Global metabolic profiling of human osteoarthritic synovium

摘要

Osteoarthritis (OA) is a debilitating disease associated with pain and loss of function in numerous diarthrodial joints of the body. Assessments of the severity and/or progression of OA are commonly based on radiographic stages and pain level, which aren't always correlated to severity of disease or joint dysfunction and may be confounded by other factors. 1. There has been recent interest in identifying a biochemical signature of OA. 1 that may be detected in serum, urine, and/or synovial fluid that would represent repeatable and predictable biomarkers of OA onset and/or progression. The objective of this study was to use global metabolic profiling to identify a distinct metabolic profile for cultured human synovial tissue from patients with end-stage OA compared to patients with little or no evidence of disease. While metabolic profiles from cultured tissues are not expected to reproduce invivo profiles, it is expected that perturbations in metabolism caused by end-stage disease would result in differences in metabolic profiles invitro compared to tissue with little or no evidence of disease. Because metabolomic perturbations often occur prior to alterations in the genome or proteome, metabolomic analysis possibly provides an earlier window to an altered biochemical profile for OA onset and/or progression, and may provide a unique set of potential drug targets. The synovium was targeted because it has been implicated in OA as a mediator of disease progression; osteoarthritic synovium has been demonstrated to express pro-inflammatory cytokines, such as Tumor Necrosis Factor - α (TNF-α), Interleukin-1 β (IL-1β), and IL-6. 2, suggesting that a diseased synovial lining could produce an ideal set of biomarkers for diagnosing OA and/or monitoring disease progression. Media from the culture of synovial explants dissected from diseased human joints (early or end-stage OA) was subjected to global metabolic profiling with a liquid chromatography (LC)/and gas chromatography (GC)/mass spectrophotometry (MS)-based technology platform. Metabolites were identified by automated comparison of the ion features in the experimental samples to a reference library of chemical standard entries developed at Metabolon, Inc (Durham, NC). Global metabolic profiling resulted in the identification of 105 distinct compounds across all sample groups, with 11 compounds showing significantly different relative concentrations between end-stage and no/early disease groups. Metabolites specific to collagen metabolism, branched-chain amino acid metabolism, energy metabolism and tryptophan metabolism were amongst the most significant compounds, suggesting an altered metabolic state with disease progression.