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BMC Genomics

BioMed Central

Open Access

Research article

The rules of gene expression in plants: Organ identity and gene
body methylation are key factors for regulation of gene expression
in Arabidopsis thaliana
Felipe F Aceituno1, Nick Moseyko2, Seung Y Rhee2 and
Rodrigo A Gutiérrez*1,3
Address: 1Departamento de Genética Molecular y Microbiología, Pontificia Universidad Católica de Chile, Avenue Libertador Bernardo O'Higgins
340, Santiago, Chile, 2Department of Plant Biology, Carnegie Institution of Washington, 260 Panama St, Stanford, CA, 94305, USA and
3Department of Biology, New York University, 100 Washington Square East, 1009 Main Building, New York, NY 10003, USA
Email: Felipe F Aceituno - feflorea@uc.cl; Nick Moseyko - nick@anm.f2s.com; Seung Y Rhee - rhee@acoma.stanford.edu;
Rodrigo A Gutiérrez* - rgutierrez@uc.cl
* Corresponding author

Published: 23 September 2008
BMC Genomics 2008, 9:438

doi:10.1186/1471-2164-9-438

Received: 5 May 2008
Accepted: 23 September 2008

This article is available from: http://www.biomedcentral.com/1471-2164/9/438
© 2008 Aceituno et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract
Background: Microarray technology is a widely used approach for monitoring genome-wide gene
expression. For Arabidopsis, there are over 1,800 microarray hybridizations representing many
different experimental conditions on Affymetrix™ ATH1 gene chips alone. This huge amount of
data offers a unique opportunity to infer the principles that govern the regulation of gene
expression in plants.
Results: We used bioinformatics methods to analyze publicly available data obtained using the
ATH1 chip from Affymetrix. A total of 1887 ATH1 hybridizations were normalized and filtered to
eliminate low-quality hybridizations. We classified and compared control and treatment
hybridizations and determined differential gene expression. The largest differences in gene
expression were observed when comparing samples obtained from different organs. On average,
ten-fold more genes were differentially expressed between organs as compared to any other
experimental variable. We defined "gene responsiveness" as the number of comparisons in which
a gene changed its expression significantly. We defined genes with the highest and lowest
responsiveness levels as hypervariable and housekeeping genes, respectively. Remarkably,
housekeeping genes were best distinguished from hypervariable genes by differences in methylation
status in their transcribed regions. Moreover, methylation in the transcribed region was inversely
correlated (R2 = 0.8) with gene responsiveness on a genome-wide scale. We provide an example
of this negative relationship using genes encoding TCA cycle enzymes, by contrasting their
regulatory responsiveness to nitrate and methylation status in their transcribed regions.
Conclusion: Our results indicate that the Arabidopsis transcriptome is largely established during
development and is comparatively stable when faced with external perturbations. We suggest a
novel functional role for DNA methylation in the transcribed region as a key determinant capable
of restraining the capacity of a gene to respond to internal/external cues. Our findings suggest a
prominent role for epigenetic mechanisms in the regulation of gene expression in plants.

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Background
Understanding the regulation of gene expression is essential to understand the form and function of living systems.
Microarray technology has been widely used in many
organisms to understand genome-wide changes in gene
expression in response to treatments [1], in different
organs [2], cell-types [3] and along developmental time
series [4]. Therefore, a large amount of microarray data
representing many different biological conditions has
accumulated over recent years. This data has been used
successfully to hypothesize on gene function on a global
scale in different organisms, such as yeast and C. elegans
[5-7], and to suggest shared regulatory mechanisms. Promoters of genes with strongly correlated expression patterns in multiple experiments are likely to be bound by a
common transcription factor [8], and conserved regulatory motifs have been identified based solely on expression data [9]. From a systems view, however, we believe
that this data has been underutilized as a resource to
understand the basic rules of gene expression.
To learn the general rules that govern gene expression in
plants, we took advantage of a large microarray database
available for Arabidopsis in the NASCarrays database
[10]. Using this data, we defined the internal and external
cues that regulate the expression of all of the Arabidopsis
genes that are represented in the Affymetrix ATH1 gene
chips. We quantified the effect of the different experimental conditions on gene expression, which revealed tissue
type to be the most influential variable. We also analyzed
different structural features and correlated it with the
capacity of the genes to respond to the different stimuli.
We found evidence for a mechanistic relationship
between DNA methylation in the body of the gene (i.e.,
the transcript region) and the regulation of gene expression, thus assigning a novel and important role for the
methylation of the body of the gene in eukaryotic
genomes.

Results and discussion
The Arabidopsis transcriptome is robust to most
perturbations but strongly influenced by organ type
In an effort to discover new principles that govern gene
expression in Arabidopsis thaliana, we integrated and analyzed publicly available whole-genome microarray data
for this model plant. From this data, we defined 474 biologically relevant comparisons (i.e. control vs. treatment)
as described in Materials and Methods (Additional File 1).
These comparisons spanned a wide variety of experimental conditions and plant organs (Figure 1). We wished to
evaluate the effect of the different experimental factors
that defined each comparison on genome-wide gene
expression patterns. To do so, we defined differential gene
expression using the RankProducts method [11]. This
method outperformed other methods to determine regu-

Figure 1
Classification of experiments from the NASCarrays database
Classification of experiments from the NASCarrays
database. Pie charts with the classification of microarray
experiments according to the experimental factor categories
defined by TAIR (A) or the organ used to extract RNA to
perform the microarray experiments (B).

lation of gene expression in previous studies [11,12] and
in our own evaluation (see Materials and Methods), particularly in datasets with a small number of replicates.
We first examined the number of differentially regulated
genes per comparison. We found their distribution to be
far from normal. As shown in Figure 2A, some comparisons exhibit more than 4,000 differentially expressed
genes. These outliers were exclusively comparisons
between different organs. In fact, organ type was the
strongest experimental factor contributing to the number
of differentially expressed genes. Other experimental factors, regardless of their nature, showed an approximately
10-fold smaller impact on gene expression with an average of 337 genes regulated per comparison (Figure 2B).
Moreover, approximately 10% of the Arabidopsis genes
did not respond to any of the stimuli in the dataset and

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Figure 2
Global characteristics of the Arabidopsis transcriptome
Global characteristics of the Arabidopsis transcriptome. A) Histogram of the number of genes (X-axis) regulated in a
given number of comparisons (Y-axis). B) Average number of genes regulated by each experimental category as defined in Figure 1A. C) Histogram of the number of comparisons (X-axis) for which the specified number of genes (Y-axis) show significant
regulation.

