Neural mechanisms of stimulus generalization in auditory fear conditioning

Dissertation presented to obtain the Ph.D degree in Neuroscience Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa

Metabolic flux analysis of neural cell metabolism in primary cultures

Dissertation presented to obtain the Ph.D degree in Biochemistry, Neuroscience

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 computational role of short-term plasticity and the balance of excitation and inhibition in neural microcircuits: experimental and theoretical analysis

The computations performed by the brain ultimately rely on the functional connectivity between neurons embedded in complex networks. It is well known that the neuronal connections, the synapses, are plastic, i.e. the contribution of each presynaptic neuron to the firing of a postsynaptic neuron can be independently adjusted. The modulation of effective synaptic strength can occur on time scales that range from tens or hundreds of milliseconds, to tens of minutes or hours, to days, and may involve pre- and/or post-synaptic modifications. The collection of these mechanisms is generally believed to underlie learning and memory and, hence, it is fundamental to understand their consequences in the behavior of neurons.(...)

Neural correlations during brain activation in arithmetical tasks – an approach using electroencephalographic data

Dissertação apresentada na Faculdade de Ciências e Tecnologiea da Universidade Nova de Lisboa, para obtenção do Grau de Mestre em Engenharia Biomédica

Yeast as a model system to study genetic and post-translational regulation of metabolic pathways in mammals

Dissertation presented to obtain the Ph.D degree in Biology

Structural and functional studies of PpcA: a key protein in the electron transfer pathways of Geobacter sulfurreducens

Dissertação para obtenção do Grau de Doutor em Bioquímica, ramo de Biotecnologia

Signal transduction pathways involving the hypertension-related WNK1 and WNK4 protein kinases

Dissertação apresentada para obtenção do Grau de Doutor em Biologia, na especialidade de Genética Molecular, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Overexpressing BDNF in Neural Stem Cells using non-viral gene delivery strategies

Dissertação para obtenção do Grau de Mestre em Biotecnologia

Tbx1 and Bmp2 in the development of the ear, neural crest and pharyngeal system in mice

Dissertation presented to obtain the Ph.D degree in Biology

Uncertainty, generalization, and neural representation of relevant variables for decision making

Dissertation presented to obtain the Ph.D degree in Biology, Computational Biology.

Interaction of malaria parasites with host late endocytic and autophagic pathways is essential for Plasmodium liver stage development

