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      Two genomes of highly polyphagous lepidopteran pests ( Spodoptera frugiperda, Noctuidae) with different host-plant ranges

      research-article
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      Scientific Reports
      Nature Publishing Group UK

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          Abstract

          Emergence of polyphagous herbivorous insects entails significant adaptation to recognize, detoxify and digest a variety of host-plants. Despite of its biological and practical importance - since insects eat 20% of crops - no exhaustive analysis of gene repertoires required for adaptations in generalist insect herbivores has previously been performed. The noctuid moth Spodoptera frugiperda ranks as one of the world’s worst agricultural pests. This insect is polyphagous while the majority of other lepidopteran herbivores are specialist. It consists of two morphologically indistinguishable strains (“C” and “R”) that have different host plant ranges. To describe the evolutionary mechanisms that both enable the emergence of polyphagous herbivory and lead to the shift in the host preference, we analyzed whole genome sequences from laboratory and natural populations of both strains. We observed huge expansions of genes associated with chemosensation and detoxification compared with specialist Lepidoptera. These expansions are largely due to tandem duplication, a possible adaptation mechanism enabling polyphagy. Individuals from natural C and R populations show significant genomic differentiation. We found signatures of positive selection in genes involved in chemoreception, detoxification and digestion, and copy number variation in the two latter gene families, suggesting an adaptive role for structural variation.

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          The genome of the model beetle and pest Tribolium castaneum.

          Tribolium castaneum is a member of the most species-rich eukaryotic order, a powerful model organism for the study of generalized insect development, and an important pest of stored agricultural products. We describe its genome sequence here. This omnivorous beetle has evolved the ability to interact with a diverse chemical environment, as shown by large expansions in odorant and gustatory receptors, as well as P450 and other detoxification enzymes. Development in Tribolium is more representative of other insects than is Drosophila, a fact reflected in gene content and function. For example, Tribolium has retained more ancestral genes involved in cell-cell communication than Drosophila, some being expressed in the growth zone crucial for axial elongation in short-germ development. Systemic RNA interference in T. castaneum functions differently from that in Caenorhabditis elegans, but nevertheless offers similar power for the elucidation of gene function and identification of targets for selective insect control.
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            InParanoid 7: new algorithms and tools for eukaryotic orthology analysis

            The InParanoid project gathers proteomes of completely sequenced eukaryotic species plus Escherichia coli and calculates pairwise ortholog relationships among them. The new release 7.0 of the database has grown by an order of magnitude over the previous version and now includes 100 species and their collective 1.3 million proteins organized into 42.7 million pairwise ortholog groups. The InParanoid algorithm itself has been revised and is now both more specific and sensitive. Based on results from our recent benchmarking of low-complexity filters in homology assignment, a two-pass BLAST approach was developed that makes use of high-precision compositional score matrix adjustment, but avoids the alignment truncation that sometimes follows. We have also updated the InParanoid web site (http://InParanoid.sbc.su.se). Several features have been added, the response times have been improved and the site now sports a new, clearer look. As the number of ortholog databases has grown, it has become difficult to compare among these resources due to a lack of standardized source data and incompatible representations of ortholog relationships. To facilitate data exchange and comparisons among ortholog databases, we have developed and are making available two XML schemas: SeqXML for the input sequences and OrthoXML for the output ortholog clusters.
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              The monarch butterfly genome yields insights into long-distance migration.

