Bacteriophage Φ21's receptor-binding protein evolves new functions through destabilizing mutations that generate non-genetic phenotypic heterogeneity.

How viruses evolve to expand their host range is a major question with implications for predicting the next pandemic. Gain-of-function experiments have revealed that host-range expansions can occur through relatively few mutations in viral receptor-binding proteins, and the search for molecular mechanisms that explain such expansions is underway. Previous research on expansions of receptor use in bacteriophage λ has shown that mutations that destabilize λ's receptor-binding protein cause it to fold into new conformations that can utilize novel receptors but have weakened thermostability. These observations led us to hypothesize that other viruses may take similar paths to expand their host range. Here, we find support for our hypothesis by studying another virus, bacteriophage 21 (Φ21), which evolves to use two new host receptors within 2 weeks of laboratory evolution. By measuring the thermodynamic stability of Φ21 and its descendants, we show that as Φ21 evolves to use new receptors and expands its host range, it becomes less stable and produces viral particles that are genetically identical but vary in their thermostabilities. Next, we show that this non-genetic heterogeneity between particles is directly associated with receptor use innovation, as phage particles with more derived receptor-use capabilities are more unstable and decay faster. Lastly, by manipulating the expression of protein chaperones during Φ21 infection, we demonstrate that heterogeneity in receptor use of phage particles arises during protein folding. Altogether, our results provide support for the hypothesis that viruses can evolve new receptor-use tropisms through mutations that destabilize the receptor-binding protein and produce multiple protein conformers.

More evolvable bacteriophages better suppress their host.

The number of multidrug-resistant strains of bacteria is increasing rapidly, while the number of new antibiotic discoveries has stagnated. This trend has caused a surge in interest in bacteriophages as anti-bacterial therapeutics, in part because there is near limitless diversity of phages to harness. While this diversity provides an opportunity, it also creates the dilemma of having to decide which criteria to use to select phages. Here we test whether a phage's ability to coevolve with its host (evolvability) should be considered and how this property compares to two previously proposed criteria: fast reproduction and thermostability. To do this, we compared the suppressiveness of three phages that vary by a single amino acid yet differ in these traits such that each strain maximized two of three characteristics. Our studies revealed that both evolvability and reproductive rate are independently important. The phage most able to suppress bacterial populations was the strain with high evolvability and reproductive rate, yet this phage was unstable. Phages varied due to differences in the types of resistance evolved against them and their ability to counteract resistance. When conditions were shifted to exaggerate the importance of thermostability, one of the stable phages was most suppressive in the short-term, but not over the long-term. Our results demonstrate the utility of biological therapeutics' capacities to evolve and adjust in action to resolve complications like resistance evolution. Furthermore, evolvability is a property that can be engineered into phage therapeutics to enhance their effectiveness.

An intron endonuclease facilitates interference competition between coinfecting viruses.

Introns containing homing endonucleases are widespread in nature and have long been assumed to be selfish elements that provide no benefit to the host organism. These genetic elements are common in viruses, but whether they confer a selective advantage is unclear. In this work, we studied intron-encoded homing endonuclease gp210 in bacteriophage ΦPA3 and found that it contributes to viral competition by interfering with the replication of a coinfecting phage, ΦKZ. We show that gp210 targets a specific sequence in ΦKZ, which prevents the assembly of progeny viruses. This work demonstrates how a homing endonuclease can be deployed in interference competition among viruses and provide a relative fitness advantage. Given the ubiquity of homing endonucleases, this selective advantage likely has widespread evolutionary implications in diverse plasmid and viral competition as well as virus-host interactions.

Viral Receptor-Binding Protein Evolves New Function through Mutations That Cause Trimer Instability and Functional Heterogeneity.

