Genetic complementation fosters evolvability in complex fitness landscapes

doi:10.21203/rs.3.rs-2111456/v1

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Genetic complementation fosters evolvability in complex fitness landscapes

https://doi.org/10.21203/rs.3.rs-2111456/v1

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published 12 Jan, 2023

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The ability of natural selection to optimize traits depends on the topology of the genotype-fitness map (fitness landscape). Epistatic interactions produce rugged fitness landscapes, where adaptation is constrained by the presence of low-fitness intermediates. Here, we used simulations to explore how evolvability in rugged fitness landscapes is influenced by genetic complementation, a process whereby different sequence variants can compensate for their deleterious mutations. We designed our model inspired by viral populations, in which genetic variants are known to interact frequently through coinfection. Our simulations indicate that genetic complementation enables a more efficient exploration of rugged fitness landscapes. Although this benefit may be undermined by genetic parasites, its overall effect on evolutionary fitness remains positive in populations that exhibit strong relatedness between interacting sequences. Similar processes could operate in contexts other than viral coinfection, such as in the evolution of ploidy.

fitness landscape

evolvability

genetic complementation

viral evolution

epistasis

Evolvability is determined by the topology of the genotype-fitness map (fitness landscape) among other factors such as populations size, reproduction mode, and mutation rate. The topology of fitness landscapes depends on gene-by-gene interactions (epistasis) [1–4]. With no epistasis, gradual evolutionary optimization is straightforward because selection can operate on a succession of mutations, each conferring a certain fitness benefit independent of other mutations. In contrast, when traits are encoded by multiple genes exhibiting abundant epistasis, maladaptive allele combinations introduce low-fitness valleys between local fitness peaks. Such ruggedness represents a major obstacle to evolutionary optimization, since natural selection per se cannot favor intermediate, low-fitness states. Evidence from experimental evolution and mutant analysis studies suggests that epistasis is widespread and that, consequently, most fitness landscapes are rugged [5–8]. Epistasis among variants within a gene (conformational epistasis) may also play a key role in protein evolution [9].

Several explanations have been put forward to explain how populations evolve in complex fitness landscapes. First, with sufficiently high mutation rates, certain combinations of favorable alleles could appear simultaneously, avoiding low-fitness intermediates [10]. However, high mutation rates are unlikely to supply complex adaptations in a single step, and would strongly increase mutational load, which in turn burdens adaptation. Second, episodes of random genetic drift may allow populations to traverse local fitness valleys and subsequently reach new fitness peaks under the action of natural selection [11]. Third, theoretical and experimental work has revealed that organisms exhibit certain features, such as genetic redundancy and other mutational tolerance mechanisms that favor the accumulation of cryptic genetic variation in populations. These conditionally neutral variants can promote evolvability, as they may become beneficial upon changes in genetic background or environment [12, 13]. It has also been shown that the very action of natural selection can promote evolvability by increasing mutational robustness [14]. Finally, mechanisms of phenotypic plasticity, stochastic gene expression, epigenetics and resiliency under adverse conditions (e.g. latency) might facilitate the evolution of novel traits in the long term [1]. However, the genetic and population-level determinants of evolvability remain to be investigated further.

Here, we examine whether complementation between different genetic variants can foster evolvability in rugged fitness landscapes. To implement this, we simulated populations in which mutants can compensate for their genetic defects, promoting their joint survival. We contextualized our hypothesis within the field of viral evolution. This allowed us to investigate the implications and limitations of the model in a specific type of biological system, although the model should in principle be applicable to other systems. There are at least two reasons why viruses should be particularly prone to genetic complementation. First, they typically exhibit extremely high levels of sequence diversity as a result of their high rates of spontaneous mutation [15–17]. Second, coinfections are relatively frequent [18–20]. Coinfection of a given cell with multiple viral particles of the same species can occur when there is a high local ratio of viral particles to susceptible cells (multiplicity of infection), but also when groups of viral infectious particles are jointly transmitted [21]. An example of the later process is provided by extracellular vesicles containing multiple viral particles [22], which have been described in widely different types of viruses including hepatitis A virus [23], enteroviruses, [24], rotaviruses [25], noroviruses [25], and marseilleviruses [26]. Baculovirus occlusion bodies constitute another example of collective viral spread in which transmission between insect hosts involves pools of nucleocapsids embedded in polyhedrin protein crystals [27]. Yet another example of collective viral spread is aggregation of infectious particles in the extracellular milieu, which has been described in different types of viruses including human immunodeficiency virus particles in semen [28], and vesicular stomatitis virus particles in saliva [29]. The delivery of multiple genetic variants of a given virus to the same cell allows different viral genomes to share gene products in cooperative or antagonistic manners [30].

