Dynamic changes in genome-wide histone H3 lysine 4 methylation patterns in response to dehydration stress in Arabidopsis thaliana.

Abstract

The molecular mechanisms of genome reprogramming during transcriptional responses to stress are associated with specific chromatin modifications. Available data, however, describe histone modifications only at individual plant genes induced by stress. We have no knowledge of chromatin modifications taking place at genes whose transcription has been down-regulated or on the genome-wide chromatin modification patterns that occur during the plant's response to dehydration stress.
Using chromatin immunoprecipitation and deep sequencing (ChIP-Seq) we established the whole-genome distribution patterns of histone H3 lysine 4 mono-, di-, and tri-methylation (H3K4me1, H3K4me2, and H3K4me3, respectively) in Arabidopsis thaliana during watered and dehydration stress conditions. In contrast to the relatively even distribution of H3 throughout the genome, the H3K4me1, H3K4me2, and H3K4me3 marks are predominantly located on genes. About 90% of annotated genes carry one or more of the H3K4 methylation marks. The H3K4me1 and H3K4me2 marks are more widely distributed (80% and 84%, respectively) than the H3K4me3 marks (62%), but the H3K4me2 and H3K4me1 levels changed only modestly during dehydration stress. By contrast, the H3K4me3 abundance changed robustly when transcripts levels from responding genes increased or decreased. In contrast to the prominent H3K4me3 peaks present at the 5'-ends of most transcribed genes, genes inducible by dehydration and ABA displayed atypically broader H3K4me3 distribution profiles that were present before and after the stress.
A higher number (90%) of annotated Arabidopsis genes carry one or more types of H3K4me marks than previously reported. During the response to dehydration stress the changes in H3K4me1, H3K4me2, and H3K4me3 patterns show different dynamics and specific patterns at up-regulated, down-regulated, and unaffected genes. The different behavior of each methylation mark during the response process illustrates that they have distinct roles in the transcriptional response of implicated genes. The broad H3K4me3 distribution profiles on nucleosomes of stress-induced genes uncovered a specific chromatin pattern associated with many of the genes involved in the dehydration stress response.

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RESEARCH ARTICLEOpen Access
Dynamic changes in genome-wide histone
H3 lysine 4 methylation patterns in response
to dehydration stress in Arabidopsis thaliana
Karin van Dijk1,5, Yong Ding1, Sridhar Malkaram1, Jean-Jack M Riethoven1,2, Rong Liu1,6, Jingyi Yang1,7,
Peter Laczko1, Han Chen1, Yuannan Xia1, Istvan Ladunga1,3, Zoya Avramova2, Michael Fromm1,4*
Abstract
Background: The molecular mechanisms of genome reprogramming during transcriptional responses to stress are
associated with specific chromatin modifications. Available data, however, describe histone modifications only at
individual plant genes induced by stress. We have no knowledge of chromatin modifications taking place at genes
whose transcription has been down-regulated or on the genome-wide chromatin modification patterns that occur
during the plant’s response to dehydration stress.
Results: Using chromatin immunoprecipitation and deep sequencing (ChIP-Seq) we established the whole-
genome distribution patterns of histone H3 lysine 4 mono-, di-, and tri-methylation (H3K4me1, H3K4me2, and
H3K4me3, respectively) in Arabidopsis thaliana during watered and dehydration stress conditions. In contrast to the
relatively even distribution of H3 throughout the genome, the H3K4me1, H3K4me2, and H3K4me3 marks are
predominantly located on genes. About 90% of annotated genes carry one or more of the H3K4 methylation
marks. The H3K4me1 and H3K4me2 marks are more widely distributed (80% and 84%, respectively) than the
H3K4me3 marks (62%), but the H3K4me2 and H3K4me1 levels changed only modestly during dehydration stress.
By contrast, the H3K4me3 abundance changed robustly when transcripts levels from responding genes increased
or decreased. In contrast to the prominent H3K4me3 peaks present at the 5’-ends of most transcribed genes,
genes inducible by dehydration and ABA displayed atypically broader H3K4me3 distribution profiles that were
present before and after the stress.
Conclusions: A higher number (90%) of annotated Arabidopsis genes carry one or more types of H3K4me marks
than previously reported. During the response to dehydration stress the changes in H3K4me1, H3K4me2, and
H3K4me3 patterns show different dynamics and specific patterns at up-regulated, down-regulated, and unaffected
genes. The different behavior of each methylation mark during the response process illustrates that they have
distinct roles in the transcriptional response of implicated genes. The broad H3K4me3 distribution profiles on
nucleosomes of stress-induced genes uncovered a specific chromatin pattern associated with many of the genes
involved in the dehydration stress response.
Background
Plants respond to external signals by activating signaling
pathways that rapidly alter physiological reactions and
transcription rates of responding genes. The molecular
mechanisms regulating gene expression are coordinated
at the genome level where the accessibility of DNA
sequences is determined by the structure of chromatin.
