Deciphering the genetic bases of pathogen adaptation to its host is a key question in ecology and evolution. To understand how the fungus Magnaporthe oryzae adapts to different plants, we sequenced eight M. oryzae isolates differing in host specificity (rice, foxtail millet, wheat, and goosegrass), and one Magnaporthe grisea isolate specific of crabgrass. Analysis of Magnaporthe genomes revealed small variation in genome sizes (39-43 Mb) and gene content (12,283-14,781 genes) between isolates. The whole set of Magnaporthe genes comprised 14,966 shared families, 63% of which included genes present in all the nine M. oryzae genomes. The evolutionary relationships among Magnaporthe isolates were inferred using 6,878 single-copy orthologs. The resulting genealogy-was mostly bifurcating among the different host-specific lineages, but was reticulate inside the rice lineage. We detected traces of introgression from a nonrice genome in the rice reference 70-15genome. Among M. oryzae isolates and host-specific lineages, the genome composition in terms of frequencies of genes putatively involved in pathogenicity (effectors, secondary metabolism, cazome) was conserved. However, 529 shared families were found only in nonrice lineages, whereas the rice lineage possessed 86 specific families absent from the nonrice genomes. Our results confirmed that the host specificity of M. oryzae isolates was associated with a divergence between lineages without major gene flow and that, despite the strong conservation of gene families between lineages, adaptation to different hosts, especially to rice, was associated with the presence of a small number of specific gene families. All information was gathered in a public database (http://genome.jouy.inra.fr/gemo).

Over the past decade, considerable advances have been made in the understanding of the role of fungal effectors, and especially small secreted proteins (SSPs), in the infectious process. NGS technologies offer powerful tools to study, at the genomic scale, how deep are SSPs involved in the adaptation of fungal populations to different host plants. We addressed this question in the plant pathogenic fungus Magnaporthe oryzae, the agent of blast on rice and other Poaceae. This species encompasses isolated genetic lineages specifically attacking different hosts. In the GEMO project, we sequenced eight strains of M. oryzae representing different genetic groups pathogenic of different species of Poacees (5 strains attacking rice Oryza sativa, 1 attacking wheat Triticum sp., 1 attacking foxtail millet Setaria sp., 1 attacking finger millet Eleusine sp.), and one strain of the sister species M. grisea (attacking fonio millet Digitaria sp). The nine genomes have been sequenced using NGS technologies (454 and Solexa/Illumina) and assembled by the Genoscope (Evry, France). We included the public reference strain of M. oryzae 70-15 in our analyses. Gene annotation and orthology predictions have been carried out. We also annotated transposable elements and assessed the amount of horizontal transfers. Here we present the characterization of the repertoires of SSPs in the nine genomes, established using classical predictors of peptide signals (SignalP), transmembrane domains (TMHMM), GPI anchors (PrediGPI) and subcellular location assignment (TargetP). These lists were then curated using two complementary approaches: systematic tBlastn searches of the SSP predicted in each genome against the nine genomic databases of the project (including its own), and gene mining through the RNAseq analysis of the in planta transcriptome of one of the strain. We will compare these lists with orthology predictions to analyze the core-secretome and the dynamics of gains/losses/duplications of SSPs in the different lineages. We will also address the question of co-localization of SSPs with transposable elements. Finally we will search for signatures of adaptive evolution in SSPs.

The rapid evolution of particular genes is essential for the adaptation of pathogens to new hosts and new environments. Powerful methods have been developed for detecting targets of selection in the genome. Here we used divergence data to compare genes among four closely related fungal pathogens adapted to different hosts to elucidate the functions putatively involved in adaptive processes. For this goal, ESTs were sequenced in the specialist fungal pathogens Botrytis tulipae and Botrytis ficariarum, and compared with genome sequences of Botrytis cinerea and Sclerotinia sclerotiorum, responsible for diseases on over 200 plant species. A maximum likelihood-based analysis of 642 predicted orthologs detected 21 genes showing footprints of positive selection. These results were validated by resequencing nine of these genes in additional Botrytis species, showing they have also been rapidly evolving in other related species. Twenty of the 21 genes had not previously been identified as pathogenicity factors in B. cinerea, but some had functions related to plant–fungus interactions. The putative functions were involved in respiratory and energy metabolism, protein and RNA metabolism, signal transduction or virulence, similarly to what was detected in previous studies using the same approach in other pathogens. Mutants of B. cinerea were generated for four of these genes as a first attempt to elucidate their functions.

