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

<div><p>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.</p></div

Integration of epigenetic regulatory mechanisms in heart failure

The number of “omics” approaches is continuously growing. Among others, epigenetics has appeared as an attractive area of investigation by the cardiovascular research community, notably considering its association with disease development. Complex diseases such as cardiovascular diseases have to be tackled using methods integrating different omics levels, so called “multi-omics” approaches. These approaches combine and co-analyze different levels of disease regulation. In this review, we present and discuss the role of epigenetic mechanisms in regulating gene expression and provide an integrated view of how these mechanisms are interlinked and regulate the development of cardiac disease, with a particular attention to heart failure. We focus on DNA, histone, and RNA modifications, and discuss the current methods and tools used for data integration and analysis. Enhancing the knowledge of these regulatory mechanisms may lead to novel therapeutic approaches and biomarkers for precision healthcare and improved clinical outcomes

Recent advances in functional annotation and prediction of the epitranscriptome

RNA modifications, in particular N(6)-methyladenosine (m(6)A), 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

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

Recent studies have revealed that the RNA N6-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

Computational Methods for the Analysis of Genomic Data and Biological Processes

In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality

Prediction of RNA Methylation Status From Gene Expression Data Using Classification and Regression Methods

RNA N6-methyladenosine (m6A) has emerged as an important epigenetic modification for its role in regulating the stability, structure, processing, and translation of RNA. Instability of m6A homeostasis may result in flaws in stem cell regulation, decrease in fertility, and risk of cancer. To this day, experimental detection and quantification of RNA m6A modification are still time-consuming and labor-intensive. There is only a limited number of epitranscriptome samples in existing databases, and a matched RNA methylation profile is not often available for a biological problem of interests. As gene expression data are usually readily available for most biological problems, it could be appealing if we can estimate the RNA methylation status from gene expression data using in silico methods. In this study, we explored the possibility of computational prediction of RNA methylation status from gene expression data using classification and regression methods based on mouse RNA methylation data collected from 73 experimental conditions. Elastic Net-regularized Logistic Regression (ENLR), Support Vector Machine (SVM), and Random Forests (RF) were constructed for classification. Both SVM and RF achieved the best performance with the mean area under the curve (AUC) = 0.84 across samples; SVM had a narrower AUC spread. Gene Site Enrichment Analysis was conducted on those sites selected by ENLR as predictors to access the biological significance of the model. Three functional annotation terms were found statistically significant: phosphoprotein, SRC Homology 3 (SH3) domain, and endoplasmic reticulum. All 3 terms were found to be closely related to m6A pathway. For regression analysis, Elastic Net was implemented, which yielded a mean Pearson correlation coefficient = 0.68 and a mean Spearman correlation coefficient = 0.64. Our exploratory study suggested that gene expression data could be used to construct predictors for m6A methylation status with adequate accuracy. Our work showed for the first time that RNA methylation status may be predicted from the matched gene expression data. This finding may facilitate RNA modification research in various biological contexts when a matched RNA methylation profile is not available, especially in the very early stage of the study

M6A RNA methylation in diabetes induced endothelial damage and ischaemic disease

Diabetes mellitus exposes endothelial cells (ECs) to a chronic hyperglycaemic milieu, leading to dysfunction of the vascular endothelium. The resulting microvasculature rarefaction leads to tissue hypoperfusion and propagates the occurrence of ischaemic events, such as critical limb ischaemia and ischaemic heart disease. Moreover, hyperglycaemia impairs the angiogenic potential of ECs thereby compromising post-ischaemic reparative neovascularisation. N6-methyladenosine (m6A) is emerging as a new layer for fine-tuning gene expression. The functional importance of m6A has been revealed in a plethora of fundamental bioprocesses, while its dysregulation has been linked to several diseases including diabetes. However, our understanding of the precise roles of m6A in the cardiovascular system is still in its infancy and its significance in diabetes associated complications of the vasculature remains completely unexplored. This study elucidates a novel role for METTL3, the primary m6A methylase, in the regulation of angiogenesis. Loss and gain of function studies reveal METTL3 to be crucial in the modulation of EC processes that are conducive to angiogenesis in vitro and in vivo. Mechanistically, METTL3 modulates angiogenesis by mediating the endothelial bioprocessing of the angiogenic miRNAs let-7e and the miR-17-92 cluster. Expressional analysis revealed a dysregulation of m6A and METTL3 in human ECs exposed to diabetic and ischaemic mimicking conditions, ECs derived from a murine model of diabetic LI and in left ventricular tissue and ECs isolated from diabetic mouse hearts. The therapeutic potential of endothelial METTL3 was demonstrated using murine models of diabetic limb ischaemia and myocardial infarction. Here, the adenovirus mediated overexpression of METTL3 in ischaemic limb muscles improved post-ischaemic muscular neovascularisation. Additionally, infarcted hearts treated with Ad.METTL3 showed an increase in arteriole and capillary densities while exhibiting improved contractile function. Thus, the findings in this thesis suggest that the modulation of METTL3 could represent novel therapeutic target for ischaemic complications in diabetic patients.Open Acces

m7GHub: deciphering the location, regulation and pathogenesis of internal mRNA N7-methylguanosine (m7G) sites in human

Motivation Recent progress in N7-methylguanosine (m7G) RNA methylation studies has focused on its internal (rather than capped) presence within mRNAs. Tens of thousands of internal mRNA m7G sites have been identified within mammalian transcriptomes, and a single resource to best share, annotate and analyze the massive m7G data generated recently are sorely needed. Results We report here m7GHub, a comprehensive online platform for deciphering the location, regulation and pathogenesis of internal mRNA m7G. The m7GHub consists of four main components, including: the first internal mRNA m7G database containing 44 058 experimentally validated internal mRNA m7G sites, a sequence-based high-accuracy predictor, the first web server for assessing the impact of mutations on m7G status, and the first database recording 1218 disease-associated genetic mutations that may function through regulation of m7G methylation. Together, m7GHub will serve as a useful resource for research on internal mRNA m7G modification