Expression of sestrins in skeletal muscle with acute exercise and aging
Bibliographic record
Abstract
Regular exercise can maintain skeletal muscle mass as we age. However, the molecular mechanisms that contribute to muscle health with exercise are incompletely defined. Recently, a group of three p53‐regulated proteins called Sestrins (Sesn1–3) have been identified. Whether these proteins are altered with age, or are induced by acute exercise in muscle is currently unknown. Thus, the purposes of our investigation were to determine: 1) if Sesns are expressed in the presence or absence of p53 after acute exercise, and 2) whether the expression of Sesns is altered in aging muscle. p53 wild‐type (WT) and knock‐out (KO) mice were run on a treadmill for 90mins and either sacrificed immediately or allowed to recover for 3hrs. Basally, muscle from p53 KO animals had 27% lower mRNA expression of Sesn2, while Sesn1 mRNA was increased by 2.3‐fold compared to WT animals. Acute exercise had no effect on Sesn2 expression, but resulted in a 1.9‐fold increase in Sesn1 which was evident in the recovery phase, but only in WT animals. To examine the effect of age, muscle from 6‐ and 36‐month old rats was analyzed. Aged animals displayed 71% and 26% reductions in Sesn1 and Sesn2 mRNA, respectively, compared to younger animals. Similar trends were observed at the protein level. Our results suggest that Sesn expression is reduced with age, and that Sesn1 can be induced by acute exercise in a p53‐dependent manner. 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) | 0.001 | 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".