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Identification of inflammatory gene modules based
on variations of human endothelial cell responses
to oxidized lipids
Peter S. Gargalovic,
Minori Imura, Bin Zhang, Nima M. Gharavi, Michael J. Clark,
Joanne Pagnon, Wen-Pin Yang, Aiqing He, Amy Truong, Shilpa
Patel , Stanley F. Nelson , Steve Horvath, Judith A. Berliner,
Todd G. Kirchgessner, and Aldons J. Lusis
GOAL: Understand the complex biological system/disease
Evolution of approaches:
1. gene cloning and single gene regulation
2.
identification of gene-gene relationships (pathways)
3.
regulation of a pathway in the given system
4. integration of a given pathway/genome into complex
and dynamic biological system (current challenge)
NEW TECHNOLOGIES (Expression arrays):
Initial use in gene expression mapping:
Identify all genes regulated by Inflammatory
Stimuli (Oxidized Lipids)
Classical approach to exploratory expression array
experiments
Dose response
oxPAPC (4hrs)
10 μg/ml
30 μg/ml
HAEC
50 μg/ml
Data
analysis
LPS (2ng/ml)
Time course
Multiple time points 0 - 4hrs (50 μg/ml)
HAEC
Data
analysis
Major Differences in Gene Regulation Between LPS
and OxPAPC
oxPAPC (50 ug/ml)
Bacterial LPS (2 ng/ml)
459
70
vs.
283
742 genes
17
87 genes
Many Genes and Pathways are Regulated by Oxidized Lipids
(complex system!!!)
LDL
Inflammatory response
Endothelial Cells
Oxidation
Oxidized
Nitric Oxide
Phospholipids
Unfolded
Protein
Response
SREBP
~ 800 genes
ERK/EGR-1
CREB/HO-1
Src/Jak/STAT
GPCR,
cAMP
Can we take advantage of the large amount of data collected from
differentially perturbed states to learn more about the biological system?
Approach: Weighted Gene Co-expression NETWORK Analysis
(WGCNA)
• Identifies network modules that can be used to explain gene
regulation and function (pathway analysis)
•Hierarchical clustering with the topological overlap matrix
• Uses intramodular connectivity to identify important genes
•References
•Bin Zhang and Steve Horvath (2005) "A General Framework for Weighted
Gene Co-Expression Network Analysis", Statistical Applications in Genetics
and Molecular Biology: Vol. 4: No. 1, Article 17.
• Horvath S, Zhang B, Carlson M, Lu KV, Zhu S, Felciano RM, Laurance MF,
Zhao W, Shu, Q, Lee Y, Scheck AC, Liau LM, Wu H, Geschwind DH, Febbo
PG, Kornblum HI, Cloughesy TF, Nelson SF, Mischel PS (2006) "Analysis of
Oncogenic Signaling Networks in Glioblastoma Identifies ASPM as a Novel
Molecular Target", PNAS
Hypothesis:
Genetic variation modulates inflammatory
responses to oxidized phospholipids in
human population
Interleukin 8:
 Pro-inflammatory cytokine implicated in atherogenesis
 Mediates adhesion of monocytes to EC
 Highly induced by oxPAPC
 IL8 levels are higher in patients with unstable CAD then in healthy individuals
 Elevated plasma IL8 levels are associated with increased risk for future CAD
Genetic background influences inflammatory
responses to oxidized lipids in human EC
1400
oxPAPC
PAPC
1200
IL8 (pg/ml)
1000
800
600
400
200
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
DONOR HAEC #
Inflammatory Responses are Preserved Between
Cell Passages
IL8 ELISA
1600
correlation=0.825 p<0.001
1400
2nd (pg/ml)
1200
1000
800
600
400
200
0
0
200
400
600
800
1st (pg/ml)
1000
1200
1400
Co-Expression Network of Endothelial Responses to Oxidized
Phospholipids
ENDOTHELIAL CELL DONORS
1
2
3
4
5
6
7
8
Oxidized Phospholipids
IL8
Gene X
Gene Y
EXPRESSION PATTERNS
9
10
11
12
Experimental Design:
ENDOTHELIAL CELL DONORS
1
2
3
4
5
6
7
8
9
10
11
TREATMENT (4hrs)
1.
