Integrated metagenomic data analysis demonstrates that a loss of diversity in oral microbiota is associated with periodontitis

Abstract

Background

Periodontitis is an inflammatory disease affecting the tissues supporting teeth (periodontium). Integrative analysis of metagenomic samples from multiple periodontitis studies is a powerful way to examine microbiota diversity and interactions within host oral cavity.

Methods

A total of 43 subjects were recruited to participate in two previous studies profiling the microbial community of human subgingival plaque samples using shotgun metagenomic sequencing. We integrated metagenomic sequence data from those two studies , including six healthy controls, 14 sites representative of stable periodontitis, 16 sites representative of progressing periodontitis, and seven periodontal sites of unknown status. We applied phylogenetic diversity, differential abundance, and network analyses, as well as clustering, to the integrated dataset to compare microbiological community profiles among the different disease states.

Results

We found alpha-diversity, i.e., mean species diversity in sites or habitats at a local scale, to be the single strongest predictor of subjects’ periodontitis status ( P < 0.011). More specifically, healthy subjects had the highest alpha-diversity, while subjects with stable sites had the lowest alpha-diversity. From these results, we developed an alpha-diversity logistic model-based naive classifier able to perfectly predict the disease status of the seven subjects with unknown periodontal status (not used in training). Phylogenetic profiling resulted in the discovery of nine marker microbes, and these species are able to differentiate between stable and progressing periodontitis, achieving an accuracy of 94.4%. Finally, we found that the reduction of negatively correlated species is a notable signature of disease progression.

Conclusions

Our results consistently show a strong association between the loss of oral microbiota diversity and the progression of periodontitis, suggesting that metagenomics sequencing and phylogenetic profiling are predictive of early periodontitis, leading to potential therapeutic intervention. Our results also support a keystone pathogen-mediated polymicrobial synergy and dysbiosis (PSD) model to explain the etiology of periodontitis. Apart from P. gingivalis, we identified three additional keystone species potentially mediating the progression of periodontitis progression based on pathogenic characteristics similar to those of known keystone pathogens.