Deep learning modeling of RNA ac4C deposition reveals the importance of plant alternative splicing.
Alternative splicing
Deep learning
Epigenetics
N4-acetylcytidine
RNA methylation
Journal
Plant molecular biology
ISSN: 1573-5028
Titre abrégé: Plant Mol Biol
Pays: Netherlands
ID NLM: 9106343
Informations de publication
Date de publication:
28 Oct 2024
28 Oct 2024
Historique:
received:
14
05
2024
accepted:
03
10
2024
medline:
29
10
2024
pubmed:
29
10
2024
entrez:
29
10
2024
Statut:
epublish
Résumé
The N4-acetylcytidine (ac4C) modification has recently been characterized as a noncanonical RNA marker in plants. While the precise installation of ac4C sites in individual plant transcripts continues to present challenges, the biological roles of ac4C in specific plant species are gradually being deciphered. Herein, we utilized a deep learning technique called iac4C (intelligent ac4C) to predict ac4C sites in mRNA. ac4C deposition was effectively forecasted by the iac4C model (AUROC = 0.948), revealing a reliable distribution pattern primarily situated in the transcribing area as opposed to regions that are not translated. The iac4C deep learning approach using a combination of BiGRU and self-attention mechanisms both validates previous studies showing a positive correlation between ac4C and RNA splicing in plant species and reveals new examples of other splicing events associated with ac4C. Our advanced deep learning algorithm for analyzing ac4C enables swift identification of important biological phenomena that would otherwise be challenging to uncover through traditional experimental approaches. These findings provide insight into the essential regulatory function of site-specific ac4C deposition in alternative splicing processes. The source code and datasets for iac4C are available at https://github.com/xlwei507/iac4C .
Identifiants
pubmed: 39467957
doi: 10.1007/s11103-024-01512-2
pii: 10.1007/s11103-024-01512-2
doi:
Substances chimiques
RNA, Plant
0
Cytidine
5CSZ8459RP
RNA, Messenger
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
118Subventions
Organisme : National Natural Science Foundation of China Grant
ID : 32372147
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Nature B.V.
Références
Andrews S (2014) FastQC a quality control tool for high throughput sequence data
Arango D, Sturgill D, Alhusaini N, Dillman AA, Sweet TJ, Hanson G, Hosogane M, Sinclair WR, Nanan KK, Mandler MD, Fox SD, Zengeya TT, Andresson T, Meier JL, Coller J, Oberdoerffer S (2018) Acetylation of cytidine in mRNA promotes translation efficiency. Cell 175:1872–1886. https://doi.org/10.1016/j.cell.2018.10.030
doi: 10.1016/j.cell.2018.10.030
pubmed: 30449621
pmcid: 6295233
Ataee S, Brochet X, Peña-Reyes CA (2022) Bacteriophage genetic edition using LSTM. Front Bioinform 2:932319
doi: 10.3389/fbinf.2022.932319
pubmed: 36353213
pmcid: 9639385
Bano S, Khalid S, Tairan NM, Shah H, Khattak HA (2023) Summarization of scholarly articles using BERT and BiGRU: deep learning-based extractive approach. J King Saud Univ-Comput Inf Sci 35:101739. https://doi.org/10.1016/j.jksuci.2023.101739
doi: 10.1016/j.jksuci.2023.101739
Boccaletto P, Machnicka MA, Purta E, Piątkowski P, Bagiński B, Wirecki TK, de Crécy-Lagard V, Ross R, Limbach PA, Kotter A, Helm M, Bujnicki JM (2018) MODOMICS: a database of RNA modification pathways. 2017 update. Nucleic Acids Res 46:D303–D307. https://doi.org/10.1093/nar/gkx1030
