Florida State University Libraries
Honors Theses
The Division of Undergraduate Studies
2011
Crosstalk between Chromatin Structure and
DNA Methylation and the Regulation of
DNA Templated Processes
Tarreq Noori
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Abstract
Methylation of DNA is one of the earliest described epigenetic modifications.
Hypermethylation is associated with gene silencing, while the inhibition of methylation is
generally associated with reactivating silenced genes. The packaging of DNA in the
nucleus into chromatin also plays a role in regulating gene expression. We sought to
understand the crosstalk between changes in methylation status of the genome and
changes in chromatin structure.
5-azacytidine (5-azaC), a potent DNA methytransferase inhibitor, has recently
generated interest as a potential anti-cancer drug, possibly functioning by reactivating
silenced tumor suppressor genes. We treated the hematologic cancer cell lines U-937 and
T-HP1 with 5-aza for varying lengths of time. We then harvested DNA for methylation
studies, RNA for gene expression studies and chromatin for nuclease accessibility
studies. The chromatin accessibility was further measured at two different levels of
resolution; the domain level (10s of kb) and nucleosome position (10s of bp). This was
achieved using an innovative DNA microarray assay.
Results were analyzed to correlate changes in chromatin structure with changes in
methylation and gene expression. We have identified that the class II, major
histocompatibility complex, transactivator (CIITA) shows chromatin structural changes.
The results provide a model for understanding the regulatory chromatin structure
involved in the immune response.
Keywords: nucleosome distribution, DNA methyltransferase inhibitor, microarray
THE FLORIDA STATE UNIVERSITY
College of Arts & Sciences
CROSSTALK BETWEEN CHROMATIN STRUCTURE AND DNA METHYLATION
AND THE REGULATION OF DNA TEMPLATED PROCESSES
By
TARREQ NOORI
A Thesis submitted to the
Department of Biological Science
in partial fulfillment of the requirements for graduation with
Honors in the Major
Degree Awarded:
Spring 2011
The members of the Defense Committee approve the thesis of Tarreq Noori defended on
April 13, 2011.
______________________________
Assistant Professor. Dr. Jonathan H. Dennis
Thesis Director
______________________________
Professor and Assistant Dean for
Admissions. Dr. Graham A. Patrick
Outside Committee Member
______________________________
Associate Professor. Dr. Laura R. Keller
Committee Member
Introduction
Approximately two meters of identical genomic DNA is tightly packaged into the nucleus
of every human eukaryotic cell (Fig. 1). The genome achieves this organization by
spooling DNA around histone proteins to create chromatin. Nucleosomes are the
fundamental packing unit of chromatin consisting of about 150 base pairs of DNA
wrapped around a histone octamer about 1.7 times. The octamer is composed of two
copies each of the proteins H2A, H2B, H3, and H4. This nuclear organization is
significant as this DNA packaging results in architectures that facilitate or impede DNAbinding interactions required for nuclear processes such as transcription, replication,
recombination, repair, and transposition (Koerber et al., 2009). A complete
understanding of gene regulation requires a thorough description of regulatory chromatin
structure.
tight packaging
nucleosome
loose packaging
Figure 1. Overview of DNA packaging in the nucleus.
(Bouvier 2007)
Chromatin Structure
The structure of proteins is commonly classified to be arranged into the primary,
secondary, tertiary, and quaternary structures. There is an analogy between the structure
of protein and chromatin structure (Fig. 2). The primary structure of proteins describes
the linear arrangement of amino acid residues. Interactions between peptide backbones
define secondary structure. The two types of secondary structure interacts further to
create tertiary structure. These fully folded polypeptides interact with one another to
create the quaternary structure. By analogy, chromatin packing follows the same
hierarchal pattern. The primary level is widely known as “beads on a string” depicting
the translation positions of nucleosomes with respect to the underlying DNA sequence.
This string of nucleosomes, approximately 10 nm in diameter is further packaged into a
proposed helical 30 nm fiber. These fibers can continue to organize themselves into
chromosomes, analogous to protein tertiary structure. Finally the chromosomes can
interact with one another similar to polypeptides in protein quaternary structure.
