The central 5-hydroxytryptamine ( Serotonin, 5-HT ) system play an important role in the brain sexual differentiation in tilapia,Oreochromis mossambicus. ¡@The 5-HT1A receptor is involved in the neural develop- ment of brain.¡@The cDNA sequences of 5-HT1A receptor of tilapia, Ore- ochromis mossambicus have been cloned in our laboratory.¡@In the present study, 1633 bp of DNA sequence of 5-HT1A receptor from the transcription start site ( TSS ) were cloned by 5¡¦ rapid amplification of cDNA end ( 5¡¦ RACE ) and Genome Walker DNA Walking.¡@The 1633 bp of DNA sequence divided into six different fragments to detect the luciferase activity in the HeLa cell.¡@The bioinformatic analysis was performed on the fragment which showed the highest luciferase activity for predicting the transcription factor binding site.¡@These results showed that there is a activator protein-1 ( AP-1 ) binding site in the fragment form -52 bp to -46 bp.¡@The site-direct mutagenesis and electrophoresis mobility shift assay was performed on the fragment form -52 bp to -46 bp .¡@The present results indicate that there is a transcription factor binding site in the fragment form -52 bp to -46 bp.¡@This transcription factor binding site could be a AP-1 binding site.

Thesis (Master, Biology) -- Queen's University, 2009-09-10 13:10:31.576 / Ca2+ ions participate as second messengers in many stress-response and developmental pathways. Among eukaryotes, plants possess a remarkable diversity of Ca2+ binding proteins (Ca2+ sensors) such as calmodulin (CaM) and CaM-related proteins (CMLs) that regulate downstream targets and coordinate signal transduction events in response to stimuli. Previous studies have shown that a small subfamily of CMLs (CML37, CML38, CML39) in Arabidopsis show differential tissue expression as well as a dramatic induction of expression in response to environmental stress. For example, CML37 and CML38 respond very strongly to wounding, while CML39 is induced significantly by jasmonate. In order to understand the underlying regulatory mechanisms of the genes, promoter analysis experiments using the 5 upstream regions of these CMLs driving -glucuronidase (GUS) reporter expression were conducted. This empirical approach is a critical complement to algorithm-based prediction methods. It was found that the gateway vector pMDC163 was unsuitable for 5 deletion analyses. Three regions within the CML37 promoter were identified as having wound-responsiveness. Several known wound-responsive cis-elements were identified in these regions. A putative cis-element that is overrepresented in genes coexpressed with CML37 was also identified. Together, these data should lay the groundwork to identify the transcriptional regulators that direct stress-responsive CML gene expression. / Master

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La mucoviscidose est la maladie autosomique récessive grave la plus fréquente dans la population d'origine caucasienne. Elle est due a des anomalies du gène CFTR, dont les multiples mutations décrites rendent compte en partie de la grande variabilité phénotypique. A l'heure du développement de thérapies ciblées selon les mutations portées par les patients, mieux comprendre les mécanismes sous-jacents des relations génotype-phénotype semble de première importance. La première partie de ce travail est focalisée sur la relation génotype-phénotype. Par une étude rétrospective de 694 demandes de diagnostic prénatal de la mucoviscidose sur signes d'appel échographique, nous définissons les profils d'anomalies digestives les plus discriminants, et proposons en conséquence une révision de la stratégie d'analyse moléculaire du gène CFTR. La deuxième partie concerne la mise en place d’outils nécessaires à l’exploration fonctionnelle du promoteur CFTR. En effet, dans les formes atypiques de la maladie, la fonction résiduelle de CFTR peut expliquer le phénotype. Des anomalies de régulation de la transcription peuvent parfois être à l’origine de telles formes modérées. La mise en place des outils d’analyse des variants du promoteur permettra de mieux interpréter leur pathogénicité et d’ouvrir de nouvelles pistes pour la compréhension de la régulation de ce gène. La troisième partie s'intéresse a l'inflammation pulmonaire anormalement régulée qui est une caractéristique phénotypique et le premier facteur de morbidité et de mortalité de la mucoviscidose. La protéine COMMD1 est une protéine pleiotrope participant a de nombreux processus cellulaires, principalement par un mécanisme de stabilisation d'interactions protéiques. Elle est impliquée dans les trois voies thérapeutiques : modulation de CFTR, restauration du liquide de surface des voies aériennes et inhibition de l'inflammation. Notre étude a permis d'observer l'activité anti-inflammatoire de COMMD1 dans le contexte d'inflammation exacerbée décrite chez les patients atteints de mucoviscidose. La réduction de cette réaction exacerbée fait partie des enjeux thérapeutiques actuels et nous montrons ici que la protéine COMMD1 est un bon candidat comme modérateur de l'inflammation mediee par NF-kB dans cette pathologie. / Cystic fibrosis (CF) is the most common severe autosomal recessive disorder in the Caucasian population. Apart from classical CF, there is a broad range of phenotypes associated with a huge genotypic variability concerning the mutations in the CFTR gene. In order to develop a mutation specific therapeutic approach, a better understanding of the phenotype]genotype correlation and its underlying mechanism is primordial. In the first part of our work, we focused on genotype‐phenotype correlation. With a retrospective study on 694 cases of prenatal diagnosis of CF for fetal bowel anomalies, we report on the most evocative digestive abnormal patterns and propose to revise current strategies for the CFTR gene analysis. The second part concerns the CFTR promoter functional analysis. Mutations which conserve a residual CFTR channel function, such as mutations affecting the gene regulation, can be involved in atypical phenotypes. However, knowledge about the CFTR promoter reminds poor and the clinical significance of new variants identified in this region is difficult to evaluate. Our implementation of functional analysis tools will improve the appreciation of such new variants in the CFTR promoter and open new insights for the gene regulation study. In the third part, we contributed to study the inappropriate pulmonary inflammation which characterizes CF, the respiratory affection being the major factor of morbidity and mortality in the disease. COMMD1 is a pleiotropic protein involved in CFTR trafficking, ionic exchanges in the airways surface liquid and inflammation inhibition. In our study, we show the anti]inflammatory role of COMMD1 in the context of cystic fibrosis. Modulation of the exaggerated inflammation belongs to currently therapeutic challenges, and we show the ability of COMMD1, a protein partner of CFTR, to buffer the NF-kB pathway activation

