Molecular biology. Internal mRNA methylation finally finds functions

Comprehensive analysis of the expression patterns and function of the FTO-LINE1 axis in yak tissues and muscle satellite cells

Background

The long interspersed nuclear element 1 (LINE1) retrotransposon has been identified as a specific substrate for fat mass and obesity-related gene (FTO), which facilitates the removal of N6-methyladenosine modifications from its targeted RNAs.

Methods

This study examined the dynamic interaction between FTO and LINE1 in yak tissues and muscle satellite cells, utilizing RT-qPCR, RNA immunoprecipitation (RIP), immunofluorescence staining, and techniques involving overexpression and interference of FTO and LINE1 to elucidate the relationship between FTO and LINE1 in yak tissues and muscle satellite cells.

Results

Cloning and analysis of the FTO coding sequence in Jiulong yak revealed a conserved protein structure across various Bos breeds, with notable homology observed with domestic yak, domestic cattle, and Java bison. Comprehensive examination of FTO and LINE1 gene expression patterns in Jiulong yaks revealed consistent trends across tissues in both sexes. FTO mRNA levels were markedly elevated in the heart and kidney, while LINE1 RNA was predominantly expressed in the heart. Immunoprecipitation confirmed the direct interaction between the FTO protein and LINE1 RNA in yak tissues and muscle satellite cells. The FTO-LINE1 axis was confirmed by a significant decrease in LINE1 RNA enrichment following its expression interference in yak muscle satellite cells. Overexpression of FTO substantially reduced the expression of recombinant myogenic factor 5 (MYF5). However, FTO interference had no discernible effect on MYF5 and myoblast determination protein 1 (MYOD1) mRNA levels. Immunofluorescence analysis revealed no alterations in Ki-67 protein expression following FTO interference or overexpression. However, phalloidin staining demonstrated enhancement in the myotube fusion rate of yak muscle satellite cells upon LINE1 interference.

Conclusion

This comprehensive mapping of the FTO and LINE1 mRNA expression patterns establishes a direct interaction between the FTO protein and LINE1 RNA in yak. The findings suggest that FTO overexpression promotes muscle satellite cells differentiation, whereas LINE1 negatively regulates myotube fusion. The study provides fundamental insights into the role of the FTO-LINE1 axis in determining the fate of muscle satellite cells in yak, laying a solid theoretical foundation for future investigations.

Prediction of Multiple Types of RNA Modifications via Biological Language Model

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

The landscape of implantation and placentation: deciphering the function of dynamic RNA methylation at the maternal-fetal interface

The maternal-fetal interface is defined as the interface between maternal tissue and sections of the fetus in close contact. RNA methylation modifications are the most frequent kind of RNA alterations. It is effective throughout both normal and pathological implantation and placentation during pregnancy. By influencing early embryo development, embryo implantation, endometrium receptivity, immune microenvironment, as well as some implantation and placentation-related disorders like miscarriage and preeclampsia, it is essential for the establishment of the maternal-fetal interface. Our review focuses on the role of dynamic RNA methylation at the maternal-fetal interface, which has received little attention thus far. It has given the mechanistic underpinnings for both normal and abnormal implantation and placentation and could eventually provide an entirely novel approach to treating related complications.

DNMT3B activates FGFR3-mediated endoplasmic reticulum stress by regulating PTPN2 promoter methylation to promote the development of atherosclerosis

Endoplasmic reticulum (ER) stress is closely associated with atherosclerosis (AS). Nevertheless, the regulatory mechanism of ER stress in endothelial cells during AS progression is unclear. Here, the role and regulatory mechanism of DNA (cytosine-5-)- methyltransferase 3 beta (DNMT3B) in ER stress during AS progression were investigated. ApoE^-/- mice were fed with high fat diet to construct AS model in vivo. HE and Masson staining were performed to analyze histopathological changes and collagen deposition. HUVECs stimulated by ox-LDL were used as AS cellular model. Cell apoptosis was examined using flow cytometry. DCFH-DA staining was performed to examine ROS level. The levels of pro-inflammatory cytokines were assessed using ELISA. In addition, MSP was employed to detect PTPN2 promoter methylation level. Our results revealed that DNMT3B and FGFR3 were significantly upregulated in AS patient tissues, whereas PTPN2 was downregulated. PTPN2 overexpression attenuate ox-LDL-induced ER stress, inflammation and apoptosis in HUVECs and ameliorated AS symptoms in vivo. PTPN2 could suppress FGFR3 expression in ox-LDL-treated HUVECs, and FGFR3 knockdown inhibited ER stress to attenuate ox-LDL-induced endothelial cell apoptosis. DNMT3B could negatively regulate PTPN2 expression and positively FGFR2 expression in ox-LDL-treated HUVECs; DNMT3B activated FGFR2 expression by increasing PTPN2 promoter methylation level. DNMT3B downregulation repressed ox-LDL-induced ER stress, inflammation and cell apoptosis in endothelial cells, which was reversed by PTPN2 silencing. DNMT3B activated FGFR3-mediated ER stress by increasing PTPN2 promoter methylation level and suppressed its expression, thereby boosting ER stress to facilitate AS progression.

The therapeutic targets of N6-methyladenosine (m6A) modifications on tumor radioresistance

Radiation therapy is an important tool for malignant tumors, and its tolerance needs to be addressed. In recent years, several studies have shown that regulators of aberrant m6A methylation play an important role in the formation, development and invasion and metastasis of tumors. A large number of studies have confirmed aberrant m6A methylation as a new target for tumour therapy, but research on whether it can play a role in tumor sensitivity to radiotherapy has not been extensive and thorough enough. Recent studies have shown that all three major enzymes of m6A methylation have significant roles in radioresistance, and that the enzymes that play a role differ in different tumor types and by different mechanisms, including regulating tumor cell stemness, affecting DNA damage and repair, and controlling the cell cycle. Therefore, elucidating the mechanisms of m6A methylation in the radiotherapy of malignant tumors is essential to counteract radioresistance, improve the efficacy of radiotherapy, and even propose targeted treatment plans for specific tumors. The latest research progress on m6A methylation and radioresistance is reviewed in this article.

