Abstract
N6-methyladenosine (m6A) and pseudouridine (Ψ) are the two most abundant modifications in mammalian messenger RNA, but the coordination of their biological functions remains poorly understood. We develop a machine learning-based nanopore direct RNA sequencing method (NanoSPA) that simultaneously analyzes m6A and Ψ in the human transcriptome. Applying NanoSPA to polysome profiling, we reveal opposing transcriptomic co-occurrence of m6A and Ψ and synergistic, hierarchical effects of m6A and Ψ on the polysome.
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Data availability
The nanopore sequencing data of this study have been submitted to NCBI GEO (accession no. GSE230936)57. The published nanopore WT samples (replicates 1 and 2) used to validate the m6A model are GSM5467024 and GSM5467025 under GSE180656. The published WT GLORI (GSM6432590, GSM6432591) and eTAM-seq data (GSE211303, GSE211303_hela.polya.wt.ftom.ftop.rep1.deep.hits.txt.gz) are derived from the GEO database. The mESC nanopore (GSM5841801) and miCLIP2 samples (GSE163500) are derived from the GEO database. Human genome (GRCh38.p13) and annotation (gencode.v41.annotation.gff3) and mouse genome (GRCm38.primary_assembly.genome.fa) are derived from the GENCODE database (https://www.gencodegenes.org). GO information is derived from Gene Ontology Resource (http://geneontology.org).
Code availability
The NanoSPA package and tutorials are available on GitHub (https://github.com/sihaohuanguc/NanoSPA)58.
References
Roundtree, I. A., Evans, M. E., Pan, T. & He, C. Dynamic RNA modifications in gene expression regulation. Cell 169, 1187–1200 (2017).
Frye, M., Harada, B. T., Behm, M. & He, C. RNA modifications modulate gene expression during development. Science 361, 1346–1349 (2018).
Jackson, L. A. et al. An mRNA vaccine against SARS-CoV-2 – preliminary report. N. Engl. J. Med. 383, 1920–1931 (2020).
Kariko, K. et al. Incorporation of pseudouridine into mRNA yields superior nonimmunogenic vector with increased translational capacity and biological stability. Mol. Ther. 16, 1833–1840 (2008).
Anderson, B. R. et al. Incorporation of pseudouridine into mRNA enhances translation by diminishing PKR activation. Nucleic Acids Res. 38, 5884–5892 (2010).
Eyler, D. E. et al. Pseudouridinylation of mRNA coding sequences alters translation. Proc. Natl Acad. Sci. USA 116, 23068–23074 (2019).
Martinez, N. M. et al. Pseudouridine synthases modify human pre-mRNA co-transcriptionally and affect pre-mRNA processing. Mol. Cell 82, 645–659 (2022).
Workman, R. E. et al. Nanopore native RNA sequencing of a human poly(A) transcriptome. Nat. Methods 16, 1297–1305 (2019).
Liu, H. et al. Accurate detection of m(6)A RNA modifications in native RNA sequences. Nat. Commun. 10, 4079 (2019).
Lorenz, D. A., Sathe, S., Einstein, J. M. & Yeo, G. W. Direct RNA sequencing enables m(6)A detection in endogenous transcript isoforms at base-specific resolution. RNA 26, 19–28 (2020).
Huang, S. et al. Interferon inducible pseudouridine modification in human mRNA by quantitative nanopore profiling. Genome Biol. 22, 330 (2021).
Pratanwanich, P. N. et al. Identification of differential RNA modifications from nanopore direct RNA sequencing with xPore. Nat. Biotechnol. 39, 1394–1402 (2021).
Leger, A. et al. RNA modifications detection by comparative Nanopore direct RNA sequencing. Nat. Commun. 12, 7198 (2021).
Begik, O. et al. Quantitative profiling of pseudouridylation dynamics in native RNAs with nanopore sequencing. Nat. Biotechnol. 39, 1278–1291 (2021).
