Dataset Information

Untargeted metabolomics profiling reveals exercise intensity-dependent alterations in Thoroughbred racehorses’ plasma after routine conditioning sessions

ABSTRACT:

Thoroughbred (TB) racehorses are subjected to rigorous conditioning programs involving exercise sessions of various types, distances and intensities. These programs aim to optimize both their physical and mental capabilities, which are essential to achieve maximal performance. Conventional investigations of exercise-induced metabolic responses typically focus on a limited set of targeted hematological and biochemical parameters. This field study was conducted to comprehensively assess the metabolic responses triggered by exercise sessions routinely used in TB conditioning. To accomplish this, we employed an untargeted metabolomics approach. Blood samples were collected from 10 racehorses at rest and after completing a routine exercise session. The population was subsequently divided into two groups based on the intensity of the exercise performed: a high-intensity group (n = 6, gallop at ± 13.38 m/s, 1400 m) and a moderate-intensity group (n = 4, soft canter at ± 7.63 m/s, 2500 m). The exercise intensity was evaluated through monitoring of speed, heart rate, and targeted hematological and biochemical parameters (e.g., lactatemia, hematocrit, etc). Resting and 30 min post-exercise plasma samples were analyzed using an untargeted metabolomics approach based on ultra-performance liquid chromatography coupled with high-resolution mass spectrometry. Unsupervised principal component analysis revealed changes in the metabolome from pre- to post-exercise samples with greater alterations following high-intensity exercise. 54 metabolites, mainly related to amino acid, fatty acid, nucleic acid and vitamin metabolism, were altered following high-intensity exercise versus 23 metabolites, mainly related to fatty acid and amino acid metabolism, following moderate-intensity exercise. Untargeted metabolomics complemented the routine biochemical analyses, allowing a more comprehensive exploration of the exercise-induced response to exercises used in routine conditioning. As previously reported by traditional biochemistry, exercise-induced changes in energy metabolism in TB racehorses have been confirmed. Through metabolomics, the involvement of lipid and amino acid metabolism during routine exercise and recovery has been highlighted, aspects that had previously been largely overlooked in Thoroughbred racehorses.

INSTRUMENT(S): Liquid Chromatography MS - negative - reverse phase, Liquid Chromatography MS - positive - reverse phase

SUBMITTER: Maelle M. Bonhomme

PROVIDER: MTBLS8285 | MetaboLights | 2024-01-16

REPOSITORIES: MetaboLights

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Dataset's files

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			Action	DRS
	MTBLS8285	Other
	FILES	Other
	a_MTBLS8285_LC-MS_negative_reverse-phase_metabolite_profiling.txt	Txt
	a_MTBLS8285_LC-MS_positive_reverse-phase_metabolite_profiling.txt	Txt
	i_Investigation.txt	Txt

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Untargeted Metabolomics Profiling Reveals Exercise Intensity-Dependent Alterations in Thoroughbred Racehorses' Plasma after Routine Conditioning Sessions.

Bonhomme Maëlle M MM Patarin Florence F Kruse Caroline-J CJ François Anne-Christine AC Renaud Benoît B Couroucé Anne A Leleu Claire C Boemer François F Toquet Marie-Pierre MP Richard Eric A EA Seignot Jérôme J Wouters Clovis P CP Votion Dominique-Marie DM

ACS omega 20231207 50

Thoroughbred (TB) racehorses undergo rigorous conditioning programs to optimize their physical and mental capabilities through varied exercise sessions. While conventional investigations focus on limited hematological and biochemical parameters, this field study employed untargeted metabolomics to comprehensively assess metabolic responses triggered by exercise sessions routinely used in TB conditioning. Blood samples were collected pre- and post-exercise from ten racehorses, divided into two gr ...[more]

PMID: 38144146

Similar Datasets

Project description:This file describes the SBML version of the mathematical model in the following journal article: Linking Pulmonary Oxygen Uptake, Muscle Oxygen Utilization and Cellular Metabolism during Exercise, Ann Biomed Eng. 2007 Jun;35(6):956-69. (Pubmed ID: 17380394). This mathematical model simulates oxygen transport and metabolism in skeletal muscle in response to a step change from a warm-up steady state to a higher work rate corresponding to exercise at different levels of intensity: moderate (M), heavy (H) and very heavy (VH). The model parameter values are listed in the tables of this article. The parameter values that are independent of the exercise level are reported in Table 2. The parameter values that depend on the exercise level are reported in Tables 1A, 3 and 4. The model simulations (Figures 2, 3, 4 and 5) were obtained for a representative subject with a set of parameter values different from those in Table 1A, 3 and 4. In the sbml model, these model parameters are used to simulate exercise at a very heavy (VH) intensity for the representative subject. Additionally, the parameter values needed to simulate exercise at moderate (M) and heavy (H) intensity are reported in the list of parameters of the file. The model simulates dynamics of (1) the concentrations of free (F) and total (T) oxygen concentration in blood (CFcap, CTcap) and tissue (CFtis, CTtis), Adenosine Triphosphate (ATP), Adenosine Diphosphate (ADP), Phosphocreatine (PCr) and Creatine (Cr); (2) the metabolic flux of oxidative phosphorylation, creatine kinase and ATPase; (3) the oxygen uptake in blood and oxygen transport rate from blood to tissue during exercise. The simulation also computes muscle oxygen saturation (StO2m) and relative muscle oxygen saturation (RStO2m) in order to compare simulated and experimental responses of human muscle oxygenation during exercise. The model was successfully tested with Roadrunner of the Systems Biology Workbench (SBW). The model simulations obtained with Roadrunner match those obtained with the mathematical model represented in Fortran and Matlab for relative and absolute tolerance smaller than 10-7. To allow for simulations at varying levels of exercise, the parameter exercise_level was introduced. A value of 1 means medium, 2 heavy and 3 very heavy exercise. Setting this parameter assigns the parameters Vmax, KatpaseE, dQMm and tauQm with the relevant parameters. The warmup steady state is influenced by the parameter changes for this representative subject and the model has to be brought into steady state after each change of exercise level. This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.For more information see the terms of use.To cite BioModels Database, please use Le Nov��re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.