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      Analysis of the Effects of Dietary Pattern on the Oral Microbiome of Elite Endurance Athletes

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          Abstract

          Although the oral microbiota is known to play a crucial role in human health, there are few studies of diet x oral microbiota interactions, and none in elite athletes who may manipulate their intakes of macronutrients to achieve different metabolic adaptations in pursuit of optimal endurance performance. The aim of this study was to investigate the shifts in the oral microbiome of elite male endurance race walkers from Europe, Asia, the Americas and Australia, in response to one of three dietary patterns often used by athletes during a period of intensified training: a High Carbohydrate (HCHO; n = 9; with 60% energy intake from carbohydrates; ~8.5 g kg −1 day −1 carbohydrate, ~2.1 g kg −1 day −1 protein, 1.2 g kg −1 day −1 fat) diet, a Periodised Carbohydrate (PCHO; n = 10; same macronutrient composition as HCHO, but the intake of carbohydrates is different across the day and throughout the week to support training sessions with high or low carbohydrate availability) diet or a ketogenic Low Carbohydrate High Fat (LCHF; n = 10; 0.5 g kg −1 day −1 carbohydrate; 78% energy as fat; 2.1 g kg −1 day −1 protein) diet. Saliva samples were collected both before (Baseline; BL) and after the three-week period (Post treatment; PT) and the oral microbiota profiles for each athlete were produced by 16S rRNA gene amplicon sequencing. Principal coordinates analysis of the oral microbiota profiles based on the weighted UniFrac distance measure did not reveal any specific clustering with respect to diet or athlete ethnic origin, either at baseline (BL) or following the diet-training period. However, discriminant analyses of the oral microbiota profiles by Linear Discriminant Analysis (LDA) Effect Size (LEfSe) and sparse Partial Least Squares Discriminant Analysis (sPLS-DA) did reveal changes in the relative abundance of specific bacterial taxa, and, particularly, when comparing the microbiota profiles following consumption of the carbohydrate-based diets with the LCHF diet. These analyses showed that following consumption of the LCHF diet the relative abundances of Haemophilus, Neisseria and Prevotella spp. were decreased, and the relative abundance of Streptococcus spp. was increased. Such findings suggest that diet, and, in particular, the LCHF diet can induce changes in the oral microbiota of elite endurance walkers.

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          Calypso: a user-friendly web-server for mining and visualizing microbiome–environment interactions

          Abstract Calypso is an easy-to-use online software suite that allows non-expert users to mine, interpret and compare taxonomic information from metagenomic or 16S rDNA datasets. Calypso has a focus on multivariate statistical approaches that can identify complex environment-microbiome associations. The software enables quantitative visualizations, statistical testing, multivariate analysis, supervised learning, factor analysis, multivariable regression, network analysis and diversity estimates. Comprehensive help pages, tutorials and videos are provided via a wiki page. Availability and Implementation: The web-interface is accessible via http://cgenome.net/calypso/. The software is programmed in Java, PERL and R and the source code is available from Zenodo (https://zenodo.org/record/50931). The software is freely available for non-commercial users. Contact: l.krause@uq.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.
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            Bacterial diversity in the oral cavity of 10 healthy individuals.

            The composition of the oral microbiota from 10 individuals with healthy oral tissues was determined using culture-independent techniques. From each individual, 26 specimens, each from different oral sites at a single point in time, were collected and pooled. An 11th pool was constructed using portions of the subgingival specimens from all 10 individuals. The 16S ribosomal RNA gene was amplified using broad-range bacterial primers, and clone libraries from the individual and subgingival pools were constructed. From a total of 11,368 high-quality, nonchimeric, near full-length sequences, 247 species-level phylotypes (using a 99% sequence identity threshold) and 9 bacterial phyla were identified. At least 15 bacterial genera were conserved among all 10 individuals, with significant interindividual differences at the species and strain level. Comparisons of these oral bacterial sequences with near full-length sequences found previously in the large intestines and feces of other healthy individuals suggest that the mouth and intestinal tract harbor distinct sets of bacteria. Co-occurrence analysis showed significant segregation of taxa when community membership was examined at the level of genus, but not at the level of species, suggesting that ecologically significant, competitive interactions are more apparent at a broader taxonomic level than species. This study is one of the more comprehensive, high-resolution analyses of bacterial diversity within the healthy human mouth to date, and highlights the value of tools from macroecology for enhancing our understanding of bacterial ecology in human health.
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              MixMC: A Multivariate Statistical Framework to Gain Insight into Microbial Communities

              Culture independent techniques, such as shotgun metagenomics and 16S rRNA amplicon sequencing have dramatically changed the way we can examine microbial communities. Recently, changes in microbial community structure and dynamics have been associated with a growing list of human diseases. The identification and comparison of bacteria driving those changes requires the development of sound statistical tools, especially if microbial biomarkers are to be used in a clinical setting. We present mixMC, a novel multivariate data analysis framework for metagenomic biomarker discovery. mixMC accounts for the compositional nature of 16S data and enables detection of subtle differences when high inter-subject variability is present due to microbial sampling performed repeatedly on the same subjects, but in multiple habitats. Through data dimension reduction the multivariate methods provide insightful graphical visualisations to characterise each type of environment in a detailed manner. We applied mixMC to 16S microbiome studies focusing on multiple body sites in healthy individuals, compared our results with existing statistical tools and illustrated added value of using multivariate methodologies to fully characterise and compare microbial communities.
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                Author and article information

                Journal
                Nutrients
                Nutrients
                nutrients
                Nutrients
                MDPI
                2072-6643
                13 March 2019
                March 2019
                : 11
                : 3
                : 614
                Affiliations
                [1 ]Faculty of Medicine, Translational Research Institute, University of Queensland Diamantina Institute, Brisbane, QLD 4102, Australia; nida.murtaza@ 123456uqconnect.edu.au (N.M.); l.krause@ 123456uq.edu.au (L.K.)
                [2 ]Centre for Exercise & Nutrition, Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC 3000, Australia; meg.ross@ 123456ausport.gov.au
                [3 ]Australian Institute of Sport, Canberra, ACT 2617, Australia; Nicole.Vlahovich@ 123456ausport.gov.au (N.V.); bronwen.charlesson@ 123456outlook.com (B.C.)
                [4 ]Faculty of Health Sciences & Medicine, Bond University, Robina, QLD 4226, Australia; haoneill@ 123456bond.edu.au (H.M.O.); Katrina.Campbell@ 123456health.qld.gov.au (K.L.C.)
                Author notes
                [* ]Correspondence: Louise.Burke@ 123456ausport.gov.au (L.M.B.); m.morrison1@ 123456uq.edu.au (M.M.); Tel.: +61-2-621-41351 (L.M.B.); +61-7-344-36957 (M.M.)
                Author information
                https://orcid.org/0000-0001-8866-5637
                https://orcid.org/0000-0001-6812-6580
                https://orcid.org/0000-0002-4479-1284
                Article
                nutrients-11-00614
                10.3390/nu11030614
                6471070
                30871219
                7402be77-be45-4c29-afb3-d7bc4cec2557
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 17 January 2019
                : 04 March 2019
                Categories
                Article

                Nutrition & Dietetics
                oral microbiome,elite athletes,diet
                Nutrition & Dietetics
                oral microbiome, elite athletes, diet

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