A Combined Metabolomic and Proteomic Investigation of the Effects of

Apr 3, 2008 - A Combined Metabolomic and Proteomic Investigation of the Effects of a Failure to Express Dystrophin in the Mouse Heart. Melanie K. Guls...
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A Combined Metabolomic and Proteomic Investigation of the Effects of a Failure to Express Dystrophin in the Mouse Heart Melanie K. Gulston,† Denis V. Rubtsov,† Helen J. Atherton,† Kieran Clarke,‡ Kay E. Davies,‡ Kathryn S. Lilley,† and Julian L. Griffin*,† The Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QW, U.K., and Department of Physiology, Anatomy and Genetics, University of Oxford, Parks Road, Oxford OX1 3PT Received January 28, 2008

Muscle degeneration in the heart of 1–9 month-old mdx mice (a model for Duchenne muscular dystrophy) has been monitored using metabolomic and proteomic approaches. In both data sets, a pronounced aging trend was detected in control and mdx mice, and this trend was separate from the disease process. In addition, the characteristic increase in taurine associated with dystrophic tissue is correlated with proteins associated with oxidative phosphorylation and mitochondrial metabolism. Keywords: metabonomics • taurine • pattern recognition • metabolic profiling

Introduction Duchenne muscular dystrophy (DMD) is an X-linked recessive disorder characterized by progressive muscle wasting, culminating in death from either respiratory or cardiac failure.1 One of the most widely used models of this disease is the mdx mouse which also possesses a mis-sense mutation in the gene for dystrophin.2 While the mdx mouse has a pronounced hunch and lower muscle regenerative capacity compared with wildtype controls,3,4 the phenotype of the disorder is milder in the mouse compared with human sufferers. This suggests that other proteins may compensate the failure to express dystrophin in the mouse but not humans and also highlights the difficulties that remain moving from a genomic description of a disease to an understanding of the functional phenotype in different organisms. In this respect, one intriguing event in the development of the mdx mouse is that muscle tissue undergoes a wave of necrosis followed by recovery at 2–3 months. Identification of the mechanisms involved in muscle recovery may identify potential mechanisms to treat the disorder in humans. A number of previous studies have noted profound metabolic perturbations in dystrophic muscle tissue.3,5–8 High resolution NMR spectroscopy based metabolomics has demonstrated that dystrophic muscle tissues, including that from the heart and diaphragm, are similarly perturbed in terms of metabolism and are characterized by an increase in taurine and lactate alongside a decrease in creatine.7 Furthermore, McIntosh and co-workers3,4 correlated muscle taurine content to regeneration of muscle tissue in the mdx mouse. There is also a relationship between the concentration of taurine and utrophin expression in dystrophic muscle tissue.9 Utrophin is a functionally related protein to dystrophin10–13 that can reverse * Author to whom correspondences should be addressed. E-mail: [email protected]. Tel.: 44-(0)1223 764922. Fax: 44-(0)1223 333345. † University of Cambridge. ‡ University of Oxford. 10.1021/pr800070p CCC: $40.75

 2008 American Chemical Society

the dystrophic phenotype normally observed in mdx mice when overexpressed.14 These metabolic studies suggest that the effects of a failure to express dystrophin, and how this interrelates with the expression of other proteins, can be monitored by NMR-based metabolomics. However, these studies have largely focused on a single time point, and hence it is not clear how these metabolite perturbations change during the disease progression. In the present study, a combined metabolomic and proteomic approach has been conducted to monitor muscle degeneration in the heart of the mdx mouse. The multivariate statistical approaches of principal component analysis (PCA) and prediction to latent structures through partial least-squares (PLS) have been used to model the metabolomic and proteomic data sets.

Materials and Methods 1. Animal Handling. All mice were maintained according to the UK Home Office guidelines. Male mice were removed from stable colonies of C57BL/10 control mice and mdx mice (n ) 4 for each time point; 1, 3, 5, 7, and 9 months old). Animals were killed by cervical dislocation, the heart rapidly removed and a 100 mg wet weight section of the left ventricle removed (typically within 15 s). Tissue was immediately frozen in liquid nitrogen and stored at -80 °C prior to NMR and proteomic analysis. The genetic integrity of the colonies was monitored throughout by standard genotyping techniques. 2. Sample Preparation. Frozen heart tissue was pulverized using a pestle and mortar, and metabolites were extracted using a chloroform/methanol extraction procedure (100 mg of wet weight tissue in 0.6 mL of 2:1 chloroform/methanol). To this, 0.2 mL of chloroform and water was added, and the mixture was centrifuged for 20 min at 13 000 rev/s. The resultant aqueous solution was lyophilised, and the extracts were redissolved in D2O containing 1 mM TSP for NMR analysis. The protein pellet was dried in air prior to storage at -20 °C for proteomic analysis. Journal of Proteome Research 2008, 7, 2069–2077 2069 Published on Web 04/03/2008

