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

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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.