Metabolomic Signatures of CAD Associated Genotypes

CAD 相关基因型的代谢组学特征

• 批准号: 9334928
• 负责人: DONALD W BOWDEN
• 金额: $ 62.25万
• 依托单位: WAKE FOREST UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9334928
• 关键词: Analytical Chemistry; Behavior; Biochemistry; Bioinformatics; Biological; Biological Assay; Biological Markers; Biology; Cardiovascular Diseases; Cardiovascular system; Cohort Studies; Collaborations; Communities; Computational Biology; Consensus; Coronary Arteriosclerosis; Coronary heart disease; Data; Data Analytics; Databases; Development; Diagnostic tests; Disease; Enrollment; Environmental Exposure; Environmental Risk Factor; Event; Financial cost; Genetic; Genotype; Goals; Health; Human; Individual; International; Intervention; Investigation; Knowledge; Lead; Lipids; Low-Density Lipoproteins; Mass Spectrum Analysis; Medicine; Meta-Analysis; Metabolic; Metabolic Pathway; Metabolism; Methods; Molecular; Monitor; Multi-Ethnic Study of Atherosclerosis; NMR Spectroscopy; NOESY; Network-based; Nuclear Magnetic Resonance; Pathogenesis; Pathway Analysis; Pathway interactions; Physiological; Population; Prevention; Prevention strategy; Resolution; Risk; Sampling; Science; Scientist; Supervision; Systems Biology; Variant; analytical method; cardiovascular risk factor; clinical risk; cohort; data resource; diagnostic screening; genetic risk factor; genetic variant; genome wide association study; genome-wide analysis; genomic data; improved; insight; member; metabolome; metabolomics; new therapeutic target; novel; novel diagnostics; novel therapeutics; pleiotropism; web site

Project Summary Currently there are 153 consensus coronary artery disease (CAD) associated common genetic variants identified through meta-analysis of genome-wide association studies (GWAS).1 For some, the molecular mechanisms explaining their association with CAD are well-defined (e.g. PCSK9, LDL-R). However, for the vast majority, the molecular mechanisms are less well understood or completely unknown. A more comprehensive characterization of the mechanisms for these CAD GWAS variants could lead to new insights concerning the pathogenesis of coronary disease or suggest novel therapeutic or preventive strategies. Recent advances in high-throughput nuclear magnetic resonance (NMR) spectroscopy and mass-spectrometry (MS) make it possible to perform highly accurate, precise, and sensitive metabolomic profiling on thousands of biologic samples 2-6. Unlike conventional targeted metabolomics, un-targeted metabolomics uses a combination of NMR and MS assays to access a broader range of metabolites (both known and unknown) than possible from any single metabolomic assay or target list. The overall goal of this proposal is to use un-targeted metabolomics to characterize the metabolomic signatures associated with each consensus CAD GWAS hit. We aim to generate new knowledge about the mechanisms and biological pathways involved in the pathogenesis of CAD. We propose to use previously obtained GWAS and metabolomic data from subsets of MESA (N=4,000), RHS (N=2,000), Airwave (N=4,000), 1. To perform univariate and multivariate metabolome-wide association analyses with each of the consensus CAD GWAS hits. The metabolomic data will include two NMR assays (NOESY and CPMG) and four MS assays (lipid+/-, HILIC+/-) representing >100,000 distinct metabolomic features. 2. To use statistical, bioinformatic and analytic chemistry methods to identify the specific metabolites represented by the NMR and MS features identified in Specific Aim 1. 3. To use unsupervised and supervised network and systems biology analyses to characterize the groups of metabolites and pathways associated with each GWAS hit. 4. To create a data repository of all the metabolomic data and the generated association and network analyses for the benefit of the wider scientific community. This project will be carried out by a collaborating group of international scientists with expertise in cardiovascular disease, metabolomics, biochemistry, statistical genetics, computational and systems biology, and project management. The resulting data may provide novel insights concerning the metabolic and physiologic mechanisms through which CAD GWAS hits influence cardiovascular risk factors and risk for clinical cardiovascular events.

项目概要 目前有 153 种共识的冠状动脉疾病 (CAD) 相关的常见遗传变异 通过全基因组关联研究 (GWAS) 的荟萃分析确定。1 对于某些人来说，分子 解释它们与 CAD 关联的机制是明确的（例如 PCSK9、LDL-R）。然而，对于 绝大多数的分子机制尚不清楚或完全未知。一个更多 对这些 CAD GWAS 变体机制的全面表征可能会带来新的见解 有关冠状动脉疾病的发病机制或提出新的治疗或预防策略。 高通量核磁共振 (NMR) 光谱和质谱的最新进展 (MS) 使对数千个样本进行高度准确、精密和灵敏的代谢组分析成为可能 生物样品2-6。与传统的靶向代谢组学不同，非靶向代谢组学使用 NMR 和 MS 检测相结合，可获取更广泛的代谢物（已知和未知） 可能来自任何单一代谢组学测定或目标列表。 该提案的总体目标是使用非靶向代谢组学来表征代谢组学 与每个共识 CAD GWAS 命中相关的签名。我们的目标是产生关于 CAD发病机制和生物学途径。我们建议先使用 从 MESA (N=4,000)、RHS (N=2,000)、Airwave (N=4,000) 子集获得 GWAS 和代谢组数据， 1. 对每个共识进行单变量和多变量全代谢组关联分析 CAD GWAS 上线。代谢组数据将包括两项 NMR 测定（NOESY 和 CPMG）和四项 MS 代表>100,000种不同代谢组学特征的测定（脂质+/-、HILIC+/-）。 2. 利用统计、生物信息学和分析化学方法鉴定特定代谢物 由具体目标 1 中确定的 NMR 和 MS 特征表示。 3. 使用无监督和监督网络和系统生物学分析来表征群体 与每个 GWAS 命中相关的代谢物和途径。 4. 创建所有代谢组数据以及生成的关联和网络的数据存储库 为更广泛的科学界的利益进行分析。 该项目将由具有专业知识的国际科学家合作小组进行 心血管疾病、代谢组学、生物化学、统计遗传学、计算和系统生物学、 和项目管理。由此产生的数据可能提供有关代谢和 CAD GWAS 影响心血管危险因素和风险的生理机制 临床心血管事件。