RNA m5C Research Articles

Folate regulates RNA m5C modification and translation in neural stem cells

BackgroundFolate is an essential B-group vitamin and a key methyl donor with important biological functions including DNA methylation regulation. Normal neurodevelopment and physiology are sensitive to the cellular folate levels. Either deficiency or excess of folate may lead to neurological disorders. Recently, folate has been linked to tRNA cytosine-5 methylation (m5C) and translation in mammalian mitochondria. However, the influence of folate intake on neuronal mRNA m5C modification and translation remains largely unknown. Here, we provide transcriptome-wide landscapes of m5C modification in poly(A)-enriched RNAs together with mRNA transcription and translation profiles for mouse neural stem cells (NSCs) cultured in three different concentrations of folate.ResultsNSCs cultured in three different concentrations of folate showed distinct mRNA methylation profiles. Despite uncovering only a few differentially expressed genes, hundreds of differentially translated genes were identified in NSCs with folate deficiency or supplementation. The differentially translated genes induced by low folate are associated with cytoplasmic translation and mitochondrial function, while the differentially translated genes induced by high folate are associated with increased neural stem cell proliferation. Interestingly, compared to total mRNAs, polysome mRNAs contained high levels of m5C. Furthermore, an integrative analysis indicated a transcript-specific relationship between RNA m5C methylation and mRNA translation efficiency.ConclusionsAltogether, our study reports a transcriptome-wide influence of folate on mRNA m5C methylation and translation in NSCs and reveals a potential link between mRNA m5C methylation and mRNA translation.

Emerging Role of RNA m5C Modification in Cardiovascular Diseases.

Epitranscriptomics is the emerging field of research that comprises the study of epigenetics changes in RNAs. Progressing development in the field of epigenetics has helped to manage and comprehend human diseases. RNA methylation regulates all aspects of RNA functions, which are involved in the pathogenesis of human diseases. Interestingly, RNA m5C methylation is significantly linked to various types of human disease, including cardiovascular diseases (CVD). The m5C methylation is controlled by m5C regulatory proteins, which act as methyltransferase, demethyltransferase, and RNA-binding protein. Dysregulated expression in m5C regulatory proteins is significantly associated with cardiovascular disease, and these regulatory proteins have crucial roles in biological and cellular functions. This review is mainly focused on the role of RNA m5C modification in CVD and mitochondrial dysfunction. Thus, m5C will contribute to discovering the new diagnostic marker and therapeutic target for CVD.

RNA m5C regulator-mediated modification patterns and the cross-talk between tumor microenvironment infiltration in gastric cancer.

The effect of immunotherapy strategy has been affirmed in the treatment of various tumors. Nevertheless, the latent role of RNA 5-methylcytosine (m5C) modification in gastric cancer (GC) tumor microenvironment (TME) cell infiltration is still unclear. We systematically explore the m5C modification patterns of 2,122 GC patients from GEO and TCGA databases by 16 m5C regulators and related these patterns to TME characteristics. LASSO Cox regression was employed to construct the m5Cscore based on the expression of regulators and DEGs, which was used to evaluate the prognosis. All the GC patients were divided into three m5C modification clusters with distinct gene expression characteristics and TME patterns. GSVA, ssGSEA, and TME cell infiltration analysis showed that m5C clusters A, B, and C were classified as immune-desert, immune-inflamed, and immune-excluded phenotype, respectively. The m5Cscore system based on the expression of eight genes could effectively predict the prognosis of individual GC patients, with AUC 0.766. Patients with a lower m5Cscore were characterized by the activation of immunity and experienced significantly longer PFS and OS. Our study demonstrated the non-negligible role of m5C modification in the development of TME complexity and inhomogeneity. Assessing the m5C modification pattern for individual GC patients will help recognize the infiltration characterization and guide more effective immunotherapy treatment.