Our simulations show that genetic complementation can increase evolvability by allowing low-fitness allele combinations to be maintained in the population. In this way, populations gain the ability to traverse fitness valleys and reach high-fitness peaks that are not directly accessible through a simple mutation-selection process. We also investigate the stability of this form of cooperation in the face of genetic parasites and find that, although parasites severely undermine population fitness and evolvability, this interference can be overcome in highly structured populations where interactions take place preferentially among genealogically related sequences.

Basic mutation-selection model. We developed a stochastic model in which haplotypes or sequences consist of a binary string of length N. Each string had a certain probability of being selected for the next generation depending on its fitness (selection), and each of the N positions had probability α of undergoing mutation by changing '0' to '1', or vice versa (mutation). The population was composed of S sequences (typically 10,000), and in each generation S new sequences were selected by drawing S times a sequence from the population. In each draw, a sequence i had a probability \({P}_{i}={f}_{i}/\sum _{j}^{S}{f}_{j}\) of being selected, where f_i is fitness. Thus, each sequence had a probability \(1-{(1-{f}_{i}/\sum _{j}^{S}{f}_{j}) }^{S}\)of contributing to the next generation. The fitness of each sequence was obtained from a predefined fitness landscape, which assigned a fitness value to each binary string.

The simplest scenario considered was a two-locus system with two alleles per locus. In this model, there were only four possible sequences: 0–0, 0–1, 1 − 0 and 1–1 (Fig. 1A). To introduce epistasis, we defined a landscape with a local maximum in the 0–0 sequence and a global maximum in the 1–1 sequence. These two peaks were separated by the low-fitness states 1 − 0 and 0–1. To generalize the model for multiple loci and epistasis, we used random NK models [31]. These fitness landscapes are built randomly, creating sequences of length N with K epistatic interactions. The fitness values of the sequences are calculated as a random contribution of each of each of the N positions, depending not only on its state (0 or 1), but also on the state of other K random positions. K = 0 produces a purely additive (non-epistatic) fitness landscape, while K = N − 1 produces a random landscape with zero correlation between neighboring sequences (House of Cards). Increasing K increases the ruggedness of the landscape and, consequently, the number of local peaks.

Genetic complementation. To allow for genetic complementation, we created groups of sequences and calculated a shared fitness value for each group. Specifically, the population of S sequences was structured in S/m of groups of size m. We used different rules to calculate the fitness value associated to groups. First, we considered classical genetic trans-complementation, whereby genes from different genomes share their gene products to generate a phenotype. For this, we obtained all possible allele combinations produced by the sequences in a group, and used the same predefined fitness landscape used in the basic selection model to calculate the fitness of each of these combinations. For example, in groups of size two formed by sequences (1–1) and (0–0) there are four possible allele combinations: (1–1), (1 − 0), (0–1) and (0–0). Group fitness was calculated in two alternative ways: as the fitness of the fittest allele combination present in the group (Fig. 1B), or as the average fitness of all these allele combinations (Fig. 1C). The former represents a scenario in which a single copy of a gene is sufficient to recover the fittest phenotype (full trans-complementation), while the latter represents a system in which gene dosage determines the phenotype (average trans-complementation).

