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      Eco-evolutionary dynamics of host–microbiome interactions in a natural population of closely related mouse subspecies and their hybrids

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          Abstract

          Closely related host species share similar symbionts, but the effects of host genetic admixture and environmental conditions on these communities remain largely unknown. We investigated the influence of host genetic admixture and environmental factors on the intestinal prokaryotic and eukaryotic communities (fungi, parasites) of two house mouse subspecies ( Mus musculus domesticus and M. m. musculus) and their hybrids in two settings: (i) wild-caught mice from the European hybrid zone and (ii) wild-derived inbred mice in a controlled laboratory environment before and during a community perturbation (infection). In wild-caught mice, environmental factors strongly predicted the overall microbiome composition. Subspecies' genetic distance significantly influenced the overall microbiome composition, and each component (bacteria, parasites and fungi). While hybridization had a weak effect, it significantly impacted fungal composition. We observed similar patterns in wild-derived mice, where genetic distances and hybridization influenced microbiome composition, with fungi being more stable to infection-induced perturbations than other microbiome components. Subspecies' genetic distance has a stronger and consistent effect across microbiome components than differences in expected heterozygosity among hybrids, suggesting that host divergence and host filtering play a key role in microbiome divergence, influenced by environmental factors. Our findings offer new insights into the eco-evolutionary processes shaping host–microbiome interactions.

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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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            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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              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
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draft
                Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: SupervisionRole: Writing – review and editing
                Role: Writing – review and editing
                Role: InvestigationRole: Writing – review and editing
                Role: InvestigationRole: Writing – review and editing
                Role: Funding acquisitionRole: Writing – review and editing
                Role: Funding acquisitionRole: Writing – review and editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: SupervisionRole: Writing – review and editing
                Journal
                Proc Biol Sci
                Proc Biol Sci
                RSPB
                royprsb
                Proceedings of the Royal Society B: Biological Sciences
                The Royal Society
                0962-8452
                1471-2954
                December 2024
                December 18, 2024
                December 18, 2024
                : 291
                : 2037
                : 20241970
                Affiliations
                [ 1 ]Division of Computational Systems Biology, Center for Microbiology and Ecological Systems Science, University of Vienna, Djerassipl. 1; , Vienna 1030, Austria
                [ 2 ]Department of Molecular Parasitology, Institute for Biology, Humboldt University Berlin (HU). Philippstr. 13 Haus 14; , Berlin 10115, Germany
                [ 3 ]Department of Interdisciplinary Life Sciences, Research Institute of Wildlife Ecology, University of Veterinary Medicine Vienna, Savoyenstraße 1; , Vienna A-1160, Austria
                [ 4 ]Experimental and Clinical Research Center, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Lindenberger Weg 80; , Berlin 13125, Germany
                [ 5 ]Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC). Robert-Rössle-Str. 10; , Berlin 13125, Germany
                [ 6 ]Research Group Ecology and Evolution of Molecular Parasite–Host Interactions, Leibniz Institute for Zoo and Wildlife Research (IZW). Alfred-Kowalke-Straße 17; , Berlin 10315, Germany
                [ 7 ]Experimental and Clinical Research Center, a cooperation between the Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association and the Charité–Universitätsmedizin Berlin; , Berlin, Germany
                [ 8 ]Department of Ecological Dynamics, Leibniz Institute for Zoo and Wildlife Research (IZW). Alfred-Kowalke-Straße 17; , Berlin 10315, Germany
                [ 9 ]Research Facility Studenec, Institute of Vertebrate Biology, Czech Academy of Sciences; , Brno, Czech Republic
                [ 10 ]Institute of Ecology, Chair of Planning-related Animal Ecology, Technische Universität Berlin (TUB), Rothenburgstr. 12; , Berlin 12165, Germany
                Author notes

                Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare.c.7571803.

                [ † ]

                These authors contributed equally to the study.

                Author information
                https://orcid.org/0000-0003-3758-1091
                https://orcid.org/0000-0002-9269-4446
                Article
                rspb20241970
                10.1098/rspb.2024.1970
                11651899
                39689880
                f7d7ac0f-d6ae-4e64-8aed-25c238583e80
                © 2024 The Author(s).

                Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

                History
                : June 19, 2024
                : November 1, 2024
                : November 8, 2024
                Funding
                Funded by: Deutsche Forschungsgemeinschaft, FundRef http://dx.doi.org/10.13039/501100001659;
                Funded by: Bundesministerium für Bildung und Forschung, FundRef http://dx.doi.org/10.13039/501100002347;
                Categories
                1001
                1001
                1001
                60
                200
                70
                Ecology
                Research Articles

                Life sciences
                host–microbiome interactions,species barriers,hybridization,microbiome,spatial environment

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