Aplicación móvil para la enseñanza de genética

El presente trabajo tiene su origen en la necesidad de herramientas de apoyo al aprendizaje para los alumnos en las clases de Genética de la Facultad de Biología de la Universidad Complutense de Madrid. En esta asignatura, el equipo docente ha desarrollado aplicaciones para dispositivos móviles destinadas a los alumnos. Las aplicaciones les permiten trabajar con materiales relacionados con aspectos clave de la asignatura. Estas aplicaciones contienen apartados de teoría y ejercicios. Los ejercicios cuentan con asistentes automatizados que guían al alumno para su realización y autocorrección. En su forma actual, las aplicaciones presentan limitaciones tanto desde el punto de vista de su diseño como de la funcionalidad que ofrecen. El actual diseño no aplica las técnicas comunes de Ingeniería del Software respecto a aplicaciones cliente-servidor. Ello las hace difíciles de mantener cuando se plantea abordar nuevas funcionalidades y plataformas, o facilitar la creación de nuevos materiales de la asignatura. Ello ha limitado su expansión para incorporar nuevos tipos de materiales (en particular diferentes tipos de ejercicios), integrarlas con otras herramientas (por ejemplo, el Campus Virtual de la universidad) o permitir un apoyo efectivo a la comunidad de aprendizaje formada por alumnos y docentes (por ejemplo, para que los docentes supervisen la evolución de los alumnos y estos puedan obtener información adicional de los profesores). Para abordar esta situación se propone una aplicación móvil que engobe a todas las aplicaciones anteriores que se habían creado para las clases de Genética. Se utilizará un modelo cliente-servidor para mejorar sus capacidades funcionales, de modo que cumpla con los requisitos establecidos. Entre estos se incluye un control de los usuarios que utilizan la aplicación, y que se optimice la memoria local utilizada por la aplicación, permitiendo así el uso de imágenes más pesadas. Además, este modelo facilitará las tareas de mantenimiento de la aplicación, por ejemplo incluir nuevo material. Por otro lado, también se propone rediseñar la interfaz de la aplicación, de modo que sea más accesible desde el punto de vista de la usabilidad.

Aceleración de algoritmos de estimación de movimiento mediante OpenCL

El flujo óptico y la estimación de movimiento es área de conocimiento muy importante usado en otros campos del conocimiento como el de la seguridad o el de la bioinformática. En estos sectores, se demandan aplicaciones de flujo óptico que realicen actividades muy importantes con tiempos de ejecución lo más bajos posibles, llegando a tiempo real si es posible. Debido a la gran complejidad de cálculos que siguen a este tipo de algoritmos como se observará en la sección de resultados, la aceleración de estos es una parte vital para dar soporte y conseguir ese tiempo real tan buscado. Por lo que planteamos como objetivo para este TFG la aceleración de este tipo de algoritmos mediante diversos tipos de aceleradores usando OpenCL y de paso demostrar que OpenCL es una buena herramienta que permite códigos paralelizados con un gran Speedup a la par que funcionar en toda una diversa gama de dispositivos tan distintos como un GPU y una FPGA. Para lo anteriormente mencionado trataremos de desarrollar un código para cada algoritmo y optimizarlo de forma no especifica a una plataforma para posteriormente ejecutarlo sobre las diversas plataformas y medir tiempos y error para cada algoritmo. Para el desarrollo de este proyecto partimos de la teoría de dos algoritmos ya existentes: Lucas&Kanade monoescala y el Horn&Schunck. Además, usaremos estímulos para estos algoritmos muy aceptados por la comunidad como pueden ser el RubberWhale o los Grove, los cuales nos ayudarán a establecer la corrección de estos algoritmos y analizar su precisión, dando así un estudio referencia para saber cual escoger.

