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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.

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.

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.

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.

Noncanonical base pairs contribute crucially to the three-dimensional architecture of large RNA molecules; however, how to accurate model them remains an open challenge in RNA 3D structure prediction. Here we reported a promising coarse-grained IsRNA2 model to predict noncanonical base pairs in large RNAs through molecular dynamics simulations. By introducing a five-bead per nucleotide coarse-grained representation to reserve the three interacting edges of nucleobases, IsRNA2 accurately models various base pairing interactions, including both canonical and noncanonical base pairs. A benchmark test indicated that IsRNA2 achieves a comparable performance to the atomic model in de novo modeling of noncanonical RNA structures. In addition, IsRNA2 was able to refine the 3D structure predictions for large RNAs in RNA-Puzzles challenges. Finally, the graphics processing unit (GPU) acceleration was introduced to speed up the sampling efficiency in IsRNA2 for very large RNA molecules. Therefore, the coarse-grained IsRNA2 model reported here offers a reliable approach to predict structures and dynamics of large RNAs.

Accurate prediction of RNA structure and folding stability has a far-reaching impact on our understanding of RNA functions. Here we develop Vfold2D-MC, a new physics-based model, to predict RNA structure and folding thermodynamics from the sequence. The model employs virtual bond-based coarse-graining of RNA backbone conformation and generates RNA conformations through Monte Carlo sampling of the bond angles and torsional angles of the virtual bonds. Using a coarse-grained statistical potential derived from the known structures, we assign each conformation with a statistical weight. The weighted average over the conformational ensemble gives the entropy and free energy parameters for the hairpin, bulge, and internal loops, and multi-way junctions. From the thermodynamic parameters, we predict RNA structures, melting curves, and structural changes from the sequence. Theory-experiment comparisons indicate that Vfold2D-MC not only gives improved structure predictions but also enables the interpretation of thermodynamic results for different RNA structures, including multibranched junctions. This new model sets a promising framework to treat more complicated RNA structures, such as pseudoknotted and intramolecular kissing loops, for which experimental thermodynamic parameters are often unavailable.

HIV-1 reverse transcription initiates at the primer binding site (PBS) in the viral genomic RNA (gRNA). Although the structure of the PBS-segment undergoes substantial rearrangement upon tRNALys3 annealing, the proper folding of the PBS-segment during gRNA packaging is important as it ensures loading of beneficial host factors. DHX9/RNA helicase A (RHA) is recruited to gRNA to enhance the processivity of reverse transcriptase. Because the molecular details of the interactions have yet to be defined, we solved the solution structure of the PBS-segment preferentially bound by RHA. Evidence is provided that PBS-segment adopts a previously undefined adenosine-rich three-way junction structure encompassing the primer activation stem (PAS), tRNA-like element (TLE) and tRNA annealing arm. Disruption of the PBS-segment three-way junction structure diminished reverse transcription products and led to reduced viral infectivity. Because of the existence of the tRNA annealing arm, the TLE and PAS form a bent helical structure that undergoes shape-dependent recognition by RHA double-stranded RNA binding domain 1 (dsRBD1). Mutagenesis and phylogenetic analyses provide evidence for conservation of the PBS-segment three-way junction structure that is preferentially bound by RHA in support of efficient reverse transcription, the hallmark step of HIV-1 replication.

To investigate the water-air diffusive greenhouse gases (GHGs) fluxes from the Three Gorges Reservoir (TGR), a field experiment on carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) fluxes from water surface was carried out from March 2011 to August 2012 by floating static chamber method. The results showed that CO2 was released to the atmosphere all the time and was less in autumn than in other seasons (P 0.05). Additionally, the annual fluxes of CO2, CH4 and N2O were 140.45 ± 12.57 mg CO2·m−2 h−1, 1.35 ± 0.14 mg CH4·m−2 h−1 and 34.34 ± 11.64 μg N2O·m−2 h−1, respectively. When compared to other reservoirs worldwide, the CO2 and N2O fluxes from TGR were higher than those from boreal and temperate reservoirs, but much lower than those from tropical reservoirs. CH4 flux was lower than those from boreal, temperate and most tropical reservoirs. In our study, the surface area of the TGR emitted 1.42 × 106 t CO2, 1.19 × 104 t CH4 and 589.93 t N2O in a year. The total GWP was 17.68 t CO2-eq ha−1 yr−1, of which CO2 flux was dominant (74.38%). Therefore, CO2 was the main contributor of GHGs fluxes in our study and thus future researches should focus on how to reduce CO2 fluxes from the surface of the TGR. TGR has a considerable contribution to regional GHG emissions.

