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Zhang Y, Yan H, Wei Z, Hong H, Huang D, Liu G, Qin Q, Rong R, Gao P, Meng J, Ying B. NanoMUD: Profiling of pseudouridine and N1-methylpseudouridine using Oxford Nanopore direct RNA sequencing. Int J Biol Macromol 2024; 270:132433. [PMID: 38759861 DOI: 10.1016/j.ijbiomac.2024.132433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 05/13/2024] [Accepted: 05/14/2024] [Indexed: 05/19/2024]
Abstract
Nanopore direct RNA sequencing provided a promising solution for unraveling the landscapes of modifications on single RNA molecules. Here, we proposed NanoMUD, a computational framework for predicting the RNA pseudouridine modification (Ψ) and its methylated analog N1-methylpseudouridine (m1Ψ), which have critical application in mRNA vaccination, at single-base and single-molecule resolution from direct RNA sequencing data. Electric signal features were fed into a bidirectional LSTM neural network to achieve improved accuracy and predictive capabilities. Motif-specific models (NNUNN, N = A, C, U or G) were trained based on features extracted from designed dataset and achieved superior performance on molecule-level modification prediction (Ψ models: min AUC = 0.86, max AUC = 0.99; m1Ψ models: min AUC = 0.87, max AUC = 0.99). We then aggregated read-level predictions for site stoichiometry estimation. Given the observed sequence-dependent bias in model performance, we trained regression models based on the distribution of modification probabilities for sites with known stoichiometry. The distribution-based site stoichiometry estimation method allows unbiased comparison between different contexts. To demonstrate the feasibility of our work, three case studies on both in vitro and in vivo transcribed RNAs were presented. NanoMUD will make a powerful tool to facilitate the research on modified therapeutic IVT RNAs and provides useful insight to the landscape and stoichiometry of pseudouridine and N1-pseudouridine on in vivo transcribed RNA species.
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Affiliation(s)
- Yuxin Zhang
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China; Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L69 7ZB Liverpool, United Kingdom
| | - Huayuan Yan
- Suzhou Abogen Biosciences Co., Ltd., Suzhou 215123, China
| | - Zhen Wei
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China; Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L69 7ZB Liverpool, United Kingdom
| | - Haifeng Hong
- Suzhou Abogen Biosciences Co., Ltd., Suzhou 215123, China
| | - Daiyun Huang
- Wisdom Lake Academy of Pharmacy, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China
| | - Guopeng Liu
- Suzhou Abogen Biosciences Co., Ltd., Suzhou 215123, China
| | - Qianshan Qin
- Suzhou Abogen Biosciences Co., Ltd., Suzhou 215123, China
| | - Rong Rong
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China
| | - Peng Gao
- Suzhou Abogen Biosciences Co., Ltd., Suzhou 215123, China.
| | - Jia Meng
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China; AI University Research Centre, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China; Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L69 7ZB Liverpool, United Kingdom.
| | - Bo Ying
- Suzhou Abogen Biosciences Co., Ltd., Suzhou 215123, China.
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Acera Mateos P, J Sethi A, Ravindran A, Srivastava A, Woodward K, Mahmud S, Kanchi M, Guarnacci M, Xu J, W S Yuen Z, Zhou Y, Sneddon A, Hamilton W, Gao J, M Starrs L, Hayashi R, Wickramasinghe V, Zarnack K, Preiss T, Burgio G, Dehorter N, E Shirokikh N, Eyras E. Prediction of m6A and m5C at single-molecule resolution reveals a transcriptome-wide co-occurrence of RNA modifications. Nat Commun 2024; 15:3899. [PMID: 38724548 PMCID: PMC11082244 DOI: 10.1038/s41467-024-47953-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 04/15/2024] [Indexed: 05/12/2024] Open
Abstract
The epitranscriptome embodies many new and largely unexplored functions of RNA. A significant roadblock hindering progress in epitranscriptomics is the identification of more than one modification in individual transcript molecules. We address this with CHEUI (CH3 (methylation) Estimation Using Ionic current). CHEUI predicts N6-methyladenosine (m6A) and 5-methylcytosine (m5C) in individual molecules from the same sample, the stoichiometry at transcript reference sites, and differential methylation between any two conditions. CHEUI processes observed and expected nanopore direct RNA sequencing signals to achieve high single-molecule, transcript-site, and stoichiometry accuracies in multiple tests using synthetic RNA standards and cell line data. CHEUI's capability to identify two modification types in the same sample reveals a co-occurrence of m6A and m5C in individual mRNAs in cell line and tissue transcriptomes. CHEUI provides new avenues to discover and study the function of the epitranscriptome.
