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

TransRM: Weakly supervised learning of translation-enhancing N6-methyladenosine (m6A) in circular RNAs

As our understanding of Circular RNAs (circRNAs) continues to expand, accumulating evidence has demonstrated that circRNAs can interact with microRNAs and RNA-binding proteins to modulate gene expression. More importantly, a subset of circRNAs has been reported to possess coding potential, enabling them to translate into functional proteins. Recent studies also indicate that the N6-methyladenosine (m6A)-modified start codon may function as an Internal Ribosome Entry Site (IRES), influencing the translation of circRNAs. Therefore, elucidating how m6A regulates circRNA translation potential could significantly advance circRNA research, including the development of circRNA-based vaccines. However, to our knowledge, there are currently no computational tools specifically designed for this purpose. To bridge this gap, we have developed the first computational model, termed TransRM, to predict the impact of base-resolution m6A sites on circRNA translation. Our model employs weakly supervised learning with two convolution layers. These layers extract RNA modification features, and a bidirectional gated recurrent unit predicts the contribution of each RNA modification to circRNA translation. Subsequently, the RNA modification features are then integrated with their contribution to assess the probability of circRNA translation using a random forest algorithm. TransRM has demonstrated efficiency in identifying translation-enhancing m6A sites, with an AUROC of 0.9188 and an AUPRC of 0.9371, respectively. We hope that our newly proposed model could help to broaden our understanding of circRNA regulation at the epitranscriptome layer, particularly in identifying translated circRNAs, thereby contributing to the development of more effective circular RNA-based therapeutics.

Charting the epitranscriptomic landscape across RNA biotypes using native RNA nanopore sequencing

RNA modifications are conserved chemical features found in all domains of life and across diverse RNA biotypes, shaping gene expression profiles and enabling rapid responses to environmental changes. Their broad chemical diversity and dynamic nature pose significant challenges for studying them comprehensively. These limitations can now be addressed through direct RNA nanopore sequencing (DRS), which allows simultaneous identification of diverse RNA modification types at single-molecule and single-nucleotide resolution. Here, we review recent efforts pioneering the use of DRS to better understand the epitranscriptomic landscape. We highlight how DRS can be applied to investigate different RNA biotypes, emphasizing the use of specialized library preparation protocols and downstream bioinformatic workflows to detect both natural and synthetic RNA modifications. Finally, we provide a perspective on the future role of DRS in epitranscriptomic research, highlighting remaining challenges and emerging opportunities from improved sequencing yields and accuracy enabled by the latest DRS chemistry.

Consistent RNA expression and RNA modification patterns in cardiotoxicity induced by Matrine and Evodiamine

Recent research has demonstrated the efficacy of traditional Chinese medicine (TCM) and its active compounds in combating cancer, leading to an increasing utilization of TCM as adjunctive therapy in clinical oncology. However, the optimal dosage of TCM remains unclear, and excessive use may result in cardiotoxicity, which poses a significant health concern for patients undergoing systemic treatment. Therefore, elucidating the underlying mechanisms of cytotoxicity induced by TCM can provide valuable insights for clinical management. In this study, we employed a comprehensive bioinformatics analysis to present sequencing data obtained from AC16 myocardial cells treated with two bioactive derived from botanical drugs: Matrine and Evodiamine. We aim to investigate the dysregulated signaling pathways associated with cardiotoxicity induced by these compounds. Based on our sequencing results, we observed consistent patterns of gene expression and epitranscriptome regulation (m6A and A-to-I modifications) across various drugs-treated AC16 cells when analyzed using KEGG pathway enrichment and gene ontology analyses. Furthermore, m6A writers VIRMA and A-to-I writers ADARB1 is consistent target of Evodiamine and Matrine. In general, our findings suggest that different Chinese botanical drugs induced cardiotoxicity may share common therapeutic strategies.

Statistical modeling of single-cell epitranscriptomics enabled trajectory and regulatory inference of RNA methylation

As a fundamental mechanism for gene expression regulation, post-transcriptional RNA methylation plays versatile roles in various biological processes and disease mechanisms. Recent advances in single-cell technology have enabled simultaneous profiling of transcriptome-wide RNA methylation in thousands of cells, holding the promise to provide deeper insights into the dynamics, functions, and regulation of RNA methylation. However, it remains a major challenge to determine how to best analyze single-cell epitranscriptomics data. In this study, we developed SigRM, a computational framework for effectively mining single-cell epitranscriptomics datasets with a large cell number, such as those produced by the scDART-seq technique from the SMART-seq2 platform. SigRM not only outperforms state-of-the-art models in RNA methylation site detection on both simulated and real datasets but also provides rigorous quantification metrics of RNA methylation levels. This facilitates various downstream analyses, including trajectory inference and regulatory network reconstruction concerning the dynamics of RNA methylation.

Sci-ModoM: a quantitative database of transcriptome-wide high-throughput RNA modification sites

We present Sci-ModoM, the first next-generation RNome database offering a holistic view of the epitranscriptomic landscape. Sci-ModoM has a simple yet powerful interface, underpinned by FAIR data principles, a standardized nomenclature, and interoperable formats, fostering the use of common standards within the epitranscriptomics community. Sci-ModoM provides quantitative measurements per site and dataset, enabling users to assess confidence levels based on score, coverage, and stoichiometry. Data in Sci-ModoM is directly traceable to its sources. Users can Search and Compare over six million modifications across 156 datasets, Browse or download datasets, and retrieve metadata. A comparison tool offers a novel and unique opportunity to compare modifications site-wise across datasets, with the ability to securely upload and compare user data against latest published research. Sci-ModoM empowers researchers, including non-experts, to access a broad spectrum of recent quantitative RNA modification data, thereby enhancing the utility and impact of latest discoveries, and opening new avenues in biological and medical research.

