UCLA

Our capabilities to define cardiovascular health and disease using highly multivariate “omics” datasets have substantially increased in recent years. Advances in acquisition technologies as well as bioinformatics methods have paved the way for ultimately resolving every biomolecule comprising various human “omes”. Understanding how different “omes” change and interact with one another temporally will ultimately unveil multi-omic molecular signatures that inform pathologic mechanisms, indicate disease phenotypes, and identify new therapeutic targets. Herein we describe a thesis project that creates novel, contemporary data science methods and workflows to extract temporal molecular signatures of disease from multi-omics analyses, and develops integrated omics knowledgebases for the cardiovascular community at-large.

Chapter 1 provides an overview of cardiac physiology and pathophysiology involved in cardiac remodeling and heart failure (HF). A description of the systematic characterization of cardiac proteomes and metabolomes is included, including methodologies for multi-omics phenotyping. Finally, an overview of bioinformatics methods for driver molecule discovery is provided, discussing strategies for characterizing temporal patterns and conducting functional enrichment. Chapter 2 describes computational approaches to discern the oxidative posttranslational modification (O-PTM) proteome, an important factor in cardiac remodeling. We developed a novel platform involving a customized, quantitative biotin switch pipeline and advanced analytic workflow to profile O-PTMs in an isoproterenol (ISO)-induced cardiac remodeling mouse model. We identified 1,655 proteins containing 3,324 oxidized sites, and unveiled temporal progression of O-PTM in disease. Chapter 3 discusses computational approaches for identifying temporal metabolomics fingerprints in HF treatment. Pathologic remodeling from a healthy to diseased heart involves a series of alterations over time. Mechanical circulatory support devices (MCSD) are a promising strategy for unloading the heart and reversing this process. We sought to identify molecular drivers of pathologic remodeling and reverse remodeling in the plasma metabolome; however, machine learning (ML)-empowered technological platforms required for these analyses are lacking. Thus, we established a Multiple Reaction Monitoring (MRM)-based MS quantitative platform and ML-based computational workflow to discern metabolomics fingerprints. We quantified 610 plasma metabolites and identified those exhibiting high correlation to cardiac phenotype, demonstrating a novel platform for biomarker discovery. Finally, Chapter 4 integrates all aforementioned innovations into one unified, cloud-based computational knowledgebase, MetProt, equipped to analyze, annotate, and integrate metabolomics and proteomics information. This pipeline fully characterizes the plasma metabolome in HF, unveils the interplay of proteomes and metabolomes, and derives new knowledge in cardiovascular medicine. Innovations include engineered features for addressing large-scale clinical datasets as well as algorithms to connect various types of molecules (e.g., proteins and metabolites). Chapter 4 is subdivided into 3 projects: Project 1 describes a computational pipeline to characterize the plasma metabolome using datasets from the ISO mouse model of HF and human HF; Project 2 develops bioinformatics strategies to integrate proteome and metabolome datasets from six genetically distinct mouse strains; and Project 3 establishes the cloud-based MetProt to disseminate the above computational pipelines for the cardiovascular community at-large. Taken together, these innovations offer new approaches and workflows for integrated omics investigations that enable novel discovery and ultimately advance precision cardiovascular science and medicine.