Epigenetic priming of memory updating during reconsolidation to attenuate remote fear memories.

Traumatic events generate some of the most enduring forms of memories. Despite the elevated lifetime prevalence of anxiety disorders, effective strategies to attenuate long-term traumatic memories are scarce. The most efficacious treatments to diminish recent (i.e., day-old) traumata capitalize on memory updating mechanisms during reconsolidation that are initiated upon memory recall. Here, we show that, in mice, successful reconsolidation-updating paradigms for recent memories fail to attenuate remote (i.e., month-old) ones. We find that, whereas recent memory recall induces a limited period of hippocampal neuroplasticity mediated, in part, by S-nitrosylation of HDAC2 and histone acetylation, such plasticity is absent for remote memories. However, by using an HDAC2-targeting inhibitor (HDACi) during reconsolidation, even remote memories can be persistently attenuated. This intervention epigenetically primes the expression of neuroplasticity-related genes, which is accompanied by higher metabolic, synaptic, and structural plasticity. Thus, applying HDACis during memory reconsolidation might constitute a treatment option for remote traumata.

Architecture-driven quantitative nanoscopy maps cytoskeleton remodeling.

Cytoskeleton remodeling which generates force and orchestrates signaling and trafficking to govern cell migration remains poorly understood, partly due to a lack of an investigation tool with high system flexibility, spatiotemporal resolution, and computational sensitivity. Herein, we developed a multimodal superresolution imaging system-based architecture-driven quantitative (ADQ) framework in spatiotemporal-angular hyperspace to enable both identification of the optimal imaging mode with well-balanced fidelity and phototoxicity and accurate postcharacterization of microtubule remodeling. In the ADQ framework, a pixel/voxel-wise metric reflecting heterogeneous intertubule alignment was proposed with improved sensitivity over previous efforts and further incorporated with temporal features to map dynamic microtubule rearrangements. The ADQ framework was verified by assessing microtubule remodeling in drug-induced (de)polymerization, lysosome transport, and migration. Different remodeling patterns from two migration modes were successfully revealed by the ADQ framework, with a front-rear polarization for individual directed migration and a contact site-centered polarization for cell-cell interaction-induced migration in an immune response model. Meanwhile, these migration modes were found to have consistent orientation changes, which exhibited the potential of predicting migration trajectory.

m6A-Atlas v2.0: updated resources for unraveling the N6-methyladenosine (m6A) epitranscriptome among multiple species.

N 6-Methyladenosine (m6A) is one of the most abundant internal chemical modifications on eukaryote mRNA and is involved in numerous essential molecular functions and biological processes. To facilitate the study of this important post-transcriptional modification, we present here m6A-Atlas v2.0, an updated version of m6A-Atlas. It was expanded to include a total of 797 091 reliable m6A sites from 13 high-resolution technologies and two single-cell m6A profiles. Additionally, three methods (exomePeaks2, MACS2 and TRESS) were used to identify >16 million m6A enrichment peaks from 2712 MeRIP-seq experiments covering 651 conditions in 42 species. Quality control results of MeRIP-seq samples were also provided to help users to select reliable peaks. We also estimated the condition-specific quantitative m6A profiles (i.e. differential methylation) under 172 experimental conditions for 19 species. Further, to provide insights into potential functional circuitry, the m6A epitranscriptomics were annotated with various genomic features, interactions with RNA-binding proteins and microRNA, potentially linked splicing events and single nucleotide polymorphisms. The collected m6A sites and their functional annotations can be freely queried and downloaded via a user-friendly graphical interface at: http://rnamd.org/m6a.

m7GHub V2.0: an updated database for decoding the N7-methylguanosine (m7G) epitranscriptome.

With recent progress in mapping N7-methylguanosine (m7G) RNA methylation sites, tens of thousands of experimentally validated m7G sites have been discovered in various species, shedding light on the significant role of m7G modification in regulating numerous biological processes including disease pathogenesis. An integrated resource that enables the sharing, annotation and customized analysis of m7G data will greatly facilitate m7G studies under various physiological contexts. We previously developed the m7GHub database to host mRNA m7G sites identified in the human transcriptome. Here, we present m7GHub v.2.0, an updated resource for a comprehensive collection of m7G modifications in various types of RNA across multiple species: an m7GDB database containing 430 898 putative m7G sites identified in 23 species, collected from both widely applied next-generation sequencing (NGS) and the emerging Oxford Nanopore direct RNA sequencing (ONT) techniques; an m7GDiseaseDB hosting 156 206 m7G-associated variants (involving addition or removal of an m7G site), including 3238 disease-relevant m7G-SNPs that may function through epitranscriptome disturbance; and two enhanced analysis modules to perform interactive analyses on the collections of m7G sites (m7GFinder) and functional variants (m7GSNPer). We expect that m7Ghub v.2.0 should serve as a valuable centralized resource for studying m7G modification. It is freely accessible at: www.rnamd.org/m7GHub2.

