Assessing the natural variability in crop composition

Regulatory Toxicology and Pharmacology 58 (2010) S13–S20

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Assessing the natural variability in crop composition George G. Harrigan, Kevin C. Glenn, William P. Ridley ⇑ Monsanto Company, Product Safety Center, 800 North Lindbergh Blvd., St. Louis, MO 63167, USA

a r t i c l e

i n f o

Article history: Available online 9 September 2010 Keywords: Crop composition Natural variability Nutrients Soybean Maize ILSI

a b s t r a c t The number of evaluations of the nutrient composition of food and feed crops has increased over the past 15 years due to the introduction of new crops using the tools of modern biotechnology. The composition of these crops has been extensively compared with conventional (non-transgenic) controls as an integral part of the comparative safety assessment process. Following guidelines outlined in the Organization of Economic Co-operation and Development (OECD) Consensus Documents, most of these studies have incorporated field trials at multiple geographies and a diverse range of commercially available varieties/hybrids that are analyzed to understand natural variability in composition due to genetic and environmental influences. Using studies conducted in the US, Argentina and Brazil over multiple growing seasons, this report documents the effect of geography, growing season, and genetic background on soybean composition where fatty acids and isoflavones were shown to be particularly variable. A separate investigation of 96 different maize hybrids grown at three locations in the US demonstrated that levels of free amino acids, sugars/polyols, and molecules associated with stress response can vary to a greater degree than that observed for more abundant components. The International Life Sciences Institute (ILSI) crop composition database has proven to be an important resource for collecting and disseminating nutrient composition data to promote a further understanding of the variability that occurs naturally in crops used for food and feed. Ó 2010 Elsevier Inc. All rights reserved.

1. Introduction An understanding of the natural variability in nutrient composition of maize, soybeans, and other crops is an important consideration in the development of diets that promote the healthy growth of humans and livestock animals. Many factors are known to affect nutrient composition, including the germplasm of the crop, soil conditions, weather conditions, weed pressure, insect infestations, and other environmental variables. Studies on maize hybrids, for example, have demonstrated that endogenous levels of grain components can be affected by genetics and by location; levels of compositional analytes of a single hybrid corn grown at different locations can vary quite significantly (Reynolds et al., 2005). Some crop components may be more variable than others; after a genetic evaluation of soybean (Gutierrez-Gonzalez et al., 2009), the authors of one study observed ‘‘the range of values in isoflavones is overwhelming, even for homozygous genotypes growing in the same year (Gutierrez-Gonzalez et al., 2009)”. The variability in composition of harvested seed and grain is unsurprising given the plasticity of the plant genome and diverse environmental influences on development as illustrated by, for example, induction of plant defenses to insect attack (Kessler et al., 2004), ⇑ Corresponding author. Fax: +1 314 694 5071. E-mail address: [email protected] (W.P. Ridley). 0273-2300/$ - see front matter Ó 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.yrtph.2010.08.023

the influence of genotype and the environment on embryo dormancy (Jones et al., 1997), and the integration of environmental signals to control flowering times (Bernir and Perillux, 2004). Jeffery and colleagues (Dixon et al., 2006; Kushad et al., 1999) have described how developmental processes impact levels of bioactive components within food plants thus highlighting the importance of environmental conditions on nutrients, anti-nutrients, and secondary metabolites of the mature crop. Comparative compositional studies are a significant aspect of safety assessments of food and feed derived from novel (typically GM) crops (Codex, 2003; Delaney, 2007; Kok et al., 2008). These compositional analyses are typically conducted on a crop-specific basis according to the principles outlined in the Organization for Economic Cooperation and Development (OECD) consensus documents and focus on quantitative measurements of key biochemical components of the ‘‘articles of commerce” (usually grain and forage) (OECD, 1998, 2002, 2006). These components include major constituents such as proximates, (fat, protein, ash, carbohydrate, and fiber), key nutrients such as amino acids, fatty acids, vitamins, minerals, and crop-specific anti-nutrients and secondary metabolites such as phytic acid, ferulic acid, p-coumaric acid in maize, isoflavones in soybean, and gossypol in cotton. Direct comparisons between endogenous levels of these key components in the novel food and a near isogenic conventional control form the bases of compositional assessments. There are,

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however, two key considerations that are also incorporated into compositional assessments because of their importance in defining natural variability in crop composition. Firstly, multiple replicated field trials typically encompassing diverse geographic regions and multiple growing seasons are used to establish environmental diversity. This allows an evaluation of the novel crop grown in several distinct environments. Secondly, the composition of a range of unique commercially available crops (termed reference substances) grown concurrently and at the same sites as the novel crop and its near-isogenic control is assessed. This provides an overview of the impact of genetic variation on compositional variability. Ranges in levels of endogenous crop components reported in the literature can also be incorporated to understand compositional variability. Compositional equivalence of GM crops and their conventional comparators has now been demonstrated in a range of crops including potato (Rogan et al., 2000; Shepherd et al., 2006), cotton (Berberich et al., 1996; Hamilton et al., 2004; Nida et al., 1996), soybean (Berman et al., 2009, 2010; Harrigan et al., 2007c; Lundry et al., 2008; McCann et al., 2005; Padgette et al., 1996; Taylor et al., 1999), corn (Drury et al., 2008; Harrigan et al., 2009; McCann et al., 2007; Ridley et al., 2002; Sidhu et al., 2000), rice (Oberdoerfer et al., 2005), wheat (Obert et al., 2004), and alfalfa (McCann et al., 2006) products. These studies have highlighted that differences due to transgene insertion are minor, and generally not reproducible, when contrasted to differences attributable to natural variation associated with environmental and/or genetic diversity. One review of the composition of seven GM crop varieties, from a total of nine countries and a total of eleven growing seasons, concluded that the contribution of modern biotechnology has had a negligible contribution to compositional variation and that the composition of agronomically equivalent GM and conventional crops cannot be distinguished from one another (Harrigan et al., 2010). Over the past decade, a large number of studies following OECD guidelines has allowed the generation of an abundance of data on genetic and environmental influences on composition of conventional crops. This report presents a preliminary analysis of compositional variability in soybean grown under normal practices from a range of geographic regions (US, Argentina, and Brazil). Also presented are results from studies (some non-OECD) of conventional corn and an overview of the effect of environmental stress (drought) on crop composition. Finally, a discussion of the ILSI Crop Composition Database is presented.

