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      Discrimination of Bacterial Community Structures among Healthy, Gingivitis, and Periodontitis Statuses through Integrated Metatranscriptomic and Network Analyses

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          ABSTRACT

          Periodontal disease is an inflammatory condition caused by polymicrobial infection. The inflammation is initiated at the gingiva (gingivitis) and then extends to the alveolar bone, leading to tooth loss (periodontitis). Previous studies have shown differences in bacterial composition between periodontal healthy and diseased sites. However, bacterial metabolic activities during the health-to-periodontitis microbiome shift are still inadequately understood. This study was performed to investigate the bacterial characteristics of healthy, gingivitis, and periodontitis statuses through metatranscriptomic analysis. Subgingival plaque samples of healthy, gingivitis, and periodontitis sites in the same oral cavity were collected from 21 patients. Bacterial compositions were then determined based on 16S rRNA reads; taxonomic and functional profiles derived from genes based on mRNA reads were estimated. The results showed clear differences in bacterial compositions and functional profiles between healthy and periodontitis sites. Co-occurrence networks were constructed for each group by connecting two bacterial species if their mRNA abundances were positively correlated. The clustering coefficient values were 0.536 for healthy, 0.600 for gingivitis, and 0.371 for periodontitis sites; thus, network complexity increased during gingivitis development, whereas it decreased during progression to periodontitis. Taxa, including Eubacterium nodatum, Eubacterium saphenum, Filifactor alocis, and Fretibacterium fastidiosum, showed greater transcriptional activities than those of red complex bacteria, in conjunction with disease progression. These taxa were associated with periodontal disease progression, and the health-to-periodontitis microbiome shift was accompanied by alterations in bacterial network structure and complexity.

          IMPORTANCE The characteristics of the periodontal microbiome influence clinical periodontal status. Gingivitis involves reversible gingival inflammation without alveolar bone resorption. In contrast, periodontitis is an irreversible disease characterized by inflammatory destruction in both soft and hard tissues. An imbalance of the microbiome is present in both gingivitis and periodontitis. However, differences in microbiomes and their functional activities in the healthy, gingivitis, and periodontitis statuses are still inadequately understood. Furthermore, some inflamed gingival statuses do not consistently cause attachment loss. In this study, metatranscriptomic analyses were used to investigate the specific bacterial composition and gene expression patterns of the microbiomes of the healthy, gingivitis, and periodontitis statuses. In addition, co-occurrence network analysis revealed that the gingivitis site included features of networks observed in both the healthy and periodontitis sites. These results provide transcriptomic evidence to support gingivitis as an intermediate state between the healthy and periodontitis statuses.

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            KEGG: kyoto encyclopedia of genes and genomes.

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            KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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              Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities.

              mothur aims to be a comprehensive software package that allows users to use a single piece of software to analyze community sequence data. It builds upon previous tools to provide a flexible and powerful software package for analyzing sequencing data. As a case study, we used mothur to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the alpha and beta diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments. This analysis of more than 222,000 sequences was completed in less than 2 h with a laptop computer.
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                Author and article information

                Contributors
                Role: Editor
                Role: ad hoc peer reviewer
                Journal
                mSystems
                mSystems
                msystems
                mSystems
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2379-5077
                26 October 2021
                Nov-Dec 2021
                26 October 2021
                : 6
                : 6
                : e00886-21
                Affiliations
                [a ] Department of Periodontology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU)grid.265073.5, , Tokyo, Japan
                [b ] Department of Chemistry, Nihon University School of Dentistry, Tokyo, Japan
                [c ] Department of Regenerative and Reconstructive Dental Medicine, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU)grid.265073.5, , Tokyo, Japan
                University of Georgia
                University of Texas Health Science Center at Houston
                Author notes

                Citation Nemoto T, Shiba T, Komatsu K, Watanabe T, Shimogishi M, Shibasaki M, Koyanagi T, Nagai T, Katagiri S, Takeuchi Y, Iwata T. 2021. Discrimination of bacterial community structures among healthy, gingivitis, and periodontitis statuses through integrated metatranscriptomic and network analyses. mSystems 6:e00886-21. https://doi.org/10.1128/mSystems.00886-21.

                Author information
                https://orcid.org/0000-0002-7659-1965
                https://orcid.org/0000-0002-5765-2742
                Article
                mSystems00886-21 msystems.00886-21
                10.1128/mSystems.00886-21
                8547322
                34698525
                11c62efe-9ad5-4e99-9c92-3096f0744296
                Copyright © 2021 Nemoto et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 9 July 2021
                : 20 September 2021
                Page count
                supplementary-material: 10, Figures: 4, Tables: 2, Equations: 0, References: 58, Pages: 16, Words: 9049
                Funding
                Funded by: MEXT | Japan Society for the Promotion of Science (JSPS), FundRef https://doi.org/10.13039/501100001691;
                Award ID: 17H06662
                Award ID: 19K19016
                Award ID: 21K16987
                Award Recipient :
                Funded by: MEXT | Japan Society for the Promotion of Science (JSPS), FundRef https://doi.org/10.13039/501100001691;
                Award ID: 19K10164
                Award Recipient :
                Funded by: MEXT | Japan Society for the Promotion of Science (JSPS), FundRef https://doi.org/10.13039/501100001691;
                Award ID: 17K11981
                Award ID: 20K09934
                Award Recipient :
                Categories
                Research Article
                open-peer-review, Open Peer Review
                microbial-pathogenesis, Microbial Pathogenesis
                Custom metadata
                November/December 2021

                gingivitis,periodontitis,metatranscriptome,co-occurrence network,oral microbiome,dysbiosis

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