Above the Epitranscriptome: RNA Modifications and Stem Cell Identity

Morena, Francesco; Argentati, Chiara; Bazzucchi, Martina; Emiliani, Carla; Martino, Sabata

doi:10.3390/genes9070329

Cited by 41 publications

(32 citation statements)

References 245 publications

(342 reference statements)

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“…Neural stem cells maintain cell populations through self-renewal, and can differentiate into various nerve cells such as neurons, astrocytes, and oligodendrocytes [64][65][66][67]. A number of studies have shown that the mRNA m 6 A modification can affect the self-renewal and differentiation of neural stem cells [68][69][70].…”

Section: Effect Of M 6 a On Neural Stem Cellsmentioning

confidence: 99%

The role of mRNA m6A methylation in the nervous system

Yang

et al. 2019

Cell Biosci

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Epitranscriptomics, also known as "RNA epigenetics", is a chemical modification for RNA regulation. Ribonucleic acid (RNA) methylation is considered to be a major discovery following the deoxyribonucleic acid (DNA) and histone methylation. Messenger RNA (mRNA) methylation modification accounts for more than 60% of all RNA modifications and N6-methyladenosine (m 6 A) is known as one of the most common type of eukaryotic mRNA methylation modifications in current. The m 6 A modification is a dynamic reversible modification, which can directly or indirectly affect biological processes, such as RNA degradation, translation and splicing, and can play important biological roles in vivo. This article introduces the mRNA m 6 A methylation modification enzymes and binding proteins, and reviews the research progress and related mechanisms of the role of mRNA m 6 A methylation in the nervous system from the aspects of neural stem cells, learning and memory, brain development, axon growth and glioblastoma.

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Section: Effect Of M 6 a On Neural Stem Cellsmentioning

confidence: 99%

The role of mRNA m6A methylation in the nervous system

Yang

et al. 2019

Cell Biosci

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“…RNA modification, represented by the epitranscriptome [1], refers to biochemical modifications of RNAs that are involved in functional regulations such as translation efficiency and mRNA stability without a change in the RNA sequence. Over 100 types of RNA modifications have been reported [2].…”

Section: Introductionmentioning

confidence: 99%

MoAIMS: efficient software for detection of enriched regions of MeRIP-Seq

Zhang

Hamada

2020

BMC Bioinformatics

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Background: Methylated RNA immunoprecipitation sequencing (MeRIP-Seq) is a popular sequencing method for studying RNA modifications and, in particular, for N6-methyladenosine (m6A), the most abundant RNA methylation modification found in various species. The detection of enriched regions is a main challenge of MeRIP-Seq analysis, however current tools either require a long time or do not fully utilize features of RNA sequencing such as strand information which could cause ambiguous calling. On the other hand, with more attention on the treatment experiments of MeRIP-Seq, biologists need intuitive evaluation on the treatment effect from comparison. Therefore, efficient and user-friendly software that can solve these tasks must be developed. Results: We developed a software named "model-based analysis and inference of MeRIP-Seq (MoAIMS)" to detect enriched regions of MeRIP-Seq and infer signal proportion based on a mixture negative-binomial model. MoAIMS is designed for transcriptome immunoprecipitation sequencing experiments; therefore, it is compatible with different RNA sequencing protocols. MoAIMS offers excellent processing speed and competitive performance when compared with other tools. When MoAIMS is applied to studies of m6A, the detected enriched regions contain known biological features of m6A. Furthermore, signal proportion inferred from MoAIMS for m6A treatment datasets (perturbation of m6A methyltransferases) showed a decreasing trend that is consistent with experimental observations, suggesting that the signal proportion can be used as an intuitive indicator of treatment effect. Conclusions: MoAIMS is efficient and easy-to-use software implemented in R. MoAIMS can not only detect enriched regions of MeRIP-Seq efficiently but also provide intuitive evaluation on treatment effect for MeRIP-Seq treatment datasets.

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“…In recent years, with the advent and development of high-throughput sequencing technologies coupled with direct RNA-sequencing technologies, the focus has shifted to the study of epigenetic modifications of RNA [3,4]. Based on these sequencing technologies, published data reveals that RNA contains multiple dynamic modifications, among which the most studied are N6-methyladenosine (m 6 A), 5-methylcytosine (m 5 C), N1-methyladenosine (m 1 A), N7methylguanosine(N 7 G), and ribose 2'-O-methylation as well as pseudouridine (Ψ) and inosine (I) [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Clinicopathological signature and prognostic value of RNA:m5C methyltransferases in gliomas

Wang

Huang

et al. 2020

Preprint

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Background: Glioma is the most common primary intracranial tumor, accounting for the vast majority of intracranial malignant tumors. Aberrant expression of RNA:5-methylcytosine(m5C) methyltransferases has recently been the focus of research relating to the occurrence and progression of tumors. However, the prognostic value of RNA:m5C methyltransferases in glioma remains unclear. This study investigated RNA: m5C methyltransferase expression and defined its clinicopathological signature and prognostic value in gliomas. Methods: We systematically studied the RNA-sequence data of RNA:m5C methyltransferases underlying gliomas in the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets and identified different subtypes using Consensus clustering analysis. Gene Ontology (GO) and Gene Set Enrichment analysis (GSEA) was used to annotate the function of these genes. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm analyses were performed to construct the risk score model. Kaplan-Meier method and Receiver operating characteristic (ROC) curves were used to assess the overall survival of glioma patients. Additionally, Cox proportional regression model analysis was developed to address the connections between the risk scores and clinical factors. Results: Consensus clustering of RNA:m5C methyltransferases identified three clusters of gliomas with different prognostic and clinicopathological features. Meanwhile, Functional annotations demonstrated that RNA:m5C methyltransferases were significantly associated with the malignant progression of gliomas. Thereafter, five RNA:m5C methyltransferase genes were screened to construct a risk score model which can be used to predict not only overall survival but also clinicopathological features in gliomas. ROC curves revealed the significant prognostic ability of this signature. In addition, Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for glioma outcome. Conclusion: We demonstrated the role of RNA:m5C methyltransferases in the initiation and progression of glioma. We have expanded on the understanding of the molecular mechanism involved, and provided a unique approach to predictive biomarkers and targeted therapy.

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Above the Epitranscriptome: RNA Modifications and Stem Cell Identity

Cited by 41 publications

References 245 publications

The role of mRNA m6A methylation in the nervous system

The role of mRNA m6A methylation in the nervous system

MoAIMS: efficient software for detection of enriched regions of MeRIP-Seq

Clinicopathological signature and prognostic value of RNA:m5C methyltransferases in gliomas

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