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ORIGINAL ARTICLE
Magnitude and sign epistasis among deleterious mutations
in a positive-sense plant RNA virus
J Lalic
´1and SF Elena1,2
How epistatic interactions between mutations determine the genetic architecture of fitness is of central importance in evolution.
The study of epistasis is particularly interesting for RNA viruses because of their genomic compactness, lack of genetic
redundancy, and apparent low complexity. Moreover, interactions between mutations in viral genomes determine traits such as
resistance to antiviral drugs, virulence and host range. In this study we generated 53 Tobacco etch potyvirus genotypes carrying
pairs of single-nucleotide substitutions and measured their separated and combined deleterious fitness effects. We found that
up to 38% of pairs had significant epistasis for fitness, including both positive and negative deviations from the null hypothesis
of multiplicative effects. Interestingly, the sign of epistasis was correlated with viral protein–protein interactions in a model
network, being predominantly positive between linked pairs of proteins and negative between unlinked ones. Furthermore, 55%
of significant interactions were cases of reciprocal sign epistasis (RSE), indicating that adaptive landscapes for RNA viruses
maybe highly rugged. Finally, we found that the magnitude of epistasis correlated negatively with the average effect of
mutations. Overall, our results are in good agreement to those previously reported for other viruses and further consolidate the
view that positive epistasis is the norm for small and compact genomes that lack genetic robustness.
Heredity (2012) 109, 71–77; doi:10.1038/hdy.2012.15; published online 11 April 2012
Keywords: epistasis; fitness landscapes; genome architecture; virus evolution
INTRODUCTION
Epistasis has been the focus of intensive research since the beginning
of genetics as a scientific discipline (Phillips, 2008). In general,
epistasis is the interaction between genes or mutations in determining
phenotypes. The direction, magnitude and prevalence of epistasis is
central to theories seeking to explain the origin of characteristics of
genetic systems, such as sex and recombination (De Visser and Elena,
2007), dominance (Bagheri and Wagner, 2004), ploidy (Kondrashov
and Crow, 1991), phenotypic plasticity (Remold and Lenski, 2004),
robustness (De Visser et al., 2003), the ruggedness of adaptive
landscapes (Weinreich et al., 2006; Poelwijk et al., 2007), or attempt-
ing to mechanistically explain dynamic biological processes such as
the accumulation of mutations in finite populations (Kondrashov,
1994), and speciation by reproductive isolation (Coyne, 1992). Very
recently, the evolutionary causes of epistasis, and not only their
evolutionary consequences, have also attracted attention (Sanjua
´nand
Nebot, 2008; De Visser et al., 2011; Macı
´aet al., 2012).
Broadly speaking, epistatic interactions can be classified as uni- or
multi-dimensional (Kondrashov and Kondrashov, 2001). Uni-dimen-
sional epistasis is defined as deviations from a linear relationship
between mean multiplicative fitness and the number of mutations
affecting fitness. By contrast, multi-dimensional epistasis includes all
the possible individual interactions among a set of mutations. Multi-
dimensional epistasis provides a more complete description of the
interactions within the fitness landscape defined by a set of mutations.
Interactions can be further classified as magnitude or as sign epistasis.
Magnitude epistasis (ME) occurs when that the fitness value
associated to a mutation, but not its sign, changes upon the genetic
background wherein it appears (Weinreich et al., 2005; Poelwijk et al.,
2007). Moreover, ME can be positive or negative, depending on
whether the fitness of the double mutant is larger or smaller than
expected under the multiplicative null model, respectively. ME is a
widespread phenomenon observed in organisms of different complex-
ity (Sanjua
´n and Elena, 2006). Sign epistasis (SE) refers to cases in
which the sign of the fitness effect of a mutation is under epistatic
control; thus, such a mutation is beneficial in some genetic back-
grounds and deleterious in others (Weinreich et al., 2005; Poelwijk
et al., 2007). In few instances where it has been sought, SE seems to be
quite common, although perhaps not as ubiquitous as ME (Weinreich
et al., 2006 Poelwijk et al., 2007; Franke et al., 2011; Kvitek and
Sherlock, 2011).
