Key result
The S2M stochastic simulation model demonstrated that increasing rates of replication error and recombination synergistically drive the genetic transition of poliovirus from the Sabin-1 vaccine strain toward a neurovirulent Mahoney-like state.
The S2M stochastic simulation model provides a computational tool to understand the mechanisms driving genetic state transitions in poliovirus, such as reversion to neurovirulence.
May inform Sabin-derived poliovirus reversion monitoring; leaves open empirical validation in clinical isolates.
Modeling the molecular mechanisms that govern genetic variation can be useful in understanding the dynamics that drive genetic state transition in quasispecies viruses. For example, there is considerable interest in understanding how the relatively benign vaccine strains of poliovirus eventually revert to forms that confer neurovirulence and cause disease (ie, vaccine-derived poliovirus). This report describes a stochastic simulation model, S2M, which can be used to generate hypothetical outcomes based on known mechanisms of genetic diversity. S2M begins with predefined genotypes based on the Sabin-1 and Mahoney wild-type sequences, constructs a set of independent cell-based populations, and performs in-cell replication and cell-to-cell infection cycles while quantifying genetic changes that track the transition from Sabin-1 toward Mahoney. Realism is incorporated into the model by assigning defaults for variables that constrain mechanisms of genetic variability based roughly on metrics reported in the literature, yet these values can be modified at the command line in order to generate hypothetical outcomes driven by these parameters. To demonstrate the utility of S2M, simulations were performed to examine the effects of the rates of replication error and recombination and the presence or absence of defective interfering particles, upon reaching the end states of Mahoney resemblance (semblance of a vaccine-derived state), neurovirulence, genome fitness, and cloud diversity. Simulations provide insight into how modeled biological features may drive hypothetical outcomes, independently or in combination, in ways that are not always intuitively obvious.
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Carol Zhou (2016) studied Poliovirus genetic state transition. S2M stochastic simulation model was evaluated on Simulated genetic state transition from Sabin-1 to Mahoney wild type. The S2M stochastic simulation model demonstrated that increasing rates of replication error and recombination synergistically drive the genetic transition of poliovirus from the Sabin-1 vaccine strain toward a neurovirulent Mahoney-like state.
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