The role of long non-coding RNAs in plants

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Abstract

Long non-coding RNAs (lncRNAs) are a class of linear or circular RNA molecules longer than 200 nucleotides without open reading frames. Experimental studies have shown the involvement of lncRNAs in the regulation of resistance to cold, salt, and heat stress, and in fruit, root, and leaf development. However, experimental methods are labor-intensive and costly approaches and cannot yet be used for genome-wide mass studies of lncRNAs. For this purpose, bioinformatic approaches that aim at large-scale recognition of lncRNA sequences in genomes and transcriptomes have been applied. However, despite the growing number of studies devoted to the structural and functional analysis of lncRNAs, this type of molecule remains poorly understood. This is due to the many factors that need to be considered when identifying lncRNAs. The use of pan-genomes and pan-transcriptomes will improve the efficiency of the study and the total number of predicted lncRNAs compared to using the genome of a single species representative. This review focuses on describing the molecular and biological functions of lncRNAs, experimental and bioinformatic methods of identification, patterns of evolution, detection and analysis of lncRNAs at the scale of pan-genomes and pan-transcriptomes.

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About the authors

А. Y. Pronozin

Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences; Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences

Author for correspondence.
Email: pronozinartem95@gmail.com

Kurchatov Genomic Center

Russian Federation, Novosibirsk; Novosibirsk

D. A. Afonnikov

Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences; Institute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences; Novosibirsk National Research State University

