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Antagonistic interactions are widespread in the microbial world and affect microbial evolutionary dynamics. Natural microbial communities often display spatial structure, which affects biological interactions, but much of what we know about microbial antagonism comes from laboratory studies of well-mixed communities. To overcome this limitation, we manipulated two killer strains of the budding yeastSaccharomyces cerevisiae, expressing different toxins, to independently control the rate at which they released their toxins. We developed mathematical models that predict the experimental dynamics of competition between toxin-producing strains in both well-mixed and spatially structured populations. In both situations, we experimentally verified theory’s prediction that a stronger antagonist can invade a weaker one only if the initial invading population exceeds a critical frequency or size. Finally, we found that toxin-resistant cells and weaker killers arose in spatially structured competitions between toxin-producing strains, suggesting that adaptive evolution can affect the outcome of microbial antagonism in spatial settings.
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This content will become publicly available on April 8, 2026
Selection for toxin production in spatially structured environments increases with growth rate
Abstract Microbes adopt a diversity of strategies to successfully compete with coexisting strains for space and resources. One common strategy is the production of toxic compounds to inhibit competitors, but the strength and direction of selection for this strategy varies depending on the environment. Existing theoretical and experimental evidence suggests growth in spatially structured environments makes toxin production more beneficial because competitive interactions are localized. Because higher growth rates reduce the length-scale of interactions in structured environments, theory predicts that toxin production should be especially beneficial under these conditions. We tested this hypothesis by developing a genome-scale metabolic modeling approach and complementing it with comparative genomics to investigate the impact of growth rate on selection for costly toxin production. Our modeling approach expands the current abilities of the dynamic flux balance analysis platform COMETS to incorporate signaling and toxin production. Using this capability, we find that our modeling framework predicts that the strength of selection for toxin production increases as growth rate increases. This finding is supported by comparative genomics analyses that include diverse microbial species. Our work emphasizes that toxin production is more likely to be maintained in rapidly growing, spatially structured communities, thus improving our ability to manage microbial communities and informing natural product discovery.
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- Award ID(s):
- 1935458
- PAR ID:
- 10585299
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- The ISME Journal
- ISSN:
- 1751-7362
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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