Search

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.

RNA functions are often kinetically controlled. The folding kinetics of RNAs involves global structural changes and local nucleotide movement, such as base flipping. The most elementary step in RNA folding is the closing and opening of a basepair. By integrating molecular dynamics simulation, master equation, and kinetic Monte Carlo simulation, we investigate the kinetics mechanism of RNA helix-terminal basepairing. The study reveals a six-state folding scheme with three dominant folding pathways of tens, hundreds, and thousands of nanoseconds of folding timescales, respectively. The overall kinetics is rate limited by the detrapping of a misfolded state with the overall folding time of 10(−5) s. Moreover, the analysis examines the different roles of the various driving forces, such as the basepairing and stacking interactions and the ion binding/dissociation effects on structural changes. The results may provide useful insights for developing a basepair opening/closing rate model and further kinetics models of large RNAs.

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.

With rapid advances in computer algorithms and hardware, fast and accurate virtual screening has led to a drastic acceleration in selecting potent small molecules as drug candidates. Computational modeling of RNA-small molecule interactions has become an indispensable tool for RNA-targeted drug discovery. The current models for RNA-ligand binding have mainly focused on the docking-and-scoring method. Accurate docking and scoring should tackle four crucial problems: (1) conformational flexibility of ligand, (2) conformational flexibility of RNA, (3) efficient sampling of binding sites and binding poses, and (4) accurate scoring of different binding modes. Moreover, compared with the problem of protein-ligand docking, predicting ligand binding to RNA, a negatively charged polymer, is further complicated by additional effects such as metal ion effects. Thermodynamic models based on physics-based and knowledge-based scoring functions have shown highly encouraging success in predicting ligand binding poses and binding affinities. Recently, kinetic models for ligand binding have further suggested that including dissociation kinetics (residence time) in ligand docking would result in improved performance in estimating in vivo drug efficacy. More recently, the rise of deep-learning approaches has led to new tools for predicting RNA-small molecule binding. In this review, we present an overview of the recently developed computational methods for RNA-ligand docking and their advantages and disadvantages.

RNA three-dimensional (3D) structures are critical for RNA cellular functions. However, structure prediction for large and complex RNAs remains a challenge, which hampers our understanding of RNA structure-function relationship. We here report a new web server, the VfoldLA server (http://rna.physics.missouri.edu/vfoldLA), for the prediction of RNA 3D structures from nucleotide sequences and base-pair information (2D structure). This server is based on the recently developed VfoldLA, a model that classifies the single-stranded loops (junctions) into four different types and according to the loop-helix connections, assembles RNA 3D structures from the loop/junction templates. The VfoldLA web server provides a user-friendly online interface for a fully automated prediction of putative 3D RNA structures using VfoldLA. With a single-RNA or RNA-RNA complex sequence and 2D structure as input, the server generates structure(s) with the JSmol visualization along with a downloadable PDB file. The output result may serve as useful scaffolds for future structure refinement studies.

Ion correlation and fluctuation can play a potentially significant role in metal ion–nucleic acid interactions. Previous studies have focused on the effects for multivalent cations. However, the correlation and fluctuation effects can be important also for monovalent cations around the nucleic acid surface. Here, we report a model, gMCTBI, that can explicitly treat discrete distributions of both monovalent and multivalent cations and can account for the correlation and fluctuation effects for the cations in the solution. The gMCTBI model enables investigation of the global ion binding properties as well as the detailed discrete distributions of the bound ions. Accounting for the ion correlation effect for monovalent ions can lead to more accurate predictions, especially in a mixed monovalent and multivalent salt solution, for the number and location of the bound ions. Furthermore, although the monovalent ion-mediated correlation does not show a significant effect on the number of bound ions, the correlation may enhance the accumulation of monovalent ions near the nucleic acid surface and hence affect the ion distribution. The study further reveals novel ion correlation-induced effects in the competition between the different cations around nucleic acids.

Clustered regularly interspaced short palindromic repeats (CRISPR) editing is being considered as a potential gene repair therapy to treat Duchenne muscular dystrophy, a dystrophin-deficient lethal muscle disease affecting all muscles in the body. A recent preliminary study from the Olson laboratory (Amoasii et al. Science 2018;362:89–91) showed robust dystrophin restoration in a canine Duchenne muscular dystrophy model following intramuscular or intravenous delivery of the CRISPR editing machinery by adeno-associated virus serotype 9. Despite the limitation of the small sample size, short study duration, and the lack of muscle function data, the Olson lab findings have provided important proof of principle for scaling up CRISPR therapy from rodents to large mammals. Future large-scale, long-term, and comprehensive studies are warranted to establish the safety and efficacy of CRISPR editing therapy in large mammals.