Protein Structure
Chromatin
Structure
Figure 2. Analogy to levels of protein structure.
Chromatin Structure Regulates Gene Expression
The hierarchal nature of chromatin packaging plays a major role in regulating gene
expression. This is explained by the idea that some of the genome is tightly compacted,
whereas other parts may exist in a more open form. This more open chromatin is critical
because it is more likely accessible to soluble regulatory factors. Likewise, parts of the
genome that are tightly condensed would be inaccessible to proteins and enzymes that
can affect templated processes. The differential access to regulatory elements is built into
the chromatin structure itself. Thus, inactive chromatin structure is linked to
transcriptional repression while active chromatin structure is linked to transcriptional
activation (Gilbert, 2004). It has been proposed that a complete understanding of the
regulation of chromatin structure and gene expression requires a thorough description of
the network of histone modifications, chromatin remodelers, and DNA methylation
associated with that structure (Collins, 2010).
Modifications of Chromatin
In addition to chromatin structure, covalent modifications have an influence on regulating
gene expression. Each of the two components of chromatin, DNA and protein histones
may each be modified with striking effects on nuclear processes. The influence of one
mark affecting the nature of another mark has been termed “crosstalk” in chromatin
biology.
Covalent Modifications of Histones
Common histone modifications such as the acetylation of lysine nine on histone 3
(H3K9) and the trimethylation of lysine 27 on histone 3 (H3K27me3) are associated with
transcriptional repression while H3K4me and H3K36me are associated with gene
activation (Kouzarides, 2007). A summary of the common histone modifications is
shown in Table 1.
H3K36me are related to transcriptional activation (See Figure 3).
Table 1. Common Histone Modifications.
Modifications to histones are not the only marks that regulation chromatin structure.
Modifications to the DNA sequence in the form of cytosine methylation also play a role
in regulation.
DNA Methylation
Areas of the genome containing high levels of methylated cytosines are associated with
repressive marks and inactive transcription. Areas of the genome containing low levels
of DNA methylation corresponds to active transcription. DNA becomes modified when a
methyl group is covalently bound to the five carbon of a cytosine nucleotide to give 5methylcytosine. This reaction is catalyzed by way of DNA methyltransferase, an enzyme
that transfers the methyl groups at CpG islands throughout the genome. The CpG islands
are found near a majority of promoters, and hotspots for transcriptional activation (Lin et
al, 2007). Areas of hypermethylation are known to be associated with transcriptional
repression. Hypomethylated areas are associated with gene activation.
DNA methyltransferases (DNMTs) maybe be classified into two categories: de
novo and maintenance DNMT. The de novo methyltransferase, DNMT1, is able to
methylate previously unmethylated genomic loci. Alternatively, the maintenance
methyltransferases, DNMT3a and DNMT3b, duplicate the methylated mark to confer
mitotic stability. DNA methylation is fundamental to balancing allelic expression such as
genomic imprinting X-chromosome inactivation, or imprinting. This phenomenon of
genomic imprinting as well as DNA methylation is critical for normal nuclear events.
Thus, when any of these methylating processes are interrupted or altered nuclear
processes are disregulated and the cellular phenotypes change.
Cancer Relatedness
The role of methylation, either hyper or hypo, in affecting gene regulation is a major
focus of cancer related research. In general cancer cells are maintained through the
differential maintenance of methylation status in the genome (Fig. 3).
(Melki, 2002)
Figure 3. Levels of DNA methylation reduced in many cancers.
This differential gene expression resulting from aberrant methylation can lead to different
phenotypes in cancer cells including the inactivation of tumor suppressor genes or to the
proliferation of tumors. Given the close links between DNA methylation, chromatin
structure, and the transformed phenotype it is important to understand the crosstalk
between chromatin structure and methylation in cancer therapy research.
