Реконструкция и компьютерный анализ генной сети, отражающей роль микроРНК в регуляции ответа пшеницы на засуху

Недостаток влаги – критический фактор, ограничивающий продуктивность мягкой пшеницы (Triticum aestivum L.), одной из ключевых сельскохозяйственных культур. Адаптация пшеницы к водному дефициту обеспечивается комплексными молекулярно-генетическими механизмами, включающими согласованную работу множества генов, регулируемых транскрипционными факторами и сигнальными некодирующими РНК, в частности микроРНК. микроРНК – опосредованная регуляция экспрессии генов – рассматривается как один из основных механизмов устойчивости растений к абиотическим стрессам. Изучение этих сложных молекулярно-генетических механизмов требует применения методов компьютерной системной биологии. Цель данной работы – реконструкция и компьютерный анализ генной сети, связанной с микроРНК-регуляцией адаптации мягкой пшеницы к условиям недостаточного увлажнения. Для достижения этой цели использованы программно-информационная система ANDSystem и специализированная база знаний Smart crop, адаптированная для области генетики и селекции пшеницы. Нами была реконструирована генная сеть ответа пшеницы на водный дефицит, включающая 144 гена, 1017 белков и 21 микроРНК пшеницы. Анализ сети выявил, что микроРНК преимущественно регулируют гены, контролирующие процессы морфогенеза побегов и корней растений, что играет важную роль в морфологических адаптациях к засухе. Ключевыми компонентами генной сети, регулируемыми микроРНК, оказались транскрипционные факторы семейств MYB и WRKY, а также белок теплового шока HSP90 и белок RPM1. Эти белки связаны с сигнальными путями фитогормонов и кальций-зависимыми протеинкиназами, играющими существенную роль в адаптации растений к водному дефициту. Было идентифицировано несколько микроРНК (MIR7757, MIR9653a, MIR9671, MIR9672b), ранее не обсуждавшихся в контексте адаптации пшеницы к засухе, которые являются кандидатами для дальнейших экспериментальных исследований, направленных на усиление устойчивости пшеницы к недостатку влаги. Полученные результаты могут быть полезными для создания новых сортов пшеницы с повышенной устойчивостью к водному дефициту, что имеет существенное значение для сельского хозяйства в условиях изменения климата.

1. Abbas A., Shah A.N., Tanveer M., Ahmed W., Shah A.A., Fiaz S., Waqas M.M., Ullah S. MiRNA fine tuning for crop improvement: using advance computational models and biotechnological tools. Mol. Biol. Rep. 2022;49(6):5437-5450. doi 10.1007/s11033-022-07231-5

2. Alptekin B., Langridge P., Budak H. Abiotic stress miRNomes in the Triticeae. Funct. Integr. Genomics. 2017;17(2-3):145-170. doi 10.1007/s10142-016-0525-9

3. Antropova E.A., Khlebodarova T.M., Demenkov P.S., Volianskaia A.R., Venzel A.S., Ivanisenko N.V., Gavrilenko A.D., Ivanisenko T.V., Adamovskaya A.V., Revva P.M., Kolchanov N.A., Lavrik I.N., Ivanisenko V.A. Reconstruction of the regulatory hypermethylation network controlling hepatocellular carcinoma development during hepatitis C viral infection. J. Integr. Bioinform. 2023;20(3):20230013. doi 10.1515/jib-2023-0013

4. Asano T., Hayashi N., Kikuchi S., Ohsugi R. CDPK-mediated abiotic stress signaling. Plant Signal Behav. 2012;7(7):817-821. doi 10.4161/psb.20351

5. Baillo E.H., Kimotho R.N., Zhang Z., Xu P. Transcription factors associated with abiotic and biotic stress tolerance and their potential for crops improvement. Genes (Basel). 2019;10(10):771. doi 10.3390/genes10100771

6. Bragina E.Y., Tiys E.S., Freidin M.B., Koneva L.A., Demenkov P.S., Ivanisenko V.A., Kolchanov N.A., Puzyrev V.P. Insights into pathophysiology of dystropy through the analysis of gene networks: an example of bronchial asthma and tuberculosis. Immunogenetics. 2014;66(7-8):457-465. doi 10.1007/s00251-014-0786-1

