Download Transcriptional profiling of wheat caryopsis development using

Plant Mol Biol (2007) 63:651–668
DOI 10.1007/s11103-006-9114-y
Transcriptional profiling of wheat caryopsis development using
cDNA microarrays
Debbie L. Laudencia-Chingcuanco Æ
Boryana S. Stamova Æ Frank M. You Æ
Gerard R. Lazo Æ Diane M. Beckles Æ
Olin D. Anderson
Received: 6 March 2006 / Accepted: 6 November 2006 / Published online: 9 January 2007
Springer Science+Business Media B.V. 2007
Abstract The expression of 7,835 genes in developing
wheat caryopses was analyzed using cDNA arrays.
Using a mixed model analysis of variance (ANOVA)
method, 29% (2,237) of the genes on the array were
identified to be differentially expressed at the 6 different time-points examined, which covers the developmental stages from coenocytic endosperm to
physiological maturity. Comparison of genes differentially expressed between two time-points revealed a
dynamic transcript accumulation profile with major
re-programming events that occur at 3–7, 7–14 and
21–28 DPA. A k-means clustering algorithm grouped
the differentially expressed genes into 10 clusters,
revealing co-expression of genes involved in the same
pathway such as carbohydrate and protein synthesis or
preparation for desiccation. Functional annotation of
genes that show peak expression at specific time-points
Electronic supplementary material The online version of this
article (doi: 10.1007/s11103-006-9114-y) contains supplementary
material, which is available to authorized users.
Boryana S. Stamova share first co-authorship
D. L. Laudencia-Chingcuanco (&) G. R. Lazo O. D. Anderson
Genomics and Gene Discovery, USDA-ARS-WRRC, 800
Buchanan Street, Albany, CA 94710, USA
e-mail: [email protected]
B. S. Stamova
Genetic Resources Conservation Program, University of
California-Davis, One Shields Avenue, Davis, CA 95616,
USA
F. M. You D. M. Beckles
Plant Sciences Department, University of California-Davis,
One Shields Avenue, Davis, CA 95616, USA
correlated with the developmental events associated
with the respective stages. Results provide information on the temporal expression during caryopsis
development for a significant number of differentially
expressed genes with unknown function.
Keywords Wheat Endosperm Profiling Transcriptome Caryopsis Development Microarray cDNA ANOVA
Introduction
We are using cDNA microarrays to study global patterns of gene expression in developing wheat caryopsis.
The caryopsis is the fruit of grasses in which the pericarp is fused to the seed coat at maturity and it is
interchangeably referred to as the grain in cereals. The
caryopsis consists of three major structures: embryo,
endosperm and seed coat with the endosperm being
the tissue of economic value. The endosperm is the
primary site of storage of starch and protein, which are
both important for global agriculture (Aquino et al.
1999; Triboi and Triboi-Blondel 2002). The endosperm
starch and storage proteins are critical to bread and
pasta quality and are exclusive to wheat such that no
other cereal can be used similarly (Bechtel et al. 1990;
Shewry et al. 2002). Wheat milling, baking, cooking
and eating qualities are largely determined by the
biochemical composition of the seed (Stoddard 1999a)
and processed wheat starch and protein are also finding
use as biopolymers (Davies et al. 2003; Woerdeman
et al. 2004). To enhance wheat seed quality and
functionality with the intention of broadening existing
markets, it will be necessary to dissect the underlying
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molecular mechanisms determining grain storage
product accumulation (Davies et al. 2003; Francki and
Appels 2002; Wilson et al. 2004).
The physiological, biochemical and morphological
changes that culminate during grain development
determine the nutritional profile of the seed at harvest.
These fundamental processes are almost certainly
regulated at the level of transcription. Wheat endosperm development can be generally divided into five
phases (Simmonds and O’Brien 1981): Phase I [0 days
post-anthesis (DPA)] fertilization, Phase II (1–5 DPA)
‘‘coenocytic’’ endosperm, Phase III (6–13 DPA)
cellularization and early grain filling, Phase IV
(14–24 DPA) maximum grain filling, and Phase V
(25–38 DPA) desiccation. Phase II is also commonly
known as Grain Growth stage, Phase III and Phase IV
as the grain filling stage, and Phase V as dry-down
stage—where each stage describes the relationship
between cell development and storage product accumulation. During Grain Growth, free nuclear division
in the embryo sac produces the watery coenocytic tissue. Final cell number of the endosperm is established
and differentiation into the outer aleurone layer and
inner endosperm cells occurs. Grain filling is dominated by the rapid synthesis of starch and protein in the
endosperm (Bechtel et al. 1990; Shewry and Halford
2002; Stoddard 1999b) and to a lesser degree, lipids.
Cell division ceases and cell enlargement takes place to
accommodate reserve storage. Dry down is characterized by cessation of most metabolic processes. The
organ reaches maximal dry weight and the cells
undergo a type of programmed cell death in preparation for dormancy (Olsen 2001; Olsen et al. 1999;
Young and Gallie 2000).
The changes in storage reserve accumulation during
wheat grain maturation are well established; however,
identifying key molecular determinants controlling
carbon flux to the grain and the partitioning of carbon
to starch and protein are more elusive. Much of our
current knowledge is based on biochemical assays of
protein and enzymatic activities of starch and protein
biosynthesis during caryopsis development. Our
understanding of how each pathway is controlled is
complicated by the occurrence of multi-gene families
encoding many of the enzymes in these biochemical
pathways [reviewed in Morell and Myers (2005)], the
interconnectedness of these different pathways (Giroux et al. 1994; Simmonds 1995; Triboi and TriboiBlondel 2002) and the strong influence of the environment on the amount and nature of the starch and
protein made (Dupont and Altenbach 2003; Triboi
et al. 2003). Gene networks and transcriptional regulators synchronously expressed with the biochemical
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and physiological changes observed during grain
development (Appels et al. 2003) may be informative.
Classifying genes based on similarities or difference in
transcript profile with phenotype can confirm existing
knowledge, lead to the dissection and revelation of
novel mechanisms determining nutrient partitioning,
and generate new unbiased hypotheses (Leader 2005;
Zhu et al. 2003).
cDNA microarrays are used to provide a comprehensive description of transcript level in an organism
after perturbation or during development [reviewed
in Schaffer et al. (2000)]. They have been valuable in
understanding aspects of grain growth and filling in
cereals (Close et al. 2004; Gregersen et al. 2005;
Sreenivasulu et al. 2004; Zhu et al. 2003). However,
because of its recalcitrant genome (Langridge et al.
2001), this type of transcriptional analysis has only just
seen wider utility in wheat (Clarke and Rahman 2005;
Gregersen et al. 2005; Kawaura et al. 2006; Leader
2005; Lu et al. 2005; Wilson et al. 2004). In spite of
their increasing use and vast contribution to opening
new paradigms in plant biology the limitations of
cDNA microarrays have been well documented (Alba
et al. 2004; Donson et al. 2002; Leader 2005; Meyers
et al. 2004). Inherent problems include cross-hybridization of homologous DNA and the poor reproducibility of the PCR amplicons spotted on the array
(Meyers et al. 2004). Superior resources for global
expression profiling such as tag-based technologies are
available [reviewed in Meyers et al. (2004)]. However,
they are not widely disseminated because they are
technically demanding and relatively expensive. cDNA
microarrays in contrast are cheap and relatively portable. The biological relevance of results obtained by
this method may be enhanced by rigorous biological
and technical replication, validation of results by
independent methods and attention to quality control
(Brazma et al. 2001; Yang and Speed 2002).
We produced a cDNA microarray for wheat endosperm transcriptomics. Our aim was to establish an inhouse platform suitable for robust gene expression
profiling of wheat caryopsis development. To support
our objectives we created a ‘‘boutique’’ cDNA microarray with high coverage of clones isolated from
developing caryopsis in order to focus on this process.
Recent wheat transcriptional profiling studies have
provided analyses of the changes underpinning caryopses development (Clarke and Rahman 2005; Gregersen et al. 2005; Leader 2005; Lu et al. 2005; Wilson
et al. 2004; Xue et al. 2006). We wish to describe these
changes, and to use the information as the basis to
further explore changes in the wheat caryopsis transcriptome after genetic perturbation.
Plant Mol Biol (2007) 63:651–668
In this paper we report, (1) the development and
validation of the wheat cDNA microarray (2) the
classification of the temporal patterns of gene expression during wheat grain filling, and (3) the correlation
of those changes with storage product accumulation
and caryopsis development. We show that the array is
highly reproducible and validate our measurements by
semi-quantitative reverse-transcription PCR. Finally
we show that our array and methodology is stringent
enough to further examine the mechanistic forces that
underscore carbon partitioning in wheat.