Induced PSIG expression by Herbacetin contributes to suppressing the proliferation, migration, and invasion of melanoma cells

Melanoma is a very common malignant tumor with poor prognosis. Herbacetin is a flavonol compound with outstanding anti-tumor effects. Our work investigated the biological effects and mechanism of Herbacetin in melanoma. In our study, the mRNA and protein expressions were assessed using qRT-PCR, Western blot and IHC. MSP was performed to evaluated PGIS promoter methylation level. Cell viability, migration and invasion were examined by MTT assay, transwell migration and invasion assay, respectively. Our results revealed that DNMT3B was markedly upregulated in melanoma, while PGIS was lowly expressed. Herbacetin treatment could not only inhibit the proliferation, migration, invasion of melanoma cells and inhibit the growth of melanoma in vivo. Herbacetin could also restore the abnormal expressions of DNMT3B and PGIS in melanoma cells and tumor tissues. PGIS silencing neutralized the inhibitory effects of Herbacetin on the malignant behaviors of melanoma cells. Besides, DNMT3B knockdown promoted PGIS expression via reducing PGIS promoter methylation level in melanoma cells, thereby inhibiting malignant behaviors of melanoma cells. And as expected, the inhibitory effects of Herbacetin on malignant behaviors of melanoma cells were all abolished by DNMT3B overexpression. Collectively, Herbacetin reduced DNMT3B expression to upregulate PGIS in melanoma cells and participated in suppressing the proliferation, migration, and invasion of melanoma cells.

The m6A reader YTHDC2 promotes SIRT3 expression by reducing the stabilization of KDM5B to improve mitochondrial metabolic reprogramming in diabetic peripheral neuropathy

AIMS

Diabetic peripheral neuropathy (DPN) is a common diabetic complication. Aberrant mitochondrial function causes neurodegeneration under hyperglycemia-induced metabolic stress, which in turn results in DPN progression. m⁶A and m⁶A reader (YTHDC2) are closely related to diabetes and diabetes complications, while the role of YTHDC2 in regulating mitochondrial metabolism in DPN needs to be further probed.

METHODS

For HG treatment, Schwann cells (RSC96) were subjected to D-glucose for 72 h. db/db mice were used as the diabetic mouse model. Me-RIP assay was performed to evaluate KDM5B m⁶A level. RNA degradation assay was conducted to examine KDM5B mRNA stability. In addition, OCR and ECAR were examined by XF96 Analyzer. Moreover, the content of ATP and PDH activity in RSC96 cells were detected using kits, and the level of ROS was detected using MitoSOX staining. RIP, RNA pull-down and dual-luciferase reporter gene assays were carried out to verify the binding relationships between YTHDC2, KDM5B and SIRT3.

RESULTS

We first observed that KDM5B expression and KDM5B mRNA stabilization were significantly increased in DPN. The m⁶A reader YTHDC2 was lowly expressed in DPN. Meanwhile, YTHDC2 over expression decreased KDM5B mRNA stability in an m⁶A-dependent manner. Our results also revealed that YTHDC2 overexpression resulted in reduced ROS level and increased ATP level, PDH activity, OCR and ECAR in HG-treated Schwann cells, while these effects were reversed by KDM5B overexpression. Additionally, SIRT3 served as the target of YTHDC2/KDM5B axis in regulating mitochondrial metabolism in DPN.

CONCLUSIONS

Taken together, YTHDC2 promoted SIRT3 expression by reducing the stabilization of KDM5B to improve mitochondrial metabolic reprogramming in DPN.

The m6A demethylase FTO promotes renal epithelial-mesenchymal transition by reducing the m6A modification of lncRNA GAS5

BACKGROUND

Renal interstitial fibrosis (RIF) is the main pathological change of a variety of chronic kidney diseases (CKD). Epigenetic modifications of fibrosis-prone genes regulate RIF progression. This study aimed to investigate long non-coding RNA (lncRNA) N6-methyladenosine (m⁶A) modification and its role in regulating RIF progression.

METHODS

Unilateral ureteral occlusion (UUO) was employed to construct the RIF in vivo model; and TGF-β1-treated HK-2 and HKC-8 cells were used for in vitro experiments. The mRNA and protein expressions were assessed using qRT-PCR and western blot. The proliferation and migration were evaluated by EdU assay and transwell assay, respectively. In addition, levels of inflammatory cytokines were determined by ELISA assay and qRT-PCR. Moreover, lncRNA GAS5 m⁶A level was detected using Me-RIP assay. HE and Masson staining were employed to evaluate fibrotic lesions of the kidney.

RESULTS

FTO expression was elevated in HK-2 and HKC-8 cells after TGF-β1 treatment and mouse kidney tissue following UUO, and lncRNA GAS5 was downregulated. LncRNA GAS5 overexpression or FTO silencing suppressed TGF-β1-induced the increase of EMT-related proteins (Vimentin, Snail and N-cadherin) and inflammatory cytokines (IL-6, IL-1β and TNF-α) levels in HK-2 cells. FTO suppressed lncRNA GAS5 expression by reducing the m6A modification of lncRNA GAS5. Additionally, FTO knockdown could suppress EMT process and inflammation response induced by TGF-β1 and UUO in vitro and in vivo. As expected, FTO knockdown abrogated the promotion effects of lncRNA GAS5 silencing on TGF-β1-induced EMT process and inflammation response in HK-2 and HKC-8 cells.

CONCLUSION

FTO promoted EMT process and inflammation response through reducing the m⁶A modification of lncRNA GAS5.

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.

The m6 A reader MhYTP2 regulates MdMLO19 mRNA stability and antioxidant genes translation efficiency conferring powdery mildew resistance in apple

N⁶ -methyladenosine (m⁶ A) reader protein plays an important role in trichome morphology, developmental timing and morphogenesis in Arabidopsis. However, the function of m⁶ A readers in plant-microbe interaction remains unclear. Here, a Malus YTH-domain family protein MhYTP2 was initially characterized as an m⁶ A reader. MhYTP2 overexpression increased mRNA m⁶ A modification level and translation efficiency. The m⁶ A in the exon regions appeared to destabilize the mRNAs, whereas m⁶ A in the untranslated regions positively correlated with the associated mRNA abundance. MhYTP2 overexpression enhanced apple powdery mildew resistance, possibly by rapidly degrading the bound mRNAs of MdMLO19 and MdMLO19-X1 and improving the translation efficiency of the antioxidant genes. To conclude, the results shed light on the apple m⁶ A profile, the effect of MhYTP2 on m⁶ A profile, and the m⁶ A roles in MdMLO19 and MdMLO19-X1 mRNAs stability and glutamate dehydrogenase 1-like MdGDH1L mRNA translation efficiency.