Chemical modifications of the histone amino-terminal
tails are implicated in facilitating nucleosome remodel-
ing and repositioning, and in the recruitment of specific
transcription factors. Changes in DNA methylation and/
or histone modifications are associated with altered gene
expression under various stresses in plant and cell cul-
ture systems [1-5]. Changes in nucleosome occupancy
and in the levels of histone H3 tri-methylation (lysine 4)
or acetylation (lysine 9, 14 or 27) during dehydration
* Correspondence: mfromm@unlnotes.unl.edu
1Center for Biotechnology, 1901 Vine St., University of Nebraska, Lincoln, NE,
68588, USA
Full list of author information is available at the end of the article
van Dijk et al. BMC Plant Biology 2010, 10:238
http://www.biomedcentral.com/1471-2229/10/238
© 2010 van Dijk 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.

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stress occurred at four inducible Arabidopsis genes [6].
Dehydration stress induces biosynthesis of abscisic acid
(ABA), which as a second messenger activates signaling
cascades that trigger stomatal closure and altered gene
expression via chromatin remodeling. In turn, ABA reg-
ulation is achieved through genetic and chromatin mod-
ification mechanisms [6,7]. Thus, chromatin structure
and chromatin modifications are emerging as critical
factors in plants’ responses to environmental cues.
It is important to note, however, that currently avail-
able data describe histone modifications only at indivi-
dual stress-induced plant genes. We also have limited
knowledge of chromatin modifications taking place at
genes whose transcription has been down-regulated.
Moreover, there are no data on global modification
trends occurring during the reprogramming of the
entire genome in response to stress.
Here, we provide a genome-wide view and analysis of
the histone H3 lysine 4 mono-, di-, and tri-methylation
(H3K4me1, H3K4me2, H3K4me3, respectively) patterns
in chromatin isolated from Arabidopsis rosette leaves
before and after dehydration stress. Genome-wide tran-
script patterns in watered and dehydration stressed
plants were compared with changes in the H3K4me1,
H3K4me2, and H3K4me3 levels of nucleosomes asso-
ciated with responding genes. Using chromatin immuno-
precipitation (ChIP) with H3K4 methylation specific
antibodies and genome-wide sequencing (ChIP-Seq), we
revealed different dynamics and different magnitudes of
the changes in H3K4me1, H3K4me2, and H3K4me3
profiles taking place upon dehydration stress. We demon-
strate specific patterns of the H3K4me1, H3K4me2, and
H3K4me3 distributions at up-regulated, down-regulated,
and unaffected genes during the stress.
Methods
Plant materials and growth conditions
Col-0 plants were grown in 12 h light for 4 weeks in
pots with soil covered with miracloth to prevent soil
contamination of leaf tissues at harvest. Control plants
continued to receive water while water was withheld
from water-deficit treated plants until a relative water
content (RWC) of 65% was reached in 4 to 6 days.
Vegetative rosettes were harvested and frozen for RNA
isolation or immediately fixed in formaldehyde for isola-
tion of chromatin as described below.
Isolation and immunoprecipitation of chromatin
Watered or water-deficit treated vegetative tissues were
fixed in 1% formaldehyde for 10 min after vacuum infil-
tration of the tissues in phosphate buffered saline (PBS)
with 0.007% silwet. Fixation was stopped by quenching
with 125 mM glycine. Next, the tissue was rinsed twice
in ice cold water, blotted dry and flash frozen in liquid
nitrogen. Chromatin was isolated and resuspended in
300 μl nuclei lysis buffer as described by [8] and soni-
cated for four 15 sec pulses using a Branson sonifier
450 at an output between 10 - 15%. The sheared chro-
matin was used in chromatin immunoprecipitation
assays as described [8] with some modifications. In
brief, 100 μl of sheared chromatin was diluted in 900 μl
ChIP dilution buffer and precleared, using 15 μl of pro-
tein A Dynabeads (Invitrogen, Carlsbad, CA). Protein A
beads were collected using a magnet, and the precleared
chromatin was incubated overnight at 4 °C with antibo-
dies against either H3 (6 μL, Abcam ab1791, Cambridge,
MA), mono-methylated H3K4 (10 μL, Abcam ab8895,
Cambridge, MA), dimethylated H3K4 (6 μL, Upstate 07-
212, Millipore, Billerica, MA), or trimethylated H3K4
(6 μL, Abcam ab8580, Cambridge, MA). Western blot-
ting of the H3K4 methylation antibodies against mono-,
di- or tri-methylated H3K4 or monomethylated H3K9
synthetic peptides was performed and confirmed their
expected specificity (Additional file 1, Figure S1). A
sample without antibody addition was also used as a
control. Next the antibody/chromatin complex was pre-
cipitated by incubating the mix with 40 μl of protein
A beads for 1 h followed by collection of the beads
using a magnet. The beads were washed, chromatin
eluted, and DNA de-crosslinked using procedures as
described [8]. DNA was recovered by phenol/chloroform
extraction and ethanol precipitation in the presence of
Novagen pellet paint (EMD Chemicals, Gibbstown, NJ)
and resuspended in 30 μL of TE buffer. Purified DNA
was end modified, ligated to amplification primers,
amplified, and sequenced according to the manufac-
turer’s protocols (Illumina, San Diego, CA).
RNA isolation and labeling
Total RNA was extracted from the frozen rosette tissues
using grinding and TRIzol (Invitrogen, Carlsbad, CA)
reagent. Three independent replicates of watered and
dehydration-stressed tissues were used. Fifteen μg of
total RNA from each sample was used to produce
labeled cRNA for hybridization to Affymetrix Arabidop-
sis ATH microarrays using the manufacturer’s protocols
(Affymetrix, Santa Clara, CA).
