m6A Sites Research Articles

Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation.

As the most pervasive epigenetic mark present on mRNA and lncRNA, N6-methyladenosine (m6A) RNA methylation regulates all stages of RNA life in various biological processes and disease mechanisms. Computational methods for deciphering RNA modification have achieved great success in recent years; nevertheless, their potential remains underexploited. One reason for this is that existing models usually consider only the sequence of transcripts, ignoring the various regions (or geography) of transcripts such as 3′UTR and intron, where the epigenetic mark forms and functions. Here, we developed three simple yet powerful encoding schemes for transcripts to capture the submolecular geographic information of RNA, which is largely independent from sequences. We show that m6A prediction models based on geographic information alone can achieve comparable performances to classic sequence-based methods. Importantly, geographic information substantially enhances the accuracy of sequence-based models, enables isoform- and tissue-specific prediction of m6A sites, and improves m6A signal detection from direct RNA sequencing data. The geographic encoding schemes we developed have exhibited strong interpretability, and are applicable to not only m6A but also N1-methyladenosine (m1A), and can serve as a general and effective complement to the widely used sequence encoding schemes in deep learning applications concerning RNA transcripts.

Integrative Analysis of N6-Methyladenosine-Related Enhancer RNAs Identifies Distinct Prognosis and Tumor Immune Micro-Environment Patterns in Head and Neck Squamous Cell Carcinoma.

Simple SummaryHead and neck squamous cell carcinoma (HNSCC) has high morbidity and mortality. The interaction between immune cells and tumor cells in the tumor micro-environment is an important factor affecting the tumor progression and prognosis of HNSCC patients. More biomarkers and targets need to be explored to improve patient outcomes. The m6A modification on enhancer RNAs (eRNAs) is associated with the signature of active enhancer, and the function of m6A driving eRNAs in tumor progression has not been reported. In this study, we screened and identified a risk model containing 5 m6A-related eRNA, which can better predict the survival and immunotherapy outcome of patients. The role of m6A-related eRNA in HNSCC cells was verified in vitro. We also combined the risk score and multiple clinical features to construct a nomogram for predicting OS of HNSCC patients, which provides an effective quantitative analysis tool for guiding the personalized precise treatment for patients.At present, the prognostic value of N6-methyladenosine (m6A)-related enhancer RNAs (eRNAs) for head and neck squamous cell carcinoma (HNSCC) still remains unclear. Our study aims to explore the prognostic value of m6A-related eRNAs in HNSCC patients and their potential significance in immune infiltration and immunotherapy. We constructed a 5 m6A-related eRNAs risk model from The Cancer Genome Atlas (TCGA) HNSCC dataset, using univariate and multivariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis. Based on the SRAMP website and in vitro experiments, it was verified that these 5 m6A-related eRNAs had m6A sites, the expression of which was regulated by corresponding m6A regulators. Moreover, we constructed a nomogram base on 5 m6A-related eRNAs and confirmed the consistency and robustness of an internal TCGA testing set. Further analysis found that the risk score was positively associated with low overall survival (OS), tumor cell metastasis, metabolic reprogramming, low immune surveillance, lower expression of immune-related genes, and higher expression of targeted genes. Finally, we verified that silencing MIR4435-2HG inhibited HNSCC cell migration and invasion. This study contributes to the understanding of the characteristics of m6A-related eRNAs in HNSCC and provides a reference for effective immunotherapy and targeted therapy.

Exercise training ameliorates myocardial phenotypes in heart failure with preserved ejection fraction by changing N6-methyladenosine modification in mice model.

