Barski, A. et al. Chromatin poises miRNA- and protein-coding genes for expression. Genome Res. 19, 1742-1751

Laboratory of Molecular Immunology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA.
Genome Research (Impact Factor: 14.63). 09/2009; 19(10):1742-51. DOI: 10.1101/gr.090951.109
Source: PubMed


Chromatin modifications have been implicated in the regulation of gene expression. While association of certain modifications with expressed or silent genes has been established, it remains unclear how changes in chromatin environment relate to changes in gene expression. In this article, we used ChIP-seq (chromatin immunoprecipitation with massively parallel sequencing) to analyze the genome-wide changes in chromatin modifications during activation of total human CD4(+) T cells by T-cell receptor (TCR) signaling. Surprisingly, we found that the chromatin modification patterns at many induced and silenced genes are relatively stable during the short-term activation of resting T cells. Active chromatin modifications were already in place for a majority of inducible protein-coding genes, even while the genes were silent in resting cells. Similarly, genes that were silenced upon T-cell activation retained positive chromatin modifications even after being silenced. To investigate if these observations are also valid for miRNA-coding genes, we systematically identified promoters for known miRNA genes using epigenetic marks and profiled their expression patterns using deep sequencing. We found that chromatin modifications can poise miRNA-coding genes as well. Our data suggest that miRNA- and protein-coding genes share similar mechanisms of regulation by chromatin modifications, which poise inducible genes for activation in response to environmental stimuli.


Available from: Kairong Cui
Chromatin poises miRNA- and protein-coding
genes for expression
Artem Barski,
Raja Jothi,
Suresh Cuddapah,
Kairong Cui,
Tae-Young Roh,
Dustin E. Schones,
and Keji Zhao
Laboratory of Molecular Immunology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda,
Maryland 20892, USA
Chromatin modifications have been implicated in the regulation of gene expression. While association of certain mod-
ifications with expressed or silent genes has been established, it remains unclear how changes in chromatin environment
relate to changes in gene expression. In this article, we used ChIP-seq (chromatin immunoprecipitation with massively
parallel sequencing) to analyze the genome-wide changes in chromatin modifications during activation of total human
T cells by T-cell receptor (TCR) signaling. Surprisingly, we found that the chromatin modification patterns at many
induced and silenced genes are relatively stable during the short-term activation of resting T cells. Active chromatin
modifications were already in place for a majority of inducible protein-coding genes, even while the genes were silent in
resting cells. Similarly, genes that were silenced upon T-cell activation retained positive chromatin modifications even after
being silenced. To investigate if these observations are also valid for miRNA-coding genes, we systematically identified
promoters for known miRNA genes using epigenetic marks and profiled their expression patterns using deep sequencing.
We found that chromatin modifications can poise miRNA-coding genes as well. Our data suggest that miRNA- and
protein-coding genes share similar mechanisms of regulation by chromatin modifications, which poise inducible genes for
activation in response to environmental stimuli.
[Supplemental material is available online at The raw and processed data from this study have
been submitted to NCBI Short Read Archive ( under accession nos. SRP000201 and
SRP000200 for resting cells, and to NCBI Gene Expression Omnibus (http://ncbi/ under accession no.
GSE16657 for activated cells.]
Activation of T cells by T-cell receptor (TCR) signaling is a well-
studied physiological process (Kane et al. 2000). In living organ-
isms, T-cell activation is initiated by TCR encountering a specific
antigen presented by major histocompatibility complex (MHC) on
the surface of antigen presenting cells, which triggers a cascade of
signaling events leading to cell proliferation and expression of
certain cytokines ( Janeway 2001). The process is accompanied by
global chromatin decondensation and transcriptional activation
of numerous genes, which is mediated by TCR signaling-activated
kinases and transcription factors (Crabtree 1989; Chandok and
Farber 2004).
Initiation of transcription involves binding of transcription
factors to cis elements, followed by recruitment of coactivator
enzymes, chromatin remodeling factors, mediators, and RNA poly-
merase (Li et al. 2007). Histone modifications and substitution of
core histones by certain histone variants are believed to play an im-
portant role in this process (Weisbrod 1982; Jenuwein and Allis
2001; Stallcup 2001; Vermaak et al. 2003; Roh et al. 2005; Bernstein
et al. 2007; Kouzarides 2007). Indeed, recent large-scale analyses
indicate that specific chromatin modifications are enriched at ac-
tive or silent promoters (Kim et al. 2005; Roh et al. 2006; Barski
et al. 2007; Guenther et al. 2007; Heintzman et al. 2007; Mikkelsen
et al. 2007; for review, see Bernstein et al. 2005, 2007; Schones
and Zhao 2008).
The patterns of histone modifications on protein-coding
genes in the human genome have been extensively characterized
(Bernstein et al. 2005; Kim et al. 2005; Roh et al. 2006; Barski et al.
2007; Guenther et al. 2007; Mikkelsen et al. 2007; Wang et al. 2008;
Kolasinska-Zwierz et al. 2009). However, very little is known re-
garding the regulation of miRNA genes, even though they have
been implicated in various important biological functions (Bartel
and Chen 2004; Pedersen and David 2008). While some miRNAs
were shown to be transcribed by RNA polymerase III (Pol III)
(Borchert et al. 2006), it is believed that a majority of miRNA genes
require Pol II for expression (Lee et al. 2004; Chuang and Jones
2007). Most of the transcription start sites (TSSs) of miRNA genes
were not known, although attempts have been made to identify
TSSs of some individual miRNAs (Woods et al. 2007). A recent
study suggests that histone modification patterns may be used to
predict TSSs of miRNA genes (Marson et al. 2008).
We recently mapped the genome-wide distribution of many
histone methylation (Barski et al. 2007) and acetylation marks
(Wang et al. 2008), as well as nucleosome positions (Schones et al.
2008) in resting human CD4
T cells. Among the 38 modifications
examined, five modifications (H3K9me2, H3K9me3, H3K27me2,
H3K27me3, and H4K20me3) were associated with gene repression,
whereas the others were associated with gene activation, or had
no clear association (Wang et al. 2008). Consequently, the for-
mer modifications are considered ‘repressive’ and the latter are
These authors contributed equally to this work.
Present addresses:
Biostatistics Branch, National Institute of Envi-
ronmental Health Sciences, National Institutes of Health, Research
Triangle Park, NC 27709, USA;
Department of Life Science, Pohang
University of Science and Technology (POSTECH), Pohang 790-784,
Republic of Korea.
Corresponding author.
E-mail; fax (301) 480-0961.
Article published online before print. Article and publication date are at
1742 Genome Research
19:1742–1751; ISSN 1088-9051/09;
Page 1
considered ‘active.’ The specific association of histone mod-
ifications with transcriptional activity appears to confirm the no-
tion that chromatin modifications regulate gene expression.
However, even though the active modifications generally correlate
with gene transcription, some of them have also been found at
silent genes (Raisner et al. 2005; Roh et al. 2006; Barski et al. 2007;
Guenther et al. 2007; Mikkelsen et al. 2007). Studies in yeast sug-
gest that some histone modifications result from active transcrip-
tion (Li et al. 2002; Ng et al. 2003) and have been proposed to be
a memory of past transcriptional events (Ng et al. 2003). On the
other hand, experiments in mice have shown that changes in
chromatin modifications might precede changes in gene expres-
sion (Chambeyron and Bickmore 2004).
