Computational Prediction of N6-methyladenosine (m6A) RNA Methylation in SARS-CoV-2 Viral Transcripts
Authors: 
Qingru Xu, Xiangyu Wu, Jia MengAuthors Info & Claims
Article No.: 11, Pages 1 - 4
Abstract
SARS-CoV-2 caused atypical pneumonia (COVID-19) is an ongoing pandemic that seriously threat the global public health. Many people die from this disease with severe symptoms. The most prevalent m6A RNA modification may be involved in by assisting the virus escaping from the host cell immune system attack. We provided here the first computational prediction study of RNA methylation sites in SARS-CoV-2. Based on virus sequence information, we predict the potential virus m6A sites and hope to make anyhow contributions to this unprecedented situation. As a result, we found 27 most frequent m6A sequences (41 bp) in SARS-CoV-2, and two of them are quite near to the spike protein stop codon position.
References
[1]
Drosten, C., Günther, S., Preiser, W., Van Der Werf, S., Brodt, H.-R., Becker, S., Rabenau, H., Panning, M., Kolesnikova, L. and Fouchier, R. A. (2003) J. N. E. j. o. m. Identification of a novel coronavirus in patients with severe acute respiratory syndrome, 348, 20, 1967-1976.
[2]
Zaki, A. M., Van Boheemen, S., Bestebroer, T. M., Osterhaus, A. D. and Fouchier, R. A. (2012) J. N. E. J. o. M. Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabia, 367, 19, 1814-1820.
[3]
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J. and Gu, X. (2020) J. T. l. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China, 395, 10223, 497-506.
[4]
Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W. and Lu, R. (2020) J. N. E. J. o. M. A novel coronavirus from patients with pneumonia in China, 2019.
[5]
Lu, M., Zhang, Z., Xue, M., Zhao, B. S., Harder, O., Li, A., Liang, X., Gao, T. Z., Xu, Y. and Zhou, J. (2020) J. N. M. N 6-methyladenosine modification enables viral RNA to escape recognition by RNA sensor RIG-I, 5, 4, 584-598.
[6]
Barabasi, A.-L. and Oltvai, Z. N. (2004) J. N. r. g. Network biology: understanding the cell's functional organization, 5, 2, 101-113.
[7]
Chen, K., Wei, Z., Zhang, Q., Wu, X., Rong, R., Lu, Z., Su, J., de Magalhaes, J. P., Rigden, D. J. and Meng, J. (2019) J. N. a. r. WHISTLE: a high-accuracy map of the human N 6-methyladenosine (m6A) epitranscriptome predicted using a machine learning approach, 47, 7, e41-e41.
[8]
Boccaletto, P., Machnicka, M. A., Purta, E., Piątkowski, P., Bagiński, B., Wirecki, T. K., de Crécy-Lagard, V., Ross, R., Limbach, P. A. and Kotter, A. (2018) J. N. a. r. MODOMICS: a database of RNA modification pathways. 2017 update, 46, D1, D303-D307.
[9]
Machnicka, M. A., Milanowska, K., Osman Oglou, O., Purta, E., Kurkowska, M., Olchowik, A., Januszewski, W., Kalinowski, S., Dunin-Horkawicz, S. and Rother, K. M. (2012) J. N. a. r. MODOMICS: a database of RNA modification pathways—2013 update, 41, D1, D262-D267.
[10]
Liu, H., Flores, M. A., Meng, J., Zhang, L., Zhao, X., Rao, M. K., Chen, Y. and Huang, Y. (2015) J. N. A. R. MeT-DB: a database of transcriptome methylation in mammalian cells, 43, D1, D197-D203.
[11]
Tang, Y., Chen, K., Song, B., Ma, J., Wu, X., Xu, Q., Wei, Z., Su, J., Liu, G. and Rong, R. (2020) J. N. A. R. m6A-Atlas: a comprehensive knowledgebase for unraveling the N6-methyladenosine (m6A) epitranscriptome.
[12]
Zheng, Y., Nie, P., Peng, D., He, Z., Liu, M., Xie, Y., Miao, Y., Zuo, Z. and Ren, J. (2018) J. N. a. r. m6AVar: a database of functional variants involved in m6A modification, 46, D1, D139-D145.
[13]
Luo, X., Li, H., Liang, J., Zhao, Q., Xie, Y., Ren, J. and Zuo, Z. (2020) J. N. A. R. RMVar: an updated database of functional variants involved in RNA modifications.