Microarray data analysis
Affymetrix® GeneChip Operating Software (GCOS) gen-
erated the image and probe set signal values as CEL
files, which were loaded into Rosetta Resolver® (version
6.0, build 6.0.0.311; Rosetta Inpharmatics LLC, Seattle,
WA). The water deficit and control condition replicate
arrays were combined into their respective intensity pro-
files, and subsequently used to generate ratio experi-
ments in Rosetta Resolver and analyzed using the
software’s background correction, normalization, and
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proprietary ratio error model [9]. The gene expression
arrays have been submitted to the NCBI Gene Expres-
sion Omnibus under GSE11538 (part of SuperSeries
record GSE11658).
Computational Methods for the analysis of sequence data
The primary analysis of the Illumina Genome Analyzer
output was performed by the manufacturer’s ELAND
program. ELAND mapped sequencing reads of 35 bases
uniquely to the Arabidopsis genome with no more than
2 mismatches. As a quality control, we used BLAST [10]
to re-map all reads to the Arabidopsis genome [11] with
a word length of 9 bases and low-complexity filtering
turned off. This analysis revealed a small number of
further non-unique sequencing reads. From the remain-
ing reads that uniquely map to the genome with no
more than 2 mismatches, the coverage of each genomic
position by the sequencing reads was computed. The
DNA sequencing reads for total genomic DNA from
Arabidopsis thaliana cultivar Col-0 were obtained and
mapped to the Arabidopsis thaliana Col-0 genome,
which is used as the control data set. To check for
biases in Illumina sequencing, the distribution of
sequencing coverage along the genome was calculated.
Statistical analyses were performed in MATLAB (Math-
Works, Nantucket, MA). Tests for the statistical signifi-
cance in the difference in the coverage profiles of
H3K4me3 along the normalized gene length between
the sets of all expressed and ABA induced genes under
both watered and soil water deficit conditions were per-
formed by the non-parametric Wilcoxon rank sum test
on the total (peak-normalized) coverage over the nor-
malized gene length for individual genes.
Coverage peaks were identified as maximal scoring
segments using a modified dynamic programming algo-
rithm, analogous to pairwise local alignments [12]. Back-
ground noise was estimated as the mean coverage of the
intergenic regions (ranging from 1.10 to 3.25 depending
on the sequencing run) and was subtracted from the
raw coverage values. We extended peaks from each
genomic position that had an above background cover-
age so as to reach the maximum of the cumulative cov-
erage, analogous to the cumulative score of a BLAST
High-Scoring Segment Pair. During peak extension, we
allowed for local drops in the peaks provided that they
were compensated by subsequent above-background
regions.
Position-wise coverage data for the three H3K4
methylation levels for watered and dehydration-stressed
conditions, peak calls, our gene expression results,
exons, UTRs, alternative transcription start sites are dis-
played in the implementation of the UC Santa Cruz
Genome Browser [13] at the Bioinformatics Core Facil-
ity at the University of Nebraska-Lincoln. Exons,
transcription start sites and untranslated regions were
downloaded from the TAIR [11]ftp://ftp.Arabidopsis.
org/home/tair/Genes/. The results can be accessed at:
http://bioinformatics.unl.edu/h3k4me/cgi-bin/hgGate-
way. The ChIP-Seq data has been deposited at the
NCBI Gene Expression Omnibus under series record
GSE11657 (part of SuperSeries record GSE11658).
Results
Changes in gene expression in plants exposed to soil
water deficit conditions
Plants were watered or subjected to dehydration stress
by withholding water until a relative water content
(RWC) of 65% was reached. A RWC of 65% causes leaf
wilting and is considered a moderate level of dehydra-
tion stress [14]. The mRNAs from rosette leaves of non-
stressed (control) and dehydration-stressed Arabidopsis
plants were analyzed by microarrays (Additional file 2,
Table S1). Further analysis indicated 640 genes
increased, and 525 genes decreased, their transcript
levels by 4-fold or more (p ≤ 0.001) as result of the
dehydration stress (Additional file 3, Table S2). Analysis
of the most highly represented gene categories (using
the Gene Ontology classification) indicated that the
majority of the genes with altered expression was
involved in various stress-responses (Additional file 4,
Table S3). For example, within the list of genes respond-
ing to dehydration-stress, many genes have been also
recognized as components of pathways stimulated by
cold, temperature, light, and radiation (~750 genes), by
hormones, endogenous, and chemical stimuli (~300
genes), as well as genes encoding transcription factors
and regulators (131 genes). Genes for metabolic
enzymes, and for oxidoreductase functions, were also
affected (Additional file 4, Table S3). Among the genes
up-regulated by dehydration stress, well-known genes
implicated in this response in other studies were pre-
sent, including genes ABF3 (ABA responsive element
binding factor 3, AT4G34000, [15]), CBF4 (C-repeat
binding factor 4, AT5G51990 [16]), RD26 (Responsive to
desiccation 26, AT4G27410, [17]), RD29A (AT5G52310),
RD29B (AT5G52300), ATHB7 (Arabidopsis thaliana
homeobox 7, AT2G46680) and ATHB12 (AT3G61890;
[18]).