Epistasis is particularly relevant for our understanding of adaptive
evolution, as it determines the ruggedness of the adaptive landscape
(Withlock et al., 1995; Poelwijk et al., 2011) as well as the accessibility
of adaptive pathways throughout the landscape (Weinreich, 2005
Welch and Waxman, 2005; Franke et al., 2011). Evolutionary
trajectories may end at suboptimal fitness peaks due to the ruggedness
of the fitness landscape. Epistasis can therefore hamper the efficiency
of natural selection and thus slow down the rate of adaptation
(Withlock et al., 1995). Moreover, epistasis can make certain evolu-
tionary pathways towards higher fitness genotypes selectively inacces-
sible because of troughs and valleys in the fitness landscape:
intermediate genotypes have reduced fitness compared with sur-
rounding genotypes. Weinreich et al. (2005) were the first to notice
this evolutionary constraint and to postulate that such limitation
would arise only as a consequence of SE. Indeed, a particular type of
1Instituto de Biologı
´a Molecular y Celular de Plantas, Consejo Superior de Investigaciones Cientı
´ficas-UPV, Vale
`ncia, Spain and and 2Santa Fe Institute, Santa Fe NM, USA
Correspondence: Professor SF Elena, Instituto de Biologı
´a Molecular y Celular de Plantas, CSIC-UPV, Campus UPV CPI 8E, Ingeniero Fausto Elio s/n, 46022 Vale
`ncia, Spain.
E-mail: santiago.elena@csic.es
Received 3 October 2011; revised 21 February 2012; accepted 28 February 2012; published online 11 April 2012
Heredity (2012) 109, 71–77
&
2012 Macmillan Publishers Limited. All rights reserved 0018-067X/12
www.nature.com/hdy
SE known as RSE, that is, the sign of the fitness effect of a mutation is
conditional upon the state of another locus and vice versa, has been
shown to be a necessary condition for an adaptive landscape to be
rugged (Poelwijk et al., 2011).
RNA viruses are ideal experimental systems for exploring the nature
of epistatic interactions: their compact genomes often code for
overlapping reading frames, contain functional RNA secondary
structures and encode for multi-functional proteins. Altogether, these
properties are expected to lead to strong epistasis. Indeed, recent
studies exploring uni- and multi-dimensional epistasis have provided
empirical evidences that ME is common for RNA viruses such as
Foot-and-mouth disease virus (Elena, 1999), bacteriophage f6 (Burch
and Chao, 2004), Vesicular stomatitis virus (VSV, Sanjua
´net al., 2004),
Human immunodeficiency virus type 1 (Bonhoeffer et al., 2004; Van
Opijnen et al., 2006; Parera et al., 2009; Da Silva et al., 2010; Martı
´nez
et al., 2011), Rous sarcoma virus (Sanjua
´n, 2006), or Tobacco etch virus
(TEV; De la Iglesia and Elena, 2007b), among others, as well as for
ssDNA bacteriophages such as ID11 (Rokyta et al., 2011) or fX174
(Pepin and Wichman, 2007). Furthermore, in most of these studies
positive epistasis is more abundant than negative epistasis, although
variability exists within each virus. Positive epistasis may appear as a
consequence of individual mutations having a large negative impact
on fitness such that any additional mutation that still produces a
viable virus must necessarily exert a minor impact (Elena et al., 2010).
SE, by contrast, has been detected only among compensatory
mutations for fX174 (Poon and Chao, 2006) and among pairs of
beneficial mutations for ID11 (Rokyta et al., 2011). By contrast, no
evidence of SE was found for combinations of beneficial mutations in
the RNA bacteriophage MS2 (Betancourt, 2010).
