Download Interpreting DNA Methylation in terms of TFBS

Meng Li
School of Informatics
Interdisciplinary Program of Biochemistry
Indiana University
Advisors: Dr. Sun Kim, Dr. Kenneth Nephew
4/25/2008
1
Overview
 Biology background and experimental model
 Objective
 High-throughput data and data analysis
 Combinatorial study and data mining
 Conclusions
2
Ovarian cancer and drug resistance
 Ovarian cancer
 Most deadly gynecological malignancy
 Cisplatin
 Widely used chemotherapeutic drug
 DNA intercalating agent
 Cisplatin resistance
 70% to 80% of patients develop resistance after 2year treatment
3
DNA methylation
CpG dinucleotides: 5’- AATACGCCACGA
4
DNA methylation
CpG island
CG
CG
MCG
MCG
MCGMCG MCG MCG
CG
CG
CG
CG CG CG
Normal
Cancer
X
C: cytosine
mC:
De novo methylation: acquired methylation
methylcytosine
5
Objective
 Does de novo DNA methylation plays a role in the
development of chemotherapeutic drug resistance
in ovarian cancer cells?
 How does de novo DNA methylation affect drug
resistance development?
6
In vitro drug resistance system
Cisplatin
Cisplatin
Drug-sensitive
parental cells
A2780 R-
IC50 (uM)
Drug-resistant
Cells
A2780 R+
Rounds of Cisplatin treatment
7
High-throughput data and data analysis
 Global promoter methylation data
 Global gene expression data
Louis Staudt, The nation’s investment in cancer research (NCI)
8
Global promoter methylation profiling
A2780 R-
A2780 R+
 Differential Methylation
Hybridization (DMH)
 44,000 probes representing
10,000 genes
 Two-color microarray analysis
 Data processing
 Estimate methylation level
CpG Island Microarray (44K)
9
Global promoter methylation profiling
 Loess normalization: correcting technical bias
Raw Data
Normalized Data
 Fold-change analysis: extracting gene methylation level
M =
Red
log 2
Green
10
Global gene expression profiling
 Affymetrix U133 plus 2.0
microarray
 54,675 probes representing
20,606 genes
mRNA
 Single-color microarray
cRNA
analysis
 Data processing
 Estimate gene expression
levels
U133 plus 2.0 array
11
Global gene expression profiling
 Clustering
 Fold change
A2780R+ expression
A2780R- expression
 Welch’s t-test p-values
A2780 R+ A2780 RCutoffs:
- p-value < 0.01
- fold change >= 1.5
12
Combinatorial study and data mining
 Does de novo DNA methylation plays a role in the
development of chemotherapeutic drug resistance
in ovarian cancer cells?
 How does de novo DNA methylation affect drug
resistance development?
13
The number of hypermethylated genes
positively correlated with the increase of IC50
14
DNA methyl-transferases (DNMT) are
up_regulated in resistant cells
Welch's t-test
Fold change
Gene title
DNMT1
0.0011
1.63
DNA (cytosine-5-)-methyltransferase 1
DNMT2
0.3673
1.17
DNA (cytosine-5-)-methyltransferase 2
DNMT3A
0.1009
1.20
DNA (cytosine-5-)-methyltransferase 3 alpha
DNMT3B
0.0004
1.80
DNA (cytosine-5-)-methyltransferase 3 beta
DNMT1: maintain genomic DNA methylation
DNMT3B: de novo methylation
15
Resistant cells re-establish cisplatin sensitivity
after methylation inhibitor treatment
16
Key questions
 Does de novo DNA methylation plays a role in the
development of chemotherapeutic drug resistance?
Yes
 How does de novo DNA methylation affect drug
resistance development?
 Does de novo methylation selectively blocking transcription factor
binding?
x
17
Surveying Transcription Factor Binding Sites
(TFBS) on differentially methylated regions
 Scan TFBS on hypermethylation (S+), hypomethylation (S-),
or hypermethylated CGI (SCpG) regions with match program
against TRANSFAC database
.
.
.
.
.
*
S+
.*
.
.
.
.
*
S-
*
*
.
.
.
.
.