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were only differentially expressed between organ samples.
Thus, organ is by far the most important factor in determining genome-wide expression levels. Furthermore, the
upper 5th percentile (ordered by the number of genes regulated) of the 77 mutant vs wt comparisons involved only
genes whose mutations have well documented developmental phenotypes. These genes were AP2-6[13],
ARR21[14], GLABROUS1[15] and LFY-12 mutations [16].
They regulated 1475, 1420, 1379 and 1362 genes, respectively – a much more than the category average (471
genes). These results indicate that global gene expression
patterns are established during plant development. The
results also suggest that the Arabidopsis transcriptome is
robust to most perturbations, with only an estimated
1.5% of the genome on average responding in a single
experiment to experimental factors such as chemical or
hormone treatments, pathogen challenges or environmental stress. A detail of the categories in which each of
the Arabidopsis genes responds is presented in Additional
File 2. Additional Files 3 to 10 contain the genes that
respond in exclusively one category, including organ type.
Given its impact on global gene expression levels, we next
wished to evaluate the importance of organ type in the
context of typical experimental factors that are tested in
the laboratory. We compared the number of genes
responding in shoots or roots for each of the nine treatments in the AtGenExpress abiotic stress series. On average, only 13% of the total genes that responded to a
treatment responded in both organs. By contrast, a much
higher proportion of genes (88%) were regulated by the
treatment in an organ-specific manner (Additional File
11). This data indicate that plant responses to external
stimuli are strongly organ-dependent and underscore the
need for a more thorough survey of organ-specific and, by
extension, cell-specific responses in Arabidopsis and other
plants [3].
Housekeeping and hypervariable genes possess marked
structural differences
To identify properties that explain the capacity of a gene
to respond to stimuli, we ranked genes based on the
number of comparisons in which they are differentially
expressed. As shown in Figure 2C, the Arabidopsis
genome contains genes that are regulated in a wide range
of comparisons, with an average of 14 comparisons, or
3% of the total comparisons in our dataset. The underlying data is provided in Additional File 12. We expect structural differences to be maximized at the extremes of this
distribution. We defined housekeeping genes based on
three criteria: (1) genes that were not differentially
expressed in any of the 474 comparisons, (2) genes with
signal intensities higher than the median intensity across
the entire dataset and (3) genes with the lowest signal variability (measured with the interquartile range, see Mate-

http://www.biomedcentral.com/1471-2164/9/438

rials and Methods) across the entire dataset. In contrast,
we defined hypervariable genes based on the following
three criteria: (1) genes that were within the top 1% of the
gene responsiveness distribution, (2) genes with the largest signal variability, and (3) genes that show differential
expression by stimuli from six of the eight categories
described in Figure 1A. These criteria defined 384 housekeeping genes and 123 hypervariable genes (Additional
files 13 and 14).
A previous study positively correlated expression levels
with gene size in plants [17]. To understand how gene
responses to stimuli relate to gene size and other structural features, we analyzed the structure of housekeeping
and hypervariable genes. Housekeeping genes were significantly larger and had more introns than do hypervariable
genes and were above genome averages for both criteria
(Table 1). By contrast, hypervariable genes were significantly shorter and contained fewer introns than average
(Table 1). Interestingly, a functional annotation of the
hypervariable gene set indicates that it is enriched for
genes involved in responses to internal and external stimuli (Additional File 15). Most hypervariable genes were
plant specific as defined in a previous study [18], and the
set was enriched for genes that code for unstable transcripts [19] (Table 1). These results suggest that plants
favored the evolution of small, hypervariable genes to
respond quickly and economically to multiple environmental signals.
Eukaryotic genes are transcriptionally regulated by the
coordinated interaction of multiple protein factors that
interact with discrete binding sites and with each other
[20]. These binding sites are usually located upstream of
the transcribed region they regulate [20]. The promoters
of hypervariable genes often have a TATA-box sequence
and contain a larger number of predicted transcription
factor binding sites as compared to the housekeeping
genes or the genome average (Table 1 and Additional File
16). These data suggest that the presence of a TATA box
and the number of transcription factor binding sites in the
promoter region of some of the most responsive genes in
Arabidopsis may explain their capacity to respond to stimuli, as was previously found in an analysis of a smaller
expression dataset [21]. However, it is clear that this simple rule does not always apply and that other factors are
necessary to explain gene expression responses.
In addition to gene structure, epigenetic mechanisms such
as DNA methylation are known to have an impact on gene
expression in eukaryotes, particularly in heterochromatic
regions [22,23]. To evaluate the potential role of DNA
methylation in the gene expression responses observed
for housekeeping and hypervariable genes, we analyzed
the methylation patterns of these two groups of genes. We

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Table 1: Contrasting features of housekeeping and hypervariable genes.

Gene feature

Housekeeping

Hypervariable

Genome

CDS length (bp)a
Gene length (bp)a
Total exon length (bp)a
Total intron length (bp)a
Number of exons (pb)a
Genes without introns

2624 (s.e. = 89)
3117 (s.e. = 87)
1941 (s.e. = 52)
1173 (s.e. = 52)
8 (s.e. = 0.31)
6% (p = 5E-16)

1178 (s.e. = 73)
1493 (s.e. = 78)
1169 (s.e. = 50)
323 (s.e. = 44)
3 (s.e. = 0.24)
33% (p = 0.0007)

1931 (s.e. = 8)
2229 (s.e. = 8)
1568 (s.e. 6)
660 (s.e. = 4)
5 (s.e = 0.03)
28%

Average number of transcription factor binding sitesb

27 ± 1.2 (p < 0.01)

46 ± 1.8 (p < 0.0001)

30 ± 0.1

TATA-containing genesc

5% (p = 1.3E-6)

45% (p = 6.1E-15)

15%

Genes coding for unstable transcriptsd

0% (n.a.)

8% (p = 9E-11)

1%

Shared among eukaryotese
Plant-specifice

18% (p = 0.002)
11%

7%
34% (p = 2E-10)

14%
14%

Body methylationf
Promoter methylationf
Body methylationg

63% (p = 1.5E-35)
3%
36% (p = 9.1E-21)

8% (p = 2E-10)
3%
2% (p = 3.8E-8)

34%
5%
20%

The first column lists various features analyzed for housekeeping genes (second column), hypervariable genes (third column) and the whole genome
(fourth column). Rows report average and standard error or percentage values. P values for significant (p < 0.01) enrichment or depletion as
compared to the genome occurrence are shown in parenthesis. a, differences between all groups are significant (p < 0.01) as determined by
ANOVA.b, average number of cis-acting regulatory elements as defined in the AGRIS database [47]. p-value was determined by a t-test. C, presence
of TATA-box as determined by the MotifSearch algorithm [26]. Similar results were obtained with an alternative TATA-box definition [27]. d,
unstable transcripts as defined in [19]. e, phylogenetic profiles as defined previously [18]. Only significantly enriched profiles are listed. f, methylation
patterns as determined in [24]. g, methylation patterns as determined in [25]. n.a., not applicable.

