Identification of Salivary Microbiota and Its Association With Host Inflammatory Mediators in Periodontitis

Abstract

Periodontitis is a microbial-induced chronic inflammatory disease, which may not only result in tooth loss, but can also contribute to the development of various systemic diseases. The transition from healthy to diseased periodontium depends on microbial dysbiosis and impaired host immune response. Although periodontitis is a common disease as well as associated with various systemic inflammatory conditions, the taxonomic profiling of the salivary microbiota in periodontitis and its association with host immune and inflammatory mediators has not been reported. Therefore, the aim of this study was to identify key pathogens and their potential interaction with the host's inflammatory mediators in saliva samples for periodontitis risk assessment. The microbial 16S rRNA gene sequencing and the levels of inflammatory mediators were performed in saliva samples from patients with chronic periodontitis and periodontally healthy control subjects. The salivary microbial community composition differed significantly between patients with chronic periodontitis and healthy controls. Our analyses identified a number of microbes, including bacteria assigned to Eubacterium saphenum, Tannerella forsythia, Filifactor alocis, Streptococcus mitis/parasanguinis, Parvimonas micra, Prevotella sp., Phocaeicola sp., and Fretibacterium sp. as more abundant in periodontitis, compared to healthy controls. In samples from healthy individuals, we identified Campylobacter concisus, and Veillonella sp. as more abundant. Integrative analysis of the microbiota and inflammatory mediators/cytokines revealed associations that included positive correlations between the pathogens Treponema sp. and Selenomas sp. and the cytokines chitinase 3-like 1, sIL-6Rα, sTNF-R1, and gp130/sIL-6Rβ. In addition, a negative correlation was identified between IL-10 and Filifactor alocis. Our results reveal distinct and disease-specific patterns of salivary microbial composition between patients with periodontitis and healthy controls, as well as significant correlations between microbiota and host-mediated inflammatory cytokines. The positive correlations between the pathogens Treponema sp. and Selenomas sp. and the cytokines chitinase 3-like 1, sIL-6Rα, sTNF-R1, and gp130/sIL-6Rβ might have the future potential to serve as a combined bacteria-host salivary biomarker panel for diagnosis of the chronic infectious disease periodontitis. However, further studies are required to determine the capacity of these microbes and inflammatory mediators as a salivary biomarker panel for periodontitis.

Keywords: 16S rRNA sequencing; cytokines; inflammatory mediators; microbiome; microbiota; periodontitis; saliva.

Figure 1

Relative abundance distribution at phylum level of saliva samples from periodontitis patients and healthy controls. The bar plots depict the relative abundance of microbiota at phylum level for all samples, grouped according to their disease status (periodontitis or healthy). The median relative abundance of each phylum in saliva, in samples from patients with periodontitis and healthy controls, are also demonstrated together with the adjusted P-value. Significantly (adjusted P < 0.05) different levels are indicated with boldface.

Figure 2

Principal Coordinate Analysis (PCoA) plots on the first two coordinates with UniFrac distances. Variations explained by the coordinates are given within parenthesis. (A) A sample projections plot with the samples color-coded by disease status. Saliva samples from patients with periodontitis are highlighted in red and healthy controls are highlighted in blue. (B) The contributions of individual sequence variants (ASVs) are visualized in the same PCoA space as the samples and shaded according to their phylum assignment. (C) The contributions of the ASVs to the PCoA, split by phyla.

Figure 3

Differentially abundant amplicon sequence variants (ASVs) identified through DESeq2 testing. Each ASV is demonstrated at its lowest annotated taxonomic rank together with its ASV ID. The ASVs are color-coded according to the phyla they belong to and plotted according to their log2 fold change, calculated as the levels in samples from patients with periodontitis relative the levels in samples from healthy controls, with size factor normalization as implemented in DESeq2. Dot sizes correspond to mean counts, after normalization as implemented in DESeq2, and ranges from 3.4 (Treponema parvum, ASV681) to 6023.0 (Streptococcus sp. ASV1). Streptococcus mitis/parasanguinis is shortened to Streptococcus m/p (ASV11).

Figure 4

Amplicon sequence variants (ASVs) identified by random forest analysis. The 20 most important ASVs for the classification of saliva samples from patients with periodontitis and healthy controls are included. The variable importances, corresponding to mean decrease Gini are demonstrated together with the log2 Fold Changes, calculated on the relative abundances of the ASVs. The ASVs more abundant in samples from patients with periodontitis are highlighted in green and ASVs more abundant in samples from healthy controls are highlighted in orange. The dot sizes correspond to the variable importances, or mean decrease Gini, with Tannerella forsythia having the highest variable importance, 1.14, and Prevotella sp. (ASV149) having the lowest variable importance, 0.32. The lowest annotated taxonomic rank is shown for each ASV as well as the IDs of the ASVs used for the analysis. These ASVs showed some variations in fold change compared to the univariate analysis, which was performed with size factor normalization and shrinkage estimation of fold changes, as implemented in DESeq2, while in the random forest model relative abundances were used. Streptococcus mitis/parasanguinis is shortened to Streptococcus m/p (ASV11).

Figure 5

Levels of inflammatory mediators/cytokines in saliva samples from patients with periodontitis and healthy controls. Periodontitis is color-coded in red and healthy controls are color-coded in blue. Gp130/sIL-6Rβ, glycoprotein 130/soluble interleukin-6 receptor β; IL-10, interlukin-10; IL-19, interleukin-19; sTNF-R1, soluble tumor necrosis factor receptor 1; *P < 0.05; **P < 0.05 with Benjamini-Hochberg post-hoc test.

Figure 6

Sparse partial least squares discriminant analysis (sPLS-DA) sample representation. Samples are demonstrated regarding the first and second component based on (A) the microbial and (B) the cytokine data. Red represents saliva samples from patients with periodontitis and blue represents samples from healthy controls.

Figure 7

Circos plot depicting correlations between microbiota and cytokines. The panel of amplicon sequence variants (ASVs) and cytokines identified by the sparse partial least squares discriminant analysis (sPLS-DA), regarding the first and second component, are included in the Circos plot. Blue variables represent ASVs and green variables represent cytokines. Orange and blue lines outside the circle represent the abundance of ASVs or the levels of cytokines in samples from patients with periodontitis and healthy controls, respectively. Red and blue lines inside the circle represent positive and negative correlations, respectively, between ASVs and cytokines, at a correlation cutoff of 0.5. The lowest annotated taxonomic rank is shown for each ASV that has a significant correlation with a cytokine, together with its ASV ID used in the analyses. Streptococcus mitis/parasanguinis is shortened to Streptococcus m/p (ASV11).