We also modeled a different type of complementation, in which the elements of a sequence did not represent different loci, but different positions within a locus (nucleotides, amino acids, cis-acting elements). We called this ‘cis-complementation’. Here, we no longer considered all possible combinations of alleles, but only each actual sequence present in a group (Fig. 1D-E). Conformational epistasis [9] is an example of this situation, where a single gene must accumulate several mutations to produce a protein with a different phenotype. As in the previous case, the fitness of the group was calculated as the fitness of the fittest sequence (full cis-complementation, Fig. 1D), or as the average of all sequences within the group (average cis-complementation, Fig. 1E).

Selection in the presence of complementation. When groups of complementing sequences were defined, we calculated the fitness of each sequence in a two-step manner. First, we computed the fitness of each group (f_g) using the above-defined rules. The probability that a group contributed to the next generation was \({P}_{g}={f}_{g}/\sum _{j}^{G}{f}_{j}\). Then, we obtained the fitness of each individual sequence within a group. In the absence of intra-group conflict, all group members had the same fitness, meaning that the probability of an individual i within group g of being selected in each draw was \({P}_{i|g}=\frac{1}{m}{P}_{g}\). In contrast, in the presence of genetic parasites, some sequences took more benefits from the group than others, thus behaving as social cheaters in a cooperative system. Specifically, the probability that individual i within group g was drawn became \({P}_{i|g}={{P}_{g}\beta }_{i}/\sum _{k}^{m}{\beta }_{k}\). In all simulations we used β_i = 1 for ‘normal’ (helper) sequences and β_i = 5 for genetic parasites. The simulations started with a population of helper sequences but, in each generation, helpers had a certain probability of mutating to parasites (α_C). In viruses, genetic parasites often consist of genomes with large deletions [32–33], called defective interfering particles, which are unable to successfully infect cells on their own or to complement deleterious genes from other viruses. To capture these features, we made the following assumptions: first, fitness was null for groups containing parasites only. Second, parasitic sequences contained a random number of deleted genes (starting by the first position, we deleted the first I genes, being I a uniformly distributed integer between 1 and N) such that, for these genes, the parasites were unable to complement deleterious alleles present in other sequences.

Simulations were implemented in MATLAB R2018. Scripts are available upon request to E. S-O. ([email protected]).

Genetic complementation promotes evolvability in a two-sequence, two-locus, two-allele model. We started our simulations with the simplest possible case, a two-locus, two-allele system with groups of two sequences, which yields four possible individual sequences (0–0, 1 − 0, 0–1 and 1–1) and 10 different sequence pairs. This will be later generalized to multiple loci and larger groups. To investigate how evolutionary optimization takes place in the presence of deleterious intermediates, we assigned each of the four sequences the following fitness values: f₁₁ = 1, f₀₀ = 0.2 and f₁₀ = f₀₁ = 0 (Fig. 2A). The population initially consisted exclusively of 0–0 sequences, and we explored whether the 1–1 sequence was reached by a stochastic mutation-selection process. In the absence of interactions between different sequences, this could only occur via a double mutation transforming directly 0–0 into 1–1, since each mutation alone was lethal. To introduce complementation, we formed random pairs of individuals and allowed them to mutually compensate for their genetic defects (trans-complementation; Fig. 1B-C). We first assumed that the fitness of a pair was determined by the best allele present in each gene (full trans-complementation; Fig. 2B). For instance, pairs of 0–1 and 1 − 0 sequences would rescue both, allowing each of them to reach a fitness value equal to f₁₁. As intuitively expected, we found that this form of cooperation accelerated evolutionary optimization in this simple model (Fig. 2C-D).

