Division of Rheumatology, University of California, San Diego, 9500 Gilman Drive, San Diego, CA, 92093, USA.
Department of Medicine, Autonomous University of Barcelona, Plaça Cívica, 08193 Bellaterra, Barcelona, Spain.
Arthritis Res Ther. 2018 Aug 3;20(1):164. doi: 10.1186/s13075-018-1655-3.
Metabolomics is an emerging field of biomedical research that may offer a better understanding of the mechanisms of underlying conditions including inflammatory arthritis. Perturbations caused by inflamed synovial tissue can lead to correlated changes in concentrations of certain metabolites in the synovium and thereby function as potential biomarkers in blood. Here, we explore the hypothesis of whether characterization of patients' metabolomic profiles in blood, utilizing H-nuclear magnetic resonance (NMR), predicts synovial marker profiling in rheumatoid arthritis (RA).
Nineteen active, seropositive patients with RA, on concomitant methotrexate, were studied. One of the involved joints was a knee or a wrist appropriate for arthroscopy. A Bruker Avance 700 MHz spectrometer was used to acquire NMR spectra of serum samples. Gene expression in synovial tissue obtained by arthroscopy was analyzed by real-time PCR. Data processing and statistical analysis were performed in Python and SPSS.
Analysis of the relationships between each synovial marker-metabolite pair using linear regression and controlling for age and gender revealed significant clustering within the data. We observed an association of serine/glycine/phenylalanine metabolism and aminoacyl-tRNA biosynthesis with lymphoid cell gene signature. Alanine/aspartate/glutamate metabolism and choline-derived metabolites correlated with TNF-α synovial expression. Circulating ketone bodies were associated with gene expression of synovial metalloproteinases. Discriminant analysis identified serum metabolites that classified patients according to their synovial marker levels.
The relationship between serum metabolite profiles and synovial biomarker profiling suggests that NMR may be a promising tool for predicting specific pathogenic pathways in the inflamed synovium of patients with RA.
代谢组学是生物医学研究中一个新兴的领域，它可能提供对潜在疾病机制的更好理解，包括炎症性关节炎。炎症性滑膜组织引起的干扰会导致滑膜中某些代谢物浓度的相关变化，从而成为血液中潜在的生物标志物。在这里，我们探索了这样一种假设，即利用 H-核磁共振（NMR）对患者血液代谢组特征进行表征是否可以预测类风湿关节炎（RA）患者滑膜标志物的特征。
研究了 19 名接受甲氨蝶呤联合治疗的活动期、血清阳性的 RA 患者。其中一个受累关节为适合关节镜检查的膝关节或腕关节。使用 Bruker Avance 700 MHz 光谱仪采集血清样本的 NMR 光谱。通过关节镜获得的滑膜组织的基因表达通过实时 PCR 进行分析。使用 Python 和 SPSS 进行数据处理和统计分析。
使用线性回归分析并控制年龄和性别，对每个滑膜标志物-代谢物对之间的关系进行分析，发现数据内存在显著聚类。我们观察到丝氨酸/甘氨酸/苯丙氨酸代谢和氨酰-tRNA 生物合成与淋巴样细胞基因特征相关。丙氨酸/天冬氨酸/谷氨酸代谢和胆碱衍生代谢物与 TNF-α 滑膜表达相关。循环酮体与滑膜金属蛋白酶的基因表达相关。判别分析确定了根据患者滑膜标志物水平对其进行分类的血清代谢物。
血清代谢物谱与滑膜生物标志物谱之间的关系表明，NMR 可能是预测 RA 患者炎症滑膜中特定致病途径的有前途的工具。
