Molecular analysis of dimethyl sulphide dehydrogenase from Rhodovulum sulfidophilum: its place in the dimethyl sulphoxide reductase family of microbial molybdopterin-containing enzymes

Dimethyl sulphide dehydrogenase catalyses the oxidation of dimethyl sulphide to dimethyl sulphoxide (DMSO) during photoautotrophic growth of Rhodovulum sulfidophilum . Dimethyl sulphide dehydrogenase was shown to contain bis (molybdopterin guanine dinucleotide)Mo, the form of the pterin molybdenum cofactor unique to enzymes of the DMSO reductase family. Sequence analysis of the ddh gene cluster showed that the ddhA gene encodes a polypeptide with highest sequence similarity to the molybdop-terin-containing subunits of selenate reductase, ethylbenzene dehydrogenase. These polypeptides form a distinct clade within the DMSO reductase family. Further sequence analysis of the ddh gene cluster identified three genes, ddhB , ddhD and ddhC . DdhB showed sequence homology to NarH, suggesting that it contains multiple iron-sulphur clusters. Analysis of the N-terminal signal sequence of DdhA suggests that it is secreted via the Tat secretory system in complex with DdhB, whereas DdhC is probably secreted via a Sec-dependent mechanism. Analysis of a ddhA mutant showed that dimethyl sulphide dehydrogenase was essential for photolithotrophic growth of Rv. sulfidophilum on dimethyl sulphide but not for chemo-trophic growth on the same substrate. Mutational analysis showed that cytochrome c (2) mediated photosynthetic electron transfer from dimethyl sulphide dehydrogenase to the photochemical reaction centre, although this cytochrome was not essential for photoheterotrophic growth of the bacterium.

The DMSO reductase family of microbial molybdenum enzymes; Molecular properties and role in the dissimilatory reduction of toxic elements

The dimethylsulfoxide (DMSO) reductase family of molybdenum enzymes is a large and diverse group that is found in bacteria and archaea. These enzymes are characterised by a bis(molybdopterin guanine dinucleotide)Mo form of the molybdenum cofactor, and they are particularly important in anaerobic respiration including the dissimilatory reduction of certain toxic oxoanions. The structural and phylogenetic relationship between the proteins of this family is discussed. High-resolution crystal structures of enzymes of the DMSO reductase family have revealed a high degree of similarity in tertiary structure. However, there is considerable variation in the structure of the molybdenum active site and it seems likely that these subtle but important differences lead to the great diversity of function seen in this family of enzymes. This diversity of catalytic capability is associated with several distinct pathways of electron transport.

One-dimensional quantum walk with unitary noise

The effect of unitary noise on the discrete one-dimensional quantum walk is studied using computer simulations. For the noiseless quantum walk, starting at the origin (n=0) at time t=0, the position distribution P-t(n) at time t is very different from the Gaussian distribution obtained for the classical random walk. Furthermore, its standard deviation, sigma(t) scales as sigma(t)similar tot, unlike the classical random walk for which sigma(t)similar toroott. It is shown that when the quantum walk is exposed to unitary noise, it exhibits a crossover from quantum behavior for short times to classical-like behavior for long times. The crossover time is found to be Tsimilar toalpha(-2), where alpha is the standard deviation of the noise.

Biochemical and molecular studies using human autopsy brain tissue

The use of human brain tissue obtained at autopsy for neurochemical, pharmacological and physiological analyses is reviewed. RNA and protein samples have been found suitable for expression profiling by techniques that include RT-PCR, cDNA microarrays, western blotting, immunohistochemistry and proteomics. The rapid development of molecular biological techniques has increased the impetus for this work to be applied to studies of brain disease. It has been shown that most nucleic acids and proteins are reasonably stable post-mortem. However, their abundance and integrity can exhibit marked intra- and intercase variability, making comparisons between case-groups difficult. Variability can reveal important functional and biochemical information. The correct interpretation of neurochemical data must take into account such factors as age, gender, ethnicity, medicative history, immediate ante-mortem status, agonal state and post-mortem and post-autopsy intervals. Here we consider issues associated with the sampling of DNA, RNA and proteins using human autopsy brain tissue in relation to various ante- and post-mortem factors. We conclude that valid and practical measures of a variety of parameters may be made in human brain tissue, provided that specific factors are controlled.