7. Bragina E.Y., Tiys E.S., Rudko A.A., Ivanisenko V.A., Freidin M.B. Novel tuberculosis susceptibility candidate genes revealed by the reconstruction and analysis of associative networks. Infect. Genet. Evol. 2016;46:118-123. doi 10.1016/j.meegid.2016.10.030

8. Bragina E.Y., Gomboeva D.E., Saik O.V., Ivanisenko V.A., Freidin M.B., Nazarenko M.S., Puzyrev V.P. Apoptosis genes as a key to identification of inverse comorbidity of huntington’s disease and cancer. Int. J. Mol. Sci. 2023;24(11):9385. doi 10.3390/ijms24119385

9. Chao H., Zhang S., Hu Y., Ni Q., Xin S., Zhao L., Ivanisenko V.A., Orlov Y.L., Chen M. Integrating omics databases for enhanced crop breeding. J. Integr. Bioinform. 2023;20(4):20230012. doi 10.1515/jib-2023-0012

10. Chen H., Lai Z., Shi J., Xiao Y., Chen Z., Xu X. Roles of arabidopsis WRKY18, WRKY40 and WRKY60 transcription factors in plant responses to abscisic acid and abiotic stress. BMC Plant Biol. 2010; 10:281. doi 10.1186/1471-2229-10-281

11. Cheval C., Aldon D., Galaud J.P., Ranty B. Calcium/calmodulin-mediated regulation of plant immunity. Biochim. Biophys. Acta. 2013; 1833(7):1766-1771. doi 10.1016/j.bbamcr.2013.01.031

12. Demenkov P.S., Ivanisenko T.V., Kolchanov N.A., Ivanisenko V.A. ANDVisio: a new tool for graphic visualization and analysis of literature mined associative gene networks in the ANDSystem. In Silico Biol. 2012;11(3-4):149-161. doi 10.3233/ISB-2012-0449

13. Demenkov P.S., Saik O.V., Ivanisenko T.V., Kolchanov N.A., Kochetov A.V., Ivanisenko V.A. Prioritization of potato genes involved in the formation of agronomically valuable traits using the SOLANUM TUBEROSUM knowledge base. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2019;23(3): 312-319. doi 10.18699/VJ19.501

14. di Donato M., Geisler M. HSP90 and co-chaperones: a multitaskers’ view on plant hormone biology. FEBS Lett. 2019;593(13):1415-1430. doi 10.1002/1873-3468.13499

15. Ferdous J., Hussain S.S., Shi B.J. Role of microRNAs in plant drought tolerance. Plant Biotechnol. J. 2015;13(3):293-305. doi 10.1111/pbi.12318

16. Gahlaut V., Jaiswal V., Kumar A., Gupta P.K. Transcription factors involved in drought tolerance and their possible role in developing drought tolerant cultivars with emphasis on wheat (Triticum aestivum L.). Theor. Appl. Genet. 2016;129(11):2019-2042. doi 10.1007/s00122-016-2794-z

17. Gupta O.P., Meena N.L., Sharma I., Sharma P. Differential regulation of microRNAs in response to osmotic, salt and cold stresses in wheat. Mol. Biol. Rep. 2014;41(7):4623-4629. doi 10.1007/s11033-014-3333-0

18. Haider M.S., Kurjogi M.M., Khalil-Ur-Rehman M., Fiaz M., Pervaiz T., Jiu S., Haifeng J., Chen W., Fang J. Grapevine immune signaling network in response to drought stress as revealed by transcriptomic analysis. Plant Physiol. Biochem. 2017;121:187-195. doi 10.1016/j.plaphy.2017.10.026

19. Hong M.J., Kim D.Y., Kang S.Y., Kim D.S., Kim J.B., Seo Y.W. Wheat F-box protein recruits proteins and regulates their abundance during wheat spike development. Mol. Biol. Rep. 2012;39(10):9681-9696. doi 10.1007/s11033-012-1833-3

20. Ivanisenko T.V., Sayk O.V., Demenkov P.S., Khlestkin V.K., Khlestkina E.K., Kolchanov N.A., Ivanisenko V.A. The SOLANUM TUBEROSUM knowledge base: the section on molecular-genetic regulation of metabolic pathways. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2018;22(1): 8-17. doi 10.18699/VJ18.325 (in Russian)

