Matriptase is a novel initiator of cartilage matrix degradation in osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: Increasing evidence implicates serine proteinases in pathologic tissue turnover. The aim of this study was to assess the role of the transmembrane serine proteinase matriptase in cartilage destruction in osteoarthritis (OA). METHODS: Serine proteinase gene expression in femoral head cartilage obtained from either patients with hip OA or patients with fracture to the neck of the femur (NOF) was assessed using a low-density array. The effect of matriptase on collagen breakdown was determined in cartilage degradation models, while the effect on matrix metalloproteinase (MMP) expression was analyzed by real-time polymerase chain reaction. ProMMP processing was determined using sodium dodecyl sulfate-polyacrylamide gel electrophoresis/N-terminal sequencing, while its ability to activate proteinase-activated receptor 2 (PAR-2) was determined using a synovial perfusion assay in mice. RESULTS: Matriptase gene expression was significantly elevated in OA cartilage compared with NOF cartilage, and matriptase was immunolocalized to OA chondrocytes. We showed that matriptase activated proMMP-1 and processed proMMP-3 to its fully active form. Exogenous matriptase significantly enhanced cytokine-stimulated cartilage collagenolysis, while matriptase alone caused significant collagenolysis from OA cartilage, which was metalloproteinase-dependent. Matriptase also induced MMP-1, MMP-3, and MMP-13 gene expression. Synovial perfusion data confirmed that matriptase activates PAR-2, and we demonstrated that matriptase-dependent enhancement of collagenolysis from OA cartilage is blocked by PAR-2 inhibition. CONCLUSION: Elevated matriptase expression in OA and the ability of matriptase to activate selective proMMPs as well as induce collagenase expression make this serine proteinase a key initiator and inducer of cartilage destruction in OA. We propose that the indirect effects of matriptase are mediated by PAR-2, and a more detailed understanding of these mechanisms may highlight important new therapeutic targets for OA treatment.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".