Nanopore RNA sequencing shows promise as a method for discriminating and identifying different RNA modifications in native RNA. Expanding on the ability of nanopore sequencing to detect N6-methyladenosine (m6A), we show that other modifications, in particular pseudouridine (Ѱ) and 2'-O-methylation (Nm), also result in characteristic base-calling 'error' signatures in the nanopore data. Focusing on Ѱ modification sites, we detect known and uncover previously unreported Ѱ sites in mRNAs, ncRNAs and rRNAs, including a Pus4dependent Ѱ modification in yeast mitochondrial rRNA. To explore the dynamics of pseudouridylation, we treat yeast cells with oxidative, cold and heat stresses and detect heatsensitive Ѱ-modified sites in snRNAs, snoRNAs and mRNAs. Finally, we develop a software, nanoRMS, that estimates per-site modification stoichiometries by identifying single-molecule reads with altered current intensity and trace profiles. This work demonstrates that Nm and Ѱ RNA modifications can be detected in cellular RNAs and that Ѱ RNA can be identified in a quantitative manner by nanopore sequencing of native RNA.
Transfer RNAs (tRNAs) play a central role in protein translation. Studying them has been difficult in part because a simple method to simultaneously quantify their abundance and chemical modifications is lacking. Here we introduce Nano-tRNAseq, a nanopore-based approach to sequence native tRNA populations that provides quantitative estimates of both tRNA abundances and modification dynamics in a single experiment. We show that default nanopore sequencing settings discard the vast majority of tRNA reads, leading to poor sequencing yields and biased representations of tRNA abundances based on their transcript length. Re-processing of raw nanopore current intensity signals leads to a 12-fold increase in the number of recovered tRNA reads and enables recapitulation of accurate tRNA abundances. We then apply Nano-tRNAseq to Saccharomyces cerevisiae tRNA populations, revealing crosstalks and interdependencies between different tRNA modification types within the same molecule and changes in tRNA populations in response to oxidative stress.
The biological relevance and dynamics of mRNA modifications have been extensively studied in the past few years, revealing their key roles in major cellular processes, such as cellular differentiation or sex determination. However, whether rRNA modifications are dynamically regulated, and under which conditions, remains largely unclear. Here, we performed a systematic characterization of bacterial rRNA modification dynamics upon exposure to diverse antibiotics using native RNA nanopore sequencing. To identify significant rRNA modification changes, we developed NanoConsensus, a novel pipeline that integrates the estimates from multiple RNA modification detection algorithms, predicting differentially modified rRNA sites with very low false positive rates and high replicability. We showed that NanoConsensus is robust across RNA modification types, stoichiometries and coverage, and outperforms all individual algorithms tested. Using this approach, we identified multiple rRNA modifications that are lost upon the presence of antibiotics, showing that rRNA modification profiles are altered in an antibiotic-specific manner. We found that significantly altered rRNA modified sites upon antibiotic exposure are located in the vicinity of the A and P-sites of the ribosome, possibly contributing to antibiotic resistance. We then systematically examined whether loss of "antibiotic-sensitive" rRNA modifications may be sufficient to confer antibiotic resistance, finding that depletion of some rRNA modification enzymes guiding dysregulated rRNA modifications confers increased antibiotic resistance. Altogether, our work reveals that rRNA modification profiles can be rapidly altered in response to environmental exposures, and that nanopore sequencing can accurately identify dysregulated rRNA modifications, contributing to the mechanistic dissection of antibiotic resistance. Moreover, we provide a novel, robust workflow to study rRNA modification dynamics in any species using nanopore sequencing in a scalable and reproducible manner.
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