RMVar 2.0: an updated database of functional variants in RNA modifications

Evaluating the impact of genetic variants on RNA modifications (RMs) is crucial for identifying disease-associated variants and understanding the pathogenic mechanisms underlying human diseases. Previously, we developed a database called RMVar to catalog variants linked to RNA modifications in humans and mice. Here, we present an updated version RMVar 2.0 (http://rmvar.renlab.cn). In this updated version, we applied an enhanced analytical pipeline to the latest RNA modification datasets and genetic variant information to identify RM-associated variants. A notable advancement in RMVar 2.0 is our incorporation of allele-specific RNA modification analysis to identify RM-associated variants, a novel approach not utilized in RMVar 1.0 or other comparable databases. Furthermore, the database offers comprehensive annotations for various molecular events, including RNA-binding protein (RBP) interactions, RNA-RNA interactions, splicing events, and circular RNAs (circRNAs), which facilitate investigations into how RM-associated variants influence post-transcriptional regulation. Additionally, we provide disease-related information sourced from ClinVar and GWAS to help researchers explore the connections between RNA modifications and various diseases. We believe that RMVar 2.0 will significantly enhance our understanding of the functional implications of genetic variants affecting RNA modifications within the context of human disease research.

Deciphering the pseudouridine nucleobase modification in human diseases: From molecular mechanisms to clinical perspectives

RNA pseudouridylation, a dynamic and reversible post-transcriptional modification found in diverse RNA species, is crucial for various biological processes, including tRNA homeostasis, tRNA transport, translation initiation regulation, pre-mRNA splicing, enhancement of mRNA translation, and translational fidelity. Disruption of pseudouridylation impairs cellular homeostasis, contributing to pathological alterations. Recent studies have highlighted its regulatory role in human diseases, particularly in tumourigenesis. Cellular stresses trigger RNA pseudouridylation in organisms, suggesting that pseudouridylation-mediated epigenetic reprogramming is essential for maintaining cellular viability and responding to stress. This review examines the regulatory mechanisms and pathological implications of pseudouridylation in human diseases, with a focus on its involvement in tumourigenesis. Additionally, it explores the therapeutic potential of targeting pseudouridylation, presenting novel strategies for disease treatment. HIGHLIGHTS: Methods to detect pseudouridine were introduced from classic mass spectrometry-based methods to newer approaches such as nanopore-based technologies and BID sequencing, each with its advantages and limitations. RNA pseudouridylation is crucial for various biological processes, including tRNA homeostasis, tRNA transport, translation initiation regulation, pre-mRNA splicing, enhancement of mRNA translation, and translational fidelity. Increased pseudouridylation is frequently associated with tumour initiation, progression, and poor prognosis, whereas its reduction is predominantly implicated in non-tumour diseases. A comprehensive understanding of the inducing factors for RNA pseudouridylation will be essential for elucidating its role in diseases. Such insights can provide robust evidence for how pseudouridylation influences disease progression and offer new avenues for therapeutic strategies targeting pseudouridylation dysregulation. The therapeutic potential of RNA pseudouridylation in diseases is enormous, including inhibitors targeting pseudouridine synthases, the application of RNA pseudouridylation in RNA therapeutics, and its role as a biological marker.

Interpretable deep cross networks unveiled common signatures of dysregulated epitranscriptomes across 12 cancer types

Cancer is a complex and multifaceted group of diseases characterized by uncontrolled cell growth that leads to the formation of malignant tumors. Recent studies suggest that N6-methyladenosine (m6A) RNA methylation plays pivotal roles in cancer pathology by influencing various cellular processes. However, the degree to which these mechanisms are shared across different cancer types remains unclear. In this study, we analyze an expansive array of 167 m6A epitranscriptome profiles covering 12 distinct cancer types and their originating normal tissues. We trained 12 distinct, cancer type-specific interpretable deep cross network models, which successfully distinguish between specific pairs of normal and cancer m6A contexts using integrated information from both the sequences and curated genomic knowledge. Interestingly, cross-cancer type testing indicated the existence of shared genomic patterns across various cancers at the epitranscriptome level. A pan-cancer model was subsequently developed to identify these shared patterns that could not be observed in a single cancer type. Our analysis uncovered, for the first time, a common epitranscriptome signature shared across multiple cancer types, particularly associated with RNA hybridization process and aberrant splicing. This highlights the importance of a comprehensive understanding of the pan-cancer epitranscriptome and holding potential implications in the development of RNA methylation-based therapeutics for various cancers.

RNA methylation and breast cancer: insights into m6A, m7G and m5C

Breast cancer remains the most commonly diagnosed cancer in female worldwide, marked by its molecular diversity and complex subtypes. Despite progress in targeted therapies, tumor heterogeneity and treatment resistance continue to present major challenges. Recent studies emphasize the crucial role of RNA modifications in cancer biology, with nearly 200 distinct modifications identified. Among these, methylation is particularly significant, with methylation-related factors emerging as key regulators of RNA metabolism, influencing cancer progression, metastasis, and treatment resistance. This review focuses on the roles of key RNA methylation in breast cancer, particularly N6-methyladenosine (m6A), N7-methylguanosine (m7G), 5-methylcytosine (m5C), N1-methyladenosine (m1A), and N3-methylcytidine (m3C). We examine the functions of m6A "writers" like METTL3 and METTL14, and "readers" such as the YTH domain family in modulating tumor behavior. Dysregulation of m6A "erasers" like FTO and ALKBH5 are noticed too, highlighting their impact on cancer stem cell phenotypes, chemoresistance, and immune evasion. Additionally, the role of m7G modifications in mRNA stability and translation, facilitated by METTL1/WDR4 and RNMT, is discussed as a potential therapeutic target. The involvement of m5C, m1A, and m3C modifications, particularly those mediated by NSUN2 and NSUN6, in breast cancer tumorigenesis and prognosis is also reviewed. Despite coding RNAs, the interplay between these RNA methylations and non-coding RNAs, such as lncRNAs and miRNAs, is explored, shedding light on their roles in cancer cell proliferation, invasion, and immune response modulation. This review highlights the potential of RNA methylations as novel therapeutic targets in breast cancer, offering insights for precision medicine and improved patient outcomes.

