Les améliorations des technologies de séquençage offrent aujourd’hui la possibilité d’explorer la variabilité intraspécifique au sein d’une espèce à travers le séquençage complet du génome d’un grand nombre d’individus. Dans ce contexte, mes travaux de thèse se sont basés sur l’étude et la comparaison de la variabilité génomique à travers des études de génomique des populations au sein de plusieurs espèces de levures. Dans un premier temps, j’ai réalisé une étude systématique de la variabilité intraspécifique au sein de 6 espèces de levures, me donnant notamment la possibilité d’étudier la variabilité du contenu en gènes entre les espèces. Dans un second temps, je me suis focalisé sur l’utilisation des dernières technologies de séquençage dans l’objectif de produire une séquence de référence de Dekkera bruxellensis, dont l’absence pour un grand nombre d’espèces limite l’établissement d’étude de génomique des populations. Cette séquence a été utilisée dans un dernier temps afin d’étudier l’évolution de l’espèce. Dans l’ensemble, ces travaux apportent de solides fondations dans l’exploration de la diversité génétique au sein d’espèces non-modèles. / Advent of high throughput technologies as well as the reduction of their price open the way to the exploration of the intraspecific genetic variation at the species level by sequencing the complete genome of a wide range of individuals. Doing so, I first produced populations genomics studies of 6 yeast species based on the same framework, allowing the exploration and comparison of the genes repository of each species. I then used new sequencing technologies to produce a reference sequence for the yeast species Dekkera bruxellensis. Using this sequence, I was then able to produce for the first time a population genomic study at the genome wide scale for this species.

It is well known that nucleotide diversity varies across the genomes of eukaryotic species in ways consistent with the effects of natural selection. However, the contribution of selection on advantageous and deleterious mutations to the observed variation is not well understood. In this thesis, I aim to disentangle the contribution of background selection and selective sweeps to patterns of genetic diversity in the mouse genome, thus furthering our understanding of natural selection in mammals. In chapter 1, I introduce core concepts in evolutionary genetics and describe how recombination and selection interact to shape patterns of genetic diversity. I will then describe three projects in which I examine aspects of molecular evolution in house mice. In the first of these, I estimate the landscape of recombination rate variation in wild mice using population genomic data. In the second, I estimate the distribution of fitness effects for new mutations, based on the site frequency spectrum, then analyse population genomic simulations parametrized using my estimates. In the third, I use a model of selective sweeps to estimate and compare the strength of selection in protein-coding and regulatory regions of the mouse genome. This thesis demonstrates that selective sweeps are responsible for a large amount of the variation in genetic diversity across the mouse genome.