line selected at random and bulked for compositional analyses (Harrigan et al., 2007a). 2.2. Compositional analyses All compositional analyses were performed using methods based on internationally-recognized procedures and literature publications (OECD, 2002, 2006). Brief descriptions of these methods have been referenced in the published compositional reports that form the basis of this report (Berman et al., 2009). Components measured in soybean seed included proximates (moisture, fat, ash, protein), carbohydrates by calculation, acid detergent fiber, neutral detergent fiber, total amino acid composition, (all 18 proteinogenic amino acids) fatty acid composition (myristic, palmitic, palmitoleic, heptadecanoic, stearic, oleic, linoleic, linolenic, arachidic, eicosenoic, behenic), isoflavones (total daidzein, total genistein, total glycitein), raffinose, stachyose, phytic acid, trypsin inhibitor, lectin, and vitamin E. Mean values were determined on biological replicates from samples harvested from each block of the randomized design for each location, with the exception of reference substances from the US production where only a single measurement (n = 1) was conducted. Components measured in corn grain (Harrigan et al., 2007a) included proximates (moisture, fat, ash, protein), carbohydrates by calculation, acid detergent fiber, neutral detergent fiber, total and free amino acid composition, fatty acid composition, selected sugars and polyols, absicisic acid, and glycine betaine. Methods for sugars and polyols, absicisic acid, and glycine betaine used LC/MS-MS. 2.3. Multivariate statistical analyses Hierarchical cluster analysis (HCA) and principal component analysis (PCA) of compositional data were performed using JMP 8.1 software. The purpose of the HCA conducted on the soybean samples was to group these samples into subsets or ‘‘clusters”, such that the composition of those within each cluster was more closely related to the composition of one another than to the composition of soybean samples assigned to different clusters. PCA is a data reduction technique and was used here in an exploratory and qualitative evaluation to determine how growing regions and germplasm affected differences in compositional space. The multivariate analyses were conducted using mean values for all compositional components analysed above.

2. Materials and methods

3. Results and discussion

2.1. Biological material

3.1. Variation in composition of seed harvested from soybean

All cultivations of the conventional soybean varieties assessed here were conducted under normal agronomic practices specific to each growing region (Berman et al., 2009, 2010). The cultivations were conducted at multiple replicated sites in the US during the 2007 growing season, Argentina during the 2007–2008 growing season, and Brazil during the 2007–2008 and 2008–2009 growing seasons. All sites used a randomized complete block design with three blocks for the US and Argentina productions, and four blocks for the Brazil productions. Specific locations of the growing sites are listed in Tables 1 and 2. Corn cultivations were conducted under normal agronomic or specific drought conditions as described in the appropriate references (Harrigan et al., 2007a,b). For the data presented in Table 3, seed of the various corn hybrids were planted at three different locations in Iowa; Cambridge, Huxley, and South Amana. Ninety-six plots representing 48 inbred lines crossed with the two different testers were planted per location. Mature grain was collected from five plants for each

Monsanto Company has developed two soybean products that contain biotechnology-derived traits that offer benefits in weed and pest management. MON 89788 is tolerant to glyphosate, a broad-spectrum herbicide and MON 87701 is resistant to targeted lepidopteran pests. Compositional evaluations of seed harvested from these products have now been conducted at multiple replicated sites in the US during the 2007 growing season, Argentina during the 2007– 2008 growing season, and Brazil during the 2007–2008 and 2008– 2009 growing seasons. Components assessed included proximates (moisture, fat, ash, and protein), carbohydrates by calculation, acid detergent fiber, neutral detergent fiber, total amino acid composition, fatty acid composition, isoflavones (daidzein, genistein, glycitein), raffinose, stachyose, phytic acid, trypsin inhibitor, lectin, and vitamin E. Results showed that MON 87988 and MON 87701 and their conventional counterparts were compositionally equivalent across the diverse geographic regions.

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G.G. Harrigan et al. / Regulatory Toxicology and Pharmacology 58 (2010) S13–S20 Table 1 Values for key components from A5547 (US, Argentina, Brazil South) and Monsoy 8329 Soybean (Brazil North). Field production US 2007 Argentina 2007–8 Brazil South 2007–8 Brazil South 2008–9 Brazil North 2007–8 Brazil North 2008–9 All

