Abstract 221: PDGF-Mediated Activation of CREB in Vascular Smooth Muscle Cells Alters Cell Cycling via Rb, p27kip1 and Fox01
Bibliographic record
Abstract
Introduction: Vascular injury initiates signals which lead to the secretion of humoral factors and ultimately to vessel repair. PDGF is one such factor that promotes conversion of smooth muscle cells (SMC) from their contractile state to the synthetic phenotype, which is characterized by the ability to migrate and proliferate. If not properly controlled, such changes can lead to neointimal hyperplasia or restenosis. As CREB has been shown to participate in vascular remodeling, it is plausible that it is an important player in PDGF-mediated phenotypic switch. Hypothesis and aim: We assessed the hypothesis that a relationship exists between CREB and PDGF-dependent proliferation of coronary artery SMCs. We also aimed to identify the pathways both upstream and downstream of CREB activation. Methods: Vascular SMCs from porcine explants and Western blotting were used to monitor protein levels and changes in phosphorylation resulting from various treatments. Multiple cell growth methods were applied to study cell proliferation. A dominant-negative CREB mutant and constitutive expression of p27kip1 were used to assess the involvement of these proteins in mediating the actions of PDGF. Results: Phosphorylation of CREB by PDGF was dependent on both Src and PI3 kinase, and partially dependent on MAPK. Dominant-negative CREB decreased PDGF-dependent cell proliferation by approximately 75%, and both PCNA expression and Ser-780 phosphorylation of Rb were inhibited. While no change in either cyclin D or cdk4 levels was observed, PCNA and Rb mediate the signals transduced through CREB. These cell cycle proteins likely controlled via p27kip1 expression in response to CREB-dependent Fox01 acetylation. Conclusion: CREB phosphorylation is required for SMC proliferation in response to PDGF. Additionally, p27kip1 mediates the actions of CREB in SMCs, likely as a result of changes in Fox01 activity.
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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".