RNA 5-methylcytosine Research Articles

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.

M5C-Atlas: a comprehensive database for decoding and annotating the 5-methylcytosine (m5C) epitranscriptome.

5-Methylcytosine (m5C) is one of the most prevalent covalent modifications on RNA. It is known to regulate a broad variety of RNA functions, including nuclear export, RNA stability and translation. Here, we present m5C-Atlas, a database for comprehensive collection and annotation of RNA 5-methylcytosine. The database contains 166 540 m5C sites in 13 species identified from 5 base-resolution epitranscriptome profiling technologies. Moreover, condition-specific methylation levels are quantified from 351 RNA bisulfite sequencing samples gathered from 22 different studies via an integrative pipeline. The database also presents several novel features, such as the evolutionary conservation of a m5C locus, its association with SNPs, and any relevance to RNA secondary structure. All m5C-atlas data are accessible through a user-friendly interface, in which the m5C epitranscriptomes can be freely explored, shared, and annotated with putative post-transcriptional mechanisms (e.g. RBP intermolecular interaction with RNA, microRNA interaction and splicing sites). Together, these resources offer unprecedented opportunities for exploring m5C epitranscriptomes. The m5C-Atlas database is freely accessible at https://www.xjtlu.edu.cn/biologicalsciences/m5c-atlas.

Rapid Analysis and Prediction of Drug Resistance in Leukemia By Novel Nascent RNA-Driven Flow Imaging Technologies and Biomarkers

Background:Drug selectivity and resistance is a major obstacle to successful cancer therapy, including traditional chemotherapies (Vasan, N. et al. Nature 2019), immunotherapy (Hu-Lieskovan, S. et al. Future Oncol, 2021), and epigenetic therapy (Saliba, A.N. et al. Cancer Drug Resist,2021). However, no reliable technologies and biomarkers have been developed to analyze and predict drug resistance in clinical settings. Nascent RNAs composed of mostly non-coding RNAs undergo extensive modifications at co- and post-transcriptional levels. Nascent RNAs, and their associated modifications and modifying proteins (RMPs), such as RNA 5-methylcytosine (RNA:m 5C) and RNA cytosine methyltransferases (RCMTs), regulate almost all essential bioprocesses, including chromatin remodeling, DNA transcription, RNA processing/splicing and protein translation. Largely due to technology limitation, the role of nascent RNAs and RNA epigenetics remain largely unknown. Our previous publication first demonstrated distinct cell lineage-associated, RCMTs/BRD4-mediated, drug (azacitidine)-resistant transcriptionally active chromatin structures (TAC) at nascent (newly synthesized) RNAs in leukemia cells (Cheng, J.X. et al. Nat Commun. 2018). The goal of this study is to develop novel nascent RNA/TAC-driven technologies and biomarkers that enable us to rapidly analyze and predict drug resistance in clinical settings.Results: 1.Our experimental data demonstrated that TAC and RNA epigenetics regulate the resistance to venetoclax, a selective inhibitor of the anti-apoptotic protein BCL2, in leukemia cells. There is a significant, lineage-associated, increase in specific RCMTs, such as NSUN2 and NSUN1/NOP2, in venetoclax-resistant leukemia cells. Knockdown of NSUN2 and/or NSUN1 overcome venetoclax resistance in these leukemia cells.2.Our data also demonstrated distinct patterns of drug- and lineage-specific RNA synthesis dynamics in drug-sensitive vs. -resistant leukemia cells. In drug-sensitive leukemia cells, dinaciclib, a potent a potent, selective small molecule inhibitor of CDKs inhibiting CDK1, CDK2, CDK5 and CDK9 at nano-molar concentrations (Parry, D. et al. Mol.Cancer Ther, 2010), completely inhibit nascent RNA synthesis within 3-5 min, while venetoclax and azacitidine partially inhibit nascent RNA synthesis within 15 min and 240 min, respectively. Such drug-induced inhibition of nascent RNA synthesis is completely independent of apoptosis and program cell death. In contrast, no drug-induced inhibition of nascent RNA synthesis is observed in drug-resistant leukemia cells.3.Based on our data, we have developed a novel multifactorial system that targets the unique drug- and lineage-specific features of nascent RNA synthesis, TAC and RNA epigenetics for rapid analysis and prediction of drug resistance in clinical settings.Conclusion:Our data demonstrated distinct drug- and lineage-specific patterns of RNA synthesis in drug-sensitive vs. -resistant leukemia cells, which enabled us to develop novel nascent RNA/TAC-drive technologies and biomarkers for rapid analysis and prediction of anticancer drug resistance. DisclosuresNo relevant conflicts of interest to declare.

