Autophagic Signaling and Proteolytic Enzyme Activity in Cardiac and Skeletal Muscle of Spontaneously Hypertensive Rats following Chronic Aerobic Exercise
Bibliographic record
Abstract
Hypertension is a cardiovascular disease associated with deleterious effects in skeletal and cardiac muscle. Autophagy is a degradative process essential to muscle health. Acute exercise can alter autophagic signaling. Therefore, we aimed to characterize the effects of chronic endurance exercise on autophagy in skeletal and cardiac muscle of normotensive and hypertensive rats. Male Wistar Kyoto (WKY) and spontaneously hypertensive rats (SHR) were assigned to a sedentary condition or 6 weeks of treadmill running. White gastrocnemius (WG) of hypertensive rats had higher (p<0.05) caspase-3 and proteasome activity, as well as elevated calpain activity. In addition, skeletal muscle of hypertensive animals had elevated (p<0.05) ATG7 and LC3I protein, LAMP2 mRNA, and cathepsin activity, indicative of enhanced autophagic signaling. Interestingly, chronic exercise training increased (p<0.05) Beclin-1, LC3, and p62 mRNA as well as proteasome activity, but reduced (p<0.05) Beclin-1 and ATG7 protein, as well as decreased (p<0.05) caspase-3, calpain, and cathepsin activity. Left ventricle (LV) of hypertensive rats had reduced (p<0.05) AMPKα and LC3II protein, as well as elevated (p<0.05) p-AKT, p-p70S6K, LC3I and p62 protein, which collectively suggest reduced autophagic signaling. Exercise training had little effect on autophagy-related signaling factors in LV; however, exercise training increased (p<0.05) proteasome activity but reduced (p<0.05) caspase-3 and calpain activity. Our results suggest that autophagic signaling is altered in skeletal and cardiac muscle of hypertensive animals. Regular aerobic exercise can effectively alter the proteolytic environment in both cardiac and skeletal muscle, as well as influence several autophagy-related factors in skeletal muscle of normotensive and hypertensive rats.
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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 | 0.001 |
| 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".