Multiomics profiling uncovers curdione-induced reproductive toxicity in HTR-8/SVneo cells

The assessment of medication toxicity and safety is pivotal during pregnancy. Curdione, a sesquiterpene compound extracted from Curcumae Radix, displays beneficial properties in terms of anti-inflammatory, tumor growth suppression, and anti-coagulative effects. However, its reproductive toxicity and precise mechanism remain unclear. This study aims to explore the mechanism of curdione-induced toxicity damage in HTR-8/SVneo cells through the epigenetics, proteomics, and metabolomics, and experimental verification. The results showed that curdione elicited alterations in m6A modification, gene expression, protein levels, and cellular metabolism of HTR-8/SVneo cells. Additionally, curdione induces oxidative stress, mitochondrial and DNA damage, while also downregulating the expression of Wnt6, β-catenin, ZO-1, and CLDN1 proteins. Curdione has the potential to modulate oxidative stress, mitochondrial dysfunction, and disruption of tight junctions via the Wnt/β-catenin signaling pathway, which contributes to cellular damage in HTR-8/SVneo cells.

Deep learning modeling of RNA ac4C deposition reveals the importance of plant alternative splicing

The N4-acetylcytidine (ac4C) modification has recently been characterized as a noncanonical RNA marker in plants. While the precise installation of ac4C sites in individual plant transcripts continues to present challenges, the biological roles of ac4C in specific plant species are gradually being deciphered. Herein, we utilized a deep learning technique called iac4C (intelligent ac4C) to predict ac4C sites in mRNA. ac4C deposition was effectively forecasted by the iac4C model (AUROC = 0.948), revealing a reliable distribution pattern primarily situated in the transcribing area as opposed to regions that are not translated. The iac4C deep learning approach using a combination of BiGRU and self-attention mechanisms both validates previous studies showing a positive correlation between ac4C and RNA splicing in plant species and reveals new examples of other splicing events associated with ac4C. Our advanced deep learning algorithm for analyzing ac4C enables swift identification of important biological phenomena that would otherwise be challenging to uncover through traditional experimental approaches. These findings provide insight into the essential regulatory function of site-specific ac4C deposition in alternative splicing processes. The source code and datasets for iac4C are available at https://github.com/xlwei507/iac4C .

Pan-cancer Analysis Reveals m6A Variation and Cell-specific Regulatory Network in Different Cancer Types

As the most abundant messenger RNA (mRNA) modification, N6-methyladenosine (m6A) plays a crucial role in RNA fate, impacting cellular and physiological processes in various tumor types. However, our understanding of the role of the m6A methylome in tumor heterogeneity remains limited. Herein, we collected and analyzed m6A methylomes across nine human tissues from 97 m6A sequencing (m6A-seq) and RNA sequencing (RNA-seq) samples. Our findings demonstrate that m6A exhibits different heterogeneity in most tumor tissues compared to normal tissues, which contributes to the diverse clinical outcomes in different cancer types. We also found that the cancer type-specific m6A level regulated the expression of different cancer-related genes in distinct cancer types. Utilizing a novel and reliable method called "m6A-express", we predicted m6A-regulated genes and revealed that cancer type-specific m6A-regulated genes contributed to the prognosis, tumor origin, and infiltration level of immune cells in diverse patient populations. Furthermore, we identified cell-specific m6A regulators that regulate cancer-specific m6A and constructed a regulatory network. Experimental validation was performed, confirming that the cell-specific m6A regulator CAPRIN1 controls the m6A level of TP53. Overall, our work reveals the clinical relevance of m6A in various tumor tissues and explains how such heterogeneity is established. These results further suggest the potential of m6A in cancer precision medicine for patients with different cancer types.

Comprehensive Profiling of Transcriptome and m6A Epitranscriptome Uncovers the Neurotoxic Effects of Yunaconitine on HT22 Cells

Objective

To explore different mRNA transcriptome patterns and RNA N6-methyladenosine (m6A) alteration in yunaconitine (YA)-treated HT22 mouse hippocampal neuron, and uncover the role of abnormal mRNA expression and RNA m6A modification in YA-induced neurotoxicity.

Methods

HT22 cells were treated with 0, 5, 10, and 50 μM of YA for 72 h to evaluate their viability and GSH content. Subsequently, mRNA-seq and MeRIP-seq analyses were performed on HT22 cells treated with 0 and 10 μM YA for 72 h, and molecular docking was used to simulate interactions between YA and differentially expressed m6A regulators. The mitochondrial membrane potential was examined using the JC-10 probe, and RT-qPCR was conducted to verify the expression levels of differentially expressed m6A regulatory factors, as well as to assess alterations in the mRNA expression levels of antioxidant genes.

Results

YA treatment significantly reduced the viability of HT22 cells and decreased GSH content. The mRNA-seq analysis obtained 1018 differentially expressed genes, KEGG and GO enrichment results of differentially expressed genes mainly comprise the nervous system development, cholinergic synapse, response to oxidative stress, and mitochondrial inner membrane. A total of 7 differentially expressed m6A regulators were identified by MeRIP-seq. Notably, molecular docking results suggested a stable interaction between YA and most of the differentially expressed m6A regulators.