Le travail présenté dans ce manuscrit porte sur la recherche de nouveaux membres de la famille des dioxygénases α-cétoglutarate et fer dépendantes (α-KAO) et leur application en synthèse organique. Dans un premier, ce travail a consisté à chercher de nouvelles enzymes selon une approche génomique basée sur l’homologie de séquence et le partage d’un motif InterPro. Deux criblages haut débit avec 79 et 127 enzymes candidates ont ensuite été effectués sur des panels constitués respectivement de 23 et 36 substrats, structurellement plus ou moins proches des substrats métaboliques. Huit nouvelles α-KAO ont ainsi pu être découvertes. Parmi ces huit nouvelles α-KAO, quatre ont été étudiées plus en détail. Après optimisation des conditions de réaction pour chaque enzyme, des montées en échelle ont été réalisées pour caractériser les produits formés. A partir de ces quatre enzymes, la (3S)-3-hydroxy-L-lysine, un dérivé cyclisé de la (4R)-4-hydroxy-L-lysine, (3S)-3-hydroxy-L-ornithine et un dérivé de la (3S)-3-hydroxy-L-arginine ont pu être produits. Nous avons proposé une synthèse biocatalytique de mono et dihydroxydiamines en couplant une ou deux α-KAO avec une décarboxylase. Les (2S)-1,5-diamino-2-pentanol, 1,5-diamino-3-pentanol, (2S)-1,4-diamino-2-butanol et (2S,3S)-1,5-diamino-2,3-pentanediol ont ainsi été obtenus avec de bonnes conversions. / The work described in this manuscript deals with the search of new members of the α-ketoglutarate and Iron-dependent dioxygenases family (α-KAO) and their applications in organic synthesis. The first part of this work presents the search of new enzymes through a genomic approach based on sequence homology and InterPro motif sharing. Two high-throughput screenings with 79 and 127 candidate enzymes have been performed on 23 and 36 substrates more or less structurally close to known metabolic substrates. 8 new α-KAOs have been discovered. Among these new enzymes, four were studied in more details. After optimization of the enzymatic reaction conditions for each enzyme, scale-up allowed to obtain compounds for isolation and characterization. With these four enzymes, (3S)-3-hydroxy-L-lysine, (4R)-4-hydroxy-L-lysine as its cyclic derivative, (3S)-3-hydroxy-L-ornithine and a derivative of (3S)-3-hydroxy-L-arginine were produced. Two of the new α-KAO were combined in a cascade process to afford the (3R,4R)-3,4-dihydroxy-L-lysine as its cyclic derivative. We proposed a biocatalytic synthesis of mono and hydroxydiamines by coupling one or two α-KAO with a decarboxylase enzyme. (2S)-1,5-diamino-2-pentanol, 1,5-diamino-3-pentanol, (2S)-1,4-diamino-2-butanol and (2S,3S)-1,5-diamino-2,3-pentanediol were obtained with good overall conversions.

Copy Number Variants (CNVs) are defined as DNA segments of 1kb or more in length and present in a variable number of copies in the human genome. It has been recently shown that many human genetic diseases including organ malformations are caused by CNVs in a patient's genome. However, the genetic and molecular basis for Renal Agenesis (RA), which is a medical condition whereby unilateral or bilateral fetal kidneys fail to develop, has not yet been extended to CNV studies. By using array-based Comparative Genomic Hybridization, we are analyzing DNA from patients who have RA in order to identify CNVs that are causative for RA; genes within the CNVs will then be assessed for their potential involvement in RA by altering their dose in Xenopus embryos.