*
SCpG
18
Methylation selectively occurs at
certain TFBS
32.5
Occurrence percentage
30
Hypermethylated
27.5
25
Hypomethylated
22.5
20
17.5
15
12.5
10
7.5
5
2.5
0
19
Statistical scoring
• Fisher exact test
• Multiple test correction – False discovery rate (FDR)
TFBS
S+ vs. S-
S+ vs. Sr
S+ vs. SCpG
NCX
HMGIY
CEBP
BRCA
0.000698
0.00113
0.008758
0.01259
5.49E-10
6.14E-16
1.53E-09
1.67E-06
0.0007561
2.88E-05
0.0001531
0.001406
20
Key questions
 Does de novo DNA methylation plays a role in the
development of chemotherapeutic drug resistance?
Yes
 How does de novo DNA methylation affect drug
resistance development?
 Does de novo methylation selectively blocking transcription factor
binding? Yes
 Does de novo methylation selectively regulates certain pathways?
21
Methylation regulated pathways
• Hypomethylation up-regulated pathways
UpRegulated Genes fc > 1.5 (1037)
HypoMethyl fc <-1.5 and UpReg genes
p<0.01 fc >1.5 (55)
Impact
Input genes
in pathway
Corrected
p-value
Impact
Input genes in
pathway
Corrected
p-value
fisher.test
p-value
Glioma
6.09
6
0.016
11.909
3
8.69E-5
0.013
Melanoma
4.516
5
0.060
10.268
3
3.91E-4
0.009
Pancreatic cancer
5.237
8
0.033
9.09
3
0.0011
0.023
Prostate cancer
5.064
9
0.038
8.991
3
0.0012
0.029
Colorectal cancer
3.108
6
0.184
8.758
3
0.0015
0.012
Chronic myeloid leukemia
3.169
6
0.175
8.221
3
0.0025
0.012
Non-small cell lung cancer
1.376
2
0.600
5.59
2
0.0246
0.044
All are human cancer related pathways
22
Hypomethylated and up-regulated
pathways
 Genes involved for each pathway:
PIK3R3, PDGFRA, E2F1, TGFBR2
E2F
Smad2/3
Smad4
23
Methylation regulated pathways
• Hypermethylation down-regulated pathways
DownReg Genes fc < -1.5 (1286)
HyperMethyl fc >1.5 and DownReg
genes p<0.01 fc <-1.5 (55)
Input
genes in
pathway
Corrected
p-value
Impact
Cell adhesion molecules
384.485
(CAMs)
14
1.95E-164
897.568
4
0
0.005052
Tight junction
8.868
13
0.0014
17.229
3
9.93E-7
0.02503
PPAR signaling pathway
3.68
6
0.1172
7.377
2
0.0052
0.03946
Impact
Input genes Corrected
in pathway p-value
fisher.test
p-value
Cell adhesion molecules (CAMs): Environmental information processing
Tight junction: Experimental processes -> Cell communication
PPAR signaling pathway: Experimental processes -> Endocrine system
24
Hypermethylated and down-regulated
pathways
 Genes involved for each pathway:
CAMs (ITGAV, CLDN11, NEO1, CDH2)
Tight junction (CLDN11, PPP2R4, INADL)
PPAR signaling (CPT1A, SLC27A6)
25
Key questions
 Does de novo DNA methylation plays a role in the
development of chemotherapeutic drug resistance?
Yes
 How does de novo DNA methylation affect drug
resistance development?
 Does de novo methylation selectively blocking transcription factor
binding? Yes
 Does de novo methylation selectively regulates certain pathways?
Yes
26
Conclusion
 Promoter CpG island methylation
 Participates in the development of drug resistance of
ovarian cancer cells
 Regulates gene expression alteration through drug
resistance development by selectively occurring at
certain TFBS
 Regulates cellular functions by methylating key players
in certain pathways
27
Acknowledgements
 Advisors
Sun Kim
Kenneth Nephew
 Committee
Curt Balch
Haixu Tang
 OSU ICBP center
Dustin Potter
Pearlly Yan
Tim H-M. Huang
 IUPUI
Lang Li
Jeanette McClintick
 Colleagues
Fang Fang
Shu Zhang
Henry Paik
John Montgomery
Mikyoung Jeong
Fuxiao Xin
Nicolas Berry
Xinghua Long
Nicole Nickerson
Xi Rao
Cori Hartiman-Frey
 Funding Agencies
NCI U54 CA11300
NCI R01 CA85289
28
F. Coste, J. M. Malinge, L. Serre, W. Shepard, M. Roth, M. Leng and C. Zelwer, "Crystal structure
of a double-stranded DNA containing a cisplatin interstrand cross-link at 1.63 A resolution:
hydration at the platinated site", Nucleic Acids Res, 1999, 27, 1837.
29