The effect of exercise and beta<sub>2</sub>-adrenergic stimulation on glutathionylation and function of the Na,K-ATPase in human skeletal muscle
Bibliographic record
Abstract
Potassium and sodium displacements across the skeletal muscle membrane during exercise may cause fatigue and are in part controlled by the Na,K-ATPase. Regulation of the Na,K-ATPase is therefore important for muscle functioning. We investigated the effect of oxidative stress (glutathionylation) on Na,K-ATPase activity. Ten male subjects performed three bouts of 4-min submaximal exercise followed by intense exercise to exhaustion with and without beta2-adrenergic stimulation with terbutaline. Muscle biopsies were obtained from m. vastus lateralis at rest (Control samples) and at exhaustion. In vitro glutathionylation reduced (P < 0.05) maximal Na,K-ATPase activity in a dose-dependent manner. Na,K-ATPase α subunits, purified by immunoprecipitation and tested by glutathione (GSH) antibodies, had a basal glutathionylation in Control samples and no further glutathionylation with exercise and beta2-adrenergic stimulation. Immunoprecipitation with an anti-GSH antibody and subsequent immunodetection with β1 antibodies showed approximately 20% glutathionylation in Control samples and further glutathionylation after exercise (to 32%) and beta2-adrenergic stimulation (to 38%, P < 0.05). Combining exercise and beta2-adrenergic stimulation raised the β1 glutathionylation to 45% (P < 0.05). In conclusion, both α and β1 subunits of the Na,K-ATPase were glutathionylated in Control samples, which indicates that the maximal Na,K-ATPase activity is overestimated if based on protein density only. β1 subunits are further glutathionylated by exercise and beta2-adrenergic stimulation. Our data suggest that glutathionylation contributes to the complex regulation of Na,K-ATPase function in human skeletal muscle. Glutathionylation of the Na,K-ATPase may explain reductions in maximal Na,K-ATPase activity after exercise, which may be involved in muscle fatigue.
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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".