There are three main categories of algorithms for identifying small transcription regulatory sequences in the promoters of genes, phylogenetic comparison, expectation maximization and combinatorial. For convenience, the combinatorial methods typically define motifs in terms of a canonical sequence and a set of sequences that have a small number of differences compared to the canonical sequence. Such motifs are referred to as (l, d)-motifs where l is the length of the motif and d indicates how many mismatches are allowed between an instance of the motif and the canonical motif sequence. There are limits to the complexity of the patterns of motifs that can be found by combinatorial methods. For some values of l and d, there will exist many sets of random words in a cluster of gene promoters that appear to form an (l, d)-motif. For these motifs, it will be impossible to distinguish biological motifs from randomly generated motifs. A better formalization of motifs is the (l, f, d)-motif that is derived from a biological consideration of motifs. The motivation for (l, f, d)-motifs comes from an examination of known transcription factor binding sites where typically a few positions in the motif are invariant. It is shown that there exist (l, f, d)-motifs that can be found in the promoters of gene clusters that would not be recognizable from random sequences if they were described as (l, d)-motifs. The inclusion of the f-value in the definition of motifs suggests that the sequence space that is occupied by a motif will consist of a several clusters of closely related sequences. An algorithm, CM, has been developed that identifies small sets of overabundant sequences in the promoters from a cluster of genes and then combines these simple sets of sequences to form complex (l, f, d)-motif models. A dataset from a yeast gene expression experiment is analyzed with CM. Known biological motifs and novel motifs are identified by CM. The performance of CM is compared to that of a popular expectation maximization algorithm, AlginACE, and to that from a simple combinatorial motif finding program.

We first identified the transcriptional regulatory element of the human dynamin IV gene (Hdyn IV; dymple). The Hdyn IV belongs to a large GTPase family. This protein has a N-terminal highly conserved tripartite GTP-binding domain, coiled-coil (CC) region, but it lacks the pleckstrin homology (PH) domain and a modestly conserved C-terminal proline rich domain (PRD). Hdyn IV gene is enriched in subcellular membrane fractions of cytoplasmic vesicles and endoplasmic reticulum, and the function of Hdyn IV gene is considered to be associated with the functions of mitochondria. The Hdyn IV is expressed as four alternative splicing variants in all eukaryotic organisms. Our question concerning why expressions of four alternative splicing variants in brain tumor tissues? To elucidate the regulatory mechanism and the transcription factors involved, we firstly determined the transcriptional start site by 5¡¦ RACE. We next cloned the 5¡¦-flanking region of the Hdyn IV gene and determined the nucleotide sequence of 999 bases upstream from the transcription start site. The promoter has several potential binding sites for AP2, Sp1 binding protein, but it lacks TATA and CAAT boxes. Transfection studies using a series of Hdyn IV promoter luciferase constructs in HeLa cell demonstrate that the 5¡¦flanking region has a promoter activity. Functional promoter element of the Hdyn IV gene was located within the ¡V140~ +29 region. Deletion analyses demonstrated that the minimal promoter activity for the transcriptional element of Hdyn IV was detected in the sequence between nucleotides ¡V110 and ¡V100. Electorphoretic mobility shift assay demonstrated that a putative transcriptional factor bound to the ¡V119 to ¡V90 region. Site-directed mutagenesis analysis of this region revealed that nucleotides at positions ¡V108 to ¡V100 were essential for transactivation mediated by this element. To summary, the data indicated that the ¡¦¡¦CTCCCAGCA¡¦¡¦ (-108~ -100) sequence is capable of regulating Hdyn IV gene expression. However, the protein involved in the binding of this novel sequence requires further study.

