Identification of clinically relevant strains of the Streptococcus mitis group based on sequences comparisons of housekeeping genes 16S rRNA, GROEL, and RPOB

Abstract

The Streptococcus Mitis group (SMG) is composed of closely related species, making it often difficult to distinguish them from each other. It represents a significant challenge for both taxonomy and the routines by clinical diagnostics laboratories. Precise identification of SMG members is crucial, since it contains highly pathogenic species, such as S. pneumoniae and S. mitis, which are major causes of invasive infections worldwide. Misidentification can lead to wrong antimicrobial therapy and contribute to the development of antimicrobial resistance. While single-gene sequencing methods, such as 16S rRNA gene analysis, are widely applied, it is not recommended for identification of species of the SMG because of their limited discriminatory power. In this study, 33 bacterial strains are isolated from clinical samples, previously classified by the Culture Collection Gothenburg University (CCUG), as S. mitis, S. mitis complex or S. mitis group, were analyzed using multi-locus sequence analysis (MLSA) with three housekeeping genes: 16S rRNA, groEL, and rpoB. Of these, 31 strains produced high-quality 16S rRNA gene sequences, while 30 yielded reliable sequences for groEL and rpoB. Comparative analysis against GenBank references revealed that 16S rRNA gene alone was insufficient for species-level resolution due to the high sequence similarity among SMG members. In contrast, groEL and rpoB provided greater discriminatory power, particularly when used in combination. The results in this study demonstrated that the combination of multiple housekeeping genes significantly improves resolution compared to single-gene approaches. It emphasized the importance of MLSA as a robust method when identifying SMG with high accuracy. The implementation of such strategies in diagnostic microbiology can raise the knowledge about SMG’s functional structure, improve clinical decision-making in health care, strengthen epidemiological surveillance, and reduce the risks associated with antimicrobial resistance.