Abstract

Graphical Abstract

INTRODUCTION

Predicting evolution is a key goal in virology, especially for pathogenic viruses (Holmes, 2013). Accurately forecasting viral evolutionary dynamics has implications for vaccine design and the maintenance, drug design, and surveillance of viral pathogens. Ultimately, the evolutionary path of a viral population is shaped by selection. The deterministic influence of natural selection tends to increase the fitness of the population over time, through positive selection of beneficial mutations. Natural selection constrains the evolutionary path by removing lethal and deleterious mutations from the population through negative, or purifying, selection. As a result, certain evolutionary trajectories are more likely than others. Therefore, understanding the selective forces that drive and constrain the evolutionary trajectories of specific viruses is critical to evolutionary prediction. New approaches and technologies to study specific phenotypic transitions that underlie host transmission or pathogenesis, could inform future prediction, surveillance, and intervention strategies.

Although RNA viruses have very limited genetic capacity, and a correspondingly limited repertoire of evolutionary solutions, they present unique challenges to evolutionary prediction. Stochastic influences of genetic drift, mutation, and recombination all limit our ability to anticipate evolutionary events. RNA viruses display high mutation rates (as high as 1 mutation in 1000 bases) and many engage in frequent recombination and reassortment, creating novel genotypes from co-circulating strains. RNA viruses also undergo frequent population bottlenecks as they pass from host to host, and encounter changes in their selective environment (Grubaugh et al., 2015, 2017; Varble et al., 2014). These bottlenecks are especially important in the evolution of viruses that transmit between highly divergent host species (Recently reviewed in (McCrone and Lauring, 2017)).

The feasibility of predicting viral evolution depends on the breadth and scale of the question being asked, and requires careful consideration of the relevant selective pressures and evolutionary parameters. The life histories of individual viruses, and the biological complexity associated with specific evolutionary challenges, influence the predictability of their evolutionary trajectories. Predicting the evolutionary response to a strong and specific selective pressure, such as treatment with a single direct-acting antiviral, is a more tractable question than predicting the next zoonotic spillover event. In the latter case, prediction is impeded by several ecological and biological factors influencing the likelihood of a specific outcome. It follows that there is significant disagreement about the utility and feasibility of approaches to predict virus evolution, and the resources that should be devoted to surveillance initiatives (Geoghegan and Holmes, 2017).

Despite the challenges in anticipating evolutionary events, there are on-going efforts to predict virus evolution in response to specific selection. One of the most notable examples is the forecasting of seasonal influenza dynamics to guide vaccine strain selection. Current methods of vaccine formulation, based on the observation of currently circulating strains, have been moderately, but variably, successful in maintaining efficacious vaccine stocks year-to-year, with efficacy normally around 30–50% against Influenza A virus (IAV) strains (Flannery et al., 2017; Jackson et al., 2017). Predictive models of RNA virus evolution could increase these rates as they are adopted into strain selection procedures (Du et al., 2017; Luksza and Lässig, 2014; Neher et al., 2016). Moreover, predictive methods could be used to anticipate and monitor for potential egg-adaptive mutations in the influenza glycoprotein hemagglutinin, which arise during some vaccine production procedures, leading to reduced efficacy (Zost et al., 2017). Efforts to anticipate and predict other viral evolutionary transitions, such as the emergence of drug resistance or increased pathogenesis, would also benefit from improved predictive models of virus evolution.

Here, we provide a perspective on current experimental approaches to the study of evolutionary landscapes of viruses and the forces that drive the dynamics of evolving virus populations. We highlight how combining empirical approaches to study the effect of individual mutations with historical analyses of virus evolution can inform our efforts to anticipate the evolution and emergence of new viral genotypes. We also draw examples from experimental and theoretical work outside of virology that define the limits of prediction and inform future study of viral evolutionary biology. We argue that, although prediction at certain scales is currently out of the question, mapping the biological and physical constraints on evolutionary trajectories can inform our understanding of the evolutionary potential of RNA viruses. These data can also leverage historical studies by testing mechanistic hypotheses they raise and their improving underlying evolutionary models. Together these approaches can elucidate evolutionary mechanisms and pathways underlying important phenotypic transitions, and better anticipate, monitor, and respond to evolutionary events in nature.

