Establishing a generalizable model for predictable antisense RNA repression

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RNA is mostly known as a messenger to convey genetic information, while small RNA plays important roles in gene regulation. Such regulatory RNAs have been found to exist naturally in many organisms, but they have been used in a relatively limited number of bacteria as a tool to regulate gene expression. In this project, the investigators will develop generalizable RNA regulators that can be used to understand and engineer diverse biotechnologically important bacteria. Specifically, design principles for antisense RNA (asRNA), one of the common regulatory RNAs in bacteria, will be determined to provide biologists and bioengineers with predictable RNA tools for gene regulation. Additionally, this project is broadly impactful by cultivating the next generation. Multiple graduate students will be trained to perform the interdisciplinary project. Moreover, our outreach program will introduce state-of-the-art synthetic biology RNA tools to high school teachers, and provide them with teaching kits that will be used in their classroom, broadening student participation in science and engineering.The long-term goal of this project is to determine quantitative design principles for asRNAs and provide a generalizable model that enables predictably tunable asRNA-mediated gene repression in diverse organisms. asRNA is one of several types of small regulatory RNAs, and it represses gene expression by binding to its target messenger RNA. Although this binding occurs following the simple RNA base-pairing rule, predictably tunable asRNA-mediated repression is still challenging. The most important design parameters for asRNA in Escherichia coli DH10B have been identified by using three target reporter genes in plasmids. A predictive model for asRNA-mediated repression, which relates repression efficiency to the identified parameters by a multiple linear regression analysis, will be developed. To demonstrate the generalizability of this model, three specific aims are proposed. First, the model developed with plasmid reporters in E. coli DH10B will be validated by expanding it to chromosomal gene targets. This task will allow for testing of forward-designed asRNAs in different genetic contexts. Second, the predictive model will be tested in three different bacterial species. This task will test the hypothesis that asRNA design principles are consistent among diverse bacteria. Third, the model will be further validated by building and testing additional asRNAs. A user-friendly online version of the model will also be developed to provide a way to collaboratively advance this RNA-based technology.