BACKGROUNDMulti-locus sequence typing (MLST) is widely used to explore the population structure of numerous bacterial pathogens. However, for genotypically-restricted pathogens, the sensitivity of MLST is limited by a paucity of variation within selected loci. For Bartonella henselae (B. henselae), although the MLST scheme currently used has been proven useful in defining the overall population structure of the species, its reliability for the accurate delineation of closely-related sequence types, between which allelic variation is usually limited to, at most, one or two nucleotide polymorphisms. Exploitation of high-throughput sequencing data allows a more informed selection of MLST loci and thus, potentially, a means of enhancing the sensitivity of the schemes they comprise.

METHODSWe carried out SOLiD resequencing on 12 representative B. henselae isolates and explored these data using single nucleotide polymorphism (SNP) analysis. We determined the number and distribution of SNPs in the genes targeted by the established MLST scheme and modified the position of loci within these genes to capture as much genetic variation as possible.

RESULTSUsing genome-wide SNP data, we found the distribution of SNPs within each open reading frame (ORF) of MLST loci, which were not represented by the established B. henselae MLST scheme. We then modified the position of loci in the MLST scheme to better reflect the polymorphism in the ORF as a whole. The use of amended loci in this scheme allowed previously indistinguishable ST1 strains to be differentiated. However, the diversity of B. henselae was still rare in China.

CONCLUSIONSOur study demonstrates the use of SNP analysis to facilitate the selection of MLST loci to augment the currently-described scheme for B. henselae. And the diversity among B. henselae strains in China is markedly less than that observed in B. henselae populations elsewhere in the world.

Bartonella henselae ; genetics ; Molecular Sequence Data ; Multilocus Sequence Typing ; methods ; Open Reading Frames ; genetics ; Polymorphism, Single Nucleotide ; genetics