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m(6)A Regulator-Mediated Methylation Modification Patterns and Characteristics of Immunity in Blood Leukocytes of COVID-19 Patients
Both RNA N6-methyladenosine (m(6)A) 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 m(6)A methylation modification of leukocytes responds to the virus infection remains unknown. Based on the RNA-seq of 126...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8669770/ https://www.ncbi.nlm.nih.gov/pubmed/34917088 http://dx.doi.org/10.3389/fimmu.2021.774776 |
Sumario: | Both RNA N6-methyladenosine (m(6)A) 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 m(6)A 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 m(6)A 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, m(6)A cluster A characterized by T cell activation had a higher prognosis than m(6)A cluster B. Elevated metabolism level, blockage of the immune checkpoint, and lower level of m6A score were observed in m(6)A 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 m(6)A methylation modification and host lymphocytes in patients infected with SARS-CoV-2 and provided a well-performing model to predict the patients’ outcomes. |
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