Both Protein Kinase A and Exchange Protein Activated by cAMP Coordinate Adhesion of Human Vascular Endothelial Cells
Bibliographic record
Abstract
cAMP regulates integrin-dependent adhesions of vascular endothelial cells (VECs) to extracellular matrix proteins, their vascular endothelial cadherin-dependent intercellular adhesions, and their proliferation and migration in response to growth and chemotactic factors. Previously, we reported that cAMP-elevating agents differentially inhibited migration of human VECs isolated from large vascular structures (macro-VECs, human aortic endothelial cells [HAECs]) or small vascular structures (micro-VECs, human microvascular endothelial cells [HMVECs]) and that cAMP hydrolysis by phosphodiesterase (PDE)3 and PDE4 enzymes was important in coordinating this difference. Here we report that 2 cAMP-effector enzymes, namely protein kinase (PK)A and exchange protein activated by cAMP (EPAC), each regulate extracellular matrix protein-based adhesions of both macro- and micro-VECs. Of interest and potential therapeutic importance, we report that although specific pharmacological activation of EPAC markedly stimulated adhesion of micro-VECs to extracellular matrix proteins when PKA was inhibited, this treatment only modestly promoted adhesion of macro-VECs. Consistent with an important role for cAMP PDEs in this difference, PDE3 or PDE4 inhibitors promoted EPAC-dependent adhesions in micro-VECs when PKA was inhibited but not in macro-VECs. At a molecular level, we identify multiple, nonoverlapping, PKA- or EPAC-based signaling protein complexes in both macro- and micro-VECs and demonstrate that each of these complexes contains either PDE3B or PDE4D but not both of these PDEs. Taken together, our data support the concept that adhesion of macro- and micro-VECs is differentially regulated by cAMP and that these differences are coordinated through selective actions of cAMP at multiple nonoverlapping signaling complexes that contain PKA or EPAC and distinct PDE variants.
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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".