Analytical method evaluation and discovery of variation within maize varieties in the context of...

Journal of Agricultural and Food Chemistry is published by the American ChemicalSociety. 1155 Sixteenth Street N.W., Washington, DC 20036Published by American Chemical Society. Copyright American Chemical Society.However, no copyright claim is made to original U.S. Government works, or worksproduced by employees of any Commonwealth realm Crown government in the courseof their duties.

ArticleAnalytical method evaluation and discovery of variation within maize

varieties in the context of food safety: Transcript profiling and metabolomics.Weiqing Zeng, Jan Hazebroek, Mary Beatty, Kevin Hayes, Christine Ponte, Carl A. Maxwell, and Cathy Zhong

J. Agric. Food Chem., Just Accepted Manuscript DOI: 10.1021/jf405652j Publication Date (Web): 24 Feb 2014Downloaded from http://pubs.acs.org on March 16, 2014

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1

Analytical method evaluation and discovery of variation within maize 1

varieties in the context of food safety: Transcript profiling and metabolomics. 2

3

Weiqing Zeng1*, Jan Hazebroek

2, Mary Beatty

2, Kevin Hayes

3, Christine Ponte

1, Carl Maxwell

1, 4

and Cathy Xiaoyan Zhong1* 5

6

1DuPont Pioneer, Regulatory Sciences, Wilmington, DE 19880 7

2DuPont Pioneer, Analytical & Genomics Technologies, Johnson, IA 50131 8

3DuPont Pioneer, Trait Characterization, Johnson, IA 50131 9

*Corresponding Authors 10

11

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ACS Paragon Plus Environment

Journal of Agricultural and Food Chemistry

2

SUMMARY 12

Profiling techniques such as microarrays, proteomics, and metabolomics are used widely to 13

assess the overall effects of genetic background, environmental stimuli, growth stage, or 14

transgene expression in plants. To assess the potential regulatory use of these techniques in 15

agricultural biotechnology, we carried out microarray and metabolomic studies of three different 16

tissues from eleven conventional maize varieties. We measured technical variations for both 17

microarrays and metabolomics, compared results from individual plants and corresponding 18

pooled samples, and documented variations detected among different varieties with individual 19

plants or pooled samples. Both microarray and metabolomic technologies are reproducible, and 20

can be used to detect plant-to-plant and variety-to-variety differences. A pooling strategy 21

lowered sample variations for both microarray and metabolomics, while capturing variety-to-22

variety variation. However, unknown genomic sequences differing between maize varieties 23

might hinder the application of microarrays. High throughput metabolomics could be useful as a 24

tool for the characterization of transgenic crops. However, researchers will have to take into 25

consideration the impact on the detection and quantitation of a wide range of metabolites on 26

experimental design as well as validation and interpretation of results. 27

KEYWORDS 28

Metabolomics, zea mays, maize, microarray 29

30

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INTRODUCTION 31

Global demand for food is increasing rapidly, a trend that is expected to continue for many years. 32

This trend coincides with the growth of the world population, the limited availability of arable 33

land and irrigation water, and global environmental changes.1-3

In addition to traditional plant 34

breeding, biotechnology has become a main focus in the effort to meet the global food demand. 35

The main crops targeted for genetic engineering include maize, soy, cotton, oilseed, 36

canola/rapeseed, rice, potato, staple cereal plants, and vegetables.2 37

The introduction of genetically modified (GM) crops has presented technical, regulatory, 38

and social challenges.4,5

Detailed studies are required to demonstrate that food and feed produced 39

from agricultural products developed through biotechnology are as safe as conventional 40

counterparts, not posing risks to the environment or human health.6-8

In the early 2000s, the 41

concept of substantial equivalence emerged for testing the equivalence of GM and corresponding 42

conventional crops.5,9

The introduction of a single gene of interest should preferably affect only 43

the desired trait. The biochemical composition of the crop should otherwise be comparable to a 44

parental strain or a variety similar to the parental line.10

Therefore, compositional analysis 45

covering key nutrients and anti-nutrients is recommended by the Organization for Economic 46

Cooperation and Development (OECD). This targeted approach, focusing on the majority of the 47

compositional components,11-14

has been widely accepted by international regulatory agencies as 48

part of the concept of substantial equivalence and applied to the assessment of the safety of GM 49

crops.9,14,15

50

The development of -omics profiling offers powerful high-throughput tools for 51

biomedical and agricultural studies. Since non-targeted profiling technologies can screen many 52

components simultaneously, they have the potential to provide insight into complicated 53

metabolic pathways and their interconnections. Such technologies therefore could represent 54

valuable analytical approaches for the assessment of substantial equivalence for GM plants.10,15-

55

17 The challenges in the use of these methods are due to the complexity of the data sets and the 56

use of different technological platforms and software that might generate artifacts, biases, and 57

non-uniform data representations.18

58

Although non-targeted surveys of the overall transcriptome, proteome, or metabolome of 59

a plant at one snapshot in time and tissue are gaining attention,19,20

these technologies are not yet 60

fully validated within the regulatory framework and therefore not at present officially 61

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recommended for safety evaluations of GM plants. A major challenge is to determine whether 62

any detected differences are due to genetic manipulation through biotechnology or are due to 63

natural variation resulting from genetic and environmental effects, interaction of genotypes with 64

environments, or even stochastic differences between plants. For this purpose it is necessary to 65

evaluate reproducibility of these analytical methods and natural variation of the results of 66

applying these methods to crop species, such as maize. Without this understanding it would be 67

impossible to interpret the omics data and declare equivalence. Therefore, the International Life 68

Sciences Institute (ILSI) recommended establishing baseline ranges for natural variations and 69

validating these -omics technologies before they can be used for regulatory assessment of 70

biotech crops.15

This paper is directed towards fulfilling this function for transcriptomic and 71

metabolomic methods. 72

Microarray analysis of transcriptomes is available for both model and crop plants, 73

including Arabidopsis, maize, rice, potato, tomato, soy, pepper, barley, Brassica, and 74

sugarcane.21

Microarrays provide high-throughput, simultaneous detection of differences in 75

mRNA abundance between samples for thousands of genes. Use of microarray technology for 76

safety assessment of GM crops faces some challenges. First, nucleic acid probe hybridization is 77

not able to detect genes expressed at very low level or genes with alternate splicing forms. 78

Second, it is difficult to achieve high reproducibility for microarray experiments due to 79

variations resulting from sample handling, experiment processes, environmental impact on 80

plants, and crop variety differences.22-24

81

Technologies for simultaneous analysis of metabolites have been developed,25,26

and 82

offer the possibility of surveying significantly more metabolites than conventional chemical 83

analyses in a much shorter time and with much lower cost per analyte. However, comparing data 84

from different laboratories remains challenging. This challenge is usually due to relative rather 85

than absolute quantification and to different methodologies adopted by different groups, 86

including equipment platforms and statistical analysis methods. High sample-to-sample and 87

experiment-to-experiment variability, even within the same laboratory, and the wide 88

concentration range of the same metabolite between plants add to the complexity of the 89

analysis.10

We applied microarray and metabolomic technologies to a randomized field study as 90

conventionally used in regulatory studies. To evaluate the reproducibility and technical 91

variations of the microarray and metabolomic technologies, the samples were tested individually 92

