Functional & Integrative Genomics

, Volume 10, Issue 3, pp 383–392 | Cite as

Meta-analysis of transcripts associated with race-specific resistance to stripe rust in wheat demonstrates common induction of blue copper-binding protein, heat-stress transcription factor, pathogen-induced WIR1A protein, and ent-kaurene synthase transcripts

  • Tristan E. CoramEmail author
  • Xueling Huang
  • Gangming Zhan
  • Matthew L. Settles
  • Xianming Chen
Original Paper


Resistance to stripe rust in wheat is a preferred method of disease prevention. Race-specific all-stage resistance usually provides complete protection; thus an understanding of the molecular control of race-specific resistance is important. To build on previous studies of race-specific resistance controlled by the Yr5 gene, this study reports the construction and use of a custom oligonucleotide microarray to perform a meta-analysis of the transcriptional response involved in race-specific resistance conferred by Yr1, Yr5, Yr7, Yr8, Yr9, Yr10, Yr15, and Yr17. By profiling the response of eight resistance genes in a common background, we identified 28 transcripts significantly involved in the resistance phenotype across all genotypes. The most significant of these were annotated as blue copper-binding protein, heat-stress transcription factor, pathogen-induced WIR1A protein, and ent-kaurene synthase transcripts. Unique transcripts significant in each genotype were also identified, which highlighted some transcriptional events specific to certain genotypes. The approach was effective in narrowing down the list of candidate genes in comparison to studying individual genotypes. Annotation revealed key gene expression events involved in race-specific resistance. The results confirm the activity of known R-gene-mediated pathway race-specific resistance, including an oxidative burst that likely contributes to a hypersensitive response, as well as pathogenesis-related protein expression and activity of the phenylpropanoid pathway. However, several identified transcripts remained unknown and may prove interesting candidates for further characterization.


Wheat Stripe rust Microarray Gene expression Resistance 



This research was supported in part by the US Department of Agriculture, Agricultural Research Service (project no. 5348-22000-014-00D), USDA-ARS Postdoctoral Program, and Washington Wheat Commission (project no. 13C-3061-3923). PPNS no. XXXX, Department of Plant Pathology, College of Agricultural, Human, and Natural Resources Research Center, project numbers WNP00823. The authors acknowledge Lisa Orfe and Dr. Douglas Call for printing the microarrays.

Supplementary material

10142_2009_148_MOESM1_ESM.doc (125 kb)
Supplementary Table The transcripts identified as uniquely significant (p < 0.10, fold change>2.0) for the incompatible interaction of each genotype in reference to mock-inoculated controls. Note that no unique transcripts were identified for genotypes Yr5 and Yr9. Functional categories were based on the Munich Information Center for Protein Sequence classifications and putative function shows the best significant BLASTX database hit from HarvEST. (DOC 125 kb)


