Vol 13, Issue 3, 327-340, March 2003
Gene Expression Analyses of Arabidopsis Chromosome 2 Using a Genomic DNA Amplicon Microarray
Heenam Kim1,
Erik C. Snesrud1,
Brian Haas,
Foo Cheung,
Christopher D. Town and
John Quackenbush2
The Institute for Genomic Research, Rockville, MD 20850, USA.
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ABSTRACT
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The gene predictions and accompanying functional assignments
resulting from the sequencing and annotation of a genome represent
hypotheses that can be tested and used to develop a more complete
understanding of the organism and its biology. In the model plant
Arabidopsis thaliana, we developed a novel approach to
constructing whole-genome microarrays based on PCR amplification of the
3' ends of each predicted gene from genomic DNA, and constructed an
array representing more than 94% of the predicted genes and
pseudogenes on chromosome 2. With this array, we examined various
tissues and physiological conditions, providing expression-based
validation for 84% of the gene predictions and providing clues as to
the functions of many predicted genes. Further, by examining the
distribution of expression along the physical chromosome, we were able
to identify a region of repressed transcription that may represent a
previously undescribed heterochromatic region.
[The sequence
data from this study have been submitted to ArrayExpress under
accession nos.: For the Array Design, A-TIGR-2. For the three subgroups
of experiments: AbioticStress, E-TIGR-2; BioticStress, E-TIGR-3;
Tissues, E-TIGR-4.]
The sequencing of the whole Arabidopsis
genome by an international consortium,
Arabidopsis Genome Initiative (AGI), began in 1996.
Chromosomes 2 and 4 were published in December 1999 (Lin et al. 1999 ;
Mayer et al. 1999), and the remainder of the genome, chromosomes 1, 3,
and 5, was completed and published in the winter of 2000
(Arabidopsis Genome Initiative 2000; European Union
Chromosome 3 Arabidopsis Sequencing Consortium 2000; Kazusa
DNA Research Institute et al. 2000; Theologis et al. 2000).
The goal of a genome project is not the collection of the organism's
DNA sequence, but rather the identification of the genes encoded
within. Consequently, as the Arabidopsis sequence became
available, significant effort was devoted to gene prediction and
sequence annotation. Gene identification in eukaryotes remains a
significant challenge; various existing gene prediction programs
frequently provide contradictory results, and consequently, their
predictions are best viewed as models that must be confirmed by other
data, including alignments to EST, gene, or protein sequences. In
Arabidopsis, <50% of the annotated genes had strong EST
support. Further, while nearly 69% of the annotated genes were
assigned putative functions, only 9% had been previously
characterized. Although recent cDNA sequencing efforts have provided
additional support for some predictions (Seki et al. 2002), many of the
annotated gene structures and functional assignments remain hypotheses
that must be tested to evaluate the quality of the annotation and to
refine annotation techniques.
Microarray expression analysis allows monitoring of gene expression
patterns on a global scale and provides an opportunity to both validate
the gene predictions and to develop experimental evidence for
functional assignments. There are a number of approaches to
constructing microarrays, including mechanical spotting of cDNA clones
(Schena et al. 1995) or long oligonucleotides (Kane et al. 2000; Call
et al. 2001) onto derivatized glass and the in situ synthesis of short
oligonucleotide probes directly on a glass microarray surface (Chee et
al. 1996). In Arabidopsis, however, each of these approaches
suffers significant limitations. Publicly available cDNA clones even
now represent <60% of the predicted genes (Seki et al. 2002), while
oligomer-based approaches rely on accurate gene structure predictions
to effectively select target regions.
To circumvent these limitations, we developed a novel approach in which
we constructed arrays consisting of PCR-amplified genomic segments
representing nearly the entirety of the annotated genes on
Arabidopsis chromosome 2 spotted onto aminosaline-coated
microscope slides. Using these arrays, we set out to evaluate the
validity of genomic annotation and to place the predicted genes in a
biological context. Our results demonstrate expression of at least 84%
of the predicted genes under one or more of the conditions tested and
allow us to identify genes expressed in stress response and in
particular tissues. Further, we have identified a region that appears
to be transcriptionally repressed; the composition of the genes in this
region resembles known heterochromatic regions in the chromosome 4 and
in other plant chromosomes.
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RESULTS AND DISCUSSION
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A Novel Approach to Construction of the Genomic Amplicon Microarrays
The lack of cDNA clones representing the majority of the predicted
genes on chromosome 2, coupled with the inability of ab initio gene
prediction programs to accurately deduce gene structures led us to
develop a novel PCR-based approach targeting the 3' ends of the
predicted genes (Fig. 1). Briefly, starting
at the 3' end of the predicted transcribed region of each gene (Lin et
al. 1999; available through http://www.tigr.org/tdb/e2k1/ath1/), we
selected a 1000 base-pair region immediately upstream of the predicted
stop codon. If an annotated 3' untranslated region (UTR) existed, we
added the complete UTR, otherwise, we included 150 base pairs of
sequence downstream of the predicted stop. The selected target
sequences provided with a minimum of 1150 base pairs for all predicted
genes from which we designed PCR primers using Primer 3.0 (Whitehead
Institute, http://www-genome.wi.mit.edu/genome_software) with
optimized design parameters that can be used to amplify >5/6 of the
target. The resulting PCR products are  1 kb in length, which is
large enough to assure the presence of sufficient coding sequence in
the target genomic region for efficient hybridization, while small
enough not to contain multiple genes. Using this approach, we were able
to design primers for 4437 of the 4442 predicted genes and pseudogenes
identified on the chromosome 2 and have successfully amplified 4180
(94.2%) from genomic DNA using standardized amplification conditions,
with approximately equal numbers either giving no clear amplification
product or showing multiple bands (see
http://atarrays.tigr.org/arabdata.shtml for primer sequences and
amplification data, as well as the perl script used for primer
selection). It should be noted that this represents a lower bound for
representation on the arrays, as some of the products that gave no
visible product on an agarose gel yielded good hybridization data;
subsequent reanalysis suggests that the majority of these "undetected
products" represent misloaded samples or samples at low
concentrations. Purified PCR amplicons were spotted in duplicate at
high density on aminosaline-coated microscope slides and the resulting
microarrays used to assess gene expression in a wide range of tissues
and physiological states.