Li, F. et al. Porpoise: a new approach for accurate prediction of RNA pseudouridine sites. Brief. Bioinform. 22, bbab245 (2021).
Fleming, A. M., Mathewson, N. J., Howpay Manage, S. A. & Burrows, C. J. Nanopore dwell time analysis permits sequencing and conformational assignment of pseudouridine in SARS-CoV-2. ACS Cent. Sci. 7, 1707–1717 (2021).
Jenjaroenpun, P. et al. Decoding the epitranscriptional landscape from native RNA sequences. Nucleic Acids Res. 49, e7 (2021).
Liu, H., Begik, O. & Novoa, E. M. EpiNano: detection of m(6)A RNA modifications using Oxford Nanopore Direct RNA Sequencing. Methods Mol. Biol. 2298, 31–52 (2021).
Gao, Y. et al. Quantitative profiling of N(6)-methyladenosine at single-base resolution in stem-differentiating xylem of Populus trichocarpa using Nanopore direct RNA sequencing. Genome Biol. 22, 22 (2021).
Hassan, D., Acevedo, D., Daulatabad, S. V., Mir, Q. & Janga, S. C. Penguin: a tool for predicting pseudouridine sites in direct RNA nanopore sequencing data. Methods 203, 478–487 (2022).
Piechotta, M., Naarmann-de Vries, I. S., Wang, Q., Altmuller, J. & Dieterich, C. RNA modification mapping with JACUSA2. Genome Biol. 23, 115 (2022).
Ramasamy, S. et al. An informatics approach to distinguish RNA modifications in nanopore direct RNA sequencing. Genomics 114, 110372 (2022).
Hendra, C. et al. Detection of m6A from direct RNA sequencing using a multiple instance learning framework. Nat. Methods 19, 1590–1598 (2022).
Liu, R. et al. Mixed-weight neural bagging for detecting m(6)A modifications in SARS-CoV-2 RNA sequencing. IEEE Trans. Biomed. Eng. 69, 2557–2568 (2022).
Qin, H. et al. DENA: training an authentic neural network model using Nanopore sequencing data of Arabidopsis transcripts for detection and quantification of N(6)-methyladenosine on RNA. Genome Biol. 23, 25 (2022).
Zhang, Y., Huang, D., Wei, Z. & Chen, K. Primary sequence-assisted prediction of m(6)A RNA methylation sites from Oxford nanopore direct RNA sequencing data. Methods 203, 62–69 (2022).
Ramasamy, S. et al. Chemical probe-based nanopore sequencing to selectively assess the RNA modifications. ACS Chem. Biol. 17, 2704–2709 (2022).
Tavakoli, S. et al. Semi-quantitative detection of pseudouridine modifications and type I/II hypermodifications in human mRNAs using direct long-read sequencing. Nat. Commun. 14, 334 (2023).
Yu, F. et al. Identifying RNA modifications by direct RNA sequencing reveals complexity of epitranscriptomic dynamics in rice. Genomics Proteomics Bioinformatics 21, 788–804 (2023).
Stoiber, M. et al. De novo identification of DNA modifications enabled by genome-guided nanopore signal processing. Preprint at BioRxiv https://doi.org/10.1101/094672 (2017).
Parker, M. T. et al. Nanopore direct RNA sequencing maps the complexity of Arabidopsis mRNA processing and m(6)A modification. eLife 9, e49658 (2020).
Price, A. M. et al. Direct RNA sequencing reveals m(6)A modifications on adenovirus RNA are necessary for efficient splicing. Nat. Commun. 11, 6016 (2020).
Hu, L. et al. m(6)A RNA modifications are measured at single-base resolution across the mammalian transcriptome. Nat. Biotechnol. 40, 1210–1219 (2022).
Liu, C. et al. Absolute quantification of single-base m(6)A methylation in the mammalian transcriptome using GLORI. Nat. Biotechnol. 41, 355–366 (2023).
