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      The effect of different sweeteners on the oral microbiome: a randomized clinical exploratory pilot study

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          ABSTRACT

          Introduction

          The aim of the study was to evaluate the modulating effects of five commonly used sweetener (glucose, inulin, isomaltulose, tagatose, trehalose) containing mouth rinses on the oral microbiome.

          Methods

          A single-centre, double-blind, parallel randomized clinical trial was performed with healthy, 18–55-year-old volunteers (N = 65), who rinsed thrice-daily for two weeks with a 10% solution of one of the allocated sweeteners. Microbiota composition of supragingival dental plaque and the tongue dorsum coating was analysed by 16S RNA gene amplicon sequencing of the V4 hypervariable region (Illumina MiSeq). As secondary outcomes, dental plaque red fluorescence and salivary pH were measured.

          Results

          Dental plaque microbiota changed significantly for two groups: inulin (F = 2.0239, p = 0.0006 PERMANOVA, Aitchison distance) and isomaltulose (F = 0.67, p = 0.0305). For the tongue microbiota, significant changes were observed for isomaltulose (F = 0.8382, p = 0.0452) and trehalose (F = 1.0119, p = 0.0098). In plaque, 13 species changed significantly for the inulin group, while for tongue coating, three species changed for the trehalose group (ALDEx2, p < 0.1). No significant changes were observed for the secondary outcomes.

          Conclusion

          The effects on the oral microbiota were sweetener dependant with the most pronounced effect on plaque microbiota. Inulin exhibited the strongest microbial modulating potential of the sweeteners tested. Further full-scale clinical studies are required.

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          Most cited references74

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          phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data

          Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.
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            Search and clustering orders of magnitude faster than BLAST.

            Biological sequence data is accumulating rapidly, motivating the development of improved high-throughput methods for sequence classification. UBLAST and USEARCH are new algorithms enabling sensitive local and global search of large sequence databases at exceptionally high speeds. They are often orders of magnitude faster than BLAST in practical applications, though sensitivity to distant protein relationships is lower. UCLUST is a new clustering method that exploits USEARCH to assign sequences to clusters. UCLUST offers several advantages over the widely used program CD-HIT, including higher speed, lower memory use, improved sensitivity, clustering at lower identities and classification of much larger datasets. Binaries are available at no charge for non-commercial use at http://www.drive5.com/usearch.
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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

                Journal
                J Oral Microbiol
                J Oral Microbiol
                Journal of Oral Microbiology
                Taylor & Francis
                2000-2297
                24 June 2024
                2024
                24 June 2024
                : 16
                : 1
                : 2369350
                Affiliations
                [a ]Department of Preventive Dentistry, Academic Centre for Dentistry Amsterdam, Vrije Universiteit Amsterdam and University of Amsterdam; , Amsterdam, The Netherlands
                [b ]Department of Cariology, Academic Centre for Dentistry Amsterdam, Vrije Universiteit Amsterdam and University of Amsterdam; , Amsterdam, The Netherlands
                [c ]Research Group Microbiology and Systems Biology, TNO; , Leiden, The Netherlands
                Author notes
                CONTACT Egija Zaura e.zaura@ 123456acta.nl Department of Preventive Dentistry, Academic Centre for Dentistry Amsterdam, Vrije Universiteit Amsterdam and University of Amsterdam, Gustav Mahlerlaan 3004, Amsterdam 1081 LA, The Netherlands
                Author information
                https://orcid.org/0000-0002-1155-1989
                https://orcid.org/0000-0002-6407-3714
                https://orcid.org/0000-0003-1597-2948
                https://orcid.org/0009-0004-0884-2736
                https://orcid.org/0000-0002-4049-2914
                https://orcid.org/0000-0003-1432-6194
                Article
                2369350
                10.1080/20002297.2024.2369350
                11198155
                38919384
                54509125-3cd9-4a28-bf4e-a645624397b2
                © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

                History
                Page count
                Figures: 5, Tables: 3, References: 75, Pages: 1
                Categories
                Research Article
                Microbiome Modulators and Oral Health

                Microbiology & Virology
                oral microbiome,microbiome modulation,oral health,dental plaque,tongue microbiome,clinical trial,sequence analysis,sweetening agents

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