To test if similar mechanisms exist in the human genome, we
decided to examine changes in histone modifications in the genes
induced or repressed by TCR signaling during activation of human
T cells. Surprisingly, we found that the induced genes were
associated with the active modifications in resting T cells even
before TCR signaling and, little or no change in histone mod-
ifications was detected upon gene induction in the short-term
activation of T cells. This suggests that these inducible genes are
poised for activation by chromatin in the resting cells. Further-
more, we identified promoters of miRNA genes using chromatin
signatures and found that some silent miRNA genes in resting cells
are also poised for expression by chromatin.
Changes in chromatin modifications and gene expression
To examine if dynamic changes in chromatin modifications are
associated with gene activation or repression, we combined gene
expression analysis with the mapping of several chromatin mod-
ifications in resting and activated human CD4
T cells. Total rest-
ing CD4
T cells were isolated from blood to 95%–98% purity, and
activated with anti-CD3/28 beads for 18 h resulting in the
activation of 85%–95% of the cells (Supplemental Fig. S1A,B). The
genome-wide distribution of eight histone methylation marks
(H3K4me1, H3K4me3, H3K9me1, H3K27me1, H3K27me3, H3K36
me3, H3K79me2, and H4K20me1), his-
tone variant H2A.Z, and RNA polymerase
II (Pol II) in activated cells were mapped
using ChIP-seq, as described previously
(Barski et al. 2007) and compared with
the distribution patterns of 29 histone
modifications in resting T cells (Barski
et al. 2007; Wang et al. 2008; Supple-
mental Table S1). At the same time, we
profiled the mRNA levels using micro-
array analysis and analyzed miRNA pro-
files using deep sequencing. We found
that 3244 protein-coding genes were
constitutively expressed (see Methods) in
both resting and activated cells (hence-
forth, referred to as E!E), 5717 genes
were constitutively silent in both cell
states (S!S), 167 genes were silent in
resting cells, but expressed in activated
cells (S!E), and 271 genes were expressed
in resting cells, but silenced upon activa-
tion (E!S). The S!E subset was signifi-
cantly enriched in genes associated with
immune response, response to stimuli, and cell proliferation
(Supplemental Table S2). Among the 541 known miRNAs in the
human genome, 194 miRs were detected (>5 tags, P < 10
on a Poisson background model) in resting and/or activated T cells
by deep sequencing (Supplemental Table S3).
Similar to our findings in resting T cells (Roh et al. 2006; Barski
et al. 2007; Wang et al. 2008), the promoters of expressed genes
in activated T cells were associated with high levels of Pol II and
‘active’ histone modifications such as H3K4me1/3, H3K9me1,
H3K27me1, H4K20me1, and histone variant H2A.Z. Many silent
genes were associated with the repressive H3K27me3 modification.
Examination of the induced genes (S!E set) revealed that
many of them already had active chromatin modifications at their
promoters, while silent in the resting state. For example, the E2F1
gene, whose expression was not detected in resting T cells, but
was induced upon T-cell activation, had peaks of active marks
H3K4me3, H2A.Z, and H3K9me1, but not repressive mark
H3K27me3 (Fig. 1A). Similar patterns were observed for LIF and
CISH genes, which were induced upon TCR signaling (Supple-
mental Fig. S2). Thus, although these genes were not expressed,
they had a chromatin environment resembling that of expressed
genes, suggesting that their genomic loci were prepared for ex-
pression. Such genes that are currently not expressed, but possess
‘active’ chromatin modifications will henceforth be referred to as
‘poised’ genes. We also observed silent, but poised genes in acti-
vated T cells, especially among genes that were silenced upon T-cell
activation. Many of these genes retained active chromatin mod-
ifications, even after being silenced. For example, the NKTR gene
retained H3K4me3, H3K27me1, and H4K20me1 modifications,
and histone variant H2A.Z at the promoter, even though it was
silenced (Fig. 1B), suggesting that poising could be a result of pre-
vious transcription.
To investigate the poising of genes on a global scale, we cal-
culated the average tag density profiles (see Methods) of histone
modifications at promoter regions and in the gene bodies.
As expected, constitutively expressed (E!E) genes were en-
riched in active modifications, including H3K4me1/3, H3K9me1,
H3K27me1, H4K20me1, and H2A.Z at their promoters and/or in
the gene bodies in both resting and activated T cells, and had low
Figure 1. E2F1 and NKTR genes do not change their chromatin status upon induction o r silencing,
respectively. The chromatin modification patterns of the E2F1 gene (A) and the NKTR gene (B) are shown
in resting (top panel) and activated (botto m panel) T cells.
Genome Research 1743
Chromatin poises genes for expression
Page 2
levels of the repressive modification H3K27me3 (Fig. 2; Supple-
mental Figs. S3, S4). In contrast, constitutively silent (S!S) genes
had lower levels of active modifications and higher levels of
repressive H3K27me3. Interestingly, induced genes (S!E) had
high levels of active modifications not only in activated cells,
where they were expressed, but also in resting cells, where they
were silent. These genes had low levels of H3K27me3 in both cell
states. Similarly, repressed genes (E!S) possessed high levels of
active modifications in both resting and activated T cells and low
levels of H3K27me3. Pol II was present at a higher level in the
bodies (Fig. 2C, arrow; Supplemental Fig. S5) of expressed genes
compared to those of silent genes, thus confirming that the ab-
sence of mRNA product from silent genes was due to the absence
of transcription. However, high levels of Pol II were present at
Figure 2. A majority of the genes do not change their chromatin status upon induction or silencing. Average ChIP-seq tag density for the four gene sets
are shown in resting (top panel) and activated (bottom panel) CD4
T cells. The four gene sets: S!E (silent in resting and expressed in activated T cells), red
dashed line in density plot and light red in box plot; E!S, blue dashed line and light blue; E!E, red line and red; S!S, blue line and blue. The average tag
density values for resting and activated cells are not directly comparable as they have not been normalized across samples. Box plot summarizes the
distribution of the number of tags in the region of interest (highlighted in yellow) for each gene set. Box plot captures the median, the middle 50% of the
data points, and the outliers. The data points for each gene set are divided into quartiles, and the interquartile range (IQR) is calculated as the difference
between the first and the third quartiles. The filled box denotes the middle 50% of the data points, with the horizontal line in-between and the notch
representing the median and confidence intervals, respectively. Data points more than 1.5 times IQR lower or higher than first or third quartiles, re-
spectively, represent outliers and are shown as dots. The horizontal line connected by vertical dashed lines above and below the filled box (whiskers)
represents the largest and the smallest nonoutlier data points. The cluster of horizontal red and black lines below each box plot signifies whether or not the
difference between the medians of two gene sets are statistically significant, respectively (P < 0.01; two-tailed Wilcoxon rank-sum test). (A) H3K4me3, (B)
H3K27me3, (C ) Pol II, and (D) H3K79me2. Profiles for other modifications are presented in Supplemental Figures S3 and S4.
Barski et al.
1744 Genome Research
Page 3
induced gene (S!E) promoters in both resting and activated T
cells. In addition, Pol II did not disappear from the repressed gene
(E!S) promoters after the genes were silenced in activated T cells.