used two recently published genome-wide methylation
data sets [24,25] to analyze methylation in the promoter
and transcribed regions of each gene. Using the methylome data produced by Zhang et al. [24], we found that a
large proportion of housekeeping genes were methylated
in their transcribed regions (a significant enrichment
compared to the expected genome frequency; p = 1.5E-35,
Table 1). By contrast, only 8% of the hypervariable genes
were methylated in their transcribed regions (a significant
depletion; p = 2E-10, Table 1). Similar results were
obtained with an independently generated methylome
data set [25]. These results suggest that the capacity of Arabidopsis housekeeping and hypervariable genes to
respond to stimuli not only depends on structural features
in their promoter or transcribed regions, such as transcription factor binding sites, but may also have an important
epigenetic component.
Transcript region methylation is the most important factor
to explain genome-wide responses to internal/external
stimuli
To evaluate the importance of these features for gene
expression responses on a genomic scale, we performed a
regression analysis of the gene responsiveness for all Arabidopsis genes as a function of each of the structural features described above. We used a linear model of the
form: Y ~ αX + β, where Y was the observed gene respon-

siveness of all genes and X was the structural feature under
evaluation (e.g. presence of TATA-box, cis-acting binding
sites in the promoter or gene body methylation). Thus,
the effects detected were free from any bias arising from
gene selection, as could be the case when analyzing this
relatively small group of housekeeping and hypervariable
genes.
Notably, using the two independently generated methylome datasets [24,25], gene responsiveness showed a
remarkably high negative correlation with the presence of
methylation in the transcribed region of the gene. Both
datasets generated models with a coefficient of determination (R2) of 0.8 (share of explained variability, Figure 3A–
B). A similar result was obtained using average foldchange ≥ |2| (treatment versus control) as a criterion to
determine gene responsiveness (Additional Files 17 and
18). This correlation was independent of the type of
experimental factor, as similar trends were observed when
analyzing each experimental category individually for
both methylome datasets (Figure 3C–F and Additional
File 19). Next, to transcript region methylation, the presence of a TATA-box was the second best factor to explain
gene responsiveness, and it had a positive effect. R2 for
two definitions of TATA-box [26,27] were 0.49 or 0.68.
Two factor models that included transcript region methylation and the presence of a TATA-box slightly improved

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the R2 over those obtained with methylation alone (Table
2). Two factor ANOVA models (Additional File 20) confirmed the stronger effect of gene body methylation on
responsiveness, as determined by the Tukey comparison
procedure [28]. However, goodness of fit estimation by
the Bayesian Information Criteria [29] suggests that additive models, including TATA-box and methylation, are
better than one-factor ANOVA models. (Additional File
20). Interestingly, this also suggests that the effect of
TATA-box and methylation are independent, as interaction terms are not significant in these models (not
shown). None of the other structural features (gene size,
presence of introns, number of binding sites, etc) yielded
models with such high R2 on a genomic scale. Thus, gene
body methylation and, to a lesser extent, TATA-box presence explained gene responsiveness on a global scale. It is
not possible, however, to infer from this data the mechanistic relationships between TATA-related factors, gene
body methylation status and regulation of gene expression.

frequency of methylation to the median expression level
of the whole dataset revealed no such trend (Figure 4).
The most and the least highly expressed genes are likely to
lack methylation within their body, as previously reported
[25]. Similarly, no correlation was found between the
presence of a TATA-box and gene expression levels. (Figure 4). Moreover, no relationship was evident between
expression level and gene responsiveness in our data set
(Additional File 21).
We also evaluated the relationship between the presence
of modified histones and gene responsiveness. We used a
recently published genomic survey of trimethylation in
lysine 27 of histone H3 (H3K27me3) f[30]. We found a
weak correlation between the frequency of H3K27me3
gene targets and gene responsiveness, with an R2 of 0.12
(Figure 3F and Additional File 19). This finding is consistent with the hypothesis that H3K27me3 mostly acts in a
DNA methylation-independent manner, as previously
suggested [30]. Other histone modifications, such as
H3K4 or H3K9 methylation [31] or combinations thereof
[32], may be related to gene body methylation in Arabidopsis, thus "marking" the corresponding chromatin
region for or against the regulation of gene expression
[33].

The effect of DNA methylation on gene responsiveness
could be explained by a simple transcriptional gene
silencing effect [22,23]. Silencing a gene would render it
unable to be regulated. If so, transcript region methylation
should correlate with expression levels. Comparing the
Table 2: Results of the simple and multiple linear regression analyses

Explanatory variable(s)

Data Source

r2

p

Coefficient

Methylation frequency

[24,25]

0.8
0.8

<2E-16
<2E-16

n.r.
n.r.

Frequency of genes target of H3k27me3

[30]

0.12

0.000207

n.r.

Gene size

TAIR Genome v6.0

0.02

>0.01

n.r.

Cis-acting elements

[48]

0.05

>0.01

n.r.

TATA-box frequency

(MotifSearch, [26])
(PlantProm, [26])

0.49
0.68

<2E-16
<2E-16

n.r.
n.r.

Methylation + TATA-box

[24]+ (MotifSearch, [26])

0.84

<2E-16a
0.0002b

-201.5a
35b

[24] + (PlantProm, [26])

0.86

<2E-16a
1.00E-09b

-168a
50.5b

[25] + (MotifSearch, [26])

0.87

2.00E-16a
5.00E-09b

-158.6a
54.8b

[25] + (PlantProm, [26])

0.84

<2E-16a
0.0006b

-194.3a
39b

Column 1 reports the explanatory variables used to model gene responsiveness. Column 2 indicates the source of the data (reference). Columns 3
and 4 report the different statistics obtained with the linear regression. n.r., not reported; n.d., not determined. a, statistics for methylation variable.
b, statistics for TATA-box variable. Column 5 shows the coefficients from the linear regression analysis.

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Figure 3
Correlation between methylation and gene responsiveness
Correlation between methylation and gene responsiveness. (A) Plot of the frequency of methylated genes (according
to Zhang et al. [24]; X-axis) within a group of genes against the number of comparisons in which that group of genes is regulated (Y-axis). The dotted line represents the regression line. B) Same as (A) except using data from Zilberman et al [25]. C) to
E). Same as (A) except with the different experimental categories defined in Figure 1A, using methylome data from Zhang et al
[24]. G) Same as (A) except the X-axis represents the frequency of genes that are the target of trimethylation on H3K27 [30].

