Multi-Organ Expression Profiling Uncovers a Gene Module
in Coronary Artery Disease Involving Transendothelial
Migration of Leukocytes and LIM Domain Binding 2: The
Stockholm Atherosclerosis Gene Expression (STAGE) Study
Sara Ha ¨gg1,2,3., Josefin Skogsberg1,3., Jesper Lundstro ¨m1,2,3., Peri Noori1,3, Roland Nilsson2,3, Hua
Zhong4, Shohreh Maleki1, Ming-Mei Shang1,3, Bjo ¨rn Brinne2, Maria Bradshaw1,2,3, Vladimir B. Bajic5,6,
Ann Samnega ˚rd7, Angela Silveira8, Lee M. Kaplan9, Bruna Gigante10, Karin Leander10, Ulf de Faire10,
Stefan Rosfors11, Ulf Lockowandt12,13, Jan Liska12,13, Peter Konrad14, Rabbe Takolander14, Anders
Franco-Cereceda12,13, Eric E. Schadt4, Torbjo ¨rn Ivert12,13, Anders Hamsten8, Jesper Tegne ´r1,2,3, Johan
1The Computational Medicine Group, Atherosclerosis Research Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden, 2Department of Computational
Biology, Linko ¨ping Institute of Technology, Linko ¨ping University, Linko ¨ping, Sweden, 3Clinical Gene Networks AB, Karolinska Science Park, Stockholm, Sweden, 4Rosetta
Inpharmatics, Merck, Seattle,Washington, UnitedStatesofAmerica, 5South AfricanNationalBioinformaticsInstitute (SANBI),Universityofthe Western Cape, CapeTown, South
Africa, 6Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia, 7Department of
Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden, 8Cardiovascular Genetics Group, Atherosclerosis Research Unit, Department of Medicine,
Karolinska Institutet, Stockholm, Sweden,9Massachusetts General Hospital(MGH) WeightCenterand DepartmentofMedicine,Harvard MedicalSchool, Boston, Massachusetts,
Karolinska Institutet, Stockholm, Sweden, 12Department of Thoracic Surgery and Anesthesiology, Karolinska University Hospital, Stockholm, Sweden, 13Department of
Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden, 14Department of Surgery, Stockholm So ¨der Hospital, Karolinska Institutet, Stockholm, Sweden
Environmental exposures filtered through the genetic make-up of each individual alter the transcriptional repertoire in organs
central to metabolic homeostasis, thereby affecting arterial lipid accumulation, inflammation, and the development of coronary
artery disease (CAD). The primary aim of the Stockholm Atherosclerosis Gene Expression (STAGE) study was to determine whether
there are functionally associated genes (rather than individual genes) important for CAD development. To this end, two-way
clustering was used on 278 transcriptional profiles of liver, skeletal muscle, and visceral fat (n=66/tissue) and atherosclerotic and
unaffected arterial wall (n=40/tissue) isolated from CAD patients during coronary artery bypass surgery. The first step, across all
mRNA signals (n=15,042/12,621 RefSeqs/genes) in each tissue, resulted in a total of 60 tissue clusters (n=3958 genes). In the
second step (performed within tissue clusters), one atherosclerotic lesion (n=49/48) and one visceral fat (n=59) cluster segregated
the patients into two groups that differed in the extent of coronary stenosis (P=0.008 and P=0.00015). The associations of these
clusters with coronary atherosclerosis were validated by analyzing carotid atherosclerosis expression profiles. Remarkably, in one
cluster (n=55/54) relating to carotid stenosis (P=0.04), 27 genes in the two clusters relating to coronary stenosis were confirmed
clusters, referred to as the atherosclerosis module (A-module). In a second validation step, using three independent cohorts, the A-
modulewasfound to begeneticallyenrichedwith CAD riskby 1.8-fold(P,0.004).The transcription co-factorLIMdomainbinding2
(LDB2) was identified as a potential high-hierarchy regulator of the A-module, a notion supported by subnetwork analysis, by
cellular and lesion expression of LDB2, and by the expression of 13 TEML genes in Ldb2–deficient arterial wall. Thus, the A-module
appears to be important for atherosclerosis development and, together with LDB2, merits further attention in CAD research.
