m6A-Driver: Identifying Context-Specific mRNA m6A Methylation-Driven Gene Interaction Networks

m6Aexpress-enet: Predicting the regulatory expression m6A sites by an enet-regularization negative binomial regression model

As the most abundant mRNA modification, m6A controls and influences many aspects of mRNA metabolism including the mRNA stability and degradation. However, the role of specific m6A sites in regulating gene expression still remains unclear. In additional, the multicollinearity problem caused by the correlation of methylation level of multiple m6A sites in each gene could influence the prediction performance. To address the above challenges, we propose an elastic-net regularized negative binomial regression model (called m6Aexpress-enet) to predict which m6A site could potentially regulate its gene expression. Comprehensive evaluations on simulated datasets demonstrate that m6Aexpress-enet could achieve the top prediction performance. Applying m6Aexpress-enet on real MeRIP-seq data from human lymphoblastoid cell lines, we have uncovered the complex regulatory pattern of predicted m6A sites and their unique enrichment pathway of the constructed co-methylation modules. m6Aexpress-enet proves itself as a powerful tool to enable biologists to discover the mechanism of m6A regulatory gene expression. Furthermore, the source code and the step-by-step implementation of m6Aexpress-enet is freely accessed at https://github.com/tengzhangs/m6Aexpress-enet.

epidecodeR: a functional exploration tool for epigenetic and epitranscriptomic regulation

Recent technological advances in sequencing DNA and RNA modifications using high-throughput platforms have generated vast epigenomic and epitranscriptomic datasets whose power in transforming life science is yet fully unleashed. Currently available in silico methods have facilitated the identification, positioning and quantitative comparisons of individual modification sites. However, the essential challenge to link specific 'epi-marks' to gene expression in the particular context of cellular and biological processes is unmet. To fast-track exploration, we generated epidecodeR implemented in R, which allows biologists to quickly survey whether an epigenomic or epitranscriptomic status of their interest potentially influences gene expression responses. The evaluation is based on the cumulative distribution function and the statistical significance in differential expression of genes grouped by the number of 'epi-marks'. This tool proves useful in predicting the role of H3K9ac and H3K27ac in associated gene expression after knocking down deacetylases FAM60A and SDS3 and N6-methyl-adenosine-associated gene expression after knocking out the reader proteins. We further used epidecodeR to explore the effectiveness of demethylase FTO inhibitors and histone-associated modifications in drug abuse in animals. epidecodeR is available for downloading as an R package at https://bioconductor.riken.jp/packages/3.13/bioc/html/epidecodeR.html.

Epigenetic modification of m6A methylation: Regulatory factors, functions and mechanism in inflammatory bowel disease

Although the exact cause of inflammatory bowel disease (IBD) is still unknown, there is a lot of evidence to support the notion that it results from a combination of environmental factors, immune system issues, gut microbial changes, and genetic susceptibility. In recent years, the role of epigenetics in the pathogenesis of IBD has drawn increasing attention. The regulation of IBD-related immunity, the preservation of the intestinal epithelial barrier, and autophagy are all significantly influenced by epigenetic factors. The most extensive epigenetic methylation modification of mammalian mRNA among them is N6-methyladenosine (m6A). It summarizes the general structure and function of the m6A regulating factors, as well as their complex effects on IBD by regulating the intestinal mucous barrier, intestine mucosal immunity, epidermal cell death, and intestinal microorganisms.This paper provides key insights for the future identification of potential new targets for the diagnosis and treatment of IBD.

Bioinformatics identification and experimental validation of m6A-related diagnostic biomarkers in the subtype classification of blood monocytes from postmenopausal osteoporosis patients

BACKGROUND

Postmenopausal osteoporosis (PMOP) is a common bone disorder. Existing study has confirmed the role of exosome in regulating RNA N6-methyladenosine (m6A) methylation as therapies in osteoporosis. However, it still stays unclear on the roles of m6A modulators derived from serum exosome in PMOP. A comprehensive evaluation on the roles of m6A modulators in the diagnostic biomarkers and subtype identification of PMOP on the basis of GSE56815 and GSE2208 datasets was carried out to investigate the molecular mechanisms of m6A modulators in PMOP.

METHODS

We carried out a series of bioinformatics analyses including difference analysis to identify significant m6A modulators, m6A model construction of random forest, support vector machine and nomogram, m6A subtype consensus clustering, GO and KEGG enrichment analysis of differentially expressed genes (DEGs) between different m6A patterns, principal component analysis, and single sample gene set enrichment analysis (ssGSEA) for evaluation of immune cell infiltration, experimental validation of significant m6A modulators by real-time quantitative polymerase chain reaction (RT-qPCR), etc.

RESULTS

In the current study, we authenticated 7 significant m6A modulators via difference analysis between normal and PMOP patients from GSE56815 and GSE2208 datasets. In order to predict the risk of PMOP, we adopted random forest model to identify 7 diagnostic m6A modulators, including FTO, FMR1, YTHDC2, HNRNPC, RBM15, RBM15B and WTAP. Then we selected the 7 diagnostic m6A modulators to construct a nomogram model, which could provide benefit with patients according to our subsequent decision curve analysis. We classified PMOP patients into 2 m6A subtypes (clusterA and clusterB) on the basis of the significant m6A modulators via a consensus clustering approach. In addition, principal component analysis was utilized to evaluate the m6A score of each sample for quantification of the m6A subgroups. The m6A scores of patients in clusterB were higher than those of patients in clusterA. Moreover, we observed that the patients in clusterA had close correlation with immature B cell and gamma delta T cell immunity while clusterB was linked to monocyte, neutrophil, CD56dim natural killer cell, and regulatory T cell immunity, which has close connection with osteoclast differentiation. Notably, m6A modulators detected by RT-qPCR showed generally consistent expression levels with the bioinformatics results.

