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Biofilm structure promotes coexistence of phage-resistant and phage-susceptible bacteria

Simmons, M.; Bond, M. C.; Koskella, B.; Drescher, K.; Bucci, V.; Nadell, C. D.

2019-10-08 microbiology
10.1101/552265 bioRxiv
Show abstract

Interactions among bacteria and their viral predators, the bacteriophages, are likely among the most common ecological phenomena on Earth. The constant threat of phage infection to bacterial hosts, and the imperative of achieving infection on the part of phages, drives an evolutionary contest in which phage-resistant bacteria emerge, often followed by phages with new routes of infection. This process has received abundant theoretical and experimental attention for decades and forms an important basis for molecular genetics and theoretical ecology and evolution. However, at present, we know very little about the nature of phage-bacteria interaction - and the evolution of phage resistance - inside the surface-bound communities that microbes usually occupy in natural environments. These communities, termed biofilms, are encased in a matrix of secreted polymers produced by their microbial residents. Biofilms are spatially constrained such that interactions become limited to neighbors or near-neighbors; diffusion of solutes and particulates is reduced; and there is pronounced heterogeneity in nutrient access and therefore physiological state. These factors can dramatically impact the way phage infections proceed even in simple, single-strain biofilms, but we still know little of their effect on phage resistance evolutionary dynamics. Here we explore this problem using a computational simulation framework customized for implementing phage infection inside multi-strain biofilms. Our simulations predict that it is far easier for phage-susceptible and phage-resistant bacteria to coexist inside biofilms relative to planktonic culture, where phages and hosts are well-mixed. We characterize the negative frequency dependent selection that underlies this coexistence, and we then test and confirm this prediction using an experimental model of biofilm growth measured with confocal microscopy at single-cell and single-phage resolution.

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