Materials and methods
Plant material and total RNA extraction
Triticum aestivum cv. Bobwhite was grown to maturity in 10-inch pots (6 plants per pot) in the greenhouse with maximum daytime temperature of 24C
and minimum nighttime temperature of 17C. Natural
light was supplemented with 100 W high-pressure
sodium lights to maintain a day length of 16 h. Plants
were fertilized with Plantex 20-20-20 using an automatic drip irrigation system. Several measures were
implemented to reduce the variability of the biological samples. Pots were rotated every two-weeks to
reduce the effects of environmental variations. The
primary spike of each plant was tagged at anthesis
and only grains at the middle of each spike were
harvested. Grains were collected at 3, 7, 10, 14, 18, 21,
25, 28, 31, 35, 42, 45 and 50 DPA and immediately
frozen with liquid nitrogen. For microarray experiments, RNA was isolated from developing grains at 3,
7, 14, 21, 28 and 35 DPA using a modified TRIZOL
(Invitrogen, Carlsbad, CA). Frozen grains from each
spike were ground using a mortar and pestle and the
powdered tissue resuspended and incubated in a
starch pre-extraction buffer (50 mM Tris–HCl pH 9.0,
200 mM NaCl, 1% Sarcosyl, 20 mM EDTA and
5 mM DTT). Starch was separated from the aqueous
phase by phenol/chloroform extraction before proceeding with the TRIZOL protocol. Total RNA was
treated with RQ1 DNAse (Promega, Madison, WI)
and was re-purified using RNA Easy columns (QIAGEN Inc, Valencia, CA). The quality and concentration of RNA was measured by spectroscopy. RNA
integrity and assessment of DNA contamination
was done by resolving an aliquot of RNA sample in a
2% agarose gel with ethidium bromide and then UV–
visualized. Alternatively, RNA integrity was determined using the Agilent 2100 Bioanalyzer (Agilent,
Palo Alto, CA).
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Determination of physiological parameters during
grain development
To determine the change in grain fresh and dry weight
during development, grains from 9 to 20 single heads
were collected, counted and weighed. Grains from a
single head were freeze-dried to determine dry weight.
Water content was calculated by subtracting dry weight
from fresh weight. Values from individual heads were
averaged for each time point.
To determine the starch and protein content, freezedried grains were ground to a powder using an UDY
mill (UDY Corporation, Fort Collins, CO, USA).
Total starch content per 100 mg sample was assayed
using the Megazyme total starch determination assay
kit (Megazyme International, County Wicklow, Ireland). Total protein content was determined on 30 mg
samples by N combustion analysis with a Leco nitrogen
analyzer (Leco Corporation, St Joseph, MI, USA)
using a protein to N ratio of 5.7:1. For both starch and
proteins assays, three determinations were done per
biological sample, and 2–5 biological samples were
used per time point. Values were averaged for each
time point.
Wheat 8K cDNA array construction
Two sets of clones were used for the construction of
the wheat 8K cDNA arrays: 2,304 unique clones from
developing wheat grain ESTs and the set of approximately 6,000 expressed sequence tag (EST) clones
mapped to the wheat hexaploid genome (Qi et al.
2004). The cDNA clones for the developing wheat
grain ESTs were derived from two non-normalized
cDNA libraries: TA001E1X, generated from RNA
isolated from 5 to 30 DPA developing endosperm
tissue of T. aestivum cv. Cheyenne, and TA059E1X,
generated from developing grains of T. aestivum
cv. Butte plants subjected to different abiotic
stresses (http://wheat.pw.usda.gov/cgi-bin/nsf/nsf_library.
cgi). The set of mapped EST clones were derived from
48 different cDNA libraries generated from different
plant tissues of normally grown plants and those subjected to different biotic and abiotic stresses. The
description of the cDNA libraries and sequences of the
mapped probes are available at http://wheat.pw.usda.
gov/NSF/progress_mapping.html.
The
GenBank
accession number (when available) of a clone spotted
on the array is used as its unique ID. Clones that are
duplicated from the two sources are assigned probe
names appended with a letter. Clones that fail to
generate a PCR product either due to poor growth or
give a double or multiple bands when amplified
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were not removed from the array. Instead the original
clone was re-arrayed and new plasmid DNA isolated to
generate a quality probe. The GenBank accession
number of the re-arrayed clone was also appended with
a letter. The new array contained 10,800 spots; aside
from the control clones, 9,167 of the spots were wheat
probes representing 7,835 unique genes.
Clone inserts were amplified using a modified pair of
universal M13 forward and M13 reverse primers:
GTTTTCCCAGTCACGACGTTG and TGAGCGGATAACAATTTC ACACAG, respectively. PCR
amplifications were carried out in 100 ll volume in an
MJR Tetrad Thermal Cycler (Bio-Rad Laboratories,
Hercules, CA) for 30 cycles with 57C annealing temperature and 2.5 min extension time. The reaction
cocktail contained plasmid DNA, 1.5 mM MgCl2,
200 lM each of deoxynucleotides dATP, dCTP, dGTP
and dTTP, 200 nM each of M13 forward and M13 reverse primers, 1.25 unit Taq polymerase (Promega
Biosciences, Inc., San Luis Obispo, CA) and 1· reaction buffer (50 mM KCl, 10 mM Tris–HCl pH 9.0,
0.1% Triton X-100). Amplicon size, yield and integrity
were determined by resolving 5 ll of the PCR product
in a 1% agarose gel. Amplicons were purified using
QIAquick 96 PCR purification kit (QIAGEN Inc,
Valencia, CA), dried and resuspended in 50% DMSO
as printing buffer.
Probe DNA (approximately 300 ng/ll) were spotted
from 384-well microtiter plates onto Corning UltraGAPS slides (Corning Inc, NY) using an Omnigrid 100
arraying machine (Genomics Solutions, Ann Arbor,
MI) fitted with 48 CMP3 printing pens from Telechem
(Sunnyvale, CA). Each array has 48 subgrids and each
subgrid had 15 · 15 spots. The same pen prints all the
spots on a subgrid. The printed slides were UV-crosslinked at 300 mJ and dried for 2 h in an 80C oven
before use. Clone insert identities were verified by resequencing using the ABI Big Dye terminator mix
from Perkin-Elmer and analyzed in a 3700 or 3730 ABI
DNA analyzer.
mation marker genes. The 23 Lucidea Score Card
control DNA are artificial genes composed of sequences from yeast intergenic regions. Each of the 10
AFGC spiking controls was spotted on the first row of
44 of the 48 subgrids on the array. The 23 Lucidea
Score Card control DNAs were spotted on 4 of the 48
subgrids on the array. The four subgrids (7, 18, 31 and
42) were evenly spaced along the entire length of the
printed array. The mRNA for the 10 AFGC spiking
controls were in vitro transcribed using Promega RiboProbe in vitro Transcription System-T3 (Promega,
Madison, WI). The AFGC control clones are available
at the European Arabidopsis Stock Center (http://
seeds.nottingham.ac.uk/Nasc). The set of in vitro
transcribed mRNA for the Lucidea Score Card controls were purchased from Amersham.
Microarray controls
Hybridized slides were scanned using a Genepix 4000B
microarray scanner (Axon Instruments, Union City,
CA) and raw spot fluorescence intensities were collected using GenePix Pro version 5.0. A quality control
filter is used to flag questionable spots on the array so
they can be removed from analysis. The criteria for a
fair array feature included intensity signal >55% of
background +1 standard deviation (SD), less than 3%
pixel saturation for both channels and feature shape or
circularity. Array features annotated as ‘‘printing buffer’’, ‘‘blank’’ or ‘‘empty’’ were flagged and excluded
from analysis.
The DNA of several control clones were spotted on the
array which could be used for normalization and data
quality assessment: The control clones include the 18
Arabidopsis Functional Genomics Consortium
(AFGC) microarray control set and 23 Lucidea Score
Card (Amersham Biosciences, Palo Alto, CA) calibration, ratio, utility and negative controls. The AFGC
control set includes 10 spiking controls derived from
human genes which do not show significant cross
hybridization with plant DNA and 8 plant transfor-
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RNA labeling and microarray hybridization
RNA was indirectly labeled with Alexa 555 and Alexa
647 fluorophores (Molecular Probes, Inc, Eugene,
OR) using the protocol recommended by the manufacturer. Briefly, 10 lg total RNA was annealed to
0.5 lg oligo-dT and reverse transcribed using the
following reaction cocktail: 200 units Superscript II
(Invitrogen, Carlsbad, CA), 500 lM each of dATP,
dCTP and dGTP, 150 lM dTTP, 300 lM amino-allyldUTP, 10 mM dithiothreitol, and 1· Superscipt II
Strand buffer. The fluorophores were coupled to the
cDNA for at least 2 h, quenched and the unbound
fluorophores removed using Microcon YM-30 filters
(Millipore, Billerica, MA). Equal amounts of in vitro
transcribed mRNA for 10 mammalian spiking controls
(0.05 ng each/lg sample RNA) and 8 Lucidea calibration controls were added to each labeling reaction
mix. Hybridization was carried out using the Pronto!
hybridization kit reagents (Corning Inc, NY) following
the supplied protocol.
Data collection and preprocessing
Plant Mol Biol (2007) 63:651–668
Although sources of variance can be included in the
analysis of variance (ANOVA) gene model, the data
set were pre-normalized before ANOVA analysis was
performed to further reduce non-biologically significant variations. The logarithms (base 2) of the original
intensity values, not the ratios, for each probe obtained
from each fluorescent channel were used. To reduce
any dye bias introduced during RNA labeling, the
global median value of all the signals per channel on
each individual array was subtracted from each spot
raw intensity signal and divided by the standard deviation. The expression values per gene were centered
based on the median value of expression of the gene
across all time points.
