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Repeated Sprints Alter Signaling Related to Mitochondrial Biogenesis in Humans


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Medicine & Science in Sports & Exercise: May 2012 - Volume 44 - Issue 5 - p 827-834
doi: 10.1249/MSS.0b013e318240067e
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Physical exercise is used, both in acute and in chronic form, as a method to investigate the mechanisms of mitochondrial adaptations in human skeletal muscle. The mitochondria play an important role in the etiopathogenesis of chronic diseases (24); therefore, it is important to understand the molecular events that contribute to exercise-induced mitochondrial adaptations. To date, continuous exercise, performed at a low or moderate intensity, has predominantly been used as the experimental model and has been shown to activate the molecular pathways associated with mitochondrial biogenesis (10,21,26). However, prolonged continuous exercise might not represent an attractive option for many individuals to engage in regular physical activity (17). Consequently, different experimental models have been explored, for example, acute and chronic high-intensity intermittent exercise. In particular, submaximal intervals of 4–5 min in duration (2,13) or repeated 30-s all-out bouts (12) have been used. However, the practical applications of such models seem to be limited because these exercises do not reflect typical patterns of physical activities and are often perceived as very demanding (8). Repeated-sprint exercise (RSE) comprises short maximal sprints (<6 s in duration) interspersed with recovery of less than 30 s. This type of exercise is usually well tolerated (28) and is a typical component of many sports, in particular, team sports (29). However, despite this, there is almost no research that has investigated the effects of RSE on molecular pathways associated with mitochondrial biogenesis.

A coordinated interaction of signaling molecules, transcription coactivators, transcription factors, and repressors is responsible for exercise-induced mitochondrial adaptations in the skeletal muscle. The mitochondrial transcription factor A (TFAM) (11), nuclear respiratory factor 1 (NRF1) (34), and myocyte enhancer factor 2 (MEF2) (22) have been identified as key transcription factors. The peroxisome proliferator–activated receptor γ coactivator 1α (PGC-1α) is considered the master regulator of these transcription factors and is targeted, among other pathways, by the 5′ AMP-activated protein kinase (AMPK), Ca2+ calmodulin-dependent protein kinase II (CaMK II) (33), and p38 mitogen-activated protein kinase (p38 MAPK) (1) signaling pathways. Also, repressors interact either with coactivators or with transcription factors, for example, the histone deacetylases (HDAC) (22), to help regulate mitochondrial biogenesis.

Despite the remarkably low exercise volume, RSE might represent an effective physiological stimulus to upregulate the signaling processes associated with mitochondrial adaptations. For example, repeated 4-s sprints performed with 25 s of recovery produce a ∼15% decrease in adenosine triphosphate (ATP) content in the human vastus lateralis (30). Because the change in the AMP-to-ATP ratio during muscle contraction is the primary trigger of AMPK activation (14), this suggests that RSE may provide an ideal exercise stimulus to activate AMPK and its downstream acetyl-CoA carboxylase (ACC). Also, the intracellular concentration of Ca2+ is the main initiator of the CaMK II signaling cascade, and it has been demonstrated that CaMK II is able to decode high-frequency Ca2+ spikes better than low-frequency ones (7). Therefore, the repetition of maximal sprints may phosphorylate CaMK II protein, ultimately leading to mitochondrial biogenesis.

It has been shown that a single set of intermittent sprints (10 × 6-s sprints, 1-min recovery) increases AMPK protein phosphorylation and PGC-1α messenger RNA (mRNA) expression (5,6). However, there is paucity of information about the effects of multiple-set RSE on a more comprehensive sequence of molecular events. Also, the effects of chronic RSE, i.e., repeated-sprint training, have not been assessed. Therefore, our aim was to investigate the effects of acute and chronic RSE on skeletal muscle mRNA expression and protein abundance/phosphorylation associated with mitochondrial adaptations. It was hypothesized that acute RSE would increase the expression of transcriptional regulators of mitochondrial biogenesis via large activation of associated signaling pathways and result in a training-induced increase in PGC-1α protein.



