A Functional Analysis of Mouse Models of Cardiac Disease through Metabolic Profiling
2004; Elsevier BV; Volume: 280; Issue: 9 Linguagem: Inglês
10.1074/jbc.m410200200
ISSN1083-351X
AutoresGareth L.A.H. Jones, Elizabeth Sang, Catharine A. Goddard, Russell J. Mortishire‐Smith, Brian C. Sweatman, John N. Haselden, Kay E. Davies, Andrew A. Grace, Kieran Clarke, Julian L. Griffin,
Tópico(s)Molecular Biology Techniques and Applications
ResumoSince the completion of the human and mouse genomes, the focus in mammalian biology has been on assessing gene function. Tools are needed for assessing the phenotypes of the many mouse models that are now being generated, where genes have been "knocked out," "knocked in," or mutated, so that gene expression can be understood in its biological context. Metabolic profiling of cardiac tissue through high resolution NMR spectroscopy in conjunction with multivariate statistics has been used to classify mouse models of cardiac disease. The data sets included metabolic profiles from mouse models of Duchenne muscular dystrophy, two models of cardiac arrhythmia, and one of cardiac hypertrophy. The metabolic profiles demonstrate that the strain background is an important component of the global metabolic phenotype of a mouse, providing insight into how a given gene deletion may result in very different responses in diverse populations. Despite these differences associated with strain, multivariate statistics were capable of separating each mouse model from its control strain, demonstrating that metabolic profiles could be generated for each disease. Thus, this approach is a rapid method of phenotyping mouse models of disease. Since the completion of the human and mouse genomes, the focus in mammalian biology has been on assessing gene function. Tools are needed for assessing the phenotypes of the many mouse models that are now being generated, where genes have been "knocked out," "knocked in," or mutated, so that gene expression can be understood in its biological context. Metabolic profiling of cardiac tissue through high resolution NMR spectroscopy in conjunction with multivariate statistics has been used to classify mouse models of cardiac disease. The data sets included metabolic profiles from mouse models of Duchenne muscular dystrophy, two models of cardiac arrhythmia, and one of cardiac hypertrophy. The metabolic profiles demonstrate that the strain background is an important component of the global metabolic phenotype of a mouse, providing insight into how a given gene deletion may result in very different responses in diverse populations. Despite these differences associated with strain, multivariate statistics were capable of separating each mouse model from its control strain, demonstrating that metabolic profiles could be generated for each disease. Thus, this approach is a rapid method of phenotyping mouse models of disease. Following the sequencing of the mouse (1Landers E.S. Linton L.M. Birren B. Nusbaum C. Zody M.C. Baldwin J. Devon K. Dewar K. Doyle M. FitzHugh W. Funke R. Gage D. Harris K. Heaford A. Howland J. Kann L. Lehoczky J. LeVine R. McEwan P. McKernan K. Meldrim J. Mesirov J.P. Miranda C. Morris W. Naylor J. Raymond C. Rosetti M. Santos R. Sheridan A. Sougnez C. Stange-Thomann N. Stojanovic N. Subramanian A. Wyman D. Rogers J. Sulston J. Ainscough R. Beck S. 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Biotechnol. 2001; 19: 45-50Crossref PubMed Scopus (840) Google Scholar) suggested an approach described as Functional ANalysis by Co-responses in Yeast (FANCY), which uses global analytical tools such as 1H NMR spectroscopy (7Raamsdonk L.M. Teusink B. Broadhurst D. Zhang N. Hayes A. Walsh M.C. Berden J.A. Brindle K.M. Kell D.B. Rowland J.J. Westerhoff H.V. van Dam K. Oliver S.G. Nat. Biotechnol. 2001; 19: 45-50Crossref PubMed Scopus (840) Google Scholar) or mass spectrometry (8Castrillo J.I. Hayes A. Mohammed S. Gaskell S.J. Oliver S.G. Phytochemistry. 2003; 62: 929-937Crossref PubMed Scopus (186) Google Scholar) to study the metabolic changes induced in different yeast mutants. These profiles were then used to classify samples, clustering mutants that arise from similar deletions together. For example, yeast mutants involving the deletion of one of two genes encoding the same enzyme, 6-phosphofructo-2-kinase, produced the same metabolic phenotype, and deletions involving oxidative phosphorylation also clustered together (7Raamsdonk L.M. Teusink B. Broadhurst D. Zhang N. Hayes A. Walsh M.C. Berden J.A. Brindle K.M. Kell D.B. Rowland J.J. Westerhoff H.V. van Dam K. Oliver S.G. Nat. Biotechnol. 