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      Inference from the analysis of genetic structure of Helicobacter pylori strains isolates from two paediatric patients with recurrent infection

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          Abstract

          Background

          Helicobacter pylori recurrence after successful eradication is an important problem. Children are particularly vulnerable to reinfection, by intrafamilial transmission which facilitates the acquisition or recombination of new genetic information by this bacterium. We investigated the evolutionary dynamics of 80 H. pylori strains isolated from two paediatric patients with recurrent infection (recrudescence and reinfection).

          Results

          We characterized the virulence genes vacA ( s1, m1, s2, and m2), cagA, cagE, and babA2 and performed multilocus sequence typing (MLST) on 7 housekeeping genes ( atpA, efp, ureI, ppa, mutY, trpC, and yphC) to infer the evolutionary dynamics of the H. pylori strains through phylogenetic and genealogic inference analyses, genetic diversity analysis and the exploration of recombination events during recurrent infections. The virulence genotype vacAs1m1/cagA+/cagE+/babA2 was present at a high frequency, as were the EPIYA motifs EPIYA-A, −B and -C. Furthermore, the housekeeping genes of the H. pylori strains exhibited high genetic variation, comprising 26 new alleles and 17 new Sequence Type (ST). In addition, the hpEurope (76.5%) and hspWAfrica (23.5%) populations predominated among the paediatric strains. All strains, regardless of their ancestral affiliation, harboured western EPIYA motifs.

          Conclusions

          This study provides evidence of the evolutionary dynamics of the H. pylori strains in two paediatric patients during recrudescence and reinfection events. In particular, our study shows that the strains changed during these events, as evidenced by the presence of different STs that emerged before and after treatment; these changes may be due to the accumulation of mutations and recombination events during the diversification process and recolonization of the patients by different genotypes.

          Electronic supplementary material

          The online version of this article (10.1186/s12866-019-1554-z) contains supplementary material, which is available to authorized users.

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          Most cited references61

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          Neighbor-net: an agglomerative method for the construction of phylogenetic networks.

          We present Neighbor-Net, a distance based method for constructing phylogenetic networks that is based on the Neighbor-Joining (NJ) algorithm of Saitou and Nei. Neighbor-Net provides a snapshot of the data that can guide more detailed analysis. Unlike split decomposition, Neighbor-Net scales well and can quickly produce detailed and informative networks for several hundred taxa. We illustrate the method by reanalyzing three published data sets: a collection of 110 highly recombinant Salmonella multi-locus sequence typing sequences, the 135 "African Eve" human mitochondrial sequences published by Vigilant et al., and a collection of 12 Archeal chaperonin sequences demonstrating strong evidence for gene conversion. Neighbor-Net is available as part of the SplitsTree4 software package.
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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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              SplitsTree: analyzing and visualizing evolutionary data.

              D Huson (1998)
              Real evolutionary data often contain a number of different and sometimes conflicting phylogenetic signals, and thus do not always clearly support a unique tree. To address this problem, Bandelt and Dress (Adv. Math., 92, 47-05, 1992) developed the method of split decomposition. For ideal data, this method gives rise to a tree, whereas less ideal data are represented by a tree-like network that may indicate evidence for different and conflicting phylogenies. SplitsTree is an interactive program, for analyzing and visualizing evolutionary data, that implements this approach. It also supports a number of distances transformations, the computation of parsimony splits, spectral analysis and bootstrapping.
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                Author and article information

                Contributors
                smendoza@himfg.edu.mx
                yurico_cts@hotmail.com
                capotezu@hotmail.com
                renecerritos@gmail.com
                pvalencia@himfg.edu.mx
                draalejandraconsuelo@yahoo.com.mx
                hectorolivaresclavijo@gmail.com
                +52 (55) 52289917 , normave@himfg.edu.mx
                Journal
                BMC Microbiol
                BMC Microbiol
                BMC Microbiology
                BioMed Central (London )
                1471-2180
                8 August 2019
                8 August 2019
                2019
                : 19
                : 184
                Affiliations
                [1 ]ISNI 0000 0004 0633 3412, GRID grid.414757.4, Infectology Laboratory, Hospital Infantil de México Federico Gómez, ; Dr. Márquez 162, Col. Doctores, Cuauhtémoc, 06720 Mexico City, Mexico
                [2 ]ISNI 0000 0001 2165 8782, GRID grid.418275.d, Biological Chemistry Sciences Postgraduate, Escuela Nacional de Ciencias Biológicas, , Instituto Politécnico Nacional, ; Mexico City, Mexico
                [3 ]ISNI 0000 0001 2165 8782, GRID grid.418275.d, Biological Variation and Evolution Laboratory, Department of Zoology, Escuela Nacional de Ciencias Biológicas, , Instituto Politécnico Nacional, ; Mexico City, Mexico
                [4 ]ISNI 0000 0001 2159 0001, GRID grid.9486.3, Center of Research in Population and Health Policy, UNAM, ; Mexico City, Mexico
                [5 ]ISNI 0000 0004 0633 3412, GRID grid.414757.4, Department of Pathology, , Hospital Infantil de México Federico Gómez, ; Mexico City, Mexico
                [6 ]ISNI 0000 0004 0633 3412, GRID grid.414757.4, Department of Gastroenterology and Nutrition, , Hospital Infantil de México Federico Gómez, ; Mexico City, Mexico
                [7 ]ISNI 0000 0004 0633 3412, GRID grid.414757.4, Hemerobiblioteca, Hospital Infantil de México Federico Gómez, ; México City, Mexico
                Article
                1554
                10.1186/s12866-019-1554-z
                6686460
                31395006
                54fca2ea-1c50-482c-b5e7-c00997a242d0
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

                History
                : 5 January 2019
                : 26 July 2019
                Funding
                Funded by: Ministry of Health and Assistance (SSA), México
                Award ID: HIM/2011/080-SSA 1005
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2019

                Microbiology & Virology
                helicobacter pylori,recurrent infection,reinfection,paedriatric patients,genetic variability,evolutionary relationship

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