Abstract

   Background: Like Duchenne muscular dystrophy (DMD), the Golden
   Retriever Muscular Dystrophy (GRMD) dog model of DMD is characterized
   by muscle necrosis, progressive paralysis, and pseudohypertrophy in
   specific skeletal muscles. This severe GRMD phenotype includes moderate
   atrophy of the biceps femoris (BF) as compared to unaffected normal
   dogs, while the long digital extensor (LDE), which functions to flex
   the tibiotarsal joint and serves as a digital extensor, undergoes the
   most pronounced atrophy. A recent microarray analysis of GRMD
   identified alterations in genes associated with lipid metabolism and
   energy production. Methods: We, therefore, undertook a non-targeted
   metabolomics analysis of the milder/earlier stage disease GRMD BF
   muscle versus the more severe/chronic LDE using GC-MS to identify
   underlying metabolic defects specific for affected GRMD skeletal
   muscle. Results: Untargeted metabolomics analysis of
   moderately-affected GRMD muscle (BF) identified eight significantly
   altered metabolites, including significantly decreased stearamide
   (0.23-fold of controls, p = 2.89 × 10^−3), carnosine (0.40-fold of
   controls, p = 1.88 × 10^−2), fumaric acid (0.40-fold of controls, p =
   7.40 × 10^−4), lactamide (0.33-fold of controls, p = 4.84 × 10^−2),
   myoinositol-2-phosphate (0.45-fold of controls, p = 3.66 × 10^−2), and
   significantly increased oleic acid (1.77-fold of controls, p = 9.27 ×
   10^−2), glutamic acid (2.48-fold of controls, p = 2.63 × 10^−2), and
   proline (1.73-fold of controls, p = 3.01 × 10^−2). Pathway enrichment
   analysis identified significant enrichment for arginine/proline
   metabolism (p = 5.88 × 10^−4, FDR 4.7 × 10^−2), where alterations in
   L-glutamic acid, proline, and carnosine were found. Additionally,
   multiple Krebs cycle intermediates were significantly decreased (e.g.,
   malic acid, fumaric acid, citric/isocitric acid, and succinic acid),
   suggesting that altered energy metabolism may be underlying the
   observed GRMD BF muscle dysfunction. In contrast, two pathways,
   inosine-5′-monophosphate (VIP Score 3.91) and 3-phosphoglyceric acid
   (VIP Score 3.08) mainly contributed to the LDE signature, with two
   metabolites (phosphoglyceric acid and inosine-5′-monophosphate) being
   significantly decreased. When the BF and LDE were compared, the most
   significant metabolite was phosphoric acid, which was significantly
   less in the GRMD BF compared to control and GRMD LDE groups.
   Conclusions: The identification of elevated BF oleic acid (a long-chain
   fatty acid) is consistent with recent microarray studies identifying
   altered lipid metabolism genes, while alterations in arginine and
   proline metabolism are consistent with recent studies identifying
   elevated L-arginine in DMD patient sera as a biomarker of disease.
   Together, these studies demonstrate muscle-specific alterations in
   GRMD-affected muscle, which illustrate previously unidentified
   metabolic changes.

   Keywords: Duchenne muscular dystrophy, golden retriever muscular
   dystrophy, skeletal muscle, metabolism, non-targeted metabolomics

1. Introduction

   Duchene muscular dystrophy (DMD) is a neuromuscular disorder
   characterized by progressive loss of muscle mass that renders the
   mobility of its victims to wheelchair before the age of 12 [[44]1].
   Genetically, X-linked Duchenne muscular dystrophy is caused by the
   absence of dystrophin protein and is a lethal inherited disorder that
   mainly affects male. The disease progresses with a gradual development
   of respiratory insufficiency, cardiomyopathy, and skeletal muscle
   weakness, frequently causing death by the late teens or early twenties
   [[45]2,[46]3]. Of the three naturally-occurring mammalian (murine,
   feline, and canine) DMD models [[47]4], in addition to the two
   non-mammalian (zebrafish and Caenorhabditis elegans) models [[48]5],
   the canine models are thought to be the most suitable ones for studies
   because of their pathological resemblance and clinical relevance to
   human patients of DMD [[49]5,[50]6]. Like DMD, the genetically
   homologous Golden Retriever Muscular Dystrophy (GRMD) dog model of DMD
   is characterized by muscle necrosis, progressive paralysis,
   differential weakness of flexor and extensor muscles muscles, and mixed
   atrophy and hypertrophy of specific skeletal muscles [[51]7,[52]8]. The
   GRMD phenotype includes marked atrophy of the long digital extensor
   (LDE) as compared to sibling unaffected normal dogs, while the biceps
   femoris (BF), a member of the hamstring muscle group, undergoes less
   pronounced atrophy [[53]7,[54]8].

