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      Phylosymbiosis: Relationships and Functional Effects of Microbial Communities across Host Evolutionary History

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          Abstract

          Phylosymbiosis was recently proposed to describe the eco-evolutionary pattern, whereby the ecological relatedness of host-associated microbial communities parallels the phylogeny of related host species. Here, we test the prevalence of phylosymbiosis and its functional significance under highly controlled conditions by characterizing the microbiota of 24 animal species from four different groups ( Peromyscus deer mice, Drosophila flies, mosquitoes, and Nasonia wasps), and we reevaluate the phylosymbiotic relationships of seven species of wild hominids. We demonstrate three key findings. First, intraspecific microbiota variation is consistently less than interspecific microbiota variation, and microbiota-based models predict host species origin with high accuracy across the dataset. Interestingly, the age of host clade divergence positively associates with the degree of microbial community distinguishability between species within the host clades, spanning recent host speciation events (~1 million y ago) to more distantly related host genera (~108 million y ago). Second, topological congruence analyses of each group's complete phylogeny and microbiota dendrogram reveal significant degrees of phylosymbiosis, irrespective of host clade age or taxonomy. Third, consistent with selection on host–microbiota interactions driving phylosymbiosis, there are survival and performance reductions when interspecific microbiota transplants are conducted between closely related and divergent host species pairs. Overall, these findings indicate that the composition and functional effects of an animal's microbial community can be closely allied with host evolution, even across wide-ranging timescales and diverse animal systems reared under controlled conditions.

          Author Summary

          Studies on the assembly and function of host-microbiota symbioses are inherently complicated by the diverse effects of diet, age, sex, host genetics, and endosymbionts. Central to unraveling one effect from the other is an experimental framework that reduces confounders. Using common rearing conditions across four animal groups (deer mice, flies, mosquitoes, and wasps) that span recent host speciation events to more distantly related host genera, this study tests whether microbial community assembly is generally random with respect to host relatedness or "phylosymbiotic," in which the phylogeny of the host group is congruent with ecological relationships of their microbial communities. Across all four animal groups and one external dataset of great apes, we apply several statistics for analyzing congruencies and demonstrate phylosymbiosis to varying degrees in each group. Moreover, consistent with selection on host–microbiota interactions driving phylosymbiosis, transplanting interspecific microbial communities in mice significantly decreased their ability to digest food. Similarly, wasps that received transplants of microbial communities from different wasp species had lower survival than those given their own microbiota. Overall, this experimental and statistical framework shows how microbial community assembly and functionality across related species can be linked to animal evolution, health, and survival.

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          Most cited references 74

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          Search and clustering orders of magnitude faster than BLAST.

           Robert Edgar (2010)
          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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            RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

            Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
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              New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0.

              PhyML is a phylogeny software based on the maximum-likelihood principle. Early PhyML versions used a fast algorithm performing nearest neighbor interchanges to improve a reasonable starting tree topology. Since the original publication (Guindon S., Gascuel O. 2003. A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:696-704), PhyML has been widely used (>2500 citations in ISI Web of Science) because of its simplicity and a fair compromise between accuracy and speed. In the meantime, research around PhyML has continued, and this article describes the new algorithms and methods implemented in the program. First, we introduce a new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves. The parsimony criterion is used here to filter out the least promising topology modifications with respect to the likelihood function. The analysis of a large collection of real nucleotide and amino acid data sets of various sizes demonstrates the good performance of this method. Second, we describe a new test to assess the support of the data for internal branches of a phylogeny. This approach extends the recently proposed approximate likelihood-ratio test and relies on a nonparametric, Shimodaira-Hasegawa-like procedure. A detailed analysis of real alignments sheds light on the links between this new approach and the more classical nonparametric bootstrap method. Overall, our tests show that the last version (3.0) of PhyML is fast, accurate, stable, and ready to use. A Web server and binary files are available from http://www.atgc-montpellier.fr/phyml/.
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                Author and article information

                Affiliations
                [1 ]Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America
                [2 ]Vanderbilt Genetics Institute, Vanderbilt University, Nashville, Tennessee, United States of America
                [3 ]The Rowland Institute at Harvard, Harvard University, Cambridge, Massachusetts, United States of America
                [4 ]Department of Pathology, Microbiology, and Immunology, Vanderbilt University, Nashville, Tennessee, United States of America
                Stanford University School of Medicine, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                ‡ These authors share first authorship on this work.

