Genome analysis of the necrotrophic fungal pathogens Sclerotinia sclerotiorum and Botrytis cinerea

Sclerotinia sclerotiorum and Botrytis cinerea are closely related necrotrophic plant pathogenic fungi notable for their wide host ranges and environmental persistence. These attributes have made these species models for understanding the complexity of necrotrophic, broad host-range pathogenicity. Despite their similarities, the two species differ in mating behaviour and the ability to produce asexual spores. We have sequenced the genomes of one strain of S. sclerotiorum and two strains of B. cinerea. The comparative analysis of these genomes relative to one another and to other sequenced fungal genomes is provided here. Their 38–39 Mb genomes include 11,860–14,270 predicted genes, which share 83% amino acid identity on average between the two species. We have mapped the S. sclerotiorum assembly to 16 chromosomes and found large-scale co-linearity with the B. cinerea genomes. Seven percent of the S. sclerotiorum genome comprises transposable elements compared t

Genomic analysis of the necrotrophic fungal pathogens Sclerotinia sclerotiorum and Botrytis cinerea

Sclerotinia sclerotiorum and Botrytis cinerea are closely related necrotrophic plant pathogenic fungi notable for their wide host ranges and environmental persistence. These attributes have made these species models for understanding the complexity of necrotrophic, broad host-range pathogenicity. Despite their similarities, the two species differ in mating behaviour and the ability to produce asexual spores. We have sequenced the genomes of one strain of S. sclerotiorum and two strains of B. cinerea. The comparative analysis of these genomes relative to one another and to other sequenced fungal genomes is provided here. Their 38–39 Mb genomes include 11,860–14,270 predicted genes, which share 83% amino acid identity on average between the two species. We have mapped the S. sclerotiorum assembly to 16 chromosomes and found large-scale co-linearity with the B. cinerea genomes. Seven percent of the S. sclerotiorum genome comprises transposable elements compared to <1% of B. cinerea. The arsenal of genes associated with necrotrophic processes is similar between the species, including genes involved in plant cell wall degradation and oxalic acid production. Analysis of secondary metabolism gene clusters revealed an expansion in number and diversity of B. cinerea–specific secondary metabolites relative to S. sclerotiorum. The potential diversity in secondary metabolism might be involved in adaptation to specific ecological niches. Comparative genome analysis revealed the basis of differing sexual mating compatibility systems between S. sclerotiorum and B. cinerea. The organization of the mating-type loci differs, and their structures provide evidence for the evolution of heterothallism from homothallism. These data shed light on the evolutionary and mechanistic bases of the genetically complex traits of necrotrophic pathogenicity and sexual mating. This resource should facilitate the functional studies designed to better understand what makes these fungi such successful and persistent pathogens of agronomic crops.Fil: Ten Have, Arjen. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Mar del Plata. Instituto de Investigaciones Biológicas; Argentina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Biológicas; ArgentinaFil: Amselem, Joelle. Institut National de la Recherche Agronomique; FranciaFil: Cuomo, Christina A.. Broad Institute of MIT and Harvard; Estados UnidosFil: Jan, A. L. van Kan. Wageningen University; Países BajosFil: Viaud, Muriel. Institut National de la Recherche Agronomique; FranciaFil: Benito, Ernesto P.. Universidad de Salamanca; EspañaFil: Couloux, Arnaud. Centre National de Séquençage. Genoscope; FranciaFil: Coutinho, Pedro M.. Centre National de la Recherche Scientifique; FranciaFil: Vries, Ronald P. de. Microbiology and Kluyver Centre for Genomics of Industrial Fermentations; Países Bajos. Fungal Biodiversity Centre; Países BajosFil: Dyer, Paul S.. The University Of Nottingham; Reino UnidoFil: Fillinger, Sabine. Institut National de la Recherche Agronomique; FranciaFil: Fournier, Elisabeth. Institut National de la Recherche Agronomique; Francia. Centre de coopération internationale en recherche agronomique pour le développement; FranciaFil: Gout, Lilian. Institut National de la Recherche Agronomique; FranciaFil: Hahn, Matthias. University Of Kaiserlautern; AlemaniaFil: Kohn, Linda. University Of Toronto; CanadáFil: Lapalu, Nicolas. Institut National de la Recherche Agronomique; FranciaFil: Plummer, Kim M.. la Trobe University; AustraliaFil: Pradier, Jean-Marc. Institut National de la Recherche Agronomique; FranciaFil: Quévillon, Emmanuel. Institut National de la Recherche Agronomique; Francia. Centre National de la Recherche Scientifique; FranciaFil: Sharon, Amir. Tel Aviv University. Department of Molecular Biology and Ecology of Plants; IsraelFil: Simon, Adeline. Institut National de la Recherche Agronomique; FranciaFil: Tudzynski, Bettina. Institut für Biologie und Biotechnologie der Pflanzen; AlemaniaFil: Tudzynski, Paul. Institut für Biologie und Biotechnologie der Pflanzen; AlemaniaFil: Wincker, Patrick. Centre National de Séquençage. Genoscope; FranciaFil: Andrew, Marion. University Of Toronto; CanadáFil: Anthouard, Véronique. Centre National de Séquençage. Genoscope; FranciaFil: Beever, Ross E.. Landcare Research; Nueva ZelandaFil: Beffa, Rolland. Centre National de la Recherche Scientifique; FranciaFil: Benoit, Isabelle . Microbiology and Kluyver Centre for Genomics of Industrial Fermentations; Países BajosFil: Bouzid, Ourdia. Microbiology and Kluyver Centre for Genomics of Industrial Fermentations; Países Bajo

