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      Characteristics and genetic diversity of multi-drug resistant extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli isolated from bovine mastitis

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          Abstract

          A characterization of the drug resistance profiles, identification of PCR-based replicon typing, and multilocus sequence typing (MLST) and analysis of 46 ESBL-producing Escherichia coli from cows with mastitis are described. All multidrug-resistant isolates of various phylogenetic groups (A = 31, B1= 3, B2 = 2, D = 10) were ESBL-producers of genotypes CTX-M-15 (29), CTX-M-55 (4), CTX-M-14 (4), CTX-M-3 (1), CTX-M-1 (1), TEM (22) and SHV (8) that were found on conjugative plasmids of diverse incompatibility groups (primarily IncF). Transconjugation experiments indicated successful (100%) trans-conjugation, which was verified phenotypically and genotypically. A total of 28 sequence types (ST) were identified, with 10% of isolates being ST410, and 9 other ST that were assigned arbitrary numbers, reflecting the degree of diversity. Multilocus sequence analysis revealed two lineages, a dominant and a small lineage. Split-decomposition showed intraspecies recombination clearly contributed in genetic recombination generating genotypic diversity among the isolates, and a lack of interspecies recombination. This coherent analysis on genetic structure of multidrug-resistant pathogenic E. coli population isolated from mastitic-milk weaponized with resistance elements from a large, rapidly developing country will be a helpful contribution for epidemiology and surveillance of drug resistance patterns, and understanding their global diversity.

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          DnaSP, DNA polymorphism analyses by the coalescent and other methods.

          DnaSP is a software package for the analysis of DNA polymorphism data. Present version introduces several new modules and features which, among other options allow: (1) handling big data sets (approximately 5 Mb per sequence); (2) conducting a large number of coalescent-based tests by Monte Carlo computer simulations; (3) extensive analyses of the genetic differentiation and gene flow among populations; (4) analysing the evolutionary pattern of preferred and unpreferred codons; (5) generating graphical outputs for an easy visualization of results. The software package, including complete documentation and examples, is freely available to academic users from: http://www.ub.es/dnasp
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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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              Global optimal eBURST analysis of multilocus typing data using a graphic matroid approach

              Background Multilocus Sequence Typing (MLST) is a frequently used typing method for the analysis of the clonal relationships among strains of several clinically relevant microbial species. MLST is based on the sequence of housekeeping genes that result in each strain having a distinct numerical allelic profile, which is abbreviated to a unique identifier: the sequence type (ST). The relatedness between two strains can then be inferred by the differences between allelic profiles. For a more comprehensive analysis of the possible patterns of evolutionary descent, a set of rules were proposed and implemented in the eBURST algorithm. These rules allow the division of a data set into several clusters of related strains, dubbed clonal complexes, by implementing a simple model of clonal expansion and diversification. Within each clonal complex, the rules identify which links between STs correspond to the most probable pattern of descent. However, the eBURST algorithm is not globally optimized, which can result in links, within the clonal complexes, that violate the rules proposed. Results Here, we present a globally optimized implementation of the eBURST algorithm – goeBURST. The search for a global optimal solution led to the formalization of the problem as a graphic matroid, for which greedy algorithms that provide an optimal solution exist. Several public data sets of MLST data were tested and differences between the two implementations were found and are discussed for five bacterial species: Enterococcus faecium, Streptococcus pneumoniae, Burkholderia pseudomallei, Campylobacter jejuni and Neisseria spp.. A novel feature implemented in goeBURST is the representation of the level of tiebreak rule reached before deciding if a link should be drawn, which can used to visually evaluate the reliability of the represented hypothetical pattern of descent. Conclusion goeBURST is a globally optimized implementation of the eBURST algorithm, that identifies alternative patterns of descent for several bacterial species. Furthermore, the algorithm can be applied to any multilocus typing data based on the number of differences between numeric profiles. A software implementation is available at .
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                Author and article information

                Journal
                Oncotarget
                Oncotarget
                Oncotarget
                ImpactJ
                Oncotarget
                Impact Journals LLC
                1949-2553
                27 October 2017
                4 October 2017
                : 8
                : 52
                : 90144-90163
                Affiliations
                1 Department of Clinical Veterinary Medicine, College of Veterinary Medicine, China Agricultural University, Beijing, P.R. China
                2 College of Veterinary Sciences and Animal Husbandry, Abdul Wali Khan University, Garden Campus, Mardan, Pakistan
                3 Department of Dairy Science, University of Wisconsin, Madison, WI, USA
                4 Department of Animal Health, The University of Agriculture, Peshawar, Pakistan
                Author notes
                Correspondence to: Bo Han, hanbo@ 123456cau.edu.cn
                [*]

                These authors have contributed equally and co-first authors

                Article
                21496
                10.18632/oncotarget.21496
                5685738
                793ffd2a-b24a-4afd-a5d3-e5f68b3669d3
                Copyright: © 2017 Ali et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 15 June 2017
                : 23 August 2017
                Categories
                Research Paper

                Oncology & Radiotherapy
                e. coli,esbl,multilocus sequence typing,pcr-based replicon typing,split network analysis

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