Distinguishing species of the Burkholderia cepacia complex and Burkholderia gladioli by automated ribotyping

Several species belonging to the genus Burkholderia are clinically relevant , opportunistic pathogens that inhabit major environmental reservoirs. Cons equently, the availability of means for adequate identification and epidemi ological characterization of individual environmental or clinical isolates is mandatory. In the present communication we describe the use of the Ribop rinter microbial characterization system (Qualicon, Warwick, United Kingdom ) for automated ribotyping of 104 strains of Burkholderia species from dive rse sources, including several publicly accessible collections. The main ou tcome of this analysis was that all strains were typeable and that strains of Burkholderia gladioli and of each species of the B. cepacia complex, inc luding 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 classific ation based on restriction fragment analysis of 16S ribosomal amplicons, bu t the resolution of ribotyping was much higher. This enabled automated mole cular typing below the species level. Cluster analysis of the patterns obta ined by ribotyping (riboprints) showed that within B, gladioli, B. multivor ans, and B. cepacia genomovar VI, the different riboprints identified alway s clustered together. Riboprints of B. cepacia genomovars I and III, B. sta bilis, and B. vietnamiensis did not show distinct clustering but rather exh ibited the formation of loose assemblages within which several smaller, gen omovar-specific clusters were delineated. Therefore, ribotyping proved usef ul for genomovar identification. Analysis of serial isolates from individua l patients demonstrated that infection with a single ribotype had occurred, despite minor genetic differences that were detected by pulsed-field gel e lectrophoresis of DNA macrorestriction fragments. The automated approach al lows very rapid and reliable identification and epidemiological characteriz ation of strains and generates an easily manageable database suited for exp ansion with information on additional bacterial isolates.