Temporal Stability of the Salivary Microbiota in Oral Health

Abstract

Objectives: Saliva is a biological fluid suitable for biomarker analysis, and differences in the salivary microbiota in oral health and disease have been reported. For such comparative analyses, time of sampling is critical since the bacterial composition may vary throughout the day, i.e., diurnal variation. The purpose of this study is to compare the salivary microbiome over time to determine the optimal time for sampling.

Design: Stimulated saliva samples were collected from 5 orally healthy individuals in 4 h intervals for 24 h, and collection was repeated 7 days later (number of samples per person, n = 12, total number of samples, n = 60). Salivary microbiota was analyzed using the Human Oral Microbe Identification using Next Generation Sequencing (HOMINGS), and statistical analysis was performed using the Kruskal-Wallis test with Benjamini-Hochberg's correction for multiple comparisons, cluster analysis, principal component analysis and correspondence analysis.

Results: From a total of 60 saliva samples, 477 probe targets were collectively identified with a mean number of probes per sample of 207 (range: 153-307). Little or no variation in microbial profiles within subjects was observed over time.

Conclusions: Although there was considerable variation between subjects, microbial profiles within subjects were stable throughout a 24 hour period and after 1 week. Since there is little or no evidence of diurnal variation of the salivary microbiome, time of sampling of saliva is not critical for perturbation or other microbial studies.

Fig 1. Cluster analysis.

Cluster analysis based on Spearman Rank Correlation. Participant 1: green, Participant 2: blue, Participant 3: red, Participant 4: yellow, Participant 5: purple. Sample denotation: P1-P5 (Participant 1-Participant 5), D1-D2 (Day 1-Day 2), S1-S6 (Sample 1-Sample 6).

Fig 2. Principal component analysis.

Principal component analysis visualized 2-dimensionally with axes expressed as the second and third most crucial components accounting for 36.6% of the variation of the dataset. Individual 1: blue square, individual 2: red square, individual 3: green triangle, individual 4: purple cross, individual 5: light blue cross.

Fig 3. Correspondence analysis.

Correspondence analysis visualized 2-dimensionally with axes expressed as the 2 most crucial inertia values accounting for a cumulative inertia of 37.6%. Individual 1: blue square, individual 2: red square, individual 3: green triangle, individual 4: purple cross, individual 5: light blue cross.