Abstract Title

Self-Cleaving RNA Model Experimentation

Additional Funding Sources

The project described was supported by Institutional Development Awards (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under Grant Nos. P20GM103408 and P20GM109095, and National Science Foundation S-STEM Gateway Scholarships in Biological Sciences under Grant Award No. DUE-1644233. We also acknowledge support from the Biomolecular Research Center at Boise State with funding from the National Science Foundation, Grant Nos. 0619793 and 0923535, the M.J. Murdock Charitable Trust, and the Idaho State Board of Education.

Abstract

RNA molecules are important for many biological processes. They are a component of ribosomes, and are necessary for protein production and gene expression. The function of an RNA molecule is often dependent upon its formation of a 3D structure, which is itself dependent on the particular RNA sequence. Advances in sequencing technologies are rapidly identifying previously unknown RNA molecules. However, our ability to determine the structure and function of these newly discovered RNA molecules lags behind the rate of discovery. There are currently few tools available to predict the structure of an RNA molecule given its sequence. Catalytically active RNA molecules that cut their own phosphodiester backbone, called self-cleaving ribozymes, provide a good model system to develop these new tools. They are a good model because the sequence, structure, and function being analyzed are contained in a single molecule that we can easily mutate, replicate, and then measure the self-cleavage of through high throughput sequencing. The data provides structural information in the form of rescue interactions. When a single mutation reduces the self-cleaving activity, but the activity is then restored by a second mutation, it indicates the two positions are base-paired. Analysis of previously obtained sequence data has indicated that undesired side products of one of the reactions reduced the number of usable sequencing reads. In this project we optimized biochemical reaction conditions and generated higher quality samples for sequencing. In the future, these data sets paired with computational analysis may be useful for predicting unknown RNA structures and functions.

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Self-Cleaving RNA Model Experimentation

RNA molecules are important for many biological processes. They are a component of ribosomes, and are necessary for protein production and gene expression. The function of an RNA molecule is often dependent upon its formation of a 3D structure, which is itself dependent on the particular RNA sequence. Advances in sequencing technologies are rapidly identifying previously unknown RNA molecules. However, our ability to determine the structure and function of these newly discovered RNA molecules lags behind the rate of discovery. There are currently few tools available to predict the structure of an RNA molecule given its sequence. Catalytically active RNA molecules that cut their own phosphodiester backbone, called self-cleaving ribozymes, provide a good model system to develop these new tools. They are a good model because the sequence, structure, and function being analyzed are contained in a single molecule that we can easily mutate, replicate, and then measure the self-cleavage of through high throughput sequencing. The data provides structural information in the form of rescue interactions. When a single mutation reduces the self-cleaving activity, but the activity is then restored by a second mutation, it indicates the two positions are base-paired. Analysis of previously obtained sequence data has indicated that undesired side products of one of the reactions reduced the number of usable sequencing reads. In this project we optimized biochemical reaction conditions and generated higher quality samples for sequencing. In the future, these data sets paired with computational analysis may be useful for predicting unknown RNA structures and functions.