The evolution of collective infectious units in viruses

Abstract

Viruses frequently spread among cells or hosts in groups, with multiple viral genomes inside the same infectious unit. These collective infectious units can consist of multiple viral genomes inside the same virion, or multiple virions inside a larger structure such as a vesicle. Collective infectious units deliver multiple viral genomes to the same cell simultaneously, which can have important implications for viral pathogenesis, antiviral resistance, and social evolution. However, little is known about why some viruses transmit in collective infectious units, whereas others do not. We used a simple evolutionary approach to model the potential costs and benefits of transmitting in a collective infectious unit. We found that collective infectious units could be favoured if cells infected by multiple viral genomes were significantly more productive than cells infected by just one viral genome, and especially if there were also efficiency benefits to packaging multiple viral genomes inside the same infectious unit. We also found that if some viral sequences are defective, then collective infectious units could evolve to become very large, but that if these defective sequences interfered with wild-type virus replication, then collective infectious units were disfavoured.

Keywords: Bloc transmission; Collective infection; Collective infectious unit; Defective interfering genome; Multiplicity of infection; Virus evolution.

Fig. 1

Group infection benefits and CIU evolution. (a) plots the opportunity cost of larger CIUs. All else being equal, fewer CIUs can be produced if each CIU contains more genomes. In our model, we only use integer values of k. (b) plots the relationship between the success of a CIU and the number of genomes it contains. (c) and (d) plot the optimal size of a CIU (k^*) when CIU success has a diminishing (c) or threshold (d) relationship with CIU size. The red dashed line in (c) plots the analytical condition for when CIUs evolve. When infectious unit success has diminishing returns (c), larger CIUs (k* > 2) only evolve when the success slope is relatively flat (a is high). In contrast, when there are threshold effects (d), only larger CIUs (k* > 2) are found, but these are found over less of the parameter space.

Fig. 2

The influence of efficiency benefits on CIU evolution. (a) plots the potential increase in genome availability which comes from transmitting in CIUs of larger sizes. The increased genome availability depends on α, which reflects the extent to which increased efficiency of genome packaging results in more viral genome copies being produced. (b) plots the optimal size of CIU (k^*) which is reached for a spherical CIU with α = 0, reflecting the largest possible efficiency gains from larger CIUs. Compared to Fig. 1c, where there are no efficiency gains, CIUS evolve in a larger region of parameter space and are larger when they do evolve.

Fig. 3

Defective and interfering genomes and CIU evolution. (a) and (b) plot the relationship between the success of an infectious unit and its size when the proportion of genomes which are defective (μ) (a) or which are defective and interfering (ι) (b) varies. In (a), as μ increases, virions need to be larger to achieve the same success, because there is a larger chance that the genomes inside a virion are defective. However, when some defective genomes are interfering (ι > 0) (b), there is a cost to larger CIUs, because larger CIUs have a greater chance of including an interfering genome. This cost reduces both the value of k at which success peaks and the success experienced by an infectious unit containing k genomes. In (b), 25% of viral progeny are defective (μ = 0.25). (c) and (d) plot the optimal size of infectious unit (k^*) as the proportion of defective genomes (μ) increases. The dashed line plots k_t (the number of complete genomes that results in maximum infectious unit success) and the dotted line plots k^* = 1 (when CIUs are not favoured). In (c), a is the shape parameter for the diminishing returns success curve, with higher values indicating a more linear curve. As defective genomes become more prevalent, the optimal size of CIU increases and can reach values which are substantially higher than k_t. However, increases in μ by themselves cannot drive the evolution of CIUs from no CIUs. In (d), higher values of ι indicate that a higher proportion of defective genomes are interfering. As the proportion of interfering genomes (ι) increases, the optimal size of CIU decreases, and the likelihood that CIUs are favoured at all also decreases. Interfering genomes (ι) have a larger impact on CIU evolution when defective genomes are common (high μ).