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Abstract The translocation of YAP from the cytoplasm to the nucleus is critical for its activation and plays a key role in tumor progression. However, the precise molecular mechanisms governing the nuclear import of YAP are not fully understood. In this study, we have uncovered a crucial role of SOX9 in the activation of YAP. SOX9 promotes the nuclear translocation of YAP by direct interaction. Importantly, we have identified that the binding between Asp-125 of SOX9 and Arg-124 of YAP is essential for SOX9-YAP interaction and subsequent nuclear entry of YAP. Additionally, we have discovered a novel asymmetrical dimethylation of YAP at Arg-124 (YAP-R124me2a) catalyzed by PRMT1. YAP-R124me2a enhances the interaction between YAP and SOX9 and is associated with poor prognosis in multiple cancers. Furthermore, we disrupted the interaction between SOX9 and YAP using a competitive peptide, S-A1, which mimics an α-helix of SOX9 containing Asp-125. S-A1 significantly inhibits YAP nuclear translocation and effectively suppresses tumor growth. This study provides the first evidence of SOX9 as a pivotal regulator driving YAP nuclear translocation and presents a potential therapeutic strategy for YAP-driven human cancers by targeting SOX9-YAP interaction.

RNA molecules play multifaceted functional and regulatory roles within cells and have garnered significant attention in recent years as promising therapeutic targets. With remarkable successes achieved by artificial intelligence (AI) in different fields such as computer vision and natural language processing, there is a growing imperative to harness AI’s potential in computer-aided drug design (CADD) to discover novel drug compounds that target RNA. Although machine-learning (ML) approaches have been widely adopted in the discovery of small molecules targeting proteins, the application of ML approaches to model interactions between RNA and small molecule is still in its infancy. Compared to protein-targeted drug discovery, the major challenges in ML-based RNA-targeted drug discovery stem from the scarcity of available data resources. With the growing interest and the development of curated databases focusing on interactions between RNA and small molecule, the field anticipates a rapid growth and the opening of a new avenue for disease treatment. In this review, we aim to provide an overview of recent advancements in computationally modeling RNA-small molecule interactions within the context of RNA-targeted drug discovery, with a particular emphasis on methodologies employing ML techniques.

Cell-to-cell communication plays a cardinal role in the biology of multicellular organisms. H2O2 is an important cell-to-cell signaling molecule involved in the response of mammalian cells to wounding and other stimuli. We previously identified a signaling pathway that transmits wound-induced cell-to-cell H2O2 signals within minutes over long distances, measured in centimeters, in a monolayer of cardiomyocytes. Here we report that this long-distance H2O2 signaling pathway is accompanied by enhanced accumulation of cytosolic H2O2 and altered redox state in cells along its path. We further show that it requires the production of superoxide, as well as the function of gap junctions, and that it is accompanied by changes in the abundance of hundreds of proteins in cells along its path. Our findings highlight the existence of a unique and rapid long-distance H2O2 signaling pathway that could play an important role in different inflammatory responses, wound responses/healing, cardiovascular disease, and/or other conditions.

The International Society of RNA Nanotechnology and Nanomedicine (ISRNN) serves to further the development of a wide variety of functional nucleic acids and other related nanotechnology platforms. To aid in the dissemination of the most recent advancements, a biennial discussion focused on biomotors, viral assembly, and RNA nanobiotechnology has been established where international experts in interdisciplinary fields such as structural biology, biophysical chemistry, nanotechnology, cell and cancer biology, and pharmacology share their latest accomplishments and future perspectives. The results summarized here highlight advancements in our understanding of viral biology and the structure–function relationship of frame-shifting elements in genomic viral RNA, improvements in the predictions of SHAPE analysis of 3D RNA structures, and the understanding of dynamic RNA structures through a variety of experimental and computational means. Additionally, recent advances in the drug delivery, vaccine design, nanopore technologies, biomotor and biomachine development, DNA packaging, RNA nanotechnology, and drug delivery are included in this critical review. We emphasize some of the novel accomplishments, major discussion topics, and present current challenges and perspectives of these emerging fields.

The Cas9-specific T cell response has been speculated to impair CRISPR therapy. Here, the authors show that local and systemic AAV CRISPR therapy induces cytotoxic killing and eliminates rescued dystrophin in canine models of Duchenne muscular dystrophy.

