Metabolism and global protein glycosylation are differentially expressed in healthy and osteoarthritic equine carpal synovial fluid

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.

Keywords: O-glycosylation; glycomics; horse; lectin microarray; lubricin/proteoglycan 4; mass spectrometry; metabolomics.

FIGURE 1

Comparison of the metabolome between healthy and osteoarthritis (OA) synovial fluid. (A) OPLS-DA loading S-plot to identify metabolites that are increased or decreased in OA. The model covariance represents the magnitude of the difference between healthy and OA of a metabolite, and the model correlation represents the reliability of the difference between healthy and OA. The metabolites situated in the extremes of the S-plot, in either positive or negative covariance, represent possible discriminating variables between healthy and OA synovial fluid samples. (B) Heat map of relative changes between metabolites (P < .05). Increases in OA are indicated in red, and decreases in OA are indicated in blue; the darker the colour, the larger the difference in concentration. (C) PCA score plot formed two distinct clusters of metabolites segregating healthy (green) and OA (red) between principal component 1 and principal component 2. Shading represents the 95% confidence interval. (D) Loading plots of the first two principal components. For clarity, some metabolite names were excluded from the figure

FIGURE 2

Pathway over representation analysis showing all matched pathways according to P-value (pathway enrichment analysis) and pathway impact (pathway topology analysis) derived from KEGG pathway codes. The P-value is indicated by colour ranging from yellow as least significant to red as most significant. Pathway impact is indicated by circle size where the pathways with the lowest impact factor have the smallest circle to those with the highest impact factor having the largest circle

FIGURE 3

Pathway map indicating the directionality of metabolites recognised to impact the top five KEGG pathways. Metabolites which were increased in OA are indicated in red, and those which were decreased are indicated in blue

FIGURE 4

Glycomic analysis between healthy and osteoarthritis (OA) synovial fluid. (A) The volcano plot indicates eight lectins (including duplicate lectins from different sources) that differed significantly between healthy and OA synovial fluid; six with a fold change greater that 50%; one with a fold change greater than −50% and one with a fold change less than −50%. (B) Heat map of the relative changes (logarithmic, base = 2) between the eight lectins identified in the volcano plot. (C) Fluorescence intensity values for four lectins showing an overall predominance of O-glycans (AIA and MNA-G) to N-glycans (PHA-E and LcH) in both healthy and OA synovial fluid samples. (D) Dual colour lectin microarray image of a representative healthy synovial fluid sample, demonstrating the predominance of O-glycans. (E) Lubricin ELISA showed increased concentrations of lubricin in equine OA synovial fluid