In most instances when the isolates were not identified by MALDI-ToF this was because MALDI-ToF was unable to differentiate between S. pyogenes and S. dysgalactiae. By removing two S. pyogenes reference spectra from the MALDI-ToF database the proportion of correctly identified isolates increased to 96% overall. MALDI-ToF is a promising method for discriminating between S. dysgalactiae, S. canis, and S. equi, although more strains need to be tested to clarify this.

The mitis group of streptococci (MGS) is a member of the healthy human microbiome in the oral cavity and upper respiratory tract. Troublingly, some MGS are able to escape this niche and cause infective endocarditis, a severe and devastating disease. Genome-scale models have been shown to be valuable in investigating metabolism of bacteria. Here we present the first genome-scale model, iCJ415, for Streptococcus oralis SK141. We validated the model using gene essentiality and amino acid auxotrophy data from closely related species. iCJ415 has 71-76% accuracy in predicting gene essentiality and 85% accuracy in predicting amino acid auxotrophy. Further, the phenotype of S. oralis was tested using the Biolog Phenotype microarrays, giving iCJ415 a 82% accuracy in predicting carbon sources. iCJ415 can be used to explore the metabolic differences within the MGS, and to explore the complicated metabolic interactions between different species in the human oral cavity.

Streptococcus gordonii and Streptococcus sanguinis belong to the Mitis group streptococci, which mostly are commensals in the human oral cavity. Though they are oral commensals, they can escape their niche and cause infective endocarditis, a severe infection with high mortality. Several virulence factors important for the development of infective endocarditis have been described in these two species. However, the background for how the commensal bacteria, in some cases, become pathogenic is still not known. To gain a greater understanding of the mechanisms of the pathogenic potential, we performed a comparative analysis of 38 blood culture strains, S. sanguinis (n = 20) and S. gordonii (n = 18) from patients with verified infective endocarditis, along with 21 publicly available oral isolates from healthy individuals, S. sanguinis (n = 12) and S. gordonii (n = 9). Using whole genome sequencing data of the 59 streptococci genomes, functional profiles were constructed, using protein domain predictions based on the translated genes. These functional profiles were used for clustering, phylogenetics and machine learning. A clear separation could be made between the two species. No clear differences between oral isolates and clinical infective endocarditis isolates were found in any of the 675 translated core-genes. Additionally, random forest-based machine learning and clustering of the pan-genome data as well as amino acid variations in the core-genome could not separate the clinical and oral isolates. A total of 151 different virulence genes was identified in the 59 genomes. Among these homologs of genes important for adhesion and evasion of the immune system were found in all of the strains. Based on the functional profiles and virulence gene content of the genomes, we believe that all analysed strains had the ability to become pathogenic. The oral cavity is covered by a mixed-species biofilm. The composition of the biofilm, as well as the abundance of specific species has a great impact on the oral health 1. Among the pioneer colonizers of the oral cavity, we find Streptococcus sanguinis and Streptococcus gordonii 2,3. Both species are non-hemolytic streptococci and commensal members of the Mitis group. They are found to prevent dental caries by inhibiting the bacterial growth of the plaque forming Mutans streptococci 1,4. The oral cavity is an extreme environment; the bacteria have to cope with variations in temperature and pH, oxidative stress and strong hydrodynamic as well as mechanical forces caused by food consumption, chewing,

A correct identification of Streptococcus pseudopneumoniae is a prerequisite for investigating the clinical impact of the bacterium. The identification has traditionally relied on phenotypic methods. However, these phenotypic traits have been shown to be unreliable, with some S. pseudopneumoniae giving conflicting results. Therefore, sequence based identification methods have increasingly been used for identification of S. pseudopneumoniae. In this study we used 64 S. pseudopneumoniae strains, 59 S. pneumoniae strains, 23 S. mitis strains, 25 S. oralis strains, seven S. infantis, and one S. peroris to test the capability of three single genes (rpoB, gyrB, recA), two MLSA schemes, the SNP based phylogeny tool CSI phylogeny, a k-mer based identification method (KmerFinder), Average Nucleotide Identity using fastANI and core genome analysis to identify S. pseudopneumoniae. Core genome analysis and CSI phylogeny were able to cluster all strains into distinct clusters related to their respective species. It was not possible to identify all S. pseudopneumoniae correctly using only one of the single genes. The MLSA schemes were unable to identify some of the S. pseudopneumoniae strains, and some strains could be misidentified. KmerFinder identified all S. pseudopneumoniae strains but misidentified one S. mitis strain as S. pseudopneumoniae and fastANI could differentiate between S. pseudopneumoniae and S. pneumoniae using an ANI cut-off of 96%.