Dataset Information

Quantifying the RNA cap epitranscriptome reveals novel caps in cellular and viral RNA.

ABSTRACT: Chemical modification of transcripts with 5' caps occurs in all organisms. Here, we report a systems-level mass spectrometry-based technique, CapQuant, for quantitative analysis of an organism's cap epitranscriptome. The method was piloted with 21 canonical caps-m7GpppN, m7GpppNm, GpppN, GpppNm, and m2,2,7GpppG-and 5 'metabolite' caps-NAD, FAD, UDP-Glc, UDP-GlcNAc, and dpCoA. Applying CapQuant to RNA from purified dengue virus, Escherichia coli, yeast, mouse tissues, and human cells, we discovered new cap structures in humans and mice (FAD, UDP-Glc, UDP-GlcNAc, and m7Gpppm6A), cell- and tissue-specific variations in cap methylation, and high proportions of caps lacking 2'-O-methylation (m7Gpppm6A in mammals, m7GpppA in dengue virus). While substantial Dimroth-induced loss of m1A and m1Am arose with specific RNA processing conditions, human lymphoblast cells showed no detectable m1A or m1Am in caps. CapQuant accurately captured the preference for purine nucleotides at eukaryotic transcription start sites and the correlation between metabolite levels and metabolite caps.

SUBMITTER: Wang J

PROVIDER: S-EPMC6847653 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Quantifying the RNA cap epitranscriptome reveals novel caps in cellular and viral RNA.

Wang Jin J Alvin Chew Bing Liang BL Lai Yong Y Dong Hongping H Xu Luang L Balamkundu Seetharamsingh S Cai Weiling Maggie WM Cui Liang L Liu Chuan Fa CF Fu Xin-Yuan XY Lin Zhenguo Z Shi Pei-Yong PY Lu Timothy K TK Luo Dahai D Jaffrey Samie R SR Dedon Peter C PC

Nucleic acids research 20191101 20

Chemical modification of transcripts with 5' caps occurs in all organisms. Here, we report a systems-level mass spectrometry-based technique, CapQuant, for quantitative analysis of an organism's cap epitranscriptome. The method was piloted with 21 canonical caps-m7GpppN, m7GpppNm, GpppN, GpppNm, and m2,2,7GpppG-and 5 'metabolite' caps-NAD, FAD, UDP-Glc, UDP-GlcNAc, and dpCoA. Applying CapQuant to RNA from purified dengue virus, Escherichia coli, yeast, mouse tissues, and human cells, we discover ...[more]

PMID: 31504804

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Project description:Biochemical modifications to mRNA, especially N6-methyladenosine (m6A) and 5-methylcytosine (m5C), have been recently shown to be associated with crucial biological functions. Despite the intriguing advancements, little is known so far about the dynamic landscape of RNA methylome across different cell types and how the epitranscriptome is regulated at the system level by enzymes, i.e., RNA methyltransferases and demethylases. To investigate this issue, a meta-analysis of m6A MeRIP-Seq datasets collected from 10 different experimental conditions (cell type/tissue or treatment) is performed, and the combinatorial epitranscriptome, which consists of 42?758 m6A sites, is extracted and divided into 3 clusters, in which the methylation sites are likely to be hyper- or hypo-methylated simultaneously (or co-methylated), indicating the sharing of a common methylation regulator. Four different clustering approaches are used, including K-means, hierarchical clustering (HC), Bayesian factor regression model (BFRM) and nonnegative matrix factorization (NMF) to unveil the co-methylation patterns. To validate whether the patterns are corresponding to enzymatic regulators, i.e., RNA methyltransferases or demethylases, the target sites of a known m6A regulator, fat mass and obesity-associated protein (FTO), are identified from an independent mouse MeRIP-Seq dataset and lifted to human. Our study shows that 3 out of the 4 clustering approaches used can successfully identify a group of methylation sites overlapping with FTO target sites at a significance level of 0.05 (after multiple hypothesis adjustment), among which, the result of NMF is the most significant (p-value 2.81×10(-06)). We defined a new approach evaluating the consistency between two clustering results which shows that clustering results of different methods are highly correlated strongly indicating the existence of co-methylation patterns. Consistent with recent studies, a number of cancer and neuronal disease-related bimolecular functions are enriched in the identified clusters, which are biological functions that can be regulated at the epitranscriptional level, indicating the pharmaceutical prospect of RNA N6-methyladenosine-related studies. This result successfully reveals the linkage between the global RNA co-methylation patterns embedded in the epitranscriptomic data under multiple experimental conditions and the latent enzymatic regulators, suggesting a promising direction towards a more comprehensive understanding of the epitranscriptome.

Dataset Information

Quantifying the RNA cap epitranscriptome reveals novel caps in cellular and viral RNA.

Publications

Quantifying the RNA cap epitranscriptome reveals novel caps in cellular and viral RNA.

Similar Datasets

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