Identification of Potential Oral Microbial Biomarkers for the Diagnosis of Periodontitis

Abstract

Periodontitis is a chronic and multifactorial inflammatory disease that can lead to tooth loss. At present, the diagnosis for periodontitis is primarily based on clinical examination and radiographic parameters. Detecting the periodontal pathogens at the subgingival plaque requires skilled professionals to collect samples. Periodontal pathogens are also detected on various mucous membranes in patients with periodontitis. In this study, we characterized the oral microbiome profiles from buccal mucosa and supragingival space in a total of 272 healthy subjects as a control group, and periodontitis patients as a disease group. We identified 13 phyla, 193 genera, and 527 species and determined periodontitis-associated taxa. Porphyromonas gingivalis, Tannerella forsythia, Treponema denticolar, Filifactor alocis, Porphyromonas endodontalis, Fretibacterium fastiosum and Peptostreptococcus species were significantly increased in both the buccal mucosa and the supragingival space in periodontitis patients. The identified eight periodontitis-associated bacterial species were clinically validated in an independent cohort. We generated the prediction model based on the oral microbiome profiles using five machine learning algorithms, and validated its capability in predicting the status of patients with periodontitis. The results showed that the oral microbiome profiles from buccal mucosa and supragingival space can represent the microbial composition of subgingival plaque and further be utilized to identify potential microbial biomarkers for the diagnosis of periodontitis. Besides, bacterial community interaction network analysis found distinct patterns associated with dysbiosis in periodontitis. In summary, we have identified oral bacterial species from buccal and supragingival sites which can predict subgingival bacterial composition and can be used for early diagnosis of periodontitis. Therefore, our study provides an important basis for developing easy and noninvasive methods to diagnose and monitor periodontitis.

Keywords: Bioinformatics; Biomarkers; Microbiome; Oral bacteria; Periodontal disease(s)/periodontitis.

Figure 1

Relative abundance of bacterial phyla grouped by the disease status, sampling sites, and four subject groups. (a) Disease status; Healthy (H) vs. Periodontitis (P) and sampling sites; Buccal mucosa (b) vs. Supragingival space (S) (B) four subject groups; Healthy_Buccal (HB), Healthy_Supragingival (HS), Periodontitis_Buccal (PB), and Periodontitis_Supragingival (PS).

Figure 2

Alpha and beta diversities of bacterial communities grouped by the disease status, sampling sites, and four subject groups. Kruskal–Wallis pairwise test was used to assess the statistical significance between groups for the alpha diversities. In case of beta diversities, permanova test was used to evaluate the statistical significance between groups. (a) Alpha diversity—richness (abundance-based coverage estimator; Ace) (b) alpha diversity—evenness (Shannon diversity index), (c) Principal coordinate analysis plots based on unweighted UniFrac distance. Adjusted P values provided on each plot are for the indicated comparisons.

Figure 3

A heatmap of 12 highly correlated bacterial species. Pearson correlation was used to evaluate the association between bacterial abundances and clinical parameters (age, gender, PD, CAL, GI, and PI) and if the adjusted p-value was less than 0.001, the bacterial species was defined as a highly correlated bacterial species (Table S3). The heatmap was generated using the relative abundance normalized by centered log-ratio (CLR) methods (see supplementary methods).

Figure 4

Bacterial species enriched in the H and periodontitis groups. (a) Volcano plots for the bacterial species for the different bacteria species abundance. DESeq2 was to use to test the statistical significance. Differential abundance of bacteria species was defined as those with changes of at least 4-fold between samples at a false discovery rate (FDR) of 0.001%. The x and y axes in the plot present the magnitude of fold changes (log2 transformed) and the adjusted p-value (-log10) by Benjamini–Hochberg correction, respectively. Dominant bacterial species in the P group were represented as red dots and dominant bacterial species in the H group were shown as blue dots in the plot. (b) Venn diagram of the number of differential bacterial species common/unique to the buccal (green) and supragingival (red) sites. (c) The ROC plots and AUC for four different feature sets. ALL; all bacteria species, DESeq1; the enriched bacterial species identified DESeq2, LefSe; the enriched bacterial species identified LefSe, and DESeq2+LefSe; union of DESeq2 and LefSe sets. Right panel: buccal site samples with logistic model tree (LMT) algorithm and left panel; supragingival site samples with LogitBoost algorithm.

Figure 5

Clinical validation of the identified microbiome related to periodontitis. Real-time PCR (RT-PCR) was performed to measure a relative abundance of bacterial species in the sample. The difference between two groups (H: healthy group, P: periodontitis group) was assessed using the Wilcoxon rank sum test. The * indicates that the p-value for the test is less than 0.01.

Figure 6

Bacterial community interaction network analysis. The co-occurrence bacterial community networks were constructed using SparCC method and visualized in Cytoscape software. The intersection networks between two networks between buccal and supragingival sites were obtained using Cytoscape merge tools. (a) Interaction networks in buccal site, (b) interaction networks in supragingival site, and (c) intersection networks in the H and periodontitis groups. Diamond shape node represents the highly correlated bacterial species with clinical parameters. The differential bacterial species between healthy and periodontitis groups were colored as blue (healthy) or red (periodontitis). The directions of the correlation between two bacterial species were annotated as edge colors; red color: negative correlation, green color: positive correlation.