Advertisement

Vegetos

pp 1–14 | Cite as

In-silico prediction of microRNA targets and finding genes suggesting significant involvement in the development of Glycine max seed

  • Nivedita Yadav
  • Kavita Goswami
  • Budhayash Gautam
  • Pramod Kumar YadavEmail author
Research Articles

Abstract

Glycine max is a worldwide leading economic crop and its seeds are deepening with proteins and oils which supply food and sustenance to all being. Various amounts of alimentary constituents are racked up in the G. max seed in the period of its ontogenesis. Thus, grasping the regulation of biological functions during seed enlargement belong to the basics for crop enhancement. The gene regulatory characteristics of miRNAs in G. max attracted us to focus on its target gene prediction, gene ontology (GO) analysis and expression pattern to their miRNA target genes, which suggest significant involvement in the development of G. max seed. Seven miRNAs have been found from the differential gene expression analysis of development stage 0–4 mm vs. 12–16 mm of G. max seed on the statistical parameter of p value ≤ 0.05 by computational-based microarray data analysis for miRNA target gene prediction. The miRNA target prediction analysis showed total 23 genes that were cleaved from 6 miRNAs, and computationally validated by identifying t-plots of miRNA targets using CleaveLand tool. GO results confirmed that the differentially expressed target genes could be classified into 20 molecular function categories, 73 biological process categories, and 10 cell components categories. On the basis of GO results, two genes were found to be significantly involved in the developmental process of G.max seed. The first miRNA target gene Glyma.01g119500 was predicted to annotate for embryo development ending in seed dormancy, seed dormancy, seed maturation, and seed germination. The second miRNA target gene Glyma.15g005300 was found to be involved in the regulation of seed germination. The Soybean eFP browser analysis suggests that the gene Glyma.01g119500 and Glyma.15g005300 reaches its maximum expression level of 35.88 and 26.6 respectively in the Soybean data source. The present study provides an avenue to explore more genomic and proteomic information about G. max seed developmental stage-specific miRNA target genes.

Keywords

miRNAs Degradome analysis miRNA target prediction GO analysis Expression pattern 

Notes

Acknowledgement

Authors are grateful to the Department of Computational Biology and Bioinformatics, Jacob Institute of Biotechnology and Bioengineering, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, for providing infrastructure facilities. First author is also thankful to the Ministry of Social Justice and Empowerment, Govt. of India, New Delhi for providing fellowship.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Supplementary material

42535_2019_75_MOESM1_ESM.xlsx (21 kb)
Supplementary material 1 (XLSX 21 kb)
42535_2019_75_MOESM2_ESM.xlsx (23 kb)
Supplementary material 2 (XLSX 22 kb)

