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Abstract While different stages of mutualism can be observed in natural communities, the dynamics and mechanisms underlying the gradual erosion of independence of the initially autonomous organisms are not yet fully understood. In this study, by conducting the laboratory evolution on an engineered microbial community, we reproduce and molecularly track the stepwise progression towards enhanced partner entanglement. We observe that the evolution of the community both strengthens the existing metabolic interactions and leads to the emergence of de novo interdependence between partners for nitrogen metabolism, which is a common feature of natural symbiotic interactions. Selection for enhanced metabolic entanglement during the community evolution repeatedly occurred indirectly, via pleiotropies and trade-offs within cellular regulatory networks, and with no evidence of group selection. The indirect positive selection of metabolic dependencies between microbial community members, which results from the direct selection of other coupled traits in the same regulatory network, may therefore be a common but underappreciated driving force guiding the evolution of natural mutualistic communities.

Abstract The operation of the central metabolism is typically assumed to be deterministic, but dynamics and high connectivity of the metabolic network make it potentially prone to generating fluctuations. However, time-resolved measurements of metabolite levels in individual cells that are required to characterize such fluctuations remained a challenge, particularly in small bacterial cells. Here we use single-cell metabolite measurements based on Förster resonance energy transfer, combined with computer simulations, to explore the real-time dynamics of the metabolic network of Escherichia coli. We observe that steplike exposure of starved E. coli to glycolytic carbon sources elicits large periodic fluctuations in the intracellular concentration of pyruvate in individual cells. These fluctuations are consistent with predicted oscillatory dynamics of E. coli metabolic network, and they are primarily controlled by biochemical reactions around the pyruvate node. Our results further indicate that fluctuations in glycolysis propagate to other cellular processes, possibly leading to temporal heterogeneity of cellular states within a population.

Cellular signalling networks provide information to the cell, but the trade-off between accuracy of information transfer and energetic cost of doing so has not been assessed. Here, the authors investigate a MAPK signalling cascade in budding yeast and find that information is maximised per unit energetic cost.

Gram-positive bacteria can release signaling peptides that are ‘probed’ by intracellular receptors after being pumped into the cytoplasm. Here, Babel et al. show that these pump-probe networks can infer the fraction of signal-producing cells in a mixed population, and do not necessarily mediate typical quorum-sensing control.

Swimming bacteria perform collective motion at high cell density, yet it is unclear how this behaviour affects their ability to follow substance gradients in the environment. Here, Colin et al. address this question by studying motion of Escherichia coli in controlled chemical gradients.

Bacteria use chemotaxis receptors to perceive environmental factors. Here, the authors show that mutations in a chemotaxis receptor can invert the sensory response, e.g. from attractant to repellent, and use these mutants to map regions that enable the receptor to sense multiple environmental factors.

A small molecule (autoinducer 2, or AI-2) regulates biofilm formation and virulence in several bacteria, but its role in Escherichia coli is unknown. Here, Laganenka et al. show that chemotaxis towards self-produced AI-2 mediates autoaggregation and promotes stress resistance and biofilm formation in E. coli.

Cells of the yeast Saccharomyces cerevisiaecan mate with other cells of opposite mating type. Here, the authors show that the combination of a pheromone and a pheromone-degrading enzyme allows yeast cells to monitor relative mate abundance within a population and adjust their commitment to sexual reproduction.

Nature Communications 7: Article number: 12984 (2016); Published: 30 September 2016; Updated: 15 December 2016 Two previous studies describing the effects of autoinducer 2 (AI-2) on biofilm formation and chemotaxis in Escherichia coli were inadvertently omitted from the reference list of this Article.