Ten healthy young adults (seven males, three females) gave written informed consent and participated in this study, which was approved by the Victoria University Human Research Ethics Committee and conforms to the Declaration of Helsinki. Their physical characteristics were (mean ± SD) age = 22.3 ± 4.1 yr, height = 174.4 ± 9 cm, mass = 70.2 ± 11.6 kg, and V˙O2peak = 53.7 ± 6.9 mL·kg−1·min−1. All participants were recreationally active, and seven were involved in team sports at a recreational level.

Experimental design

Before training, participants performed an incremental exercise test on a motorized treadmill, followed by a first habituation of the nonmotorized treadmill used for the RSE, as previously described in detail (28). At least 48 h later, participants returned to the laboratory for the main RSE familiarization trial. After 1 wk, participants performed the pretraining RSE trial. Then, participants commenced a 4-wk training program, three times per week, with each session composed of a standardized warm-up followed by a repeated-sprint protocol that replicated the pretraining RSE. The posttraining RSE trial was conducted 48 h after the last training session, and the incremental test was then performed a further 48 h later. Muscle biopsies and venous blood samples were obtained during pre- and posttraining RSE trials. Participants refrained from exercise and from alcohol and caffeine consumption for 24 h before all tests. Participants were asked to complete a dietary report, with a detailed description of the last three meals consumed before the pretraining RSE trial. The same diet was then replicated before the posttraining RSE trial.

RSE trial

The participants arrived at the laboratory between 08:00 and 10:00 a.m. After remaining supine for 10 min, a resting venous blood sample and a muscle biopsy were taken. Immediately after the biopsy, participants completed a standardized warm-up consisting of two parts. First, they ran on a treadmill for 4 min at a velocity corresponding to 60% of the velocity associated with their V˙O2peak. Participants then quickly moved to the nonmotorized treadmill and performed two 4-s runs at 70% of their peak sprinting velocity (measured from the sprints during the familiarization trial) with 20 s of passive recovery; after 1 min of rest, one 4-s run at 90% of the peak sprinting velocity was performed. Participants were required, from a standing start, to reach the required velocity indicated on the treadmill as rapidly as possible and to maintain that velocity for 4 s. The same warm-up was subsequently used for all training sessions and the posttraining RSE. The RSE commenced 1 min after the completion of the warm-up and consisted of three sets of 5 × 4-s sprints with 20 s of passive recovery between sprints and 4.5 min of passive rest between sets. Laboratory temperature and relative humidity during the trials were 22.3°C ± 0.2°C and 43.0% ± 4.1%, respectively, and did not differ between pre- and posttraining RSE trials.

Venous blood sampling and analyses

A 20-gauge catheter (Optiva; Smiths Medical, Rossendale, UK) was inserted in the antecubital vein, and a blood sample (∼3 mL) was drawn at rest, after each set of sprints, and at 1, 2, 5, 10, 20, and 30 min after RSE and immediately placed in a tube containing lithium heparin. Glucose ([Glu]) and lactate ([Lac]) concentrations were measured in duplicate using an automated analyzer (2300 STAT plus; YSI, Inc., Yellow Springs, OH). Total hemoglobin concentration and the hematocrit were measured in duplicate using an automated hematology analyzer (Sysmex K-800; TOA Medical Electronics, Kobe, Japan) to assess the change in plasma volume during exercise and recovery. The typical error, expressed as a coefficient of variation (%) of duplicates for [Glu], [Lac], hemoglobin concentration, and hematocrit, was 3.8%, 3.9%, 0.6%, and 1.0%, respectively.

Muscle biopsies

Skeletal muscle samples were taken from the vastus lateralis muscle using a biopsy needle with suction. After injection of a local anesthetic into the skin and fascia (Xylocaine 1%; AstraZeneca, North Ryde, Australia), four small incisions were made 1 cm apart. Biopsies were taken before, immediately after, and 1 and 4 h after RSE, both before and after training. Opposite legs were chosen for the pre- and posttraining biopsies. After sampling, muscle samples were rapidly blotted on a filter paper to remove excess blood and immediately frozen in liquid N2. All samples were then stored at −80°C before being analyzed.