While CRISPR/Cas9 is a powerful tool in genome engineering, the on-target activity and off-target effects of the system widely vary because of the differences in guide RNA (gRNA) sequences and genomic environments. Traditional approaches rely on separate models and parameters to treat on- and off-target cleavage activities. Here, we demonstrate that a free-energy scheme dominates the Cas9 editing efficacy and delineate a method that simultaneously considers on-target activities and off-target effects. While data-driven machine-learning approaches learn rules to model particular datasets, they may not be as transferrable to new systems or capable of producing new mechanistic insights as principled physical approaches. By integrating the energetics of R-loop formation under Cas9 binding, the effect of the protospacer adjacent motif sequence, and the folding stability of the whole single guide RNA, we devised a unified, physical model that can apply to any cleavage-activity dataset. This unified framework improves predictions for both on-target activities and off-target efficiencies of spCas9 and may be readily transferred to other systems with different guide RNAs or Cas9 ortholog proteins.

Metal ions play essential roles in nucleic acids folding and stability. The interaction between metal ions and nucleic acids can be highly complicated because of the interplay between various effects such as ion correlation, fluctuation, and dehydration. These effects may be particularly important for multivalent ions such as Mg 2+ ions. Previous efforts to model ion correlation and fluctuation effects led to the development of the Monte Carlo tightly bound ion model. Here, by incorporating ion hydration/dehydration effects into the Monte Carlo tightly bound ion model, we develop a, to our knowledge, new approach to predict ion binding. The new model enables predictions for not only the number of bound ions but also the three-dimensional spatial distribution of the bound ions. Furthermore, the new model reveals several intriguing features for the bound ions such as the mutual enhancement/inhibition in ion binding between the fully hydrated (diffuse) ions, the outer-shell dehydrated ions, and the inner-shell dehydrated ions and novel features for the monovalent-divalent ion interplay due to the hydration effect.

Modeling structures and functions of large ribonucleic acid (RNAs) especially with complicated topologies is highly challenging due to the inefficiency of large conformational sampling and the presence of complicated tertiary interactions. To address this problem, one highly promising approach is coarse-grained modeling. Here, following an iterative simulated reference state approach to decipher the correlations between different structural parameters, we developed a potent coarse-grained RNA model named as IsRNA1 for RNA studies. Molecular dynamics simulations in the IsRNA1 can predict the native structures of small RNAs from a sequence and fold medium-sized RNAs into near-native tertiary structures with the assistance of secondary structure constraints. A large-scale benchmark test on RNA 3D structure prediction shows that IsRNA1 exhibits improved performance for relatively large RNAs of complicated topologies, such as large stem-loop structures and structures containing long-range tertiary interactions. The advantages of IsRNA1 include the consideration of the correlations between the different structural variables, the appropriate characterization of canonical base-pairing and base-stacking interactions, and the better sampling for the backbone conformations. Moreover, a blind screening protocol was developed based on IsRNA1 to identify good structural models from a pool of candidates without prior knowledge of the native structures.

Selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) chemical probing provides local RNA flexibility information at single-nucleotide resolution. In general, SHAPE is thought of as a secondary structure (2D) technology, but we find evidence that robust tertiary structure (3D) information is contained in SHAPE data. Here, we report a new model that achieves a higher correlation between SHAPE data and native RNA 3D structures than the previous 3D structure-SHAPE relationship model. Furthermore, we demonstrate that the new model improves our ability to discern between SHAPE-compatible and -incompatible structures on model decoys. After identifying sequence-dependent bias in SHAPE experiments, we propose a mechanism driving sequence-dependent bias in SHAPE experiments, using replica-exchange umbrella sampling simulations to confirm that the SHAPE sequence bias is largely explained by the stability of the unreacted SHAPE reagent in the binding pocket. Taken together, this work represents multiple practical advances in our mechanistic and predictive understanding of SHAPE technology.

The ever-increasing discoveries of noncoding RNA functions draw a strong demand for RNA structure determination from the sequence. In recently years, computational studies for RNA structures, at both the two-dimensional and the three-dimensional levels, led to several highly promising new developments. In this chapter, we describe a hybrid method, which combines the motif template-based Vfold3D model and the loop template-based VfoldLA model, to predict RNA 3D structures. The main emphasis is placed on the definition of motifs and loops, the treatment of no-template motifs, and the 3D structure assembly from templates of motifs and loops. For illustration, we use the ZIKV xrRNA1 as an example to show the template-based prediction of RNA 3D structures from the 2D structure. The web server for the hybrid model is freely accessible at http://rna.physics.missouri.edu/vfold3D2 .