When proteins evolve new activity, a concomitant decrease in stability is often observed because the mutations that confer new activity can destabilize the native fold. In the conventional model of protein evolution, reduced stability is considered a purely deleterious cost of molecular innovation because unstable proteins are prone to aggregation and are sensitive to environmental stressors. However, recent work has revealed that nonnative, often unstable protein conformations play an important role in mediating evolutionary transitions, raising the question of whether instability can itself potentiate the evolution of new activity. We explored this question in a bacteriophage receptor-binding protein during host-range evolution. We studied the properties of the receptor-binding protein of bacteriophage λ before and after host-range evolution and demonstrated that the evolved protein is relatively unstable and may exist in multiple conformations with unique receptor preferences. Through a combination of structural modeling and in vitro oligomeric state analysis, we found that the instability arises from mutations that interfere with trimer formation. This study raises the intriguing possibility that protein instability might play a previously unrecognized role in mediating host-range expansions in viruses.

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

Virus population dynamics are driven by counter-balancing forces of production and loss. Whereas viral production arises from complex interactions with susceptible hosts, the loss of infectious virus particles is often approximated as a first-order kinetic process. As such, experimental protocols to measure infectious virus loss are not typically designed to identify non-exponential decay processes. Here, we propose methods to evaluate if an experimental design is adequate to identify multiphasic virus particle decay and to optimize the sampling times of decay experiments, accounting for uncertainties in viral kinetics. First, we evaluate synthetic scenarios of biphasic decays, with varying decay rates and initial proportions of subpopulations. We show that 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. Moreover, design optimization involving non-equal spacing between observations increases the precision of estimation while reducing the number of samples. We then apply these methods to infer multiple decay rates associated with the decay of bacteriophage ('phage') Φ D 9 , an evolved isolate derived from phage Φ 21 . A pilot experiment confirmed that Φ D 9 decay is multiphasic, but was unable to resolve the rate or proportion of the fast decaying subpopulation(s). We then applied a Fisher information matrix-based design optimization method to propose non-equally spaced sampling times. Using this strategy, we were able to robustly estimate multiple decay rates and the size of the respective subpopulations. Notably, we conclude that the vast majority (94%) of the phage Φ D 9 population decays at a rate 16-fold higher than the slow decaying population. Altogether, these results provide both a rationale and a practical approach to quantitatively estimate heterogeneity in viral decay.

Bacterial resistance response and resource availability mediate viral coexistence.

Viruses that infect bacteria, known as bacteriophages or phages, are the most prevalent entities on Earth. Their genetic diversity in nature is well documented, and members of divergent lineages can be found sharing the same ecological niche. This viral diversity can be influenced by a number of factors, including productivity, spatial structuring of the environment, and host-range trade-offs. Rapid evolution is also known to promote diversity by buffering ecological systems from extinction. There is, however, little known about the impact of coevolution on the maintenance of viral diversity within a microbial community. To address this, we developed a 4 species experimental system where two bacterial hosts, a generalist and a specialist phage, coevolved in a spatially homogenous environment over time. We observed the persistence of both viruses if the resource availability was sufficiently high. This coexistence occurred in the absence of any detectable host-range trade-offs that are costly for generalists and thus known to promote viral diversity. However, the coexistence was lost if two bacteria were not permitted to evolve alongside the phages or if two phages coevolved with a single bacterial host. Our findings indicate that a host's resistance response in mixed-species communities plays a significant role in maintaining viral diversity in the environment.

Inferring strain-level mutational drivers of phage-bacteria interaction phenotypes.

The enormous diversity of bacteriophages and their bacterial hosts presents a significant challenge to predict which phages infect a focal set of bacteria. Infection is largely determined by complementary -and largely uncharacterized- genetics of adsorption, injection, and cell take-over. Here we present a machine learning (ML) approach to predict phage-bacteria interactions trained on genome sequences of and phenotypic interactions amongst 51 Escherichia coli strains and 45 phage λ strains that coevolved in laboratory conditions for 37 days. Leveraging multiple inference strategies and without a priori knowledge of driver mutations, this framework predicts both who infects whom and the quantitative levels of infections across a suite of 2,295 potential interactions. The most effective ML approach inferred interaction phenotypes from independent contributions from phage and bacteria mutations, predicting phage host range with 86% mean classification accuracy while reducing the relative error in the estimated strength of the infection phenotype by 40%. Further, transparent feature selection in the predictive model revealed 18 of 176 phage λ and 6 of 18 E. coli mutations that have a significant influence on the outcome of phage-bacteria interactions, corroborating sites previously known to affect phage λ infections, as well as identifying mutations in genes of unknown function not previously shown to influence bacterial resistance. While the genetic variation studied was limited to a focal, coevolved phage-bacteria system, the method's success at recapitulating strain-level infection outcomes provides a path forward towards developing strategies for inferring interactions in non-model systems, including those of therapeutic significance.