Therapeutics
It has been proposed that reducing levels of DNA methylation can lead to the
expression of otherwise silenced genes. This control of gene expression could lead to
enhanced cancer therapies. 5-azacytidine (5-aza) is a widely used and popular inhibitor
of DNA methylation. This potent drug inhibits the enzymatic activity of DNA
methyltransferase 1 (DNMT1). We hypothesized that 5-azaC does not allow for the
methylation of specific regions within the genome and may play a role in activating
cancer related genes.
Rationale
In order to test this hypothesis 5-azaC will be used to treat blood cancer cell lines in order
to act as a stimulus and initiate an immune response. This research allows measure the
degree to which chromatin structure is influenced by methylation status in cancer cells.
Previously, there have been no large scale systematic genome wide experiments that can
test the close relationship between chromatin structure, DNA methylation, and gene
expression. This highly unique research allows for the systematic analysis of chromatin
structure, DNA methylation, and gene expression in cancer cells that have been treated
with this potent anti-cancer drug. The goal of this research is to measure the degree to
which chromatin structure is influenced by methylation status in cancer cells. We
hypothesized that there is a relationship between chromatin structure and DNA
methylation allowing for the identification of loci in hematopoetic cancer cell lines whose
chromatin structure changed in response to treatment with the DNMT inhibitor 5-azaC.
Methods
Tissue Culture
This experiment used the hemapoeitic cancer cell lines THP1 and U937. THP1 cells are
blood cells taken from a 1 year old male infant with acute monocytic leukemia (Tsuchiya,
1980). The U937 cells are monocytes taken from a 37 year old male with histiocytic
lymphoma, a cancer of lymphoid tissue (Nilsson, 1976). This experiment includes the
use of 5-azacytidine (5-azaC), a potent DNA methyltransferase inhibitor. The cells of
interest are grown in RPMI-1640 full growth media containing fetal bovine serum,
gentamyacin, and beta mercaptethanol until a concentration of 1x107 cells is achieved.
Here a potent DNA methyltransferase inhibitor, 5-azacytidine, is added to a
concentration of 2uM. This concentration allows for cell growth without the induction of
apoptosis. Once the treatment is applied, the cells are grown for 12 and 36 hours.
Formaldehyde Crosslinking
At the appropriate time points the individual plates of cells are harvested. Each plate is
harvested for chromatin, RNA, and DNA studies (Fig. 4).
Figure 4. Experimental Overview.
Immediately, 1% formaldehyde is added to the cells and allowed to incubate at room
temperature for 10 minutes. An inexpensive amino acid, glycine is then added to quench
the reaction. This crosslinking technique locks in the native state of the chromatin
structure.
Analysis of Nucleosome Positioning
In order to analyze the primary structure of chromatin from Fig. 2, first I isolated the
nuclei from cells using resuspension buffer (0.3M sucrose, 2mM MgAc2, 3mM CaCl2,
1% Triton X-100, 10 mM HEPES at pH 7.8). Once the nuclei have been spun down and
isolated. Micrococcal nuclease (MNase), an internucleosomal cleaving enzyme, is used to
cleave the DNA. In order to enable this enzyme it must be placed in calcium containing
MNase cleavage buffer (25 mM KCl, 4 mM MgCl2, 12.5% glycerol, and 50 mM Tris at
pH 7.5). This enzyme cuts the DNA between the nucleosomes, thus the enzyme cuts
outside the bounds of the nucleosome. This creates 150 base pair fragments from the
DNA wrapped around the histone octamer. Higher concentrations of MNase produce
more mononucleosomal fragments. In order to represent the whole genome a titration of
MNase at concentration of 8, 2, 1 units of 2.6x106 cells in 500 uL (Fig. 5).
1
Units
2
Units
8
Units
MNase concentration
600 bp
400 bp
mononucleosomes
200 bp
Figure 5. An MNase titration of increasing concentration, more specifically 1, 2, and 8 Units,
was performed on all samples. The red box is the region on a 2% agarose gel corresponding
to the location of mononucleosomes (about 150bp).
