RMBase: a resource for decoding the landscape of RNA modifications from high-throughput sequencing data

DHUpredET: A comparative computational approach for identification of dihydrouridine modification sites in RNA sequence

Laboratory-based detection of D sites is laborious and expensive. In this study, we developed effective machine learning models employing efficient feature encoding methods to identify D sites. Initially, we explored various state-of-the-art feature encoding approaches and 30 machine learning techniques for each and selected the top eight models based on their independent testing and cross-validation outcomes. Finally, we developed DHUpredET using the extra tree classifier methods for predicting DHU sites. The DHUpredET model demonstrated balanced performance across all evaluation criteria, outperforming state-of-the-art models by 8 % and 14 % in terms of accuracy and sensitivity, respectively, on an independent test set. Further analysis revealed that the model achieved higher accuracy with position-specific two nucleotide (PS2) features, leading us to conclude that PS2 features are the best suited for the DHUpredET model. Therefore, our proposed model emerges as the most favorite choice for predicting D sites. In addition, we conducted an in-depth analysis of local features and identified a particularly significant attribute with a feature score of 0.035 for PS2_299 attributes. This tool holds immense promise as an advantageous instrument for accelerating the discovery of D modification sites, which contributes too many targeting therapeutic and understanding RNA structure.

Computational limitations and future needs to unravel the full potential of 2'-O-Methylation and C/D box snoRNAs

This review evaluates the current state of C/D snoRNA databases and prediction tools in relation to 2'-O-methylation (2'-O-Me). It highlights the limitations of existing resources in accurately annotating and predicting guide snoRNAs, particularly for newly identified 2"-O-Me sites. We emphasize the need for advanced computational approaches specifically tailored to 2"-O-Me to enable the discovery and functional analysis of snoRNAs. Given the growing importance of 2'-O-Me in areas such as cancer epitranscriptomics, ribosome biogenesis, and heterogeneity, existing tools remain inadequate. As 2'-O-Me gains recognition as a potential biomarker and therapeutic target, more sophisticated methods are urgently needed to improve snoRNA annotation and prediction, facilitating biomedical advancements.

RNA methylation of CD47 mediates tumor immunosuppression in EGFR-TKI resistant NSCLC

BACKGROUND

Although immune checkpoint inhibitors (ICIs) have been successfully utilized in patients with non-small cell lung cancer (NSCLC), EGFR-mutated patients didn't benefit from ICIs. The underlying mechanisms for the poor efficacy of this subgroup remain unclear.

METHODS

CD8+T cells cytotoxicity, DCs phagocytosis and immunofluorescence assay were applied to examine the immunosuppressive microenvironment of NSCLC. m6A RNA immunoprecipitation, luciferase assay and immunohistochemistry were used to explore the relationship between CD47 and ALKBH5 in EGFR-TKI resistant NSCLC. Autochthonous EGFR-driven lung tumor mouse model and PDXs were performed to explore the therapeutic potential of CD47 antibody and EGFR-TKI combination.

RESULTS

We found that EGFR-TKI resistance promoted a more immunosuppressive tumor microenvironment and inhibited anti-tumor functions of CD8+ T cells. Mechanistically, the m6A eraser ALKBH5 was inhibited in EGFR-TKI resistant NSCLC, which subsequently upregulates CD47 by catalyzing m6A demethylation and causes immunosuppression. Combined treatment with EGFR-TKI and inhibitors of CD47 enhances antitumor immunity and EGFR-TKI efficacy in vivo.

CONCLUSIONS

Collectively, our findings reveal the possible underlying mechanism for poor immune response of ICIs in EGFR-TKI resistant NSCLC and provide preclinical evidence that targeted therapy combined with innate immune checkpoint blockade may provide synergistic effects in NSCLC treatment.

The IGF2BP1 oncogene is a druggable m6A-dependent enhancer of YAP1-driven gene expression in ovarian cancer

The Hippo/YAP1 signaling pathway regulates normal development by controlling contact inhibition of growth. In cancer, YAP1 activation is often dysregulated, leading to excessive tumor growth and metastasis. SRC kinase can cross talk to Hippo signaling by disrupting adherens junctions, repressing the Hippo cascade, or activating YAP1 to promote proliferation. Here, we demonstrate that the IGF2 messenger RNA-binding protein 1 (IGF2BP1) impedes the repression of YAP1 by Hippo signaling in carcinomas. IGF2BP1 stabilizes the YAP1 messenger RNA (mRNA) and enhances YAP1 protein synthesis through an m6A-dependent interaction with the 3' untranslated region of the YAP1 mRNA, thereby increasing YAP1/TAZ-driven transcription to bypass contact inhibition of tumor cell growth. Inhibiting IGF2BP1-mRNA binding using BTYNB reduces YAP1 levels and transcriptional activity, leading to significant growth inhibition in carcinoma cells and ovarian cancer organoids. In contrast, SRC inhibition with Saracatinib fails to inhibit YAP1/TAZ-driven transcription and cell growth in general. This is particularly significant in de-differentiated, rather mesenchymal carcinoma-derived cells, which exhibit high IGF2BP1 and YAP1 expression, rendering them less reliant on SRC-directed growth stimulation. In such invasive carcinoma models, the combined inhibition of SRC, IGF2BP1, and YAP1/TAZ proved superior over monotherapies. These findings highlight the therapeutic potential of targeting IGF2BP1, a key regulator of oncogenic transcription networks.

N6-methyladenosine in inflammatory diseases: Important actors and regulatory targets

N6-methyladenosine (m6A) is one of the most prevalent epigenetic modifications in eukaryotic cells. It regulates RNA function and stability by modifying RNA methylation through writers, erasers, and readers. As a result, m6A plays a critical role in a wide range of biological processes. Inflammation is a common and fundamental pathological process. Numerous studies have investigated the role of m6A modifications in inflammatory diseases. This review highlights the mechanisms by which m6A contributes to inflammation, focusing on pathogen-induced infectious diseases, autoimmune disorders, allergic conditions, and metabolic disorder-related inflammatory diseases.

RNA sequence analysis landscape: A comprehensive review of task types, databases, datasets, word embedding methods, and language models

Deciphering information of RNA sequences reveals their diverse roles in living organisms, including gene regulation and protein synthesis. Aberrations in RNA sequence such as dysregulation and mutations can drive a diverse spectrum of diseases including cancers, genetic disorders, and neurodegenerative conditions. Furthermore, researchers are harnessing RNA's therapeutic potential for transforming traditional treatment paradigms into personalized therapies through the development of RNA-based drugs and gene therapies. To gain insights of biological functions and to detect diseases at early stages and develop potent therapeutics, researchers are performing diverse types RNA sequence analysis tasks. RNA sequence analysis through conventional wet-lab methods is expensive, time-consuming and error prone. To enable large-scale RNA sequence analysis, empowerment of wet-lab experimental methods with Artificial Intelligence (AI) applications necessitates scientists to have a comprehensive knowledge of both DNA and AI fields. While molecular biologists encounter challenges in understanding AI methods, computer scientists often lack basic foundations of RNA sequence analysis tasks. Considering the absence of a comprehensive literature that bridges this research gap and promotes the development of AI-driven RNA sequence analysis applications, the contributions of this manuscript are manifold: It equips AI researchers with biological foundations of 47 distinct RNA sequence analysis tasks. It sets a stage for development of benchmark datasets related to 47 distinct RNA sequence analysis tasks by facilitating cruxes of 64 different biological databases. It presents word embeddings and language models applications across 47 distinct RNA sequence analysis tasks. It streamlines the development of new predictors by providing a comprehensive survey of 58 word embeddings and 70 language models based predictive pipelines performance values as well as top performing traditional sequence encoding based predictors and their performances across 47 RNA sequence analysis tasks.

