Fitness Declines in Tobacco Etch Virus upon Serial Bottleneck Transfers

It has been well established that populations of RNA virusestransmitted throughout serial bottlenecks suffer from significantfitness declines as a consequence of the accumulation of deleteriousmutations by the onset of Muller's ratchet. Bottlenecks areunavoidably linked to different steps of the infectious cycleof most plant RNA viruses, such as vector-mediated transmissionsand systemic colonization of new leaves. Here we report evidencefor fitness declines by the accumulation of deleterious mutationsin the potyvirus Tobacco etch virus (TEV). TEV was inoculatedinto the nonsystemic host Chenopodium quinoa, and local lesionswere isolated and used to initiate 20 independent mutation accumulationlineages. Weekly, a random lesion from each lineage was isolatedand used to inoculate the next set of plants. At each transfer,the Malthusian growth rate was estimated. After 11 consecutivetransfers, all lineages suffered significant fitness losses,and one even became extinct. The average rate of fitness declinewas 5% per day. The average pattern of fitness decline was consistentwith antagonistic epistasis between deleterious mutations, aspostulated for antiredundant genomes. Temporal fitness fluctuationswere not explained by random noise but reflected more complexunderlying processes related to emergence and self-organizationphenomena.

INTRODUCTION

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INTRODUCTION

Due to the lack of proofreading of their replicases, RNA virusesshow the highest mutation rates in nature (12). For this reason,along with short generation times and large population sizes,RNA virus populations usually consist of highly heterogeneousmixtures known as quasispecies (11). In a defined environment,the mutant spectrum of a viral population is generally centeredon one or several sequences that are fitter, but nonethelessoften represent a minor proportion of the mutant distribution(11). Therefore, associated with the inherent stochasticityof transmission or tissue colonization events, viral populationsare continuously regenerated from a few individual genomes,and, thus, a finite probability exists that the new populationcarries an increased mutational load relative to the most-fitmembers of the ancestral population. Repeated genetic bottleneckevents followed by low-fidelity genome replication may leadto substantial fitness losses. In large populations, where purifyingselection is a strong force, genomes carrying reversion or second-sitecompensatory mutations quickly increase in frequency. However,when the effective population size is small, compensatory mutationsmight not arise (3, 49). Muller (42) predicted that when mutationrates are high and population sizes small, this process occursin an irreversible ratchet-like manner that leads to the gradualdecrease of fitness in a population, particularly in asexualorganisms (24). Indeed, the onset of Muller's ratchet furtherreduces population size, thus accelerating additional mutationaccumulation and leading to extinction in a process known asmutational meltdown (23).

The operation of Muller's ratchet in RNA virus populations hasbeen well documented. In a seminal work, Chao (6) found thatunder demographic conditions in which genetic drift was themajor driving force, the mean fitness of segmented RNA bacteriophage ${phi}$ 6 was systematically decreased. Soon after, this observationwas extended to Vesicular stomatitis virus (13-15, 17, 43, 44),Foot-and-mouth disease virus (19), Human immunodeficiency virustype 1 (HIV-1) (59, 60), and bacteriophage MS2 (8). Later, theresistance of Foot-and-mouth disease virus populations to extinctionafter prolonged exposure to periodic bottleneck events was reported(10, 20, 36). Interestingly, these later experiments showedthat fitness loss trajectories are biphasic, with a first phaseof rapid decline followed by a long stationary phase in whichfitness fluctuates around a constant value (10, 36). Furthermore,the magnitude of the fluctuations was not simply reflectingrandom noise but was the result of a more complex dynamic governedby the sporadic arising of beneficial mutations within a growingplaque and the existence of extinction thresholds below whichindividual genomes are removed (35, 36, 40).

Unfortunately, similar observations have never been made fora plant virus, despite the fact that bottlenecks are commonduring vector-mediated transmissions (1, 46) and also duringthe systemic colonization of new leaves (22, 37, 47), the latterleading to the establishment of genetically diverse populationsin different parts of the plant (22, 27). Here, we take a firststep toward covering this gap and extend the study of Muller'sratchet to plant virus populations. We report results of anexperiment designed to quantitatively explore the fitness lossesassociated with serial bottleneck transfers of Tobacco etchvirus (TEV) on the local lesion host Chenopodium quinoa. Inaddition to inferring the rate of fitness decline, the existenceand sign of epistasis among deleterious mutations, and theirrelationship, we will also explore whether the temporal patternof fluctuations in fitness can be trivially explained by randomnoise or, more interestingly, as a consequence of complex dynamicsrelated to self-organized critical (SOC) phenomena.

