A-Kinase Anchoring Proteins Coordinate Inflammatory Responses To Cigarette Smoke In Airway Smooth Muscle
Bibliographic record
Abstract
beta2-Agonist inhibitors can relieve chronic obstructive pulmonary disease (COPD) symptoms by stimulating cyclic AMP (cAMP) signaling. A-kinase-anchoring proteins (AKAPs) compartmentalize cAMP signaling by establishing protein complexes. We previously reported that the beta2-agonist fenoterol, direct activation of protein kinase A (PKA), and exchange factor directly activated by cAMP decrease cigarette smoke extract (CSE)-induced release of neutrophil attractant interleukin-8 (IL-8) from human airway smooth muscle (ASM) cells. In the present study, we tested the role of AKAPs in CSE-induced IL-8 release from ASM cells and assessed the effect of CSE on the expression levels of different AKAPs. We also studied mRNA and protein expression of AKAPs in lung tissue from patients with COPD. Our data show that CSE exposure of ASM cells decreases AKAP5 and AKAP12, both capable of interacting with beta2-adrenoceptors. In lung tissue of patients with COPD, mRNA levels of AKAP5 and AKAP12 were decreased compared with lung tissue from controls. Using immunohistochemistry, we detected less AKAP5 protein in ASM of patients with COPD Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage II compared with control subjects. St-Ht31, which disrupts AKAP-PKA interactions, augmented CSE-induced IL-8 release from ASM cells and diminished its suppression by fenoterol, an effect mediated by disturbed ERK signaling. The modulatory role of AKAP-PKA interactions in the anti-inflammatory effects of fenoterol in ASM cells and the decrease in expression of AKAP5 and AKAP12 in response to cigarette smoke and in lungs of patients with COPD suggest that cigarette smoke-induced changes in AKAP5 and AKAP12 in patients with COPD may affect efficacy of pharmacotherapy.
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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".