Virus evolution and predictability

As with all genetic organisms, the forces of mutation, drift, and natural selection shape the course of virus evolution and determine its predictability (Kryazhimskiy et al., 2014; Lenski et al., 1991). The sequence space of an RNA virus constitutes a vast network of genotypes connected by single mutational steps (Figure 1A). Individuals in a population sample adjacent genotypes through mutation. Due to their high mutation rates and often large population sizes, RNA viruses rapidly sample adjacent genotypes in sequence space, enabling rapid adaptation to changes in the selective environment. Each mutation has a characteristic effect on the reproductive fitness of the virus, or a mutational fitness effect (MFE), and selection acts on individual alleles based on their MFE. Beneficial alleles, those with positive fitness effects, are driven to fixation by positive selection. Deleterious and lethal alleles are removed from the population by negative selection. Together, these selective forces shape the evolutionary trajectories of virus populations across sequence space.

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Figure 1

Example genotype-fitness landscapes

A.) The network of genotypes corresponding to a simple 5-locus, 2-mutation system, showing all 32 possible genotype combinations (2⁵) connected by single nucleotide substitutions. The likelihood of traversing a given link (acquiring a mutation) is shown as the line weight. Interactions between mutations (epistasis) shapes the complexity of the network. B.) From a starting point (grey dot), populations explore the topography of the genotype-fitness landscape by acquiring mutations. The slope of the landscape represents the change in fitness associated with genotypic change. Natural selection drives populations (grey lines) up the fitness gradient to local fitness maxima.

Sewall Wright described genotype-fitness landscapes (Figure 1B) in 1932 as a representation of phenotype, or fitness, as a function of genotype of a population (Wright, 1932). The topography of the landscape represents the fitness effects associated with movement (evolutionary change) through sequence space (McCandlish, 2011). The trajectories of viral populations across these landscapes are shaped by stochastic and deterministic forces. The deterministic force of natural selection tends to drive populations towards fitness maxima, or peaks in the fitness landscape, by fixing adaptive mutations. Conversely, genetic drift tends to shift the population to lower fitness by primarily fixing deleterious mutations.

Understanding why populations evolve along one trajectory, or a set of trajectories, and not others requires an understanding of how positive and negative selection act on viral genomes and their encoded proteins. The maintenance of viral fitness, enzyme function and protein stability constrains the repertoire of viable mutations (Acevedo et al., 2014; Wylie and Shakhnovich, 2011). Protein stability is a particularly important determinant of mutational fitness effects, and mutations which destabilize protein structures are rapidly removed by negative selection (Bloom et al., 2007). Because their genomes are dense with coding regions, models of mutational fitness based on calculations of the difference in thermodynamic stability between WT and mutant residues can correlate surprisingly well with empirically determined mutational fitness effects on viral genomes (Wylie and Shakhnovich, 2011). Environmental influences, such as host immunity or drug pressure, also shape the fitness landscape. In contrast to negative selection, which limits diversity, these extrinsic selective pressures are not static, and their fluctuations drive positive selection of new adaptive alleles in the viral population.

