Quantitative profiling of native RNA modifications and their dynamics using nanopore sequencing

Abstract

<jats:title>SUMMARY</jats:title><jats:p>A broad diversity of modifications decorate RNA molecules. Originally conceived as static components, evidence is accumulating that some RNA modifications may be dynamic, contributing to cellular responses to external signals and environmental circumstances. A major difficulty in studying these modifications, however, is the need of tailored protocols to map each modification individually. Here, we present a new approach that uses direct RNA nanopore sequencing to identify diverse RNA modification types present in native RNA molecules, using rRNA as the exemplar, and show that each RNA modification type results in distinct and characteristic base-calling ‘error’ signatures. We demonstrate the value of these signatures for <jats:italic>de novo</jats:italic> prediction of pseudouridine (Y) modifications transcriptome-wide, confirming known Y modifications in rRNAs, snRNAs and mRNAs, and uncovering a novel Pus4-dependent Y modification in yeast mitochondrial rRNA. Using a machine learning classifier, we show that the stoichiometry of modified sites can be quantified by identifying current intensity alterations in individual RNA reads. Finally, we explore the dynamics of pseudouridylation across a battery of environmental stresses, revealing novel heat-sensitive Y-modified sites in both snRNAs and snoRNAs. Altogether, our work demonstrates that Y RNA modifications can be predicted <jats:italic>de novo</jats:italic> and in a quantitative manner using native RNA nanopore sequencing.</jats:p>

The Stress-Dependent Dynamics of Saccharomyces cerevisiae tRNA and rRNA Modification Profiles

“…After 60 min of stress exposure, we found only minor changes in the natural epitranscriptomes of both rRNAs. This is in accordance with a recent study from the Novoa laboratory [ 5 ]. However, in rRNA from MMS exposed yeast, we found high numbers of the potential damage products m 1 A, m 7 G, m 3 C and m 6 A and a damage-methylated 2′-O-methyladenosine (m x Am).…”

“…A fundamental study was presented by Chan et al They provided insight into the changes to modifications in small RNA (<200 nts) as a cause of chemical stress exposure. This study coined the term “stress-dependent RNA modification reprogramming,” and there is clear evidence that RNA modifications are regulated by stress [ 4 , 5 ]. The methodological foundation of this and later studies was quantitative mass spectrometry, which allows one to assess changes in RNA modification abundance and compare, e.g., stressed samples with controls.…”

The m6A landscape of polyadenylated nuclear (PAN) RNA and its related methylome in the context of KSHV replication

“…Unfortunately, high throughput epitranscriptomic mapping approaches, which involve multiple chemical handling steps, often result in higher than desired background, that obscures that relevant measurement. Also, the quantification of m 6 A stoichiometry based on DRS readouts has limitations as detection of modifications is partly dependent on sequence context and stoichiometry prediction is heavily affected by choice of resquiggling algorithms (67). To gain accurate insight into KSHV replication state-dependent variations in PAN m 6 A at each identified site, we developed a new quantitative method, termed Selenium-modified deoxythymidine triphosphate Reverse Transcription and Ligation Assisted PCR analysis (SLAP).…”

Methylartist: Tools for Visualising Modified Bases from Nanopore Sequence Data

“…This has utility for identification of modified bases in assay-specific contexts which include GpC methylation (NOMe-seq), and 6mA (SMAC-seq, DamID in a 5'-GATC-3' context, as well as native RNA base modifications). With few exceptions [14,15], most currently available models for calling modified bases involve some form of methylation or hydroxymethylation, so modifications will be referred to collectively as "methylation", without loss of generality.…”