Division of Biomedical Convergence, College of Biomedical Science, Institute of Bioscience & Biotechnology, Kangwon National University, Chuncheon, 24341, Korea.
Division of Healthcare and Artificial Intelligence, Department of Precision Medicine, Korea National Institute of Health, Osong, 28159, Korea.
BMC Infect Dis. 2024 Nov 4;24(1):1237. doi: 10.1186/s12879-024-10139-z.
Numerous studies have investigated the molecular properties that contribute to the symptoms of COVID-19, such as the virus's genetic makeup, its replication mechanisms, and how it interacts with host cells. However, identifying the immunopathological properties, such as the immune system's response, cytokine levels, and the presence of specific biomarkers, that are associated with the severity of the infection remains crucial for developing effective treatments and preventions.
We analyzed blood protein factor profiles from 420 individuals to identify features differentiating between test-negative healthy, asymptomatic, and symptomatic individuals using statistical comparison and the least absolute shrinkage and selection operator (i.e., LASSO) algorithm. Additionally, we examined single-cell RNA sequencing data from 141 individuals to identify specific cell types associated with the COVID-19 symptoms.
Healthy individuals who tested negative had distinct blood protein factor levels compared to asymptomatic individuals. We identified two key protein factors, Serpin A10 and Complement C9, that differentiate between asymptomatic and symptomatic patients. Symptomatic patients showed lower levels of CD4 T naïve, CD4 T effector & memory, and CD8 T naïve cells, along with higher levels of CD14 classical monocytes compared to asymptomatic patients. Additionally, CD16 non-classical monocytes, major producers of C1QA/B/C, appeared to contribute to the observed Complement C9 levels.
These findings advance our understanding of the immunopathological mechanisms underlying COVID-19 and may inform the development of targeted therapies and preventative measures. Future research should focus on further elucidating these mechanisms and exploring their potential clinical applications in managing COVID-19 severity.
许多研究已经探究了导致 COVID-19 症状的分子特性，例如病毒的遗传构成、复制机制以及它与宿主细胞的相互作用。然而，确定与感染严重程度相关的免疫病理特性，如免疫系统的反应、细胞因子水平和特定生物标志物的存在，对于开发有效的治疗和预防方法仍然至关重要。
我们分析了 420 个人的血液蛋白因子谱，使用统计比较和最小绝对收缩和选择算子（即 LASSO）算法来识别区分阴性健康、无症状和有症状个体的特征。此外，我们还检查了 141 个人的单细胞 RNA 测序数据，以识别与 COVID-19 症状相关的特定细胞类型。
阴性健康个体与无症状个体的血液蛋白因子水平存在明显差异。我们确定了两个关键的蛋白因子，Serpin A10 和 Complement C9，它们可以区分无症状和有症状的患者。与无症状患者相比，有症状患者的 CD4+T 幼稚细胞、CD4+T 效应器和记忆细胞以及 CD8+T 幼稚细胞水平较低，而 CD14+经典单核细胞水平较高。此外，CD16+非经典单核细胞，C1QA/B/C 的主要产生者，似乎对观察到的 Complement C9 水平有贡献。
这些发现加深了我们对 COVID-19 免疫病理机制的理解，并可能为靶向治疗和预防措施的发展提供信息。未来的研究应重点进一步阐明这些机制，并探索它们在管理 COVID-19 严重程度方面的潜在临床应用。
