Reservoir displacement by an invasive rodent reduces Lassa virus zoonotic spillover risk

The black rat (Rattus rattus) is a globally invasive species that has been widely introduced across Africa. Within its invasive range in West Africa, R. rattus may compete with the native rodent Mastomys natalensis, the primary reservoir host of Lassa virus, a zoonotic pathogen that kills thousands annually. Here, we use rodent trapping data from Sierra Leone and Guinea to show that R. rattus presence reduces M. natalensis density within the human dwellings where Lassa virus exposure is most likely to occur. Further, we integrate infection data from M. natalensis to demonstrate that Lassa virus zoonotic spillover risk is lower at sites with R. rattus. While non-native species can have numerous negative effects on ecosystems, our results suggest that R. rattus invasion has the indirect benefit of decreasing zoonotic spillover of an endemic pathogen, with important implications for invasive species control across West Africa.

Developing transmissible vaccines for animal infections

Intrinsically safe designs and a staged transparent development process will be essential.

Recombinant transmissible vaccines will be intrinsically contained despite the ability to superinfect

INTRODUCTION

Transmissible vaccines offer a novel approach to suppressing viruses in wildlife populations, with possible applications against viruses that infect humans as zoonoses - Lassa, Ebola, rabies. To ensure safety, current designs propose a recombinant vector platform in which the vector is isolated from the target wildlife population. Because using an endemic vector creates the potential for preexisting immunity to block vaccine transmission, these designs focus on vector viruses capable of superinfection, spreading throughout the host population following vaccination of few individuals.

AREAS COVERED

We present original theoretical arguments that, regardless of its R0 value, a recombinant vaccine using a superinfecting vector is not expected to expand its active infection coverage when released into a wildlife population that already carries the vector. However, if superinfection occurs at a high rate such that individuals are repeatedly infected throughout their lives, the immunity footprint in the population can be high despite a low incidence of active vaccine infections. Yet we provide reasons that the above expectation is optimistic.

EXPERT OPINION

High vaccine coverage will typically require repeated releases or release into a population lacking the vector, but careful attention to vector choice and vaccine engineering should also help improve transmissible vaccine utility.

Predicting the fine-scale spatial distribution of zoonotic reservoirs using computer vision

Zoonotic diseases threaten human health worldwide and are often associated with anthropogenic disturbance. Predicting how disturbance influences spillover risk is critical for effective disease intervention but difficult to achieve at fine spatial scales. Here, we develop a method that learns the spatial distribution of a reservoir species from aerial imagery. Our approach uses neural networks to extract features of known or hypothesized importance from images. The spatial distribution of these features is then summarized and linked to spatially explicit reservoir presence/absence data using boosted regression trees. We demonstrate the utility of our method by applying it to the reservoir of Lassa virus, Mastomys natalensis, within the West African nations of Sierra Leone and Guinea. We show that, when trained using reservoir trapping data and publicly available aerial imagery, our framework learns relationships between environmental features and reservoir occurrence and accurately ranks areas according to the likelihood of reservoir presence.

Optimizing the delivery of self-disseminating vaccines in fluctuating wildlife populations

Zoonotic pathogens spread by wildlife continue to spill into human populations and threaten human lives. A potential way to reduce this threat is by vaccinating wildlife species that harbor pathogens that are infectious to humans. Unfortunately, even in cases where vaccines can be distributed en masse as edible baits, achieving levels of vaccine coverage sufficient for pathogen elimination is rare. Developing vaccines that self-disseminate may help solve this problem by magnifying the impact of limited direct vaccination. Although models exist that quantify how well these self-disseminating vaccines will work when introduced into temporally stable wildlife populations, how well they will perform when introduced into populations with pronounced seasonal population dynamics remains unknown. Here we develop and analyze mathematical models of fluctuating wildlife populations that allow us to study how reservoir ecology, vaccine design, and vaccine delivery interact to influence vaccine coverage and opportunities for pathogen elimination. Our results demonstrate that the timing of vaccine delivery can make or break the success of vaccination programs. As a general rule, the effectiveness of self-disseminating vaccines is optimized by introducing after the peak of seasonal reproduction when the number of susceptible animals is near its maximum.

Identifying the genetic basis of viral spillover using Lassa virus as a test case

The rate at which zoonotic viruses spill over into the human population varies significantly over space and time. Remarkably, we do not yet know how much of this variation is attributable to genetic variation within viral populations. This gap in understanding arises because we lack methods of genetic analysis that can be easily applied to zoonotic viruses, where the number of available viral sequences is often limited, and opportunistic sampling introduces significant population stratification. Here, we explore the feasibility of using patterns of shared ancestry to correct for population stratification, enabling genome-wide association methods to identify genetic substitutions associated with spillover into the human population. Using a combination of phylogenetically structured simulations and Lassa virus sequences collected from humans and rodents in Sierra Leone, we demonstrate that existing methods do not fully correct for stratification, leading to elevated error rates. We also demonstrate, however, that the Type I error rate can be substantially reduced by confining the analysis to a less-stratified region of the phylogeny, even in an already-small dataset. Using this method, we detect two candidate single-nucleotide polymorphisms associated with spillover in the Lassa virus polymerase gene and provide generalized recommendations for the collection and analysis of zoonotic viruses.

