Transcriptome analysis in Brassica rapa under the abiotic stresses using Brassica 24K oligo microarray.
ABSTRACT Genome wide transcription analysis in response to stresses is essential to provide the basis of effective engineering strategies to improve stress tolerance in crop plants. In order to perform transcriptome analysis in Brassica rapa, we constructed a B. rapa oligo microarray, KBGP-24K, using sequence information from approximately 24,000 unigenes and analyzed cold (4 degrees C), salt (250 mM NaCl), and drought (air-dry) treated B. rapa plants. Among the B. rapa unigenes represented on the microarray, 417 (1.7%), 202 (0.8%), and 738 (3.1%) were identified as responsive genes that were differently expressed 5-fold or more at least once during a 48-h treatment with cold, salt, and drought, respectively. These results were confirmed by RT-PCR analysis. In the abiotic stress responsive genes identified, we found 56 transcription factor genes and 60 commonly responsive genes. It suggests that various transcriptional regulatory mechanisms and common signaling pathway are working together under the abiotic stresses in B. rapa. In conclusion, our new developed 24K oligo microarray will be a useful tool for transcriptome profiling and this work will provide valuable insight in the response to abiotic stress in B. rapa.
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ABSTRACT: Arabidopsis thaliana RD26 cDNA, isolated from dehydrated plants, encodes a NAC protein. Expression of the RD26 gene was induced not only by drought but also by abscisic acid (ABA) and high salinity. The RD26 protein is localized in the nucleus and its C terminal has transcriptional activity. Transgenic plants overexpressing RD26 were highly sensitive to ABA, while RD26-repressed plants were insensitive. The results of microarray analysis showed that ABA- and stress-inducible genes are upregulated in the RD26-overexpressed plants and repressed in the RD26-repressed plants. Furthermore, RD26 activated a promoter of its target gene in Arabidopsis protoplasts. These results indicate that RD26 functions as a transcriptional activator in ABA-inducible gene expression under abiotic stress in plants.The Plant Journal 10/2004; 39(6):863-76. · 6.58 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: Plant growth and productivity are greatly affected by environmental stresses such as drought, high salinity, and low temperature. Expression of a variety of genes is induced by these stresses in various plants. The products of these genes function not only in stress tolerance but also in stress response. In the signal transduction network from perception of stress signals to stress-responsive gene expression, various transcription factors and cis-acting elements in the stress-responsive promoters function for plant adaptation to environmental stresses. Recent progress has been made in analyzing the complex cascades of gene expression in drought and cold stress responses, especially in identifying specificity and cross talk in stress signaling. In this review article, we highlight transcriptional regulation of gene expression in response to drought and cold stresses, with particular emphasis on the role of transcription factors and cis-acting elements in stress-inducible promoters.Annual review of plant biology 02/2006; 57:781-803. · 23.65 Impact Factor
- [show abstract] [hide abstract]
ABSTRACT: Homeodomain-leucine zipper (HD-Zip) proteins are putative transcription factors encoded by a class of recently discovered homeobox genes as yet found only in plants. This paper reports on the characterization of one of these genes, ATHB-7, in Arabidopsis thaliana. ATHB-7 transcripts were present in all organs of the plant at low levels, but expression was induced several-fold by water deficit, osmotic stress as well as by exogenous treatment with abscisic acid (ABA), a response being detectable at 10(-8) M and reaching a maximum at 10(-6) M ABA. The ATHB-7 transcript was detected within 30 min after treatment with ABA and the transcript level was rapidly reduced after removal of the hormone. The induction of ATHB-7 was shown to be mediated strictly via ABA, since no induction of ATHB-7 was detectable in the ABA-deficient mutant aba-3 subjected to drought treatment. Induction levels in two ABA-insensitive mutants abi2 and abi3 were similar to the wild-type response. In the abi1 mutant, however, induction was impaired as 100-fold higher concentrations of ABA were required for a maximum induction as compared with wild-type. In this mutant the ATHB-7 response was reduced also after drought and osmotic stress treatments. These results indicate that ATHB-7 is transcriptionally regulated in an ABA-dependent manner and may act in a signal transduction pathway which mediates a drought response and also includes ABI1.The Plant Journal 09/1996; 10(2):375-81. · 6.58 Impact Factor
Mol. Cells OS, 595-605, December 31, 2008
Transcriptome Analysis in Brassica rapa under the
Abiotic Stresses Using Brassica 24K Oligo
Sang-Choon Lee1,4, Myung-Ho Lim1,4, Jin A Kim1, Soo-In Lee1, Jung Sun Kim1, Mina Jin1, Soo-Jin Kwon1,
Jeong-Hwan Mun1, Yeon-Ki Kim2, Hyun Uk Kim1, Yoonkang Hur3, and Beom-Seok Park1,*
Genome wide transcription analysis in response to stresses
is essential to provide the basis of effective engineering
strategies to improve stress tolerance in crop plants. In
order to perform transcriptome analysis in _ê~ëëáÅ~=ê~é~I
we constructed a _K= ê~é~ oligo microarray, KBGP-24K,
using sequence information from approximately 24,000
unigenes and analyzed cold (4°C), salt (250 mM NaCl), and
drought (air-dry) treated _K=ê~é~ plants. Among the _K=ê~é~
unigenes represented on the microarray, 417 (1.7%), 202
(0.8%), and 738 (3.1%) were identified as responsive genes
that were differently expressed 5-fold or more at least once
