The Journal of Immunology
Histone Methylation Patterns Are Cell-Type Specific in
Human Monocytes and Lymphocytes and Well Maintained
at Core Genes1
Feng Miao,* Xiwei Wu,† Lingxiao Zhang,* Arthur D. Riggs,‡ and Rama Natarajan2*
Different immune cells are expected to have unique, obligatory, and stable epigenomes for cell-specific functions. Histone methylation is recognized as a major layer of the cellular epigenome. However, the discovery of histone demethylases raises questions
about the stability of histone methylation and its role in the epigenome. In this study, we used chromatin-immunoprecipitation
combined with microarrays to map histone H3K9 dimethylation (H3K9Me2) patterns in gene coding and CpG island regions in
human primary monocytes and lymphocytes. This chromosomal mark showed consistent distribution patterns in either monocytes
or lymphocytes from multiple volunteers despite age or gender, but the pattern in monocytes was clearly distinct from lymphocytes
of the same population. Gene Set Enrichment analysis, a bioinformatics tool, revealed that H3K9Me2 candidate genes are enriched
in many tightly controlled signaling and cell-type specific pathways. These results demonstrate that monocytes and lymphocytes
have distinct epigenomes and H3K9Me2 may play regulatory roles in the transcription of genes indispensable for maintaining
immune responses and cell-type specificity. The Journal of Immunology, 2008, 180: 2264 –2269.
V
arious cells in humans have uniform genomes but diverse phenotypes. The development from a single cell to
an embryo is largely an epigenetic process, in which
some 23,000 genes are expressed in specific cells in a time-dependent manner by epigenetic modulation of the chromatin without
changing genome sequences (1). Gene expression patterns in specific cell types are established during this process of development.
It is therefore thought that the epigenome determines the differential expression of genes in specific cell types. The key layers of
epigenetic control of gene expression include histone post translational modifications (PTMs),3 DNA methylation, and protein occupancies. Ample evidence now demonstrates that histone PTMs
in the chromatin, such as methylation, acetylation, and phosphorylation, play vital roles in determining whether a gene is activated,
repressed, or silenced and that specific patterns of histone PTMs
are required for cellular development (2–5). DNA methylation and
histone acetylation patterns have been shown to change over time.
Recently, it was reported that homozygous twins are epigenetically
indistinguishable in their early years but exhibit marked variations
in DNA methylation and histone acetylation patterns later in their
*Department of Diabetes, †Department of Biomedical Informatics, and ‡Department
of Biology, Beckman Research Institute of City of Hope, Duarte, CA 91010
Received for publication September 28, 2007. Accepted for publication December
9, 2007.
The costs of publication of this article were defrayed in part by the payment of page
charges. This article must therefore be hereby marked advertisement in accordance
with 18 U.S.C. Section 1734 solely to indicate this fact.
1
This work was supported by grants from the National Institutes of Health (R01
DK065073) and the Juvenile Diabetes Research Foundation International (to R.N.)
and in part by a General Clinical Research Center grant from the National Center for
Research Resources (M01RR00043 to City of Hope).
2
Address correspondence and reprint requests to Dr. Rama Natarajan, Department of
Diabetes, Beckman Research Institute of City of Hope, 1500 East Duarte Road, Duarte, CA 91010. E-mail address: [email protected]
3
Abbreviations used in this paper: PTM, post translational modification; H3K9Me2,
H3 lysine-9 dimethylation; ChIP-chip, chromatin immunoprecipitation coupled to
DNA microarray analysis; GSEA, gene set enrichment analysis; ES, enrichment
score; FDR, false discovery rate.
Copyright © 2008 by The American Association of Immunologists, Inc. 0022-1767/08/$2.00
www.jimmunol.org
lifetime (6). In addition, the more recent discoveries of histone
demethylases provide convincing evidence of the dynamic nature
of histone methylation (7, 8). These studies raise questions about
the stability of histone marks, especially if histone methylation, the
most stable histone PTM, is dynamically altered (9). However,
specific human cell types have to maintain distinct, stable core
epigenomes if they are involved in controlling cell-specific gene
expression patterns and function. We sought to obtain evidence for
this by mapping a key chromatin mark in a genome-wide scale in
human blood cells.