The methylation of histone H3K36 and H3K79 residues in the
gene body is considered to be associated with active transcription
(Li et al. 2002, 2007; Krogan et al. 2003; Steger et al. 2008). How-
ever, we detected increased levels of H3K36me3 in induced genes
(S!E) even in resting cells (Supplemental Fig. S3E), suggesting that
this modification might have remained from previous rounds
of transcription or be generated in a transcription-independent
manner. We found that only H3K79me2 was somewhat correlated
with the gene expression status in resting cells. Upon T-cell acti-
vation, H3K79me2 levels increased in induced genes, but repressed
genes (E!S) did not lose the methylation (Fig. 2D), suggesting that
longer times may be required to erase this modification. Our
observations remained the same even when we used alternative
gene sets defined using more stringent criteria (see Methods for
To ensure that the observed global trends are not due to a few
genes with extremely high levels of histone modifications, we
examined promoter modifi cations on a gene-by-gene basis. For
each gene set, we calculated the percentage of genes possessing
statistically significant (P-value < 10
) levels of H3K4me3, H2A.Z,
and Pol II tags at gene promoters (Methods). We found H3K4me3
at 83% and 81% of constitutively expressed gene (E!E) promoters
in resting and activated T cells, respectively (Fig. 3A). Interestingly,
68% and 64% of induced genes (S!E) had H3K4me3 in resting
and activated T cells, respectively. In fact, only three out of the
167 induced genes (S!E) actually became H3K4me3-positive
upon T-cell activation. Similarly, a ma-
jority of repressed genes (E!S) had
H3K4me3 in both resting (65%) and ac-
tivated (58%) cells. Only 18 of the 271
repressed (E!S) genes lost H3K4me3
upon T-cell activation. Two-thirds of the
constitutively silent genes (S!S) lacked
H3K4me3. We observed a largely similar
pattern for H2A.Z and Pol II (Fig. 3B,C).
While we found no changes in regard to
the presence or absence of chromatin
modifications and Pol II at promoters,
their levels at some individual genes
could have changed.
Nature of poised genes
as histone variant H2A.Z and Pol II, occu-
pied promoters of 15%–30% of all silent
genes. This is consistent with recent ob-
servations that a large fraction of silent
genes had H3K4me3 at their promoters
(Barski et al. 2007; Guenther et al. 2007;
Mikkelsen et al. 2007). To better under-
stand the nature of the poised genes, we
performed Gene Ontology (GO) analysis
of the silent genes having significant lev-
els of Pol II at their promoters in resting
cells. This analysis revealed that this gene
set was enriched with functions related to
metabolic processes and cell cycle (Sup-
plemental Table S5). This was not surpris-
ing given that the genes involved in basic cellular processes would
have to be prepared for expression in case certain contingencies
arise. When T cells receive a signal (TCR signaling being just one
example), some of these genes begin to be expressed. One would
expect the cell-type specific genes that are not needed in a given cell
type not to be poised. Indeed, H3K4me3 and Pol II tag density
profiles of silent genes involved in muscle or organ development,
which are not needed for T cells, showed no enrichment in
H3K4me3 or Pol II. Conversely, profiles for cell cycle or metabolism-
related silent genes showed high enrichment at the promoter
(Fig. 3D,E).
Systematic identification of miRNA gene promoters
using chromatin signatures
Our data above indicate that the majority of inducible protein-
coding genes are poised by chromatin for possible future expres-
sion. Next, we sought to find if the poising mechanism also applies
to miRNA-coding genes. Since most miRNAs are believed to be
transcribed by Pol II (Lee et al. 2004; Chuang and Jones 2007), we
hypothesized that their promoter structure and regulation may be
similar to that of protein-coding genes. Indeed, we found that
transcription of pri-miRNAs might be initiated from promoters
that bear chromatin modifications similar to that of protein-cod-
ing genes. For example, we detected peaks of chromatin mod-
ifications including H3K4me3, H3K9ac, and H2A.Z, as well as Pol II
upstream of a cluster of three highly expressed miRNA genes
MIRLET7A1, MIRLET7F2, and MIRLET7D located in an intergenic
region on chromosome 9 (Fig. 4A; Supplemental Table S3). High
Figure 3. Inducible genes are poised for expression. (A–C ) A majority of inducible genes have
H3K4me3 (A), H2A.Z (B), and Pol II (C ) at their promoters. The percentage of genes that have significant
(P-value < 10
) number of tags at the promoter for each set of genes is shown in resting (top panel) and
activated (bottom panel) T cells. (D,E ) Silent genes associated with cell cycle and metabolism, but not
muscle or organ function and development, are poised in T cells. H3K4me3 ( D ) and Pol II (E ) tag density
profiles for silent genes taking part in specific biological processes.
Chromatin poises genes for expression
Genome Research 1745
Page 4
levels of H3K27me1, H3K9me1, H3K79me2, and H3K36me3,
which are typically associated with actively transcribed regions of
protein-coding genes, were found immediately downstream from
these peaks. Presence of expressed sequence tags (ESTs) starting
near the peaks and extending past the miRNAs provides further
evidence of transcriptional initiation from the predicted TSS
regions for the miRNA genes.
Based on these observations, we designed an algorithm that
uses a kernel density estimation function (see Methods) to identify
putative promoters for all known miRNAs using H3K4me3, H2A.Z,
and Pol II data in resting cells. The algorithm starts from the 59 end
of the pre-miRNA, and traverses upstream until it finds statistically
significant peaks for each of the three modifications. A promoter
for a miRNA-coding gene is predicted if the peaks of at least two out
of the three colocalize, a pattern typically observed at the pro-
moters of protein-coding genes (see Methods for details). When
this algorithm was tested to predict the promoters of 1000 highly
expressed genes, ;90% of the predicted promoters were within
a few hundred base pairs of the annotated TSSs (Supplemental Fig.
S6). Of the 541 human miRNAs in miRBase (Griffiths-Jones et al.
2006), we identified promoters for 234 miRNAs, 129 of which are
located within a gene, and 105 of which are intergenic (Supple-
mental Table S3). About 67% either shared the promoter with
a protein-coding gene that hosts the miRNA or had an EST initi-
ating at the predicted promoter and extending past the miRNA,
suggesting that the transcription of pri-miRNAs is indeed initiated
from the promoters. Another 15% had ESTs starting at the pro-
moter, extending toward the miRNA, but ending short of it. Of the
85 intergenic miRNAs that did not share a promoter with a protein-
coding gene, 36 had an EST starting at the predicted promoter and
extending past the miRNA, and 19 had an EST starting at the
promoter, but ending short of the miRNA (Fig. 4; Supplemental
Table S3).
To ensure that the predicted miRNA promoters are accurate,
we picked a few predicted promoters for which no EST support was
available and performed validation experiments. First, we per-
formed 59 RACE experiments to detect pri-miRNA transcripts
downstream from predicted TSSs of seven clusters of miRNAs (16
miRNAs total) that were expressed in T cells (Supplemental Fig.
