RECK-Mediated β1-Integrin Regulation by TGF-β1 Is Critical for Wound Contraction in Mice
Bibliographic record
Abstract
Fibroblasts are critical for wound contraction; a pivotal step in wound healing. They produce and modify the extracellular matrix (ECM) required for the proper tissue remodeling. Reversion-inducing cysteine-rich protein with Kazal motifs (RECK) is a key regulator of ECM homeostasis and turnover. However, its role in wound contraction is presently unknown. Here we describe that Transforming growth factor type β1 (TGF-β1), one of the main pro-fibrotic wound-healing promoting factors, decreases RECK expression in fibroblasts through the Smad and JNK dependent pathways. This TGF-β1 dependent downregulation of RECK occurs with the concomitant increase of β1-integrin, which is required for fibroblasts adhesion and wound contraction through the activation of focal adhesion kinase (FAK). Loss and gain RECK expression experiments performed in different types of fibroblasts indicate that RECK downregulation mediates TGF-β1 dependent β1-integrin expression. Also, reduced levels of RECK potentiate TGF-β1 effects over fibroblasts FAK-dependent contraction, without affecting its cognate signaling. The above results were confirmed on fibroblasts derived from the Reck+/- mice compared to wild type-derived fibroblasts. We observed that Reck+/- mice heal dermal wounds more efficiently than wild type mice. Our results reveal a critical role for RECK in skin wound contraction as a key mediator in the axis: TGF-β1-RECK-β1-integrin.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".