Fatores de transcrição (FT) são proteínas que se ligam em sequências específicas e bem conservadas de nucleotídeos no DNA, denominadas sítios de ligação dos fatores de transcrição (SLFT), localizadas em regiões de regulação gênica conhecidas como módulos cis-reguladores (CRM). Ao reconhecer o SLFT, o fator de transcrição se liga naquele sítio e influencia a transcrição gênica positiva ou negativamente. Existem técnicas experimentais para a identificação dos locais dos SLFTs em um genoma, como footprinting, ChIP-chip ou ChIP-seq. Entretanto, a execução de tais técnicas implica em custos e tempo elevados. Alternativamente, pode-se utilizar as sequências de SLFTs já conhecidas para um determinado fator de transcrição e aplicar técnicas de aprendizado computacional supervisionado para criar um modelo computacional para tal sítio e então realizar a predição computacional no genoma. Entretanto, a maioria das ferramentas computacionais existentes para esse fim considera independência entre as posições entre os nucleotídeos de um sítio - como as baseadas em PWMs (position weight matrix) - o que não é necessariamente verdade. Este projeto teve como objetivo avaliar a utilização de gramáticas regulares estocásticas (GRE) como técnica alternativa às PWMs neste problema, uma vez que GREs são capazes de caracterizar dependências entre posições consecutivas dos sítios. Embora as diferenças de desempenho tenham sido sutis, GREs parecem mesmo ser mais adequadas do que PWMs na presença de valores mais altos de dependência de bases, e PWMs nos demais casos. Por fim, uma ferramenta de predição computacional de SLFTs foi criada baseada tanto em GREs quanto em PWMs. / Transcription factors (FT) are proteins that bind to specific and well-conserved sequences of nucleotides in the DNA, called transcription factor binding sites (TFBS), contained in regions of gene regulation known as cis-regulatory modules (CRM). By recognizing TFBA, the transcription factor binds to that site and positively or negatively influence the gene transcription. There are experimental procedures for the identification of TFBS in a genome such as footprinting, ChIP-chip or ChIP-Seq. However, the implementation of these techniques involves high costs and time. Alternatively, one may utilize the TFBS sequences already known for a particular transcription factor and applying computational supervised learning techniques to create a computational model for that site and then perform the computational prediction in the genome. However, most existing software tools for this purpose considers independence between nucleotide positions in the site - such as those based on PWMs (position weight matrix) - which is not necessarily true. This project aimed to evaluate the use of stochastic regular grammars (SRG) as an alternative technique to PWMs in this problem, since SRGs are able to characterize dependencies between consecutive positions in the sites. Although differences in performance have been subtle, SRGs appear to be more suitable than PWMs in the presence of higher base dependency values, and PWMs in other cases. Finally, a computational TFBS prediction tool was created based on both SRGs and PWMs.

It has been suggested that organismal complexity correlates with the complexity of gene regulation. Transcriptional control of gene expression is mediated by binding of regulatory proteins to cis-acting sequences on the genome. Hence, it is crucial to identify the chromosomal targets of transcription factors (TFs) to delineate transcriptional regulatory networks underlying gene expression programs. The development of ChIP-chip technology has enabled high throughput mapping of TF binding sites across the genome. However, there are many limitations to the technology including the availability of whole genome arrays for complex organisms such human or mouse. To circumvent these limitations, we developed the Sequence Tag Analysis of Genomic Enrichment (STAGE) methodology that is based on extracting short DNA sequences or “tags” from ChIP-enriched DNA. With improvements in sequencing technologies, we applied the recently developed ChIP-Seq technique i.e. ChIP followed by ultra high throughput sequencing, to identify binding sites for the TF E2F4 across the human genome. We identified previously uncharacterized E2F4 binding sites in intergenic regions and found that several microRNAs are potential E2F4 targets. Binding of TFs to their respective chromosomal targets requires access of the TF to its regulatory element, which is strongly influenced by nucleosomal remodeling. In order to understand nucleosome remodeling in response to transcriptional perturbation, we used ultra high throughput sequencing to map nucleosome positions in yeast that were subjected to heat shock or were grown normally. We generated nucleosome remodeling profiles across yeast promoters and found that specific remodeling patterns correlate with specific TFs active during the transcriptional reprogramming. Another important aspect of gene regulation operates at the post-transcriptional level. MicroRNAs (miRNAs) are ~22 nucleotide non-coding RNAs that suppress translation or mark mRNAs for degradation. MiRNAs regulate TFs and in turn can be regulated by TFs. We characterized a TF-miRNA network involving the oncofactor Myc and the miRNA miR-22 that suppresses the interferon pathway as primary fibroblasts enter a stage of rapid proliferation. We found that miR-22 suppresses the interferon pathway by inhibiting nuclear translocation of the TF NF-kappaB. Our results show how the oncogenic TF Myc cross-talks with other TF regulatory pathways via a miRNA intermediary. / text