Citation: Ha ¨gg S, Skogsberg J, Lundstro ¨m J, Noori P, Nilsson R, et al. (2009) Multi-Organ Expression Profiling Uncovers a Gene Module in Coronary Artery Disease
Involving Transendothelial Migration of Leukocytes and LIM Domain Binding 2: The Stockholm Atherosclerosis Gene Expression (STAGE) Study. PLoS Genet 5(12):
Editor: Kathleen Kerr, University of Washington, United States of America
Received May 26, 2009; Accepted November 4, 2009; Published December 4, 2009
Copyright: ? 2009 Ha ¨gg et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by grants from the Swedish Research Council (JB, JT, JS),the Karolinska Institute (JB, AH), the Stockholm County Council (JB, AH),
the Swedish Foundation for Strategic Research (JB, JT), the Swedish Heart-Lung Foundation (JB), the King Gustaf V and Queen Victoria Foundation (JB), the Swedish
Society of Medicine (JB, JT), the Hans and Loo Osterman Foundation for Geriatric Research (JB, JS), the Professor Nanna Swartz Fund (JB), the Foundation for Old
Servants (JB, JS), the Magnus Bergvalls Foundation (JB), Ake Wiberg Stiftelse (JB, JT), Wennergren Foundation (JT), Vinnova Sweden-Japan (JB, JT), Vinnova research
grant (SM, JT, JB), Vinnova SAMPOST grant (M-MS, JB), the PhD Programme in Medical Bioinformatics (JB, JT), Linkoping University and Stockholm Bioinformatics
Center (JT) and CarlTryggers Foundation (JT),Swedish Match(unconditional researchgrantto JT), AstraZeneca (unconditionalresearch grants to JB, AH), and Clinical
Gene Networks (JB, JT). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: Clinical Gene Networks AB with Johan Bjo ¨rkegren and Jesper Tegne ´r as major shareholders has filed a PCT application for a screening
method using genes in the atherosclerosis module including LDB2 (PCT/SE2007/00864).
* E-mail: email@example.com
. These authors contributed equally to this work.
PLoS Genetics | www.plosgenetics.org1 December 2009 | Volume 5 | Issue 12 | e1000754
The mapping of the human genome resulted in new
technologies for studying complex diseases such as coronary
artery disease (CAD) from a functional genomic perspective. By
revealing comprehensive repertoires of molecular activities, these
technologies combined with systems biology analyses will pave the
way for a more detailed understanding of the complexity
underlying common disorders—a prerequisite to advance molec-
ular diagnostics for early identification of disease and to identify
central disease pathways for therapies tailored to specific disease
The aim of the Stockholm Atherosclerosis Gene Expression
(STAGE) study was to identify functionally associated genes
important for CAD using whole-genome expression profiles from
multiple organs. To this end, we used a modified version of a two-
way clustering approach [4–6]. In the first step, the algorithm
processed all mRNA signals within one organ to define a number
of tissue clusters. The individual genes of the tissue clusters are
defined by the level of associations between mRNA signals across
all patients. In the second step, the patients are clustered according
to the mRNA signals within each tissue cluster to identify signals
related to clinical phenotypes. In this study, the clinical endpoint
was the extent of coronary atherosclerotic lesions as judged from
the degree of coronary stenosis, measured by quantitative
coronary angiography (QCA). A secondary hypothesis was to
reveal the extent to which any tissue cluster related to coronary
stenosis acts in isolation in one organ or across several organs.
A multi-organ biopsy approach is primarily motivated by the
nature of CAD development: atherosclerotic diseases are believed
to start in adolescence and develop throughout life . The pace
of development depends on genetic and environmental risk factors.
Of particular importance are metabolic disturbances (e.g.
overweight, diabetes and dyslipidemias) that originate in organs
central to energy metabolism, including liver, skeletal muscle, and
fat deposits. Thus, molecular activities (mirrored by mRNA levels)
distant from the actual site of CAD are likely to influence the
progression and extent of coronary atherosclerosis.
The STAGE study comprises 114 carefully characterized
patients, including a compendium of 278 global gene-expression
profiles from five CAD-relevant tissues isolated during coronary
artery bypass grafting (CABG). Using a two-way clustering
approach, we analyzed this compendium to test our main
hypothesis that there are groups of functionally associated genes
(rather than individual genes) of importance for CAD and to
determine whether those groups of genes act in isolation in each
tissue or across several tissues.
