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2000
Volume 21, Issue 1
  • ISSN: 1574-8936
  • E-ISSN: 2212-392X

Abstract

Background

The gene encodes a core component of m6A methyltransferase complex, which plays a crucial role in the post-transcriptional modification of RNA that influences many vital processes in plants. However, due to the constraint of embryonic lethality in knockout mutation, the molecular function of gene has yet to be comprehensively investigated.

Objective

The aim of this study is to investigate the expression and regulation of in .

Methods

A large-scale transcriptome and genome analysis were carried out for the expression and nsSNP (non-synonymous Single Nucleotide Polymorphism) studies. Structured-based virtual screening, molecular dynamics simulation, binding free energy calculation and m6A modification level assay were employed to mine and validate regulators from COCONUT natural product database.

Results

Tissue-specific expression and stress-responsive expression patterns of were observed in nsSNPs from the 1,001 project were not detected in the binding site of the methyl-donor substrate S-adenosylmethionine (SAM) in . 10 small molecules were identified as potential regulators, among which CNP0251613 (adenosine diphosphate glucose, ADPG) was selected and validated to decrease m6A levels at 10 µM the control in .

Conclusion

Our results provide a new insight and chemical entity into the in-depth study of RNA m6A writer in plants.

© 2026 Bentham Science Publishers
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References

