Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection.
Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection.
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DOI：
10.1371/journal.pcbi.1009735
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发表时间：
2021-12
影响因子：
4.3
通讯作者：
Forrest S
中科院分区：
文献类型：
--
作者：
Moses ME;Hofmeyr S;Cannon JL;Andrews A;Gridley R;Hinga M;Leyba K;Pribisova A;Surjadidjaja V;Tasnim H;Forrest S
SARS-CoV-2感染中的一个关键问题是为什么病毒载量和患者结果在个体之间存在显着差异。由于病毒传播和免疫反应的时空动力学在体内研究具有挑战性，我们开发了冠状病毒的空间免疫模型（SIMCoV），这是一种可扩展的计算模型，可以模拟数亿个肺细胞，包括呼吸道上皮细胞和T细胞。SIMCoV复制了在患者中观察到的病毒生长动力学，并显示了空间分散的感染如何导致病毒载量增加。该模型还显示了T细胞反应的时间和强度如何影响病毒的持续性，振荡和控制。通过整合空间相互作用，SIMCoV通过仅改变初始感染部位的数量以及T细胞免疫应答的幅度和时间，为患者之间显著不同的病毒载量轨迹提供了一个简洁的解释。当明确表示肺的分支气道结构时，我们发现病毒传播速度比在2D上皮细胞层中更快，但比未分化的3D网格或混合良好的微分方程模型中慢得多。这些结果说明了如何现实的，空间明确的计算模型可以提高理解宿主内的SARS-CoV-2感染的动力学。SARS-CoV-2感染的一个关键问题是为什么病毒载量和患者结果在个体之间如此不同。由于很难看到病毒如何在感染者的肺部传播，我们开发了冠状病毒的空间免疫模型（SIMCoV），这是一种模拟数亿个细胞的计算模型，包括肺细胞和免疫细胞。SIMCoV模拟了病毒如何生长然后下降，模拟结果与在患者中观察到的数据相匹配。SIMCoV表明，当初始感染部位较多时，病毒会生长到更高的峰值。该模型还显示了免疫反应的时间，特别是T细胞反应，如何影响病毒持续的时间以及最终是否从肺部清除。SIMCoV表明，不同患者的不同病毒载量可以通过病毒最初在肺部的不同位置进行解释。我们明确地将肺的分支气道结构添加到模型中，并表明病毒的传播速度略快于肺细胞的2D层，但比基于微分方程的传统数学模型慢得多。这些结果说明了现实的空间计算模型如何提高对SARS-CoV-2感染如何在肺部传播的理解。
A key question in SARS-CoV-2 infection is why viral loads and patient outcomes vary dramatically across individuals. Because spatial-temporal dynamics of viral spread and immune response are challenging to study in vivo, we developed Spatial Immune Model of Coronavirus (SIMCoV), a scalable computational model that simulates hundreds of millions of lung cells, including respiratory epithelial cells and T cells. SIMCoV replicates viral growth dynamics observed in patients and shows how spatially dispersed infections can lead to increased viral loads. The model also shows how the timing and strength of the T cell response can affect viral persistence, oscillations, and control. By incorporating spatial interactions, SIMCoV provides a parsimonious explanation for the dramatically different viral load trajectories among patients by varying only the number of initial sites of infection and the magnitude and timing of the T cell immune response. When the branching airway structure of the lung is explicitly represented, we find that virus spreads faster than in a 2D layer of epithelial cells, but much more slowly than in an undifferentiated 3D grid or in a well-mixed differential equation model. These results illustrate how realistic, spatially explicit computational models can improve understanding of within-host dynamics of SARS-CoV-2 infection. A key question in SARS-CoV-2 infection is why viral loads and patient outcomes are so different across individuals. Because it’s difficult to see how the virus spreads in the lungs of infected people, we developed Spatial Immune Model of Coronavirus (SIMCoV), a computational model that simulates hundreds of millions of cells, including lung cells and immune cells. SIMCoV simulates how virus grows and then declines, and the simulations match data observed in patients. SIMCoV shows that when there are more initial infection sites, the virus grows to a higher peak. The model also shows how the timing of the immune response, particularly the T cell response, can affect how long the virus persists and whether it is ultimately cleared from the lungs. SIMCoV shows that the different viral loads in different patients can be explained by how many different places the virus is initially seeded inside their lungs. We explicitly add the branching airway structure of the lung into the model and show that virus spreads slightly faster than it would in a 2D layer of lung cells, but much slower than in traditional mathematical models based on differential equations. These results illustrate how realistic spatial computational models can improve understanding of how SARS-CoV-2 infection spreads in the lung.
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影响因子：
64.8
作者：
Harris, Tajie H.;Banigan, Edward J.;Christian, David A.;Konradt, Christoph;Wojno, Elia D. Tait;Norose, Kazumi;Wilson, Emma H.;John, Beena;Weninger, Wolfgang;Luster, Andrew D.;Liu, Andrea J.;Hunter, Christopher A.
通讯作者：
Hunter, Christopher A.
影响因子：
4.6
作者：
Kassin MT;Varble N;Blain M;Xu S;Turkbey EB;Harmon S;Yang D;Xu Z;Roth H;Xu D;Flores M;Amalou A;Sun K;Kadri S;Patella F;Cariati M;Scarabelli A;Stellato E;Ierardi AM;Carrafiello G;An P;Turkbey B;Wood BJ
通讯作者：
Wood BJ
影响因子：
9.4
作者：
Hernandez-Vargas EA;Velasco-Hernandez JX
通讯作者：
Velasco-Hernandez JX
抗病毒疗法的效力和时机是SARS-COV-2脱落持续时间的决定因素和炎症反应的强度。
影响因子：
13.6
作者：
Goyal A;Cardozo-Ojeda EF;Schiffer JT
通讯作者：
Schiffer JT
影响因子：
3.5
作者：
Goncalves, Antonio;Bertrand, Julie;Guedj, Jeremie
通讯作者：
Guedj, Jeremie