Email: pronozinartem95@gmail.com

Kurchatov Genomic Center

Russian Federation, Novosibirsk; Novosibirsk; Novosibirsk

References

  1. Deng P., Liu S., Nie X., Wu L. Conservation analysis of long non-coding RNAs in plants // Sci. China Life Sci. 2018. V. 61. P. 190–198.
  2. Wu H.-J., Wang Z-M., Wang M., Wang X-J. Widespread long noncoding RNAs as endogenous target mimics for microRNAs in plants // Plant Physiol. 2013. V. 161. № 4. P. 1875–1884.
  3. Zhu Q.-H., Wang M.-B. Molecular functions of long non-coding RNAs in plants // Genes. 2012. V. 3. № 1. P. 176–190.
  4. Назипова Н.Н. Разнообразие некодирующих РНК в геномах эукариот // Матем. биол. и биоинформат. 2021. Т. 16. № 2. С. 256–298.
  5. Joshi A., Romanowska J. Recent advances in computational-based approaches in epigenetics studies // Epigenetics Methods. 2020. P. 569–590.
  6. Kim E.-D., Sung S. Long noncoding RNA: Unveiling hidden layer of gene regulatory networks // Trends Plant Sci. 2012. V. 17. № 1. P. 16–21.
  7. Karlik E., Ari S., Gozukirmizi N. LncRNAs: Genetic and epigenetic effects in plants // Biotechnol. Biotechnol. 2019. V. 33. № 1. P. 429–439. https://doi.org/10.1080/13102818.2019.1581085
  8. Tsai M.-C., Manor O., Wan Y. et al. Long noncoding RNA as modular scaffold of histone modification complexes // Science. 2010. V. 329. № 5992. P. 689–693. https://doi.org/10.1126/science.1192002
  9. Sousa C., Johansson C., Charonet C. et al. Translational and structural requirements of the early nodulin gene enod40 , a short-open reading frame-containing RNA, for elicitation of a cell-specific growth response in the alfalfa root cortex // Mol. Cell. Biol. 2001. V. 21. № 1. P. 354–366. https://doi.org/10.1128/MCB.21.1.354-366.2001
  10. Medvedeva Y.A., Lennartsson A., Ehsani R. et al. EpiFactors: A comprehensive database of human epigenetic factors and complexes // Database. 2015. V. 2015. P. bav067.
  11. Frankish A., Diekhans M., Jungreis I. et al. GENCODE 2021 // Nucl. Acids Res. 2021. V. 49. № D1. P. D916–D923.
  12. Гордиюк В.В. Длинные некодирующие РНК – камертон в регуляции клеточных процессов // Ukr. Biochem. J. 2014. V. 86. № 2. С. 5–15.
  13. Zhao X., Li J., Lian B. et al. Global identification of Arabidopsis lncRNAs reveals the regulation of MAF4 by a natural antisense RNA // Nat. Commun. 2018. V. 9. № 1. P. 5056.
  14. Li X., Wu Z., Fu X. et al. lncRNAs: Insights into their function and mechanics in underlying disorders // Mutat. Res. Mutat. Res. 2014. V. 762. P. 1–21.
  15. Ahmad P., Bensaoud C., Mekki I. et al. Long non-coding RNAs and their potential roles in the vector–host–pathogen triad // Life. MDPI. 2021. V. 11. № 1. P. 56.
  16. De Quattro C., Pè M.E., Bertolini E. Long noncoding RNAs in the model species Brachypodium distachyon // Sci. Rep. 2017. V. 7. № 1. P. 11252.
  17. Ma L., Bajic V.B., Zhang Z. On the classification of long non-coding RNAs // RNA Biol. 2013. V. 10. № 6. P. 924–933. https://doi.org/10.4161/rna.24604
  18. Chen L., Zhu Q.-H., Kaufmann K. Long non-coding RNAs in plants: Emerging modulators of gene activity in development and stress responses // Planta. 2020. V. 252. № 5. P. 92. https://doi.org/10.1007/s00425-020-03480-5
  19. Griffiths-Jones S. miRBase: The microRNA sequence database // MicroRNA Protocols. 2006. V. 342. P. 129–138.
  20. Amaral P.P., Mattick J.S. Noncoding RNA in development // Mamm. Genome. 2008. V. 19. P. 454–492.
  21. Бейлерли О.А., Гареев И.Ф. Длинные некодирующие РНК: какие перспективы? // Профилактическая медицина. 2020. Т. 23. № 2. С. 124–128.
  22. Blythe A.J., Fox A.H., Bond C.S. The ins and outs of lncRNA structure: How, why and what comes next? // Biochim. Biophys. Acta BBA-Gene Regul. Mech. 2016. V. 1859. № 1. P. 46–58.
  23. Bryzghalov O., Makałowska I., Szcześniak M.W. lncEvo: Automated identification and conservation study of long noncoding RNAs // BMC Bioinformatics. 2021. V. 22. № 1. P. 59. https://doi.org/10.1186/s12859-021-03991-2