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or as pools of plants, RNA and metabolites were extracted and analyzed by microarray and 93

GC/MS. Overall, we evaluated the reproducibility of the microarray and metabolomic 94

technologies in order to explore the capability of these methodologies in our experimental 95

settings to detect the natural variation of gene expression and metabolite levels between plants 96

and maize varieties. 97

98

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MATERIALS AND METHODS 99

100

Plant Tissue 101

Seven inbred and four non-GM commercial hybrid maize varieties were planted in a randomized 102

plot at DuPont Stine Haskell Research Center, Newark, Delaware, USA. Twenty-five seeds were 103

sowed per row for each variety. Leaves at the V5 growth stage and immature kernels at 25 days 104

after pollination (DAP) were collected in the morning between 8:30 and 12 AM for microarray 105

and GC/MS-based metabolomics. 106

Three leaf punches avoiding midribs were collected at the middle of the V5 leaf area and placed 107

on dry ice immediately after harvest, transported to the lab on dry ice, and stored at -80C before 108

processing for metabolomic analysis. The remaining leaf was collected and frozen in liquid 109

nitrogen immediately after harvest, transported to the lab on dry ice, and stored at -80C before 110

processing for microarray analysis. 111

For 25 DAP kernels, 10 kernels in the middle row of the ear were collected for metabolomics, 112

and the remaining kernels were used for microarray analysis. The ears at 25 DAP were removed 113

from the plants and placed on the wet ice immediately after harvest and transported to the lab on 114

wet ice. Immature kernels were removed from the cobs, frozen in the liquid nitrogen, and stored 115

at -80C before processing for microarray and metabolomics analyses. 116

Mature kernels at R6 growth stage (about 60 DAP) were also collected for metabolomics 117

analysis. The ears at R6 stage were removed from the plants and placed on the wet ice 118

immediately after harvest and transported to the lab on wet ice. Ten mature kernels in the middle 119

row of the ear were removed from the cob, frozen in the liquid nitrogen, and stored at -80C 120

before processing for metabolomics analyses. 121

For microarray analysis, tissues were ground into fine powders. For metabolomics analysis, 122

tissues were lyophilized before grinding to fine powders. Additional pooled samples were 123

obtained by combining equivalent amounts of ground material from three individual plants. 124

125

Microarray 126

Total RNA was isolated from ground frozen tissue using the EZNA SQ RNA Isolation Kit 127

(Omega Bio-Tek, Norcross, GA), treated with DNase-I, and used for mRNA isolation with the 128

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Illustra mRNA Purification Kit (GE Biosciences, Pittsburgh, PA). The total RNA and mRNA 129

samples were visualized and quantified on a Bioanalyzer 2100 (Agilent Technologies, Santa 130

Clara, CA). Each mRNA sample was converted into double-stranded DNA by an in-vitro 131

transcription reaction and labeled with Cy3 fluorescent dye using the Low RNA Input 132

Fluorescent Linear Amplification Kit (Agilent Technologies, Santa Clara, CA). The cRNA 133

product was purified with an Agencourt RNAClean Kit (Beckman Coulter, Indianapolis, IN). 134

Hybridizations were performed overnight with equal amounts of labeled cRNA to a custom 135

4x44K Maize Oligo Microarray from Agilent Technologies (Santa Clara, CA) according to 136

Agilents One-Color Microarray-Based Gene Expression Analysis protocol. After hybridization, 137

the microarray slides were washed and immediately scanned with the G2505C DNA Microarray 138

Scanner (Agilent Technologies, Santa Clara, CA). The images were visually inspected for 139

artifacts and feature intensities were extracted, filtered, and normalized with the Feature 140

Extraction Software (v 10.5.1.1) (Agilent Technologies, Santa Clara, CA). Quality control and 141

downstream analysis were performed using data analysis tools in Genedata Expressionist and the 142

statistical language R. Further data analysis and bioinformatic analyses were carried out 143

according to methods described in Hayes et al.27

144

145

Metabolomics 146

Metabolites were extracted from approximately 3 mg (dry weight) lyophilized tissues for each 147

sample. In a 1.1-mL polypropylene microtube containing two -5/32 inch stainless steel ball 148

bearings, each sample was added with 500 L of chloroform:methanol:water (2:5:2, v/v/v) 149

solution containing a 0.015 mg ribitol internal standard. Samples were homogenized in a 2000 150

Geno/Grinder ball mill at setting 1,650 for 1 min and then rotated at 4C for 30 min before being 151

centrifuged at 1,454g for 15 min at 4C. Aliquots (300-L) were transferred to 1.8-mL high 152

recovery glass autosampler vials, evaporated to dryness in a speed vac, and re-dissolved in 50 L 153

of 20 mg mL-1

methoxyamine hydrochloride in pyridine. The vials were capped, agitated with a 154

vortex mixer, and incubated in an orbital shaker at 30C for 90 min to form methoxyamine 155

derivatives. Next, 80 L of N-methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA) were added 156

to each sample to form trimethylsilyl (TMS) derivatives by a Gerstel autosampler 30 min prior to 157

injection to minimize sample variations due to derivatization differences. This just in time 158

derivatization eliminates variation due to differences in reaction time or temperature. 159

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Furthermore, the gas chromatograph inlet liner and septum were replaced daily, mitigating 160

against the known influence of sample residue in the inlet on trimethylsilyation completeness.28

161

However, trimethylsilylation can vary with the sample matrix.28

Thus, for molecules such as 162

amino acids that present multiple reaction sites leading to the possibility of two or more chemical 163

derivatives, the relative abundance of these trimethylsilyled forms can vary among the three 164

different tissue types assayed in this study. 165

The derivatized samples were separated by gas chromatography on a Restek 30m x 0.25mm x 166

0.25m film thickness Rtx

-5Sil MS column with a 10 m Integra-Guard column. One 167

microliter injections were made with a 1:30 split ratio using the Gerstel autosampler. The 168

Agilent 6890N gas chromatograph was programmed for an initial temperature of 80C for 0.5 169

min, increased to 350C at a rate of 18 min-1

where it was held for 2 min, before being cooled 170

rapidly to 80C and held there for 5 min in preparation for the next run. The injector and transfer 171

line temperatures were 230C and 250C, respectively, and the source temperature was 200C. 172

Helium was used as the carrier gas with a constant flow rate of 1 mL min-1

maintained by 173

electronic pressure control. Data acquisition was performed on a LECO Pegasus III time-of-174

flight mass spectrometer with an acquisition rate of 10 spectra sec-1

in the mass range of m/z 45-175

600. An electron beam of 70eV was used to generate spectra. Detector voltage was 1,750 V. An 176

instrument auto-tune for mass calibration using PFTBA (perfluorotributylamine) was performed 177

prior to each sample sequence. 178

179

Metabolomics Data Processing and Analysis 180

Raw Leco GC/MS .peg datafiles were converted into .netcdf (Andi) formats using the Leco 181