Magnesium ions (Mg2+) are vital for RNA structure and cellular functions. Present efforts in RNA structure determination and understanding of RNA functions are hampered by the inability to accurately locate Mg2+ ions in an RNA. Here we present a machine-learning method, originally developed for computer visual recognition, to predict Mg2+ binding sites in RNA molecules. By incorporating geometrical and electrostatic features of RNA, we capture the key ingredients of Mg2+-RNA interactions, and from deep learning, predict the Mg2+ density distribution. Five-fold cross-validation on a dataset of 177 selected Mg2+-containing structures and comparisons with different methods validate the approach. This new approach predicts Mg2+ binding sites with notably higher accuracy and efficiency. More importantly, saliency analysis for eight different Mg2+ binding motifs indicates that the model can reveal critical coordinating atoms for Mg2+ ions and ion-RNA inner/outer-sphere coordination. Furthermore, implementation of the model uncovers new Mg2+ binding motifs. This new approach may be combined with X-ray crystallography structure determination to pinpoint the metal ion binding sites.

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.

The three-dimensional (3D) structures of Ribonucleic acid (RNA) molecules are essential to understanding their various and important biological functions. However, experimental determination of the atomic structures is laborious and technically difficult. The large gap between the number of sequences and the experimentally determined structures enables the thriving development of computational approaches to modeling RNAs. However, computational methods based on all-atom simulations are intractable for large RNA systems, which demand long time simulations. Facing such a challenge, many coarse-grained (CG) models have been developed. Here, we provide a review of CG models for modeling RNA 3D structures, compare the performance of the different models, and offer insights into potential future developments.

Selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) chemical probing serves as a convenient and efficient experiment technique for providing information about RNA local flexibility. The local structural information contained in SHAPE reactivity data can be used as constraints in 2D/3D structure predictions. Here, we present SHAPE predictoR (SHAPER), a web server for fast and accurate SHAPE reactivity prediction. The main purpose of the SHAPER web server is to provide a portal that uses experimental SHAPE data to refine 2D/3D RNA structure selection. Input structures for the SHAPER server can be obtained through experimental or computational modeling. The SHAPER server can accept RNA structures with single or multiple conformations, and the predicted SHAPE profile and correlation with experimental SHAPE data (if provided) for each conformation can be freely downloaded through the web portal. The SHAPER web server is available at http://rna.physics.missouri.edu/shaper/.

RNA is a unique biomolecule that is involved in a variety of fundamental biological functions, all of which depend solely on its structure and dynamics. Since the experimental determination of crystal RNA structures is laborious, computational 3D structure prediction methods are experiencing an ongoing and thriving development. Such methods can lead to many models; thus, it is necessary to build comparisons and extract common structural motifs for further medical or biological studies. Here, we introduce a computational pipeline dedicated to reference-free high-throughput comparative analysis of 3D RNA structures. We show its application in the RNA-Puzzles challenge, in which five participating groups attempted to predict the three-dimensional structures of 5′- and 3′-untranslated regions (UTRs) of the SARS-CoV-2 genome. We report the results of this puzzle and discuss the structural motifs obtained from the analysis. All simulated models and tools incorporated into the pipeline are open to scientific and academic use.

Abstract Background Our previous study has demonstrated that hepatocyte nuclear factor 1α (HNF1α) exerts potent therapeutic effects on hepatocellular carcinoma (HCC). However, the molecular mechanisms by which HNF1α reverses HCC malignancy need to be further elucidated. Methods lncRNA microarray was performed to identify the long noncoding RNAs (lncRNAs) regulated by HNF1α. Chromatin immunoprecipitation and luciferase reporter assays were applied to clarify the mechanism of the transcriptional regulation of HNF1α to HNF1A antisense RNA 1 (HNF1A-AS1). The effect of HNF1A-AS1 on HCC malignancy was evaluated in vitro and in vivo. RNA pulldown, RNA-binding protein immunoprecipitation and the Bio-Layer Interferometry assay were used to validate the interaction of HNF1A-AS1 and Src homology region 2 domain-containing phosphatase 1 (SHP-1). Results HNF1α regulated the expression of a subset of lncRNAs in HCC cells. Among these lncRNAs, the expression levels of HNF1A-AS1 were notably correlated with HNF1α levels in HCC cells and human HCC tissues. HNF1α activated the transcription of HNF1A-AS1 by directly binding to its promoter region. HNF1A-AS1 inhibited the growth and the metastasis of HCC cells in vitro and in vivo. Moreover, knockdown of HNF1A-AS1 reversed the suppressive effects of HNF1α on the migration and invasion of HCC cells. Importantly, HNF1A-AS1 directly bound to the C-terminal of SHP-1 with a high binding affinity (KD = 59.57 ± 14.29 nM) and increased the phosphatase activity of SHP-1. Inhibition of SHP-1 enzymatic activity substantially reversed the HNF1α- or HNF1A-AS1-induced reduction on the metastatic property of HCC cells. Conclusions Our data revealed that HNF1A-AS1 is a direct transactivation target of HNF1α in HCC cells and involved in the anti-HCC effect of HNF1α. HNF1A-AS1 functions as phosphatase activator through the direct interaction with SHP-1. These findings suggest that regulation of the HNF1α/HNF1A-AS1/SHP-1 axis may have beneficial effects in the treatment of HCC.