Data analysis and visualization
Mixed model ANOVA was used to assess the significance of the difference in expression of each gene
across the 6 time-points surveyed (Kerr et al. 2000;
Wolfinger et al. 2001). With the multiple steps required
to carry out a successful microarray experiment, it is
not unusual to have ‘‘noisy’’ data. To extract reliable
information from the data, non-biologically significant
sources of signal variation were identified and their
effects removed. The following gene model was used to
identify genes that were differentially expressed:
Yijklm ¼ l þ Arrayi þ Dyej þ Treatmentk
þ BioRepl þ ðDye TreatmentÞjk þ
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Genesis software (Sturn et al. 2002). Genespring GX
software version 7.3 (Agilent Technologies, Santa
Clara, CA) was used to identify genes with pattern of
expression correlating with the accumulation and rate
of accumulation of physiological markers.
RT-PCR
To confirm the expression patterns observed by
microarray data analysis, one-step semi-quantitative
reverse transcription polymerase chain reaction (RTPCR) experiments were performed. Probes that
exhibited different patterns of expression based on
microarray data analysis were chosen. Primer pairs
were selected and designed from sequences near the 3¢
end of the gene using PrimeSelect software (DNASTAR Inc. Madison, WI). If a clone belonged to a
contig, the primer pair was designed from the consensus sequence of the virtual gene. An 18S rRNA
gene was selected as a control. Total RNA isolated
from grains of appropriately staged spikes of a fourth
biological sample (different from the three sets used
for the microarray experiment) was used as templates
for amplification. The QIAGEN One Step RT-PCR kit
and MJR Peltier thermal cycler were used to carry out
the amplifications. RT-PCR fragments were resolved
on an agarose gel, stained with ethidium bromide and
visualized with UV light.
Microarray probe annotation and GO functional
categorization
þ ðBioRep TreatmentÞlk þ ðBioRep DyeÞjl
þ ðDye BioRep TreatmentÞjkl
þ eijklm
Yijkl denotes the transformed intensity for a gene, l
denotes the average intensity and eijklm captures the
random errors. The variation due to microarray slide
used (Array) was designated as random effect,
whereas, variations due to RNA fluorescent labeling
(Dye), biological sample RNA (BioRep) and caryopsis
developmental stage (Treatment) were treated as fixed
effects. Only the main effects interacting with Treatment were included in the model. The Statistical
Analysis Software version 9.0 (SAS Institute Inc,
Cary, NC) PROC MIXED protocol was used for
analysis.
The data for the differentially expressed genes were
visualized by implementing the hierarchical and nonhierarchical (k-means) clustering methods in the
The DNA sequence for each of the unique probes
spotted on the array was searched against the
non-redundant GenBank DNA and protein databases
(release 144) using BLASTN and BLASTX (Altschul
et al. 1990, 1997). The best match was extracted using
an in-house Perl script and used as a basis for
obtaining annotations for each probe based on sequence identity. The DNA sequence for each of
the unique probes was also searched against the
UniProt database (Release 1.5, TrEMBL, Swiss-Prot,
and PIR, http://www.ebi.ac.uk/uniprot) resources
using BLASTX, and best matches (E value < 10–10)
were compared to terms of the Gene OntologyTM
(GO) Consortium. Using GO/UniProt comparison
tables, candidate GO assignments were predicted
based on EST matches to the UniProt reference
sequences. Categories were assigned based on
biological, functional, and molecular annotations
available from GO (http://www.geneontology.org/).
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Plant Mol Biol (2007) 63:651–668
Results and discussion
Timing of developmental events in wheat caryopsis
The environmental conditions by which wheat is
grown affect the development and quality of the grain
(Altenbach et al. 2003; Bhullar and Jenner 1986;
Brooks et al. 1982; Daniel and Triboi 2000; Dupont
and Altenbach 2003). The timing of grain development
in particular is dramatically influenced by temperature
(Altenbach and Kothari 2004; Dupont and Altenbach
2003). Altenbach and Kothari showed that high temperature advance and compress the transcriptional
programs in developing wheat grain. However, they
also observed that when grains grown under two different temperature regimes were staged based on
physiological markers rather than on chronological
time, the pattern of transcript accumulation in grains
grown in high temperature compared with those grown
in moderate temperature were equivalent. Presumably
this results because the network of the grain transcriptional program is dependent on environmental
conditions. Thus, the physiological markers of plant
growth may serve as a better reference for analyzing
the timing of gene expression.
To associate the timing of caryopsis development
with changes in gene expression, several key physiological markers of growth were determined for the
materials used in the microarray experiments: caryopsis fresh weight, dry weight, water, protein and starch
content. Fresh weight (FW) reflects the accumulation
of both dry matter and uptake or loss of water. Dry
weight (DW) is primarily influenced by the accumulation of the major seed storage molecules, proteins
and starch, in the endosperm. Water content (WC),
determined as the difference between FW and DW of
each grain, can be used to mark grain cellular expansion and desiccation. Grains from appropriately staged
spikes were collected, counted, weighed and dried. The
dried caryopses in each spike were ground for protein
and starch content determination.
The results of the physiological marker determinations for 14 different time points are shown in Fig. 1.
Our data show that caryopsis FW increased linearly to
31 DPA reaching a maximum of 62 mg/caryopsis
(Fig. 1A). The rate of FW accumulation was at its
maximum rate at 14 DPA. Caryopsis DW increased
steadily and plateau at 35–38 DPA at 37–39 mg/caryopsis. The rate of DW accumulation was maximal at
21 DPA. Although the caryopsis accumulated water up
to 21 DPA, the maximum %WC per caryopsis was at
10 DPA. The rate of water accumulation was maximal
between 7 DPA and 14 DPA. The caryopsis began to
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Fig. 1 Physiological marker of developmental events in wheat
caryopsis. (A) Fresh weight (circle), dry matter (square) and
water content (triangle), shown as amount in mg per caryopsis,
were determined at 14 different time points in wheat caryopsis
development. (B) Accumulation of storage compounds, starch
(circle) and protein (square), during caryopsis development. (C)
Classes of expressed genes expected and predicted to be
associated with the biological events at each stage in caryopsis
development
lose water at 28–35 DPA and reached 4–5% of FW
at 45–50 DPA. The caryopsis began to turn brown at
around 28 DPA and reached physiological maturity at
35–38 DPA, with water content between 35% and 40%
FW (Calderini et al. 2000). Starch accumulated at a
maximum rate between 10–14 DPA and peaked at 35–
38 DPA. Total protein accumulated linearly between
7–28 DPA and reached its peak level at 38–42 DPA.
Six time points in caryopsis development were used
for the microarray experiment. These time-points (3, 7,
14, 21, 28 and 35 DPA) cover the different stages of
development from coenocytic stage to desiccation. The
time-points encompass the transition points in dry
matter accumulation and water uptake and loss, which
Plant Mol Biol (2007) 63:651–668
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could reflect control points in the programming of gene
expression in the developing caryopsis. The expected
and predicted genes that may be expressed in these
stages are shown in Fig. 1C. At 3 DPA, the caryopsis is
at the coenocytic stage. It is small and has not begun
accumulating grain storage molecules. At 7 DPA, the
endosperm is actively undergoing cellularization and
storage molecule accumulation is initiated. The cells
are undergoing expansion as reflected by increasing
rate of water uptake, which peaks at 14 DPA. At
14 DPA, the rate of dry matter increases and peaks at
21 DPA. At 21 DPA water content reaches its maximum and begins to decline at 28 DPA. At 35 DPA
caryopsis attains maximum dry matter content and
reaches physiological maturity.
RNA profiling experimental design and data
analysis
We used an in-house generated wheat cDNA array
containing probes for 7,835 genes to examine the gene
expression dynamics in the developing caryopsis. The
probe set is enriched for genes that are expressed in the
endosperm and 75% of the probes (5,817 genes) have
been mapped in the hexaploid wheat genome. Data
analysis of the embedded spiking and hybridization
controls on the arrays showed that we can reliably
detect a signal from 0.3 pg of the calibration control
RNA for every 1 lg of total RNA labeled for hybridization (see Supplemental Data I). Assuming that the
amplification efficiency of the endogenous wheat RNA
is similar to that of the spiked-in calibration controls
and using the estimated 100–500 pg of total RNA in a
plant cell (Goldberg et al. 1978; Kerk et al. 2003), and
an average of 1,500 nucleotides/molecule of plant
mRNA (Alexandrov et al. 2006; Goldberg et al. 1978;
Ogihara et al. 2004), the cDNA microarrays could
detect the expression of genes with as low as 36–181
RNA transcripts per cell. Using Goldberg and
colleagues’ estimate of 17 transcripts, 340 transcripts
and 4,500 transcripts per cell for low, moderate and
highly expressed genes, respectively, the wheat 8K
cDNA arrays can reliably detect moderately expressed
genes and perhaps some of the low expressed genes.
