Defining bacterial species in the genomic era: insights from the genus Acinetobacter.

backgroundMicrobial taxonomy remains a conservative discipline, relying on phenotypic information derived from growth in pure culture and techniques that are time-consuming and difficult to standardize, particularly when compared to the ease of modern high-throughput genome sequencing. Here, drawing on the genus Acinetobacter as a test case, we examine whether bacterial taxonomy could abandon phenotypic approaches and DNA-DNA hybridization and, instead, rely exclusively on analyses of genome sequence data.

resultsIn pursuit of this goal, we generated a set of thirteen new draft genome sequences, representing ten species, combined them with other publically available genome sequences and analyzed these 38 strains belonging to the genus. We found that analyses based on 16S rRNA gene sequences were not capable of delineating accepted species. However, a core genome phylogenetic tree proved consistent with the currently accepted taxonomy of the genus, while also identifying three misclassifications of strains in collections or databases. Among rapid distance-based methods, we found average-nucleotide identity (ANI) analyses delivered results consistent with traditional and phylogenetic classifications, whereas gene content based approaches appear to be too strongly influenced by the effects of horizontal gene transfer to agree with previously accepted species.

conclusionWe believe a combination of core genome phylogenetic analysis and ANI provides an appropriate method for bacterial species delineation, whereby bacterial species are defined as monophyletic groups of isolates with genomes that exhibit at least 95% pair-wise ANI. The proposed method is backwards compatible; it provides a scalable and uniform approach that works for both culturable and non-culturable species; is faster and cheaper than traditional taxonomic methods; is easily replicable and transferable among research institutions; and lastly, falls in line with Darwin's vision of classification becoming, as far as is possible, genealogical.