Stability of Cross-Feeding Polymorphisms in Microbial Communities

Abstract

Cross-feeding, a relationship wherein one organism consumes metabolites excreted by another, is a ubiquitous feature of natural and clinically-relevant microbial communities and could be a key factor promoting diversity in extreme and/or nutrient-poor environments. However, it remains unclear how readily cross-feeding interactions form, and therefore our ability to predict their emergence is limited. In this paper we developed a mathematical model parameterized using data from the biochemistry and ecology of an E. coli cross-feeding laboratory system. The model accurately captures short-term dynamics of the two competitors that have been observed empirically and we use it to systematically explore the stability of cross-feeding interactions for a range of environmental conditions. We find that our simple system can display complex dynamics including multi-stable behavior separated by a critical point. Therefore whether cross-feeding interactions form depends on the complex interplay between density and frequency of the competitors as well as on the concentration of resources in the environment. Moreover, we find that subtly different environmental conditions can lead to dramatically different results regarding the establishment of cross-feeding, which could explain the apparently unpredictable between-population differences in experimental outcomes. We argue that mathematical models are essential tools for disentangling the complexities of cross-feeding interactions.

A shared limiting resource leads to competitive exclusion in a cross‐feeding system

“…also suggest, the more similar the metabolism is between cross‐feeding strains, the more likely it is that nutrient addition will shift a mutualism to a negative interaction. Consistent with this idea, mathematical predictions suggest that minimizing competition is critical for stabilizing cross‐feeding interactions (Doebeli, ; Pfeiffer and Bonhoeffer, ; Gudelj et al ., ; McCully et al ., ). It will be interesting to examine whether stable cross‐feeding interactions in natural systems are more common between species with smaller degrees of niche overlap.…”

Enhanced production of heterologous proteins by a synthetic microbial community: Conditions and trade-offs

“…5). While coexistence in natural and synthetic ecosystems has been studied in previous work [30,31,33], and is an obvious prerequisite for the possibility of increasing productivity by a consortium, the observation that this dependency works in both directions, in that the production of a heterologous protein affects coexistence as well, is a novel finding of this study. In particular, the analysis of the model shows that the coexistence region is shifted towards lower dilution rates (Fig.…”

“…The synthetic consortium considered here consists of two E. coli strains, one growing on glucose and producing the heterologous protein, and the other preferentially growing on acetate and thus removing this growth-inhibitory by-product from the environment [20]. While the overall structure of the model is similar to other population-based models of synthetic mutualistic consortia [28][29][30][31][32][33], it also differs on key points from previous work in order to account for regulatory mechanisms specific to E. coli. We here explain the model for the producer strain in detail and then appropriately modify the model for the cleaner in The consortium consists of two bacterial species.…”

“…2). Like in other work on the mathematical modeling of community dynamics [28][29][30][31][32][33], we have developed coarsegrained models of the physiology of the individual species in the consortium, which requires making stark simplying assumptions (see [24,27] for other perspectives, based on genome-wide flux balance models). Despite the simplifications, however, a critical requirement to assess the potential for productivity gains is the ability of the model to qualitatively and quantitatively reproduce nutrient production and consumption patterns, as well as to predict the growth rates of individual species.…”

“…The models we develop differ in two main respects from existing coarse-grained models of similarly structured communities [28][29][30][31][32][33]. First, we take into account a range of mechanisms controlling the growth phenotypes of the E. coli strains constituting our consortium, with the aim of obtaining quantitatively predictive models.…”