Integrated proteomic and metabolomics analysis of thrombotic myocardial infarction

血栓性心肌梗死的蛋白质组学和代谢组学综合分析

• 批准号: 10337207
• 负责人: Patrick J Trainor
• 金额: $ 22.2万
• 依托单位: NEW MEXICO STATE UNIVERSITY LAS CRUCES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10337207
• 关键词: Acute myocardial infarction; Address; Affect; Arterial Fatty Streak; Biochemical Reaction; Biological; Biological Markers; Biological Process; Blinded; Blood coagulation; Blood flow; Cardiac Myocytes; Cause of Death; Cell Death; Cell-Cell Adhesion; Cessation of life; Characteristics; Classification; Clinical; Coagulation Process; Coronary; Coronary Arteriosclerosis; Data; Detection; Development; Diagnostic; Diagnostic Procedure; Diagnostic tests; Ensure; Etiology; Evaluation; Event; Fibrinolysis; Guidelines; Hour; Human; Infarction; International; Investigation; Knowledge; Ligand Binding; Ligands; Lipids; Mass Spectrum Analysis; Measures; Metabolic; Metabolic Pathway; Metabolism; Methods; Modeling; Monitor; Myocardial; Myocardial Infarction; Myocardial Ischemia; Myocardium; Necrosis; Oxygen; Pathologic; Patients; Performance; Plasma; Plasma Proteins; Platelet Activation; Procedures; Process; Proteins; Proteome; Proteomics; Reaction; Reporting; Research Design; Resolution; Rupture; Sampling; Secondary to; Statistical Models; System; Systems Biology; Therapeutic Intervention; Thrombosis; Thrombus; Time; United States; Vascular blood supply; Work; atherosclerotic plaque rupture; base; cohort; design; diagnostic criteria; differential expression; experience; feature selection; follow-up; genome-wide; human subject; improved; insight; lipid metabolism; lipidomics; liquid chromatography mass spectrometry; metabolome; metabolomics; mortality; multiple omics; new therapeutic target; noninvasive diagnosis; novel diagnostics; optimal treatments; patient safety; protein metabolite; proteomic signature; receptor; safety outcomes; statistical and machine learning; targeted treatment; thrombotic; treatment strategy

Project Summary / Abstract: Acute myocardial infarction (MI) is defined as myocardial ischemia, the inadequate supply of blood to heart muscle, followed by myocardial cell death. Multiple causes of acute MI are widely recognized and can be categorized as thrombotic, non-thrombotic, and acute MI secondary to coronary procedures. The mechanistic cause of a thrombotic MI is the rupture or erosion of an atherosclerotic plaque that results in the formation of a thrombus, or blood clot, which occludes the flow of blood. In contrast, non-thrombotic MI occurs secondary to mechanisms which create an oxygen supply and demand imbalance, but are not associated with atherosclerotic plaque rupture or disruption. Given that myocardial cell death is the pathological characteristic that is common to all acute MI, non-invasive diagnostics for acute MI are based on the detection of myocardial cell death. Currently non-invasive diagnostics for differentiating thrombotic MI from non-thrombotic MI do not exist which results in sub-optimal treatment and diminished patient safety. Further, it is not known how the impacts on metabolism and biological processes differ between thrombotic and non-thrombotic MI. In this project, we will address both of these problems. We will develop a diagnostic method for the non-invasive differentiation of thrombotic MI versus non-thrombotic MI that will enable earlier, safer, and more precise targeting of therapeutics to patients suffering from acute MI. We will determine biological processes that differ between thrombotic and non-thrombotic MI, which will suggest targets for therapeutic intervention that are specific to the underlying cause of an acute MI. In Aim 1 we will utilize high resolution mass spectrometry to determine the absolute concentration of over 500 proteins in previously collected plasma samples from human subjects who were experiencing an acute MI for which the cause (thrombotic versus non-thrombotic) was determined. This will enable us to determine which proteins report on the cause of the acute MI as opposed to the presence of myocardial cell death. A critical advantage of our study design is that we have repeated measures from the same human subjects: at the time of presentation, 6 hours post-presentation, and at a stable event-free follow-up timepoint 3 months after the acute MI. In Aim 2 we will integrate this data with our existing data on the abundances of metabolites and lipids generated from the same human subject samples. This integrated data will facilitate an in-depth analysis of the differences between thrombotic and non-thrombotic MI in the activities of metabolic pathways, receptor-ligand binding events, and other biochemical reactions. Further we will conduct data- dependent systems biology analyses that will highlight proteins, metabolites, and lipids that are co-abundant in plasma and will evaluate how the topology of these related entities differs between thrombotic and non- thrombotic MI. In Aim 3 we will develop a statistical classifier for the determination of the underlying cause of an acute MI. We will conduct a blinded evaluation of the performance of this classifier in a second cohort.

项目摘要 /摘要： 急性心肌梗塞（MI）定义为心肌缺血，血液供应不足 肌肉，然后是心肌细胞死亡。急性MI的多种原因被广泛认可，可以是 被归类为冠状动脉程序继发的血栓形成，非栓性和急性MI。机械 血栓形成MI的原因是动脉粥样硬化斑块的破裂或侵蚀，导致形成A 血栓或血凝块，会阻塞血液的流动。相反，非栓性MI发生于 产生氧气供应不平衡但与动脉粥样硬化无关的机制 斑块破裂或破坏。鉴于心肌细胞死亡是常见的病理特征 对于所有急性MI，急性MI的非侵入性诊断基于检测心肌细胞死亡。 目前不存在用于区分血栓性MI与非栓性MI的非侵入性诊断，而不是存在 导致次优治疗和患者安全性降低。此外，尚不知道如何影响 血栓形成和非栓性MI之间的代谢和生物过程不同。在这个项目中，我们将 解决这两个问题。我们将开发一种诊断方法，用于非侵入性分化 血栓形成MI与非栓性MI，可以更早，更安全，更精确地靶向治疗剂 给患有急性MI的患者。我们将确定血栓形成和之间不同的生物过程 非血栓形成MI，这将提出针对基本原因的治疗干预措施的靶标 急性mi。在AIM 1中，我们将利用高分辨率质谱来确定绝对浓度 从以前收集的人类受试者的血浆样本中的500多种蛋白质中 确定原因（血栓形成与非栓性）的急性MI。这将使我们能够 确定哪些蛋白质报告了急性MI的原因而不是心肌细胞的存在 死亡。我们的研究设计的一个关键优势是我们已经重复了同一人类的措施 受试者：在演讲时，后期6小时，在稳定的无活动随访时间点3 急性MI几个月后。在AIM 2中，我们将将这些数据与我们的现有数据集成 由相同的人类受试者样品产生的代谢产物和脂质。该集成数据将有助于 深入分析代谢活性中血栓形成和非栓性MI之间的差异 途径，受体配体结合事件和其他生化反应。此外，我们将进行数据 - 依赖性系统生物学分析将突出蛋白质，代谢产物和脂质，这些蛋白质和脂质在 等离子体并将评估这些相关实体的拓扑学如何在血栓形成和非 - 血栓形成MI。在AIM 3中，我们将开发一个统计分类器，以确定一个根本原因 急性mi。我们将对第二个队列中该分类器的性能进行盲目评估。