Low-intensity Resistance Exercise Stimulates Mitochondria Protein Synthesis And PGC-1α mRNA Expression
Bibliographic record
Abstract
Limited data exists on the acute effects of resistance exercise on rates of mitochondria protein synthesis and mRNA expression of peroxisome proliferator-activated receptor gamma coactivator (PGC)-1α, a key regulator of mitochondrial biogenesis, during post-exercise recovery. PURPOSE: To determine if changes in mitochondrial protein synthesis and PGC-1α mRNA expression after low-intensity resistance exercise are influenced by the time the muscle is under tension (TUT). METHODS: Eight men (23±1 y, BMI= 26.5±1.0 kg·m-2) received a primed, constant infusion of L-[ring-13C6]phenylalanine and biopsies of the vastus lateralis were obtained in the fed (i.e., 20g whey protein) and fasted-states at rest (FAST), 5hFED, 24hFAST, 29hFED post-exercise. Subjects performed 3 sets of unilateral knee extension exercise at 30% 1RM performed at a slow (SLOW) TUT (6-s concentric, 1-s hold, 6-s eccentric) or an external work-matched standard TUT control (1-s concentric, 0-s hold, 1-s eccentric; CTL). RESULTS: Exercise at SLOW and CTL were identical in the number of contractions performed for set 1 (12±1), set 2 (7±0.5), and set 3 (6±0.4). TUT was greater (P<0.05) for all 3 sets in SLOW (set 1: 3.3min; set 2: 2min; set 3: 1.5min) as compared to CTL (set 1: 0.4min; set 2: 0.2min, set 3: 0.2min). Exercise-induced rates of mitochondrial protein synthesis were elevated by 114% above fasted rates at 5hFED only after the SLOW condition; however, at 29hFED both CTL (126%) and SLOW (175%) conditions were elevated above FAST (all P<0.05). PGC-1α mRNA expression was significantly increased above FAST at 5hFED in SLOW (3.1-fold) and CTL (2.7-fold) conditions CONCLUSIONS: TUT affects the acute amplitude (1-5h) of rates of mitochondrial synthesis; however, a carryover effect of resistance exercise, regardless of TUT, exists on mitochondria protein synthesis 24-29h later. Further, PGC-1α may, in part, mediate the response at further time points after resistance exercise. Supported by NSERC
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".