Stress-induced mutagenesis and complex adaptation

Because mutations are mostly deleterious, mutation rates should be reduced by natural selection. However, mutations also provide the raw material for adaptation. Therefore, evolutionary theory suggests that the mutation rate must balance between adaptability—the ability to adapt—and adaptedness—the ability to remain adapted. We model an asexual population crossing a fitness valley and analyse the rate of complex adaptation with and without stress-induced mutagenesis (SIM)—the increase of mutation rates in response to stress or maladaptation. We show that SIM increases the rate of complex adaptation without reducing the population mean fitness, thus breaking the evolutionary trade-off between adaptability and adaptedness. Our theoretical results support the hypothesis that SIM promotes adaptation and provide quantitative predictions of the rate of complex adaptation with different mutational strategies.     

Quasispecies made simple

Quasispecies are clouds of genotypes that appear in a population at mutation–selection balance. This concept has recently attracted the attention of virologists, because many RNA viruses appear to generate high levels of genetic variation that may enhance the evolution of drug resistance and immune escape. The literature on these important evolutionary processes is, however, quite challenging. Here we use simple models to link mutation–selection balance theory to the most novel property of quasispecies: the error threshold—a mutation rate below which populations equilibrate in a traditional mutation–selection balance and above which the population experiences an error catastrophe, that is, the loss of the favored genotype through frequent deleterious mutations. These models show that a single fitness landscape may contain multiple, hierarchically organized error thresholds and that an error threshold is affected by the extent of back mutation and redundancy in the genotype-to-phenotype map. Importantly, an error threshold is distinct from an extinction threshold, which is the complete loss of the population through lethal mutations. Based on this framework, we argue that the lethal mutagenesis of a viral infection by mutation-inducing drugs is not a true error catastophe, but is an extinction catastrophe.     

Theory of lethal mutagenesis for viruses

Mutation is the basis of adaptation. Yet, most mutations are detrimental, and elevating mutation rates will impair a population's fitness in the short term. The latter realization has led to the concept of lethal mutagenesis for curing viral infections, and work with drugs such as ribavirin has supported this perspective. As yet, there is no formal theory of lethal mutagenesis, although reference is commonly made to Eigen's error catastrophe theory. Here, we propose a theory of lethal mutagenesis. With an obvious parallel to the epidemiological threshold for eradication of a disease, a sufficient condition for lethal mutagenesis is that each viral genotype produces, on average, less than one progeny virus that goes on to infect a new cell. The extinction threshold involves an evolutionary component based on the mutation rate, but it also includes an ecological component, so the threshold cannot be calculated from the mutation rate alone. The genetic evolution of a large population undergoing mutagenesis is independent of whether the population is declining or stable, so there is no runaway accumulation of mutations or genetic signature for lethal mutagenesis that distinguishes it from a level of mutagenesis under which the population is maintained. To detect lethal mutagenesis, accurate measurements of the genome-wide mutation rate and the number of progeny per infected cell that go on to infect new cells are needed. We discuss three methods for estimating the former. Estimating the latter is more challenging, but broad limits to this estimate may be feasible.     

Evolution at a High Imposed Mutation Rate: Adaptation Obscures the Load in Phage T7

