PROJECT SUMMARY / ABSTRACT Subcellular mRNA localization enables post-transcriptional regulation of gene expression through the spatial and temporal control of translation. Control of gene expression through mRNA localization is important in many different contexts including development, cell polarization and cell cycle progression. This mechanism relies on localization elements (LE) within the transcript which interact with trans-acting factors, (RNA binding proteins), to drive localization to a specific subcellular location. LEs are often contained within the 3’ UTR and range in size from 100 to 250nt. The mechanisms driving mRNA localization to the midbody (MB), an organelle which forms during a cell division, are only just emerging and are the main focus of this proposal. The MB is a microtubule-rich organelle that forms between the two dividing daughter cells and is a critical mitotic regulator. The MB mediates the conclusion of mitosis, abscission. Abscission is the highly regulated separation of the two daughter cells that relies on the tightly controlled recruitment of specific abscission regulating proteins to the MB and the site of abscission. Abscission is important during tissue development and function, and defects in this process contribute to increases in aneuploidy and genomic instability. Yet, how abscission regulators are localized to and activated at the MB remains largely unclear and is the main focus of this proposal. In this project I propose to use high-throughput experimental techniques, single molecule imaging, and machine learning to identify and characterize RNA localization elements that drive RNA localization to the MB. I will accomplish this by using a massively parallel reporter assay (MPRA) to generate ~20,000 sequences spanning the 3’UTRs of MB localized RNAs. These 260nt long sequences will be systematically tested for their ability to drive a reporter RNA to the MB. After identification of localization elements (LE) in each 3’UTR tested, I will then test the necessity of each newly identified LE for MB RNA localization by deleting it from its endogenous location using CRISPR-Cas9. Given that previously identified LEs that traffic RNA to neuronal projections also traffic RNAs to MBs, I will combine data from the MPRA performed in MBs with MPRA data from neurons produced by our lab to generate a predictive model and identify new LEs within other MB localized RNAs. The minimally active size of previously identified LEs is over 200 nt, making it difficult to easily identify the features of LEs necessary for their localization activity. To extract the active LE features from this data, I will use machine learning techniques to uncover features (kmer/motif content, RNA secondary structures, etc.) found in the active sequences but lacking in the inactive sequences. These experiments will be the first to identify localization elements capable of localizing RNAs to the MB and will represent the first step in defining mec...