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      Genetic diversity and population structure of Tenacibaculum maritimum, a serious bacterial pathogen of marine fish: from genome comparisons to high throughput MALDI-TOF typing

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          Abstract

          Tenacibaculum maritimum is responsible for tenacibaculosis, a devastating marine fish disease. This filamentous bacterium displays a very broad host range and a worldwide geographical distribution. We analyzed and compared the genomes of 25 T. maritimum strains, including 22 newly draft-sequenced genomes from isolates selected based on available MLST data, geographical origin and host fish. The genome size (~3.356 Mb in average) of all strains is very similar. The core genome is composed of 2116 protein-coding genes accounting for ~75% of the genes in each genome. These conserved regions harbor a moderate level of nucleotide diversity (~0.0071 bp −1) whose analysis reveals an important contribution of recombination (r/m ≥ 7) in the evolutionary process of this cohesive species that appears subdivided into several subgroups. Association trends between these subgroups and specific geographical origin or ecological niche remains to be clarified. We also evaluated the potential of MALDI-TOF-MS to assess the variability between T. maritimum isolates. Using genome sequence data, several detected mass peaks were assigned to ribosomal proteins. Additionally, variations corresponding to single or multiple amino acid changes in several ribosomal proteins explaining the detected mass shifts were identified. By combining nine polymorphic biomarker ions, we identified combinations referred to as MALDI-Types (MTs). By investigating 131 bacterial isolates retrieved from a variety of isolation sources, we identified twenty MALDI-Types as well as four MALDI-Groups (MGs). We propose this MALDI-TOF-MS Multi Peak Shift Typing scheme as a cheap, fast and an accurate method for screening T. maritimum isolates for large-scale epidemiological surveys.

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          eBURST: inferring patterns of evolutionary descent among clusters of related bacterial genotypes from multilocus sequence typing data.

          The introduction of multilocus sequence typing (MLST) for the precise characterization of isolates of bacterial pathogens has had a marked impact on both routine epidemiological surveillance and microbial population biology. In both fields, a key prerequisite for exploiting this resource is the ability to discern the relatedness and patterns of evolutionary descent among isolates with similar genotypes. Traditional clustering techniques, such as dendrograms, provide a very poor representation of recent evolutionary events, as they attempt to reconstruct relationships in the absence of a realistic model of the way in which bacterial clones emerge and diversify to form clonal complexes. An increasingly popular approach, called BURST, has been used as an alternative, but present implementations are unable to cope with very large data sets and offer crude graphical outputs. Here we present a new implementation of this algorithm, eBURST, which divides an MLST data set of any size into groups of related isolates and clonal complexes, predicts the founding (ancestral) genotype of each clonal complex, and computes the bootstrap support for the assignment. The most parsimonious patterns of descent of all isolates in each clonal complex from the predicted founder(s) are then displayed. The advantages of eBURST for exploring patterns of evolutionary descent are demonstrated with a number of examples, including the simple Spain(23F)-1 clonal complex of Streptococcus pneumoniae, "population snapshots" of the entire S. pneumoniae and Staphylococcus aureus MLST databases, and the more complicated clonal complexes observed for Campylobacter jejuni and Neisseria meningitidis.
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            Guidelines for the validation and application of typing methods for use in bacterial epidemiology.

            For bacterial typing to be useful, the development, validation and appropriate application of typing methods must follow unified criteria. Over a decade ago, ESGEM, the ESCMID (Europen Society for Clinical Microbiology and Infectious Diseases) Study Group on Epidemiological Markers, produced guidelines for optimal use and quality assessment of the then most frequently used typing procedures. We present here an update of these guidelines, taking into account the spectacular increase in the number and quality of typing methods made available over the past decade. Newer and older, phenotypic and genotypic methods for typing of all clinically relevant bacterial species are described according to their principles, advantages and disadvantages. Criteria for their evaluation and application and the interpretation of their results are proposed. Finally, the issues of reporting, standardisation, quality assessment and international networks are discussed. It must be emphasised that typing results can never stand alone and need to be interpreted in the context of all available epidemiological, clinical and demographical data relating to the infectious disease under investigation. A strategic effort on the part of all workers in the field is thus mandatory to combat emerging infectious diseases, as is financial support from national and international granting bodies and health authorities.
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              Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching.