After incubation at 37ºC for 5 minutes, digestion terminated by adding EDTA to a
concentration of 50 uM. The mononucleosomes created from these digests are pooled
together to represent the entire genome, not only the open areas accessible at low MNase
concentrations. SDS and proteinase K are added to denature and digest the proteins and
is then placed at 65ºC to remove the crosslinks. After a phenol chloroform extraction
separates the nucleic acid from nuclear debris, the isolated DNA is qualitatively analyzed
on a 2% agarose gel at a ratio of 5:10:10 ug of different MNase concentrations from the
12 and 36 hour timepoint are added together. This 25 ug is then loaded onto a gel and is
analyzed with UV light to locate the 200bp molecular weight marker band, which is
consistent with the size of mononucleosomally protected DNA . This marker then allows
for the gel to be cut all the way across retrieving only mononucleosomes. The gel slices
are electro eluted at 50 V for 3 hours to isolate pure mononucleosomes from the
individual gel slices (Fig. 6). In this process gel slices are individually placed into
dialysis bags and are secured to avoid leaking. 500uL of 1% TAE is added into each bag
before placing into the gel rig.
U937
0 hrs.
12 hrs.
THP-1
36 hrs.
0 hrs. 12 hrs.
36 hrs.
mononucleosomes
200
bp
Figure 6. After separation and elution, this gel shows the presence of the 150 bp
mononucleosomes of the combined titration samples.
To isolate the DNA from solution, ethanol precipiatation using glycogen as a carrier
creates a DNA pellet to resuspend in Tris-EDTA (TE). The nucleosomally protected
DNA is labeled with a fluorescent dye; Cy3 while bare genomic DNA is labeled with
Cy5 giving a two color experiment. Samples were labeled using the Nimblegen dualcolor labeling kit, containing Klenow exonuclease allows for efficient labeling per
manufacturers instructions. Subsequently, these samples are hybridized to a custom
designed 12-plex Comparative Genomic Hybridization (CGH) microarray containing
transcription start sites of 425 cancer related genes. The hybridization was carried out
according to manufacturer’s instructions. These data are used to measure nucleosome
distribution around the TSS of these 425 genes.
Analysis of Nucleosome Accessibility
To determine if there is a link between global and local chromatin structure, a MNase
digestion of 0.1 unit for 5 minutes at 37ºC was performed, stopping the reaction with
EDTA. The small concentration allows for enough digestion to create an equal amount
of global (inaccessible) and local (accessible) chromatin structure (Fig. 7).
U937
0 hrs.
12 hrs. 36 hrs.
THP-1
0 hrs. 12 hrs.
36 hrs.
7000 bp
3000 bp
2000 bp
1000 bp
800 bp
Nucleosomal
Ladder
600 bp
400 bp
200 bp
Figure 7. The gel represents a 0.1 unit MNase digest of 1x107 cells. This creates a
nucleosomal ladder expected from an MNase digestion.
Once the creation of a nucleosomal ladder is confirmed, ensuring proper MNase
digestion, 25 ug of the material is loaded onto a 1.5% agarose gel. Next the MNase
cleaved DNA is cut out of the gel at the 5000 base pair mark giving two populations:
greater than 5000 bp and less than 5000bp. The DNA 5000 base pairs and above is
associated with the inaccessible regions while everything smaller is associated with the
accessible regions. DNA is electro eluted at 50 V for 3 hours in order to ensure the
maximum amount of DNA recovery. Ethanol precipitation isolates the DNA from
solution and the pellet is resuspended in TE. Afterward, these fractions are analyzed
qualitatively on a 2% agarose gel to confirm separation of inaccessible and accessible
regions (Fig. 8).
U937
THP-1
0 hrs. 12 hrs. 36 hrs. 0 hrs. 12 hrs. 36 hrs.
H L H
L H L
H
L H
L
H L
Molecular weight
fractions
7000 bp
5000 bp
3000 bp
Figure 8. After separation and electro elution, this gel shows a complete separation of the whole
genome into two regions. Now the entire genome can be analyzed based on inaccessible and
accessible regions.