ChromaTof

ver. 4.13 software. Data preprocessing was performed with Genedata Refiner MS

182

ver. 5.2.1 software. For each .netcdf file, retention times were converted into retention indices 183

using an in-house program. Preprocessing consisted of gridding chromatograms in the m/z value 184

(80-437) and retention index dimensions, subtracting chemical noise, aligning the retention 185

indices of each selected ion chromatogram, and detecting nominal mass peaks, using empirically 186

optimized settings for each process. Data from each of the three tissue types were processed 187

separately to maximize alignment and peak peaking. The resulting three matrices consisted of 188

intensities for each m/z value_retention index combination and each sample. The aligned and 189

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de-noised data matrices were passed to Genedata Analyst ver. 2.1 software where each 190

intensity value by sample was normalized for both the ribitol internal standard signal and sample 191

dry weight. 192

Since m/z value_retention index fingerprint data is redundant, significant signatures were 193

reduced to named known metabolites based on matching both the retention index and mass 194

spectrum to those of authentic standards. Relative quantitation of each metabolite in each sample 195

was derived from the intensity of each metabolites representative m/z value obtained from the 196

Genedata Analyst output. In a few cases, peak heights obtained from ChromaTof

197

quantification ion chromatograms were used instead when signals were below the threshold set 198

for fingerprinting and thus not present in the Genedata Analyst output. Metabolite detection 199

from either source was dependent on reaching a conservative limit of detection to mitigate 200

against false positive peaks that would have an undue effect on subsequent statistical analyses. 201

Percent CV values were calculated for each metabolite across selected samples. Data matrices 202

were reformatted and imported into the PLS_Toolbox version 7.0.1 (Eigenvector Research, Inc.), 203

with which principle component analysis (PCA) was performed on autoscaled (mean centered 204

and each variable scaled to unit variance) data. 205

206

Experimental Design 207

For both microarray and metabolomics experiments, 11 maize varieties were used, including (1) 208

seven inbred lines PHG9B (high oil), H31(low oil), PH2WBS (high protein), PH2WBR (low 209

protein), PH0GP (median starch), PH14T(median starch), and 658 (low starch); and (2) 4 210

commodity hybrid lines 38B85, 37Y12, 34A15, and 34P88. These lines were chosen as a partial 211

representation of the range of U.S. cultivated maize diversity, and include lines differing in 212

protein, oil and starch content. Three types of tissues, V5 leaf, 25 DAP immature kernel, and 213

mature kernel, were used for metabolomic experiments. Because the mature kernels are dormant 214

and have very limited gene expression,29,30

only the V5 leaf and the 25 DAP immature kernels 215

were used for microarray experiments. Due to limited tissue availability for some varieties, some 216

microarray or metabolomic experiments were not conducted.. 217

For microarray technical repeat controls, eight independent RNA samples were isolated 218

from either V5 leaves or 25 DAP immature kernels of a single plant from a high oil variety 219

PHG9B and a low oil line H31, and used for eight different microarray hybridizations. The 220

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signal differences among these hybridizations were considered as the technical variation of the 221

microarray methodology. Similarly, eight independent metabolite extractions were made from 222

bulk collections of V5 leaves or 25 DAP immature kernels of PHG9B and H31, and from bulk 223

mature kernels of PH2WBS (high protein line) and PHG9B. They were used for independent 224

GC/MS analyses and technical variation assessment. The multiple sample preparation and testing 225

steps were used to evaluate the reproducibility of both technical methods. Sample variations 226

were also evaluated by comparing data from different individual plants and different pooled 227

plants. 228

229

qRT-PCR 230

Genes, primers and probes are listed in Supplementary Table 1. Primers and probes were 231

designed with Primer Express 3.0.1 (Applied Biosystems, Carlsbad, CA) and purchased from 232

Integrated DNA Technologies, Inc. (Coralville, IA). First-strand cDNA was synthesized from the 233

same mRNA samples used for microarray. Fifteen pooled samples from either V5 leaf or 25 234

DAP kernel were chosen based on sample availability. For each RT reaction, 240 ng mRNA was 235

used as a template in a total volume of 80 l following the manufacturers instruction for the 236

SuperScript VILO cDNA synthesis kit (Invitrogen, Carlsbad, CA). All qRT-PCR primers and 237

Taqman probes were designed using the Primer Express program (Applied Biosystems, 238

Carlsbad, CA), and tested for specificity by Blast search against the NCBI public sequence 239

database. The qPCR reactions were carried out in 384-well plates in a ViiA 7 real-time-PCR 240

machine (Applied Biosystems, Carlsbad, CA) using the TaqMan Gene Expression Master Mix 241

(Applied Biosystems). The qPCR program was 50C for 2 min, 95C for 10 min, followed with 242

40 cycles of 95C for 15 sec and 60C for 1 min. Each reaction contains 200 nM of each primer, 243

100 nM probe, and 2 l of the RT reaction solution as template in a final volume of 20 l. Every 244

reaction was repeated 3 times. The ViiA 7 Software V1.2 was used to record and process the 245

data. The Rn (Normalized Reporter) values of each reaction for every cycle was exported and 246

used to calculate the single-well qPCR efficiencies using a Real-Time PCR Miner program.31

247

248

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RESULTS 249

Microarray Reproducibility 250

To compare the reproducibility of expression levels of the same genes on repeated microarrays, 251

the data were analyzed using correlation statistics. The coefficient of variation (CV) for each set 252

of repeats was calculated and compared as an indication of reproducibility.32

Mean CV of gene 253

transcripts for technical repeats were 0.25, 0.23, 0.33, and 0.23 for 25 DAP PHG9B, 25 DAP 254

H31, V5 leaf PHG9B, and V5 leaf H31 samples, respectively, relatively low compared to the 255

microarray literature,33-35

indicating good technical reproducibility. Expression of most of genes 256

on the microarrays had low CV values, with 82.4%, 92.1%, 90.4%, and 91.6% of genes from V5 257

leaf PHG9B, V5 leaf H31, 25 DAP PHG9B and 25 DAP H31 microarrays exhibiting CV values 258

below 0.5 (Figure 1A). In addition, these CV values showed log-normal distribution centered at 259

0.1 (Figure 1B), indicating good reproducibility. However, the reproducibility of 8 technical 260

repeat microarrays for PHG9B V5 leaves was little higher than other technical repeat 261

microarrays (Figure 1A). Alternatively, the CV values were log transformed and plotted against 262

the log transformed mean values. Polynomial curve fitting showed as expected that CV values 263

decreased as the mean intensities increased (Figure 1C). The inflection points calculated based 264

on the polynomial curves showed that technical repeat microarrays for PHG9B V5 leaves have 265

higher background noise (Figure 1D), similar to that shown by the CV distributions (Figure 1A). 266