doi: 10.1093/nar/gkx1030
pubmed: 29106616
Bozdag S, Niu M, Zou Q, Lin C (2022) CRBPDL: identification of circRNA-RBP interaction sites using an ensemble neural network approach. PLOS Comput Biol 18:e1009798. https://doi.org/10.1371/journal.pcbi.1009798
doi: 10.1371/journal.pcbi.1009798
Chen S, Zhou Y, Chen Y, Gu J (2018) fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34:i884–i890. https://doi.org/10.1093/bioinformatics/bty560
doi: 10.1093/bioinformatics/bty560
pubmed: 30423086
pmcid: 6129281
Chimnaronk S, Suzuki T, Manita T, Ikeuchi Y, Yao M, Suzuki T, Tanaka I (2009) RNA helicase module in an acetyltransferase that modifies a specific tRNA anticodon. EMBO J 28:1362–1373. https://doi.org/10.1038/emboj.2009.69
doi: 10.1038/emboj.2009.69
pubmed: 19322199
pmcid: 2683049
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29:15–21. https://doi.org/10.1093/bioinformatics/bts635
doi: 10.1093/bioinformatics/bts635
pubmed: 23104886
Dominissini D, Moshitch-Moshkovitz S, Schwartz S, Salmon-Divon M, Ungar L, Osenberg S, Cesarkas K, Jacob-Hirsch J, Amariglio N, Kupiec M, Sorek R, Rechavi G (2012) Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature 485:201–206. https://doi.org/10.1038/nature11112
doi: 10.1038/nature11112
pubmed: 22575960
El Allali A, Elhamraoui Z, Daoud R (2021) Machine learning applications in RNA modification sites prediction. Comput Struct Biotechnol J 19:5510–5524. https://doi.org/10.1016/j.csbj.2021.09.025
doi: 10.1016/j.csbj.2021.09.025
pubmed: 34712397
pmcid: 8517552
Fu L, Niu B, Zhu Z, Wu S, Li W (2012) CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28:3150–3152. https://doi.org/10.1093/bioinformatics/bts565
doi: 10.1093/bioinformatics/bts565
pubmed: 23060610
pmcid: 3516142
Ito S, Horikawa S, Suzuki T, Kawauchi H, Tanaka Y, Suzuki T, Suzuki T (2014) Human NAT10 is an ATP-dependent RNA acetyltransferase responsible for N4-acetylcytidine formation in 18 S ribosomal RNA (rRNA)*. J Biol Chem 289:35724–35730. https://doi.org/10.1074/jbc.C114.602698
doi: 10.1074/jbc.C114.602698
pubmed: 25411247
pmcid: 4276842
Jia J, Cao X, Wei Z (2023a) DLC-ac4C: a prediction model for N4-acetylcytidine sites in human mRNA based on DenseNet and bidirectional LSTM methods. Curr Genomics 24:171–186. https://doi.org/10.2174/0113892029270191231013111911
doi: 10.2174/0113892029270191231013111911
pubmed: 38178985
pmcid: 10761336
Jia J, Wei Z, Cao X, Muneer A, Fati SM, Akbar NA, Agustriawan D, Wahyudi ST (2023b) EMDL-ac4C: identifying N4-acetylcytidine based on ensemble two-branch residual connection DenseNet and attention iVaccine-Deep: prediction of COVID-19 mRNA vaccine degradation using deep learning. Front Genet 14:7419–7432. https://doi.org/10.3389/fgene.2023.1232038
doi: 10.3389/fgene.2023.1232038
Lai F-L, Gao F (2023) LSA-ac4C: a hybrid neural network incorporating double-layer LSTM and self-attention mechanism for the prediction of N4-acetylcytidine sites in human mRNA. Int J Biol Macromol 253:126837. https://doi.org/10.1016/j.ijbiomac.2023.126837
doi: 10.1016/j.ijbiomac.2023.126837
pubmed: 37709212
Li Z, Jiang H, Kong L, Chen Y, Lang K, Fan X, Zhang L, Pian C (2021) Deep6mA: a deep learning framework for exploring similar patterns in DNA N6-methyladenine sites across different species. PLoS Comput Biol 17:e1008767. https://doi.org/10.1371/journal.pcbi.1008767