21. Ivanisenko T.V., Saik O.V., Demenkov P.S., Ivanisenko N.V., Savostianov A.N., Ivanisenko V.A. ANDDigest: a new web-based module of ANDSystem for the search of knowledge in the scientific literature. BMC Bioinformatics. 2020;21(Suppl. 11):228. doi 10.1186/s12859-020-03557-8

22. Ivanisenko T.V., Demenkov P.S., Kolchanov N.A., Ivanisenko V.A. The new version of the ANDDigest tool with improved ai-based short names recognition. Int. J. Mol. Sci. 2022;23(23):14934. doi 10.3390/ijms232314934

23. Ivanisenko V.A., Saik O.V., Ivanisenko N.V., Tiys E.S., Ivanisenko T.V., Demenkov P.S., Kolchanov N.A. ANDSystem: an associative network discovery system for automated literature mining in the field of biology. BMC Syst. Biol. 2015;9(Suppl. 2):S2. doi 10.1186/1752-0509-9-S2-S2

24. Ivanisenko V.A., Demenkov P.S., Ivanisenko T.V., Mishchenko E.L., Saik O.V. A new version of the ANDSystem tool for automatic extraction of knowledge from scientific publications with expanded functionality for reconstruction of associative gene networks by considering tissue-specific gene expression. BMC Bioinformatics. 2019; 20(Suppl. 1):34. doi 10.1186/s12859-018-2567-6

25. Ivanisenko V.A., Gaisler E.V., Basov N.V., Rogachev A.D., Cheresiz S.V., Ivanisenko T.V., Demenkov P.S., Mishchenko E.L., Khripko O.P., Khripko Y.I., Voevoda S.M., Karpenko T.N., Velichko A.J., Voevoda M.I., Kolchanov N.A., Pokrovsky A.G. Plasma metabolomics and gene regulatory networks analysis reveal the role of nonstructural SARS-CoV-2 viral proteins in metabolic dysregulation in COVID-19 patients. Sci. Rep. 2022;12(1):19977. doi 10.1038/s41598-022-24170-0

26. Ivanisenko V.A., Basov N.V., Makarova A.A., Venzel A.S., Rogachev A.D., Demenkov P.S., Ivanisenko T.V., Kleshchev M.A., Gaisler E.V., Moroz G.B., Plesko V.V., Sotnikova Y.S., Patrushev Y.V., Lomivorotov V.V., Kolchanov N.A., Pokrovsky A.G. Gene networks for use in metabolomic data analysis of blood plasma from patients with postoperative delirium. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2023;27(7):768-775. doi 10.18699/VJGB-23-89

27. Jeyasri R., Muthuramalingam P., Satish L., Pandian S.K., Chen J.T., Ahmar S., Wang X., Mora-Poblete F., Ramesh M. An overview of abiotic stress in cereal crops: negative impacts, regulation, biotechnology and integrated omics. Plants (Basel). 2021;10(7):1472. doi 10.3390/plants10071472

28. Jiang J., Ma S., Ye N., Jiang M., Cao J., Zhang J. WRKY transcription factors in plant responses to stresses. J. Integr. Plant Biol. 2017;59(2):86-101. doi 10.1111/jipb.12513

29. Khlebodarova T.M., Demenkov P.S., Ivanisenko T.V., Antropova E.A., Lavrik I.N., Ivanisenko V.A. Primary and secondary micro-RNA modulation the extrinsic pathway of apoptosis in hepatocellular carcinoma. Mol. Biol. 2023;57(2):165-175. doi 10.1134/S0026893323020103

30. Khoso M.A., Hussain A., Ritonga F.N., Ali Q., Channa M.M., Alshegaihi R.M., Meng Q., Ali M., Zaman W., Brohi R.D., Liu F., Manghwar H. WRKY transcription factors (TFs): Molecular switches to regulate drought, temperature, and salinity stresses in plants. Front. Plant Sci. 2022;13:1039329. doi 10.3389/fpls.2022.1039329

31. Kumar R.R., Pathak H., Sharma S.K., Kala Y.K., Nirjal M.K., Singh G.P., Goswami S., Rai R.D. Novel and conserved heat-responsive microRNAs in wheat (Triticum aestivum L.). Funct. Integr. Genomics. 2015;15(3):323-348. doi 10.1007/s10142-014-0421-0

