In vitro reproducible model of subgingival microbiome for high throughput screening for microbiome modulators

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Project summary Periodontitis continues to be a global health problem: combined with edentulism and severe tooth loss, it constitutes the 6th most prevalent long-term disease worldwide, accounting for 11 million years lived with disability and lost productivity of 117 billion USD. Currently, periodontitis is viewed as an immunological destruction of the periodontium, orchestrated by low abundance pathogens in an unbalanced subgingival microbial community--the so called microbial dysbiosis hypothesis. This entails that selectively targeting pathogens or/and stimulating growth of commensals to reverse subgingival microbial dysbiosis (or promote normobiosis) represents a promising strategy for prevention and adjunctive treatment of periodontitis. Such microbiome modulation can be achieved by using agents like prebiotics and probiotics. Remarkably, while a number of in vitro dental biofilm/microbiome models has been described in the literature, none has been developed for the purpose of exploring microbiome modulators. This two-year R03 has a single aim: to develop a robust, high- throughput, reproducible in vitro subgingival microbiome model specifically optimized for testing of microbiome modulators. The model will include a dysbiotic (experimental) microbiome grown from periodontitis-associated subgingival samples, and a normobiotic (reference) microbiome grown from health- associated subgingival plaques samples. The growth conditions will be fine-tuned to maximize similarity between the in vitro microbiomes and the original samples. We will also explore the possibility of reproducing the generated microbiomes by passaging or using frozen stocks, eliminating the need to obtain more patient samples. In addition, a novel subgingival microbial dysbiosis index (SMDI) as a measure of dysbiosis in the microbiomes will be developed. The microbial composition of the microbiomes as well as original samples will be assessed using 16S rRNA sequencing coupled with our BLASTN-based, species-level taxonomy assignment algorithm. Microbiomes will be compared using distance matrices, principle component analysis and a similarity index. The model characterized here will provide the scientific community with an important tool to screen large numbers of candidate modulators and quantitatively assess their effects on subgingival microbiome, before they can be considered for further testing in animals, and eventually, humans.