Epidermal growth factor upregulates expression of MUC5AC via TMEM16A, in chronic rhinosinusitis with nasal polyps
Bibliographic record
Abstract
BACKGROUND: Mucus hypersecretion and goblet cell upregulation are common features of chronic rhinosinusitis with nasal polyps (CRSwNP). Although epidermal growth factor (EGF) has been reported to stimulate the expression of MUC5AC, the major macro-molecular constituent of airway mucus, the precise mechanisms underlying the regulation of MUC5AC expression are still not fully understood. The aim of this study therefore was to investigate the role of EGF in regulation of mucin MUC5AC expression and define the involvement of transmembrane protein 16A (TMEM16A) in mediating the EGF-induced mucus overexpression. METHODS: Human nasal epithelial cells (HNECs) derived from tissue of patients with CRSwNP and control subjects were established as air-liquid interface (ALI) cultures. Differentiated cultures were treated with different concentrations of EGF for 4-24 h, and assessed for the expression of TMEM16A and MUC5AC by real-time RT-PCR, Western blotting, ELISA and immunofluorescence. Cultures pretreated for 30 min with T16Ainh-A01 (a specific TMEM16A inhibitor) or LY294002 (a phosphoinositide 3-kinase (PI3K) inhibitor) were also assessed similarly following EGF treatment. RESULTS: EGF treatment (10-100 ng/ml for 4-24 h) significantly increased the expression of both TMEM16A and MUC5AC mRNA and protein, as well as the percentage of TMEM16A-positive cells, MUC5AC-positive cells and cells coexpressing TMEM16A and MUC5AC in HNECs compared to control non-EGF-treated HNECs. Pretreatment of the HNECs with T16Ainh-A01 and LY294002 attenuated these EGF-induced effects. CONCLUSIONS: This study demonstrated that EGF upregulates the expression of MUC5AC in HNECs from CRSwNP patients. Furthermore, the EGF-mediated regulation of MUC5AC expression is likely to involve a PI3K-TMEM16A signalling pathway in CRSwNP.
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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 | 0.000 |
| 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".