Conclusion

This study showed that YA-induced HT22 cell damage was associated with the increased methylation modification level of target gene m6A and abnormal expression of m6A regulators.

Nmix: a hybrid deep learning model for precise prediction of 2'-O-methylation sites based on multi-feature fusion and ensemble learning

RNA 2'-O-methylation (Nm) is a crucial post-transcriptional modification with significant biological implications. However, experimental identification of Nm sites is challenging and resource-intensive. While multiple computational tools have been developed to identify Nm sites, their predictive performance, particularly in terms of precision and generalization capability, remains deficient. We introduced Nmix, an advanced computational tool for precise prediction of Nm sites in human RNA. We constructed the largest, low-redundancy dataset of experimentally verified Nm sites and employed an innovative multi-feature fusion approach, combining one-hot, Z-curve and RNA secondary structure encoding. Nmix utilizes a meticulously designed hybrid deep learning architecture, integrating 1D/2D convolutional neural networks, self-attention mechanism and residual connection. We implemented asymmetric loss function and Bayesian optimization-based ensemble learning, substantially improving predictive performance on imbalanced datasets. Rigorous testing on two benchmark datasets revealed that Nmix significantly outperforms existing state-of-the-art methods across various metrics, particularly in precision, with average improvements of 33.1% and 60.0%, and Matthews correlation coefficient, with average improvements of 24.7% and 51.1%. Notably, Nmix demonstrated exceptional cross-species generalization capability, accurately predicting 93.8% of experimentally verified Nm sites in rat RNA. We also developed a user-friendly web server (https://tubic.org/Nm) and provided standalone prediction scripts to facilitate widespread adoption. We hope that by providing a more accurate and robust tool for Nm site prediction, we can contribute to advancing our understanding of Nm mechanisms and potentially benefit the prediction of other RNA modification sites.

m7GRegpred: substrate prediction of N7-methylguanosine (m7G) writers and readers based on sequencing features

N7-Methylguanosine (m7G) is important RNA modification at internal and the cap structure of five terminal end of message RNA. It is essential for RNA stability of RNA, the efficiency of translation, and various intracellular RNA processing pathways. Given the significance of the m7G modification, numerous studies have been conducted to predict m7G sites. To further elucidate the regulatory mechanisms surrounding m7G, we introduce a novel bioinformatics framework, m7GRegpred, designed to forecast the targets of the m7G methyltransferases METTL1 and WDR4, and m7G readers QKI5, QKI6, and QKI7 for the first time. We integrated different features to build predictors, with AUROC scores of 0.856, 0.857, 0.780, 0.776, 0.818 for METTL1, WDR4, QKI5, QKI6, and QKI7, respectively. In addition, the effect of window lengths and algorism were systemically evaluated in this work. The finial model was summarized in a user-friendly webserver: http://modinfor.com/m7GRegpred/. Our research indicates that the substrates of m7G regulators can be identified and may potentially advance the study of m7G regulators under unique conditions.

Interaction of the intestinal cytokines-JAKs-STAT3 and 5 axes with RNA N6-methyladenosine to promote chronic inflammation-induced colorectal cancer

Colorectal cancer (CRC) is one of the most common cancers, with a high mortality rate worldwide. Mounting evidence indicates that mRNA modifications are crucial in RNA metabolism, transcription, processing, splicing, degradation, and translation. Studies show that N6-methyladenosine (m6A) is mammalians' most common epi-transcriptomic modification. It has been demonstrated that m6A is involved in cancer formation, progression, invasion, and metastasis, suggesting it could be a potential biomarker for CRC diagnosis and developing therapeutics. Cytokines, growth factors, and hormones function in JAK/STAT3/5 signaling pathway, and they could regulate the intestinal response to infection, inflammation, and tumorigenesis. Reports show that the JAK/STAT3/5 pathway is involved in CRC development. However, the underlying mechanism is still unclear. Signal Transducer and Activator of Transcription 3/5 (STAT3, STAT5) can act as oncogenes or tumor suppressors in the context of tissue types. Also, epigenetic modifications and mutations could alter the balance between pro-oncogenic and tumor suppressor activities of the STAT3/5 signaling pathway. Thus, exploring the interaction of cytokines-JAKs-STAT3 and/or STAT5 with mRNA m6A is of great interest. This review provides a comprehensive overview of the characteristics and functions of m6A and JAKs-STAT3/5 and their relationship with gastrointestinal (GI) cancers.

Involvement of autophagy in mesaconitine-induced neurotoxicity in HT22 cells revealed through integrated transcriptomic, proteomic, and m6A epitranscriptomic profiling

Background: Mesaconitine (MA), a diester-diterpenoid alkaloid extracted from the medicinal herb Aconitum carmichaelii, is commonly used to treat various diseases. Previous studies have indicated the potent toxicity of aconitum despite its pharmacological activities, with limited understanding of its effects on the nervous system and the underlying mechanisms. Methods: HT22 cells and zebrafish were used to investigate the neurotoxic effects of MA both in vitro and in vivo, employing multi-omics techniques to explore the potential mechanisms of toxicity. Results: Our results demonstrated that treatment with MA induces neurotoxicity in zebrafish and HT22 cells. Subsequent analysis revealed that MA induced oxidative stress, as well as structural and functional damage to mitochondria in HT22 cells, accompanied by an upregulation of mRNA and protein expression related to autophagic and lysosomal pathways. Furthermore, methylated RNA immunoprecipitation sequencing (MeRIP-seq) showed a correlation between the expression of autophagy-related genes and N6-methyladenosine (m6A) modification following MA treatment. In addition, we identified METTL14 as a potential regulator of m6A methylation in HT22 cells after exposure to MA. Conclusion: Our study has contributed to a thorough mechanistic elucidation of the neurotoxic effects caused by MA, and has provided valuable insights for optimizing the rational utilization of traditional Chinese medicine formulations containing aconitum in clinical practice.

