Roles of long noncoding RNAs in neural differentiation

Cell differentiation relies on extensive changes in gene expression affecting virtually all categories of transcripts. Mammalian genomes encode thousands of long noncoding RNAs (lncRNAs) that lack functional open reading frames. Although these RNA species have attracted considerable research interest over the last few years, their roles in cell proliferation, differentiation and survival remain poorly understood. We have recently identified a new class of lncRNAs enriched in short tandem repeats (STRs),multiple iterations of 2-12 nucleotide-long "words" [1]. An interesting property of these transcripts is that they can interact with multiple copies of specific RNA-binding proteins (RBPs), a diverse group of regulators of cellular RNA metabolism. So far, we have analysed a single example of STR-lncRNAs, a transcript that we termed PNCTR. PNCTR contains STRs interacting with pyrimidine tract-binding protein (PTBP1), which allow this RNA to promote cell survival by sequestering PTBP1 in a nuclear body called perinucleolar compartment [1]. Our ongoing bioinformatics analyses suggest that the human and mouse genomes encode hundreds of STR-containing lncRNAs including many transcripts expressed from heterochromatic regions. This in turn points at an exciting possibility that lncRNAs control activity of a large network of RBPs with possible roles in chromatin and nuclear organization. Given the importance of RNA-based regulation mechanisms in the nervous system and our long-standing interest in molecular mechanisms responsible for acquisition and maintenance of neural cell identities [2-4], we propose to investigate the role of STR-lncRNAs in the developing brain.
The project will pursue the following interrelated objectives: 1. Understanding biological functions of heterochromatin-associated repeat-containing lncRNAs in human induced pluripotent stem cells and the neural lineage. 2. Systematic discovery of STR-containing lncRNAs differentially regulated in developing neural lineage using bioinformatics approaches. 3. In-depth analysis of functional significance of the most interesting candidate(s) identified by the bioinformatics survey.