m5C-Seq: Machine learning-enhanced profiling of RNA 5-methylcytosine modifications

Epigenetic modifications, particularly RNA methylation and histone alterations, play a crucial role in heredity, development, and disease. Among these, RNA 5-methylcytosine (m5C) is the most prevalent RNA modification in mammalian cells, essential for processes such as ribosome synthesis, translational fidelity, mRNA nuclear export, turnover, and translation. The increasing volume of nucleotide sequences has led to the development of machine learning-based predictors for m5C site prediction. However, these predictors often face challenges related to training data limitations and overfitting due to insufficient external validation. This study introduces m5C-Seq, an ensemble learning approach for RNA modification profiling, designed to address these issues. m5C-Seq employs a meta-classifier that integrates 15 probabilities generated from a novel, large dataset using systematic encoding methods to make final predictions. Demonstrating superior performance compared to existing predictors, m5C-Seq represents a significant advancement in accurate RNA modification profiling. The code and the newly established datasets are made available through GitHub at https://github.com/Z-Abbas/m5C-Seq.

Comprehensive Review and Assessment of Computational Methods for Prediction of N6-Methyladenosine Sites

N6-methyladenosine (m6A) plays a crucial regulatory role in the control of cellular functions and gene expression. Recent advances in sequencing techniques for transcriptome-wide m6A mapping have accelerated the accumulation of m6A site information at a single-nucleotide level, providing more high-confidence training data to develop computational approaches for m6A site prediction. However, it is still a major challenge to precisely predict m6A sites using in silico approaches. To advance the computational support for m6A site identification, here, we curated 13 up-to-date benchmark datasets from nine different species (i.e., H. sapiens, M. musculus, Rat, S. cerevisiae, Zebrafish, A. thaliana, Pig, Rhesus, and Chimpanzee). This will assist the research community in conducting an unbiased evaluation of alternative approaches and support future research on m6A modification. We revisited 52 computational approaches published since 2015 for m6A site identification, including 30 traditional machine learning-based, 14 deep learning-based, and 8 ensemble learning-based methods. We comprehensively reviewed these computational approaches in terms of their training datasets, calculated features, computational methodologies, performance evaluation strategy, and webserver/software usability. Using these benchmark datasets, we benchmarked nine predictors with available online websites or stand-alone software and assessed their prediction performance. We found that deep learning and traditional machine learning approaches generally outperformed scoring function-based approaches. In summary, the curated benchmark dataset repository and the systematic assessment in this study serve to inform the design and implementation of state-of-the-art computational approaches for m6A identification and facilitate more rigorous comparisons of new methods in the future.

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.

Virtual Screening and Molecular Docking: Discovering Novel METTL3 Inhibitors

Methyltransferase-like 3 (METTL3) is an RNA methyltransferase that catalyzes the N6 -methyladenosine (m6A) modification of mRNA in eukaryotic cells. Past studies have shown that METTL3 is highly expressed in various cancers and is closely related to tumor development. Therefore, METTL3 inhibitors have received widespread attention as effective treatments for different types of tumors. This study proposes a hybrid high-throughput virtual screening (HTVS) protocol that combines structure-based methods with geometric deep learning-based DeepDock algorithms. We identified unique skeleton inhibitors of METTL3 from our self-built internal database. Among them, compound C3 showed significant inhibitory activity on METTL3, and further molecular dynamics simulations were performed to provide more details about the binding conformation. Overall, our research demonstrates the effectiveness of hybrid virtual algorithms, which is of great significance for understanding the biological functions of METTL3 and developing treatment methods for related diseases.

PseU-KeMRF: A Novel Method for Identifying RNA Pseudouridine Sites

Pseudouridine is a type of abundant RNA modification that is seen in many different animals and is crucial for a variety of biological functions. Accurately identifying pseudouridine sites within the RNA sequence is vital for the subsequent study of various biological mechanisms of pseudouridine. However, the use of traditional experimental methods faces certain challenges. The development of fast and convenient computational methods is necessary to accurately identify pseudouridine sites from RNA sequence information. To address this, we introduce a novel pseudouridine site prediction model called PseU-KeMRF, which can identify pseudouridine sites in three species, H. sapiens, S. cerevisiae, and M. musculus. Through comprehensive analysis, we selected four RNA coding schemes, including binary feature, position-specific trinucleotide propensity based on single strand (PSTNPss), nucleotide chemical property (NCP) and pseudo k-tuple composition (PseKNC). Then the support vector machine-recursive feature elimination (SVM-RFE) method was used for feature selection and the feature subset was optimized. Finally, the best feature subsets are input into the kernel based on multinomial random forests (KeMRF) classifier for cross-validation and independent testing. As a new classification method, compared with the traditional random forest, KeMRF not only improves the node splitting process of decision tree construction based on multinomial distribution, but also combines the easy to interpret kernel method for prediction, which makes the classification performance better. Our results indicate superior predictive performance of PseU-KeMRF over other existing models, which can prove that PseU-KeMRF is a highly competitive predictive model that can successfully identify pseudouridine sites in RNA sequences.

The RNA Demethylases ALKBH5 and FTO Regulate the Translation of ATF4 mRNA in Sorafenib-Treated Hepatocarcinoma Cells

Translation is one of the main gene expression steps targeted by cellular stress, commonly referred to as translational stress, which includes treatment with anticancer drugs. While translational stress blocks the translation initiation of bulk mRNAs, it nonetheless activates the translation of specific mRNAs known as short upstream open reading frames (uORFs)-mRNAs. Among these, the ATF4 mRNA encodes a transcription factor that reprograms gene expression in cells responding to various stresses. Although the stress-induced translation of the ATF4 mRNA relies on the presence of uORFs (upstream to the main ATF4 ORF), the mechanisms mediating this effect, particularly during chemoresistance, remain elusive. Here, we report that ALKBH5 (AlkB Homolog 5) and FTO (FTO: Fat mass and obesity-associated protein), the two RNA demethylating enzymes, promote the translation of ATF4 mRNA in a transformed liver cell line (Hep3B) treated with the chemotherapeutic drug sorafenib. Using the in vitro luciferase reporter translational assay, we found that depletion of both enzymes reduced the translation of the reporter ATF4 mRNA upon drug treatment. Consistently, depletion of either protein abrogates the loading of the ATF3 mRNA into translating ribosomes as assessed by polyribosome assays coupled to RT-qPCR. Collectively, these results indicate that the ALKBH5 and FTO-mediated translation of the ATF4 mRNA is regulated at its initiation step. Using in vitro methylation assays, we found that ALKBH5 is required for the inhibition of the methylation of a reporter ATF4 mRNA at a conserved adenosine (A235) site located at its uORF2, suggesting that ALKBH5-mediated translation of ATF4 mRNA involves demethylation of its A235. Preventing methylation of A235 by introducing an A/G mutation into an ATF4 mRNA reporter renders its translation insensitive to ALKBH5 depletion, supporting the role of ALKBH5 demethylation activity in translation. Finally, targeting either ALKBH5 or FTO sensitizes Hep3B to sorafenib-induced cell death, contributing to their resistance. In summary, our data show that ALKBH5 and FTO are novel factors that promote resistance to sorafenib treatment, in part by mediating the translation of ATF4 mRNA.