  1. Barrett T, Suzek TO, Troup DB, Wilhite SE, Ngau WC, Ledoux P, Rudnev D, Lash AE, Fujibuchi W, Edgar R (2005) NCBI GEO: mining millions of expression profiles—database and tools. Nucleic Acids Res 33:D562–D566CrossRefPubMedGoogle Scholar
  2. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B 57:289–300Google Scholar
  3. Bethke PC, Fath A, Spiegel YN, Hwang YS, Jones RL (2002) Abscisic acid, gibberellin and cell viability in cereal aleurone. Euphytica 126:3–11CrossRefGoogle Scholar
  4. Bindschedler L, Dewdney J, Blee K, Stone J, Asai T, Plotnikov J, Denoux C, Hayes T, Gerrish C, Davies D, Ausubel F, Bolwell G (2006) Peroxidase-dependent apoplastic oxidative burst in Arabidopsis required for pathogen resistance. Plant J 47:851–863CrossRefPubMedGoogle Scholar
  5. Boddu J, Cho S, Kruger W, Muehlbauer G (2006) Transcriptome analysis of the barley–Fusarium graminearum interaction. Mol Plant-Microbe Interact 19:407–417CrossRefPubMedGoogle Scholar
  6. Boddu J, Cho S, Muehlbauer G (2007) Transcriptome analysis of trichothecene-induced gene expression in barley. Mol Plant–Microbe Interact 20:1364–1375CrossRefPubMedGoogle Scholar
  7. Bouche N, Yellin A, Snedden W, Fromm H (2005) Plant-specific calmodulin-binding proteins. Annu Rev Plant Biol 56:435–466CrossRefPubMedGoogle Scholar
  8. Buckley M (2002) The spot user’s guide. CSIRO Mathematical and Information Sciences.
  9. Bull J, Mauch F, Hertig C, Rebmann G, Dudler R (1992) Sequence and expression of a wheat gene that encodes a novel protein associated with pathogen defense. Mol Plant–Microbe Interact 5:516–519PubMedGoogle Scholar
  10. Chen X (2005) Epidemiology and control of stripe rust (Puccinia striiformis f. sp. tritici) on wheat. Can J Plant Pathol 27:314–337Google Scholar
  11. Cliftona R, Millara A, Whelan J (2006) Alternative oxidases in Arabidopsis: a comparative analysis of differential expression in the gene family provides new insights into function of non-phosphorylating bypasses. BBA-Bioenergetics 1757:730–741CrossRefGoogle Scholar
  12. Coram T, Settles M, Chen X (2008a) Transcriptome analysis of high-temperature adult-plant resistance conditioned by Yr39 during the wheat–Puccinia striiformis f. sp. tritici interaction. Mol Plant Pathol 9:479–493CrossRefPubMedGoogle Scholar
  13. Coram T, Wang M, Chen X (2008b) Transcriptome analysis of the wheat–Puccinia striiformis f. sp. tritici interaction. Mol Plant Pathol 9:157–169CrossRefPubMedGoogle Scholar
  14. Dangl J, Jones J (2001) Plant pathogens and integrated defense responses to infection. Nature 411:826–833CrossRefPubMedGoogle Scholar
  15. DeYoung BJ, Innes RW (2006) Plant NBS-LRR proteins in pathogen sensing and host defense. Nat Immunol 7:1243–1249. doi: 10.1038/ni1410 CrossRefPubMedGoogle Scholar
  16. Dittrich H, Kutchan T (1991) Molecular cloning, expression, and induction of berberine bridge enzyme, an enzyme essential to the formation of benzophenanthridine alkaloids in the response of plants to pathogenic attack. Proc Natl Acad Sci U S A 88:9969–9973CrossRefPubMedGoogle Scholar
  17. Dixon R, Achnine L, Kota P, Lui C, Reddy M, Wang L (2002) The phenylpropanoid pathway and plant defence—a genomics perspective. Mol Plant Pathol 3:371–390CrossRefPubMedGoogle Scholar
  18. Gautier L, Cope L, Bolstad B, Irizarry R (2004) affy-analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20:307–315CrossRefPubMedGoogle Scholar
  19. Gentleman R, Carey V, Bates D, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn TWH, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini A, Sawitzki G, Smith C, Smyth G, Tierney L, Yang J, Zhang J (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5:R80CrossRefPubMedGoogle Scholar
  20. Gjetting T, Hagedorn P, Schweizer P, Thordal-Christensen H, Carver T, Lyngkjær M (2007) Single-cell transcript profiling of barley attacked by the powdery mildew fungus. Mol Plant–Microbe Interact 20:235–246CrossRefPubMedGoogle Scholar
  21. Greenberg J (1997) Programmed cell death in plant–pathogen interactions. Annu Rev Plant Phys 48:525–545CrossRefGoogle Scholar
  22. Hammond-Kosack K, Parker J (2003) Deciphering plant–pathogen communication: fresh perspectives for molecular resistance breeding. Curr Opin Biotech 14:177–193CrossRefPubMedGoogle Scholar
