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.

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.

Modeling shield immunity to reduce COVID-19 transmission in long-term care facilities.

PURPOSE: Nursing homes and long-term care facilities have experienced severe outbreaks and elevated mortality rates of COVID-19. When available, vaccination at-scale has helped drive a rapid reduction in severe cases. However, vaccination coverage remains incomplete among residents and staff, such that additional mitigation and prevention strategies are needed to reduce the ongoing risk of transmission. One such strategy is that of "shield immunity", in which immune individuals modulate their contact rates and shield uninfected individuals from potentially risky interactions. METHODS: Here, we adapt shield immunity principles to a network context, by using computational models to evaluate how restructured interactions between staff and residents affect SARS-CoV-2 epidemic dynamics. RESULTS: First, we identify a mitigation rewiring strategy that reassigns immune healthcare workers to infected residents, significantly reducing outbreak sizes given weekly testing and rewiring (48% reduction in the outbreak size). Second, we identify a preventative prewiring strategy in which susceptible healthcare workers are assigned to immunized residents. This preventative strategy reduces the risk and size of an outbreak via the inadvertent introduction of an infectious healthcare worker in a partially immunized population (44% reduction in the epidemic size). These mitigation levels derived from network-based interventions are similar to those derived from isolating infectious healthcare workers. CONCLUSIONS: This modeling-based assessment of shield immunity provides further support for leveraging infection and immune status in network-based interventions to control and prevent the spread of COVID-19.

Timescales modulate optimal lysis-lysogeny decision switches and near-term phage reproduction.

Temperate phage can initiate lysis or lysogeny after infecting a bacterial host. The genetic switch between lysis and lysogeny is mediated by phage regulatory genes as well as host and environmental factors. Recently, a new class of decision switches was identified in phage of the SPbeta group, mediated by the extracellular release of small, phage-encoded peptides termed arbitrium. Arbitrium peptides can be taken up by bacteria prior to infection, modulating the decision switch in the event of a subsequent phage infection. Increasing the concentration of arbitrium increases the chance that a phage infection will lead to lysogeny, rather than lysis. Although prior work has centered on the molecular mechanisms of arbitrium-induced switching, here we focus on how selective pressures impact the benefits of plasticity in switching responses. In this work, we examine the possible advantages of near-term adaptation of communication-based decision switches used by the SPbeta-like group. We combine a nonlinear population model with a control-theoretic approach to evaluate the relationship between a putative phage reaction norm (i.e. the probability of lysogeny as a function of arbitrium) and the extent of phage reproduction at a near-term time horizon. We measure phage reproduction in terms of a cellular-level metric previously shown to enable comparisons of near-term phage fitness across a continuum from lysis to latency. We show the adaptive potential of communication-based lysis-lysogeny responses and find that optimal switching between lysis and lysogeny increases the near-term phage reproduction compared to fixed responses, further supporting both molecular- and model-based analyses of the putative benefits of this class of decision switches. We further find that plastic responses are robust to the inclusion of cellular-level stochasticity, variation in life history traits, and variation in resource availability. These findings provide further support to explore the long-term evolution of plastic decision systems mediated by extracellular decision-signaling molecules and the feedback between phage reaction norms and ecological context.

Origins and Evolution of the Global RNA Virome.

Viruses with RNA genomes dominate the eukaryotic virome, reaching enormous diversity in animals and plants. The recent advances of metaviromics prompted us to perform a detailed phylogenomic reconstruction of the evolution of the dramatically expanded global RNA virome. The only universal gene among RNA viruses is the gene encoding the RNA-dependent RNA polymerase (RdRp). We developed an iterative computational procedure that alternates the RdRp phylogenetic tree construction with refinement of the underlying multiple-sequence alignments. The resulting tree encompasses 4,617 RNA virus RdRps and consists of 5 major branches; 2 of the branches include positive-sense RNA viruses, 1 is a mix of positive-sense (+) RNA and double-stranded RNA (dsRNA) viruses, and 2 consist of dsRNA and negative-sense (-) RNA viruses, respectively. This tree topology implies that dsRNA viruses evolved from +RNA viruses on at least two independent occasions, whereas -RNA viruses evolved from dsRNA viruses. Reconstruction of RNA virus evolution using the RdRp tree as the scaffold suggests that the last common ancestors of the major branches of +RNA viruses encoded only the RdRp and a single jelly-roll capsid protein. Subsequent evolution involved independent capture of additional genes, in particular, those encoding distinct RNA helicases, enabling replication of larger RNA genomes and facilitating virus genome expression and virus-host interactions. Phylogenomic analysis reveals extensive gene module exchange among diverse viruses and horizontal virus transfer between distantly related hosts. Although the network of evolutionary relationships within the RNA virome is bound to further expand, the present results call for a thorough reevaluation of the RNA virus taxonomy.IMPORTANCE The majority of the diverse viruses infecting eukaryotes have RNA genomes, including numerous human, animal, and plant pathogens. Recent advances of metagenomics have led to the discovery of many new groups of RNA viruses in a wide range of hosts. These findings enable a far more complete reconstruction of the evolution of RNA viruses than was attainable previously. This reconstruction reveals the relationships between different Baltimore classes of viruses and indicates extensive transfer of viruses between distantly related hosts, such as plants and animals. These results call for a major revision of the existing taxonomy of RNA viruses.

