Horizontal gene transfer enables acquisition and dissemination of novel traits including antibiotic resistance and virulence among bacteria. Frequently such traits are gained through the acquisition of clusters of functionally related genes, often referred to as genomic islands (GIs). Quantifying horizontal flow of GIs and assessing their contributions to the emergence and evolution of novel metabolic traits in bacterial organisms are central to understanding the evolution of bacteria in general and the evolution of pathogenicity and antibiotic resistance in particular, a focus of this dissertation study. Methods for GI detection have also evolved with advances in sequencing and bioinformatics, however, comprehensive assessment of these methods has been lacking. This motivated us to assess the performance of current methods for identifying islands on broad datasets of well-characterized bacterial genomes and synthetic genomes, and leverage this information to develop a novel approach that circumvents the limitations of the current state-of-the-art in GI detection. The main findings from our assessment studies were 1) the methods have complementary strengths, 2) a gene-clustering method utilizing codon usage bias as the discriminant criterion, namely, JS-CB, is most efficient in localizing genomic islands, specifically the well-studied SCCmec resistance island in methicillin resistant Staphylococcus aureus (MRSA) genomes, and 3) in general, the bottom up, gene by gene analysis methods, are inherently limited in their ability to decipher large structures such as GIs as single entities within bacterial genomes. We adapted a top-down approach based on recursive segmentation and agglomerative clustering and developed a GI prediction tool, GEMINI, which combined compositional features with segment context information to localize GIs in the Liverpool epidemic strain of Pseudomonas aeruginosa. Application of GEMINI to the genome of P. aeruginosa LESB58 demonstrated its ability to delineate experimentally verified GIs in the LESB58 genome. GEMINI identified several novel islands including pathogenicity islands and revealed the mosaic structure of several LESB58 harbored GIs. A new GI identification approach, CAFE, with broad applicability was developed. CAFE incorporates biological information encoded in a genome within the statistical framework of segmentation and clustering to more robustly localize GIs in the genome. CAFE identifies genomic islands lacking markers by virtue of their association with genomic islands with markers originating from the same source. This is made possible by performing marker enrichment and phyletic pattern analyses within the integrated framework of recursive segmentation and clustering. CAFE compared favorably with frequently used methods for genomic island detection on synthetic test datasets and on a test-set of known islands from 15 well-characterized bacterial species. These tools can be readily adapted for cataloging GIs in just sequenced, yet uncharacterized genomes.

Le développement d’un organisme multicellulaire requière la coordination de la croissance, détermination tissulaire et différenciation cellulaire. Cependant, alors que les bases de la génétique de la morphogenèse ont été rigoureusement étudiées, le processus permettant la conversion de l’activité génétique en des structures biologiques complexes est bien moins compris. Chez Arabidopsis thaliana, les feuilles et fleurs initiés à partir du Méristème Apical Primaire (MAP) ont une expression génétique casi similaire. Toutefois, leur forme est considérablement différente dès les premières étapes de leur développement. Une compréhension de ce paradoxe requière avant tout de précisément quantifier la croissance dans toutes les dimensions de ces organes. Dans cet article, je présente une méthode de quantification spatio-temporelle complète de la croissance et de la prolifération des feuilles et des fleurs chez A. thaliana. En analysant des séries d’images confocales, j’en ai conclu que la différence morphologique observée entre feuilles et fleurs émerge principalement d’une asymétrie de la distribution de la croissance entre leurs côtés abaxial et adaxial, tôt dans leur développement. Je montre que le tissue contribuant principalement au développement des primordia est la couche 2 (L2) chez les feuilles et la couche 3 (L3) chez les fleurs. Mes résultats préliminaires démontrent que les premiers signes de l’initiation d’organes est un changement de distribution de la croissance, et non de la prolifération. Dans le futur, en appliquant, par exemple, cette méthodologie à l’étude de gènes de développement, il sera possible de finalement réconcilier la morphogenèse et la génétique de l’initiation des plantes. / The development of a multicellular organism requires the proper coordination of growth, pattern determination and cell differentiation. Still, while the genetic basis of morphogenesis has been extensively studied, the process converting gene activity into intricate biological shapes is less understood. In Arabidopsis thaliana, flowers and leaves, both initiated from the shoot apical meristem (SAM), have a very similar genetic expression profile. Yet, their shape differs considerably from early developmental stages. A full comprehension of this paradox requires an accurate quantification of cellular growth in those organs. In this paper, I am presenting a methodology for the complete spatio-temporal quantitative analysis of growth and proliferation of initiating leaves and flowers in wild type Arabidopsis thaliana. By analyzing time series of leaf and flower confocal images, I conclude that the morphological differences observed between flowers and leaves mainly arises from asymmetrical distributions of growth between their adaxial and abaxial sides during their initiation. I show that the tissue that mainly contributes to the development of early primordium is the layer 2 (L2) in leaves, and the layer 3 (L3) in flowers. My preliminary results also demonstrate that the first signs of organ initiation are a change in growth distribution, not cell proliferation. In the future, by applying this methodology, for example, to study morphogen reporter lines, it could finally bridge the gap between the morphogenesis and the genetics of plant initiation.