We further investigated the reproducibility of microarray results by a linear regression 267

model correlating data between any pair of microarrays within each group. Four groups were 268

analyzed this way for both V5 leaf and 25 DAP kernel samples, including the 8 technical arrays 269

for H31, the 6 individual biological repeats for H31, the 8 technical arrays for PHG9B, and the 9 270

individual biological repeats for PHG9B. The box plots represent the distributions of R square 271

values of all pair-wise comparisons of linear regression modeling (Figure 2). The technical 272

replicates had consistently higher correlations than the biological replicates (Figure 2). We 273

conclude that gene expression variation from microarrays resulted primarily from maize variety 274

differences rather than from plant-to-plant differences, pooled sample-to-sample differences, or 275

technical variations, indicating that the method is sensitive enough to detect biological variation 276

among individual or pooled plant samples. 277

278

Correlation between gene expression of individual and pooled samples 279

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Next, CV values for the microarrays from the 6 varieties analyzed both as individual samples (I) 280

and pooled samples (P) were calculated. For each variety, 6 or 9 individual plants and 2 or 3 281

pools of samples (3 plants per pool) were analyzed. The pools were created by combining equal 282

amount of RNA extracts from individual plants. Overall, 73.7% (34A15_I) 93.3% (38B85_P) 283

of the genes had CV values below 0.5 from V5 leaf samples, and 73.8% (38B85_I) 96.8% 284

(PHG9B_P) of genes had CV values below 0.5 from 25 DAP kernel samples (Supplementary 285

Table 2), representing very good experimental reproducibility. The distributions of the CV 286

values from both 25 DAP kernel and V5 leaf samples are shown in Supplementary Figure 1. 287

When the overall CV distribution patterns of individual or pooled samples were compared, 288

microarrays for 25 DAP kernel samples showed larger CV differences, compared to V5 leaf 289

samples. Additionally, log10 (CV) vs. log10 (mean) plots were generated for all the microarrays 290

to reveal the relationship between CV and mean intensities (data not shown). The inflection point 291

values were very similar to what was shown by the CV distribution patterns (Supplementary 292

Figure 1). 293

The Pearsons correlation coefficient was calculated by comparing mean gene 294

expressions between individual samples and pooled samples for each variety and tissue type. The 295

R values were between 0.9743 and 0.9959 (Table 1), indicating that the signals obtained from 296

individual plants and pooled plants were highly correlated and similar. When samples from the 297

same variety but different tissue types were compared, the Pearson correlation R values were 298

between 0.234 and 0.317 (Table 1), indicating significant gene expression differences between 299

leaves and kernels, as expected. For every variety-tissue combination, the pooled samples 300

showed a smaller mean CV value than the one from corresponding individual samples 301

(Supplementary Table 3). Therefore, the plant-to-plant variation detected from the same variety 302

was reduced by pooling 3 plants into a single sample, essentially transforming plant-to-plant 303

variation into sample-to-sample variation. In addition, the distribution patterns of CV values 304

from I and P samples were very similar (Supplementary Figure 2), indicating that our pooling 305

strategy was efficient in capturing the variations existing among maize varieties, while realizing 306

a cost savings. 307

308

Gene expression differences between varieties 309

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To evaluate gene expression variation between maize varieties, mean microarray spot intensities 310

from 6 or 9 individual samples (I) and 2 or 3 pooled samples, with 3 individuals per each pool 311

(P) were determined and used for CV calculations comparing the 6 varieties that had both I and P 312

samples. 313

When the CV distributions of individual samples representing variety-to-variety 314

variations (Supplementary Figure 2 and Supplementary Table 4) were compared to the CV 315

distributions representing plant-to-plant variation within a certain variety (Supplementary Figure 316

3 and Supplementary Table 2), we found that the former were larger. For V5 leaves, 65.4% of 317

genes showed a CV value less than 0.5 comparing different maize varieties (Supplementary 318

Figure 2, Supplementary Table 4), but 73.7% (34A15) to 93.1% (38B85) of genes had a CV less 319

than 0.5 when comparing individual plants within a given variety (Supplementary Figure 1, 320

Supplementary Table 2). For 25 DAP kernels, 68% of genes showed a CV value less than 0.5 321

comparing different maize varieties (Supplementary Figure 2, Supplementary Table 4), but 322

73.8% (38B85) to 85.1% (H31) of genes had a CV less than 0.5 when comparing individual 323

plants within a given variety (Supplementary Figure 1, Supplementary Table 2). These results 324

indicate that higher variations exist among different maize varieties compared to those among 325

individual plants of the same variety, likely due to the genetic differences and/or genetic and 326

environmental interactions affecting gene expression among varieties. 327

In addition, the variety-to-variety variation detected in 25 DAP kernels is similar to that 328

among V5 leaf tissues based on their CV distributions (Supplementary Figure 2), indicating that 329

gene expression variations among different maize varieties are similar between these two tissue 330

types. 331

332

Confirmation of Microarray Results by qRT-PCR 333

To confirm the gene expression levels measured by the microarray experiments, two groups of 334

18 different genes were chosen for V5 leaf and 25 DAP kernel, respectively (Supplementary 335

Table 1). The expression levels of these genes are ranked across all microarrays at 80% or 50% 336

percentiles. Expression of these genes was measured by qRT-PCR reactions using the same RNA 337

samples used for microarrays. Due to limited sample availability and possible polymorphisms 338

among different maize varieties at primer annealing locations, we used a Real-Time PCR Miner 339

program31

that has been validated by many other groups36-43

to monitor the single-well qRT-PCR 340

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efficiency. The dapA gene was used as a control for comparison between microarray and qRT-341

PCR data. Gene expression levels from qRT-PCR reactions were calculated based on the dapA 342

expression and compared to levels detected on microarrays that were also quantified based on the 343

dapA expression. The ratio of expression levels for each gene detected by these two techniques 344

was log transformed for proper comparison (Figure 3). 345

For V5 leaf tissue samples, two genes (pco602011 and pco603626) were not amplified 346

from any of the 15 templates by qRT-PCR and therefore not included in the analysis. Two genes, 347

pco627753 and pco643043 (gene 2 and 11 in Figure 3A, respectively), had expression detected 348

only in some of the samples, and 4 genes, pco624384, pco521467, pco652567, and pco658406 349

(gene 13, 14, 15, and 16 in Figure 3A, respectively), showed higher expression (ca. 2-32 fold 350

higher), relative to microarray, in all 15 samples. For the remaining genes tested, the expression 351

levels were close to those measured by the microarrays, although there were some variety-352

specific expression differences between the two techniques (Figure 3A). For 25 DAP kernel 353

samples, one gene (pco621453) did not show any amplification from qRT-PCR and was not 354

included for further analysis. Two genes, pco653893 and pco598383 (gene14 and 15 in Figure 355

3B, respectively), were amplified from 11 and 8 samples out of 15 varieties, respectively. Two 356

genes, pco601999, pco632057 (gene 16 and 17 in Figure 3B, respectively), had very different 357

expression levels from microarray across all varieties (Figure 3B). Gene 16 showed 4-60 times 358

lower expression and gene 17 showed 8-60 times higher expression when detected by qRT-PCR 359

compared to microarray results. The rest of genes tested in 25 DAP kernel samples showed good 360

consistency with microarray data (Figure 3B). A few qRT-PCR expression data points were 361

different from the microarray data, but only for one or two maize varieties. 362

363

Metabolomic Data Analysis 364

The three processed data matrices contained 3,891 metabolomic signatures or fingerprints (m/z 365

value_RI combinations) for V5 leaves, 4,300 for 25 DAP immature kernels, and 3,891 for 366

mature kernels. Of these, 87-103 metabolites were successfully identified in tissues examined. 367

