Granzyme B Mediates Both Direct and Indirect Cleavage of Extracellular Matrix in Skin After Chronic Low‐Dose Ultraviolet Light Irradiation
Bibliographic record
Abstract
Extracellular matrix (ECM) degradation is a hallmark of tissue aging, as well as many other chronic inflammatory diseases that can lead to a loss of function. Granzyme B (GzmB), a serine protease that is expressed by a variety cells, has been shown to accumulate in the extracellular space during chronic inflammation and cleave a number of proteins. Using a chronic low‐grade UV‐irradiation murine model, we hypothesized that GzmB contributes to skin damage and ECM degradation through processes involving both direct ECM cleavage and indirect ECM cleavage through the induction of other proteinases. Wild‐type and GzmB‐knockout (KO) mice were repeatedly exposed to minimal erythemal doses of solar simulated UV‐irradiation for up to 20 weeks. GzmB expression was significantly increased in wild‐type UV‐treated skin compared to non‐irradiated controls. GzmB deficiency significantly protected against the formation of wrinkles and the loss of dermal collagen density. GzmB‐KO mice were also protective against the loss of fibronectin (FN) in vivo, and FN fragments were released from the cell‐derived matrix of cultured fibroblasts after GzmB treatment. GzmB‐mediated FN fragments increased the release of metalloproteinase (MMP)‐1 and MMP‐3 from fibroblasts. Furthermore, GzmB cleavage of decorin predisposed collagen fibrils more susceptible to attack by MMP‐1. Finally, GzmB reduced the barrier function of a monolayer of keratinocytes in vitro as measured by electrical impedance. Collectively, these findings indicate a significant role for GzmB in ECM degradation, which may have implications in many aged‐related dermatological indications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".