Multi-omic dissection of clonal hematopoiesis-associated diseases

克隆造血相关疾病的多组学解剖

• 批准号: 10644764
• 负责人: Zhi Yu
• 金额: $ 13.73万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2025-09-14
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10644764
• 关键词: Address; Adult; Aging; Attention; Biological Process; Blood; Chronic; Chronic Disease; Chronic Obstructive Pulmonary Disease; Clonal Expansion; Coronary Arteriosclerosis; DNA; DNMT3a; Data; Development; Dimensions; Disease; Dissection; Gene Expression; Gene Expression Profile; Genes; Genetic; Genomics; Goals; Heart failure; Hematological Disease; Hematopoiesis; Hematopoietic stem cells; Immune; Individual; Inflammasome; Inflammation; Interleukin-1 beta; Interleukin-6; Lead; Liver diseases; Measures; Mediating; Mediation; Methods; Modeling; Modification; Molecular; Multiomic Data; Mutation; Myocardial Infarction; National Heart, Lung, and Blood Institute; Normal tissue morphology; Osteoporosis; Participant; Pathologic; Pathway Analysis; Pathway interactions; Phase; Plasma Proteins; Population; Proteins; Proteomics; Proxy; Risk; Sampling; Somatic Mutation; Statistical Methods; Stroke; Structure; Testing; Tissue-Specific Gene Expression; Trans-Omics for Precision Medicine; Work; age related; biobank; cell type; chronic liver disease; deep learning; disease phenotype; disorder risk; early onset; improved; insight; instrument; learning strategy; machine learning method; multiple omics; personalized strategies; precision medicine; prevent; protein protein interaction; proteomic signature; response; self renewing cell; single-cell RNA sequencing; statistical and machine learning; therapeutic target; trait; transcriptome sequencing; transcriptomics; venous thromboembolism

Project Summary Self-renewing cell populations accumulate somatic mutations with aging and/or in response to environmental insult and chronic inflammation. Most of these mutations in normal tissues have no functional consequence. In rare cases, these somatic mutations can confer a selective advantage leading to clonal expansion in hematopoietic stem cells, a phenomenon termed 'Clonal Hematopoiesis of Indeterminate Potential' (CHIP). CHIP has been associated with a selective range of non-hematopoietic conditions, including coronary artery disease, stroke, heart failure, venous thromboembolism, chronic obstructive pulmonary disease, chronic liver diseases, and osteoporosis. However, the molecular mechanisms by which CHIP specifically promotes these diseases remain largely unknown. The overarching goal of this proposal is to molecularly connect CHIP with the development of associated diseases through developing and applying statistical and machine learning methods to multi-omics data in the NHLBI Trans-Omics for Precision Medicine (TOPMed), UK Biobank, and Mass General Brigham Biobank (MGBB). Specifically, I propose the following aims. In Aim 1, I will develop a statistical method that combines multiple proteins implicated in the same pathway to improve discovery and facilitate translational insights for proteomics-based association analyses. The combined protein quantitative traits generated by this method will be used in the following aims. In Aim 2, I will examine how the presence of CHIP and each CHIP driver gene specifically promote certain diseases through (1) identifying the proteomic signatures of CHIP and quantifying the mediation effects to associated diseases and (2) evaluating the potential modifications of CHIP-associated diseases by the expression levels of genes implicated in diverse molecular factors. In Aim 3, I will evaluate how the progression of CHIP promotes associated diseases. I will examine associations between longitudinal change in the number and size of CHIP clones and incident disease risks, as well as generate bulk and single-cell RNA sequencing data among CHIP carriers with or without osteoporosis, a disease strongly associated with CHIP but understudied, and test for differential gene expressions between the two groups. This project's successful execution will identify the mechanisms underlying CHIP-associated disease risks to prioritize therapeutic targets and advance precision medicine goals.

项目摘要 自我更新细胞群体会因衰老和/或对环境而累积体细胞突变 侮辱和慢性炎症。正常组织中的大多数突变没有功能后果。在 极少数情况下，这些躯体突变可以赋予选择性优势，从而导致克隆扩张 造血干细胞，这种现象称为“不确定潜力的克隆造血症”（芯片）。 CHIP与选择性范围的非脊髓性疾病有关，包括冠状动脉 疾病，中风，心力衰竭，静脉血栓栓塞，慢性阻塞性肺疾病，慢性肝脏 疾病和骨质疏松症。但是，芯片专门促进这些分子机制 疾病在很大程度上仍然未知。该提案的总体目标是将芯片分子连接 通过开发和应用统计和机器学习来开发相关疾病 NHLBI Trans-omics中多词数据的方法（TopMed），英国生物银行和 群众杨百翰生物库（MGBB）。具体来说，我提出以下目标。在AIM 1中，我将发展一个 统计方法结合了多种蛋白质，与相同的途径有关，以改善发现和 促进基于蛋白质组学的关联分析的转化见解。蛋白质合并定量 以下目的将使用此方法生成的特征。在AIM 2中，我将研究如何存在 芯片和每个芯片驱动器基因通过（1）识别蛋白质组学特异性促进某些疾病 芯片的签名并量化对相关疾病的调解作用，（2）评估 与不同的基因表达水平与涉及多样的基因的表达水平对芯片相关疾病的潜在修饰 分子因子。在AIM 3中，我将评估芯片的进展如何促进相关疾病。我会 检查芯片克隆数量和大小的纵向变化与事件之间的关联 疾病风险，以及与OR或 没有骨质疏松症，这种疾病与芯片密切相关，但研究了，并测试了差异基因 两组之间的表情。该项目的成功执行将确定机制 基本的芯片相关疾病风险优先考虑治疗靶点并提高精度医学 目标。