Types Of RNA Modifications Research Articles

Regulations of m6A and other RNA modifications and their roles in cancer.

RNA modification is an essential component of the epitranscriptome, regulating RNA metabolism and cellular functions. Several types of RNA modifications have been identified to date; they include N6-methyladenosine (m6A), N1-methyladenosine (m1A), 5-methylcytosine (m5C), N7-methylguanosine (m7G), N6,2'-O-dimethyladenosine (m6Am), N4-acetylcytidine (ac4C), etc. RNA modifications, mediated by regulators including writers, erasers, and readers, are associated with carcinogenesis, tumor microenvironment, metabolic reprogramming, immunosuppression, immunotherapy, chemotherapy, etc. A novel perspective indicates that regulatory subunits and post-translational modifications (PTMs) are involved in the regulation of writer, eraser, and reader functions in mediating RNA modifications, tumorigenesis, and anticancer therapy. In this review, we summarize the advances made in the knowledge of different RNA modifications (especially m6A) and focus on RNA modification regulators with functions modulated by a series of factors in cancer, including regulatory subunits (proteins, noncoding RNA or peptides encoded by long noncoding RNA) and PTMs (acetylation, SUMOylation, lactylation, phosphorylation, etc.). We also delineate the relationship between RNA modification regulator functions and carcinogenesis or cancer progression. Additionally, inhibitors that target RNA modification regulators for anticancer therapy and their synergistic effect combined with immunotherapy or chemotherapy are discussed.

Transfer learning enables identification of multiple types of RNA modifications using nanopore direct RNA sequencing

Nanopore direct RNA sequencing (DRS) has emerged as a powerful tool for RNA modification identification. However, concurrently detecting multiple types of modifications in a single DRS sample remains a challenge. Here, we develop TandemMod, a transferable deep learning framework capable of detecting multiple types of RNA modifications in single DRS data. To train high-performance TandemMod models, we generate in vitro epitranscriptome datasets from cDNA libraries, containing thousands of transcripts labeled with various types of RNA modifications. We validate the performance of TandemMod on both in vitro transcripts and in vivo human cell lines, confirming its high accuracy for profiling m6A and m5C modification sites. Furthermore, we perform transfer learning for identifying other modifications such as m7G, Ψ, and inosine, significantly reducing training data size and running time without compromising performance. Finally, we apply TandemMod to identify 3 types of RNA modifications in rice grown in different environments, demonstrating its applicability across species and conditions. In summary, we provide a resource with ground-truth labels that can serve as benchmark datasets for nanopore-based modification identification methods, and TandemMod for identifying diverse RNA modifications using a single DRS sample.

Simultaneous Detection of Adenosine-to-Inosine Editing and N6-Methyladenosine at Identical RNA Sites through Deamination-Assisted Reverse Transcription Stalling.

Adenosine-to-inosine (A-to-I) editing and N6-methyladenosine (m6A) modifications are pivotal RNA modifications with widespread functional significance in physiological and pathological processes. Although significant effort has been dedicated to developing methodologies for identifying and quantifying these modifications, traditional approaches have often focused on each modification independently, neglecting the potential co-occurrence of A-to-I editing and m6A modifications at the same adenosine residues. This limitation has constrained our understanding of the intricate regulatory mechanisms governing RNA function and the interplay between different types of RNA modifications. To address this gap, we introduced an innovative technique called deamination-assisted reverse transcription stalling (DARTS), specifically designed for the simultaneous quantification of A-to-I editing and m6A at the same RNA sites. DARTS leverages the selective deamination activity of the engineered TadA-TadA8e protein, which converts adenosine residues to inosine, in combination with the unique property of Bst 2.0 DNA polymerase, which stalls when encountering inosine during reverse transcription. This approach enables the accurate quantification of A-to-I editing, m6A, and unmodified adenosine at identical RNA sites. The DARTS method is remarkable for its ability to directly quantify two distinct types of RNA modifications simultaneously, a capability that has remained largely unexplored in the field of RNA biology. By facilitating a comprehensive analysis of the co-occurrence and interaction between A-to-I editing and m6A modifications, DARTS opens new avenues for exploring the complex regulatory networks modulated by different RNA modifications.

