Journal of Experimental Botany, Vol. 60, No. 1, pp. 153–167, 2009
doi:10.1093/jxb/ern270 Advance Access publication 17 November, 2008
This paper is available online free of all access charges (see http://jxb.oxfordjournals.org/open_access.html for further details)
A pathway-specific microarray analysis highlights the
complex and co-ordinated transcriptional networks of
the developing grain of field-grown barley
Michael Hansen1, Carsten Friis2, Steve Bowra3, Preben Bach Holm1and Eva Vincze1,*
1Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, Research Centre Flakkebjerg,
DK-4200 Slagelse, Denmark
2Center for Biological Sequence Analysis, BioCentrum, Technical University of Denmark, Building 208, DK-2800, Lyngby, Denmark
3Verzyme (UK) Ltd., Plas Gogerddan, Aberystwyth, Wales SY23 3EB, UK
Received 30 July 2008; Revised 8 October 2008; Accepted 9 October 2008
The aim of the study was to describe the molecular and biochemical interactions associated with amino acid
biosynthesis and storage protein accumulation in the developing grains of field-grown barley. Our strategy was to
analyse the transcription of genes associated with the biosynthesis of storage products during the development of
field-grown barley grains using a grain-specific microarray assembled in our laboratory. To identify co-regulated
genes, a distance matrix was constructed which enabled the identification of three clusters corresponding to early,
middle, and late grain development. The gene expression pattern associated with the clusters was investigated using
pathway-specific analysis with specific reference to the temporal expression levels of a range of genes involved
mainly in the photosynthesis process, amino acid and storage protein metabolism. It is concluded that the grain-
specific microarray is a reliable and cost-effective tool for monitoring temporal changes in the transcriptome of the
major metabolic pathways in the barley grain. Moreover, it was sensitive enough to monitor differences in the gene
expression profiles of different homologues from the storage protein families. The study described here should
provide a strong complement to existing knowledge assisting further understanding of grain development and thereby
provide a foundation for plant breeding towards storage proteins with improved nutritional quality.
Key words: Amino acid metabolism, cDNA microarray, field trial, hordein, Hordeum vulgare, storage proteins.
The content and quality of proteins are major determinants
of the nutritional value of cereal grains and grain-derived
products. Storage protein composition is the result of
a complex interaction between the plant’s genetic back-
ground and its environment; the latter encompasses nutrient
availability (Shewry et al., 2001; Oury et al., 2003). Nitrogen
is an essential nutrient and plays a dominant role in
determining the amount of protein stored in cereal grains.
Intensive agriculture is driven by high inputs, in particular
N fertilizer. However, increasing environmental concerns
have prompted the need to improve nitrogen use efficiency
(NUE) of cereals which currently only utilize approximately
30–40% of the available nitrogen (Raun and Johnson,
The study of storage protein accumulation in the cereal
grain has a long history and a range of biochemical and
molecular techniques have been used successfully to dissect
the complex regulation of individual genes and proteins
associated with storage product accumulation during grain
filling (see recent reviews by Jolliffe et al., 2005; Vicente-
Carbajosa and Carbonero, 2005, and references therein).
While significant understanding was achieved by analysing
single or a small subset of genes in isolation it also
underpinned the development of molecular plant breeding
* To whom correspondence should be addressed: E-mail: firstname.lastname@example.org
ª 2008 The Author(s).
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-
nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
techniques such as marker assisted breeding and Quantita-
tive Trait Loci (QTL) mapping (Thomas, 2003). In recent
years, a rapidly increasing number of microarray analyses
have been implemented to enable ‘global’ gene expression
analysis. Microarray data can readily be integrated with
traditional biochemical and physiological analysis, which
has apportioned function to a gene and its gene product.
Therefore, microarray has the potential to enable the
identification of candidate genes related to a wide range of
important quality traits such as protein content and
To date, microarray technology has been used to study
global gene expression during grain filling and seed
formation in a number species, for example, in rice (Zhu
et al., 2003; Duan et al., 2005), Arabidopsis (Girke et al.,
2000; Ruuska et al., 2002), wheat (Gregersen et al., 2005;
Baudo et al., 2006; Kan et al., 2006), barley (Sreenivasulu
et al., 2004, 2006; Radchuk et al., 2005, Druka et al., 2006),
and Medicago truncatula (Firnhaber et al., 2005). Further-
more, the work of Druka et al. (2006) has provided a global
framework of whole plant gene expression analysis for
barley, which underpins the database named BarleyBase
(http://www.plexdb.org). However, it is important to note
that a common feature of the studies cited above is that the
experimental plant material was grown under controlled
conditions either in greenhouses or growth cabinets. Given
the fact that a ‘systems approach’ must integrate the impact
of the environment and since environment has a significant
impact on plant performance, extrapolation of the results
from glasshouse-grown material to field-grown material is
not straightforward. Recent studies have demonstrated the
general utility of microarray analysis of field-grown plants
(Duan and Sun, 2005; Lu et al., 2005). Moreover, when
comparing plant material grown in controlled conditions
with field-grown material, significant differences have been
illustrated (Dhanaraj et al., 2007). Therefore ‘real life’
systems analysis, where field-grown material is analysed,
has a greater relevance when addressing plant performance
in particular complex traits such as yield and quality.
As stated, applied nitrogen is highly important with
respect to barley grain quality, however, to our knowledge,
despite the very significant body of work in this area,
microarray analysis has not yet been applied to barley grain
development with the sole object of describing the in-
teraction of genes associated with amino acid and protein
synthesis. Our rationale was that using microarray analysis
to obtain a synthesis of gene expression was a necessary first
step towards studying the impact of nitrogen treatment on
the expression of genes that influence grain quality traits.
To achieve this; a pathway-specific analysis of microarray
data derived from a custom-made cDNA microarray with
1035 genes has been performed. The array contains
a comprehensive set of genes involved in nitrogen mobiliza-
tion, transport, and amino acid metabolism. The results of
the study will provide the opportunity to unify global gene
expression with existing biochemical and molecular data
with the aim of ultimately altering or regulating the
nutritional quality of barley grains.
Materials and methods
Spring barley (Hordeum vulgare L. cv. Barke) was grown in
three field plots of 19.8 m2(12 m 31.65 m) during the
summer of 2005, at the Research Centre Flakkebjerg,
Denmark. After sowing, the plots were fertilized with NS24-
7 (DLA Agro) which contains 12% ammonium, 12% nitrate,
and 7% sulphur, at a rate of 120 kg nitrogen ha?1. After
1 week the plots were fertilized again with PK 0-4-21 (DLA
Agro) at a rate of 25 kg phosphorus ha?1and 60 kg
potassium ha?1. The plots were sprayed one month after
sowing with a broad-spectrum herbicide mixture containing
Express ST (Tribenuron-methyl 50%; E.L. du Pont de
Nemours & Co), Oxitril CM (loxynil 17.32%; Bayer Crop
Science) and Starane 180s (Fluroxypyr 180 g l?1; Dow
Agrosciences) herbicides. The plant material was both
morphologically and chronologically staged in accordance
with internationally recognized criteria (Fig. 1) (Zadoks code,
Zadoks et al., 1974). Individual spikes were tagged at
flowering and harvested in the morning (08.00–09.00 h) at
10, 15, 18, and 25 d after pollination (DAP). Developing
grains were immediately frozen in liquid nitrogen and stored
at –80 ?C until analysis.
The grains harvested were analysed for water (%), starch (%),
and protein content (%) using a near-infrared spectroscopy
analyser (Foss Tecator, Infratec 1241, Grain Analyser
v.3.40). The near-infrared spectroscopy analyser was cali-
brated and linked to the Danish NIT network (Buchmann
et al., 2001).
Construction of barley cDNA microarrays
A set of genes (1035) was assembled from two EST libraries
obtained from Clemson University Genomics Institute
(termed HVSMEi and HVSMEk; (http://www.genome.
clemson.edu/projects/barley/) and the microarray slides were
prepared as described by Hansen et al. (2007). The list of
Fig. 1. Development of barley (Hordeum vulgare cv. Barke) grains
used for the expression profiling. DAP: days after pollination.
154 | Hansen et al.
the 1035 genes is available as Supplementary material at
JXB online in Hansen et al. (2007).
RNA isolation and labelling of target material
Three biological replicates were sampled; each sample
consisted of two grains collected from the midrib of in-
dependent barley spikes. After grinding the grains in liquid
nitrogen the total RNA was extracted according to the
manufacturer’s protocol (FastRNA Pro Green Kit, Bio101
Systems, France). Messenger RNA was extracted from the
total RNA using-Dynabeads (610–05, Dynal, N) according
to the manufacturer’s protocol. The synthesis of first and
second strand cDNA and labelling with Cyanine3/Cyanine5
were performed according to Eisen and Brown (1999).