La sélection variétale a pour objectif la génération de variétés toujours plus performantes pour des caractères agronomiques d'intérêt. Pour les caractères quantitatifs, qui sont sous contrôle génétique polygénique, la sélection variétale consiste à réunir progressivement dans les nouvelles variétés des allèles favorables pour un maximum de gènes. Les processus de sélection évoluent, notamment par l'intégration des progrès concernant les connaissances génétiques et outils biotechnologiques. La sélection génomique est une méthode qui peut prédire la valeur génétique d'individus à partir de données génomiques et d'un modèle d'effets génétiques appris sur une population de référence. Nos études ont porté sur la possibilité d'insérer la sélection génomique dans le processus de sélection pour en augmenter l'efficacité. Notre sujet a été appliqué à un programme privé qui vise l'obtention de variétés de soja performantes pour le rendement et le contenu des graines en protéines, pour répondre à un besoin de protéines d'origine végétale. Des études génétiques sur une population de lignées générées lors de cycles de sélection successifs ont mis en évidence une structuration génétique en deux sous-populations qui ne sont pas "hermétiques". Nous avons étudié par échantillonnages de populations de test la précision de prédiction obtenue dans nos deux groupes avec différents modèles de GS : des modèles GBLUP additifs avec différentes populations d'apprentissage, puis des modèles d'architectures génétiques plus complexes. Les précisions de prédiction de nos modèles étaient proches les unes des autres. Cependant, nos résultats suggèrent que le modèle GBLUP le plus adapté pour obtenir des prédictions précises au sein de nos deux groupes est un modèle appris sur une population représentative du groupe à prédire et comprenant une composante additive et une composante épistatique additive x additive. Nous avons mis en place une application de la GS dans un cycle de sélection en cours de réalisation. Nous avons estimé le potentiel des croisements de départ par simulation de descendants virtuels et prédiction génomique de leurs performances, ce qui nous a permis de choisir trois populations biparentales prometteuses à l'intérieur desquelles nous avons effectué une sélection sur la base de prédictions génomiques. Nous avons développé un outil permettant de simuler des schémas de sélection sur plusieurs cycles consécutifs. Il s'agit d'un outil flexible et générique du point de vue de la définition des schémas de sélection. Cet outil permet notamment de comparer le gain génétique obtenu avec deux schémas différents à partir d'une même population de départ et d'un même modèle des effets génétiques et environnementaux agissant sur l'expression phénotypique d'un caractère. Avec cet outil, nous avons étudié la précision d'évaluation et les composantes de la variance de deux modèles GBLUP (avec ou sans modélisation de l'épistasie) après simulation de différentes architectures génétiques. Nous avons également comparé le schéma de sélection classique et différents schémas incluant une utilisation de la GS. Avec une comparaison sur un cycle, nous n'avons pas observé de gain à utiliser des schémas intégrant la GS pour augmenter l'efficacité de sélection de nouvelles variétés, à coût constant. Par contre, nous avons observé un gain à utiliser la GS pour choisir les croisements en début de cycle : la valeur génétique moyenne des lignées produites augmente de cycle en cycle. Concernant les alternatives au schéma de sélection classique du soja, des études plus approfondies seront nécessaires. Elles permettront notamment d'inclure la simulation des étapes de sélection sur le contenu des graines en protéines et d'étudier la question du gain génétique à long terme. / Varietal selection aims at the generation of increasingly more performing varieties for agronomic traits of interest. In the case of quantitative traits, which are under polygenic genetic control, varietal selection consists in gradually joining together in the new varieties favorable alleles for a maximum number of genes. Selection processes are evolving, in particular by integrating advances in genetic knowledge and biotechnological tools. Genomic selection is a method that can predict the genetic value of individuals from genomic data and a model of genetic effects learned on a reference population. Our studies have focused on the possibility of including genomic selection in the selection process to increase its efficiency. Our subject has been applied to a private program aimed at obtaining soybean varieties performing for yield and seed protein content to meet a need for proteins of plant origin. Genetic studies on a population of lines generated during successive breeding cycles have shown genetic structuration in two subpopulations that are not "hermetic". We studied by samplings of test populations the prediction accuracies obtained within our two groups with different GS models: additive GBLUP models with different learning populations, and then models of more complex genetic architectures. The prediction accuracies of our models were close to one another. However, our results suggest that the most suitable GBLUP model for obtaining accurate predictions within our two groups is a model learned on a population representative of the group to be predicted and including an additive component and an additive x additive epistatic component. We have implemented an application of GS in a selection cycle in progress. We evaluated the potential of initial crosses by simulation of virtual descendants and genomic prediction of their performances, which allowed us to select three promising biparental populations within which we made a selection based on genomic predictions. We have developed a tool to simulate selection schemes over several consecutive cycles. It is a flexible and generic tool from the point of view of selection schemes definition. This tool makes it possible, in particular, to compare the genetic gain obtained with two different schemes starting from a same starting population and from a same model of genetic and environmental effects acting on the phenotypic expression of a trait. With this tool, we studied evaluation accuracy and variance components of two GBLUP models (with or without epistasy modeling) after simulation of different genetic architectures. We also compared the classic selection scheme and different schemes including a use of GS. With a comparison on one cycle, we did not observe any gain in using schemes integrating GS to increase efficiency of selection of new varieties, at constant cost. On the other hand, we observed a gain in using GS to choose crosses at the beginning of cycle: mean genetic value of produced lines increases from one cycle to another. Regarding alternatives to the traditional soybean selection scheme, further studies will be required. In particular, they will include simulation of selection stages on seed protein content and study of long-term genetic gain.

