Abstract Human prostatic acid phosphatase (hPAP) was the first laboratory parameter used for prostate cancer diagnosis, whereas the mechanisms behind the androgen regulation and tissue-specific expression of this prostate epithelium-specific differentiation antigen are not yet clear. In this study, a transient transfection model and transgenic animal model have been set up for functional analysis of the promoter and first intron region of the hPAP gene. The promoter constructs covering the region-734/+467 of the gene were functional in both prostatic and nonprostatic cells. Although hPAP constructs included two putative AREs with in vitro AR-binding ability at -178 and +336, androgen treatment had little effect on the promoter activity of the gene in transiently transfected cells. The hPAP fragment -734/+467 could trigger the expression of the CAT reporter gene and restrict the expression mainly in the prostates of transgenic mice. The DNA-binding site with the sequence GAAAATATGATA of a regulatory protein involved in prostate-specific and androgen receptor-dependent gene expression was identified from rPB promoter. The exact same 12 bp sequence was found in the first intron +1144/+1155 of the hPAP gene. Five homologous sequence, A, B, C, D and E, were located in the -734/+467 region of the hPAP gene, where site C and E could bind the regulatory protein in EMSA. Deletion of site C decreased the transcriptional activities significantly compared to those of corresponding wild-type constructs in LNCaP cells when androgens were present. Deletion of site E or both sites D and E increased the promoter activity in LNCaP when androgens were absent. In conclusion, androgens could not directly regulate hPAP expression via receptor-binding to the AREs in LNCaP cells. The promoter and first intron fragment -734/+467 of the hPAP gene could direct and restrict the gene expression mainly in prostate epithelium. A prostatic regulatory protein binds to multiple sites with the GAAAATATGATA or homologous sequences along the regulatory areas of the hPAP gene with different affinities, modulating the prostate-specific expression of the gene in a bidirectional manner, depending on the hormone status.

M.Sc. (Biochemistry) / Lipopolysaccharide (LPS) is a complex lipoglycan that is found in the outer membrane of Gram-negative bacteria and is composed of three regions namely the fatty acid Lipid A, a core region of short oligosaccharide chains and an O-antigen region of polysaccharides. When LPS is recognized as a microbe-/pathogen-associated molecular pattern (M/PAMP), it not only induces an innate immune response in plants but also stimulates the development of defence responses such as the immediate release of reactive oxygen species/intermediates (ROS/I), pathogen-related (PR) gene expression and activation of the hypersensitive response (HR), resulting in stronger subsequent pathogen interactions. The identification and characterisation of the elusive LPS receptor/receptor complex in plants is thus of importance, since understanding the mechanism of perception and specific signal transduction pathways will clarify, and lead to the advancement of, basal resistance in plants in order to decrease crop plant losses due to pathogen attack. In mammals, LPS binds to a LPS binding protein (LBP) to form a LPS-LBP complex which is translocated to myeloid differentiation 2 (MD2) with the presence/absence of its co-receptor, a glycosylphosphatidylinositol (GPI)-linked protein, CD14. The interaction occurs on the host membrane and triggers an inflammatory defence response through the signalling cascade activated by the interaction with Toll-like receptor 4 (TLR4). A similar LPS-receptor interaction is, however, unknown in plants. To address the LPS perception mechanism in plants, biological binding studies with regard to concentration, incubation time and temperature, affinity, specificity and saturation were conducted in Arabidopsis thaliana protoplasts using LPS labeled with Alexa 488 hydrazide. Quantum dots (Qdots), which allow non-covalent hydrophobic labeling of LPS, were further also employed in similar Arabidopsis protoplast binding studies. These studies were conducted by fluorescence determination through the use of a BD FACS Aria flow cytometer. Although Alexa-labeling does not affect the biological activity in mammalian studies, the same cannot necessarily be said for plant systems, and hence Qdots were included to address this question. The conjugation of Qdots to LPS was confirmed by transmission electron microscopy (TEM) and results illustrated higher fluorescence values as compared to Alexa-LPS fluorescence analysis. Furthermore, inhibition of the perception process is also reported using Wortmannin and Brefeldin A as suitable endo- and exocytosis inhibitors. Affinity, specificity and saturability as well as the role of endo- and exocytosis inhibition in LPS binding to protoplasts was ultimately demonstrated by both fluorophores, with the use of Qdots as a label proving to be a more sensitive strategy in comparison to the conventional Alexa 488 hydrazide label.

No description available.

