Glycogen synthase kinase-3 regulates cigarette smoke extract- and IL-1β-induced cytokine secretion by airway smooth muscle
Bibliographic record
Abstract
Glycogen synthase kinase-3 (GSK-3) is a constitutively active kinase that regulates multiple signaling proteins and transcription factors involved in inflammation. Its role in inflammatory lung diseases, including chronic obstructive pulmonary disease (COPD), is largely unknown. We investigated the role of GSK-3 in the secretion of chemokines and growth factors by human airway smooth muscle cells after exposure to cigarette smoke extract (CSE) or interleukin-1β (IL-1β), important factors involved in the development of COPD. Cultured human airway smooth muscle cells were exposed to increasing concentrations of CSE (1-15%) and IL-1β (0.01-1.0 ng/ml), which induced the secretion of VEGF-A and IL-8, whereas eotaxin secretion was induced by IL-1β only. Inhibition of GSK-3 by the selective inhibitor SB216763 or CHIR/CT99021 attenuated the cytokine and growth factor release induced by CSE and/or IL-1β, without affecting the basal release. Secretion of the cytokines by airway smooth muscle partially depends on NF-κB signaling, and GSK-3 has been implicated in regulating multiple steps in activating the NF-κB signaling pathway. IL-1β treatment induced degradation of the NF-κB inhibitory protein Iκ-Bα followed by nuclear translocation and DNA binding of p65 NF-κB, which were unaffected by inhibition of GSK-3. However, induction of NF-κB-dependent transcriptional activity by IL-1β and CSE was largely reduced upon GSK-3 inhibition by SB216763. Collectively, we demonstrate that CSE and IL-1β activate airway smooth muscle cells to secrete the proinflammatory cytokines IL-8, eotaxin, and VEGF-A. Furthermore, we show that GSK-3 regulates the release of these cytokines induced by CSE and IL-1β by promoting NF-κB-dependent gene transcription.
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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".