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      Structure and Function of Oral Microbial Community in Periodontitis Based on Integrated Data

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          Abstract

          Objective

          Microorganisms play a key role in the initiation and progression of periodontal disease. Research studies have focused on seeking specific microorganisms for diagnosing and monitoring the outcome of periodontitis treatment. Large samples may help to discover novel potential biomarkers and capture the common characteristics among different periodontitis patients. This study examines how to screen and merge high-quality periodontitis-related sequence datasets from several similar projects to analyze and mine the potential information comprehensively.

          Methods

          In all, 943 subgingival samples from nine publications were included based on predetermined screening criteria. A uniform pipeline (QIIME2) was applied to clean the raw sequence datasets and merge them together. Microbial structure, biomarkers, and correlation network were explored between periodontitis and healthy individuals. The microbiota patterns at different periodontal pocket depths were described. Additionally, potential microbial functions and metabolic pathways were predicted using PICRUSt to assess the differences between health and periodontitis.

          Results

          The subgingival microbial communities and functions in subjects with periodontitis were significantly different from those in healthy subjects. Treponema, TG5, Desulfobulbus, Catonella, Bacteroides, Aggregatibacter, Peptostreptococcus, and Eikenella were periodontitis biomarkers, while Veillonella, Corynebacterium, Neisseria, Rothia, Paludibacter, Capnocytophaga, and Kingella were signature of healthy periodontium. With the variation of pocket depth from shallow to deep pocket, the proportion of Spirochaetes, Bacteroidetes, TM7, and Fusobacteria increased, whereas that of Proteobacteria and Actinobacteria decreased. Synergistic relationships were observed among different pathobionts and negative relationships were noted between periodontal pathobionts and healthy microbiota.

          Conclusion

          This study shows significant differences in the oral microbial community and potential metabolic pathways between the periodontitis and healthy groups. Our integrated analysis provides potential biomarkers and directions for in-depth research. Moreover, a new method for integrating similar sequence data is shown here that can be applied to other microbial-related areas.

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          Most cited references49

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          edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

          Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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            Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

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              Metagenomic biomarker discovery and explanation

              This study describes and validates a new method for metagenomic biomarker discovery by way of class comparison, tests of biological consistency and effect size estimation. This addresses the challenge of finding organisms, genes, or pathways that consistently explain the differences between two or more microbial communities, which is a central problem to the study of metagenomics. We extensively validate our method on several microbiomes and a convenient online interface for the method is provided at http://huttenhower.sph.harvard.edu/lefse/.
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                Author and article information

                Contributors
                Journal
                Front Cell Infect Microbiol
                Front Cell Infect Microbiol
                Front. Cell. Infect. Microbiol.
                Frontiers in Cellular and Infection Microbiology
                Frontiers Media S.A.
                2235-2988
                17 June 2021
                2021
                : 11
                : 663756
                Affiliations
                [1] 1 State Key Laboratory of Oral Diseases, West China College of Stomatology, Sichuan University , Chengdu, China
                [2] 2 National Clinical Research Center for Oral Diseases, West China College of Stomatology, Sichuan University , Chengdu, China
                [3] 3 Department of Periodontics, West China Hospital of Stomatology, Sichuan University , Chengdu, China
                Author notes

                Edited by: Thuy Do, University of Leeds, United Kingdom

                Reviewed by: Marcelo Freire, J. Craig Venter Institute (La Jolla), United States; Daniel Hagenfeld, University Hospital of Münster, Germany

                *Correspondence: Lei Zhao, jollyzldoc@ 123456163.com

                This article was submitted to Microbiome in Health and Disease, a section of the journal Frontiers in Cellular and Infection Microbiology

                Article
                10.3389/fcimb.2021.663756
                8248787
                34222038
                1301b521-0311-4f8a-a9ac-bb3119649622
                Copyright © 2021 Cai, Lin, Hu and Zhao

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 03 February 2021
                : 31 May 2021
                Page count
                Figures: 4, Tables: 4, Equations: 0, References: 49, Pages: 13, Words: 6304
                Categories
                Cellular and Infection Microbiology
                Original Research

                Infectious disease & Microbiology
                16s,periodontitis,bacteria,microbiome,metabolite,biomarker,high-throughput nucleotide sequencing

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