Genome-wide distributions of H3 and mono-, di-, and
tri-methylated H3K4 in watered and in dehydration
stressed plants
To examine whether and how chromatin reacted during
the stress response at the whole-genome level, we used
ChIP-Seq to analyze the changes in the global chroma-
tin methylation marks on H3K4 between the watered
and dehydration stressed samples (see Additional file 5,
Table S4 for the number of sequences analyzed for each
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sample). The ChIP-Seq chromatin data was visualized
using a local implementation of the UCSC Genome Brow-
ser [19]. A representative chromosomal region demon-
strating the types of coverage obtained from total input
DNA, and from antibodies to total H3 or specific to
mono-, di- or tri-methylated H3K4 is shown (Figure 1).
The antibodies for the H3K4me1, H3K4me2, and
H3K4me3 forms of H3 were analyzed for their ability to
recognize a panel of peptides containing these modifica-
tions. The antibodies to H3K4me1 and H3K4me3 were
highly specific, while the antibody to H3K4me2 had weak
recognition of H3K4me3 (Additional file 1, Figure S1).
Figure 1 UCSC browser view of genomic data of a representative chromosomal region. A representative region of chromosome 1
representing 250,000 bp and an expanded region of 20,000 bp are shown. For simplicity, only data from samples from the watered condition
are shown. The chromosomal coordinates are shown at the top of each region. The amount of sequence coverage for the different samples is
shown to the left of each track (y-axis), and represents the number of times each region was recovered by sequencing. The tracks are: tri-
methylated H3K4 (H3K4Me3, blue) di- methylated H3K4 (H3K4Me2, green), and mono-methylated H3K4 (H3K4Me1, red), total H3 (H3, gray), and
total input DNA (Input, black). The magnified section shows the mRNA levels at the top as a color scale with the values shown in each colored
bar. The gene models below indicate the gene name and direction.
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This weak cross reactivity did not appear to affect the
ChIP-SEQ results as the profile of H3K4me2 was very dif-
ferent than the profile of H3K4me3 (Figure 1). Samples
containing total input DNA (not subjected to chromatin
immunoprecipitation), or DNA from samples immunopre-
cipitated with antibody that recognized H3 regardless of
its methylation state, did not show the gene-centric
enrichment identified by the antibodies to the methylated
forms of H3K4 (Figure 1). The distributions of the methy-
lated forms of H3K4 were further analyzed below. The
accuracy of the ChIP-Seq method was verified by quantita-
tive PCR (qPCR) of arbitrarily selected gene regions (Addi-
tional file 6, Table S5) and a comparison of the data sets
indicated they had a good quantitative agreement, with a
correlation coefficient of 0.7.
A detailed analysis of the genomic locations of the peaks
of H3K4 methylation indicated that the majority of the
peaks mapped to genes. The percentage of the peaks for
the individual mono-, di-, or tri-methylated H3K4 types
that were located on gene regions ranged from 80% to
88% of the total peaks (Additional file 7, Table S6).
Although the majority of peaks mapped to genes, not all
genes contained each type of H3K4 methylation: the
percentage of genes containing mono-, di-, or tri-methy-
lation of H3K4 ranged from 62 to 84% (Table 1). 9.9%
of annotated genes did not contain any form of H3K4
methylation (Table 1). The majority of the genes lacking
any type of H3K4 methylation were in the two lowest
expression quintiles (data not shown).
Genome-wide distribution of histone H3, H3K4me1, me2
and me3 at gene sequences: correlation with
transcription activity
Next, we examined how overall gene transcription activ-
ity was associated with H3 or H3K4 methylation levels
on genes. Genes were grouped into expression quintiles
based on their transcript levels. The amount of H3 or
mono-, di-, or tri-methylated H3K4 was plotted at their
5’- and 3’-ends (Figure 2). We observed that histone H3
occupancy was slightly enriched within genes (Figure
2A). There was a significant dip in the H3 (nucleosome)
occupancy immediately 5’ of the transcription start site
(TSS) for the highly expressed genes (the top three
quintiles, Figure 2A). This region could correspond to a
nucleosome free region (NRF) that occurs at actively
transcribed genes [20]. At the 3’-end of the genes, this
transcriptional effect on H3 levels was much less
pronounced.
The profiles of mono-, di-, and tri-methylated H3K4
displayed specific patterns associated with transcript
levels. H3K4me1 levels remained fairly constant
throughout genes (Figure 2B). One pattern of interest
was that the 5’-regions of the genes with the lowest
expression levels had higher H3K4me1 levels than more
highly expressed genes (Figure 2B). The profiles of
H3K4me2 distribution had no correlation with tran-
script levels, and were relatively constant throughout the
transcribed regions (Figure 2C). As such, H3K4me2
marked 84% of genes (Table 1) irrespective of the tran-
script levels produced from these genes. Both H3K4me1
and H3K4me2 were fairly constant across the middle
and end of the genes, and accurately marked the ends of
the genes.