[14]
Chen, K., Song, B., Tang, Y., Wei, Z., Xu, Q., Su, J., de Magalhães, J. P., Rigden, D. J. and Meng, J. (2020) J. N. A. R. RMDisease: a database of genetic variants that affect RNA modifications, with implications for epitranscriptome pathogenesis.
[15]
Han, Y., Feng, J., Xia, L., Dong, X., Zhang, X., Zhang, S., Miao, Y., Xu, Q., Xiao, S. and Zuo, Z. (2019) J. C. CVm6A: a visualization and exploration database for m6As in cell lines, 8, 2, 168.
[16]
Tang, Y., Chen, K., Wu, X., Wei, Z., Zhang, S.-Y., Song, B., Zhang, S.-W., Huang, Y. and Meng, J. (2019) J. F. i. g. DRUM: inference of disease-associated m6A RNA methylation sites from a multi-layer heterogeneous network, 10, 266.
[17]
Wu, X., Wei, Z., Chen, K., Zhang, Q., Su, J., Liu, H., Zhang, L. and Meng, J. (2019) J. B. b. m6Acomet: large-scale functional prediction of individual m 6 A RNA methylation sites from an RNA co-methylation network, 20, 1, 1-12.
[18]
Dominissini, D., Moshitch-Moshkovitz, S., Schwartz, S., Salmon-Divon, M., Ungar, L., Osenberg, S., Cesarkas, K., Jacob-Hirsch, J., Amariglio, N. and Kupiec, M. (2012) J. N. Topology of the human and mouse m 6 A RNA methylomes revealed by m 6 A-seq, 485, 7397, 201-206.
[19]
Linder, B., Grozhik, A. V., Olarerin-George, A. O., Meydan, C., Mason, C. E. and Jaffrey, S. R. (2015) Single-nucleotide-resolution mapping of m6A and m6Am throughout the transcriptome. Nature methods, 12, 8, 767-772.
[20]
Meyer, K. D., Saletore, Y., Zumbo, P., Elemento, O., Mason, C. E. and Jaffrey, S. R. (2012) Comprehensive analysis of mRNA methylation reveals enrichment in 3' UTRs and near stop codons. Cell, 149, 7, 1635-1646.
[21]
Pendleton, K. E., Chen, B., Liu, K., Hunter, O. V., Xie, Y., Tu, B. P. and Conrad, N. K. (2017) The U6 snRNA m(6)A Methyltransferase METTL16 Regulates SAM Synthetase Intron Retention. Cell, 169, 5, 824-835.e814.
[22]
Shima, H., Matsumoto, M., Ishigami, Y., Ebina, M., Muto, A., Sato, Y., Kumagai, S., Ochiai, K., Suzuki, T. and Igarashi, K. (2017) S-Adenosylmethionine Synthesis Is Regulated by Selective N(6)-Adenosine Methylation and mRNA Degradation Involving METTL16 and YTHDC1. Cell reports, 21, 12, 3354-3363.
[23]
Warda, A. S., Kretschmer, J., Hackert, P., Lenz, C., Urlaub, H., Höbartner, C., Sloan, K. E. and Bohnsack, M. T. (2017) Human METTL16 is a N(6)-methyladenosine (m(6)A) methyltransferase that targets pre-mRNAs and various non-coding RNAs. EMBO reports, 18, 11, 2004-2014.
[24]
Eichler, D. C., Craig, N. (1994) J. P. i. n. a. r. and biology, m. Processing of eukaryotic ribosomal RNA, 49, 197-239.
[25]
Tollervey, D., Lehtonen, H., Jansen, R., Kern, H. and Hurt, E. C. (1993) J. C. Temperature-sensitive mutations demonstrate roles for yeast fibrillarin in pre-rRNA processing, pre-rRNA methylation, and ribosome assembly, 72, 3, 443-457.
[26]
Schimmang, T., Tollervey, D., Kern, H., Frank, R. and Hurt, E. (1989) J. T. E. j. A yeast nucleolar protein related to mammalian fibrillarin is associated with small nucleolar RNA and is essential for viability, 8, 13, 4015-4024.
[27]
Henriquez, R., Blobel, G. and Aris, J. (1990) J. J. o. B. C. Isolation and sequencing of NOP1. A yeast gene encoding a nucleolar protein homologous to a human autoimmune antigen, 265, 4, 2209-2215.
