Comparative Analyses of Subgingival Microbiome in Chronic Periodontitis Patients with and Without IgA Nephropathy by High Throughput 16S rRNA Sequencing

Abstract

Background/Aims: Periodontitis is a prevalent chronic inflammatory disease caused by enhanced inflammation induced by dysbiotic microbes forming on subgingival tooth sites, which may disturb the balance of the microbial composition in the biofilm and finally result in the progressive destruction of the periodontal ligament and alveolar bone with periodontal pocket formation and/or gingival recession. Methods: To elucidate the correlation between subgingival microbiome and IgAN incidence in CP (chronic periodontitis at severe levels) patients, subgingival plaque samples were collected from CP patients without IgAN (Control) and CP patients with IgAN (Disease). 16S rRNA sequencing and comparative analyses of plaque bacterial microbiome between Control and Disease were performed. Results: Subgingival microbial diversity in Disease was a little higher than that in Control. Besides, significant differences were found in subgingival microbiome between Disease and Control. Compared with that in Control, at phylum level, the abundances of Proteobacteria and Actinobacteria were significantly higher while the abundances of Bacteroidetes, Fusobacteria, Spirochaetae, Synergistetes, and Saccharibacteria were significantly lower in Disease; at class level, the abundances of Betaproteobacteria, Bacilli, Actinobacteria, Flavobacteriia, and Gammaproteobacteria were significantly higher while the abundances of Bacteroidia, Fusobacteriia, Negativicutes, Clostridia, and Spirochaetes were significantly lower in Disease; at genus level, the abundances of Bergeyella, Capnocytophaga, Actinomyces, Corynebacterium, Comamonas, Lautropia, and Streptococcus were significantly higher while the abundances of Treponema and Prevotella were significantly lower in Disease. Conclusions: Our data indicated a correlation between the changes in subgingival microbial structure and IgAN incidence in CP patients, which might be used to predict IgAN incidence in CP patients.

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