In this study we sought to characterize the patterns of multi-
dimensional epistasis for the RNA plant virus TEV (genus Potyvirus,
family Potyviridae). TEV has a single-stranded positive-sense RNA
genome of ca. 9.5 Kb that encodes for a single polyprotein that self-
processes into 10 mature peptides. An additional peptide is translated
from an overlapping ORF after þ2 frameshifting. To this end, we
generated a collection of 53 double mutants by randomly combining
20 individual mutations whose deleterious fitness effect had been
previously quantified (Carrasco et al., 2007b). The fitness of all single
and double mutants was evaluated in the primary host Nicotiana
tabacum. We characterized the statistical properties of the distribution
of epistatic interactions and found a mixture of positive and negative
effects (including some examples of synthetic lethals (SLs)). Next, we
found that RSE was the most common type of epistasis. We also
explored the negative association between the average fitness effect of
deleterious mutations and the strength of the epistastic interaction in
which they were involved. Finally, we tried to frame the observed
epistatic effects within a model of the protein–protein interaction
network (PPIN) formed by all 11 TEV proteins.
There are many novelties within our study. First, this is the first
description of extensive SE, particularly of the reciprocal type,
contributing to the architecture of fitness of an RNA virus. Second,
we contextualize epistasis in the network of interactions among viral
proteins. Third, it is the first report of epistasis for a eukaryotic virus
in its natural host rather than in in vitro cell cultures, which represent
an artificial and oversimplified environment. Last, but not least, this is
the first analysis of multidimensional epistasis for any plant pathogen.
MATERIALS AND METHODS
Virus genotypes
A subset of 20 mutants non-lethal in N. tabacum (Supplementary
Table 1) was randomly chosen from a larger collection generated in a
previous study (Carrasco et al., 2007b). Six were synonymous
mutations, whereas the rest were nonsynonymous. Plasmid pMTEV
(Bedoya and Daro
`s, 2010) was used to reconstitute the wild-type TEV
and to generate the mutant genotypes. These 20 mutations were
randomly combined to generate a set of 53 double mutants
(Supplementary Table 2) by site-directed mutagenesis using the
QuikChange II XL Site-Directed Mutagenesis Kit (Stratagene, Santa
Clara, CA, USA) as described by Carrasco et al. (2007a). The kit
incorporates PfuUltra high fidelity DNA polymerase that minimizes
the introduction of undesired mutations. The uniqueness of each
mutation was confirmed by sequencing an 800-bp fragment encom-
passing the mutated nucleotide.
Infectious RNA of each genotype was obtained by in vitro
transcription after BglII linearization of the corresponding plasmid
as described in Carrasco et al.(2007a).
Inoculation experiments
All N. tabacum plants were inoculated at an identical growth stage to
minimize variations in defense response to infection with develop-
mental stage. All inoculations were done in a single experimental
block. Nine plants per TEV genotype were inoculated by rubbing the
first true leaf with 5 mlcontaining5mgRNAin vitro transcript of the
virus and 10% Carborundum (100 mg ml 1).
Ten days post-inoculation, the whole infected plant, except the
inoculated leaf, was collected. The collected tissue was frozen in liquid
nitrogen and grounded with mortar and pestle.
RNA purification and virus quantification
An aliquot of approximately 100 mg of grounded tissue was taken and
mixed with 200 ml of e xtraction buffer (0. 2 MTri s , 0 . 2 MNaCl, 50 mM
EDTA, 2% SDS; pH 8). An equal volume of phenol:chloroform:
isoamylic alcohol (25:25:1) was added, thoroughly vortexed and
centrifuged at 14 000 g for 5 min at 25 1C. Ca. 160 ml of the upper
aqueous phase was mixed with 80 ml of a solution containing 7.5 M
LiCl an d 50 mMEDTA and incubated overnight on ice at 4 1C. The
precipitated RNA was centrifuged at 14000 g for 15 min at 41C,
washed once with 70% ice-cold ethanol, dried in a SpeedVac (Thermo
Fisher Scientific, Waltham, MA, USA) and resuspended in 30 mlof
DEPC-treated ultrapure water. RNA concentration was measured
spectrophotometrically and the samples were diluted to a final
concentration of 50 ng ml1.
Within-plant virus accumulation was measured by absolute
RTqPCR using an external standard (Pfaffl, 2004). Standard curves
were constructed using five serial dilutions of TEV RNA produced by
in vitro transcription and diluted in RNA obtained from the host plant.