Xiao, Y.-L. et al. Transcriptome-wide profiling and quantification of N6-methyladenosine by enzyme-assisted adenosine deamination. Nat. Biotechnol. 41, 993–1003 (2023).
Zhong, Z. D. et al. Systematic comparison of tools used for m(6)A mapping from nanopore direct RNA sequencing. Nat. Commun. 14, 1906 (2023).
Safra, M., Nir, R., Farouq, D., Vainberg Slutskin, I. & Schwartz, S. TRUB1 is the predominant pseudouridine synthase acting on mammalian mRNA via a predictable and conserved code. Genome Res. 27, 393–406 (2017).
Dai, Q. et al. Quantitative sequencing using BID-seq uncovers abundant pseudouridines in mammalian mRNA at base resolution. Nat. Biotechnol. 41, 344–354 (2023).
Borchardt, E. K., Martinez, N. M. & Gilbert, W. V. Regulation and function of RNA pseudouridylation in human cells. Annu. Rev. Genet. 54, 309–336 (2020).
Ingolia, N. T., Hussmann, J. A. & Weissman, J. S. Ribosome profiling: global views of translation. Cold Spring Harb. Perspect. Biol. 11, a032698 (2019).
Choi, J. et al. N 6-methyladenosine in mRNA disrupts tRNA selection and translation-elongation dynamics. Nat. Struct. Mol. Biol. 23, 110–115 (2016).
Wang, X. et al. N6-methyladenosine modulates messenger RNA translation efficiency. Cell 161, 1388–1399 (2015).
Meyer, K. D. et al. 5′ UTR m6A promotes cap-independent translation. Cell 163, 999–1010 (2015).
Svitkin, Y. V. et al. N1-methyl-pseudouridine in mRNA enhances translation through eIF2α-dependent and independent mechanisms by increasing ribosome density. Nucleic Acids Res. 45, 6023–6036 (2017).
Wang, X. et al. N6-methyladenosine-dependent regulation of messenger RNA stability. Nature 505, 117–120 (2014).
Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
McKinney, W. Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference Vol. 445 (eds van der Walt, S. and Millman, J.) 51–56 (Scipy, 2010).
Abadi, M. et al. TensorFlow: large-scale machine learning on heterogeneous distributed systems. Preprint at https://arxiv.org/abs/1603.04467 (2016).
Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).
Körtel, N. et al. Deep and accurate detection of m6A RNA modifications using miCLIP2 and m6Aboost machine learning. Nucleic Acids Res. 49, e92 (2021).
Frankish, A. et al. Gencode 2021. Nucleic Acids Res. 49, D916–D923 (2021).
Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).
Gene Ontology Consortium. The Gene Ontology resource: enriching a GOld mine. Nucleic Acids Res. 49, D325–D334 (2021).
Huang, S., Wylder, A. & Pan, T. Simultaneous nanopore profiling of mRNA m6A and pseudouridine reveals translation coordination. Gene Expression Omnibus https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE230936 (2024).
Huang, S., Wylder, A. & Pan, T. NanoSPA. GitHub https://github.com/sihaohuanguc/NanoSPA (2024).
Acknowledgements
We thank L. Zhang and C. He for providing analysis of m6A-SAC-seq data before publication and D. Pan for contribution to coding. This work was supported by NIH (no. RM1 HG008935 to T.P.).
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S.H. performed all computational work including NanoSPA pipeline development and sequencing data analysis. A.C.W. performed all experimental work including siRNA knockdown, polysome profiling and nanopore sequencing. S.H. and T.P. conceived the project. S.H., A.C.W. and T.P. designed the experiments and wrote the paper.
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Huang, S., Wylder, A.C. & Pan, T. Simultaneous nanopore profiling of mRNA m6A and pseudouridine reveals translation coordination. Nat Biotechnol (2024). https://doi.org/10.1038/s41587-024-02135-0
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DOI: https://doi.org/10.1038/s41587-024-02135-0