Variable calling of m6A and associated features in databases: a guide for end-users

N6-methyladenosine (m$^{6}$A) is a widely-studied methylation to messenger RNAs, which has been linked to diverse cellular processes and human diseases. Numerous databases that collate m$^{6}$A profiles of distinct cell types have been created to facilitate quick and easy mining of m$^{6}$A signatures associated with cell-specific phenotypes. However, these databases contain inherent complexities that have not been explicitly reported, which may lead to inaccurate identification and interpretation of m$^{6}$A-associated biology by end-users who are unaware of them. Here, we review various m$^{6}$A-related databases, and highlight several critical matters. In particular, differences in peak-calling pipelines across databases drive substantial variability in both peak number and coordinates with only moderate reproducibility, and the inclusion of peak calls from early m$^{6}$A sequencing protocols may lead to the reporting of false positives or negatives. The awareness of these matters will help end-users avoid the inclusion of potentially unreliable data in their studies and better utilize m$^{6}$A databases to derive biologically meaningful results.

A comprehensive epigenetic network can influence the occurrence of thyroid-associated ophthalmopathy by affecting immune and inflammatory response

The primary objective of this study is to understand the regulatory role of epigenetics in thyroid-associated ophthalmopathy (TAO) using multi-omics sequencing data. We utilized tRFs sequencing data, DNA methylation sequencing data, and lncRNA/circRNA/mRNA sequencing data, as well as several RNA methylation target prediction websites, to analyze the regulatory effect of DNA methylation, non-coding RNA, and RNA methylation on TAO-associated genes. Through differential expression analysis, we identified 1019 differentially expressed genes, 985 differentially methylated genes, and 2601 non-coding RNA. Functional analysis showed that differentially expressed genes were mostly associated with the PI3K signaling pathway and the IL17 signaling pathway. Genes regulated by DNA epigenetic regulatory networks were mainly related to the Cytokine-cytokine receptor interaction pathway, whereas genes regulated by RNA epigenetic regulatory networks were primarily related to the T cell receptor signaling pathway. Finally, our integrated regulatory network analysis revealed that epigenetics mainly impacts the occurrence of TAO through its effects on key pathways such as cell killing, cytokine production, and immune response. In summary, this study is the first to reveal a new mechanism underlying the development of TAO and provides new directions for future TAO research.

Comprehensive review on lipid metabolism and RNA methylation: Biological mechanisms, perspectives and challenges

Adipose tissue plays a crucial role in maintaining energy balance, regulating hormones, and promoting metabolic health. To address disorders related to obesity and develop effective therapies, it is essential to have a deep understanding of adipose tissue biology. In recent years, RNA methylation has emerged as a significant epigenetic modification involved in various cellular functions and metabolic pathways. Particularly in the realm of adipogenesis and lipid metabolism, extensive research is ongoing to uncover the mechanisms and functional importance of RNA methylation. Increasing evidence suggests that RNA methylation plays a regulatory role in adipocyte development, metabolism, and lipid utilization across different organs. This comprehensive review aims to provide an overview of common RNA methylation modifications, their occurrences, and regulatory mechanisms, focusing specifically on their intricate connections to fat metabolism. Additionally, we discuss the research methodologies used in studying RNA methylation and highlight relevant databases that can aid researchers in this rapidly advancing field.

PseUpred-ELPSO Is an Ensemble Learning Predictor with Particle Swarm Optimizer for Improving the Prediction of RNA Pseudouridine Sites

RNA pseudouridine modification exists in different RNA types of many species, and it has a significant role in regulating the expression of biological processes. To understand the functional mechanisms for RNA pseudouridine sites, the accurate identification of pseudouridine sites in RNA sequences is essential. Although several fast and inexpensive computational methods have been proposed, the challenge of improving recognition accuracy and generalization still exists. This study proposed a novel ensemble predictor called PseUpred-ELPSO for improved RNA pseudouridine site prediction. After analyzing the nucleotide composition preferences between RNA pseudouridine site sequences, two feature representations were determined and fed into the stacking ensemble framework. Then, using five tree-based machine learning classifiers as base classifiers, 30-dimensional RNA profiles are constructed to represent RNA sequences, and using the PSO algorithm, the weights of the RNA profiles were searched to further enhance the representation. A logistic regression classifier was used as a meta-classifier to complete the final predictions. Compared to the most advanced predictors, the performance of PseUpred-ELPSO is superior in both cross-validation and the independent test. Based on the PseUpred-ELPSO predictor, a free and easy-to-operate web server has been established, which will be a powerful tool for pseudouridine site identification.

Fuzzy kernel evidence Random Forest for identifying pseudouridine sites

Pseudouridine is an RNA modification that is widely distributed in both prokaryotes and eukaryotes, and plays a critical role in numerous biological activities. Despite its importance, the precise identification of pseudouridine sites through experimental approaches poses significant challenges, requiring substantial time and resources.Therefore, there is a growing need for computational techniques that can reliably and quickly identify pseudouridine sites from vast amounts of RNA sequencing data. In this study, we propose fuzzy kernel evidence Random Forest (FKeERF) to identify pseudouridine sites. This method is called PseU-FKeERF, which demonstrates high accuracy in identifying pseudouridine sites from RNA sequencing data. The PseU-FKeERF model selected four RNA feature coding schemes with relatively good performance for feature combination, and then input them into the newly proposed FKeERF method for category prediction. FKeERF not only uses fuzzy logic to expand the original feature space, but also combines kernel methods that are easy to interpret in general for category prediction. Both cross-validation tests and independent tests on benchmark datasets have shown that PseU-FKeERF has better predictive performance than several state-of-the-art methods. This new method not only improves the accuracy of pseudouridine site identification, but also provides a certain reference for disease control and related drug development in the future.

Stack-DHUpred: Advancing the accuracy of dihydrouridine modification sites detection via stacking approach

Dihydrouridine (DHU, D) is one of the most abundant post-transcriptional uridine modifications found in tRNA, mRNA, and snoRNA, closely associated with disease pathogenesis and various biological processes in eukaryotes. Identifying D sites is important for understanding the modification mechanisms and/or epigenetic regulation. However, biological experiments for detecting D sites are time-consuming and expensive. Given these challenges, computational methods have been developed for accurately identifying the D sites in genome-wide datasets. However, existing methods have some limitations, and their prediction performance needs to be improved. In this work, we have developed a new computational predictor for accurately identifying D sites called Stack-DHUpred. Briefly, we trained 66 baseline models or single-feature models by connecting six machine learning classifiers with eleven different feature encoding methods and stacked different baseline models to build stacked ensemble learning models. Subsequently, the optimal combination of the baseline models was identified for the construction of the final stacked model. Remarkably, the Stack-DHUpred outperformed the existing predictors on our new independent dataset, indicating that the stacking approach significantly improved the prediction performance. We have made Stack-DHUpred available to the public through a web server (http://kurata35.bio.kyutech.ac.jp/Stack-DHUpred) and a standalone program (https://github.com/kuratahiroyuki/Stack-DHUpred). We believe that Stack-DHUpred will be a valuable tool for accelerating the discovery of D modifications and understanding their role in post-transcriptional regulation.