  1. DesrosiersR. FridericiK. RottmanF. Identification of methylated nucleosides in messenger RNA from Novikoff hepatoma cells.Proc. Natl. Acad. Sci. USA197471103971397510.1073/pnas.71.10.3971 4372599
     [Google Scholar]
  2. YueY. LiuJ. HeC. RNA N6 -methyladenosine methylation in post-transcriptional gene expression regulation.Genes Dev.201529131343135510.1101/gad.262766.115 26159994
     [Google Scholar]
  3. MolinieB. WangJ. LimK.S. m6A-LAIC-seq reveals the census and complexity of the m6A epitranscriptome.Nat. Methods201613869269810.1038/nmeth.3898 27376769
     [Google Scholar]
  4. MeyerK.D. PatilD.P. ZhouJ. 5′ UTR m6A promotes cap-independent translation.Cell20151634999101010.1016/j.cell.2015.10.012 26593424
     [Google Scholar]
  5. WangX. LuZ. GomezA. N6-methyladenosine-dependent regulation of messenger RNA stability.Nature2014505748111712010.1038/nature12730 24284625
     [Google Scholar]
  6. FustinJ.M. DoiM. YamaguchiY. RNA-methylation-dependent RNA processing controls the speed of the circadian clock.Cell2013155479380610.1016/j.cell.2013.10.026 24209618
     [Google Scholar]
  7. WangX. FengJ. XueY. Structural basis of N6-adenosine methylation by the METTL3–METTL14 complex.Nature2016534760857557810.1038/nature18298 27281194
     [Google Scholar]
  8. TzafrirI. Pena-MurallaR. DickermanA. Identification of genes required for embryo development in Arabidopsis.Plant Physiol.200413531206122010.1104/pp.104.045179 15266054
     [Google Scholar]
  9. ZhongS. LiH. BodiZ. MTA is an Arabidopsis messenger RNA adenosine methylase and interacts with a homolog of a sex-specific splicing factor.Plant Cell20082051278128810.1105/tpc.108.058883 18505803
     [Google Scholar]
  10. DespresB. DelsenyM. DevicM. Partial complementation of embryo defective mutations: A general strategy to elucidate gene function.Plant J.200127214915910.1046/j.1365‑313x.2001.01078.x 11489192
     [Google Scholar]
  11. QianH. LuN. XueL. Reduced MTA1 expression by RNAi inhibits in vitro invasion and migration of esophageal squamous cell carcinoma cell line.Clin. Exp. Metastasis200522865366210.1007/s10585‑006‑9005‑2 16703414
     [Google Scholar]
  12. HuJ. CaiJ. ParkS.J. N6-methyladenosine mRNA methylation is important for salt stress tolerance in Arabidopsis.Plant J.202110661759177510.1111/tpj.15270 33843075
     [Google Scholar]
  13. WangX. JiangB. GuL. A photoregulatory mechanism of the circadian clock in Arabidopsis.Nat. Plants20217101397140810.1038/s41477‑021‑01002‑z 34650267
     [Google Scholar]
  14. GovindanG. SharmaB. LiY.F. mRNA N 6 ‐methyladenosine is critical for cold tolerance in arabidopsis.Plant J.202211141052106810.1111/tpj.15872 35710867
     [Google Scholar]
  15. HouN. LiC. HeJ. MdMTA-mediated m 6 A modification enhances drought tolerance by promoting mRNA stability and translation efficiency of genes involved in lignin deposition and oxidative stress.New Phytol.202223441294131410.1111/nph.18069 35246985
     [Google Scholar]
  16. ZhouL. TangR. LiX. TianS. LiB. QinG. N6-methyladenosine RNA modification regulates strawberry fruit ripening in an ABA-dependent manner.Genome Biol.202122116810.1186/s13059‑021‑02385‑0 34078442
     [Google Scholar]
  17. O’ ConnorC.J. LaraiaL. SpringD.R. Chemical genetics.Chem. Soc. Rev.20114084332434510.1039/c1cs15053g 21562678
     [Google Scholar]
  18. StockwellB.R. Frontiers in chemical genetics.Trends Biotechnol.2000181144945510.1016/S0167‑7799(00)01499‑2 11058785
     [Google Scholar]
  19. WalshD.P. ChangY.T. Chemical genetics.Chem. Rev.200610662476253010.1021/cr0404141 16771457
     [Google Scholar]
  20. LeiB. SongM. LiX. SMART v1.0: A database for small molecules with functional implications in plants.Interdiscip. Sci.202214127928310.1007/s12539‑021‑00480‑1 34648133