   Recent studies have begun to implicate metabolic defects in muscular
   dystrophies, including DMD. Qualitative and quantitative lipid analysis
   of DMD, Becker’s Muscular Dystrophy, facioscapulohumeral muscular
   dystrophy (FSHD), and limb girdle muscular dystrophy-2B (LGMD-2B)
   patient biopsies found significant increases in glycogen [[55]9]. Fatty
   acids were also altered in DMD, BMD, FSHD, and LGMD-2B, although DMD
   biopsies exhibited unique increases in cholesterol in DMD patients
   [[56]9]. A recent microarray analysis of GRMD muscle identified the
   first evidence of potential alterations in metabolism, specifically
   alterations in genes associated with lipid metabolism and energy
   production [[57]10]. Impairment of bioenergetics has recently been
   reported in congenital muscle dystrophy Type 1A (MDC1A) and Leigh
   Syndrome, linking metabolism to disturbances in skeletal muscle cell
   apoptosis [[58]11]. In the present study, we undertook a non-targeted
   metabolomics analysis to determine how GRMD skeletal muscle compared to
   age-matched control muscle metabolically using gas chromatography-mass
   spectrometry (GC-MS) to identify underlying metabolic defects specific
   for GRMD skeletal muscle.

2. Results

2.1. Determination of Metabolomics Changes in the GRMD Biceps Femoris vs.
Control (t-Test)

   The first statistical analysis we ran was designed to determine if
   there were significant alterations in the GRMD BF muscle compared to
   controls. To do this, we analyzed biopsy samples from GRMD-affected
   (and non-affected sibling) dogs at 6 months of age, at which time
   phenotypic changes have occurred [[59]7,[60]8]. Previous studies
   identified marked atrophy of the LDE compared to sibling unaffected
   normal dogs, while the BF, a member of the hamstring muscle group,
   undergoes less pronounced atrophy [[61]7,[62]8]. The limited number of
   samples run (6 GRMD and 4 controls) are due to the cost of housing and
   maintaining ill dogs for 6 months and the limited number of animals in
   the colony. A t-test was then performed on the metabolites to determine
   BF metabolites affected. Analysis of the less atrophied, early stage
   disease BF yielded 75 metabolites muscle ([63]Figure S1A), including 19
   imputed ([64]Figure S1B), which upon partial least squares discriminant
   analysis (PLS-DA) produced separation of the control and GRMD groups
   from the first two components ([65]Figure 1A). PLS-DA VIP analysis
   identified pyrophosphate (VIP Score 2.65) and stearamide (VIP Score
   2.24) with the highest contributions to this signature ([66]Figure 1B).
   t-Test analysis of the two groups identified eight significant
   metabolites (carnosine, fumaric acid, steramide,
   myoinositol-2-phosphate, lactamide, proline, oleic acid, glutamic acid,
   [67]Figure 1C).

Figure 1.

   [68]Figure 1
   [69]Open in a new tab

   Untargeted metabolomics analysis of golden retriever muscular dystrophy
   (GRMD) biceps femoris (BF) muscle. (A) Supervised clustering of GRMD BF
   metabolites using Partial least squares discriminant analysis (PLS-DA);
   (B) The top metabolites ranked by VIP scores; (C) Heatmap of t-test
   significant metabolites identified in GRMD BF vs. age-matched controls.
   Analysis by Metaboanalyst analysis of GRMD (N = 6) vs. control (N = 4)
   BF metabolites.

   We next investigated what these eight significant metabolites had in
   common in terms of metabolic pathways. To do this, we determined their
   enrichment, or likelihood of being significantly altered by random
   chance, in metabolic pathways. Pathway analysis of these eight
   significant metabolites in GRMD BF identified four affected pathways,
   including: (1) arginine and proline metabolism; (2) alanine, aspartic
   acid, and glutamic acid metabolism; (3) butanoate metabolism, and (4)
   histidine metabolism, with false discovery rates (FDR) of 5–9%
   ([70]Figure 2A). The FDR estimates the likelihood that a conclusion
   that a relationship exists, whereas in reality it does not for an
   individual comparison [[71]12]. Its use in the the current context
   represents the likelihood that the pathways identified as significantly
   enriched may be false and is generally set at 5%. In the current
   studies, we have presented data with just higher FDR, as we are
   necessarily analyzing a smaller number of biological biopsies.
   Enrichment analysis of the BF t-test significant metabolites against a
   disease-associated metabolite set (blood) identified > 20-fold
   enrichment in histidine metabolism and alanine metabolism ([72]Figure
   2B). Enrichment of arginine and proline metabolism had the lowest p
   value (p = 5.88 × 10^−4, FDR = 4.7 × 10^−2) enriched > 15-fold
   ([73]Figure 2B). To further identify metabolic pathways to which our
   significant metabolites may be related, we compared them to changes in
   other disease sets, including urine, CSF, and location-based sets for
   further clues to the metabolic pathways affected. Metabolic enrichment
   BF t-test significant metabolites were analyzed against
   disease-associated metabolite sets (urine, CSF) and location-based
   metabolite sets and identified nearly 80-fold enrichment for
   carnosinuria/carnosinemia ([74]Figure S2A), > 10-fold for
   spinocerebellar degeneration ([75]Figure S2B), and 5-fold for skeletal
   muscle ([76]Figure S2C). Of the eight (8) t-test significant
   metabolites, five (5) were significantly decreased (stearamide,
   carnosine, fumaric acid, lactamide, and myoinositol-2-phosphate), while
   three (3) were significantly increased (oleic acid, glutamic acid, and
   proline) ([77]Figure 2C).