CONCLUSION

In general, m6A modulators exert integral function in the pathological process of PMOP. Our study of m6A patterns may provide diagnostic biomarkers and immunotherapeutic strategies for future PMOP treatment.

Analysis of N6-Methyladenosine RNA Methylation Regulators in Diagnosis and Distinct Molecular Subtypes of Ankylosing Spondylitis

The most frequent internal modification in eukaryotic mRNA is N6-methyladenosine (m6A). However, what we know about the m6A regulators in Ankylosing spondylitis (AS) is still limited. In our study, eight distinct m6A regulators were selected utilizing Differentially Expressed Gene (DEG) analysis of the Gene Expression Omnibus GSE73754 dataset for making comparisons between AS (Ankylosing spondylitis) and non-AS patients. The random forest model and the nomogram model were used to screen the eight candidate m6A regulators and evaluate their prediction accuracy for the occurrence of AS. Furthermore, based on the selected m6A regulators, the AS patients were divided into two subgroups, and we applied principal component analysis algorithms to calculate their m6A score and evaluate the m6A patterns. Our findings revealed that patients in cluster A were linked to activated CD4 T cell immunity and activated CD8 T cell immunity. With its major contributions in the area of immunology, our research in m6A patterns may benefit the future diagnosis and treatment strategies of AS.

Advances in detecting N6-methyladenosine modification in circRNAs

Circular RNAs (circRNAs) are a class of noncoding RNAs with covalently single-stranded closed loop structures derived from back-splicing event of linear precursor mRNAs (pre-mRNAs). N6-methyladenosine (m⁶A), the most abundant epigenetic modification in eukaryotic RNAs, has been shown to play a crucial role in regulating the fate and biological function of circRNAs, and thus affecting various physiological and pathological processes. Accurate identification of m⁶A modification in circRNAs is an essential step to fully elucidate the crosstalk between m⁶A and circRNAs. In recent years, the rapid development of high-throughput sequencing technology and bioinformatic methodology has propelled the establishment of a multitude of approaches to detect circRNAs and m⁶A modification, including in vitro-based and in silico methods. Based on this, the research community has started on a new journey to develop methods for identification of m⁶A modification in circRNAs. In this review, we provide a comprehensive review and evaluation of the existing methods responsible for detecting circRNAs, m⁶A modification, and especially, m⁶A modification in circRNAs, which mainly focused on those developed based on high-throughput technologies and methodology of bioinformatics. This handy reference can help researchers figure out towards which direction this field will go.

m 6 Aexpress-BHM: predicting m6A regulation of gene expression in multiple-groups context by a Bayesian hierarchical mixture model

As the most abundant RNA modification, N6-methyladenosine (m6A) plays an important role in various RNA activities including gene expression and translation. With the rapid application of MeRIP-seq technology, samples of multiple groups, such as the involved multiple viral/ bacterial infection or distinct cell differentiation stages, are extracted from same experimental unit. However, our current knowledge about how the dynamic m6A regulating gene expression and the role in certain biological processes (e.g. immune response in this complex context) is largely elusive due to lack of effective tools. To address this issue, we proposed a Bayesian hierarchical mixture model (called m6Aexpress-BHM) to predict m6A regulation of gene expression (m6A-reg-exp) in multiple groups of MeRIP-seq experiment with limited samples. Comprehensive evaluations of m6Aexpress-BHM on the simulated data demonstrate its high predicting precision and robustness. Applying m6Aexpress-BHM on three real-world datasets (i.e. Flaviviridae infection, infected time-points of bacteria and differentiation stages of dendritic cells), we predicted more m6A-reg-exp genes with positive regulatory mode that significantly participate in innate immune or adaptive immune pathways, revealing the underlying mechanism of the regulatory function of m6A during immune response. In addition, we also found that m6A may influence the expression of PD-1/PD-L1 via regulating its interacted genes. These results demonstrate the power of m6Aexpress-BHM, helping us understand the m6A regulatory function in immune system.

m6A echoes with DNA methylation: Coordinated DNA methylation and gene expression data analysis identified critical m6A genes associated with asthma

Asthma is a chronic inflammatory disease that involves complex gene-environment interactions. Methylation of nucleotides, such as 5-methylcytosine (5mC) in DNA and N6-methyladenosine (m6A) in mRNA, carries important information for gene regulation. Our study screened m6A genes and genes associated with asthma from the Gene Expression Omnibus (GEO) databases GSE63383, GSE119580, GSE38003, GSE34313, GSE13168, and GSE35643. GSE52778, GSE35643, GSE40996, and GSE64744), and DNA methylation data from GSE85568 and GSE146377. We screened out 6 m6A related genes (FTO, IGF2BP2, RBM15, RBMX, WTAP, and YTHDC1) that were significantly dysregulated in asthma or proinflammatory conditions. A correlation study showed a high correlation between m6A genes and gene pairs such as WTAP, IL7R, and TLR2; RBMX, SLC22A4, IL33, TNC, FLG, and IL6R (|r| ≥ 0.8). Following DNA methylation dataset analysis, we proposed several DNA methylation-m6A modification asthma-related gene axes such as cg19032951/cg15153914-IGF2BP2-SMAD3. Interestingly, several target genes, such as SMAD3, possess the ability to participate in DNA methylation processes, which may reciprocally regulate the expression of m6A genes and form a closed-loop regulation axis. Some classic DNA methylation-related genes, such as TET1, UHRF1, and ZBTB4, were also involved. We identified an integrated profile of m6A gene expression in asthma and proposed a novel potential interplay between DNA methylation and m6A modification in asthma pathogenesis. Using the CMAP database, we found that resveratrol may target these dysregulated m6A genes, and therefore may serve as a potential therapeutic agent for asthma.

