Anna Karenina and the subgingival microbiome associated with periodontitis

Abstract

Background: Although localized aggressive periodontitis (LAP), generalized aggressive periodontitis (GAP), and chronic periodontitis (CP) are microbially driven diseases, our inability to separate disease-specific associations from those common to all three forms of periodontitis has hampered biomarker discovery. Therefore, we aimed to map the genomic content of, and the biological pathways encoded by, the microbiomes associated with these clinical phenotypes. We also estimated the extent to which these biomes are governed by the Anna Karenina principle (AKP), which states that eubiotic communities are similar between individuals while disease-associated communities are highly individualized.

Methods: We collected subgingival plaque from 25 periodontally healthy individuals and diseased sites of 59 subjects with stage 3 periodontitis and used shotgun metagenomics to characterize the aggregate of bacterial genes.

Results: Beta-dispersion metrics demonstrated that AKP was most evident in CP, followed by GAP and LAP. We discovered broad dysbiotic signatures spanning the three phenotypes, with over-representation of pathways that facilitate life in an oxygen-poor, protein- and heme-rich, pro-oxidant environment and enhance capacity for attachment and biofilm formation. Phenotype-specific indicators were more readily evident in LAP microbiome than GAP or CP. Genes that enable acetate-scavenging lifestyle, utilization of alternative nutritional sources, oxidative and nitrosative stress responses, and siderophore production were unique to LAP. An attenuation of virulence-related functionalities and stress response from LAP to GAP to CP was apparent. We also discovered that clinical phenotypes of disease resolved variance in the microbiome with greater clarity than the newly established grading system. Importantly, we observed that one third of the metagenome of LAP is unique to this phenotype while GAP shares significant functional and taxonomic features with both LAP and CP, suggesting either attenuation of an aggressive disease or an early-onset chronic disease.

Conclusion: Within the limitations of a small sample size and a cross-sectional study design, the distinctive features of the microbiomes associated with LAP and CP strongly persuade us that these are discrete disease entities, while calling into question whether GAP is a separate disease, or an artifact induced by cross-sectional study designs. Further studies on phenotype-specific microbial genes are warranted to explicate their role in disease etiology. Video Abstract.

Keywords: Chronic periodontitis; Comparative metagenomics; Generalized aggressive periodontitis; Localized aggressive periodontitis; Microbiome.

Fig. 1

Between-class analysis of phylogenetic and functional profiles in health and disease. Nonmetric multidimensional scaling (NMDS) and receiver operating characteristic (ROC) curves shown. The first three dimensions of species-level (a) and gene-level (b) Bray-Curtis distances are shown (p-value < 0.001 for a and b). Each purple circle represents one of 59 subjects with periodontitis and each yellow circle represents one of 25 periodontally healthy individuals. The ability of disease-specific and health-specific indicators to predict each state is shown in c (phylogenetic metrics) and d (functional metrics)

Fig. 2

Factors that explain variance in the subgingival microbiome. k-means clustering of Bray-Curtis distances revealed three clusters (LAP blue, CP red, GAP green) based on function (a, p-value < 0.001) and taxa (b, p-value < 0.001). The ellipses represent the centroids of each cluster. Linear discriminant analysis of relative abundances of function (top panels) and phylogeny (bottom panels) revealed that disease phenotype (c, d), but not disease grade (e, f), was able to discriminate between subjects with disease. The differences based on age-decade (g, h) and ethnicity (i, j) were explained by the disease phenotype. In each cluster, the larger ellipse indicates the 95% confidence region to contain the true mean for the group, and the smaller (inner) ellipse represents the region estimated to contain 50% of the population for the group. The misclassification rates of each variable are shown within each panel

Fig. 3

Disease-specific taxonomical indicators. Density curves of alpha diversity (ACE) are shown in a. The peak indicates the median values for each group, and the x-axis shows the data range. LAP exhibited significantly lower alpha diversity than the other 2 groups (p < 0.0001, Dunn’s test). Distribution of species-level taxa by gram staining characteristics and oxygen requirements in is shown in b. GAP patients demonstrated significantly greater gram-negative anaerobic bacteria and lower gram-negative aerobic bacteria when compared to the other two groups (p < 0.01, Dunn’s test for multiple comparisons). Percent of the microbiome that is shared by 80% or more of individuals (common core microbiome) with CP, GAP, and LAP are graphically indicated by the Euler graphs c (i–iii), as well as the number of core species shared by all three diseases is shown in c (iv). Phylogenetic tree of species that were significantly different between groups (p < 0.05, FDR-adjusted Wald test) are shown in 3D. Bars represent normalized mean relative abundances, while the solid circles indicate species that belong to the common disease core. Data supporting this figure can be found in Supplemental Table 1

Fig. 4

Same players, different teams. Zi-Pi plots of co-occurrence networks in generalized aggressive periodontitis (a), chronic periodontitis (b), and localized aggressive periodontitis (c). The text box illustrates the general topographical characteristics of each network. The plots show nodes with low connectivity (peripherals), and the putative keystone species, which are separated into local/module key players (module hubs), key players across different modules (connectors), and the combination of the two categories (network hubs). Network graphs of chronic periodontitis and localized aggressive periodontitis (d, e) show the ego network of species that have first-degree edges with the putative keystone species. A similar network graph was not possible for generalized aggressive periodontitis since there no keystone pathogens were identified in the Zi-Pi plot. Nodes are colored based on their modules. Thick edges represent positive correlations, and faint lines represent negative correlations. Data supporting this figure can be found in Supplemental Table 2

Fig. 5

Disease-specific functional indicators. Barycentric plots of significantly different virulence functions in the three groups (p < 0.05, FDR adjusted Wald test) are shown. Each dot represents a gene. The three groups generalized aggressive periodontitis, chronic periodontitis, and localized aggressive periodontitis (GAP, CP, and GAP) are used as vertices. Within each plot, the coordinates of each gene are determined by the weighted average of the coordinates of all genes, and the weights are given by the relative abundance of the gene in that group (LAP, GAP, and CP). Data supporting this figure can be found in Supplemental Table 3