Article pubs.acs.org/JAFC
Genotypic and Environmental Impact on Natural Variation of Nutrient Composition in 50 Non Genetically Modified Commercial Maize Hybrids in North America Bin Cong,† Carl Maxwell,† Stanley Luck,† Deanne Vespestad,‡ Keith Richard,§ James Mickelson,# and Cathy Zhong*,† †
DuPont Pioneer, 200 Powder Mill Road, P.O. Box 8352, Wilmington, Delaware 19803, United States Eurofins AgroSciences, Fort Walton Beach, Florida 32547, United States § EPL Bio Analytical Services, 9095 West Harristown Boulevard, Niantic, Illinois 62551, United States # DuPont Pioneer, 8325 N.W. 62nd Avenue, Johnston, Iowa 50131, United States ‡
S Supporting Information *
ABSTRACT: This study was designed to assess natural variation in composition and metabolites in 50 genetically diverse non genetically modified maize hybrids grown at six locations in North America. Results showed that levels of compositional components in maize forage were affected by environment more than genotype. Crude protein, all amino acids except lysine, manganese, and β-carotene in maize grain were affected by environment more than genotype; however, most proximates and fibers, all fatty acids, lysine, most minerals, vitamins, and secondary metabolites in maize grain were affected by genotype more than environment. A strong interaction between genotype and environment was seen for some analytes. The results could be used as reference values for future nutrient composition studies of genetically modified crops and to expand conventional compositional data sets. These results may be further used as a genetic basis for improvement of the nutritional value of maize grain by molecular breeding and biotechnology approaches. KEYWORDS: maize, hybrid, natural variation, nutrient composition, substantial equivalence, compositional analysis
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INTRODUCTION Maize is a major crop providing not only animal and human nutrition but also raw materials for manufacturing industrial products. World production and consumption of maize were about 989,608 and 953,133 thousand metric tons in 2013/2014, respectively.1 Maize yield has improved substantially in the past two decades through conventional breeding combined with molecular marker-assisted selection and biotechnology.2 To feed an expanding world population, projected to reach 9.6 billion in 2050,3 and to supply feedstock for industrial products, biotechnology is needed to improve crop productivity and sustainability. Genetically modified (GM) crop varieties were grown on >175.2 million hectares worldwide in 2013.4 GM crops have been stringently scrutinized by global regulatory agencies in many aspects including agronomic, environmental, toxicological, and nutritional compositional data since the initial application of biotechnology to crops in the 1990s. One of the risk assessments, substantial equivalence evaluation for nutrient composition of GM crops, was first outlined by the Food and Agriculture Organization of the United Nations (FAO)/World Health Organization (WHO) consultation5,6 and further developed by the Organization for Economic Co-operation and Development (OECD)7,8 and additional FAO/WHO consultation.9 Currently, many international organizations including FAO, WHO, and the International Life Sciences Institute (ILSI) have adopted substantial equivalence as an accepted principle in the safety assessment of GM plants. © XXXX American Chemical Society
