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Characteristics of the Salivary Microbiota in Periodontal Diseases and Potential Roles of Individual Bacterial Species To Predict the Severity of Periodontal Disease
The purposes of this study were to examine the compositional changes in the salivary microbiota according to the severity of periodontal disease and to verify whether the distribution of specific bacterial species in saliva can distinguish the severity of disease. Saliva samples were collected from...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Microbiology
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10269672/ https://www.ncbi.nlm.nih.gov/pubmed/37191548 http://dx.doi.org/10.1128/spectrum.04327-22 |
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author | Ji, Suk Kook, Joong-Ki Park, Soon-Nang Lim, Yun Kyong Choi, Geum Hee Jung, Jae-Suk |
author_facet | Ji, Suk Kook, Joong-Ki Park, Soon-Nang Lim, Yun Kyong Choi, Geum Hee Jung, Jae-Suk |
author_sort | Ji, Suk |
collection | PubMed |
description | The purposes of this study were to examine the compositional changes in the salivary microbiota according to the severity of periodontal disease and to verify whether the distribution of specific bacterial species in saliva can distinguish the severity of disease. Saliva samples were collected from 8 periodontally healthy controls, 16 patients with gingivitis, 19 patients with moderate periodontitis, and 29 patients with severe periodontitis. The V3 and V4 regions of the 16S rRNA gene in the samples were sequenced, and the levels of 9 bacterial species showing significant differences among the groups by sequencing analysis were identified using quantitative real-time PCR (qPCR). The predictive performance of each bacterial species in distinguishing the severity of disease was evaluated using a receiver operating characteristic curve. Twenty-nine species, including Porphyromonas gingivalis, increased as the severity of disease increased, whereas 6 species, including Rothia denticola, decreased. The relative abundances of P. gingivalis, Tannerella forsythia, Filifactor alocis, and Prevotella intermedia determined by qPCR were significantly different among the groups. The three bacterial species P. gingivalis, T. forsythia, and F. alocis were positively correlated with the sum of the full-mouth probing depth and were moderately accurate at distinguishing the severity of periodontal disease. In conclusion, the salivary microbiota showed gradual compositional changes according to the severity of periodontitis, and the levels of P. gingivalis, T. forsythia, and F. alocis in mouth rinse saliva had the ability to distinguish the severity of periodontal disease. IMPORTANCE Periodontal disease is one of the most widespread medical conditions and the leading cause of tooth loss, imposing high economic costs and an increasing burden worldwide as life expectancy increases. Changes in the subgingival bacterial community during the progression of periodontal disease can affect the entire oral ecosystem, and bacteria in saliva can reflect the degree of bacterial imbalance in the oral cavity. This study explored whether the specific bacterial species in saliva can distinguish the severity of periodontal disease by analyzing the salivary microbiota and suggested P. gingivalis, T. forsythia, and F. alocis as biomarkers for distinguishing the severity of periodontal disease in saliva. |
format | Online Article Text |
id | pubmed-10269672 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Society for Microbiology |
record_format | MEDLINE/PubMed |
spelling | pubmed-102696722023-06-16 Characteristics of the Salivary Microbiota in Periodontal Diseases and Potential Roles of Individual Bacterial Species To Predict the Severity of Periodontal Disease Ji, Suk Kook, Joong-Ki Park, Soon-Nang Lim, Yun Kyong Choi, Geum Hee Jung, Jae-Suk Microbiol Spectr Research Article The purposes of this study were to examine the compositional changes in the salivary microbiota according to the severity of periodontal disease and to verify whether the distribution of specific bacterial species in saliva can distinguish the severity of disease. Saliva samples were collected from 8 periodontally healthy controls, 16 patients with gingivitis, 19 patients with moderate periodontitis, and 29 patients with severe periodontitis. The V3 and V4 regions of the 16S rRNA gene in the samples were sequenced, and the levels of 9 bacterial species showing significant differences among the groups by sequencing analysis were identified using quantitative real-time PCR (qPCR). The predictive performance of each bacterial species in distinguishing the severity of disease was evaluated using a receiver operating characteristic curve. Twenty-nine species, including Porphyromonas gingivalis, increased as the severity of disease increased, whereas 6 species, including Rothia denticola, decreased. The relative abundances of P. gingivalis, Tannerella forsythia, Filifactor alocis, and Prevotella intermedia determined by qPCR were significantly different among the groups. The three bacterial species P. gingivalis, T. forsythia, and F. alocis were positively correlated with the sum of the full-mouth probing depth and were moderately accurate at distinguishing the severity of periodontal disease. In conclusion, the salivary microbiota showed gradual compositional changes according to the severity of periodontitis, and the levels of P. gingivalis, T. forsythia, and F. alocis in mouth rinse saliva had the ability to distinguish the severity of periodontal disease. IMPORTANCE Periodontal disease is one of the most widespread medical conditions and the leading cause of tooth loss, imposing high economic costs and an increasing burden worldwide as life expectancy increases. Changes in the subgingival bacterial community during the progression of periodontal disease can affect the entire oral ecosystem, and bacteria in saliva can reflect the degree of bacterial imbalance in the oral cavity. This study explored whether the specific bacterial species in saliva can distinguish the severity of periodontal disease by analyzing the salivary microbiota and suggested P. gingivalis, T. forsythia, and F. alocis as biomarkers for distinguishing the severity of periodontal disease in saliva. American Society for Microbiology 2023-05-16 /pmc/articles/PMC10269672/ /pubmed/37191548 http://dx.doi.org/10.1128/spectrum.04327-22 Text en Copyright © 2023 Ji et al. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Article Ji, Suk Kook, Joong-Ki Park, Soon-Nang Lim, Yun Kyong Choi, Geum Hee Jung, Jae-Suk Characteristics of the Salivary Microbiota in Periodontal Diseases and Potential Roles of Individual Bacterial Species To Predict the Severity of Periodontal Disease |
title | Characteristics of the Salivary Microbiota in Periodontal Diseases and Potential Roles of Individual Bacterial Species To Predict the Severity of Periodontal Disease |
title_full | Characteristics of the Salivary Microbiota in Periodontal Diseases and Potential Roles of Individual Bacterial Species To Predict the Severity of Periodontal Disease |
title_fullStr | Characteristics of the Salivary Microbiota in Periodontal Diseases and Potential Roles of Individual Bacterial Species To Predict the Severity of Periodontal Disease |
title_full_unstemmed | Characteristics of the Salivary Microbiota in Periodontal Diseases and Potential Roles of Individual Bacterial Species To Predict the Severity of Periodontal Disease |
title_short | Characteristics of the Salivary Microbiota in Periodontal Diseases and Potential Roles of Individual Bacterial Species To Predict the Severity of Periodontal Disease |
title_sort | characteristics of the salivary microbiota in periodontal diseases and potential roles of individual bacterial species to predict the severity of periodontal disease |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10269672/ https://www.ncbi.nlm.nih.gov/pubmed/37191548 http://dx.doi.org/10.1128/spectrum.04327-22 |
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