Evaluation of Genetically Engineered Crops Using Transcriptomic, Proteomic, and Metabolomic Profiling Techniques

Barley

Using field-grown barley (Hordeum vulgare) lines expressing either a chitinase or a β-glucanase, Kogel et al. (2010) compared changes in the leaf transcriptome and metabolome caused by transgenes, cultivar, or biotic interactions in the root. Transgene effects were negligible in the first case and low in the second, while the difference caused by the genetic background of cultivars (even if down to a low number of alleles) was of a greater magnitude. Effects of exposing roots to the spores of mycorrhizal fungi could be visualized by metabolome but not transcriptome analysis. Based on this result, the authors conclude that the metabolome represents a more immediate probe of the physiological status of the plant.

Maize

When performing transcriptomic studies using in vitro- or field-grown maize (Zea mays) plants, Coll et al. (2008, 2009) found differential expression for a minority of transcripts between in vitro-grown MON810 (insect-resistant of Bt type) and control lines, and most of these differences were not observed in the field. In real agricultural conditions, under two farming practices (conventional and low-nitrogen fertilization), Coll et al. (2010a) found differential expression for only 0.14% of the analyzed sequences (approximately one-third of the maize genome). Analysis of the expression of a subset of sequences in a different MON810/non-GE pair indicated that varietal differences had the highest impact on gene expression patterns, followed by nitrogen availability, while the MON810 characteristic had the lowest impact.

Coll et al. (2010b) found the grain proteome of two field-grown MON810/non-GE variety pairs to be virtually identical, with very few spots showing variations in the 1- to 1.8-fold range, which were all variety specific. Previously, Albo et al. (2007) had also found limited changes in the grain proteome of two different MON810 varieties (also field grown). Zolla et al. (2008) also used two MON810 variety pairs but found more differences, although environment (field versus growth chamber) induced more changes. To explain the differences resulting from genetic modifications, these authors speculated about genome rearrangement induced by the transformation method but did not consider the possibility that the control lines were certainly not fully isogenic. The discrepancy between these results remains unexplained, especially since one of the two pairs used by Coll et al. (2010b) was the MON810/non-GE pair used by Zolla et al. (2008).

In a first grain metabolome analysis, carried out on a greenhouse-grown MON810 line, Manetti et al. (2006) found differences in the levels of compounds from primary nitrogen metabolism in transgenic grain samples. Using a different MON810 line, grown in a growth chamber, Piccioni et al. (2009) identified 40 water-soluble metabolites and found a higher concentration for five compounds in the GE extracts (all different from those of Manetti et al. [2006]). Leon et al. (2009) found increases in some metabolites from specific metabolisms (purine, amino acid, arachidonic acid, linoleic acid) in three field-grown MON810 lines compared with their controls. There were only 10 metabolites with increased levels when two different technologies were compared. One of them, carnitine, had been proposed in a previous study by the same team (Levandi et al., 2008) to be a biomarker for Bt maize (note, however, that both studies analyzed the same samples, which provides no additional perspective). It should be pointed out that these various teams did not find similar results, which may be explained by their use of different genetic backgrounds and/or different growth conditions and also different technologies.

In this context, the work of Barros et al. (2010) is important. Using transcriptome, proteome, and metabolome profiling to compare two GE maize lines (MON810 and glyphosate tolerant) with the respective control lines, they found that the environment (plants were grown over three seasons in one location) affected gene expression, protein distribution, and metabolite content more strongly than the genetic modification. In addition, the authors found distinct profiles for the three locations that were also part of their comparisons during one season.

Natural plant-to-plant variability also exists. Using MON810 and control lines, Batista and Oliveira (2010) compared 2DE-separated protein spots from samples obtained either from individual plants (five different ears of five different maize plants) or from pooled plants. For some spots, they noticed a high variability between individual samples from the same line and that these differences were masked in the pools. For other spots, variability was observed between individual samples and also between pools. The authors concluded that differences not related to the genetic engineering, such as natural plant-to-plant variability, need to be eliminated when using omics.