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Affiliation(s)
- P Acera Mateos
- EMBL Australia Partner Laboratory Network at the Australian National University, Canberra, ACT, 2601, Australia
- The Shine-Dalgarno Centre for RNA Innovation, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia
- The Centre for Computational Biomedical Sciences, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia
| | - A J Sethi
- EMBL Australia Partner Laboratory Network at the Australian National University, Canberra, ACT, 2601, Australia
- The Shine-Dalgarno Centre for RNA Innovation, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia
- The Centre for Computational Biomedical Sciences, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia
| | - A Ravindran
- EMBL Australia Partner Laboratory Network at the Australian National University, Canberra, ACT, 2601, Australia
- The Shine-Dalgarno Centre for RNA Innovation, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia
- The Centre for Computational Biomedical Sciences, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia
| | - A Srivastava
- EMBL Australia Partner Laboratory Network at the Australian National University, Canberra, ACT, 2601, Australia
- The Shine-Dalgarno Centre for RNA Innovation, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia
- The Centre for Computational Biomedical Sciences, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia
| | - K Woodward
- The Shine-Dalgarno Centre for RNA Innovation, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia
| | - S Mahmud
- The Shine-Dalgarno Centre for RNA Innovation, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia
| | - M Kanchi
- The Shine-Dalgarno Centre for RNA Innovation, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia
| | - M Guarnacci
- The Shine-Dalgarno Centre for RNA Innovation, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia
| | - J Xu
- EMBL Australia Partner Laboratory Network at the Australian National University, Canberra, ACT, 2601, Australia
| | - Z W S Yuen
- EMBL Australia Partner Laboratory Network at the Australian National University, Canberra, ACT, 2601, Australia
- The Shine-Dalgarno Centre for RNA Innovation, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia
- The Centre for Computational Biomedical Sciences, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia
| | - Y Zhou
- Buchmann Institute for Molecular Life Sciences (BMLS) & Faculty of Biological Sciences, Goethe University Frankfurt, 60438, Frankfurt am Main, Germany
| | - A Sneddon
- EMBL Australia Partner Laboratory Network at the Australian National University, Canberra, ACT, 2601, Australia
- The Shine-Dalgarno Centre for RNA Innovation, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia
- The Centre for Computational Biomedical Sciences, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia
| | - W Hamilton
- Peter MacCallum Cancer Centre, Melbourne, VIC, 3052, Australia
| | - J Gao
- The Shine-Dalgarno Centre for RNA Innovation, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia
| | - L M Starrs
- The Shine-Dalgarno Centre for RNA Innovation, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia
| | - R Hayashi
- The Shine-Dalgarno Centre for RNA Innovation, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia
| | | | - K Zarnack
- Buchmann Institute for Molecular Life Sciences (BMLS) & Faculty of Biological Sciences, Goethe University Frankfurt, 60438, Frankfurt am Main, Germany
| | - T Preiss
- The Shine-Dalgarno Centre for RNA Innovation, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia
- Victor Chang Cardiac Research Institute, Sydney, NSW, 2010, Australia
| | - G Burgio
- The Shine-Dalgarno Centre for RNA Innovation, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia
- The Centre for Computational Biomedical Sciences, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia
| | - N Dehorter
- The Eccles Institute of Neuroscience, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia
- The Queensland Brain Institute, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - N E Shirokikh
- The Shine-Dalgarno Centre for RNA Innovation, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia.
| | - E Eyras
- EMBL Australia Partner Laboratory Network at the Australian National University, Canberra, ACT, 2601, Australia.
- The Shine-Dalgarno Centre for RNA Innovation, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia.
- The Centre for Computational Biomedical Sciences, The John Curtin School of Medical Research, Australian National University, Canberra, ACT, 2601, Australia.
- Catalan Institution for Research and Advanced Studies (ICREA), 08010, Barcelona, Spain.
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Fleming AM, Bommisetti P, Xiao S, Bandarian V, Burrows CJ. Direct Nanopore Sequencing for the 17 RNA Modification Types in 36 Locations in the E. coli Ribosome Enables Monitoring of Stress-Dependent Changes. ACS Chem Biol 2023; 18:2211-2223. [PMID: 37345867 PMCID: PMC10594579 DOI: 10.1021/acschembio.3c00166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 06/06/2023] [Indexed: 06/23/2023]
Abstract
The bacterium Escherichia coli possesses 16S and 23S rRNA strands that have 36 chemical modification sites with 17 different structures. Nanopore direct RNA sequencing using a protein nanopore sensor and helicase brake, which is also a sensor, was applied to the rRNAs. Nanopore current levels, base calling profile, and helicase dwell times for the modifications relative to unmodified synthetic rRNA controls found signatures for nearly all modifications. Signatures for clustered modifications were determined by selective sequencing of writer knockout E. coli and sequencing of synthetic RNAs utilizing some custom-synthesized nucleotide triphosphates for their preparation. The knowledge of each modification's signature, apart from 5-methylcytidine, was used to determine how metabolic and cold-shock stress impact rRNA modifications. Metabolic stress resulted in either no change or a decrease, and one site increased in modification occupancy, while cold-shock stress led to either no change or a decrease. The double modification m4Cm1402 resides in 16S rRNA, and it decreased with both stressors. Using the helicase dwell time, it was determined that the N4 methyl group is lost during both stressors, and the 2'-OMe group remained. In the ribosome, this modification stabilizes binding to the mRNA codon at the P-site resulting in increased translational fidelity that is lost during stress. The E. coli genome has seven rRNA operons (rrn), and the earlier studies aligned the nanopore reads to a single operon (rrnA). Here, the reads were aligned to all seven operons to identify operon-specific changes in the 11 pseudouridines. This study demonstrates that direct sequencing for >16 different RNA modifications in a strand is achievable.
