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Ectomycorrhizal fungi decompose soil organic matter using oxidative mechanisms adapted from saprotrophic ancestors

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      • Ectomycorrhizal fungi are thought to have a key role in mobilizing organic nitrogen that is trapped in soil organic matter ( SOM). However, the extent to which ectomycorrhizal fungi decompose SOM and the mechanism by which they do so remain unclear, considering that they have lost many genes encoding lignocellulose‐degrading enzymes that are present in their saprotrophic ancestors.

      • Spectroscopic analyses and transcriptome profiling were used to examine the mechanisms by which five species of ectomycorrhizal fungi, representing at least four origins of symbiosis, decompose SOM extracted from forest soils.

      • In the presence of glucose and when acquiring nitrogen, all species converted the organic matter in the SOM extract using oxidative mechanisms. The transcriptome expressed during oxidative decomposition has diverged over evolutionary time. Each species expressed a different set of transcripts encoding proteins associated with oxidation of lignocellulose by saprotrophic fungi. The decomposition ‘toolbox’ has diverged through differences in the regulation of orthologous genes, the formation of new genes by gene duplications, and the recruitment of genes from diverse but functionally similar enzyme families.

      • The capacity to oxidize SOM appears to be common among ectomycorrhizal fungi. We propose that the ancestral decay mechanisms used primarily to obtain carbon have been adapted in symbiosis to scavenge nutrients instead.

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        MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

        We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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          RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies with maximum likelihood (ML). Low-level technical optimizations, a modification of the search algorithm, and the use of the GTR+CAT approximation as replacement for GTR+Gamma yield a program that is between 2.7 and 52 times faster than the previous version of RAxML. A large-scale performance comparison with GARLI, PHYML, IQPNNI and MrBayes on real data containing 1000 up to 6722 taxa shows that RAxML requires at least 5.6 times less main memory and yields better trees in similar times than the best competing program (GARLI) on datasets up to 2500 taxa. On datasets > or =4000 taxa it also runs 2-3 times faster than GARLI. RAxML has been parallelized with MPI to conduct parallel multiple bootstraps and inferences on distinct starting trees. The program has been used to compute ML trees on two of the largest alignments to date containing 25,057 (1463 bp) and 2182 (51,089 bp) taxa, respectively.

            Author and article information

            [ 1 ] Department of Biology Microbial Ecology GroupLund University Ecology Building SE‐223 62 LundSweden
            [ 2 ] Centre for Environmental SciencesHasselt University Building D Agoralaan 3590 Diepenbeek LimburgBelgium
            [ 3 ] Biology Department Lasry Center for BioscienceClark University 950 Main Street Worcester MA 01610‐1477USA
            [ 4 ] Department of Pharmaceutical Microbiology at the Hans Knöll InstituteFriedrich‐Schiller‐Universität Beutenbergstrasse 11a 07745 JenaGermany
            [ 5 ] Centre National de la Recherche Scientifique (CNRS)UMR7257 Université Aix‐Marseille Marseille 13288France
            [ 6 ] Department of Biological SciencesKing Abdulaziz University JeddahSaudi Arabia
            [ 7 ] Bioinformatics Infrastructures for Life Sciences (BILS) Department of BiologyLund University Ecology Building SE‐223 62 LundSweden
            [ 8 ] Institut de la Recherche Agronomique (INRA) Laboratory of Excellence ARBREUMR INRA‐Université de Lorraine ‘Interactions Arbres/Micro‐organismes’ INRA‐Nancy 54280 ChampenouxFrance
            [ 9 ] Centre for Environmental and Climate Research (CEC)Lund University Ecology Building SE‐223 62 LundSweden
            Author notes
            [* ] Author for correspondence:

            Anders Tunlid

            Tel: +46 46 222 37 57

            Email: anders.tunlid@

            New Phytol
            New Phytol
            The New Phytologist
            John Wiley and Sons Inc. (Hoboken )
            March 2016
            03 November 2015
            : 209
            : 4 ( doiID: 10.1111/nph.2016.209.issue-4 )
            : 1705-1719
            26527297 5061094 10.1111/nph.13722 NPH13722 2015-19899
            © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust

            This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

            Pages: 15
            Funded by: Swedish Research Council
            Funded by: Knut and Alice Wallenberg Foundation
            Funded by: Biodiversity and Ecosystem Services in a Changing Climate
            Funded by: French National Research Agency
            Funded by: Laboratory of Excellence ARBRE
            Award ID: ANR‐11‐LABX‐0002‐01
            Funded by: Office of Science of the US Department of Energy
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            March 2016
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