RNA processing events that take place on the transcribed pre-mRNA include capping, splicing, editing, 3′ processing, and polyadenylation. Most of these processes occur co-transcriptionally while the RNA polymerase II (Pol II) enzyme is engaged in transcriptional elongation. How Pol II elongation rates are influenced by splicing is not well understood. We generated a family of inducible gene constructs containing increasing numbers of introns and exons, which were stably integrated in human cells to serve as actively transcribing gene loci. By monitoring the association of the transcription and splicing machineries on these genes in vivo, we showed that only U1 snRNP localized to the intronless gene, consistent with a splicing-independent role for U1 snRNP in transcription. In contrast, all snRNPs accumulated on intron-containing genes, and increasing the number of introns increased the amount of spliceosome components recruited. This indicates that nascent RNA can assemble multiple spliceosomes simultaneously. Kinetic measurements of Pol II elongation in vivo, Pol II ChIP, as well as use of Spliceostatin and Meayamycin splicing inhibitors showed that polymerase elongation rates were uncoupled from ongoing splicing. This study shows that transcription elongation kinetics proceed independently of splicing at the model genes studied here. Surprisingly, retention of polyadenylated mRNA was detected at the transcription site after transcription termination. This suggests that the polymerase is released from chromatin prior to the completion of splicing, and the pre-mRNA is post-transcriptionally processed while still tethered to chromatin near the gene end.

This work addresses two novel problems in the field of computational genomics. The first is whole genome alignment and the second is inferring horizontal gene transfer using posets. We define these two problems and present algorithmic approaches for solving them. For the whole genome alignment, we define alignment graphs for representing different evolutionary events, and define a scoring function for those graphs. The problem defined is proven to be NP-complete. Two heuristics are presented to solve the problem, one is a dynamic programming approach that is optimal for a class of sequences that we define in this work as breakable arrangements. And, the other is a greedy approach that is not necessarily optimal, however, unlike the dynamic programming approach, it allows for reversals. For inferring horizontal gene transfer, we define partial order sets among species, with respect to different genes, and infer genes involved in horizontal gene transfer by comparing posets for different genes. The posets are used to construct a tree for each gene. Those trees are then compared and tested for contradiction, where contradictory trees correspond to genes that are candidates of horizontal gene transfer. / Ph. D.

The evolutionary options of a population are strongly influenced by the avail- ability of adaptive mutants. In this thesis, I use the concept of neutral networks to show that neutral drift can actually increase the accessibility of adaptive mu- tants, and therefore facilitate adaptive evolutionary change. Neutral networks are groups of unique genotypes which all code for the same phenotype, and are connected by simple point mutations. I calculate the size and shape of the networks in a small but exhaustively enumerated space of RNA genotypes by mapping the sequences to RNA secondary structure phenotypes. The qual- itative results are similar to those seen in many other genotype–phenotype map models, despite some significant methodological differences. I show that the boundary of each network has single point–mutation connections to many more phenotypes than the average individual genotype within that network. This means that paths involving a series of neutral point–mutation steps across a network can allow evolution to adaptive phenotypes which would otherwise be extremely unlikely to arise spontaneously. This can be likened to walking along a flat ridge in an adaptive landscape, rather than traversing or jumping across a lower fitness valley. Within this model, when a genotype is made up of just 10 bases, the mean neutral path length is 1.88 point mutations. Furthermore, the map includes some networks that are so convoluted that the path through the network is longer than the direct route between two sequences. A minimum length adaptive walk across the genotype space usually takes as many neutral steps as adaptive ones on its way to the optimum phenotype. Finally I show that the shape of a network can have a very important affect on the number of generations it takes a population to drift across it, and that the more routes between two sequences, the fewer generations required for a population to find an advantageous sequence. My conclusion is that, within the RNA map at least, the size, shape and connectivity of neutral networks all have a profound effect on the way that sequences change and populations evolve, and by not considering them, we risk missing an important evolutionary mechanism.

