אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

The major spliceosome is a multi-component and highly dynamic complex that carries out the tightly regulated steps of splicing. It is composed of hundreds of proteins, including five small nuclear ribonucleoprotein complexes (snRNPs) that catalyze>99% of all pre-mRNA splicing events. In addition, there are alternative splicing regulators, such as the SR and hnRNP proteins, that either activate or repress a subset of the splicing events. Members of these protein families are known to interact with components of the spliceosome; however, which interactions are direct and functional, and whether they participate in spliceosome assembly is unknown. I will present a high-throughput proteomic approach to assemble and explore the interactome network of four well known alternative splicing factors: SRSF1 (SF2/ASF), SRSF6 (SRp55), hnRNPA1 and the brain/muscle specific splicing factor RBFOX1. Using a combination of affinity purification and mass spectrometry, we generated protein-protein interaction (PPI) networks of each splicing factor. The analysis of the reconstructed networks revealed specific points of interaction between these splicing factors and the spliceosome, as well as differences in their global connectivity. Unexpectedly, we also observed links between splicing factors and other biological networks, as in the case of RBFOX1 and a complex of proteins involved in Alzheimer. Another useful way to address the relationship of splicing to other biological networks would be through the study of Protein-RNA interactions, between specific splicing factors and their alternative splicing targets. It is possible to uncover such connections using high throughput technologies like RNA deep sequencing. I will present SpliceTrap, our newly developed method to quantify exon inclusion levels using paired-end RNA-seq data. Unlike other tools, which focus on the assembly of full-length transcript isoforms, SpliceTrap approaches the expression level estimation of each exon as an independent problem. In addition, SpliceTrap can identify alternative splicing events under a single cellular condition, without requiring a background set of reads to estimate relative splicing changes. I will show a case of study in which we applied SpliceTrap to uncover splicing targets of SRSF1. In summary, through the combination of high throughput proteomics and transcriptomics, we are aiming to uncover connections that would help us to understand how the process of splicing is integrated in the global network of the cell.