The serine proteinase hepsin is an activator of pro-matrix metalloproteinases: molecular mechanisms and implications for extracellular matrix turnover
Bibliographic record
Abstract
Increasing evidence implicates serine proteinases in the proteolytic cascades leading to the pathological destruction of extracellular matrices such as cartilage in osteoarthritis (OA). We have previously demonstrated that the type II transmembrane serine proteinase (TTSP) matriptase acts as a novel initiator of cartilage destruction via the induction and activation of matrix metalloproteinases (MMPs). Hepsin is another TTSP expressed in OA cartilage such that we hypothesized this proteinase may also contribute to matrix turnover. Herein, we demonstrate that addition of hepsin to OA cartilage in explant culture induced significant collagen and aggrecan release and activated proMMP-1 and proMMP-3. Furthermore, hepsin directly cleaved the aggrecan core protein at a novel cleavage site within the interglobular domain. Hepsin expression correlated with synovitis as well as tumour necrosis factor α expression, and was induced in cartilage by a pro-inflammatory stimulus. However, a major difference compared to matriptase was that hepsin demonstrated markedly reduced capacity to activate proteinase-activated receptor-2. Overall, our data suggest that hepsin, like matriptase, induces potent destruction of the extracellular matrix whilst displaying distinct efficiencies for the cleavage of specific substrates.
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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".