  24. Zhao Q., Sun Y., Wang D. et al. LncPipe: A nextflow-based pipeline for identification and analysis of long non-coding RNAs from RNA-Seq data // J. Genet. Genomics. 2018. V. 45. https://doi.org/10.1016/j.jgg.2018.06.005
  25. Talyan S., Filipów S., Ignarski M. et al. CALINCA – a novel pipeline for the identification of lncRNAs in podocyte disease // Cells. MDPI. 2021. V. 10. № 3. P. 692.
  26. Campalans A., Kondorosi A., Crespi M. Enod40, a short open reading frame – containing mRNA, induces cytoplasmic localization of a nuclear RNA binding protein in Medicago truncatula // Plant Cell. 2004. V. 16. № 4. P. 1047–1059.
  27. Unver T., Tombuloglu H. Barley long non-coding RNAs (lncRNA) responsive to excess boron // Genomics. 2020. V. 112. № 2. P. 1947–1955.
  28. Khorkova O., Hsiao J., Wahlestedt C. Basic biology and therapeutic implications of lncRNA // Adv. Drug Deliv. Rev. 2015. V. 87. P. 15–24.
  29. Duret L., Chureau С., Samain S. et al. The Xist RNA Gene evolved in eutherians by pseudogenization of a protein-coding gene // Science. 2006. V. 312. № 5780. P. 1653–1655. https://doi.org/10.1126/science.1126316
  30. Graf J., Kretz M. From structure to function: Route to understanding lncRNA mechanism // BioEssays. 2020. V. 42. № 12. https://doi.org/10.1002/bies.202000027
  31. Golicz A.A., Singh M.B., Bhalla P.L. The long intergenic noncoding RNA (LincRNA) landscape of the soybean genome // Plant Physiol. 2018. V. 176. № 3. P. 2133–2147.
  32. Cheng F., Wu J., Fang L., Wang X. Syntenic gene analysis between Brassica rapa and other Brassicaceae species // Front. Plant Sci. Frontiers. 2012. V. 3. P. 30895.
  33. Huang L., Dong H., Zhou D. et al. Systematic identification of long non-coding RNAs during pollen development and fertilization in Brassica rapa // Plant J. 2018. V. 96. № 1. P. 203–222. https://doi.org/10.1111/tpj.14016
  34. Derrien T., Johnson R., Bussotti G. et al. The GENCODE v7 catalog of human long noncoding RNAs: Analysis of their gene structure, evolution, and expression // Genome Res. 2012. V. 22. № 9. P. 1775–1789.
  35. Ponting C.P., Oliver P.L., Reik W. Evolution and functions of long noncoding RNAs // Cell. 2009. V. 136. № 4. P. 629–641.
  36. Golicz A.A., Bayer P.E., Barker G.C. et al. The pangenome of an agronomically important crop plant Brassica oleracea // Nat. Commun. 2016. V. 7. № 1. P. 13390.
  37. Meile L., Croll D., Brunner P.C. et al. A fungal avirulence factor encoded in a highly plastic genomic region triggers partial resistance to septoria tritici blotch // New Phytol. 2018. V. 219. № 3. P. 1048–1061. https://doi.org/10.1111/nph.15180
  38. Alcaraz L.D., Moreno-Hagelsieb G., Eguiarte L.E. et al. Understanding the evolutionary relationships and major traits of Bacillus through comparative genomics // BMC Genomics. 2010. V. 11. № 1. P. 332. https://doi.org/10.1186/1471-2164-11-332
  39. Rasko D.A., Rosovitz M.J., Myers G.S.A. et al. The pangenome structure of Escherichia coli: Comparative genomic analysis of E. coli commensal and pathogenic isolates // J. Bacteriol. 2008. V. 190. № 20. P. 6881–6893. https://doi.org/10.1128/JB.00619-08
  40. Merot-L’anthoene V., Tournebize R., Darracq O. et al. Development and evaluation of a genome – wide Coffee 8.5K SNP array and its application for high-density genetic mapping and for investigating the origin of Coffea arabica L. // Plant Biotechnol. J. 2019. V. 17. № 7. P. 1418–1430. https://doi.org/10.1111/pbi.13066
  41. Budak H., Kaya S.B., Cagirici H.B. Long non-coding RNA in plants in the era of reference sequences // Front. Plant Sci. Frontiers. 2020. V. 11. P. 441273.
  42. Britto-Kido S. de A., Neto J.R.C.F., Pandolfi V. et al. Natural antisense transcripts in plants: A review and identification in soybean infected with Phakopsora pachyrhizi SuperSAGE Library // Sci. World J. 2013. V. 2013.
  43. Kapusta A., Kronenberg Z., Lynch V.J. et al. Transposable elements are major contributors to the origin, diversification, and regulation of vertebrate long noncoding RNAs // PLoS Genet. 2013. V. 9. № 4. P. e1003470.
