No description available.

Nowadays, it is very important to study gene expression mechanism in molecular biology. Gene expression is also regulated by sequence specific transcription factors which binds to regulatory regions of the genes. Searching for this specific sequences can be very problematic because transcription factor binding sites can be very degenerative. There are several possible methods that can be aplied to this problem. First part of this paper describes few algorithms for transcription binding sites search. Second part contains design and implementation of algorithm for searching binding sites of transcription factor p53.

Os projetos genoma têm fornecido uma grande quantidade de informação sobre a arquitetura gênica e sobre a configuração física de suas respectivas regiões flanqueadoras (RF). Estas RF contêm informações com o potencial de auxiliar na elucidação de vários processos biológicos, como os mecanismos de expressão gênica e de sua regulação. Estes mecanismos são de extrema importância para a compreensão do correto funcionamento dos organismos e das patologias que os afetam. Uma parte significativa dos mecanismos de controle de expressão gênica atuam na fase transcricional. Na base destes mecanismos está o recrutamento de proteínas que se ligam às regiões promotoras da transcrição, as quais são segmentos específicos de DNA que podem estar localizados tanto próximos à região de início da transcrição (TSS) quanto a centenas ou até a milhares de pares de bases dela. Essas proteínas compõem a maquinaria transcricional e podem ativar ou inibir o processo de transcrição. Experimentalmente, os segmentos regulatórios podem ser identificadas utilizando métodos complexos de biologia molecular, tais como SELEX, ChiP-ChiP, ChIP-Seq, dentre outros. Uma estratégia alternativa aos métodos experimentais é a utilização de metodologias computacionais. Análises computacionais tendem a ser mais rápidas, baratas e flexíveis do que protocolos experimentais, além de poderem ser utilizadas em larga escala. Atualmente, os métodos computacionais disponíveis necessitam de informações experimentais para a definição de padrões globais de preferências de sequências de DNA para a ligação de fatores de transcrição (TFBS, em inglês transcription factor binding sites). Entretanto, esses métodos apresentam uma elevada taxa de falso positivos e, por vezes, apresentam também taxas significativas de falso negativos, além de serem limitados ao estudo de fatores de transcrição de espécies bem conhecidas, o que diminui a área de aplicação dos mesmos. Diante deste cenário, o uso de métodos computacionais que não necessitem da informação referente aos sítios de ligação, bem como os que utilizem parâmetros mais robustos de detecção dos resultados, em detrimento dos escores de pontuação provindos de alinhamentos, podem acrescentar uma sensível melhoria ao processos de predição de regiões regulatórias. Neste projeto, foi desenvolvido um novo modelo computacional (TFBSAnalyzer) para análise e identificação de TFBS em elementos regulatórios, que utiliza técnicas de modelagem molecular para a construção de complexos entre um fator de transcrição ancorado a estruturas de DNA com sequências variáveis de bases e, através de cálculos termodinâmicos de entalpia de ligação, determina uma função de pontuação baseada na energia de ligação e realiza a predição de sítios de ligação ao DNA para o fator de transcrição em análise. Esta abordagem foi testada com três fatores de transcrição como sistemas-modelo, pertencentes à família dos receptores nucleares, a saber: o receptor de estrógeno ER-alfa (Estrogen Receptor Alpha), o receptor de ácido retinoico RAR-beta (Retinoid Acid Receptor Beta) e o receptor X retinóico RXR (Retinoid X Receptor). Os modelos previstos computacionalmente foram comparados aos dados experimentais disponíveis para estes receptores nucleares, os quais apresentaram as seguintes taxas de FP/FN: 10%/0 para RAR-beta e RXR, 21%/6% para ER-alfa. Também simulamos um experimento de ChIP-seq do ER-alfa no genoma humano, cujos genes selecionados foram submetidos a uma análise de enriquecimento de fatores de transcrição curados experimentalmente, que fazem sua regulação, revelando que o receptor de estrógeno está realmente envolvido no processo. Para mostrar a aplicabilidade geral de nosso método, nós modelamos a distribuição de energia de ligação para o receptor NHR-28 isoforma a de Caenorhabditis elegans com