during a 48-h treatment with cold, salt, and drought, re-
spectively. These results were confirmed by RT-PCR
analysis. In the abiotic stress responsive genes identified,
we found 56 transcription factor genes and 60 commonly
responsive genes. It suggests that various transcriptional
regulatory mechanisms and common signaling pathway
are working together under the abiotic stresses in _K=ê~é~K
In conclusion, our new developed 24K oligo microarray
will be a useful tool for transcriptome profiling and this
work will provide valuable insight in the response to
abiotic stress in _K=ê~é~K
Abiotic stress, which includes cold, high salinity and drought,
induces various physiological and molecular responses in
plants. Changes at the molecular level include altered gene
expression, metabolic change, and osmotic adjustment (Ingram
and Bartels, 1996; Thomashow, 1999). These changes influ-
ence plant distribution, survival, and crop yield worldwide. Fur-
thermore, it is anticipated that plants will be more frequently
exposed to these types of abiotic stresses as a consequence of
global climate change that may occur in the near future (IPCC,
2007). A large percentage of genes that show altered expres-
sion following exposure to abiotic stress have been reported to
be involved in stress tolerance, in the regulation of other gene
expression, and in stress signal transduction (Shinozaki et al.,
2003; Xiong et al., 2002). Therefore, the study of these genes is
important for understanding mechanisms involved in regulating
the stress-response as well as to fully elucidate the mecha-
nisms of tolerance against abiotic stress. A number of studies
have demonstrated that the use of microarray analysis can be
utilized to examine changes in genome-wide gene expression
in response to specific stimuli. Consistent with these studies, a
number of investigators also have utilized microarray experi-
ments to analyze gene expression changes in a number of crop
species including rice (Rabbani et al., 2003), maize (Yu and
Setter, 2003), potato (Rensink et al., 2005), barley (Talamè et
al., 2006), and pepper (Lee and Yun, 2006). However, in _ê~ëJ
ëáÅ~=species, the technology has not been widely used due, in
part, to absence of a microarray to efficiently cover the entire
_ê~ëëáÅ~ genome. Since a genome-wide microarray has yet to
be developed for _ê~ëëáÅ~= species, a number of microarray
experiments in _ê~ëëáÅ~=have used the ^ê~ÄáÇçéëáë microarray
or low density cDNA chip (Carlsson et al., 2007; Fei et al.,
2007; Li et al., 2005; Soeda et al., 2005; Yang et al., 2005; Yin
et al., 2006).
The genus _ê~ëëáÅ~ includes a number of important crops
that provide oil, vegetables, condiments, dietary fiber, and vita-
min C (Fahey et al., 1995). Among species represented in this
genus, _ê~ëëáÅ~=ê~é~ includes morphologically diverse crops
such as Chinese cabbage, PakChoi, turnip rape, and sarson
(Gomez-Campo and Prakash, 1999). Chinese cabbage (_K=
ê~é~ ssp. éÉâáåÉåëáë) is widely grown in Asia and one of the
most important vegetables in Korea, Japan, and China. Fur-
thermore, this subspecies is typical of the _ê~ëëáÅ~ A genome
and has a large number of genomic resources including a
mapping population and BAC libraries, due to its relatively small
genome (529 Mb) among other _ê~ëëáÅ~ species. During culti-
vation in the field, _K=ê~é~ plant is adversely affected by various
abiotic stresses; for example, as a consequence of cold and/or
drought, seedlings and mature plants of this species show
stress-related symptoms including delayed germination, poor
growth, frost damage, and poor head (RDA, 2007). In addition,
1Brassica Genomics Team, National Institute of Agricultural Biotechnology, Rural Development Administration, Suwon 441-707, Korea, 2GreenGene
Biotech Inc. Genomics and Genetics Institute, Yongin 449-030, Korea, 3Plant Genome Research Institute, Chungnam National University, Daejeon 305-
764, Korea, 4These authors contributed equally to this work.
Received August 6, 2008; accepted September 11, 2008; published online September 17, 2008
Keywords: abiotic stress, _ê~ëëáÅ~=ê~é~, microarray, transcriptome
596 Transcriptome Analysis in _ê~ëëáÅ~=ê~é~
the productivity of _ê~ëëáÅ~ crops including _K=ê~é~ is also af-
fected by soil salinization that occurs as a consequence of artifi-
cial irrigation (Ashraf and McNeilly, 1990; Francois, 1994; Munshi
et al., 1986). Collectively, these culture limitations have resulted
in poor quality and insufficient supply of this vegetable crop. Un-
fortunately, despite the pressing need to understand abiotic
stress in _ê~ëëáÅ~ species, compared to more traditional model
plant organisms like ^ê~ÄáÇçéëáë and rice, the study abiotic
stress-responsive mechanism in _ê~ëëáÅ~ has progressed slowly.
Therefore, in order to enhance the capacity to study abiotic
stress-responsive mechanism in _ê~ëëáÅ~ species, a high
throughput method such as microarray analysis will be required.
Thus, we have constructed a microarray and utilized it to analyze
a number of _K=ê~é~ samples. Here we report the development of
the first high density _K=ê~é~ oligo microarray and its use to ana-
lyze transcriptome of abiotic stress-treated _K=ê~é~=crop=plantK
MATERIALS AND METHODS
Plant material, stress treatments, and RNA isolation
Seeds of _K= ê~é~ ssp. éÉâáåÉåëáë (inbred line ‘Chiifu’) were
germinated in soil and then grown for approximately three
weeks in a growth chamber at 25°C (16 h day/8 h night, RH 40-
70%, light intensity 290 μmol m-2 sec-1). For cold treatment, the
3-week-old plants were transferred to 4°C growth chamber with
continuous light. For salt treatment, the plants were transferred
to and grown in water containing 250 mM NaCl under continu-
ous light. For drought treatment, the plants were removed from
pot together with soil, and then air-dried in a growth chamber.