MRM-BERT: a novel deep neural network predictor of multiple RNA modifications by fusing BERT representation and sequence features

RNA modifications play crucial roles in various biological processes and diseases. Accurate prediction of RNA modification sites is essential for understanding their functions. In this study, we propose a hybrid approach that fuses a pre-trained sequence representation with various sequence features to predict multiple types of RNA modifications in one combined prediction framework. We developed MRM-BERT, a deep learning method that combined the pre-trained DNABERT deep sequence representation module and the convolutional neural network (CNN) exploiting four traditional sequence feature encodings to improve the prediction performance. MRM-BERT was evaluated on multiple datasets of 12 commonly occurring RNA modifications, including m6A, m5C, m1A and so on. The results demonstrate that our hybrid model outperforms other models in terms of area under receiver operating characteristic curve (AUC) for all 12 types of RNA modifications. MRM-BERT is available as an online tool (http://117.122.208.21:8501) or source code (https://github.com/abhhba999/MRM-BERT), which allows users to predict RNA modification sites and visualize the results. Overall, our study provides an effective and efficient approach to predict multiple RNA modifications, contributing to the understanding of RNA biology and the development of therapeutic strategies.

Analysis approaches for the identification and prediction of N6-methyladenosine sites

The global dynamics in a variety of biological processes can be revealed by mapping transcriptional m⁶A sites, in particular full-transcriptome m⁶A. And individual m⁶A sites have contributed to biological function, which can be evaluated by stoichiometric information obtained from the single nucleotide resolution. Currently, the identification of m⁶A sites is mainly carried out by experiment and prediction methods, based on high-throughput sequencing and machine learning model respectively. This review summarizes the recent topics and progress made in bioinformatics methods of deciphering the m⁶A methylation, including the experimental detection of m⁶A methylation sites, techniques of data analysis, the way of predicting m⁶A methylation sites, m⁶A methylation databases, and detection of m⁶A modification in circRNA. At the end, the essay makes a brief discussion for the development perspective in this area.

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.

Comprehensive landscape of RNA N6-methyladenosine modification in lens epithelial cells from normal and diabetic cataract

To gain more insight into the mechanism of cataract formation from the perspective of epigenetics in the diabetic population, lens epithelium from diabetic cataract patients and health individuals were collected separately and analyzed for N6-methyladenosine (m6A)-modified RNA using methylated RNA immunoprecipitation sequencing (MeRIP-Seq). Subsequently, differential expression analysis was performed on m6A-regulated messenger RNA (mRNA), circular RNA (circRNA), and long non-coding RNA (lncRNA), followed by functional annotation using the Gene Ontology (GO) database. Furthermore, analysis of single-cell data of lens complemented the intrinsic association and cellular heterogeneity of cataract and m6A regulators. In this study, both the global expression levels and peak intensity of m6A-tagged RNAs were increased in patients with diabetic cataract. And we noted multiple core enzymes were upregulated in the diabetic cataract (DC) samples. Besides, single-cell RNA sequencing analysis of the lens revealed the heterogeneous expression of RNA m6A regulators across different cell types, and we noted that the early fiber cell cluster was also closely associated with the onset of cataract and m6A modification. The results comprehensively revealed the dynamic modification landscape of m6A on mRNA, circRNA, and lncRNA, which might provide valuable resources for future studies of the pathogenesis of DCs.

Shining a spotlight on m6A and the vital role of RNA modification in endometrial cancer: a review

RNA modifications are mostly dynamically reversible post-transcriptional modifications, of which m6A is the most prevalent in eukaryotic mRNAs. A growing number of studies indicate that RNA modification can finely tune gene expression and modulate RNA metabolic homeostasis, which in turn affects the self-renewal, proliferation, apoptosis, migration, and invasion of tumor cells. Endometrial carcinoma (EC) is the most common gynecologic tumor in developed countries. Although it can be diagnosed early in the onset and have a preferable prognosis, some cases might develop and become metastatic or recurrent, with a worse prognosis. Fortunately, immunotherapy and targeted therapy are promising methods of treating endometrial cancer patients. Gene modifications may also contribute to these treatments, as is especially the case with recent developments of new targeted therapeutic genes and diagnostic biomarkers for EC, even though current findings on the relationship between RNA modification and EC are still very limited, especially m6A. For example, what is the elaborate mechanism by which RNA modification affects EC progression? Taking m6A modification as an example, what is the conversion mode of methylation and demethylation for RNAs, and how to achieve selective recognition of specific RNA? Understanding how they cope with various stimuli as part of in vivo and in vitro biological development, disease or tumor occurrence and development, and other processes is valuable and RNA modifications provide a distinctive insight into genetic information. The roles of these processes in coping with various stimuli, biological development, disease, or tumor development in vivo and in vitro are self-evident and may become a new direction for cancer in the future. In this review, we summarize the category, characteristics, and therapeutic precis of RNA modification, m6A in particular, with the purpose of seeking the systematic regulation axis related to RNA modification to provide a better solution for the treatment of EC.

Cell Differentiation and Aging Lead To Up-Regulation of FTO, While the ALKBH5 Protein Level Was Stable During Aging but Up-Regulated During in vitro-Induced Cardiomyogenesis

FTO and ALKBH5 proteins are essential erasers of N6-adenosine methylation in RNA. We studied how levels of FTO and ALKBH5 proteins changed during mouse embryonic development, aging, cardiomyogenesis, and neuroectodermal differentiation. We observed that aging in male and female mice was associated with FTO up-regulation in mouse hearts, brains, lungs, and kidneys, while the ALKBH5 level remained stable. FTO and ALKBH5 proteins were up-regulated during experimentally induced cardiomyogenesis, but the level of ALKBH5 protein was not changed when neuroectodermal differentiation was induced. HDAC1 depletion in mouse ES cells caused FTO down-regulation. In these cells, mRNA, carrying information from genes that regulate histone signature, RNA processing, and cell differentiation, was characterized by a reduced level of N6-adenosine methylation in specific gene loci, primarily regulating cell differentiation into neuroectoderm. Together, when we compared both RNA demethylating proteins, the FTO protein level undergoes the most significant changes during cell differentiation and aging. Thus, we conclude that during aging and neuronal differentiation, m6A RNA demethylation is likely regulated by the FTO protein but not via the function of ALKBH5.

m6A-TSHub: Unveiling the Context-specific m6A Methylation and m6A-affecting Mutations in 23 Human Tissues

As the most pervasive epigenetic marker present on mRNAs and long non-coding RNAs (lncRNAs), N6-methyladenosine (m6A) RNA methylation has been shown to participate in essential biological processes. Recent studies have revealed the distinct patterns of m6A methylome across human tissues, and a major challenge remains in elucidating the tissue-specific presence and circuitry of m6A methylation. We present here a comprehensive online platform, m6A-TSHub, for unveiling the context-specific m6A methylation and genetic mutations that potentially regulate m6A epigenetic mark. m6A-TSHub consists of four core components, including (1) m6A-TSDB, a comprehensive database of 184,554 functionally annotated m6A sites derived from 23 human tissues and 499,369 m6A sites from 25 tumor conditions, respectively; (2) m6A-TSFinder, a web server for high-accuracy prediction of m6A methylation sites within a specific tissue from RNA sequences, which was constructed using multi-instance deep neural networks with gated attention; (3) m6A-TSVar, a web server for assessing the impact of genetic variants on tissue-specific m6A RNA modifications; and (4) m6A-CAVar, a database of 587,983 The Cancer Genome Atlas (TCGA) cancer mutations (derived from 27 cancer types) that were predicted to affect m6A modifications in the primary tissue of cancers. The database should make a useful resource for studying the m6A methylome and the genetic factors of epitranscriptome disturbance in a specific tissue (or cancer type). m6A-TSHub is accessible at www.xjtlu.edu.cn/biologicalsciences/m6ats.

