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Practically delineating bacterial species with genealogical concordance

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Abstract

Bacterial species are commonly defined by applying a set of predetermined criteria, including DNA–DNA hybridization values, 16S rRNA gene sequence similarity, phenotypic data as well as genome-based criteria such as average nucleotide identity or digital DNA-DNA hybridization. These criteria mostly allow for the delimitation of taxa that resemble typical bacterial species. Their application is often complicated when the objective is to delineate new species that are characterized by significant population-level diversity or recent speciation. However, we believe that these complexities and limitations can be easily circumvented by recognizing that bacterial species represent unique and exclusive assemblages of diversity. Within such a framework, methods that account for the population processes involved in species evolution are used to infer species boundaries. A method such as genealogical concordance analysis is well suited to delineate a putative species. The existence of the new taxon is then interrogated using an array of traditional and genome-based characters. By making use of taxa in the genera Pantoea, Paraburkholderia and Escherichia we demonstrate in a step-wise process how genealogical concordance can be used to delimit a bacterial species. Genetic, phenotypic and biological criteria were used to provide independent lines of evidence for the existence of that taxon. Our six-step approach to species recognition is straightforward and applicable to bacterial species especially in the post-genomic era, with increased availability of whole genome sequences. In fact, our results indicated that a combined genome-based comparative and evolutionary approach would be the preferred alternative for delineating coherent bacterial taxa.

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Acknowledgements

We are grateful to the South African National Research Foundation (NRF) and the Department of Science and Technology for funding through their Center of Excellence programme. Dr. Seth Walk from the Department of Microbiology and Immunology, Montana State University for sharing the original E. coli MLST dataset published in Walk et al. (2009).

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  1. Department of Microbiology and Plant Pathology, Forestry and Agricultural Biotechnology Institute (FABI), University of Pretoria, Pretoria, South Africa

    Stephanus N. Venter, Marike Palmer, Chrizelle W. Beukes, Wai-Yin Chan, Giyoon Shin, Elritha van Zyl, Tarren Seale, Teresa A. Coutinho & Emma T. Steenkamp

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  1. Stephanus N. Venter
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  2. Marike Palmer
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Correspondence to Stephanus N. Venter.

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Venter, S.N., Palmer, M., Beukes, C.W. et al. Practically delineating bacterial species with genealogical concordance. Antonie van Leeuwenhoek 110, 1311–1325 (2017). https://doi.org/10.1007/s10482-017-0869-8

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