research articles 3. NMR Spectroscopy. All solution state spectra were acquired using a 9.6 T superconducting magnet interfaced to an INOVA spectrometer (Varian, CA, USA). Solvent suppressed spectra were acquired into 16 K data points and summed over 128 scans, using a solvent suppression pulse sequence based on the start of the 2D NOESY pulse sequence (relaxation delay ) 3 s; mixing time ) 150 ms, t1 ) 4 µs; solvent suppression during the mixing time and relaxation time). Spectra were processed using ACD NMR manager software (ACD, Toronto, Canada). Free Induction Decays (FIDs) were Fourier transformed from the time to frequency domain following multiplication by a 1 Hz exponential function. Spectra were phased, baseline corrected, and referenced to the singlet of TSP at δ 0.0. Spectra were integrated across an average of 0.01 ppm integral regions between 0.2 and 9.72 ppm using an intelligent bucketing routine within ACD NMR manager. This approach ensures that the integral regions do not straddle NMR resonance peaks, by ensuring the edge of the integral regions is placed in the troughs of the peaks. The output vector representing each spectrum was normalized across the integral regions, excluding the water resonance (4.5–5.2 ppm), effectively standardizing all the individual integrals to the total integral of all the low molecular weight metabolites.15,16 In addition, the region from 3.32 to 3.38 was excluded as variable amounts of methanol from the extraction procedure were detected in this region. This produced a data set consisting of 924 variables. 4. Proteomic Analysis of Cardiac Tissue Using 2D DIGE. Protein pellets were resuspended in ASB14 lysis buffer (8 M urea, 2% w/w ASB14, 5 mM magnesium acetate, 10 mM Tris, pH 8.0). A soluble protein fraction was obtained by taking the supernatant after centrifugation at 13k rpm for 10 min. Protein concentration was determined using the detergent-compatible Bio-Rad DC protein assay. For each time point, 2D-DIGE was performed as previously described.17 Individual protein samples (50 µg) were labeled with 200 pmol of Cy3 or Cy5 minimal dyes. A protein pool consisting of all protein samples included in the 3 month age group was used as an internal standard for all DIGE analyses to allow comparisons across all ages of animals and was labeled with Cy2. This labeling was performed at the time of the Cy3 and Cy5 labeling using a stored aliquot of all the proteins from the 3 month time period. Three protein samples labeled with Cy2 (pool), Cy3, and Cy5 were mixed and separated by iso-electric focusing (IEF) using 13 cm nonlinear IPG DryStrips, pH 3–10NL (GE Healthcare), according to manufacturers instructions. Further orthogonal separation according to molecular weight was achieved using 12% SDS-polyacrylamide gels. Following electrophoresis, gels were scanned at appropriate wavelengths for Cy2, Cy3, and Cy5 fluorescence using a Typhoon 9400 (GE Healthcare). Gel images were cropped to remove extraneous regions of the gel image using ImageQuant v5.2 (GE Healthcare), and protein expression was quantified (standardized abundance) using DeCyder v5 (GE Healthcare). For each biological replicate, a single standardized abundance value was obtained. Following multivariate analysis to identify which protein spots were significantly changed by age or disease (see below), spots were excised from gels, stained with colloidal-Coomassie G250, digested with trypsin, and subjected to tandem mass spectrometric analysis (QTof2, Waters) coupled to reverse phase liquid chromatography (capLC, Waters). Protein identification was achieved by submitting fragment ion data to the Mascot search engine (http://www.matrixscience.com). 5. Description of Proteomic Data Sets. Four analyses were performed on the total proteomic data set. For all these comparisons, it was ensured that groups were made up of 2070

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Gulston et al. samples labeled with Cy3 and Cy5 dyes with equal distribution so as dye bias would not confound a given comparison and the intensities were normalized to the Cy2 dye channel representing the total protein content in all the samples from 3 months. The first analyses were of those protein spots that were important in the separation of disease and control samples at each individual time point. The second set was created by combining the data across all time points by normalizing to the Cy2 concentration. This produced a smaller data set which contained spots largely common across all the individual time points. The third set of proteins for analysis was generated from the top 15 most discriminatory proteins important for classification at each time point and in the time trend and was used for cross correlation with metabolomic data. The fourth analysis was performed on a subset of the third, where protein abundance was large enough to allow identification using tandem mass spectrometric analysis and a subsequent Mascot search. 6. Pattern Recognition of Metabolomic and Proteomic Data. Data sets (metabolomic and proteomic) were imported into the SIMCA package (version 11, Umetrics, Umea, Sweden) and then preprocessed using Pareto scaling by multiplying each variable by (1/sk)1/2 where sk is the standard deviation of the variable. This scaling effectively increases the importance of low concentration metabolites or proteins in the resultant models but not to an extent where the noise significantly contributes to the model. Data were analyzed using PCA and the supervised regression extension, partial least-squares discriminant analysis (PLS-DA) within the package SIMCA. 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. PLSDA 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. To monitor temporal progressions, the multivariate regression technique of partial least squares (PLS) was used. This is a generalization of PCA where a projection model is calculated to predict a Y variable (here time) from X via scores of X (here either NMR spectra or proteomic expression). This algorithm is a generalized multiple regression method that can model multiple collinear X and Y variables. Cross validation of PLS-DA and PLS models was carried out by leaving out every sixth observation and predicting the observation’s class membership or associated Y score (age) on a new model. 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 as Q2cum ) 1 - Σ(PRESS/SS)

