Modulation of autophagy signaling with resistance exercise and protein ingestion following short-term energy deficit
Bibliographic record
Abstract
Autophagy contributes to remodeling of skeletal muscle and is sensitive to contractile activity and prevailing energy availability. We investigated changes in targeted genes and proteins with roles in autophagy following 5 days of energy balance (EB), energy deficit (ED), and resistance exercise (REX) after ED. Muscle biopsies from 15 subjects (8 males, 7 females) were taken at rest following 5 days of EB [45 kcal·kg fat free mass (FFM)(-1)·day(-1)] and 5 days of ED (30 kcal·kg FFM(-1)·day(-1)). After ED, subjects completed a bout of REX and consumed either placebo (PLA) or 30 g whey protein (PRO) immediately postexercise. Muscle biopsies were obtained at 1 and 4 h into recovery in each trial. Resting protein levels of autophagy-related gene protein 5 (Atg5) decreased after ED compared with EB (∼23%, P < 0.001) and remained below EB from 1 to 4 h postexercise in PLA (∼17%) and at 1 h in PRO (∼18%, P < 0.05). In addition, conjugated Atg5 (cAtg12) decreased below EB in PLA at 4 h (∼20, P < 0.05); however, its values were increased above this time point in PRO at 4 h alongside increases in FOXO1 above EB (∼22-26%, P < 0.05). Notably, these changes were subsequent to increases in unc-51-like kinase 1(Ser757) phosphorylation (∼60%) 1 h postexercise in PRO. No significant changes in gene expression of selected autophagy markers were found, but EGR-1 increased above ED and EB in PLA (∼417-864%) and PRO (∼1,417-2,731%) trials 1 h postexercise (P < 0.001). Postexercise protein availability, compared with placebo, can selectively promote autophagic responses to REX in ED.
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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 | 0.000 | 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) | 0.001 | 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".