These numbers, reduced relatively to the raw data set take into account the elimination of the 368

inherent redundancy in metabolomics signatures and ignoring metabolites the identity of which 369

could not be unambiguously established. The substantial reductions are due to (1) eliminating the 370

inherent redundancy in metabolomics signatures wherein each metabolite can be represented by 371

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multiple m/z values in its electron impact mass spectrum and (2) ignoring metabolites the 372

identity of which could not be unambiguously established. 373

To evaluate technical variations including the sampling, analytical, and data analysis 374

variability, 8 technical repeats were produced from V5 leaves of PHG9B and H31, 25 DAP 375

kernels from PHG9B and H31, and mature kernels from PH2WBS and PHG9B. For each tissue-376

variety combination, a single bulk tissue sample was aliquoted into 8 extraction tubes, producing 377

8 metabolomic samples. Mean CV values calculated from technical repeat metabolite relative 378

levels were between 0.33-0.54, and median values were between 0.27-0.46, indicating good 379

reproducibility in spite of some outliers in the upper ranges (Supplementary Figure 3). The 380

majority of metabolites detected showed CV values less than 0.6, with 76.7% and 87.4% 381

metabolites from V5 leaves of PHG9B and H31, 76.1% and 80.2% of metabolites from 25 DAP 382

immature kernels of PHG9B and H31, and 77.6% and 60.9% of metabolites from mature seeds 383

of PH2WBS and PHG9B, respectively (Supplementary Figure 3). We also found that pooled 384

samples had lower variances compared to the individual samples (Figure 4, Supplementary Table 385

5), similar to what was observed from the transcript data. Particularly, metabolomics for mature 386

seed samples showed much less variation than for immature seeds (Supplementary Table 5), 387

probably due to a less complex metabolome and terminal differentiation state of mature kernels. 388

Mean metabolite levels detected from individual and pooled samples of the same tissue-389

variety combination are highly correlated, with Pearson correlation R values all close to 1 (Table 390

2). For PHG9B and H31, the high correlations are observed despite the fact that the individual 391

samples were from 9 different plants, compared to the technical variation tests where metabolites 392

were extracted from 8 aliquots of a same plant sample. These results also demonstrate consistent 393

derivatization, GC chromatography, MS data acquisition, and data processing. 394

395

Tissue or Variety Separation Based on Metabolomics 396

When the metabolites detected from the three different tissues were compared, PCA analysis 397

clearly indicated tissue separation (Figure 5), reflecting tissue specificity of metabolic processes, 398

as expected. However, PCA analysis revealed variety specificity for only certain variety-tissue 399

combinations. For example, for V5 leaf tissues, there was clear separation of PH2WBR, PH14T, 400

and H31 from other varieties based on PC1 and PC3 (Figure 6A). Likewise, PH2WBS and 401

PH2WBR in 25 DAP kernels were readily distinguished from other varieties with PC1 and PC4 402

Page 15 of 44



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(Figure 6E). For mature kernels, PH2WBS and PHG9B showed good separation from other 403

varieties based on PC2 and PC3 (Figure 6I). 404

By and large, the tissue and variety classifications observed with individual plants were 405

also evident in pooled plant samples, although sometimes with different principal component 406

projections (Figures 5 and 6A, J). This result suggests that pooling did not degrade the 407

discriminating power afforded by individual samples. Interestingly, the combined percent 408

variance included in the PCA scores plots was slightly higher for pooled samples compared to 409

that generated for analogous individual samples, suggesting that pooling removed some 410

uninformative signal. 411

Loadings associated with examples of the above variety classifications were selected 412

graphically (Figures 6C, D; G, H; and K, L; in purple) and listed in Supplementary Table 6. The 413

very significant increases in the amount of amino acids in developing kernels, including glutamic 414

acid, glutamine, histidine, leucine, lysine, pyroglutamic acid (which could be derived from 415

glutamine during sample preparation), and tryptophan, are expected for PH2WBS, a genotype 416

with elevated grain protein. Explanations for the genotype-specific differences (loadings) in the 417

other tissues are less obvious. For the three examples shown, loadings from pooled plants were 418

very similar to those from individual plants. Thus, pooling generated similar PCA scores and 419

loadings, maintaining the ability to classify sample groups (varieties) as well as to identify the 420

prominent metabolites underlying said classifications. 421

In this GC/MS metabolomic study, we also found that some metabolites were only 422

detected in one or two tissue types. Among individual plant samples, there were 19 metabolites 423

uniquely detected in V5 leaves, 2 only in immature kernels and 3 only in mature kernels (Table 424

3). It is expected that the metabolome of leaves is more divergent than that of immature or 425

mature kernels. Some of these metabolites are present but not detected in other tissues, given our 426

conservative limit of peak detection. Moreover, immature and mature kernels contain more 427

polysaccharides by weight than leaves. Since approximately 3 mg dry weight samples were used 428

for all three tissue types, it is expected that the concentration of many small molecule metabolites 429

will be greater in leaf than in kernel samples. This could result in apparent tissue specificity, as 430

seen in Table 3. 431

432

Range and Variations of Metabolite Abundances 433

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We observed large ranges in relative levels for many metabolites across all varieties. The ratio 434

between the maximum value and the minimum value detected for a metabolite in individual 435

samples ranged from 1.8 to 1,663 for V5 leaf tissues, from 3.3 to 16,815 for immature kernels, 436

and 2.7 to 585 for mature kernels (Supplementary Table 7). However, when samples were 437

pooled, the range narrowed to 1.4 to 167 for V5 leaves, 2.1 to 4,828 for immature kernels, and 438

1.6 to 86 for mature kernels (Supplementary Table 7). Similarly, when the mean values within 439

each variety for each metabolite from either individual samples or pooled samples were 440

compared across all varieties with box plots, the pooled samples showed much narrower 441

distribution compared to the individual samples (data not shown). This observation indicated that 442

the biological variation among individual plants combined with variety variation was very large. 443

However, our pooling strategy effectively decreased the biological variation between plants. The 444

actual relative levels are specific to the current dataset and should not be compared to other 445

datasets, unless they were processed (aligned and scaled) together. 446

Multiple derivative forms for certain metabolites are characteristic of GC/MS-based 447

metabolomics, as illustrated by asparagine in Supplementary Table 7. Asparagine with four TMS 448

groups (one attached to the carboxyl and three to the amines) was found in mature kernels while 449

asparagine with just three TMS moieties (one attached to the carboxyl and two to the amines) 450

was specific to V5 leaves. This dichotomy might be explained by differential trimethylsilylation 451

due to the different sample matrices.28

Consequently, comparing metabolomes across tissue 452

types or species should be undertaken with caution. 453

We also compared the levels of metabolites in all samples across all varieties for a given 454

tissue type, and identified metabolites that are quite stable as well as those that are highly 455

variable among varieties. There were 21 metabolites from V5 leaves, 20 metabolites from 25 456

