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Prediction of whole-genome DNA–DNA similarity, determination of G+C content and phylogenetic analysis within the family Pasteurellaceae by multilocus sequence analysis (MLSA)

Boena M. Korczak

Institute of Veterinary Bacteriology, Vetsuisse Faculty of the University of Bern, Laenggass-Str. 122, CH-3001 Bern, Switzerland

Correspondence
Peter Kuhnert
peter.kuhnert{at}vbi.unibe.ch

Genome predictions based on selected genes would be a very welcome approach for taxonomic studies, including DNA–DNA similarity, G+C content and representative phylogeny of bacteria. At present, DNA–DNA hybridizations are still considered the gold standard in species descriptions. However, this method is time-consuming and troublesome, and datasets can vary significantly between experiments as well as between laboratories. For the same reasons, full matrix hybridizations are rarely performed, weakening the significance of the results obtained. The authors established a universal sequencing approach for the three genes recN, rpoA and thdF for the Pasteurellaceae, and determined if the sequences could be used for predicting DNA–DNA relatedness within the family. The sequence-based similarity values calculated using a previously published formula proved most useful for species and genus separation, indicating that this method provides better resolution and no experimental variation compared to hybridization. By this method, cross-comparisons within the family over species and genus borders easily become possible. The three genes also serve as an indicator of the genome G+C content of a species. A mean divergence of around 1 % was observed from the classical method, which in itself has poor reproducibility. Finally, the three genes can be used alone or in combination with already-established 16S rRNA, rpoB and infB gene-sequencing strategies in a multisequence-based phylogeny for the family Pasteurellaceae. It is proposed to use the three sequences as a taxonomic tool, replacing DNA–DNA hybridization.

Individual phylogenetic trees for six genes are available as supplementary data with the online version of this paper.

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