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Open Access Research article

Microbial transformation from normal oral microbiota to acute endodontic infections

William W L Hsiao1, Kevin L Li2, Zhenqiu Liu3, Cheron Jones1, Claire M Fraser-Liggett1 and Ashraf F Fouad2*

Author Affiliations

1 University of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, MD, 20201, USA

2 University of Maryland School of Dentistry, Department of Endodontics, Prosthodontics and Operative Dentistry, Baltimore, MD, 20201, USA

3 University of Maryland School of Medicine, Department of Epidemiology and Preventive Medicine, Baltimore, MD, 20201, USA

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BMC Genomics 2012, 13:345  doi:10.1186/1471-2164-13-345

Published: 28 July 2012

Abstract

Background

Endodontic infections are a leading cause of oro-facial pain and tooth loss in western countries, and may lead to severe life-threatening infections. These infections are polymicrobial with high bacterial diversity. Understanding the spatial transition of microbiota from normal oral cavities through the infected root canal to the acute periapical abscess can improve our knowledge of the pathogenesis of endodontic infections and lead to more effective treatment. We obtained samples from the oral cavity, infected root canal and periapical abscess of 8 patients (5 with localized and 3 with systemic infections). Microbial populations in these samples were analyzed using next-generation sequencing of 16S rRNA amplicons. Bioinformatics tools and statistical tests with rigorous criteria were used to elucidate the spatial transition of the microbiota from normal to diseased sites.

Results

On average, 10,000 partial 16S rRNA gene sequences were obtained from each sample. All sequences fell into 11 different bacterial phyla. The microbial diversity in root canal and abscess samples was significantly lower than in the oral samples. Streptococcus was the most abundant genus in oral cavities while Prevotella and Fusobacterium were most abundant in diseased samples. The microbiota community structures of root canal and abscess samples were, however, more similar to each other than to the oral cavity microbiota. Using rigorous criteria and novel bioinformatics tools, we found that Granulicatella adiacens, Eubacterium yurii, Prevotella melaninogenica, Prevotella salivae, Streptococcus mitis, and Atopobium rimae were over-represented in diseased samples.

Conclusions

We used a novel approach and high-throughput methodologies to characterize the microbiota associated normal and diseased oral sites in the same individuals.

Keywords:
Endodontic infection; Endodontic microbiome; Periapical abscess; Oral microbiota; Next generation sequencing; 16S rRNA gene; Bacterial diversity