References

  1. Addo-Quaye C, Eshoo TW, Bartel DP, Axtell MJ (2008) Endogenous siRNA and miRNA targets identified by sequencing of the Arabidopsis degradome. Curr Biol 18(10):758–762.  https://doi.org/10.1016/j.cub.2008.04.042 CrossRefPubMedPubMedCentralGoogle Scholar
  2. Addo-Quaye C, Miller W, Axtell MJ (2009) CleaveLand: a pipeline for using degradome data to find cleaved small RNA targets. Bioinformatics 25:130–131.  https://doi.org/10.1093/bioinformatics/btn604 CrossRefPubMedGoogle Scholar
  3. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP et al (2000) Gene Ontology: tool for the unification of biology. Nature Genet 25:25–29.  https://doi.org/10.1038/75556 CrossRefPubMedGoogle Scholar
  4. Blein T, Patrick P (2016) MicroRNAs (miRNAs) and Plant Development. Wiley online library.  https://doi.org/10.1002/9780470015902.a0020106.pub2 CrossRefGoogle Scholar
  5. Fahlgren N, Howell MD, Kasschau KD et al (2007) High-throughput sequencing of Arabidopsis microRNAs: evidence for frequent birth and death of MIRNA genes. PLoS One 2(2):e219.  https://doi.org/10.1371/journal.pone.0000219 CrossRefPubMedPubMedCentralGoogle Scholar
  6. Fu C, Sunkar R, Zhou C, Shen H et al (2012) Overexpression of miR156 in switchgrass (Panicum virgatum L.) results in various morphological alterations and leads to improved biomass production. Plant Biotechnol J 10:443–452.  https://doi.org/10.1111/j.1467-7652.2011.00677.x CrossRefPubMedPubMedCentralGoogle Scholar
  7. Gai YP, Li YQ et al (2014) Analysis of phytoplasma-responsive sRNAs provide insight into the pathogenic mechanisms of mulberry yellow dwarf disease. Sci Rep 4:5378.  https://doi.org/10.1038/srep05378 CrossRefPubMedPubMedCentralGoogle Scholar
  8. Gaudet P, Dessimoz C (2017) Gene Ontology: Pitfalls, Biases, and Remedies. In: Dessimoz C, Skunca N (eds) The Gene Ontology Handbook. Methods Mol Biol, vol 1446. Humana Press, NYGoogle Scholar
  9. German MA, Pillay M, Jeong DH et al (2008) Global identification of microRNA-target RNA pairs by parallel analysis of RNA ends. Nat Biotechnol 26:941–946.  https://doi.org/10.1038/nbt1417 CrossRefPubMedGoogle Scholar
  10. Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ (2006) miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res 34:D140–D144.  https://doi.org/10.1093/nar/gkj112 CrossRefPubMedPubMedCentralGoogle Scholar
  11. Guo C, Xu Y, Shi M et al (2017) Repression of miR156 by miR159 regulates the timing of the juvenile-to-adult transition in Arabidopsis. Plant Cell 29(6):1293–1304.  https://doi.org/10.1105/tpc.16.00975 CrossRefPubMedPubMedCentralGoogle Scholar
  12. Huang SQ, Xiang AL, Che LL et al (2010) A set of miRNAs from Brassica napus in response to sulphate deficiency and cadmium stress. Plant Biotechnol J 8(8):887–899.  https://doi.org/10.1111/j.1467-7652.2010.00517.x CrossRefPubMedGoogle Scholar
  13. Jones-Rhoades MW, Bartel DP (2004) Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Mol Cell 14(6):787–799.  https://doi.org/10.1016/j.molcel.2004.05.027 CrossRefPubMedGoogle Scholar
  14. Libault M, Farmer A, Joshi T et al (2010) An integrated transcriptome atlas of the crop model Glycine max, and its use in comparative analyses in plants. Plant J. 63:86–99.  https://doi.org/10.1111/j.1365-313X.2010.04222.x CrossRefPubMedGoogle Scholar
  15. Millar AA, Waterhouse PM (2005) Plant and animal microRNAs: similarities and differences. Funct Integr Genomics 5:129–135.  https://doi.org/10.1007/s10142-005-0145-2 CrossRefPubMedGoogle Scholar
  16. Nakano M, Nobuta K et al (2006) Plant MPSS databases: signature-based transcriptional resources for analyses of mRNA and small RNA. Nucleic Acids Res 34:D731–D735.  https://doi.org/10.1093/nar/gkj077 CrossRefPubMedGoogle Scholar
  17. Schmutz J, Cannon SB, Schlueter J et al (2010) Genome sequence of the palaeopolyploid soybean. Nature 463(7278):178–183.  https://doi.org/10.1038/nature08670 CrossRefPubMedPubMedCentralGoogle Scholar
  18. Semih E, Sahin K (2014) Computational methods for MicroRNA target prediction. Genes 5(3):671–683.  https://doi.org/10.3390/genes5030671 CrossRefGoogle Scholar
