FMRP phosphorylation modulates neuronal translation through YTHDF1.

RNA-binding proteins (RBPs) control messenger RNA fate in neurons. Here, we report a mechanism that the stimuli-induced neuronal translation is mediated by phosphorylation of a YTHDF1-binding protein FMRP. Mechanistically, YTHDF1 can condense with ribosomal proteins to promote the translation of its mRNA targets. FMRP regulates this process by sequestering YTHDF1 away from the ribosome; upon neuronal stimulation, FMRP becomes phosphorylated and releases YTHDF1 for translation upregulation. We show that a new small molecule inhibitor of YTHDF1 can reverse fragile X syndrome (FXS) developmental defects associated with FMRP deficiency in an organoid model. Our study thus reveals that FMRP and its phosphorylation are important regulators of activity-dependent translation during neuronal development and stimulation and identifies YTHDF1 as a potential therapeutic target for FXS in which developmental defects caused by FMRP depletion could be reversed through YTHDF1 inhibition.

Kinetic pathway of HIV-1 TAR cotranscriptional folding.

The Trans-Activator Receptor (TAR) RNA, located at the 5'-end untranslated region (5' UTR) of the human immunodeficiency virus type 1 (HIV-1), is pivotal in the virus's life cycle. As the initial functional domain, it folds during the transcription of viral mRNA. Although TAR's role in recruiting the Tat protein for trans-activation is established, the detailed kinetic mechanisms at play during early transcription, especially at points of temporary transcriptional pausing, remain elusive. Moreover, the precise physical processes of transcriptional pause and subsequent escape are not fully elucidated. This study focuses on the folding kinetics of TAR and the biological implications by integrating computer simulations of RNA folding during transcription with nuclear magnetic resonance (NMR) spectroscopy data. The findings reveal insights into the folding mechanism of a non-native intermediate that triggers transcriptional pause, along with different folding pathways leading to transcriptional pause and readthrough. The profiling of the cotranscriptional folding pathway and identification of kinetic structural intermediates reveal a novel mechanism for viral transcriptional regulation, which could pave the way for new antiviral drug designs targeting kinetic cotranscriptional folding pathways in viral RNAs.

RNAJP: enhanced RNA 3D structure predictions with non-canonical interactions and global topology sampling.

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.

Machine learning in RNA structure prediction: Advances and challenges.

RNA molecules play a crucial role in various biological processes, with their functionality closely tied to their structures. The remarkable advancements in machine learning techniques for protein structure prediction have shown promise in the field of RNA structure prediction. In this perspective, we discuss the advances and challenges encountered in constructing machine learning-based models for RNA structure prediction. We explore topics including model building strategies, specific challenges involved in predicting RNA secondary (2D) and tertiary (3D) structures, and approaches to these challenges. In addition, we highlight the advantages and challenges of constructing RNA language models. Given the rapid advances of machine learning techniques, we anticipate that machine learning-based models will serve as important tools for predicting RNA structures, thereby enriching our understanding of RNA structures and their corresponding functions.

Selective PPARγ modulator diosmin improves insulin sensitivity and promotes browning of white fat.

Peroxisome proliferator-activated receptor γ (PPARγ) is a master regulator of adipocyte differentiation, glucolipid metabolism, and inflammation. Thiazolidinediones are PPARγ full agonists with potent insulin-sensitizing effects, whereas their oral usage is restricted because of unwanted side effects, including obesity and cardiovascular risks. Here, via virtual screening, microscale thermophoresis analysis, and molecular confirmation, we demonstrate that diosmin, a natural compound of wide and long-term clinical use, is a selective PPARγ modulator that binds to PPARγ and blocks PPARγ phosphorylation with weak transcriptional activity. Local diosmin administration in subcutaneous fat (inguinal white adipose tissue [iWAT]) improved insulin sensitivity and attenuated obesity via enhancing browning of white fat and energy expenditure. Besides, diosmin ameliorated inflammation in WAT and liver and reduced hepatic steatosis. Of note, we determined that iWAT local administration of diosmin did not exhibit obvious side effects. Taken together, the present study demonstrated that iWAT local delivery of diosmin protected mice from diet-induced insulin resistance, obesity, and fatty liver by blocking PPARγ phosphorylation, without apparent side effects, making it a potential therapeutic agent for the treatment of metabolic diseases.

Broadband Room-Temperature Photodetection via InBiTe3 Nanosheet.

