Поиск перспективных генетических маркеров, ассоциированных с молекулярными механизмами снижения устойчивости риса к Rhizoctonia solani при избытке азотных удобрений, методом реконструкции и анализа генных сетей

Азотные удобрения, повышающие урожайность риса, при избытке могут снижать устойчивость растений к заболеваниям, в частности к ризоктониозу, вызываемому Rhizoctonia solani. Этот патоген способен уничтожить до 50 % урожая, однако механизмы, лежащие в основе снижения устойчивости при избытке азота, остаются малоизученными. Данное исследование направлено на выявление потенциальных генов-маркеров для повышения устойчивости риса к R. solani в условиях избытка азота. Применен комплексный биоинформатический подход, включающий анализ дифференциальной экспрессии генов, реконструкцию генных сетей, анализ перепредставленности биологических процессов, филостратиграфический анализ и анализ коэкспрессии некодирующих РНК. Использованы когнитивная система Smart crop, ANDSystem, база данных ncPlantDB и другие биоинформатические ресурсы. Анализ молекулярно-генетической сети взаимодействий выявил три потенциальных механизма, объясняющих снижение устойчивости риса к R. solani при избытке азота: OsGSK2-опосредованный путь, путь OsMYB44-OsWRKY6-OsPR1 и путь SOG1-Rad51-PR1/PR2. Идентифицированы потенциальные маркеры для селекции: 7 генов, контролирующих ответы риса на широкий круг стрессов, и 11 генов-модуляторов иммунной системы. Особое внимание уделено ключевым участникам регуляторных путей в условиях избытка азота. Анализ некодирующих РНК выявил 30 микроРНК, мишенями которых являются гены из реконструированной генной сети. Для двух микроРНК (Osa-miR396 и Osa-miR7695) обнаружено около 7400 тыс. уникальных длинных некодирующих РНК (днРНК) с различными индексами коэкспрессии. Выделены топ-50 днРНК с наибольшим индексом коэкспрессии для каждой микроРНК, что открывает новые перспективы в изучении регуляторных механизмов устойчивости риса к патогенам. Полученные результаты создают теоретическую основу для экспериментальных работ по созданию новых сортов риса с повышенной устойчивостью к патогенам в условиях избыточного азотного питания.

1. 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

2. 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

3. 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

4. Cao X., Wei Y., Shen B., Liu L., Mao J. Interaction of the transcription factors BES1/BZR1 in plant crowth and stress response. Int. J. Mol. Sci. 2024;25(13):6836. doi 10.3390/ijms25136836

5. 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

6. Charagh S., Hui S., Wang J., Raza A., Zhou L., Xu B., Zhang Y., Sheng Z., Tang S., Hu S., Hu P. Unveiling innovative approaches to mitigate metals/metalloids toxicity for sustainable agriculture. Physiol. Plant. 2024;176(2):e14226. doi 10.1111/ppl.14226

7. Chen K., Li G.J., Bressan R.A., Song C.P., Zhu J.K., Zhao Y. Abscisic acid dynamics, signaling, and functions in plants. J. Integr. Plant Biol. 2020;62(1):25-54. doi 10.1111/jipb.12899

8. 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. 2011-2012;11(3-4):149-161. doi 10.3233/ISB-2012-0449

9. 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

10. Diédhiou C.J., Popova O.V., Dietz K.J., Golldack D. The SNF1-type serine-threonine protein kinase SAPK4 regulates stress-responsive gene expression in rice. BMC Plant Biol. 2008;8:49. doi 10.1186/1471-2229-8-49

11. Domazet-Lošo T., Tautz D. A phylogenetically based transcriptome age index mirrors ontogenetic divergence patterns. Nature. 2010; 468(7325):815-818. doi 10.1038/nature09632

12. Friis E.M., Pedersen K.R., Crane P.R. Araceae from the Early Cretaceous of Portugal: evidence on the emergence of monocotyledons. Proc. Natl. Acad. Sci. USA. 2004;101(47):16565-16570. doi 10.1073/pnas.0407174101