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Figure 1. Metabolomic analysis of dystrophic cardiac tissue across time. (a) The summed high-resolution 1H NMR spectra from all the tissue extracts used in this study. Spectra were acquired at 400 MHz, and the summation represents 40 individual spectra. Peak assignments: (1) ADP, (2) ATP, (3) NAD, (4) Adenosine (C2 ring), (5) Adenosine (ribose), (6) Lactate, (7) Serine, (8) Creatine, (9) Glutamate, (10) Glycine, (11) Taurine, (12) Phosphocholine, (13) Glycerophosphocholine, (14) Choline, (15) Malonate, (16) Aspartate, (17) Glutamine, (18) Acetate, (19) Alanine, (20) Valine, (21/22) Leucine/Isoleucine. (b) Principal components analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) of the entire metabolomic data set identified the presence of a time trend which dominated any differences between the dystrophic and control tissue (9, 1 month, O, 3 months, (, 5 months, 0, 7 months, 4, 9 months). However, PLS-DA separated dystrophic and control tissues for (c) the entire data set with a low Q2 (9, Control, O, Disease) or (d) for an individual time point with a high Q2. PLS-DA plot shown for 9-month-old mice (9, Control, O, Disease). (e) A plot of Q2 for the individual time points.

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. The variable importance parameter (VIP) 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.18 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 cross-validation routine described previously and the goodness of fit algorithm used to determine whether a metabolite was responsible for a statistically significant classification.

scores intercepts near or below zero, and (iii) all the R2 values for the random models are below the R2 score for the original model. 7. Cross Correlation of Metabolomic and Proteomic Data Sets. To investigate correlates between the metabolomic and proteomic data sets, Pearson’s correlation coefficients were calculated between the two data sets within the statistics toolbox of Matlab (Mathworks, www.mathworks.com). These correlates were calculated for all mice, mdx mice alone, and control mice alone. To minimize the possible influence of outliers and obtain empirical confidence intervals for correlation coefficients, a jack-knife procedure was used for correlation calculations, whereby samples were left out to investigate the influence this has on given correlation coefficients. Only significant correlations (p < 0.01) and where R > 0.7 are reported. These were displayed as heat maps within the Matlab package.

To further test the robustness of PLS models, the goodness of fit in terms of the Q2 and R2 values was compared with those obtained from models where the Y value was randomly permutated, while the X matrix was kept constant. This cross validation indicates a robust, predictive model when: (i) all the Q2 values generated from the random models are below the Q2 from the original model; (ii) the regression line of the Q2

1. Failure to Express Dystrophin Results in Metabolic Perturbations in Heart Tissue. The metabolic status of the heart was assessed using 1H NMR spectroscopy of the tissue extracts (Figure 1a) Initially, PCA was applied to the entire metabolomic data set to identify the dominant trends. In each case, the data were dominated by a trend associated with age of animal rather than disease status (R2 ) 49% for heart tissue

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research articles for the first two components; Figure 1b). This was particularly apparent using the supervised approach of PLS-DA (clustered according to age, ignoring effects of disease; R2(X) ) 49%; R2(Y) ) 46%; Q2 ) 27% for heart tissue across the first two components; Figure 1b). Despite the aging trend detected, the application of the supervised approach PLS-DA distinguished tissue from control and dystrophic animals across the entire time course examined (Figure 1c). However, this model had a low predictive capability as indicated by a Q2 value of 8% (1 component significant; R2 ) 27%). This was further increased by the exclusion of the 1-month-old animals which were still being weaned at this stage (2 significant components, Q2 ) 21%; R2 ) 42%). Thus, individual time points were investigated. PLS-DA separated control and diseased tissues at all time points (Figure 1d). To assess the significance of these separations, the Q2 value was calculated for each PLS-DA model (Figure 1e), with its value increasing from 2% (weakly predictive) at 1 month to 94% (strongly predictive) at 9 months, signifying that the metabolic perturbations associated with dystrophic tissue increased with age. Using a combination of the loadings and VIP scores, the most perturbed metabolite regions that were significantly different as judged by a crossvalidation routine were identified for the heart at each time point examined (Table 1.) One caveat associated with this analysis is that each comparison contained only four samples from either the control or mdx mouse group. This limits the statistical validity of any cross validation process. To address this, adjacent time points were also combined and analyzed (e.g., 1 + 3 months, 3 + 5 months, etc). This analysis produced robust PLS-DA models for all comparisons except 1 + 3 months (Supplementary Figure 1). 2. Metabolic Perturbation in Cardiac Tissue Is Similarly Reflected in Changes in the Proteome of Dystrophic Cardiac Tissue. To assess changes in the protein complement of dystrophic cardiac tissue, each time point was initially considered separately using PCA and PLS-DA to identify protein changes at a particular time point (Figure 2a). Considering the number of proteins detected at any given time point, the least number of common protein features was detected at 5 months (1814 spots), while the largest number was detected at 1 month (2084 spots). Using the Q2 goodness of fit algorithm to assess the robustness of a given separation, the weakest separation was detected at 1 month (Q2 ) 25%), with the strongest separation at 5 months (Q2 ) 86%) (Figure 2b). The protein expression patterns for each individual time point were compared across the whole time series by ensuring that the Cy2 dye in each gel represented the combined 3 month sample set. This channel was normalized across the whole data set and ensured that a protein spot number at one time point would relate to the same protein spot at the other time points. This produced a data set consisting of 1605 protein spots. Applying PCA to this data set again resulted in separation according to the age of animal the heart tissue was taken from (R2 ) 33% for the first two components; Figure 2c). In an analogous manner to the previous analyses of the individual time points, as expected, dystrophic cardiac tissue could be classified as distinct from control tissue at each time point (Figure 2d for 3 months; R2 ) 58%; Q2 ) 46% across two components). However, it was not possible to separate heart tissue according to dystrophin status when considering all time points together (data not shown). However, a PLS-DA model 2072

