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      Genomic diversity of Salmonella enterica -The UoWUCC 10K genomes project

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          Abstract

          Background: Most publicly available genomes of Salmonella enterica are from human disease in the US and the UK, or from domesticated animals in the US.

          Methods: Here we describe a historical collection of 10,000 strains isolated between 1891-2010 in 73 different countries. They encompass a broad range of sources, ranging from rivers through reptiles to the diversity of all S. enterica isolated on the island of Ireland between 2000 and 2005. Genomic DNA was isolated, and sequenced by Illumina short read sequencing.

          Results: The short reads are publicly available in the Short Reads Archive. They were also uploaded to EnteroBase, which assembled and annotated draft genomes. 9769 draft genomes which passed quality control were genotyped with multiple levels of multilocus sequence typing, and used to predict serovars. Genomes were assigned to hierarchical clusters on the basis of numbers of pair-wise allelic differences in core genes, which were mapped to genetic Lineages within phylogenetic trees.

          Conclusions: The University of Warwick/University College Cork (UoWUCC) project greatly extends the geographic sources, dates and core genomic diversity of publicly available S. enterica genomes. We illustrate these features by an overview of core genomic Lineages within 33,000 publicly available Salmonella genomes whose strains were isolated before 2011. We also present detailed examinations of HC400, HC900 and HC2000 hierarchical clusters within exemplar Lineages, including serovars Typhimurium, Enteritidis and Mbandaka. These analyses confirm the polyphyletic nature of multiple serovars while showing that discrete clusters with geographical specificity can be reliably recognized by hierarchical clustering approaches. The results also demonstrate that the genomes sequenced here provide an important counterbalance to the sampling bias which is so dominant in current genomic sequencing.

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          Open-access bacterial population genomics: BIGSdb software, the PubMLST.org website and their applications

          The PubMLST.org website hosts a collection of open-access, curated databases that integrate population sequence data with provenance and phenotype information for over 100 different microbial species and genera.  Although the PubMLST website was conceived as part of the development of the first multi-locus sequence typing (MLST) scheme in 1998 the software it uses, the Bacterial Isolate Genome Sequence database (BIGSdb, published in 2010), enables PubMLST to include all levels of sequence data, from single gene sequences up to and including complete, finished genomes.  Here we describe developments in the BIGSdb software made from publication to June 2018 and show how the platform realises microbial population genomics for a wide range of applications.  The system is based on the gene-by-gene analysis of microbial genomes, with each deposited sequence annotated and curated to identify the genes present and systematically catalogue their variation.  Originally intended as a means of characterising isolates with typing schemes, the synthesis of sequences and records of genetic variation with provenance and phenotype data permits highly scalable (whole genome sequence data for tens of thousands of isolates) means of addressing a wide range of functional questions, including: the prediction of antimicrobial resistance; likely cross-reactivity with vaccine antigens; and the functional activities of different variants that lead to key phenotypes.  There are no limitations to the number of sequences, genetic loci, allelic variants or schemes (combinations of loci) that can be included, enabling each database to represent an expanding catalogue of the genetic variation of the population in question.  In addition to providing web-accessible analyses and links to third-party analysis and visualisation tools, the BIGSdb software includes a RESTful application programming interface (API) that enables access to all the underlying data for third-party applications and data analysis pipelines.
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            The global burden of nontyphoidal Salmonella gastroenteritis.

            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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              Validating the AMRFinder Tool and Resistance Gene Database by Using Antimicrobial Resistance Genotype-Phenotype Correlations in a Collection of Isolates

