m6A Regulator-Mediated Methylation Modification Patterns and Characteristics of Immunity in Blood Leukocytes of COVID-19 Patients

Frontiers in Immunology

Xiaoliang Hua¹  Xiangmin Qiu³  Qianyin Li³  Juan Chen³  Qin Zhou³ 

[1] Anhui Province Key Laboratory of Genitourinary Diseases, Anhui Medical University, Hefei, China;Department of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, China;The Ministry of Education Key Laboratory of Laboratory Medical Diagnostics, The College of Laboratory Medicine, Chongqing Medical University, Chongqing, China;

关键词: COVID-19;    immune characteristics;    m6A methylation modification;    protective model;    leukocytes;   

DOI  :  10.3389/fimmu.2021.774776

来源: DOAJ

【 摘 要 】

Both RNA N6-methyladenosine (m6A) modification of SARS-CoV-2 and immune characteristics of the human body have been reported to play an important role in COVID-19, but how the m6A methylation modification of leukocytes responds to the virus infection remains unknown. Based on the RNA-seq of 126 samples from the GEO database, we disclosed that there is a remarkably higher m6A modification level of blood leukocytes in patients with COVID-19 compared to patients without COVID-19, and this difference was related to CD4+ T cells. Two clusters were identified by unsupervised clustering, m6A cluster A characterized by T cell activation had a higher prognosis than m6A cluster B. Elevated metabolism level, blockage of the immune checkpoint, and lower level of m6A score were observed in m6A cluster B. A protective model was constructed based on nine selected genes and it exhibited an excellent predictive value in COVID-19. Further analysis revealed that the protective score was positively correlated to HFD45 and ventilator-free days, while negatively correlated to SOFA score, APACHE-II score, and crp. Our works systematically depicted a complicated correlation between m6A methylation modification and host lymphocytes in patients infected with SARS-CoV-2 and provided a well-performing model to predict the patients’ outcomes.

【 授权许可】

Unknown