Myc is a powerful oncogene frequently deregulated in cancer yet deregulated Myc alone does not lead to cellular transformation due to the intrinsic safety mechanism of deregulated Myc potentiating apoptosis. The mechanism by which Myc potentiates apoptosis remains unclear, however, because the regions of Myc essential for apoptosis are also required for Myc to function as a regulator of gene transcription, it is thought that Myc’s role in apoptosis is a function of its regulation of an apoptotic genetic program. We hypothesize that under apoptotic conditions, Myc differentially binds and/or regulates a specific cohort of genes to potentiate apoptosis. The foremost approach to addressing this hypothesis is the employment of ChIP-chip coupled with expression microarray analyses. Here, using the MCF10A breast epithelial and SHEP neuroblastoma cell lines, we developed and characterized two independent human systems for subsequent ChIP-chip and expression array analyses to elucidate the genetic program of Myc-potentiated apoptosis.

Myc is a powerful oncogene frequently deregulated in cancer yet deregulated Myc alone does not lead to cellular transformation due to the intrinsic safety mechanism of deregulated Myc potentiating apoptosis. The mechanism by which Myc potentiates apoptosis remains unclear, however, because the regions of Myc essential for apoptosis are also required for Myc to function as a regulator of gene transcription, it is thought that Myc’s role in apoptosis is a function of its regulation of an apoptotic genetic program. We hypothesize that under apoptotic conditions, Myc differentially binds and/or regulates a specific cohort of genes to potentiate apoptosis. The foremost approach to addressing this hypothesis is the employment of ChIP-chip coupled with expression microarray analyses. Here, using the MCF10A breast epithelial and SHEP neuroblastoma cell lines, we developed and characterized two independent human systems for subsequent ChIP-chip and expression array analyses to elucidate the genetic program of Myc-potentiated apoptosis.

<p>Transcriptional regulation largely determines which proteins and the protein levels that are found in a cell, and this is crucial in development, differentiation and responses to environmental stimuli. The major effectors of transcriptional regulation are a group of proteins known as transcription factors, which importance is supported by their frequent involvement in mendelian and complex diseases.</p><p>In paper I, we attempted to establish the importance of DNA sequence variation in transcriptional control, by analyzing the potential functionality of polymorphic short repetitive elements as cis-regulatory elements. However, the relevance of this study was constrained by the limited number of analyzed sequences and the <i>in vitro</i> nature of the experiments. To overcome these limitations, (paper II) we optimized an <i>in vivo</i> large-scale technology named ChIP-chip, which couples chromatin immunoprecipitation and microarray hybridization. We successfully identified the binding profiles of metabolic-disease associated transcription factors in 1% of the human genome, using a liver cellular model, and inferred the binding sites at base pair resolution.</p><p>Another important characteristic of transcriptional regulation is its plasticity, which allows adjusting the cellular transcriptome to cellular and environmental stimuli. In paper III, we investigated such plasticity by treating HepG2 cells with butyrate, a histone deacetylase inhibitor (HDACi) and interrogating the changes in histone H3 and H4 acetylation levels in 1% of the genome. Observation of frequent deacetylation around transcription start sites and hyperacetylation at the nuclear periphery challenges pre-assumed HDACi mechanisms of action.</p><p>Finally, in paper IV we extended the DNA binding profiles of the medically relevant transcription factors, USF1 and USF2, and H3 acetylation to the whole non-repetitive fraction of the human genome. Using motif finding tools and chromatin profiling, we uncovered the major determinants of USF-DNA interactions. Furthermore, USFs and H3ac were clearly localized around transcription start sites, frequently in the context of bidirectional promoters.</p>

Transcriptional regulation largely determines which proteins and the protein levels that are found in a cell, and this is crucial in development, differentiation and responses to environmental stimuli. The major effectors of transcriptional regulation are a group of proteins known as transcription factors, which importance is supported by their frequent involvement in mendelian and complex diseases. In paper I, we attempted to establish the importance of DNA sequence variation in transcriptional control, by analyzing the potential functionality of polymorphic short repetitive elements as cis-regulatory elements. However, the relevance of this study was constrained by the limited number of analyzed sequences and the in vitro nature of the experiments. To overcome these limitations, (paper II) we optimized an in vivo large-scale technology named ChIP-chip, which couples chromatin immunoprecipitation and microarray hybridization. We successfully identified the binding profiles of metabolic-disease associated transcription factors in 1% of the human genome, using a liver cellular model, and inferred the binding sites at base pair resolution. Another important characteristic of transcriptional regulation is its plasticity, which allows adjusting the cellular transcriptome to cellular and environmental stimuli. In paper III, we investigated such plasticity by treating HepG2 cells with butyrate, a histone deacetylase inhibitor (HDACi) and interrogating the changes in histone H3 and H4 acetylation levels in 1% of the genome. Observation of frequent deacetylation around transcription start sites and hyperacetylation at the nuclear periphery challenges pre-assumed HDACi mechanisms of action. Finally, in paper IV we extended the DNA binding profiles of the medically relevant transcription factors, USF1 and USF2, and H3 acetylation to the whole non-repetitive fraction of the human genome. Using motif finding tools and chromatin profiling, we uncovered the major determinants of USF-DNA interactions. Furthermore, USFs and H3ac were clearly localized around transcription start sites, frequently in the context of bidirectional promoters.