Exploratory Clustering of Gene-Expression Profiles in the
To test the main hypothesis of the study we explored the gene
expression profiles of the STAGE cohort. Gene expression profiles
could not be obtained from all tissues in all patients of the STAGE
cohort (n=114). Therefore, it was important to examine whether
the two subgroups of patients in which gene expression profiles
were obtained—66 patients with gene expression profile from
visceral fat, liver, and skeletal muscle and 40 in whom expression
profiles were also obtained from atherosclerotic and unaffected
arterial wall—had similar clinical phenotypes. Indeed, this
appeared to be the case (Table 1).
In the first step of the two-way clustering analysis, mRNA signals
of 15,042 Reference Sequence transcripts (RefSeq) were examined
in each tissue (Figure 1, Text S1, Figure S1). Importantly, the first
step was performed without preconceptions about the extent of
coronary atherosclerosis in the CABG patients. Instead, tissue-
specific mRNA signals across the patients were analyzed solely to
determine whether or not a given RefSeq belonged to a group of
functionally associated genes in a tissue cluster. The first clustering
step generated 60 tissue clusters representing 4007 RefSeqs/3958
genes (Table S1). Thus, 73% of the RefSeqs or 11,035 RefSeqs
(8663 genes) were excluded from further analysis (i.e., the second
clustering step). Of these 60 tissue clusters, 15 were identified from
the liver gene expression profiles, 11 from skeletal muscle profiles,
20 from visceral fat profiles, and 14 from gene expression profiles of
the atherosclerotic arterial wall (Table S1). To assess the
repeatability and reliability of these clusters, resampling using
Jackknife analysis was performed (Table S1).
In the second step of clustering, the mRNA signals within each
of the 60 tissue clusters were used to cluster the patients. The
extent of coronary stenosis, determined by QCA, was then
compared in the resulting patient groups. Two of the 60 tissue
clusters (n=49 RefSeqs/48 genes, Table S2, (90% CI: 28–49) and
n=59 RefSeqs/genes, Table S3, (90% CI: 38–59), respectively)
segregated the patients into groups according to the extent of
coronary stenosis: one cluster in atherosclerotic arterial wall and
one in visceral fat (P=0.008 (Figure 2) and P=0.00015 (Figure 3),
To determine whether the identified tissue clusters relating to
coronary atherosclerosis are tissue-specific or present in several
tissues, we assessed the gene overlap between the atherosclerosis-
related clusters in atherosclerotic arterial wall and visceral fat.
Seven genes (12%, 14% respectively) were present in both tissue
clusters. Although this overlap may appear small, the statistical
likelihood of observing an overlap of this size by chance was less
than 10210. Thus, this overlap indicates atherosclerosis-related
gene activity common to both visceral fat and atherosclerotic
The WHO predicts that coronary artery disease (CAD) will
become the leading cause of death worldwide in 2010.
Currently, major research efforts are focused on under-
standing the genetics of CAD through multi-center,
genome-wide association studies of tens of thousands of
patients and controls. Such studies can identify common
variants of general importance throughout the entire
population, which are likely relatively few. The number of
rare genetic variants and variants that act in the context of
environmental risk factors for CAD is probably much
higher. We performed whole-genome expression analyses
in several organs to identify functionally associated genes
important for CAD development. We found an atheroscle-
rosis module (A-module) consisting of 128 genes, enriched
with genetic risk for CAD, involving transendothelial
migration of leukocytes (TEML) and LIM domain binding
2 (LDB2) as its high-hierarchy regulator. Our study design
represents a novel way of understanding the molecular
underpinnings of CAD, focusing on genome-wide expres-
sion sensing both environmental and genetic influences.