RTqPCR reactions were performed in 20 ml volume using One
Step SYBR PrimeScript RTPCR Kit II (TaKaRa, Bio Inc, Otsu,
Japan) following the instructions provided by the manufacturer. The
forward TEVCP 50-TTGGTCTTGATGGCAACGTG and reverse
TEVCP 50-TGTGCCGTTCAGTGTCTTCCT primers amplify a 71
nt fragment within the TEV CP cistron. CP was chosen because it is
located in the 30end of TEV genome and hence would only quantify
complete genomes. Each RNA sample was quantified three times in
independent experiments. Amplifications were done using the ABI
PRISM Sequence Analyzer 7000 (Applied Biosystems, Carlsbad, CA,
USA). The thermal profile was: RT phase consisted of 5min at 42 1C
followed by 10 s at 95 1C; and PCR phase of 40 cycles of 5 s at
95 1C and 31 s at 60 1C. Quantification results were examined using
SDS7000 software v. 1.2.3 (Applied Biosystems).
For each genotype, a Malthusian growth rate per day was
computed as m¼(1/t)log(Qt), where Qtare the pg of TEV RNA
Epistasis in RNA genomes
JLalic
´and SF Elena
72
Heredity
per 100 ng of total plant RNA quantified at t¼10 days post-
inoculation. Absolute fitness was then defined as W¼em(Crow and
Kimura, 1970).
Estimation of epistasis among pairs of mutations
Epistasis among pair of mutations xand y,Exy, was calculated as
Exy ¼W00Wxy Wx0W0y(Kouyos et al., 2007), where W00,Wxy,Wx0,
and W0ycorrespond to the absolute fitness of the wild-type, the
double mutant and each single mutant, respectively. A value of Exy40
corresponds to the case of positive (antagonistic) epistasis, whereas a
value of Exyo0 is indicative of negative (synergistic) epistasis. Values
of Exy not significantly deviating from zero were qualified as multi-
plicative (that is, non-epistatic) mutational effects.
In all cases, reported error intervals correspond to ±1 s.e.m. All
statistical analyses were performed using IBM SPSS v. 19 (Armonk,
NY, USA).
RESULTS
Epistasis among pairs of deleterious mutations
Figure 1 shows the relationship between observed and expected fitness
values for the set of 53 double mutant genotypes synthetized for this
study (Supplementary Table 2). The solid line represents the null
hypothesis of non-epistatic fitness effects. The observed fitness values of
20 double mutant genotypes significantly departed from this null
expectation (Supplementary Table 2; t-tests, in all cases Pp0.049).
Nine of these cases were SLs, which means that two mutations that
were viable by themselves become lethal when combined. These SLs
represent an extreme case of negative epistasis. All other significant
cases corresponded to positive epistasis. Therefore, we found variability
in the sign and strength of epistasis. However, only the nine SLs
remained significant after applying the more stringent sequential
Bonferroni correction for multiple tests of the same hypothesis (Rice,
1989). Nonetheless, for all analyses presented below, we used all 20
significant cases, unless otherwise indicated. This decision represents a
compromise between reducing the data set to only the nine SLs (which
precludes running any additional analysis) and using the whole data set
irrespective of the significance of observed fitness values.
Three double mutants contained two synonymous mutations, 22
combined one synonymous and one nonsynonymous mutation and
28 carried two nonsynonymous mutations. No differences existed,
however, in the magnitude of epistasis among these genotypic classes
(Kruskal–Wallis test: w2¼0.405, 2 df, P¼0.817).
Using the whole data set, we sought to test whether the distribution
of SL and viable mutations were homogeneous among pairs of
mutations within the same cistron or among affecting different
cistrons. In two out of nine SLs both mutations were at the same
cistron (22.2%), whereas in the case of viable double mutant
genotypes, only one genotype out of 44 had both mutations in
the same cistron (2.3%), a significant difference (w2¼5.569, 1 df,
P¼0.018) despite the small sample size. Furthermore, the average
epistasis coefficient computed for mutations within the same cistron
was 1.142±0.617, whereas it was reduced to 0.171±0.090 for
pairs of mutations affecting different cistrons. This 85.1% relaxation
in the strength of epistasis was also significant (t51 ¼2.477, P¼0.017).
