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      Gene Expansion Shapes Genome Architecture in the Human Pathogen Lichtheimia corymbifera: An Evolutionary Genomics Analysis in the Ancient Terrestrial Mucorales (Mucoromycotina)

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          Abstract

          Lichtheimia species are the second most important cause of mucormycosis in Europe. To provide broader insights into the molecular basis of the pathogenicity-associated traits of the basal Mucorales, we report the full genome sequence of L. corymbifera and compared it to the genome of Rhizopus oryzae, the most common cause of mucormycosis worldwide. The genome assembly encompasses 33.6 MB and 12,379 protein-coding genes. This study reveals four major differences of the L. corymbifera genome to R. oryzae: (i) the presence of an highly elevated number of gene duplications which are unlike R. oryzae not due to whole genome duplication (WGD), (ii) despite the relatively high incidence of introns, alternative splicing (AS) is not frequently observed for the generation of paralogs and in response to stress, (iii) the content of repetitive elements is strikingly low (<5%), (iv) L. corymbifera is typically haploid. Novel virulence factors were identified which may be involved in the regulation of the adaptation to iron-limitation, e.g. LCor01340.1 encoding a putative siderophore transporter and LCor00410.1 involved in the siderophore metabolism. Genes encoding the transcription factors LCor08192.1 and LCor01236.1, which are similar to GATA type regulators and to calcineurin regulated CRZ1, respectively, indicating an involvement of the calcineurin pathway in the adaption to iron limitation. Genes encoding MADS-box transcription factors are elevated up to 11 copies compared to the 1–4 copies usually found in other fungi. More findings are: (i) lower content of tRNAs, but unique codons in L. corymbifera, (ii) Over 25% of the proteins are apparently specific for L. corymbifera. (iii) L. corymbifera contains only 2/3 of the proteases (known to be essential virulence factors) in comparision to R. oryzae. On the other hand, the number of secreted proteases, however, is roughly twice as high as in R. oryzae.

          Author Summary

          Lichtheimia species are ubiquitous saprophytic fungi, which cause life-threating infections in humans. In contrast to the mucoralean pathogen R. oryzae, Lichtheimia species belong to the ancient mucoralean lineages. We determined the genome of L. corymbifera (formerly Mycocladus corymbifer ex Absidia corymbifera) and found high dissimilarities between L. corymbifera and other sequenced mucoralean fungi in terms of gene families and syntenies. A highly elevated number of gene duplications and expansions was observed, which comprises virulence-associated genes like proteases, transporters and iron uptake genes but also transcription factors and genes involved in signal transduction. In contrast to R. oryzae, we did not find evidence for a recent whole genome duplication in Lichtheimia. However, gene duplications create functionally diverse paralogs in L. corymbifera, which are differentially expressed in virulence-related compared to standard conditions. In addition, new potential virulence factors could be identified which may play a role in the regulation of the adaptation to iron-limitation. The L. corymbifera genome and the phylome will advance further research and better understanding of virulence mechanisms of these medically important pathogens at the level of genome architecture and evolution.

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          The roles of segmental and tandem gene duplication in the evolution of large gene families in Arabidopsis thaliana

          Background Most genes in Arabidopsis thaliana are members of gene families. How do the members of gene families arise, and how are gene family copy numbers maintained? Some gene families may evolve primarily through tandem duplication and high rates of birth and death in clusters, and others through infrequent polyploidy or large-scale segmental duplications and subsequent losses. Results Our approach to understanding the mechanisms of gene family evolution was to construct phylogenies for 50 large gene families in Arabidopsis thaliana, identify large internal segmental duplications in Arabidopsis, map gene duplications onto the segmental duplications, and use this information to identify which nodes in each phylogeny arose due to segmental or tandem duplication. Examples of six gene families exemplifying characteristic modes are described. Distributions of gene family sizes and patterns of duplication by genomic distance are also described in order to characterize patterns of local duplication and copy number for large gene families. Both gene family size and duplication by distance closely follow power-law distributions. Conclusions Combining information about genomic segmental duplications, gene family phylogenies, and gene positions provides a method to evaluate contributions of tandem duplication and segmental genome duplication in the generation and maintenance of gene families. These differences appear to correspond meaningfully to differences in functional roles of the members of the gene families.
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            BIONJ: an improved version of the NJ algorithm based on a simple model of sequence data.

