Signals of Epigenetic Modification in Sjogrens Syndrome

干燥综合征的表观遗传修饰信号

基本信息

批准号：
8910299
负责人：
Michael Cole
金额：
$ 3.87万
依托单位：
UNIVERSITY OF CALIFORNIA BERKELEY
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-04-01 至 2020-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8910299
关键词：
Aberrant DNA Methylation Address Affect Age Algorithms Alleles Autoimmune Diseases Autoimmunity B-Lymphocytes Binding Biological Biological Assay Biological Markers Biopsy Carbon Cell Separation Cells ChIP-seq Characteristics Chronic Classification Clinical Clinical Data Collection Complex Country CpG dinucleotide Cytosine DNA DNA Methylation Data Data Set Development Disease Enrollment Epigenetic Process Etiology European Exhibits Fellowship Female Frequencies Future Gene Expression Genes Genetic Genetic Code Genome Genomic Segment Genomics Genotype Goals Heterogeneity Human Inflammatory International Labial Salivary Gland Lasso Link Logistic Regressions Machine Learning Measures Mediating Mediator of activation protein Mentors Methods Methylation Modification Not Hispanic or Latino Parents Participant Pathogenesis Pathway interactions Patients Pattern Phenotype Play Population Predoctoral Individual National Research Service Award Principal Component Analysis Quantitative Trait Loci RNA Resources Rheumatoid Arthritis Risk Role Salivary Gland Tissue Salivary Glands Sampling Serological Severity of illness Signal Transduction Site Sjogren&apos s Syndrome Staging Subgroup System Systemic Lupus Erythematosus T-Lymphocyte Techniques Testing Therapeutic Tissues Trans-Activators Transcriptional Regulation Woman Work abstracting base bead chip case control cell type chromatin remodeling disease phenotype disorder risk dosage epigenetic variation epigenome forest genetic risk factor genetic variant genome wide association study genome-wide high throughput technology histone modification insight methylation pattern novel outcome forecast peripheral blood public health relevance repository risk variant transcription factor

项目摘要

DESCRIPTION (provided by applicant): The objective of the project is to characterize the potential role of DNA methylation patterns in Sjögren's Syndrome (SS) disease etiology and mechanism. A growing body of evidence indicates that epigenetic changes, in particular, altered patterns of DNA methylation, contribute to the development of autoimmune disease and can mediate genetic risk factors. To date, aberrant DNA methylation has been associated with several human autoimmune diseases, including rheumatoid arthritis and systemic lupus erythematosus. My proposal focuses on the identification of unique epigenetic profiles in SS, focusing primarily on T cells, B cells, and salivary gland tissue, a primary site of the inflammatory disease. An understanding of these profiles in the context of genetic background, publicly available genomic data (RNA, ChIP-seq, WGBS) and annotation data (TF binding motifs, genomic QTLs), and cell types have the potential to significantly transform our understanding of SS etiology, particularly given the well-established heterogeneity in clinical manifestations. Further, these DNA methylation profiles could become useful clinical biomarkers for determining risk and prognosis in SS, especially if their biological relevance can be validated in future functional studies. The overall hypothesis of this project is that DNA methylation changes in key genomic regions are associated with SS status and phenotype. The project will use genome-wide SNP data, epigenome-wide methylation array data, and clinical characteristics abstracted from 100 SS cases and 20 controls to address 3 related hypotheses: First, that epigenome-wide DNA methylation patterns in T cells, B cells (both derived from peripheral blood), and salivary gland biopsy tissue are associated with primary SS status after correcting for cell-type heterogeneity. Case-control differences will be tested using supervised machine learning algorithms, new region-discovery methods, and cell-mixture inference techniques. Publicly available data (listed above) will be used to infer biological relevance. Second, that epigenome-wide DNA methylation patterns in these samples are representative of SS sub-phenotypes. Cases (and initially controls) will be clustered using principal component analysis (PCA) applied to all relevant DNA methylation probes and subsets of probes measuring CpGs implicated in specific pathways relevant to SS. The reduced representations will be tested for association with specific clinical and serological profiles. Third, that risk alleles identifie by genome-wide association studies (GWAS) are directly or indirectly associated with SS-specific DNA methylation patterns. Allelic dosage and random forest selection frequency (RFSF) analyses will be used to identify novel methylation quantitative trait loci within SS-associated genes in cases and controls (separately), and we will test whether cell-proportion or eQTLs can explain SS-specific methylation patterns.

描述（由适用提供）：该项目的目的是表征DNA甲基化模式在Sjögren综合征（SS）病病因和机制中的潜在作用。越来越多的证据表明，表观遗传学的变化，尤其是DNA甲基化的模式改变，有助于自身免疫性疾病的发展，并可以介导遗传危险因素。迄今为止，异常的DNA甲基化与几种人类自身免疫性疾病有关，包括类风湿关节炎和全身性红斑狼疮。我的建议着重于鉴定SS中独特的表观遗传谱，主要集中在T细胞，B细胞和唾液腺组织（炎症性疾病的主要部位）上。在遗传背景，公开可用的基因组数据（RNA，CHIP-SEQ，WGB）和注释数据（TF结合基序，基因组QTL）和细胞类型的情况下，对这些概况的理解，有可能显着改变我们对SS病因的理解，尤其是鉴于鉴于临床上良好成熟的异质性，尤其是鉴于临床上的杂质。此外，这些DNA甲基化谱可能成为确定SS风险和预后的有用临床生物标志物，尤其是在可以验证其生物学相关性的情况下在未来的功能研究中。该项目的总体假设是，关键基因组区域的DNA甲基化变化与SS状态和表型有关。 The project will use genome-wide SNP data, epigenome-wide methylation array data, and clinical characteristics abstracted from 100 SS cases and 20 controls to address 3 related hypotheses: First, that epigenome-wide DNA methylation patterns in T cells, B cells (both derived) from peripheral blood), and salivariate gland biopsy tissue are associated with primary SS status after correcting for cell-type异质性。病例对照差异将使用监督的机器学习算法，新的区域发现方法和细胞混合推理技术进行测试。公开可用的数据（上面列出）将用于推断生物学相关性。其次，这些样品中的表观组范围内的DNA甲基化模式代表了SS亚表征。使用主成分分析（PCA）将病例（和最初的控制）聚集在所有相关的DNA甲基化问题和测量与SS相关的特定途径中隐含的CPG的问题的子集中。降低的表示形式将与特定的临床和血清学特征相关。第三，通过全基因组关联研究（GWAS）可识别的风险等位基因直接或间接与SS特异性DNA甲基化模式相关。等位基因剂量和随机森林选择频率（RFSF）分析将用于鉴定新型的甲基化定量性状基因座（分别）在SS相关基因内（分别），我们将测试细胞成绩或EQTL是否可以解释SS特异性甲基化模式。