Quantum dynamical characterization of unimolecular resonances

We give a selective review of quantum mechanical methods for calculating and characterizing resonances in small molecular systems, with an emphasis on recent progress in Chebyshev and Lanczos iterative methods. Two archetypal molecular systems are discussed: isolated resonances in HCO, which exhibit regular mode and state specificity, and overlapping resonances in strongly bound HO2, which exhibit irregular and chaotic behavior. Future directions in this field are also discussed.

Experimental investigation of continuous-variable quantum teleportation

We report the experimental demonstration of quantum teleportation of the quadrature amplitudes of a light field. Our experiment was stably locked for long periods, and was analyzed in terms of fidelity F and with signal transfer T-q=T++T- and noise correlation V-q=Vinparallel to out+Vinparallel to out-. We observed an optimum fidelity of 0.64+/-0.02, T-q=1.06+/-0.02, and V-q=0.96+/-0.10. We discuss the significance of both T-q>1 and V-q

Quantum theory of the far-off-resonance continuous-wave Raman laser: Heisenberg-Langevin approach

We present the quantum theory of the far-off-resonance continuous-wave Raman laser using the Heisenberg-Langevin approach. We show that the simplified quantum Langevin equations for this system are mathematically identical to those of the nondegenerate optical parametric oscillator in the time domain with the following associations: pump pump, Stokes signal, and Raman coherence idler. We derive analytical results for both the steady-state behavior and the time-dependent noise spectra, using standard linearization procedures. In the semiclassical limit, these results match with previous purely semiclassical treatments, which yield excellent agreement with experimental observations. The analytical time-dependent results predict perfect photon statistics conversion from the pump to the Stokes and nonclassical behavior under certain operational conditions.

Tailoring teleportation to the quantum alphabet

We introduce a refinement of the standard continuous variable teleportation measurement and displacement strategies. This refinement makes use of prior knowledge about the target state and the partial information carried by the classical channel when entanglement is nonmaximal. This gives an improvement in the output quality of the protocol. The strategies we introduce could be used in current continuous variable teleportation experiments.

O complexo Myosotella 'myosotis/denticulata': uma perspetiva morfológica, molecular e ecológica

Dissertação de Mestrado, Biodiversidade e Ecologia Insular, 29 de Maio de 2013, Universidade dos Açores.

Molecular screening of 246 Portuguese Aspergillus isolates among different clinical and environmental sources

Clinical and environmental samples from Portugal were screened for the presence of Aspergillus and the distributions of the species complexes were determined in order to understand how their distributions differ based on their source. Fifty-seven Aspergillus isolates from clinical samples were collected from 10 health institutions. Six species complexes were detected by internal transcribed spacer sequencing; Fumigati, Flavi, and Nigri were found most frequently (50.9%, 21.0%, and 15.8%, respectively). β-tubulin and calmodulin sequencing resulted in seven cryptic species (A. awamorii, A. brasiliensis, A. fructus, A. lentulus, A. sydowii, A. tubingensis, Emericella echinulata) being identified among the 57 isolates. Thirty-nine isolates of Aspergillus were recovered from beach sand and poultry farms, 31 from swine farms, and 80 from hospital environments, for a total 189 isolates. Eleven species complexes were found in these 189 isolates, and those belonging to the Versicolores species complex were found most frequently (23.8%). There was a significant association between the different environmental sources and distribution of the species complexes; the hospital environment had greater variability of species complexes than other environmental locations. A high prevalence of cryptic species within the Circumdati complex was detected in several environments; from the isolates analyzed, at least four cryptic species were identified, most of them growing at 37ºC. Because Aspergillus species complexes have different susceptibilities to antifungals, knowing the species-complex epidemiology for each setting, as well as the identification of cryptic species among the collected clinical isolates, is important. This may allow preventive and corrective measures to be taken, which may result in decreased exposure to those organisms and a better prognosis.