Genomic analysis of the necrotrophic fungal pathogens Sclerotinia sclerotiorum and Botrytis cinerea

This is the final version of the article. Available from the publisher via the DOI in this record.Sclerotinia sclerotiorum and Botrytis cinerea are closely related necrotrophic plant pathogenic fungi notable for their wide host ranges and environmental persistence. These attributes have made these species models for understanding the complexity of necrotrophic, broad host-range pathogenicity. Despite their similarities, the two species differ in mating behaviour and the ability to produce asexual spores. We have sequenced the genomes of one strain of S. sclerotiorum and two strains of B. cinerea. The comparative analysis of these genomes relative to one another and to other sequenced fungal genomes is provided here. Their 38-39 Mb genomes include 11,860-14,270 predicted genes, which share 83% amino acid identity on average between the two species. We have mapped the S. sclerotiorum assembly to 16 chromosomes and found large-scale co-linearity with the B. cinerea genomes. Seven percent of the S. sclerotiorum genome comprises transposable elements compared to <1% of B. cinerea. The arsenal of genes associated with necrotrophic processes is similar between the species, including genes involved in plant cell wall degradation and oxalic acid production. Analysis of secondary metabolism gene clusters revealed an expansion in number and diversity of B. cinerea-specific secondary metabolites relative to S. sclerotiorum. The potential diversity in secondary metabolism might be involved in adaptation to specific ecological niches. Comparative genome analysis revealed the basis of differing sexual mating compatibility systems between S. sclerotiorum and B. cinerea. The organization of the mating-type loci differs, and their structures provide evidence for the evolution of heterothallism from homothallism. These data shed light on the evolutionary and mechanistic bases of the genetically complex traits of necrotrophic pathogenicity and sexual mating. This resource should facilitate the functional studies designed to better understand what makes these fungi such successful and persistent pathogens of agronomic crops.The Sclerotinia sclerotiorum genome project was supported by the USDA Cooperative State Research, Education and Extension Service (USDA-NRI 2004). Sclerotinia sclerotiorum ESTs were funded by a grant to JA Rollins from USDA specific cooperative agreement 58-5442-4-281. The genome sequence of Botrytis cinerea strain T4 was funded by Genoscope, CEA, France. M Viaud was funded by the “Projet INRA Jeune-Equipe”. PM Coutinho and B Henrissat were funded by the ANR to project E-Tricel (grant ANR-07-BIOE-006). The CAZy database is funded in part by GIS-IBiSA. DM Soanes and NJ Talbot were partly funded by the UK Biotechnology and Biological Sciences Research Council. KM Plummer was partially funded by the New Zealand Bio-Protection Research Centre, http://bioprotection.org.nz/. BJ Howlett and A Sexton were partially funded by the Australian Grains Research and Development Corporation, www.grdc.com.au. L Kohn was partially funded by NSERC Discovery Grant (Natural Sciences and Engineering Research Council of Canada) - Grant number 458078. M Dickman was supported by the NSF grant MCB-092391 and BARD grant US-4041-07C. O Yarden was supported by BARD grant US-4041-07C. EG Danchin obtained financial support from the European Commission (STREP FungWall grant, contract: LSHB - CT- 2004 - 511952). A Botrytis Genome Workshop (Kaiserslautern, Germany) was supported by a grant from the German Science Foundation (DFG; HA1486) to M Hahn

