King Connor R, Berezin Casey-Tyler, Peccoud Jean
Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, Colorado, United States of America.
PLoS Comput Biol. 2024 Feb 7;20(2):e1011373. doi: 10.1371/journal.pcbi.1011373. eCollection 2024 Feb.
We present the first complete stochastic model of vesicular stomatitis virus (VSV) intracellular replication. Previous models developed to capture VSV's intracellular replication have either been ODE-based or have not represented the complete replicative cycle, limiting our ability to understand the impact of the stochastic nature of early cellular infections on virion production between cells and how these dynamics change in response to mutations. Our model accurately predicts changes in mean virion production in gene-shuffled VSV variants and can capture the distribution of the number of viruses produced. This model has allowed us to enhance our understanding of intercellular variability in virion production, which appears to be influenced by the duration of the early phase of infection, and variation between variants, arising from balancing the time the genome spends in the active state, the speed of incorporating new genomes into virions, and the production of viral components. Being a stochastic model, we can also assess other effects of mutations beyond just the mean number of virions produced, including the probability of aborted infections and the standard deviation of the number of virions produced. Our model provides a biologically interpretable framework for studying the stochastic nature of VSV replication, shedding light on the mechanisms underlying variation in virion production. In the future, this model could enable the design of more complex viral phenotypes when attenuating VSV, moving beyond solely considering the mean number of virions produced.
我们提出了首个完整的水泡性口炎病毒(VSV)细胞内复制的随机模型。此前为描述VSV细胞内复制而开发的模型,要么是基于常微分方程的,要么没有呈现完整的复制周期,这限制了我们理解早期细胞感染的随机性对细胞间病毒粒子产生的影响,以及这些动态如何因突变而变化的能力。我们的模型准确预测了基因改组的VSV变体中平均病毒粒子产生的变化,并能捕捉所产生病毒数量的分布情况。该模型使我们能够加深对病毒粒子产生过程中细胞间变异性的理解,这种变异性似乎受到感染早期阶段持续时间的影响,以及变体之间的差异影响,这些差异源于基因组处于活跃状态的时间、新基因组整合到病毒粒子中的速度以及病毒成分的产生之间的平衡。作为一个随机模型,我们还可以评估突变的其他影响,不仅仅是所产生病毒粒子的平均数量,包括感染中止的概率以及所产生病毒粒子数量的标准差。我们的模型为研究VSV复制的随机性提供了一个具有生物学可解释性的框架,揭示了病毒粒子产生变异背后的机制。未来,在减毒VSV时,该模型可以超越单纯考虑所产生病毒粒子的平均数量,实现设计更复杂的病毒表型。