Evolution at high mutation rates is expected to reduce population fitness deterministically by the accumulation of deleterious mutations. A high enough rate should even cause extinction (lethal mutagenesis), a principle motivating the clinical use of mutagenic drugs to treat viral infections. The impact of a high mutation rate on long-term viral fitness was tested here. A large population of the DNA bacteriophage T7 was grown with a mutagen, producing a genomic rate of 4 nonlethal mutations per generation, two to three orders of magnitude above the baseline rate. Fitness—viral growth rate in the mutagenic environment—was predicted to decline substantially; after 200 generations, fitness had increased, rejecting the model. A high mutation load was nonetheless evident from (i) many low- to moderate-frequency mutations in the population (averaging 245 per genome) and (ii) an 80% drop in average burst size. Twenty-eight mutations reached high frequency and were thus presumably adaptive, clustered mostly in DNA metabolism genes, chiefly DNA polymerase. Yet blocking DNA polymerase evolution failed to yield a fitness decrease after 100 generations. Although mutagenic drugs have caused viral extinction in vitro under some conditions, this study is the first to match theory and fitness evolution at a high mutation rate. Failure of the theory challenges the quantitative basis of lethal mutagenesis and highlights the potential for adaptive evolution at high mutation rates.THE evolutionary consequences of a high mutation rate are mysterious. It is widely considered that mutations are essential for adaptation, but that the rate maximizing adaptation is far below what can be tolerated (e.g., Trobner and Piechocki 1984; Sniegowski 1997, 2001). In this “twilight zone” of higher-than-optimal mutation rates, the population experiences unique challenges. In one process, the “error catastrophe,” the best genotype is driven out of the population deterministically because the onslaught of viable, mutant genotypes simply overwhelms it (Eigen et al. 1988). With Muller''s ratchet, a phenomenon of finite asexual populations, high mutation rates and genetic drift combine to cause loss of the wild-type genome, and the absence of recombination blocks its recreation (Muller 1964); fitness gradually decays as mutations continue their stochastic accumulation. Yet another high mutation rate process is the straightforward, deterministic decline in population fitness as deleterious mutations accumulate (Kimura and Maruyama 1966), leading to extinction if fecundity is too low to compensate (Maynard Smith 1978; Bull et al. 2007).The problem with our understanding of evolution at a high mutation rate is that it is piecemeal. We do not yet know how to combine these different processes nor do we know their relative importance. For example, the fitness loss at a high mutation rate can be offset both by adaptation and by the error catastrophe, but for realistic models, there is no formal basis for predicting the magnitude of adaptation or even for recognizing an error catastrophe (Bull et al. 2005, 2007). Empirical studies are needed. Several studies of viruses have explored extinction through elevated mutation rate (lethal mutagenesis) (Domingo et al. 2001; Anderson et al. 2004; also see discussion), but they have not been tied to any quantitative model. The practical value of such work is that mutagenic drugs are sometimes used to treat viral infections, yet we do not know how the elevated mutation rate is affecting the virus.Here we develop an empirical system to enforce viral evolution at a high mutation rate and test theory developed for lethal mutagenesis. A mutagen is applied to the culture in which the DNA bacteriophage T7 is grown, the mutation input per generation is measured on a genomewide scale, and the system is used to observe both molecular and fitness evolution. Comparison of data and theory provides new insights into the process that underlies lethal mutagenesis. However, existing theory must also be modified to address some empirical properties of the system.

Theory of fitness evolution at high mutation rate:

