COVID-19 virtual patient cohort suggests immune mechanisms driving disease outcomes.
COVID-19 virtual patient cohort suggests immune mechanisms driving disease outcomes.
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DOI：
10.1371/journal.ppat.1009753
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发表时间：
2021-07
期刊：
影响因子：
6.7
通讯作者：
Craig M
中科院分区：
文献类型：
--
作者：
Jenner AL;Aogo RA;Alfonso S;Crowe V;Deng X;Smith AP;Morel PA;Davis CL;Smith AM;Craig M
为了了解SARS-CoV-2免疫反应的多样性和区分易患严重新冠肺炎的个体的特征，我们开发了一个机械性的、宿主内的数学模型和虚拟患者队列。我们的结果表明，与早期和强劲的干扰素应答相比，感染细胞来源的干扰素产生率低的虚拟患者随后经历了高度炎症性疾病表型。在这些矽肺患者中，IL-6的最大浓度也是CD8+T细胞耗竭的主要预测因素。我们的分析预测，严重新冠肺炎患者还可以通过增加IL-6和减少I型干扰素信号来加速单核细胞向巨噬细胞的分化。综上所述，这些发现表明，生物标志物推动了严重新冠肺炎的发展，并支持旨在减少炎症的早期干预。了解对SARS-CoV-2感染的免疫反应的多样性对于改进诊断和治疗方法至关重要。确定哪些免疫机制导致不同的结果在临床上可能很困难，实验模型和纵向数据才刚刚开始出现。作为回应，我们开发了新冠肺炎免疫病理学的机制、数学和计算模型，该模型根据广泛的实验和临床免疫学数据进行校准和独立验证。为了研究严重新冠肺炎的驱动因素，我们使用我们的模型扩大了虚拟患者的队列，每个患者都具有真实的疾病动力学。我们的结果提示了调节虚拟患者对SARS-CoV-2感染的免疫反应的关键过程，并提出了可行的治疗靶点，强调了使用宿主内模型研究新病原体的合理、多方面方法的重要性。
To understand the diversity of immune responses to SARS-CoV-2 and distinguish features that predispose individuals to severe COVID-19, we developed a mechanistic, within-host mathematical model and virtual patient cohort. Our results suggest that virtual patients with low production rates of infected cell derived IFN subsequently experienced highly inflammatory disease phenotypes, compared to those with early and robust IFN responses. In these in silico patients, the maximum concentration of IL-6 was also a major predictor of CD8+ T cell depletion. Our analyses predicted that individuals with severe COVID-19 also have accelerated monocyte-to-macrophage differentiation mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings suggest biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation. Understanding of the diversity of immune responses to SARS-CoV-2 infections is critical for improving diagnostic and treatment approaches. Identifying which immune mechanisms lead to divergent outcomes can be clinically difficult, and experimental models and longitudinal data are only beginning to emerge. In response, we developed a mechanistic, mathematical and computational model of the immunopathology of COVID-19 calibrated to and independently validated against a broad set of experimental and clinical immunological data. To study the drivers of severe COVID-19, we used our model to expand a cohort of virtual patients, each with realistic disease dynamics. Our results suggest key processes that regulate the immune response to SARS-CoV-2 infection in virtual patients and suggest viable therapeutic targets, underlining the importance of a rational, multifaceted approach to studying novel pathogens using intra-host models.