Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 12 June 2019 doi:10.20944/preprints201906.0106.v1 Cheating in the viral world Asher Leeks1, , Melanie Ghoul1, , and Stuart A. West1, 1Department of Zoology, University of Oxford, Oxford, UK The extent to which cheating occurs in the natural world has ferent viruses, we can study the natural dynamics of cheat proved contentious. We suggest that viruses offer an excep- populations in parallel across a broad range of viruses and tional opportunity for studying cheats, individuals that exploit hosts. Furthermore, viruses are excellent candidates for ex- the cooperative behaviour of others. In particular, we show that: perimental evolution studies, since they evolve quickly in the (1) cheating is common in viruses; (2) there are many different lab and allow a range of experimental manipulations (Elena types of viral cheat; (3) viral cheats offer novel problems for so- & Sanjuán, 2007). This potentially allows us to document the cial evolution theory; (4) viruses offer excellent empirical oppor- full evolutionary process, from genetic basis to population tunities for studying cheating; (5) cheating shows that viral pop- ulations experience substantial conflict, changing how we think dynamics in the wild (Barrett et al., 2019). Viruses therefore about how viral infections evolve; (6) evolutionary theory about offer an excellent opportunity to test fundamental ideas about cheating could help us understand viral evolution; (7) a greater the evolution of cheating and cooperation, many of which understanding of cheating in viruses could aid viral intervention were originally developed with animal behaviour in mind. strategies. We first define cheating, and provide a detailed example of how this definition can be applied to viruses. We argue that virus evolution | cheat | cooperation | social evolution | defective interfering genome | satellite virus the biology of viruses could lead to cheating being much Correspondence: [email protected] more common in viruses than in some other taxa, such as multicellular animals. We then use our definition of cheat- ing as a framework to identify and classify different kinds of Introduction cheating in the viral world. We cover known examples, and suggest where further cheats may be found. Finally, we dis- The existence of cooperation provides an opportunity for cuss how studying cheating in viruses could challenge and cheats to evolve, that spread by exploiting cooperation (West extend social evolution theory, and how applying ideas from et al., 2007a; Ghoul et al., 2013; Jones et al., 2015). Given social evolution could be useful in virology. that cooperation occurs at all levels of biology, from genes to complex animal societies, then this allows the possibility for cheating at all levels of biology (Maynard Smith & Szath- What is a cheat? mary, 1995; Bourke, 2011). However, the extent to which Cheats are selfish individuals that avoid paying the costs of cheating occurs in nature has proved contentious, leading cooperation but still benefit from the cooperation of other in- some authors to suggest that it is more important in theory dividuals (Ghoul et al., 2013). One example is brood par- than in reality (Ghoul et al., 2013; Jones et al., 2015; Freder- asites such as the cuckoo, where individuals of one species ickson, 2017). trick parents of a different species into neglecting their We suggest that viruses offer an exceptional opportunity for own chicks and instead feeding the brood parasite’s chicks studying cheating in the natural world. Viruses cooperateDRAFT in (Davies, 2010). Another example is individuals in the bac- a number of ways, and a range of different viral cheats ex- terial pathogen Pseudomonas aeruginosa which do not pro- ist, that exploit these different forms of cooperation (Díaz- duce siderophores (iron-scavenging molecules) (Griffin et al., Muñoz et al., 2017). For example, viruses that exploit gene 2004; Cordero et al., 2012; Andersen et al., 2015). The pro- products produced by others, or viruses that grow unsustain- duction of siderophores is a costly cooperative behaviour be- ably fast. Furthermore, viral cheats can span different evolu- cause individuals pay a cost to produce siderophores, but the tionary timescales. While some viral cheats originate rapidly benefits of siderophores are experienced by the group of bac- in new infections but do not persist, others persist over evolu- teria as a whole (a public good). Some cells do not produce tionary timescales and become closely adapted to exploiting siderophores, but retain the ability to uptake siderophores others. Finally, viral cheats seem to have particularly devas- produced by others, and so act as cheats. tating effects on the fitness of cooperator viruses, suggesting that there is the potential for strong selection pressures driv- ing coevolution between cooperators and cheats. Testing for cheats Viruses also offer a number of practical advantages for study- To count as a cheat, an individual must exploit a coopera- ing cheating. The wealth of publicly available sequencing tive behaviour (Ghoul et al., 2013; Jones et al., 2015). Given data means that we can study the evolutionary dynamics of that a viral genome can potentially exploit products encoded viral cheats in nature (Firth & Lipkin, 2013; Geoghegan & by another genome, we consider a genome as an individual, Holmes, 2018). Because cheats arise de novo in many dif- and ask whether viral genomes can cheat (Díaz-Muñoz et al., © 2019 by the author(s). Distributed under a Creative Commons CC BY license. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 12 June 2019 doi:10.20944/preprints201906.0106.v1 ports progeny viral sequences to new cells. Capsid proteins encoded by one viral genome can produce capsids that can potentially contain other viral genomes, and so they can be a public good (Fig. 2). Consequently, a viral sequence could potentially be a cheat if: (i) it lacks the genes for encoding capsid proteins itself; and: (ii) it is able to exploit capsids constructed by proteins encoded by other sequences. A simi- lar line of argument applies to a number of other viral genes, including proteins that suppress host cell defences (Fig. 2). Any viral gene that can be complemented in trans can poten- tially be cheated. There are multiple reasons why mutants lacking these func- Fig. 1. Testing for cheats. In order for an entity to be classified as tions may gain a replicative advantage over cooperative wild- a cheat, it must (a) be less fit than the cooperator when each are type viruses, and therefore act as cheats (Fig. 3). The sim- grown alone; and (b) be more fit than the cooperator when both plest reason is that shorter genome sequences may accu- are grown together in mixture. In many cases, theory predicts mulate more quickly inside a cell because the shorter a se- that cheats will be most fit when they are rare, and least fit when quence is, the more copies can be produced by a replicase in they are common. a given amount of time (Spiegelman et al., 1965; Mills et al., 1967). Alternatively, because they don’t produce coopera- 2017). The first step in testing for this is to compare the tive gene products, they can allocate more time towards repli- growth rates of potential cooperators and cheats on their own cating copies of themselves (Holland, 1985; Chao & Elena, and in mixture (Fig. 1). For example, genomes that do and 2017). Another example is that cheats may replace coop- do not encode a cooperative trait such as a replicase. If one erative genes with sequences that increase their affinity for genome is a cooperator, and another is a cheat, then: (i) the binding to replicase, allowing them to out-compete the wild- cooperator will have a higher growth rate when grown on its type cooperators for a limited supply of replicases (Lee & own, but (ii) the cheat will have a higher growth rate when Nathans, 1979). Most of these advantages come about by both are grown together (Fig. 1). These results follow from allowing cheats to escape constraints on viral genome sizes. the fact that cooperation provides a benefit at the group level, Consequently, cheats may be more likely to gain a large ad- but can also be exploited by cheats that avoid the costs of vantage in RNA viruses, which are highly constrained by cooperating. their small maximum genome size (Holmes, 2003; Belshaw et al., 2008). When testing for cheats, it is important to rule out alterna- tive explanations for the different growth rates of the two A clear example of cheating exists in defective interfering strains. For example, a cooperator’s growth rate will be genomes of poliovirus. When poliovirus is grown under con- slow if grown in conditions under which cooperation is not ditions allowing for frequent multiple infections, defective favoured. Therefore, it is important to conduct a test for interfering mutants can emerge (Cole et al., 1971). One such cheating in conditions that reflect the natural environment mutant, ‘DI PV1’ contains a deletion consisting of the en- that cheats and cooperators evolve in as faithfully as possi- tire capsid protein region (Shirogane et al., 2019). When ble. In particular, conditions where the cooperative trait be- grown on its own, this mutant is unable to package progeny ing examined is required and beneficial (Ghoul et al., 2013, sequences into virions. However, when cooperator and de- 2014). DRAFTfective interfering genome are grown together, the defec- tive interfering genome can exploit the capsid proteins pro- duced by the cooperator, achieving more than 1,000 times as How could viruses cheat? many genomes inside virions as the cooperator (Shirogane et al., 2019). In this example, the shorter defective inter- In viruses, completing an infection cycle inside a host cell can fering “cheat” genome has advantages at two stages of the be a cooperative process.