Therefore, we can conclude that a tendency exists for mutations
affecting the same cistron to generate a SL phenotype and to interact
in a stronger and more negative manner, whereas mutations affecting
different viral proteins presented weaker interactions.
Statistical properties of the epistasis distribution
Figure 2 illustrates the distribution of epistasis parameters for all pairs
of point mutations analyzed. The distribution had a bimodal shape,
with SLs representing the left probability mass and the viable
genotypes being on the right side of the distribution. The average
epistasis was
E¼0:226 0:095, a value that departs from the null
hypothesis of multiplicative effects (t52 ¼2.376, P¼0.021). Further-
more, the distribution had a significant negative skewness
(g1¼1.806±0.327; t52 ¼5.515, Po0.001), that is, the tail contain-
ing negative epistasis is heavier than the Gaussian and thus asym-
metric. Similarly, the distribution was significantly leptokurtic
(g2¼1.326±0.644, t52 ¼2.058, P¼0.045), indicating that it had a
more acute peak around the mean value compared with the Gaussian.
Given that lethal mutations are largely irrelevant for evolutionary
dynamics, we sought to reanalyze the epistasis distribution
after removing SLs. The main consequence of this removal was
that the average epistasis then becomes significantly positive
(
E¼0:084 0:005; t43 ¼17.438, Po0.001). Regarding the shape of
the distribution, it still remained asymmetric with significant negative
Figure 1 Relationship between observed and expected multiplicative fitness
for 53 TEV genotypes carrying pairs of nucleotide substitutions. The solid
line represents the null hypothesis of multiplicative fitness effects.
Deviations from this line arise as a consequence of the existence of
epistatic fitness effects.
Figure 2 Distribution of epistasis. Epistasis, E, was computed as the
difference between the observed fitness of the double mutant (W00Wxy)and
the value expected from subtracting the effects of each single mutant from
the wild-type value (Wx0W0y).
Epistasis in RNA genomes
JLalic
´and SF Elena
73
Heredity
skewness (g1¼1.050±0.358; t43 ¼2.936, P¼0.005), although the
skewness parameter was 41.9% smaller than when SLs were included
in the data set. In contrast, the distribution became 77.1% more
leptokurtic (g2¼2.348±0.702, t43 ¼3.346, P¼0.002), as a conse-
quence of the removing the cases from the left tail extreme.
Pervasive RSE
We were interested in evaluating the extent to which SE was present in
our data set. Poelwijk et al. (2011) defined mathematically the
condition for SE as
jWx0W00 þWxy W0yjojWx0W00jþjWxy W0yj
Twelve out of the twenty TEV double-mutant genotypes for which we
had detected significant epistasis (Supplementary Table 2) fulfilled
this condition and thus can be classified as cases of SE. The other
eight, hence, correspond to cases of ME. Is this 3:2 proportion
expected given the observed fitness values of individual mutations
and of the double mutants? To tackle this question we applied the
above inequality to the 33 non-epistatic pairs of mutations, founding
that 26 fulfilled it, despite not being significant. A Fisher’s exact test
failed to detect significant differences among epistatic and non-
epistatic pairs fulfilling the inequality (1-tailed P¼0.124), thus
confirming that the observed proportion of ME and of SE was not
significantly enriched in the later class. Therefore, we conclude that SE
makes a major contribution (60%) to all cases of significant epistasis.
Next, we specifically evaluated the contribution of RSE to the
observed pattern of SE. According to Poelwijk et al.(2011)the
following additional condition must be met by a pair of mutations
showing SE in order to be considered as cases of RSE:
jW0yW00 þWxy Wx0jojW0yW00jþ jWxy Wx0j:
Herewith, this condition was fulfilled by 11 out of 12 cases of SE
(91.7%). Indeed, only synthetic lethal PC6/PC76 did not match it. As
before, given the fitness of single and double mutants, we tested
whether this extremely high prevalence of RSE among cases of SE is to
be expected. We counted the number of cases that fulfilled this second
inequality (25) among the 26 non-epistatic cases that matched the
first one. A Fisher’s exact test also showed no significant enrichment
in cases of RSE among cases of SE (1-tailed P¼0.538). Therefore, we
conclude that RSE is common in TEV genome.