     [Google Scholar]
  21. SpringD.R. Chemical genetics to chemical genomics: Small molecules offer big insights.Chem. Soc. Rev.200534647248210.1039/b312875j 16137160
     [Google Scholar]
  22. StockwellB.R. Chemical genetics: Ligand-based discovery of gene function.Nat. Rev. Genet.20001211612510.1038/35038557 11253651
     [Google Scholar]
  23. TóthR. van der HoornR.A.L. Emerging principles in plant chemical genetics.Trends Plant Sci.2010152818810.1016/j.tplants.2009.11.005 20036182
     [Google Scholar]
  24. DejongheW. RussinovaE. Plant chemical genetics: From phenotype-based screens to synthetic biology.Plant Physiol.2017174152010.1104/pp.16.01805 28275150
     [Google Scholar]
  25. McCourtP. DesveauxD. Plant chemical genetics.New Phytol.20101851152610.1111/j.1469‑8137.2009.03045.x 19825020
     [Google Scholar]
  26. BlackwellH.E. ZhaoY. Chemical genetic approaches to plant biology.Plant Physiol.2003133244845510.1104/pp.103.031138 14555772
     [Google Scholar]
  27. WaadtR. SellerC.A. HsuP.K. TakahashiY. MunemasaS. SchroederJ.I. Plant hormone regulation of abiotic stress responses.Nat. Rev. Mol. Cell Biol.2022231068069410.1038/s41580‑022‑00479‑6 35513717
     [Google Scholar]
  28. HooperC.M. CastledenI.R. TanzS.K. AryamaneshN. MillarA.H. SUBA4: The interactive data analysis centre for Arabidopsis subcellular protein locations.Nucleic Acids Res.201745D1D1064D107410.1093/nar/gkw1041 27899614
     [Google Scholar]
  29. KlepikovaA.V. KasianovA.S. GerasimovE.S. LogachevaM.D. PeninA.A. A high resolution map of the Arabidopsis thaliana developmental transcriptome based on RNA-seq profiling.Plant J.20168861058107010.1111/tpj.13312 27549386
     [Google Scholar]
  30. WaeseJ. FanJ. PashaA. ePlant: Visualizing and exploring multiple levels of data for hypothesis generation in plant biology.Plant Cell20172981806182110.1105/tpc.17.00073 28808136
     [Google Scholar]
  31. ZhangH. ZhangF. YuY. A comprehensive online database for exploring ∼20,000 public Arabidopsis RNA-Seq Libraries.Mol. Plant20201391231123310.1016/j.molp.2020.08.001 32768600
     [Google Scholar]
  32. ZhaoY. LiM.C. KonatéM.M. TPM, FPKM, or normalized counts? A comparative study of quantification measures for the analysis of RNA-seq data from the NCI patient-derived models repository.J. Transl. Med.202119126910.1186/s12967‑021‑02936‑w 34158060
     [Google Scholar]
  33. KochL. 1001 genomes and epigenomes.Nat. Rev. Genet.201617950310.1038/nrg.2016.99 27452113
     [Google Scholar]
  34. WeigelD. MottR. The 1001 genomes project for Arabidopsis thaliana.Genome Biol.200910510710.1186/gb‑2009‑10‑5‑107 19519932
     [Google Scholar]
  35. NgP.C. HenikoffS. SIFT: Predicting amino acid changes that affect protein function.Nucleic Acids Res.200331133812381410.1093/nar/gkg509 12824425
     [Google Scholar]
  36. SimN.L. KumarP. HuJ. HenikoffS. SchneiderG. NgP.C. SIFT web server: Predicting effects of amino acid substitutions on proteins.Nucleic Acids Res.201240W452-710.1093/nar/gks539
     [Google Scholar]
  37. MiH. EbertD. MuruganujanA. PANTHER version 16: A revised family classification, tree-based classification tool, enhancer regions and extensive API.Nucleic Acids Res.202149D1D394D40310.1093/nar/gkaa1106 33290554
     [Google Scholar]
  38. AdzhubeiI.A. SchmidtS. PeshkinL. A method and server for predicting damaging missense mutations.Nat. Methods20107424824910.1038/nmeth0410‑248 20354512
     [Google Scholar]
  39. PejaverV. UrrestiJ. Lugo-MartinezJ. Inferring the molecular and phenotypic impact of amino acid variants with MutPred2.Nat. Commun.2020111591810.1038/s41467‑020‑19669‑x 33219223
     [Google Scholar]