  44. Лукина С.С., Бурденный А.М., Филиппова Е.А. et al. Роль длинных некодирующих РНК и ДНК-метилирования в патогенезе рака яичников // Патол. физиол. и эксперим. терапия. 2022. Т. 66. № 4. С. 143–156.
  45. Ulitsky I., Bartel D.P. lincRNAs: Genomics, evolution, and mechanisms // Cell. 2013. V. 154. № 1. P. 26–46.
  46. Dinger M.E., Pang K.C., Mercer T.R. et al. Differentiating protein-coding and noncoding RNA: Challenges and ambiguities // PLoS Comput. Biol. 2008. V. 4. № 11. P. e1000176.
  47. Xie C., Yuan J., Li H. et al. NONCODEv4: Exploring the world of long non-coding RNA genes // Nucleic Acids Res. 2014. V. 42. № D1. P. D98–D103.
  48. Pal D., Rao M.R.S. Long noncoding RNAs in pluripotency of stem cells and cell fate specification // Long Non Coding RNA Biology / Ed. Rao M.R.S. Singapore: Springer, 2017. V. 1008. P. 223–252.
  49. Татосян К.А., Зиневич Л.С., Демин Д.Э., Шварц А.М. Функциональные особенности длинных некодирующих РНК, содержащих последовательности мобильных генетических элементов // Мол. биол. 2020. Т. 54. № 5. P. 718–724.
  50. Zhao Z., Zang S., Zou W. et al. Long non-coding RNAs: New players in plants // Int. J. Mol. Sci. MDPI. 2022. V. 23. № 16. P. 9301.
  51. Kopp F., Mendell J.T. Functional classification and experimental dissection of long noncoding RNAs // Cell. Elsevier. 2018. V. 172. № 3. P. 393–407.
  52. Huarte M., Rinn J.L. Large non-coding RNAs: missing links in cancer? // Hum. Mol. Genet. 2010. V. 19. № R2. P. R152–R161.
  53. Hung Т., Wang Y., Lin M.F. et al. Extensive and coordinated transcription of noncoding RNAs within cell-cycle promoters // Nat. Genet. 2011. V. 43. № 7. P. 621–629.
  54. Loewer S., Cabili M.N., Guttman M. et al. Large intergenic non-coding RNA-RoR modulates reprogramming of human induced pluripotent stem cells // Nat. Genet. 2010. V. 42. № 12. P. 1113–1117.
  55. Wang K.C., Chang H.Y. Molecular mechanisms of long noncoding RNAs // Mol. Cell. 2011. V. 43. № 6. P. 904–914.
  56. Chen J., Wang H., Yao Y. Experimental study of nonlinear ultrasonic behavior of soil materials during the compaction // Ultrasonics. 2016. V. 69. P. 19–24.
  57. Kotake Y., Nakagawa T., Kitagawa K. et al. Long non-coding RNA ANRIL is required for the PRC2 recruitment to and silencing of p15INK4B tumor suppressor gene // Oncogene. 2011. V. 30. № 16. P. 1956–1962.
  58. Ye X., Wang S., Zhao X. et al. Role of lncRNAs in cis- and trans-regulatory responses to salt in Populus trichocarpa // Plant J. 2022. V. 110. № 4. P. 978–993. https://doi.org/10.1111/tpj.15714
  59. Yang L., Froberg J.E., Lee J.T. Long noncoding RNAs: Fresh perspectives into the RNA world // Trends Biochem. Sci. 2014. V. 39. № 1. P. 35–43.
  60. Lam M.T.Y., Cho H., Lesch H.P. et al. Rev-Erbs repress macrophage gene expression by inhibiting enhancer-directed transcription // Nature. 2013. V. 498. № 7455. P. 511–515.
  61. Kim T.-K., Hemberg M., Gray J.M. Enhancer RNAs: A class of long noncoding RNAs synthesized at enhancers // Cold Spring Harb. Perspect. Biol. 2015. V. 7. № 1. P. a018622.
  62. Kim T.-K., Hemberg M., Gray J.M. et al. Widespread transcription at neuronal activity-regulated enhancers // Nature. 2010. V. 465. № 7295. P. 182–187.
  63. Lai F., Orom U.A., Cesaroni M. et al. Activating RNAs associate with Mediator to enhance chromatin architecture and transcription // Nature. 2013. V. 494. № 7438. P. 497–501.
  64. Melo C.A., Drost J., Wijchers P.J. et al. eRNAs are required for p53-dependent enhancer activity and gene transcription // Mol. Cell. 2013. V. 49. № 3. P. 524–535.
  65. Zhang P., Meng J., Luan Y. et al. Plant miRNA–lncRNA interaction prediction with the ensemble of CNN and IndRNN // Interdiscip. Sci. Comput. Life Sci. 2020. V. 12. P. 82–89.
  66. Chen K., Rajewsky N. The evolution of gene regulation by transcription factors and microRNAs // Nat. Rev. Genet. 2007. V. 8. № 2. P. 93–103.