A connected loop design (Kerr and Churchill 2001a,
b) was used to compare the expression of genes between time points (Fig. 2A). To reduce the effect of
dye-bias, dye swaps for each RNA sample were performed for each comparison The experiment was carried out using three biological replicates per time
point, wherein RNA was independently isolated from
spikes of three different plants at the same stage of
development. Overall 54 slides were used for 108
Fig. 2 Experimental design and data normalization. (A) Loop
design: RNA level from a specific time-point, T, is directly
compared with the RNA level from two adjacent time points and
the time-point 3 times removed. The arrow represents independent RNA labeling and hybridization, wherein the RNA at the
tail end of the arrow is labeled with Alexa 555 fluorophore and
the RNA at the head of the arrow is labeled with Alexa 647
fluorophore. (B) Distribution of normalized data: The left panel
shows the distribution of close to 1.17 million microarray spots
raw intensity values after log (base 2) transformation. The right
panel shows the distribution of all the data points after
normalization (dye correction and median centering). Note that
after data pre-processing all the data points were normally
distributed and centered on zero
hybridizations, which yielded 18 data-points per gene
per time point. Raw signals from each slide were preprocessed to remove flagged data points and were prenormalized using the spiking control signals. The logarithmic (base 2) transformed signals across all slides
showed a skewed distribution that was fixed after
normalization (Fig. 2B). The normalized median-centered log2 transformed signals for each probe were
used as input for a mixed model analysis of variance
(ANOVA) to identify genes that were differentially
expressed during development.
Dynamics of gene expression in developing
caryopsis
We identified 2,295 validated probes on our array to be
differentially expressed (at P £ 0.01) during caryopsis
development (listed in Supplemental Data II). Examination of the distribution of the genes that are differentially expressed between two time-points (Fig. 3,
solid bars) indicates that a significant number of genes
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Numbre of DE Trnarcspits
1200
963
1000
800
725
600
400
364
401
525
460
256
200
88
69
0
3 to 7
7 to 14
14 to 21
21 to 28
13
28 to 35
Consecutive Time-Points Comparison (DPA)
Fig. 3 Differentially expressed genes between two consecutive
time points. Solid bars represent differentially expressed genes
between two consecutive time points. The hatched bars represent
genes that show significant differential expression (P-value = 0.01 or less) in two consecutive time-points only and not
significantly expressed in other time points
showed differential expression throughout development and that major reprogramming occurs during the
early stages of caryopsis development especially between 7 DPA and 14 DPA which coincides with the
onset for grain filling. To further gain insight into the
transcriptional control of biological events at specific
stages of grain development, we examined the transition points for genes that are differentially expressed
between two time points. Transcripts that show rapid
up-regulation or down-regulation only during a particular time of the grain development could be characteristic of a specific developmental stage. Genes
differentially expressed between two time points were
queried for genes that were differentially expressed
ONLY in the two consecutive time-points chosen (at
P £ 0.01) and not in other stages of development
(Fig. 3, hatched bars). A significant number of the
genes expressed in this manner were observed in three
stages: between 3–7, 7–14 and 21–28 DPA. These three
stages correspond to distinct developmental and metabolic events occurring in the caryopsis. Caryopsis
endosperm at 3–7 DPA is transitioning from a coenocyte to a multi-cellular tissue. The onset of grain filling
occurs at 7–14 DPA with a maximal rate of accumulation of transcripts encoding storage proteins and
enzymes involved in starch synthesis. At 21–28 DPA,
the caryopsis is beginning to undergo maturation and
desiccation.
A major proportion (48–55%) of the differentially
expressed genes have not been ascribed a GO biological function annotation. For those genes with biological
GO annotation, the majority falls into the cellular
macromolecule metabolism category or generation of
precursor metabolites and energy category. About 20%
of these genes reach a maximum transcript level between 14 DPA and 21 DPA, when maximum grain
filling occurs. The group of genes (69 transcripts) that
were differentially expressed only between 14 DPA
and 21 DPA contained three 14-3-3b interacting
proteins, which were down regulated during that timespan. 14-3-3 are a family of proteins that play a regulatory role in several processes in plant development
such as signal transduction, checkpoint control, apoptosis and nutrient-sensing pathways (Ferl 1996; Fulgosi
et al. 2002). There are three genes of unknown function
that closely follow the pattern of expression of 14-3-3.
This co-expression can suggest their possible coinvolvement with these regulatory proteins. It has been
proposed that 14-3-3b may inhibit starch biosynthesis
(Fulgosi et al. 2002; Sehnke et al. 2001). This is consistent with our observation that 14-3-3b transcript
accumulation is inversely related to starch accumulation. Among the genes that are significantly up-regulated only at 14–21 DPA are genes involved in
generation of metabolite precursors and energy and
those that are possibly involved in defense mechanisms.
About half of the genes in this group are significantly
down-regulated. These include EF1-a, actin, a-tubulin,
carbohydrate metabolism and unknown genes.
123
Global patterns of transcript accumulation in
developing caryopsis
To assimilate the data generated, hierarchical and
k-means clustering methods were used to discriminate
and visualize patterns of gene expression during development. Hierarchical clustering (Eisen et al. 1998)
allows for the analysis of the relationship of each gene to
every other gene on the array, whereas, non-hierarchical
clustering, like k-means clustering (Sturn et al. 2002),
allows the separation of the genes (or the expression
profiles of the transcripts) into distinct classes.
To classify the genes into groups with a similar
pattern of expression, k-means clusters of 5, 8, 10, 12,
15, 20 and 25 were tested (data not shown). Clustering
into 10 groups was selected (Fig. 4) since it gave
Plant Mol Biol (2007) 63:651–668
659
Fig. 4 Patterns of gene expression in developing wheat caryopsis
The 2,295 differentially expressed genes across all 6 time points
were grouped into 10 clusters using the k-means algorithm. The
mean centered relative gene expression value (in log2 scale) for
each gene is plotted on the y-axis and the time of development in
days post-anthesis (DPA) is on the x-axis. The magenta curve
represents the median of the gene expression values in each
cluster. Tick marks on the x-axis represent the developmental
time 3, 7, 14, 21, 28 and 35 DPA. Tick marks on the x-axis for
3 DPA and 35 DPA overlap with the sides of the cluster box
enough resolution (after 50 iterations) of the different
expression profiles without significant redundancy. The
number of genes that grouped into the 10 different
clusters ranged from 10 (Cluster 10) to 771 (Cluster 9).
Based on trends of mRNA accumulation, the 10 kmeans cluster of gene expression patterns can be sorted
into four classes: up-regulated (Fig. 4, Clusters 2, 3, 6, 7
and 10), down-regulated (Fig. 4, Clusters 1 and 5), bellshaped (Fig. 4, Cluster 8), and modulated (Fig. 4,
Cluster 4 and 9). The range of gene expression based
on fold change is shown in Table 1 and the distribution
of the gene biological GO annotations for each cluster
is in Table 2. Clusters 1, 4, 6 and 9 contain the majority
of the genes and have a narrow range of change in gene
expression (1.2–4.9) compared to the other clusters.
Gene GO annotation shows a significant number involved in cellular and macromolecule synthesis; 41–
67% of the genes in these clusters have unknown
biological roles in the cell.
The activity of the genes in Clusters 1 and 5, which
show peak of expression at 3–7 DPA, are consistent
with the cellular events occurring during Grain Growth
stage. These genes are involved in cell division and
nucleic acid metabolism (e.g. histones, tubulins), photosynthesis (i.e. chlorophyll a/b binding proteins, ferredoxin) and protein metabolism (i.e. ribosomal
Table 1 Level of expression of the developmentally regulated genes within each cluster
Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7 Cluster 8 Cluster 9 Cluster 10
a
No. of genes
Fold changeb
Maximum fold D
Minimum fold D
Mean fold D
‡2.0 fold D
Peak stagec
3 DPA
7 DPA
14 DPA
21 DPA
28 DPA
35 DPA
424
53
92
456
51
337
67
34
771
10
4.9
1.4
1.9
28%
37.2
5.7
11.9
100%
15.7
2.3
4.2
100%
2.6
1.2
1.4
1%
11.1
2.2
5.0
100%
3.2
1.2
1.7
22%
114.6
17.0
42.2
100%
9.7
2.2
4.2
100%
2.7
1.2
1.4
2%
13.3
4.3
9.0
100%
125
298
1
0
0
0
0
0
1
42
5
5
0
0
3
67
8
14
421
9
20
4
0
2
36
15
0
0
0
0
0
1
50
212
10
64
0
0
0
50
7
10
0
0
29
5
0
0
70
468
115
118
0
0
0
0
0
4
0
6
a
The number of genes that group to a cluster as described in Fig. 4
Fold change (D) in level of gene expression was determined by subtracting the lowest from the highest value of the relative
expression of a gene across all the 6 time points; since the gene relative expression value is in log2 the difference was converted to its
natural number Maximum fold change is the fold-change in expression of the gene within a cluster with the highest fold-change value,
whereas, the minimum fold-change is the fold-change in expression of the gene in a cluster with lowest value. Mean fold-change is the
average fold-change in expression of all the genes in the cluster. Also shown is the portion of genes in a cluster that had ‡2.0-fold
change in expression during development
c
The developmental time point when the maximum transcript level of a gene is reached is designated as ‘‘Peak Stage’’. Indicated in
each row are the numbers of genes in each cluster that reached a peak stage at a defined time point
b
123
660
Plant Mol Biol (2007) 63:651–668
Table 2 Functional annotation of genes in each cluster
Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7 Cluster 8 Cluster 9 Cluster 10
Carbohydrate metabolism
Cellular biosynthesis
Cellular catabolism
Defense response
Energy molecular
precursor
Lipid metabolism
Macromolecule synthesis
Nucleic acid metabolism
Polymer biosynthesis
Polymer catabolism
Protein metabolism
Unknown
9
24
11
5
15
8
2
8
23
6
5
8
4
11
5
6
14
10
3
7
10
10
1
12
8
5
10
7
4
6
3
1
3
21
10
29
35
26
6
18
7
14
12
4
8
0
0
10
10
0
3
36
14
1
5
23
41
0
19
6
0
9
11
49
0
10
14
1
5
8
61
2
23
11
1
7
14
54
4
51
35
0
2
35
35
1
13
7
1
5
7
67
3
4
3
0
6
3
64
3
47
9
15
15
18
35
4
27
13
1
7
16
52
20
10
0
0
0
10
50
The distribution of genes in each cluster based on selected GO functional annotations are reported in percent of total number of genes
in a cluster. The percent total per cluster is not equal to 100% because some of the genes have two or more assigned biological
functions in the cell
proteins, translation initiation and elongation factors,
proteosomal proteins). A significant number of the
genes in these clusters (35–41%) still have unknown
biological functions in the cell.