Multi-task adaptive pooling enabled synergetic learning of RNA modification across tissue, type and species from low-resolution epitranscriptomes.

Post- and co-transcriptional RNA modifications are found to play various roles in regulating essential biological processes at all stages of RNA life. Precise identification of RNA modification sites is thus crucial for understanding the related molecular functions and specific regulatory circuitry. To date, a number of computational approaches have been developed for in silico identification of RNA modification sites; however, most of them require learning from base-resolution epitranscriptome datasets, which are generally scarce and available only for a limited number of experimental conditions, and predict only a single modification, even though there are multiple inter-related RNA modification types available. In this study, we proposed AdaptRM, a multi-task computational method for synergetic learning of multi-tissue, type and species RNA modifications from both high- and low-resolution epitranscriptome datasets. By taking advantage of adaptive pooling and multi-task learning, the newly proposed AdaptRM approach outperformed the state-of-the-art computational models (WeakRM and TS-m6A-DL) and two other deep-learning architectures based on Transformer and ConvMixer in three different case studies for both high-resolution and low-resolution prediction tasks, demonstrating its effectiveness and generalization ability. In addition, by interpreting the learned models, we unveiled for the first time the potential association between different tissues in terms of epitranscriptome sequence patterns. AdaptRM is available as a user-friendly web server from http://www.rnamd.org/AdaptRM together with all the codes and data used in this project.

Quantitative profiling N1-methyladenosine (m1A) RNA methylation from Oxford nanopore direct RNA sequencing data.

With the recent advanced direct RNA sequencing technique that proposed by the Oxford Nanopore Technologies, RNA modifications can be detected and profiled in a simple and straightforward manner. Majority nanopore-based modification studies were devoted to those popular types such as m6A and pseudouridine. To address current limitations on studying the crucial regulator, m1A modification, we conceived this study. We have developed an integrated computational workflow designed for the detection of m1A modifications from direct RNA sequencing data. This workflow comprises a feature extractor responsible for capturing signal characteristics (such as mean, standard deviations, and length of electric signals), a single molecule-level m1A predictor trained with features extracted from the IVT dataset using classical machine learning algorithms, a confident m1A site selector employing the binomial test to identify statistically significant m1A sites, and an m1A modification rate estimator. Our model achieved accurate molecule-level prediction (Average AUC = 0.9689) and reliable m1A site detection and quantification. To show the feasibility of our workflow, we conducted a study on in vivo transcribed human HEK293 cell line, and the results were carefully annotated and compared with other techniques (i.e., Illumina sequencing-based techniques). We believed that this tool will enabling a comprehensive understanding of the m1A modification and its functional mechanisms within cells and organisms.

DirectRMDB: a database of post-transcriptional RNA modifications unveiled from direct RNA sequencing technology.

With advanced technologies to map RNA modifications, our understanding of them has been revolutionized, and they are seen to be far more widespread and important than previously thought. Current next-generation sequencing (NGS)-based modification profiling methods are blind to RNA modifications and thus require selective chemical treatment or antibody immunoprecipitation methods for particular modification types. They also face the problem of short read length, isoform ambiguities, biases and artifacts. Direct RNA sequencing (DRS) technologies, commercialized by Oxford Nanopore Technologies (ONT), enable the direct interrogation of any given modification present in individual transcripts and promise to address the limitations of previous NGS-based methods. Here, we present the first ONT-based database of quantitative RNA modification profiles, DirectRMDB, which includes 16 types of modification and a total of 904,712 modification sites in 25 species identified from 39 independent studies. In addition to standard functions adopted by existing databases, such as gene annotations and post-transcriptional association analysis, we provide a fresh view of RNA modifications, which enables exploration of the epitranscriptome in an isoform-specific manner. The DirectRMDB database is freely available at: http://www.rnamd.org/directRMDB/.

