Bioinformatics approaches for deciphering the epitranscriptome: Recent progress and emerging topics

Role of m5C methylation in digestive system tumors (Review)

Currently, the incidence of digestive system tumors has been increasing annually, thus becoming a prevalent cause of cancer‑related mortalities. Although significant strides have been made in targeting the molecular mechanisms that underpin the development of these tumors, their treatment and prognosis still pose substantial challenges. This is primarily due to the ambiguity of early diagnostic indicators and the fact that most digestive system tumors are detected at an advanced stage. However, epigenetic modifications are capable of altering the expression of oncogenes and regulating biological processes in cancer. In recent years, the study of methylation in relation to tumor pathogenesis has become a focus of prominent research. Among the various types of methylation, 5‑methylcytosine (m5C) methylation plays a crucial role in the development of digestive system tumors and is anticipated to serve as a novel therapeutic target. However, to date, a comprehensive and systematic review concerning the role of m5C methylation in digestive system tumors is lacking. Consequently, the present study reviewed the role of m5C methylation in digestive system tumors such as esophageal cancer, gastric cancer and hepatocellular carcinoma, with the aim of providing a valuable reference for future research endeavors.

YTHDF2 promotes ATP synthesis and immune evasion in B cell malignancies

Long-term durable remission in patients with B cell malignancies following chimeric antigen receptor (CAR)-T cell immunotherapy remains unsatisfactory, often due to antigen escape. Malignant B cell transformation and oncogenic growth relies on efficient ATP synthesis, although the underlying mechanisms remain unclear. Here, we report that YTHDF2 facilitates energy supply and antigen escape in B cell malignancies, and its overexpression alone is sufficient to cause B cell transformation and tumorigenesis. Mechanistically, YTHDF2 functions as a dual reader where it stabilizes mRNAs as a 5-methylcytosine (m5C) reader via recruiting PABPC1, thereby enhancing their expression and ATP synthesis. Concomitantly, YTHDF2 also promotes immune evasion by destabilizing other mRNAs as an N6-methyladenosine (m6A) reader. Small-molecule-mediated targeting of YTHDF2 suppresses aggressive B cell malignancies and sensitizes them to CAR-T cell therapy.

Exploring m6A methylation in skin Cancer: Insights into molecular mechanisms and treatment

N6-methyladenosine (m6A) is the most common and prevalent internal mRNA modification in eukaryotes. m6A modification is a dynamic and reversible process regulated by methyltransferases, demethylases, and m6A binding proteins. Skin cancers, including melanoma and nonmelanoma skin cancers (NMSCs), are among the most commonly diagnosed cancers worldwide. m6A methylation is involved in the regulation of RNA splicing, translation, degradation, stability, translocation, export, and folding. Aberrant m6A modification participates in the pathophysiological processes of skin cancers and is associated with tumor cell proliferation, invasion, migration, and metastasis during cancer progression. In this review, we provide a comprehensive summary of the biological functions of m6A and the most up-to-date evidence related to m6A RNA modification in skin cancer. We also emphasize the potential clinical applications in the diagnosis and treatment of skin cancers.

Prognostic analysis and risk assessment based on RNA editing in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, and prognosis assessment is crucial for guiding treatment decisions. In this study, we aimed to develop a personalized prognostic model for HCC based on RNA editing. RNA editing is a post-transcriptional process that can affect gene expression and, in some cases, play a role in cancer development. By analyzing RNA editing sites in HCC, we sought to identify a set of sites associated with patient prognosis and use them to create a prognostic model. We gathered RNA editing data from the Synapse database, comprising 9990 RNA editing sites and 250 HCC samples. Additionally, we collected clinical data for 377 HCC patients from the Cancer Genome Atlas (TCGA) database. We employed a multi-step approach to identify prognosis-related RNA editing sites (PR-RNA-ESs). We assessed how patients in the high-risk and low-risk groups, as defined by the model, fared in terms of survival. A nomogram was developed to predict the precise survival prognosis of HCC patients and assessed the prognostic model's utility through a receiver operating characteristic (ROC) analysis and decision curve analysis (DCA). Our analysis identified 33 prognosis-related RNA editing sites (PR-RNA-ESs) associated with HCC patient prognosis. Using a combination of LASSO regression and cross-validation, we constructed a prognostic model based on 13 PR-RNA-ESs. Survival analysis demonstrated significant differences in the survival outcomes of patients in the high-risk and low-risk groups defined by this model. Additionally, the differential expression of the 13 PR-RNA-ESs played a role in shaping patient survival. Risk-prognostic investigations further distinguished patients based on their risk levels. The nomogram enabled precise survival prognosis prediction. Our study has successfully developed a highly personalized and accurate prognostic model for individuals with HCC, leveraging RNA editing data. This model has the potential to revolutionize clinical evaluation and medical management by providing individualized prognostic information. The identification of specific RNA editing sites associated with HCC prognosis and their incorporation into a predictive model holds promise for improving the precision of treatment strategies and ultimately enhancing patient outcomes in HCC.

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.