Figure 2.

   [78]Figure 2
   [79]Open in a new tab

   Pathway enrichment analysis of t-test significant metabolites from GRMD
   biceps femoris (BF) muscle. (A) Pathway analysis of t-test significant
   metabolites; (B) Enrichment analysis of t-test significant metabolites
   using pathway dataset for comparison; (C) Comparison of Peak values of
   t-test significant metabolites. Analysis by Metaboanalyst analysis of
   GRMD (N = 6) vs. control (N = 4) BF metabolites. Data is presented as
   the mean +/- SEM.

2.2. GRMD vs. Control Long Digital Extensor Muscle vs. Control (t-Test)

   The second statistical analysis we ran was designed to determine if
   there were significant alterations in the more atrophied, later stage
   disease GRMD LDE compared to controls. The LDE functions to flex the
   tibiotarsal joint and also serves as a digital extensor [[80]13] and is
   significantly atrophied in GRMD to a greater extent than the BF muscle
   [[81]7,[82]8]. In this analysis, we compared biopsy samples from
   GRMD-affected and non-affected sibling LDE samples at 6 months of age
   and performed a t-test to determine if there were any significant
   alterations in the GRMD LDE muscle compared to controls ([83]Figure 3).
   Of the 75 metabolites identified in LDE muscles ([84]Figure S3A), 14
   were imputed ([85]Figure S3B), separation on partial least squares
   discriminant analysis (PLS-DA) was seen ([86]Figure 3A). PLS-DA VIP
   analysis identified inosine-5’-monophosphate (VIP Score 3.91) and
   3-phosphoglyceric acid (VIP Score 3.08) with the highest contributions
   to this signature. t-Test analysis of the two groups identified two (2)
   significant metabolites (phosphoglyceric aid and
   inosine-5’-monophosphate ([87]Figure 3C), which were both decreased
   ([88]Figure 3D).

Figure 3.

   [89]Figure 3
   [90]Open in a new tab

   Untargeted metabolomics analysis of GRMD long digital extensor (LDE)
   muscle. (A) Supervised clustering of GRMD LDE metabolites using Partial
   least squares discriminant analysis (PLS-DA); (B) The top metabolites
   ranked by VIP scores; (C) Heatmap of t-test significant metabolites
   identified in GRMD BF vs. age-matched controls. Analysis by
   Metaboanalyst analysis of GRMD (N = 6) vs. control (N = 4) long digital
   extensor metabolites; (D) Peak values of significant metabolites
   identified in GRMD LDE vs. control LDE.

2.3. GRMD LDE vs. GRMD BF vs. Control LDE vs. Control BF (ANOVA)

   A third statistical analysis analysis was performed to identify if
   there were any common changes in the more atrophied later stage disase
   GRMD LDE and less atrophied, early stage GRMD BF compared to control
   LDE and BF dogs (four groups total). To do this, an ANOVA analysis was
   performed to identify any common changes in GRMD muscles, a question
   that the first two t-tests did not address ([91]Figure 4). By PLS-DA
   analysis, considerable overlap of the BF metabolic profiles (GRMD and
   Control) and LDE (GRMD and Control) by both component 1 and component 2
   was seen when all four groups were analyzed together ([92]Figure S5).
   Of the 75 metabolites from each group ([93]Figure S4), imputed as
   described above ([94]Figure S1B, Figure S3B), eight (8) were
   significant, including carnosine, fumaric acid, stearamide,
   myoinositol-2-phosphate, lactamide, and phosphoric acid ([95]Figure
   4A). Pathway analysis identified three pathways as significant: (1) TCA
   cycle; (2) alanine, aspartic acid, glutamic acid metabolism; and (3)
   beta-alanine metabolism ([96]Figure 4B), with > 6-fold enrichment for
   protein biosynthesis ([97]Figure 4C). The unique significant metabolite
   in this analysis compared to the t-test run on the BF group was
   phosphoric acid, which was significantly less in the GRMD BF as
   compared to control and GRMD LDE groups ([98]Figure 5A). While ANOVA
   analysis of stearamide revealed a single difference between control and
   GRMD BF muscles only ([99]Figure 5B), the significant decreases in
   lactamide and myoinositol-2-phosphate seen in GRMD BF were
   significantly attenuated in GRMD LDE ([100]Figure 5C,D).

Figure 4.

   [101]Figure 4
   [102]Open in a new tab

   One-Way ANOVA analysis of GRMD long digital extensor (LDE) and biceps
   femoris (BF). (A) Heatmap of ANOVA significant metabolites from control
   and GRMD LDE and BF; (B) Pathway analysis of ANOVA significant
   metabolites; (C) Pathway analysis of ANOVA significant metabolites.
   Analysis by Metaboanalyst analysis of GRMD (N = 6) vs. control (N = 4)
   LDE metabolites.

