Design, optimization, and inference of multiphasic decay of infectious virus particles.

The loss of virus particles is typically considered to arise from a first-order kinetic process. Signals of deviations from this exponential decay are often de-prioritized. Here, we propose methods to evaluate if a design is adequate to evaluate evidence for multiphasic virus particle decay and to optimize the sampling times of decay experiments, accounting for uncertainties in viral kinetics. First, we evaluate 1500 synthetic scenarios of biphasic decays, with varying decay rates and initial proportions of subpopulations. Robust inference of multiphasic decay is more likely when the faster decaying subpopulation predominates insofar as early samples are taken to resolve the faster decay rate. Overall, we find that design optimization leads to a better precision of estimation while reducing the number of samples. It helps to estimate adequately the fastest decay in 54% of situations vs. 41% using a non-optimized design. We then apply these methods to infer multiple decay rates associated with the decay of ΦD9, an evolved isolate derived from phage Φ21. A pilot experiment confirmed that ΦD9 decay is multiphasic, but was unable to resolve the rate or proportion of the fast decay subpopulation(s). We then applied optimal design methods to propose new ΦD9 sampling times. Using this strategy, we were able to robustly estimate both decay rates and their respective subpopulations. Notably, we conclude that the vast majority (94%) of the population decays at a rate 16-fold higher than a slow decaying population. Altogether, these results provide methods to quantitatively estimate heterogeneity in viral decay.

Rapid bacteria-phage coevolution drives the emergence of multiscale networks.

Interactions between species catalyze the evolution of multiscale ecological networks, including both nested and modular elements that regulate the function of diverse communities. One common assumption is that such complex pattern formation requires spatial isolation or long evolutionary timescales. We show that multiscale network structure can evolve rapidly under simple ecological conditions without spatial structure. In just 21 days of laboratory coevolution, Escherichia coli and bacteriophage Φ21 coevolve and diversify to form elaborate cross-infection networks. By measuring ~10,000 phage-bacteria infections and testing the genetic basis of interactions, we identify the mechanisms that create each component of the multiscale pattern. Our results demonstrate how multiscale networks evolve in parasite-host systems, illustrating Darwin's idea that simple adaptive processes can generate entangled banks of ecological interactions.

Comparison of bacterial suppression by phage cocktails, dual-receptor generalists, and coevolutionarily trained phages.

The evolution and spread of antibiotic-resistant bacteria have renewed interest in phage therapy, the use of bacterial viruses (phages) to combat bacterial infections. The delivery of phages in cocktails where constituent phages target different modalities (e.g., receptors) may improve treatment outcomes by making it more difficult for bacteria to evolve resistance. However, the multipartite nature of cocktails may lead to unintended evolutionary and ecological outcomes. Here, we compare a 2-phage cocktail with a largely unconsidered group of phages: generalists that can infect through multiple, independent receptors. We find that λ phage generalists and cocktails that target the same receptors (LamB and OmpF) suppress Escherichia coli similarly for ~2 days. Yet, a "trained" generalist phage, which previously adapted to its host via 28 days of coevolution, demonstrated superior suppression. To understand why the trained generalist was more effective, we measured the resistance of bacteria against each of our phages. We find that, when bacteria were assailed by two phages in the cocktail, they evolved mutations in manXYZ, a host inner-membrane transporter that λ uses to move its DNA across the periplasmic space and into the cell for infection. This provided cross-resistance against the cocktail and untrained generalist. However, these mutations were ineffective at blocking the trained generalist because, through coevolutionary training, it evolved to bypass manXYZ resistance. The trained generalist's past experiences in training make it exceedingly difficult for bacteria to evolve resistance, further demonstrating the utility of coevolutionary phage training for improving the therapeutic properties of phages.