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Record W4380291424 · doi:10.1113/jp284856

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

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

Bibliographic record

VenueThe Journal of Physiology · 2023
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsYork University
Fundersnot available
KeywordsAthletesMitochondrionChemistryPhysical therapyPhysical medicine and rehabilitationMedicineBiologyCell biology

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.010
Scholarly communication0.0080.011
Open science0.0010.003
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0070.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.236
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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