DNA . Obtivemos distribuições de energia semelhantes àquelas encontradas para os NRs modelos, portanto seria possível aplicar o método para buscar possíveis TFBSs para este receptor no genoma de C. elegans. Os dados gerados e as metodologias desenvolvidas neste projeto devem acrescentar uma sensível melhoria aos processos de predição de regiões regulatórias e consequentemente auxiliar no entendimento dos mecanismos envolvidos no processo de expressão gênica e de sua regulação. / The genome projects have provided a lot of information about the genetic architecture, as well as on the physical configuration of their flanking regions (FR). These FR have the potential to aid in the elucidation of many biological processes, such as the mechanisms involved in gene expression and its regulation. These mechanisms are extremely important for undeerstanfind the correct functioning of organisms as well as the pathologies that affect them. A significant part of the control mechanisms of gene expression act during transcription. On the basis of this mechanisms is the recruitment of proteins that bind to promoter regions of transcription, which are specific segments of DNA that can be located either near the transcription start site or at hundreds or even thousands of base pairs away. These proteins form the transcription machinery, which can activate or inhibit the transcription process. The regulatory segments can be identified experimentally using complex methods of molecular biology, such as SELEX, ChIP-chip, ChIP-seq, among others. An alternative strategy to these experimental methods is the use of computational methodologies for predicting regulatory regions. Computational analysis tend to be faster, cheaper and more flexible than the experimental protocols, and can be used on a larger scale. Currently, the available computational methods require information previously obtained from experiments in order to define global standards of preference of DNA-Binding sequences for transcription factors (TFBS - Transcription Factor Binding Sites). However, these methods have a high rate of false positives and sometimes also have significant rates of false negatives, besides being limited to the study of transcription factors of well-known species, which decreases their application area. In this scenario, the use of computational methods that do not require previous information concerning the binding sites and use more robust parameters of results detection, instead of alignment scores, may add significant improvement to the processes of predicting regulatory regions. In this project, we developed a new computational model TFBSAnalyzer) for analysis and identification of regulatory elements using molecular modeling techniques for the construction of complexes between a transcription factor bound to specific DNA structures with variable sequences of bases and, by means of thermodynamic calculations of bond enthalpy, provides a scoring function based on the binding energy and predicts the DNA binding sites for the transcription factor in analysis. This approach was tested initially with three transcription factors as models, belonging to the nuclear receptor family, namely estrogen receptor ER-alpha (Estrogen Receptor Alpha), the retinoic acid receptor RAR-beta (Retinoid Acid Receptor Beta) and the retinoic X receptor RXR (Retinoid X Receptor). The computationally predicted models were compared to experimental data available for these nuclear receptors, and presented the following rates of FP/FN: 10%/0 for RAR-beta and RXR, 21%/6% for ER-alpha. We also simulated an experiment of ChIP-seq with ER-alpha with the human genome, where the selected genes were subjected to a transcription factor enrichment analysis, with curated information, revealing that the estrogen receptor is indeed involved in their regulation. To show that our method has a general applicability, we modeled the binding energy distribution for the NHR-28 receptor, isoform a, from Caenorhabditis elegans. The energy distributions obtained were similar to the ones obtained for the model NR, so it would be possible to use the method and search for possible TFBS in the C. elegans genome. The data generated and the methodologies developed in this project should add a significant improvement to the prediction processes of regulatory regions and, consequently, help to understand the mechanisms involved in the gene expression process and its regulation.

