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

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          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. 1H-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.

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          CV-ANOVA for significance testing of PLS and OPLS® models

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            Metabolic signatures of exercise in human plasma.

            Exercise provides numerous salutary effects, but our understanding of how these occur is limited. To gain a clearer picture of exercise-induced metabolic responses, we have developed comprehensive plasma metabolite signatures by using mass spectrometry to measure >200 metabolites before and after exercise. We identified plasma indicators of glycogenolysis (glucose-6-phosphate), tricarboxylic acid cycle span 2 expansion (succinate, malate, and fumarate), and lipolysis (glycerol), as well as modulators of insulin sensitivity (niacinamide) and fatty acid oxidation (pantothenic acid). Metabolites that were highly correlated with fitness parameters were found in subjects undergoing acute exercise testing and marathon running and in 302 subjects from a longitudinal cohort study. Exercise-induced increases in glycerol were strongly related to fitness levels in normal individuals and were attenuated in subjects with myocardial ischemia. A combination of metabolites that increased in plasma in response to exercise (glycerol, niacinamide, glucose-6-phosphate, pantothenate, and succinate) up-regulated the expression of nur77, a transcriptional regulator of glucose utilization and lipid metabolism genes in skeletal muscle in vitro. Plasma metabolic profiles obtained during exercise provide signatures of exercise performance and cardiovascular disease susceptibility, in addition to highlighting molecular pathways that may modulate the salutary effects of exercise.
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              Multivariate paired data analysis: multilevel PLSDA versus OPLSDA

              Metabolomics data obtained from (human) nutritional intervention studies can have a rather complex structure that depends on the underlying experimental design. In this paper we discuss the complex structure in data caused by a cross-over designed experiment. In such a design, each subject in the study population acts as his or her own control and makes the data paired. For a single univariate response a paired t-test or repeated measures ANOVA can be used to test the differences between the paired observations. The same principle holds for multivariate data. In the current paper we compare a method that exploits the paired data structure in cross-over multivariate data (multilevel PLSDA) with a method that is often used by default but that ignores the paired structure (OPLSDA). The results from both methods have been evaluated in a small simulated example as well as in a genuine data set from a cross-over designed nutritional metabolomics study. It is shown that exploiting the paired data structure underlying the cross-over design considerably improves the power and the interpretability of the multivariate solution. Furthermore, the multilevel approach provides complementary information about (I) the diversity and abundance of the treatment effects within the different (subsets of) subjects across the study population, and (II) the intrinsic differences between these study subjects.
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                Author and article information

                Contributors
                Journal
                Front Physiol
                Front Physiol
                Front. Physiol.
                Frontiers in Physiology
                Frontiers Media S.A.
                1664-042X
                15 July 2015
                2015
                : 6
                : 198
                Affiliations
                [1] 1Unité de Biologie Intégrative et Adaptation à l'Exercice EA 7362, Université d'Evry Val D'Essonne Evry, France
                [2] 2Génétique Animale et Biologie Intégrative, UMR1313, Institut National de la Recherche Agronomique (INRA) Jouy-en-Josas, France
                [3] 3Chimie Structures et Propriétés de Biomatériaux et d'Agents Thérapeutiques (CSPBAT), Centre National de la Recherche Scientifique, Université Paris 13, Sorbonne Paris Cité, UMR 7244 Bobigny, France
                [4] 4Ecole Nationale Vétérinaire d'Alfort, Université Paris Est Maisons-Alfort, France
                Author notes

                Edited by: Eugene Nalivaiko, University of Newcastle, Australia

                Reviewed by: Kenneth Harrington McKeever, Rutgers, The State University of New Jersey, USA; Toshiro Arai, Nippon Veterinary and Life Science University, Japan

                *Correspondence: Laurence Le Moyec, Unité de Biologie Intégrative et Adaptation à l'Exercice EA 7362, Université d'Evry Val d'Essonne, Bd François Mitterrand, 91025 Evry, France laurence.lemoyec@ 123456univ-evry.fr

                This article was submitted to Integrative Physiology, a section of the journal Frontiers in Physiology

                †These authors have contributed equally to this work.

                Article
                10.3389/fphys.2015.00198
                4544308
                26347654
                f8f025c2-22a6-4082-b49c-7453b173ae68
                Copyright © 2015 Luck, Le Moyec, Barrey, Triba, Bouchemal, Savarin and Robert.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 19 February 2015
                : 29 June 2015
                Page count
                Figures: 4, Tables: 6, Equations: 0, References: 30, Pages: 12, Words: 9067
                Funding
                Funded by: Fonds Eperon
                Funded by: Institut Français du Cheval et de l'Equitation (IFCE)
                Funded by: Association du Cheval Arabe (ACA)
                Funded by: Institut National de Recherche Agronomique (INRA)
                Funded by: Ecole Nationale Vétérinaire d'Alfort (ENVA)
                Categories
                Physiology
                Original Research

                Anatomy & Physiology
                horses,plasma,endurance,1h nmr,metabolomics,energetics
                Anatomy & Physiology
                horses, plasma, endurance, 1h nmr, metabolomics, energetics

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