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Related Experiment Video

Updated: Jan 7, 2026

Oral Biofilm Sampling for Microbiome Analysis in Healthy Children

Oral Biofilm Sampling for Microbiome Analysis in Healthy Children

Published on: December 31, 2017

In-Depth Multi-Approach Analysis of WGS Metagenomics Data Reveals Signatures Potentially Explaining Features in

Ignat V Sonets¹, Iulia S Galeeva¹, Danil V Krivonos^1,2

¹Department of Mathematical Biology and Bioinformatics, Research Institute for Systems Biology and Medicine, Moscow 117246, Russia.

Dentistry Journal

|December 24, 2025

Summary

This summary is machine-generated.

This study reveals that periodontitis severity is influenced by a complex interplay of oral microbiome composition and host factors like cardiovascular disease. Identifying these microbial signatures is key for personalized periodontitis treatments.

Keywords:

analysis of variance dentistry metabolic pathways metagenome periodontitis whole genome sequencing

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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

Area of Science:

Oral microbiome research
Periodontal disease pathogenesis
Metagenomics

Background:

Periodontitis involves tooth-supporting structure destruction, often linked to Porphyromonas gingivalis.
The role of other periodontal bacteria in periodontitis severity is not well understood.

Purpose of the Study:

To characterize the periodontal microbiome in mild versus severe periodontitis using whole-genome shotgun metagenomics.
To identify microbial and non-microbial factors associated with periodontitis severity.

Main Methods:

Whole-genome shotgun (WGS) metagenomics applied to 28 patients with mild and severe periodontitis.
Analysis of taxonomic composition, predicted metabolic pathways, and metagenome-assembled genomes (MAGs).
Inclusion of non-microbial factors like cardiovascular disease and antibiotic history.

Main Results:

Identified key bacteria from periodontal complexes (red, orange, green) associated with disease heterogeneity.
Non-microbial factors (cardiovascular disease, antibiotic use) correlated with disease severity.
Metabolic traits and microbial network interactions linked to severe periodontitis.

Conclusions:

Periodontitis pathogenesis is multifactorial, involving microbial, host, and environmental elements.
Integrated data approaches are crucial for understanding disease progression.
Findings support future research into personalized periodontitis diagnostics and therapeutics.