For control samples, whole plants were immediately sampled
before cold, salt, or drought treatment. The plants were sub-
jected to stress conditions for the indicated periods of time: 0.5,
3, 12, 24, and 48 h treatment under cold and salt stress and 6,
12, 24, 36 and 48 h treatment under drought stress. Whole
plants at each treatment time point were sampled and frozen in
liquid nitrogen for further analysis. Total RNA was isolated us-
ing RNeasy plant mini kit (Qiagen) according to manufacturer’s
instruction, and the RNA quantity and quality were monitored
using RNA gel electrophoresis and spectrophotometer.
_ê~ëëáÅ~=ê~é~ EST analysis and oligo microarray
EST sequences were analyzed after random selection of
clones from the cDNA libraries. Finally, approximately 24,000
unigenes were produced by clustering and assembling using
megablast-Cap3 assembly program with a parameter of 97%
sequence identity and 30 bp minimum match length. The oligo
microarray was designed using the unigene sequences.
Among these, the direction of open reading frame (ORF) was
known for 19,900 unigenes, and therefore six probes per gene
were designed in the sense direction (5′UTR to 3′UTR). How-
ever, because the direction of remaining 5,063 genes was un-
known, 12 probes (six sense and six antisense) per gene were
designed. A set of 180,156 probes were designed, and dupli-
cated in two separated block on a single chip. The 60 nucleo-
tide long probes with Tm value 75 to 85°C were synthesized on
the slide using NimbleGen System (http://www.nimblegen.
com/). Random GC probes to monitor the hybridization effi-
ciency and four corners to overlay the grid on the image were
included. Information of the unigenes and microarray is avail-
able on the Brassica rapa Genome Project website (http://www.
Two biological replicates of total RNA were prepared from each
plant sample and 10 μg of total RNA were used for cDNA syn-
thesis by using Superscript Double-Stranded cDNA Synthesis
Kit (Invitrogen). Following cDNA synthesis, remaining RNA was
removed by the addition of RNaseA and the cDNA was precipi-
tated following phenol/chloroform extraction. The cDNA pellet
was rehydrated and used for Cy3-labelling. For the synthesis of
Cy3-labeled target DNA fragments, 1 μg of double strand
cDNA was mixed with 40 μl (1 OD) of Cy3-9 mer primers
(Sigma-Aldrich) and the total volume was adjusted to 80 μl with
deionized water. After heating at 98°C for 10 min, 10 μL of
dNTP mix (10 mM each) and 2 μl of Klenow fragment (NEB; 50
units/μl) were added and the reaction was incubated at 37°C
for 2 h. Finally, the reaction was stopped by the addition of 10
μl of 0.5 M EDTA. Labeled DNA fragments were precipitated
with isopropanol and rehydrated with water. The concentration
of each sample was determined using spectrophotometer. For
hybridization, 13 μg of the labeled DNA fragment was mixed
with hybridization buffer (NimbleGen) and then hybridized with
the microarray using a MAUI hybridization chamber (Biomicro)
at 42°C for 16 h. Following hybridization, the microarray was
washed with wash solution I, II, and III (NimbleGen), and then
dried in a dark desiccator. The microarray slide was scanned
using GenePix scanner 4000B (Axon) and the spot intensities
were analyzed using a NimbleScan (NimbleGen). The normal
distribution of Cy3 intensities was tested by qqline. The data
was normalized and processed with cubic spline normalization
using quantiles to adjust signal variations between chips and
with Rubust Multi-Chip Analysis (RMA) using a median polish
algorithm implemented in NimbleScan software (NimbleGen)
(Irizarry et al., 2003; Workman et al., 2002). Finally, perfect match
(PM) values of the six probes were used for selection of respon-
sive genes. After removing genes with less than 1,000 PM value
at all time point, genes with more than 2-fold and 5-fold change at
any treatment time point in each of the repeated microarray
analyses were selected. Information of raw data and selected
genes is available on the _ê~ëëáÅ~=ê~é~ Genome Project website
Semi-quantitative RT-PCR analysis
The gene-specific primers of the selected genes were used for
PCR analysis (Table 1). Specific primers were designed with _K=
ê~é~ unigene sequences corresponding to probe sequences ID
(SeqID) on the microarray chip. As a control, we used the prim-
ers specific to _K= ê~é~ actin gene sequence, _ê^ÅíáåN (EX
087730), that is highly similarity to the ^ê~ÄáÇçéëáë actin gene,
^ÅíN (NM_179953) (Shin et al., 2004) and the _K=å~éìë actin
gene (AF111812) (Zhao et al., 2006). Ten μg of total RNA was
reverse transcribed to first strand cDNAs using oligo (dT)20
primer and SuperScript II reverse transcriptase (Invitrogen)
according to manufacturer’s instruction. The synthesized
cDNAs were used as templates for PCR amplification as fol-
lowed condition: 94°C for 5 min; followed by cycles of 94°C for
1 min, 54°C for 1 min, and 72°C for 1 min; and final extension
of 72°C for 7 min. The PCR cycles numbered 22 to 35 for spe-
cific amplification of the genes. Amplified PCR products were
electrophoresed on a 1.3% (w/v) agarose gel and then band
intensity of PCR products was measured by TINA software
(Raytest) after ethidium bromide staining.
