UC Santa Cruz

The human heart is one of the first organs to form during embryogenesis. By week 3, heart is a linear tube; then, between week 3 and 8, it undergoes a complex looping process to form heart chambers. From week 8 up until birth, the heart undergoes a period of rapid growth and expansion as the various cardiovascular lineages mature to support the developing fetus. Defects in this process lead to congenital heart defects. Therefore, understanding heart development at the molecular level will give insight into the mechanisms leading to the disease, and principles can further be applied for re- generative medicine purposes. In addition to genetic factors, lifestyle also significantly contributes to maintaining a healthy heart; therefore, lowering the risk factors that contribute to heart diseases such as obesity is vital. This thesis investigates computational and statistical approaches that help understand the influences of molecular, genetic, and lifestyle components in maintaining a healthy heart.Single-cell sequencing has provided a unique opportunity to study the heart at cellular resolution. Recent advances in single-cell sequencing allow scientists to sequence different modalities, such as proteomics, spatial transcriptomics, methylation, and chromatin accessibility at the level of individual cells. These advances offer a unique opportunity to develop computational methods for integrative single-cell data analysis to benefit from the information in multiple modalities. In the first chapter of this thesis, I present Single-cell Integrative Gene Regulatory Network (sci-GRN) inference framework that uses single-cell transcriptomics and epigenomics to infer gene regulatory networks (GRNs), and I demonstrate its application to the human heart development. Here, I first describe how each omic is processed independently to cluster the cells. Next, I describe how we annotated cell types from scRNA-seq data using known marker genes. Third, I present how I used the scRNA-seq data to infer the expression and cell type of each ATAC cell. Lastly, I present how sci-GRN uses the integrated data to build a gene regulatory network. In addition to genetic factors, lifestyle also significantly contributes to maintaining a healthy heart; therefore, lowering the risk factors contributing to heart diseases is vital. Studies have shown that obesity is associated with an increase in cardiovascular diseases. Therefore, it is crucial to tackling obesity systematically. Enara Health is a medical company based in San Mateo, California that offers a unique hybrid digital and in-person weight loss program. By combining mobile technology with healthcare visits, the program provides patients with personalized physician-driven medical weight loss programs to tackle obesity[2]. In the second chapter of my thesis, I present our study on analyzing one of the cardiovascular diseases” risk factors, obesity. I demonstrate how my statistical analysis implies the significant role of anti-obesity medication initiation and duration on weight loss in obese patients enrolled in Enara’s weight loss program. In the final chapter, I further analyze the impact of Enara’s weight-loss pro- gram mobile application (mHealth) on weight loss in obese patients. The mobile application under study allows for examining and comparing self-monitoring, feedback, support, and educational features within the same mHealth program, which may shed insight into which features and usage are better for sustained weight loss.[2]