The inaccessible regions are then labeled, as described above, with Cy5 while the
accessible regions are labeled with Cy3 to give a two color experiment. This material is
hybridized to a 12-plex CGH microarray spanning all 3.4 billion bases of the human
genome for three days. This array consists of 135,000 probes spaced 12.5 kilobases
apart. The hybridization was carried out according to manufacturer’s instructions. The
slide is then scanned using a state of the art 2 micron scanner.
Methylated DNA Immunoprecipitation
This protocol is an adaptation of a common methylated DNA immunoprecipitation
protocol (Weber, 2007). DNA is sonicated to a range of 200-1000 base pairs using the
Diagenoide Bioruptor UC-200 set on high power with 30 second ON/OFF cycles for 7-10
minutes provides the ideal fragments. This provides a fragment size that can facilitate the
binding of primary antibodies. Sheep anti 5-methylcytosine antibody is used to bind
methylated CpG islands. 5 uL of this antibody is added to 10 ug of sonicated DNA. 1 ug
of DNA was saved to use as an input control. Once this primary antibody is allowed to
bind with the DNA in a 250 uL reaction at 4ºC for 2 hours, these regions can be
selectively pulled down to obtain only methylated DNA. Using 40 uL of magnetic
Dynabeads® M-280 sheep anti mouse was added to magnetically pull down the primary
5-methylcytosine antibody. The samples are incubated with the secondary antibody at
4ºC for 3 hours, magnetic recovery is used to isolate methylated DNA from the
unmethylated genome. Biochemically this works because the beads are made to
specifically bind any sheep isolated antibodies. From here the pull down methylated
DNA is isolated from the proteins through a phenol chloroform extraction. A whole
genome amplification kit (WGA) was used to amplify the isolated methylated DNA.
This kit requires two steps, first a library is prepared to have a template to for
amplification. Then an amplification step is used to prepare microgram quantities of
target DNA. Although the material has been amplified, it still can not be used for further
analysis since it must go through a PCR purification kit to remove the excess dNTPs and
primers. This kit contains a well known procedure consisting of a series of washes in a
spin column. Upon completion the material is in microgram quantities representation our
target methylated DNA and can be labeled with Cy3 while the input sonicated DNA is
labeled with Cy5, creating another two color experiment. These samples are then loaded
onto the same microarray slide as nucleosome distribution data in order to see whether
methylation status differs in cancer related genes.
RNA preparation
RNAzol RT® allows for a single-step RNA isolation with yielding RNA ready for RTPCR without additional purification or DNase treatment (Chomczynski, et al, 2010). The
cells are placed into 500 uL of RNAzol RT®, from here the supernatant is drawn off and
the RNA is isolated with isoproponal. After two 75% ethanol washes pure RNA is
isolated from cells (Fig. 9). After confirming the isolation of the 28S, 18S, and 5S
subunits on a 2% agarose gel, the next step is to make cDNA. This task is accomplished
with the superscript III first strand synthesis supermix for qRT-PCR with E. coli RNase
H to degrade the rest of the RNA present. The cDNA can then be labeled as a one color
experiment and hybridized to a 12-plex gene expression array. This data is used to
analyze gene expression.
Figure 9. Experion Bioanalyzer results of RNA and cDNA quality analysis.
Data Analysis
Using the public access statistical computing program R and a set of unique tools written
by the Dennis lab, we are able to measure the distribution of nucleosomes centered on the
transcription start site (Fig. 10). Many nucleosome mapping experiments are centered
around the transcription start site (TSS) due to its regulatory importance. TSS’s are
flanked by strongly positioned nucleosomes both upstream and downstream. The –1
nucleosome is normally found at –300 to –150 relative to the start site and is often the
target of covalent histone modifications associated with gene activation or repression
(Jiang and Pugh, 2009). Our analysis of chromatin structure occurs at two levels of
resolution. At the primary structure level we identify the coordinates of nucleosome
distribution in response to 5-azaC. Nucleosome position refers to the likelihood that
nucleosomes from a population of cells will be located at a precise location along the
genomic sequence occupying 150 base pairs (Pugh, 2010). Positioned nucleosomes as
well as nucleosome free regions are believed to have an important function in regulation
of transcription. We have also measured chromatin accessibility at the tertiary structure
level. We have created whole genome maps based on accessibility status: inaccessible or
accessible. This will allow us to identify loci in the genome where crosstalk between
chromatin structure and DNA methylation may regulate gene expression.