S7B; see Methods). We were able to detect the pri-miRNA tran-
scripts and their TSSs were within ;200 base pair (bp) of their
predicted promoters (Supplemental Fig. S7). This resolution is
similar to that achieved for a set of protein-coding genes (Supple-
mental Fig. S6), and is reasonable given that the resolution of the
chromatin modification data used to predict the promoters was
;150 bp. Second, we used a promoter reporter assay to test the
promoter activity at the predicted miRNA promoters. We cloned
;650 bp surrounding putative promoters of six miRNA genes that
were not expressed in T cells into a pGL3 enhancer luciferase re-
porter vector containing an SV40 enhancer, but no promoter. Two
sequences that did not display Pol II binding and H3K4me3 en-
richment in ChIP-seq were used as controls. When transfected into
Jurkat cells, all six miRNA promoter constructs had significantly
higher activity than controls (P < 0.005), thus confirming that
predicted promoters indeed possess promoter activity (Fig. 5; see
Methods). A chromatin-based approach to promoter prediction
has also been used and validated by others (Marson et al. 2008;
Guttman et al. 2009). A comparison of the miRNA promoters
predicted by our approach with that predicted by Marson et al.
(2008), which used H3K4me3 mark and DNA sequence features,
revealed that a majority of our predictions are the same as that
reported in this other study.
Figure 4. miRNA promoters have chromatin modification patterns similar to that of protein-coding genes. Chromatin modification patterns in the
region surrounding the intergenic MIRLE T7 miRNA cluster (A), and intragenic MIR98 and MIRLE T7F2 (B), and MIR491 (C) are shown. Putative promoters
are marked by H3K4me3, H2A.Z, and Pol II peaks. The green bars extend from the predicted transcription start site to the 39 end of the pre-miRNA.
H3K27me1, H3K9me1, H3K79me2, and H3K36me3 modifications, typically found within gene bodies of protein-coding genes, are seen within the
putative miRNA coding transcript.
Barski et al.
1746 Genome Research
Page 5
We found examples of intragenic miRNAs having indepen-
dent promoters or sharing promoters with the protein-coding
genes. For example, MIR98 and MIRLET7F2 located within the
HUWE1 gene on chromosome X, had peak signals of Pol II,
H3K4me3, H2A.Z, and other modifications ;25 kb upstream of
the annotated HUWE1 promoter with H3K36me3 extending from
this putative promoter toward the miRNAs, suggesting that the
miRNA transcription actually starts upstream of the host gene.
ESTs were also found to start in this region (Fig. 4B). Another ex-
ample of an intragenic miRNA starting downstream from the host
gene’s TSS is shown in Figure 4C. However, most of the predicted
promoters for intragenic miRNAs were the same as those of the
host genes (Supplemental Fig. S8).
To investigate the chromatin environment at miRNA pro-
moters, we examined the average tag density profiles of chromatin
modifications at these loci (Supplemental Fig. S9). While H3K4
me3, H2A.Z, and Pol II were used for promoter prediction and thus,
one can expect to see their peaks at the promoters, the rest of the
modifications were enriched at promoters independent of the
prediction algorithm. In particular, H3K9me1, H4K20me1, and
H3K36me3 were elevated downstream from the TSSs (Supple-
mental Fig. S9G,J,K). These results show that miRNA genes have
a chromatin environment similar to that of protein-coding genes.
Poising of miRNA genes by chromatin
Since peaks of Pol II, H3K4me3, and H2A.Z in resting cells were
used to predict miRNA promoters, the miRNAs (i.e., the pri-miRNAs
coding for these miRNAs) whose transcription starts at these pre-
dicted promoters must be either expressed or poised in resting
cells. To avoid a circular argument, instead of asking whether in-
duced miRNA genes were poised in resting cells, we asked how
many of the 234 predicted miRNA promoters (which, by defini-
tion, are associated with ‘‘active’ modifications) were silent in
resting cells. To account for the fact that two or more miRNAs often
share the same promoter, we considered 175 promoters driving the
expression of 234 miRNAs (Supplemental Table S3). Our deep se-
quencing of miRNAs indicated that 42% of these promoters had
zero sequence reads mapped to their product miRNA(s), suggesting
that the miRNAs coded by these transcripts are silent in resting
cells. These silent miRNAs already contain active chromatin
modifications at their promoters suggesting that they might be
poised for future expression. Indeed, we found that several of these
miRNA genes were induced by TCR signaling. For example, there
were zero tags for intragenic MIR877 in resting T cells and 80 tags in
activated T cells; for intergenic MIR301B, the expression increased
from one to 11 tags. (Fig. 6; for more examples, see Supplemental
Fig. S10). This suggested that chromatin modifications poise some
miRNA genes for expression.
We have provided evidence that the chromatin modification pat-
terns at a majority of induced and repressed genes are stable during
short-term activation of resting human CD4
T cells. This suggests
that changes in the chromatin environment of induced or re-
pressed genes take a relatively long time. Given that, according to
FRAP studies (Hager et al. 2006), chromatin states can be changed
relatively fast, the stability of chromatin modification patterns we
observe has to be purposefully maintained.
In general, as shown in this and other studies, active chro-
matin modifications were found to be associated with expressed
genes. As suggested by Ruthenburg et al. (2007), the association
between expressed genes and active modifications can be ex-
plained in four ways: histone modifying enzymes (e.g., histone
methyl transferases [HMTs]) are recruited (1) by TFs before tran-
scription initiation, which helps to recruit Pol II machinery; (2) by
the polymerase machinery itself concurrently with or after initia-
tion; (3) by other histone modifications via effector proteins; or (4)
by RNA. However, we and others have found that some silent
genes are also associated with active modifications including
H3K4me3 (yeast [Ng et al. 2003], mouse [Mikkelsen et al. 2007],
and human [Barski et al. 2007; Guenther et al. 2007]), H2A.Z (yeast
[Raisner et al. 2005]) and human (this study), and other mod-
ifications (human [this study]). In this context, gene poising
or presence of active chromatin marks at silent genes can be
Figure 5. Promoter-reporter assay. Predicted miRNA promoters or
control sequences were cloned into pGL3 enhancer vector containing
SV40 enhancer, but no promoter. Plasmids were transfected into Jurkat
cells together with Renilla luciferase control. Luciferase activity was mea-
sured after two days. Ratio of firefly and Renilla luciferase signal + SD (n = 3)
is shown. *P-value < 0.005 in Student t-tests vs. both controls 1 and 2.
Figure 6. Inducible miRNA genes are poised for expression. The chro-
matin modification patterns at the intragenic MIR877 (A) and intergenic
MIR301B (B) genes that are induced upon T-cell activation.
Chromatin poises genes for expression
Genome Research 1747
Page 6
explained by either de novo poising of silent genes for future ex-
pression (assuming recruitment mechanism 1 or 3), or as a mem-
ory of past transcription (mechanisms 2–4).
In yeast, the only known H3K4 HMT, Set1, is recruited to
active genes via Ser5 phosphorylated Pol II, suggesting that the
H3K4me3 mark remains at gene promoters mostly as a memory of
past transcription (Ng et al. 2003). However, more evolutionarily
advanced organisms, such as flies and mammals have several H3K4
HMTs. These are recruited not only by transcribing Pol II, but also
by sequence-specific transcription factors, via interaction with other
histone modifications, or even RNA (for review, see Ruthenburg
et al. 2007). We found evidence suggesting both de novo poising
and memory of past transcription in our experiments with human
cells. While only 15%–20% of all silent genes have active mod-
ifications, as many as 60%–70% of inducible silent genes are as-
sociated with active modifications and Pol II. This suggests that
active modifications are deposited to poise inducible genes for
future expression. At the same time, a majority of recently silenced
genes retained the active modifications, suggesting the ‘‘memory
of past transcription’ principle. In this study, the only modifica-
tion that can be clearly explained in only one way seems to be
H3K79me2. We detected little enrichment of this modification at
inducible genes before gene induction, but significant levels at the
genes that were recently silenced, suggesting that this modifica-
tion is deposited only during active transcription and its presence
at silent genes is due to short-term ‘transcriptional memory.’