Number of sites e

5 5f 2g 2g 2h 2h 18

Proteina,i

Fata,i

Oleic acidb,i

Linoleic acidb,i

Linolenic acidb,i

Vitamin Ec,i

Daidzeind,i

Stachyosea,i

35.3–41.5 36.3–38.6 37.0–38.4 35.9–37.5 37.3–39.8 37.3–37.4 35.3–41.5

18.0–22.4 18.0–18.6 18.4–20.2 20.0–20.1 20.1–21.2 20.8–21.5 18.0–22.4

20.6–27.4 18.0–19.7 20.8–24.4 19.6–24.1 37.7–43.1 29.4–33.7 18.0–43.1

48.5–53.8 53.6–55.2 51.2–53.3 51.2–54.4 37.7–41.8 46.1–48.6 37.7–55.2

5.5–8.2 8.5–9.7 6.9–8.6 7.0–8.2 5.0–5.4 5.1–5.8 5.0–9.7

5.0–7.8 2.9–4.0 3.7–6.1 6.4–8.0 4.8–4.9 4.8–6.1 2.9–8.0

216–803 851–1027 705–1313 575–1023 227–342 200–312 200–1313

2.4–6.1 3.8–4.2 3.8–4.0 4.1–4.6 3.9–4.4 4.0–4.1 2.4–6.1

a

% Dry weight. % Total fatty acid. c mg/100 g Dry weight. d mg/kg Dry weight. e Baldwin county, Alabama (AL), Jackson county, Arkansas (AR), Clarke county, Georgia (GA), Jackson county, Illinois (IL), Wayne county, North Carolina (NC) (Berman et al., 2009). f Tacuari, Buenos Aires (B1), Gahan, Buenos Aires (B2), Berdier, Buenos Aires (B3), Alejo Ledesma, Cordoba (CB), San Francisco, Santa Fe (SF) (Berman et al., 2009). g Nao-Me-Toque, Rio Grande do Sul (NT), Rolandia, Parana (RO). (Berman et al., 2010, 2008–9 data not published). h Cachoeira Dourada, Minas Gerais (CD), Sorriso, Mato Grosso (SR) (Berman et al., 2010, 2008–9 data not published). i Values are presented as a range of the means recorded at each site; each individual mean is derived from three replicates (US and Argentina) or four replicates (Brazil). b

Table 2 Values for key components from different soybean grown in the US, Argentina, and Brazil. Field production e

US 2007 Argentina 2007–8f Brazil South 2007–8g Brazil South 2007–8 (GM)g Brazil South 2008–9g Brazil South 2008–9 (GM)g Brazil North 2007–8h Brazil North 2007–8 (GM)h Brazil North 2008–9h Brazil North 2008–9 (GM)h All conventional All GM

Number of varieties

Proteina,i

Fata,i

Oleic acidb,i

Linoleic acidb,i

Linolenic acidb,i

Vitamin Ec,i

Daidzeind,i

Stachyosea,i

20 20 4 4 3 5 2 6 2 6 50 22

35.3–42.7 35.2–40.9 36.3–38.5 36.3–38.1 37.3–40.0 36.2–39.7 37.7–40.5 38.1–42.7 37.3–38.9 37.3–39.4 35.2–42.7 36.2–42.7

17.9–23.6 16.4–19.7 19.4–22.4 19.1–20.6 20.0–21.2 17.7–21.4 20.8–22.8 19.6–23.0 18.5–20.5 18.4–22.1 16.4–23.6 17.7–22.1

16.7–35.2 17.4–22.8 21.0–24.3 19.7–23.6 26.1–27.4 19.0–27.8 27.9–31.6 21.7–33.5 23.8–24.1 17.1–33.1 16.7–43.1 17.1–33.5

44.2–57.7 51.7–56.3 51.8–54.6 52.6–54.5 49.4–51.4 49.2–56.8 47.3–50.1 45.4–55.6 52.9–53.4 46.6–59.3 37.7–57.7 45.4–59.3

4.3–8.8 7.6–9.7 6.6–7.9 6.9–8.4 6.1–6.2 6.0–8.4 5.9–6.0 5.7–7.0 6.2–6.2 5.3–7.7 4.3–9.7 5.3–8.4

1.7–8.1 1.2–6.4 2.2–3.2 1.1–4.6 4.0–4.3 2.1–5.3 3.6–3.9 2.5–4.4 3.2–3.6 3.0–4.1 1.2–8.1 1.1–5.3

214–1274 382–1392 618–1873 457–994 305–743 372–1270 351–393 236–401 273–411 250–336 200–1873 236–1270

2.0–6.1 3.1–4.5 3.4–4.4 4.0–4.4 4.0–4.2 3.5–4.3 3.6–4.0 2.7–4.8 3.6–4.0 3.5–4.7 2.0–6.1 2.7–4.8

a

% Dry weight. % Total fatty acid. mg/100 g Dry weight. d mg/kg Dry weight. e Baldwin county, Alabama (AL), Jackson county, Arkansas (AR), Clarke county, Georgia (GA), Jackson county, Illinois (IL), Wayne county, North Carolina (NC) (Berman et al., 2009). f Tacuari, Buenos Aires (B1), Gahan, Buenos Aires (B2), Berdier, Buenos Aires (B3), Alejo Ledesma, Cordoba (CB), San Francisco, Santa Fe (SF) (Berman et al., 2009). g Nao-Me-Toque, Rio Grande do Sul (NT), Rolandia, Parana (RO). (Berman et al., 2010, 2008–9 data not published). h Cachoeira Dourada, Minas Gerais (CD), Sorriso, Mato Grosso (SR) (Berman et al., 2010, 2008–9 data not published). i Values are presented as a range of the means recorded at each site; each individual mean is derived from one replicate (US) three replicates (Argentina) or four replicates (Brazil). b

c

Table 3 Values for components from conventional corn grown at three sites in Iowa, US. Component

Number of analytes measured > LOQ

Location effect

Tester effect

G  E effect

Fold variation

Oil Protein Fatty acids Total amino acids Free amino acids Sugars/Polyols Organic acids Glycerol Glycine betaine Abscisic acid

1 1 5 18 19 7 8 1 1 1

– – – 13 5 6 4 1 – –

– – 1 3 8 4 4 1 – –

1 1 3 4 8 – – – 1 –

1.75 1.75 1.25–1.9 1.65–2.3 2.8–24.0 10.0–42.0 3.7–16.3 60.3 52.3 21.6

To evaluate the impact of genetics, geographies, and growing season on soybean composition, an analysis of the composition of the conventional controls is presented, as well as the composition of reference substances that were incorporated in the above referenced studies. The samples included seed from;

(i) US (2007); one variety (A5547 used as the isogenic control for the GM products) grown in five different states (AL, AR, GA, IL, NC, see Table 1) and 20 different commercially available references with four unique reference substances per site; all reference substances were conventional soybean.

