Proteinase-Mediated Macrophage Signaling in Psoriatic Arthritis
Bibliographic record
Abstract
Objective Multiple proteinases are present in the synovial fluid (SF) of an arthritic joint. We aimed to identify inflammatory cell populations present in psoriatic arthritis (PsA) SF compared to osteoarthritis (OA) and rheumatoid arthritis (RA), identify their proteinase-activated receptor 2 (PAR2) signaling function and characterize potentially active SF serine proteinases that may be PAR2 activators. Methods Flow cytometry was used to characterize SF cells from PsA, RA, OA patients; PsA SF cells were further characterized by single cell 3’-RNA-sequencing. Active serine proteinases were identified through cleavage of fluorogenic trypsin- and chymotrypsin-like substrates, activity-based probe analysis and proteomics. Fluo-4 AM was used to monitor intracellular calcium cell signaling. Cytokine expression was evaluated using a multiplex Luminex panel. Results PsA SF cells were dominated by monocytes/macrophages, which consisted of three populations representing classical, non-classical and intermediate cells. The classical monocytes/macrophages were reduced in PsA compared to OA/RA, whilst the intermediate population was increased. PAR2 was elevated in OA vs. PsA/RA SF monocytes/macrophages, particularly in the intermediate population. PAR2 expression and signaling in primary PsA monocytes/macrophages significantly impacted the production of monocyte chemoattractant protein-1 (MCP-1). Trypsin-like serine proteinase activity was elevated in PsA and RA SF compared to OA, while chymotrypsin-like activity was elevated in RA compared to PsA. Tryptase-6 was identified as an active serine proteinase in SF that could trigger calcium signaling partially via PAR2. Conclusion PAR2 and its activating proteinases, including tryptase-6, can be important mediators of inflammation in PsA. Components within this proteinase-receptor axis may represent novel therapeutic targets.
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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) | 0.002 | 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".