Investigating the relative enrichment of genetic CAD risk in
functional groups (modules and networks) is an alternative
approach to extract additional relevant information from
genome-wide association studies. The A-module and LDB2
are attractive targets for treatments to modulate TEML and
PLoS Genetics | www.plosgenetics.org2 December 2009 | Volume 5 | Issue 12 | e1000754
Confirmatory Clustering of Gene-Expression Profiles of
The molecular underpinnings of atherosclerosis are believed to
be very similar in all major arteries . Accordingly, if the two
atherosclerosis-related tissue clusters identified in the STAGE
cohort are of general importance for atherosclerosis, they should
be possible to confirm, at least in part, in another atherosclerotic
tissue sample. To this end, total RNA samples from atherosclerotic
carotid lesions were isolated from patients undergoing carotid
stenosis surgery (Figure 1 and Table 1). Both the gene expression
profiling and the subsequent two-way clustering analysis were
performed exactly according to the protocol used for the STAGE
cohort. A well-established surrogate measure of the extent of
carotid atherosclerosis , the intima-media thickness (IMT), was
determined preoperatively using ultrasound. The first clustering
step generated a total of eight tissue clusters (Table S1)
Table 1. Basic characteristics of the STAGE cohort.
Characteristics STAGE Carotid patients
expression profilesp-Value expression profiles
n (% of total)114 (100) 66 (58)40 (35) 25 (100)
Age, y (mean6 6SD)6668 6668 6668 69611
Male, n (%) 102 (89)59 (89) 37 (93)15 (60)
Body-mass index, kg/m2(mean6 6SD)26.663.7 26.463.926.363.925.363.2
Waist-to-hip ratio (mean6 6SD)0.9460.060.9360.060.9360.06 0.9160.07
Blood pressure, mm Hg (mean6 6SD)
Diastolic8069 806107868 7769
Insulin, pmol/L (mean6 6SD)62647 59649 61653 44616
Proinsulin, pmol/L (mean6 6SD)5.622.214.171.124 5.566.94.662.4
HbA1c, % (mean6 6SD) 5.261.35.060.75.060.64.860.4
Cholesterol, mmol/L (mean6 6SD)
Total4.0861.013.9761.08 3.8361.02 4.7461.21
VLDL 0.3260.25 0.2960.25 0.2660.25 0.2260.17
Triglycerides, mmol/L (mean6 6SD)
Total 1.4160.73 1.3660.701.4160.761.2360.49
VLDL1.0460.67 0.9760.64 0.9860.68 0.7960.42
LDL 0.2660.090.2760.09 0.2860.090.2960.09
Current smoker, n (%) 8 (7) 4 (6) 2 (5)1 (4)
Former smoker, n (%) 70 (61)42 (64) 25 (63)18 (67)
Alcohol consumption, g/week (mean6 6SD) 120696 117689124682 1176106
Stenosis score (mean6 6SD)- 5.0662.41 5.3762.43NA
IMT, mm (mean6 6SD)NA NANA 1.2460.24
Diabetes mellitus, n (%) 24 (21) 11 (17) 5 (13)
,0.05 2 (8)
Insulin-requiring 23 (20)9 (14) 5 (13)1 (4)
Hyperlipidemia, n (%) 84 (74)49 (74) 27 (68)13 (52)
Statins 101 (89) 61 (92)37 (93)15 (60)
Hypertension, n (%)72 (63) 43 (65)25 (63) 16 (64)
Betablocker103 (90)62 (94) 38 (95)11 (44)
ACE inhibitors42 (37)25 (38)15 (38)5 (20)
Thiazide diuretics0 (0)0 (0) 0 (0) 1 (4)
Loop diuretics26 (23) 13 (20)10 (25)3 (12)
Calcium-channel blockers15 (13)7 (11)4 (10) 5 (20)
p-Values were calculated using unpaired t-tests comparing subgroups in STAGE with the entire STAGE cohort (n=114). Subgroups are included in the entire cohort.
NA indicates not available. HbA1c, glycated haemoglobin; VLDL, very low density lipoprotein; LDL, low density lipoprotein; HDL, high density lipoprotein; IMT, intima-
media thickness; ACE, angiotensin-converting enzyme.
PLoS Genetics | www.plosgenetics.org3 December 2009 | Volume 5 | Issue 12 | e1000754
representing 904 RefSeqs/894 genes. In the second clustering
step, one of the eight tissue clusters (n=55 RefSeqs/54 genes,
Table S4, (90% CI: 32–55)) segregated the patients into two
groups according to IMT score (P=0.039, Figure 4). Remarkably,
16 of the 55 RefSeqs overlapped with genes in the visceral fat
cluster (P=10227), and 17 with genes in the atherosclerotic arterial
wall cluster (P=10230) (Figure 5A). Six RefSeqs (representing the
genes encoding C-type lectin domain family-14, cadherin-5,
chromosome 20 open reading frame-160, endothelial differentia-
tion sphingolipid G-protein-coupled receptor-1, G protein-coupled
receptor-116, and LIM domain binding 2 (LDB2)) were in all three
clusters (P=10223); the union of the clusters contained 129
RefSeqs/128 genes (Figure 5A, Table S5).