Analysis of bacterial transcriptome and epitranscriptome using nanopore direct RNA sequencing

Bacterial gene expression is a complex process involving extensive regulatory mechanisms. Along with growing interests in this field, Nanopore Direct RNA Sequencing (DRS) provides a promising platform for rapid and comprehensive characterization of bacterial RNA biology. However, the DRS of bacterial RNA is currently deficient in the yield of mRNA-mapping reads and has yet to be exploited for transcriptome-wide RNA modification mapping. Here, we showed that pre-processing of bacterial total RNA (size selection followed by ribosomal RNA depletion and polyadenylation) guaranteed high throughputs of sequencing data and considerably increased the amount of mRNA reads. This way, complex transcriptome architectures were reconstructed for Escherichia coli and Staphylococcus aureus and extended the boundaries of 225 known E. coli operons and 89 defined S. aureus operons. Utilizing unmodified in vitro-transcribed (IVT) RNA libraries as a negative control, several Nanopore-based computational tools globally detected putative modification sites in the E. coli and S. aureus transcriptomes. Combined with Next-Generation Sequencing-based N6-methyladenosine (m6A) detection methods, 75 high-confidence m6A candidates were identified in the E. coli protein-coding transcripts, while none were detected in S. aureus. Altogether, we demonstrated the potential of Nanopore DRS in systematic and convenient transcriptome and epitranscriptome analysis.

Transcriptomics and epigenetic data integration learning module on Google Cloud

Multi-omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, etc.) research approaches are vital for understanding the hierarchical complexity of human biology and have proven to be extremely valuable in cancer research and precision medicine. Emerging scientific advances in recent years have made high-throughput genome-wide sequencing a central focus in molecular research by allowing for the collective analysis of various kinds of molecular biological data from different types of specimens in a single tissue or even at the level of a single cell. Additionally, with the help of improved computational resources and data mining, researchers are able to integrate data from different multi-omics regimes to identify new prognostic, diagnostic, or predictive biomarkers, uncover novel therapeutic targets, and develop more personalized treatment protocols for patients. For the research community to parse the scientifically and clinically meaningful information out of all the biological data being generated each day more efficiently with less wasted resources, being familiar with and comfortable using advanced analytical tools, such as Google Cloud Platform becomes imperative. This project is an interdisciplinary, cross-organizational effort to provide a guided learning module for integrating transcriptomics and epigenetics data analysis protocols into a comprehensive analysis pipeline for users to implement in their own work, utilizing the cloud computing infrastructure on Google Cloud. The learning module consists of three submodules that guide the user through tutorial examples that illustrate the analysis of RNA-sequence and Reduced-Representation Bisulfite Sequencing data. The examples are in the form of breast cancer case studies, and the data sets were procured from the public repository Gene Expression Omnibus. The first submodule is devoted to transcriptomics analysis with the RNA sequencing data, the second submodule focuses on epigenetics analysis using the DNA methylation data, and the third submodule integrates the two methods for a deeper biological understanding. The modules begin with data collection and preprocessing, with further downstream analysis performed in a Vertex AI Jupyter notebook instance with an R kernel. Analysis results are returned to Google Cloud buckets for storage and visualization, removing the computational strain from local resources. The final product is a start-to-finish tutorial for the researchers with limited experience in multi-omics to integrate transcriptomics and epigenetics data analysis into a comprehensive pipeline to perform their own biological research.This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [16] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

HIGHLIGHTS

RNA methylation-related inhibitors: Biological basis and therapeutic potential for cancer therapy

RNA methylation is widespread in nature. Abnormal expression of proteins associated with RNA methylation is strongly associated with a number of human diseases including cancer. Increasing evidence suggests that targeting RNA methylation holds promise for cancer treatment. This review specifically describes several common RNA modifications, such as the relatively well-studied N6-methyladenosine, as well as 5-methylcytosine and pseudouridine (Ψ). The regulatory factors involved in these modifications and their roles in RNA are also comprehensively discussed. We summarise the diverse regulatory functions of these modifications across different types of RNAs. Furthermore, we elucidate the structural characteristics of these modifications along with the development of specific inhibitors targeting them. Additionally, recent advancements in small molecule inhibitors targeting RNA modifications are presented to underscore their immense potential and clinical significance in enhancing therapeutic efficacy against cancer. KEY POINTS: In this paper, several important types of RNA modifications and their related regulatory factors are systematically summarised. Several regulatory factors related to RNA modification types were associated with cancer progression, and their relationships with cancer cell migration, invasion, drug resistance and immune environment were summarised. In this paper, the inhibitors targeting different regulators that have been proposed in recent studies are summarised in detail, which is of great significance for the development of RNA modification regulators and cancer treatment in the future.

Decoding epitranscriptomic regulation of viral infection: mapping of RNA N6-methyladenosine by advanced sequencing technologies

Elucidating the intricate interactions between viral pathogens and host cellular machinery during infection is paramount for understanding pathogenic mechanisms and identifying potential therapeutic targets. The RNA modification N6-methyladenosine (m6A) has emerged as a significant factor influencing the trajectory of viral infections. Hence, the precise and quantitative mapping of m6A modifications in both host and viral RNA is pivotal to understanding its role during viral infection. With the rapid advancement of sequencing technologies, scientists are able to detect m6A modifications with various quantitative, high-resolution, transcriptome approaches. These technological strides have reignited research interest in m6A, underscoring its significance and prompting a deeper investigation into its dynamics during viral infections. This review provides a comprehensive overview of the historical evolution of m6A epitranscriptome sequencing technologies, highlights the latest developments in transcriptome-wide m6A mapping, and emphasizes the innovative technologies for detecting m6A modification. We further discuss the implications of these technologies for future research into the role of m6A in viral infections.

