Initiation of cell locomotility is a morphogenetic checkpoint in thyroid epithelial cells regulated by ERK and PI3-kinase signals

Epithelial locomotility is a fundamental determinant of tissue patterning that is subject to strict physiological regulation. The current, study sought to identify cellular signals that initiate cell migration in cultured thyroid epithelial cells. Porcine thyroid cells cultured as 3-dimensional follicles convert to 2-dimensional monolayers when deprived of agents that stimulate cAMP/PKA signaling. This morphogenetic event is driven by the activation of cell-on-substrate locomotility, providing a convenient assay for events that regulate the initiation of locomotion. In this system, the extracellular signal regulated kinase (ERK) pathway became activated as follicles converted to monolayer, as demonstrated by immunoblotting for activation-specific phosphorylation and nuclear accumulation of ERK. Inhibition of ERK activation using the drug PD98059 effectively prevented cells from beginning to migrate. PD98059 inhibited cell spreading, actin filament reorganization and the assembly of focal adhesions, cellular events that mediate the initiation of thyroid cell locomotility. Akt (PKB) signaling was also activated during follicle-to-monolayer conversion and the phosphoinositide 3-kinase (PI3-kinase) inhibitor, wortmannin, also blocked the initiation of cell movement. Wortmannin did not, however, block activation of ERK signaling. These findings, therefore, identify the ERK and PI3-kinase signaling pathways as important stimulators of thyroid cell locomotility. These findings are incorporated into a model where the initiation of thyroid cell motility constitutes a morphogenetic checkpoint regulated by coordinated changes in stimulatory (ERK, PI3-kinase) and tonic inhibitory (cAMP/PKA) signaling pathways. Cell Motil. Cytoskeleton 49:93-103, 2001. (C) 2001 Wiley-Liss, Inc.

The expression of tubulin cofactor A (TBCA) is regulated by a noncoding antisense Tbca RNA during testis maturation

Abstract - Recently, long noncoding RNAs have emerged as pivotal molecules for the regulation of coding genes' expression. These molecules might result from antisense transcription of functional genes originating natural antisense transcripts (NATs) or from transcriptional active pseudogenes. TBCA interacts with β-tubulin and is involved in the folding and dimerization of new tubulin heterodimers, the building blocks of microtubules. Methodology/Principal findings: We found that the mouse genome contains two structurally distinct Tbca genes located in chromosomes 13 (Tbca13) and 16 (Tbca16). Interestingly, the two Tbca genes albeit ubiquitously expressed, present differential expression during mouse testis maturation. In fact, as testis maturation progresses Tbca13 mRNA levels increase progressively, while Tbca16 mRNA levels decrease. This suggests a regulatory mechanism between the two genes and prompted us to investigate the presence of the two proteins. However, using tandem mass spectrometry we were unable to identify the TBCA16 protein in testis extracts even in those corresponding to the maturation step with the highest levels of Tbca16 transcripts. These puzzling results led us to re-analyze the expression of Tbca16. We then detected that Tbca16 transcription produces sense and natural antisense transcripts. Strikingly, the specific depletion by RNAi of these transcripts leads to an increase of Tbca13 transcript levels in a mouse spermatocyte cell line. Conclusions/Significance: Our results demonstrate that Tbca13 mRNA levels are post-transcriptionally regulated by the sense and natural antisense Tbca16 mRNA levels. We propose that this regulatory mechanism operates during spermatogenesis, a process that involves microtubule rearrangements, the assembly of specific microtubule structures and requires critical TBCA levels.

Espresso coffee residues: a valuable source of unextracted compounds

Espresso spent coffee grounds were chemically characterized to predict their potential, as a source of bioactive compounds, by comparison with the ones from the soluble coffee industry. Sampling included a total of 50 samples from 14 trademarks, collected in several coffee shops and prepared with distinct coffee machines. A high compositional variability was verified, particularly with regard to such water-soluble components as caffeine, total chlorogenic acids (CGA), and minerals, supported by strong positive correlations with total soluble solids retained. This is a direct consequence of the reduced extraction efficiency during espresso coffee preparation, leaving a significant pool of bioactivity retained in the extracted grounds. Besides the lipid (12.5%) and nitrogen (2.3%) contents, similar to those of industrial coffee residues, the CGA content (478.9 mg/100 g), for its antioxidant capacity, and its caffeine content (452.6 mg/100 g), due to its extensive use in the food and pharmaceutical industries, justify the selective assembly of this residue for subsequent use.

