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      Diurnal oscillations in gut bacterial load and composition eclipse seasonal and lifetime dynamics in wild meerkats

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          Abstract

          Circadian rhythms in gut microbiota composition are crucial for metabolic function, yet the extent to which they govern microbial dynamics compared to seasonal and lifetime processes remains unknown. Here, we investigate gut bacterial dynamics in wild meerkats ( Suricata suricatta) over a 20-year period to compare diurnal, seasonal, and lifetime processes in concert, applying ratios of absolute abundance. We found that diurnal oscillations in bacterial load and composition eclipsed seasonal and lifetime dynamics. Diurnal oscillations were characterised by a peak in Clostridium abundance at dawn, were associated with temperature-constrained foraging schedules, and did not decay with age. Some genera exhibited seasonal fluctuations, whilst others developed with age, although we found little support for microbial senescence in very old meerkats. Strong microbial circadian rhythms in this species may reflect the extreme daily temperature fluctuations typical of arid-zone climates. Our findings demonstrate that accounting for circadian rhythms is essential for future gut microbiome research.

          Abstract

          Circadian rhythms in gut microbiota composition are crucial for metabolic function, yet the extent to which they govern microbial dynamics in comparison to seasonal and lifetime processes remains unknown. This study of gut bacterial dynamics in wild meerkats over a 20-year period finds that diurnal oscillations in bacterial load and composition eclipse seasonal and lifetime dynamics.

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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
                alice.risely@uni-ulm.de
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                14 October 2021
                14 October 2021
                2021
                : 12
                : 6017
                Affiliations
                [1 ]Institute for Evolutionary Ecology and Conservation Genomics, Ulm, Germany
                [2 ]GRID grid.5335.0, ISNI 0000000121885934, Large Animal Research Group, Department of Zoology, , University of Cambridge, ; Cambridge, UK
                [3 ]GRID grid.49697.35, ISNI 0000 0001 2107 2298, University of Pretoria, Mammal Research Institute, ; Pretoria, South Africa
                [4 ]Kalahari Research Trust, Kuruman River Reserve, Northern Cape, South Africa
                [5 ]GRID grid.7400.3, ISNI 0000 0004 1937 0650, Department of Evolutionary Biology and Environmental Studies, , University of Zurich, ; Zurich, Switzerland
                Author information
                http://orcid.org/0000-0002-0731-2934
                http://orcid.org/0000-0001-5583-2777
                http://orcid.org/0000-0001-8110-8969
                http://orcid.org/0000-0001-8787-5667
                http://orcid.org/0000-0002-5148-8136
                Article
                26298
                10.1038/s41467-021-26298-5
                8516918
                34650048
                fe81baf5-30e6-42ac-9507-11378b8a6739
                © 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
                : 8 March 2021
                : 29 September 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100010663, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council);
                Award ID: 294494
                Award ID: 742808
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100000854, Human Frontier Science Program (HFSP);
                Award ID: RGP0051/2017
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft (German Research Foundation);
                Award ID: DFG SO 428/15-1
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                microbial ecology,microbiome
                Uncategorized
                microbial ecology, microbiome

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