Profiling of healthy and asthmatic airway smooth muscle cells following interleukin-1β treatment: a novel role for CCL20 in chronic mucus hypersecretion
Bibliographic record
Abstract
Chronic mucus hypersecretion (CMH) contributes to the morbidity and mortality of asthma, and remains uncontrolled by current therapies in the subset of patients with severe, steroid-resistant disease. Altered cross-talk between airway epithelium and airway smooth muscle cells (ASMCs), driven by pro-inflammatory cytokines such as interleukin (IL)-1β, provides a potential mechanism that influences CMH. This study investigated mechanisms underlying CMH by comparing IL-1β-induced gene expression profiles between asthma and control-derived ASMCs and the subsequent paracrine influence on airway epithelial mucus productionin vitro. IL-1β-treated ASMCs from asthmatic patients and healthy donors were profiled using microarray analysis and ELISA. Air–liquid interface (ALI)-cultured CALU-3 and primary airway epithelial cells were treated with identified candidates and mucus production assessed. The IL-1β-inducedCCL20expression and protein release was increased in ASMCs from moderate compared with mild asthmatic patients and healthy controls.IL-1β induced lowerMIR146Aexpression in asthma-derived ASMCs compared with controls. DecreasedMIR146Aexpression was validatedin vivoin bronchial biopsies from 16 asthmatic patientsversus39 healthy donors. miR-146a-5p overexpression abrogated CCL20 release in ASMCs. CCL20 treatment of ALI-cultured CALU-3 and primary airway epithelial cells induced mucus production, while CCL20 levels in sputum were associated with increased levels of CMH in asthmatic patients. Elevated CCL20 production by ASMCs, possibly resulting from dysregulated expression of the anti-inflammatory miR-146a-5p, may contribute to enhanced mucus production in asthma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".