Les exposicions del Guernica de Picasso

Aproximació al procés de creació del Guernica de Pablo Picasso i a la seva presentació pública, a través de les exposicions de caràcter temporal i les corresponents a les col·leccions dels museus en les quals es va dipositar, per a poder reconstruir la història d’una de les icones mundials de l’art modern universal. El treball atent a la creació, contingut simbòlic i característiques principals de l’obra, a les circumstàncies històriques que la van envoltar, als itineraris expositius, els discursos museogràfics i les condicions de la seva presentació pública: al Pavelló espanyol de l’Exposició Internacional de París de 1937, al Museu d’Art Modern de New York (MoMA), al Museo Nacional del Prado i al Museu Nacional Centre de Artes Reina Sofía de Madrid, institució que acull actualment l’obra. Al llarg del treball es descriu el procés de transformació de la seva significació en funció del context en el qual s’insereix en cada moment, des d’un instrument de propaganda política, icona de l’art modern occidental, fins a la seva consideració com a obra mestra en la articulació de l’art espanyol modern i contemporani

A comprehensive survey of integron-associated genes present in metagenomes

Background: Integrons are genomic elements that mediate horizontal gene transfer by inserting and removing genetic material using site-specific recombination. Integrons are commonly found in bacterial genomes, where they maintain a large and diverse set of genes that plays an important role in adaptation and evolution. Previous studies have started to characterize the wide range of biological functions present in integrons. However, the efforts have so far mainly been limited to genomes from cultivable bacteria and amplicons generated by PCR, thus targeting only a small part of the total integron diversity. Metagenomic data, generated by direct sequencing of environmental and clinical samples, provides a more holistic and unbiased analysis of integron-associated genes. However, the fragmented nature of metagenomic data has previously made such analysis highly challenging. Results: Here, we present a systematic survey of integron-associated genes in metagenomic data. The analysis was based on a newly developed computational method where integron-associated genes were identified by detecting their associated recombination sites. By processing contiguous sequences assembled from more than 10 terabases of metagenomic data, we were able to identify 13,397 unique integron-associated genes. Metagenomes from marine microbial communities had the highest occurrence of integron-associated genes with levels more than 100-fold higher than in the human microbiome. The identified genes had a large functional diversity spanning over several functional classes. Genes associated with defense mechanisms and mobility facilitators were most overrepresented and more than five times as common in integrons compared to other bacterial genes. As many as two thirds of the genes were found to encode proteins of unknown function. Less than 1% of the genes were associated with antibiotic resistance, of which several were novel, previously undescribed, resistance gene variants. Conclusions: Our results highlight the large functional diversity maintained by integrons present in unculturable bacteria and significantly expands the number of described integron-associated genes

Comprehensive screening of genomic and metagenomic data reveals a large diversity of tetracycline resistance genes

Tetracyclines are broad-spectrum antibiotics used to prevent or treat a variety of bacterial infections. Resistance is often mediated through mobile resistance genes, which encode one of the three main mechanisms: active efflux, ribosomal target protection or enzymatic degradation. In the last few decades, a large number of new tetracycline-resistance genes have been discovered in clinical settings. These genes are hypothesized to originate from environmental and commensal bacteria, but the diversity of tetracycline-resistance determinants that have not yet been mobilized into pathogens is unknown. In this study, we aimed to characterize the potential tetracycline resistome by screening genomic and metagenomic data for novel resistance genes. By using probabilistic models, we predicted 1254 unique putative tetracycline resistance genes, representing 195 gene families (<70 % amino acid sequence identity), whereof 164 families had not been described previously. Out of 17 predicted genes selected for experimental verification, 7 induced a resistance phenotype in an Escherichia coli host. Several of the predicted genes were located on mobile genetic elements or in regions that indicated mobility, suggesting that they easily can be shared between bacteria. Furthermore, phylogenetic analysis indicated several events of horizontal gene transfer between bacterial phyla. Our results also suggested that acquired efflux pumps originate from proteobacterial species, while ribosomal protection genes have been mobilized from Firmicutes and Actinobacteria. This study significantly expands the knowledge of known and putatively novel tetracycline resistance genes, their mobility and evolutionary history. The study also provides insights into the unknown resistome and genes that may be encountered in clinical settings in the future

ECOdrug: A database connecting drugs and conservation of their targets across species

Pharmaceuticals are designed to interact with specific molecular targets in humans and these targets generally have orthologs in other species. This provides opportunities for the drug discovery community to use alternative model species for drug development. It also means, however, there is potential for mode of action related effects in non-target wildlife species as many pharmaceuticals reach the environment through patient use and manufacturing wastes. Acquiring insight in drug target ortholog predictions across species and taxonomic groups has proven difficult because of the lack of an optimal strategy and because necessary information is spread across multiple and diverse sources and platforms. We introduce a new research platform tool, ECOdrug, that reliably connects drugs to their protein targets across divergent species. It harmonizes ortholog predictions from multiple sources via a simple user interface underpinning critical applications for a wide range of studies in pharmacology, ecotoxicology and comparative evolutionary biology. ECOdrug can be used to identify species with drug targets and identify drugs that interact with those targets. As such, it can be applied to support intelligent targeted drug safety testing by ensuring appropriate and relevant species are selected in ecological risk assessments. ECOdrug is freely accessible and available at: Http://www.ecodrug.org

