Plant Molecular Biology

, Volume 68, Issue 1–2, pp 105–117 | Cite as

Lack of repeatable differential expression patterns between MON810 and comparable commercial varieties of maize

  • Anna Coll
  • Anna Nadal
  • Montserrat Palaudelmàs
  • Joaquima Messeguer
  • Enric Melé
  • Pere Puigdomènech
  • Maria PlaEmail author


The introduction of genetically modified organisms (GMO) in many countries follows strict regulations to assure that only products that have been safety tested in relation to human health and the environment are marketed. Thus, GMOs must be authorized before use. By complementing more targeted approaches, profiling methods can assess possible unintended effects of transformation. We used microarrays to compare the transcriptome profiles of widely commercialized maize MON810 varieties and their non-GM near-isogenic counterparts. The expression profiles of MON810 seedlings are more similar to those of their corresponding near-isogenic varieties than are the profiles of other lines produced by conventional breeding. However, differential expression of ∼1.7 and ∼0.1% of transcripts was identified in two variety pairs (AristisBt/Aristis and PR33P67/PR33P66) that had similar cryIA(b) mRNA levels, demonstrating that commercial varieties of the same event have different similarity levels to their near-isogenic counterparts without the transgene (note that these two pairs also show phenotypic differences). In the tissues, developmental stage and varieties analyzed, we could not identify any gene differentially expressed in all variety-pairs. However, a small set of sequences were differentially expressed in various pairs. Their relation to the transgenesis was not proven, although this is likely to be modulated by the genetic background of each variety.


GMO (Genetically Modified Organism) MON810 Maize Transcriptome Unintended effects Expression profile 



Complementary DNA


Certified reference material




European Bioinformatics Institute


European Food Safety Authority


European Union


Food and Agriculture Organization / World Health Organization


Genetically Modified


Genetically Modified Organism


Institute for Reference Materials and Measurements


International Service for the Acquisition of Agri-biotech Applications


messenger RNA


Organisation for Economic Co-operation and Development

real-time RT-PCR

Reverse transcription—real-time polymerase chain reaction


Robust Multichip Average


ribosomal RNA


Vegetative two-leaf stage



We specially thank Prof. E. Montesinos (INTEA, UdG) for critically reading of the manuscript. We thank R. Collado (UdG) and J.M. García-Cantalejo (Parque Científico de Madrid) for technical assistance; T. Esteve (CRAG), J. Serra and J. Salvia (E.E.A. Mas Badia) for valuable suggestions. This work was financially supported by the Spanish MEC project with ref. AGL2007-65903/AGR. AC received a studentship from the Generalitat de Catalunya (2005FI 00144).