_ê~ëëáÅ~=ê~é~=24K oligo microarray
In order to design an oligo microarray, we analyzed 127,144
EST sequences from more than 20 cDNA libraries and then
generated 23,929 unigenes that can represent about 50% of
Sang-Choon Lee et al. 597
Table 1. List of primers used in RT-PCR analysis
Primers Nucleotide sequence (5′-3′)
598 Transcriptome Analysis in _ê~ëëáÅ~=ê~é~
Fig. 1. Functional classification of the 21,361 genes with ^ê~ÄáÇçéJ
ëáë homologues, among unigenes present on the microarray. The
genes were classified into 14 functional categories using TAIR GO
annotation tool with AGI numbers of ^ê~ÄáÇçéëáë homologues simi-
lar to the unigenes.
estimated total 46,000 _K=ê~é~ genes (Yang et al., 2006). The
EST sequences were deposited in GenBank EST database
(http://www.ncbi.nlm.nih.gov/dbEST/index.html) under acces-
sion number EX015357 to EX142500. Using sequences infor-
mation from these unigenes, a total 180,156 probes were de-
signed to represent the 23,929 unigenes=and a 60mer oligo
microarray, Korea Brassica Genome Project 24K (KBGP-24K),
was manufactured by NimbleGen Systems Inc. Of the repre-
sented unigenes, 16,792 (70.2%) were similar to sequences of
known function, whereas 6,488 (27.1%) were similar to un-
known, hypothetical, or unnamed proteins. The remaining 649
(2.7%) did not show any significant similarity with genes in the
current database. Furthermore, 21,361 (89.3%) of the repre-
sented unigenes were found to have ^ê~ÄáÇçéëáë homologous
genes, while the remaining 2,568 (10.7%) showed no similarity
with known ^ê~ÄáÇçéëáë=genes when analyzed by blast search
(cutoff ≥ E-9). The 21,361 unigenes present on the microarray
were classified into 14 functional categories using the TAIR GO
annotation tools (http://www.arabidopsis.org/tools/bulk/go/index.
jsp) based on similarity with ^ê~ÄáÇçéëáë genes (Fig. 1).
Microarray analyses using abiotic stress-treated _K= ê~é~=
In order to massively identify _K=ê~é~ abiotic stress-responsive
genes, total RNAs were extracted from _K=ê~é~ plants that were
treated by cold (4°C), salt (250 mM NaCl), and drought (air-dry)
stress for various times (0.5 to 48 h) and were used for microar-
ray analysis. For drought treated samples, sampling after 6 h
and 36 h treatment was also carried out in the 48-h duration of
the drought treatment since the seedlings were not wilted until
after 24 h of drought treatment. Microarray analysis was con-
ducted in duplicate using two biologically repeated samples of
each treatment. The correlation coefficient values between
biologically replicated microarray analyses were found to be
greater more than 0.93. Moreover, the correlation coefficient
values were greater than 0.92 among microarray analyses
using control (0 h) samples of each treatment. Collectively
these results demonstrated that the microarray analyses using
the KBGP-24K oligo chip and _K=ê~é~ samples have no prob-
lem in relation to reproducibility.
Identification of cold, salt, and drought responsive genes
To identify responsive gene that showed similar expression
pattern in each of the replicated microarray analyses, we com-
pared the fold-change of genes between the replicated sam-
ples at the same time point. After the exclusion of these genes
Fig. 2. Venn diagrams comparing cold, salt, and drought respon-
sive genes with a greater than 5-fold change at any treatment time
point. The number of genes in each set is displayed within a circle.
Common genes are shown in the intersection of the two sets.
that exhibited a PM value of less than 1,000 at all time point of
each treatment, genes with a similar fold change in each of the
duplicated samples were selected. As for the genes with more
than 2-fold change in at least one time point, 3,251 (13.6%),
1,786 (7.5%), and 4,366 (18.2%) were selected as cold, salt,
and drought responsive gene, respectively. In total, 6,486
(27.1%) genes were identified as being responsive to at least
one form of abiotic stress. As for the genes with more than 5-
fold change in at least one time point, 417 (1.7%), 202 (0.8%),
and 738 (3.1%) were identified as being responsive to cold, salt,
or drought conditions, respectively (Fig. 2). In total, 1,074
(4.5%) genes were responsive to at least one form of abiotic
stress. In this study, we focused on the genes that showed a
greater than 5-fold change because of their strong responsive-
ness to abiotic stress. By cross comparison among the respon-
sive genes with more than 5-fold change, 261, 60, and 530
genes were identified as being specific to cold, salt, and
drought, respectively, while 60 genes were found to be respon-
sive to all three forms of stress. Among these responsive genes,
SeqID BRAS0001S00000014, BRAS0001S00001571, and
BRAS0001S00001722 are identical to reported _ê~ëëáÅ~ abiotic
stress-responsive genes, _åOU~ (Orr et al., 1992), _åNNR
(Weretilnyk et al., 1993), and _çopN (Tang et al., 2004), re-
spectively. SeqID BRAS0001S00005204 is the same as _K=
å~éìë CRT/DRE-binding factor gene, _å`_cNT (Gao et al.,
2002). These four _K=ê~é~ genes from our chip are highly up-
regulated by treated stresses, which are almost similar to their
abiotic stress-induced regulation from reported results (Fig. 3).
These results, consistent with previous studies, indicate that
use of this microarray analysis is efficient to identify stress-
induced changes of gene expression.
In order to confirm the fidelity of our microarray analysis, sev-
eral genes were analyzed using RT-PCR method. After the 417
cold-responsive genes were grouped into 9 clusters on the
basis of expression pattern (Fig. 4A), 21 genes from 7 clusters
were selected to perform RT-PCR analyses (Fig. 4B). After
electrophoresis of RT-PCR product, band intensity was cali-
brated using transcript level of _ê^ÅíáåN and the expression
Sang-Choon Lee et al. 599
Fig. 3. Expression pattern of cold, salt, and drought responsive
genes. Expression changes of the selected responsive genes dur-
ing treatment (0.5 to 48 h) are presented in a graph format. The
graphs plot log of fold-changes over time. Thick colored lines indi-
cate expression pattern of some positive control genes for abiotic
stress treatment: Red line, _åOU~; green line, _åNNR; blue line,=
_çopN; pink line, _å`_cNT.
ratio to control sample was calculated. When the ratio was
compared with that of our microarray results, we found that
there was a positive correlation between the ratios of both ex-
periments (Fig. 4C). The correlation coefficient values of the
tested genes were greater than 0.7. In addition, when RT-PCR
analysis was performed with several genes responding to all
three stress conditions, the expression ratio of the tested genes
was also similar to that of the microarray results (data not
shown). Taken together, these data provide the validity and
reliability of our microarray results.