International audience; RNA-Puzzles is a collective endeavor dedicated to the advancement and improvement of RNA 3D structure prediction. With agreement from crystallographers, the RNA structures are predicted by various groups before the publication of the crystal structures. We now report the prediction of 3D structures for six RNA sequences: four nucleolytic ribozymes and two riboswitches. Systematic protocols for comparing models and crystal structures are described and analyzed. In these six puzzles, we discuss (i) the comparison between the automated web servers and human experts; (ii) the prediction of coaxial stacking; (iii) the prediction of structural details and ligand binding; (iv) the development of novel prediction methods; and (v) the potential improvements to be made. We show that correct prediction of coaxial stacking and tertiary contacts is essential for the prediction of RNA architecture, while ligand binding modes can only be predicted with low resolution and simultaneous prediction of RNA structure with accurate ligand binding still remains out of reach. All the predicted models are available for the future development of force field parameters and the improvement of comparison and assessment tools.

With the rapid development of China's social economy, the real estate industry has become one of the pillar industries. In this paper, the expert intuitive judgment method is used to identify the risks in real estate development projects, AHP is used to evaluate the risk factors, and the corresponding risk response strategies are formulated, and risk monitoring measures are proposed to help real estate enterprises effectively manage real estate projects.

The novel C/Fe-FeVO₄ composite photocatalyst were synthesized by using a two-step hydrothermal synthesis method. Through a detailed exploration on the chemical and phisical properties by some spectroscopic and analytical techniques, the as-prepared C/Fe-FeVO₄ exhibted a nanosheet and meso porosity structure. Accordingly, we further utilized this C/Fe-FeVO₄ composite as a photocatalist for degradating the notorious ciprofloxacin (CIP) under simulated solar light (SSL) irradiation. Due to its outstanding catalytic properties, the C/Fe-FeVO₄ exhibited superior photocatalytic activity. The possible photocatalytic mechanism has been discussed.

RNA aptamers that bind HIV-1 reverse transcriptase (RT) inhibit RT in enzymatic and viral replication assays. Some aptamers inhibit RT from only a few viral clades, while others show broad-spectrum inhibition. Biophysical determinants of recognition specificity are poorly understood. We investigated the interface between HIV-1 RT and a broad–spectrum UCAA-family aptamer. SAR and hydroxyl radical probing identified aptamer structural elements critical for inhibition and established the role of signature UCAA bulge motif in RT-aptamer interaction. HDX footprinting on RT ± aptamer shows strong contacts with both subunits, especially near the C-terminus of p51. Alanine scanning revealed decreased inhibition by the aptamer for mutants P420A, L422A and K424A. 2D proton nuclear magnetic resonance and SAXS data provided constraints on the solution structure of the aptamer and enable computational modeling of the docked complex with RT. Surprisingly, the aptamer enhanced proteolytic cleavage of precursor p66/p66 by HIV-1 protease, suggesting that it stabilizes the productive conformation to allow maturation. These results illuminate features at the RT-aptamer interface that govern recognition specificity by a broad-spectrum antiviral aptamer, and they open new possibilities for accelerating RT maturation and interfering with viral replication.

A field experiment was conducted at the Key Field Station for Monitoring of Eco-Environment of Purple Soil of the Ministry of Agriculture of China at the farm of Southwest University in Chongqing. In the study, static chamber and gas chromatography methods were used to study the effects of plastic film mulching treatment on CO

On the basis of a helix-based transition rate model, we developed a new method for sampling cotranscriptional RNA conformational ensemble and the prediction of cotranscriptional folding kinetics. Applications to E. coli. SRP RNA and pbuE riboswitch indicate that the model may provide reliable predictions for the cotranscriptional folding pathways and population kinetics. For E. coli. SRP RNA, the predicted population kinetics and the folding pathway are consistent with the SHAPE profiles in the recent cotranscriptional SHAPE-seq experiments. For the pbuE riboswitch, the model predicts the transcriptional termination efficiency as a function of the force. The theoretical results show (a) a force-induced transition from the aptamer (antiterminator) to the terminator structure and (b) the different folding pathways for the riboswitch with and without the ligand (adenine). More specifically, without adenine, the aptamer structure emerges as a short-lived kinetic transient state instead of a thermodynamically stable intermediate state. Furthermore, from the predicted extension-time curves, the model identifies a series of conformational switches in the pulling process, where the predicted relative residence times for the different structures are in accordance with the experimental data. The model may provide a new tool for quantitative predictions of cotranscriptional folding kinetics, and results can offer useful insights into cotranscriptional folding-related RNA functions such as regulation of gene expression with riboswitches.