Reservoir population ecology, viral evolution and the risk of emerging infectious disease

The ecology and life history of wild animals influences their potential to harbour infectious disease. This observation has motivated studies identifying empirical relationships between traits of wild animals and historical patterns of spillover and emergence into humans. Although these studies have identified compelling broad-scale patterns, they are generally agnostic with respect to underlying mechanisms. Here, we develop mathematical models that couple reservoir population ecology with viral epidemiology and evolution to clarify existing verbal arguments and pinpoint the conditions that favour spillover and emergence. Our results support the idea that average lifespan influences the likelihood of an animal serving as a reservoir for human infectious disease. At the same time, however, our results show that the magnitude of this effect is sensitive to the rate of viral mutation. Our results also demonstrate that viral pathogens causing persistent infections or a transient immune response within the reservoir are more likely to fuel emergence. Genetically explicit stochastic simulations enrich these mathematical results by identifying relationships between the genetic basis of transmission and the risk of spillover and emergence. Together, our results clarify the scope of applicability for existing hypotheses and refine our understanding of emergence risk.

One step closer to a transmissible vaccine for rabies virus

This Primer explores a recent study in PLOS Biology showing that a betaherpesvirus circulating in the vampire bat could serve as an effective vector for a transmissible vaccine capable of reducing the risk of rabies virus spillover in Peru.

Methods for measuring the evolutionary stability of engineered genomes to improve their longevity

Diverse applications rely on engineering microbes to carry and express foreign transgenes. This engineered baggage rarely benefits the microbe and is thus prone to rapid evolutionary loss when the microbe is propagated. For applications where a transgene must be maintained for extended periods of growth, slowing the rate of transgene evolution is critical and can be achieved by reducing either the rate of mutation or the strength of selection. Because the benefits realized by changing these quantities will not usually be equal, it is important to know which will yield the greatest improvement to the evolutionary half-life of the engineering. Here, we provide a method for jointly estimating the mutation rate of transgene loss and the strength of selection favoring these transgene-free, revertant individuals. The method requires data from serial transfer experiments in which the frequency of engineered genomes is monitored periodically. Simple mathematical models are developed that use these estimates to predict the half-life of the engineered transgene and provide quantitative predictions for how alterations to mutation and selection will influence longevity. The estimation method and predictive tools have been implemented as an interactive web application, MuSe.

Coevolutionary Arms Races and the Conditions for the Maintenance of Mutualism

AbstractEmpirical evidence suggests that coevolutionary arms races between flowering plants and their pollinators can occur in wild populations. In extreme cases, trait escalation may result in evolutionary switching from mutualism to parasitism. However, theoretical approaches to studying coevolution typically assume fixed types of ecological interactions and ignore the evolution of absolute fitness. Here, we introduce a novel approach to track the evolution of absolute fitness as a framework to determine when escalatory coevolution results in a switch from mutualism to parasitism. We apply our approach to two previously studied mechanisms mediating selection as a function of phenotype. Our results demonstrate that interactions mediated by a "bigger-is-better" mechanism evolve toward parasitism. In contrast, generalizing the classical trait-matching mechanism so that the fitness of each species is optimized when trait values mismatch by a particular amount, we find theoretical support for indefinite trait exaggeration that preserves mutualistic interactions. Building on our results, we discuss the consequences of coevolutionary arms races for the maintenance of cheating. Moving beyond pairwise interactions, we consider the ramifications of coevolution in a South African pollination network for the evolution of parasitism. Future work extending our approach beyond pairwise interactions can lead to a framework for understanding the evolution of parasitism in mutualistic networks and further insights into the structure and dynamic nature of ecological communities in general.

A white noise approach to evolutionary ecology

Although the evolutionary response to random genetic drift is classically modelled as a sampling process for populations with fixed abundance, the abundances of populations in the wild fluctuate over time. Furthermore, since wild populations exhibit demographic stochasticity and since random genetic drift is in part due to demographic stochasticity, theoretical approaches are needed to understand the role of demographic stochasticity in eco-evolutionary dynamics. Here we close this gap for quantitative characters evolving in continuously reproducing populations by providing a framework to track the stochastic dynamics of abundance density across phenotypic space using stochastic partial differential equations. In the process we develop a set of heuristics to operationalize the powerful, but abstract theory of white noise and diffusion-limits of individual-based models. Applying these heuristics, we obtain stochastic ordinary differential equations that generalize classical expressions of ecological quantitative genetics. In particular, by supplying growth rate and reproductive variance as functions of abundance densities and trait values, these equations track population size, mean trait and additive genetic variance responding to mutation, demographic stochasticity, random genetic drift, deterministic selection and noise-induced selection. We demonstrate the utility of our approach by formulating a model of diffuse coevolution mediated by exploitative competition for a continuum of resources. In addition to trait and abundance distributions, this model predicts interaction networks defined by niche-overlap, competition coefficients, or selection gradients. Using a high-richness approximation, we find linear selection gradients and competition coefficients are uncorrelated, but magnitudes of linear selection gradients and quadratic selection gradients are both positively correlated with competition coefficients. Hence, competing species that strongly affect each other's abundance tend to also impose selection on one another, but the directionality is not predicted. This approach contributes to the development of a synthetic theory of evolutionary ecology by formalizing first principle derivations of stochastic models tracking feedbacks of biological processes and the patterns of diversity they produce.

Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus spillover in West Africa

Forecasting the risk of pathogen spillover from reservoir populations of wild or domestic animals is essential for the effective deployment of interventions such as wildlife vaccination or culling. Due to the sporadic nature of spillover events and limited availability of data, developing and validating robust, spatially explicit, predictions is challenging. Recent efforts have begun to make progress in this direction by capitalizing on machine learning methodologies. An important weakness of existing approaches, however, is that they generally rely on combining human and reservoir infection data during the training process and thus conflate risk attributable to the prevalence of the pathogen in the reservoir population with the risk attributed to the realized rate of spillover into the human population. Because effective planning of interventions requires that these components of risk be disentangled, we developed a multi-layer machine learning framework that separates these processes. Our approach begins by training models to predict the geographic range of the primary reservoir and the subset of this range in which the pathogen occurs. The spillover risk predicted by the product of these reservoir specific models is then fit to data on realized patterns of historical spillover into the human population. The result is a geographically specific spillover risk forecast that can be easily decomposed and used to guide effective intervention. Applying our method to Lassa virus, a zoonotic pathogen that regularly spills over into the human population across West Africa, results in a model that explains a modest but statistically significant portion of geographic variation in historical patterns of spillover. When combined with a mechanistic mathematical model of infection dynamics, our spillover risk model predicts that 897,700 humans are infected by Lassa virus each year across West Africa, with Nigeria accounting for more than half of these human infections.

The 2019 emergence of SARS-CoV-2 is a grim reminder of the threat animal-borne pathogens pose to human health. Even prior to SARS-CoV-2, the spillover of pathogens from animal reservoirs was a persistent problem, with pathogens such as Ebola, Nipah, and Lassa regularly but unpredictably causing outbreaks. Machine-learning models that anticipate when and where pathogen transmission from animals to humans is likely to occur would help guide surveillance efforts and preemptive countermeasures like information campaigns or vaccination programs. We develop a novel machine learning framework that uses datasets describing the distribution of a virus within its host and the range of its animal host, along with data on spatial patterns of human immunity, to infer rates of animal-to-human transmission across a region. By training the model on data from the animal host alone, our framework allows rigorous validation of spillover predictions using human data. We apply our framework to Lassa fever, a viral disease of West Africa that is spread to humans by rodents, and use the predictions to update estimates of Lassa virus infections in humans. Our results suggest that Nigeria is most at risk for the emergence of Lassa virus, and should be prioritized for outbreak-surveillance.

Suppressing evolution in genetically engineered systems through repeated supplementation

Genetically engineered organisms are prone to evolve in response to the engineering. This evolution is often undesirable and can negatively affect the purpose of the engineering. Methods that maintain the stability of engineered genomes are therefore critical to the successful design and use of genetically engineered organisms. One potential method to limit unwanted evolution is by taking advantage of the ability of gene flow to counter local adaption, a process of supplementation. Here, we investigate the feasibility of supplementation as a mechanism to offset the evolutionary degradation of a transgene in three model systems: a bioreactor, a gene drive, and a transmissible vaccine. In each model, continual introduction from a stock is used to balance mutation and selection against the transgene. Each system has its unique features. The bioreactor system is especially tractable and has a simple answer: The level of supplementation required to maintain the transgene at a frequency p ^ is approximatelyp ^ s , where s is the selective disadvantage of the transgene. Supplementation is also feasible in the transmissible vaccine case but is probably not practical to prevent the evolution of resistance against a gene drive. We note, however, that the continual replacement of even a small fraction of a large population can be challenging, limiting the usefulness of supplementation as a means of controlling unwanted evolution.

Designing transmissible viral vaccines for evolutionary robustness and maximum efficiency

The danger posed by emerging infectious diseases necessitates the development of new tools that can mitigate the risk of animal pathogens spilling over into the human population. One promising approach is the development of recombinant viral vaccines that are transmissible, and thus capable of self-dissemination through hard to reach populations of wild animals. Indeed, mathematical models demonstrate that transmissible vaccines can greatly reduce the effort required to control the spread of zoonotic pathogens in their animal reservoirs, thereby limiting the chances of human infection. A key challenge facing these new vaccines, however, is the inevitability of evolutionary change resulting from their ability to self-replicate and generate extended chains of transmission. Further, carrying immunogenic transgenes is often costly, in terms of metabolic burden, increased competition with the pathogen, or due to unintended interactions with the viral host regulatory network. As a result, natural selection is expected to favor vaccine strains that down-regulate or delete these transgenes resulting in increased rates of transmission and reduced efficacy against the target pathogen. In addition, efficacy and evolutionary stability will often be at odds; as when longer, more efficacious antigens experience faster rates of evolutionary decay. Here, we ask how such trade-offs influence the overall performance of transmissible vaccines. We find that evolutionary instability can substantially reduce performance, even for vaccine candidates with the ideal combination of efficacy and transmission. However, we find that, at least in some cases, vaccine stability and overall performance can be improved by the inclusion of a second, redundant antigen. Overall, our results suggest that the successful application of recombinant transmissible vaccines will require consideration of evolutionary dynamics and epistatic effects, as well as basic measurements of epidemiological features.