  1. Baker JM, Hawkins ND, Ward JL, Lovegrove A, Napier JA, Shewry PR et al (2006) A metabolomic study of substantial equivalence of field-grown genetically modified wheat. Plant Biotechnol J 4:381–392. doi: 10.1111/j.1467-7652.2006.00197.x PubMedCrossRefGoogle Scholar
  2. Batista R, Saibo N, Lourenco T, Oliveira MM (2008) Microarray analyses reveal that plant mutagenesis may induce more transcriptomic changes than transgene insertion. Proc Natl Acad Sci USA 105:3640–3645. doi: 10.1073/pnas.0707881105 PubMedCrossRefGoogle Scholar
  3. Baudo MM, Lyons R, Powers S, Pastori GM, Edwards KJ, Holdsworth MJ et al (2006) Transgenesis has less impact on the transcriptome of wheat grain than conventional breeding. Plant Biotechnol J 4:369–380. doi: 10.1111/j.1467-7652.2006.00193.x PubMedCrossRefGoogle Scholar
  4. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57:289–300Google Scholar
  5. Bradford KJ, Van Deynze A, Gutterson N, Parrott W, Strauss SH (2005) Regulating transgenic crops sensibly: lessons from plant breeding, biotechnology and genomics. Nat Biotechnol 23:439–444. doi: 10.1038/nbt1084 PubMedCrossRefGoogle Scholar
  6. Catchpole GS, Beckmann M, Enot DP, Mondhe M, Zywicki B, Taylor J et al (2005) Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops. Proc Natl Acad Sci USA 102:14458–14462. doi: 10.1073/pnas.0503955102 PubMedCrossRefGoogle Scholar
  7. Cellini F, Chesson A, Colquhoun I, Constable A, Davies HV, Engel KH et al (2004) Unintended effects and their detection in genetically modified crops. Food Chem Toxicol 42:1089–1125. doi: 10.1016/j.fct.2004.02.003 PubMedCrossRefGoogle Scholar
  8. Chassy B, Egnin M, Gao Y, Glenn K, Kleter GA, Nestel P, Newell-McGloughlin M, Phipps RH, Shillito R (2008) Nutritional and safety assessments of foods and feeds nutritionally improved through biotechnology: case studies. Comp Rev Food Sci Food Safety 7:65–74CrossRefGoogle Scholar
  9. Dallas PB, Gottardo NG, Firth MJ, Beesley AH, Hoffmann K, Terry PA et al (2005) Gene expression levels assessed by oligonucleotide microarray analysis and quantitative real-time RT-PCR—how well do they correlate? BMC Genomics:6Google Scholar
  10. Dubouzet JG, Ishihara A, Matsuda F, Miyagawa H, Iwata H, Wakasa K (2007) Integrated metabolomic and transcriptomic analyses of high-tryptophan rice expressing a mutant anthranilate synthase alpha subunit. J Exp Bot 58:3309–3321. doi: 10.1093/jxb/erm179 PubMedCrossRefGoogle Scholar
  11. EFSA (2004) Guidance document of the scientific panel on genetically modified organisms for the risk assessment of genetically modified plants and derived food and feed. EFSA J 99:1–94Google Scholar
  12. El Ouakfaoui S, Miki B (2005) The stability of the Arabidopsis transcriptome in transgenic plants expressing the marker genes nptII and uidA. Plant J 41:791–800. doi: 10.1111/j.1365-313X.2005.02350.x PubMedCrossRefGoogle Scholar
  13. FAO/WHO (2001) Evalutaion of allergenicity of Genetically Modified Foods. In: Report of a Joint FAO/WHO Expert Consultation on Foods Derived from Biotechnology. Food and Agriculture Organisation of the United Nations. Accessed 15 Nov 2007
  14. FAO/WHO (2002) Report of the Third Session of the Codex Ad Hoc Intergovernmental Task Force on Foods Derived from Biotechnology (ALINORM 01/34). In: Codex Ad Hoc Intergovernmental Task Force on Foods Derived from Biotechnology. Food and Agriculture Organisation of the United Nations. Accessed 15 Nov 2007
  15. Filipecki M, Malepszy S (2006) Unintended consequences of plant transformation: a molecular insight. J Appl Genet 47:277–286PubMedGoogle Scholar
  16. Gregersen PL, Brinch-Pedersen H, Holm PB (2005) A microarray-based comparative analysis of gene expression profiles during grain development in transgenic and wild type wheat. Transgenic Res 14:887–905. doi: 10.1007/s11248-005-1526-y PubMedCrossRefGoogle Scholar
  17. Hernández M, Esteve T, Pla M (2005) Real-time PCR based methods for quantitative detection of barley, rice, sunflower and wheat. J Agric Food Chem 53:7003–7009. doi: 10.1021/jf050797j PubMedCrossRefGoogle Scholar