Coarse-grained RNA folding models promise great potential for RNA structure prediction. A key component in a coarse-grained folding model is the force field. One of the challenges in the coarse-grained force field calculation is how to treat the correlation between the different degrees of freedoms. Here, we describe a new approach (IsRNA) to extract the correlated energy functions from the known structures. Through iterative molecular dynamics simulations, we build the correlation effects into the reference states, from which we extract the energy functions. The validity of IsRNA is supported by the close agreement between the simulated Boltzmann-like probability distributions for all the structure parameters and those observed from the experimentally determined structures. The correlated energy functions derived here may provide a new tool for RNA 3D structure prediction.

The interaction between metal ions, especially Mg 2+ ions, and RNA plays a critical role in RNA folding. Upon binding to RNA, a metal ion that is fully hydrated in bulk solvent can become dehydrated. Here we use molecular dynamics simulation to investigate the dehydration of bound hexahydrated Mg 2+ ions. We find that a hydrated Mg 2+ ion in the RNA groove region can involve significant dehydration in the outer hydration shell. The first or innermost hydration shell of the Mg 2+ ion, however, is retained during the simulation because of the strong ion-water electrostatic attraction. As a result, water-mediated hydrogen bonding remains an important form for Mg 2+ -RNA interaction. Analysis for ions at different binding sites shows that the most pronounced water deficiency relative to the fully hydrated state occurs at a radial distance of around 11 A from the center of the ion. Based on the independent 200 ns molecular dynamics simulations for three different RNA structures (Protein Data Bank: 1TRA, 2TPK, and 437D), we find that Mg 2+ ions overwhelmingly dominate over monovalent ions such as Na + and K + in ion-RNA binding. Furthermore, application of the free energy perturbation method leads to a quantitative relationship between the Mg 2+ dehydration free energy and the local structural environment. We find that ΔΔ G hyd , the change of the Mg 2+ hydration free energy upon binding to RNA, varies linearly with the inverse distance between the Mg 2+ ion and the nearby nonbridging oxygen atoms of the phosphate groups, and ΔΔ G hyd can reach −2.0 kcal/mol and −3.0 kcal/mol for an Mg 2+ ion bound to the surface and to the groove interior, respectively. In addition, the computation results in an analytical formula for the hydration ratio as a function of the average inverse Mg 2+ -O distance. The results here might be useful for further quantitative investigations of ion-RNA interactions in RNA folding.

The current RNA structure prediction methods cannot keep up the pace of the rapidly increasing number of sequences and the emerging new functions of RNAs. Template-based RNA three-dimensional structure prediction methods are restricted by the limited number of known RNA structures, and traditional motif-based search for the templates does not always lead to successful results. Here we report a new template search and assembly algorithm, the hierarchical loop template-assembly method (VfoldLA). The method searches for templates for single strand loop/junctions instead of the whole motifs, which often renders no available templates, or short fragments (several nucleotides), which requires a long computational time to assemble and refine. The VfoldLA method has the advantage of accounting for local and nonlocal interloop interactions. Benchmark tests indicate that this new method can provide low-resolution predictions for RNA conformations at different levels of structural complexities. Furthermore, the VfoldLA-predicted conformations may also serve as reliable putative models for further structure prediction and refinements. VfoldLA is accessible at http://rna.physics.missouri.edu/vfoldLA .

This work reports the synthesis of a unique positively-charged copolymer obtained from the reaction between iodomethane and a random copolymer of styrene and 4-vinylpyridine. This copolymer, referred to as qP(S-r-4-VP), was fully characterized by 1H NMR, FT-IR, XPS and GPC, and then mixed with poly(vinylidene fluoride) (PVDF) to form porous membranes able to kill bacteria upon contact. Membranes were prepared by a dual-bath procedure using methanol (10 min) and water (24 h). They exhibited typical bi-continuous structure with large pores (mean pore flow diameter: 0.2–0.45 µm) that decreased with the copolymer content, and large porosity (77%-80%). The presence of the copolymer on the membrane surface was ascertained by FT-IR. All membranes exhibited a high water contact angle in air (>140°), but hydration capacity was enhanced with the copolymer content in the membrane (from 10 mg/cm3 for the virgin membrane up to 225 mg/cm3 for modified membranes). The surface charge of the membrane increased with the copolymer content. Q3 membrane (containing 3 wt% copolymer) was selected as the best membrane able to kill various bacteria including Escherichia coli, Stenotrophomonas maltophilia and Staphylococcus epidermidis. Besides, washing with a saline solution of sodium citrate or sodium hexametaphosphate disturbed electrostatic and hydrophobic interactions between dead bacteria and membrane surface, making bacterial release possible. Membrane regeneration properties were also demonstrated from multiple killing/release cycles. Finally, it was shown that membranes were able to kill bacteria during a dead-end filtration process, while achieving 100% rejection. The material and membrane presented in the frame of this work could be of interest to decrease the pathogenicity of a feed during separation

