Plant Molecular Biology

, Volume 73, Issue 3, pp 349–362 | Cite as

Natural variation explains most transcriptomic changes among maize plants of MON810 and comparable non-GM varieties subjected to two N-fertilization farming practices

  • Anna Coll
  • Anna Nadal
  • Rosa Collado
  • Gemma Capellades
  • Mikael Kubista
  • Joaquima Messeguer
  • Maria PlaEmail author


The introduction of genetically modified organisms (GMO) in many countries follows strict regulations to ensure that only safety-tested products are marketed. Over the last few years, targeted approaches have been complemented by profiling methods to assess possible unintended effects of transformation. Here we used a commercial (Affymertix) microarray platform (i.e. allowing assessing the expression of ~1/3 of the genes of maize) to evaluate transcriptional differences between commercial MON810 GM maize and non-transgenic crops in real agricultural conditions, in a region where about 70% of the maize grown was MON810. To consider natural variation in gene expression in relation to biotech plants we took two common MON810/non-GM variety pairs as examples, and two farming practices (conventional and low-nitrogen fertilization). MON810 and comparable non-GM varieties grown in the field have very low numbers of sequences with differential expression, and their identity differs among varieties. Furthermore, we show that the differences between a given MON810 variety and the non-GM counterpart do not appear to depend to any major extent on the assayed cultural conditions, even though these differences may slightly vary between the conditions. In our study, natural variation explained most of the variability in gene expression among the samples. Up to 37.4% was dependent upon the variety (obtained by conventional breeding) and 31.9% a result of the fertilization treatment. In contrast, the MON810 GM character had a very minor effect (9.7%) on gene expression in the analyzed varieties and conditions, even though similar cryIA(b) expression levels were detected in the two MON810 varieties and nitrogen treatments. This indicates that transcriptional differences of conventionally-bred varieties and under different environmental conditions should be taken into account in safety assessment studies of GM plants.


GMO (Genetically Modified Organism) MON810 Maize Nitrogen stress Transcriptome Unintended effects Agricultural field Natural variation 



Complementary DNA


Certified reference material




European Bioinformatics Institute


European Union


Genetically Modified


Genetically Modified Organism


Glutamine Synthase 2


International Service for the Acquisition of Agri-biotech Applications


Messenger RNA




Organisation for Economic Co-operation and Development


Principal Component Analysis


Reverse transcription—real-time polymerase chain reaction


Robust Multichip Average


Ribosomal RNA


Self-organizing Map


Vegetative six-leaf stage


Vegetative eight-leaf stage


Vegetative Tasseling



We thank J. M. García-Cantalejo (Parque Científico de Madrid) for technical assistance; J. Serra (E. E. A. Mas Badia), E. Melé and M. Palaudelmàs (CRAG), and Ales Tichopad (Prague) for valuable suggestions; and Prof. P. Puigdomènech (CRAG) for critical reading of the manuscript. 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).

Supplementary material

11103_2010_9624_MOESM1_ESM.doc (32 kb)
Supplementary material 1 (DOC 31 kb)
11103_2010_9624_MOESM2_ESM.doc (30 kb)
Supplementary material 2 (DOC 31 kb)
11103_2010_9624_MOESM3_ESM.doc (28 kb)
Supplementary material 3 (DOC 28 kb)