Nous avons étudié le transcriptome de neuf échantillons d'ARN extraits de cultures primaires de cellules non tumorales de l’épithélium de surface de l’ovaire (NOSE) provenant de quatre donneuses non porteuses de mutation, deux mutées sur BRCA1 et trois sur BRCA2, ainsi que de quatre échantillons d’ARN extraits de cultures primaires de cellules tumorales de l’ovaire (TOV) provenant de trois donneuses porteuses de mutation sur BRCA1 et une sur BRCA2. Nous avons identifié, pour la première fois, les signatures moléculaires associées à la présence d’une mutation de BRCA1 et BRCA2 dans les cellules NOSEs ainsi que la signature associée à la transformation tumorale des cellules NOSEs en TOVs chez les porteuses de mutation de BRCA1. Nous avons également localisé les domaines chromosomiques comportant des gènes corégulés en association avec la présence d’une mutation de BRCA1 dans les cellules NOSEs. Les allèles sauvage et muté de BRCA2 étaient exprimés dans les cellules TOVs provenant des porteuses de la mutation 8765delAG sur BRCA2. Nous avons observé que le niveau d’expression des transcrits de BRCA2 était plus élevé dans les cellules provenant des tumeurs ovariennes les plus agressives chez les femmes porteuses de la mutation 8765delAG sur BRCA2, les transcrits correspondants à l’allèle muté contribuant avec un pourcentage élevé du niveau d’expression total du gène. Le phénotype tumoral observé chez les Canadiennes Françaises porteuses de cette mutation pourrait résulter d’un effet de dosage de l’allèle muté. / We analyzed the transcriptome of nine primary cultures of non-tumor ovarian surface epithelium cells (NOSE) from four non-carriers, two BRCA1 and three BRCA2 carriers, and four primary cultures of tumor ovarian cells (TOV) from three BRCA1 and one BRCA2 carriers. We identified the first molecular signatures associated with the presence of BRCA1 and BRCA2 mutations in NOSEs and the first molecular signature associated with the transformation from NOSEs to TOVs in French Canadian women carriers of BRCA1 mutation. Moreover, we localized some co-regulated chromosomal domains associated with the presence of a BRCA1 mutation in NOSE cells. Wild-type and mutated BRCA2 allelic transcripts were expressed in tumor cells from 8765delAG BRCA2 mutation carriers, with the highest level of BRCA2 transcript expression and the highest contribution of the mutated allele in cells originating from the most aggressive ovarian tumors. The observed phenotype in BRCA2-mutated cells as well as the aggressiveness of the tumor could result from a dosage effect of the BRCA2 mutated allele.