Noncanonical contribution of microglial transcription factor NR4A1 to post-stroke recovery through TNF mRNA destabilization

Microglia-mediated neuroinflammation is involved in various neurological diseases, including ischemic stroke, but the endogenous mechanisms preventing unstrained inflammation is still unclear. The anti-inflammatory role of transcription factor nuclear receptor subfamily 4 group A member 1 (NR4A1) in macrophages and microglia has previously been identified. However, the endogenous mechanisms that how NR4A1 restricts unstrained inflammation remain elusive. Here, we observed that NR4A1 is up-regulated in the cytoplasm of activated microglia and localizes to processing bodies (P-bodies). In addition, we found that cytoplasmic NR4A1 functions as an RNA-binding protein (RBP) that directly binds and destabilizes Tnf mRNA in an N6-methyladenosine (m6A)-dependent manner. Remarkably, conditional microglial deletion of Nr4a1 elevates Tnf expression and worsens outcomes in a mouse model of ischemic stroke, in which case NR4A1 expression is significantly induced in the cytoplasm of microglia. Thus, our study illustrates a novel mechanism that NR4A1 posttranscriptionally regulates Tnf expression in microglia and determines stroke outcomes.

Epigenetic regulation of epithelial-mesenchymal transition during cancer development

Epithelial-mesenchymal transition (EMT) plays essential roles in promoting malignant transformation of epithelial cells, leading to cancer progression and metastasis. During EMT-induced cancer development, a wide variety of genes are dramatically modified, especially down-regulation of epithelial-related genes and up-regulation of mesenchymal-related genes. Expression of other EMT-related genes is also modified during the carcinogenic process. Especially, epigenetic modifications are observed in the EMT-related genes, indicating their involvement in cancer development. Mechanically, epigenetic modifications of histone, DNA, mRNA and non-coding RNA stably change the EMT-related gene expression at transcription and translation levels. Herein, we summarize current knowledge on epigenetic regulatory mechanisms observed in EMT process relate to cancer development in humans. The better understanding of epigenetic regulation of EMT during cancer development may lead to improvement of drug design and preventive strategies in cancer therapy.

Evaluation and development of deep neural networks for RNA 5-Methyluridine classifications using autoBioSeqpy

Post-transcriptionally RNA modifications, also known as the epitranscriptome, play crucial roles in the regulation of gene expression during development. Recently, deep learning (DL) has been employed for RNA modification site prediction and has shown promising results. However, due to the lack of relevant studies, it is unclear which DL architecture is best suited for some pyrimidine modifications, such as 5-methyluridine (m⁵U). To fill this knowledge gap, we first performed a comparative evaluation of various commonly used DL models for epigenetic studies with the help of autoBioSeqpy. We identified optimal architectural variations for m⁵U site classification, optimizing the layer depth and neuron width. Second, we used this knowledge to develop Deepm5U, an improved convolutional-recurrent neural network that accurately predicts m⁵U sites from RNA sequences. We successfully applied Deepm5U to transcriptomewide m⁵U profiling data across different sequencing technologies and cell types. Third, we showed that the techniques for interpreting deep neural networks, including LayerUMAP and DeepSHAP, can provide important insights into the internal operation and behavior of models. Overall, we offered practical guidance for the development, benchmark, and analysis of deep learning models when designing new algorithms for RNA modifications.

YTHDF1 Promotes Bladder Cancer Cell Proliferation via the METTL3/YTHDF1-RPN2-PI3K/AKT/mTOR Axis

N6-methyladenosine (m6A) is the most common mRNA modification and it plays a critical role in tumor progression, prognoses and therapeutic response. In recent years, more and more studies have shown that m6A modifications play an important role in bladder carcinogenesis and development. However, the regulatory mechanisms of m6A modifications are complex. Whether the m6A reading protein YTHDF1 is involved in the development of bladder cancer remains to be elucidated. The aims of this study were to determine the association between METTL3/YTHDF1 and bladder cancer cell proliferation and cisplatin resistance to explore the downstream target genes of METTL3/YTHDF1 and to explore the therapeutic implications for bladder cancer patients. The results showed that the reduced expression of METTL3/YTHDF1 could lead to decreased bladder cancer cell proliferation and cisplatin sensitivity. Meanwhile, overexpression of the downstream target gene, RPN2, could rescue the effect of reduced METTL3/YTHDF1 expression on bladder cancer cells. In conclusion, this study proposes a novel METTL3/YTHDF1-RPN2-PI3K/AKT/mTOR regulatory axis that affects bladder cancer cell proliferation and cisplatin sensitivity.

i2OM: Toward a better prediction of 2'-O-methylation in human RNA

2'-O-methylation (2OM) is an omnipresent post-transcriptional modification in RNAs. It is important for the regulation of RNA stability, mRNA splicing and translation, as well as innate immunity. With the increase in publicly available 2OM data, several computational tools have been developed for the identification of 2OM sites in human RNA. Unfortunately, these tools suffer from the low discriminative power of redundant features, unreasonable dataset construction or overfitting. To address those issues, based on four types of 2OM (2OM-adenine (A), cytosine (C), guanine (G), and uracil (U)) data, we developed a two-step feature selection model to identify 2OM. For each type, the one-way analysis of variance (ANOVA) combined with mutual information (MI) was proposed to rank sequence features for obtaining the optimal feature subset. Subsequently, four predictors based on eXtreme Gradient Boosting (XGBoost) or support vector machine (SVM) were presented to identify the four types of 2OM sites. Finally, the proposed model could produce an overall accuracy of 84.3 % on the independent set. To provide a convenience for users, an online tool called i2OM was constructed and can be freely access at i2om.lin-group.cn. The predictor may provide a reference for the study of the 2OM.

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.

DL-m6A: Identification of N6-Methyladenosine Sites in Mammals Using Deep Learning Based on Different Encoding Schemes

N6-methyladenosine (m6A) is a common post-transcriptional alteration that plays a critical function in a variety of biological processes. Although experimental approaches for identifying m6A sites have been developed and deployed, they are currently expensive for transcriptome-wide m6A identification. Some computational strategies for identifying m6A sites have been presented as an effective complement to the experimental procedure. However, their performance still requires improvement. In this study, we have proposed a novel tool called DL-m6A for the identification of m6A sites in mammals using deep learning based on different encoding schemes. The proposed tool uses three encoding schemes which give the required contextual feature representation to the input RNA sequence. Later these contextual feature vectors individually go through several neural network layers for shallow feature extraction after which they are concatenated to a single feature vector. The concatenated feature map is then used by several other layers to extract the deep features so that the insight features of the sequence can be used for the prediction of m6A sites. The proposed tool is firstly evaluated on the tissue-specific dataset and later on a full transcript dataset. To ensure the generalizability of the tool we assessed the proposed model by training it on a full transcript dataset and test on the tissue-specific dataset. The achieved results by the proposed model have outperformed the existing tools. The results demonstrate that the proposed tool can be of great use for the biology experts and therefore a freely accessible web-server is created which can be accessed at: http://nsclbio.jbnu.ac.kr/tools/DL-m6A/.

