The evolution of costly acquired immune memory

A key feature of the vertebrate adaptive immune system is acquired immune memory, whereby hosts launch a faster and heightened response when challenged by previously encountered pathogens, preventing full infection. Here, we use a mathematical model to explore the role of ecological and epidemiological processes in shaping selection for costly acquired immune memory. Applying the framework of adaptive dynamics to the classic SIR (Susceptible‐Infected‐Recovered) epidemiological model, we focus on the conditions that may lead hosts to evolve high levels of immunity. Linking our work to previous theory, we show how investment in immune memory may be greatest at long or intermediate host lifespans depending on whether immunity is long lasting. High initial costs to gain immunity are also found to be essential for a highly effective immune memory. We also find that high disease infectivity and sterility, but intermediate virulence and immune period, increase selection for immunity. Diversity in host populations through evolutionary branching is found to be possible but only for a limited range of parameter space. Our model suggests that specific ecological and epidemiological conditions have to be met for acquired immune memory to evolve.     

Within-host parasite dynamics,emerging trade-off,and evolution of virulence with immune system

Abstract.— Virulence is an evolutionary paradox because parasites never benefit from their host's death. The adaptive explanation of virulence is classically based upon the existence of physiological constraints that create a trade-off between parasites' epidemiological traits (virulence, transmissibility, and clearance). Here we develop an epidemiological model where infections are dynamic processes and we demonstrate how these dynamics generate a trade-off between emerging epidemiological parameters. We then study how host's immune strength modifies this trade-off and hence influences virulence evolution. We found that in acute infections, where parasites are engaged in a race with immune cells, immunity restrains more the duration of the infection than its intensity. As a consequence parasites evolve to provoke more virulent but shorter infections in strongly immunized hosts.     

Bottom‐up regulation of malaria population dynamics in mice co‐infected with lung‐migratory nematodes

When and how populations are regulated by bottom up vs. top down processes, and how those processes are affected by co‐occurring species, are poorly characterised across much of ecology. We are especially interested in the community ecology of parasites that must share a host. Here, we quantify how resources and immunity affect parasite propagation in experiments in near‐replicate ‘mesocosms’’ – i.e. mice infected with malaria (Plasmodium chabaudi) and nematodes (Nippostrongylus brasiliensis). Nematodes suppressed immune responses against malaria, and yet malaria populations were smaller in co‐infected hosts. Further analyses of within‐host epidemiology revealed that nematode co‐infection altered malaria propagation by suppressing target cell availability. This is the first demonstration that bottom‐up resource regulation may have earlier and stronger effects than top‐down immune mechanisms on within‐host community dynamics. Our findings demonstrate the potential power of experimental ecology to disentangle mechanisms of population regulation in complex communities.     

Vector preference and host defense against infection interact to determine disease dynamics

Vector preference based on host infection status has long been recognized for its importance in disease dynamics. Prior theoretical work has assumed that all hosts are uniformly susceptible to the pathogen. Here we investigated disease dynamics when this assumption is relaxed using a series of vector–host epidemiological compartment models with variable levels of host resistance or tolerance to infection – collectively termed defense. In our models, vectors cannot acquire the infection from resistant hosts but can acquire from tolerant hosts. Specifically, we investigated the interacting effects of vector preference and host defense in a series of single‐ and two‐patch models. Results indicate that resistant host types generally reduce disease prevalence and pathogen spillover, independent of vector preference. The epidemiological consequences of host tolerance, however, depended on vector preference. When vectors preferred diseased hosts, tolerance reduced incidence compared to susceptible hosts; when vectors avoided diseased hosts, tolerance enhanced disease prevalence. Finally, a variation of the model that included preference‐based vector patch leaving rates suggests that both resistance and tolerance can promote pathogen spillover if vectors prefer diseased hosts, because of increased vector dispersal into susceptible patches. Collectively, we found complex, context‐dependent effects of vector preference and host defense on disease dynamics. In the context of management programs for vector‐borne diseases, managers should consider both the precise form of host defense present in a population, breed, or cultivar, as well as vector feeding behavior.     