RESUMO: A Malária é causada por parasitas do género Plasmodium, sendo a doença parasitária mais fatal para o ser humano. Apesar de, durante o século passado, o desenvolvimento económico e a implementação de diversas medidas de controlo, tenham permitido erradicar a doença em muitos países, a Malária continua a ser um problema de saúde grave, em particular nos países em desenvolvimento. A Malária é transmitida através da picada de uma fêmea de mosquito do género Anopheles. Durante a picada, os esporozoítos são injetados na pele do hospedeiro, seguindo-se a fase hepática e obrigatória do ciclo de vida. No fígado, os esporozoítos infetam os hepatócitos onde se replicam, dentro de um vacúolo parasitário (VP) e de uma forma imunitária silenciosa, em centenas de merozoitos. Estas novas formas do parasita são as responsáveis por infetar os eritrócitos, iniciando a fase sanguínea da doença, onde se os primeiros sintomas se manifestam, tais como a característica febre cíclica. A fase hepática da doença é a menos estudada e compreendida. Mais ainda, as interações entre o VP e os organelos da células hospedeira estão ainda pouco caracterizados. Assim, neste estudo, as interações entre os organelos endocíticos e autofágicos da célula hospedeira e o VP foram dissecados, observando-se que os anfisomas, que são organelos resultantes da intersecção do dois processos de tráfego intracelular, interagem com o parasita. Descobrimos que a autofagia tem também uma importante função imunitária durante a fase hepática inicial, ao passo, que durante o desenvolvimento do parasita, já numa fase mais tardia, o parasita depende da interação com os endossomas tardios e anfisomas para crescer. Vesiculas de BSA, EGF e LC3, foram, também, observadas dentro do VP, sugerindo que os parasitas são capazes de internalizar material endocítico e autofágico do hospedeiro. Mais ainda, mostramos que esta interação depende da cinase PIKfyve, responsável pela conversão do fosfoinositidio-3-fosfato no fosfoinositidio-3,5-bifosfato, uma vez que inibindo esta cinase o parasita não é capaz de crescer normalmente. Finalmente, mostramos que a proteína TRPML1, uma proteína efetora do fosfoinositidio-3,5-bifosfato, e envolvida no processo de fusão das membranas dos organelos endocíticos e autofágicos, também é necessária para o crescimento do parasita. Desta forma, o nosso estudo sugere que a membrana do VP funde com vesiculas endocíticas e autofágicas tardias, de uma forma dependente do fositidio-3,5-bifosfato e do seu effetor TRPML1, permitindo a troca de material com a célula hospedeira. Concluindo, os nossos resultados evidenciam que o processo autofágico que ocorre na célula hospedeira tem um papel duplo durante a fase hepática da malaria. Enquanto numa fase inicial os hepatócitos usam o processo autofágico como forma de defesa contra o parasita, já durante a fase de replicação o VP funde com vesiculas autofágicas e endocíticas de forma a obter os nutrientes necessários ao seu desenvolvimento.--------- ABSTRACT: Malaria, which is caused by parasites of the genus Plasmodium, is the most deadly parasitic infection in humans. Although economic development and the implementation of control measures during the last century have erradicated the disease from many areas of the world, it remains a serious human health issue, particularly in developing countries. Malaria is transmitted by female mosquitoes of the genus Anopheles. During the mosquito blood meal, Plasmodium spp. sporozoites are injected into the skin dermis of the vertebrate host, followed by an obligatory liver stage. Upon entering the liver, Plasmodium parasites infect hepatocytes and silently replicate inside a host cell-derived parasitophorous vacuole (PV) into thousands of merozoites. These new parasite forms can infect red blood cells initiating the the blood stage of the disease which shows the characteristic febrile malaria episodes. The liver stage is the least characterized step of the malaria infection. Moreover, the interactions between the Plasmodium spp. PV and the host cell trafficking pathways are poorly understood. We dissected the interaction between Plasmodium parasites and the host cell endocytic and autophagic pathways and we found that both pathways intersect and interconnect in the close vicinity of the parasite PV, where amphisomes are formed and accumulate. Interestingly, we observed a clearance function for autophagy in hepatocytes infected with Plasmodium berghei parasites at early infection times, whereas during late liver stage development late endosomes and amphisomes are required for parasite growth. Moreover, we found the presence of internalized BSA, EGF and LC3 inside parasite vacuoles, suggesting that the parasites uptake endocytic and autophagic cargo. Furthermore, we showed that the interaction between the PV and host traffic pathways is dependent on the kinase PIKfyve, which converts the phosphoinositide PI(3)P into PI(3,5)P2, since PIKfyve inhibition caused a reduction in parasite growth. Finally, we showed that the PI(3,5)P2 effector protein TRPML1, which is involved in late endocytic and autophagic membrane fusion, is also required for parasite development. Thus, our studies suggest that the parasite parasitophorous vacuole membrane (PVM) is able to fuse with late endocytic and autophagic vesicles in a PI(3,5)P2- and TRPML1-dependent manner, allowing the exchange of material between the host cell and the parasites, necessary for the rapid development of the latter that is seen during the liver stage of infection. In conclusion, we present evidence supporting a specific and essential dual role of host autophagy during the course of Plasmodium liver infection. Whereas in the initial hours of infection the host cell uses autophagy as a cell survival mechanism to fight the infection, during the replicative phase the PV fuses with host autophagic and endocytic vesicles to obtain nutrients required for parasite growth.

Role of Notch signaling on the differentiation of early lymphoid progenitors cells: a view throughout the development of the embryonic chicken thymus and spleen

Dissertação para a obtenção do grau de Mestre em Genética Molecular e Biomedicina

Study of novel energy metabolism pathways in anaerobic bacteria

Energy conservation in chemotrophic anaerobic bacteria is achieved by two possible processes, substrate level phosphorylation (SLP) and electron transfer phosphorylation (ETP). This second mechanism, also known as respiration, involves chemiosmotic coupling. However, a third mechanism for energy coupling was recently proposed: the flavin-based electron bifurcation (FBEB). (...)

Nanostructuring silicon probes via electrodeposition: characterization of electrode coatings for acute in vivo neural recordings

Understanding how the brain works will require tools capable of measuring neuron elec-trical activity at a network scale. However, considerable progress is still necessary to reliably increase the number of neurons that are recorded and identified simultaneously with existing mi-croelectrode arrays. This project aims to evaluate how different materials can modify the effi-ciency of signal transfer from the neural tissue to the electrode. Therefore, various coating materials (gold, PEDOT, tungsten oxide and carbon nano-tubes) are characterized in terms of their underlying electrochemical processes and recording ef-ficacy. Iridium electrodes (177-706 μm2) are coated using galvanostatic deposition under different charge densities. By performing electrochemical impedance spectroscopy in phosphate buffered saline it is determined that the impedance modulus at 1 kHz depends on the coating material and decreased up to a maximum of two orders of magnitude for PEDOT (from 1 MΩ to 25 kΩ). The electrodes are furthermore characterized by cyclic voltammetry showing that charge storage capacity is im-proved by one order of magnitude reaching a maximum of 84.1 mC/cm2 for the PEDOT: gold nanoparticles composite (38 times the capacity of the pristine). Neural recording of spontaneous activity within the cortex was performed in anesthetized rodents to evaluate electrode coating performance.