Acute exercise activates AMPK and eNOS in the mouse aorta
Bibliographic record
Abstract
Exercise can prevent endothelial cell (EC) dysfunction and atherosclerosis even in the absence of improvements in plasma lipids. However, the mechanisms responsible for these effects are incompletely understood. In this study we examined in mice whether an acute bout of exercise activates enzymes that could prevent EC dysfunction, such as AMP-activated protein kinase (AMPK) and endothelial nitric oxide synthase (eNOS). We also examined whether exercise alters known regulators of these enzymes. C57BL/6 mice underwent a single bout of exhaustive treadmill exercise after which their aortas were analyzed for activation of AMPK, AMPK regulatory proteins, eNOS, and various enzymes that, like AMPK, activate eNOS. We found that such exercise acutely activates both AMPK and eNOS in the whole aorta and that the magnitude of these effects correlated with both the distance run and activation of the AMPK regulatory proteins silent information regulator-1 (SIRT1)-LKB1 and CaMKKβ. In contrast, Akt, PKA, PKG, and Src, other kinases known to activate eNOS, were unaffected. Immunohistochemical analysis revealed that AMPK and eNOS were both activated in the ECs of the aorta. This study provides the first evidence that an acute bout of exercise activates AMPK and eNOS in the endothelium of the aorta. The results also suggest that AMPK likely is the principal activator of eNOS in this setting and that its own activation may be mediated by both SIRT1-LKB1 and CaMKKβ.
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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 | 0.001 |
| 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".