Peripheral blood T cells, B cells, neutrophils, and monocytes are
involved in innate and adaptive immunity and are derived by hemopoietic stem cell differentiation. Beyond transcription factors, it
is now obvious that chromatin modifications and structure play key
roles in cell differentiation and development (10, 11). Examining
and mapping the landscape of histone modifications in T cells
could provide a wealth of resources to understand T cell-specific
functions (12). However, it is not clear whether the global histone
lysine methylation patterns of genes in blood cells are cell-type
specific and whether they differ between individuals. In the current
study, we have examined these aspects because such data will be
an invaluable epigenomic resource, particularly due to the recent
discoveries of several histone methylases and demethylases.
Chromatin immunoprecipitation coupled to DNA microarray
analysis, or ChIP-chip, is currently a widely used approach for
acquiring genome-wide information on histone modifications (12–
17). In this study, by evaluating histone H3K9Me2 for variability,
as a measure of epigenetic stability within cell types, we compared
the profile of histone H3K9Me2 in primary lymphocytes vs monocytes isolated from peripheral blood of normal volunteers.
H3K9Me2 was chosen because it is widespread in chromatin, is
frequently associated with DNA methylation, and is both a repressive mark in euchromatin and a hallmark feature of heterochromatin (3–5). Increases or decreases in H3K9 methylation can
change chromatin structure and affect gene expression (18). Most
importantly, we observed that the chromatin mark, histone
H3K9Me2, is maintained in a relatively stable and inheritable state
within the coding and promoter regions of core genes in human
The Journal of Immunology
2265
FIGURE 1. Comparison of histone
H3K9Me2 profiles in monocytes and
lymphocytes within coding regions
and CpG islands. Lymphocytes and
monocytes were isolated from normal
volunteers. Each sample was processed for ChIP-chips with antiH3K9Me2 Ab. A, Hierarchical clustering with Spearman Correlation and
average linkage applied to selected
probes from cDNA arrays. Each column represents methylation profile in
one individual, and the columns represent H3K9Me2 profiles in eight
monocyte and eight lymphocyte samples. Color bar shows H3K9Me2 enrichment level compared with the average of all samples, where red
indicates increased methylation and
green indicates decreased methylation. Color intensity correlates to the
magnitude of change. B, Similar hierarchical clustering patterns from 12K
CpG arrays. C, Venn diagram of selected histone H3K9 methylated
genes from 12K cDNA array. These
candidates in lymphocyte and monocytes were selected separately and
only probes with p ⬍ 0.05- and ⬎1.5fold were selected. F ⫽ Female; M ⫽
male. D, Venn diagram of selected histone H3K9 candidates from CpG
arrays.
primary lymphocytes or monocytes despite differences in age and
gender, but the pattern of histone H3K9Me2 was highly specific to
cell type.
Materials and Methods
Materials
Abs specific to H3K9Me2 (07– 441) and H3K4Me2 (07– 030) were purchased from Upstate Biotechnology. Human 12K cDNA arrays were from
the University of Pennsylvania Functional Genomics Core. Human 12K
CpG island arrays were from the Universal Health Network Microarray
Center. The sequences of CpG islands on the array and alignment data are
available through http://data.microarrays.ca/. Human 6K promoter arrays
were from Aviva Systems Biology. Human promoter tiling arrays were
produced by NimbleGen Systems. The design is a two-array set, containing
5.0 kb of each promoter region (from build HG17) that extends 4.2 kb
upstream and 800 bp downstream of the transcription start site. Where
individual 5.0-kb regions overlap, they are merged into a single larger
region, preventing redundancy of coverage. The promoter regions thus
range in size from 5.0 to 50 kb. These regions are tiled at a 110-bp interval
using variable length probes with a target melting temperature of 76°C.
