Background: The metabolic and physiologic responses to exercise are increasingly interesting, given that regular physical activity enhances antioxidant capacity, improves cardiac function, and protects against type 2 diabetes. The metabolic interactions between tissues and the heart illustrate a critical cross-talk we know little about. Methods: To better understand the metabolic changes induced by exercise, we investigated skeletal muscle (plantaris, soleus), liver, serum, and heart from exercise trained (or sedentary control) animals in an established rat model of exercise-induced aerobic training via non-targeted GC-MS metabolomics. Results: Exercise-induced alterations in metabolites varied across tissues, with the soleus and serum affected the least. The alterations in the plantaris muscle and liver were most alike, with two metabolites increased in each (citric acid/isocitric acid and linoleic acid). Exercise training additionally altered nine other metabolites in the plantaris (C13 hydrocarbon, inosine/adenosine, fructose-6-phosphate, glucose-6-phosphate, 2-aminoadipic acid, heptadecanoic acid, stearic acid, alpha-tocopherol, and oleic acid). In the serum, we identified significantly decreased alpha-tocopherol levels, paralleling the increases identified in plantaris muscle. Eleven unique metabolites were increased in the heart, which were not affected in the other compartments (malic acid, serine, aspartic acid, myoinositol, glutamine, gluconic acid-6-phosphate, glutamic acid, pyrophosphate, campesterol, phosphoric acid, creatinine). These findings complement prior studies using targeted metabolomics approaches to determine the metabolic changes in exercise-trained human skeletal muscle. Specifically, exercise trained vastus lateralus biopsies had significantly increased linoleic acid, oleic acid, and stearic acid compared to the inactive groups, which were significantly increased in plantaris muscle in the present study. Conclusions: While increases in alpha-tocopherol have not been identified in muscle after exercise to our knowledge, the benefits of vitamin E (alpha-tocopherol) supplementation in attenuating exercise-induced muscle damage has been studied extensively. Skeletal muscle, liver, and the heart have primarily different metabolic changes, with few similar alterations and rare complementary alterations (alpha-tocopherol), which may illustrate the complexity of understanding exercise at the organismal level.

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 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.

The field of metabolomics as applied to human disease and health is rapidly expanding. In recent efforts of metabolomics research, greater emphasis has been placed on quality control and method validation. In this study, we report an experience with quality control and a practical application of method validation. Specifically, we sought to identify and modify steps in gas chromatography-mass spectrometry (GC-MS)-based, non-targeted metabolomic profiling of human plasma that could influence metabolite identification and quantification. Our experimental design included two studies: (1) a limiting-dilution study, which investigated the effects of dilution on analyte identification and quantification; and (2) a concentration-specific study, which compared the optimal plasma extract volume established in the first study with the volume used in the current institutional protocol. We confirmed that contaminants, concentration, repeatability and intermediate precision are major factors influencing metabolite identification and quantification. In addition, we established methods for improved metabolite identification and quantification, which were summarized to provide recommendations for experimental design of GC-MS-based non-targeted profiling of human plasma.