Food and Nutrition Sciences, 2013, 4, 335-341
http://dx.doi.org/10.4236/fns.2013.43044 Published Online March 2013 (http://www.scirp.org/journal/fns)
Omics Technologies Reveal Abundant Natural Variation in
Metabolites and Transcripts among Conventional Maize
Hybrids
Xiaofeng S. Yang1*, Jeffrey M. Staub1*, Anand Pandravada2, Susan G. Riordan1, Yongpan Yan1,
Gary A. Bannon1, Susan J. Martino-Catt1#
1Monsanto Company, St. Louis, USA; 2DuPont Pioneer, Thondhebhavi, India.
Received January 28th, 2013; revised February 28th, 2013; accepted March 8th, 2013
ABSTRACT
In this report we have evaluated metabolite and RNA profiling technologies to begin to und erstand the natural variation
in these biomolecules found in commercial-quality, conventional (non-GM) maize hybrids. Our analyses focus on ma-
ture grain, the article of commerce that is most typically subjected to the rigorous studies involved in the comparative
safety assessment of GM products. We have used a population of conventionally-bred maize hybrids that derive from
closely related inbred parents grown under standard field conditions across g eographically similar locati ons. This study
highlights the large amount of natural variation in metabolites and transcripts across conventional maize germplasm
grown under normal field conditions, and underscores the critical need for further extensive studies before these tech-
nologies can be seriously considered for utility in the comparative safety assessment of GM crops.
Keywords: Metabolite Profiling; RNA Profiling; Maize; Conventional Hybrids; GM Crops; Natural Variation;
Omics-Technologies; Safety Assessment
1. Introduction
There is an urgent need to accelerate agricultural produc-
tivity on a global scale to feed our rapidly increasing
human population. It is estimated that by the year 2050,
global food production must double [1,2]. This is a tre-
mendous challenge when coupled with ongoing pressures
to preserve the environment and minimize the impact of
global warming. Both traditional breeding and biotech-
nology methods of crop improvement must be utilized to
meet these growing demands.
First-generation commercial g enetically-mod ified ( GM)
crops were developed to begin to meet the critical need
for increased productivity from current agricultural prac-
tices. These first-generation GM crops have focused on
agronomic traits, including herbicide tolerance and insect
resistance, and carry transgenes that impart new and eas-
ily measured biochemical properties to the plant [3,4].
The impending next generation of GM crops will address
multigenic yield traits such as drought tolerance or im-
proved utilization of nitrogenous fertilizers, whose mo-
lecular genetic basis of the engineered trait is only now
beginning to be understood [5]. Due to the multigenic
nature of these next generation yield traits, GM crops
may carry one or more transgenes required to modulate
the expression of several endogenous plant genes or bio-
chemical pathways.
The current safety assessment process for products
improved through modern biotechnology includes in-
depth studies of phenotypic, agronomic, morphological,
and compositional profiles to identify potential harmful
effects that could affect product safety [6]. The applica-
tion of this safety assessment pro cess has worked well to
protect public safety. Since commercialization of the first
GM crop in 1996 [4], farmers have planted more than
690 million h ectares (1.7 billion acres) [7] without a sin-
gle confirmed incidence of health or environmental harm.
We believe it is therefore appropriate that the safety as-
sessment of the next generation GM crops utilize the
current well-established and proven regulatory processes
[8-10]. This rational approach will enable the develop-
ment and commercial use of new products that are criti-
cal to meeting the next generation’s agricultural chal-
lenges.
Advances in technology, such as open-ended profiling
technologies (i.e. Omics) to assess metabolite and gene
*These authors contributed equally to this work.
#Corresponding author.

Omics Technologies Reveal Abundant Natural Variation in Metabolites and Transcripts among
Conventional Maize Hybrids
336
expression profiles, raises the question of whether these
methods should also be included as part of the safety
assessment process. These methods have the ability to
identify and quantify a wide variety of specific bio-
molecules to determine differences between GM and
non-GM crops. Recent publications have suggested that
the application of open-ended profiling technologies mig ht
be informative to the safety assessment of GM crops,
particularly to identify potential unintended effects re-
sulting from the transgenic modification [11-15]. Appli-
cation of these powerful methods to the regulatory deci-
sion-making process requires that the data generated be
both reproducible and have probative value to influence
product safety.