PseU-ST: A new stacked ensemble-learning method for identifying RNA pseudouridine sites

Background: Pseudouridine (Ψ) is one of the most abundant RNA modifications found in a variety of RNA types, and it plays a significant role in many biological processes. The key to studying the various biochemical functions and mechanisms of Ψ is to identify the Ψ sites. However, identifying Ψ sites using experimental methods is time-consuming and expensive. Therefore, it is necessary to develop computational methods that can accurately predict Ψ sites based on RNA sequence information. Methods: In this study, we proposed a new model called PseU-ST to identify Ψ sites in Homo sapiens (H. sapiens), Saccharomyces cerevisiae (S. cerevisiae), and Mus musculus (M. musculus). We selected the best six encoding schemes and four machine learning algorithms based on a comprehensive test of almost all of the RNA sequence encoding schemes available in the iLearnPlus software package, and selected the optimal features for each encoding scheme using chi-square and incremental feature selection algorithms. Then, we selected the optimal feature combination and the best base-classifier combination for each species through an extensive performance comparison and employed a stacking strategy to build the predictive model. Results: The results demonstrated that PseU-ST achieved better prediction performance compared with other existing models. The PseU-ST accuracy scores were 93.64%, 87.74%, and 89.64% on H_990, S_628, and M_944, respectively, representing increments of 13.94%, 6.05%, and 0.26%, respectively, higher than the best existing methods on the same benchmark training datasets. Conclusion: The data indicate that PseU-ST is a very competitive prediction model for identifying RNA Ψ sites in H. sapiens, M. musculus, and S. cerevisiae. In addition, we found that the Position-specific trinucleotide propensity based on single strand (PSTNPss) and Position-specific of three nucleotides (PS3) features play an important role in Ψ site identification. The source code for PseU-ST and the data are obtainable in our GitHub repository (https://github.com/jluzhangxinrubio/PseU-ST).

Predicting Pseudouridine Sites with Porpoise

Pseudouridine is a ubiquitous RNA modification and plays a crucial role in many biological processes. However, it remains a challenging task to identify pseudouridine sites using expensive and time-consuming experimental research. To this end, we present Porpoise, a computational approach to identify pseudouridine sites from RNA sequence data. Porpoise builds on a stacking ensemble learning framework with several informative features and achieves competitive performance compared with state-of-the-art approaches. This protocol elaborates on step-by-step use and execution of the local stand-alone version and the webserver of Porpoise. In addition, we also provide a general machine learning framework that can help identify the optimal stacking ensemble learning model using different combinations of feature-based features. This general machine learning framework can facilitate users to build their pseudouridine predictors using their in-house datasets.

Dynamic m6A-ncRNAs association and their impact on cancer pathogenesis, immune regulation and therapeutic response

Several types of modifications have been proven to participate in the metabolism and processing of different RNA types, including non-coding RNAs (ncRNAs). N-6-methyladenosine (m6A) is a dynamic and reversible RNA modification that is closely involved in the ncRNA homeostasis, and serves as a crucial regulator for multiple cancer-associated signaling pathways. The ncRNAs usually regulate the epigenetic modification, mRNA transcription and other biological processes, displaying enormous roles in human cancers. In this review, we summarized the significant implications of m6A-ncRNA interaction in various types of cancers. In particular, the interplay between m6A and ncRNAs in cancer pathogenesis and therapeutic resistance are being widely recognized. We also discussed the relevance of m6A-ncRNA interaction in immune regulation, followed by the interference on cancer immunotherapeutic procedures. In addition, we briefly highlighted the computation tools that could identify the accurate features of m6A methylome among ncRNAs. In summary, this review would pave the way for a better understanding of the biological functions of m6A-ncRNA crosstalk in cancer research and treatment.

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.

Advancement of epigenetics in stroke

A wide plethora of intervention procedures, tissue plasminogen activators, mechanical thrombectomy, and several neuroprotective drugs were reported in stroke research over the last decennium. However, against this vivid background of newly emerging pieces of evidence, there is little to no advancement in the overall functional outcomes. With the advancement of epigenetic tools and technologies associated with intervention medicine, stroke research has entered a new fertile. The stroke involves an overabundance of inflammatory responses arising in part due to the body's immune response to brain injury. Neuroinflammation contributes to significant neuronal cell death and the development of functional impairment and even death in stroke patients. Recent studies have demonstrated that epigenetics plays a key role in post-stroke conditions, leading to inflammatory responses and alteration of the microenvironment within the injured tissue. In this review, we summarize the progress of epigenetics which provides an overview of recent advancements on the emerging key role of secondary brain injury in stroke. We also discuss potential epigenetic therapies related to clinical practice.

MTDeepM6A-2S: A two-stage multi-task deep learning method for predicting RNA N6-methyladenosine sites of Saccharomyces cerevisiae

N6-methyladenosine (m6A) is one of the most important RNA modifications, which is involved in many biological activities. Computational methods have been developed to detect m6A sites due to their high efficiency and low costs. As one of the most widely utilized model organisms, many methods have been developed for predicting m6A sites of Saccharomyces cerevisiae. However, the generalization of these methods was hampered by the limited size of the benchmark datasets. On the other hand, over 60,000 low resolution m6A sites and more than 10,000 base resolution m6A sites of Saccharomyces cerevisiae are recorded in RMBase and m6A-Atlas, respectively. The base resolution m6A sites are often obtained from low resolution results by post calibration. In view of these, we proposed a two-stage deep learning method, named MTDeepM6A-2S, to predict RNA m6A sites of Saccharomyces cerevisiae based on RNA sequence information. In the first stage, a multi-task model with convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) deep framework was built to not only detect the low resolution m6A sites but also assign a reasonable probability for the predicted site. In the second stage, a transfer-learning strategy was used to build the model to predict the base resolution m6A sites from those low resolution m6A sites. The effectiveness of our model was validated on both training and independent test sets. The results show that our model outperforms other state-of-the-art models on the independent test set, which indicates that our model holds high potential to become a useful tool for epitranscriptomics analysis.

Comparative functional RNA editomes of neural differentiation from human PSCs

RNA editing is a fundamental mechanism that constitutes the epitranscriptomic complexity. A-to-G editing is the predominant type catalyzed by ADAR1 and ADAR2 in human. Using a CRISPR/Cas9 approach to knockout ADAR1/2, we identified a regulatory role of RNA editing in directed differentiation of human embryonic stem cells (hESCs) toward neural progenitor cells (NPCs). Genome-wide landscapes of A-to-G editing in hESCs and four derivative cell lineages representing all three germ layers and the extraembryonic cell fate were profiled, with a particular focus on neural differentiation. Furthermore, a bioinformatics-guided case study identified a potential functional editing event in ZYG11B 3'UTR that might play a role in regulation of NPC differentiation through gain of miR6089 targeting. Collectively, our study established the functional role of A-to-G RNA editing in neural lineage differentiation; illustrated the RNA editing landscapes of hESCs and NPC differentiation; and shed new light on molecular insights thereof.

