Abstract
The exchange of metabolites among different bacterial genotypes is key for determining the structure and function of microbial communities. However, the factors that govern the establishment of these cross-feeding interactions remain poorly understood. While kin selection theory predicts that individuals should direct benefits preferentially to close relatives, the potential benefits resulting from a metabolic exchange may be larger for more distantly related species. Here we distinguish between these two possibilities by performing pairwise cocultivation experiments between auxotrophic recipients and 25 species of potential amino acid donors. Auxotrophic recipients were able to grow in the vast majority of pairs tested (78%), suggesting that metabolic cross-feeding interactions are readily established. Strikingly, both the phylogenetic distance between donor and recipient as well as the dissimilarity of their metabolic networks was positively associated with the growth of auxotrophic recipients. Finally, this result was corroborated in an in-silico analysis of a co-growth of species from a gut microbial community. Together, these findings suggest metabolic cross-feeding interactions are more likely to establish between strains that are metabolically more dissimilar. Thus, our work identifies a new rule of microbial community assembly, which can help predict, understand, and manipulate natural and synthetic microbial systems.
Significance Metabolic cross-feeding is critical for determining the structure and function of natural microbial communities. However, the rules that determine the establishment of these interactions remain poorly understood. Here we systematically analyze the propensity of different bacterial species to engage in unidirectional cross-feeding interactions. Our results reveal that synergistic growth was prevalent in the vast majority of cases analyzed. Moreover, both phylogenetic and metabolic dissimilarity between donors and recipients favored a successful establishment of metabolite exchange interactions. This work identifies a new rule of microbial community assembly that can help predict, understand, and manipulate microbial communities for diverse applications.
Competing Interest Statement
The authors have declared no competing interest.