Metabolism and global protein glycosylation are differentially expressed in healthy and osteoarthritic equine carpal synovial fluid
Bibliographic record
Abstract
BACKGROUND: Carpal osteochondral fragmentation and subsequent post-traumatic osteoarthritis (PTOA) are leading causes of wastage in the equine athlete. Identification of synovial fluid biomarkers could contribute to the diagnosis and understanding of osteoarthritis (OA) pathophysiology. OBJECTIVE: The aim of this study was to identify differentially expressed metabolic and glycosylation pathways in synovial fluid from healthy horses and horses with naturally occurring carpal OA. STUDY DESIGN: Cross-sectional, in vivo metabolomics and glycomics study. METHODS: In cohort 1, carpal synovial fluid (n = 12 horses; n = 6 healthy, n = 6 OA) was analysed using high-resolution liquid chromatography mass spectrometry (LC-MS). In cohort 2 (n = 40 horses; n = 20 healthy, n = 20 OA), carpal synovial fluid was analysed using lectin microarrays and a lubricin sandwich ELISA. RESULTS: Metabolomic analysis identified >4900 LC-MS features of which 84 identifiable metabolites were differentially expressed (P < .05) between healthy and OA joints, including key pathways related to inflammation (histidine and tryptophan metabolism), oxidative stress (arginine biosynthesis) and collagen metabolism (lysine metabolism). Principle Component Analysis and Partial Least Squares Discriminant Analysis demonstrated separation between healthy and OA synovial fluid. Lectin microarrays identified distinct glycosylation patterns between healthy and OA synovial fluid, including increased Core 1/Core 3 O-glycosylation, increased α-2,3 sialylation and decreased α-1,2 fucosylation in OA. O-glycans predominated over N-glycans in all synovial fluid samples, and synovial fluid lubricin was increased in OA joints as compared to controls. MAIN LIMITATIONS: The sample size in cohort 1 was limited, and there is inherent variation in severity and duration of joint injury in naturally occurring OA. However, LC-MS identified up to 5000 unique features. CONCLUSIONS: These data suggest new potential diagnostic and therapeutic targets for equine OA. Future targeted metabolomic and glycomic studies should be performed to verify these results. Lectin microarrays could be investigated as a potential screening tool for the diagnosis and therapeutic monitoring of equine 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 | 0.001 | 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".