Non-coding RNAs (ncRNAs) account for a higher percent of the genome than coding mRNAs, and are implicated in human disease such as cancer, neurological, cardiac and many others. While the majority of ncRNAs involved in disease were originally attributed to a class of RNAs called micro RNAs (miRNAs) with a small size of only about 19 -24 base pairs, emerging research has now demonstrated a class of long non-coding RNAs (lncRNAs) that have a size of over 200 base pairs to be responsible for gene regulation and other functional roles and have also found to contribute to pathogenesis in humans. The increased size and structural complexity require novel tools to study their interactions beyond RNA interference. Synthetic antibodies are classic tools and therapeutics utilized to study and treat proteins involved in human disease. Likewise we hypothesize that structured RNAs can also take advantage of synthetic antibodies to probe their functions and be utilized as therapeutics. Currently, antibodies have been raised against microbial riboswitches and other structured RNAs of single-celled organisms, and only one human structured RNA to the best of our knowledge. However, no one has yet to create a synthetic antibody capable of behaving as a therapeutic against a structured RNA. We therefore sought to raise an antibody Fab against a structured RNA, human initiator tRNA, a model oncogenic non-coding RNA and demonstrate its efficacy in vitro. We then characterized the antibody and explored delivery options in cancer cells including the use of nanoparticle delivery systems. With the emerging transcriptome revealing new ncRNAs implicated in human disease, our research has begun to address a new therapeutic strategy, laying down the foundation for the future of structured RNA-targeted therapies.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-2198 |
| Date | 01 January 2014 |
| Creators | Archer, Jennifer |
| Publisher | University of Central Florida |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
Page generated in 0.0015 seconds