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病原体免疫逃逸的生态进化动力学:推导群体水平的系统发育动力学曲线。

Eco-evolutionary dynamics of pathogen immune-escape: deriving a population-level phylodynamic curve.

作者信息

Nielsen Bjarke Frost, Saad-Roy Chadi M, Metcalf C Jessica E, Viboud Cécile, Grenfell Bryan T

机构信息

High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA.

Miller Institute for Basic Research in Science, University of California, Berkeley, CA, USA.

出版信息

J R Soc Interface. 2025 Apr;22(225):20240675. doi: 10.1098/rsif.2024.0675. Epub 2025 Apr 2.

DOI:10.1098/rsif.2024.0675
PMID:40172571
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11963905/
Abstract

The phylodynamic curve (Grenfell . 2004 , 327-332 (doi:10.1126/science.1090727)) conceptualizes how immunity shapes the rate of viral adaptation in a non-monotonic fashion, through its opposing effects on viral abundance and the strength of selection. However, concrete and quantitative model realizations of this influential concept are rare. Here, we present an analytic, stochastic framework in which a population-scale phylodynamic curve emerges dynamically, allowing us to address questions regarding the risk and timing of the emergence of viral immune escape variants. We explore how pathogen- and population-specific parameters such as strength of immunity, transmissibility, seasonality and antigenic constraints affect the emergence risk. For pathogens exhibiting pronounced seasonality, we find that the timing of likely immune-escape variant emergence depends on the level of case importation between regions. Motivated by the COVID-19 pandemic, we probe the likely effects of non-pharmaceutical interventions (NPIs), and the lifting thereof, on the risk of viral escape variant emergence. Looking ahead, the framework has the potential to become a useful tool for probing how natural immunity, as well as choices in vaccine design and distribution and the implementation of NPIs, affect the evolution of common viral pathogens.

摘要

系统动力学曲线(Grenfell. 2004, 327 - 332 (doi:10.1126/science.1090727))以概念化的方式展示了免疫如何通过对病毒丰度和选择强度的相反作用,以非单调的方式塑造病毒适应率。然而,对这一有影响力概念的具体定量模型实现却很少见。在这里,我们提出了一个分析性的随机框架,在这个框架中,群体规模的系统动力学曲线动态出现,使我们能够解决有关病毒免疫逃逸变体出现的风险和时间问题。我们探讨了诸如免疫强度、传播性、季节性和抗原限制等病原体和群体特异性参数如何影响出现风险。对于表现出明显季节性的病原体,我们发现可能出现免疫逃逸变体的时间取决于地区之间的病例输入水平。受新冠疫情的启发,我们探究了非药物干预措施(NPIs)及其解除对病毒逃逸变体出现风险的可能影响。展望未来,该框架有可能成为一个有用的工具,用于探究自然免疫以及疫苗设计、分发选择和非药物干预措施的实施如何影响常见病毒病原体的进化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2401/11963905/2f499f747aa8/rsif.2024.0675.f006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2401/11963905/adb935fc53cb/rsif.2024.0675.f001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2401/11963905/b9ab1a268675/rsif.2024.0675.f002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2401/11963905/3df8c7aaf092/rsif.2024.0675.f003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2401/11963905/9fe92f0063c3/rsif.2024.0675.f004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2401/11963905/6b961a078615/rsif.2024.0675.f005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2401/11963905/2f499f747aa8/rsif.2024.0675.f006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2401/11963905/adb935fc53cb/rsif.2024.0675.f001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2401/11963905/b9ab1a268675/rsif.2024.0675.f002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2401/11963905/3df8c7aaf092/rsif.2024.0675.f003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2401/11963905/9fe92f0063c3/rsif.2024.0675.f004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2401/11963905/2f499f747aa8/rsif.2024.0675.f006.jpg

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