Genetic parasites undermine complementation but can be avoided in highly structured populations. We then allowed for genetic parasites, which function as social cheaters. Similar to defective interfering particles in viruses, these were defined as sequences that contained large deletions and hence contributed only partially to complementation (defective), but in addition took a greater share of group-associated fitness to the detriment of helper sequences (interfering). Simulations were started from 0–0 sequences capable of complementing but, in each generation, helper sequences had a certain probability of mutating to parasites. We found that parasites took over the population, and that this had a negative impact on mean population fitness at endpoint (Fig. 3A-C). Initially, trans-complementation fostered evolvability, but this effect was lost after the emergence of parasites. To try to avoid parasite invasion, we restricted interactions to progeny derived from a common parental pair of sequences. We will refer to this type of structure as “kin groups”, as opposed to the randomly formed groups explored above. This describes the way viruses are transmitted using extracellular vesicles or occlusion bodies, as well as direct cell-to-cell viral spread using specialized structures such as viral synapses or plasmodesmata [34]. In all these types of collective spread, group members are viral particles derived from the same producer cell. Unless cells are coinfected with multiple such groups or free viral particles, the resulting virus-virus interactions are necessarily restricted to progeny derived from a common parental group. We found that this type of structure increased genetic relatedness enough to prevent parasite invasion, yet allowed trans-complementation to promote evolvability (Fig. 3D-F). Complementation was revealed by an increase in intra-group genetic diversity, which was concomitant to an increase in population fitness. This diversity peak was nevertheless transient, since intra-group diversity regained low values after this episode, leaving fewer opportunities for parasites to take over the population.

Partial trans-complementation can also promote evolvability. In the above simulations, we assumed that genetic defects were fully compensated in trans. Here, we assumed a different situation (average trans-complementation), in which the fitness assigned to a given group (here, a pair) was equal to the average fitness of all allele combinations in the group. For instance, in interactions involving 0–1 and 1 − 0 sequences, the resulting fitness was equal to the average of fitness values for 0–1, 1 − 0, 0–0, and 1–1 sequences whereas, in the full trans-complementation model, the resulting fitness was determined solely by the fittest possible combination 1–1. We found that evolvability was substantially reduced compared to the full trans-complementation case, but that partial complementation still had a positive effect on evolvability (Figure Supplementary S1 online).

Cis-complementation can also foster evolvability. Next, we explored a different type of interaction that could be relevant in the context of conformational epistasis. This form of epistasis takes place when one or more sites in the sequence of a protein affect the stability of other sites [13]. Since this concerns variants of a given locus, effects in trans are excluded. Thus, the main difference between this situation and the previous complementation model is that, here, the fitness of the group was a function of the individual performance of each existing sequence, rather than being determined by all combinations of alleles present in different sequences. As above, we allowed for genetic parasites but assumed that populations were structured in kin groups. We first considered a situation in which the fitness value associated with a group was equal to the fitness of the best sequence present in the group (full cis-complementation), and found that under these conditions, evolvability was promoted (Fig. 4).

We then considered a different model in which the fitness value assigned to a group was equal to the average of the fitness values of each group member (average cis-complementation). Similar to what we found with trans-complementation, the positive effects on evolvability were reduced, but still observed (Supplementary Figure S2 online).

Effects of genetic complementation in a multi-locus epistatic model. To generalize our analysis, we used random NK models [31] to create arbitrary fitness landscapes from N genes and K gene-gene interactions. We also considered groups of m > 2 individuals, using the same complementation rules as above. In these simulations, genetic parasites were also allowed but populations were structured in kin groups. We changed K (which determines the ruggedness of the fitness landscape) keeping gene number constant (N = 5). As expected, increasing K generally hampered evolutionary optimization. With minimal epistasis (K = 1), full trans-complementation was slightly detrimental for evolvability (Fig. 5A-B). In contrast, in landscapes with abundant epistasis (K = 4), full trans-complementation strongly favored evolvability despite the presence of parasites (Fig. 5C-D). This effect became more pronounced as group size increased, since mutual compensation of genetic defects was more likely in larger groups. However, this positive effect on evolvability was reversed when trans-complementation was only partial (average trans-complementation; Supplementary Figure S3 online).

We also applied the NK model to the cis-complementation case. As above, we allowed for parasites but restricted interactions to progeny derived from the same parental group. We found that evolvability was also fostered by this type of complementation, but that this effect strongly depended on whether group-associated fitness was determined by the fittest sequence (full cis-complementation; Figure S4 online) or by the average fitness value of all group members (average cis-complementation; Supplementary Figure S5 online). Since these results are qualitatively similar to those obtained with the trans-complementation model, we conclude that the main benefit of sequence interactions did not lie in the ability to explore novel allele combinations. Instead, we suggest that complementation fostered evolvability by allowing low-fitness sequences to be maintained in the population, thereby promoting the ability of the population to traverse rugged fitness landscapes.