M5C Regulator-Mediated Methylation Modification Patterns and Tumor Microenvironment Infiltration Characterization in Papillary Thyroid Carcinoma.

Recently, immune response modulation at the epigenetic level is illustrated in studies, but the possible function of RNA 5-methylcytosine (m5C) modification in cell infiltration within the tumor microenvironment (TME) is still unclear. Three different m5C modification patterns were identified, and high differentiation degree was observed in the cell infiltration features within TME under the above three identified patterns. A low m5C-score, which was reflected in the activated immunity, predicted the relatively favorable prognostic outcome. A small amount of effective immune infiltration was seen in the high m5C-score subtype, indicating the dismal patient survival. Our study constructed a diagnostic model using the 10 signature genes highly related to the m5C-score, discovered that the model exhibited high diagnostic accuracy for PTC, and screened out five potential drugs for PTC based on this m5C-score model. m5C modification exerts an important part in forming the TME complexity and diversity. It is valuable to evaluate the m5C modification patterns in single tumors, so as to enhance our understanding towards the infiltration characterization in TME.

NSUN6, an RNA methyltransferase of 5-mC controls glioblastoma response to temozolomide (TMZ) via NELFB and RPS6KB2 interaction

ABSTRACT Nop2/Sun RNA methyltransferase (NSUN6) is an RNA 5-methyl cytosine (5mC) transferase with little information known of its function in cancer and response to cancer therapy. Here, we show that NSUN6 methylates both large and small RNA in glioblastoma and controls glioblastoma response to temozolomide with or without influence of the MGMT promoter status, with high NSUN6 expression conferring survival benefit to glioblastoma patients and in other cancers. Mechanistically, our results show that NSUN6 controls response to TMZ therapy via 5mC-mediated regulation of NELFB and RPS6BK2. Taken together, we present evidence that show that NSUN6-mediated 5mC deposition regulates transcriptional pause by accumulation of NELFB and the general transcription factor complexes (POLR2A, TBP, TFIIA, and TFIIE) on the preinitiation complex at the TATA binding site to control translation machinery in glioblastoma response to alkylating agents. Our findings open a new frontier into controlling of transcriptional regulation by RNA methyltransferase and 5mC.

NSUN2-mediated RNA 5-methylcytosine promotes esophageal squamous cell carcinoma progression via LIN28B-dependent GRB2 mRNA stabilization

5-Methylcytosine (m5C) is a posttranscriptional RNA modification participating in many critical bioprocesses, but its functions in human cancer remain unclear. Here, by detecting the transcriptome-wide m5C profiling in esophageal squamous cell carcinoma (ESCC), we showed increased m5C methylation in ESCC tumors due to the overexpressed m5C methyltransferase NSUN2. Aberrant expression of NSUN2 was positively regulated by E2F Transcription Factor 1 (E2F1). High NSUN2 levels predicted poor survival of ESCC patients. Moreover, silencing NSUN2 suppressed ESCC tumorigenesis and progression in Nsun2 knockout mouse models. Mechanistically, NSUN2 induced m5C modification of growth factor receptor-bound protein 2 (GRB2) and stabilized its mRNA, which was mediated by a novel m5C mediator, protein lin-28 homolog B (LIN28B). Elevated GRB2 levels increased the activation of PI3K/AKT and ERK/MAPK signalling. These results demonstrate that NSUN2 enhances the initiation and progression of ESCC via m5C-LIN28B dependent stabilization of GRB2 transcript, providing a promising epitranscriptomic-targeted therapeutic strategy for ESCC.