[28]
Lapeyre, B., Mariottini, P., Mathieu, C., Ferrer, P., Amaldi, F., Amalric, F., Caizergues-Ferrer, M. (1990) J. M. and Biology, C. Molecular cloning of Xenopus fibrillarin, a conserved U3 small nuclear ribonucleoprotein recognized by antisera from humans with autoimmune disease, 10, 1, 430-434.
[29]
Aris, J. P. and Blobel, G. (1991) J. P. o. t. N. A. o. S. cDNA cloning and sequencing of human fibrillarin, a conserved nucleolar protein recognized by autoimmune antisera, 88, 3, 931-935.
[30]
Turley, S. J., Tan, E. M., Pollard, K. M. (1993) J. B. e. B. A.-G. S. and Expression Molecular cloning and sequence analysis of U3 snoRNA-associated mouse fibrillarin, 1216, 1, 119-122.
[31]
Chen, H., Wurm, T., Britton, P., Brooks, G. and Hiscox, J. A. (2002) J. J. o. V. Interaction of the coronavirus nucleoprotein with nucleolar antigens and the host cell, 76, 10, 5233-5250.
[32]
Puvion-Dutilleul, F. and Christensen, M. (1993) J. E. j. o. c. b. Alterations of fibrillarin distribution and nucleolar ultrastructure induced by adenovirus infection, 61, 1, 168.
[33]
Zhou, Y., Zeng, P., Li, Y.-H., Zhang, Z. and Cui, Q. (2016) J. N. a. r. SRAMP: prediction of mammalian N6-methyladenosine (m6A) sites based on sequence-derived features, 44, 10, e91-e91.
[34]
Ke, S., Alemu, E. A., Mertens, C., Gantman, E. C., Fak, J. J., Mele, A., Haripal, B., Zucker-Scharff, I., Moore, M. J., Park, C. Y. (2015) J. G. and development A majority of m6A residues are in the last exons, allowing the potential for 3′ UTR regulation, 29, 19, 2037-2053.
[35]
Tortorici, M. A. and Veesler, D. (2019) J. A. i. v. r. Structural insights into coronavirus entry, 105, 93-116.
[36]
Edgar, R. C. (2004) J. N. a. r. MUSCLE: multiple sequence alignment with high accuracy and high throughput, 32, 5, 1792-1797.
- Computational Prediction of N6-methyladenosine (m6A) RNA Methylation in SARS-CoV-2 Viral Transcripts
Recommendations
Computational study and design of effective siRNAs to silence structural proteins associated genes of Indian SARS-CoV-2 strains
AbstractSARS-CoV-2 is a highly transmissible and pathogenic coronavirus that first emerged in late 2019 and has since triggered a pandemic of acute respiratory disease named COVID-19 which poses a significant threat to all public health ...
Graphical AbstractDisplay Omitted
Highlights- We designed and analyzed siRNAs from the wide array of 811 Indian SARS-CoV-2 strains that mediate PTGS of target mRNA.
Identifying potential human and medicinal plant microRNAs against SARS-CoV-2 3′UTR region: A computational genomics assessment
AbstractThe coronavirus disease of 2019 (COVID-19) began as an outbreak and has taken a toll on human lives. The current pandemic requires scientific attention; hence we designed a systematic computational workflow to identify the cellular ...
Graphical abstractDisplay Omitted
Highlights- Analysis of 5024 SARS-CoV-2 complete genomes from 98 countries/territories worldwide.
Molecular insight into the genomic variation of SARS-CoV-2 strains from current outbreak
Graphical abstractDisplay Omitted
Highlights- Genomic variation analysis of 715 SARS-CoV-2 genome sequences from 39 countries was performed.
AbstractCoronavirus disease 2019 (COVID-19) is the newly emerging viral disease, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The epidemic sparked in December 2019 at Wuhan city, China that causes a large global ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
July 2021
231 pages
ISBN:9781450389297
DOI:10.1145/3469678
Copyright © 2021 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 28 July 2021
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
BIBE2021
BIBE2021: The Fifth International Conference on Biological Information and Biomedical Engineering
July 20 - 22, 2021
Hangzhou, China
Acceptance Rates
Overall Acceptance Rate 36 of 116 submissions, 31%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 55Total Downloads
- Downloads (Last 12 months)4
- Downloads (Last 6 weeks)2
Reflects downloads up to 03 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format