Chinese Science Bulletin

, Volume 55, Issue 13, pp 1263–1270 | Cite as

Prediction of sorghum miRNAs and their targets with computational methods

  • JiangFeng Du
  • YongJun Wu
  • XiaoFeng Fang
  • JunXia Cao
  • Liang Zhao
  • ShiHeng Tao
Articles Bioinformatics


microRNAs are a class of ∼21-nt long, non-coding and newly-identified RNAs that play critical roles in post-transcriptional gene regulation. Their targets are involved in various biological processes, including development, metabolism, and stress response. Though a large number of miRNAs have been reported in many species, reports of miRNAs in sorghum are limited. Using a homology search based on the genomic survey sequence (GSS) and the microRNA (miRNA) secondary structure, a total of 17 new miRNAs were identified in this work. They were found to be distributed unevenly among 11 miRNA families. Some miRNA genes were found at multiple locations and in more than one genomic context. Most miRNAs are conserved within the same kingdom, but we found in sorghum that sbi-miR127 and sbi-miR466 showed conservation with H. sapiens and M. musculus, respectively. Analysis of those 17 new miRNAs via online software miRU showed that they might regulate 64 target genes, most of which are involved in RNA processing, metabolism, cell cycle, protein degradation, stress response and transportation. At least 7 of 11 miRNA families target proteins that are necessary in metabolism and stress response, including NADPH-cytochrome P450 reductase, nucleoside diphosphate kinase and superoxide dismutase, suggesting that miRNAs play an essential role in biological processes.