RNA extraction and reverse transcriptase polymerase chain reaction

Total cellular RNA was extracted as previously described (31). Reverse transcriptase polymerase chain reaction (PCR) was performed using a Real-Time PCR System (7500; Applied Biosystems, Mulgrave, Australia). PCR was performed in duplicate with reaction volumes of 20 μL, containing Power SYBR Green 1 (Applied Biosystems), forward and reverse primers, and complementary DNA template (1.25 ng·μL−1). Data were analyzed using a comparative critical threshold (Ct) method, where the amount of target gene normalized to the amount of endogenous control relative to control value is given by 2−ΔΔCt. The efficacy of 18s as an endogenous control was examined using the equation 2−ΔCt. The 18s was considered an appropriate control for this study when no changes in the expression of the mRNA were observed (data not shown). Primers were designed using the Primer Express Software package version 3.0 (Applied Biosystems) from gene sequences obtained from GenBank (Table 1). Primers were designed spanning intron–exon boundaries to prevent amplification of the target region for any contaminating DNA. Primer sequence specificity was also confirmed using the Basic Local Alignment Search Tool. A melting point dissociation curve was generated by the PCR instrument for all PCR products to confirm the presence of a single amplified product.

Primer sequences and accession numbers for reverse transcriptase PCR of skeletal muscle genes.


Approximately 40 mg of frozen muscle samples was homogenized in ice-cold buffer containing 20 mM of Tris, pH 7.8 (Bio-Rad Laboratories, Hercules, CA), 137 mM of NaCl, 2.7 mM of KCl (Merck, Darmstadt, Germany), 1 mM of MgCl2, 5 mM of Na4O7P2, 10 mM of NaF, 1% Triton X-100, 10% glycerol (Ajax Finechem, Taren Point, Australia), 0.5 mM of Na4VO3, 1 μg·mL−1 of leupeptin, 1 μg·mL−1 of aprotinin, 200 mM of Phenylmethanesulfonyl fluoride, 1 mM of DL-Dithiothreitol, and 1 mM of benzamidine. All reagents were analytical grade (Sigma-Aldrich, St. Louis, MO), unless otherwise specified. Samples were homogenized (1:37.5 dilution (w/v)) for 2 × 20 s, using a tissue homogenizer (TH220; Omni International, Kennesaw, GA). Homogenates were then rotated for 60 min at 4°C and centrifuged at 15,000g for 10 min at 4°C, and protein concentration of the resulting supernatant was determined using a DC Protein Assay kit (Bio-Rad). Aliquots of the muscle lysate were mixed with Laemmli sample buffer, and 60 μg of total protein per sample was separated by 6%–20% gradient SDS-PAGE, for 2 h at 80 mA and overnight at 16 mA, in a standard vertical electrophoresis unit (SE 600 Chroma®; Hoefer, Inc., Holliston, MA). After electrophoresis, proteins were transferred to polyvinylidene fluoride membranes (Bio-Rad) for 3 h at 320 mA using a semidry blotter (TE70X; Hoefer). Membranes were blocked in TBST buffer (10 mM of Tris, 100 mM of NaCl, 0.02% Tween 20) containing 7.5% nonfat milk, for 1 h at room temperature. After being washed 3 × 10 min in TBST, membranes were incubated with the appropriate primary antibody overnight at 4°C. Primary antibodies were diluted in TBS buffer containing 0.1% NaN3 and 0.1% albumin bovine serum. All membranes were incubated with the same volume of buffer.

