Heat Shock Protein 27 modulate mesothelial and epithelial to mesenchymal transition (EMT)
Bibliographic record
Abstract
Introduction: Pulmonary fibrosis (PF) has currently no treatment. We have shown that adenoviral gene transfer of TGF-β1 (AdTGF-β1) to the pleura induces a severe pleural fibrosis that invades the parenchyma. In this process, mesothelial cells differentiate into myofibroblastes (α-SMA positive cells) through an EMT-like process suggesting a key role of mesothelial cells in PF. Heat Shock Protein 27 (HSP27), is a chaperon for actin. Its role in fibrogenesis is unknown. Methods: Sprague Dawley rats received intrapleural injection of AdTGF-β1 or AdDL (empty vector). Mesothelial Met-5A and A549 cells were treated with rTGF-β1. Results: in vitro: 1) mesothelial cells are susceptible to rTGF-β1 induced EMT 2) HSP27 is strongly linked to α-SMA during EMT (colocalisation and co-immunoprecipitation). 3) HSP27 overexpression induces an EMT and siRNA mediated HSP27 inhibition blocks TGF-β1 induced EMT and mesothelial cell migration 4) HSP27 modulates the TGF-β1/SMAD pathway. Data were reproduced in A549 epithelial cells. In vivo: 7 days after AdTGF-β1 injection, HSP27 and α-SMA are overexpressed and colocalize in fibrotic sub-pleura areas. AdTGF-β1 rats treated by intrapleural injections of OGX427 (AntiSens Oligonucleotide, ASO, directed against HSP27) have a strong decrease in HPS27, α-SMA expression, mesothelial cells migration into the parenchyma and fibrosis compare to AdTGF-β1 rats treated with control ASO. Conclusion: HSP27 plays a major role in EMT and could be a key target to inhibit EMT in PF and others diseases involving EMT. This work is supported by: – the EU, 7th FP, HEALTH-F2-2007-202224 eurIPFnet – La “Recherche en santé Respiratoire” et la Société de Pneumologie de Langue Française
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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) | 0.003 | 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".