Orientador: Henrique Nunes de [UNESP] Oliveira / Resumo: Nos últimos anos, o rápido desenvolvimento de tecnologias de sequenciamento de alto rendimento permitiu a genotipagem em larga escala de milhares de marcadores genéticos. Diversos modelos estatísticos foram desenvolvidos para predizer os valores genéticos para traços complexos usando as informações de marcadores moleculares em alta densidade, pedigrees ou ambos. Esses modelos incluem, entre outros, as redes neurais artificiais (RNA) que têm sido amplamente utilizadas em problemas de previsão em outros campos de aplicação e, mais recentemente, para predição genômica. O objetivo deste trabalho foi avaliar o desempenho de redes neurais artificiais na predição genômica de bovinos Nelore. Para isso foram testadas diferentes arquiteturas de rede (1 a 4 neurônios em camada oculta), 5 estratégias para seleção de animais com base na acurácia do EBV a serem declaradas para a rede de treinamento como entrada e avaliação de matrizes de relacionamento (NN_G (G como entrada); NN_GD (combinados G com D); e N_Guar (Guar como entrada)) a serem utilizados como entrada para predição genômica em características de peso corporal de bovinos Nelore em relação a modelos de regressão lineares bayesianos hierárquicos (BayesB). . Para isso, utilizou-se o dEBV de 8652 animais genotipados para peso corporal aos 120 dias, 240 dias, 365 dias e 455 dias. Esses animais foram divididos pela acurácia do EBV em população de treinamento e na validação. Todas as estratégias foram repetidas 5 vezes e a correlação ... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: In recent years, the fast development of high-throughput sequencing technologies has enabled large-scale genotyping of thousands of genetic markers. Several statistical models have been developed for predicting breeding genetic values for complex traits using the information on dense molecular markers, pedigrees, or both. These models include, among others, the artificial neural networks (ANN) that have been widely used in prediction problems in other fields of application and, more recently, for genome-enabled prediction. The objective of this work was to evaluate the performance of artificial neural networks in the genomic prediction of complex trait in Nellore cattle. For this, we has been tested different network architectures (1 to 4 neurons on hidden layer), 5 strategies to select animals based on their EBV accuracy to be declared for the training network as input and evaluation of relationship matrices [ NN_G (G as input); NN_GD(combined G with D), and N_Guar (Guar as input)] to be used as input for genomic prediction in body weight traits in Nellore cattle relative to hierarchical linear Bayesian regression models (BayesB) . The dEBV of 8652 animals genotyped for body weight at 120 days, 240 days, 365 days, and 455 days was used. Animals were divided into training population and validation by the predicted EBV accuracy. All strategies were repeated five times, and the correlation between dEBV and predicted dEBV was used as the accuracy measure of the models tested. Th... (Complete abstract click electronic access below) / Doutor

Hepatitis C, a life threatening disease, caused by the hepatitis C virus (HCV) currently affects over 170-200 million people worldwide (~3% of global human population), more than five times the percentage of total HIV infections. HCV infection has been shown to be a major cause of chronic hepatitis, liver cirrhosis, and hepatocellular carcinoma and is the leading cause of liver transplantation in the U.S. HCV has escaped every therapeutic target to date by means of its error-prone RNA polymerase, which allows it to mutate prolifically. The current standard anti-HCV therapy, which is pegylated interferon a combined with ribavirin, is difficult to tolerate, and more than 50% of HCV patients are refractory to it. No protective vaccine or therapeutic antibody is available, making the need for the development of an efficacious immunoprophylactic and therapeutic agent imperative. HCV is an enveloped virus with a positive sense RNA genome of ~9.6 kilobases (kb), which carries a large open reading frame (ORF), flanked by 5'- and 3'- untranslated regions (UTRs). Interestingly, within the highly mutational HCV RNA, there are a limited number of 100% conserved and functionally vital motifs, located in the 5' UTR, coding region and in the 3' UTR. Within the HCV genome, these motifs have been proposed to be involved in multiple exclusive interactions with each other and furthermore, these interactions have been demonstrated to be essential for HCV replication and/or translation of the viral proteins. / Bayer School of Natural and Environmental Sciences; / Chemistry and Biochemistry / PhD; / Dissertation;

No description available.