We develop a partial charge-based Tightly Bound Ion (PCTBI) model for the ion effects in RNA folding. Based on the Monte Carlo Tightly Bound Ion (MCTBI) approach, the model can account for ion fluctuation and correlation effects, and can predict the ion distribution around the RNA. Furthermore, unlike the previous coarse-grained RNA charge models, where negative charges are placed on the phosphates only, the current new model considers the detailed all-atom partial charge distribution on the RNA. Thus, the model not only keeps the advantage of the MCTBI model but also has the potential to provide important detailed information unattainable by the previous MCTBI models. For example, the model predicts the reduction in ion binding upon protein binding and ion-induced conformational switches. For Hepatitis C virus (HCV) genomic RNA, the model predicts a Mg2+-induced stabilization of a kissing motif for a cis-acting regulatory element in the genomic RNA. Extensive theory-experiment comparisons support the reliability of the theoretical predictions. Therefore, the model may serve as a robust starting point for further development of an accurate method for ion effects in RNA conformational equilibrium and RNA-cofactor interactions.

Metal ions play critical roles in RNA structure and function. However, web servers and software packages for predicting ion effects in RNA structures are notably scarce. Furthermore, the existing web servers and software packages mainly neglect ion correlation and fluctuation effects, which are potentially important for RNAs. We here report a new web server, the MCTBI server (http://rna.physics.missouri.edu/MCTBI), for the prediction of ion effects for RNA structures. This server is based on the recently developed MCTBI, a model that can account for ion correlation and fluctuation effects for nucleic acid structures and can provide improved predictions for the effects of metal ions, especially for multivalent ions such as Mg2+ effects, as shown by extensive theory-experiment test results. The MCTBI web server predicts metal ion binding fractions, the most probable bound ion distribution, the electrostatic free energy of the system, and the free energy components. The results provide mechanistic insights into the role of metal ions in RNA structure formation and folding stability, which is important for understanding RNA functions and the rational design of RNA structures.

CRISPR editing of muscle stem cells (MuSCs) with adeno-associated virus serotype-9 (AAV9) holds promise for sustained gene repair therapy for muscular dystrophies. However, conflicting evidence exists on whether AAV9 transduces MuSCs. To rigorously address this question, we used a muscle graft model. The grafted muscle underwent complete necrosis before regenerating from its MuSCs. We injected AAV9.Cre into Ai14 mice. These mice express tdTomato upon Cre-mediated removal of a floxed stop codon. About 28%–47% and 24%–89% of Pax7(+) MuSCs expressed tdTomato in pre-grafts and regenerated grafts (p > 0.05), respectively, suggesting AAV9 efficiently transduced MuSCs, and AAV9-edited MuSCs renewed successfully. Robust MuSC transduction was further confirmed by delivering AAV9.Cre to Pax7-ZsGreen-Ai14 mice in which Pax7(+) MuSCs are genetically labeled by ZsGreen. Next, we co-injected AAV9.Cas9 and AAV9.gRNA to dystrophic mdx mice to repair the mutated dystrophin gene. CRISPR-treated and untreated muscles were grafted to immune-deficient, dystrophin-null NSG.mdx4cv mice. Grafts regenerated from CRISPR-treated muscle contained the edited genome and yielded 2.7-fold more dystrophin(+) cells (p = 0.015). Importantly, increased dystrophin expression was not due to enhanced formation of revertant fibers or de novo transduction by residual CRISPR vectors in the graft. We conclude that AAV9 effectively transduces MuSCs. AAV9 CRISPR editing of MuSCs may provide enduring therapy.

