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Enregistrement W4380291424 · doi:10.1113/jp284856

Strength athletes and mitochondria: it's about ‘time’

2023· letter· en· W4380291424 sur OpenAlexaff
Madison C. Garibotti, Christopher G. R. Perry

Notice bibliographique

RevueThe Journal of Physiology · 2023
Typeletter
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueMuscle metabolism and nutrition
Établissements canadiensYork University
Organismes subventionnairesnon disponible
Mots-clésAthletesMitochondrionChemistryPhysical therapyPhysical medicine and rehabilitationMedicineBiologyCell biology

Résumé

récupéré en direct d'OpenAlex

Mitochondrial adaptations to endurance exercise training have been well characterized, whereas the ability of mitochondria to adapt to strength training has received less attention. The topic is interesting given the relatively short period of muscle contractions during a single strength/resistance exercise session compared to more sustained endurance exercise sessions raises questions regarding the amount of cellular stress required to stimulate mitochondrial adaptations. The recent report in The Journal of Physiology (Botella et al., 2023) addresses this gap in knowledge by performing extensive high-resolution imaging with electron microscopy that identified diverse ultrastructural reorganizations within skeletal muscle mitochondria in strength trained individuals and in response to acute resistance exercise. Mitochondria have a remarkable ability to adapt to acute and chronic stressors over time. While mitochondria provide numerous functions vital for cell survival, their contribution to energy homeostasis is perhaps more commonly appreciated. During exercise, the creation of an energy imbalance is 'sensed' by mitochondria through a variety of feed-forward and feed-back loops that acutely activate rate-limiting transporters and enzymes regulating glucose and fat catabolism including oxidation in the mitochondria (Hargreaves & Spriet, 2020). This demand-driven system allows mitochondria to respond rapidly to 'acute' perturbations to energy homeostasis so that contraction, and hence exercise, can be sustained without compromising muscle cell survival. In addition to this acute response system, these same stressors — if presented repeatedly — can invoke adaptive responses that are more sustained. In this way, repeated exercise bouts increase mitochondrial content markers as first reported in 1967 by John Holloszy using rodent models (Holloszy, 1967). Decades of research have identified numerous mechanisms by which a variety of cellular stressors activate gene expression from both nuclear and mitochondrial DNA to upregulate substrate catabolic pathways that ultimately support mitochondrial oxidative phosphorylation. The repetition of stressors is key to this model and is believed to explain, in part, a metabolic basis for how a person's fitness improves over time with regular exercise. Indeed, electron microscopy studies by Hoppeler's group in the 1970s and beyond (Hoppeler et al., 1985) identified increased mitochondrial volume in skeletal muscle from endurance-trained people, thereby verifying much biochemical evidence that mitochondria adapt to chronic exercise stress. However, whether such mitochondrial adaptations occur in response to strength training was largely unknown. In the past few years, at least one report has shown that several weeks of strength training increases Complex I and II-linked mitochondrial respiration in human muscle (Porter et al., 2015). These findings followed earlier observations that resistance exercise in untrained individuals activate AMPK (Dreyer et al., 2006) — a key energy sensing pathway that has been linked to transcriptional regulation of mitochondrial biogenesis, at least following endurance exercise. The degree to which such functional responses to resistance exercise are linked to altered mitochondrial remodelling remained largely unknown. The recent study by Botella et al. addressed this gap in knowledge. High resolution electron microscopy in strength trained athletes showed mitochondria in strength-trained athletes had increased cristae density compared to untrained individuals — a finding that is often a hallmark sign of endurance-trained muscle. For example, greater cristae density is thought to be central to increased content and integration of electron transport chain system protein complexes. This finding suggests that strength training creates repeated challenges to energy homeostasis over time that improves mitochondrial oxidative phosphorylation consistent with the prior reports of increased respiration (Porter et al., 2015). Of interest, while a single resistance exercise session in untrained individuals increases AMPK activity (Dreyer et al., 2006), chronically trained strength athletes do not show such activation with a single bout of resistance exercise (Coffey et al., 2006) which is consistent with the greater ability to produce ATP to maintain energy homeostasis reported previously (Porter et al., 2015). This notion is consistent with the current findings of increased cristae density by Botella et al. Despite greater cristae density, mitochondrial volume density was not larger in strength-trained athletes versus untrained individuals. In fact, mitochondrial size was decreased which may be consistent with the greater cristae density. The authors distinguish volume density from size in terms of the amount of mitochondria visible in a given image separate from the size of each cross-sectional mitochondrial image itself. These findings are intriguing given it is often expected that both cristae remodelling and increased mitochondrial volume and/or size following endurance training work in tandem to increase the ability of mitochondria to maintain energy homeostasis. In this way, the lack of change in volume density, smaller size, and greater cristae density (and increased surface-to-volume ratio) inspire new questions regarding why such adaptations are seemingly distinct from classic endurance training literature. To this end, the work by Botella et al. provide a foundation for designing new studies that track the time-dependent relationship between metabolic and related stressors of resistance exercise to this unique mitochondrial remodelling with parallel comparisons to the pattern during endurance training. Such directions could consider the additional discoveries by Botella et al. that acute resistance exercise caused mild but apparently stressful morphological changes to mitochondria that might reflect the very stress that invokes adaptations to strength training. They also demonstrated mitochondrial morphology varies across the subcellular landscape of a muscle fibre with relatively minor differences between Type I and Type II fibres. This finding raises questions regarding how compartment-specific changes in metabolic demand might drive location-specific remodelling of mitochondria precisely where they are needed, and whether this is occurring more so in both fibre types given they are both recruited during higher intensity contractions. Such examples demonstrate the opportunity to build on their novel findings to better understand the precision and complexity by which mitochondria remodel themselves in response to different types of exercise. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. The authors have no conflicts of interest. M.G.: Conception or design of the work; Drafting the work or revising it critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work C.P.: Conception ordesign of the work; Drafting the work or revising it critically for important intellectual content; Final approval of the version to be published; Agreement to be accountable for all aspects of the work. This work did not receive funding.

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)0,003
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,024

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,004
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,010
Communication savante0,0080,011
Science ouverte0,0010,003
Intégrité de la recherche0,0060,008
Charge utile insuffisante (le modèle a refusé de juger)0,0070,002

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 Opus0,010
Tête enseignante GPT0,236
Écart entre enseignants0,226 · 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'étudeSans objet
Domainenon disponible
GenreCommentaire

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

En bref

Citations4
Publié2023
Routes d'admission1
Résumé présentoui

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