Microarray design, data pre-procession, and
identification of differential expression
The hybridization protocol was performed according to
Eisen and Brown (1999) with modifications according to
Hansen et al. (2007).
The hybridization of the grain-specific microarray was
carried out with three biological replicates. The array
contained 1035 genes. Each gene was spotted in triplicate in
three subgrids across the slide to control for potential
sources of variation in hybridization across the area of the
slide (technical replicates). The microarray experiments were
performed using samples collected from field-grown barley
subject to three different nitrogen regimes (50, 120, and 150
kg ha?1) at four time points (15, 18, 20, and 25 DAP). An
interwoven loop experimental design was chosen (Altman
and Hua, 2006) in combination with three biological
replicates per treatment resulting in 18 hybridizations (see
Supplementary Fig. S1 at JXB online). Data acquisition
and analysis was performed using an arrayWoRx micro-
array scanner (BioChipReader, Applied Precision, USA)
using the arrayWoRx 2.0 software suite. The spots of each
individual slide were quantified using a ‘well-defined’ grid.
The experimental design dictated that two different
factors (time and treatment) were combined for each
hybridization, thus strengthening the statistical analysis.
Although the different nitrogen regimes are not of interest
in the present study, including them in a two factor set-up,
such as a two-way ANOVA, allows for the inference of
differential expression with a higher degree of confidence
(Shrout and Fleiss, 1979). The other P-values returned by
the test (for the nitrogen regimes and the conjugated
P-value) were not used in the present article. A two-way
ANOVA was performed treating the technical replicates as
independent data points rather than means, thereby avoid-
ing loss of variation and in turn increasing the confidence in
the resulting P-values (Altman and Hua, 2006). The slides
were pre-processed to ensure uniformity of hybridization
before being subject to ANOVA.
The raw data files can be obtained from the ArrayExpress
microarray repository at the European Bioinformatics In-
stitute (EBI) (http://www.ebi.ac.uk/arrayexpress/, accession
number: E-MEXP-1013) supplemented with the available
sequences for the genes used in the experiment. Annotations
of the genes can be found at http://www.genome.clemson.
edu/projects/barley/. The microarray data were normalized
using the non-linear Qspline algorithm (Workman et al.,
2002). The data reported in this article were extracted from
the interwoven loop experiment design in order to identify
significantly regulated genes during grain development in
the barley grown at 120 kg N ha?1.
Clustering using Partitioning Around Medoids (PAM)
Co-regulated genes were identified by generating a distance
matrix using a Pearson correlation between the expression
values with the highest confidence limits. The statistical
package used was R (Becker et al., 1988) (http://www.
r-project.org/). The distance matrix was subsequently clus-
tered by the Partitioning Around Medoids method (PAM)
(Kaufman and Rousseeuw, 1990) using the cluster package
in R. The PAM algorithm is a robust version of k-means
and it searches for a specified number of medoids (repre-
sentatives), k, around which clusters are constructed.
Minimizing the sum of the dissimilarities of all observations
and assigning them to their closest medoid generated the
clusters. The value of k¼3 was identified by manual
inspection as the optimal number of clusters and it divided
the data into three categories, one having the highest
expression at day 10, another at 15–18 d, while the last
cluster showed the highest expression at 25 d (Fig. 2).
Heat map and supervised hierarchical clustering
The heat map diagram (Fig. 3) shows the result of the one-
way hierarchical clustering of genes of the samples (Eisen
et al., 1998). Every horizontal row represents an individual
gene and the gene clustering tree is shown on the left.
Developmental stages (days after flowering), assigned in 10,
15, 18, and 25 d intervals at the top, are represented in vertical
columns. The clustering is performed on the log2 transformed
expression values. A Z-score is calculated and used for the
clustering by subtracting the mean value from the absolute
expression value for each gene followed by the division with
standard deviation across samples. The colour scale shown at
the bottom illustrates the relative expression level of a gene
across all samples: the red colour represents an expression
level above the mean and the blue colour represents
expression levels lower than the mean (Eisen et al., 1998).
Real-time RT-PCR expression analysis
Total RNA was isolated from a pool of three individual
grains taken from the middle of three independent barley
spikes using FastRNA Pro Green Kit (Bio101, Systems,
France) and resuspended in 50 ll DEPC-treated water
according to the manufacturer’s manual. The diluted RNA
was quantified using a GeneQuant II DNA/RNA calculator
(Pharmacia Biotech, Piscataway NJ, USA). First strand
cDNA synthesis and real time RT-PCR were carried out as
described by Hansen et al. (2007). Primer Express software
(Applied Biosystems, Foster City, CA, USA) was used to
Transcriptional network of a developing barley grain | 155
design the primer to the same region as the microarray probe
(see Supplementary Table S1 at JXB online). Primers for the
hordein family were designed towards homologous regions
identified using sequence alignments generated from acces-
sions recovered from the GenBank database. To investigate
the specificity of each primer set (see Supplementary Table
S1 at JXB online) a dissociation curve analysis was
implemented. Expression level units of each gene of interest
were calculated relative to the housekeeping gene, actin, in
the samples (Livak and Schmittgen, 2001). For the individual
candidate genes, the expression at 18 DAP and 25 DAP was
calibrated to the expression at 10 DAP (calibrator). The Ct
value was obtained for each specific gene in the samples,
followed by a quality check of linear regression (R2) and
relative expression calculation for each gene using parameters
of the software RESTªaccording to Pfaffl et al. (2002).
To assess parity between our experimental material and
commercially grown barley the starch and protein contents
of the experimental field-grown material was determined
and found to be 61.6% and 10.4%, respectively, which was
in agreement with Danish field trials (http://orgprints.org/
8019/01/8019.pdf). The yield of our field trial, based on a
19.8 m2plot, was calculated to 55.5 kg ha?1, which again
compared favourably to the 60.4 kg ha?1reported for the
2005 national trials.
Gene expression profile, cluster analysis
The focus of this study was on the developmental phase
between 10 DAP and 25 DAP, because, during this period,
the grains undergo highly orchestrated programmed events
that synchronize the synthesis and deposition of storage
products (Coruzzi and Bush, 2001; Coruzzi and Zhou, 2001;
Palenchar et al., 2004).
A pathway-specific analysis was conducted on a subset of
data generated from the grain-specific microarray. This
established the temporal expression profile of genes associ-
ated with nitrogen mobilization, transport, and amino acid
metabolism and offered an insight to the basic metabolic and
biosynthetic pathways in the developing grain of field-grown
barley. Upon manual inspection, three gene expression
clusters were created which correlated with the development
stages described above (Fig. 2). In an attempt to establish the
robustness of the clusters, the level of significance was
iteratively increased to P <6.58E-06 (300 probes correspond-
ing to the genes clustered have this limit). The set of data
with 1200 probes corresponds to 501 genes (P <0.01328)
covering 51 out of the 55 significantly expressed genes
discussed in the article and is available at JXB online in
Supplementary Fig. S2 and Table S2. The extra four genes
labelled with an asterisk (*) had lower P values (P <0.1) and
were chosen to extend the discussion. We proceeded with
pathway-specific analysis of the whole data set, focusing
primarily on the genes associated with carbon provision and
primary metabolism of aspartate-, arginine-, ornithine-, and
proline-derived amino acids. These data were correlated with
transcriptional profiles of the highly significant expression of
storage protein genes (Tables 1–3).
The use of the Partitioning Around Medoids (PAM)
method for clustering allowed the gene expression profiles to
be collated with early, middle, and late developmental stages
of grain development. To represent individual gene expres-
sion patterns, the gene expression values of the 55 genes
selected for discussion were displayed in a heat map format
(Fig. 3). The heat map is a graphical representation of data
where the values taken by a variable in a two-dimensional
map are represented as colours. Each coloured cell in the
heat map represented the gene expression value for a probe
in a sample. The largest gene expression values are
displayed in red, the smallest values in blue and intermedi-
ate values in shades of red (pink) or blue (Eisen et al., 1998).
The provision of carbon for storage product
In our field experiment the barley grains remained green
until approximately 20 DAP whereafter they began to lose
Fig. 2. Cluster analysis. The gene expression profile of the first
300 most significantly regulated probes representing early-, mid-,
and late phases of the field-grown barley grain. The relative
expression is depicted by the Z-score (obtained for each
measurement by subtracting from it the mean intensity for the
given probe and dividing the result with the corresponding
standard deviation) separated by the sampling time points. The
red line indicated the average gene expression during development.