To determine protein abundance and phosphorylation, membranes were incubated with antibodies for ACC (#3662; Cell Signaling Technology, Danvers, MA), phospho-ACC (Ser79) (#3661; Cell Signaling), AMPKα (#2532; Cell Signaling), phospho-AMPKα (Thr172) (#2535; Cell Signaling), phospho-CaMK II (Thr286) (#3361; Cell Signaling), cytochrome oxidase subunit IV (COX IV) (#4844; Cell Signaling), p38 MAPK (#9212; Cell Signaling), phospho-p38 MAPK (Thr180/Tyr182) (#9211; Cell Signaling), PGC-1α (#KP9803; Calbiochem, Merck), phospho-HDAC5 (Ser259) (kindly donated by Dr. Sean McGee and raised as previously described [23]), and Receptor-interacting protein 140 (#ab3425; Abcam, Sapphire Bioscience, Waterloo, Australia). All membranes were normalized for loading with glyceraldehyde-3-phosphate dehydrogenase (GAPDH; #sc-25778; Santa Cruz Biotechnology, Santa Cruz, CA). A dilution of 1:1000 was used for most antibodies, with the exception of ACC/phospho-ACC (1:750), RIP 140 (1:500), COX IV (1:6000), and GAPDH (1:2000). After incubation with the primary antibodies, membranes were washed 3 × 10 min in TBST buffer and incubated with the appropriate antirabbit (#NEF812001EA; PerkinElmer, Waltham, MA) or antimouse (#NEF822001EA; PerkinElmer) horseradish peroxidase–conjugated secondary antibodies for 1 h at room temperature. Secondary antibodies were diluted 1:5000 in TBST buffer containing 5% nonfat milk. After washing the membranes in TBST, immunoreactive proteins were detected using chemiluminescence reagents (Immobilon HRP Substrate; Millipore, Billerica, MA) and quantified by densitometric scanning (VersaDoc Imaging System; Bio-Rad). Only linear adjustments to the whole images were performed with a dedicated software (Quantity One v.4.6.6; Bio-Rad), with no modifications to the gamma settings and no external image-processing software used. Where possible, images are presented with a height of approximately five bands above and below the band of interest (32).

Statistical analyses

Data are presented as mean ± SD. Results were tested for normal distribution using a Shapiro–Wilk W test, and when this assumption was not met (P < 0.05), data were log transformed to reduce bias due to the nonuniformity of error. For balanced data sets (i.e., no missing values), a two-way ANOVA with repeated measures and Bonferroni post hoc test were applied. For the data sets with missing values, a linear mixed-model analysis was used, with training and time as fixed effects and with restricted maximum likelihood as the estimation method for missing values. For each variable, three covariance structures were tested, i.e., compound symmetry, first-order autoregressive, and unstructured. The appropriate structure was then chosen by comparing the Akaike information criterion of each covariance type and accounting for the number of parameters included in the estimation. Statistical significance was set at P < 0.05. All analyses were performed using SPSS 15.0 for Windows (IBM Corporation, Armonk, NY). The magnitude of the changes was assessed using an effect size (ES) statistic with 90% confidence intervals and percentage changes. ES were classified as follows: <0.2 was defined as trivial, 0.2–0.6 was defined as small, 0.6–1.2 was defined as moderate, 1.2–2.0 was defined as large, and >2.0 was defined as very large (15).


Performance and physiological responses

During the pretraining RSE, mean power was 710 ± 180, 711 ± 163, and 688 ± 152 W for sets 1, 2, and 3, respectively. Peak power was 2106 ± 724, 2056 ± 608, and 1974 ± 535 W for sets 1, 2, and 3, respectively. Training increased mean power (P < 0.001 main effect), being 802 ± 196 W (+13.0%), 780 ± 182 W (+9.6%), and 750 ± 164 W (+9.5%) for sets 1, 2, and 3, respectively. Peak power did not change after training (+2.4%, 6.1%, and 4.4% for sets 1, 2, and 3, respectively). The [Lac] and [Glu] in response to acute RSE before and after training are presented in Figure 1. There were no differences in plasma volume changes between the pre- and posttraining RSE. Training did not change V˙O2peak (53.7 ± 6.9 and 54.8 ± 6.6 mL·kg−1·min−1, before and after training, respectively), the onset of blood [Lac] accumulation (12.4 ± 1.8 and 12.4 ± 1.8 km·h−1, before and after training, respectively), or the [Lac] threshold (11.4 ± 2 km·h−1 before training, 11.0 ± 1.7 km·h−1 after training).

Blood [Glu] (A) and blood [Lac] (B) concentrations during acute RSE and recovery. Filled circles indicate before training; open circles indicate after training. Data are presented as mean ± SD; n = 10. *Significantly less than before training (main effect, P < 0.05).