Results
Nucleosome Distribution
We wanted to determine how many of the 425 genes show a significant change in
nucleosome distribution. We observed that treatment with 5-azacytidine cause’s similar
changes in the distribution of nucleosome position between two hematopoietic cell lines.
In order to statistically analyze the data in an unbiased manner we have generated a t-test
value for every gene using the tools in R. The probes associated with one specific gene
are averaged, over a user-defined sliding window, gives an overall t-test mean value for
each of the 425 genes. A high t-test value means there is a change in nucleosome
distribution while the low values indicate a similarity in distribution. Next the highest ttest mean highest values down to a cutoff of .525 were selected with their respective gene
names. To accomplish this task the data calculated from unique R tools were sorted in
descending order based on t-test mean value. More specifically U937 12 hour data had
62 genes out of 425 on the array that fell within this data range while the THP1 12 hour
data has 110 out of 425 genes, about 50% less than the U937 12 hour genes. In addition
the U937 and THP1 36 hour data have 155 and 106 genes out of 425 in the range,
respectively. To determine how many of these genes were in common between the U937
12 hour and 36 hour time point. We created Venn diagrams of these data (Fig. 11).
The Venn diagram A was created by comparing the 62 genes of U937 12 hour and 155 of
U937 36 hour, resulting in 43 common genes. Next we wanted to do this same Venn
diagram for the THP1 data. The Venn diagram B, shown in Fig. 11B, compares the 110
genes from THP1 12 hour and the 106 genes of THP1 36 hour, resulting in 66 more
genes in common.
A
C
19
U937 12, 36h
43
112
B
U937, THP1 12h
79
31
31
D
44
66
40
43
63
92
U937, THP1 36h
THP1 12, 36h
Figure 11. Common genes within cell lines and between time points. The Venn diagrams
represent the number of genes in common between the sample groups. Each population of genes
was chosen by selecting mean t-test values of .525 or higher. Venn A and Venn B were created
from data within the U937 and THP1 lines respectively. Venn C and Venn D were created from
data between cell lines and within the time points of 12 and 36 hours respectively.
To find the common genes between U937 and THP1, the Venn diagram C, show
in Fig. 11C, was created with the 110 genes from THP1 12 hour and the 62 genes from
U937 12 hour. This diagram showed 31 genes in common between the cell lines. The
Venn diagram D, shown in Fig. 11D, was created from the 106 THP1 36 hour genes and
the 155 U937 36 hour genes, showing 63 genes in common between the two. The 31 and
63 common genes between the two cell lines were compared to
narrow down our research to a list of 19 potentially interesting genes
that have similar changes between cell lines (See Table 2).
Table 2. List of common
genes between time points
and within time points.
Gene Name
RELA
MYST3
BCL3
CDK5
PRMT1 CDC25B
CDK10 CSK
ATM
RHOC
VEGFA DNMT1
PRMT3 BAX
CUL1
SUV39H1
CRAT
BCL2L1
CRHR1
With the scope of the analysis is narrowed down to a set of 19 genes we next wanted to
determine how 5-azaC affects cancer cells. The common genes were researched to
determine if their importance in cancer, and those genes were graphed based on the log
ratio of the Cy5 (mononucleosomal) and Cy3 (bare genomic DNA) at each time point
through both U937 and THP1 (Fig. 12). We observed that these genes have a common
change in nucleosome distribution, for example in the BCL3, CDK5, and DNMT1 gene
at the asterisk marker.
