Department of Mathematics, University of Tennessee, Knoxville, TN, United States.
Department of Electrical Engineering & Computer Science, University of Tennessee, Knoxville, TN, United States.
Front Immunol. 2021 Oct 28;12:754127. doi: 10.3389/fimmu.2021.754127. eCollection 2021.
COVID-19 presentations range from mild to moderate through severe disease but also manifest with persistent illness or viral recrudescence. We hypothesized that the spectrum of COVID-19 disease manifestations was a consequence of SARS-CoV-2-mediated delay in the pathogen-associated molecular pattern (PAMP) response, including dampened type I interferon signaling, thereby shifting the balance of the immune response to be dominated by damage-associated molecular pattern (DAMP) signaling. To test the hypothesis, we constructed a parsimonious mechanistic mathematical model. After calibration of the model for initial viral load and then by varying a few key parameters, we show that the core model generates four distinct viral load, immune response and associated disease trajectories termed "patient archetypes", whose temporal dynamics are reflected in clinical data from hospitalized COVID-19 patients. The model also accounts for responses to corticosteroid therapy and predicts that vaccine-induced neutralizing antibodies and cellular memory will be protective, including from severe COVID-19 disease. This generalizable modeling framework could be used to analyze protective and pathogenic immune responses to diverse viral infections.
COVID-19 的表现形式从轻度到中度到重度疾病不等,但也表现为持续的疾病或病毒复发。我们假设 COVID-19 疾病表现的范围是 SARS-CoV-2 介导的病原体相关分子模式(PAMP)反应延迟的结果,包括减弱的 I 型干扰素信号,从而使免疫反应的平衡转向以损伤相关分子模式(DAMP)信号为主导。为了验证这一假设,我们构建了一个简约的机械数学模型。在对初始病毒载量进行模型校准后,然后通过改变几个关键参数,我们表明核心模型产生了四种不同的病毒载量、免疫反应和相关疾病轨迹,称为“患者原型”,其时间动态反映了住院 COVID-19 患者的临床数据。该模型还解释了皮质类固醇治疗的反应,并预测疫苗诱导的中和抗体和细胞记忆将具有保护作用,包括预防严重的 COVID-19 疾病。这种可推广的建模框架可用于分析针对不同病毒感染的保护性和致病性免疫反应。