To investigate the effects of plastic film mulching and nitrogen fertilizer application on CH

To investigate the effects of plastic film mulching and nitrogen fertilizer application on N

To investigate the effect of dam impounding on CH4 and N2O emissions from the drawdown area of the Three Gorges Reservoir (TGR), CH4 and N2O fluxes were measured at the elevations of 180 m, 175 m, 165 m and 155 m (above the sea level) from September 2010 to August 2012 using the static chamber technique. The elevations of 175 m, 165 m and 155 m were located in the drawdown area and the non-flooded elevation of 180 m was taken as the control for the drawdown area. The drawdown area studied here acted as the sources of CH4 and N2O as a whole. Seasonal, inter-annual and spatial CH4, but not N2O, fluxes varied significantly between September 2010 and August 2012. The CH4 fluxes were highest in winter but lowest in summer, and significantly higher in the wet year than in the dry year. The annually cumulative CH4 emissions were 14.09 ± 4.27, 12.61 ± 3.59, 68.92 ± 13.09 and 77.41 ± 9.42 kg CH4 ha−1 from the elevations of 180 m, 175 m, 165 m and 155 m, respectively. Compared with 180 m elevation, the annual CH4 emission was insignificantly decreased by 11% at 175 m elevation (P > 0.05), and substantially increased by 389% and 449% at 165 m (P 0.05). These results showed that CH4 emissions were increased but N2O emissions were not affected in the drawdown area after the dam impounding in the TGR, and CH4 emissions were increased with the decrease in the elevations while N2O emissions were not affected by the elevation in the drawdown area.

We develop a new statistical mechanical model to predict the molecular crowding effects in ion-RNA interactions. By considering discrete distributions of the crowders, the model can treat the main crowder-induced effects, such as the competition with ions for RNA binding, changes of the electrostatic interaction due to crowder-induced changes in the dielectric environment, and changes in the nonpolar hydration state of the crowder-RNA system. To enhance the computational efficiency, we sample the crowder distribution using a hybrid approach: for crowders in the close vicinity of RNA surface, we sample their discrete distributions; for crowders in the bulk solvent away from the RNA surface, we use a continuous mean-field distribution for the crowders. Moreover, using the Tightly Bound Ion (TBI) model, the model accounts for ion fluctuation and correlation effects in the calculation for ion-RNA interactions. Applications of the model to a variety of simple RNA structures such as RNA helices show a crowder-induced increase in free energy and decrease in ion binding. Such crowding effects tend to contribute to the destabilization of RNA structure. Further analysis indicates that these effects are associated with the crowder-ion competition in RNA binding and the effective decrease in the dielectric constant. This simple ion effect model may serve as a useful framework for modeling more realistic crowders with larger, more complex RNA structures.

RNA aptamers represent an emerging class of biologics that can be easily adapted for personalized and precision medicine. Several therapeutic aptamers with desirable binding and functional properties have been developed and evaluated in preclinical studies over the past 25 years. However, for the majority of these aptamers, their clinical potential has yet to be realized. A significant hurdle to the clinical adoption of this novel class of biologicals is the limited information on their secondary and tertiary structure. Knowledge of the RNA’s structure would greatly facilitate and expedite the post-selection optimization steps required for translation, including truncation (to reduce costs of manufacturing), chemical modification (to enhance stability and improve safety) and chemical conjugation (to improve drug properties for combinatorial therapy). Here we describe a structural computational modeling methodology that when coupled to a standard functional assay, can be used to determine key sequence and structural motifs of an RNA aptamer. We applied this methodology to enable the truncation of an aptamer to prostate specific membrane antigen (PSMA) with great potential for targeted therapy that had failed previous truncation attempts. This methodology can be easily applied to optimize other aptamers with therapeutic potential.

Pseudoknots are a fundamental RNA tertiary structure with important roles in regulation of mRNA translation. Molecular force spectroscopic approaches such as optical tweezers can track the pseudoknot’s unfolding intermediate states by pulling the RNA chain from both ends, but the kinetic unfolding pathway induced by this method may be different from that in vivo, which occurs during translation and proceeds from the 5′ to 3′ end. Here we developed a ribosome-mimicking, nanopore pulling assay for dissecting the vectorial unfolding mechanism of pseudoknots. The pseudoknot unfolding pathway in the nanopore, either from the 5′ to 3′ end or in the reverse direction, can be controlled by a DNA leader that is attached to the pseudoknot at the 5′ or 3′ ends. The different nanopore conductance between DNA and RNA translocation serves as a marker for the position and structure of the unfolding RNA in the pore. With this design, we provided evidence that the pseudoknot unfolding is a two-step, multistate, metal ion-regulated process depending on the pulling direction. Most notably, unfolding in both directions is rate-limited by the unzipping of the first helix domain (first step), which is Helix-1 in the 5′ → 3′ direction and Helix-2 in the 3′ → 5′ direction, suggesting that the initial unfolding step in either pulling direction needs to overcome an energy barrier contributed by the noncanonical triplex base-pairs and coaxial stacking interactions for the tertiary structure stabilization. These findings provide new insights into RNA vectorial unfolding mechanisms, which play an important role in biological functions including frameshifting.

The selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) method probes RNA local structural and dynamic information at single nucleotide resolution. To gain quantitative insights into the relationship between nucleotide flexibility, RNA 3D structure, and SHAPE reactivity, we develop a 3D Structure-SHAPE Relationship model (3DSSR) to rebuild SHAPE profiles from 3D structures. The model starts from RNA structures and combines nucleotide interaction strength and conformational propensity, ligand (SHAPE reagent) accessibility, and base-pairing pattern through a composite function to quantify the correlation between SHAPE reactivity and nucleotide conformational stability. The 3DSSR model shows the relationship between SHAPE reactivity and RNA structure and energetics. Comparisons between the 3DSSR-predicted SHAPE profile and the experimental SHAPE data show correlation, suggesting that the extracted analytical function may have captured the key factors that determine the SHAPE reactivity profile. Furthermore, the theory offers an effective method to sieve RNA 3D models and exclude models that are incompatible with experimental SHAPE data.

The chemical properties and biological mechanisms of RNAs are determined by their tertiary structures. Exploring the tertiary structure folding processes of RNA enables us to understand and control its biological functions. Here, we report a nanopore snapshot approach combined with coarse-grained molecular dynamics simulation and master equation analysis to elucidate the folding of an RNA pseudoknot structure. In this approach, single RNA molecules captured by the nanopore can freely fold from the unstructured state without constraint and can be programmed to terminate their folding process at different intermediates. By identifying the nanopore signatures and measuring their time-dependent populations, we can “visualize” a series of kinetically important intermediates, track the kinetics of their inter-conversions, and derive the RNA pseudoknot folding pathway. This approach can potentially be developed into a single-molecule toolbox to investigate the biophysical mechanisms of RNA folding and unfolding, its interactions with ligands, and its functions., 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.

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.

The 3′-end of the genomic RNA of the hepatitis C virus (HCV) embeds conserved elements that regulate viral RNA synthesis and protein translation by mechanisms that have yet to be elucidated. Previous studies with oligo-RNA fragments have led to multiple, mutually exclusive secondary structure predictions, indicating that HCV RNA structure may be context-dependent. Here we employed a nuclear magnetic resonance (NMR) approach that involves long-range adenosine interaction detection, coupled with site-specific 2H labeling, to probe the structure of the intact 3′-end of the HCV genome (385 nucleotides). Our data reveal that the 3′-end exists as an equilibrium mixture of two conformations: an open conformation in which the 98 nucleotides of the 3′-tail (3′X) form a two-stem–loop structure with the kissing-loop residues sequestered and a closed conformation in which the 3′X rearranges its structure and forms a long-range kissing-loop interaction with an upstream cis-acting element 5BSL3.2. The long-range kissing species is favored under high-Mg2+ conditions, and the intervening sequences do not affect the equilibrium as their secondary structures remain unchanged. The open and closed conformations are consistent with the reported function regulation of viral RNA synthesis and protein translation, respectively. Our NMR detection of these RNA conformations and the structural equilibrium in the 3′-end of the HCV genome support its roles in coordinating various steps of HCV replication.

The interaction between metal ions, especially Mg(2+) ions, and RNA plays a critical role in RNA folding. Upon binding to RNA, a metal ion that is fully hydrated in bulk solvent can become dehydrated. Here we use molecular dynamics simulation to investigate the dehydration of bound hexahydrated Mg(2+) ions. We find that a hydrated Mg(2+) ion in the RNA groove region can involve significant dehydration in the outer hydration shell. The first or innermost hydration shell of the Mg(2+) ion, however, is retained during the simulation because of the strong ion-water electrostatic attraction. As a result, water-mediated hydrogen bonding remains an important form for Mg(2+)-RNA interaction. Analysis for ions at different binding sites shows that the most pronounced water deficiency relative to the fully hydrated state occurs at a radial distance of around 11 Å from the center of the ion. Based on the independent 200 ns molecular dynamics simulations for three different RNA structures (Protein Data Bank: 1TRA, 2TPK, and 437D), we find that Mg(2+) ions overwhelmingly dominate over monovalent ions such as Na(+) and K(+) in ion-RNA binding. Furthermore, application of the free energy perturbation method leads to a quantitative relationship between the Mg(2+) dehydration free energy and the local structural environment. We find that [Formula: see text] the change of the Mg(2+) hydration free energy upon binding to RNA, varies linearly with the inverse distance between the Mg(2+) ion and the nearby nonbridging oxygen atoms of the phosphate groups, and [Formula: see text] can reach −2.0 kcal/mol and −3.0 kcal/mol for an Mg(2+) ion bound to the surface and to the groove interior, respectively. In addition, the computation results in an analytical formula for the hydration ratio as a function of the average inverse Mg(2+)-O distance. The results here might be useful for further quantitative investigations of ion-RNA interactions in RNA folding.

