Comparative genomics of Aeschynomene symbionts : insights into the ecological lifestyle of nod-independent photosynthetic Bradyrhizobia

Tropical aquatic species of the legume genus Aeschynomene are stem- and root-nodulated by bradyrhizobia strains that exhibit atypical features such as photosynthetic capacities or the use of a nod gene-dependent (ND) or a nod gene-independent (NI) pathway to enter into symbiosis with legumes. In this study we used a comparative genomics approach on nine Aeschynomene symbionts representative of their phylogenetic diversity. We produced draft genomes of bradyrhizobial strains representing different phenotypes: five NI photosynthetic strains (STM3809, ORS375, STM3847, STM4509 and STM4523) in addition to the previously sequenced ORS278 and BTAi1 genomes, one photosynthetic strain ORS285 hosting both ND and NI symbiotic systems, and one NI non-photosynthetic strain (STM3843). Comparative genomics allowed us to infer the core, pan and dispensable genomes of Aeschynomene bradyrhizobia, and to detect specific genes and their location in Genomic Islands (GI). Specific gene sets linked to photosynthetic and NI/ND abilities were identified, and are currently being studied in functional analyses

Heart rate variability in sleeping neonates

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Genome Sequence of the Saprophyte Leptospira biflexa Provides Insights into the Evolution of Leptospira and the Pathogenesis of Leptospirosis

Leptospira biflexa is a free-living saprophytic spirochete present in aquatic environments. We determined the genome sequence of L. biflexa, making it the first saprophytic Leptospira to be sequenced. The L. biflexa genome has 3,590 protein-coding genes distributed across three circular replicons: the major 3,604 chromosome, a smaller 278-kb replicon that also carries essential genes, and a third 74-kb replicon. Comparative sequence analysis provides evidence that L. biflexa is an excellent model for the study of Leptospira evolution; we conclude that 2052 genes (61%) represent a progenitor genome that existed before divergence of pathogenic and saprophytic Leptospira species. Comparisons of the L. biflexa genome with two pathogenic Leptospira species reveal several major findings. Nearly one-third of the L. biflexa genes are absent in pathogenic Leptospira. We suggest that once incorporated into the L. biflexa genome, laterally transferred DNA undergoes minimal rearrangement due to physical restrictions imposed by high gene density and limited presence of transposable elements. In contrast, the genomes of pathogenic Leptospira species undergo frequent rearrangements, often involving recombination between insertion sequences. Identification of genes common to the two pathogenic species, L. borgpetersenii and L. interrogans, but absent in L. biflexa, is consistent with a role for these genes in pathogenesis. Differences in environmental sensing capacities of L. biflexa, L. borgpetersenii, and L. interrogans suggest a model which postulates that loss of signal transduction functions in L. borgpetersenii has impaired its survival outside a mammalian host, whereas L. interrogans has retained environmental sensory functions that facilitate disease transmission through water

In silico prioritisation of candidate genes for prokaryotic gene function discovery: an application of phylogenetic profiles

Background: In silico candidate gene prioritisation (CGP) aids the discovery of gene functions by ranking genes according to an objective relevance score. While several CGP methods have been described for identifying human disease genes, corresponding methods for prokaryotic gene function discovery are lacking. Here we present two prokaryotic CGP methods, based on phylogenetic profiles, to assist with this task. Results: Using gene occurrence patterns in sample genomes, we developed two CGP methods (statistical and inductive CGP) to assist with the discovery of bacterial gene functions. Statistical CGP exploits the differences in gene frequency against phenotypic groups, while inductive CGP applies supervised machine learning to identify gene occurrence pattern across genomes. Three rediscovery experiments were designed to evaluate the CGP frameworks. The first experiment attempted to rediscover peptidoglycan genes with 417 published genome sequences. Both CGP methods achieved best areas under receiver operating characteristic curve (AUC) of 0.911 in Escherichia coli K-12 (EC-K12) and 0.978 Streptococcus agalactiae 2603 (SA-2603) genomes, with an average improvement in precision of >3.2-fold and a maximum of >27-fold using statistical CGP. A median AUC of >0.95 could still be achieved with as few as 10 genome examples in each group of genome examples in the rediscovery of the peptidoglycan metabolism genes. In the second experiment, a maximum of 109-fold improvement in precision was achieved in the rediscovery of anaerobic fermentation genes in EC-K12. The last experiment attempted to rediscover genes from 31 metabolic pathways in SA-2603, where 14 pathways achieved AUC >0.9 and 28 pathways achieved AUC >0.8 with the best inductive CGP algorithms. Conclusion: Our results demonstrate that the two CGP methods can assist with the study of functionally uncategorised genomic regions and discovery of bacterial gene-function relationships. Our rediscovery experiments also provide a set of standard tasks against which future methods may be compared.12 page(s

Intuitive Visualization and Analysis of Multi-Omics Data and Application to Escherichia coli Carbon Metabolism