Os projetos genoma têm fornecido uma grande quantidade de informação sobre a arquitetura gênica e sobre a configuração física de suas respectivas regiões flanqueadoras (RF). Estas RF contêm informações com o potencial de auxiliar na elucidação de vários processos biológicos, como os mecanismos de expressão gênica e de sua regulação. Estes mecanismos são de extrema importância para a compreensão do correto funcionamento dos organismos e das patologias que os afetam. Uma parte significativa dos mecanismos de controle de expressão gênica atuam na fase transcricional. Na base destes mecanismos está o recrutamento de proteínas que se ligam às regiões promotoras da transcrição, as quais são segmentos específicos de DNA que podem estar localizados tanto próximos à região de início da transcrição (TSS) quanto a centenas ou até a milhares de pares de bases dela. Essas proteínas compõem a maquinaria transcricional e podem ativar ou inibir o processo de transcrição. Experimentalmente, os segmentos regulatórios podem ser identificadas utilizando métodos complexos de biologia molecular, tais como SELEX, ChiP-ChiP, ChIP-Seq, dentre outros. Uma estratégia alternativa aos métodos experimentais é a utilização de metodologias computacionais. Análises computacionais tendem a ser mais rápidas, baratas e flexíveis do que protocolos experimentais, além de poderem ser utilizadas em larga escala. Atualmente, os métodos computacionais disponíveis necessitam de informações experimentais para a definição de padrões globais de preferências de sequências de DNA para a ligação de fatores de transcrição (TFBS, em inglês transcription factor binding sites). Entretanto, esses métodos apresentam uma elevada taxa de falso positivos e, por vezes, apresentam também taxas significativas de falso negativos, além de serem limitados ao estudo de fatores de transcrição de espécies bem conhecidas, o que diminui a área de aplicação dos mesmos. Diante deste cenário, o uso de métodos computacionais que não necessitem da informação referente aos sítios de ligação, bem como os que utilizem parâmetros mais robustos de detecção dos resultados, em detrimento dos escores de pontuação provindos de alinhamentos, podem acrescentar uma sensível melhoria ao processos de predição de regiões regulatórias. Neste projeto, foi desenvolvido um novo modelo computacional (TFBSAnalyzer) para análise e identificação de TFBS em elementos regulatórios, que utiliza técnicas de modelagem molecular para a construção de complexos entre um fator de transcrição ancorado a estruturas de DNA com sequências variáveis de bases e, através de cálculos termodinâmicos de entalpia de ligação, determina uma função de pontuação baseada na energia de ligação e realiza a predição de sítios de ligação ao DNA para o fator de transcrição em análise. Esta abordagem foi testada com três fatores de transcrição como sistemas-modelo, pertencentes à família dos receptores nucleares, a saber: o receptor de estrógeno ER-alfa (Estrogen Receptor Alpha), o receptor de ácido retinoico RAR-beta (Retinoid Acid Receptor Beta) e o receptor X retinóico RXR (Retinoid X Receptor). Os modelos previstos computacionalmente foram comparados aos dados experimentais disponíveis para estes receptores nucleares, os quais apresentaram as seguintes taxas de FP/FN: 10%/0 para RAR-beta e RXR, 21%/6% para ER-alfa. Também simulamos um experimento de ChIP-seq do ER-alfa no genoma humano, cujos genes selecionados foram submetidos a uma análise de enriquecimento de fatores de transcrição curados experimentalmente, que fazem sua regulação, revelando que o receptor de estrógeno está realmente envolvido no processo. Para mostrar a aplicabilidade geral de nosso método, nós modelamos a distribuição de energia de ligação para o receptor NHR-28 isoforma a de Caenorhabditis elegans com DNA . Obtivemos distribuições de energia semelhantes àquelas encontradas para os NRs modelos, portanto seria possível aplicar o método para buscar possíveis TFBSs para este receptor no genoma de C. elegans. Os dados gerados e as metodologias desenvolvidas neste projeto devem acrescentar uma sensível melhoria aos processos de predição