Extracellular RNA Expression Biomarkers in Osteoarthritis Disease and Progression

项目摘要 骨关节炎（OA）是一种进行性和退行性关节疾病，随着年龄的增长，其发病率越来越高 针对疼痛的主要症状的现有治疗仅获得最小成功的人群。 OA的表现是异质性的，个体之间的病理学不同。这种疾病 异质性使得鉴定可靠和一致的分子生物标志物变得困难。这在一定程度上 阻碍了特定治疗方法的发展。因此， 需要OA临床表型和疾病进展纵向变化的特异性生物标志物。 为了开始解决这个问题，在这里，我们提出了一个倡议，发现血浆细胞外RNA 在不同的临床和进行性表型中，OA的外链RNA（exRNA）表达特征基因组特征。 exRNA已被证明在多种复杂疾病中具有预后和生物学价值。的 实验将利用骨关节炎倡议（OAI），这是一个前瞻性、纵向多中心队列研究 近5000名被诊断为放射学膝关节OA或有患病风险的受试者的研究。我们首先将集群 根据生化测定、成像数据和临床表现，将整个OAI队列分为不同的临床表型。 症状问卷和使用高通量鉴定与那些表型相关的exRNA标签 RNA测序（SA 1）。此外，我们将通过以下方式确定患有进展性OA的个体， X光片随时间变化以及随时间增加的疼痛。我们将产生纵向时间- exRNA表达的过程概况以推断该进展的生物学机制（SA 2）。 通过产生特定的表型定义，这项研究增加了识别分子特征的能力 这些OA群体。这将是利用功能基因组学的价值作为 OA精准医学的生物标志物。

PROJECT SUMMARY Osteoarthritis (OA) is a progressive and degenerative joint disease with an increasing prevalence in the aging population for which existing treatments targeting the primary symptom of pain are only minimally successful. The manifestation of OA is heterogeneous with symptomology differing across individuals. This disease heterogeneity has made identification of reliable and consistent molecular biomarkers elusive. This has in part hindered the progress toward development of specific therapeutic treatments. Therefore, the identification of specific biomarkers of OA clinical phenotypes and longitudinal changes over disease progression is required. To begin addressing this issue, here we propose an initiative for the discovery of plasma extracellular RNA (exRNA) expression signatures genomic signatures of OA among diverse clinical and progressive phenotypes. exRNA has been demonstrated in a variety of complex diseases to have prognostic and biological value. The experiments will utilize the Osteoarthritis Initiative (OAI) which is a prospective, longitudinal multicenter cohort study of nearly 5000 subjects diagnosed with radiographic knee OA or at risk for the disease. We will first cluster the entire OAI cohort into distinct clinical phenotypes based on biochemical assays, imaging data, and clinical symptom questionnaires and identify exRNA signatures associated with those phenotypes using high throughput RNA sequencing (SA1). Furthermore, we will identify individuals with progressive OA defined as progression by radiograph changes over time as well as increasing pain over time. We will then generate longitudinal time- course profiles of exRNA expression to deduce biological mechanisms of that progression (SA2). By generating specific phenotype definitions, this study has increased power to identify molecular signatures of these OA groups. This will be an important step toward harnessing the value of functional genomics as a biomarker in the precision medicine of OA.