Vascular Endothelial Growth Factor (VEGF)-driven Actin-based Motility Is Mediated by VEGFR2 and Requires Concerted Activation of Stress-activated Protein Kinase 2 (SAPK2/p38) and Geldanamycin-sensitive Phosphorylation of Focal Adhesion Kinase
Bibliographic record
Abstract
In endothelial cells, vascular endothelial growth factor (VEGF) induces an accumulation of stress fibers associated with new actin polymerization and rapid formation of focal adhesions at the ventral surface of the cells. This cytoskeletal reorganization results in an intense motogenic activity. Using porcine endothelial cells expressing one or the other type of the VEGF receptors, VEGFR1 or VEGFR2, or human umbilical vein endothelial cells pretreated with a VEGFR2 neutralizing antibody, we show that VEGFR2 is responsible for VEGF-induced activation of the stress-activated protein kinase-2/p38 (SAPK2/p38), phosphorylation of focal adhesion kinase (FAK), and enhanced migratory activity. Activation of SAPK2/p38 triggered actin polymerization whereas FAK, which was phosphorylated independently of SAPK2/p38, initiated assembly of focal adhesions. Both processes contributed to the formation of stress fibers. Geldanamycin, an inhibitor of HSP90 blocked tyrosine phosphorylation of FAK, assembly of focal adhesions, actin reorganization, and cell migration, all of which were reversed by overexpressing HSP90. We conclude that VEGFR2 mediates the physiological effect of VEGF on cell migration and that two independent pathways downstream of VEGFR2 regulate actin-based motility. One pathway involves SAPK2/p38 and leads to enhanced actin polymerization activity. The other involves HSP90 as a permissive signal transduction factor implicated in FAK phosphorylation and assembly of focal adhesions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".