Self-disseminating vaccines to suppress zoonoses

The SARS-CoV-2 epidemic is merely the most recent demonstration that our current approach to emerging zoonotic infectious disease is ineffective. SARS, MERS, Ebola, Nipah and an array of arenavirus infections sporadically spillover into human populations and are often contained only as a result of their poor transmission in human hosts, coupled with intense public health control efforts in the early stages of an emerging epidemic. It is now more apparent than ever that we need a better and more proactive approach. One possibility is to eliminate the threat of spillover before it occurs using vaccines capable of autonomously spreading through wild animal reservoirs. We are now poised to begin developing self-disseminating vaccines targeting a wide range of human pathogens, but important decisions remain about how they can be most effectively designed and used to target pathogens with a high risk of spillover and/or emergence. In this Perspective, we first review the basic epidemiological theory establishing the feasibility and utility of self-disseminating vaccines. We then outline a road map for overcoming remaining technical challenges: identifying high-risk pathogens before they emerge, optimizing vaccine design with an eye to evolution, behaviour and epidemiology, and minimizing the risk of unintended consequences.

Patterns of floral morphology in relation to climate and floral visitors

BACKGROUND AND AIMS

The diversity of floral morphology among plant species has long captured the interest of biologists and led to the development of a number of explanatory theories. Floral morphology varies substantially within species, and the mechanisms maintaining this diversity are diverse. One possibility is that spatial variation in the pollinator fauna drives the evolution of spatially divergent floral ecotypes adapted to the local suite of pollinators. Another possibility is that geographic variation in the abiotic environment and local climatic conditions favours different floral morphologies in different regions. Although both possibilities have been shown to explain floral variation in some cases, they have rarely been competed against one another using data collected from large spatial scales. In this study, we assess floral variation in relation to climate and floral visitors in four oil-reward-specialized pollination interactions.

METHODS

We used a combination of large-scale plant and pollinator samplings, morphological measures and climatic data. We analysed the data using spatial approaches, as well as traditional multivariate and structural equation modelling approaches.

KEY RESULTS

Our results indicate that the four species have different levels of specialization, and that this can be explained by their climatic niche breadth. In addition, our results show that, at least for some species, floral morphology can be explained by the identity of floral visitors, with climate having only an indirect effect.

CONCLUSIONS

Our results demonstrate that, even in very specialized interactions, both biotic and abiotic variables can explain a substantial amount of intraspecific variation in floral morphology.

When to vaccinate a fluctuating wildlife population: Is timing everything?

Wildlife vaccination is an important tool for managing the burden of infectious disease in human populations, domesticated livestock and various iconic wildlife. Although substantial progress has been made in the field of vaccine designs for wildlife, there is a gap in our understanding of how to time wildlife vaccination, relative to host demography, to best protect a population.We use a mathematical model and computer simulations to assess the outcomes of vaccination campaigns that deploy vaccines once per annual population cycle.Optimal timing of vaccination is an important consideration in animals with short to intermediate life spans and a short birthing season. Vaccines that are deployed shortly after the birthing season best protect the host population.The importance of timing is greater in wildlife pathogens that have a high rate of transmission and a short recovery period. Vaccinating at the end of the birthing season best reduces the mean abundance of pathogen-infected hosts. Delaying vaccination until later in the year can facilitate pathogen elimination. Policy Implications. Tuning wildlife vaccination campaigns to host demography and pathogen traits can substantially increase the effectiveness of a campaign. Our results suggest that, for a fluctuating population, vaccinating at, or shortly after, the end of the birthing season, best protects the population against an invading pathogen. If the pathogen is already endemic, delaying vaccination until after the birthing season is over can help facilitate pathogen elimination. Our results highlight the need to better understand and predict host demography in wildlife populations that are targeted for vaccination.

Evolution and containment of transmissible recombinant vector vaccines

Transmissible vaccines offer a revolutionary approach for controlling infectious disease and may provide one of the few feasible methods for eliminating pathogens from inaccessible wildlife populations. Current efforts to develop transmissible vaccines use recombinant vector technology whereby pathogen antigens are engineered to be expressed from innocuous infectious viral vectors. The resulting vaccines can transmit from host to host, amplifying the number of vaccine-protected individuals beyond those initially vaccinated directly through parenteral inoculation. One main engineering challenge is the potential for natural selection to favor vaccine mutants that eliminate or reduce expression of antigenic inserts, resulting in immunogenic decay of the vaccine over time. Here, we study a mathematical model of vector mutation whereby continuous elimination of the antigenic insert results in reversion of the vaccine back into the insert-free vector. We use this model to quantify the maximum allowable rate of reversion that can be tolerated for a transmissible vaccine to maintain a critical threshold level of immunogenicity against a target pathogen. Our results demonstrate that even for transmissible vaccines where reversion is frequent, performance will often substantially exceed that of conventional, directly administered vaccines. Further, our results demonstrate the feasibility of designing transmissible vaccines that yield desired levels of immunogenicity, yet degrade at a rate sufficient for persistence of the recombinant vaccine within the environment to be minimized.

Recombinant vector vaccine evolution

Replicating recombinant vector vaccines consist of a fully competent viral vector backbone engineered to express an antigen from a foreign transgene. From the perspective of viral replication, the transgene is not only dispensable but may even be detrimental. Thus vaccine revertants that delete or inactivate the transgene may evolve to dominate the vaccine virus population both during the process of manufacture of the vaccine as well as during the course of host infection. A particular concern is that this vaccine evolution could reduce its antigenicity—the immunity elicited to the transgene. We use mathematical and computational models to study vaccine evolution and immunity. These models include evolution arising during the process of manufacture, the dynamics of vaccine and revertant growth, plus innate and adaptive immunity elicited during the course of infection. Although the selective basis of vaccine evolution is easy to comprehend, the immunological consequences are not. One complication is that the opportunity for vaccine evolution is limited by the short period of within-host growth before the viral population is cleared. Even less obvious, revertant growth may only weakly interfere with vaccine growth in the host and thus have a limited effect on immunity to vaccine. Overall, we find that within-host vaccine evolution can sometimes compromise vaccine immunity, but only when the extent of evolution during vaccine manufacture is severe, and this evolution can be easily avoided or mitigated.

