A tale of two risks: smoking, diabetes and the subgingival microbiome.

Although smoking and diabetes have been established as the only two risk factors for periodontitis, their individual and synergistic impacts on the periodontal microbiome are not well studied. The present investigation analyzed 2.7 million 16S sequences from 175 non-smoking normoglycemic individuals (controls), smokers, diabetics and diabetic smokers with periodontitis as well as periodontally healthy controls, smokers and diabetics to assess subgingival bacterial biodiversity and co-occurrence patterns. The microbial signatures of periodontally healthy smokers, but not diabetics, were highly aligned with the disease-associated microbiomes of their respective cohorts. Diabetics were dominated by species belonging to Fusobacterium, Parvimonas, Peptostreptococcus, Gemella, Streptococcus, Leptotrichia, Filifactor, Veillonella, TM7 and Terrahemophilus. These microbiomes exhibited significant clustering based on HbA1c levels (pre-diabetic (<6.5%), diabetic (6.5-9.9%), diabetics >10%). Smokers with periodontitis evidenced a robust core microbiome (species identified in at least 80% of individuals) dominated by anaerobes, with inter-individual differences attributable largely to the 'rare biosphere'. Diabetics and diabetic smokers, on the other hand, were microbially heterogeneous and enriched for facultative species. In smokers, microbial co-occurrence networks were sparse and predominantly congeneric, while robust inter-generic networks were observed in diabetics and diabetic smokers. Smoking and hyperglycemia impact the subgingival microbiome in distinct ways, and when these perturbations intersect, their synergistic effect is greater than what would be expected from the sum of each effect separately. Thus, this study underscores the importance of early intervention strategies in maintaining health-compatible microbiomes in high-risk individuals, as well as the need to personalize these interventions based on the environmental perturbation.

PhyloToAST: Bioinformatics tools for species-level analysis and visualization of complex microbial datasets.

The 16S rRNA gene is widely used for taxonomic profiling of microbial ecosystems; and recent advances in sequencing chemistry have allowed extremely large numbers of sequences to be generated from minimal amounts of biological samples. Analysis speed and resolution of data to species-level taxa are two important factors in large-scale explorations of complex microbiomes using 16S sequencing. We present here new software, Phylogenetic Tools for Analysis of Species-level Taxa (PhyloToAST), that completely integrates with the QIIME pipeline to improve analysis speed, reduce primer bias (requiring two sequencing primers), enhance species-level analysis, and add new visualization tools. The code is free and open source, and can be accessed at http://phylotoast.org.