2001; 19: 45-50Crossref PubMed Scopus (840) Google Scholar). Thus, such a process of defining a phenotype through the global changes induced in metabolism may be used to predict the function of genes deleted or up-regulated in a given system through comparative metabolomics. The definition of a metabolic phenotype, or metabotype, by large scale analysis of metabolites using either 1H NMR spectroscopy or mass spectrometry has found a number of applications in genetic engineering, toxicology, and disease diagnosis in plants, animals, and microbes (9Fiehn O. Kopka J. Dormann P. 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A. 1984; 81: 1189-1192Crossref PubMed Scopus (1348) Google Scholar), two models of cardiac arrhythmia (a heterozygous gene deletion of the cardiac sodium channel (Scn–/+) as well as a heterozygous gain in function for the previous gene deletion (ScnΔ/+) (14Papadatos G.A. Wallerstein P.M.R. Ratcliff R.R. Head C.E. Huang C.L.H. Saumarez R.C. Colledge W.H. Grace A.A. Proc. Natl. Acad. Sci. U. S. A. 2002; 99: 6210-6215Crossref PubMed Scopus (311) Google Scholar)) and one of cardiac hypertrophy (muscle LIM protein knock out (MLPKO) (15Arber S. Hunter J.J. Ross J. Hongo M. Sansig G. Borg J. Perriard J.-C. Chien K.R. Caroni P. Cell. 1997; 88: 393-403Abstract Full Text Full Text PDF PubMed Scopus (689) Google Scholar)), were used to classify and co-cluster cardiac tissues. Table I summarizes the reported phenotypical characteristics of these mouse models in terms of visual observations and gross pathology. In each model the primary genetic lesion is not associated with an enzyme or metabolic pathway, indicating that such approaches of deriving metabolic phenotypes can be applied generally to functional genomic studies. We have termed this approach "phenotype information by metabolic profiles." Furthermore, we demonstrate that the strain background can have a profound impact on the resultant metabolism of the mouse, which may provide insights into how a given gene deletion results in very different responses across genetically diverse populations.Table IPhenotype characteristics of mouse models previously reportedDisease modelCharacterized phenotypeRef.DMDMild, nonprogressive muscle destruction with subsequent regeneration. Mild phenotype, mice identified by measuring plasma creative kinase activity with mutants showing consistent elevation. Histologically, mutants showed reduced dystrophin antibody staining13Bullfield G. Siller W.G. Wight P.A.L. Moore K.J. Proc. Natl. Acad. Sci. U. S. A. 1984; 81: 1189-1192Crossref PubMed Scopus (1348) Google Scholar, 39Dangain J. Vrbova G. Muscle Nerve. 1984; 7: 700-704Crossref PubMed Scopus (262) Google ScholarMuscle LIM protein knock-out model of cardiac hypertrophy (MLPKO)At birth, hearts are not hypertrophic but already abnormally soft, with cell-autonomous and muscle LIM protein-sensitive alterations in cytoarchitecture. Mild hypertrophy at 3-6 months. Both ventricular and atrial hypertrophy produce a pointed shaping to the heart15Arber S. Hunter J.J. Ross J. Hongo M. Sansig G. Borg J. Perriard J.-C. Chien K.R. Caroni P. Cell. 1997; 88: 393-403Abstract Full Text Full Text PDF PubMed Scopus (689) Google ScholarCardiac arrhythmia (heterozygous cardiac sodium channel knock out: Scn+/-)Hearts have several defects including impaired atrioventricular conduction, delayed intramyocardial conduction, increased ventricular refractoriness, and ventricular tachycardia with characteristics of re-entrant excitation14Papadatos G.A. Wallerstein P.M.R. Ratcliff R.R. Head C.E. Huang C.L.H. Saumarez R.C. Colledge W.H. Grace A.A. Proc. Natl. Acad. Sci. U. S. A. 2002; 99: 6210-6215Crossref PubMed Scopus (311) Google ScholarCardiac arrhythmia (heterozygous cardiac sodium channel modification, ScnΔ/+)First characterization Open table in a new tab Animal Handling—All mice were maintained according to the UK Home Office guidelines. Male 4–5-month-old mice were removed from stable colonies of C57BL/10 control mice (n = 15), mdx mice (n = 16), MLPKO mice (n = 6), B6129S2Svs control mice to MLPKO (n = 6), Scn–/+ (n = 8), ScnΔ/+ (n = 8), and 129Sv control mice (n = 16). Animals were sacrificed by cervical dislocation, and cardiac tissue was removed rapidly. Tissue was immediately frozen in liquid nitrogen and stored at –80 °C prior to NMR analysis. Genetic integrity