Abstract 487: EBP50 Promotes Epidermal Growth Factor-Dependent Activation of Focal Adhesion Kinase and Vascular Smooth Muscle Cells Migration
Bibliographic record
Abstract
The Ezrin-Radixin-Moesin-Binding Phosphoprotein 50 (EBP50) is a scaffolding protein that regulates a variety of physiological functions. Previous studies showed that EBP50 promotes vascular smooth muscle cells (VSMC) proliferation and neointima formation following arterial injury. In this study the role of EBP50 on VSMC migration was characterized. VSMC migration was determined by wound-healing assays. The motility of primary VSMC isolated from EBP50 KO mice was significantly reduced compared to WT cells (25 um/h WT vs. 19 um/h KO, p<0.05) resulting in a 50% reduction in wound coverage. Conversely, EBP50 expression increased VSMC migration. EBP50-null VSMC had fewer and larger focal adhesions than EBP50-expressing cells. Both assembly and disassembly of focal adhesion in response to epidermal growth factor (EGF) (determined by real time TIRF microscopy) were significantly reduced in KO cells. Immunoprecipitation experiments showed that EBP50 interacts with both focal adhesion kinase (FAK) and EGF receptor (EGFR) and the formation of a complex containing both EGFR and FAK was increased by EBP50. Stimulation of VSMC with EGF induced FAK phosphorylation on Tyr925 in WT, but not in KO cell, whereas phosphorylation of Tyr397 was unaffected. Collectively, these observations indicate that EBP50 facilitates growth factors-dependent activation of FAK, and consequently, migration of VSMC. Therefore, in addition to increasing VSMC proliferation, EBP50 promotes neointima formation following arterial injury by increasing VSMC motility.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".