National Key Laboratory for Shock Wave and Detonation Physics Research, Institute of Fluid Physics, Chinese Academy of Engineering Physics, Mianyang, China.
Department of Neurosurgery and Key Laboratory of Neurotrauma, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqin, China.
Front Immunol. 2023 Jan 26;14:974343. doi: 10.3389/fimmu.2023.974343. eCollection 2023.
The COVID-19 pandemic has posed a major burden on healthcare and economic systems across the globe for over 3 years. Even though vaccines are available, the pathogenesis is still unclear. Multiple studies have indicated heterogeneity of immune responses to SARS-CoV-2, and potentially distinct patient immune types that might be related to disease features. However, those conclusions are mainly inferred by comparing the differences of pathological features between moderate and severe patients, some immunological features may be subjectively overlooked.
In this study, the relevance scores(RS), reflecting which features play a more critical role in the decision-making process, between immunological features and the COVID-19 severity are objectively calculated through neural network, where the input features include the immune cell counts and the activation marker concentrations of particular cell, and these quantified characteristic data are robustly generated by processing flow cytometry data sets containing the peripheral blood information of COVID-19 patients through PhenoGraph algorithm.
Specifically, the RS between immune cell counts and COVID-19 severity with time indicated that the innate immune responses in severe patients are delayed at the early stage, and the continuous decrease of classical monocytes in peripherial blood is significantly associated with the severity of disease. The RS between activation marker concentrations and COVID-19 severity suggested that the down-regulation of IFN-γ in classical monocytes, Treg, CD8 T cells, and the not down-regulation of IL_17a in classical monocytes, Tregs are highly correlated with the occurrence of severe disease. Finally, a concise dynamic model of immune responses in COVID-19 patients was generalized.
These results suggest that the delayed innate immune responses in the early stage, and the abnormal expression of IL-17a and IFN-γ in classical monocytes, Tregs, and CD8 T cells are primarily responsible for the severity of COVID-19.
COVID-19 大流行已经对全球的医疗保健和经济系统造成了超过 3 年的重大负担。尽管疫苗已经可用，但发病机制仍不清楚。多项研究表明，针对 SARS-CoV-2 的免疫反应存在异质性，并且可能存在与疾病特征相关的不同患者免疫类型。然而，这些结论主要是通过比较中度和重度患者之间的病理特征差异推断出来的，一些免疫特征可能会被主观忽略。
在这项研究中，通过神经网络客观地计算了免疫特征与 COVID-19 严重程度之间的相关性评分（RS），反映了哪些特征在决策过程中起着更关键的作用，其中输入特征包括免疫细胞计数和特定细胞的激活标志物浓度，这些量化特征数据是通过 PhenoGraph 算法处理包含 COVID-19 患者外周血信息的流式细胞数据集稳健生成的。
具体而言，免疫细胞计数与 COVID-19 严重程度随时间的 RS 表明，重度患者的固有免疫反应在早期延迟，外周血中经典单核细胞的持续减少与疾病严重程度显著相关。激活标志物浓度与 COVID-19 严重程度之间的 RS 表明，经典单核细胞、Treg 和 CD8 T 细胞中 IFN-γ的下调以及经典单核细胞、Tregs 中 IL_17a 的下调与重度疾病的发生高度相关。最后，概括了 COVID-19 患者免疫反应的简明动态模型。
这些结果表明，早期固有免疫反应的延迟，以及经典单核细胞、Treg 和 CD8 T 细胞中 IL-17a 和 IFN-γ的异常表达是 COVID-19 严重程度的主要原因。