Correlation between fitness effects and epistasis
It has been observed that average deleterious mutational effects and
the strength of positive epistasis are not independent parameters but,
instead, are negatively correlated (Wilke and Adami, 2001 You and
Yin, 2002; Wilke et al., 2003; Bershtein et al., 2006; Sanjua
´net al.,
2006; De la Iglesia and Elena, 2007). We sought to investigate if this
negative relationship holds for TEV. Figure 3 shows the relationship
between the mean fitness of the two mutations combined and the
estimated epistasis for all 53 double mutants. A first observation is
that two different and significant relationships exist in correspondence
to different phenotypic classes: one for the nine SLs (Spearman’s
rS¼1.000, 7 df, Po0.001) and another one for the viable genotypes
(rS¼0.416, 42 df, P¼0.005). However, overall a significant
negative correlation existed after controlling for the difference within
two phenotypic classes (partial r¼0.331, 50 df, P¼0.017). The
slope for the viable genotypes was significantly smaller than the slope
for the SLs (analysis of covariance test for the homogeneity of slopes
in Figure 3: F1,49 ¼9.212, P¼0.004), suggesting that the underlying
mechanisms for the observed relationships were different for each
phenotypic class. Indeed, the correlation observed for the SLs is trivial
because it is expected based on the definition of epistasis used here.
If the observed fitness of the double mutant is Wxy ¼0, then
Exy ¼Wx0W0y¼W
2,whereW
is the geometric mean fitness
of mutations xand y. The validity of this explanation was confirmed
by the fact that linear regression throughout the origin of epistasis on
W
2for the SLs data rendered the expected slope of 1.000±0.000.
These correlations suggest that mutational effects and epistasis are
not independent traits, but instead, they may evolve hand in hand.
Stronger mutational effects are associated with more positive inter-
actions, whereas milder effects are associated with more relaxed
positive interactions. Therefore, a reduction in the magnitude of
mutational effects translates into a relaxation of the positive epistasis.
Epistasis in the context of TEV PPIN
Mutations were grouped according to the mature protein they affect.
By doing so, we focused on the analyses of interaction among proteins
rather than among individual nucleotide residues. Rodrigo et al.
(2011) inferred the undirected PPIN shown in Figure 4 using a
compendium of physical interactions experimentally determined by
the yeast two-hybrid method. We were interested in correlating the
network properties with the characteristics of the distribution of
epistasis inferred in this study.
First, we sought to test whether the number of significant epistatic
and non-epistatic interactions was homogeneously distributed among
pairs of proteins directly linked in the PPIN graph or unlinked
(Figure 4). A Fisher’s exact test failed to reveal a significant association
(P¼0.151), thus rejecting the hypothesis that a direct interaction
between two proteins is a necessary condition to generate significant
epistasis.
Second, we explored whether the number of pairs of proteins
involved in positive and negative epistatic interactions was evenly
distributed among pairs directly connected in the PPIN and those that
are not (Figure 4). It has been argued for modularly organized PPINs
that mutations affecting independent modules would show a pattern
of positive epistasis, although PPINs organized as a single-functional
module would be more sensitive to the effect of mutations and show a
pattern of negative epistasis (Sanjua
´n and Elena, 2006; Sanjua
´nand
Nebot, 2008; Macı
´aet al., 2012). In agreement to this expectation,
Figure 3 Association between average mutational effects and the magnitude
of epistasis. Two apparent relationships exist: one for pairs of mutations
generating viable genotypes (upper cloud) and a different one associated to
the SLs (lower cloud). The regression lines are included to illustrate the
difference in the underlying relationship between epistasis and average
mutational effects between both types of phenotypes.
Epistasis in RNA genomes
JLalic
´and SF Elena
74
Heredity
we found that nine out of fourteen (64.3%) positive interactions
between linked elements, whereas only two out of six (33.3%)
interactions between unlinked elements were positive. Thus, we
conclude that mutations affecting connected elements in the PPIN
tend to be involved in more positive epistatic interactions than those
affecting non-connected components.
