Department of Medicine and Immunology, Duke University Medical Center and Medical Research Service, VA Medical Center, Box 151G, 508 Fulton Street, Durham, NC 27705, United States.
Semin Arthritis Rheum. 2020 Jun;50(3):373-379. doi: 10.1016/j.semarthrit.2020.02.010. Epub 2020 Mar 5.
Personalized medicine is an important goal for the treatment of rheumatic disease that seeks to improve outcomes by matching therapy more precisely with the underlying pathogenetic disturbances in the individual patient. Realization of this goal requires actionable biomarkers to identify these disturbances as well as pathways that can be targeted for novel therapy. Among advances in characterizing pathogenesis, Big Data provides an unprecedented picture of pathogenesis, with analysis of tissue lesions revealing disturbances that may not be apparent in blood. Big Data approaches include single cell RNAseq (scRNAseq) which can elucidate patterns of gene expression by individual cells. Galvanized by the Accelerating Medicines Partnership, a public-private initiative of the NIH, investigative teams have analyzed gene expression in cells in the synovium for rheumatoid arthritis and kidney for systemic lupus erythematosus. A review of basic and translational research for 2018-2019 provides the progress in these areas. Thus, the studies on rheumatoid arthritis have identified subpopulations of immune cells and fibroblasts implicated in synovitis. For lupus, transcriptomic studies have provided evidence for widespread effects of type 1 interferon. Studies in progressive sclerosis have demonstrated changes associated with stem cell therapy as well as potential new targets for anti-fibrotic agents. Other studies using molecular approaches have defined new mechanisms for vasculitis as well as the potential role of the microbiome in inflammatory arthritis and systemic lupus erythematosus. Future studies with Big Data will incorporate the spatial relationships of cells in inflammation as well as changes in gene expression over time.
个体化医学是治疗风湿性疾病的一个重要目标，旨在通过更精确地将治疗与个体患者的潜在致病紊乱相匹配来改善治疗效果。要实现这一目标，需要有可行的生物标志物来识别这些紊乱，以及可以针对新疗法的途径。在对发病机制进行特征描述的进展中，大数据提供了发病机制的前所未有的图景，对组织损伤的分析揭示了在血液中可能不明显的紊乱。大数据方法包括单细胞 RNAseq（scRNAseq），它可以通过单个细胞阐明基因表达模式。在 NIH 公私合营的加速药物研发倡议的推动下，研究小组已经分析了类风湿关节炎滑膜和系统性红斑狼疮肾脏细胞中的基因表达。对 2018-2019 年基础和转化研究的综述提供了这些领域的进展情况。因此，关于类风湿关节炎的研究已经确定了与滑膜炎有关的免疫细胞和成纤维细胞亚群。对于狼疮，转录组研究提供了 1 型干扰素广泛作用的证据。在进行性硬化症的研究中，已经证明了与干细胞治疗相关的变化以及抗纤维化药物的潜在新靶点。其他使用分子方法的研究定义了血管炎的新机制以及微生物组在炎症性关节炎和系统性红斑狼疮中的潜在作用。未来的大数据研究将纳入炎症中细胞的空间关系以及随时间变化的基因表达变化。