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(ii) Argentina (2007–2008); one variety (A5547 used as the isogenic control for the GM products) grown in five different regions (see Tables 1 and 2) and 20 different commercially available references with four unique reference substances per site; all reference substances were conventional soybean. (iii) Brazil [2007–2008 and 2008–2009]; one variety (A5547 used as the isogenic control for evaluation of the GM products) grown at two different sites in the southern region during both growing seasons, and Monsoy 8329 also used as a control for evaluation of the GM products at two different sites in the northern region (see Tables 1 and 2). A total of 16 different commercially available reference substances (four per site) were grown during both seasons. The reference substances included both conventional and GM soybean. An overview of variation in levels of representative components (protein, fat, oleic acid, linoleic acid, linolenic acid, vitamin E, daidzein, and stachyose) from seed from A5547 and Monsoy 8329 are presented in Table 1. There were differences in levels of all components both within and across field productions. For some components, values were similar and showed extensive overlap across field productions (for example, protein and stachyose). Other components showed appreciable differences in measured levels. Thus, comparisons of fatty acid values from the US and Argentina field productions showed no overlap for oleic acid and linolenic acid and only modest overlap for linoleic acid. Values for vitamin E and daidzein were also clearly differentiated across these two growing regions. Comparison of the US and Argentina compositional data with that of the Brazil field productions also highlighted differences in the levels of key components in seed from A5547 (see Table 1). There were also compositional differences across the different growing seasons and different growing regions (North and South) within the Brazil data set. For example, results showed that in the Brazil South region, levels of vitamin E and stachyose differed in seed from A5547 harvested from the two different growing seasons (Table 1). In the Brazil North region, growing season differences were more evident for levels of oleic acid and linoleic acid. The most striking differences however were between the two conventional controls (Monsoy 8329 in the North and A5547 in the South) particularly in levels of fatty acid and daidzein. This was true over both growing seasons although differences in fatty acid levels were greatest in the 2007–2008 season (Table 1). As a representative illustration of the above discussion, Fig. 1 presents values for fat, linolenic acid, and daidzein across the entire data set. Table 2 presents an overview of levels of representative components from seed harvested from all of the commercially available reference substances used during the US, Argentina, and Brazil field productions. Across these productions, a total of 72 reference substances, of which 41 were unique, were grown. The US and Argentina field productions shared 19 reference substances, none of which were grown in Brazil. There were a total of 20 unique reference substances in the Brazil production in which seven were represented at more than one site or growing season. The inclusion of an increased number of distinct soybean varieties expanded the range of component values when compared with values derived from a single soybean variety. These expanded ranges served to increase overlap between component values recorded at the different geographic regions. However, differences in ranges associated with genetic and environmental diversity are still evident for several components when comparing values from different regions and growing seasons (Table 2). For the US and Argentina data set, there were consistent differences in levels of components of each identical reference substance grown in the two different field productions; with these differences often being quite pronounced. To illustrate, for seed from

Fig. 1. Fat, linoleic acid, and daidzein levels in seed from conventional soybean A5547 (US, Argentina [Arg], Brazil South [Sites NT, RO], and Monsoy 8329 (Brazil North, [Sites CD, SR]). Tables 1 and 2 list sites denoted by above codes. Br1 indicates the first growing season in Brazil, Br2 indicates the second growing season.

one reference substance (CMC5901COC, grown at GA, US), the highest value for fat was 23.6% DW and for oleic acid, 35.2% Total FA; whereas corresponding values for seed from the same reference substance grown at Argentina were 19.3% DW and 17.9% Total FA, respectively. Hierarchical cluster analysis (HCA, Fig. 2A) of all compositional components shows graphically the effect of location, both within and across the two field productions; in which no examples of identical reference substances are seen to cluster together. HCA of the Brazil data set (Fig. 2B) showed that, as a general rule, seeds from reference substances grown at sites from the South (both growing seasons) cluster separately from those of sites in the North. HCA (Fig. 2) also showed that growing season had an impact on the composition of seed from these soybeans (as was true for AA5547 and Monsoy 8329). Changes in the levels of most endogenous components were observed (data not shown). Table 2 shows the effect of growing season on a selected number of metabolites. A striking example of the impact of growing season on metabolite levels was observed for daidzein levels in the CD-215 and VMax soybean at RO [Brazil South] where levels were markedly decreased in the second growing season for the RO samples (Fig. 3). Table 2, as well as data for A5547 and Monsoy 8329, however, suggests that GM is not a major contributor to compositional variation. HCA of the Brazil data set (Fig. 2B) as well as prin-