Reconstruction of Biological Networks for Integrative Analysis

Biological systems can be very complex and consist of several thousand components that interact with each other in the cell. One of the goals of systems biology is to study biological systems from a systemic viewpoint in order to get an increased understanding of the behavior of the cell. Biological network reconstructions are important tools in systems biology in order to model the behavior of different biological systems. The biological networks can also be used as a scaffold for integrative analysis where high-throughput data from different conditions or different strains are integrated into the biological network to reduce the dimension of the data and to group the response between conditions or strains into biological pathways or key metabolites etc. The biological interpretation and discovery using integrative analysis can be facilitated by constructing more comprehensive and diverse biological networks.In this thesis I expanded current biological network reconstructions for the yeast Saccharomyces cereveisae in three steps and used them as a scaffold for biological interpretation and discovery. First I constructed an up-to-date yeast genome-scale metabolic model. The model is a comprehensive description of yeast metabolism and contains more genes, reactions and metabolites than previous models. The model performs well in simulating the metabolism under different conditions. Second, I studied the transcriptional regulatory network of yeast in terms of topology and structure of the network and compared it to transcriptional regulation in E. coli, human and mouse. I also used high-throughput data from many different conditions to study the condition-dependent response of the yeast transcriptional regulatory network. Third, I was involved in reconstruction of models of the protein secretion machinery in S. cerevisiae and for the high protein producer Aspergillus oryzae, describing protein folding, post-translational modifications and protein transport etc. High-throughput data from several different strains producing α-amylase were integrated into the models in order to get an insight in the mechanisms and bottlenecks of protein secretion in these organisms.The biological networks presented here were also used for data integration and the results and interpretation of the cellular behavior under different conditions can give us a deeper understanding and insight in for example condition-specific transcriptional regulation and protein production

Integrative analysis of omics data

Data generation and analysis are essential parts of systems biology. Today, large amounts of omics data can be generated fast and cost-efficiently thanks to the development of modern high-throughput measurement techniques. Their interpretation is, however, challenging because of the high dimensionality and the often substantial levels of noise. Integrative analysis provides a framework for analysis of the omics data from a biological perspective, starting from the raw data, via preprocessing and statistical analysis, to the interpretation of the results. By integrating the data into structures created from biological information available in resources, databases, or genome-scale models, the focus moves from the individual transcripts or proteins to the entire pathways and other relevant biochemical functions present in the cell. The result provides a context-based interpretation of the omics data, which can be used to form a holistic and unbiased view of biological systems at a molecular level.The concept of integrative analysis can be used formany forms of omics data, including genome sequencing, transcriptomics, and proteomics, and can be applied to a wide range of fields within the life sciences

Variability in Metagenomic Count Data and Its Influence on the Identification of Differentially Abundant Genes.

Metagenomics is the study of microorganisms in environmental and clinical samples using high-throughput sequencing of random fragments of their DNA. Since metagenomics does not require any prior culturing of isolates, entire microbial communities can be studied directly in their natural state. In metagenomics, the abundance of genes is quantified by sorting and counting the DNA fragments. The resulting count data are high-dimensional and affected by high levels of technical and biological noise that make the statistical analysis challenging. In this article, we introduce an hierarchical overdispersed Poisson model to explore the variability in metagenomic data. By analyzing three comprehensive data sets, we show that the gene-specific variability varies substantially between genes and is dependent on biological function. We also assess the power of identifying differentially abundant genes and show that incorrect assumptions about the gene-specific variability can lead to unacceptable high rates of false positives. Finally, we evaluate shrinkage approaches to improve the variance estimation and show that the prior choice significantly affects the statistical power. The results presented in this study further elucidate the complex variance structure of metagenomic data and provide suggestions for accurate and reliable identification of differentially abundant genes

Modelling of zero-inflation improves inference of metagenomic gene count data

Metagenomics enables the study of gene abundances in complex mixtures of microorganisms and has become a standard methodology for the analysis of the human microbiome. However, gene abundance data is inherently noisy and contains high levels of biological and technical variability as well as an excess of zeros due to non-detected genes. This makes the statistical analysis challenging. In this study, we present a new hierarchical Bayesian model for inference of metagenomic gene abundance data. The model uses a zero-inflated overdispersed Poisson distribution which is able to simultaneously capture the high gene-specific variability as well as zero observations in the data. By analysis of three comprehensive datasets, we show that zero-inflation is common in metagenomic data from the human gut and, if not correctly modelled, it can lead to substantial reductions in statistical power. We also show, by using resampled metagenomic data, that our model has, compared to other methods, a higher and more stable performance for detecting differentially abundant genes. We conclude that proper modelling of the gene-specific variability, including the excess of zeros, is necessary to accurately describe gene abundances in metagenomic data. The proposed model will thus pave the way for new biological insights into the structure of microbial communities

Variability in Metagenomic Count Data and Its Influence on the Identification of Differentially Abundant Genes.

Metagenomics is the study of microorganisms in environmental and clinical samples using high-throughput sequencing of random fragments of their DNA. Since metagenomics does not require any prior culturing of isolates, entire microbial communities can be studied directly in their natural state. In metagenomics, the abundance of genes is quantified by sorting and counting the DNA fragments. The resulting count data are high-dimensional and affected by high levels of technical and biological noise that make the statistical analysis challenging. In this article, we introduce an hierarchical overdispersed Poisson model to explore the variability in metagenomic data. By analyzing three comprehensive data sets, we show that the gene-specific variability varies substantially between genes and is dependent on biological function. We also assess the power of identifying differentially abundant genes and show that incorrect assumptions about the gene-specific variability can lead to unacceptable high rates of false positives. Finally, we evaluate shrinkage approaches to improve the variance estimation and show that the prior choice significantly affects the statistical power. The results presented in this study further elucidate the complex variance structure of metagenomic data and provide suggestions for accurate and reliable identification of differentially abundant genes