BackgroundIn the bacterium Caulobacter crescentus, CtrA coordinates DNA replication, cell division, and polar morphogenesis and is considered the cell cycle master regulator. CtrA activity varies during cell cycle progression and is modulated by phosphorylation, proteolysis and transcriptional control. In a phosphorylated state, CtrA binds specific DNA sequences, regulates the expression of genes involved in cell cycle progression and silences the origin of replication. Although the circuitry regulating CtrA is known in molecular detail in Caulobacter, its conservation and functionality in the other alpha-proteobacteria are still poorly understood.ResultsOrthologs of Caulobacter factors involved in the regulation of CtrA were systematically scanned in genomes of alpha-proteobacteria. In particular, orthologous genes of the divL-cckA-chpT-ctrA phosphorelay, the divJ-pleC-divK two-component system, the cpdR-rcdA-clpPX proteolysis system, the methyltransferase ccrM and transcriptional regulators dnaA and gcrA were identified in representative genomes of alpha-proteobacteria. CtrA, DnaA and GcrA binding sites and CcrM putative methylation sites were predicted in promoter regions of all these factors and functions controlled by CtrA in all alphas were predicted.ConclusionsThe regulatory cell cycle architecture was identified in all representative alpha-proteobacteria, revealing a high diversification of circuits but also a conservation of logical features. An evolutionary model was proposed where ancient alphas already possessed all modules found in Caulobacter arranged in a variety of connections. Two schemes appeared to evolve: a complex circuit in Caulobacterales and Rhizobiales and a simpler one found in Rhodobacterales.

In this article we present MeDuSa (Multi-Draft based Scaffolder), an algorithm for genome scaffolding. MeDuSa exploits information obtained from a set of (draft or closed) genomes from related organisms to determine the correct order and orientation of the contigs. MeDuSa formalizes the scaffolding problem by means of a combinatorial optimization formulation on graphs and implements an efficient constant factor approximation algorithm to solve it. In contrast to currently used scaffolders, it does not require either prior knowledge on the microrganisms dataset under analysis (e.g. their phylogenetic relationships) or the availability of paired end read libraries. This makes usability and running time two additional important features of our method. Moreover, benchmarks and tests on real bacterial datasets showed that MeDuSa is highly accurate and, in most cases, outperforms traditional scaffolders. The possibility to use MeDuSa on eukaryotic datasets has also been evaluated, leading to interesting results.

Staphylococcus aureus is a preeminent bacterial pathogen capable of colonizing diverse ecological niches within its human host. We describe here the pangenome of S. aureus based on analysis of genome sequences from 64 strains of S. aureus spanning a range of ecological niches, host types, and antibiotic resistance profiles. Based on this set, S. aureus is expected to have an open pangenome composed of 7,411 genes and a core genome composed of 1,441 genes. Metabolism was highly conserved in this core genome; however, differences were identified in amino acid and nucleotide biosynthesis pathways between the strains. Genome-scale models (GEMs) of metabolism were constructed for the 64 strains of S. aureus. These GEMs enabled a systems approach to characterizing the core metabolic and panmetabolic capabilities of the S. aureus species. All models were predicted to be auxotrophic for the vitamins niacin (vitamin B3) and thiamin (vitamin B1), whereas strain-specific auxotrophies were predicted for riboflavin (vitamin B2), guanosine, leucine, methionine, and cysteine, among others. GEMs were used to systematically analyze growth capabilities in more than 300 different growth-supporting environments. The results identified metabolic capabilities linked to pathogenic traits and virulence acquisitions. Such traits can be used to differentiate strains responsible for mild vs. severe infections and preference for hosts (e.g., animals vs. humans). Genome-scale analysis of multiple strains of a species can thus be used to identify metabolic determinants of virulence and increase our understanding of why certain strains of this deadly pathogen have spread rapidly throughout the world.systems biology | mathematical modeling | pathogenicity | core genome | pangenome

Background: The pioneering ancestor of land plants that conquered terrestrial habitats around 500 million years ago had to face dramatic stresses including UV radiation, desiccation, and microbial attack. This drove a number of adaptations, among which the emergence of the phenylpropanoid pathway was crucial, leading to essential compounds such as flavonoids and lignin. However, the origin of this specific land plant secondary metabolism has not been clarified.

The genome of about 10% of bacterial species is divided among two or more large chromosome-sized replicons. The contribution of each replicon to the microbial life cycle (for example, environmental adaptations and/or niche switching) remains unclear. Here we report a genome-scale metabolic model of the legume symbiont Sinorhizobium meliloti that is integrated with carbon utilization data for 1,500 genes with 192 carbon substrates. Growth of S. meliloti is modelled in three ecological niches (bulk soil, rhizosphere and nodule) with a focus on the role of each of its three replicons. We observe clear metabolic differences during growth in the tested ecological niches and an overall reprogramming following niche switching. In silico examination of the inferred fitness of gene deletion mutants suggests that secondary replicons evolved to fulfil a specialized function, particularly host-associated niche adaptation. Thus, genes on secondary replicons might potentially be manipulated to promote or suppress host interactions for biotechnological purposes.

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Plasmids are vessels of genetic exchange in microbial communities. They are known to transfer between different host organisms and acquire diverse genetic elements from chromosomes and/or other plasmids. Therefore, they constitute an important element in microbial evolution by rapidly disseminating various genetic properties among different communities. A paradigmatic example of this is the dissemination of antibiotic resistance (AR) genes that has resulted in the emergence of multiresistant pathogenic bacterial strains. To globally analyze the evolutionary dynamics of plasmids, we built a large graph in which 2,343 plasmids (nodes) are connected according to the proteins shared by each other. The analysis of this gene-sharing network revealed an overall coherence between network clustering and the phylogenetic classes of the corresponding microorganisms, likely resulting from genetic barriers to horizontal gene transfer between distant phylogenetic groups. Habitat was not a crucial factor in clustering as plasmids from organisms inhabiting different environments were often found embedded in the same cluster. Analyses of network metrics revealed a statistically significant correlation between plasmid mobility and their centrality within the network, providing support to the observation that mobile plasmids are particularly important in spreading genes in microbial communities. Finally, our study reveals an extensive (and previously undescribed) sharing of AR genes between Actinobacteria and Gammaproteobacteria, suggesting that the former might represent an important reservoir of AR genes for the latter.

The spatial distribution of microbes on our planet is famously formulated in the Baas Becking hypothesis as “everything is everywhere but the environment selects.” While this hypothesis does not strictly rule out patterns caused by geographical effects on ecology and historical founder effects, it does propose that the remarkable dispersal potential of microbes leads to distributions generally shaped by environmental factors rather than geographical distance. By constructing sequence similarity networks from uncultured environmental samples, we show that microbial gene pool distributions are not influenced nearly as much by geography as ecology, thus extending the Bass Becking hypothesis from whole organisms to microbial genes. We find that gene pools are shaped by their broad ecological niche (such as sea water, fresh water, host, and airborne). We find that freshwater habitats act as a gene exchange bridge between otherwise disconnected habitats. Finally, certain antibiotic resistance genes deviate from the general trend of habitat specificity by exhibiting a high degree of cross-habitat mobility. The strong cross-habitat mobility of antibiotic resistance genes is a cause for concern and provides a paradigmatic example of the rate by which genes colonize new habitats when new selective forces emerge.