The objective is to develop a theory for data that are readily obtained. The most basic theory requires one population property (the deleterious mutation rate) to predict another population property (mean fitness), but other properties are not predicted. In experimental systems, mean fitness is easily measured, and the deleterious mutation rate can be estimated within bounds. A fully comprehensive model of evolution at a high mutation rate, one predicting full distributions of genotypes, could be developed if mutation rates and fitness effects were known for each individual mutation and for combinations of mutations, including recombination frequencies. However, the full spectrum of mutations and their fitness effects is too vast to allow those measurements in any biological system, so the only applicable theory describes just mean fitness.If the fitness (e.g., viability) of the mutation-free genotype is assigned the value 1, the mean fitness of an infinite, asexual population at equilibrium is e^−U, where U is the genomic deleterious mutation rate (discrete generations) (Kimura and Maruyama 1966). By itself, this result does not indicate whether a population will survive or not, but one simple modification extends the model to address lethal mutagenesis: fecundity. For an asexual population to survive, a minimal condition is that each parent must produce at least one surviving offspring. In the case of a virus, if each infection produces b viable progeny (in the absence of mutation), the inequality be^−U < 1 ensures eventual extinction. When this inequality is met, the number of progeny in each generation starts out smaller than the number in the parent generation, so the population size declines (Bull et al. 2007).This decline in fitness is not due to stochastic effects in small populations; extinction in this model formally requires a finite population, but the effect of deleterious mutations is treated deterministically. Finite population size can contribute to extinction at mutation rates below the threshold (e.g., from Muller''s ratchet), but we limit ourselves to nearly infinite population sizes.A useful property of the model is that the fitness effects of deleterious mutations and their individual rates need not be known, only the overall rate. Yet this elegance of the Kimura–Maruyama result starts to fade when considering empirical reality. The model considers only deleterious mutations, including lethals; neutral mutations are allowed but ignored, and beneficial mutations are not even allowed. Maximum fitness is assigned to the starting, mutation-free genotype, so any mutation that elevates fitness is excluded. Compensatory mutations that ameliorate the effect of deleterious mutations, and thus are beneficial only within mutated genomes, are also not allowed.To consider a simple model with beneficial mutations, if the initial genotype does not have maximum possible fitness, but a fitness of W relative to the starting genotype is attainable by beneficial mutations (W > 1), then a modified equilibrium is simply We^−U relative to a starting fitness of 1.0. In a virus whose initial fitness is b progeny, adaptive evolution could be accommodated in the model by increasing fecundity to B. The extent to which B exceeds b represents the extent to which the initial (wild-type) virus is poorly adapted to the mutagenic environment, which is unknown. Furthermore, this threshold relaxation omits compensatory mutations that ameliorate specific deleterious mutations and neglects any interference of deleterious mutations on the ascent of beneficial ones.Two further empirical limitations of the Kimura–Maruyama model are evident. Following the onset of an increased mutation rate, the fitness equilibrium may require few or many generations to be approached closely and potentially could require more generations than would be experienced by any real population (Crow and Kimura 1970; Bull and Wilke 2008). The rate of approach depends on the details of the mutation rate and fitness effects, whereas the equilibrium mean fitness does not. We thus attempt to carry out experiments long enough to assume that fitness has neared equilibrium. Second, the Kimura–Maruyama model was developed explicitly for asexuals; the same equilibrium applies with free recombination and no epistasis, but not necessarily when either of these conditions is violated (Maynard Smith 1978; Kondrashov 1982, 1984; Keightley and Otto 2006).In the Kimura–Maruyama model (Kimura and Maruyama 1966), fitness is measured per discrete generation as relative number of surviving offspring. In our viral study, fitness is measured as a growth rate, essentially the log of fitness in the Kimura–Maruyama model. This discrepancy can be resolved by deriving new results for growth rate, again assuming asexuality. Neglecting viral loss from death and other causes, a model of viral growth rate (r) is given by(1)where C is cell (host) density, k is the adsorption rate of virus to cells, b is burst size (average number of progeny per infected cell), and L is lysis time in minutes (Bull 2006). Cell density is assumed to be constant, and cells always outnumber virus (a condition that can be enforced experimentally). r is an exponential or geometric growth rate: at equilibrium, the number of virus at time t, N_t, as a function of initial density, N₀, is given by N_t = e^rtN₀. This model is tailored to the conditions used here, and a model for treatment of a mammalian infection would need to contend with spatial structure and the possibility that the viral population had reached a dynamic equilibrium in which exponential growth no longer applied (see also Steinmeyer and Wilke 2009).With a deleterious, genomic mutation rate U per generation, the deterministic growth rate of the mutation-free class is simply(2)By assumption, all mutation classes in the population are derived ultimately from the mutation-free class and, because all mutations in U are deleterious (neutral mutations are allowed but not counted), all mutants have slower growth rates than the mutation-free genotype. Back mutations and other forms of beneficial mutations are not allowed. It follows that the growth rate of the entire population at mutation–selection equilibrium is given by (2). This result is convenient because the average population growth rate can be understood from the growth rate of the mutation-free class. It is important to emphasize that the solution to (2) [and (1)] is an equilibrium that may require thousands of generations to be reached. Thus, if the solution is negative (r < 0), implying that the population will ultimately decline, the population may go extinct before attaining approximate equilibrium.Equation 2 does not lend itself to an explicit solution, but it is easily solved numerically. Although the parameters in (2) are meant to apply across all mutation rates, the reality for any chemical mutagen or drug is that higher doses of mutagen will not only increase U but also directly reduce viral fitness, such as by reducing burst size. To address this issue, parameters should be estimated in the mutagenic environment. In turn, estimating parameters in the mutagenic environment creates the complication that lethal mutations kill progeny and reduce the apparent burst size (when burst size is determined by plaque counts). To overcome this latter problem, we partition the total deleterious mutation rate into the sum of the lethal rate (U_X) and the nonlethal rate (U_d), U = U_X + U_d, and rewrite Equation 2 as(3)where , the viable burst size. Now, the direct effect of mutagen on burst size is inseparable from the effects of lethal mutations.