microRNA sorghum precursor genome survey sequence (GSS) prediction targets function 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hattori T, Sonobe K, Araki H, et al. Silicon application by sorghum through the alleviation of stress-induced increase in hydraulic resistance. J Plant Nutr, 2008, 31: 1482–1495CrossRefGoogle Scholar
  2. 2.
    Prasad P V V, Pisipati S R, Mutava R N, et al. Sensitivity of grain sorghum to high temperature stress during reproductive development. Crop Sci, 2008, 48: 1911–1917CrossRefGoogle Scholar
  3. 3.
    Bartel D P. microRNAs: Genomics, biogenesis, mechanism, and function. Cell, 2007, 131: 11–29Google Scholar
  4. 4.
    Hunter C, Poethig R S. Missing links: miRNAs and plant development. Curr Opin Genet Dev, 2003, 13: 372–378CrossRefGoogle Scholar
  5. 5.
    Ke X S, Liu C M, Liu D P, et al. MicroRNAs: Key participants in gene regulatory networks-Commentary. Curr Opin Chem Biol, 2003, 7: 516–523CrossRefGoogle Scholar
  6. 6.
    Kidner C A, Martienssen R A. Macro effects of microRNAs in plants. Trends Genet, 2003, 19: 13–16CrossRefGoogle Scholar
  7. 7.
    Murchison E P, Hannon G J. MiRNAs on the move: miRNA biogenesis and the RNAi machinery. Curr Opin Cell Biol, 2004, 16: 223–229CrossRefGoogle Scholar
  8. 8.
    Lee R C, Feinbaum R L, Ambros V. The C. telegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell, 1993, 75: 843–854CrossRefGoogle Scholar
  9. 9.
    Lagos-Quintana M, Rauhut R, Lendeckel W, et al. Identification of novel genes coding for small expressed RNAs. Science, 2001, 294: 853–858CrossRefGoogle Scholar
  10. 10.
    Lau N C, Lim L P, Weinstein E G, et al. An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science, 2001, 294: 858–862CrossRefGoogle Scholar
  11. 11.
    Khvorova A, Reynolds A, Jayasena S D. Functional siRNAs and miRNAs exhibit strand bias. Cell, 2007, 131: 41–49Google Scholar
  12. 12.
    Hake S. MicroRNAs: A role in plant development. Curr Biol, 2003, 13: R851–R852CrossRefGoogle Scholar
  13. 13.
    Valencia-Sanchez M A, Liu J D, Hannon G J, et al. Control of translation and mRNA degradation by miRNAs and siRNAs. Gene Dev, 2006, 20: 515–524CrossRefGoogle Scholar
  14. 14.
    Doench J G, Petersen C P, Sharp P A. siRNAs can function as miRNAs. Gene Dev, 2003, 17: 438–442CrossRefGoogle Scholar
  15. 15.
    Zeng Y, Yi R, Cullen B R. microRNAs and small interfering RNAs can inhibit mRNA expression by similar mechanisms. Proc Natl Acad Sci USA, 2003, 100: 9779–9784CrossRefGoogle Scholar
  16. 16.
    Wightman B, Ha I, Ruvkun G. Post transcriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern-formation in C. elegans. Cell, 1993, 75: 855–862CrossRefGoogle Scholar
  17. 17.
    Olsen P H, Ambros V. The lin-4 regulatory RNA controls developmental timing in Caenorhabditis elegans by blocking LIN-14 protein synthesis after the initiation of translation. Dev Biol, 1999, 216: 671–680CrossRefGoogle Scholar
  18. 18.
    Seggerson K, Tang L J, Moss E G. Two genetic circuits repress the Caenorhabditis elegans heterochronic gene lin-28 after translation initiation. Dev Biol, 2002, 243: 215–225CrossRefGoogle Scholar
  19. 19.
    Moss E G, Lee R C, Ambros V. The cold shock domain protein LIN-28 controls developmental timing in C. elegans and is regulated by the lin-4 RNA. Cell, 1997, 88: 637–646CrossRefGoogle Scholar
  20. 20.
    Llave C, Kasschau K D, Rector M A, et al. Endogenous and silencing-associated small RNAs in plants. Plant Cell, 2002, 14: 1605–1619CrossRefGoogle Scholar
  21. 21.
    Park W, Li J, Song R, et al. CARPEL factory, a Dicer homolog, and HEN1, a novel protein, act in microRNA metabolism in Arabidopsis thaliana. Curr Biol, 2002, 12: 1484–1495CrossRefGoogle Scholar
  22. 22.
    Palatnik J F, Allen E, Wu X, et al. Control of leaf morphogenesis by microRNAs. Nature, 2003, 425: 257–263CrossRefGoogle Scholar
  23. 23.
    Rhoades M W, Reinhart B J, Lim L P, et al. Prediction of plant microRNA targets. Cell, 2002, 110: 513–520CrossRefGoogle Scholar
  24. 24.
    Tay Y, Zhang J Q, Thomson A M, et al. microRNAs to Nanog, Oct4 and Sox2 coding regions modulate embryonic stem cell differentiation. Nature, 2008, 455: 1124–1128CrossRefGoogle Scholar
  25. 25.
    Grad Y, Aach J, Hayes G D, et al. Computational and experimental identification of C. elegans microRNAs. Mol Cell, 2003, 11: 1253–1263CrossRefGoogle Scholar
  26. 26.
    Lee R C, Ambros V. An extensive class of small RNAs in Caenorhabditis elegans. Science, 2001, 294: 862–864CrossRefGoogle Scholar
  27. 27.
    Mourelatos Z, Dostie J, Paushkin S, et al. MiRNPs: A novel class of ribonucleoproteins containing numerous microRNAs. Gene Dev, 2002, 16: 720–728CrossRefGoogle Scholar
  28. 28.
    Reinhart B J, Weinstein E G, Rhoades M W, et al. microRNAs in plants. Gene Dev, 2002, 16: 1616–1626CrossRefGoogle Scholar
  29. 29.
    Yoon S, Micheli G D. Computational identification of microRNAs and their targets. Birth Defects Res, 2006, 78: 118–128CrossRefGoogle Scholar
  30. 30.
    Wang X J, Reyes J L, Chua N H, et al. Prediction and identification of Arabidopsis thaliana microRNAs and their mRNA targets. Genome Biol, 2004, 5: R65CrossRefGoogle Scholar
  31. 31.
    Adai A, Johnson C, Mlotshwa S, et al. Computational prediction of miRNAs in Arabidopsis thaliana. Genome Res, 2005, 15: 78–91CrossRefGoogle Scholar
  32. 32.
    Jones-Rhoades M W, Bartel D P. Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Mol Cell, 2004, 14: 787–799CrossRefGoogle Scholar
  33. 33.