Isolation of human peripheral blood lymphocytes and monocytes
Informed consents were obtained from all volunteers before blood sampling. A total of 50 ml of blood from unrelated adult normal, healthy
volunteers (n ⫽ 8) was collected in the presence of anticoagulant in accordance with an approved Institutional Review Board protocol. PBMCs
were isolated by Ficoll-Paque density gradient centrifugation. Blood was
diluted with equal volumes of PBS. An equal volume of diluted blood was
overlaid on Ficoll-Paque-plus in 1:1 ratio and centrifuged at 1200 ⫻ g for
20 min at 18 –20°C. The leukocyte population was collected from the interface and washed with PBS several times to remove plasma and Ficoll.
Approximately 50 million washed cells in 10 ml of RPMI 1640 medium
containing 10% FCS were plated in 100-mm culture dishes to allow monocytes to adhere on the surface of the dish for 2–3 h. The nonadherent cells
(lymphocyte population) were removed, washed with fresh medium, and
cultured in RPMI 1640 medium. Attached monocytes were washed twice
with warm RPMI 1640 medium containing 10% FCS and allowed to remain in the dish overnight at 37°C in 5% CO2. During this period, the
monocytes detached from the dish. They were collected in fresh RPMI
1640 medium. Monocyte purity is ⬃85%, and this is similar (86%) to what
we get with the monocyte isolation kit II from Miltenyi Biotec. Lymphocyte preparations contain ⬍5% monocytes based on CD14 expression. Viability was 98% and 99% in monocyte and lymphocyte fractions, respectively (trypan blue staining). Both lymphocytes and monocytes were used
for ChIP experiments.
ChIP-chips
Purified blood monocytes and lymphocytes (⬃107) from normal and T1D
subjects were crosslinked in 1% formaldehyde, washed, and then sonicated
to shear DNA. IP was then performed with anti-H3K9Me2 or -H3K4Me2,
as described earlier (15). One-tenth of the total lysate was used for “no
antibody” control. IP was then performed with the methylated histone Abs.
Precipitates are washed, eluted, and crosslinks reversed. Part of the DNA
was retained for conventional ChIP PCR. The remaining ChIP-enriched
2266
HISTONE METHYLATION IN MONOCYTES AND LYMPHOCYTES
Table I. GSEA of histone H3K9Me2-methylated candidates in lymphocytes and monocytes (cDNA array)
Lymphocytes
Size
ES
NESa
Nominal
p Val
FDR
q Val
Thrombin signaling
Wnt/␤ catenin signaling
Rhodopsin-like G protein receptor
T cell signaling
Myocyte adrenergic Receptor
11
18
27
23
12
⫺0.62
⫺0.50
⫺0.48
⫺0.46
⫺0.53
⫺1.87
⫺1.81
⫺1.77
⫺1.76
⫺1.74
0.00
0.00
0.01
0.01
0.01
0.40
0.36
0.36
0.30
0.29
B cell Ag receptor
25
⫺0.43 ⫺1.72
0.01
0.29
Oxidative phosphorylation
VIPb inhibit the apoptosis in T
cells
G ␣ 13 pathway
45
15
⫺0.42 ⫺1.69
⫺0.56 ⫺1.66
0.01
0.00
0.32
0.36
23
⫺0.41 ⫺1.66
0.01
0.34
Adrenergic receptor signaling
20
⫺0.45 ⫺1.64