Figure 5.

   [103]Figure 5
   [104]Open in a new tab

   Comparison of Peak values of ANOVA metabolites in GRMD LDE and BF
   muscles by untargeted metabolomics. Peak values of GRMD LDE and BF (A)
   phosphoric acid; (B) stearamide; (C) lactamide; and (D)
   myosinositol-2-phosphate. Analysis by Metaboanalyst analysis of GRMD (N
   = 6) vs. control (N = 4) long digital extensor metabolites. Data is
   presented as the mean +/- SEM.

   In the preceeding three analyses, pathway analyses identified that
   beta-alanine metabolism, arginine/proline metabolism, and the TCA cycle
   were the pathways most affected based on both the t-test and ANOVA
   significant metabolites identified in the GRMD BF ([105]Figure 2A,
   [106]Figure 4B). To further delineate the context of these alterations
   in context of their metabolic pathways, we next documented the changes
   in the pathways themselves ([107]Figure 6). In the beta-alanine and
   arginine/proline metabolic pathways, significant decreases in carnosine
   were identified ([108]Figure 6A), along with significant increases in
   glutamic acid and proline ([109]Figure 6B,C). In BF, significant
   decreases in fumaric ([110]Figure 7A) and malic acid ([111]Figure 7B)
   were identified, with decreases in citric/isocitric acid ([112]Figure
   7C) and succinic acid also seen (not significant) ([113]Figure 7D).

Figure 6.

   [114]Figure 6
   [115]Open in a new tab

   Significantly altered metabolites in the b-Alanine and Arginine/Proline
   metabolic pathways. (A) Carnosine decreased in BF by t-test and ANOVA;
   (B) Glutamic acid increased by in BF by t-test and ANOVA; (C) Proline
   increased in BF by t-test.

Figure 7.

   [116]Figure 7
   [117]Open in a new tab

   Significantly altered metabolites in the Krebs (TCA) Cycle in GRMD BF
   muscle by untargeted metabolomics. (A) Significantly decreased fumaric
   acid (One-Way ANOVA); (B) significantly decreased malic acid (t-test),
   with decreased (not significant by post-hoc t-test analysis); in (C)
   Citric/Isocitric acid; and (D) Succinic acid. Data is presented as the
   mean +/- SEM.

2.4. Integrated Metabolomics Analysis

   The two primary approaches in functional analysis of metabolomics
   consist of metabolite set enrichment [[118]14] and metabolic pathway
   analysis [[119]15], as performed in the described in the prior two
   analyses [[120]16]. A new approach that has been developed utilizes
   analysis of metabolomics experiments in combination with
   transcriptomics studies to exploit the models from KEGG (Kyoto
   Encyclopedia of Genes and Genomes) pathways using a method called
   integrated pathway analysis [[121]15]. This integrated pathway analysis
   combining evidence from both metabolite concentrations and gene
   expression was developed to pinpoint pathways involved in the
   underlying biological processes [[122]15]. In the present study, we
   performed an integrated pathway analysis using GRMD BF/LDE (6 months of
   age) significant metabolites caronosine, oleic acid, pyrophosphate,
   3-phosphoglyceric acid, campesterol ([123]Table S2) and 55
   significantly altered genes from the GRMD gastrocnemius (6 months of
   age) ([124]Table S3). This analysis was the first time that both
   metabolomics and microarray evidence were compared in combination, to
   determine how these complementary datasets mights point to common
   alterations in metabolism.

   The integrated pathway analysis gives two values for each metabolic
   pathway: enrichment and topology. Topology uses the structure of a
   given pathway to identify the relative importance of genes and
   compounds. In our first analysis, we analyzed the data sets to look at
   gene-metabolite pathways and found the most enriched/topology pathways
   to be “Biosynthesis of unsaturated fatty acids”, “Histidine
   metabolism”, and “β-Alanine metabolism” ([125]Figure 8A). When the
   analysis was run in a gene-centric manner so that the transcriptomics
   were the main focus of analysis, we identified the most
   enriched/topology pathways to be “cardiac muscle contraction”,
   “hypertrophic cardiomyopathy”, and “tight junction” ([126]Figure 8B).
   These findings expand upon the conclusions of the initial published
   microarray analysis of the GRMD heart and gastrocnemius tissues,
   finding a common alteration in lipid metabolism, energy production, and
   inflammatory responses [[127]10] and the primary finding that the
   highest induced transcript in the GRMD gastrocnemius muscle was SPP1
   [[128]10], previously shown to be an indicator of muscle injury prior
   to microarray analyses [[129]17].

Figure 8.