156 | Hansen et al.
chlorophyll and exhibited signs of senescence (Fig. 1). By
contrast the flag leaves showed signs of senescence around
15–18 DAP (data not shown). Dissecting the gene expres-
sion profiles that make up clusters 1 and 2 (Fig. 2) revealed
differential expression of genes associated with photosyn-
thesis (Fig. 3).
The need for reductant and ATP was supported by the
expression of genes encoding a photosystem II protein
Fig. 3. Hierarchical clustering of genes. Heat map of hierarchical clustering for 55 selected differentially expressed genes: horizontal
rows represent individual genes and vertical rows represent individual time points. Red and blue indicate transcript level above and
below the median for that gene across all samples, respectively. Distinct clusters of differentially expressed genes can be seen for
early, middle, and late developmental stages.
Transcriptional network of a developing barley grain | 157
(HVSMEk0001E12) and the protochlorophyllide reductase A
(HVSMEi0011M10), an enzyme involved in the production of
the chlorophyll antenna components (Fig. 3).
The results indicated that the gene encoding the Rubisco
binding assembly protein (HVSMEk0013C18) was expressed
at a high level in cluster 1; this preceded the gene encoding
Rubisco large subunit (HVSMEi0008P04), which peaked
later around 15–18 DAP and fell into cluster 2 (Fig. 3). The
grains were green and contained substantial amounts of
mRNA coding for the Rubisco large subunit, despite the
absence of sufficient light for the operation of the Calvin
cycle. Moreover, the genes encoding for Calvin cycle
enzymes, for example, phosphoglycerate kinase (EC 18.104.22.168)
or glyceraldehyde-3-phoshate dehydrogenase (GAPDH, EC
22.214.171.124) were exclusively expressed during the first phase
of development while Rubisco peaked in the mid-stage
Similar to Rubisco, pyruvate orthophosphate dikinase
(PPDK, EC 126.96.36.199) was highly up-regulated during the
second phase of development and corresponds to cluster
Table 1. Genes with early expression profile of cluster 1
The listed genes corresponded to a cluster 1 profile and were significantly expressed (P <0.05). Asterisks indicate P <0.1.
Library name Swissprot nameAnnotation P-values
RuBisCO subunit – binding-protein b-subunit
Phosphoglycerate kinase – cytosolic (EC 188.8.131.52)
GAPDH; glyceraldehyde-3-phosphate dehydrogenase (GAPDH. EC 184.108.40.206)
Glutamine synthetase – root isozyme 2 (EC 220.127.116.11)
Glutamine synthetase – root isozyme 4 (EC 18.104.22.168)
Glutamate dehydrogenase 2 (EC 22.214.171.124)
Aspartate aminotransferase – cytoplasmic (EC 126.96.36.199)
Aspartate aminotransferase – chloroplast (EC 188.8.131.52)
Aspartate aminotransferase – mitochondria (EC 184.108.40.206)
Saccharopine dehydrogenase (EC 220.127.116.11)
Cystathionine c-synthase (EC 18.104.22.168)
SAM synthetase 1; S-adenosylmethionine synthetase 1 (EC 22.214.171.124)
Adenosine kinase 2 (EC 126.96.36.199)
SAM decarboxylase; S-adenosylmethionine decarboxylase (EC 188.8.131.52)
Ketol-acid reductoisomerase (EC 184.108.40.206)
P5CS; delta 1-pyrroline-5-carboxylate synthetase (EC 220.127.116.11)
Argininosuccinate synthase (EC 18.104.22.168)
Argininosuccinate lyase (EC 22.214.171.124)
LMW – B hordein
Table 2. Genes with mid expression profile of cluster 2
The listed corresponded to a cluster 2 profile and were significantly expressed (P <0.05). Asterisks indicate P <0.1.
Library nameSwissprot nameAnnotationP-values
Photosystem II 10 kDa polypeptide, chloroplast precursor
Protochlorophyllide reductase A (EC 126.96.36.199)
RuBisCO – large subunit (EC 188.8.131.52)
Pyruvate phosphate dikinase. chloroplast precursor (EC 184.108.40.206)
Glutamine synthetase (EC 220.127.116.11)
L-asparaginase (EC 18.104.22.168)
Aspartate-semialdehyde dehydrogenase (EC 22.214.171.124)
Probable S-adenosylmethionine synthetase 1 (EC 126.96.36.199)
Branched-chain-amino-acid aminotransferase-like protein 1 (EC 188.8.131.52)
P5CR; delta-(1)-pyrroline-5-carboxylate reductase (EC 184.108.40.206)
LMW – B hordein
Hordein D (glutenin HMW)
158 | Hansen et al.
2, pyruvate kinase (PK, EC 220.127.116.11) belonged to cluster 3
Amino acid biosynthesis related genes of primary
Glutamate, aspartate, and serine are the most abundant
amino acids that are translocated in the phloem of barley
and thus provide the primary source of nitrogen from the
leaves to the sink tissue (grain) during barley grain filling
(Winter et al., 1992).
Glutamate is assimilated principally by the cytosolic
glutamine synthetase (GS, EC 18.104.22.168) which catalyses the
ATP-dependent conversion of glutamate and ammonia into
glutamine (Miflin and Habash, 2002; Kichey et al., 2006).
The microarray used in this study included three GS probes
and sequence analysis suggests these homologues were
cytosolic. The steady-state level of two of the cytosolic GS
(HVSMEk0009J22; HVSMEi0007N22) genes was high at
the beginning of grain filling (10 DAP), whereas the third
GS (HVSMEk0005B15) appears to obtain the highest
steady-state level of expression at 18 DAP (Fig. 3). The
product of GS, glutamine, reacts subsequently with 2-
oxoglutarate from the Krebs’ cycle leading to the creation
of two molecules of glutamate, a step catalysed by
glutamine 2-oxoglutarate amino transferase which is also
named glutamate synthase (NADH-GOGAT, EC 22.214.171.124;
Fd-GOGAT, EC 126.96.36.199). In our field experiment it was
found that GOGAT expression was not temporally regu-
lated (data not shown).
As an alternative, glutamate can be formed by glutamate
dehydrogenase (GDH, EC 188.8.131.52–184.108.40.206) via reductive
amination of 2-oxoglutarate, although the reaction is known
to be reversible (Purnell et al., 2005). In our field exper-
iment, the temporal expression profiles of the two homo-
logues of GS which were expressed early (HVSMEk0009J22;
HVSMEi0007N22) were accompanied by glutamate dehy-
drogenase 2 (GDH2, EC 220.127.116.11) (Fig. 3).
which convert glutamate and oxaloacetate to aspartate,
exhibited a similar temporal expression profile to the early
expressing homologues of GS and GDH (Fig. 3). The
steady-state mRNA level was highest at 10 DAP and
Aspartate produced via AspAT is the substrate for
asparagine synthetase (AS, EC 18.104.22.168), which transfers the
amide group of glutamine to aspartate, generating aspara-
gine and glutamate in a reaction driven by ATP. There was
an AS2 probe (HVSMEk0015E21) in the array and the
steady-state level of the AS2 transcript was high at the
beginning of the experiment (10 DAP) and declined until 18
DAP where upon it increased again from 18 DAP to 25
DAP (Fig. 3).
L-Asparaginase (ASase, EC 22.214.171.124) hydrolyses the amide
group of asparagine to produce aspartate and ammonia and
thus provides a route where asparagine is utilized for the
synthesis of amino acids and proteins. The mRNA level of
the gene encoding ASase (potassium-independent isoform)
increased from 10 DAP to 18 DAP in this experiment
126.96.36.199) produces L-aspartate-semialdehyde by the reduc-
tive dephosphorylation of L-b-aspartyl phosphate utilizing
NADPH. This enzyme lies at the first branch point in the
biosynthetic pathway from
leading to the formation of the amino acids lysine,
isoleucine, methionine, and threonine (Cohen, 1983). The
expression profile of the AspSD gene belonged to cluster
2 (Fig. 3).
L-aspartic acid in plants,
Table 3. Genes with late expression profile of cluster 3
The listed corresponded to a cluster 3 profile and were significantly expressed (P <0.05).
