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.

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.