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Figure 4
gene body methylation or TATA-box presence
Lack of linear correlation between expression levels and
Lack of linear correlation between expression levels
and gene body methylation or TATA-box presence.
(A) Plot of the median expression level across the whole
NASC arrays dataset in 10% bins (X-axis) versus the frequency of methylated genes in the bin (Y-axis), as determined
by Zhang et al. [24]. (B) Same as (A), except using data from
Zilberman et al. [25]. C) Same as (A), except the Y-axis represents the frequency of TATA-containing genes according
to the MotifSearch definition [26]. D) Same as (C), but using
the PlantProm definition [27].

Gene body methylation and regulation of expression by
nitrate in TCA cycle genes
As a case-study and to provide a concrete example of the
influence of methylation patterns on the regulation of
gene expression, we focused on a discrete biological process and experimental factor: nitrate. Nitrate has been
shown to be a signal to regulate gene expression in plants
[34]. We chose four microarray experiments in which
wild-type seedlings were treated with different nitrate concentrations. These nitrate experiments were not included
in the microarray database used in the previous sections.
We found that nitrate regulates many genes in central metabolic pathways such as the TCA cycle [34-37]. We analyzed responsiveness and nitrate regulation for all genes
coding for TCA cycle enzymes. Most of the genes (29 out
of 36, data not shown) did not respond to the nitrate
treatments, as expected due to the robustness of expression patterns in Arabidopsis (see Figure 2B). Among the
genes regulated by nitrate, we found a malate dehydrogenase gene (MDH, At3g47520), two genes coding for
NAD+ dependent isocitrate dehydrogenases (At5g03290
and At4g35260) and a putative NADP+ dependent isocitrate dehydrogenase (At1g65930) (Table 3). Remarkably,
these four genes were classified as unmethylated in studies
by both Zhang et al. [24] and Zilberman et al. [25]. Moreover, body methylated genes were enriched among the
analyzed genes that were not regulated by nitrate (Table
3). For instance, among eight genes coding for malate
dehydrogenase that are not regulated by nitrate, five are
methylated according to the two methylome datasets.

Table 3: Relationship between the methylation status and nitrate regulation of TCA cycle genes.

AGI number

Gene Annotation

At3g47520
At1g04410
At1g53240
At2g22780

MDH (malate dehydrogenase); malate dehydrogenase
malate dehydrogenase, cytosolic, putative
malate dehydrogenase (NAD), mitochondrial
PMDH1 (PEROXISOMAL NAD-MALATE DEHYDROGENASE 1); malate
dehydrogenase
malate dehydrogenase (NAD), mitochondrial, putative
PMDH2 (PEROXISOMAL NAD-MALATE DEHYDROGENASE 2), PMDH2
(PEROXISOMAL NAD-MALATE DEHYDROGENASE 2); malate dehydrogenase
malate dehydrogenase, cytosolic, putative
malate dehydrogenase (NADP), chloroplast, putative
malate dehydrogenase, cytosolic, putative
isocitrate dehydrogenase, putative/NAD+ isocitrate dehydrogenase, putative
IDH1 (ISOCITRATE DEHYDROGENASE 1); isocitrate dehydrogenase (NAD+)
isocitrate dehydrogenase, putative/NADP+ isocitrate dehydrogenase, putative
isocitrate dehydrogenase, putative/NAD+ isocitrate dehydrogenase, putative
isocitrate dehydrogenase, putative/NAD+ isocitrate dehydrogenase, putative
isocitrate dehydrogenase, putative/NADP+ isocitrate dehydrogenase, putative
ICDH (ICDH); isocitrate dehydrogenase (NADP+)

At3g15020
At5g09660
At5g56720
At5g58330
At5g43330
At5g03290
At4g35260
At1g65930
At3g09810
At4g35650
At5g14590
At1g54340

Responsiveness to nitrate Methylation statusa
3
0
0
0

U
A
M
M

0
0

U
M

0
0
0
2
2
1
0
0
0
0

M
M
U
U
U
U
M
U
M
M

This table provides the AGI number, the gene annotation, regulation by nitrate as determined from four independent experiments (see main text)
and the methylation status according to the two methylome datasets used in this work. This table includes all the different malate dehydrogenase
and isocitrate dehydrogenase isozyme-coding genes present in the Arabidopsis genome, according to VirtualPlant http://www.virtualplant.org.
aMethylation code: U, unmethylated in both datasets; M, methylated in both datasets; A, ambiguous according to Zilberman et al. [25] but
unmethylated according to Zhang et al. [24].

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This is a much higher frequency than is expected by
chance (p < 0.05), as only 20–34% of the genes were
methylated according to the two methylome datasets. The
same was true for the isocitrate dehydrogenases, with
enrichment of methylated genes for those that did not
respond to the nitrate treatment (p < 0.05). These results
agree with the proposed relationship between gene body
methylation and the regulation of gene expression in
response to regulatory signals (in this case, nitrate). Moreover, it suggests gene body methylation plays a role in the
regulation of gene expression in physiological processes
such as the reprogramming of carbon metabolism in
response to nitrogen nutrient availability [38].

Conclusion
The analysis of the large and heterogeneous wholegenome microarray dataset available in the public
domain proved useful to evaluate principles that govern
regulation of gene expression in plants. Our global and
systematic analysis of the quantitative effect of different
experimental factors (e.g., mutations, stress and organ
identity) on the plant transcriptome revealed the key role
of developmental processes for establishing mRNA levels
throughout the plant. This process in turn determines
how cells, organs and tissues respond to exogenous cues.
Our data indicate that plant responses to external stimuli
are strongly organ-dependent and underscore the need for
a more thorough survey of organ-specific and, by extension, cell-specific responses in Arabidopsis and other
plants [3].
The second part of our analysis provided a weighted
insight into the role of different molecular mechanisms in
the global regulation of gene expression in Arabidopsis.
The data indicate that DNA methylation within the body
of Arabidopsis genes is a key factor that may determine or
negatively influence the capacity of genes to respond to
internal or external cues. The presence of a TATA-box may
favor gene responsiveness but to a lesser extent than the
negative effect of DNA methylation. Surprisingly, our data
indicate that other gene structural features (e.g., number
of cis-acting elements, gene size, presence and number of
introns) are less important than DNA methylation and
the presence of a TATA-box. These results highlight the
importance of epigenetic mechanisms for the global control of gene expression. As a concrete example, we found
consistency between regulation by an external stimulus
(nitrate) and gene body methylation for a discrete biological process, the TCA cycle, beyond what would be
expected by chance. The results presented here suggest a
model whereby gene body DNA methylation restrains the
ability of a gene to be regulated, regardless of regulatory
signals (e.g., binding sites for specific transcription factors
in the promoter region). This effect would not be directly
dependent on basal gene expression levels. Moreover, our

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results provide a plausible functional role for the DNA
methylation that is found in the body of a large number
of Arabidopsis genes. This new role differs from the proposed role for DNA methylation in suppressing spurious
transcriptional initiation [25,39] and reinforces the link
between the regulation of gene expression and DNA
methylation in eukaryotes.