  1. Baker JM, Hawkins ND, Ward JL, Lovegrove A, Napier JA, Shewry PR, Beale MH (2006) A metabolomic study of substantial equivalence of field-grown genetically modified wheat. Plant Biotechnol J 4:381–392CrossRefPubMedGoogle 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 105:3640–3645CrossRefPubMedGoogle Scholar
  3. Baudo MM, Lyons R, Powers S, Pastori GM, Edwards KJ, Holdsworth MJ, Shewry PR (2006) Transgenesis has less impact on the transcriptome of wheat grain than conventional breeding. Plant Biotechnol J 4:369–380CrossRefPubMedGoogle Scholar
  4. Baudo MM, Powers SJ, Mitchell RA, Shewry PR (2009) Establishing substantial equivalence: transcriptomics. Methods Mol Biol 478:247–272CrossRefPubMedGoogle Scholar
  5. Beale MH, Ward JL, Baker JM (2009) Establishing substantial equivalence: metabolomics. Methods Mol Biol 478:289–303CrossRefPubMedGoogle Scholar
  6. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and poweful approach to multiple testing. J Royal Stat Soc B 57:289–300Google Scholar
  7. Bi YM, Wang RL, Zhu T, Rothstein SJ (2007) Global transcription profiling reveals differential responses to chronic nitrogen stress and putative nitrogen regulatory components in Arabidopsis. BMC Genomics 8:281CrossRefPubMedGoogle Scholar
  8. Blackmer TM, Schepers JS (1995) Use of chlorophyll meter to monitor nitrogen status and schedule fertigation for corn. J Prod Agric 8:56–60Google Scholar
  9. 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 23:439–444CrossRefPubMedGoogle Scholar
  10. 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 102:14458–14462CrossRefPubMedGoogle Scholar
  11. 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 42:1089–1125CrossRefPubMedGoogle Scholar
  12. Chassy B, Egnin M, Gao Y, Glenn K, Kleter GA, Nestel P, Newell-McGloughlin M, 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
  13. Cheng KC, Beaulieu J, Iquira E, Belzile FJ, Fortin MG, Stromvik MV (2008) Effect of transgenes on global gene expression in soybean is within the natural range of variation of conventional cultivars. J Agric Food Chem 56:3057–3067CrossRefPubMedGoogle Scholar
  14. Coll A, Nadal A, Palaudelmas M, Messeguer J, Mele E, Puigdomenech P, Pla M (2008) Lack of repeatable differential expression patterns between MON810 and comparable commercial varieties of maize. Plant Mol Biol 68:105–117CrossRefPubMedGoogle Scholar
  15. Coll A, Nadal A, Collado R, Capellades G, Messeguer J, Mele E, Palaudelmas M, Pla M (2009) Gene expression profiles of MON810 and comparable non-GM maize varieties cultured in the field are more similar than are those of conventional lines. Transgenic Res 18:801–808CrossRefPubMedGoogle Scholar
  16. Coruzzi G (2003) Primary N-assimilation into amino acids in Arabidopsis. In: Meyerowitz EM, Rockville MD (eds) The arabidopsis book. American Society of Plant Biologists. doi:  10.1199/tab.0010,
  17. Dallas PB, Gottardo NG, Firth MJ, Beesley AH, Hoffmann K, Terry PA, Freitas JR, Boag JM, Cummings AJ, Kees UR (2005) Gene expression levels assessed by oligonucleotide microarray analysis and quantitative real-time RT-PCR—how well do they correlate? BMC Genomics 6:59CrossRefPubMedGoogle Scholar
  18. Di Carli M, Villani ME, Renzone G, Nardi L, Pasquo A, Franconi R, Scaloni A, Benvenuto E, Desiderio A (2009) Leaf proteome analysis of transgenic plants expressing antiviral antibodies. J Proteome Res 8:838–848CrossRefPubMedGoogle Scholar
  19. Domingo F, Díaz-Pereira E, Mayol F, Lasa B, Lópied H, Irañeta I, Maturano M, Roselló-Martínez A (2006) FENIMAR, a tool for nitrogen recomendation at field scale in irrigated maize. Biblioteca Fragmenta Agronomica 11:383–385Google Scholar
  20. 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 58:3309–3321CrossRefPubMedGoogle Scholar
  21. El Ouakfaoui S, Miki B (2005) The stability of the Arabidopsis transcriptome in transgenic plants expressing the marker genes nptII and uidA. Plant J 41:791–800CrossRefPubMedGoogle Scholar
  22. Frink CR, Waggoner PE, Ausubel JH (1999) Nitrogen fertilizer: retrospect and prospect. Proc Natl Acad Sci USA 96:1175–1180CrossRefPubMedGoogle Scholar
