Paradoxical effects of endurance training and chronic hypoxia on myofibrillar ATPase activity

The Use of Fitness-Fatigue Models for Sport Performance Modelling: Conceptual Issues and Contributions from Machine-Learning

The emergence of the first Fitness-Fatigue impulse responses models (FFMs) have allowed the sport science community to investigate relationships between the effects of training and performance. In the models, athletic performance is described by first order transfer functions which represent Fitness and Fatigue antagonistic responses to training. On this basis, the mathematical structure allows for a precise determination of optimal sequence of training doses that would enhance the greatest athletic performance, at a given time point. Despite several improvement of FFMs and still being widely used nowadays, their efficiency for describing as well as for predicting a sport performance remains mitigated. The main causes may be attributed to a simplification of physiological processes involved by exercise which the model relies on, as well as a univariate consideration of factors responsible for an athletic performance. In this context, machine-learning perspectives appear to be valuable for sport performance modelling purposes. Weaknesses of FFMs may be surpassed by embedding physiological representation of training effects into non-linear and multivariate learning algorithms. Thus, ensemble learning methods may benefit from a combination of individual responses based on physiological knowledge within supervised machine-learning algorithms for a better prediction of athletic performance.In conclusion, the machine-learning approach is not an alternative to FFMs, but rather a way to take advantage of models based on physiological assumptions within powerful machine-learning models.

Training load responses modelling and model generalisation in elite sports

This study aims to provide a transferable methodology in the context of sport performance modelling, with a special focus to the generalisation of models. Data were collected from seven elite Short track speed skaters over a three months training period. In order to account for training load accumulation over sessions, cumulative responses to training were modelled by impulse, serial and bi-exponential responses functions. The variable dose-response (DR) model was compared to elastic net (ENET), principal component regression (PCR) and random forest (RF) models, while using cross-validation within a time-series framework. ENET, PCR and RF models were fitted either individually (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$M_{I}$$\end{document}MI) or on the whole group of athletes (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$M_{G}$$\end{document}MG). Root mean square error criterion was used to assess performances of models. ENET and PCR models provided a significant greater generalisation ability than the DR model (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p = 0.018$$\end{document}p=0.018, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p < 0.001$$\end{document}p<0.001, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p = 0.004$$\end{document}p=0.004 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p < 0.001$$\end{document}p<0.001 for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ENET_{I}$$\end{document}ENETI, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ENET_{G}$$\end{document}ENETG, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$PCR_{I}$$\end{document}PCRI and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$PCR_{G}$$\end{document}PCRG, respectively). Only \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ENET_{G}$$\end{document}ENETG and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$RF_{G}$$\end{document}RFG were significantly more accurate in prediction than DR (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p < 0.001$$\end{document}p<0.001 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p < 0.012$$\end{document}p<0.012). In conclusion, ENET achieved greater generalisation and predictive accuracy performances. Thus, building and evaluating models within a generalisation enhancing procedure is a prerequisite for any predictive modelling.

Modelling performance and skeletal muscle adaptations with exponential growth functions during resistance training

System theory is classically applied to describe and to predict the effects of training load on performance. The classic models are structured by impulse-type transfer functions, nevertheless, most biological adaptations display exponential growth kinetics. The aim of this study was to propose an extension of the model structure taking into account the exponential nature of skeletal muscle adaptations by using a genetic algorithm. Thus, the conventional impulse-type model was applied in 15 resistance trained rodents and compared with exponential growth-type models. Even if we obtained a significant correlation between actual and modelled performances for all the models, our data indicated that an exponential model is associated with more suitable parameters values, especially the time constants that correspond to the positive response to training. Moreover, positive adaptations predicted with an exponential component showed a strong correlation with the main structural adaptations examined in skeletal muscles, i.e. hypertrophy (R² = 0.87, 0.96 and 0.99, for type 1, 2A and 2X cross-sectional area fibers, respectively) and changes in fiber-type composition (R² = 0.81 and 0.79, for type 1 and 2A fibers, respectively). Thus, an exponential model succeeds to describe both performance variations with relevant time constants and physiological adaptations that take place during resistance training.

Histone Deacetylase 3 (HDAC3)-dependent Reversible Lysine Acetylation of Cardiac Myosin Heavy Chain Isoforms Modulates Their Enzymatic and Motor Activity

Reversible lysine acetylation is a widespread post-translational modification controlling the activity of proteins in different subcellular compartments. We previously demonstrated that a class II histone deacetylase (HDAC), HDAC4, and a histone acetyltransferase, p300/CREB-binding protein-associated factor, associate with cardiac sarcomeres and that a class I and II HDAC inhibitor, trichostatin A, enhances contractile activity of myofilaments. In this study we show that a class I HDAC, HDAC3, is also present at cardiac sarcomeres. By immunohistochemical and electron microscopic analyses, we found that HDAC3 was localized to A-band of sarcomeres and capable of deacetylating myosin heavy chain (MHC) isoforms. The motor domains of both cardiac α- and β-MHC isoforms were found to be reversibly acetylated. Biomechanical studies revealed that lysine acetylation significantly decreased the Km for the actin-activated ATPase activity of MHC isoforms. By in vitro motility assay, we found that lysine acetylation increased the actin-sliding velocity of α-myosin by 20% and β-myosin by 36% compared with their respective non-acetylated isoforms. Moreover, myosin acetylation was found to be sensitive to cardiac stress. During induction of hypertrophy, myosin isoform acetylation increased progressively with duration of stress stimuli independently of isoform shift, suggesting that lysine acetylation of myosin could be an early response of myofilaments to increase contractile performance of the heart. These studies provide the first evidence for localization of HDAC3 at myofilaments and uncover a novel mechanism modulating the motor activity of cardiac MHC isoforms.

HDAC3-dependent reversible lysine acetylation of cardiac myosin heavy chain isoforms modulates their enzymatic and motor activity

Reversible lysine acetylation is a widespread post-translational modification controlling the activity of proteins in different subcellular compartments. We previously demonstrated that a class II histone deacetylase (HDAC), HDAC4, and a histone acetyltransferase, PCAF, associate with cardiac sarcomeres, and a class I and II HDAC inhibitor, trichostatin A, enhances contractile activity of myofilaments. In this study, we show that a class I HDAC, HDAC3, is also present at cardiac sarcomeres. By immunohistochemical and electron microscopic analyses, we found that HDAC3 was localized to the A band of sarcomeres and was capable of deacetylating myosin heavy chain (MHC) isoforms. The motor domains of both cardiac α- and β-MHC isoforms were found to be reversibly acetylated. Biomechanical studies revealed that lysine acetylation significantly decreased the K(m) for the actin-activated ATPase activity of both α- and β-MHC isoforms. By an in vitro motility assay, we found that lysine acetylation increased the actin sliding velocity of α-myosin by 20% and β-myosin by 36%, compared to their respective non-acetylated isoforms. Moreover, myosin acetylation was found to be sensitive to cardiac stress. During induction of hypertrophy, myosin isoform acetylation increased progressively with duration of stress stimuli, independent of isoform shift, suggesting that lysine acetylation of myosin could be an early response of myofilaments to increase contractile performance of the heart. These studies provide the first evidence for localization of HDAC3 at myofilaments and uncover a novel mechanism modulating the motor activity of cardiac MHC isoforms.