Recombinant vector vaccines are live replicating viruses that are engineered to carry extra genes derived from a pathogen—and these extra genes produce proteins against which we want to generate immunity. These vaccine genomes may evolve to lose the extra genes during the process of manufacture of the vaccine or during replication within an individual, and there is a concern that this evolution might severely limit the vaccine’s efficacy. The dynamics of this process are studied here with mathematical models. The potential for vaccine evolution within the host is somewhat limited by the short-term growth of the vaccine population before it is suppressed by the immune response. We find that evolution is a problem only when the process of manufacture results in the majority of the vaccine virus being revertant. We show that increasing the vaccine inoculum size or reducing the level of revertant in the vaccine inoculum can largely avoid the loss of immunity arising from evolution.

Transmissible vaccines in heterogeneous populations: Implications for vaccine design

Transmissible vaccines may provide a promising solution for improving the control of infectious disease, particularly zoonotic pathogens with wildlife reservoirs. Although it is well known that heterogeneity in pathogen transmission impacts the spread of infectious disease, the effects of heterogeneity on vaccine transmission are largely unknown. Here we develop and analyze a mathematical model that quantifies the potential benefits of a transmissible vaccine in a population where transmission is heterogeneous between two subgroups. Our results demonstrate that the effect of heterogeneity on the benefit of vaccine transmission largely depends on the vaccine design and the pattern of vaccine administration across subgroups. Specifically, our results show that in most cases a transmissible vaccine designed to mirror the transmission of the pathogen is optimal. If the vaccination effort can be preferentially biased towards a given subgroup, a vaccine with a pattern of transmission opposite to that of the pathogen can become optimal in some cases. To better understand the consequences of heterogeneity on the effectiveness of a transmissible vaccine in the real world, we parameterized our model using data from Sin Nombre virus in deer mice (Peromyscus maniculatus). The results of this analysis reveal that when a vaccination campaign is limited in vaccine availability, a traditional vaccine must be administered primarily to males for the spread of Sin Nombre virus to be prevented. In contrast, a transmissible vaccine remains effective even when it cannot be preferentially administered to males.

The measurement of coevolution in the wild

Coevolution has long been thought to drive the exaggeration of traits, promote major evolutionary transitions such as the evolution of sexual reproduction and influence epidemiological dynamics. Despite coevolution's long suspected importance, we have yet to develop a quantitative understanding of its strength and prevalence because we lack generally applicable statistical methods that yield numerical estimates for coevolution's strength and significance in the wild. Here, we develop a novel method that derives maximum likelihood estimates for the strength of direct pairwise coevolution by coupling a well-established coevolutionary model to spatially structured phenotypic data. Applying our method to two well-studied interactions reveals evidence for coevolution in both systems. Broad application of this approach has the potential to further resolve long-standing evolutionary debates such as the role species interactions play in the evolution of sexual reproduction and the organisation of ecological communities.

Transmissible vaccines whose dissemination rates vary through time, with applications to wildlife

Transmission is a potential property of live viral vaccines that remains largely unexploited but may lie within the realm of many engineering designs. While likely unacceptable for vaccines of humans, transmission may be highly desirable for vaccines of wildlife, both to protect natural populations and also to limit zoonotic transmissions into humans. Defying intuition, transmission alone does not guarantee that a vaccine will perform well: the benefit of transmission over no transmission depends on and increases with the basic reproductive number of the vaccine, R₀. The R₀ of an infectious agent in a homogeneous population is typically considered to be a fixed number, but some evidence suggests that dissemination of transmissible vaccines may change through time. One obvious possibility is that transmission will be greater from hosts directly vaccinated than from hosts who acquire the vaccine passively, but other types of change might also accrue. Whenever transmission changes over time, the R₀ estimated from directly vaccinated hosts will not reflect the vaccine's long term impact. As there is no theory on the consequences of changing transmission rates for a vaccine, we derive conditions for a transmissible vaccine with varying transmission rates to protect a population from pathogen invasion. Being the first in the transmission chain, the R₀ from directly vaccinated hosts has a larger effect than those from later steps in the chain. This mathematical property reveals that a transmissible vaccine with low long term transmission may nonetheless realize a big impact if early transmission is high. Furthermore, there may be ways to artificially elevate early transmission, thereby achieving high herd immunity from transmission while ensuring that the vaccine will ultimately die out.

Controlling epidemics with transmissible vaccines

As the density of human and domestic animal populations increases, the threat of localized epidemics and global pandemics grows. Although effective vaccines have been developed for a number of threatening pathogens, manufacturing and disseminating vaccines in the face of a rapidly spreading epidemic or pandemic remains a formidable challenge. One potentially powerful solution to this problem is the use of transmissible vaccines. Transmissible vaccines are capable of spreading from one individual to another and are currently being developed for a range of infectious diseases. Here we develop and analyze mathematical models that allow us to quantify the benefits of vaccine transmission in the face of an imminent or ongoing epidemic. Our results demonstrate that even a small amount of vaccine transmission can greatly increase the rate at which a naïve host population can be protected against an anticipated epidemic and substantially reduce the size of unanticipated epidemics if vaccination is initiated shortly after pathogen detection. In addition, our results identify key biological properties and implementation practices that maximize the impact of vaccine transmission on infectious disease.