  40. VenselaarH. te BeekT.A.H. KuipersR.K.P. HekkelmanM.L. VriendG. Protein structure analysis of mutations causing inheritable diseases. An e-Science approach with life scientist friendly interfaces.BMC Bioinformatics201011154810.1186/1471‑2105‑11‑548 21059217
     [Google Scholar]
  41. VriendG. WHAT IF: A molecular modeling and drug design program.J Mol Graph1990815256, 2910.1016/0263‑7855(90)80070‑V2268628
     [Google Scholar]
  42. CapriottiE. FariselliP. CasadioR. Predicting stability changes upon mutation from the protein sequence or structure.Nucleic Acids Res.200533W306-10
     [Google Scholar]
  43. RodriguesC.H.M. PiresD.E.V. AscherD.B. DynaMut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations.Protein Sci.2021301606910.1002/pro.3942 32881105
     [Google Scholar]
  44. ZhouY. PanQ. PiresD.E.V. RodriguesC.H.M. AscherD.B. DDMut: Predicting effects of mutations on protein stability using deep learning.Nucleic Acids Res.202351W1W122-810.1093/nar/gkad472 37283042
     [Google Scholar]
  45. JumperJ. EvansR. PritzelA. Highly accurate protein structure prediction with alphafold.Nature2021596787358358910.1038/s41586‑021‑03819‑2 34265844
     [Google Scholar]
  46. WiedersteinM. SipplM.J. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins.Nucleic Acids Res.200735Suppl. 2W407-1010.1093/nar/gkm290 17517781
     [Google Scholar]
  47. TianW. ChenC. LeiX. ZhaoJ. LiangJ. CASTp 3.0: Computed atlas of surface topography of proteins.Nucleic Acids Res.201846W1W363-710.1093/nar/gky473 29860391
     [Google Scholar]
  48. DeLanoW.L. LamJ.W. PyMOL: A communications tool for computational models.Abstr Pap Am Chem Soc2005230U1371U1372
     [Google Scholar]
  49. SorokinaM. MerseburgerP. RajanK. YirikM.A. SteinbeckC. COCONUT online: Collection of open natural products database.J. Cheminform.2021131210.1186/s13321‑020‑00478‑9 33423696
     [Google Scholar]
  50. YoshikawaN. HutchisonG.R. Fast, efficient fragment-based coordinate generation for Open Babel.J. Cheminform.20191114910.1186/s13321‑019‑0372‑5 31372768
     [Google Scholar]
  51. O’BoyleN.M. BanckM. JamesC.A. MorleyC. VandermeerschT. HutchisonG.R. Open Babel: An open chemical toolbox.J. Cheminform.2011313310.1186/1758‑2946‑3‑33 21982300
     [Google Scholar]
  52. JonesG. WillettP. GlenR.C. Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation.J. Mol. Biol.19952451435310.1016/S0022‑2836(95)80037‑9 7823319
     [Google Scholar]
  53. JonesG. WillettP. GlenR.C. LeachA.R. TaylorR. Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen.J Mol Biol1997267372774810.1006/jmbi.1996.0897 9126849
     [Google Scholar]
  54. VerdonkM.L. ColeJ.C. HartshornM.J. MurrayC.W. TaylorR.D. Improved protein–ligand docking using GOLD.Proteins200352460962310.1002/prot.10465 12910460
     [Google Scholar]
  55. LovellS.C. WordJ.M. RichardsonJ.S. RichardsonD.C. The penultimate rotamer library.Proteins200040338940810.1002/1097‑0134(20000815)40:3<389::AID‑PROT50>3.0.CO;2‑2 10861930
     [Google Scholar]
  56. Salomon-FerrerR. CaseD.A. WalkerR.C. An overview of the Amber biomolecular simulation package.Wiley Interdiscip. Rev. Comput. Mol. Sci.20133219821010.1002/wcms.1121
     [Google Scholar]
  57. CaseD.A. CheathamT.E.III DardenT. The Amber biomolecular simulation programs.J. Comput. Chem.200526161668168810.1002/jcc.20290 16200636
     [Google Scholar]
  58. MaierJ.A. MartinezC. KasavajhalaK. WickstromL. HauserK.E. SimmerlingC. ff14SB: Improving the accuracy of protein side chain and backbone parameters from ff99SB.J. Chem. Theory Comput.20151183696371310.1021/acs.jctc.5b00255 26574453
     [Google Scholar]
  59. FrischM.J. TrucksG.W. SchlegelH.B. Gaussian 09, Revision A.02.Gaussian, Inc. Wallingford, CT2009
     [Google Scholar]
  60. DardenT. YorkD. PedersenL. Particle mesh Ewald: An N ⋅log(N) method for ewald sums in large systems.J. Chem. Phys.19939812100891009210.1063/1.464397