Despite the potential of Omics technologies, many
challenges must be addressed before these could add
useful information to the current regulatory safety as-
sessment. These challenges include the multiple number
of available Omics platforms, as well as the lack of stan-
dardized methods and protocols for sample preparation,
data generation and data analysis. Recognizing these
shortcomings, international efforts to standardize the tech-
nologies have recently been initiated [16-20].
Another significant challenge to the use of Omics tec h-
nologies in safety assessment of GM crops is the rational
determination of biologically meaningful differences in
relation to control samples. To this end, the extent of the
inherent natural variation in GM and non-GM crops must
first be known to ascertain if changes detected by an
analytical technology are due to the introduced transgene
or are the result of changes due to genetic and environ-
mental variability. To address th is deficiency for compo-
sition data currently required for safety assessment, the
International Life Sciences Institute (ILSI) formed a lar ge
consortium representing academics, private industry and
government agencies to collect comprehensive composi-
tional data from grain in a range of genotypes across
several crops. The ILSI Compositional Database
(www.cropcomposition.org) now provides a data-rich
baseline of the natural variation in crops that is used as
the current standard when assessing substantial equiva-
lence of compositional data for GM crops [21]. To date,
there are no standardized and universally accepted data-
bases available that describe the natural variation in
transcripts, proteins or metabolites.
2. A preliminary Assessment of Natural
Variation by Metabolite and Transcript
Profiling Technologies
In this report we have evaluated metabolite (metabolom-
ics) and RNA (transcriptomics) profiling technologies to
begin to understand the natural variation in these bio-
molecules found in commercial-quality, conventional
(non-GM) maize hybrids. Our analyses focus on mature
grain, the article of commerce that is most typically sub-
jected to the rigorous studies involved in the comparative
safety assessment of GM products. We generated popu-
lation of 30 genetically-related maize hybrids by crossing
6 female inbred lines with 5 different male inbred lines to
produce hybrid seeds. The female inbred lines can trace
their lineages to one of the common Stiff-Stalk (BSSS)
progenitors and have a ~72% - 87% marker-based simi-
larity to B73, while the male inbreds are of Non-Stiff
Stalk (NSS) lineages and have a ~50% marker-based
similarity to either B73 or MO17. To minimize environ-
ment and genotype variables, we have used this popula-
tion derived from closely related inbred parents grown
under standard field conditions across two geographi-
cally similar locations at Jerseyville and Jacksonville in
Illinois, which represent typical commercial corn pro-
duction environment. To our knowledge, this is the first
use of Omics technologies to characterize the large
amount of natural variability in transcripts and metabo-
lites across maize germplasm, and underscores the criti-
cal need for further extensive studies before these tech-
nologies can be seriously considered for utility in the
comparative safety assessment of GM crops. We also
conducted the standard composition analysis as a refer-
ence to compare the variability revealed by the Omics
technologies.
3. Compositional Analysis
Composition analysis of total protein, fat, ash, amino
acids, and fatty acids from mature grain is a standard
assay to establish equivalence and safety of GM crops
(see the guidelines of the OECD (http://www.oecd.org)).
These compositional metabolites accumulate as the result
of diverse metabolic activities, particularly during the
late vegetative and reproductive stages of the life cycle.
We used accepted protocols [22-24] to mea sure the var ia-
tion in those standard composition metabolites in grain
samples from a set of 30 hybrids used in this study
(analysis performed by Covance, Madison, WI).