Emerging role of N6-methyladenosine RNA modification in regulation of SARS-CoV-2 infection and virus-host interactions

Since December 2019, the infection caused by Severe Acute Respiratory Syndrome Coronavirus Type 2 (SARS-CoV-2) has posed an enormous threat to human health security worldwide. Constant mutation of viral genome and varying therapeutic responses of patients infected with this virus prompted efforts to uncover more novel regulators in the pathogenesis. The involvement of N6-methyladenosine, a modified form of RNA, plays a crucial role in viral replication, viral pathogenicity, and intricate signaling pathways connected with immune responses. This review discusses research advances revealing the regulation of the life cycle of SARS-CoV-2 and antiviral responses of host cells by RNA m6A modification, highlights the biological functions of N6-methyladenosine components in SARS-CoV-2 infection and virus-host interactions, and outlines current challenges and future directions for exploring the potential clinical value of m6A modification in COVID-19.

BiPSTP: Sequence feature encoding method for identifying different RNA modifications with bidirectional position-specific trinucleotides propensities

RNA modification, a posttranscriptional regulatory mechanism, significantly influences RNA biogenesis and function. The accurate identification of modification sites is paramount for investigating their biological implications. Methods for encoding RNA sequence into numerical data play a crucial role in developing robust models for predicting modification sites. However, existing techniques suffer from limitations, including inadequate information representation, challenges in effectively integrating positional and sequential information, and the generation of irrelevant or redundant features when combining multiple approaches. These deficiencies hinder the effectiveness of machine learning models in addressing the performance challenges associated with predicting RNA modification sites. Here, we introduce a novel RNA sequence feature representation method, named BiPSTP, which utilizes bidirectional trinucleotide position-specific propensities. We employ the parameter ξ to denote the interval between the current nucleotide and its adjacent forward or backward dinucleotide, enabling the extraction of positional and sequential information from RNA sequences. Leveraging the BiPSTP method, we have developed the prediction model mRNAPred using support vector machine classifier to identify multiple types of RNA modification sites. We evaluate the performance of our BiPSTP method and mRNAPred model across 12 distinct RNA modification types. Our experimental results demonstrate the superiority of the mRNAPred model compared to state-of-art models in the domain of RNA modification sites identification. Importantly, our BiPSTP method enhances the robustness and generalization performance of prediction models. Notably, it can be applied to feature extraction from DNA sequences to predict other biological modification sites.

Regulatory role of m6A epitranscriptomic modifications in normal development and congenital malformations during embryogenesis

The discovery of N6-methyladenosine (m6A) methylation and its role in translation has led to the emergence of a new field of research. Despite accumulating evidence suggesting that m6A methylation is essential for the pathogenesis of cancers and aging diseases by influencing RNA stability, localization, transformation, and translation efficiency, its role in normal and abnormal embryonic development remains unclear. An increasing number of studies are addressing the development of the nervous and gonadal systems during embryonic development, but only few are assessing that of the immune, hematopoietic, urinary, and respiratory systems. Additionally, these studies are limited by the requirement for reliable embryonic animal models and the difficulty in collecting tissue samples of fetuses during development. Multiple studies on the function of m6A methylation have used suitable cell lines to mimic the complex biological processes of fetal development or the early postnatal phase; hence, the research is still in the primary stage. Herein, we discuss current advances in the extensive biological functions of m6A methylation in the development and maldevelopment of embryos/fetuses and conclude that m6A modification occurs extensively during fetal development. Aberrant expression of m6A regulators is probably correlated with single or multiple defects in organogenesis during the intrauterine life. This comprehensive review will enhance our understanding of the pivotal role of m6A modifications involved in fetal development and examine future research directions in embryogenesis.

m6A-TCPred: a web server to predict tissue-conserved human m6A sites using machine learning approach

BACKGROUND

N6-methyladenosine (m6A) is the most prevalent post-transcriptional modification in eukaryotic cells that plays a crucial role in regulating various biological processes, and dysregulation of m6A status is involved in multiple human diseases including cancer contexts. A number of prediction frameworks have been proposed for high-accuracy identification of putative m6A sites, however, none have targeted for direct prediction of tissue-conserved m6A modified residues from non-conserved ones at base-resolution level.

RESULTS

We report here m6A-TCPred, a computational tool for predicting tissue-conserved m6A residues using m6A profiling data from 23 human tissues. By taking advantage of the traditional sequence-based characteristics and additional genome-derived information, m6A-TCPred successfully captured distinct patterns between potentially tissue-conserved m6A modifications and non-conserved ones, with an average AUROC of 0.871 and 0.879 tested on cross-validation and independent datasets, respectively.

CONCLUSION

Our results have been integrated into an online platform: a database holding 268,115 high confidence m6A sites with their conserved information across 23 human tissues; and a web server to predict the conserved status of user-provided m6A collections. The web interface of m6A-TCPred is freely accessible at: www.rnamd.org/m6ATCPred .

