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      Diet and gut microbiome enterotype are associated at the population level in African buffalo

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          Abstract

          Studies in humans and laboratory animals link stable gut microbiome “enterotypes” with long-term diet and host health. Understanding how this paradigm manifests in wild herbivores could provide a mechanistic explanation of the relationships between microbiome dynamics, changes in dietary resources, and outcomes for host health. We identify two putative enterotypes in the African buffalo gut microbiome. The enterotype prevalent under resource-abundant dietary regimes, regardless of environmental conditions, has high richness, low between- and within-host beta diversity, and enrichment of genus Ruminococcaceae-UCG-005. The second enterotype, prevalent under restricted dietary conditions, has reduced richness, elevated beta diversity, and enrichment of genus Solibacillus. Population-level gamma diversity is maintained during resource restriction by increased beta diversity between individuals, suggesting a mechanism for population-level microbiome resilience. We identify three pathogens associated with microbiome variation depending on host diet, indicating that nutritional background may impact microbiome-pathogen dynamics. Overall, this study reveals diet-driven enterotype plasticity, illustrates ecological processes that maintain microbiome diversity, and identifies potential associations between diet, enterotype, and disease.

          Abstract

          There are stable relationships between diet and microbiome in humans and lab animals. A study on African buffalo finds that diet influences microbiome variation and enterotype formation. Three pathogens may associate with microbiome depending on host diet, suggesting nutrition impacts relationships between gut microbiome and host health.

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          Most cited references63

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          Fitting Linear Mixed-Effects Models Usinglme4

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            DADA2: High resolution sample inference from Illumina amplicon data

            We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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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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                Author and article information

                Contributors
                claire.couch@oregonstate.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                15 April 2021
                15 April 2021
                2021
                : 12
                : 2267
                Affiliations
                [1 ]GRID grid.4391.f, ISNI 0000 0001 2112 1969, Department of Integrative Biology, , Oregon State University, ; Corvallis, OR USA
                [2 ]GRID grid.4391.f, ISNI 0000 0001 2112 1969, Department of Microbiology, , Oregon State University, ; Corvallis, OR USA
                [3 ]GRID grid.4391.f, ISNI 0000 0001 2112 1969, Department of Fisheries & Wildlife, , Oregon State University, ; Corvallis, OR USA
                [4 ]GRID grid.4391.f, ISNI 0000 0001 2112 1969, Department of Statistics, , Oregon State University, ; Corvallis, OR USA
                [5 ]GRID grid.4391.f, ISNI 0000 0001 2112 1969, Carlson College of Veterinary Medicine, , Oregon State University, ; Corvallis, OR USA
                Author information
                http://orcid.org/0000-0003-2815-4530
                http://orcid.org/0000-0002-3182-1894
                http://orcid.org/0000-0002-9711-4340
                http://orcid.org/0000-0003-3074-9912
                Article
                22510
                10.1038/s41467-021-22510-8
                8050287
                33859184
                71dfd140-2fef-47be-91d8-d57e6bf0a03d
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 29 May 2020
                : 2 March 2021
                Funding
                Funded by: NSF grant #1840998 American Genetic Association American Association of Zoological Veterinarians grant #57 USDA-NIFA AFRI grant #2013-67015-21291 ARCS Foundation Scholars Program (Oregon Chapter) Robert and Clarice MacVicar Fund NSF grant #1557192
                Categories
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                Custom metadata
                © The Author(s) 2021

                Uncategorized
                ecology,ecological epidemiology,microbial ecology,microbiology,microbial communities
                Uncategorized
                ecology, ecological epidemiology, microbial ecology, microbiology, microbial communities

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