An Overview of Current Detection Methods for RNA Methylation

Epitranscriptomic mechanisms, which constitute an important layer in post-transcriptional gene regulation, are involved in numerous cellular processes under health and disease such as stem cell development or cancer. Among various such mechanisms, RNA methylation is considered to have vital roles in eukaryotes primarily due to its dynamic and reversible nature. There are numerous RNA methylations that include, but are not limited to, 2'-O-dimethyladenosine (m6Am), N7-methylguanosine (m7G), N6-methyladenosine (m6A) and N1-methyladenosine (m1A). These biochemical modifications modulate the fate of RNA by affecting the processes such as translation, target site determination, RNA processing, polyadenylation, splicing, structure, editing and stability. Thus, it is highly important to quantitatively measure the changes in RNA methylation marks to gain insight into cellular processes under health and disease. Although there are complicating challenges in identifying certain methylation marks genome wide, various methods have been developed recently to facilitate the quantitative measurement of methylated RNAs. To this end, the detection methods for RNA methylation can be classified in five categories such as antibody-based, digestion-based, ligation-based, hybridization-based or direct RNA-based methods. In this review, we have aimed to summarize our current understanding of the detection methods for RNA methylation, highlighting their advantages and disadvantages, along with the current challenges in the field.

MSCAN: multi-scale self- and cross-attention network for RNA methylation site prediction

BACKGROUND

Epi-transcriptome regulation through post-transcriptional RNA modifications is essential for all RNA types. Precise recognition of RNA modifications is critical for understanding their functions and regulatory mechanisms. However, wet experimental methods are often costly and time-consuming, limiting their wide range of applications. Therefore, recent research has focused on developing computational methods, particularly deep learning (DL). Bidirectional long short-term memory (BiLSTM), convolutional neural network (CNN), and the transformer have demonstrated achievements in modification site prediction. However, BiLSTM cannot achieve parallel computation, leading to a long training time, CNN cannot learn the dependencies of the long distance of the sequence, and the Transformer lacks information interaction with sequences at different scales. This insight underscores the necessity for continued research and development in natural language processing (NLP) and DL to devise an enhanced prediction framework that can effectively address the challenges presented.

RESULTS

This study presents a multi-scale self- and cross-attention network (MSCAN) to identify the RNA methylation site using an NLP and DL way. Experiment results on twelve RNA modification sites (m6A, m1A, m5C, m5U, m6Am, m7G, Ψ, I, Am, Cm, Gm, and Um) reveal that the area under the receiver operating characteristic of MSCAN obtains respectively 98.34%, 85.41%, 97.29%, 96.74%, 99.04%, 79.94%, 76.22%, 65.69%, 92.92%, 92.03%, 95.77%, 89.66%, which is better than the state-of-the-art prediction model. This indicates that the model has strong generalization capabilities. Furthermore, MSCAN reveals a strong association among different types of RNA modifications from an experimental perspective. A user-friendly web server for predicting twelve widely occurring human RNA modification sites (m6A, m1A, m5C, m5U, m6Am, m7G, Ψ, I, Am, Cm, Gm, and Um) is available at http://47.242.23.141/MSCAN/index.php .

CONCLUSIONS

A predictor framework has been developed through binary classification to predict RNA methylation sites.

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.

Global hypermethylation of the N6-methyladenosine RNA modification associated with apple heterografting

Grafting can facilitate better scion performance and is widely used in plants. Numerous studies have studied the involvement of mRNAs, small RNAs, and epigenetic regulations in the grafting process. However, it remains unclear whether the mRNA N6-methyladenosine (m6A) modification participates in the apple (Malus x domestica Borkh.) grafting process. Here, we decoded the landscape of m6A modification profiles in 'Golden delicious' (a cultivar, Gd) and Malus prunifolia 'Fupingqiuzi' (a unique rootstock with resistance to environmental stresses, Mp), as well as their heterografted and self-grafted plants. Interestingly, global hypermethylation of m6A occurred in both heterografted scion and rootstock compared with their self-grafting controls. Gene Ontology (GO) term enrichment analysis showed that grafting-induced differentially m6A-modified genes were mainly involved in RNA processing, epigenetic regulation, stress response, and development. Differentially m6A-modified genes harboring expression alterations were mainly involved in various stress responses and fatty acid metabolism. Furthermore, grafting-induced mobile mRNAs with m6A and gene expression alterations mainly participated in ABA synthesis and transport (e.g. carotenoid cleavage dioxygenase 1 [CCD1] and ATP-binding cassette G22 [ABCG22]) and abiotic and biotic stress responses, which might contribute to the better performance of heterografted plants. Additionally, the DNA methylome analysis also demonstrated the DNA methylation alterations during grafting. Downregulated expression of m6A methyltransferase gene MdMTA (ortholog of METTL3) in apples induced the global m6A hypomethylation and distinctly activated the expression level of DNA demethylase gene MdROS1 (REPRESSOR OF SILENCING 1) showing the possible association between m6A and 5mC methylation in apples. Our results reveal the m6A modification profiles in the apple grafting process and enhance our understanding of the m6A regulatory mechanism in plant biological processes.

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.

