In Molecular systems biology
DNA methylation comprises a cumulative record of lifetime exposures superimposed on genetically determined markers. Little is known about methylation dynamics in humans following an acute perturbation, such as infection. We characterized the temporal trajectory of blood epigenetic remodeling in 133 participants in a prospective study of young adults before, during, and after asymptomatic and mildly symptomatic SARS-CoV-2 infection. The differential methylation caused by asymptomatic or mildly symptomatic infections was indistinguishable. While differential gene expression largely returned to baseline levels after the virus became undetectable, some differentially methylated sites persisted for months of follow-up, with a pattern resembling autoimmune or inflammatory disease. We leveraged these responses to construct methylation-based machine learning models that distinguished samples from pre-, during-, and postinfection time periods, and quantitatively predicted the time since infection. The clinical trajectory in the young adults and in a diverse cohort with more severe outcomes was predicted by the similarity of methylation before or early after SARS-CoV-2 infection to the model-defined postinfection state. Unlike the phenomenon of trained immunity, the postacute SARS-CoV-2 epigenetic landscape we identify is antiprotective.
Mao Weiguang, Miller Clare M, Nair Venugopalan D, Ge Yongchao, Amper Mary Anne S, Cappuccio Antonio, George Mary-Catherine, Goforth Carl W, Guevara Kristy, Marjanovic Nada, Nudelman German, Pincas Hanna, Ramos Irene, Sealfon Rachel S G, Soares-Schanoski Alessandra, Vangeti Sindhu, Vasoya Mital, Weir Dawn L, Zaslavsky Elena, Kim-Schulze Seunghee, Gnjatic Sacha, Merad Miriam, Letizia Andrew G, Troyanskaya Olga G, Sealfon Stuart C, Chikina Maria
DNA methylation, SARS-CoV-2, machine learning model, temporal dynamics, trained immunity