It is important to note that our experiments have been per-
formed using total CD4
T cells, including both naı
ve and memory
T cells. Unlike naı
ve T cells that have never met an antigen,
memory T cells have been activated by interaction with antigen,
but returned to a quiescent, resting state. Therefore, the chromatin
modifications at the inducible genes could reflect the TCR signal-
ing history of memory T cells. Indeed, IL2, IFNG, and IL4 pro-
moters are marked with H3K4me3 in our cells. Our analysis of
chromatin modifications in naı
ve CD4
T cells in mouse (Wei et al.
2009) showed that several cytokine gene promoters, including Il2,
Il4, and Ifng, were not marked by active modifications in naı
cells. This observation suggests that the poising we observed in
total human CD4
T cells could have come from memory cells,
which have experienced transcription of certain cytokine genes
due to TCR signaling in the past. On the other hand, poised genes
related to general cellular machinery and cell cycle (e.g., E2F1, SRF)
are poised in mouse even in naı
ve T cells. These genes have obvi-
ously been expressed in the past during T-cell development. This
suggests that some genes expressed in the past can stay poised,
possibly, to facilitate transcription in the future. When naı
ve CD4
T cells were differentiated to various T-helper cell lineages, signif-
icant changes of histone modification at critical cytokine and
transcription factor genes were detected (Wei et al. 2009). Similarly,
dramatic chromatin changes were also detected when hemato-
poietic stem cells were differentiated to erythrocyte precursor cells
(Cui et al. 2009) and in other studies of cell differentiation
(Mikkelsen et al. 2007; Mohn et al. 2008). In contrast, even though
many genes were induced, the short-term TCR signaling of CD4
cells in this study did not cause dramatic changes in chromatin
modifications. Altogether this leads us to hypothesize that exe-
cution of cell function, such as quick production of cytokines
upon activation, is facilitated by preexisting chromatin mod-
ifications or, in other words, gene poising. At the same time long-
term cell state changes, such as naı
ve to Th1/2 T-helper cell
differentiation are accompanied by changes in chromatin mod-
ifications that serve as a robust epigenetic mechanism stabiliz-
ing the changed cell states and supporting future execution of
cell function. However, further experiments are required to vali-
date this hypothesis.
In this work we analyzed only histone methylation and
acetylation modifications. It is possible that other histone mod-
ifications, both known and undiscovered, can play a significant
role in the regulation of gene expression. In addition to acetyla-
tions and methylations these might include phosphorylations,
ubiquitinations, and other modifications. These remain interest-
ing areas for future studies.
The poised genes are marked by active chromatin mod-
ifications and possess Pol II at their promoters, but are not tran-
scribed, suggesting that recruitment of Pol II is not the final step in
the regulation of transcription for a significant percentage of
genes. The presence of paused Pol II at the promoters of silent
genes has been observed in both the Drosophila and human (Barski
et al. 2007; Guenther et al. 2007; Muse et al. 2007; Zeitlinger et al.
2007; Schones et al. 2008). While it is not clear what prevents Pol II
from transcribing after its recruitment to promoter, transition from
transcriptional initiation to elongation is known to be regulated by
a P-TEFb complex (Core and Lis 2008; Steger et al. 2008). However,
the manner of gene-specific recruitment of P-TEFb remains to be
Our data suggest that chromatin modifications poise genes,
which creates a chromatin environment that can accommodate
transcriptional activation by specific transcription factors induced
by TCR signaling or other environmental stimuli. We found that
a poising mechanism is employed in the regulation of not only
protein-coding, but also miRNA genes. This suggests that control of
miRNA expression works similarly to that of protein-coding genes.
Cells, antibodies, and ChIP-seq
Human CD4
T cells were prepared by negative selection as de-
scribed previously (Barski et al. 2007). Activation was performed by
addition of anti-CD3/anti-CD28 beads (Invitrogen) to cells for 18 h
according to manufacturer’s instructions. Chromatin was prepared
by formaldehyde cross-linking and sonication for H3K79me2 and
Pol II ChIPs and by micrococcal nuclease digestion for the rest of
the ChIP experiments. ChIP-seq was performed as described pre-
viously (Barski et al. 2007; Wang et al. 2008; see also Barski and
Zhao 2009; Cuddapah et al. 2009 for review and protocol, re-
spectively). Antibodies used for ChIP are listed in Supplemental
Table S1 and their specificity was assessed in a previous study
(Wang et al. 2008).
Illumina pipeline analysis
Sequenced tags were mapped to the human genome (hg18) using
the Illumina Analysis Pipeline. The mapped tags for each sample
were converted to a browser extensible data (BED) file, detailing
the genomic coordinate of each tag. Summary files, displaying the
number of tags in 200-bp windows, in BED format were used for
viewing in the UCSC Genome Browser, and to generate screen-
shots. The read numbers for each sample are listed in Supplemental
Table S1.
Gene Ontology analysis
GO analysis was performed using DAVID functional annotation
tool, using a P-value cutoff of 0.01 (
(Huang da et al. 2007).
Barski et al.
1748 Genome Research
Page 7
Expression analysis and gene sets
Total RNA was prepared using an RNeasy kit (Qiagen) and analyzed
using Human Genome U133 Plus 2.0 array (Affymetrix). Data were
previously deposited to GEO as GSE10437.The Affymetrix Micro-
array Suite 5 (MAS5.0) algorithm (Affymetrix 2002) was used on
the expression data to make absent/present calls. Probes that were
designated as present (or absent) across all replicates of a given
sample were considered expressed (or silent, respectively) in that
sample. Only those Affymetrix probe sets, which were un-
equivocally mapped to UCSC known genes (Hsu et al. 2006), were
considered for further analysis, leaving us with 9399 genes for
further analysis. Set E!E (or S!S) was defined as the set of genes
expressed (or silent) in both resting and activated T cells. Genes
that were silent in resting cells, but expressed in activated cells
were defined as set S!E, and those that were expressed in resting
cells and silent in activated cells were defined as set E!S.
To ensure that our observations do not hinge critically on the
definition of the gene sets, and that they will hold even if one uses
a more stringent definition for gene sets, we repeated the analyses
using alternative gene sets defined based on (1) P/A calls, (2) the
requirement that there is at least a twofold change in expression
for genes switching states from P to A and A to P in all replicates,
and (3) the requirement that the expression of genes in S!S are
below the median expression levels in both cell states in all repli-
cates, and that the expression of genes in E!E are above the me-
dian expression levels in both cell states. These restrictions reduced
the number of genes in E!E, S!E, E!S, and S!S sets from 3250,
168, 271, and 5729 to 2544, 45, 44, and 4941, respectively. The
average tag density plots for these strictly defined gene sets
revealed no changes in the observed trends (see Supplemental Fig.
S11) compared to those plotted using gene sets defined using only
A/P calls (Fig. 2; Supplemental Figs. S3, S4).
