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      Comparative analysis of core genome MLST and SNP typing within a European Salmonella serovar Enteritidis outbreak

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          Abstract

          Multi-country outbreaks of foodborne bacterial disease present challenges in their detection, tracking, and notification. As food is increasingly distributed across borders, such outbreaks are becoming more common. This increases the need for high-resolution, accessible, and replicable isolate typing schemes. Here we evaluate a core genome multilocus typing (cgMLST) scheme for the high-resolution reproducible typing of Salmonella enterica ( S. enterica) isolates, by its application to a large European outbreak of S. enterica serovar Enteritidis. This outbreak had been extensively characterised using single nucleotide polymorphism (SNP)-based approaches. The cgMLST analysis was congruent with the original SNP-based analysis, the epidemiological data, and whole genome MLST (wgMLST) analysis. Combination of the cgMLST and epidemiological data confirmed that the genetic diversity among the isolates predated the outbreak, and was likely present at the infection source. There was consequently no link between country of isolation and genetic diversity, but the cgMLST clusters were congruent with date of isolation. Furthermore, comparison with publicly available Enteritidis isolate data demonstrated that the cgMLST scheme presented is highly scalable, enabling outbreaks to be contextualised within the Salmonella genus. The cgMLST scheme is therefore shown to be a standardised and scalable typing method, which allows Salmonella outbreaks to be analysed and compared across laboratories and jurisdictions.

          Highlights

          • cgMLST is proposed as a universal typing scheme for Salmonella.

          • cgMLST is congruent with SNP analyses and easier to implement across laboratories.

          • Genomic data are consistent with the epidemiology of the outbreak.

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          Most cited references 40

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          Multilocus sequence typing: a portable approach to the identification of clones within populations of pathogenic microorganisms.

          Traditional and molecular typing schemes for the characterization of pathogenic microorganisms are poorly portable because they index variation that is difficult to compare among laboratories. To overcome these problems, we propose multilocus sequence typing (MLST), which exploits the unambiguous nature and electronic portability of nucleotide sequence data for the characterization of microorganisms. To evaluate MLST, we determined the sequences of approximately 470-bp fragments from 11 housekeeping genes in a reference set of 107 isolates of Neisseria meningitidis from invasive disease and healthy carriers. For each locus, alleles were assigned arbitrary numbers and dendrograms were constructed from the pairwise differences in multilocus allelic profiles by cluster analysis. The strain associations obtained were consistent with clonal groupings previously determined by multilocus enzyme electrophoresis. A subset of six gene fragments was chosen that retained the resolution and congruence achieved by using all 11 loci. Most isolates from hyper-virulent lineages of serogroups A, B, and C meningococci were identical for all loci or differed from the majority type at only a single locus. MLST using six loci therefore reliably identified the major meningococcal lineages associated with invasive disease. MLST can be applied to almost all bacterial species and other haploid organisms, including those that are difficult to cultivate. The overwhelming advantage of MLST over other molecular typing methods is that sequence data are truly portable between laboratories, permitting one expanding global database per species to be placed on a World-Wide Web site, thus enabling exchange of molecular typing data for global epidemiology via the Internet.
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            The global burden of nontyphoidal Salmonella gastroenteritis.

             B. O’Brien,  ,  Martyn Kirk (2010)
            To estimate the global burden of nontyphoidal Salmonella gastroenteritis, we synthesized existing data from laboratory-based surveillance and special studies, with a hierarchical preference to (1) prospective population-based studies, (2) "multiplier studies," (3) disease notifications, (4) returning traveler data, and (5) extrapolation. We applied incidence estimates to population projections for the 21 Global Burden of Disease regions to calculate regional numbers of cases, which were summed to provide a global number of cases. Uncertainty calculations were performed using Monte Carlo simulation. We estimated that 93.8 million cases (5th to 95th percentile, 61.8-131.6 million) of gastroenteritis due to Salmonella species occur globally each year, with 155,000 deaths (5th to 95th percentile, 39,000-303,000 deaths). Of these, we estimated 80.3 million cases were foodborne. Salmonella infection represents a considerable burden in both developing and developed countries. Efforts to reduce transmission of salmonellae by food and other routes must be implemented on a global scale.
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              Distinguishing homologous from analogous proteins.

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                Author and article information

                Contributors
                Journal
                Int J Food Microbiol
                Int. J. Food Microbiol
                International Journal of Food Microbiology
                Elsevier Science Publishers
                0168-1605
                1879-3460
                02 June 2018
                02 June 2018
                : 274
                : 1-11
                Affiliations
                [a ]Department of Zoology, University of Oxford, Peter Medawar Building for Pathogen Research, South Parks Road, Oxford OX1 3SY, United Kingdom
                [b ]National Institute for Health Research, Health Protection Research Unit, Gastrointestinal Infections, University of Oxford, United Kingdom
                [c ]Warwick Medical School, University of Warwick, Coventry CV4 7AL, United Kingdom
                [d ]Public Health England, Gastrointestinal Bacteria Reference Unit, 61 Colindale Avenue, London NW9 5EQ, United Kingdom
                Author notes
                [* ]Corresponding author at: Department of Zoology, University of Oxford, Peter Medawar Building for Pathogen Research, South Parks Road, Oxford OX1 3SY, United Kingdom. Madison.pearce@ 123456zoo.ox.ac.uk
                Article
                S0168-1605(18)30074-6
                10.1016/j.ijfoodmicro.2018.02.023
                5899760
                29574242
                © 2018 The Authors. Published by Elsevier B.V.

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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