Selective targeting of CREB‐binding protein/β‐catenin inhibits growth of and extracellular matrix remodelling by airway smooth muscle
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Asthma is a heterogeneous chronic inflammatory disease, characterized by the development of structural changes (airway remodelling). β-catenin, a transcriptional co-activator, is fundamentally involved in airway smooth muscle growth and may be a potential target in the treatment of airway smooth muscle remodelling. EXPERIMENTAL APPROACH: We assessed the ability of small-molecule compounds that selectively target β-catenin breakdown or its interactions with transcriptional co-activators to inhibit airway smooth muscle remodelling in vitro and in vivo. KEY RESULTS: ICG-001, a small-molecule compound that inhibits the β-catenin/CREB-binding protein (CBP) interaction, strongly and dose-dependently inhibited serum-induced smooth muscle growth and TGFβ1-induced production of extracellular matrix components in vitro. Inhibition of β-catenin/p300 interactions using IQ-1 or inhibition of tankyrase 1/2 using XAV-939 had considerably less effect. In a mouse model of allergic asthma, β-catenin expression in the smooth muscle layer was found to be unaltered in control versus ovalbumin-treated animals, a pattern that was found to be similar in smooth muscle within biopsies taken from asthmatic and non-asthmatic donors. However, β-catenin target gene expression was highly increased in response to ovalbumin; this effect was prevented by topical treatment with ICG-001. Interestingly, ICG-001 dose-dependently reduced airway smooth thickness after repeated ovalbumin challenge, but had no effect on the deposition of collagen around the airways, mucus secretion or eosinophil infiltration. CONCLUSIONS AND IMPLICATIONS: Together, our findings highlight the importance of β-catenin/CBP signalling in the airways and suggest ICG-001 may be a new therapeutic approach to treat airway smooth muscle remodelling in asthma.
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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) | 0.002 | 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".