Figure 1 graphically displays the amino acid composi-
tion data measured in mature grain samples among the 30
hybrids at each of the two locations tested. For comparison,
we also list the known variation for each amino acid, as
found in the ILSI composition database. It is interesting
to note that the natural range of variation for some amino
acids observed across the germplasm available in the
ILSI database varies broadly, such as tryptophan that has
a variation of almost 700%. This is not surprising since
the ILSI database is reflective of grain from many dif-
ferent genetic backgrounds grown in many different en-
vironments. As expected, a large amount of variation wit h-
in our dataset was also observed in some key metabolites

Omics Technologies Reveal Abundant Natural Variation in Metabolites and Transcripts among
Conventional Maize Hybrids
337
Figure 1. Variation in amino acid composition among conventional maize hybrids. Metabolite measurements for amino acids
are represented as the range of values observed among 30 hybrids at each of two locations (JA: Jacksonville; JE: Jerseyville)
in Illinois during the 2006 growing season. The variation reported for standardized metabolite data from the ILSI database
(version 2.0) is also shown for comparison to the hybrids analyzed in this study .
across the 30 hybrids tested at the two locations. How-
ever, not all metabolites exhibited such high variability
and the accumulation of the majority of metabolites var-
ied by less than 100%. Despite th e observed v ariation, all
of the values determined for the 30 hybrids fell within
the documented ranges reported by the ILSI database,
indicating that the hybrids were compositionally equiva-
lent to commercial hybrids. The range of data across the
30 hybrids within each single location also demonstrates
the inherent variability of common metabolites observed
even when the starting materials are genetically very
similar. This compositional data will be submitted to the
ILSI database for public access.
4. Metabolic Profiling
To understand the application of Omics technologies to
characterize the nature and variability of additional me-
tabolites observed in grain samples, we employed a com-
mercially available, non-targeted metabolite profiling
platform (Metabolon, Durham, NC). This platform uses
gas and liquid chro matography in combinatio n with mass
spectrometry to identify and quantify small molecule me-
tabolites [25,26]. We used this metabolite profiling plat-
form on the identical 30 grain samples as used for stan-
dard compositional analysis. Of the >400 metabolites
that were detected in at least 50% of the samples, it was
surprising that only ~130 were identified as previously
known compounds. This analysis suggests that the ma-
jority of metabolites in maize grain are currently u nchar-
acterized and demonstrates that the current standard com-
positional analyses represent the most common and abun-
dant metabolites.
Table 1 shows the quantification of the metabolites
analyzed, and the number whose concentrations across
grain samples from the two measured locations varies
from 2-fold to 16-fold. The vast majority (>96%) of me-
tabolites vary more than 2-fold when compared at each
location tested. Furthermore, more than half of the me-
tabolites vary more than 4-fold, ~20% vary by at least
8-fold and 6% - 8% of metabolites vary as much as
16-fold. This result indicates that dramatic variations in
metabolite concentrations occur in both known and un-
characterized metabolites even among genetically similar
maize plants.
Application of Omics technology in a food safety as-
sessment context requires that consistent platforms and
validated methods be used so that reasonably skilled and
trained individuals would obtain the same values for a
specific sample. To evaluate this, we compared the results
from the standard compositional analysis (Covance, Ma-
dison, WI) and the open-ended metabolite analysis (Me-
tabolon, Durham, NC) performed on the 30 hybrid grain
samples to determine the consistency in the measured
variation of identical metabolites across the two plat-
forms. Because each platform measures a different com-
plement of metabolites, the comparison was made only
between identically annotated metabolites that were
measured across both of the analytical platforms. Table 2
lists the 19 common metabolites measured in grain sam-
ples of hybrids at each of two field locations. To elimi-
nate large differences observed in absolute metabolite
intensity va lues due to differen ces in the in tern al stand ards

Omics Technologies Reveal Abundant Natural Variation in Metabolites and Transcripts among
Conventional Maize Hybrids
338
Table 1. Variation across metabolites measured from an
open-ended profiling platform.
