Abstract

SummaryMany viruses cause both lytic infections, where they release viral particles, and dormant infections, where they await future opportunities to reactivate.1 The benefits of each transmission mode depend on the density of susceptible hosts in the environment.2, 3, 4 Some viruses infecting bacteria use molecular signaling to respond plastically to changes in host availability.5 These viruses produce a signal during lytic infection and regulate, based on the signal concentration in the environment, the probability with which they switch to causing dormant infections.5,6 We present an analytical framework to examine the adaptive significance of plasticity in viral life-history traits in fluctuating environments. Our model generalizes and extends previous theory7 and predicts that host density fluctuations should select for plasticity in entering lysogeny as well as virus reactivation once signal concentrations decline. Using Bacillus subtilis and its phage phi3T, we experimentally confirm the prediction that phages use signal to make informed decisions over prophage induction. We also demonstrate that lysogens produce signaling molecules and that signal is degraded by hosts in a density-dependent manner. Declining signal concentrations therefore potentially indicate the presence of uninfected hosts and trigger prophage induction. Finally, we find that conflict over the responses of lysogenization and reactivation to signal is resolved through the evolution of different response thresholds for each trait. Collectively, these findings deepen our understanding of the ways viruses use molecular communication to regulate their infection strategies, which can be leveraged to manipulate host and phage population dynamics in natural environments.

Highlights

  • We generated an epidemiological model of a well-mixed bacterial population made up of susceptible cells, lysogenic cells, and free virus particles

  • We use this model to establish when temperate phages should evolve to respond to changes in signal concentration, and whether they should regulate the transition from lysis to lysogeny and the transition from lysogeny to lysis.[11]

  • Our model assumes that the influx of susceptible cells may vary with time, and we allow lysogenization and reactivation rates to be functions of the concentration of arbitrium in the environment

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Summary

Introduction

We generated an epidemiological model (see STAR Methods for details) of a well-mixed bacterial population made up of susceptible cells, lysogenic cells, and free virus particles. Our model assumes that the influx of susceptible cells may vary with time, and we allow lysogenization and reactivation rates to be functions of the concentration of arbitrium in the environment This model tracks the densities of bacteria (uninfected and lysogens), phages (free phage and lysogens), and signal concentrations. We determine the fate of viral mutants with altered lysogeny or prophage induction in response to changes in signal concentration in a fluctuating environment This evolutionary analysis shows that the selection for the mutant varies with the availability of susceptible cells in the environment (see STAR Methods for details).

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