The Genealogical Population Dynamics of HIV-1 in a Large Transmission Chain: Bridging within and among Host Evolutionary Rates

Transmission lies at the interface of human immunodeficiency virus type 1 (HIV-1) evolution within and among hosts and separates distinct selective pressures that impose differences in both the mode of diversification and the tempo of evolution. In the absence of comprehensive direct comparative analyses of the evolutionary processes at different biological scales, our understanding of how fast within-host HIV-1 evolutionary rates translate to lower rates at the between host level remains incomplete. Here, we address this by analyzing pol and env data from a large HIV-1 subtype C transmission chain for which both the timing and the direction is known for most transmission events. To this purpose, we develop a new transmission model in a Bayesian genealogical inference framework and demonstrate how to constrain the viral evolutionary history to be compatible with the transmission history while simultaneously inferring the within-host evolutionary and population dynamics. We show that accommodating a transmission bottleneck affords the best fit our data, but the sparse within-host HIV-1 sampling prevents accurate quantification of the concomitant loss in genetic diversity. We draw inference under the transmission model to estimate HIV-1 evolutionary rates among epidemiologically-related patients and demonstrate that they lie in between fast intra-host rates and lower rates among epidemiologically unrelated individuals infected with HIV subtype C. Using a new molecular clock approach, we quantify and find support for a lower evolutionary rate along branches that accommodate a transmission event or branches that represent the entire backbone of transmitted lineages in our transmission history. Finally, we recover the rate differences at the different biological scales for both synonymous and non-synonymous substitution rates, which is only compatible with the ‘store and retrieve’ hypothesis positing that viruses stored early in latently infected cells preferentially transmit or establish new infections upon reactivation.     

Expression of parasite genetic variation changes over the course of infection: implications of within-host dynamics for the evolution of virulence

How infectious disease agents interact with their host changes during the course of infection and can alter the expression of disease-related traits. Yet by measuring parasite life-history traits at one or few moments during infection, studies have overlooked the impact of variable parasite growth trajectories on disease evolution. Here we show that infection-age-specific estimates of host and parasite fitness components can reveal new insight into the evolution of parasites. We do so by characterizing the within-host dynamics over an entire infection period for five genotypes of the castrating bacterial parasite Pasteuria ramosa infecting the crustacean Daphnia magna. Our results reveal that genetic variation for parasite-induced gigantism, host castration and parasite spore loads increases with the age of infection. Driving these patterns appears to be variation in how well the parasite maintains control of host reproduction late in the infection process. We discuss the evolutionary consequences of this finding with regard to natural selection acting on different ages of infection and the mechanism underlying the maintenance of castration efficiency. Our results highlight how elucidating within-host dynamics can shed light on the selective forces that shape infection strategies and the evolution of virulence.     

Vector-Borne Pathogen and Host Evolution in a Structured Immuno-Epidemiological System

Vector-borne disease transmission is a common dissemination mode used by many pathogens to spread in a host population. Similar to directly transmitted diseases, the within-host interaction of a vector-borne pathogen and a host’s immune system influences the pathogen’s transmission potential between hosts via vectors. Yet there are few theoretical studies on virulence–transmission trade-offs and evolution in vector-borne pathogen–host systems. Here, we consider an immuno-epidemiological model that links the within-host dynamics to between-host circulation of a vector-borne disease. On the immunological scale, the model mimics antibody-pathogen dynamics for arbovirus diseases, such as Rift Valley fever and West Nile virus. The within-host dynamics govern transmission and host mortality and recovery in an age-since-infection structured host-vector-borne pathogen epidemic model. By considering multiple pathogen strains and multiple competing host populations differing in their within-host replication rate and immune response parameters, respectively, we derive evolutionary optimization principles for both pathogen and host. Invasion analysis shows that the \({\mathcal {R}}_0\) maximization principle holds for the vector-borne pathogen. For the host, we prove that evolution favors minimizing case fatality ratio (CFR). These results are utilized to compute host and pathogen evolutionary trajectories and to determine how model parameters affect evolution outcomes. We find that increasing the vector inoculum size increases the pathogen \({\mathcal {R}}_0\), but can either increase or decrease the pathogen virulence (the host CFR), suggesting that vector inoculum size can contribute to virulence of vector-borne diseases in distinct ways.     

