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      A reappraisal of microbiome dysbiosis during experimental periodontitis

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          Abstract

          Periodontitis is a chronic inflammatory disease associated with the presence of dysbiotic microbial communities. Several studies interrogating periodontitis pathogenesis have utilized the murine ligature‐induced periodontitis (LIP) model and have further examined the ligature‐associated microbiome relying on 16S rRNA‐based sequencing techniques. However, it is often very challenging to compare microbial profiles across studies due to important differences in bioinformatic processing and databases used for taxonomic assignment. Thus, our study aim was to reanalyze microbiome sequencing datasets from studies utilizing the LIP model through a standardized bioinformatic analysis pipeline, generating a comprehensive overview of microbial dysbiosis during experimental periodontitis.We conducted a reanalysis of 16S rDNA gene sequencing datasets from nine published studies utilizing the LIP model. Reads were grouped according to the hypervariable region of the 16S rDNA gene amplified (V1‐V3 and V4), preprocessed, binned into operational taxonomic units and classified utilizing relevant databases. Alpha‐ and beta‐diversity analyses were conducted, along with relative abundance profiling of microbial communities.

          Our findings revealed similar microbial richness and diversity across studies and determined shifts in microbial community structure determined by periodontitis induction and study of origin. Clear variations in the relative abundance of bacterial taxa were observed starting on day 5 after ligation and onward, consistent with a distinct microbial composition during health and experimental periodontitis. We also uncovered differentially represented bacterial taxa across studies, dominating periodontal health and LIP‐associated communities.

          Collectively, this reanalysis provides a unified overview of microbial dysbiosis during the LIP model, providing new insights that aim to inform further studies dedicated to unraveling oral host–microbial interactions.

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          The SILVA ribosomal RNA gene database project: improved data processing and web-based tools

          SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.
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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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              Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy.

              The Ribosomal Database Project (RDP) Classifier, a naïve Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98%) were of high estimated confidence (> or = 95%) and high accuracy (98%). In addition to being tested with the corpus of 5,014 type strain sequences from Bergey's outline, the RDP Classifier was tested with a corpus of 23,095 rRNA sequences as assigned by the NCBI into their alternative higher-order taxonomy. The results from leave-one-out testing on both corpora show that the overall accuracies at all levels of confidence for near-full-length and 400-base segments were 89% or above down to the genus level, and the majority of the classification errors appear to be due to anomalies in the current taxonomies. For shorter rRNA segments, such as those that might be generated by pyrosequencing, the error rate varied greatly over the length of the 16S rRNA gene, with segments around the V2 and V4 variable regions giving the lowest error rates. The RDP Classifier is suitable both for the analysis of single rRNA sequences and for the analysis of libraries of thousands of sequences. Another related tool, RDP Library Compare, was developed to facilitate microbial-community comparison based on 16S rRNA gene sequence libraries. It combines the RDP Classifier with a statistical test to flag taxa differentially represented between samples. The RDP Classifier and RDP Library Compare are available online at http://rdp.cme.msu.edu/.
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                Author and article information

                Contributors
                Journal
                Molecular Oral Microbiology
                Molecular Oral Microbiology
                Wiley
                2041-1006
                2041-1014
                October 2022
                August 2022
                October 2022
                : 37
                : 5
                : 180-195
                Affiliations
                [1 ] Laboratory of Oral Microbiology Faculty of Dentistry University of Chile Santiago Chile
                [2 ] Laboratory for Craniofacial Translational Research Faculty of Dentistry University of Chile Santiago Chile
                [3 ] Department of Conservative Dentistry Faculty of Dentistry University of Chile Santiago Chile
                [4 ] Department of Pathology and Oral Medicine Faculty of Dentistry University of Chile Santiago Chile
                Article
                10.1111/omi.12382
                6bf83d38-79f4-4acc-9108-f45a57435736
                © 2022

                http://onlinelibrary.wiley.com/termsAndConditions#vor

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