13. Han G.Z. Origin and evolution of the plant immune system. New Phytol. 2019;222(1):70-83. doi 10.1111/nph.15596

14. Hückelhoven R. Cell wall-associated mechanisms of disease resistance and susceptibility. Annu. Rev. Phytopathol. 2007;45(1):101-127. doi 10.1146/annurev.phyto.45.062806.094325

15. Im J.H., Choi C., Park S.R., Hwang D.J. The OsWRKY6 transcriptional cascade functions in basal defense and Xa1-mediated defense of rice against Xanthomonas oryzae pv. oryzae. Planta. 2022; 255(2):47. doi 10.1007/s00425-022-03830-5

16. Im J.H., Choi C., Jung M.Y., Park S.R., Hwang D.J. The OsICS1 is directly regulated by OsWRKY6 and increases resistance against Xanthomonas oryzae pv. oryzae. Planta. 2024;259(6):124. doi 10.1007/s00425-024-04405-2

17. Ivanisenko T.V., Saik 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)

18. 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

19. 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

20. 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

21. 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

22. Javed T., Wang W., Yang B., Shen L., Sun T., Gao S.J., Zhang S. Pathogenesis related-1 proteins in plant defense: regulation and functional diversity. Crit. Rev. Biotechnol. 2024;1-9. doi 10.1080/07388551.2024.2344583

23. Johnson L.Y.D., Major I.T., Chen Y., Yang C., Vanegas-Cano L.J., Howe G.A. Diversification of JAZ-MYC signaling function in immune metabolism. New Phytol. 2023;239(6):2277-2291. doi 10.1111/nph.19114

24. Junior A.T.D., Farias D.D., dos Santos R.S., do Amaral M.N., Arge L.W.P., Oliveira D.D.C., Silveira S.F.S., Sousa R.O., Braga E.J.B., Maia L.C., Oliveira A.C. The quest for more tolerant rice: How high concentrations of iron affect alternative splicing? Transcriptomics. 2015;3:2. doi 10.4172/2329-8936.1000122

25. Kolchanov N.A., Ignatyeva E.V., Podkolodnaya O.A., Likhoshvai V.A., Matushkin Yu.G. Gene networks. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2013;17(4/2):833-850 (in Russian)

26. Kumar K., Mandal S.N., Neelam K., de Los Reyes B.G. MicroRNAmediated host defense mechanisms against pathogens and herbivores in rice: balancing gains from genetic resistance with tradeoffs to productivity potential. BMC Plant Biol. 2022;22(1):351. doi 10.1186/s12870-022-03723-5

27. Kumar S., Shah S.H., Vimala Y., Jatav H.S., Ahmad P., Chen Y., Siddique K.H.M. Abscisic acid: metabolism, transport, crosstalk with other plant growth regulators, and its role in heavy metal stress mitigation. Front. Plant Sci. 2022;13:972856. doi 10.3389/fpls.2022.972856

28. Kumeiko Yu.V., Paraschenko V.N., Kremzin N.M. Application of nitrification inhibitor to reduce nitrogen losses and increase the efficiency of nitrogen fertilizers in rice growing. Sbornik Nauchnykh Trudov Stavropolskogo NII Zhivotnovodstva i Kormoproizvodstva = Proceedings of the Stavropol Research Institute of Animal Husbandry and Forage Production. 2013;3(6):144-147 (in Russian)

29. Li Q.F., Lu J., Zhou Y., Wu F., Tong H.N., Wang J.D., Yu J.W., Zhang C.Q., Fan X.L., Liu Q.Q. Abscisic acid represses rice lamina joint inclination by antagonizing brassinosteroid biosynthesis and signaling. Int. J. Mol. Sci. 2019;20(19):4908. doi 10.3390/ijms20194908

30. Mustafin Z.S., Lashin S.A., Matushkin Yu.G. Phylostratigraphic analysis of gene networks of human diseases. Vavilovskii Zhurnal Genetiki i Selektsii = Vavilov Journal of Genetics and Breeding. 2021;25(1):46-56. doi 10.18699/VJ21.006

