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Signature of Microbial Dysbiosis in Periodontitis

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Signature of Microbial Dysbiosis in Periodontitis

Vincent Meuric et al. Appl Environ Microbiol.

Abstract

Periodontitis is driven by disproportionate host inflammatory immune responses induced by an imbalance in the composition of oral bacteria; this instigates microbial dysbiosis, along with failed resolution of the chronic destructive inflammation. The objectives of this study were to identify microbial signatures for health and chronic periodontitis at the genus level and to propose a model of dysbiosis, including the calculation of bacterial ratios. Published sequencing data obtained from several different studies (196 subgingival samples from patients with chronic periodontitis and 422 subgingival samples from healthy subjects) were pooled and subjected to a new microbiota analysis using the same Visualization and Analysis of Microbial Population Structures (VAMPS) pipeline, to identify microbiota specific to health and disease. Microbiota were visualized using CoNet and Cytoscape. Dysbiosis ratios, defined as the percentage of genera associated with disease relative to the percentage of genera associated with health, were calculated to distinguish disease from health. Correlations between the proposed dysbiosis ratio and the periodontal pocket depth were tested with a different set of data obtained from a recent study, to confirm the relevance of the ratio as a potential indicator of dysbiosis. Beta diversity showed significant clustering of periodontitis-associated microbiota, at the genus level, according to the clinical status and independent of the methods used. Specific genera (Veillonella, Neisseria, Rothia, Corynebacterium, and Actinomyces) were highly prevalent (>95%) in health, while other genera (Eubacterium, Campylobacter, Treponema, and Tannerella) were associated with chronic periodontitis. The calculation of dysbiosis ratios based on the relative abundance of the genera found in health versus periodontitis was tested. Nonperiodontitis samples were significantly identifiable by low ratios, compared to chronic periodontitis samples. When applied to a subgingival sample set with well-defined clinical data, the method showed a strong correlation between the dysbiosis ratio, as well as a simplified ratio (Porphyromonas, Treponema, and Tannerella to Rothia and Corynebacterium), and pocket depth. Microbial analysis of chronic periodontitis can be correlated with the pocket depth through specific signatures for microbial dysbiosis.IMPORTANCE Defining microbiota typical of oral health or chronic periodontitis is difficult. The evaluation of periodontal disease is currently based on probing of the periodontal pocket. However, the status of pockets "on the mend" or sulci at risk of periodontitis cannot be addressed solely through pocket depth measurements or current microbiological tests available for practitioners. Thus, a more specific microbiological measure of dysbiosis could help in future diagnoses of periodontitis. In this work, data from different studies were pooled, to improve the accuracy of the results. However, analysis of multiple species from different studies intensified the bacterial network and complicated the search for reproducible microbial signatures. Despite the use of different methods in each study, investigation of the microbiota at the genus level showed that some genera were prevalent (up to 95% of the samples) in health or disease, allowing the calculation of bacterial ratios (i.e., dysbiosis ratios). The correlation between the proposed ratios and the periodontal pocket depth was tested, which confirmed the link between dysbiosis ratios and the severity of the disease. The results of this work are promising, but longitudinal studies will be required to improve the ratios and to define the microbial signatures of the disease, which will allow monitoring of periodontal pocket recovery and, conceivably, determination of the potential risk of periodontitis among healthy patients.

Keywords: chronic periodontitis; dysbiosis ratio; health; microbiota.

Figures

FIG 1
FIG 1
Different views of 3D PCoA plots illustrating the beta diversity of bacterial populations as a function of sampling site and diagnosis. Light blue, supragingival samples; dark blue, healthy subgingival samples; green, outgroups (saliva, midvagina, and dentine caries samples); red, CP samples. Percentages represent percent explained variance.
FIG 2
FIG 2
Unrooted tree displaying genus Bray-Curtis beta-diversity clustering of microbiota and pie charts related to the sample origins within each cluster. The tree was prepared using Figtree 1.4.2 software. The distribution of microbiota in each cluster is represented by pie charts, with different colors representing different sampling sites (supragingival [Sup] in light blue, saliva in light green, dentine caries in medium green, and midvagina in dark green) and diagnosis for subgingival (Sub) samples (healthy in dark blue, shallow in yellow, and CP in red). Percentages correspond to the number of samples from a specific sampling site in a given cluster relative to the total number of samples from the same sampling site.
FIG 3
FIG 3
Alpha diversity index values. Comparisons of microbiota sampling depth, observed richness (number of different taxa per sample), and diversity (Shannon-Weaver index) in subgingival samples of healthy clusters 1 and 2 (dark blue) and either CP samples (red) or healthy subgingival samples (light blue) of cluster 5 were performed. *, P < 0.05; **, P < 0.01.
FIG 4
FIG 4
Patterns of subgingival microbial communities. (A) Patterns of genera present in at least 95% of all healthy subgingival samples. (B) Patterns of genera present in at least 95% of all CP samples from cluster 5. Edges represent 1 (thin line) or 2 or 3 (thick line) significant correlations between genera (green, positive; red, negative). Node colors represent the numbers of partners, ranging from 1 (green) to 7 (dark orange). Node sizes represent the abundance of each taxon.
FIG 5
FIG 5
Subgingival dysbiosis ratios of Eubacterium, Campylobacter, Treponema, and Tannerella to Veillonella, Neisseria, Rothia, Corynebacterium, and Actinomyces. (A) Comparisons between healthy, shallow, and CP samples from all clusters. (B) Comparisons between clusters 1 and 2 and cluster 5 for healthy, shallow, and CP samples.
FIG 6
FIG 6
Correlation between pocket depth (PD) and dysbiosis. Samples from Bizzarro et al. (11) were analyzed by VAMPS, followed by calculation of the dysbiosis ratios. (A) Ratios of Eubacterium, Campylobacter, Treponema, and Tannerella to Veillonella, Neisseria, Rothia, Corynebacterium, and Actinomyces. (B) Simplified ratios of Porphyromonas, Treponema, and Tannerella to Rothia and Corynebacterium.

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