Genomic Analysis of the Necrotrophic Fungal Pathogens Sclerotinia sclerotiorum and Botrytis cinerea

Sclerotinia sclerotiorum and Botrytis cinerea are closely related necrotrophic plant pathogenic fungi notable for their wide host ranges and environmental persistence. These attributes have made these species models for understanding the complexity of necrotrophic, broad host-range pathogenicity. Despite their similarities, the two species differ in mating behaviour and the ability to produce asexual spores. We have sequenced the genomes of one strain of S. sclerotiorum and two strains of B. cinerea. The comparative analysis of these genomes relative to one another and to other sequenced fungal genomes is provided here. Their 38–39 Mb genomes include 11,860–14,270 predicted genes, which share 83% amino acid identity on average between the two species. We have mapped the S. sclerotiorum assembly to 16 chromosomes and found large-scale co-linearity with the B. cinerea genomes. Seven percent of the S. sclerotiorum genome comprises transposable elements compared to <1% of B. cinerea. The arsenal of genes associated with necrotrophic processes is similar between the species, including genes involved in plant cell wall degradation and oxalic acid production. Analysis of secondary metabolism gene clusters revealed an expansion in number and diversity of B. cinerea–specific secondary metabolites relative to S. sclerotiorum. The potential diversity in secondary metabolism might be involved in adaptation to specific ecological niches. Comparative genome analysis revealed the basis of differing sexual mating compatibility systems between S. sclerotiorum and B. cinerea. The organization of the mating-type loci differs, and their structures provide evidence for the evolution of heterothallism from homothallism. These data shed light on the evolutionary and mechanistic bases of the genetically complex traits of necrotrophic pathogenicity and sexual mating. This resource should facilitate the functional studies designed to better understand what makes these fungi such successful and persistent pathogens of agronomic crops

Incidence of Genome Structure, DNA Asymmetry, and Cell Physiology on T-DNA Integration in Chromosomes of the Phytopathogenic Fungus Leptosphaeria maculans

The ever-increasing generation of sequence data is accompanied by unsatisfactory functional annotation, and complex genomes, such as those of plants and filamentous fungi, show a large number of genes with no predicted or known function. For functional annotation of unknown or hypothetical genes, the production of collections of mutants using Agrobacterium tumefaciens–mediated transformation (ATMT) associated with genotyping and phenotyping has gained wide acceptance. ATMT is also widely used to identify pathogenicity determinants in pathogenic fungi. A systematic analysis of T-DNA borders was performed in an ATMT-mutagenized collection of the phytopathogenic fungus Leptosphaeria maculans to evaluate the features of T-DNA integration in its particular transposable element-rich com- partmentalized genome. A total of 318 T-DNA tags were recovered and analyzed for biases in chromo- some and genic compartments, existence of CG/AT skews at the insertion site, and occurrence of microhomologies between the T-DNA left border (LB) and the target sequence. Functional annotation of targeted genes was done using the Gene Ontology annotation. The T-DNA integration mainly tar- geted gene-rich, transcriptionally active regions, and it favored biological processes consistent with the physiological status of a germinating spore. T-DNA integration was strongly biased toward regulatory regions, and mainly promoters. Consistent with the T-DNA intranuclear-targeting model, the density of T-DNA insertion correlated with CG skew near the transcription initiation site. The existence of micro- homologies between promoter sequences and the T-DNA LB flanking sequence was also consistent with T-DNA integration to host DNA mediated by homologous recombination based on the microhomology- mediated end-joining pathway

Convolutional and Recurrent Neural Networks for Activity Recognition in Smart Environment

International audienceConvolutional Neural Networks (CNN) are very useful for fully automatic extraction of discriminative features from raw sensor data. This is an important problem in activity recognition, which is of enormous interest in ambient sensor environments due to its universality on various applications. Activity recognition in smart homes uses large amounts of time-series sensor data to infer daily living activities and to extract effective features from those activities, which is a challenging task. In this paper we demonstrate the use of the CNN and a comparison of results, which has been performed with Long Short Term Memory (LSTM), recurrent neural networks and other machine learning algorithms, including Naive Bayes, Hidden Markov Models, Hidden Semi-Markov Models and Conditional Random Fields. The experimental results on publicly available smart home datasets demonstrate that the performance of 1D-CNN is similar to LSTM and better than the other probabilistic models