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      The Genomes of the Fungal Plant Pathogens Cladosporium fulvum and Dothistroma septosporum Reveal Adaptation to Different Hosts and Lifestyles But Also Signatures of Common Ancestry

      1 , 2 , * , 1 , 3 , 1 , 1 , 2 , 4 , 5 , 6 , 7 , 1 , 8 , 8 , 9 , 10 , 11 , 12 , 13 , 1 , 8 , 12 , 14 , 8 , 1 , 15 , 16 , 11 , 6 , 17 , 18 , 6 , 1 , 19 , 6 , 8 , 20 , 6 , 8 , 21 , 22 , 6 , 1 , 2 , 23 , 9 , 10 , 8 , 24 , 25 , 6 , 1 , 2 , 8 , *

      PLoS Genetics

      Public Library of Science

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          We sequenced and compared the genomes of the Dothideomycete fungal plant pathogens Cladosporium fulvum (Cfu) (syn. Passalora fulva) and Dothistroma septosporum (Dse) that are closely related phylogenetically, but have different lifestyles and hosts. Although both fungi grow extracellularly in close contact with host mesophyll cells, Cfu is a biotroph infecting tomato, while Dse is a hemibiotroph infecting pine. The genomes of these fungi have a similar set of genes (70% of gene content in both genomes are homologs), but differ significantly in size ( Cfu >61.1-Mb; Dse 31.2-Mb), which is mainly due to the difference in repeat content (47.2% in Cfu versus 3.2% in Dse). Recent adaptation to different lifestyles and hosts is suggested by diverged sets of genes. Cfu contains an α-tomatinase gene that we predict might be required for detoxification of tomatine, while this gene is absent in Dse. Many genes encoding secreted proteins are unique to each species and the repeat-rich areas in Cfu are enriched for these species-specific genes. In contrast, conserved genes suggest common host ancestry. Homologs of Cfu effector genes, including Ecp2 and Avr4, are present in Dse and induce a Cf-Ecp2- and Cf-4-mediated hypersensitive response, respectively. Strikingly, genes involved in production of the toxin dothistromin, a likely virulence factor for Dse, are conserved in Cfu, but their expression differs markedly with essentially no expression by Cfu in planta. Likewise, Cfu has a carbohydrate-degrading enzyme catalog that is more similar to that of necrotrophs or hemibiotrophs and a larger pectinolytic gene arsenal than Dse, but many of these genes are not expressed in planta or are pseudogenized. Overall, comparison of their genomes suggests that these closely related plant pathogens had a common ancestral host but since adapted to different hosts and lifestyles by a combination of differentiated gene content, pseudogenization, and gene regulation.

          Author Summary

          We compared the genomes of two closely related pathogens with very different lifestyles and hosts: C. fulvum ( Cfu), a biotroph of tomato, and D. septosporum ( Dse), a hemibiotroph of pine. Some differences in gene content were identified that can be directly related to their different hosts, such as the presence of a gene involved in degradation of a tomato saponin only in Cfu. However, in general the two species share a surprisingly large proportion of genes. Dse has functional homologs of Cfu effector genes, while Cfu has genes for biosynthesis of dothistromin, a toxin probably associated with virulence in Dse. Cfu also has an unexpectedly large content of genes for biosynthesis of other secondary metabolites and degradation of plant cell walls compared to Dse, contrasting with its host preference and lifestyle. However, many of these genes were not expressed in planta or were pseudogenized. These results suggest that evolving species may retain genetic signatures of the host and lifestyle preferences of their ancestor and that evolution of new genes, gene regulation, and pseudogenization are important factors in adaptation.

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          Most cited references 103

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          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from
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              We describe and validate a new membrane protein topology prediction method, TMHMM, based on a hidden Markov model. We present a detailed analysis of TMHMM's performance, and show that it correctly predicts 97-98 % of the transmembrane helices. Additionally, TMHMM can discriminate between soluble and membrane proteins with both specificity and sensitivity better than 99 %, although the accuracy drops when signal peptides are present. This high degree of accuracy allowed us to predict reliably integral membrane proteins in a large collection of genomes. Based on these predictions, we estimate that 20-30 % of all genes in most genomes encode membrane proteins, which is in agreement with previous estimates. We further discovered that proteins with N(in)-C(in) topologies are strongly preferred in all examined organisms, except Caenorhabditis elegans, where the large number of 7TM receptors increases the counts for N(out)-C(in) topologies. We discuss the possible relevance of this finding for our understanding of membrane protein assembly mechanisms. A TMHMM prediction service is available at Copyright 2001 Academic Press.

