IL-22 contributes to TGF-β1-mediated epithelial-mesenchymal transition in asthmatic bronchial epithelial cells
Bibliographic record
Abstract
BACKGROUND: Allergic asthma is characterized by airway inflammation in response to antigen exposure, leading to airway remodeling and lung dysfunction. Epithelial-mesenchymal transition (EMT) may play a role in airway remodeling through the acquisition of a mesenchymal phenotype in airway epithelial cells. TGF-β1 is known to promote EMT; however, other cytokines expressed in severe asthma with extensive remodeling, such as IL-22, may also contribute to this process. In this study, we evaluated the contribution of IL-22 to EMT in primary bronchial epithelial cells from healthy and asthmatic subjects. METHODS: Primary bronchial epithelial cells were isolated from healthy subjects, mild asthmatics and severe asthmatics (n=5 patients per group). The mRNA and protein expression of epithelial and mesenchymal cell markers and EMT-associated transcription factors was evaluated following stimulation with TGF-β1, IL-22 and TGF-β1+IL-22. RESULTS: Primary bronchial epithelial cells stimulated with TGF-β1 underwent EMT, demonstrated by decreased expression of epithelial markers (E-cadherin and MUC5AC) and increased expression of mesenchymal markers (N-cadherin and vimentin) and EMT-associated transcription factors. IL-22 alone had no effect on epithelial or mesenchymal gene expression. However, IL-22+TGF-β1 promoted the expression of some EMT transcription factors (Snail1 and Zeb1) and led to a more profound cadherin shift, but only in cells obtained from severe asthmatics. CONCLUSION: The impact of IL-22 on airway epithelial cells depends on the cytokine milieu and the clinical phenotype of the patient. Further studies are required to determine the molecular mechanism of IL-22 and TGF-β1 cooperativity in driving EMT in primary human bronchial epithelial cells.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".