ALKBH5 promotes the progression of infantile hemangioma through regulating the NEAT1/miR-378b/FOSL1 axis

Our work aims to investigate long non-coding RNA (lncRNA) N6-methyladenosine (m⁶A) modification and its role in infantile hemangioma (IH). The mRNA and protein expression levels were assessed using quantitative real-time polymerase chain reaction, western blot and immunohistochemistry. Me-RIP assay was performed to evaluate lncRNA NEAT1 m⁶A levels. Cell proliferation, migration and invasion were evaluated using cell counting kit-8 assay, transwell migration and invasion assay, respectively. Photo-activatable ribonucleoside-enhanced crosslinking and immunoprecipitation assay was conducted to verify the binding relationship between lncRNA nuclear paraspeckle assembly transcript 1 (NEAT1) and ALKBH5 (an RNA demethylase). The binding relationship between lncRNA NEAT1, microRNA (miR)-378b and FOS-like antigen 1 (FOSL1) was verified using dual-luciferase reporter gene assay and/or RNA immunoprecipitation assay. ALKBH5, lncRNA NEAT1 and FOLS1 expression was elevated in IH tissues, while miR-378b was downregulated. ALKBH5 knockdown suppressed cell proliferation, migration and invasion of IH cells, while promoting cell apoptosis. ALKBH5 promoted lncRNA NEAT1 expression by reducing the m⁶A modification of lncRNA NEAT1. In addition, miR-378b was the target of lncRNA NEAT1, and its overexpression reversed the promotion effect of lncRNA NEAT1 overexpression on IH cell tumor-like behaviors. Moreover, FOLS1 was the target of miR-378b, and its overexpression reversed the inhibitory effect of miR-378b overexpression on IH cell tumor-like behaviors in vitro. ALKBH5 might have great potential as therapeutic target for IH, since ALKBH5 silencing suppressed IH progression by regulation of the NEAT1/miR-378b/FOSL1 axis.

Analysis of N6-Methyladenosine Methylome in Adenocarcinoma of Esophagogastric Junction

Background: From previous studies, we found that there are more than 100 types of RNA modifications in RNA molecules. m⁶A methylation is the most common. The incidence rate of adenocarcinoma of the esophagogastric junction (AEG) at home and abroad has increased faster than that of stomach cancer at other sites in recent years. Here, we systematically analyze the modification pattern of m⁶A mRNA in adenocarcinoma at the esophagogastric junction.

Methods: m⁶A sequencing, RNA sequencing, and bioinformatics analysis were used to describe the m⁶A modification pattern in adenocarcinoma and normal tissues at the esophagogastric junction.

Results: In AEG samples, a total of 4,775 new m⁶A peaks appeared, and 3,054 peaks disappeared. The unique m6A-related genes in AEG are related to cancer-related pathways. There are hypermethylated or hypomethylated m⁶A peaks in AEG in differentially expressed mRNA transcripts.

Conclusion: This study preliminarily constructed the first m⁶A full transcriptome map of human AEG. This has a guiding role in revealing the mechanism of m⁶A-mediated gene expression regulation.

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.

Integration Analysis of m6A Related Genes in Skin Cutaneous Melanoma and the Biological Function Research of the SPRR1B

Melanoma has gradually entered the public view because of its high morbidity and rising prevalence rate, which is a serious threat to human life and health. Recently, N6-methyladenine (m6A) modification has been increasingly confirmed as a potential role in the development of tumogenesis. The purpose of this study is to explore the role and function of m6a-related regulators in the development of melanoma disease at the molecular, cellular and clinical levels through bioinformatics and traditional experiments. We screened and validated differential expression genes (DEGs) in m6A regulators via the GEO, GTEx, TCGA database. The biological processes and signaling pathway involved by DEGs were improved by constructing bioinformational methods such as PPI, GO enrichment, KEGG enrichment, GSEA enrichment, and immune infiltration analysis. And then, we explored the biological function of the key gene, SPRR1B, through cell invasion, migration, infiltration, and tissue chips. The gene IGF2BP3 which was differentially expressed in m6A regulatory factor gene was screened. The results of the enrichment analysis are significantly enriched in the biological processes and pathways of the skin barrier, epidermal differentiation, cytoskeleton, lymphocyte migration and other pathways, pointing to the direction of tumor immunity and tumor metastasis. Tumor immune-related genes YTHDC1, YTHDC2 and ALKBH5 were found. Knock SPRR1B reduction group had a significantly lower invasive ability, the ability to migrate. Nomogram prediction model shows that SPRR1B increased, expressing a worse prognosis. For this purpose, the relationship between m6A regulatory factor and melanoma progression was explored. At the same time, it was found that the abnormal up-regulated expression of SPRR1B before metastasis would lead to poor prognosis of melanoma. SPRR1B promotes the proliferation, invasion and migration of human melanoma cells.

Integration Analysis of m6A Regulators and m6A-Related Genes in Hepatocellular Carcinoma

Abstract Background: N6-Methyladenosine (m6A) RNA methylation of eukaryotic mRNA is involved in the progression of various tumors. We aimed to investigate m6A-related genes and m6A regulators in hepatocellular carcinoma (HCC) and their association with prognosis in HCC.Methods: We downloaded liver cancer sample data from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium database. A total of 21 m6A regulators and 1258 m6A-related genes were then analyzed by consensus clustering, Spearman’s correlation, GO, KEGG, LASSO Cox regression, and univariate Cox regression analyses. Finally, we constructed a risk prognostic model.Results: We obtained 192 candidate m6A-related genes and 3 m6A regulators, including YTHDF1, YTHDF2, and YTHDC1. The expression of these genes and regulators differed significantly in different stages of HCC. Based on Cox regression analysis, 19 of 98 m6A-related prognostic genes were obtained to construct a risk score model. The 1- and 3-year area under the curves (AUCs) among HCC patients were greater than 0.7. Finally, based on analysis of mutation differences between high- and low-risk score groups, we determined that TP53 had the highest mutation frequency in the high-risk HCC patient group, whereas titin (TTN) had the highest mutation frequency in the low-risk HCC patient group.Conclusion: This study comprehensively analyzed m6A regulators and m6A-related genes through an integrated bioinformatic analysis, including expression, clustering, protein‐protein interaction, and prognosis, thus providing novel insights into the roles of m6A regulators and m6A-related genes in HCC.