In substantial equivalence analysis, measurements of analytes representing key nutrients, antinutrients, toxicants, and secondary metabolites from GM crops are compared to data collected from non-GM near-isogenic plants and non-GM conventional hybrids grown under identical conditions and are also compared to historic values from the literature and the ILSI crop composition database.10,11 This comparative safety assessment approach provides assurance that foods and feeds derived from GM crops are as safe as their conventional counterparts. Compositional studies of grain as well as forage have been conducted in both non-GM conventional crops and GM crops including maize,12 soybean,13 cotton,14 rice,15 wheat,16 potato,17 and others.18,19 Many studies have shown that transgenes have less of an effect on nutrient composition than genotypes and environments.20,21 Maize was domesticated about 8000 years ago and has undergone extensive human selection for agronomic performance for more than a century. Whereas genetic improvement contributes about 70% of the yield gained in the development of modern hybrids, grain quality and composition also have been changed.22 This study was designed to assess the degree of genotypic and environmental impact on forage and grain composition in 50 non-GM commercial maize hybrids that Received: January 22, 2015 Revised: May 12, 2015 Accepted: May 14, 2015
A
DOI: 10.1021/acs.jafc.5b01764 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
a
B
7.4 5.2−9.2 3.6 2.1−4.7 3.4 2.3−4.7 85.6 82.2−88.4 5 3.9−6.3 18.2 12.5−23.6 24.9 18.6−31.9 41.5 33.3−51.9 0.24 0.12−0.38 0.24 0.17−0.30
ON mean rangea
8.6 6.5−10.6 3.3 1.9−5.0 4.5 3.2−5.9 83.6 80.4−87.1 6.7 4.4−9.5 19.6 14.3−24.5 25.7 18.8−32.4 41.9 32.9−62.7 0.17 0.089−0.27 0.31 0.22−0.37
MN mean rangea 7.0 5.0−9.2 3 1.9−3.9 4.3 2.3−5.5 85.6 82.2−89.2 5.8 4.4−9.5 23.7 16.0−32.4 32.2 23.1−39.9 50.0 39.4−58.6 0.18 0.11−0.26 0.18 0.12−0.25
IL mean rangea 7.7 5.8−9.6 3.6 2.3−4.9 6.4 4.9−9.1 82.3 77.5−85.4 6.2 4.7−8.3 22.5 17.7−31.6 30.3 23.1−41.7 45.6 34.7−57.5 0.21 0.12−0.32 0.31 0.22−0.40
NE mean rangea 8.8 6.6−10.3 2.3 1.4−4.3 5.8 4.4−9.0 83.1 79.5−86.0 8.2 6.6−10.2 25.6 20.9−30.8 32.1 26.6−38.2 50.0 37.9−58.8 0.19 0.13−0.29 0.25 0.13−0.36
KS mean rangea 9.0 7.2−10.8 3.6 2.1−4.7 6.9 4.3−9.5 80.4 77.9−83.5 5.5 4.0−7.5 22.5 16.4−30.1 30.7 23.4−40.4 46.6 32.5−58.1 0.25 0.14−0.40 0.25 0.16−0.32
TX mean rangea 8.1 5.0−10.8 3.2 1.4−5.0 5.4 2.3−9.5 83.3 77.5−89.2 6.3 3.9−10.2 22.3 12.5−32.4 29.6 18.6−41.7 46.2 32.5−62.7 0.21 0.089−0.40 0.26 0.12−0.40
combined mean rangeb
0.0936−0.37
0.0714−0.577
20.3−63.7
16.1−47.4
35.9−62.8
NA
76.4−92.1
1.53−9.64
0.296−4.57
3.14−11.57
ILSI rangec
0.0936−0.55
0.0714−0.6
20.3−63.7
16.1−47.4
19−62.8
12.5−46.7
76.4−92.1
1.3−10.5
0.296−6.7
3.14−15.9
literature range
The minimum and maximum of sample values for hybrids grown at each location. bThe combined range for all six sites. cILSI ranges reported in version 3.0.
phosphorus
calcium
NDF
ADF
crude fiber
moisture
carbohydrates
ash
crude fat
crude protein
analyte
Table 1. Means and Ranges of Proximates, Fibers, and Minerals Measured from Forage across All Six Sites (Percent Dry Weight)
15.2
18.6
33
36.4
38
28.1
24
28.1
21.2
25.5
genotype
61.3
28.2
36
40.50
44
57.2
59.2
67.8
38.9
42.6
location
% variability
25.8
21
31.2
34.9
33.5
31.3
30.7
34.6
33.1
30.4
G×E
Journal of Agricultural and Food Chemistry Article
DOI: 10.1021/acs.jafc.5b01764 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
Article
Journal of Agricultural and Food Chemistry