Au cours des dernières années, de nouvelles évidences semblent indiquer que, tout autant que sa séquence, l’organisation d’un génome dans l’espace et le temps est importante pour comprendre la fonction de celui-ci. Une des avancées fonda- mentales sur le sujet a été de présenter à l’échelle du génome la carte des inter- actions ADN-ADN. Ces interactions sont essentiellement de 2 types, soit entre chromosomes ou entre régions du même chromosome. Par la suite, la modélisa- tion a permis de visualiser et appréhender la structure tridimensionnelle (3D) du génome à partir des données 3C, ou d’une modélisation purement théorique. Une question importante et centrale demeure, soit de résoudre les mécanismes res- ponsables de l’organisation spatiale et fonctionnelle du génome. Notamment, une question est de savoir comment des processus nucléaires tels que la transcription affectent la structure du génome. Cependant, l’idée selon laquelle les données de types 3C capturent cette information dans la levure est remise en question par le fait que les modèles théoriques du génome récapitulent les caractéristiques mar- quantes soulignées par 3C. Pour répondre à cette question, nous avons conçu une approche qui, pour évaluer l’importance d’une interaction, se base sur la distri- bution d’interactions entre les 2 régions d’ADN mises en contacts. Nos résultats supportent l’hypothèse selon laquelle les éléments fonctionnels et propres aux données expérimentales de la structure 3D du génome se forment d’une manière spécifique à l’échelle de l’interaction et au type d’interactions. Par ailleurs, nos résultats indiquent qu’un grand nombre de facteurs de transcription induisent la proximité spatiale des gènes dont ils régulent l’expression. / Over the last decade, accumulating empirical evidence suggest that, as much as its sequence, a genome spatiotemporal organization is essential to understand it’s biological function. One of the major breakthroughs has been chromosome conformation capture (3C) experiments presenting DNA-DNA contact for whole genomes at unprecedented resolution (5-10kb). Along with genome-wide maps of DNA contacts came genome 3D modelling from experimental 3C data, and even from purely theoretical and biophysical basis. However, the mechanisms underlying the regulation of the genome spatial functional organization are still not well understood. Among other questions, how the regulation and event of nuclear processes such as transcription modulate genome structure or how genome structure affect these in turn is still not fully resolved. Moreover, computational models of S.cerevisae genome have recapitulated the hallmarks at larger scale of its 3D features. In order to contrast genome structural features arising from the event of biochemical and molecular activity, we have develop a method assessing the significance of structural features. The underlying principle is to consider for a given interaction, the two DNA regions put in contact and the distribution of existing interactions between these before assigning significance to the selected interaction. Using this method, we demonstrate that structural features resulting from potential biochemically active processes occur at precise scale on the genome. Our results also highlight that exact nature of the interaction (between vs across chromosomes) is crucial to such events. Finally, we have also found that a large portion of transcription factors have their targeted genes in spatial proximity.

Genome wide association studies (GWAS) have revolutionized our approach to mapping genetic determinants of complex human diseases. However, even with success from recent studies, we have typically been able to explain only a fraction of the trait heritability. GWAS are typically analysed by testing for the marginal effects of single variants. Consequently, it has been suggested that gene-gene interactions might contribute to the missing heritability of complex diseases. GWAS incorporating interaction effects have not been routinely applied because of statistical and computational challenges relating to the number of tests performed, genome-wide. To overcome this issue, I have developed novel methodology to allow rapid testing of pairwise interactions in GWAS of complex traits, implemented in the IntRapid software. Simulations demonstrated that the power of this approach was equivalent to computationally demanding exhaustive searches of the genome, but required only a fraction of the computing time. Application of IntRapid to GWAS of a range of complex human traits undertaken by the Wellcome Trust Case Control Consortium (WTCCC) identified several interaction effects at nominal significance, which warrant further investigation in independent studies. In an attempt to fine-map the identified interacting loci, I undertook imputation of the WTCCC genotype data up to the 1000 Genomes Project reference panel (Phase 1 integrated release, March 2012) in the neighbourhood of the lead SNPs. I modified the IntRapid software to take account of imputed genotypes, and identified stronger signals of interaction after imputation at the majority of loci, where the lead SNP often had moved by hundreds of kilobases. The X-chromosome is often overlooked in GWAS of complex human traits, primarily because of the difference in the distribution of genotypes in males and females. I have extended IntRapid to allow for interactions with the X chromosome by considering males and females separately, and combining effect estimates across the sexes in a fixed-effects meta-analysis. Application to genotype data from the WTCCC failed to identify any strong signals of association with the X-chromosome, despite known epidemiological differences between the sexes for the traits considered. The novel methods developed as part of this doctoral work enable a user friendly, computationally efficient and powerful way of implementing genome-wide gene-gene interaction studies. Further work would be required to allow for more complex interaction modelling and deal with the associated computational burden, particularly when using next-generation sequencing (NGS) data which includes a much larger set of SNPs. However, IntRapid is demonstrably efficient in exhaustively searching for pairwise interactions in GWAS of complex traits, potentially leading to novel insights into the genetic architecture and biology of human disease.

