Distinguishing Species of the Burkholderia cepacia Complex and Burkholderia gladioli by Automated Ribotyping

Abstract

ABSTRACT Several species belonging to the genus Burkholderia are clinically relevant, opportunistic pathogens that inhabit major environmental reservoirs. Consequently, the availability of means for adequate identification and epidemiological characterization of individual environmental or clinical isolates is mandatory. In the present communication we describe the use of the Riboprinter microbial characterization system (Qualicon, Warwick, United Kingdom) for automated ribotyping of 104 strains of Burkholderia species from diverse sources, including several publicly accessible collections. The main outcome of this analysis was that all strains were typeable and that strains of Burkholderia gladioli and of each species of the B. cepacia complex, including B. multivorans , B. stabilis , and B. vietnamiensis , were effectively discriminated. Furthermore, different ribotypes were discerned within each species. Ribotyping results were in general agreement with strain classification based on restriction fragment analysis of 16S ribosomal amplicons, but the resolution of ribotyping was much higher. This enabled automated molecular typing below the species level. Cluster analysis of the patterns obtained by ribotyping (riboprints) showed that within B. gladioli , B. multivorans , and B. cepacia genomovar VI, the different riboprints identified always clustered together. Riboprints of B. cepacia genomovars I and III, B. stabilis , and B. vietnamiensis did not show distinct clustering but rather exhibited the formation of loose assemblages within which several smaller, genomovar-specific clusters were delineated. Therefore, ribotyping proved useful for genomovar identification. Analysis of serial isolates from individual patients demonstrated that infection with a single ribotype had occurred, despite minor genetic differences that were detected by pulsed-field gel electrophoresis of DNA macrorestriction fragments. The automated approach allows very rapid and reliable identification and epidemiological characterization of strains and generates an easily manageable database suited for expansion with information on additional bacterial isolates.