For miRNA analysis, RNA was prepared using miRNeasy kit
(Qiagen). Cloning was performed by a protocol similar to that of
Lau et al. (2001) followed by deep sequencing on Illumina genetic
analyzer (A Barski, unpubl.). Read numbers corresponding to each
human miRNA are shown in Supplemental Table S3.
Chromatin modification profiles
To examine modification patterns near the TSSs of each gene set,
all genes in the set were aligned relative to their TSS coordinates.
Tag density was calculated in 100-bp windows relative to the TSS,
and the average tag density (used in the plots) denotes the average
number of tags per base pair. Modification patterns in the gene
bodies of each gene set were examined in a similar fashion, except
that the genes were aligned relative to their TSS and transcription
end site (TSS). The tag density within the gene body was calculated
using windows, whose length is 5% of the gene length.
It should be noted that tag densities have not been normal-
ized across cell states, and thus are not directly comparable be-
tween different cell states. Tag density depends on a number of
factors including total number of tags sequenced, background
modification levels of chromatin, and ChIP enrichment which are
different for different samples. For that reason we only compare tag
densities for various gene sets within the same sample.
Presence/absence of a chromatin modification
For each chromatin modification, a 500-bp region (either in the
promoter or within the gene body) with maximal difference in the
enrichment of modification between the constitutively expressed
(E!E) and constitutively silent (S!S) genes was identified (Sup-
plemental Table S1). A given chromatin modification is said to be
present at a gene if the number of tags mapped within the iden-
tified 500-bp window is statistically significant. To assess statistical
significance, we modeled the distribution of tags throughout the
genome as a Poisson process and calculated the number of reads
necessary in 500-bp windows for a P-value threshold of 10
. Since
less than 80% of the genome length could be uniquely mapped
using 25 bp reads, the background model assumed the genome
size to be 2.46 Gb (80% of 3.08 Gb). The number of tags necessary
to satisfy the chosen P-value threshold is listed in Supplemental
Table S1.
Functional gene sets
Genes related to certain biological processes were selected using
MetaSearch software from the MetaCore package (Ekins et al. 2007)
from GeneGo, Inc. ( The following GO bi-
ological process terms were used to select the genes: muscle:
‘muscle system process’’ or ‘‘muscle cell differentiation’’; organ
development: ‘‘liver development,’ ‘kidney development,’ ‘‘di-
gestive tract morphogenesis,’ or ‘‘digestion’’; ‘cell cycle’’; and
‘metabolic process.’
MicroRNA promoter prediction
For each modification (Pol II, H3K4me3, and H2A.Z), the tag
density profile of tags at position i in the genome is approximated
using kernel density estimation (KDE) profiles. For Pol II, the tag
density profile at position i is given by
j = i +3h
Kðj i=hÞ:Cðj + 75Þ;
where h is the kernel density bandwidth (smoothing parameter,
set to 20),
is the standard Gaussian kernel density function, and C(x) is the
sum of the number of 59 read ends at position x 80 on the sense
strand and position x + 80 on the antisense strand. For H3K4me3
and H2A.Z, the tag density profiles at position i are given by
j = i +3h
Kðj i=hÞ:Cðj + 125Þ
j = i +3h
Kðj i=hÞ:CðjÞ;
Putative TSS for each miRNA is predicted using a trace-back
algorithm, which starts from the 59 end of the pre-miRNA and
traces back upstream looking for statistically significant peaks of
Pol II, H3K4me3, and H2A.Z. Statistical significance of peaks was
assessed by modeling the distribution of tags throughout the ge-
nome as a Poisson process and calculating the number of reads
necessary in 400-bp windows for a P-value threshold of 10
. For
each modification, the KDE profiles are used to keep track of the
tallest peak seen so far. The trace back stops if a peak has been
found for all three modifications or 250 kb has been explored,
whichever happens first. If a peak has been identified for all three
modifications, the TSS for the miRNA is predicted as follows: If the
Pol II peak colocalizes with peaks of H3K4me3 and/or H2A.Z, then
the coordinate marking the Pol II peak is the TSS. Or else, if the
H3K4me3 peak colocalizes with the H2A.Z peak, then the co-
ordinate marking the H3K4me3 peak is the TSS. If neither of the
Chromatin poises genes for expression
Genome Research 1749
Page 8
above conditions holds, or if peaks for all three modifications have
not been identified, then a prediction is not made. The region
surrounding the predicted miRNA TSS is defined as the promoter.
Predicted TSSs are expected to be within 500 bp of the actual TSSs
based on the TSS predictions of 1000 highly expressed genes in
resting T cells (Supplemental Fig. S6). Predicted TSSs were manually
inspected to filter out obvious false-positives by taking into ac-
count the three modifications used for prediction, as well as
H3K36me3, H3K79me2, and the coordinates of known genes.
Total RNA from resting and activated CD4+ T cell was prepared
using RNeasy kit (Qiagen). Human placenta RNA was obtained
from Clontech. Reverse transcription was performed using 1 mgof
RNA and a mixture of RT primers (Supplemental Fig. S7A; Sup-
plemental Table S6) using a Smart RACE cDNA amplification kit
(Clontech). PCR was performed using Universal primer (anneals to
Clontech Smart Oligo) and individual RT primer. If no product was
obtained, nested primer was used (Supplemental Table S6).
Promoter reporter assay
Approximately 650 bp fragments surrounding putative promoters
were amplified by PCR using high fidelity Phusion DNA poly-
merase (NEB) and cloned into KpnI/ BglII sites of pGL3 enhancer
vector (Promega). Primers are listed in Supplemental Table S7.
Jurkat cells were plated at 6 3 10
per well of six-well plate and
transfected with 500 ng of reporter construct, 50 ng of pRLTK
Renilla luciferase control plasmid, and 1 mg of carrier DNA (pBSK)
using Superfect reagent (Qiagen). Dual luciferase assay was per-
formed 2 d later, and firefly luciferase signal was normalized by
that of Renilla luciferase.
This work was supported by the Intramural Research Program of
the National Heart, Lung and Blood Institute (NHLBI), National
Institutes of Health. The gene expression analysis using the Affy-
metrix microarrays was performed by the Genomics Core Facility
Author contributions: A.B., R.J., S.C., and K.Z. designed the
study and wrote the paper; A.B., S.C., K.C. and T-Y.R. performed the
experiments; R.J. analyzed the data with contributions from A.B.
and D.E.S.
Barski A, Zhao K. 2009. Genomic location analysis by ChIP-seq. J Cell
Biochem 107: 11–18.
Barski A, Cuddapah S, Cui K, Roh TY, Schones DE, Wang Z, Wei G, Chepelev
I, Zhao K. 2007. High-resolution profiling of histone methylations in the
human genome. Cell 129: 823–837.
Bartel DP, Chen CZ. 2004. Micromanagers of gene expression: The
potentially widespread influence of metazoan microRNAs. Nat Rev Genet
5: 396–400.
Bernstein BE, Kamal M, Lindblad-Toh K, Bekiranov S, Bailey DK, Hue bert DJ,
McMahon S, Karlsson EK, Kulbokas EJ III, Gingeras TR, et al. 2005.
Genomic maps and comparative analysis of histone modifications in
human and mouse. Cell 120: 169–181.
Bernstein BE, Meissner A, Lander ES. 2007. The mammalian epigenome. Cell
128: 669–681.