While RNA folding is critical for its function, study of this process is challenging. Here, the authors combine nanopore single-molecule manipulation with theoretical analysis to follow the folding of an RNA pseudoknot, monitoring the intermediate states and the kinetics of their interconversion.

Abstract The CRISPR/Cas9 complex, a bacterial immune response system, has been widely adopted for RNA-guided genome editing and transcription regulation in applications such as targeted genome modification and site-directed mutagenesis. However, the physical basis for its target specificity is not fully understood. In this study, based on a statistical mechanical analysis for the whole ensemble of sgRNA-target complex conformations, we identify a strong correlation between Cas9 cleavage efficiency and the stability of the DNA-RNA (R-loop) complex structures, with a Pearson correlation coefficient ranging from 0.775 to 0.886 for the tested systems. The finding leads to quantitative insights into important experimental results, such as the greater Cas9 tolerance to single-base mismatches in PAM-distal region than to PAM-proximal region and the high specificity and efficiency for shorter protospacers. Moreover, the results from the genome-wide off-target assessments, compared with other off-target scoring tools, indicate that the statistical mechanics-based approach provides more reliable off-target analyses and sgRNA design. To facilitate the genome engineering applications, a new web-based tool for genome-wide off-target assessment is established.

The long-range base pairing between the 5BSL3. 2 and 3′X domains in hepatitis C virus (HCV) genomic RNA is essential for viral replication. Experimental evidence points to the critical role of metal ions, especially Mg2+ ions, in the formation of the 5BSL3.2:3′X kissing complex. Furthermore, NMR studies suggested an important ion-dependent conformational switch in the kissing process. However, for a long time, mechanistic understanding of the ion effects for the process has been unclear. Recently, computational modeling based on the Vfold RNA folding model and the partial charge-based tightly bound ion (PCTBI) model, in combination with the NMR data, revealed novel physical insights into the role of metal ions in the 5BSL3.2-3′X system. The use of the PCTBI model, which accounts for the ion correlation and fluctuation, gives reliable predictions for the ion-dependent electrostatic free energy landscape and ion-induced population shift of the 5BSL3.2:3′X kissing complex. Furthermore, the predicted ion binding sites offer insights about how ion-RNA interactions shift the conformational equilibrium. The integrated theory-experiment study shows that Mg2+ ions may be essential for HCV viral replication. Moreover, the observed Mg2+-dependent conformational equilibrium may be an adaptive property of the HCV genomic RNA such that the equilibrium is optimized to the intracellular Mg2+ concentration in liver cells for efficient viral replication.

A variety of neurotrophic factors have been shown to repair the damaged peripheral nerve. However, in clinical practice, nerve growth factor, neurotrophin-3 and brain-derived neurotrophic factor are all peptides or proteins that may be rapidly deactivated at the focal injury site; their local effective concentration time following a single medication cannot meet the required time for spinal axons to regenerate and cross the glial scar. In this study, we produced polymer sustained-release microspheres based on the polylactic-co-glycolic acid copolymer; the microspheres at 300-μm diameter contained nerve growth factor, neurotrophin-3 and brain-derived neurotrophic factor. Six microspheres were longitudinally implanted into the sciatic nerve at the anastomosis site, serving as the experimental group; while the sciatic nerve in the control group was subjected to the end-to-end anastomosis using 10/0 suture thread. At 6 weeks after implantation, the lower limb activity, weight of triceps surae muscle, sciatic nerve conduction velocity and the maximum amplitude were obviously better in the experimental group than in the control group. Compared with the control group, more regenerating nerve fibers were observed and distributed in a dense and ordered manner with thicker myelin sheaths in the experimental group. More angiogenesis was also visible. Experimental findings indicate that polylactic-co-glycolic acid composite microspheres containing nerve growth factor, neurotrophin-3 and brain-derived neurotrophic factor can promote the restoration of sciatic nerve in rats after injury.

RNA-RNA interactions are essential for genomic RNA dimerization, mRNA splicing, and many RNA-related gene expression and regulation processes. The prediction of the structure and folding stability of RNA-RNA complexes is a problem of significant biological importance and receives substantial interest in the biological community. The VfoldCPX server provides a new web interface to predict the two-dimensional (2D) structures of RNA-RNA complexes from the nucleotide sequences. The VfoldCPX server has several novel advantages including the ability to treat RNAs with tertiary contacts (crossing base pairs) such as loop-loop kissing interactions and the use of physical loop entropy parameters. Based on a partition function-based algorithm, the server enables prediction for structure with and without tertiary contacts. Furthermore, the server outputs a set of energetically stable structures, ranked by their stabilities. The results allow users to gain extensive physical insights into RNA-RNA interactions and their roles in RNA function. The web server is freely accessible at "http://rna.physics.missouri.edu/vfoldCPX".

