Leukocyte Gene Expression and Genetic Biomarkers of OA Incidence and Progression

描述(由申请人提供)：本次竞争性更新将建立在前一时期的生物标记物发现的基础上。在四个目标中，我们将重点放在总体假设上，即“炎症性”候选生物标记物识别出有发生症状性膝骨性关节炎(SKOA)的风险的患者，或那些已确定有SKOA的患者，他们疾病进展的风险更高。与我们在这笔赠款上的合作者一起，我们建议使用四个OA队列来验证和复制我们最初的发现：1)NIH骨关节炎倡议(OAI，M.Hochberg)；2)第二周期OAI扩展(E)纽约大学(OAI ENYU)使用高质量放射照片的发现队列；3)约翰斯顿县骨关节炎项目(JoCo，J.Jordan)；以及4)全面性骨关节炎的遗传学纵向研究(Gogo Long，V.Kraus)。在目标1中，我们将确定候选基因组生物标记物(炎症基因的外周血白细胞表达)或血浆蛋白/脂(GPL)生物标记物是否预测有症状的膝骨性关节炎患者疾病严重程度或进展的风险增加。我们将：a)在OAI中验证从纽约大学发现的GPL生物标记物小组；b)从OAI eNYU构建GPL生物标记物小组；c)在Gogo Long和JoCo复制该小组。在目标2中，我们将确定候选遗传生物标记物(来自GWAS分析的映射到炎症基因的SNPs和来自全外显子测序分析的外显子变异)是否预测放射学膝骨性关节炎患者疾病进展的风险增加。我们将：a)构建来自OAI eNYU的SNP生物标记物小组；b)在Gogo JoCo中验证该小组；c)构建来自eNYU OAI的外显子变异生物标记小组；d)通过在eNYU OAI中坚持来验证该小组。在目标3中，我们将进行综合分析，应用预测性多分析方法来确定遗传变异、基因表达和血浆蛋白/脂表达之间潜在的致病相互作用。我们将：a)在OAI eNYU中使用GPL SNP生物标记物进行整合建模；b)在Gogo和JoCo中验证该模型；c)在OAI eNYU中使用GPL SNP外显子变异生物标记物进行整合建模。在目标4中，我们将确定基因组、遗传和蛋白质/脂肪生物标记物是否能预测膝关节骨性关节炎发病风险的增加。相关性：骨关节炎是一种影响多个关节的常见疾病，导致关节破坏、功能丧失和残疾。根据我们的初步数据，我们预计这项建议将确定预后生物标记物测试(S)，可以预测有发病风险或迅速进展到关节破坏的个体。这些预后生物标志物的验证将有助于开发新的治疗疾病的骨性关节炎药物，并在未来可能加强对这种疾病的临床管理，就像对药物管理的决定一样。

DESCRIPTION (provided by applicant): This Competitive Renewal will build upon biomarker discoveries from the previous period. In four Aims we will focus upon the overall hypothesis that "inflammatory" candidate biomarkers identify patients at risk for incident symptomatic knee OA (SKOA), or those patients with established SKOA, who are at increased risk for disease progression. Together with our collaborators on this grant, we propose to validate and replicate our initial discoveries using four OA cohorts: 1) the NIH Osteoarthritis Initiative (OAI, M. Hochberg); 2) a second cycle OAI+expanded (e)NYU (OAI+eNYU) discovery cohort using high quality radiographs; 3) the Johnston County Osteoarthritis Project (JoCo, J. Jordan); and 4) the Genetics of Generalized Osteoarthritis longitudinal study (GOGO Long, V. Kraus). In Aim 1, we will determine whether candidate Genomic biomarkers (peripheral blood leukocyte expression of inflammatory genes) or plasma Protein/Lipid (GPL) biomarkers predict increased risk of disease severity or progression in subjects with symptomatic radiographic knee OA. We will: a) validate discovered GPL biomarker panel from NYU in the OAI; b) construct a panel of GPL biomarkers from OAI+eNYU; c) replicate this panel in GOGO Long and JoCo. In Aim 2 we will determine whether candidate genetic biomarkers (SNPs from GWAS assays that map to inflammatory genes and exonic variants from whole-exome sequencing assay) predict increased risk of disease progression in subjects with radiographic knee OA. We will: a) construct a panel of SNP biomarkers from OAI+eNYU; b) validate this panel in GOGO+JoCo; c) construct a panel of exonic variant biomarkers from eNYU+OAI; d) validate this panel by holdout in eNYU+OAI. In Aim 3 we will perform an integrative analysis, applying a predictive multi-assay analyses approach to identify potential pathogenic interactions among genetic variations, gene expression and plasma protein/lipid expression. We will: a) perform integrative modeling using GPL+SNP biomarkers in OAI+eNYU; b) validate this model in GOGO and JoCo; c) perform integrative modeling using GPL+SNP+exonic variant biomarkers in OAI+eNYU. In Aim 4 we will determine whether genomic, genetic and protein/lipid biomarkers predict increased risk of incident symptomatic knee OA. Relevance: Osteoarthritis is a common disease that affects multiple joints, resulting in joint destruction, loss of function and disability. Base on our preliminary data we expect that this proposal will identify prognostic biomarker test(s) that can predict individuals at risk for disease onset or of progressing rapidly to joint destruction. Validation of such prognostic biomarkers will facilitate the development new disease-modifying OA drugs, and, in the future, could enhance clinical management of the disease, as in decisions regarding administration of drugs.