Transforming growth factor-β1 and SMAD signalling pathway in the small airways of smokers and patients with COPD: potential role in driving fibrotic type-2 epithelial mesenchymal transition
Bibliographic record
Abstract
Background COPD is a common disease characterized by respiratory airflow obstruction. TGF-β1 and SMAD pathway is believed to play a role in COPD pathogenesis by driving epithelial mesenchymal transition (EMT). Methods We investigated TGF-β1 signalling and pSmad2/3 and Smad7 activity in resected small airway tissue from patients with; normal lung function and a smoking history (NLFS), current smokers and ex-smokers with COPD GOLD stage 1 and 2 (COPD-CS and COPD-ES) and compared these with normal non-smoking controls (NC). Using immunohistochemistry, we measured activity for these markers in the epithelium, basal epithelium, and reticular basement membrane (RBM). Tissue was also stained for EMT markers E-cadherin, S100A4 and vimentin. Results The Staining of pSMAD2/3 was significantly increased in the epithelium, and RBM of all COPD groups compared to NC (p <0.0005). There was a less significant increase in COPD-ES basal cell numbers compared to NC (p= 0.02). SMAD7 staining showed a similar pattern (p <0.0001). All COPD group levels of TGF-β1 in the epithelium, basal cells, and RBM cells were significantly lower than NC (p <0.0001). Ratio analysis showed a disproportionate increase in SMAD7 levels compared to pSMAD2/3 in NLFS, COPD-CS and COPD-ES. pSMAD negatively correlated with small airway calibre (FEF 25–75% ; p= 0.03 r= -0.36). EMT markers were active in the small airway epithelium of all the pathological groups compared to patients with COPD. Conclusion Activation of the SMAD pathway via pSMAD2/3 is triggered by smoking and active in patients with mild to moderate COPD. These changes correlated to decline in lung function. Activation of the SMADs in the small airways is independent of TGF-β1, suggesting factors other than TGF-β1 are driving these pathways. These factors may have implications for small airway pathology in smokers and COPD through the process of EMT, however more mechanistic work is needed to prove these correlations.
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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) | 0.002 | 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".