It is common to test many hypotheses simultaneously in the application of statistics. The probability of making a false discovery grows with the number of statistical tests performed. When all the null hypotheses are true, and the test statistics are indepen- dent and continuous, the error rates from the family wise error rate (FWER)- and the false discovery rate (FDR)-controlling procedures are equal to the nominal level. When some of the null hypotheses are not true, both procedures are conservative. In the first part of this study, we review the background of the problem and propose methods to estimate the number of true null hypotheses. The estimates can be used in FWER- and FDR-controlling procedures with a consequent increase in power. We conduct simulation studies and apply the estimation methods to data sets with bio- logical or clinical significance. In the second part of the study, we propose a mixture model approach for the analysis of ChIP-chip high density oligonucleotide array data to study the interac- tions between proteins and DNA. If we could identify the specific locations where proteins interact with DNA, we could increase our understanding of many important cellular events. Most experiments to date are performed in culture on cell lines, bac- teria, or yeast, and future experiments will include those in developing tissues, organs, or cancer biopsies, and they are critical in understanding the function of genes and proteins. Here we investigate the ChIP-chip data structure and use a beta-mixture model to help identify the binding sites. To determine the appropriate number of components in the mixture model, we suggest the Anderson-Darling testing. Our study indicates that it is a reasonable means of choosing the number of components in a beta-mixture model. The mixture model procedure has broad applications in biology and is illustrated with several data sets from bioinformatics experiments.

La recherche dans le domaine de la génomique génère de données colossales dont la dimension ne cesse de s'accroître avec la technologie. Pour traiter cette masse d'information, la statistique est devenue un outil indispensable. Ce nouveau type de données représente un véritable challenge dans la mesure où ces données sont de très grande dimension, qu'elles sont très "bruitées" et qu'il n'existe généralement pas de "golden standard" permettant de valider les résultats. Au cours de cette thèse, nous nous sommes intéressés à l'analyse statistique de trois types de données : les puces à ADN, les ChIP-chip et les ChIP-Seq. Pour chacune d'entres elles, une nouvelle approche a été mise au point. Dans le cas des données de puces à ADN, la méthode GAGG permet de détecter les gènes différentiellement exprimés et de les grouper par type de profils. Pour ce faire, un Algorithme Génétique est utilisé de manière à optimiser deux critères liés à des méthodes voisines de l'ACP et des k-means. Pour les données de ChIP-chip, la méthode POTChIPS a été réalisée. Elle permet de repérer sur le génome, les sites de fixation d'une protéine d'intérêt (ex : un facteur de transcription). Dans cette méthode, une extraction des pics du signal est réalisée puis un seuil de significativité est déterminé à partir d'une modélisation POT. Enfin, pour ce qui est des données de ChIP-Seq, l'objectif est le même que pour les ChIP-chip, à savoir, repérer les sites de fixation d'une protéine sur l'ADN. La méthode POTSeq, mise au point au cours de cette thèse, est une adaptation de POTChIPS aux données de ChIP-Seq. / Research in Genomics produces very huge data which still increase with technology. Statistics is becoming essential to treat this amount of information. These new kind of data represent a great challenge in data analysis because of the great dimensions, the important background and the absence of "golden standard" which could allow to validate the obtained results. During this PhD thesis, we focused on statistical analysis for three kinds of data: DNA microarray, ChIP-chip and ChIP-Seq. For each one, a new approach have been proposed. For DNA microarrays, the GAGG method allows to detect differentially expressed genes and to cluster them by profile types. To do so, a Genetic Algorithm is used in order to optimize two criteria related to two nearby methods of PCA and $k$-means. In the case of ChIP-chip data, the POTChIPS method have been proposed. It allows to detect the binding sites of a protein of interest (a transcription factor e.g.) along the genome. In this method a peak extraction i realized then a significant threshold is obtained from a POT modelization. Finally, for ChIP-Seq data, the goal is the same that the one of chIP-chip, i.e., to find on DNA the binding sites of a protein of interest. The POTSeq method is an adaptation of POTChIPS for ChIP-Seq.La méthode POTSeq, mise au point au cours de cette thèse, est une adaptation de POTChIPS aux données de ChIP-Seq.

