Leukotriene D<sub>4</sub>‐induced, epithelial cell‐derived transforming growth factor β1 in human bronchial smooth muscle cell proliferation
Bibliographic record
Abstract
BACKGROUND: Cysteinyl-leukotrienes (cys-LTs) orchestrate many pathognomonic features of asthma in animal models of allergic airway inflammation, including bronchial smooth muscle cell (BSMC) hyperplasia. However, because cys-LTs alone do not induce mitogenesis in monocultures of human BSMC, the effect observed in vivo seemingly involves indirect mechanisms, which are still undefined. OBJECTIVE: This study aims to investigate the regulatory role of leukotriene (LT)D(4) on TGF-beta1 expression in airway epithelial cells and the consequence of this interplay on BSMC proliferation. METHODS: HEK293 cells stably transfected with cys-LT receptor 1 (CysLT1) (293LT1) were stimulated with LTD(4) and TGF-beta1 mRNA and protein expression was measured using Northern blot and ELISA, respectively. Conditioned medium (CM) harvested from LTD(4)-treated cells was then assayed for its proliferative effect on primary human BSMC. TGF-beta1 mRNA expression was also determined in tumoural type II pneumocytes A549 and in normal human bronchial epithelial cells (NHBE) following LTD(4) stimulation. RESULTS: The results demonstrated that LTD(4)-induced TGF-beta1 mRNA production in a time- and concentration-dependent manner in 293LT1. TGF-beta1 secretion was also up-regulated and CM from LTD(4)-treated 293LT1 was shown to increase BSMC proliferation in a TGF-beta1-dependent manner. The increased expression of TGF-beta1 mRNA by LTD(4) also occured in A549 and NHBE cells via a CysLT1-dependent mechanism. CONCLUSION: In conclusion, elevated expression of cys-LTs in asthmatic airways might contribute to BSMC hyperplasia and concomitant clinical features of asthma such as airway hyperresponsiveness via a paracrine loop involving TGF-beta1 production by airway 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 | 0.000 | 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".