Methods
Data processing
The CEL data files comprising all ATH1 Affymetrix hybridizations through the end of 2005 were obtained from
NASCArrays through the AffyWatch Subscription Service.
This data comprised 1887 hybridizations corresponding
to 108 different experiments. The entire hybridization set
was normalized using the Robust Multiarray Analysis
method [40] available from Bioconductor http://
www.bioconductor.org. Once normalized, the hybridizations were quality-controlled using the method devised by
Persson et al [41]. Briefly, this method uses a Kolmogorov-Smirnov goodness-of-fit test to evaluate
whether the distribution of deleted residuals for an individual hybridization deviates from a "t" distribution.
According to Persson et al [41], this occurs when the value
of the D statistic from the goodness-of-fit test is more than
0.15. The CEL files with a D statistic over this cut-off value
were excluded from the analysis. This step resulted in the
exclusion of 186 CEL files.

For the analysis of differential expression, the remaining
1701 hybridizations were mapped to their corresponding
experiments. Controls and biologically meaningful tests
were identified and grouped with their replicates. Comparisons in which the control or treatment hybridizations
had less than 2 replicates were discarded. This process
resulted in a list of 474 biologically meaningful comparisons (control versus test), including 1295 hybridizations.
In the case of tissue comparisons, we used rosette leaves as
a control, and all other tissues were considered tests.
Rosette leaves were chosen as the reference because they
are the prototypical organ system [2]. We classified the
comparisons according the experimental variable
involved using the criteria defined by TAIR [42], and
according to the RNA source organ (Figure 1)
Differential expression analysis
The comparisons were analyzed for differential gene
expression using the RankProducts method [11], implemented as a Bioconductor package [43]. This method outperformed other methods to define differential expression
in a study comparing ten different methods [12], particularly in high-noise, low-replicate datasets. Our comparisons have a low number of replicates (average = 2.7) and
a high variability (pooled variance of the whole dataset =
4.04). We also evaluated the performance of RankProd-

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ucts as compared to other popular alternative methods
based on biological criteria. We defined regulation using
RankProducts, average fold change and t-test with different FDR corrections for multiple testing [44,45]. To evaluate the methods, we randomly chose five test
comparisons from different experimental categories (e.g.
biotic, abiotic, tissue).
We evaluated the functional coherence of the differentially expressed genes by the different methods by evaluating enriched gene ontology (GO) terms in the resulting
lists. For most of the comparisons tested, visual inspection
revealed enriched GO terms that were obviously related to
the experimental factor. This was not the case for the other
methods. As an example, 245 genes were found to be differentially expressed in the comparison DO.1.1 (Additional File 1). Out of these 245 genes, 217 were previously
identified as regulated in these experiments using a different method in a prior study [46]. In addition, the 140
down-regulated genes determined by RankProducts
showed an overrepresentation of "transport" and other
functional terms previously known to be related to the
experimental factor [46]. Similarly, the abscisic acid
response evaluated in comparison AQ.4.4 (Additional
File 1) identified 241 differentially expressed genes.
Among the up-regulated genes, we found that the 'abscisic
acid response' functional term was overrepresented.
With the results of the differential expression analysis, a
"regulation matrix" was created. This matrix contained the
p-value for the down- and up-regulation of all of the
ATH1 Affymetrix chip probes across the 474 comparisons.
The cut-off for defining a probe as differentially expressed
was 0.05. The complete data file with ratios is available
from
http://virtualplant.bio.puc.cl/cgi-bin/Lab/down
load.cgi. Additional data files are available upon request.
Housekeeping and hypervariable gene definition
The least responsive genes (housekeeping genes) were
defined as follows: first, we selected genes which did not
show differential expression in any comparison (5652
genes). Second, these genes were filtered for expression
above the median of the entire NASC dataset (1758
genes). Third, we choose only those having a signal difference between the 1st and 3rd quartile (interquartile range)
that was in the bottom 5 percentile of the signal interquartile ranges from the whole dataset. This ensured the selection of 384 expressed Arabidopsis genes that exhibit the
lowest expression variability.

For the most responsive genes (hypervariable genes), we
first choose genes that were regulated in 86 or more comparisons, corresponding to the top 1% most responsive
genes from Figure 2C. Second, we selected genes that were
regulated in at least six out of the eight categories defined
in Figure 1A to avoid any bias due to large categories (e.g.,

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abiotic stress experiments). We did not use an expression
cutoff, since as expected hypervariable genes were sufficiently expressed, with a median signal of 8.4 across the
NASC dataset (the global median is 7.4). From the 185
genes selected by these criteria, we choose those with a signal interquartile range in the upper 5% of the entire dataset. Thus, we defined a group of 123 "hypervariable
genes".
Structural and phylogenetic analyses and correlation with
gene responsiveness
Gene structural features (gene, CDS, exon, intron lengths
and numbers) – were obtained from the TAIR 6.0 Arabidopsis genome [42]. Phylogenetic classifications of the
genes were obtained from the Plant-Specific Database
[18]. Methylation status of the different genes (body
methylated, body unmethylated and promoter methylated) was obtained from Zhang et al. [24] or Zilberman et
al. [25]. TATA-box presence or absence in the promoter
region of Arabidopsis genes was obtained from Molina
and Grotewold[26]. The number of transcription factor
binding sites in gene promoters was calculated from the
data in the AtCis Database from AGRIS [47]. Unstable
transcripts were extracted from the data generated by Gutierrez et al. [19]. All data were processed using custommade scripts in R http://www.R-project.org and Perl languages. Statistical analyses and graphs were done in R,
GraphPad Prisma 4.0 software or Microsoft Excel.
Statistical and regression analysis
Calculation of significant enrichment or depletion was
done in R using the hypergeometric distribution. t-tests
were carried out with the GraphPad Prisma 4.0 software.
Simple and multiple linear regression models used to predict gene responsiveness as a function of various structural
parameters were done in R. We used simple models of the
form: Y ~ αX + β, where Y, the response variable, is the
gene responsiveness and X is the value of the structural
feature under evaluation. In the case of categorical features, such as methylation or the presence of TATA-box, X
represented the frequency of the feature in a group of
genes sharing the same responsiveness. For multiple linear regressions, we used models of the form: Y ~ αX + βZ
+ γW... where Y was the gene responsiveness and X, Z, W,
etc. corresponded to different features to evaluate. Models
were fitted using the lm function from the R statistical
software. We used the R2 parameter to evaluate the quality
of the model, since R2 represents the extent of data variability explained by the model. As a complementary
approach for categorical features, we used one factor
ANOVA models. They have the form Y ~ αX + β, where X
was a factor encoding the presence or absence of those features at two different levels. We used the 'aov' function in
R to fit the model. We used the F statistic to estimate the
significance of the contribution of the factors to the
response. To estimate the differences between the levels of
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BMC Genomics 2008, 9:438

the factors, we followed the Tukey procedure, using the
'glht' function from the 'multcomp' package in R. The
Bayesian Information Criteria was calculated in R using
the 'BIC' function in the package 'nlme'. Graphs were
done in R, GraphPad Prisma 4.0 software or Microsoft
Excel.
Gene body methylation and regulation by nitrate for TCA
cycle genes
We retrieved the genes corresponding to the TCA cycle
from AraCyc [48]. We then determined the gene responsiveness of these genes in four previously published
microarray data sets [34-37] that were not included in the
NASCarrays database and were therefore not used to
derive our genome-wide conclusions. We intersected the
methylation status [24,25] and regulation by nitrate of the
genes encoding malate dehydogenases and isocitrate
dehydrogenases using the VirtualPlant software platform
http://www.virtualplant.org. Statistical analysis of enrichment was performed as described above.

