Cyclic AMP-independent Activation of Protein Kinase A by Vasoactive Peptides
Bibliographic record
Abstract
Protein kinase A (PKA) is an important effector enzyme commonly activated by cAMP. The present study focuses on our finding that the vasoactive peptide endothelin-1 (ET1), whose signaling is not coupled to cAMP production, stimulates PKA in two independent cellular models. Using an in vivo assay for PKA activity, we found that ET1 stimulated PKA in HeLa cells overexpressing ET1 receptors and in aortic smooth muscle cells expressing endogenous levels of ET1 receptors. In these cell models, ET1 did not stimulate cAMP production, indicating a novel mechanism for PKA activation. The ET1-induced activation of PKA was found to be dependent on the degradation of inhibitor of kappaB, which was previously reported to bind and inhibit PKA. ET1 potently stimulated the nuclear factor-kappaB pathway, and this effect was inhibited by overexpression of the inhibitor of kappaB dominant negative mutant (IkappaBalpham) and by treatment with the proteasome inhibitor MG-132. Importantly, IkappaBalpham and MG-132 had similar inhibitory effects on ET1-induced activation of PKA without affecting G(s)-mediated activation of PKA or ET1-induced phosphorylation of mitogen-activated protein kinase. Finally, another vasoactive peptide, angiotensin II, also stimulated PKA in a cAMP-independent manner in aortic smooth muscle cells. These findings suggest that cAMP-independent activation of PKA might be a general response to vasoactive peptides.
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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) | 0.001 | 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".