Nascent Epitranscriptomics - Insights into RNA processing

The current thesis studies the RNA processing dynamics in a high-resolution time scale manner. The method used, called BrU Chase Seq, determined the processing kinetics of pri-miRNAs within intact cells over time using a pulse-chase approach to obtain nascent RNA within a 1-hour window after transcription. Further analysis showed that primiRNAs exhibit different processing kinetics ranging from fast over intermediate to slow processing. In addition, polycistronic pri-miRNAs show differential processing. The second part of the thesis describes for the first time the role of m6A RNA modification impact on RNA splicing kinetics. Two techniques have been developed namely TNT-Seq and qTNT-Chase Seq. These techniques provide the first time-resolved high-resolution assessment of m6A on nascent RNA transcripts and unveil its importance for the control of RNA splicing kinetics. More specifically, the early co-transcriptional m6A deposition near splice junctions promotes fast splicing, while m6A modifications in introns are associated with long, slowly processed introns and alternative splicing events. In conclusion, the early m6A deposition specifies the fate of transcripts regarding splicing kinetics and alternative splicing

LncRNA-OIS1 regulates DPP4 activation to modulate senescence induced by RAS

Oncogene-induced senescence (OIS), provoked in response to oncogenic activation, is considered an important tumor suppressor mechanism. Long noncoding RNAs (lncRNAs) are transcripts longer than 200 nt without a protein-coding capacity. Functional studies showed that deregulated lncRNA expression promote tumorigenesis and metastasis and that lncRNAs may exhibit tumor-suppressive and oncogenic function. Here, we first identified lncRNAs that were differentially expressed between senescent and non-senescent human fibroblast cells. Using RNA interference, we performed a loss-function screen targeting the differentially expressed lncRNAs, and identified lncRNA-OIS1 (lncRNA#32, AC008063.3 or ENSG00000233397) as a lncRNA required for OIS. Knockdown of lncRNA-OIS1 triggered bypass of senescence, higher proliferation rate, lower abundance of the cell-cycle inhibitor CDKN1A and high expression of cell-cycle-associated genes. Subcellular inspection of lncRNA-OIS1 indicated nuclear and cytosolic localization in both normal culture conditions as well as following oncogene induction. Interestingly, silencing lncRNA-OIS1 diminished the senescent-associated induction of a nearby gene (Dipeptidyl Peptidase 4, DPP4) with established role

SPLICE-q: a Python tool for genome-wide quantification of splicing efficiency

Background Introns are generally removed from primary transcripts to form mature RNA molecules in a post-transcriptional process called splicing. An efficient splicing of primary transcripts is an essential step in gene expression and its misregulation is related to numerous human diseases. Thus, to better understand the dynamics of this process and the perturbations that might be caused by aberrant transcript processing it is important to quantify splicing efficiency. Results Here, we introduce SPLICE-q, a fast and user-friendly Python tool for genome-wide SPLICing Efficiency quantification. It supports studies focusing on the implications of splicing efficiency in transcript processing dynamics. SPLICE-q uses aligned reads from strand-specific RNA-seq to quantify splicing efficiency for each intron individually and allows the user to select different levels of restrictiveness concerning the introns’ overlap with other genomic elements such as exons of other genes. We applied SPLICE-q to globally assess the dynamics of intron excision in yeast and human nascent RNA-seq. We also show its application using total RNA-seq from a patient-matched prostate cancer sample. Conclusions Our analyses illustrate that SPLICE-q is suitable to detect a progressive increase of splicing efficiency throughout a time course of nascent RNA-seq and it might be useful when it comes to understanding cancer progression beyond mere gene expression levels. SPLICE-q is available at: https://github.com/vrmelo/SPLICE-