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Prediction of Plant miRNA Targets

  • Priyanka Pandey
  • Prashant K. Srivastava
  • Shree P. PandeyEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1932)

Abstract

microRNAs (miRNAs) are the central component of an important layer of regulation of gene expression at posttranscriptional level. In plants, miRNAs target the transcripts in a highly complementary sequence-dependent manner. Extensive research is being made to study genome-wide miRNA-mediated regulation of gene expression, which has resulted in the development of many tools for in silico prediction of miRNA targets. Although several tools have been developed for predicting miRNA targets in model plants, genome-wide analysis of miRNA targets is still a challenge for non-model species that lack dedicated tools. Here, we describe an in silico procedure for studying miRNA-mediated interactions in plants, which is based on the fact that canonical miRNA-target sites are highly complementary, the miRNAs negatively regulate the expression of their target genes, and miRNAs may form regulatory networks as one miRNA may target more than one transcript and vice versa to modulate and fine-tune expression of the genome.

Key words

miRNA Target prediction Genome-wide analysis Next-generation sequencing NGS Network analysis 

Notes

Acknowledgment

SPP acknowledges financial support by Max Planck Society and Max Planck India partner group program.

References

  1. 1.
    Axtell MJ, Meyers BC (2018) Revisiting criteria for plant MicroRNA annotation in the era of big data. Plant Cell 30:272–284CrossRefGoogle Scholar
  2. 2.
    Pandey SP, Baldwin IT (2007) RNA-directed RNA polymerase 1 (RdR1) mediates the resistance of Nicotiana attenuata to herbivore attack in nature. Plant J 50:40–53CrossRefGoogle Scholar
  3. 3.
    Pandey SP, Somssich IE (2009) The role of WRKY transcription factors in plant immunity. Plant Physiol 150:1648–1655CrossRefGoogle Scholar
  4. 4.
    Bozorov TA, Pandey SP, Dinh ST, Kim SG, Heinrich M, Gase K, Baldwin IT (2012) DICER-like proteins and their role in plant-herbivore interactions in Nicotiana attenuata. J Integr Plant Biol 54:189–206CrossRefGoogle Scholar
  5. 5.
    Pradhan M, Pandey P, Gase K, Sharaff M, Singh RK, Sethi A, Baldwin IT, Pandey SP (2017) Argonaute 8 (AGO8) mediates the elicitation of direct defenses against herbivory. Plant Physiol 175:927–946PubMedPubMedCentralGoogle Scholar
  6. 6.
    Ferdous J, Hussain SS, Shi BJ (2015) Role of microRNAs in plant drought tolerance. Plant Biotechnol J 13:293–305CrossRefGoogle Scholar
  7. 7.
    Li C, Zhang B (2016) MicroRNAs in control of plant development. J Cell Physiol 231:303–313CrossRefGoogle Scholar
  8. 8.
    Axtell MJ (2013) Classification and comparison of small RNAs from plants. Annu Rev Plant Biol 64:137–159CrossRefGoogle Scholar
  9. 9.
    Liu Q, Wang F, Axtell MJ (2014) Analysis of complementarity requirements for plant microRNA targeting using a Nicotiana benthamiana quantitative transient assay. Plant Cell 26:741–753CrossRefGoogle Scholar
  10. 10.
    Brodersen P, Voinnet O (2009) Revisiting the principles of microRNA target recognition and mode of action. Nat Rev Mol Cell Biol 10:141–148CrossRefGoogle Scholar
  11. 11.
    Luo Y, Guo Z, Li L (2013) Evolutionary conservation of microRNA regulatory programs in plant flower development. Dev Biol 380:133–144CrossRefGoogle Scholar
  12. 12.
    Chorostecki U, Crosa VA, Lodeyro AF, Bologna NG, Martin AP, Carrillo N, Schommer C, Palatnik JF (2012) Identification of new microRNA-regulated genes by conserved targeting in plant species. Nucleic Acids Res 40:8893–8904CrossRefGoogle Scholar
  13. 13.
    Srivastava PK, Moturu TR, Pandey P, Baldwin IT, Pandey SP (2014) A comparison of performance of plant miRNA target prediction tools and the characterization of features for genome-wide target prediction. BMC Genomics 15:348CrossRefGoogle Scholar
  14. 14.
    Fahlgren N, Howell MD, Kasschau KD, Chapman EJ, Sullivan CM, Cumbie JS, Givan SA, Law TF, Grant SR, Dangl JL, Carrington JC (2007) High-throughput sequencing of Arabidopsis microRNAs: evidence for frequent birth and death of MIRNA genes. PLoS One 2:e219CrossRefGoogle Scholar
  15. 15.
    Dai X, Zhao PX (2011) psRNATarget: a plant small RNA target analysis server. Nucleic Acids Res 39:W155–W159CrossRefGoogle Scholar
  16. 16.
    Bonnet E, He Y, Billiau K, Van de Peer Y (2010) TAPIR, a web server for the prediction of plant microRNA targets, including target mimics. Bioinformatics 26:1566–1568CrossRefGoogle Scholar
  17. 17.
    Chorostecki U, Palatnik JF (2014) comTAR: a web tool for the prediction and characterization of conserved microRNA targets in plants. Bioinformatics 30:2066–2067CrossRefGoogle Scholar
  18. 18.
    Zhang W, Le TD, Liu L, Zhou ZH, Li J (2016) Predicting miRNA targets by integrating gene regulatory knowledge with expression profiles. PLoS One 11:e0152860CrossRefGoogle Scholar
  19. 19.
    Zhang Z, Jiang L, Wang J, Gu P, Chen M (2015) MTide: an integrated tool for the identification of miRNA-target interaction in plants. Bioinformatics 31:290–291CrossRefGoogle Scholar
  20. 20.
    Ma X, Liu C, Gu L, Mo B, Cao X, Chen X (2018) TarHunter, a tool for predicting conserved microRNA targets and target mimics in plants. Bioinformatics 34:1574–1576CrossRefGoogle Scholar
  21. 21.
    Maere S, Heymans K, Kuiper M (2005) BiNGO: a cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21:3448–3449CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Priyanka Pandey
    • 1
  • Prashant K. Srivastava
    • 2
  • Shree P. Pandey
    • 3
    Email author
  1. 1.National Institute of Biomedical GenomicsKalyaniIndia
  2. 2.Division of Brain Sciences, Department of MedicineImperial CollegeLondonUK
  3. 3.Department of Molecular EcologyMax Planck Institute for Chemical EcologyJenaGermany

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