Wnt Signaling Is Deranged in Asthmatic Bronchial Epithelium and Fibroblasts
Bibliographic record
Abstract
Both canonical and non-canonical Wnt signaling pathway alterations have been documented in pulmonary disease pathogenesis and progression; therefore, they can be an attractive target for pharmaceutical management of severe asthma. Wnt/β-catenin signaling was shown to link early embryonic lung development impairment to later in life asthmatic airway remodeling. Here we explored the changes in Wnt signaling associated with asthma initiation and progression in epithelial and fibroblasts using a comprehensive approach based on in silico analysis and followed by in vitro validation. In summary, the in silico analysis showed that the bronchial epithelium of severe asthmatic patients showed a deranged balance between Wnt enhancer and Wnt inhibitors. A Th2-high phenotype is associated with upregulated Wnt-negative regulators, while inflammatory and neutrophilic severe asthmatics showed higher canonical Wnt signaling member enrichment. Most of these genes are regulators of healthy lung development early in life and, if disturbed, can make people susceptible to developing asthma early in life and prone to developing a severe phenotype. Most of the Wnt members are secreted, and their effect can be in an autocrine fashion on the bronchial epithelium, paracrine on nearby adjacent structural cells like fibroblasts and smooth muscles, or systemic in blood. Our results showed that canonical Wnt signaling is needed for the proper response of cells to proliferative stimuli, which puts cells under stress. Cells in response to this proliferative stress will activate the senescence mechanism, which is also dependent on Wnt signaling. Inhibition of Wnt signaling using FH535 inhibits both proliferation and senescence markers in bronchial fibroblasts compared to DMSO-treated cells. In fibroblasts from asthmatic patients, inhibition of Wnt signaling did not show that effect as the Wnt signaling is deranged besides other pathways that might be non-functional.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".