Objective: To investigate the clinical characteristics, operative methods, and diffusion tensor imaging (DTI) in the resection of intrinsic insular gliomas via transsylvian approach. Methods: From June 2008 to June 2010, 12 patients with intrinsic insular gliomas were treated via transsylvian microsurgical approach, with preoperative magnetic resonance imaging diffusion tensor imaging (MR DTI) evaluation. The data of these patients were retrospectively analyzed. Results: All patients had astrocytoma, including 8 patients of Grades I to II, 2 patients of Grades III to IV, and 2 patients of mixed glial tumors. The insular tumors were completely removed in 9 patients, whereas they were only partially removed from 3 patients. No death was related to the operations. Two patients had transient aphasia, 2 experienced worsened hemiplegia on opposite sides of their bodies, and 2 had mild hemiplegia and language function disturbance. Conclusions: Most of the insular gliomas are of low grade. By evaluating the damage of the corticospinal tract through DTI and using ultrasonography to locate the tumors during operation, microsurgery treatment removes the lesions as much as possible, protects the surrounding areas, reduces the mobility rate, and improves the postoperative quality of life.

Metal ions play a critical role in the stabilization of RNA structures. Therefore, accurate prediction of the ion effects in RNA folding can have a far-reaching impact on our understanding of RNA structure and function. Multivalent ions, especially Mg²⁺, are essential for RNA tertiary structure formation. These ions can possibly become strongly correlated in the close vicinity of RNA surface. Most of the currently available software packages, which have widespread success in predicting ion effects in biomolecular systems, however, do not explicitly account for the ion correlation effect. Therefore, it is important to develop a software package/web server for the prediction of ion electrostatics in RNA folding by including ion correlation effects.The TBI web server http://rna.physics.missouri.edu/tbi_index.html provides predictions for the total electrostatic free energy, the different free energy components, and the mean number and the most probable distributions of the bound ions. A novel feature of the TBI server is its ability to account for ion correlation and ion distribution fluctuation effects.By accounting for the ion correlation and fluctuation effects, the TBI server is a unique online tool for computing ion-mediated electrostatic properties for given RNA structures. The results can provide important data for in-depth analysis for ion effects in RNA folding including the ion-dependence of folding stability, ion uptake in the folding process, and the interplay between the different energetic components.

One of the key issues in the theoretical prediction of RNA folding is the prediction of loop structure from the sequence. RNA loop free energies are dependent on the loop sequence content. However, most current models account only for the loop length-dependence. The previously developed "Vfold" model (a coarse-grained RNA folding model) provides an effective method to generate the complete ensemble of coarse-grained RNA loop and junction conformations. However, due to the lack of sequence-dependent scoring parameters, the method is unable to identify the native and near-native structures from the sequence. In this study, using a previously developed iterative method for extracting the knowledge-based potential parameters from the known structures, we derive a set of dinucleotide-based statistical potentials for RNA loops and junctions. A unique advantage of the approach is its ability to go beyond the the (known) native structures by accounting for the full free energy landscape, including all the nonnative folds. The benchmark tests indicate that for given loop/junction sequences, the statistical potentials enable successful predictions for the coarse-grained 3D structures from the complete conformational ensemble generated by the Vfold model. The predicted coarse-grained structures can provide useful initial folds for further detailed structural refinement.

Bone age is an important indicator of human growth and development, which can objectively reflect the growth level and maturity of individuals. Traditional manual bone age assessment usually compares the X-ray of the left wrist with the reference standard to obtain the corresponding bone age. This method is time-consuming and its results vary with different observers. In recent years, with the continuous development of computer science, bone age assessment has began to change from traditional manual assessment to automatic assessment. Although there has already been numerous researches on automatic bone age assessment, most of them are still in the experimental stage. This paper reviews related research and progress on automatic bone age assessment at home and abroad in recent years, in order to provide reference and research ideas for relevant researchers.

In CASP15, we used an integrated hierarchical and hybrid approach to predict RNA structures. The approach involves three steps. First, with the use of physics-based methods, Vfold2D-MC and VfoldMCPX, we predict the 2D structures from the sequence. Second, we employ template-based methods, Vfold3D and VfoldLA, to build 3D scaffolds for the predicted 2D structures. Third, using the 3D scaffolds as initial structures and the predicted 2D structures as constraints, we predict the 3D structure from coarse-grained molecular dynamics simulations, IsRNA and RNAJP. Our approach was evaluated on 12 RNA targets in CASP15 and ranked second among all the 34 participating teams. The result demonstrated the reliability of our method in predicting RNA 2D structures with high accuracy and RNA 3D structures with moderate accuracy. Further improvements in RNA structure prediction for the next round of CASP may come from the incorporation of the physics-based method with machine learning techniques. [ABSTRACT FROM AUTHOR]