The fibronectin ED-A domain enhances recruitment of latent TGF-β-binding protein-1 to the fibroblast matrix
Bibliographic record
Abstract
Dysregulated secretion and extracellular activation of TGF-β1 stimulates myofibroblasts to accumulate disordered and stiff extracellular matrix (ECM) leading to fibrosis. Fibronectin immobilizes latent TGF-β-binding protein-1 (LTBP-1) and thus stores TGF-β1 in the ECM. Because the ED-A fibronectin splice variant is prominently expressed during fibrosis and supports myofibroblast activation, we investigated whether ED-A promotes LTBP-1-fibronectin interactions. Using stiffness-tuneable substrates for human dermal fibroblast cultures, we showed that high ECM stiffness promotes expression and colocalization of LTBP-1 and ED-A-containing fibronectin. When rescuing fibronectin-depleted fibroblasts with specific fibronectin splice variants, LTBP-1 bound more efficiently to ED-A-containing fibronectin than to ED-B-containing fibronectin and fibronectin lacking splice domains. Function blocking of the ED-A domain using antibodies and competitive peptides resulted in reduced LTBP-1 binding to ED-A-containing fibronectin, reduced LTBP-1 incorporation into the fibroblast ECM and reduced TGF-β1 activation. Similar results were obtained by blocking the heparin-binding stretch FNIII12-13-14 (HepII), adjacent to the ED-A domain in fibronectin. Collectively, our results suggest that the ED-A domain enhances association of the latent TGF-β1 by promoting weak direct binding to LTBP-1 and by enhancing heparin-mediated protein interactions through HepII in fibronectin.
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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".