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Figure 1. Primer design strategy for amplification of the 3' ends of the
annotated genes identified on the chromosome 2. Starting at the
predicted stop codon of each annotated gene, we selected a region 1000
bp upstream and 150 bp, or the length of the annotated 3' untranslated
region if available and extracted it from the genomic sequence. Primer
3.0.9 from the Whitehead Institute was used to design primers spanning
>5/6 the length of the selected region. Amplification success from
genomic DNA was 94.2% using this approach.
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Validation of the Gene Predictions on Arabidopsis Chromosome 2
Of the 4437 genes for which we were able to design primers, 273
(6.2%) were previously known genes, 1807 (40.7%) were assigned
putative functions based on protein sequence homology, 866 (19.5%)
were classified as encoding unknown proteins as they shared similarity
with other proteins of unknown function, 1094 (24.7%) were annotated
as hypothetical indicating that they encode novel proteins of unknown
function, and 397 (8.9%) were classified as pseudogenes. While the
chromosome 2 microarrays represent nearly the entire complement of the
genes on the chromosome, at any particular instant in time, a given
tissue or physiological state is likely to express only a subset of the
genes encoded within the genome. Consequently, we chose to survey a
broad range of tissues and developmental stages, as well as plants
challenged by biotic and abiotic stressors, in order to assess the
validity of the gene predictions (Fig. 2).
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Figure 2. Paired Arabidopsis samples surveyed with microarrays in this
study. A total of 19 samples were grouped into 20 hybridization pairs
representing abiotic and biotic stressors and tissue-specific sets;
subsets of experiments are color-coded as in Figures 6 and 8. mRNA from
each plant sample was labeled with Cy3 or Cy5 fluorescent dye as
indicated and the collection of hybridizations was replicated with dye
labels reversed.
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In total, 40 cohybridization assays were performed, representing 20
direct comparisons and dye-reversed replicas. As each gene on the
chromosome was printed in duplicate, each pair of samples provides four
opportunities to detect expression. We scored the genes "expressed"
when they exhibited a measurable signal above background in at least
two of these four replicas. Using this definition, we found 3720
(83.7%) of the 4442 genes on the chromosome to be expressed in at
least one sample, providing transcriptional evidence for these
predictions (Fig. 3A). We detected
expression of 894 (81.7%) of the 1094 annotated hypothetical genes and
783 (90.4%) of the 866 genes encoding unknown proteins.
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Figure 3. Validation of gene predictions by expression as detected by microarray
analysis. (A) Various levels of support can be inferred based
on how often expression was detected in the 40 assays performed. Of
4437 genes surveyed, 83.7% provides evidence of expression in at least
one assay, while 12.4% are expressed in all assays. (B) Genes
assigned to functional classes, shown for the chromosome and for those
genes that were expressed in every sample or that failed to be detected
in any assay. Genes of previously known function are relatively
overrepresented among those ubiquitously expressed and underrepresented
among those not detected, while "hypothetical" genes display the
opposite behavior.
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A total of 550 genes (12.4%) were detected as expressed in all 40
hybridizations. These ubiquitously expressed genes include many of the
known genes, as well as those unknown genes annotated based on their
conservation in other species. Only 36 of the hypothetical genes, which
were annotated solely on the basis of ab initio predictions, fell into
this class. Only 717 (16.2%) genes were undetected in any of the
assays performed; in this set, hypothetical genes were highly
represented. Taken together, these data suggest, not surprisingly, that
gene predictions without supporting EST or protein alignment evidence
are most likely to be of questionable validity. These results are
summarized in Figure 3; all expression data from this study can be
found at http://atarrays.tigr.org/data/.
One interesting observation can be made by looking at the
representation of known genes in various subsets of the data (Fig. 3B).
For the entirety of chromosome 2, the known genes represent only 6.5%
of the total 4437 annotated genes. However, when we examine the 550
genes that appear in all of the conditions surveyed in this study, we
find that the known genes represent 20.2% of the total, while in the
set of 717 genes that showed no discernable expression in any of our
assays, the known genes represent only 3.2% of the total. What this
suggests is subtle but profound for microarray studies. The "known
genes" are likely known because they are nearly ubiquitously
expressed and consequently more likely to be identified and assigned a
functional role in standard biological experiments. In contrast, many
genes of unknown function appear in only a small number of tissues or
states or in response to specific stressors. This observation is
important for microarray construction where the goal is to elucidate
patterns of gene expression. Many people argue that arrays should be
limited to genes of known function to facilitate interpretation of the
data. This could, however, have the effect of eliminating from
consideration the very genes that may well be important for a
particular response in favor of genes that play a more general role in
the cell.
Genes Responsive to Abiotic Stresses
Compared to validating expression of annotated genes, confirming
functional role assignments for putative genes and determining
functions for hypothetical and unknown genes is significantly more
difficult. It often is not easy to find the proper conditions under
which those genes are significantly regulated, and precise functional
assignments generally require serial biochemical and genetic analyses
to confirm a gene product's action. Nevertheless, microarray data
provides information on patterns of gene expression that can be used to
infer possible functions for these genes that can be further tested in
directed studies.
The conditions we surveyed included three independent abiotic stresses,
heat, cold, and salt, with response to salt stress measured 12 and 24 h
after exposure. Of the genes on chromosome 2, we were able to identify
497 that were differentially expressed at 95% confidence under one or
more of the conditions. These included 43 that had been previously
characterized; 247 were genes coding for putative functions, 106 genes
encoded unknown proteins, 83 were hypothetical genes, and 18 had been
annotated as pseudogenes. Figure
4 shows the
297 genes for which expression data were available in all four
conditions organized into 10 clusters using k-means clustering
with a Euclidean distance metric.
