Molecular Biosciences Program, Montana State University, Bozeman, USA; Department of Cell Biology & Neuroscience, Montana State University, Bozeman, USA.
Molecular Biosciences Program, Montana State University, Bozeman, USA; Department of Microbiology & Immunology, Montana State University, Bozeman, USA.
Biochem Biophys Res Commun. 2018 May 5;499(2):182-188. doi: 10.1016/j.bbrc.2018.03.117. Epub 2018 Mar 24.
Osteoarthritis affects over 250 million individuals worldwide. Currently, there are no options for early diagnosis of osteoarthritis, demonstrating the need for biomarker discovery. To find biomarkers of osteoarthritis in human synovial fluid, we used high performance liquid-chromatography mass spectrometry for global metabolomic profiling. Metabolites were extracted from human osteoarthritic (n = 5), rheumatoid arthritic (n = 3), and healthy (n = 5) synovial fluid, and a total of 1233 metabolites were detected. Principal components analysis clearly distinguished the metabolomic profiles of diseased from healthy synovial fluid. Synovial fluid from rheumatoid arthritis patients contained expected metabolites consistent with the inflammatory nature of the disease. Similarly, unsupervised clustering analysis found that each disease state was associated with distinct metabolomic profiles and clusters of co-regulated metabolites. For osteoarthritis, co-regulated metabolites that were upregulated compared to healthy synovial fluid mapped to known disease processes including chondroitin sulfate degradation, arginine and proline metabolism, and nitric oxide metabolism. We utilized receiver operating characteristic analysis to determine the diagnostic value of each metabolite and identified 35 metabolites as potential biomarkers of osteoarthritis, with an area under the receiver operating characteristic curve >0.9. These metabolites included phosphatidylcholine, lysophosphatidylcholine, ceramides, myristate derivatives, and carnitine derivatives. This pilot study provides strong justification for a larger cohort-based study of human osteoarthritic synovial fluid using global metabolomics. The significance of these data is the demonstration that metabolomic profiling of synovial fluid can identify relevant biomarkers of joint disease.
骨关节炎影响全球超过 2.5 亿人。目前,尚无早期诊断骨关节炎的方法,这表明需要发现生物标志物。为了在人类滑液中寻找骨关节炎的生物标志物,我们使用高效液相色谱-质谱联用进行了全面的代谢组学分析。从人类骨关节炎(n=5)、类风湿关节炎(n=3)和健康(n=5)滑液中提取代谢物,共检测到 1233 种代谢物。主成分分析清楚地区分了患病和健康滑液的代谢组学特征。类风湿关节炎患者的滑液中含有与疾病的炎症性质一致的预期代谢物。同样,无监督聚类分析发现,每种疾病状态都与独特的代谢组学特征和共同调节代谢物簇相关。对于骨关节炎,与健康滑液相比上调的共同调节代谢物映射到已知的疾病过程,包括硫酸软骨素降解、精氨酸和脯氨酸代谢以及一氧化氮代谢。我们利用接收者操作特征分析来确定每种代谢物的诊断价值,并确定了 35 种代谢物作为骨关节炎的潜在生物标志物,其接收者操作特征曲线下面积>0.9。这些代谢物包括磷脂酰胆碱、溶血磷脂酰胆碱、神经酰胺、豆蔻酸衍生物和肉碱衍生物。这项初步研究为使用代谢组学对人类骨关节炎滑液进行更大的基于队列的研究提供了强有力的依据。这些数据的意义在于证明了滑液代谢组学分析可以识别与关节疾病相关的生物标志物。