0.01
0.33
fMLP-induced chemokine
activation
B cell immune responses (TACI)b
Steroids biosynthesis
IL 4 signaling
GATA3 transcription
Methane metabolism
Programmed cell death
Myosin phosphorylation
Regulation of ck1/cdk5
27
⫺0.44 ⫺1.63
0.02
0.33
10
11
9
10
8
9
8
10
⫺0.56
⫺0.52
⫺0.60
⫺0.62
⫺0.60
⫺0.52
⫺0.56
⫺0.61
⫺1.62
⫺1.62
⫺1.61
⫺1.60
⫺1.59
⫺1.57
⫺1.52
⫺1.51
0.03
0.02
0.03
0.03
0.03
0.03
0.04
0.03
0.33
0.31
0.31
0.31
0.31
0.33
0.41
0.42
Erk1/Erk2 MAPK signaling
24
⫺0.42 ⫺1.50
0.05
0.41
N-Glycan degradation
Heme biosynthesis
Protein kinase C signaling
CD40 signaling
10
6
6
11
⫺0.53
⫺0.58
⫺0.58
⫺0.45
0.05
0.08
0.02
0.04
0.39
0.39
0.44
0.44
⫺1.50
⫺1.49
⫺1.47
⫺1.46
Monocytes
Size
ES
NESa
Nominal
p Val
FDR
q Val
Fibrinolysis
Cell cycle G1 to S
IL17 signaling
TNFR1 signaling
Cytokine/matrix
metalloproteinases Connection
ETS-mediated macrophage
differentiation
Cell cycle KEGGb
TNF silencer of death domain
signaling
Cascade of cyclin gene
expression
Cytokines and inflammatory
response
Secretin-like G protein-coupled
receptors
IL10 signaling
NK T cell pathway
MAPK signaling
Cysteine metabolism
Wnt signaling
Cyclin E destruction
Tyrosine metabolism
Cytokines-mediated
hematopoiesis
Cell cycle morphological
progression
Ubiquitin-mediated proteolysis
FASb signaling
Methionine metabolism
Keratinocyte differentiation
5
34
5
19
9
0.80
0.42
0.81
0.56
0.79
1.79
1.76
1.76
1.75
1.74
0.00
0.00
0.00
0.02
0.00
1.00
0.72
0.49
0.40
0.35
13
0.73 1.73
0.00
0.31
37
7
0.39 1.70
0.74 1.65
0.00
0.01
0.33
0.43
5
0.81 1.65
0.01
0.39
12
0.67 1.64
0.01
0.36
5
0.83 1.63
0.01
0.37
8
9
64
5
45
7
10
6
0.54
0.67
0.39
0.68
0.45
0.66
0.67
0.78
1.63
1.60
1.59
1.58
1.56
1.56
1.55
1.55
0.01
0.04
0.04
0.01
0.08
0.04
0.05
0.05
0.34
0.39
0.37
0.36
0.41
0.38
0.38
0.36
37
0.34 1.54
0.03
0.34
21
17
6
35
0.46
0.48
0.56
0.37
0.01
0.03
0.05
0.09
0.35
0.34
0.36
0.39
1.55
1.54
1.53
1.51
a
NES: Normalized enrichment score.
KEGG: Kyoto Encyclopedia of Genes and Genomes; VIP: Vasoactive intestinal peptide; TACI: TNF receptor superfamily member 13B; FAS: TNF receptor superfamily
member 6.
b
DNA was amplified by ligation-mediated PCR. DNA was blunted with T4
DNA polymerase, purified, and ligated with linker (5⬘-GCGGTGAC
CCGGGAGATCTGAATTC-3⬘ and 5⬘-GAATTCAGATC-3⬘). DNA was
purified on Qiagen spin columns, and used for PCR amplification (20 cycles) with the primer 5⬘-GCGGTGACCCGGGAGATCTGAATTC-3⬘. The
PCR products were purified using Qiagen spin columns used in ChIP-chip
experiments. The ChIP-chips were performed with human 12K cDNA,
12K CpG, and 6K promoter arrays using protocols described by us (15).
All NimbleGen Systems arrays were hybridized, and the data were extracted according to standard operating procedures by NimbleGen Systems.
Signal map software provided by NimbleGen Systems was used to visualize the array peaks.
