Dataset Information

Forced Running Endurance Is Influenced by Gene(s) on Mouse Chromosome 10.

ABSTRACT: Phenotypic diversity between laboratory mouse strains provides a model for studying the underlying genetic mechanisms. The A/J strain performs poorly in various endurance exercise models. The aim of the study was to test if endurance capacity and contractility of the fast- and slow-twitch muscles are affected by the genes on mouse chromosome 10. The C57BL/6J (B6) strain and C57BL/6J-Chr 10A/J/NaJ (B6.A10) consomic strain which carries the A/J chromosome 10 on a B6 strain background were compared. The B6.A10 mice compared to B6 were larger in body weight (p < 0.02): 27.2 ± 1.9 vs. 23.8 ± 2.7 and 23.4 ± 1.9 vs. 22.9 ± 2.3 g, for males and females, respectively, and in male soleus weight (p < 0.02): 9.7 ± 0.4 vs. 8.6 ± 0.9 mg. In the forced running test the B6.A10 mice completed only 64% of the B6 covered distance (p < 0.0001). However, there was no difference in voluntary wheel running (p = 0.6) or in fatigability of isolated soleus (p = 0.24) or extensor digitorum longus (EDL, p = 0.7) muscles. We conclude that chromosome 10 of the A/J strain contributes to reduced endurance performance. We also discuss physiological mechanisms and methodological aspects relevant to interpretation of these findings.

SUBMITTER: Kvedaras M

PROVIDER: S-EPMC5253375 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Forced Running Endurance Is Influenced by Gene(s) on Mouse Chromosome 10.

Kvedaras Mindaugas M Minderis Petras P Fokin Andrej A Ratkevicius Aivaras A Venckunas Tomas T Lionikas Arimantas A

Frontiers in physiology 20170123

Phenotypic diversity between laboratory mouse strains provides a model for studying the underlying genetic mechanisms. The A/J strain performs poorly in various endurance exercise models. The aim of the study was to test if endurance capacity and contractility of the fast- and slow-twitch muscles are affected by the genes on mouse chromosome 10. The C57BL/6J (B6) strain and C57BL/6J-Chr 10<sup>A/J</sup>/NaJ (B6.A10) consomic strain which carries the A/J chromosome 10 on a B6 strain background we ...[more]

PMID: 28167917

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Project description:Background: Properly performed training is a matter of importance for endurance athletes (EA). It allows for achieving better results and safer participation. Recently, the development of machine learning methods has been observed in sports diagnostics. Velocity at anaerobic threshold (VAT), respiratory compensation point (VRCP), and maximal velocity (Vmax) are the variables closely corresponding to endurance performance. The primary aims of this study were to find the strongest predictors of VAT, VRCP, Vmax, to derive and internally validate prediction models for males (1) and females (2) under TRIPOD guidelines, and to assess their machine learning accuracy. Materials and Methods: A total of 4001 EA (nmales = 3300, nfemales = 671; age = 35.56 ± 8.12 years; BMI = 23.66 ± 2.58 kg·m−2; VO2max = 53.20 ± 7.17 mL·min−1·kg−1) underwent treadmill cardiopulmonary exercise testing (CPET) and bioimpedance body composition analysis. XGBoost was used to select running performance predictors. Multivariable linear regression was applied to build prediction models. Ten-fold cross-validation was incorporated for accuracy evaluation during internal validation. Results: Oxygen uptake, blood lactate, pulmonary ventilation, and somatic parameters (BMI, age, and body fat percentage) showed the highest impact on velocity. For VAT R2 = 0.57 (1) and 0.62 (2), derivation RMSE = 0.909 (1); 0.828 (2), validation RMSE = 0.913 (1); 0.838 (2), derivation MAE = 0.708 (1); 0.657 (2), and validation MAE = 0.710 (1); 0.665 (2). For VRCP R2 = 0.62 (1) and 0.67 (2), derivation RMSE = 1.066 (1) and 0.964 (2), validation RMSE = 1.070 (1) and 0.978 (2), derivation MAE = 0.832 (1) and 0.752 (2), validation MAE = 0.060 (1) and 0.763 (2). For Vmax R2 = 0.57 (1) and 0.65 (2), derivation RMSE = 1.202 (1) and 1.095 (2), validation RMSE = 1.205 (1) and 1.111 (2), derivation MAE = 0.943 (1) and 0.861 (2), and validation MAE = 0.944 (1) and 0.881 (2). Conclusions: The use of machine-learning methods allows for the precise determination of predictors of both submaximal and maximal running performance. Prediction models based on selected variables are characterized by high precision and high repeatability. The results can be used to personalize training and adjust the optimal therapeutic protocol in clinical settings, with a target population of EA.

Dataset Information

Forced Running Endurance Is Influenced by Gene(s) on Mouse Chromosome 10.

Publications

Forced Running Endurance Is Influenced by Gene(s) on Mouse Chromosome 10.

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