Antioxidants and NOX1/NOX4 inhibition blocks TGFβ1-induced CCN2 and α-SMA expression in dermal and gingival fibroblasts
Bibliographic record
Abstract
TGFbeta induces fibrogenic responses in fibroblasts. Reactive oxygen species (ROS)/nicotinamide adenine dinucleotide phosphate (NADPH) oxidase (NOX) may contribute to fibrogenic responses. Here, we examine if the antioxidant N-acetylcysteine (NAC), the NOX inhibitor diphenyleneiodonium (DPI) and the selective NOX1/NOX4 inhibitor GKT-137831 impairs the ability of TGFbeta to induce profibrotic gene expression in human gingival (HGF) and dermal (HDF) fibroblasts. We also assess if GKT-137831 can block the persistent fibrotic phenotype of lesional scleroderma (SSc) fibroblasts. We use real-time polymerase chain reaction and Western blot analysis to evaluate whether NAC and DPI impair the ability of TGFbeta1 to induce expression of fibrogenic genes in fibroblasts. The effects of GKT-137831 on TGFbeta-induced protein expression and the persistent fibrotic phenotype of lesional scleroderma (SSc) fibroblasts were tested using Western blot and collagen gel contraction analyses. In HDF and HGF, TGFbeta1 induces CCN2, CCN1, endothelin-1 and alpha-smooth muscle actin (SMA) in a fashion sensitive to NAC. Induction of COL1A1 mRNA was unaffected. Similar results were seen with DPI. NAC and DPI impaired the ability of TGFbeta1 to induce protein expression of CCN2 and alpha-SMA in HDF and HGF. GKT-137831 impaired TGFbeta-induced CCN2 and alpha-SMA protein expression in HGF and HDF. In lesional SSc dermal fibroblasts, GKT-137831 reduced alpha-SMA and CCN2 protein overexpression and collagen gel contraction. These results are consistent with the hypothesis that antioxidants or NOX1/4 inhibition may be useful in blocking profibrotic effects of TGFbeta on dermal and gingival fibroblasts and warrant consideration for further development as potential antifibrotic agents.
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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".