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Splicing regulatory elements (SREs) are short, degenerate sequences on pre-mRNA molecules that enhance or inhibit the splicing process via the binding of splicing factors, proteins that regulate the functioning of the spliceosome. Existing methods for identifying SREs in a genome are either experimental or computational. This work tackles the limitations in the current approaches for identifying SREs. It addresses two major computational problems, identifying variable length SREs utilizing a graph-based model with de Bruijn graphs and discovering co-occurring sets of SREs (combinatorial SREs) utilizing graph mining techniques. In addition, I studied and analyzed the effect of alternative splicing on tissue specificity in human. First, I have used a formalism based on de Bruijn graphs that combines genomic structure, word count enrichment analysis, and experimental evidence to identify SREs found in exons. In my approach, SREs are not restricted to a fixed length (i.e., k-mers, for a fixed k). Consequently, the predicted SREs are of different lengths. I identified 2001 putative exonic enhancers and 3080 putative exonic silencers for human genes, with lengths varying from 6 to 15 nucleotides. Many of the predicted SREs overlap with experimentally verified binding sites. My model provides a novel method to predict variable length putative regulatory elements computationally for further experimental investigation. Second, I developed CoSREM (Combinatorial SRE Miner), a graph mining algorithm for discovering combinatorial SREs. The goal is to identify sets of exonic splicing regulatory elements whether they are enhancers or silencers. Experimental evidence is incorporated through my graph-based model to increase the accuracy of the results. The identified SREs do not have a predefined length, and the algorithm is not limited to identifying only SRE pairs as are current approaches. I identified 37 SRE sets that include both enhancer and silencer elements in human genes. These results intersect with previous results, including some that are experimental. I also show that the SRE set GGGAGG and GAGGAC identified by CoSREM may play a role in exon skipping events in several tumor samples. Further, I report a genome-wide analysis to study alternative splicing on multiple human tissues, including brain, heart, liver, and muscle. I developed a pipeline to identify tissue-specific exons and hence tissue-specific SREs. Utilizing the publicly available RNA-Seq data set from the Human BodyMap project, I identified 28,100 tissue-specific exons across the four tissues. I identified 1929 exonic splicing enhancers with 99% overlap with previously published experimental and computational databases. A complicated enhancer regulatory network was revealed, where multiple enhancers were found across multiple tissues while some were found only in specific tissues. Putative combinatorial exonic enhancers and silencers were discovered as well, which may be responsible for exon inclusion or exclusion across tissues. Some of the enhancers are found to be co-occurring with multiple silencers and vice versa, which demonstrates a complicated relationship between tissue-specific enhancers and silencers. / Ph. D.

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No description available.

In eukaryotes the coding regions of most genes are interrupted by introns that must be removed by splicing to form a coding mRNA. However, while the splicing mechanism has received a lot of attention, much less is known about the metabolism of introns. This is partly due to the difficulties in studying introns as both aberrantly spliced transcripts and spliced introns are rapidly degraded. In this study, I have analysed intron metabolism in two respects: first I have investigated how introns are degraded following the completion of splicing. Second, I investigate the fate of transcripts, in which introns are retained due to splicing failure. In order to study the degradation of introns following splicing, I performed siRNA mediated knock down of the debrancing enzyme (Dbr1). Following splicing, introns are present in a circular lariat structure and Dbr1 is the enzyme thought to be responsible for opening this. Indeed, I found that knockdown of Dbr1 increased the amount of stabilised introns. Interestingly, introns were found to be stabilised in the cytoplasm and not in the nucleus as expected, even though immunofluoresence showed that Dbr1 is clearly nuclear. However, western blot analysis localised Dbr1 in the cytoplasm. Further investigation showed widely used methods to separate nuclear and cytoplasmic fractions are prone to generating artefacts which result in nucleoplasmic proteins delocalised to the cytoplasm. This finding may prevent future misinterpretation of data obtained by these methods. To investigate splicing failure, it was necessary to generated a sufficiently large pool of unspliced transcripts. To do this I used antisense morpholinos (AMOs) that bind to specific snRNAs (small nuclear RNAs). They are designed to block interaction surfaces that are important for splicing. Using this approach, I investigated the localisation of RNA transcripts and selected RNA processing and degradation factors in normal conditions and where splicing was inhibited. When splicing is inhibited I found splicing factors and unspliced, polyadenylated RNA localising to nuclear, splicing speckle marker SC35 positive speckles. I further discovered that for RNA to localise to nuclear speckles, polyadenylation and RNA cleavage are essential, indicating that SC-35 speckles might sequester unspliced transcripts preventing translation of potentially harmful transcripts. These transcripts remain functional however, and can be spliced where functional spliceosomes can be assembled.

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In addressing the pre-mRNA substrate, the splicing machinery requires rearrangement of multiple RNA and protein components. The classical model of spliceosome formation begins with the U1 snRNA recognition of the 5" splice site and U2 snRNP interaction with the branch point. This process is followed by the engagement of a pre-assembled U4/U6·U5 tri-snRNP to form the A2-1 complex. The spliceosome is subsequently activated through a number of structural rearrangements. Among these is the unwinding of the U4/U6 intermolecular helix by the tri-snRNP component Brr2p. While numerous protein components of the tri-snRNP have been identified, the function of many of these remain unknown. The nonessential Snu66p (U4/U6·U5-110K in humans) stably associates only with the U4/U6·U5 tri-snRNP while the similarly nonessential Snu40p (U5-52K in humans) associates exclusively with the U5 snRNP. To understand why two non-essential pre-mRNA splicing factors have been so well conserved through great evolutionary distances, we examined their roles in the assembly and function of the tri-snRNP. Removal of SNU40 alone does not affect snRNP levels, however deletion of SNU66 results in reduced levels of tri-snRNP. The U4/U6·U5 snRNPs in [Delta]snu66 cells are resistant to the ATP-dependent U4/U6 unwinding by Brr2p, and profound U4/U6 accumulation occurs at reduced temperatures. Remarkably, subsequent removal of SNU40 in a [Delta]snu66 strain bypasses the tri-snRNP formation defect while unwinding of U4/U6 remains defective. Additional investigation revealed that Prp6p, another tri-snRNP protein, is destabilized from the complex. Based upon this data in total, I present a model in which Snu40p and Snu66p interact sequentially with Prp6p to maintain directionality for proper biogenesis of the tri-snRNP. Further, the U4/U6 unwinding defect of the double mutant should theoretically arrest the A2-1 spliceosome. Indeed, native gel analysis confirms the buildup of a large complex later determined to be A2-1. I have purified this complex, functionally tested its catalytic viability, and identified its components via mass spectrometry. This is the first full characterization of the A2-1 precatalytic spliceosome complex in Saccharomyces cerevisiae. / text

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No description available.