Biotoxicity assays of quantum dots in in vitro cultures of Medicago sativa and Medicago truncatula

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Biotecnologia

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.

Quantum dots: synthesis, functionalization and bioconjugation for biological applications

Dissertation presented to obtain the Ph.D degree in Engineering Sciences and Technology

Solution processed quantum dot photodetectors

To find sustainable solutions for the production of energy, it is necessary to create photovoltaic technologies that make every photon count. To pursue this necessity, in the present work photodetectors of zinc oxide embedded with nano-structured materials, that significantly raise the conversion of solar energy to electric energy, were developed. The novelty of this work is on the development of processing methodologies in which all steps are in solution: quantum dots synthesis, passivation of their surface and sol-gel deposition. The quantum dot solutions with different capping agents were characterized by UVvisible absorption spectroscopy, spectrofluorimetry, dynamic light scattering and transmission electron microscopy. The obtained quantum dots have dimensions between 2 and 3nm. These particles were suspended in zinc acetate solutions and used to produce doped zinc oxide films with embedded quantum dots, whose electric response was tested. The produced nano-structured zinc oxide materials have a superior performance than the bulk, in terms of the produced photo-current. This indicates that an intermediate band material should have been produced that acts as a photovoltaic medium for solar cells. The results are currently being compiled in a scientific article, that is being prepared for possible submission to Energy and Environmental Science or Nanoscale journals.

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.

Assessment of climate change statistical downscaling methods: Application and comparison of two statistical methods to a single site in Lisbon

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia do Ambiente

New fluorescent chemosensors based on bio-inspired ligands and macrocycles: from single molecules to nanoparticles

Dissertação apresentada para obtenção do grau de Doutor em Biotecnologia pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia. A presente dissertação foi preparada no âmbito do protocolo de acordo bilateral de educação avançada (ERASMUS) entre a Universidade de Vigo e a Universidade Nova de Lisboa

Produção de SCO (Single Cell Oil) para biodiesel a partir da microalga Chlorella protothecoides em diversos bioreactores com troca simbiótica de correntes gasosas

Dissertação para obtenção do Grau de Mestre em Biotecnologia

Study of forbidden transitions in atomic systems

A Thesis submitted for the co-tutelle degree of Doctor in Physics at Universidade Nova de Lisboa and Université Pierre et Marie Curie

Single operation multiple data - paralelismo de dados ao nível da sub-rotina

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Caracterização de estirpes Mycobacterium Tuberculosis da família Lisboa: análise de regiões de diferença e single nucleotide polymorphisms

Dissertação apresentada para a obtenção do Grau de Mestre em Genética Molecular e Biomedicina, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

A nanopore-based stochastic detection method: Single molecule characterisation of nanoparticles using ()- hemolysin

Dissertation presented to obtain the Ph.D degree in Chemistry.

Caracterização de múltiplos polimorfismos de Pneumocystis Jirovecii por multiplex-PCR/Single base extension

