Enzimas lipolíticas possuem enorme potencial biotecnológico. O objetivo foi prospectar genes para a codificação de enzimas lipolíticas em biblioteca metagenômica com 4224 clones. A atividade lipolítica foi avaliada pela formação de halo ao redor das colônias através do cultivo dos clones em meio de cultura suplementado com tributirina, sendo positiva para 30 clones, e dois foram selecionados e tiveram o DNA sub-clonado. Os DNAs das sub-bibliotecas foram sequenciados, gerando um contig completo para o clone PL28.F10, que foi comparado com as sequências do banco NCBI. Uma ORF codificadora de esterase/lipase de 303 aminoácidos e 61% de identidade com micro-organismo não cultivável foi encontrada. Árvores filogenéticas indicam que o clone possui a ORF15 mais próxima da família IV das esterases/lipases. Foi possível identificar os sítios ativos representativos da família, confirmando o resultado das árvores filogenéticas. Com sequências já patenteadas, a ORF15 é um grupo irmão das sequências de esterases/lipases da BASF e de uma proteína não identificada da CAMBIA. / Lipolytic enzymes have show enormous biotechnological potential. The work was done to find genes which codify lipolytic enzymes in a metagenomic library with 4224 clones. Clones were selected according to lipolytic activity and were assessed by cultivation in medium supplemented with tributyrin. Assessment was done by observation of halos formed around the colonies, with 30 clones producing halos. Of these, two were selected. DNA from the sub libraries was sequenced, generating a complete contig for clone PL28.F10 that was compared to sequences from the NCBI. An ORF of 303 amino acids with 61% of identity with uncunturable microorganism were found. The clone presented the ORF15 similar to that of lipolytic enzyme family IV. The alignments made possible the identification of active sites which represent the family, confirming the results obtained with the construction of the cladograms. The ORF15 showed similarities to patented BASF esterase/lipase and an unnamed protein of CAMBIA.

As enzimas lipolíticas vêm atraindo atenção no mercado global devido ao enorme potencial biotecnológico, como: na formulação de detergentes; na indústria de couro; produção de cosméticos, fármacos, aromas, biodiesel, etc. O objetivo deste trabalho foi prospectar genes codificadores de enzimas lipolíticas em biblioteca metagenômica de um consórcio microbiano degradador de óleo diesel. A seleção foi feita pela atividade lipolítica através do cultivo dos clones em placa de petri e a avaliação foi pela observação de halo ao redor da colônia, sendo positiva para 30 clones dentre os quais dois se destacaram. Estes dois clones foram selecionados e subclonados. Os DNAs das sub-bibliotecas foram sequenciados, gerando um contig completo para cada clone. Através do ORF Finder foi identificado cinco ORFs de esterase/lipase, dentre as quais uma alcançou 58% de identidade com uma bactéria não cultivável. As árvores filogenéticas indicam que duas ORFs são similares à família IV das enzimas lipolíticas, enquanto que as outras três ORFs à família V. / Lipolytic enzymes have been attracting global market attention because they show enormous biotechnological potential. The present work was done as an attempt to find genes which codify lipolytic enzymes in a metagenomic library composed of diesel oil degradation microbe consortia. Clones were selected according to lipolytic activity and were then evaluated after cultivation in Petri dishes by observation of halo formation around the colonies. 30 clones produced halo formations and were identified as positives, two of which showed prominent results. These two were then selected and sub cloned. DNA from the sub libraries was sequenced, generating a complete contig for each clone. Using the ORF Finder five esterase/lipase ORFs were identified, with one of these attaining 58% of identity to a non cultivatable bacteria species. Assessment of the cladograms showed that two ORFs were similar to lipolytic enzyme family IV, while the other three ORFs were similar to family V.

No description available.

Beluga whales in Canadian waters are subdivided into at least six genetically distinct stocks maintained by geographic separation and philopatry to estuaries in summer. Belugas in eastern and western Hudson Bay have previously been shown to be compose genetically distinct populations using mitochondrial restriction analysis. It is not known whether these stocks are further subdivided on the basis of specific estuarine use. Mitochondrial DNA control region sequences were used to investigate variation among belugas sampled at several sites along eastern Hudson Bay, Hudson Strait and Ungava Bay. 320 bp were sequenced, including the highly variable 5' region of control region, in 126 belugas. 17 variable sites and 17 haplotypes, which clustered into 2 related groups, were detected among the whales sequenced. Haplotypes of group A were found mostly in eastern Hudson Bay sites, while B group haplotypes were predominant in northern populations. Significant differences in frequencies of haplotype groups were found between eastern Hudson Bay and Southern Hudson Strait/Ungava Bay populations, indicating they are genetically distinct populations. Haplotype distribution patterns also suggested possible differences between belugas using different estuaries along eastern Hudson Bay. The presence of both groups in each population indicated some exchange of individuals between populations, and/or between eastern and western Hudson Bay. Multilocus DNA fingerprinting was used to investigate the extent of gene flow between eastern and western Hudson Bay belugas via interbreeding on common wintering grounds in Hudson Strait. Belugas from St. Lawrence estuary and the Mackenzie Delta were also analyzed to measure their genetic relatedness to Hudson Bay whales as well as for purposes of comparison to earlier fingerprinting analyses. While results supported lower genetic diversity within the St. Lawrence population, the range of bandsharing within and between populations was otherwise low (0.09 -0.17 for Jeffreys 33.15 and 0.12-0.22 for Jeffreys 33.6). Mantel tests showed differences among St. Lawrence, Hudson Bay, and Mackenzie Delta populations, but not within Hudson Bay. The conflicting nature of the data did not allow conclusions regarding gene flow. Therefore, DNA fingerprinting was not considered to have provided sufficient resolution in addressing this issue. / Thesis / Master of Science (MS)