of the colonies was monitored throughout by standard genotyping techniques. NMR Spectroscopy—Frozen cardiac tissue was pulverized using a pestle and mortar, and metabolites were extracted using perchloric acid (100 mg wet weight tissue in 1 ml of 6% perchloric acid). To further aid extraction, the perchloric acid/tissue powder mixture was further mixed using a Polytron (3 bursts for 30 s). The solution was neutralized with KOH and the precipitate discarded. The resultant solution was lyophilized, and the extracts were redissolved in D2O containing 1 mm TSP. 1The abbreviations used are: TSP, sodium 3-trimethylsilyl-[2,2,3,3-2H4]-1-propionate; MRS, magnetic resonance spectroscopy; DMD, Duchenne muscular dystrophy; MLPKO, muscle LIM protein knock out; HRMAS, high resolution magic angle spinning; PC, principal components; PCA, PC analysis; PLS-DA, partial least squares discriminant analysis. All solution state spectra were acquired using a 9.6-tesla superconducting magnet interfaced to an INOVA spectrometer (Varian, CA). Solvent suppressed spectra were acquired into 16,384 data points, summed over 128 scans, using continuous wave solvent suppression pulse sequence (relaxation delay = 2 s). To derive spectra from intact cardiac tissue, 10 mg of tissue was soaked in D2O and packed into zirconium oxide rotors, with 5 μl of D2O and 10 mm TSP, using a rotor spacer to ensure packing homogeneity. The rotors were spun at 6000 Hz at 300 K in a high resolution magic angle spinning (HRMAS) probe placed in a 16.5-tesla superconducting magnet interfaced with an AVANCE spectrometer (Bruker Gmbh, Rheinstetten, Germany). HRMAS spectra were acquired using a solvent suppression sequence based on the nuclear Overhauser effect spectroscopy pulse sequence. Free induction decays were collected into 16,384 data points and summed over 256 scans. All spectra were processed using XWINNMR software (version 3.1; Bruker Gmbh, Germany). Spectra were Fourier transformed from the time to frequency domain following multiplication by either a 0.3- or 1-Hz exponential function for solution and HRMAS spectra, respectively. Spectra were phased, base-line corrected, and referenced to the singlet of TSP at δ 0.0. Pattern Recognition of Tissue Spectra—Spectra were integrated across 0.04 ppm integral regions between 0.4 and either 4.2 or 9.4 ppm for HRMAS and solution state NMR, respectively, using a software routine written for Matlab (Mathworks). The output vector representing each spectrum was normalized across the integral regions, excluding the water resonance, effectively standardizing all the individual integrals to the total integral of all the low molecular weight metabolites (16Anthony M.L. Sweatman B.C. Beddell C.R. Lindon J.C. Nicholson J.K. Mol. Pharmacol. 1994; 46: 199-211PubMed 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 (231) Google Scholar). Data sets were imported into the SIMCA package (Umetrics, Umea, Sweden) and then preprocessed using Pareto scaling by multiplying each variable by (1/sk)1/2 where sk is the variance of the variable. This scaling effectively increases the importance of low concentration metabolites in the resultant models but not to an extent where the noise significantly contributes to the model. Data were analyzed by using principal components analysis (PCA) and the supervised regression extension, partial least squares discriminant analysis (PLS-DA), within the package SIMCA (Umetrics, Umea, Sweden). PCA is a quantitatively rigorous method for replacing a group of variables with a smaller number of new variables, called principal components (PC), which are linear combinations of the original variables. All the principal components are orthogonal to each other so there is no redundant information. Projecting the observations on one of these axes generates another new variable designed to maximize the description of the variance in the data set. PLS-DA is a generalization of PCA where a projection model is developed predicting class membership from the variables (X matrix) via scores of these variables through a generalized multiple regression method that can cope with a number of variables being correlated with class membership. Cross-validation of PLS-DA was carried out by leaving out every 6th observation and predicting the observation's class membership on a new model as part of a jack-knifing routine. The prediction error sum of squares (PRESS) is the squared differences between observed and predicted values for