Anti-Inflammatory Role of the cAMP Effectors Epac and PKA: Implications in Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
Cigarette smoke-induced release of pro-inflammatory cytokines including interleukin-8 (IL-8) from inflammatory as well as structural cells in the airways, including airway smooth muscle (ASM) cells, may contribute to the development of chronic obstructive pulmonary disease (COPD). Despite the wide use of pharmacological treatment aimed at increasing intracellular levels of the endogenous suppressor cyclic AMP (cAMP), little is known about its exact mechanism of action. We report here that next to the β(2)-agonist fenoterol, direct and specific activation of either exchange protein directly activated by cAMP (Epac) or protein kinase A (PKA) reduced cigarette smoke extract (CSE)-induced IL-8 mRNA expression and protein release by human ASM cells. CSE-induced IκBα-degradation and p65 nuclear translocation, processes that were primarily reversed by Epac activation. Further, CSE increased extracellular signal-regulated kinase (ERK) phosphorylation, which was selectively reduced by PKA activation. CSE decreased Epac1 expression, but did not affect Epac2 and PKA expression. Importantly, Epac1 expression was also reduced in lung tissue from COPD patients. In conclusion, Epac and PKA decrease CSE-induced IL-8 release by human ASM cells via inhibition of NF-κB and ERK, respectively, pointing at these cAMP effectors as potential targets for anti-inflammatory therapy in COPD. However, cigarette smoke exposure may reduce anti-inflammatory effects of cAMP elevating agents via down-regulation of Epac1.
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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 | 0.001 | 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".