     [Google Scholar]
  61. MillerB.R.III McGeeT.D.Jr SwailsJ.M. HomeyerN. GohlkeH. RoitbergA.E. MMPBSA.py : An efficient program for end-state free energy calculations.J. Chem. Theory Comput.2012893314332110.1021/ct300418h 26605738
     [Google Scholar]
  62. LarkinM.A. BlackshieldsG. BrownN.P. Clustal W and clustal X version 2.0.Bioinformatics200723212947294810.1093/bioinformatics/btm404 17846036
     [Google Scholar]
  63. ThompsonJ. GibsonT.J. PlewniakF. JeanmouginF. HigginsD.G. The CLUSTAL_X windows interface: Flexible strategies for multiple sequence alignment aided by quality analysis tools.Nucleic Acids Res.199725244876488210.1093/nar/25.24.4876 9396791
     [Google Scholar]
  64. RobertX GouetP Deciphering key features in protein structures with the new endscript server.Nucleic Acids Res201442W320W32410.1093/nar/gku316
     [Google Scholar]
  65. HuongT.T. NgocL.N.T. KangH. Functional characterization of a putative RNA demethylase ALKBH6 in Arabidopsis growth and abiotic stress responses.Int. J. Mol. Sci.20202118670710.3390/ijms21186707 32933187
     [Google Scholar]
  66. WangJ. ChitsazF. DerbyshireM.K. The conserved domain database in 2023.Nucleic Acids Res.202351D1D384D38810.1093/nar/gkac1096 36477806
     [Google Scholar]
  67. WinterD. VinegarB. NahalH. AmmarR. WilsonG.V. ProvartN.J. An electronic fluorescent pictograph browser for exploring and analyzing large-scale biological data sets.PLoS One200728e71810.1371/journal.pone.0000718 17684564
     [Google Scholar]
  68. WangS. WangH. XuZ. m6A mRNA modification promotes chilling tolerance and modulates gene translation efficiency in Arabidopsis.Plant Physiol.202319221466148210.1093/plphys/kiad112 36810961
     [Google Scholar]
  69. VaserR. AdusumalliS. LengS.N. SikicM. NgP.C. SIFT missense predictions for genomes.Nat. Protoc.20161111910.1038/nprot.2015.123 26633127
     [Google Scholar]
  70. YankovaE. BlackabyW. AlbertellaM. Small-molecule inhibition of METTL3 as a strategy against myeloid leukaemia.Nature2021593786059760110.1038/s41586‑021‑03536‑w 33902106
     [Google Scholar]
  71. DuY. YuanY. XuL. Discovery of METTL3 small molecule inhibitors by virtual screening of natural products.Front. Pharmacol.20221387813510.3389/fphar.2022.878135 35571106
     [Google Scholar]
  72. DolboisA. BediR.K. BochenkovaE. 1,4,9-Triazaspiro[5.5]undecan-2-one derivatives as potent and selective METTL3 inhibitors.J. Med. Chem.20216417127381276010.1021/acs.jmedchem.1c00773 34431664
     [Google Scholar]
  73. BallicoraM.A. IglesiasA.A. PreissJ. ADP-glucose pyrophosphorylase, a regulatory enzyme for bacterial glycogen synthesis.Microbiol. Mol. Biol. Rev.200367221322510.1128/MMBR.67.2.213‑225.2003 12794190
     [Google Scholar]
  74. UelandP.M. Pharmacological and biochemical aspects of S-adenosylhomocysteine and S-adenosylhomocysteine hydrolase.Pharmacol. Rev.1982343223253 6760211
     [Google Scholar]
  75. TalukdarA. MukherjeeA. BhattacharyaD. Fascinating transformation of SAM-competitive protein methyltransferase inhibitors from nucleoside analogues to non-nucleoside analogues.J. Med. Chem.20226531662168410.1021/acs.jmedchem.1c01208 35014841
     [Google Scholar]
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Screening of Candidate Chemical Regulators for the m6A Writer MTA in Arabidopsis
Curr Bioinform 21, 35 (2026); https://doi.org/10.2174/0115748936324036241016070320
/content/journals/cbio/10.2174/0115748936324036241016070320
/content/journals/cbio/10.2174/0115748936324036241016070320
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  • Article Type:     Research Article
Keyword(s): Chemical regulator; embryo-defective gene; m6A writer; MTA gene; N6-methyladenosine modification; virtual screening

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