Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

In silico Characterization of microRNAs and Their Target Transcripts from Cranberry (Vaccinium macrocarpon)

Abstract

MicroRNAs (miRNAs) are highly conserved, non-coding, 20–24 nucleotides long RNA molecules that play important regulatory roles in plants and animals. Due to several limitations involved in the experimental validation of potent miRNAs, in silico prediction of miRNAs and their target(s) from various organisms have been successfully employed. Cranberries are one of the healthiest fruits due to their high nutrient and antioxidant contents. In this study applying genome-wide computational-based approaches and following a set of strict filtering criteria a total of 23 potentially conserved microRNAs belonging to 15 families were identified from cranberry. All the precursors of identified miRNAs formed stable minimum free energy (MFE) stem-loop structure as their orthologues form and possessed high minimum free energy index (MFEI) values. psRNATarget tool detected a total of 92 potential miRNA targets including binding proteins, transcription factors, kinases that are involved in biosyntheses, different metabolic processes, and signal transduction. Among the detected targets, 9 targets (SPLs, proline-rich family proteins, F-Box proteins, HD proteins, Scarecrow proteins, zinc finger proteins, cytochrome P450, sulfate transporters and ABC transporters) were found to have a specific role in phytochemical biosynthesis. To the best of our knowledge, this is the first report of cranberry microRNAs and their targets.

This is a preview of subscription content, log in to check access.

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.

REFERENCES

  1. 1

    Paul, S., Kundu, A., and Pal, A., Identification and expression profiling of Vigna mungo microRNAs from leaf small RNA transcriptome by deep sequencing, J. Integr. Plant Biol., 2014, vol. 56, no. 1, pp. 15–23.

  2. 2

    Bartel, D.P., MicroRNAs: genomics, biogenesis, mechanism, and function, Cell, 2004, vol. 116, no. 2, pp. 281–297.

  3. 3

    Zhang, B., Pan, X., Cobb, G.P., and Anderson, T.A., Plant microRNA: a small regulatory molecule with big impact, Dev. Biol., 2006, vol. 289, no. 1, pp. 3–16.

  4. 4

    Paul, S., Kundu, A., and Pal, A., Identification and validation of conserved microRNAs along with their differential expression in roots of Vigna unguiculata grown under salt stress, Plant Cell Tissue Organ Cult., 2011, vol. 105, no. 2, pp. 233–242.

  5. 5

    Mallory, A.C., Reinhart, B.J., Jones-Rhoades, M.W., et al., MicroRNA control of PHABULOSA in leaf development: importance of pairing to the microRNA 5' region, EMBO J., 2004, vol. 23, no. 16, pp. 3356–3364.

  6. 6

    Naya, L., Paul, S., Valdes-Lopez, O., et al., Regulation of copper homeostasis and biotic interactions by microRNA 398b in common bean, PLoS One, 2014, vol. 9, no. 1. e84416. https://doi.org/10.1371/journal.pone.0084416

  7. 7

    Zhang, B., Pan, X., Cannon, C.H., et al., Conservation and divergence of plant microRNA genes, Plant J., 2006, vol. 46, no. 2, pp. 243–259.

  8. 8

    Zhang, B., Wang, Q., Wang, K., et al., Identification of cotton microRNAs and their targets, Gene, 2007, vol. 397, no. 1, pp. 26–37.

  9. 9

    Zhang, B., Pan, X., and Stellwag, E.J., Identification of soybean microRNAs and their targets, Planta, 2008, vol. 229, no. 1, pp. 161–182.

  10. 10

    Jin, W., Li, N., Zhang, B., et al., Identification and verification of microRNA in wheat (Triticum aestivum), J. Plant Res., 2008, vol. 121, no. 3, pp. 351–355.

  11. 11

    Xie, F., Frazier, T.P., and Zhang, B., Identification, characterization and expression analysis of microRNAs and their targets in the potato (Solanum tuberosum), Gene, 2011, vol. 473, no. 1, pp. 8–22.

  12. 12

    Gleave, A.P., Ampomah-Dwamena, C., Berthold, S., et al., Identification and characterisation of primary microRNAs from apple (Malus domestica cv. Royal Gala) expressed sequence tags, Tree Genet. Genomes, 2008, vol. 4, no. 2, pp. 343–358.

  13. 13

    Pappas, E. and Schaichm, K.M., Phytochemicals of cranberries and cranberry products: Characterization, potential health effects, and processing stability, Crit. Rev. Food. Sci. Nutr., 2009, vol. 49, no. 9, pp. 741–81.

