Genetic and Genomic Dissection of Psoriatic Arthritis

银屑病关节炎的遗传学和基因组解析

• 批准号: 10208711
• 负责人: JAMES TILFORD ELDER
• 金额: $ 60.85万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-12 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10208711
• 关键词: Address; Alleles; Area; Autoimmune; Autoimmune Diseases; Behavior; Biochemical; Biological Markers; Blood; Blood Banks; Blood specimen; CD8B1 gene; Cardiovascular Diseases; Cellular Assay; Chronic small plaque psoriasis; Clinic; Clinical; Clustered Regularly Interspaced Short Palindromic Repeats; Code; Collaborations; Collection; Cutaneous; DNA; Data; Dermatology; Dissection; Elements; Exhibits; Genes; Genetic; Genetic Load; Genetic Markers; Genetic study; Genomics; Genotype; HLA-C Antigens; Health; Hospitals; Immunity; In Vitro; Individual; Inflammatory; Interferons; International; Life; Longitudinal cohort; Machine Learning; Memory; Messenger RNA; Meta-Analysis; Michigan; MicroRNAs; Modeling; Mutagenesis; Osteoblasts; Osteoclasts; Pathogenesis; Patients; Predisposition; Proteins; Psoriasis; Psoriatic Arthritis; RNA; Regulatory Element; Regulatory T-Lymphocyte; Research; Resources; Rheumatology; Risk; Role; STAT3 gene; Sampling; Serum; Signal Transduction; Site; Skin; Small Interfering RNA; Suggestion; T-Lymphocyte; TNFSF15 gene; TYK2; Testing; Transcript; United States; Untranslated RNA; Variant; arthropathies; base; biomarker development; case control; causal variant; clinically relevant; cohort; comorbidity; differential expression; exome; follow-up; genetic variant; genome wide association study; high risk; innovation; insight; memory CD4 T lymphocyte; metabolome; metabolomics; novel; osteoblast differentiation; predictive marker; rare variant; recruit; systemic autoimmunity; tool; tool development; transcriptome; transcriptome sequencing

Psoriatic arthritis (PsA) is distinctive amongst the inflammatory/autoimmune joint diseases in that its onset is commonly preceded by cutaneous psoriasis (PsC). This provides an unparalleled opportunity for the identification of predictive biomarkers to determine which of the approximately 25% of psoriasis vulgaris (PsV) patients will develop PsA. Over the past decade, we have expanded our genetic study of PsV to focus on PsA, resulting in the collection of 1,279 PsA patients at Michigan, 743 of whom have already been subjected to GWAS. Initiated in 2007, the International Psoriatic Arthritis Research Team (IPART) has accumulated 1,919 Canadian PsA patients, of whom 1,370 have already been subjected to GWAS. In 2015, we completed a meta- GWAS of PsC and PsA involving a discovery cohort of 1,430 PsA cases and 1,417 controls, with 9,293 additional PsV replication samples (3,061 PsA, 3,110 PsC) and 13,670 controls. We detected 10 associations for PsA and 11 for PsC, as well as a new association for PsV. Utilizing an innovative core exome array to genotype additional cases and controls, we carried out the largest meta-GWAS of PsV to date (~40,000 subjects) and found 16 more susceptibility regions, highlighting the roles of interferon signaling and the NFB cascade, and demonstrating strong enrichment for psoriasis genetic signals in T-cell regulatory elements. Using machine learning to model ~200 genetic variants in our PsA vs. PsC GWAS, we achieved 82% area under receiver operator curve for distinguishing PsA vs PsC, with 98% accuracy among the top 10% of patients with the highest genetic load. We also carried out RNA-seq on mRNA and miRNA from 65 pairs of pre- and post-conversion samples from PsC patients who developed PsA. Suggestive of a shift from skin- focused to systemic autoimmunity, we found significant post-conversion enrichment for central memory CD4+ T-cell (CD4-Tcm) transcripts among the up-regulated genes. We also observed highly correlated pre- and post- conversion behaviors of 54 differentially-expressed miRNAs and their mRNA targets, as well as multiple serum miRNAs that are significantly differentially expressed in PsA vs. PsC. A metabolomic study of 50 paired converter sera revealed 293 biochemicals with significant alterations, 275 of which were increased. Finally, we identified noncoding eQTLs for IL23R that correlate strikingly with a region of selective PsA association. Based on these results, we hypothesize that PsA and PsC have pathogenetic mechanisms that are T-cell, osteoblast and osteoclast-driven and progress from skin to systemic during transition to PsA. We propose that this paradigm can be used to develop a useful test to predict PsA in PsC patients, while increasing our basic understanding of PsA. To test this hypothesis, we propose four aims: (1) to maintain and grow our longitudinal clinical resource; (2) To identify biomarkers for the development of PsA in PsC patients; (3) To integrate the biomarkers identified in Aim 2 into a clinically-useful tool for PsA identification using machine learning ; and (4) to explore the mechanisms by which the IL23R gene contributes to PsA pathogenesis.