  18. Hernández M, Pla M, Esteve T, Prat S, Puigdomènech P, Ferrando A (2003) A specific real-time quantitative PCR detection system for event MON810 in maize YieldGard based on the 3′-transgene integration sequence. Transgenic Res 12:179–189. doi: 10.1023/A:1022979624333 PubMedCrossRefGoogle Scholar
  19. Herrero M, Ibanez E, Martin-Alvarez PJ, Cifuentes A (2007) Analysis of chiral amino acids in conventional and transgenic maize. Anal Chem 79:5071–5077. doi: 10.1021/ac070454f PubMedCrossRefGoogle Scholar
  20. Holst-Jensen A, De Loose M, Van den Eede G (2006) Coherence between legal requirements and approaches for detection of genetically modified organisms (GMOs) and their derived products. J Agric Food Chem 54:2799–2809. doi: 10.1021/jf052849a PubMedCrossRefGoogle Scholar
  21. Ioset JR, Urbaniak B, Ndjoko-Ioset K, Wirth J, Martin F, Gruissem W et al (2007) Flavonoid profiling among wild type and related GM wheat varieties. Plant Mol Biol 65:645–654. doi: 10.1007/s11103-007-9229-9 PubMedCrossRefGoogle Scholar
  22. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U et al (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4:249–264. doi: 10.1093/biostatistics/4.2.249 PubMedCrossRefGoogle Scholar
  23. Ishida Y, Saito H, Ohta S, Hiei Y, Komari T, Kumashiro T (1996) High efficiency transformation of maize (Zea mays L.) mediated by Agrobacterium tumefaciens. Nat Biotechnol 14:745–750. doi: 10.1038/nbt0696-745 PubMedCrossRefGoogle Scholar
  24. Jain M, Nijhawan A, Tyagi AK, Khurana JP (2006) Validation of housekeeping genes as internal control for studying gene expression in rice by quantitative real-time PCR. Biochem Biophys Res Commun 345:646–651. doi: 10.1016/j.bbrc.2006.04.140 PubMedCrossRefGoogle Scholar
  25. James C (2007) Global status of commercialized biotech/GM Crops: 2007. ISAAA Briefs 37. ISAAA, Ithaca, NYGoogle Scholar
  26. Jia JP, Fu JJ, Zheng J, Zhou X, Huai JL, Wang JH et al (2006) Annotation and expression profile analysis of 2073 full-length cDNAs from stress-induced maize (Zea mays L.) seedlings. Plant J 48:710–727. doi: 10.1111/j.1365-313X.2006.02905.x PubMedCrossRefGoogle Scholar
  27. Kok EJ, Keijer J, Kleter GA, Kuiper HA (2008) Comparative safety assessment of plant-derived foods. Regul Toxicol Pharmacol 50:98–113. doi: 10.1016/j.yrtph.2007.09.007 PubMedCrossRefGoogle Scholar
  28. Kok EJ, Kuiper HA (2003) Comparative safety assessment for biotech crops. Trends Biotechnol 21:439–444. doi: 10.1016/j.tibtech.2003.08.003 PubMedCrossRefGoogle Scholar
  29. Kristensen C, Morant M, Olsen CE, Ekstrom CT, Galbraith DW, Moller BL et al (2005) Metabolic engineering of dhurrin in transgenic Arabidopsis plants with marginal inadvertent effects on the metabolome and transcriptome. Proc Natl Acad Sci USA 102:1779–1784. doi: 10.1073/pnas.0409233102 PubMedCrossRefGoogle Scholar
  30. Larkin PJ, Scowcroft WR (1981) Somaclonal variation—a novel source of variability from cell cultures for plant improvement. Theor Appl Genet 60:197–214. doi: 10.1007/BF02342540 CrossRefGoogle Scholar
  31. Lehesranta SJ, Davies HV, Shepherd LV, Nunan N, McNicol JW, Auriola S et al (2005) Comparison of tuber proteomes of potato varieties, landraces, and genetically modified lines. Plant Physiol 138:1690–1699. doi: 10.1104/pp.105.060152 PubMedCrossRefGoogle Scholar
  32. Manetti C, Bianchetti C, Casciani L, Castro C, Di Cocco ME, Miccheli A et al (2006) A metabonomic study of transgenic maize (Zea mays) seeds revealed variations in osmolytes and branched amino acids. J Exp Bot 57:2613–2625. doi: 10.1093/jxb/erl025 PubMedCrossRefGoogle Scholar
  33. Millstone E, Brunner E, Mayer S (1999) Beyond ‘substantial equivalence’. Nature 401:525–526. doi: 10.1038/44006 PubMedCrossRefGoogle Scholar
  34. Murashige T, Skoog F (1962) A revised medium for rapid growth and bio assays with tobacco tissue cultures. Physiol Plant 15:473–497. doi: 10.1111/j.1399-3054.1962.tb08052.x CrossRefGoogle Scholar
