TGFβ1-induced extracellular matrix production enhances airway smooth muscle cell proliferation
Bibliographic record
Abstract
The fibrogenic cytokine transforming growth factor-β1 (TGFβ1) is an important mediator in airway remodelling. TGFβ1 overexpression in mice induces airway smooth muscle (ASM) hyperplasia, one of the characteristics of airway remodelling in asthma. The mechanisms underlying this response are unclear. As studies of ASM cell proliferation showed that TGFβ1 alone has very weak mitogenic properties, combined effects with other factors such as Gq- or Gi-protein coupled receptor agonists may be involved. Here, we hypothesized that the mitogenic effects of TGFβ1 on ASM are indirect and require prolonged exposure to allow deposition of extracellular matrix (ECM) proteins. To address this hypothesis, we investigated the effects of acute and prolonged treatment with TGFβ1 (2 ng/mL), alone and in combination with the muscarinic receptor agonist methacholine (MCh, 10μM) on human ASM cell (hASMc) proliferation. TGFβ1 had no acute effect on hASMc proliferation. However, pretreatment with TGFβ1 for 7 days increased hASMc proliferation, and potentiated the mitogenic response to PDGF. The presence of MCh during TGFβ1 pretreatment considerably enhanced this effect of TGFβ1. Interestingly, the TGFβ1-induced effects on cell proliferation as well as the potentiating effects of MCh were inhibited by the integrin-blocking peptide RGDS (Arg-Gly-Asp-Ser), whereas RGDS had no direct effect on hASMc proliferation. Accordingly, pretreatment with TGFβ1 induced an increase in fibronectin protein expression, which was enhanced by MCh stimulation. In conclusion, our results indicate that pretreatment with TGFβ1 enhances hASMc proliferation, which is mediated by ECM proteins and enhanced by muscarinic receptor agonists.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".