G.G. Harrigan et al. / Regulatory Toxicology and Pharmacology 58 (2010) S13–S20

A

A4922_Arg_CB H4994_Arg_CB A5427 _Arg_CB DP 5989_Arg_SF Hutcheson_Arg_SF USG 5601T_Arg_SF H5218_Arg_CB UA 4805_Arg_B2 Anand_US_AR Fowler_US_NC Ozark_US_AR UA 4805_US_AR A5403_Arg_B3 A5560_Arg_B3 LEE 74_Arg_B3 A5843_Arg_B1 USG 5002T_Arg_B1 Hornbeck C5894_Arg_B2 Ozark_Arg_B2 Anand_Arg_B2 Fowler_Arg_SF A5959_Arg_B1 CMA 5804AOC_Arg_B1 CMC 5901COC_Arg_B3 A4922_US_IL USG 5601T_US_NC DP 5989_US_NC H6686_US_AL Hornbeck C5894_US_AR LEE 74_US_GA A5843US_AL Hutcheson_US_NC A5959_US_AL CMA 5804AOC_US_AL A5403_US_GA A5560_US_GA A5427 _US_IL H4994_US_IL H5218_US_IL CMC 5901COC_US_GA

B

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A5547_Br1_NT CD-213_Br1_NT_gm CD 215_Br1_NT CD 215_Br1_RO A5547_Br1_RO CD-213_Br1_RO_gm Fundacep 53_Br2_NT_gm A5547_Br2_NT Impacto_Br2_NT_gm A5547_Br2_RO Magna_Br2_RO_gm CD-214_Br1_NT_gm CD-214_Br1_RO_gm TMG 103_Br1_SR_gm V-Max_Br1_NT V-Max_Br1_RO Apollo_Br2_NT_gm CD-226_Br2_NT_gm TMG 103_Br2_SR_gm BRS Conquista_Br1_CD BRS-Favorita_Br1_CD_gm BRS Valiosa_Br1_CD_gm Monsoy 8352_Br1_CD_gm Monsoy 8360_Br1_SR_gm Monsoy 8360_Br2_SR_gm BRS Valiosa_Br2_CD_gm Monsoy 8360_Br2_CD_gm CD-225_Br2_RO_gm V-Max_Br2__RO Monsoy 8329_Br1_CD Monsoy 8329_Br1_SR Monsoy 8757_Br1_SR TMG 115_Br1_SR_gm BRS GO Luziânia_Br_CD CD-215_Br2_RO TMG 115_Br2_SR_gm Monsoy 8757_Br2_SR Monsoy 8329_Br2_CD Monsoy 8329_Br2_SR Monsoy 7908_Br2_CD_gm

Fig. 2. (A) Hierarchical cluster analysis (HCA) of compositional data on seed from commercially available soybean grown in Argentina and the US. The suffix for each entry indicates the site code (see Tables 1 and 2). (B) HCA of compositional data on seed from commercially available soybean grown in Brazil. The suffix for each entry indicates the site code and whether the reference substance is GM or conventional. Br1 indicates the first growing season in Brazil, Br2 indicates the second growing season. HCA of the Brazil set includes A5547 and Monsoy 8329 data.

genetic and environmental diversity that leads to natural variation. Levels of endogenous components may be differentially impacted by geography and growing season. In broad terms, fatty acid and daidzein levels appeared to show greatest variation over the entire data set. The fact that the conventional and GM varieties share similar compositional space in PCA evaluation regardless of geography or growing season also confirms that modern biotechnology is not a major contributor to compositional variability in agronomically equivalent soybean. This is expected since insertion of a small number of transgenes that do not interact with the biochemical pathways of plants are far less likely to influence plant composition compared with the movement of thousands of genes, many integrally tied to endogenous biochemical pathways, which are recombined during development of conventionally bred crops. Fig. 3. Levels of daidzein in seed from soybean reference substances grown in more than one growing season in Brazil [South]. The suffix for each entry indicates the site code; Br1 indicates the first growing season in Brazil, Br2 indicates the second growing season.

3.2. Contribution of environmental stress to compositional variation in conventional corn

cipal component analysis (PCA) shown in Fig. 4 confirm that the conventional and GM soybean tend to cluster primarily according to site and growing season, not according to whether or not the soybean variety contains a GM trait. In summary, results from a diverse range of conventional and GM varieties indicate that soybean composition is influenced by both

Comparative safety assessments are generally conducted on crops grown under normal agronomic practices for a given geographic region (Codex, 2003; Delaney, 2007; Kok et al., 2008). With the development of drought (or other stress)-tolerant crops, field trials may incorporate different environmental conditions. For example, a recent compositional assessment of a new

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Fig. 4. Principal component analysis (PCA) of compositional data on seed from commercially available soybean grown in Brazil. Symbols are as follows; Circles, First Growing Season; green, Brazil North; yellow, Brazil South; Squares, second growing season; blue, Brazil North; purple, Brazil South. Closed shapes are conventional soybean, open shapes are GM soybean.

drought-tolerant corn, MON 87460, used normal agronomic practices in the US during the 2006 growing season (six replicated sites) and well-watered and water-limited conditions in a 2006– 2007 Chile production (three replicated sites) (Harrigan et al., 2009). This study highlighted the compositional equivalence of MON 87460 to its conventional counterpart when both are grown under this diverse range of agronomic practices and geographic conditions. The study further highlighted significant compositional variation of corn when grown at different sites within and across these two productions. More germane perhaps, water restriction appeared to affect levels of endogenous corn components although changes were generally modest and not consistent across the three replicated sites of the Chile production. Surprisingly, however, the effect of water restriction on corn composition has not been extensively studied. It is known that there is an inverse correlation between corn yield and protein level (Uribelarrea et al., 2004) so, at least in principle, where yield is impacted by stress, so too will protein levels. A three year study of tropical corn varieties subjected to pre-anthesis drought concluded that there was no significant impact of water restriction (Feil et al., 2005). Restricting water availability after silking also showed no effect on the levels of measured minerals (P and K) despite a 33% grain yield reduction (Harder et al., 1982). Recently, two interrelated studies were conducted that were designed to assess compositional variation in conventional corn grown under water restriction, and a genetically diverse corn population grown under normal agronomic practices. In addition to standard OECD components, grain levels of known osmoprotectants, selected stress metabolites, and a range of other relevant small molecule metabolites were assessed. The goal was to establish the range of variability in levels of components that, potentially, could be impacted by drought or by modified drought tolerance. In the first study, seven conventional corn hybrids were grown under well-watered and water-limited conditions (Harrigan et al., 2007b). Analyses of the harvested grain indicated that droughtinduced changes in composition, including levels of osmoprotectants, stress metabolites, and other small molecules, were modest