32. Langridge P., Reynolds M. Breeding for drought and heat tolerance in wheat. Theor. Appl. Genet. 2021;134(6):1753-1769. doi 10.1007/s00122-021-03795-1

33. Larina I.M., Pastushkova L.Kh., Tiys E.S., Kireev K.S., Kononikhin A.S., Starodubtseva N.L., Popov I.A., Custaud M.A., Dobrokhotov I.V., Nikolaev E.N., Kolchanov N.A., Ivanisenko V.A. Permanent proteins in the urine of healthy humans during the Mars-500 experiment. J. Bioinform. Comput. Biol. 2015;13(1):1540001. doi 10.1142/S0219720015400016

34. Li C., Hou N., Fang N., He J., Ma Z., Ma F., Guan Q., Li X. Cold shock protein 3 plays a negative role in apple drought tolerance by regulating oxidative stress response. Plant Physiol. Biochem. 2021a;168:83-92. doi 10.1016/j.plaphy.2021.10.003

35. Li C., Li L., Reynolds M.P., Wang J., Chang X., Mao X., Jing R. Recognizing the hidden half in wheat: root system attributes associated with drought tolerance. J. Exp. Bot. 2021b;72(14):5117-5133. doi 10.1093/jxb/erab124

36. Li J., Brader G., Kariola T., Palva E.T. WRKY70 modulates the selection of signaling pathways in plant defense. Plant J. 2006;46(3): 477-491. doi 10.1111/j.1365-313X.2006.02712.x

37. Li N., Liu T., Guo F., Yang J., Shi Y., Wang S., Sun D. Identification of long non-coding RNA-microRNA-mRNA regulatory modules and their potential roles in drought stress response in wheat (Triticum aestivum L.). Front. Plant Sci. 2022;13:1011064. doi 10.3389/fpls.2022.1011064

38. Li Y., Wei K. Comparative functional genomics analysis of cytochrome P450 gene superfamily in wheat and maize. BMC Plant Biol. 2020; 20(1):93. doi 10.1186/s12870-020-2288-7

39. Li Y., Zhang H., Dong F., Zou J., Gao C., Zhu Z., Liu Y. Multiple roles of wheat calmodulin genes during stress treatment and TaCAM2-D as a positive regulator in response to drought and salt tolerance. Int. J. Biol. Macromol. 2022;220:985-997. doi 10.1016/j.ijbiomac.2022.08.124

40. Li Y., Han S., Qi Y. Advances in structure and function of auxin response factor in plants. J. Integr. Plant Biol. 2023;65(3):617-632. doi 10.1111/jipb.13392

41. Li Y.-F., Zheng Y., Jagadeeswaran G., Sunkar R. Characterization of small RNAs and their target genes in wheat seedlings using sequencing-based approaches. Plant Sci. 2013;203-204:17-24. doi 10.1016/j.plantsci.2012.12.014

42. Liebsch D., Palatnik J.F. MicroRNA miR396, GRF transcription factors and GIF co-regulators: a conserved plant growth regulatory module with potential for breeding and biotechnology. Curr. Opin. Plant Biol. 2020;53:31-42. doi 10.1016/j.pbi.2019.09.008

43. Liu J., Feng L., Li J., He Z. Genetic and epigenetic control of plant heat responses. Front. Plant Sci. 2015;6:267. doi 10.3389/fpls.2015.00267

44. Ma Z., Hu L. MicroRNA: a dynamic player from signalling to abiotic tolerance in plants. Int. J. Mol. Sci. 2023;24(14):11364. doi 10.3390/ijms241411364

45. Manna M., Thakur T., Chirom O., Mandlik R., Deshmukh R., Salvi P. Transcription factors as key molecular target to strengthen the drought stress tolerance in plants. Physiol. Plant. 2021;172(2):847-868. doi 10.1111/ppl.13268

46. Mao G., Meng X., Liu Y., Zheng Z., Chen Z., Zhang S. Phosphorylation of a WRKY transcription factor by two pathogen-responsive MAPKs drives phytoalexin biosynthesis in Arabidopsis. Plant Cell. 2011;23(4):1639-1653. doi 10.1105/tpc.111.084996

