Bacterial community composition of chronic periodontitis and novel oral sampling sites for detecting disease indicators

Abstract

Background: Periodontitis is an infectious and inflammatory disease of polymicrobial etiology that can lead to the destruction of bones and tissues that support the teeth. The management of chronic periodontitis (CP) relies heavily on elimination or at least control of known pathogenic consortia associated with the disease. Until now, microbial plaque obtained from the subgingival (SubG) sites has been the primary focus for bacterial community analysis using deep sequencing. In addition to the use of SubG plaque, here, we investigated whether plaque obtained from supragingival (SupG) and tongue dorsum sites can serve as alternatives for monitoring CP-associated bacterial biomarkers.

Results: Using SubG, SupG, and tongue plaque DNA from 11 healthy and 13 diseased subjects, we sequenced V3 regions (approximately 200 bases) of the 16S rRNA gene using Illumina sequencing. After quality filtering, approximately 4.1 million sequences were collapsed into operational taxonomic units (OTUs; sequence identity cutoff of >97%) that were classified to a total of 19 phyla spanning 114 genera. Bacterial community diversity and overall composition was not affected by health or disease, and multiresponse permutation procedure (MRPP) on Bray-Curtis distance measures only supported weakly distinct bacterial communities in SubG and tongue plaque depending on health or disease status (P < 0.05). Nonetheless, in SubG and tongue sites, the relative abundance of Firmicutes was increased significantly from health to disease and members of Synergistetes were found in higher abundance across all sites in disease. Taxa indicative of CP were identified in all three locations (for example, Treponema denticola, Porphyromonas gingivalis, Synergistes oral taxa 362 and 363).

Conclusions: For the first time, this study demonstrates that SupG and tongue dorsum plaque can serve as alternative sources for detecting and enumerating known and novel bacterial biomarkers of CP. This finding is clinically important because, in contrast with SubG sampling that requires trained professionals, obtaining plaque from SupG and tongue sites is convenient and minimally-invasive and offers a novel means to track CP-biomarker organisms during treatment outcome monitoring.

Keywords: 16S rRNA gene; Bacterial community; Chronic periodontitis; Oral microbiome; Plaque; Subgingival; Supragingival; Tongue.

Figure 1

Alpha diversity calculations for healthy (red) and diseased (blue) samples. No significant differences between healthy and diseased samples were observed for various α-diversity measures. Calculations and plots were generated using the Phyloseq software package. (A) All samples (SubG, SupG, and Tongue) pooled. (B) SubG samples. (C) SupG samples. (D) Tongue samples.

Figure 2

Bray-Curtis based non-metric multidimensional scaling (NMDS) plot of all samples. NMDS plot (stress 0.22) shows the clear clustering of tongue samples distinct from SubG and SupG samples. Ellipsoids represent a 95% confidence interval surrounding each group. MRPP analysis concluded that the members of the tongue and SubG/SupG groups were more dissimilar than expected by chance (A = 0.06, T = -22.7, P < 0.001).

Figure 3

Principal coordinate analysis (PCoA) plot with Bray-Curtis dissimilarity. Results revealed that tongue samples clustered separately from tooth samples (SubG and SupG), suggesting that the tongue community is relatively unique from that in SupG and SubG sites. No obvious clustering was apparent within or between SupG and SubG samples. (A) Bray-Curtis PCoA ordination of pooled SupG, SubG, and tongue samples. (B) Ordination of SubG samples. Healthy patient samples are shown in green, diseased patient samples in pale red. (C) Ordination of SupG samples. (D) Ordination of tongue samples.

Figure 4

Health and disease associated phyla, by relative abundance. The top five most abundant phyla with significant differences between health and disease are shown. Significance was assessed with a Mann-Whitney test (P < 0.05). Disease abundances are shown in pale red, and health abundances in green. (A) All samples (SubG, SupG, and tongue). (B) SubG samples. (C) SupG samples. (D) Tongue samples. *0.01 < P < 0.05; **0.001 < P < 0.01; ***P < 0.001; ****P < 0.0001.

Figure 5

Numbers of taxa associated with health and disease by indicator species analysis. Dufrêne-Legendre indicator species analysis revealed OTU associations with either health or disease. The number of indicator taxa at phylum, genus, and species levels (OTU indicator value >0.5 and taxa sequence abundance ≥100) of health (green), disease (pale red), or both (light blue) are shown. Taxa that were present in the samples but not an indicator of either health or disease are shown in grey. (A) SubG samples. (B) SupG samples. (C) Tongue samples.

Figure 6

Bubble plot of indicator OTUs associated with health and disease. Each bubble represents an OTU identified by Dufrêne-Legendre indicator species analysis as being associated with health (green) or disease (pale red). (A) Pooled SupG, SubG, and tongue samples; (B) SubG samples; (C) SupG samples; and (D) tongue samples. Bubble area is proportional to OTU sequence abundance. Only OTUs with indicator values >0.5 and taxa with a total of ≥100 sequences are shown. Complete indicator OTU lists are available in Additional file 7.

Figure 7

Quantitative PCR-derived relative abundance of indicator organisms. Data for indicators are presented as reciprocals of the mean normalized cycle threshold (Ct) values (that is, Ct_{indicator species}/Ct_{total bacteria}). Individual points represent average normalized expression values for each individual based on duplicate runs in both health (solid circles) and CP (inverted open triangles) for (A) Total bacterial load. (B)Filifactor alocis, (C)Porphyromonas gingivalis, and (D)Tannerella forsythia. Significant differences between healthy cohorts and those with chronic periodontitis within each of the respective locations in the oral cavity is based on non-parametric Mann-Whitney Test with outliers removed using Grubbs’ Outlier Test. *0.01 < P < 0.05; **0.001 < P < 0.01; ***P < 0.001; ****P < 0.0001.