Identification of abiotic stress-responsive transcription
Fifty six genes encoding putative transcription factors respon-
sive to abiotic stresses in _K=ê~é~ were identified by comparison
with ^ê~ÄáÇçéëáë information. These transcription factors were
divided into 15 groups on the basis of the classification of their
^ê~ÄáÇçéëáë homologues (Table 2). Five transcription factor
families containing AP2-EREBP, NAC, bHLH, C2C2-CO-like,
and C2H2 domains are composed of 74% and nine families,
bZIP, Cupin, C2C2-Gata, C3H, G2-like, Heat stress transcrip-
tion factor, Homeobox, MYB, and Trihelix, are composed of
26%, respectively, in total 56 abiotic responsive genes. Five
genes (BRAS0001S00004267, BRAS0001S00005204, BRAS
0001S00011350, BRAS0001S00011629, and BRAS0001S
00012021) encoding CBF/DREB in AP2-EREBP family and
four w^q genes (BRAS0001S00001871, BRAS0001S00004393,
BRAS0001S00014584, and BRAS0001S00019266) containing
C2CH2 domain were identified as the up-regulated transcrip-
tion factors in cold. These corresponding ^ê~ÄáÇçéëáë= `_c/
aob_ and w^q genes have known a key regulator in abiotic
stress signaling pathways (Yamaguchi-Shinozaki and Shino-
zaki, 2006). All 8 genes in NAC family are up-regulated at least
one of abiotic stresses and 5 genes (BRAS0001S00004492,
BRAS0001S00010332, BRAS0001S00014457, BRAS0001S
00014640, and BRAS0001S00017334) among them are com-
monly up-regulation in cold, salt and drought. Four WRKY tran-
scription factors were up-regulated in cold. In contrast to NAC
and WRKY, most bHLH (6 genes out of 7) transcription factors
are down regulated in abiotic stresses. Eight transcription fac-
tors, BRAS0001S00003176 and BRAS0001S00004525 in
AP2-EREBP, BRAS0001S00019512 in cupin, BRAS0001S
00004465 in C2C2-Gata, BRAS0001S00019048 in C3H,
BRAS0001S00005628 in G2-like, BRAS0001S00020548 in
Trihelix have similarity with poorly identified ^ê~ÄáÇçéëáë tran-
Stress-responsive pattern of 56 transcription factors was
compared with ^ê~ÄáÇçéëáë homologue retrieved from AtGen-
Express database (http://www.arabidopsis.org/info/expression/
ATGenExpress.jsp; Kilian et al., 2007). The most of identified
transcription factors showed almost similar expression pattern
in both _K=ê~é~ and ^ê~ÄáÇçéëáë. The pattern of 14 genes was
quite similar and 37 genes were somewhat different in both _K=
ê~é~ and ^ê~ÄáÇçéëáë. The differences observed in 37 genes,
potentially as a consequence of different treatment and/or dif-
ferences in microarray analysis between experiments of both
species (Table 2). Interestingly, two genes, BRAS0001S0000
4525 and BRAS0001S00019512, showed a negative correla-
tion with their ^ê~ÄáÇçéëáë homologues. The BRAS 0001S0000
4525 encodes putative AP2-EREBP transcription factor. Under
drought stress, BRAS0001S00004525 was more than 5-fold
down-regulated, while its
AT1G21910, was transiently up- and then down-regulated
while exhibiting a greater than 2-fold change in expression
levels. This _ê~ëëáÅ~ AP2-EREBP gene was up-regulated by
cold, but did not respond to salt, whereas AT1G21910 was
transiently up- and down-regulated and down- and up-
regulated by cold and salt, respectively. The BRAS0001S
00019512 and its ^ê~ÄáÇçéëáë homologues, AT3G20810, en-
code jumonji (jmjC) domain-containing protein that was known
as transcriptional repressor in animals (Takeuchi et al., 2006).
While BRAS0001S00019512 showed a more than 5-fold in-
crease in expression following cold, salt, or drought treatment,
its ^ê~ÄáÇçéëáë homologue, AT3G20810 was down-regulated
by cold or transiently up-regulated by salt or drought.
Six of the 56 transcription factor genes were commonly
responsive to all three stresses. The BRAS0001S00010332
and BRAS0001S00014640 are homologues of ^ê~ÄáÇçéëáë
NAC transcription factor proteins, ANAC072 and ANAC019
(Tran et al., 2004), while BRAS0001S 00018287, BRAS0001
S000 18707, and BRAS0001S00019512 encode putative tran-
scription factors similar to the zinc finger protein CONSTANS-
LIKE 10, ATHB-7 (^ê~ÄáÇçéëáë homeoboxleucine zipper pro-
tein) (Söderman et al., 1996), and jumonji-domain contained
600 Transcriptome Analysis in _ê~ëëáÅ~=ê~é~
protein, respectively. The BRAS0001S000 19389 encodes for
a basic helix-loop-helix (bHLH) type tran scription factor. These
five genes were more than 5-fold up-regulated, while the re-
maining gene, BRAS0001S00019389 was drastically down-
regulated under cold, salt, and drought stresses.