Figure 1. Phylogenetic analysis of 50 hybrids based on SNP genotype data. Fifty hybrids (entry codes 1−50) are clustered in five major distinct groups labeled I, II, III, IV, and V. Five hybrids (entry codes 1−5) were selected to grow at all six sites. Forage and Grain Sample Collection. One forage sample and one grain sample were collected from each hybrid in each block for compositional analysis. Each forage sample was obtained by cutting the aerial portion of three plants from the root system, approximately 2.5 cm above the soil surface line. The plants were then cut into sections approximately ≤7.5 cm, the cut material was mixed, and one-third of the cut material was collected into a prelabeled, plastic-lined cloth bag and placed on dry ice until transfer to frozen storage (≤ −10 °C). Each grain sample was obtained by harvesting the grain from five hand-pollinated ears. Grain samples were collected at typical harvest maturity and placed in prelabeled, plastic-lined cloth bags. Samples were kept on dry ice until transfer to frozen storage (≤ −10 °C). Processing. Grain and forage samples were shipped frozen to EPL Bio Analytical Services (EPL BAS, Niantic, IL, USA) and maintained at approximately −20 °C until processing. Forage samples were dried at 60 °C in a forced-air oven and then ground with a Wiley laboratory mill (model 4, Thomas Scientific, Swedsboro, NJ, USA) to pass a 1 mm screen. Grain samples were not dried prior to grinding in an ultra centrifugal mill fitted with a 0.75 mm distance ring/sieve (ZM 200, Retsch, Newton, PA, USA). Ground samples were stored at −20 °C. Nutrient Composition Analysis. Compositional analyses were conducted by EPL BAS using GLP validated methods. Both forage and grain were analyzed for crude protein, crude fat, ash, carbohydrates, crude fiber, acid detergent fiber (ADF), neutral detergent fiber (NDF), and minerals (calcium and phosphorus). Grain was further analyzed for fatty acids, amino acids, additional minerals (manganese, iron, magnesium, copper, potassium, sodium, and zinc), vitamins (β-carotene, thiamin, riboflavin, niacin, pantothenic acid, pyridoxine, folic acid, and α-, β-, γ-, and δ-tocopherol), secondary metabolites, and antinutrients (inositol, raffinose, furfural, ferulic acid, p-coumaric acid, phytic acid, and
represent different genetic backgrounds and maturity groups. These hybrids were grown in six different geographical regions in North America during the 2010 growing season with each site having 20 unique genotypes. Forage and grain samples collected from the commercial maize hybrids were analyzed for key nutritional components. The genotype data for approximately 3000 single-nucleotide polymorphisms (SNPs) were used in genome-wide association studies (GWAS) for several nutrients. The results for oleic acid are reported here to demonstrate that the genetic variation in the selected hybrids is sufficient in providing an independent confirmation of previous GWAS results for oleic acid. Furthermore, the Pearson correlation matrix was applied to calculate the distance matrix for the analytes. This study provides insights into natural variation in maize nutrient composition by identifying effects of genotype, environment, and their interaction at the analyte level. In addition, the results may offer opportunities for further genetic improvement of maize forage and grain composition.
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MATERIALS AND METHODS
Materials. Fifty DuPont Pioneer non-GM commercial maize hybrids (entry codes 1−50) with diverse genetic backgrounds and different maturity groups were grown at six sites in North America (Illinois (IL), Kansas (KS), Minnesota (MN), Nebraska (NE), Texas (TX), and Ontario (ON)). Twenty genotypes were grown at each location as shown in Table 1 of the Supporting Information reported by Asiago et al.23 At each location, the selected genotypes were planted in three blocks and two replicates per block. C
DOI: 10.1021/acs.jafc.5b01764 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
Article 30.2
14.1
a
carbohydrates
ash
NDF
ADF
crude fiber
The minimum and maximum of sample values for hybrids grown at each location. bThe combined range for all six sites. cILSI ranges reported in version 3.0.
