, 251:49 | Cite as

Computational screening of miRNAs and their targets in leaves of Hypericum spp. by transcriptome-mining: a pilot study

  • Linda PetijováEmail author
  • Zuzana Jurčacková
  • Eva Čellárová
Original Article


Main conclusion

Our work provides a survey of mature miRNAs, their target genes and primary precursors identified by in-silico approach in leaf transcriptomes of five selected Hypericum species.


MiRNAs are small non-coding RNA molecules found in animals, terrestrial plants, several algae and molds. As their role lies in the post-transcriptional gene silencing, these tiny molecules regulate many biological processes. Phyto-miRNAs are considered the important regulators of secondary metabolism in medicinal plants. The genus Hypericum comprises many producers of bioactive compounds, mainly unique naphtodianthrones with a great therapeutic potential. The main goal of our work was to identify genetically conserved miRNAs, characterize their primary precursors and target sequences in the leaf transcriptomes of five Hypericum species using in-silico approach. We found 20 sequences of potential Hypericum pri-miRNAs, and predicted and computationally validated their secondary structures. The mature miRNAs were identified by target genes screening analysis. Whereas predicted miRNA profiles differed in less genetically conserved families, the highly conserved miRNAs were found in almost all studied species. Moreover, we detected several novel highly likely miRNA–mRNA interactions, such as mir1171 with predicted regulatory role in the biosynthesis of melatonin in plants. Our work contributes to the knowledge of Hypericum miRNAome and miRNA–mRNA interactions.


Bioinformatic analysis Primary miRNAs Melatonin Secondary metabolism 



The work was supported by the Slovak Research and Development Agency under Grant number APVV-18-0125 and the Scientific Grant Agency of Slovak Republic under Grant number VEGA 1/0013/19.

Supplementary material

425_2020_3342_MOESM1_ESM.xlsx (58 kb)
Online resource S1 Putative miRNAs and their target genes identified in H. perforatum leaf transcriptome (XLSX 58 kb)
425_2020_3342_MOESM2_ESM.xlsx (69 kb)
Online resource S2 Putative miRNAs and their target genes identified in H. androsaemum leaf transcriptome (XLSX 69 kb)
425_2020_3342_MOESM3_ESM.xlsx (65 kb)
Online resource S3 Putative miRNAs and their target genes identified in H. kalmianum leaf transcriptome (XLSX 65 kb)
425_2020_3342_MOESM4_ESM.xlsx (56 kb)
Online resource S4 Putative miRNAs and their target genes identified in H. annulatum leaf transcriptome (XLSX 56 kb)
425_2020_3342_MOESM5_ESM.xlsx (59 kb)
Online resource S5 Putative miRNAs and their target genes identified in H. tomentosum leaf transcriptome (XLSX 59 kb)


