Predicting bacterial interaction outcomes from monoculture growth and supernatant assays

Abstract

How to derive principles of community dynamics and stability is a central question in microbial ecology. To answer this, bottom-up experimental approaches, in which a small number of bacterial species are mixed, have become popular. However, experimental setups are typically limited because species are difficult to distinguish in mixed cultures and co-culture experiments are labor-intensive. Here, we use a community of four bacterial species to show that information from monoculture growth and inhibitory effects caused by secreted compounds can be combined to reliably predict pairwise species interactions in co-cultures. Specifically, integrative parameters from growth curves allow to build a competitive rank order, which is then adjusted using inhibitory compound effects from supernatant assays. While our procedure worked for two media examined, we observed differences in species rank orders between media. We further used computer simulations, parameterized with our empirical data, to show that higher-order species interactions largely follow the dynamics predicted from pairwise interactions with one important exception. The impact of inhibitory compounds was reduced in higher-order communities because these compounds are spread across multiple species, thereby diluting their effects. Altogether, our results lead to the formulation of three simple rules of how monoculture growth and supernatant assay data can be combined to establish competitive species rank orders in bacterial communities.