Heart failure with preserved ejection fraction (HFpEF) shows complicated and not clearly defined etiology and pathogenesis. Although no pharmacotherapeutics have improved the survival rate in HFpEF, exercise training has become an efficient intervention to improve functional outcomes. Here, we investigated N6-methyladenosine (m6A) RNA methylation modification in a “two-hit” mouse model with HFpEF and HFpEF with exercise (HFpEF + EXT). The manner of m6A in HFpEF and HFpEF + EXT hearts was explored via m6A-specific methylated RNA immunoprecipitation followed by high-throughput and RNA sequencing methods. A total amount of 3992 novel m6A peaks were spotted in HFpEF + EXT, and 426 differently methylated sites, including 371 hypermethylated and 55 hypomethylated m6A sites, were singled out for further analysis (fold change >2, p < 0.05). According to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, unique m6A-modified transcripts in HFpEF + EXT were associated with apoptosis-related pathway and myocardial energy metabolism. HFpEF + EXT had higher total m6A levels and downregulated fat mass and obesity-related (FTO) protein levels. Overexpression of FTO cancels out the benefits of exercise in HFpEF + EXT mice by promoting myocyte apoptosis, myocardial fibrosis and myocyte hypertrophy. Totally, m6A is a significant alternation of epitranscriptomic processes, which is also a potentially meaningful therapeutic target.

LRTCLS: low-rank tensor completion with Laplacian smoothing regularization for unveiling the post-transcriptional machinery of N6-methylation (m6A)-mediated diseases.

Recently, N6-methylation (m6A) has recently become a hot topic due to its key role in disease pathogenesis. Identifying disease-related m6A sites aids in the understanding of the molecular mechanisms and biosynthetic pathways underlying m6A-mediated diseases. Existing methods treat it primarily as a binary classification issue, focusing solely on whether an m6A-disease association exists or not. Although they achieved good results, they all shared one common flaw: they ignored the post-transcriptional regulation events during disease pathogenesis, which makes biological interpretation unsatisfactory. Thus, accurate and explainable computational models are required to unveil the post-transcriptional regulation mechanisms of disease pathogenesis mediated by m6A modification, rather than simply inferring whether the m6A sites cause disease or not. Emerging laboratory experiments have revealed the interactions between m6A and other post-transcriptional regulation events, such as circular RNA (circRNA) targeting, microRNA (miRNA) targeting, RNA-binding protein binding and alternative splicing events, etc., present a diverse landscape during tumorigenesis. Based on these findings, we proposed a low-rank tensor completion-based method to infer disease-related m6A sites from a biological standpoint, which can further aid in specifying the post-transcriptional machinery of disease pathogenesis. It is so exciting that our biological analysis results show that Coronavirus disease 2019 may play a role in an m6A- and miRNA-dependent manner in inducing non-small cell lung cancer.

METTL3-induced DLGAP1-AS2 promotes non-small cell lung cancer tumorigenesis through m6A/c-Myc-dependent aerobic glycolysis

ABSTRACT The critical roles of N6-methyladenosine (m6A) modification have been demonstrated by more and more evidence. However, the cross talk of m6A and long noncoding RNAs (lncRNAs) in non-small cell lung cancer (NSCLC) tumorigenesis is still unclear. Here, this work focused on the functions and molecular mechanism of m6A-modified lncRNA DLGAP1 antisense RNA 2 (DLGAP1-AS2) in NSCLC. Microarray analysis found that lncRNA DLGAP1-AS2 is upregulated in NSCLC cells. Clinical data showed that DLGAP1-AS2 high-expression was correlated with advanced pathological stage and poor prognosis. Functionally, DLGAP1-AS2 overexpression promoted the aerobic glycolysis and DLGAP1-AS2 knockdown suppressed the tumor growth of NSCLC cells. Mechanistically, m6A methyltransferase METTL3 enhanced the stability of DLGAP1-AS2 via m6A site binding. Moreover, DLGAP1-AS2 interacted with YTHDF1 to enhance the stability of c-Myc mRNA through DLGAP1-AS2/YTHDF1/m6A/c-Myc mRNA. In conclusion, our work indicates the functions of m6A-modified DLGAP1-AS2 in the NSCLC aerobic glycolysis, disclosing a potential m6A-dependent manner for NSCLC treatment.

YTHDF3 Is Involved in the Diapause Process of Bivoltine Bombyx mori Strains by Regulating the Expression of Cyp307a1 and Cyp18a1 Genes in the Ecdysone Synthesis Pathway.