Trans-complementation fosters exploration of rugged fitness landscapes. To better understand how complementation promoted evolvability, we inspected evolutionary trajectories in the rugged fitness landscape (N = 5, K = 4). As above, in these simulations we allowed for parasites, but populations were structured in kin groups. We plotted mean population fitness among the 150-generation simulation as a function of the Hamming distance to the highest-fitness sequence (global optimum), starting each simulation from a different position in the landscape. In the absence of interactions (m = 1), populations were systematically trapped in local sub-optimal peaks (Fig. 6). In contrast, when full trans-complementation was allowed, populations reached maximal fitness. This positive effect was accentuated as group size, m, increased. However, the Hamming distance to the optimum was not always zero, since adaptation was achievable by a set of sequences that mutually compensated for their genetic defects. This indicates that trans-complementation allowed exploration of complex fitness landscapes by buffering the deleterious effects of mutations. Hence, the downside of this enhanced evolvability was a higher abundance of conditionally deleterious mutations at equilibrium. Systematic analysis of all possible single mutations at endpoint confirmed that, in populations displaying trans-complementation, mutations had much lower fitness effects than in populations of non-interacting sequences, as expected in a relaxed selection scenario.

We have shown through simulations that a form of cooperation based on compensation of genetic defects promotes adaptability by enhancing the capacity of the population to explore complex fitness landscapes. While the population and genetic factors that determine the evolutionary stability of cooperation have been extensively investigated [35–44], how cooperation feeds back on evolvability is a less explored topic. Moreover, many previous studies have analyzed how populations evolve in complex fitness landscapes [1–3, 10–14], but these have not addressed how cooperation among genetically distinct individuals determine evolvability. Only in [45] is this connection explored, where the authors determine that cooperation by public goods enhances adaptability by promoting peak shifts in complex fitness landscapes. Our results continue this line, showing a clear connection between cooperation and adaptability.

A fundamental question in evolutionary biology is how cooperation can be favored by natural selection despite the costs associated with helping others. This conundrum has been addressed by different approaches, introducing kin and group selection, and solved using frequency-dependent selection models and game theory, among other approaches [35–42]. In general, for cooperation to be evolutionarily stable, cheaters need to be segregated from cooperative interactions. In the absence of such assortment, cheaters are favored by selection because they reap the benefits of cooperation but do not pay the costs of helping others. Cheater invasion leads to a reduction of mean population fitness and, potentially, to loss of the cooperative trait. Both kin and group selection models show that cheater invasion can be avoided when interactions occur preferentially between genetically related individuals. Not surprisingly, we also found that genetic complementation was severely jeopardized by genetic parasites, and that restricting interactions to groups of genealogically related sequences could prevent parasite invasion. Intuitively, it may be expected that, by increasing relatedness, such assortment should also curtail the benefits of genetic complementation for adaptation in rugged landscapes. However, interestingly, our model shows that there is room to avoid parasites while still promoting evolvability.

In our simulations, we have referred to individual sequences that establish pairwise or higher-order interactions involving different types of genetic complementation. As discussed, the model could be applied to highly diverse RNA virus populations exhibiting frequent coinfections. Recent work has shown that viruses often undergo inter-host transmission and intra-host dissemination as groups of viral particles [21], increasing coinfection probability and, thus, the opportunities for genetic complementation. This possibility was suggested verbally in previous work [46–47], but the effects of complementation on evolvability were not explored by simulations, models, or experiments, and the problem of genetic parasites was not addressed.

