Human osteoarthritis knee joint synovial fluids cleave and activate Proteinase-Activated Receptor (PAR) mediated signaling
Bibliographic record
Abstract
Osteoarthritis (OA) is the most prevalent joint disorder with increasing worldwide incidence. Mechanistic insights into OA pathophysiology are evolving and there are currently no disease-modifying OA drugs. An increase in protease activity is linked to progressive degradation of the cartilage in OA. Proteases also trigger inflammation through a family of G protein-coupled receptors (GPCRs) called the Proteinase-Activated Receptors (PARs). PAR signaling can trigger pro-inflammatory responses and targeting PARs is proposed as a therapeutic approach in OA. Several enzymes can cleave the PAR N-terminus, but the endogenous protease activators of PARs in OA remain unclear. Here we characterized PAR activating enzymes in knee joint synovial fluids from OA patients and healthy donors using genetically encoded PAR biosensor expressing cells. Calcium signaling assays were performed to examine receptor activation. The class and type of enzymes cleaving the PARs was further characterized using protease inhibitors and fluorogenic substrates. We find that PAR1, PAR2 and PAR4 activating enzymes are present in knee joint synovial fluids from healthy controls and OA patients. Compared to healthy controls, PAR1 activating enzymes are elevated in OA synovial fluids while PAR4 activating enzyme levels are decreased. Using enzyme class and type selective inhibitors and fluorogenic substrates we find that multiple PAR activating enzymes are present in OA joint fluids and identify serine proteinases (thrombin and trypsin-like) and matrix metalloproteinases as the major classes of PAR activating enzymes in the OA synovial fluids. Synovial fluid driven increase in calcium signaling was significantly reduced in cells treated with PAR1 and PAR2 antagonists, but not in PAR4 antagonist treated cells. OA associated elevation of PAR1 cleavage suggests that targeting this receptor may be beneficial in the treatment of OA.
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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) | 0.003 | 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".