Network Control Models With Personalized Genomics Data for Understanding Tumor Heterogeneity in Cancer

Due to rapid development of high-throughput sequencing and biotechnology, it has brought new opportunities and challenges in developing efficient computational methods for exploring personalized genomics data of cancer patients. Because of the high-dimension and small sample size characteristics of these personalized genomics data, it is difficult for excavating effective information by using traditional statistical methods. In the past few years, network control methods have been proposed to solve networked system with high-dimension and small sample size. Researchers have made progress in the design and optimization of network control principles. However, there are few studies comprehensively surveying network control methods to analyze the biomolecular network data of individual patients. To address this problem, here we comprehensively surveyed complex network control methods on personalized omics data for understanding tumor heterogeneity in precision medicine of individual patients with cancer.

m6A-express: uncovering complex and condition-specific m6A regulation of gene expression

N⁶-methyladenosine (m⁶A) is the most abundant form of mRNA modification and controls many aspects of RNA metabolism including gene expression. However, the mechanisms by which m⁶A regulates cell- and condition-specific gene expression are still poorly understood, partly due to a lack of tools capable of identifying m⁶A sites that regulate gene expression under different conditions. Here we develop m⁶A-express, the first algorithm for predicting condition-specific m⁶A regulation of gene expression (m⁶A-reg-exp) from limited methylated RNA immunoprecipitation sequencing (MeRIP-seq) data. Comprehensive evaluations of m⁶A-express using simulated and real data demonstrated its high prediction specificity and sensitivity. When only a few MeRIP-seq samples may be available for the cellular or treatment conditions, m⁶A-express is particularly more robust than the log-linear model. Using m⁶A-express, we reported that m⁶A writers, METTL3 and METTL14, competitively regulate the transcriptional processes by mediating m⁶A-reg-exp of different genes in Hela cells. In contrast, METTL3 induces different m⁶A-reg-exp of a distinct group of genes in HepG2 cells to regulate protein functions and stress-related processes. We further uncovered unique m⁶A-reg-exp patterns in human brain and intestine tissues, which are enriched in organ-specific processes. This study demonstrates the effectiveness of m⁶A-express in predicting condition-specific m⁶A-reg-exp and highlights the complex, condition-specific nature of m⁶A-regulation of gene expression.

Network control principles for identifying personalized driver genes in cancer

To understand tumor heterogeneity in cancer, personalized driver genes (PDGs) need to be identified for unraveling the genotype-phenotype associations corresponding to particular patients. However, most of the existing driver-focus methods mainly pay attention on the cohort information rather than on individual information. Recent developing computational approaches based on network control principles are opening a new way to discover driver genes in cancer, particularly at an individual level. To provide comprehensive perspectives of network control methods on this timely topic, we first considered the cancer progression as a network control problem, in which the expected PDGs are altered genes by oncogene activation signals that can change the individual molecular network from one health state to the other disease state. Then, we reviewed the network reconstruction methods on single samples and introduced novel network control methods on single-sample networks to identify PDGs in cancer. Particularly, we gave a performance assessment of the network structure control-based PDGs identification methods on multiple cancer datasets from TCGA, for which the data and evaluation package also are publicly available. Finally, we discussed future directions for the application of network control methods to identify PDGs in cancer and diverse biological processes.

HIV Modifies the m6A and m5C Epitranscriptomic Landscape of the Host Cell

The study of RNA modifications, today known as epitranscriptomics, is of growing interest. The N6-methyladenosine (m6A) and 5-methylcytosine (m5C) RNA modifications are abundantly present on mRNA molecules, and impact RNA interactions with other proteins or molecules, thereby affecting cellular processes, such as RNA splicing, export, stability, and translation. Recently m6A and m5C marks were found to be present on human immunodeficiency (HIV) transcripts as well and affect viral replication. Therefore, the discovery of RNA methylation provides a new layer of regulation of HIV expression and replication, and thus offers novel array of opportunities to inhibit replication. However, no study has been performed to date to investigate the impact of HIV replication on the transcript methylation level in the infected cell. We used a productive HIV infection model, consisting of the CD4+ SupT1 T cell line infected with a VSV-G pseudotyped HIVeGFP-based vector, to explore the temporal landscape of m6A and m5C epitranscriptomic marks upon HIV infection, and to compare it to mock-treated cells. Cells were collected at 12, 24, and 36 h post-infection for mRNA extraction and FACS analysis. M6A RNA modifications were investigated by methylated RNA immunoprecipitation followed by high-throughput sequencing (MeRIP-Seq). M5C RNA modifications were investigated using a bisulfite conversion approach followed by high-throughput sequencing (BS-Seq). Our data suggest that HIV infection impacted the methylation landscape of HIV-infected cells, inducing mostly increased methylation of cellular transcripts upon infection. Indeed, differential methylation (DM) analysis identified 59 m6A hypermethylated and only 2 hypomethylated transcripts and 14 m5C hypermethylated transcripts and 7 hypomethylated ones. All data and analyses are also freely accessible on an interactive web resource (http://sib-pc17.unil.ch/HIVmain.html). Furthermore, both m6A and m5C methylations were detected on viral transcripts and viral particle RNA genomes, as previously described, but additional patterns were identified. This work used differential epitranscriptomic analysis to identify novel players involved in HIV life cycle, thereby providing innovative opportunities for HIV regulation.