There are around 20.000 genes in the human genome all of which could potentially be expressed. However, it is obvious that not all of them can be active at the same time. Thus, there is a need for coordination achieved through the regulation of transcription. Transcriptional regulation is a crucial multi-component process involving genetic and epigenetic factors, which determine when and how genes are expressed. The aim of this thesis was to study two of these components, the transcription factors and the DNA sequence elements with which they interact. In papers I and II, we tried to characterize the regulatory role of repeated elements in the regulatory sequences of nitric oxide synthase 2 gene. We found that this type of repeat is able to adopt non B-DNA conformations in vitro and that it binds nuclear factors, in addition to RNA polymerase II. Therefore it is probable that these types of repeats can participate in the regulation of genes. In papers III-V, we intended to analyze the genome-wide binding sites for six transcription factors involved in fatty acid and cholesterol metabolism and the sites of an epigenetic mark in a human liver cell line. For this, we applied the chromatin immunoprecipitation (ChIP) method together with detection on microarrays (ChIP-chip) or by detection with the new generation massively parallel sequencers (ChIP-seq). We found that all of these transcription factors are involved in other liver-specific processes than metabolism, for example cell proliferation. We were also able to define two sets of transcription factors depending on the position of their binding relative to gene promoters. Finally, we demonstrated that the patterns of the epigenetic mark reflect the structure and transcriptional activity of the promoters. In conclusion, this thesis presents experiments, which moves our view from genetics to genomics, from in vitro to in vivo, and from low resolution to high resolution analysis of transcriptional regulation.

Regulation of transcription is a central mechanism in all living cells that now can be investigated with high-throughput technologies. Data produced from such experiments give new insights to how transcription factors (TFs) coordinate the gene transcription and thereby regulate the amounts of proteins produced. These studies are also important from a medical perspective since TF proteins are often involved in disease. To learn more about transcriptional regulation, we have developed strategies for analysis of data from microarray and massively parallel sequencing (MPS) experiments. Our computational results consist of methods to handle the steadily increasing amount of data from high-throughput technologies. Microarray data analysis tools have been assembled in the LCB-Data Warehouse (LCB-DWH) (paper I), and other analysis strategies have been developed for MPS data (paper V). We have also developed a de novo motif search algorithm called BCRANK (paper IV). The analysis has lead to interesting biological findings in human liver cells (papers II-V). The investigated TFs appeared to bind at several thousand sites in the genome, that we have identified at base pair resolution. The investigated histone modifications are mainly found downstream of transcription start sites, and correlated to transcriptional activity. These histone marks are frequently found for pairs of genes in a bidirectional conformation. Our results suggest that a TF can bind in the shared promoter of two genes and regulate both of them. From a medical perspective, the genes bound by the investigated TFs are candidates to be involved in metabolic disorders. Moreover, we have developed a new strategy to detect single nucleotide polymorphisms (SNPs) that disrupt the binding of a TF (paper IV). We further demonstrated that SNPs can affect transcription in the immediate vicinity. Ultimately, our method may prove helpful to find disease-causing regulatory SNPs.

Zugl.: Hamburg, Techn. Univ., Diss., 2009

La chromatine est un système de compaction de l’ADN jouant un rôle important dans la régulation de l’expression génique. L’acétylation de la chromatine provoque un relâchement de sa structure, facilitant le recrutement de facteurs de transcription. Inversement, des complexes histones déacétylases favorisent une structure compacte, réprimant l’expression de gènes. Un complexe HDAC, Rpd3S, est recruté par l’ARN polymérase II phosphorylée sur les régions codantes transcrites. Cette activité HDAC est stimulée par la déposition de la marque H3K36me générée par l’histone méthyltransférase Set2. Par approche génomique, en utilisant comme organisme modèle Saccharomyces cerevisiae, j’ai optimisé la méthode de ChIP-chip puis démontré que les sous unités Hos2 et Set3 d’un autre complexe HDAC, Set3C, étaient recrutées sur des régions codantes de gènes transcrits. De plus, Set3C est connu pour être recruté soit par H3K4me ou par la Pol II. La suite du projet portera sur le recrutement de Set3C qui semble similaire à Rpd3S. / Chromatin is a DNA compaction system and the main mecanism of gene expression regulation. Acetylation of histones loosens DNA compaction and thus facilitates the recruitment of transcription factors. By opposition, histone deacetylase (HDAC) complexes increase the compaction of chromatin and thus repress transcription. Rpd3S, a HDAC complex, is recruited to chromatin by interaction with a phosphorylated form of RNA polymerase II (Pol II) on actively transcribed genes and its activity is stimulated by the presence of the mark H3K36me mark. During my Master degree, I optimized ChIP-chip, using Saccharomyces cerevisiae as model organism, in order to investigate the localisation of two subunits (Hos2 and Set3) of another HDAC complex, Set3C. My datas demonstrate that Set3C subunits localized on actively transcribed genes, in a fashion similar to Rpd3S. Further, Set3C is known to be recruited either by Pol II or H3K4me suggesting that Set3C has a similar recruitment mechanism than Rpd3S. Future experiments in the laboratory will investigate the recruitment of Set3C.