Library nameSwissprot nameAnnotationP-values
Pyruvate kinase A – chloroplastic (EC 188.8.131.52)
Asparagine synthetase (EC 184.108.40.206)
DAP; diaminopimelate decarboxylase(EC 220.127.116.11)
Adenosylhomocysteinase (EC 18.104.22.168)
P5CS; delta-(1)-pyrroline-5-carboxylate synthetase (EC 22.214.171.124)
Hordein D (glutenin. HMW)
Globulin-1 S allele precursor
Globulin-1 S allele precursor
Globulin-1 S allele precursor
12S seed storage globulin precursor
12S seed storage globulin precursor
Vicilin (alpha globulin)
Glutelin type-B 1 precursor rice homologue)
Glutelin type-B 1 precursor(rice homologue)
Glutelin type-B 1 precursor(rice homologue)
Transcriptional network of a developing barley grain | 159
Aspartate-derived amino acids
The ‘aspartate family’ of amino acids takes its name from
aspartate, the main precursor in the biosynthetic pathway
towards lysine, methionine, threonine, and isoleucine.
Lysine metabolism is highly controlled at both the
anabolic and catabolic level. During development, the catab-
olism of lysine is thought to be controlled by saccharopine
dehydrogenase (SDH; EC 126.96.36.199) while lysine itself regu-
lates biosynthesis through product feedback inhibition
(Stepansky et al., 2006). A high steady-state mRNA level
was measured for SDH (HVSMEi0005N08) at 10 DAP, the
mRNA level decreased dramatically until 18 DAP when it
again increased (Fig. 3). Unfortunately, the feedback
sensitive aspartate kinase was not present in the cDNA
library. The gene involved in the terminal step of the lysine
pathway encoding diaminopimelate (DAP) decarboxylase
(Lys A, EC 188.8.131.52), increased up to 18 DAP and there-
after remained constant (Fig. 3), while diaminopimelate
epimerase 2 (EC 184.108.40.206) was not differentially regulated
over time (data not shown).
The biosynthesis of methionine is initiated from the
‘aspartate family’ intermediate o-phosphohomoserine (OPH)
and is further metabolized through to methionine and
S-adenosylmethionine (SAM) via a suite of enzymes.
Cluster 1 included a gene encoding cystathionine c-synthase
(EC 220.127.116.11), which is linked to methionine biosynthesis
and S-adenosylmethionine synthetase (SAM-S, EC 18.104.22.168)
(Fig. 3). The second SAM-S gene present in the microarray
has an expression profile characteristic for cluster 2,
emphasizing the importance of SAM in the later stage of
development as well (Fig. 3). Similarly, the gene encoding
S-adenosylmethionine decarboxylase (EC 22.214.171.124), which
facilitates many important methylation events (Poulton,
1981) showed an expression profile characteristic of cluster
1 (Fig. 3). Adenosine kinase (ADK 2, EC 126.96.36.199) and
S-adenosylhomocysteine hydrolase (SAHH, EC 188.8.131.52),
which are both required for the maintenance and recycling
of S-adenosylmethionine-dependent methylation in plants,
belonged to cluster 1, while SAHH gene belonged to
cluster 3 (Fig. 3; Table 3).
The precursor aspartate is further allocated through the
threonine biosynthesis pathway towards the production of
leucine, isoleucine, and valine. Significant temporal regula-
tion of the gene coding for acetolactate synthase (ALS; EC
184.108.40.206) was not observed, the first common enzyme in the
biosynthetic pathway of branched-chain amino acids. How-
ever, the genes coding ketol-acid reductoisomerase (EC
220.127.116.11) andthe branched-chain-amino-acid aminotransferase-
like protein 1 (EC 18.104.22.168) exhibited significant temporal
expression (Fig. 3).
Arginine, ornithine, and proline amino acids
In higher plants, there are two possible pathways for
proline biosynthesis: one utilizes glutamine as a precursor,
the other one uses ornithine (Delauney and Verma, 1993).
The precise contribution of the glutamine pathway and the
ornithine pathway to proline biosynthesis, especially dur-
ing fruit or seed development, remains uncertain (Hare
et al., 1998, and references therein; Stines et al., 1999).
In the field experiment, the expression profile of two
allelic isoforms encoding delta 1-pyrroline-5-carboxylate
synthetase (P5CS; EC 22.214.171.124), the rate-limiting enzymes
of the proline biosynthesis pathway differed. One P5CS
homologue (HVSMEk0024A17) was apportioned to cluster
1 due to the apparent high initial steady-state level of
mRNA, which thereafter decreased during development
(Fig. 3). The expression profile of the second P5CS
homologue (HVSMEi0015K09) fell into cluster 3, where
the expression continued to increase from 15 DAP through
to 25 DAP, the period of the experiment (Fig. 3). The genes
coding for the enzymes of the ornithine pathway, acetylor-
nithine aminotransferases (EC 126.96.36.199) and ornithine
carbamoyltransferase (EC 188.8.131.52) did not change signifi-
cantly during the period studied (data not shown). Delta 1-
pyrroline-5-carboxylate reductase (P5CR; EC 184.108.40.206), the
last enzyme of the proline biosynthetic pathway, is situated
at the confluence of both proline biosynthesis pathways
(Delauney and Verma, 1993). The expression pattern of the
gene encoding P5CR belonged to cluster 2, with the mRNA
level peaking around 15 DAP (Fig. 3).
It is noteworthy that the transcriptional data for one of
the P5CS homologues (HVSMEk0024A17) belonging to
cluster 1 in our experiments does not compare with the
similar homologue deposited at http://www.plexdb.org.
Further analysis of the Affymetrix Barley1 22K GeneChip?
(Close et al., 2004) probe sets illustrated errors (the last two
out of the 11 probes are outside of the 3’ untranslated
region and may be related to activities of a gene other than
The gene corresponding to the arginine pathway, encod-
ing argininosuccinate synthase (EC 220.127.116.11) exhibited a high
level of expression at 10 DAP and belonged to cluster 1,
while argininosuccinate lyase (EC 18.104.22.168) had a more
constant level of expression and belonged to cluster 1 with
lower P value (0.08) only.
The transcriptional profile of storage proteins
Barley storage protein is made up of glutelin, albumins,
globulin, and hordeins that are encoded by multi-gene
families (for review see Shewry and Halford, 2002). To
assess the expression of these families, homologues of barley
storage proteins were included in the microarray and
considerable variation was observed in the temporal gene
expression profile of members of the family (Fig. 3). For
example, three out of the five B hordein genes represented
on the array showed an expression profile within cluster 1,
a single probe exhibited expression characteristics of cluster
2, while one gene appeared to be expressed late in
development and corresponded to cluster 3 (Fig. 3). Similar
differences were observed for the expression patterns of the
five c-hordein genes, two were present in cluster 1, two
160 | Hansen et al.
others belonged to cluster 2, and one to cluster 3. The two
D hordein genes were represented in both cluster 2 and
cluster 3 (Fig. 3). All six significantly expressed globulin
genes belonged to cluster 3 (Fig. 3). The expression patterns
of genes coding for a hordein C homologue and the lysine-
rich glutelin genes were all associated with cluster 3, where
the respective mRNA levels increased late in development
When some of our expression patterns were com-
pared with those reported in the BarleyBase expression
library (http://www.plexdb.org), further mistakes were
found in the Affymetrix probe sets. The probe sets are
wrongly assigned in the case of one of the D hordeins and
the C-hordein in the Affymetrix Barley1 22K GeneChip?.
Validation by real-time RT-PCR
The gene expression profiles obtained from the microarray
experiments were validated by real-time RT-PCR for
a selection of genes (Fig. 4). Primers homologous to all
members of the appropriate genes families present on the
microarray were used (see Supplementary Table S1 at JXB
online), so the real-time RT-PCR results represented an
average expression level among the family members. This
was confirmed when an average profile for the different
homologues was created from the microarray absolute
expression values (Fig. 5). The profile of the C-hordein and
the three glutelin homologues indicated that transcription
of the respective genes continued to increase up to 25
DAP, which correlated with the results of the cDNA
microarray and confirmed that the genes belong to cluster
3 (Fig. 5). Similarly the real-time PCR results matched the
average profile pattern for the B-, D- and gamma-hordeins
(Fig. 5). In addition, the profiles of a Rubisco large
subunit unigene were analysed by real-time RT-PCR (Fig.
4). The profiles indicated a comparative trend to the
microarray results, although suggested sharper increase
from 10 to 18 DAP, while the real-time RT-PCR showed
a stronger decrease at 25 DAP (Fig. 5). No changes were
detected in the expression profiles of SAM-S genes by
real-time PCR. This result was very different from the
microarray results where one SAM-S gene present in the
microarray had an expression profile characteristic for
cluster 1 and a second for cluster 2. The real-time PCR did
not match either of the SAM-S gene expression profiles
detected in the microarray experiments, but it was in good
agreement with the average expression profile for the two
SAM-S genes (Fig. 5).