RMDisease V2.0: an updated database of genetic variants that affect RNA modifications with disease and trait implication.

Recent advances in epitranscriptomics have unveiled functional associations between RNA modifications (RMs) and multiple human diseases, but distinguishing the functional or disease-related single nucleotide variants (SNVs) from the majority of 'silent' variants remains a major challenge. We previously developed the RMDisease database for unveiling the association between genetic variants and RMs concerning human disease pathogenesis. In this work, we present RMDisease v2.0, an updated database with expanded coverage. Using deep learning models and from 873 819 experimentally validated RM sites, we identified a total of 1 366 252 RM-associated variants that may affect (add or remove an RM site) 16 different types of RNA modifications (m6A, m5C, m1A, m5U, Ψ, m6Am, m7G, A-to-I, ac4C, Am, Cm, Um, Gm, hm5C, D and f5C) in 20 organisms (human, mouse, rat, zebrafish, maize, fruit fly, yeast, fission yeast, Arabidopsis, rice, chicken, goat, sheep, pig, cow, rhesus monkey, tomato, chimpanzee, green monkey and SARS-CoV-2). Among them, 14 749 disease- and 2441 trait-associated genetic variants may function via the perturbation of epitranscriptomic markers. RMDisease v2.0 should serve as a useful resource for studying the genetic drivers of phenotypes that lie within the epitranscriptome layer circuitry, and is freely accessible at: www.rnamd.org/rmdisease2.

The Prognostic Value of CD39 as a Marker of Tumor-Specific T Cells in Triple-Negative Breast Cancer in Asian Women.

Triple-negative breast cancer (TNBC) has a poor prognosis with limited therapeutic options available for affected patients. Efforts are ongoing to identify surrogate markers for tumor-specific CD8+ T cells that can predict the response to immune checkpoint inhibitor (ICI) therapies, such as programmed cell death protein 1 or programmed cell death ligand-1 blockade. We have previously identified tumor-specific CD39+CD8+ T cells in non-small cell lung cancer that might help predict patient responses to programmed cell death protein 1 or programmed cell death ligand-1 blockade. Based on this finding, we conducted a comparative interrogation of TNBC in an Asian cohort to evaluate the potential of CD39 as a surrogate marker of tumor-specific CD8+ T cells. Using ICI-treated TNBC mouse models (n = 24), flow cytometric analyses of peripheral blood mononuclear cells and tumor-infiltrating lymphocytes revealed that >99% of tumor-specific CD8+ T cells also expressed CD39. To investigate the relationship between CD39+CD8+ T-cell density and CD39 expression with disease prognosis, we performed multiplex immunohistochemistry staining on treatment-naive human TNBC tissues (n = 315). We saw that the proportion of CD39+CD8+ T cells in human TNBC tumors correlated with improved overall survival, as did the densities of other CD39+ immune cell infiltrates, such as CD39+CD68+ macrophages. Finally, increased CD39 expression on CD8+ T cells was also found to predict the response to ICI therapy (pembrolizumab) in a separate cohort of 11 TNBC patients. These findings support the potential of CD39+CD8+ T-cell density as a prognostic factor in Asian TNBC patients.

Hepatitis B Virus X Protein Contributes to Hepatocellular Carcinoma via Upregulation of KIAA1429 Methyltransferase and mRNA m6A Hypermethylation of HSPG2/Perlecan.

Chronic hepatitis B virus (HBV) remains to be the most common risk factor of hepatocellular carcinoma (HCC). While previous work has primarily focussed on understanding the direct and indirect mechanisms of Hepatitis B virus X protein (HBx)-mediated hepatocarcinogenesis, from genetic and epigenetic perspectives, its influence on RNA modification mediated onset of liver malignancies is less well understood. This study explored the role of HBV-encoded HBx in altering the m6A methylome profile and its implications on the pathogenesis of HCC. We established HBx-expressing stable HCC cell lines, Huh7-HBx and HepG2-HBx, and explored the transcriptomic and epitranscriptomic profiles by RNA-seq and MeRIP-seq, respectively. Preliminary results suggest that HBx promotes liver cell proliferation, migration, survival and overall m6A methylation in HCC cells and is involved in modulating the extracellular matrix. We show that HBx mediates liver cell transformation by upregulating KIAA1429 methyltransferase. HBx also drives the expression and hypermethylation of the extracellular matrix protein HSPG2/Perlecan and promotes tumourigenesis. Furthermore, we observed a potential interaction between KIAA1429 and HSPG2 in HCC liver cancer cells and demands further investigation.