Tissue-specific RNA methylation prediction from gene expression data using sparse regression models

N6-methyladenosine (m6A) is a highly prevalent and conserved post-transcriptional modification observed in mRNA and long non-coding RNA (lncRNA). Identifying potential m6A sites within RNA sequences is crucial for unraveling the potential influence of the epitranscriptome on biological processes. In this study, we introduce Exp2RM, a novel approach that formulates single-site-based tissue-specific elastic net models for predicting tissue-specific methylation levels utilizing gene expression data. The resulting ensemble model demonstrates robust predictive performance for tissue-specific methylation levels, with an average R-squared value of 0.496 and a median R-squared value of 0.482 across all 22 human tissues. Since methylation distribution varies among tissues, we trained the model to incorporate similar patterns, significantly improves accuracy with the median R-squared value increasing to 0.728. Additonally, functional analysis reveals Exp2RM's ability to capture coefficient genes in relevant biological processes. This study emphasizes the importance of tissue-specific methylation distribution in enhancing prediction accuracy and provides insights into the functional implications of methylation sites.

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.

A review on the role of RNA methylation in aging-related diseases

Senescence is the underlying mechanism of organism aging and is robustly regulated at the post-transcriptional level. This regulation involves the chemical modifications, of which the RNA methylation is the most common. Recently, a rapidly growing number of studies have demonstrated that methylation is relevant to aging and aging-associated diseases. Owing to the rapid development of detection methods, the understanding on RNA methylation has gone deeper. In this review, we summarize the current understanding on the influence of RNA modification on cellular senescence, with a focus on mRNA methylation in aging-related diseases, and discuss the emerging potential of RNA modification in diagnosis and therapy.

The functions and mechanisms of post-translational modification in protein regulators of RNA methylation: Current status and future perspectives

RNA methylation, an epigenetic modification that does not alter gene sequence, may be important to diverse biological processes. Protein regulators of RNA methylation include "writers," "erasers," and "readers," which respectively deposit, remove, and recognize methylated RNA. RNA methylation, particularly N6-methyladenosine (m6A), 5-methylcytosine (m5C), N3-methylcytosine (m3C), N1-methyladenosine (m1A) and N7-methylguanosine (m7G), has been suggested as disease therapeutic targets. Despite advances in the structure and pharmacology of RNA methylation regulators that have improved drug discovery, regulating these proteins by various post-translational modifications (PTMs) has received little attention. PTM modifies protein structure and function, affecting all aspects of normal biology and pathogenesis, including immunology, cell differentiation, DNA damage repair, and tumors. It is becoming evident that RNA methylation regulators are also regulated by diverse PTMs. PTM of RNA methylation regulators induces their covalent linkage to new functional groups, hence modifying their activity and function. Mass spectrometry has identified many PTMs on protein regulators of RNA methylation. In this review, we describe the functions and PTM of protein regulators of RNA methylation and summarize the recent advances in the regulatory mode of human disease and its underlying mechanisms.

DPred_3S: identifying dihydrouridine (D) modification on three species epitranscriptome based on multiple sequence-derived features

Introduction: Dihydrouridine (D) is a conserved modification of tRNA among all three life domains. D modification enhances the flexibility of a single nucleotide base in the spatial structure and is disease- and evolution-associated. Recent studies have also suggested the presence of dihydrouridine on mRNA. Methods: To identify D in epitranscriptome, we provided a prediction framework named "DPred_3S" based on the machine learning approach for three species D epitranscriptome, which used epitranscriptome sequencing data as training data for the first time. Results: The optimal features were evaluated by the F-score and integration of different features; our model achieved area under the receiver operating characteristic curve (AUROC) scores 0.955, 0.946, and 0.905 for Saccharomyces cerevisiae, Escherichia coli, and Schizosaccharomyces pombe, respectively. The performances of different machine learning algorithms were also compared in this study. Discussion: The high performances of our model suggest the D sites can be distinguished based on their surrounding sequence, but the lower performance of cross-species prediction may be limited by technique preferences.

Molecular Characterization and Establishment of a Prognostic Model Based on Primary Immunodeficiency Features in Association with RNA Modifications in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer. Although immunotherapy is effective for some patients, most find it difficult to benefit from it. This study aims to explore the impact of specific immune pathways and their regulated molecular mechanisms in TNBC. The gene expression data of breast cancer patients were obtained from the TCGA and METABRIC databases. Gene set variation analysis (GSVA) revealed specific upregulation or abnormal expression of immunodeficiency pathways in TNBC patients. Multi-omics data showed significant differential expression of Primary Immunodeficiency Genes (PIDGs) in TNBC patients, who are prone to genomic-level variations. Consensus clustering was used in two datasets to classify patients into two distinct molecular subtypes based on PIDGs expression patterns, with each displaying different biological features and immune landscapes. To further explore the prognostic characteristics of PIDGs-regulated molecules, we constructed a four-gene prognostic PIDG score model and a nomogram using least absolute shrinkage and selection operator (LASSO) regression analysis in combination with clinicopathological parameters. The PIDG score was closely associated with the immune therapy and drug sensitivity of TNBC patients, providing potential guidance for clinical treatment. Particularly noteworthy is the close association of this scoring with RNA modifications; patients with different scores also exhibited different mutation landscapes. This study offers new insights for the clinical treatment of TNBC and for identifying novel prognostic markers and therapeutic targets in TNBC.

