Publications by authors named "Sebastien Ravel"

Endogenous banana streak virus (eBSV) integrants derived from three distinct species, present in Musa balbisiana (B) but not Musa acuminata (A) banana genomes are able to reconstitute functional episomal viruses causing banana streak disease in interspecific triploid AAB banana hybrids but not in the diploid (BB) parent line, which harbours identical eBSV loci. Here, we investigated the regulation of these eBSV. In-depth characterization of siRNAs, transcripts and methylation derived from eBSV using Illumina and bisulfite sequencing were carried out on eBSV-free Musa acuminata AAA plants and BB or AAB banana plants with eBSV.

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Plants have powerful defense mechanisms and extensive immune receptor repertoires, yet crop monocultures are prone to epidemic diseases. Rice (Oryza sativa) is susceptible to many diseases, such as rice blast caused by Magnaporthe oryzae. Varietal resistance of rice to blast relies on intracellular nucleotide binding, leucine-rich repeat (NLR) receptors that recognize specific pathogen molecules and trigger immune responses.

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Different fungal species of the family infect rice, causing similar symptoms. Reference genomic sequences are useful tools to study the evolution of these species and to develop accurate molecular diagnostic tools. Here, we report the complete genome sequences of , , and .

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Plant pathogens secrete proteins called effectors that target host cellular processes to promote disease. Recently, structural genomics has identified several families of fungal effectors that share a similar three-dimensional structure despite remarkably variable amino-acid sequences and surface properties. To explore the selective forces that underlie the sequence variability of structurally-analogous effectors, we focused on MAX effectors, a structural family of effectors that are major determinants of virulence in the rice blast fungus Pyricularia oryzae.

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Traditional agrosystems, where humans, crops and microbes have coevolved over long periods, can serve as models to understand the ecoevolutionary determinants of disease dynamics and help the engineering of durably resistant agrosystems. Here, we investigated the genetic and phenotypic relationship between rice (Oryza sativa) landraces and their rice blast pathogen (Pyricularia oryzae) in the traditional Yuanyang terraces of flooded rice paddies in China, where rice landraces have been grown and bred over centuries without significant disease outbreaks. Analyses of genetic subdivision revealed that indica rice plants clustered according to landrace names.

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In recent years, Brown spot disease of rice (BSR) has been observed on leaves and seeds of rice in all rice-growing areas of Burkina Faso. and are the main fungal species isolated from BSR infected tissues and they are frequently observed in the same field. However, we are lacking information on the genetic diversity and population structure of these fungi in Burkina Faso.

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Many species of fungal plant pathogens coexist as multiple lineages on the same host, but the factors underlying the origin and maintenance of population structure remain largely unknown. The rice blast fungus Pyricularia oryzae is a widespread model plant pathogen displaying population subdivision. However, most studies of natural variation in P.

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Multipartite viruses have a segmented genome, with each segment encapsidated separately. In all multipartite virus species for which the question has been addressed, the distinct segments reproducibly accumulate at a specific and host-dependent relative frequency, defined as the 'genome formula'. Here, we test the hypothesis that the multipartite genome organization facilitates the regulation of gene expression via changes of the genome formula and thus via gene copy number variations.

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Rapid and repeatable polymorphism analyses have become a necessity with the current amount of genomic data that can be collected in many organisms. Traditionally, such analyses are conducted using a variety of tools in combination, often requiring numerous format translation and manipulation. Here, we present a massively updated version of our previous software package egglib, intended to alleviate such costly and error-prone tinkering with the data.

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Plant pathogens often adapt to plant genetic resistance so characterization of the architecture underlying such an adaptation is required to understand the adaptive potential of pathogen populations. Erosion of banana quantitative resistance to a major leaf disease caused by polygenic adaptation of the causal agent, the fungus Pseudocercospora fijiensis, was recently identified in the northern Caribbean region. Genome scan and quantitative genetics approaches were combined to investigate the adaptive architecture underlying this adaptation.

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Plant pathogens can adapt to quantitative resistance, eroding its effectiveness. The aim of this work was to reveal the genomic basis of adaptation to such a resistance in populations of the fungus , a major devastating pathogen of banana, by studying convergent adaptation on different cultivars. Samples from populations showing a local adaptation pattern on new banana hybrids with quantitative resistance were compared, based on a genome scan approach, with samples from traditional and more susceptible cultivars in Cuba and the Dominican Republic.

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Rice blast, caused by the filamentous ascomycete , is one of the most devastating diseases of rice. Four genetic clusters were previously identified, and three have a large geographic distribution. Asia is the center of diversity and the origin of most migrations to other continents, and sexual reproduction persisted only in the South China-Laos-North Thailand region, which was identified as the putative center of origin of all populations on rice.

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Blast disease is a notorious fungal disease leading to dramatic yield losses on major food crops such as rice and wheat. The causal agent, , encompasses different lineages, each having a different host range. Host shifts are suspected to have occurred in this species from spp.

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The rice blast fungus (syn., ) is both a threat to global food security and a model for plant pathology. Molecular pathologists need an accurate understanding of the origins and line of descent of populations in order to identify the genetic and functional bases of pathogen adaptation and to guide the development of more effective control strategies.

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Delineating species and epidemic lineages in fungal plant pathogens is critical to our understanding of disease emergence and the structure of fungal biodiversity and also informs international regulatory decisions. (syn. ) is a multihost pathogen that infects multiple grasses and cereals, is responsible for the most damaging rice disease (rice blast), and is of growing concern due to the recent introduction of wheat blast to Bangladesh from South America.

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Background: In February 2016, a new fungal disease was spotted in wheat fields across eight districts in Bangladesh. The epidemic spread to an estimated 15,000 hectares, about 16 % of the cultivated wheat area in Bangladesh, with yield losses reaching up to 100 %. Within weeks of the onset of the epidemic, we performed transcriptome sequencing of symptomatic leaf samples collected directly from Bangladeshi fields.

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Article Synopsis
  • The increasing amount of Next Generation Sequencing (NGS) data necessitates efficient bioinformatics tools, so a modular toolbox called TOGGLE was created to streamline the process of developing data analysis pipelines.
  • TOGGLE allows users to easily configure and manipulate different software options through a single basic configuration file, facilitating SNP discovery analysis across various computing environments.
  • By improving pipeline creation and offering reliable tracking, TOGGLE enables biologists to focus more on interpreting their results and adjusting experimental conditions without altering the code.
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The whole genome sequence of Coffea canephora, the perennial diploid species known as Robusta, has been recently released. In the context of the C. canephora genome sequencing project and to support post-genomics efforts, we developed the Coffee Genome Hub (http://coffee-genome.

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