Imperfect vaccines and the evolution of pathogens causing acute infections in vertebrates

A study by Gandon et al. (2001) considered the potential ways pathogens may evolve in response to vaccination with imperfect vaccines. In this paper, by focusing on acute infections of vertebrate hosts, we examine whether imperfect vaccines that do not completely block a pathogen's replication (antigrowth) or transmission (antitransmission) may lead to evolution of more or less virulent pathogen strains. To address this question, we use models of the within-host dynamics of the pathogen and the host's immune responses. One advantage of the use of this within-host approach is that vaccination can be easily incorporated in the models and the trade-offs between pathogen transmissibility, host recovery, and virulence that drive evolution of pathogens in these models can be easily estimated. We find that the use of either antigrowth or antitransmission vaccines leads to the evolution of pathogens with an increased within-host growth rate; infection of unvaccinated hosts with such evolved pathogens results in high host mortality and low pathogen transmission. Vaccination of only a fraction of hosts with antigrowth vaccines may prevent pathogens from evolving high virulence due to pathogen adaptation to unvaccinated hosts and thus protection of vaccinated hosts from pathogen-induced disease. In contrast, antitransmission vaccines may be beneficial only if they are effective enough to cause pathogen extinction. Our results suggest that particular mechanisms of action of vaccines and their efficacy are crucial in predicting longterm evolutionary consequences of the use of imperfect vaccines.     

Within-host competition between Borrelia afzelii ospC strains in wild hosts as revealed by massively parallel amplicon sequencing

Infections frequently consist of more than one strain of a given pathogen. Experiments have shown that co-infecting strains often compete, so that the infection intensity of each strain in mixed infections is lower than in single strain infections. Such within-host competition can have important epidemiological and evolutionary consequences. However, the extent of competition has rarely been investigated in wild, naturally infected hosts, where there is noise in the form of varying inoculation doses, asynchronous infections and host heterogeneity, which can potentially alleviate or eliminate competition. Here, we investigated the extent of competition between Borrelia afzelii strains (as determined by ospC genotype) in three host species sampled in the wild. For this purpose, we developed a protocol for 454 amplicon sequencing of ospC, which allows both detection and quantification of each individual strain in an infection. Each host individual was infected with one to six ospC strains. The infection intensity of each strain was lower in mixed infections than in single ones, showing that there was competition. Rank-abundance plots revealed that there was typically one dominant strain, but that the evenness of the relative infection intensity of the different strains in an infection increased with the multiplicity of infection. We conclude that within-host competition can play an important role under natural conditions despite many potential sources of noise, and that quantification by next-generation amplicon sequencing offers new possibilities to dissect within-host interactions in naturally infected hosts.     

Bridging scales in the evolution of infectious disease life histories: application

Within- and between-host disease processes occur on the same timescales, therefore changes in the within-host dynamics of parasites, resources, and immunity can interact with changes in the epidemiological dynamics to affect evolutionary outcomes. Consequently, studies of the evolution of disease life histories, that is, infection-age-specific patterns of transmission and virulence, have been constrained by the need for a mechanistic understanding of within-host disease dynamics. In a companion paper (Day et al. 2011), we develop a novel approach that quantifies the relevant within-host aspects of disease through genetic covariance functions. Here, we demonstrate how to apply this theory to data. Using two previously published datasets from rodent malaria infections, we show how to translate experimental measures into disease life-history traits, and how to quantify the covariance in these traits. Our results show how patterns of covariance can interact with epidemiological dynamics to affect evolutionary predictions for disease life history. We also find that the selective constraints on disease life-history evolution can vary qualitatively, and that "simple" virulence-transmission trade-offs that are often the subject of experimental investigation can be obscured by trade-offs within one trait alone. Finally, we highlight the type and quality of data required for future applications.     