Richard Jackson (1940–2020) ‐ A towering presence in translation

Richard Jackson (1940–2020) ‐ A towering presence in translation

Dynamic transcriptomic m5 C and its regulatory role in RNA processing.

RNA 5-methylcytosine (m5 C) is a prevalent RNA modification in multiple RNA species, including messenger RNAs (mRNAs), transfer RNAs (tRNAs), ribosomal RNAs (rRNAs), and noncoding RNAs (ncRNAs), and broadly distributed from archaea, prokaryotes to eukaryotes. The multiple detecting techniques of m5 C have been developed, such as m5 C-RIP-seq, miCLIP-seq, AZA-IP-seq, RNA-BisSeq, TAWO-seq, and Nanopore sequencing. These high-throughput techniques, combined with corresponding analysis pipeline, provide a precise m5 C landscape contributing to the deciphering of its biological functions. The m5 C modification is distributed along with mRNA and enriched around 5'UTR and 3'UTR, and conserved in tRNAs and rRNAs. It is dynamically regulated by its related enzymes, including methyltransferases (NSUN, DNMT, and TRDMT family members), demethylases (TET families and ALKBH1), and binding proteins (ALYREF and YBX1). So far, accumulative studies have revealed that m5 C participates in a variety of RNA metabolism, including mRNA export, RNA stability, and translation. Depletion of m5 C modification in the organism could cause dysfunction of mitochondria, drawback of stress response, frustration of gametogenesis and embryogenesis, abnormality of neuro and brain development, and has been implicated in cell migration and tumorigenesis. In this review, we provide a comprehensive summary of dynamic regulatory elements of RNA m5 C, including methyltransferases (writers), demethylases (erasers), and binding proteins (readers). We also summarized the related detecting technologies and biological functions of the RNA 5-methylcytosine, and provided future perspectives in m5 C research. This article is categorized under: RNA Processing > RNA Editing and Modification.

Quantitative and Single-Nucleotide Resolution Profiling of RNA 5-Methylcytosine.

RNA has coevolved with numerous posttranscriptional modifications to sculpt interactions with proteins and other molecules. One of these modifications is 5-methylcytosine (m5C) and mapping the position and quantifying the level in different types of cellular RNAs and tissues is an important objective in the field of epitranscriptomics. Both in plants and animals bisulfite conversion has long been the gold standard for detection of m5C in DNA but it can also be applied to RNA. Here, we detail methods for highly reproducible bisulfite treatment of RNA, efficient locus-specific PCR amplification, detection of candidate sites by sequencing on the Illumina MiSeq platform, and bioinformatic calling of non-converted sites.

RNA 5-Methylcytosine Controls Plant Development and Environmental Adaptation.

Despite increasing understanding of 5-methylcytosine (m5C) on RNAs in mammals, the function of m5C in plants remains obscure. A dynamic function of m5C in plant development and stress adaptation had been suggested, and Tang et al. have now uncovered a role for RNA m5C methyltransferase in rice adaptation to high temperature.

OsNSUN2-Mediated 5-Methylcytosine mRNA Modification Enhances Rice Adaptation to High Temperature.

Extreme weather events can cause heat stress that decreases crop production. Recent studies have demonstrated that protein degradation and rRNA homeostasis as well as transcription factors are involved in the thermoresponse in plants. However, how RNA modifications contribute to temperature stress response in plant remains largely unknown. Herein, we identified OsNSUN2 as an RNA 5-methylcytosine (m5C) methyltransferase in rice. osnsun2 mutant displayed severe temperature- and light-dependent lesion-mimic phenotypes and heat-stress hypersensitivity. Heat stress enhanced the OsNSUN2-dependent m5C modification of mRNAs involved in photosynthesis and detoxification systems, such as β-OsLCY, OsHO2, OsPAL1, and OsGLYI4, which increased protein synthesis. Furthermore, the photosystem of osnsun2 mutant was vulnerable to high ambient temperature and failed to undergo repair under tolerable heat stress. Thus, OsNSUN2 mutation reduced photosynthesis efficiency and accumulated excessive reactive oxygen species upon heat treatment. Our findings demonstrate an important mechanism of mRNA m5C-dependent heat acclimation in rice.