  19. Song QX, Liu YF, Hu XY et al (2011) Identification of miRNAs and their target genes in developing soybean seeds by deep sequencing. BMC Plant Biol 11:5.  https://doi.org/10.1186/1471-2229-11-5 CrossRefPubMedPubMedCentralGoogle Scholar
  20. Tripathi RK, Goel R, Kumari S, Dahuja A (2017) Genomic organization, phylogenetic comparison, and expression profiles of the SPL family genes and their regulation in soybean. Dev Genes Evol 227:101.  https://doi.org/10.1007/s00427-017-0574-7 CrossRefPubMedGoogle Scholar
  21. Turner M, Yu O, Subramanian S (2012) Genome organization and characteristics of soybean microRNAs. BMC Genomics 13:169.  https://doi.org/10.1186/1471-2164-13-169 CrossRefPubMedPubMedCentralGoogle Scholar
  22. Wang Y, Li P, Cao X, Wang X, Zhang A, Li X (2009) Identification and expression analysis of miRNAs from nitrogen-fixing soybean nodules. Biochem Biophys Res Commun 378:799–803.  https://doi.org/10.1016/j.bbrc.2008.11.140 CrossRefPubMedGoogle Scholar
  23. Wang Y, Wang Z, Amyot L, Tian L, Xu Z, Gruber MY, Hannoufa A (2014) Ectopic expression of miR156 represses nodulation and causes morphological and developmental changes in Lotus japonicus. Mol Genet Genomics 290:471–484.  https://doi.org/10.1007/s00438-014-0931-4 CrossRefPubMedPubMedCentralGoogle Scholar
  24. Wang Y, Li K, Chen L, Zou Y et al (2015) MicroRNA167-directed regulation of the auxin response factors GmARF8a and GmARF8b is required for soybean nodulation and lateral root development. Plant Physiol 168(3):984–999.  https://doi.org/10.1104/pp.15.00265 CrossRefPubMedPubMedCentralGoogle Scholar
  25. Wang Y, Lan Q, Zhao X, Xu W, Li F, Wang Q, Chen R (2016) Comparative profiling of microRNA expression in soybean seeds from genetically modified plants and their near-isogenic parental lines. PLoS One 11(5):e0155896.  https://doi.org/10.1371/journal.pone.0155896 CrossRefPubMedPubMedCentralGoogle Scholar
  26. Winter D, Vinegar B et al (2007) An “electronic fluorescent pictograph” browser for exploring and analyzing large-scale biological data sets. PLoS One 2(8):e718.  https://doi.org/10.1371/journal.pone.0000718 CrossRefPubMedPubMedCentralGoogle Scholar
  27. Wu G, Park MY, Conway SR, Wang JW et al (2009) The sequential action of miR156 and miR172 regulates developmental timing in arabidopsis. Cell 138:750–759.  https://doi.org/10.1016/j.cell.2009.06.031 CrossRefPubMedPubMedCentralGoogle Scholar
  28. Nivedita Yadav, Yadav PK, Gautam B (2015) Gene expression profiling of transcription factors of Arabidopsis thaliana using microarray data analysis. IJARCSSE 5(4):783–793Google Scholar
  29. Yadav N, Gautam B, Yadav PK (2019) Computational analysis of microarray-based gene expression profiling and unveiling the functional traits in the developmental phases of Glycine max seed. Vegetos.  https://doi.org/10.1007/s42535-019-00008-5 CrossRefGoogle Scholar
  30. Yan Z, Hossain MS, Wang J, Valdes-Lopez O, Liang Y, Libault M, Qiu L, Stacey G (2013) miR172 regulates soybean nodulation. Mol Plant Microbe Interact 26:1371–1377.  https://doi.org/10.1094/MPMI-04-13-0111-R CrossRefPubMedGoogle Scholar
  31. Yin XC, Wang J, Cheng H, Wang XL, Yu DY (2013) Detection and evolutionary analysis of soybean miRNAs responsive to soybean mosaic virus. Planta 237:1213–1225.  https://doi.org/10.1007/s00425-012-1835-3 CrossRefPubMedGoogle Scholar
  32. Zhang B, Pan X, Stellwag EJ (2008) Identification of soybean microRNAs and their targets. Planta 229:161–182.  https://doi.org/10.1007/s00425-008-0818-x CrossRefPubMedGoogle Scholar

Copyright information

© Society for Plant Research 2019

Authors and Affiliations

  • Nivedita Yadav
    • 1
  • Kavita Goswami
    • 1
  • Budhayash Gautam
    • 1
  • Pramod Kumar Yadav
    • 1
    Email author
  1. 1.Jacob Institute of Biotechnology and Bioengineering, Department of Computational Biology and BioinformaticsSam Higginbottom University of Agriculture, Technology and SciencesPrayagrajIndia

Personalised recommendations