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      Horizontally acquired papGII-containing pathogenicity islands underlie the emergence of invasive uropathogenic Escherichia coli lineages

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          Abstract

          Escherichia coli is the leading cause of urinary tract infection, one of the most common bacterial infections in humans. Despite this, a genomic perspective is lacking regarding the phylogenetic distribution of isolates associated with different clinical syndromes. Here, we present a large-scale phylogenomic analysis of a spatiotemporally and clinically diverse set of 907 E. coli isolates, including 722 uropathogenic E. coli (UPEC) isolates. A genome-wide association approach identifies the (P-fimbriae-encoding) papGII locus as the key feature distinguishing invasive UPEC, defined as isolates associated with severe UTI, i.e., kidney infection (pyelonephritis) or urinary-source bacteremia, from non-invasive UPEC, defined as isolates associated with asymptomatic bacteriuria or bladder infection (cystitis). Within the E. coli population, distinct invasive UPEC lineages emerged through repeated horizontal acquisition of diverse papGII-containing pathogenicity islands. Our findings elucidate the molecular determinants of severe UTI and have implications for the early detection of this pathogen.

          Abstract

          Escherichia coli is a major cause of urinary tract infection. Here, Biggel et al. provide a phylogenomic analysis of 907 clinical E. coli isolates and identify the P-fimbriae-encoding locus associated with invasive uropathogenic E. coli isolates.

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          Most cited references113

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          MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

          The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.
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            RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

            Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
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              SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing.

              The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.
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                Author and article information

                Contributors
                michaelbiggel@gmail.com
                Sandra.VanPuyvelde@uantwerpen.be
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                24 November 2020
                24 November 2020
                2020
                : 11
                : 5968
                Affiliations
                [1 ]GRID grid.5284.b, ISNI 0000 0001 0790 3681, Laboratory of Medical Microbiology, Vaccine and Infectious Disease Institute, , University of Antwerp, ; Antwerp, Belgium
                [2 ]GRID grid.17635.36, ISNI 0000000419368657, Veterans Affairs Medical Center and University of Minnesota, ; Minneapolis, MN USA
                [3 ]GRID grid.475435.4, Department of Clinical Microbiology, Rigshospitalet, ; Copenhagen, Denmark
                [4 ]GRID grid.411414.5, ISNI 0000 0004 0626 3418, Laboratory of Clinical Microbiology, , Antwerp University Hospital, ; Antwerp, Belgium
                [5 ]GRID grid.5335.0, ISNI 0000000121885934, Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID), Department of Medicine, , University of Cambridge, ; Cambridge, CB2 0SP UK
                Author information
                http://orcid.org/0000-0002-1337-2132
                http://orcid.org/0000-0002-5897-0240
                http://orcid.org/0000-0002-7912-1985
                http://orcid.org/0000-0002-7217-3643
                http://orcid.org/0000-0001-8434-5732
                Article
                19714
                10.1038/s41467-020-19714-9
                7686366
                33235212
                2c5d3e63-0e3e-4265-8340-58002e89efcf
                © Crown 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 6 April 2020
                : 27 October 2020
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                © The Author(s) 2020

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                clinical microbiology,bacterial genetics,bacterial infection,urinary tract infection

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