LncAY controls BMI1 expression and activates BMI1/Wnt/β-catenin signaling axis in hepatocellular carcinoma

BACKGROUND & AIMS

Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver. Long non-coding RNAs as master gene regulators play important roles in tumorigenesis and progression. However, the significance of lncRNAs and their regulatory mechanisms in HCC are largely unknown. Our study was to define the role of lncAY (long noncoding RNA AY927503) in HCC.

METHODS

Methylated RNA immunoprecipitation qPCR combined with bioinformatics were used to identify the m6A modification of lncAY. qRT-PCR, western blotting and immunofluorescence were used to identify the expression of the lncAY/YTHDF2/BMI1/Wnt axis in HCC tissues and cell lines. Gain- and loss-of functions of lncAY and BMI1 were implemented to confirm their roles in the behaviors of HCC cells.

RESULTS

Our findings suggested that m6A-modified lncAY expression relied on m6A "reader" protein YTHDF2. LncAY upregulated BMI1 expression in HCC cells and a notably positive relevance is evident between lncAY and BMI1 expression in TCGA HCC datasets. BMI1 was upregulated in HCC tissues and patients with higher BMI1 expression had a poor clinical prognosis. Besides, GSEA analysis showed remarkable enrichment of high BMI1 expression in gene sets associated with Wnt/β-catenin signaling. Rescue results revealed that BMI1 reversed the suppressive effects of lncAY depletion in HCC cells.

CONCLUSIONS

Our work suggested that lncAY might elevate BMI1 expression and further activate the Wnt/β-catenin signaling. BMI1 reverses the suppressive effects of lncAY depletion in HCC cells. Collectively, our work uncovers a novel undefined regulatory signaling pathway, namely lncAY/BMI1/Wnt/β-catenin axis, involved in liver cancer progression.

M6A-BiNP: predicting N6-methyladenosine sites based on bidirectional position-specific propensities of polynucleotides and pointwise joint mutual information

N⁶-methyladenosine (m⁶A) plays an important role in various biological processes. Identifying m⁶A site is a key step in exploring its biological functions. One of the biggest challenges in identifying m⁶A sites is how to extract features comprising rich categorical information to distinguish m⁶A and non-m⁶A sites. To address this challenge, we propose bidirectional dinucleotide and trinucleotide position-specific propensities, respectively, in this paper. Based on this, we propose two feature-encoding algorithms: Position-Specific Propensities and Pointwise Mutual Information (PSP-PMI) and Position-Specific Propensities and Pointwise Joint Mutual Information (PSP-PJMI). PSP-PMI is based on the bidirectional dinucleotide propensity and the pointwise mutual information, while PSP-PJMI is based on the bidirectional trinucleotide position-specific propensity and the proposed pointwise joint mutual information in this paper. We introduce parameters α and β in PSP-PMI and PSP-PJMI, respectively, to represent the distance from the nucleotide to its forward or backward adjacent nucleotide or dinucleotide, so as to extract features containing local and global classification information. Finally, we propose the M6A-BiNP predictor based on PSP-PMI or PSP-PJMI and SVM classifier. The 10-fold cross-validation experimental results on the benchmark datasets of non-single-base resolution and single-base resolution demonstrate that PSP-PMI and PSP-PJMI can extract features with strong capabilities to identify m⁶A and non-m⁶A sites. The M6A-BiNP predictor based on our proposed feature encoding algorithm PSP-PJMI is better than the state-of-the-art predictors, and it is so far the best model to identify m⁶A and non-m⁶A sites.

EDLm6APred: ensemble deep learning approach for mRNA m6A site prediction

BACKGROUND

As a common and abundant RNA methylation modification, N6-methyladenosine (m⁶A) is widely spread in various species' transcriptomes, and it is closely related to the occurrence and development of various life processes and diseases. Thus, accurate identification of m⁶A methylation sites has become a hot topic. Most biological methods rely on high-throughput sequencing technology, which places great demands on the sequencing library preparation and data analysis. Thus, various machine learning methods have been proposed to extract various types of features based on sequences, then occupied conventional classifiers, such as SVM, RF, etc., for m⁶A methylation site identification. However, the identification performance relies heavily on the extracted features, which still need to be improved.

RESULTS

This paper mainly studies feature extraction and classification of m⁶A methylation sites in a natural language processing way, which manages to organically integrate the feature extraction and classification simultaneously, with consideration of upstream and downstream information of m⁶A sites. One-hot, RNA word embedding, and Word2vec are adopted to depict sites from the perspectives of the base as well as its upstream and downstream sequence. The BiLSTM model, a well-known sequence model, was then constructed to discriminate the sequences with potential m⁶A sites. Since the above-mentioned three feature extraction methods focus on different perspectives of m⁶A sites, an ensemble deep learning predictor (EDLm⁶APred) was finally constructed for m⁶A site prediction. Experimental results on human and mouse data sets show that EDLm⁶APred outperforms the other single ones, indicating that base, upstream, and downstream information are all essential for m⁶A site detection. Compared with the existing m⁶A methylation site prediction models without genomic features, EDLm⁶APred obtains 86.6% of the area under receiver operating curve on the human data sets, indicating the effectiveness of sequential modeling on RNA. To maximize user convenience, a webserver was developed as an implementation of EDLm⁶APred and made publicly available at www.xjtlu.edu.cn/biologicalsciences/EDLm6APred .

CONCLUSIONS

Our proposed EDLm⁶APred method is a reliable predictor for m⁶A methylation sites.

m6AGE: A Predictor for N6-Methyladenosine Sites Identification Utilizing Sequence Characteristics and Graph Embedding-Based Geometrical Information

N6-methyladenosine (m6A) is one of the most prevalent RNA post-transcriptional modifications and is involved in various vital biological processes such as mRNA splicing, exporting, stability, and so on. Identifying m6A sites contributes to understanding the functional mechanism and biological significance of m6A. The existing biological experimental methods for identifying m6A sites are time-consuming and costly. Thus, developing a high confidence computational method is significant to explore m6A intrinsic characters. In this study, we propose a predictor called m6AGE which utilizes sequence-derived and graph embedding features. To the best of our knowledge, our predictor is the first to combine sequence-derived features and graph embeddings for m6A site prediction. Comparison results show that our proposed predictor achieved the best performance compared with other predictors on four public datasets across three species. On the A101 dataset, our predictor outperformed 1.34% (accuracy), 0.0227 (Matthew's correlation coefficient), 5.63% (specificity), and 0.0081 (AUC) than comparing predictors, which indicates that m6AGE is a useful tool for m6A site prediction. The source code of m6AGE is available at https://github.com/bokunoBike/m6AGE.

