Synthesis of fluorosugar reagents for the construction of well-defined fluoroglycoproteins.

2-Deoxy-2-fluoroglycosyl iodides are privileged glycosyl donors for the stereoselective preparation of 1-Nu-β-fluorosugars, which are useful reagents for chemical site-selective protein glycosylation. Ready access to such β-fluorosugars enables the mild and efficient construction of well-defined fluoroglycoproteins.We thank the European Commission (Marie Curie CIG, O.B. and G.J.L.B.), MICINN, Spain (Juan de la Cierva Fellowship, O.B.), MINECO, Spain (CTQ2011-22872BQU) and Generalitat de Catalunya (M.S.) for generous financial support. We also thank Mr. Adrià Cardona-Benages (URV) for technical assis-tance. G.J.L.B. thanks the Royal Society (University Research Fellowship), Fundação para a Ciência a Tecnologia, Portugal (FCT Investigator), and the EPSRC for funding.This is the final version of the article. It first appeared from ACS via http://pubs.acs.org/doi/abs/10.1021/acs.orglett.5b01259

MARVEL, a Tool for Prediction of Bacteriophage Sequences in Metagenomic Bins

Here we present MARVEL, a tool for prediction of double-stranded DNA bacteriophage sequences in metagenomic bins. MARVEL uses a random forest machine learning approach. We trained the program on a dataset with 1,247 phage and 1,029 bacterial genomes, and tested it on a dataset with 335 bacterial and 177 phage genomes. We show that three simple genomic features extracted from contig sequences were sufficient to achieve a good performance in separating bacterial from phage sequences: gene density, strand shifts, and fraction of significant hits to a viral protein database. We compared the performance of MARVEL to that of VirSorter and VirFinder, two popular programs for predicting viral sequences. Our results show that all three programs have comparable specificity, but MARVEL achieves much better performance on the recall (sensitivity) measure. This means that MARVEL should be able to identify many more phage sequences in metagenomic bins than heretofore has been possible. In a simple test with real data, containing mostly bacterial sequences, MARVEL classified 58 out of 209 bins as phage genomes; other evidence suggests that 57 of these 58 bins are novel phage sequences. MARVEL is freely available at https://github.com/LaboratorioBioinformatica/MARVEL

Causes of mortality in laying hens in different housing systems in 2001 to 2004

<p>Abstract</p> <p>Background</p> <p>The husbandry systems for laying hens were changed in Sweden during the years 2001 – 2004, and an increase in the number of submissions for necropsy from laying hen farms was noted. Hence, this study was initiated to compare causes of mortality in different housing systems for commercial laying hens during this change.</p> <p>Methods</p> <p>Based on results from routine necropsies of 914 laying hens performed at the National Veterinary Institute (SVA) in Uppsala, Sweden between 2001 and 2004, a retrospective study on the occurrence of diseases and cannibalism, i.e., pecking leading to mortality, in different housing systems was carried out. Using the number of disease outbreaks in caged flocks as the baseline, the expected number of flocks with a certain category of disease in the other housing systems was estimated having regard to the total number of birds in the population. Whether the actual number of flocks significantly exceeded the expected number was determined using a Poisson distribution for the variance of the baseline number, a continuity correction and the exact value for the Poisson distribution function in Excel 2000.</p> <p>Results</p> <p>Common causes of mortality in necropsied laying hens included colibacillosis, erysipelas, coccidiosis, red mite infestation, lymphoid leukosis and cannibalism. Less common diagnoses were Newcastle Disease, pasteurellosis and botulism. Considering the size of the populations in the different housing systems, a larger proportion of laying hens than expected was submitted for necropsy from litter-based systems and free range production compared to hens in cages (<it>P </it>< 0.001). The study showed a significantly higher occurrence of bacterial and parasitic diseases and cannibalism in laying hens kept in litter-based housing systems and free-range systems than in hens kept in cages (<it>P </it>< 0.001). The occurrence of viral diseases was significantly higher in indoor litter-based housing systems than in cages (<it>P </it>< 0.001).</p> <p>Conclusion</p> <p>The results of the present study indicated that during 2001–2004 laying hens housed in litter-based housing systems, with or without access to outdoor areas, were at higher risk of infectious diseases and cannibalistic behaviour compared to laying hens in cages. Future research should focus on finding suitable prophylactic measures, including efficient biosecurity routines, to reduce the risk of infectious diseases and cannibalism in litter-based housing systems for laying hens.</p

Table_2_MARVEL, a Tool for Prediction of Bacteriophage Sequences in Metagenomic Bins.XLSX

