Identifying, Characterizing, and Exploring the Profiles of Ribosomal RNA Fragments (rRFs) and Their Implications for Personalized Medicine Contexts

Personalized medicine is a way to treat and monitor a patient based not only their symptoms but also on the unique factors that contribute to their disease. With the advent of new technologies like Next Generation Sequencing, scientists have explored many of these factors, including noncoding RNAs, a type of genetic material that does not make proteins. Short noncoding RNAs like miRNA, miRNA isoforms, and tRNA-derived fragments are one type of material under active investigation. These RNAs have been shown to be important, playing regulatory roles in diseases and fluctuating based on personal attributes such as population of origin, sex, disease status, etc. Through computational and experimental approaches, this thesis aims to explore a recently-emerged short RNA class called ribosomal RNA fragments (rRFs). To identify and characterize rRFs, we developed a computational analysis pipeline, which for the first time systematically maps, identifies, and characterizes all the rRFs present in a human cell type (Lymphoblastoid Cell Line). We observed both universal and population-specific profiles of rRFs in cells. In an effort to uncover the role that rRFs play in cells, we developed a new method called Fraction-seq. The method separates, sequences, analyzes, models, and validates the short RNA from model cell lines of Triple Negative Breast Cancer (BT-20, MDA-MB-231, and MDA-MB468) into its cell compartments (nucleus, cytoplasm, mitochondria, and mitoplast). We found that rRFs, in addition to isomiRs and tRFs, exhibit dynamic localization profiles that are cell-line-specific. Taken together, this work supports rRFs acting as important players in health and disease contexts, especially when it comes to personalized medicine. The knowledge gained from this study provides a springboard for future work directed at identifying rRF biogenesis and function in the cell. In addition, the context-specific features of rRFs align with those of isomiRs and tRFs and can be used as important biomarkers for disease and potential therapeutics