Automatic learning for the classification of chemical reactions and in statistical thermodynamics

This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds. NMR-based classification of photochemical and enzymatic reactions. Photochemical and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen SOMs) and Random Forests (RFs) taking as input the difference between the 1H NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data. A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases was able to correctly classify 75% of an independent test set in terms of the EC number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used. This study was performed with NMR data simulated from the molecular structure by the SPINUS program. In the design of one test set, simulated data was combined with experimental data. The results support the proposal of linking databases of chemical reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions. Genome-scale classification of enzymatic reactions from their reaction equation. The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken, changed, and made during a chemical reaction. The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods should be available to automatically compare metabolic reactions and for the automatic assignment of EC numbers to reactions still not officially classified. In this study, the genome-scale data set of enzymatic reactions available in the KEGG database was encoded by the MOLMAP descriptors, and was submitted to Kohonen SOMs to compare the resulting map with the official EC number classification, to explore the possibility of predicting EC numbers from the reaction equation, and to assess the internal consistency of the EC classification at the class level. A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions. RFs were also used to assign the four levels of the EC hierarchy from the reaction equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases (for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively. In the course of the experiments with metabolic reactions we suggested that the MOLMAP / SOM concept could be extended to the representation of other levels of metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different types of metabolism and pathways that do not share similarities in terms of EC numbers. Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function. The results indicated that for LJ-type potentials, NNs can be trained to generate accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown. The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo, the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes. Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task. The data consisted of energy values, from Density Functional Theory (DFT) calculations, at different distances, for several molecular orientations and three electrode adsorption sites. The results indicate that NNs require a data set large enough to cover well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions. Therefore, they can be used in molecular simulations, particularly for the ethanol/Au (111) interface which is the case studied in the present Thesis. Once properly trained, the networks are able to produce, as output, any required number of energy points for accurate interpolations.

The impact of CdSe/ZnS Quantum Dots in cells of Medicago sativa in suspension culture

Abstract Background: Nanotechnology has the potential to provide agriculture with new tools that may be used in the rapid detection and molecular treatment of diseases and enhancement of plant ability to absorb nutrients, among others. Data on nanoparticle toxicity in plants is largely heterogeneous with a diversity of physicochemical parameters reported, which difficult generalizations. Here a cell biology approach was used to evaluate the impact of Quantum Dots (QDs) nanocrystals on plant cells, including their effect on cell growth, cell viability, oxidative stress and ROS accumulation, besides their cytomobility. Results: A plant cell suspension culture of Medicago sativa was settled for the assessment of the impact of the addition of mercaptopropanoic acid coated CdSe/ZnS QDs. Cell growth was significantly reduced when 100 mM of mercaptopropanoic acid -QDs was added during the exponential growth phase, with less than 50% of the cells viable 72 hours after mercaptopropanoic acid -QDs addition. They were up taken by Medicago sativa cells and accumulated in the cytoplasm and nucleus as revealed by optical thin confocal imaging. As part of the cellular response to internalization, Medicago sativa cells were found to increase the production of Reactive Oxygen Species (ROS) in a dose and time dependent manner. Using the fluorescent dye H2DCFDA it was observable that mercaptopropanoic acid-QDs concentrations between 5-180 nM led to a progressive and linear increase of ROS accumulation. Conclusions: Our results showed that the extent of mercaptopropanoic acid coated CdSe/ZnS QDs cytotoxicity in plant cells is dependent upon a number of factors including QDs properties, dose and the environmental conditions of administration and that, for Medicago sativa cells, a safe range of 1-5 nM should not be exceeded for biological applications.