Broadband room-temperature photodetection has become a pressing need as application requirements for communication, imaging, spectroscopy, and sensing have evolved. Topological insulators (TIs) have narrow bandgap structures with a wide absorption spectral response range, which should meet the requirements of broadband detection. However, owing to their high carrier concentration and low carrier mobility resulting in poor noise equivalent power (NEP), they are generally considered unsuitable for photodetection. Here, InBiTe3 alloy nanosheet formed by doping In2Te3 into Bi2Te3(≈ 1:1) is utilized, effectively improving carrier mobility by over ten times while maintaining a narrow bandgap structure, to fabricate a broadband photodetector covering a wide response range from visible to millimeter wave (MMW). Under the synergistic multi-mechanism of the photoelectric effect in the visible-infrared region and the electromagnetic-induced potential well (EIW) effect in Terahertz band, the performance of NEP = 75 pW Hz-1/2 and response time τ ≈100 µs in visible to infrared band and the performance of NEP = 6.7 × 10-3 pW Hz-1/2, τ ≈8 µs in Terahertz region are achieved. The results demonstrate the promising prospects of topological insulator alloy (like InBiTe3) nanosheet in optoelectronic detection applications and provide a direction for the research into high-performance broadband photoelectric detectors via TIs.

Bruceantinol targeting STAT3 exerts promising antitumor effects in in vitro and in vivo osteosarcoma models.

Bruceantinol (BOL) is a quassinoid compound found in the fruits of Brucea javanica. Previous research has highlighted the manifold physiological and pharmacological activities of BOL. Notably, BOL has demonstrated antitumor cytotoxic and antibacterial effects, lending support to its potential as a promising therapeutic agent for various diseases. Despite being recognized as a potent antitumor inhibitor in multiple cancer types, its efficacy against osteosarcoma (OS) has not been elucidated. In this work, we investigated the antitumor properties of BOL against OS. Our findings showed that BOL significantly decreased the proliferation and migration of OS cells, induced apoptosis, and caused cell death without affecting the cell cycle. We further confirmed that BOL potently suppressed tumor growth in vivo. Mechanismly, we discovered that BOL directly bound to STAT3, and prevent the activation of STAT3 signaling at low nanomolar concentrations. Overall, our study demonstrated that BOL potently inhibited the growth and metastasis of OS, and efficiently suppressed STAT3 signaling pathway. These results suggest that BOL could be a promising therapeutic candidate for OS.

VfoldMCPX: predicting multistrand RNA complexes.

Multistrand RNA complexes play a critical role in RNA-related biological processes. The understanding of RNA functions and the rational design of RNA nanostructures require accurate prediction of the structure and folding stability of the complexes, including those containing pseudoknots. Here, we present VfoldMCPX, a new model for predicting two-dimensional (2D) structures and folding stabilities of multistrand RNA complexes. Based on a partition function-based algorithm combined with physical loop free energy parameters, the VfoldMCPX model predicts not only the native structure but also the folding stability of the complex. An important advantage of the model is the ability to treat pseudoknotted structures. Extensive tests on structure predictions show the VfoldMCPX model provides improved accuracy for multistranded RNA complexes, especially for RNA complexes with three or more strands and/or containing pseudoknots. We have developed a freely accessible VfoldMCPX web server at http://rna.physics.missouri.edu/vfoldMCPX2.

An RNA aptamer that shifts the reduction potential of metabolic cofactors.

The discovery of ribozymes has inspired exploration of RNA's potential to serve as primordial catalysts in a hypothesized RNA world. Modern oxidoreductase enzymes employ differential binding between reduced and oxidized forms of redox cofactors to alter cofactor reduction potential and enhance the enzyme's catalytic capabilities. The utility of differential affinity has been underexplored as a chemical strategy for RNA. Here we show an RNA aptamer that preferentially binds oxidized forms of flavin over reduced forms and markedly shifts flavin reduction potential by -40 mV, similar to shifts for oxidoreductases. Nuclear magnetic resonance structural analysis revealed π-π and donor atom-π interactions between the aptamer and flavin that cause unfavorable contacts with the electron-rich reduced form, suggesting a mechanism by which the local environment of the RNA-binding pocket drives the observed shift in cofactor reduction potential. It seems likely that primordial RNAs could have used similar strategies in RNA world metabolisms.

Nontarget Identification of Novel Organophosphorus Flame Retardants and Plasticizers in Indoor Air and Dust from Multiple Microenvironments in China.

The indoor environment is a typical source for organophosphorus flame retardants and plasticizers (OPFRs), yet the source characteristics of OPFRs in different microenvironments remain less clear. This study collected 109 indoor air samples and 34 paired indoor dust samples from 4 typical microenvironments within a university in Tianjin, China, including the dormitory, office, library, and information center. 29 target OPFRs were analyzed, and novel organophosphorus compounds (NOPs) were identified by fragment-based nontarget analysis. Target OPFRs exhibited the highest air and dust concentrations of 46.2-234 ng/m3 and 20.4-76.0 µg/g, respectively, in the information center, where chlorinated OPFRs were dominant. Triphenyl phosphate (TPHP) was the primary OPFR in office air, while tris(2-chloroethyl) phosphate dominated in the dust. TPHP was predominant in the library. Triethyl phosphate (TEP) was ubiquitous in the dormitory, and tris(2-butoxyethyl) phosphate was particularly high in the dust. 9 of 25 NOPs were identified for the first time, mainly from the information center and office, such as bis(chloropropyl) 2,3-dichloropropyl phosphate. Diphenyl phosphinic acid, two hydroxylated and methylated metabolites of tris(2,4-ditert-butylphenyl) phosphite (AO168), and a dimer phosphate were newly reported in the indoor environment. NOPs were widely associated with target OPFRs, and their human exposure risk and environmental behaviors warrant further study.