Location JA JE
#Metabolites 405 437
2× 96% 97%
4× 54% 61%
8× 20% 23%
16× 6% 8%
Identical grain samples as described in Table 1 from 30 hybrids at two
locations (JA: Jacksonville; JE: Jerseyville) in Illinois were analyzed. Me-
tabolites with detectable intensity values in at least 50% of the samples are
reported. The level of variation is defined as the percentage of metabolites
whose minimum and maximum measured values across the 30 hybrids was
at least 2-fold different (2×), four-fold different (4×), eight-fold different (8×)
or sixteen-fold differ ent (16×).
Table 2. Comparison of variation in 19 grain metabolites
measured in common across the open-ended and targeted
metabolite profiling platforms.
Location JA JE
Metabolite Open Targeted Open Targeted
Alanine 3.06 1.30 2.51 1.41
Arginine 7.64 1.36 7.00 1.40
Aspartate 2.43 1.28 2.25 1.25
Glutamate 2.11 1.34 2.49 1.40
Glycine 3.79 1.17 4.89 1.17
Histidine 3.93 1.25 6.59 1.17
Isoleucine 2.20 1.35 2.53 1.34
Leucine 2.14 1.41 2.58 1.49
Lysine 8.81 1.18 8.74 1.18
Methionine 2.69 1.68 2.12 1.36
Oleate 17.17 2.21 49.23 1.79
Palmitate 1.93 1.71 1.94 1.52
Phenylalanine 3.22 1.35 3.35 1.35
Proline 2.65 1.30 4.18 1.32
Serine 2.33 1.33 2.67 1.40
Threonine 2.37 1.19 2.30 1.17
Tryptophan 1.90 1.28 2.08 1.28
Tyrosine 2.05 2.52 2.61 2.64
Valine 2.02 1.23 2.23 1.22
The variation in metabolite accumulation within each profiling platform is
defined as the maximum/minimum values measured across the 30 hybrids at
each of two locations (JA: Jacksonville; JE: Jerseyville) in Illinois.
that were used across the platforms (data not shown), we
report the range of values expressed as the maximum
value divided by the minimum value for each metabolite.
As can be seen in Table 2 , 17 amino acids and 2 fatty
acids were measured across both profiling platforms. In
general, the range of values observed for the targeted
profiling approach (Covance) is much smaller (up to
2.6-fold) than the open profiling approach (Metabolon;
up to 49-fold). The larger range of variation and the large
discrepancy for several metabolites (for example, argin-
ine, lysine and oleate) may be due to an increased sensi-
tivity in the open prof iling methodology. However, these
platform-based differences may also be due to several
technical factors, including the metabolite extraction pro-
cedures and the technical variation of the separation and
detection equipment. In summary, these data indicate that
vastly different values can be obtained from the same
sample depending on the method used to measure the
metabolite.
5. Transcriptomic Profiling
Expression profiling in maize [27 and references within],
Arabidopsis [28] and rice [29,30] suggests that there is a
strong correlation between genetic diversity and tran-
scriptional variation. To measure the variation of gene
expression across the 30 hybrid lines of maize, we used a
custom-designed Affymetrix oligonucleotide microarray
to analyze RNA from the grain samples collected from
each hybrid. The custom microarray was designed to
detect >54,000 unique mRNA transcripts based on avail-
able EST sequences from public and proprietary data-
bases [31] .
As shown in Tab le 3, a su bset of ~30,000 unique EST
sequences represented on our microarray showed expres-
sion levels above background intensity values in the
grain samples. To determine the extent of gene expres-
sion variation due primarily to genetic factors, the data
from individual locations are reported separately. Similar
to results observed for metabolite profiling, the majority
of expressed genes (58% - 71%) have expression levels
that vary by at least 2-fold at each location. Moreover, as
many as 8% - 12% of genes have expression levels that
change more than 4-fold at each location, and the expr es-
sion level of hundreds of genes change by as much as
8-fold to 16-fold.
The large amount of variation in gene expression ob-
served in grain samples across hybrid lines is consistent
with similar large variation observed across metabolites
in the same samples. Although microarray data are hig hly
Table 3. Variation in transcript accumulation in conven-
tional maize hybrids.
