Understanding the protective and neuroinflammatory role of human brain immune cells in Alzheimer Disease

项目摘要 精准医疗是指识别风险因素和定制医疗， 每个病人的个人特征。为了推进精准医疗，我们需要更好地 了解常见和复杂性状的分子机制。加速 药物合作伙伴关系（AMP）一直在研究包括阿尔茨海默病（AD）在内的特征， 类风湿性关节炎（RA），系统性红斑狼疮（SLE），帕金森病（PD）， 精神分裂症（SCZ）和2型糖尿病（T2 D），具有复杂的和部分共享的多基因 结构，其病理生理学涉及由先天性和先天性炎症调节的炎症机制， 在大脑和周围发现的适应性免疫。我们将利用EpiXcan，一种新的机器学习， 方法，该方法利用表达参考面板（具有表达和基因型的eQTL群组 数据），以了解遗传驱动的干扰共享和不同的免疫机制 与AMP相关的特征作为此分析的输入数据，我们将整合多个GWAS摘要 人类纯化小胶质细胞中AMP相关性状的高维单细胞数据统计， 来自匹配供体和扩增内部群体的外周血单核细胞（PBMC）- 人纯化的小胶质细胞中的水平RNA-seq和ATAC-seq数据与 脑免疫细胞、PBMC和滑膜。我们的分析将确定性状-性状相关性，基因-性状 关联和分子通路与涉及AMP相关的 性状，作为未来的潜在目标，新的治疗方法。

PROJECT SUMMARY Precision Medicine refers to the identification of risk factors and customization of medical treatment to the individual characteristics of each patient. To advance Precision Medicine, we need to better understand the molecular mechanisms underlying common and complex traits. The Accelerating Medicines Partnership (AMP) has been investigating traits, including Alzheimer's disease (AD), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), Parkinson's disease (PD), Schizophrenia (SCZ) and Type 2 diabetes (T2D), which have complex and partially shared polygenic architecture, and their pathophysiology involves inflammatory mechanisms regulated by the innate and adaptive immunity found in the brain and periphery. We will utilize EpiXcan, a novel machine learning approach, which leverages expression reference panels (eQTLs cohorts with expression and genotype data) to understand the genetically driven perturbations of shared and distinct immune mechanisms across AMP-related traits. As input data to this analysis, we will integrate multiple GWAS summary statistics for AMP-related traits with high dimensional single-cell data in human purified microglia and peripheral blood mononuclear cells (PBMCs) from matched donors and expanded in-house population- level RNA-seq and ATAC-seq data in human purified microglia combined with external omics data of brain immune cells, PBMC and synovium. Our analysis will identify trait-trait correlations, gene-trait associations and molecular pathways associated with shared genes implicated across AMP-related traits, serving as potential targets for future, novel therapeutic treatments.