Identification of macrophage driver genes in fibrosis caused by different heart diseases based on omics integration.

BACKGROUND: Myocardial fibrosis, a hallmark of heart disease, is closely associated with macrophages, yet the genetic pathophysiology remains incompletely understood. In this study, we utilized integrated single-cell transcriptomics and bulk RNA-seq analysis to investigate the relationship between macrophages and myocardial fibrosis across omics integration. METHODS: We examined and curated existing single-cell data from dilated cardiomyopathy (DCM), ischemic cardiomyopathy (ICM), myocardial infarction (MI), and heart failure (HF), and analyzed the integrated data using cell communication, transcription factor identification, high dimensional weighted gene co-expression network analysis (hdWGCNA), and functional enrichment to elucidate the drivers of macrophage polarization and the macrophage-to-myofibroblast transition (MMT). Additionally, we assessed the accuracy of single-cell data from the perspective of driving factors, cell typing, anti-fibrosis performance of left ventricular assist device (LVAD). Candidate drugs were screened using L1000FWD. RESULTS: All four heart diseases exhibit myocardial fibrosis, with only MI showing an increase in macrophage proportions. Macrophages participate in myocardial fibrosis through various fibrogenic molecules, especially evident in DCM and MI. Abnormal RNA metabolism and dysregulated transcription are significant drivers of macrophage-mediated fibrosis. Furthermore, profibrotic macrophages exhibit M1 polarization and increased MMT. In HF patients, those responding to LVAD therapy showed a significant decrease in driver gene expression, M1 polarization, and MMT. Drug repurposing identified cinobufagin as a potential therapeutic agent. CONCLUSION: Using integrated single-cell transcriptomics, we identified the drivers of macrophage-mediated myocardial fibrosis in four heart diseases and confirmed the therapeutic effect of LVAD on improving HF with single-cell accuracy, providing novel insights into the diagnosis and treatment of myocardial fibrosis.

A decade of stability for wMel Wolbachia in natural Aedes aegypti populations.

Mosquitoes carrying Wolbachia endosymbionts are being released in many countries for arbovirus control. The wMel strain of Wolbachia blocks Aedes-borne virus transmission and can spread throughout mosquito populations by inducing cytoplasmic incompatibility. Aedes aegypti mosquitoes carrying wMel were first released into the field in Cairns, Australia, over a decade ago, and with wider releases have resulted in the near elimination of local dengue transmission. The long-term stability of Wolbachia effects is critical for ongoing disease suppression, requiring tracking of phenotypic and genomic changes in Wolbachia infections following releases. We used a combination of field surveys, phenotypic assessments, and Wolbachia genome sequencing to show that wMel has remained stable in its effects for up to a decade in Australian Ae. aegypti populations. Phenotypic comparisons of wMel-infected and uninfected mosquitoes from near-field and long-term laboratory populations suggest limited changes in the effects of wMel on mosquito fitness. Treating mosquitoes with antibiotics used to cure the wMel infection had limited effects on fitness in the next generation, supporting the use of tetracycline for generating uninfected mosquitoes without off-target effects. wMel has a temporally stable within-host density and continues to induce complete cytoplasmic incompatibility. A comparison of wMel genomes from pre-release (2010) and nine years post-release (2020) populations show few genomic differences and little divergence between release locations, consistent with the lack of phenotypic changes. These results indicate that releases of Wolbachia-infected mosquitoes for population replacement are likely to be effective for many years, but ongoing monitoring remains important to track potential evolutionary changes.

Identification of the central role of RNA polymerase mitochondrial for angiogenesis.

