1. Characterization of Twisters Ribozymes at Splice Sites 2. Development of a Computational Pipeline to Discover Small Self-cleaving Ribozymes with Helical Imperfections

open_access
Open Access
Author:
Chang, Benjamin
Area of Honors:
Biochemistry and Molecular Biology
Degree:
Bachelor of Science
Document Type:
Thesis
Thesis Supervisors:
Philip C Bevilacqua, Thesis Supervisor
Joseph C. Reese, Thesis Honors Advisor
Keywords:
RNA
Ribozymes
Splicing
High-throughput Pipeline
Abstract:
RNA is a molecule capable of forming various structures and therefore can have diverse functions. Small self-cleaving ribozymes are a class of relatively short RNAs capable of catalyzing biochemical reactions. Their genomic context varies from 5’ UTRs to introns and can dictate the ribozyme’s biological function. Phylogenetically, some families of small self-cleaving ribozymes are widespread while others have only been identified in bacteria. In my thesis, I primarily studied twister ribozymes, a family of small self-cleaving ribozymes characterized by double pseudoknots. While studying the genomic context of known Danio rerio (zebrafish) twister ribozymes, I discovered two examples where their self-cleavage site aligns exactly with a 3′ splice site in their respective genes. To determine the potential role of these twister ribozymes at their splice sites, I characterized different variants to investigate if the ribozyme cleaves faster in the pre-mRNA or the mature RNA. In a separate study, I aimed to expand the phylogeny and find more examples of small self-cleaving ribozymes by creating a high-throughput computational pipeline that integrates various RNA tools and personalized scripts. With this pipeline, I was able to identify twister, twister sister, and hatchet ribozyme candidates from a diverse set of organisms. Three mammalian twister candidates were selected to test experimentally, and I was able to characterize the first mammalian twister ribozyme, in a dolphin. This research will provide a possible biological function of twister ribozymes and showcase a computational pipeline capable of identifying active ribozymes.