Background Reactivation of Epstein Barr virus (EBV) leads to modulation of the viral and cellular epitranscriptome. N6-methyladenosine (m6A) modification is a type of RNA modification that regulates metabolism of mRNAs. Previous reports demonstrated that m6A modification affects the stability and metabolism of EBV encoded mRNAs. However, the effect of reactivation on reprograming of the cellular mRNAs, and how this contributes to successful induction of lytic reactivation is not known. Methods Methylated RNA immunoprecipitation sequencing (MeRIP-seq), transcriptomic RNA sequencing (RNA-seq) and RNA pull-down PCR were used to screen and validate differentially methylated targets. Western blotting, quantitative real-time PCR (RT-qPCR) and immunocytochemistry were used to investigate the expression and localization of different proteins. RNA stability and polysome analysis assays were used to detect the half-lives and translation efficiencies of downstream genes. Insertion of point mutation to disrupt the m6A methylation sites was used to verify the effect of m6A methylation on its stability and expression levels. Results We report that during EBV reactivation the m6A eraser ALKBH5 is significantly downregulated leading to enhanced methylation of the cellular transcripts DTX4 and TYK2, that results in degradation of TYK2 mRNAs and higher efficiency of translation of DTX4 mRNAs. This resulted in attenuation of IFN signaling that promoted progression of viral lytic replication. Furthermore, inhibition of m6A methylation of these transcripts led to increased production of IFN, and a substantial reduction in viral copy number, which suggests abrogation of lytic viral replication. Conclusion Our findings illuminate the significance of m6A modification in overcoming the innate immune response during EBV reactivation. We now report that during lytic reactivation EBV targets the RNA methylation system of the host to attenuate the innate immune response by suppressing the interferon signaling which facilitates successful lytic replication of the virus.
Spatial-resolved scRNA-seq (sp-scRNA-seq) technologies provide the potential to comprehensively profile the gene expression pattern in the tissue context. However, the development of computational methods does not catch up with the fast advances of technologies and fails to fully fulfill their potential. In this study, we develop a deep learning approach for clustering sp-scRNA-seq data, named Deep Spatial-constrained Single-cell Clustering (DSSC). In this model, we integrate the spatial information of cells into the clustering process by two steps: 1) the spatial information is encoded by using a graphical neural network model; 2) cell-to-cell constraints are built based on the spatially expression pattern of the marker genes and added in the model to guide the clustering process. Then, a deep embedding clustering is performed on the bottle-neck layer of autoencoder by Kullback-Leibler (KL) divergence along with the learning of feature representation. DSSC is the first model which can utilize the information from both the spatial coordinates and the marker genes to guide the cell/spot clustering. Extensive experiments on both simulated and real datasets demonstrate that DSSC boosts clustering performance significantly compared to the state-of-art methods. It has a robust performance over different datasets with various cell type/tissue organization and/or cell type/tissue spatial dependency. We conclude that DSSC is a promising tool for clustering sp-scRNA-seq data.
BackgroundImmunotherapy is a promising anti-cancer strategy in hepatocellular carcinoma (HCC). However, a limited number of patients can benefit from it. There are currently no reliable biomarkers available to find the potential beneficiaries. Methylcytosine (m5C) is crucial in HCC, but its role in forecasting clinical responses to immunotherapy has not been fully clarified.MethodsIn this study, we analyzed 371 HCC patients from The Cancer Genome Atlas (TCGA) database and investigated the expression of 18 m5C regulators. We selected 6 differentially expressed genes (DEGs) to construct a prognostic risk model as well as 2 m5C-related diagnostic models.ResultsThe 1-, 3-, and 5-year area under the curve (AUC) of m5C scores for the overall survival (OS) was 0.781/0.762/0.711, indicating the m5C score system had an ideal distinction of prognostic prediction for HCC. The survival analysis showed that patients with high-risk scores present a worse prognosis than the patients with low-risk scores (p< 0.0001). We got consistent results in 6 public cohorts and validated them in Xiangya real-world cohort by quantitative real-time PCR and immunohistochemical (IHC) assays. The high-m5C score group was predicted to be in an immune evasion state and showed low sensitivity to immunotherapy, but high sensitivity to chemotherapy and potential targeted drugs and agents, such as sepantronium bromide (YM-155), axitinib, vinblastine and docetaxel. Meanwhile, we also constructed two diagnostic models to distinguish HCC tumors from normal liver tissues or liver cirrhosis.ConclusionIn conclusion, our study helps to early screen HCC patients and select patients who can benefit from immunotherapy. Step forwardly, for the less likely beneficiaries, this study provides them with new potential targeted drugs and agents for choice to improve their prognosis.
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