Fast and Efficient Design of Deep Neural Networks for Predicting N7-Methylguanosine Sites Using autoBioSeqpy

N⁷-Methylguanosine (m⁷G) is a crucial post-transcriptional RNA modification that plays a pivotal role in regulating gene expression. Accurately identifying m⁷G sites is a fundamental step in understanding the biological functions and regulatory mechanisms associated with this modification. While whole-genome sequencing is the gold standard for RNA modification site detection, it is a time-consuming, expensive, and intricate process. Recently, computational approaches, especially deep learning (DL) techniques, have gained popularity in achieving this objective. Convolutional neural networks and recurrent neural networks are examples of DL algorithms that have emerged as versatile tools for modeling biological sequence data. However, developing an efficient network architecture with superior performance remains a challenging task, requiring significant expertise, time, and effort. To address this, we previously introduced a tool called autoBioSeqpy, which streamlines the design and implementation of DL networks for biological sequence classification. In this study, we utilized autoBioSeqpy to develop, train, evaluate, and fine-tune sequence-level DL models for predicting m⁷G sites. We provided detailed descriptions of these models, along with a step-by-step guide on their execution. The same methodology can be applied to other systems dealing with similar biological questions. The benchmark data and code utilized in this study can be accessed for free at http://github.com/jingry/autoBioSeeqpy/tree/2.0/examples/m7G.

Concepts and methods for transcriptome-wide prediction of chemical messenger RNA modifications with machine learning

The expanding field of epitranscriptomics might rival the epigenome in the diversity of biological processes impacted. In recent years, the development of new high-throughput experimental and computational techniques has been a key driving force in discovering the properties of RNA modifications. Machine learning applications, such as for classification, clustering or de novo identification, have been critical in these advances. Nonetheless, various challenges remain before the full potential of machine learning for epitranscriptomics can be leveraged. In this review, we provide a comprehensive survey of machine learning methods to detect RNA modifications using diverse input data sources. We describe strategies to train and test machine learning methods and to encode and interpret features that are relevant for epitranscriptomics. Finally, we identify some of the current challenges and open questions about RNA modification analysis, including the ambiguity in predicting RNA modifications in transcript isoforms or in single nucleotides, or the lack of complete ground truth sets to test RNA modifications. We believe this review will inspire and benefit the rapidly developing field of epitranscriptomics in addressing the current limitations through the effective use of machine learning.

BERT2OME: Prediction of 2'-O-Methylation Modifications From RNA Sequence by Transformer Architecture Based on BERT

Recent work on language models has resulted in state-of-the-art performance on various language tasks. Among these, Bidirectional Encoder Representations from Transformers (BERT) has focused on contextualizing word embeddings to extract context and semantics of the words. On the other hand, post-transcriptional 2'-O-methylation (Nm) RNA modification is important in various cellular tasks and related to a number of diseases. The existing high-throughput experimental techniques take longer time to detect these modifications, and costly in exploring these functional processes. Here, to deeply understand the associated biological processes faster, we come up with an efficient method Bert2Ome to infer 2'-O-methylation RNA modification sites from RNA sequences. Bert2Ome combines BERT-based model with convolutional neural networks (CNN) to infer the relationship between the modification sites and RNA sequence content. Unlike the methods proposed so far, Bert2Ome assumes each given RNA sequence as a text and focuses on improving the modification prediction performance by integrating the pretrained deep learning-based language model BERT. Additionally, our transformer-based approach could infer modification sites across multiple species. According to 5-fold cross-validation, human and mouse accuracies were 99.15% and 94.35% respectively. Similarly, ROC AUC scores were 0.99, 0.94 for the same species. Detailed results show that Bert2Ome reduces the time consumed in biological experiments and outperforms the existing approaches across different datasets and species over multiple metrics. Additionally, deep learning approaches such as 2D CNNs are more promising in learning BERT attributes than more conventional machine learning methods.

Tool and techniques study to plant microbiome current understanding and future needs: an overview

Microorganisms are present in the universe and they play role in beneficial and harmful to human life, society, and environments. Plant microbiome is a broad term in which microbes are present in the rhizo, phyllo, or endophytic region and play several beneficial and harmful roles with the plant. To know of these microorganisms, it is essential to be able to isolate purification and identify them quickly under laboratory conditions. So, to improve the microbial study, several tools and techniques such as microscopy, rRNA, or rDNA sequencing, fingerprinting, probing, clone libraries, chips, and metagenomics have been developed. The major benefits of these techniques are the identification of microbial community through direct analysis as well as it can apply in situ. Without tools and techniques, we cannot understand the roles of microbiomes. This review explains the tools and their roles in the understanding of microbiomes and their ecological diversity in environments.

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.

Primary sequence-assisted prediction of m6A RNA methylation sites from Oxford nanopore direct RNA sequencing data

Traditional epitranscriptome profiling approach relies on specific antibodies or chemical treatments to capture modified RNA molecules and then applies high throughput sequencing to identify their transcriptomic locations. However, due to the lack of suitable or high-quality antibodies, only a small proportion of the 170 documented RNA modifications were profiled with those approaches. Direct sequencing of native RNA molecules using Oxford Nanopore Technologies (ONT) enabled straight inspection of RNA modifications and offered a promising alternative solution. N6-methyladenosine (m6A) is known to cause characteristic changes and increased base call errors of ONT signals compared with non-modified adenosines, based on which, the m6A sites can be identified directly from ONT signals. Meanwhile, a number of studies have shown that it is possible to predict m6A sites from RNA primary sequences. Using the m6A sites revealed by Illumina technology as benchmark, we showed that, the accuracy of ONT-based m6A site prediction can be further increased by integrating additional information from the primary sequences of RNA (AUROC of 0.918), compared with using ONT signals only (AUROC 0.878 using Base call error features, and 0.804 using Tombo features), providing a new perspective for more reliable mining of the relatively noisy ONT signals.