Finally, we hypothesized that highly linked nodes would have a
stronger tendency to be epistatic, whereas less connected nodes will be
less so. To test this hypothesis, we first computed the tendency of a
protein to be involved in significant epistasis interactions (that is,
epistasiness) for each protein as the absolute value of the average
epistasis coefficient computed across all interactions in which this
protein was involved and using the whole data set. Absolute values
were used because we tested for the tendency for involvement in
significant interactions regardless of their sign. Then, we computed
the connectivity of each node as the number of links it has in
Figure 4. A non-parametric correlation coefficient failed to detect a
significant association between these two variables (rS¼0.221, 6 df,
P¼0.599). Therefore, we conclude that the tendency of a protein to
be involved in epistatic interactions is not a necessary consequence of
the amount of interactions itself.
DISCUSSION
In this study, the distribution of epistatic interactions on fitness for a
plant RNA virus has been evaluated by constructing genotypes
carrying pairs of single-nucleotide substitutions, each having a
deleterious fitness effect. We detected cases of both positive and
negative epistasis, although positive epistases were significantly more
abundant than negative ones, such that the combined effect of
mutations is significantly less harmful than expected under the null
multiplicative model. This dominance of positive epistasis is particu-
larly frequent among mutations affecting two different proteins,
whereas, on average, mutations affecting the same protein interact
in a negative manner. These findings are in good agreement with
observations accumulated in recent years for other RNA viruses,
including retroviruses, and small ssDNA viruses (reviewed by Elena
et al., 2010), both using experimental approaches to characterize uni-
and multi-dimensional epistasis. Given this heterogeneity in viral
systems, it thus seems highly likely that positive epistasis among
deleterious mutations is a general feature of most small viruses. What
may be the mechanistic reason for this excess of positive epistasis?
Several reasons can be brought forward. First, the compactness of
viral genomes, many of which even had adopted the strategy of
overlapping genes and multifunctional proteins, necessarily implies
that the deleterious effects of different mutations can partially overlap,
hence producing positive epistasis. Indeed, this expectation is well
fulfilled by our finding that interactions are, on average, more positive
when mutations occur in two different proteins than when they both
occur in the same one. Second, positive epistasis can also be a
consequence of the existence of elements of RNA secondary structure.
Indeed, it was shown by computer simulations of RNA folding that
mutations affecting the same structural element may restore it and
thus generate positive epistasis (Wilke et al., 2003 Sanjua
´net al.,
2006). Another observation that seems to be common among
experiments of multi-dimensional epistasis in RNA viruses is the
existence of frequent cases of synthetic lethality, for example, for the
mammalian rhabdovirus VSV (Sanjua
´net al., 2004).
The dominance of positive epistasis among deleterious mutations
and the existence of frequent cases of synthetic lethality are both
fingerprints of another phenomenon: the low genetic robustness of
viral genomes. It has been postulated that epistasis and robustness are
two sides of the same coin and that negative epistasis must be a
hallmark for genetic robustness (De Visser et al., 2003, 2011; Proulx
and Phillips, 2005; Desai et al., 2007). Indeed, the observed negative
correlation between epistasis and mutational effects shown in Figure 3
provides additional support for this hypothesis and is consistent with
observations made in systems as diverse as artificial life (Wilke and
Adami, 2001; Edlund and Adami, 2004), computer simulations of
genetic systems (You and Yin, 2002; Macı
´aet al., 2012), RNA (Wilke
et al., 2003; Sanjua
´net al., 2006) and protein folding (Bershtein et al.,
2006), and in a mutation–accumulation experiment done with TEV
NIa
Pro
6K1
P3
VPg
6K2
HC
Pro
NIb
CP
PIPO
CI
P1
Figure 4 TEV PPIN inferred from yeast two-hybrid data published
elsewhere. The 11 mature peptides are indicated as nodes. Black edges
correspond to interactions for which we did not detect significant epistasis.
Red edges correspond to cases of negative epistasis and green edges
correspond to cases of positive epistasis. Double green lines correspond to
two pairs of mutations affecting the same proteins. The dashed blue line
corresponds to a case in which a first pair of mutations showed positive
epistasis (PC19/PC95) but a second pair had negative epistasis (PC22/
PC95). The PPIN was drawn using Cytoscape (Killcoyne et al., 2009).
