Exchange protein activated by cyclic-adenosine monophosphate (Epac) regulates atrial fibroblast function and controls cardiac remodelling
Bibliographic record
Abstract
Aims: Heart failure (HF) produces left atrial (LA)-selective fibrosis and promotes atrial fibrillation. HF also causes adrenergic activation, which contributes to remodelling via a variety of signalling molecules, including the exchange protein activated by cAMP (Epac). Here, we evaluate the effects of Epac1-signalling on LA fibroblast (FB) function and its potential role in HF-related atrial remodelling. Methods and results: HF was induced in adult male mongrel dogs by ventricular tachypacing (VTP). Epac1-expression decreased in LA-FBs within 12 h (-3.9-fold) of VTP onset. The selective Epac activator, 8-pCPT (50 µM) reduced, whereas the Epac blocker ESI-09 (1 µM) enhanced, collagen expression in LA-FBs. Norepinephrine (1 µM) decreased Epac1-expression, an effect blocked by prazosin, and increased FB collagen production. The β-adrenoceptor (AR) agonist isoproterenol increased Epac1 expression, an effect antagonized by ICI (β2-AR-blocker), but not by CGP (β1-AR-blocker). β-AR-activation with isoproterenol decreased collagen expression, an effect mimicked by the β2-AR-agonist salbutamol and blocked by the Epac1-antagonist ESI-09. Transforming growth factor-β1, known to be activated in HF, suppressed Epac1 expression, an effect blocked by the Smad3-inhibitor SIS3. To evaluate effects on atrial fibrosis in vivo, mice subjected to myocardial infarction (MI) received the Epac-activator Sp-8-pCPT or vehicle for 2 weeks post-MI; Sp-8-pCPT diminished LA fibrosis and attenuated cardiac dysfunction. Conclusions: HF reduces LA-FB Epac1 expression. Adrenergic activation has complex effects on FBs, with α-AR-activation suppressing Epac1-expression and increasing collagen expression, and β2-AR-activation having opposite effects. Epac1-activation reduces cardiac dysfunction and LA fibrosis post-MI. Thus, Epac1 signalling may be a novel target for the prevention of profibrillatory cardiac remodelling.
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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".