Involvement of the Akt-dependent CREB signaling pathway in hydrogen-peroxide-induced early growth response protein-1 expression in rat vascular smooth muscle cells
Bibliographic record
Abstract
Increased generation of reactive oxygen species is believed to play a key role in the pathophysiology of cardiovascular diseases. Excessive growth and proliferation of vascular smooth muscle cells (VSMCs) have been suggested to be major contributors to vascular dysfunction. Potential involvement of early growth response protein-1 (Egr-1), a zinc finger transcription factor, in the development of vascular diseases has been suggested. Recent studies have shown that the reactive oxygen species hydrogen peroxide (H2O2) increases Egr-1 expression in VSMCs; however, signaling events leading to H2O2-induced Egr-1 expression are not fully understood. Therefore, we aimed to determine the signaling pathways implicated in H2O2-induced Egr-1 expression in rat VSMCs. Pharmacological blockade of the phosphatidylinositol 3-kinase/Akt pathway by wortmannin or SC66 significantly inhibited the protein and mRNA levels of Egr-1 induced by H2O2. H2O2-induced Egr-1 expression was associated with increased phosphorylation of cyclic AMP response element-binding (CREB) protein, and pharmacological inhibition or silencing of Akt attenuated both H2O2-induced CREB phosphorylation and Egr-1 expression. Moreover, RNA interference-mediated depletion of CREB almost completely suppressed the stimulatory effect of H2O2 on Egr-1 expression. Pharmacological blockade or silencing of c-Src resulted in significant suppression of H2O2-induced Egr-1 expression as well as Akt and CREB phosphorylation. These data show that H2O2 enhances the expression of Egr-1, which was associated with increased phosphorylation of Akt, and H2O2 triggers its effects on Egr-1 expression through c-Src–mediated Akt and CREB-dependent signaling events in VSMCs.
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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 | 0.001 |
| 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".