Lethal mutagenesis and evolutionary epidemiology

The lethal mutagenesis hypothesis states that within-host populations of pathogens can be driven to extinction when the load of deleterious mutations is artificially increased with a mutagen, and becomes too high for the population to be maintained. Although chemical mutagens have been shown to lead to important reductions in viral titres for a wide variety of RNA viruses, the theoretical underpinnings of this process are still not clearly established. A few recent models sought to describe lethal mutagenesis but they often relied on restrictive assumptions. We extend this earlier work in two novel directions. First, we derive the dynamics of the genetic load in a multivariate Gaussian fitness landscape akin to classical quantitative genetics models. This fitness landscape yields a continuous distribution of mutation effects on fitness, ranging from deleterious to beneficial (i.e. compensatory) mutations. We also include an additional class of lethal mutations. Second, we couple this evolutionary model with an epidemiological model accounting for the within-host dynamics of the pathogen. We derive the epidemiological and evolutionary equilibrium of the system. At this equilibrium, the density of the pathogen is expected to decrease linearly with the genomic mutation rate U. We also provide a simple expression for the critical mutation rate leading to extinction. Stochastic simulations show that these predictions are accurate for a broad range of parameter values. As they depend on a small set of measurable epidemiological and evolutionary parameters, we used available information on several viruses to make quantitative and testable predictions on critical mutation rates. In the light of this model, we discuss the feasibility of lethal mutagenesis as an efficient therapeutic strategy.     

How well can the exponential-growth coalescent approximate constant-rate birth–death population dynamics?

One of the central objectives in the field of phylodynamics is the quantification of population dynamic processes using genetic sequence data or in some cases phenotypic data. Phylodynamics has been successfully applied to many different processes, such as the spread of infectious diseases, within-host evolution of a pathogen, macroevolution and even language evolution. Phylodynamic analysis requires a probability distribution on phylogenetic trees spanned by the genetic data. Because such a probability distribution is not available for many common stochastic population dynamic processes, coalescent-based approximations assuming deterministic population size changes are widely employed. Key to many population dynamic models, in particular epidemiological models, is a period of exponential population growth during the initial phase. Here, we show that the coalescent does not well approximate stochastic exponential population growth, which is typically modelled by a birth–death process. We demonstrate that introducing demographic stochasticity into the population size function of the coalescent improves the approximation for values of R₀ close to 1, but substantial differences remain for large R₀. In addition, the computational advantage of using an approximation over exact models vanishes when introducing such demographic stochasticity. These results highlight that we need to increase efforts to develop phylodynamic tools that correctly account for the stochasticity of population dynamic models for inference.     

Multiple host transfers,but only one successful lineage in a continent-spanning emergent pathogen

Emergence of a new disease in a novel host is thought to be a rare outcome following frequent pathogen transfers between host species. However, few opportunities exist to examine whether disease emergence stems from a single successful pathogen transfer, and whether this successful lineage represents only one of several pathogen transfers between hosts. We examined the successful host transfer and subsequent evolution of the bacterial pathogen Mycoplasma gallisepticum, an emergent pathogen of house finches (Haemorhous (formerly Carpodacus) mexicanus). Our principal goals were to assess whether host transfer has been a repeated event between the original poultry hosts and house finches, whether only a single host transfer was ultimately responsible for the emergence of M. gallisepticum in these finches, and whether the spread of the pathogen from east to west across North America has resulted in spatial structuring in the pathogen. Using a phylogeny of M. gallisepticum based on 107 isolates from domestic poultry, house finches and other songbirds, we infer that the bacterium has repeatedly jumped between these two groups of hosts but with only a single lineage of M. gallisepticum persisting and evolving in house finches; bacterial evolution has produced monophyletic eastern and western North American subclades.     

Intracellular demography and the dynamics of Salmonella enterica infections

An understanding of within-host dynamics of pathogen interactions with eukaryotic cells can shape the development of effective preventive measures and drug regimes. Such investigations have been hampered by the difficulty of identifying and observing directly, within live tissues, the multiple key variables that underlay infection processes. Fluorescence microscopy data on intracellular distributions of Salmonella enterica serovar Typhimurium (S. Typhimurium) show that, while the number of infected cells increases with time, the distribution of bacteria between cells is stationary (though highly skewed). Here, we report a simple model framework for the intensity of intracellular infection that links the quasi-stationary distribution of bacteria to bacterial and cellular demography. This enables us to reject the hypothesis that the skewed distribution is generated by intrinsic cellular heterogeneities, and to derive specific predictions on the within-cell dynamics of Salmonella division and host-cell lysis. For within-cell pathogens in general, we show that within-cell dynamics have implications across pathogen dynamics, evolution, and control, and we develop novel generic guidelines for the design of antibacterial combination therapies and the management of antibiotic resistance.     

