The past few decades have witnessed an array of advances in DNA science including the introduction of genomics and bioinformatics. The quest for complete genome sequences has driven the development of microarray and massively parallel sequencing technologies at a rapid pace, yielding numerous scientific discoveries. My thesis applies several of these genome-scale technologies to understand genomic response to perturbation as well as chromatin structure, and it is divided into three major studies. The first study describes a method I developed to identify drug targets by overexpressing human genes in yeast. This chemical genomic assay makes use of the human ORFeome collection and oligonucleotide microarrays to identify potential novel human drug targets. My second study applies genome resequencing of yeast that have evolved resistance to antifungal drug combinations. Using massively parallel genomic sequencing, I identified novel genomic variations that were responsible for this resistance and it was confirmed in vivo. Lastly, I report the characterization of chromatin structure in a non-eukaryotic species, an archaeon. The conservation of the nucleosomal landscape in archaea suggests that chromatin is not solely a hallmark of eukaryotes, and that its role in transcriptional regulation is ancient. Together, these 3 studies illustrate how maturation of genomic technology for research applications has great utility for the identification of potential human and antifungal drug targets and offers an encompassing glance at the structure of genomes.

Cancer is a complex and deadly disease that is caused by genetic lesions in somatic cells. Further research in computational methodology for detecting and characterizing somatic mutations is necessary in order to understand the comprehensive systems level model of the roles of those lesions in cancer development. In the first project, I trained a list of supervised machine learning classifiers that classify false positive versus true positive somatic single nucleotide variants (SNVs). I was able to show an improvement of somatic SNV detection on the data set over the reported classifier. In the second project, we developed PhyloSub model that uses a nonparametric Bayesian prior over a set of trees to cluster SNVs, and infer the subclonal phylogenetic structure of tumors with uncertainty from SNV sequencing data. Experiments showed that PhyloSub model could infer the subclonal phylogenetic structure from both single and multiple tumor samples.

Cancer is a complex and deadly disease that is caused by genetic lesions in somatic cells. Further research in computational methodology for detecting and characterizing somatic mutations is necessary in order to understand the comprehensive systems level model of the roles of those lesions in cancer development. In the first project, I trained a list of supervised machine learning classifiers that classify false positive versus true positive somatic single nucleotide variants (SNVs). I was able to show an improvement of somatic SNV detection on the data set over the reported classifier. In the second project, we developed PhyloSub model that uses a nonparametric Bayesian prior over a set of trees to cluster SNVs, and infer the subclonal phylogenetic structure of tumors with uncertainty from SNV sequencing data. Experiments showed that PhyloSub model could infer the subclonal phylogenetic structure from both single and multiple tumor samples.

The first aim of this thesis is to examine relationships between microRNAs targeting gene networks, combining knowledge from microRNA prediction databases into our microRNA Data Integration Portal (mirDIP). Modeling the microRNA:transcript interactome – referred to as the micronome – to build microRNA interaction networks of signalling pathways, we find genes within signalling pathways to be co-targeted by common microRNAs suggesting an unexpected level of transcriptional control. We identify two distinct classes of microRNAs; universe microRNAs, which are involved in many signalling pathways; and intra-pathway microRNAs, which target multiple genes within one signalling pathway. We find universe microRNAs to have more targets, to be more studied and more involved in cancer signalling than their intrapathway counterparts. The second aim was to undertake a more focused view, analyzing the characteristics of microRNAs within the micronome itself beginning with a focus on the under-examined microRNA terminal loop across the micronome to determine if this region of the microRNA structure might contribute to microRNA functioning. We have identified 2 main classes of microRNAs based on loop structure – perfect and occluded, which show biological relevance. We found regulatory motifs within microRNA terminal loops and found a large number of Frequently Occurring Words (FOWs) significantly overrepresented across the micronome. Set analysis of in vitro secreted microRNAs, microRNA expression across a panel of normal tissues, and microRNAs shown to be secreted in lung cancer shows that specific microRNA loop motifs within these groups are significantly overreperesented – suggesting that microRNA terminal loops harbour sequences bearing microRNA processing and localization signals.

This work focuses on the use of network graph theory in biological networks. I explore how network graph theory informs our understanding of biological networks such as protein interaction networks. I show that the human protein interaction network forms dynamic, modular structures that organize cell signaling pathways into higher order units. The misregulation of the dynamic, modular structure of the protein interaction network in breast cancer tumours is associated with outcome of the disease, suggesting that the altered structure of the protein interaction network is directly related to the phenotype of the tumour. I also demonstrate that the human protein interaction network is fractal in nature and thus forms self-similar structures within the network. The fractal skeletons of the protein interaction network contain critical information and therefore can be used alone in determining the phenotype of breast cancer tumours by examing the disruption of dynamic network structures. The self-similar fractal backbones deconvolve the protein interaction network into layers of independent function, resulting in improved description of breast cancer outcome using the dynamic network modularity algorithm. Finally, I discuss how the discoveries and technologies described within can be improved and how these discoveries can lead to a network based modality of medicine.