Network and Bioinformatic Analyses of the
The highly significant overlaps between the three clusters in the
atherosclerotic arterial wall, visceral fat and carotid stenosis
suggest that the union of all genes may represent a module
harboring biological activity important for human atherosclerosis
(referred to as the A-module). To investigate interactions between
genes in the A-module, gene expression profiles from these tissues
were reused to infer a total of three gene networks (Text S1). In
Figure 5B, a network supported by nodes and edges in at least two
of the three networks is shown. The network of A-module genes
consisted of 49 nodes (genes) interacting with a total of 55 edges, of
which LDB2 had 19 edges and BCL6B had 14 edges.
To learn more about the functional representation of the A-
module, bioinformatic analysis using Gene Ontology (GO) and
KEGG pathway was performed (Table S6). Thirty-one of the 128
genes had previously been related to atherosclerosis (Table S9), 40
had no GO annotation, and six participated in regulatory activity
(Text S1). Only 39 of the 128 genes had annotation in KEGG
pathways. Twenty-three of these 39 genes (,60%) were associated
with the transendothelial migration of leukocyte (TEML) pathway
with a statistical significant enrichment score  (P=6.661025,
FDR=0.01; Figure 5C).
Enrichment of Genetic Risk for CAD in the Atherosclerosis
If gene activity in the A-module is casually important for
atherosclerosis development (and not merely reactive marker for
the extent of atherosclerosis), functionally associated single
nucleotide polymorphisms (SNPs) in the vicinity of the 128 A-
module genes should be enriched for CAD risk. In addition, such
enrichment would further strengthen our notion that the A-
module genes as being important in atherogenesis. To investigate
this, we first identified SNPs in the A-module that were
significantly associated with gene expression (eSNPs, indicating a
functional relation between the SNP allele distribution and gene
expression (Text S1)) using two genetics of gene expression (GGE)
studies . Next, to test whether the identified eSNPs also were
enriched for association with CAD, we assembled results from a
recent genome-wide association study (GWAS), the Wellcome
Figure 1. Analytical scheme of multi-organ clustering steps in the STAGE study. Sixty-six gene profiles (15,042 RefSeqs each) from liver,
skeletal muscle, and visceral fat and 40 from atherosclerotic aortic wall were clustered by a coupled two-way approach. First, the RefSeqs were
clustered according to their average probe signal values on the chip (mRNA level, see figure ‘‘clustering’’) resulting in 11 skeletal muscle, 20 visceral
fat, 15 liver, and 14 atherosclerotic arterial wall clusters together representing 4007 RefSeqs/3958 genes. Second, clustering within each tissue cluster
was performed to sort patients by mRNA levels. Clusters that sorted the patients according to extent of coronary stenosis were considered further. To
validate these atherosclerosis-related clusters, we performed cluster analysis of 25 gene-expression profiles of carotid atherosclerosis lesions. Of eight
clusters representing 903 RefSeqs/894 genes, one segregated patients according to IMT. The extent of overlap between this cluster relating to carotid
atherosclerosis and the two clusters relating to coronary atherosclerosis was used as the confirmatory measure. Genetic enrichment and functional
gene classifications were then assessed by bioinformatic and TRANSFAC analyses. Animal and cell models were used for functional validation of
PLoS Genetics | www.plosgenetics.org4 December 2009 | Volume 5 | Issue 12 | e1000754
Figure 2. Heat map of an atherosclerotic arterial wall cluster related to coronary stenosis. The cluster was defined by related mRNA levels
(indicated by average probe signals on the arrays) and identified as one of fourteen atherosclerotic arterial wall clusters by the second step of
coupled two-way clustering of mRNA profiles from STAGE patients (Text S1). Columns represent individual patients, and rows individual RefSeqs with
corresponding gene symbols and mRNA ratios of the two patient groups. Above heat map: individual patient numbers, below heat map: bars
indicating individual stenosis score together with means 6 SD and average ratios in each group and P-values for comparing groups. EVA1 is
represented by two RefSeqs.
