The massive generation of sequence data has enabled phylogenetic analyses of unprecedented quality, leading to overdue revisions in bacterial taxonomy, including the class Mollicutes. The need to consider as many informative sites as possible is reflected in the use of average nucleotide identity (ANI) and average amino acid identity (AAI) analyses. Uncertainty, and thus problems in assigning taxonomic rank, arise in rapidly evolving genomes, such as the obligate bacterial parasites of the provisional taxon 'Candidatus Phytoplasma'.

We investigated the complementary use of qualitative scoring of percentage of conserved proteins (POCP) and signature-based approaches such as codon usage and tetranucleotide frequency analysis.

A total of 143 complete and draft genome sequences were selected and protein-coding sequences were predicted by Prodigal to ensure comparable data sets [1]. ANI, AAI, POCP, codon usage and tetranucleotide frequency analyses were carried out [2,3].

The results are consistent with the ANI and AAI analyses and the most recent taxonomic revisions. Using the taxonomy recommended by the International Committee on Systematics of Prokaryotes, the POCP analysis of species within families showed only weak significance. In contrast, genome-wide codon usage analysis shows species-specific patterns and provides a stable phylogenetic assignment. Tetranucleotide analysis also allows differentiation below the species level. Thresholds are suggested using the phytoplasma group as an example.

Codon usage analysis should be considered in future phylogenetic analyses, as their lineage-specific signatures allow reliable phylogenetic clustering.

References

[1] Vesth T, Lagesen K, Acar Ö, Ussery D (2013). CMG-biotools, a free workbench for basic comparative microbial genomics. PLoS One. 8(4):e60120.

[2] Richter M, Rosselló-Móra R, Glöckner FO, Peplies J (2016). JSpeciesWS: a web server for prokaryotic species circumscription based on pairwise genome comparison. Bioinformatics. 32(6): 929–931.

[3] Kim D, Park S, Chun J (2021). Introducing EzAAI: a pipeline for high throughput calculations of prokaryotic average amino acid identity. J Microbiol 59(5):476-480.