Omics Phenotyping for Identifying Molecular Signatures of the Healthy and Failing Heart: An Integrated Data Science Platform

用于识别健康和衰竭心脏分子特征的组学表型分析：集成数据科学平台

• 批准号: 10327640
• 负责人: Peipei Ping
• 金额: $ 91.07万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10327640
• 关键词: Address; Area; Automobile Driving; Big Data to Knowledge; Biological; Biological Models; Cardiac; Cardiovascular system; Clinical Data; Cloud Computing; Communities; Data; Data Commons; Data Science; Data Set; Disease; Environment; Fostering; Foundations; Future; Genetic Variation; Genomics; Health; Heart; Human; Informatics; Infrastructure; Knowledge; Leadership; Link; Medicine; Mission; Mitochondria; Modeling; Molecular; Molecular Profiling; National Heart, Lung, and Blood Institute; Pathway interactions; Phenotype; Production; Proteins; Proteome; Proteomics; Research; Research Personnel; Resources; Services; Time; Training; United States National Institutes of Health; Vision; analytical tool; cohort; computerized tools; data acquisition; data ecosystem; data management; data tools; data-driven model; graphical user interface; heart cell; improved; innovation; interoperability; kinetic model; large datasets; metabolomics; molecular phenotype; novel; phenotypic data; precision medicine; protein metabolite; regression algorithm; spatiotemporal

PROJECT SUMMARY/ABSTRACT The inception of the R35 mechanism is a testament to the foresight and vision of NHLBI leadership. Clearly, this will provide unparalleled opportunities for driving discovery and enhancing human health. Capitalizing on a 22-year track record of scientific innovation, training and service to the community, as well as unique abilities in leveraging the technical foundation built by the NIH BD2K Center of Excellence at UCLA, this application presents a multi-pronged strategy for identifying molecular signatures that drive cardiac phenotypes. This application addresses two critical biomedical challenges. Firstly, there is a knowledge gap in how we conceptualize proteins, including how they interplay with other omes, and how their dynamics contribute to functional phenotypes. Secondly, there is an inadequacy of computational tools for systematically linking phenotypic and molecular data, and the cardiovascular community lacks a shared informatics management environment where both datasets and resources are accessible and interoperable for integrative analyses. Accordingly, this R35 proposes two areas of focus for breaking new ground. The first focus area will be to unveil how cardiac mitochondrial spatio-temporal proteomes and their interplay with metabolomic and genomic information drive cardiac phenotypes. This involves the advancement of technological platforms to characterize global spatio-temporal dynamics of cardiac proteins, metabolites, and pathways, producing both valuable molecular datasets from model systems and human cohorts and optimized kinetic models for enabling global dynamic analyses. The second focus area will be to build analytical tools for integrating molecular and phenotypic data, and to construct a prototypic cardiovascular data commons for supporting on-demand interactions among data, tools, resources, and users. These efforts will enable the elucidation of interconnected biological networks from proteomic, metabolomic, genetic variation, and clinical data types through a novel mixed model regression algorithm. Moreover, this will result in novel components for supporting a specialized data commons in cardiovascular medicine, including new APIs, graphical user interface, cloud-computing infrastructure, and data management pipeline. In summary, this R35 proposes to build a translational data ecosystem of seamless data acquisition and informatics platforms for enabling a new model of data-driven knowledge production. Discoveries will propel the NHLBI mission forward, including unveiling molecular signatures of disease in model systems and humans, fostering the training of future biomedical professionals, and disseminating advances to the scientific community and public via a specialized cardiovascular commons, all in the realization of precision medicine.

项目总结/摘要 R35机制的启动证明了NHLBI领导层的远见和远见。很显然， 这将为推动发现和增进人类健康提供前所未有的机会。利用 22-一年的科学创新，培训和服务社会的记录，以及独特的能力， 利用UCLA的NIH BD 2K卓越中心建立的技术基础，该应用程序 提出了一个多管齐下的策略，用于识别驱动心脏表型的分子特征。这 该应用解决了两个关键的生物医学挑战。首先，在我们如何 概念化蛋白质，包括它们如何与其他omes相互作用，以及它们的动力学如何有助于 功能表型第二，系统地联系的计算工具不足 表型和分子数据，心血管社区缺乏共享的信息管理 环境中的数据集和资源都是可访问的和可互操作的综合分析。 因此，R35提出了两个重点领域，以开辟新的领域。第一个重点领域将是 揭示心脏线粒体时空蛋白质组及其与代谢组学和基因组学的相互作用 信息驱动心脏表型。这涉及到技术平台的进步， 心脏蛋白质、代谢物和途径的全球时空动态， 来自模型系统和人类队列的分子数据集以及优化的动力学模型， 动态分析第二个重点领域将是建立分析工具， 表型数据，并构建支持按需的原型心血管数据共享 数据、工具、资源和用户之间的交互。这些努力将有助于阐明 来自蛋白质组学、代谢组学、遗传变异和临床数据类型的互连生物网络 通过一种新的混合模型回归算法。此外，这将产生用于制造电子设备的新部件。 支持心血管医学领域的专业数据共享，包括新的API、图形用户界面、 接口、云计算基础设施和数据管理管道。 总而言之，R35建议建立一个无缝数据采集的转换数据生态系统， 信息学平台，以实现数据驱动的知识生产的新模式。新发现将推动 NHLBI未来使命包括揭示模型系统和人类疾病的分子特征， 促进未来生物医学专业人员的培训，传播科学进步， 社区和公众通过一个专门的心血管共用，所有在实现精准医疗。