de regiões regulatórias e consequentemente auxiliar no entendimento dos mecanismos envolvidos no processo de expressão gênica e de sua regulação. / The genome projects have provided a lot of information about the genetic architecture, as well as on the physical configuration of their flanking regions (FR). These FR have the potential to aid in the elucidation of many biological processes, such as the mechanisms involved in gene expression and its regulation. These mechanisms are extremely important for undeerstanfind the correct functioning of organisms as well as the pathologies that affect them. A significant part of the control mechanisms of gene expression act during transcription. On the basis of this mechanisms is the recruitment of proteins that bind to promoter regions of transcription, which are specific segments of DNA that can be located either near the transcription start site or at hundreds or even thousands of base pairs away. These proteins form the transcription machinery, which can activate or inhibit the transcription process. The regulatory segments can be identified experimentally using complex methods of molecular biology, such as SELEX, ChIP-chip, ChIP-seq, among others. An alternative strategy to these experimental methods is the use of computational methodologies for predicting regulatory regions. Computational analysis tend to be faster, cheaper and more flexible than the experimental protocols, and can be used on a larger scale. Currently, the available computational methods require information previously obtained from experiments in order to define global standards of preference of DNA-Binding sequences for transcription factors (TFBS - Transcription Factor Binding Sites). However, these methods have a high rate of false positives and sometimes also have significant rates of false negatives, besides being limited to the study of transcription factors of well-known species, which decreases their application area. In this scenario, the use of computational methods that do not require previous information concerning the binding sites and use more robust parameters of results detection, instead of alignment scores, may add significant improvement to the processes of predicting regulatory regions. In this project, we developed a new computational model TFBSAnalyzer) for analysis and identification of regulatory elements using molecular modeling techniques for the construction of complexes between a transcription factor bound to specific DNA structures with variable sequences of bases and, by means of thermodynamic calculations of bond enthalpy, provides a scoring function based on the binding energy and predicts the DNA binding sites for the transcription factor in analysis. This approach was tested initially with three transcription factors as models, belonging to the nuclear receptor family, namely estrogen receptor ER-alpha (Estrogen Receptor Alpha), the retinoic acid receptor RAR-beta (Retinoid Acid Receptor Beta) and the retinoic X receptor RXR (Retinoid X Receptor). The computationally predicted models were compared to experimental data available for these nuclear receptors, and presented the following rates of FP/FN: 10%/0 for RAR-beta and RXR, 21%/6% for ER-alpha. We also simulated an experiment of ChIP-seq with ER-alpha with the human genome, where the selected genes were subjected to a transcription factor enrichment analysis, with curated information, revealing that the estrogen receptor is indeed involved in their regulation. To show that our method has a general applicability, we modeled the binding energy distribution for the NHR-28 receptor, isoform a, from Caenorhabditis elegans. The energy distributions obtained were similar to the ones obtained for the model NR, so it would be possible to use the method and search for possible TFBS in the C. elegans genome. The data generated and the methodologies developed in this project should add a significant improvement to the prediction processes of regulatory regions and, consequently, help to understand the mechanisms involved in the gene expression process and its regulation.

No description available.