                Contributors
                Role: Conceptualization, Role: Data curation, Role: Formal analysis, Role: Investigation, Role: Methodology, Role: Software, Role: Validation, Role: Visualization, Role: Writing – original draft, Role: Writing – review & editing
                Role: Conceptualization, Role: Formal analysis, Role: Funding acquisition, Role: Investigation, Role: Methodology, Role: Visualization, Role: Writing – original draft, Role: Writing – review & editing
                Role: Conceptualization, Role: Data curation, Role: Formal analysis, Role: Funding acquisition, Role: Investigation, Role: Methodology, Role: Project administration, Role: Resources, Role: Software, Role: Supervision, Role: Validation, Role: Visualization, Role: Writing – original draft, Role: Writing – review & editing
                Role: Conceptualization, Role: Formal analysis, Role: Investigation, Role: Methodology, Role: Validation, Role: Visualization, Role: Writing – review & editing
                Role: Conceptualization, Role: Funding acquisition, Role: Investigation, Role: Methodology, Role: Project administration, Role: Resources, Role: Software, Role: Supervision, Role: Validation, Role: Visualization, Role: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                18 November 2016
                November 2016
                18 November 2016
                : 14
                : 11
                27861590 5115861 10.1371/journal.pbio.2000225 pbio.2000225
                © 2016 Brooks et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Counts
                Figures: 6, Tables: 0, Pages: 29
                Product
                Funding
                Rowland Institute at Harvard University Junior Fellowship to RMB. National Science Foundation—Division of Integrative Organismal Systems https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=503623 (grant number 1456778). Received by SRB. National Science Foundation—Division of Biological Infrastructure http://www.nsf.gov/div/index.jsp?div=DBI (grant number 1400456). Received by KDK. National Institute of Health—Predoctoral Training Grant https://researchtraining.nih.gov (grant number 5T32GM080178). Received by AWB. National Science Foundation—Division of Environmental Biology http://www.nsf.gov/div/index.jsp?div=DEB (grant number 1046149). Received by SRB. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Microbiology
                Medical Microbiology
                Microbiome
                Biology and Life Sciences
                Genetics
                Genomics
                Microbial Genomics
                Microbiome
                Biology and Life Sciences
                Microbiology
                Microbial Genomics
                Microbiome
                Biology and Life Sciences
                Organisms
                Animals
                Vertebrates
                Amniotes
                Mammals
                Rodents
                Peromyscus
                Biology and Life Sciences
                Microbiology
                Microbial Evolution
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                Evolutionary Biology
                Organismal Evolution
                Microbial Evolution
                Research and Analysis Methods
                Model Organisms
                Animal Models
                Drosophila Melanogaster
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                Invertebrates
                Arthropoda
                Insects
                Drosophila
                Drosophila Melanogaster
                Biology and Life Sciences
                Evolutionary Biology
                Evolutionary Systematics
                Phylogenetics
                Animal Phylogenetics
                Biology and Life Sciences
                Taxonomy
                Evolutionary Systematics
                Phylogenetics
                Animal Phylogenetics
                Computer and Information Sciences
                Data Management
                Taxonomy
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                Phylogenetics
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                Biology and Life Sciences
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                Medicine and Health Sciences
                Epidemiology
                Disease Vectors
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                Biology and Life Sciences
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                Biology and Life Sciences
                Molecular Biology
                Molecular Biology Techniques
                Molecular Biology Assays and Analysis Techniques
                Phylogenetic Analysis
                Research and Analysis Methods
                Molecular Biology Techniques
                Molecular Biology Assays and Analysis Techniques
                Phylogenetic Analysis
                Biology and Life Sciences
                Organisms
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                Invertebrates
                Arthropoda
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                Custom metadata
                All sequencing and mapping files are available from the Dryad database repository: doi: 10.5061/dryad.n3v49. A GitHub repository contains custom analysis scripts and all of the necessary data for figure reconstruction (including BIOM Tables and Mapping files) for each clade are also publicly available: https://github.com/awbrooks19/phylosymbiosis.

                Life sciences

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