Molecular Biosciences Program, Montana State University, Bozeman, USA.
Molecular Biosciences Program, and Department of Microbiology and Immunology, Montana State University, Bozeman, USA.
Clin Exp Rheumatol. 2019 May-Jun;37(3):393-399. Epub 2019 Jan 3.
The objective of this study was to analyse the metabolomic profiles of rheumatoid arthritis synovial fluid to test the use of global metabolomics by liquid chromatography-mass spectrometry for clinical analysis of synovial fluid.
Metabolites were extracted from rheumatoid arthritis (n=3) and healthy (n=5) synovial fluid samples using 50:50 water: acetonitrile. Metabolite extracts were analysed in positive mode by normal phase liquid chromatography-mass spectrometry for global metabolomics. Statistical analyses included hierarchical clustering analysis, principal component analysis, Student's t-test, and volcano plot analysis. Metabolites were matched with known metabolite identities using METLIN and enriched for relevant pathways using IMPaLA.
1018 metabolites were detected by LC-MS analysis in synovial fluid from rheumatoid arthritis and healthy patients, with 162 metabolites identified as significantly different between diseased and control. Pathways upregulated with disease included ibuprofen metabolism, glucocorticoid and mineralocorticoid metabolism, alpha-linolenic acid metabolism, and steroid hormone biosynthesis. Pathways downregulated with disease included purine and pyrimidine metabolism, biological oxidations, arginine and proline metabolism, the citrulline-nitric oxide cycle, and glutathione metabolism. Receiver operating characteristic analysis identified 30 metabolites as putative rheumatoid arthritis biomarkers including various phospholipids, diol and its derivatives, arsonoacetate, oleananoic acid acetate, docosahexaenoic acid methyl ester, and linolenic acid and eicosatrienoic acid derivatives.
This study supports the use of global metabolomic profiling by liquid chromatography-mass spectrometry for synovial fluid analysis to provide insight into the aetiology of disease.
本研究旨在分析类风湿关节炎滑膜液的代谢组学特征,以检验液相色谱-质谱联用的全局代谢组学在滑膜液临床分析中的应用。
采用水-乙腈(50:50)混合液从类风湿关节炎(n=3)和健康(n=5)滑膜液样本中提取代谢物。采用正相液相色谱-质谱联用对代谢物提取物进行全局代谢组学分析。统计分析包括层次聚类分析、主成分分析、学生 t 检验和火山图分析。采用 METLIN 对代谢物进行匹配,以确定其已知的代谢物身份,并利用 IMPaLA 对相关通路进行富集。
通过 LC-MS 分析,在类风湿关节炎和健康患者的滑膜液中检测到 1018 种代谢物,其中 162 种代谢物被鉴定为疾病和对照组之间存在显著差异。与疾病相关的上调通路包括布洛芬代谢、糖皮质激素和盐皮质激素代谢、α-亚麻酸代谢和甾体激素生物合成。与疾病相关的下调通路包括嘌呤和嘧啶代谢、生物氧化、精氨酸和脯氨酸代谢、瓜氨酸-一氧化氮循环和谷胱甘肽代谢。受试者工作特征分析确定了 30 种代谢物作为潜在的类风湿关节炎生物标志物,包括各种磷脂、二醇及其衍生物、砷酸酯、齐墩果酸乙酸酯、二十二碳六烯酸甲酯、亚油酸和二十碳三烯酸衍生物。
本研究支持采用液相色谱-质谱联用的全局代谢组学方法对滑膜液进行分析,以深入了解疾病的发病机制。