The objective of the study was to correlate global gene
expression analysis with the molecular and biochemical
interactions associated with amino acid biosynthesis and
storage protein accumulation in the developing grains of
field-grown barley. Some of our results are in disagreement
with the reported expression patterns in the barley database
(http://www.plexdb.org). The analysis of the Affymetrix
Barley1 22K GeneChip?probe sets (Close et al., 2004)
revealed errors in the design, resulting in specific probes
lying outside the 3’ untranslated region of target genes.
Mistakes were found in the Affymetrix Barley1 22K
GeneChip?in connection with P5CS and some of the
hordein coding genes. In the recent microarray literature,
similar problems with the probe set were described for rat
and human Affymetrix arrays (Cambon et al., 2007; Lu
et al., 2007).
Gene clusters correlated with real life gene expression
The development of barley grains can be divided into three
broad stages: (Phase 1) an initial phase of approximately
2 weeks post-anthesis where the endosperm cellularizes and
organelles proliferate; (Phase 2) a phase characterized by the
rapid synthesis of storage products; (Phase 3) a phase, which
occurs around 30 d after anthesis, where the dry matter
accumulation rate decreases and grains begin to desiccate
(Ma and Smith, 1992; Goldberg et al., 1994).
The choice of an appropriate clustering algorithm is
a complex one, since no given method is universally superior
(Fraley and Raftery, 1998; Jain et al., 1999). The best choice
will depend on the size of data set. As hierarchical methods
generally scale poorly with increasing-sized data sets and the
resulting dendrograms become harder to interpret, the
partitioned algorithm was chosen for the larger data set. The
three clusters created by manual inspections correlated with
the developmental stages described above (see Supplemen-
tary Fig. S2 and Table S2 at JXB online). Based on these
three major clusters, 55 genes of interest, showing the three
distinct gene expression profiles, were represented by hierar-
chical methods (Fig. 3).
The provision of carbon for storage product
accumulation: differential expression of genes
associated with photosynthesis
Developing barley grains source photosynthate from the
flag leaf, awns of the spike, and the pericarp (Frey-Wyssling
log2 (fold change)
Fig. 4. Genes of interest validated by real-time RT-PCR. Values of
fold changes calculated relatively to 10 DAP are presented in
logarithmic scale. Gene expression profiles: (open diamonds)
C-hordein; (filled circles) glutelin; (filled squares) B-hordein; (filled
triangles) D-hordein; (crosses) c-hordein; (open triangles) SAM-S;
(open circles) Rubisco large subunit.
Transcriptional network of a developing barley grain | 161
and Buttrose 1959; Thorne, 1963; Duffus and Rosie, 1973;
Kjack and Witters, 1974; Watson and Duffus, 1988; Ma and
Smith 1992). Therefore, the level of photosynthesis is
assumed to be high in the green grains during the early and
middle stages of development. Our findings showed elevated
expression of a number of photosynthesis-related genes in the
early stage of development (Fig. 3). A similar pattern was
reported in the developing seeds of Arabidopsis, where the
major group of photosynthesis-related genes represented by
light-harvesting complex II, photosystem II, and a Rubisco
small subunit peaked around 11 DAP (Ruuska et al., 2002).
In Arabidopsis seeds, the decline in gene expression did not
correlate with photosynthetic activity. It was reported that
the system was still functional until the seeds began to
desiccate around 17 DAP (Fait et al., 2006).
Interestingly, the expression pattern of genes encoding the
Rubisco large subunit and pyruvate orthophosphate diki-
nase (PPDK) belonged to cluster 2 (Fig. 2). The recent work
of Schwender et al. (2004) using B. napus has demonstrated
a metabolic route, not previously described, that accounts
for Rubisco activity in the absence of Calvin cycle-related
enzymes thus increasing the efficiency of carbon partition-
ing into oil. The net result was improved carbon efficiency
of the developing green seeds. The authors concluded that
this might explain why seeds of many species are green and
contain substantial Rubisco activity during development,
Rubisco (large sub)
Fig. 5. The absolute expression profiles of the genes mentioned in the real-time RT-PCR experiments: absolute expression values and
the created average profile for the different homologous of the genes present on the microarray chips. The (filled diamonds, dashed line)
represents average gene expression. The B-hordein diagrams shows profiles for: (filled squares) HVSMEi0006A15, (filled triangles)
HVSMEk0006I09, (crosses) HVSMEk0005A14, (filled circles) HVSMEk0006P03, (open squares) HVSMEk0012H15 (right y-axis). The
gamma hordein profiles are: (filled triangles) HVSMEi0011I18, (crosses) HVSMEi0011I01, (filled squares) HVSMEi0003C02, (filled circles)
HVSMEk0012D09, (open squares) HVSMEi0011M13 (right y-axis). The gene expression profiles for D-hordein: (filled squares)
HVSMEi0004I12, (filled triangles) HVSMEk0002P07. The glutelins expressions: (filled squares) HVSMEk0015F23, (filled triangles)
HVSMEk0003D11, (filled circles) HVSMEk0017D10. The profiles of SAM-S: (filled squares) HVSMEk0019N08, (filled triangles)
162 | Hansen et al.
despite the absence of sufficient light for the operation of
the Calvin cycle. Our results perhaps support this as the
genes encoding for Calvin cycle enzymes were exclusively
expressed during the first phase of development, while
Rubisco peaked in the mid-stage.
While PPDK is known to play an important role as
a photosynthetic enzyme in C4plants, it has been suggested
to have a non-photosynthetic function in C3plants (Chastain
et al., 2006). In C4maize the PPDK enzyme peak occurs at
the end of starch accumulation (21 DAP) and suggests
a critical role in the starch–protein balance (Mechin et al.,
2007). In C3plants, expression of PPDK is detected in the
early stages of grain development similar to our results
(wheat, Aoyagi and Chua, 1988; rice, Chastain et al., 2006;
Arabidopsis, Parsley and Hibberd, 2006). It is concluded that
cytoplasmic PPDK serves as the major means of producing
cellular ATP during early grain development by competing
with ADP-dependent cytoplasmic pyruvate kinase (PK) and
thus bypassing the default route of phosphoenolpyruvate
(PEP) to pyruvate (Pyr) via PK (Chastain et al., 2006).
Amino acid biosynthesis related genes of the primary
metabolism are interconnected
It was shown for leaves, roots, and seeds of Arabidopsis
that the glutamate and aspartate pathways are intercon-
nected and GDH, GS, GOGAT, AS, AspAT, and ASase
interact in the synthesis of glutamine/glutamate and
asparagine/aspartate (Lam et al., 1995; Zhu et al., 2003).
A similar picture can be drawn from our study of
developing barley grains. The recycled nitrogen from
transported amino acids, together with the photorespir-
atory NH4(reactions catalysed by GDH and GS), would
appear to be available to enter the aspartate- and
glutamate-pathways. The importance of these routes is
supported by the early expression of genes coding for
GDH, the GS1 (two isoforms), AS2, and AspAT (all three
isoforms). These genes are highly expressed at the early
stage of seed development, as their encoded proteins are
involved in producing intermediates for the synthesis of
other amino acids and proteins. ASase expression peaked
during the mid-stage, suggesting that ASase catalysed
asparagine catabolism starts to be important at this point
in grain development. The increase in the steady-state level
of ASase observed was concomitant with the decreased
level of AS mRNA, further suggesting that the high level
of ASase is involved in the mobilization of asparagine
during the mid-stage of development The expression of the
third GS isoform was similar to ASase and therefore might
indicate the involvement of the gene product in NH4
detoxification. Similarly, the increased steady-state level of
AS2 mRNA at the late stage of seed development might
accommodate increased demand on aspartate and gluta-
The fate of aspartate-derived amino acids
In the metabolite study of Arabidopsis seeds, the pool of
free lysine significantly declined from 10 DAP to 17 DAP
before dramatically increasing during the desiccation period
(Fait et al., 2006). Our results were in good agreement with
the reported results, as at 10 DAP a high steady-state
mRNA level of saccharopine dehydrogenase was measured,
a gene thought to be involved in the catabolism of lysine,
while the gene encoding diaminopimelate (DAP) decarbox-
ylase, involved in the terminal step of the lysine pathway,
increased up to 15 DAP and thereafter remained constant.
SAM-S carries out the terminal step producing SAM,
a major methyl donor in plants (Azevedo et al., 1997). The
significant differential expression of genes associated with
methionine and SAM biosynthesis and metabolism can be
explained by their essential and perhaps ubiquitous nature
supporting homeostasis of the grain cell during develop-
ment. As an example in plants, the metabolite SAM is used
as the methyl donor for the synthesis of ethanolamine,
pectins, chlorophyll, lipids, and nucleic acids (Ravanel
et al., 1998).