Population variation:

An important but subtle implication of the theory is that, when the mutation rate is high, the population will be genetically heterogeneous for deleterious mutations maintained at low to moderate frequencies (Haldane 1927; Crow and Kimura 1970; Eigen et al. 1988). Although every genome may contain many deleterious mutations, different genomes have different sets of deleterious mutations. Only a small proportion of the population may be of the best genotype, in which case, most individuals sampled will have lower fitness than that characterizing the population''s growth (Rouzine et al. 2003, 2008). This heterogeneity has the effect of complicating one means of estimating population fitness. When fitness involves component life history parameters such as burst size and lysis time, a fitness calculation based on separate estimates of life history components appears to underestimate actual population fitness. We have observed this effect in unpublished simulations and suspect that it is a parallel to the principle that the average of a ratio is not the ratio of averages. The T7 system that we use here has the advantage that the intrinsic mutation rate of the virus is low. Thus the starting phage and isolates are genetically uniform and are not subject to this problem. Estimation of fitness directly (as population growth rate rather than from separate fitness components) avoids this problem as well.     

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.     

Fluctuating natural selection accounts for the evolution of diversification bet hedging

Natural environments are characterized by unpredictability over all time scales. This stochasticity is expected on theoretical grounds to result in the evolution of ‘bet-hedging’ traits that maximize the long term, or geometric mean fitness even though such traits do not maximize fitness over shorter time scales. The geometric mean principle is thus central to our interpretation of optimality and adaptation; however, quantitative empirical support for bet hedging is lacking. Here, I report a quantitative test using the timing of seed germination—a model diversification bet-hedging trait—in Lobelia inflata under field conditions. In a phenotypic manipulation study, I find the magnitude of fluctuating selection acting on seed germination timing—across 70 intervals throughout five seasons—to be extreme: fitness functions for survival are complex and multimodal within seasons and significantly dissimilar among seasons. I confirm that the observed magnitude of fluctuating selection is sufficient to account for the degree of diversification behaviour characteristic of individuals of this species. The geometric mean principle has been known to economic theory for over two centuries; this study now provides a quantitative test of optimality of a bet-hedging trait in nature.     

A Multi-Step Process of Viral Adaptation to a Mutagenic Nucleoside Analogue by Modulation of Transition Types Leads to Extinction-Escape

Resistance of viruses to mutagenic agents is an important problem for the development of lethal mutagenesis as an antiviral strategy. Previous studies with RNA viruses have documented that resistance to the mutagenic nucleoside analogue ribavirin (1-β-D-ribofuranosyl-1-H-1,2,4-triazole-3-carboxamide) is mediated by amino acid substitutions in the viral polymerase that either increase the general template copying fidelity of the enzyme or decrease the incorporation of ribavirin into RNA. Here we describe experiments that show that replication of the important picornavirus pathogen foot-and-mouth disease virus (FMDV) in the presence of increasing concentrations of ribavirin results in the sequential incorporation of three amino acid substitutions (M296I, P44S and P169S) in the viral polymerase (3D). The main biological effect of these substitutions is to attenuate the consequences of the mutagenic activity of ribavirin —by avoiding the biased repertoire of transition mutations produced by this purine analogue—and to maintain the replicative fitness of the virus which is able to escape extinction by ribavirin. This is achieved through alteration of the pairing behavior of ribavirin-triphosphate (RTP), as evidenced by in vitro polymerization assays with purified mutant 3Ds. Comparison of the three-dimensional structure of wild type and mutant polymerases suggests that the amino acid substitutions alter the position of the template RNA in the entry channel of the enzyme, thereby affecting nucleotide recognition. The results provide evidence of a new mechanism of resistance to a mutagenic nucleoside analogue which allows the virus to maintain a balance among mutation types introduced into progeny genomes during replication under strong mutagenic pressure.     

