Ribavirin inhibits the growth and ascites formation of hepatocellular carcinoma through downregulation of type I CARM1 and type II PRMT5.

Type I co-activator-associated arginine methyltransferase 1 (CARM1) and type II protein arginine methyltransferase 5 (PRMT5) are highly expressed in multiple cancers including liver cancer and their overexpression contributes to poor prognosis, thus making them promising therapeutic targets. Here, we evaluated anti-tumor activity of ribavirin in hepatocellular carcinoma (HCC). We found that ribavirin significantly inhibited the proliferation of HCC cells in a time- and dose-dependent manner. Furthermore, ribavirin suppressed the growth of subcutaneous and orthotopic xenograft of HCC in mice, decreased vascular endothelial growth factor (VEGF) and peritoneal permeability to reduce ascites production, and prolonged the survival of mice in HCC ascites tumor models. Mechanistically, ribavirin potently down-regulated global protein expression of CARM1 and PRMT5, and concurrently decreased accumulation of H3R17me2a and H3R8me2s/H4R3me2s. However, ribavirin did not affect the activity and mRNA levels of both CARM1 and PRMT5 in vivo and in vitro HCC cells. In addition, our ChIP results shown that ribavirin inhibited CARM1 which in turn decreased the H3R17me2a, binds to the eukaryotic translation initiation factor 4E (eIF4E) and VEGF promoter region, and reduced the relative mRNA expression level of eIF4E and VEGF in HCC cells. Our findings suggested a potential therapeutic strategy for patients with HCC through inhibition of the abnormal activation/expression of both CARM1 and PRMT5.

Transcriptome-Wide Annotation of m5C RNA Modifications Using Machine Learning.

The emergence of epitranscriptome opened a new chapter in gene regulation. 5-methylcytosine (m5C), as an important post-transcriptional modification, has been identified to be involved in a variety of biological processes such as subcellular localization and translational fidelity. Though high-throughput experimental technologies have been developed and applied to profile m5C modifications under certain conditions, transcriptome-wide studies of m5C modifications are still hindered by the dynamic and reversible nature of m5C and the lack of computational prediction methods. In this study, we introduced PEA-m5C, a machine learning-based m5C predictor trained with features extracted from the flanking sequence of m5C modifications. PEA-m5C yielded an average AUC (area under the receiver operating characteristic) of 0.939 in 10-fold cross-validation experiments based on known Arabidopsis m5C modifications. A rigorous independent testing showed that PEA-m5C (Accuracy [Acc] = 0.835, Matthews correlation coefficient [MCC] = 0.688) is remarkably superior to the recently developed m5C predictor iRNAm5C-PseDNC (Acc = 0.665, MCC = 0.332). PEA-m5C has been applied to predict candidate m5C modifications in annotated Arabidopsis transcripts. Further analysis of these m5C candidates showed that 4nt downstream of the translational start site is the most frequently methylated position. PEA-m5C is freely available to academic users at: https://github.com/cma2015/PEA-m5C.

Corrigendum: Transcriptome-Wide Annotation of m5C RNA Modifications Using Machine Learning.

[This corrects the article DOI: 10.3389/fpls.2018.00519.].