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基于人体挑战研究的 SARS-CoV-2 感染动力学。

The kinetics of SARS-CoV-2 infection based on a human challenge study.

机构信息

Theoretical Division, Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87545.

出版信息

Proc Natl Acad Sci U S A. 2024 Nov 12;121(46):e2406303121. doi: 10.1073/pnas.2406303121. Epub 2024 Nov 7.


DOI:10.1073/pnas.2406303121
PMID:39508770
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11573497/
Abstract

Studying the early events that occur after viral infection in humans is difficult unless one intentionally infects volunteers in a human challenge study. Here, we use data about severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in such a study in combination with mathematical modeling to gain insights into the relationship between the amount of virus in the upper respiratory tract and the immune response it generates. We propose a set of dynamic models of increasing complexity to dissect the roles of target cell limitation, innate immunity, and adaptive immunity in determining the observed viral kinetics. We introduce an approach for modeling the effect of humoral immunity that describes a decline in infectious virus after immune activation. We fit our models to viral load and infectious titer data from all the untreated infected participants in the study simultaneously. We found that a power-law with a power < 1 describes the relationship between infectious virus and viral load. Viral replication at the early stage of infection is rapid, with a doubling time of ~2 h for viral RNA and ~3 h for infectious virus. We estimate that adaptive immunity is initiated ~7 to 10 d postinfection and appears to contribute to a multiphasic viral decline experienced by some participants; the viral rebound experienced by other participants is consistent with a decline in the interferon response. Altogether, we quantified the kinetics of SARS-CoV-2 infection, shedding light on the early dynamics of the virus and the potential role of innate and adaptive immunity in promoting viral decline during infection.

摘要

研究人类感染病毒后早期发生的事件是困难的,除非在人类挑战研究中故意感染志愿者。在这里,我们结合数学建模,使用关于严重急性呼吸综合征冠状病毒 2(SARS-CoV-2)的此类研究中的数据,深入了解上呼吸道病毒数量与其引发的免疫反应之间的关系。我们提出了一组具有递增复杂性的动态模型,以剖析靶细胞限制、先天免疫和适应性免疫在确定观察到的病毒动力学中的作用。我们引入了一种描述免疫激活后传染性病毒减少的体液免疫效应建模方法。我们同时拟合了研究中所有未经治疗的感染参与者的病毒载量和传染性滴度数据。我们发现,具有幂 < 1 的幂律描述了传染性病毒与病毒载量之间的关系。感染早期病毒复制迅速,病毒 RNA 的倍增时间约为 2 小时,传染性病毒的倍增时间约为 3 小时。我们估计适应性免疫在感染后 7 至 10 天启动,似乎有助于一些参与者经历的多相病毒下降;其他参与者经历的病毒反弹与干扰素反应的下降一致。总的来说,我们量化了 SARS-CoV-2 感染的动力学,揭示了病毒的早期动态以及先天和适应性免疫在感染过程中促进病毒下降的潜在作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f7da/11573497/47857ab4caae/pnas.2406303121fig04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f7da/11573497/4cd1ae421202/pnas.2406303121fig01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f7da/11573497/445989a50d7d/pnas.2406303121fig02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f7da/11573497/44647df5ba4b/pnas.2406303121fig03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f7da/11573497/47857ab4caae/pnas.2406303121fig04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f7da/11573497/4cd1ae421202/pnas.2406303121fig01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f7da/11573497/445989a50d7d/pnas.2406303121fig02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f7da/11573497/44647df5ba4b/pnas.2406303121fig03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f7da/11573497/47857ab4caae/pnas.2406303121fig04.jpg

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[4]
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[7]
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Access Microbiol. 2025-2-17

[8]
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本文引用的文献

[1]
Human SARS-CoV-2 challenge uncovers local and systemic response dynamics.

Nature. 2024-7

[2]
Modeling the emergence of viral resistance for SARS-CoV-2 during treatment with an anti-spike monoclonal antibody.

PLoS Pathog. 2024-4

[3]
How robust are estimates of key parameters in standard viral dynamic models?

PLoS Comput Biol. 2024-4

[4]
Diversity begets stability: Sublinear growth and competitive coexistence across ecosystems.

Science. 2024-3-15

[5]
Mucosal and systemic immune correlates of viral control after SARS-CoV-2 infection challenge in seronegative adults.

Sci Immunol. 2024-9-2

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SARS-CoV-2 Virologic Rebound With Nirmatrelvir-Ritonavir Therapy : An Observational Study.

Ann Intern Med. 2023-12

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PLoS Comput Biol. 2023-8

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