Identification of commonly responsive gene to all of cold,
salt, and drought
Among _K=ê~é~ abiotic responsive genes showing more than a
5-fold change, 60 genes were identified to be responsive to all
of cold, salt, and drought treatment (Fig. 5). Among 60 genes,
nine genes (BRAS0001S00002903, BRAS0001S00003736,
BRAS0001S00003748, BRAS0001S00004439, BRAS0001S
00015743, BRAS0001S00019389, BRAS0001S00019556,
BRAS0001S00020826, and BRAS0001S00022937) in cluster
3 were down regulated by all three treatments, while only one
gene similar to epmSMI=BRAS0001S00012328, in cluster 5 was
down-regulated in response to cold treatment. The remaining
50 genes were highly up-regulated by all three treatments,
although the up-regulated expression pattern of each gene was
varied during the course of treatment. These commonly re-
sponsive genes included 46 genes that encode homologues of
reported genes, 13 genes similar to unknown genes, and one
gene that has no similarity with any reported gene when ana-
lyzed by blast search. Among the 46 genes, eight were similar
to reported abiotic stress-responsive genes. BRAS0001S
00000014, BRAS0001S00001571, and BRAS0001S00001722
are identical to _ê~ëëáÅ~ genes, _kOU~I=_kNNR, and _çopN,
which have been reported to be highly up-regulated by abiotic
stresses (Orr et al.,1992; Tang et al., 2004; Weretilnyk et al.,
1993); The BRAS0001S00024369 is similar to _åOSI _K=å~éìë
low-temperature responsive gene (Weretilnyk et al., 1993). The
BRAS0001S00004092, BRAS0001S00011162, and BRAS
0001S00019907 are highly similar to ib^NQI= boaT, and=
o`fO^ which were reported as ^ê~ÄáÇçéëáë abiotic stress-
responsive genes (Capel et al., 1997; Kimura et al., 2003); and
the BRAS0001S00013942 encodes homologue of AtCOR413-
PM1 (^ê~ÄáÇçéëáë cold regulated 413 plasma membrane 1) that
is similar to the cold acclimation protein WCOR413 in wheat
(Breton et al., 2003). In addition, six genes, BRAS0001S
00000502, BRAS0001S00001723, BRAS0001S00018287, BRAS
0001S00019519, BRAS0001S00019544, and BRAS0001S000
19683, have similarity with genes that may be involved in cir-
cadian rhythm (Doyle et al., 2002; Heintzen et al., 1994; 1997;
Schultz et al., 2001). Finally, BRAS0001S00006773 encodes a
homologue of ABI2 that may function as a negative regulator in
ABA signaling pathway (Merlot et al., 2001). Among the com-
monly responsive genes, six were identified as a gene encod-
ing putative transcription factor, as previously mentioned in
The characterization of abiotic stress-responsive genes is es-
sential for elucidating the responsive mechanisms, by which
plants can be adapted to the stresses. Recently, transcriptome
studies using microarray analyses are accelerating to identify
massive key genes in developmental and different environ-
mental conditions. Despite this advance, microarray technology
has not been widely applied to the study of _ê~ëëáÅ~ species
because of the absence of a high density _ê~ëëáÅ~ microarray
with sufficient coverage of the entire genome. Instead, ^ê~ÄáJ
Ççéëáë microarrays have been used for _ê~ëëáÅ~ species be-
cause both species belong to the mustard family (_ê~ëëáÅ~J=
ÅÉ~É) and have evolutionary close relationship. The _ê~ëëáÅ~
genome is generally believed to have triplicated following diver-
Fig. 4. K-means clustering and semi-
quantitative RT-PCR analyses of cold
responsive genes. (A) Grouping of cold
responsive genes on the basis of simi-
larities in expression pattern during 0 to
48 h of cold treatment. The responsive
genes were classified into nine clusters
by K-means clustering of TIGR MeV
s/w (http://www.tm4.org/mev.html). The
number of genes in each cluster is
indicated on the graph. (B) RT-PCR
analyses of several cold-responsive
genes. Numbers on the left side indi-
cate the cluster number of each gene in
the nine groups of 417 cold responsive
genes. a, Genes that were not identified
as cold responsive when selected by 2-
or 5-fold change. Total RNA from cold-
treated plants for indicated time was
used as templates for RT-PCR. _ê
^ÅíáåN was used for PCR control. C,
control. (C) Comparison of fold-change
ratios from RT-PCR analyses with
those from microarray data. After elec-
trophoresis and ethidium bromide stain-
ing, band intensity of PCR products
was measured and fold ratio to control
Sang-Choon Lee et al. 601
Table 2. List of _K=ê~é~ 56 genes encoding putative transcription factor, among the selected abiotic responsive genes
Gene regulationd ^ê~ÄáÇçéëáë homologuee / Gene regulationf
Gene family namea SeqIDb _ê~ëëáÅ~ geneC=
Cold Salt DroughtAGI no. %ID Gene name Cold Salt Drought
Up AT1G2837088 ^qbocNN= Up* Up* Up*
Down* Down DownAT5G61590 92 = Down* DownDown
Up*AT5G5199088 `_cQ=L=aob_Na= Up* Up*
Up&down Down&up Up&down
AT4G2549085 `_cN=L=aob_N_= Up* Up* Up*
Up*UpUp AT3G1521093 ^qbocQ=L=o^mOKR= Up Up* Up