the data. For every component, the overall PRESS/SS was calculated, where SS is the residual sum of squares of the previous dimension. The final PRESS score then has contributions from all data. The goodness of fit algorithm was used to determine whether a correlation was significant (Q2 > 0.05), and is defined in Equation 1.Qcum2=1−∑(PRESS/SS)(Eq. 1) The major metabolic perturbations between cardiac tissues from different mouse models were determined from loadings scores and the variable importance parameters for each pattern recognition model. Loading plots display the correlation between the X variables, in the first dimension, or the residuals of the X variables in subsequent dimensions, and class membership. X variables with large weights (positive or negative) are highly correlated with class. Variable importance parameter is the influence on class membership (Y) of every term in the model, summed over all model dimensions, and is equal to the squared PLS weight of that term, multiplied by the explained SS of that PLS dimension (18Eriksson L. Johansson E. Kettaneh-Wold N. Wold S. Introduction to Multi- and Megavariate Data Analysis Using Projection Methods (PCA and PLS). Umetrics, Umea, Sweden1999Google Scholar). To confirm the importance of these metabolites, the integral regions were excluded from the analysis to examine their leverage on the models produced. New models were produced in an analogous manner to the jackknifing routine described previously, and the goodness of fit algorithm was used to determine whether a metabolite was responsible for a statistically significant classification. Overt Signs of Disease Detected Visually—Overall, overt pathological signs were minor for the four disease models for the age range examined, with all mice showing similar levels of activity, and no increase in mortality rate was detected for any of the mouse mutants at this age. Consistent with previous literature reports there was no significant difference in cardiac mass for the mdx and sodium channel modification mouse models (data not shown). However, as previously reported, the MLPKO mouse heart demonstrated mild hypertrophy at this age (mass of MLPKO hearts, 0.255 ± 0.051 g; B6129S2Svs control strain, 0.185 ± 0.023 g; unpaired t test p = 0.0119). No pathological changes were detected post-mortem by visual inspection of the hearts from mdx, Scn–/+, and ScnΔ/+ during dissection, but the MLPKO mice had signs of both ventricular and atrial hypertrophy producing a pointed shaping to the heart. Deriving Metabolic Profiles Using NMR Spectroscopy—High resolution 1H NMR spectra of aqueous cardiac tissue extracts allowed the quantification of the concentrations of lactate, alanine, leucine, isoleucine, valine, β-hydroxybutyrate, acetate, glutamate, glutamine, aspartate, citrate, succinate, creatine, choline, phosphocholine, taurine, glucose, and adenosine in the tissues (Fig. 1A). As lipid metabolites also significantly influence the metabolism of the heart, HRMAS 1H NMR spectra (Fig. 1B) were acquired on tissue from the bottom of the left ventricle from 4-month-old Scn–/+ (n = 5), ScnΔ/+ (n = 8), MLPKO (n = 8), and mdx (n = 6) mice as well as the control wild type strains C57BL/10 (n = 3), 129Sv (n = 8), and B6129S2Svs control strain (n = 8). As well as resonances from lipids and glycogen, a number of water-soluble metabolites could be detected including lactate, alanine, leucine, isoleucine, valine, β-hydroxybutyrate, acetate, glutamate, creatine, choline, phosphocholine, taurine, and glucose. Visual inspection of the NMR spectra failed to distinguish either disease status or strain type for the animal models of cardiac diseases investigated for either data set. Cardiac Tissue from Different Mouse Strains Could Be Distinguished by the Concentration of Water-soluble Metabolites—By applying PCA to the analysis of the solution state spectra, clusterings were detected arising from strain differences between the different mouse models (Fig. 2A). This strain difference was represented by the first two PCs and accounted for 44% of the total variation in the data set. The metabolites contributing to these clusterings were identified from the loadings scores, representing the weighting of the contribution each metabolite made to a particular PC (Fig. 2B). The 129Sv strain background mice all had increased concentrations of creatine (chemical shift of resonances identified in the loadings scores (δ): 3.04 and 3.92), lactate (δ 