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Identification of nucleoside monophosphates and their epigenetic modifications using an engineered nanopore

Nature Nanotechnology volume 17, pages 976–983 (2022) Cite this article

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Abstract

RNA modifications play critical roles in the regulation of various biological processes and are associated with many human diseases. Direct identification of RNA modifications by sequencing remains challenging, however. Nanopore sequencing is promising, but the current strategy is complicated by sequence decoding. Sequential nanopore identification of enzymatically cleaved nucleoside monophosphates may simultaneously provide accurate sequence and modification information. Here we show a phenylboronic acid-modified hetero-octameric Mycobacterium smegmatis porin A nanopore, with which direct distinguishing between monophosphates of canonical nucleosides, 5-methylcytidine, N6-methyladenosine, N7-methylguanosine, N1-methyladenosine, inosine, pseudouridine and dihydrouridine was achieved. A custom machine learning algorithm, which reports an accuracy of 0.996, was also applied to the quantitative analysis of modifications in microRNA and natural transfer RNA. It is generally suitable for sensing of a variety of other nucleoside or nucleotide derivatives and may bring new insights to epigenetic RNA sequencing.

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Fig. 1: Discrimination of canonical NMPs using a PBA-modified MspA.
The alternative text for this image may have been generated using AI.
Fig. 2: Epigenetic NMPs identified by MspA-PBA.
The alternative text for this image may have been generated using AI.
Fig. 3: Machine learning assisted NMP identification.
The alternative text for this image may have been generated using AI.
Fig. 4: Detection of epigenetic modifications from RNA.
The alternative text for this image may have been generated using AI.
Fig. 5: Quantitative detection of epigenetic modifications of yeast tRNAPhe.
The alternative text for this image may have been generated using AI.

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Data availability

The datasets generated and/or analysed during the current study are available within the source data provided with this paper. All data presented in this work can be requested from the corresponding author upon reasonable request.

Code availability

The custom machine learning code is shared on GitHuB as ‘NMP classifier’ at https://github.com/sonic220/NMP-Classifier.

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Acknowledgements

We acknowledge H. Bayley (University of Oxford) for valuable suggestions concerning preparation of the manuscript. We acknowledge Z. Guo, S. Zhu, C. Zhu, J. Li, R. Xie, Y. Guo, X. Chen and J. Xu in Nanjing University for inspiring discussions. This project was funded by the National Natural Science Foundation of China (grant Nos. 31972917, 91753108 and 21675083, to S.H.), and supported by the Fundamental Research Funds for the Central Universities (grant Nos. 020514380257 and 020514380261, to S.H.), programmes for high-level entrepreneurial and innovative talents introduction of Jiangsu Province (individual and group programme, to S.H.), the Natural Science Foundation of Jiangsu Province (grant No. BK20200009, to S.H.), the Excellent Research Programme of Nanjing University (grant No. ZYJH004, to S.H.), the Shanghai Municipal Science and Technology Major Project (S.H.), the State Key Laboratory of Analytical Chemistry for Life Science (grant No. 5431ZZXM2204, to S.H.), the Technology innovation fund programme of Nanjing University (S.H.) and the China Postdoctoral Science Foundation (grant No. 2021M691508, to Y.Q.W.).

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  1. These authors contributed equally: Yuqin Wang, Shanyu Zhang, Wendong Jia.

Authors and Affiliations

  1. State Key Laboratory of Analytical Chemistry for Life Sciences, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, China

    Yuqin Wang, Shanyu Zhang, Wendong Jia, Pingping Fan, Liying Wang, Xinyue Li, Jialu Chen, Zhenyuan Cao, Xiaoyu Du, Yao Liu, Kefan Wang, Chengzhen Hu, Jinyue Zhang, Jun Hu, Panke Zhang, Hong-Yuan Chen & Shuo Huang

  2. Chemistry and Biomedicine Innovation Center (ChemBIC), Nanjing University, Nanjing, China

    Yuqin Wang, Shanyu Zhang, Wendong Jia, Pingping Fan, Liying Wang, Xinyue Li, Jialu Chen, Zhenyuan Cao, Xiaoyu Du, Yao Liu, Kefan Wang, Chengzhen Hu, Jinyue Zhang & Shuo Huang

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  1. Yuqin Wang
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Contributions

S.H., Y.Q.W., S.Y.Z. and J.W.D conceived the project. Y.Q.W, S.Y.Z., P.P.F., K.F.W. and X.Y.D. prepared the MspA nanopores. Y.Q.W, S.Y.Z., P.P.F., L.Y.W., X.Y.L., J.L.C., Z.Y.C. and C.Z.H. performed the measurements. Y.Q.W. and Y.L. designed the machine learning algorithms. Y.Q.W and J.Y.Z. performed RNA extraction. Y.Q.W and J.H. performed the mass spectroscopy measurement. P.K.Z. set up the instruments. S.H. and Y.Q.W. wrote the paper. S.H. and H.Y.C. supervised the project.

Corresponding author

Correspondence to Shuo Huang.

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Competing interests

S.H., S.Y.Z., Y.Q.W., K.F.W. and Y.L. have filed patents describing the preparation of heterogeneous MspA and its applications thereof. The remaining authors declare no competing interests.

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Nature Nanotechnology thanks Sukanya Punthambaker and Manisha Gupta for their contribution to the peer review of this work.

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Supplementary information

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Supplementary materials, Tables 1–10 and Figs. 1–39.

Supplementary Video 1 (download MP4 )

Simultaneous sensing of 11 types of NMPs. Electrophysiology measurements were performed as described in Methods in a 1.5 M KCl buffer (1.5 M KCl, 10 mM MOPS, pH 7.0). A transmembrane potential of +200 mV was continually applied. NMPs were simultaneously added to cis with a final concentration of 100 μM for each analyte. Characteristic events of different NMPs were clearly observed from the trace. Assisted by the machine learning algorithm, each event was automatically identified and labelled with C, U, A, G, m5C, m6C, ψ, I, D, m7G or m1A, respectively. For demonstration, the movie was played back at 1.0× speed of the actual data acquisition.

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Wang, Y., Zhang, S., Jia, W. et al. Identification of nucleoside monophosphates and their epigenetic modifications using an engineered nanopore. Nat. Nanotechnol. 17, 976–983 (2022). https://doi.org/10.1038/s41565-022-01169-2

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