The fibronectin ED-A domain enhances recruitment of latent TGF-β-binding protein-1 to the fibroblast matrix
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it