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Gulston et al. Table 1. Key Metabolite Changes Detected Using 1H NMR Spectroscopy and Partial Least-Squares Discriminant Analysis of Extracts of Heart Tissue age of animal

heart increased

1

Myo-inositol (3.46–3.48, 3.50–3.51, 3.52–3.55)

3

Lactate (1.32–1.33), Myo-inositol (3.52–3.55), phosphocholine (3.20–3.22)

5

Choline (3.19–3.20), adenosine (8.34), taurine (3.26–3.27, 3.42)

7

Citrate (2.50–2.64), glucose/myo-inositol (3.46–3.72, 3.79–3.86), taurine (3.27, 3.42)

9

Leucine/isoleucine/valine (0.96–1.08), glutamine (2.30–2.34, 2.38, 2.40– 2.41), taurine (3.27–3.30, 3.42–3.43)

decreased

Creatine (3.05–3.06, 3.92–3.96), lactate (1.32–1.34), taurine (3.25–3.28, 3.40–3.45), phosphocholine (3.20–3.22), alanine (1.47–1.50), glutamate/ glutamine (2.08–2.16, 2.33–2.34), glutamine (2.45–2.46) (4.00–4.04), adenosine (6.15, 8.60–8.62) Creatine (3.03–3.06, 3.92–3.95), choline (3.18–3.19), glutamine (2.45–2.46), glucose (3.50), adenosine (8.60) Alanine (1.47–1.49), phosphocholine (3.23), ribose sugar (6.15–6.16), creatine (3.05, 3.94), leucine, isoleucine and valine (0.98–1.05), glycerol (4.03) Lactate (1.30–1.31), glutamine (2.12–2.13, 2.40, 2.42–2.43), creatine (3.03), ribose sugar (6.16), adenosine (8.27, 8.43, 8.60), NAD (9.34) Butyrate (1.16–1.18), lactate (1.30–1.34, 4.10–4.14), alanine (1.45–1.47), phosphocholine (3.21–3.22), glucose (3.23–3.25, 3.71–3.76, 3.80–3.90), creatine (3.04–3.05, 3.92)

was formed when the 1 month group was excluded (Supplementary Figure 2). Similarly to the metabolomic data set, to increase the number of gels compared, adjacent time points were also combined and analyzed. This analysis produced robust PLSDA models for all comparisons except 1 + 3 months (Supplementary Figure 3). Thus, the largest trend found in the total data set was aging, then a failure to express dystrophin. Table 2 shows the most discriminatory proteins for these separations as determined by examinations of the loading plots and VIP scores. 3. Temporal Trends Are the Same for Both Dystrophic and Control Tissues in Metabolomic and Proteomic Data Sets. In human sufferers of DMD, the disease is progressive, and one would expect the metabolic phenotype between control and diseased tissue to become more distinct. However, in the mouse, there is a reduced progression in muscular dysfunction. To examine this using metabolomics and proteomics, a series of PLS models were constructed. For each data set, the first model consisted of both diseased and control

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Figure 2. Proteomic analysis of dystrophic cardiac tissue across time. (a) PCA and PLS-DA readily separated the two tissue types for the BVA analysis performed on individual time points (9, Control, O, Disease). A three-dimensional PCA plot and a PLS-DA plot is shown for heart tissue taken from mice at 7 months of age (2054 spots; 1920 with presence >50% for this time point; Q2 ) 80%; R2(X) ) 64% for the PLS-DA plot). (b) A plot of Q2 for the individual time points. (c) PCA of the proteomic changes detected across the time course for proteins found at all time points (R2 ) 33% for first two components) (9, 1 month; O, 3 months; (, 5 months; 3, 7 months; 2, 9 months). (d) PLS-DA analysis of an individual time point from the data set used in (c) the PLS-DA plot for 3 months (Q2 ) 58%; R2(X) ) 46% across two components) (9, Control, O, Disease).