Pereira et al. (2007) reported a co-ordinated and probably
transcriptional regulation of ADK and SAHH genes in
most organs of Arabidopsis, while SAHH abundance was
distinctly higher in seeds and roots, which suggests that it
may have a non-methyl-related role in these organs. In our
case, ADK and SAHH transcript amounts were shown to
fluctuate independently (Fig. 3). ADK was expressed at an
early stage of grain development, while SAHH was
expressed in the late stage, indicating that SAHH may have a
non-methyl-related role during the late stage of barley seed
Arginine, ornithine, and proline amino acids: glutamate
versus ornithine pathways
In addition to its incorporation into polypeptides, free
proline may have a role in osmoprotection, both maintain-
ing homeostasis and acting as an osmoprotectant in re-
sponse to water and NaCl stress (Hare and Cress, 1997;
Zhu et al., 1998; Sawahel and Hassan, 2002).
The expression profile of the second P5CS homologue
(HVSMEi0015K09) fell into cluster 3, while the gene
encoding P5CR belonged to cluster 2, therefore suggesting
a decoupling of expression of the gene from the major phase
of desiccation. The synthesis of glutamate pathway inter-
mediates, glutamic-c-semialdehyde (GSA) and pyrroline-
5-carboxylate (P5C), would seem to indicate additional
diverse metabolic roles (Hua et al., 2001). Interestingly, it
has been reported that P5C/GSA triggers a salicylic acid–
mediated signalling cascade and if not metabolized rapidly,
leads to cell death. Furthermore, an increase in the capacity
to metabolize P5C/GSA leads to protection from cell death
(Deuschle et al., 2004). Therefore P5C is not only involved
in proline biosynthesis and degradation, but also in the
metabolism of ornithine and arginine and citrulline.
Argininosuccinate synthase and argininosuccinate lyase,
two genes corresponding to the arginine pathway, belonged
to cluster 1 (Fig. 3). This may support the importance of the
ornithine pathway, as their substrate is ornithine. Similarly,
the recent metabolomic study carried out by Fait et al.
Transcriptional network of a developing barley grain | 163
(2006) found a high proportion of arginine accumulation
during the period of desiccation of Arabidopsis seeds, which
again supports a role for the ornithine pathway.
The developmental transcriptional profile of storage
proteins: cross-talk between the primary metabolism
and storage product pathways
It is widely suggested that storage product accumulation
occurs in the later phase of grain or seed development in
preparation for a period of dormancy before germination,
which would be fuelled by the utilization of the storage
products (Shewry and Halford, 2002). However, in our
studies, storage product gene expression was observed not
only in the later period of development but also in the early
stages. This is in line with a recent study which reported the
early expression of hordeins in microspore-derived embryos
(Pulido et al., 2006). Pulido and colleagues (2006) suggested
that these proteins might be synthesized and consumed
according to the requirements of the embryogenic micro-
spores and early embryos. Therefore, the presence of
storage products, albeit transiently in some cases, early in
development may not reflect a genetically programmed
response, but a metabolic response to the significant flux of
metabolites into the developing grain from the host plant
i.e. the flag leaf, the awns and the pericarp. This hypothesis
is perhaps supported by our observation that cluster 1 was
dominated by genes associated with primary metabolism,
for example, genes coding TCA cycle enzymes as well as
genes involved in glycolysis (TCA cycle enzymes encoding
genes, e.g. pyruvate carboxylase, ATP-citrate synthase,
isocitrate dehydrogenase, succinyl-CoA ligase, succinate
dehydrogenase, malate dehydrogenase; genes involved in
glycolysis, e.g. aldolases, glyceraldehyde phosphate dehy-
drogenase, enolase, glucose-6-phosphate isomerase, triose-
phosphate isomerase; see Supplementary Table S2 available
at JXB online). Combined with genes associated with
photosynthesis described above, a picture emerges which
suggests a significant flux of carbon skeletons, which are
sequestered albeit temporally in a storage ‘vehicle’. This
intense activity is likely to produce free radicals and cause
REDOX stress. Free proline accumulates in plant tissues
during abiotic stresses (Skopelitis et al., 2006) and contrib-
utes to the scavenging of surplus free radicals (Kaul et al.,
2006). This could explain the high steady-state level of the
P5CR gene expression at 10 DAP, as P5CR is the terminal
gene of the proline biosynthetic pathway.
The temporal expression profiles of the homologues
observed within the storage protein families seem to
coincide with protein production reported by Rahman
et al. (1984). The interplay among the differential temporal
expression, suggests that the genes of each family of
proteins are subject to different transcriptional regulation,
implying that the regulatory units of the genes respond to
different developmental and environmental stimuli. This
opens the intriguing possibility of breeding selectively for
specific alleles/homologues to confer enhanced amino acid
profile of the barley storage proteins.
Validation by real-time RT-PCR: pros and cons of the
Real-time PCR is commonly used for validation of micro-
array results (Mackay et al., 2002). A review of the
literature illustrates that microarray data generally are in
good agreement, but not always confirmed by real-time RT
PCR (Jason et al., 2008; Linton et al., 2008). In their fruit
development study of watermelon, Wechter et al. (2008)
reported that 72 of the 750 (9.6%) tissue-type quantitative-
reactions were in conflict with the microarray results, thus
90.4% were in agreement. The failure to validate microarray
data with real-time PCR is frequently explained by the
possibility of the use of the tissue sources at different
developmental stages (Gregersen et al., 2005; Lee et al.,
The real-time RT PCR validation performed as part of
this study was conducted with primers designed to
homologous regions within selected gene families. Our
rationale was that it was desirable to capture the ‘average’
expression level within a gene family; hence the real-time
PCR results were compared with an average of the micro-
array data which combined the expression of the alleles.
Amplification of multiple family members which may have
slightly different melting curves (depending on the level of
sequence conservation) could affect the calculation of the
relative expression levels. In spite of this possible error,
adopting such an approach resulted in a good correlation
between the microarray and the real-time PCR results,
although the allelic variation observed and reported as
part of this study was lost (Fig. 5). Extending this
argument, we would like to urge caution when designing
primers for real-time PCR validation, as it is clear that to
design primers to a gene requires full knowledge of the
allelic complement in any given genome. Without full
sequence information and carefully considering the region
used for primer design, gene expression observed using
real-time PCR can be an over or underestimation of
relevant gene expression, thus compromising the attempt
to get valid microarray-derived results.
The data presented here provides a comprehensive, pub-
licly accessible transcriptomic analysis of cereal grain
development of field-grown material. It is based on a set
of genes chosen from cDNA libraries of developing barley
seeds. Although the available microarray data set de-
posited in the BarleyBase (http://www.plexdb.org) is very
comprehensive, it is limited to greenhouse material with 20
DAP being the oldest developmental stage reported.
Coupled to this, a number of errors were identified in the
primers used. It is beyond the scope of this article to
describe the inconsistency between the data sets in greater
detail except to say that data derived from Affymetrix
probe sets should be treated with care given the reported
problems with the probe sets in rat and human databases
164 | Hansen et al.
(Cambon et al., 2007; Lu et al., 2007). The temporal
expression profiles of a range of genes involved in photosyn-
thesis, amino acid metabolism, and storage protein accumu-
lation are described and discussed. It is concluded that the
grain-specific microarray coupled pathway-specific analysis is
a fast, reliable, and cost-effective tool for monitoring
temporal changes in the transcriptome of the major meta-
bolic pathways in the barley grain. Therefore, microarray
analysis could provide the knowledge required for the
rational design of an optimal AA profile with the intriguing
possibility of breeding selectively for specific alleles/homo-
logues to confer an enhanced amino acid profile of the barley
storage proteins and so increase its utility as animal feed.
The supplementary data, which can be found at JXB online,
consists of two tables and two figures.
Table S1. Specific primers used for real-time RT-PCR.
Table S2. Significantly regulated genes from the develop-
ing grain-specific microarray. The 501 genes (P <0.05) are
described with a specific library name from Clemson Univer-
Fig. S1. The array design of hybridizations.
Fig. S2. Cluster analysis. The gene expression profile of
the first 501 most significantly regulated genes (1200 probes)
representing the early-, mid- and late phases of the field-
grown barley grain.
We would like to thank KB Nellerup and OB Hansen for
their excellent technical support and HB Rasmussen for
the photographic work. This work was supported by
a grant (93S-943-F07-00047) from The Danish Directorate
for Food, Fisheries and Agri Business.
