A Unified Atlas of Dynamic tRNA Function

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The broad objective of this project is to better understand function, regulation, and the varied transcripts produced by each individual human tRNA gene. We propose to sequence full-length tRNA and tRNA-derived small RNA transcripts across many different tissue types for both human and the preferred mammalian model, mouse. These data will complement existing large-scale data sets focusing only on protein coding genes, giving an integrated view of tRNAs in the context of all other genes. With this new data, we will be able to better annotate and predict tRNA expression breadth across tissues, identify atypical tRNAs with potentially novel function, and recognize potentially disease-contributing mutations in human tRNA genes. The tRNA research field will also greatly benefit by being able to apply the revolutionary CRISPR/Cas9 gene targeting technique to individual tRNA genes for study – an experimental resource which we propose to develop. Thus, the aims of this grant are as follow: (1) Creation of new tRNA gene predictive models leveraging existing functional data with new insights in tRNA gene variation and the importance of external genomic features. (2) Train new predictive models with state-of-the-art tRNA transcriptome analyses, currently absent from the public databases. We will generate a comparative atlas of tRNA expression in samples from a broad range of healthy tissues from human and mouse, matching the tissue distribution of the NIH Genotype-Tissue Expression (GTEx) project to complement gene expression data for protein coding genes. (3) Integrate tRNA Atlas data with epigenomic and other published functional data within the Genomic tRNA Database. Using the framework of this database, we will establish robust functional ortholog maps between species, starting with human and mouse, to enable researchers to identify the best candidates for study of human genes in model organisms. (4) Develop a library of human CRISPR/Cas9 guide RNAs to enable individual tRNA gene targeting and characterization. We will select a representative subset of fifteen tRNAs of special interest for study, based on functional predictions and expression data collected from Aims 1 and 2. These new data, integrated into the most widely used tRNA gene database, along with improved tools for predicting and testing gene function, will enable and accelerate biomedical research in the tRNA community. Our interdisciplinary research group is ideally suited to carry out these aims, as we have demonstrated expertise in both computational analysis and development of molecular biology techniques enabling study of tRNA biology.