   [130]Figure 8
   [131]Open in a new tab

   Integrated metabolomics analysis using recently published microarray
   analysis. Fisher’s exact test using degree centrality was performed
   using (A) Gene-metabolite pathways or (B) Gene-centric pathways in
   Metaboanalyst. GRMD significant metabolites (t-test, VIP >2.0 listed in
   [132]Table S2) and mRNA >1.9 or < −1.3 fold from GRMD muscle
   (downloaded from GEO, as published in Pediatr Res. 2016
   Apr;79(4):629-36) and listed in [133]Table S3 with fold change
   calculations) were included in the Metaboanalyst integrated analysis.

3. Discussion

   The metabolic component of DMD has not been previously analyzed
   directly. At the transcriptional level, studies of GRMD skeletal, but
   not cardiac, muscle have been described as consistent with a “metabolic
   crisis” [[134]18], with the down-regulation of dozens of energy
   production-associated mRNAs and multiple electron transport component
   mRNAs in the TCA cycle [[135]18]. In the present study, we investigated
   two GRMD pelvic limb muscles, the more proximal BF muscle which is a
   member of the hamstring group and is relatively spared and the more
   distal LDE that undergoes progressive atrophy. We hypothesized that the
   BF and LDE would have different metabolic profiles consistent with
   their milder/earlier versus more severe/chronic stages of disease,
   respectively. There were several differences in metabolism, with nine
   significantly altered metabolites in the GRMD BF muscle (eight by
   t-test, [136]Figure 2C, one additional by ANOVA, [137]Figure 4A) as
   compared to only 2 in the more chronically affected GRMD muscle (LDE).
   In addition, we identified for the first time in this system
   significant increases in metabolites involved in β-alanine metabolism
   and arginine/proline metabolism ([138]Figure 6).

   There are non-specific conditions that have been linked to muscle
   catabolism in humans, which may lower muscle glutamic acid levels,
   including emphysema [[139]19], COPD [[140]20], elderly fraility
   [[141]21], and in acute sepsis and surgical trauma [[142]19]. Several
   studies have been suggestive of these metabolic changes in DMD, using
   NMR analyses. In the mdx mouse, recent studies using ^1H-magnetic
   resonance spectroscopy to study the metabolic profile of quadriceps and
   diaphragm muscle identified increases in glutamic acid and carnosine
   [[143]22]. Other studies using NMR analysis of vastus lateralis muscle
   biopsies from DMD patients (N = 11 vs. 7 normal controls) identified
   significant decreases in glutamate/glutamine (not discernable from each
   other) [[144]23].

   Skeletal muscles most abundantly require glutamic acid [[145]24].
   Glutamic acid is the only amino acid that is actively taken up from the
   circulation by the muscles in the post-absorptive state [[146]25].
   Intracellular degradation of myofibrillar proteins also release
   glutamic acid with large amounts contributing to the muscle glutamic
   acid pool [[147]26]. The role of glutamic acid in muscular dystrophy
   was studied in the early 1960s in rabbit models (due to vitamin E
   deprivation) [[148]27]. In these studies, body weight loss and the
   onset of terminal disease was accelerated when animals were given
   exogenous glutamic acid [[149]27]. While the reasons for and sources of
   elevated BF glutamic acid levels (~2.5-fold controls) are not clear
   ([150]Figure 6), their presence could potentially play a role in the
   eventual worsening of the disease process.

   The role of oleic acid in DMD disease has been investigated in the mdx
   mouse model, where dietary oleic acid was replaced with the n-3
   poly-unsaturated fatty acid (PUFA), alpha-linolenic acid (ALA)
   [[151]28]. Interestingly, these studies found that shifting to a high
   PUFA diet resulted in the expected higher n-3 PUFA content in muscle,
   while reducing arachidonic acid content in skeletal muscle
   phospholipids, but also made the mdx muscle more susceptible to
   sarcolemmal leakiness [[152]28]. When DMD patient erythrocytes have
   been studied for long chain fatty acid composition, decreases in oleic,
   linoleic, and arachidonic acids have been identified [[153]29]. Where
   these fatty acids are being utilized, however, has not been
   investigated, nor have their levels in skeletal muscle been determined,
   to our knowledge. In the present study, the significantly elevated
   oleic acid (18:0) in GRMD BF muscle ([154]Figure 2C) may represent
   alternate energy substrate utilization and be directly related to the
   decreased oleic acid (18:0) metabolite stearamide ([155]Figure 2C).
   Alternatively, the significant increase and GRMD BF oleic acid may be a
   consequence of a greater accumulation of muscle triacylglyceride (TAG),
   a storage form of oleic acid. In the GRMD model, fatty infiltration is
   rare before 4 months of age, but common in those > 13 months of age
   [[156]8], so would be expected to be present in the 6 month old muscle
   investigated here. If this is the case, its possible that mitochondrial
   dysfunction may be responsible, as mitochondrial dysfunction has been
   reported to induce lipid droplet formation (including triacyglycerol
   and cholesterol esters) in reponse to stress [[157]30].

