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      Deciphering the Cryptic Genome: Genome-wide Analyses of the Rice Pathogen Fusarium fujikuroi Reveal Complex Regulation of Secondary Metabolism and Novel Metabolites

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          Abstract

          The fungus Fusarium fujikuroi causes “bakanae” disease of rice due to its ability to produce gibberellins (GAs), but it is also known for producing harmful mycotoxins. However, the genetic capacity for the whole arsenal of natural compounds and their role in the fungus' interaction with rice remained unknown. Here, we present a high-quality genome sequence of F. fujikuroi that was assembled into 12 scaffolds corresponding to the 12 chromosomes described for the fungus. We used the genome sequence along with ChIP-seq, transcriptome, proteome, and HPLC-FTMS-based metabolome analyses to identify the potential secondary metabolite biosynthetic gene clusters and to examine their regulation in response to nitrogen availability and plant signals. The results indicate that expression of most but not all gene clusters correlate with proteome and ChIP-seq data. Comparison of the F. fujikuroi genome to those of six other fusaria revealed that only a small number of gene clusters are conserved among these species, thus providing new insights into the divergence of secondary metabolism in the genus Fusarium. Noteworthy, GA biosynthetic genes are present in some related species, but GA biosynthesis is limited to F. fujikuroi, suggesting that this provides a selective advantage during infection of the preferred host plant rice. Among the genome sequences analyzed, one cluster that includes a polyketide synthase gene ( PKS19) and another that includes a non-ribosomal peptide synthetase gene ( NRPS31) are unique to F. fujikuroi. The metabolites derived from these clusters were identified by HPLC-FTMS-based analyses of engineered F. fujikuroi strains overexpressing cluster genes. In planta expression studies suggest a specific role for the PKS19-derived product during rice infection. Thus, our results indicate that combined comparative genomics and genome-wide experimental analyses identified novel genes and secondary metabolites that contribute to the evolutionary success of F. fujikuroi as a rice pathogen.

          Author Summary

          Fungi produce numerous “secondary metabolites” (SMs) that are not essential for life but can provide an advantage under natural conditions, e.g. in fungal-host interactions. Here, we conducted the most comprehensive analysis to date of secondary metabolism in fungi using Fusarium fujikuroi. This fungus causes “bakanae” disease of rice and is best known for its ability to produce gibberellins (GAs). We show that GA production is limited to F. fujikuroi and provides a selective advantage during infection of the preferred host plant rice. Generation and analysis of a high-quality de novo F. fujikuroi genome sequence combined with comparisons to six other Fusarium genomes revealed the presence of 45 mostly unknown SM gene clusters. We provide a broad spectrum of experimental data including epigenetic, transcriptional, proteomic and chemical product analyses under different nitrogen and pH conditions. Two of the SM clusters (PKS19 and NRPS31) are not present in any other sequenced fungal genome. In planta expression studies revealed that the otherwise silent PKS19 cluster is induced on rice, but not on maize, suggesting a specific role for the PKS19-derived product during rice infection. Together, our results demonstrate the tremendous potential of a single fungal species to produce a diversity of SMs that likely contributes to adaptation to environmental changes.

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          Most cited references106

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          TANDEM: matching proteins with tandem mass spectra.

          Tandem mass spectra obtained from fragmenting peptide ions contain some peptide sequence specific information, but often there is not enough information to sequence the original peptide completely. Several proprietary software applications have been developed to attempt to match the spectra with a list of protein sequences that may contain the sequence of the peptide. The application TANDEM was written to provide the proteomics research community with a set of components that can be used to test new methods and algorithms for performing this type of sequence-to-data matching. The source code and binaries for this software are available at http://www.proteome.ca/opensource.html, for Windows, Linux and Macintosh OSX. The source code is made available under the Artistic License, from the authors.
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            The generic genome browser: a building block for a model organism system database.

            The Generic Model Organism System Database Project (GMOD) seeks to develop reusable software components for model organism system databases. In this paper we describe the Generic Genome Browser (GBrowse), a Web-based application for displaying genomic annotations and other features. For the end user, features of the browser include the ability to scroll and zoom through arbitrary regions of a genome, to enter a region of the genome by searching for a landmark or performing a full text search of all features, and the ability to enable and disable tracks and change their relative order and appearance. The user can upload private annotations to view them in the context of the public ones, and publish those annotations to the community. For the data provider, features of the browser software include reliance on readily available open source components, simple installation, flexible configuration, and easy integration with other components of a model organism system Web site. GBrowse is freely available under an open source license. The software, its documentation, and support are available at http://www.gmod.org.
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              Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training.

