EPAC activation inhibits acetaldehyde-induced activation and proliferation of hepatic stellate cell via Rap1
Bibliographic record
Abstract
Hepatic stellate cells (HSCs) activation represents an essential event during alcoholic liver fibrosis (ALF). Previous studies have demonstrated that the rat HSCs could be significantly activated after exposure to 200 μmol/L acetaldehyde for 48 h, and the cAMP/PKA signaling pathways were also dramatically upregulated in activated HSCs isolated from alcoholic fibrotic rat liver. Exchange protein activated by cAMP (EPAC) is a family of guanine nucleotide exchange factors (GEFs) for the small Ras-like GTPases Rap, and is being considered as a vital mediator of cAMP signaling in parallel with the principal cAMP target protein kinase A (PKA). Our data showed that both cAMP/PKA and cAMP/EPAC signaling pathways were involved in acetaldehyde-induced HSCs. Acetaldehyde could reduce the expression of EPAC1 while enhancing the expression of EPAC2. The cAMP analog Me-cAMP, which stimulates the EPAC/Rap1 pathway, could significantly decrease the proliferation and collagen synthesis of acetaldehyde-induced HSCs. Furthermore, depletion of EPAC2, but not EPAC1, prevented the activation of HSC measured as the production of α-SMA and collagen type I and III, indicating that EPAC1 appears to have protective effects on acetaldehyde-induced HSCs. Curiously, activation of PKA or EPAC perhaps has opposite effects on the synthesis of collagen and α-SMA: EPAC activation by Me-cAMP increased the levels of GTP-bound (activated) Rap1 while PKA activation by Phe-cAMP had no significant effects on such binding. These results suggested that EPAC activation could inhibit the activation and proliferation of acetaldehyde-induced HSCs via Rap1.
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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) | 0.002 | 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".