Factors influencing oral microbiome analysis: from saliva sampling methods to next-generation sequencing platforms.

The exploration of oral microbiome has been increasing due to its relatedness with various systemic diseases, but standardization of saliva sampling for microbiome analysis has not been established, contributing to the lack of data comparability. Here, we evaluated the factors that influence the microbiome data. Saliva samples were collected by the two collection methods (passive drooling and mouthwash) using three saliva-preservation methods (OMNIgene, DNA/RNA shield, and simple collection). A total of 18 samples were sequenced by both Illumina short-read and Nanopore long-read next-generation sequencing (NGS). The component of the oral microbiome in each sample was compared with alpha and beta diversity and the taxonomic abundances, to find out the effects of factors on oral microbiome data. The alpha diversity indices of the mouthwash sample were significantly higher than that of the drooling group with both short-read and long-read NGS, while no significant differences in microbial diversities were found between the three saliva-preservation methods. Our study shows mouthwash and simple collection are not inferior to other sample collection and saliva-preservation methods, respectively. This result is promising since the convenience and cost-effectiveness of mouthwash and simple collection can simplify the saliva sample preparation, which would greatly help clinical operators and lab workers.

Exploration of the interplay between spatially distinct microbial habitats through comparative analysis.

Objectives: The oral microbiome is closely associated with systemic diseases, indicating the presence of bacteremia and inflammatory mediators in the systemic circulation. Our research aims to investigate the relationship between the oral microbiome and other microbial habitats. Methods: We analyzed 180 specimens from 36 patients, including saliva, buccal swab, plaque, stool, and blood samples from a healthy group (Non_PD, n = 18) and a periodontitis group (PD, n = 18). The final analysis included 147 specimens, with varying sample sizes for each group. Metagenomic analysis was performed using prokaryotic 16S rRNA on the MiSeq platform (Illumina). Results: PD saliva showed significant richness differences (P's < 0.05), similar to plaque. Buccal swabs had slight variations. Microbial network analysis revealed altered microbial interactions in the PD group, with decreased interactions in saliva and buccal swabs, and increased interactions in plaque. In our analysis of nine specimens where all paired habitat samples could be analyzed, microorganisms linked to oral periodontitis were found in sterile blood samples, resembling the oral cavity's composition. Conclusions: Microbiome differences should consider overall microbial-environment interactions, alongside diversity and richness. Our data cautiously suggest that disease-related changes in the salivary microbiome may be reflected in blood specimens through the oral-blood axis.