Borchert GM, Lanier W, Davidson BL. 2006. RNA polymerase III transcribes
human microRNAs. Nat Struct Mol Biol 13: 1097–1101.
Chambeyron S, Bickmore WA. 2004. Chromatin decondensation and
nuclear reorgan ization of the HoxB locus upon induction of
transcription. Genes & Dev 18: 1119–1130.
Chandok MR, Farber DL. 2004. Signaling control of memory T cell
generation and function. Semin Immunol 16: 285–293.
Chuang JC, Jones PA. 2007. Epi genetics and microRNAs. Pediatr Res 61: 24R–
Core LJ, Lis JT. 2008. Transcription regulation through promoter-proximal
pausing of RNA polymerase II. Science 319: 1791–1792.
Crabtree GR. 1989. Contingent genetic regulator y events in T lymphocyte
activation. Science 243: 355–361.
Cuddapah S, Barski A, Cui K, Schones DE, Wang Z, Wei G, Zhao K. 2009.
Native chrom atin preparation and Illumina/Solexa Library
Construction. Cold Spring Harb Protoc doi: 10.1101/pdb.prot5237.
Cui K, Zang C, Roh T-Y, Schones DE, Childs RW, Peng W, Zhao K. 2009.
Chromatin signatures in multipotent human hematopoietic stem cells
indicate the fate of bivalent genes during differentiation. Cell Stem Cell
4: 80–93.
Ekins S, Nikolsky Y, Bugrim A, Kirillov E, Nikolskaya T. 2007. Pathway
mapping tools for analysis of high content data. Methods Mol Biol 356:
Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ. 2006.
miRBase: MicroRNA sequences, targets and gene nomenclature. Nucleic
Acids Res 34: D140–D144.
Guenther MG, Levine SS, Boyer LA, Jaenisch R, Young RA. 2007. A
chromatin landmark and transcription initiation at most promoters in
human cells. Cell 130: 77–88.
Guttman M, Amit I, Garber M, French C, Lin MF, Feldser D, Huarte M, Zuk O,
Carey BW, Cassady JP, et al. 2009. Chromatin signature reveals over
a thousand highly conserved large non-coding RNAs in mammals.
Nature 458: 223–227.
Hager GL, Elbi C, Johnson TA, Voss T, Nagaich AK, Schiltz RL, Qiu Y, John S.
2006. Chromatin dynamics and the evolution of alternate promoter
states. Chromosome Res 14: 107–116.
Heintzman ND, Stuart RK, Hon G, Fu Y, Ching CW, Hawkins RD, Barrera LO,
Van Calcar S, Qu C, Ching KA, et al. 2007. Distinct and predictive
chromatin signatures of transcriptional promoters and enhancers in the
human genome. Nat Genet 39: 311–318.
Hsu F, Kent WJ, Clawson H, Kuhn RM, Diekhans M, Haussler D. 2006. The
UCSC known genes.
Bioinformatics 22: 1036–1046.
Huang da W, Sherman BT, Tan Q, Collins JR, Alvord WG, Roayaei J, Stephens
R, Baseler MW, Lane HC, Lempicki RA. 2007. The DAVID Gene
Functional Classification Tool: A novel biological module-centric
algorithm to functionally analyze large gene lists. Genome Biol 8: R183.
doi: 10.1186/gb-2007-8-9-r183.
Janeway C. 2001. Immunobiology 5: The immune system in health and disease.
Garland, New York.
Jenuwein T, Allis CD. 2001. Translating the histone code. Science 293: 1074–
Kane LP, Lin J, Weiss A. 2000. Signal transduction by the TCR for antigen.
Curr Opin Immunol 12: 242–249.
Kim TH, Barrera LO, Zheng M, Qu C, Singer MA, Richmond TA, Wu Y, Green
RD, Ren B. 2005. A high-resolution map of active promoters in the
human genome. Nature 436: 876–880.
Kolasinska-Zwierz P, Down T, Latorre I, Liu T, Liu XS, Ahringer J. 2009.
Differential chromatin marking of introns and expressed exons by
H3K36me3. Nat Genet 41: 376–381.
Kouzarides T. 2007. Chromatin modifications and their function. Cell 128:
Krogan NJ, Dover J, Wood A, Schneider J, Heidt J, Boateng MA, Dean K, Ryan
OW, Golshani A, Johnston M, et al. 2003. The Paf1 complex is required
for histone H3 methylation by COMPASS and Dot1p: Linking
transcriptional elongation to histone methylation. Mol Cell 11: 721–
Lau NC, Lim LP, Weinstein EG, Bartel DP. 2001. An abundant class of tiny
RNAs with probable regulatory roles in Caenorhabditis elegans. Science
294: 858–862.
Lee Y, Kim M, Han J, Yeom KH, Lee S, Baek SH, Kim VN. 2004. MicroRNA
genes are transcribed by RNA polymerase II. EMBO J 23: 4051–4060.
Li B, Carey M, Workman JL. 2007. The role of chromatin during
transcription. Cell 128: 707–719.
Li J, Moazed D, Gygi SP. 2002. Association of the histone methyltransferase
Set2 with RNA polymerase II plays a role in transcription elongation.
J Biol Chem 277: 49383–49388.
Marson A, Levine SS, Cole MF, Frampton GM, Brambrink T, Johnstone S,
Guenther MG, Johnston WK, Wernig M, Newman J, et al. 2008.
Connecting microRNA genes to the core transcriptional regulatory
circuitry of embryonic stem cells. Cell 134: 521–533.
Mikkelsen TS, Ku M, Jaffe DB, Issac B, Lieberman E, Giannoukos G, Alvarez P,
Brockman W, Kim TK, Koche RP, et al. 2007. Genome-wide maps of
chromatin state in pluripotent and lineage-committed cells. Nature 448:
Mohn F, Weber M, Rebhan M, Roloff TC, Richter J, Stadler MB, Bibel M,
Schubeler D. 2008. Lineage-specific polycomb targets and de novo DNA
Barski et al.
1750 Genome Research
Page 9
methylation define restriction and potential of neuronal progenitors.
Mol Cell 30: 755–766.
Muse GW, Gilchrist DA, Nechaev S, Shah R, Parker JS, Grissom SF, Zeitlinger
J, Adelman K. 2007. RNA polymerase is poised for activation across the
genome. Nat Genet 39: 1507–1511.
Ng HH, Robert F, Young RA, Struhl K. 2003. Targeted recruitment of
Set1 histone methylase by elongating Pol II provides a localized
mark and memory of recent transcriptional activity. Mol Cell 11: 709–
Pedersen I, David M. 2008. MicroRNAs in the immune response. Cytokine
43: 391–394.
Raisner RM, Hartley PD, Meneghini MD, Bao MZ, Liu CL, Schreiber SL,
Rando OJ, Madhani HD. 2005. Histone variant H2A.Z marks the 59 ends
of both active and inactive genes in euchromatin. Cell 123: 233–248.
Roh TY, Cuddapah S, Zhao K. 2005. Active chromatin domains are defined
by acetylation islands revealed by genome-wide mapping. Genes & Dev
19: 542–552.
Roh TY, Cuddapah S, Cui K, Zhao K. 2006. The genomic landscape of
histone modifications in human T cells. Proc Natl Acad Sci 103: 15782–
Ruthenburg AJ, Allis CD, Wysocka J. 2007. Methylation of lysine 4 on
histone H3: Intricacy of writing and reading a single epigenetic mark.