<p>Here we present MARVEL, a tool for prediction of double-stranded DNA bacteriophage sequences in metagenomic bins. MARVEL uses a random forest machine learning approach. We trained the program on a dataset with 1,247 phage and 1,029 bacterial genomes, and tested it on a dataset with 335 bacterial and 177 phage genomes. We show that three simple genomic features extracted from contig sequences were sufficient to achieve a good performance in separating bacterial from phage sequences: gene density, strand shifts, and fraction of significant hits to a viral protein database. We compared the performance of MARVEL to that of VirSorter and VirFinder, two popular programs for predicting viral sequences. Our results show that all three programs have comparable specificity, but MARVEL achieves much better performance on the recall (sensitivity) measure. This means that MARVEL should be able to identify many more phage sequences in metagenomic bins than heretofore has been possible. In a simple test with real data, containing mostly bacterial sequences, MARVEL classified 58 out of 209 bins as phage genomes; other evidence suggests that 57 of these 58 bins are novel phage sequences. MARVEL is freely available at https://github.com/LaboratorioBioinformatica/MARVEL.</p

Image_1_MARVEL, a Tool for Prediction of Bacteriophage Sequences in Metagenomic Bins.pdf

<p>Here we present MARVEL, a tool for prediction of double-stranded DNA bacteriophage sequences in metagenomic bins. MARVEL uses a random forest machine learning approach. We trained the program on a dataset with 1,247 phage and 1,029 bacterial genomes, and tested it on a dataset with 335 bacterial and 177 phage genomes. We show that three simple genomic features extracted from contig sequences were sufficient to achieve a good performance in separating bacterial from phage sequences: gene density, strand shifts, and fraction of significant hits to a viral protein database. We compared the performance of MARVEL to that of VirSorter and VirFinder, two popular programs for predicting viral sequences. Our results show that all three programs have comparable specificity, but MARVEL achieves much better performance on the recall (sensitivity) measure. This means that MARVEL should be able to identify many more phage sequences in metagenomic bins than heretofore has been possible. In a simple test with real data, containing mostly bacterial sequences, MARVEL classified 58 out of 209 bins as phage genomes; other evidence suggests that 57 of these 58 bins are novel phage sequences. MARVEL is freely available at https://github.com/LaboratorioBioinformatica/MARVEL.</p

Bacterial Diversification in the Light of the Interactions with Phages: The Genetic Symbionts and Their Role in Ecological Speciation

Phages have a major impact on microbial populations. In this work, we discuss how predation, transduction, lysogeny, and phage domestication lead to symbio-centric genomic interactions between bacteria and phages, ranging from antagonistic to mutualistic. Furthermore, these interactions influence bacterial diversification and ecotype formation. We then propose an additional consideration in the form of a symbio-centric ecological speciation framework for bacteria. Our framework builds upon classical morphological and molecular taxonomy by also considering bacteria and their phages as a unit of evolutionary selection. This framework acknowledges the considerable effect that phage interaction has on bacterial genomic content, regulation, and evolution, and will advance our understanding of bacterial evolution

Table_1_MARVEL, a Tool for Prediction of Bacteriophage Sequences in Metagenomic Bins.XLSX

<p>Here we present MARVEL, a tool for prediction of double-stranded DNA bacteriophage sequences in metagenomic bins. MARVEL uses a random forest machine learning approach. We trained the program on a dataset with 1,247 phage and 1,029 bacterial genomes, and tested it on a dataset with 335 bacterial and 177 phage genomes. We show that three simple genomic features extracted from contig sequences were sufficient to achieve a good performance in separating bacterial from phage sequences: gene density, strand shifts, and fraction of significant hits to a viral protein database. We compared the performance of MARVEL to that of VirSorter and VirFinder, two popular programs for predicting viral sequences. Our results show that all three programs have comparable specificity, but MARVEL achieves much better performance on the recall (sensitivity) measure. This means that MARVEL should be able to identify many more phage sequences in metagenomic bins than heretofore has been possible. In a simple test with real data, containing mostly bacterial sequences, MARVEL classified 58 out of 209 bins as phage genomes; other evidence suggests that 57 of these 58 bins are novel phage sequences. MARVEL is freely available at https://github.com/LaboratorioBioinformatica/MARVEL.</p

Detection of Hepatitis E Virus Genotype 3 in Feces of Capybaras (Hydrochoeris hydrochaeris) in Brazil

Hepatitis E virus (HEV) is an emerging zoonotic pathogen associated with relevant public health issues. The aim of this study was to investigate HEV presence in free-living capybaras inhabiting urban parks in São Paulo state, Brazil. Molecular characterization of HEV positive samples was undertaken to elucidate the genetic diversity of the virus in these animals. A total of 337 fecal samples were screened for HEV using RT-qPCR and further confirmed by conventional nested RT-PCR. HEV genotype and subtype were determined using Sanger and next-generation sequencing. HEV was detected in one specimen (0.3%) and assigned as HEV-3f. The IAL-HEV_921 HEV-3f strain showed a close relationship to European swine, wild boar and human strains (90.7–93.2% nt), suggesting an interspecies transmission. Molecular epidemiology of HEV is poorly investigated in Brazil; subtype 3f has been reported in swine. This is the first report of HEV detected in capybara stool samples worldwide.</jats:p