and not consistently expressed across the seven different corn hybrids. It was concluded that there was no trend associating compositional changes in grain with drought exposure when assessed over a range of conventional corn hybrids. While this observation is in itself significant, this study further emphasized the high degree of genetic and environmental variability in levels of osmoprotectants and stress metabolites. The second study was undertaken to define variability in the metabolite composition of conventional corn grain and to provide insights into the effect of interactions between plant genetics and the environment on grain composition (Harrigan et al., 2007a). This study included a diverse genetic range of ninety-six different hybrids grown at three different locations in the US. Overall, recorded variability in free amino acids, sugars/polyols, and organic acids was markedly higher than that observed for analytes typically evaluated under OECD guidelines, and exceeded the variability in metabolites observed in the above study on drought restriction (Table 3). As confirmed in Table 3, the variability in nutrient levels across the 96 hybrids was due to environmental variability, genetic diversity, as well as the interaction between the two variables. The generation of large datasets in this study (17,856 datapoints [96 hybrids  3 locations  62 analytes]) highlighted the need for databases that can store compositional data in a form that is retrievable based upon user-specified criteria. 3.3. International Life Sciences Institute (ILSI) crop composition database Recognizing the need for an up to date, comprehensive, and high quality source of data, the International Life Sciences Institute released the first version of an online crop composition database (www.cropcomposition.org) in 2003 (Ridley et al., 2004). The database is a compilation of data on the nutrients, anti-nutrients, and secondary metabolites for conventionally bred maize, soybean, and cotton samples obtained from controlled field trials, in multiple world-wide locations generated by six biotechnology companies and donated to ILSI. The analyses of the samples were conducted using internationally accepted and validated analytical methods, in either accredited/certified laboratories or laboratories experienced with specific analytical methodology. Quality control checks were included with the analytical runs using certified or historically verified standards. Each data point in the database is linked to the analytical method used to generate it, in addition to also being linked to the year and location in which the sample was generated. ILSI has made a number of improvements to the database since its initial introduction and the current version, Version 3.0, contains approximately 118,000 data points representing nine production years from North America (United States and Canada), South America (Brazil, Argentina, and Chile), Europe (Spain, France, Germany, Hungary, Italy and Bulgaria), Asia (Philippines), and Australia. The database is open access on line and data can be extracted based upon user selected criteria. ILSI has recently developed an updated version of the database planned for launch in 2010. Version 4.0 represents a new user-friendly interface, significantly increased search speeds, added security, and additional search and output features including a unit conversion feature and multiple output format options. As was previously described (Ridley et al., 2004), the natural variability in composition documented in this database across genetic and environmental diversity is consistent with the results described in the preceding sections of this report. 4. Conclusions The large and ever increasing number of compositional assessments of food and feed crops conducted over the past 15 years, due

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primarily to the introduction of new crops using the tools of modern biotechnology, has facilitated a further appreciation of the effect of genetics and environment on compositional variation. Following OECD guidelines most compositional studies offer the advantages of rigorous quantitative assays. These quantitative data supply much of the crop composition data now recorded in the ILSI database allowing this resource to promote a further understanding of the variability that occurs naturally in crops, which serve as a source of nutrients for humans and animals. Data from studies conducted in the US, Argentina and Brazil over multiple growing seasons described in this report have further illustrated the effect of geography, growing season, and genetic background on soybean composition. Fatty acids and isoflavones were shown to be particularly variable in these studies. A separate investigation of 96 different maize hybrids grown at three locations in the US demonstrated that levels of free amino acids, sugars/polyols, and molecules associated with stress response, such as glycerol and glycine betaine, can vary to a greater degree than that observed for more abundant components. Together, this report provides further understanding of the variability that occurs naturally in crops which serve as a source of nutrients for healthy diets in humans and animals.

Acknowledgments The authors gratefully acknowledge the contributions of colleagues at Monsanto (Sao Paulo, Brazil)–Wladecir Oliveira, Daniella Braga and Geraldo Berger; Monsanto (St. Louis, MO)–Kristina Berman, Susan Riordan, Margaret Nemeth and Eddie Zhu; EPL Bio-Analytical Services (Niantic, IL)-Christy Hanson and Michelle Smith; Certus International, Inc. (Chesterfield, MO)–Roy Sorbet who contributed to the design, generation and/or interpretation of the composition data cited in this manuscript.