Harrigan et al. (2010) reviewed compositional data for GE maize and soybean (Glycine max) varieties (seven GE crop varieties) from a total of nine countries and 11 growing seasons. From their analysis, which is not based on omic technologies but represents the most comprehensive compilation of GE crop composition data to date, the authors conclude that compositional differences between GE varieties and their conventional comparators are “encompassed within the natural variability of the conventional crop and that the composition of GM and conventional crops cannot be disaggregated.”

Pea

Analyzing two pea (Pisum sativum) cultivars producing a bean (Phaseolus vulgaris) α-amylase inhibitor (AI1), Islam et al. (2009) found around 30 seed protein spots showing changes in abundance in each transgenic/control pair (generally not the same spots, although AI1 was produced at similar levels in both cultivars). While differences were minor for one pair, they were more pronounced quantitatively and qualitatively (appearance and disappearances of 36 protein spots) for the second pair. The authors suggest that differences of “similar magnitude” occur between cultivars. In a different cultivar, Chen et al. (2009) reported that 33 proteins differentially accumulated in AI1-expressing lines compared with the parental line, three of which were associated with the expression of AI1. The remaining 30 proteins were associated with the transformation events. A number of the increased spots corresponded to seed storage proteins. Since such proteins are common food allergens, the authors suggested that these increases might be linked to food antigens detected in mice fed with GE peas (attempts to use 2DE of proteins and proteomics to detect new allergens are listed in Supplemental Table S2).

Rice

Montero et al. (2010) found around 0.40% transcriptomic differences in leaves of in vitro-grown experimental rice (Oryza sativa) lines producing an antifungal protein. They could distinguish differences due to transgene insertion (15%), transgene expression (50%), and regeneration (35%). Around half of the genes whose expression was affected by the transgene itself also had their expression affected in non-GE plants after wounding.

Zhou et al. (2009) compared profiles of compounds from primary metabolism in three GE lines (each independently transformed with the same two insect resistance genes; their data were averaged) with those of the wild-type line (field grown side by side). They found three metabolites to be present in greater amounts in the GE group (up to 3-fold). Differences in other metabolites were within the same range as those of the wild type under various growth conditions (location and/or sowing time). It should be mentioned that wild-type lines planted at different times contained varying amounts of trehalose (up to 40-fold) and change in location influenced the levels of four compounds.

The work by Jiao et al. (2010) provides some perspective on transgenic changes in the context of varietal changes in rice. Comparing two lines with different sets of antifungal genes and one with two insect resistance genes with their respective controls, the authors found decreases or increases, inconsistent between lines, ranging from 20% to 74% for amino acids, 19% to 38% for fatty acids, 25% to 57% for vitamins, and 20% to 50% for elements. These changes were all within the range occurring among varieties (according to OECD values). A 25% reduction in protein content was observed for one antifungal GE line, which was therefore considered by the authors to be less nutritious.

Batista et al. (2008) addressed the following question: which of the mutagenized or transgenic plants are more susceptible to present unintended modification? Gene expression was analyzed in duplicated samples of four types of rice plants (irradiated stable mutants and transgenic plants producing an antibody or developed for improved stress tolerance) and their respective controls. In all cases studied, the modification in transcriptome was greater in mutagenized than in transgenic plants. Since these results were obtained with seedlings grown on tissue culture medium, wider confirmation is necessary.

Soybean

Cheng et al. (2008) found that gene expression in leaves (grown in a growth chamber) differs more between conventional varieties than between two GE glyphosate-tolerant varieties (carrying the same transgenic event) and their closest conventional varieties. The authors also note that the older the soybean variety, the larger the difference in gene expression (recently developed cultivars are more inbred), which raises the question of which varieties should be chosen to create a reference set for the crop species. Also using a glyphosate-tolerant variety (not specified) grown in a growth chamber, but analyzing seeds, García-Villalba et al. (2008) identified and quantified the main metabolites: in general, the same metabolites, in similar amounts, were found in GE glyphosate-resistant soybean and in its corresponding parental line. However, significant differences were observed in some specific cases: among the 45 metabolites examined, higher amounts were found for three and lower amounts for five (one was not detected) in the GE line. At least some of these differences could be explained by modification in the regulation of the shikimate pathway in GE soybean (glyphosate tolerance is conferred by a transgenic 5-enolpyruvylshikimate-3-phosphate synthase enzyme that bypasses the endogenous glyphosate-sensitive enzyme).

