Secretion of IL-13 by Airway Epithelial Cells Enhances Epithelial Repair via HB-EGF
Bibliographic record
Abstract
Inappropriate repair after injury to the epithelium generates persistent activation, which may contribute to airway remodeling. In the present study we hypothesized that IL-13 is a normal mediator of airway epithelial repair. Mechanical injury of confluent airway epithelial cell (AEC) monolayers induced expression and release of IL-13 in a time-dependent manner coordinate with repair. Neutralizing of IL-13 secreted from injured epithelial cells by shIL-13Ralpha2.FC significantly reduced epithelial repair. Moreover, exogenous IL-13 enhanced epithelial repair and induced epidermal growth factor receptor (EGFR) phosphorylation. We examined secretion of two EGFR ligands, epidermal growth factor (EGF) and heparin-binding EGF (HB-EGF), after mechanical injury. Our data showed a sequential release of the EGF and HB-EGF by AEC after injury. Interestingly, we found that IL-13 induces HB-EGF, but not EGF, synthesis and release from AEC. IL-13-induced EGFR phosphorylation and the IL-13-reparative effect on AEC are mediated via HB-EGF. Finally, we demonstrated that inhibition of EGFR tyrosine kinase activity by tyrphostin AG1478 increases IL-13 release after injury, suggesting negative feedback between EGFR and IL-13 during repair. Our data, for the first time, showed that IL-13 plays an important role in epithelial repair, and that its effect is mediated through the autocrine release of HB-EGF and activation of EGFR. Dysregulation of EGFR phosphorylation may contribute to a persistent repair phenotype and chronically increased IL-13 release, and in turn result in airway remodeling.
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 | 0.001 |
| 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) | 0.001 | 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".