Among the known genes were some previously associated with abiotic
stress-response in plants, and these served as positive controls for
our analysis. For example, a gene encoding a glutathione S-transferase
(GST, At2g29450) was up-regulated in response to all
stressors. GST enzymes are known to be involved in numerous
biotic and abiotic stress responses including those assayed here (Marrs
1996; Edwards et al. 2000). Genes coding for cold-regulated protein
cor15a precursor (At2g42540) and cold-regulated protein cor15b
precursor (At2g42530) were up-regulated in response to cold stress,
consistent with the involvement of these proteins in acclimation to
cold stress (Wilhem and Thomashow 1993; Steponkus et al. 1998).
Induction of actin depolymerizing factor (ADF, At2g16700)
under cold stress is consistent with the previous observation that low
temperatures induce the accumulation of an ADF protein in
Gramineae species (Ouellet et al. 2001). A change in the
abundance of ADF proteins is believed to lead to changes in the actin
cytoskeletal architecture during low-temperature acclimation, and these
modifications may be related to cell survival under freezing conditions
(Staiger et al. 1997; Lappaleinen et al. 1998; Aon et al. 2000).
Delta-9 desaturase (At2g31360) was specifically up-regulated under cold
stress. Production of delta9 desaturase under cold stress may be a way
to acclimate to the cold conditions. Transgenic tobacco plants
expressing cyanobacterial delta-9 desaturase have been shown to have
highly reduced level of saturated fatty acid in membrane lipids and
exhibited a significant increase in chilling resistance
(Ishizaki-Nishizawa et al. 1996). Delta-1-pyrroline 5-carboxylase
synthetase (P5C1; At2g39800) was induced in response to cold
and salt stresses. This enzyme is required for the synthesis of
proline, which is known to play an important role as an osmoprotectant
in plants subjected to hyperosmotic stresses such as cold, drought, and
soil salinity (Delauney and Verma 1993; Hong et al. 2000).
Other genes with known and putative functions, which were found to be
differentially expressed, can be used to generate hypotheses regarding
the mechanism of stress response. Induction of a gene encoding a
K+ transporter (AKT1; At2g26650) is
intriguing because it is known that high concentrations of
Na+ caused by salt stresses can cause
K+ deficiency in the cell (Hanegawa et al. 2000).
AKT1 is predominantly expressed in root cortex and root epidermis, and
is responsible for inward rectifying K+ currents in
these cells (Hirsch et al. 1998; Reintanz et al. 2002). Moreover, when
1 mM Na+ was applied in the presence of 30 mM
K+ in the bath solution, inward
K+ currents remained largely unaffected (Reintanz
et al. 2002). Induction of this transporter may alleviate
K+ deficiency caused by increased concentration of
Na+ in the cell. Salt stress also induced expression
of 12-oxophytodienoate-10, 11-reductase (At2g06050), which is required
for jasmonate synthesis, suggesting that salt stresses result in the
synthesis of this chemical. Nitric oxide signaling may also play a role
in salt-stress response as cytoplasmic aconitate hydratase (At2g05710),
which plays a role as a nitric oxide sensor, is up-regulated.
One of the values of the microarray data is that they provide support
for the genes coding for proteins of putative function. The
differentially expressed genes we identified include a number of genes
encoding putative transcription factors and various proteins that may
have roles in signal transduction pathways, and their patterns of
expression provide the first experimental evidence for their
assignments. For instance, a gene for a putative
low-temperature–regulated protein (At2g15970) indeed was
significantly and specifically induced under cold stress (Fig. 4B).
At2g03760, which encodes a putative steroid sulfotransferase, was also
up-regulated. Steroid sulfotransferases are the enzymes that inactivate
steroid hormones and recently have been shown induced by salicylic acid
in plant (Rouleau et al. 1999). These authors suggested that plants
might respond to stresses by modulating steroid-dependent growth and
developmental processes. We observed the induction of At2g47600 coding
for a putative Na+/Ca2+ antiporter under salt
stress. Although this is not a surprising response to the osmotic
pressure induced by high salt, to our knowledge this is the first
report of salt-induced expression of this transporter. Induction of
putative amine oxidase (At2g43020) under salt stress suggests that
reactive oxygen species (ROS) signaling may also play a role in the
plant's response. This hypothesis is consistent with the induction of
12-oxophytodienoate-10, 11-reductase (At2g06050), which is a key enzyme
for jasmonate synthesis and which is known to both be produced in
response to ROS and to play a role in modulating oxidative signaling
(Schaller et al. 1998; Rao et al. 2000). A putative inositol
polyphosphate 5'-phosphatase (At2g43900) was also induced. These
enzymes are known to be involved in abscisic acid (ABA) signaling, and
it is known that ABA accumulates in vegetative cells in response to
water deficit, salinity, cold temperature, and light variation, and it
is thought to act as a signal for the initiation of acclimation to
these stresses.
We also found 83 hypothetical and 106 "unknown" genes to be
differentially regulated in response to abiotic stresses. This suggests
that these genes may play a role in stress response, although with
these limited data it is not possible to deduce precise functions. A
more comprehensive expression analysis of stress response in
combination with traditional genetic studies would help to refine the
roles that these unknown genes might play.
