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      Genomic Insights into the Atopic Eczema-Associated Skin Commensal Yeast Malassezia sympodialis

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          ABSTRACT

          Malassezia commensal yeasts are associated with a number of skin disorders, such as atopic eczema/dermatitis and dandruff, and they also can cause systemic infections. Here we describe the 7.67-Mbp genome of Malassezia sympodialis, a species associated with atopic eczema, and contrast its genome repertoire with that of Malassezia globosa, associated with dandruff, as well as those of other closely related fungi. Ninety percent of the predicted M. sympodialis protein coding genes were experimentally verified by mass spectrometry at the protein level. We identified a relatively limited number of genes related to lipid biosynthesis, and both species lack the fatty acid synthase gene, in line with the known requirement of these yeasts to assimilate lipids from the host. Malassezia species do not appear to have many cell wall-localized glycosylphosphatidylinositol (GPI) proteins and lack other cell wall proteins previously identified in other fungi. This is surprising given that in other fungi these proteins have been shown to mediate interactions (e.g., adhesion and biofilm formation) with the host. The genome revealed a complex evolutionary history for an allergen of unknown function, Mala s 7, shown to be encoded by a member of an amplified gene family of secreted proteins. Based on genetic and biochemical studies with the basidiomycete human fungal pathogen Cryptococcus neoformans, we characterized the allergen Mala s 6 as the cytoplasmic cyclophilin A. We further present evidence that M. sympodialis may have the capacity to undergo sexual reproduction and present a model for a pseudobipolar mating system that allows limited recombination between two linked MAT loci.

          IMPORTANCE

          Malassezia commensal yeasts are associated with a number of skin disorders. The previously published genome of M. globosa provided some of the first insights into Malassezia biology and its involvement in dandruff. Here, we present the genome of M. sympodialis, frequently isolated from patients with atopic eczema and healthy individuals. We combined comparative genomics with sequencing and functional characterization of specific genes in a population of clinical isolates and in closely related model systems. Our analyses provide insights into the evolution of allergens related to atopic eczema and the evolutionary trajectory of the machinery for sexual reproduction and meiosis. We hypothesize that M. sympodialis may undergo sexual reproduction, which has important implications for the understanding of the life cycle and virulence potential of this medically important yeast. Our findings provide a foundation for the development of genetic and genomic tools to elucidate host-microbe interactions that occur on the skin and to identify potential therapeutic targets.

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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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            Estimating synonymous and nonsynonymous substitution rates under realistic evolutionary models.

            Q. Z. Yang (2000)
            Approximate methods for estimating the numbers of synonymous and nonsynonymous substitutions between two DNA sequences involve three steps: counting of synonymous and nonsynonymous sites in the two sequences, counting of synonymous and nonsynonymous differences between the two sequences, and correcting for multiple substitutions at the same site. We examine complexities involved in those steps and propose a new approximate method that takes into account two major features of DNA sequence evolution: transition/transversion rate bias and base/codon frequency bias. We compare the new method with maximum likelihood, as well as several other approximate methods, by examining infinitely long sequences, performing computer simulations, and analyzing a real data set. The results suggest that when there are transition/transversion rate biases and base/codon frequency biases, previously described approximate methods for estimating the nonsynonymous/synonymous rate ratio may involve serious biases, and the bias can be both positive and negative. The new method is, in general, superior to earlier approximate methods and may be useful for analyzing large data sets, although maximum likelihood appears to always be the method of choice.
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              Insights from the genome of the biotrophic fungal plant pathogen Ustilago maydis.

              Ustilago maydis is a ubiquitous pathogen of maize and a well-established model organism for the study of plant-microbe interactions. This basidiomycete fungus does not use aggressive virulence strategies to kill its host. U. maydis belongs to the group of biotrophic parasites (the smuts) that depend on living tissue for proliferation and development. Here we report the genome sequence for a member of this economically important group of biotrophic fungi. The 20.5-million-base U. maydis genome assembly contains 6,902 predicted protein-encoding genes and lacks pathogenicity signatures found in the genomes of aggressive pathogenic fungi, for example a battery of cell-wall-degrading enzymes. However, we detected unexpected genomic features responsible for the pathogenicity of this organism. Specifically, we found 12 clusters of genes encoding small secreted proteins with unknown function. A significant fraction of these genes exists in small gene families. Expression analysis showed that most of the genes contained in these clusters are regulated together and induced in infected tissue. Deletion of individual clusters altered the virulence of U. maydis in five cases, ranging from a complete lack of symptoms to hypervirulence. Despite years of research into the mechanism of pathogenicity in U. maydis, no 'true' virulence factors had been previously identified. Thus, the discovery of the secreted protein gene clusters and the functional demonstration of their decisive role in the infection process illuminate previously unknown mechanisms of pathogenicity operating in biotrophic fungi. Genomic analysis is, similarly, likely to open up new avenues for the discovery of virulence determinants in other pathogens.
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                Author and article information

                Journal
                mBio
                MBio
                mbio
                mbio
                mBio
                mBio
                American Society of Microbiology (1752 N St., N.W., Washington, DC )
                2150-7511
                22 January 2013
                Jan-Feb 2013
                : 4
                : 1
                : e00572-12
                Affiliations
                Science for Life Laboratory, Translational Immunology Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden [ a ],
                Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden [ b ];
                Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina, USA [ c ];
                Procter & Gamble Co., Miami Valley Innovation Center, Cincinnati, Ohio, USA [ d ];
                Translational Immunology Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden [ e ];
                Max Planck Institute for Terrestrial Microbiology, Marburg, Germany [ f ];
                CBS-Fungal Biodiversity Centre, Utrecht, The Netherlands [ g ];
                Institute of Medical Sciences, University of Aberdeen, Aberdeen, United Kingdom [ h ];
                Cancer Proteomics Mass Spectrometry, Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden [ i ];
                Department of Plant Pathology & Microbiology [ j ] and
                Institute for Integrative Genome Biology, [ k ] University of California, Riverside, California, USA;
                Department of Genetics, Carolina Center for Genome Science, School of Medicine, University of North Carolina, at Chapel Hill, Chapel Hill, North Carolina, USA [ l ];
                Department of Biology, Saint Louis University, St. Louis, Missouri, USA [ m ];
                Science for Life Laboratory School of Biotechnology, Division of Gene Technology, Royal Institute of Technology, Stockholm, Sweden [ n ];
                Department of Internal Medicine and Infectious Diseases, University Medical Center, Utrecht, The Netherlands [ o ];
                Regional Center for Biomedical Research, Albacete Science & Technology Park, University of Castilla-La Mancha, Albacete, Spain [ p ]; and
                School of Biomolecular and Biomedical Science, University College Dublin, Dublin, Ireland [ q ]
                Author notes
                Address correspondence to Annika Scheynius, annika.scheynius@ 123456ki.se .

                Editor Judith Berman, University of Minnesota

                Article
                mBio00572-12
                10.1128/mBio.00572-12
                3560662
                23341551
                6544beec-7562-41b8-989c-1ad9bd130090
                Copyright © 2013 Gioti et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-ShareAlike 3.0 Unported license, which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 6 December 2012
                : 11 December 2012
                Page count
                Pages: 16
                Categories
                Research Article
                Custom metadata
                January/February 2013

                Life sciences
                Life sciences

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