O Brasil tem a maior produção de cana-de-açúcar do mundo. O cultivo de cana-de-açúcar no Brasil está voltado principalmente para a produção de açúcar ou Etanol e nos últimos anos para a produção de bioeletricidade através da utilização da biomassa do bagaço e da palha. Apesar da importância econômica e do potencial sustentável que a cana-de-açúcar apresenta, o genoma de referência para esta cultura ainda não está disponível na literatura. A principal justificativa para isso está na complexidade do mesmo, em especial pela alopoliploidia e autopoliploidia. De fato esta característica é a principal barreira para o desenvolvimento de novas variedades comerciais. Na literatura há diferentes estratégias que visam contribuir com o conhecimento genômico de cana-de-açúcar sendo mais prevalente dados de transcriptoma e pouca informação sobre o processo de regulação gênica. Além disso, diferente do que é observado em outras culturas comerciais, em cana-de-açúcar não há trabalhos associados com a caracterização in silico da região Promotora, assim como na identificação de sítios de ligação para Fatores de Transcrição (TFBSs). Por esta razão, o nosso trabalho foi direcionado para a caracterização in silico de regiões regulatórias em cana-de-açúcar. Para esta tarefa nós realizamos apenas a rotulação de sequências de DNA não codificante que estavam a upstream de cada gene anotado em cana-de-açúcar. Todos os genes foram selecionados de dados de transcriptoma e a sequência de DNA da região Promotora foi isolada do Genespace de cana-de-açúcar SP80-3280 gerado pelo projeto de sequenciamento do genoma de referência do nosso grupo. A rotulação da região regulatória em cana-de-açúcar foi executada em duas subsequências: Core Promoter e Promotor Proximal. Na região Core Promoter nós realizamos a identificação do sítio de inicio de transcrição (TSS), a estimativa do tamanho da região 5\' UTR e a classificação da região Core Promoter em TATA-box ou TATA-less. Todos os processos foram realizados através da ferramenta TSSPlant. A utilização da ferramenta TSSPlant motivou o desenvolvimento de uma nova ferramenta para predição do sinal de TSS que aqui chamamos de TSSFinder. A ferramenta TSSinder apresentou resultados de predição do sinal de TSS superior aos seus pares, além disso esta ferramenta foi bem sucedida em diferentes organismos como Arabidopsis thaliana, Gallus gallus e Saccharomyces cerevisiae. Na região Promotora Proximal nós realizamos a identificação de TFBSs através de duas metodologias: predição de novo e mapeamento de matrizes de TFBS (PSSM). O processo de predição de novo foi realizada por meio de dois modelos: Maximização da expectativa e Gibbs Sampler e esse processo foi executado apenas para o subgrupo de genes co-expressos ou apenas para o conjunto de sequências homeólogas de cada gene de cana-de-açúcar selecionado. Para o restante das sequências foi realizado apenas o mapeamento das matrizes de TFBSs identificadas durante o processo de predição de novo. Em paralelo todos TFBSs identificados no nosso trabalho foram comparados com o banco de TFBS para plantas. Através desse procedimento foi possível estimar qual classe de Fator de Transcrição está interagindo com o TFBS identificado na região Promotora Proximal dos genes Scdr1, ScSuSy, ScPAL. Com este trabalho, nós cobrimos parte da lacuna observada em estudos in silico paras regiões regulatórias de cana-de-açúcar. Além disso, nós aperfeiçoamos o processo de identificação do sinal de TSS para diferentes organismos; inclusive para plantas Dicotiledôneas e Monocotiledôneas. / Brazil has the highest production of sugarcane in the world. Its cultivation in Brazil is aimed at producing of sugar or ethanol and in recent years, biomass for bioenergy from bagasse and straw. Despite the economic importance and the sustainable potential that sugarcane presents, a reference genome for this crop is not yet available in the literature. One justification for this absence lies in the sugarcane genome complexity, allopolyploidy and autopolyploidy. In fact these characteristics are the main barrier for the development of new commercial varieties. In the literature different strategies aimed at contributing to genomic sugarcane mostly on the transcriptome and little information on the process of gene regulation. Furthermore, unlike other commercial crops, sugarcane has no reported in silico characterization of its promoter regions and identification of Transcription Factor binding sites. For this reason, our work was directed to an in silico characterization of regulatory regions in sugarcane. For this task we performed the