Response to Biotic Stress
We also investigated plant response to bacterial infection. For
this, we infiltrated Arabidopsis rosette leaves with buffer
suspensions of Pseudomonas syringae pv. tomato
(Pst) strain DC 3000 (Staskawicz et al. 1987) carrying either
the avirulent gene avrRpt2 (Whalen et al. 1991; Mudgett and
Staskawicz 1999; Chen et al. 2000) or the vector control (pLAFR3) for
the gene construct (Staskawicz et al. 1987). The avrRpt2 gene
encodes a virulence factor that is quickly detected by the
Arabidopsis surveillance system and induces an avirulence
response (Mudgett and Staskawicz 1999; Chen et al. 2000). We also
challenged plants with Xanthomonas campestris pv.campestris, which causes black rot disease in both crucifers and
some noncrucifers including Arabidopsis (Bent et al. 1992).
Buffer without bacteria was used as a negative control.
A total of 344 genes showed a significant response to at least one
treatment (Fig. 5), of which
12 are of previously known function and some of which can serve as
positive controls for our assays. At2g37040 codes for phenylalanine
ammonia lyase (PAL1) was up-regulated in response to
infiltration with P. syringae DC 3000 (avrRpt2),
P. syringae DC 3000 (vector control), and buffer alone. This
is consistent with the fact that PAL1 has been implicated in pathogen
and wound response in plants (Logemann et al. 1995; Weisshaar and
Jenkins 1998). Induction of At2g40940 coding for ethylene response
sensor (ERS) and At2g06050 coding for 12-oxophytodienoate-10,
11-reductase, a key enzyme for jasmonate biosynthesis, is consistent
with published observations that ethylene and jasmonate are involved in
pathogen-responsive interactions (Pieterse and van Loon 1999).
At2g14580, which encodes pathogenesis-related PR-1-like protein, was
strongly induced in response to P. syringae DC 3000
(avrRpt2) but only weakly to P. syringae DC 3000
(vector control), suggesting the protein is expressed in response to
the avrRpt2 product.
Of the 344 response genes we found to be differentially expressed in
response to at least one treatment, 228 had measurable expression in
all four. These were clustered using k-means (k = 6,
Euclidean distance; Fig. 5). Clusters A and B contain genes that are
highly up-regulated in response to P. syringae DC 3000
(avrRpt2) relative to other treatments. It is possible that
many of these may be involved in avirulence responses to the
avrRpt2 gene product. Clusters C and D include genes
specifically down-regulated by X. campestris infection. Many
of these are involved in gene expression and signal transduction and
were up-regulated in response to salt and other abiotic stresses. This
suggests that X. campestris may have a strategy to suppress
host defense systems to effectively establish pathogenesis. Finally, we
found 167 putative, 59 hypothetical, and 85 unknown genes significantly
regulated in these biotic stress-response experiments, suggesting
potential roles for these genes.
Gene Expression Profiles in Tissue Samples
We also surveyed gene expression in a variety of paired tissues and
whole seedlings (Fig. 2). We identified 738 genes differentially
expressed in at least one pair of samples, of which 179 had measurable
expression in all assays. Patterns of expression are shown in Figure
6. Although direct comparison between all
assays are difficult because different reference samples were used for
each pair, the dataset allows interesting observations to be made. For
example, in comparison of flowers, stems, and leaves with whole aerial
tissue, we observe distinct patterns of expression for each tissue.
Among genes down-regulated in flowers are those associated with
photosynthesis. A gene (At2g37040) encoding phenylalanine ammonia lyase
(PAL1) (Weisshaar and Jenkins 1998) was up-regulated in stem
but significantly down-regulated in leaves and flowers, implying the
rapid cell wall synthesis (growth) in the stem. These suggest that our
array approach coupled with detailed tissue and developmental sampling
of tissues can lead to a better understanding of the genes that are
specifically expressed in various tissues and in tissue
differentiation.
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Figure 6. Gene expression in various tissues. A total of 179 genes that showed
significant differences in expression compared to corresponding
reference samples were subjected to average linkage hierarchical
clustering with a Euclidean distance metric. Predicted role categories
are denoted by color-coded squares; genes also found to be
significantly regulated in response to both abiotic and biotic stresses
are denoted with blue circles.
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Functional Distribution of Differentially Expressed Genes
If one examines the functional distribution of genes differentially
expressed in all three subsets of the samples we analyzed (Fig.
7A), it is apparent that hypothetical and
pseudogenes are significantly underrepresented relative to the
functional distribution of genes on the chromosome. While it is
possible that the conditions that we surveyed are those in which these
genes are not expressed, it is more likely that the pseudogenes are
simply not expressed because of loss of their promoter sequences, and
that many of the hypotheticals, predicted without support from ESTs or
known proteins, are not real genes or are rarely transcribed and
consequently do not appear in our assays.
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Figure 7. A comparison of genes found to be significantly regulated in the
various experimental subsets. (A) Distribution of role
categories for each of the biotic, abiotic, and tissue classes of
assays. (B). Venn diagram analysis showing the number of
significantly regulated genes overlapping between sets.
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One other interesting observation is that there are a number of genes
that are transcriptionally regulated in response to both biotic and
abiotic stressors, as well as in a tissue-specific fashion (Fig. 7B).
Many stress-responsive genes are known to be involved in normal
physiology in plants. Moreover, it is not surprising that a range of
stressors activate the same repair and protective mechanisms and
signaling pathways. Many stressors also cause oxidative damage (Bowler
and Fluhr 2000) and these result in production of antioxidants and
scavenging enzymes, as we have seen induction of the GST
genes.
Despite considerable overlap between many stress signaling pathways,
our data also provides clear evidence for stress-specific responses.
Examples include the Na+/Ca2+ antiporter and
K+ channel we observed up-regulated in response to salt
stress (Fig. 4). These can be important for the salt stress response,
however may not be as important for other stresses that do not involve
ionic stress.
Chromosomal Organization and Gene Expression
One particular application of gene expression analysis that is not
possible without a comprehensive survey of the genome of an organism
(or its chromosomes) is the analysis of chromosomal position effects on
patterns of gene expression (Fig. 8). While
it has been known that such effects exist (Fransz et al. 2000;
McCombie et al. 2000), the whole chromosome 2 Arabidopsis
microarray represents the first opportunity to directly study this in a
comprehensive way in a higher eukaryote.