Mol Cell 25: 15–30.
Schones DE, Zhao K. 2008. Genome-wide approaches to studying
chromatin modifications. Nat Rev Genet 9: 179–191.
Schones DE, C ui K, Cuddapah S, Roh TY, Barski A, Wang Z, Wei G, Zhao K.
2008. Dynamic regulation of nucleosome positioning in the human
genome. Cell 132: 887–898.
Stallcup MR. 2001. Role of protein methylation in chromatin remodeling
and transcriptional regulation. Oncogene 20: 3014–3020.
Steger DJ, Lefterova MI, Ying L, Stonestrom AJ, Schupp M, Zhuo D, Vakoc
AL, Kim JE, Chen J, Lazar MA, et al. 2008. DOT1L/KMT4 recruitment and
H3K79 methylation are ubiquitously coupled with gene transcription in
mammalian cells. Mol Cell Biol 28: 2825–2839.
Vermaak D, Ahmad K, Henikoff S. 2003. Maintenance of chromatin states:
An open-and-shut case. Curr Opin Cell Biol 15: 266–274.
Wang Z, Zang C, Rosenfeld JA, Schones DE, Barski A, Cuddapah S, Cui K,
Roh TY, Peng W, Zhang MQ, et al. 2008. Combinatorial patterns of
histone acetylations and methylations in the huma n genome. Nat Genet
40: 897–90 3.
Wei G, Wei L, Zhu J, Zang C, Hu-Li J, Yao Z, Cui K, Kanno Y, Roh T-Y, Watford
W, et al. 2009. Global mapping of histone H3 K4 and K27 trimethylation
reveals specif icity and plasticity in lineage fate determination of
differentiating CD4+ T cells. Immunity 30: 155–167.
Weisbrod S. 1982. Active chromatin. Nature 297: 289–295.
Woods K, Thomson JM, Hammond SM. 2007. Direct regulation of an
oncogenic micro-RNA cluster by E2F transcription factors. J Biol Chem
282: 2130–2134.
Zeitlinger J, Stark A, Kellis M, Hong JW, Nechaev S, Adelman K, Levine M,
Young RA. 2007. RNA polymerase stalling at developmental control
genes in the Drosophila melanogaster embr yo. Nat Genet
39: 1512 –
Received January 7, 2009; accepted in revised form June 2, 2009.
Chromatin poises genes for expression
Genome Research 1751
Page 10
  • Source
    • "miR-17 and miR-20a form an incoherent positive feedforward loop with the target mRNA Cd69 miRNA expression responds to T-cell activation signals [34, 35,39404142434445. Many microRNAs are downregulated upon T-cell activation40414243, but the expression of the miR-17-92 cluster is upregulated in activated mouse and human T cells [45]. Since the miR-17-92 cluster encodes microRNAs that target the Cd69 3'UTR, including miR-17 and miR-20a (Fig. 3), we investigated how the expression of miR-17 and miR-20a was affected by the activation of DP thymocytes. "
    [Show abstract] [Hide abstract] ABSTRACT: Author Summary microRNAs are integral to many developmental processes and may 'canalise' development by reducing cell-to-cell variation in gene expression. This idea is supported by computational studies that have modeled the impact of microRNAs on the expression of their targets and the construction of artificial incoherent feedforward loops using synthetic biology tools. Here we show that this interesting principle of microRNA regulation actually occurs in a mammalian developmental system. We examine cell-to-cell variation of protein expression in developing mouse thymocytes by quantitative flow cytometry and find that the absence of microRNAs results in increased cell-to-cell variation in the expression of the microRNA target Cd69. Mechanistically, T cell receptor signaling induces both Cd69 and miR-17 and miR-20a, two microRNAs that target Cd69. Co-regulation of microRNAs and their target mRNA dampens the expression of Cd69 and forms an incoherent feedforward loop that reduces cell-to-cell variation on CD69 expression. In addition, miR-181, which also targets Cd69 and is a known modulator of T cell receptor signaling, also affects cell-to-cell variation of CD69 expression. The ability of microRNAs to control the uniformity of gene expression across mammalian cell populations may be important for normal development and for disease.
    Full-text · Article · Feb 2015 · PLoS Genetics
  • Source
    • "Importantly, miRNAs play critical roles in the tissue response to environmental stimuli without changing DNA sequence with a rapid and reversible means of gene regulation. miRNAs may also themselves be epigenetically regulated, as histone modifications and changes in chromatin structure also affect miRNA transcription and expression [128] . Furthermore , miRNAs and other non-coding RNAs can also interact with transcriptional coregulators and thereby further exert epigenetic control through transcriptional regulation [129, 130] . "
    [Show abstract] [Hide abstract] ABSTRACT: There is evidence demonstrating that genetic factors contribute to the risk of diabetic retinopathy (DR). Genetics variants, structural variants (copy number variation, CNV) and epigenetic changes play important roles in the development of DR. Genetic linkage and association studies have uncovered a number of genetic loci and common genetic variants susceptibility to DR. CNV and interactions of gene by environment have also been detected by association analysis. Apart from nucleus genome, mitochondrial DNA plays critical roles in regulation of development of DR. Epigenetic studies have indicated epigenetic changes in chromatin affecting gene transcription in response to environmental stimuli, which provided a large body of evidence of regulating development of diabetes mellitus. Identification of genetic variants and epigenetic changes contributed to risk or protection of DR will benefit uncovering the complex mechanism underlying DR. This review focused on the current knowledge of the genetic and epigenetic basis of DR.
    Full-text · Article · Sep 2014 · Journal of endocrinological investigation
  • Source
    • "Core promoters of miRNA genes were promptly identified for depicting full-length primary transcripts [3] [4] [5]. Highthroughput sequencing datasets derived from epigenetic signature and TSS-relevant experiments unfold transcriptional start sites (TSSs) of miRNAs and offer a practical strategy to determine miRNA promoters [6] [7] [8] [9]. "
    [Show abstract] [Hide abstract] ABSTRACT: Noncoding, endogenous microRNAs (miRNAs) are fairly well known for regulating gene expression rather than protein coding. Dysregulation of miRNA gene, either upregulated or downregulated, may lead to severe diseases or oncogenesis, especially when the miRNA disorder involves significant bioreactions or pathways. Thus, how miRNA genes are transcriptionally regulated has been highlighted as well as target recognition in recent years. In this study, a large-scale investigation of novel cis- and trans-elements was undertaken to further determine TF-miRNA regulatory relations, which are necessary to unravel the transcriptional regulation of miRNA genes. Based on miRNA and annotated gene expression profiles, the term "coTFBS" was introduced to detect common transcription factors and the corresponding binding sites within the promoter regions of each miRNA and its coexpressed annotated genes. The computational pipeline was successfully established to filter redundancy due to short sequence motifs for TFBS pattern search. Eventually, we identified more convinced TF-miRNA regulatory relations for 225 human miRNAs. This valuable information is helpful in understanding miRNA functions and provides knowledge to evaluate the therapeutic potential in clinical research. Once most expression profiles of miRNAs in the latest database are completed, TF candidates of more miRNAs can be explored by this filtering approach in the future.
    Full-text · Article · Jun 2014 · BioMed Research International
Show more