Bioinformatics analysis of the Pedobacter sp. NL19 genome

The last decades of the 20th century defined the genetic engineering advent, climaxing in the development of techniques, such as PCR and Sanger sequencing. This, permitted the appearance of new techniques to sequencing whole genomes, identified as next-generation sequencing. One of the many applications of these techniques is the in silico search for new secondary metabolites, synthesized by microorganisms exhibiting antimicrobial properties. The peptide antibiotics compounds can be classified in two classes, according to their biosynthesis, in ribosomal or nonribosomal peptides. Lanthipeptides are the most studied ribosomal peptides and are characterized by the presence of lanthionine and methylanthionine that result from posttranslational modifications. Lanthipeptides are divided in four classes, depending on their biosynthetic machinery. In class I, a LanB enzyme dehydrate serine and threonine residues in the C-terminus precursor peptide. Then, these residues undergo a cyclization step performed by a LanC enzyme, forming the lanthionine rings. The cleavage and the transport of the peptide is achieved by the LanP and LanT enzymes, respectively. Although, in class II only one enzyme, LanM, is responsible for the dehydration and cyclization steps and also only one enzyme performs the cleavage and transport, LanT. Pedobacter sp. NL19 is a Gram-negative bacterium, isolated from sludge of an abandon uranium mine, in Viseu (Portugal). Antibacterial activity in vitro was detected against several Gram-positive and Gram-negative bacteria. Sequencing and in silico analysis of NL19 genome revealed the presence of 21 biosynthetic clusters for secondary metabolites, including nonribosomal and ribosomal peptides biosynthetic clusters. Four lanthipeptides clusters were predicted, comprising the precursor peptides, the modifying enzymes (LanB and LanC), and also a bifunctional LanT. This result revealed the hybrid nature of the clusters, comprising characteristics from two distinct classes, which are poorly described in literature. The phylogenetic analysis of their enzymes showed that they clustered within the bacteroidetes clade. Furthermore, hybrid gene clusters were also found in other species of this phylum, revealing that it is a common characteristic in this group. Finally, the analysis of NL19 colonies by MALDI-TOF MS allowed the identification of a 3180 Da mass that corresponds to the predicted mass of a lanthipeptide encoded in one of the clusters. However, this result is not fully conclusive and further experiments are needed to understand the full potential of the compounds encoded in this type of clusters. In conclusion, it was determined that NL19 strain has the potential to produce diverse secondary metabolites, including lanthipeptides that were not functionally characterized so far.

CD81 interacting proteins

Fertilization is a multistep and complex process culminating in the merge of gamete membranes, cytoplasmic unity and fusion of genome. CD81 is a tetraspanin protein that participates in sperm-oocyte interaction, being present at the oocyte surface. CD81 has also been implicated in other biological processes, however its specific function and molecular mechanisms of action remain to be elucidated. The interaction between CD81 and its binding partner proteins may underlie the CD81 involvement in a variety of cellular processes and modulate CD81/interactors specific functions. Interestingly, in a Yeast two Hybrid system previously performed in our lab, CD81 has emerged as a putative interactor of the Amyloid Precursor Protein (APP). In the work here described, bioinformatics analyses of CD81 interacting proteins were performed and the retrieved information used to construct a protein-protein interaction network, as well as to perform Gene Ontology enrichment analyses. CD81 expression was further evaluated in CHO, GC-1 and SH-SY5Y cell lines, and in human sperm cells. Additionally, its subcellular localization was analyzed in sperm cells and in the neuronal-like SH-SY5Y cell line. Subsequently, coimmunoprecipitation assays were performed in CHO and SH-SY5Y cells to attempt to prove the physical interaction between CD81 and APP. A functional interaction between these two proteins was accessed thought the analyses of the effects of CD81 overexpression on APP levels. A co-localization analysis of CD81 and some interactors proteins retrieved from the bioinformatics analyses, such as APP, AKT1 and cytoskeleton-related proteins, was also performed in sperm cells and in SH-SY5Y cells. The effects of CD81 in cytoskeleton remodeling was evaluated in SH-SY5Y cells through monitoring the effects of CD81 overexpression in actin and tubulin levels, and analyzing the colocalization between overexpressed CD81 and F-actin. Our results showed that CD81 is expressed in all cell lines tested, and also provided the first evidence of the presence of CD81 in human sperm cells. CD81 immunoreactivity was predominantly detected in the sperm head, including the acrosome membrane, and in the midpiece, where it co-localized with APP, as well as in the post-acrosomal region. Furthermore, CD81 co-localizes with APP in the plasma membrane and in cellular projections in SH-SY5Y cells, where CD81 overexpression has an influence on APP levels, also visible in CHO cells. The analysis of CD81 interacting proteins such as AKT1 and cytoskeletonrelated proteins showed that CD81 is involved in a variety of pathways that may underlie cytoskeleton remodeling events, related to processes such as sperm motility, cell migration and neuritogenesis. These results deepen our understanding on the functions of CD81 and some of its interactors in sperm and neuronal cells.