We present a general method for assessing threading score significance. The threading score of a protein sequence, thread onto a given structure, should be compared with the threading score distribution of a random amino-acid sequence, of the same length, thread on the same structure; small p-values point significantly high scores. We claim that, due to general protein contact map properties, this reference distribution is a Weibull extreme value distribution whose parameters depend on the threading method, the structure, the length of the query and the random sequence simulation model used. These parameters can be estimated off-line with simulated sequence samples, for different sequence lengths. They can further be interpolated at the exact length of a query, enabling the quick computation of the p-value.

En genetique des populations, les modeles sont souvent complexes ce qui rend l'evaluation de la vraisemblance difficile, si ce n'est impossible. En revanche, des mecanismes de generation de donnees sont parfois disponibles ce qui explique pourquoi les methodes d'inference bayesienne sans vraisemblance sont si utilisees dans ce domaine. Nous nous interessons ici a la detection des effets de la selection darwinienne sur un gene. Les donnees sont alors constituees d'un ensemble de sequences homologues. Notre objectif est d'estimer la distribution a posteriori de certains parametres, les autres etant consideres comme des parametres de nuisance. Ces derniers incluent un arbre phylogenetique qui est ici represente par un arbre de coalescence, ce qui correspond au modele de Moran. Sous cette hypothese, nous pouvons construire un mecanisme de generation de donnees. Notre procedure d'estimation repose sur l'algorithme ABC-SMC (Del Moral et al., 2009). Nous montrerons ici les resultats obtenus sur le gene \textitenv du virus HIV.

Numerous genes in diverse organisms have been shown to be under positive selection, especially genes involved in reproduction, adaptation to contrasting environments, hybrid inviability, and host-pathogen interactions. Looking for genes under positive selection in pathogens has been a priority in efforts to investigate coevolution dynamics and to develop vaccines or drugs. To elucidate the functions involved in host specialization, here we aimed at identifying candidate sequences that could have evolved under positive selection among closely related pathogens specialized on different hosts. For this goal, we sequenced c. 17,000-32,000 ESTs from each of four Microbotryum species, which are fungal pathogens responsible for anther smut disease on host plants in the Caryophyllaceae. Forty-two of the 372 predicted orthologous genes showed significant signal of positive selection, which represents a good number of candidate genes for further investigation. Sequencing 16 of these genes in 9 additional Microbotryum species confirmed that they have indeed been rapidly evolving in the pathogen species specialized on different hosts. The genes showing significant signals of positive selection were putatively involved in nutrient uptake from the host, secondary metabolite synthesis and secretion, respiration under stressful conditions and stress response, hyphal growth and differentiation, and regulation of expression by other genes. Many of these genes had transmembrane domains and may therefore also be involved in pathogen recognition by the host. Our approach thus revealed fruitful and should be feasible for many non-model organisms for which candidate genes for diversifying selection are needed.

BACKGROUND:The increasing availability of fungal genome sequences provides large numbers of proteins for evolutionary and phylogenetic analyses. However the heterogeneity of data, including the quality of genome annotation and the difficulty of retrieving true orthologs, makes such investigations challenging. The aim of this study was to provide a reliable and integrated resource of orthologous gene families to perform comparative and phylogenetic analyses in fungi.DESCRIPTION:FUNYBASE is a database dedicated to the analysis of fungal single-copy genes extracted from available fungal genomes sequences, their classification into reliable clusters of orthologs, and the assessment of their informative value for phylogenetic reconstruction based on amino acid sequences. The current release of FUNYBASE contains two types of protein data: (i) a complete set of protein sequences extracted from 30 public fungal genomes and classified into clusters of orthologs using a robust automated procedure, and (ii) a subset of 246 reliable ortholog clusters present as single copy genes in 21 fungal genomes. For each of these 246 ortholog clusters, phylogenetic trees were reconstructed based on their amino acid sequences. To assess the informative value of each ortholog cluster, each was compared to a reference species tree constructed using a concatenation of roughly half of the 246 sequences that are best approximated by the WAG evolutionary model. The orthologs were classified according to a topological score, which measures their ability to recover the same topology as the reference species tree. The full results of these analyses are available on-line with a user-friendly interface that allows for searches to be performed by species name, the ortholog cluster, various keywords, or using the BLAST algorithm. Examples of fruitful utilization of FUNYBASE for investigation of fungal phylogenetics are also presented.CONCLUSION:FUNYBASE constitutes a novel and useful resource for two types of analyses: (i) comparative studies can be greatly facilitated by reliable clusters of orthologs across sets of user-defined fungal genomes, and (ii) phylogenetic reconstruction can be improved by identifying genes with the highest informative value at the desired taxonomic level.