H2Opred: a robust and efficient hybrid deep learning model for predicting 2'-O-methylation sites in human RNA

2'-O-methylation (2OM) is the most common post-transcriptional modification of RNA. It plays a crucial role in RNA splicing, RNA stability and innate immunity. Despite advances in high-throughput detection, the chemical stability of 2OM makes it difficult to detect and map in messenger RNA. Therefore, bioinformatics tools have been developed using machine learning (ML) algorithms to identify 2OM sites. These tools have made significant progress, but their performances remain unsatisfactory and need further improvement. In this study, we introduced H2Opred, a novel hybrid deep learning (HDL) model for accurately identifying 2OM sites in human RNA. Notably, this is the first application of HDL in developing four nucleotide-specific models [adenine (A2OM), cytosine (C2OM), guanine (G2OM) and uracil (U2OM)] as well as a generic model (N2OM). H2Opred incorporated both stacked 1D convolutional neural network (1D-CNN) blocks and stacked attention-based bidirectional gated recurrent unit (Bi-GRU-Att) blocks. 1D-CNN blocks learned effective feature representations from 14 conventional descriptors, while Bi-GRU-Att blocks learned feature representations from five natural language processing-based embeddings extracted from RNA sequences. H2Opred integrated these feature representations to make the final prediction. Rigorous cross-validation analysis demonstrated that H2Opred consistently outperforms conventional ML-based single-feature models on five different datasets. Moreover, the generic model of H2Opred demonstrated a remarkable performance on both training and testing datasets, significantly outperforming the existing predictor and other four nucleotide-specific H2Opred models. To enhance accessibility and usability, we have deployed a user-friendly web server for H2Opred, accessible at https://balalab-skku.org/H2Opred/. This platform will serve as an invaluable tool for accurately predicting 2OM sites within human RNA, thereby facilitating broader applications in relevant research endeavors.

m5U-GEPred: prediction of RNA 5-methyluridine sites based on sequence-derived and graph embedding features

5-Methyluridine (m5U) is one of the most common post-transcriptional RNA modifications, which is involved in a variety of important biological processes and disease development. The precise identification of the m5U sites allows for a better understanding of the biological processes of RNA and contributes to the discovery of new RNA functional and therapeutic targets. Here, we present m5U-GEPred, a prediction framework, to combine sequence characteristics and graph embedding-based information for m5U identification. The graph embedding approach was introduced to extract the global information of training data that complemented the local information represented by conventional sequence features, thereby enhancing the prediction performance of m5U identification. m5U-GEPred outperformed the state-of-the-art m5U predictors built on two independent species, with an average AUROC of 0.984 and 0.985 tested on human and yeast transcriptomes, respectively. To further validate the performance of our newly proposed framework, the experimentally validated m5U sites identified from Oxford Nanopore Technology (ONT) were collected as independent testing data, and in this project, m5U-GEPred achieved reasonable prediction performance with ACC of 91.84%. We hope that m5U-GEPred should make a useful computational alternative for m5U identification.

Combined signature of N7-methylguanosine regulators with their related genes and the tumor microenvironment: a prognostic and therapeutic biomarker for breast cancer

Background

Identifying predictive markers for breast cancer (BC) prognosis and immunotherapeutic responses remains challenging. Recent findings indicate that N7-methylguanosine (m7G) modification and the tumor microenvironment (TME) are critical for BC tumorigenesis and metastasis, suggesting that integrating m7G modifications and TME cell characteristics could improve the predictive accuracy for prognosis and immunotherapeutic responses.

Methods

We utilized bulk RNA-sequencing data from The Cancer Genome Atlas Breast Cancer Cohort and the GSE42568 and GSE146558 datasets to identify BC-specific m7G-modification regulators and associated genes. We used multiple m7G databases and RNA interference to validate the relationships between BC-specific m7G-modification regulators (METTL1 and WDR4) and related genes. Single-cell RNA-sequencing data from GSE176078 confirmed the association between m7G modifications and TME cells. We constructed an m7G-TME classifier, validated the results using an independent BC cohort (GSE20685; n = 327), investigated the clinical significance of BC-specific m7G-modifying regulators by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis, and performed tissue-microarray assays on 192 BC samples.

Results

Immunohistochemistry and RT-qPCR results indicated that METTL1 and WDR4 overexpression in BC correlated with poor patient prognosis. Moreover, single-cell analysis revealed relationships between m7G modification and TME cells, indicating their potential as indicators of BC prognosis and treatment responses. The m7G-TME classifier enabled patient subgrouping and revealed significantly better survival and treatment responses in the m7Glow+TMEhigh group. Significant differences in tumor biological functions and immunophenotypes occurred among the different subgroups.

Conclusions

The m7G-TME classifier offers a promising tool for predicting prognosis and immunotherapeutic responses in BC, which could support personalized therapeutic strategies.

EMDL-ac4C: identifying N4-acetylcytidine based on ensemble two-branch residual connection DenseNet and attention

Introduction: N4-acetylcytidine (ac4C) is a critical acetylation modification that has an essential function in protein translation and is associated with a number of human diseases. Methods: The process of identifying ac4C sites by biological experiments is too cumbersome and costly. And the performance of several existing computational models needs to be improved. Therefore, we propose a new deep learning tool EMDL-ac4C to predict ac4C sites, which uses a simple one-hot encoding for a unbalanced dataset using a downsampled ensemble deep learning network to extract important features to identify ac4C sites. The base learner of this ensemble model consists of a modified DenseNet and Squeeze-and-Excitation Networks. In addition, we innovatively add a convolutional residual structure in parallel with the dense block to achieve the effect of two-layer feature extraction. Results: The average accuracy (Acc), mathews correlation coefficient (MCC), and area under the curve Area under curve of EMDL-ac4C on ten independent testing sets are 80.84%, 61.77%, and 87.94%, respectively. Discussion: Multiple experimental comparisons indicate that EMDL-ac4C outperforms existing predictors and it greatly improved the predictive performance of the ac4C sites. At the same time, EMDL-ac4C could provide a valuable reference for the next part of the study. The source code and experimental data are available at: https://github.com/13133989982/EMDLac4C.