47. Millar A.A., Lohe A., Wong G. Biology and function of miR159 in plants. Plants (Basel). 2019;8(8):255. doi 10.3390/plants8080255

48. Nagy Z., Németh E., Guóth A., Bona L., Wodala B., Pécsváradi A. Metabolic indicators of drought stress tolerance in wheat: glutamine synthetase isoenzymes and Rubisco. Plant Physiol. Biochem. 2013; 67:48-54. doi 10.1016/j.plaphy.2013.03.001

49. Ni Z., Hu Z., Jiang Q., Zhang H. GmNFYA3, a target gene of miR169, is a positive regulator of plant tolerance to drought stress. Plant Mol. Biol. 2013;82(1-2):113-129. doi 10.1007/s11103-013-0040-5

50. Niu C.F., Wei W., Zhou Q.Y., Tian A.G., Hao Y.J., Zhang W.K., Ma B., Lin Q., Zhang Z.B., Zhang J.S., Chen S.Y. Wheat WRKY genes TaWRKY2 and TaWRKY19 regulate abiotic stress tolerance in transgenic Arabidopsis plants. Plant Cell Environ. 2012;35(6):1156-1170. doi 10.1111/j.1365-3040.2012.02480.x

51. Pakul A.L., Lapshinov N.A., Bozhanova G.V., Pakul V.N. The main factors influencing efficiency of spring common wheat agrocenosis. Sibirskii Vestnik Sel’skokhozyajstvennoi Nauki = Siberian Herald of Agricultural Science. 2018;48(6):21-29. doi 10.26898/0370-8799-2018-6-3 (in Russian)

52. Pandian B.A., Sathishraj R., Djanaguiraman M., Prasad P.V.V., Jugulam M. Role of cytochrome P450 enzymes in plant stress response. Antioxidants (Basel). 2020;9(5):454. doi 10.3390/antiox9050454

53. Park C.Y., Lee J.H., Yoo J.H., Moon B.C., Choi M.S., Kang Y.H., Lee S.M., Kim H.S., Kang K.Y., Chung W.S., Lim C.O., Cho M.J. WRKY group IId transcription factors interact with calmodulin. FEBS Lett. 2005;579(6):1545-1550. doi 10.1016/j.febslet.2005.01.057

54. Park S.Y., Grabau E. Differential isoform expression and protein localization from alternatively spliced Apetala2 in peanut under drought stress. J. Plant Physiol. 2016;206:98-102. doi 10.1016/j.jplph.2016.09.007

55. Pastushkova L., Kashirina D.N., Brzhozovskiy A.G., Kononikhin A.S., Tiys E.S., Ivanisenko V.A., Koloteva M.I., Nikolaev E.N., Larina I.M. Evaluation of cardiovascular system state by urine proteome after manned space flight. Acta Astronaut. 2019;160:594-600. doi 10.1016/j.actaastro.2019.02.015

56. Pellegrineschi A., Reynolds M., Pacheco M., Brito R.M., Almeraya R., Yamaguchi-Shinozaki K., Hoisington D. Stress-induced expression in wheat of the Arabidopsis thaliana DREB1A gene delays water stress symptoms under greenhouse conditions. Genome. 2004; 47(3):493-500. doi 10.1139/g03-140

57. Ranty B., Aldon D., Cotelle V., Galaud J.P., Thuleau P., Mazars C. Calcium sensors as key hubs in plant responses to biotic and abiotic stresses. Front. Plant Sci. 2016;7:327. doi 10.3389/fpls.2016.00327

58. Raza A., Charagh S., Karikari B., Sharif R., Yadav V., Mubarik M.S., Habib M., Zhuang Y., Zhang C., Chen H., Varshney R.K., Zhuang W. miRNAs for crop improvement. Plant Physiol. Biochem. 2023;201: 107857. doi 10.1016/j.plaphy.2023.107857

59. Ren L., Zhang T., Wu H., Ge X., Wan H., Chen S., Li Z., Ma D., Wang A. Blocking IbmiR319a impacts plant architecture and reduces drought tolerance in sweet potato. Genes (Basel). 2022;13(3): 404. doi 10.3390/genes13030404

