Dynamical modelling of viral infection and cooperative immune protection in COVID-19 patients.

IF 4.3 2区生物学 PLoS Computational Biology Pub Date : 2023-09-01 DOI:10.1371/journal.pcbi.1011383

Zhengqing Zhou, Dianjie Li, Ziheng Zhao, Shuyu Shi, Jianghua Wu, Jianwei Li, Jingpeng Zhang, Ke Gui, Yu Zhang, Qi Ouyang, Heng Mei, Yu Hu, Fangting Li

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Abstract

Once challenged by the SARS-CoV-2 virus, the human host immune system triggers a dynamic process against infection. We constructed a mathematical model to describe host innate and adaptive immune response to viral challenge. Based on the dynamic properties of viral load and immune response, we classified the resulting dynamics into four modes, reflecting increasing severity of COVID-19 disease. We found the numerical product of immune system's ability to clear the virus and to kill the infected cells, namely immune efficacy, to be predictive of disease severity. We also investigated vaccine-induced protection against SARS-CoV-2 infection. Results suggested that immune efficacy based on memory T cells and neutralizing antibody titers could be used to predict population vaccine protection rates. Finally, we analyzed infection dynamics of SARS-CoV-2 variants within the construct of our mathematical model. Overall, our results provide a systematic framework for understanding the dynamics of host response upon challenge by SARS-CoV-2 infection, and this framework can be used to predict vaccine protection and perform clinical diagnosis.

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新冠肺炎患者病毒感染和协同免疫保护的动态模型。

一旦受到严重急性呼吸系统综合征冠状病毒2型病毒的攻击，人类宿主免疫系统就会触发一个对抗感染的动态过程。我们构建了一个数学模型来描述宿主对病毒攻击的先天和适应性免疫反应。根据病毒载量和免疫反应的动态特性，我们将由此产生的动态分为四种模式，反映了新冠肺炎疾病日益严重。我们发现免疫系统清除病毒和杀死受感染细胞的能力的数字乘积，即免疫效力，可以预测疾病的严重程度。我们还研究了疫苗对严重急性呼吸系统综合征冠状病毒2型感染的保护作用。结果表明，基于记忆T细胞和中和抗体滴度的免疫效力可用于预测群体疫苗保护率。最后，我们在数学模型的构建中分析了严重急性呼吸系统综合征冠状病毒2型变异株的感染动力学。总的来说，我们的研究结果为了解宿主对严重急性呼吸系统综合征冠状病毒2型感染的反应动力学提供了一个系统框架，该框架可用于预测疫苗保护和进行临床诊断。

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来源期刊

PLoS Computational Biology 生物-生化研究方法

CiteScore

7.10

自引率

4.70%

发文量

820

期刊介绍： PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.