Determing the genetic basis of responses to biomechanical strain in an in vitro model of osteoarthritis

确定骨关节炎体外模型中生物力学应变反应的遗传基础

• 批准号: 10348171
• 负责人: Anthony Hung
• 金额: $ 5.18万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10348171
• 关键词: ATAC-seq; Abbreviations; Accounting; Affect; Biochemical; Biological; Biology; Biomechanics; Bone structure; Cell Culture Techniques; Cell Line; Cells; Chondrocytes; Chromatin; Chronic; Complex; DNA Methylation; Data; Degenerative polyarthritis; Development; Disease; Disease Progression; Environmental Risk Factor; Epigenetic Process; Gene Expression; Gene Expression Profile; Gene Expression Regulation; Genes; Genetic; Genetic Transcription; Genetic Variation; Genomics; Genotype; Genotype-Tissue Expression Project; Health; Heritability; Heterogeneity; Homeostasis; Human; In Vitro; Individual; Individual Differences; Joints; Link; Maps; Measures; Mediating; Mentors; Methods; Modeling; Nature; Onset of illness; Pain; Pathogenesis; Pathway interactions; Pattern; Periodicity; Phenotype; Pilot Projects; Process; Publishing; Quantitative Trait Loci; Replacement Arthroplasty; Research; Resort; Risk; Sampling; Specificity; Stimulus; Stress; Structure; System; Testing; Time; Tissue-Specific Gene Expression; Tissues; Trauma; Treatment Protocols; United States; Variant; Work; arthropathies; articular cartilage; cartilage cell; disability; experimental study; gene environment interaction; genetic association; genome wide association study; genomic locus; improved; in vitro Model; induced pluripotent stem cell; insight; inter-individual variation; joint inflammation; joint loading; mechanical load; molecular phenotype; response; risk variant; single-cell RNA sequencing; skills; subchondral bone; tissue culture; transcriptome sequencing

Osteoarthritis (OA), a degenerative joint disorder characterized by articular cartilage damage and alterations to the structure of subchondral bone, is the most common joint disease worldwide1. Numerous genetic loci2 and environmental factors such as biomechanical stress3–8 have been associated with joint health and may modulate the regulation of gene expression on the road to mediate disease. However, how these factors interact to initiate OA pathogenesis is still unclear. To evaluate the effects of gene-by-environment interactions on gene regulation in the context of human OA development, the work proposed here will study inter-individual variation in gene expression responses to biomechanical stress in chondrocytes, the primary cells of cartilage. Specifically, in Aim 1, I will characterize gene expression in stressed and control chondrocytes using an iPSC-derived biomechanical strain model of OA. I have optimized a cyclic tensile strain treatment regimen model of OA9–12 for use on iPSC-derived chondrocytes13. I have further applied these methods to three individuals from a panel of 58 Yoruba (abbreviation: YRI) human iPSC lines14. Using bulk and single-cell RNA-seq data from this experiment, I have identified patterns of differential gene expression between biomechanical strain conditions. In the continuation of this work, I will differentiate all 58 YRI iPSC lines into chondrocytes and characterize bulk and single-cell transcription in strained and control chondrocytes. In Aim 2, I will identify biomechanical strain dynamic expression quantitative trait loci (eQTLs) in differentiated chondrocytes. I have used data from a small-scale pilot study to establish the viability of this strain model of OA for mapping eQTLs. I will use RNA- seq data collected in Aim 1 to identify dynamic eQTLs that vary in effect between treatment conditions while assessing and accounting for disparities in differentiation efficiency and heterogeneity in the response to cyclic tensile strain. In Aim 3, I will integrate mapped dynamic eQTLs with genome-wide association study (GWAS) and epigenetic data to better understand the functional consequences of variation at genetic loci associated with OA. I will test for enrichment and colocalization of my dynamic eQTLs among published significant OA GWAS loci15 to determine if OA genetic associations could influence OA risk through context- specific gene regulation. I will also evaluate the tissue-specificity of my dynamic eQTLs using data from the Genotype-Tissue Expression Project16. Finally, I will collect DNA methylation and chromatin accessibility data from my in vitro system to assess how these molecular phenotypes change in response to biomechanical stress and potentially mediate transcriptional changes. Overall, my work will elucidate the genetic basis of how biomechanical stress impacts human joint health. More broadly, my project will deepen our understanding of how genetic associations with complex diseases may be mediated through gene-by-environment interactions. At the same time, this work will provide me abundant opportunities to grow my wet lab and computational research skills while benefitting from the support of a wide group of collaborators and mentors.

骨关节炎 (OA)，一种退行性关节疾病，其特征是关节软骨损伤和关节软骨改变 软骨下骨的结构是世界范围内最常见的关节疾病1。许多遗传位点2和 生物力学压力等环境因素3-8 与关节健康有关，并且可能会调节 介导疾病过程中基因表达的调节。然而，这些因素如何相互作用来启动 OA的发病机制尚不清楚。评估基因与环境相互作用对基因调控的影响 在人类 OA 发展的背景下，这里提出的工作将研究基因的个体间变异 软骨细胞（软骨的主要细胞）对生物力学应激的表达反应。具体来说，在目标 1，我将使用 iPSC 衍生的方法来表征应激软骨细胞和对照软骨细胞中的基因表达 OA 的生物力学应变模型。我优化了OA9-12的循环拉伸应变治疗方案模型 用于 iPSC 衍生的软骨细胞13。我进一步将这些方法应用于小组中的三个人 58 个约鲁巴人（缩写：YRI）人类 iPSC 系14。使用来自此的批量和单细胞 RNA-seq 数据 通过实验，我确定了生物力学应变条件之间差异基因表达的模式。 在这项工作的后续工作中，我将把所有 58 个 YRI iPSC 系分化为软骨细胞并表征体积 以及应变软骨细胞和对照软骨细胞中的单细胞转录。在目标 2 中，我将识别生物力学应变 分化软骨细胞中动态表达数量性状位点（eQTL）。我使用过的数据来自 一项小规模试点研究，旨在建立 OA 菌株模型用于绘制 eQTL 的可行性。我将使用RNA- 目标 1 中收集的 seq 数据用于识别动态 eQTL，这些动态 eQTL 在处理条件之间的效果有所不同，同时 评估和解释循环响应中分化效率和异质性的差异 拉伸应变。在目标 3 中，我将把映射的动态 eQTL 与全基因组关联研究 (GWAS) 结合起来 和表观遗传数据，以更好地了解遗传位点变异的功能后果 与 OA 相关。我将测试已发表的动态 eQTL 的富集和共定位 显着的 OA GWAS 位点15，以确定 OA 遗传关联是否可以通过背景影响 OA 风险 特异性基因调控。我还将使用来自以下组织的数据评估动态 eQTL 的组织特异性： 基因型组织表达项目16。最后，我将收集 DNA 甲基化和染色质可及性数据 从我的体外系统评估这些分子表型如何响应生物力学压力而变化 并可能介导转录变化。总的来说，我的工作将阐明如何的遗传基础 生物力学压力影响人类关节健康。更广泛地说，我的项目将加深我们对 如何通过基因与环境的相互作用来介导与复杂疾病的遗传关联。 同时，这项工作将为我提供丰富的机会来发展我的湿实验室和计算能力 研究技能，同时受益于广泛的合作者和导师的支持。