Epac1 and Epac2 are differentially involved in inflammatory and remodeling processes induced by cigarette smoke
Bibliographic record
Abstract
Cigarette smoke (CS) induces inflammatory responses characterized by increase of immune cells and cytokine release. Remodeling processes, such as mucus hypersecretion and extracellular matrix protein production, are also directly or indirectly induced by CS. Recently, we showed that activation of the exchange protein directly activated by cAMP (Epac) attenuates CS extract‐induced interleukin (IL)‐8 release from cultured airway smooth muscle cells. Using an acute, short‐term model of CS exposure, we now studied the role of Epac1, Epac2, and the Epac effector phospholipase‐Cε (PLCε) in airway inflammation and remodeling in vivo. Compared to wild‐type mice exposed to CS, the number of total inflammatory cells, macrophages, and neutrophils and total IL‐6 release was lower in Epac2 ‐/‐ mice, which was also the case for neutrophils and IL‐6 in PLCε ‐/‐ mice. Taken together, Epac2, acting partly via PLCε, but not Epac1, enhances CS‐induced airway inflammation in vivo. In total lung homogenates of Epac1 ‐/‐ mice, MUC5AC and matrix remodeling parameters (transforming growth factor‐β1, collagen I, and fibronectin) were increased at baseline. Our findings suggest that Epac1 primarily is capable of inhibiting remodeling processes, whereas Epac2 primarily increases inflammatory processes in vivo. —Oldenburger, A., Timens, W., Bos, S., Smit, M., Smrcka, A. V., Laurent, A‐C., Cao, J., Hylkema, M., Meurs, H., Maarsingh, H., Lezoualc'h, F., and Schmidt, M., Epac1 and Epac2 are differentially involved in inflammatory and remodeling processes induced by cigarette smoke. FASEB J. 28, 4617–4628 (2014). www.fasebj.org
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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".