High-Throughput-Methyl-Reading (HTMR) assay: a solution based on nucleotide methyl-binding proteins enables large-scale screening for DNA/RNA methyltransferases and demethylases.

Epigenetic therapy has significant potential for cancer treatment. However, few small potent molecules have been identified against DNA or RNA modification regulatory proteins. Current approaches for activity detection of DNA/RNA methyltransferases and demethylases are time-consuming and labor-intensive, making it difficult to subject them to high-throughput screening. Here, we developed a fluorescence polarization-based 'High-Throughput Methyl Reading' (HTMR) assay to implement large-scale compound screening for DNA/RNA methyltransferases and demethylases-DNMTs, TETs, ALKBH5 and METTL3/METTL14. This assay is simple to perform in a mix-and-read manner by adding the methyl-binding proteins MBD1 or YTHDF1. The proteins can be used to distinguish FAM-labelled substrates or product oligonucleotides with different methylation statuses catalyzed by enzymes. Therefore, the extent of the enzymatic reactions can be coupled with the variation of FP binding signals. Furthermore, this assay can be effectively used to conduct a cofactor competition study. Based on the assay, we identified two natural products as candidate compounds for DNMT1 and ALKBH5. In summary, this study outlines a powerful homogeneous approach for high-throughput screening and evaluating enzymatic activity for DNA/RNA methyltransferases and demethylases that is cheap, easy, quick, and highly sensitive.

Design of a Transformer Oil Viscosity, Density, and Dielectric Constant Simultaneous Measurement System Based on a Quartz Tuning Fork.

Transformer oil, crucial for transformer and power system safety, demands effective monitoring. Aiming to address the problems of expensive and bulky equipment, poor real-time performance, and single parameter detection of traditional measurement methods, this study proposes a quartz tuning fork-based simultaneous measurement system for online monitoring of the density, viscosity, and dielectric constant of transformer oil. Based on the Butterworth-Van Dyke quartz tuning fork equivalent circuit model, a working mechanism of transformer oil density, viscosity, and dielectric constant was analyzed, and a measurement model for oil samples was obtained. A miniaturized simultaneous measurement system was designed based on a dedicated chip for vector current-voltage impedance analysis for data acquisition and a Savitzky-Golay filter for data filtering. A transformer oil test platform was built to verify the simultaneous measurement system. The results showed that the system has good repeatability, and the measurement errors of density, viscosity, and dielectric constant are lower than 2.00%, 5.50%, and 3.20%, respectively. The online and offline results showed that the system meets the requirements of the condition maintenance system for online monitoring accuracy and real-time detection.

The development and training effect of innovative undergraduate dental talents training project: A 6-year retrospective study.

INTRODUCTION: To summarize the development of Innovative Undergraduate Dental Talents Training Project (IUDTTP) and investigate the training effect of this extracurricular dental basic research education activity from 2015 to 2020 to obtain educational implications. MATERIALS AND METHODS: The Guanghua School of Stomatology established the IUDTTP in 2015. The authors recorded the development process and analysed the participation situation, training effect, academic performance and overall satisfaction during 2015-2020 through documental analysis, questionnaire and quiz. The t-test, chi-square test and ANOVA were used to test the difference. RESULTS: The educational goal, education module and assessment system of IUDTTP evolved and developed every year. A total of 336 students and 79 mentors attended the IUDTTP from 2015 to 2020, with the participation rate increasing from 45.1% to 73.5%. The participants exhibited favourable basic research abilities, manifesting as the increase of funded projects and published papers and satisfying quiz scores. Almost all students (94.94%) admitted their satisfaction with the IUDTTP. Moreover, the attended students surpassed the non-participants in terms of GPA, the number of acquired scholarships and outstanding graduates (p < .05). Likewise, the enrolment rate of postgraduate participants was significantly higher than non-participants. CONCLUSIONS: To date, the training effect indicated that the IUDTTP has fulfilled the education aim. It brought positive effects on promoting research interest, cultivating research capacities and enhancing academic performance. The potential deficiencies of extracurricular educational activities, including inflexibility in schedule and insufficiency in systematisms, may be remedied by more systematic educational settings in the future.