银屑病关节炎(PSA)在炎症性/自身免疫性关节疾病中的独特之处在于它的发病 通常先于皮肤牛皮癣(PSC)。这为我们提供了无与伦比的机会 识别预测生物标志物以确定大约25%的寻常型银屑病(PSV)中的哪些 患者会患上PSA。在过去的十年里，我们扩大了对PSV的遗传研究，将重点放在PSA上， 导致密歇根州收集了1279名PSA患者，其中743人已经接受了 GWARD。成立于2007年的国际银屑病关节炎研究团队(IPart)已积累了1,919 加拿大的PSA患者，其中1,370人已经接受了GWAS。2015年，我们完成了一次元- PSC和PSA的GWA涉及1,430例PSA病例和1,417例对照，其中9,293例 额外的PSV复制样本(3,061个PSA，3,110个PSC)和13,670个对照。我们发现了10个关联 用于PSA和11用于PSC，以及用于PSV的新关联。利用创新的核心外显体阵列 在其他病例和对照中，我们进行了迄今为止最大规模的PSV Meta-Gwas(约40,000例) 受试者)，并发现了另外16个易感区域，突出了干扰素信号和NFB的作用 银屑病遗传信号在T细胞调节元件中表现出强烈的富集性。 使用机器学习对我们的PSA和PSC GWAS中的大约200个遗传变异进行建模，我们获得了82%的面积 在区分PSA和PSC的接收器操作曲线下，前10%的准确率为98% 遗传负荷最高的患者。我们还对65对MRNA和miRNA进行了rna-seq。 转换前和转换后的样本来自患有PSA的PSC患者。暗示着皮肤的变化- 聚焦于系统自身免疫，我们发现转换后显著丰富了中枢记忆中的CD4+ 在上调的基因中，T细胞(CD4-Tcm)转录。我们还观察到前后高度相关的 54个差异表达miRNAs及其mRNA靶点和多血清的转化行为 在PSA和PSC中显著差异表达的miRNAs。50对配对人群的代谢组学研究 转炉血清检测出293种生物化学物质有显著变化，其中275种增加。最后，我们 确定了IL23R的非编码eQTL，该eQTL与选择性PSA关联区域显著相关。 基于这些结果，我们假设PSA和PSC具有T细胞的致病机制， 在向PSA过渡的过程中，成骨细胞和破骨细胞驱动并从皮肤向全身进展。我们建议 这一范例可以用来开发一种有用的测试来预测PSC患者的PSA，同时增加我们的基础 了解PSA。为了验证这一假设，我们提出了四个目标：(1)保持和发展我们的纵向 临床资源；(2)确定PSC患者PSA发生的生物标志物；(3)整合 将AIM 2中确定的生物标记物转化为使用机器学习进行PSA鉴定的临床有用工具；以及(4) 探讨IL23R基因在PSA发病机制中的作用。