The emerging significance of RNA 5-methylcytosine modification in human cancers

Abstract RNA methylation, the most prevalent type of RNA modification, encompasses more than 60 % of all known RNA modifications. With the advancement of methylation sequencing technologies, a diverse range of biological functions associated with RNA methylation in eukaryotes has been revealed. Specifically, 5-methylcytosine (m5C) modifications have been extensively observed in various RNA molecules, including messenger RNAs (mRNAs), ribosomal RNAs (rRNAs), transfer RNAs (tRNAs), and non-coding RNAs. These m5C modifications have been shown to impact critical RNA processes, such as mRNA translation, rRNA assembly, and tRNA stability. Notably, emerging evidence suggests that m5C modifications play significant roles in the initiation and progression of human cancers. However, a comprehensive understanding of the intricate m5C networks involved in human cancers is yet to be fully realized. In this comprehensive review, we provide an up-to-date summary of the roles and potential mechanisms underlying m5C modification in human cancers.

Genetic variants in m5C modification genes are associated with survival of patients with HBV-related hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with a high mortality rate. The 5-methylcytosine (m5C), a type of RNA modification, plays crucial regulatory roles in HCC carcinogenesis, metastasis, and prognosis. However, a few studies have investigated the effect of genetic variants in m5C modification genes on survival of patients with hepatitis B virus (HBV)-related HCC. In the present study, we evaluated associations between 144 SNPs in 15 m5C modification genes and overall survival (OS) in 866 patients with the HBV-related HCC. Expression quantitative trait loci (eQTL) analysis and differential expression analysis were conducted to investigate biological mechanisms. As a result, we identified that two SNPs (NSUN7 rs2437325 A > G and TRDMT1 rs34434809 G > C) were significantly associated with HBV-related HCC OS with adjusted allelic hazards ratios of 1.25 (95% confidence interval = 1.05-1.48 and P = 0.011) and 1.19 (1.02-1.38 and P = 0.027), respectively, with a trend of combined risk genotypes (Ptrend < 0.001). Moreover, the results of eQTL analyses showed that both NSUN7 rs2437325 G and TRDMT1 rs34434809 C alleles were associated with a reduced mRNA expression level in 208 normal liver tissues (P = 0.007 and P < 0.001, respectively). Taken together, genetic variants in the m5C modification genes may be potential prognostic biomarkers of HBV-related HCC after hepatectomy, likely through mediating the mRNA expression of corresponding genes.

TGF-β2-induced alterations of m6A methylation in hTERT RPE-1 cells

N6-methyladenosine (m6A) is a major type of RNA modification implicated in various pathophysiological processes. Transforming growth factor β2 (TGF-β2) induces epithelial-mesenchymal transition (EMT) in retinal pigmental epithelial (RPE) cells and promotes the progression of proliferative vitreoretinopathy (PVR). However, the role of m6A methylation in the EMT of human telomerase reverse transcriptase (hTERT) retinal pigmental epithelium (RPE)-1 cells has not been clarified. Here, we extracted RNA from RPE cells subjected to 0 or 20 ng/mL TGF-β2 for 72 h and identified differentially methylated genes (DMGs) by m6A-Seq and differentially expressed genes (DEGs) by RNA-Seq. We selected the genes related to EMT by conjoint m6A-Seq/RNA-Seq analysis and verified them by qRT-PCR. We then confirmed the function of m6A methylation in the EMT of RPE cells by knocking down the methyltransferase METTL3 and the m6A reading protein YTHDF1. Sequencing yielded 5814 DMGs and 1607 DEGs. Conjoint analysis selected 467 genes altered at the m6A and RNA levels that are closely associated with the EMT-related TGF-β, AGE-RAGE, PI3K-Akt, P53, and Wnt signaling pathways. We also identified ten core EMT genes ACTG2, BMP6, CDH2, LOXL2, SNAIL1, SPARC, BMP4, EMP3, FOXM1, and MYC. Their RNA levels were evaluated by qRT-PCR and were consistent with the sequencing results. We observed that METTL3 knockdown enhanced RPE cell migration and significantly upregulated the EMT markers N-cadherin (encoded by CDH2), fibronectin (FN), Snail family transcription repressor (SLUG), and vimentin. However, YTHDF1 knockdown had the opposite effects and decreased both cell migration and the N-cadherin, FN, and SLUG expression levels. The present study clarified TGF-β2-induced m6A- and RNA-level differences in RPE cells, indicated that m6A methylation might regulate EMT marker expression, and showed that m6A could regulate TGF-β2-induced EMT.