  67. Jin Q., Zhao Z., Zhao Q. et al. Long noncoding RNAs: Emerging roles in pulmonary hypertension // Heart Fail. Rev. 2020. V. 25. P. 795–815.
  68. Huntzinger E., Izaurralde E. Gene silencing by microRNAs: Contributions of translational repression and mRNA decay // Nat. Rev. Genet. 2011. V. 12. № 2. P. 99–110.
  69. Yoon J.-H., Abdelmohsen K., Kim J. et al. Scaffold function of long non-coding RNA HOTAIR in protein ubiquitination // Nat. Commun. 2013. V. 4. № 1. P. 2939.
  70. Mukherjee N., Corcoran D.L., Nusbaum J.D. et al. Integrative regulatory mapping indicates that the RNA-binding protein HuR couples pre-mRNA processing and mRNA stability // Mol. Cell. 2011. V. 43. № 3. P. 327–339.
  71. Thomson D.W., Dinger M.E. Endogenous microRNA sponges: Evidence and controversy // Nat. Rev. Genet. 2016. V. 17. № 5. P. 272–283.
  72. Franco-Zorrilla J.M., Valli A., Todesco M. et al. Target mimicry provides a new mechanism for regulation of microRNA activity // Nat. Genet. 2007. V. 39. № 8. P. 1033–1037.
  73. Du Q., Wang K., Zou C. et al. The PILNCR1-miR399 regulatory module is important for low phosphate tolerance in maize // Plant Physiol. 2018. V. 177. № 4. P. 1743–1753.
  74. Wang T., Zhao M., Zhang X. et al. Novel phosphate deficiency-responsive long non-coding RNAs in the legume model plant Medicago truncatula // J. Exp. Bot. 2017. V. 68. № 21–22. P. 5937–5948.
  75. Faghihi M.A., Zhang M., Huang J. et al. Evidence for natural antisense transcript-mediated inhibition of microRNA function // Genome Biol. 2010. V. 11. № 5. https://doi.org/10.1186/gb-2010-11-5-r56
  76. Kimura T., Jiang S., Nishizawa M. et al. Stabilization of human interferon-α1 mRNA by its antisense RNA // Cell. Mol. Life Sci. 2013. V. 70. № 8. P. 1451–1467. https://doi.org/10.1007/s00018-012-1216-x
  77. Wang Y., Pang W.J., Wei N. et al. Identification, stability and expression of Sirt1 antisense long non-coding RNA // Gene. 2014. V. 539. № 1. P. 117–124.
  78. Cai X., Cullen B.R. The imprinted H19 noncoding RNA is a primary microRNA precursor // RNА. 2007. V. 13. № 3. P. 313–316.
  79. Augoff K., McCue B., Plow E.F. et al. miR-31 and its host gene lncRNA LOC554202 are regulated by promoter hypermethylation in triple-negative breast cancer // Mol. Cancer. 2012. V. 11. № 1. https://doi.org/10.1186/1476-4598-11-5
  80. Kallen A.N., Zhou X.B., Xu J. et al. The imprinted H19 lncRNA antagonizes let-7 microRNAs // Mol. Cell. 2013. V. 52. № 1. P. 101–112.
  81. Amor B.B., Wirth S., Merchan F. et al. Novel long non-protein coding RNAs involved in Arabidopsis differentiation and stress responses // Genome Res. 2009. V. 19. № 1. P. 57–69.
  82. Hirsch J., Lefort V., Vankersschaver M. et al. Characterization of 43 non-protein-coding mRNA genes in Arabidopsis, including the MIR162a-derived transcripts // Plant Physiol. 2006. V. 140. № 4. P. 1192–1204.
  83. Lamin-Samu A.T., Zhuo S., Ali M., Lu G. Long non-coding RNA transcriptome landscape of anthers at different developmental stages in response to drought stress in tomato // Genomics. 2022. V. 114. № 4. P. 110383.
  84. Kryuchkova-Mostacci N., Robinson-Rechavi M. A benchmark of gene expression tissue-specificity metrics // Brief. Bioinform. 2017. V. 18. № 2. P. 205–214.
  85. Li L., Eichten S.R., Shimizu R. et al. Genome-wide discovery and characterization of maize long non-coding RNAs // Genome Biol. 2014. V. 15. № 2. P. https://doi.org/10.1186/gb-2014-15-2-r40.
  86. Han L., Mu Z., Luo Z. et al. New lncRNA annotation reveals extensive functional divergence of the transcriptome in maize // J. Integr. Plant Biol. 2019. V. 61. № 4. P. 394–405. https://doi.org/10.1111/jipb.12708
  87. Subramanian S., Kumar S. Gene expression intensity shapes evolutionary rates of the proteins encoded by the vertebrate genome // Genetics. 2004. V. 168. № 1. P. 373–381.
  88. Yanai I., Benjamin H., Shmoish M. et al. Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification // Bioinformatics. 2005. V. 21. № 5. P. 650–659.
