, Volume 5, Issue 2, pp 239–252 | Cite as

Metabolic profiling of transgenic wheat over-expressing the high-molecular-weight Dx5 glutenin subunit

  • Boryana S. Stamova
  • Ute Roessner
  • Suganthi Suren
  • Debbie Laudencia-Chingcuanco
  • Antony Bacic
  • Diane M. Beckles
Original Article


The primary aim of this work was to evaluate potential changes in the metabolic network of transgenic wheat grain over-expressing the high-molecular-weight (HMW) glutenin Dx5-subunit gene. GC–MS and multivariate analyses were used to compare the metabolite profiles of developing caryopses of two independently transformed lines over-expressing Dx5 and another two independently transformed lines expressing only the selectable-marker gene (controls). Developing grain at 7, 14 and 21 Days Post-Anthesis (DPA) was studied to observe differences in metabolically active tissues. There was no distinction between the Dx5 transformants and the controls by principal component analysis (PCA) suggesting that their metabolite compositions were similar. Most changes in metabolite levels and starch occurred at 14 DPA but tapered off by 21 DPA. Only 3 metabolites, guanine, 4-hydroxycinnamic acid and Unknown 071306a, were altered due to Dx5 expression after correction for false discovery rates (P < 0.0005). However, discriminant function analysis (DFA) and correlative analyses of the metabolites showed that Dx5-J, which had the highest level of Dx5 protein in ripe caryopses, could be distinguished from the other genotypes. The second aim of this work was to determine the influence of gene transformation on the metabolome. Cross-comparison of the transformed controls to each other, and to the Dx5 genotypes showed that approximately 50% of the metabolic changes in the Dx5 genotypes were potentially due to variations arising from gene transformation and not from the expression of the Dx5-gene per se. This study therefore suggests the extent to which plant transformation by biolistics can potentially influence phenotype.


Transgenic wheat Storage protein GC–MS Multivariate analysis 



We thank Dr. Ann Blechl for the generous gift of the transgenic seeds and information on the lines. We are indebted to Dr. Ron Haff, USDA-ARS Albany, for help with SAS Software and to Nick Petkov and Dobromir Tzankov for technical assistance. We thank Drs. Belinda Martineau and Olin Anderson for comments on the manuscript. This work was supported by grants to the Australian Centre for Plant Functional Genomics from the Grains Research and Development Corporation, the Australian Research Council, the South Australian Government, the University of Adelaide and the University of Melbourne (SS, UR, AB); and to Metabolomics Australia provided by the Australian Government through the National Collaborative Research Infrastructure Strategy (UR, AB), USDA-ARS CRIS Project 5325-21000-011 (DLC, BS) and National Science Foundation Grant: NSF-MCB-0620001 (DMB).

Supplementary material

11306_2008_146_MOESM1_ESM.ppt (102 kb)
11306_2008_146_MOESM2_ESM.doc (453 kb)


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Boryana S. Stamova
    • 1
    • 2
    • 3
    • 4
  • Ute Roessner
    • 5
  • Suganthi Suren
    • 5
  • Debbie Laudencia-Chingcuanco
    • 2
  • Antony Bacic
    • 5
  • Diane M. Beckles
    • 1
  1. 1.Department of Plant Sciences-Mail Stop 3University of California-DavisDavisUSA
  2. 2.Genomics and Gene Discovery UnitUSDA-AlbanyAlbanyUSA
  3. 3.Genetics Resources Conservation ProgramUniversity of California-DavisDavisUSA
  4. 4.M.I.N.D Institute, Department of NeurologySchool of Medicine, University of California Medical CenterSacramento USA
  5. 5.Australian Centre for Plant Functional Genomics and Metabolomics AustraliaSchool of Botany, University of Melbourne ParkvilleAustralia

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