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.

The ability to accurately predict the binding site, binding pose, and binding affinity for ligand-RNA binding is important for RNA-targeted drug design. Here, we describe a new computational method, RLDOCK, for predicting the binding site and binding pose for ligand-RNA binding. By developing an energy-based scoring function, we sample exhaustively all of the possible binding sites with flexible ligand conformations for a ligand-RNA pair based on the geometric and energetic scores. The model distinguishes from other approaches in three notable features. First, the model enables exhaustive scanning of all of the possible binding sites, including multiple alternative or coexisting binding sites, for a given ligand-RNA pair. Second, the model is based on a new energy-based scoring function developed here. Third, the model employs a novel multistep screening algorithm to improve computational efficiency. Specifically, first, for each binding site, we use a gird-based energy map to rank the binding sites according to the minimum Lennard-Jones potential energy for the different ligand poses. Second, for a given selected binding site, we predict the ligand pose using a two-step algorithm. In the first step, we quickly identify the probable ligand poses using a coarse-grained simplified energy function. In the second step, for each of the probable ligand poses, we predict the ligand poses using a refined energy function. Tests of the RLDOCK for a set of 230 RNA-ligand-bound structures indicate that RLDOCK can successfully predict ligand poses for 27.8, 58.3, and 69.6% of all of the test cases with the root-mean-square deviation within 1.0, 2.0, and 3.0 Å, respectively, for the top three predicted docking poses. The computational method presented here may enable the development of a new, more comprehensive framework for the prediction of ligand-RNA binding with an ensemble of RNA conformations and the metal-ion effects.

The ability to accurately predict RNA hairpin structure and stability for different loop sequences and salt conditions is important for understanding, modeling, and designing larger RNA folds. However, traditional RNA secondary structure models cannot treat loop-sequence and ionic effects on RNA hairpin folding. Here, we describe a general, three-dimensional (3D) conformation-based computational method for modeling salt concentration-dependent conformational distributions and the detailed 3D structures for a set of three RNA hairpins that contain a variable, 15-nucleotide loop sequence. For a given RNA sequence, the new, to our knowledge, method integrates a Vfold2D two-dimensional structure folding model with IsRNA coarse-grained molecular dynamics 3D folding simulations and Monte Carlo tightly bound ion estimations of ion-mediated electrostatic interactions. The model predicts free-energy landscapes for the different RNA hairpin-forming sequences with variable salt conditions. The theoretically predicted results agree with the experimental fluorescence measurements, validating the strategy. Furthermore, the theoretical model goes beyond the experimental results by enabling in-depth 3D structural analysis, revealing energetic mechanisms for the sequence- and salt-dependent folding stability. Although the computational framework presented here is developed for RNA hairpin systems, the general method may be applied to investigate other RNA systems, such as multiway junctions or pseudoknots in mixed metal ion solutions.

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.

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An RNA global fold can be described at the level of helix orientations and relatively flexible loop conformations that connect the helices. The linkage between the helices plays an essential role in determining the structural topology, which restricts RNA local and global folds, especially for RNA tertiary structures involving cross-linked base pairs. We quantitatively analyze the topological constraints on RNA 3D conformational space, in particular, on the distribution of helix orientations, for pseudoknots and loop-loop kissing structures. The result shows that a viable conformational space is predominantly determined by the motif type, helix size, and loop size, indicating a strong topological coupling between helices and loops in RNA tertiary motifs. Moreover, the analysis indicates that (cross-linked) tertiary contacts can cause much stronger topological constraints on RNA global fold than non-cross-linked base pairs. Furthermore, based on the topological constraints encoded in the 2D structure and the 3D templates, we develop a 3D structure prediction approach. This approach can be further combined with structure probing methods to expand the capability of computational prediction for large RNA folds.

