Heterogeneity of chemical modifications on RNA.

The chemical modifications of RNAs broadly impact almost all cellular events and influence various diseases. The rapid advance of sequencing and other technologies opened the door to global methods for profiling all RNA modifications, namely the "epitranscriptome." The mapping of epitranscriptomes in different cells and tissues unveiled that RNA modifications exhibit extensive heterogeneity, in type, amount, and in location. In this mini review, we first introduce the current understanding of modifications on major types of RNAs and the methods that enabled their discovery. We next discuss the tissue and cell heterogeneity of RNA modifications and briefly address the limitations of current technologies. With much still remaining unknown, the development of the epitranscriptomic field lies in the further developments of novel technologies.

Detection of m6A from direct RNA sequencing using a multiple instance learning framework.

RNA modifications such as m6A methylation form an additional layer of complexity in the transcriptome. Nanopore direct RNA sequencing can capture this information in the raw current signal for each RNA molecule, enabling the detection of RNA modifications using supervised machine learning. However, experimental approaches provide only site-level training data, whereas the modification status for each single RNA molecule is missing. Here we present m6Anet, a neural-network-based method that leverages the multiple instance learning framework to specifically handle missing read-level modification labels in site-level training data. m6Anet outperforms existing computational methods, shows similar accuracy as experimental approaches, and generalizes with high accuracy to different cell lines and species without retraining model parameters. In addition, we demonstrate that m6Anet captures the underlying read-level stoichiometry, which can be used to approximate differences in modification rates. Overall, m6Anet offers a tool to capture the transcriptome-wide identification and quantification of m6A from a single run of direct RNA sequencing.

Identification of differential RNA modifications from nanopore direct RNA sequencing with xPore.

RNA modifications, such as N6-methyladenosine (m6A), modulate functions of cellular RNA species. However, quantifying differences in RNA modifications has been challenging. Here we develop a computational method, xPore, to identify differential RNA modifications from nanopore direct RNA sequencing (RNA-seq) data. We evaluate our method on transcriptome-wide m6A profiling data, demonstrating that xPore identifies positions of m6A sites at single-base resolution, estimates the fraction of modified RNA species in the cell and quantifies the differential modification rate across conditions. We apply xPore to direct RNA-seq data from six cell lines and multiple myeloma patient samples without a matched control sample and find that many m6A sites are preserved across cell types, whereas a subset exhibit significant differences in their modification rates. Our results show that RNA modifications can be identified from direct RNA-seq data with high accuracy, enabling analysis of differential modifications and expression from a single high-throughput experiment.

METTL4 catalyzes m6Am methylation in U2 snRNA to regulate pre-mRNA splicing.

N 6-methylation of 2'-O-methyladenosine (Am) in RNA occurs in eukaryotic cells to generate N6,2'-O-dimethyladenosine (m6Am). Identification of the methyltransferase responsible for m6Am catalysis has accelerated studies on the function of m6Am in RNA processing. While m6Am is generally found in the first transcribed nucleotide of mRNAs, the modification is also found internally within U2 snRNA. However, the writer required for catalyzing internal m6Am formation had remained elusive. By sequencing transcriptome-wide RNA methylation at single-base-resolution, we identified human METTL4 as the writer that directly methylates Am at U2 snRNA position 30 into m6Am. We found that METTL4 localizes to the nucleus and its conserved methyltransferase catalytic site is required for U2 snRNA methylation. By sequencing human cells with overexpressed Mettl4, we determined METTL4's in vivo target RNA motif specificity. In the absence of Mettl4 in human cells, U2 snRNA lacks m6Am thereby affecting a subset of splicing events that exhibit specific features such as 3' splice-site weakness and an increase in exon inclusion. These findings suggest that METTL4 methylation of U2 snRNA regulates splicing of specific pre-mRNA transcripts.

Atlas of quantitative single-base-resolution N6-methyl-adenine methylomes.

Various methyltransferases and demethylases catalyse methylation and demethylation of N6-methyladenosine (m6A) and N6,2'-O-dimethyladenosine (m6Am) but precise methylomes uniquely mediated by each methyltransferase/demethylase are still lacking. Here, we develop m6A-Crosslinking-Exonuclease-sequencing (m6ACE-seq) to map transcriptome-wide m6A and m6Am at quantitative single-base-resolution. This allows for the generation of a comprehensive atlas of distinct methylomes uniquely mediated by every individual known methyltransferase or demethylase. Our atlas reveals METTL16 to indirectly impact manifold methylation targets beyond its consensus target motif and highlights the importance of precision in mapping PCIF1-dependent m6Am. Rather than reverse RNA methylation, we find that both ALKBH5 and FTO instead maintain their regulated sites in an unmethylated steady-state. In FTO's absence, anomalous m6Am disrupts snRNA interaction with nuclear export machinery, potentially causing aberrant pre-mRNA splicing events.