31. Ogita N., Okushima Y., Tokizawa M., Yamamoto Y.Y., Tanaka M., Seki M., Makita Y., Matsui M., Okamoto-Yoshiyama K., Sakamoto T., Kurata T., Hiruma K., Saijo Y., Takahashi N., Umeda M. Identifying the target genes of SUPPRESSOR OF GAMMA RESPONSE 1, a master transcription factor controlling DNA damage response in Arabidopsis. Plant J. 2018;94(3):439-453. doi 10.1111/tpj.13866

32. 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

33. Rose J.K.C., Catalá C., Gonzalez‐Carranza Z.H., Roberts J.A. Cell wall disassembly. In: Annual Plant Reviews online. Vol. 8. The Plant Cell Wall. Wiley, 2018;264-324. doi 10.1002/9781119312994.apr0075

34. 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

35. 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

36. 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

37. Senapati M., Tiwari A., Sharma N., Chandra P., Bashyal B.M., Ellur R.K., Bhowmick P.K., Bollinedi H., Vinod K.K., Singh A.K., Krishnan S.G. Rhizoctonia solani Kühn pathophysiology: status and prospects of sheath blight disease management in rice. Front. Plant Sci. 2022;13:881116. doi 10.3389/fpls.2022.881116

38. Shen J., Wang X., Song H., Wang M., Niu T., Lei H., Qin C., Liu A. Physiology and transcriptomics highlight the underlying mechanism of sunflower responses to drought stress and rehydration. iScience. 2023;26(11):108112. doi 10.1016/j.isci.2023.108112

39. Shim J.S., Jung C., Lee S., Min K., Lee Y.W., Choi Y., Lee J.S., Song J.T., Kim J.K., Choi Y.D. AtMYB44 regulates WRKY70 expression and modulates antagonistic interaction between salicylic acid and jasmonic acid signaling. Plant J. 2013;73(3):483-495. doi 10.1111/tpj.12051

40. Sircar S., Musaddi M., Parekh N. NetREx: Network-based Rice Expression Analysis Server for abiotic stress conditions. Database (Oxford). 2022;2022:baac060. doi 10.1093/database/baac060

41. Son S., An H.K., Seol Y.J., Park S.R., Im J.H. Rice transcription factor WRKY114 directly regulates the expression of OsPR1a and Chitinase to enhance resistance against Xanthomonas oryzae pv. oryzae. Biochem. Biophys. Res. Commun. 2020;533(4):1262-1268. doi 10.1016/j.bbrc.2020.09.141

42. Song H., Duan Z., Zhang J. WRKY transcription factors modulate flowering time and response to environmental changes. Plant Physiol. Biochem. 2024;210:108630. doi 10.1016/j.plaphy.2024.108630

43. Song L., Fang Y., Chen L., Wang J., Chen X. Role of non-coding RNAs in plant immunity. Plant Commun. 2021;2(3):100180. doi 10.1016/j.xplc.2021.100180

44. Song W.Y., Wang G.L., Chen L.L., Kim H.S., Pi L.Y., Holsten T., Gardner J., Wang B., Zhai W.X., Zhu L.H., Fauquet C., Ronald P. A receptor kinase-like protein encoded by the rice disease resistance gene, Xa21. Science. 1995;270(5243):1804-1806. doi 10.1126/science.270.5243.1804

45. Song Y., Zhai Y., Li L., Yang Z., Ge X., Yang Z., Zhang C., Li F., Ren M. BIN2 negatively regulates plant defence against Verticillium dahliae in Arabidopsis and cotton. Plant Biotechnol. J. 2021;19(10):2097-2112. doi 10.1111/pbi.13640.

46. Statello L., Guo C.J., Chen L.L., Huarte M. Gene regulation by long non-coding RNAs and its biological functions. Nat. Rev. Mol. Cell Biol. 2021;22(2):96-118. doi 10.1038/s41580-020-00315-9

47. Steber C.M., McCourt P. A role for brassinosteroids in germination in Arabidopsis. Plant Physiol. 2001;125(2):763-769. doi 10.1104/pp.125.2.763.