              We present the draft 273 Mb genome of the migratory monarch butterfly (Danaus plexippus) and a set of 16,866 protein-coding genes. Orthology properties suggest that the Lepidoptera are the fastest evolving insect order yet examined. Compared to the silkmoth Bombyx mori, the monarch genome shares prominent similarity in orthology content, microsynteny, and protein family sizes. The monarch genome reveals a vertebrate-like opsin whose existence in insects is widespread; a full repertoire of molecular components for the monarch circadian clockwork; all members of the juvenile hormone biosynthetic pathway whose regulation shows unexpected sexual dimorphism; additional molecular signatures of oriented flight behavior; microRNAs that are differentially expressed between summer and migratory butterflies; monarch-specific expansions of chemoreceptors potentially important for long-distance migration; and a variant of the sodium/potassium pump that underlies a valuable chemical defense mechanism. The monarch genome enhances our ability to better understand the genetic and molecular basis of long-distance migration. Copyright © 2011 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Nicolas.Negre@univ-montp2.fr
                claire.lemaitre@inria.fr
                emmanuelle.d-alencon@inra.fr
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                25 September 2017
                25 September 2017
                2017
                : 7
                : 11816
                Affiliations
                [1 ]ISNI 0000 0001 2298 7270, GRID grid.420225.3, INRIA, IRISA, GenScale, Campus de Beaulieu, ; Rennes, 35042 France
                [2 ]ISNI 0000 0001 2191 9284, GRID grid.410368.8, INRA, UMR Institut de Génétique, Environnement et Protection des Plantes (IGEPP), BioInformatics Platform for Agroecosystems Arthropods (BIPAA), Campus Beaulieu, ; Rennes, 35042 France
                [3 ]ISNI 0000 0001 2298 7270, GRID grid.420225.3, INRIA, IRISA, GenOuest Core Facility, Campus de Beaulieu, ; Rennes, 35042 France
                [4 ]ISNI 0000 0001 2097 0141, GRID grid.121334.6, DGIMI, INRA, Univ. Montpellier, ; 34095 Montpellier, France
                [5 ]ISNI 0000 0004 0641 2997, GRID grid.434728.e, CEA, Genoscope, 2 rue Gaston Crémieux, ; 91000 Evry, France
                [6 ]Université Côte d’Azur, INRA, CNRS, Institut Sophia Agrobiotech, 06903 Sophia-Antipolis, France
                [7 ]GRID grid.462350.6, Sorbonne Universités, UPMC University Paris 06, Institute of Ecology and Environmental Sciences of Paris, ; 75005 Paris, France
                [8 ]ISNI 0000 0004 0491 7131, GRID grid.418160.a, Department of Entomology, Max Planck Institute for Chemical Ecology, ; D-07745 Jena, Germany
                [9 ]GRID grid.418070.a, URGI, INRA, Université Paris-Saclay, ; 78026 Versailles, France
                [10 ]GRID grid.417653.2, CSIRO Ecosystem Sciences, Black Mountain, ; Canberra, ACT 2600 Australia
                [11 ]ISNI 0000 0004 0436 6763, GRID grid.1025.6, School of Veterinary and Life Sciences, Murdoch University, ; Murdoch, 6150 Australia
                [12 ]GRID grid.418070.a, INRA, Institute of Ecology and Environmental Sciences, ; 78000 Versailles, France
                [13 ]ISNI 0000 0001 2173 938X, GRID grid.5338.d, Department of Genetics, Universitat de València, ; 46100 Burjassot, Valencia Spain
                [14 ]ISNI 0000 0001 2173 938X, GRID grid.5338.d, Estructura de Recerca Interdisciplinar en Biotecnologia i Biomedicina (ERI-BIOTECMED), Universitat de València, ; 46100 Burjassot, Valencia Spain
                [15 ]EPHE, PSL Research University, UMR1333 - DGIMI, Pathologie comparée des Invertébrés CC101, F-34095 Montpellier cedex 5, France
                [16 ]Laboratory of Mammalian Genetics, Center for DNA Fingerprinting and Diagnostics (CDFD), Lab block: Tuljaguda (Opp. MJ Market), Nampally, Hyderabad, 500 001 India
                [17 ]ISNI 0000000084992262, GRID grid.7177.6, Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, Science Park 904, ; 1090 GE Amsterdam, The Netherlands
                [18 ]Plateforme MGX, C/o institut de Génomique Fonctionnelle, 141, rue de la Cardonille, 34094 Montpellier cedex 05, France
                [19 ]ISNI 0000 0004 1761 2943, GRID grid.412720.2, Key Laboratory of Forest Disaster Warning and Control of Yunnan Province, Southwest Forestry University, ; Kunming, 650224 China
                [20 ]GRID grid.1016.6, CSIRO, Clunies Ross St, (GPO Box 1700), ; Acton, ACT 2601 Australia
                [21 ]ISNI 0000 0004 1937 0722, GRID grid.11899.38, Departamento de Entomologia e Acarologia, Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo, Av. Pádua Dias 11, ; 13418-900 Piracicaba, Brazil
                [22 ]ISNI 0000 0001 2097 4281, GRID grid.29857.31, Department of Biology, , 208 Mueller Laboratory, The Pennsylvania State University, ; University Park, 16802 Pennsylvania USA
                [23 ]ISNI 0000 0001 2097 4281, GRID grid.29857.31, Department of Plant Science, , 102 Tyson Building, The Pennsylvania State University, ; University Park, 16802 Pennsylvania USA
                [24 ]CNRS UMR 8030, 2 rue Gaston Crémieux, 91000 Evry, France
                [25 ]ISNI 0000 0001 2180 5818, GRID grid.8390.2, Université d’Evry Val D’Essonne, ; 91000 Evry, France
                [26 ]INRA, UMR1062 CBGP, IRD, CIRAD, Montpellier SupAgro, 755 Avenue du campus Agropolis, 34988 Montferrier/Lez, France
                Author information
                http://orcid.org/0000-0003-1718-3010
                http://orcid.org/0000-0002-3613-2609
                http://orcid.org/0000-0001-8055-596X
                http://orcid.org/0000-0001-8758-3568
                http://orcid.org/0000-0002-6574-1358
                http://orcid.org/0000-0002-8108-9910
                http://orcid.org/0000-0003-3372-9423
                http://orcid.org/0000-0002-6052-4178
                http://orcid.org/0000-0001-6810-6504
                http://orcid.org/0000-0003-1973-7136
                http://orcid.org/0000-0002-2287-0782
                http://orcid.org/0000-0001-5690-2108
                http://orcid.org/0000-0003-3001-4908
                http://orcid.org/0000-0001-9727-3416
                http://orcid.org/0000-0001-8675-170X
                http://orcid.org/0000-0001-8994-0119
                Article
                10461
                10.1038/s41598-017-10461-4
                5613006
                28947760
                735e6d24-8f65-4261-a952-8b6834a4b990
                © The Author(s) 2017

                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
                : 7 February 2017
                : 19 April 2017
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