Cobamide-based interactions between soil bacteria can be predicted based on monoculture growth
Alvarez-Aponte, Z. I.; Anagu, H. I.; Mok, K. C.; Trotter, V. V.; Taga, M. E.
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Interactions between microbes shape the structure and function of microbial communities. While studying interactions is key to understanding microbial communities as a whole, gaining a detailed mechanistic view is challenging due to the scale of co-occurring interactions. The model nutrient approach enables the study of a subset of interactions involving a single nutrient class and can shed light on broader interaction mechanisms involving other nutrients. Here, we focus on cobamides, the cobalamin (vitamin B12) family of enzyme cofactors, to study nutrient competition and nutrient-sharing interactions in co-cultures and tri-cultures. We examined bacteria that were previously isolated from a grassland soil and were characterized as "dependents" (require cobamides but cannot synthesize them) or "producers" (synthesize cobamides). The outcome of competition between a pair of dependents was predictable based on monoculture growth characteristics, with the dominant microbe determined by its adaptation to a specific cobamide concentration range. Moreover, cobamide producers could support the cobamide-dependent growth of dependents in co-culture and influenced the outcome of competition between dependents in tri-culture. We analyzed the metabolic capacity encoded in the genomes of producers and dependents and found that cobamides are likely the main shared nutrient in our co- and tri-cultures. These results highlight the utility of the model nutrient approach to characterize and predict interactions in bacterial consortia of increasing complexity. ImportanceInteractions between microbes shape their communities and the world around them. Because microbial communities are physically and chemically complex, predicting microbial interactions is often difficult. Using a set of four bacteria isolated from the same soil environment, we generated and tested predictions about the interactions involving a family of model nutrients related to vitamin B12. Understanding and predicting interactions can lead to greater insights into microbiome function.
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