  23. 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 14:887–905CrossRefPubMedGoogle Scholar
  24. Hawkins JA, Sawyer JE, Barker DW, Lundva JP (2009) Using relative chlorophyll meter values to determine nitrogen application rates for corn. Agron J 99:1034–1040CrossRefGoogle Scholar
  25. 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 12:179–189CrossRefPubMedGoogle Scholar
  26. 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 53:7003–7009CrossRefPubMedGoogle Scholar
  27. Herrero M, Ibáñez E, Martín-Álvarez PJ, Cifuentes A (2007) Analysis of chiral amino acids in conventional and transgenic maize. Anal Chem 79:5071–5077CrossRefPubMedGoogle Scholar
  28. Hoekenga OA (2008) Using metabolomics to estimate unintended effects in transgenic crop plants: problems, promises, and opportunities. J Biomol Tech 19:159–166PubMedGoogle Scholar
  29. Howarth JR, Parmar S, Jones J, Shepherd CE, Corol DI, Galster AM et al (2008) Co-ordinated expression of amino acid metabolism in response to N and S deficiency during wheat grain filling. J Exp Bot 59:3675–3689CrossRefPubMedGoogle Scholar
  30. Ioset JR, Urbaniak B, Ndjoko-Ioset K, Wirth J, Martin F, Gruissem W, Hostettmann K, Sautter C (2007) Flavonoid profiling among wild type and related GM wheat varieties. Plant Mol Biol 65:645–654CrossRefPubMedGoogle Scholar
  31. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4:249–264CrossRefPubMedGoogle Scholar
  32. James C (2008) Global status of commercialized biotech/GM Crops: 2008. ISAAA Briefs 39. ISAAA, IthacaGoogle Scholar
  33. Kok EJ, Keijer J, Kleter GA, Kuiper HA (2008) Comparative safety assessment of plant-derived foods. Regul Toxicol Pharmacol 50:98–113CrossRefPubMedGoogle Scholar
  34. König A, Cockburn A, Crevel RW, Debruyne E, Grafstroem R, Hammerling U et al (2004) Assessment of the safety of foods derived from genetically modified (GM) crops. Food Chem Toxicol 42:1047–1088CrossRefPubMedGoogle Scholar
  35. Kuiper HA, Kok EJ, Engel KH (2003) Exploitation of molecular profiling techniques for GM food safety assessment. Curr Opin Biotechnol 14:238–243CrossRefPubMedGoogle Scholar
  36. Lalonde S, Wipf D, Frommer WB (2004) Transport mechanisms for organic forms of carbon and nitrogen between source and sink. Annu Rev Plant Biol 55:341–372CrossRefPubMedGoogle Scholar
  37. Less H, Galili G (2008) Principal transcriptional programs regulating plant amino acid metabolism in response to abiotic stresses. Plant Physiol 147:316–330CrossRefPubMedGoogle Scholar
  38. Levandi T, Leon C, Kaljurand M, Garcia-Canas V, Cifuentes A (2008) Capillary electrophoresis time-of-flight mass spectrometry for comparative metabolomics of transgenic versus conventional maize. Anal Chem 80:6329–6335CrossRefPubMedGoogle Scholar
  39. Lian X, Wang S, Zhang J, Feng Q, Zhang L, Fan D, Li X, Yuan D, Han B, Zhang Q (2006) Expression profiles of 10, 422 genes at early stage of low nitrogen stress in rice assayed using a cDNA microarray. Plant Mol Biol 60:617–631CrossRefPubMedGoogle Scholar
  40. Lovegrove A, Salt L, Shewry PR (2009) Establishing substantial equivalence: proteomics. Methods Mol Biol 478:273–288CrossRefPubMedGoogle Scholar
  41. Manetti C, Bianchetti C, Casciani L, Castro C, Di Cocco ME, Miccheli A, Motto M, Conti F (2006) A metabonomic study of transgenic maize (Zea mays) seeds revealed variations in osmolytes and branched amino acids. J Exp Bot 57:2613–2625CrossRefPubMedGoogle Scholar
  42. Metzdorff SB, Kok EJ, Knuthsen P, Pedersen J (2006) Evaluation of a non-targeted “omic” approach in the safety assessment of genetically modified plants. Plant Biol (Stuttg) 8:662–672CrossRefGoogle Scholar
  43. Millstone E, Brunner E, Mayer S (1999) Beyond ‘substantial equivalence’. Nature 401:525–526CrossRefPubMedGoogle Scholar
  44. OECD (1993) Safety evaluation of foods derived by modern biotechnology. Available via OECD Accessed 22 Sep 2009
  45. Piccioni F, Capitani D, Zolla L, Mannina L (2009) NMR metabolic profiling of transgenic maize with the Cry1Ab gene. J Agric Food Chem 57:6041–6049CrossRefPubMedGoogle Scholar
  46. 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 34:1508–1518CrossRefPubMedGoogle Scholar
