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Prediction of sorghum miRNAs and their targets with computational methods

  • Articles
  • Bioinformatics
  • Published:
Chinese Science Bulletin

Abstract

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.

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References

  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: 1482–1495

    Article  Google Scholar 

  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: 1911–1917

    Article  Google Scholar 

  3. Bartel D P. microRNAs: Genomics, biogenesis, mechanism, and function. Cell, 2007, 131: 11–29

    Google Scholar 

  4. Hunter C, Poethig R S. Missing links: miRNAs and plant development. Curr Opin Genet Dev, 2003, 13: 372–378

    Article  Google Scholar 

  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: 516–523

    Article  Google Scholar 

  6. Kidner C A, Martienssen R A. Macro effects of microRNAs in plants. Trends Genet, 2003, 19: 13–16

    Article  Google Scholar 

  7. Murchison E P, Hannon G J. MiRNAs on the move: miRNA biogenesis and the RNAi machinery. Curr Opin Cell Biol, 2004, 16: 223–229

    Article  Google Scholar 

  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: 843–854

    Article  Google Scholar 

  9. Lagos-Quintana M, Rauhut R, Lendeckel W, et al. Identification of novel genes coding for small expressed RNAs. Science, 2001, 294: 853–858

    Article  Google Scholar 

  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: 858–862

    Article  Google Scholar 

  11. Khvorova A, Reynolds A, Jayasena S D. Functional siRNAs and miRNAs exhibit strand bias. Cell, 2007, 131: 41–49

    Google Scholar 

  12. Hake S. MicroRNAs: A role in plant development. Curr Biol, 2003, 13: R851–R852

    Article  Google Scholar 

  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: 515–524

    Article  Google Scholar 

  14. Doench J G, Petersen C P, Sharp P A. siRNAs can function as miRNAs. Gene Dev, 2003, 17: 438–442

    Article  Google Scholar 

  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: 9779–9784

    Article  Google Scholar 

  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: 855–862

    Article  Google Scholar 

  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: 671–680

    Article  Google Scholar 

  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: 215–225

    Article  Google Scholar 

  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: 637–646

    Article  Google Scholar 

  20. Llave C, Kasschau K D, Rector M A, et al. Endogenous and silencing-associated small RNAs in plants. Plant Cell, 2002, 14: 1605–1619

    Article  Google Scholar 

  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: 1484–1495

    Article  Google Scholar 

  22. Palatnik J F, Allen E, Wu X, et al. Control of leaf morphogenesis by microRNAs. Nature, 2003, 425: 257–263

    Article  Google Scholar 

  23. Rhoades M W, Reinhart B J, Lim L P, et al. Prediction of plant microRNA targets. Cell, 2002, 110: 513–520

    Article  Google Scholar 

  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: 1124–1128

    Article  Google Scholar 

  25. Grad Y, Aach J, Hayes G D, et al. Computational and experimental identification of C. elegans microRNAs. Mol Cell, 2003, 11: 1253–1263

    Article  Google Scholar 

  26. Lee R C, Ambros V. An extensive class of small RNAs in Caenorhabditis elegans. Science, 2001, 294: 862–864

    Article  Google Scholar 

  27. Mourelatos Z, Dostie J, Paushkin S, et al. MiRNPs: A novel class of ribonucleoproteins containing numerous microRNAs. Gene Dev, 2002, 16: 720–728

    Article  Google Scholar 

  28. Reinhart B J, Weinstein E G, Rhoades M W, et al. microRNAs in plants. Gene Dev, 2002, 16: 1616–1626

    Article  Google Scholar 

  29. Yoon S, Micheli G D. Computational identification of microRNAs and their targets. Birth Defects Res, 2006, 78: 118–128

    Article  Google Scholar 

  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: R65

    Article  Google Scholar 

  31. Adai A, Johnson C, Mlotshwa S, et al. Computational prediction of miRNAs in Arabidopsis thaliana. Genome Res, 2005, 15: 78–91

    Article  Google Scholar 

  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: 787–799

    Article  Google Scholar 

  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: 336–360

    Article  Google Scholar 

  34. Zhang B H, Wang Q, Wang K, et al. Identification of cotton microRNAs and their targets. Gene, 2007, 397: 26–37

    Article  Google Scholar 

  35. Lai E C, Tomancak P, Williams R W, et al. Computational identification of Drosophila microRNA genes. Genome Biol, 2003, 4:R42