Silencing the gip gene of Phytophthora cinnamomi by iRNA and studying the subcellular localization of GIP and NPP1 proteins

Ink Disease is considered one of the most important causes of the decline of chestnut orchards. The break in yield of Castanea sativa Mill is caused by two species: Phytophthora cinnamomi and Phytophthora cambivora, being the first one the foremost pathogen of ink disease in Portugal. P. cinnamomi is one of the most aggressive and widespread plant pathogen with nearly 1,000 host species. This oomycete causes enormous economic losses and it is responsible for the decline of many plant species in Europe and worldwide. Up to now no efficient treatments are available to fight these pathogens. Because of the importance of chestnut at economical and ecological levels, especially in Portugal, it becomes essential to explore the molecular mechanisms that determine the interaction between Phytophthora species and host plants through the study of proteins GIP (glucanase inhibitor protein) and NPP1 (necrosis-inducing Phytophthora protein 1) produced by P. cinnamomi during the infection. The technique of RNA interference was used to knockdown the gip gene of P. cinnamomi. Transformants obtained with the silenced gene have been used to infect C. sativa, in order to determine the effect of gene silencing on the plant phenotype. To know more about the function of GIP and NPP1 involved in the mechanism of infection, the ORF’s of gip and npp1 genes have been cloned to the pTOR-eGFP vector for a future observation of P. cinnamomi transformants with fluorescent microscopy and determination of the subcellular localization. Moreover the prediction by bioinformatics tools indicates that both GIP and NPP1 proteins are secreted. The results allow to predict the secretory destination of both GIP and NPP1 proteins and confirm RNAi as a potential alternative biological tool in the control and management of P. cinnamomi. Keywords:

Metagenômica comparativa de solo de regiões de mata atlântica e caatinga do estado do Rio Grande do Norte - Brasil

The microorganisms play very important roles in maintaining ecosystems, which explains the enormous interest in understanding the relationship between these organisms as well as between them and the environment. It is estimated that the total number of prokaryotic cells on Earth is between 4 and 6 x 1030, constituting an enormous biological and genetic pool to be explored. Although currently only 1% of all this wealth can be cultivated by standard laboratory techniques, metagenomic tools allow access to the genomic potential of environmental samples in a independent culture manner, and in combination with third generation sequencing technologies, the samples coverage become even greater. Soils, in particular, are the major reservoirs of this diversity, and many important environments around us, as the Brazilian biomes Caatinga and Atlantic Forest, are poorly studied. Thus, the genetic material from environmental soil samples of Caatinga and Atlantic Forest biomes were extracted by direct techniques, pyrosequenced, and the sequences generated were analyzed by bioinformatics programs (MEGAN MG-RAST and WEBCarma). Taxonomic comparative profiles of the samples showed that the phyla Proteobacteria, Actinobacteria, Acidobacteria and Planctomycetes were the most representative. In addition, fungi of the phylum Ascomycota were identified predominantly in the soil sample from the Atlantic Forest. Metabolic profiles showed that despite the existence of environmental differences, sequences from both samples were similarly placed in the various functional subsystems, indicating no specific habitat functions. This work, a pioneer in taxonomic and metabolic comparative analysis of soil samples from Brazilian biomes, contributes to the knowledge of these complex environmental systems, so far little explored

