Identification of Whole-Blood DNA Methylation Signatures and Rules Associated with COVID-19 Severity

Abstract

Background: Different severities of coronavirus disease 2019 (COVID-19) cause different levels of respiratory symptoms and systemic inflammation. DNA methylation, a heritable epigenetic process, also shows differential changes in different severities of COVID-19. DNA methylation is involved in regulating the activity of various immune cells and influences immune pathways associated with viral infections. It may also be involved in regulating the expression of genes associated with the progression of COVID-19. Methods: In this study, a sophisticated machine-learning workflow was designed to analyze whole-blood DNA methylation data from COVID-19 patients with different severities versus healthy controls. We aimed to understand the role of DNA methylation in the development of COVID-19. The sample set contained 101 negative controls, 360 mildly infected individuals, and 113 severely infected individuals. Each sample involved 768,067 methylation sites. Three feature-ranking algorithms (least absolute shrinkage and selection operator (LASSO), light gradient-boosting machine (LightGBM), and Monte Carlo feature selection (MCFS)) were used to rank and filter out sites highly correlated with COVID-19. Based on the obtained ranking results, a high-performance classification model was constructed by combining the feature incremental approach with four classification algorithms (decision tree (DT), k-nearest neighbor (kNN), random forest (RF), and support vector machine (SVM)). Results: Some essential methylation sites and decision rules were obtained. Conclusions: The genes (IGSF6, CD38, and TLR2) of some essential methylation sites were confirmed to play important roles in the immune system.

Keywords

• covid-19 severity
• dna methylation
• machine learning
• rules