PAPC
(40 ug/ml)
2.
oxPAPC (40ug/ml)
1043 Genes Regulated by OxPAPC
Data Analysis Using Gene Co-expression Network Approach
12
Genetic Perturbation Approach to Study
Gene Regulation
oxPAPC
Endothelial cell line (1)
SREBP activity
(+) LOW
Expression of SREBP- regulated genes
(+) LOW
oxPAPC
Endothelial cell line (2)
SREBP activity
(+++) HIGH
Expression of SREBP- regulated genes
(+++) HIGH
1043 genes in the oxPAPC network are separated
into 15 modules
Topological Overlap Matrix Plot
12 cell lines
Brown Module is enriched in SREBP Pathway Genes
gene
INSIG1
6.257772
Highest
INSIG1
6.194221
SLC2A3
6.061201
INSIG1
5.695922
SLC2A14
5.606994
SLC2A14
5.227064
SLC2A14
4.260267
NQO1
3.984579
SQLE
3.5742
SLC2A3
3.483622
LPIN2
3.087652
Ranking
ADRB2
based
2.922237
SC4MOL
on
connectivity
2.915552
CYP51A1
2.373458
CPNE8
2.241534
SQSTM1
1.861886
CYP51A1
1.784242
---
1.722028
LOC285148
1.674725
---
1.602659
---
1.528179
SQLE
1.36481
LTB4DH
0.84509
LOC283219
0.790956
ID3
0.691711
---
0.255479
Brown module has
26 genes
8 of 14 SREBP
targets are in Brown
module
(p-value 1.26x10-10 )
Blue and Red module are enriched in UPR genes
RED MODULE (52 genes)
BLUE MODULE (256 genes)
5 out of top 10 genes are UPR
genes
22 out of top 100 genes are UPR
genes
CEBPB
10.82586
GIT2
9.623114
ATF4
9.178292
SLC7A5
8.612143
CEBPG
7.563844
MGC4504
7.446907
Ranking
7.270555on
based
network
7.019388
connectivity
XBP1
KIAA0582
MTHFD2
Ranking
based on
network
connectivity
6.86908
SPTLC2
6.824852
DDIT4
6.682974
EEF2K
6.475676
KIAA0582
6.40407
KIAA0121
6.288301
VEGF
6.155599
RALA
6.062034
LOC148418
6.031962
C14orf27
5.904039
IMAP1
5.65993
MLYCD
5.586476
RED module UPR
enrichment
(p-value 0.049 )
BLUE module UPR
enrichment
(p-value 1.3x10-13 )
Gene network separates genes into modules based on
mechanism of regulation
SREBP genes (Brown module)
(p-value 1.26x10-10 )
UPR genes (Blue and Red module)
(p-value 1.3x10-13 and 0.049)
IL8 (Blue module)
IL8 expression in cell lines is highly correlated with UPR genes
Screen for UPR regulatory sites in 1043 network
genes
Endoplasmic Reticulum
IRE1
PERK
ATF6
XBP1
ATF4
UPR genes
UPRE
5’-TGACGTGG-3’)
ERSE-I
5’- CCAAT(N9)CCACG -3’
ERSE-II
5 –ATTGGNCCACG- 3’
C/EBP-ATF
5’-TTGCATCA -3’
CRE-like site found in IL8 promoter
XBP1 and ATF6
ATF4
ATF4 siRNA inhibits IL8 expression in
primary human aortic ECs
mRNA (% of control)
400
p=0.001
IL8
Scrambled siRNA
ATF4 siRNA
300
UPR
Blue
module
p=0.0002
200
p=0.0006
68%
100
72%
74%
0
400
ATF4
ATF4
OX
TUN
p=0.003
300
p<0.0001
1000
200
Scrambled siRNA
ATF4 siRNA
INSIG1
p<0.0001
100
81%
71%
85%
0
CONT
OX
TUN
mRNA (% of control)
mRNA (% of control)
CONT
Scrambled siRNA
ATF4 siRNA
800
600
400
200
0
CONT
OX
TUN
SREBP
Brown
module
Co-expression network can be applied to
new gene-function discovery (MGC4504 in
red module is regulated by ATF4)
Gene of unknown function
present in UPR module
400
8000
ATF4
ATF4
7000
p=0.003
300
p<0.0001
200
p<0.0001
100
81%
71%
85%
0
mRNA (% of control)
mRNA (% of control)
MGC4504
Scrambled siRNA
ATF4 siRNA
6000
OX
TUN
p=0.0007
5000
p=0.003
4000
3000
2000
p=0.0008
1000
0
CONT
Scrambled siRNA
ATF4 siRNA
MGC4504
89%
CONT
96%
OX
97%
TUN
SUMMARY
 Common genetic variations in human population have
significant impact on inflammatory responses to oxidized
lipids
 Genetic variation-based gene co-expression network
approach was used to:
 subdivide genes into pathways based on mechanism of
regulation (UPR versus SREBP pathway)
 predict UPR involvement in regulation of IL8 and MGC4504
 ER homeostasis and associated stress pathways may play a
central role in mediating endothelial inflammatory
responsiveness to oxidized phospholipids and possibly other
stimuli