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RNA 3D structures are critical for understanding their functions. However, only a limited number of RNA structures have been experimentally solved, so computational prediction methods are highly desirable. Nevertheless, accurate prediction of RNA 3D structures, especially those containing multiway junctions, remains a significant challenge, mainly due to the complicated non-canonical base pairing and stacking interactions in the junction loops and the possible long-range interactions between loop structures. Here we present RNAJP (‘RNA Junction Prediction’), a nucleotide- and helix-level coarse-grained model for the prediction of RNA 3D structures, particularly junction structures, from a given 2D structure. Through global sampling of the 3D arrangements of the helices in junctions using molecular dynamics simulations and in explicit consideration of non-canonical base pairing and base stacking interactions as well as long-range loop–loop interactions, the model can provide significantly improved predictions for multibranched junction structures than existing methods. Moreover, integrated with additional restraints from experiments, such as junction topology and long-range interactions, the model may serve as a useful structure generator for various applications.

Supplementary Table S3. Pathway analysis of differentially expressed genes in RNA-seq

Supplementary Figure S1. p300 is a potential target in HCC. Supplementary Figure S2. B029-2 is a potential and selective catalytic p300/CBP inhibitor that targets HCC.Supplementary Figure S3. Targeting p300 suppresses the cell-proliferation of HCC cells.Supplementary Figure S4. B029-2 regulates the expression of ribosome protein genes.Supplementary Figure S5. The expression status of metabolic enzymes in liver hepatocellular carcinoma (LIHC) patients.Supplementary Figure S6. Epigenetic regulation of metabolic enzymes in HCC via the histone acetyltransferase activity of p300/CBP.Supplementary Figure S7. B029-2 treatment reduced the occupation of p300 and BRD4 on the promoters of the six metabolic genes.Supplementary Figure S8. Suppressing the expression of PSPH and DTYMK inhibits HCC progression.Supplementary Figure S9. Overexpression of PSPH and DTYMK partially reversed the inhibitory effects of B029-2 on HCC cells.Supplementary Figure S10. Overexpression of PSPH and DTYMK partially reversed the inhibitory effects of B029-2 on HCC xenografts.

Targeting epigenetics in cancer has emerged as a promising anticancer strategy. p300/CBP is a central regulator of epigenetics and plays an important role in hepatocellular carcinoma (HCC) progression. Tumor-associated metabolic alterations contribute to the establishment and maintenance of the tumorigenic state. In this study, we used a novel p300 inhibitor, B029-2, to investigate the effect of targeting p300/CBP in HCC and tumor metabolism. p300/CBP–mediated acetylation of H3K18 and H3K27 increased in HCC tissues compared with surrounding noncancerous tissues. Conversely, treatment with B029-2 specifically decreased H3K18Ac and H3K27Ac and displayed significant antitumor effects in HCC cells in vitro and in vivo. Importantly, ATAC-seq and RNA-seq integrated analysis revealed that B029-2 disturbed metabolic reprogramming in HCC cells. Moreover, B029-2 decreased glycolytic function and nucleotide synthesis in Huh7 cells by reducing H3K18Ac and H3K27Ac levels at the promoter regions of amino acid metabolism and nucleotide synthesis enzyme genes, including PSPH, PSAT1, ALDH18A1, TALDO1, ATIC, and DTYMK. Overexpression of PSPH and DTYMK partially reversed the inhibitory effect of B029-2 on HCC cells. These findings suggested that p300/CBP epigenetically regulates the expression of glycolysis-related metabolic enzymes through modulation of histone acetylation in HCC and highlights the value of targeting the histone acetyltransferase activity of p300/CBP for HCC therapy.Significance:This study demonstrates p300/CBP as a critical epigenetic regulator of glycolysis-related metabolic enzymes in HCC and identifies the p300/CBP inhibitor B029-2 as a potential therapeutic strategy in this disease.

Adeno-associated virus (AAV)-mediated clustered regularly interspaced short palindromic repeats (CRISPR) editing holds promise to restore missing dystrophin in Duchenne muscular dystrophy (DMD). Intramuscular coinjection of CRISPR-associated protein 9 (Cas9) and guide RNA (gRNA) vectors resulted in robust dystrophin restoration in short-term studies in the mdx mouse model of DMD. Intriguingly, this strategy failed to yield efficient dystrophin rescue in muscle in a long-term (18-month) systemic injection study. In-depth analyses revealed a selective loss of the gRNA vector after long-term systemic, but not short-term local injection. To determine whether preferential gRNA vector depletion is due to the mode of delivery (local vs. systemic) or the duration of the study (short term vs. long term), we conducted a short-term systemic injection study. The gRNA (4e12 vg/mouse in the 1:1 group or 1.2e13 vg/mouse in the 3:1 group) and Cas9 (4e12 vg/mouse) vectors were coinjected intravenously into 4-week-old mdx mice. The ratio of the gRNA to Cas9 vector genome copy dropped from 1:1 and 3:1 at injection to 0.4:1 and 1:1 at harvest 3 months later, suggesting that the route of administration, rather than the experimental duration, determines preferential gRNA vector loss. Consistent with our long-term systemic injection study, the vector ratio did not influence Cas9 expression. However, the 3:1 group showed significantly higher dystrophin expression and genome editing, better myofiber size distribution, and a more pronounced improvement in muscle function and electrocardiography. Our data suggest that the gRNA vector dose determines the outcome of systemic AAV CRISPR therapy for DMD.

