Myosin phosphorylation potentiated steady state work output without altering contractile economy of mouse fast skeletal muscles
Bibliographic record
Abstract
Skeletal myosin light chain kinase (skMLCK) catalyzed phosphorylation of the myosin regulatory light chain (RLC) increases (i.e. potentiates) mechanical work output of fast skeletal muscle. The influence of this event on contractile economy (i.e. energy cost/work performed) remains controversial, however. Our purpose was to quantify contractile economy of potentiated extensor digitorum longus (EDL) muscles from mouse skeletal muscles with (wildtype, WT) and without (skMLCK ablated, skMLCK−/−) the ability to phosphorylate the RLC. Contractile economy was calculated as the ratio of total work performed to high-energy phosphate consumption (HEPC) during a period of repeated isovelocity contractions that followed a potentiating stimulus (PS). Consistent with genotype, the PS increased RLC phosphorylation measured during before and after isovelocity contractions in WT but not skMLCK−/− muscles (i.e. 0.65 and 0.05 mol phos mol RLC, respectively). In addition, although the PS enhanced work during repeated isovelocity contractions in both genotypes the increase was significantly greater in WT than in skMLCK−/− muscles (1.51±0.03 vs. 1.10±0.05, respectively) (all data P<0.05, n=8). Interestingly, the HEPC determined during repeated isovelocity contractions was statistically similar between genotypes at 19.03±3.37 and 16.02±3.41 µmol∼P; respectively (P<0.27). As a result, despite performing significantly more work, the contractile economy calculated for WT muscles was similar to that calculated for skMLCK−/− muscles (i.e. 5.74±0.67 and 4.61±0.71 J•kg−1μmol∼P−1; respectively (P<0.27). In conclusion, our results support the notion that myosin RLC phosphorylation enhances dynamic contractile function of mouse fast skeletal muscle but does so without decreasing contractile economy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".