Epigenetic and transcriptomic determinants of Sjogren's Syndrome subtypes utilizing data from the Sjogren's International Collaborative Clinical Alliance (SICCA) cohort

利用干燥综合征国际合作临床联盟 (SICCA) 队列的数据，研究干燥综合征亚型的表观遗传学和转录组决定因素

• 批准号: 10041649
• 负责人: CAROLINE Helene SHIBOSKI
• 金额: $ 16.15万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-09 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10041649
• 关键词: Accounting; Affect; American; Arthralgia; Autoantibodies; Biological; Biological Specimen Banks; Biopsy; Blood Cells; Caring; Cells; Classification; Clinical; Clinical Data; Clinical Trials; Cohort Studies; Collaborations; DNA Methylation; Data; Development; Diagnosis; Disease; Disease Pathway; Epidemiology; Epigenetic Process; Etiology; European; Exanthema; Exhibits; Exocrine System; Fatigue; Functional disorder; Funding; Future; Gene Expression; Genes; Genetic; Genetic Heterogeneity; Genetic study; Genome; Genomics; Genotype; Hereditary Disease; Immunologics; Infrastructure; International; Knowledge; Labial Salivary Gland; Laboratories; Lacrimal gland structure; Lymphoma; Machine Learning; Measures; Mediating; Methylation; Modeling; Modification; Molecular; Multiomic Data; National Institute of Dental and Craniofacial Research; Oral; Participant; Pathogenesis; Pathway interactions; Patients; Peripheral Blood Mononuclear Cell; Pharmaceutical Preparations; Phenotype; Positioning Attribute; Process; Quantitative Trait Loci; Randomized; Registries; Research; Research Personnel; Resources; Rheumatism; Rheumatology; Risk; Risk Factors; Salivary Gland Tissue; Salivary Glands; Secure; Site; Sjogren' s Syndrome; Standardization; Symptoms; Systemic Lupus Erythematosus; Testing; United States National Institutes of Health; Validation; Variant; Work; Xerostomia; biobank; cell type; cohort; college; differential expression; disease heterogeneity; epigenome; eye dryness; genetic association; genetic epidemiology; genetic risk factor; genetic variant; genome wide association study; genomic data; improved; methylation testing; monocyte; multiple omics; novel therapeutics; repository; single-cell RNA sequencing; success; systemic autoimmune disease; transcriptome; transcriptome sequencing; transcriptomics; whole genome

ABSTRACT Sjögren's Syndrome (SS) is a systemic autoimmune disease affecting the exocrine system, with hallmark symptoms of dry mouth and/or dry eyes caused by the dysfunction of salivary and/or lacrimal glands, respectively. While Genome-Wide Association Studies (GWAS) and other studies have increased our knowledge of genetic risk factors for SS, the disease etiology remains not well understood, and such risk factors have not been translatable to any immunological treatment options for SS. The NIH-NIDCR-funded Sjögren's International Collaborative Clinical Alliance (SICCA) was established to improve the understanding, diagnosis and treatment of patients with SS by developing/validating standardized classification criteria for SS; and developing a rich biospecimen repository with clinical data to be used for future epidemiologic, pathogenesis, and genetic studies of SS.[1, 2] For this project, we will focus on genomic data and measures of the 2016 ACR-EULAR classification criteria, involving ocular, oral, and autoantibody manifestations. As shown in our previous work, the genetics of SS varies with ancestry; thus, we will cluster patients by both the criteria subphenotypes and genetic ancestry. We believe that accounting for disease heterogeneity in this way will enable us to more precisely identify disease pathways and mechanisms. Using previously secured funding, we are completing DNA methylation typing on LSGs in 373 SICCA patients and single-cell RNA sequencing (scRNAseq) on PBMCs of 86 SICCA patients who also have DNA methylation profiling. This data provides a unique opportunity for multi-omics analysis to determine correlates between LSG tissue epigenetics, peripheral blood cell-type distributions and cell-specific gene expression by SS subsets. First, using GWAS data and DNA methylation data from LSG biopsies, we will identify genetic and epigenetic modifications associated with subtypes of SS in SICCA patients. We will then examine the relationships between them by testing for genotype-specific methylation and expression, and utilizing mendelian randomization and causal inference testing to investigate causality between these measures. Second, we will analyze scRNAseq data to identify how cell types, states and cell- specific gene expression correlate with SS subtypes. Finally, we will integrate genetics, epigenetics, and transcriptomics to determine multi-omics profiles associated with SS subtypes. We will jointly model associated features from the genomic data to investigate causal pathways via correlation networks, conditional analysis, and machine learning. We anticipate that SS subtypes will exhibit specific relationships within the multi- omics data and that this will advance our understanding of SS disease processes, leading to better treatment targets.

摘要