Publication Date

8-2022

Date of Final Oral Examination (Defense)

5-20-2022

Type of Culminating Activity

Dissertation

Degree Title

Doctor of Philosophy in Biomolecular Sciences

Department

Biology

Supervisory Committee Chair

Eric J. Hayden, Ph.D.

Supervisory Committee Member

Allan R. Albig, Ph.D.

Supervisory Committee Member

Matthew L. Ferguson, Ph.D.

Abstract

Self-cleaving ribozymes are a naturally occurring class of catalytically active RNA molecules which cleave their own phosphate backbone. In nature, self-cleaving ribozymes are best known for their role in processing concatamers of viral genomes into monomers during viral replication in some RNA viruses, but to a lesser degree have also been implicated in mRNA regulation and processing in bacteria and eukaryotes. In addition to their biological relevance, these RNA enzymes have been harnessed as important biomolecular tools with a variety of applications in fields such as bioengineering. Self-cleaving ribozymes are relatively small and easy to generate in the lab using common molecular biology approaches, and have therefore been accessible and well exploited model systems used to interrogate RNA sequence-structure-function relationships. Furthermore, self-cleaving ribozymes are also being implemented as parts in the development of various biomolecular tools such as biosensors and gene regulatory elements. While much progress has been made in these areas, there are still challenges associated with the performance and implementation of such tools.

The work contained in this dissertation aims to address several of these challenges and improve the ribozyme toolbox in several diverse areas. Chapter one provides an introduction to pertinent background information for this dissertation. Chapter two aims to improve the ribozyme toolbox by providing and analyzing new high-throughput sequence-structure-function data sets on five different self-cleaving ribozymes, and identifying how trends in epistasis relate to distinct structural elements. Chapter three uses such high-throughput data to train machine learning models that accurately predict the historically difficult to predict functional effects of higher order mutations in functional RNA’s. Finally, in chapter four, I developed a biologically relevant platform to study the real time performance and kinetics of self-cleaving ribozyme-based gene regulatory elements directly at the site of transcription in mammalian cells.

DOI

https://doi.org/10.18122/td.1991.boisestate

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