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      Distinct signatures of dental plaque metabolic byproducts dictated by periodontal inflammatory status

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          Abstract

          Onset of chronic periodontitis is associated with an aberrant polymicrobial community, termed dysbiosis. Findings regarding its etiology obtained using high-throughput sequencing technique suggested that dysbiosis holds a conserved metabolic signature as an emergent property. The purpose of this study was to identify robust biomarkers for periodontal inflammation severity. Furthermore, we investigated disease-associated metabolic signatures of periodontal microbiota using a salivary metabolomics approach. Whole saliva samples were obtained from adult subjects before and after removal of supragingival plaque (debridement). Periodontal inflamed surface area (PISA) was employed as an indicator of periodontal inflammatory status. Based on multivariate analyses using pre-debridement salivary metabolomics data, we found that metabolites associated with higher PISA included cadaverine and hydrocinnamate, while uric acid and ethanolamine were associated with lower PISA. Next, we focused on dental plaque metabolic byproducts by selecting salivary metabolites significantly decreased following debridement. Metabolite set enrichment analysis revealed that polyamine metabolism, arginine and proline metabolism, butyric acid metabolism, and lysine degradation were distinctive metabolic signatures of dental plaque in the high PISA group, which may be related to the metabolic signatures of disease-associated communities. Collectively, our findings identified potential biomarkers of periodontal inflammatory status and also provide insight into metabolic signatures of dysbiotic communities.

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          Most cited references30

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          PERIODONTAL DISEASE IN PREGNANCY. II. CORRELATION BETWEEN ORAL HYGIENE AND PERIODONTAL CONDTION.

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            The subgingival microbiome in health and periodontitis and its relationship with community biomass and inflammation.

            The goals of this study were to better understand the ecology of oral subgingival communities in health and periodontitis and elucidate the relationship between inflammation and the subgingival microbiome. Accordingly, we used 454-pyrosequencing of 16S rRNA gene libraries and quantitative PCR to characterize the subgingival microbiome of 22 subjects with chronic periodontitis. Each subject was sampled at two sites with similar periodontal destruction but differing in the presence of bleeding, a clinical indicator of increased inflammation. Communities in periodontitis were also compared with those from 10 healthy individuals. In periodontitis, presence of bleeding was not associated with different α-diversity or with a distinct microbiome, however, bleeding sites showed higher total bacterial load. In contrast, communities in health and periodontitis largely differed, with higher diversity and biomass in periodontitis. Shifts in community structure from health to periodontitis resembled ecological succession, with emergence of newly dominant taxa in periodontitis without replacement of primary health-associated species. That is, periodontitis communities had higher proportions of Spirochetes, Synergistetes, Firmicutes and Chloroflexi, among other taxa, while the proportions of Actinobacteria, particularly Actinomyces, were higher in health. Total Actinomyces load, however, remained constant from health to periodontitis. Moreover, an association existed between biomass and community structure in periodontitis, with the proportion of specific taxa correlating with bacterial load. Our study provides a global-scale framework for the ecological events in subgingival communities that underline the development of periodontitis. The association, in periodontitis, between inflammation, community biomass and community structure and their role in disease progression warrant further investigation.
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              MetAlign: interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data preprocessing.

              Hyphenated full-scan MS technology creates large amounts of data. A versatile easy to handle automation tool aiding in the data analysis is very important in handling such a data stream. MetAlign softwareas described in this manuscripthandles a broad range of accurate mass and nominal mass GC/MS and LC/MS data. It is capable of automatic format conversions, accurate mass calculations, baseline corrections, peak-picking, saturation and mass-peak artifact filtering, as well as alignment of up to 1000 data sets. A 100 to 1000-fold data reduction is achieved. MetAlign software output is compatible with most multivariate statistics programs.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                21 February 2017
                2017
                : 7
                Affiliations
                [1 ]Department of Preventive Dentistry, Osaka University Graduate School of Dentistry, 1–8 Yamadaoka , Suita, Osaka 565–0871, Japan
                [2 ]AMED-CREST, Japan Agency for Medical Research and Development, 1-7-1 Otemachi , Chiyoda-ku, Tokyo, 100-0004, Japan
                [3 ]Project ‘Challenge to Intractable Oral Disease’, Osaka University Dental Hospital; 1-8 Yamadaoka , Suita, Osaka 565-0871, Japan
                [4 ]Division of Metabolomics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi , Higashi-ku, Fukuoka 812-8582, Japan
                [5 ]Department of Biotechnology, Osaka University Graduate School of Engineering, 2–1 Yamadaoka , Suita, Osaka 565–0871, Japan
                Author notes
                Article
                srep42818
                10.1038/srep42818
                5318866
                28220901
                f484af4f-3ff6-42d9-b3ad-b0469b5b8047
                Copyright © 2017, The Author(s)

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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