U937
12 h
36 h
THP1
12 h
36 h
BCL3
*
*
*
*
RELA
CDK10
ATM
BCL2L1
DNMT1
*
*
*
*
CDK5
*
*
*
*
Figure 12. Plots genes in common with cancer implications. The y-axis is the
log2(mononucleosomal DNA/bare genomic DNA). The x-axis is the genomic coordinates
of human genome build 19 of the most common genes between U937 and THP1 cell
lines. The graph is centered at the TSS ± 1000bp. The black line represents the
untreated sample, while the red line represent the 5-aza treated sample with respective
treatment time at the top of the column. These graphs are centered at the TSS ± 1000bp.
Since several of the genes previously represented had similar changes in
nucleosome distribution between both cell lines, we wanted to see if we could generalize
this result to ontological classes grouped by molecular function. To do this all of the 425
cancer genes on the microarray were researched on the AmiGO website and the
molecular functions were logged for each and every gene. First in order to confirm the
presence of canonical nucleosome positioning we plotted all 425 gene loci on the array
for both cell lines (Fig. 13). These graphs show the presence of the +1 and -1
nucleosome and thus a common structure between all genes. Since graphing based on
molecular function shows nucleosome position consistent between both cell lines we
classified the genes in this analysis into specific molecular functions. The GO categories
of transcription factor binding, cyclin dependent protein kinase activity, and ubiquitinligase activity were graphed due to their importance in cancer progression. These graphs
are based on an average of all the genes that fall within their respective molecular
function category.
*Top Half= THP1 cell line
12h
36h
Transcription Factor
Binding (28 gene avg.)
Cyclin Dependent
Protein Kinase Activity
(7 gene avg.)
Ubiquitin-Ligase
Activity (12 gene avg.)
All Gene Loci
(404 gene
avg.)
12h
36h
*Bottom Half= U937 cell line
Figure 13. Generalized effect of 5-azaC treatment on GO category. The y-axis
is the log2(mononucleosomal DNA/bare genomic DNA). The x-axis is the
genomic positions of genes associated with the appropriate GO category. An
average of all the data within a GO category is taken and then plotted. The
last column is an average of all 404 gene loci. The black line represents the
untreated sample, while the red line represent the 5-aza treated sample with
respective treatment time at each row. These graphs are centered at the TSS ±
1000bp.
Nuclease Accessibility
Since we initially measured changes in chromatin structure at the primary level,
we wondered if these changes were rooted in the tertiary, global structure of chromatin.
Here using the same kind of suite tools in R we are able to map the tertiary structure of
chromatin on a whole genome scale (Fig. 14a).
THP1 12h
THP1 36h
U937 12h
U937 36h
Figure 14a. Whole genome plots based on DNA accessibility. The x-axis are is the genomic
coordinates of human genome build 18 The y-axis is log2(inaccessible DNA/accessible DNA).. The
black line represents the untreated sample, while the red line represent the 5-aza treated sample
with respective treatment time at the top of the column. The dotted lines represents the individual
chromosomes. The strongest signals represent the greatest regions of inaccessibility.
Each graph represents a whole genome plot of their respective treatment times. There is
a large amount of data (135,000 data points) in each one of the graphs. The THP1 12
hour and THP1 36 hour do not show many changes on a genome wide scale.
Alternatively, the U937 12 hour shows more changes and that diminish through the U937
36 time point. But there is a consistent pattern between the untreated black line in both
cell lines. In order to analyze this experiment in a manageable group of data, the
chromosomes from THP1 12 hour data were individually plotted (Fig. 14b).
DNMT1
RELA
BCL3
ATM
BCL2L1
CDK5
CDK10
Figure 14b. Whole genome plots broken down by chromosome. The x-axis is
the genomic coordinates of human genome build 18 The y-axis is
log2(inaccessible DNA/accessible DNA).. The black line represents the
untreated sample, while the red line represent the 5-aza treated sample. The
most common cancer related genes have been emphasized.