Novel insight into RNA modifications in tumor immunity: Promising targets to prevent tumor immune escape

An immunosuppressive state is a typical feature of the tumor microenvironment. Despite the dramatic success of immune checkpoint inhibitor (ICI) therapy in preventing tumor cell escape from immune surveillance, primary and acquired resistance have limited its clinical use. Notably, recent clinical trials have shown that epigenetic drugs can significantly improve the outcome of ICI therapy in various cancers, indicating the importance of epigenetic modifications in immune regulation of tumors. Recently, RNA modifications (N⁶-methyladenosine [m⁶A], N¹-methyladenosine [m¹A], 5-methylcytosine [m⁵C], etc.), novel hotspot areas of epigenetic research, have been shown to play crucial roles in protumor and antitumor immunity. In this review, we provide a comprehensive understanding of how m⁶A, m¹A, and m⁵C function in tumor immunity by directly regulating different immune cells as well as indirectly regulating tumor cells through different mechanisms, including modulating the expression of immune checkpoints, inducing metabolic reprogramming, and affecting the secretion of immune-related factors. Finally, we discuss the current status of strategies targeting RNA modifications to prevent tumor immune escape, highlighting their potential.

Epitranscriptomics in the development, functions, and disorders of cancer stem cells

Biomolecular modifications play an important role in the development of life, and previous studies have investigated the role of DNA and proteins. In the last decade, with the development of sequencing technology, the veil of epitranscriptomics has been gradually lifted. Transcriptomics focuses on RNA modifications that affect gene expression at the transcriptional level. With further research, scientists have found that changes in RNA modification proteins are closely linked to cancer tumorigenesis, progression, metastasis, and drug resistance. Cancer stem cells (CSCs) are considered powerful drivers of tumorigenesis and key factors for therapeutic resistance. In this article, we focus on describing RNA modifications associated with CSCs and summarize the associated research progress. The aim of this review is to identify new directions for cancer diagnosis and targeted therapy.

Integrated Profiles of Transcriptome and mRNA m6A Modification Reveal the Intestinal Cytotoxicity of Aflatoxin B1 on HCT116 Cells

Aflatoxin B1 (AFB1) is widely prevalent in foods and animal feeds and is one of the most toxic and carcinogenic aflatoxin subtypes. Existing studies have proved that the intestine is targeted by AFB1, and adverse organic effects have been observed. This study aimed to investigate the relationship between AFB1-induced intestinal toxicity and N6-methyladenosine (m6A) RNA methylation, which involves the post-transcriptional regulation of mRNA expression. The transcriptome-wide m6A methylome and transcriptome profiles in human intestinal cells treated with AFB1 are presented. Methylated RNA immunoprecipitation sequencing and mRNA sequencing were carried out to determine the distinctions in m6A methylation and different genes expressed in AFB1-induced intestinal toxicity. The results showed that there were 2289 overlapping genes of the differentially expressed mRNAs and differentially m6A-methylation-modified mRNAs. After enrichment of the signaling pathways and biological processes, these genes participated in the terms of the cell cycle, endoplasmic reticulum, tight junction, and mitophagy. In conclusion, the study demonstrated that AFB1-induced HCT116 injury was related to the disruptions to the levels of m6A methylation modifications of target genes and the abnormal expression of m6A regulators.

Dynamic regulation and key roles of ribonucleic acid methylation

Ribonucleic acid (RNA) methylation is the most abundant modification in biological systems, accounting for 60% of all RNA modifications, and affects multiple aspects of RNA (including mRNAs, tRNAs, rRNAs, microRNAs, and long non-coding RNAs). Dysregulation of RNA methylation causes many developmental diseases through various mechanisms mediated by N ⁶-methyladenosine (m⁶A), 5-methylcytosine (m⁵C), N ¹-methyladenosine (m¹A), 5-hydroxymethylcytosine (hm⁵C), and pseudouridine (Ψ). The emerging tools of RNA methylation can be used as diagnostic, preventive, and therapeutic markers. Here, we review the accumulated discoveries to date regarding the biological function and dynamic regulation of RNA methylation/modification, as well as the most popularly used techniques applied for profiling RNA epitranscriptome, to provide new ideas for growth and development.

RNA Epigenetics in Chronic Lung Diseases

Chronic lung diseases are highly prevalent worldwide and cause significant mortality. Lung cancer is the end stage of many chronic lung diseases. RNA epigenetics can dynamically modulate gene expression and decide cell fate. Recently, studies have confirmed that RNA epigenetics plays a crucial role in the developing of chronic lung diseases. Further exploration of the underlying mechanisms of RNA epigenetics in chronic lung diseases, including lung cancer, may lead to a better understanding of the diseases and promote the development of new biomarkers and therapeutic strategies. This article reviews basic information on RNA modifications, including N⁶ methylation of adenosine (m⁶A), N¹ methylation of adenosine (m¹A), N⁷-methylguanosine (m⁷G), 5-methylcytosine (m⁵C), 2'O-methylation (2'-O-Me or Nm), pseudouridine (5-ribosyl uracil or Ψ), and adenosine to inosine RNA editing (A-to-I editing). We then show how they relate to different types of lung disease. This paper hopes to summarize the mechanisms of RNA modification in chronic lung disease and finds a new way to develop early diagnosis and treatment of chronic lung disease.

RNA modifications: an overview of select web-based tools

The field of epitranscriptomics has expanded dramatically in recent years, both in the number of identified RNA modifications and the number of researchers studying them. As knowledge of post-transcriptional modifications continues to expand, numerous new methods have been developed to detect these modifications. Additionally, modifications are being extended to therapeutic settings, such as with recent mRNA vaccines. With this increase in knowledge and use, the community is recognizing the necessity for user-friendly databases to (i) store information from both high- and low-throughput studies and (ii) provide prediction software on how RNA modifications contribute to RNA function and disease. This mini-review highlights select RNA modification databases and their key attributes with the aim of providing a resource to researchers in the field of epitranscriptomics.