Literature DB >> 27343985

Genotyping of Salmonella with lineage-specific genes: correlation with serotyping.

Qing-Hua Zou1, Ren-Qing Li2, Gui-Rong Liu3, Shu-Lin Liu4.   

Abstract

BACKGROUND: The bacterial genus Salmonella encompasses a large number of serotypes that are genetically very similar but biologically quite different, especially in pathogenic properties and host specificity. Serotyping has been used for the classification, identification, and epidemiological investigation due to its excellent discriminating power, but it cannot distinguish the different pathogenic lineages within a polyphyletic serotype. Additionally, very few institutions have the comprehensive set of antisera for typing. Therefore various studies have been performed to explore alternative assays to differentiate Salmonella isolates, such as the search for genes that can be used as potential molecular substitutes for serotyping. However, the genes tested so far have often given inconsistent results.
METHODS: In this study, the discriminating power of seven genes to differentiate 309 Salmonella strains representing 26 serotypes was evaluated and the results were compared with those of other methods.
RESULTS: The seven newly selected genes have a good power to differentiate different serovars. The tree based on the concatenated sequences of these genes revealed phylogenetic relationships of the bacteria consistent with that of the whole genome tree.
CONCLUSION: Individual Salmonella lineages each have specific genes that can be used to differentiate Salmonella isolates on a phylogenetic basis.
Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Entities:  

Keywords:  MLST; Salmonella; Serotype-specific gene; Serotyping; rpoB

Mesh:

Year:  2016        PMID: 27343985     DOI: 10.1016/j.ijid.2016.05.029

Source DB:  PubMed          Journal:  Int J Infect Dis        ISSN: 1201-9712            Impact factor:   3.623


  8 in total

Review 1.  Genomic epidemiology of multidrug-resistant Gram-negative organisms.

Authors:  Shawn E Hawken; Evan S Snitkin
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2.  Quantifying the Survival of Multiple Salmonella enterica Serovars In Vivo via Massively Parallel Whole-Genome Sequencing To Predict Zoonotic Risk.

Authors:  Prerna Vohra; Marie Bugarel; Frances Turner; Guy H Loneragan; Jayne C Hope; John Hopkins; Mark P Stevens
Journal:  Appl Environ Microbiol       Date:  2018-01-31       Impact factor: 4.792

3.  Quinolone resistance phenotype and genetic characterization of Salmonella enterica serovar Pullorum isolates in China, during 2011 to 2016.

Authors:  Xiaodong Guo; Honglin Wang; Yiluo Cheng; Wenting Zhang; Qingping Luo; Guoyuan Wen; Guijun Wang; Huabin Shao; Tengfei Zhang
Journal:  BMC Microbiol       Date:  2018-12-27       Impact factor: 3.605

4.  In silico Identification of Serovar-Specific Genes for Salmonella Serotyping.

Authors:  Xiaomei Zhang; Michael Payne; Ruiting Lan
Journal:  Front Microbiol       Date:  2019-04-24       Impact factor: 5.640

5.  Serotype-specific evolutionary patterns of antimicrobial-resistant Salmonella enterica.

Authors:  Jingqiu Liao; Renato Hohl Orsi; Laura M Carroll; Jasna Kovac; Hongyu Ou; Hailong Zhang; Martin Wiedmann
Journal:  BMC Evol Biol       Date:  2019-06-21       Impact factor: 3.260

Review 6.  Typing methods based on whole genome sequencing data.

Authors:  Laura Uelze; Josephine Grützke; Maria Borowiak; Jens Andre Hammerl; Katharina Juraschek; Carlus Deneke; Simon H Tausch; Burkhard Malorny
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7.  The Efficacy of a Trivalent Inactivated Salmonella Vaccine Combined with the Live S. Gallinarum 9R Vaccine in Young Layers after Experimental Infections with S. Enteritidis, S. Typhimurium, and S. Infantis.

Authors:  Yosef Daniel Huberman; Melanie Caballero-García; Rober Rojas; Silvia Ascanio; Leandro Hipólito Olmos; Rosana Malena; Jorgelina Lomónaco; Paula Nievas; Paula Chero; Julio Lévano-Gracía; Alfredo Mendoza-Espinoza
Journal:  Vaccines (Basel)       Date:  2022-07-12

8.  Performance and Accuracy of Four Open-Source Tools for In Silico Serotyping of Salmonella spp. Based on Whole-Genome Short-Read Sequencing Data.

Authors:  Laura Uelze; Maria Borowiak; Carlus Deneke; István Szabó; Jennie Fischer; Simon H Tausch; Burkhard Malorny
Journal:  Appl Environ Microbiol       Date:  2020-02-18       Impact factor: 4.792

  8 in total

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