Protein signaling

Representative Western blot images are presented in Figure 2. Acute RSE was associated with a small change in AMPKα phosphorylation (32%, ES = 0.7 ± 0.4), when 1 h after exercise was compared with rest (Table 2). ACC abundance (51%, ES = 0.7 ± 0.3), as well as ACC phosphorylation (86%, ES = 1.4 ± 0.8), was increased immediately after exercise compared with rest (P < 0.001; Fig. 3A). Acute RSE increased CaMK II phosphorylation 1 h after exercise compared with rest (69%, ES = 0.7 ± 0.6; Fig. 3B), but there were only small changes in p38 MAPK abundance (17%, ES = 0.3 ± 0.3) and phosphorylation (19%, ES = 0.3 ± 0.3) immediately after exercise compared with rest. Training resulted in a small change in resting AMPKα protein abundance (18%, ES = 0.6 ± 0.8; P = 0.027 main effect) and moderate changes in AMPKα phosphorylation (38%, ES = 0.7 ± 0.4; P < 0.05) and changed ACC abundance by (46% ES = 0.8 ± 0.8). After training, acute RSE increased ACC phosphorylation by 83% (ES = 1.4 ± 0.8; P < 0.001).

Skeletal muscle protein abundance and phosphorylation in response to acute RSE before and after 4 wk of repeated-sprint training (three times per week).
Representative Western blot images for one individual. R, rest; E, end of exercise; 1 h, 1 h after exercise; pre, before training; post, after training.
Skeletal muscle ACC (A) and CaMK II (B) protein phosphorylation in response to acute RSE before (filled bars) and after (open bars) 4wk of repeated-sprint training. Data are presented as mean ± SD; n = 9 for p-ACC; n = 10 for p-CaMK II. *Significantly greater compared with rest (P < 0.05). ES, effect size statistic ± 90% confidence interval.

PGC-1α, phospho-HDAC5, and RIP 140

Before training, acute RSE increased PGC-1α mRNA expression by 208% at 4 h after exercise (ES = 1.5 ± 0.7, P = 0.002; Fig. 4A). There was a small change in the phosphorylation of HDAC5 immediately after exercise compared with rest (92%, ES = 0.6 ± 0.8) but no change in RIP 140 abundance (−10%, ES = −0.2 ± 0.6). Four weeks of repeated-sprint training induced a 33% increase in PGC-1α protein abundance at rest (ES = 0.9 ± 0.7; Fig. 4B). After training, acute RSE resulted in an increase of PGC-1α mRNA expression at 4 h after exercise (ES = 1.4 ± 1.3) and a small change in phospho-HDAC5 immediately after exercise, both compared with rest.

Skeletal muscle PGC-1α gene expression in response to acute RSE (A) and resting protein abundance (B), before (filled bars) and after training (open bars). Data are presented as mean ± SD; n = 10. *Significantly greater compared with rest (P < 0.05). ES, effect size statistic ± 90% confidence interval.

Transcription factors

As a result of acute RSE, the mRNA expression at 4 h after exercise and compared with rest was moderately changed for NRF1 (92%, ES = 0.7 ± 0.8) and MEF2a (202%, ES = 1.0 ± 1.3). Training produced a trivial change in the resting mRNA expression of NRF1 (33%, ES = 0.1 ± 0.6), TFAM (18%, ES = 0.1 ± 1.0), and MEF2a (16%, ES = 0.1 ± 0.9). After training, acute RSE resulted in a moderate change of mRNA expression at 4 h after exercise compared with rest for NRF1 (105%, ES = 0.9 ± 1.5) and TFAM (34%, ES = 0.7 ± 1.0).


Acute RSE induced a trivial change in COX IV mRNA expression at 4 h after exercise compared with rest (25%, ES = 0.1 ± 0.8) and no change in the protein abundance (Table 2). Training resulted in a small change in protein abundance at rest (14%, ES = 0.3 ± 0.7). After the acute RSE after training, COX IV mRNA expression was moderately changed at 4 h after exercise compared with rest (74%, ES = 0.8 ± 1.5).


There were three main findings in this study: a) acute RSE produced a large increase in ACC protein phosphorylation and a moderate increase of CaMK II protein phosphorylation, b) acute RSE induced a large increase in PGC-1α mRNA expression, and c) 4 wk of training induced a moderate change in resting PGC-1α protein abundance.

