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Whole genome sequencing as a tool for phylogenetic analysis of clinical strains of Mitis group streptococci

L H Rasmussen1,2, R Dargis1, K Højholt1,3

  • 1Department of Clinical Microbiology, Slagelse Hospital, Slagelse, Denmark.

European Journal of Clinical Microbiology & Infectious Diseases : Official Publication of the European Society of Clinical Microbiology
|June 22, 2016
PubMed

Insights

Accurate identification of Mitis group streptococci (MGS) is vital for diagnosing infective endocarditis. Whole genome sequencing and core-genome analyses offer superior species identification compared to single-gene methods.

Area of Science:

  • Microbiology
  • Genomics
  • Infectious Diseases

Background:

  • Accurate species-level identification of Mitis group streptococci (MGS) is difficult in routine microbiology laboratories.
  • Correct MGS identification is critical for diagnosing infective endocarditis, treatment failure, and infection relapse.

Purpose of the Study:

  • To compare the effectiveness of different phylogenetic analyses for MGS species identification.
  • To evaluate the utility of whole genome sequencing for MGS identification in clinical isolates.

Main Methods:

  • Whole genome sequencing of 80 MGS isolates from Danish patients with infective endocarditis.
  • Phylogenetic analysis using single genes (recA, sodA, gdh), multilocus sequence analysis (MLSA), single nucleotide polymorphisms (SNPs), and core-genome sequences.

Main Results:

  • Six phylogenetic analyses generally revealed similar patterns with six monophyletic clusters.
  • Single-gene analyses demonstrated limitations in species identification with increased strain numbers.
  • MLSA, SNP, and core-genome analyses provided more distinct clustering.
  • Core-genome tree analysis exhibited the most distinct clustering for MGS species.

Conclusions:

  • Whole genome sequencing provides robust data for MGS phylogenetic analysis.
  • Core-genome analysis is the most effective method for accurate MGS species identification, surpassing single-gene approaches.

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