Analysis of global mRNA expression in human skeletal muscle during recovery from endurance exercise
Bibliographic record
Abstract
To search for novel transcriptional pathways that are activated in skeletal muscle after endurance exercise, we used cDNA microarrays to measure global mRNA expression after an exhaustive bout of high-intensity cycling (approximately 75 min). Healthy, young, sedentary males performed the cycling bout, and skeletal muscle biopsies were taken from the vastus lateralis before, and at 3 and 48 h after exercise. We examined mRNA expression in individual muscle samples from four subjects using cDNA microarrays, used repeated-measures significance analysis of microarray (SAM) to determine statistically significant expression changes, and confirmed selected results using real-time RT-PCR. In total, the expression of 118 genes significantly increased 3 h postcycling and 8 decreased. At 48 h, the expression of 29 genes significantly increased and 5 decreased. Many of these are potentially important novel genes involved in exercise recovery and adaptation, including several involved in 1) metabolism and mitochondrial biogenesis (FOXO1, PPARdelta, PPARgamma, nuclear receptor binding protein 2, IL-6 receptor, ribosomal protein L2, aminolevulinate delta-synthase 2); 2) the oxidant stress response (metalothioneins 1B, 1F, 1G, 1H, 1L, 2A, 3, interferon regulatory factor 1); and 3) electrolyte transport across membranes [Na+-K+-ATPase (beta3), SERCA3, chloride channel 4]. Others include genes involved in cell stress, proteolysis, apoptosis, growth, differentiation, and transcriptional activation, as well as all three nuclear receptor subfamily 4A family members (Nur77, Nurr1, and Nor1). This study is the first to characterize global mRNA expression during recovery from endurance exercise, and the results provide potential insight into 1) the transcriptional contributions to homeostatic recovery in human skeletal muscle after endurance exercise, and 2) the transcriptional contributions from a single bout of endurance exercise to the adaptive processes that occur after a period of endurance exercise training.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".