State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, China.
Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China.
Front Immunol. 2021 Aug 24;12:677025. doi: 10.3389/fimmu.2021.677025. eCollection 2021.
The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a global crisis; however, our current understanding of the host immune response to SARS-CoV-2 infection remains limited. Herein, we performed RNA sequencing using peripheral blood from acute and convalescent patients and interrogated the dynamic changes of adaptive immune response to SARS-CoV-2 infection over time. Our results revealed numerous alterations in these cohorts in terms of gene expression profiles and the features of immune repertoire. Moreover, a machine learning method was developed and resulted in the identification of five independent biomarkers and a collection of biomarkers that could accurately differentiate and predict the development of COVID-19. Interestingly, the increased expression of one of these biomarkers, , a molecule related to nervous system damage, was associated with the clustering of severe symptoms. Importantly, analyses on immune repertoire metrics revealed the distinct kinetics of T-cell and B-cell responses to SARS-CoV-2 infection, with B-cell response plateaued in the acute phase and declined thereafter, whereas T-cell response can be maintained for up to 6 months post-infection onset and T-cell clonality was positively correlated with the serum level of anti-SARS-CoV-2 IgG. Together, the significantly altered genes or biomarkers, as well as the abnormally high levels of B-cell response in acute infection, may contribute to the pathogenesis of COVID-19 through mediating inflammation and immune responses, whereas prolonged T-cell response in the convalescents might help these patients in preventing reinfection. Thus, our findings could provide insight into the underlying molecular mechanism of host immune response to COVID-19 and facilitate the development of novel therapeutic strategies and effective vaccines.
新型冠状病毒病 2019（COVID-19）大流行是由严重急性呼吸综合征冠状病毒 2（SARS-CoV-2）感染引起的全球危机；然而，我们目前对宿主对 SARS-CoV-2 感染的免疫反应的理解仍然有限。在此，我们使用急性和恢复期患者的外周血进行了 RNA 测序，并探究了适应性免疫反应随时间推移对 SARS-CoV-2 感染的动态变化。我们的结果表明，在这些队列中，基因表达谱和免疫库特征都发生了许多改变。此外，我们开发了一种机器学习方法，结果确定了五个独立的生物标志物和一组生物标志物，这些标志物可以准确地区分和预测 COVID-19 的发生。有趣的是，其中一个标志物的表达增加，即与神经系统损伤有关的分子，与严重症状的聚类有关。重要的是，对免疫库指标的分析揭示了 T 细胞和 B 细胞对 SARS-CoV-2 感染的反应动力学明显不同，B 细胞反应在急性期达到平台期，随后下降，而 T 细胞反应可在感染后持续长达 6 个月，T 细胞克隆性与抗 SARS-CoV-2 IgG 的血清水平呈正相关。总之，显著改变的基因或生物标志物，以及急性感染中 B 细胞反应的异常升高，可能通过介导炎症和免疫反应导致 COVID-19 的发病机制，而恢复期的延长的 T 细胞反应可能有助于这些患者预防再感染。因此，我们的发现可以深入了解宿主对 COVID-19 的免疫反应的潜在分子机制，并促进新型治疗策略和有效疫苗的开发。