PLoS Genetics | www.plosgenetics.org5 December 2009 | Volume 5 | Issue 12 | e1000754
Figure 3. Heat map of a visceral fat cluster related to coronary stenosis. The cluster was defined by related mRNA levels (indicated by
average probe signals on the arrays) and identified as one of 20 visceral fat clusters by the second step of coupled two-way clustering of mRNA
profiles from STAGE patients (Text S1). Columns represent individual patients, and rows individual RefSeqs with corresponding gene symbols and
mRNA ratios of the two patient groups. Above heat map: individual patient numbers, below heat map: bars indicating individual stenosis score
together with means 6 SD and average ratios in each group and P-values for comparing groups. Red highlighting indicates genes also found in the
cluster in Figure 2.
PLoS Genetics | www.plosgenetics.org6 December 2009 | Volume 5 | Issue 12 | e1000754
Trust Case Control Cohort (WTCCC) study . Since the GGE
and WTCCC studies used different SNP-microarray platforms,
strong linkage disequilibrium (LD) (R.0.84) was used to confer
matches between eSNPs and WTCCC SNPs resulting in a set of
484 eSNPs. The distribution of P-values for CAD associations
according to the WTCCC study for these 484 eSNPs is shown in
Figure 5D. To determine whether this distribution was signifi-
cantly enriched for CAD risk, we empirically estimated the null
distribution of 100,000 random sets of 484 WTCCC eSNPs.
10.3% of the 484 eSNPs in the A-module had a significant
association to CAD (P,0.05), compared to an average of 5.8% of
the eSNPs (95% CI: 2.5%–9.2%) in the random sets (Z=2.64;
P=0.004), representing a 1.8-fold enrichment of CAD risk in the
A-module. When instead all SNPs were considered, the enrich-
ment of CAD risk in the A-module was 1.4-fold (Z=2.71;
Identifying a Putative Regulator of the Atherosclerosis
Of the six genes in the intersection of all three clusters making
up the A-module (Figure 5A), LDB2 was the only transcriptional
regulator. The re-occurrence of this transcriptional co-factor in
three separate genome-wide analyses suggested a regulatory role of
the A-module genes. A notion supported by the interconnectivity
of LDB2 in the network analysis (Figure 5B). To investigate this
possibility further, we first identified seven transcription factors
(TFs) (ISL-1alpha, Lmo2, Lhx3a, Lhx3b, LHX2, LHX4, and
BRCA1) having LIM-binding domains  or otherwise previ-
ously been shown to interact with LDB2 . We then performed
in silico sequence matching for 161 promoters (Ensembl) found in
122 of the 128 A-module genes using TRANSFAC (v11.2) .
Of these 161 promoters (target promoters), 81% had binding site(s)
for at least one of the seven TFs, suggesting that LDB2 could
regulate the A-module via these TFs. In relation to a background
of 10,255 human promoters covering a [-600,-1] region relative to
transcription start sites, binding to the target promoters was
enriched 1.2- to 5-fold (Text S1, Table S10). The enrichment for
the entire family of 7 TFs was statistically significant (P=0.011).
Functional Validation of LDB2 in Atherosclerosis
Next, we investigated the possible role of LDB2 in atheroscle-
rosis in vitro in three major atherosclerosis cell types as well as in vivo
in atherosclerosis-free arterial wall and in early and late
atherosclerotic lesions in atherosclerosis-prone Ldlr2/2Apob100/100
mice . The presence of LDB2 in the arterial endothelium was
first assessed by co-localization of LDB2 with the endothelial
marker von Willebrand factor (VWF). LDB2 expression was most
obvious in the endothelium before an atherosclerotic lesion had
developed and generally co-localized with VWF (Figure 6A, 406).
In late and early lesions, LDB2 endothelial expression was patchy
and subtler, and the co-localization with VWF was less clear
except in the endothelium of lesion-free areas (e.g., cusps;
Figure 6A). LDB2 expression in endothelial cells was confirmed
by RT-PCR analyses in a human endothelial cell line (EAHY926)
and in human umbilical vein endothelial cells (HUVECs)
(Figure 6B). In accordance with the immunohistochemical results,
the mRNA levels were higher in noninduced than in induced
EAHY926 cells (Figure 6B).