            O. Gascuel (1997)
            We propose an improved version of the neighbor-joining (NJ) algorithm of Saitou and Nei. This new algorithm, BIONJ, follows the same agglomerative scheme as NJ, which consists of iteratively picking a pair of taxa, creating a new mode which represents the cluster of these taxa, and reducing the distance matrix by replacing both taxa by this node. Moreover, BIONJ uses a simple first-order model of the variances and covariances of evolutionary distance estimates. This model is well adapted when these estimates are obtained from aligned sequences. At each step it permits the selection, from the class of admissible reductions, of the reduction which minimizes the variance of the new distance matrix. In this way, we obtain better estimates to choose the pair of taxa to be agglomerated during the next steps. Moreover, in comparison with NJ's estimates, these estimates become better and better as the algorithm proceeds. BIONJ retains the good properties of NJ--especially its low run time. Computer simulations have been performed with 12-taxon model trees to determine BIONJ's efficiency. When the substitution rates are low (maximum pairwise divergence approximately 0.1 substitutions per site) or when they are constant among lineages, BIONJ is only slightly better than NJ. When the substitution rates are higher and vary among lineages,BIONJ clearly has better topological accuracy. In the latter case, for the model trees and the conditions of evolution tested, the topological error reduction is on the average around 20%. With highly-varying-rate trees and with high substitution rates (maximum pairwise divergence approximately 1.0 substitutions per site), the error reduction may even rise above 50%, while the probability of finding the correct tree may be augmented by as much as 15%.
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              Sea anemone genome reveals ancestral eumetazoan gene repertoire and genomic organization.

              Sea anemones are seemingly primitive animals that, along with corals, jellyfish, and hydras, constitute the oldest eumetazoan phylum, the Cnidaria. Here, we report a comparative analysis of the draft genome of an emerging cnidarian model, the starlet sea anemone Nematostella vectensis. The sea anemone genome is complex, with a gene repertoire, exon-intron structure, and large-scale gene linkage more similar to vertebrates than to flies or nematodes, implying that the genome of the eumetazoan ancestor was similarly complex. Nearly one-fifth of the inferred genes of the ancestor are eumetazoan novelties, which are enriched for animal functions like cell signaling, adhesion, and synaptic transmission. Analysis of diverse pathways suggests that these gene "inventions" along the lineage leading to animals were likely already well integrated with preexisting eukaryotic genes in the eumetazoan progenitor.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                1553-7390
                1553-7404
                August 2014
                14 August 2014
                : 10
                : 8
                : e1004496
                Affiliations
                [1 ]University of Jena, Institute of Microbiology, Department of Microbiology and Molecular Biology, Jena, Germany
                [2 ]Leibniz Institute for Natural Product Research and Infection Biology, Department of Molecular and Applied Microbiology, Hans Knöll Institute, Jena, Germany
                [3 ]University of Jena, Department of Bioinformatics, Jena, Germany
                [4 ]Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Systems Biology/Bioinformatics, Jena, Germany
                [5 ]Centre for Genomic Regulation (CRG), Barcelona, Spain
                [6 ]Universitat Pompeu Fabra (UPF), Barcelona, Spain
                [7 ]Centre Nacional d'Anàlisi Genòmica (CNAG), Functional Bioinformatics, Barcelona, Spain
                [8 ]Laboratório Nacional de Computação Científica (LNCC), Petrópolis, Rio de Janeiro, Brazil
                [9 ]Ruhr University Bochum, Department of General and Molecular Botany, Bochum, Germany
                [10 ]Leibniz Institute for Natural Product Research and Infection Biology, Hans Knöll Institute, Department of Microbial Immunology, Jena, Germany
                Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: VUS SWi ES MMH FH MS MN VV JL IDJ MM AAB TG SB KV. Performed the experiments: VUS SWi ES MMH FH MS MN VV JL IDJ KK KR SWe. Analyzed the data: VUS SWi ES MMH FH MS MN VV JL IDJ KR SWe MM TG SB KV. Contributed reagents/materials/analysis tools: SWi ES FH MS MN VV JL IDJ KR SWe MM AAB TG SB KV. Wrote the paper: VUS SWi ES MMH FH MS MN VV MM TG SB KV. Designed the software used in analysis: SB MS SWi KR MM FH JL MMH TG.

                ¶ These authors share senior authorship on this work.

                Article
                PGENETICS-D-13-00007
                10.1371/journal.pgen.1004496
                4133162
                25121733
                8935522e-f6dc-4b11-a9e0-20a9b749bdd0
                Copyright @ 2014

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

                History
                : 2 January 2013
                : 24 May 2014
                Page count
                Pages: 16
                Funding
                TG group is funded in part by a grant from the Spanish Ministry of Science and Innovation (BFU2009-09168). MM group was in part supported by the German Science Foundation (DFG) MA5082/1-1 (KR). MN would like to thank Ulrich Kück for generous support, and the DFG for funding (project NO407/4-1). SB group was in part supported by DFG BO 1910/8-1 (SWi). KV and AAB acknowledge financial support of the Leibniz Institute of Natural Product Research and Infection Biology - Hans Knöll Institute Jena, Germany. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Genomic Databases
                Comparative Genomics
                Genome Complexity
                Genome Evolution
                Evolutionary Biology
                Evolutionary Systematics
                Phylogenetics
                Organismal Evolution
                Microbial Evolution
                Evolutionary Genetics
                Genetics
                Genomics
                Microbiology
                Medical Microbiology
                Microbial Pathogens
                Molecular Biology
                Molecular Biology Techniques
                Sequencing Techniques
                Sequence Analysis
                Sequence Databases
                Genome Sequencing
                Mycology
                Fungal Evolution
                Organisms
                Fungi
                Medicine and Health Sciences
                Pathology and Laboratory Medicine
                Pathogenesis

                Genetics
                Genetics

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