tissue, with metabolite or protein changes regressed against the age of the animal. The second model was trained using just the control tissue, and then the age of the dystrophic animals predicted from the metabolite/protein profiles of the tissue. In the third model, the metabolic or proteomic profiles of dystrophic tissue were used to predict the age of the animals the control tissue had been obtained from. In the metabolomic data set, a highly robust model was produced from the combined control and mdx data set with R2(X) ) 57%, R2(Y) ) 94%, and Q2 ) 86% across three components (Figure 3a). The model was deemed to be highly robust using a random permutation to assess the Q2 of the true model (Figure 3b). The metabolite regions most important to this regression model were increases in taurine (δ 3.25–3.28, δ 3.38–3.46), creatine (δ 3.02–3.05, δ 3.90–3.94), glutamine and glutamate (δ 2.38–2.50), and lysine (δ 2.98–3.02) and decreases in lactate (δ 1.30–1.38), β-hydroxy-butyrate (δ 1.18–1.23), and glutamate (δ 2.34–2.38). For each PLS model subsequently generated, the same metabolites were identified as being responsible for the trend. Using the control data set to predict the age of the mdx mice, the mean error in age was -0.4 ( 1.8 months, indicating across the model there was no significant difference between the two trends. Repeating this procedure using the mdx data as a train set and the control data as a test set, the mean error was 0.5 ( 1.3 months. Applying a similar analysis to the proteomic data set produced similar PLS models that demonstrated a distinct

temporal trend across the proteomic data set (Figure 3c). Analyzing the combined control and mdx data set, a PLS model was produced with an R2(X) ) 35%, R2(Y) ) 97%, and Q2 ) 69% across three components. This model was validated by randomly permuting the values of Y (Figure 3d). The model was driven by increases in protein spots 540, 947, 1415, 642, and 1206 and decreases in spots 2017, 2016, 1232, 1989, and 1664. Producing a model containing only mdx tissue, a threecomponent model was produced with an R2(X) ) 41%, R2(Y) ) 99%, and Q2 ) 73% which was validated by random permutation. The most significant protein changes across the age data set were increases in protein spots 540, 1415, 947, 690, and 642 and decreases in spots 1829, 1803 (ATP synthase, H+ transporting, F0 complex, subunit d), 699, 1865, and 706. Predicting the age of the control tissue data, the mean difference was 2.3 ( 1.7 months. No model could be produced by the control data set alone, but a robust model was built after excluding the 1 month group (three components, with an R2(X) ) 44%; R2(Y) ) 99%; Q2 ) 64%). Predicting the age of the mdx tissue data, the mean difference was 0.3 ( 1.7 months. 4. Data Fusion of Metabolomic and Proteomic Data. Having identified discriminatory proteins for lack of dystrophin and aging, the correlations between proteomic and metabolomic profiles were investigated (see Supplementary Figure 3 for an excerpt of this analysis). Examining the control animal data set revealed 80 positive correlates with R > 0.70 and 61 Journal of Proteome Research • Vol. 7, No. 5, 2008 2073

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Gulston et al. a

Table 2. Key Changes in Identified Proteins Detected Using 2D-DIGE on Extracts of Heart Tissue

mascot score, no. of matched queries

protein spot number

protein name

1793 1575 1663 579 646 864 1794 1803

Adenylate Kinase 1 Alpha globin Alpha-globin ATP synthase (b subunit) ATP synthase (b subunit) ATP synthase (b subunit) ATP synthase B chain, mitochondrial precursor ATP synthase, H+ Transporting mitochondrial F0 complex (subunit d) ATP synthase, H+ Transporting mitochondrial F1 complex (b subunit) ATP synthase, H+ Transporting, mitochondrial F0 complex (subunit d) ATP synthase, H+ transporting, mitochondrial F1 complex, g polypeptide Beta Actin Contraspin (protease inhibitor) Dihydrolipoamide branched chain transacylase E2 Electron transfer flavoprotein β subunit Electron transferring flavoprotein, R polypeptide Glyceraldehyde-3-phosphate dehydrogenase Hemoglobin β & R globin Hemoglobin β adult major chain Phosphatidylethanolamine binding protein Prohibitin Putative TSA, thiol specific antioxidant Serine proteinase inhibitor (CA,1a) Serine proteinase inhibitor (CA,1d) Serine proteinase inhibitor (CA,1d) Succinate dehydrogenase complex (subunit A) Trifunctional Enzyme (b subunit) Trifunctional Enzyme (b subunit) Trifunctional enzyme (Hydroxyacyl-Coenzyme A dehydrogenase/3-ketoacyl-Coenzyme A thiolase/ enoyl-Coenzyme A hydratase) Tu translation elongation factor, mitochondrial Α globin β-1 globin

589 1802 1370 522 384 700 1506 1232 1150 1507 1578 1830 1380 1789 564 447 542 380 808 819 273

837 1465 1434 a

275, 6 256, 6 87, 2 718, 18 1236, 90 95, 2 243, 4 191, 4

7m

134, 2

1m

221, 7

TC

222, 6

7m

61, 2 344, 7 103, 2 199, 5 125, 3 72, 2 139, 9 242, 5 141, 3 140, 4 202, 7 472, 15 161, 7 399, 13 197, 4 478, 28 66, 2 760, 18