Acute RSE activates AMPKα and CaMK II signaling pathways

We report for the first time the effects of RSE on the main molecular pathways associated with mitochondrial biogenesis. As hypothesized, despite the extremely low exercise volume (only 60 s), the repetition of maximal-intensity efforts and short recovery periods achieved during RSE activated AMPKα and CaMK II protein signaling. This suggests that intensity plays an important role in the exercise-induced adaptations associated with mitochondrial biogenesis. In support of this, 10 min of exercise at 50% of V˙O2peak followed by 3.5 min at 100% of V˙O2peak increased CaMK II autonomous activity to an extent similar to that measured in response to 40 min of exercise at 76% of V˙O2peak, despite the volume of the latter being more than three times greater (27). However, maximal-intensity exercise does not seem to influence all signaling proteins in the same manner. In a recent study, the effect of 4 × 30-s all-out exercise bouts on protein signaling was investigated (12). The authors detected a similar increase in the phosphorylation of AMPK compared with our study but an approximately sixfold greater phosphorylation of ACC. Because AMPK is mainly triggered by a change in the metabolic status in the skeletal muscle cell, it is of interest to consider the differences in ATP depletion after a 6 × 4-s RSE (30) and one bout of all-out 30-s exercise (12). Both exercises had similar duration and peak power, and both were performed on air-braked cycle ergometers. The two protocols show a similar reduction in muscle ATP content (−16% and −20%, respectively) compared with rest. This might explain the similar increase in AMPKα protein phosphorylation in the study from our group and the one by Gibala et al. (12). However, the marked difference in ACC protein phosphorylation remains undetermined. It has been shown that contraction-induced ACC phosphorylation is AMPK independent in a model of transgenic mice expressing a kinase-dead AMPK (9). Our results suggest that the exercise characteristics, such as intensity, duration, and volume, might be differently involved in the regulation of signaling proteins within this pathway.

Acute RSE increases PGC-1α mRNA expression

Together with the activation of the AMPKα and CaMK II protein signaling, acute RSE was associated with a >200% increase in PGC-1α mRNA expression 4 h after exercise compared with rest. A similar increase in PGC-1α mRNA expression has been shown in response to other low-volume exercise methods, such as combined intermittent-sprint and resistance exercise (5) and repeated 30-s all-out efforts (12). The study by Gibala et al. (12) also reported, similarly to our study, an unchanged PGC-1α protein abundance 3 h after exercise. These results might depend on the time course of PGC-1α protein synthesis. In fact, PGC-1α protein abundance was elevated 24 h but not 4 h after an exercise comprising 10 × 4-min cycling at 90% of V˙O2peak (25). The absence of an increase in PGC-1α protein abundance in our study does not signify an interruption in the sequence of molecular events. In fact, 4 × 30-s all-out efforts increased the nuclear abundance but not the whole-muscle abundance of PGC-1α protein at 3 h after exercise (18), suggesting an important role for the protein localization. Also, other types of posttranslational modification of PGC-1α protein, such as phosphorylation (16) and deacetylation (4), might be of more importance in the short-term adaptation to exercise rather than the simple increase in abundance. It is acknowledged that the specificity of the PGC-1α antibody represents an area of current discussion, and the use of different commercially available antibodies might partly explain the differences in the results obtained from different research groups. In the present study, we detected a band of a molecular weight of approximately 100 kDa.

In this study, we also report for the first time an increased phosphorylation of HDAC5 protein in response to RSE. At rest, transcription is inhibited by the association of HDAC5 with MEF2 transcription factor (20). During exercise, AMPK and CaMK II phosphorylate HDAC5 causing its export from the nucleus, ultimately allowing gene transcription to occur (22). Sixty minutes of stimulation with 5-aminoimidazole-4-carboxamide-1-β-D-ribonucleoside induced a ∼65% increase of HDAC5 phosphorylation at Ser259 in human primary myotubes (23). The fact that the magnitude of the change reported in that study is similar to the change that we observed suggests that the intensity of exercise might be more important than its duration or volume. This is supported by the observation that 36 min of cycling at ∼80% of V˙O2peak induced a phosphorylation of HDAC 4/5/7 that was almost double that after a 70-min isocaloric exercise performed at ∼40% of V˙O2peak (10). Conversely, we did not find any increase in the abundance of RIP 140 protein after acute or chronic RSE. This is of interest considering that research conducted by our group shows a sevenfold increase in the abundance of RIP 140 in response to 1 h of exercise at 70% of V˙O2peak (unpublished data, NKS, 2012). Further research is required for a comprehensive understanding of the role of repressors in the exercise-induced molecular adaptations, with particular focus on exercise intensity and volume.