  1. Axtell MJ, Meyers BC (2018) Revisiting criteria for plant microRNA Annotation in the era of big data. Plant Cell 30:272. CrossRefPubMedPubMedCentralGoogle Scholar
  2. Bartel DP (2018) Metazoan microRNAs. Cell 173:20–51. CrossRefPubMedPubMedCentralGoogle Scholar
  3. Borges F, Martienssen RA (2015) The expanding world of small RNAs in plants. Nat Rev Mol Cell Biol 16:727–741. CrossRefPubMedPubMedCentralGoogle Scholar
  4. Bulgakov VP, Avramenko TV (2015) New opportunities for the regulation of secondary metabolism in plants: focus on microRNAs. Biotechnol Lett 37:1719–1727. CrossRefPubMedGoogle Scholar
  5. Camacho C, Coulouris G, Avagyan V et al (2009) BLAST+: architecture and applications. BMC Bioinform 10:421. CrossRefGoogle Scholar
  6. Cavalieri D, Rizzetto L, Tocci N et al (2016) Plant microRNAs as novel immunomodulatory agents. Sci Rep 6:25761CrossRefGoogle Scholar
  7. Clokie SJH, Lau P, Kim HH et al (2012) MicroRNAs in the pineal gland: miR-483 regulates melatonin synthesis by targeting arylalkylamine N-acetyltransferase. J Biol Chem 287:25312–25324. CrossRefPubMedPubMedCentralGoogle Scholar
  8. Dai X, Zhuang Z, Zhao PX (2018) psRNATarget: a plant small RNA target analysis server (2017 release). Nucleic Acids Res 46:W49–W54. CrossRefPubMedPubMedCentralGoogle Scholar
  9. Djami-Tchatchou AT, Sanan-Mishra N, Ntushelo K, Dubery IA (2017) Functional roles of microRNAs in agronomically important plants—potential as targets for crop improvement and protection. Front Plant Sci 8:378. CrossRefPubMedPubMedCentralGoogle Scholar
  10. Ellis J, Dodds P, Pryor T (2000) Structure, function and evolution of plant disease resistance genes. Curr Opin Plant Biol 3:278–284CrossRefGoogle Scholar
  11. Ergun S (2019) Cross-kingdom gene regulation via miRNAs of Hypericum perforatum (St. John’s wort) flower dietetically absorbed: an in silico approach to define potential biomarkers for prostate cancer. Comput Biol Chem 80:16–22. CrossRefPubMedGoogle Scholar
  12. Fu L, Niu B, Zhu Z et al (2012) CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28:3150–3152. CrossRefPubMedPubMedCentralGoogle Scholar
  13. Galla G, Volpato M, Sharbel TF, Barcaccia G (2013) Computational identification of conserved microRNAs and their putative targets in the Hypericum perforatum L. flower transcriptome. Plant Reprod 26:209–229. CrossRefPubMedGoogle Scholar
  14. Gamborg OL, Miller RA, Ojima K (1968) Nutrient requirements of suspension cultures of soybean root cells. Exp Cell Res 50:151–158. CrossRefGoogle Scholar
  15. Grabherr MG, Haas BJ, Yassour M et al (2011) Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol 29:644–652. CrossRefPubMedPubMedCentralGoogle Scholar
  16. Gupta OP, Karkute SG, Banerjee S et al (2017) Contemporary understanding of miRNA-based regulation of secondary metabolites biosynthesis in plants. Front Plant Sci 8:374. CrossRefPubMedPubMedCentralGoogle Scholar
  17. Jeyaraj A, Liu S, Zhang X et al (2017) Genome-wide identification of microRNAs responsive to Ectropis oblique feeding in tea plant (Camellia sinensis L.). Sci Rep 7:13634. CrossRefPubMedPubMedCentralGoogle Scholar
  18. Jones-Rhoades MW, Bartel DP, Bartel B (2006) MicroRNAS and their regulatory roles in plants. Annu Rev Plant Biol 57:19–53. CrossRefPubMedGoogle Scholar
  19. Kalvari I, Argasinska J, Quinones-Olvera N et al (2018) Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families. Nucleic Acids Res 46:D335–D342. CrossRefPubMedGoogle Scholar
  20. Kang K, Kong K, Park S et al (2011) Molecular cloning of a plant N-acetylserotonin methyltransferase and its expression characteristics in rice. J Pineal Res 50:304–309. CrossRefPubMedGoogle Scholar
  21. Kang Y-J, Yang D-C, Kong L et al (2017) CPC2: a fast and accurate coding potential calculator based on sequence intrinsic features. Nucleic Acids Res 45:W12–W16. CrossRefPubMedPubMedCentralGoogle Scholar
  22. Kim H, Kim SW, Seok KH et al (2018) Hypericin-assisted photodynamic therapy against anaplastic thyroid cancer. Photodiagnosis Photodyn Ther 24:15–21. CrossRefPubMedGoogle Scholar
  23. Kozomara A, Griffiths-Jones S (2011) miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 39:D152–157. CrossRefPubMedGoogle Scholar
