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.

Public Health Relevance

There is a gap in our knowledge and understanding regarding the conceptualization of proteins in space and time throughout their lifetime in a heart cell. Moreover, there is a lack of computational tools to extract meaning from large datasets and to enable researchers to collaborate on data-intensive projects. This proposal addresses these challenges and provides innovative solutions for advancing cardiovascular research in order to improve human health.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Unknown (R35)
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Study Section
Special Emphasis Panel (ZHL1)
Program Officer
Schwartz, Lisa
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University of California Los Angeles
Schools of Medicine
Los Angeles
United States
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Liem, David A; Murali, Sanjana; Sigdel, Dibakar et al. (2018) Phrase mining of textual data to analyze extracellular matrix protein patterns across cardiovascular disease. Am J Physiol Heart Circ Physiol 315:H910-H924
Wang, Jie; Choi, Howard; Chung, Neo C et al. (2018) Integrated Dissection of Cysteine Oxidative Post-translational Modification Proteome During Cardiac Hypertrophy. J Proteome Res :
Caufield, John Harry; Liem, David A; Garlid, Anders O et al. (2018) A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts. J Vis Exp :
Lau, Edward; Cao, Quan; Lam, Maggie P Y et al. (2018) Integrated omics dissection of proteome dynamics during cardiac remodeling. Nat Commun 9:120
Caufield, J Harry; Zhou, Yijiang; Garlid, Anders O et al. (2018) A reference set of curated biomedical data and metadata from clinical case reports. Sci Data 5:180258
Ping, Peipei; Hermjakob, Henning; Polson, Jennifer S et al. (2018) Biomedical Informatics on the Cloud: A Treasure Hunt for Advancing Cardiovascular Medicine. Circ Res 122:1290-1301
McClatchy, Daniel B; Ma, Yuanhui; Liem, David A et al. (2018) Quantitative temporal analysis of protein dynamics in cardiac remodeling. J Mol Cell Cardiol 121:163-172
Lindsey, Merry L; Bolli, Roberto; Canty Jr, John M et al. (2018) Guidelines for experimental models of myocardial ischemia and infarction. Am J Physiol Heart Circ Physiol 314:H812-H838
Ping, Peipei; Watson, Karol; Han, Jiawei et al. (2017) Individualized Knowledge Graph: A Viable Informatics Path to Precision Medicine. Circ Res 120:1078-1080