77.4−89.5
0.616−6.28
77.4−89.5
0.616−6.28
52.2
61.6
42.1
19.50
31.2 9.3 5.59−22.6
1.82−11.3
5.59−22.6
1.82−11.3
0.49−5.5 0.49−3.26
2.47−5.9
43.5
36.4
43.1
37.2
27.3
23.5
33.1
24.4 43.8 2.47−5.9
6−17.3 6.15−17.3
9.8 6.5−13.4 4.3 2.9−7.7 2.6 1.8−3.4 4.1 2.3−5.7 10.9 7.6−22.3 1.4 1.1−1.8 84.5 80.7−88.2 10.3 9.1−12.2 4.4 3.3−5.4 2.5 2.0−2.9 4.1 3.4−4.9 12 8.2−22.3 1.4 1.3−1.6 83.8 81.6−85.4 11.6 9.9−13.4 4.1 3.0−5.0 2.5 1.8−3.0 3.9 3.0−5.1 10.5 7.9−12.7 1.4 1.2−1.6 82.9 80.7−84.7 9.3 7.6−11.0 4.5 3.6−7.7 2.8 2.5−3.4 4.3 3.7−5.2 11.2 9.0−14.8 1.4 1.3−1.6 84.8 81.8−87.2 9.3 8.5−10.4 4.0 3.2−4.9 2.8 2.2−3.2 3.9 2.3−4.6 10.9 9.2−13.1 1.3 1.1−1.4 85.4 84.1−87.0 9.7 8.2−11.2 4.3 2.9−5.0 2.7 2.2−3.3 3.8 3.2−4.5 10.5 7.6−12.4 1.4 1.2−1.8 84.6 82.4−87.1 8.5 6.5−11.8 4.5 3.7−6.9 2.6 2.0−3.1 4.6 3.7−5.7 10.5 8.5−13.5 1.3 1.1−1.7 85.6 81.5−88.2 crude protein
crude fat
G×E ILSI rangec combined mean rangeb TX mean rangea KS mean rangea NE mean rangea IL mean rangea MN mean rangea ON mean rangea analyte
Table 2. Means and Ranges of Proximates and Fibers Measured from Grain across All Six Sites (Percent Dry Weight) D
13.8
58.5
location genotype
% variability
literature range
46
trypsin inhibitor). Most compositional analyses were performed as described by Herman et al.24 Modified procedures used for measuring fatty acids, minerals, inositol, and raffinose and procedures used for measuring crude fat, niacin, pantothenic acid, and pyridoxine are briefly described below. Crude Fat. Crude fat content was quantified by an automated hydrolysis method using an ANKOMHCl hydrolysis system (Ankom Technology, Macedon, NY, USA) and an automated extraction method using an ANKOMXT15 extraction system.25−27 Samples were hydrolyzed at 90 °C for 80 and 60 min for forage and grain, respectively, and then extracted using a mixed solvent of petroleum ether/ethyl ether/ethanol (45:45:10). Extracts were evaporated, and the fat residue remaining was determined gravimetrically. Fatty Acids. Lipid material was extracted from the grain with petroleum ether using a Soxhlet apparatus.28 Lipids were saponified with 2% methanolic NaOH followed by methylation with 14% BF3/methanol reagent. The fatty acid methyl esters were extracted with hexane and analyzed by gas chromatography with flame ionization detection (GC-FID). Minerals. The concentrations of calcium and phosphorus in forage and grain as well as those of copper, iron, magnesium, manganese, potassium, sodium, and zinc in grain were measured by inductively coupled plasma−optical emission spectrometry (ICP-OES) after digestion in 28% aqueous nitric acid with a MarsXpress (CEM Corp., Matthews, NC, USA) microwave.29,30 Emission intensity at the appropriate wavelength was determined for each element and compared to that of external standards for quantification. Niacin (Vitamin B3). Grain sample analysis was based on a method from the AACC.31 Aqueous extracts were prepared of samples and then an aliquot of the extract was mixed with a niacin-free bacterial growth medium. The mixture was inoculated with Lactobacillus plantarum. The niacin concentration was determined by measuring the turbidity of the L. plantarum growth response in the sample and comparing it with the growth response in standard concentrations of niacin. Pantothenic Acid (Vitamin B5). Grain sample analysis was based on a method from the AOAC.32 Panthothenic acid was extracted with a pH 5.65 acetate buffer and autoclaving. The extract was mixed with a pantothenic acid-free bacterial growth medium and then the mixture inoculated with L. plantarum. The pantothenic acid concentration was determined by measuring the turbidity of the L. plantarum growth response in the sample and comparing it with the growth response in standard concentrations of pantothenic acid. Pyridoxine, Pyridoxal, and Pyridoxamine (Vitamin B6). Grain sample analysis was based on a method from the AACC.33 Vitamin B6 was extracted with 0.44 N sulfuric acid and autoclaving. The extract was mixed with a vitamin B6-free microbiological growth medium and then the mixture inoculated with Saccharomyces cerevisiae. The vitamin B6 concentration was determined by measuring the turbidity of the S. cerevisiae growth response in the sample and comparing it with the growth response in standard concentrations of pyridoxine. Inositol and Raffinose. Measurement of inositol and raffinose was based on three methods.34,35 Grain samples were extracted with 50% aqueous ethanol. The ethanol extract was partitioned against chloroform and then split into aliquots and dried. One aliquot was resuspended in 50% aqueous acetonitrile and then analyzed by reverse phase HPLC with refractive index (RI) detection to quantify raffinose. External standards were used to calibrate the HPLC-RI. The other aliquot was treated with butylboronic acid, and butylboronic acid-derivatized inositol was quantified by GC-FID. External standards were used to calibrate the GC-FID. Statistical Analyses. Statistical analyses were conducted using R36 and SciPy.37 For each nutrient composition analyte, the arithmetic means and ranges at each site and across sites were calculated across hybrid maize lines. In each location, measurements were taken for each hybrid with replication in three different blocks. Additive corrections were applied to the data for the replicates to remove the variation between blocks. A two-way ANOVA procedure was used to calculate the explained variance for genotype, location, and genotype by location interaction.
