Physical Integrity of Smooth Muscle Myosin Filaments is Enhanced by Phosphorylation of the Regulatory Myosin Light Chain
Bibliographic record
Abstract
BACKGROUND AND AIMS: Smooth muscle myosin monomers self-assemble in solution to form filaments. Phosphorylation of the 20-kD regulatory myosin light chain (MLC20) enhances filament formation. It is not known whether the phosphorylated and non-phosphorylated filaments possess the same structural integrity. METHODS: We purified myosin from bovine trachealis to form filaments, in ATP-containing zero-calcium solution during a slow dialysis that gradually reduced the ionic strength. Sufficient myosin light chain kinase and phosphatase, as well as calmodulin, were retained after the myosin purification and this enabled phosphorylation of MLC20 within 20-40s after addition of calcium to the filament suspension. The phosphorylated and non-phosphorylated filaments were then partially disassembled by ultrasonification. The extent of filament disintegration was visualized and quantified by atomic force microscopy. RESULTS: MLC20 phosphorylation reduced the diameter of the filaments and rendered the filaments more resistant to ultrasonic agitation. Electron microscopy revealed a similar reduction in filament diameter in intact smooth muscle when the cells were activated. CONCLUSION: Modification of the structural and physical properties of myosin filaments by MLC20 phosphorylation may be a key regulation step in smooth muscle where formation and dissolution of the filaments are required in the cells' adaptation to different cell length.
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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".