  23. Hulbert S, Bai J, Fellers J, Pacheco M, Bowden R (2007) Gene expression patterns in near isogenic lines for wheat rust resistance gene Lr34/Yr18. Phytopathology 97:1083–1093CrossRefPubMedGoogle Scholar
  24. Jansen C, Korell M, Eckey C, Biedenkopf D, Kogel K (2005) Identification and transcriptional analysis of powdery-mildew induced barley genes. Plant Sci 168:373–380CrossRefGoogle Scholar
  25. Jasinski M, Ducos E, Martinoia E, Boutry M (2003) The ATP-binding cassette transporters: structure, function, and gene family comparison between rice and Arabidopsis. Plant Physiol 131:1169–1177CrossRefPubMedGoogle Scholar
  26. Kong L, Anderson J, Ohm H (2005) Induction of wheat defense and stress-related genes in response to Fusarium graminearum. Genome 48:29–40CrossRefPubMedGoogle Scholar
  27. Lamb C, Dixon R (1997) The oxidative burst in plant disease response. Annu Rev Plant Phys 48:251–275CrossRefGoogle Scholar
  28. Marcel T, Varshney R, Barbieri M, Jafary H, de Kock M, Graner A, Niks R (2007) A high-density consensus map of barley to compare the distribution of QTLs for partial resistance to Puccinia hordei and of defence gene homologues. Theor Appl Genet 114:487–500CrossRefPubMedGoogle Scholar
  29. Mohammadi M, Kazemi H (2002) Changes in peroxidase and polyphenol oxidase activities in susceptible and resistant wheat heads inoculated with Fusarium graminearum and induced resistance. Plant Sci 162:491–498CrossRefGoogle Scholar
  30. Narusaka Y, Narusaka M, Seki M, Umezawa T, Ishida J, Nakajima M, Enju A, Shinozaki K (2004) Crosstalk in the responses to abiotic and biotic stresses in Arabidopsis: analysis of gene expression in cytochrome P450 gene superfamily by cDNA microarray. Plant Mol Biol 55:327–342CrossRefPubMedGoogle Scholar
  31. Neu C, Keller B, Feuillet C (2003) Cytological and molecular analysis of the Hordeum vulgare–Puccinia triticina nonhost interaction. Mol Plant–Microbe Interact 16:626–633CrossRefPubMedGoogle Scholar
  32. Pritsch C, Muehlbauer G, Bushnell W, Somers D, Vance C (2000) Fungal development and induction of defense response genes during early infection of wheat spikes by Fusarium graminearum. Mol Plant–Microbe Interact 13:159–169CrossRefPubMedGoogle Scholar
  33. R Development Core Team (2006) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  34. Richards K, Schott E, Sharma Y, Davis K, Gardner R (1998) Aluminum induces oxidative stress genes in Arabidopsis thaliana. Plant Physiol 116:409–418CrossRefPubMedGoogle Scholar
  35. Sculer M, Werck-Reichhart D (2003) Functional genomics of P450s. Annu Rev Plant Biol 54:629–667CrossRefGoogle Scholar
  36. Smyth G (2005) Limma: linear models for microarray data. In: Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W (eds) Bioinformatics and computational biology solutions using R and bioconductor. Springer, New York, pp 397–420CrossRefGoogle Scholar
  37. Van Ooijen G, Mayr G, Kasiem MMA, Albrecht M, Cornelissen BJC, Takken FLW (2008) Structure–function analysis of the NB-ARC domain of plant disease resistance proteins. J Exp Bot 59:1383–1397. doi: 10.1093/jxb/ern045 CrossRefPubMedGoogle Scholar
  38. Wellings C, Singh R, McIntosh R, Pretorius Z (2004) The development and application of near isogenic lines for the wheat stripe (yellow) rust pathosystem. In 11th International Cereal Rusts and Powdery Mildew Conference. John Innes Centre, Norwich, p 39Google Scholar
  39. Yang KY, Im YJ, Chung GC, Cho BH (2002) Activity of the Arabidopsis blue copper-binding protein gene promoter in transgenic tobacco plants upon wounding. Plant Cell Rep 20:987–991. doi: 10.1007/s00299-002-0436-1 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Tristan E. Coram
    • 1
    Email author
  • Xueling Huang
    • 2
    • 5
  • Gangming Zhan
    • 2
    • 5
  • Matthew L. Settles
    • 3
  • Xianming Chen
    • 2
    • 4
  1. 1.USDA-ARSPlant Science Research UnitRaleighUSA
  2. 2.Department of Plant PathologyWashington State UniversityPullmanUSA
  3. 3.Department of Molecular BiosciencesWashington State UniversityPullmanUSA
  4. 4.USDA-ARSWheat Genetics, Quality, Physiology, and Disease Research UnitPullmanUSA
  5. 5.College of Plant ProtectionNorthwest A&F UniversityXianyangChina

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