1.34), phosphocholine (δ 3.24), and choline (δ 3.20) compared with the other strains, whereas the C57BL/10 strain background was characterized by increased concentrations of glucose (δ 3.48, 3.52, and 3.56) and β-hydroxybutyrate (δ 1.12 and 1.16). Aqueous extracts of cardiac tissue from the B6129S2Svs strain background exhibited a higher concentration of taurine (δ 3.28 and 3.44). To examine whether these differences in relative metabolite concentrations in tissue extracts could be used to predict the mouse strain from which the tissue was derived, a supervised PLS-DA model was built with the data set (Fig. 2C), and class membership was predicted for each strain. This model correctly predicted the strain for both mice on C57BL/10 and 129Sv backgrounds with 100% accuracy, but only produced a model that was 53% accurate for the B6129S2Svs background strain. To define further the difference between strains measured in this data set, a Cooman's plot was formed using PCA models of the C57BL/10 and the 129Sv backgrounds (Fig. 2D). For this a PCA model was built to describe the variation in just the subset of data relating to a particular strain. These two models were then used to predict whether each observation belonged to the C57BL/10 background, the 129Sv background, could not be separated (i.e. the model predicted the sample to be both C57BL/10 and 129Sv), or belonged to neither model. Only one observation from the 129Sv background subset was outside the 95% confidence limit for this strain, whereas all the C57BL/10 background mice were within the 95% confidence limit for this strain (i.e. the strain of only one mouse was not correctly predicted for the 129Sv and C57BL/10 mice). By using this Cooman's plot, two spectra from the B6129S2Svs background were misclassified, and the rest of the data set were determined to lie outside the two PCA models (i.e. were correctly predicted as being neither 129Sv nor C57BL/10 strain mice). To examine whether the different strain backgrounds also had different proportions of NMR-detectable lipids, a similar analysis was performed on the HRMAS 1H NMR spectra data set. Again, strain differences dominated the PCA of the complete HRMAS data set. This separation was further improved by building a PLS-DA model with this model predicting class membership for all 48 spectra in terms of the strain (Fig. 2E). The 10 most important resonance regions responsible for this classification as identified by the variable importance parameter were from the metabolites taurine (δ 3.28 and 3.44), CH2CH2CH2 lipids (boldface type indicates the resonance observed; δ 1.24–1.36), lactate (δ 1.32), choline (δ 3.2), CH3CH2 lipid (δ 0.92), phosphocholine (δ 3.24), and creatine (δ 3.04) (Fig. 2F). Despite Strain Differences, Distinct Metabolic Profiles Are Still Detectable for Each Mouse Model—As strain differences dominated the first components of the PCA model, and PLS-DA models were used to distinguish the four animal models of disease. We used three different routines to identify metabolic changes with the disease process (Fig. 3). For the first set of models the data were correlated to distinguish one disease model (one group) from the total remaining data set (the other group representing all other disease models and all control mice). The most significant model in terms of the goodness of fit algorithm (Q2) was produced for Scn–/+ mice (Q2cum = 0.46), then ScnΔ/+ mice (Q2cum = 0.38), then MLPKO mice (Q2cum = 0.25), and the least for the model of DMD (Q2cum = 0.17) (models not shown). These models were used to predict disease status across the complete group (Table II).Table IIComparing one disease group against all miceMouse modelsNMR experimentComponentsR2cumQ2cumCorrect predictions for mouse modelCorrect predictions for other modelsScnΔ/+Solution20.380.395/864/64HRMAS20.310.144/840/40Scn-/+Solution40.510.467/865/65HRMAS20.310.296/737/41MLPKOSolution20.360.254/863/64HRMAS20.380.298/840/40MdxSolution10.180.178/1458/58HRMAS10.330.073/642/42 Open table in a new tab However, because of the large differences associated with strain, the data from the individual disease models and their respective control strain were processed alone using PLS-DA. Highly predictive models were produced for each disease Fig. 4, A–C. Each disease state had a distinctive metabolic profile and could be distinguished by the variable importance parameter scores of key metabolites (Fig. 4, D–
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