   The primary amides of oleic (18:0), palmitic, palmitoleic, eladic, and
   linoleic acid were identified in human plasma in 1989, although their
   significance was not understood [[158]31]. Subsequently, other fatty
   acid amides (including stearamide) have been isolated from human tear
   gland secretions [[159]32]. The role of fatty acid amides is not
   completely clear, but the fact that the arachidonic acid metabolite
   arachidonamide is the best substrate for the membrane-bound serine
   hydrolase fatty acid amide hydrolase (FAAH) suggests a role in
   signaling [[160]33]. In muscle, primary fatty acid amides reduced
   sarco/endoplasmic reticulum Ca^2+-ATPase (SERCA) activity in the
   micromolar (μM) range, although stearamide inhibition was relatively
   minimal [[161]34]. The significance of the reduced GRMD BF stearamide
   (0.2-fold control muscle) is not clear but may reflect altered
   signaling pathways within affected skeletal muscle not seen in
   unaffected GRMD muscle (LDE, [162]Figure 5B).

   Carnosine (β-alanyl-L-histidine) is a dipeptide found to be highly
   concentrated in skeletal muscles [[163]35]. At least three roles have
   been attributed to carnosine, including: a lactic acid neutralizer
   under anaerobic conditions [[164]36], a potent scavenger of singlet
   oxygen protecting the cells from oxidative damage [[165]37], and a
   physiological activator of myofibrillar ATPase activity [[166]38]. The
   significant decrease in GRMD BF carnosine (0.4-fold control BF levels,
   [167]Figure 2C) could set the stage for eventual muscle damage (e.g.
   due to lactic acid or oxidative damage). It may also be a cause of
   limited myosin ATPase activity, which relies on carnosine [[168]39],
   and be a contributing factor to muscle fatigue. It has been reported
   that vitamin B6 plays a pivotal role in maintaining the carnosine
   concentrations in the skeletal muscles [[169]40,[170]41]; however,
   vitamin B6 (pyridoxine) was not measured in the present study in order
   to determine if this was an underlying cause. Interestingly, GRMD BF
   β-alanine levels were significantly increased (2.9-fold of control
   levels), while carnosine was decreased ([171]Figure 6). Since carnosine
   is hydrolyzed to its constituent amino acids (β-alanine and histidine)
   by the enzyme carnosinase [[172]42], increased carnosinase may be a
   contributing cause of the elevated GRMD β-alanine and decreased
   carnosine.

   In the present study, we identified elevated proline and factors
   associated with the arginine/proline metabolic pathway. Arginine is
   taken up by cells, metabolized to proline, which can be used as a
   precursor for collagen [[173]43]. This is significant because a major
   finding in chronic myopathies, such as DMD, is collagen
   deposition/fibrosis. Previous studies of the GRMD cranial sartorius
   muscle identified patchy increases in endomysial space as early as 6
   months of age [[174]8]. In previous studies, the GRMD dog muscles were
   found to have higher levels of hydroxyproline, a biomarker of fibrosis
   and collagen content, across muscles (cranial sartorius, vastus
   lateralis, long digital extensor, and lateral head of the
   gastrocnemius) compared to controls at 8–9 months of age [[175]44].
   Similarly, increased fibrosis has been reported in 12 month old GRMD
   carrier hearts, along with inflammation and/or fatty changes, compared
   to control hearts [[176]45]. Since elevated levels of proline are
   detected in the pathophysiology of fibrosis, including liver fibrosis
   [[177]46], sarcoidosis [[178]47], and rheumatoid arthritis
   [[179]47,[180]48], the increases in the GRMD proline/arginine metabolic
   pathway could be linked to increases in inflammation-related collagen
   synthesis/muscle fibrosis known to the be a central part of the
   pathogensis of DMD.

   There are weaknesses and assumptions in the present study to consider.
   These include the limited number of biological replicates analyzed per
   group, due to the expense and rarity of the GRMD model itself. In
   particular, the identification of metabolites that were decreased, but
   not significant (e.g., citric/isocitric acid, and succinic acid,
   [181]Figure 7) may reflect either a lack of samples (i.e., power), the
   variability of the phenotype, or may actually not be different.
   Therefore, caution should be exercised in interpreting these data and
   the findings should not be used as evidence to support a hypothesis.
   Additionally, the data presented here may be a consequence of the
   observed muscular dysfunction, and not a cause of the effects on the
   TCA cycle intermediate pools. For example, brief periods of endurance
   training can reduce TCA intermediate pools, as does overweight-to-obese
   sedentary lifestyles [[182]49], whereas dynamic exercise can increase
   TCA intermediates [[183]50] in the absence of disease.