doi: 10.1371/journal.pcbi.1008767
pubmed: 33600435
pmcid: 7924747
Li B, Li D, Cai L, Zhou Q, Liu C, Lin J, Li Y, Zhao X, Li L, Liu X, He C (2023a) Transcriptome-wide profiling of RNA N4-cytidine acetylation in Arabidopsis thaliana and Oryza sativa. Mol Plant 16:1082–1098. https://doi.org/10.1016/j.molp.2023.04.009
doi: 10.1016/j.molp.2023.04.009
pubmed: 37073130
Li B, Qu L, Yang J (2023b) RNA-guided RNA modifications: biogenesis, functions, and applications. Acc Chem Res 56:3198–3210. https://doi.org/10.1021/acs.accounts.3c00474
doi: 10.1021/acs.accounts.3c00474
pubmed: 37931323
Li D, Li W, Zhao Y, Liu X (2024a) The analysis of deep learning recurrent neural network in english grading under the Internet of Things. IEEE Access 12:44640–44647. https://doi.org/10.1109/ACCESS.2024.3380480
doi: 10.1109/ACCESS.2024.3380480
Li F, Zhang J, Li K, Peng Y, Zhang H, Xu Y, Yu Y, Zhang Y, Liu Z, Wang Y, Huang L, Zhou F (2024b) GANSamples-ac4C: Enhancing ac4C site prediction via generative adversarial networks and transfer learning. Anal Biochem 689:115495. https://doi.org/10.1016/j.ab.2024.115495
doi: 10.1016/j.ab.2024.115495
pubmed: 38431142
Li Z, Jin B, Fang J (2024c) MetaAc4C: a multi-module deep learning framework for accurate prediction of N4-acetylcytidine sites based on pre-trained bidirectional encoder representation and generative adversarial networks. Genomics 116:110749. https://doi.org/10.1016/j.ygeno.2023.110749
doi: 10.1016/j.ygeno.2023.110749
pubmed: 38008265
Ling Y, Mahfouz MM, Zhou S (2021) Pre-mRNA alternative splicing as a modulator for heat stress response in plants. Trends Plant Sci 26:1153–1170. https://doi.org/10.1016/j.tplants.2021.07.008
doi: 10.1016/j.tplants.2021.07.008
pubmed: 34334317
Liu D, Liu Z, Xia Y, Wang Z, Song J, Yu DJ (2024) TransC-ac4C: identification of N4-acetylcytidine (ac4C) sites in mRNA using deep learning. IEEE/ACM Trans Comput Biol Bioinform. https://doi.org/10.1109/TCBB.2024.3386972
doi: 10.1109/TCBB.2024.3386972
pubmed: 39453793
Louloupi A, Ntini E, Conrad T, Ørom UAV (2018) Transient N-6-Methyladenosine transcriptome sequencing reveals a regulatory role of m6A in splicing efficiency. Cell Rep 23:3429–3437. https://doi.org/10.1016/j.celrep.2018.05.077
doi: 10.1016/j.celrep.2018.05.077
pubmed: 29924987
Lv H, Dao FY, Lin H (2022) DeepKla: an attention mechanism-based deep neural network for protein lysine lactylation site prediction. iMeta 1:e11. https://doi.org/10.1002/imt2.11
doi: 10.1002/imt2.11
pubmed: 38867734
pmcid: 10989745
Muneer A, Fati SM, Arifin Akbar N, Agustriawan D, Tri Wahyudi S (2022) iVaccine-deep: prediction of COVID-19 mRNA vaccine degradation using deep learning. J King Saud Univ-Comput Inf Sci 34:7419–7432. https://doi.org/10.1016/j.jksuci.2021.10.001
doi: 10.1016/j.jksuci.2021.10.001
pubmed: 38620874
Pennington J, Socher R, Manning C (2014) GloVe: global vectors for word representation. In: Association for Computational Linguistics. Doha, Qatar, pp 1532–1543
Sas-Chen A, Thomas JM, Matzov D, Taoka M, Nance KD, Nir R, Bryson KM, Shachar R, Liman GLS, Burkhart BW, Gamage ST, Nobe Y, Briney CA, Levy MJ, Fuchs RT, Robb GB, Hartmann J, Sharma S, Lin Q, Florens L, Washburn MP, Isobe T, Santangelo TJ, Shalev-Benami M, Meier JL, Schwartz S (2020) Dynamic RNA acetylation revealed by quantitative cross-evolutionary mapping. Nature 583:638–643. https://doi.org/10.1038/s41586-020-2418-2