Gibbs random fields are polymorphous statistical models that can be used to analyse different types of dependence, in particular for spatially correlated data. However, when those models are faced with the challenge of selecting a dependence structure from many, the use of standard model choice methods is hampered by the unavailability of the normalising constant in the Gibbs likelihood. In particular, from a Bayesian perspective, the computation of the posterior probabilities of the models under competition requires special likelihood-free simulation techniques like the Approximate Bayesian Computation (ABC) algorithm that is intensively used in population Genetics. We show in this paper how to implement an ABC algorithm geared towards model choice in the general setting of Gibbs random fields, demonstrating in particular that there exists a sufficient statistic across models. The accuracy of the approximation to the posterior probabilities can be further improved by importance sampling on the distribution of the models. The practical aspects of the method are detailed through two applications, the test of an iid Bernoulli model versus a first-order Markov chain, and the choice of a folding structure for a protein of Thermotoga maritima implicated into signal transduction processes.

Phylogenies involving nonmodel species are based on a few genes, mostly chosen following historical or practical criteria. Because gene trees are sometimes incongruent with species trees, the resulting phylogenies may not accurately reflect the evolutionary relationships among species. The increase in availability of genome sequences now provides large numbers of genes that could be used for building phylogenies. However, for practical reasons only a few genes can be sequenced for a wide range of species. Here we asked whether we can identify a few genes, among the single-copy genes common to most fungal genomes, that are sufficient for recovering accurate and well-supported phylogenies. Fungi represent a model group for phylogenomics because many complete fungal genomes are available. An automated procedure was developed to extract single-copy orthologous genes from complete fungal genomes using a Markov Clustering Algorithm (Tribe-MCL). Using 21 complete, publicly available fungal genomes with reliable protein predictions, 246 single-copy orthologous gene clusters were identified. We inferred the maximum likelihood trees using the individual orthologous sequences and constructed a reference tree from concatenated protein alignments. The topologies of the individual gene trees were compared to that of the reference tree using three different methods. The performance of individual genes in recovering the reference tree was highly variable. Gene size and the number of variable sites were highly correlated and significantly affected the performance of the genes, but the average substitution rate did not. Two genes recovered exactly the same topology as the reference tree, and when concatenated provided high bootstrap values. The genes typically used for fungal phylogenies did not perform well, which suggests that current fungal phylogenies based on these genes may not accurately reflect the evolutionary relationships among species. Analyses on subsets of species showed that the phylogenetic performance did not seem to depend strongly on the sample. We expect that the best-performing genes identified here will be very useful for phylogenetic studies of fungi, at least at a large taxonomic scale. Furthermore, we compare the method developed here for finding genes for building robust phylogenies with previous ones and we advocate that our method could be applied to other groups of organisms when more complete genomes are available.

This paper provides a theoretical description of the chromosome architecture resulting from a given number of generations in a back-cross. It is worth considering chromosome architecture as being dependent on a marked point process, whose properties themselves depend on the crossing-over model used. The resulting architecture is presented here for two different models, one without interference, the other with complete interference. Exact distributions, with easy-to-compute formulae, are derived for quantities of interest, as the lengths of donor or receiver fragments, for any chromosome length and for both crossing-over models. Examples are presented to illustrate the use of these distributions in introgression programs.

BACKGROUND:The basidiomycete fungus Microbotryum violaceum is responsible for the anther-smut disease in many plants of the Caryophyllaceae family and is a model in genetics and evolutionary biology. Infection is initiated by dikaryotic hyphae produced after the conjugation of two haploid sporidia of opposite mating type. This study describes M. violaceum ESTs corresponding to nuclear genes expressed during conjugation and early hyphal production.RESULTS:A normalized cDNA library generated 24,128 sequences, which were assembled into 7,765 unique genes; 25.2% of them displayed significant similarity to annotated proteins from other organisms, 74.3% a weak similarity to the same set of known proteins, and 0.5% were orphans. We identified putative pheromone receptors and genes that in other fungi are involved in the mating process. We also identified many sequences similar to genes known to be involved in pathogenicity in other fungi. The M. violaceum EST database, MICROBASE, is available on the Web and provides access to the sequences, assembled contigs, annotations and programs to compare similarities against MICROBASE.CONCLUSION:This study provides a basis for cloning the mating type locus, for further investigation of pathogenicity genes in the anther smut fungi, and for comparative genomics.