Although we have discussed our model in the context of viral evolution, there may be other biological systems that exhibit similar interactions. For instance, by sharing gene products encoded on different chromosomes, sexually-reproducing diploid or polyploid organisms tend to buffer the effects of maladaptive alleles, as evidenced by the fact that most strongly deleterious alleles are recessive [48–50]. The potential evolutionary advantages and disadvantages of diploids over haploids have been investigated [51–52], including with respect to evolvability. Genetic algorithm models have pointed out that sexual reproduction in diploid organisms can accelerate optimization due to recombination, which allows for a more rapid emergence of novel allelic combinations [53–54]. However, to our knowledge, previous work has not considered genetic complementation as a process that increases the evolvability of diploids relative to haploids. On the other hand, the role of gene or genome duplications in promoting long-term evolvability has been addressed previously. Provided that the original gene function is conserved in at least one copy, duplicated genes can accumulate cryptic variation that can lead to the evolution of novel functions in the long term [55–57]. It has also been pointed out that duplicated genes can be preserved if different copies undergo mutually compensable deleterious mutations [58]. However, as we have shown, increased ploidy may not per se foster evolvability unless selfish DNA elements are prevented.

Genetic complementation has also been suggested to play a role in evolutionary transitions such as from unsegmented to segmented viruses [59], and in the zoonotic transmission of influenza A virus [60]. Furthermore, complementation could be relevant to the evolution of symbiotic relationships in which certain metabolic pathways become redundant [61], allowing one or both partners to evolve new functions. Finally, in complex social species, compensation of deleterious mutations through community-level interactions might allow the accumulation of genetic diversity that could yield new adaptations.

Funding. This work was funded by ERC Consolidator Grant 724519 Vis-a-Vis to R.S. E S-O. was funded by a PhD fellowship from Spanish Ministerio de Educación.

Data Availability. All data showed are generated from simulations with the models described in the Methods section. Scripts are available upon request to E. S-O. ([email protected]).

Competing interests. The authors declare no competing interests.

Author contribution.