In contrast, the H3K4me3 distribution showed a clear
peak around 300 bp downstream of the TSS. However,
the magnitudes of signal values for the five expression-
based quintiles were in reverse to those displayed by
H3K4me1: the highest H3K4me3 peaks were for genes
with the highest transcript levels. The H3K4me3 signal
at the 5’-ends of the least actively transcribed genes dis-
played the lowest coverage values (Figure 2D). Charac-
teristically, the peak of H3K4me3 distribution tapers off
towards the 3’ end of the genes and reaches intergenic
levels at the 3’-gene ends.
H3K4 methylation changes when transcript levels change
To determine whether/how stress-altered gene expression
correlated with changes in H3K4 methylation, we exam-
ined genes whose transcripts increased or decreased by
4-fold or more, and had highly significant p-values (p ≤
0.001; Additional file 3, Table S2). The 1165 genes meet-
ing these criteria were sorted into five up-regulated and
five down-regulated change-of-expression quintiles, i.e.,
the genes with the biggest increase or decrease in tran-
script levels were in the highest (5th) up or down regu-
lated change-of-expression quintile, respectively. The
median and range of changes in H3K4me1, me2, or me3
methylation amounts were plotted for each of the up-
and down-regulated expression quintiles (Figure 3). The
median H3K4me1 levels slightly decreased as the magni-
tude of the up-regulated transcript changes increased and
slightly increased as the magnitude of the transcript
Table 1 Percentage of genes with H3K4 methylation
regions1
Treatment and type of H3K4
methylation
Number of
genes
Percentage of
genes
Water: H3K4me1
Dry: H3K4me1
Water: H3K4me2
Dry: H3K4me2
Water: H3K4me3
Dry: H3K4me3
Genes with one or more types of H3K4
methylation
21875
22167
22708
22953
16769
18072
24487
80.5
81.6
83.6
84.5
61.7
66.5
90.1
Genes lacking H3K4 methylation
2682 9.9
1H3K4 methylation region analyzed includes 200 bp upstream and
downstream of the transcribed regions of 27,169 annotated genes.
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reductions increased. Thus, H3K4me1 levels were inver-
sely or negatively associated with changes in transcript
levels. The median for the H3K4me2 values slightly
decreased for up-regulated quintiles, and was little chan-
ged for down-regulated quintiles (Figure 3).
The most significant changes were displayed by the
H3K4 tri-methylation profiles. The median and the
majority of the genes within one standard deviation of
the median increased for all up-regulated quintiles and
decreased for all down regulated quintiles (Figure 3). In
addition, the largest increase in H3K4me3 levels was
found on nucleosomes of the genes in the highest
expression quintile, indicating that the magnitude of the
H3K4me3 increase was positively associated with
increased transcript levels. Similarly, the down-regulated
quintile with the largest decrease in transcript levels had
the largest decrease in H3K4me3 levels (Figure 3).
As a control for our method of analysis, we analyzed
genes that did not change transcript levels and found
that these genes did not show significant variation in
their H3K4 methylation levels. Correlation coefficients
of 0.95, 0.88, and 0.93 were observed between the
watered and dehydration-stressed samples for the H3K4
mono-, di-, or tri-methylation comparisons of genes that
did not change transcript levels, respectively. These high
correlation coefficients indicated that there was very lit-
tle change in H3K4 methylation in this set of genes
(Additional file 8, Figure S2).
H3K4 methylation profiles of selected genes during
watered or dehydration stressed conditions
A specific chromosomal region of interest demonstrating
changes in H3K4 methylation status after dehydration
stress is the region containing the ABA and dehydration-
Figure 2 H3K4 methylation profiles of genes with different transcript levels. Genes were sorted into quintiles according to their transcript
levels. These sets are indicated by the different colors shown in the insert box at the top of the Figure, with yellow (Q5) representing the 20%
of genes with the most abundant transcripts. Total H3 or the type of H3K4 methylation measured is indicated above each graph. The y-axis of
each graph shows the amount of H3 or H3K4 methylation after normalization (using the input DNA coverage profile) as measured by the
number of times that region was detected by sequencing (Average Coverage). The x-axis shows the gene position in bp relative to either the
transcription start site (TSS on left side of the Figure) or stop codon (on right side of the Figure) of the genes.
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induced genes RD29A (AT5G52310) and RD29B
(AT5G52300, Figure 4). Microarray data indicated that
transcript levels of RD29A and RD29B were induced 9
and 147 fold by dehydration stress, respectively (tran-
script levels are shown as a color scale with insert values
in Figure 4). The neighboring genes (AT5G52280,
AT5G52290, and AT5G52320) did not change their
expression (color scale at top of Figure 4) or H3K4
methylation levels. RD29A had a higher basal transcrip-
tion rate than RD29B in the watered control treatment
and had a higher basal level of H3K4me3. Their expres-
sion increased during dehydration stress as did the
H3K4me3 levels on the nucleosomes of both genes.
There was a corresponding decrease in H3K4me1 levels
while H3K4me2 levels showed little change.