Infection dynamics of endemic malaria in a wild bird population: parasite species-dependent drivers of spatial and temporal variation in transmission rates

1.?Investigating the ecological context in which host-parasite interactions occur and the roles of biotic and abiotic factors in forcing infection dynamics is essential to understanding disease transmission, spread and maintenance. 2.?Despite their prominence as model host-pathogen systems, the relative influence of environmental heterogeneity and host characteristics in influencing the infection dynamics of avian blood parasites has rarely been assessed in the wild, particularly at a within-population scale. 3.?We used a novel multievent modelling framework (an extension of multistate mark-recapture modelling) that allows for uncertainty in disease state, to estimate transmission parameters and assess variation in the infection dynamics of avian malaria in a large, longitudinally sampled data set of breeding blue tits infected with two divergent species of Plasmodium parasites. 4.?We found striking temporal and spatial heterogeneity in the disease incidence rate and the likelihood of recovery within this single population and demonstrate marked differences in the relative influence of environmental and host factors in forcing the infection dynamics of the two Plasmodium species. 5.?Proximity to a permanent water source greatly influenced the transmission rates of P.?circumflexum, but not of P.?relictum, suggesting that these parasites are transmitted by different vectors. 6.?Host characteristics (age/sex) were found to influence infection rates but not recovery rates, and their influence on infection rates was also dependent on parasite species: P.?relictum infection rates varied with host age, whilst P.?circumflexum infection rates varied with host sex. 7.?Our analyses reveal that transmission of endemic avian malaria is a result of complex interactions between biotic and abiotic components that can operate on small spatial scales and demonstrate that knowledge of the drivers of spatial and temporal heterogeneity in disease transmission will be crucial for developing accurate epidemiological models and a thorough understanding of the evolutionary implications of pathogens.     

Disease evolution across a range of spatio-temporal scales

Traditional explorations of infectious disease evolution have considered the competition between two cross-reactive strains within the standard framework of disease models. Such techniques predict that diseases should evolve to be highly transmissible, benign to the host and possess a long infectious period: in general, diseases do not conform to this ideal. Here we consider a more holistic approach, suggesting that evolution is a trade-off between adaptive pressures at different scales: within host, between hosts and at the population level. We present a model combining within-host pathogen dynamics and transmission between individuals governed by an explicit contact network, where transmission dynamics between hosts are a function of the interaction between the pathogen and the hosts' immune system, though ultimately constrained by the contacts each infected host possesses. Our results show how each of the scales places constraints on the evolutionary behavior, and that complex dynamics may emerge due to the feedbacks between epidemiological and evolutionary dynamics. In particular, multiple stable states can occur with switching between them stochastically driven.     

Backward bifurcations and multiple equilibria in epidemic models with structured immunity

Many disease pathogens stimulate immunity in their hosts, which then wanes over time. To better understand the impact of this immunity on epidemiological dynamics, we propose an epidemic model structured according to immunity level that can be applied in many different settings. Under biologically realistic hypotheses, we find that immunity alone never creates a backward bifurcation of the disease-free steady state. This does not rule out the possibility of multiple stable equilibria, but we provide two sufficient conditions for the uniqueness of the endemic equilibrium, and show that these conditions ensure uniqueness in several common special cases. Our results indicate that the within-host dynamics of immunity can, in principle, have important consequences for population-level dynamics, but also suggest that this would require strong non-monotone effects in the immune response to infection. Neutralizing antibody titer data for measles are used to demonstrate the biological application of our theory.     

The Price equation framework to study disease within-host evolution

The evolution of infectious diseases is known to affect epidemiological dynamics, but, for some viruses and bacteria, this evolution also takes place inside a host during the course of an infection. I develop an original approach to study intrahost evolutionary dynamics of quantitative disease traits. This approach can be expressed mathematically using the ‘Price equation’ framework recently developed in evolutionary epidemiology. This framework combines population genetics and within-host population dynamics models to identify trade-offs that affect disease intrahost evolution and to predict short-term evolutionary dynamics of life-history traits. I show that this can be applied to study the evolution of viruses competing for host cells or to study the coevolution between parasites and the immune system of the host. This framework can also easily incorporate experimental data. Studying intrahost evolutionary dynamics provides insight at the within-host level, because it allows us to better understand the course of chronic infections, and at the epidemiological level, because it helps to study multi-scale evolutionary processes. This framework can be used to address important biological issues, from immune escape to disease evolutionary response to treatments.     