60. Rogachev A.D., Alemasov N.A., Ivanisenko V.A., Ivanisenko N.V., Gaisler E.V., Oleshko O.S., Cheresiz S.V., Mishinov S.V., Stupak V.V., Pokrovsky A.G. Correlation of metabolic profiles of plasma and cerebrospinal fluid of high-grade glioma patients. Metabolites. 2021;11(3):133. doi 10.3390/metabo11030133

61. Saik O.V., Ivanisenko T.V., Demenkov P.S., Ivanisenko V.A. Interactome of the hepatitis C virus: Literature mining with ANDSystem. Virus Res. 2016;218:40-48. doi 10.1016/j.virusres.2015.12.003

62. Saik O.V., Demenkov P.S., Ivanisenko T.V., Bragina E.Y., Freidin M.B., Dosenko V.E., Zolotareva O.I., Choynzonov E.L., Hofestaedt R., Ivanisenko V.A. Search for new candidate genes involved in the comorbidity of asthma and hypertension based on automatic analysis of scientific literature. J. Integr. Bioinform. 2018;15(4):20180054. doi 10.1515/jib-2018-0054

63. Saik O.V., Nimaev V.V., Usmonov D.B., Demenkov P.S., Ivanisenko T.V., Lavrik I.N., Ivanisenko V.A. Prioritization of genes involved in endothelial cell apoptosis by their implication in lymphedema using an analysis of associative gene networks with ANDSystem. BMC Med. Genomics. 2019;12(Suppl. 2):47. doi 10.1186/s12920-019-0492-9

64. Saxena H., Negi H., Sharma B. Role of F-box E3-ubiquitin ligases in plant development and stress responses. Plant Cell Rep. 2023; 42(7):1133-1146. doi 10.1007/s00299-023-03023-8

65. Shamloo-Dashtpagerdi R., Shahriari A.G., Tahmasebi A., Vetukuri R.R. Potential role of the regulatory miR1119-MYC2 module in wheat (Triticum aestivum L.) drought tolerance. Front. Plant Sci. 2023;14: 1161245. doi 10.3389/fpls.2023.1161245

66. Shojaee S., Ravash R., Shiran B., Ebrahimie E. Meta-analysis highlights the key drought responsive genes in genes: PEPC and TaSAG7 are hubs response networks. J. Genet. Eng. Biotechnol. 2022;20(1): 127. doi 10.1186/s43141-022-00395-4

67. Srivastava R., Kumar R. The expanding roles of APETALA2/Ethylene Responsive Factors and their potential applications in crop improvement. Brief. Funct. Genomics. 2018;18(4):240-254. doi 10.1093/bfgp/elz001

68. Szlachtowska Z., Rurek M. Plant dehydrins and dehydrin-like proteins: characterization and participation in abiotic stress response. Front. Plant Sci. 2023;14:1213188. doi 10.3389/fpls.2023.1213188

69. Tokizawa M., Enomoto T., Chandnani R., Mora-Macías J., Burbridge C., Armenta-Medina A., Kobayashi Y., Yamamoto Y.Y., Koyama H., Kochian L.V. The transcription factors, STOP1 and TCP20, are required for root system architecture alterations in response to nitrate deficiency. Proc. Natl. Acad. Sci. USA. 2023;120(35):e2300446120. doi 10.1073/pnas.2300446120

70. Virdi A.S., Pareek A., Singh P. Evidence for the possible involvement of calmodulin in regulation of steady state levels of Hsp90 family members (Hsp87 and Hsp85) in response to heat shock in sorghum. Plant Signal. Behav. 2011;6(3):393-399. doi 10.4161/psb.6.3.13867

71. Virdi A.S., Singh S., Singh P. Abiotic stress responses in plants: roles of calmodulin-regulated proteins. Front. Plant Sci. 2015;6:809. doi 10.3389/fpls.2015.00809

72. Volyanskaya A.R., Antropova E.A., Zubairova U.S., Demenkov P.S., Venzel A.S., Orlov Y.L., Makarova A.A., Ivanisenko T.V., Gorshkova T.A., Aglyamova A.R., Kolchanov N.A., Chen M., Ivanisenko V.A. Reconstruction and analysis of the gene regulatory network for cell wall function in Arabidopsis thaliana L. leaves in response to water deficit. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2023;27(8):1031-1041. doi 10.18699/VJGB-23-118