48. Sun H.L., Wang X.J., Ding W.H., Zhu S.Y., Zhao R., Zhang Y.X., Xin Q., Wang X.F., Zhang D.P. Identification of an important site for function of the type 2C protein phosphatase ABI2 in abscisic acid signalling in Arabidopsis. J. Exp. Bot. 2011;62(15):5713-5725. doi 10.1093/jxb/err274

49. Supriya P., Srividya G.K., Solanki M., Manvitha D., Prakasam V., Balakrishnan M., Neeraja C.N., Srinivasa Rao Ch, Sundaram R.M., Mangrauthia S.K. Identification and expression analysis of long noncoding RNAs of rice induced during interaction with Rhizoctonia solani. Physiol. Mol. Plant Pathol. 2024;134:102389. doi 10.1016/j.pmpp.2024.102389

50. Tong Y.-B., Shi M.-W., Qian S.H., Chen Y.-J., Luo Z.-H., Tu Y.-X., Xiong Y.-L., Geng Y.-J., Chen C., Chen Z.-X. GenOrigin: a comprehensive protein-coding gene origination database on the evolutionary timescale of life. J. Genet. Genomics. 2021;48(12):1122-1129. doi 10.1016/j.jgg.2021.03.018

51. van der Kooi C.J., Ollerton J. The origins of flowering plants and pollinators. Science. 2020;368(6497):1306-1308. doi 10.1126/science.aay3662

52. Vincentz M., Cara F.A., Okura V.K., da Silva F.R., Pedrosa G.L., Hemerly A.S., Capella A.N., Marins M., Ferreira P.C., França S.C., Grivet L., Vettore A.L., Kemper E.L., Burnquist W.L., Targon M.L., Siqueira W.J., Kuramae E.E., Marino C.L., Camargo L.E., Carrer H., Coutinho L.L., Furlan L.R., Lemos M.V., Nunes L.R., Gomes S.L., Santelli R.V., Goldman M.H., Bacci M. Jr, Giglioti E.A., Thiemann O.H., Silva F.H., Van Sluys M.A., Nobrega F.G., Arruda P., Menck C.F. Evaluation of monocot and eudicot divergence using the sugarcane transcriptome. Plant Physiol. 2004;134(3):951-959. doi 10.1104/pp.103.033878

53. 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. Vavilov J. Genet. Breed. 2023;27(8):1031-1041. doi 10.18699/VJGB-23-118

54. Wang F., Wang C., Liu P., Lei C., Hao W., Gao Y., Liu Y.G., Zhao K. Enhanced rice blast resistance by CRISPR/Cas9-targeted mutagenesis of the ERF transcription factor gene OsERF922. PLoS One. 2016;11(4):e0154027. doi 10.1371/journal.pone.0154027

55. Wang F., Yang F., Zhu D., Saniboere B., Zhou B., Peng D. MYB44 plays key roles in regulating plant responses to abiotic and biotic stress, metabolism, and development. J. Plant Biochem. Biotechnol. 2024;33(4):462-473. doi 10.1007/s13562-023-00864-y

56. Wang G.L., Song W.Y., Ruan D.L., Sideris S., Ronald P.C. The cloned gene, Xa21, confers resistance to multiple Xanthomonas oryzae pv. oryzae isolates in transgenic plants. Mol. Plant-Microbe Interact. 1996;9(9):850-855. doi 10.1094/mpmi-9-0850

57. Wang H., Tang J., Liu J., Hu J., Liu J., Chen Y., Cai Z., Wang X. Abscisic acid signaling inhibits brassinosteroid signaling through dampening the dephosphorylation of BIN2 by ABI1 and ABI2. Mol. Plant. 2018;11:315-325. doi 10.1016/j.molp.2017.12.013