When viral population sizes are very large, they exist as swarms of related genotypes, often referred to as a quasispecies (Andino and Domingo, 2015; Eigen, 1971, 1996; Lauring and Andino, 2010). Although driven by the same evolutionary forces, selection in these populations acts on the entire mutational distribution due to intense mutational pressure (Wilke, 2005). Although not a universal model for virus evolution across all spatial and temporal scales (Holmes and Moya, 2002), quasispecies theory conveys the nature of large RNA virus populations as distributions of genotypes across sequence space. It also makes important predictions about the evolution of viral populations. First, the consensus sequence of the population does not necessarily represent the most common sequences in the population. Instead, the population exists as a dynamic equilibrium of variants surrounding a master sequence, or master sequences, that emerge in response to selection on the population. Second, the master genotypes are positioned in sequence space by past selection to maximize accessibility to neutral and adaptive neighbors and buffer mutation by increasing the probability of neutral mutations (i.e. mutational robustness) (Forster et al., 2006; Lauring et al., 2012; Montville et al., 2005). Third, under given selective conditions, specific quasispecies distributions emerge reproducibly as a function of the fitness landscape. These complex population and dynamic population structures appear to be relevant to evolution within and between hosts (Korboukh et al., 2014; Stern et al., 2014; Vignuzzi et al., 2006; Xiao et al., 2017). Quantifying the dynamics of viral quasispecies and understanding their relevance to viral biology and pathogenesis represents a major technical challenge in experimental RNA virus evolution.

Identifying patterns of positive selection

Determining the diversity of adaptive paths available to a population in response to selection is key to predicting viral evolution (Figure 2A). The traditional methods for identifying potential evolutionary trajectories are historical methods, such as phylogenetics, and experimental selection and clonal sequencing. These tools have been used for decades to map the patterns of mutations that escape antibody recognition or confer resistance to drug pressure. Although evolution is largely unpredictable at some scales (Kryazhimskiy et al., 2014; Nelson et al., 2006; Sailer and Harms, 2017a), the existence of parallel evolution in phenotypic or genotypic evolution has emerged as a common theme in evolutionary studies (Sackman et al., 2017; Stewart et al., 1988; Xiao et al., 2017; Xue et al., 2017). In cases where viruses are subject to similar selective pressures in parallel or repeatedly over time, for example within individual cells, hosts, or host populations, common adaptive mutations are often selected (Morley and Turner, 2017; Xue et al., 2017). In large populations, selection drives the emergence of reproducible quasispecies structures under common selective pressures. These repeated patterns often reflect the key selective pressures underlying important phenotypic transitions, such as escape from immune selection, changes in virulence, or shifts in host tropism. As such, these common signatures could potentially serve as indicators for future surveillance or intervention.

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Figure 2

Experimental techniques to study viral evolutionary paths and aid in prediction

A. A 2D genotype-fitness landscape underlying a hypothetical phenotypic transition (from purple to gold). B. Traversal of a viral population through such a landscape can be examined in a number of experimental ways. Phylogenetic reconstruction examines the evolutionary history of a viral population. Experimental evolution techniques measure the precise trajectories a given viral population takes to navigate a particular selective environment. Library-based screening allows further manipulation of the viral population, by exploring the local topography of the genotype-fitness landscape. C.) High resolution sequencing, such as Circular Sequencing (CirSeq), allows for the tracking of most single nucleotide mutations in a viral population, even at low frequencies. The error correction of CirSeq relies on the generation of head-to-tail cDNA repeats from circularized, fragmented RNA. Only mutations occurring in a majority of these repeats are identified as true mutations. If combined with serial passaging, such high resolution sequencing experiments can be used to estimate fitness effects across the genome and describe the evolutionary landscapes of viruses. D.) Deep mutational scanning (DMS) uses successive rounds of mutagenic PCR to generate a library of all possible codon or nucleotide substitutions of a given sequence. This library is then cloned back into the viral sequence for rescue and creation of mutant viruses. This population undergoes some form of selection (e.g. antibody selection), and the frequency of codon mutants postselection is determined by deep sequencing. A variety of methods can be used to determine amino acid preference at individual site across the gene of interest. Such preferences can be used to better inform phylogenetic reconstruction of viral evolution. E.) Microfluidics allow for massively parallel evolutionary experimentation and fine control of evolutionary and selective parameters influencing the evolution of viral populations. The ability to observe individual host cells, and individual virus particles and genomes, enables the quantification of important biological heterogeneity and dynamics underlying the infection process.

