Pinpointing how single-cell states affect genetic regulation of HLA expression in autoimmune diseases

项目摘要： 自身免疫性疾病，如类风湿性关节炎和系统性红斑狼疮， 以及由致病性炎症反应引起的高度流行的慢性疾病。主要 组织相容性（MHC）区域，其含有人白细胞抗原（HLA）， 其他免疫相关基因，与自身免疫性疾病有最强的遗传关联，但确切的 MHC疾病风险背后的分子机制尚未解决。以前的研究主要探索 编码变体如何影响HLA蛋白质结构和抗原结合，但最近的研究强调了 非编码变体在调节HLA表达中的作用。HLA表达增加可能在以下方面起因果作用： 疾病通过更高水平的抗原呈递给自身反应性T细胞。迫切需要更好地 了解细胞的生物学状态如何影响HLA表达的遗传控制。 这项拟议中的研究将验证MHC区域的遗传变异调节HLA的假设 以细胞状态依赖性和疾病相关的方式表达。申请人将开发创新 计算方法来整合从发炎的细胞中取样的遗传和单细胞转录组学数据， 多种人类免疫介导疾病背景下的组织和对照，包括> 1，088，000个细胞 384人。具体而言，该研究旨在（1）量化遗传变异对HLA的影响 在关键免疫和基质细胞状态（T、B、成纤维细胞和骨髓细胞）中表达，（2）鉴定表达 调节HLA调节变体作用的程序和转录调节因子，以及（3）连接HLA 自身免疫性疾病风险基因座的调节变异。这项工作将产生一个资源，详细介绍HLA 在不同的细胞状态下表达，并确定遗传变异调节HLA的特定背景 表情这将加深我们对自身免疫性疾病机制的基本理解 风险，并可能为更好的知情治疗策略铺平道路。 拟议的培训计划将使申请人能够：（A）加强对遗传学的理解 和人类疾病的免疫基础，（B）培养较强的计算基因组学方法的技能 发展，（C）发展统计遗传学和计算免疫学的数据科学技能，（D） 提高对自身免疫性疾病临床方面的认识，以及（E）发展专业的科学 沟通能力丰富和支持性的培训环境以及专家的密切指导， 复杂的性状遗传学，免疫学和单细胞方法的发展将使申请人具备 知识和技能，成为一个有效的医生，科学家谁可以有助于疾病领域- 专注于计算免疫基因组学

Project Summary: Autoimmune diseases, such as rheumatoid arthritis and systemic lupus erythematosus, are debilitating and highly prevalent chronic conditions that result from pathogenic inflammatory responses. The major histocompatibility (MHC) region on chromosome 6, which contains the human leukocyte antigen (HLA) and other immune-related genes, has the strongest genetic association with autoimmune diseases, but the exact molecular mechanisms behind MHC disease risk are yet unsolved. Previous research has primarily explored how coding variants affect HLA protein structure and antigen binding, but recent studies highlight the potential role of noncoding variants in regulating HLA expression. Increased HLA expression could play a causal role in disease through higher levels of antigen presentation to autoreactive T cells. There is a critical need to better understand how a cell’s biological state impacts genetic control of HLA expression. The proposed research will test the hypothesis that genetic variation in the MHC region modulates HLA expression in a cell-state-dependent and disease-relevant manner. The applicant will develop innovative computational methods to integrate both genetic and single-cell transcriptomic data sampled from inflamed tissues and controls across multiple human immune-mediated disease contexts, comprising >1,088,000 cells from 384 individuals. Specifically, the study aims to (1) quantify the effect of genetic variation on HLA expression in key immune and stromal cell states (T, B, fibroblast, and myeloid cells), (2) identify expression programs and transcriptional regulators that modulate the effect of HLA regulatory variants, and (3) link HLA regulatory variation to autoimmune disease risk loci. This work will generate a resource detailing HLA expression across diverse cell states and identify the specific contexts in which genetic variants regulate HLA expression. This will deepen our fundamental understanding of mechanisms underlying autoimmune disease risk and may pave the way for better informed therapeutic strategies. The proposed training plan will enable the applicant to: (A) strengthen an understanding of the genetic and immune basis of human diseases, (B) cultivate strong skills in computational genomics methods development, (C) develop data science skills in statistical genetics and computational immunology, (D) improve understanding of the clinical aspects of autoimmune diseases, and (E) develop professional scientific communication skills. An enriching and supportive training environment and close mentorship by experts in complex trait genetics, immunology, and single-cell methods development will equip the applicant with knowledge and skills to become an effective physician-scientist who can contribute to the field of disease- focused computational immunogenomics.