RNA-Puzzles is a collective experiment in blind 3D RNA structure prediction. We report here a third round of RNA-Puzzles. Five puzzles, 4, 8, 12, 13, 14, all structures of riboswitch aptamers and puzzle 7, a ribozyme structure, are included in this round of the experiment. The riboswitch structures include biological binding sites for small molecules (S-adenosyl methionine, cyclic diadenosine monophosphate, 5-amino 4-imidazole carboxamide riboside 5′-triphosphate, glutamine) and proteins (YbxF), and one set describes large conformational changes between ligand-free and ligand-bound states. The Varkud satellite ribozyme is the most recently solved structure of a known large ribozyme. All puzzles have established biological functions and require structural understanding to appreciate their molecular mechanisms. Through the use of fast-track experimental data, including multidimensional chemical mapping, and accurate prediction of RNA secondary structure, a large portion of the contacts in 3D have been predicted correctly leading to similar topologies for the top ranking predictions. Template-based and homology-derived predictions could predict structures to particularly high accuracies. However, achieving biological insights from de novo prediction of RNA 3D structures still depends on the size and complexity of the RNA. Blind computational predictions of RNA structures already appear to provide useful structural information in many cases. Similar to the previous RNA-Puzzles Round II experiment, the prediction of non-Watson–Crick interactions and the observed high atomic clash scores reveal a notable need for an algorithm of improvement. All prediction models and assessment results are available at http://ahsoka.u-strasbg.fr/rnapuzzles/.

In addition to continuous rapid progress in RNA structure determination, probing, and biophysical studies, the past decade has seen remarkable advances in the development of a new generation of RNA folding theories and models. In this article, we review RNA structure prediction models and models for ion–RNA and ligand–RNA interactions. These new models are becoming increasingly important for a mechanistic understanding of RNA function and quantitative design of RNA nanotechnology. We focus on new methods for physics-based, knowledge-based, and experimental data–directed modeling for RNA structures and explore the new theories for the predictions of metal ion and ligand binding sites and metal ion-dependent RNA stabilities. The integration of these new methods with theories about the cellular environment effects in RNA folding, such as molecular crowding and cotranscriptional kinetic effects, may ultimately lead to an all-encompassing RNA folding model.

In order to carry out biological functions, RNA molecules must fold into specific three-dimensional (3D) structures. Current experimental methods to determine RNA 3D structures are expensive and time consuming. With the recent advances in computational biology, RNA structure prediction is becoming increasingly reliable. This chapter describes a recently developed RNA structure prediction software, Vfold, a virtual bond-based RNA folding model. The main features of Vfold are the physics-based loop free energy calculations for various RNA structure motifs and a template-based assembly method for RNA 3D structure prediction. For illustration, we use the yybP-ykoY Orphan riboswitch as an example to show the implementation of the Vfold model in RNA structure prediction from the sequence.

It has long been appreciated that Mg(2+) is essential for the stabilization of RNA tertiary structure. However, the problem of quantitative prediction for the ion effect in tertiary structure folding remains. By using the virtual bond RNA folding model (Vfold) to generate RNA conformations and the newly improved tightly bound ion model (TBI) to treat ion-RNA interactions, we investigate Mg(2+)-facilitated tetraloop-receptor docking. For the specific construct of the tetraloop-receptor system, the theoretical analysis shows that the Mg(2+)-induced stabilizing force for the docked state is predominantly entropic and the major contribution comes from the entropy of the diffusive ions. Furthermore, our results show that Mg(2+) ions promote tetraloop-receptor docking mainly through the entropy of the diffusive ions. The theoretical prediction agrees with experimental analysis. The method developed in this paper, which combines the theory for the (Mg(2+)) ion effects in RNA folding and RNA conformational sampling, may provide a useful framework for studying the ion effect in the folding of more complex RNA structures.