Defining genome-wide negative selection with deep-sequencing

Negative selection, or purifying selection, constrains the evolutionary trajectory by removing deleterious genotypes from populations. Mapping the regions of the genome that evolve under strong negative selection provides insights into the structural and functional constraints on the viral genome and encoded proteins. Sites evolving under strong purifying selection can also suggest targets for vaccine or antiviral development with improved resistance profiles. However, identifying sites evolving under negative selection has historically been a challenge experimentally and phylogenetically, as it is often impossible to distinguish from sampling bias (Zhai et al., 2009). In other words, distinguishing those mutations which cannot occur from those that have not occurred. Recent advances in deep sequencing and library-based mutation screening allow the robust measurement of site-specific negative selection through the observation of dynamics of low frequency alleles in naturally evolving populations (Acevedo et al., 2014; Jabara et al., 2011; Kennedy et al., 2014; Kosik et al., 2018) and the high-resolution screening of mutant libraries before and after selection.

Future challenges and opportunities

Dynamic Landscapes

The concept of a single, static fitness landscape is useful conceptually, but in reality RNA virus evolution takes place across dynamic, responsive genotype-fitness landscapes (Fig 3A). Determining the fitness effects of individual mutations experimentally provides a picture of the selective pressures that influence the evolutionary trajectory contingent on a given genotype, and a given selective environment. However, any change in selective pressure will rapidly reshape the viral fitness landscape (Figure 3A–B). In this way, fitness landscapes can instead be considered as time-dependent fitness seascapes (Merrell, 1994; Mustonen and Lässig, 2009). A changing fitness landscape can render previously beneficial mutations neutral, or even detrimental (Figure 3C). Depending on the timescale and frequency of selective change, mutations can behave as neutral over time if the fitness seascape continuously shifts during the time of fixation (Mustonen and Lässig, 2009).

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Figure 3

Challenges in predicting viral evolution

A) The fitness landscapes encountered by viruses are dynamic, making the maximal fitness a moving target. B.) Contour plot showing the movement of the optimal genotype in response to the shifting landscape in A. C.) Changes in the selective environment change the fitness effects of specific mutations. D.) Bottleneck effect. In transmitting a population of mutants, smaller samples will not accurately represent the distribution of mutations in the parental population and will result in the loss of rare beneficial mutations in the most-fit class. E.) When small populations are transmitted, the mean fitness of the transmitted population is highly variable. F.) Epistatic interactions create historical contingency in the path of evolution and constrain populations evolution (0 = wild-type locus; 1 = mutant). Mutation at the second position (mutant B) is lethal on its own, but viable when epistatic with a mutation at the third position (mutants C and D).

In the case of RNA viruses, selective pressures change especially rapidly. Whether from host transmission, intrahost spread, or host antiviral responses, the influence of selection on specific genomic loci changes over time. The evolutionary responses to particular transient selective pressures often lead to fitness tradeoffs by destabilizing proteins or perturbing viral metabolic processes, thus requiring compensatory mutations (Gong et al., 2013; Wu et al., 2016). In this way, a shifting landscape creates a moving target for the virus, forcing the virus to constantly adapt while balancing adaptive mutations with those that keep core viral functions intact (Lässig et al., 2017). Accurate prediction must not only take into account a virus’ long-term response to a fixed landscape, but also its evolutionary response as the landscape itself shifts.

Identifying and recapitulating the selective landscapes a virus will face constitutes a major challenge to the experimental study of RNA virus evolution in vivo and in vitro. In vivo, host tissues constitute distinct selective microenvironments, each presenting a unique repertoire of selective pressures to the virus population and, therefore, defining a unique fitness landscape. In the case of poliovirus (PV), subpopulations of virus isolated from distinct tissues of transgenic mice expressing the human poliovirus receptor (hPVR) exhibited unique patterns of genetic diversity (Xiao et al., 2017). By analyzing the host gene expression and viral evolution over the course of infection, the authors found the ability of the virus to rapidly evolve is required to overcome the distinct selective challenges within individual tissues. Rapid evolution is only required for spread in immune-competent mice, suggesting the ability to evolve rapidly is required in response to changes in the selective environment due to immune activation.