Construction and Validation of a m7G-Related Gene-Based Prognostic Model for Gastric Cancer

Background

Gastric cancer (GC) is one of the most common malignant tumors of the digestive system. Chinese cases of GC account for about 40% of the global rate, with approximately 1.66 million people succumbing to the disease each year. Despite the progress made in the treatment of GC, most patients are diagnosed at an advanced stage due to the lack of obvious clinical symptoms in the early stages of GC, and their prognosis is still very poor. The m7G modification is one of the most common forms of base modification in post-transcriptional regulation, and it is widely distributed in the 5′ cap region of tRNA, rRNA, and eukaryotic mRNA.

Methods

RNA sequencing data of GC were downloaded from The Cancer Genome Atlas. The differentially expressed m7G-related genes in normal and tumour tissues were determined, and the expression and prognostic value of m7G-related genes were systematically analysed. We then built models using the selected m7G-related genes with the help of machine learning methods.The model was then validated for prognostic value by combining the receiver operating characteristic curve (ROC) and forest plots. The model was then validated on an external dataset. Finally, quantitative real-time PCR (qPCR) was performed to detect gene expression levels in clinical gastric cancer and paraneoplastic tissue.

Results

The model is able to determine the prognosis of GC samples quantitatively and accurately. The ROC analysis of model has an AUC of 0.761 and 0.714 for the 3-year overall survival (OS) in the training and validation sets, respectively. We determined a correlation between risk scores and immune cell infiltration and concluded that immune cell infiltration affects the prognosis of GC patients. NUDT10, METTL1, NUDT4, GEMIN5, EIF4E1B, and DCPS were identified as prognostic hub genes and potential therapeutic agents were identified based on these genes.

Conclusion

The m7G-related gene-based prognostic model showed good prognostic discrimination. Understanding how m7G modification affect the infiltration of the tumor microenvironment (TME) cells will enable us to better understand the TME’s anti-tumor immune response, and hopefully guide more effective immunotherapy methods.

Role of main RNA modifications in cancer: N6-methyladenosine, 5-methylcytosine, and pseudouridine

Cancer is one of the major diseases threatening human life and health worldwide. Epigenetic modification refers to heritable changes in the genetic material without any changes in the nucleic acid sequence and results in heritable phenotypic changes. Epigenetic modifications regulate many biological processes, such as growth, aging, and various diseases, including cancer. With the advancement of next-generation sequencing technology, the role of RNA modifications in cancer progression has become increasingly prominent and is a hot spot in scientific research. This review studied several common RNA modifications, such as N⁶-methyladenosine, 5-methylcytosine, and pseudouridine. The deposition and roles of these modifications in coding and noncoding RNAs are summarized in detail. Based on the RNA modification background, this review summarized the expression, function, and underlying molecular mechanism of these modifications and their regulators in cancer and further discussed the role of some existing small-molecule inhibitors. More in-depth studies on RNA modification and cancer are needed to broaden the understanding of epigenetics and cancer diagnosis, treatment, and prognosis.

m5CRegpred: Epitranscriptome Target Prediction of 5-Methylcytosine (m5C) Regulators Based on Sequencing Features

5-methylcytosine (m5C) is a common post-transcriptional modification observed in a variety of RNAs. m5C has been demonstrated to be important in a variety of biological processes, including RNA structural stability and metabolism. Driven by the importance of m5C modification, many projects focused on the m5C sites prediction were reported before. To better understand the upstream and downstream regulation of m5C, we present a bioinformatics framework, m5CRegpred, to predict the substrate of m5C writer NSUN2 and m5C readers YBX1 and ALYREF for the first time. After features comparison, window lengths selection and algorism comparison on the mature mRNA model, our model achieved AUROC scores 0.869, 0.724 and 0.889 for NSUN2, YBX1 and ALYREF, respectively in an independent test. Our work suggests the substrate of m5C regulators can be distinguished and may help the research of m5C regulators in a special condition, such as substrates prediction of hyper- or hypo-expressed m5C regulators in human disease.

m6A-mediated modulation coupled with transcriptional regulation shapes long noncoding RNA repertoire of the cGAS-STING signaling

The cGAS-STING signaling plays pivotal roles not only in host antiviral defense but also in various noninfectious contexts. Compared with protein-coding genes, much less was known about long noncoding RNAs involved in this pathway. Here, we performed an integrative study to elucidate the lncRNA repertoire and the mechanisms modulating lncRNA’s expression following cGAS-STING signaling activation. We uncovered a reliable set of 672 lncRNAs closely linked to cGAS-STING signaling activation (cs-lncRNA), which might be associated with type-I interferon response and infection-related phenotypes. The ChIP-seq analysis demonstrated that cs-lncRNA was strongly regulated at the transcriptional level. We further found N6-methyladenosine (m⁶A) regulatory machinery was indispensable for establishing cs-lncRNA repertoire via modulating m⁶A modification on cs-lncRNA transcripts and promoting the expression of signaling transduction key components, including IFNAR1. Loss of IFNAR1 led to the dysregulation of cs-lncRNAs resembled that of loss of an essential subunit of m⁶A writer METTL14. We also found m⁶A system affected transcriptional machinery to modulate cs-lncRNAs by targeting multiple crucial transcription factors. Inhibiting an m⁶A modification regulated transcription factor, EZH2, markedly enhanced the expression pattern of cs-lncRNAs. Taken together, our results uncovered the composition of the cs-lncRNAs and revealed m⁶A-mediated modulation coupled with transcriptional regulation significantly shaped cs-lncRNA repertoire.