Theoretical approaches to disclosing the emergence and adaptive advantages of multipartite viruses.

Multipartite viruses have a segmented genome encapsidated in different viral particles that, in principle, propagate independently. Current empirical knowledge on the molecular, ecological and evolutionary features underlying the very existence of multipartitism is fragmented and puzzling. Although it is generally assumed that multipartitism is viable only when propagation occurs at high multiplicity of infection, evidence indicates that severe population bottlenecks are common. Mathematical models aimed at describing the dynamics of multipartite viruses typically assign an advantage to the multipartite form to compensate for the cost of high multiplicity of infection. Since progress in the theoretical understanding of the evolutionary ecology of multipartitism is strongly conditioned by empirical advances, both aspects are jointly revised in this contribution.

Multipartite viruses: adaptive trick or evolutionary treat?

Multipartitism counts amongst the weirdest lifestyles found in the virosphere. Multipartite viruses have genomes segmented in pieces enclosed in different capsids that are independently transmitted. Since all segments have to meet in the host for complementation and completion of the viral cycle, multipartite viruses are bound to fight the loss of genomic information. While this is an obvious disadvantage of this strategy, no consensus on its actual advantages has been reached. In this review we present an exhaustive summary of all multipartite viruses described to date. Based on evidence, we discuss possible mechanistic and evolutionary origins of different groups, as well as their mutual relationships. We argue that the ubiquitous interactions of viruses with other unrelated viruses and with subviral elements might be regarded as a plausible first step towards multipartitism. In agreement with the view of the Virosphere as a deeply entangled network of gene sharing, we contend that the power of multipartitism relies on its dynamical and opportunistic nature, because it enables immediate adaptive responses to environmental changes. As such, perhaps the reasons for its success should be shought in multipartitism itself as an adaptive mechanism, to which its evolutionarily short-lived products (that is, the extant ensemble of multipartite viral species) are subordinated. We close by discussing how our understanding of multipartitism would improve by using concepts and tools from systems biology.

Internal Disequilibria and Phenotypic Diversification during Replication of Hepatitis C Virus in a Noncoevolving Cellular Environment.

Viral quasispecies evolution upon long-term virus replication in a noncoevolving cellular environment raises relevant general issues, such as the attainment of population equilibrium, compliance with the molecular-clock hypothesis, or stability of the phenotypic profile. Here, we evaluate the adaptation, mutant spectrum dynamics, and phenotypic diversification of hepatitis C virus (HCV) in the course of 200 passages in human hepatoma cells in an experimental design that precluded coevolution of the cells with the virus. Adaptation to the cells was evidenced by increase in progeny production. The rate of accumulation of mutations in the genomic consensus sequence deviated slightly from linearity, and mutant spectrum analyses revealed a complex dynamic of mutational waves, which was sustained beyond passage 100. The virus underwent several phenotypic changes, some of which impacted the virus-host relationship, such as enhanced cell killing, a shift toward higher virion density, and increased shutoff of host cell protein synthesis. Fluctuations in progeny production and failure to reach population equilibrium at the genomic level suggest internal instabilities that anticipate an unpredictable HCV evolution in the complex liver environment.IMPORTANCE Long-term virus evolution in an unperturbed cellular environment can reveal features of virus evolution that cannot be explained by comparing natural viral isolates. In the present study, we investigate genetic and phenotypic changes that occur upon prolonged passage of hepatitis C virus (HCV) in human hepatoma cells in an experimental design in which host cell evolutionary change is prevented. Despite replication in a noncoevolving cellular environment, the virus exhibited internal population disequilibria that did not decline with increased adaptation to the host cells. The diversification of phenotypic traits suggests that disequilibria inherent to viral populations may provide a selective advantage to viruses that can be fully exploited in changing environments.