Summary

   While transcriptional analysis of GRMD skeletal muscle has suggested a
   metabolic component by the down-regulation of dozens of energy
   production-associated mRNAs and multiple electron transport component
   mRNAs in the TCA cycle, the present study is the first to our knowledge
   to analyze the metabolic component directly. We utilized untargeted
   metabolomics analysis to assess two GRMD pelvic limb muscles, the more
   proximal BF muscle which is a member of the hamstring group and is
   relatively spared, and the more distal LDE that undergoes progressive
   atrophy. We hypothesized that the BF and LDE would have diffent
   metabolic profiles consistent with their milder/earlier versus more
   severe/chronic stages of disease, respectively. There were eight (8)
   significantly altered metabolites in the milder/earlier stage disease
   GRMD BF muscle, including five (5) which were decreased (stearamide,
   carnosine, fumaric acid, lactamide, and myoinositol-2-phosphate) and
   three (3) which were increased (oleic acid, glutamic acid, and
   proline). Pathway analysis of these metabolites identified enrichment
   for arginine/proline metabolism and β-alanine metabolism.
   Identification of alterations across all four groups (GRMD BF, Control
   BF, GRMD LDE, Control BF) additionally identified phosphoric acid.
   Overall, many Krebs cycle intermediates were significantly decreased
   (e.g., malic acid, fumaric acid) and other intermediates were decreased
   but not significant (e.g., citric/isocitric acid, and succinic acid).
   These findings support the first direct evidence for decreases in TCA
   cycle intermediates and suggest a mechanism of altered energy
   metabolism may be underlying the observed dysfunction. We also
   identified elevated levels of the monounsaturated fatty acid oleic acid
   (18:0) in GRMD BF muscle, which has been related to increased
   inflammation in DMD that would be expected in earlier stages of
   disease, with associated necrosis.

4. Materials and Methods

4.1. Golden Retriever Muscular Dystrophy Dog Model

   All GRMD dogs used in this study were from a colony at the University
   of North Carolina at Chapel Hill (UNC-CH), now located at Texas A&M
   University. Dogs were produced by mating a heterozygote affected male
   to a carrier female, such that ~ 25% each of the pups should be either
   normal males, affected males, heterozygous (carrier) females, or
   homozygous (affected) females. Normal females are not produced with
   this mating. Blood creatine kinase levels taken shortly after birth
   were used to diagnose neonates with GRMD [[184]51], along with PCR, as
   previously described [[185]52]. Samples of the BF and LDE muscles were
   taken at necropsy from GRMD (n = 6; 4 males and 2 females) and
   littermate wild type (control) (n = 4; all male) dogs at 6 months of
   age immediately following barbiturate euthanasia. These dogs were part
   of a larger cohort that was studied previously with a genome wide
   association study (GWAS) [[186]53]. Dogs were used and cared for
   according to principles outlined in the National Research Council Guide
   for the Care and Use of Laboratory Animals. The experiments were
   approved by the Institutional Animal Care and Use Committee (IACUC)
   review board at UNC-CH and performed in accordance with federal
   guidelines.

4.2. Metabolomics Determination by GC–MS Instrumentation

   Bicep femoris (BF) and long digital extensor (LDE) muscles were flash
   frozen after biopsy in liquid nitrogen and stored at –80 °C. A fraction
   of the muscle was then weighed (~25–50 mg wet weight), finely minced,
   and quickly added to fresh ice-cold pre-made buffer (50% acetonitrile,
   50% water, 0.3% formic acid) at a standard concentration of 25 mg/475
   μL, homogenized on ice for 20–25 seconds and stored at −80 °C. The
   samples were “crash” deproteinized by methanol precipitation and then
   spiked with D27-deuterated myristic acid (D27-C14:0) as an internal
   standard for retention-time locking only and dried. The deuterated
   standard was not used for relative quantification/correction of analyte
   peak responses due to the limited number of samples processed in single
   batches for the different tissues. The derivatized trimethylsilyl
   (TMS)-D27-C14:0 standard retention time (RT) was set at ~16.727 min.
   Reactive carbonyls were stabilized at 50 °C with methoxyamine
   hydrochloride in dry pyridine. Metabolites were made volatile with TMS
   groups using N-methyl-N (trimethylsilyl) trifluoroacetamide or MSTFA
   with catalytic trimethylchlorosilane at 50 °C. GC/MS methods generally
   follow those of Roessner et al., [[187]54], Fiehn et al., [[188]55],
   and Kind et al., [[189]56], and used a 6890N GC connected to a 5975B
   inert single-quadrupole MS (Agilent Technologies, Santa Clara, CA,
   USA). The two wall-coated, open-tubular (WCOT) GC columns connected in
   series were both from J&W/Agilent (part 122–5512), DB5-MS, 15 meters in
   length, 0.25 mm in diameter, with an 0.25 μm luminal film. Positive
   ions generated with conventional electron-ionization (EI) at 70 eV were
   scanned broadly from 600 to 50 m/z in the detector throughout the
   45-min cycle time.