labeling of non-coding DNA sequences that were upstream of each gene annotated in sugarcane. All genes were using from transcriptome data and the promoter region DNA sequence was isolated from Genespace of the SP80-3280 reference genome obtained of our group. The labeling of the regulatory region in sugarcane was carried out in two subsections: Core Promoter and Proximal Promoter. In the Core Promoter region we performed the identification of the TSS signal, the estimation of the size of the 5\' UTR region and the classification of the Core Promoter region in TATA-box or TATA-less. All processes were performed using the TSSPlant tool. The use of the TSSPlant tool motivated the development of a new tool to predict the TSS signal that we call TSSFinder. The TSSinder tool presented TSS signal prediction results superior to its peers, moreover this tool was successful in different organisms - Arabidopsis thaliana, Gallus gallus and Saccharomyces cerevisiae. In the Proximal Promoter region we performed the identification of TFBSs through two methodologies: de novo prediction and mapping of TFBS matrices (PSSM). The de novo prediction process was performed using two models: Expectancy Maximization and Gibbs Sampler and this process was performed only for subgroups of coexpressed genes or only for the set of homeologues sequences from each sugarcane gene. For the rest of the sequences only the mapping of the matrices of TFBSs identified during the de novo prediction process was conducted. In parallel all TFBSs identified in our work were compared with the TFBS database for plants. Through this procedure it was estimated which class of Transcription Factor is interacting with the TFBS identified in the Proximal Promoter region of the Scdr1, ScSuSy, ScPAL genes.With this work, we cover part of the gap observed in in silico studies for the regulatory region of sugarcane. In addition, we improved the process of identification the TSS signal for different organisms including dicotyledonous and monocotyledonous plants.

Les facteurs de transcription sont des protéines spécialisées qui jouent un rôle important dans différents processus biologiques tel que la différenciation, le cycle cellulaire et la tumorigenèse. Ils régulent la transcription des gènes en se fixant sur des séquences d’ADN spécifiques (éléments cis-régulateurs). L’identification de ces éléments est une étape cruciale dans la compréhension des réseaux de régulation des gènes. Avec l’avènement des technologies de séquençage à haut débit, l’identification de tout les éléments fonctionnels dans les génomes, incluant gènes et éléments cis-régulateurs a connu une avancée considérable. Alors qu’on est arrivé à estimer le nombre de gènes chez différentes espèces, l’information sur les éléments qui contrôlent et orchestrent la régulation de ces gènes est encore mal définie. Grace aux techniques de ChIP-chip et de ChIP-séquençage il est possible d’identifier toutes les régions du génome qui sont liées par un facteur de transcription d’intérêt. Plusieurs approches computationnelles ont été développées pour prédire les sites fixés par les facteurs de transcription. Ces approches sont classées en deux catégories principales: les algorithmes énumératifs et probabilistes. Toutefois, plusieurs études ont montré que ces approches génèrent des taux élevés de faux négatifs et de faux positifs ce qui rend difficile l’interprétation des résultats et par conséquent leur validation expérimentale. Dans cette thèse, nous avons ciblé deux objectifs. Le premier objectif a été de développer une nouvelle approche pour la découverte des sites de fixation des facteurs de transcription à l’ADN (SAMD-ChIP) adaptée aux données de ChIP-chip et de ChIP-séquençage. Notre approche implémente un algorithme hybride qui combine les deux stratégies énumérative et probabiliste, afin d’exploiter les performances de chacune d’entre elles. Notre approche a montré ses performances, comparée aux outils de découvertes de motifs existants sur des jeux de données simulées et des jeux de données de ChIP-chip et de ChIP-séquençage. SAMD-ChIP présente aussi l’avantage d’exploiter les propriétés de distributions des sites liés par les facteurs de transcription autour du centre des régions liées afin de limiter la prédiction aux motifs qui sont enrichis dans une fenêtre de longueur fixe autour du centre de ces régions. Les facteurs de transcription agissent rarement seuls. Ils forment souvent des complexes pour interagir avec l’ADN pour réguler leurs gènes cibles. Ces interactions impliquent des facteurs de transcription dont les sites de fixation à