Significance: The evolution of antibiotic-resistant bacteria threatens to claim over 10 million lives annually by 2050. This crisis has renewed interest in phage therapy, the use of bacterial viruses to treat infections. A major barrier to successful phage therapy is that bacteria readily evolve phage resistance. One idea proposed to combat resistance is “training” phages by using their natural capacity to evolve to counter resistance. Here, we show that training phages by coevolving them with their hosts for 1 mo repeatably enhances their capacity for suppressing bacterial growth and delays the emergence of resistance. Enhanced suppression was caused by several mechanisms, suggesting that the coevolutionary training protocol produces a robust therapeutic that employs complementary modes of action.

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.

Nongenetic variation drives viral evolution: Bacteriophage λ is a virus that infects bacteria by exploiting various membrane proteins in a well-characterized manner. Petrie et al. show how the evolution of variable folding conformations of isogenic proteins, which do not differ in their genetic sequences, contributed to λ's ability to exploit an additional host receptor for infection. Because the protein can take on two shapes, this genotype can have two phenotypes. Natural selection may thus be able to act on this nongenetic heterogeneity to connect phenotypic heterogeneity, evolvability, and protein stability. Science , this issue p. 1542

The origin of new and complex structures and functions is fundamental for shaping the diversity of life. Such key innovations are rare because they require multiple interacting changes. We sought to understand how the adaptive landscape led to an innovation whereby bacteriophage λ evolved the new ability to exploit a receptor, OmpF, onEscherichia colicells. Previous work showed that this ability evolved repeatedly, despite requiring four mutations in one virus gene. Here, we examine how this innovation evolved by studying six intermediate genotypes of λ isolated during independent transitions to exploit OmpF and comparing them to their ancestor. All six intermediates showed large increases in their adsorption rates on the ancestral host. Improvements in adsorption were offset, in large part, by the evolution of host resistance, which occurred by reduced expression of LamB, the usual receptor for λ. As a consequence of host coevolution, the adaptive landscape of the virus changed such that selection favouring four of the six virus intermediates became stronger after the host evolved resistance, thereby accelerating virus populations along the path to using the new OmpF receptor. This dependency of viral fitness on host genotype thus shows an important role for coevolution in the origin of the new viral function.

During the struggle for survival, populations occasionally evolve new functions that give them access to untapped ecological opportunities. Theory suggests that coevolution between species can promote the evolution of such innovations by deforming fitness landscapes in ways that open new adaptive pathways. We directly tested this idea by using high-throughput gene editing-phenotyping technology (MAGE-Seq) to measure the fitness landscape of a virus, bacteriophage λ, as it coevolved with its host, the bacterium Escherichia coli. An analysis of the empirical fitness landscape revealed mutation-by-mutation-by-host-genotype interactions that demonstrate coevolution modified the contours of λ’s landscape. Computer simulations of λ’s evolution on a static versus shifting fitness landscape showed that the changes in contours increased λ’s chances of evolving the ability to use a new host receptor. By coupling sequencing and pairwise competition experiments, we demonstrated that the first mutation λ evolved en route to the innovation would only evolve in the presence of the ancestral host, whereas later steps in λ’s evolution required the shift to a resistant host. When time-shift replays of the coevolution experiment were run where host evolution was artificially accelerated, λ did not innovate to use the new receptor. This study provides direct evidence for the role of coevolution in driving evolutionary novelty and provides a quantitative framework for predicting evolution in coevolving ecological communities.

Viruses and their hosts can undergo coevolutionary arms races where hosts evolve increased resistance and viruses evolve counter‐resistance. Given these arms race dynamics (ARD), both players are predicted to evolve along a single trajectory as more recently evolved genotypes replace their predecessors. By coupling phenotypic and genomic analyses of coevolving populations of bacteriophage λ and Escherichia coli, we find conflicting evidence for ARD. Virus‐host infection phenotypes fit the ARD model, yet genomic analyses revealed fluctuating selection dynamics. Rather than coevolution unfolding along a single trajectory, cryptic genetic variation emerges and is maintained at low frequency for generations until it eventually supplants dominant lineages. These observations suggest a hybrid ‘leapfrog’ dynamic, revealing weaknesses in the predictive power of standard coevolutionary models. The findings shed light on the mechanisms that structure coevolving ecological networks and reveal the limits of using phenotypic or genomic data alone to differentiate coevolutionary dynamics.