Reorganising the order Bacillales through phylogenomics.

Syst Appl Microbiol

Centre for Microbial Ecology and Genomics, University of Pretoria, South Africa. Electronic address:

Published: March 2019

AI Article Synopsis

  • Current bacterial classification at higher taxonomic levels relies on 16S rRNA analysis and shared traits, but these methods may not accurately reflect true relationships among bacteria.
  • In the order Bacillales, traditional definitions don't account for variations like anaerobic and asporogenic species, highlighting limitations in existing classification methods.
  • A whole genome-based phylogenomic approach was used to reassess the taxonomy of Bacillales, revealing inconsistencies and proposing a consensus method for more accurate classifications.

Article Abstract

Bacterial classification at higher taxonomic ranks such as the order and family levels is currently reliant on phylogenetic analysis of 16S rRNA and the presence of shared phenotypic characteristics. However, these may not be reflective of the true genotypic and phenotypic relationships of taxa. This is evident in the order Bacillales, members of which are defined as aerobic, spore-forming and rod-shaped bacteria. However, some taxa are anaerobic, asporogenic and coccoid. 16S rRNA gene phylogeny is also unable to elucidate the taxonomic positions of several families incertae sedis within this order. Whole genome-based phylogenetic approaches may provide a more accurate means to resolve higher taxonomic levels. A suite of phylogenomic approaches were applied to re-evaluate the taxonomy of 80 representative taxa of eight families (and six family incertae sedis taxa) within the order Bacillales. This showed several anomalies in the current family and order level classifications including the existence of four Bacillaceae and two Paenibacillaceae "family" clades. Furthermore, the families Staphylococcaceae and Listeriaceae belong to the sister order Lactobacillales. Finally, we propose a consensus phylogenomic approach which may diminish algorithmic biases associated with single approaches and facilitate more accurate classification of a broad range of taxa at the higher taxonomic levels.

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http://dx.doi.org/10.1016/j.syapm.2018.10.007DOI Listing

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