Distribution of fitness and virulence effects caused by single-nucleotide substitutions in Tobacco Etch virus

Little is known about the fitness and virulence consequences of single-nucleotide substitutions in RNA viral genomes, and most information comes from the analysis of nonrandom sets of mutations with strong phenotypic effect or which have been assessed in vitro, with their relevance in vivo being unclear. Here we used site-directed mutagenesis to create a collection of 66 clones of Tobacco etch potyvirus, each carrying a different, randomly chosen, single-nucleotide substitution. Competition experiments between each mutant and the ancestral nonmutated clone were performed in planta to quantitatively assess the relative fitness of each mutant genotype. Among all mutations, 40.9% were lethal, and among the viable ones, 36.4% were significantly deleterious and 22.7% neutral. Not a single case of beneficial effects was observed within the level of resolution of our measures. On average, the fitness of a genotype carrying a deleterious but viable mutation was 49% smaller than that for its unmutated progenitor. Deleterious mutational effects conformed to a beta probability distribution. The virulence of a subset of viable mutants was assessed as the reduction in the number of viable seeds produced by infected plants. Mutational effects on virulence ranged between 17% reductions and 24.4% increases. Interestingly, the only mutations showing a significant effect on virulence were hypervirulent. Competitive fitness and virulence were uncorrelated traits.     

The Distribution of Fitness Effects of Beneficial Mutations in Pseudomonas aeruginosa

Understanding how beneficial mutations affect fitness is crucial to our understanding of adaptation by natural selection. Here, using adaptation to the antibiotic rifampicin in the opportunistic pathogen Pseudomonas aeruginosa as a model system, we investigate the underlying distribution of fitness effects of beneficial mutations on which natural selection acts. Consistent with theory, the effects of beneficial mutations are exponentially distributed where the fitness of the wild type is moderate to high. However, when the fitness of the wild type is low, the data no longer follow an exponential distribution, because many beneficial mutations have large effects on fitness. There is no existing population genetic theory to explain this bias towards mutations of large effects, but it can be readily explained by the underlying biochemistry of rifampicin–RNA polymerase interactions. These results demonstrate the limitations of current population genetic theory for predicting adaptation to severe sources of stress, such as antibiotics, and they highlight the utility of integrating statistical and biophysical approaches to adaptation.     

Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics

The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to empirically quantify. As these 2 parameters determine the pace of evolutionary change in a population, the dynamics of adaptive evolution may enable inference of their values. Copy number variants (CNVs) are a pervasive source of heritable variation that can facilitate rapid adaptive evolution. Previously, we developed a locus-specific fluorescent CNV reporter to quantify CNV dynamics in evolving populations maintained in nutrient-limiting conditions using chemostats. Here, we use CNV adaptation dynamics to estimate the rate at which beneficial CNVs are introduced through de novo mutation and their fitness effects using simulation-based likelihood–free inference approaches. We tested the suitability of 2 evolutionary models: a standard Wright–Fisher model and a chemostat model. We evaluated 2 likelihood-free inference algorithms: the well-established Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) algorithm, and the recently developed Neural Posterior Estimation (NPE) algorithm, which applies an artificial neural network to directly estimate the posterior distribution. By systematically evaluating the suitability of different inference methods and models, we show that NPE has several advantages over ABC-SMC and that a Wright–Fisher evolutionary model suffices in most cases. Using our validated inference framework, we estimate the CNV formation rate at the GAP1 locus in the yeast Saccharomyces cerevisiae to be 10^−4.7 to 10⁻⁴ CNVs per cell division and a fitness coefficient of 0.04 to 0.1 per generation for GAP1 CNVs in glutamine-limited chemostats. We experimentally validated our inference-based estimates using 2 distinct experimental methods—barcode lineage tracking and pairwise fitness assays—which provide independent confirmation of the accuracy of our approach. Our results are consistent with a beneficial CNV supply rate that is 10-fold greater than the estimated rates of beneficial single-nucleotide mutations, explaining the outsized importance of CNVs in rapid adaptive evolution. More generally, our study demonstrates the utility of novel neural network–based likelihood–free inference methods for inferring the rates and effects of evolutionary processes from empirical data with possible applications ranging from tumor to viral evolution.

This study shows that simulation-based inference of evolutionary dynamics using neural networks can yield parameter values for fitness and mutation rate that are difficult to determine experimentally, including those of copy number variants (CNVs) during experimental adaptive evolution of yeast.     

Pathways to extinction: beyond the error threshold

Since the introduction of the quasispecies and the error catastrophe concepts for molecular evolution by Eigen and their subsequent application to viral populations, increased mutagenesis has become a common strategy to cause the extinction of viral infectivity. Nevertheless, the high complexity of virus populations has shown that viral extinction can occur through several other pathways apart from crossing an error threshold. Increases in the mutation rate enhance the appearance of defective forms and promote the selection of mechanisms that are able to counteract the accelerated appearance of mutations. Current models of viral evolution take into account more realistic scenarios that consider compensatory and lethal mutations, a highly redundant genotype-to-phenotype map, rough fitness landscapes relating phenotype and fitness, and where phenotype is described as a set of interdependent traits. Further, viral populations cannot be understood without specifying the characteristics of the environment where they evolve and adapt. Altogether, it turns out that the pathways through which viral quasispecies go extinct are multiple and diverse.     