Optimal immune specificity at the intersection of host life history and parasite epidemiology

Hosts diverge widely in how, and how well, they defend themselves against infection and immunopathology. Why are hosts so heterogeneous? Both epidemiology and life history are commonly hypothesized to influence host immune strategy, but the relationship between immune strategy and each factor has commonly been investigated in isolation. Here, we show that interactions between life history and epidemiology are crucial for determining optimal immune specificity and sensitivity. We propose a demographically-structured population dynamics model, in which we explore sensitivity and specificity of immune responses when epidemiological risks vary with age. We find that variation in life history traits associated with both reproduction and longevity alters optimal immune strategies–but the magnitude and sometimes even direction of these effects depends on how epidemiological risks vary across life. An especially compelling example that explains previously-puzzling empirical observations is that depending on whether infection risk declines or rises at reproductive maturity, later reproductive maturity can select for either greater or lower immune specificity, potentially illustrating why studies of lifespan and immune variation across taxa have been inconclusive. Thus, the sign of selection on the life history-immune specificity relationship can be reversed in different epidemiological contexts. Drawing on published life history data from a variety of chordate taxa, we generate testable predictions for this facet of the optimal immune strategy. Our results shed light on the causes of the heterogeneity found in immune defenses both within and among species and the ultimate variability of the relationship between life history and immune specificity.     

Gyro-scope: an individual-based computer model to forecast gyrodactylid infections on fish hosts

Individual-based computer models (IBM) feature prominently in current theoretical ecology but have only been applied in a small number of parasitological studies. Here we designed an IBM to simulate the infection dynamics of gyrodactylid parasites and immune defence of na?ve hosts (i.e. fish previously not exposed to these parasites). We compared the results of the model with empirical data from guppies (Poecilia reticulata) infected with Gyrodactylus parasites. The laboratory experiments on guppies showed that larger fish acquired a heavier parasite load at the peak of the infection. The survival probability declined with increased body size and no fish survived a parasite load of 80 or more worms in this experiment (i.e. lethal load). The model was a good predictor of the Gyrodactylus infection dynamics of guppies and the model output was congruent with previously published data on Gyrodactylus salaris infections of salmon (Salmo salar). Computer simulations indicated that the infections persisted longer on larger hosts and that the parasite load increased exponentially with the body size of the host. Simulations furthermore predicted that the parasite load of fish with a standard length in excess of 17mm (i.e. the size of adult guppies) reached a lethal load. This suggests that in the conditions of the experiment, the immune defence of na?ve guppies can offer moderate protection against gyrodactylid infections to juveniles, but not to na?ve adult guppies. The model is a useful tool to forecast the development of gyrodactylid infections on single hosts and make predictions about optimal life history strategies of parasites.     

Effect of deer density on tick infestation of rodents and the hazard of tick-borne encephalitis. II: population and infection models

Tick-borne encephalitis is an emerging vector-borne zoonotic disease reported in several European and Asiatic countries with complex transmission routes that involve various vertebrate host species other than a tick vector. Understanding and quantifying the contribution of the different hosts involved in the TBE virus cycle is crucial in estimating the threshold conditions for virus emergence and spread. Some hosts, such as rodents, act both as feeding hosts for ticks and reservoirs of the infection. Other species, such as deer, provide important sources of blood for feeding ticks but they do not support TBE virus transmission, acting instead as dead-end (i.e., incompetent) hosts. Here, we introduce an eco-epidemiological model to explore the dynamics of tick populations and TBE virus infection in relation to the density of two key hosts. In particular, our aim is to validate and interpret in a robust theoretical framework the empirical findings regarding the effect of deer density on tick infestation on rodents and thus TBE virus occurrence from selected European foci. Model results show hump-shaped relationships between deer density and both feeding ticks on rodents and the basic reproduction number for TBE virus. This suggests that deer may act as tick amplifiers, but may also divert tick bites from competent hosts, thus diluting pathogen transmission. However, our model shows that the mechanism responsible for the dilution effect is more complex than the simple reduction of tick burden on competent hosts. Indeed, while the number of feeding ticks on rodents may increase with deer density, the proportion of blood meals on competent compared with incompetent hosts may decrease, triggering a decline in infection. As a consequence, using simply the number of ticks per rodent as a predictor of TBE transmission potential could be misleading if competent hosts share habitats with incompetent hosts.     