Effects of repeated-sprint training

One of the findings of this study is that short-term repeated-sprint training resulted in a moderate increase in PGC-1α resting protein abundance. The training protocol used in this research comprised only 12 min of RSE, probably representing an unprecedented low training volume in an experimental study. It is of interest to compare our results with those from similar exercise protocols. Other intermittent training interventions, all with an exercise volume higher than our protocol, returned discordant results. On one hand, 2 wk (six sessions) of training comprising 8–12 × 60-s cycling at 100% of the peak power output did not change PGC-1α protein abundance at rest (19), whereas 18 sessions of 10 × 4-min exercise at 90% of V˙O2peak produced only a 16% increase (13). On the other hand, 6 wk (18 sessions) of 4–6 × 30-s all-out efforts resulted in a ∼100% increase in PGC-1α abundance, an adaptation comparable to that induced by endurance training with 10 times higher volume (3). This suggests that the mechanisms of molecular adaptations to repeated-sprint training, in particular those regarding the role of PGC-1α, might be more complex than a simple dose–response concept. Supportive to this is the analysis of the molecular adaptations induced by acute RSE after training compared with before training. Despite that the change in the abundance/phosphorylation of almost all proteins considered was blunted in response to acute exercise after training, the magnitude of PGC-1α mRNA expression was fully preserved. This might signify that only small changes in signaling are required to maintain PGC-1α mRNA gene expression in a trained muscle.


This study shows for the first time the effects of acute and chronic RSE, with short maximal sprints and brief recovery, on the molecular events associated with mitochondrial biogenesis. As hypothesized, acute RSE was capable of altering the signaling pathways dependent on the metabolic state and the Ca2+ signaling in the skeletal muscle cell, in turn, increasing PGC-1α mRNA expression. The fact that such a low-volume intervention is capable of altering the signaling pathways and the PGC-1α mRNA expression in the skeletal muscle is, without a doubt, of interest. Possibly as a result of the repeated acute effect of exercise, a moderate increase in PGC-1α protein abundance was detected after training. However, the changes in PGC-1α mRNA expression and protein abundance do not seem to be sufficient to promote beneficial adaptations in the systemic aerobic capacity, as highlighted by the absence of changes in V˙O2peak and [Lac] threshold/onset of blood [Lac] accumulation.

Despite these changes described previously, the trivial-to-small changes of many genes and proteins observed in this study, either before or after the training intervention, are surprising. On one hand, the small changes in the expression of the transcription factor genes after acute RSE and in the abundance of COX IV protein after training are in contrast with the hypothesis that RSE would produce an ideal stimulus for mitochondrial biogenesis. However, the observation of a greater COX IV mRNA expression after acute RSE after training compared with before training, together with a preserved increase in PGC-1α mRNA expression, is encouraging to extend on research regarding the effects of low-volume high-intensity physical activity on muscle adaptations associated with mitochondrial adaptations. In particular, further investigation is required to understand whether an RSE intervention alone, perhaps with a greater training volume, might be sufficient to induce mitochondrial biogenesis in skeletal muscle or whether RSE can be more effective in combination with other physical activity regimens to even better mimic the activity patterns of team sports.

Fabio R. Serpiello was supported by a 2008 Endeavour Europe Award (no. 407_2008), granted by the Department of Education, Employment and Workplace Relations, Australia. Dr. Nigel Stepto is supported by a National Health and Medical Research Council grant (no. 606553).

The authors thank Mr. Bradley Gatt for invaluable assistance during the project and Ms. Victoria Wyckelsma, Ms. Emma Gallaher, Ms. Collene Steward, and Mr. Ed MacDonald for their help with data collection.

No funding was received for the present study.

The authors declare that they have no conflict of interest.

The experiments in this study were performed in accordance with the laws of the country in which the study was conducted.

The results of this study do not constitute endorsement by the American College of Sports Medicine.


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