Mitochondria are central to endothelial cell activation and angiogenesis, with the RNA polymerase mitochondrial (POLRMT) serving as a key protein in regulating mitochondrial transcription and oxidative phosphorylation. In our study, we examined the impact of POLRMT on angiogenesis and found that its silencing or knockout (KO) in human umbilical vein endothelial cells (HUVECs) and other endothelial cells resulted in robust anti-angiogenic effects, impeding cell proliferation, migration, and capillary tube formation. Depletion of POLRMT led to impaired mitochondrial function, characterized by mitochondrial depolarization, oxidative stress, lipid oxidation, DNA damage, and reduced ATP production, along with significant apoptosis activation. Conversely, overexpressing POLRMT promoted angiogenic activity in the endothelial cells. In vivo experiments demonstrated that endothelial knockdown of POLRMT, by intravitreous injection of endothelial specific POLRMT shRNA adeno-associated virus, inhibited retinal angiogenesis. In addition, inhibiting POLRMT with a first-in-class inhibitor IMT1 exerted significant anti-angiogenic impact in vitro and in vivo. Significantly elevated expression of POLRMT was observed in the retinal tissues of streptozotocin-induced diabetic retinopathy (DR) mice. POLRMT endothelial knockdown inhibited pathological retinal angiogenesis and mitigated retinal ganglion cell (RGC) degeneration in DR mice. At last, POLRMT expression exhibited a substantial increase in the retinal proliferative membrane tissues of human DR patients. These findings collectively establish the indispensable role of POLRMT in angiogenesis, both in vitro and in vivo.

The Effects of Excipients on Freeze-dried Monoclonal Antibody Formulation Degradation and Sub-Visible Particle Formation during Shaking.

PURPOSES: We previously reported an unexpected phenomenon that shaking stress could cause more protein degradation in freeze-dried monoclonal antibody (mAb) formulations than liquid ones (J Pharm Sci, 2022, 2134). The main purposes of the present study were to investigate the effects of shaking stress on protein degradation and sub-visible particle (SbVP) formation in freeze-dried mAb formulations, and to analyze the factors influencing protein degradation during production and transportation. METHODS: The aggregation behavior of mAb-X formulations during production and transportation was simulated by shaking at a rate of 300 rpm at 25°C for 24 h. The contents of particles and monomers were analyzed by micro-flow imaging, dynamic light scattering, size exclusion chromatography, and ultraviolet - visible (UV-Vis) spectroscopy to compare the protective effects of excipients on the aggregation of mAb-X. RESULTS: Shaking stress could cause protein degradation in freeze-dried mAb-X formulations, while surfactant, appropriate pH, polyol mannitol, and high protein concentration could impact SbVP generation. Water content had little effect on freeze-dried protein degradation during shaking, as far as the water content was controlled in the acceptable range as recommended by mainstream pharmacopoeias (i.e., less than 3%). CONCLUSIONS: Shaking stress can reduce the physical stability of freeze-dried mAb formulations, and the addition of surfactants, polyol mannitol, and a high protein concentration have protective effects against the degradation of model mAb formulations induced by shaking stress. The experimental results provide new insight for the development of freeze-dried mAb formulations.

Detection and Quantification of 5moU RNA Modification from Direct RNA Sequencing Data.

Background: Chemically modified therapeutic mRNAs have gained momentum recently. In addition to commonly used modifications (e.g., pseudouridine), 5moU is considered a promising substitution for uridine in therapeutic mRNAs. Accurate identification of 5-methoxyuridine (5moU) would be crucial for the study and quality control of relevant in vitro-transcribed (IVT) mRNAs. However, current methods exhibit deficiencies in providing quantitative methodologies for detecting such modification. Utilizing the capabilities of Oxford nanopore direct RNA sequencing, in this study, we present NanoML-5moU, a machine-learning framework designed specifically for the read-level detection and quantification of 5moU modification for IVT data. Materials and Methods: Nanopore direct RNA sequencing data from both 5moU-modified and unmodified control samples were collected. Subsequently, a comprehensive analysis and modeling of signal event characteristics (mean, median current intensities, standard deviations, and dwell times) were performed. Furthermore, classical machine learning algorithms, notably the Support Vector Machine (SVM), Random Forest (RF), and XGBoost were employed to discern 5moU modifications within NNUNN (where N represents A, C, U, or G) 5-mers. Results: Notably, the signal event attributes pertaining to each constituent base of the NNUNN 5-mers, in conjunction with the utilization of the XGBoost algorithm, exhibited remarkable performance levels (with a maximum AUROC of 0.9567 in the "AGTTC" reference 5-mer dataset and a minimum AUROC of 0.8113 in the "TGTGC" reference 5-mer dataset). This accomplishment markedly exceeded the efficacy of the prevailing background error comparison model (ELIGOs AUC 0.751 for site-level prediction). The model's performance was further validated through a series of curated datasets, which featured customized modification ratios designed to emulate broader data patterns, demonstrating its general applicability in quality control of IVT mRNA vaccines. The NanoML-5moU framework is publicly available on GitHub (https://github.com/JiayiLi21/NanoML-5moU). Conclusion: NanoML-5moU enables accurate read-level profiling of 5moU modification with nanopore direct RNA-sequencing, which is a powerful tool specialized in unveiling signal patterns in in vitro-transcribed (IVT) mRNAs.