Microarray data collection and statistical analyses
After washing, hybridized microarray slides were scanned using GenePix
4000B scanner (Axon Instruments). Acquired microarray images were analyzed with Genepix v.6 software. Preprocessing of raw data and statistical
analyses were performed (15). To examine the methylation pattern in
monocytes and lymphocytes, an unsupervised hierarchical clustering
method was used to group the samples. We focused on probes whose
ChIP-enriched signal was 1.5-fold higher than the no-antibody control signal in at least four of the 16 samples. A total of 2682 probes on cDNA array
and 2878 probes on CpG array satisfied the above criteria and were applied
to Cluster v2.11 to generate a hierarchical clustering diagram. Pearson
correlation was used as distance measurement and average linkage method
was used to generate the dendrogram, which was visualized using Java
Treeview V1.0.12. The unbiased probe selection criterion assures that these
observations are not due to probes that either already shows consistent
patterns within the cell type groups or show difference between the groups.
To generate the venn diagrams, dimethylation candidates in lymphocyte
and monocyte samples were selected separately using Bioconductor package LIMMA. The criteria used in candidate selection were ⬎1.5-fold enrichment in ChIP-enriched DNA compared with input DNA and with a
value of p ⬍ 0.05.
Gene set enrichment analysis (GSEA) was performed as described (6).
This method analyzes expression data at the level of predefined gene sets
instead of individual genes to detect significant concordant differences in
biological processes between two phenotypes. GSEA 2.0 software and generic pathways in the Molecular Signature Database of genesets C2 version
2 were used for the analysis. All genes with known symbols in the data set
were ranked based on their correlation to the lymphocyte phenotype, and
the rank positions of all members of a given gene set were used to calculate
an enrichment score (ES). Subsequently, 1000 permutations were used to
determine which gene sets were significantly enriched in monocytes or
lymphocytes.
Results
Profiling of histone H3K9Me2 in human primary lymphocyte
and monocytes
Genome-wide histone methylation data sets for histone H3K9Me2
were obtained by the ChIP-chip approach (13, 19). Peripheral
blood lymphocyte and monocyte fractions were prepared from
eight normal healthy volunteers, ages ranging from 36 to 71 (three
males and five females) according to standard procedures as described earlier (19). All blood samples were obtained under protocols approved by the City of Hope IRB. Blood cells were then
processed for conventional ChIP assays and ChIP-chip profiling.
In brief, isolated cells (lymphocytes or monocytes) from peripheral
blood were crosslinked with 1% formaldehyde. Anti-dimethyl-histone H3K9 (Upstate 07-441), specific to H3K9Me2 but not to
H3K9Me or H3K9Me3, was used in our ChIP-chip experiments.
The Ab-enriched DNA samples and no-antibody controls were
prepared from lymphocytes and monocytes of each volunteer separately, amplified by ligation-mediated PCR, and labeled with Cy5
The Journal of Immunology
and Cy3 dyes. They were then analyzed by ChIP-chips using human 12K CpG island arrays and 12K cDNA arrays as described
under Materials and Methods.
To analyze histone H3K9Me2 profiling data sets, we used an
unsupervised hierarchical clustering method to group the samples.
Fig. 1A depicts the 12K cDNA array data. Two striking features
are observed. First, the H3K9Me2 distribution patterns in the coding regions are remarkably similar in all the individuals within
their monocyte or lymphocyte groups irrespective of age or gender. Second, the distribution patterns are clearly distinct between
lymphocyte and monocyte groups. To elaborate the cell-type distinct patterns, we selected probes consistently methylated in all
samples using LIMMA (detailed candidate list in Tables S1–S4,
published as online supplement information).4 The venn diagram
(Fig. 1C) shows a striking difference with very little overlap of
methylated probes between monocytes and lymphocytes. Similar
results were also found using CpG island arrays that are mostly
representative of promoter regions (20) (Fig. 1, B and D). This
genome-wide profiling demonstrates for the first time, with resolution at the gene level, that two closely related human blood cells
with different functions have distinct H3K9Me2 patterns, whereas
patterns within a specific cell type are remarkably similar despite
age or gender, suggesting that stability of core epigenome integrity
is indispensable for cell-specific functions. However, in analyzing
lymphocytes and monocytes separately, we did find that a portion
of the epigenome does indeed show key differences among individuals, with person-to-person variations as previously indicated
(6). This emphasizes a critical need to analyze blood cells separately, rather than whole blood when performing these kinds of
profiling studies.