   Data were acquired using MSD ChemStation (Agilent Technologies) and
   metabolites annotated based on their mass fragmentation patterns and
   RT. Raw data formatted files were exported for further analysis in
   Automatic Mass Spectral Deconvolution and Identification Software
   (AMDIS version 2.72, build 140.24, freeware developed by Steve Stein,
   W. Gary Mallard, and their coworkers at National Institute of Standards
   and Technology or NIST [[190]57,[191]58,[192]59]). Deconvoluted spectra
   were annotated as metabolites, to the extent possible, using an
   orthogonal approach that incorporates both RT from GC and the
   fragmentation pattern observed in EI-MS. Peak annotation is based
   primarily on our own RT-locked spectral library of metabolites. The
   library is built upon the Fiehn GC/MS Metabolomics RTL Library (a gift
   from Agilent, part number G1676-90000), Golm Metabolome Library
   [[193]56] (courtesy of Joachim Kopka and coworkers at the Max Planck
   Institute of Molecular Plant Physiology, Golm, Germany [[194]60]), the
   Wiley 9th-NIST 2011 commercial library (Agilent G1730-64000), and other
   spectral libraries. Once annotation was complete, a cross-tabulated
   spreadsheet was created, listing the integrated peak area for each
   metabolite versus sample identity. This was accomplished using a custom
   Visual Basic program in Microsoft Excel that grouped peaks across
   samples based on identical metabolite annotation and RT proximity. Peak
   alignment across samples was further confirmed using SpectConnect
   [[195]61] to assess similarity in spectral fragmentation patterns and
   by manual curation. The raw, transformed, and sorted data used for each
   of the three comparisons in the metabolomic analyses (next) can be
   found in [196]Table S1. The data obtained in this study is accessible
   at the NIH Common Fund’s Data Repository and Coordinating Center
   (supported by NIH grant, U01-DK097430) website,
   [197]http://www.metabolomicsworkbench.org.

4.3. Metabolomic Statistical Analysis

   Metaboanalyst (v3.0) run on the statistical package R (v2.14.0) used
   metabolite peak areas (as representative of concentration)
   [[198]16,[199]62,[200]63,[201]64]. The control group contained 4
   biological replicates, while the GRMD group contained 6. If more than
   one individual in the control group did not have a metabolite detected
   in a group (of 4 total), that metabolite was excluded from further
   analysis. Likewise, if more than 2 individuals in the GRMD group did
   not have a metabolite detected in a group (of 6 total), that metabolite
   was excluded. In groups with values missing, the lowest value of that
   group was used to impute those values. These data were scaled using the
   Pareto scaling feature. Initially, a t-test comparing control to GRMD
   muscles was performed on both the BF and LDE muscle. In a second
   analysis, a One-Way Analysis of Variance (ANOVA) and Fisher LSD
   post-hoc test across the groups (control BF, GRMD BF, control LDE, GRMD
   LDE) was performed using Metaboanalyst v3.0. The data were analyzed by
   both unsupervised principal component analysis (PCA) and supervised
   partial least squares discriminant analysis (PLS-DA) to further
   determine the metabolites that separated groups. The specific
   metabolites contributing most significantly to the differences
   identified by PLS-DA between control BF and GRMD BF (or control LDE and
   GRMD LDE) muscles were determined using the variable importance in
   projection (VIP) analysis in the Metaboanalyst environment. Both t-test
   and ANOVA significant metabolites were matched to metabolomics pathways
   using the Pathway Analysis feature in Metaboanalyst 3.0. Only
   metabolites identified in the one-way ANOVA were included. All data
   from this study are available in [202]Table S1. All heat maps were
   generated using the heat map analysis feature in Metaboanalyst v3.0.
   Data are presented as mean +/- SEM, unless otherwise indicated.

4.4. Integrated Microarray and Metabolomics Statistical Analysis

   The metabolites used in the integrated microarray analysis included
   t-test significant and VIP > 2 metabolites from BF and LDE, limited to
   those with HMDB designations (carnosine, oleic acid, pyrophosphate,
   3-phosphoglyceric acid, and campesterol) ([203]Table S2). The microarry
   datasets used in parallel were accessed from GEO ([204]GSE68626) and
   extracted using the GEO2R online tool, which were recently published
   [[205]10]. Briefly, datasets from skeletal muscle (gastrocnemius)
   tissue from normal golden retriever (or golden retriever muscular
   dystrophy model) as were used in the current analysis. The normal
   golden retriever medial head of the gastrocnemius muscles (N = 3) were
   from animals ages 6, 6, and 7.5 months (accession number
   [206]GSM1677427, [207]GSM1677428, [208]GSM1677429, respectively). The
   GRMD gastrocnemius medial head of the gastrocnemius muscles (N = 3)
   were from animals ages 6.75, 6, and 6 months ([209]GSM1677430,
   [210]GSM1677431, [211]GSM1677432, respsectively). Data included GRMD
   mRNA expressed > 1.9 fold (vs. control) and < −1.3 fold (found in
   [212]Table S3).

4.5. Other Statistical Analysis

   Metabolites not found to be significant were analyzed in a post-hoc
   analysis by a Student’s t-test in Microsoft Excel (Version 15.34
   (170515, Office 365, Seattle, WA, USA) using a one-tailed test assuming
   the two-samples had heteroscedastic (unequal) variance and plotted in
   Prism 7.0 (GraphPad Software, Inc., La Jolla, CA, USA).

Acknowledgments