To investigate LDB2 protein expression in other atherosclerosis
cell types, CD68 was used as a marker of lesion macrophage/foam
cellsandSM22 (transgelin) asa markeroflesionsmooth musclecells
(SMCs). In early lesions, LDB2 staining was subtle (but clearly
present compared to control) and appeared to co-localize with both
CD68 and SM22 (Figure 6C). In late lesions, LDB2 staining was
marked, and in all locations of LDB2 staining there was also CD68
staining. Inthissense,there was co-localization ofLDB2 andCD68.
However,the CD68 stainingwasgenerallystronger,andsomeareas
with CD68 staining had little or no LDB2 staining. LDB2 also co-
localized with SM22, but some areas with marked LDB2 staining
had no SM22 staining (Figure 6B, ovals). LDB2 was also expressed
in macrophages/foam cells in human carotid lesions (Figure S2).
Figure 4. Heat map of a carotid stenosis cluster related to IMT.
The cluster was defined by related mRNA levels (indicated by average
probe signals on the arrays) and identified as one of eight carotid
stenosis clusters by the second step of coupled two-way clustering of
mRNA profiles from Carotid Stenosis patients (Text S1). Columns
represent individual patients, and rows individual RefSeqs with
corresponding gene symbols and mRNA ratios of the two patient
groups. Below heat map: bars indicating individual IMT together with
means 6 SD and average ratios in each group and P-values for
comparing groups. Red highlighting indicates genes also identified in
the clusters in Figure 2 and Figure 3. EVA1 is represented by two
PLoS Genetics | www.plosgenetics.org7 December 2009 | Volume 5 | Issue 12 | e1000754
The immunohistochemical results were largely confirmed by
RT-PCR analyses of primary SMCs and macrophages and a
human monocytic cell line (THP-1) (Figure 6D). Consistent with
the higher protein expression in late lesions than in early lesions,
LDB2 mRNA levels increased with differentiation of THP-1
monocytes to macrophages and foam cells (panel 1). The
expression of LDB2 in THP-1 was also confirmed in primary
macrophages (panel 2). In primary SMCs isolated from human
pulmonary artery, there was also clear expression of LDB2, which
in comparison with the immunohistochemical results was
surprisingly high (panel 3).
In summary, LDB2 was expressed by all three major
atherosclerosis cell types; before lesion formation and in early
lesions primarily in the endothelium and in late lesions, mainly in
macrophages/foam cells but also in SMCs. The generally higher
LDB2 expression in late lesions was confirmed by RT-PCR of
total RNA from early and late lesions isolated from mouse aortic
arch samples (Figure 6E).
Last, we examined mRNA levels of 20 genes central to TEML
in the arterial wall of 6-week-old Ldb22/2mice. Our goal was to
investigate a possible role of LDB2 as a regulator of TEML genes
in general and specifically as a regulator of A-module genes. All 20
genes had higher levels of expression in Ldb22/2than in wild-type
mice whereof 13 was significantly higher (Table 2). Eight of these
13 genes were specific to the A-module, and five were not. Of
note, five of the investigated genes have previously been targeted
in mouse models of atherosclerosis and found to be affecting lesion
Figure 5. Intersection, network and bioinformatic analyses of the A-module. (A) Venn diagrams showing overlaps of genes in the A-module
(three clusters related to extent of atherosclerosis) (Figure 2, Figure 3, Figure 4). Seven genes were found in both the atherosclerotic arterial wall and
visceral fat clusters (P=10210), 17 in the atherosclerotic arterial wall and carotid stenosis clusters (P=10230), and 16 in the visceral and carotid
stenosis clusters (P=10227). Six genes were found in all three clusters (P=10223). The union of all three clusters represented 128 genes. (B) A gene
regulatory network inferred by co-expression of A-module genes using genome-wide expression data from the atherosclerotic arterial wall, carotid
stenosis tissue, and visceral fat. Network edges are supported by at least two of the datasets, resulting in a total of 49 nodes. Marked in black are
nodes (genes) with known regulatory activity, which are prioritized by the algorithm (Text S1). Marked as diamonds are 24 genes present in
intersections between at least two of the clusters in Figure 5A (n=27). (C) The TEML pathway. Marked in red are eight genes in the A-module that
perfectly matched genes in the TEML pathway (P=6.661025). Marked in blue are 15 genes in the A-module that were associated with the TEML
pathway according to Panther family annotation in DAVID. For a list of all genes in the TEML pathway and Panther families see Table S7 and Table S8,
respectively. (D) The P-value distribution of 484 eSNPs (SNPs with allele distribution affecting gene expression) in the A-module indicating association
with CAD according to a recent GWAS, the WCTTT study .