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

ABSTRACT 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.

FTO ameliorates doxorubicin-induced cardiotoxicity by inhibiting ferroptosis via P53–P21/Nrf2 activation in a HuR-dependent m6A manner

Doxorubicin (DOX)-induced cardiotoxicity seriously limits its clinical applicability, and no therapeutic interventions are available. Ferroptosis, an iron-dependent regulated cell death characterised by lipid peroxidation, plays a pivotal role in DOX-induced cardiotoxicity. N6-methyladenosine (m6A) methylation is the most frequent type of RNA modification and involved in DOX-induced ferroptosis, however, its underlying mechanism remains unclear. P21 was recently found to inhibit ferroptosis by interacting with Nrf2 and is regulated in a P53-dependent or independent manner, such as through m6A modification. In the present study, we investigated the mechanism underlying m6A modification in DOX-induced ferroptosis by focusing on P21. Our results show that fat mass and obesity–associated protein (FTO) down-regulation was associated with DOX-induced cardiotoxicity. FTO over-expression significantly improved cardiac function and cell viability in DOX-treated mouse hearts and H9C2 cells. FTO over-expression significantly inhibited DOX-induced ferroptosis, and the Fer-1 inhibition of ferroptosis significantly reduced DOX-induced cardiotoxicity. P21 was significantly upregulated by FTO and activated Nrf2, playing a crucial role in the anti-ferroptotic effect. FTO upregulated P21/Nrf2 in a P53-dependent manner by mediating the demethylation of P53 or in a P53-independent manner by mediating P21/Nrf2 directly. Human antigen R (HuR) is crucial for FTO-mediated regulation of ferroptosis and P53–P21/Nrf2. Notably, we also found that P21 inhibition in turn inhibited HuR and P53 expression, while HuR inhibition further inhibited FTO expression. RNA immunoprecipitation assay showed that HuR binds to the transcripts of FTO and itself. Collectively, FTO inhibited DOX-induced ferroptosis via P21/Nrf2 activation by mediating the m6A demethylation of P53 or P21/Nrf2 in a HuR-dependent manner and constituted a positive feedback loop with HuR and P53–P21. Our findings provide novel insight into key functional mechanisms associated with DOX-induced cardiotoxicity and elucidate a possible therapeutic approach.

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

BackgroundEpi-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.ResultsThis 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.ConclusionsA predictor framework has been developed through binary classification to predict RNA methylation sites.

MeRIP-Seq for Identifying Stress-Responsive Transcriptome-Wide m6A Profiles in Plants.

Recent advancements in detection and mapping methods have enabled researchers to uncover the biological importance of RNA chemical modifications, which play a vital role in post-transcriptional gene regulation. Although numerous types of RNA modifications have been identified in higher eukaryotes, only a few have been extensively studied for their biological functions. Of these, N6-methyladenosine (m6A) is the most prevalent and important mRNA modification that influences various aspects of RNA metabolism, including mRNA stability, degradation, splicing, alternative polyadenylation, export, and localization, as well as translation. Thus, they have implications for a variety of biological processes, including growth, development, and stress responses. The m6A deposition or removal on transcripts is dynamic and is altered in response to internal and external cues. Because this mark can alter gene expression under stress conditions, it is essential to identify the transcripts that can acquire or lose this epitranscriptomic mark upon exposure to stress conditions. Here we describe a step-by-step protocol for identifying stress-responsive transcriptome-wide m6A changes using RNA immunoprecipitation followed by high-throughput sequencing (MeRIP-seq).

Discovery of a PROTAC degrader for METTL3-METTL14 complex

N6-methyladenosine (m6A) methylation is the most abundant type of RNA modification that is mainly catalyzed by the METTL3-METTL14 methyltransferase complex. This complex has been linked to multiple cancers and is considered a promising therapeutic target for acute myeloid leukemia (AML). However, only a few METTL3 inhibitors targeting the catalytic activity were developed recently. Here, we present the discovery of WD6305 as the potent and selective proteolysis-targeting chimera (PROTAC) degrader of METTL3-METTL14 complex. WD6305 suppresses m6A modification and the proliferation of AML cells, and promotes apoptosis much more effectively than its parent inhibitor. WD6305 also affects a variety of signaling pathways related to the development and proliferation of AML. Collectively, our study reveals PROTAC degradation of METTL3-METTL14 complex as a potential anti-leukemic strategy and provides desirable chemical tool for further understanding METTL3-METTL14 protein functions.