The study on natural variation in soybean crop composition and the impact of transgenesis by Harrigan et al. (2010) has been mentioned above.

Using 2DE protein analysis, soybean endogenous allergen expression was found not to be altered after genetic modification (see related refs. in Supplemental Table S2).

Wheat

Gregersen et al. (2005) found that the strong expression of a phytase gene had no significant effect on the overall gene expression patterns in the developing wheat (Triticum aestivum) seed. Samples from greenhouse-grown plants were taken at three different seed development times. The slight differences observed concerned primarily genes strongly expressed over a shorter period of seed development. This highlights the necessity of careful interpretation of microarray results when extensive progressive developmental changes occur, as is the case for seeds, and when minor asynchrony is hard to avoid. Ioset et al. (2007) analyzed lines with either a combination of three transgenes or a single one (KP4, of viral origin) for increased defense against fungal pathogens. For greenhouse-grown plants, they found only minor differences in the flavonoid profile between GE lines and their conventional control lines. In contrast, the different genetic background of the control lines resulted in a quantitatively different (up to 2-fold for some compounds) flavonoid content. In a field test, KP4 did not influence flavonoid content either, whether the lines were infected by pathogens or not.

Conclusion

These profiling studies are highly heterogeneous (plant tissues, growth parameters, range of comparators, technologies). They have to be considered as exploratory (i.e. not normalized validated approaches for the routine assessment of GE plants).

This survey on the profiling of GE crop lines with agronomic traits, but without deliberate modifications to metabolic pathways, reveals that some differences exist when compared with control lines. However, the available data on various conventional lines consistently show more differences. This has to be linked to the fact that GE lines have been selected by a process based not only on the suitable expression of a new trait but also on phenotypic and compositional equivalence with a close comparator, followed by a number of crosses to introgress the new trait into elite lines. A number of environmental factors (field location, sampling time during the season or at different seasons, mineral nutrition) have also been shown, consistently, to exert a greater influence than transgenesis.

Maize

Huang et al. (2005) generated maize lines with an elevated content of free and total Lys in the kernels due to the combined deregulation of its synthesis and reduced levels of a Lys-poor storage protein. Kernels from field-grown plants showed, in addition, strong increases in the content of two Lys metabolites and up to 2-fold higher content of other free amino acids but with only marginal changes for total amino acids.

Potato

Lehesranta et al. (2005) demonstrated major qualitative and quantitative differences in the tuber proteome of field-grown varieties and landraces but found only limited quantitative differences between GE lines (affected either in cell wall structure or ethylene/polyamine metabolism) and their controls. Using the same lines, plus related ones as well as lines expressing a sense and antisense fructokinase gene (all grown in pots), similar conclusions were reached using metabolic profiling (Defernez et al., 2004) or targeted compositional analysis (Shepherd et al., 2006). The most obvious differences were found between the two non-GE varieties. Differences were also found between tissue culture-derived tubers and tubers derived from transformation with the empty vector. This raises the possibility that somaclonal variation (known to occur significantly in potato, depending on genotype) may be responsible for an unknown proportion of differences.

Similarly, using field-grown tubers engineered to produce inulin-type fructans, Catchpole et al. (2005) found their metabolite composition to be similar to the progenitor line and variations to be within the range found in classical cultivars, apart from the predictable increase in fructans and derivatives. Baroja-Fernández et al. (2009) found numerous transcriptomic changes in tubers with altered levels of Suc synthase, but their data were not compared with varietal changes.

An additional perspective (i.e. influence of sampling time) is provided by Kim et al. (2009), who found that 1 week of storage significantly modified tuber metabolite patterns, but the constitutive expression of β-amyloid, curdlan synthase, or glycogen synthase triggered neither quantitative nor qualitative differences.