Authors' contributions
FFA carried out the bioinformatics and statistical analyses
and wrote the manuscript. NM and SYR revised the manuscript critically for important intellectual content. RAG
carried out some of the bioinformatics analyses, wrote the
manuscript and was responsible for the conception of the
study, the design of the data analysis and the interpretation of the results. All authors read and approved the final
manuscript.

Additional material

http://www.biomedcentral.com/1471-2164/9/438

Additional file 4
Genes regulated specifically in one experimental category. Each file
provides the individual genes responding exclusively in abiotic, biotic, ecotype, chemical, hormone, mutant, nutrient or organ comparisons, respectively.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-9-438-S4.xls]

Additional file 5
Genes regulated specifically in one experimental category. Each file
provides the individual genes responding exclusively in abiotic, biotic, ecotype, chemical, hormone, mutant, nutrient or organ comparisons, respectively.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-9-438-S5.xls]

Additional file 6
Genes regulated specifically in one experimental category. Each file
provides the individual genes responding exclusively in abiotic, biotic, ecotype, chemical, hormone, mutant, nutrient or organ comparisons, respectively.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-9-438-S6.xls]

Additional file 7
Genes regulated specifically in one experimental category. Each file
provides the individual genes responding exclusively in abiotic, biotic, ecotype, chemical, hormone, mutant, nutrient or organ comparisons, respectively.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-9-438-S7.xls]

Additional file 8
Control vs. tests comparisons. List of the analyzed 474 comparisons in
the NASCarrays database, annotated according to the experimental factor
and plant structure categories. NASC experiment numbers are provided.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-9-438-S1.xls]

Genes regulated specifically in one experimental category. Each file
provides the individual genes responding exclusively in abiotic, biotic, ecotype, chemical, hormone, mutant, nutrient or organ comparisons, respectively.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-9-438-S8.xls]

Additional File 2

Additional file 9

Additional File 1

Gene responsiveness by categories. Table detailing the number of experiments, within the eight experimental categories, in which each Arabidopsis gene is regulated. The number in parenthesis in the header of the Table
indicates the total number of experiments in each category.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-9-438-S2.xls]

Genes regulated specifically in one experimental category. Each file
provides the individual genes responding exclusively in abiotic, biotic, ecotype, chemical, hormone, mutant, nutrient or organ comparisons, respectively.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-9-438-S9.xls]

Additional file 3

Additional file 10

Genes regulated specifically in one experimental category. Each file
provides the individual genes responding exclusively in abiotic, biotic, ecotype, chemical, hormone, mutant, nutrient or organ comparisons, respectively.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-9-438-S3.xls]

Genes regulated specifically in one experimental category. Each file
provides the individual genes responding exclusively in abiotic, biotic, ecotype, chemical, hormone, mutant, nutrient or organ comparisons, respectively.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-9-438-S10.xls]

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BMC Genomics 2008, 9:438

http://www.biomedcentral.com/1471-2164/9/438

Additional File 11

Additional file 17

Importance of organ type in the response to abiotic stress in Arabidopsis. Percentage of genes responding to various stresses in either roots,
shoots or both. Data corresponds to the AtGenExpress Abiotic Stress series
present in the NASCarrays database. The black zone indicates the percentage of genes responding only in roots; the white zone indicates those
responding only in shoots, and the black squares region indicates the genes
responding in both tissues
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-9-438-S11.pdf]

Correlation between gene responsiveness as determined by the foldchange method and gene body methylation. Table listing gene responsiveness as determined by the fold-change method (≥ |2|), and the corresponding frequencies of methylated genes.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-9-438-S17.xls]

Additional file 12
Gene responsiveness. Gene responsiveness as determined by the Rank
Products and fold-change method.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-9-438-S12.xls]

Additional file 13
Housekeeping and hypervariable genes and their methylation status
(1). List of Housekeeping and hypervariable genes, classified according
their methylation status as defined in:Zhang X, et al: Genome-wide
high-resolution mapping and functional analysis of DNA methylation in arabidopsis. Cell 2006, 126(6): 1189–1201. Gene annotation
was provided by the VirtualPlant system http://www.virtualplant.org.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-9-438-S13.xls]

Additional file 18
Plot of the correlation between gene responsiveness determined by the
fod-change method versus gene body methylation. This graphs shows
the linear correlation between gene responsiveness as determined by fold
change ((≥ |2|) and gene body methylation.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-9-438-S18.pdf]

Additional file 19
Results of simple regression models, given by experimental category.
Description is as Table 2, see main text.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-9-438-S19.xls]

Additional file 20

Housekeeping and hypervariable genes and their methylation status
(2). List of Housekeeping and hypervariable genes, classified according
their methylation status as defined in: Zilberman et al: Genome-wide
analysis of Arabidopsis thaliana DNA methylation uncovers an
interdependence between methylation and transcription. Nat
Genet 2007, 39(1): 61–69. Gene annotation was provided by the VirtualPlant system http://www.virtualplant.org
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-9-438-S14.xls]

ANOVA models for the effect of methylation and TATA-box presence
on gene responsiveness, by category of experimental treatment. The
models have the form Y ~ aX + b, where X was a factor encoding the presence or absence of those features as two different levels. We used the 'aov'
function in R to fit the model. The F statistic estimates the significance of
the contribution of the factors to the response. The differences between the
levels of the factors were estimated by the Tukey procedure, using the 'glht'
function from the 'multcomp' package in R. This is equivalent to comparing the coefficients of the factors. The Bayesian Information Criteria was
calculated in R using the 'BIC' function in the package 'nlme'. This
parameter represents the "a posteriori" probability of the model to be true,
being maximized when the magnitude of the parameter is minimized.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-9-438-S20.xls]

