Repeatability of exercise‐induced changes in mRNA expression and technical considerations for qPCR analysis in human skeletal muscle
Bibliographic record
Abstract
New Findings What is the central question of this study? Are individual changes in exercise‐induced mRNA expression repeatable (i.e. representative of the true response to exercise rather than random error)? What is the main finding and its importance? Exercise‐induced changes in mRNA expression are not repeatable even under identical experimental conditions, thereby challenging the use of mRNA expression as a biomarker of adaptive potential and/or individual responsiveness to exercise. Abstract It remains unknown if (1) the observed change in mRNA expression reflects an individual's true response to exercise or random (technical and/or biological) error, and (2) the individual responsiveness to exercise is protocol‐specific. We examined the repeatability of skeletal muscle PGC‐1α, PDK4, NRF‐1, VEGF‐A, HSP72 and p53 mRNA expression following two identical endurance exercise (END) bouts (END‐1, END‐2; 30 min of cycling at 65% of peak work rate (WRpeak), n = 11) and inter‐individual variability in PGC‐1α and PDK4 mRNA expression following END and sprint interval training (SIT; 8 × 20 s cycling intervals at ∼170% WRpeak, n = 10) in active young males. The repeatability of key gene analysis steps (RNA extraction, reverse transcription, qPCR) and within‐sample fibre‐type distribution (n = 8) was also determined to examine potential sources of technical error in our analyses. Despite highly repeatable exercise bout characteristics (work rate, heart rate, blood lactate; ICC > 0.71; CV < 10%; r > 0.85, P < 0.01), gene analysis steps (ICC > 0.73; CV < 24%; r > 0.75, P < 0.01), and similar group‐level changes in mRNA expression, individual changes in PGC‐1α, PDK4, VEGF‐A and p53 mRNA expression were not repeatable (ICC < 0.22; CV > 20%; r < 0.21). Fibre‐type distribution in two portions of the same muscle biopsy was highly variable and not significantly related (ICC = 0.39; CV = 26%; r = 0.37, P = 0.37). Since individual changes in mRNA expression following identical exercise bouts were not repeatable, inferences regarding individual responsiveness to END or SIT were not made. Substantial random error exists in changes in mRNA expression following acute exercise, thereby challenging the use of mRNA expression for analysing individual responsiveness to exercise.
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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 | 0.037 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".