and Data using and Scholar). an of how of the within a data be by the of the model. The how is by the total model. for a the first three represent of the total found within a data how the or be and this is more to pattern over a model that is a between a highly This is by out samples from the model and or of or the variable in and in GABA Tissue from Cln3 1 1H NMR spectral profiles from the cortex, the cerebellum, and the remaining brain tissue of type and Cln3 mice were for metabolic differences NMR profiles from the tissue of mice aged 1 month were by in all three brain regions not of the similar changes to be responsible for the all In particular, of glutamate, myo-inositol, and were increased in Cln3 tissue to GABA were Furthermore, perturbations including in glutamine, N-acetyl-aspartate and were also not in all brain regions of metabolic deficits detected between type and Cln3 cerebral in decrease in glutamine, myo-inositol, creatine, glutamine, myo-inositol, creatine, myo-inositol, creatine, myo-inositol, creatine, creatine, myo-inositol, creatine, creatine, myo-inositol, myo-inositol, myo-inositol, glutamine, myo-inositol, in a To common metabolic changes the brain of metabolic differences between different regions, spectral profiles from all three brain regions were analyzed by This pattern was in to the associated with the different brain regions while the associated with the mouse model type in the model. the with the that the same discriminatory metabolites identified in individual regions are responsible for of Metabolic with changes associated with the Cln3 disease spectral profiles derived from mice aged 2, 3, and 6 months were also type and Cln3 spectra were for all age although for and mice was in of the three brain regions The to in all regions may result from the number of samples analyzed to changes or from the of changes associated with the disease. the changes in glutamate and GABA for all time with at 1 month of Specifically, glutamate was increased in Cln3 the concentration of GABA was in all age one an in GABA in Cln3 tissue from at all time and in all brain regions the of was increased in Cln3 in regions where the pattern analysis not in and creatine, in Cln3 were also in mice aged 2, 3, and 6 with at 3 months analysis of the that several of the causative metabolic deficits were consistent with identified in deficits an glutamine in Cln3 mice were in mice 1 that was an decrease in the was found to be increased in and Cln3 mice in Cln3 mice aged 3 and 6 in and changes was identified. and Cln3 at the between 1 and 6 the a of analyzed the metabolic changes in the Cln3 mouse of age a analysis was to the metabolic associated with type and Cln3 a highly model that 1H NMR spectral profiles with age a metabolic aging common to type and Cln3 pattern models using were type and Cln3 NMR profiles were analyzed not similar metabolites with age in all In particular, glutamate, glutamine, myo-inositol, creatine, and increased with and with neurological diseases are associated with an accelerated aging the of this aging between type and Cln3 mice were models using type or Cln3 spectral data were used to the of of an accelerated aging was the type model be to Cln3 mice to be older they the Cln3 model type mice to be they In was between the age of type and Cln3 mice 3, and This result that the metabolic that the Cln3 mutant are distinct from associated with normal In this 1H NMR has been used to metabolic profiles from brain tissue of a Cln3 knock-out mouse model. metabolic changes have been detected the three brain regions the first of metabolic changes at 1 month of age in the Cln3 mouse to neuronal cell detected from months of age (14Mitchison H.M. 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The NMR based approach used to brain tissue from the Cln3 mouse model was in and in of the of the The analysis is also on a it for of is that it is to as metabolic from This is not to is also a of and where a is of a or of as the glutamate/glutamine cycling in the brain by NMR spectroscopy are time and Furthermore, metabolic analysis that the of concentration changes be more to metabolic perturbations of this analysis is that the multivariate statistical approach all of the metabolic changes Furthermore, Pareto scaling of the data high and low concentration metabolites to a to the pattern the variations in the of glutamate, glutamine, and GABA the multivariate approach was to changes of metabolites with to one is these perturbations that the detected in the models changes in analysis of is to metabolic In the present of glutamate were increased the three regions GABA were in the brains of Cln3 mice to In glutamine were also from months of age in Cln3 mice that the glutamine to glutamate was The in glutamate concentration to GABA and glutamine is consistent with and the of a which a similar in glutamate concentration at 3 months of age S. 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