Research Resources: Epigenomic and Transcriptomic Profiles of Human Immune Cells

描述（由申请人提供）：全基因组关联研究（GWAS）已经确定了与许多人类疾病易感性增加相关的遗传变异（通常是单核苷酸多态性，SNP）。这些变异体中的绝大多数位于非编码基因组区域，因此被认为通过扰乱控制相同等位基因上基因表达的顺式调控DNA元件的功能来影响疾病结果。为了了解常见的遗传变异在人类疾病中的作用，我们在这里建议对来自健康人类受试者的13种纯化的循环免疫细胞类型进行表观基因组学和转录组学分析。目标1：对于来自每个供体的每种免疫细胞类型，我们将确定H3 K4 me 2和H3 K27 Ac的全基因组位置，这两种组蛋白修饰与功能增强子相关;我们还将获得细胞的转录谱以及基因型和全基因组单倍型信息。这将使我们能够确定每种人类细胞类型的基因表达谱以及顺式调控元件的强度和细胞类型特异性。目标二：我们将鉴定携带疾病相关变体的顺式调节元件，预测它们是否破坏转录因子结合位点，并使用来自杂合受试者的数据来确定顺式调节活性的等位基因特异性差异是否与顺式等位基因上的基因表达的相应变化相关。这将使我们能够建立一个计算管道，预测每一组疾病相关的遗传变异，哪些顺式调节元件，转录因子，基因，免疫细胞类型和分子途径最有可能被破坏。目标3：我们将在一个专门的网站上提供我们的实验结果和分析工具，该网站将经过设计和优化，为免疫学家提供直观的界面，最大限度地提高回答研究问题的能力，而无需先进的生物信息学专业知识。 相关性：对于许多人类疾病，大规模基因组研究已经确定了常见的遗传变异，这些变异在患有心血管、自身免疫、炎症和传染性疾病、糖尿病和哮喘的人中比在没有这些疾病的人中更频繁地发生。在这里，我们建议了解这些变异如何导致对疾病的易感性，重点关注与免疫系统相关的疾病，并利用这些信息为这些疾病寻找新的治疗靶点。

DESCRIPTION (provided by applicant): Genome-wide association studies (GWAS) have identified genetic variants (often single-nucleotide polymorphisms, SNPs) associated with increased susceptibility to many human diseases. The vast majority of these variants are located in non-coding genomic regions, and so are thought to influence disease outcome by perturbing the functions of cis-regulatory DNA elements that control gene expression on the same allele. To understand the role of common genetic variations in human disease, we propose here to perform epigenomic and transcriptomic analyses of 13 purified circulating immune cell types from healthy human subjects. Aim 1: For each immune cell type from each donor, we will determine the genome-wide locations of H3K4me2 and H3K27Ac, two histone modifications associated with functional enhancers; we will also obtain the transcriptional profiles of the cells as well as genotype and whole-genome haplotype information. This will allow us to determine, for each human cell type, the gene expression profiles as well as the strength and cell type-specificity of cis-regulatory elements. Aim 2: We will identify cis-regulatoy elements that harbor disease- associated variants, predict if they disrupt transcription factor binding sites, and use data from heterozygous subjects to determine if allele-specific differences in cis-regulatory activity are linked to corresponding changes in gene expression on the cis allele. This will allow us to establish a computational pipeline that predicts, for each set of disease-associated genetic variations, which cis-regulatory elements, transcription factors, genes, immune cell types and molecular pathways are most likely to be disrupted. Aim 3: We will make our experimental results and analysis tools available on a dedicated website that will be designed and optimized to provide immunologists with intuitive interfaces that maximize the ability to answer research questions without requiring advanced bioinformatics expertise. RELEVANCE: For many human diseases, large-scale genomic studies have identified common genetic variants that occur more frequently in people with cardiovascular, autoimmune, inflammatory and infectious diseases, diabetes and asthma than in those without these diseases. Here we propose to understand how these variants cause susceptibility to disease, focusing on diseases related to the immune system, and use this information to find novel therapeutic targets for these diseases.