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.

项目总结