doi: 10.1038/s41586-020-2418-2
pubmed: 32555463
pmcid: 8130014
Song B, Wang X, Liang Z, Ma J, Huang D, Wang Y, de Magalhães JP, Rigden DJ, Meng J, Liu G, Chen K, Wei Z (2023) RMDisease V2.0: an updated database of genetic variants that affect RNA modifications with disease and trait implication. Nucleic Acids Res 51:D1388–D1396. https://doi.org/10.1093/nar/gkac750
doi: 10.1093/nar/gkac750
pubmed: 36062570
Stern L, Schulman LH (1978) The role of the minor base N4-acetylcytidine in the function of the Escherichia coli noninitiator methionine transfer RNA. J Biol Chem 253:6132–6139. https://doi.org/10.1016/S0021-9258(17)34590-8
doi: 10.1016/S0021-9258(17)34590-8
pubmed: 355249
Su W, Xie X, Liu X-W, Gao D, Ma C-Y, Zulfiqar H, Yang H, Lin H, Yu X-L, Li Y-W (2022) iRNA-ac4C: a novel computational method for effectively detecting N4-acetylcytidine sites in human mRNA. Int J Biol Macromol 227:1174–1181
doi: 10.1016/j.ijbiomac.2022.11.299
pubmed: 36470433
Taniguchi T, Miyauchi K, Sakaguchi Y, Yamashita S, Soma A, Tomita K, Suzuki T (2018) Acetate-dependent tRNA acetylation required for decoding fidelity in protein synthesis. Nat Chem Biol 14:1010–1020. https://doi.org/10.1038/s41589-018-0119-z
doi: 10.1038/s41589-018-0119-z
pubmed: 30150682
Wang C, Ju Y, Zou Q, Lin C (2021a) DeepAc4C: a convolutional neural network model with hybrid features composed of physicochemical patterns and distributed representation information for identification of N4-acetylcytidine in mRNA. Bioinformatics 38:52–57. https://doi.org/10.1093/bioinformatics/btab611
doi: 10.1093/bioinformatics/btab611
pubmed: 34427581
Wang D, Zhang Z, Jiang Y, Mao Z, Wang D, Lin H, Xu D (2021b) DM3Loc: multi-label mRNA subcellular localization prediction and analysis based on multi-head self-attention mechanism. Nucleic Acids Res 49:e46. https://doi.org/10.1093/nar/gkab016
doi: 10.1093/nar/gkab016
pubmed: 33503258
pmcid: 8096227
Wang S, Xie H, Mao F, Wang H, Wang S, Chen Z, Zhang Y, Xu Z, Xing J, Cui Z, Gao X, Jin H, Hua J, Xiong B, Wu Y (2022) N4-acetyldeoxycytosine DNA modification marks euchromatin regions in Arabidopsis thaliana. Genome Biol 23:5. https://doi.org/10.1186/s13059-021-02578-7
doi: 10.1186/s13059-021-02578-7
pubmed: 34980211
pmcid: 8722123
Wang S, Zhou L, Ji N, Sun C, Sun L, Sun J, Du Y, Zhang N, Li Y, Liu W, Lu W (2023a) Targeting ACYP1-mediated glycolysis reverses lenvatinib resistance and restricts hepatocellular carcinoma progression. Drug Resist Updates 69:100976. https://doi.org/10.1016/j.drup.2023.100976
doi: 10.1016/j.drup.2023.100976
Wang W, Liu H, Wang F, Liu X, Sun Y, Zhao J, Zhu C, Gan L, Yu J, Witte C-P, Chen M (2023b) N4-acetylation of cytidine in mRNA plays essential roles in plants. Plant Cell 35:3739–3756. https://doi.org/10.1093/plcell/koad189
doi: 10.1093/plcell/koad189
pubmed: 37367221
pmcid: 10533332
Zhang G, Luo W, Lyu J, Yu Z-G, Huang G (2022) CNNLSTMac4CPred: a hybrid model for N4-acetylcytidine prediction. Interdiscip Sci 14:439–451. https://doi.org/10.1007/s12539-021-00500-0
doi: 10.1007/s12539-021-00500-0
pubmed: 35106702
Zhang L, Zhang Y, Liu J, Li H, Liu B, Zhao T (2023) N6-methyladenosine mRNA methylation is important for the light response in soybean. Front Plant Sci. https://doi.org/10.3389/fpls.2023.1153840
doi: 10.3389/fpls.2023.1153840
pubmed: 38510833
pmcid: 10786348