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Record W2885222683 · doi:10.1113/jp276802

The importance of exercise intensity, volume and metabolic signalling events in the induction of mitochondrial biogenesis

2018· letter· en· W2885222683 on OpenAlexaffabout
Heather L. Petrick, Kaitlyn M.J.H. Dennis, Paula M. Miotto

Bibliographic record

VenueThe Journal of Physiology · 2018
Typeletter
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMitochondrial biogenesisCell biologyAMPKProtein kinase ADownregulation and upregulationPDK4MitochondrionBiologyAMP-activated protein kinaseOrganelle biogenesisKinaseChemistryBiogenesisBiochemistry

Abstract

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A classical consequence of exercise training is the induction of mitochondrial biogenesis, a highly coordinated process acting to enhance oxidative capacity and aerobic energy production. Mediated by acute transcriptional events following each individual bout of exercise, a key factor initiating mitochondrial biogenesis is the transient upregulation of peroxisome proliferator-activated receptor γ coactivator 1-α (PGC-1α) expression, activating several downstream transcription factors implicated in the control of mitochondrial content and structure. However, our understanding of the signalling events inducing the exercise-mediated increase in skeletal muscle PGC-1α mRNA expression remains incomplete. Traditionally, literature has largely focused on the role of intracellular metabolites (AMP, ADP, Ca2+) during exercise as potent activators of AMP-activated protein kinase (AMPK), Ca2+/calmodulin-dependent protein kinase II (CaMKII), and p38 mitogen-activated protein kinase (p38 MAPK) phosphorylation events mediating cellular adaptations. Furthermore, other lines of evidence suggest glycogen depletion and lactate accumulation, representing a metabolic profile indicative of energetic stress, are linked to the upregulation of PGC-1α mRNA content (Egan et al. 2010). It is therefore conceivable that the mitochondrial biogenic responses to exercise are dependent on the degree of metabolic stress; however, this has remained a contentious subject in human studies as a graded relationship between exercise-mediated signalling events and PGC-1α expression has not been consistently established (Brandt et al. 2016). This is of particular interest with respect to low-volume sprint interval training, in which the extent of energetic demands could be important in overcoming the reduction in volume to elicit mitochondrial adaptations classically observed following continuous prolonged exercise. To provide novel insight into the role of energetic stress in PGC-1α expression during intense exercise, a recent paper published by Fiorenza et al. in The Journal of Physiology (Fiorenza et al. 2018) examined the metabolic perturbations and mitochondrial biogenic responses to three distinct exercise protocols. Specifically, 12 trained men completed an acute bout of repeated-sprint exercise (RS, 18 × 5 s all out with 30 s recovery), work-matched speed endurance exercise (SE, 6 × 20 s all out with 120 s recovery), and traditional continuous moderate intensity exercise (CM, 50 min at 70% ). Fiorenza et al. reported that (1) between high intensity exercise bouts, metabolic stress was a predictor of skeletal muscle mRNA responses indicative of mitochondrial biogenesis, while in contrast, (2) PGC-1α mRNA expression following moderate intensity exercise was not dependent on energetic perturbations when these responses were directly compared to high intensity exercise bouts. Specifically, while all types of exercise utilized by Fiorenza et al. increased skeletal muscle PGC-1α mRNA expression at a 3 h post-exercise time point, this response was greater following SE and CM compared to RS. In a similar trend, the associated transcription factors Tfam and Nrf2, mediating mitochondrial DNA transcription, and Mfn2 and Drp1, implicated in mitochondrial structural remodelling, were only upregulated with SE and CM, but not RS. Despite the work-matched nature of SE and RS exercise bouts, greater CaMKII and p38MAPK phosphorylation, and a greater rise in muscle lactate concentrations, as well as higher plasma adrenaline levels, were identified following SE, likely contributing to the enhanced transcriptional responses to SE exercise. In addition, the novel finding that heat shock protein 72 (HSP72) mRNA levels were elevated to a greater extent following SE exercise compared to both CM and RS further indicates a heightened metabolic stress response to this low-volume intense exercise bout. This evidence would therefore implicate metabolic stress as a key factor required for high intensity exercise to overcome the reduction in training volume and elicit cellular transcription events mediating downstream mitochondrial adaptations. However, in contrast to these findings, AMPK phosphorylation was elevated to a similar extent in skeletal muscle obtained immediately following all three exercise bouts, regardless of the different PGC-1α transcriptional responses. In addition, CaMKII phosphorylation, p38 MAPK phosphorylation, and the accumulation