A Bayesian MCMC Approach to Assess the Complete Distribution of Fitness Effects of New Mutations: Uncovering the Potential for Adaptive Walks in Challenging Environments

The role of adaptation in the evolutionary process has been contentious for decades. At the heart of the century-old debate between neutralists and selectionists lies the distribution of fitness effects (DFE)—that is, the selective effect of all mutations. Attempts to describe the DFE have been varied, occupying theoreticians and experimentalists alike. New high-throughput techniques stand to make important contributions to empirical efforts to characterize the DFE, but the usefulness of such approaches depends on the availability of robust statistical methods for their interpretation. We here present and discuss a Bayesian MCMC approach to estimate fitness from deep sequencing data and use it to assess the DFE for the same 560 point mutations in a coding region of Hsp90 in Saccharomyces cerevisiae across six different environmental conditions. Using these estimates, we compare the differences in the DFEs resulting from mutations covering one-, two-, and three-nucleotide steps from the wild type—showing that multiple-step mutations harbor more potential for adaptation in challenging environments, but also tend to be more deleterious in the standard environment. All observations are discussed in the light of expectations arising from Fisher’s geometric model.     

Selection for robustness in mutagenized RNA viruses

Mutational robustness is defined as the constancy of a phenotype in the face of deleterious mutations. Whether robustness can be directly favored by natural selection remains controversial. Theory and in silico experiments predict that, at high mutation rates, slow-replicating genotypes can potentially outcompete faster counterparts if they benefit from a higher robustness. Here, we experimentally validate this hypothesis, dubbed the “survival of the flattest,” using two populations of the vesicular stomatitis RNA virus. Characterization of fitness distributions and genetic variability indicated that one population showed a higher replication rate, whereas the other was more robust to mutation. The faster replicator outgrew its robust counterpart in standard competition assays, but the outcome was reversed in the presence of chemical mutagens. These results show that selection can directly favor mutational robustness and reveal a novel viral resistance mechanism against treatment by lethal mutagenesis.     

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.     

Mutational robustness of an RNA virus influences sensitivity to lethal mutagenesis

The ability to extinguish a viral population of fixed reproductive capacity by causing small changes in the mutation rate is referred to as lethal mutagenesis and is a corollary of population genetics theory. Here we show that coxsackievirus B3 (CVB3) exhibits reduced mutational robustness relative to poliovirus, manifesting in enhanced sensitivity of CVB3 to lethal mutagens that is dependent on the size of the viral population. We suggest that mutational robustness may be a useful measure of the sensitivity of a virus to lethal mutagenesis.     

An inclusive fitness analysis of synergistic interactions in structured populations

We study the evolution of a pair of competing behavioural alleles in a structured population when there are non-additive or ‘synergistic’ fitness effects. Under a form of weak selection and with a simple symmetry condition between a pair of competing alleles, Tarnita et al. provide a surprisingly simple condition for one allele to dominate the other. Their condition can be obtained from an analysis of a corresponding simpler model in which fitness effects are additive. Their result uses an average measure of selective advantage where the average is taken over the long-term—that is, over all possible allele frequencies—and this precludes consideration of any frequency dependence the allelic fitness might exhibit. However, in a considerable body of work with non-additive fitness effects—for example, hawk–dove and prisoner''s dilemma games—frequency dependence plays an essential role in the establishment of conditions for a stable allele-frequency equilibrium. Here, we present a frequency-dependent generalization of their result that provides an expression for allelic fitness at any given allele frequency p. We use an inclusive fitness approach and provide two examples for an infinite structured population. We illustrate our results with an analysis of the hawk–dove game.     