Role of promoters in regulating alternative splicing

Alternative splicing (AS) plays a critical role in enhancing proteome complexity in higher eukaryotes. Almost all the multi intron-containing genes undergo AS in humans. Splicing mainly occurs co-transcriptionally, where RNA polymerase II (RNA pol II) plays a crucial role in coordinating transcription and pre-mRNA splicing. Aberrant AS leads to non-functional proteins causative in various pathophysiological conditions such as cancers, neurodegenerative diseases, and muscular dystrophies. Transcription and pre-mRNA splicing are deeply interconnected and can influence each other's functions. Several studies evinced that specific promoters employed by RNA pol II dictate the RNA processing decisions. Promoter-specific recruitment of certain transcriptional factors or transcriptional coactivators influences splicing, and the extent to which these factors affect splicing has not been discussed in detail. Here, in this review, various DNA-binding proteins and their influence on promoter-specific AS are extensively discussed. Besides, this review highlights how the promoter-specific epigenetic changes might regulate AS.

NADP modulates RNA m6A methylation and adipogenesis via enhancing FTO activity

Metabolism is often regulated by the transcription and translation of RNA. In turn, it is likely that some metabolites regulate enzymes controlling reversible RNA modification, such as N6-methyladenosine (m6A), to modulate RNA. This hypothesis is at least partially supported by the findings that multiple metabolic diseases are highly associated with fat mass and obesity-associated protein (FTO), an m6A demethylase. However, knowledge about whether and which metabolites directly regulate m6A remains elusive. Here, we show that NADP directly binds FTO, independently increases FTO activity, and promotes RNA m6A demethylation and adipogenesis. We screened a set of metabolites using a fluorescence quenching assay and NADP was identified to remarkably bind FTO. In vitro demethylation assays indicated that NADP enhances FTO activity. Furthermore, NADP regulated mRNA m6A via FTO in vivo, and deletion of FTO blocked NADP-enhanced adipogenesis in 3T3-L1 preadipocytes. These results build a direct link between metabolism and RNA m6A demethylation.

Advances in the role of m6A RNA modification in cancer metabolic reprogramming

N6-methyladenosine (m6A) modification is the most common internal modification of eukaryotic mRNA and is widely involved in many cellular processes, such as RNA transcription, splicing, nuclear transport, degradation, and translation. m6A has been shown to plays important roles in the initiation and progression of various cancers. The altered metabolic programming of cancer cells promotes their cell-autonomous proliferation and survival, leading to an indispensable hallmark of cancers. Accumulating evidence has demonstrated that this epigenetic modification exerts extensive effects on the cancer metabolic network by either directly regulating the expression of metabolic genes or modulating metabolism-associated signaling pathways. In this review, we summarized the regulatory mechanisms and biological functions of m6A and its role in cancer metabolic reprogramming.

Emerging translation strategies during virus-host interaction

Translation control is crucial during virus-host interaction. On one hand, viruses completely rely on the protein synthesis machinery of host cells to propagate and have evolved various mechanisms to redirect the host's ribosomes toward their viral mRNAs. On the other hand, the host rewires its translation program in an attempt to contain and suppress the virus early on during infection; the antiviral program includes specific control on protein synthesis to translate several antiviral mRNAs involved in quenching the infection. As the infection progresses, host translation is in turn inhibited in order to limit viral propagation. We have learnt of very diverse strategies that both parties utilize to gain or retain control over the protein synthesis machinery. Yet novel strategies continue to be discovered, attesting for the importance of mRNA translation in virus-host interaction. This review focuses on recently described translation strategies employed by both hosts and viruses. These discoveries provide additional pieces in the understanding of the complex virus-host translation landscape. This article is categorized under: Translation > Translation Mechanisms Translation > Translation Regulation.

Prediction of N6-methyladenosine sites using convolution neural network model based on distributed feature representations

N⁶-methyladenosine (m⁶A) is a well-studied and most common interior messenger RNA (mRNA) modification that plays an important function in cell development. N⁶A is found in all kingdoms​ of life and many other cellular processes such as RNA splicing, immune tolerance, regulatory functions, RNA processing, and cancer. Despite the crucial role of m⁶A in cells, it was targeted computationally, but unfortunately, the obtained results were unsatisfactory. It is imperative to develop an efficient computational model that can truly represent m⁶A sites. In this regard, an intelligent and highly discriminative computational model namely: m6A-word2vec is introduced for the discrimination of m⁶A sites. Here, a concept of natural language processing in the form of word2vec is used to represent the motif of the target class automatically. These motifs (numerical descriptors) are automatically targeted from the human genome without any clear definition. Further, the extracted feature space is then forwarded to the convolution neural network model as input for prediction. The developed computational model obtained 83.17%, 92.69%, and 90.50% accuracy for benchmark datasets S₁, S₂, and S₃, respectively, using a 10-fold cross-validation test. The predictive outcomes validate that the developed intelligent computational model showed better performance compared to existing computational models. It is thus greatly estimated that the introduced computational model "m6A-word2vec" may be a supportive and practical tool for elementary and pharmaceutical research such as in drug design along with academia.

LncRNA SOX2OT promotes temozolomide resistance by elevating SOX2 expression via ALKBH5-mediated epigenetic regulation in glioblastoma

Temozolomide (TMZ) resistance is a major cause of recurrence and poor prognosis in glioblastoma (GBM). Recently, increasing evidences suggested that long noncoding RNAs (LncRNAs) modulate GBM biological processes, especially in resistance to chemotherapy, but their role in TMZ chemoresistance has not been fully illuminated. Here, we found that LncRNA SOX2OT was increased in TMZ-resistant cells and recurrent GBM patient samples, and abnormal expression was correlated with high risk of relapse and poor prognosis. Knockdown of SOX2OT suppressed cell proliferation, facilitated cell apoptosis, and enhanced TMZ sensitivity. In addition, we identified that SOX2OT regulated TMZ sensitivity by increasing SOX2 expression and further activating the Wnt5a/β-catenin signaling pathway in vitro and in vivo. Mechanistically, further investigation revealed that SOX2OT recruited ALKBH5, which binds with SOX2, demethylating the SOX2 transcript, leading to enhanced SOX2 expression. Together, these results demonstrated that LncRNA SOX2OT inhibited cell apoptosis, promoted cell proliferation, and TMZ resistance by upregulating SOX2 expression, which activated the Wnt5a/β-catenin signaling pathway. Our findings indicate that LncRNA SOX2OT may serve as a novel biomarker for GBM prognosis and act as a therapeutic target for TMZ treatment.

