We propose to develop and validate a technology for matching known RNAs with corresponding inhibitory microRNAs. MicroRNAs are a class of small, non-protein-coding RNAs that control gene expression by translational repression or mRNA degradation. More than 600 microRNAs are expressed in humans, and up to 30% of human genes may be regulated by microRNAs. Mounting evidence suggests that microRNAs play a very broad role in a variety of cellular pathways, including development, differentiation, cell proliferation, and apoptosis. The importance of microRNAs in human diseases, and in particular cancer, is becoming increasingly appreciated. Although a number of tools have been developed for identifying and profiling microRNAs, cataloguing microRNAs and their expression patterns does not provide a complete picture of their cellular roles. In order to gain such an understanding, it is important to identify those cellular mRNAs regulated by microRNAs. However, imperfect base pairing of microRNAs to target RNAs causes difficulty in the prediction of microRNA targets. Moreover, current experimental approaches to verifying predicted interactions are laborious and time-consuming. Consequently, relatively few predicted interactions have been functionally confirmed. We propose to develop a systematic tool to rapidly and routinely identify microRNAs that hybridize to any query RNA sequence. This microRNA identification system will be comprised of: 1) a puromycin resistance reporter vector into which query RNA sequences can be introduced and 2) stably-transduced cell populations that as a group over-express 550 different microRNAs, but comprised of individual cells over-expressing on average a single microRNA. To use this system, reporter vectors containing target RNA sequences of interest are delivered into microRNA over-expressing cells by lentiviral infection. Puromycin is then added. Only individual cells that over-express a microRNA that hybridizes to the target RNA sequences will grow in the presence of puromycin. These puromycin resistant cells are then analyzed to determine the microRNA(s) that they express. This technology will be developed and validated by completing three specific tasks. First, the lentivector will be developed based on a prototype selection vector that we have shown can detect interactions between microRNAs and their known target mRNAs. Second, we will create cell lines comprised of mixed populations of cells, each of which over-expresses on average one of 550 human microRNA precursors. Third, the feasibility of using this new technology to detect interactions between microRNAs and target RNAs will be validated by screening the 3'untranslated regions (3'UTRs) of 10 RNAs that contain known microRNA binding sites for microRNA effectors. To achieve our objective, we must identify all known interactions between microRNAs and these target RNAs. The technology developed in this proposal will provide an inexpensive and easy to use tool for identification of microRNAs that hybridize to any target RNA of interest. A systematic approach to identification of microRNA regulators of key cellular mRNAs will alleviate a critical bottleneck in microRNA studies and contribute to our understanding of the roles these molecules play in cancer and other human diseases.
A system for identifying microRNAs that bind a target RNA Principal Investigator: Davies, Joan Project Narrative A goal of biological research is to better understand human disease in the interest of improved health. A new class of RNA molecules called microRNAs has been show to play key roles in cancer and other human diseases. However, we need new tools to study this new type of molecule. This proposal describes an easy way to find the proteins affected by microRNAs so we can better understand how microRNAs cause disease and may suggest new ways to treat diseases.
