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Energetics of endurance exercise in young horses determined by nuclear magnetic resonance metabolomics.

Luck MM, Le Moyec L, Barrey E, Triba MN, Bouchemal N, Savarin P, Robert C - Front Physiol (2015)

Bottom Line: The statistical parameters showed the robustness of the model (R2Y = 0.947, Q2Y = 0.856 and cros-validated ANOVA p < 0.001).For confirmation of the predictive value of the model, a test set of 104 sample spectra were projected by the model, which provided perfect predictions as the area under the receiving-operator curve was 1.The metabolomic profile determined with the OPLS model showed that glycemia after the race was lower than glycemia before the race, despite the involvement of lipid and protein catabolism.

View Article: PubMed Central - PubMed

Affiliation: Unité de Biologie Intégrative et Adaptation à l'Exercice EA 7362, Université d'Evry Val D'Essonne Evry, France.

ABSTRACT
Long-term endurance exercise severely affects metabolism in both human and animal athletes resulting in serious risk of metabolic disorders during or after competition. Young horses (up to 6 years old) can compete in races up to 90 km despite limited scientific knowledge of energetic metabolism responses to long distance exercise in these animals. The hypothesis of this study was that there would be a strong effect of endurance exercise on the metabolomic profiles of young horses and that the energetic metabolism response in young horses would be different from that of more experienced horses. Metabolomic profiling is a powerful method that combines Nuclear Magnetic Resonance (NMR) spectrometry with supervised Orthogonal Projection on Latent Structure (OPLS) statistical analysis. (1)H-NMR spectra were obtained from plasma samples drawn from young horses (before and after competition). The spectra obtained before and after the race from the same horse (92 samples) were compared using OPLS. The statistical parameters showed the robustness of the model (R2Y = 0.947, Q2Y = 0.856 and cros-validated ANOVA p < 0.001). For confirmation of the predictive value of the model, a test set of 104 sample spectra were projected by the model, which provided perfect predictions as the area under the receiving-operator curve was 1. The metabolomic profile determined with the OPLS model showed that glycemia after the race was lower than glycemia before the race, despite the involvement of lipid and protein catabolism. An OPLS model was calculated to compare spectra obtained on plasma taken after the race from 6-year-old horses and from experienced horses (cross-validated ANOVA p < 0.001). The comparison of metabolomic profiles in young horses to those from experienced horses showed that experienced horses maintained their glycemia with higher levels of lactate and a decrease of plasma lipids after the race.

No MeSH data available.


Related in: MedlinePlus

CPMG proton 1D NMR spectra of young horse plasma samples before exercise (A) and after exercise (B). The main metabolites are labeled in the spectra and metabolites appearing in both spectra are not repeated. The main metabolites are labeled as follows: 1–9, metabolites from lipid metabolism; 1, β-hydroxybutyrate; 2, glycerol; 3, choline; 4, phosphocholine; 5, alkene; 6, methyl; 7, methylene; 8, methylene-α-ester; 9, methylene-β-ester; 10–14, metabolites from carbohydrate metabolism; 10, lactate; 11, fumarate; 12, citrate; 13, acetate; 14, glucose; 15–24, metabolites from amino acid metabolism and glycoproteins; 15, creatine; 16, creatinine; 17, urea; 18, phenylalanine; 19, tyrosine; 20, glutamate; 21, 2-3-methylvalerate; 22, N-Acetyl; 23, alanine; 24, branched chain amino acids (valine, leucine, isoleucine).
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Figure 1: CPMG proton 1D NMR spectra of young horse plasma samples before exercise (A) and after exercise (B). The main metabolites are labeled in the spectra and metabolites appearing in both spectra are not repeated. The main metabolites are labeled as follows: 1–9, metabolites from lipid metabolism; 1, β-hydroxybutyrate; 2, glycerol; 3, choline; 4, phosphocholine; 5, alkene; 6, methyl; 7, methylene; 8, methylene-α-ester; 9, methylene-β-ester; 10–14, metabolites from carbohydrate metabolism; 10, lactate; 11, fumarate; 12, citrate; 13, acetate; 14, glucose; 15–24, metabolites from amino acid metabolism and glycoproteins; 15, creatine; 16, creatinine; 17, urea; 18, phenylalanine; 19, tyrosine; 20, glutamate; 21, 2-3-methylvalerate; 22, N-Acetyl; 23, alanine; 24, branched chain amino acids (valine, leucine, isoleucine).

Mentions: The NMR spectra of young horse plasma samples obtained with the CPMG sequence at BE and AE are plotted in Figure 1 with metabolite assignments. These spectra and the NOESY 1D spectra (not shown here) were similar to those reported previously (Le Moyec et al., 2014). The region between 2 and 2.1 ppm appears to be different from that for the same region in human plasma spectra. Indeed, in human plasma, only one broad resonance is detectable, while in horse plasma spectra, three peaks are detected. They have been previously assigned to N-acetyl moieties of glycoproteins and could arise from the extracellular matrix of most tissues and/or joint synovial fluid (Hodavance et al., 2007; Keller et al., 2011; Nahon et al., 2012; Le Moyec et al., 2014). Other metabolites involved in several pathways commonly found in mammalian tissues were also identified such as amino acids (alanine, tyrosine), organic acids (lactate), lipids, and carbohydrates (α- and β-glucose).