La vitesse de réplication du HIV-1(Human Immunodeficiency Virus type 1), qui semble corrélée de manière directe à la vitesse de progression de la maladie vers le stade SIDA, est essentiellement contrôlée au niveau transcriptionnel. La transcription du HIV-1 est régulée par la structure chromatinienne, des éléments agissant en cis localisés dans les LTRs, des facteurs de transcription agissant en trans et par la protéine virale trans-activatrice Tat (revu dans Quivy et al. 2007, Bisgrove et al. 2005, Rohr et al. 2003, Rabson and Graves 1997). En plus de l’enhancer localisé dans le LTR5’ du HIV-1, un enhancer intragénique, localisé dans le gène pol du HIV-1, inductible par le phorbol 12-myristate 13-acétate (PMA) a été identifié. La localisation progressive de l’activité enhancer a permis de définir deux domaines distincts et indépendants dans cet enhancer intragénique :les fragments 5103 et 5105 localisés respectivement dans la partie centrale du gène pol et dans une région couvrant la fin du gène pol, le gène vif, le gène vpr et le premier exon codant des gènes tat et rev (Verdin et al. 1990). Les fragments 5103 et 5105 se comportent tous deux comme des enhancers inductibles par le PMA lorsqu’ils sont clonés en amont du promoteur de la thymidine kinase dans un vecteur rapporteur en cellules HeLa. Notre laboratoire a précédemment identifié trois sites de liaison pour les facteurs de transcription AP-1 dans le fragment 5103 (Van Lint et al. 1991). <p><p>Au cours de notre thèse, nous avons poursuivi la caractérisation de ces sites de liaison AP-1 et avons montré que les facteurs c Fos, JunB et JunD interagissent in vitro avec ces motifs. Pour chaque site, nous avons identifié des mutations qui abolissent la liaison des facteurs AP-1 sans altérer la séquence en acides aminés sous-jacente de la transcriptase inverse. Par des expériences de transfection transitoire, nous avons démontré que les sites AP 1 intragéniques sont entièrement responsables de l’activité enhancer PMA-dépendante du fragment 5103. De plus, l’activité PMA-inductible du fragment 5103 est inhibée par le mutant dominant négatif A-Fos à condition que les sites ne soient pas mutés. A l’inverse, l’expression ectopique de dimères forcés AP-1 affecte positivement l’activité enhancer du fragment 5103. Enfin, nous avons étudié le rôle biologique des sites AP-1 intragéniques dans la réplication virale et avons montré que ces sites contribuent positivement à l’infectivité du virus.<p><p>Durant la seconde partie de notre thèse, nous avons entamé la caractérisation physique et fonctionnelle du fragment 5105. Nos résultats de transfection transitoire montrent que l’activité PMA inductible du fragment 5105 est localisée dans le dernier tiers de ce dernier :le sous fragment 5105.3. L’analyse bioinformatique de cette région a permis de mettre en évidence un site de liaison pour les facteurs AP-1 in vitro. Des mutations ponctuelles permettent d’abolir la liaison des facteurs à leur site mais altèrent la séquence en acides aminés sous-jacente codant pour les protéines Tat et Rev. Nous avons montré que ce site est impliqué dans l’activité transcriptionnelle de ce fragment. L’expression ectopique du mutant dominant négatif A-Fos inhibe l’activité transcriptionnelle PMA-inductible du fragment 5105. Une analyse bioinformatique plus large nous a ensuite permis d’identifier in vitro, par retard de migration sur gel, 5 sites de liaison pour le facteur YY1 et 2 sites de liaison pour le facteur PU.1 dont les implications pour le virus restent encore à déterminer.<p> / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