The variable diapause features of bivoltine silkworm (Bombyx mori) strains regulated by environmental signals in the embryonic stage are closely related to epigenetics. Previously, we showed that the expression of YTHDF3 is significantly different in the pupae of the bivoltine silkworm Qiufeng developed from eggs incubated at a normal temperature (QFHT, diapause egg producer) compared to those from eggs incubated at a low temperature (QFLT, nondiapause egg producer), indicating that the expression of diapause-associated genes is regulated by the m6A modification level. However, how YTHDF3 regulates the expression of diapause-related genes remains unclear. In this study, we observed that the knockdown of B. mori YTHDF3 resulted in delayed embryo development, while the overexpression of YTHDF3 resulted in the transformation of nondiapause-destined eggs into a mixture of diapause and nondiapause eggs. Further studies showed that YTHDF3, as a reading protein, can recognize the m6A site of Cyp307a1 and Cyp18a1 genes in the ecdysone synthesis pathway (ESP), and the overexpression of YTHDF3 affects the diapause traits of the silkworm by decreasing the stabilities of mRNAs of Cyp307a1 and Cyp18a1 and inhibiting their translation. The above results demonstrate that m6A modification mediates YTHDF3 to affect the expression levels of its target genes, Cyp307a1 and Cyp18a1, in the ESP to regulate diapause in bivoltine B. mori. This is the first report of the m6A methylation regulation mechanism in diapause in B. mori and provides new experimental data for clarifying the diapause regulation network.

Novel insight into m6A regulator-mediated methylation modification patterns and immune characteristics in intracranial aneurysm.

BackgroundGrowing evidence demonstrated that m6A modification in cardiovascular diseases. However, how it is involved in the intracranial aneurysm (IA) is still unclear. This study aimed to identify the role of m6A modification in IA.MethodsThree datasets downloaded from the Gene Expression Omnibus (GEO) database were used, including GSE122897, GSE15629, and GSE3679. The landscapes of 24 m6A regulators were depicted using the STRING database, Pearson’s correlation analysis, and Wilcoxon test. The targets of differentially expressed m6A (DEm6A) were predicted in the m6A2Target database and the modification m6A sites of hub targets were identified in SRAMP online tool. A diagnostic model based on DEm6A was constructed and verified in training and test databases. A consensus clustering algorithm was performed to classify IA patients into distinct m6A-related clusters. Functional analyses including gene ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set variation analysis, and gene set enrichment analysis analyses were conducted to elucidate the underlying mechanisms. ssGSEA algorithm was performed to uncover the immune characteristics. A PCA method was adopted to quantify the m6A score.ResultsNine DEm6A (IGF2BP1, IGF2BP3, YTHDF2, ZNF217, RBM15, YTHDF3, YTHDC1, FTO, and LRPPRC) significantly differed between IA and controls. Biological annotations showed that immune-related pathways (such as complement activation, inflammatory response, and interleukin signaling) and apoptosis were more enriched in IAs than in controls. Immune analyses indicate that the abundance of immune cells, immune responses, and HLA gene expression were elevated in IA samples than in controls. PCA results showed that IA has a lower m6A score than controls. An immune/apoptosis-related network modified by DEm6A was constructed. The m6A sites of six hub targets (CDK1, ASPM, AURKB, BUB1B, MKI67, and TPX2) were predicted with very high confidence. A diagnostic model with four genes (LRPPRC, YTHDF3, IGF2BP1, and ZNF217) was constructed and verified. Two m6A modification subtypes were identified with unsupervised cluster analysis. Immune infiltration analysis revealed that cluster 1 had higher immune activation than cluster 2. Further study showed that cluster 1 had a larger proportion of ruptured IAs.ConclusionThe m6A modification may shape the IAs microenvironment and participates in the formation and rupture of IAs by regulating immune infiltration.

The S. cerevisiae m6A-reader Pho92 promotes timely meiotic recombination by controlling key methylated transcripts.