              We describe a new ab initio algorithm, GeneMark-ES version 2, that identifies protein-coding genes in fungal genomes. The algorithm does not require a predetermined training set to estimate parameters of the underlying hidden Markov model (HMM). Instead, the anonymous genomic sequence in question is used as an input for iterative unsupervised training. The algorithm extends our previously developed method tested on genomes of Arabidopsis thaliana, Caenorhabditis elegans, and Drosophila melanogaster. To better reflect features of fungal gene organization, we enhanced the intron submodel to accommodate sequences with and without branch point sites. This design enables the algorithm to work equally well for species with the kinds of variations in splicing mechanisms seen in the fungal phyla Ascomycota, Basidiomycota, and Zygomycota. Upon self-training, the intron submodel switches on in several steps to reach its full complexity. We demonstrate that the algorithm accuracy, both at the exon and the whole gene level, is favorably compared to the accuracy of gene finders that employ supervised training. Application of the new method to known fungal genomes indicates substantial improvement over existing annotations. By eliminating the effort necessary to build comprehensive training sets, the new algorithm can streamline and accelerate the process of annotation in a large number of fungal genome sequencing projects.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Pathog
                PLoS Pathog
                plos
                plospath
                PLoS Pathogens
                Public Library of Science (San Francisco, USA )
                1553-7366
                1553-7374
                June 2013
                June 2013
                27 June 2013
                : 9
                : 6
                : e1003475
                Affiliations
                [1 ]Institut für Biologie und Biotechnologie der Pflanzen, Molecular Biology and Biotechnology of Fungi, Westfälische Wilhelms-Universität Münster, Münster, Germany
                [2 ]Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany
                [3 ]Institute for Food Chemistry, Westfälische Wilhelms-Universität Münster, Corrensstraße 45, Münster, Germany
                [4 ]Institut für Biologie und Biotechnologie der Pflanzen, Plant Biochemistry and Biotechnology, Westfälische Wilhelms-Universität Münster, Münster, Germany
                [5 ]Department of Biochemistry and Biophysics, Center for Genome Research and Biocomputing, Oregon State University, Corvallis, Oregon, United States of America
                [6 ]Institut of Genetics/Developmental Genetics, Martin-Luther-Universität Halle-Wittenberg, Halle, Germany
                [7 ]Department of Genetics, University of Pretoria, Hatfield, Pretoria, South Africa
                [8 ]Institute of Plant Sciences, Genomics, Agricultural Research Organization (ARO), The Volcani Center, Bet-Dagan, Israel
                [9 ]Department of Plant Pathology, Agricultural Research Organization (ARO), The Volcani Center, Bet-Dagan, Israel
                [10 ]National Center for Agricultural Utilization Research, United States Department of Agriculture, Peoria, Illinois, United States of America
                Carnegie Mellon University, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: BT UG PW HUH MF MH GR. Performed the experiments: KWvB PW LS EMN KH SA DW JJE SVB LRC AF KK. Analyzed the data: KWvB PW LS JJE AF EMN UG CMKS MM CBM TB KMS RO DWB RHP. Contributed reagents/materials/analysis tools: BDW SF BT HUH MF. Wrote the paper: BT PW UG DWB RHP MF MH.

                [¤]

                Current address: Department of Medical Microbiology and Immunology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.

                Article
                PPATHOGENS-D-13-00685
                10.1371/journal.ppat.1003475
                3694855
                23825955
                aff85fc5-a4c6-489e-bd70-059ca458db44
                Copyright @ 2013

                This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                : 8 March 2013
                : 18 May 2013
                Page count
                Pages: 35
                Funding
                This work was supported by funds of the Deutsche Forschungsgesellschaft (DFG TU 101/16; HU 730/9; GU 1205/1, GU 1205/2) and by grants from the NIH (GM097637) and ACS (RSG-08-030-01-CCG) to MF. UG was funded by the Austrian Science Fund FWF (special research project Fusarium, F3705). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Genetics
                Epigenetics
                Gene Expression
                Gene Function
                Genome-Wide Association Studies
                Molecular Genetics
                Genomics
                Comparative Genomics
                Functional Genomics
                Genome Analysis Tools
                Genome Databases
                Genome Expression Analysis
                Genome Sequencing
                Microbiology
                Mycology
                Fungal Evolution
                Fungal Physiology
                Fungi
                Host-Pathogen Interaction
                Microbial Metabolism
                Microbial Pathogens
                Pathogenesis
                Proteomics
                Protein Abundance
                Proteomic Databases
                Sequence Analysis
                Spectrometric Identification of Proteins

                Infectious disease & Microbiology
                Infectious disease & Microbiology

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