cAMP‐signaling via EPAC1 mediates vascular endothelial cell adaptation to fluid‐shear stress
Bibliographic record
Abstract
Herein, we report a novel role for cAMP‐signaling in human aortic endothelial cell (HAEC) adaptation to fluid‐mediated shear stress (FSS). Our results showed that increases in cAMP enhanced elongation and alignment of HAECs cultured under LSS for 48hrs and that this effect was largely mediated by the cAMP‐effector molecule, exchange protein activated by cAMP (EPAC)‐1, but not Protein Kinase A (PKA). Additionally, major HAEC cAMP‐hydrolyzing enzyme, phosphodiesterase (PDE)4D, was also shown to play a crucial role in integrating FSS signals in these cells. Our results demonstrated that flow‐mediated increases in atheroprotective genes, Kruppel‐like factor, endothelial nitric oxide synthase and thrombomodulin mRNA expression were reduced in EPAC‐1 and PDE4D knockdown HAECs exposed to both LSS and HSS. Furthermore, EPAC‐1 knockdown increased leukocyte extravasation across HAEC monolayers in vitro and activation of EPAC significantly reduced LPS‐induced leukocyte rolling flux in mice. VECs have numerous mechanosensors which facilitate the integration of FSS signals from the flowing blood. One such sensor consists of platelet‐endothelial cell adhesion molecule (PECAM)‐1, vascular endothelial‐cadherin (VECAD) and vascular endothelial growth factor receptor (VEFGR)‐2. Our current data suggests that EPAC1 and PDE4D can additionally mediate flow‐sensing through this mechanosensory complex. All together, this data provides important mechanistic insight providing therapeutic targets in endothelial dysfunction and atherosclerosis.
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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) | 0.001 | 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".