  14. 14

    Weh, K., Clarke, J., and Kresty, L., Cranberries and cancer: an update of preclinical studies evaluating the cancer inhibitory potential of cranberry and cranberry derived constituents, Antioxidants, 2016, vol. 5, no. 3, p. 27.

  15. 15

    Gupta, O.P., Karkute, S.G., Banerjee, S., et al., Contemporary understanding of miRNA-based regulation of secondary metabolites biosynthesis in plants, Front. Plant Sci., 2017, vol. 8, p. 374. https://doi.org/10.3389/fpls.2017.00374

  16. 16

    Polashock, J., Zelzion, E., Fajardo, D., et al., The American cranberry: first insights into the whole genome of a species adapted to bog habitat, BMC Plant Biol., 2014, vol. 14, no. 1, p. 165.

  17. 17

    Paul, S., Identification and characterization of microRNAs and their targets in high-altitude stress-adaptive plant maca (Lepidium meyenii Walp), 3 Biotech, 2017, vol. 7, no. 2, p. 103.

  18. 18

    Yang, W., Liu, X., Zhang, J., et al., Prediction and validation of conservative microRNAs of Solanum tuberosum L., Mol. Biol. Rep., 2010, vol. 37, no. 7, pp. 3081–3087.

  19. 19

    Barozai, M.Y.K., Baloch, I.A., and Din, M., Identification of microRNAs and their targets in Helianthus,Mol. Biol. Rep., 2012, vol. 39, no. 3, pp. 2523–2532.

  20. 20

    Han, Y., Zhu, B., Luan, F., et al., Conserved miRNAs and their targets identified in lettuce (Lactuca) by EST analysis, Gene, 2010, vol. 463, no. 1, pp. 1–7.

  21. 21

    Paul, S. and Pal, A., Genome-wide Characterization of microRNAs from mungbean (Vigna radiata L.), Biotech. J. Int., 2017, vol. 17, no. 9, pp. 1–9. https://doi.org/10.9734/BJI/2017/30984

  22. 22

    Wang, L., Liu, H., Li, D., et al., Identification and characterization of maize microRNAs involved in the very early stage of seed germination, BMC Genomics, 2011, vol. 12, no. 1, p. 154.https://doi.org/10.1186/1471-2164-12-154

  23. 23

    Huang, Y., Zou, Q., and Wang, Z.B., Computational identification of miRNA genes and their targets in mulberry, Russ. J. Plant Physiol., 2014, vol. 61, no. 4, pp. 537–542.

  24. 24

    Bulgakov, V.P. and Avramenko, T.V., New opportunities for the regulation of secondary metabolism in plants: focus on microRNAs, Biotechnol. Lett., 2015, vol. 37, no. 9, pp. 1719–1727.

  25. 25

    Pillet, J., Yu, H.W., Chambers, A.H., et al., Identification of candidate flavonoid pathway genes using transcriptome correlation network analysis in ripe strawberry (Fragaria × ananassa) fruits, J. Exp. Bot., 2015, vol. 66, no. 15, pp. 4455–4467.

  26. 26

    Feder, A., Burger, J., Gao, S., et al., A Kelch domain-containing F-box coding gene negatively regulates flavonoid accumulation in Cucumis melo L., Plant Physiol., 2015, vol. 169, no. 3, pp. 1714–1726.

  27. 27

    Kubo, H., Peeters, A.J., Aarts, M.G., et al., ANTHOCYANINLESS2, a homeobox gene affecting anthocyanin distribution and root development in Arabidopsis,Plant Cell, 1999, vol. 11, no. 7, pp. 1217–1226.

  28. 28

    Ghosh, S., Triterpene structural diversification by plant cytochrome P450 enzymes, Front. Plant Sci., 2017, vol. 8, p. 1886. https://doi.org/10.3389/fpls.2017.01886

  29. 29

    Gigolashvili, T. and Kopriva, S., Transporters in plant sulfur metabolism, Front. Plant Sci., 2014, vol. 5, p. 442. https://doi.org/10.3389/fpls.2014.00442

Download references

Author information

Correspondence to Sujay Paul.

Ethics declarations

The authors declare that they have no conflict of interest. This article does not contain any studies involving animals or human participants performed by any of the authors.

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sangita Chowdhury Paul, Sharma, A., Mehta, R. et al. In silico Characterization of microRNAs and Their Target Transcripts from Cranberry (Vaccinium macrocarpon). Cytol. Genet. 54, 82–90 (2020). https://doi.org/10.3103/S0095452720010120

Download citation

Keywords:

  • Cranberry
  • phytochemicals
  • microRNA (miRNA)
  • computational identification
  • MFEI
  • miRNA target