Potential implications of granzyme B in keloids and hypertrophic scars through extracellular matrix remodeling and latent TGF-β activation
Bibliographic record
Abstract
Keloid scars (KS) and hypertrophic scars (HS) are fibroproliferative wound healing defects characterized by excessive accumulation of extracellular matrix (ECM) in the dermis of affected individuals. Although transforming growth factor (TGF)-β is known to be involved in the formation of KS and HS, the molecular mechanisms responsible for its activation remain unclear. In this study we investigated Granzyme B (GzmB), a serine protease with established roles in fibrosis and scarring through the cleavage of ECM proteins, as a potential new mediator of TGF-β activation in KS and HS. Increased GzmB-positive mast cells were identified in the dermis of KS and HS but not healthy skin controls. Elevated levels of substance P, a neuropeptide involved in mast cell degranulation, suggest that GzmB is released extracellularly, as confirmed by the significant reduction of the established extracellular GzmB substrate decorin in KS and HS. Similarly, presence of latent TGF-β binding protein 1 (LTBP1), a protein involved in the extracellular tethering of latent TGF-β, was disrupted proximal to the dermal-epidermal junction (DEJ) of GzmBhigh KS and HS lesions. Using LTBP1-enriched medium as well as purified LTBP1, its cleavage by GzmB was confirmed in vitro. Increased TGF-β/Smad signaling pathway was observed in keratinocytes treated with GzmB-digested LTBP1 and was abolished by the addition of a pan-TGF-β inhibitor, suggesting that GzmB cleavage of LTBP1 contributes to TGF-β activation. In dermal fibroblasts, GzmB also cleaved cell-derived LTBP1 and induced TGF-β activation through the cleavage of one or more unidentified fibroblast-secreted proteins. Altogether, the present results suggest that GzmB contributes to KS and HS through ECM remodeling and TGF-β activation.
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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 | 0.001 | 0.001 |
| 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".