MATERIALS AND METHODS

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MATERIALS AND METHODS

TEV infectious clone and production of a large stock of TEV infectious particles. The pTEV-7DA infectious clone, kindly provided by James C. Carrington(Oregon State University, Corvallis), was used as an ancestorsurrogated wild-type virus (9).

All basic protocols have been described elsewhere (5). In short,pTEV-7DA was linearized with BglII (Fermentas) and transcribedinto 5'-capped RNAs using the SP6 mMESSAGE mMACHINE kit (Ambion,Inc.). Transcripts were precipitated, collected, and resuspendedin diethyl pyrocarbonate-treated water. RNA integrity and quantitywere assessed by gel electrophoresis. Transcripts were mixedwith a 1:10 volume of inoculation buffer (100 mg/ml carborundum,0.5 M K₂HPO₄) (5). Five 4-week-old Nicotiana tabacum cv. Xanthiplants were inoculated by abrasion with 5 µl of inoculumapplied to the third true leaf. Plants were maintained in thegreenhouse at 25°C with 16 h of light. Symptoms appeared4 to 5 days postinoculation (dpi). Eight or 9 dpi, TEV virionswere partially purified from symptomatic tissue, as describedin reference 5, and pooled in a single large stock.

Inoculation of and virus purification from C. quinoa leaves. Four different leaves from each one of five different 4-week-oldC. quinoa plants were rub inoculated with 10 µl of undiluted,5-, 10-, and 100-fold-diluted virus, respectively. This randomizedinoculation design minimizes plant effects since each dilutionis present on each plant, as classically suggested (29, 30).Viral titers, measured as the number of lesion-forming units(LFU) per µl of inoculum, were inferred from the regressionof the observed number of local lesions, corrected by a factoralso to be estimated from the data, to the dilution factor (29).

TEV particles were purified from local lesions as follows. Awell-isolated lesion was cut off from the leaf using the wideside of a 5-µl micropipette tip (approximately 4-mm-diameterdisc), immediately frozen with liquid N₂, and ground in a mortarwith 20 µl of extraction buffer (50 mM phosphate, 3% polyethyleneglycol 8000). The extract was carefully collected and centrifugedfor 5 min at 13,000 rpm. The supernatant was finally collected,glycerol was added at a final concentration of 20% (vol/vol),and the mixture was frozen at –80°C.

MA experiment. Mutation accumulation (MA) experiments are a classic geneticapproach to the study of deleterious mutations. They are conceptuallyand mechanistically simple, yet very informative and easy toadapt to widely different experimental systems (38). By imposinga strong bottleneck at each population transfer, the effectof random drift is maximized while the effect of natural selectionis minimized, allowing for the accumulation of deleterious mutationsthat otherwise will be purged from the populations by purifyingselection. Figure 1 shows an outline of the MA experiment carriedout here with TEV. The TEV stock was serially diluted, and eachdilution was inoculated, as described in the previous section,into C. quinoa leaves. Nine days postinoculation, the numberof local lesions produced was recorded and 20 lesions were randomlyisolated. These lesions constitute the starting point for the20 independent MA lineages. Virus was extracted from each lesionand serially diluted (0-, 5-, 10-, and 100-fold), and each dilutionwas used to inoculate a new batch of C. quinoa plants, as describedabove. Nine days postinoculation, the number of local lesionswas recorded for each MA lineage, and a lesion per lineage wasrandomly chosen and used for the next bottleneck transfer. (Passages4 and 11 were done after 8 days due to reasons beyond our control.)

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FIG. 1. Scheme of the serial lesion-to-lesion transfer of a TEV population. At each transfer (t), a random lesion was taken, virus purified, and serially diluted. Four dilutions were inoculated on five leaves of each of the next C. quinoa set (t + 1), as described in the text. Only one leaf at each transfer is illustrated here. Nine days postinoculation, newly developed lesions were counted and the number of LFU of the previous lesion was estimated as N(t). Malthusian growth rates were estimated as described in Materials and Methods.