Additional file 15

Additional file 21

Function of housekeeping and hypervariable genes. Analysis of overrepresentation of gene ontology functional terms in housekeeping and
hypervariable genes (performed in VirtualPlant – http://www.virtual
plant.org
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-9-438-S15.xls]

Lack of linear correlation between expression levels and gene responsiveness. Box plot of the signal of a gene across the whole NASC arrays
dataset (X-axis) versus gene responsiveness (the number of comparisons
in which it is significantly regulated, Y-axis). A simple linear regression
model cannot explain the variability in the data (R2 = 0.04).
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-9-438-S21.pdf]

Additional file 14

Additional file 16
Enrichment of cis-acting motifs in the promoter of hypervariable
genes. Frequency distribution of the number of predicted transcription
binding sites in the promoter of housekeeping and hypervariable genes and
the whole genome. The genes were ranked according to the number of cisacting regulatory elements in their promoters according to the AGRIS
database (X-axis). The points represent the fraction of genes in a bin of
10 motifs.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-9-438-S16.pdf]

Acknowledgements
We thank Xiaoyu Zhang, Dr. Steve Jacobsen and Dr. Joseph Ecker for
kindly providing genome-wide DNA methylation data in a custom format,
and Juanita Larraín-Linton for her proof-reading. This work was funded by
grants from: ICGEB (CRPCHI0501), FONDECYT (1060457), MILLENNIUM NUCLEUS FOR PLANT FUNCTIONAL GENOMICS (P006-09-F),
FUNDACION ANDES (C14060/62) and NSF (DBI0445666) to R.A.G.
F.F.A. was funded by a Ph.D. fellowship from CONICYT.

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References
1.

2.

3.
4.
5.

6.
7.
8.
9.

10.
11.

12.
13.
14.

15.
16.
17.
18.

19.

20.
21.

Kilian J, Whitehead D, Horak J, Wanke D, Weinl S, Batistic O,
D'Angelo C, Bornberg-Bauer E, Kudla J, Harter K: The AtGenExpress global stress expression data set: protocols, evaluation
and model data analysis of UV-B light, drought and cold
stress responses. Plant J 2007, 50(2):347-363.
Schmid M, Davison TS, Henz SR, Pape UJ, Demar M, Vingron M,
Scholkopf B, Weigel D, Lohmann JU: A gene expression map of
Arabidopsis thaliana development.
Nat Genet 2005,
37(5):501-506.
Birnbaum K, Shasha DE, Wang JY, Jung JW, Lambert GM, Galbraith
DW, Benfey PN: A Gene Expression Map of the Arabidopsis
Root. Science 2003, 302(5652):1956-1960.
Spencer MW, Casson SA, Lindsey K: Transcriptional profiling of
the Arabidopsis embryo. Plant Physiol 2007, 143(2):924-940.
Hughes TR, Marton MJ, Jones AR, Roberts CJ, Stoughton R, Armour
CD, Bennett HA, Coffey E, Dai H, He YD, Kidd MJ, King AM, Meyer
MR, Slade D, Lum PY, Stepaniants SB, Shoemaker DD, Gachotte D,
Chakraburtty K, Simon J, Bard M, Friend SH: Functional discovery
via a compendium of expression profiles.
Cell 2000,
102(1):109-126.
Kim SK, Lund J, Kiraly M, Duke K, Jiang M, Stuart JM, Eizinger A, Wylie
BN, Davidson GS: A Gene Expression Map for Caenorhabditis
elegans. Science 2001, 293(5537):2087.
Stuart JM, Segal E, Koller D, Kim SK: A gene-coexpression network for global discovery of conserved genetic modules. Science 2003, 302(5643):249-255.
Allocco D, Kohane I, Butte A: Quantifying the relationship
between co-expression, co-regulation and gene function.
BMC Bioinformatics 2004, 5(1):18.
Harmer SL, Hogenesch JB, Straume M, Chang HS, Han B, Zhu T,
Wang X, Kreps JA, Kay SA: Orchestrated transcription of a key
pathway in Arabidopsis by the circadian clock. Science 2000,
290:2110-2113.
Craigon DJ, James N, Okyere J, Higgins J, Jotham J, May S: NASCArrays: a repository for microarray data generated by NASC's
transcriptomics service. Nucleic Acids Res 2004:D575-577.
Breitling R, Armengaud P, Amtmann A, Herzyk P: Rank products: a
simple, yet powerful, new method to detect differentially
regulated genes in replicated microarray experiments. FEBS
Lett 2004, 573(1–3):83-92.
Jeffery IB, Higgins DG, Culhane AC: Comparison and evaluation
of methods for generating differentially expressed gene lists
from microarray data. BMC Bioinformatics 2006, 7:359.
Kunst L, Klenz JE, Martinez-Zapater J, Haughn GW: AP2 Gene
Determines the Identity of Perianth Organs in Flowers of
Arabidopsis thaliana. Plant Cell 1989, 1(12):1195-1208.
Tajima Y, Imamura A, Kiba T, Amano Y, Yamashino T, Mizuno T:
Comparative studies on the type-B response regulators
revealing their distinctive properties in the His-to-Asp phosphorelay signal transduction of Arabidopsis thaliana. Plant Cell
Physiol 2004, 45(1):28-39.
Schiefelbein J: Cell-fate specification in the epidermis: a common patterning mechanism in the root and shoot. Curr Opin
Plant Biol 2003, 6(1):74-78.
Weigel D, Alvarez J, Smyth DR, Yanofsky MF, Meyerowitz EM:
LEAFY controls floral meristem identity in Arabidopsis. Cell
1992, 69(5):843-859.
Ren X-Y, Vorst O, Fiers MWEJ, Stiekema WJ, Nap J-P: In plants,
highly expressed genes are the least compact. Trends in Genetics 2006, 22(10):528-532.
Gutierrez RA, Green PJ, Keegstra K, Ohlrogge JB: Phylogenetic
profiling of the Arabidopsis thaliana proteome: what proteins distinguish plants from other organisms? Genome Biol
2004, 5(8):R53.
Gutierrez RA, Ewing RM, Cherry JM, Green PJ: Identification of
unstable transcripts in Arabidopsis by cDNA microarray
analysis: rapid decay is associated with a group of touch- and
specific clock-controlled genes. Proc Natl Acad Sci USA 2002,
99(17):11513-11518.
Orphanides G, Reinberg D: A Unified Theory of Gene Expression. Cell 2002, 108(4):439-451.
Walther D, Brunnemann R, Selbig J: The regulatory code for transcriptional response diversity and its relation to genome
structural properties in Arabidopsis thaliana. PLoS Genetics
2006. preprint(2006):e11.eor.

http://www.biomedcentral.com/1471-2164/9/438

22.
23.
24.

25.

26.
27.
28.
29.
30.

31.
32.

33.

34.

35.

36.

37.

38.
39.
40.

