TGF-β1 increases proliferation of airway smooth muscle cells by phosphorylation of map kinases
Bibliographic record
Abstract
BACKGROUND: Airway remodeling in asthma is the result of increased expression of connective tissue proteins, airway smooth muscle cell (ASMC) hyperplasia and hypertrophy. TGF-beta1 has been found to increase ASMC proliferation. The activation of mitogen-activated protein kinases (MAPKs), p38, ERK, and JNK, is critical to the signal transduction associated with cell proliferation. In the present study, we determined the role of phosphorylated MAPKs in TGF-beta1 induced ASMC proliferation. METHODS: Confluent and growth-arrested bovine ASMCs were treated with TGF-beta1. Proliferation was measured by [3H]-thymidine incorporation and cell counting. Expressions of phosphorylated p38, ERK1/2, and JNK were determined by Western analysis. RESULTS: In a concentration-dependent manner, TGF-beta1 increased [3H]-thymidine incorporation and cell number of ASMCs. TGF-beta1 also enhanced serum-induced ASMC proliferation. Although ASMCs cultured with TGF-beta1 had a significant increase in phosphorylated p38, ERK1/2, and JNK, the maximal phosphorylation of each MAPK had a varied onset after incubation with TGF-beta1. TGF-beta1 induced DNA synthesis was inhibited by SB 203580 or PD 98059, selective inhibitors of p38 and MAP kinase kinase (MEK), respectively. Antibodies against EGF, FGF-2, IGF-I, and PDGF did not inhibit the TGF-beta1 induced DNA synthesis. CONCLUSION: Our data indicate that ASMCs proliferate in response to TGF-beta1, which is mediated by phosphorylation of p38 and ERK1/2. These findings suggest that TGF-beta1 which is expressed in airways of asthmatics may contribute to irreversible airway remodeling by enhancing ASMC proliferation.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".