RNA Modifications in Cancer: Functions, Mechanisms, and Therapeutic Implications

Over 170 chemical modifications have been identified in protein-coding and noncoding RNAs and shown to exhibit broad impacts on gene expression. Dysregulation of RNA modifications caused by aberrant expression of or mutations in RNA modifiers aberrantly reprograms the epitranscriptome and skews global gene expression, which in turn leads to tumorigenesis and drug resistance. Here we review current knowledge of the functions and underlying mechanisms of aberrant RNA modifications in human cancers, particularly several common RNA modifications, including N6-methyladenosine (m6A), A-to-I editing, pseudouridine (ψ), 5-methylcytosine (m5C), 5-hydroxymethylcytosine (hm5C), N1-methyladenosine (m1A), and N4-acetylcytidine (ac4C), providing insights into therapeutic implications of targeting RNA modifications and the associated machineries for cancer therapy.

A Mini-review of the Computational Methods Used in Identifying RNA 5-Methylcytosine Sites.

RNA 5-methylcytosine (m5C) is one of the pillars of post-transcriptional modification (PTCM). A growing body of evidence suggests that m5C plays a vital role in RNA metabolism. Accurate localization of RNA m5C sites in tissue cells is the premise and basis for the in-depth understanding of the functions of m5C. However, the main experimental methods of detecting m5C sites are limited to varying degrees. Establishing a computational model to predict modification sites is an excellent complement to wet experiments for identifying m5C sites. In this review, we summarized some available m5C predictors and discussed the characteristics of these methods.

IRNA-m5C_NB: A Novel Predictor to Identify RNA 5-Methylcytosine Sites Based on the Naive Bayes Classifier

As one of the widespread RNA post-transcriptional modifications (PTCMs), 5-Methylcytosine (m5C) plays vital roles in better understanding of basic biological mechanisms and major disease treatments. In experiments, traditional high-throughput approaches to find m5C sites are usually expensive and laborious. Additionally, facing with a large number of RNA sequences, developing accurate computational methods to distinguish m5C and non-m5C sites is an efficient solution. Here we introduced a novel predictor, called iRNA-m5C_NB, to identify m5C sites in Home sapiens using Naive Bayes (NB) algorithm. In this method, unbalanced dataset Met935 is firstly analyzed using efficient hybrid-sampling strategy SMOTEEEN. Then top 57 features are selected by the ANOVA F-value from four kinds of well-performance feature extraction techniques, including Bi-profile Bayes (BPB), enhanced Nucleic Acid Composition (ENAC), electron-ion interaction pseudopotentials (EIIP) and mMGap_1. Based on the jackknife test, the evaluated recall for the unbalanced training dataset Met935 is up to 82.81% with MCC of 0.63. And for the independent dataset Test1157, the predictor still shows high recall of 70.06% and MCC of 0.34. It is the first m5C predictor constructed using the unbalanced dataset, and the recall scores are increased by 19.82% and 59.23% for jackknife and independent tests compared with the latest tool RNAm5CPred, respectively. We demonstrate that the proposed predictor iRNA-m5C_NB outperforms other state-of-art models, which hopes to be an efficient and reliable method to identify m5C sites.

YTHDF2 Binds to 5-Methylcytosine in RNA and Modulates the Maturation of Ribosomal RNA.

5-Methylcytosine is found in both DNA and RNA; although its functions in DNA are well established, the exact role of 5-methylcytidine (m5C) in RNA remains poorly defined. Here we identified, by employing a quantitative proteomics method, multiple candidate recognition proteins of m5C in RNA, including several YTH domain-containing family (YTHDF) proteins. We showed that YTHDF2 could bind directly to m5C in RNA, albeit at a lower affinity than that toward N6-methyladenosine (m6A) in RNA, and this binding involves Trp432, a conserved residue located in the hydrophobic pocket of YTHDF2 that is also required for m6A recognition. RNA bisulfite sequencing results revealed that, after CRISPR-Cas9-mediated knockout of the YTHDF2 gene, the majority of m5C sites in rRNA (rRNA) exhibited substantially augmented levels of methylation. Moreover, we found that YTHDF2 is involved in pre-rRNA processing in cells. Together, our data expanded the functions of the YTHDF2 protein in post-transcriptional regulations of RNA and provided novel insights into the functions of m5C in RNA biology.