33.4
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DOI: 10.1021/acs.jafc.5b01764 J. Agric. Food Chem. XXXX, XXX, XXX−XXX
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Journal of Agricultural and Food Chemistry
Figure 2. Environmental and genetic impacts on natural variation of analytes. Bar plot of the explained variance from a two-way ANOVA analysis of genotype by environment effects. Variability (from the highest to the lowest) of each analyte explained by location effects was used to order analytes on the x-axis.
hybrids.40 Mean and range values for each analyte from 20 hybrids grown at each site are reported (Tables 1−7). The composition data collected from this study could expand conventional compositional data sets and be used as reference values for substantial equivalence assessment for GM crops. In maize breeding programs the standard procedure is to test only the best adopted hybrids in a given location, which are classified into maturity zones. Hybrids with flowering time outside the range for a zone would present a problem for crop management of the field testing and commercial production. This maturity zone constraint resulted in an unbalanced experiment design for this study, but every effort was made to include hybrids across locations that could be adopted to the zones.21 In the study, five hybrids, representing clades I, III, IV, and V, were grown in all six locations (Figure 1). Data analysis was performed with these 5 hybrids, and the results showed similar trends with the results analyzed from the 50 hybrids (Supplemental Figure 1). Furthermore, two previously published forage and grain metabolomics studies have shown similar results.23,41 These two studies used samples collected from the same set of non-GM DuPont Pioneer commercial hybrids as reported in this study, with one study focusing on data analysis of metabolites measured from the 50 hybrids and the other one on metabolomics data measured only from the 5 hybrids grown at all locations.23,41 We regard the hybrids as constituting a representative population for a given location and the data as providing estimates for the population mean and variance for nutrient. Our use of the two-way ANOVA method for the analysis of genotype by environment effects relies on the validity of this approximation. Therefore, our conclusions about genotype by environment interactions are best interpreted as corresponding to elite maize germplasm subject to the maturity zone restriction. The effects of genotype, environment, and their interactions are also reported (Tables 1−7). Proximate, Fiber, Calcium, and Phosphorus Contents in Forage. Crude protein, fat, carbohydrates, fiber including ADF and NDF, calcium, phosphorus, ash, and moisture were measured and/or calculated for forage from the 50 hybrids across six locations. Statistical analysis demonstrated that natural variation for all components measured in forage was attributed largely to environmental effects rather than genotypic effects (Table 1). The similar results have been previously reported.23,41
The inbred parents for the 50 non-GM commercial hybrids were genotyped using Illumina custom bead arrays for 3072 SNPs. Genotypes for the hybrids were inferred from the homozygous base calls for each of the parents, with lexicographic ordering of the base calls. The median for the missing genotype calls was 8%, and the maximum was 32%. Additional genotype data for 2000 SNPs were also included with missing genotype calls up to 78%. The neighbor-joining phylogenetic analysis with 1000 bootstrap iterations was carried out using QuickTree.38 The phylogeny analysis result file was imported into Archaeopteryx to produce the tree graph.39 The genotype data for approximately 3000 SNPs were used in a GWAS for oleic acid. Genotype data for an individual SNP were used as the predictors in a linear statistical model for the phenotype. In the GWAS graph, explained variation, r2, is plotted versus genetic coordinate for the SNPs. SNPs with minor allele counts of