References Berberich, S.A., Ream, J.E., Jackson, T.L., Wood, R., Stipanovic, R., Harvey, P., Patzer, S., Fuchs, R.L., 1996. The composition of insect-protected cottonseed is equivalent to that of conventional cottonseed. J. Agric. Food Chem. 44, 365–371. Berman, K.H., Harrigan, G.G., Riordan, S.G., Nemeth, M.A., Hanson, C., Smith, M., Sorbet, R., Zhu, E., Ridley, W.P., 2009. Compositions of seed, forage, and processed fractions from insect-protected soybean MON 87701 are equivalent to those of conventional soybean. J. Agric. Food Chem. 57, 11360–11369. Berman, K.H., Harrigan, G.G., Riordan, S.G., Nemeth, M.A., Hanson, C., Smith, M., Sorbet, R., Zhu, E., Ridley, W.P., 2010. Compositions of forage and seed from second-generation glyphosate-tolerant soybean MON 89788 and insectprotected soybean MON 87701 from Brazil are equivalent to those of conventional soy bean (Glycine max). J. Agric.Food Chem. 58, 6270–6276. Bernir, G., Perillux, C., 2004. A physiological overview of the genetics of flowering time control. Plant Biotechnol. J. 3, 3–16. Codex, 2003. Guideline for the Conduct of Food Safety Assessment of Foods Derived from Recombinant-DNA Plants (CAC/GL 45-2003). Delaney, B., 2007. Strategies to evaluate the safety of bioengineered foods. Int. J. Toxicol. 26, 389–399. Dixon, R.A., Gang, D.R., Charlton, A.J., Fiehn, O., Kuiper, H.A., Reynolds, T.L., Tjeerdema, R.S., Jeffery, E.H., German, J.B., Ridley, W.P., Seiber, J.N., 2006. Applications of metabolomics in agriculture. J. Agric. Food Chem. 54, 8984–8994. Drury, S.M., Reynolds, T.L., Ridley, W.P., Bogdanova, N., Riordan, S., Nemeth, M.A., Sorbet, R., Trujillo, W.A., Breeze, M.L., 2008. Composition of forage and grain from second-generation insect-protected corn MON 89034 is equivalent to that of conventional corn (Zea mays L.). J. Agric. Food Chem. 56, 4623–4630. Feil, B., Moser, S.B., Jampatong, S., Stamp, P., 2005. Mineral composition of the grains of tropical maize varieties as affected by pre-anthesis drought and rate of nitrogen fertilization. Crop Sci. 45, 516–523. Gutierrez-Gonzalez, J.J., Wu, X., Zhang, J., Lee, J.D., Ellersieck, M., Shannon, J.G., Yu, O., Nguyen, H.T., Sleper, D.A., 2009. Genetic control of soybean seed isoflavone content: importance of statistical model and epistasis in complex traits. Theor. Appl. Genet. 119, 1069–1083. Hamilton, K.A., Pyla, P.D., Breeze, M., Olson, T., Li, M., Robinson, E., Gallagher, S.P., Sorbet, R., Chen, Y., 2004. Bollgard II cotton: compositional analysis and feeding studies of cottonseed from insect-protected cotton (Gossypium hirsutum L.) producing the Cry1Ac and Cry2Ab2 proteins. J. Agric. Food Chem. 52, 6969– 6976.

