Inhibition of β-Catenin/CREB Binding Protein Signaling Attenuates House Dust Mite-Induced Goblet Cell Metaplasia in Mice
Bibliographic record
Abstract
Excessive mucus production is a major feature of allergic asthma. Disruption of epithelial junctions by allergens such as house dust mite (HDM) results in the activation of β-catenin signaling, which has been reported to stimulate goblet cell differentiation. β-catenin interacts with various co-activators including CREB binding protein (CBP) and p300, thereby regulating the expression of genes involved in cell proliferation and differentiation, respectively. We specifically investigated the role of the β-catenin/CBP signaling pathway in goblet cell metaplasia in a HDM-induced allergic airway disease model in mice using ICG-001, a small molecule inhibitor that blocks the binding of CBP to β-catenin. Female 6- 8-week-old BALB/c mice were sensitized to HDM/saline on days 0, 1, and 2, followed by intranasal challenge with HDM/saline with or without subcutaneous ICG-001/vehicle treatment from days 14 to 17, and samples harvested 24 h after the last challenge/treatment. Differential inflammatory cells in bronchoalveolar lavage (BAL) fluid were enumerated. Alcian blue (AB)/Periodic acid-Schiff (PAS) staining was used to identify goblet cells/mucus production, and airway hyperresponsiveness (AHR) was assessed using invasive plethysmography. Exposure to HDM induced airway inflammation, goblet cell metaplasia and increased AHR, with increased airway resistance in response to the non-specific spasmogen methacholine. Inhibition of the β-catenin/CBP pathway using treatment with ICG-001 significantly attenuated the HDM-induced goblet cell metaplasia and infiltration of macrophages, but had no effect on eosinophils, neutrophils, lymphocytes or AHR. Increased β-catenin/CBP signaling may promote HDM-induced goblet cell metaplasia in mice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".