58. Wang S., Durrant W.E., Song J., Spivey N.W., Dong X. Arabidopsis BRCA2 and RAD51 proteins are specifically involved in defense gene transcription during plant immune responses. Proc. Natl. Acad. Sci. USA. 2010;107(52):22716-22721. doi 10.1073/pnas.1005978107

59. Wang X., Wang Y., Piñeros M.A., Wang Z., Wang W., Li C., Wu Z., Kochian L.V., Wu P. Phosphate transporters OsPHT1;9 and OsPHT1;10 are involved in phosphate uptake in rice. Plant Cell Environ. 2014; 37(5):1159-1170. doi 10.1111/pce.12224

60. Wolf Y.I., Novichkov P.S., Karev G.P., Koonin E.V., Lipman D.J. The universal distribution of evolutionary rates of genes and distinct characteristics of eukaryotic genes of different apparent ages. Proc. Natl. Acad. Sci. USA. 2009;106(18):7273-7280. doi10.1073/pnas.0901808106

61. Xie K., Wu C., Xiong L. Genomic organization, differential expression, and interaction of SQUAMOSA promoter-binding-like transcription factors and microRNA156 in rice. Plant Physiol. 2006;142(1): 280-293. doi 10.1104/pp.106.084475

62. Xing J., Cao X., Zhang M., Wei X., Zhang J., Wan X. Plant nitrogen availability and crosstalk with phytohormones signallings and their biotechnology breeding application in crops. Plant Biotechnol. J. 2023;21(7):1320-1342. doi 10.1111/pbi.13971

63. Xiong Q., Hu J., Wei H., Zhang H., Zhu J. Relationship between plant roots, rhizosphere microorganisms, and nitrogen and its special focus on rice. Agriculture. 2021;11(3):234. doi 10.3390/agriculture11030234

64. Yang J., Duan G., Li C., Liu L., Han G., Zhang Y., Wang C. The crosstalks between jasmonic acid and other plant hormone signaling highlight the involvement of jasmonic acid as a core component in plant response to biotic and abiotic stresses. Front. Plant Sci. 2019; 10:1349. doi 10.3389/fpls.2019.01349

65. Yang Y.X., Ahammed G.J., Wu C., Fan S.Y., Zhou Y.H. Crosstalk among jasmonate, salicylate and ethylene signaling pathways in plant disease and immune responses. Curr. Protein Pept. Sci. 2015; 16(5):450-461. doi 10.2174/1389203716666150330141638

66. Yokotani N., Tsuchida-Mayama T., Ichikawa H., Mitsuda N., Ohme-Takagi M., Kaku H., Minami E., Nishizawa Y. OsNAC111, a blast disease-responsive transcription factor in rice, positively regulates the expression of defense-related genes. Mol. Plant-Microbe Interact. 2014;27(10):1027-1034. doi 10.1094/MPMI-03-14-0065-R

67. Yoshiyama K.O., Kimura S. Ser-Gln sites of SOG1 are rapidly hyperphosphorylated in response to DNA double-strand breaks. Plant Signal. Behav. 2018;13(6):e1477904. doi 10.1080/15592324.2018.1477904

68. Zhang S., Su J., Ma S., Wang H., Wang X., He K., Wang H., Canfield D.E. Eukaryotic red and green algae populated the tropical ocean 1400 million years ago. Precambrian Res. 2021;357:106166. doi 10.1016/j.precamres.2021.106166

69. Zhang X., Zhao H., Gao S., Wang W.C., Katiyar-Agarwal S., Huang H.D., Raikhel N., Jin H. Arabidopsis Argonaute 2 regulates innate immunity via miRNA393(*)-mediated silencing of a Golgilocalized SNARE gene MEMB12. Mol. Cell. 2011;42(3):356-366. doi 10.1016/j.molcel.2011.04.010

70. Zhou X., Zha M., Huang J., Li L., Imran M., Zhang C. StMYB44 negatively regulates phosphate transport by suppressing expression of PHOSPHATE1 in potato. J. Exp. Bot. 2017;68(5):1265-1281. doi 10.1093/jxb/erx026

71. 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