5-methylcytosine (m5C) RNA modification controls the innate immune response to virus infection by regulating type I interferons

5-methylcytosine (m5C) is one of the most prevalent modifications of RNA, playing important roles in RNA metabolism, nuclear export, and translation. However, the potential role of RNA m5C methylation in innate immunity remains elusive. Here, we show that depletion of NSUN2, an m5C methyltransferase, significantly inhibits the replication and gene expression of a wide range of RNA and DNA viruses. Notably, we found that this antiviral effect is largely driven by an enhanced type I interferon (IFN) response. The antiviral signaling pathway is dependent on the cytosolic RNA sensor RIG-I but not MDA5. Transcriptome-wide mapping of m5C following NSUN2 depletion in human A549 cells revealed a marked reduction in the m5C methylation of several abundant noncoding RNAs (ncRNAs). However, m5C methylation of viral RNA was not noticeably altered by NSUN2 depletion. In NSUN2-depleted cells, the host RNA polymerase (Pol) III transcribed ncRNAs, in particular RPPH1 and 7SL RNAs, were substantially up-regulated, leading to an increase of unshielded 7SL RNA in cytoplasm, which served as a direct ligand for the RIG-I-mediated IFN response. In NSUN2-depleted cells, inhibition of Pol III transcription or silencing of RPPH1 and 7SL RNA dampened IFN signaling, partially rescuing viral replication and gene expression. Finally, depletion of NSUN2 in an ex vivo human lung model and a mouse model inhibits viral replication and reduces pathogenesis, which is accompanied by enhanced type I IFN responses. Collectively, our data demonstrate that RNA m5C methylation controls antiviral innate immunity through modulating the m5C methylome of ncRNAs and their expression.

Reactivity-dependent profiling of RNA 5-methylcytidine dioxygenases

Epitranscriptomic RNA modifications can regulate fundamental biological processes, but we lack approaches to map modification sites and probe writer enzymes. Here we present a chemoproteomic strategy to characterize RNA 5-methylcytidine (m5C) dioxygenase enzymes in their native context based upon metabolic labeling and activity-based crosslinking with 5-ethynylcytidine (5-EC). We profile m5C dioxygenases in human cells including ALKBH1 and TET2 and show that ALKBH1 is the major hm5C- and f5C-forming enzyme in RNA. Further, we map ALKBH1 modification sites transcriptome-wide using 5-EC-iCLIP and ARP-based sequencing to identify ALKBH1-dependent m5C oxidation in a variety of tRNAs and mRNAs and analyze ALKBH1 substrate specificity in vitro. We also apply targeted pyridine borane-mediated sequencing to measure f5C sites on select tRNA. Finally, we show that f5C at the wobble position of tRNA-Leu-CAA plays a role in decoding Leu codons under stress. Our work provides powerful chemical approaches for studying RNA m5C dioxygenases and mapping oxidative m5C modifications and reveals the existence of novel epitranscriptomic pathways for regulating RNA function.

Neuronal Depolarization Induced RNA m5C Methylation Changes in Mouse Cortical Neurons.

Simple SummaryWhen neurons are activated, two waves of gene expression changes occur to achieve neuronal adaptation to their environment. Currently, little is known about the changes in post-transcriptional modifications of neuronal transcripts in response to environmental stimuli. This study aims to investigate the dynamics of m5C modification on neuronal mRNAs upon depolarization using potassium chloride. We found that neuronal mRNAs carrying m5C modifications are enriched for genes with important neuronal functions. The numbers of m5C sites and methylated transcripts increased at 2 h after neuronal depolarization and slightly dropped at 6 h. Interestingly, for differentially methylated genes, a negative correlation between the levels of gene expression and RNA methylation is more prevalent. We anticipate our findings may shed new lights on post-transcriptional regulation in activated neurons, and the transcriptomic plus epi-transcriptomic datasets generated in this study would provide a valuable resource to the scientific community.Neuronal activity is accomplished via substantial changes in gene expression, which may be accompanied by post-transcriptional modifications including RNA cytosine-5 methylation (m5C). Despite several reports on the transcriptome profiling of activated neurons, the dynamics of neuronal mRNA m5C modification in response to environmental stimuli has not been explored. Here, we provide transcriptome-wide maps of m5C modification, together with gene expression profiles, for mouse cortical neurons at 0 h, 2 h, and 6 h upon membrane depolarization. Thousands of differentially expressed genes (DEGs) were identified during the neuronal depolarization process. In stimulated neurons, the majority of early response genes were found to serve as expression regulators of late response genes, which are involved in signaling pathways and diverse synaptic functions. With RNA bisulfite sequencing data, a union set of 439 m5C sites was identified with high confidence, and approximately 30% of them were shared by neurons at all three time points. Interestingly, over 41% of the m5C sites showed increased methylation upon neuronal activation and were enriched in transcripts coding for proteins with synaptic functions. In addition, a modest negative correlation was observed between RNA expression and methylation. In summary, our study provided dynamic transcriptome-wide landscapes of RNA m5C methylation in neurons, and revealed that mRNA m5C methylation is associated with the regulation of gene expression.

