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

项目总结： 自身免疫性疾病，如类风湿性关节炎和系统性红斑狼疮，使人虚弱。 以及由致病性炎症反应引起的高度普遍的慢性疾病。少校 6号染色体上的组织相容性(MHC)区，包含人类白细胞抗原(HL A)和 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。之前的研究主要探索了 编码变异如何影响人类白细胞抗原蛋白结构和抗原结合，但最近的研究强调了这种可能性 非编码变异体在调节人类白细胞抗原表达中的作用。人类白细胞抗原表达增加可能在慢性粒细胞白血病中起因果作用 通过更高水平的抗原递呈给自身反应性T细胞而导致的疾病。迫切需要更好地 了解细胞的生物状态如何影响对人类白细胞抗原表达的遗传控制。 这项拟议的研究将检验MHC区域的遗传变异调节人类白细胞抗原的假设 以细胞状态依赖和疾病相关的方式表达。申请者将发展创新能力 整合来自炎症性疾病样本的遗传和单细胞转录数据的计算方法 在多种人类免疫介导的疾病背景下的组织和对照，包括1,088,000个细胞 来自384个个体。具体地说，本研究的目的是(1)量化遗传变异对人类白细胞抗原的影响 在关键免疫和基质细胞状态(T、B、成纤维细胞和髓样细胞)中的表达，(2)识别表达 调节人类白细胞抗原调节变异体效应的程序和转录调节因子，以及(3)将人类白细胞抗原 自身免疫性疾病风险基因的调控变异。这项工作将生成一个详细介绍HLA的资源 在不同的细胞状态下表达，并确定基因变体调节人类白细胞抗原的特定背景 表情。这将加深我们对自身免疫性疾病潜在机制的基本理解。 风险，并可能为更有见地的治疗策略铺平道路。 拟议的培训计划将使申请人能够：(A)加强对遗传基因的了解 和人类疾病的免疫基础，(B)培养强大的计算基因组学方法技能 发展，(C)发展统计遗传学和计算免疫学方面的数据科学技能，(D) 提高对自身免疫性疾病临床方面的了解，并(E)发展专业科学 沟通技巧。丰富和支持的培训环境和专家的密切指导 复杂的性状遗传学、免疫学和单细胞方法的开发将使申请者具备 知识和技能，成为一名有效的内科医生-科学家，可以为疾病领域做出贡献- 聚焦计算免疫基因组学。

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.