598, 11 116, 3 63, 2

increase in control mice

1m TC 1m 9m 3m 7m TC

TC 1, 7 m TC 1m TC 3m 1m 1m 1m 1, 5 m 3, 5 m, TC 3m 1m 1, 3, 9 m TC 9m 9m 9m

3m 1m 1m

TC stands for time course.

negative correlates with R e 0.70. In contrast, the diseased data set had 33 positive correlates with R > 0.70 and 136 negative correlates with R e 0.70 when correlating the metabolomic and proteomic data. While too many correlates were detected to allow a detailed discussion of all, given the importance of taurine for the classification of tissue from mdx mice and its role in the aging trend detected, we examined the correlates between the major resonances associated with taurine at δ 3.25–3.27 and δ 3.42–3.46 and the reduced proteomic data set of identified proteins for diseased, control, and all animals across the time range examined in the study (Table 3; Figure 4). Between the diseased and control groups, a number of correlates were found to occur in both groups, with a correlation value >0.5, including correlates between taurine and spots numbers 1434 (β-1 globin; negative correlate), 1465 (R-globin; negative), 1506 (electron transfer flavoprotein β subunit; negative), 1507 (hemoglobin β and R globin; negative), 1575 (R globin; negative), and 1578 (hemoglobin β adult major chain; negative). However, spot 1794 (ATP synthase β chain, mitochondrial precursor) was negatively correlated in control tissue but positively correlated in diseased tissue. In addition, the control tissue showed further correlates with 1830 (phosphatidylethanolamine binding protein; negative 2074

increase in MDX mice

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correlate), 1793 (adenylate kinase; negative), 1789 (thiol specific antioxidant; negative), 1578 (hemoglobin β-adult major chain; negative), 1370 (ATP synthase, H+ transporting, mitochondrial F1 complex, g polypeptide; positive), 1232 (electron transferring flavoprotein, R polypeptide; positive), and 819 (mitochondrial trifunctional enzyme (b subunit); positive).

Discussion 1. Metabolomic and Proteomic Changes Similarly Classify Cardiac Tissue as Dystrophic in the mdx Mouse. Using both proteomic and metabolomic data sets, heart tissue was successfully classified as dystrophic for the majority of time points, with only the 1 month data sets for proteomics and metabolomics building weakly statistically significant models according to the Q2 measure of model robustness. Furthermore, metabolic and proteomic changes increased across the time period as signified by the increase in group separation and Q2 scores for the plots. In the mdx mouse muscle necrosis and apoptosis occurs at ∼3 weeks-1 month of age2 followed by waves of regeneration and necrosis from 6 weeks until ∼3 months.19–21 In our present study, the 1 month group produced the least robust PLS-DA

Effects of Failure to Express Dystrophin in the Mouse Heart

Figure 3. Examination of time-associated trends using partial least-squares (PLS). (a) A robust PLS model for the metabolomic data set containing both dystrophic and control tissue (R2(X) ) 57%, R2(Y) ) 94%, and Q2 ) 86% across three components). (b) A random permutation for the model in (a) to assess the robustness of the model (2, signifies permuted values of Q2, 9, signifies permuted values of R2). (c) A robust PLS model for the proteomic data set containing both dystrophic and control tissue (R2(X) ) 35%, R2(Y) ) 97%, and Q2 ) 69%). (d) A random permutation for the model in (c) to assess the robustness of the model (2, signifies permuted values of Q2, 9, signifies permuted values of R2).

models indicating that the function of the heart was only mildly perturbed during this stage of the disease process. However, in older mice, the regenerative capacity of skeletal muscle declines, with necrotic processes dominating.22 In heart tissue, profound metabolic and protein changes were detected at 5 months and older using our combined “omic” approach, with significant proteomic changes at 3 months, albeit without large metabolic changes. At this stage, heart function is normal as measured by left-ventricular (LV) morphology by magnetic resonance imagining, LV passive resistance as measured during ex vivo perfusion, and cytoskeletal protein levels measured by Western blotting.23 Previously, we have demonstrated that metabolomics can distinguish dystrophic tissue in adult mice older than 5 months for a range of tissues including heart tissue according to their metabolite profiles7,9 and an accumulation of lipid.8 Furthermore, in human patients and using a transcriptional approach, Chen and co-workers24 reported a general downregulation of nuclear-encoded mitochondrial transcripts in dystrophic tissue. The DIGE proteomic approach used does not resolve all the proteins present within the proteome and in particular will over represent soluble proteins compared with membrane-associated proteins, and integral membrane proteins will be largely absent. Thus, our approach was probably not sensitive to many of the changes concerning dystrophin-associated proteins which localize to the dystrophin-associated surface glycoprotein complex at the sarcolemmal membrane.25 Indeed, in the present study, the majority of the detected and identified proteins was associated with mitochondrial metabolism and