Episo: quantitative estimation of RNA 5-methylcytosine at isoform level by high-throughput sequencing of RNA treated with bisulfite

MotivationRNA 5-methylcytosine (m5C) is a type of post-transcriptional modification that may be involved in numerous biological processes and tumorigenesis. RNA m5C can be profiled at single-nucleotide resolution by high-throughput sequencing of RNA treated with bisulfite (RNA-BisSeq). However, the exploration of transcriptome-wide profile and potential function of m5C in splicing remains to be elucidated due to lack of isoform level m5C quantification tool.ResultsWe developed a computational package to quantify Epitranscriptomal RNA m5C at the transcript isoform level (named Episo). Episo consists of three tools: mapper, quant and Bisulfitefq, for mapping, quantifying and simulating RNA-BisSeq data, respectively. The high accuracy of Episo was validated using an improved m5C-specific methylated RNA immunoprecipitation (meRIP) protocol, as well as a set of in silico experiments. By applying Episo to public human and mouse RNA-BisSeq data, we found that the RNA m5C is not evenly distributed among the transcript isoforms, implying the m5C may subject to be regulated at isoform level.Availability and implementationEpiso is released under the GNU GPLv3+ license. The resource code Episo is freely accessible from https://github.com/liujunfengtop/Episo (with Tophat/cufflink) and https://github.com/liujunfengtop/Episo/tree/master/Episo_Kallisto (with Kallisto).Supplementary information Supplementary data are available at Bioinformatics online.

RNAm5CPred: Prediction of RNA 5-Methylcytosine Sites Based on Three Different Kinds of Nucleotide Composition

5-methylcytosine (m5C) is one of the most common and abundant post-transcriptional modifications (PTCMs) in RNA. Recent studies showed that m5C plays important roles in many biological functions such as RNA metabolism and cell fate decision. Because most experimental methods that determine m5C sites across the transcriptome are time-consuming and expensive, it is urgent to develop accurate computational methods to identify m5C sites effectively. A benchmark dataset is important for developing and evaluating computational methods. In this work, we constructed four different datasets according to the data redundancy and imbalance. Based on these datasets, we generated three different kinds of features, i.e., KNFs (K-nucleotide frequencies), KSNPFs (K-spaced nucleotide pair frequencies), and pseDNC (pseudo-dinucleotide composition), and then used a support vector machine (SVM) to build our models. Based on the imbalanced and nonredundant dataset, Met935, we extensively studied the three kinds of features and determined an optimal combination of the features. Based on the feature combination, we built models on the three different datasets and compared them with state-of-the-art models. According to the predictive results of the stringent jackknife test, the models based on the three features, 4NF, 1SNPF, and pseDNC, are superior or comparable to other methods. To determine the best model between the models based on the imbalanced dataset Met935 and the balanced dataset Met240, we further evaluated the two models on an independent test set Test1157. Our results demonstrate that the model based on the balanced dataset Met240 achieved the highest recall (68.79%) and the highest Matthews correlation coefficient (MCC) (0.154). In addition, the model is also superior to other state-of-the-art methods according to the integrated parameter MCC on the independent test set. Thus, we selected the model based on Met240 as our final model, which was named RNAm5CPred. In addition, a web server for RNAm5CPred (http://zhulab.ahu.edu.cn/RNAm5CPred/) has been provided to facilitate experimental research.