The aim of this thesis was to investigate different aspects of male breast cancer (MBC), and to compare these with findings in female breast cancer (FBC). In paper I, a population–based study was performed to investigate possible differences in treatment and outcome between MBC and FBC patients. MBC and FBC presented with a similar distribution of stage. Although no differences in primary treatment strategy were demonstrated, MBC patients had significantly poorer overall and relative survival, indicating a more aggressive disease. Paper II aimed to assess the value of clinicopathological factors and molecular subtypes in MBC. One hundred and ninety-seven MBC tumors were characterized using immunohistochemistry (IHC) and the findings were correlated to outcome. Lymph node positivity, larger tumor size and ER-negativity were independent risk factors for breast cancer death. Tumor grade, HER2, Ki 67 or IHC classification into molecular subtypes did not demonstrate any prognostic information. In paper III, the same patient material as in paper II was used for evaluation of proliferation markers. High levels of cyclin A and cyclin B expression and an elevated mitotic count were predictive of breast cancer death. Ki-67 was re-evaluated using different cut-offs, but no prognostic value could be demonstrated. Contrarily, overexpression of cyclin D1 was associated with a lower risk of breast cancer death. In papers IV-V, the molecular background of MBC tumors was investigated.  Global GEX analyses were performed and two novel subgroups of MBC tumors were identified; luminal M1 and luminal M2. When comparing the degree of similarity with the “intrinsic” subtypes in FBC tumors, more than half of the MBC tumors remained unclassified.  Comparative genomic hybridization was used to investigate DNA aberrations. Two MBC subgroups were identified, of which one did not resemble any of the female subgroups. In both studies on the molecular level, a majority of patients were classified into the subgroup with a more aggressive tumor behavior. In conclusion, MBC seems to be a unique tumor entity. The established molecular subtypes in FBC are not applicable in MBC. Other prognostic profiles, specific for MBC, need to be identified.

Epigenetic states constitute heritable features of the chromatin to regulate when, where and how genes are expressed in the developing conceptus. A special case of epigenetic regulation, genomic imprinting, is defined as parent of origin-dependent monoallelic expression. The Igf2-H19 locus is considered as paradigm of genomic imprinting with a growth-promoting gene, Igf2, expressed paternally and a growth antagonist, H19 encoding a non-coding transcript, expressed only from the maternal allele. The monoallelic expression patterns are regulated by the epigenetic status at an imprinting control region (ICR) in the 5´-flank of the H19 gene. The chromatin insulator protein CTCF interacts with only the maternal H19 ICR allele to prevent downstream enhancers to communicate with the Igf2 promoters. Mutations of these CTCF binding sites lead to biallelic Igf2 expression, increased size of the conceptus and predisposition for cancer. Reasoning that these effects cannot be explained by the regulation of Igf2 expression alone, a technique was invented to examine long-range chromatin interactions without prior knowledge of the interacting partners. Applying the circular chromosomal conformation capture (4C) technique to mouse neonatal liver cells, it was observed that 114 unique sequences interacted with the H19 ICR. A majority of these interactors was in complex with only the maternal H19 ICR allele and depended on the presence of functional CTCF binding sites. The functional consequence of chromosomal networks was demonstrated by the observation that the maternal H19 ICR allele regulated the transcription of two genes on another chromosome. As the chromosomal networks underwent reprogramming during the maturation of embryonic stem cells, attention was turned to human cancer cells, displaying features common with mouse embryonic stem cells. Subsequently, chromatin folding at the human H19 ICR suggested that stable chromatin loops were organized by synergistic interactions within and between baits and interactors. The presence of these interactions was linked to DNA methylation patterns involving repeat elements. A "flower" model of chromatin networks was formulated to explain these observations. This thesis has unravealed a novel feature of the epigenome and its functions to regulate gene expression in trans. The identified roles for CTCF as an architectural factor in the organization of higher order chromatin conformations may be of importance in understanding development and disease ontogeny from novel perspectives.