Extraction of molecular signature of HFpEF via a machine learning-empowered proteomic characterization: A study of the BCAA pathway

通过机器学习支持的蛋白质组表征提取 HFpEF 的分子特征：BCAA 途径的研究

• 批准号: 10440446
• 负责人: Chun Ming Dominic Ng
• 金额: $ 64.97万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10440446
• 关键词: Address; Affect; Age; Aging; Atlases; Biology; Branched-Chain Amino Acids; Cardiac; Catabolism; Cell physiology; Cells; Clinical; Complex; Data Analyses; Data Science; Defect; Diabetes Mellitus; Diagnostic; Disease; Disease Progression; Disease model; EFRAC; Enzymes; Exposure to; Failure; Functional disorder; Genetic Predisposition to Disease; Glucose; Heart; Heart Atrium; Heart Diseases; Heart failure; Homeostasis; Hypertension; Knowledge; Lead; Link; Machine Learning; Mechanical Stress; Mediating; Metabolic; Metabolic Diseases; Metabolic Pathway; Metabolic stress; Metabolism; Methodology; Mitochondria; Modeling; Molecular; Molecular Genetics; Molecular Profiling; Molecular Target; Mus; Myocardium; Nature; Obese Mice; Obesity; Organ; Oxidation-Reduction; Oxidative Stress; Pathogenesis; Pathogenicity; Pathologic; Pathway interactions; Patients; Phenotype; Post-Translational Protein Processing; Procedures; Process; Production; Proteins; Proteome; Proteomics; Reactive Oxygen Species; Regulation; Research; Research Personnel; Risk Factors; Role; Stress; Symptoms; System; Technology; Thinness; Treatment Failure; Uncertainty; Woman; antioxidant enzyme; base; computational platform; constriction; diagnostic criteria; effective therapy; empowered; exhaustion; fatty acid metabolism; heart function; in vivo; insight; knowledge graph; machine learning method; men; molecular phenotype; mouse model; new therapeutic target; novel; novel diagnostics; preservation; pressure; protein function; therapeutic target; tool

PROJECT SUMMARY Heart failure with preserved ejection fraction (HFpEF), characterized by heart failure symptoms with normal ejection fraction, is highly prevalent. However, most HFpEF patients do not respond to standard therapy for heart failure with reduced ejection fraction (HFrEF), and there are no clear and uniform diagnostic criteria to stratify and differentiate HFpEF from HFrEF. Therefore, there is a pressing unmet need for us to better understand HFpEF at the molecular and system levels. Unbiased approaches such as machine learning (ML) offer a powerful means to tease out the molecular signatures of HFpEF in relevant disease models. The emerging evidence implicates that metabolism and redox homeostasis are two significant disruptions in cellular processes evidenced by clinical symptoms of HFpEF. Previous studies have identified branched-chain amino acid (BCAA) catabolic defect as another major metabolic hallmark in heart failure as well as in metabolic disorders. Moreover, BCAA catabolic defects have been demonstrated to directly impact mitochondrial function and elevate reactive oxygen species (ROS) production, resulting in oxidative stress-sensitive post-translational modifications (O-PTMs) that govern protein function and pathways. These exciting discoveries lead to our new hypothesis that O-PTM-mediated proteome remodeling is a dynamic and pervasive molecular change in diseased hearts, affecting proteins with central function in cardiac homeostasis and pathophysiology. To investigate the unique molecular features and pathogenic mechanisms of HFpEF, we highlight a novel HFpEF mouse model that incorporates both genetic predisposition for obesity/diabetes and pressure-overload, the two major risk factors for HFpEF, by performing trans-aortic constriction (TAC) in the ob/ob mice. We have also perfected the experimental tools and data analysis platform to provide O-PTM profiling at the whole-proteome level in hearts. Accordingly, we have strategically formulated the following aims according to three phenotypic levels: At the systemic level, Aim 1 will establish and characterize in vivo mouse models of HFpEF vs. HFrEF by cardiac and mitochondrial function as well as redox status. At the organellar level, Aim 2 will conduct targeted proteomics profiling of the cardiac mitochondria and extract O-PTM signatures using ML-based methods to achieve deep phenotyping of HFpEF and HFrEF. This information will then be integrated and enriched in an O- PTM molecular atlas and knowledge graph. At the molecular level, Aim 3 will target the BCAA catabolic pathway to exhaustively scrutinize its role in HFpEF and HFrEF. A multilevel understanding of the HFpEF phenotype, from its global profiling to molecular targets, will provide valuable new insights into the disease process that can lead to potential novel diagnostic and therapeutic targets.

项目摘要 心力衰竭和保留的射血分数（HFPEF），其特征是心力衰竭症状正常 射血分数高度普遍。但是，大多数HFPEF患者对心脏的标准疗法没有反应 射血分数减少（HFREF）的失败，并且没有明确且均匀的诊断标准进行分层 并将HFPEF与HFREF区分开。因此，我们有迫切需要我们更好地理解 HFPEF在分子和系统水平上。公正的方法（例如机器学习（ML））提供了强大的方法 意味着在相关疾病模型中取消HFPEF的分子特征。 新兴的证据暗示了新陈代谢和氧化还原稳态是两个重大干扰 HFPEF的临床症状证明了细胞过程。先前的研究已经确定了分支链 氨基酸（BCAA）分解代谢缺陷是心力衰竭以及代谢中的另一个主要代谢标志 疾病。此外，已经证明了BCAA分解代谢缺陷直接影响线粒体功能 并升高活性氧（ROS）产生，导致氧化应激敏感性翻译后 修改（O-PTMS），这些（O-PTM）控制蛋白质功能和途径。这些令人兴奋的发现导致了我们的新发现 O-PTM介导的蛋白质组重塑的假设是动态和普遍的分子变化 患病的心脏，影响心脏稳态和病理生理学中心功能的蛋白质。 为了研究HFPEF的独特分子特征和致病机制，我们重点介绍了一种新型的HFPEF 小鼠模型既结合了肥胖/糖尿病和压力超负荷的遗传易感性 HFPEF的主要危险因素，通过在OB/OB小鼠中执行反应收缩（TAC）。我们也有 完善实验工具和数据分析平台，以在全蛋白质体上提供O-PTM分析 心中的水平。因此，我们从战略上根据三种表型制定了以下目标 级别：在系统级别，AIM 1将在HFPEF与HFREF的体内鼠标模型中建立和表征 通过心脏和线粒体功能以及氧化还原状态。在细胞器级别，AIM 2将进行针对性 心脏线粒体的蛋白质组学分析并使用基于ML的方法提取O-PTM特征 实现HFPEF和HFREF的深层表型。然后将将这些信息集成并丰富在O-中 PTM分子图集和知识图。在分子水平上，AIM 3将针对BCAA分解代谢途径 详尽地审查其在HFPEF和HFREF中的作用。对HFPEF表型的多层次理解， 从全球分析到分子靶标，将为疾病过程提供宝贵的新见解 导致潜在的新型诊断和治疗靶标。