TGFβ1 induces resistance of human lung myofibroblasts to cell death via down‐regulation of TRPA1 channels
Bibliographic record
Abstract
Background and Purpose TGFβ1‐mediated myofibroblast activation contributes to pathological fibrosis in many diseases including idiopathic pulmonary fibrosis (IPF), where myofibroblast resistance to oxidant‐mediated apoptosis is also evident. We therefore investigated the involvement of redox‐sensitive TRPA1 ion channels on human lung myofibroblasts (HLMFs) cell death and TGFβ1‐mediated pro‐fibrotic responses. Experimental approach The effects of TGFβ1 stimulation on TRPA1 expression and cell viability was studied in HLMFs derived from IPF patients and non‐fibrotic patients. We also examined a model of TGFβ1‐dependent fibrogenesis in human lung. We used qRT‐PCR, immunofluorescent assays, overexpression with lentiviral vectors and electrophysiological methods. Key Results TRPA1 mRNA, protein and ion currents were expressed in HLMFs derived from both non‐fibrotic patient controls and IPF patients, and expression was reduced by TGFβ1. TRPA1 mRNA was also down‐regulated by TGFβ1 in a model of lung fibrogenesis in human lung. TRPA1 over‐expression or activation induced HLMF apoptosis, and activation of TRPA1 channel activation by H 2 O 2 induced necrosis. TRPA1 inhibition following TGFβ1 down‐regulation or pharmacological inhibition, protected HLMFs from both apoptosis and necrosis. Lentiviral vector mediated TRPA1 expression was also found to induce sensitivity to H 2 O 2 induced cell death in a TRPA1‐negative HEK293T cell line. Conclusion and Implications TGFβ1 induces resistance of HLMFs to TRPA1 agonist‐ and H 2 O 2 ‐mediated cell death via down‐regulation of TRPA1 channels. Our data suggest that therapeutic strategies which prevent TGFβ1‐dependent down‐regulation of TRPA1 may reduce myofibroblast survival in IPF and therefore improve clinical outcomes.
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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".