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Whole-Genome Sequencing Reveals Distinct Mutational Patterns in Closely Related Laboratory and Naturally Propagated Francisella tularensis Strains

Overview of attention for article published in PLOS ONE, July 2010
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Title
Whole-Genome Sequencing Reveals Distinct Mutational Patterns in Closely Related Laboratory and Naturally Propagated Francisella tularensis Strains
Published in
PLOS ONE, July 2010
DOI 10.1371/journal.pone.0011556
Pubmed ID
Authors

Andreas Sjödin, Kerstin Svensson, Marie Lindgren, Mats Forsman, Pär Larsson

Abstract

The F. tularensis type A strain FSC198 from Slovakia and a second strain FSC043, which has attenuated virulence, are both considered to be derivatives of the North American F. tularensis type A strain SCHU S4. These strains have been propagated under different conditions: the FSC198 has undergone natural propagation in the environment, while the strain FSC043 has been cultivated on artificial media in laboratories. Here, we have compared the genome sequences of FSC198, FSC043, and SCHU S4 to explore the possibility that the contrasting propagation conditions may have resulted in different mutational patterns. We found four insertion/deletion events (INDELs) in the strain FSC043, as compared to the SCHU S4, while no single nucleotide polymorphisms (SNPs) or variable number of tandem repeats (VNTRs) were identified. This result contrasts with previously reported findings for the strain FSC198, where eight SNPs and three VNTR differences, but no INDELs exist as compared to the SCHU S4 strain. The mutations detected in the laboratory and naturally propagated type A strains, respectively, demonstrate distinct patterns supporting that analysis of mutational spectra might be a useful tool to reveal differences in past growth conditions. Such information may be useful to identify leads in a microbial forensic investigation.

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Geographical breakdown

Country Count As %
United States 3 7%
Sweden 1 2%
Italy 1 2%
Australia 1 2%
Unknown 36 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 45%
Student > Ph. D. Student 8 19%
Professor > Associate Professor 5 12%
Student > Master 4 10%
Professor 3 7%
Other 1 2%
Unknown 2 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 60%
Immunology and Microbiology 4 10%
Biochemistry, Genetics and Molecular Biology 4 10%
Computer Science 2 5%
Chemical Engineering 1 2%
Other 5 12%
Unknown 1 2%