DAP immature kernels and 11 metabolites from the mature kernels that showed a CV value less 457

than 0.4 across all varieties (Table 4), representing tissue-specific stable metabolomes. Among 458

them, sucrose and myo-inositol were identified from all three tissues, and another ten metabolites 459

appeared in two tissue types. On the other hand, there were 17 metabolites from the V5 leaves, 460

13 from the 25 DAP immature kernels, and 16 from the mature kernels that showed CV values 461

larger than 1, indicating that these metabolites are highly variable among different maize 462

varieties (Table 4). A partial derivative form of glutamine seemed to be highly variable in all 463

three tissue types, and another three metabolites were highly variable for two tissue types. The 464

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18

high variability of the partial derivative of glutamine may be due, at least in part, to inconsistent 465

transformation to pyroglutamic acid, which was also highly variable in two of the tissues. The 466

inconsistent transformation of pyroglutamic acid is a process known to be associated with 467

trimethylsilylation. 468

As with gene expression levels detected from microarrays, mean metabolite abundances 469

from individual samples or pooled samples were calculated for each variety and used to calculate 470

CVs among varieties. For all three tissue types, metabolite variations among different varieties 471

detected from individual or pooled samples are well-correlated. Linear regression R2 values are 472

0.90, 0.92, and 0.82 for V5 leaves, 25 DAP developing kernels, and mature kernels, respectively. 473

Furthermore, the CV distribution patterns are very similar between individual and pooled 474

samples (Supplementary Figure 4). For V5 leaves, 43.7% of metabolites showed higher CVs in 475

pooled samples compared to individual samples. In 25 DAP developing kernels and mature 476

kernels, the numbers are 61.5% and 47.6%. This observation indicated that using pooled samples 477

revealed variety-to-variety metabolomic variation similar to that using individual samples. 478

479

DISCUSSION 480

Thorough evaluation of the applicability and limitations of the -omics technologies for food 481

safety assessment is necessary before their acceptance for this purpose. Towards this end, we 482

evaluated high-throughput gene expression and metabolomic technologies by characterizing the 483

transcriptomes and metabolomes of several conventional maize varieties using alternative 484

protocols. Our observations lead us to conclude that in applying these methods to regulatory 485

issues, consideration should be given to natural variation in maize transcriptome and to the high 486

degree of variation in metabolite concentrations between plant varieties and individuals of the 487

same variety. 488

489

Technical Variation 490

To validate methods for both microarray and metabolomics, selected samples were analyzed 491

multiple times to serve as technical repeats. The CV distribution for the technical microarrays 492

showed small variations between different microarray runs for the same sample (Figure 1), 493

validating the method and technical consistency. When compared to CVs detected from 494

individual plant samples, the technical CVs are much smaller (Figure 1A, Supplementary Table 495

Page 18 of 44



19

2), indicating that our microarray technology is consistent and sensitive enough to detect 496

biological variations outside of technical variations. The data correlation analysis among repeat 497

arrays comparing individual samples and technical repeat samples confirmed this conclusion 498

(Figure 2). Technical plus biological CVs detected from metabolomics, however, were much 499

larger compared to microarrays (Supplemetary Figure 3). This increase is not unexpected since 500

expression of many metabolites is dynamically affected by microenvironment. Furthermore, 501

different metabolites have very different physical and biochemical properties as well as ranges of 502

expression, and therefore can be affected by the extraction and derivatization methods employed. 503

Nevertheless, biological variability was found to be greater than analytical variability. The mean 504

CVs observed are similar to those reported in the plant metabolomic literature. 505

Sample Pooling 506

Profiling techniques are a powerful tool for gene discovery research as long as appropriate 507

statistical tools are used to analyze the data. Pooling of mRNA samples from different 508

individuals of the same variety for microarray hybridizations has the following advantages: 1) 509

controls cost, 2) generates data when the amounts of individual samples are insufficient, and 3) 510

decreases variation between individuals. A design of multiple pools with multiple individual 511

samples in each pool was established as a compromise.44-47

Thus, the ability to detect the 512

difference between biological subject-to-subject variations and the experimental technical 513

variations is combined with the efficiency of the pooling strategy designed to reduce overall 514

variance. The larger the individual-to-individual variability is, as compared to technical 515

variability, the greater the reduction of variability is achieved by pooling samples.44,45

516

We designed the microarray and metabolomics experiments to include both individual 517

samples and sample pools. Gene expression levels detected from microarray and metabolite 518

abundances both showed very good correlation between individual and pooled samples within a 519

same tissue type and variety (Table 1, 2). Using pooled samples lowered sample-to-sample 520

variation resulting in lower CVs (Figure 4, Supplementary Figure 1, Supplementary Table 2, 3, 5 521

6). Interestingly, pooling microarray samples reduced the CVs more dramatically for 25 DAP 522

samples compared to V5 leaf samples (Supplementary Figure 1C, D), presumably due to the 523

higher transcriptome variation among 25 DAP individual samples compared to V5 leaf 524

individual samples. 525

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20

The mean CVs for each variety calculated from either individual plants or pooled 526

samples represent the variety-to-variety variations. The CVs representing variety-to-variety 527

variations were similar when obtained from either individual plants or pooled samples for both 528

microarrays and metabolomics (Supplementary Figures 2,4). Furthermore, the distribution 529

patterns of variety CVs were similar for both microarrays and metabolomics. A slight increase of 530

variation in pooled samples compared with individual samples from microarrays was detected 531

(Supplementary Figure 2 and Supplementary Table 3), presumably due to fewer pooled samples. 532

Pooling plants prior to analysis also did not adversely affect the ability to classify tissues 533

or varieties, or identify discriminating metabolites by PCA (Figures 5, 6). In fact, pooling 534

appeared to enhance discriminating power, presumably by eliminating some noise from the 535

datasets. Overall, our pooling strategy of three sample pools of three is a cost-saving design that 536

does not sacrifice analytical power. 537

538

qRT-PCR and Microarray 539

The use of microarray profiling for comparative assessment of biotech crops requires a gene 540

expression sequence database for probe design, gene annotation, and expression level 541

interpretation. For many plant species, genomes or transcriptomes have not been completely 542

sequenced except for a few model genotypes. The maize genome has an especially high level of 543

DNA sequence polymorphisms, approximately an order of magnitude higher than that in 544

humans.48-50

High level of genotypic variation in maize introduces challenges for gene 545

expression profiling such as microarray or PCR-based technologies, since experimental designs 546

are based on knowledge obtained from just one or two varieties. As most of the genomic 547

sequence and transcriptome for the varieties used in this study are not available, microarray 548

hybridization efficiency is expected to vary between varieties. In the microarray study and in 549

qRT-PCR, the primers and probes were designed using gene sequences of the B73 reference 550

genome. Consequently, we observed substantial variation in single-well qRT-PCR efficiencies 551

for the amplification of the same gene from different maize variety samples (data not shown). 552