A Prediction Model for Clinical Outcomes of COVID-19 Hospitalized Patients: Construction and Accuracy Assessment.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic spread rapidly with considerable morbidity nationwide since China's liberalization in December 2022. Our work has focused on identifying different predictive factors from the laboratory examination of critically ill patients, and forecasting the unfavorable outcome of critically ill patients with COVID-19 through a combined diagnosis of biological markers. METHODS: We conducted a retrospective study at the Department of First Affiliated Hospital of Wenzhou Medical University, China, from December 24, 2022, to January 10, 2023, where 434 critically ill patients who met the inclusion criteria were involved. Machine analysis was employed to search for the parameters with the highest predictive value to calculate COVID-19 mortality by exploiting 66 typical laboratory results. RESULTS: Combined diagnosis of serum albumin (ALB), lactate dehydrogenase (LDH), direct bilirubin (Dbil), ferritin, pulse oxygen saturation (SpO2), and neutrophil count (NEUT#) was evaluated, and the result with the highest predictive value (NEUT#) was selected as the predictor for COVID-19 mortality with a sensitivity of 89.2% and a specificity of 77.4%. CONCLUSIONS: The increased levels of LDH, Dbil, ferritin, and NEUT#, along with lowered ALB and SpO2 levels are the most decisive variables for forecasting the mortality for COVID-19 according to our machine-learning-based model. The combined diagnosis could be used to improve further diagnostic performance.

Effectiveness of Physiotherapeutic Scoliosis-Specific Exercises on 3-Dimensional Spinal Deformities in Patients With Adolescent Idiopathic Scoliosis: A Systematic Review and Meta-analysis.

OBJECTIVE: To investigate the effects of physiotherapeutic scoliosis-specific exercises (PSSE) on coronal, horizontal, and sagittal deformities of the spine in adolescent idiopathic scoliosis (AIS) as well as how curve severity, intervention duration, and intervention type could modify these effects. DATA SOURCES: Data sources included PubMed, Web of Science, Embase, Cochrane Library, and Scopus databases, which were searched from their inception to September 5, 2023. STUDY SELECTION: Clinical controlled trials reporting the effects of PSSE on the Cobb angle, angle of trunk rotation (ATR), thoracic kyphosis (TK), or lumbar lordosis in patients with AIS aged 10-18 years. The experimental groups received PSSE; the control groups received standard care (observation or bracing) or conventional exercise such as core stabilization exercise, Pilates, proprioceptive neuromuscular facilitation, and other nonspecific exercises. DATA EXTRACTION: Two researchers independently extracted key information from eligible studies. The quality of the studies was assessed using the Cochrane Handbook version 5.1.0 risk of bias assessment and the JBI Center for Evidence-Based Health Care (2016) of quasi-experimental research authenticity assessment tool. The level and certainty of evidence were rated according to the Grading of Recommendations, Assessment, Development, and Evaluation framework. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. The protocol for this study was registered in PROSPERO (CRD42023404996). DATA SYNTHESIS: Twelve randomized controlled trials (RCTs) and 5 non-RCTs were meta-analyzed separately. The results indicated that compared with other nonsurgical management, PSSE significantly improved the Cobb angle, ATR, and TK, whereas the lumbar lordosis improvement was not statistically significant. Additionally, the efficacy of PSSE on Cobb angle was not significant in patients with curve severity ≥30° compared with controls. Nevertheless, the pooled effect of PSSE on Cobb angle was not significantly modified by intervention duration and intervention type and that on ATR was not significantly modified by intervention duration. The overall quality of evidence according to Grading of Recommendations, Assessment, Development, and Evaluation was moderate to low for RCT and very low for non-RCT. CONCLUSIONS: PSSE exhibited positive benefits on the Cobb angle, ATR, and TK in patients with AIS compared with other nonsurgical therapies. In addition, the effectiveness of PSSE may be independent of intervention duration and intervention type but may be influenced by the initial Cobb angle. However, more RCTs are needed in the future to validate the efficacy of PSSE in moderate AIS with a mean Cobb angle ≥30°. Current evidence is limited by inconsistent control group interventions and small sample size of the studies.

Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation.

As the most pervasive epigenetic mark present on mRNA and lncRNA, N6-methyladenosine (m6A) RNA methylation regulates all stages of RNA life in various biological processes and disease mechanisms. Computational methods for deciphering RNA modification have achieved great success in recent years; nevertheless, their potential remains underexploited. One reason for this is that existing models usually consider only the sequence of transcripts, ignoring the various regions (or geography) of transcripts such as 3'UTR and intron, where the epigenetic mark forms and functions. Here, we developed three simple yet powerful encoding schemes for transcripts to capture the submolecular geographic information of RNA, which is largely independent from sequences. We show that m6A prediction models based on geographic information alone can achieve comparable performances to classic sequence-based methods. Importantly, geographic information substantially enhances the accuracy of sequence-based models, enables isoform- and tissue-specific prediction of m6A sites, and improves m6A signal detection from direct RNA sequencing data. The geographic encoding schemes we developed have exhibited strong interpretability, and are applicable to not only m6A but also N1-methyladenosine (m1A), and can serve as a general and effective complement to the widely used sequence encoding schemes in deep learning applications concerning RNA transcripts.

m5C-Atlas: a comprehensive database for decoding and annotating the 5-methylcytosine (m5C) epitranscriptome.

5-Methylcytosine (m5C) is one of the most prevalent covalent modifications on RNA. It is known to regulate a broad variety of RNA functions, including nuclear export, RNA stability and translation. Here, we present m5C-Atlas, a database for comprehensive collection and annotation of RNA 5-methylcytosine. The database contains 166 540 m5C sites in 13 species identified from 5 base-resolution epitranscriptome profiling technologies. Moreover, condition-specific methylation levels are quantified from 351 RNA bisulfite sequencing samples gathered from 22 different studies via an integrative pipeline. The database also presents several novel features, such as the evolutionary conservation of a m5C locus, its association with SNPs, and any relevance to RNA secondary structure. All m5C-atlas data are accessible through a user-friendly interface, in which the m5C epitranscriptomes can be freely explored, shared, and annotated with putative post-transcriptional mechanisms (e.g. RBP intermolecular interaction with RNA, microRNA interaction and splicing sites). Together, these resources offer unprecedented opportunities for exploring m5C epitranscriptomes. The m5C-Atlas database is freely accessible at https://www.xjtlu.edu.cn/biologicalsciences/m5c-atlas.

Gene Expression in Synovium of Rotator Cuff Tear Patients Determined by RNA Sequencing.

Rotator cuff tear (RCT) is a common shoulder disorder related to pain and dysfunction. However, the pathological mechanism of RCT remains unclear. Thus, this study aims to investigate the molecular events in RCT synovium and identify possible target genes and pathways as determined by RNA sequencing (RNA-Seq). The synovial tissue was biopsied from 3 patients with RCT (RCT group) and 3 patients with shoulder instability (Control group) during arthroscopic surgery. Then, differentially expressed (DE) mRNAs, long non-coding RNAs (lncRNAs) and micro RNAs (miRNAs) were comprehensively profiled by RNA-Seq. Gene ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and competing endogenous RNA (ceRNA) network analysis were performed to identify the potential functions of these DE genes. 447 mRNAs, 103 lncRNAs and 15 miRNAs were identified differentially expressed. The DE mRNAs were highlighted in inflammatory pathway including up-regulated T cell costimulation, positive regulation of T cell activation, and T cell receptor signaling. Down-regulated fatty acid degradation pathway and 5'-AMP-activated protein kinase (AMPK) signaling in RCT group are also enriched. Validation assay showed that the expression of pro-inflammatory molecules including IL21R, CCR5, TNFSF11, and MMP11 was significantly increased in RCT group compared with Control group. CeRNA analysis further revealed lncRNA-miRNA-mRNA regulatory networks involving IL21R and TNFSF11 in RCT. Activated synovial inflammation is the remarkable event of RCT. Importantly, increased T cell activation and disordered fatty acid metabolism signaling might play a significant role. ceRNA networks involving IL21R and TNFSF11 identified could potentially control the progression of RCT. In conclusion, our findings could provide new evidence for the molecular mechanisms of RCT and might identify new therapeutic targets.