Energetics of endurance exercise in young horses determined by nuclear magnetic resonance metabolomics.

Luck MM, Le Moyec L, Barrey E, Triba MN, Bouchemal N, Savarin P, Robert C - Front Physiol (2015)

CPMG proton 1D NMR spectra of young horse plasma samples before exercise (A) and after exercise (B). The main metabolites are labeled in the spectra and metabolites appearing in both spectra are not repeated. The main metabolites are labeled as follows: 1–9, metabolites from lipid metabolism; 1, β-hydroxybutyrate; 2, glycerol; 3, choline; 4, phosphocholine; 5, alkene; 6, methyl; 7, methylene; 8, methylene-α-ester; 9, methylene-β-ester; 10–14, metabolites from carbohydrate metabolism; 10, lactate; 11, fumarate; 12, citrate; 13, acetate; 14, glucose; 15–24, metabolites from amino acid metabolism and glycoproteins; 15, creatine; 16, creatinine; 17, urea; 18, phenylalanine; 19, tyrosine; 20, glutamate; 21, 2-3-methylvalerate; 22, N-Acetyl; 23, alanine; 24, branched chain amino acids (valine, leucine, isoleucine).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4544308&req=5

Figure 1: CPMG proton 1D NMR spectra of young horse plasma samples before exercise (A) and after exercise (B). The main metabolites are labeled in the spectra and metabolites appearing in both spectra are not repeated. The main metabolites are labeled as follows: 1–9, metabolites from lipid metabolism; 1, β-hydroxybutyrate; 2, glycerol; 3, choline; 4, phosphocholine; 5, alkene; 6, methyl; 7, methylene; 8, methylene-α-ester; 9, methylene-β-ester; 10–14, metabolites from carbohydrate metabolism; 10, lactate; 11, fumarate; 12, citrate; 13, acetate; 14, glucose; 15–24, metabolites from amino acid metabolism and glycoproteins; 15, creatine; 16, creatinine; 17, urea; 18, phenylalanine; 19, tyrosine; 20, glutamate; 21, 2-3-methylvalerate; 22, N-Acetyl; 23, alanine; 24, branched chain amino acids (valine, leucine, isoleucine).
Mentions: The NMR spectra of young horse plasma samples obtained with the CPMG sequence at BE and AE are plotted in Figure 1 with metabolite assignments. These spectra and the NOESY 1D spectra (not shown here) were similar to those reported previously (Le Moyec et al., 2014). The region between 2 and 2.1 ppm appears to be different from that for the same region in human plasma spectra. Indeed, in human plasma, only one broad resonance is detectable, while in horse plasma spectra, three peaks are detected. They have been previously assigned to N-acetyl moieties of glycoproteins and could arise from the extracellular matrix of most tissues and/or joint synovial fluid (Hodavance et al., 2007; Keller et al., 2011; Nahon et al., 2012; Le Moyec et al., 2014). Other metabolites involved in several pathways commonly found in mammalian tissues were also identified such as amino acids (alanine, tyrosine), organic acids (lactate), lipids, and carbohydrates (α- and β-glucose).

Bottom Line: The statistical parameters showed the robustness of the model (R2Y = 0.947, Q2Y = 0.856 and cros-validated ANOVA p < 0.001).For confirmation of the predictive value of the model, a test set of 104 sample spectra were projected by the model, which provided perfect predictions as the area under the receiving-operator curve was 1.The metabolomic profile determined with the OPLS model showed that glycemia after the race was lower than glycemia before the race, despite the involvement of lipid and protein catabolism.

View Article: PubMed Central - PubMed

Affiliation: Unité de Biologie Intégrative et Adaptation à l'Exercice EA 7362, Université d'Evry Val D'Essonne Evry, France.

ABSTRACT
Long-term endurance exercise severely affects metabolism in both human and animal athletes resulting in serious risk of metabolic disorders during or after competition. Young horses (up to 6 years old) can compete in races up to 90 km despite limited scientific knowledge of energetic metabolism responses to long distance exercise in these animals. The hypothesis of this study was that there would be a strong effect of endurance exercise on the metabolomic profiles of young horses and that the energetic metabolism response in young horses would be different from that of more experienced horses. Metabolomic profiling is a powerful method that combines Nuclear Magnetic Resonance (NMR) spectrometry with supervised Orthogonal Projection on Latent Structure (OPLS) statistical analysis. (1)H-NMR spectra were obtained from plasma samples drawn from young horses (before and after competition). The spectra obtained before and after the race from the same horse (92 samples) were compared using OPLS. The statistical parameters showed the robustness of the model (R2Y = 0.947, Q2Y = 0.856 and cros-validated ANOVA p < 0.001). For confirmation of the predictive value of the model, a test set of 104 sample spectra were projected by the model, which provided perfect predictions as the area under the receiving-operator curve was 1. The metabolomic profile determined with the OPLS model showed that glycemia after the race was lower than glycemia before the race, despite the involvement of lipid and protein catabolism. An OPLS model was calculated to compare spectra obtained on plasma taken after the race from 6-year-old horses and from experienced horses (cross-validated ANOVA p < 0.001). The comparison of metabolomic profiles in young horses to those from experienced horses showed that experienced horses maintained their glycemia with higher levels of lactate and a decrease of plasma lipids after the race.

No MeSH data available.


Related in: MedlinePlus