In this thesis I apply new sequencing technologies and analytical methods derived from genomics and computer science to the neuroanatomy of gene expression. The first project explores characteristics of gene expression across adult neocortical layers in a representative mammal – the mouse. Amongst the thousands of genes and transcripts differentially expressed across layers, I found common functional characteristics of genes that define certain layers, candidate cases of isoform switching, and over a thousand apparent long intergenic non-coding RNA transcripts. The second project compares patterns of gene expression in the structurally diverged adult derivatives of the pallium in mice and chickens. Overall, gene expression levels were moderately correlated between the two species. While expression patterns of ‘marker’ genes were only poorly conserved in these regions, there nevertheless was significant conservation of cross-species marker genes for homologous structures, cell types and functionally analogous regions. Many aspects of these data from both projects can now be easily browsed and searched from custom-built web interfaces. In addition to generating unprecedented genome-wide resources for the neuroscience community to explore the functional and structural dimensions of gene expression amongst different pallial regions in mammals and birds, this work also provides new insights into the widespread evolutionary shuffling of adult marker gene expression.

Le rôle important joué par la mitochondrie dans la cellule eucaryote est admis depuis longtemps. Cependant, la composition exacte des mitochondries, ainsi que les processus biologiques qui sy déroulent restent encore largement inconnus. Deux facteurs principaux permettent dexpliquer pourquoi létude des mitochondries progresse si lentement : le manque defficacité des méthodes didentification des protéines mitochondriales et le manque de précision dans lannotation de ces protéines. En conséquence, nous avons développé un nouvel outil informatique, YimLoc, qui permet de prédire avec succès les protéines mitochondriales à partir des séquences génomiques. Cet outil intègre plusieurs indicateurs existants, et sa performance est supérieure à celle des indicateurs considérés individuellement. Nous avons analysé environ 60 génomes fongiques avec YimLoc afin de lever la controverse concernant la localisation de la bêta-oxydation dans ces organismes. Contrairement à ce qui était généralement admis, nos résultats montrent que la plupart des groupes de Fungi possèdent une bêta-oxydation mitochondriale. Ce travail met également en évidence la diversité des processus de bêta-oxydation chez les champignons, en corrélation avec leur utilisation des acides gras comme source dénergie et de carbone. De plus, nous avons étudié le composant clef de la voie de bêta-oxydation mitochondriale, lacyl-CoA déshydrogénase (ACAD), dans 250 espèces, couvrant les 3 domaines de la vie, en combinant la prédiction de la localisation subcellulaire avec la classification en sous-familles et linférence phylogénétique. Notre étude suggère que les gènes ACAD font partie dune ancienne famille qui a adopté des stratégies évolutionnaires innovatrices afin de générer un large ensemble denzymes susceptibles dutiliser la plupart des acides gras et des acides aminés. Finalement, afin de permettre la prédiction de protéines mitochondriales à partir de données autres que les séquences génomiques, nous avons développé le logiciel TESTLoc qui utilise comme données des Expressed Sequence Tags (ESTs). La performance de TESTLoc est significativement supérieure à celle de tout autre outil de prédiction connu. En plus de fournir deux nouveaux outils de prédiction de la localisation subcellulaire utilisant différents types de données, nos travaux démontrent comment lassociation de la prédiction de la localisation subcellulaire à dautres méthodes danalyse in silico permet daméliorer la connaissance des protéines mitochondriales. De plus, ces travaux proposent des hypothèses claires et faciles à vérifier par des expériences, ce qui présente un grand potentiel pour faire progresser nos connaissances des métabolismes mitochondriaux. / The important role of mitochondria in the eukaryotic cell has long been appreciated, but their exact composition and the biological processes taking place in mitochondria are not yet fully understood. The two main factors that slow down the progress in this field are inefficient recognition and imprecise annotation of mitochondrial proteins. Therefore, we developed a new computational tool, YimLoc, which effectively predicts mitochondrial proteins from genomic sequences. This tool integrates the strengths of existing predictors and yields higher performance than any individual predictor. We applied YimLoc to ~60 fungal genomes in order to address the controversy about the localization of beta oxidation in these organisms. Our results show that in contrast to previous studies, most fungal groups do possess mitochondrial beta oxidation. This work also revealed the diversity of beta oxidation in fungi, which correlates with their utilization of fatty acids as energy and carbon sources. Further, we conducted an investigation of the key component of the mitochondrial beta oxidation pathway, the acyl-CoA dehydrogenase (ACAD). We combined subcellular localization prediction with subfamily classification and phylogenetic inference of ACAD enzymes from 250 species covering all three domains of life. Our study suggests that ACAD genes are an ancient family with innovative evolutionary strategies to generate a large enzyme toolset for utilizing most diverse fatty acids and amino acids. Finally, to enable the prediction of mitochondrial proteins from data beyond genome sequences, we designed the tool TESTLoc that uses expressed sequence tags (ESTs) as input. TESTLoc performs significantly better than known tools. In addition to providing two new tools for subcellular localization designed for different data, our studies demonstrate the power of combining subcellular localization prediction with other in silico analyses to gain insights into the function of mitochondrial proteins. Most importantly, this work proposes clear hypotheses that are easily testable, with great potential for advancing our knowledge of mitochondrial metabolism.