UpUpUp*AT5G0541084 aob_O^= Up* Up*
AT4G2549084 `_cN=L=aob_N_= Up* Up* Up*
Up*AT5G0541086 aob_O^= Up* Up*
Up AT1G2837088 ^qbocNN= Up* Up* Up*
AT2G1830087 ^íÄeieSQ= Down* Down*Down
Down Down Down*AT3G5906089 ^íÄeieSR=L=mfcR=L=mfiS= Down* Down* Down*
Up*AT2G4651089 ^íÄeieNT= Up* Up* Up*
AT4G3453085 ^íÄeieSP=not assayed (na) na na
AT5G3986091 mobN=Down DownDown
Down*Down* Down*AT2G1830090 ^íÄeieSQ= Down* Down*Down
UpUp*AT2G4627091 ^íÄwfmRR=L=d_cP=Up Up* Up Basic region leucine
Up*AT4G3459091 ^íÄwfmNN=L=^q_O=L=d_cS=Up Up
Cupin super family BRAS0001S00019512
Up*Up*Up*AT3G2081092 =Down Down&upDown&up*
Down* Down DownAT4G3896088 =
Up*Up*Up*AT5G4825095 `liNM= Up* Up Up
Down*AT1G2544087 `liNS=Na Na Na
Down*AT1G6852089 `liS= Down* Down
Up*Up*Up AT3G0765094 `liV= Up* Up Up
Down Down*AT5G1585085 `liN= Up* DownDown
Down*AT4G1614192 =Na Na Na
Up AT1G2773087 w^qNM= Up* Up* Up*
AT5G5982084 w^qNO=L=oeiQN= Up* Up* Up
BRAS0001S00014584 _åwcO=Up*UpUp*AT5G0434083 w^qS= Up* Up* Up
AT5G5982086 w^qNO=L=oeiQN= Up* Up* Up
Down Down Down*AT5G4419090 ^qdihO=L=dmofO=Down Down
AT3G0299083 ^qepc^Nb====Up Up*
Up*Up*Up*AT2G4668089 ^qe_JT=Up Up*
Down Down* Down*AT2G4683085 ``^N= Up* Down* Down*MYB
Down*AT3G4613090 ^qjv_QU=L=jv_NNN= Down* Down*Down
Up*AT1G6949089 ^k^`MOV=L=^qk^m=Up Up* Up
UpUpUp*AT3G1550086 ^k^`MRR=L=^qk^`P=Up Up* Up
BRAS0001S00010332 _åk^`QUR=Up*Up*Up*AT4G2741096 ^k^`MTO=L=oaOS= Up* Up* Up
BRAS0001S00014457 _åk^`NU=UpUpUp*AT1G0172089 ^k^`MMO=L=^q^cN=Up Up* Up
Up*Up*Up*AT1G5289087 ^k^`MNV=Up Up* Up*
BRAS0001S00017334 _åk^`NQ= UpUpUp*AT1G7745091 ^k^`MPO== Up* Up* Up
Down*AT1G1185094 =Down Down
AT4G2381087 ^qtohvRP=Up Up* Up
Up AT1G8084089 ^qtohvQM= Up* Up* Up*
Up*UpUp AT1G8084090 ^qtohvQM= Up* Up* Up*
Up AT2G3847090 ^qtohvPP=Up Up* Up
aName of transcription factor family based on classification of ^ê~ÄáÇçéëáë homologous gene in TAIR (http://www.arabidopsis.org/)
bUnique designation provided by NimbleGen for each probe sequence on _K=ê~é~ KBGP-24K chip
cReported _ê~ëëáÅ~ gene that was encoded by the selected gene in this study
dRegulation of the selected gene by cold (4°C), salt (250 mM NaCl), and drought (air-dry) from microarray analyses in this study. Blank line indicates no-
response when judged by more than 2-fold change.
e^ê~ÄáÇçéëáë homologous gene identified by blast search using nucleotide sequence of the _K=ê~é~ gene as a query
fRegulation of ^ê~ÄáÇçéëáë homologous gene by cold (4°C), salt (150 mM NaCl), and drought (air-dry) from AtGenExpress database. Blank line indicates
no-response when judged by more than 2-fold change. up* and down* indicate that the gene showed more than 5-fold up- and down-regulation under
indicated stress, respectively. Up&down and down&up indicate that expression of the gene was transiently up- and down-regulated and down- and up-
regulated under indicated stress, respectively.
602 Transcriptome Analysis in _ê~ëëáÅ~=ê~é~
Fig. 5. Hierarchical cluster display of 60 commonly responsive genes. Sixty genes responsive to all of cold, salt, and drought treatment were
grouped into 9 clusters by K-means clustering and their expression changes during cold [cold (0.5 h) to cold (48 h)], salt [salt (0.5 h) to salt (48
h)], and drought [dr (6 h) to dr (48 h)] treatment were displayed with color bar. The color scale bar shown upper the cluster indicates the maxi-
mum and minimum brightness values that represent expression ratios in log2. aCluster number in gene grouping based on its expression pat-
tern under cold, salt, and drought. bUnique designation provided by NimbleGen for each probe sequence on _K=ê~é~ KBGP-24K chip. cGene
description from blast search.
Sang-Choon Lee et al. 603
gence from ^ê~ÄáÇçéëáë (O’Neill and Bancroft, 2000; Rana et al.,
2004) and thus consist of approximately 46,000 genes (Yang et
al., 2006). Furthermore, physiologically and morphologically,
_ê~ëëáÅ~ species are different from ^ê~ÄáÇçéëáëK These indicate
that all _ê~ëëáÅ~ genes might not be represented in the ^ê~ÄáJ
Ççéëáë genome and not be regulated in a same manner of
^ê~ÄáÇçéëáë. Somewhat an ^ê~ÄáÇçéëáë microarray chip may
provide some information in the gene regulation of _ê~ëëáÅ~
plants; however, it is insufficient to provide some insight into
_ê~ëëáÅ~ biology. In fact, about 10% of _K=ê~é~ unigenes identi-
fied in this study are unique and has no ^ê~ÄáÇçéëáë counter-
parts. Therefore, in order to accurately perform transcriptome
studies in the species, a _ê~ëëáÅ~=specific=microarray is essen-
tial. Thus, we newly developed an oligo microarray, KBGP-24K,
which represents about 24,000 unigenes amount to about 50%
of estimated total genes in _K=ê~é~. Moreover, considering evo-
lutionary relationship or synteny among the _ê~ëëáÅ~ species
(Morinaga, 1933; 1934; U, 1935), this microarray will also be
useful for the study of other _ê~ëëáÅ~ species including _K=
å~éìë and _K=çäÉê~ÅÉ~.