E. S-O. conceived the study, formulated the model, analyzed the data, and wrote the manuscript.

R. S. conceived the study, formulated the model, analyzed the data, and wrote the manuscript.

Payne, J. L. & Wagner, A. The causes of evolvability and their evolution. Nat Rev Genet 20, 24–38 (2019).
Fragata, I., Blanckaert, A., Dias Louro, M. A., Liberles, D. A. & Bank, C. Evolution in the light of fitness landscape theory. Trends Ecol Evol 34, 69–82 (2019).
Kondrashov, D. A. & Kondrashov, F. A. Topological features of rugged fitness landscapes in sequence space. Trends Genet 31, 24–33 (2015).
Gros, P.-A., Le Nagard, H. & Tenaillon, O. The Evolution of Epistasis and Its Links With Genetic Robustness, Complexity and Drift in a Phenotypic Model of Adaptation. Genetics 182, 277–293 (2009).
Weinreich DM, Delaney NF, Depristo MA, Hartl DL. Darwinian evolution can follow only very few mutational paths to fitter proteins. Science 312(5770):111-4 (2006).
Silva, J. da, Coetzer, M., Nedellec, R., Pastore, C. & Mosier, D. E. Fitness Epistasis and Constraints on Adaptation in a Human Immunodeficiency Virus Type 1 Protein Region. Genetics 185, 293–303 (2010).
Schenk, M. F., Szendro, I. G., Salverda, M. L. M., Krug, J. & de Visser, J. A. G. M. Patterns of Epistasis between beneficial mutations in an antibiotic resistance gene. Mol Biol Evol 30, 1779–1787 (2013).
O’Maille, P. E. et al. Quantitative exploration of the catalytic landscape separating divergent plant sesquiterpene synthases. Nat Chem Biol 4, 617–623 (2008).
Ortlund, E. A., Bridgham, J. T., Redinbo, M. R. & Thornton, J. W. Crystal Structure of an Ancient Protein: Evolution by Conformational Epistasis. Science 317, 1544–1548 (2007).
Iwasa, Y., Michor, F. & Nowak, M. A. Stochastic tunnels in evolutionary dynamics. Genetics 166, 1571–1579 (2004).
Barton, N. H. Sewall Wright on Evolution in Mendelian Populations and the “Shifting Balance”. Genetics 202, 3–4 (2016).
Zheng, J., Payne, J. L. & Wagner, A. Cryptic genetic variation accelerates evolution by opening access to diverse adaptive peaks. Science 365(6451):347-353 (2019).
Paaby, A. B. & Rockman, M. V. Cryptic genetic variation: evolution’s hidden substrate. Nat Rev Genet 15, 247–258 (2014).
Zheng J, Guo N, Wagner A. Selection enhances protein evolvability by increasing mutational robustness and foldability. Science 370(6521) (2020).
Dolan, P. T., Whitfield, Z. J. & Andino, R. Mechanisms and Concepts in RNA Virus Population Dynamics and Evolution. Annu. Rev. Virol. 5, 69–92 (2018).
Sanjuán, R. & Domingo-Calap, P. Genetic Diversity and Evolution of Viral Populations. Encyclopedia of Virology 53–61 (2021)
Sanjuán, R. & Domingo-Calap, P. Mechanisms of viral mutation. Cell Mol Life Sci 73, 4433–4448 (2016).
Makimaa, H., Ingle, H. & Baldridge, M. T. Enteric Viral Co-Infections: Pathogenesis and Perspective. Viruses 12, 904 (2020).
Kim, K. W. et al. Respiratory viral co-infections among SARS-CoV-2 cases confirmed by virome capture sequencing. Sci Rep 11, 3934 (2021).
Kumar, N., Sharma, S., Barua, S., Tripathi, B. N. & Rouse, B. T. Virological and Immunological Outcomes of Coinfections. Clin Microbiol Rev 31, e00111-17 (2018).
Sanjuán, R. Collective Infectious Units in Viruses. Trends Microbiol 25, 402–412 (2017).
Altan-Bonnet, N., Perales, C. & Domingo, E. Extracellular vesicles: Vehicles of en bloc viral transmission. Virus Res 265, 143–149 (2019).
Feng, Z. et al. A pathogenic picornavirus acquires an envelope by hijacking cellular membranes. Nature 496, 367–371 (2013).
Chen, Y.-H. et al. Phosphatidylserine vesicles enable efficient en bloc transmission of enteroviruses. Cell 160, 619–630 (2015).
Santiana, M. et al. Vesicle-Cloaked Virus Clusters Are Optimal Units for Inter-organismal Viral Transmission. Cell Host Microbe 24, 208-220.e8 (2018).
Arantes, T. S. et al. The Large Marseillevirus Explores Different Entry Pathways by Forming Giant Infectious Vesicles. J Virol 90, 5246–5255 (2016).
Slack, J. & Arif, B. M. The baculoviruses occlusion-derived virus: virion structure and function. Adv Virus Res 69, 99–165 (2007).
Usmani, S. M. et al. Direct visualization of HIV-enhancing endogenous amyloid fibrils in human semen. Nat Commun 5, 3508 (2014).
Cuevas, J. M., Durán-Moreno, M. & Sanjuán, R. Multi-virion infectious units arise from free viral particles in an enveloped virus. Nat Microbiol 2, 17078 (2017).
