IADR Abstract Archives
The Salivary Microbiome for Discriminating Periodontal Conditions: a Meta-Omics Analysis
Objectives: To evaluate the predictive ability of the salivary microbiota at the ASV-level to distinguish distinct periodontal health conditions: health, gingivitis, and untreated periodontitis.
Methods: One hundred and twenty-four patients (50 controls, 74 periodontitis) were selected for the collection of unstimulated saliva samples in our clinical setting. Sequencing of the V3-V4 16S rRNA gene region was performed in Illumina MiSeq, and the obtained sequences and metadata were uploaded to the sequence read archive (SRA). Searches were performed in PubMed, Scopus, Embase, and the SRA to identify previously published Illumina V3-V4 sequencing studies on the salivary microbiome in different periodontal conditions. Investigations that met the sequence and metadata quality criteria were included in the meta-omics analysis, comprising a total of 814 samples. Sequences were processed under the same bioinformatics protocol, which included the ASV-level classification and the use of an oral-specific database for taxonomic assignment. The statistical analysis was conducted using the microbiome and mixOmics packages.
Results: The predictive models for differentiating periodontal health from gingivitis and periodontitis showed AUC values of 0.925 and 0.946, respectively; and required low numbers of the detected ASVs and species (0.15% and 1.16%, and 0.46% and 3.47%, respectively). In contrast, the model to distinguish gingivitis from periodontitis required more taxa (1.29% of ASVs and 10.69% of species), with AUC values of 0.999.
Conclusions: The salivary microbiome presented an outstanding ability to discriminate between periodontal health, gingivitis, and untreated periodontitis. The main health-predictor ASVs in saliva were: Streptococcus sanguinis ASV228 and ASV619, and Rothia aeria ASV507. Conversely, for gingivitis they were: Prevotella melaninogenica ASV7, Veillonella rogosae ASV72 and ASV241, and Veillonella atypica ASV3401; and for periodontitis: Sacchari BTASV095869 ASV36 and Leptotrichia sp. HMT417 ASV33 as ASVs of higher abundance, Filifactor alocis ASV19, Tannerella forsythia ASV15, and Treponema denticola ASV38 as ASVs of lower abundance.
Methods: One hundred and twenty-four patients (50 controls, 74 periodontitis) were selected for the collection of unstimulated saliva samples in our clinical setting. Sequencing of the V3-V4 16S rRNA gene region was performed in Illumina MiSeq, and the obtained sequences and metadata were uploaded to the sequence read archive (SRA). Searches were performed in PubMed, Scopus, Embase, and the SRA to identify previously published Illumina V3-V4 sequencing studies on the salivary microbiome in different periodontal conditions. Investigations that met the sequence and metadata quality criteria were included in the meta-omics analysis, comprising a total of 814 samples. Sequences were processed under the same bioinformatics protocol, which included the ASV-level classification and the use of an oral-specific database for taxonomic assignment. The statistical analysis was conducted using the microbiome and mixOmics packages.
Results: The predictive models for differentiating periodontal health from gingivitis and periodontitis showed AUC values of 0.925 and 0.946, respectively; and required low numbers of the detected ASVs and species (0.15% and 1.16%, and 0.46% and 3.47%, respectively). In contrast, the model to distinguish gingivitis from periodontitis required more taxa (1.29% of ASVs and 10.69% of species), with AUC values of 0.999.
Conclusions: The salivary microbiome presented an outstanding ability to discriminate between periodontal health, gingivitis, and untreated periodontitis. The main health-predictor ASVs in saliva were: Streptococcus sanguinis ASV228 and ASV619, and Rothia aeria ASV507. Conversely, for gingivitis they were: Prevotella melaninogenica ASV7, Veillonella rogosae ASV72 and ASV241, and Veillonella atypica ASV3401; and for periodontitis: Sacchari BTASV095869 ASV36 and Leptotrichia sp. HMT417 ASV33 as ASVs of higher abundance, Filifactor alocis ASV19, Tannerella forsythia ASV15, and Treponema denticola ASV38 as ASVs of lower abundance.