The Properties of Adaptive Walks in Evolving Populations of Fungus

The rarity of beneficial mutations has frustrated efforts to develop a quantitative theory of adaptation. Recent models of adaptive walks, the sequential substitution of beneficial mutations by selection, make two compelling predictions: adaptive walks should be short, and fitness increases should become exponentially smaller as successive mutations fix. We estimated the number and fitness effects of beneficial mutations in each of 118 replicate lineages of Aspergillus nidulans evolving for approximately 800 generations at two population sizes using a novel maximum likelihood framework, the results of which were confirmed experimentally using sexual crosses. We find that adaptive walks do indeed tend to be short, and fitness increases become smaller as successive mutations fix. Moreover, we show that these patterns are associated with a decreasing supply of beneficial mutations as the population adapts. We also provide empirical distributions of fitness effects among mutations fixed at each step. Our results provide a first glimpse into the properties of multiple steps in an adaptive walk in asexual populations and lend empirical support to models of adaptation involving selection towards a single optimum phenotype. In practical terms, our results suggest that the bulk of adaptation is likely to be accomplished within the first few steps.     

The Fitness Effects of Random Mutations in Single-Stranded DNA and RNA Bacteriophages

Mutational fitness effects can be measured with relatively high accuracy in viruses due to their small genome size, which facilitates full-length sequencing and genetic manipulation. Previous work has shown that animal and plant RNA viruses are very sensitive to mutation. Here, we characterize mutational fitness effects in single-stranded (ss) DNA and ssRNA bacterial viruses. First, we performed a mutation-accumulation experiment in which we subjected three ssDNA (ΦX174, G4, F1) and three ssRNA phages (Qβ, MS2, and SP) to plaque-to-plaque transfers and chemical mutagenesis. Genome sequencing and growth assays indicated that the average fitness effect of the accumulated mutations was similar in the two groups. Second, we used site-directed mutagenesis to obtain 45 clones of ΦX174 and 42 clones of Qβ carrying random single-nucleotide substitutions and assayed them for fitness. In ΦX174, 20% of such mutations were lethal, whereas viable ones reduced fitness by 13% on average. In Qβ, these figures were 29% and 10%, respectively. It seems therefore that high mutational sensitivity is a general property of viruses with small genomes, including those infecting animals, plants, and bacteria. Mutational fitness effects are important for understanding processes of fitness decline, but also of neutral evolution and adaptation. As such, these findings can contribute to explain the evolution of ssDNA and ssRNA viruses.     

Can modulation of viral fitness represent a target for anti-HIV-1 strategies?

Prolonged use of anti-retroviral compounds in human immunodeficiency virus type 1 (HIV-1) infection selects for drug-resistant and often mutidrug-resistant viral variants. Drug-resistance mutations may also affect viral fitness. Interestingly, recent research has indicated that some of the unfit drug-resistant variants may be less pathogenic, suggesting that decreased viral fitness is beneficial for the host and may be driven by specific treatments during anti-HIV-1 infection. A second potential antiviral strategy starting with profound inteference with viral fitness aims at forcing viruses towards lethal mutagenesis (the so-called "error catastrophe"). This review summarizes the methods for addressing HIV-1 fitness in vitro and ex vivo, the current understanding of clinical implications of reduced HIV-1 fitness, and the potential use of anti-HIV-1 strategies aiming at modulating viral fitness. Finally, it is emphasized how the peculiar features of HIV-1 quasispecies (displaying two different forms of memory, a replicative and a non-replicative form) may sharply influence the design of future diagnostic methodologies for fitness analysis.     

INFLUENCE OF VIRAL REPLICATION MECHANISMS ON WITHIN‐HOST EVOLUTIONARY DYNAMICS

Viruses replicate their genomes using a variety of mechanisms, leading to different distributions of mutations among their progeny. Yet, models of viral evolution often only consider the mean mutation rate. To investigate when and how replication mechanisms impact viral evolution, we analyze the early dynamics of within‐host infection for two idealized cases: when all offspring virions from an infected cell carry the same genotype, mutated or not; and when mutations occur independently across offspring virions. Other replication life histories fall between these extremes. Using branching process models, we study the probability that viral infection becomes established when mutations are lethal, and in the more general case of two strains of different fitness. For a given mean mutation rate, we show that a lineage of viruses with correlated mutations is less likely to survive than with independent mutations, but when it survives, the viral population grows faster. While this holds true for all parameter regimes, replication life history has a quantitatively significant influence on viral dynamics when stochastic effects are important and when mutations are crucial for survival—conditions typical of evolutionary escape situations.