House Dust Mite–Promoted Epithelial-to-Mesenchymal Transition in Human Bronchial Epithelium
Bibliographic record
Abstract
The molecular basis of airway remodeling and loss of epithelial integrity in asthma is still undefined. We aimed to establish if exposure of human bronchial epithelium (16HBE cells) to asthma-related stimuli can induce epithelial-to-mesenchymal transition (EMT), a key process in tissue repair and remodeling associated with loss of intercellular contacts. We studied the effects of fibrogenic cytokine TGF-beta and protease-containing aeroallergen house dust mite (HDM) on mesenchymal and epithelial markers, cytoskeleton organization, and activation of beta-catenin-driven reporter TopFLASH. TGF-beta alone up-regulated vimentin and fibronectin, modestly down-regulated E-cadherin, but did not affect cytokeratin. HDM alone did not affect these markers, but promoted stress fibers. Importantly, when added to TGF-beta-primed epithelium, HDM induced E-cadherin internalization, enhanced beta-catenin-dependent transcription, and down-regulated cytokeratin. Regarding the underlying mechanisms, the stimuli together induced sustained myosin light chain phosphorylation, which was crucial for E-cadherin internalization and beta-catenin-dependent transcription. Previously, we showed that HDM signals through the epidermal growth factor receptor (EGFR). Accordingly, inhibition of EGFR prevented TGF-beta/HDM-induced mesenchymalization. TGF-beta facilitated uncoupling of EGFR from E-cadherin, its negative regulator, and prolonged EGFR signaling. Thus, we show that HDM promotes EMT in TGF-beta-primed epithelium. Analysis of primary epithelium appears consistent with this phenotypic change. We propose that TGF-beta secretion and dysregulated EGFR signaling may increase epithelial vulnerability to allergens and trigger the induction of EMT, a hitherto unrecognized contributor to airway remodeling in asthma.
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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".