73. Wang H., Wang H. The miR156/SPL module, a regulatory hub and versatile toolbox, gears up crops for enhanced agronomic traits. Mol. Plant. 2015;8(5):677-688. doi 10.1016/j.molp.2015.01.008

74. Wang M.Y., Zhao P.M., Cheng H.Q., Han L.B., Wu X.M., Gao P., Wang H.Y., Yang C.L., Zhong N.Q., Zuo J.R., Xia G.X. The cotton transcription factor TCP14 functions in auxin-mediated epidermal cell differentiation and elongation. Plant Physiol. 2013;162(3): 1669-1680. doi 10.1104/pp.113.215673

75. Wang S., Zhang M. Small-molecule ribonucleic acid (RNA) OsamiR393 for improving rice tillering and application. 2011. https://patents.google.com/patent/CN102533760A/en?oq=CN102533760A

76. Wang X., Niu Y., Zheng Y. Multiple functions of MYB transcription factors in abiotic stress responses. Int. J. Mol. Sci. 2021;22(11): 6125. doi 10.3390/ijms22116125

77. Wani S.H., Anand S., Singh B., Bohra A., Joshi R. WRKY transcription factors and plant defense responses: latest discoveries and future prospects. Plant Cell Rep. 2021;40(7):1071-1085. doi 10.1007/s00299-021-02691-8

78. Wei T., Guo D., Liu J. PtrMYB3, a R2R3-MYB transcription factor from Poncirus trifoliata, negatively regulates salt tolerance and hydrogen peroxide scavenging. Antioxidants (Basel). 2021;10(9): 1388. doi 10.3390/antiox10091388

79. Woodger F.J., Gubler F., Pogson B.J., Jacobsen J.V. A Mak-like kinase is a repressor of GAMYB in barley aleurone. Plant J. 2003;33(4): 707-717. doi 10.1046/j.1365-313x.2003.01663.x

80. Yang C., Li D., Mao D., Liu X., Ji C., Li X., Zhao X., Cheng Z., Chen C., Zhu L. Overexpression of microRNA319 impacts leaf morphogenesis and leads to enhanced cold tolerance in rice (Oryza sativa L.). Plant Cell Environ. 2013;36(12):2207-2218. doi 10.1111/pce.12130

81. Yang J., Zhang N., Mi X., Wu L., Ma R., Zhu X., Yao L., Jin X., Si H., Wang D. Identification of miR159s and their target genes and expression analysis under drought stress in potato. Comput. Biol. Chem. 2014;53(Part B):204-213. doi 10.1016/j.compbiolchem.2014.09.009

82. Yu T.F., Xu Z.S., Guo J.K., Wang Y.X., Abernathy B., Fu J.D., Chen X., Zhou Y.B., Chen M., Ye X.G., Ma Y.Z. Improved drought tolerance in wheat plants overexpressing a synthetic bacterial cold shock protein gene SeCspA. Sci Rep. 2017;7:44050. doi 10.1038/srep44050

83. Yu Y., Yu M., Zhang S., Song T., Zhang M., Zhou H., Wang Y., Xiang J., Zhang X. Transcriptomic identification of wheat AP2/ERF transcription factors and functional characterization of TaERF-6-3A in response to drought and salinity stresses. Int. J. Mol. Sci. 2022; 23(6):3272. doi 10.3390/ijms23063272

84. Zhang F., Yang J., Zhang N., Wu J., Si H. Roles of microRNAs in abiotic stress response and characteristics regulation of plant. Front. Plant Sci. 2022;13:919243. doi 10.3389/fpls.2022.919243

85. Zhou Y., Zhang L., Yu J. Application of tomato miR156e-3p gene in improvement of tomato low-temperature resistance and plant overexpression vector. 2020. https://patents.google.com/patent/CN111705077B/en?oq=CN111705077B

86. Zolotareva O., Saik O.V., Königs C., Bragina E.Y., Goncharova I.A., Freidin M.B., Dosenko V.E., Ivanisenko V.A., Hofestädt R. Comorbidity of asthma and hypertension may be mediated by shared genetic dysregulation and drug side effects. Sci. Rep. 2019;9(1):16302. doi 10.1038/s41598-019-52762-w