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in particular oxidative phosphorylation. Despite this, these proteins still distinguished the dystrophic cardiac tissue in keeping with the metabolomic changes which indicate that dystrophic tissue has profound metabolic perturbations. Using a similar proteomic approach, Doran and colleagues detected a number of high concentration proteomic changes in diaphragm tissue,26 but not in skeletal muscle.27These changes also included a number of mitochondrial proteins including dihydrolipoamide dehydrogenase, isocitrate dehydrogenase 2, electron-transferring flavoprotein, and malate dehydrogenase. One problem with our approach, which focused on a range of time points, is the small sample size that was used for individual time points. This precluded the use of a train and test routine as is commonly used to confirm that a supervised pattern recognition model is not overfitted. However, for each model the total variation modeled (R2) and the goodness of fit (Q2) were calculated to validate the models produced. Furthermore, combining two adjacent time points produced robust models for both metabolomic and proteomic data apart from the 1 month + 3 month group. 2. Proteomic and Metabolomic Changes Associated with Aging Are Distinct from the Disease Process. Using multivariate statistics, trends associated with the age of the animals were detected in both the proteomic and metabolomic data sets. However, on the whole, the metabolites and proteins responsible for these trends were the same for both dystrophic and control mice. Furthermore, using PLS we were able to investigate whether the trend associated with aging occurred at the same rate in both mouse types. In both the metabolomic and proteomic data sets, a similar rate of aging was apparent, indicating that the muscle necrosis and apoptosis associated with dystrophic tissue did not produce an accelerated aging, but instead produced metabolic and proteomic changes that were distinct to the disease process. 3. Proteomic Changes Associated with the Changes in Taurine in This Study. Previously, we reported that an increased concentration of taurine was detected in cardiac, diaphragm, and skeletal muscles of adult mdx mice.7,9,28 Furthermore, the cardiac concentration of taurine was also dependent on the expression of utrophin suggesting that changes in taurine may be part of a compensatory mechanism for the lack of dystrophin expression.9 There are a number of overlapping roles of taurine in the heart including calcium homeostasis, regulation of contractility, and osmoregulation.29 One of the major goals of this study was to use the proteomic changes to investigate what deficits led to the raised concentration of taurine in adult mice. The major trends detected were negative correlates with blood plasma proteins (R and β-1 globin and hemoglobin) and positive correlates with oxidative phosphorylation (electron transfer flavoprotein, ATP synthase, and trifunctional enzyme). One worry with both the metabolomic and proteomic changes detected is that cardiac tissue changes may have been affected by blood contained within the heart circulatory system. However, because taurine was negatively correlated with the globins, its increase in concentration in the mdx mice is unlikely to be caused by blood, despite red blood cells containing relatively high concentrations of taurine.29 For both control and mdx mice, the concentration of taurine increased with age as the contribution of bloodassociated proteins decreased. In addition, the proteome changes that formed the largest part of the aging trend were Journal of Proteome Research • Vol. 7, No. 5, 2008 2075

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Gulston et al.

Table 3. Identified Proteins Which Correlate with Taurine with a Pearson’s Correlation >0.5

a

protein number

protein name

control

1793 1575 1794 1370

Adenylate Kinase 1 Alpha globin ATP synthase B chain, mitochondrial precursor ATP synthase, H+ transporting, mitochondrial F1 complex, g polypeptide Beta Actin Contraspin Electron transfer flavoprotein β subunit Electron transferring flavoprotein, R polypeptide Hemoglobin β adult major chain Putative TSA, thiol specific antioxidant Trifunctional Enzyme (b subunit) β-1 globin

-0.64

522 384 1506 1232 1578 1789 819 1434

-0.69 +0.56

disease

combined

-0.70 +0.55

-0.63

-0.58 -0.65 +0.77 -0.77 +0.57 -0.64

-0.53 -0.66

-0.56

-0.58

-0.55

-0.63

-0.64

a

Three data sets were considered consisting of only control animals, diseased animals, and a combined data set. These correlations were calculated from the manual integral values for the two triplets of taurine at 3.27 and 3.43 ppm.

Figure 4. Correlation analysis between identified protiens and taurine. Correlates between the major resonances associated with taurine at δ 3.25–3.27 and δ 3.42–3.46 and the reduced proteomic data set of identified proteins for (a) control, (b) diseased, and (c) correlations between protein content and the integrated area under the two taurine triplets. This method avoids subtle variations in the chemical shift of taurine but is a more targeted approach necessitating manual integration of the peaks. (d) The bucketed NMR spectrum for taurine. (e) The unbucketed NMR spectrum for taurine. Spectra in (d) and (e) are aligned to their approximate buckets in the analysis.

general increases in mitochondrial associated proteins, suggesting that the role of taurine in the heart may be strongly correlated with mitochondrial function. The further increase in taurine in mdx mice most likely represents a compensatory mechanism for the increased intracellular Ca2+ in the dystrophic cells30 and a compensation for the impaired mitochondrial function of the heart tissue which has been measured at both the transcriptional and proteomic level21,26,27,31 as well as during physiological studies of muscle function.5,6 In control animals, the quantity of adenylate kinase (1793) was inversely correlated with taurine, while mitochondrial trifunctional enzyme (b subunit; 819) 2076