Recent advances in functional annotation and prediction of the epitranscriptome

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

Funm6AViewer: a web server and R package for functional analysis of context-specific m6A RNA methylation

MOTIVATION

N6-methyladenosine (m6A) is the most abundant mammalian mRNA methylation with versatile functions. To date, although a number of bioinformatics tools have been developed for location discovery of m6A modification, functional understanding is still quite limited. As the focus of RNA epigenetics gradually shifts from site discovery to functional studies, there is an urgent need for user-friendly tools to identify and explore the functional relevance of context-specific m6A methylation to gain insights into the epitranscriptome layer of gene expression regulation.

RESULTS

We introduced here Funm6AViewer, a novel platform to identify, prioritize, and visualize the functional gene interaction networks mediated by dynamic m6A RNA methylation unveiled from a case control study. By taking the differential RNA methylation (DM) data and differential gene expression (DE) data, both of which can be inferred from the widely used MeRIP-seq data, as the inputs, Funm6AViewer enables a series of analysis, including: (1) examining the distribution of differential m6A sites, (2) prioritizing the genes mediated by dynamic m6A methylation, and (3) characterizing functionally the gene regulatory networks mediated by condition-specific m6A RNA methylation. Funm6AViewer should effectively facilitate the understanding of the epitranscriptome circuitry mediated by this reversible RNA modification. Funm6AViewer is available both as a convenient web server (https://www.xjtlu.edu.cn/biologicalsciences/funm6aviewer) with graphical interface and as an independent R package (https://github.com/NWPU-903PR/Funm6AViewer) for local usage.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A brief review of RNA modification related database resources

To date, over 150 different RNA modifications have been identified, playing crucial roles in biological processes and disease pathogenesis. Thanks to the advancement of high-throughput sequencing technologies employed for transcriptome-wide mapping, a bunch of RNA modification databases have emerged as an exciting area, which promotes further investigation of the mechanisms and functions of these modified ribonucleotides. This article introduces the high-throughput sequencing technique developed for transcriptome-wide mapping of RNA modifications, as well as the procedures and main techniques of building these databases from the developers' perspective. It also reviews existing RNA modification databases in terms of their main functions, species, the number of sites they collected, the annotations, and the tools they provided. From the view of users, we further analyze and compare these databases in terms of their functions. For instance, these databases can be applied to record chemical structures and biosynthetic pathways, or unravel the epi-transcriptome comprehensively, or only investigate specific features of RNA modifications. Additionally, the limitations of the existing approaches are discussed, and some future suggestions are offered.

Significance of RNA N6-Methyladenosine Regulators in the Diagnosis and Subtype Classification of Childhood Asthma Using the Gene Expression Omnibus Database

RNA N6-methyladenosine (m6A) regulators play important roles in a variety of biological functions. Nonetheless, the roles of m6A regulators in childhood asthma remain unknown. In this study, 11 significant m6A regulators were selected using difference analysis between non-asthmatic and asthmatic patients from the Gene Expression Omnibus GSE40888 dataset. The random forest model was used to screen five candidate m6A regulators (fragile X mental retardation 1, KIAA1429, Wilm's tumor 1-associated protein, YTH domain-containing 2, and zinc finger CCCH domain-containing protein 13) to predict the risk of childhood asthma. A nomogram model was established based on the five candidate m6A regulators. Decision curve analysis indicated that patients could benefit from the nomogram model. The consensus clustering method was performed to differentiate children with asthma into two m6A patterns (clusterA and clusterB) based on the selected significant m6A regulators. Principal component analysis algorithms were constructed to calculate the m6A score for each sample to quantify the m6A patterns. The patients in clusterB had higher m6A scores than those in clusterA. Furthermore, we found that the patients in clusterA were linked to helper T cell type 1 (Th1)-dominant immunity while those in clusterB were linked to Th2-dominant immunity. In summary, m6A regulators play nonnegligible roles in the occurrence of childhood asthma. Our investigation of m6A patterns may be able to guide future immunotherapy strategies for childhood asthma.

Wilms' Tumor 1-Associated Protein Contributes to Chemo-Resistance to Cisplatin Through the Wnt/β-Catenin Pathway in Endometrial Cancer

BACKGROUND

Cisplatin remains the mainstay of endometrial cancer (EC) chemotherapy. Wilms' tumor 1-associated protein (WTAP), playing a critical role in transcriptional and post-transcriptional regulation, has been reported as an oncogene, and its expression is elevated in multiple types of human tumors. Recent evidence has shown that the increased expression of WTAP is also closely related to chemo-resistance. However, its specific role in the susceptibility of human EC cells to cisplatin remains largely unexplored.