  89. Ceriani L., Verme P. The origins of the Gini index: Extracts from Variabilità e Mutabilità (1912) by Corrado Gini // J. Econ. Inequal. 2012. V. 10. P. 421–443.
  90. Julien P., Brawand D., Soumillon M. et al. Mechanisms and evolutionary patterns of mammalian and avian dosage compensation // PLoS Biol. 2012. V. 10. № 5. P. e1001328.
  91. Xiao S.-J., Zhang C., Zou Q., Ji Z.L. TiSGeD: A database for tissue-specific genes // Bioinformatics. 2010. V. 26. № 9. P. 1273–1275.
  92. Yu X., Lin J., Zack D.J., Qian J. Computational analysis of tissue-specific combinatorial gene regulation: Predicting interaction between transcription factors in human tissues // Nucl. Acids Res. 2006. V. 34. № 17. P. 4925–4936.
  93. Huang X., Li S.Z., Wang Y. Jensen-Shannon boosting learning for object recognition // 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). IEEE. 2005. V. 2. P. 144–149.
  94. Marquardt S., Raitskin O., Wu Z. et al. Functional consequences of splicing of the antisense transcript COOLAIR on FLC transcription // Mol. Cell. 2014. V. 54. № 1. P. 156–165.
  95. Liu X., Hao L., Li D. et al. Long non-coding RNAs and their biological roles in plants // Genomics Proteomics Bioinformatics. 2015. V. 13. № 3. P. 137–147.
  96. Ding J., Lu Q., Ouyang Y. et al. A long noncoding RNA regulates photoperiod-sensitive male sterility, an essential component of hybrid rice // Proc. Natl Acad. Sci. USA. 2012. V. 109. № 7. P. 2654–2659. https://doi.org/10.1073/pnas.1121374109
  97. Song J.-H., Cao J.-S., Wang C.-G. BcMF11, a novel non-coding RNA gene from Brassica campestris, is required for pollen development and male fertility // Plant Cell Rep. 2013. V. 32. P. 21–30.
  98. Kim J., Yi H., Choi G. et al. Functional characterization of phytochrome interacting factor 3 in phytochrome-mediated light signal transduction // Plant Cell. 2003. V. 15. № 10. P. 2399–2407.
  99. Wang J., Meng X., Dobrovolskaya O.B. et al. Non-coding RNAs and their roles in stress response in plants: 5 // Genomics Proteomics Bioinformatics. 2017. V. 15. № 5. P. 301–312. https://doi.org/10.1016/j.gpb.2017.01.007
  100. Wang A., Hu J., Gao C. et al. Genome-wide analysis of long non-coding RNAs unveils the regulatory roles in the heat tolerance of Chinese cabbage (Brassica rapa ssp. chinensis) // Sci. Rep. 2019. V. 9. № 1. P. 5002
  101. Wang P., Dai L., Ai J. et al. Identification and functional prediction of cold-related long non-coding RNA (lncRNA) in grapevine // Sci. Rep. 2019. V. 9. № 1. P. 6638.
  102. Chung P.J., Jung H., Jeong D.H. et al. Transcriptome profiling of drought responsive noncoding RNAs and their target genes in rice // BMC Genomics. 2016. V. 17. № 1. P. 563. https://doi.org/10.1186/s12864-016-2997-3
  103. Calixto C.P., Tzioutziou N.A., James A.B. et al. Cold-dependent expression and alternative splicing of Arabidopsis long non-coding RNAs // Front. Plant Sci. Frontiers Media SA. 2019. V. 10. P. 235.
  104. Jha U.C., Nayyar H., Jha R. et al. Long non-coding RNAs: Emerging players regulating plant abiotic stress response and adaptation // BMC Plant Biol. 2020. V. 20. № 1. P. 466. https://doi.org/10.1186/s12870-020-02595-x
  105. Zhang X., Dong J., Deng F. et al. The long non-coding RNA lncRNA973 is involved in cotton response to salt stress // BMC Plant Biol. 2019. V. 19. № 1. P. 459. https://doi.org/10.1186/s12870-019-2088-0
  106. Wang X., Fan H., Wang B., Yuan F. Research progress on the roles of lncRNAs in plant development and stress responses // Front. Plant Sci. Frontiers Media SA. 2023. V. 14. P. 1138901.
  107. Chen J., Zhong Y., Qi X. LncRNA TCONS_00021861 is functionally associated with drought tolerance in rice (Oryza sativa L.) via competing endogenous RNA regulation // BMC Plant Biol. 2021. V. 21. № 1. P. 410. https://doi.org/10.1186/s12870-021-03195-z
  108. Kazemzadeh M., Safaralizadeh R., Orang A.V. LncRNAs: Emerging players in gene regulation and disease pathogenesis // J. Genet. 2015. V. 94. P. 771–784.