Similar tendencies were demonstrated by other dehy-
dration stress responsive genes as well. Six representa-
tive chromosomal regions, representing genes that were
induced, repressed, or unaffected by dehydration stress
conditions, are shown in a UCSC Genome Browser view
(Additional file 9, Figure S3). For induced genes, the
H3K4me3 levels increased during dehydration stress as
their expression increased, with a corresponding
decrease in H3K4me1, while H3K4me2 levels showed
little change (Additional file 9, Figure S3A, S3B). The
twodehydration-stressrepressed
decreased H3K4me3 levels, and a slight increase in
H3K4me1 (Additional file 9, Figure S3C and additional
file 10, Figure S3D). By comparison, genes without
changes in gene expression did not display significant
genesshowed
Figure 3 Changes in H3K4 methylation for genes with changes in mRNA levels. Genes that were up- or down-regulated by dehydration
stress were grouped into up- or down-regulated quintiles according to their change in transcript levels. The genes showing the largest increase
or largest decrease in transcript levels were placed into the 5thor -5thquintiles, respectively. The type of H3K4 methylation is represented by the
color key in the upper left corner of the chart. The change in the amount of H3K4 methylation for each type of H3K4 methylation is represented
on the y-axis (Change in coverage). For example, a median value (black cross bar) showing no change in H3K4 methylation would be at
y-coordinate 0 and a median value showing an increase of 15 in coverage would be placed at y-coordinate + 15. One standard deviation in
H3K4 methylation coverage is shown on each side of the median value.
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changes in any of the H3K4 methylation-forms (Addi-
tional file 10, Figure S3E, S3F). Collectively, the changes,
or lack of changes, in the H3K4 methylation patterns
displayed at individual gene loci were consistent with
the overall genomic patterns as described above.
Dehydration stress responsive genes show broader
H3K4me3 distribution profiles
Inspection of individual dehydration-stress induced
genes indicated the H3K4me3 distribution at these
genes tended to be distributed over longer gene regions
when compared to the H3K4me3 patterns at unrespon-
sive genes (see Figure 4). We then asked how general
this broader H3K4me3 distribution pattern was for
induced genes. To accomplish this, we measured the
profiles of H3K4me3 modification along the length of
three groups of genes: all expressed genes, dehydration-
induced genes, and the subset of dehydration induced
genes that were also ABA induced [21]. The median
values of H3K4me3 modification along the length of
these genes were then plotted to a gene length normal-
ized to 1500 bp. These profiles were plotted with their
actual median coverage levels or with each peak normal-
ized to the same maximum level for a better relative
comparison (Figure 5). The profiles for the dehydration
or dehydration/ABA induced genes are also shown for
the watered condition for these genes as well (Figure 5).
All five curves of H3K4me3 levels for the three sets of
genes had peaks about 300 bp downstream of the TSS
and then progressively declined towards the stop codon
of the genes. However, the profile for all expressed
genes showed a faster decrease in H3K4me3 levels,
while the dehydration-induced or subset of known
ABA-induced genes demonstrated a broader H3K4me3
distribution pattern (Figure 5B). This broader distribu-
tion of H3K4me3 was significant for the dehydration/
Figure 4 Genome browser view of the chromosomal region containing RD29A and RD29B. A region of chromosome 5, with the indicated
position numbers, containing the dehydration and ABA inducible RD29A and RD29B genes as well as three flanking genes is shown. Arrows at
the bottom show the direction of transcription below the representation of each gene. The transcript levels of the watered or dehydration
stressed samples are shown as colored bars above each gene region, with the relative transcript levels shown inside each colored bar. The type
of treatment and what was measured (mRNA levels, type of H3K4 methylation, total H3, or input DNA) are shown at the left. The histogram bars
measure the number of times each region was identified by sequencing (y-axis coverage scale with a top value range of 20 to 110, depending
on the coverage range of each sample).
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ABA induced genes (p = 1.4 × 10-10). Surprisingly, this
broad H3K4me3 distribution was also significant (p =
0.002) for these genes in the watered condition prior to
dehydration stress. This suggests that some biological
feature of the dehydration/ABA induced genes causes
these broader than normal H3K4me3 distributions in
both the watered and dehydration/ABA induced states.
Discussion
Here we report the first whole genome map of the his-
tone H3K4 methylation patterns of chromatin during a
plant’s response to dehydration stress. Using the ChIP-
Seq approach, we found that the distribution of mono-,
di-, and tri-methylation of H3K4 in Arabidopsis were
predominantly located on genes, in general agreement
with prior studies [22]. However, in watered plants we
identified 21,875 genes carrying H3K4me1 versus
15,475 genes found by the ChIP-chip method; 22,708
genes with the H3K4me2 mark versus 12,781, and
16,769 H3K4me3-tagged genes versus 15,894 genes
reported in [22]. Furthermore, our results indicated
that only 10% of expressed genes did not contain at
least one H3K4me type, while ~1/3 of the genes was
found to be free of H3K4 methylation by the ChIP-
chip approach. These differences might reflect different
sensitivities of the two methods and/or different tissue
distributions in the whole seedlings [22]versus rosette
leaves analyzed (this study). The gene-associated loca-
tion of H3K4me modifications did not change during
the dehydration stress-responding process. Within this
gene-centric framework, however, the abundances of
the individual forms of H3K4 methylation displayed
specific profile changes associated with altered tran-
scription from these genes.