RNA modification writers influence tumor microenvironment in gastric cancer and prospects of targeted drug therapy

Background: RNA adenosine modifications are crucial for regulating RNA levels. N6-methyladenosine (m6A), N1-methyladenosine (m1A), adenosine-to-inosine RNA editing, and alternative polyadenylation (APA) are four major RNA modification types. Methods: We evaluated the altered mRNA expression profiles of 27 RNA modification enzymes and compared the differences in tumor microenvironment (TME) and clinical prognosis between two RNA modification patterns using unsupervised clustering. Then, we constructed a scoring system, WM_score, and quantified the RNA modifications in patients of gastric cancer (GC), associating WM_score with TME, clinical outcomes, and effectiveness of targeted therapies. Results: RNA adenosine modifications strongly correlated with TME and could predict the degree of TME cell infiltration, genetic variation, and clinical prognosis. Two modification patterns were identified according to high and low WM_scores. Tumors in the WM_score-high subgroup were closely linked with survival advantage, CD4[Formula: see text] T-cell infiltration, high tumor mutation burden, and cell cycle signaling pathways, whereas those in the WM_score-low subgroup showed strong infiltration of inflammatory cells and poor survival. Regarding the immunotherapy response, a high WM_score showed a significant correlation with PD-L1 expression, predicting the effect of PD-L1 blockade therapy. Conclusion: The WM_scoring system could facilitate scoring and prediction of GC prognosis.

Epitranscriptome in Ischemic Cardiovascular Disease: Potential Target for Therapies

The global risk of cardiovascular disease, including ischemic disease such as stroke, remains high, and cardiovascular disease is the cause of one-third of all deaths worldwide. The main subjacent cause, atherosclerosis, is not fully understood. To improve early diagnosis and therapeutic strategies, it is crucial to unveil the key molecular mechanisms that lead to atherosclerosis development. The field of epitranscriptomics is blossoming and quickly advancing in fields like cancer research, nevertheless, poorly understood in the context of cardiovascular disease. Epitranscriptomic modifications are shown to regulate the metabolism and function of RNA molecules, which are important for cell functions such as cell proliferation, a key aspect in atherogenesis. As such, epitranscriptomic regulatory mechanisms can serve as novel checkpoints in gene expression during disease development. In this review, we describe examples of the latest research investigating epitranscriptomic modifications, in particular A-to-I editing and the covalent modification N⁶-methyladenosine and their regulatory proteins, in the context of cardiovascular disease. We additionally discuss the potential of these mechanisms as therapeutic targets and novel treatment options.

5-Methylcytosine profiles in mouse transcriptomes suggest the randomness of m5C formation catalyzed by RNA methyltransferase

OBJECTIVE

5-Methylcytosine (m⁵C) is a type of chemical modification on the nucleotides and is widespread in both DNA and RNA. Although the DNA m⁵C has been extensively studied over the past years, the distribution and biological function of RNA m⁵C still remain to be elucidated. Here, I explored the profiles of RNA m⁵C in four mouse tissues by applying a RNA cytosine methylation data analysis tool to public mouse RNA m⁵C data.

RESULTS

I found that the methylation rates of cytosine were the same with the averages of methylation level at single-nucleotide level. Furthermore, I gave a mathematical formula to describe the observed relationship and analyzed it deeply. The sufficient necessary condition for the given formula suggests that the methylation levels at most m⁵C sites are the same in four mouse tissues. Therefore, I proposed a hypothesis that the m⁵C formation catalyzed by RNA methyltransferase is random and with the same probability at most m⁵C sites, which is the methylation rate of cytosine. My hypothesis can be used to explain the observed profiles of RNA m⁵C in four mouse tissues and will be benefit to future studies of the distribution and biological function of RNA m⁵C in mammals.

Profiling of N6-Methyladenosine (m6A) Modification Landscape in Response to Drought Stress in Apple (Malus prunifolia (Willd.) Borkh)

Drought stress is a significant environmental factor limiting crop growth worldwide. Malus prunifolia is an important apple species endemic to China and is used for apple cultivars and rootstocks with great drought tolerance. N⁶-methyladenosine (m⁶A) is a common epigenetic modification on messenger RNAs (mRNAs) in eukaryotes which is critical for various biological processes. However, there are no reports on m⁶A methylation in apple response to drought stress. Here, we assessed the m⁶A landscape of M. prunifolia seedlings in response to drought and analyzed the association between m⁶A modification and transcript expression. In total, we found 19,783 and 19,609 significant m⁶A peaks in the control and drought treatment groups, respectively, and discovered a UGUAH (H: A/U/C) motif. In M. prunifolia, under both control and drought conditions, peaks were highly enriched in the 3' untranslated region (UTR) and coding sequence (CDS). Among 4204 significant differential m⁶A peaks in drought-treated M. prunifolia compared to control-treated M. prunifolia, 4158 genes with m⁶A modification were identified. Interestingly, a large number of hypermethylated peaks (4069) were stimulated by drought treatment compared to hypomethylation. Among the hypermethylated peak-related genes, 972 and 1238 differentially expressed genes (DEGs) were up- and down-regulated in response to drought, respectively. Gene ontology (GO) analyses of differential m⁶A-modified genes revealed that GO slims related to RNA processing, epigenetic regulation, and stress tolerance were significantly enriched. The m⁶A modification landscape depicted in this study sheds light on the epigenetic regulation of M. prunifolia in response to drought stress and indicates new directions for the breeding of drought-tolerant apple trees.