BLAM6A-Merge: Leveraging Attention Mechanisms and Feature Fusion Strategies to Improve the Identification of RNA N6-methyladenosine Sites.

RNA N6-methyladenosine is a prevalent and abundant type of RNA modification that exerts significant influence on diverse biological processes. To date, numerous computational approaches have been developed for predicting methylation, with most of them ignoring the correlations of different encoding strategies and failing to explore the adaptability of various attention mechanisms for methylation identification. To solve the above issues, we proposed an innovative framework for predicting RNA m6A modification site, termed BLAM6A-Merge. Specifically, it utilized a multimodal feature fusion strategy to combine the classification results of four features and Blastn tool. Apart from this, different attention mechanisms were employed for extracting higher-level features on specific features after the screening process. Extensive experiments on 12 benchmarking datasets demonstrated that BLAM6A-Merge achieved superior performance (average AUC: 0.849 for the full transcript mode and 0.784 for the mature mRNA mode). Notably, the Blastn tool was employed for the first time in the identification of methylation sites. The data and code can be accessed at https://github.com/DoraemonXia/BLAM6A-Merge.

The m7G Reader NCBP2 Promotes Pancreatic Cancer Progression by Upregulating MAPK/ERK Signaling.

PDAC is one of the most common malignant tumors worldwide. The difficulty of early diagnosis and lack of effective treatment are the main reasons for its poor prognosis. Therefore, it is urgent to identify novel diagnostic and therapeutic targets for PDAC patients. The m7G methylation is a common type of RNA modification that plays a pivotal role in regulating tumor development. However, the correlation between m7G regulatory genes and PDAC progression remains unclear. By integrating gene expression and related clinical information of PDAC patients from TCGA and GEO cohorts, m7G binding protein NCBP2 was found to be highly expressed in PDAC patients. More importantly, PDAC patients with high NCBP2 expression had a worse prognosis. Stable NCBP2-knockdown and overexpression PDAC cell lines were constructed to further perform in-vitro and in-vivo experiments. NCBP2-knockdown significantly inhibited PDAC cell proliferation, while overexpression of NCBP2 dramatically promoted PDAC cell growth. Mechanistically, NCBP2 enhanced the translation of c-JUN, which in turn activated MEK/ERK signaling to promote PDAC progression. In conclusion, our study reveals that m7G reader NCBP2 promotes PDAC progression by activating MEK/ERK pathway, which could serve as a novel therapeutic target for PDAC patients.

RMBase v3.0: decode the landscape, mechanisms and functions of RNA modifications.

Although over 170 chemical modifications have been identified, their prevalence, mechanism and function remain largely unknown. To enable integrated analysis of diverse RNA modification profiles, we have developed RMBase v3.0 (http://bioinformaticsscience.cn/rmbase/), a comprehensive platform consisting of eight modules. These modules facilitate the exploration of transcriptome-wide landscape, biogenesis, interactome and functions of RNA modifications. By mining thousands of epitranscriptome datasets with novel pipelines, the 'RNA Modifications' module reveals the map of 73 RNA modifications of 62 species. the 'Genes' module allows to retrieve RNA modification profiles and clusters by gene and transcript. The 'Mechanisms' module explores 23382 enzyme-catalyzed or snoRNA-guided modified sites to elucidate their biogenesis mechanisms. The 'Co-localization' module systematically formulates potential correlations between 14 histone modifications and 6 RNA modifications in various cell-lines. The 'RMP' module investigates the differential expression profiles of 146 RNA-modifying proteins (RMPs) in 18 types of cancers. The 'Interactome' integrates the interactional relationships between 73 RNA modifications with RBP binding events, miRNA targets and SNPs. The 'Motif' illuminates the enriched motifs for 11 types of RNA modifications identified from epitranscriptome datasets. The 'Tools' introduces a novel web-based 'modGeneTool' for annotating modifications. Overall, RMBase v3.0 provides various resources and tools for studying RNA modifications.