iMethyl-Deep: N6 Methyladenosine Identification of Yeast Genome with Automatic Feature Extraction Technique by Using Deep Learning Algorithm

One of the most common and well studied post-transcription modifications in RNAs is N6-methyladenosine (m6A) which has been involved with a wide range of biological processes. Over the past decades, N6-methyladenosine produced some positive consequences through the high-throughput laboratory techniques but still, these lab processes are time consuming and costly. Diverse computational methods have been proposed to identify m6A sites accurately. In this paper, we proposed a computational model named iMethyl-deep to identify m6A Saccharomyces Cerevisiae on two benchmark datasets M6A2614 and M6A6540 by using single nucleotide resolution to convert RNA sequence into a high quality feature representation. The iMethyl-deep obtained 89.19% and 87.44% of accuracy on M6A2614 and M6A6540 respectively which show that our proposed method outperforms the state-of-the-art predictors, at least 8.44%, 8.96%, 8.69% and 0.173 on M6A2614 and 15.47%, 28.52%, 25.54 and 0.5 on M6A6540 higher in terms of four metrics Sp, Sn, ACC and MCC respectively. Meanwhile, M6A6540 dataset never used to train a model.

Computational identification of N6-methyladenosine sites in multiple tissues of mammals

N6-methyladenosine (m6A) is the methylation of the adenosine at the nitrogen-6 position, which is the most abundant RNA methylation modification and involves a series of important biological processes. Accurate identification of m6A sites in genome-wide is invaluable for better understanding their biological functions. In this work, an ensemble predictor named iRNA-m6A was established to identify m6A sites in multiple tissues of human, mouse and rat based on the data from high-throughput sequencing techniques. In the proposed predictor, RNA sequences were encoded by physical-chemical property matrix, mono-nucleotide binary encoding and nucleotide chemical property. Subsequently, these features were optimized by using minimum Redundancy Maximum Relevance (mRMR) feature selection method. Based on the optimal feature subset, the best m6A classification models were trained by Support Vector Machine (SVM) with 5-fold cross-validation test. Prediction results on independent dataset showed that our proposed method could produce the excellent generalization ability. We also established a user-friendly webserver called iRNA-m6A which can be freely accessible at http://lin-group.cn/server/iRNA-m6A. This tool will provide more convenience to users for studying m6A modification in different tissues.

YTHDF2 reduction fuels inflammation and vascular abnormalization in hepatocellular carcinoma

BACKGROUND

Dynamic N⁶-methyladenosine (m⁶A) modification was previously identified as a ubiquitous post-transcriptional regulation that affected mRNA homeostasis. However, the m⁶A-related epitranscriptomic alterations and functions remain elusive in human cancer. Here we aim to identify the profile and outcome of m⁶A-methylation in hepatocellular carcinoma (HCC).

RESULTS

Using liquid chromatography-tandem mass spectrometry and m⁶A-immunoprecipitation in combination with high-throughput sequencing, we determined the m⁶A-mRNA levels in human HCC. Human HCC exhibited a characteristic gain of m⁶A modification in tandem with an increase of mRNA expression, owing to YTH domain family 2 (YTHDF2) reduction. The latter predicted poor classification and prognosis of HCC patients, and highly correlated with HCC m⁶A landscape. YTHDF2 silenced in human HCC cells or ablated in mouse hepatocytes provoked inflammation, vascular reconstruction and metastatic progression. Mechanistically, YTHDF2 processed the decay of m⁶A-containing interleukin 11 (IL11) and serpin family E member 2 (SERPINE2) mRNAs, which were responsible for the inflammation-mediated malignancy and disruption of vascular normalization. Reciprocally, YTHDF2 transcription succumbed to hypoxia-inducible factor-2α (HIF-2α). Administration of a HIF-2α antagonist (PT2385) restored YTHDF2-programed epigenetic machinery and repressed liver cancer.

CONCLUSION

Our results have characterized the m⁶A-mRNA landscape in human HCC and revealed YTHDF2 as a molecular 'rheostat' in epitranscriptome and cancer progression.

A Linear Regression Predictor for Identifying N6-Methyladenosine Sites Using Frequent Gapped K-mer Pattern

N6-methyladenosine (m⁶A) is one of the most common and abundant modifications in RNA, which is related to many biological processes in humans. Abnormal RNA modifications are often associated with a series of diseases, including tumors, neurogenic diseases, and embryonic retardation. Therefore, identifying m⁶A sites is of paramount importance in the post-genomic age. Although many lab-based methods have been proposed to annotate m⁶A sites, they are time consuming and cost ineffective. In view of the drawbacks of the intrinsic methods in RNA sequence recognition, computational methods are suggested as a supplement to identify m⁶A sites. In this study, we develop a novel feature extraction algorithm based on the frequent gapped k-mer pattern (FGKP) and apply the linear regression to construct the prediction model. The new predictor is used to identify m⁶A sites in the Saccharomyces cerevisiae database. It has been shown by the 10-fold cross-validation that the performance is better than that of recent methods. Comparative results indicate that our model has great potential to become a useful and effective tool for genome analysis and gain more insights for locating m⁶A sites.

N6-Methyladenosine and Viral Infection

N⁶-methyladenosine (m⁶A), as a dynamic posttranscriptional RNA modification, recently gave rise to the field of viral epitranscriptomics. The interaction between virus and host is affected by m⁶A. Multiple m⁶A-modified viral RNAs have been observed. The epitranscriptome of m⁶A in host cells are altered after viral infection. The expression of viral genes, the replication of virus and the generation of progeny virions are influenced by m⁶A modifications in viral RNAs during virus infection. Meanwhile, the decorations of m⁶A in host mRNAs can make viral infections more likely to happen or can enhance the resistance of host to virus infection. However, the mechanism of m⁶A regulation in viral infection and host immune response has not been thoroughly elucidated to date. With the development of sequencing-based biotechnologies, transcriptome-wide mapping of m⁶A in viruses has been achieved, laying the foundation for expanding its functions and corresponding mechanisms. In this report, we summarize the positive and negative effects of m⁶A in distinct viral infection. Given the increasingly important roles of m⁶A in diverse viruses, m⁶A represents a novel potential target for antiviral therapy.

iRNA(m6A)-PseDNC: Identifying N6-methyladenosine sites using pseudo dinucleotide composition

