GSK-3/β-catenin signaling axis in airway smooth muscle: role in mitogenic signaling
Bibliographic record
Abstract
Beta-catenin plays a dual role in cellular signaling by stabilizing cadherin-mediated cell-cell contact and by regulating gene transcription associated with cell cycle progression. Nonetheless, its presence and function in airway smooth muscle have not been determined. We hypothesized a central role for beta-catenin in mitogenic signaling in airway smooth muscle in response to growth factor stimulation. Immunocytochemical and biochemical analysis revealed that human airway smooth muscle cells indeed express abundant beta-catenin, which was localized primarily to the plasma membrane in quiescent cells. Treatment of airway smooth muscle cells with PDGF or FBS induced sustained phosphorylation of glycogen synthase kinase-3 (GSK-3), a negative regulator in its unphosphorylated form that promotes beta-catenin degradation. GSK-3 phosphorylation was also increased in airway smooth muscle cells with a proliferative phenotype compared with quiescent airway smooth muscle cells with a mature phenotype. Parallel with the increase in GSK-3 phosphorylation, growth factor treatment induced an increased expression and nuclear presence of beta-catenin and activated promitogenic signaling in airway smooth muscle, including the phosphorylation of retinoblastoma protein, DNA synthesis ([(3)H]thymidine incorporation), and cell proliferation. Importantly, small interfering RNA knockdown of beta-catenin strongly reduced retinoblastoma protein phosphorylation, [(3)H]thymidine incorporation, and cell proliferation induced by PDGF and FBS. Collectively, these data reveal the existence of a GSK-3/beta-catenin signaling axis in airway smooth muscle that is regulated by growth factors and of central importance to mitogenic signaling.
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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) | 0.001 | 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".