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      Diversification of Colonization Factors in a Multidrug-Resistant Escherichia coli Lineage Evolving under Negative Frequency-Dependent Selection

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          Abstract

          Infections with multidrug-resistant (MDR) strains of Escherichia coli are a significant global public health concern. To combat these pathogens, we need a deeper understanding of how they evolved from their background populations. By understanding the processes that underpin their emergence, we can design new strategies to limit evolution of new clones and combat existing clones. By combining population genomics with modelling approaches, we show that dominant MDR clones of E. coli are under the influence of negative frequency-dependent selection, preventing them from rising to fixation in a population. Furthermore, we show that this selection acts on genes involved in anaerobic metabolism, suggesting that this key trait, and the ability to colonize human intestinal tracts, is a key step in the evolution of MDR clones of E. coli.

          ABSTRACT

          Escherichia coli is a major cause of bloodstream and urinary tract infections globally. The wide dissemination of multidrug-resistant (MDR) strains of extraintestinal pathogenic E. coli (ExPEC) poses a rapidly increasing public health burden due to narrowed treatment options and increased risk of failure to clear an infection. Here, we present a detailed population genomic analysis of the ExPEC ST131 clone, in which we seek explanations for its success as an emerging pathogenic strain beyond the acquisition of antimicrobial resistance (AMR) genes. We show evidence for evolution toward separate ecological niches for the main clades of ST131 and differential evolution of anaerobic metabolism, key colonization, and virulence factors. We further demonstrate that negative frequency-dependent selection acting across accessory loci is a major mechanism that has shaped the population evolution of this pathogen.

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          Most cited references43

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          Gut inflammation provides a respiratory electron acceptor for Salmonella

          Salmonella enterica serotype Typhimurium (S. Typhimurium) causes acute gut inflammation by using its virulence factors to invade the intestinal epithelium and survive in mucosal macrophages. The inflammatory response enhances the transmission success of S. Typhimurium by promoting its outgrowth in the gut lumen through unknown mechanisms. Here we show that reactive oxygen species generated during inflammation reacted with endogenous, luminal sulphur compounds (thiosulfate) to form a new respiratory electron acceptor, tetrathionate. The genes conferring the ability to utilize tetrathionate as an electron acceptor produced a growth advantage for S. Typhimurium over the competing microbiota in the lumen of the inflamed gut. We conclude that S. Typhimurium virulence factors induce host-driven production of a new electron acceptor that allows the pathogen to use respiration to compete with fermenting gut microbes. Thus, the ability to trigger intestinal inflammation is crucial for the biology of this diarrhoeal pathogen.
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            Genomic islands in pathogenic and environmental microorganisms.

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              Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations

              Background During the most recent decade many Bayesian statistical models and software for answering questions related to the genetic structure underlying population samples have appeared in the scientific literature. Most of these methods utilize molecular markers for the inferences, while some are also capable of handling DNA sequence data. In a number of earlier works, we have introduced an array of statistical methods for population genetic inference that are implemented in the software BAPS. However, the complexity of biological problems related to genetic structure analysis keeps increasing such that in many cases the current methods may provide either inappropriate or insufficient solutions. Results We discuss the necessity of enhancing the statistical approaches to face the challenges posed by the ever-increasing amounts of molecular data generated by scientists over a wide range of research areas and introduce an array of new statistical tools implemented in the most recent version of BAPS. With these methods it is possible, e.g., to fit genetic mixture models using user-specified numbers of clusters and to estimate levels of admixture under a genetic linkage model. Also, alleles representing a different ancestry compared to the average observed genomic positions can be tracked for the sampled individuals, and a priori specified hypotheses about genetic population structure can be directly compared using Bayes' theorem. In general, we have improved further the computational characteristics of the algorithms behind the methods implemented in BAPS facilitating the analyses of large and complex datasets. In particular, analysis of a single dataset can now be spread over multiple computers using a script interface to the software. Conclusion The Bayesian modelling methods introduced in this article represent an array of enhanced tools for learning the genetic structure of populations. Their implementations in the BAPS software are designed to meet the increasing need for analyzing large-scale population genetics data. The software is freely downloadable for Windows, Linux and Mac OS X systems at .
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                Author and article information

                Contributors
                Role: Editor
                Role: Solicited external reviewer
                Role: Solicited external reviewer
                Journal
                mBio
                MBio
                mbio
                mbio
                mBio
                mBio
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2150-7511
                23 April 2019
                Mar-Apr 2019
                : 10
                : 2
                : e00644-19
                Affiliations
                [a ]Institute of Microbiology and Infection, University of Birmingham, Birmingham, United Kingdom
                [b ]Infection Genomics, Wellcome Sanger Institute, Cambridge, United Kingdom
                [c ]Department of Biostatistics, University of Oslo, Oslo, Norway
                [d ]British Society of Antimicrobial Chemotherapy, Birmingham, United Kingdom
                [e ]Department of Medicine, University of Cambridge, Cambridge, United Kingdom
                [f ]London School of Hygiene and Tropical Medicine, London, United Kingdom
                [g ]Faculty of Medicine, School of Public Health, Imperial College, London, United Kingdom
                [h ]Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
                University of British Columbia
                University of Montreal
                University of Oxford
                Author notes
                Address correspondence to Alan McNally, a.mcnally.1@ 123456bham.ac.uk , or Jukka Corander, jukka.corander@ 123456helsinki.fi .

                A.M., T.K., and C.C. as well as N.J.C. and. J.C contributed equally to this article.

                Author information
                https://orcid.org/0000-0002-7069-5958
                Article
                mBio00644-19
                10.1128/mBio.00644-19
                6479005
                31015329
                51803219-20b2-4d4d-8af7-c68d14c0a566
                Copyright © 2019 McNally et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 20 March 2019
                : 21 March 2019
                Page count
                supplementary-material: 9, Figures: 7, Tables: 0, Equations: 5, References: 57, Pages: 19, Words: 13385
                Funding
                Funded by: Wellcome Trust;
                Award ID: 104169/Z/14/Z
                Award Recipient :
                Funded by: The Wellcome Trust;
                Award ID: WT098600
                Award Recipient : Award Recipient :
                Funded by: The Wellcome Trust;
                Award ID: 2C3812/Z/16/Z
                Award Recipient : Award Recipient :
                Funded by: Norwegian Research Council;
                Award ID: 144501
                Award Recipient :
                Funded by: European research council;
                Award ID: 742158
                Award Recipient :
                Funded by: Department of Health & Social Care (DH), https://doi.org/10.13039/501100000276;
                Award ID: HICF-T5-342
                Award Recipient : Award Recipient :
                Categories
                Research Article
                Ecological and Evolutionary Science
                Editor's Pick
                Custom metadata
                March/April 2019

                Life sciences
                amr,escherichia coli,evolutionary genomics,negative frequency-dependent selection

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