l’ADN sont localisés proches les uns des autres ou bien médier par des boucles de chromatine. Notre deuxième objectif a été d’exploiter la proximité spatiale des sites liés par les facteurs de transcription dans les régions de ChIP-chip et de ChIP-séquençage pour développer une approche pour la prédiction des motifs composites (motifs composés par deux sites et séparés par un espacement de taille fixe). Nous avons testé ce module pour prédire la co-localisation entre les deux demi-sites ERE qui forment le site ERE, lié par le récepteur des œstrogènes ERα. Ce module a été incorporé à notre outil de découverte de motifs SAMD-ChIP. / Transcription factors (TF) play important roles in various biological processes such as differentiation, cell cycle progression and tumorigenesis. They regulate gene expression by binding to specific DNA sequences (TFBS). Identifying these cis-regulatory elements is a crucial step to understand gene regulatory networks. Technological developments have enhanced DNA sequencing at genomic scale. On the basis of the resulting sequences, computational biologists now attempt to localize the most important functional regions, starting with genes, but also importantly the whole genome characterization of transcription factor binding sites and allow the development of several computational DNA motif discovery tools. Although these various tools are widely used and have been successful at discovering novel motifs, they are not adapted to ChIP-chip and ChIP-sequencing data. The main drawback of these approaches is that most of the predicted motifs represent artifacts due to an inefficient assessment of their enrichment. This thesis is about transcription factor proteins and statistical analysis of their binding sites in ChIP-chip and ChIP-sequencing data. The first objective was to develop a new do novo DNA motif discovery tool adapted to ChIP-chip and ChIP-sequencing data. SAMD-ChIP combines enumerative and stochastic strategies to predict enriched motifs in the vicinity of the ChIP peak summits. Our approach is an automated pipeline that includes motif discovery, motif clustering, motif optimization and finally motif identification using transcription factor (TF) databases. SAMD-ChIP outperforms state-of-the-art motif discovery tools in term of the number of predicted motifs and the prediction of rare and degenerate motifs. In particular, SAMD-ChIP efficiently identifies gapped motifs such as inverted or direct repeats bound by nuclear receptors and composite motifs resulting from the association of different single TF binding sites. The underlying assumption of the second objective is that in regulatory regions, binding sites of interacting transcription factors co-occur more often than expected by chance in the vicinity of the ChIP-peak summits. We proposed an approach to predict transcription factor binding sites co-localization based on the prediction of single motifs by do novo motif discovery tools or by using TFBS models from TF data bases.

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.

Li-ion thin film battery technology has attracted much attention in recent years due to its highest need in portable electronic devices. Development of new materials for lithium ion battery (LIB) is very crucial for enhancement of the performance. LIB can supply higher energy density because Lithium is the most electropositive (-3.04V vs. standard hydrogen electrode) and lightest metal (M=6.94 g/mole). LIBs show many advantages over other kind of batteries such as, high energy density, high power density, long cycle life, no memory effect etc. The major work presented in this thesis is on the development of nanostructured materials for anode of Li-ion battery. It involves the synthesis and analysis of grapheme nanosheet (GNS) and its performance as anode material in Li ion battery. We studied the synthesis of GNS over different substrates and performed the anode studies. The morphology of GNS has great impact on Li storage capacity. Tin and Tin oxide nanostructures have been embedded in the GNS matrix and their electrochemical performance has been studied. Chapter 1 gives the brief introduction about the Li ion batteries (LIBs), working and background. Also the relative advantages and characterization of different electrode materials used in LIBs are discussed. Chapter 2 discusses various experimental techniques that are used to synthesize the electrode materials and characterize them. Chapter3 presents the detailed synthesis of graphene nanosheet (GNS) through electron cyclotron resonance (ECR) microwave plasma enhanced chemical