METHODS

WTAP over-expression and WTAP depletion cell lines as well as their corresponding controls were constructed by transfection with lentivirus. Western blotting analysis and quantitative real-time polymerase chain reaction (qRT-PCR) were employed to detect the expression of WTAP. Cell proliferation assay, colony formation assay, cell cycle assay, and apoptosis analysis were adopted to evaluate the effect of WTAP on the chemo-sensitivity of EC cells to cisplatin as well as its underlying mechanism. Immunofluorescence staining was used to assess the translocation of β-catenin. Moreover, a subcutaneous xenograft tumor model was established to assess the effect of WTAP on tumor growth after cisplatin treatment.

RESULTS

Depletion of WTAP in RL95-2 cells significantly enhanced the chemo-susceptibility of cells to cisplatin and increased the cell apoptosis, while WTAP over-expression in ARK-2 cells exhibited the opposite effects. Additionally, WTAP depletion significantly suppressed xenograft-tumor growth and enhanced sensitivity and apoptosis of tumor cells in vivo. Mechanistic analysis exhibited that WTAP over-expression facilitated the cytoplasm-to-nucleus translocation of β-catenin and enhanced the GSK3β phosphorylation at Ser9, while WTAP depletion revealed the opposite results, indicating that WTAP rendered chemo-resistance of EC cells to cisplatin by promoting the Wnt/β-catenin pathway.

CONCLUSIONS

WTAP might promote the chemo-resistance of EC cells to cisplatin through activating the Wnt/β-catenin pathway. Collectively, our findings offered novel insights into EC treatment.

Progression of Thyroid Carcinoma Is Promoted by the m6A Methyltransferase METTL3 Through Regulating m6A Methylation on TCF1

Objective

This study aims to uncover the progression of thyroid carcinoma influenced by the m6A methyltransferase METTL3 through regulating m⁶A methylation on TCF1 mRNA and the activated Wnt pathway.

Methods

Thyroid carcinoma tissues and paracancerous ones were collected for detecting levels of METTL3 and TCF1. Potential correlation between levels of METTL3 and TCF1 was analyzed by Pearson analysis. Survival of thyroid carcinoma patients influenced by METTL3 level was assessed by Kaplan–Meier method. Regulatory effect of METTL3 on migratory ability in TPC-1 cells was examined by wound healing assay. The interaction between METTL3 with TCF1 and IGF2BP2 was verified by RNA-Binding Protein Immunoprecipitation (RIP) assay. Meanwhile, the activity of the Wnt pathway was reflected by TOP/FOP-Flash. At last, rescue experiments were conducted to clarify the involvement of TCF1 in phenotype changes of thyroid carcinoma cells that were regulated by METTL3.

Results

METTL3 and TCF1 were upregulated in thyroid carcinoma. Similarly, METTL3 was highly expressed in thyroid carcinoma cells as well. Kaplan–Meier method uncovered poor prognosis in thyroid carcinoma patients expressing a high level of METTL3. Silence of METTL3 inhibited migratory ability and Wnt activity in TPC-1 cells. RIP assay confirmed the interaction between TCF1 and METTL3 or IGF2BP2. Moreover, METTL3 positively regulated the enrichment abundance of TCF1 in anti-IGF2BP2. Rescue experiments demonstrated that TCF1 was responsible for METTL3-regulated thyroid carcinoma progression via the m⁶A methylation.

Conclusion

Upregulated m6A methyltransferase METTL3 promotes the progression of thyroid carcinoma through m⁶A methylation on TCF1.

Predict Epitranscriptome Targets and Regulatory Functions of N 6-Methyladenosine (m6A) Writers and Erasers

Currently, although many successful bioinformatics efforts have been reported in the epitranscriptomics field for N 6-methyladenosine (m6A) site identification, none is focused on the substrate specificity of different m6A-related enzymes, ie, the methyltransferases (writers) and demethylases (erasers). In this work, to untangle the target specificity and the regulatory functions of different RNA m6A writers (METTL3-METT14 and METTL16) and erasers (ALKBH5 and FTO), we extracted 49 genomic features along with the conventional sequence features and used the machine learning approach of random forest to predict their epitranscriptome substrates. Our method achieved reasonable performance on both the writer target prediction (as high as 0.918) and the eraser target prediction (as high as 0.888) in a 5-fold cross-validation, and results of the gene ontology analysis of their preferential targets further revealed the functional relevance of different RNA methylation writers and erasers.

FunDMDeep-m6A: identification and prioritization of functional differential m6A methylation genes

MOTIVATION

As the most abundant mammalian mRNA methylation, N6-methyladenosine (m6A) exists in >25% of human mRNAs and is involved in regulating many different aspects of mRNA metabolism, stem cell differentiation and diseases like cancer. However, our current knowledge about dynamic changes of m6A levels and how the change of m6A levels for a specific gene can play a role in certain biological processes like stem cell differentiation and diseases like cancer is largely elusive.