Estimates of Malthusian growth rates. As a proxy to the fitness of a replicating viral population,we used the per-day Malthusian growth rate. For a lesion sampledat bottleneck transfer t, for the ith (i = 1, 2, ... 20) MAlineage, the number of LFU it contained was estimated as thenumber of visible local lesions produced at the next C. quinoaleaf, N_i(t + 1) (Fig. 1). Since each lesion originated froma single LFU, then the Malthusian growth rate, r_i(t), after ${tau}$ days of viral replication ( ${tau}$ = 8 or 9) can be estimated as r_i(t)= 1/ ${tau}$ log N_i(t + 1) day^–1.

Population genetics parameters and statistical analyses. Fitness time series data were analyzed to infer two importantpopulation genetics parameters: the rate of fitness declineunder MA and the average sign and intensities of the epistaticinteractions between accumulated deleterious mutations. To doso, the time series data were fitted to the power fitness function Formula , where ${alpha}$ _i stands for the per-dayrate of fitness decline and ß_i describes the formof epistasis for each lineage of MA. The per-day rate of fitnessdecline is directly proportional to the average mutational effecton fitness, (Appendix). If ß_i= 1, then mutations act multiplicatively: ß_i >1 represents the case of synergistic epistasis where mutationaleffects are amplified, and 0 < ß_i < 1 representsthe case of antagonistic epistasis where mutational effectssuffer of diminishing returns. For a compacted nonredundantRNA genome, an excess of antagonistic epistasis is expectedfrom theoretical considerations (16, 50) and supported by growingempirical evidence from phage ${phi}$ 6 (4), HIV-1 (2), Vesicular stomatitisvirus (53), Rous sarcoma virus (48), and the viroids (51).

To gain further insights into the dynamics of TEV fitness evolutionunder strong random drift, and particularly seeking scale-freelaws characteristic of complex systems self-organized into criticalstates (SOC), we proceeded to analyze the fluctuations in thetime series data. Data from the 20 MA experiments were pooledinto a single analysis after expressing the magnitude of fluctuationsas the unsigned standardized residuals (USRs) from the fit ofthe power fitness model to each MA lineage (18). By doing so,deviations from the expected value were put into a common standarddeviation scale which makes fluctuation size independent fromthe actual fitness value measured. Four probability densityfunctions (pdfs) were fitted to the fluctuation data (21). Underthe null hypothesis of pure additive sampling error, the USRsshould distribute according to a normal pdf, with the parametersthe mean, µ, and the standard deviation, ${sigma}$ . The secondmodel fitted was the log-normal pdf. Log-normal distributionsarise when errors take place at different steps acting in amultiplicative fashion. Consequently, small differences areamplified, resulting in large fluctuations. The log-normal distributionis characterized by two nonnegative parameters, the median,m, and the shape, ${sigma}$ . The third model fitted was Pareto's pdf,which represents the case of a scale-free distribution belongingto the power-law family of pdfs, and it is usually taken asthe most striking signature of SOC. Pareto's pdf is characterizedby two nonnegative parameters, the threshold, a, and the shape(or critical exponent), c. Values of 0 < c < 2 are indicativeof SOC phenomena (54). It has been suggested (33) that whenpower laws fail to describe the characteristics of empiricaldistributions, a good alternative are stretched exponentialssuch as Weibull's pdf, which is characterized by two nonnegativeparameters, the scale, ${eta}$ , and the shape, ß < 1.Differences in the goodness of fit for each of these modelswere assessed by means of the Bayesian information criterion(BIC), which takes into account not only the fit to the modelbut also its complexity and sample size (26).

All statistical analyses were done using SPSS, version 14.0(SPSS, Inc., Chicago, IL).

RESULTS

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RESULTS

The rate of fitness decline due to the onset of Muller's ratchet. Figure 2 shows the decline in Malthusian growth rates for allof the MA lineages. The time series data shown in Fig. 2 werefitted by nonlinear regression methods to the power fitnessfunction mentioned above, and the rate of fitness decline wasinferred for each MA lineage. All 20 lineages suffered fromsignificant fitness losses even after applying Bonferroni'ssequential method for correcting the significance level of multipletests of the same null hypothesis (t test; in all cases, P $≤$ 0.011); and 1 lineage was extinct after three transfers. Theper-day rate of fitness decline, ${alpha}$ , ranged from 0.020 day^–1to 0.185 day^–1, with a median value of 0.051 day^–1.The average rate (± standard error) was Formula

= 0.052 ± 0.008 day^–1, a value thatis significantly different from zero (one-sample t test; P <0.001). Thus, we conclude that, on average, TEV fitness declinesas much as 5% per day when genetic drift is the main force drivingits evolution.