RNA modifications in aging-associated cardiovascular diseases

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide that bears an enormous healthcare burden and aging is a major contributing factor to CVDs. Functional gene expression network during aging is regulated by mRNAs transcriptionally and by non-coding RNAs epi-transcriptionally. RNA modifications alter the stability and function of both mRNAs and non-coding RNAs and are involved in differentiation, development, and diseases. Here we review major chemical RNA modifications on mRNAs and non-coding RNAs, including N6-adenosine methylation, N1-adenosine methylation, 5-methylcytidine, pseudouridylation, 2′ -O-ribose-methylation, and N7-methylguanosine, in the aging process with an emphasis on cardiovascular aging. We also summarize the currently available methods to detect RNA modifications and the bioinformatic tools to study RNA modifications. More importantly, we discussed the specific implication of the RNA modifications on mRNAs and non-coding RNAs in the pathogenesis of aging-associated CVDs, including atherosclerosis, hypertension, coronary heart diseases, congestive heart failure, atrial fibrillation, peripheral artery disease, venous insufficiency, and stroke.

An integrated model of acinar to ductal metaplasia-related N7-methyladenosine regulators predicts prognosis and immunotherapy in pancreatic carcinoma based on digital spatial profiling

Acinar-to-ductal metaplasia (ADM) is a recently recognized, yet less well-studied, precursor lesion of pancreatic ductal adenocarcinoma (PDAC) developed in the setting of chronic pancreatitis. Through digital spatial mRNA profiling, we compared ADM and adjacent PDAC tissues from patient samples to unveil the bridging genes during the malignant transformation of pancreatitis. By comparing the bridging genes with the 7-methylguanosine (m7G)-seq dataset, we screened 19 m7G methylation genes for a subsequent large sample analysis. We constructed the “m7G score” model based on the RNA-seq data for pancreatic cancer in The Cancer Genome Atlas (TCGA) database and The Gene Expression Omnibus (GEO) database. Tumors with a high m7G score were characterized by increased immune cell infiltration, increased genomic instability, higher response rate to combined immune checkpoint inhibitors (ICIs), and overall poor survival. These findings indicate that the m7G score is associated with tumor invasiveness, immune cell infiltration, ICI treatment response, and overall patients’ survival. We also identified FN1 and ITGB1 as core genes in the m7Gscore model, which affect immune cell infiltration and genomic instability not only in pancreatic cancer but also in pan-cancer. FN1 and ITGB1 can inhibit immune T cell activition by upregulation of macrophages and neutrophils, thereby leading to immune escape of pancreatic cancer cells and reducing the response rate of ICI treatment.

Exploring epitranscriptomics for crop improvement and environmental stress tolerance

Climate change and stressful environmental conditions severely hamper crop growth, development and yield. Plants respond to environmental perturbations, through their plasticity provided by key-genes, governed at post-/transcriptional levels. Gene-regulation in plants is a multilevel process controlled by diverse cellular entities that includes transcription factors (TF), epigenetic regulators and non-coding RNAs beside others. There are successful studies confirming the role of epigenetic modifications (DNA-methylation/histone-modifications) in gene expression. Recent years have witnessed emergence of a highly specialized field the "Epitranscriptomics". Epitranscriptomics deals with investigating post-transcriptional RNA chemical-modifications present across the life forms that change structural, functional and biological characters of RNA. However, deeper insights on of epitranscriptomic modifications, with >140 types known so far, are to be understood fully. Researchers have identified epitranscriptome marks (writers, erasers and readers) and mapped the site-specific RNA modifications (m6A, m⁵C, 3' uridylation, etc.) responsible for fine-tuning gene expression in plants. Simultaneous advancement in sequencing platforms, upgraded bioinformatic tools and pipelines along with conventional labelled techniques have further given a statistical picture of these epitranscriptomic modifications leading to their potential applicability in crop improvement and developing climate-smart crops. We present herein the insights on epitranscriptomic machinery in plants and how epitranscriptome and epitranscriptomic modifications underlying plant growth, development and environmental stress responses/adaptations. Third-generation sequencing technology, advanced bioinformatics tools and databases being used in plant epitranscriptomics are also discussed. Emphasis is given on potential exploration of epitranscriptome engineering for crop-improvement and developing environmental stress tolerant plants covering current status, challenges and future directions.

N6-Methyladenosine Methylomic Landscape of Ureteral Deficiency in Reflux Uropathy and Obstructive Uropathy

Background

Congenital anomalies of the kidneys and urinary tracts (CAKUT) represent the most prevalent cause for renal failure in children. The RNA epigenetic modification N⁶-methyladenosine (m⁶A) methylation modulates gene expression and function post-transcriptionally, which has recently been revealed to be critical in organ development. However, it is uncertain whether m⁶A methylation plays a role in the pathogenesis of CAKUT. Thus, we aimed to explore the pattern of m⁶A methylation in CAKUT.

Methods

Using m⁶A-mRNA epitranscriptomic microarray, we investigated the m⁶A methylomic landscape in the ureter tissue of children with obstructive megaureter (M group) and primary vesicoureteral reflux (V group).

Results

A total of 228 mRNAs engaged in multiple function-relevant signaling pathways were substantially differential methylated between the “V” and “M” groups. Additionally, 215 RNA-binding proteins that recognize differentially methylated regions were predicted based on public databases. The M group showed significantly higher mRNA levels of m⁶A readers/writers (YTHDF1, YTHDF2, YTHDC1, YTHDC2 and WTAP) and significantly lower mRNA levels of m⁶A eraser (FTO) according to real-time PCR. To further investigate the differentially methylated genes, m⁶A methylome and transcriptome data were integrated to identified 298 hypermethylated mRNAs with differential expressions (265 upregulation and 33 downregulation) and 489 hypomethylated mRNAs with differential expressions (431 upregulation and 58 downregulation) in the M/V comparison.

Conclusion

The current results highlight the pathogenesis of m⁶A methylation in obstructive and reflux uropathy.

EMDLP: Ensemble multiscale deep learning model for RNA methylation site prediction

BACKGROUND

Recent research recommends that epi-transcriptome regulation through post-transcriptional RNA modifications is essential for all sorts of RNA. Exact identification of RNA modification is vital for understanding their purposes and regulatory mechanisms. However, traditional experimental methods of identifying RNA modification sites are relatively complicated, time-consuming, and laborious. Machine learning approaches have been applied in the procedures of RNA sequence features extraction and classification in a computational way, which may supplement experimental approaches more efficiently. Recently, convolutional neural network (CNN) and long short-term memory (LSTM) have been demonstrated achievements in modification site prediction on account of their powerful functions in representation learning. However, CNN can learn the local response from the spatial data but cannot learn sequential correlations. And LSTM is specialized for sequential modeling and can access both the contextual representation but lacks spatial data extraction compared with CNN. There is strong motivation to construct a prediction framework using natural language processing (NLP), deep learning (DL) for these reasons.