BACKGROUND:Comparative mapping provides new insights into the evolutionary history of genomes. In particular, recent studies in mammals have suggested a role for segmental duplication in genome evolution. In some species such as Drosophila or maize, transposable elements (TEs) have been shown to be involved in chromosomal rearrangements. In this work, we have explored the presence of interspersed repeats in regions of chromosomal rearrangements, using an updated high-resolution integrated comparative map among cattle, man and mouse.RESULTS:The bovine, human and mouse comparative autosomal map has been constructed using data from bovine genetic and physical maps and from FISH-mapping studies. We confirm most previous results but also reveal some discrepancies. A total of 211 conserved segments have been identified between cattle and man, of which 33 are new segments and 72 correspond to extended, previously known segments. The resulting map covers 91% and 90% of the human and bovine genomes, respectively. Analysis of breakpoint regions revealed a high density of species-specific interspersed repeats in the human and mouse genomes.CONCLUSION:Analysis of the breakpoint regions has revealed specific repeat density patterns, suggesting that TEs may have played a significant role in chromosome evolution and genome plasticity. However, we cannot rule out that repeats and breakpoints accumulate independently in the few same regions where modifications are better tolerated. Likewise, we cannot ascertain whether increased TE density is the cause or the consequence of chromosome rearrangements. Nevertheless, the identification of high density repeat clusters combined with a well-documented repeat phylogeny should highlight probable breakpoints, and permit their precise dating. Combining new statistical models taking the present information into account should help reconstruct ancestral karyotypes.

Proteins are major constituents of living cells, forming many cellular components and most enzymes. So, knowledge of 3D protein structures is essential to understand biological mechanisms. Researchers often use neural networks to predict secondary structure in proteins, but the networks can be hard to interpret. This alternative method uses an optimal and interpretable hidden Markov model to classify protein residues. These HMM models account for the transitions observed in 3D structures and allow a predictive approach. We've developed a method for finding an optimal HMM to classify residues into secondary-structure classes. HMMs both provide a probabilistic framework for sequence treatment and produce interpretable models.

Abstract Background Analysis of variance is a powerful approach to identify differentially expressed genes in a complex experimental design for microarray and macroarray data. The advantage of the anova model is the possibility to evaluate multiple sources of variation in an experiment. Results AnovArray is a package implementing ANOVA for gene expression data using SAS® statistical software. The originality of the package is 1) to quantify the different sources of variation on all genes together, 2) to provide a quality control of the model, 3) to propose two models for a gene's variance estimation and to perform a correction for multiple comparisons. Conclusion AnovArray is freely available at http://www-mig.jouy.inra.fr/stat/AnovArray and requires only SAS® statistical software.

We present here the use of a new statistical segmentation method on the Bacillus subtilis chromosome sequence. Maximum likelihood parameter estimation of a hidden Markov model, based on the expectation-maximization algorithm, enables one to segment the DNA sequence according to its local composition. This approach is not based on sliding windows; it enables different compositional classes to be separated without prior knowledge of their content, size and localization. We compared these compositional classes, obtained from the sequence, with the annotated DNA physical map, sequence homologies and repeat regions. The first heterogeneity revealed discriminates between the two coding strands and the non-coding regions. Other main heterogeneities arise; some are related to horizontal gene transfer, some to t-enriched composition of hydrophobic protein coding strands, and others to the codon usage fitness of highly expressed genes. Concerning potential and established gene transfers, we found 9 of the 10 known prophages, plus 14 new regions of atypical composition. Some of them are surrounded by repeats, most of their genes have unknown function or possess homology to genes involved in secondary catabolism, metal and antibiotic resistance. Surprisingly, we notice that all of these detected regions are a + t-richer than the host genome, raising the question of their remote sources.

A coding sequence is defined as a DNA sequence coding the primary structure of a protein (a polypeptide). Such a sequence must satisfy a specific constraint, which consists in coding a functional protein. As the genetic code is degenerated, there exists, for a given polypeptide, a set of synonymous sequences which would code the same polypeptide. Translation conditional models are being defined on such sets. The aim of this paper is to give a common formalism. Besides the codon bias model, a few other conditional models will be defined. Statistical estimators and comparison methods will be briefly presented. These models can be used for gene classification, or to find out, in a real sequence, remarkable features. An example will be presented on Escherichia coli genes.