Simvastatin inhibits TGFβ1-induced fibronectin in human airway fibroblasts
Bibliographic record
Abstract
BACKGROUND: Bronchial fibroblasts contribute to airway remodelling, including airway wall fibrosis. Transforming growth factor (TGF)-β1 plays a major role in this process. We previously revealed the importance of the mevalonate cascade in the fibrotic response of human airway smooth muscle cells. We now investigate mevalonate cascade-associated signaling in TGFβ1-induced fibronectin expression by bronchial fibroblasts from non-asthmatic and asthmatic subjects. METHODS: We used simvastatin (1-15 μM) to inhibit 3-hydroxy-3-methlyglutaryl-coenzyme A (HMG-CoA) reductase which converts HMG-CoA to mevalonate. Selective inhibitors of geranylgeranyl transferase-1 (GGT1; GGTI-286, 10 μM) and farnesyl transferase (FT; FTI-277, 10 μM) were used to determine whether GGT1 and FT contribute to TGFβ1-induced fibronectin expression. In addition, we studied the effects of co-incubation with simvastatin and mevalonate (1 mM), geranylgeranylpyrophosphate (30 μM) or farnesylpyrophosphate (30 μM). RESULTS: Immunoblotting revealed concentration-dependent simvastatin inhibition of TGFβ1 (2.5 ng/ml, 48 h)-induced fibronectin. This was prevented by exogenous mevalonate, or isoprenoids (geranylgeranylpyrophosphate or farnesylpyrophosphate). The effects of simvastatin were mimicked by GGTI-286, but not FTI-277, suggesting fundamental involvement of GGT1 in TGFβ1-induced signaling. Asthmatic fibroblasts exhibited greater TGFβ1-induced fibronectin expression compared to non-asthmatic cells; this enhanced response was effectively reduced by simvastatin. CONCLUSIONS: We conclude that TGFβ1-induced fibronectin expression in airway fibroblasts relies on activity of GGT1 and availability of isoprenoids. Our results suggest that targeting regulators of isoprenoid-dependent signaling holds promise for treating airway wall fibrosis.
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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 | 0.001 |
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".