β-Catenin signaling is required for TGF-β <sub>1</sub> -induced extracellular matrix production by airway smooth muscle cells
Bibliographic record
Abstract
Chronic inflammatory airway diseases like asthma and chronic obstructive pulmonary disease (COPD) are characterized by airway remodeling with altered extracellular matrix (ECM) deposition. Transforming growth factor-β(1) (TGF-β(1)) is upregulated in asthma and COPD and contributes to tissue remodeling in the airways by driving ECM production by structural cells, including airway smooth muscle. In this study, we investigated the activation of β-catenin signaling and its contribution to ECM production by airway smooth muscle cells in response to TGF-β(1). Stimulation of airway smooth muscle cells with TGF-β(1) resulted in a time-dependent increase of total and nonphosphorylated β-catenin protein expression via induction of β-catenin mRNA and inhibition of GSK-3. In addition, the TGF-β(1)-induced β-catenin activated TCF/LEF-dependent gene transcription, as determined by the β-catenin sensitive TOP-flash luciferase reporter assay. Furthermore, TGF-β(1) stimulation increased mRNA expression of collagen Iα1, fibronectin, versican, and PAI-1. Pharmacological inhibition of β-catenin by PKF115-584 or downregulation of β-catenin expression by specific small interfering RNA (siRNA) substantially inhibited TGF-β(1)-induced expression of the ECM genes. Fibronectin protein deposition by airway smooth muscle cells in response to TGF-β(1) was also inhibited by PKF115-584 and β-catenin siRNA. Moreover, transfection of airway smooth muscle cells with a nondegradable β-catenin mutant (S33Y β-catenin) was sufficient for inducing fibronectin protein expression. Collectively, these findings indicate that β-catenin signaling is activated in response to TGF-β(1) in airway smooth muscle cells, which is required and sufficient for the regulation of ECM protein production. Targeting β-catenin-dependent gene transcription may therefore hold promise as a therapeutic intervention in airway remodeling in both asthma and COPD.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".