Epigenetic Mechanisms That Drive Genetic Risk in Juvenile Arthritis

摘要 我们的目标是将遗传学应用于幼年特发性关节炎(JIA)的领域从 基因关联的识别和对遗传变异如何发挥作用的机械性理解 风险赋予效应。我们将完成目前面临的两项主要任务：(1)识别 产生导致风险的生物效应的变异(“因果变异”)；(2)基因的鉴定 其表达水平被这些变种(“靶基因”)改变。为了实现这些目标， 我们还将阐明这些变异改变基因表达和细胞 功能。 GWA对许多复杂特征的惊人发现之一，包括风湿性疾病，如 Jia是与疾病相关的遗传变异在非编码基因组中出现的频率。AS 在其他复杂性状中，JIA遗传风险基因座高度富含H3K4me1/H3K27组蛋白标记， 表观遗传特征经常与增强子功能相关。这一发现导致了假设 JIA中的遗传风险影响增强子功能，导致转录异常，可能是 在外周血细胞中观察到的。在这一应用中，我们关注的是CD4+T细胞，这是我们的初步数据 提示是JIA中可能受到因果遗传变异影响的细胞之一。 在目标1中，我们将根据不同的生物学特性识别因果变异。我们将确定组蛋白 JIA患儿CD4+T细胞中的数量性状基因座(HQTL) 与H3K4me1/H3K27ac切割并运行测序上读取深度的差异有关。我们将使用 与我们的合作调查员加夫尼博士之前在调查 系统性红斑狼疮遗传学。然后我们将确定hQTL中改变DNA拓扑的变体，一个 调节功能的关键决定因素。最后，从通过两个屏幕的变量中，我们将使用 大规模平行报告分析(MPRA)以确定hQTL中那些具有显著 对基因表达的影响。 在目标2中，我们将确定JIA风险单倍型中的靶基因。这些问题的基本前提 研究表明，尽管因果变异可能不会影响最近的基因，但大多数相关基因 相互作用将发生在相同的拓扑相关域(TADS)内。使用即采即用数据 我们在AIM 1和H3K27ac HiChIP数据中生成，并由我们发布的CTCF补充 ChIPseq/HiChIP数据，我们将识别风险单倍型上H3K27ac标记区域之间的相互作用 和基因启动子，重点是CTCF锚定的TADS中的那些。3D染色质的知识 然后，结构、患者基因和RNAseq数据将使我们能够识别变异的可能目标基因 关于风险单倍型。

Abstract We aim to move the field of genetics as applied to juvenile idiopathic arthritis (JIA) away from the identification of genetic associations and toward a mechanistic understanding of how genetic variants exert risk-conferring effects. We will accomplish two major tasks now facing the field: (1) identification of the variants that exert the biological effects that confer risk (the “causal variants”); (2) identification of the genes whose expression levels are altered by those variants (the “target genes”). In accomplishing these aims, we will also elucidate mechanisms through which those variants alter gene expression and cellular functions. One of the striking findings from GWAS for many complex traits, including rheumatic diseases such as JIA, is the frequency with which disease-associated genetic variants appear in the non-coding genome. As in other complex traits, the JIA genetic risk loci are highly enriched for H3K4me1/H3K27histone marks, epigenetic signatures frequently associated with enhancer function. This finding has led to the hypothesis that genetic risk in JIA impinges on enhancer function, leading to transcriptional abnormalities that can be observed in peripheral blood cells. In this application, we focus on CD4+ T cells, which our preliminary data suggest are among the cells likely to be impacted by causal genetic variants in JIA. In Aim 1, we will identify causal variants based on distinct biological properties. We will identify histone quantitative trait loci (hQTLs) in CD4+ T cells of children with JIA, i.e., regions where genetic variants are associated with differences in read depth on H3K4me1/H3K27ac Cut-and-Run sequencing. We will use the same approach as that previously used by our co-investigator, Dr. Gaffney, in his investigations into the genetics of systemic lupus. We will then identify the variants within the hQTLs that alter DNA topology, a critical determinant of regulatory function. Finally, from variants that pass both screens, we will use a massively parallel reporter assay (MPRA) to identify those variants within the hQTLs that have a significant influence on gene expression. In Aim 2, we will identify the target genes within the JIA risk haplotypes. The underlying premise of these studies is that, although the causal variants may not impact the nearest gene, the majority of relevant interactions will occur within the same topologically associated domains (TADs). Using Cut-and-Run data that we generate in Aim 1 as well as H3K27ac HiChIP data and supplemented by our published CTCF ChIPseq/HiChIP data, we will identify interactions between H3K27ac-marked regions on the risk haplotypes and gene promoters, focusing on those within CTCF-anchored TADs. Knowledge of the 3D chromatin structure, patient genotype, and RNAseq data will then allow us to identify the likely target genes of variants on the risk haplotypes.