Effects of stochasticity and division of labor in toxin production on two-strain bacterial competition in Escherichia coli

Abstract

In phenotypically heterogeneous microbial populations, the decision to adopt one or another phenotype is often stochastically regulated. However, how this stochasticity affects interactions between competing microbes in mixed communities is difficult to assess. One example of such an interaction system is the competition of an Escherichia coli strain C, which performs division of labor between reproducers and self-sacrificing toxin producers, with a toxin-sensitive strain S. The decision between reproduction or toxin production within a single C cell is inherently stochastic. Here, combining experimental and theoretical approaches, we demonstrate that this stochasticity in the initial phase of colony formation is the crucial determinant for the competition outcome. In the initial phase (t < 12h), stochasticity influences the formation of viable C clusters at the colony edge. In the subsequent phase, the effective fitness differences (set primarily by the degree of division of labor in the C strain population) dictate the deterministic population dynamics and consequently competition outcome. In particular, we observe that competitive success of the C strain is only found if (i) a C edge cluster has formed at the end of the initial competition phase and (ii) the beneficial and detrimental effects of toxin production are balanced, which is the case at intermediate toxin producer fractions. Our findings highlight the importance of stochastic processes during the initial phase of colony formation, which might be highly relevant for other microbial community interactions in which the random choice between phenotypes can have long-lasting consequences for community fate.

High-resolution sweep metagenomics using fast probabilistic inference

“…The co-occurrence of different organisms could be a snapshot in time of a wider process of lineage succession [26] in which the resident microbiota might resist new colonizations or be displaced by recently acquired bacteria [27,28]. Further, we suggest we may be able to infer complex interactions between organisms that occupy different microniches [29] and are not in direct competition [30,31] by analysing their co-occurrence. Therefore, this approach provides a means to investigate the nature of polymicrobial infections to improve our understanding of the spread of a specific organism between hosts and transmission to humans in addition to enabling characterization of physical and temporal variation in the distribution of lineages among multi-strain samples.…”

Evolutionary Stabilization of Cooperative Toxin Production through a Bacterium-Plasmid-Phage Interplay

“…As colicin release benefits the population but is inevitably linked to death of the producer, colicins are a prime model to investigate how costly phenotypes are evolutionarily stabilized in bacterial populations. Particular attention has been paid to group A colicin systems, where colicin production is linked to bacterial lysis (13,29,30). In this study, we focused on the Salmonella ColIb system, where colicin release is not directly linked to ColIb production but realized by temperate phage-mediated lysis (14).…”

“…Once a cell has turned on colicin expression, it produces the toxin for a short time and subsequently releases the toxin into the environment upon cell lysis (12). Especially at low cell numbers, stochastic switching can have important consequences on population dynamics and thereby determine competition outcome of colicin-producing and -sensitive strains (13).…”

Expansion, Exploitation and Extinction: Niche Construction in Ephemeral Landscapes

“…Notably, how a few stochastic events changing the connectivity of patches between populations of niche constructing and exploiting strategies lead to drastically different outcomes. Specifically, the initial conditions of these founder populations (clusters of occupied sites surrounded by destroyed sites) can exhibit both stochastic (dominated by random chance events) and deterministic phases (predicted by the proportion of NC vs. CP sites in a cluster) (56). In this case, the transition between these two phases is determined by rc.…”