M6A regulator-mediated methylation modification patterns and tumor microenvironment immune infiltration with prognostic analysis in esophageal cancer

Esophageal cancer is a highly malignant disease with poor prognosis. Despite recent advances in the study of esophageal cancer, there has been only limited improvement in the treatment and prognosis. N6-methyladenosine (m6A), a type of RNA modification, has been extensively investigated and is involved in many biological behaviors, including tumorigenesis and progression. Thus, more research on m6A modification may increase our understanding of esophageal cancer pathogenesis and provide potential targets. In our study, we integrated the public data of esophageal cancer from The Cancer Genome Atlas (TCGA) and Gene-Expression Omnibus (GEO) databases. Unsupervised clustering analysis was used to classify patients into different groups. Gene set variation analysis (GSVA) was performed in a nonparametric and unsupervised mode. We evaluated immune cell infiltration by single sample gene set enrichment analysis (ssGSEA). Differentially expressed genes (DEGs) among m6A clusters were identified using Empirical Bayesian approach. Both multivariate and univariate Cox regression models were used for prognostic analysis. We provided an overview of gene variation and expression of 23 m6A regulators in esophageal cancer, as well as their effects on survival. Based on the overall expression level of m6A regulators, patients were classified into three m6A clusters (A-C) with different immune cell infiltration abundance, gene expression signatures and prognosis. Among m6A clusters, we identified 206 DEGs, according to which patients were classified into 4 gene clusters (A-D). Quantitative m6A score was calculated for each patient based on those DEGs with significant impact on survival. The infiltration of all types of immune cells except type 2 T helper (Th2) cells were negatively correlated with m6A score. M6Acluster C exhibited the lowest m6A score, the most abundant immune cell infiltration, and the worst prognosis, suggesting an immune excluded phenotype. Consistently, gene cluster D with the lowest m6A score showed the worst prognosis. In short, patients with esophageal cancer showed different m6A modification patterns. Quantitative scoring indicated that patients with the lowest m6A score exhibited the most abundant immune cell infiltration and the poorest prognosis. This m6A scoring system is promising to assess m6A modification pattern, characterize immune infiltration and guide personalized treatment and prognostic prediction.

TransRNAm: Identifying Twelve Types of RNA Modifications by an Interpretable Multi-Label Deep Learning Model Based on Transformer.

Accurate identification of RNA modification sites is of great significance in understanding the functions and regulatory mechanisms of RNAs. Recent advances have shown great promise in applying computational methods based on deep learning for accurate prediction of RNA modifications. However, those methods generally predicted only a single type of RNA modification. In addition, such methods suffered from the scarcity of the interpretability for their predicted results. In this work, a new Transformer-based deep learning method was proposed to predict multiple RNA modifications simultaneously, referred to as TransRNAm. More specifically, TransRNAm employs Transformer to extract contextual feature and convolutional neural networks to further learn high-latent feature representations of RNA sequences relevant for RNA modifications. Importantly, by integrating the self-attention mechanism in Transformer with convolutional neural network, TransRNAm is capable of not only capturing the critical nucleotide sites that contribute significantly to RNA modification prediction, but also revealing the underlying association among different types of RNA modifications. Consequently, this work provided an accurate and interpretable predictor for multiple RNA modification prediction, which may contribute to uncovering the sequence-based forming mechanism of RNA modification sites.

The regulation of antiviral innate immunity through non-m6A RNA modifications.

The post-transcriptional RNA modifications impact the dynamic regulation of gene expression in diverse biological and physiological processes. Host RNA modifications play an indispensable role in regulating innate immune responses against virus infection in mammals. Meanwhile, the viral RNAs can be deposited with RNA modifications to interfere with the host immune responses. The N6-methyladenosine (m6A) has boosted the recent emergence of RNA epigenetics, due to its high abundance and a transcriptome-wide widespread distribution in mammalian cells, proven to impact antiviral innate immunity. However, the other types of RNA modifications are also involved in regulating antiviral responses, and the functional roles of these non-m6A RNA modifications have not been comprehensively summarized. In this Review, we conclude the regulatory roles of 2'-O-methylation (Nm), 5-methylcytidine (m5C), adenosine-inosine editing (A-to-I editing), pseudouridine (Ψ), N1-methyladenosine (m1A), N7-methylguanosine (m7G), N6,2'-O-dimethyladenosine (m6Am), and N4-acetylcytidine (ac4C) in antiviral innate immunity. We provide a systematic introduction to the biogenesis and functions of these non-m6A RNA modifications in viral RNA, host RNA, and during virus-host interactions, emphasizing the biological functions of RNA modification regulators in antiviral responses. Furthermore, we discussed the recent research progress in the development of antiviral drugs through non-m6A RNA modifications. Collectively, this Review conveys knowledge and inspiration to researchers in multiple disciplines, highlighting the challenges and future directions in RNA epitranscriptome, immunology, and virology.