H3 distribution and nucleosome free regions
The genome-wide distribution of histone H3 was used
to gain insight to whether a region lacked nucleosomes
or lacked H3K4 methylation of those nucleosomes. The
H3 distribution showed a fairly continuous presence
across genic and intergenic regions. By contrast, the
H3K4 methylated forms were practically absent from
intergenic regions. In agreement with observations made
in yeast [23] and Arabidopsis [24], we also observed an
H3 enrichment in gene regions relative to intergenic
regions. Furthermore, in agreement with other genomic
analyses in Arabidopsis [24-26], our analysis of the H3
profiles in genes grouped by transcript levels, revealed
regions with low H3 presence, “nucleosome free
regions” (NFRs), identified immediately upstream of the
TSS for more highly expressed genes. NFRs have also
been observed in yeast [27-29], Drosophila [30], and
human cells [31]. Interestingly, nucleosome density has
also been observed to be different in exon and intron
gene regions [32]. It has been suggested that the nucleo-
some occupancy is influenced by the DNA sequence,
which might facilitate nucleosome removal upon tran-
scription [33]. Our finding that NFRs are associated
with transcriptionally active genes is consistent with the
idea of nucleosome displacement by transcription
Figure 5 Dehydration induced genes show broader regions of H3K4me3. The median value of H3K4me3 coverage was calculated at
positions along the genes for three different sets of genes. Each set was further limited to contain only genes between 1,000 to 2,000 bp in
length. The three sets of genes were: all expressed genes, all dehydration induced genes, and all dehydration induced genes also known to be
ABA regulated. These groups are indicated by the color key at upper right of the Figure. The median level of H3K4me3 observed for each set of
genes (y-axis, Median coverage) was determined at different positions along the genes (x-axis in bp, with genes normalized to a standard 1500
bp size between the TSS and STOP codon and containing 500 bp upstream or downstream from these sites). (A) The actual coverage profiles
without peak normalization. (B) Profiles that were normalized for peak heights to facilitate comparison of the profile shapes. The p-values for the
normalized profiles for the dehydration/ABA induced genes in (B), which measure the probability of the broader profiles occurring by chance,
were p = 0.002 for the watered condition and p = 1.4 × 10-10for the water deficit condition.
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factors [27] or complexes involved in transcription [29].
Similarly, transcription from the Arabidopsis AP1, a
gene required for the initiation of flowering, requires
the reprogramming of the AP1 locus to an actively tran-
scribed state that is accompanied by the removal of a
nucleosome from the transcription start site and its
dynamic re-positioning in a developmentally regulated
process [34].
H3K4me1 distribution changes
The majority of genes with increased transcript levels
showed modest reductions in H3K4me1 levels. Conver-
sely, the most strongly down-regulated genes showed an
increase in H3K4me1 marks, while genes with smaller
reductions in transcript levels did not detectably change
their average H3K4me1 levels. A negative association of
H3K4me1 levels with increased transcription might be
due to conversion to the more abundant H3K4me2 and
H3K4me3 forms at higher transcription rates. Alterna-
tively, higher H3K4me1 levels, particularly near the TSS,
might repress transcription. A repressive role has been
reported in Chlamydomonas where H3K4 mono-methy-
lation was found associated with repressed transgenes
and transposable elements. The loss of this mark in
Chlamydomonas mutant Mut11 resulted in expression
of these genes [35]. The possible role of H3K4me1 in
gene repression might be unique to the plant kingdom
as H3K4me1 levels are associated with enhancers in ani-
mal cells [36].
H3K4me2 distribution changes
In our studies, 84% of Arabidopsis genes carried
H3K4me2 marks, and on average, these marks were dis-
tributed relatively uniformly along gene sequences. In
agreement with an earlier genomic study [22],
H3K4me2 levels were not associated with transcript
levels in watered plants. In response to dehydration
stress, H3K4me2 levels were only slightly reduced when
transcript levels increased, and showed little change
when transcript levels were reduced. Previous ChIP-PCR
analysis of individual genes in Arabidopsis found that
H3K4me2 marked 5’ boundaries of the tested genes
regardless of expression levels [37]. These observations
suggest H3K4me2 marks do not correlate with tran-
script levels and may play a role in gene regulation dif-
ferent from the roles of mono-, and tri-methylated
nucleosomes.
H3K4me3 distribution changes
H3K4me3 modification was predominantly associated
with actively transcribed genes. Our observation that
higher H3K4me3 levels were associated with higher
transcript levels was in agreement with studies in yeast
[38], rice [39], Arabidopsis [22,24], and human cells
[40,41]. This tendency was dynamic, as we found up-
regulated genes had increased H3K4me3 levels, and
down-regulated genes had reduced H3K4me3 levels.
These findings are in agreement with numerous results
from individual plant genes, where H3K4me3 was found
to increase with increased transcription at dehydration
induced promoters [6], or the phaseolin [42], alcohol
dehydrogenase 1 (ADH1) and pyruvate decarboxylase
1 (PDC1) genes [4]. The magnitude of the change,
however, only weakly correlated with the magnitude of
the change in transcript levels as shown by the wide
range of H3K4me3 values in each expression quintile
(Figure 3).