Les traits quantitatifs complexes sont des caractéristiques mesurables d’organismes vivants qui résultent de l’interaction entre plusieurs gènes et facteurs environnementaux. Les locus génétiques liés à un caractère complexe sont appelés «locus de traits quantitatifs » (QTL). Récemment, en considérant les niveaux d’expression tissulaire de milliers de gènes comme des traits quantitatifs, il est devenu possible de détecter des «QTLs d’expression» (eQTL). Alors que ces derniers ont été considérés comme des phénotypes intermédiaires permettant de mieux comprendre l’architecture biologique des traits complexes, la majorité des études visent encore à identifier une mutation causale dans un seul gène. Cette approche ne peut remporter du succès que dans les situations où le gène incriminé a un effet majeur sur le trait complexe, et ne permet donc pas d’élucider les situations où les traits complexes résultent d’interactions entre divers gènes. Cette thèse propose une approche plus globale pour : 1) tenir compte des multiples interactions possibles entre gènes pour la détection de eQTLs et 2) considérer comment des polymorphismes affectant l’expression de plusieurs gènes au sein de groupes de co-expression pourraient contribuer à des caractères quantitatifs complexes. Nos contributions sont les suivantes : Nous avons développé un outil informatique utilisant des méthodes d’analyse multivariées pour détecter des eQTLs et avons montré que cet outil augmente la sensibilité de détection d’une classe particulière de eQTLs. Sur la base d’analyses de données d’expression de gènes dans des tissus de souris recombinantes consanguines, nous avons montré que certains polymorphismes peuvent affecter l’expression de plusieurs gènes au sein de domaines géniques de co-expression. En combinant des études de détection de eQTLs avec des techniques d’analyse de réseaux de co-expression de gènes dans des souches de souris recombinantes consanguines, nous avons montré qu’un locus génétique pouvait être lié à la fois à l’expression de plusieurs gènes au niveau d’un domaine génique de co-expression et à un trait complexe particulier (c.-à-d. la masse du ventricule cardiaque gauche). Au total, nos études nous ont permis de détecter plusieurs mécanismes par lesquels des polymorphismes génétiques peuvent être liés à l’expression de plusieurs gènes, ces derniers pouvant eux-mêmes être liés à des traits quantitatifs complexes. / Complex quantitative traits are measurable characteristics of living organisms resulting from the interaction between multiple genes and environmental factors. Genetic loci associated with complex trait are called "quantitative trait loci" (QTL). Recently, considering the expression levels of thousands of genes as quantitative traits, it has become possible to detect "expression QTLs " (eQTL). These eQTL are considered intermediate phenotypes and are used to better understand the biological architecture of complex traits. However the majority of studies still try to identify a causal mutation in a single gene. This approach can only meet success in situations where the gene incriminate as a major effect on the complex trait, and therefore can not elucidate the situations where complex traits result from interactions between various genes. This thesis proposes a more comprehensive approach to: 1) take into account the possible interactions between multiple genes for the detection of eQTLs and 2) consider how polymorphisms affecting the expression of several genes in a module of co-expression may contribute to quantitative complex traits. Our contributions are as follows: We have developed a tool using multivariate analysis techniques to detect eQTLs, and have shown that this tool increases the sensitivity of detection of a particular class of eQTLs. Based on the data analysis of gene expression in recombinant inbred strains mice tissues, we have shown that some polymorphisms may affect the expression of several genes in domain of co-expression. Combining eQTLs detection studies with network of co-expression genes analysis in recombinant inbred strains mice, we showed that a genetic locus could be linked to both the expression of multiple genes at a domain of gene co-expression and a specific complex trait (i.e. left ventricular mass). Our studies have detected several mechanisms by which genetic polymorphisms may be associated with the expression of several genes, and may themselves be linked to quantitative complex traits.

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