P61Identification of temporally modulated lncRNAs in cardiac hypertrophy. (15th July 2014) - Stirparo, GG; Greco, CM; Kunderfranco, P; Carullo, P; Serio, S; Papait, R; Condorelli, G
Abstract: Gene expression reprogramming in cardiac myocytes is a key feature of heart hypertrophy and failure. Many mechanisms are involved in the control of gene expression. Among these, non-coding RNAs (ncRNA), such as long ncRNA (lncRNA), antisense RNA and pseudogenes, are gaining importance as regulatory elements in several cellular process, such as cell growth, apoptosis and development. Not surprisingly, the dysregulation of these RNAs has been found to cause several human diseases, such as cancer (e.g., prostate and breast cancer) and neurodevelopmental diseases (e.g., Alzheimer's disease and spinocerebellar ataxia) and, more recently, to be implicated in cardiac commitment. Despite this, little is known about their involvement in cardiac hypertrophy and how they regulate gene expression in this pathology. The aim of this study was to provide a map of ncRNAs modulated in cardiac hypertrophy. To do this, we performed ribo-depleted RNA-sequencing on cardiomyocyte RNA isolated from mice subjected to transverse aortic constriction (TAC) for 1, 2, 4 and 7 days. We identified 130 lncRNA, which we divided into different classes (antisense RNA, lincRNA, processed transcripts, etc.) according to the Ensembl glossary. We found that the lncRNA signature was time-dependent modulated and could play a central role in gene expression reprogramming during cardiac hypertrophy. Furthermore, by clustering genes by their temporal expression profiles (with Short Time-series Expression Miner), we are able to define how biological process were modulated through time. For one cluster enriched for processes involving methyltransferase, we found an over-representation of lncRNA. This is in line with the idea that lncRNA could contribute to epigenetic reprogramming by regulating or interacting with genes involved in this process. Moreover, because identification of novel lncRNAs is critical for the understanding of the intrinsic complexity of the transcriptome, we developed a method to identify novel lincRNA, exploiting both epigenetic modification and RNA-sequencing data, which allowed us to more-accurately define transcribed regions.