Genome-based analysis of the taxonomic structure of the genus Stenotrophomonas

Abstract

Objective. To analyze the current taxonomic structure of the genus Stenotrophomonas based on genomic data, taking into account limitations associated with nomenclatural validation processes. Materials and Methods. A reference dataset was constructed using taxonomic information from LPSN (List of Prokaryotic names with Standing in Nomenclature) and genome assemblies retrieved from the NCBI database. Pairwise average nucleotide identity (ANI) and average amino acid identity (AAI) values were calculated. Species delineation was performed using the commonly accepted threshold of ANI ≥ 95%. Hierarchical clustering was conducted using the average linkage method based on distance matrices. Results. Based on taxonomic information provided by LPSN, an initial reference dataset comprising 36 genomes corresponding to validly named species of the genus Stenotrophomonas was established. Pairwise ANI values confirmed clear separation of these genomes at the level of species. Analysis of pairwise AAI values revealed several genomically distinct clusters consistent with a recently proposed taxonomic framework suggesting subdivision of the genus Stenotrophomonas into multiple genera. Analysis of 2,332 Stenotrophomonas genomes from the NCBI database revealed that 15.7% had ANI values below the 95% threshold relative to reference genomes of LPSN-listed taxa, precluding assignment to any recognized species. Clustering at ANI ≥95% yielded 102 genomic clusters, some lacking genomes from LPSN-listed species. To enable classification of such genomes, an expanded reference dataset was constructed by including representative genomes of the corresponding ANI clusters. This expanded reference dataset enabled assignment of all analyzed genomes to either LPSN-listed taxa or to distinct genomic clusters lacking validated nomenclatural status. Conclusions. Even with abundant genomic data and quantitative delineation criteria, taxonomic interpretation of genomic collections remains constrained by the lag between genome-based identification of putative new taxa and their formal description with type strain designation. Our proposed analytical approach systematically accounts for such genomes and offers a reproducible framework for analyzing the taxonomic structure of the nomen genus Stenotrophomonas amid ongoing revisions.