Despite the fact that each cell in an organism has the same genetic information, it is possible that cells fundamentally differ in their function. The molecular basis for the functional diversity of cells is governed by biochemical processes that regulate the expression of genes. Key to this regulatory process are proteins called transcription factors that recognize and bind specific DNA sequences of a few nucleotides. Here we tackle the problem of identifying the binding sites of a given transcription factor. The prediction of binding preferences from the structure of a transcription factor is still an unsolved problem. For that reason, binding sites are commonly identified by searching for overrepresented sites in a given collection of nucleotide sequences. Such sequences might be known regulatory regions of genes that are assumed to be coregulated, or they are obtained from so-called ChIP-seq experiments that identify approximately the sites that were bound by a given transcription factor. In both cases, the observed nucleotide sequences are much longer than the actual binding sites and computational tools are required to uncover the actual binding preferences of a factor. Aggravated by the fact that transcription factors recognize not only a single nucleotide sequence, the search for overrepresented patterns in a given collection of sequences has proven to be a challenging problem. Most computational methods merely relied on the given set of sequences, but additional information is required in order to make reliable predictions. Here, this information is obtained by looking at the evolution of nucleotide sequences. For that reason, each nucleotide sequence in the observed data is augmented by its orthologs, i.e. sequences from related species where the same transcription factor is present. By constructing multiple sequence alignments of the orthologous sequences it is possible to identify functional regions that are under selective pressure and therefore appear more conserved than others. The processing of the additional information exerted by ortholog sequences relies on a phylogenetic tree equipped with a nucleotide substitution model that not only carries information about the ancestry, but also about the expected similarity of functional sites. As a result, a Bayesian method for the identification of transcription factor binding sites is presented. The method relies on a phylogenetic tree that agrees with the assumptions of the nucleotide substitution process. Therefore, the problem of estimating phylogenetic trees is discussed first. The computation of point estimates relies on recent developments in Hadamard spaces. Second, the statistical model is presented that captures the enrichment and conservation of binding sites and other functional regions in the observed data. The performance of the method is evaluated on ChIP-seq data of transcription factors, where the binding preferences have been estimated in previous studies.

The locus coeruleus (LC) of the rat was lesioned by microinjection of selective neurotoxins into the brainstem. 6‐Hydroxydopamine (6‐OHDA), 3 μg/μl, given unilaterally at two sites 0.6 mm apart on the rostro‐caudal axis of the LC, was used to lesion catecholamine‐containing neuronal elements. Ibotenic acid, 2.5 μg/0.5 μl, administered similarly was used to lesion nerve cell bodies. Two weeks after administration of the neurotoxin, lesion efficacy was determined based on the norepinephrine content of the cerebral cortex ipsi‐ and contralateral to the lesion. 6‐OHDA lesions of the LC caused a 46% reduction in ipsilateral cortical norepinephrine and a 60% reduction in specific 125I‐[Sar1, Ile8]‐angiotensin II (125I‐SIAII) binding in the LC. Ibotenic acid lesions of the LC caused a 73% reduction in ipsilateral cortical norepinephrine and a 81% reduction in specific 125I‐SIAII binding in the LC. These results indicate that All receptor binding sites in the LC are localized on noradrenergic nerve cell bodies or their dendritic and axonal ramifications within the LC.

Cis-regulatory elements are the short regions of DNA to which specific regulatory proteins bind and these interactions subsequently influence the level of transcription for associated genes, by inhibiting or enhancing the transcription process. It is known that much of the genetic change underlying morphological evolution takes place in these regions, rather than in the coding regions of genes. Identifying these sites in a genome is a non-trivial problem. Experimental (wet-lab) methods for finding binding sites exist, but all have some limitations regarding their applicability, accuracy, availability or cost. On the other hand computational methods for predicting the position of binding sites are less expensive and faster. Unfortunately, however, these algorithms perform rather poorly, some missing most binding sites and others over-predicting their presence. The aim of this thesis is to develop and improve computational approaches for the prediction of transcription factor binding sites (TFBSs) by integrating the results of computational algorithms and other sources of complementary biological evidence. Previous related work involved the use of machine learning algorithms for integrating predictions of TFBSs, with particular emphasis on the use of the Support Vector Machine (SVM). This thesis has built upon, extended and considerably improved this earlier work. Data from two organisms was used here. Firstly the relatively simple genome of yeast was used. In yeast, the binding sites are fairly well characterised and they are normally located near the genes that they regulate. The techniques used on the yeast genome were also tested on the more complex genome of the mouse. It is known that the regulatory mechanisms of the eukaryotic species, mouse, is considerably more complex and it was therefore interesting to investigate the techniques described here on such an organism. The initial results were however not particularly encouraging: although a small improvement on the base algorithms could be obtained, the predictions were still of low quality. This was the case for both the yeast and mouse genomes. However, when the negatively labeled vectors in the training set were changed, a substantial improvement in performance was observed. The first change was to choose regions in the mouse genome a long way (distal) from a gene over 4000 base pairs away - as regions not containing binding sites. This produced a major improvement in performance. The second change was simply to use randomised training vectors, which contained no meaningful biological information, as the negative class. This gave some improvement over the yeast genome, but had a very substantial benefit for the mouse data, considerably improving on the aforementioned distal negative training data. In fact the resulting classifier was finding over 80% of the binding sites in the test set and moreover 80% of the predictions were correct. The final experiment used an updated version of the yeast dataset, using more state of the art algorithms and more recent TFBSs annotation data. Here it was found that using randomised or distal negative examples once again gave very good results, comparable to the results obtained on the mouse genome. Another source of negative data was tried for this yeast data, namely using vectors taken from intronic regions. Interestingly this gave the best results.