                Author and article information

                Role: Editor
                PLoS Genet
                PLoS Genet
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                November 2012
                November 2012
                29 November 2012
                : 8
                : 11
                [1 ]Laboratory of Phytopathology, Wageningen University, Wageningen, The Netherlands
                [2 ]Centre for Biosystems Genomics, Wageningen, The Netherlands
                [3 ]Laboratory of Bioinformatics, Wageningen University, Wageningen, The Netherlands
                [4 ]Department of Plant Pathology, University of California Davis, Davis, California, United States of America
                [5 ]Agricultural Research Center, Plant Pathology Research Institute, Giza, Egypt
                [6 ]U.S. Department of Energy Joint Genome Institute, Walnut Creek, California, United States of America
                [7 ]King Saud University, Riyadh, Saudi Arabia
                [8 ]Institute of Molecular BioSciences, Massey University, Palmerston North, New Zealand
                [9 ]Keygene N.V., Wageningen, The Netherlands
                [10 ]Applied Bioinformatics, Plant Research International, Wageningen, The Netherlands
                [11 ]CBS–KNAW Fungal Biodiversity Centre, Utrecht, The Netherlands
                [12 ]Department of Forest Sciences, University of British Columbia, Vancouver, Canada
                [13 ]Institute of Natural Sciences, Massey University, Albany, New Zealand
                [14 ]CNRS and Aix-Marseille Université, Marseille, France
                [15 ]Department of Plant Pathology, Tarbiat Modares University, Tehran, Iran
                [16 ]Department of Bio-Interactions, Plant Research International, Wageningen, The Netherlands
                [17 ]Cancer Genome Institute, Fox Chase Cancer Center, Philadelphia, Pennsylvania, United States of America
                [18 ]INRA, Aix-Marseille Université, Marseille, France
                [19 ]Department of Wheat Breeding, Seed and Plant Improvement Institute, Karaj, Iran
                [20 ]University of Northern British Columbia, Prince George, Canada
                [21 ]Department of Plant Biology and Forest Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden
                [22 ]Department of Bioscience, Plant Research International, Wageningen, The Netherlands
                [23 ]Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
                [24 ]Industrial Research Limited, Lower Hutt, New Zealand
                [25 ]USDA–ARS/Department of Botany and Plant Pathology, Purdue University, West Lafayette, Indiana, United States of America
                Stanford University School of Medicine, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: PJGM de Wit, RE Bradshaw, J Collemare, SB Goodwin, IV Grigoriev. Performed the experiments: RE Bradshaw, A van der Burgt, B Ökmen, J Collemare, AL Aerts, SA Griffiths, H Sun, E Lindquist, A Lapidus, S Zhang, Y Guo, A Schwelm, MK Jashni, MS Kabir, S Klaubauf, HG Beenen, R Mehrabi, I Stergiopoulos, P Chettri. Analyzed the data: PJGM de Wit, RE Bradshaw, J Collemare, KA Abd-Elsalam, A van der Burgt, E Datema, MP Cox, AR Ganley, RA Ohm, B Dhillon, A Levasseur, RP de Vries, I Stergiopoulos, B Henrissat, RC Hamelin, HA van den Burg, AH Bahkali, G Kema, E Schijlen, A Salamov. Contributed reagents/materials/analysis tools: E Datema, RCHJ van Ham, TJ Owen. Wrote the paper: PJGM de Wit, RE Bradshaw, J Collemare.


                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.

                Pages: 22
                This research was supported by Wageningen University, the Royal Netherlands Academy of Arts and Sciences, Centre for Biosystems Genomics, European Research Area-Plant Genomics, Willie Commelin Scholten Foundation, Graduate School of Experimental Plant Sciences, Massey University, the New Zealand Bio-Protection Research Centre, and Royal Society of New Zealand. The work conducted by the U.S. Department of Energy Joint Genome Institute is supported by the Office of Science of the U.S. Department of Energy under contract number DE-AC02-05CH11231. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Research Article
                Computational Biology
                Comparative Genomics
                Structural Genomics
                Molecular Genetics
                Comparative Genomics
                Functional Genomics
                Plant Science
                Plant Pathology



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