              Antimicrobial resistance (AMR) is a major public health problem that requires publicly available tools for rapid analysis. To identify AMR genes in whole-genome sequences, the National Center for Biotechnology Information (NCBI) has produced AMRFinder, a tool that identifies AMR genes using a high-quality curated AMR gene reference database. The Bacterial Antimicrobial Resistance Reference Gene Database consists of up-to-date gene nomenclature, a set of hidden Markov models (HMMs), and a curated protein family hierarchy. Currently, it contains 4,579 antimicrobial resistance proteins and more than 560 HMMs. Here, we describe AMRFinder and its associated database. To assess the predictive ability of AMRFinder, we measured the consistency between predicted AMR genotypes from AMRFinder and resistance phenotypes of 6,242 isolates from the National Antimicrobial Resistance Monitoring System (NARMS). This included 5,425 Salmonella enterica , 770 Campylobacter spp., and 47 Escherichia coli isolates phenotypically tested against various antimicrobial agents. Of 87,679 susceptibility tests performed, 98.4% were consistent with predictions. To assess the accuracy of AMRFinder, we compared its gene symbol output with that of a 2017 version of ResFinder, another publicly available resistance gene detection system. Most gene calls were identical, but there were 1,229 gene symbol differences (8.8%) between them, with differences due to both algorithmic differences and database composition. AMRFinder missed 16 loci that ResFinder found, while ResFinder missed 216 loci that AMRFinder identified. Based on these results, AMRFinder appears to be a highly accurate AMR gene detection system.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data CurationRole: Formal AnalysisRole: Funding AcquisitionRole: Project AdministrationRole: ResourcesRole: SupervisionRole: VisualizationRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Role: Data CurationRole: MethodologyRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – Review & Editing
                Role: MethodologyRole: Project AdministrationRole: ResourcesRole: SoftwareRole: Supervision
                Role: Data CurationRole: Investigation
                Role: Funding AcquisitionRole: InvestigationRole: Project AdministrationRole: ResourcesRole: Supervision
                Role: Data CurationRole: Funding AcquisitionRole: InvestigationRole: ResourcesRole: Supervision
                Role: Data CurationRole: Funding AcquisitionRole: InvestigationRole: ResourcesRole: Supervision
                Role: Resources
                Role: Data CurationRole: InvestigationRole: Resources
                Role: InvestigationRole: ResourcesRole: Supervision
                Role: InvestigationRole: ResourcesRole: Supervision
                Role: ResourcesRole: Supervision
                Role: Data CurationRole: InvestigationRole: Resources
                Role: Data CurationRole: InvestigationRole: ResourcesRole: Writing – Review & Editing
                Role: Data CurationRole: InvestigationRole: Resources
                Role: Data CurationRole: ResourcesRole: Supervision
                Role: Data CurationRole: InvestigationRole: ResourcesRole: Writing – Review & Editing
                Role: Data CurationRole: InvestigationRole: Resources
                Role: ConceptualizationRole: ResourcesRole: SupervisionRole: Writing – Review & Editing
                Role: ResourcesRole: Writing – Review & Editing
                Journal
                Wellcome Open Res
                Wellcome Open Res
                Wellcome Open Res
                Wellcome Open Research
                F1000 Research Limited (London, UK )
                2398-502X
                1 February 2021
                2020
                : 5
                : 223
                Affiliations
                [1 ]Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK
                [2 ]Department of Veterinary Medicine, University of Cambridge, Cambridge, CB3 0ES, UK
                [3 ]National Salmonella, Shigella and Listeria Reference Laboratory, Galway, H91 YR71, Ireland
                [4 ]Central Regional Laboratory, Center for Diagnostics and Vaccine Development, Centers for Disease Control, Taichung, None, Taiwan
                [5 ]Statens Serum Institut, Copenhagen S, DK-2300, Denmark
                [6 ]Backweston complex, Department of Agriculture, Food and the Marine (DAFM), Celbridge, Co. Kildare, W23 X3PH, Ireland
                [7 ]Northern Ireland Public Health Laboratory, Department of Bacteriology, Belfast City Hospital, Belfast, BT9 7AD, UK
                [8 ]Animal Health and Welfare NI, Dungannon, BT71 6JT, UK
                [9 ]Institute for Medical Microbiology and Hygiene, Austrian Agency for Health and Food Safety (AGES), Graz, 8010, Austria
                [10 ]US National Poultry Research Center, USDA Agricultural Research Service, Athens, GA, 30605, USA
                [11 ]Institute for Medical Microbiology, Immunology, and Hygiene, University of Cologne, Cologne, 50935, Germany
                [12 ]Unité des bactéries pathogènes entériques, Institut Pasteur, Paris, cedex 15, France
                [13 ]Cork Institute of Technology, Cork, T12P928, Ireland
                [14 ]Dept. of Microbial Pathogenesis and Immunology, College of Medicine Texas A&M University, Bryan, TX, 77807, USA
                [15 ]Dept. of Infectious Diseases & Immunology, College of Veterinary Medicine, University of Florida, Gainesville, Florida, 32611, USA
                [16 ]UCD-Centre for Food Safety, University College Dublin, Dublin, D04 N2E5, Ireland
                [1 ]Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
                [1 ]Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
                [1 ]Center for Food Safety, Department of Food Science and Technology, University of Georgia, Griffin, GA, USA
                Author notes

                No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Author information
                https://orcid.org/0000-0001-6815-0070
                https://orcid.org/0000-0001-9783-0366
                https://orcid.org/0000-0002-1243-0767
                https://orcid.org/0000-0002-7069-5958
                https://orcid.org/0000-0002-5243-5108
                https://orcid.org/0000-0003-0572-8060
                https://orcid.org/0000-0001-9941-5799
                Article
                10.12688/wellcomeopenres.16291.2
                7869069
                33614977
                72f675c5-5f01-46f7-9aca-af5399aa86d1
                Copyright: © 2021 Achtman M et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 25 January 2021
                Funding
                Funded by: Science Foundation of Ireland
                Award ID: 05/FE1/B882
                Funded by: Wellcome Trust
                Award ID: 202792
                Funded by: Health and Social Care Research and Development Division
                Funded by: U.S. Department of Agriculture
                Award ID: 6040-32000-009-00-D
                This work was supported by the Wellcome Trust through a Investigator in Science Award to MA [202792]. This work was also supported by the Science Foundation of Ireland [05/FE1/B882 to MA]. RM is supported by USDA Agricultural Research Service Project [6040-32000-009-00-D]. Bacterial strains from Belfast City Hospital were from the Northern Ireland HSC Microbiology Culture Repository (MicroARK), Northern Ireland Public Health Laboratory and funded by the HSC Research & Development Office.
                The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Articles

                salmonella,large scale genomic database,high throughput sequencing,population genomics

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