Thermodynamic Corrections to RNA Folding Models in Cells

Restricted (Penn State Only)
- Author:
- Sieg, Jacob
- Graduate Program:
- Chemistry
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- May 23, 2023
- Committee Members:
- Andrew Patterson, Outside Field Member
Paul Babitzke, Outside Unit & Field Member
Scott Showalter, Major Field Member
Edward O'Brien, Major Field Member
Philip Bevilacqua, Chair & Dissertation Advisor
Philip Bevilacqua, Program Head/Chair - Keywords:
- RNA
metabolites
Magnesium
Thermodynamics
Nearest neighbor model - Abstract:
- RNA performs numerous functions in cells owing to its capacity to adopt complex, diverse, and functional structures. Genome-wide structure-probing techniques have provided insight into RNA structure and function in vivo. However, most biophysical techniques are not readily amenable to the cellular environment. This has caused a gap in the detailed understanding of RNA thermodynamics artificial buffer conditions in vitro and understanding of RNA thermodynamics inside organisms (in vivo). In vitro studies of RNA in artificial cytoplasm that mimic a cell, so called in vivo-like conditions, provide a link between mechanistically tractable insight in vitro and understanding of RNA structure in vivo. This thesis describes the development and application of a realistic artificial cytoplasm for detailed mechanistic studies of RNA and determines thermodynamic corrections to RNA secondary structure models used in structure prediction algorithms. Chapter 2 examines the biological network of metabolites and metal ions in diverse organisms, using absolute metabolite concentrations and metal ion-binding constants in a multi-component model. This information is applied in Chapter 3 to develop the Eco80 artificial cytoplasm, which contains 80% of Escherichia coli metabolites, and demonstrates that the metabolome weakens RNA helix stability and strengthens RNA chemical stability. Chapter 4 describes folding free energies determined for an expanded helix set in Eco80 and applies in vivo-like thermodynamic corrections to improve RNA secondary structure prediction in E. coli. Chapter 5 provides unique experimental considerations for measuring the thermodynamics of RNA helix formation in artificial cytoplasm and presents the facile data-processing software MeltR. Lastly, Chapter 6 applies LC-MS/MS to RNA studies in biological systems and describes theoretical approaches to combining sequencing-based RNA structure probing with complementary LC-MS/MS analysis, as applied to measuring tRNA structure in cells. Additionally, Chapter 6 also describes unique methods of preparing cell lysates for RNA measurements and the use of LC-MS/MS based metabolite profiling to identify ligands that bind to RNA.