Fatores de transcrição (FT) são proteínas que se ligam em sequências específicas e bem conservadas de nucleotídeos no DNA, denominadas sítios de ligação dos fatores de transcrição (SLFT), localizadas em regiões de regulação gênica conhecidas como módulos cis-reguladores (CRM). Ao reconhecer o SLFT, o fator de transcrição se liga naquele sítio e influencia a transcrição gênica positiva ou negativamente. Existem técnicas experimentais para a identificação dos locais dos SLFTs em um genoma, como footprinting, ChIP-chip ou ChIP-seq. Entretanto, a execução de tais técnicas implica em custos e tempo elevados. Alternativamente, pode-se utilizar as sequências de SLFTs já conhecidas para um determinado fator de transcrição e aplicar técnicas de aprendizado computacional supervisionado para criar um modelo computacional para tal sítio e então realizar a predição computacional no genoma. Entretanto, a maioria das ferramentas computacionais existentes para esse fim considera independência entre as posições entre os nucleotídeos de um sítio - como as baseadas em PWMs (position weight matrix) - o que não é necessariamente verdade. Este projeto teve como objetivo avaliar a utilização de gramáticas regulares estocásticas (GRE) como técnica alternativa às PWMs neste problema, uma vez que GREs são capazes de caracterizar dependências entre posições consecutivas dos sítios. Embora as diferenças de desempenho tenham sido sutis, GREs parecem mesmo ser mais adequadas do que PWMs na presença de valores mais altos de dependência de bases, e PWMs nos demais casos. Por fim, uma ferramenta de predição computacional de SLFTs foi criada baseada tanto em GREs quanto em PWMs. / Transcription factors (FT) are proteins that bind to specific and well-conserved sequences of nucleotides in the DNA, called transcription factor binding sites (TFBS), contained in regions of gene regulation known as cis-regulatory modules (CRM). By recognizing TFBA, the transcription factor binds to that site and positively or negatively influence the gene transcription. There are experimental procedures for the identification of TFBS in a genome such as footprinting, ChIP-chip or ChIP-Seq. However, the implementation of these techniques involves high costs and time. Alternatively, one may utilize the TFBS sequences already known for a particular transcription factor and applying computational supervised learning techniques to create a computational model for that site and then perform the computational prediction in the genome. However, most existing software tools for this purpose considers independence between nucleotide positions in the site - such as those based on PWMs (position weight matrix) - which is not necessarily true. This project aimed to evaluate the use of stochastic regular grammars (SRG) as an alternative technique to PWMs in this problem, since SRGs are able to characterize dependencies between consecutive positions in the sites. Although differences in performance have been subtle, SRGs appear to be more suitable than PWMs in the presence of higher base dependency values, and PWMs in other cases. Finally, a computational TFBS prediction tool was created based on both SRGs and PWMs.