This resulted in some inconsistency in expression values detected by qRT-PCR and microarrays 553

across different varieties (Figure 3). For some genes, expression values assayed by qRT-PCR 554

were very different from the corresponding microarray expression levels (Figure 3). This 555

observation raises concerns about the validity of probe homology-dependent methodologies in 556

Page 20 of 44



21

highly diverse species. In some cases, very large variation in measured expression levels 557

between varieties may be due to presence - absence variation.51

For future studies, caution should 558

be exercised when using microarray technology under similar circumstances. When comparing 559

transgenic and non-transgenic varieties, pairs of lines should be used that are isogenic except for 560

the presence of transgenes. 561

562

Metabolomics 563

The physiological concentration range of metabolites is very broad (Supplementary Table 7).52,53

564

The lower technical variation for microarrays compared to metabolomics (Figure 1, 565

Supplementary Figure 3) can be partially explained by quantile normalization of microarray data 566

which helps reduce CV. The CV ranges observed in our study are nevertheless comparable to 567

those seen by others using different systems.54-57

568

The levels of many metabolites measured by metabolomics are extremely sensitive to not 569

only the experimental procedures and instrument type used, but also to the environment where 570

the samples are collected. Nevertheless, large changes in the amount of many metabolites within 571

a plant rarely make significant overall contributions to the nutritional composition or raise safety 572

concerns.12,53,58-60

Genetic background strongly affects metabolite levels,60,61

usually more than 573

transgene insertions.16,20,63

Per sample cost for metabolomics is much lower compared to 574

microarrays, allowing more sample replicates, increasing statistical power and lowering technical 575

variation, while retaining true variation in physiological metabolite levels. 576

GC/MS-based high-throughput metabolomics requires a uniform extraction and sample 577

processing protocol for hundreds of metabolites differing in chemical properties and in vivo 578

concentrations, which leads to suboptimal analytical conditions for many metabolites. Most 579

metabolomic techniques lack sufficient analytical breadth to accurately measure hundreds of 580

metabolites with very diverse chemical properties.64-66

Analytical compromises must be made to 581

achieve high-throughput and high metabolome coverage, rendering metabolomic data 582

fundamentally different than targeted analysis of specific analytes. Even augmented with LC/MS 583

and CE/MS, metabolomics does not cover all of the compounds presumed to be present in maize 584

leaves or kernels. Also, metabolomics results include a large amount of unidentified metabolites 585

that currently cannot be mapped to a biochemical pathway. Thus, a traditional metabolic 586

pathway-centric evaluation of metabolomic data for safety assessment is not conceptually 587

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22

appropriate. The lack of knowledge of metabolic pathways and the limited availability of 588

reference standards and databases also have restricted the use of metabolomic technology for 589

tasks best served by traditional targeted analytical methods. Therefore, it might be preferable to 590

combine non-targeted methods with multivariate tools such as principle component analysis and 591

hierarchical clustering to visualize sample relationships, rather than to focus on individual 592

metabolite tolerance levels.62

593

Although our metabolomic study identified metabolites present at significantly different 594

levels in different maize varieties, the biological significance of these differences should be 595

interpreted with caution.16,67

We reported relative metabolite abundances rather than absolute 596

abundances, therefore only metabolomic data generated using the same experimental procedures, 597

detection methodologies, and internal controls should be compared to this data set directly. This 598

consideration is additional to significant biological variability. As recommended by Codex 599

Alimentarius,68

The statistical significance of any observed differences should be assessed in 600

the context of the range of natural variations for that parameter to determine its biological 601

significance. Our study strongly supports this recommendation. 602

603

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23

ABBREVIATIONS USED

GMO - genetically modified organism

qRT-PCR - quantitative reverse transcript PCR

OECD - Organization for Economic Co-operation and Development

GM - genetically modified

ILSI - International Life Sciences Institute

mRNA messenger RNA

RNA - ribonucleic acid

GC/MS - gas chromatography / mass spectrometry

DAP - days after pollination

DNA - deoxyribonucleic acid

cRNA - complementary RNA

MSTFA - N-Methyl-N-(trimethylsilyl) trifluoroacetamide

PFTBA - perfluorotributylamine

CV - coefficient of variation

PCA - principal component analysis

cDNA - complementary DNA

RT - reverse transcription

NCBI - National Center for Biotechnology Information

qPCR - quantitative PCR

LC/MS - liquid chromatography / mass spectrometry

CE/MS - capillary electrophoresis / mass spectrometry

TMS - trimethylsilyl

ACKNOWLEDGEMENTS

The authors express appreciation to the Wilmington Regulatory Science team for assistance in

tissue generation; John Nau for carrying out the microarray experiments and data processing;

Teresa Harp for carrying out the metabolomics experiments; Xiaoxiao Kong and Bonnie Hong

for assistance in data analysis; Antoni Rafalski for assistance in the preparation of the

manuscript; and Antoni Rafalski, Stan Luck, and Mary Locke for critical review of the

manuscript.

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24

SUPPORTING INFORMATION AVAILABLE

Supplementary Figure 1. CV distribution analysis of microarray data.

Supplementary Figure 2. Percent of genes with CV values at specific ranges.

Supplementary Figure 3. CV distribution of metabolite profiling technical repeats derived from

different sample groups.

Supplementary Figure 4. CVs among varieties comparing average metabolite levels.

Supplementary Table 1. Primers and probes used for qRT-PCR reactions.

Supplementary Table 2. Percentage of genes from microarrays with CV values within different

ranges.

Supplementary Table 3. CV summaries of gene expression from microarrays with individual (I)

and pooled (P) samples.

Supplementary Table 4. Percentage of genes from microarrays with CV values within different

ranges. Supplementary Table 5. CV summaries of metabolite levels from I (individual) and P

(pooled) samples.

Supplementary Table 6. Metabolites that contributed most to the classification of PH2WBR in

leaf samples and in PH2WBS in 25 DAP and mature kernel samples.

Supplementary Table 7. Max/min ratio of relative levels of each metabolite.

This material is available free of charge via the Internet at http://pubs.acs.org.

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68. Codex Alimentarius. Guideline for the conduct of food safety assessment of foods

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(accessed March 4, 2013).

FIGURE CAPTIONS

Figure 1. CVs of gene expression calculated from technical repeat microarrays. A. Percentages

of genes within different CV ranges. B. CV distributions generated by TIBCO Spotfire. X-axis,

CV values in log scale; Y-axis, gene counts. C. Plots of log10 (CV)s (y-axis) and against log10

(mean)s (x-axis). Curves are polynomial fittings generated by TIBCO Spotfire. D. Log10(CV)

values of inflection points calculated from curves in C.

Figure 2. Technical reproducibility of microarrays. Pairwise correlation coefficients between

pairs of technical replicates (T) or between pairs of biological repeats (I). Boxplots were

generated with TIBCO Spotfire. The white bar represents the median value. Edges of boxes

represent values at 75% and 25% percentiles. Edges of bars represent the ranges of values with

outside dots as outliers.

Figure 3. Gene expression analysis comparing qRT-PCR to microarray hybridization with V5

leaf (A) or 25 DAP (B) samples. Numbers are log2 transformed ratios between expression levels

detected by qRT-PCR and microarray that were defined against dapA expression levels.