The size of the human genome and complexity of histone PTMs
are major impediments to mapping the entire human epigenome
(21, 22). However, the majority of the human genome is composed
of repetitive elements with only 4% coding for proteins that include the coding and promoter regions of genes. In this study, we
used two very affordable DNA microarrays, human 12K cDNA
arrays and 12K CpG island arrays. The human 12K CpG island
array contains a significant percentage of the CpG islands found in
the human genome and ⬃68% of them were located near a transcription start site (20). Together, these cDNA and CpG arrays
cover about a third of all human genes and a significant fraction of
CpG islands and promoters. It should be noted that although these
arrays have lower resolution and accuracy than high density tiling
arrays, they are highly sensitive, requiring as little as 0.1 ␮g ChIP
DNA compared with 5 ␮g for tiling arrays. This is an important
factor for ChIP-chip analysis due to limited numbers of primary
human cells, like blood lymphocytes and monocytes obtained in
this study. Tiling arrays that cover the whole human gene coding
regions for ChIP-chip are still beyond the reach of most laboratories. Most importantly, the 12K cDNA and CpG arrays used in this
study do provide valid information at gene resolution.
Based on a previous study (6), it can be predicted that individual
epigenomes have significant variations. However, it is also reasonable that different cell types would have specific and obligatory
epigenomes for cell-specific functions. Indeed, results from this
study (Fig. 1) demonstrate that, despite noticeable variations
among individuals, the histone H3K9Me2 distribution pattern
among core genes is cell-type specific and stable.
4
The online version of this article contains supplemental material.
2267
FIGURE 2. Hierarchical clustering of histone H3K4Me2 profiling in
lymphocytes among individuals. Lymphocytes were isolated from normal
volunteers. Each sample was processed for ChIP-chip analysis with human
6K promoter arrays separately with anti-H3K4Me2 Ab. Hierarchically
clustered histone H3K9Me2 profiles of lymphocyte sample from six normal subjects (columns) and 5026 probes (rows). Only well-measured
probes were included for analysis. The filtering yielded 5026 probes, corresponding to 6000 probes. Hierarchical clustering with Spearman Correlation and average linkage applied to selected probes.
GSEA of the histone methylation data sets from cDNA arrays
An unexpected advantage of using cDNA arrays in ChIP-chip
analysis is that data mining and pathway analyses packages used in
gene expression analyses can be directly applied without any modification. Therefore, we used GSEA software (23) to examine
whether genes within specific signaling pathways are H3K9Me2
targets in monocytes or lymphocytes. GSEA, which is still not
applicable to tiling array data, is a computational method that determines whether an a priori defined set of genes in biological
pathways shows statistically significant and concordant differences
between two biological states. It is a powerful data mining bioinformatics tool that can complement single-gene studies to examine
2268
HISTONE METHYLATION IN MONOCYTES AND LYMPHOCYTES
FIGURE 3. Representative tiling array views of histone H3K4Me2 patterns across promoter regions of selected genes from histone H3K4Me2 candidates. Eight H3K4Me2 candidate genes (RPL30, RPL26, NOTCH4, SLCA1, DUSP2, CDC25A, CDCL5, and OSR1) (Table S5 in online supplement) and
four non-candidates genes (LU17A4, PLC, MOG, and ADH6) were selected to validate their promoter H3K4Me2 patterns in the promoter tiling arrays.