Pneumocystis jirovecii é conhecido por causar infecções específicas no aparelho respiratório de seus hospedeiros, principalmente em doentes imunocomprometidos, manifestando-se por uma pneumonia grave e por vezes fatal, normalmente designada por pneumonia por Pneumocystis. A caracterização da diversidade genética de P. jirovecii tem demonstrado que determinados polimorfismos de base única poderão ser reconhecidos como marcadores moleculares de eleição para o estudo da distribuição geográfica, vias de transmissão, resistência/susceptibilidade a fármacos, factores de virulência e genética populacional de subtipos genéticos. Este estudo teve como objectivo a caracterização de polimorfismos de P. jirovecii, através da metodologia PCR multiplex/Extensão de base única (do inglês single base extension), com a principal finalidade de constatar eventuais associações entre polimorfismos de base única, genótipos multilocus, e dados clínicos e demográficos da infecção. Sessenta e seis espécimes pulmonares, previamente considerados positivos para P. jirovecii, obtidos entre 2001 e 2012, a partir de doentes portugueses imunocomprometidos, foram seleccionados de forma aleatória para este estudo multilocus. PCR multiplex foi utilizada para a amplificação simultânea de três regiões genómicas: subunidade grande do rRNA mitocondrial, superóxido dismutase e dihidropteroato sintetase. Cinco polimorfismos de base única, previamente correlacionados com parâmetros da doença, foram genotipados por extensão de base única: mt85, SOD110, SOD215, DHPS165 e DHPS171. Um total de 330 polimorfismos de base única e 29 genótipos multilocus putativos de P. jirovecii foram identificados e caracterizados nos espécimes pulmonares analisados. Os padrões de distribuição dos polimorfismos foram analisados, sendo considerada a variação temporal e/ou geográfica das suas formas alélicas. Constatou-se grande diversidade genotípica entre os isolados de P. jirovecii que poderá ter influência a nível epidemiológico. Foram observadas associações estatísticas entre mt85/genótipos multilocus e parâmetros demográficos e clínicos. A correlação mais importante verificou-se entre mt85C e cargas parasitárias baixas a moderadas, enquanto mt85T foi associado com cargas parasitárias altas; MLG5, MLG9 e MLG13 foram associados com cargas parasitárias baixas, moderadas e altas, respectivamente. Tais associações demonstram que potenciais marcadores moleculares da infecção por P. jirovecii poderão existir e que polimorfismos/genótipos específicos poderão determinar perfis epidemiológicos da pneumonia por Pneumocystis. A análise genética cruzada permitiu verificar associações entre polimorfismos de base única. Os polimorfismos SOD110T e SOD215C, SOD110C e SOD215T, DHPS165A e DHPS171C, DHPS165G e DHPS171T foram associados estatisticamente. Os genótipos multilocus mais prevalentes foram considerados para o teste recombinatório d1. Dois genótipos multilocus (MLG7 e MLG9) foram observados com elevada frequência, e a análise genética indicou que estes se encontravam sobre-representados na população de P. jirovecii estudada. Estas evidências indicam que o fenómeno de desequilíbrio de ligação e a propagação clonal de subtipos genéticos é frequente, considerando que a espécie P. jirovecii poderá ser representada por uma população com estrutura epidémica. O presente trabalho confirmou a importância do estudo de polimorfismos em P. jirovecii, sugerindo que a caracterização multilocus poderá fornecer informação relevante para a compreensão dos padrões, causas e controlo da infecção, melhorando assim a investigação deste importante patogéneo.

Carbon dots: nanopartículas de carbono fluorescentes para marcação e visualização de células

A imagiologia por fluorescência é uma técnica extremamente útil em investigação biomédica. Actualmente existe uma vasta gama de fluoróforos disponíveis para marcação por fluorescência. Contudo estes marcadores possuem limitações que condicionam a sua aplicação em sistemas biológicos. As nanopartículas de carbono fluorescentes (CNPs) constituem uma recente classe de marcadores fluorescentes com elevada biocompatibilidade. O objectivo deste trabalho consistiu em produzir de CNPs através de métodos simples, a sua caracterização e aplicação como marcadores celulares para visualização de células em microscopia de fluorescência. Inicialmente foram produzidas nanopartículas (NPs) seguindo métodos mencionados na literatura. Seguidamente foram produzidas CNPs a partir de PAA, por via hidrotérmica, e a partir da carbonização de grãos de cortiça para as quais foi feito um estudo do efeito da variável temperatura de carbonização. Das amostras produzidas, nove foram devidamente estudadas. A espectroscopia de absorção no UV-Vis revelou perfis de absorção característicos para este tipo de NPs. A emissão de fluorescência das CNPs caracterizadada por espectroscopia de fluorescência evidenciou comportamentos emissivos típicos destas NPs tais como dependência do máximo de emissão com o comprimento de onda de excitação. A intensidade da fluorescência das CNPs sintetizadas por via hidrotérmica é, em geral, maior com rendimentos quânticos de fluorescência a variar entre 4 e 11%. Os rendimentos quânticos das CNPs produzidas por carbonização variam entre 2 e 5%. As imagens de microscopia electrónica demonstram que as CNPs possuíam formas esféricas. Os tamanhos determinados por SEM, TEM e DLS revelaram que as dimensões das NPs caem entre os 2 e 150nm. Por DRX constatou-se que as CNPs possuem uma estrutura atómica desorganizada. A análise FTIR mostrou que as amostras de CNPs produzidas a partir de macromoléculas pelo método hidrotérmico possuíam uma grande quantidade de precursor não degradado. Para as restantes CNPs foi verificada a presença de grupos funcionais polares que lhes conferem solubilidade em meio aquoso. Com 1H-RMN verificou-se uma diminuição de grupos alifáticos e aumento de grupos aromáticos nas CNPs de cortiça carbonizada, com o aumento da temperatura de carbonização. O potencial ζ da amostra obtida com maior temperatura de carbonização foi -25,7mV. Nos estudos in vitro realizados apenas as NPs produzidas a partir de ácido cítrico e etilenodiamina por via hidrotérmica marcaram eficazmente as linhas celulares de osteoblastos e de fibroblastos. A eficiência da marcação aparenta ser dependente do tempo de incubação com CNPs.