Altman N, Hua J. 2006. Extending the loop design for two-channel
microarray experiments. Genet Research 88, 153–163.
Aoyagi K, Chua N-H. 1988. Cell-specific expression of pyruvate, Pi
dikinase. In situ mRNA hybridization and immunolocalization labeling of
protein in wheat seed. Plant Physiology 86, 364–368.
Azevedo RA, Arruda P, Turner WL, Lea PJ. 1997. The biosynthesis
and metabolism of the aspartate derived amino acids in higher plants.
Phytochemistry 46, 395–419.
Baudo MM, Lyons R, Powers S, Pastori GM, Edwards KJ,
Holdsworth MJ, Shewry PR. 2006. Transgenesis has less impact on
the transcriptome of wheat grain than conventional breeding. Plant
Biotechnology Journal 4, 369–380.
Becker RA, Chambers JM, Wilks AR. 1988. The new S language.
Wadsworth & Brooks/Cole.
Buchmann NB, Josefsson H, Cowe IA. 2001. Performance of
European artificial neural network (ANN) calibrations for moisture and
protein in cereals using the Danish near-infrared transmission (NIT)
network. Cereal Chemistry 78, 572–577.
Cambon AC, Khalyfa A, Nigel GF, Cooper NGF, Thompson CM.
2007. Analysis of probe level patterns in Affymetrix microarray data.
BMC Bioinformatics 8, 146.
Chastain CJ, Heck JW, Colquhoun TA, Voge DG, Gu XY. 2006.
Posttranslational regulation of pyruvate, orthophosphate dikinase in
developing rice (Oryza sativa) seeds. Planta 224, 924–934.
Close TJ, Wanamaker SI, Caldo RA, Turner SM, Ashlock DA,
Dickerson JA, Wing RA, Muehlbauer GJ, Kleinhofs A, Wise R.
2004. A new resource for cereal genomics: 22K Barley GeneChip
comes of age. Plant Physiology 134, 960–968.
Cohen GN. 1983. The common pathway to lysine, methionine, and
threonine. In: Herrmann KM, Somerville RL, eds. Amino acids:
biosynthesis and genetic regulation. Reading, MA: Addison-Wesley
Publishing Co., 147–171.
Coruzzi G, Bush DR. 2001. Nitrogen and carbon nutrient and
metabolite signalling in plants. Plant Physiology 125, 61–64.
Coruzzi GM, Zhou L. 2001. Carbon and nitrogen sensing and
signalling in plants: emerging ‘matrix effects’. Current Opinion in Plant
Biology 4, 247–253.
Delauney AJ, Verma DPS. 1993. Proline biosynthesis and osmoreg-
ulation in plants. The Plant Journal 4, 215–223.
Deuschle K, Funck D, Forlani G, Stransky H, Biehl A, Leister D,
van der Graaff E, Kunzee R, Frommer WB. 2004. The role of delta-
(1)-pyrroline-5-carboxylate dehydrogenase in proline degradation. The
Plant Cell 16, 3413–3425.
Dhanaraj AL, Alkharouf NW, Beard HS, Chouikha IB,
Matthews BF, Wei H, Arora R, Rowland LJ. 2007. Major differ-
ences observed in transcript profiles of blueberry during cold
acclimation under field and cold room conditions. Planta 225, 735–751.
Druka A, Muehlbauer G, Druka I, et al. 2006. An atlas of gene
expression from seed to seed through barley development. Functional
Integrated Genomics 6, 202–211.
Duan K, Luo Y-H, Luo D, Xu Z-H, Xue H-W. 2005. New insights
into the complex and co-ordinated transcriptional regulation networks
underlying rice seed development through cDNA chip-based analysis.
Plant Molecular Biology 57, 785–804.
Duan MJ, Sun SSM. 2005. Profiling the expression of genes
controlling rice grain quality. Plant Molecular Physiology 59, 165–178.
Duffus CM, Rosie R. 1973. Some enzyme activities associated with
the chlorophyll containing layers of the immature barley pericarp.
Planta 114, 219–226.
Eisen MM, Brown PO. 1999. DNA arrays for analysis of gene
expression. Methods in Enzymology 303, 79–205.
Eisen MB, Spellman PT, Brown PO, Botstein D. 1998. Cluster
analysis and display of genome-wide expression patterns. Proceed-
ings of the National Academy of Sciences. USA 95, 14863–14868.
Fait A, Angelovici R, Less H, Ohad I, Urbanczyk-Wochniak E,
Fernie AR, Galili G. 2006. Arabidopsis seed development and
germination is associated with temporally distinct metabolic switches.
Plant Physiology 142, 839–854.
Transcriptional network of a developing barley grain | 165
Firnhaber C, Puhler A, Kuster H. 2005. EST sequencing and time-
course microarray hybridizations identify more than 700 Medicago
truncatula genes with developmental expression regulation in flowers
and pods. Planta 222, 269–283.
Fraley C, Raftery AE. 1998. How many clusters? Which clustering
methods? Answers via model-based cluster analysis. Computer
Journal 41, 578–588.
Frey-Wyssling A, Buttrose MA. 1959. Photosynthesis in the ear of
barley. Nature 184, 2031–2032.
Girke T, Todd J, Ruuska S, White J, Benning C, Ohlrogge J.
2000. Microarray analysis of developing Arabidopsis grains. Plant
Physiology 124, 1570–1581.
Goldberg RB, Depaiva G, Yadegari R. 1994. Plant embryogenesis:
zygote to seed. Science 266, 605–614.
Gregersen PL, Brinch-Pedersen H, Holm PB. 2005. A microarray-
based comparative analysis of gene expression profiles during grain
development in transgenic and wild type wheat. Transgenic Research
Hansen M, Lange M, Friis C, Dionisio G, Holm’ PB, Vincze E.
2007. Antisense mediated suppression of C-hordein biosynthesis in
the barley grain results in correlated changes in the transcriptome,
protein profile and amino acid composition. Journal of Experimental
Botany 58, 3987–3995.
Hare PD, Cress WA. 1997. Metabolic implications of stress-induced
proline accumulation in plants. Plant Growth Regulation 21, 79–102.
Hare PD, Cress WA, van Standen J. 1998. Dissecting the roles of
osmolyte accumulation during stress. Plant, Cell and Environment 21,
Hua XJ, Van de Cotte B, Van Montagu M, Verbruggen N. 2001.
The 5’ untranslated region of the At-P5R gene is involved in both
transcriptional and post-transcriptional regulation. The Plant Journal
Jain AK, Murty MN, Flynn PJ. 1999. Data clustering: a review. ACM
Computing Surveys (CSUR) 31, 264–323.
Jason A, Holliday SG, White RR, Bohlmann J, Aitken SN. 2008.
Global monitoring of autumn gene expression within and among
phenotypically divergent populations of Sitka spruce (Picea sitchensis).
New Phytologist 178, 103–122.
Jollife NA, Craddock CP, Frigerio L. 2005. Pathways for protein
transport to seed storage vacuoles. Biochemical Society Transactions
Kan YC, Wan YF, Beaudoin F, Leader DJ, Edwards K, Poole R,
Wang DW, Mitchell RAC, Shewry PR. 2006. Transcriptome analysis
reveals differentially expressed storage protein transcripts in seeds of
Aegilops and wheat. Journal of Cereal Sciences 44, 75–85.
Kaul S, Sharma SS, Mehta IK. 2006. Free radical scavenging
potential of L-proline: evidence from in vitro assays. Amino Acids 34,
Kaufman L, Rousseeuw PJ. 1990. Finding groups in data: an
introduction to cluster analysis. New York: John Wiley & Sons.
Kichey T, Heumez E, Pocholle D, Pageau K, Vanacker H,
Dubois F, Le Gouis J, Hirel B. 2006. Combined agronomic and
physiological aspects of nitrogen management in wheat highlight
a central role for glutamine synthetase. New Physiologist 169, 265–278.
Kjack JL, Witters RE. 1974. Physiological activity of awns in isolines
of Atlas barley. Crop Science 14, 243–248.
Lam H-M, Coschigano K, Schultz C, Melo-Oliveira R, Tjaden G,
Oliveira I, Ngai N, Hsieh M-H, Coruzzi G. 1995. Use of Arabidopsis
mutants and genes to study amideamino acid biosynthesis. The Plant
Cell 7, 887–898.
Lee MH, Kim B, Song SK, et al. 2008. Large-scale analysis of the
GRAS gene family in Arabidopsis thaliana. Plant Molecular Biology 67,
Linton KM, Hey Y, Saunders E, et al. 2008. Acquisition of
biologically relevant gene expression data by Affymetrix microarray
analysis of archival formalin-fixed paraffin-embedded tumours. British
Journal of Cancer 98, 1403–1414.