Ribonucleotide reductase (RNR) reduces four ribonucleoside diphosphates to corresponding deoxyribonucleoside diphosphates, which are transformed into deoxyribonucleoside triphosphates, substrates for DNA polymerase. By controlling the supply and balance of deoxyribonucleoside diphosphates, RNR regulates DNA synthesis. RNR in E. coli and in animals consists of two identical large and two identical small subunits. Until recently, little was known about RNR in plants. For cloning RNR cDNA in plants, soybean (Glycine max) cDNAs were amplified with highly degenerate primers and the Rapid Amplification of cDNA Ends techniques. The cDNAs encoding two complete large subunits, one partial large subunit and one complete small subunit of RNR in soybean were cloned and sequenced. The RNR large subunits in soybean contain a motif with 20 amino acids, which appears to be specific for the RNR large subunits in plants. Southern hybridization results imply that a gene family encodes at least three different large subunits of RNR in soybean, and that a single gene encodes the small subunit. The presence of three different large subunits of RNR in soybean suggests that RNR complex in some plants may have a non-homodimer structure; alternatively, some plants may have different RNR isozymes. Northern hybridization results show that RNR large and small subunit genes in soybean are expressed both in dark-grown and light-grown seedlings, and that light does not increase RNR mRNA levels. Multiple poly(A) sites and different lengths of the 3â untranslated regions were found in cDNAs encoding some subunits of RNR in soybean. The same cis-acting elements may imprecisely locate some multiple poly(A) sites in plants. / Ph. D.

No / The pyrrolo[2,1-c][1,4]benzodiazepines (PBDs) are a class of DNA minor groove binding agents that react covalently with guanine bases, preferably at Pu-G-Pu sites. A series of three fluorescent PBD¿coumarin conjugates with different linker architectures has been synthesized to probe correlations between DNA binding affinity, cellular localization and cytotoxicity. The results show that the linker structure plays a critical role for all three parameters. Graphical abstract A series of three fluorescent PBD¿coumarin conjugates with different linker architectures has been synthesized to probe correlations between DNA-binding affinity, cellular localization and cytotoxicity.

Position weight matrices (PWMs) have become a tool of choice for the identification of transcription factor binding sites in DNA sequences. PWMs are compiled from experimentally verified and aligned binding sequences. PWMs are then used to computationally discover novel putative binding sites for a given protein. DNA-binding proteins often show degeneracy in their binding requirement, the overall binding specificity of many proteins is unknown and remains an active area of research. Although PWMs are more reliable predictors than consensus string matching, they generally result in a high number of false positive hits. A previous study introduced a novel method to PWM training based on the known motifs to sample additional putative binding sites from a proximal promoter area. The core idea was further developed, implemented and tested in this thesis with a large scale application. Improved mono- and dinucleotide PWMs were computed for Drosophila melanogaster. The Matthews correlation coefficient was used as an optimization criterion in the PWM refinement algorithm. New PWMs keep an account of non-uniform background nucleotide distributions on the promoters and consider a larger number of new binding sites during the refinement steps. The optimization included the PWM motif length, the position on the promoter, the threshold value and the binding site location. The obtained predictions were compared for mono- and dinucleotide PWM versions with initial matrices and with conventional tools. The optimized PWMs predicted new binding sites with better accuracy than conventional PWMs.