of cellular metabolites were generally lower following CM despite the substantial elevation in PGC-1α mRNA content. This finding is consistent with previous work by the same authors (Brandt et al. 2016) in which a relationship between energetic stress and PGC-1α mRNA induction was not identified following various 60 min endurance-based exercise protocols interspersed with very brief periods at a higher intensity. Combined, these data provide interesting evidence that while PGC-1α mRNA expression is dependent on the degree of metabolic stress associated with different intensities and volumes of exercise, it also appears that other intracellular mechanisms are important for the induction of PGC-1α mRNA expression. In this respect, the increased superoxide dismutase 2 (SOD2) mRNA content observed by Fiorenza et al. (2018) following SE and CM exercise, and the enhanced HSP72 mRNA response to SE exercise, could occur as a result of greater reactive oxygen species (ROS) production, supporting a rationale to further examine the role of ROS as a cellular signalling event. Specifically, an increase in mitochondrial-derived ROS has been reported in response to acute exercise (Place et al. 2015), further influencing several redox-sensitive pathways implicated in mitochondrial adaptations. While the exact mechanistic links remain unknown, recent work has shown that mitochondrial transcriptional events and PGC-1α expression following treadmill running are blunted in mice with attenuated exercise-mediated ROS production in the presence of ADP, without changes in CaMKII and AMPK phosphorylation (Miotto & Holloway, 2018). This work would support mitochondrial ROS production as a key event influencing the mitochondrial biogenic responses to exercise; however, a direct relationship has yet to be confirmed in human skeletal muscle. It would therefore be interesting to examine cellular ROS emissions from both mitochondrial and cytosolic sources, and the influence this may have on phosphorylation events, transcriptional regulation and mitochondrial biogenesis following exercise protocols of varying energetic demands. In addition to a potential role of ROS in mitochondrial biogenesis, the current data by Fiorenza et al. could be influenced by the temporal sequence of PGC-1α expression. While gene expression has consistently been reported to peak within 2–6 h following an acute bout of exercise, it remains unknown whether this could be influenced by the intensity and duration of exercise conducted. A time course relationship could provide insight into the magnitude of PGC-1α mRNA expression following exercise of varying energetic demands, particularly given the diverse durations of each exercise protocol. A study of this nature could also build on Fiorenza et al.’s detailed account of gene transcription events, and further examine the extent to which this translates to functional outcomes. Specifically, it may be interesting to investigate subcellular localization of gene transcripts, and the downstream regulation of protein content, post-translational modifications and mitochondrial function to determine the practical implications for optimizing mitochondrial adaptations to low-volume sprint exercise training. However, despite the unknown functional outcomes, it nonetheless remains intriguing that a single bout of low-volume RS exercise utilized by Fiorenza et al. was capable of upregulating PGC-1α mRNA expression in skeletal muscle of trained individuals, who do not classically respond robustly to metabolic perturbations of exercise. Altogether, Fiorenza et al. provide intriguing data that metabolic stress may be an important factor in the acute induction of PGC-1α mRNA expression following work-matched high intensity exercise. However, as this is not a consistent finding with respect to traditional moderate-intensity exercise, other mechanisms may be involved in this process. Future work can therefore extend the findings of Fiorenza et al. by examining ROS production and redox-sensitive pathways, a temporal relationship of signalling events, and downstream functional outcomes to enhance our understanding of the capacity for low-volume sprint exercise to induce beneficial cellular adaptations. None declared. All authors have approved the final version of the manuscript and agree to be accountable for all aspects of the work. All persons designated as authors qualify for authorship, and all those who qualify for authorship are listed. H.L.P. and P.M.M. are supported by Natural Sciences and Engineering Research Council of Canada (NSERC) graduate student scholarships. The authors thank Dr Graham Holloway for his insight and helpful suggestions in preparing the manuscript.

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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)0.000
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.024
GPT teacher head0.273
Teacher spread0.249 · 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".

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Citations6
Published2018
Admission routes2
Has abstractyes

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