Dots represent the transcription start sites and arrows indicate the transcription direction. Histone H3K4Me2 data within promoter regions is visualized by
signal map software provided by NimbleGen Systems.
changes occurring in several gene members of biological networks. For each gene set, an ES is calculated to measure the degree
of enrichment in lymphocytes or monocytes. Nominal p values
derived from permutation, as well as multiple comparison corrected p values (false discovery rate (FDR) q value and familywise error rate (FWER) p values), are provided to assess the significance of the ES.
As illustrated in Table I, it is evident that histone H3K9Me2 is
substantially enriched in many tightly regulated pathways in
monocytes and lymphocytes, and especially in some cell-type specific pathways when lymphocytes and monocytes are compared.
Among the top twenty-four H3K9Me2 enriched pathways in lymphocytes, several are clearly associated with lymphocytes or lymphocyte-specific functions. Notably, T cell, IL4 signaling, and
GATA3 transcription, which are known to be T cell response pathways, are among the top pathways. Similar results are seen in
monocytes (Table I, right panel) where TNFR1, inflammation, and
matrix metalloproteinases/cytokines are present and known to
have monocyte-specific functions. Although the mechanism for
this is not completely understood, they do confirm that chromosomal H3K9Me2 is an euchromatin mark because these identified
genes are expressed or are inducible in the respective cells. They
suggest that this chromatin mark has evolved as an important modulator of gene regulation, especially for tightly controlled and celltype specific genes. As indicated by previous studies (18, 24 –26),
H3K9Me2 most likely plays a role in transient transcriptional repression of these genes. This may be mediated by G9a, the major
mammalian enzyme responsible for H3K9Me2 in cells that is also
essential for early embryogenesis (18, 27).
A recent study of G9a that greatly enhances our understanding
of the role of histone H3K9Me2 in cell development (24) showed
that, surprisingly, only eight genes were up-regulated by 2-fold in
G9a knock out cells despite a significant reduction of global
H3K9Me2. This suggests that, although H3K9Me2 is an epigenetic mark of heterochromatin formation and transcription silencing in euchromatin, this mark alone probably cannot lead to or
maintain these events unless other repression marks, such as
H3K9Me3, H3K27Me3, H4K20Me3, or DNA methylation, are
also involved. Notably, evidence shows that histone H3K9Me2
and heterochromatin protein 1␥, a protein containing a chromodomain that recognizes H3K9 methylation, are present in transcribed
genes and both were associated with elongation of RNA polymerase II (28). The wide distribution of H3K9Me2 in gene promoter
and coding regions in lymphocytes and monocytes noted in our
current study indicates that this mark has key roles in transcription
at the level of chromatin genome. It has been demonstrated that the
distribution of H3K9Me2 and H3K4Me2/3 in gene promoter and
coding regions are exclusive of each other (12, 15), indicating that
these marks define chromatin states of transcribed genes in euchromatin. Thus, chromatin with H3K4Me2/3 (along with H3K9
acetylation) would represent proactive transcription state, whereas
that with H3K9Me2 would represent repressed transcription but
not gene silencing. Such repression can be reversed to active transcription in response to proper signals such as the pathways shown
in this study (Table I). This suggests a “fine-tuning” role for
H3K9Me2 in regulating the transcription of these genes through
unknown mechanisms occurring in the coding regions of these
genes. Moreover, such repression is most likely critical for a cell
to maintain its unique identity and explain, at least in part, the
stable histone methylation distribution patterns we observed
(Fig. 1).