RESULTS

To address this, we propose in this paper FunDMDeep-m6A a novel pipeline for identifying context-specific (e.g. disease versus normal, differentiated cells versus stem cells or gene knockdown cells versus wild-type cells) m6A-mediated functional genes. FunDMDeep-m6A includes, at the first step, DMDeep-m6A a novel method based on a deep learning model and a statistical test for identifying differential m6A methylation (DmM) sites from MeRIP-Seq data at a single-base resolution. FunDMDeep-m6A then identifies and prioritizes functional DmM genes (FDmMGenes) by combing the DmM genes (DmMGenes) with differential expression analysis using a network-based method. This proposed network method includes a novel m6A-signaling bridge (MSB) score to quantify the functional significance of DmMGenes by assessing functional interaction of DmMGenes with their signaling pathways using a heat diffusion process in protein-protein interaction (PPI) networks. The test results on 4 context-specific MeRIP-Seq datasets showed that FunDMDeep-m6A can identify more context-specific and functionally significant FDmMGenes than m6A-Driver. The functional enrichment analysis of these genes revealed that m6A targets key genes of many important context-related biological processes including embryonic development, stem cell differentiation, transcription, translation, cell death, cell proliferation and cancer-related pathways. These results demonstrate the power of FunDMDeep-m6A for elucidating m6A regulatory functions and its roles in biological processes and diseases.

AVAILABILITY AND IMPLEMENTATION

The R-package for DMDeep-m6A is freely available from https://github.com/NWPU-903PR/DMDeepm6A1.0.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Discovering personalized driver mutation profiles of single samples in cancer by network control strategy

Motivation

It is a challenging task to discover personalized driver genes that provide crucial information on disease risk and drug sensitivity for individual patients. However, few methods have been proposed to identify the personalized-sample driver genes from the cancer omics data due to the lack of samples for each individual. To circumvent this problem, here we present a novel single-sample controller strategy (SCS) to identify personalized driver mutation profiles from network controllability perspective.

Results

SCS integrates mutation data and expression data into a reference molecular network for each patient to obtain the driver mutation profiles in a personalized-sample manner. This is the first such a computational framework, to bridge the personalized driver mutation discovery problem and the structural network controllability problem. The key idea of SCS is to detect those mutated genes which can achieve the transition from the normal state to the disease state based on each individual omics data from network controllability perspective. We widely validate the driver mutation profiles of our SCS from three aspects: (i) the improved precision for the predicted driver genes in the population compared with other driver-focus methods; (ii) the effectiveness for discovering the personalized driver genes and (iii) the application to the risk assessment through the integration of the driver mutation signature and expression data, respectively, across the five distinct benchmarks from The Cancer Genome Atlas. In conclusion, our SCS makes efficient and robust personalized driver mutation profiles predictions, opening new avenues in personalized medicine and targeted cancer therapy.

Availability and implementation

The MATLAB-package for our SCS is freely available from http://sysbio.sibcb.ac.cn/cb/chenlab/software.htm.

Contact

zhangsw@nwpu.edu.cn or zengtao@sibs.ac.cn or lnchen@sibs.ac.cn.

Supplementary information

Supplementary data are available at Bioinformatics online.

The dynamics of FTO binding and demethylation from the m6A motifs

N⁶-methyladenosine (m⁶A) is considered as a reversible RNA modification occurring more frequently on the GAC than AAC context in vivo, which regulates post-transcriptional gene expression in mammalian cells. m⁶A 'writers' METTL3 and METTL14 demonstrate a strong preference for binding AC-containing motifs in living cells. However, this evidence is currently lacking for m⁶A erasers, leaving the dynamics of the internal m⁶A modification under debate recently. We analysed three recently published FTO CLIP-seq data sets and two generated in this study, one of the two known m⁶A 'erasers'. FTO binding peaks from all cell lines contain RRACH motifs. Only those from K562, 3T3-L1and HeLa cells were enriched in AC-containing motifs, while those from HEK293 were not. The exogenously overexpressed FTO effectively binds to m⁶A motif-containing RNA sites. FTO overexpression specifically removed m⁶A modification from GGACU and RRACU motifs in a concentration-dependent manner. These findings underline the dynamics of FTO in target selection, which is predicted to contribute to both the m⁶A dynamics and the FTO plasticity in biological functions and diseases.

DRUM: Inference of Disease-Associated m6A RNA Methylation Sites From a Multi-Layer Heterogeneous Network

Recent studies have revealed that the RNA N 6-methyladenosine (m6A) modification plays a critical role in a variety of biological processes and associated with multiple diseases including cancers. Till this day, transcriptome-wide m6A RNA methylation sites have been identified by high-throughput sequencing technique combined with computational methods, and the information is publicly available in a few bioinformatics databases; however, the association between individual m6A sites and various diseases are still largely unknown. There are yet computational approaches developed for investigating potential association between individual m6A sites and diseases, which represents a major challenge in the epitranscriptome analysis. Thus, to infer the disease-related m6A sites, we implemented a novel multi-layer heterogeneous network-based approach, which incorporates the associations among diseases, genes and m6A RNA methylation sites from gene expression, RNA methylation and disease similarities data with the Random Walk with Restart (RWR) algorithm. To evaluate the performance of the proposed approach, a ten-fold cross validation is performed, in which our approach achieved a reasonable good performance (overall AUC: 0.827, average AUC 0.867), higher than a hypergeometric test-based approach (overall AUC: 0.7333 and average AUC: 0.723) and a random predictor (overall AUC: 0.550 and average AUC: 0.486). Additionally, we show that a number of predicted cancer-associated m6A sites are supported by existing literatures, suggesting that the proposed approach can effectively uncover the underlying epitranscriptome circuits of disease mechanisms. An online database DRUM, which stands for disease-associated ribonucleic acid methylation, was built to support the query of disease-associated RNA m6A methylation sites, and is freely available at: www.xjtlu.edu.cn/biologicalsciences/drum.

