T-RFLP-based differences in oral microbial communities as risk factor for development of oral diseases under stress

The human oral microbiome is comprised of approximately 800 different bacterial species many of which are as yet uncultivated. Their dynamics and variability in relation to health and disease are still poorly understood. Here we tested the hypothesis that the emergence of stress-induced periodontal diseases is predictable based on the composition of the initial microbiota. As a model, we analysed 58 individuals performing a challenging expedition (exposure to various stress-factors due to changes in diet, hygiene, temperature, physical and mental stress) in remote regions of the Himalayans (Annapurna Himal). Plaque samples were taken at start (Bhulbule) and destination (3000 meter difference in altitude) seven days later (Manang). Twenty-eight individuals remained symptom-free (Group I) while 30 participants developed periodontal problems, mostly gingivitis (Group II). The microbiota was monitored via T-RFLP-analysis of amplified 16S rRNA genes directly from the plaque samples. Based on the Additive-Main-Effects-Multiplicative-Interactions-model (AMMI) using the T-Rex software variation from T-RF main effects was at least 95%, indicating that most variation was due to inherent differences in microbial communities among individuals. However, an interaction signal up to 3% was consistently observed between groups I and II but not between the two time points of sampling regardless of selected analytical parameters. The data, supported by heterogeneity, diversity and similarity indices indicated marked differences between groups I and II already prior the onset of clinical symptoms. These differences may provide the basis for using ecological parameters of oral microbial communities as early diagnostic marker for the onset of oral disorders and infections.