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Figure 8. Spatial distribution of expressed genes along the chromosome for those
genes detected in all assays as well as those significantly up- or
down-regulated in particular assays. In each graph, genes are arranged
in the order they appear along the chromosome starting from the
nucleolar organizer region on the short arm. Also shown is a plot of
the average GC content 1 kb upstream of each gene. Note that gene
expression appears repressed in the region of the centromere and
telomeres, areas in which the average GC content increases. Note that a
similar increase is not observed in the other repressed regions
apparent on the long arm.
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Our expression data reveals a region near the centromere, delimited
approximately by At2g06400 and At2g14850, containing more than 600
genes (14% of the total), where gene expression appears generally
repressed relative to other regions. In this region, only plants
subject to salt stress and seedling tissue demonstrate any significant
expression. As reported previously (Copenhaver et al. 1999), this
region contains a relatively large number of genes (300) associated
with transposons, retroelements, and retroelement-like pseudogenes.
These repetitive DNA elements are consistent with
heterochromatic regions described in
Arabidopsis chromosome 4 (Fransz et al. 2000; McCombie et al.
2000), suggesting this region is also heterochromatic in nature and in
which most genes are silenced. An analysis of a region 1 kb upstream of
each of the genes also indicates an increased average guanine-cytosine
(GC) content for these centromeric genes, as well as for those falling
near the telomeres, further supporting the observation that these
regions are heterochromatic. Some genes appear to escape
silencing under specific conditions (early development and salt stress)
consistent with the fact that heterochromatin stability can change
during development (Preuss 1999; Meyer 2000) and that some activators
are known to overcome heterochromatin silencing (Ahmad and Henikoff
2001).
Finally, one should note that there are additional regions on the long
arm of chromosome 2 that also appear to be transcriptionally repressed.
Extensive analysis of the genes in these regions, including their
functional roles, GC content, and the presence of repetitive sequences,
failed to yield any clues as to what sets these regions apart. The
apparent silencing of these regions remains an open question that must
be further validated and explored.
Conclusions
The sequencing and annotation of a genome is a starting point for a
holistic analysis of the organism under study. However, the gene
predictions and their functional assignments represent hypotheses that
must be experimentally tested. We developed a novel approach to
constructing whole chromosome arrays using genomic DNA amplicons and
have demonstrated their utility in providing validation for the gene
predictions and their potential for shedding light on important
biological processes and genome-scale patterns of expression. The gene
expression profiles we have observed in this study are consistent with
previous observations and suggest new relationships between genes that
can be tested with further directed analyses. In addition, we have
provided additional validation for putative functional assignments by
demonstrating that many of these predicted genes behave as one might
expect based on sequence homology. We have also provided clues as to
potential functions for many genes annotated as hypothetical or
unknown. Our discovery of spatial effects in the patterns of gene
expression further suggests that whole chromosome analysis, and
ultimately whole-genome analysis, may reveal new features and provide
new insights on gene regulation in higher eukaryotes. Based on the
successful demonstration of the utility of this amplicon array
approach, we have expanded our efforts to the creation of a
whole-genome microarray representing the entire nuclear, chloroplast,
and mitochondrial genomes of Arabidopsis and anticipate the
first results from expression analysis using those arrays to be
available shortly. All data from this study and validated primer pair
sequences for chromosome 2 and the entire nuclear, chloroplast, and
mitochondrial genomes are available at http://atarrays.tigr.org. We
hope that this approach and these reagents become a valuable research
tool for the community.
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METHODS
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Microarray Construction
The protocols used for this study were adapted from those we
developed for the analysis of human microarrays (Hegde et al. 2000)
with minor modifications (see
http://atarrays.tigr.org/protocols.shtml). Briefly, PCR amplicons were
purified using Millipore 96 well size exclusion vacuum filter plates.
Purified products were resuspended in water and combined 1:1 with
DMSO for microarray spotting. These products were spotted in duplicate
at high density on Telechem Superamine aminosilane coated microscope
slides using a high precision spotting robot developed by Intelligent
Automation Systems. Spotted samples were allowed to dry at room
temperature and bound to the slides by ultraviolet crosslinking at 450
mJ in a Stratalinker (Stratagene). Slides were stored in a bench-top
dessicator until use.
Plant Culture and Stress Treatments
A. thaliana Columbia plants were grown at 23°C under
constant blue-white light either in liquid media or in soil. Liquid
cultured plants or callus tissues (see Fig. 2) were grown in 100 mL of
0.5x Murashige and Skoog (MS), pH 5.7 (Murashige and Skoog 1962), or
Gamborg's B5 medium (Gamborg et al. 1968) for 7-days or 14-days with
constant shaking at 100 rpm. For salt stress treatments, NaCl was added
to the flasks of plant cultures to the final concentration of 150 mM,
and whole plants were collected after 12 and 24 hours. Plants were
grown in soil to the preaerial stage (8–12 leaves) for bacterial
infection experiment. P. syringae DC3000 (avrRpt2)
(Whalen et al. 1991; Mudgett and Staskawicz 1999; Chen et al. 2000),
P. syringae DC3000 (pLAFR3) (Staskawicz et al. 1987), and
X. campestris (Bent et al. 1992) were applied to the underside
of leaves in a KHPO4 buffer using a syringe, and leaf samples
were collected after 12 h. Temperature-stressed leaves were collected
after 18 h of exposure to 4°C (cold) or to 37°C (heat). For young
and mature leaf comparisons, young leaves were determined as the ones
 3 cm and the mature ones as  4.5 cm. To obtain the aerial tissues
including flowers, plants were grown more than a month.