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Affiliation(s)
- Aaron M. Fleming
- Department of Chemistry, University of Utah, 315 S. 1400 East, Salt Lake
City, Utah 84112-0850, United States
| | - Praneeth Bommisetti
- Department of Chemistry, University of Utah, 315 S. 1400 East, Salt Lake
City, Utah 84112-0850, United States
| | - Songjun Xiao
- Department of Chemistry, University of Utah, 315 S. 1400 East, Salt Lake
City, Utah 84112-0850, United States
| | - Vahe Bandarian
- Department of Chemistry, University of Utah, 315 S. 1400 East, Salt Lake
City, Utah 84112-0850, United States
| | - Cynthia J. Burrows
- Department of Chemistry, University of Utah, 315 S. 1400 East, Salt Lake
City, Utah 84112-0850, United States
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Burrows CJ, Fleming AM. Bisulfite and Nanopore Sequencing for Pseudouridine in RNA. Acc Chem Res 2023; 56:2740-2751. [PMID: 37700703 PMCID: PMC10911771 DOI: 10.1021/acs.accounts.3c00458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
Nucleophilic addition of bisulfite to pyrimidine bases has been known for a half century, and the reaction has been in use for at least a quarter of a century for identifying 5-methylcytidine in DNA. This account focuses on the chemistry of bisulfite with pseudouridine, an isomer of the RNA nucleoside uridine in which the uracil base is connected to C1' of ribose via C5 instead of N1. Pseudouridine, Ψ, is the most common nucleotide modification found in cellular RNA overall, in part due to its abundance in rRNAs and tRNAs. It has a stabilizing influence on RNA structure because N1 is now available for additional hydrogen bonding and because the heterocycle is slightly better at π stacking. The isomerization of U to Ψ in RNA strands is catalyzed by 13 different enzymes in humans and 11 in E. coli; some of these enzymes are implicated in disease states which is testament to the biological importance of pseudouridine in cells. Recently, pseudouridine came into the limelight as the key modification that, after N1 methylation, enables mRNA vaccines to be delivered efficiently into human tissue with minimal generation of a deleterious immunogenic response. Here we describe the bisulfite reaction with pseudouridine which gives rise to a chemical sequencing method to map the modified base in the epitranscriptome. Unlike the reaction with cytidine, the addition of bisulfite to Ψ leads irreversibly to form an adduct that is bypassed during cDNA synthesis by reverse transcriptases yielding a characteristic deletion signature. Although there were hints to the structure of the bisulfite adduct(s) 30 to 50 years ago, it took modern spectroscopic and computational methods to solve the mystery. Raman spectroscopy along with extensive NMR, ECD, and computational work led to the assignment of the major product as the (R) diastereomer of an oxygen adduct at C1' of a ring-opened pseudouridine. Mechanistically, this arose from a succession of conjugate addition, E2 elimination, and a [2,3] sigmatropic rearrangement, all of which are stereodefined reactions. A minor reaction with excess bisulfite led to the (S) isomer of a S-adducted SO3- group. Understanding structure and mechanism aided the design of a Ψ-specific sequencing reaction and guided attempts to improve the utility and specificity of the method. Separately, we have been investigating the use of nanopore direct RNA sequencing, a single-molecule method that directly analyzes RNA strands isolated from cells after end-ligation of adaptor sequences. By combining the electrical current and base-calling data from the nanopore with dwell-time analysis from the helicase employed to deliver RNA to the nanopore, we were able to map Ψ sites in nearly all sequence contexts. This analysis was employed to find Ψ residues in the SARS-CoV-2 vRNA, to analyze the sequence context effects of mRNA vaccine synthesis via in vitro transcription, and to evaluate the impact of stress on chemical modifications in the E. coli ribosome. Most recently, we found that bisulfite treatment of RNA leading to Ψ adducts could modulate the nanopore signal to help in mapping modifications of low occupancy.
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Affiliation(s)
- Cynthia J Burrows
- Department of Chemistry, University of Utah, 315 S. 1400 East, Salt Lake City, Utah 84112-0850, United States
| | - Aaron M Fleming
- Department of Chemistry, University of Utah, 315 S. 1400 East, Salt Lake City, Utah 84112-0850, United States
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