Location JA JE
#Probesets 30248 29125
2× 71% 58%
4× 12% 8%
8× 3% 2%
16× 1% 1%
Microarray analysis was performed using total cellular RNA from identical
grain samples as described in the previous tables. The level of variation is
defined as the percentage of transcripts whose minimum and maximum
measured values across the 30 hybrids was at least 2-fold different (2×),
four-fold different (4×), eight-fold different (8×) or sixteen-fold different
(16×).

Omics Technologies Reveal Abundant Natural Variation in Metabolites and Transcripts among
Conventional Maize Hybrids 339
dependent on such factors as oligonucleotide probe de-
sign and the accuracy of the targeted EST sequences, the
technical variation across microarrays in our platform is
much less than 10% (data not shown). Thus, changes in
gene expression among hybrids are largely due to genetic
factors, but the moderate differences observed across
sites indicate an additional significant role of environ-
ment on gene expression.
6. Discussion
As has been documented many times throughout the
course of maize breeding, there are large var iations among
hybrid germplasm observed in basic yield components,
due to genetic background as well as environment [32,
33]. We have also seen this type of variation in yield and
yield components within our set of genetically-similar
hybrids (data not shown). Further, the set of 30 hybrids
described here also showed large variation in some stan-
dard compositional metabolites in grain, using a widely
accepted targeted analysis platform. Beyond these an-
ticipated results, the study described here was designed
to determine the extent of natural variation of less well
characterized biomolecules, transcripts and metabolites
in maize hybrids. Our results demonstrate that widely
differing levels of these biomolecules can also occur in
mature grain, with the major differences observed due to
genetic factors alone, although environmental factors
contribute to further variation. This natural variation ap-
peared to be common across the transcriptome and me-
tabolome, with no molecular or biochemical pathway
showing more or less variation than any other (data not
shown).
Omics profiling technologies are being more widely
applied to study the effects of transgenes in crops. The
conclusions drawn in much of the published literature
suggests that the presence of a transgene often results in
less variation than what may be introduced by conven-
tional breeding methods [12,15,34-38]. Given that these
Omics technologies are still evolving with increasing
detection capabilities and sensitivity of these technology
platforms expected, it is reasonable to assume that dif-
ferences in gene expression and metabolites will b e iden-
tified between crops with and without a transgene. How-
ever, one of the many challenges Omics technologies
present is the ab ility to in terpret chan ges in the contex t of
product safety. A key advancement to understand wh eth er
changes between GM and non-GM crops may impact
product safety is to first understand the natural variation
of a specific biomolecule in non-GM crops that is due to
environmental and genetic factors. Based on results from
this field-based study of 30 genetically similar maize
hybrids, there exists a broad range of variation at both the
level of gene expression and accumulated metabolites
that is dependent on the genetic backgrounds and envi-
ronment of the source material. This natural variation of
key macromolecules emphasizes the need for compre-
hensive baseline databases to characterize the natural
variation found within a species. These baseline data-
bases would provide the appropriate context for inter-
preting changes in composition and the relevance of
identified variation for safety assessment. As with the
current compositional analysis used in a comparative
safety assessment of biotech crops, if a change is de-
tected but the level still falls within the natural range of
variation, the change is most likely not due to the trans-
gene per se, but rather is a function of the underlying
genetic backgrounds or the environment. Importantly, to
date there are no reports that demonstrate a direct corre-
lation between the magnitude of a detected change in
RNA, protein or metabolites and any adverse safety ef-
fect. At the same time, dozens of GM crops have been
determined to be safe for use in food, feed and the envi-
ronment. There continues to be a growing body of litera-
ture indicating variation in metabolite content between
GM and non-GM crops is routinely smaller and within
the range of natural variation of the crop. The results
discussed in this report provide strong evidence that fur-
ther extensive studies would be needed to address the
existing challenges, including standardization of Omics
methodologies and the establishment of baseline data-
bases. This dataset will provide a solid foundation to
development of publically available reference databases
for natural variation of key macromolecules.
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