Livak KJ, Schmittgen TD. 2001. Analysis of relative gene expression
data using real-time quantitative PCR and the 2–DDCTmethod.
Methods 25, 402–408.
Lu C, Hawkesford MJ, Barraclough BJ, Poulton PR, Wilson ID,
Barker GL, Edwards KJ. 2005. Markedly different gene expression
in wheat grown with organic or inorganic fertilizer. Proceedings of the
Royal Societyof London, Biological Sciences 272, 1901–1908.
Lu J, Lee JC, Salit ML, Cam MC. 2007. Transcript-based
redefinition of grouped oligonucleotide probe sets using AceView:
high-resolution annotation for microarrays. BMC Bioinformatics 8, 108.
Mackay IM, Arden KE, Nitsche A. 2002. Real-time PCR in virology.
Nucleic Acids Research 30, 1292–1305.
Ma BL, Smith DL. 1992. Apical development of spring barley under
field conditions in north-eastern North America. Crop Science 32,
Mechin V, Thevenot C, Le Guilloux M, Prioul J-L, Damerval C.
2007. Developmental analysis of maize endosperm proteome sug-
gests a pivotal role for pyruvate orthophosphate dikinase. Plant
Physiology 143, 1203–1219.
Miflin BJ, Habash DZ. 2002. The role of glutamine synthetase and
glutamate dehydrogenase in nitrogen assimilation and possibilities for
improvement in the nitrogen utilization of crops. Journal of Experimen-
tal Botany 53, 979–987.
Oury FX, Berard P, Brancourt-Hulmel M, et al. 2003. Yield and
grain protein concentration in bread wheat: a review and study of
multi-annual data from a French breeding program. Journal of Genetic
Breeding 57, 59–68.
Palenchar PM, Kouranov A, Lejay LV, Coruzzi GM. 2004.
Genome-wide patterns of carbon and nitrogen regulation of gene
expression validate the combined carbon and nitrogen (CN)-signaling
hypothesis in plants. Genome Biology 5, R91.
Parsley K, Hibberd JM. 2006. The Arabidopsis PPDK gene is
transcribed from two promoters to produce differentially expressed
transcripts responsible for cytosolic and plastidic proteins. Plant
Molecular Biology 62, 339–349.
Pereira LAR, Todorova M, Cai X, Makaroff CA, Emery RJN,
Moffatt BA. 2007. Methyl recycling activities are co-ordinately
regulated during plant development. Journal of Experimental Botany
Pfaffl MW, Horgan GW, Dempfle L. 2002. Relative expression
software tool (RESTª) for group-wise comparison and statistical
166 | Hansen et al.
analysis of relative expression results in real-time PCR. Nucleic Acids
Research 30, e36.
Poulton JE. 1981. Transmethylation and demethylation reactions in
the metabolism of secondary plant products. In: Coon EE, ed. The
biochemistry of plants: a comprehensive treatise. New York: Academic
Pulido A, Hernando A, Bakos F, Me ´ndez E, Devic M,
Barnaba ´s B, Olmedilla A. 2006. Hordeins are expressed in
microspore-derived embryos and also during male gametophytic and
very early stages of seed development. Journal of Experimental Botany
Purnell MP, Roubelakis-Angelakis KA, Botella JR. 2005. Modula-
tion of higher-plant NAD(H)-dependent glutamate dehydrogenase
activity in transgenic tobacco via alteration of beta subunit levels.
Planta 222, 167–180.
Radchuk VV, Sreenivasulu N, Radchuk RU, Wobus U,
Weschke W. 2005. The methylation cycle and its possible functions in
barley endosperm development. Plant Molecular Physiology 59, 289–307.
Rahman S, Kreis M, Forde BG, Shewry PR, Miflin BJ. 1984.
Hordein-gene expression during development of the barley (Hordeum
vulgare) endosperm. Biochemical Journal 223, 315–322.
Raun WR, Johnson GV. 1998. Improving nitrogen use efficiency for
cereal production. Agronomy Journal 91, 357–363.
Ravanel S, Gakiere B, Job D, Douce R. 1998. The specific features
of methionine biosynthesis and metabolism in plants. Proceedings of
the National Academy of Sciences, USA 95, 7805–7812.
Ruuska SA, Girke T, Benning C, Ohlrogge JB. 2002. Contrapuntal
networks of gene expression during Arabidopsis seed filling. The Plant
Cell 14, 1191–1206.
Sawahel WA, Hassan AH. 2002. Generation of transgenic wheat
plants producing high levels of the osmoprotectant proline. Bio-
technology Letters 24, 721–725.
Schwender J, Goffman F, Ohlrogge JB, Shachar-Hill Y. 2004.
Rubisco without the Calvin cycle improves the carbon efficiency of
developing green seeds. Nature 432, 779–782.
Shewry PR, Tatham AS, Halford NG. 2001. Nutritional control of
storage protein synthesis in developing grain of wheat and barley.
Plant Growth Regulation 34, 105–111.
Shewry PR, Halford NG. 2002. Cereal seed storage proteins:
structures, properties and role in grain utilization. Journal of Exper-
imental Botany 53, 947–58.
Shrout PE, Fleiss JL. 1979. Intraclass correlations: uses in assessing
rater reliability. Psychological Bulletin 86, 420–428.
Skopelitis DS, Paranychianakis NV, Paschalidis KA,
Pliakonis ED, Delis ID, Yakoumakis DI, Kouvarakis A,
Papadakis AK. 2006. Abiotic stress generates ROS that signal
expression of anionic glutamate dehydrogenases to form glutamate for
proline synthesis in tobacco and grapevine. The Plant Cell 18, 2767–
Sreenivasulu N, Altschmeid L, Radchuk V, Gubatz S, Wobus U,
Weschke W. 2004. Transcript profiles and deduced changes of
metabolic pathways in maternal and filial tissues of developing barley
grains. The Plant Journal 37, 539–553.
Sreenivasulu N, Radchuk V, Strickert M, Miersch O,
Weschke W, Wobus U. 2006. Gene expression patterns reveal
tissue-specific signalling networks controlling programmed cell death
and ABA-regulated maturation in developing barley seeds. The Plant
Journal 47, 310–327.
Stepansky A, Less H, Angelovici R, Aharon R, Zhu X, Galili G.
2006. Lysine catabolism, an effective versatile regulator of lysine level
in plants. Amino Acids 30, 121–125.
Stines AP, Naylor DJ, Hoj PB, van Heeswijck R. 1999. Proline
accumulation in developing grapevine fruit occurs independently of
changes in the levels of Delta(1)-pyrroline-5-carboxylate synthetase
mRNA or protein. Plant Physiology 120, 923–931.
Thomas WTB. 2003. Prospects for molecular breeding in barley.
Annals of Applied Biology 142, 1–12.
Thorne GN. 1963. Varietal differences in photosynthesis of ears and
leaves of barley. Annals of Botany 27, 155–174.
Vicente-Carbajosa J, Carbonero P. 2005. Seed maturation:
developing an intrusive phase to accomplish a quiescent state.
International Journal of Developmental Biology 49, 645–651.
Watson PA, Duffus CM. 1988. Carbon dioxide fixation by detached
cereal caryopses. Plant Physiology 87, 504–509.
Wechter WP, Levi A, Harris KR, et al. 2008. Gene expression in
developing watermelon fruit. BMC Genomics 9, 275.
Winter H, Lohaus G, Heldt HW. 1992. Phloem transport of amino
acids in relation to their cytosolic levels in barley leaves. Plant
Physiology 99, 996–1004.
Workman C, Jensen LJ, Jarmer H, Berka R, Gautier L,
Nielsen HB, Saxild HH, Nielsen C, Brunak S, Knudsen S. 2002. A
new non-linear normalization method for reducing variability in DNA
microarray experiments. Genome Biology 30, 1–16.
Zadoks JC, Chang TT, Konzak CF. 1974. A decimal code for the
growth stages of cereals. Weed Research 14, 415–421.
Zhu BC, Su J, Chan MC, Verma DPS, Fan YL, Wu R. 1998.
Overexpression of a delta-(1)-pyrroline-5-carboxylate synthetase gene
and analysis of tolerance to water- and salt-stress in transgenic rice.
Plant Science 139, 41–48.
Zhu T, Budworth P, Chen W, Provart N, Guimil S, Su W, Estes B,
Zou G, Wang X. 2003. Transcriptional control of nutrient portioning
during rice grain filling. Plant Biotechnology Journal 1, 59–70.
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