  47. Price J, Laxmi A, St. Martin SK, Jang JC (2004) Global transcription profiling reveals multiple sugar signal transduction mechanisms in Arabidopsis. Plant Cell 16:2128–2150CrossRefPubMedGoogle Scholar
  48. Prinsi B, Negri AS, Pesaresi P, Cocucci M, Espen L (2009) Evaluation of protein pattern changes in roots and leaves of Zea mays plants in response to nitrate availability by two-dimensional gel electrophoresis analysis. BMC Plant Biol 9:113CrossRefPubMedGoogle Scholar
  49. Ruebelt MC, Lipp M, Reynolds TL, Schmuke JJ, Astwood JD, DellaPenna D, Engel KH, Jany KD (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 54:2169–2177CrossRefPubMedGoogle Scholar
  50. Saxena D, Stotzky G (2001) Bt corn has a higher lignin content than non-Bt corn. Am J Bot 88:1704–1706CrossRefGoogle Scholar
  51. Scheible WR, Morcuende R, Czechowski T, Fritz C, Osuna D, Palacios-Rojas N, Schindelasch D, Thimm O, Udvardi MK, Stitt M (2004) Genome-wide reprogramming of primary and secondary metabolism, protein synthesis, cellular growth processes, and the regulatory infrastructure of Arabidopsis in response to nitrogen. Plant Physiol 136:2483–2499CrossRefPubMedGoogle Scholar
  52. 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 15:409–425CrossRefPubMedGoogle Scholar
  53. Shewry PR, Baudo M, Lovegrove A, Powers S, Napier JA, Ward JL, Baker JM, Beale MH (2007) Are GM and conventionally bred cereals really different? Trends Food Sci Technol 18:201–209CrossRefGoogle Scholar
  54. Shnable PS, Ware D, Fulton RS, Stein CJ, Wei F, Pasternak S, Liang C et al (2009) The B73 maize genome: complexity, diversity, and dynamics. Science 326:1112–1115CrossRefGoogle Scholar
  55. Sidak Z (1971) On probabilities of rectangles in multivariate normal Student distributions: their dependence on correlations. Ann Math Statist 41:169–175CrossRefGoogle Scholar
  56. Stahlberg A, Elbing K, Andrade-Garda JM, Sjogreen B, Forootan A, Kubista M (2008) Multiway real-time PCR gene expression profiling in yeast Saccharomyces cerevisiae reveals altered transcriptional response of ADH-genes to glucose stimuli. BMC Genomics 9:170CrossRefPubMedGoogle Scholar
  57. Sylvester-Bradley R, Kindred DR (2009) Analysing nitrogen responses of cereals to prioritize routes to the improvement of nitrogen use efficiency. J Exp Bot 60:1939–1951CrossRefPubMedGoogle Scholar
  58. Thimm O, Blasing O, Gibon Y, Nagel A, Meyer S, Kruger P, Selbig J, Muller LA, Rhee SY, Stitt M (2004) MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J 37:914–939CrossRefPubMedGoogle Scholar
  59. van Dijk JP, Cankar K, Scheffer SJ, Beenen HG, Shepherd LV, Stewart D, Davies HV, Wilkockson SJ, Leifert C, Gruden K, Kok EJ (2009) Transcriptome analysis of potato tubers—effects of different agricultural practices. J Agric Food Chem 57:1612–1623CrossRefPubMedGoogle Scholar
  60. Wang R, Guegler K, LaBrie ST, Crawford NM (2000) Genomic analysis of a nutrient response in Arabidopsis reveals diverse expression patterns and novel metabolic and potential regulatory genes induced by nitrate. Plant Cell 12:1491–1509CrossRefPubMedGoogle Scholar
  61. Wang R, Okamoto M, Xing X, Crawford NM (2003) Microarray analysis of the nitrate response in Arabidopsis roots and shoots reveals over 1, 000 rapidly responding genes and new linkages to glucose, trehalose-6-phosphate, iron, and sulfate metabolism. Plant Physiol 132:556–567CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Anna Coll
    • 1
  • Anna Nadal
    • 2
  • Rosa Collado
    • 1
  • Gemma Capellades
    • 3
  • Mikael Kubista
    • 4
    • 5
  • Joaquima Messeguer
    • 6
  • Maria Pla
    • 1
    Email author
  1. 1.Institut de Tecnologia Agroalimentària (INTEA)Universitat de GironaGironaSpain
  2. 2.Departament Genètica MolecularCentre de Recerca en Agrigenòmica, CSIC-IRTA-UABBarcelonaSpain
  3. 3.Fundació Mas BadiaLa Tallada d’Empordà, GironaSpain
  4. 4.Institute of BiotechnologyAcademy of Sciences of the Czech RepublicPrague 4Czech Republic
  5. 5.TATAA Biocenter ABGoteborgSweden
  6. 6.Departament Genètica VegetalCentre de Recerca en Agrigenòmica. CSIC-IRTA-UABBarcelonaSpain

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