RNA Preparation and Labeling
Tissues from plant samples of interest were flash frozen in
liquid nitrogen and powdered using a cold mortar and pestle. Total RNA
was extracted using Trizol (Invitrogen Corp.), and poly(A+) RNA was
prepared using Dynabeads oligo (dT)25 (Dynal Biotech Inc.)
following the manufacturer's protocol. Fluorescently labeled probes
were prepared by direct incorporation of Cy3– or Cy5-labeled dUTP
(Amersham-Pharmacia) during oligo(dT) (Invitrogen Corp.) primed
first-strand cDNA synthesis using Superscript II reverse
transcriptase (Invitrogen Corp.). Probes were cleaned using GFX columns
(Amersham-Pharmacia) using the instructions provided by the
manufacturer.
Slide Hybridization, Scanning, and Image Analysis
To block nonspecific background during hybridization, slides were
first prehybridized in 5xSSC, 0.1% SDS, and 1% bovine serum albumin
at 42°C for 45 min. as previously described (Hegde et al. 2000).
Slides were then washed in water and isopropanol (Sigma) and dried
before hybridization. Fluorescent probes were dried after purification
and resuspended in hybridization buffer containing 50% formamide,
5xSSC, and 0.1% SDS. Cy-3 and Cy-5 labeled probes were combined and
hybridized to the slides overnight at 42°C in a humid chamber.
Following hybridization, slides were washed sequentially in 2xSSC and
0.1% SDS at 42°C for 5 min., in 0.1xSSC and 0.1% SDS at room
temperature for 5 min., and twice in 0.1xSSC at room temperature for
2.5 min., and air dried. Hybridized slides were scanned using the Axon
GenePix 4000 microarray scanner, and the independent TIFF images from
each channel were analyzed using TIGR Spotfinder
(http://www.tigr.org/softlab, TIGR) to assess relative expression
levels. Data from TIGR Spotfinder were stored in AGED, a
relational database designed to effectively capture microarray data.
Data Normalization and Analysis
Normalization is necessary to adjust for differences in labeling
and detection efficiencies of the fluorescent labels and for
differences in the quantity of starting RNA. Data was normalized using
a local regression technique, LOWESS (LOcally WEighted Scatterplot
Smoothing), using the MIDAS software tool (http://www.tigr.org/softlab,
TIGR), and the resulting data were averaged over duplicate genes on
each array and over duplicate arrays for each experiment.
All calculated gene expression ratios were
log2-transformed, and differentially expressed genes at
the 95% confidence level for each reference set were determined
by assuming the log2 ratios for each data set form a
normal distribution, and selecting genes with log2 (ratio)
values >1.96 standard deviations from the mean. This filtration of the
significantly expressed genes was conducted using MIDAS, and the
resulting lists of the genes were examined further by cross comparison
between experiments using TIGR MeV (http://www.tigr.org/softlab, TIGR).
Data Availability
All data generated by this project, including PCR primer sequences
and amplification data, as well as all primary and normalized
hybridization intensities and specific gene lists can be found at
http://atarrays.tigr.org/data/.
|
WEB SITE REFERENCES
|
|---|
http://www.tigr.org/tdb/e2k1/ath1/; hosts the TIGR Arabidopsis
thaliana database, which contains gene predictions and annotation
for the complete Arabidopsis genome.
http://www-genome.wi.mit.edu/genome_software; contains software for
genomic applications, including Primer3, which was used in this study.
http://atarrays.tigr.org; is the homepage for the NSF-funded project
that generated the data presented here.
http://atarrays.tigr.org/arabdata.shtml; includes links to all of
the data used in this analysis as well as a list of all of the primer
sequences and validation scores for amplicon arrays created for the
entire Arabidopsis nuclear, chloroplast, and mitochondrial
genomes.
http://atarrays.tigr.org/protocols.shtml; has links to all of the
laboratory protocols used for constructing the amplicon arrays.
http://www.tigr.org/software/; includes genomic analysis software
developed at TIGR, including the MADAM, Spotfinder, MIDAS, and MeV
tools used for the analysis presented here.
|
Acknowledgements
|
|---|
We thank J. White, V. Sharov, A.I. Saeed, J. Li, and W. Liang for
bioinformatics support for the microarray work. We also thank M. Heaney
and S. Lo for database support, and V. Sapiro, B. Lee, J. Shao, S.
Gregory, C. Irwin, J. Neubrech, R. Kramchedu, M. Sengamalay, and E.
Arnold for computer system support. We thank T. Vantoai, N.H. Lee, L.
Linford, L. Moy, I. Yang, S. Wang, Y. Wang, H. Wang, K. Kwong, and J.
Hasseman for technical assistance and valuable comments. This work was
supported by a grant to JQ (NSF 9975920) from the U.S. National Science
Foundation.
The publication costs of this article were defrayed in part by payment
of page charges. This article must therefore be hereby marked
"advertisement" in accordance with 18 USC section 1734 solely to
indicate this fact.
|
Footnotes
|
|---|
1 These authors contributed equally to this work. 
2 Corresponding author.
E-MAIL johnq{at}tigr.org; FAX (301) 838-0208.