This single chromosome analysis makes a higher resolution map of these data. The
individual chromosome maps allow us to mark the approximate location of the genes
within the genome. But as there are not extreme changes at the chromosomal level and
there are changes in the primary, “beads on a string” structure. We believe that there may
be a decoupling between the levels of chromatin structure.
To analyze the data we used unbiased statistical t-test averages of the transcription
start site to find the regions with greatest changes in nucleosome distribution. But
development of these algorithms is difficult for the type of data gathered in this process,
as a low t-test mean value may not reflect an outlier of a high t test value. Thus in
addition to the unbiased analysis, visual inspection is a necessary step for complete
analysis. A clear example may be seen in the B2M locus (Fig. 10). Upon further visual
inspection it was determined the class II, major histocompatibiliy complex, transactivator
(CIITA) shows the formation of a positioned nucleosome (Fig. 15).
12 hour
36 hour
THP1
*
*
U937
*
*
Figure 15. CIITA in response to 5-aza treatment. The arrow indicates the TSS.
Gene Expression
In addition, we were able to generate a simultaneous measurement of expression
status in about 45,000 human open reading frames (ORFs). This heat map of all the
genes measures the absolute expression values of these ORFs grouped on expression
levels (Fig. 16).
U0h
U12h
U36h
T0h
T12h
T36h
Figure 16. Gene expression heat map in response to 5-azaC. The
expression of all 45,033 ORFs is on the y-axis, and each cell line
and treatment are designated at the top of each column. The blue
is baseline for no expression and the light colors represent
expression.
Discussion
5-azacytidine directly or indirectly induces clear chromatin structural changes.
These differences are seen in about 15-36% (64-153) of the 425 cancer related TSS
regions. We observe that the effect of decreasing the number of methylated cytosine
residues gives specific effects on different classes of promoter regions.
These robust effects are mirrored through the same concentration of 5-azaC
treatment through two different cell lines. The similarity between two different cell lines
further supports the notion that 5-azacytidine produces specific effects on different
categories of gene promoters. For example we observe specific changes in nucleosome
positioning consistent between both cell lines in response to 5-azaC treatment. Clear
changes are seen at the B2M and CIITA major histocompatibility complex gene loci. We
infer these highly positioned nucleosome are the ultimate result of CpG island regulation.
The major histocompatibility complex (MHC) is a group of two classes of
molecules required for an immune response to occur. The MHC is a large gene family
coding for molecules responsible for the presentation of antigenic peptides to T
lymphocytes (Stoep, 2001). MHC I is present in all nucleated cells and is responsible for
displaying foreign, “non self” fragments to cytotoxic T cells, while MHC II is limited to
presentation to macrophages and B cells. Overall, these two molecules are responsible
for the presentation of foreign fragments to facilitate the immune response. Since cancer
cells are an uncontrolled cell growth that is not recognized by the immune system, the
expression of one or both of these molecules is methylated and transcription is repressed.
Methylation of CIITA was detected in seven of 32 primary acute myeloid leukemia
specimens, indicating that this modifications not a cell line-specific phenomenon
(Morimoto, 2004). But treatment with 5-azaC of the CIITA promoter in tumor cells, is a
way to enhance MHC-II expression and initiate an immune response against the tumor
(Wright, 2006). This implies that the anti cancer 5-azaC may have an ultimate effect on
the immune response and provides a model for immunotherapy (Fig. 17).
Immunotherapy
Model
*CpG island out of
probe range
Hypermethylation
Repression
Hypomethylation
Activation
Figure 17. CIITA response to 5-aza treatment. The red represents the treated sample while the
black represents the untreated. The green designates CpG islands.
We propose that the decrease in methylation recruits or removes some chromatin
regulatory machinery. Eventually a specific replacement of nucleosomes occurs near a
transcription start site. This new promoter chromatin architecture leads to an increase in
gene expression (Dennis, 2007). Here we provide a novel chromatin=based explanation
of how 5-azaC functions as an anti-cancer immunotherapeutic agent as it increases the
sensitivity of hematologic cancers.
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