Functions of RNA N6-methyladenosine modification in cancer progression

N6-methyladenosine (m6A) serves as a major RNA methylation modification and impacts the initiation and progression of various human cancers through diverse mechanisms. It has been reported that m6A RNA methylation is involved in different physiological and pathological processes, including stem cell differentiation and motility, immune response, cellular stress, tissue renewal and viral infection. In this review, the m6A modification and its regulatory functions in a few major cancers is introduced. The detection approaches for the m6A sites identification are discussed. Additionally, the potential of the RNA m6A modification in clinical application is discussed.

WT1 associated protein promotes metastasis and chemo-resistance to gemcitabine by stabilizing Fak mRNA in pancreatic cancer

WT1 associated protein (WTAP), playing an important role in several malignancies owing to its complex function in transcriptional and post-transcriptional regulation, is an independent prognostic indicator for pancreatic cancer (PC). However, its specific role and underlying mechanism in PC remain unclear. In the present study, we found that WTAP could promote migration/invasion and suppress chemo-sensitivity to gemcitabine in PC. Further mechanical investigation revealed that WTAP could bind to and stabilize Fak mRNA which in turn activated the Fak-PI3K-AKT and Fak-Src-GRB2-Erk1/2 signaling pathways. In addition, GSK2256098, a specific Fak inhibitor, could reverse WTAP-mediated chemo-resistance to gemcitabine and metastasis in PC. Taken together, Fak inhibitor might be a promising therapeutic option for PC patients with WTAP overexpression.

Functions of RNA N6-methyladenosine modification in cancer progression

N6-methyladenosine (m6A) serves as a major RNA methylation modification and impacts the initiation and progression of various human cancers through diverse mechanisms. It has been reported that m6A RNA methylation is involved in different physiological and pathological processes, including stem cell differentiation and motility, immune response, cellular stress, tissue renewal and viral infection. In this review, the m6A modification and its regulatory functions in a few major cancers is introduced. The detection approaches for the m6A sites identification are discussed. Additionally, the potential of the RNA m6A modification in clinical application is discussed.

CVm6A: A Visualization and Exploration Database for m⁶As in Cell Lines

N6-methyladenosine (m⁶A) has been identified in various biological processes and plays important regulatory functions in diverse cells. However, there is still no visualization database for exploring global m⁶A patterns across cell lines. Here we collected all available MeRIP-Seq and m⁶A-CLIP-Seq datasets from public databases and identified 340,950 and 179,201 m⁶A peaks dependent on 23 human and eight mouse cell lines respectively. Those m⁶A peaks were further classified into mRNA and lncRNA groups. To better understand the potential function of m⁶A, we then mapped m⁶A peaks in different subcellular components and gene regions. Among those human m⁶A modification, 190,050 and 150,900 peaks were identified in cancer and non-cancer cells, respectively. Finally, all results were integrated and imported into a visualized cell-dependent m⁶A database CVm6A. We believe the specificity of CVm6A could significantly contribute to the research for the function and regulation of cell-dependent m⁶A modification in disease and development.

Global analysis of N6-methyladenosine functions and its disease association using deep learning and network-based methods

N6-methyladenosine (m6A) is the most abundant methylation, existing in >25% of human mRNAs. Exciting recent discoveries indicate the close involvement of m6A in regulating many different aspects of mRNA metabolism and diseases like cancer. However, our current knowledge about how m6A levels are controlled and whether and how regulation of m6A levels of a specific gene can play a role in cancer and other diseases is mostly elusive. We propose in this paper a computational scheme for predicting m6A-regulated genes and m6A-associated disease, which includes Deep-m6A, the first model for detecting condition-specific m6A sites from MeRIP-Seq data with a single base resolution using deep learning and Hot-m6A, a new network-based pipeline that prioritizes functional significant m6A genes and its associated diseases using the Protein-Protein Interaction (PPI) and gene-disease heterogeneous networks. We applied Deep-m6A and this pipeline to 75 MeRIP-seq human samples, which produced a compact set of 709 functionally significant m6A-regulated genes and nine functionally enriched subnetworks. The functional enrichment analysis of these genes and networks reveal that m6A targets key genes of many critical biological processes including transcription, cell organization and transport, and cell proliferation and cancer-related pathways such as Wnt pathway. The m6A-associated disease analysis prioritized five significantly associated diseases including leukemia and renal cell carcinoma. These results demonstrate the power of our proposed computational scheme and provide new leads for understanding m6A regulatory functions and its roles in diseases.

trumpet: transcriptome-guided quality assessment of m6A-seq data

BACKGROUND

Methylated RNA immunoprecipitation sequencing (MeRIP-seq or m6A-seq) has been extensively used for profiling transcriptome-wide distribution of RNA N6-Methyl-Adnosine methylation. However, due to the intrinsic properties of RNA molecules and the intricate procedures of this technique, m6A-seq data often suffer from various flaws. A convenient and comprehensive tool is needed to assess the quality of m6A-seq data to ensure that they are suitable for subsequent analysis.

RESULTS

From a technical perspective, m6A-seq can be considered as a combination of ChIP-seq and RNA-seq; hence, by effectively combing the data quality assessment metrics of the two techniques, we developed the trumpet R package for evaluation of m6A-seq data quality. The trumpet package takes the aligned BAM files from m6A-seq data together with the transcriptome information as the inputs to generate a quality assessment report in the HTML format.

CONCLUSIONS

The trumpet R package makes a valuable tool for assessing the data quality of m6A-seq, and it is also applicable to other fragmented RNA immunoprecipitation sequencing techniques, including m1A-seq, CeU-Seq, Ψ-seq, etc.