The genome-wide H3K4me3 distribution in Arabidop-
sis displays a peak at +300 bp relative to the TSS (Figure
2D, [22]), similar to the location observed in human
cells [40], but slightly upstream of the peak location in
rice observed at about +500 bp [39]. A surprising devia-
tion from this pattern was the broad H3K4me3 distribu-
tion profiles present on many dehydration/ABA
inducible genes. Interestingly, this broad pattern was
present on many of these genes prior to their induction,
although the lower H3K4me3 levels made this pattern
less striking than in the induced state. For example,
both RD29A and RD29B had low levels of H3K4me3 in
a broad distribution pattern that was similar to the
broad pattern of the higher levels of H3K4me3 observed
during dehydration stress (Figure 4). Although the
occurrence of this pattern was statistically significant, its
functional significance is unclear.
Conclusions
Although there is a strong association of H3K4me3 with
transcriptionally active genes, the biochemical roles of
H3K4me3 are emerging as occurring predominantly
after transcription initiation [43-45]. H3K4me3 marks
are recognized by chromatin remodeling factors facilitat-
ing transcription by altering the structure, composition,
and positioning of nucleosomes, by components of the
spliceosome, and by proteins involved in mRNA capping
and stability [46]. Recognition and binding to H3K4me3
has been traced to plant homeodomains or chromodo-
mains present in these proteins [47]. Additionally, in
support of a post-transcriptional function, the yeast
SET1 H3K4 methyltransferase responsible for H3K4me3
modification is recruited to the +300 bp region of genes
after transcription initiation in yeast [48]. Elevated
H3K4me3 levels occurring on genes with increased tran-
script production might effectively recruit more chro-
mosomal and RNA processing proteins, thereby
facilitating the increased transcriptional activity on these
induced genes.
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Additional material
Additional File 1: Figure S1. Specificity of the H3K4 methylation
antibodies. The antibodies to H3K4me1, H3K4me2, or H3K4me3 were
tested against a panel of peptides containing or lacking these
modifications.
Additional File 2: Table S1. Microarray analysis of dehydration-
stressed Arabidopsis plants. Arabidopsis plants were watered or
subjected to dehydration stress to a relative water content of 65% prior
to RNA isolation and microarray analysis. RNA levels before and during
dehydration stress are shown.
Additional File 3: Table S2. Dehydration responsive genes that
change by at least 4-fold. A subset of genes derived from Table S1
that change expression levels by at least 4-fold.
Additional File 4: Table S3. Gene Ontology terms for the top
categories of up or down regulated genes. The genes derived from
Table S2 were classified by their gene ontology terms.
Additional File 5: Table S4. Number of sequencing reads from each
chromatin immunoprecipitation experiment. The number of
sequencing reads analyzed in the ChIP-Seq or input DNA-SEQ
experiments is shown.
Additional File 6: Table S5. Quantitative comparison of high-
throughput sequencing and qPCR measurements of ChIP samples.
The magnitude of the changes in ChIP of H3K4me3 levels recovered in
the watered or dehydration-stressed samples was measured for selected
regions by ChIP-SEQ and real time PCR. The sequences of the primers
used are also provided.
Additional File 7: Table S6. Genomic location of regions enriched
for H3K4 methylation. The intergenic or intragenic locations of the
peaks of H3K4me3 were determined across the genome for mono-, di-,
and tri-methylated H3K4 in the watered and dehydration-stressed
conditions.
Additional File 8: Figure S2. Analysis of changes in H3K4
methylation types for genes that did not change expression when
stressed. The levels of H3K4 mono-, di-, or tri-methylation for genes that
did not show changes in transcript levels were plotted for the watered
vs dehydration stress condition.
Additional File 9: Figure S3 A-C. Genome browser view of the
chromosomal region genes with up- or down-regulated expression
levels. The genome browser view of H3K4 mono-, di-, or tri-methylation
levels for genes with increased or decreased gene expression are shown.
Additional File 10: Figure S3 D-F. Genome browser view of the
chromosomal region genes with down- regulated or unchanged
expression levels. The genome browser view of H3K4 mono-, di-, or tri-
methylation levels for genes with decreased or no change in gene
expression are shown.
Acknowledgements
We thank Dr. Richard Mott for communicating unpublished data. This work
was supported by National Science Foundation grant EPS-0701892.
Author details
1Center for Biotechnology, 1901 Vine St., University of Nebraska, Lincoln, NE,
68588, USA.2School of Biological Sciences, University of Nebraska, Lincoln,
NE, 68588, USA.3Department of Statistics, University of Nebraska, Lincoln, NE,
68588, USA.4Department of Agronomy & Horticulture, University of
Nebraska, Lincoln, NE, 68583, USA.5Creighton University, Department of
Biology, 2500 California Plaza Omaha, NE 68178, USA.6Department of
Biomedical Engineering, University of California, Los Angeles, CA, 90095, USA.
7Microsoft, One Microsoft Way, Redmond, WA 98052, USA.
Authors’ contributions
KVD, YD, HC, and YX designed and performed the experiments; SM, JJMR,
RL, JY, PL, and IL computationally analyzed the data; ZA and MF wrote the
manuscript. All authors read and approved the final manuscript.
Received: 29 June 2010 Accepted: 5 November 2010
Published: 5 November 2010
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doi:10.1186/1471-2229-10-238
Cite this article as: van Dijk et al.: Dynamic changes in genome-wide
histone H3 lysine 4 methylation patterns in response to dehydration
stress in Arabidopsis thaliana. BMC Plant Biology 2010 10:238.
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