RESULTS

This study presents an ensemble multiscale deep learning predictor (EMDLP) to identify RNA methylation sites in an NLP and DL way. It organically combines the dilated convolution and Bidirectional LSTM (BiLSTM), which helps to take better advantage of the local and global information for site prediction. The first step of EMDLP is to represent the RNA sequences in an NLP way. Thus, three encodings, e.g., RNA word embedding, One-hot encoding, and RGloVe, which is an improved learning method of word vector representation based on GloVe, are adopted to decipher sites from the viewpoints of the local and global information. Then, a dilated convolutional Bidirectional LSTM network (DCB) model is constructed with the dilated convolutional neural network (DCNN) followed by BiLSTM to extract potential contributing features for methylation site prediction. Finally, these three encoding methods are integrated by a soft vote to obtain better predictive performance. Experiment results on m¹A and m⁶A reveal that the area under the receiver operating characteristic(AUROC) of EMDLP obtains respectively 95.56%, 85.24%, and outperforms the state-of-the-art models. To maximize user convenience, a user-friendly webserver for EMDLP was publicly available at http://www.labiip.net/EMDLP/index.php ( http://47.104.130.81/EMDLP/index.php ).

CONCLUSIONS

We developed a predictor for m¹A and m⁶A methylation sites.

Comprehensive Analysis of Long Noncoding RNA Modified by m6A Methylation in Oxidative and Glycolytic Skeletal Muscles

N⁶-methyladenosine (m⁶A) is the most common modification in eukaryotic RNAs. Accumulating evidence shows m⁶A methylation plays vital roles in various biological processes, including muscle and fat differentiation. However, there is a lack of research on lncRNAs’ m⁶A modification in regulating pig muscle-fiber-type conversion. In this study, we identified novel and differentially expressed lncRNAs in oxidative and glycolytic skeletal muscles through RNA-seq, and further reported the m⁶A-methylation patterns of lncRNAs via MeRIP-seq. We found that most lncRNAs have one m⁶A peak, and the m⁶A peaks were preferentially enriched in the last exon of the lncRNAs. Interestingly, we found that lncRNAs’ m⁶A levels were positively correlated with their expression homeostasis and levels. Furthermore, we performed conjoint analysis of MeRIP-seq and RNA-seq data and obtained 305 differentially expressed and differentially m⁶A-modified lncRNAs (dme-lncRNAs). Through QTL enrichment analysis of dme-lncRNAs and PPI analysis for their cis-genes, we finally identified seven key m⁶A-modified lncRNAs that may play a potential role in muscle-fiber-type conversion. Notably, inhibition of one of the key lncRNAs, MSTRG.14200.1, delayed satellite cell differentiation and stimulated fast-to-slow muscle-fiber conversion. Our study comprehensively analyzed m⁶A modifications on lncRNAs in oxidative and glycolytic skeletal muscles and provided new targets for the study of pig muscle-fiber-type conversion.

29 m6A-RNA Methylation (Epitranscriptomic) Regulators Are Regulated in 41 Diseases including Atherosclerosis and Tumors Potentially via ROS Regulation - 102 Transcriptomic Dataset Analyses

We performed a database mining on 102 transcriptomic datasets for the expressions of 29 m6A-RNA methylation (epitranscriptomic) regulators (m6A-RMRs) in 41 diseases and cancers and made significant findings: (1) a few m6A-RMRs were upregulated; and most m6A-RMRs were downregulated in sepsis, acute respiratory distress syndrome, shock, and trauma; (2) half of 29 m6A-RMRs were downregulated in atherosclerosis; (3) inflammatory bowel disease and rheumatoid arthritis modulated m6A-RMRs more than lupus and psoriasis; (4) some organ failures shared eight upregulated m6A-RMRs; end-stage renal failure (ESRF) downregulated 85% of m6A-RMRs; (5) Middle-East respiratory syndrome coronavirus infections modulated m6A-RMRs the most among viral infections; (6) proinflammatory oxPAPC modulated m6A-RMRs more than DAMP stimulation including LPS and oxLDL; (7) upregulated m6A-RMRs were more than downregulated m6A-RMRs in cancer types; five types of cancers upregulated ≥10 m6A-RMRs; (8) proinflammatory M1 macrophages upregulated seven m6A-RMRs; (9) 86% of m6A-RMRs were differentially expressed in the six clusters of CD4+Foxp3+ immunosuppressive Treg, and 8 out of 12 Treg signatures regulated m6A-RMRs; (10) immune checkpoint receptors TIM3, TIGIT, PD-L2, and CTLA4 modulated m6A-RMRs, and inhibition of CD40 upregulated m6A-RMRs; (11) cytokines and interferons modulated m6A-RMRs; (12) NF-κB and JAK/STAT pathways upregulated more than downregulated m6A-RMRs whereas TP53, PTEN, and APC did the opposite; (13) methionine-homocysteine-methyl cycle enzyme Mthfd1 downregulated more than upregulated m6A-RMRs; (14) m6A writer RBM15 and one m6A eraser FTO, H3K4 methyltransferase MLL1, and DNA methyltransferase, DNMT1, regulated m6A-RMRs; and (15) 40 out of 165 ROS regulators were modulated by m6A eraser FTO and two m6A writers METTL3 and WTAP. Our findings shed new light on the functions of upregulated m6A-RMRs in 41 diseases and cancers, nine cellular and molecular mechanisms, novel therapeutic targets for inflammatory disorders, metabolic cardiovascular diseases, autoimmune diseases, organ failures, and cancers.

The N6-Methyladenosine Modification and Its Role in mRNA Metabolism and Gastrointestinal Tract Disease

The N6-methyladenosine (m6A) modification is the most abundant internal modification of messenger RNA (mRNA) in higher eukaryotes. Under the actions of methyltransferase, demethylase and methyl-binding protein, m6A resulting from RNA methylation becomes dynamic and reversible, similar to that from DNA methylation, and this effect allows the generated mRNA to participate in metabolism processes, such as splicing, transport, translation, and degradation. The most common tumors are those found in the gastrointestinal tract, and research on these tumors has flourished since the discovery of m6A. Overall, further analysis of the mechanism of m6A and its role in tumors may contribute to new ideas for the treatment of tumors. m6A also plays an important role in non-tumor diseases of the gastrointestinal tract. This manuscript reviews the current knowledge of m6A-related proteins, mRNA metabolism and their application in gastrointestinal tract disease.

N 6-methyladenosine enhances post-transcriptional gene regulation by microRNAs

MOTIVATION

N ⁶-methyladenosine (m⁶A) is the most prevalent modification in eukaryotic messenger RNAs. MicroRNAs (miRNAs) are abundant post-transcriptional regulators of gene expression. Correlation between m⁶A and miRNA-targeting sites has been reported to suggest possible involvement of m⁶A in miRNA-mediated gene regulation. However, it is unknown what the regulatory effects might be. In this study, we performed comprehensive analyses of high-throughput data on m⁶A and miRNA target binding and regulation.

RESULTS

We found that the level of miRNA-mediated target suppression is significantly enhanced when m⁶A is present on target mRNAs. The evolutionary conservation for miRNA-binding sites with m⁶A modification is significantly higher than that for miRNA-binding sites without modification. These findings suggest functional significance of m⁶A modification in post-transcriptional gene regulation by miRNAs. We also found that methylated targets have more stable structure than non-methylated targets, as indicated by significantly higher GC content. Furthermore, miRNA-binding sites that can be potentially methylated are significantly less accessible without methylation than those that do not possess potential methylation sites. Since either RNA-binding proteins or m⁶A modification by itself can destabilize RNA structure, we propose a model in which m⁶A alters local target secondary structure to increase accessibility for efficient binding by Argonaute proteins, leading to enhanced miRNA-mediated regulation.

AVAILABILITY AND IMPLEMENTATION

N/A.