Short noncoding RNAs play roles in regulating nearly every biological process, in nearly every organism, yet the exact function and importance of these molecules remains a subject of some debate. In order to gain a better understanding of the contexts in which these regulators have evolved, I have undertaken a variety of approaches to study the evolutionary history of the components that make up these pathways, in the form of two main research efforts. In the first chapter, I have used a combination of population genetics and molecular evolution techniques to show that proteins involved in the piRNA pathway are rapidly evolving, and that different components of the pathway seem to be evolving rapidly on different timescales. These rapidly evolving piRNA pathway proteins can be loosely separated into two groups. The first group appears to evolve quickly at the species level, perhaps in response to transposons that invade across species lines, while the second group appears to evolve quickly at the level of individual populations, perhaps in response to transposons that are paternally present yet novel to the maternal genome. In the second chapter of my research, I have used molecular evolution techniques and carefully devised controls to show that the binding sites of well-conserved miRNAs are among the most slowly changing short motifs in the genome, consistent with a conserved function for these short RNAs in regulatory pathways that are ancient and extremely slow to change. I have additionally discovered a major flaw in an existing approach to motif turnover calculations, which may lead to systematic biases in the published literature toward the false inference of increased regulatory complexity over time. I have implemented a revised approach to motif turnover that addresses this flaw.

Xylella fastidiosa é uma bactéria gram-negativa, não flagelada, agente causal de doenças de importância econômica como a doença de Pierce nas videiras e a clorose variegada dos citros (CVC) nas laranjeiras. O objetivo do presente trabalho foi realizar análises in silico das sequências promotoras dos genes deste fitopatógeno em uma tentativa de arrecadar novas evidências para o melhor entendimento da dinâmica de regulação transcricional de seus genes, incluindo aqueles envolvidos em mecanismos de patogenicidade e virulência. Para tanto, duas estratégias foram utilizadas para predição de elementos cis-regulatórios em regiões promotoras do genoma da cepa referência 9a5c, comprovadamente associada à CVC. A primeira, conhecida como phylogenetic footprinting, foi empregada para identificação de elementos regulatórios conservados em promotores de unidades transcricionais ortólogas, levando em consideração o conjunto de genes de X. fastidiosa e 7 espécies comparativas. O critério para identificação de unidades transcricionais ortólogas, isto é, unidades trancricionais oriundas de espécies distintas e cujos promotores compartilham elementos cis-regulatórios, foi paralelamente estudado utilizando-se informações regulatórias das bactérias modelos: Pseudomonas aeruginosa, Bacillus subtilis e Escherichia coli. Os resultados obtidos com análise de phylogenetic footprinting nos permitiu acessar a rede regulatória transcricional da espécie de forma compreensiva (global). Foram estabelecidas 2990 interações regulatórias, compreendendo 80 motivos distribuídos nos promotores de 56.8% das unidades transcricionais do genoma de X. fastidiosa. Na segunda estratégia recuperamos informações regulatórias experimentalmente validadas em E. coli e complementamos o conhecimento de dez regulons de X. fastidiosa, através de uma metodologia de scanning (varredura), dos quais algumas interações regulatórias já haviam sido previamente descritas por outros trabalhos. Destacamos os regulons de Fur e CRP, reguladores transcricionais globais, que se mostraram responsáveis pela modulação de genes relacionados a mecanismos de invasão e colonização do hospedeiro vegetal entre outros. Por fim, análises comparativas em regiões regulatórias correspondentes entre cepas foram realizadas e diferenças possivelmente associadas a particularidades fenotípicas foram identificadas entre 9a5c e J1a12, um isolado de citros não virulento, e 9a5c e Temecula1, um isolado de videira causador da doença de Pierce. / Xylella fastidiosa is a gram-negative, non-flagellated bacterium responsible for causing economically important diseases such as Pierce\'s disease in grapevines and Citrus Variegated Clorosis (CVC) in sweet orange trees. In the present work we performed in silico analysis on promoter sequences of protein-coding genes from this phytopathogen, including those involved in virulence and pathogenic mechanisms, in an attempt to better understand the underlying transcriptional regulatory dynamics. Two strategies for cis-regulatory elements prediction were applied on promoter sequences from 9a5c strain genome, a proven causal agent of CVC. The first one, known as phylogenetic footprinting, involved the prediction of regulatory motifs conserved on promoter sequences of orthologous transcription units from X. fastidiosa and a set of 7 comparatives species. The criteria to identify orthologous transcription units, i. e., those from different species and whose promoter sequences share at least one common regulatory motif, was studied based on regulatory information available for model organisms: Pseudomonas aeruginosa, Bacillus subtilis and Escherichia coli. The results obtained with the phylogenetic footprinting analysis permitted us to access the underlying transcriptional regulatory network from the species in a comprehensive manner (genome-wide), with a total of 2990 regulatory interactions corresponding to 80 predicted motifs distributed on promoter sequences of 56.8% of all transcription units. In the second strategy regulatory information from E. coli was recovered and used to expand the knowledge of ten regulons in X. fastidiosa, through a scanning process, of which some regulatory interactions were previously described by independent studies. We emphasize some genes related to host invasion and colonization present in the Fur and CRP regulons, two global transcription regulators. Lastly, comparative analysis on corresponding regulatory regions among strains were performed and differences possibly associated to phenotypic variation were identified between 9a5c and J1a12, a non-virulent strain isolated from orange trees, and between 9a5c and Temecula1, a strain associated to Pierce\'s disease on grapevines.