  35. Parrott W (2005) The nature of change: towards sensible regulation of transgenic crops based on lessons from plant breeding, biotecnology and genomics. In: Proceedings of the 17th North American Biotechnology Council, Nahville, Tenn., June 27–29 2005. Accessed 25 March 2008
  36. Poerschmann J, Gathmann A, Augustin J, Langer U, Gorecki T (2005) Molecular composition of leaves and stems of genetically modified bt and near-isogenic non-bt maize—Characterization of lignin patterns. J Environ Qual 34:1508–1518. doi: 10.2134/jeq2005.0070 PubMedCrossRefGoogle Scholar
  37. Rasmussen R (2001) Quantification on the lightcycler. In: Meuer S, Wittwer C, Nakagawara K (eds) Rapid cycle real-time PCR, methods and applications. Springer-Verlag, BerlinGoogle Scholar
  38. Rodríguez-Lázaro D, Hernández M, Scortti M, Esteve T, Vázquez-Boland JA, Pla M (2004) Quantitative detection of Listeria monocytogenes and Listeria innocua by real-time PCR:assessment of hly, iap, and lin02483 targets and AmpliFluor technology. Appl Environ Microbiol 70:1366–1377. doi: 10.1128/AEM.70.3.1366-1377.2004 PubMedCrossRefGoogle Scholar
  39. Ruebelt MC, Lipp M, Reynolds TL, Schmuke JJ, Astwood JD, DellaPenna D et al (2006) Application of two-dimensional gel electrophoresis to interrogate alterations in the proteome of gentically modified crops, 3-Assessing unintended effects. J Agric Food Chem 54:2169–2177. doi: 10.1021/jf052358q PubMedCrossRefGoogle Scholar
  40. Saxena D, Stotzky G (2001) Bt corn has a higher lignin content than non-Bt corn. Am J Bot 88:1704–1706. doi: 10.2307/3558416 CrossRefGoogle Scholar
  41. Serra J, López A, Salvia J (2006) Varietats de blat de moro genèticament modificades (GM), amb resistència als barrinadors:productivitat i altres paràmetres agronòmics. Dossier Tecnic 10:13–18Google Scholar
  42. Shepherd LV, McNicol JW, Razzo R, Taylor MA, Davies HV (2006) Assessing the potential for unintended effects in genetically modified potatoes perturbed in metabolic and developmental processes. Targeted analysis of key nutrients and anti-nutrients. Transgenic Res 15:409–425. doi: 10.1007/s11248-006-0012-5 PubMedCrossRefGoogle Scholar
  43. Shewry PR, Baudo M, Lovegrove A, Powers S, Napier JA, Ward JL et al (2007) Are GM and conventionally bred cereals really different?. Trends Food Sci Technol 18:201–209. doi: 10.1016/j.tifs.2006.12.010 CrossRefGoogle Scholar
  44. Soitamo AJ, Piippo M, Allahverdiyeva Y, Battchikova N, Aro EM (2008) Light has a specific role in modulating Arabidopsis gene expression at low temperature. BMC Plant Biol doi: 10.1186/1471-2229-8-13
  45. van Rie J, Jansens S, Hofte H, Degheele D, Mallaert HV (1989) Specificity of Bacillus thuringiensis delta-endotoxins: importance of specific receptors on the brush border membrane of the mid-gut of target insects. Eur J Biochem 186:239–247. doi: 10.1111/j.1432-1033.1989.tb15201.x PubMedCrossRefGoogle Scholar
  46. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A et al (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3:1–12. doi: 10.1186/gb-2002-3-7-research0034 CrossRefGoogle Scholar
  47. Walia H, Wilson C, Wahid A, Condamine P, Cui X, Close TJ (2006) Expression analysis of barley (Hordeum vulgare L.) during salinity stress. Funct Integr Genomics 6:143–156. doi: 10.1007/s10142-005-0013-0 PubMedCrossRefGoogle Scholar
  48. Zhou J, Wang X, Jiao Y, Qin Y, Liu X, He K et al (2007) Global genome expression analysis of rice in response to drought and high-salinity stresses in shoot, flag leaf, and panicle. Plant Mol Biol 63:591–608. doi: 10.1007/s11103-006-9111-1 PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Anna Coll
    • 1
  • Anna Nadal
    • 2
  • Montserrat Palaudelmàs
    • 3
  • Joaquima Messeguer
    • 3
  • Enric Melé
    • 3
  • Pere Puigdomènech
    • 2
  • Maria Pla
    • 1
    Email author
  1. 1.Institut de Tecnologia Agroalimentària (INTEA)Universitat de GironaGironaSpain
  2. 2.Departament Genètica Molecular, Centre de Recerca en Agrigenòmica CSIC-IRTA-UABBarcelonaSpain
  3. 3.Departament Genètica Vegetal, Centre de Recerca en AgrigenòmicaCSIC-IRTA-UABBarcelonaSpain

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