HSM6AP: a high-precision predictor for the Homo sapiens N6-methyladenosine (m^6 A) based on multiple weights and feature stitching

Recent studies have shown that RNA methylation modification can affect RNA transcription, metabolism, splicing and stability. In addition, RNA methylation modification has been associated with cancer, obesity and other diseases. Based on information about human genome and machine learning, this paper discusses the effect of the fusion sequence and gene-level feature extraction on the accuracy of methylation site recognition. The significant limitation of existing computing tools was exposed by discovered of new features. (1) Most prediction models are based solely on sequence features and use SVM or random forest as classification methods. (2) Limited by the number of samples, the model may not achieve good performance. In order to establish a better prediction model for methylation sites, we must set specific weighting strategies for training samples and find more powerful and informative feature matrices to establish a comprehensive model. In this paper, we present HSM6AP, a high-precision predictor for the Homo sapiens N6-methyladenosine (m 6 A ) based on multiple weights and feature stitching. Compared with existing methods, HSM6AP samples were creatively weighted during training, and a wide range of features were explored. Max-Relevance-Max-Distance (MRMD) is employed for feature selection, and the feature matrix is generated by fusing a single feature. The extreme gradient boosting (XGBoost), an integrated machine learning algorithm based on decision tree, is used for model training and improves model performance through parameter adjustment. Two rigorous independent data sets demonstrated the superiority of HSM6AP in identifying methylation sites. HSM6AP is an advanced predictor that can be directly employed by users (especially non-professional users) to predict methylation sites. Users can access our related tools and data sets at the following website: http://lab.malab.cn/~lijing/HSM6AP.html The codes of our tool can be publicly accessible at https://github.com/lijingtju/HSm6AP.git.

Identification and validation of pivotal genes related to age-related meniscus degeneration based on gene expression profiling analysis and in vivo and in vitro models detection

BACKGROUND

The componential and structural change in the meniscus with aging would increase the tissue vulnerability of the meniscus, which would induce meniscus tearing. Here, we investigated the molecular mechanism of age-related meniscus degeneration with gene expression profiling analysis, and validate pivotal genes in vivo and in vitro models.

METHODS

The GSE45233 dataset, including 6 elderly meniscus samples and 6 younger meniscus samples, was downloaded from the Gene Expression Omnibus (GEO) database. To screen the differential expression of mRNAs and identify the miRNAs targeting hub genes, we completed a series of bioinformatics analyses, including functional and pathway enrichment, protein-protein interaction network, hub genes screening, and construction of a lncRNA-miRNA-mRNA network. Furthermore, crucial genes were examined in human senescent menisci, mouse senescent meniscus tissues and mouse meniscus cells stimulated by IL-1β.

RESULTS

In total, the most significant 4 hub genes (RRM2, AURKB, CDK1, and TIMP1) and 5 miRNAs (hsa-miR-6810-5p, hsa-miR-4676-5p, hsa-miR-6877-5p, hsa-miR-8085, and hsa-miR-6133) that regulated such 4 hub genes, were finally identified. Moreover, these hub genes were decreased in meniscus cells in vitro and meniscus tissues in vivo, which indicated that hub genes were related to meniscus senescence and could serve as potential biomarkers for age-related meniscus tearing.

CONCLUSIONS

In short, the integrated analysis of gene expression profile, co-expression network, and models detection identified pivotal genes, which elucidated the possible molecular basis underlying the senescence meniscus and also provided prognosis clues for early-onset age-related meniscus tearing.

Probiotics and Trained Immunity

The characteristics of innate immunity have recently been investigated in depth in several research articles, and original findings suggest that innate immunity also has a memory capacity, which has been named “trained immunity”. This notion has revolutionized our knowledge of the innate immune response. Thus, stimulation of trained immunity represents a therapeutic alternative that is worth exploring. In this context, probiotics, live microorganisms which when administered in adequate amounts confer a health benefit on the host, represent attractive candidates for the stimulation of trained immunity; however, although numerous studies have documented the beneficial proprieties of these microorganisms, their mechanisms of action are not yet fully understood. In this review, we propose to explore the putative connection between probiotics and stimulation of trained immunity.

Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data

Motivation

Increasing evidence suggests that post-transcriptional ribonucleic acid (RNA) modifications regulate essential biomolecular functions and are related to the pathogenesis of various diseases. Precise identification of RNA modification sites is essential for understanding the regulatory mechanisms of RNAs. To date, many computational approaches for predicting RNA modifications have been developed, most of which were based on strong supervision enabled by base-resolution epitranscriptome data. However, high-resolution data may not be available.

Results

We propose WeakRM, the first weakly supervised learning framework for predicting RNA modifications from low-resolution epitranscriptome datasets, such as those generated from acRIP-seq and hMeRIP-seq. Evaluations on three independent datasets (corresponding to three different RNA modification types and their respective sequencing technologies) demonstrated the effectiveness of our approach in predicting RNA modifications from low-resolution data. WeakRM outperformed state-of-the-art multi-instance learning methods for genomic sequences, such as WSCNN, which was originally designed for transcription factor binding site prediction. Additionally, our approach captured motifs that are consistent with existing knowledge, and visualization of the predicted modification-containing regions unveiled the potentials of detecting RNA modifications with improved resolution.

