The horizontal transfer of genes is fundamental for the eco-evolutionary dynamics of microbial communities, such as oceanic plankton, soil, and the human microbiome. In the case of an acquired beneficial gene, classic population genetics would predict a genome-wide selective sweep, whereby the genome spreads clonally within the community and together with the beneficial gene, removing genome diversity. Instead, several sources of metagenomic data show the existence of "gene-specific sweeps", whereby a beneficial gene spreads across a bacterial community, maintaining genome diversity. Several hypotheses have been proposed to explain this process, including the decreasing gene flow between ecologically distant populations, frequency-dependent selection from linked deleterious allelles, and very high rates of horizontal gene transfer. Here, we propose an additional possible scenario grounded in eco-evolutionary principles. Specifically, we show by a mathematical model and simulations that a metacommunity where species can occupy multiple patches, acting together with a realistic (moderate) HGT rate, helps maintain genome diversity. Assuming a scenario of patches dominated by single species, our model predicts that diversity only decreases moderately upon the arrival of a new beneficial gene, and that losses in diversity can be quickly restored. We explore the generic behaviour of diversity as a function of three key parameters, frequency of insertion of new beneficial genes, migration rates and horizontal transfer rates.Our results provides a testable explanation for how diversity can be maintained by gene-specific sweeps even in the absence of high horizontal gene transfer rates.

Growing cells adopt common basic strategies to achieve optimal resource allocation under limited resource availability. Our current understanding of such "growth laws" neglects degradation, assuming that it occurs slowly compared to the cell cycle duration. Here we argue that this assumption cannot hold at slow growth, leading to important consequences. We propose a simple framework showing that at slow growth protein degradation is balanced by a fraction of "maintenance" ribosomes. Consequently, active ribosomes do not drop to zero at vanishing growth, but as growth rate diminishes, an increasing fraction of active ribosomes performs maintenance. Through a detailed analysis of compiled data, we show that the predictions of this model agree with data from E. coli and S. cerevisiae. Intriguingly, we also find that protein degradation increases at slow growth, which we interpret as a consequence of active waste management and/or recycling. Our results highlight protein turnover as an underrated factor for our understanding of growth laws across kingdoms.

UNLABELLED: Different experimental results suggest the presence of an interplay between global transcriptional regulation and chromosome spatial organization in bacteria. The identification and clear visualization of spatial clusters of contiguous genes targeted by specific DNA-binding proteins or sensitive to nucleoid perturbations can elucidate links between nucleoid structure and gene expression patterns. Similarly, statistical analysis to assess correlations between results from independent experiments can provide the integrated analysis needed in this line of research. NuST (Nucleoid Survey tools), based on the Escherichia coli genome, gives the non-expert the possibility to analyze the aggregation of genes or loci sets along the genome coordinate, at different scales of observation. It is useful to discover correlations between different sources of data (e.g. expression, binding or genomic data) and genome organization. A user can use it on datasets in the form of gene lists coming from his/her own experiments or bioinformatic analyses, but also make use of the internal database, which collects data from many published studies. AVAILABILITY AND IMPLEMENTATION: NuST is a web server (available at http://www.lgm.upmc.fr/nust/). The website is implemented in PHP, SQLite and Ajax, with all major browsers supported, while the core algorithms are optimized and implemented in C. NuST has an extensive help page and provides a direct visualization of results as well as different downloadable file formats. A template Perl code for automated access to the web server can be downloaded at http://www.lgm.upmc.fr/nust/downloads/, in order to allow the users to use NuST in systematic bioinformatic analyses.

Abstract Motivation: Transcription networks, and other directed networks can be characterized by some topological observables (e.g. network motifs), that require a suitable randomized network ensemble, typically with the same degree sequences of the original ones. The commonly used algorithms sometimes have long convergence times, and sampling problems. We present here an alternative, based on a variant of the importance sampling Monte Carlo developed by (Chen et al.). Availability: The algorithm is available at http://wwwteor.mi.infn.it/~bassetti/downloads.html Contact: [email protected] and [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.