RNA molecules play critical roles in cellular functions at the level of gene expression and regulation. The intricate 3D structures and the functional roles of RNAs make RNA molecules ideal targets for therapeutic drugs. The rational design of RNA-targeted drug requires accurate modeling of RNA-ligand interactions. Recently a new computational tool, RLDOCK, was developed to predict ligand binding sites and binding poses. Using an iterative multiscale sampling and search algorithm and a energy-based evaluation of ligand poses, the method enables efficient and accurate predictions for RNA-ligand interactions. Here we present a detailed illustration of the computational procedure for the practical implementation of the RLDOCK method. Using Flavin mononucleotide (FMN) docking to F. nucleatum FMN riboswitch as an example, we illustrate the computational protocol for RLDOCK-based prediction of RNA- ligand interactions. The RLDOCK software is freely accessible at http://https://github.com/Vfold-RNA/RLDOCK.

Protein arginine methyltransferase 1 (PRMT1) has been reported to be involved in various diseases. The expression of PRMT1 was increased in cirrhotic livers from human patients. However, the role of PRMT1 in hepatic fibrogenesis remains largely unexplored. In this study, we investigated the effect of PRMT1 on hepatic fibrogenesis and its underlying mechanism. We found that PRMT1 expression was significantly higher in fibrotic livers of the mice treated with thioacetamide (TAA) or 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC) diet. Immunofluorescence staining revealed that PRMT1 expression was augmented in both hepatocytes and hepatic stellate cells (HSCs) in the fibrotic livers. Applying a selective inhibitor of PRMT1, PT1001B, significantly suppressed PRMT1 activity and mitigated liver fibrosis in mice. Hepatocyte-specific Prmt1 knockout did not affect liver fibrosis in mice. PRMT1 overexpression promoted the expression of fibrotic genes in the LX-2 cells, whereas knockdown of PRMT1 or treatment with PT1001B exhibited reversal effects, suggesting that PRMT1 plays an important role in HSC activation. Additionally, HSC-specific Prmt1 knockout attenuated HSC activation and liver fibrosis in TAA-induced fibrotic model. RNA-seq analysis revealed that Prmt1 knockout in HSCs significantly suppressed pro-inflammatory NF-κB and pro-fibrotic TGF-β signals, and also downregulated the expression of pro-fibrotic mediators in mouse livers. Moreover, treatment with PT1001B consistently inhibited hepatic inflammatory response in fibrotic model. In conclusion, PRMT1 plays a vital role in HSC activation. Inhibition of PRMT1 mitigates hepatic fibrosis by attenuating HSC activation in mice. Therefore, targeting PRMT1 could be a feasible therapeutic strategy for liver fibrosis.

RNA molecules fold as they are transcribed. Cotranscriptional folding of RNA plays a critical role in RNA functions in vivo. Present computational strategies focus on simulations where large structural changes may not be completely sampled. Here we describe an alternative approach to predicting cotranscriptional RNA folding by zooming in and out of the RNA folding energy landscape. By classifying the RNA structural ensemble into “partitions” based on long, stable helices, we zoom out of the landscape and predict the overall slow folding kinetics from the inter-partition kinetic network, and for each inter-partition transition, we zoom in on the landscape to simulate the kinetics. Applications of the model to the 117-nucleotide E. coli SRP RNA and the 59-nucleotide HIV-1 TAR RNA show agreements with the experimental data and new structural and kinetic insights into biologically significant conformational switches and pathways for these important systems. This approach, by zooming in/out of RNA folding landscape at different resolutions, might allow us to treat large RNAs in vivo with transcriptional pause, transcription speed, and other in vivo effects.

A spiro

The outbreak of the COVID-19 pandemic has led to intensive studies of both the structure and replication mechanism of SARS-CoV-2. In spite of some secondary structure experiments being carried out, the 3D structure of the key function regions of the viral RNA has not yet been well understood. At the beginning of COVID-19 breakout, RNA-Puzzles community attempted to envisage the three-dimensional structure of 5′- and 3′-Un-Translated Regions (UTRs) of the SARS-CoV-2 genome. Here, we report the results of this prediction challenge, presenting the methodologies developed by six participating groups and discussing 100 RNA 3D models (60 models of 5′-UTR and 40 of 3′-UTR) predicted through applying both human experts and automated server approaches. We describe the original protocol for the reference-free comparative analysis of RNA 3D structures designed especially for this challenge. We elaborate on the deduced consensus structure and the reliability of the predicted structural motifs. All the computationally simulated models, as well as the development and the testing of computational tools dedicated to 3D structure analysis, are available for further study.

Summary RNA 3D structures are critical for understanding their functions and for RNA-targeted drug design. However, experimental determination of RNA 3D structures is laborious and technically challenging, leading to the huge gap between the number of sequences and the availability of RNA structures. Therefore, the computer-aided structure prediction of RNA 3D structures from sequences becomes a highly desirable solution to this problem. Here, we present a pipeline server for RNA 3D structure prediction from sequences that integrates the Vfold2D, Vfold3D and VfoldLA programs. The Vfold2D program can incorporate the SHAPE experimental data in 2D structure prediction. The pipeline can also automatically extract 2D structural constraints from the Rfam database. Furthermore, with a significantly expanded 3D template database for various motifs, this Vfold-Pipeline server can efficiently return accurate 3D structure predictions or reliable initial 3D structures for further refinement. Availability and implementation http://rna.physics.missouri.edu/vfoldPipeline/index.html. The data underlying this article have been provided in the article and in its online supplementary material. Supplementary information Supplementary data are available at Bioinformatics online.