*omeSOM: a software for clustering and visualization of transcriptional and metabolite data mined from interspecific crosses of crop plants

Background: Modern biology uses experimental systems that involve the exploration of phenotypic variation as a result of the recombination of several genomes. Such systems are useful to investigate the functional evolution of metabolic networks. One such approach is the analysis of transcript and metabolite profiles. These kinds of studies generate a large amount of data, which require dedicated computational tools for their analysis.Results: This paper presents a novel software named *omeSOM (transcript/metabol-ome Self Organizing Map) that implements a neural model for biological data clustering and visualization. It allows the discovery of relationships between changes in transcripts and metabolites of crop plants harboring introgressed exotic alleles and furthermore, its use can be extended to other type of omics data. The software is focused on the easy identification of groups including different molecular entities, independently of the number of clusters formed. The *omeSOM software provides easy-to-visualize interfaces for the identification of coordinated variations in the co-expressed genes and co-accumulated metabolites. Additionally, this information is linked to the most widely used gene annotation and metabolic pathway databases.Conclusions: *omeSOM is a software designed to give support to the data mining task of metabolic and transcriptional datasets derived from different databases. It provides a user-friendly interface and offers several visualization features, easy to understand by non-expert users. Therefore, *omeSOM provides support for data mining tasks and it is applicable to basic research as well as applied breeding programs. The software and a sample dataset are available free of charge at http://sourcesinc.sourceforge.net/omesom/.Fil: Milone, Diego Humberto. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones en Señales e Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Stegmayer, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Centro de Investigación y Desarrollo de Ingeniería en Sistemas de Información; ArgentinaFil: Kamenetzky, Laura. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: López, Mariana. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Biotecnología; ArgentinaFil: Lee, Je M.. Cornell University; Estados UnidosFil: Giovannoni, James J.. Cornell University; Estados UnidosFil: Carrari, Fernando Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Biotecnología; Argentin

Análisis de asociaciones en escenarios de datos masivos

La cantidad de datos en estudios biológicos se ha incrementado dramáticamente en los últimos años debido a la incorporación de nuevas fuentes y capacidades de procesamiento. Los secuenciadores automáticos del ADN y los microarreglos han permitido obtener grandes volúmenes de información a nivel molecular. Asimismo, los sensores remotos y los Sistemas de Información Geográfica proveen muchos datos a escala ecosistémica. Para estudiar a los organismos en forma integrada es necesario asociar los datos producidos por diferentes tecnologías. En biología de sistemas es esencial integrar información genómica, transcriptómica, proteómica, fenómica y ambiental. Modelar estadísticamente dichas interacciones es dificultoso desde el punto de vista computacional y biológico, sin embargo, los métodos algorítmicos filtran las principales señales de la información, permitiendo estudiar asociaciones, y el posterior modelado estadístico. En este trabajo se presentan distintas técnicas multivariadas que pueden ser usadas para comparar el nivel de covariación de distintos sets de datos entre situaciones o tratamientos y encontrar asociaciones entre ellos. Se describen e ilustran con datos de especies de importancia las técnicas de Análisis de Correlaciones Canónicas, Procrustes, Regresión por Cuadrados Mínimos y mapas Autorganizativos.The amount of available information in biological studies has increased dramatically in the last years. Technologies as DNA sequencing and microarrays have swamped data bases. Additionally, remote sensing and Geografical Information Systems provide a constant influx of environmental data difficult to handle. Concomitantly, the need to study organisms as a whole and find associations between data sets of different nature has increased. In System Biology, it is essential to analyze genotype, phenotypes, environment and the interactions among them. Statistical models of such complex interactions are difficult for both computational and biological interpretation aspects. On the contrary, algorithmic methods can be used to filter the main signals of genetic data and to study the association between sets of genotypic, phenotypic and environmental data. These methods are more straightforward and provide meaningful insight for later statistical modeling. In this work we present different multivariate techniques that can be used to compare the level of covariation of different data sets between situations or treatments and to find associations. In this work, we propose the use Cannonical Correlation Analysis, Procrustes, Partial Least Squares and Self Organizing Maps for the association of different data types and and illustrate their application in agronomical important species.Sociedad Argentina de Informática e Investigación Operativ

Gene targeting in the mouse nervous system

pre-printOur understanding of the development, connectivity and function of the nervous system has been facilitated by gene targeting technology. Here we summarize the historic background and the current state of this experimental approach with specific regard to neuroscience research. I. The Pioneering Experiments. Random mutagenesis is a very powerful method for elucidating gene function in simpler model organisms. Given the size of the genome and slow reproductive cycles, however, a more direct approach is required for mammalian models. This need was met in the early 1980's by establishing gene targeting in embryonic stem cells, later nicknamed mouse knockout. This technology was born at the confluence of two lines of experimentation: Firstly, the capacity of most mammalian somatic cells to carry out homologous recombination between endogenous loci and exogenous DNA was discovered. Secondly, pluripotent embryonic stem cell lines have been established that maintained the ability to intermingle with the early mouse embryo and contribute to the germline, thereby conferring heritability, even after extensive culturing in vitro

