Epigenetic Mechanisms That Drive Genetic Risk in Juvenile Arthritis

导致幼年关节炎遗传风险的表观遗传机制

• 批准号: 10364303
• 负责人: JAMES N JARVIS
• 金额: $ 65.31万
• 依托单位: STATE UNIVERSITY OF NEW YORK AT BUFFALO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-26 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10364303
• 关键词: 3-Dimensional; ATAC-seq; Address; Affect; Alleles; Allelic Imbalance; Autoimmune Diseases; Bioinformatics; Biological; Biological Assay; Blood Cells; CD4 Positive T Lymphocytes; Cell physiology; Cells; Child; Childhood; Chromatin; Chromatin Structure; Chronic Childhood Arthritis; Chronic Disease; Clinical; Complex; DNA; Data; Disease; Elements; Enhancers; Epigenetic Process; Frequencies; Gene Expression; Genes; Genetic; Genetic Diseases; Genetic Risk; Genetic Transcription; Genome; Genotype; Haplotypes; Histones; In Vitro; Influentials; Investigation; Knowledge; Laboratories; Link; Linkage Disequilibrium; Lupus; Maps; Methods; Pathologic; Patients; Phenotype; Process; Property; Publishing; Quantitative Trait Loci; Reporter; Reporter Genes; Research Personnel; Rheumatism; Risk; Role; Signal Transduction; Testing; Transactivation; Untranslated RNA; Variant; base; causal variant; chromatin modification; disease phenotype; disorder risk; epigenome; genetic association; genetic variant; genome wide association study; genotyped patients; improved outcome; in silico; innovation; insight; novel therapeutics; nuclease; promoter; risk variant; therapy outcome; tool; trait; transcriptome sequencing

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.

抽象的 我们旨在将适用于少年特发性关节炎（JIA）远离遗传学领域 鉴定遗传关联并迈向对遗传变异的机械理解 风险支撑效应。我们将完成两项面临的领域的两个主要任务：（1）识别该领域 会产生会议风险的生物学效应的变体（“因果变体”）； （2）鉴定基因 这些变体（“靶基因”）的表达水平改变了。在完成这些目标时， 我们还将阐明这些变体改变基因表达和细胞的机制 功能。 GWA的许多复杂性状的引人注目的发现之一，包括风湿性疾病，例如 JIA是在非编码基因组中出现与疾病相关的遗传变异的频率。作为 在其他复杂性状中，JIA遗传风险基因座高度富含H3K4ME1/H3K27组织蛋白标记， 经常与增强子功能相关的表观遗传特征。这一发现导致了假设 JIA中这种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。在此应用程序中，我们专注于我们的初步数据的CD4+ T细胞 建议是可能受到JIA因果遗传变异的影响的细胞之一。 在AIM 1中，我们将根据不同的生物学特性确定因果变异。我们将识别组蛋白 JIA儿童CD4+ T细胞中的定量性状基因座（HQTL），即遗传变异的区域 与H3K4ME1/H3K27AC剪切和运行测序的读取深度差异有关。我们将使用 与我们的共同投资者Gaffney博士先前使用的方法相同的方法 系统性狼疮的遗传学。然后，我们将确定改变DNA拓扑的HQTL中的变体，A 监管功能的关键确定器。最后，从通过两个屏幕的变体中，我们将使用一个 大规模平行的记者测定法（MPRA）识别具有重要意义的HQTL中的那些变体 对基因表达的影响。 在AIM 2中，我们将确定JIA风险单倍型中的目标基因。这些的基本前提 研究是，尽管因果变异可能不会影响最近的基因，但大多数相关的基因 相互作用将发生在相同的拓扑相关域（TAD）中。使用剪切数据 我们在AIM 1以及H3K27AC HICHIP数据中生成，并由我们已发表的CTCF补充 Chipseq/Hichip数据，我们将确定H3K27AC标记区域的相互作用在风险单倍型上 和基因启动子，专注于CTCF锚定的TAD中的启动子。 3D染色质的知识 然后，结构，患者基因型和RNASEQ数据将使我们能够识别出可能的靶向基因 关于风险单倍型。