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FIG. 2. Evolution of the Malthusian growth rate for each one of the 20 mutation accumulation lineages. Values were relative to the r value estimated for the ancestral TEV-7DA clone.

Estimate of the sign and intensity of epistasis among deleterious mutations. After fitting the power fitness function to Fig. 2 data, theepistasis coefficient, ß, was also inferred. In 10cases, ß was not significantly different from thenull hypothesis of multiplicative fitness effects. In nine cases,ß was significantly smaller than 1 (t test; in allcases, P < 0.005), as is the case for antagonistic epistasis.Among those cases showing significant antagonism, ßwas found in the range 0.418 to 0.931. In only one case wassignificant synergistic epistasis (ß > 1) observed(1.178 ± 0.047; one-sample t test; P = 0.003). However,no case remained significantly different from the null hypothesisafter applying the more stringent sequential Bonferroni criterion.Nonetheless, by treating each ß estimate as an independentrealization of the true statistical distribution, the grandmean of the epistasis coefficient was Formula = 0.772 ± 0.006, a value which is significantlysmaller than the null hypothesis (one-sample t test; P <0.001). Therefore, we conclude that, on average, deleteriousmutations tend to interact antagonistically in modulating TEVfitness.

Interaction between antagonistic epistasis and mutational effect. It has been ubiquitously observed (51, 56-58) that average mutationaleffects, , and the strength of antagonisticepistasis are not independent parameters but, instead, are negativelycorrelated. We sought to investigate if such a negative relationshipholds for TEV. As an approximation to ,we used ${alpha}$ , since, as we have shown in the Appendix, these twoparameters are related by definition and therefore a correlationbetween ß and ${alpha}$ would necessarily imply a correlationof ß with . Figure 3 showsthe relationship between ß and ${alpha}$ for all MA lineages.The value of the Spearman's correlation coefficient was significantlynegative ( ${rho}$ _S = –0.714; 18 df; P = 0.001). Therefore, weconcluded that epistasis and mutational effects are not independenttraits but instead may evolve hand in hand: a reduction in themagnitude of mutational effects is associated with a reductionin the magnitude of antagonistic epistasis.

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FIG. 3. Relationship between average mutational effects and the average epistasis coefficient (± standard error).

Analysis of the distribution of fluctuations in the fitness time series data. After transforming the deviations from the predicted valuesinto USRs, values ranged over 4 orders of magnitude (from 0.0005to 0.676), a necessary condition when looking for scale-freelaws (33). Figure 4A shows the distribution of fitness fluctuationsizes. The average fluctuation size was 0.127, with a standarddeviation of 0.137. The distribution was significantly skewedtowards small fluctuations (g₁ = 1.628 ± 0.182, t₁₇₈= 8.964, P < 0.001), and consequently the median fluctuation(0.080) is well below the mean fluctuation. The distributionwas also significantly leptokurtic (g₁ = 2.467 ± 0.361,t₁₇₈ = 6.830, P < 0.001), meaning that many values lie nearthe center, and then the distribution quickly transitions toa long, evenly distributed tail.

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FIG. 4. Panel A shows the distribution of growth rate fluctuation sizes, measured as USRs, during the mutation accumulation experiment. (B) Plots showing the fit of the four alternative empirical pdfs to the observed data (big dots). The dotted line shows the fit to the normal pdf, the short-dashed line shows the fit to the log-normal pdf, the long-dashed line shows the fit to Pareto's pdf, and the continuous line shows the fit to Weibull's pdf.

Table 1 shows the estimated parameters for each of the fourdistributions fitted to the fluctuation data. Figure 4B showsthe fit of the four alternative models to the empirical pdf.Under the null hypothesis of pure sampling error, USRs shoulddistribute according to a normal pdf. As shown in Fig. 4B andTable 1, the fit to the normal pdf explained 95.8% of the observedvariability. The second model fitted was the log-normal pdf(Fig. 4B). The fit was significant (Table 1; 97.8% of variabilityexplained) and improved with respect to the normal pdf (lowerBIC; Table 1), although it still did not provide the best possiblefit among the tested models. The third model fitted was Pareto'spdf (Table 1 and Fig. 4B), which provided the worst fit amongthe four models tested (largest BIC), explaining only 91.4%of the observed variability. Nonetheless, as expected for SOC,the critical exponent was within the range (0 to 2). The lastdistribution tested was Weibull's pdf (Table 1 and Fig. 4B).The fit of Weibull's pdf was significant and explained 99.0%of the observed variability in USRs, providing the best fitamong the models tested (lowest BIC).