Research in Metabolomics via Nuclear Magnetic Resonance Spectroscopy: Data Mining, Biochemistry and Clinical Chemistry

Metabolomics entails the comprehensive characterization of the ensemble of endogenous and exogenous metabolites present in a biological specimen. Metabolites represent, at the same time, the downstream output of the genome and the upstream input from various external factors, such as the environment, lifestyle, and diet. Therefore, in the last few years, metabolomic phenotyping has provided unique insights into the fundamental and molecular causes of several physiological and pathophysiological conditions. In parallel, metabolomics has been demonstrating an emerging role in monitoring the influence of different manufacturing procedures on food quality and food safety. In light of the above, this collection includes the latest research from various fields of NMR-based metabolomics applications ranging from biomedicine to data mining and food chemistry

Dissecting the multi-functional role of heterogeneous nuclear ribonucleoprotein H1 in methamphetamine addiction traits

Both genetic and environment factors influence susceptibility to substance use disorders. However, the genetic basis of these disorders is largely unknown. We previously identified Hnrnph1 (heterogeneous nuclear ribonucleoprotein H1) as a quantitative trait gene for reduced methamphetamine (MA) stimulant sensitivity. Mutation (heterozygous deletion of a small region in the first coding exon) in Hnrnph1 also decreased MA reinforcement, reward, and dopamine release. 5’UTR genetic variants in Hnrnph1 support reduced 5’UTR usage and hnRNP H protein expression as a molecular mechanism underlying the reduced MA-induced psychostimulant response. Interestingly, Hnrnph1 mutant mice show a two-fold increase in hnRNP H protein in the striatal synaptosome with no change in whole tissue level. Proteome profiling of the synaptosome identified an increase in mitochondrial complex I and V proteins that rapidly decreased with MA in Hnrnph1 mutants. In contrast, the much lower level of basal mitochondrial proteins in the wild-type mice showed a rapid, MA-induced increase. Altered mitochondrial proteins associated with the Hnrnph1 mutation may contribute to reductions in MA behaviors. hnRNP H1 is an abundant RNA-binding protein in the brain, involved in all aspect of post-transcriptional regulation. We examined both baseline and MA-induced changes in hnRNP H-RNA interactions to identify targets of hnRNP H that could comprise the neurobiological mechanisms of cellular adaptations occurring following MA exposure. hnRNP H post-transcriptionally regulates a set of mRNA transcripts in the striatum involved in psychostimulant-induced synaptic plasticity. MA treatment induced opposite changes in binding of hnRNP H to these mRNA transcripts between Hnrnph1 mutants versus wild-types. RNA-binding, transcriptome, and spliceome analyses triangulated on hnRNP H binding to the 3’UTR of Cacna2d2, an upregulation of Cacna2d2 transcript, and decreased 3’UTR usage of Cacna2d2 in response to MA in the Hnrnph1 mutants. Cacna2d2 codes for a presynaptic, voltage-gated calcium channel subunit that could plausibly regulate MA-induced dopamine release and behavior. The multi-omics datasets point to a dysregulation of mitochondrial function and interrelated calcium signaling as potential mechanisms underlying MA-induced dopamine release and behavior in Hnrnph1 mutants

26th Fungal Genetics Conference at Asilomar

Program and abstracts from the 26th Fungal Genetics Conference, March 15-20, 2011

Evolutionary and molecular basis of host adaptation in fungal plant pathogens: insights from Zymoseptoria pathosystems

Host adaptation is considered a strong driver of pathogen evolution. With the ongoing development of modern agriculture, it is clear the importance in applying evolutionary analyses to access the risk of potential future pathogens, particularly in pathosystems that transition between wild and agricultural environments. Implementing population and functional genomics, transcriptome and infection assay approaches, we aimed to identify evolutionary and phenotypic patterns of host adaptation in fungal plant pathogens using the fungal pathogen Zymoseptoria tritici as a model of study. Unique collections of Z. tritici were isolated from wild (Aegilops spp.) and domesticated (Triticum aestivum) grass hosts, and different genomic and virulence-related aspects have been studied to identify key genomic traits playing a role in host adaptation of Z. tritici specimens in these different hosts. In Chapter 1, I describe methods to handle Z. tritici in laboratory and greenhouse conditions that are used throughout different experiments in the scope of this thesis. In Chapter 2, I focus on population genomics and virulence analyses of these host-diverging Z. tritici populations and reveal particular features of the Aegilops-infecting population that may have shaped its host adaptation and evolutionary history. In Chapter 3, I describe a new chromosome present in Aegilops-infecting Z. tritici isolates and propose its possible origin from introgression events with the closely related fungal species Zymoseptoria ardabiliae. Finally, in Chapter 4, I outline functional genetics analyses of candidate genes for host adaptation in the Aegilops-infecting Z. tritici population. As a foreseen impact, the results hereby generated are of relevance for translational evolutionary biology and plant pathology, highlighting the potential interaction between pathogens of wild and cultivated plants and a possible route of new pathogen lineages emergence