The right response at the right time: Exploring helminth immune modulation in sticklebacks by experimental coinfection

Parasites are one of the strongest selective agents in nature. They select for hosts that evolve counter‐adaptive strategies to cope with infection. Helminth parasites are special because they can modulate their hosts’ immune responses. This phenomenon is important in epidemiological contexts, where coinfections may be affected. How different types of hosts and helminths interact with each other is insufficiently investigated. We used the three‐spined stickleback (Gasterosteus aculeatus) – Schistocephalus solidus model to study mechanisms and temporal components of helminth immune modulation. Sticklebacks from two contrasting populations with either high resistance (HR) or low resistance (LR) against S. solidus, were individually exposed to S. solidus strains with characteristically high growth (HG) or low growth (LG) in G. aculeatus. We determined the susceptibility to another parasite, the eye fluke Diplostomum pseudospathaceum, and the expression of 23 key immune genes at three time points after S. solidus infection. D. pseudospathaceum infection rates and the gene expression responses depended on host and S. solidus type and changed over time. Whereas the effect of S. solidus type was not significant after three weeks, T regulatory responses and complement components were upregulated at later time points if hosts were infected with HG S. solidus. HR hosts showed a well orchestrated immune response, which was absent in LR hosts. Our results emphasize the role of regulatory T cells and the timing of specific immune responses during helminth infections. This study elucidates the importance to consider different coevolutionary trajectories and ecologies when studying host‐parasite interactions.     

Niche breadth in parasites: an evolutionarily stable strategy model, with special reference to the protozoan parasite Leishmania.

A parasite's host range essentially defines its niche breadth, which, as foraging theory predicts, is influenced by resource availability. For parasites, the interaction of infection and transmission characteristics with host population dynamics determines host availability. An epidemiological model, involving two host types and describing competition between a "generalist" parasite strain and a related "specialist" strain, is used to examine the interplay among host range, relative host availabilities, and adaptational compromises engendered by increased host range. Results show that the generalist can predominate even when it cannot maintain itself in either host alone, but that the specialist can persist if its reproductive rate attains some threshold relative to either of the generalist's respective rates in its two hosts. The model is in rough, qualitative agreement with observed dynamics of two Leishmania parasite-host systems, and overall results suggest that infection of two species with a common parasite can lead to complex, indirect coevolutionary dynamics.     

The impact of parasitoid emergence time on host–parasitoid population dynamics

We investigate the effect of parasitoid phenology on host–parasitoid population cycles. Recent experimental research has shown that parasitized hosts can continue to interact with their unparasitized counterparts through competition. Parasitoid phenology, in particular the timing of emergence from the host, determines the duration of this competition. We construct a discrete-time host–parasitoid model in which within-generation dynamics associated with parasitoid timing is explicitly incorporated. We found that late-emerging parasitoids induce less severe, but more frequent, host outbreaks, independent of the choice of competition model. The competition experienced by the parasitized host reduces the parasitoids’ numerical response to changes in host numbers, preventing the ‘boom-bust’ dynamics associated with more efficient parasitoids. We tested our findings against experimental data for the forest tent caterpillar (Malacosoma disstria Hübner) system, where a large number of consecutive years at a high host density is synonymous with severe forest damage.     

A model for the coevolution of immunity and immune evasion in vector-borne diseases with implications for the epidemiology of malaria

We describe a model of host-parasite coevolution, where the interaction depends on the investments by the host in its immune response and by the parasite in its ability to suppress (or evade) its host's immune response. We base our model on the interaction between malaria parasites and their mosquito hosts and thus describe the epidemiological dynamics with the Macdonald-Ross equation of malaria epidemiology. The qualitative predictions of the model are most sensitive to the cost of the immune response and to the intensity of transmission. If transmission is weak or the cost of immunity is low, the system evolves to a coevolutionarily stable equilibrium at intermediate levels of investment (and, generally, at a low frequency of resistance). At a higher cost of immunity and as transmission intensifies, the system is not evolutionarily stable but rather cycles around intermediate levels of investment. At more intense transmission, neither host nor parasite invests any resources in dominating its partner so that no resistance is observed in the population. These results may help to explain the lack of encapsulated malaria parasites generally observed in natural populations of mosquito vectors, despite strong selection pressure for resistance in areas of very intense transmission.     

