Microbial transformation from normal oral microbiota to acute endodontic infections
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 cavit...
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| Vydané v: | BMC genomics Ročník 13; číslo 1; s. 345 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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London
BioMed Central
28.07.2012
BioMed Central Ltd Springer Nature B.V BMC |
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| ISSN: | 1471-2164, 1471-2164 |
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| 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. |
|---|---|
| AbstractList | 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. 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. 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. We used a novel approach and high-throughput methodologies to characterize the microbiota associated normal and diseased oral sites in the same individuals. 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.BACKGROUNDEndodontic 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.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.RESULTSOn 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.We used a novel approach and high-throughput methodologies to characterize the microbiota associated normal and diseased oral sites in the same individuals.CONCLUSIONSWe used a novel approach and high-throughput methodologies to characterize the microbiota associated normal and diseased oral sites in the same individuals. 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 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. 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. 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. We used a novel approach and high-throughput methodologies to characterize the microbiota associated normal and diseased oral sites in the same individuals. Doc number: 345 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. 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. |
| ArticleNumber | 345 |
| Audience | Academic |
| Author | Fraser-Liggett, Claire M Fouad, Ashraf F Liu, Zhenqiu Hsiao, William W L Li, Kevin L Jones, Cheron |
| AuthorAffiliation | 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 1 University of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, MD, 20201, USA |
| AuthorAffiliation_xml | – name: 1 University of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, MD, 20201, USA – name: 3 University of Maryland School of Medicine, Department of Epidemiology and Preventive Medicine, Baltimore, MD, 20201, USA – name: 2 University of Maryland School of Dentistry, Department of Endodontics, Prosthodontics and Operative Dentistry, Baltimore, MD, 20201, USA |
| Author_xml | – sequence: 1 givenname: William W L surname: Hsiao fullname: Hsiao, William W L organization: University of Maryland School of Medicine, Institute for Genome Sciences – sequence: 2 givenname: Kevin L surname: Li fullname: Li, Kevin L organization: University of Maryland School of Dentistry, Department of Endodontics, Prosthodontics and Operative Dentistry – sequence: 3 givenname: Zhenqiu surname: Liu fullname: Liu, Zhenqiu organization: University of Maryland School of Medicine, Department of Epidemiology and Preventive Medicine – sequence: 4 givenname: Cheron surname: Jones fullname: Jones, Cheron organization: University of Maryland School of Medicine, Institute for Genome Sciences – sequence: 5 givenname: Claire M surname: Fraser-Liggett fullname: Fraser-Liggett, Claire M organization: University of Maryland School of Medicine, Institute for Genome Sciences – sequence: 6 givenname: Ashraf F surname: Fouad fullname: Fouad, Ashraf F email: AFouad@umaryland.edu organization: University of Maryland School of Dentistry, Department of Endodontics, Prosthodontics and Operative Dentistry |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/22839737$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | Hsiao et al.; licensee BioMed Central Ltd. 2012 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. COPYRIGHT 2012 BioMed Central Ltd. 2012 Hsiao et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright ©2012 Hsiao et al.; licensee BioMed Central Ltd. 2012 Hsiao et al.; licensee BioMed Central Ltd. |
| Copyright_xml | – notice: Hsiao et al.; licensee BioMed Central Ltd. 2012 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. – notice: COPYRIGHT 2012 BioMed Central Ltd. – notice: 2012 Hsiao et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. – notice: Copyright ©2012 Hsiao et al.; licensee BioMed Central Ltd. 2012 Hsiao et al.; licensee BioMed Central Ltd. |
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Endodontic infections are a leading cause of oro-facial pain and tooth loss in western countries, and may lead to severe life-threatening... 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... Background Endodontic infections are a leading cause of oro-facial pain and tooth loss in western countries, and may lead to severe life-threatening... Doc number: 345 Abstract Background: Endodontic infections are a leading cause of oro-facial pain and tooth loss in western countries, and may lead to severe... Background: Endodontic infections are a leading cause of oro-facial pain and tooth loss in western countries, and may lead to severe life-threatening... 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... |
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| SubjectTerms | 16S rRNA gene Acute Disease Analysis Analysis of Variance Animal Genetics and Genomics Bacteria Bacteria - genetics Bacterial diversity Bacterial Infections - microbiology Biodiversity Bioinformatics Biomedical and Life Sciences Confidence Intervals Dental Pulp Cavity - microbiology Dental Pulp Cavity - pathology Development and progression Disease DNA, Bacterial - classification DNA, Bacterial - isolation & purification Endodontic infection Endodontic microbiome Eubacterium Facial pain Female Fusobacterium Genetic aspects Genomes Genomics Granulicatella adiacens Health aspects Health care Humans Infection Infections Life Sciences Male Metagenome - genetics Microarrays Microbial Genetics and Genomics Microbiology Microbiota (Symbiotic organisms) Molecular Sequence Annotation Mouth Mucosa - microbiology Mouth Mucosa - pathology Next generation sequencing Oral microbiota Orofacial pain Periapical abscess Periapical Abscess - microbiology Phylogeny Physiological aspects Plant Genetics and Genomics Prevotella Prevotella melaninogenica Prokaryote microbial genomics Proteomics Research Article RNA Root canal therapy Sequence Analysis, DNA Software Species Specificity Streptococcus Streptococcus mitis Studies Taxonomy Teeth |
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| Title | Microbial transformation from normal oral microbiota to acute endodontic infections |
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