    Article  Google Scholar 

  36. Rajewsky N, Socci N D. Computational identification of microRNA targets. Dev Biol, 2004, 267: 529–535

    Article  Google Scholar 

  37. Altschul S F, Gish W, Miller W, et al. Basic local alignment search tool. J Mol Biol, 1990, 215: 403–410

    Google Scholar 

  38. Dezulian T, Remmert M, Palatnik J F, et al. Identification of plant microRNA homologs. Bioinformatics, 2006, 22: 359–360

    Article  Google Scholar 

  39. Sunkar R, Jagadeeswaran G. In silico identification of conserved microRNAs in large number of diverse plant species. BMC Plant Biol, 2008, 8: 37

    Article  Google Scholar 

  40. Zhang B, Pan X, Cannon C H, et al. Conservation and divergence of plant microRNA genes. Plant J, 2006, 46: 243–259

    Article  Google Scholar 

  41. Zhang Y. Mi RU: An automated plant miRNA target prediction server. Nucleic Acids Res, 2005, 33(Web Server issue): W701–W704

    Article  Google Scholar 

  42. Giegerich R, Voss B, Rehmsmeier M. Abstract shapes of RNA. Nucleic Acids Res, 2004, 32: 4843–4851

    Article  Google Scholar 

  43. Reeder J, Giegerich R. Consensus shapes: An alternative to the Sankoff algorithm for RNA consensus structure prediction. Bioinformatics, 2005, 21: 3516–3523

    Article  Google Scholar 

  44. Steffen P, Voss B, Rehmsmeier M, et al. RNAshapes: An integrated RNA analysis package based on abstract shapes. Bioinformatics, 2006, 22: 500–503

    Article  Google Scholar 

  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: 246–254

    Article  Google Scholar 

  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: e13

    Article  Google Scholar 

  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: 277–284

    Article  Google Scholar 

  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: 2124–2129

    Google Scholar 

  49. Popea R K, Pestonjamaspa K N, Smithb K P, et al. Cloning, characterization, and chromosomal localization of human supervillin (SVIL). Genomics, 1998, 52: 342–351

    Article  Google Scholar 

  50. Carrington J C, Ambros V. Role of microRNAs in plant and animal development. Science, 2003, 301: 336–338

    Article  Google Scholar 

  51. Zhang B H, Pan X P, Anderson T A. microRNA: A new player in stem cells. J Cell Physiol, 2006, 209: 266–269

    Article  Google Scholar 

  52. Zhang B H, Pan X P, Anderson T A. Identification of 188 conserved maize micro RNAs and their targets. FEBS Lett, 2006, 580: 3753–3762

    Article  Google Scholar 

  53. Zhang BH, Pan X, Cobb G P, et al. Plant microRNA: A small regulatory molecule with big impact. Dev Biol, 2006, 289: 3–16

    Article  Google Scholar 

  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: 168–172

    Article  Google Scholar 

  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: 29163–29170

    Article  Google Scholar 

  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: 910–920

    Article  Google Scholar 

  57. Federspiel N A, Palm C J, Conway A B, et al. Submitted to the EMBL/GenBank/DDBJ databases, 1999

  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: 1473–1483

    Article  Google Scholar 

  59. Hrabak E M, Chan C W, Gribskov M, et al. The Arabidopsis CDPK-SnRK superfamily of protein kinases. Plant Physiol, 2003, 132: 666–680

    Article  Google Scholar 

  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: 3089–3099

    Article  Google Scholar 

  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: 451–458

    Google Scholar 

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Authors and Affiliations

  1. Bioinformatics Center, Northwest Agricultural & Forestry University, Yangling, 712100, China

    JiangFeng Du, JunXia Cao & ShiHeng Tao

  2. College of Life Sciences, Northwest Agricultural & Forestry University, Yangling, 712100, China

    JiangFeng Du, YongJun Wu, XiaoFeng Fang, JunXia Cao, Liang Zhao & ShiHeng Tao

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  1. JiangFeng Du
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Correspondence to ShiHeng Tao.

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Du, J., Wu, Y., Fang, X. et al. Prediction of sorghum miRNAs and their targets with computational methods. Chin. Sci. Bull. 55, 1263–1270 (2010). https://doi.org/10.1007/s11434-010-0035-4

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  • DOI: https://doi.org/10.1007/s11434-010-0035-4

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