CAREER: Global Analysis of RNA-dependent RNA Polymerase Function

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Intellectual Merit. How eukaryotic organisms regulate mRNA levels is a fundamental question in biology. Early attention was focused regulation at the level of gene transcription, but recently post-transcriptional mechanisms have gained recognition for their regulatory importance. One such post-transcriptional mechanism, RNA silencing, directs sequence-specific regulation of various gene transcripts, repetitive sequences, viruses, and mobile elements. RNA silencing is triggered by production of double-stranded RNA (dsRNA) or self-complementary fold-back structures that give rise to small RNAs (smRNAs) known as microRNAs or small-interfering RNAs (siRNAs). Genetic studies aimed at uncovering factors required for RNA silencing have identified a class of enzymes called RNA-dependent RNA polymerases (RDRs), which use an RNA template to synthesize siRNA-producing dsRNA molecules. A growing body of evidence implicates RDR-dependent RNA silencing in controlling numerous aspects of eukaryotic biology ranging from gene expression to protection from pathogens. However, a comprehensive functional analysis of RDR-dependent RNA silencing is still lacking for any organism. To this end, this project seeks to uncover all smRNA-producing substrates of the six Arabidopsis RDRs, and to determine the regulatory impact of these RNAs on the transcriptome. This research will also begin to functionally characterize the effects of these smRNAs on Arabidopsis development and biology. Because smRNAs play such a key regulatory role in the plant, the findings could have important implications for future agricultural crop improvement.Broader impacts. In addition to its scientific impact, this project will have a broad impact on the development and training of the next generation of scientists. The project will provide many opportunities for undergraduates, including community college students, and even high school students, to participate in laboratory research. Furthermore, the data generated from this project will be used to develop teaching modules focused on computational-based analysis of high-throughput sequencing data. These teaching modules will be designed to be appropriate for high school level learning and will be made freely available, so that teachers and students at many institutions can make use of them.