N6-Methyladenosine (m6A), one of the most abundant internal modification of eukaryotic mRNAs, participates in the post-transcriptional control of gene expression through recruitment of specific m6A readers. In Saccharomyces cerevisiae, the m6A methyltransferase Ime4 is expressed only during meiosis and its deletion impairs this process. To elucidate how m6A control gene expression, we investigated the function of the budding yeast m6A reader Pho92. We show that Pho92 is an early meiotic factor that promotes timely meiotic progression. High-throughput RNA sequencing and mapping of Pho92-binding sites following UV-crosslinking reveal that Pho92 is recruited to specific mRNAs in an m6A-dependent manner during the meiotic prophase, preceding their down-regulation. Strikingly, point mutations altering m6A sites in mRNAs targeted by Pho92 are sufficient to delay their down-regulation and, in one case, to slow down meiotic progression. Altogether, our results indicate that Pho92 facilitate the meiotic progression by accelerating the down-regulation of timely-regulated mRNAs during meiotic recombination.

Abstract P3097: Post-transcriptional Mechanisms Of FTO-regulated Cardiac Contractility

Background: N6-methyladenosine (m6A), the most abundant and functionally relevant mRNA modification, is reversibly demethylated by FTO, an m6A eraser and a primary regulator of m6A in the heart. In failing hearts, decreased FTO increases m6A, critically reducing cardiomyocyte contraction. FTO overexpression improved cardiac contraction and attenuated remodeling. Here, we investigate post-transcriptional and translational mechanisms of FTO-regulation of cardiomyocyte contraction in vitro and in vivo. We hypothesize that FTO directly binds to selective cardiac contractile mRNAs at their m6A site and affects their nuclear export, polysome binding, cytoplasmic availability, and protein expression. Methods and Results: We performed immunostaining to examine the intracellular location and expression of FTO under different pathological stimuli in human ventricular cardiomyocyte cell line (AC16 cells), rat primary cardiomyocytes and mouse and human hearts. FTO was detected in both cytoplasmic and nuclear compartments, and was significantly reduced in pathological hearts compared to healthy controls. MeRIP (immunoprecipitation of m6A-RNA) sequencing analysis uncovered that FTO targets different sets of cardiac mRNAs in the cytoplasm and nucleus of cardiomyocytes. Knocking down FTO increased nuclear retention of cardiac mRNAs, which was reversed with FTO overexpression. Immunoprecipitation experiments demonstrated that FTO directly binds to cardiac mRNAs. To determine the effect of FTO on translation of select cardiac mRNAs, we isolated the polysome fraction from the cytoplasm of AC16 cells. Interestingly, our stable isotope labeling with amino acids in cell culture (SILAC) and mass spectrometry analyses suggested increased expression of cardiac contractile proteins with FTO overexpression. Conclusion: We demonstrate that FTO positively regulates post-transcriptional nuclear export, polysome binding of cardiac contractile mRNAs, and regulates their translation under pathological stimuli. By combining experimental and predictive models in human and mice, we have characterized the transcriptome-proteome molecular interplay regulating cardiac function.

RBM45 is an m6A-binding protein that affects neuronal differentiation and the splicing of a subset of mRNAs.

SUMMARYN6-methyladenosine (m6A) is deposited co-transcriptionally on thousands of cellular mRNAs and plays important roles in mRNA processing and cellular function. m6A is particularly abundant within the brain and is critical for neurodevelopment. However, the mechanisms through which m6A contributes to brain development are incompletely understood. RBM45 acts as an m6A-binding protein that is highly expressed during neurodevelopment. We find that RBM45 binds to thousands of cellular RNAs, predominantly within intronic regions. Rbm45 depletion disrupts the constitutive splicing of a subset of target pre-mRNAs, leading to altered mRNA and protein levels through both m6A-dependent and m6A-independent mechanisms. Finally, we find that RBM45 is necessary for neuroblastoma cell differentiation and that its depletion impacts the expression of genes involved in several neurodevelopmental signaling pathways. Altogether, our findings show a role for RBM45 in controlling mRNA processing and neuronal differentiation, mediated in part by the recognition of methylated RNA.

Plant6mA: A predictor for predicting N6-methyladenine sites with lightweight structure in plant genomes.