Adaptive evolution of butterfly wing shape: from morphology to behaviour

Butterflies display extreme variation in wing shape associated with tremendous ecological diversity. Disentangling the role of neutral versus adaptive processes in wing shape diversification remains a challenge for evolutionary biologists. Ascertaining how natural selection influences wing shape evolution requires both functional studies linking morphology to flight performance, and ecological investigations linking performance in the wild with fitness. However, direct links between morphological variation and fitness have rarely been established. The functional morphology of butterfly flight has been investigated but selective forces acting on flight behaviour and associated wing shape have received less attention. Here, we attempt to estimate the ecological relevance of morpho‐functional links established through biomechanical studies in order to understand the evolution of butterfly wing morphology. We survey the evidence for natural and sexual selection driving wing shape evolution in butterflies, and discuss how our functional knowledge may allow identification of the selective forces involved, at both the macro‐ and micro‐evolutionary scales. Our review shows that although correlations between wing shape variation and ecological factors have been established at the macro‐evolutionary level, the underlying selective pressures often remain unclear. We identify the need to investigate flight behaviour in relevant ecological contexts to detect variation in fitness‐related traits. Identifying the selective regime then should guide experimental studies towards the relevant estimates of flight performance. Habitat, predators and sex‐specific behaviours are likely to be major selective forces acting on wing shape evolution in butterflies. Some striking cases of morphological divergence driven by contrasting ecology involve both wing and body morphology, indicating that their interactions should be included in future studies investigating co‐evolution between morphology and flight behaviour.     

Linking within- and between-host dynamics in the evolutionary epidemiology of infectious diseases

Nested models (also called embedded models) explicitly link dynamical processes that occur at different scales. Recently there has been considerable interest in linking within- and between-host levels of disease dynamics in the study of pathogen evolution. Here we review the extent to which these nested models have increased our understanding of pathogen evolution. We suggest that, although such models have been useful for determining the nature of tradeoffs between epidemiological parameters and for evaluating the consequences of conflicting selection pressures at different scales, the vast majority of previous results could likely have been obtained without the use of nested models per se. Nevertheless, these models have proven very useful through their highlighting of the importance of within-host disease dynamics on pathogen evolution.     

Superinfections can induce evolutionarily stable coexistence of pathogens

Parasites reproduce and are subject to natural selection at several different, but intertwined, levels. In the recent paper, Gilchrist and Coombs (Theor. Popul. Biol. 69:145–153, 2006) relate the between-host transmission in the context of an SI model to the dynamics within a host. They demonstrate that within-host selection may lead to an outcome that differs from the outcome of selection at the host population level. In this paper we combine the two levels of reproduction by considering the possibility of superinfection and study the evolution of the pathogen’s within-host reproduction rate p. We introduce a superinfection function φ = φ(p,q), giving the probability with which pathogens with trait q, upon transmission to a host that is already infected by pathogens with trait p, “take over” the host. We consider three cases according to whether the function q → φ(p,q) (i) has a discontinuity, (ii) is continuous, but not differentiable, or (iii) is differentiable in q = p. We find that in case (i) the within-host selection dominates in the sense that the outcome of evolution at the host population level coincides with the outcome of evolution in a single infected host. In case (iii), it is the transmission to susceptible hosts that dominates the evolution to the extent that the singular strategies are the same as when the possibility of superinfections is ignored. In the biologically most relevant case (ii), both forms of reproduction contribute to the value of a singular trait. We show that when φ is derived from a branching process variant of the submodel for the within-host interaction of pathogens and target cells, the superinfection functions fall under case (ii). We furthermore demonstrate that the superinfection model allows for steady coexistence of pathogen traits at the host population level, both on the ecological, as well as on the evolutionary time scale.      

