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Enregistrement W2151428931 · doi:10.1113/jphysiol.2010.195925

Intensity-dependent activation of intracellular signalling pathways in skeletal muscle: role of fibre type recruitment during exercise

2010· letter· en· W2151428931 sur OpenAlexaff
Richard Godin, Alexis Ascah, Frédéric Daussin

Notice bibliographique

RevueThe Journal of Physiology · 2010
Typeletter
Langueen
DomaineMedicine
ThématiqueAdipose Tissue and Metabolism
Établissements canadiensUniversité de Montréal
Organismes subventionnairesnon disponible
Mots-clésMitochondrial biogenesisSkeletal muscleBiologyMitochondrionEndocrinologyCoactivatorGlycolysisInternal medicineInterval trainingIntracellularCell biologyMedicineMetabolismTranscription factorBiochemistryGene

Résumé

récupéré en direct d'OpenAlex

Physical activity elicits physiological responses in skeletal muscle that result in a number of health benefits, in particular in diseases associated with peripheral metabolic dysfunction such as diabetes, heart failure or chronic obstructive pulmonary disease. These diseases have been associated with altered skeletal muscle metabolism and, in some cases, diminished ATP production, decreased mitochondrial content, and a higher proportion of type II fast glycolytic fibre. Exercise training is one intervention that can increase the percentage of oxidative fibres (type I, slow) and have a beneficial impact on these disease states. Several studies have compared the effects of continuous vs. interval training, and have determined that interval training may be the most effective exercise strategy to promote mitochondrial biogenesis and enhance muscle oxidative capacity. The importance of exercise in regulating skeletal muscle metabolism is well appreciated; however, the molecular mechanisms that underlie the beneficial adaptations to exercise remain to be fully understood. Peroxisome proliferator-activated receptor gamma coactivator-1α (PGC-1α) is a key factor involved in the regulation of multiple myocellular signalling pathways such as those implicated in mitochondrial biogenesis and fibre type expression (Russell et al. 2003). Although muscle contraction is known to strongly modulate PGC-1α expression in human skeletal muscle, little is known about the underlying intracellular mechanisms involved. The differential activation across varying training protocols may shed light onto the regulation of signalling pathways upstream of PGC-1α. This Journal Club article discusses the paper of Egan et al. (2010) published recently in The Journal of Physiology and suggests that differences in fibre type recruitment may explain some of the results. Egan et al. compared the effects two isocaloric bouts of exercise performed at either low or high intensity on skeletal muscle signalling. Eight sedentary males performed two trials on a stationary ergocycle: the low- and high-intensity exercise consisted of continuous cycling at 40% or 80% of peak oxygen consumption (), respectively, until the caloric expenditure reached 400 kcal (1674 kJ). Muscle biopsies from the vastus lateralis were taken at rest and at +0, +3 and +19 h after both exercise bouts. The dietary intake during each experimental trial was controlled. PGC-1α mRNA increased 3 h after both exercise bouts; they observed a 3.8-fold increase after low-intensity exercise whereas it increased 10.2-fold after high-intensity training, supporting an intensity-dependent regulation of PGC-1α expression. The authors also explored the signalling pathways upstream of PGC-1α. Protein quantification by immunoblotting revealed a differential activation of multiple signalling pathways involved directly and indirectly in the regulation of PGC-1α transcription. The higher PGC-1α mRNA abundance after high-intensity exercise also coincided with a greater phosphorylation of activating transcription factor-2 (ATF-2) and of class IIa histone deacetylase (HDAC) proteins also suggesting that ATF-2 and HDAC proteins were involved in an intensity-dependent manner. The authors concluded that the intensity during a single bout of exercise regulates PGC-1α mRNA abundance by activating selected upstream signalling pathways in human skeletal muscle with an intensity-dependent response. Human skeletal muscles are heterogeneous and consist of two main fibre types, slow (type I or oxidative) and fast (type IIa and IIx, or glycolytic with varying range of oxidative potential) twitch fibres. These fibres differ in their contractile speed, metabolic profile and fatigue resistance (Coyle, 2000). As described by Ryan et al. (2006), in sedentary people with similar age as the subjects in the study by Egan et al., the vastus lateralis generally contains 40% type I fibre, 35% type IIa fibre and 25% type IIx fibre. Their recruitment during exercise depends on both intensity and duration: type I fibres are mainly recruited at low-intensity exercise (<40% of ) while increasing intensity leads to greater type II fibre recruitment (Sale, 1987). Egan et al. compared two different exercise intensities (40%vs. 80% of ) and we could speculate that the fibre type recruitment was different