    Zhang B H, Pan X P, Wang Q L, et al. Identification and characterization of new plant microRNAs using EST analysis. Cell Res, 2005, 15: 336–360CrossRefGoogle Scholar
  34. 34.
    Zhang B H, Wang Q, Wang K, et al. Identification of cotton microRNAs and their targets. Gene, 2007, 397: 26–37CrossRefGoogle Scholar
  35. 35.
    Lai E C, Tomancak P, Williams R W, et al. Computational identification of Drosophila microRNA genes. Genome Biol, 2003, 4:R42CrossRefGoogle Scholar
  36. 36.
    Rajewsky N, Socci N D. Computational identification of microRNA targets. Dev Biol, 2004, 267: 529–535CrossRefGoogle Scholar
  37. 37.
    Altschul S F, Gish W, Miller W, et al. Basic local alignment search tool. J Mol Biol, 1990, 215: 403–410Google Scholar
  38. 38.
    Dezulian T, Remmert M, Palatnik J F, et al. Identification of plant microRNA homologs. Bioinformatics, 2006, 22: 359–360CrossRefGoogle Scholar
  39. 39.
    Sunkar R, Jagadeeswaran G. In silico identification of conserved microRNAs in large number of diverse plant species. BMC Plant Biol, 2008, 8: 37CrossRefGoogle Scholar
  40. 40.
    Zhang B, Pan X, Cannon C H, et al. Conservation and divergence of plant microRNA genes. Plant J, 2006, 46: 243–259CrossRefGoogle Scholar
  41. 41.
    Zhang Y. Mi RU: An automated plant miRNA target prediction server. Nucleic Acids Res, 2005, 33(Web Server issue): W701–W704CrossRefGoogle Scholar
  42. 42.
    Giegerich R, Voss B, Rehmsmeier M. Abstract shapes of RNA. Nucleic Acids Res, 2004, 32: 4843–4851CrossRefGoogle Scholar
  43. 43.
    Reeder J, Giegerich R. Consensus shapes: An alternative to the Sankoff algorithm for RNA consensus structure prediction. Bioinformatics, 2005, 21: 3516–3523CrossRefGoogle Scholar
  44. 44.
    Steffen P, Voss B, Rehmsmeier M, et al. RNAshapes: An integrated RNA analysis package based on abstract shapes. Bioinformatics, 2006, 22: 500–503CrossRefGoogle Scholar
  45. 45.
    Zhang B H, Pan X P, Cox S B, et al. Evidence that miRNAs are different from other RNAs. Cell Mol Life Sci, 2006, 63: 246–254CrossRefGoogle Scholar
  46. 46.
    Grun D, Wang Y L, Langenberger D, et al. microRNA target predictions across seven Drosophila species and comparison to mammalian targets. PLoS Comput Biol, 2005, 1: e13CrossRefGoogle Scholar
  47. 47.
    Kurima K, Peters L M, Yang Y, et al. Dominant and recessive deafness caused by mutations of a novel gene, TMC1, required for cochlear hair-cell function. Nat Genet, 2002, 30: 277–284CrossRefGoogle Scholar
  48. 48.
    Li D M, Sun H. TEP1, encoded by a candidate tumor suppressor locus, is a novel protein tyrosine phosphatase regulated by transforming growth factor beta. Cancer Res, 1997, 57: 2124–2129Google Scholar
  49. 49.
    Popea R K, Pestonjamaspa K N, Smithb K P, et al. Cloning, characterization, and chromosomal localization of human supervillin (SVIL). Genomics, 1998, 52: 342–351CrossRefGoogle Scholar
  50. 50.
    Carrington J C, Ambros V. Role of microRNAs in plant and animal development. Science, 2003, 301: 336–338CrossRefGoogle Scholar
  51. 51.
    Zhang B H, Pan X P, Anderson T A. microRNA: A new player in stem cells. J Cell Physiol, 2006, 209: 266–269CrossRefGoogle Scholar
  52. 52.
    Zhang B H, Pan X P, Anderson T A. Identification of 188 conserved maize micro RNAs and their targets. FEBS Lett, 2006, 580: 3753–3762CrossRefGoogle Scholar
  53. 53.
    Zhang BH, Pan X, Cobb G P, et al. Plant microRNA: A small regulatory molecule with big impact. Dev Biol, 2006, 289: 3–16CrossRefGoogle Scholar
  54. 54.
    Shephard E A, Palmer C N, Segall H J, et al. Quantification of cytochrome-P450 reductase gene-expression in human tissues. Arch Biochem Biophys, 1992, 294: 168–172CrossRefGoogle Scholar
  55. 55.
    Hubbard P A, Shen A L, Paschke R, et al. NADPH-cytochrome P450 oxidoreductase — Structural basis for hydride and electron transfer. J Biol Chem, 2001, 276: 29163–29170CrossRefGoogle Scholar
  56. 56.
    Sampedro J, Sieiro C, Revilla G, et al. Cloning and expression pattern of a gene encoding an alpha-xylosidase active against xyloglucan oligosaccharides from Arabidopsis. Plant Physiol, 2001, 126: 910–920CrossRefGoogle Scholar
  57. 57.
    Federspiel N A, Palm C J, Conway A B, et al. Submitted to the EMBL/GenBank/DDBJ databases, 1999Google Scholar
  58. 58.
    Yoshida R, Hobo T, Ichimura K, et al. ABA-activated SnRK2 protein kinase is required for dehydration stress signaling in Arabidopsis. Plant Cell Physiol, 2002, 43: 1473–1483CrossRefGoogle Scholar
  59. 59.
    Hrabak E M, Chan C W, Gribskov M, et al. The Arabidopsis CDPK-SnRK superfamily of protein kinases. Plant Physiol, 2003, 132: 666–680CrossRefGoogle Scholar
  60. 60.
    Mustilli A C, Merlot S, Vavasseur A, et al. Arabidopsis OST1 protein kinase mediates the regulation of stomatal aperture by abscisic acid and acts upstream of reactive oxygen species production. Plant Cell, 2002, 14: 3089–3099CrossRefGoogle Scholar
  61. 61.
    Rochester D E, Winer J A, Shah D M. The structure and expression of maize genes encoding the major heat-shock protein, HSP70. EMBO J, 1986, 5: 451–458Google Scholar

Copyright information

© Science in China Press and Springer Berlin Heidelberg 2010

Authors and Affiliations

  • JiangFeng Du
    • 1
    • 2
  • YongJun Wu
    • 2
  • XiaoFeng Fang
    • 2
  • JunXia Cao
    • 1
    • 2
  • Liang Zhao
    • 2
  • ShiHeng Tao
    • 1
    • 2
  1. 1.Bioinformatics CenterNorthwest Agricultural & Forestry UniversityYanglingChina
  2. 2.College of Life SciencesNorthwest Agricultural & Forestry UniversityYanglingChina

Personalised recommendations