NSUN2-mediated RNA m5C modification modulates uveal melanoma cell proliferation and migration

ABSTRACT RNA 5-methylcytosine (m5C) is a widespread post-transcriptional modification involved in diverse biological processes through controlling RNA metabolism. However, its roles in uveal melanoma (UM) remain unknown. Here, we describe the biological roles and regulatory mechanisms of RNA m5C in UM. Initially, we identified significantly elevated global RNA m5C levels in both UM cells and tissue specimens using ELISA assay and dot blot analysis. Meanwhile, NOP2/Sun RNA methyltransferase family member 2 (NSUN2) was upregulated in both types of these samples, whereas NSUN2 knockdown significantly decreased RNA m5C level. Such declines inhibited UM cell migration and suppressed cell proliferation through cell cycle G1 arrest. Furthermore, bioinformatic analyses, m5C-RIP-qPCR, and luciferase assay identified β-Catenin (CTNNB1) as a direct target of NSUN2-mediated m5C modification in UM cells. Additionally, overexpression of miR-124a in UM cells diminished NSUN2 expression levels indicating that it is an upstream regulator of this response. Our study suggests that NSUN2-mediated RNA m5C methylation provides a potential novel target to improve the therapeutic management of UM pathogenesis.

Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy

As one of the most prevalent post-transcriptional epigenetic modifications, N5-methylcytosine (m5C) plays an essential role in various cellular processes and disease pathogenesis. Therefore, it is important accurately identify m5C modifications in order to gain a deeper understanding of cellular processes and other possible functional mechanisms. Although a few computational methods have been proposed, their respective models have been developed using small training datasets. Hence, their practical application is quite limited in genome-wide detection. To overcome the existing limitations, we propose Deepm5C, a bioinformatics method for identifying RNA m5C sites throughout the human genome. To develop Deepm5C, we constructed a novel benchmarking dataset and investigated a mixture of three conventional feature-encoding algorithms and a feature derived from word-embedding approaches. Afterward, four variants of deep-learning classifiers and four commonly used conventional classifiers were employed and trained with the four encodings, ultimately obtaining 32 baseline models. A stacking strategy is effectively utilized by integrating the predicted output of the optimal baseline models and trained with a one-dimensional (1D) convolutional neural network. As a result, the Deepm5C predictor achieved excellent performance during cross-validation with a Matthews correlation coefficient and an accuracy of 0.697 and 0.855, respectively. The corresponding metrics during the independent test were 0.691 and 0.852, respectively. Overall, Deepm5C achieved a more accurate and stable performance than the baseline models and significantly outperformed the existing predictors, demonstrating the effectiveness of our proposed hybrid framework. Furthermore, Deepm5C is expected to assist community-wide efforts in identifying putative m5Cs and to formulate the novel testable biological hypothesis.

Systematic evaluation of parameters in RNA bisulfite sequencing data generation and analysis.