S19

Harder, H.J., Carlson, R.E., Shaw, R.H., 1982. Yield, yield components, and nutrient content of corn grain as influenced by post-silking moisture stress. Agron. J. 74, 275–278. Harrigan, G.G., Stork, L.G., Riordan, S.G., Reynolds, T.L., Ridley, W.P., Masucci, J.D., Macisaac, S., Halls, S.C., Orth, R., Smith, R.G., Wen, L., Brown, W.E., Welsch, M., Riley, R., McFarland, D., Pandravada, A., Glenn, K.C., 2007a. Impact of genetics and environment on nutritional and metabolite components of maize grain. J. Agric. Food Chem. 55, 6177–6185. Harrigan, G.G., Stork, L.G., Riordan, S.G., Ridley, W.P., Macisaac, S., Halls, S.C., Orth, R., Rau, D., Smith, R.G., Wen, L., Brown, W.E., Riley, R., Sun, D., Modiano, S., Pester, T., Lund, A., Nelson, D., 2007b. Metabolite analyses of grain from maize hybrids grown in the United States under drought and watered conditions during the 2002 field season. J. Agric. Food Chem. 55, 6169–6176. Harrigan, G.G., Ridley, W.P., Riordan, S.G., Nemeth, M.A., Sorbet, R., Trujillo, W.A., Breeze, M.L., Schneider, R.W., 2007c. Chemical composition of glyphosatetolerant soybean 40–3-2 grown in Europe remains equivalent with that of conventional soybean (Glycine max L.). J. Agric. Food Chem. 55, 6160–6168. Harrigan, G.G., Ridley, W.P., Miller, K.D., Sorbet, R., Riordan, S.G., Nemeth, M.A., Reeves, W., Pester, T.A., 2009. The forage and grain of MON 87460, a drought tolerant corn hybrid, are compositionally equivalent to that of conventional corn. J. Agric. Food Chem. 57, 9754–9763. Harrigan, G.G., Lundry, D., Drury, S., Berman, K., Riordan, S.G., Nemeth, M.A., Ridley, W.P., Glenn, K.C., 2010. Natural variation in crop composition and the impact of transgenesis. Nature Biotechnol. 28, 402–404. Jones, H.D., Peters, N.C., Holdsworth, M.J., 1997. Genotype and environment interact to control dormancy and differential expression of the VIVIPAROUS 1 homologue in embryos of Avena fatua. Plant J. 12, 911–920. Kessler, A., Halitschke, R., Baldwin, I.T., 2004. Silencing the jasmonate cascade: induced plant defenses and insect populations. Science 305, 665–668. Kok, E.J., Keijer, J., Kleter, G.A., Kuiper, H.A., 2008. Comparative safety assessment of plant-derived foods. Regul. Toxicol. Pharm. 50, 98–113. Kushad, M.M., Brown, A.F., Kurilich, A.C., Juvik, J.A., Klein, B.P., Wallig, M.A., Jeffery, E.H., 1999. Variation of glucosinolates in vegetable crops of Brassica oleracea. J. Agric. Food Chem. 47, 1541–1548. Lundry, D.R., Ridley, W.P., Meyer, J.J., Riordan, S.G., Nemeth, M.A., Trujillo, W.A., Breeze, M.L., Sorbet, R., 2008. Composition of grain, forage, and processed fractions from second-generation glyphosate-tolerant soybean, MON 89788 Is equivalent to that of conventional soybean (Glycine max L.). J. Agric. Food Chem. 56, 4611–4622. McCann, M.C., Liu, K., Trujillo, W.A., Dobert, R.C., 2005. Glyphosate-tolerant soybeans remain compositionally equivalent to conventional soybeans (Glycine max L.) during three years of field testing. J. Agric. Food Chem 53, 5331–5335. McCann, M.C., Rogan, G.J., Fitzpatrick, S., Trujillo, W.A., Sorbet, R., Hartnell, G.F., Riodan, S.G., Nemeth, M.A., 2006. Glyphosate-tolerant alfalfa is compositionally equivalent to conventional alfalfa (Medicago sativa L.). J. Agric. Food Chem. 54, 7187–7192. McCann, M.C., Trujillo, W.A., Riordan, S.G., Sorbet, R., Bogdanova, N.N., Sidhu, R.S., 2007. Comparison of the forage and grain composition from insect-protected and glyphosate-tolerant MON 88017 corn to conventional corn (Zea mays L.). J. Agric. Food Chem. 55, 4034–4042. Nida, D.L., Patzer, S., Harvey, P., Stipanovic, R., Wood, R., Fuchs, R.L., 1996. Glyphosate-tolerant cotton: the composition of the cottonseed is equivalent to that of conventional cottonseed. J. Agric. Food Chem. 44, 1967–1974. Oberdoerfer, R.B., Shillito, R.D., de Beuckeleer, M., Mitten, D.H., 2005. Rice (Oryza sativa L.) containing the bar gene is compositionally equivalent to the nontransgenic counterpart. J. Agric. Food Chem. 53, 1457–1465. Obert, J.C., Ridley, W.P., Schneider, R.W., Riordan, S.G., Nemeth, M.A., Trujillo, W.A., Breeze, M.L., Sorbet, R., Astwood, J.D., 2004. The composition of grain and forage from glyphosate tolerant wheat MON 71800 is equivalent to that of conventional wheat (Triticum aestivum L.). J. Agric. Food Chem. 52, 1375–1384. OECD, 1998. Report of the OECD Workshop on the Toxicological and Nutritional Testing of Novel Foods. Organisation for Economic Co-operation and Development, Paris, France. OECD, 2002. Consensus Document on Compositional Considerations for New Varieties of Maize (Zea Mays): Key Food and Feed Nutrients, Anti-Nutrients and Secondary Plant Metabolites. Organization for Economic Cooperation and Development, Paris, France. OECD, 2006. An Introduction to the Food/Feed Safety Consensus Documents of the Task Force. Organization for Economic Cooperation and Development, Paris, France. Padgette, S.R., Taylor, N.B., Nida, D.L., Bailey, M.R., MacDonald, J., Holden, L.R., Fuchs, R.L., 1996. The composition of glyphosate-tolerant soybean seeds is equivalent to that of conventional soybeans. J Nutr. 126, 702–716. Reynolds, T.L., Nemeth, M.A., Glenn, K.C., Ridley, W.P., Astwood, J.D., 2005. Natural variability of metabolites in maize grain: differences due to genetic background. J. Agric. Food Chem. 53, 10061–10067. Ridley, W.P., Sidhu, R.S., Pyla, P.D., Nemeth, M.A., Breeze, M.L., Astwood, J.D., 2002. Comparison of the nutritional profile of glyphosate-tolerant corn event NK603 with that of conventional corn (Zea mays L.). J. Agric. Food Chem. 50, 7235–7243. Ridley, W.P., Shillito, R.D., Coats, I., Steiner, H.Y., Shawgo, M., Phillips, A., Dussold, P., Kurtyka, L., 2004. Development of the international life sciences institute crop composition database. J. Food Compos. Anal. 17, 423–438. Rogan, G.J., Bookout, J.T., Duncan, D.R., Fuchs, R.L., Lavrik, P.B., Love, S.L., Mueth, M., Olson, T., Owens, E.D., Raymond, P.J., Zalewski, J., 2000. Compositional analysis of tubers from insect and virus resistant potato plants. J. Agric. Food Chem. 48, 5936– 5945.

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Shepherd, L.V., McNicol, J.W., Razzo, R., Taylor, M.A., Davies, H.V., 2006. Assessing the potential for unintended effects in genetically modified potatoes perturbed in metabolic and developmental processes. Targeted analysis of key nutrients and anti-nutrient. Transgenic Res. 15, 409–425. Sidhu, R.S., Hammond, B.G., Fuchs, R.L., Mutz, J.N., Holden, L.R., George, B., Olson, T., 2000. Glyphosate-tolerant corn: the composition and feeding value of grain from glyphosate-tolerant corn is equivalent to that of conventional corn (Zea mays L.). J. Agric. Food Chem. 48, 2305–2312.

Taylor, N.B., Fuchs, R.L., MacDonald, J., Shariff, A.R., Padgette, S.R., 1999. Compositional analysis of glyphosate-tolerant soybeans treated with glyphosate. J. Agric. Food Chem. 47, 4469–4473. Uribelarrea, M., Below, F.E., Moose, S.P., 2004. Grain composition and productivity of maize hybrids derived from the Illinois protein strains in response to variable nitrogen supply. Crop Sci. 44, 1593–1600.