A Simulation Framework for Modeling the Within-Patient Evolutionary Dynamics of SARS-CoV-2.

A Simulation Framework for Modeling the Within-Patient Evolutionary Dynamics of SARS-CoV-2.
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DOI:
10.1093/gbe/evad204
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发表时间:
2023-11-01
影响因子:
3.3
通讯作者:
Jensen, Jeffrey D.
Jensen, Jeffrey D.
中科院分区:
生物学2区
文献类型:
--
作者:
Terbot II, John W.;Cooper, Brandon S.;Good, Jeffrey M.;Jensen, Jeffrey D.

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严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)的全球影响引起了人们对检测新的有益突变和其他基因组变化的极大兴趣,这些变化可能标志着关注变体(VOC)的发展。准确检测个体患者样本中这些变化的能力对于实现VOC的早期检测非常重要。通过比较经验数据与模拟数据来最好地进行这种很少作用的正选择的基因组扫描,其中可以仔细考虑和参数化共同作用的进化因素,包括突变和重组,生殖和感染动力学,以及纯化和背景选择。虽然已经有工作来量化这些因素在SARS-CoV-2,他们还没有被整合到一个基线模型描述宿主内的进化动力学。为了构建这样一个基线模型,我们开发了一个模拟框架,使人们能够建立对患者水平变化的潜在水平和模式的期望。通过改变8个关键参数,我们评估了12,096种不同的模型参数组合,并将其与现有的经验数据进行了比较。其中,根据所得的平均预期分离变体数量,592个模型(0.5%)是合理的。这些看似合理的模型有几个共同点,揭示了宿主内SARS-CoV-2的进化动力学:严重的感染瓶颈,低水平的生殖偏斜,以及健身效应的分布偏向于强烈有害的突变。我们还描述了模型不确定性的重要领域,并强调了可能有助于进一步完善基线模型的其他序列数据。这项研究为改进现有和未来SARS-CoV-2患者内数据的分析奠定了基础。
The global impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to considerable interest in detecting novel beneficial mutations and other genomic changes that may signal the development of variants of concern (VOCs). The ability to accurately detect these changes within individual patient samples is important in enabling early detection of VOCs. Such genomic scans for rarely acting positive selection are best performed via comparison of empirical data with simulated data wherein commonly acting evolutionary factors, including mutation and recombination, reproductive and infection dynamics, and purifying and background selection, can be carefully accounted for and parameterized. Although there has been work to quantify these factors in SARS-CoV-2, they have yet to be integrated into a baseline model describing intrahost evolutionary dynamics. To construct such a baseline model, we develop a simulation framework that enables one to establish expectations for underlying levels and patterns of patient-level variation. By varying eight key parameters, we evaluated 12,096 different model–parameter combinations and compared them with existing empirical data. Of these, 592 models (∼5%) were plausible based on the resulting mean expected number of segregating variants. These plausible models shared several commonalities shedding light on intrahost SARS-CoV-2 evolutionary dynamics: severe infection bottlenecks, low levels of reproductive skew, and a distribution of fitness effects skewed toward strongly deleterious mutations. We also describe important areas of model uncertainty and highlight additional sequence data that may help to further refine a baseline model. This study lays the groundwork for the improved analysis of existing and future SARS-CoV-2 within-patient data.
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