The major metabolic activity occurring in the grain
filling (14–24 DPA) stage of grain development is the
synthesis and accumulation of storage molecules,
starch and proteins. The main storage proteins are the
prolamins, glutenins, and gliadins, which are the major
components of the gluten polymer that determines the
economic significance of wheat (Shewry and Halford
2002). Most of the storage protein genes are found in
Clusters 2 and 7. Genes in these clusters show a dramatic rate of increase in transcript level between
7 DPA and 14 DPA, peak expression at 21 DPA and
then plateau thereafter. The average fold change in
gene expression in Cluster 2 is 12, those for Cluster 7 is
42. Cluster 7 contains the most highly differentially
expressed genes in the endosperm, with a minimum
fold change of 17 and the highest of 115. Most of which
the genes in Cluster 7 are involved in grain filling:
storage protein genes, grain softness protein, and aamylase inhibitors (involved in metabolic and possibly
defense functions). 9 of the 67 genes in Cluster 7 are
unknown proteins. The maximum rate of storage protein transcript and protein accumulation both occur at
the same time between 7 DPA and 14 DPA, which is
consistent with results obtained by others (Altenbach
and Kothari 2004; Clarke et al. 2000). This observation
supports the hypothesis that storage protein gene
transcription, poly (A)+ RNA processing, and translation are closely coupled in the developing wheat
caryopsis, suggesting that a major control of storage
protein accumulation is at the transcriptional level.
123
Cluster 8, which contains 34 genes, shows a bell-shaped
pattern of expression with a peak in transcript level at
14 DPA. The cluster represents mainly metabolic enzymes involved in carbohydrate and protein metabolism:
orthophosphate dikinase, aspartic protease, aspartate
aminotransferase, alanine aminotransferase, sucrose
synthase 2 and ADP-glucose pyrophosphorylase small
subunit, which is a component of an enzyme involved in
the first committed step in starch biosynthesis (Tetlow
et al. 2004). The bell-shaped expression of the carbohydrate and protein metabolic genes correlates with the
observed rate of starch and protein accumulation in the
grain (Fig. 1), except that the maximal rate of accumulation of these storage products is observed between
14 DPA and 21 DPA, following the maximum of the
transcript expression. This is expected since the mRNA is
the template for protein synthesis. It should be noted, that
the accumulation of transcripts involved in starch biosynthesis occurs as early as 3 DPA. This could be from the
synthesis of starch in the pericarp tissue, which is still
photosynthetic at this stage. However, the presence of
starch biosynthesis genes at 3 DPA could also support the
morphological observation that starch granules are present as early as the coenocytic stage of endosperm development (Hughes 1976; Simmonds and O’Brien 1981).
Cluster 10, which contains 10 genes, shows a maximum rate of transcript accumulation between 7 DPA
and 21 DPA, and the transcripts continue to be
up-regulated even after the desiccation program
has begun late in grain development. The transcript
level of 4 of the genes peaked at 21 DPA and the other
6 continue increasing thru the last data point at
35 DPA. Among the members of this cluster are
gamma-purothionin, a well-characterized defense
Plant Mol Biol (2007) 63:651–668
protein (Bruix et al. 1993; Colilla et al. 1990) and
Ec-protein (‘early’ cysteine-labeled metalloprotein).
Not much is known about the function of Ec in wheat
(Hanley-Bowdoin and Lane 1983). A review of plant
metalloproteins (Bilecen et al. 2005), suggested that
these proteins may be involved in stress responses,
programmed cell death, developmental regulation and
heavy-metal metabolism.
The cluster analysis, which classified genes with
similar expression profiles across development,
revealed the enrichment of genes associated in similar
metabolic or developmental pathways. The co-expression of genes in a pathway has been observed in other
large-scale expression analyses (the most recent ones
include Gachon et al. 2005; Wellmer et al. 2006; Zhang
et al. 2005). This association within the cluster could
provide insight into the function of genes with unknown function. Clustering of similar or paralogous/
orthologous genes involved in the same pathway could
also serve to improve approaches to manipulate these
pathways by gene silencing. Finally, the genes in each
cluster could provide a good source of promoters to
drive the transcription of transgenes to a specific
expression profile during caryopsis development.
Genes of unknown function
As described in the previous section, the majority of
genes that are differentially expressed during grain
development have unknown function. Many of these
genes cluster together with annotated genes involved
in defined biological processes such as cell division,
carbohydrate and protein synthesis, and desiccation.
Analysis of gene expression patterns combined with
gene sequence analysis could provide testable
hypotheses as to the function of these unknown genes.
To test the assumption of ‘‘guilt-by-association’’ and
gain insight into the character of unknown genes we
looked more closely at the genes in Cluster 7 (Fig. 4).
The majority of the 67 genes that grouped to Cluster 7
are storage proteins and defense proteins. Nine genes
in this cluster could not be ascribed a function based
on BLASTX and BLASTN best match hits.
Using the TIGR wheat index EST annotator (http://
www.tigr.org/tigr-scripts/tgi/est_ann.pl?db=wheatest),
we checked whether the probe for the unknown genes
in Cluster 7 assembled with other ESTs to form a
contig. If so, then the contig sequence was used to
compare against the non-redundant GenBank DNA
and protein databases. If the unknown gene had no
match to known genes, the protein domain database
was searched for common protein motifs (MarchlerBauer et al. 2005). With this strategy, the unknown
661
gene encoded by BG262302 showed identity to a seed
storage protein (GenBank accession Q7XYF0) and
BE606942 is similar to a ferrochelatase protein
(GenBank accession Q9FEK8). Interestingly, the derived translation protein of BQ804784 sequence
showed similarity to an mRNA binding-like protein,
whereas, BQ167790 sequence has 3 RRM domains, an
RNA recognition motif (Marchler-Bauer et al. 2005).
The other 5 genes contain no recognizable domains,
therefore, remained unknown.
Expression of developmental stage-specific genes
To further examine gene expression at different stages
of grain development, the genes that showed peaks of
transcript level at each stage were selected (see Supplemental Data II). Genes highly expressed at a particular stage may indicate relevance for stage-specific
developmental functions. Of the 2,295 genes analyzed,
652 showed peaks of expression at 3 DPA, 791 genes
peaked at 7 DPA, 219 at 14 DPA, 502 at 21 DPA, 30 at
28 DPA and 101 genes at 35 DPA (see Supplemental
Data II). Genes that peaked in expression in the same
stage showed clustering of genes of defined function. For
example, the maximally expressed genes at 35 DPA
include dehydrins and late embryo abundant (LEA)
genes, protein synthesis inhibitors, including tritin,
proteinase inhibitors, serpin and thaumatin. Dehydrins
and LEA proteins (Farrant et al. 2004) are produced in
response to environmental stimuli with a dehydrative
nature, such as drought, low temperature, salinity, and
developmental stages such as seed and pollen maturation (Campbell and Close 1997; Danyluk et al. 1998).
Their up-regulation was characteristic only during the
last stages of the grain development studied and can
signify entering of the grain into the dry-down stage.
Tritin is a ribosome inactivating protein (Nielsen and
Boston 2001) and has been proposed to have a defense
role (Massiah and Hartley 1995) or a suggested role in
the programmed senescence of the endosperm at
maturity in barley (Leah et al. 1991). Its transcript
abundance is significantly increasing between 28 DPA
and 35 DPA, when protein biosynthesis is terminating
and the seed is preparing for desiccation.