Competition-driven eco-evolutionary feedback reshapes bacteriophage lambda's fitness landscape and enables speciation.

A major challenge in evolutionary biology is explaining how populations navigate rugged fitness landscapes without getting trapped on local optima. One idea illustrated by adaptive dynamics theory is that as populations adapt, their newly enhanced capacities to exploit resources alter fitness payoffs and restructure the landscape in ways that promote speciation by opening new adaptive pathways. While there have been indirect tests of this theory, to our knowledge none have measured how fitness landscapes deform during adaptation, or test whether these shifts promote diversification. Here, we achieve this by studying bacteriophage [Formula: see text], a virus that readily speciates into co-existing receptor specialists under controlled laboratory conditions. We use a high-throughput gene editing-phenotyping technology to measure [Formula: see text]'s fitness landscape in the presence of different evolved-[Formula: see text] competitors and find that the fitness effects of individual mutations, and their epistatic interactions, depend on the competitor. Using these empirical data, we simulate [Formula: see text]'s evolution on an unchanging landscape and one that recapitulates how the landscape deforms during evolution. [Formula: see text] heterogeneity only evolves in the shifting landscape regime. This study provides a test of adaptive dynamics, and, more broadly, shows how fitness landscapes dynamically change during adaptation, potentiating phenomena like speciation by opening new adaptive pathways.

Upper Bound on the Mutational Burden Imposed by a CRISPR-Cas9 Gene-Drive Element.

CRISPR-Cas9 gene drives (CCGDs) are powerful tools for genetic control of wild populations, useful for eradication of disease vectors, conservation of endangered species and other applications. However, Cas9 alone and in a complex with gRNA can cause double-stranded DNA breaks at off-target sites, which could increase the mutational load and lead to loss of heterozygosity (LOH). These undesired effects raise potential concerns about the long-term evolutionary safety of CCGDs, but the magnitude of these effects is unknown. To estimate how the presence of a CCGD or a Cas9 alone in the genome affects the rates of LOH events and de novo mutations, we carried out a mutation accumulation experiment in yeast Saccharomyces cerevisiae. Despite its substantial statistical power, our experiment revealed no detectable effect of CCGD or Cas9 alone on the genome-wide rates of mutations or LOH events, suggesting that these rates are affected by less than 30%. Nevertheless, we found that Cas9 caused a slight but significant shift towards more interstitial and fewer terminal LOH events, and the CCGD caused a significant difference in the distribution of LOH events on Chromosome V. Taken together, our results show that these genetic elements impose a weak and likely localized additional mutational burden in the yeast model. Although the mutagenic effects of CCGDs need to be further evaluated in other systems, our results suggest that the effect of CCGDs on off-target mutation rates and genetic diversity may be acceptable.

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.

A mobile intron facilitates interference competition between co-infecting viruses.

Mobile introns containing homing endonucleases are widespread in nature and have long been assumed to be selfish elements that provide no benefit to the host organism. These genetic elements are common in viruses, but whether they confer a selective advantage is unclear. Here we studied a mobile intron in bacteriophage ΦPA3 and found its homing endonuclease gp210 contributes to viral competition by interfering with the virogenesis of co-infecting phage ΦKZ. We show that gp210 targets a specific sequence in its competitor ΦKZ, preventing the assembly of progeny viruses. This work reports the first demonstration of how a mobile intron can be deployed to engage in interference competition and provide a reproductive advantage. Given the ubiquity of introns, this selective advantage likely has widespread evolutionary implications in nature.

Competition-driven eco-evolutionary feedback reshapes bacteriophage lambda's fitness landscape and enables speciation.