Sanjuán, R. The Social Life of Viruses. Annu. Rev. Virol. 8, null (2021).
Kauffman, S. A. & Weinberger, E. D. The NK model of rugged fitness landscapes and its application to maturation of the immune response. J. Theor. Biol. 141, 211–245 (1989).
Vignuzzi, M. & López, C. B. Defective viral genomes are key drivers of the virus-host interaction. Nat Microbiol 4, 1075–1087 (2019).
Rezelj, V. V., Levi, L. I. & Vignuzzi, M. The defective component of viral populations. Curr Opin Virol 33, 74–80 (2018).
Sanjuán, R. & Thoulouze, M.-I. Why viruses sometimes disperse in groups†. Virus Evol 5, (2019).
Bourke, A. F. G. The validity and value of inclusive fitness theory. Proc Biol Sci 278, 3313–3320 (2011).
Frank, S. A. Natural selection. VII. History and interpretation of kin selection theory. J Evol Biol 26, 1151–1184 (2013).
Lehtonen, J. Multilevel Selection in Kin Selection Language. Trends Ecol. Evol 31, 752–762 (2016).
Nowak MA, Sigmund K. Evolutionary dynamics of biological games. Science 303(5659):793-9 (2004).
Nowak, M. A. Five rules for the evolution of cooperation. Science 314, 1560–1563 (2006).
Perc, M. & Szolnoki, A. Coevolutionary games—A mini review. Biosystems 99, 109–125 (2010).
West, S. A., Griffin, A. S. & Gardner, A. Evolutionary Explanations for Cooperation. Curr. Biol. 17, R661–R672 (2007).
Fletcher, J. A. & Doebeli, M. A simple and general explanation for the evolution of altruism. Proc. Royal Soc. B 276, 13–19 (2009).
Bourke, A. F. G. Hamilton’s rule and the causes of social evolution. Philos Trans R Soc Lond B Biol Sci 369, 20130362 (2014).
Kuzdzal-Fick J.J., Fox S.A., Strassmann J.E., Queller D.C. High relatedness is necessary and sufficient to maintain multicellularity in Dictyostelium. Science 334(6062):1548-51 (2011).
Obolski, U., Lewin-Epstein, O., Even-Tov, E. et al. With a little help from my friends: cooperation can accelerate the rate of adaptive valley crossing. BMC Evol Biol 17, 143 (2017).
Shirogane, Y., Watanabe, S. & Yanagi, Y. Cooperation between different variants: A unique potential for virus evolution. Virus Res 264, 68–73 (2019).
Andino, R. & Domingo, E. Viral quasispecies. Virology 479–480, 46–51 (2015).
Bagheri, H. C. Unresolved boundaries of evolutionary theory and the question of how inheritance systems evolve: 75 years of debate on the evolution of dominance. J Exp Zool B Mol Dev Evol 306, 329–359 (2006).
Agrawal, A. F. & Whitlock, M. C. Inferences about the distribution of dominance drawn from yeast gene knockout data. Genetics 187, 553–566 (2011).
Huber, C. D., Durvasula, A., Hancock, A. M. & Lohmueller, K. E. Gene expression drives the evolution of dominance. Nat Commun 9, 2750 (2018).
Madlung, A. Polyploidy and its effect on evolutionary success: old questions revisited with new tools. Heredity 110, 99–104 (2013).
Zeyl, C. Experimental studies on ploidy evolution in yeast. FEMS Microbiol Lett 233, 187–192 (2004).
Kanade, V. Evolution with recombination. In 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science (pp. 837-846). IEEE.
Qi, Y., Hou, Z., Yin, M., Sun, H., & Huang, J. An immune multi-objective optimization algorithm with differential evolution inspired recombination. Appl. Soft Comput., 29, 395-410 (2015).
Otto, S. P. The evolutionary consequences of polyploidy. Cell 131, 452–462 (2007).
Freeling, M. & Thomas, B. C. Gene-balanced duplications, like tetraploidy, provide predictable drive to increase morphological complexity. Genome Res 16, 805–814 (2006).
Lynch, M. & Conery, J. S. The origins of genome complexity. Science 302, 1401–1404 (2003).
Lynch, M. & Force, A. The probability of duplicate gene preservation by subfunctionalization. Genetics 154, 459–473 (2000).
García-Arriaza, J., Manrubia, S. C., Toja, M., Domingo, E. & Escarmís, C. Evolutionary Transition Toward Defective RNAs That Are Infectious by Complementation. J Virol 78, 11678–11685 (2004).
Murcia, Pablo R et al. Evolution of an Eurasian avian-like influenza virus in naïve and vaccinated pigs. PLoS Pathog 8,5 (2012).
Bennett, G. M. & Moran, N. A. Heritable symbiosis: The advantages and perils of an evolutionary rabbit hole. PNAS 112, 10169–10176 (2015).

No competing interests reported.

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Genetic complementation fosters evolvability in complex fitness landscapes

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