              A major problem for current peak detection algorithms is that noise in mass spectrometry (MS) spectra gives rise to a high rate of false positives. The false positive rate is especially problematic in detecting peaks with low amplitudes. Usually, various baseline correction algorithms and smoothing methods are applied before attempting peak detection. This approach is very sensitive to the amount of smoothing and aggressiveness of the baseline correction, which contribute to making peak detection results inconsistent between runs, instrumentation and analysis methods. Most peak detection algorithms simply identify peaks based on amplitude, ignoring the additional information present in the shape of the peaks in a spectrum. In our experience, 'true' peaks have characteristic shapes, and providing a shape-matching function that provides a 'goodness of fit' coefficient should provide a more robust peak identification method. Based on these observations, a continuous wavelet transform (CWT)-based peak detection algorithm has been devised that identifies peaks with different scales and amplitudes. By transforming the spectrum into wavelet space, the pattern-matching problem is simplified and in addition provides a powerful technique for identifying and separating the signal from the spike noise and colored noise. This transformation, with the additional information provided by the 2D CWT coefficients can greatly enhance the effective signal-to-noise ratio. Furthermore, with this technique no baseline removal or peak smoothing preprocessing steps are required before peak detection, and this improves the robustness of peak detection under a variety of conditions. The algorithm was evaluated with SELDI-TOF spectra with known polypeptide positions. Comparisons with two other popular algorithms were performed. The results show the CWT-based algorithm can identify both strong and weak peaks while keeping false positive rate low. The algorithm is implemented in R and will be included as an open source module in the Bioconductor project.
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                Author and article information

                Contributors
                sebastien-bridel@live.fr
                f.bourgeon@biochenevert.fr
                a.marie@labofarm.com
                Denis.Saulnier@ifremer.fr
                sophiepasek@gmail.com
                pierre.nicolas@inrae.fr
                jean-francois.bernardet@inrae.fr
                eric.duchaud@inrae.fr
                Journal
                Vet Res
                Vet. Res
                Veterinary Research
                BioMed Central (London )
                0928-4249
                1297-9716
                7 May 2020
                7 May 2020
                2020
                : 51
                : 60
                Affiliations
                [1 ]GRID grid.12832.3a, ISNI 0000 0001 2323 0229, Université Paris-Saclay, INRAE, UVSQ, ; VIM 78350 Jouy-En-Josas, France
                [2 ]Labofarm, Finalab, 22603 Loudéac, France
                [3 ]GRID grid.12832.3a, ISNI 0000 0001 2323 0229, Université de Versailles Saint-Quentin-En-Yvelines, ; 78180 Montigny-Le-Bretonneux, France
                [4 ]Bio Chêne Vert, Finalab, Rue Blaise Pascal, 35220 Châteaubourg, France
                [5 ]Ifremer, UMR EIO 241, Labex Corail, Centre du Pacifique, BP 49, Taravao, 98719 Tahiti, French Polynesia
                [6 ]GRID grid.463994.5, ISNI 0000 0004 0370 7618, Institut de Systématique Evolution, Biodiversité, UMR 7205 Sorbonne Université MNHN CNRS EPHE, ; Paris, France
                [7 ]Université Paris-Saclay, INRAE, MaIAGE 78350 Jouy-en-Josas, France
                Author information
                http://orcid.org/0000-0003-3353-4714
                Article
                782
                10.1186/s13567-020-00782-0
                7204230
                32381115
                ed4fad14-7d8f-48a0-92ef-7f41466d65a1
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 20 January 2020
                : 8 April 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100011900, Institut Carnot Santé Animale;
                Funded by: FundRef http://dx.doi.org/10.13039/501100003032, Association Nationale de la Recherche et de la Technologie;
                Award ID: 2006/0707
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © L'Institut National de Recherche en Agriculture, Alimentation et Environnement (INRAE) 2020

                Veterinary medicine
                Veterinary medicine

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