FRET analysis of actin–myosin interaction in contracting rat aortic smooth muscle
Bibliographic record
Abstract
We examined the interaction of smooth muscle myosin with alpha-actin and beta-actin isoforms during the contraction of A7r5 smooth muscle cells and rat aortic smooth muscle. The techniques of confocal microscopy and fluorescence resonance energy transfer (FRET) analysis were utilized in examining A7r5 cells and rat aortic rings contracted with phorbol 12,13-dibutyrate. Visual evaluation of confocal images of A7r5 smooth muscle cells contracted by phorbol 12,13-dibutyrate indicated significant disassociation of myosin from alpha-actin but not beta-actin. Whole-cell FRET analysis confirmed these observations (alpha-actin-myosin -67%, beta-actin-myosin -2%). Time course studies further showed that alpha-actin-myosin complex increased significantly (40%) within 1.5 min after the addition of phorbol 12,13-dibutyrate and then declined as contraction progressed. FRET analysis of rat aortic rings at different intervals of contraction indicated significant increases in alpha-actin-myosin at the initiation (79%) and plateau (67%) in force development, but not during the intermediate period of slowly developing tension (-4%). By comparison, beta-actin-myosin complex was unchanged except during slow force development, in which the association was significantly decreased (-30%). Similar to that of alpha-actin-myosin, Alexa 488 - phalloidin staining fluorescence indicated increased tissue F-actin content at the initiation (21%) and plateau (62%) in force. FRET images indicated the development of thickened cables and patches of alpha-actin-myosin in tissue throughout the interval of contraction. The results provide direct evidence of dynamic remodeling of the contractile protein during vascular smooth muscle contraction and suggest that FRET analysis may be a powerful tool for assessment of tissue protein-protein associations.
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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".