A major challenge in evolutionary biology is explaining how populations navigate rugged fitness landscapes without getting trapped on local optima. One idea illustrated by adaptive dynamics theory is that as populations adapt, their newly enhanced capacities to exploit resources alter fitness payoffs and restructure the landscape in ways that promote speciation by opening new adaptive pathways. While there have been indirect tests of this theory, none have measured how fitness landscapes deform during adaptation, or test whether these shifts promote diversification. Here, we achieve this by studying bacteriophage λ, a virus that readily speciates into co-existing receptor specialists under controlled laboratory conditions. We used a high-throughput gene editing-phenotyping technology to measure λ's fitness landscape in the presence of different evolved-λ competitors and found that the fitness effects of individual mutations, and their epistatic interactions, depend on the competitor. Using these empirical data, we simulated λ's evolution on an unchanging landscape and one that recapitulates how the landscape deforms during evolution. λ heterogeneity only evolved in the shifting landscape regime. This study provides a test of adaptive dynamics, and, more broadly, shows how fitness landscapes dynamically change during adaptation, potentiating phenomena like speciation by opening new adaptive pathways.

Fecal virome transplantation is sufficient to alter fecal microbiota and drive lean and obese body phenotypes in mice.

The gastrointestinal microbiome plays a significant role in modulating numerous host processes, including metabolism. Prior studies show that when mice receive fecal transplants from obese donors on high-fat diets (HFD) (even when recipient mice are fed normal diets after transplantation), they develop obese phenotypes, demonstrating the prominent role that gut microbiota play in determining lean and obese phenotypes. While much of the credit has been given to gut bacteria, the impact of gut viruses on these phenotypes is understudied. To address this shortcoming, we gavaged mice with viromes isolated from donors fed HFD or normal chow over a 4-week study. By characterizing the gut bacterial biota via 16S rRNA amplicon sequencing and measuring mouse weights over time, we demonstrate that transplanted viruses affect the gut bacterial community, as well as weight gain/loss. Notably, mice fed chow but gavaged with HFD-derived viromes gained more weight than their counterparts receiving chow-derived viromes. The converse was also true: mice fed HFD but gavaged with chow-derived viromes gained less weight than their counterparts receiving HFD-derived viromes. Results were replicated in two independent experiments and phenotypic changes were accompanied by significant and identifiable differences in the fecal bacterial biota. Due to methodological limitations, we were unable to identify the specific bacterial strains responsible for respective phenotypic changes. This study confirms that virome-mediated perturbations can alter the fecal microbiome in vivo and indicates that such perturbations are sufficient to drive lean and obese phenotypes in mice.

Canonical Host-Pathogen Trade-Offs Subverted by Mutations with Dual Benefits.

AbstractHost-parasite coevolution is expected to drive the evolution of genetic diversity because the traits used in arms races-namely, host range and parasite resistance-are hypothesized to trade off with traits used in resource competition. We therefore tested data for several trade-offs among 93 isolates of bacteriophage λ and 51 Escherichia coli genotypes that coevolved during a laboratory experiment. Surprisingly, we found multiple trade-ups (positive trait correlations) but little evidence of several canonical trade-offs. For example, some bacterial genotypes evaded a trade-off between phage resistance and absolute fitness, instead evolving simultaneous improvements in both traits. This was surprising because our experimental design was predicted to expose resistance-fitness trade-offs by culturing E. coli in a medium where the phage receptor, LamB, is also used for nutrient acquisition. On reflection, LamB mediates not one but many trade-offs, allowing for more complex trait interactions than just pairwise trade-offs. Here, we report that mathematical reasoning and laboratory data highlight how trade-ups should exist whenever an evolutionary system exhibits multiple interacting trade-offs. Does this mean that coevolution should not promote genetic diversity? No, quite the contrary. We deduce that whenever positive trait correlations are observed in multidimensional traits, other traits may trade off and so provide the right circumstances for diversity maintenance. Overall, this study reveals that there are predictive limits when data account only for pairwise trait correlations, and it argues that a wider range of circumstances than previously anticipated can promote genetic and species diversity.

Fecal virome transplantation is sufficient to alter fecal microbiota and drive lean and obese body phenotypes in mice.

