Transforming Growth Factor β1 Induces αvβ3 Integrin Expression in Human Lung Fibroblasts via a β3 Integrin-, c-Src-, and p38 MAPK-dependent Pathway
Bibliographic record
Abstract
In response to transforming growth factor beta1 (TGFbeta) stimulation, fibroblasts modify their integrin repertoire and adhesive capabilities to certain extracellular matrix proteins. Although TGFbeta has been shown to increase the expression of specific alphav integrins, the mechanisms underlying this are unknown. In this study we demonstrate that TGFbeta1 increased both beta3 integrin subunit mRNA and protein levels as well as surface expression of alphavbeta3 in human lung fibroblasts. TGFbeta1-induced alphavbeta3 expression was strongly adhesion-dependent and associated with increased focal adhesion kinase and c-Src kinase phosphorylation. Inhibition of beta3 integrin activation by the Arg-Gly-Asp tripeptide motif-specific disintegrin echistatin or alphavbeta3 blocking antibody prevented the increase in beta3 but not beta5 integrin expression. In addition, echistatin inhibited TGFbeta1-induced p38 MAPK but not Smad3 activation. Furthermore, inhibition of the Src family kinases, but not focal adhesion kinase, completely abrogated TGFbeta1-induced expression of alphavbeta3 and p38 MAPK phosphorylation but not beta5 integrin expression and Smad3 activation. The TGFbeta1-induced alphavbeta3 expression was blocked by pharmacologic and genetic inhibition of p38 MAPK- but not Smad2/3-, Sp1-, ERK-, phosphatidylinositol 3-kinase, and NF-kappaB-dependent pathways. Our results demonstrate that TGFbeta1 induces alphavbeta3 integrin expression via a beta3 integrin-, c-Src-, and p38 MAPK-dependent pathway. These data identify a novel mechanism for TGFbeta1 signaling in human lung fibroblasts by which they may contribute to normal and pathological wound healing.
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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".