Combinations of ‘omics’ investigations (i.e, transcriptomic, proteomic, metabolomic and/or fluxomic) are increasingly applied to get comprehensive understanding of biological systems. Because the latter are organized as complex networks of molecular and functional interactions, the intuitive interpretation of multi-omics datasets is difficult. Here we describe a simple strategy to visualize and analyze multi-omics data. Graphical representations of complex biological networks can be generated using Cytoscape where all molecular and functional components could be explicitly represented using a set of dedicated symbols. This representation can be used i) to compile all biologically-relevant information regarding the network through web link association, and ii) to map the network components with multi-omics data. A Cytoscape plugin was developed to increase the possibilities of both multi-omic data representation and interpretation. This plugin allowed different adjustable colour scales to be applied to the various omics data and performed the automatic extraction and visualization of the most significant changes in the datasets. For illustration purpose, the approach was applied to the central carbon metabolism of Escherichia coli. The obtained network contained 774 components and 1232 interactions, highlighting the complexity of bacterial multi-level regulations. The structured representation of this network represents a valuable resource for systemic studies of E. coli, as illustrated from the application to multi-omics data. Some current issues in network representation are discussed on the basis of this work

P-value based visualization of codon usage data

Two important and not yet solved problems in bacterial genome research are the identification of horizontally transferred genes and the prediction of gene expression levels. Both problems can be addressed by multivariate analysis of codon usage data. In particular dimensionality reduction methods for visualization of multivariate data have shown to be effective tools for codon usage analysis. We here propose a multidimensional scaling approach using a novel similarity measure for codon usage tables. Our probabilistic similarity measure is based on P-values derived from the well-known chi-square test for comparison of two distributions. Experimental results on four microbial genomes indicate that the new method is well-suited for the analysis of horizontal gene transfer and translational selection. As compared with the widely-used correspondence analysis, our method did not suffer from outlier sensitivity and showed a better clustering of putative alien genes in most cases

MicroScope: a platform for microbial genome annotation and comparative genomics

The initial outcome of genome sequencing is the creation of long text strings written in a four letter alphabet. The role of in silico sequence analysis is to assist biologists in the act of associating biological knowledge with these sequences, allowing investigators to make inferences and predictions that can be tested experimentally. A wide variety of software is available to the scientific community, and can be used to identify genomic objects, before predicting their biological functions. However, only a limited number of biologically interesting features can be revealed from an isolated sequence. Comparative genomics tools, on the other hand, by bringing together the information contained in numerous genomes simultaneously, allow annotators to make inferences based on the idea that evolution and natural selection are central to the definition of all biological processes. We have developed the MicroScope platform in order to offer a web-based framework for the systematic and efficient revision of microbial genome annotation and comparative analysis (http://www.genoscope.cns.fr/agc/microscope). Starting with the description of the flow chart of the annotation processes implemented in the MicroScope pipeline, and the development of traditional and novel microbial annotation and comparative analysis tools, this article emphasizes the essential role of expert annotation as a complement of automatic annotation. Several examples illustrate the use of implemented tools for the review and curation of annotations of both new and publicly available microbial genomes within MicroScope’s rich integrated genome framework. The platform is used as a viewer in order to browse updated annotation information of available microbial genomes (more than 440 organisms to date), and in the context of new annotation projects (117 bacterial genomes). The human expertise gathered in the MicroScope database (about 280,000 independent annotations) contributes to improve the quality of microbial genome annotation, especially for genomes initially analyzed by automatic procedures alone

Life in an arsenic-containing gold mine: genome and physiology of the autotrophic arsenite-oxidizing bacterium rhizobium sp. NT-26

Arsenic is widespread in the environment and its presence is a result of natural or anthropogenic activities. Microbes have developed different mechanisms to deal with toxic compounds such as arsenic and this is to resist or metabolize the compound. Here, we present the first reference set of genomic, transcriptomic and proteomic data of an Alphaproteobacterium isolated from an arsenic-containing goldmine: Rhizobium sp. NT-26. Although phylogenetically related to the plant-associated bacteria, this organism has lost the major colonizing capabilities needed for symbiosis with legumes. In contrast, the genome of Rhizobium sp. NT-26 comprises a megaplasmid containing the various genes, which enable it to metabolize arsenite. Remarkably, although the genes required for arsenite oxidation and flagellar motility/biofilm formation are carried by the megaplasmid and the chromosome, respectively, a coordinate regulation of these two mechanisms was observed. Taken together, these processes illustrate the impact environmental pressure can have on the evolution of bacterial genomes, improving the fitness of bacterial strains by the acquisition of novel functions

Experimental Evolution of a Plant Pathogen into a Legume Symbiont

Following acquisition of a rhizobial symbiotic plasmid, adaptive mutations in the virulence pathway allowed pathogenic Ralstonia solanacearum to evolve into a legume symbiont under plant selection