2,833 research outputs found

    Association between urinary metabolic profile and the intestinal effects of cocoa in rats

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    The aim of this study was to elucidate the relationship between the urinary metabolic fingerprint and the effects of cocoa and cocoa fibre on body weight, hormone metabolism, intestinal immunity and microbiota composition. To this effect, Wistar rats were fed, for 3 weeks, a diet containing 10 % cocoa (C10) or two other diets with same the proportion of fibres: one based on cocoa fibre (CF) and another containing inulin as a reference (REF) diet. The rats' 24 h urine samples were analysed by an untargeted 1H NMR spectroscopy-based metabonomic approach. Concentrations of faecal IgA and plasma metabolic hormones were also quantified. The C10 diet decreased the intestinal IgA, plasma glucagon-like peptide-1 and glucagon concentrations and increased ghrelin levels compared with those in the REF group. Clear differences were observed between the metabolic profiles from the C10 group and those from the CF group. Urine metabolites derived from cocoa correlated with the cocoa effects on body weight, immunity and the gut microbiota. Overall, cocoa intake alters the host and bacterial metabolism concerning energy and amino acid pathways, leading to a metabolic signature that can be used as a marker for consumption. This metabolic profile correlates with body weight, metabolic hormones, intestinal immunity and microbiota composition.</p

    PRIDB: a protein–RNA interface database

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    The Protein–RNA Interface Database (PRIDB) is a comprehensive database of protein–RNA interfaces extracted from complexes in the Protein Data Bank (PDB). It is designed to facilitate detailed analyses of individual protein–RNA complexes and their interfaces, in addition to automated generation of user-defined data sets of protein–RNA interfaces for statistical analyses and machine learning applications. For any chosen PDB complex or list of complexes, PRIDB rapidly displays interfacial amino acids and ribonucleotides within the primary sequences of the interacting protein and RNA chains. PRIDB also identifies ProSite motifs in protein chains and FR3D motifs in RNA chains and provides links to these external databases, as well as to structure files in the PDB. An integrated JMol applet is provided for visualization of interacting atoms and residues in the context of the 3D complex structures. The current version of PRIDB contains structural information regarding 926 protein–RNA complexes available in the PDB (as of 10 October 2010). Atomic- and residue-level contact information for the entire data set can be downloaded in a simple machine-readable format. Also, several non-redundant benchmark data sets of protein–RNA complexes are provided. The PRIDB database is freely available online at http://bindr.gdcb.iastate.edu/PRIDB

    Total Absorption Spectroscopy Study of 92^{92}Rb Decay: A Major Contributor to Reactor Antineutrino Spectrum Shape

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    The antineutrino spectra measured in recent experiments at reactors are inconsistent with calculations based on the conversion of integral beta spectra recorded at the ILL reactor. 92^{92}Rb makes the dominant contribution to the reactor spectrum in the 5-8 MeV range but its decay properties are in question. We have studied 92^{92}Rb decay with total absorption spectroscopy. Previously unobserved beta feeding was seen in the 4.5-5.5 region and the GS to GS feeding was found to be 87.5(25)%. The impact on the reactor antineutrino spectra calculated with the summation method is shown and discussed.Comment: 6 pages, 3 figure

    Automatic Selection of Molecular Descriptors using Random Forest: Application to Drug Discovery

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    The optimal selection of chemical features (molecular descriptors) is an essential pre-processing step for the efficient application of computational intelligence techniques in virtual screening for identification of bioactive molecules in drug discovery. The selection of molecular descriptors has key influence in the accuracy of affinity prediction. In order to improve this prediction, we examined a Random Forest (RF)-based approach to automatically select molecular descriptors of training data for ligands of kinases, nuclear hormone receptors, and other enzymes. The reduction of features to use during prediction dramatically reduces the computing time over existing approaches and consequently permits the exploration of much larger sets of experimental data. To test the validity of the method, we compared the results of our approach with the ones obtained using manual feature selection in our previous study (Perez-Sanchez et al., 2014). The main novelty of this work in the field of drug discovery is the use of RF in two different ways: feature ranking and dimensionality reduction, and classification using the automatically selected feature subset. Our RF-based method out-performs classification results provided by Support Vector Machine (SVM) and Neural Networks (NN) approaches

    RNase H2, mutated in Aicardi-Goutières syndrome, promotes LINE-1 retrotransposition

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    Long INterspersed Element class 1 (LINE-1) elements are a type of abundant retrotransposons active in mammalian genomes. An average human genome contains ~100 retrotransposition-competent LINE-1s, whose activity is influenced by the combined action of cellular repressors and activators. TREX1, SAMHD1 and ADAR1 are known LINE-1 repressors and when mutated cause the autoinflammatory disorder Aicardi-Goutières syndrome (AGS). Mutations in RNase H2 are the most common cause of AGS, and its activity was proposed to similarly control LINE-1 retrotransposition. It has therefore been suggested that increased LINE-1 activity may be the cause of aberrant innate immune activation in AGS. Here, we establish that, contrary to expectations, RNase H2 is required for efficient LINE-1 retrotransposition. As RNase H1 overexpression partially rescues the defect in RNase H2 null cells, we propose a model in which RNase H2 degrades the LINE-1 RNA after reverse transcription, allowing retrotransposition to be completed. This also explains how LINE-1 elements can retrotranspose efficiently without their own RNase H activity. Our findings appear to be at odds with LINE-1-derived nucleic acids driving autoinflammation in AGS.M.B.-G. is funded by a “Formacion Profesorado Universitario” (FPU) PhD fellowship from the Government of Spain (MINECO, Ref FPU15/03294), and this paper is part of her thesis project (“Epigenetic control of the mobility of a human retrotransposon”). R.V.-A. is funded by a PFIS Fellowship from the Government of Spain (ISCiii, FI16/00413). O.M. is funded by an EMBO Long-Term Fellowship (ALTF 7-2015), the European Commission FP7 (Marie Curie Actions, LTFCOFUND2013, GA-2013-609409) and the Swiss National Science Foundation (P2ZHP3_158709). S.R.H. is funded by the Government of Spain (MINECO, RYC-2016-21395 and SAF2015-71589-P). A.P.J’s laboratory is supported by the UK Medical Research Council (MRC University Unit grant U127527202). J.L.G.P’s laboratory is supported by CICEFEDER- P12-CTS-2256, Plan Nacional de I+D+I 2008-2011 and 2013-2016 (FISFEDER- PI14/02152), PCIN-2014-115-ERA-NET NEURON II, the European Research Council (ERC-Consolidator ERC-STG-2012-233764), by an International Early Career Scientist grant from the Howard Hughes Medical Institute (IECS-55007420), by The Wellcome Trust-University of Edinburgh Institutional Strategic Support Fund (ISFF2) and by a private donation from Ms Francisca Serrano (Trading y Bolsa para Torpes, Granada, Spain)
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