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      Global phylogenetic analysis of Escherichia coli and plasmids carrying the mcr-1 gene indicates bacterial diversity but plasmid restriction

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          Abstract

          To understand the dynamics behind the worldwide spread of the mcr-1 gene, we determined the population structure of Escherichia coli and of mobile genetic elements (MGEs) carrying the mcr-1 gene. After a systematic review of the literature we included 65 E. coli whole genome sequences (WGS), adding 6 recently sequenced travel related isolates, and 312 MLST profiles. We included 219 MGEs described in 7 Enterobacteriaceae species isolated from human, animal and environmental samples. Despite a high overall diversity, 2 lineages were observed in the E. coli population that may function as reservoirs of the mcr-1 gene, the largest of which was linked to ST10, a sequence type known for its ubiquity in human faecal samples and in food samples. No genotypic clustering by geographical origin or isolation source was observed. Amongst a total of 13 plasmid incompatibility types, the IncI2, IncX4 and IncHI2 plasmids accounted for more than 90% of MGEs carrying the mcr-1 gene. We observed significant geographical clustering with regional spread of IncHI2 plasmids in Europe and IncI2 in Asia. These findings point towards promiscuous spread of the mcr-1 gene by efficient horizontal gene transfer dominated by a limited number of plasmid incompatibility types.

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          Antibiotic resistance-the need for global solutions.

          The causes of antibiotic resistance are complex and include human behaviour at many levels of society; the consequences affect everybody in the world. Similarities with climate change are evident. Many efforts have been made to describe the many different facets of antibiotic resistance and the interventions needed to meet the challenge. However, coordinated action is largely absent, especially at the political level, both nationally and internationally. Antibiotics paved the way for unprecedented medical and societal developments, and are today indispensible in all health systems. Achievements in modern medicine, such as major surgery, organ transplantation, treatment of preterm babies, and cancer chemotherapy, which we today take for granted, would not be possible without access to effective treatment for bacterial infections. Within just a few years, we might be faced with dire setbacks, medically, socially, and economically, unless real and unprecedented global coordinated actions are immediately taken. Here, we describe the global situation of antibiotic resistance, its major causes and consequences, and identify key areas in which action is urgently needed. Copyright © 2013 Elsevier Ltd. All rights reserved.
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            eBURST: inferring patterns of evolutionary descent among clusters of related bacterial genotypes from multilocus sequence typing data.

            The introduction of multilocus sequence typing (MLST) for the precise characterization of isolates of bacterial pathogens has had a marked impact on both routine epidemiological surveillance and microbial population biology. In both fields, a key prerequisite for exploiting this resource is the ability to discern the relatedness and patterns of evolutionary descent among isolates with similar genotypes. Traditional clustering techniques, such as dendrograms, provide a very poor representation of recent evolutionary events, as they attempt to reconstruct relationships in the absence of a realistic model of the way in which bacterial clones emerge and diversify to form clonal complexes. An increasingly popular approach, called BURST, has been used as an alternative, but present implementations are unable to cope with very large data sets and offer crude graphical outputs. Here we present a new implementation of this algorithm, eBURST, which divides an MLST data set of any size into groups of related isolates and clonal complexes, predicts the founding (ancestral) genotype of each clonal complex, and computes the bootstrap support for the assignment. The most parsimonious patterns of descent of all isolates in each clonal complex from the predicted founder(s) are then displayed. The advantages of eBURST for exploring patterns of evolutionary descent are demonstrated with a number of examples, including the simple Spain(23F)-1 clonal complex of Streptococcus pneumoniae, "population snapshots" of the entire S. pneumoniae and Staphylococcus aureus MLST databases, and the more complicated clonal complexes observed for Campylobacter jejuni and Neisseria meningitidis.
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              Hierarchical and Spatially Explicit Clustering of DNA Sequences with BAPS Software

              Phylogeographical analyses have become commonplace for a myriad of organisms with the advent of cheap DNA sequencing technologies. Bayesian model-based clustering is a powerful tool for detecting important patterns in such data and can be used to decipher even quite subtle signals of systematic differences in molecular variation. Here, we introduce two upgrades to the Bayesian Analysis of Population Structure (BAPS) software, which enable 1) spatially explicit modeling of variation in DNA sequences and 2) hierarchical clustering of DNA sequence data to reveal nested genetic population structures. We provide a direct interface to map the results from spatial clustering with Google Maps using the portal http://www.spatialepidemiology.net/ and illustrate this approach using sequence data from Borrelia burgdorferi. The usefulness of hierarchical clustering is demonstrated through an analysis of the metapopulation structure within a bacterial population experiencing a high level of local horizontal gene transfer. The tools that are introduced are freely available at http://www.helsinki.fi/bsg/software/BAPS/.
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                Author and article information

                Contributors
                s.p.matamoros@amc.nl
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                10 November 2017
                10 November 2017
                2017
                : 7
                : 15364
                Affiliations
                [2 ]ISNI 000000040459992X, GRID grid.5645.2, Department of Medical Microbiology and Infectious Diseases, , Erasmus University Medical Center, ; Rotterdam, The Netherlands
                [3 ]GRID grid.412966.e, School for Public Health and Primary Care (Caphri), Department of Medical Microbiology, Maastricht University Medical Center (MUMC), ; Maastricht, The Netherlands
                [8 ]ISNI 0000000090126352, GRID grid.7692.a, Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, ; Utrecht, The Netherlands
                [9 ]ISNI 0000000120346234, GRID grid.5477.1, Department of Mathematics, Faculty of Science, Utrecht University, ; Utrecht, The Netherlands
                [10 ]Department of Internal Medicine, Havenziekenhuis - Institute for Tropical Diseases, Rotterdam, The Netherlands
                [11 ]ISNI 0000000404654431, GRID grid.5650.6, Center of Tropical Medicine and Travel Medicine, Academic Medical Centre (AMC), ; Amsterdam, The Netherlands
                [1 ]ISNI 0000000404654431, GRID grid.5650.6, Department of Medical Microbiology, , Academic Medical Center (AMC), ; Amsterdam, The Netherlands
                [4 ]GRID grid.412966.e, School for Nutrition and Translational Research in Metabolism (NUTRIM), MUMC, ; Maastricht, The Netherlands
                [5 ]ISNI 0000000404654431, GRID grid.5650.6, Department of Global Health-Amsterdam Institute for Global Health and Development, AMC, ; Amsterdam, The Netherlands
                [6 ]ISNI 0000 0004 0429 6814, GRID grid.412433.3, Oxford University Clinical Research Unit, Centre for Tropical Medicine, ; Ho Chi Minh City, Vietnam
                [7 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Centre for Tropical Medicine, Nuffield Department of Medicine, University of Oxford, ; Oxford, UK
                Author information
                http://orcid.org/0000-0001-6132-2298
                Article
                15539
                10.1038/s41598-017-15539-7
                5681592
                29127343
                d1a85fe2-6961-4df8-88c0-e97e5e2ae582
                © The Author(s) 2017

                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
                : 15 June 2017
                : 27 October 2017
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