  109. Zhu Y., Chen L., Hong X. et al. Revealing the novel complexity of plant long non-coding RNA by strand-specific and whole transcriptome sequencing for evolutionarily representative plant species // BMC Genomics. 2022. V. 23. № S4. P. 381. https://doi.org/10.1186/s12864-022-08602-9
  110. Ulitsky I. Evolution to the rescue: Using comparative genomics to understand long non-coding RNAs // Nat. Rev. Genet. 2016. V. 17. № 10. P. 601–614.
  111. Wang H., Niu Q.W., Wu H.W. et al. Analysis of non-coding transcriptome in rice and maize uncovers roles of conserved lnc RNAs associated with agriculture traits // Plant J. 2015. V. 84. № 2. P. 404–416. https://doi.org/10.1111/tpj.13018
  112. Nitsche A., Stadler P.F. Evolutionary clues in lncRNAs // WIREs RNA. 2017. V. 8. № 1. https://doi.org/10.1002/wrna.1376
  113. Sang S., Chen W., Zhang D. et al. Data integration and evolutionary analysis of long non-coding RNAs in 25 flowering plants: 3 // BMC Genomics. 2021. V. 22. № 3. P. 739. https://doi.org/10.1186/s12864-021-08047-6
  114. Zhang Y.-C., Liao J.Y., Li Z.Y. et al. Genome-wide screening and functional analysis identify a large number of long noncoding RNAs involved in the sexual reproduction of rice // Genome Biol. 2014. V. 15. № 12. P. 512. https://doi.org/10.1186/s13059-014-0512-1
  115. Pronozin A.Y., Bragina M.K., Salina E.A. Crop pangenomes // Vavilov. J. Genet. Breed. 2021. V. 25. № 1. P. 57.
  116. Vernikos G., Medini D., Riley D.R., Tettelin H. et al. Ten years of pan-genome analyses // Curr. Opin. Microbiol. 2015. V. 23. P. 148–154.
  117. Lapierre P., Gogarten J.P. Estimating the size of the bacterial pan-genome // Trends Genet. 2009. V. 25. № 3. P. 107–110.
  118. Chekanova J.A., Gregory B.D., Reverdatto S.V. et al. Genome-wide high-resolution mapping of exosome substrates reveals hidden features in the Arabidopsis transcriptome // Cell. 2007. V. 131. № 7. P. 1340–1353.
  119. Medina C.A., Samac D.A., Yu L.-X. Pan-transcriptome identifying master genes and regulation network in response to drought and salt stresses in Alfalfa (Medicago sativa L.) // Sci. Rep. 2021. V. 11. № 1. P. 17203.
  120. Jin M., Liu H., He C. et al. Maize pan-transcriptome provides novel insights into genome complexity and quantitative trait variation // Sci. Rep. 2016. V. 6. № 1. P. 18936.
  121. Chowdhary A., Satagopam V., Schneider R. Long non-coding RNAs: Mechanisms, experimental, and computational approaches in identification, characterization, and their biomarker potential in cancer // Front. Genet. 2021. V. 12. https://doi.org/10.3389/fgene.2021.649619
  122. Svergun D.I., Koch M.H. Small-angle scattering studies of biological macromolecules in solution // Rep. Prog. Phys. 2003. V. 66. № 10. P. 1735.
  123. Schön P. Atomic force microscopy of RNA: State of the art and recent advancements // Seminars in Cell & Developmental Biology. 2018. V. 73. P. 209–219.
  124. Chillón I., Marcia M., Legiewicz M. et al. Native purification and analysis of long RNAs // Methods in Enzymology. 2015. V. 558. P. 3–37.
  125. Cheung F., Haas B.J., Goldberg S.M.D. et al. Sequencing Medicago truncatula expressed sequenced tags using 454 Life Sciences technology // BMC Genomics. 2006. V. 7. № 1. https://doi.org/10.1186/1471-2164-7-272
  126. Au P.C.K., Zhu Q.-H. Identification of lncRNAs using computational and experimental approaches // Regulatory RNAs / Eds Mallick B., Ghosh Z. Berlin; Heidelberg: Springer, 2012. P. 319–340.
  127. Babak T., Blencowe B.J., Hughes T.R. A systematic search for new mammalian noncoding RNAs indicates little conserved intergenic transcription // BMC Genomics. 2005. V. 6. № 1. P. 104. https://doi.org/10.1186/1471-2164-6-104
  128. Shiraki T., Kondo S., Katayama S. et al. Cap analysis gene expression for high-throughput analysis of transcriptional starting point and identification of promoter usage // Proc. Natl Acad. Sci. USA. 2003. V. 100. № 26. P. 15776–15781. https://doi.org/10.1073/pnas.2136655100