The presence of 5-methylcytosine (m5C) in RNA molecules has been known for decades and its importance in regulating RNA metabolism has gradually become appreciated. Despite recent advances made in the functional and mechanistic understanding of RNA m5C modifications, the detection and quantification of methylated RNA remains a challenge. In this study, we compared four library construction procedures for RNA bisulfite sequencing and implemented an analytical pipeline to assess the key parameters in the process of m5C calling. We found that RNA fragmentation after bisulfite conversion increased the yield significantly, and an additional high temperature treatment improved bisulfite conversion efficiency especially for sequence reads mapped to the mitochondrial transcriptome. Using Unique Molecular Identifiers (UMIs), we observed that PCR favors the amplification of unmethylated templates. The low sequencing quality of bisulfite-converted bases is a major contributor to the methylation artifacts. In addition, we found that mitochondrial transcripts are frequently resistant to bisulfite conversion and no p-m5C sites with high confidence could be identified on mitochondrial mRNAs. Taken together, this study reveals the various sources of artifacts in RNA bisulfite sequencing data and provides an improved experimental procedure together with analytical methodology.

M5Cpred-XS: A New Method for Predicting RNA m5C Sites Based on XGBoost and SHAP.

As one of the most important post-transcriptional modifications of RNA, 5-cytosine-methylation (m5C) is reported to closely relate to many chemical reactions and biological functions in cells. Recently, several computational methods have been proposed for identifying m5C sites. However, the accuracy and efficiency are still not satisfactory. In this study, we proposed a new method, m5Cpred-XS, for predicting m5C sites of H. sapiens, M. musculus, and A. thaliana. First, the powerful SHAP method was used to select the optimal feature subset from seven different kinds of sequence-based features. Second, different machine learning algorithms were used to train the models. The results of five-fold cross-validation indicate that the model based on XGBoost achieved the highest prediction accuracy. Finally, our model was compared with other state-of-the-art models, which indicates that m5Cpred-XS is superior to other methods. Moreover, we deployed the model on a web server that can be accessed through http://m5cpred-xs.zhulab.org.cn/, and m5Cpred-XS is expected to be a useful tool for studying m5C sites.

RNA 5-Methylcytosine Regulators Contribute to Metabolism Heterogeneity and Predict Prognosis in Ovarian Cancer.

5-Methylcytosine (m5C) is an abundant and highly conserved modification in RNAs. The dysregulation of RNA m5C methylation has been reported in cancers, but the regulatory network in ovarian cancer of RNA m5C methylation-related genes and its implication in metabolic regulation remain largely unexplored. In this study, RNA-sequencing data and clinical information of 374 ovarian cancer patients were downloaded from The Cancer Genome Atlas database, and a total of 14 RNA m5C regulators were included. Through unsupervised consensus clustering, two clusters with different m5C modification patterns were identified with distinct survivals. According to enrichment analyses, glycosaminoglycan and collagen metabolism–related pathways were specifically activated in cluster 1, whereas fatty acid metabolism–related pathways were enriched in cluster 2, which had better overall survival (OS). Besides the metabolism heterogeneity, the higher sensitivity to platinum and paclitaxel in cluster 2 can further explain the improved OS. Ultimately, a least absolute shrinkage and selection operator prediction model formed by ALYREF, NOP2, and TET2 toward OS was constructed. In conclusion, distinct m5C modification pattern exhibited metabolism heterogeneity, different chemotherapy sensitivity, and consequently survival difference, providing evidence for risk stratification.

Predicting RNA 5-Methylcytosine Sites by Using Essential Sequence Features and Distributions.

Methylation is one of the most common and considerable modifications in biological systems mediated by multiple enzymes. Recent studies have shown that methylation has been widely identified in different RNA molecules. RNA methylation modifications have various kinds, such as 5-methylcytosine (m5C). However, for individual methylation sites, their functions still remain to be elucidated. Testing of all methylation sites relies heavily on high-throughput sequencing technology, which is expensive and labor consuming. Thus, computational prediction approaches could serve as a substitute. In this study, multiple machine learning models were used to predict possible RNA m5C sites on the basis of mRNA sequences in human and mouse. Each site was represented by several features derived from k-mers of an RNA subsequence containing such site as center. The powerful max-relevance and min-redundancy (mRMR) feature selection method was employed to analyse these features. The outcome feature list was fed into incremental feature selection method, incorporating four classification algorithms, to build efficient models. Furthermore, the sites related to features used in the models were also investigated.

