Myoferlin Regulates Vascular Endothelial Growth Factor Receptor-2 Stability and Function
Bibliographic record
Abstract
Myoferlin and dysferlin are members of the ferlin family of membrane proteins. Recent studies have shown that mutation or genetic disruption of myoferlin or dysferlin promotes muscular dystrophy-related phenotypes in mice, which are the result of impaired plasma membrane integrity. However, no biological functions have been ascribed to myoferlin in non-muscle tissues. Herein, using a proteomic analysis of endothelial cell (EC) caveolae/lipid raft microdomains we identified myoferlin in these domains and show that myoferlin is highly expressed in ECs and vascular tissues. The loss of myoferlin results in lack of proliferation, migration, and nitric oxide (NO) release in response to vascular endothelial growth factor (VEGF). Western blotting and surface biotinylation experiments show that loss of myoferlin reduces the expression level and autophosphorylation of VEGF receptor-2 (VEGFR-2) in native ECs. In a reconstituted cell system, transfection of myoferlin increases VEGFR-2 membrane expression and autophosphorylation in response to VEGF. In vivo, VEGFR-2 levels and VEGF-induced permeability are impaired in myoferlin-deficient mice. Mechanistically, myoferlin forms a complex with dynamin-2 and VEGFR-2, which prevents CBL-dependent VEGFR-2 polyubiquitination and proteasomal degradation. These data are the first to report novel biological activities for myoferlin and reveal the role of membrane integrity to VEGF signaling.
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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".