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Multi-Locus Sequence Typing of a Geographically and Temporally Diverse Sample of the Highly Clonal Human Pathogen Bartonella quintana

Overview of attention for article published in PLOS ONE, March 2010
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Title
Multi-Locus Sequence Typing of a Geographically and Temporally Diverse Sample of the Highly Clonal Human Pathogen Bartonella quintana
Published in
PLOS ONE, March 2010
DOI 10.1371/journal.pone.0009765
Pubmed ID
Authors

Mardjan Arvand, Didier Raoult, Edward J. Feil

Abstract

Bartonella quintana is a re-emerging pathogen and the causative agent of a variety of disease manifestations in humans including trench fever. Various typing methods have been developed for B. quintana, but these tend to be limited by poor resolution and, in the case of gel-based methods, a lack of portability. Multilocus sequence typing (MLST) has been used to study the molecular epidemiology of a large number of pathogens, including B. henselae, a close relative of B. quintana. We developed a MLST scheme for B. quintana based on the 7 MLST loci employed for B. henselae with two additional loci to cover underrepresented regions of the B. quintana chromosome. A total of 16 B. quintana isolates spanning over 60 years and three continents were characterized. Allelic variation was detected in five of the nine loci. Although only 8/4270 (0.002%) of the nucleotide sites examined were variable over all loci, these polymorphisms resolved the 16 isolates into seven sequence types (STs). We also demonstrate that MLST can be applied on uncultured isolates by direct PCR from cardiac valve tissue, and suggest this method presents a promising approach for epidemiological studies in this highly clonal organism. Phylogenetic and clustering analyses suggest that two of the seven STs form a distinct lineage within the population.

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Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 21%
Student > Ph. D. Student 5 18%
Professor > Associate Professor 4 14%
Student > Doctoral Student 3 11%
Student > Master 3 11%
Other 4 14%
Unknown 3 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 39%
Medicine and Dentistry 4 14%
Veterinary Science and Veterinary Medicine 3 11%
Biochemistry, Genetics and Molecular Biology 2 7%
Economics, Econometrics and Finance 1 4%
Other 1 4%
Unknown 6 21%