Arcos Sarah, Lauring Adam S
Department of Microbiology and Immunology, University of Michigan, Ann Arbor.
Department of Internal Medicine, University of Michigan, Ann Arbor.
bioRxiv. 2025 May 21:2025.05.16.654520. doi: 10.1101/2025.05.16.654520.
Lethal mutagenesis is a strategy to achieve viral extinction by drugging viral mutation rates beyond an extinction threshold. Accurate estimation of the extinction threshold is critical, as elevating viral mutation rates near, but not past this threshold increases the likelihood of mutations that could result in drug resistance, vaccine escape, or increased pathogenesis. Traditional models of lethal mutagenesis rely on the Poisson distribution, which assumes a uniform mutation rate across individuals. Yet, RNA viruses like influenza A virus (IAV) can have varied mutation rates due to mutations in the polymerase complex. This variability suggests that lethal mutagenesis models incorporating mutation rate diversity, such as ones using the gamma-Poisson distribution, may be more accurate for RNA viruses. Poisson models assume count data have equal mean and variance, while gamma-Poisson counts are overdispersed (variance greater than mean). Here we provide experimental data showing that IAV mutations are overdispersed, indicating that the gamma-Poisson distribution is more appropriate for modeling IAV mutations. Modeling of lethal mutagenesis using the gamma-Poisson distribution reveals that the degree of overdispersion is critical in determining survival or extinction. Increased overdispersion shifts the extinction threshold higher, indicating that Poisson-based models have underestimated the mutation rate required to achieve viral extinction and avoid viral escape or accelerated evolution. Furthermore, time to extinction in simulated populations is significantly longer with gamma-Poisson-based models than Poisson-based. This investigation of how mutation rate variability affects lethal mutagenesis will directly impact antiviral drug design and strategy, thus advancing efforts to combat virus outbreaks and future pandemics.
致死性诱变是一种通过使病毒突变率超过灭绝阈值来实现病毒灭绝的策略。准确估计灭绝阈值至关重要，因为将病毒突变率提高到接近但不超过该阈值会增加产生可能导致耐药性、疫苗逃逸或致病性增加的突变的可能性。传统的致死性诱变模型依赖泊松分布，该分布假设个体间的突变率是均匀的。然而，像甲型流感病毒（IAV）这样的RNA病毒由于聚合酶复合物中的突变，其突变率可能会有所不同。这种变异性表明，纳入突变率多样性的致死性诱变模型，例如使用伽马-泊松分布的模型，可能对RNA病毒更准确。泊松模型假设计数数据的均值和方差相等，而伽马-泊松计数则是过度离散的（方差大于均值）。在这里，我们提供的实验数据表明IAV突变是过度离散的，这表明伽马-泊松分布更适合对IAV突变进行建模。使用伽马-泊松分布对致死性诱变进行建模表明，过度离散程度在决定生存或灭绝方面至关重要。过度离散程度的增加会使灭绝阈值升高，这表明基于泊松的模型低估了实现病毒灭绝并避免病毒逃逸或加速进化所需的突变率。此外，基于伽马-泊松模型的模拟种群中的灭绝时间比基于泊松模型的显著更长。这项关于突变率变异性如何影响致死性诱变的研究将直接影响抗病毒药物设计和策略，从而推动应对病毒爆发和未来大流行的努力。