In the cultivation of _K=ê~é~, the productivity is adversely af-
fected by various environmental stresses including cold, salt,
and drought stress. Thus, to identify key responsive genes
involved in the abiotic stress-responsive mechanism, we used
KBGP-24K microarray. These experiments were performed in
duplicate and the data from the biological replicates showed
high correlation reciprocally. Furthermore, this high level of
correlation was also observed between microarray data of con-
trol samples in three stress treatments. Together with the high
correlation coefficient between RT-PCR analysis and microar-
ray data, this indicate that our data from KBGP-24K microarray
was reliable and reproducible. We identified 417 (1.7%) cold
responsive, 202 (0.8%) salt responsive, and 738 (3.1%)
drought responsive genes that showed a greater than 5-fold
change in expression (Fig. 2). Comparing with ^ê~ÄáÇçéëáë
microarray analyses in similar conditions, the percentage of
responsive genes showed somewhat different. By selecting
genes with a more than 5-fold change, 0.8%, 2.8%, and 4.0%
of about 7,000 ^ê~ÄáÇçéëáë genes were identified as a cold
(4°C), salt (250 mM NaCl), and drought (air-dry) responsive
gene (Seki et al., 2002). Although these differences in gene
expression may be due to slightly different conditions in stress
treatment or in microarray analysis, it is also mainly possible
from the difference in physiological response in two species
under the abiotic stresses.
We identified 56 putative transcription factors response to
abiotic stresses and divided them into 15 transcription factor
families (Table 2). This result suggests that various transcription
factors are involving and functioning in the responsive mecha-
nisms under abiotic stresses in _K=ê~é~K=We searched a set of
homologous genes which consist in ^ê~ÄáÇçéëáë stress-
signaling pathway. In addition to five genes encoding CBF/
DREB-like transcription factors (Table 2), we identified five their
putative targeted genes, BRAS0001S00000571, BRAS0001S
00000575, BRAS0001S00001722, BRAS0001S00009565, and
BRAS0001S00026194 encoding bêÇNMI= oÇOV~L`çêTUI= and=
`çêQTLoÇNT. It was determined that the expression of these
genes were similar to those of their ^ê~ÄáÇçéëáë homologues,
suggesting that a CBF/DREB-involved stress-signaling path-
way= may also be present in= _K= ê~é~K Therefore, the cold-
responsive mechanism in _K=ê~é~ may be very similar to that of
^ê~ÄáÇçéëáë. However, considering differences in cold sensitiv-
ity, it is possible that _K=ê~é~ may have differences in cold re-
sponsive and protective mechanisms as compared to more
cold tolerant plants like ^ê~ÄáÇçéëáë. Furthermore, we found
several _K=ê~é~ genes similar to ^ê~ÄáÇçéëáë plp genes in-
volved in hyper-osmotic and ionic stress signaling pathways
(Yokoi et al., 2002). However, salt induction of these putative _K=
ê~é~ plp genes could not be confirmed by the microarray
analyses in this study because the genes did not show more
than 2-fold change.
In order to select important genes to enhance stress tolerance
in _K=ê~é~I we identified 60 commonly responsive genes that
strongly responded to all three stresses (Fig. 5). Interestingly,
these genes also included six transcription factor genes, of which
two genes, BR0001S00010332 and BR0001S00014640,
encode NAC domain containing protein. These genes, at both
nucleotide and protein level, are highly homologous with ^ê~ÄáJ
Ççéëáë NAC transcription factors such as ANAC072 and
ANAC019 that were up-regulated by cold, salt, and drought
stress (Tran et al., 2004). The NAC transcription factors are a
MYC-like sequence-binding protein and are capable of up-
regulation of several stress-inducible genes (Fujita et al., 2004;
Tran et al., 2004). In ^ê~ÄáÇçéëáë and rice, the overexpression
of the k^` genes enhanced abiotic stress tolerance in trans-
genic plants (Hu et al., 2006; Nakashima et al., 2007; Tran et
al., 2004). In _ê~ëëáÅ~ species, eight NAC transcription factors
including BnNAC14 and BnNAC18 have been identified
(Hegedus et al., 2003). These NAC transcription factor genes,
together with other commonly responsive genes, will be good
candidates to increase tolerance of _K=ê~é~=to multiple forms of
abiotic stress. Of commonly responsive genes, six are similar to
genes that may be involved in regulating in circadian rhythm.
Specifically, BRAS0001S00018287 and BRAS0001S
00019544 were similar to the `lkpq^kpJifhb and b^oiv=
ciltbofkd= Q (bicQ) genes which are believed to be in-
volved in flowering (Doyle et al., 2002; Kim et al., 2003). Since
_K= ê~é~ plants need vernalization by low-temperature below
13°C in order to flowering (Eguchi et al., 1963), it is reasonable
that these genes were also induced in response to cold stress.
Furthermore, as discussed by Yang et al. (2005), these genes
as well as other commonly responsive genes may be involved
in both survival and vernalization in response to cold stress.
In conclusion, we presented the validity and accuracy of the
newly developed _K= ê~é~ 24K oligo microarray through the
analysis of _K=ê~é~ plants under various stress conditions and
identified a number of abiotic stress-responsive genes including
several transcription factor genes and commonly responsive
genes. Therefore, this work will provide a tool for _ê~ëëáÅ~
large-scale transcriptome study and valuable insight in the re-
sponse of _ê~ëëáÅ~ species to abiotic stress.
We thank technicians of the Brassica Genomics Team at Na-
tional Institute of Agricultural Biotechnology and the members of
National Institute of Agricultural Biotechnology bioinformatics
center for their technical assistance. This work was supported by
grants from the National Institute of Agricultural Biotechnology
(Code # 04-1-12-2-3) and the BioGreen 21 Program (Code #
20050301034438), the Rural Development Administration, Korea.
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