eQTL mapping in response to osteoarthritis induction in differentiated skeletal cell types

项目概要/摘要 骨关节炎（OA）是一种以软骨和底层骨退化为特征的关节疾病， 是全世界残疾1和经济负担2、3的主要根源。虽然已取得进展 认识到 OA 是一种复杂的疾病4，引发和介导 OA 发病的机制 发病机制尚不清楚。遗传标记5-19 和环境影响，例如生物力学 压力20-24与关节健康有关，基因表达调控的改变25-36可能与关节健康有关。 并加强这些因素对疾病进展的参与。为了更好地表征基因的程度 骨骼细胞中的表达模式与潜在的基因型相关，并根据生物力学而改变 力，我们将使用大量分化的人类软骨细胞和成骨细胞来识别个体间的差异 基因表达对机械应力处理反应的变化。具体来说，在目标 1 中，我将区分 来自 70 个人类诱导多能干细胞 (iPSC) 的软骨细胞和成骨细胞并表征 使用批量和单细胞数据分析这些细胞类型中的基因表达。拟议的研究将包括 先前对来自 70 个完全测序的 Hutterite 个体的 iPSC 组进行了表征37,38，并将使用已建立的 软骨细胞和成骨细胞分化方案39。 Drop-seq 单细胞 RNA-seq 数据将收集在 除了批量 RNA-seq 数据之外，我还可以表征基因表达的个体间变异性 解释样本中细胞组成的变化。在目标 2 中，我将治疗分化的软骨细胞和 具有生物力学应激的成骨细胞，以确定反应表达数量性状位点（eQTL）。至 为此，我将使终末分化的软骨细胞和成骨细胞承受既定的循环拉伸应变 作为 OA40-43 体外模型的治疗。响应 eQTL 将使用批量 RNA 测序来确定 数据，以及生物力学应激反应的稳健程度将使用单细胞数据进行估计。 最后，在目标 3 中，我将把生物力学应激反应 eQTL 与全基因组关联整合起来 研究 (GWAS) 数据，用于识别与 OA 风险相关的变异及其潜在分子 机制。已在主要欧洲人的个体中发现了与 OA 的多种遗传关联 体面6,8–19。由于拟议研究中的细胞组由代表的同质群体组成 欧洲遗传多样性的大部分44，它是一个理想的比较样本集。我将确定疾病- 通过测试 OA GWAS 命中的富集并评估共定位，我们的响应 eQTL 的相关性。 总的来说，这项研究将识别和表征人群中个体间的基因表达反应—— OA的规模细胞培养模型。此外，这项工作将产生最大的人类 iPSC 衍生组 软骨细胞和成骨细胞，有望对基因与环境产生深入的了解 导致骨骼系统疾病表型的相互作用。

Project Summary/Abstract Osteoarthritis (OA) is a joint disease characterized by the degradation of cartilage and underlying bone, and it is a major source of disability1 and financial burden2,3 worldwide. While progress has been made towards recognizing OA as a complex disorder4, the mechanisms that initiate and mediate the onset of OA pathogenesis are still unclear. Genetic markers5–19 and environmental influences such as biomechanical stress20–24 have been associated with joint health, and alterations in gene expression regulation25–36 may connect and reinforce these factors’ involvement in disease progression. To better characterize the degree to which gene expression patterns in skeletal cells relate to underlying genotypes and are altered in response to biomechanical forces, we will use a large panel of differentiated human chondrocytes and osteoblasts to identify inter-individual variation in gene expression responses to mechanical stress treatments. Specifically, in Aim 1, I will differentiate chondrocytes and osteoblasts from 70 human induced pluripotent stem cells (iPSCs) and characterize gene expression in these cell types using bulk and single-cell data. The proposed study will include a previously characterized panel of iPSCs from 70 fully sequenced Hutterite individuals37,38 and will use established chondrocyte and osteoblast differentiation protocols39. Drop-seq single-cell RNA-seq data will be collected in addition to bulk RNA-seq data so that I can characterize inter-individual variability in gene expression and account for variation in cell composition across samples. In Aim 2, I will treat differentiated chondrocytes and osteoblasts with biomechanical stress to identify response expression quantitative trait loci (eQTLs). To do this, I will subject terminally differentiated chondrocytes and osteoblasts to established cyclic tensile strain treatments that serve as an in vitro model of OA40–43. Response eQTLs will be determined using bulk RNA-seq data, and the degree to which biomechanical stress response is robust will be estimated using single-cell data. Finally, in Aim 3, I will integrate biomechanical stress response eQTLs with genome-wide association study (GWAS) data to identify variants associated with OA risk and the underlying molecular mechanisms. Several genetic associations with OA have been identified in individuals of primarily European decent6,8–19. Since the panel of cells in the proposed study consists of a homogeneous population that represents much of European genetic diversity44, it is an ideal comparative sample set. I will determine the disease- relevance of our response eQTLs by testing for enrichment of OA GWAS hits and evaluating colocalization. Overall, this research will identify and characterize inter-individual gene expression responses in a population- scale cell culture model of OA. Further, this work will produce the largest panel of human iPSC-derived chondrocytes and osteoblasts and is expected to yield substantial insight into the gene-by-environment interactions that contribute to disease phenotypes in the skeletal system.