Background: 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.
Methods: 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.
Results: 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.
Conclusions: 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.
Keywords: Asymptomatic; COVID-19; Machine learning; Severity; Single-cell RNA sequencing.
© 2024. The Author(s).