Fluorescent-BOX-PCR for resolving bacterial genetic diversity, endemism and biogeography

BACKGROUND: BOX-A1R-based repetitive extragenic palindromic-PCR (BOX-PCR) is one of the most used techniques in biogeography studies of microbial isolates. However the traditional separation of BOX-PCR patterns by agarose gel electrophoresis suffers many limitations. The aim of this research was to set up a fluorescent BOX-PCR (F-BOX-PCR) assay in which separation of PCR products is automated in a capillary electrophoresis system. F-BOX-PCR was compared with the traditional BOX-PCR using bacterial strains with different G+C content (Bacillus cereus; Escherichia coli; isolates of the family Geodermatophilaceae). Resolution, discriminatory power and reproducibility were evaluated by assaying different electrophoretic runs, PCR reactions and independent DNA extractions. BOX-PCR and F-BOX-PCR were compared for the analysis of 29 strains of Modestobacter multiseptatus isolated from three different microsites in an altered carbonatic wall from Cagliari, Italy, and 45 strains of Streptococcus thermophilus isolated from 34 samples of the hand-made, yogurt-like product Matsoni, collected in different locations in Georgia. RESULTS: Fluorophore 6-FAM proved more informative than HEX and BOX-PCR both in agarose gel electrophoresis (p < 0.004 and p < 0.00003) and in capillary electrophoresis (compared only with HEX, p < 2 × 10(-7)). 6-FAM- and HEX-based F-BOX-PCR respectively detected up to 12.0 and 11.3 times more fragments than BOX-PCR. Replicate separations of F-BOX-PCR showed an accuracy of the size calling of ± 0.5 bp until 500 bp, constantly decreasing to ± 10 bp at 2000 bp. Cluster analysis of F-BOX-PCR profiles grouped M. multiseptatus strains according to the microsite of isolation and S. thermophilus strains according to the geographical origin of Matsoni, but resulted intermixed when a BOX-PCR dataset was used. CONCLUSION: F-BOX-PCR represents an improved method for addressing bacterial biogeography studies both in term of sensitivity, reproducibility and data analysis.