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      A depauperate immune repertoire precedes evolution of sociality in bees

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          Abstract

          Background

          Sociality has many rewards, but can also be dangerous, as high population density and low genetic diversity, common in social insects, is ideal for parasite transmission. Despite this risk, honeybees and other sequenced social insects have far fewer canonical immune genes relative to solitary insects. Social protection from infection, including behavioral responses, may explain this depauperate immune repertoire. Here, based on full genome sequences, we describe the immune repertoire of two ecologically and commercially important bumblebee species that diverged approximately 18 million years ago, the North American Bombus impatiens and European Bombus terrestris.

          Results

          We find that the immune systems of these bumblebees, two species of honeybee, and a solitary leafcutting bee, are strikingly similar. Transcriptional assays confirm the expression of many of these genes in an immunological context and more strongly in young queens than males, affirming Bateman’s principle of greater investment in female immunity. We find evidence of positive selection in genes encoding antiviral responses, components of the Toll and JAK/STAT pathways, and serine protease inhibitors in both social and solitary bees. Finally, we detect many genes across pathways that differ in selection between bumblebees and honeybees, or between the social and solitary clades.

          Conclusions

          The similarity in immune complement across a gradient of sociality suggests that a reduced immune repertoire predates the evolution of sociality in bees. The differences in selection on immune genes likely reflect divergent pressures exerted by parasites across social contexts.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13059-015-0628-y) contains supplementary material, which is available to authorized users.

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          Most cited references56

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Molecular signatures of natural selection.

            There is an increasing interest in detecting genes, or genomic regions, that have been targeted by natural selection. The interest stems from a basic desire to learn more about evolutionary processes in humans and other organisms, and from the realization that inferences regarding selection may provide important functional information. This review provides a nonmathematical description of the issues involved in detecting selection from DNA sequences and SNP data and is intended for readers who are not familiar with population genetic theory. Particular attention is placed on issues relating to the analysis of large-scale genomic data sets.
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              Codon-substitution models for heterogeneous selection pressure at amino acid sites.

              Comparison of relative fixation rates of synonymous (silent) and nonsynonymous (amino acid-altering) mutations provides a means for understanding the mechanisms of molecular sequence evolution. The nonsynonymous/synonymous rate ratio (omega = d(N)d(S)) is an important indicator of selective pressure at the protein level, with omega = 1 meaning neutral mutations, omega 1 diversifying positive selection. Amino acid sites in a protein are expected to be under different selective pressures and have different underlying omega ratios. We develop models that account for heterogeneous omega ratios among amino acid sites and apply them to phylogenetic analyses of protein-coding DNA sequences. These models are useful for testing for adaptive molecular evolution and identifying amino acid sites under diversifying selection. Ten data sets of genes from nuclear, mitochondrial, and viral genomes are analyzed to estimate the distributions of omega among sites. In all data sets analyzed, the selective pressure indicated by the omega ratio is found to be highly heterogeneous among sites. Previously unsuspected Darwinian selection is detected in several genes in which the average omega ratio across sites is 1. Genes undergoing positive selection include the beta-globin gene from vertebrates, mitochondrial protein-coding genes from hominoids, the hemagglutinin (HA) gene from human influenza virus A, and HIV-1 env, vif, and pol genes. Tests for the presence of positively selected sites and their subsequent identification appear quite robust to the specific distributional form assumed for omega and can be achieved using any of several models we implement. However, we encountered difficulties in estimating the precise distribution of omega among sites from real data sets.
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                Author and article information

                Contributors
                barribeaus14@ecu.edu
                bmsadd@ilstu.edu
                louis.duplessis@env.ethz.ch
                mark.brown@rhul.ac.uk
                severine.buechel@env.ethz.ch
                kaat.cappelle@ugent.be
                james.carolan@nuim.ie
                olchrist.christiaens@ugent.be
                tcolgan@tcd.ie
                silvio.erler@zoologie.uni-halle.de
                jay.evans@ars.usda.gov
                sophie.helbing@zoologie.uni-halle.de
                elke.karaus@env.ethz.ch
                lattorff@zoologie.uni-halle.de
                monika.marxer@env.ethz.ch
                ivan.meeus@ugent.be
                kathrin.naepflin@env.ethz.ch
                jinzhi.niu@ugent.be
                regula.schmid@env.ethz.ch
                guy.smagghe@ugent.be
                robert.waterhouse@unige.ch
                na.yu@ugent.be
                evgeny.zdobnov@unige.ch
                paul.schmid-hempel@env.ethz.ch
                Journal
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1465-6906
                1465-6914
                24 April 2015
                24 April 2015
                2015
                : 16
                : 1
                : 83
                Affiliations
                [ ]Experimental Ecology, Institute of Integrative Biology, ETH Zürich, CH-8092 Zürich, Switzerland
                [ ]Department of Biology, East Carolina University, Greenville, NC 27858 USA
                [ ]School of Biological Sciences, Illinois State University, Normal, IL 61790 USA
                [ ]Theoretical Biology, Institute of Integrative Biology, ETH Zürich, CH-8092 Zürich, Switzerland
                [ ]Computational Evolution, Department of Biosystems Science and Evolution, ETH Zürich, 4058 Basel, Switzerland
                [ ]Swiss Institute of Bioinformatics, 1211 Lausanne, Switzerland
                [ ]School of Biological Sciences, Royal Holloway University of London, London, TW20 0EX UK
                [ ]Department of Crop Protection, Faculty of Bioscience Engineering, Ghent University, 9000 Ghent, Belgium
                [ ]Maynooth University Department of Biology, Maynooth University, Maynooth, Kildare, Ireland
                [ ]Department of Zoology, School of Natural Sciences, Trinity College Dublin, Dublin, 2 Ireland
                [ ]School of Biological and Chemical Sciences, Queen Mary University of London, E1 41NS London, UK
                [ ]Department of Apiculture and Sericulture, University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, Cluj-Napoca, 400372 Romania
                [ ]Institut für Biologie, Molekulare Ökologie, Martin-Luther-Universität Halle-Wittenberg, Wittenberg, 06120 Germany
                [ ]USDA-ARS Bee Research Laboratory, Beltsville, MD 20705 USA
                [ ]German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, 04103 Leipzig, Germany
                [ ]Institut für Biologie, Tierphysiologie, Martin-Luther-Universität Halle-Wittenberg, Wittenberg, 06099 Germany
                [ ]College of Plant Protection, Southwest University, Chongqing, 400716 PR China
                [ ]Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland
                [ ]Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
                [ ]The Broad Institute of MIT and Harvard, Cambridge, MA 02142 USA
                Article
                628
                10.1186/s13059-015-0628-y
                4408586
                25908406
                b744842e-1d4a-4203-af3d-db2394816de8
                © Barribeau et al.; licensee BioMed Central. 2015

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 11 August 2014
                : 11 March 2015
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                Research
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                © The Author(s) 2015

                Genetics
                Genetics

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