N6-methyladenine (6mA) in DNA, a type of DNA methylation in epigenetic modification, has attracted extensive attention in recent years. In order to improve our understanding of 6mA biological activities and mechanisms in plant genomes, we need to be able to accurately identify 6mA sites. Because traditional wet-lab experiments frequently necessitate a large amount of manpower and time, a plethora of computational methods, particularly machine learning, have emerged to achieve fast and accurate 6mA site prediction. Traditional machine learning methods, on the other hand, rely heavily on manual features and integrated learning to improve performance, resulting in a reliance on prior knowledge and a large model scale. Furthermore, many models are only trained and tested for one species, with no comparison of model generalization performance, resulting in models with limited practical usability. In order to increase the generalization capability of the model, we propose a lightweight structure predictor Plant6mA based on Transformer encoder. Based on results on independent test sets, our proposed Plant6mA has better generalization performance than the most advanced methods in predicting 6mA location in plant genomes. Plant6mA's MultiHead Attention mechanism effectively enhances its expressive ability by capturing potential biological information from multiple scales of the input sequence. Furthermore, we used a dimensionality reduction tool to visualize Plant6mA's training process and visually demonstrate the effectiveness of our model.

ELAVL1 promotes prostate cancer progression by interacting with other m6A regulators.

N6-Methyladenosine (m6A) imbalance is an important factor in the occurrence and development of prostate cancer (PCa). Many m6A regulators have been found to be significantly dysregulated in PCa. ELAVL1 is an m6A binding protein that can promote the occurrence and development of tumors in an m6A-dependent manner. In this study, we found that most m6A regulators were significantly dysregulated in PCa, and some m6A regulators were associated with the progression-free interval. Mutations and copy number variations of these m6A regulators can alter their expression. However, ELAVL1 mutations were not found in PCa. Nevertheless, ELAVL1 upregulation was closely related to PCa proliferation. High ELAVL1 expression was also related to RNA metabolism. Further experiments showed that ELAVL1 interacted with other m6A regulators and that several m6A regulatory mRNAs have m6A sites that can be recognized by ELAVL1. Additionally, protein–protein interactions occur between ELAVL1 and other m6A regulators. Finally, we found that the dysregulation of ELAVL1 expression occurred in almost all tumors, and interactions between ELAVL1 and other m6A regulators also existed in almost all tumors. In summary, ELAVL1 is an important molecule in the development of PCa, and its interactions with other m6A regulators may play important roles in PCa progression.

Deep transformers and convolutional neural network in identifying DNA N6-methyladenine sites in cross-species genomes.

As one of the most common post-transcriptional epigenetic modifications, N6-methyladenine (6mA), plays an essential role in various cellular processes and disease pathogenesis. Therefore, accurately identifying 6mA modifications is necessary for a deep understanding of cellular processes and other possible functional mechanisms. Although a few computational methods have been proposed, their respective models were developed with small training datasets. Hence, their practical application is quite limited in genome-wide detection. To overcome the existing limitations, we present a novel model based on transformer architecture and deep learning to identify DNA 6mA sites from the cross-species genome. The model is constructed on a benchmark dataset and explored a feature derived from pre-trained transformer word embedding approaches. Subsequently, a convolutional neural network was employed to learn the generated features and generate the prediction outcomes. As a result, our predictor achieved excellent performance during independent test with the accuracy and Matthews correlation coefficient (MCC) of 79.3% and 0.58, respectively. Overall, its performance achieved better accuracy than the baseline models and significantly outperformed the existing predictors, demonstrating the effectiveness of our proposed hybrid framework. Furthermore, our model is expected to assist biologists in accurately identifying 6mAs and formulate the novel testable biological hypothesis. We also release source codes and datasets freely at https://github.com/khanhlee/bert-dna for front-end users.

GC6mA-Pred: A deep learning approach to identify DNA N6-methyladenine sites in the rice genome.