A protein, B0238.11, was identified in a yeast one-hybrid screen to bind to the ribosomal intergenic spacer region (IGS) of Caenorhabditis elegans. Proteins interacting with this region of the DNA have been implicated in ribosome biogenesis in other model organisms, so it is also possible that B0238.11 plays a role in RNA transcription by interacting with RNA polymerase I or other transcription machinery. Thus, the goal of this study was to further characterize the structure and function of B0238.11. I used yeast two-hybrid experiments to identify proteins that interact with B0238.11 within the nucleus. RPS-0, K04G2.2, DPY-4, EFT-3, PAL-1, and B0238.11, itself, were found to bind to B0238.11. Additionally, I analysed the amino acid sequence of B0238.11 using in silico bioinformatics methods to determine its structure and putative function and also to identify and characterize the other interacting proteins. I found that B0238.11 contains a high-mobility group box domain, which is also found in HMO1P in yeast and UBF in vertebrates. These other proteins also bind to the IGS, are known to form homodimers and have been implicated in the initiation of ribosomal RNA transcription. Here I scrutinize the validity of the interaction between each protein and B0238.11. I conclude that B0238.11 is likely to be a C. elegans homolog of UBF and present an updated interactome map for B0238.11. / Synopsis: I carried out yeast two-hybrid assay to find proteins interacting with B0238.11 (O16487_CAEEL). I found that this protein's DNA-binding profile and protein interaction profile mimic other HMG-box containing proteins UBF and HMO1P which are involved in ribosomal RNA transcription initiation. Acknowledgements: I would like to thank my supervisor, Dr. Teresa J. Crease, for not only giving me the opportunity to investigate an interesting topic in Molecular Biology, but also for her patient guidance, encouragement and sound advice. I feel extremely lucky to have a supervisor who cared so much about my work, who responded to my questions and queries so promptly, and was always available to discuss project and career related matters. I would also like to thank Dr. Todd Gillis and Dr. Terry Van Raay for their careful consideration of this project and timely constructive criticisms that helped shape my project. I would like to thank all the members of my committee for helping me see things from different perspectives and helping me develop and critical and mature understanding of the scientific process. I must also express my gratitude to Dr. Robin Floyd for allowing me to build upon his work and Dr. Marian Walhout, at the University of Massachusetts, for providing the Caenorhabditis elegans complimentary DNA library. A large part of this project would not have been possible without the people at the genomics facility in the Department of Integrative Biology, I commend their professionalism and punctuality in delivering results. Completing this work would have been all the more difficult were it not for the support and friendship provided by my peers Shannon Eagle, Tyler Elliott, Nick Jeffery, Joao Lima, Sabina Stanescu, Fatima Mitterboeck and Paola Pierossi. And finally, I would like to thank my parents and siblings Sara Omar and Ali Omar for their continued support through good times and bad, and letting me use their laptops when mine broke down.

Anxiety and depression are highly prevalent diseases with common heritable elements, but the particular genetic mechanisms and biological pathways underlying them are poorly understood. Part of the challenge in understanding the genetic basis of these disorders is that they are polygenic and often context-dependent. In my thesis, I apply a series of modern statistical tools to ascertain some of the myriad genetic and environmental factors that underlie fear-related behaviours in nearly two thousand heterogeneous stock mice, which serve as animal models of anxiety and depression. Using a Bayesian method called Sparse Partitioning and a frequentist method called Bagphenotype, I identify gene-by-sex interactions that contribute to variation in fear-related behaviours, such as those displayed in the elevated plus maze and the open field test, although I demonstrate that the contributions are generally small. Also using Bagphenotype, I identify hundreds of gene-by-environment interactions related to these traits. The interacting environmental covariates are diverse, ranging from experimenter to season of the year. With gene expression data from a brain structure associated with anxiety called the hippocampus, I generate modules of co-expressed genes and map them to the genome. Two of these modules were enriched for key nervous system components — one for dendritic spines, another for oligodendrocyte markers — but I was unable to find significant correlations between them and fear-related behaviours. Finally, I employed another Bayesian technique, Sparse Instrumental Variables, which takes advantage of conditional probabilities to identify hippocampus genes whose expression appears not just to be associated with variation in fear-related behaviours, but cause variation in those phenotypes.