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      One Health Genomic Surveillance of Escherichia coli Demonstrates Distinct Lineages and Mobile Genetic Elements in Isolates from Humans versus Livestock

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          Abstract

          The increasing prevalence of E. coli bloodstream infections is a serious public health problem. We used genomic epidemiology in a One Health study conducted in the East of England to examine putative sources of E. coli associated with serious human disease. E. coli from 1,517 patients with bloodstream infections were compared with 431 isolates from livestock farms and meat. Livestock-associated and bloodstream isolates were genetically distinct populations based on core genome and accessory genome analyses. Identical antimicrobial resistance genes were found in livestock and human isolates, but there was limited overlap in the mobile elements carrying these genes. Within the limitations of sampling, our findings do not support the idea that E. coli causing invasive disease or their resistance genes are commonly acquired from livestock in our region.

          ABSTRACT

          Livestock have been proposed as a reservoir for drug-resistant Escherichia coli that infect humans. We isolated and sequenced 431 E. coli isolates (including 155 extended-spectrum β-lactamase [ESBL]-producing isolates) from cross-sectional surveys of livestock farms and retail meat in the East of England. These were compared with the genomes of 1,517 E. coli bacteria associated with bloodstream infection in the United Kingdom. Phylogenetic core genome comparisons demonstrated that livestock and patient isolates were genetically distinct, suggesting that E. coli causing serious human infection had not directly originated from livestock. In contrast, we observed highly related isolates from the same animal species on different farms. Screening all 1,948 isolates for accessory genes encoding antibiotic resistance revealed 41 different genes present in variable proportions in human and livestock isolates. Overall, we identified a low prevalence of shared antimicrobial resistance genes between livestock and humans based on analysis of mobile genetic elements and long-read sequencing. We conclude that within the confines of our sampling framework, there was limited evidence that antimicrobial-resistant pathogens associated with serious human infection had originated from livestock in our region.

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          Toward almost closed genomes with GapFiller

          De novo assembly is a commonly used application of next-generation sequencing experiments. The ultimate goal is to puzzle millions of reads into one complete genome, although draft assemblies usually result in a number of gapped scaffold sequences. In this paper we propose an automated strategy, called GapFiller, to reliably close gaps within scaffolds using paired reads. The method shows good results on both bacterial and eukaryotic datasets, allowing only few errors. As a consequence, the amount of additional wetlab work needed to close a genome is drastically reduced. The software is available at http://www.baseclear.com/bioinformatics-tools/.
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            NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy

            The National Center for Biotechnology Information (NCBI) Reference Sequence (RefSeq) database is a collection of genomic, transcript and protein sequence records. These records are selected and curated from public sequence archives and represent a significant reduction in redundancy compared to the volume of data archived by the International Nucleotide Sequence Database Collaboration. The database includes over 16 000 organisms, 2.4 × 106 genomic records, 13 × 106 proteins and 2 × 106 RNA records spanning prokaryotes, eukaryotes and viruses (RefSeq release 49, September 2011). The RefSeq database is maintained by a combined approach of automated analyses, collaboration and manual curation to generate an up-to-date representation of the sequence, its features, names and cross-links to related sources of information. We report here on recent growth, the status of curating the human RefSeq data set, more extensive feature annotation and current policy for eukaryotic genome annotation via the NCBI annotation pipeline. More information about the resource is available online (see http://www.ncbi.nlm.nih.gov/RefSeq/).
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              Improving the accuracy of demographic and molecular clock model comparison while accommodating phylogenetic uncertainty.

              Recent developments in marginal likelihood estimation for model selection in the field of Bayesian phylogenetics and molecular evolution have emphasized the poor performance of the harmonic mean estimator (HME). Although these studies have shown the merits of new approaches applied to standard normally distributed examples and small real-world data sets, not much is currently known concerning the performance and computational issues of these methods when fitting complex evolutionary and population genetic models to empirical real-world data sets. Further, these approaches have not yet seen widespread application in the field due to the lack of implementations of these computationally demanding techniques in commonly used phylogenetic packages. We here investigate the performance of some of these new marginal likelihood estimators, specifically, path sampling (PS) and stepping-stone (SS) sampling for comparing models of demographic change and relaxed molecular clocks, using synthetic data and real-world examples for which unexpected inferences were made using the HME. Given the drastically increased computational demands of PS and SS sampling, we also investigate a posterior simulation-based analogue of Akaike's information criterion (AIC) through Markov chain Monte Carlo (MCMC), a model comparison approach that shares with the HME the appealing feature of having a low computational overhead over the original MCMC analysis. We confirm that the HME systematically overestimates the marginal likelihood and fails to yield reliable model classification and show that the AICM performs better and may be a useful initial evaluation of model choice but that it is also, to a lesser degree, unreliable. We show that PS and SS sampling substantially outperform these estimators and adjust the conclusions made concerning previous analyses for the three real-world data sets that we reanalyzed. The methods used in this article are now available in BEAST, a powerful user-friendly software package to perform Bayesian evolutionary analyses.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                mBio
                MBio
                mbio
                mbio
                mBio
                mBio
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2150-7511
                22 January 2019
                Jan-Feb 2019
                : 10
                : 1
                : e02693-18
                Affiliations
                [a ]London School of Hygiene & Tropical Medicine, London, United Kingdom
                [b ]Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
                [c ]Department of Medicine, University of Cambridge, Addenbrooke’s Hospital, Cambridge, United Kingdom
                [d ]Clinical Microbiology and Public Health Laboratory, Public Health England, Cambridge, United Kingdom
                [e ]Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
                [f ]British Society for Antimicrobial Chemotherapy, Birmingham, United Kingdom
                [g ]Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom
                [h ]Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom
                Pasteur Institute
                Author notes
                Address correspondence to Catherine Ludden, catherine.ludden@ 123456lshtm.ac.uk .
                Author information
                https://orcid.org/0000-0002-7069-5958
                Article
                mBio02693-18
                10.1128/mBio.02693-18
                6343043
                30670621
                f57b73d4-e70e-488b-bc8c-c502f3be5217
                Copyright © 2019 Ludden et al.

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

                History
                : 5 December 2018
                : 11 December 2018
                Page count
                supplementary-material: 10, Figures: 4, Tables: 0, Equations: 0, References: 56, Pages: 12, Words: 9193
                Funding
                Funded by: Wellcome Trust;
                Award ID: 098051
                Award Recipient :
                Funded by: Wellcome Trust;
                Award ID: 103387/Z/13/Z
                Award Recipient :
                Funded by: Wellcome Trust;
                Award ID: 201344/Z/16/Z
                Award Recipient :
                Funded by: RCUK | Medical Research Council (MRC), https://doi.org/10.13039/501100000265;
                Award ID: MR/K021133/1
                Award Recipient :
                Funded by: Department of Health & Social Care (DHSC), https://doi.org/10.13039/501100000276;
                Award ID: HICF-T5-342
                Award ID: WT098600
                Award Recipient :
                Categories
                Research Article
                Clinical Science and Epidemiology
                Editor's Pick
                Custom metadata
                January/February 2019

                Life sciences
                esbl,escherichia coli,antimicrobial resistance,genomics,livestock
                Life sciences
                esbl, escherichia coli, antimicrobial resistance, genomics, livestock

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