Epistasis in RNA genomes
JLalic
´and SF Elena
75
Heredity
(De la Iglesia and Elena, 2007). The negative correlation between
epistasis and mutational effects means that the milder the
average mutational effect is, the more negative the epistatic interac-
tions between mutations will be. This results in a genotype
that is more mutationally robust against genetic perturbations. In
contrast, positive epistasis reflects strong mutational effects
and, therefore, low genetic robustness. Sanjua
´n and Elena (2006)
postulated that robustness would scale up with genetic complexity
and that it may result from the fact that more complex genetic
systems may contain more redundant structures capable of buffering
the effect of mutations. Very recently, Macı
´aet al. (2012) tested this
hypothesis by simulating the evolution of genetic circuits under
variable selection for robustness. They found that, as predicted,
negative epistasis was caused by the existence of genetic redundancy
in complex networks and not due to complexity itself, as
the correlation disappeared when the formation of redundant
structures was not allowed during the evolution of complex networks.
In this sense, RNA viruses will occupy the lower side of the
complexity spectrum and, therefore, would be highly sensitive (that
is, non-robust) to mutations.
Within cases of significant epistatic interactions, we found a large
contribution of SE relative to the contribution of ME. This represents
the first description of SE for an RNA virus, as previous studies of
multi-dimensional epistasis in RNA viruses did not explicitly look for
SE (for example, Bonhoeffer et al., 2004; Sanjua
´net al., 2004; Sanjua
´n,
2006; Van Opijnen et al., 2006) or simply failed to find them
(Betancourt, 2010). In contrast, SE has been shown to be common
during adaptation of b-lactamase to cefotaxime (Weinreich et al.,
2006; Salverda et al., 2011), in evolution experiments compensating
for the cost of antibiotic resistance in bacteria (Schrag et al., 1997;
Maisnier-Patin et al., 2002) and viruses (Molla et al., 1996; Cong et al.,
2007; Martı
´nez-Picado and Martı
´nez, 2009), in experimental evolu-
tion of asexual Saccharomyces cerevisiae (Kvitek and Sherlock, 2011),
and in multi-dimensional tests of epistasis in Aspergillus niger (Franke
et al., 2011). All but one cases of SE detected in TEV corresponded to
RSE, perhaps making this observation even more interesting. This
type of epistasis is particularly relevant from the perspective of
describing fitness landscapes. Poelwijk et al. (2011) have shown that
the existence of multiple adaptive peaks in a fitness landscape, that is,
ruggedness, requires RSE. Furthermore, Kwitek and Sherlock (2011)
experimentally confirmed that RSE caused the ruggedness of a fitness
landscape. The ruggedness of adaptive landscapes is critical to predict
whether evolving populations may reach the global optima or may get
stuck into suboptimal peaks (Weinreich, 2005; Withlock et al., 1995).
Our finding of a predominance of RSE suggests that the fitness
landscape for TEV, and maybe for other RNA viruses, must be highly
rugged.
In conclusion, the results reported here, together with previous
findings, contribute to the perspective that viral genomes are
dominated by positive epistasis, which may result from their
compactness and lack of genetic redundancy. In addition, we provide
the first direct proof that SE, in particular RSE, contributes in a large
extent to the architecture of viral fitness. The high frequency of RSE
suggests that adaptive landscapes for RNA viruses maybe highly
rugged. This ruggedness may impose harsh constraints on the
often-invoked but not empirically grounded limitless adaptability
of RNA viruses.
DATA ARCHIVING
Data have been archived at Dryad: doi: 10.5061/dryad.bq4pp7f9.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
ACKNOWLEDGEMENTS
We thank Francisca de la Iglesia and A
`ngels Pro
`sper for their excellent technical
assistance, Ste
´phanie Bedhomme and Mark P Zwart for the discussion and
Mario A Fares for statistical advice. Jose
´ADaro
`s generously gifted us the
pMTEV plasmid. This research was supported by the Spanish Ministry of
Science and Innovation grant BFU2009-06993 to SFE. JL was supported by the
JAE program from CSIC.
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