There are three main categories of algorithms for identifying small transcription regulatory sequences in the promoters of genes, phylogenetic comparison, expectation maximization and combinatorial. For convenience, the combinatorial methods typically define motifs in terms of a canonical sequence and a set of sequences that have a small number of differences compared to the canonical sequence. Such motifs are referred to as (l, d)-motifs where l is the length of the motif and d indicates how many mismatches are allowed between an instance of the motif and the canonical motif sequence. There are limits to the complexity of the patterns of motifs that can be found by combinatorial methods. For some values of l and d, there will exist many sets of random words in a cluster of gene promoters that appear to form an (l, d)-motif. For these motifs, it will be impossible to distinguish biological motifs from randomly generated motifs. A better formalization of motifs is the (l, f, d)-motif that is derived from a biological consideration of motifs. The motivation for (l, f, d)-motifs comes from an examination of known transcription factor binding sites where typically a few positions in the motif are invariant. It is shown that there exist (l, f, d)-motifs that can be found in the promoters of gene clusters that would not be recognizable from random sequences if they were described as (l, d)-motifs. The inclusion of the f-value in the definition of motifs suggests that the sequence space that is occupied by a motif will consist of a several clusters of closely related sequences. An algorithm, CM, has been developed that identifies small sets of overabundant sequences in the promoters from a cluster of genes and then combines these simple sets of sequences to form complex (l, f, d)-motif models. A dataset from a yeast gene expression experiment is analyzed with CM. Known biological motifs and novel motifs are identified by CM. The performance of CM is compared to that of a popular expectation maximization algorithm, AlginACE, and to that from a simple combinatorial motif finding program.

Temperature affects the sexual differentiation and the development of brain of tilapia, Oreochromis mossambicus. Expressed sequence tags ( EST ) derived from the developing tilapia brain had been cloned in our laboratory. In the present study, we focus on the promoter of cold-induced protein. The promoter sequence of cold-induced protein from the transcription start site ( TSS ) were cloned by 5¡¦ rapid amplification of cDNA end ( 5¡¦-RACE ) and Genome Walker DNA Walking. The bioinformatic analysis was performed on the fragment for predicting the transcription factor binding site. We used the digestion method of restriction enzymes and an electrophoresis mobility shift assay to find transcription factor binding site. The results indicated that there is a putative POU3F2 binding site in the fragment form -157 bp to -149 bp. The luciferase activity assay was performed on this site and results indicated that wild type showed the enhanced promoter activity. However, site-direct mutagenesis of this site did not result in the reduction of the promoter activity.

Promoters are key regions that are involved in differential transcription regulation of protein-coding and RNA genes. The gene-specific architecture of promoter sequences makes it extremely difficult to devise a general strategy for their computational identification. Accurate prediction of promoters is fundamental for interpreting gene expression patterns, and for constructing and understanding genetic regulatory networks. In the last decade, genomes of many organisms have been sequenced and their gene content was mostly identified. Promoters and transcriptional start sites (TSS), however, are still left largely undetermined and efficient software able to accurately predict promoters in newly sequenced genomes is not yet available in the public domain. While there are many attempts to develop computational promoter identification methods, reliable tools to analyze long genomic sequences are still lacking. In this dissertation, I present the methods I have developed for prediction of promoters for different organisms. The first two methods, TSSPlant and PromCNN, achieved state-of-the-art performance for discriminating promoter and non-promoter sequences for plant and eukaryotic promoters respectively. For TSSPlant, a large number of features were crafted and evaluated to train an optimal classifier. Prom- CNN was built using a deep learning approach that extracts features from the data automatically. The trained model demonstrated the ability of a deep learning approach to grasp complex promoter sequence characteristics. For the latest method, DeeReCT-PromID, I focus on prediction of the exact positions of the TSSs inside the eukaryotic genomic sequences, testing every possible location. This is a more difficult task, requiring not only an accurate classifier, but also appropriate selection of unique predictions among multiple overlapping high scoring genomic segments. The new method significantly outperform the previous promoter prediction programs by considerably reducing the number of false positive predictions. Specifically, to reduce the false positive rate, the models are adaptively and iteratively trained by changing the distribution of samples in the training set based on the false positive errors made in the previous iteration. The new methods are used to gain insights into the design principles of the core promoters. Using model analysis, I have identified the most important core promoter elements and their effect on the promoter activity. Furthermore, the importance of each position inside the core promoter was analyzed and validated using a large single nucleotide polymorphisms data set. I have developed a novel general approach to detect long range interactions in the input of a deep learning model, which was used to find related positions inside the promoter region. The final model was applied to the genomes of different species without a significant drop in the performance, demonstrating a high generality of the developed method.

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