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      A novel cgMLST for genomic surveillance of Yersinia enterocolitica infections in France allowed the detection and investigation of outbreaks in 2017–2021

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          ABSTRACT

          Enteric yersiniosis, the third most common food-borne zoonosis in Europe, is mainly caused by the pathogen Yersinia enterocolitica. In France , the yersiniosis microbiological surveillance is conducted at the Yersinia National Reference Laboratory (YNRL). Since 2017, isolates have been characterized by whole genome sequencing (WGS) followed by a 500-gene Yersinia-cgMLST. We report here the data of the WGS-based surveillance on Y. enterocolitica isolates for the 2017–2021 period. The YNRL characterized 7,642 Y. enterocolitica strains distributed in 2,497 non-pathogenic isolates from lineages 1Aa and 1Ab, and 5,145 specimens belonging to 8 pathogenic lineages. Among pathogenic isolates, lineage 4 was the most common (87.2%) followed by lineages 2/3-9b (10.6%), 2/3-5a (1.2%), 2/3-9a (0.6%), 3-3b, 3-3c, 1B, and 3-3d (0.1% per each). Importantly, we developed a routine surveillance system based on a new typing method consisting of a 1,727-genes core genome Multilocus Sequence Typing (cgMLST) specific to the species Y. enterocolitica followed by isolate clustering. Thresholds of allelic distances (AD) were determined and fixed for the clustering of isolates: AD ≤ 5 for lineages 4, 2/3-5a, and 2/3-9a, and AD ≤ 3 for lineage 2/3-9b. Clustering programs were implemented in 2019 in routine surveillance to detect genomic clusters of pathogenic isolates. In total, 419 clusters with at least 2 isolates were identified, representing 2,504 of the 3,503 isolates characterized between 2019 and 2021. Most clusters ( n = 325) comprised 2 to 5 isolates. The new typing method proved to be useful for the molecular investigation of unusual grouping of cases as well as for the detection of genomic clusters in routine surveillance.

          IMPORTANCE

          We describe here the new typing method used for molecular surveillance of Yersinia enterocolitica infections in France based on a novel core genome Multilocus Sequence Typing (cgMLST) specific to Y. enterocolitica species. This method can reliably identify the pathogenic Y. enterocolitica subspecies and compare the isolates with a high discriminatory power. Between 2017 and 2021, 5,145 pathogenic isolates belonging to 8 lineages were characterized and lineage 4 was by far the most common followed by lineage 2/3-9b. A clustering program was implemented, and detection thresholds were cross-validated by the molecular and epidemiological investigation of three unusual groups of Y. enterocolitica infections. The routine molecular surveillance system has been able to detect genomic clusters, leading to epidemiological investigations.

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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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            BIGSdb: Scalable analysis of bacterial genome variation at the population level

            Background The opportunities for bacterial population genomics that are being realised by the application of parallel nucleotide sequencing require novel bioinformatics platforms. These must be capable of the storage, retrieval, and analysis of linked phenotypic and genotypic information in an accessible, scalable and computationally efficient manner. Results The Bacterial Isolate Genome Sequence Database (BIGSDB) is a scalable, open source, web-accessible database system that meets these needs, enabling phenotype and sequence data, which can range from a single sequence read to whole genome data, to be efficiently linked for a limitless number of bacterial specimens. The system builds on the widely used mlstdbNet software, developed for the storage and distribution of multilocus sequence typing (MLST) data, and incorporates the capacity to define and identify any number of loci and genetic variants at those loci within the stored nucleotide sequences. These loci can be further organised into 'schemes' for isolate characterisation or for evolutionary or functional analyses. Isolates and loci can be indexed by multiple names and any number of alternative schemes can be accommodated, enabling cross-referencing of different studies and approaches. LIMS functionality of the software enables linkage to and organisation of laboratory samples. The data are easily linked to external databases and fine-grained authentication of access permits multiple users to participate in community annotation by setting up or contributing to different schemes within the database. Some of the applications of BIGSDB are illustrated with the genera Neisseria and Streptococcus. The BIGSDB source code and documentation are available at http://pubmlst.org/software/database/bigsdb/. Conclusions Genomic data can be used to characterise bacterial isolates in many different ways but it can also be efficiently exploited for evolutionary or functional studies. BIGSDB represents a freely available resource that will assist the broader community in the elucidation of the structure and function of bacteria by means of a population genomics approach.
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              GrapeTree: visualization of core genomic relationships among 100,000 bacterial pathogens

              Current methods struggle to reconstruct and visualize the genomic relationships of large numbers of bacterial genomes. GrapeTree facilitates the analyses of large numbers of allelic profiles by a static “GrapeTree Layout” algorithm that supports interactive visualizations of large trees within a web browser window. GrapeTree also implements a novel minimum spanning tree algorithm (MSTree V2) to reconstruct genetic relationships despite high levels of missing data. GrapeTree is a stand-alone package for investigating phylogenetic trees plus associated metadata and is also integrated into EnteroBase to facilitate cutting edge navigation of genomic relationships among bacterial pathogens.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                Microbiol Spectr
                Microbiol Spectr
                spectrum
                Microbiology Spectrum
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2165-0497
                June 2024
                23 April 2024
                23 April 2024
                : 12
                : 6
                : e00504-24
                Affiliations
                [1 ]Institut Pasteur, Université de Paris Cité, Yersinia Research Unit, Yersinia National Reference Laboratory, WHO Collaborating Centre for Plague Fra-140; , Paris, France
                [2 ]Santé publique France, Infectious Diseases Division; , Saint-Maurice, France
                [3 ]Santé publique France, Regions Division, Bourgogne-Franche-Comté Office; , Dijon, France
                [4 ]Institut Pasteur, Université de Paris Cité, Bioinformatics and Biostatistic Hub; , Paris, France
                University Paris-Saclay; , Clamart, France
                Author notes
                Address correspondence to Anne-Sophie Le Guern, anne-sophie.le-guern@ 123456pasteur.fr
                Address correspondence to Javier Pizarro-Cerdá, javier.pizarro-cerda@ 123456pasteur.fr

                The authors declare no conflict of interest.

                Author information
                https://orcid.org/0000-0003-1051-6740
                https://orcid.org/0000-0002-4343-0508
                Article
                00504-24 spectrum.00504-24
                10.1128/spectrum.00504-24
                11237650
                38651883
                02215426-36ec-4c52-bb88-72bf35e7cf40
                Copyright © 2024 Le Guern et al.

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

                History
                : 27 February 2024
                : 08 April 2024
                Page count
                supplementary-material: 0, authors: 7, Figures: 5, Tables: 6, References: 36, Pages: 17, Words: 8646
                Funding
                Funded by: Institut Pasteur;
                Award ID: ANR LBX-62 IBEID
                Award Recipient :
                Categories
                Research Article
                clinical-microbiology, Clinical Microbiology
                Custom metadata
                June 2024

                yersinia enterocolitica,enteric yersiniosis,cgmlst,outbreak,epidemiological investigation

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