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Quantitative profiling of pseudouridylation dynamics in native RNAs with nanopore sequencing

Nature Biotechnology volume 39, pages 1278–1291 (2021)Cite this article

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Abstract

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, we show that other modifications, in particular pseudouridine (Ψ) and 2′-O-methylation (Nm), also result in characteristic base-calling ‘error’ signatures in the nanopore data. Focusing on Ψ modification sites, we detected known and uncovered previously unreported Ψ sites in mRNAs, non-coding RNAs and rRNAs, including a Pus4-dependent Ψ modification in yeast mitochondrial rRNA. To explore the dynamics of pseudouridylation, we treated yeast cells with oxidative, cold and heat stresses and detected heat-sensitive Ψ-modified sites in small nuclear RNAs, small nucleolar RNAs and mRNAs. Finally, we developed 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 Ψ RNA modifications can be detected in cellular RNAs and that their modification stoichiometry can be quantified by nanopore sequencing of native RNA.

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Fig. 1: Systematic analysis of base-calling and mapping algorithms for the detection of RNA modifications in direct RNA sequencing datasets.
Fig. 2: RNA modifications can be detected in yeast rRNA in the form of base-calling errors, and each RNA modification type shows a distinct ‘error’ signature.
Fig. 3: Pseudouridylation and 2′-O-methylations cause systematic base-calling ‘errors’ as well as altered current intensities, and their signature disappears upon depletion of snoRNAs guiding the modification.
Fig. 4: Loss of specific Ψ rRNA modifications causes deviations in current intensity in regions surrounding the Ψ sites.
Fig. 5: De novo prediction of Ψ modifications reveals a novel Pus4-dependent mitochondrial rRNA modification.
Fig. 6: Comparative analysis of yeast rRNA and snRNA Ψ modifications upon distinct environmental stresses identifies known and previously unknown heat-sensitive snRNA and snoRNA Ψ modifications.
Fig. 7: Quantitative prediction of pseudouridine stoichiometry transcriptome-wide and systematic benchmarking of nanoRMS using RNA molecules with diverse modification stoichiometries.

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Data availability

For in vitro transcribed datasets, FAST5 files used in this work were already publicly available (UNM and m6A: PRJNA511582) or are publicly available in the Sequence Read Archive (SRA) (m5C: PRJNA563591; hm5C: PRJNA548268; Ψ: PRJNA549001; UNM-S: PRJNA575545; Ψ with varying stoichiometries: PRJNA695584). Base-called and demultiplexed FASTQ from all yeast RNA direct RNA sequencing data runs are publicly available in the Gene Expression Omnibus (GEO) under accession number GSE148603, including processed EpiNano outputs. FAST5 files for all yeast RNA direct RNA sequencing are available in the European Nucleotide Archive (ENA) under accession numbers PRJEB37798 and PRJEB41495. A detailed description of the datasets used and sequenced in this work, with their corresponding GEO and ENA/SRA IDs, can be found in Supplementary Table 14.

Code availability

All scripts and code used in this work are available in GitHub: https://github.com/novoalab/Best_Practices_dRNAseq_analysis (analysis of in vitro curlcake datasets), https://github.com/novoalab/yeast_RNA_Mod (analysis of in vivo datasets) and https://github.com/novoalab/nanoRMS (prediction of RNA modifications and estimation of RNA modification stoichiometries).

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Acknowledgements

We thank all the members of the Novoa lab for their valuable insights and discussion. We thank V. Malhotra for sharing the Pus1 and Pus4 knockout strains. O.B. is supported by a University of New South Wales International PhD Fellowship. M.C.L. is supported by an FPI Severo-Ochoa Fellowship by the Spanish Ministry of Economy, Industry and Competitiveness (MEIC). I.M. and S.C. are supported by ‘la Caixa’ INPhINIT PhD Fellowships (LCF/BQ/DI18/11660028 and LCF/BQ/DI19/11730036, respectively). This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Marie Skodowska-Curie grant agreement no. 713673. This work was supported by the Australian Research Council (DP180103571 to E.M.N.) and the MEIC (PGC2018-098152-A-100 to E.M.N.). We acknowledge the support of the MEIC to the EMBL partnership, the Centro de Excelencia Severo Ochoa and the CERCA Programme/Generalitat de Catalunya.

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  1. These authors contributed equally: Oguzhan Begik, Morghan C. Lucas.

Authors and Affiliations

  1. Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain

    Oguzhan Begik, Morghan C. Lucas, Leszek P. Pryszcz, Jose Miguel Ramirez, Rebeca Medina, Ivan Milenkovic, Sonia Cruciani, Huanle Liu, Helaine Graziele Santos Vieira & Eva Maria Novoa

  2. Garvan Institute of Medical Research, Darlinghurst, NSW, Australia

    Oguzhan Begik

  3. UNSW Sydney, Kensington, NSW, Australia

    Oguzhan Begik & John S. Mattick

  4. Universitat Pompeu Fabra (UPF), Barcelona, Spain

    Morghan C. Lucas, Ivan Milenkovic, Sonia Cruciani & Eva Maria Novoa

  5. International Institute of Molecular and Cell Biology, Warsaw, Poland

    Leszek P. Pryszcz

  6. Weizmann Institute of Science, Rehovot, Israel

    Aldema Sas-Chen & Schraga Schwartz

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Contributions

O.B. and M.C.L. performed most wet lab experiments, including RNA extraction and nanopore library preparation. O.B. and L.P.P. performed bioinformatic analysis of the data, together with J.M.R. and E.M.N. O.B. conceived and performed nanoCMC-seq experiments. M.C.L. produced the in vitro transcribed sequences with modifications and their corresponding nanopore libraries. O.B. prepared and sequenced the in vitro transcribed sequences with different pseudouridine stoichiometries. L.P.P. benchmarked and wrote the nanoRMS code, together with O.B. and E.M.N. J.M.R. performed bioinformatic analyses on in vitro transcribed constructs and compared base-calling and mapping algorithms. I.M. built polysome gradients and helped with their corresponding nanopore libraries. S.C. and I.M. prepared and sequenced the 2′-O-methylation mutant strains. R.M. and H.G.S.V. cultured the S. cerevisiae strains under different stress conditions. H.L. contributed with code for the analysis of current intensity values. A.S.C. and S.S. cultured all snoRNA-depleted yeast mutant strains and extracted their total RNA. E.M.N. conceived the project. E.M.N. supervised the work, with the assistance of J.S.M. M.C.L., O.B. and E.M.N. built the figures. O.B., M.C.L. and E.M.N. wrote the paper, with contributions from all authors.

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Correspondence to Eva Maria Novoa.

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E.M.N. has received travel and accommodation expenses to speak at Oxford Nanopore Technologies conferences. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Begik, O., Lucas, M.C., Pryszcz, L.P. et al. Quantitative profiling of pseudouridylation dynamics in native RNAs with nanopore sequencing. Nat Biotechnol 39, 1278–1291 (2021). https://doi.org/10.1038/s41587-021-00915-6

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  • DOI: https://doi.org/10.1038/s41587-021-00915-6

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