Self-assembly in chains, rings, and branches: a single component system with two critical points

Agências financiadoras: FCT - PEstOE/FIS/UI0618/2011; PTDC/FIS/098254/2008 ERC-PATCHYCOLLOIDS e MIUR-PRIN

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.

A survey of technologies and applications for climbing robots locomotion and adhesion

The interest in the development of climbing robots has grown rapidly in the last years. Climbing robots are useful devices that can be adopted in a variety of applications, such as maintenance and inspection in the process and construction industries. These systems are mainly adopted in places where direct access by a human operator is very expensive, because of the need for scaffolding, or very dangerous, due to the presence of an hostile environment. The main motivations are to increase the operation efficiency, by eliminating the costly assembly of scaffolding, or to protect human health and safety in hazardous tasks. Several climbing robots have already been developed, and other are under development, for applications ranging from cleaning to inspection of difficult to reach constructions. A wall climbing robot should not only be light, but also have large payload, so that it may reduce excessive adhesion forces and carry instrumentations during navigation. These machines should be capable of travelling over different types of surfaces, with different inclinations, such as floors, walls, or ceilings, and to walk between such surfaces (Elliot et al. (2006); Sattar et al. (2002)). Furthermore, they should be able of adapting and reconfiguring for various environment conditions and to be self-contained. Up to now, considerable research was devoted to these machines and various types of experimental models were already proposed (according to Chen et al. (2006), over 200 prototypes aimed at such applications had been developed in the world by the year 2006). However, we have to notice that the application of climbing robots is still limited. Apart from a couple successful industrialized products, most are only prototypes and few of them can be found in common use due to unsatisfactory performance in on-site tests (regarding aspects such as their speed, cost and reliability). Chen et al. (2006) present the main design problems affecting the system performance of climbing robots and also suggest solutions to these problems. The major two issues in the design of wall climbing robots are their locomotion and adhesion methods. With respect to the locomotion type, four types are often considered: the crawler, the wheeled, the legged and the propulsion robots. Although the crawler type is able to move relatively faster, it is not adequate to be applied in rough environments. On the other hand, the legged type easily copes with obstacles found in the environment, whereas generally its speed is lower and requires complex control systems. Regarding the adhesion to the surface, the robots should be able to produce a secure gripping force using a light-weight mechanism. The adhesion method is generally classified into four groups: suction force, magnetic, gripping to the surface and thrust force type. Nevertheless, recently new methods for assuring the adhesion, based in biological findings, were proposed. The vacuum type principle is light and easy to control though it presents the problem of supplying compressed air. An alternative, with costs in terms of weight, is the adoption of a vacuum pump. The magnetic type principle implies heavy actuators and is used only for ferromagnetic surfaces. The thrust force type robots make use of the forces developed by thrusters to adhere to the surfaces, but are used in very restricted and specific applications. Bearing these facts in mind, this chapter presents a survey of different applications and technologies adopted for the implementation of climbing robots locomotion and adhesion to surfaces, focusing on the new technologies that are recently being developed to fulfill these objectives. The chapter is organized as follows. Section two presents several applications of climbing robots. Sections three and four present the main locomotion principles, and the main "conventional" technologies for adhering to surfaces, respectively. Section five describes recent biological inspired technologies for robot adhesion to surfaces. Section six introduces several new architectures for climbing robots. Finally, section seven outlines the main conclusions.

Optimal location of supports in beam structures using genetic algorithms

Real structures can be thought as an assembly of components, as for instances plates, shells and beams. This later type of component is very commonly found in structures like frames which can involve a significant degree of complexity or as a reinforcement element of plates or shells. To obtain the desired mechanical behavior of these components or to improve their operating conditions when rehabilitating structures, one of the eventual parameters to consider for that purpose, when possible, is the location of the supports. In the present work, a beam-type structure is considered, and for a set of cases concerning different number and types of supports, as well as different load cases, the authors optimize the location of the supports in order to obtain minimum values of the maximum transverse deflection. The optimization processes are carried out using genetic algorithms. The results obtained, clearly show a good performance of the approach proposed. © 2014 IEEE.

