High-resolution taxonomic profiling of the subgingival microbiome for biomarker discovery and periodontitis diagnosis.

2.50
Hdl Handle:
http://hdl.handle.net/10033/578537
Title:
High-resolution taxonomic profiling of the subgingival microbiome for biomarker discovery and periodontitis diagnosis.
Authors:
Szafranski, Szymon P; Wos-Oxley, Melissa L; Vilchez-Vargas, Ramiro ( 0000-0002-7220-258X ) ; Jáuregui, Ruy; Plumeier, Iris; Klawonn, Frank ( 0000-0001-9613-182X ) ; Tomasch, Jürgen; Meisinger, Christa; Kühnisch, Jan; Sztajer, Helena; Pieper, Dietmar H; Wagner-Döbler, Irene
Abstract:
The oral microbiome plays a key role for caries, periodontitis, and systemic diseases. A method for rapid, high-resolution, robust taxonomic profiling of subgingival bacterial communities for early detection of periodontitis biomarkers would therefore be a useful tool for individualized medicine. Here, we used Illumina sequencing of the V1-V2 and V5-V6 hypervariable regions of the 16S rRNA gene. A sample stratification pipeline was developed in a pilot study of 19 individuals, 9 of whom had been diagnosed with chronic periodontitis. Five hundred twenty-three operational taxonomic units (OTUs) were obtained from the V1-V2 region and 432 from the V5-V6 region. Key periodontal pathogens like Porphyromonas gingivalis, Treponema denticola, and Tannerella forsythia could be identified at the species level with both primer sets. Principal coordinate analysis identified two outliers that were consistently independent of the hypervariable region and method of DNA extraction used. The linear discriminant analysis (LDA) effect size algorithm (LEfSe) identified 80 OTU-level biomarkers of periodontitis and 17 of health. Health- and periodontitis-related clusters of OTUs were identified using a connectivity analysis, and the results confirmed previous studies with several thousands of samples. A machine learning algorithm was developed which was trained on all but one sample and then predicted the diagnosis of the left-out sample (jackknife method). Using a combination of the 10 best biomarkers, 15 of 17 samples were correctly diagnosed. Training the algorithm on time-resolved community profiles might provide a highly sensitive tool to detect the onset of periodontitis.
Affiliation:
Helmholtz Centre for infection research, Inhoffenstr. 7, D-38124 Braunschweig, Germany.
Citation:
High-resolution taxonomic profiling of the subgingival microbiome for biomarker discovery and periodontitis diagnosis. 2015, 81 (3):1047-58 Appl. Environ. Microbiol.
Journal:
Applied and environmental microbiology
Issue Date:
Feb-2015
URI:
http://hdl.handle.net/10033/578537
DOI:
10.1128/AEM.03534-14
PubMed ID:
25452281
Type:
Article
Language:
en
ISSN:
1098-5336
Appears in Collections:
collections of the research group microbial communication (KOM)

Full metadata record

DC FieldValue Language
dc.contributor.authorSzafranski, Szymon Pen
dc.contributor.authorWos-Oxley, Melissa Len
dc.contributor.authorVilchez-Vargas, Ramiroen
dc.contributor.authorJáuregui, Ruyen
dc.contributor.authorPlumeier, Irisen
dc.contributor.authorKlawonn, Franken
dc.contributor.authorTomasch, Jürgenen
dc.contributor.authorMeisinger, Christaen
dc.contributor.authorKühnisch, Janen
dc.contributor.authorSztajer, Helenaen
dc.contributor.authorPieper, Dietmar Hen
dc.contributor.authorWagner-Döbler, Ireneen
dc.date.accessioned2015-09-21T12:10:32Zen
dc.date.available2015-09-21T12:10:32Zen
dc.date.issued2015-02en
dc.identifier.citationHigh-resolution taxonomic profiling of the subgingival microbiome for biomarker discovery and periodontitis diagnosis. 2015, 81 (3):1047-58 Appl. Environ. Microbiol.en
dc.identifier.issn1098-5336en
dc.identifier.pmid25452281en
dc.identifier.doi10.1128/AEM.03534-14en
dc.identifier.urihttp://hdl.handle.net/10033/578537en
dc.description.abstractThe oral microbiome plays a key role for caries, periodontitis, and systemic diseases. A method for rapid, high-resolution, robust taxonomic profiling of subgingival bacterial communities for early detection of periodontitis biomarkers would therefore be a useful tool for individualized medicine. Here, we used Illumina sequencing of the V1-V2 and V5-V6 hypervariable regions of the 16S rRNA gene. A sample stratification pipeline was developed in a pilot study of 19 individuals, 9 of whom had been diagnosed with chronic periodontitis. Five hundred twenty-three operational taxonomic units (OTUs) were obtained from the V1-V2 region and 432 from the V5-V6 region. Key periodontal pathogens like Porphyromonas gingivalis, Treponema denticola, and Tannerella forsythia could be identified at the species level with both primer sets. Principal coordinate analysis identified two outliers that were consistently independent of the hypervariable region and method of DNA extraction used. The linear discriminant analysis (LDA) effect size algorithm (LEfSe) identified 80 OTU-level biomarkers of periodontitis and 17 of health. Health- and periodontitis-related clusters of OTUs were identified using a connectivity analysis, and the results confirmed previous studies with several thousands of samples. A machine learning algorithm was developed which was trained on all but one sample and then predicted the diagnosis of the left-out sample (jackknife method). Using a combination of the 10 best biomarkers, 15 of 17 samples were correctly diagnosed. Training the algorithm on time-resolved community profiles might provide a highly sensitive tool to detect the onset of periodontitis.en
dc.language.isoenen
dc.subject.meshBacteriaen
dc.subject.meshBiological Markersen
dc.subject.meshBiotaen
dc.subject.meshChronic Diseaseen
dc.subject.meshCluster Analysisen
dc.subject.meshDNA, Bacterialen
dc.subject.meshDNA, Ribosomalen
dc.subject.meshGingivaen
dc.subject.meshHumansen
dc.subject.meshPeriodontitisen
dc.subject.meshRNA, Ribosomal, 16Sen
dc.subject.meshSequence Analysis, DNAen
dc.titleHigh-resolution taxonomic profiling of the subgingival microbiome for biomarker discovery and periodontitis diagnosis.en
dc.typeArticleen
dc.contributor.departmentHelmholtz Centre for infection research, Inhoffenstr. 7, D-38124 Braunschweig, Germany.en
dc.identifier.journalApplied and environmental microbiologyen

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