Abstract
Cocktail combinations of bacteria-infecting viruses (bacteriophages) can suppress pathogenic bacterial growth. However, predicting how phage cocktails influence microbial communities with complex ecological interactions, specifically cross-feeding interactions in which bacteria exchange nutrients, remains challenging. Here, we used experiments and mathematical simulations to determine how to best suppress a model pathogen, E. coli, when obligately cross-feeding with S. enterica. We tested whether the duration of pathogen suppression caused by a two-lytic phage cocktail was maximized when both phages targeted E. coli, or when one phage targeted E. coli and the other its cross-feeding partner, S. enterica. Experimentally, we observed that cocktails targeting both cross-feeders suppressed E. coli growth longer than cocktails targeting only E. coli. Two non-mutually exclusive mechanisms could explain these results: (i) we found that treatment with two E. coli phage led to the evolution of a mucoid phenotype that provided cross-resistance against both phages, and (ii) S. enterica set the growth rate of the coculture, and therefore, targeting S. enterica had a stronger effect on pathogen suppression. Simulations suggested that cross-resistance and the relative growth rates of cross-feeders modulated the duration of E. coli suppression. More broadly, we describe a novel bacteriophage cocktail strategy for pathogens that cross-feed.
Highlights
Phage have been used to treat pathogenic bacteria in human health, agriculture, and the food industry
We wondered whether the pathogen, E. coli, would be suppressed for longer by a phage cocktail combining two E. coli-targeting phage (‘pathogen-targeting cocktail’) or combining an E
We grew control cocultures without phage (‘phage-free’), and treatment cocultures with combinations of T7 and P1vir as E. coli-targeting phage and P22vir as a S. enterica-targeting phage
Summary
Phage have been used to treat pathogenic bacteria in human health, agriculture, and the food industry. We understand less about how treatment outcomes are affected by complex interactions among bacteria in a microbial community (Fazzino et al., 2020). One bacterial interaction of particular interest is cross-feeding, in which metabolites secreted by one bacterium are used as a nutrient source by another. This is a common interaction in natural systems (Schink, 2002; D’Souza et al, 2014; Mee et al, 2014; Zelezniak et al, 2015; Adamowicz et al, 2018). Understanding how complex ecological interactions involving pathogens affect phage treatment outcomes will be critical for designing effective therapies. We explore how two important factors - the potential for cross-resistance evolution and relative bacterial growth rates - interact with targeting strategies to suppress growth of a focal pathogen cross-feeding in an engineered coculture
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