Bacterial infections are often polymicrobial, leading to intricate pathogen–pathogen and pathogen–host interactions. There is increasing interest in studying the molecular basis of pathogen interactions and how such mechanisms impact host morbidity. However, much less is known about the ecological dynamics between pathogens and how they affect virulence and host survival. Here we address these open issues by co-infecting larvae of the insect model host Galleria mellonella with one, two, three or four bacterial species, all of which are opportunistic human pathogens. We found that host mortality was always determined by the most virulent species regardless of the number of species and pathogen combinations injected. In certain combinations, the more virulent pathogen simply outgrew the less virulent pathogen. In other combinations, we found evidence for negative interactions between pathogens inside the host, whereby the more virulent pathogen typically won a competition. Taken together, our findings reveal positive associations between a pathogen's growth inside the host, its competitiveness towards other pathogens and its virulence. Beyond being generalizable across species combinations, our findings predict that treatments against polymicrobial infections should first target the most virulent species to reduce host morbidity, a prediction we validated experimentally.
Bacterial infections are often polymicrobial, leading to intricate pathogen-pathogen and pathogen-host interactions. There is increasing interest in studying the molecular basis of pathogen interactions and how such mechanisms impact host morbidity. However, much less is known about the ecological dynamics between pathogens and how they affect virulence and host survival. Here we address these open issues by co-infecting larvae of the insect model host Galleria mellonella with one, two, three or four bacterial species, all of which are opportunistic human pathogens. We found that virulence was always driven by the most virulent species regardless of the number of species and pathogen combinations injected. Moreover, we observed a link between a pathogen's virulence and its growth within the host. In certain cases, the more virulent pathogen simply outgrew the less virulent pathogen. In other cases, we found evidence for negative interactions between pathogens inside the host, whereby the more virulent pathogen typically won a competition. Taken together, our findings reveal positive links between a pathogen's growth inside the host, its competitiveness towards other pathogens, and its virulence. Beyond being generalizable across species combinations, our findings suggest that treatment strategies against polymicrobial infections should target the most virulent species.
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.
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