Efficient discrimination within a Corynebacterium diphtheriae epidemic clonal group by a novel macroarray-based method
- PMID: 15814981
- PMCID: PMC1081353
- DOI: 10.1128/JCM.43.4.1662-1668.2005
Efficient discrimination within a Corynebacterium diphtheriae epidemic clonal group by a novel macroarray-based method
Erratum in
- J Clin Microbiol. 2005 Jul;43(7):3590
Abstract
A large diphtheria epidemic in the 1990s in Russia and neighboring countries was caused by a clonal group of closely related Corynebacterium diphtheriae strains (ribotypes Sankt-Peterburg and Rossija). In the recently published complete genome sequence of C. diphtheriae strain NCTC13129, representative of the epidemic clone (A. M. Cerdeno-Tarraga et al., Nucleic Acids Res. 31:6516-6523, 2003), we identified in silico two direct repeat (DR) loci 39 kb downstream and 180 kb upstream of the oriC region, consisting of minisatellite (27- to 36-bp) alternating DRs and variable spacers. We designated these loci DRA and DRB, respectively. A reverse-hybridization macroarray-based method has been developed to study polymorphism (the presence or absence of 21 different spacers) in the larger DRB locus. We name it spoligotyping (spacer oligonucleotide typing), analogously to a similar method of Mycobacterium tuberculosis genotyping. The method was evaluated with 154 clinical strains of the C. diphtheriae epidemic clone from the St. Petersburg area in Russia from 1997 to 2002. By comparison with the international ribotype database (Institut Pasteur, Paris, France), these strains were previously identified as belonging to ribotypes Sankt-Peterburg (n = 79) and Rossija (n = 75). The 154 strains were subdivided into 34 spoligotypes: 14 unique strains and 20 types shared by 2 to 46 strains; the Hunter Gaston discriminatory index (HGDI) was 0.85. DRB locus-based spoligotyping allows fast and efficient discrimination within the C. diphtheriae epidemic clonal group and is applicable to both epidemiological investigations and phylogenetic reconstruction. The results are easy to interpret and can be presented and stored in a user-friendly digital database (Excel file), allowing rapid type determination of new strains.
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