Graphical representation of the expression profiles
of genes that peaked at different stages (Fig. 5) displays the continuous expression of these genes from
one stage to another. For example, genes with a peak
of expression at 14 DPA continue their expression
thereafter, suggesting a continued functional requirement at later stages in grain development. Several
genes involved in starch biosynthesis peak at 14 DPA
(i.e. ADP-glucose pyrophosphorylase, starch synthase
123
662
Plant Mol Biol (2007) 63:651–668
Fig. 5 Gene expression at
different stages of grain
development The left panel
shows the morphology of the
whole caryopsis at the
different time points of
development used on the
microarray experiments. The
yellow bar in the topmost
panel is 2 mm. The middle
panel shows the pattern of
expression of genes that peak
at each specific stage. The
relative expression value (yaxis) is plotted against
developmental time (x-axis).
The right panel shows the
major physiological and
biological processes occurring
in the endosperm at different
developmental stage based on
histological sections and
literature
IIa-3 and starch branching enzyme IIb) and continue to
be expressed up to 35 DPA.
Overall, the analysis of the temporal expression
patterns of developmentally regulated genes showed
gene activities that were well correlated with the
developmental events at the respective stages.
Physiological markers of caryopsis development
and associated differentially expressed genes
We examined the relationship of the 497 genes that have
changed at least 2-fold or greater in expression across
the 6 time-points studied with the physiological markers
FW, WC and DW of a developing caryopsis (Fig. 6A).
Caryopsis FW is a function of DW and WC accumulation. FW is primarily due to WC at the early stages of
development. At 14 DPA the accumulation of WC tapers off whereas that of DW continuous to rise. The
increase in FW at the later stage is due to the accumulation of the storage compounds specially starch. Our
123
analysis showed that the pattern of expression of 234
genes correlated well (Pearson correlation = or greater
than 0.95) with the pattern of accumulation of these
physiological markers (Listed in Supplemental Data
IV-a). A majority (196 genes) correlated with WC, 102
of which are shared with FW; 136 genes correlated with
FW, 20 of which are unique to FW. Only 18 correlated
with DW, 14 shared with FW. All of the transcripts from
storage protein genes within this group of 497 genes
showed similar pattern of accumulation to WC.
To identify genes that may potentially play a role in
the control of the accumulation of the physiological
markers, we examined the pattern of expression of
genes that correlates with the RATE of DW, FW and
WC accumulation (Fig. 6B). Our data showed 36 genes
with significant correlation (Listed in Supplemental
Data IV-b). The majority of the genes (35) correlated
with DW rate of accumulation. The expression of one
gene (encoding a hypothetical protein) correlated
with the rate of accumulation of FW and none
Plant Mol Biol (2007) 63:651–668
663
Fig. 6 Physiological markers
of development and coexpressed genes. Genes with
transcript expression profile
pattern similar to the (A)
pattern of accumulation (or
loss) FW, DW and WC per
caryopsis and (B) the pattern
of the rate of accumulation of
each of the designated
physiological markers. The
numbers outside the circles
corresponds to the number of
genes that did not show
similar pattern (at Pearson
correlation >0.95) as the
physiological markers. DW,
Dry weight; FW, Fresh
weight; WC, water content
correlated with WC. The genes that correlated with the
pattern of DW rate of accumulation include 11 hypothetical proteins and proteins without known function.
Genes with annotations include those involve in the
synthesis, accumulation and probable protection of the
major storage compounds, starch and storage proteins,
both of which substantially contribute to caryopsis
DW. These include genes that code for starch synthesis
enzymes, starch branching enzyme I and ADP-glucose
pyrophosphorylase large subunit, a component of an
enzyme complex, the activity of which catalyzes the
first committed step in the biosynthesis of starch;
inhibitors of protein degradation, protease inhibitor
II, a putative Hageman factor (a serine protease
inhibitor) and PUP88 protein (a member of the trypsin/a-amylase family of inhibitors); and probable
defense proteins—puroindoline, spodomicin and
purothionin.
Interestingly, the expression of several genes
involved in protein degradation also correlated with
the rate of DW accumulation. These include serine
carboxypeptidase I and II, and a putative 26S proteosome regulatory subunit. Serine carboxypeptidases are
proteolytic enzymes reported to be expressed in the
123
664
Plant Mol Biol (2007) 63:651–668
Fig. 7 Transcription factors: A hierarchical clustering of the 49
transcription factors that are differentially expressed across all
time-points examined using average linkage clustering and
Euclidian distance. The hierarchical cluster color code: yellow
bar represents the median value of gene expression during
development; red bar represents higher expression than median
value of gene expression and the green bar represents lower than
median value of gene expression. k-Means clustering was used to
group the developmentally regulated genes into 4 classes. The
color of the k-means class letter ID corresponds to the color of
the vertical bar between the GenBank accession number of each
gene and its description; the vertical bar color marks which gene
assembled to each of the k-means cluster
aleurone layer and participate in the mobilization of
endosperm storage proteins during cereal grain germination (Dal Degan et al. 1994). These genes are also
expressed in the vascular tissue, where they might be
involved in programmed cell death in germinating
grains; and in the scutellum, where their function is still
unclear (Dominguez et al. 2002). The 26S proteosome,
which is a large ATP-dependent protease composed of
two multiprotein complexes, a 19S regulatory complex
and the 20S proteosome, degrades short-lived intra-
cellular proteins marked by ubiquitin (Smalle and
Vierstra 2004). Proteases are necessary for protein
turnover and development—degradation of damaged,
and misfolded proteins provides free amino acids required for the synthesis of new proteins. The breakdown of selected regulatory proteins by the ubiquitin/
proteosome pathway could control key aspects of grain
growth and development; and limited proteolysis at
highly specific sites is required in the maturation and
sub-cellular targeting of enzymes and storage proteins.
123
Plant Mol Biol (2007) 63:651–668
Grain weight, which is a major component of grain
yield, is determined by the rate and duration at which
the grain accumulates dry matter. It has been reported
that wheat cultivars show significant variations in the
rate of dry matter accumulation (Bruckner and Fruhberg 1987; Chanda et al.1999) and that increases in rate
of caryopsis dry matter accumulation may be an
important factor leading to improved yield (Hartung
et al. 1989). One could test whether the genes with
expression correlating with the pattern of DW rate of
accumulation could be used as possible markers in the
selection for increased rate of dry matter accumulation
that may result in greater yield potentials in crops.
Transcription factors involved in grain development
In spite of the limitations in the sensitivity of the arrays
for detecting low expressed genes the expression of a
significant number of transcription factors were detected. Of the 175 genes on the whole array that
showed similarity to transcription factors (BLASTX at
E-value £ 10–5), 49 were differentially expressed across
the 6 time-points examined. Cluster analysis identified
4 groups of transcription factors (A–D) each with distinct patterns of expression (Fig. 7). Clusters A and C
show maximal expression during later anthesis but with
different kinetics. Cluster A shows a low expression at
3 DPA, rapidly increasing between 7 DPA and
14 DPA, peaking at 21 DPA then slightly decline but
still highly expressed thereafter. This cluster is composed of 7 genes including three no apical meristem
(NAM) transcription factors, a WRKY transcription
factor, a negative regulator of transcription and an
F-box containing protein. NAM is a member of a
Fig. 8 Data validation by RT-PCR Comparison of gene expression profile of two genes using microarray and RT-PCR: the top
panels show the expression profile of MADS-box 9 transcription
factor and tritin at the 6 time-points examined using cDNA
665
family of transcription factors found only in plants
(Ooka et al. 2003). NAM plays a role in the establishment of the shoot apical meristem. The bZIP
transcription factors in this cluster show the highest
change difference (5.4-fold) in expression from early
anthesis to late anthesis. Their expression profile correlates with that of the storage proteins (gliadins)
especially those involved in gluten formation. Previous
studies have shown that gluten protein genes are regulated by bZIP domain containing transcription factors
(Albani et al. 1997). Cluster B contains 4 genes; two
MADS-box containing transcription factors, a putative
transcription factor and a zinc-finger containing protein.Clusters C and D have a narrow range of change in
gene expression from early to late anthesis. The average change of expression for these groups is 1.4-fold.
These clusters include some of the general transcription factors like the TATA- and CCAAT-box binding
proteins.
Validation of wheat microarray data
Semi-quantitative RT-PCR was used to determine the
expression of selected clones with distinct patterns of
expression to confirm the trends in mRNA accumulation determined by cDNA arrays (Fig. 8). RNAs isolated from a fourth biological sample (different from
the set of biological samples used for hybridization on
the array) were used as templates for amplification.
The pattern of the MADS-box and tritin gene transcript accumulation correlated well with that obtained
by cDNA arrays. The good agreement of gene
expression on a different biological sample other than
what was used on the array provides further support to
microarrays. The middle panels show the PCR fragments
amplified by RT-PCR from RNA isolated from developing
grains using gene-specific primers. Bottom panel show the
amplification product for the 18S rRNA control
123
666
the reliability of the microarray results. The patterns of
expression of 5 other clones examined by RT-PCR
were consistent with the pattern of expression of the
same clones obtained using the cDNA arrays (data not
shown). Our results indicate that the trend of RNA
accumulation obtained with the use of the cDNA
microarrays we generated is consistent with the data
obtained with other methods used for measuring gene
expression.