Profiling of histone H3K4Me2 in human primary lymphocyte
Evidence shows that H3K4Me2 and H3K9Me2 are present in partitioned chromosome regions that are structurally and functionally
distinct in eukaryotes (29). H3K4Me2 is closely associated with
H3K4Me3, which mainly occurs concomitantly on active loci (14,
15, 30), especially in the first exon. H3K4Me2 can be present on
active as well as inactive genes. Its distribution can extend to promoter regions and gene coding regions (29, 30). We therefore
tested whether patterns of K4Me2, a chromosomal mark associated
with gene activation, are also conserved among the different individuals. We performed ChIP-chip experiments in primary lymphocytes from the volunteers using anti-histone H3K4Me2 and human
6K promoter arrays (Aviva Systems Biology). Similar to chromatin mark histone H3K9Me2, H3K4Me2 distribution patterns at
promoter region are comparable in lymphocytes among individuals (Fig. 2). Analysis results in 1240 candidate genes were identified as enriched H3K4Me2 in promoter region. Detailed genes
list in Tables S5, published as online supplement.
The accuracy of the analysis of histone H3K4Me2-methylated
candidates (Table S5, online supplement) were further validated by
an independent ChIP-chip experiment with the same Ab but this
time with a high resolution human promoter tiling array (NimbleGen Systems). Fig. 3 shows the promoter regions of selected candidate methylated genes (RPL30, RPL26, NOTCH4, SLCA1,
The Journal of Immunology
DUSP2, CDC25A, CDCL5, AND OSR1) from Table S5 (online
supplement) and demonstrates that promoter region of these gene
are, indeed, nicely enriched in H3K4Me2 peaks. In contrast, no
peaks are seen in promoters of none candidate genes (LU17A4,
PLC, MOG, and ADH6) that are not determined to be methylated
candidates from the analysis (Fig. 3) and provide further evidence
for the accuracy of the microarray data (Fig. 2). Together, these
experiments are consistence with histone H3K9Me2 results (Fig.
1) and prove that H3K4Me2 distribution patterns in lymphocytes
are also cell-specific among individuals regardless of age or
gender.
Discussion
This study shows, for the first time, that monocytes and lymphocytes of different individuals, regardless of age or gender, have
stable, consistent, and cell-type specific histone methylation patterns in gene coding, promoter, and CpG islands regions. It is
highly likely that this conclusion can be extended to the whole
human genome. Although our result could be anticipated, this genome-wide study provides direct experimental proof that histone
methylation distributions are not random but exhibit specific and
stable patterns among different individuals. Notably, this is analogous to gene expression patterns, but there are key differences
between them. Whereas gene expression profiling measures thousands of RNA transcripts simultaneously, histone methylation profiling provides genome-wide information of chromatin status depending on the type of microarray used.
The implications of our results are the following. First, the specific blood-cell type maintains its histone methylation patterns and
perhaps the epigenome too, although factors such as age, gender,
and cellular states may contribute to significant variations. Second,
the methylation patterns of core genes in differentiated cells are
relatively stable and very likely heritable. Thus, a cell might have
system(s) to repair imbalances or “damages” involving nucleosome deletion or alterations in PTMs induced by events such as
DNA repair. Third, both histone methylation and gene expression
have cell-type specific patterns and provide strong evidence that
histone PTMs control gene expression. Fourth, histone H3K9Me2
is enriched in specific cell-type genes and pathways, suggesting
that the H3K9Me2 methyltransferase, G9a, plays a regulatory role
in the transcription of these genes, and also explaining the requirement for this chromosomal mark and G9a during differentiation.
Last, but not least, human peripheral blood remains the most easily
accessible and noninvasive source of human tissue, which can provide valuable information such as genomic mutations and gene
expression, particularly with respect to immune and inflammation
responses. Approaches such as DNA sequence analyses, mRNA
profiling, and proteomics have yielded immense amounts of valuable data for medical research. However, there is little data in the
context of histone methylation in specific human blood cells. Our
results demonstrate that histone PTMs have the potential to become chromatin information databases for individuals that can be
enriched by profiling these chromatin marks in human primary
tissues and cells using the ChIP-chip approach.
Acknowledgments
We are deeply grateful to all the volunteers who donated blood.
Disclosures
The authors have no financial conflict of interest.
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