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TABLE 1. Statistics describing the fit of fluctuation size data to alternative pdf models^a

In conclusion, the pattern of fluctuations observed during fitnessdecay due to mutation accumulation, rather than being triviallyexplained by random noise, suggests that at least some of theprocesses behind viral evolution are related to a broad categoryof phenomena previously documented in many fields, includingphysics, biology, and economics, which are related to emergingproperties (33, 54).

DISCUSSION

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DISCUSSION

Our first result confirms that whenever genetic drift is themajor force driving viral evolution, fitness substantially declinesas a consequence of the onset of Muller's ratchet. Indeed, TEVclones lost as much as 5% of their fitness per day, which meansthat after each bottleneck transfer, they lost 7 x 5% (35%)of their fitness. This result extends previous findings fromanimal and bacterial viruses (6, 8, 13-15, 17, 19, 20, 36, 43,44, 59, 60) to plant viruses. This result is particularly relevantgiven the importance of bottlenecks during the infectious cycleof plant viruses (1, 22, 27, 37, 46, 47). If bottlenecks areso common and their impact on viral fitness so large, then thequestion is how viruses can continue to keep replicating withoutheading towards extinction? Three potential reasons can be putforward. In principle, all of these reasons may work for anyviral system, although we will make clarifications for the caseof plant viruses. First, right after the bottleneck, large populationsizes can be generated and, in the process of replication, newgenetic variation can be created. This genetic variation mayinclude reversions to the wild-type sequence and second-sitecompensatory mutations that will spread into the replicatingpopulation quite fast. During the systemic infection of a plant,however, the virus suffers from strong bottlenecks (22, 37,47) which may favor not necessarily the fittest variant. Inaddition, in contrast to lytic viruses, plant viruses are notreleased to a common pool in which they compete for resourcesand, therefore, selection should not be as strong as requiredfor this explanation to work. Second, it has been recently proposedthat the entire viral populations may be robust against mutationaleffects, despite the fact that individual genomes may be highlysensitive to the effect of mutations (16). The reason for thisapparent paradox is that the efficiency of natural selectionpurging out deleterious variability is proportional to the productN_es, where N_e is the effective population size and s is thedeleterious fitness effect of a given mutation. In moderateto large populations, such as those typical of RNA viruses,very deleterious mutations (large s) are removed from the populationinstantaneously, and even those mutations with a mild effect(small s) are removed after short times, preserving a high averagepopulation fitness (32). Furthermore, the frequency of lethalmutations has been reported to be quite high (52). In the caseof plant viruses, N_e has been estimated to be very low (22,47), on the order of a few units, and, therefore, the aboveargument appears to be weak, unless s tends to be very large.Indeed, this is the case for Tobacco mosaic virus, for whichthe spectrum of mutations is dominated by insertions and deletionsrather than by base substitutions (39). Most of these insertionsand deletions will be almost immediately lethal (s $->$ ${infty}$ ) and willnot contribute to the next viral generation. Third, virusesmay turn cellular chaperones to their own benefit to bufferthe effect on protein stability of their mutational load. Ithas been shown that most viruses need cellular chaperones duringtheir life cycle, both to solve their own protein-folding problemsand to interfere with cellular processes (41). The fact thatmutant misfolded viral proteins elicit the expression of chaperoneshas been illustrated by Jockush et al. (25). They showed thatthe presence of Tobacco mosaic virus mutant misfolded coat proteins(CP) triggers the overexpression of Hsp70, with the intensityof the response proportional to the amount of mutant CP. Incontrast, the presence of wild-type CP does not elicit suchresponse.

Our second finding is that, on average, deleterious mutationsinteract in an antagonistic manner, which means that successivemutations have relatively less impact on viral fitness thanexpected under a multiplicative model. This result is also extendingprevious findings on animal and bacterial viruses (2, 4, 48,53) to a plant virus. Given the great heterogeneity in the viralmodels studied, it seems very plausible that antagonistic epistasisis a general feature of RNA virus genomes. Although epistasisis central for many evolutionary theories, perhaps the mostintriguing one is the maintenance of sex and recombination.According to the mutational deterministic hypothesis (31), anexcess of synergistic epistasis among deleterious mutationsis required to compensate for the twofold advantage of clonalreproduction. In such a situation, recombination would be beneficialfor RNA viruses in purging a deleterious mutational load. Hence,the predominance of antagonistic epistasis in TEV invalidatesthis explanation and raises the question of why positive-strandedviruses, and in particular Potyvirus, have evolved the capacityto recombine (7, 34). In theory, drift can favor the evolutionof recombination even for very large populations, as long asthere are a sufficient number of loci under selection (28, 45).Whether drift-based explanations for the benefit of recombinationare applicable to positive stranded viruses remains to be investigated.