Post-transcriptional regulation is carried out by RNA-binding proteins (RBPs) that bind to specific RNA molecules and control their processing, localization, stability and degradation. Experimental studies have successfully identified RNA targets associated with specific RBPs. However, because the locations of the binding sites within the targets are unknown and because RBPs recognize both sequence and structure elements in their binding sites, identification of RBP binding preferences from these data remains challenging. The unifying theme of this thesis is to identify RBP binding preferences from experimental data. First, we propose a protocol to design a complex RNA pool that represents diverse sets of sequence and structure elements to be used in an in vitro assay to efficiently measure RBP binding preferences. This design has been implemented in the RNAcompete method, and applied genome-wide to human and Drosophila RBPs. We show that RNAcompete-derived motifs are consistent with established binding preferences. We developed two computational models to learn binding preferences of RBPs from large-scale data. Our first model, RNAcontext uses a novel representation of secondary structure to infer both sequence and structure preferences of RBPs, and is optimized for use with in vitro binding data on short RNA sequences. We show that including structure information improves the prediction accuracy significantly. Our second model, MaLaRKey, extends RNAcontext to fit motif models to sequences of arbitrary length, and to incorporate a richer set of structure features to better model in vivo RNA secondary structure. We demonstrate that MaLaRKey infers detailed binding models that accurately predict binding of full-length transcripts.

The FRIABLE1 (FRB1) gene is identified to be a novel glycosyltransferase involved in cell adhesion, based on reverse genetics and immunocytochemistry studies. A total of 31 FRB1 paralogues were found in Arabidopsis thaliana using a bioinformatics approach. The following expression analysis has revealed 6 FRB1 paralogues to be pollen-specific. One pollen-specific FRB1 paralogue, At1g14970, exhibits longer silique lengths when exposed to higher than normal temperature at 28oC in its T-DNA insertional knockout when compared to Columbia wildtype plants. This may be due to the loss of temperature sensing and the continuous stimulated pollen tube cell wall growth or the up-regulation of genes that encode other glycosyltransferases. Thus, the identification of FRB1 paralogues and homologues in both rice and poplar may have tremendous potential to increase their yield in global warming for agricultural and industrial benefits.

The past few decades have witnessed an array of advances in DNA science including the introduction of genomics and bioinformatics. The quest for complete genome sequences has driven the development of microarray and massively parallel sequencing technologies at a rapid pace, yielding numerous scientific discoveries. My thesis applies several of these genome-scale technologies to understand genomic response to perturbation as well as chromatin structure, and it is divided into three major studies. The first study describes a method I developed to identify drug targets by overexpressing human genes in yeast. This chemical genomic assay makes use of the human ORFeome collection and oligonucleotide microarrays to identify potential novel human drug targets. My second study applies genome resequencing of yeast that have evolved resistance to antifungal drug combinations. Using massively parallel genomic sequencing, I identified novel genomic variations that were responsible for this resistance and it was confirmed in vivo. Lastly, I report the characterization of chromatin structure in a non-eukaryotic species, an archaeon. The conservation of the nucleosomal landscape in archaea suggests that chromatin is not solely a hallmark of eukaryotes, and that its role in transcriptional regulation is ancient. Together, these 3 studies illustrate how maturation of genomic technology for research applications has great utility for the identification of potential human and antifungal drug targets and offers an encompassing glance at the structure of genomes.

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal. / L'inférence d'un modèle tridimensionnel (3-D) d'une molécule d'ARN à partir d'informations biochimiques de faible résolution est une tâche complexe. Le développement de méthodes efficaces et objectives de modélisation est essentiel à la compréhension des mécanismes moléculaires impliquant des ARN. Le présent travail propose trois ajouts importants de ce domaine. Dans un premier temps, une définition de l'espace des conformations d'un ARN est établie et la technique de retour-arrière est décrite de façon à permettre une exploration complète de cet espace. Ensuite, un formalisme basé sur la logique floue est présenté. Cette approche permet d'utiliser l'information provenant de séquences multiples et est appliquée pour la modélisation du ribozyme activé par le plomb. Finalement, la flexibilité d'un système de modélisation 3-D utilisant une approche de satisfaction de contrainte est mise en évidence par l'ajout d'un nouveau type de contrainte dans l'engin de recherche, permettant la modélisation efficace de multimères cycliques. Ces ajouts permettent d'élargir le spectre des informations utiles à la modélisation 3-D des ARN et facilite l'intégration de nouveaux types d'informations à l'intérieur d'une méthode systématique. Les résultats obtenus sur des projets de modélisation spécifiques (ribozyme activé par le plomb et pRNA de çi>29) permettent de confirmer la pertinence de cette approche.

Master of Science / Department of Human Nutrition / Weiqun Wang / Weight control through either dietary calorie restriction (DCR) or exercise is associated with cancer prevention in animal models. However, the underlying mechanisms are not fully defined. Bioinformatics approaches using genomics, proteomics, and lipidomics were employed to elucidate the profiling changes of genes, proteins, and phospholipids in response to weight loss by DCR or exercise in a mouse skin cancer model. SENCAR mice were randomly assigned into 4 groups for 10 weeks: ad lib-fed sedentary control, ad lib-fed exercise (AE), exercise but pair-fed isocaloric amount of control (PE), and 20% DCR. Two hours after topical TPA treatment, skin epidermis was analyzed by Affymetrix for gene expression, DIGE for proteomics, and lipidomics for phospholipids. Body weights were significantly reduced in both DCR and PE but not AE mice versus the control. Among 39,000 transcripts, 411, 67, and 110 genes were significantly changed in DCR, PE, and AE, respectively. The expression of genes relevant to PI3K-Akt and Ras-MAPK signaling was effectively reduced by DCR and PE as measured through GenMAPP software. Proteomics analysis identified ~120 proteins, with 22 proteins significantly changed by DCR, including upregulated apolipoprotein A-1, a key antioxidant protein that decreases Ras-MAPK activity. Of the total 338 phospholipids analyzed by lipidomics, 57 decreased by PE including 5 phophatidylinositol species that serve as PI3K substrates. Although there were many impacts that we still need to characterize, it appears that both Ras-MAPK and PI3K-Akt signaling pathways are the key cancer preventive targets that have been consistently demonstrated by three bioinformatics approaches.