Concluding remarks
This study describes the expression dynamics of global
gene expression during wheat caryopsis development
using DNA microarrays. We report the construction
and validation of an 8K wheat cDNA microarray for
transcriptome analysis. We provide a comprehensive
list of developmentally regulated wheat caryopsis
genes and their expression profiles, including the
information on the temporal expression of a significant
number of uncharacterized genes. Several files containing supplemental data are provided for more
comprehensive information on the differentially expressed genes discussed in this paper (also available at
http://wheat.pw.usda.gov/pubs/2007/Laudencia).
Acknowledgements We would like to thank Adaku Ude, Sarah
Vela and Joseph Pham for their excellent technical assistance
and Dr. Grace Chen, Dr. Michael Gitt, Dr. Kent McCue and Dr.
Craig Parker for the critical reading of the manuscript. We
apologize to those whose works we have not cited owing to
restrictions in the length of this article. The USDA-ARS CRIS
Project 5325-21000-011 funded this work.
References
Alba R, Fei ZJ, Payton P, Liu Y, Moore SL, Debbie P, Cohn J,
D’Ascenzo M, Gordon JS, Rose JKC, Martin G, Tanksley SD,
Bouzayen M, Jahn MM, Giovannoni J (2004) ESTs, cDNA
microarrays, and gene expression profiling: tools for dissecting
plant physiology and development. Plant J 39:697–714
Albani D, Hammond-Kosack MC, Smith C, Conlan S, Colot V,
Holdsworth M, Bevan MW (1997) The wheat transcriptional activator SPA: a seed-specific bZIP protein that
recognizes the GCN4-like motif in the bifactorial endosperm box of prolamin genes. Plant Cell 9:171–184
Alexandrov NN, Troukhan ME, Brover VV, Tatarinova T,
Flavell RB, Feldmann KA (2006) Features of Arabidopsis
genes and genome discovered using full-length cDNAs.
Plant Mol Biol 60:69–85
Altenbach SB, DuPont FM, Kothari KM, Chan R, Johnson EL,
Lieu D (2003) Temperature, water and fertilizer influence
the timing of key events during grain development in a US
spring wheat. J Cereal Sci 37:9–20
Altenbach SB, Kothari KM (2004) Transcript profiles of genes
expressed in endosperm tissue are altered by high temperature during wheat grain development. J Cereal Sci 40:115–126
123
Plant Mol Biol (2007) 63:651–668
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990)
Basic local alignment search tool. J Mol Biol 215:403–410
Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z,
Miller W, Lipman DJ (1997) Gapped BLAST and PSIBLAST: a new generation of protein database search
programs. Nucleic Acids Res 25:3389–3402
Appels R, Francki M, Chibbar R (2003) Advances in cereal
functional genomics. Funct Integr Genom 3:1–24
Aquino P, Carron F, Calvo R (1999) Selected wheat statistics.
CIMMYT, Mexico
Bechtel DB, Zayas I, Kaleikau L, Pomeranz Y (1990) Sizedistribution of wheat–starch granules during endosperm
development. Cereal Chem 67:59–63
Bhullar SS, Jenner CF (1986) Effects of temperature on the
conversion of sucrose to starch in the developing wheat
endosperm. Aust J Plant Physiol 13:605
Bilecen K, Ozturk UH, Duru AD, Sutlu T, Petoukhov MV,
Svergun DI, Koch MH, Sezerman UO, Cakmak I, Sayers Z
(2005) Triticum durum metallothionein. Isolation of the
gene and structural characterization of the protein using
solution scattering and molecular modeling. J Biol Chem
280:13701–13711
Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P,
Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC,
Gaasterland T, Glenisson P, Holstege FCP, Kim IF,
Markowitz V, Matese JC, Parkinson H, Robinson A,
Sarkans U, Schulze-Kremer S, Stewart J, Taylor R, Vilo J,
Vingron M (2001) Minimum information about a microarray experiment (MIAME) – toward standards for microarray data. Nat Genet 29:365–371
Brooks A, Jenner CF, Aspinall D (1982) Effects of water deficit
on endosperm starch granules and on grain physiology of
wheat and barley. Aust J Plant Physiol 9:423
Bruckner PL, Frohberg RC (1987) Rate and duration of grain fill
in spring wheat. Crop Science 27:451–455
Bruix M, Jimenez MA, Santoro J, Gonzalez C, Colilla FJ,
Mendez E, Rico M (1993) Solution structure of gamma 1-H
and gamma 1-P thionins from barley and wheat endosperm
determined by 1H-NMR: a structural motif common to toxic
arthropod proteins. Biochemistry 32:715–724
Calderini DF, Abeledo LG, Slafer GA (2000) Physiological
maturity in wheat based on kernel water and dry matter.
Agron J 92:895–901
Campbell SA, Close TJ (1997) Dehydrins: genes, proteins, and
associations with phenotypic traits. New Phytol 137:61–74
Chanda SV, Narmada K, Singh YD (1999) Dry matter accumulation and associated changes in biochemical parameters
during wheat grain development. J Agron Crop Sci 182:153
Clarke B, Rahman S (2005) A microarray analysis of wheat grain
hardness. Theor Appl Genet 110:1259–1267
Clarke BC, Appels R, Hobbs M, Skylas D (2000) Genes active in
developing wheat endosperm. Funct Integr Genomics 1:44
Close TJ, Wanamaker SI, Caldo RA, Turner SM, Ashlock DA,
Dickerson JA, Wing RA, Muehlbauer GJ, Kleinhofs A,
Wise RP (2004) A new resource for cereal genomics: 22K
barley GeneChip comes of age. Plant Physiol 134:960–968
Colilla FJ, Rocher A, Mendez E (1990) gamma-Purothionins:
amino acid sequence of two polypeptides of a new family of
thionins from wheat endosperm. FEBS Lett 270:191–194
Dal Degan F, Rocher A, Cameron-Mills V, Von Wettstein D
(1994) The expression of serine carboxypeptidases during
maturation and germination of the barley grain. Proc Nat
Acad Sci USA 91:8209
Daniel C, Triboi E (2000) Effects of temperature and nitrogen
nutrition on the grain composition of winter wheat: effects
on gliadin content and composition. J Cereal Sci 32:45
Plant Mol Biol (2007) 63:651–668
Danyluk J, Perron A, Houde M, Limin A, Fowler B, Benhamou
N, Sarhan F (1998) Accumulation of an acidic dehydrin in
the vicinity of the plasma membrane during cold acclimation
of wheat. Plant Cell 10:623–638
Davies EJ, Tetlow IJ, Bowsher CG, Emes MJ (2003) Molecular
and biochemical characterization of cytosolic phosphoglucomutase in wheat endosperm (Triticum aestivum L. cv.
Axona). J Exp Bot 54:1351–1360
Dominguez F, Gonzalez MC, Cejudo FJ (2002) A germinationrelated gene encoding a serine carboxypeptidase is expressed during the differentiation of the vascular tissue in
wheat grains and seedlings. Planta 215:727
Donson J, Fang YW, Espiritu-Santo G, Xing WM, Salazar A,
Miyamoto S, Armendarez V, Volkmuth W (2002) Comprehensive gene expression analysis by transcript profiling.
Plant Mol Biol 48:75–97
Dupont FM, Altenbach SB (2003) Molecular and biochemical
impacts of environmental factors on wheat grain development and protein synthesis. J Cereal Sci 38:133–146
Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster
analysis and display of genome-wide expression patterns.
Proc Natl Acad Sci USA 95:14863–14868
Farrant JM, Bailly C, Leymarie J, Hamman B, Come D,
Corbineau F (2004) Wheat seedlings as a model to understand desiccation tolerance and sensitivity. Physiol Plant
120:563–574
Ferl RJ (1996) 14-3-3 Proteins and signal transduction. Ann Rev
Plant Physiol Plant Mol Biol 47:49–73
Francki M, Appels R (2002) Wheat functional genomics and
engineering crop improvement. Genome Biol 3:Rev 1013
Fulgosi H, Soll J, de Faria Maraschin S, Korthout HA, Wang M,
Testerink C (2002) 14-3-3 Proteins and plant development.
Plant Mol Biol 50:1019–1029
Gachon CM, Langlois-Meurinne M, Henry Y, Saindrenan P
(2005) Transcriptional co-regulation of secondary metabolism enzymes in Arabidopsis: functional and evolutionary
implications. Plant Mol Biol 58:229–245
Giroux MJ, Boyer C, Feix G, Hannah LC (1994) Coordinated
transcriptional regulation of storage product genes in the
maize endosperm. Plant Physiol 106:713–722
Goldberg RB, Hoschek G, Kamalay JC (1978) Sequence
complexity of nuclear and polysomal RNA in leaves of the
tobacco plant. Cell 14:123–131
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.
Transgen Res 14:887–905
Hanley-Bowdoin L, Lane BG (1983) A novel protein programmed by the mRNA conserved in dry wheat embryos.