Changes in m6A RNA methylation are associated with male sterility in wolfberry

BackgroundN6-methyladenosine (m6A) modification is the most abundant type of RNA modification in eukaryotic cells, playing pivotal roles in multiple plant growth and development processes. Yet the potential role of m6A in conferring the trait of male sterility in plants remains unknown.ResultsIn this study, we performed RNA-sequencing (RNA-Seq) and m6A-sequencing (m6A-Seq) of RNAs obtained from the anther tissue of two wolfberry lines: ‘Ningqi No.1’ (LB1) and its natural male sterile mutant ‘Ningqi No.5’ (LB5). Based on the newly assembled transcriptome, we established transcriptome-wide m6A maps for LB1 and LB5 at the single nucleus pollen stage. We found that the gene XLOC_021201, a homolog of m6A eraser-related gene ALKBH10 in Arabidopsis thaliana, was significantly differentially expressed between LB1 and LB5. We also identified 1642 and 563 m6A-modified genes with hypermethylated and hypomethylated patterns, respectively, in LB1 compared with LB5. We found the hypermethylated genes significantly enriched in biological processes related to energy metabolism and lipid metabolism, while hypomethylation genes were mainly linked to cell cycle process, gametophyte development, and reproductive process. Among these 2205 differentially m6A methylated genes, 13.74% (303 of 2205) were differentially expressed in LB1 vis-à-vis LB5.ConclusionsThis study constructs the first m6A transcriptome map of wolfberry and establishes an association between m6A and the trait of male sterility in wolfberry.

Prediction of multiple types of RNA modifications via biological language model.

It has been demonstrated that RNA modifications play essential roles in multiple biological processes. Accurate identification of RNA modifications in the transcriptome is critical for providing insights into the biological functions and mechanisms. Many tools have been developed for predicting RNA modifications at single-base resolution, which employ conventional feature engineering methods that focus on feature design and feature selection processes that require extensive biological expertise and may introduce redundant information. With the rapid development of artificial intelligence technologies, end-to-end methods are favorably received by researchers. Nevertheless, each well-trained model is only suitable for a specific RNA methylation modification type for nearly all of these approaches. In this study, we present MRM-BERT by feeding task-specific sequences into the powerful BERT (Bidirectional Encoder Representations from Transformers) model and implementing fine-tuning, which exhibits competitive performance to the state-of-the-art methods. MRM-BERT avoids repeated de novo training of the model and can predict multiple RNA modifications such as pseudouridine, m6A, m5C, and m1A in Mus musculus, Arabidopsis thaliana, and Saccharomyces cerevisiae. In addition, we analyse the attention heads to provide high attention regions for the prediction, and conduct saturated in silico mutagenesis of the input sequences to discover potential changes of RNA modifications, which can better assist researchers in their follow-up research. MRM-BERT is freely accessible at http://csbio.njust.edu.cn/bioinf/mrmbert/.

A-to-I RNA editing shows dramatic up-regulation in osteosarcoma and broadly regulates tumor-related genes by altering microRNA target regions.

A-to-I RNA editing is a prevalent type of RNA modification in animals. The dysregulation of RNA editing has led to multiple human cancers. However, the role of RNA editing has never been studied in osteosarcoma, a complex bone cancer with unknown molecular basis. We retrieved the RNA-sequencing data from 24 primary osteosarcoma patients and 3 healthy controls. We systematically profiled the RNA editomes in these samples and quantitatively identified reliable differential editing sites (DES) between osteosarcoma and normal samples. RNA editing efficiency is dramatically increased in osteosarcoma, presumably due to the significant up-regulation of editing enzymes ADAR1 and ADAR2. Up-regulated DES in osteosarcoma are enriched in 3'UTRs. Strikingly, such 3'UTR sites are further enriched in microRNA binding regions of gene EMP2 and other oncogenes, abolishing the microRNA suppression on target genes. Accordingly, the expression of these tumor-promoting genes is elevated in osteosarcoma. There might be an RNA editing-dependent pathway leading to osteosarcoma. We expanded our knowledge on the potential roles of RNA editing in oncogenesis. Based on these molecular features, our work is valuable for future prognosis and diagnosis of osteosarcoma.