Localization studies of the FemXAB protein family in Staphylococcus aureus

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

Climbing robots: a survey of technologies and applications

The interest in the development of climbing robots is growing rapidly. Motivations are typically to increase the operation efficiency by obviating the costly assembly of scaffolding or to protect human health and safety in hazardous tasks. Climbing robots are starting to be developed for applications ranging from cleaning to inspection of difficult to reach constructions. These robots should be capable of travelling on different types of surfaces, with varying inclinations, such as floors, walls, ceilings, and to walk between such surfaces. Furthermore, these machines should be capable of adapting and reconfiguring for various environment conditions and to be self-contained. Regarding the adhesion to the surface, they should be able to produce a secure gripping force using a light-weight mechanism. This paper presents a survey of different applications and technologies proposed for the implementation of climbing robots.

The potential of molecular imprinting as a biosensing devices for monitoring the CEA cancer biomarker

6th Graduate Student Symposium on Molecular Imprinting

Bioactive beads for local sensing of proteases in 3D engineered tissues

Dissertação para obtenção do Grau de Doutor em Bioengenharia (MIT)

Empirical models for quantification of machining damage in composite materials

Dissertação para obtenção do Grau de Doutor em Engenharia Mecânica

Structural investigation of the Bacillus subtilis morphogenic factor RodZ

A thesis to obtain a Master degree in Structural and Functional Biochemistry

Towards modification of Medicago truncatula epigenome: genome editing with engineered nucleases

Periodic drought is the primary limitation of plant growth and crop yield. The rise of water demand caused by the increase in world population and climate change, leads to one of the biggest challenges of modern agriculture: to increase food and feed production. De novo DNA methylation is a process regulated by small interfering RNA (siRNAs), which play a role in plant response and adaptation to abiotic stress. In the particular case of water deficit, growing evidences suggest a link between the siRNA pathways and drought response in the model legume Medicago truncatula. As a first step to understand the role of DNA methylation under water stress, we have set up several bioinformatics and molecular methodologies allowing the design of Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 systems and the assembly of TALENs (transcription activator-like effector nucleases), to target both dicer-like 3 (MtDCL3) and RNA-Dependent RNA polymerase (MtRDR2), enzymes of the RNA-directed DNA methylation pathway. TALENs efficiency was evaluated prior to plant transformation by a yeast-based assay using two different strategies to test TALENs activity: Polyacrylamide gel electrophoresis (PAGE) and Single strand conformation polymorphisms (SSCP). In this assay, yeast cells triple transformation emerged as good and rapid alternative to laborious yeast mating strategies. PAGE analysis might be a valuable tool to test TALENs efficacy in vivo if we could increase TALENs activity. SSCP-based approach proved to be ineffective due to the generation of several false positives. TALENs and CRISPR/Cas9 system constructed and designed in this work will in the future certainly enable the successful disruption of DCL3 and RDR2 genes and shed the light on the relationship between plant stress resistance and epigenetic regulation mediated by siRNAs in M.truncatula.

Production and characterization of electrospun composite fibers: confinement of thermosensitive microgels

Materials engineering focuses on the assembly of materials´ properties to design new products with the best performance. By using sub-micrometer size materials in the production of composites, it is possible to obtain objects with properties that none of their compounds show individually. Once three-dimensional materials can be easily customized to obtain desired properties, much interest has been paid to nanostructured poly-mers in order to build biocompatible devices. Over the past years, the thermosensitive microgels have become more common in the framework of bio-materials with potential applicability in therapy and/or diagnostics. In addition, high aspect ratio biopolymers fibers have been produced using the cost-effective method called electrospinning. Taking advantage of both microgels and electrospun fibers, surfaces with enhanced functionalities can be obtained and, therefore employed in a wide range of applications. This dissertation reports on the confinement of stimuli-responsive microgels through the colloidal electro-spinning process. The process mainly depends on the composition, properties and patterning of the precur-sor materials within the polymer jet. Microgels as well as the electrospun non-woven mats were investigated to correlate the starting materials with the final morphology of the composite fibers. PNIPAAm and PNIPAAm/Chitosan thermosensitive microgels with different compositions were obtained via surfactant free emulsion polymerization (SFEP) and characterized in terms of chemical structure, morphology, thermal sta-bility, swelling properties and thermosensitivity. Finally, the colloidal electrospinning method was carried out from spinning solutions composed of the stable microgel dispersions (up to a concentration of about 35 wt. % microgels) and a polymer solution of PEO/water/ethanol mixture acting as fiber template solution. The confinement of microgels was confirmed by Scanning Electron Microscopy (SEM). The electrospinning process was statistically analysed providing the optimum set of parameters aimed to minimize the fiber diameter, which give rise to electrospun nanofibers of PNIPAAm microgels/PEO with a mean fiber diameter of 63 ± 25 nm.