Predictive models in spatially and temporally variable freshwater systems

Abstract This paper discusses the relationships between scaling and predictability in ecosystems. The logical basis of ecosystem modelling is explored using ideas first developed in complexity theory and analogies with the behaviour of complex adaptive systems. Any ecological model is a scale-dependent entity and both empirical and dynamic models of freshwater systems have their strengths and weaknesses. The logical basis of modelling using functional groups is explored. I conclude that such an approach can be justified and that such models have predictive power. Any predictive model of freshwater systems must take the major scales of external (atmospheric and catchment) forcing into account as well as the scales of key processes in the ecosystem itself. The importance of so-called ‘pink noise’ spectra, which arise both from external forcing and the internal dynamics of dynamic systems, is noted. The key scales of pattern and process in freshwater ecosystems are discussed in relation to the properties of the major functional groups. In order to have predictive power, I conclude that models of freshwater systems must include sediment exchanges and the properties of aquatic macrophytes as well as water column interactions and the pelagic components. When viewed at the scale of functional groups and the major biogeochemical processes, freshwater ecosystems may not be as complex as is often assumed.     

Coevolution of recovery ability and virulence.

Most models for coevolution of hosts and parasites are based on the assumption that resistance of hosts to parasites is an all-or-nothing effect. In many cases, for example where parasites require an appropriate receptor on host cells, this is a reasonable assumption. However, in many other cases, for example where hosts mount an immune response, this picture may be too simple. An immune system is expensive to maintain, which poses a question as to how much of its resources a host should allocate to resist parasites: if the risk of infection is low, natural selection may favour hosts with less effective immune systems. As optimal allocation to defence will depend on the force of infection, and the force of infection, in turn, depends on the level of defence in the rest of the host population, a game-theoretic approach is necessary. Here I analyse a simple model for the evolution of the ability to recover from infection. If parasites are not allowed to coevolve, the outcome is a single evolutionarily stable strategy (ESS). If the parasites coevolve, multiple evolutionary outcomes are possible, one in which the parasites are relatively avirulent and common and the hosts invest little in recovery ability, and another (the escalated arms race) where parasites are rare but virulent and the hosts invest heavily in defence.     

Multiple infections, immune dynamics, and the evolution of virulence

Understanding the effect of multiple infections is essential for the prediction (and eventual control) of virulence evolution. Some theoretical studies have considered the possibility that several strains coexist in the same host (coinfection), but few have taken their within-host dynamics explicitly into account. Here, we develop a nested approach based on a simple model for the interaction of parasite strains with their host's immune system. We study virulence evolution by linking the within-host dynamics to an epidemiological framework that incorporates multiple infections. Our model suggests that antigenically similar parasite strains cannot coexist in the long term inside a host. We also find that the optimal level of virulence increases with the efficiency of multiple infections. Finally, we notice that coinfections create heterogeneity in the host population (with susceptible hosts and infected hosts), which can lead to evolutionary branching in the parasite population and the emergence of a hypervirulent parasite strategy. We interpret this result as a parasite specialization to the infectious state of the hosts. Our study has experimental and theoretical implications in a virulence management perspective.     

Methods of modelling viral disease dynamics across the within- and between-host scales: the impact of virus dose on host population immunity

We study the epidemiology of a viral disease with dose-dependent replication and transmission by nesting a differential-equation model of the within-host viral dynamics inside a between-host epidemiological model. We use two complementary approaches for nesting the models: an agent-based (AB) simulation and a mean-field approximation called the growth-matrix (GM) model. We find that although infection rates and predicted case loads are somewhat different between the AB and GM models, several epidemiological parameters, e.g. mean immunity in the population and mean dose received, behave similarly across the methods. Further, through a comparison of our dose-dependent replication model against two control models that uncouple dose-dependent replication from transmission, we find that host immunity in a population after an epidemic is qualitatively different than when transmission depends on time-varying viral abundances within hosts. These results show that within-host dynamics and viral dose should not be neglected in epidemiological models, and that the simpler GM approach to model nesting provides a reasonable tradeoff between model complexity and accuracy of results.