  24. Kurihara Y, Watanabe Y (2004) Arabidopsis micro-RNA biogenesis through Dicer-like 1 protein functions. Proc Natl Acad Sci USA 101:12753–12758. CrossRefPubMedGoogle Scholar
  25. Lelandais-Briere C, Sorin C, Declerck M et al (2010) Small RNA diversity in plants and its impact in development. Curr Genomics 11:14–23. CrossRefPubMedPubMedCentralGoogle Scholar
  26. Li B, Fillmore N, Bai Y et al (2014) Evaluation of de novo transcriptome assemblies from RNA-Seq data. Genome Biol 15:553. CrossRefPubMedPubMedCentralGoogle Scholar
  27. Luo R, Liu B, Xie Y et al (2012) SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Gigascience 1:18. CrossRefPubMedPubMedCentralGoogle Scholar
  28. Margis R, Fusaro AF, Smith NA et al (2006) The evolution and diversification of Dicers in plants. FEBS Lett 580:2442–2450. CrossRefPubMedGoogle Scholar
  29. Murashige T, Skoog F (1962) A revised medium for rapid growth and bio assays with tobacco tissue cultures. Physiol Plant 15:473–497. CrossRefGoogle Scholar
  30. Murch SJ, Simmons CB, Saxena PK (1997) Melatonin in feverfew and other medicinal plants. Lancet 350:1598–1599. CrossRefPubMedGoogle Scholar
  31. Robson NKB (2016) And then came molecular phylogenetics—reactions to a monographic study of Hypericum (Hypericaceae). Phytotaxa 255(3):181 198. CrossRefGoogle Scholar
  32. Sabzehzari M, Naghavi MR (2019) Phyto-miRNA: a molecule with beneficial abilities for plant biotechnology. Gene 683:28–34. CrossRefPubMedGoogle Scholar
  33. Samad AFA, Sajad M, Nazaruddin N et al (2017) MicroRNA and transcription factor: key players in plant regulatory network. Front Plant Sci 8:565. CrossRefPubMedPubMedCentralGoogle Scholar
  34. Song X, Li Y, Cao X, Qi Y (2019) MicroRNAs and their regulatory roles in plant–environment interactions. Annu Rev Plant Biol 70:489–525. CrossRefPubMedGoogle Scholar
  35. Soták M, Czeranková O, Klein D et al (2016) Comparative transcriptome reconstruction of four Hypericum species focused on hypericin biosynthesis. Front Plant Sci 7:1039. CrossRefPubMedPubMedCentralGoogle Scholar
  36. Tan D-X, Zheng X, Kong J et al (2014) Fundamental issues related to the origin of melatonin and melatonin isomers during evolution: relation to their biological functions. Int J Mol Sci 15:15858–15890. CrossRefPubMedPubMedCentralGoogle Scholar
  37. Wang J, Mei J, Ren G (2019) Plant microRNAs: biogenesis, homeostasis, and degradation. Front Plant Sci 10:360. CrossRefPubMedPubMedCentralGoogle Scholar
  38. Xu L, Zhang X, Cheng W et al (2019) Hypericin-photodynamic therapy inhibits the growth of adult T-cell leukemia cells through induction of apoptosis and suppression of viral transcription. Retrovirology 16:5. CrossRefPubMedPubMedCentralGoogle Scholar
  39. Yang JH, Han SJ, Yoon EK, Lee WS (2006) Evidence of an auxin signal pathway, microRNA167-ARF8-GH3, and its response to exogenous auxin in cultured rice cells. Nucleic Acids Res 34:1892–1899. CrossRefPubMedPubMedCentralGoogle Scholar
  40. Zhang J-W, Long Y, Xue M et al (2017) Identification of microRNAs in response to drought in common wild rice (Oryza rufipogon Griff.) shoots and roots. PLoS ONE 12:e0170330. CrossRefPubMedPubMedCentralGoogle Scholar
  41. Zhang K, Gao S, Guo J et al (2018) Hypericin-photodynamic therapy inhibits proliferation and induces apoptosis in human rheumatoid arthritis fibroblast-like synoviocytes cell line MH7A. Iran J Basic Med Sci 21:130–137. CrossRefPubMedPubMedCentralGoogle Scholar
  42. Zhao D, Yu Y, Shen Y et al (2019) Melatonin synthesis and function: Evolutionary history in animals and plants. Front Endocrinol (Lausanne) 10:249. CrossRefGoogle Scholar
  43. Zinati Z, Shamloo-Dashtpagerdi R, Behpouri A (2016) In silico identification of miRNAs and their target genes and analysis of gene co-expression network in saffron (Crocus sativus L.) stigma. Mol Biol Res Commun 5:233–246PubMedPubMedCentralGoogle Scholar
  44. Zuker M (2003) Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res 31:3406–3415. CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  • Linda Petijová
    • 1
    Email author
  • Zuzana Jurčacková
    • 1
  • Eva Čellárová
    • 1
  1. 1.Department of Genetics, Faculty of Science, Institute of Biology and EcologyP. J. Šafárik University in KošiceKošiceSlovak Republic

Personalised recommendations