ABSTRACT
Mitochondrial antiviral signaling (MAVS) protein is the core signaling adaptor in the RNA signaling pathway. Thus, appropriate regulation of MAVS expression is essential for antiviral immunity against RNA virus infection. However, the regulation of MAVS expression at the mRNA level especially at the post transcriptional level is not well-defined. Here, it is reported that the MAVS mRNA undergoes N6 -methyladenosine (m6 A) modification through methyltransferase-like protein 14 (METTL14), which leads to a fast turnover of MAVS mRNA. Knockdown or deficiency of METTL14 increases MAVS mRNA stability, and downstream phosphorylation of TBK1/IRF3 and interferon-ß production in response to RNA viruses. Compared to wild-type mice, heterozygotes Mettl14+/- mice better tolerate RNA virus infection. The authors' findings unveil a novel mechanism to regulate the stability of MAVS transcripts post-transcriptionally through m6 A modification.
Subject(s)
Adaptor Proteins, Signal Transducing/immunology , Adaptor Proteins, Signal Transducing/metabolism , Adenosine/analogs & derivatives , Methyltransferases/immunology , Methyltransferases/metabolism , Adaptor Proteins, Signal Transducing/genetics , Adenosine/genetics , Adenosine/immunology , Adenosine/metabolism , Animals , Disease Models, Animal , Humans , Immunity, Innate/genetics , Immunity, Innate/immunology , Methyltransferases/genetics , Mice , Mice, Inbred C57BL , Signal Transduction/genetics , Signal Transduction/immunologyABSTRACT
Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with a poor prognosis. The current coronavirus disease 2019 (COVID-19) shares some similarities with IPF. SARS-CoV-2 related genes have been reported to be broadly regulated by N6-methyladenosine (m6A) RNA modification. Here, we identified the association between m6A methylation regulators, COVID-19 infection pathways, and immune responses in IPF. The characteristic gene expression networks and immune infiltration patterns of m6A-SARS-CoV-2 related genes in different tissues of IPF were revealed. We subsequently evaluated the influence of these related gene expression patterns and immune infiltration patterns on the prognosis/lung function of IPF patients. The IPF cohort was obtained from the Gene Expression Omnibus dataset. Pearson correlation analysis was performed to identify the correlations among genes or cells. The CIBERSORT algorithm was used to assess the infiltration of 22 types of immune cells. The least absolute shrinkage and selection operator (LASSO) and proportional hazards model (Cox model) were used to develop the prognosis prediction model. Our research is pivotal for further understanding of the cellular and genetic links between IPF and SARS-CoV-2 infection in the context of the COVID-19 pandemic, which may contribute to providing new ideas for prognosis assessment and treatment of both diseases.