Article and publication are at
http://www.genome.org/cgi/doi/10.1101/gr.552003.
|
REFERENCES
|
|---|
Ahmad, K. and Henikof, S. 2001. Modulation of a transcription factor counteracts heterochromatic gene silencing in Drosophila. Cell 104: 839-847.[CrossRef][Medline]
Aon, M.A., Cortassa, S., Gomez, C.D.F., and Iglesias, A.A. 2000. Effects of stress on cellular infrastructure and metabolic organization in plant cells. Int. Rev. Cytol. 194: 239-273.[Medline]
The Arabidopsis Genome Initiative 2000. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408: 796-815.[CrossRef][Medline]
Bent, A.F., Innes, R.W., Ecker, J.R., and Staskawicz, B.J. 1992. Disease development in ethylene-insensitive Arabidopsis thaliana infected with virulent and avirulent Pseudomonas and Xanthomonas pathogens. Mol. Plant Microbe. Interact. 5: 372-378.[Medline]
Bowler, C. and Fluhr, R. 2000. The role of calcium and activated oxygens as signals for controlling cross-tolerance. Trends Plant Sci. 5: 241-246.[CrossRef][Medline]
Call, D.R., Chandler, D.P., and Brockman, F. 2001. Fabrication of DNA microarrays using unmodified oligonucleotide probes. BioTechniques 30: 368-372.[Medline]
Chee, M., Yang, R., Hubbell, E., Berno, A., Huang, X.C., Stern, D., Winkler, J., Lockhart, D.J., Morris, M.S., and Fodor, S.P. 1996. Accessing genetic information with high-density DNA arrays. Science 274: 610-614.[Abstract/Free Full Text]
Chen, Z., Kloek, A.P., Boch, J., Katagiri, F., and Kunkel, B.N. 2000. The Pseudomonas syringae avirRpt2 gene product promotes pathogen virulence from inside plant cells. Mol. Plant-Microbe Interact. 13: 1312-1321.[Medline]
Copenhaver, G.P., Nickel, K., Kuromori, T., Benito, M.-I., Kaul, S., Lin, X., Bevan, M., Murphy, G., Harris, B., Parnell, L.D., et al. 1999. Genetic definition and sequence analysis of Arabidopsis centromeres. Science 286: 2468-2474.[Abstract/Free Full Text]
Delauney, A.J. and Verma, D.P.S. 1993. Proline biosynthesis and osmoregulation in plants. Plant J. 4: 215-223.
Edwards, R., Dixon, D.P., and Walbot, V. 2000. Plant glutathione S-transferases: Enzymes with multiple functions in sickness and in health. Trends Plant Sci. 5: 193-198.[CrossRef][Medline]
European Union Chromosome 3 Arabidopsis Sequencing ConsortiumThe Institute for Genomic Research and Kazusa DNA Research Institute 2000. Sequence and analysis of chromosome 3 of the plant Arabidopsis thaliana.. Nature 408: 820-823.[CrossRef][Medline]
Fransz, P.F., Armstrong, S., de Long, J.H., Parnell, L.D., van Drunen, C., Dean, C., Zabel, P., Bisseling, T., and Jones, G.H. 2000. Integrated cytogenetic map of chromosome arm 4S of A. thaliana: Structural organization of heterochromatic knob and centromere region. Cell 100: 367-376.[CrossRef][Medline]
Gamborg, O.L., Miller, R.A., and Ojima, K. 1968. Nutrient requirements of suspension cultures of soybean root cells. Exp. Cell Res. 50: 151-158.[CrossRef][Medline]
Hanegawa, P.M., Bressan, R.A., Zhu, J.K., and Bohnert, H.J. 2000. Plant cellular and molecular responses to high salinity. Annu. Rev. Plant. Mol. Plant Physiol. 51: 463-499.[CrossRef]
Hegde, P., Qi, R., Abernathy, R., Gay, C., Dharap, S., Gaspard, R., EarleHughes, J., Snesrud, E., Lee, N.H., and Quackenbush, J. 2000. A concise guide to cDNA microarray analysis. BioTechniques 29: 548-562.[Medline]
Hirsch, R.E., Lewis, B.D., Spalding, E.P., and Sussman, M.R. 1998. A role for the AKT1 potassium channel in plant nutrition. Science 280: 918-920.[Abstract/Free Full Text]
Hong, Z., Lakkineni, K., Zhang, Z., and Verma, D.P.S. 2000. Removal of feedback inhibition of  1-Pyrroline-5-carboxylate synthetase results in increased proline accumulation and protection of plants from osmotic stress. Plant Physiol. 122: 1129-1136.[Abstract/Free Full Text]
Ishizaki-Nishizawa, O., Fujii, T., Azuma, M., Sekiguchi, K., Murata, N., Ohtani, T., and Toguri, T. 1996. Low-temperature resistance of higher plants is significantly enhanced by a nonspecific cyanobacterial desaturase. Nat. Biotechnol. 14: 1003-1006.[CrossRef][Medline]
Kane, M.D., Jatkoe, T.A., Stumpf, C.R., Lu, J., Thomas, J.D., and Madore, S.J. 2000. Assessment of the sensitivity and specificity of oligonucleotide (50mer) microarrays. Nucleic Acids Res. 28: 4552-4557.[Abstract/Free Full Text]
Kazusa DNA Research InstituteThe Cold Spring Harbor and Washington University Sequencing ConsortiumThe European Union Arabidopsis Genome Sequencing ConsortiumInstitute of Plant Genetics and Crop Research (IPK) 2000. Sequence and analysis of chromosome 5 of the plant Arabidopsis thaliana. Nature 408: 823-826.[CrossRef][Medline]
Lappaleinen, P., Kessels, M.M., Cope, M.J.T.V., and Drubin, D. 1998. The ADF homology (ADF-H) domain: A highly exploited actin-binding module. Mol. Biol. Cell 9: 1951-1959.[Free Full Text]
Lin, X., Kaul, S., Rounsley, S., Shea, T.P., Benito, M.I., Town, C.D., Fujii, C.Y., Mason, T., Bowman, C.L., Barnstead, M., et al. 1999. Sequence and analysis of chromosome 2 of Arabidopsis thaliana. Nature 402: 761-768.[CrossRef][Medline]