Evaluating the promise of recombinant transmissible vaccines

Transmissible vaccines have the potential to revolutionize infectious disease control by reducing the vaccination effort required to protect a population against a disease. Recent efforts to develop transmissible vaccines focus on recombinant transmissible vaccine designs (RTVs) because they pose reduced risk if intra-host evolution causes the vaccine to revert to its vector form. However, the shared antigenicity of the vaccine and vector may confer vaccine-immunity to hosts infected with the vector, thwarting the ability of the vaccine to spread through the population. We build a mathematical model to test whether a RTV can facilitate disease management in instances where reversion is likely to introduce the vector into the population or when the vector organism is already established in the host population, and the vector and vaccine share perfect cross-immunity. Our results show that a RTV can autonomously eradicate a pathogen, or protect a population from pathogen invasion, when cross-immunity between vaccine and vector is absent. If cross-immunity between vaccine and vector exists, however, our results show that a RTV can substantially reduce the vaccination effort necessary to control or eradicate a pathogen only when continuously augmented with direct manual vaccination. These results demonstrate that estimating the extent of cross-immunity between vector and vaccine is a critical step in RTV design, and that herpesvirus vectors showing facile reinfection and weak cross-immunity are promising.

Eradicating infectious disease using weakly transmissible vaccines

Viral vaccines have had remarkable positive impacts on human health as well as the health of domestic animal populations. Despite impressive vaccine successes, however, many infectious diseases cannot yet be efficiently controlled or eradicated through vaccination, often because it is impossible to vaccinate a sufficient proportion of the population. Recent advances in molecular biology suggest that the centuries-old method of individual-based vaccine delivery may be on the cusp of a major revolution. Specifically, genetic engineering brings to life the possibility of a live, transmissible vaccine. Unfortunately, releasing a highly transmissible vaccine poses substantial evolutionary risks, including reversion to high virulence as has been documented for the oral polio vaccine. An alternative, and far safer approach, is to rely on genetically engineered and weakly transmissible vaccines that have reduced scope for evolutionary reversion. Here, we use mathematical models to evaluate the potential efficacy of such weakly transmissible vaccines. Our results demonstrate that vaccines with even a modest ability to transmit can significantly lower the incidence of infectious disease and facilitate eradication efforts. Consequently, weakly transmissible vaccines could provide an important tool for controlling infectious disease in wild and domestic animal populations and for reducing the risks of emerging infectious disease in humans.

Transmissible Viral Vaccines

Genetic engineering now enables the design of live viral vaccines that are potentially transmissible. Some designs merely modify a single viral genome to improve on the age-old method of attenuation whereas other designs create chimeras of viral genomes. Transmission has the benefit of increasing herd immunity above that achieved by direct vaccination alone but also increases the opportunity for vaccine evolution, which typically undermines vaccine utility. Different designs have different epidemiological consequences but also experience different evolution. Approaches that integrate vaccine engineering with an understanding of evolution and epidemiology will reap the greatest benefit from vaccine transmission.

Rethinking Conventional Wisdom: Are Locally Adapted Parasites Ahead in the Coevolutionary Race?

The metaphors of the Red Queen and the arms race have inspired a large amount of research aimed at understanding the process of antagonistic coevolution between hosts and parasites. One approach that has been heavily used is to estimate the strength of parasite local adaptation using a reciprocal cross infection or transplant study. These studies frequently conclude that the locally adapted species is ahead in the coevolutionary race. Here, I use mathematical models to decompose parasite infectivity into components attributable to local versus global adaptation and to develop a robust index of which species is ahead in the coevolutionary race, which I term coevolutionary advantage. Computer simulations of coevolving host-parasite interactions demonstrate that because the magnitudes of local and global adaptation are largely independent, the link between the sign of local adaptation and coevolutionary advantage is tenuous. A consequence of the weak coupling between local adaptation and coevolutionary advantage is that the bulk of existing empirical studies do not sample enough populations for any reliable conclusions to be drawn. Together, these results suggest that the long-standing conventional wisdom holding that locally adapted parasites are ahead in the coevolutionary race should be reconsidered.

Identifying coevolving loci using interspecific genetic correlations

Evaluating the importance of coevolution for a wide range of evolutionary questions, such as the role parasites play in the evolution of sexual reproduction, requires that we understand the genetic basis of coevolutionary interactions. Despite its importance, little progress has been made identifying the genetic basis of coevolution, largely because we lack tools designed specifically for this purpose. Instead, coevolutionary studies are often forced to re-purpose single species techniques. Here, we propose a novel approach for identifying the genes mediating locally adapted coevolutionary interactions that relies on spatial correlations between genetic marker frequencies in the interacting species. Using individual-based multi-locus simulations, we quantify the performance of our approach across a range of coevolutionary genetic models. Our results show that when one species is strongly locally adapted to the other and a sufficient number of populations can be sampled, our approach accurately identifies functionally coupled host and parasite genes. Although not a panacea, the approach we outline here could help to focus the search for coevolving genes in a wide variety of well-studied systems for which substantial local adaptation has been demonstrated.