Position weight matrices (PWMs) have become a tool of choice for the identification of transcription factor binding sites in DNA sequences. PWMs are compiled from experimentally verified and aligned binding sequences. PWMs are then used to computationally discover novel putative binding sites for a given protein. DNA-binding proteins often show degeneracy in their binding requirement, the overall binding specificity of many proteins is unknown and remains an active area of research. Although PWMs are more reliable predictors than consensus string matching, they generally result in a high number of false positive hits. A previous study introduced a novel method to PWM training based on the known motifs to sample additional putative binding sites from a proximal promoter area. The core idea was further developed, implemented and tested in this thesis with a large scale application. Improved mono- and dinucleotide PWMs were computed for Drosophila melanogaster. The Matthews correlation coefficient was used as an optimization criterion in the PWM refinement algorithm. New PWMs keep an account of non-uniform background nucleotide distributions on the promoters and consider a larger number of new binding sites during the refinement steps. The optimization included the PWM motif length, the position on the promoter, the threshold value and the binding site location. The obtained predictions were compared for mono- and dinucleotide PWM versions with initial matrices and with conventional tools. The optimized PWMs predicted new binding sites with better accuracy than conventional PWMs.

Position weight matrices (PWMs) have become a tool of choice for the identification of transcription factor binding sites in DNA sequences. PWMs are compiled from experimentally verified and aligned binding sequences. PWMs are then used to computationally discover novel putative binding sites for a given protein. DNA-binding proteins often show degeneracy in their binding requirement, the overall binding specificity of many proteins is unknown and remains an active area of research. Although PWMs are more reliable predictors than consensus string matching, they generally result in a high number of false positive hits. A previous study introduced a novel method to PWM training based on the known motifs to sample additional putative binding sites from a proximal promoter area. The core idea was further developed, implemented and tested in this thesis with a large scale application. Improved mono- and dinucleotide PWMs were computed for Drosophila melanogaster. The Matthews correlation coefficient was used as an optimization criterion in the PWM refinement algorithm. New PWMs keep an account of non-uniform background nucleotide distributions on the promoters and consider a larger number of new binding sites during the refinement steps. The optimization included the PWM motif length, the position on the promoter, the threshold value and the binding site location. The obtained predictions were compared for mono- and dinucleotide PWM versions with initial matrices and with conventional tools. The optimized PWMs predicted new binding sites with better accuracy than conventional PWMs.

DNA sequence alignments are usually not homogeneous. Mosaic structures may result as a consequence of recombination or rate heterogeneity. Interspecific recombination, in which DNA subsequences are transferred between different (typically viral or bacterial) strains may result in a change of the topology of the underlying phylogenetic tree. Rate heterogeneity corresponds to a change of the nucleotide substitution rate. Various methods for simultaneously detecting recombination and rate heterogeneity in DNA sequence alignments have recently been proposed, based on complex probabilistic models that combine phylogenetic trees with factorial hidden Markov models or multiple changepoint processes. The objective of my thesis is to identify potential shortcomings of these models and explore ways of how to improve them. One shortcoming that I have identified is related to an approximation made in various recently proposed Bayesian models. The Bayesian paradigm requires the solution of an integral over the space of parameters. To render this integration analytically tractable, these models assume that the vectors of branch lengths of the phylogenetic tree are independent among sites. While this approximation reduces the computational complexity considerably, I show that it leads to the systematic prediction of spurious topology changes in the Felsenstein zone, that is, the area in the branch lengths configuration space where maximum parsimony consistently infers the wrong topology due to long-branch attraction. I demonstrate these failures by using two Bayesian hypothesis tests, based on an inter- and an intra-model approach to estimating the marginal likelihood. I then propose a revised model that addresses these shortcomings, and demonstrate its improved performance on a set of synthetic DNA sequence alignments systematically generated around the Felsenstein zone. The core model explored in my thesis is a phylogenetic factorial hidden Markov model (FHMM) for detecting two types of mosaic structures in DNA sequence alignments, related to recombination and rate heterogeneity. The focus of my work is on improving the modelling of the latter aspect. Earlier research efforts by other authors have modelled different degrees of rate heterogeneity with separate hidden states of the FHMM. Their work fails to appreciate the intrinsic difference between two types of rate heterogeneity: long-range regional effects, which are potentially related to differences in the selective pressure, and the short-term periodic patterns within the codons, which merely capture the signature of the genetic code. I have improved these earlier phylogenetic FHMMs in two respects. Firstly, by sampling the rate vector from the posterior distribution with RJMCMC I have made the modelling of regional rate heterogeneity more flexible, and I infer the number of different degrees of divergence directly from the DNA sequence alignment, thereby dispensing with the need to arbitrarily select this quantity in advance. Secondly, I explicitly model within-codon rate heterogeneity via a separate rate modification vector. In this way, the within-codon effect of rate heterogeneity is imposed on the model a priori, which facilitates the learning of the biologically more interesting effect of regional rate heterogeneity a posteriori. I have carried out simulations on synthetic DNA sequence alignments, which have borne out my conjecture. The existing model, which does not explicitly include the within-codon rate variation, has to model both effects with the same modelling mechanism. As expected, it was found to fail to disentangle these two effects. On the contrary, I have found that my new model clearly separates within-codon rate variation from regional rate heterogeneity, resulting in more accurate predictions.