41.
42.

Chan SW, Henderson IR, Jacobsen SE: Gardening the genome:
DNA methylation in Arabidopsis thaliana. Nat Rev Genet 2005,
6(5):351-360.
Gehring M, Henikoff S: DNA methylation dynamics in plant
genomes. Biochim Biophys Acta 2007.
Zhang X, Yazaki J, Sundaresan A, Cokus S, Chan SW, Chen H, Henderson IR, Shinn P, Pellegrini M, Jacobsen SE, Ecker JR: Genome-wide
high-resolution mapping and functional analysis of DNA
methylation in arabidopsis. Cell 2006, 126(6):1189-1201.
Zilberman D, Gehring M, Tran RK, Ballinger T, Henikoff S: Genomewide analysis of Arabidopsis thaliana DNA methylation
uncovers an interdependence between methylation and
transcription. Nat Genet 2007, 39(1):61-69.
Molina C, Grotewold E: Genome wide analysis of Arabidopsis
core promoters. BMC Genomics 2005, 6(1):25.
Shahmuradov IA, Gammerman AJ, Hancock JM, Bramley PM, Solovyev VV: PlantProm: a database of plant promoter sequences.
Nucleic Acids Res 2003, 31(1):114-117.
Tukey J: Multiple comparisons. J Am Stat Assoc 1953, 48:624-625.
Schwarz G: Estimating the dimension of a model. Annls Statistics
1978, 6:461-464.
Zhang X, Clarenz O, Cokus S, Bernatavichute YV, Pellegrini M,
Goodrich J, Jacobsen SE: Whole-genome analysis of histone H3
lysine 27 trimethylation in Arabidopsis. PLoS Biol 2007,
5(5):e129.
Shi J, Dawe RK: Partitioning of the Maize Epigenome by the
Number of Methyl Groups on Histone H3 Lysines 9 and 27.
Genetics 2006, 173(3):1571-1583.
Bernstein BE, Mikkelsen TS, Xie X, Kamal M, Huebert DJ, Cuff J, Fry
B, Meissner A, Wernig M, Plath K, Jaenisch R, Wagschal A, Feil R, Schreiber SL, Lander ES: A Bivalent Chromatin Structure Marks
Key Developmental Genes in Embryonic Stem Cells. Cell
2006, 125(2):315-326.
Garcia-Bassets I, Kwon Y-S, Telese F, Prefontaine GG, Hutt KR,
Cheng CS, Ju B-G, Ohgi KA, Wang J, Escoubet-Lozach L, Rose DW,
Glass CK, Fu X-D, Rosenfeld MG: Histone Methylation-Dependent Mechanisms Impose Ligand Dependency for Gene Activation by Nuclear Receptors. Cell 2007, 128(3):505-518.
Wang R, Tischner R, Gutierrez RA, Hoffman M, Xing X, Chen M,
Coruzzi G, Crawford NM: Genomic analysis of the nitrate
response using a nitrate reductase-null mutant of Arabidopsis. Plant Physiol 2004, 136(1):2512-2522.
Scheible WR, Morcuende R, Czechowski T, Fritz C, Osuna D, Palacios-Rojas N, Schindelasch D, Thimm O, Udvardi MK, Stitt M:
Genome-wide reprogramming of primary and secondary
metabolism, protein synthesis, cellular growth processes,
and the regulatory infrastructure of Arabidopsis in response
to nitrogen. Plant Physiol 2004, 136(1):2483-2499.
Palenchar P, Kouranov A, Lejay L, Coruzzi G: Genome-wide patterns of carbon and nitrogen regulation of gene expression
validate the combined carbon and nitrogen (CN)-signaling
hypothesis in plants. Genome Biology 2004, 5(11):R91.
Wang R, Okamoto M, Xing X, Crawford NM: Microarray analysis
of the nitrate response in Arabidopsis roots and shoots
reveals over 1,000 rapidly responding genes and new linkages to glucose, trehalose-6-phosphate, iron, and sulfate
metabolism. Plant Physiology 2003, 132:556-567.
Stitt M, Muller C, Matt P, Gibon Y, Carillo P, Morcuende R, Scheible
WR, Krapp A: Steps towards an integrated view of nitrogen
metabolism. J Exp Bot 2002, 53(370):959-970.
Suzuki MM, Kerr ARW, De Sousa D, Bird A: CpG methylation is
targeted to transcription units in an invertebrate genome.
Genome Res 2007, 17(5):625-631.
Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ,
Scherf U, Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data.
Biostat 2003, 4(2):249-264.
Persson S, Wei H, Milne J, Page GP, Somerville CR: Identification of
genes required for cellulose synthesis by regression analysis
of public microarray data sets. PNAS 2005, 102(24):8633-8638.
Rhee SY, Beavis W, Berardini TZ, Chen GH, Dixon D, Doyle A, Garcia-Hernandez M, Huala E, Lander G, Montoya M, Miller N, Mueller
LA, Mundodi S, Reiser L, Tacklind J, Weems DC, Wu YH, Xu I, Yoo
D, Yoon J, Zhang PF: The Arabidopsis Information Resource
(TAIR): a model organism database providing a centralized,

Page 13 of 14
(page number not for citation purposes)

BMC Genomics 2008, 9:438

43.

44.
45.
46.

47.

48.

http://www.biomedcentral.com/1471-2164/9/438

curated gateway to Arabidopsis biology, research materials
and community. Nucleic Acids Research 2003, 31(1):224-228.
Hong F, Breitling R, McEntee CW, Wittner BS, Nemhauser JL, Chory
J: RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis. Bioinformatics 2006,
22(22):2825-2827.
Benjamini Y, Hochberg Y: Controlling the False Discovery Rate:
a practical and powerful approach to multiple testing. Journal
of the Royal Statistical Society, Series B 1995, 57:289-300.
Storey JD, Tibshirani R: Statistical significance for genomewide
studies. Proc Natl Acad Sci USA 2003, 100(16):9440-9445.
Deeken R, Engelmann JC, Efetova M, Czirjak T, Muller T, Kaiser WM,
Tietz O, Krischke M, Mueller MJ, Palme K, Dandekar T, Hedrich R:
An integrated view of gene expression and solute profiles of
Arabidopsis tumors: a genome-wide approach. Plant Cell 2006,
18(12):3617-3634.
Davuluri R, Sun H, Palaniswamy S, Matthews N, Molina C, Kurtz M,
Grotewold E: AGRIS: Arabidopsis Gene Regulatory Information Server, an information resource of Arabidopsis cis-regulatory elements and transcription factors. BMC Bioinformatics
2003, 4(1):25.
Mueller LA, Zhang P, Rhee SY: AraCyc: A Biochemical Pathway
Database for Arabidopsis. Plant Physiol 2003, 132(2):453.

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