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Current Microbiology

, Volume 59, Issue 2, pp 123–129 | Cite as

Core Genome Haplotype Diversity and vacA Allelic Heterogeneity of Chinese Helicobacter pylori Strains

  • Y. L. Liao
  • G. Guo
  • X. H. Mao
  • Q. H. Xie
  • W. J. Zhang
  • X. F. Liu
  • Q. M. ZouEmail author
Article

Abstract

The human gastric pathogen, Helicobacter pylori, has co-evolved with its host and established itself in the human stomach possibly millions of years ago. Therefore, the diversity of this bacterium is important in its clinical manifestations. Our aim has been to evaluate the genetic diversity of 40 H. pylori clinical isolates from four different parts of China. The methods of multi-locus sequence typing and vacA allele genotyping were used to assess their genetic diversity. To discriminate MLST, the vacA genotype method was used to identify strains. Patients from the northern, eastern, southern, and southwestern parts of China were recruited randomly from the cities of Beijing, Shanghai, Guangzhou, and Chongqing, respectively. Most of the sequence types are new and have never been reported in the database of the H. pylori multi-locus sequence typing system. The most prevalent vacA genotype in patients was s1a/m2 (80.0%), followed by s1b/m2 (17.5%). In contrast, the s1a/m1 genotype was scarcely represented (2.5%). The vacA genotype varied for each ST. These results showed that the MLST method offers high resolution of the H. pylori isolates in China when compared to vacA genotyping. The vacA allelic s1a has been correlated with the peptic ulcer. Because of the paucity of data on human isolates due to the absence of systematic investigations of H. pylori in China, the data provide useful information for understanding the epidemiology of H. pylori in China from the viewpoint of nucleotide sequence databases.

Keywords

Peptic Ulcer Pylorus Strain Clonal Grouping cagA Gene Pylorus Isolate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported by a grant-in-aid for the Major State Basic Research Development Program of China (973 Program) (No. 2009CB522606). We thank Yiqi Du, Jide Wang, and Fukun Wang for technical assistance.

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Y. L. Liao
    • 1
  • G. Guo
    • 1
  • X. H. Mao
    • 1
  • Q. H. Xie
    • 1
  • W. J. Zhang
    • 1
  • X. F. Liu
    • 1
  • Q. M. Zou
    • 1
    Email author
  1. 1.Department of Clinical Microbiology and Immunology, Faculty of Medical Laboratory ScienceThird Military Medical UniversityChongqingChina

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