Availability implementation

The source code for the WeakRM algorithm, along with the datasets used, are freely accessible at: https://github.com/daiyun02211/WeakRM

Supplementary information

Supplementary data are available at Bioinformatics online.

ncRDense: A novel computational approach for classification of non-coding RNA family by deep learning

With the rapidly growing importance of biological research, non-coding RNAs (ncRNA) attract more attention in biology and bioinformatics. They play vital roles in biological processes such as transcription and translation. Classification of ncRNAs is essential to our understanding of disease mechanisms and treatment design. Many approaches to ncRNA classification have been developed, several of which use machine learning and deep learning. In this paper, we construct a novel deep learning-based architecture, ncRDense, to effectively classify and distinguish ncRNA families. In a comparative study, our model produces comparable results with existing state-of-the-art methods. Finally, we built a freely accessible web server for the ncRDense tool, which is available at http://nsclbio.jbnu.ac.kr/tools/ncRDense/.

Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications

Recent studies suggest that epi-transcriptome regulation via post-transcriptional RNA modifications is vital for all RNA types. Precise identification of RNA modification sites is essential for understanding the functions and regulatory mechanisms of RNAs. Here, we present MultiRM, a method for the integrated prediction and interpretation of post-transcriptional RNA modifications from RNA sequences. Built upon an attention-based multi-label deep learning framework, MultiRM not only simultaneously predicts the putative sites of twelve widely occurring transcriptome modifications (m6A, m1A, m5C, m5U, m6Am, m7G, Ψ, I, Am, Cm, Gm, and Um), but also returns the key sequence contents that contribute most to the positive predictions. Importantly, our model revealed a strong association among different types of RNA modifications from the perspective of their associated sequence contexts. Our work provides a solution for detecting multiple RNA modifications, enabling an integrated analysis of these RNA modifications, and gaining a better understanding of sequence-based RNA modification mechanisms.

Recent advances in functional annotation and prediction of the epitranscriptome

RNA modifications, in particular N⁶-methyladenosine (m⁶A), participate in every stages of RNA metabolism and play diverse roles in essential biological processes and disease pathogenesis. Thanks to the advances in sequencing technology, tens of thousands of RNA modification sites can be identified in a typical high-throughput experiment; however, it remains a major challenge to decipher the functional relevance of these sites, such as, affecting alternative splicing, regulation circuit in essential biological processes or association to diseases. As the focus of RNA epigenetics gradually shifts from site discovery to functional studies, we review here recent progress in functional annotation and prediction of RNA modification sites from a bioinformatics perspective. The review covers naïve annotation with associated biological events, e.g., single nucleotide polymorphism (SNP), RNA binding protein (RBP) and alternative splicing, prediction of key sites and their regulatory functions, inference of disease association, and mining the diagnosis and prognosis value of RNA modification regulators. We further discussed the limitations of existing approaches and some future perspectives.

Status of Bioinformatics Education in South Asia: Past and Present

Bioinformatics education has been a hot topic in South Asia, and the interest in this education peaks with the start of the 21^st century. The governments of South Asian countries had a systematic effort for bioinformatics. They developed the infrastructures to provide maximum facility to the scientific community to gain maximum output in this field. This article renders bioinformatics, measures, and its importance of implementation in South Asia with proper ways of improving bioinformatics education flaws. It also addresses the problems faced in South Asia and proposes some recommendations regarding bioinformatics education. The information regarding bioinformatics education and institutes was collected from different existing research papers, databases, and surveys. The information was then confirmed by visiting each institution's website, while problems and solutions displayed in the article are mostly in line with South Asian bioinformatics conferences and institutions' objectives. Among South Asian countries, India and Pakistan have developed infrastructure and education regarding bioinformatics rapidly as compared to other countries, whereas Bangladesh, Sri Lanka, and Nepal are still in a progressing phase in this field. To advance in a different sector, the bioinformatics industry has to be revolutionized, and it will contribute to strengthening the pharmaceutical, agricultural, and molecular sectors in South Asia. To advance in bioinformatics, universities' infrastructure needs to be on a par with the current international standards, which will produce well-trained professionals with skills in multiple fields like biotechnology, mathematics, statistics, and computer science. The bioinformatics industry has revolutionized and strengthened the pharmaceutical, agricultural, and molecular sectors in South Asia, and it will serve as the standard of education increases in the South Asian countries. A framework for developing a centralized database is suggested after the literature review to collect and store the information on the current status of South Asian bioinformatics education. This will be named as the South Asian Bioinformatics Education Database (SABE). This will provide comprehensive information regarding the bioinformatics in South Asian countries by the country name, the experts of this field, and the university name to explore the top-ranked outputs relevant to queries.

Transcriptomic Analysis of the Mechanisms for Alleviating Psoriatic Dermatitis Using Taodan Granules in an Imiquimod-Induced Psoriasis-like Mouse Model

Taodan granules (TDGs) are clinically efficacious for treating psoriasis, buttheir specific mechanisms of action are unclear. In this study, we determined the concentrations of tanshinone IIA and curcumol using high-performance liquid chromatography (HPLC) to establish quality control parameters for assessing the mechanism of TDGs in treating psoriasis. Thereafter, a mouse model of psoriasis was treated with TDGs. TDGs attenuated imiquimod-induced typical erythema, scales, and thickening of the back and ear lesions in the psoriatic mouse model. Furthermore, PCNA and Ki67-positive cells were reduced in the epidermis of psoriatic lesions following TDG treatment. Finally, the sequencing results were verified using a multitude of methods, and the mechanism of action of TDGs against psoriasis was found to be via the upregulation of metabolic signaling pathways such as the Gly-Ser-Thr axis, the downregulation of immune and inflammatory pathways, and the decrease in Rac2 and Arhgdib concentrations. Overall, this study clarified the mechanism of TDG treatment for psoriasis and provided evidence for its clinical application.