Identification of Proteinase Activated Receptor (PAR) cleaving enzymes in human osteoarthritis knee joint synovial fluids
Bibliographic record
Abstract
ABSTRACT Objective Osteoarthritis (OA) is the most prevalent joint disorder with incidence increasing worldwide. Mechanistic insights into OA pathophysiology are still evolving and there are currently no disease-modifying OA drugs available. It is well established that an increase in proteolytic enzyme activity is linked to progressive degradation of the cartilage in OA. Proteolytic enzymes can also trigger inflammation through activation of a family of G-protein coupled receptors (GPCRs) called the Proteinase Activated Receptors (PARs). Here we sought to characterize the PAR activating enzyme repertoire in human OA knee joint fluids. Methods Human knee joint synovial fluids derived from twenty-five OA patients and four healthy donors were screened for PAR cleavage activity using novel genetically encoded human PAR biosensor expressing cells. The class or type of enzymes cleaving the PARs was further characterized using enzyme-selective inhibitors and enzyme-specific fluorogenic substrates. Results Activity of PAR1, PAR2 and PAR4 activating enzymes were identified at substantially different levels in OA patients relative to healthy knee joint synovial fluids. Using enzyme class or type selective inhibitors and fluorogenic substrates we found that serine proteinases, including thrombin-like enzymes, trypsin-like enzymes, and matrix metalloproteinases are the major PAR activating enzymes present in the OA knee synovial fluids. Conclusions Multiple enzymes activating PAR1, PAR2 and PAR4 are present in OA joint fluids. PAR signalling can trigger pro-inflammatory responses and targeting PARs has been proposed as a therapeutic approach in OA. Knowledge of the PAR activators present in the human knee joint will guide study of relevant signaling events and enable future development of novel PAR targeted therapies for OA and other inflammatory joint diseases.
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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 | 0.001 | 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".