between the two exercise bouts: the 40% exercise would be associated with mainly type I fibre recruitment while, the 80% exercise would involve a greater proportion of type II fibre. This may explain the differences reported between the two exercise intensities used. Several studies have focused on specific fibre type characteristics and response to exercise. The expression of PGC-1α, in response to exercise in human vastus lateralis differs between fibre types. Russell et al. (2003) observed more than a 3-fold higher PGC-1α protein content in type IIa fibres than in type I fibres after 6 weeks of interval training consisting of 5 to 6 intervals of 1–3 min at 70–80% of with 1 min of recovery at 50% of . The training intensity was similar to the 80% that was used during the high-intensity exercise in the study by Egan et al. suggesting a higher PGC-1α gene activation would be observed when type II fibres are stimulated such as during high-intensity exercise. AMPK, one of the activators of PGC-1α that were investigated in the study of Egan et al., was also shown to differ between fibre types. In young untrained humans, Lee-Young et al. (2009) reported a higher baseline AMPK phosphorylation indicating activation in type II fibres as well as an increase in AMPK phosphorylation which was more pronounced in type II fibres than in type I fibres after an acute exercise bout at 65% of . These results support a fibre type-specific regulation of PGC-1α. Egan et al. proposed that CaMKII was also regulated by exercise intensity. To our knowledge, the fibre type specificity of CaMKII activity has still not been explored. However, the motor unit firing frequency determines both the amplitude and duration of the Ca2+ transient in skeletal muscle, which are modulators of CAMKII activity. At rest, intracellular calcium concentration ([Ca2+]i) measured in isolated single muscle fibres is 30–50 nm (Wu et al., 2000). In contrast, when muscles are stimulated at physiological frequencies, [Ca2+]i reaches 100–300 nm in slow-twitch (type I) fibres, but may reach concentration that are 10-fold higher (1–2 μm) in fast-twitch fibres (Hennig & Lomo, 1985). These dramatic fluxes in intracellular calcium concentration, as well as the duration for which these amplitudes are achieved, are thought to encode signals that will be recognized by different downstream Ca2+-dependent pathways. Therefore, we could speculate that the higher phosphorylation of CaMKII after high-intensity exercised observed by Egan et al. may be due to higher type II fibre recruitment. Finally, Egan et al. reported an intensity-dependent increase of ATF-2 phosphorylation. ATF-2 can be activated by p38 mitogen-activated protein kinase (MAPK) and to date, the group of Blomstand is to our knowledge the only group that explored the phosphorylation of p38 MAPK in different fibre types in response to exercise. For example, Tannerstedt et al. (2009) submitted six subjects to a high mechanical stress consisting of ten eccentric contractions at 50% of maximal force. In response to this resistance exercise, they observed an increase in phophorylated p38 MAPK in both muscle fibre types with a markedly higher increase in type II fibres. Even if this exercise did not involve the metabolic stress that is characteristic of an aerobic exercise, these results show a fibre type-specific response to high-intensity contractions such as those experienced during high-intensity aerobic session. This suggests a higher capacity of type II fibres to phosphorylate p38 MAPK and its downstream target ATF-2, ultimately leading to higher PGC1α transcription in the fast twitch fibres. Taken together, the results presented by Egan et al. show an intensity-dependent response of PGC1α mRNA expression to exercise. However, some previous studies described a fibre type-specific response to exercise. Assuming that fibre type recruitment is dependent on exercise intensity, we could speculate that the results of Egan et al. could be influenced by the specific fibre type recruitment associated with each training intensity. Future studies should consider the compartmentalization of exercise adaptations according to the typology in response to varying training intensities. Techniques such as laser microdissection, which allow for individual fibres to be selected prior to homogenisation and the ensuing analyses, should be used to discriminate the fibre type-specific responses. Research in this field is needed because full elucidation of exercise-mediated signalling pathways would represent a significant step toward the treatment or prevention of the peripheral metabolic dysfunction that are associated to numerous chronic diseases. The authors thank Drs Yan Burelle and François Peronnet for their suggestions and assistance in the preparation of this manuscript.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,008

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,030
Tête enseignante GPT0,261
Écart entre enseignants0,231 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations25
Publié2010
Routes d'admission1
Résumé présentoui

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