DNA N6-methyladenine (6mA) is a pivotal DNA modification for various biological processes. More accurate prediction of 6mA methylation sites plays an irreplaceable part in grasping the internal rationale of related biological activities. However, the existing prediction methods only extract information from a single dimension, which has some limitations. Therefore, it is very necessary to obtain the information of 6mA sites from different dimensions, so as to establish a reliable prediction method. In this study, a neural network based bioinformatics model named GC6mA-Pred is proposed to predict N6-methyladenine modifications in DNA sequences. GC6mA-Pred extracts significant information from both sequence level and graph level. In the sequence level, GC6mA-Pred uses a three-layer convolution neural network (CNN) model to represent the sequence. In the graph level, GC6mA-Pred employs graph neural network (GNN) method to integrate various information contained in the chemical molecular formula corresponding to DNA sequence. In our newly built dataset, GC6mA-Pred shows better performance than other existing models. The results of comparative experiments have illustrated that GC6mA-Pred is capable of producing a marked effect in accurately identifying DNA 6mA modifications.

Deep6mAPred: A CNN and Bi-LSTM-based deep learning method for predicting DNA N6-methyladenosine sites across plant species

DNA N6-methyladenine (6mA) is a key DNA modification, which plays versatile roles in the cellular processes, including regulation of gene expression, DNA repair, and DNA replication. DNA 6mA is closely associated with many diseases in the mammals and with growth as well as development of plants. Precisely detecting DNA 6mA sites is of great importance to exploration of 6mA functions. Although many computational methods have been presented for DNA 6mA prediction, there is still a wide gap in the practical application. We presented a convolution neural network (CNN) and bi-directional long-short term memory (Bi-LSTM)-based deep learning method (Deep6mAPred) for predicting DNA 6mA sites across plant species. The Deep6mAPred stacked the CNNs and the Bi-LSTMs in a paralleling manner instead of a series-connection manner. The Deep6mAPred also employed the attention mechanism for improving the representations of sequences. The Deep6mAPred reached an accuracy of 0.9556 over the independent rice dataset, far outperforming the state-of-the-art methods. The tests across plant species showed that the Deep6mAPred is of a remarkable advantage over the state of the art methods. We developed a user-friendly web application for DNA 6mA prediction, which is freely available at http://106.13.196.152:7001/ for all the scientific researchers. The Deep6mAPred would enrich tools to predict DNA 6mA sites and speed up the exploration of DNA modification.

Targeting HOTAIRM1 ameliorates glioblastoma by disrupting mitochondrial oxidative phosphorylation and serine metabolism

SummarySerine hydroxymethyltransferase 2 (SHMT2), which catalyzes the conversion of serine to glycine and one-carbon transfer reactions in mitochondria, is significantly upregulated in glioblastoma (GBM). However, the mechanism by which the stability of SHMT2 gene expression is maintained to drive GBM tumorigenesis has not been clarified. Herein, through microarray screening, we identified that HOXA Transcript Antisense RNA, Myeloid-Specific 1 (HOTAIRM1) modulates the SHMT2 level in various GBM cell lines. Serine catabolism and mitochondrial oxidative phosphorylation activities were decreased by HOTAIRM1 inhibition. Mechanistically, according to our mass spectrometry and eCLIP-seq results, HOTAIRM1 can bind to PTBP1 and IGF2BP2. Furthermore, HOTAIRM1 maintains the stability of SHMT2 by promoting the recognition of an m6A site and the interaction of PTBP1/IGF2BP2 with SHMT2 mRNA. The stability of HOTAIRM1 can also be enhanced and results in positive feedback regulation to support the progression of GBM. Thus, targeting HOTAIRM1 could be a promising metabolic therapy for GBM.

XNA probe and CRISPR/Cas12a-powered flexible fluorescent and electrochemical dual-mode biosensor for sensitive detection of m6A site-specific RNA modification

