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Genome-based phylogenetic analysis of Streptomyces and its relatives

By Eriko Takano, Maria Elena Merlo, Mohammad Tauqeer Alam and Rainer Breitling

Abstract

Motivation: Streptomyces is one of the best-studied genera of the order Actinomycetales due to its great importance in medical science, ecology and the biotechnology industry. A comprehensive, detailed and robust phylogeny of Streptomyces and its relatives is needed for understanding how this group emerged and maintained such a vast diversity throughout evolution and how soil-living mycelial forms (e.g., Streptomyces s. str.) are related to parasitic, unicellular pathogens (e.g., Mycobacterium tuberculosis) or marine species (e.g., Salinispora tropica). The most important application area of such a phylogenetic analysis will be in the comparative re-annotation of genome sequences and the reconstruction of Streptomyces metabolic networks for biotechnology. Methods: Classical 16S-rRNA-based phylogenetic reconstruction does not guarantee to produce well-resolved robust trees that reflect the overall relationship between bacterial species with widespread horizontal gene transfer. In our study we therefore combine three whole genome-based phylogenies with eight different, highly informative single-gene phylogenies to determine a new robust consensus tree of 45 Actinomycetales species with completely sequenced genomes. Results: None of the individual methods achieved a resolved phylogeny of Streptomyces and its relatives. Single-gene approaches failed to yield a detailed phylogeny; even though the single trees are in good agreement among each other, they show very low resolution of inner branches. The three whole genome-based methods improve resolution considerably. Only by combining the phylogenies from single gene-based and genome-based approaches we finally obtained a consensus tree with well-resolved branches for the entire set of Actinomycetales species. This phylogenetic information is stable and informative enough for application to the system-wide comparative modeling of bacterial physiology.

Year: 2010
OAI identifier: oai:ub.rug.nl:dbi/4b84ec0050e93
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