vapor deposition (ECR PECVD) method. Various substrates such as metallic (copper, Ni and Pt coated copper) and insulating (Si, amorphous SiC and Quartz) were used for deposition of GNS. Morphology, structure and chemical bonding were analyzed using SEM, TEM, Raman, XRD and XPS techniques. GNS is a unique allotrope of carbon, which forms highly porous and vertically aligned graphene sheets, which consist of many layers of graphene. The morphology of GNS varies with substrate. Chapter 4 deals with the electrochemical studies of GNS films. The anode studies of GNS over various substrates for Li thin film batteries provides better discharge capacity. Conventional Li-ion batteries that rely on a graphite anode have a limitation in the capacity (372 mAh/g). We could show that the morphology of GNS has great effect in the electrochemical performance and exceeds the capacity limitation of graphite. Among the electrodes PtGNS shown as high discharge capacity of ~730 mAh/g compare to CuGNS (590 mAh/g) and NiGNS (508 mAh/g) for the first cycle at a current density of 23 µA/cm2. Electrochemical impedance spectroscopy provides the various cell parameters of the electrodes. Chapter 5 gives the anodic studies of Tin (Sn) nanoparticles decorated over GNS matrix. Sn nanoparticles of 20 to 100nm in size uniformly distributed over the GNS matrix provides a discharge capacity of ~1500 mAh/g mAh/g for as deposited and ~950 mAh/g for annealed Sn@GNS composites, respectively. The cyclic voltammogram (CV) also shows the lithiation and delithiation process on GNS and Sn particles. Chapter 6 discusses the synthesis of Tinoxide@GNS composite and the details of characterization of the electrode. SnO and SnO2 phases of Tin oxide nanostructures differing in morphologies were embedded in the GNS matrix. The anode studies of the electrode shows a discharge capacity of ~1400 mAh/g for SnO phase (platelet morphology) and ~950 mAh/g for SnO2 phase (nanoparticle morphology). The SnO phase also exhibits a good coulumbic efficiency of ~95%. Chapter 7 describes the use of SnO2 nanowire attached to the side walls of the GNS matrix. A discharge capacity of ~1340 mAh/g was obtained. The one dimensional wire attached to the side walls of GNS film and increases the surface area of active material for Li diffusion. Discharge capacity obtained was about 1335 mAhg-1 and the columbic efficiency of ~86% after the 50th cycle. The research work carried out as part of this thesis, and the results have summarized in chapter 8.

Human complex diseases, like Diabetes and Cancer, affect many people worldwide today. Despite existing knowledge, many of these diseases are still not preventable. Complex diseases are known to be caused by a combination of genetic factors, as well as environmental and life style factors. The scope of this investigation covered the genomics of Type 1 Diabetes (T1D). There are 49 human genomic regions that are known to carry markers (disease-associated single nucleotide mutations) for T1D, and these were extensively studied in this research. The aim was to find out in how far this disease may be caused by problems in gene regulation rather than in gene coding. For this, the genetic factors associated with T1D, including the single point mutations and susceptibility regions, were characterised on the basis of their genomic attributes. Furthermore, mutations that occur in binding sites for transcription factors were analysed for change in the conspicuousness of their binding region, caused by allele substitution. This is called SNP (Single nucleotide polymorphism) sensitivity. From this study, it was found that the markers for T1D are mostly non-coding SNPs that occur in introns and non-coding gene transcripts, these are structures known to be involved in gene regulatory activity. It was also discovered that the T1D susceptibility regions contain an abundance of intronic, non-coding transcript and regulatory nucleotides, and that they can be split into three distinct groups on the basis of their structural and functional genomic contents. Finally, using an algorithm designed for this study, thirty-seven SNPs that change the representation of their surrounding region were identified. These regulatory mutations are non-associated T1D-SNPs that are mostly characterised by Cytosine to Thymine (C-T) transition mutations. They were found to be closer in average distance to the disease-associated SNPs than other SNPs in binding sites, and also to occur frequently in the binding motifs for the USF (Upstream stimulatory factor) protein family which is linked to problems in Type 2 diabetes.