N6-methyladenosine (m6A) in RNAs is closely related to various biological progresses, but the specific regulatory mechanisms are still unclear. The existing m6A single-base resolution analysis techniques have problems of specificity and sensitivity to be improved, which can hardly meet the urgent needs of basic research and clinical applications. This work proposes a new strategy based on xeno nucleic acid (XNA) probe and CRISPR/Cas12a signal amplification for the sensitive detection of site-specific m6A modifications. According to the difference in the thermodynamic stability of hybridization between XNA probe with m6A-RNA and A-RNA, XNA was designed as a block probe to mediate m6A-RNA specific reverse transcription polymerase chain reaction (MsRT-PCR). Therefore, m6A can be specifically distinguished by converting difficult-to-test m6A modifications into easily detectable dsDNA fragments. Integration of CRISPR/Cas12a technology, skilfully designed sequences of crRNAs targeting m6A site-specific amplification dsDNA. The specificity was significantly improved through dual specific recognition of XNA probe and crRNA. Furthermore, the sensitivity of the assay was also greatly increased by the combined signal amplification of PCR and CRISPR/Cas12a. Additionally, we extend the application of CRISPR/Cas12a to flexible fluorescent and electrochemical biosensing system, which can accurately detect m6A modifications with different ranges of methylation fractions. The analysis results of m6A sites in MALAT1, ACTB and TPT1 further demonstrated the feasibility of the constructed biosensor for the accurate detection of hypomethylated samples in cells. The implementation of this work will provide strong technical support to promote the in-depth research on m6A in disease regulation mechanisms and in vitro molecular diagnosis.

Genome-wide identification m6A modified circRNAs revealed their key roles in skin ulceration syndrome disease development in Apostichopus japonicus

Circular RNAs (circRNAs) are novel endogenous non-coding RNAs (ncRNAs) and can be acted as competing endogenous RNAs (ceRNAs) to regulate microRNA (miRNA) and downstream gene expression. Recently, m6A modification has been found in circRNA, and m6A circRNAs also play important roles in various biological processes and a variety of diseases. Our previous study had been demonstrated that circRNAs were differentially expressed in skin ulceration syndrome (SUS) diseased sea cucumber Apostichopus japonicus. However, whether the function of circRNAs are dependent on m6A levels are largely unknown. Here, we firstly investigated the genome-wide map of m6A circRNAs in sea cucumbers with different stages of Vibrio splendidus challenge, that's Control group, SUS-diseased group, and SUS-resistant group. MeRIP-seq revealed that m6A abundances were enriched in circRNAs in all three groups, especially for SUS-resistant group. Among them, more than 62% of modified circRNAs harbor only a single m6A peak and about 55% of m6A sites in circRNAs were derived from sense overlapping in each group. After V. splendidus infection, we found that most of m6A peaks in circRNAs were upregulated and less were downregulated in both SUS-diseased and SUS-resistant groups when compared with Control. Furthermore, GO analysis indicated that the host genes of circRNAs with dysregulated m6A peaks in SUS-diseased and SUS-resistant groups were both mainly enriched in the adhesion pathway. More importantly, we discovered that more than 50% m6A circRNAs showed a positive correlation between the circRNAs expression and m6A methylation levels both in SUS-diseased and SUS-resistant groups. Therefore, a core circRNA-miRNA-mRNA (ceRNA) network whether influenced by m6A modification was constructed based on conjoint analysis. Our results indicated that several selected m6A circRNAs bind with miRNAs were mainly targeting to ubiquitylation system and adhesion pathway. What's more, three candidate m6A circRNAs and three target genes were validated by MeRIP-qPCR and qPCR, whose m6A levels in circRNA and mRNA expressions were consistent with disease occurrence or disease resistance. All of our current findings suggested that m6A circRNAs could play important roles during pathogen infection and might be served as a new molecular biomarker in SUS disease diagnose of A. japonicus.

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.

Mapping single-nucleotide m6A by m6A-REF-seq.

The past few years have witnessed rapid progress in the field of RNA modifications. As the most prevailing modification on eukaryotic mRNA, m6A is characterized to play a vital role in various cellular activities. However, limitations of the detection method impede functional studies of m6A. Here we introduce m6A-REF-seq, a powerful and straightforward method to identify m6A at single-nucleotide resolution. m6A-REF-seq relies on the recognition of RNA endonuclease MazF towards m6A at the ACA motif, providing an orthogonal method independent of the m6A antibody being adopted by most of current methods. We describe a detailed protocol to perform m6A-REF-seq, including NGS library construction and sequencing data analysis. In particular, we describe an optimized assay to validate individual m6A sites identified by m6A-REF-seq, which can also be applied to detect any candidate m6A sites.