Exploring the Early Molecular Pathogenesis of Osteoarthritis Using Differential Network Analysis of Human Synovial Fluid.

The molecular mechanisms that drive the onset and development of osteoarthritis (OA) remain largely unknown. In this exploratory study, we used a proteomic platform (SOMAscan assay) to measure the relative abundance of more than 6000 proteins in synovial fluid (SF) from knees of human donors with healthy or mildly degenerated tissues, and knees with late-stage OA from patients undergoing knee replacement surgery. Using a linear mixed effects model, we estimated the differential abundance of 6251 proteins between the three groups. We found 583 proteins upregulated in the late-stage OA, including MMP1, collagenase 3 and interleukin-6. Further, we selected 760 proteins (800 aptamers) based on absolute fold changes between the healthy and mild degeneration groups. To those, we applied Gaussian Graphical Models (GGMs) to analyze the conditional dependence of proteins and to identify key proteins and subnetworks involved in early OA pathogenesis. After regularization and stability selection, we identified 102 proteins involved in GGM networks. Notably, network complexity was lost in the protein graph for mild degeneration when compared to controls, suggesting a disruption in the regular protein interplay. Furthermore, among our main findings were several downregulated (in mild degeneration versus healthy) proteins with unique interactions in the healthy group, one of which, SLCO5A1, has not previously been associated with OA. Our results suggest that this protein is important for healthy joint function. Further, our data suggests that SF proteomics, combined with GGMs, can reveal novel insights into the molecular pathogenesis and identification of biomarker candidates for early-stage OA.

Genetic Influence on Osteoarthritis Versus Other Rheumatic Diseases.

OBJECTIVE: We aimed to compare the genetic contribution to osteoarthritis (OA) versus other rheumatic/musculoskeletal diseases (RMDs) in the same population and to explore the role for any shared genetics between OA and other RMDs. METHODS: In 59,970 Swedish twins aged 35 years or older, we estimated the heritability (the variance explained by genetic factors) of OA in peripheral joints, back and neck pain, shoulder pain (adhesive capsulitis, impingement syndrome, etc), rheumatoid arthritis, spondyloarthritis (SpA) and psoriatic arthritis, myalgia, and osteoporosis diagnosed in specialist and inpatient care. We also studied how much covariance between OA and each of the RMDs could be explained by genetics by studying phenotypic correlations in bivariate classical twin models. RESULTS: Any-site OA and hip OA (50% and 64%) were among the most heritable RMDs (as compared with 23% for fibromyalgia [lowest] and 63% for SpA [highest]). The highest phenotypic correlations were between OA (any joint site) and shoulder pain in the same individual (r = 0.33, 95% confidence interval 0.31-0.35), of which 70% (95% confidence interval 52-88) could be explained by shared genetics. The phenotypic correlation between OA and back/neck pain was r = 0.25, with 25% to 75% explained by genetics. Phenotypic correlations between OA and each of the other RMDs were lower (r ~ 0.1 to r ~ 0.2), with inconclusive sources of variation. CONCLUSION: OA has relatively large heritability as compared with other RMDs. The coexistence of OA and shoulder pain, as well as back pain, was common and could often be explained by genetic factors. Findings imply similar etiologies of OA and several pain conditions.

A human meniscus explant model for studying early events in osteoarthritis development by proteomics.

Degenerative meniscus lesions have been associated with both osteoarthritis etiology and its progression. We, therefore, sought to establish a human meniscus ex vivo model to study the meniscal response to cytokine treatment using a proteomics approach. Lateral menisci were obtained from five knee-healthy donors. The meniscal body was cut into vertical slices and further divided into an inner (avascular) and outer region. Explants were either left untreated (controls) or stimulated with cytokines. Medium changes were conducted every 3 days up to Day 21 and liquid chromatography-mass spectrometry was performed at all the time points for the identification and quantification of proteins. Mixed-effect linear regression models were used for statistical analysis to estimate the effect of treatments versus control on protein abundance. Treatment by IL1ß increased release of cytokines such as interleukins, chemokines, and matrix metalloproteinases but a limited catabolic effect in healthy human menisci explants. Further, we observed an increased release of matrix proteins (collagens, integrins, prolargin, tenascin) in response to oncostatin M (OSM) + tumor necrosis factor (TNF) and TNF+interleukin-6 (IL6) + sIL6R treatments, and analysis of semitryptic peptides provided additional evidence of increased catabolic effects in response to these treatments. The induced activation of catabolic processes may play a role in osteoarthritis development.

Identification and quantification of degradome components in human synovial fluid reveals an increased proteolytic activity in knee osteoarthritis patients vs controls.

Synovial fluid (SF) may contain cleavage products of proteolytic activities. Our aim was to characterize the degradome through analysis of proteolytic activity and differential abundance of these components in a peptidomic analysis of SF in knee osteoarthritis (OA) patients versus controls (n = 23). SF samples from end-stage knee osteoarthritis patients undergoing total knee replacement surgery and controls, that is, deceased donors without known knee disease were previously run using liquid chromatography mass spectrometry (LC-MS). This data was used to perform new database searches generating results for non-tryptic and semi-tryptic peptides for studies of degradomics in OA. We used linear mixed models to estimate differences in peptide-level expression between the two groups. Known proteolytic events (from the MEROPS peptidase database) were mapped to the dataset, allowing the identification of potential proteases and which substrates they cleave. We also developed a peptide-centric R tool, proteasy, which facilitates analyses that involve retrieval and mapping of proteolytic events. We identified 429 differentially abundant peptides. We found that the increased abundance of cleaved APOA1 peptides is likely a consequence of enzymatic degradation by metalloproteinases and chymase. We identified metalloproteinase, chymase, and cathepsins as the main proteolytic actors. The analysis indicated increased activity of these proteases irrespective of their abundance.