Advancing RNA 3D structure prediction: Exploring hierarchical and hybrid approaches in CASP15.

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.

Vfold-Pipeline: a web server for RNA 3D structure prediction from sequences.

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

The long non-coding RNA KLF3-AS1/miR-10a-3p/ZBTB20 axis improves the degenerative changes in human nucleus pulposus cells.

Excessive apoptosis of intervertebral disc cells, namely nucleus pulposus (NP) cells, results in decreased cell density and extracellular matrix (ECM) catabolism, hence leading to intervertebral disc degeneration (IVDD). As a cell model in the present study, a commercially available human NP cell line was utilized. Long noncoding RNAs and microRNAs may regulate the proliferation or apoptosis of human NP cells, hence exerting a significant influence on the occurrence of IVDD. KLF3-AS1 was discovered to be abnormally downregulated in IVDD tissues. Overexpression of KLF3-AS1 enhanced NP cell viability, prevented cell apoptosis, boosted ECM synthesis, and lowered MMP-13 and ADAMTS4 levels. ZBTB20 and KLF3-AS1 were co-expressed in IVDD; ZBTB20 overexpression had similar effects on NP cells, ECM production, and MMP-13 and ADAMTS4 levels as KLF3-AS1 overexpression. miR-10a-3p may target KLF3-AS1 and ZBTB20 and inhibit the expression of ZBTB20. Inhibition of miR-10a-3p enhanced NP cell viability, reduced apoptosis, and enhanced ECM synthesis. KLF3-AS1 overexpression increased ZBTB20 expression, whereas miR-10a-3p overexpression decreased ZBTB20 expression; miR-10a-3p overexpression reduced the effects of KLF3-AS1 on ZBTB20. Overexpression of miR-10a-3p consistently decreased the effects of KLF3-AS1 overexpression on NP cell survival, apoptosis, and ECM synthesis. In conclusion, KLF3-AS1 overexpression may ameliorate degenerative NP cell alterations through the miR-10a-3p/ZBTB20 axis.

Inhibition of protein arginine methyltransferase 1 alleviates liver fibrosis by attenuating the activation of hepatic stellate cells in mice.

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-ß 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.

RLDOCK method for predicting RNA-small molecule binding modes.

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.

Discovery of new small molecule inhibitors of the BPTF bromodomain.

Chromatin remodeling regulates many basic cellular processes, such as gene transcription, DNA repair, and programmed cell death. As the largest member of nucleosome remodeling factor (NURF), BPTF plays a vital role in the occurrence and development of cancer. Currently, BPTF bromodomain inhibitors are still in development. In this study, by conducting homogenous time-resolved fluorescence resonance energy transfer (HTRF) assay, we identified a potential, novel BPTF inhibitor scaffold Sanguinarine chloride with the IC50 value of 344.2 ± 25.1 nM. Biochemical analysis revealed that compound Sanguinarine chloride exhibited high binding affinity to the BPTF bromodomain. Molecular docking predicted the binding mode of Sanguinarine chloride and elucidated the activities of its derivatives. Moreover, Sanguinarine chloride showed a potent anti-proliferative effect in MIAPaCa-2 cells and inhibited the expression of BPTF target gene c-Myc. Taken together, Sanguinarine chloride provides a qualified chemical tool for developing potent BPTF bromodomain inhibitors.

Discovery of a novel OGT inhibitor through high-throughput screening based on Homogeneous Time-Resolved Fluorescence (HTRF).

O-GlcNAcylation is a specific type of post-translational glycosylation modification, which is regulated by two enzymes, O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA). Aberrant overexpression of OGT is associated with the development of many solid tumors. In this study, we have developed and optimized a sensitive Homogeneous Time-Resolved Fluorescence (HTRF) assay then identified a novel OGT inhibitor CDDO (also called Bardoxolone) through a high-throughput screening (HTS) based on HTRF assay. Further characterization suggested that CDDO is an effective OGT inhibitor with an IC50 value of 6.56 ± 1.69 µM. CPMG-NMR analysis confirmed that CDDO is a direct binder of OGT with a binding affinity (Kd) of approximately 1.7 µM determined by the MST analysis. Moreover, HDX-MS analysis indicated that CDDO binds to the TPR domain and N-Terminal domain of OGT, which was further confirmed by the enzymatic competition experiments as the binding of CDDO to OGT was not affected by the catalytic site binding inhibitor OSMI-4. Our docking modeling analysis further predicted the possible interactions between CDDO and OGT, providing informative molecular basis for further optimization of the inhibitor in the future. Together, our results suggested CDDO is a new inhibitor of OGT with a distinct binding pocket from the reported OGT inhibitors. Our work paved a new direction for developing OGT inhibitors driven by novel mechanisms.