Figure 4. CV distribution of metabolite levels detected from V5 leaf (A), 25 DAP immature

kernel (B), and mature kernels (C).

Figure 5. PCA score plots from individual or pooled plants showing tissue specificity of

metabolomes.

Figure 6. PCA scores and loadings plots from individual plants (A, C; E, G; I, K) or pooled

plants (B, D; F, H; J, L) showing classifications of PH2WBR from the leaf metabolome (A-D),

PH2WBS from the 25 DAP kernel metabolome (E-H) and PH2WBS from the mature kernel

metabolome (I-L). Significant loadings shown in purple.

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TABLES

Table 1. Rows 1,2: Mean gene expression levels detected in individual-plant (I) and pooled-

plants (P) for the same tissue type of a variety are highly correlated. Values are Pearson

correlation coefficients (R) comparing mean gene expression intensities between all I and all P

samples for each variety-tissue combination, calculated using Excel function PEARSON.

Rows 3,4: Mean gene expressions detected microarrays are not correlated between V5 leaf and

25 DAP. Values are Pearson correlation coefficients (R) comparing gene expression intensities

from V5 leaf and 25 DAP for the same plant samples , calculated using Excel function

PEARSON, for individual-plant (I) or pooled-plants (P)

Row Sample 34A15 38B85 PH2WBR PH2WBS PHG9B H31

1 V5Leaf 0.9932 0.9885 0.9959 0.9893 0.9937 0.9846

2 25DAP 0.9938 0.9743 0.989 0.9836 0.9889 0.9899

3 I 0.238 0.2527 0.317 0.2757 0.283 0.2472

4 P 0.234 0.2551 0.3092 0.2595 0.2849 0.239

Table 2. Mean metabolite levels detected from individual-plant (I) and pooled-plants (P)

samples for the same tissue type of a variety are highly correlated. Pearson correlation

coefficients (R) by Excel function PEARSON.

Variety V5Leaf 25DAP Mature

34A15 0.9976 0.9996 0.9732

37Y12 0.9988 0.9987 0.9921

38B85 0.9988 0.9994

PH2WBS 0.9089 0.9952

PH2WBR 0.9985

PH0GP 0.9970 0.9971

658 0.9981 0.9948 0.9984

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PH14T 0.9994 0.9981 0.9992

PHG9B 0.9993 0.9933 0.9940

H31 0.9980 0.9891

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Table 3. Apparent tissue specific metabolites.

Tissue Analyte Class

V5 leaf

Tyramine polyamine

L-Tryptophan, N,1-bis(trimethylsilyl)-, amino acid

Chlorogenic acid phenolic acid

Citramalic acid organic acid

Dehydroascorbic acid, secondary peak 1 vitamin



Heptadecanoic acid fatty acid

Itaconic acid organic acid

Maleic acid organic acid

Pyruvic acid organic acid

Salicylic acid phenolic acid

cis-Caffeic acid phenolic acid

trans-Caffeic acid phenolic acid

alpha-Tocopherol vitamin

Rhamnose sugar

Trehalose sugar

Glyceric acid-3-phosphate phosphorylated acid

Phytol alkane alcohol

Margaric acid fatty acid

25 DAP Myristic acid fatty acid

Cysteine, partial derivative amino acid

mature

kernel

Adenosine-5-monophosphate nucleic acid

Pipecolic acid organic acid

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Table 4. Metabolites with relatively stable or highly variable levels among different maize

varieties and their CV values. CV values were calculated from metabolite levels from all

individual (I) and pooled (P) samples for all varieties. Only metabolites that were detected from

all I and P samples of all varieties were included for the calculation. Only metabolites with CV

values of less than 0.30 (relatively stable) and those with CV values greater than 1.00 (highly

variable) are shown. Metabolites in italic were found in all three tissues and metabolites in bold

were found in two tissues for the same category. ).

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V5 Leaf 25 DAP Immature Kernel Mature Kernel

0.31 Acetic acid 0.30 Acetic acid 0.29 beta-Sitosterol

0.30 Arabinose 0.39 Arabinose 0.28 Campesterol

0.13 beta-Sitosterol 0.34 Aspartic acid part. deri. 0.36 Erythritol

0.23 Campesterol 0.32 Benzoic acid 0.37 Glycerol-3-phosphate 0.40 Cellobiose deri. 1 0.30 Ethanolamine 0.37 Linoleic acid 0.38 cis-Aconitic acid 0.37 Fructose deri. 1 0.38 Malic acid 0.39 Ferulic acid 0.39 Galactose 0.35 myo-Inositol 0.33 Galactitol 0.24 Glucose deri. 1 0.31 Palmitic acid 0.24 Glycerol 0.38 Glyceric acid 0.31 Stigmasterol 0.38 Glycerol-3-phosphate 0.36 Isoleucine part. deri. 0.19 Sucrose 0.40 Heptadecanoic acid 0.34 Leucine part. deri. 0.39 Tyrosine

0.30 Linoleic acid 0.36 Malic acid 0.17 myo-Inositol 0.36 Mannose

0.30 Palmitic acid 0.23 myo-Inositol 0.17 Phytol 0.25 Phosphoric acid

0.37 Serine part. deri.

0.35 Stigmasterol 0.30 Succinic acid 0.14 Sucrose 0.38 Sucrose 0.36 trans-Caffeic acid 0.31 Threonine part. deri. 0.31 Xylitol 0.35 Tyrosine 0.28 Xylose deri. 1 0.32 Xylose deri. 2

1.60 2-Aminobutyric acid 1.10 beta-Alanine part. deri. 1.24 Asparagine 1.79 Asparagine part. deri. 2.14 cis-Aconitic acid 2.15 beta-Amyrin 1.52 Aspartic acid 1.39 Citric acid 2.63 Cellobiose deri. 1 2.04 Benzoic acid 2.26 Gluconic acid 1.05 Cysteine part. deri.

1.21 Dehydroascorbic acid 1.49 Glutamic acid 1.28 Dehydroascorbic acid

1.54 Dehydroascorbic acid

2nd peak 1 2.70 Glutamine part. deri. 1.03 Gaba

1.71 Ethanolamine 1.84 Histidine 1.04 Glucose deri. 2

1.36 Glutamic acid 1.63 Isocitric acid 1.83 Glucose-6-phosphate deri. 2 2.14 Glutamine part. deri. 1.25 Linolenic acid 1.70 Glutamine part. deri. 1.19 Glycine 2.56 Myristic acid 1.04 Glyceric acid 1.22 Glycine part. deri. 1.03 Oleic acid 1.60 Maltose 1.07 Isoleucine 3.43 para-Coumaric acid 1.25 Pipecolic acid 1.11 Leucine 1.57 Pyroglutamic acid 1.14 Pipecolic Acid part. deri.

1.45 Ornithine 1.32 Pyroglutamic acid 1.06 Serine 1.16 Xylitol

1.60 Trehalose 1.64 Xylose deri. 2

1.21 Tyrosine

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FIGURES

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Images for TOC

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