Characterization of the salivary microbiome before and after antibiotic therapy via separation technique

In the present research, the MALDI-TOF MS technique was applied as a tool to rapidly identify the salivary microbiome. In this fact, it has been monitored the changes occurred in molecular profiles under different antibiotic therapy. Significant changes in the composition of the salivary microbiota were noticed not only in relation to the non antibiotic (non-AT) and antibiotic treatment (AT) groups, but also to the used media, the antibiotic therapy and co-existed microbiota. Each antibiotic generates specific changes in molecular profiles. The highest number of bacterial species was isolated in the universal culture medium (72%) followed by the selective medium (48% and 38%). In the case of non-AT patients, the prevalence of Streptococcus salivarius (25%), Streptococcus vestibularis (19%), Streptococcus oralis (13%), and Staphylococcus aureus (6%) was identified while in the case of AT, Streptococcus salivarius (11%), Streptococcus parasanguinis (11%), Staphylococcus epidermidis (12%), Enterococcus faecalis (9%), Staphylococcus hominis (8%), and Candida albicans (6%) were identified. Notable to specified that the Candida albicans was noticed only in AT samples, indicating a negative impact on the antibiotic therapy. The accuracy of the MALDI-TOF MS technique was performed by the 16S rRNA gene sequencing analysis-as a reference method. Conclusively, such an approach highlighted in the present study can help in developing the methods enabling a faster diagnosis of disease changes at the cellular level before clinical changes occur. Once the MALDI tool allows for the distinguishing of the microbiota of non-AT and AT, it may enable to monitor the diseases treatment and develop a treatment regimen for individual patients in relation to each antibiotic. KEY POINTS: The salivary microbiota of antibiotic-treated patients was more bacteria variety MALDI-TOF MS is a promising tool for recording of reproducible molecular profiles Our data can allow to monitor the treatment of bacterial diseases for patients.