New Strategies on Molecular Biology Applied to Microbial Systematics

Systematics is the study of diversity of the organisms and their relationships comprising classification, nomenclature and identification. The term classification or taxonomy means the arrangement of the organisms in groups (rate) and the nomenclature is the attribution of correct international scientific names to organisms and identification is the inclusion of unknown strains in groups derived from classification. Therefore, classification for a stable nomenclature and a perfect identification are required previously. The beginning of the new bacterial systematics era can be remembered by the introduction and application of new taxonomic concepts and techniques, from the 50’s and 60’s. Important progress were achieved using numerical taxonomy and molecular taxonomy. Molecular taxonomy, brought into effect after the emergence of the Molecular Biology resources, provided knowledge that comprises systematics of bacteria, in which occurs great evolutionary interest, or where is observed the necessity of eliminating any environmental interference. When you study the composition and disposition of nucleotides in certain portions of the genetic material, you study searching their genome, much less susceptible to environmental alterations than proteins, codified based on it. In the molecular taxonomy, you can research both DNA and RNA, and the main techniques that have been used in the systematics comprise the build of restriction maps, DNA-DNA hybridization, DNA-RNA hybridization, sequencing of DNA sequencing of sub-units 16S and 23S of rRNA, RAPD, RFLP, PFGE etc. Techniques such as base sequencing, though they are extremely sensible and greatly precise, are relatively onerous and impracticable to the great majority of the bacterial taxonomy laboratories. Several specialized techniques have been applied to taxonomic studies of microorganisms. In the last years, these have included preliminary electrophoretic analysis of soluble proteins and isoenzymes, and subsequently determination of deoxyribonucleic acid base composition and assessment of base sequence homology by means of DNA-RNA hybrid experiments beside others. These various techniques, as expected, have generally indicated a lack of taxonomic information in microbial systematics. There are numberless techniques and methodologies that make bacteria identification and classification study possible, part of them described here, allowing establish different degrees of subspecific and interspecific similarity through phenetic-genetic polymorphism analysis. However, was pointed out the necessity of using more than one technique for better establish similarity degrees within microorganisms. Obtaining data resulting from application of a sole technique isolatedly may not provide significant information from Bacterial Systematics viewpoint

Phenotypic and molecular characterization of Salmonella Enteritidis strains isolated in São Paulo, Brazil

In São Paulo State, Brazil, the epidemic increase in isolation of Salmonella Enteritidis has been observed since 1994. A total of 105 S. Enteritidis strains (72 from human and 33 from non-human sources) isolated during the period 1975-1995, previously characterized by phage typing, was analyzed by antimicrobial susceptibility, plasmid profile, and ribotyping. Over 70% of the strains were susceptible to all antimicrobial agents tested, however, multiple resistance to antimicrobials was observed among the studied strains, mainly those from hospitalized patients. Phage type 8 (PT-8) was predominant among the strains isolated during the period of 1975-1992, but in the following years, PT-4 was the most frequent phage type identified. Seven different plasmid profiles were detected and 96% of the isolates harbored a plasmid of approximately 36 MDa. Ribotyping discriminated fourteen ribotypes (R1 to R14) among the strains examined. By analysis of dendrogram the strains were included in three groups with similarity level of 60%. The obtained results indicate that, a single ribotype (R11), determined for PT-4 strains isolated from 1993, characterizes the epidemic clone of S. Enteritidis in our region.

Febrifugine derivative antimalarial activity: quantum mechanical predictors

Plasmodium falciparum resistant strain development has encouraged the search for new antimalarial drugs. Febrifugine is a natural substance with high activity against P. falciparum presenting strong emetic property and liver toxicity, which prevent it from being used as a clinical drug. The search for analogues that could have a better clinical performance is a current topic. We aim to investigate the theoretical electronic structure by means of febrifugine derivative family semi-empirical molecular orbital calculations, seeking the electronic indexes that could help the design of new efficient derivatives. The theoretical results show there is a clustering in well-defined ranges of several electronic indexes of the most selective molecules. The model proposed for achieving high selectivity was tested with success.