Using serious games for improving medical education: an application to cytopathology

El objetivo principal del proyecto es desarrollar una plataforma compuesta por aplicaciones educativas gamificadas para el entrenamiento de personal médico en países de recursos limitados en citopatología mediante dispositivos Android de bajo presupuesto. Antes de desplegar la plataforma en países con recursos limitados, va a ser probada en un curso de Introducción a Citopatología de la Escuela Médica de Harvard. El proyecto final debe funcionar tanto en PCs como en dispositivos Android de bajo coste (p.e. 50 dólares americanos, Amazon Kindle Fire 7 pulgadas) y no puede depender de una conexión a internet continua. Se han analizado algunas aplicaciones con propósito de juego y simulaciones gamificadas para tener una base de conocimiento común entre expertos médicos y desarrolladores. También se han estudiado juegos y aplicaciones cuyo objetivo es hacer uso de imágenes médicas para entrenamiento de personal médico o están enfocadas al diagnóstico mediante colaboración por parte de personal no-médico. Esto nos ha permitido identificar las mejores mecánicas de juego para nuestro caso de uso. A continuación, se han comparado diferentes herramientas de edición y motores de juegos desde el punto de vista del rendimiento ofrecido, las plataformas soportadas, su documentación y licencia. Todo ello nos ha permitido elegir la tecnología de desarrollo (libGDX). Finalmente, diseñamos e implementamos un sistema integrado de aplicaciones (editor de contenido y generador de juegos). El sistema está enfocado a reducir la dependencia entre el personal experto y los desarrolladores para crear y mantener contenido educativo. Se trata de una arquitectura formada por un servicio RESTful, y un editor asociado, orientado a la gestión de contenido educativo orientado para citopatología y dos clientes para diferentes plataformas (PC y Android) que consumen dicho servicio. Finalmente, se presentan las conclusiones y el trabajo futuro del proyecto.

Towards the Knowledge-based Design of Universal Influenza Epitope Ensemble Vaccines

Motivation: Influenza A viral heterogeneity remains a significant threat due to unpredictable antigenic drift in seasonal influenza and antigenic shifts caused by the emergence of novel subtypes. Annual review of multivalent influenza vaccines targets strains of influenza A and B likely to be predominant in future influenza seasons. This does not induce broad, cross protective immunity against emergent subtypes. Better strategies are needed to prevent future pandemics. Cross-protection can be achieved by activating CD8+ and CD4+ T cells against highly-conserved regions of the influenza genome. We combine available experimental data with informatics-based immunological predictions to help design vaccines potentially able to induce cross-protective T-cells against multiple influenza subtypes. Results: To exemplify our approach we designed two epitope ensemble vaccines comprising highlyconserved and experimentally-verified immunogenic influenza A epitopes as putative non-seasonal influenza vaccines; one specifically targets the US population and the other is a universal vaccine. The USA-specific vaccine comprised 6 CD8+ T cell epitopes (GILGFVFTL, FMYSDFHFI, GMDPRMCSL, SVKEKDMTK, FYIQMCTEL, DTVNRTHQY) and 3 CD4+ epitopes (KGILGFVFTLTVPSE, EYIMKGVYINTALLN, ILGFVFTLTVPSERG). The universal vaccine comprised 8 CD8+ epitopes: (FMYSDFHFI, GILGFVFTL, ILRGSVAHK, FYIQMCTEL, ILKGKFQTA, YYLEKANKI, VSDGGPNLY, YSHGTGTGY) and the same 3 CD4+ epitopes. Our USA-specific vaccine has a population protection coverage (portion of the population potentially responsive to one or more component epitopes of the vaccine, PPC) of over 96% and 95% coverage of observed influenza subtypes. The universal vaccine has a PPC value of over 97% and 88% coverage of observed subtypes.