The RNA m5C Methyltransferase NSUN2 Promotes Progression of Hepatocellular Carcinoma by Enhancing PKM2-Mediated Glycolysis

The RNA m5C Methyltransferase NSUN2 Promotes Progression of Hepatocellular Carcinoma by Enhancing PKM2-Mediated Glycolysis

RNA 5-methylcytosine regulates YBX2-dependent liquid-liquid phase separation

5-Methylcytosine (m5C) is one of the most prevalent internal modifications of messenger RNA (mRNA) in higher eukaryotes. Here we report that Y box protein 2 (YBX2) serves as a novel mammalian m5C binding protein to undergo liquid-liquid phase separation (LLPS) both in vivo and in vitro, and this YBX2-dependent LLPS is enhanced by m5C marked RNA. Furthermore, the crystal structure assay revealed that W100, as a distinct m5C binding site of YBX2, is critical in mediating YBX2 phase separation. Our study resolved the relationship between RNA m5C and phase separation, providing a clue for a new regulatory layer of epigenetics.

5-Methylcytosine RNA Methyltransferases-Related Long Non-coding RNA to Develop and Validate Biochemical Recurrence Signature in Prostate Cancer.

The effects of 5-methylcytosine in RNA (m5C) in various human cancers have been increasingly studied recently; however, the m5C regulator signature in prostate cancer (PCa) has not been well established yet. In this study, we identified and characterized a series of m5C-related long non-coding RNAs (lncRNAs) in PCa. Univariate Cox regression analysis and least absolute shrinkage and selector operation (LASSO) regression analysis were implemented to construct a m5C-related lncRNA prognostic signature. Consequently, a prognostic m5C-lnc model was established, including 17 lncRNAs: MAFG-AS1, AC012510.1, AC012065.3, AL117332.1, AC132192.2, AP001160.2, AC129510.1, AC084018.2, UBXN10-AS1, AC138956.2, ZNF32-AS2, AC017100.1, AC004943.2, SP2-AS1, Z93930.2, AP001486.2, and LINC01135. The high m5C-lnc score calculated by the model significantly relates to poor biochemical recurrence (BCR)-free survival (p < 0.0001). Receiver operating characteristic (ROC) curves and a decision curve analysis (DCA) further validated the accuracy of the prognostic model. Subsequently, a predictive nomogram combining the prognostic model with clinical features was created, and it exhibited promising predictive efficacy for BCR risk stratification. Next, the competing endogenous RNA (ceRNA) network and lncRNA–protein interaction network were established to explore the potential functions of these 17 lncRNAs mechanically. In addition, functional enrichment analysis revealed that these lncRNAs are involved in many cellular metabolic pathways. Lastly, M AFG-AS1 was selected for experimental validation; it was upregulated in PCa and probably promoted PCa proliferation and invasion in vitro. These results offer some insights into the m5C's effects on PCa and reveal a predictive model with the potential clinical value to improve the prognosis of patients with PCa.

Target-Specific Profiling of RNA m5C Methylation Level Using Amplicon Sequencing.

Mapping the position and quantifying the level of 5-methylcytosine (m5C) as a modification in different types of cellular RNA is an important objective in the field of epitranscriptomics. Bisulfite conversion has long been the gold standard for the detection of m5C in DNA, but it can also be applied to RNA. Here, we detail methods for bisulfite treatment of RNA, locus-specific PCR amplification, and detection of candidate sites by sequencing on the Illumina MiSeq platform.

Staem5: A novel computational approach for accurate prediction of m5C site

5-Methylcytosine (m5C) is an important post-transcriptional modification that has been extensively found in multiple types of RNAs. Many studies have shown that m5C plays vital roles in many biological functions, such as RNA structure stability and metabolism. Computational approaches act as an efficient way to identify m5C sites from high-throughput RNA sequence data and help interpret the functional mechanism of this important modification. This study proposed a novel species-specific computational approach, Staem5, to accurately predict RNA m5C sites in Mus musculus and Arabidopsis thaliana. Staem5 was developed by employing feature fusion tactics to leverage informatic sequence profiles, and a stacking ensemble learning framework combined five popular machine learning algorithms. Extensive benchmarking tests demonstrated that Staem5 outperformed state-of-the-art approaches in both cross-validation and independent tests. We provide the source code of Staem5, which is publicly available at https://github.com/Cxd-626/Staem5.git.