Design and engineering of a transmissible antiviral defense

Background

We propose, model, and implement a novel system of population-level intervention against a virus. One context is a treatment against a chronic infection such as HIV. The underlying principle is a form of virus ‘wars’ in which a benign, transmissible agent is engineered to protect against infection by and spread of a lethal virus. In our specific case, the protective agent consists of two entities, a benign virus and a gene therapy vector mobilized by the benign virus.

Results

Numerical analysis of a mathematical model identified parameter ranges in which adequate, population-wide protection is achieved. The protective system was implemented and tested using E. coli, bacteriophage M13 and a phagemid vector mobilized by M13 to block infection by the lethal phage T5. Engineering of M13 profoundly improved its dynamical properties for facilitating spread of the gene therapy vector. However, the gene therapy vector converts the host cell to resist T5 too slowly for protection on a time scale appropriate for T5.

Conclusions

Overall, there is a reasonable marriage between the mathematical model and the empirical system, suggesting that such models can be useful guides to the design of such systems even before the models incorporate most of the relevant biological details.

Electronic supplementary material

The online version of this article (doi:10.1186/s13036-016-0033-4) contains supplementary material, which is available to authorized users.

Virus wars: using one virus to block the spread of another

The failure of traditional interventions to block and cure HIV infections has led to novel proposals that involve treating infections with therapeutic viruses-infectious viruses that specifically inhibit HIV propagation in the host. Early efforts in evaluating these proposals have been limited chiefly to mathematical models of dynamics, for lack of suitable empirical systems. Here we propose, develop and analyze an empirical system of a therapeutic virus that protects a host cell population against a lethal virus. The empirical system uses E. coli bacteria as the host cell population, an RNA phage as the lethal virus and a filamentous phage as the therapeutic virus. Basic dynamic properties are established for each virus alone and then together. Observed dynamics broadly agree with those predicted by a computer simulation model, although some differences are noted. Two cases of dynamics are contrasted, differing in whether the therapeutic virus is introduced before the lethal virus or after the lethal virus. The therapeutic virus increases in both cases but by different mechanisms. With the therapeutic virus introduced first, it spreads infectiously without any appreciable change in host dynamics. With the therapeutic virus introduced second, host abundance is depressed at the time therapy is applied; following an initial period of therapeutic virus spread by infection, the subsequent rise of protection is through reproduction by hosts already protected. This latter outcome is due to inheritance of the therapeutic virus state when the protected cell divides. Overall, the work establishes the feasibility and robustness to details of a viral interference using a therapeutic virus.

Trait dimensionality explains widespread variation in local adaptation

All species are locked in a continual struggle to adapt to local ecological conditions. In cases where species fail to locally adapt, they face reduced population growth rates, or even local extinction. Traditional explanations for limited local adaptation focus on maladaptive gene flow or homogeneous environmental conditions. These classical explanations have, however, failed to explain variation in the magnitude of local adaptation observed across taxa. Here we show that variable levels of local adaptation are better explained by trait dimensionality. First, we develop and analyse mathematical models that predict levels of local adaptation will increase with the number of traits experiencing spatially variable selection. Next, we test this prediction by estimating the relationship between dimensionality and local adaptation using data from 35 published reciprocal transplant studies. This analysis reveals a strong correlation between dimensionality and degree of local adaptation, and thus provides empirical support for the predictions of our model.

Genetics, adaptation, and invasion in harsh environments

We analyze mathematical models to examine how the genetic basis of fitness affects the persistence of a population suddenly encountering a harsh environment where it would go extinct without evolution. The results are relevant for novel introductions and for an established population whose existence is threatened by a sudden change in the environment. The models span a range of genetic assumptions, including identical loci that contribute to absolute fitness, a two-locus quantitative genetic model with nonidentical loci, and a model with major and minor genes affecting a quantitative trait. We find as a general (though not universal) pattern that prospects for persistence narrow as more loci contribute to fitness, in effect because selection per locus is increasingly weakened with more loci, which can even overwhelm any initial enhancement of fitness that adding loci might provide. When loci contribute unequally to fitness, genes of small effect can significantly reduce extinction risk. Indeed, major and minor genes can interact synergistically to reduce the time needed to evolve growth. Such interactions can also increase vulnerability to extinction, depending not just on how genes interact but also on the initial genetic structure of the introduced, or newly invaded, population.

Multidimensional (co)evolutionary stability

The complexity of biotic and abiotic environmental conditions is such that the fitness of individuals is likely to depend on multiple traits. Using a synthetic framework of phenotypic evolution that draws from adaptive dynamics and quantitative genetics approaches, we explore how the number of traits under selection influences convergence stability and evolutionary stability in models for coevolution in multidimensional phenotype spaces. Our results allow us to identify three different effects of trait dimensionality on stability. First are (i) a "combinatorial effect": without epistasis and genetic correlations, a higher number of trait dimensions offers more opportunities for equilibria to be unstable; and (ii) epistatic interactions, that is, fitness interactions between traits, which tend to destabilize evolutionary equilibria; this effect increases with the dimension of phenotype space. These first two effects influence both convergence stability and evolutionary stability, while (iii) genetic correlations (due, e.g., to pleiotropy or linkage disequilibrium) can affect only convergence stability. We illustrate the general prediction that increased dimensionality destabilizes evolutionary equilibria using examples drawn from well-studied classical models of frequency-dependent competition for resources, adaptation to a spatially heterogeneous environment, and antagonistic coevolution. In addition, our analyses show that increased dimensionality can favor diversification, for example, in the form of local adaptation, as well as evolutionary escape.

