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Effective microbial molecular diagnosis of periodontitis-related pathogen Porphyromonas gingivalis from salivary samples using rgpA gene
Genomics & Informatics ; : e13-2023.
Artigo em Inglês | WPRIM | ID: wpr-976791
ABSTRACT
Importance of accurate molecular diagnosis and quantification of particular disease-related pathogenic microorganisms is highlighted as an introductory step to prevent and care for diseases. In this study, we designed a primer/probe set for quantitative real-time polymerase chain reaction (qRT-PCR) targeting rgpA gene, known as the specific virulence factor of periodontitis-related pathogenic bacteriaPorphyromonas gingivalis’, and evaluated its diagnostic efficiency by detecting and quantifying relative bacterial load of P. gingivalis within saliva samples collected from clinical subjects. As a result of qRT-PCR, we confirmed that relative bacterial load of P. gingivalis was detected and quantified within all samples of positive control and periodontitis groups. On the contrary, negative results were confirmed in both negative control and healthy groups. Additionally, as a result of comparison with next-generation sequencing (NGS)–based 16S metagenome profiling data, we confirmed relative bacterial load of P. gingivalis, which was not identified on bacterial classification table created through 16S microbiome analysis, in qRT-PCR results. It showed that an approach to quantifying specific microorganisms by applying qRT-PCR method could solve microbial misclassification issues at species level of an NGS-based 16S microbiome study. In this respect, we suggest that P. gingivalis–specific primer/probe set introduced in present study has efficient applicability in various oral healthcare industries, including periodontitis-related microbial molecular diagnosis field.
Texto completo: DisponíveL Índice: WPRIM (Pacífico Ocidental) Idioma: Inglês Revista: Genomics & Informatics Ano de publicação: 2023 Tipo de documento: Artigo

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Texto completo: DisponíveL Índice: WPRIM (Pacífico Ocidental) Idioma: Inglês Revista: Genomics & Informatics Ano de publicação: 2023 Tipo de documento: Artigo