Diagnóstico automático de patologías cardíacas de bloqueo

Los electrocardiogramas (ECG) registran la actividad eléctrica del corazón a través de doce señales principales denominadas derivaciones. Estas derivaciones son analizadas por expertos médicos observando aquellos segmentos de la señal eléctrica que determinan cada una de las patologías que pueden afectar al corazón. Este hecho en general, es un condicionante muy importante para el diseño de sistemas expertos de diagnóstico médico, ya que es preciso conocer, delimitar y extraer de la señal eléctrica aquellos segmentos que determinan la patología. Dar solución a estos problemas, sería fundamental para facilitar el diseño de sistemas expertos para el diagnóstico de enfermedades cardiacas. El objetivo de este trabajo es demostrar que es posible identificar patologías cardiacas analizando la señal completa de las diferentes derivaciones de los ECGs, y determinar puntos concretos que determinan la patología en lugar de segmentos de la señal. Para ello se ha utilizado una BBDD de electrocardiogramas y se ha determinado mediante un algoritmo, los puntos de la señal que determinan la patología. Se ha aplicado a la patología de bloqueos de rama y los puntos obtenidos con el algoritmo se han utilizado para el diseño de un clasificador automático basado en redes neuronales artificiales, obteniendo un coeficiente de sensibilidad del 100% y de especificad del 99.24%, demostrando su validez para el diseño de sistemas expertos de clasificación.

Epigenetics and alternative splicing

Dissertação de Mestrado, Oncobiologia: Mecanismos Moleculares do Cancro, Departamento de Ciências Biomédicas e Medicina, Universidade do Algarve, 2015

Automatic learning of pre-miRNAs from different species.

Discovery of microRNAs (miRNAs) relies on predictive models for characteristic features from miRNA precursors (pre-miRNAs). The short length of miRNA genes and the lack of pronounced sequence features complicate this task. To accommodate the peculiarities of plant and animal miRNAs systems, tools for both systems have evolved differently. However, these tools are biased towards the species for which they were primarily developed and, consequently, their predictive performance on data sets from other species of the same kingdom might be lower. While these biases are intrinsic to the species, their characterization can lead to computational approaches capable of diminishing their negative effect on the accuracy of pre-miRNAs predictive models. We investigate in this study how 45 predictive models induced for data sets from 45 species, distributed in eight subphyla/classes, perform when applied to a species different from the species used in its induction. Results: Our computational experiments show that the separability of pre-miRNAs and pseudo pre-miRNAs instances is species-dependent and no feature set performs well for all species, even within the same subphylum/class. Mitigating this species dependency, we show that an ensemble of classifiers reduced the classification errors for all 45 species. As the ensemble members were obtained using meaningful, and yet computationally viable feature sets, the ensembles also have a lower computational cost than individual classifiers that rely on energy stability parameters, which are of prohibitive computational cost in large scale applications. Conclusion: In this study, the combination of multiple pre-miRNAs feature sets and multiple learning biases enhanced the predictive accuracy of pre-miRNAs classifiers of 45 species. This is certainly a promising approach to be incorporated in miRNA discovery tools towards more accurate and less species-dependent tools.