Prostate-specific membrane antigen (PSMA) is a well-characterized tumor marker associated with prostate cancer and neovasculature of most solid tumors. PSMA-specific ligands are thus being developed to deliver imaging or therapeutic agents to cancer cells. Here, we report on a crystal structure of human PSMA in complex with A9g, a 43-bp PSMA-specific RNA aptamer, that was determined to the 2.2 Å resolution limit. The analysis of the PSMA/aptamer interface allows for identification of key interactions critical for nanomolar binding affinity and high selectivity of A9g for human PSMA. Combined with in silico modeling, site-directed mutagenesis, inhibition experiments and cell-based assays, the structure also provides an insight into structural changes of the aptamer and PSMA upon complex formation, mechanistic explanation for inhibition of the PSMA enzymatic activity by A9g as well as its ligand-selective competition with small molecules targeting the internal pocket of the enzyme. Additionally, comparison with published protein–RNA aptamer structures pointed toward more general features governing protein-aptamer interactions. Finally, our findings can be exploited for the structure-assisted design of future A9g-based derivatives with improved binding and stability characteristics.

INTRODUCTION: Duchenne muscular dystrophy (DMD) is an X-linked handicapping disease due to the loss of an essential muscle protein dystrophin. Dystrophin-null animals have been extensively used to study disease mechanisms and to develop experimental therapeutics. Despite decades of research, however, treatment options for DMD remain very limited. AREAS COVERED: High-throughput high-content screening and precision medicine offer exciting new opportunities. Here, the authors review animal models that are suitable for these studies. EXPERT OPINION: Nonmammalian models (worm, fruit fly, and zebrafish) are particularly attractive for cost-effective large-scale drug screening. Several promising lead compounds have been discovered using these models. Precision medicine for DMD aims at developing mutation-specific therapies such as exon-skipping and genome editing. To meet these needs, models with patient-like mutations have been established in different species. Models that harbor hotspot mutations are very attractive because the drugs developed in these models can bring mutation-specific therapies to a large population of patients. Humanized hDMD mice carry the entire human dystrophin gene in the mouse genome. Reagents developed in the hDMD mouse-based models are directly translatable to human patients.

Oxidative stress is involved in the pathogenesis of vascular dementia. Studies have shown that lycopene can significantly inhibit oxidative stress; therefore, we hypothesized that lycopene can reduce the level of oxidative stress in vascular dementia. A vascular dementia model was established by permanent bilateral ligation of common carotid arteries. The dosage groups were treated with lycopene (50, 100 and 200 mg/kg) every other day for 2 months. Rats without bilateral carotid artery ligation were prepared as a sham group. To test the ability of learning and memory, the Morris water maze was used to detect the average escape latency and the change of search strategy. Hematoxylin-eosin staining was used to observe changes of hippocampal neurons. The levels of oxidative stress factors, superoxide dismutase and malondialdehyde, were measured in the hippocampus by biochemical detection. The levels of reactive oxygen species in the hippocampus were observed by dihydroethidium staining. The distribution and expression of oxidative stress related protein, neuron-restrictive silencer factor, in hippocampal neurons were detected by immunofluorescence histochemistry and western blot assays. After 2 months of drug administration, (1) in the model group, the average escape latency was longer than that of the sham group, and the proportion of straight and tend tactics was lower than that of the sham group, and the hippocampal neurons were irregularly arranged and the cytoplasm was hyperchromatic. (2) The levels of reactive oxygen species and malondialdehyde in the hippocampus of the model group rats were increased, and the activity of superoxide dismutase was decreased. (3) Lycopene (50, 100 and 200 mg/kg) intervention improved the above changes, and the lycopene 100 mg/kg group showed the most significant improvement effect. (4) Neuron-restrictive silencer factor expression in the hippocampus was lower in the sham group and the lycopene 100 mg/kg group than in the model group. (5) The above data indicate that lycopene 100 mg/kg could protect against the learning-memory ability impairment of vascular dementia rats. The protective mechanism was achieved by inhibiting oxidative stress in the hippocampus. The experiment was approved by the Animal Ethics Committee of Fujian Medical University, China (approval No. 2014-025) in June 2014.