Rice

In seeds of two high-Trp rice lines (field grown), Wakasa et al. (2006) found an increase in the content of other free amino acids (but to a lesser extent than that of Trp) and of indole acetic acid, which was predictable given the relation between the Trp biosynthetic pathway and the production of this growth regulator. However, they found no major change for other phenolic compounds. The same laboratory (Dubouzet et al., 2007) also found limited metabolic and transcriptomic differences in 8-d-old seedlings of lines with high Trp. Beatty et al. (2009) reported limited transcriptional changes in roots and shoots of “nitrogen use-efficient” rice obtained by overexpression of Ala aminotransferase.

Tomato

Le Gall et al. (2003) analyzed metabolic profiles during tomato (Solanum lycopersicum) fruit ripening and the potential unintended effects when two transcription factors were simultaneously overexpressed to increase flavonol content. The levels of at least 15 other metabolites were found to be different between the red GE and non-GE tomato types, but according to the authors (who did not specify the growth conditions), these changes are within the natural variation normally observed in a field-grown crop.

Long et al. (2006) found no perturbation in phenolic metabolites in mutant and transgenic lines altered in structural genes for carotenoid biosynthesis, and reciprocally, the down-regulation of ferulate 5-hydroxylase did not affect carotenoid content in red fruit from greenhouse-grown plants.

In a more comprehensive study, but also limited to greenhouse conditions, Fraser et al. (2007) characterized the fruit metabolic changes associated with the overproduction of carotenoids. Specific sectors of metabolism were altered in green fruit, resembling some metabolic changes normally associated with ripening. Ripe fruit showed the least change in overall metabolites, although levels of 43% of the metabolites were altered. Thus, perturbation in carotenoid synthesis has profound regulatory implications for tomato fruit development, but these effects arise without altering the general phenotype of the plant and fruit ripening.

In addition, as expected, several metabolisms can be altered, either in conventional mutants or in transgenic lines, when regulatory genes are affected, such as those involved in light perception (Long et al., 2006; see other refs. in Supplemental Table S3) or growth regulator biosynthesis (Mattoo and Handa, 2008).

Wheat

Baudo et al. (2009) report the transcriptomic comparison of GE and conventionally bred lines (grown in a greenhouse) expressing a given set of seed storage proteins (glutelins) known to determine bread-making quality. Differences in endosperm and leaf transcriptome between GE and parent lines were rare (up to six genes). More differences (up to 527 genes in endosperm) were observed between this parent line and another conventionally bred line. The latter, although of different overall background, contains the same set of glutelins as the GE line and unexpectedly showed fewer differences (up to 154 genes) with the GE line than with the parent of the GE line. Baker et al. (2006) performed metabolomic comparisons also using lines differing in their set of glutelins. They found some differences in polar metabolites between GE and parental lines, but generally, they were in the range of differences caused by the environment (plants grown in fields on different sites and in different years). Larger differences were often observed between two parental lines, between years, and between different sites than between the GE and control lines. Additional articles analyzing wheat or barley lines with a modified set of seed storage proteins are listed in Supplemental Table S3.

Conclusion

Few of these studies brought their results in perspective with the potential effects of the environment. Nevertheless, the available data are noteworthy since they indicate that GE lines with altered metabolic traits do not necessarily exhibit pleiotropic changes. This is encouraging for the future use of transgenesis to improve food and feed quality. However, some pleiotropic effects do occur when certain pathways are modified.

A key consideration for crops with altered composition, in a substantial equivalence perspective, is the choice of a comparator for GE lines. The published omic studies did not yet examine the question of what the appropriate comparator should be (the progenitor or a crop that most closely resembles the new variety with respect to the intentionally altered metabolic trait). On the other hand, it can be stressed that, up to now, choosing a comparator has not posed a major problem. GE crops (as well as conventional varieties) with altered composition have already been assessed and approved by regulators (e.g. crops with high oleic acid content).