Genomics of clownfish adaptive radiation

How species diversify, creating the astonishing biodiversity observed on Earth, has been a central question since Darwin’s On the Origin of Species. Thanks to the sequencing revolution, this question can be approached today from a genomic perspective, examining how intrinsic genomic architecture and extrinsic biological and ecological factors interplay to shape the diversification of organisms. In this sense, clownfishes, which experienced an adaptive radiation following the acquisition of mutualism with sea anemones, represent a fascinating system. Thus, in this thesis, I combine comparative and population genomics approaches to study the genomic architecture underlying the diversification of this group. In the first two sections, I generate genomic resources for ten closely related but ecologically divergent clownfish species and the damselfish Pomacentrus moluccensis. Using this data, I question the genetic mechanisms underlying the acquisition of mutualism. I identify several candidate genes that experienced positive selection at the basis of clownfish radiation and show functions associated with sea anemones toxins discharge, thus likely involved in the evolution of clownfishes' ability to live unharmed within their otherwise-toxic hosts. In the last two sections, I dive into the diversification process of clownfishes. Through comparative genomics approaches, I show that the group experienced bursts of transposable elements, overall accelerated molecular evolution, and ancestral hybridization events, which likely facilitated the radiation of the group by generating the genomic variations necessary for natural selection to act on. I identify genes undergoing differential selective pressures linked with ecological divergence, suggesting that parallel evolution is shaping clownfish diversification, and I pinpoint candidate genes involved in the evolution of the particular size- based hierarchical social structure observed in the group. I finally focus on the mechanisms underlying the evolution of a clownfish clade, the skunk complex. Through population genomics approaches, I demonstrate that gene flow occurred throughout the diversification of the group. Indeed, the species experienced moderate ancestral gene flow, which lessened but still persists in sympatry. Moreover, contrary to what was previously suggested, I demonstrate that A. sandaracinos did not originate from hybrid speciation. I finally pinpoint candidate regions of introgression between species that likely played a role in the diversification of the complex. Overall, my work provides the first insights into the genomic mechanisms underlying clownfish adaptive radiation. -- Comprendre comment les espèces se diversifient et créent l'étonnante biodiversité observée sur Terre sont des questions centrales depuis l’écriture de l’Origine des espèces par Darwin. Grâce à la révolution du séquençage, ces questions peuvent être abordées aujourd'hui en examinant comment l'architecture génomique et les facteurs biologiques et écologiques interagissent et mènent à la diversification des organismes. En ce sens, les poissons-clowns, qui ont connu une radiation adaptative suite à l'acquisition du mutualisme avec les anémones de mer, représentent un système fascinant. Ainsi, dans cette thèse, j’étudie l'architecture génomique qui sous-tend la diversification de ce groupe. Dans les deux premières sections, je génère des ressources génomiques pour dix espèces de poissons-clowns ainsi que pour l’espèce de demoiselle Pomacentrus moluccensis. À partir de ces données, je questionne les mécanismes génétiques qui sous-tendent l'acquisition du mutualisme. J’identifie plusieurs gènes ayant subi une sélection positive à la base du rayonnement des poissons-clowns. Ces gènes ont des fonctions associées à la décharge des toxines des anémones, suggérant donc une implication dans l'évolution de la capacité des poissons-clowns à vivre au sein de leurs hôtes normalement toxiques. Dans les deux dernières sections, je me plonge dans le processus de diversification des poissons-clowns. Grâce à des approches de génomique comparative, je montre non seulement que ce groupe compte une importante quantité d'éléments transposables au sein de son génome, mais qu’il a également subi une évolution moléculaire accélérée ainsi que des événements d'hybridation ancestrale. La combinaison de ces différents éléments a probablement facilité leur diversification en générant les variations génomiques nécessaires à l'action de la sélection naturelle. J’identifie également des gènes ayant subi des pressions de sélection différentielles en lien avec des divergences écologiques, suggérant donc un processus d’évolution parallèle impliquée dans la diversification des poissons-clowns. Finalement, je me suis concentrée sur les mécanismes liés à l'évolution d'un clade de poisson- clown - le clade “skunk”. Je démontre qu’un flux de gènes modéré s'est produit tout au long de la diversification de ce groupe, et - bien que son amplitude ait diminué avec le temps - il persiste encore en sympatrie. De plus, j’identifie des régions candidates d'introgression entre espèces qui ont probablement joué un rôle dans la diversification du complexe. Dans l'ensemble, mon travail fournit les premières informations concernant les mécanismes génomiques impliqués dans la radiation adaptative des poissons-clowns

Integrative Advances in Rice Research

This book describes some recent advances in rice research in terms of crop breeding and improvement (Section 1), crop production and protection (Section 2), and crop quality control and food processing (Section 3). It contains fourteen chapters that cover such topics as two-line rice breeding in India, the different aspects of aromatic rice, bacterial diseases of rice, quality control and breeding strategies, and much more. This volume is a useful reference for professionals and graduate students working in all areas of rice science and technology