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was positively correlated. This indicates that as fat metabolism increases in importance in the heart there is less dephosphorylation of ADP to AMP as occurs under extremes of exercise. These changes do not appear to be evident in the heart tissue of mdx mice. Confusingly, spot 1794 (ATP synthase β chain, mitochondrial precursor) is negatively correlated with taurine in the heart of control animals but positively correlated in the heart of mdx mice. This is despite other components of ATP synthase being positively correlated with taurine. A similar discrepancy existed in heart tissue following ischemia-reperfusion injury in dog hearts with the R subunit of the ATP synthase isoform precursor

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Effects of Failure to Express Dystrophin in the Mouse Heart decreasing while the mitochondrial ATP synthase D chain increased in relative terms,32 suggesting that some of these components may be lost more readily following tissue damage. In conclusion, the combined metabolomic and proteomic approach successfully distinguished dystrophic cardiac tissue in adult mice. The application of the multivariate approach PLS detected distinct aging trends in both control and mdx mice, with this aging trend being distinct from the metabolite and proteomic changes associated with the disease. Finally, the correlations between the metabolomic and proteomic changes suggested that the increase in taurine was correlated with increased expression of proteins involved in oxidative phosphorylation, demonstrating the usefulness of a combined metabolomic-proteomic analysis.

Acknowledgment. This work is supported by a grant from the Wellcome Trust (ME029321MES). J.L.G. is grateful to the Royal Society for a University Research Fellowship. M.K.G. and K.D. are funded by the Wellcome Trust. K.C. is funded by the British Heart Foundation. D.V.R., H.J.A., J.L.G., and K.S.L. are funded by the BBSRC. Supporting Information Available: Supplementary Figures 1-3. This material is available free of charge via the Internet at http://pubs.acs.org. References (1) Emery, A. E. Clinical and molecular studies in Duchenne muscular dystrophy. Prog. Clin. Biol. Res. 1989, 306, 15–28. (2) Bulfield, G.; Siller, W. G.; Wight, P. A. L.; Moore, K. J. X chromosome-linked muscular dystrophy (mdx) in the mouse. Proc. Natl. Acad. Sci. U.S.A. 1984, 81, 1189–1192. (3) McIntosh, L. M.; Baker, R. E.; Anderson, J. E. Magnetic resonance imaging of regenerating and dystrophic mouse muscle. Biochem. Cell Biol. 1998, 76 (2–3), 532–41. (4) McIntosh, L. M.; Garrett, K. L.; Megeney, L.; Rudnicki, M. A.; Anderson, J. E. Regeneration and myogenic cell proliferation correlate with taurine levels in dystrophin- and MyoD-deficient muscles. Anatomical Rec. 1998, 252 (2), 311–24. (5) Kemp, G. J.; Taylor, D. J.; Dunn, J. F.; Frostick, S. P.; Radda, G. K. Cellular energetics of dystrophic muscle. J. Neurol. Sci. 1993, 116 (2), 201–6. (6) Even, P. C.; Decrouy, A.; Chinet, A. Defective regulation of energy metabolism in mdx-mouse skeletal muscles. Biochem. J. 1994, 304 (2), 649–54. (7) Griffin, J. L.; Williams, H. J.; Sang, E.; Clarke, K.; Rae, C.; Nicholson, J. K. Metabolic profiling of genetic disorders: a multitissue (1)H nuclear magnetic resonance spectroscopic and pattern recognition study into dystrophic tissue. Anal. Biochem. 2001, 293 (1), 16–21. (8) Griffin, J. L.; Williams, H. J.; Sang, E.; Nicholson, J. K. Abnormal lipid profile of dystrophic cardiac tissue as demonstrated by oneand two-dimensional magic-angle spinning (1)H NMR spectroscopy. Magn. Reson. Med. 2001, 46 (2), 249–55. (9) Griffin, J. L.; Sang, E.; Evens, T.; Davies, K.; Clarke, K. Metabolic profiles of dystrophin and utrophin expression in mouse models of Duchenne Muscular dystrophy. FEBS Lett. 2002, 530 (1–3), 109– 16. (10) Khurana, T. S.; Hoffman, E. P.; Kunkel, L. M. Identification of a chromosome 6-encoded dystrophin-related protein. J. Biol. Chem. 1990, 265 (28), 16717–20. (11) Khurana, T. S.; Watkins, S. C.; Chafey, P.; Chelly, J.; Tome, F. M.; Fardeau, M.; Kaplan, J. C.; Kunkel, L. M. Immunolocalization and developmental expression of dystrophin related protein in skeletal muscle. Neuromuscular Disord. 1991, 1 (3), 185–94. (12) Hoffman, E. P.; Brown, R. H.; Kunkel, L. M. Dystrophin: the protein product of the Duchenne muscular dystrophy locus. Cell 1987, 51 (6), 919–28. (13) Rybakova, I. N.; Patel, J. R.; Davies, K. E.; Yurchenco, P. D.; Ervasti, J. M. Utrophin binds laterally along Actin filaments and can couple

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