The identification of genetic variants such as single nucleotide polymorphisms (SNPs), which affect cancer progression, survival and response to treatments could help in the design of better prevention and treatment strategies. Genome-wide association studies (GWAS) have provided the first step of identifying SNPs associating with cancer risk. However, identifying the causal SNPs responsible for the associations has proven challenging, and GWAS have not been successful for time-to-event phenotypes such as cancer progression, due to the insurmountable obstacle of the large sample size needed. The aim of this thesis is to design and implement strategies that combine the identification of SNPs significantly associated with cancer, focusing on time-to-event phenotypes, with detailed bioinformatics analysis to allow for further experimental validation and modelling, to better understand cancer-associated genomic loci and accelerate their incorporation into the clinic. First, a methodology that utilises the Random Survival Forest is developed and combined with a bioinformatics analysis that ranks SNPs according to their potential to result in differential protein levels or activity, in order to identify SNPs that affect the progression of B-cell chronic lymphocytic leukaemia. Next, an analysis that aims to extend our understanding of the role of SNPs in mediating the cellular responses to chemotherapeutic agents is applied. SNPs that could associate with differential cellular growth responses in cancer cell line panels are identified, and their association with the differential survival of cancer patients is explored. Finally, the potential roles of SNPs in affecting the transcriptional regulation of key cancer genes resulting in differential cancer risk are assessed. First, by focusing on SNPs in an important transcription factor binding motif that has been shown to be extremely sensitive to single base pair changes (the E-box) and next, by exploring the possibility that polymorphic transcription factor binding sites could underlie the significant associations noted in cancer GWAS.

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.

The polyamines spermine and spermidine play important roles in many cellular processes, and unusual levels of these polyamines have been associated with numerous human diseases. Spermidine/spermine N¹-acetyltransferase (SSAT) is an enzyme involved in polyamine regulation, where acetylation of polyamines by SSAT ultimately leads to their degradation or export from the cell. In this dissertation, x-ray crystallography and nuclear magnetic resonance (NMR) are used to provide insights into the structure and function of this important enzyme. X-ray crystallography provided two distinct views of SSAT: one of the enzyme in complex with coenzyme A (CoA), and another of the enzyme in complex with CoA and the polyamine spermine. Together, the two structures reveal structural plasticity in the active site of the enzyme. The complex with spermine provides a direct view of polyamine binding by SSAT, and shows that the enzyme relies heavily on associated water molecules to bind spermine; these water molecules also appear to form a "proton relay" between the primary amine of spermine and the side-chain of a conserved glutamate residue. Guided by the structural results, NMR methods were used to test hypotheses regarding the enzyme mechanism of SSAT. The activity of the enzyme over a range of solution conditions, and towards different polyamine substrates, was determined; the effects of mutating single amino acids in the enzyme were also evaluated. The enzyme appeared to be most active between pH 8.5 and 9.5, and mutation of the aforementioned glutamate significantly altered this behavior. This suggests the glutamate is directly involved in the acetyltransfer reaction, where it likely functions as a catalytic base though the proton relay in the enzyme active site. These studies advance our general understanding of how polyamines are regulated in mammalian cells, and have the potential to assist in developing new therapeutic options for human diseases involving polyamines. / text

The tumour suppressor p53 protein plays a central role in the DNA damage response/checkpoint pathways leading to DNA repair, cell cycle arrest, apoptosis and senescence. The activation of p53-mediated pathways is primarily facilitated by the binding of tetrameric p53 to two 'half-sites', each consisting of a decameric p53 response element (RE). Functional REs are directly adjacent or separated by a small number of 1-13 'spacer' base pairs (bp). The p53 RE is detected by exact or inexact matches to the palindromic sequence represented by the regular expression [AG][AG][AG]C[AT][TA]G[TC][TC][TC] or a position weight matrix (PWM). The use of matrix-based and regular expression pattern-matching techniques, however, leads to an overwhelming number of false positives. A more specific model, which combines multiple factors known to influence p53-dependent transcription, is required for accurate detection of the binding sites. In this thesis, we present a logistic regression based model which integrates sequence information and epigenetic information to predict human p53 binding sites. Sequence information includes the PWM score and the spacer length between the two half-sites of the observed binding site. To integrate epigenetic information, we analyzed the surrounding region of the binding site for the presence of mono- and trimethylation patterns of histone H3 lysine 4 (H3K4). Our model showed a high level of performance on both a high-resolution data set of functional p53 binding sites from the experimental literature (ChIP data) and the whole human genome. Comparing our model with a simpler sequence-only model, we demonstrated that the prediction accuracy of the sequence-only model could be improved by incorporating epigenetic information, such as the two histone modification marks H3K4me1 and H3K4me3.