Structural equation modelling analysis of evolutionary and ecological patterns in Australian Banksia

Evolutionary history of species, their geographic ranges, ecological ranges, genetic diversity, and resistance to pathogen infection, have been viewed as being mutually linked through a complex network of interactions. Previous studies have described simple correlations between pairs of these factors, while rarely separated the direct effects among multiple interacting factors. This study was to separate the effect of multiple interacting factors, to reveal the strength of the interactions among these factors, and to explore the mechanisms underlying the ecological and evolutionary processes shaping the geographic range, genetic diversity and fitness of species. I assembled comparative data on evolutionary history, geographic range, ecological range, genetic diversity, and resistance to pathogen infection for thirteen Banksia species from Australia. I used structural equation modelling to test multivariate hypotheses involving evolutionary history, geographic range, genetic diversity and fitness. Key results are: (1) Species with longer evolutionary times tend to occupy larger geographic ranges; (2) higher genetic diversity is directly associated with longer flowering duration in Banksia; and (3) species with higher genotypic diversity have higher level of resistance to infection caused by the pathogen Phytophthora cinnamomi, whereas heterozygosity has the opposite relationship with capacity of resistance to the infections caused by this pathogen. These results revealed a mutually linked and complex network of interactions among gene, species, environment and pathogen in evolutionary and ecological scales. These findings also have great practical significance and help to provide preemptive management options in pathogen control.     

Arabidopsis thaliana Genes Encoding Defense Signaling and Recognition Proteins Exhibit Contrasting Evolutionary Dynamics

The interplay between pathogen effectors, their host targets, and cognate recognition proteins provides various opportunities for antagonistic cycles of selection acting on plant and pathogen to achieve or abrogate resistance, respectively. Selection has previously been shown to maintain diversity in plant proteins involved in pathogen recognition and some of their cognate pathogen effectors. We analyzed the signatures of selection on 10 Arabidopsis thaliana genes encoding defense signal transduction proteins in plants, which are potential targets of pathogen effectors. There was insufficient evidence to reject neutral evolution for 6 genes encoding signaling components consistent with these proteins not being targets of effectors and/or indicative of constraints on their ability to coevolve with pathogen effectors. Functional constraints on effector targets may have provided the driving selective force for the evolution of guard proteins. PBS1, a known target of an effector, showed little variation but is known to be monitored by a variable guard protein. Evidence of selection maintaining diversity was present at NPR1, PAD4, and EDS1. Differences in the signatures of selection observed may reflect the numbers of effectors that target a particular protein, the presence or absence of a cognate guard protein, as well as functional constraints imposed by biochemical activities or interactions with plant proteins.     

The age and evolution of an antiviral resistance mutation in Drosophila melanogaster

What selective processes underlie the evolution of parasites and their hosts? Arms-race models propose that new host-resistance mutations or parasite counter-adaptations arise and sweep to fixation. Frequency-dependent models propose that selection favours pathogens adapted to the most common host genotypes, conferring an advantage to rare host genotypes. Distinguishing between these models is empirically difficult. The maintenance of disease-resistance polymorphisms has been studied in detail in plants, but less so in animals, and rarely in natural populations. We have made a detailed study of genetic variation in host resistance in a natural animal population, Drosophila melanogaster, and its natural pathogen, the sigma virus. We confirm previous findings that a single (albeit complex) mutation in the gene ref(2)P confers resistance against sigma and show that this mutation has increased in frequency under positive selection. Previous studies suggested that ref(2)P polymorphism reflects the progress of a very recent selective sweep, and that in Europe during the 1980s, this was followed by a sweep of a sigma virus strain able to infect flies carrying this mutation. We find that the ref(2)P resistance mutation is considerably older than the recent spread of this viral strain and suggest that—possibly because it is recessive—the initial spread of the resistance mutation was very slow.     