We have also found a negative correlation between mutationaleffects and the strength of antagonistic epistasis (Fig. 3).This correlation seems to be a ubiquitous phenomenon that hasbeen observed with digital organisms (56), in silico modelsof bacteriophage T7 infectious cycle (58), and simulated (57)and viroid (51) RNA folding. The dependence of epistasis onmutational effects means that both parameters cannot be evolutionarilyoptimized independently. If individual genetic robustness hasto evolve (i.e., mutational effects become smaller), then thestrength of antagonistic epistasis would be relaxed. Antagonisticepistasis is a hallmark of nonredundant genomes where a firstmutation has a strong effect on fitness but subsequent mutations,by hitting an already damaged function, may have an increasinglysmaller marginal contribution to fitness (16, 50). Therefore,the parasitic runaway lifestyle of RNA viruses, by favoringgenomic compaction, is creating antagonistic epistasis and strongsensitivity to deleterious mutations.

Finally, we have observed that fluctuations in fitness duringthe MA experiments, rather than simply reflecting statisticaluncertainty and sampling errors, can be better explained bythe stretched exponentials of Weibull's probability distribution.This finding is suggestive of the existence of SOC phenomenaassociated with viral dynamics, as previously observed for otherviruses (18, 36). But, what does it mean that RNA viruses self-organizeinto a critical state during their evolution? Perhaps the bestway of illustrating the meaning of SOC is by using a well-knownexample—bird flocks. When observing a flock moving onthe air, it is not possible to predict its behavior by simplylooking at each single bird, but the flock has to be seen asa whole, since it shows a collective behavior which is an emergingproperty of the system. Small perturbations to the flock mayhave no impact on its behavior or, with equal probability, havea catastrophic effect, disaggregating it. Similarly, RNA viruspopulations cannot be understood by simply looking at the individualgenome but by examining the entire quasispecies, which showsa series of emerging properties that are the result of the collectivebehavior of its members. For example, the entire quasispeciesmay be robustly able to handle to the effect of deleteriousmutations as far as all genotypes are connected by neutral mutations(55).

So far, the experimental analysis of viral evolution has beenlimited, with a few remarkable exceptions, to in vitro cellcultures and mostly to animal and bacterial viruses. As a concludingremark, we would like to suggest that this report extends thisvery successful approach to a more realistic situation in whichthe virus replicates and evolves into a real host.

APPENDIX

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APPENDIX

The log fitness of a genome carrying n mutations can be describedby the power-law fitness function log W_n = –n^ß. In this equation,

is the average deleterious fitness effect and ß describesthe form of epistasis. Our experimental setup, however, didnot allow us to directly estimate the number of deleteriousmutations, n. Instead, assuming that in any MA lineage the numberof mutations at time t, n_t, is Poisson distributed with parameterUt (U stands for the genomic deleterious mutation rate), themean log fitness of several replicate lineages at time t isgiven by the following equation:

Formula 1 (1)
The approximation is valid for large Ut, whichis reasonable for an RNA virus, and small deviations from theassumption of multiplicative mutational effects (ß ${approx}$ 1). Therefore, the per-day rate of fitness decline, ${alpha}$ ${approx}$ U^ß depends on theaverage mutational effects, the rate of mutation, and the signand strength of epistasis among mutations.

ACKNOWLEDGMENTS

We thank the laboratory members and J. A. Daròs for help,comments, and fruitful discussion.

This work has been supported by grants from the Spanish MEC-FEDER(BFU2005-23720-E/BMC and BFU2006-14819-C02-01/BMC), the GeneralitatValenciana (ACOMP06/015), and the EMBO Young Investigator Programto S.F.E.

FOOTNOTES

* Corresponding author. Mailing address: Instituto de Biología Molecular y Celular de Plantas (CSIC-UPV), CPI 8E, C/Ingeniero Fausto Elio s/n, 46022 València, Spain. Phone: 34 963 877 895. Fax: 34 963 877 859. E-mail: sfelena{at}ibmcp.upv.es

Published ahead of print on 7 March 2007.

REFERENCES

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