The study of non-coding RNA genes has received increased attention in recent years fuelled by accumulating evidence that larger portions of genomes than previously acknowledged are transcribed into RNA molecules of mostly unknown function, as well as the discovery of novel non-coding RNA types and functional RNA elements. Here, we demonstrate that specific properties of graphs that represent the predicted RNA secondary structure reflect functional information. We introduce a computational algorithm and an associated web-based tool (GraPPLE) for classifying non-coding RNA molecules as functional and, furthermore, into Rfam families based on their graph properties. Unlike sequence-similarity-based methods and covariance models, GraPPLE is demonstrated to be more robust with regard to increasing sequence divergence, and when combined with existing methods, leads to a significant improvement of prediction accuracy. Furthermore, graph properties identified as most informative are shown to provide an understanding as to what particular structural features render RNA molecules functional. Thus, GraPPLE may offer a valuable computational filtering tool to identify potentially interesting RNA molecules among large candidate datasets.

Group I introns are structured RNAs that have been used extensively as model systems for RNA folding because they are experimentally tractable, yet complex enough to have folding challenges associated with larger RNAs. The Tetrahymena group I intron consists of a set of conserved core helices and a set of peripheral elements. Peripheral elements surround the core helices and form long range tertiary contacts between each other and to the core. Interestingly, a long-lived misfolded state is populated that has the same long range tertiary contacts as the native state but differs locally within the core. Our lab showed that the intact periphery is necessary to specify the correct core structure, as mutating tertiary contacts or removing the P5abc peripheral element dramatically destabilized the native ribozyme relative to the misfolded form. However, we also showed that the thermodynamic benefit peripheral structure provided is accompanied by kinetic liability in folding, apparently because native tertiary contacts formed by peripheral elements around the misfolded core must come apart to allow refolding of the misfolded RNA to the native state. In addition to peripheral elements, proteins also play a role in stabilizing the native structures of many group I introns. The CYT-18 protein, which occupies the same binding site as P5abc, stabilizes the functional structures of certain group I introns by using a set of insertions that are absent in other related bacterial and mitochondrial aminoacyl tRNA synthetases. Using the P5abc deletion variant of the Tetrahymena ribozyme, I sought to further define CYT-18 roles in RNA folding by probing its thermodynamic and kinetic effects on the native state formation relative to the misfolded state. I demonstrated that CYT-18, like P5abc, provided thermodynamic stability to the native state. However, unlike P5abc, CYT-18 had no apparent effect on the refolding kinetics, suggesting that a protein co-factor can stabilize the functional structure without acquiring the associated costs in RNA folding kinetics. Furthermore, I found that the mechanism of CYT-18 action appears to be distinct from P5abc. Disruption of the long-range contact P14, which is formed between P5c and L2 and is part of the network of peripheral contacts, dramatically weakened P5abc binding to the native ribozyme core by ~10⁸ fold. Interestingly, CYT-18 maintained specific and tight binding to these mutants, which suggests that CYT-18 does not rely on a circular network of contacts to specifically stabilize the native state. Instead, the specificity may arise from a more direct and intimate contact of CYT-18 with the ribozyme core. This study gives insight into an evolutionary advantage of protein co-factors in RNA folding; proteins may offer thermodynamic assistance without inhibiting folding kinetics. / text

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.