Comparison of two GM maize varieties with a nearisogenic
non-GM
variety
using
transcriptomics,
proteomics and metabolomics
Eugenia Barros1,*, Sabine Lezar1, Mikko J. Anttonen2, Jeroen P. van Dijk3, Richard M. Röhlig4, Esther J. Kok3, and Karl-Heinz Engel4 1
Council for Scientific and Industrial Research (CSIR), Biosciences, Meiring Naude
Road, Brummeria, Pretoria, 0001, South Africa, 2
Department of Biosciences, University of Kuopio, Yliopistonranta 1E, P.O. Box
1627, 70211, Kuopio, Finland 3
RIKILT Institute of Food Safety, Bornesesteeg 45, P.O. Box 230, 6700 AE
Wageningen, The Netherlands, and 4
Technische
Universität
München,
Lehrstuhl
für
Allgemeine
Lebensmitteltechnologie, Am Forum 2, D-85350 Freising-Weihenstephan, Germany
* Correspondence (Dr. Eugenia Barros; CSIR Biosciences, P.O. Box 395, Pretoria 0001,
South
Africa;
tel
0027-12-8413221;
fax
0027-12-8413651;
email
[email protected])
KEYWORDS GM • transcritpomics • proteomics • metabolomics • food safety • maize
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SUMMARY
The aim of this study was to evaluate the use of four non-targeted analytical methodologies in the detection of unintended effects that could be derived during genetic manipulation of crops. Three profiling technologies were used to compare the transcriptome, proteome and metabolome of two transgenic maize lines with the respective control line. By comparing the profiles of the two transgenic lines grown in the same location over three growing seasons we could determine the extent of environmental variation, while the comparison with the control maize line allowed the investigation of effects caused by a difference in genotype. The effect of growing conditions as an additional environmental effect was also evaluated by comparing the Bt maize line with the control line from plants grown in three different locations in one growing season. The environment was shown to play an important effect in the protein, gene expression and metabolite levels of the maize samples tested where 5 proteins, 65 genes and 15 metabolites were found to be differentially expressed.
A distinct
separation between the three growing seasons was also found for all the samples grown in one location. Together, these environmental factors caused more variation in the different transcript/protein/metabolite profiles than the different genotypes.
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INTRODUCTION
Cereals are among the most important group of cultivated plants for food production worldwide. Maize (Zea mays) is the most widely grown cereal and according to a USDA report world maize production for 2007/2008 was 791.6 million tons (USDA, 2009). Genetic engineering of agricultural crops has played an important role in crop improvement where it has been used to increase resistance to disease and stresses and tolerance to herbicides as well as to improve the nutritive value of crops. However, food derived from genetically modified (GM) crops have often been surrounded by controversy, particularly in Europe, despite the lack of evidence of risks associated with GM crops and the extensive safety measures taken prior to their release. One of the concerns is about unintended effects that might result from the random integration of the transgene. This may cause gene disruptions that can lead to sequence changes, production of new proteins or formation of either new metabolites or altered levels of existing metabolites that could compromise safety (Kuiper et al, 2001; Cellini et al., 2004). This includes the potential production of new allergens or toxins. Other unintended effects, related to the genetic modification, may be secondary effects of the introduced sequences. Unintended effects can also come about during conventional breeding as a result of mutagenesis, as well as hybridization and backcrossing that are integral processes of breeding programs where the genetic variation within species and between related species is used as a major source for crop improvement. The safety assessment of GM crops is based on the principle of substantial equivalence or comparative safety analysis (OECD, 1993; FAO/WHO, 1996; Kok and
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Kuiper, 2003. To this effect the GM crop is compared to its conventional counterpart at the agronomic/phenotypic level and by compositional analysis. The latter will include analysis of macro- and micronutrients as well as toxins and anti-nutrients. Because of the varied nature of GM crops the evaluation is done on a case-by-case basis (Kleter and Kuiper, 2002). The OECD has developed consensus documents for the crops of major economic interest that provide overviews of the most relevant nutrients and anti-nutrients for these crops. These documents are used as guidelines for the comparative compositional analysis. Also for maize such a consensus document has been published (OECD, 2002; OECD, 2006). Targeted analyses of key compounds have been used extensively in substantial equivalence studies of GM crops and have contributed to the establishment of databases with detailed information on the composition of some major conventionally bred crops that serve as benchmark for the assessment of the composition of the GM crops. The best example of such a database is the ILSI Crop Composition Database that has compiled data on maize, cotton and soybean (ILSI, www.cropcomposition.org). Importantly, the only data that is included has been obtained with validated methods of targeted analysis. Profiling technologies such as transcriptomics, proteomics and metabolomics have been suggested to broaden the spectrum of detectable compounds and thus to supplement the current targeted analytical approaches (Kuiper et al., 2001; Cellini et al., 2004; Lehesranta et al, 2005; Metzdorff et al., 2006; Zolla et al., 2008, Kok et al., 2007). In this study we used non-targeted molecular profiling to provide insight into the extent of variation in the maize transcriptome, proteome and metabolome by analyzing three maize genotypes. These included two transgenic lines modified for two different genes and the respective control line. All are commercial lines that were
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grown in different locations. We report on the application of cDNA microarray for transcriptome profiling, two-dimensional gel electrophoresis for proteome profiling and 1H-NMR fingerprinting and capillary gas chromatographic / mass spectrometric (GC/MS)-based metabolite profiling for analysis of the metabolome. One of the characteristics of molecular profiling is the large amount of data generated. We used multivariate data analysis for an initial exploration followed by univariate analysis to identify the genes/proteins/metabolites that were mainly responsible for the differences between the three maize lines. The data presented serve as an exploratory study into the use of ~omics techniques for safety evaluation of GM crops. Both the added value and the current challenges are discussed.
RESULTS
In this study we evaluated the effects of genotype and environmental conditions on maize kernels at the transcriptome, proteome and metabolome levels. To this effect, two GM maize varieties (GM Bt, GM RR), each containing a single insert, were subjected to comparative profiling, using the near-isogenic non-GM variety CRN3505 as the comparator. These varieties were compared in a single location, Petit, during three consecutive years. This set-up allowed the evaluation of variation caused by year of harvest and genotype as independent factors. In addition, a comparison between the non-GM and the GM Bt variety was done between three different growing locations during one growing season. This set-up allowed evaluation of effects of the factor genotype independent from differences in growing
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conditions, including organic or conventional farming, as one of the locations (Potchefstroom) employed organic cultivation.
Effects of genotype and growing season: Comparison of two GM maize and one non-GM plant in one location (Petit) over 3 years
Microarray analysis After initial data selection a total of 3541 spots were included in the data analysis. The PCA analysis of these spots allowed the characterization of samples according to growing season or genotype (Figures 1a, 2a). For both factors a separation was observed in the score plots with different combinations of components, with the separation for growing season explaining more variation in the dataset (Figure 1a). Separate one-way ANOVAs for the factors growing season and genotype were then performed to identify differentially expressed genes. This was followed by a Tukey's HSD test to find which of the groups of samples were different from each other (P=0.01). For growing season, 69 spots were significantly different (P