Evaluating the within-host fitness effects of mutations fixed during virus adaptation to different ecotypes of a new host

The existence of genetic variation for resistance in host populations is assumed to be essential to the spread of an emerging virus. Models predict that the rate of spread slows down with the increasing frequency and higher diversity of resistance alleles in the host population. We have been using the experimental pathosystem Arabidopsis thaliana—tobacco etch potyvirus (TEV) to explore the interplay between genetic variation in host''s susceptibility and virus diversity. We have recently shown that TEV populations evolving in A. thaliana ecotypes that differ in susceptibility to infection gained within-host fitness, virulence and infectivity in a manner compatible with a gene-for-gene model of host–parasite interactions: hard-to-infect ecotypes were infected by generalist viruses, whereas easy-to-infect ecotypes were infected by every virus. We characterized the genomes of the evolved viruses and found cases of host-driven convergent mutations. To gain further insights in the mechanistic basis of this gene-for-gene model, we have generated all viral mutations individually as well as in specific combinations and tested their within-host fitness effects across ecotypes. Most of these mutations were deleterious or neutral in their local ecotype and only a very reduced number had a host-specific beneficial effect. We conclude that most of the mutations fixed during the evolution experiment were so by drift or by selective sweeps along with the selected driver mutation. In addition, we evaluated the ruggedness of the underlying adaptive fitness landscape and found that mutational effects were mostly multiplicative, with few cases of significant epistasis.     

Phenotypic variation and genomic variation in insect virulence traits reveal patterns of intraspecific diversity in a locust-specific fungal pathogen

Intraspecific pathogen diversity is crucial for understanding the evolution and maintenance of adaptation in host–pathogen interactions. Traits associated with virulence are often a significant source of variation directly impacted by local selection pressures. The specialist fungal entomopathogen, Metarhizium acridum, has been widely implemented as a biological control agent of locust pests in tropical regions of the world. However, few studies have accounted for natural intraspecific phenotypic and genetic variation. Here, we examine the diversity of nine isolates of M. acridum spanning the known geographic distribution, in terms of (1) virulence towards two locust species, (2) growth rates on three diverse nutrient sources, and (3) comparative genomics to uncover genomic variability. Significant variability in patterns of virulence and growth was shown among the isolates, suggesting intraspecific ecological specialization. Different patterns of virulence were shown between the two locust species, indicative of potential host preference. Additionally, a high level of diversity among M. acridum isolates was observed, revealing increased variation in subtilisin-like proteases from the Pr1 family. These results culminate in the first in-depth analysis regarding multiple facets of natural variation in M. acridum, offering opportunities to understand critical evolutionary drivers of intraspecific diversity in pathogens.     

Making pathogens sociable: The emergence of high relatedness through limited host invasibility

Cooperation depends upon high relatedness, the high genetic similarity of interacting partners relative to the wider population. For pathogenic bacteria, which show diverse cooperative traits, the population processes that determine relatedness are poorly understood. Here, we explore whether within-host dynamics can produce high relatedness in the insect pathogen Bacillus thuringiensis. We study the effects of host/pathogen interactions on relatedness via a model of host invasion and fit parameters to competition experiments with marked strains. We show that invasibility is a key parameter for determining relatedness and experimentally demonstrate the emergence of high relatedness from well-mixed inocula. We find that a single infection cycle results in a bottleneck with a similar level of relatedness to those previously reported in the field. The bottlenecks that are a product of widespread barriers to infection can therefore produce the population structure required for the evolution of cooperative virulence.     

Two optimal mutation rates in obligate pathogens subject to deleterious mutation

Pathogen species with high mutation rates are likely to accumulate deleterious mutations that reduce their reproductive potential within the host. By altering the within-host growth rate of the pathogen, the deleterious mutation load has the potential to affect epidemiological properties such as prevalence, mean pathogen load, and the mean duration of infections. Here, I examine an epidemiological model that allows for multiple segregating mutations that affect within-host replication efficiency. The model demonstrates a complex range of outcomes depending on pathogen mutation rate, including two distinct, widely separated mutation rates associated with high pathogen prevalence. The low mutation rate prevalence peak is associated with small amounts of genetic diversity within the pathogen population, relatively stable prevalence and infection dynamics, and genetic variation partitioned between hosts. The high mutation rate peak is characterized by considerable genetic diversity both within and between hosts, relatively frequent invasions by more virulent types, and is qualitatively similar to an RNA virus quasispecies. The two prevalence peaks are separated by a valley where natural selection favors evolution toward the optimal within-host state, which is associated with high virulence and relatively rapid host mortality. Both chronic and acute infections are examined using stochastic forward simulations.