IRNA-PseTNC: identification of RNA 5-methylcytosine sites using hybrid vector space of pseudo nucleotide composition

RNA 5-methylcytosine (m5C) sites perform a major role in numerous biological processes and commonly reported in both DNA and RNA cellular. The enzymatic mechanism and biological functions of m5C sites in DNA remain the focusing area of researchers for last few decades. Likewise, the investigators also targeted m5C sites in RNA due to its cellular functions, positioning and formation mechanism. Currently, several rudimentary roles of the m5C in RNA have been explored, but a lot of improvements are still under consideration. Initially, the identification of RNA methylcytosine sites was carried out via experimental methods, which were very hard, erroneous and time consuming owing to partial availability of recognized structures. Looking at the significance of m5C role in RNA, scientists have diverted their attention from structure to sequence-based prediction. In this regards, an intelligent computational model is proposed in order to identify m5C sites in RNA with high precision. Three RNA sequences formulation methods namely: pseudo dinucleotide composition,pseudo trinucleotide composition and pseudo tetra nucleotide composition are applied to extract variant and high profound numerical features. In a sequel, the vector spaces are fused to build a hybrid space in order to compensate the weakness of each other. Various learning hypotheses are examined to select the best operational engine, which can truly identify the pattern of the target class. The strength and generalization of the proposed model are measured using two different cross validation tests. The reported outcomes reveal that the proposed model achieved 3% better accuracy than that of the highest present approach in the literature so far.

RNA 5-Methylcytosine Facilitates the Maternal-to-Zygotic Transition by Preventing Maternal mRNA Decay.

The maternal-to-zygotic transition (MZT) is a conserved and fundamental process during which the maternal environment is converted to an environment of embryonic-driven development through dramatic reprogramming. However, how maternally supplied transcripts are dynamically regulated during MZT remains largely unknown. Herein, through genome-wide profiling of RNA 5-methylcytosine (m5C) modification in zebrafish early embryos, we found that m5C-modified maternal mRNAs display higher stability than non-m5C-modified mRNAs during MZT. We discovered that Y-box binding protein 1 (Ybx1) preferentially recognizes m5C-modified mRNAs through π-π interactions with a key residue, Trp45, in Ybx1's cold shock domain (CSD), which plays essential roles in maternal mRNA stability and early embryogenesis of zebrafish. Together with the mRNA stabilizer Pabpc1a, Ybx1 promotes the stability of its target mRNAs in an m5C-dependent manner. Our study demonstrates an unexpected mechanism of RNA m5C-regulated maternal mRNA stabilization during zebrafish MZT, highlighting the critical role of m5C mRNA modification in early development.

Deciphering the Epitranscriptomic Signatures in Cell Fate Determination and Development

Precise regulation of transcriptome modulates several vital aspects in an organism that includes gene expression, cellular activities and development, and its perturbation ensuing pathological conditions. Around 150 post-transcriptional modifications of RNA have been identified till date, which are evolutionarily conserved and likewise prevalent across RNA classes including messenger RNA (mRNA), transfer RNA (tRNA), ribosomal RNA (rRNA), and detected less frequently in small nuclear RNA (snRNA) and microRNAs (miRNA). Among the RNA modifications documented, N6-methyladenosine (m6A) is the best characterised till date. Also, N1-methyladenosine (m1A), 5-methylcytosine (m5C) and pseudouridine (Ψ) are some of the other prominent modifications detected in coding and non-coding RNAs. "Epitranscriptome", ensemble of these post-transcriptional RNA modifications, precisely coordinates gene expression and biological processes. Current literatures suggest the critical involvement of epitranscriptomics in several organisms during early development, contributing to cell fate specification and physiology. Indeed, epitranscriptomics similar to DNA epigenetics involves combinatorial dynamics provided by modified RNA molecules and associated protein complexes, which function as "writers", "erasers" and "readers" of these modifications. A novel code orchestrating gene expression during cell fate determination is generated by the coordinated effects of different classes of modified RNAs and its regulator proteins. In this review, we summarize the current knowhow on N6-methyladenosine (m6A), 5-methylcytosine (m5C) and pseudouridine (ψ) modifications in RNA, the associated regulator proteins and enumerate how the epitranscriptomic regulations are involved in cell fate determination.