A New Role for the Muscle Repair Protein Dysferlin in Endothelial Cell Adhesion and Angiogenesis
Bibliographic record
Abstract
OBJECTIVE: Ferlins are known to regulate plasma membrane repair in muscle cells and are linked to muscular dystrophy and cardiomyopathy. Recently, using proteomic analysis of caveolae/lipid rafts, we reported that endothelial cells (EC) express myoferlin and that it regulates membrane expression of vascular endothelial growth factor receptor 2 (VEGFR-2). The goal of this study was to document the presence of other ferlins in EC. METHODS AND RESULTS: EC expressed another ferlin, dysferlin, and that in contrast to myoferlin, it did not regulate VEGFR-2 expression levels or downstream signaling (nitric oxide and Erk1/2 phosphorylation). Instead, loss of dysferlin in subconfluent EC resulted in deficient adhesion followed by growth arrest, an effect not observed in confluent EC. In vivo, dysferlin was also detected in intact and diseased blood vessels of rodent and human origin, and angiogenic challenge of dysferlin-null mice resulted in impaired angiogenic response compared with control mice. Mechanistically, loss of dysferlin in cultured EC caused polyubiquitination and proteasomal degradation of platelet endothelial cellular adhesion molecule-1 (PECAM-1/CD31), an adhesion molecule essential for angiogenesis. In addition, adenovirus-mediated gene transfer of PECAM-1 rescued the abnormal adhesion of EC caused by dysferlin gene silencing. CONCLUSIONS: Our data describe a novel pathway for PECAM-1 regulation and broaden the functional scope of ferlins in angiogenesis and specialized ferlin-selective protein cargo trafficking in vascular settings.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".