Wilms' tumor 1-associating protein promotes renal cell carcinoma proliferation by regulating CDK2 mRNA stability

Background

Wilms’ tumor 1-associating protein (WTAP) plays an important role in physiological processes and the development of tumor such as cell cycle regulation. The regulation of cell cycle is mainly dependent on cyclins and cyclin-dependent protein kinases (CDKs). Recent studies have shown that CDKs are closely related to the tumor diagnosis, progression and response to treatment. However, their specific biological roles and related mechanism in renal cell carcinoma (RCC) remain unknown.

Methods

Quantitative real-time PCR, western blotting and immunohistochemistry were used to detect the expression of WTAP and CDK2. The survival analysis was adopted to explore the association between WTAP expression and the prognosis of RCC. Cells were stably transfected with lentivirus approach and cell proliferation and cell cycle, as well as tumorigenesis in nude mice were performed to assess the effect of WTAP in RCC. RNA immunoprecipitation, Luciferase reporter assay and siRNA were employed to identify the direct binding sites of WTAP with CDK2 transcript. Colony formation assay was conducted to confirm the function of CDK2 in WTAP-induced growth promoting.

Results

In RCC cell lines and tissues, WTAP was significantly over-expressed. Compared with patients with low expression of WTAP, patients with high expression of WTAP had lower overall survival rate. Additionally, cell function test indicated that cell proliferation abilities in WTAP over-expressed group were enhanced, while WTAP knockdown showed the opposite results. Subcutaneous xenograft tumor model displayed that knockdown of WTAP could impede tumorigenesis in vivo. Mechanism study exhibited that CDK2 expression was positively associated with the expression of WTAP. Moreover, WTAP stabilized CDK2 transcript to enhance CDK2 expression via binding to 3′-UTR of CDK2 transcript. Additionally, specific inhibitors of CDK2 activity and small interfering RNA (siRNA) of CDK2 expression inhibited WTAP-mediated promotion of proliferation.

Conclusions

These findings suggest that WTAP may have an oncogenic role in RCC through physically binding to CDK2 transcript and enhancing its transcript stability which might provide new insights into RCC therapy.

Electronic supplementary material

The online version of this article (10.1186/s13046-018-0706-6) contains supplementary material, which is available to authorized users.

MeT-DB V2.0: elucidating context-specific functions of N6-methyl-adenosine methyltranscriptome

Methyltranscriptome is an exciting new area that studies the mechanisms and functions of methylation in transcripts. A knowledge base with the systematic collection and curation of context specific transcriptome-wide methylations is critical for elucidating their biological functions as well as for developing bioinformatics tools. Since its inception in 2014, the Met-DB (Liu, H., Flores, M.A., Meng, J., Zhang, L., Zhao, X., Rao, M.K., Chen, Y. and Huang, Y. (2015) MeT-DB: a database of transcriptome methylation in mammalian cells. Nucleic Acids Res., 43, D197-D203), has become an important resource for methyltranscriptome, especially in the N6-methyl-adenosine (m6A) research community. Here, we report Met-DB v2.0, the significantly improved second version of Met-DB, which is entirely redesigned to focus more on elucidating context-specific m6A functions. Met-DB v2.0 has a major increase in context-specific m6A peaks and single-base sites predicted from 185 samples for 7 species from 26 independent studies. Moreover, it is also integrated with a new database for targets of m6A readers, erasers and writers and expanded with more collections of functional data. The redesigned Met-DB v2.0 web interface and genome browser provide more friendly, powerful, and informative ways to query and visualize the data. More importantly, MeT-DB v2.0 offers for the first time a series of tools specifically designed for understanding m6A functions. Met-DB V2.0 will be a valuable resource for m6A methyltranscriptome research. The Met-DB V2.0 database is available at http://compgenomics.utsa.edu/MeTDB/ and http://www.xjtlu.edu.cn/metdb2.

The epitranscriptome m6A writer METTL3 promotes chemo- and radioresistance in pancreatic cancer cells

N6-methyladenosine (m6A) is the most abundant epitranscriptome modification in mammalian mRNA. Recent years have seen substantial progress in m6A epitranscriptomics, indicating its crucial roles in the initiation and progression of cancer through regulation of RNA stabilities, mRNA splicing, microRNA processing and mRNA translation. However, by what means m6A is dynamically regulated or written by enzymatic components represented by methyltransferase-like 3 (METTL3) and how m6A is significant for each of the numerous genes remain unclear. We focused on METTL3 in pancreatic cancer, the prognosis of which is not satisfactory despite the development of multidisciplinary therapies. We established METTL3-knockdown pancreatic cancer cell line using short hairpin RNA. Although morphologic and proliferative changes were unaffected, METTL3-depleted cells showed higher sensitivity to anticancer reagents such as gemcitabine, 5-fluorouracil, cisplatin and irradiation. Our data suggest that METTL3 is a potent target for enhancing therapeutic efficacy in patients with pancreatic cancer. In addition, we performed cDNA expression analysis followed by gene ontology and protein-protein interaction analysis using the Database for Annotation, Visualization, and Integrated Discovery and Search Tool for the Retrieval of Interacting Genes/Proteins databases, respectively. The results demonstrate that METTL3 was associated with mitogen-activated protein kinase cascades, ubiquitin-dependent process and RNA splicing and regulation of cellular process, suggesting functional roles and targets of METTL3.