As a prevalent post-transcriptional modification, N⁶-methyladenosine (m⁶A) plays key roles in a series of biological processes. Although experimental technologies have been developed and applied to identify m⁶A sites, they are still cost-ineffective for transcriptome-wide detections of m⁶A. As good complements to the experimental techniques, some computational methods have been proposed to identify m⁶A sites. However, their performance remains unsatisfactory. In this study, we firstly proposed an Euclidean distance based method to construct a high quality benchmark dataset. By encoding the RNA sequences using pseudo nucleotide composition, a new predictor called iRNA(m6A)-PseDNC was developed to identify m⁶A sites in the Saccharomyces cerevisiae genome. It has been demonstrated by the 10-fold cross validation test that the performance of iRNA(m6A)-PseDNC is superior to the existing methods. Meanwhile, for the convenience of most experimental scientists, established at the site http://lin-group.cn/server/iRNA(m6A)-PseDNC.php is its web-server, by which users can easily get their desired results without need to go through the detailed mathematics. It is anticipated that iRNA(m6A)-PseDNC will become a useful high throughput tool for identifying m⁶A sites in the S. cerevisiae genome.

BERMP: a cross-species classifier for predicting m6A sites by integrating a deep learning algorithm and a random forest approach

N⁶-methyladenosine (m⁶A) is a prevalent RNA methylation modification involved in several biological processes. Hundreds or thousands of m⁶A sites identified from different species using high-throughput experiments provides a rich resource to construct in-silico approaches for identifying m⁶A sites. The existing m⁶A predictors are developed using conventional machine-learning (ML) algorithms and most are species-centric. In this paper, we develop a novel cross-species deep-learning classifier based on bidirectional Gated Recurrent Unit (BGRU) for the prediction of m⁶A sites. In comparison with conventional ML approaches, BGRU achieves outstanding performance for the Mammalia dataset that contains over fifty thousand m⁶A sites but inferior for the Saccharomyces cerevisiae dataset that covers around a thousand positives. The accuracy of BGRU is sensitive to the data size and the sensitivity is compensated by the integration of a random forest classifier with a novel encoding of enhanced nucleic acid content. The integrated approach dubbed as BGRU-based Ensemble RNA Methylation site Predictor (BERMP) has competitive performance in both cross-validation test and independent test. BERMP also outperforms existing m⁶A predictors for different species. Therefore, BERMP is a novel multi-species tool for identifying m⁶A sites with high confidence. This classifier is freely available at http://www.bioinfogo.org/bermp.

M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning

N6-methyladenosine (m⁶A) modification is the most abundant RNA methylation modification and involves various biological processes, such as RNA splicing and degradation. Recent studies have demonstrated the feasibility of identifying m⁶A peaks using high-throughput sequencing techniques. However, such techniques cannot accurately identify specific methylated sites, which is important for a better understanding of m⁶A functions. In this study, we develop a novel machine learning-based predictor called M6APred-EL for the identification of m⁶A sites. To predict m⁶A sites accurately within genomic sequences, we trained an ensemble of three support vector machine classifiers that explore the position-specific information and physical chemical information from position-specific k-mer nucleotide propensity, physical-chemical properties, and ring-function-hydrogen-chemical properties. We examined and compared the performance of our predictor with other state-of-the-art methods of benchmarking datasets. Comparative results showed that the proposed M6APred-EL performed more accurately for m⁶A site identification. Moreover, a user-friendly web server that implements the proposed M6APred-EL is well established and is currently available at http://server.malab.cn/M6APred-EL/. It is expected to be a practical and effective tool for the investigation of m⁶A functional mechanisms.

Identifying RNA N6-Methyladenosine Sites in Escherichia coli Genome

N⁶-methyladenosine (m⁶A) plays important roles in a branch of biological and physiological processes. Accurate identification of m⁶A sites is especially helpful for understanding their biological functions. Since the wet-lab techniques are still expensive and time-consuming, it's urgent to develop computational methods to identify m⁶A sites from primary RNA sequences. Although there are some computational methods for identifying m⁶A sites, no methods whatsoever are available for detecting m⁶A sites in microbial genomes. In this study, we developed a computational method for identifying m⁶A sites in Escherichia coli genome. The accuracies obtained by the proposed method are >90% in both 10-fold cross-validation test and independent dataset test, indicating that the proposed method holds the high potential to become a useful tool for the identification of m⁶A sites in microbial genomes.

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.

Viruses, Mark Thy Message Well

Post-transcriptional m(6)A methylation of RNA has profound effects on RNA splicing, export, stability, and translation. A recent study by Lichinchi et al. (2016) and one in this issue of Cell Host & Microbe by Kennedy et al. (2016) demonstrate that HIV mRNA is extensively m(6)A methylated, which promotes efficient virus replication.

Splicing and transcription touch base: co-transcriptional spliceosome assembly and function

Several macromolecular machines collaborate to produce eukaryotic messenger RNA. RNA polymerase II (Pol II) translocates along genes that are up to millions of base pairs in length and generates a flexible RNA copy of the DNA template. This nascent RNA harbours introns that are removed by the spliceosome, which is a megadalton ribonucleoprotein complex that positions the distant ends of the intron into its catalytic centre. Emerging evidence that the catalytic spliceosome is physically close to Pol II in vivo implies that transcription and splicing occur on similar timescales and that the transcription and splicing machineries may be spatially constrained. In this Review, we discuss aspects of spliceosome assembly, transcription elongation and other co-transcriptional events that allow the temporal coordination of co-transcriptional splicing.

A novel algorithm for calling mRNA m6A peaks by modeling biological variances in MeRIP-seq data

Motivation:N⁶-methyl-adenosine (m⁶A) is the most prevalent mRNA methylation but precise prediction of its mRNA location is important for understanding its function. A recent sequencing technology, known as Methylated RNA Immunoprecipitation Sequencing technology (MeRIP-seq), has been developed for transcriptome-wide profiling of m⁶A. We previously developed a peak calling algorithm called exomePeak. However, exomePeak over-simplifies data characteristics and ignores the reads’ variances among replicates or reads dependency across a site region. To further improve the performance, new model is needed to address these important issues of MeRIP-seq data.