The principal site of cysteine incorporation during early
germination. Eur J Biochem 135:9–15
Hartung RC, Poneleit CG, Cornelius PL (1989) Direct and
correlated responses to selection for rate and duration of
grain fill in maize. Crop Science 29:740–745
Hughes CE (1976) The developing endosperm of Triticum aestivum (L.): an ultrastructural and morphometric study. PhD
thesis, University of Nottingham, England, I–IV
Kawaura K, Mochida K, Yamazaki Y, Ogihara Y (2006)
Transcriptome analysis of salinity stress responses in common wheat using a 22k oligo-DNA microarray. Funct Integr
Genomics 6:132–142
Kerk NM, Ceserani T, Tausta SL, Sussex IM, Nelson TM (2003)
Laser capture microdissection of cells from plant tissues.
Plant Physiol 132:27–35
Kerr MK, Churchill GA (2001a) Experimental design for gene
expression microarrays. Biostatistics 2:183–201
667
Kerr MK, Churchill GA (2001b) Statistical design and the
analysis of gene expression microarray data. Genet Res
77:123–128
Kerr MK, Martin M, Churchill GA (2000) Analysis of variance
for gene expression microarray data. J Comput Biol 7:819–
837
Langridge P, Lagudah ES, Holton TA, Appels R, Sharp PJ,
Chalmers KJ (2001) Trends in genetic and genome analyses
in wheat: a review. Aust J Agric Res 52:1043–1077
Leader DJ (2005) Transcriptional analysis and functional
genomics in wheat. J Cereal Sci 41:149–163
Leah R, Tommerup H, Svendsen I, Mundy J (1991) Biochemical
and molecular characterization of three barley seed proteins
with antifungal properties. J Biol Chem 266:1564–1573
Lu C, Hawkesford MJ, Barraclough PB, Poulton PR, Wilson ID,
Barker GL, Edwards KJ (2005) Markedly different gene
expression in wheat grown with organic or inorganic
fertilizer. Proc Biol Sci 272:1901–1908
Marchler-Bauer A, Anderson JB, Cherukuri PF, De WeeseScott C, Geer LY, Gwadz M, He S, Hurwitz DI, Jackson JD,
Ke Z, Lanczycki CJ, Liebert CA, Liu C, Lu F, Marchler
GH, Mullokandov M, Shoemaker BA, Simonyan V, Song
JS, Thiessen PA, Yamashita RA, Yin JJ, Zhang D, Bryant
SH (2005) CDD: a conserved domain database for protein
classification. Nucleic Acids Res 33:D192–D196
Massiah AJ, Hartley MR (1995) Wheat ribosome-inactivating
proteins: seed and leaf forms with different specificities and
cofactor requirements. Planta 197:633–640
Meyers BC, Galbraith DW, Nelson T, Agrawal V (2004)
Methods for transcriptional profiling in plants. Be fruitful
and replicate. Plant Physiol 135:637–652
Morell MK, Myers AM (2005) Towards the rational design of
cereal starches. Curr Opin Plant Biol 8:204–210
Nielsen K, Boston RS (2001) RIBOSOME-INACTIVATING
PROTEINS: A Plant Perspective. Annu Rev Plant Physiol
Plant Mol Biol 52:785–816
Ogihara Y, Mochida K, Kawaura K, Murai K, Seki M, Kamiya
A, Shinozaki K, Carninci P, Hayashizaki Y, Shin IT, Kohara
Y, Yamazaki Y (2004) Construction of a full-length cDNA
library from young spikelets of hexaploid wheat and its
characterization by large-scale sequencing of expressed
sequence tags. Genes Genet Syst 79:227–932
Olsen OA (2001) Endosperm development: cellularization and
cell fate specification. Ann Rev Plant Physiol Plant Mol Biol
52:233–267
Olsen OA, Linnestad C, Nichols SE (1999) Developmental
biology of the cereal endosperm. Trends Plant Sci 4:253–257
Ooka H, Satoh K, Doi K, Nagata T, Otomo Y, Murakami K,
Matsubara K, Osato N, Kawai J, Carninci P, Hayashizaki Y,
Suzuki K, Kojima K, Takahara Y, Yamamoto K, Kikuchi S
(2003) Comprehensive analysis of NAC family genes in
Oryza sativa and Arabidopsis thaliana. DNA Res 10:239–247
Qi LL, Echalier B, Chao S, Lazo GR, Butler GE, Anderson OD,
Akhunov ED, Dvorak J, Linkiewicz AM, Ratnasiri A,
Dubcovsky J, Bermudez-Kandianis CE, Greene RA, Kantety R, La Rota CM, Munkvold JD, Sorrells SF, Sorrells ME,
Dilbirligi M, Sidhu D, Erayman M, Randhawa HS, Sandhu
D, Bondareva SN, Gill KS, Mahmoud AA, Ma X-F,
Miftahudin, Gustafson JP, Conley EJ, Nduati V, Gonzalez-Hernandez JL, Anderson JA, Peng JH, Lapitan NL,
Hossain KG, Kalavacharla V, Kianian SF, Pathan MS,
Zhang DS, Nguyen HT, Choi DW, Fenton RD, Close TJ,
McGuire PE, Qualset CO, Gill BS (2004) A chromosome
bin map of 16,000 expressed sequence tag loci and distribution of genes among the three genomes of polyploid
wheat. Genetics 168:701–712
123
668
Schaffer R, Landgraf J, Perez-Amador M, Wisman E (2000)
Monitoring genome-wide expression in plants. Curr Opin
Biotechnol 11:162–167
Sehnke PC, Chung HJ, Wu K, Ferl RJ (2001) Regulation of
starch accumulation by granule-associated plant 14-3-3
proteins. Proc Natl Acad Sci USA 98:765–770
Shewry PR, Halford NG (2002) Cereal seed storage proteins:
structures, properties and role in grain utilization. J Exp Bot
53:947–958
Shewry PR, Halford NG, Belton PS, Tatham AS (2002) The
structure and properties of gluten: an elastic protein from
wheat grain. Philos Trans R Soc Lond Ser B Biol Sci
357:133–142
Simmonds D, O’Brien T (1981) Morphological and biochemical
development of the wheat endosperm. American Association of Cereal Chemists, St. Paul, Minnesota
Simmonds NW (1995) The relation between yield and protein in
cereal grain. J Sci Food Agric 67:309–315
Smalle J, Vierstra RD (2004) The ubiquitin 26S proteasome
proteolytic pathway. Ann Rev Plant Biol 55:555–590
Sreenivasulu N, Altschmied 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. Plant J 37:539–553
Stoddard FL (1999a) Survey of starch particle-size distribution in
wheat and related species. Cereal Chem 76:145–149
Stoddard FL (1999b) Variation in grain mass, grain nitrogen, and
starch B-granule content within wheat heads. Cereal Chem
76:139–144
Sturn A, Quackenbush J, Trajanoski Z (2002) Genesis: cluster
analysis of microarray data. Bioinformatics 18:207–208
Tetlow IJ, Morell MK, Emes MJ (2004) Recent developments in
understanding the regulation of starch metabolism in higher
plants. J Exp Bot 55:2131–2145
Triboi E, Martre P, Triboi-Blondel AM (2003) Environmentallyinduced changes in protein composition in developing grains
of wheat are related to changes in total protein content. J
Exp Bot 54:1731–1742
123
Plant Mol Biol (2007) 63:651–668
Triboi E, Triboi-Blondel AM (2002) Productivity and grain or
seed composition: a new approach to an old problem –
invited paper. Eur J Agron 16:163–186
Wellmer F, Alves-Ferreira M, Dubois A, Riechmann JL,
Meyerowitz EM (2006) Genome-wide analysis of gene
expression during early Arabidopsis flower development.
PLoS Genet 2:e117
Wilson ID, Barker GLA, Beswick RW, Shepherd SK, Lu CG,
Coghill JA, Edwards D, Owen P, Lyon R, Parker JS, Lenton
JR, Holdsworth MJ, Shewry PR, Edwards KJ (2004) A
transcriptomics resource for wheat functional genomics.
Plant Biotechnol J 2:495–506
Woerdeman DL, Veraverbeke WS, Parnas RS, Johnson D, Delcour
JA, Verpoest I, Plummer CJ (2004) Designing new materials
from wheat protein. Biomacromolecules 5:1262–1269
Wolfinger R, Gibson G, Wolfinger E, Bennett L, Hamadeh H,
Bushel P, Afshari C, Paules R (2001) Assessing gene
significance from cDNA microarray expression data via
mixed models. J Comput Biol 8:625–637
Xue GP, McIntyre CL, Chapman S, Bower NI, Way H, Reverter
A, Clarke B, Shorter R (2006) Differential gene expression
of wheat progeny with contrasting levels of transpiration
efficiency. Plant Mol Biol 61:863–881
Yang YH, Speed T (2002) Design issues for cDNA microarray
experiments. Nat Rev Genet 3:579–588
Young TE, Gallie DR (2000) Programmed cell death during
endosperm development. Plant Mol Biol 44:283–301
Zhang X, Feng B, Zhang Q, Zhang D, Altman N, Ma H (2005)
Genome-wide expression profiling and identification of gene
activities during early flower development in Arabidopsis.
Plant Mol Biol 58:401–419
Zhu T, Budworth P, Chen W, Provart N, Chang H-S, Guimil S,
Su W, Estes B, Zou G, Wang X (2003) Transcriptional
control of nutrient partitioning during rice grain filling. Plant
Biotechnol J 1:59–70