The study of cis-regulatory elements (CREs) will allow for increased understanding of regulation and lead to insight regarding the mechanisms governing growth, development, health, and disease. The aim of this study was to characterize the de novo in silico predictions of Arabidopsis CREs. Eight synthetic and 30 native promoter-constructs containing an eGFP/GUS reporter protein were generated for cold, genotoxic, heat, osmotic, and salt stress; the circadian clock; ABA signaling; root and epidermis tissue. Constructs were stably transformed into A. thaliana Col-0 and the effects of the CREs were evaluated by in planta stress or tissue assays using GUS expression levels. Results reveal a novel genotoxic element that specifically directs GUS expression in rosette leaves during genotoxic stress. Results also look promising for novel epidermis and root-specific elements. Results of these assays validate the de novo prediction pipeline's ability to identify novel and known CREs related to abiotic stress.

The study of cis-regulatory elements (CREs) will allow for increased understanding of regulation and lead to insight regarding the mechanisms governing growth, development, health, and disease. The aim of this study was to characterize the de novo in silico predictions of Arabidopsis CREs. Eight synthetic and 30 native promoter-constructs containing an eGFP/GUS reporter protein were generated for cold, genotoxic, heat, osmotic, and salt stress; the circadian clock; ABA signaling; root and epidermis tissue. Constructs were stably transformed into A. thaliana Col-0 and the effects of the CREs were evaluated by in planta stress or tissue assays using GUS expression levels. Results reveal a novel genotoxic element that specifically directs GUS expression in rosette leaves during genotoxic stress. Results also look promising for novel epidermis and root-specific elements. Results of these assays validate the de novo prediction pipeline's ability to identify novel and known CREs related to abiotic stress.

No description available.

Determination of cell signalling behaviour is crucial for understanding the physiological response to a specific stimulus or drug treatment. Current approaches for large-scale data analysis do not effectively incorporate critical topological information provided by the signalling network. We herein describe a novel model- and data-driven hybrid approach, or signal transduction score flow algorithm, which allows quantitative visualization of cyclic cell signalling pathways that lead to ultimate cell responses such as survival, migration or death. This score flow algorithm translates signalling pathways as a directed graph and maps experimental data, including negative and positive feedbacks, onto gene nodes as scores, which then computationally traverse the signalling pathway until a pre-defined biological target response is attained. Initially, experimental data-driven enrichment scores of the genes were computed in a pathway, then a heuristic approach was applied using the gene score partition as a solution for protein node stoichiometry during dynamic scoring of the pathway of interest. Incorporation of a score partition during the signal flow and cyclic feedback loops in the signalling pathway significantly improves the usefulness of this model, as compared to other approaches. Evaluation of the score flow algorithm using both transcriptome and ChIP-seq data-generated signalling pathways showed good correlation with expected cellular behaviour on both KEGG and manually generated pathways. Implementation of the algorithm as a Cytoscape plug-in allows interactive visualization and analysis of KEGG pathways as well as user-generated and curated Cytoscape pathways. Moreover, the algorithm accurately predicts gene-level and global impacts of single or multiple in silico gene knockouts. / Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.