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      Insights into Adaptations to a Near-Obligate Nematode Endoparasitic Lifestyle from the Finished Genome of Drechmeria coniospora

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          Nematophagous fungi employ three distinct predatory strategies: nematode trapping, parasitism of females and eggs, and endoparasitism. While endoparasites play key roles in controlling nematode populations in nature, their application for integrated pest management is hindered by the limited understanding of their biology. We present a comparative analysis of a high quality finished genome assembly of Drechmeria coniospora, a model endoparasitic nematophagous fungus, integrated with a transcriptomic study. Adaptation of D. coniospora to its almost completely obligate endoparasitic lifestyle led to the simplification of many orthologous gene families involved in the saprophytic trophic mode, while maintaining orthologs of most known fungal pathogen-host interaction proteins, stress response circuits and putative effectors of the small secreted protein type. The need to adhere to and penetrate the host cuticle led to a selective radiation of surface proteins and hydrolytic enzymes. Although the endoparasite has a simplified secondary metabolome, it produces a novel peptaibiotic family that shows antibacterial, antifungal and nematicidal activities. Our analyses emphasize the basic malleability of the D. coniospora genome: loss of genes advantageous for the saprophytic lifestyle; modulation of elements that its cohort species utilize for entomopathogenesis; and expansion of protein families necessary for the nematode endoparasitic lifestyle.

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          Most cited references 54

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          Clustal Omega, accurate alignment of very large numbers of sequences.

          Clustal Omega is a completely rewritten and revised version of the widely used Clustal series of programs for multiple sequence alignment. It can deal with very large numbers (many tens of thousands) of DNA/RNA or protein sequences due to its use of the mBED algorithm for calculating guide trees. This algorithm allows very large alignment problems to be tackled very quickly, even on personal computers. The accuracy of the program has been considerably improved over earlier Clustal programs, through the use of the HHalign method for aligning profile hidden Markov models. The program currently is used from the command line or can be run on line.
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            Surface hydrophobin prevents immune recognition of airborne fungal spores.

            The air we breathe is filled with thousands of fungal spores (conidia) per cubic metre, which in certain composting environments can easily exceed 10(9) per cubic metre. They originate from more than a hundred fungal species belonging mainly to the genera Cladosporium, Penicillium, Alternaria and Aspergillus. Although these conidia contain many antigens and allergens, it is not known why airborne fungal microflora do not activate the host innate immune cells continuously and do not induce detrimental inflammatory responses following their inhalation. Here we show that the surface layer on the dormant conidia masks their recognition by the immune system and hence prevents immune response. To explore this, we used several fungal members of the airborne microflora, including the human opportunistic fungal pathogen Aspergillus fumigatus, in in vitro assays with dendritic cells and alveolar macrophages and in in vivo murine experiments. In A. fumigatus, this surface 'rodlet layer' is composed of hydrophobic RodA protein covalently bound to the conidial cell wall through glycosylphosphatidylinositol-remnants. RodA extracted from conidia of A. fumigatus was immunologically inert and did not induce dendritic cell or alveolar macrophage maturation and activation, and failed to activate helper T-cell immune responses in vivo. The removal of this surface 'rodlet/hydrophobin layer' either chemically (using hydrofluoric acid), genetically (DeltarodA mutant) or biologically (germination) resulted in conidial morphotypes inducing immune activation. All these observations show that the hydrophobic rodlet layer on the conidial cell surface immunologically silences airborne moulds.
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              NOTUNG: a program for dating gene duplications and optimizing gene family trees.

              Large scale gene duplication is a major force driving the evolution of genetic functional innovation. Whole genome duplications are widely believed to have played an important role in the evolution of the maize, yeast, and vertebrate genomes. The use of evolutionary trees to analyze the history of gene duplication and estimate duplication times provides a powerful tool for studying this process. Many studies in the molecular evolution literature have used this approach on small data sets, using analyses performed by hand. The rapid growth of genetic sequence data will soon allow similar studies on a genomic scale, but such studies will be limited unless the analysis can be automated. Even existing data sets admit alternative hypotheses that would be too tedious to consider without automation. In this paper, we describe a program called NOTUNG that facilitates large scale analysis, using both rooted and unrooted trees. When tested on trees analyzed in the literature, NOTUNG consistently yielded results that agree with the assessments in the original publications. Thus, NOTUNG provides a basic building block for inferring duplication dates from gene trees automatically and can also be used as an exploratory analysis tool for evaluating alternative hypotheses.

                Author and article information

                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                15 March 2016
                : 6
                [1 ]Biotechnology Research Institute, Chinese Academy of Agricultural Sciences , Beijing, China
                [2 ]Key Laboratory of Agricultural Genomics (Beijing), Ministry of Agriculture , China
                [3 ]Molecular Plant Pathology, University of Amsterdam , Amsterdam, the Netherlands
                [4 ]Department of Biotechnology and Microbiology, Faculty of Science and Technology, University of Debrecen , Hungary
                [5 ]Department of Biochemistry and Molecular Biology, University of Debrecen , Debrecen, Hungary
                [6 ]Tianjin Key Laboratory of Microbial Functional Genomics, TEDA School of Biological Sciences and Biotechnology, Nankai University , Tianjin, China
                [7 ]Plant Pathology & Plant-Microbe Biology, Cornell University , Ithaca, New York, USA
                [8 ]USDA-ARS, Robert W. Holley Center for Agriculture and Health , Ithaca, New York, USA
                [9 ]Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center , Memphis, Tennessee, USA
                [10 ]State Key Laboratory of Natural Medicines, Department of Natural Medicinal Chemistry, China Pharmaceutical University , Nanjing, China
                [11 ]Natural Products Center, School of Natural Resources and the Environment, University of Arizona , Tucson, Arizona, USA
                Author notes

                These authors contributed equally to this work.

                Copyright © 2016, Macmillan Publishers Limited

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/




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