  129. Merino E.J., Wilkinson K.A., Coughlan J.L., Weeks K.M. RNA structure analysis at single nucleotide resolution by Selective 2‘-Hydroxyl Acylation and Primer Extension (SHAPE) // J. Am. Chem. Soc. 2005. V. 127. № 12. P. 4223–4231. https://doi.org/10.1021/ja043822v
  130. Kertesz M., Wan Y., Mazor E. et al. Genome-wide measurement of RNA secondary structure in yeast // Nature. 2010. V. 467. № 7311. P. 103–107.
  131. Hawkes E.J., Hennelly S.P., Novikova I.V. et al. COOLAIR antisense RNAs form evolutionarily conserved elaborate secondary structures // Cell Rep. 2016. V. 16. № 12. P. 3087–3096.
  132. Kim D.N., Thiel B.C., Mrozowich T. et al. Zinc-finger protein CNBP alters the 3-D structure of lncRNA Braveheart in solution // Nat. Commun. 2020. V. 11. № 1. P. 148.
  133. Li A., Zhang J., Zhou Z. PLEK: A tool for predicting long non-coding RNAs and messenger RNAs based on an improved k-mer scheme // BMC Bioinformatics. 2014. V. 15. № 1. P. 311. https://doi.org/10.1186/1471-2105-15-311
  134. Kang Y.-J., Yang D.C., Kong L. et al. CPC2: A fast and accurate coding potential calculator based on sequence intrinsic features // Nucl. Acids Res. 2017. V. 45. № W1. P. W12–W16.
  135. Wang L., Park H.J., Dasari S. et al. CPAT: Coding-Potential Assessment Tool using an alignment-free logistic regression model // Nucl. Acids Res. 2013. V. 41. № 6. P. e74.
  136. Da Costa Negri T., Paschoal A.R., Alves W.A.L. Comparison tools for lncRNA identification: Analysis among plants and humans // 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE. 2020. P. 1–8.
  137. Pronozin A.Y., Afonnikov D.A. ICAnnoLncRNA: A snakemake pipeline for a long non-coding-RNA search and annotation in transcriptomic sequences // Genes. MDPI. 2023. V. 14. № 7. P. 1331.
  138. Gong Y., Huang H.T., Liang Y. et al. lncRNA-screen: An interactive platform for computationally screening long non-coding RNAs in large genomics datasets // BMC Genomics. 2017. V. 18. № 1. P. 434. https://doi.org/10.1186/s12864-017-3817-0
  139. Кит О.И., Кириченко Е.Ю., Кириченко Ю.Г. и др. Длинные некодирующие РНК, ассоциированные с канцерогенезом: биологическое значение и перспективы применения в диагностике // Клин. лаб. диагностика. 2016. Т. 61. № 1. P. 13–16.
  140. Gao L., Gonda I., Sun H. et al. The tomato pan-genome uncovers new genes and a rare allele regulating fruit flavor // Nat. Genet. 2019. V. 51. № 6. P. 1044–1051.

Supplementary files

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2. 1. Classification of DNRNAS based on location and orientation relative to nearby or overlapping protein-coding genes.

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3. Fig. 2. Molecular functions of dnRNA: a – signaling, DNRNAS receive a signal for interaction with chromatin–modifying enzymes to regulate transcription; b - traps, bind proteins and prevent them from attaching to target genes; c – guides, direct transcription factors to their functioning sites, promote the necessary state of chromatin; d – scaffolds, combine proteins into complexes and initiate various biological processes; d – enhancers, can transcriptionally activate neighboring genes.

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4. 3. Mechanisms of dnRNA–microRNA interaction: a – degradation of dnRNA during microRNA insertion; b – dnRNA acts as a target (sponge) for microRNA; c – dnRNA competes with microRNA to regulate the level of mRNA expression; d – dnRNA acts as a precursor of microRNA (spliced into microRNA).

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5. 4. The biological role of dnRNA in plants. DNRNAS are involved in the regulation of genes responsible for plant resistance to various types of stress (blue rectangle), also affect plant fertility (red rectangle), participate in the development of leaves, roots (green, purple rectangle) and in the processes of vernalization (orange rectangle).

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