Viruses Roll the Dice: The Stochastic Behavior of Viral Genome Molecules Accelerates Viral Adaptation at the Cell and Tissue Levels

Recent studies on evolutionarily distant viral groups have shown that the number of viral genomes that establish cell infection after cell-to-cell transmission is unexpectedly small (1–20 genomes). This aspect of viral infection appears to be important for the adaptation and survival of viruses. To clarify how the number of viral genomes that establish cell infection is determined, we developed a simulation model of cell infection for tomato mosaic virus (ToMV), a positive-strand RNA virus. The model showed that stochastic processes that govern the replication or degradation of individual genomes result in the infection by a small number of genomes, while a large number of infectious genomes are introduced in the cell. It also predicted two interesting characteristics regarding cell infection patterns: stochastic variation among cells in the number of viral genomes that establish infection and stochastic inequality in the accumulation of their progenies in each cell. Both characteristics were validated experimentally by inoculating tobacco cells with a library of nucleotide sequence–tagged ToMV and analyzing the viral genomes that accumulated in each cell using a high-throughput sequencer. An additional simulation model revealed that these two characteristics enhance selection during tissue infection. The cell infection model also predicted a mechanism that enhances selection at the cellular level: a small difference in the replication abilities of coinfected variants results in a large difference in individual accumulation via the multiple-round formation of the replication complex (i.e., the replication machinery). Importantly, this predicted effect was observed in vivo. The cell infection model was robust to changes in the parameter values, suggesting that other viruses could adopt similar adaptation mechanisms. Taken together, these data reveal a comprehensive picture of viral infection processes including replication, cell-to-cell transmission, and evolution, which are based on the stochastic behavior of the viral genome molecules in each cell.Simulation models of cell-to-cell viral transmission illustrate how stochastic processes occurring within individual infected cells form a sophisticated adaptation system that optimizes viruses for further infection.Author Summary: Viruses rapidly adapt to environmental changes, complicating our efforts to control the spread of viral infections. Adaptation occurs in two steps: the generation of adaptive variants by random mutations and the subsequent selection of those adaptive variants. High mutation rates in viruses have been well documented; however, little is known about how adaptive variants are rapidly selected, especially given that most viral gene products are shared among viral populations within an infected cell, thereby diluting the direct benefits of each variant. Here we used an RNA virus—tomato mosaic virus—to examine the selection mechanisms hidden in its infection processes. First, we simulated the cell infection process computationally. We found that stochastic behaviors of viral genome molecules result in characteristic patterns of offspring accumulation within cells: (i) predominant replication of offspring from only a handful of founder viral genome molecules in each cell, (ii) variation in the number of offspring originating from each founder, and (iii) variation in the number of founders among cells. We then found that these characteristics enable rapid selection of adaptive variants of the virus during parallel and repeated cell infections, i.e., tissue infections. We speculate that similar selection mechanisms could also be employed by other viruses.