Role of Ccbe1 during cardiac differentiation of mouse ESCs

Tese de Doutoramento, Ciências Biomédicas, Departamento de Ciências Biomédicas e Medicina, Universidade do Algarve, 2016

Descubrimiento de fármacos basado en cribado virtual refinado con enfoques neuronales paralelos

En el campo de la medicina clínica es crucial poder determinar la seguridad y la eficacia de los fármacos actuales y además acelerar el descubrimiento de nuevos compuestos activos. Para ello se llevan a cabo ensayos de laboratorio, que son métodos muy costosos y que requieren mucho tiempo. Sin embargo, la bioinformática puede facilitar enormemente la investigación clínica para los fines mencionados, ya que proporciona la predicción de la toxicidad de los fármacos y su actividad en enfermedades nuevas, así como la evolución de los compuestos activos descubiertos en ensayos clínicos. Esto se puede lograr gracias a la disponibilidad de herramientas de bioinformática y métodos de cribado virtual por ordenador (CV) que permitan probar todas las hipótesis necesarias antes de realizar los ensayos clínicos, tales como el docking estructural, mediante el programa BINDSURF. Sin embargo, la precisión de la mayoría de los métodos de CV se ve muy restringida a causa de las limitaciones presentes en las funciones de afinidad o scoring que describen las interacciones biomoleculares, e incluso hoy en día estas incertidumbres no se conocen completamente. En este trabajo abordamos este problema, proponiendo un nuevo enfoque en el que las redes neuronales se entrenan con información relativa a bases de datos de compuestos conocidos (proteínas diana y fármacos), y se aprovecha después el método para incrementar la precisión de las predicciones de afinidad del método de CV BINDSURF.

Genome-wide copy number variation (CNV) detection in Nelore cattle reveals highly frequent variants in genome regions harboring QTLs affecting production traits.

Background: Copy number variations (CNVs) have been shown to account for substantial portions of observed genomic variation and have been associated with qualitative and quantitative traits and the onset of disease in a number of species. Information from high-resolution studies to detect, characterize and estimate population-specific variant frequencies will facilitate the incorporation of CNVs in genomic studies to identify genes affecting traits of importance. Results: Genome-wide CNVs were detected in high-density single nucleotide polymorphism (SNP) genotyping data from 1,717 Nelore (Bos indicus) cattle, and in NGS data from eight key ancestral bulls. A total of 68,007 and 12,786 distinct CNVs were observed, respectively. Cross-comparisons of results obtained for the eight resequenced animals revealed that 92 % of the CNVs were observed in both datasets, while 62 % of all detected CNVs were observed to overlap with previously validated cattle copy number variant regions (CNVRs). Observed CNVs were used for obtaining breed-specific CNV frequencies and identification of CNVRs, which were subsequently used for gene annotation. A total of 688 of the detected CNVRs were observed to overlap with 286 non-redundant QTLs associated with important production traits in cattle. All of 34 CNVs previously reported to be associated with milk production traits in Holsteins were also observed in Nelore cattle. Comparisons of estimated frequencies of these CNVs in the two breeds revealed 14, 13, 6 and 14 regions in high (>20 %), low (<20 %) and divergent (NEL > HOL, NEL < HOL) frequencies, respectively. Conclusions: Obtained results significantly enriched the bovine CNV map and enabled the identification of variants that are potentially associated with traits under selection in Nelore cattle, particularly in genome regions harboring QTLs affecting production traits.

Avaliação do desempenho relativo de bancos de dados NoSQL para arquivos de genótipos.

A bioinformática e a genômica trabalham com bases de dados fora do padrão tradicional ou clássico que, por sua vez, caracterizam-se pela organizacão tabular e pelo tratamento destas em SGBDRs. Arquivos de genótipos são exemplos de bases de dados não clássicas e são caracterizados por serem gerados como arquivos textos, com dados desbalanceados, com alta dimensionalidade e por ocuparem muito espaço, entre outros aspectos. Os SGBDRs não têm se mostrado uma boa solucão para o tratamento de tais bases e, portanto, o presente trabalho busca avaliar o desempenho relativo entre bancos de dados NoSQL que representam duas famílias de diferentes modelo de dados, a partir de cenários de teste para a manipulação de arquivos de genótipo.