These results are corroborated by recent studies that demonstrate the dynamics of RNA virus evolution are altered depending on the rate at which the environment of a virus population changes (Morley and Turner, 2017; Morley et al., 2015). When sindbis virus (SINV) populations were introduced to a novel host cell type, a majority of replicates exhibited rapid fixation of the same initial point mutation (Morley and Turner, 2017). Conversely, a more gradual introduction of environmental changes did not lead to such rapid fixation of single mutations, but rather more frequent cohorts of multiple mutations rising together, suggesting increased clonal interference when environmental factors change more slowly. A sudden change of environment also induced rapid and consistent adaptation in influenza. When cell culture- and egg-passaged influenza was inoculated into healthy subjects, strong purifying selection removed egg-adapted mutations (Sobel Leonard et al., 2016).

These studies highlight not only the role changing fitness landscapes have on viral evolution, but that the dynamics of the landscape itself can affect viral population dynamics. Host characteristics like the kinetics of the immune response, or the rate at which a new potential host enters a virus’ environment, will alter the evolutionary solutions available to a virus population and the likelihood of important phenotypic transitions. The resolution afforded by techniques such as DMS and ultra-deep sequencing will enable experimenters much greater control of important selective parameters in order to mimic realistic fitness landscapes. Future studies that apply these methods to interrogate the shifting landscapes characteristic of intrahost and interhost transmission, for example (Stern et al., 2014; Xiao et al., 2017), could further clarify the key barriers to transmission, and the potential adaptive mutations that facilitate it.

CONCLUSIONS

New technologies are allowing us to probe the dynamics of viral populations with unprecedented depth, making it possible to directly measure key evolutionary parameters in evolving populations. Massively-parallel evolutionary experiments allow us to play the ‘evolutionary tape’ (Gould, 2011) thousands of times and measure the fitness effect of every mutation in across a viral genome. Combining these approaches with high-throughput and high-content phenotypic screening enables the correlation of fitness with specific phenotypic characteristics, such as replication kinetics or translation efficiency.

Library-based screening approaches like DMS can quantify the site-specific evolutionary constraints across the viral genome. Traditional evolutionary models used in phylogenetic inference apply substitution probabilities uniformly across a sequence. However, the fitness of specific substitutions across the genome is heterogeneous, due to the unique functional and chemical context. Integration of these site-specific data from library-based screening and evolutionary experiments has the potential to greatly improve the sensitivity and quality of phylogenetic inference by informing each evolutionary model with the specific evolutionary constraints of a given molecule (Bloom, 2014; Hilton et al., 2017).

In addition to the physical and biological constraints assessed by high-throughput experimental methods, transient and dynamic selective forces, such as intrahost immune states (Ben-Shachar et al., 2016; Xiao et al., 2017), seasonal weather changes (Bedford et al., 2010; Mordecai et al., 2017), and transmission dynamics (McCrone et al., 2017) drive the evolution of pathogenic RNA virus populations in nature. The influence of these factors depends on the viral population’s life history and, therefore, careful consideration is needed to design and interpret evolutionary experiments to study the evolutionary potential of viruses and inform prediction. In the context of strong selection on specific phenotypes, the evolutionary response to selection is often repeatable and involves only a few mutations (Longdon et al., 2017; Morley and Turner, 2017; Stern et al., 2017; Weinreich et al., 2006; Xiao et al., 2017; Xue et al., 2017). However, when considering more complex outcomes, such as spillover events, environmental and epidemiological factors add layers of additional complexity that must be considered (Campbell et al., 2018; Geoghegan and Holmes, 2017). Although predictive efforts will always be probabilistic, these improved experimental techniques enable us to probe the evolutionary forces and selective pressures that lead to viral populations and novel methods for modeling and detecting outbreaks should aid in efforts to better anticipate viral evolutionary trajectories.

Acknowledgments

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