Classifying Interactions in a Synthetic Bacterial Community Is Hindered by Inhibitory Growth Medium.

Predicting the fate of a microbial community and its member species relies on understanding the nature of their interactions. However, designing simple assays that distinguish between interaction types can be challenging. Here, we performed spent medium assays based on the predictions of a mathematical model to decipher the interactions among four bacterial species: Agrobacterium tumefaciens, Comamonas testosteroni, Microbacterium saperdae, and Ochrobactrum anthropi. While most experimental results matched model predictions, the behavior of C. testosteroni did not: its lag phase was reduced in the pure spent media of A. tumefaciens and M. saperdae but prolonged again when we replenished our growth medium. Further experiments showed that the growth medium actually delayed the growth of C. testosteroni, leading us to suspect that A. tumefaciens and M. saperdae could alleviate this inhibitory effect. There was, however, no evidence supporting such "cross-detoxification," and instead, we identified metabolites secreted by A. tumefaciens and M. saperdae that were then consumed or "cross-fed" by C. testosteroni, shortening its lag phase. Our results highlight that even simple, defined growth media can have inhibitory effects on some species and that such negative effects need to be included in our models. Based on this, we present new guidelines to correctly distinguish between different interaction types such as cross-detoxification and cross-feeding. IMPORTANCE Communities of microbes colonize virtually every place on earth. Ultimately, we strive to predict and control how these communities behave, for example, if they reside in our guts and make us sick. But precise control is impossible unless we can identify exactly how their member species interact with one another. To find a systematic way to measure interactions, we started very simply with a small community of four bacterial species and carefully designed experiments based on a mathematical model. This first attempt accurately mapped out interactions for all species except one. By digging deeper, we understood that our method failed for that species as it was suffering in the growth medium that we chose. A revised model that considered that growth media can be harmful could then make more accurate predictions. What we have learned with these four species can now be applied to decipher interactions in larger communities.

Spatial structure affects phage efficacy in infecting dual-strain biofilms of Pseudomonas aeruginosa.

Bacterial viruses, or phage, are key members of natural microbial communities. Yet much research on bacterial-phage interactions has been conducted in liquid cultures involving single bacterial strains. Here we explored how bacterial diversity affects the success of lytic phage in structured communities. We infected a sensitive Pseudomonas aeruginosa strain PAO1 with a lytic phage Pseudomonas 352 in the presence versus absence of an insensitive P. aeruginosa strain PA14, in liquid culture versus colonies on agar. We found that both in liquid and in colonies, inter-strain competition reduced resistance evolution in the susceptible strain and decreased phage population size. However, while all sensitive bacteria died in liquid, bacteria in colonies could remain sensitive yet escape phage infection, due mainly to reduced growth in colony centers. In sum, spatial structure can protect bacteria against phage infection, while the presence of competing strains reduces the evolution of resistance to phage.