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

> **NIH NIH R35** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2021 · $910,734

## Abstract

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.

## Key facts

- **NIH application ID:** 10077490
- **Project number:** 5R35HL135772-05
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Peipei Ping
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $910,734
- **Award type:** 5
- **Project period:** 2017-01-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10077490

## Citation

> US National Institutes of Health, RePORTER application 10077490, Omics Phenotyping for Identifying Molecular Signatures of the Healthy and Failing Heart: An Integrated Data Science Platform (5R35HL135772-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10077490. Licensed CC0.

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