Background: The gastrointestinal microbiome plays a significant role in numerous host processes and has an especially large impact on modulating the host metabolism. Prior studies have shown that when mice receive fecal transplants from obese donors that were fed high-fat diets (HFD) (even when recipient mice are fed normal diets after transplantation), they develop obese phenotypes. These studies demonstrate the prominent role that the gut microbiota play in determining lean and obese phenotypes. While much of the credit has been given to gut bacteria, studies have not measured the impact of gut viruses on these phenotypes. To address this shortcoming, we gavaged mice with viromes isolated from donors fed HFD or normal chow. By characterizing the mice’s gut bacterial biota and weight-gain phenotypes over time, we demonstrate that viruses can shape the gut bacterial community and affect weight gain or loss. Results: We gavaged mice longitudinally over 4 weeks while measuring their body weights and collecting fecal samples for 16S rRNA amplicon sequencing. We evaluated mice that were fed normal chow or high-fat diets, and gavaged each group with either chow-derived fecal viromes, HFD-derived fecal viromes, or phosphate buffered saline controls. We found a significant effect of gavage type, where mice fed chow but gavaged with HFD-derived viromes gained significantly more weight than their counterparts receiving chow-derived viromes. The converse was also true: mice fed HFD but gavaged with chow-derived viromes gained significantly less weight than their counterparts receiving HFD-derived viromes. These results were replicated in two separate experiments and the phenotypic changes were accompanied by significant and identifiable differences in the fecal bacterial biota. Notably, there were differences in Lachnospirales and Clostridia in mice fed chow but gavaged with HFD-derived fecal viromes, and in Peptostreptococcales, Oscillospirales, and Lachnospirales in mice fed HFD but gavaged with chow-derived fecal viromes. Due to methodological limitations, we were unable to identify specific bacterial species or strains that were responsible for respective phenotypic changes. Conclusions: This study confirms that virome-mediated perturbations can alter the fecal microbiome in an in vivo model and indicates that such perturbations are sufficient to drive lean and obese phenotypes in mice.

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.

A Trait-Based Approach to Predicting Viral Host-Range Evolvability.

Predicting the evolution of virus host range has proven to be extremely difficult, in part because of the sheer diversity of viruses, each with unique biology and ecological interactions. We have not solved this problem, but to make the problem more tractable, we narrowed our focus to three traits intrinsic to all viruses that may play a role in host-range evolvability: mutation rate, recombination rate, and phenotypic heterogeneity. Although each trait should increase evolvability, they cannot do so unbounded because fitness trade-offs limit the ability of all three traits to maximize evolvability. By examining these constraints, we can begin to identify groups of viruses with suites of traits that make them especially concerning, as well as ecological and environmental conditions that might push evolution toward accelerating host-range expansion.

Host-parasite coevolution promotes innovation through deformations in fitness landscapes.

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.

Viral protein instability enhances host-range evolvability.

Viruses are highly evolvable, but what traits endow this property? The high mutation rates of viruses certainly play a role, but factors that act above the genetic code, like protein thermostability, are also expected to contribute. We studied how the thermostability of a model virus, bacteriophage λ, affects its ability to evolve to use a new receptor, a key evolutionary transition that can cause host-range evolution. Using directed evolution and synthetic biology techniques we generated a library of host-recognition protein variants with altered stabilities and then tested their capacity to evolve to use a new receptor. Variants fell within three stability classes: stable, unstable, and catastrophically unstable. The most evolvable were the two unstable variants, whereas seven of eight stable variants were significantly less evolvable, and the two catastrophically unstable variants could not grow. The slowly evolving stable variants were delayed because they required an additional destabilizing mutation. These results are particularly noteworthy because they contradict a widely supported contention that thermostabilizing mutations enhance evolvability of proteins by increasing mutational robustness. Our work suggests that the relationship between thermostability and evolvability is more complex than previously thought, provides evidence for a new molecular model of host-range expansion evolution, and identifies instability as a potential predictor of viral host-range evolution.

Leapfrog dynamics in phage-bacteria coevolution revealed by joint analysis of cross-infection phenotypes and whole genome sequencing.

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.