Connecting functional and statistical definitions of genotype by genotype interactions in coevolutionary studies

Predicting how species interactions evolve requires that we understand the mechanistic basis of coevolution, and thus the functional genotype-by-genotype interactions (G × G) that drive reciprocal natural selection. Theory on host-parasite coevolution provides testable hypotheses for empiricists, but depends upon models of functional G × G that remain loosely tethered to the molecular details of any particular system. In practice, reciprocal cross-infection studies are often used to partition the variation in infection or fitness in a population that is attributable to G × G (statistical G × G). Here we use simulations to demonstrate that within-population statistical G × G likely tells us little about the existence of coevolution, its strength, or the genetic basis of functional G × G. Combined with studies of multiple populations or points in time, mapping and molecular techniques can bridge the gap between natural variation and mechanistic models of coevolution, while model-based statistics can formally confront coevolutionary models with cross-infection data. Together these approaches provide a robust framework for inferring the infection genetics underlying statistical G × G, helping unravel the genetic basis of coevolution.

A practical guide to measuring local adaptation

Patterns of local adaptation are expected to emerge when selection is spatially heterogeneous and sufficiently strong relative to the action of other evolutionary forces. The observation of local adaptation thus provides important insight into evolutionary processes and the adaptive divergence of populations. The detection of local adaptation, however, suffers from several conceptual, statistical and methodological issues. Here, we provide practical recommendations regarding (1) the definition of local adaptation, (2) the analysis of transplant experiments and (3) the optimisation of the experimental design of local adaptation studies. Together, these recommendations provide a unified approach for measuring local adaptation and understanding the adaptive divergence of populations in a wide range of biological systems.

Coevolution in multidimensional trait space favours escape from parasites and pathogens

Almost all species are subject to continuous attack by parasites and pathogens. Because parasites and pathogens tend to have shorter generation times and often experience stronger selection due to interaction than their victims do, it is frequently argued that they should evolve more rapidly and thus maintain an advantage in the evolutionary race between defence and counter-defence. This prediction generates an apparent paradox: how do victim species survive and even thrive in the face of a continuous onslaught of more rapidly evolving enemies? One potential explanation is that defence is physiologically, mechanically or behaviourally easier than attack, so that evolution is less constrained for victims than for parasites or pathogens. Another possible explanation is that parasites and pathogens have enemies themselves and that victim species persist because parasites and pathogens are regulated from the top down and thus generally have only modest demographic impacts on victim populations. Here we explore a third possibility: that victim species are not as evolutionarily impotent as conventional wisdom holds, but instead have unique evolutionary advantages that help to level the playing field. We use quantitative genetic analysis and individual-based simulations to show that victims can achieve such an advantage when coevolution involves multiple traits in both the host and the parasite.

A unified model of autopolyploid establishment and evolution

The prevalence of polyploidy among flowering plants is surprising given the hurdles impeding the establishment and persistence of novel polyploid lineages. In the absence of strong assortative mating, reproductive assurance, or large intrinsic fitness advantages, new polyploid lineages face almost certain extinction through minority cytotype exclusion. Consequently, much work has focused on a search for adaptive advantages associated with polyploidy such as increased competitive ability, enhanced ecological tolerances, and increased resistance to pathogens. Yet, no consistent adaptive advantages of polyploidy have been identified. Here, to investigate the potential for autopolyploid establishment and persistence in the absence of any intrinsic fitness advantages, we develop a simulation model of a diploid population that sporadically gives rise to novel autopolyploids. The autopolyploids have only very small levels of initial assortative mating or niche differentiation, generated entirely by dosage effects of genome duplication, and they have realistic levels of reproductive assurance. Our results show that by allowing assortative mating and competitive interactions to evolve, establishment of novel autopolyploid lineages becomes common. Additional scenarios where adaptive optima change over time reveal that rapid environmental change promotes the replacement of diploid lineages by their autopolyploid descendants. These results help to explain recent empirical findings that suggest that many contemporary polyploid lineages arose during the Cretaceous-Tertiary extinction, without invoking adaptive advantages of polyploidy.

Neopolyploidy and diversification in Heuchera grossulariifolia

Newly formed polyploid lineages must contend with several obstacles to avoid extinction, including minority cytotype exclusion, competition, and inbreeding depression. If polyploidization results in immediate divergence of phenotypic characters these hurdles may be reduced and establishment made more likely. In addition, if polyploidization alters the phenotypic and genotypic associations between traits, that is, the P and G matrices, polyploids may be able to explore novel evolutionary paths, facilitating their divergence and successful establishment. Here, we report results from a study of the perennial plant Heuchera grossulariifolia in which the phenotypic divergence and changes in phenotypic and genotypic covariance matrices caused by neopolyploidization have been estimated. Our results reveal that polyploidization causes immediate divergence for traits relevant to establishment and results in significant changes in the structure of the phenotypic covariance matrix. In contrast, our results do not provide evidence that polyploidization results in immediate and substantial shifts in the genetic covariance matrix.