Logemann, E., Parniske, M., and Hahlbrock, K. 1995. Modes of expression and common structural features of the complete phenylalanine ammonia-lyase gene family in parsley. Proc. Natl. Acad. Sci. 92: 5905-5909.[Abstract/Free Full Text]
Marrs, K.A. 1996. The functions and regulation of glutathione S-transferases in plants. Annu. Rev. Plant Physiol. Plant Mol. Biol. 47: 127-158.[CrossRef]
Mayer, K., Schuller, C., Wambutt, R., Murphy, G., Volckaert, G., Pohl, T., Dusterhoft, A., Stiekema, W., Entian, K.D., Terryn, N., et al. 1999. Sequence and analysis of chromosome 4 of Arabidopsis thaliana. Nature 402: 769-777.[CrossRef][Medline]
Meyer, P. 2000. Transcriptional transgene silencing and chromatin components. Plant Mol. Biol. 43: 221-234.[CrossRef][Medline]
McCombie, W.R., de la Bastide, M., Habermann, K., Parnell, L.D., Dedhia, N., Gnoj, L., Schutz, K., Huang, E., Spiegel, L., Yordan, C., et al. 2000. The complete sequence of a heterochromatic island from a higher eukaryote. Cell 100: 377-386.[CrossRef][Medline]
Mudgett, M.B. and Staskawicz, B.J. 1999. Characterization of the Pseudomonas syringae pv. tomato AvrRpt2 protein: Demonstration of secretion and processing during bacterial pathogenesis. Mol. Microbiol. 32: 927-941.[CrossRef][Medline]
Murashige, T. and Skoog, F. 1962. A revised medium for rapid growth and bioassays with tobacco tissue culture. Physiol. Plant 15: 473-497.[CrossRef]
Ouellet, F., Carpentier, E., Cope, M.J.T.V., Monroy, A.F., and Sarhan, F. 2001. Regulation of a wheat actin-depolymerizing factor during cold acclimation. Plant Physiol. 125: 360-368.[Abstract/Free Full Text]
Pieterse, C.M.J. and van Loon, L.C. 1999. Salicylic acid-independent plant defense pathways. Trends Plant Sci. 4: 52-58.[CrossRef][Medline]
Preuss, D. 1999. Chromatin silencing and Arabidopsis development: A role for polycomb protein. Plant Cell. 11: 765-767.[Free Full Text]
Rao, M.V., Lee, H.-I., Creelman, R.A., Mullet, J.E., and Davis, K.R. 2000. Jasmonaic acid signaling modulates ozone-induced hypersensitive cell death. Plant Cell. 12: 1633-1646.[Abstract/Free Full Text]
Reintanz, B., Szyroki, A., Ivashikina, N., Ache, P., Godde, M., Becker, D., Palme, K., and Hedrich, R. 2002. AtKC1, a silent Arabidopsis potassium channel  -subunit modulates root hair K+ influx. Proc. Natl. Acad. Sci. 99: 4079-4084.[Abstract/Free Full Text]
Rouleau, M., Marsolais, F., Richard, M., Nicolle, L., Voigt, B., Adam, G., and Varin, L. 1999. Inactivation of brassinosteroid biological activity by a salicylate-inducible steroid sulfotransferase from Brassica napus. J. Biol. Chem. 274: 20925-20930.[Abstract/Free Full Text]
Schaller, F., Henning, P., and Weiler, E.W. 1998. 12-oxophytodienoate-10,11-reductase: Occurrence of two isoenzymes of different specificity against stereoisomers of 12-oxophytodienoic acid. Plant Physiol. 118: 1345-1351.[Abstract/Free Full Text]
Schena, M., Shalon, D., Davis, R.W., and Brown, P.O. 1995. Quantitative monitoring of gene expression patterns with complementary DNA microarray. Science 270: 467-470.[Abstract/Free Full Text]
Seki, M., Narusaka, M., Kamiya, A., Ishida, J., Satou, M., Sakurai, T., Nakajima, M., Enju, A., Akiyama, K., Oono, Y., et al. 2002. Functional annotation of a full-length Arabidopsis cDNA collection. Science 296: 141-145.[Abstract/Free Full Text]
Staiger, C.J., Gibbson, B.C., Kovar, D.R., and Zonia, L.E. 1997. Profilin and actin-depolymerizing factor: Modulators of actin organization in plants. Trends Plant Sci. 2: 275-281.[CrossRef]
Staskawicz, B., Dahlbeck, D., Keen, N., and Napoli, C. 1987. Molecular characterization of cloned avirulence genes from race 0 and race 1 of Pseudomonas syringae pv. glycinea. J. Bacteriol. 169: 5789-5794.[Abstract/Free Full Text]
Stenponkus, P.L., Uemura, M., Joseph, R.A., Gilmour, S.J., and Thomashow, M.F. 1998. Mode of action of the COR15a gene on the freezing tolerance of Arabidopsis thaliana. Proc. Natl. Acad. Sci. 95: 14570-14575.[Abstract/Free Full Text]
Theologis, A., Ecker, J.R., Palm, C.J., Federspiel, N.A., Kaul, S., White, O., Alonso, J., Altafi, H., Araujo, R., Bowman, C.L., et al. 2000. Chromosome 1 of Arabidopsis thaliana. Nature 408: 816-820.[CrossRef][Medline]
Weisshaar, B. and Jenkins, G.I. 1998. Phenylpropanoid biosynthesis and its regulation. Curr. Opin. Plant Biol. 1: 251-257.[CrossRef][Medline]
Whalen, M., Innes, R., Bent, A., and Staskawicz, B. 1991. Identification of Pseudomonas syringae pathogens of Arabidopsis thaliana and a bacterial gene determining avirulence on both Arabidopsis and soybean. Plant Cell. 3: 49-59.[Abstract/Free Full Text]
Wilhem, K.S. and Thomashow, M.F. 1993. Arabidopsis thaliana cor15b, an apparent homologue of cor15a, is strongly responsive to cold and ABA, but not drought. Plant Mol. Biol. 23: 1073-1077.[CrossRef][Medline]
Received July 3, 2002;
accepted in revised format December 20, 2002.
13:327-340 © by 2003 Cold Spring Harbor Laboratory Press ISSN 1088-9051/03 $5.00
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ABSTRACT
RESULTS AND DISCUSSION