Results: We propose a novel, graphical model-based peak calling method, MeTPeak, for transcriptome-wide detection of m⁶A sites from MeRIP-seq data. MeTPeak explicitly models read count of an m⁶A site and introduces a hierarchical layer of Beta variables to capture the variances and a Hidden Markov model to characterize the reads dependency across a site. In addition, we developed a constrained Newton’s method and designed a log-barrier function to compute analytically intractable, positively constrained Beta parameters. We applied our algorithm to simulated and real biological datasets and demonstrated significant improvement in detection performance and robustness over exomePeak. Prediction results on publicly available MeRIP-seq datasets are also validated and shown to be able to recapitulate the known patterns of m⁶A, further validating the improved performance of MeTPeak.

Availability and implementation: The package ‘MeTPeak’ is implemented in R and C ++, and additional details are available at https://github.com/compgenomics/MeTPeak

Contact:yufei.huang@utsa.edu or xdchoi@gmail.com

Supplementary information:Supplementary data are available at Bioinformatics online.

Kaposi's Sarcoma-Associated Herpesvirus Utilizes and Manipulates RNA N6-Adenosine Methylation To Promote Lytic Replication

N⁶-adenosine methylation (m⁶A) is the most common posttranscriptional RNA modification in mammalian cells. We found that most transcripts encoded by the Kaposi's sarcoma-associated herpesvirus (KSHV) genome undergo m⁶A modification. The levels of m⁶A-modified mRNAs increased substantially upon stimulation for lytic replication. The blockage of m⁶A inhibited splicing of the pre-mRNA encoding the replication transcription activator (RTA), a key KSHV lytic switch protein, and halted viral lytic replication. We identified several m⁶A sites in RTA pre-mRNA crucial for splicing through interactions with YTH domain containing 1 (YTHDC1), an m⁶A nuclear reader protein, in conjunction with serine/arginine-rich splicing factor 3 (SRSF3) and SRSF10. Interestingly, RTA induced m⁶A and enhanced its own pre-mRNA splicing. Our results not only demonstrate an essential role of m⁶A in regulating RTA pre-mRNA splicing but also suggest that KSHV has evolved a mechanism to manipulate the host m⁶A machinery to its advantage in promoting lytic replication.IMPORTANCE KSHV productive lytic replication plays a pivotal role in the initiation and progression of Kaposi's sarcoma tumors. Previous studies suggested that the KSHV switch from latency to lytic replication is primarily controlled at the chromatin level through histone and DNA modifications. The present work reports for the first time that KSHV genome-encoded mRNAs undergo m⁶A modification, which represents a new mechanism at the posttranscriptional level in the control of viral replication.

iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC

There are many different types of RNA modifications, which are essential for numerous biological processes. Knowledge about the occurrence sites of RNA modifications in its sequence is a key for in-depth understanding of their biological functions and mechanism. Unfortunately, it is both time-consuming and laborious to determine these sites purely by experiments alone. Although some computational methods were developed in this regard, each one could only be used to deal with some type of modification individually. To our knowledge, no method has thus far been developed that can identify the occurrence sites for several different types of RNA modifications with one seamless package or platform. To address such a challenge, a novel platform called "iRNA-PseColl" has been developed. It was formed by incorporating both the individual and collective features of the sequence elements into the general pseudo K-tuple nucleotide composition (PseKNC) of RNA via the chemicophysical properties and density distribution of its constituent nucleotides. Rigorous cross-validations have indicated that the anticipated success rates achieved by the proposed platform are quite high. To maximize the convenience for most experimental biologists, the platform's web-server has been provided at http://lin.uestc.edu.cn/server/iRNA-PseColl along with a step-by-step user guide that will allow users to easily achieve their desired results without the need to go through the mathematical details involved in this paper.

Identifying N6-methyladenosine sites using multi-interval nucleotide pair position specificity and support vector machine

N6-methyladenosine (m6A) refers to methylation of the adenosine nucleotide acid at the nitrogen-6 position. It plays an important role in a series of biological processes, such as splicing events, mRNA exporting, nascent mRNA synthesis, nuclear translocation and translation process. Numerous experiments have been done to successfully characterize m6A sites within sequences since high-resolution mapping of m6A sites was established. However, as the explosive growth of genomic sequences, using experimental methods to identify m6A sites are time-consuming and expensive. Thus, it is highly desirable to develop fast and accurate computational identification methods. In this study, we propose a sequence-based predictor called RAM-NPPS for identifying m6A sites within RNA sequences, in which we present a novel feature representation algorithm based on multi-interval nucleotide pair position specificity, and use support vector machine classifier to construct the prediction model. Comparison results show that our proposed method outperforms the state-of-the-art predictors on three benchmark datasets across the three species, indicating the effectiveness and robustness of our method. Moreover, an online webserver implementing the proposed predictor has been established at http://server.malab.cn/RAM-NPPS/. It is anticipated to be a useful prediction tool to assist biologists to reveal the mechanisms of m6A site functions.

Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines

As one of the most abundant RNA post-transcriptional modifications, N⁶-methyladenosine (m⁶A) involves in a broad spectrum of biological and physiological processes ranging from mRNA splicing and stability to cell differentiation and reprogramming. However, experimental identification of m⁶A sites is expensive and laborious. Therefore, it is urgent to develop computational methods for reliable prediction of m⁶A sites from primary RNA sequences. In the current study, a new method called RAM-ESVM was developed for detecting m⁶A sites from Saccharomyces cerevisiae transcriptome, which employed ensemble support vector machine classifiers and novel sequence features. The jackknife test results show that RAM-ESVM outperforms single support vector machine classifiers and other existing methods, indicating that it would be a useful computational tool for detecting m⁶A sites in S. cerevisiae. Furthermore, a web server named RAM-ESVM was constructed and could be freely accessible at http://server.malab.cn/RAM-ESVM/.

Supramolecular catalysis in the methylation of meta-phenylene ethynylene foldamer containing N,N-dimethylaminopyridine

DFT investigations show that the methylation reaction of N,N-dimethylaminopyridine (DMAP)-modified meta-phenylene ethynylene foldamer can be catalyzed by the noncovalent interactions between the foldamer and the methyl sulfonate esters.

Recent Advances in Identification of RNA Modifications

RNA modifications are involved in a broad spectrum of biological and physiological processes. To reveal the functions of RNA modifications, it is important to accurately predict their positions. Although high-throughput experimental techniques have been proposed, they are cost-ineffective. As good complements of experiments, many computational methods have been proposed to predict RNA modification sites in recent years. In this review, we will summarize the existing computational approaches directed at predicting RNA modification sites. We will also discuss the challenges and future perspectives in developing reliable methods for predicting RNA modification sites.