ena-STUDY-CENTER FOR GENOMIC REGULATION (CRG)-24-11-2020-10:56:08:643-756
Quantitative profiling of native RNA modifications and their dynamics using nanopore sequencing (mRNA Sequencing of Yeast)
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 type individually. Here, we present a new approach that uses direct RNA nanopore sequencing to identify and quantify RNA modifications present in native RNA molecules. First, we show that each RNA modification type results in a distinct and characteristic base-calling 'error' signature, which we validate using a battery of genetic strains lacking either pseudouridine (?) or 2'-O-methylation (Nm) modifications. We then demonstrate the value of these signatures for de novo prediction of ? modifications transcriptome-wide, confirming known ?-modified sites as well as uncovering novel ? sites in mRNAs, ncRNAs and rRNAs, including a previously unreported Pus4-dependent ? modification in yeast mitochondrial rRNA, which we validate using orthogonal methods. To explore the dynamics of pseudouridylation across environmental stresses, we treat the cells with oxidative, cold and heat stresses, finding that yeast ribosomal rRNA modifications do not change upon environmental exposures, contrary to the general belief. By contrast, our method reveals many novel heat-sensitive ?-modified sites in snRNAs, snoRNAs and mRNAs, in addition to recovering previously reported sites. Finally, we develop a novel software, nanoRMS, which we show can estimate per-site modification stoichiometries from individual RNA molecules by identifying the reads with altered current intensity and trace profiles, and quantify the RNA modification stoichiometry changes between two conditions. Our work demonstrates that ? RNA modifications can be predicted de novo and in a quantitative manner using native RNA nanopore sequencing.
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 type individually. Here, we present a new approach that uses direct RNA nanopore sequencing to identify and quantify RNA modifications present in native RNA molecules. First, we show that each RNA modification type results in a distinct and characteristic base-calling 'error' signature, which we validate using a battery of genetic strains lacking either pseudouridine (?) or 2'-O-methylation (Nm) modifications. We then demonstrate the value of these signatures for de novo prediction of ? modifications transcriptome-wide, confirming known ?-modified sites as well as uncovering novel ? sites in mRNAs, ncRNAs and rRNAs, including a previously unreported Pus4-dependent ? modification in yeast mitochondrial rRNA, which we validate using orthogonal methods. To explore the dynamics of pseudouridylation across environmental stresses, we treat the cells with oxidative, cold and heat stresses, finding that yeast ribosomal rRNA modifications do not change upon environmental exposures, contrary to the general belief. By contrast, our method reveals many novel heat-sensitive ?-modified sites in snRNAs, snoRNAs and mRNAs, in addition to recovering previously reported sites. Finally, we develop a novel software, nanoRMS, which we show can estimate per-site modification stoichiometries from individual RNA molecules by identifying the reads with altered current intensity and trace profiles, and quantify the RNA modification stoichiometry changes between two conditions. Our work demonstrates that ? RNA modifications can be predicted de novo and in a quantitative manner using native RNA nanopore sequencing.