Intensity-dependent activation of intracellular signalling pathways in skeletal muscle: role of fibre type recruitment during exercise
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".