Analysis of a Splice Array Experiment Elucidates Roles of Chromatin Elongation Factor Spt4–5 in Splicing

Splicing is an important process for regulation of gene expression in eukaryotes, and it has important functional links to other steps of gene expression. Two examples of these linkages include Ceg1, a component of the mRNA capping enzyme, and the chromatin elongation factors Spt4–5, both of which have recently been shown to play a role in the normal splicing of several genes in the yeast Saccharomyces cerevisiae. Using a genomic approach to characterize the roles of Spt4–5 in splicing, we used splicing-sensitive DNA microarrays to identify specific sets of genes that are mis-spliced in ceg1, spt4, and spt5 mutants. In the context of a complex, nested, experimental design featuring 22 dye-swap array hybridizations, comprising both biological and technical replicates, we applied five appropriate statistical models for assessing differential expression between wild-type and the mutants. To refine selection of differential expression genes, we then used a robust model-synthesizing approach, Differential Expression via Distance Synthesis, to integrate all five models. The resultant list of differentially expressed genes was then further analyzed with regard to select attributes: we found that highly transcribed genes with long introns were most sensitive to spt mutations. QPCR confirmation of differential expression was established for the limited number of genes evaluated. In this paper, we showcase splicing array technology, as well as powerful, yet general, statistical methodology for assessing differential expression, in the context of a real, complex experimental design. Our results suggest that the Spt4–Spt5 complex may help coordinate splicing with transcription under conditions that present kinetic challenges to spliceosome assembly or function

Human DDX3 functions in translation and interacts with the translation initiation factor eIF3

The conserved RNA helicase DDX3 is of major medical importance due to its involvement in numerous cancers, human hepatitis C virus (HCV) and HIV. Although DDX3 has been reported to have a wide variety of cellular functions, its precise role remains obscure. Here, we raised a new antibody to DDX3 and used it to show that DDX3 is evenly distributed throughout the cytoplasm at steady state. Consistent with this observation, HA-tagged DDX3 also localizes to the cytoplasm. RNAi of DDX3 in both human and Drosophila cells shows that DDX3 is required for cell viability. Moreover, using RNAi, we show that DDX3 is required for expression of protein from reporter constructs. In contrast, we did not detect a role for DDX3 in nuclear steps in gene expression. Further insight into the function of DDX3 came from the observation that its major interaction partner is the multi-component translation initiation factor eIF3. We conclude that a primary function for DDX3 is in protein translation, via an interaction with eIF3

Classifying RNA-Binding Proteins Based on Electrostatic Properties

Protein structure can provide new insight into the biological function of a protein and can enable the design of better experiments to learn its biological roles. Moreover, deciphering the interactions of a protein with other molecules can contribute to the understanding of the protein's function within cellular processes. In this study, we apply a machine learning approach for classifying RNA-binding proteins based on their three-dimensional structures. The method is based on characterizing unique properties of electrostatic patches on the protein surface. Using an ensemble of general protein features and specific properties extracted from the electrostatic patches, we have trained a support vector machine (SVM) to distinguish RNA-binding proteins from other positively charged proteins that do not bind nucleic acids. Specifically, the method was applied on proteins possessing the RNA recognition motif (RRM) and successfully classified RNA-binding proteins from RRM domains involved in protein–protein interactions. Overall the method achieves 88% accuracy in classifying RNA-binding proteins, yet it cannot distinguish RNA from DNA binding proteins. Nevertheless, by applying a multiclass SVM approach we were able to classify the RNA-binding proteins based on their RNA targets, specifically, whether they bind a ribosomal RNA (rRNA), a transfer RNA (tRNA), or messenger RNA (mRNA). Finally, we present here an innovative approach that does not rely on sequence or structural homology and could be applied to identify novel RNA-binding proteins with unique folds and/or binding motifs

Les conceptions initiales des élèves turcs de CM2 relatives aux séismes

Knowledge about earthquakes is therefore a complex knowledge, derived from a long history, transformed into knowledge to be taught through the choice of official programs, textbooks, taking into account the process of building knowledge in children, and those of the elaboration of concepts in the field of the sciences of the terrestrial space, enlightened by J. Piaget and other researchers. The recognition of representations can constitute a basis for organizing strategies adapted to the students. This article describes what specific Turkish children's conceptions of earthquakes were before they received any courses on these subjects. We conducted a series of surveys on this topic and analyzed the results

This is Your Brain on Video Games: An Examination of the Cognitive Benefits of Video Games and How Those Benefits Can Be Harnessed in the Classroom

Children today are playing video games from a very young age, many of them for hours each day. Modern brain research has shown that the brain is much more pliable than we original thought. With this neuroplasticity in mind, this paper will look at how video game play is effecting the cognitive changes in the classroom to help the students better understand the curriculum

Identifier les attitudes des étudiants en géographie après leurs études secondaires

Ce travail expose les résultats d’une étude réalisée dans le but d’analyser la démarche d’apprentissage d’un groupe d’étudiants de premier cycle de géographie. Seront traitées ici les démarches d’apprentissage qu’ils adoptent et la façon dont évolue leur niveau de confiance en soi après un an d’enseignement supérieur. Les étudiants étaient confrontés à un programme visant au développement des capacités dans le cadre de la géographie, qui mettait l’accent sur une démarche d’apprentissage en profondeur. Les résultats montrent que, bien que leur niveau de confiance en leur capacité d’étudier et d’apprendre ait augmenté, leur démarche d’apprentissage est devenue de plus en plus instrumentale. This paper shows the results of a study carried out in order to analyse the learning approach in geography of cohorts of students on entry to a geography degree. After one year of higher education, student learning approaches and their degree of confidence are examined. A program aimed at the development of the learning capacities based on a deep learning approach was proposed to students. The results indicate that although their degrees of confidence in their capacity to study increased, their learning approaches became increasingly instrumental

Perception of Climate Change: Reasons, Consequences, and Willingness to Act. How Aware are They?

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