TRDMT1 participates in the DNA damage repair of granulosa cells in premature ovarian failure.

The molecular mechanisms underlying premature ovarian failure, which seriously impacts the physical and psychological health of patients, are not fully understood. Here, we present the role of TRDMT1 in reactive oxygen species-induced granulosa cells death, which is considered an important cause of premature ovarian failure. We found that reactive oxygen species were increased in a H2O2 dose-dependent manner and accompanied by the nuclear shuttling of TRDMT1, increased DNA damage and increased apoptosis of granulosa cells. In addition, reactive oxygen species-induced granulosa cells apoptosis could be prevented by the antioxidant N-acetylcysteine or overexpression of TRDMT1. Furthermore, DNA repair following reactive oxygen species induction was severely impaired/enhanced in TRDMT1 mutants, which exhibited reduced/increased RNA m5C methylation activity. Altogether, our results reveal a novel role of TRDMT1 in the regulation of premature ovarian failure through the repair of reactive oxygen species-triggered DNA damage in granulosa cells and provide an improved understanding of the mechanisms underlying granulosa cells apoptosis, which could potentially be useful for future clinical treatments of premature ovarian failure.

The role of RNA m5C modification in cancer metastasis.

Epigenetic modification plays a crucial regulatory role in the biological processes of eukaryotic cells. The recent characterization of DNA and RNA methylation is still ongoing. Tumor metastasis has long been an unconquerable feature in the fight against cancer. As an inevitable component of the epigenetic regulatory network, 5-methylcytosine is associated with multifarious cellular processes and systemic diseases, including cell migration and cancer metastasis. Recently, gratifying progress has been achieved in determining the molecular interactions between m5C writers (DNMTs and NSUNs), demethylases (TETs), readers (YTHDF2, ALYREF and YBX1) and RNAs. However, the underlying mechanism of RNA m5C methylation in cell mobility and metastasis remains unclear. The functions of m5C writers and readers are believed to regulate gene expression at the post-transcription level and are involved in cellular metabolism and movement. In this review, we emphatically summarize the recent updates on m5C components and related regulatory networks. The content will be focused on writers and readers of the RNA m5C modification and potential mechanisms in diseases. We will discuss relevant upstream and downstream interacting molecules and their associations with cell migration and metastasis.

M5CPred-SVM: a novel method for predicting m5C sites of RNA

BackgroundAs one of the most common post-transcriptional modifications (PTCM) in RNA, 5-cytosine-methylation plays important roles in many biological functions such as RNA metabolism and cell fate decision. Through accurate identification of 5-methylcytosine (m5C) sites on RNA, researchers can better understand the exact role of 5-cytosine-methylation in these biological functions. In recent years, computational methods of predicting m5C sites have attracted lots of interests because of its efficiency and low-cost. However, both the accuracy and efficiency of these methods are not satisfactory yet and need further improvement.ResultsIn this work, we have developed a new computational method, m5CPred-SVM, to identify m5C sites in three species, H. sapiens, M. musculus and A. thaliana. To build this model, we first collected benchmark datasets following three recently published methods. Then, six types of sequence-based features were generated based on RNA segments and the sequential forward feature selection strategy was used to obtain the optimal feature subset. After that, the performance of models based on different learning algorithms were compared, and the model based on the support vector machine provided the highest prediction accuracy. Finally, our proposed method, m5CPred-SVM was compared with several existing methods, and the result showed that m5CPred-SVM offered substantially higher prediction accuracy than previously published methods. It is expected that our method, m5CPred-SVM, can become a useful tool for accurate identification of m5C sites.ConclusionIn this study, by introducing position-specific propensity related features, we built a new model, m5CPred-SVM, to predict RNA m5C sites of three different species. The result shows that our model outperformed the existing state-of-art models. Our model is available for users through a web server at https://zhulab.ahu.edu.cn/m5CPred-SVM.