PLoS Genetics | www.plosgenetics.org8 December 2009 | Volume 5 | Issue 12 | e1000754
Taken together, the functional validation supports a role for
LDB2 in TEML and atherosclerosis development. Particularly,
since endothelial LDB2 seems to regulate TEML already before
microscopic evidence of lesion formation.
In the STAGE study, we profiled five CAD-relevant tissues to
identify functionally associated genes with potential importance in
Figure 6. LDB2 expression in atherosclerotic lesions and cultured lesion cell types. Total RNA was isolated from cell cultures and
mouse aortic arch (third rib to aortic root). Consecutive mouse aortic root sections were incubated with goat anti-LDB2, rat monoclonal anti-
mouse CD68, rabbit polyclonal anti-mouse SM22 alpha, or rabbit polyclonal anti-human VWF at 4uC overnight and counterstained with
hematoxylin. RT–PCR was performed on total RNA isolated from human pulmonary artery SMCs, THP-1 monocytes, THP-1 macrophages
generated with phorbol 12-myristate 13-acetate, THP-1 foam cells cultured from THP-1 macrophages incubated with acetylated low density
lipoproteins, primary macrophages differentiated from primary monocytes isolated from human blood with AB serum, cultured EAHY926
cells, EAHY926 cells induced with 20-ng/ml human recombinant TNF-a, and HUVECs isolated with collagenase. (A) Mouse LDB2 and VWF
protein expression in serial sections of aortic roots from Ldlr2/2Apob100/100mice at 10 weeks (arterial wall without visual atherosclerosis,
‘‘non-atherosclerotic’’), 20 weeks (early lesions, fatty streaks), and 50 weeks (late lesion, plaques). Ovals indicate areas of overlapping LDB2
and VWF staining in relation to negative controls. (B) LDB2 mRNA levels in EAHY926 cells, induced EAHY926 cells, and HUVECs (n=4 per cell
type; scales on Y-axes are comparable because the RT-PCR was performed in one single run). (C) Mouse LDB2, CD68, and SM22 alpha protein
expression in serial sections of aortic roots from Ldlr2/2Apob100/100mice at 20 and 50 weeks. (D) LDB2 mRNA levels in primary human SMCs,
THP-1 monocytes, THP-1 monocytes differentiated into THP-1 macrophages, THP-1 foam cells, and primary human monocytes differentiated
into macrophages (n=4 per experiment). Ovals indicate areas of overlap between LDB2 and CD68 but no or very subtle SM22 staining in
relation to negative controls. (E) mRNA levels measured by real-time PCR from late (40 weeks, plaques, n=5) and early (20 weeks, fatty
streaks, n=5; lesions from the aortic arch in Ldlr2/2Apob100/100mice.
PLoS Genetics | www.plosgenetics.org9 December 2009 | Volume 5 | Issue 12 | e1000754
coronary atherosclerosis. This analysis revealed 128 genes that
were strongly associated with atherosclerosis severity (A-module).
The A-module was found to be enriched with genetic risk for CAD
and involve the TEML pathway. Parts of the A-module were
active in both atherosclerotic arterial wall and visceral fat. The
latter may be a local source of inflammation contributing to
coronary atherosclerosis. We also identified a putative high-
hierarchy regulator of the A-module, LDB2, which was robustly
expressed in all major lesion cell types both in lesion-free and in
late atherosclerosis lesions. Interestingly, key genes in the TEML
pathway were differentially regulated in the arterial wall of Ldb2-
deficient mice. Our findings suggest that the A-module, including
LDB2, is important in the regulation of TEML and in
TEML is an established pathway in atherosclerosis and
other inflammatory diseases . Transendothelial migration
of monocytes is essential for foam-cell formation and for early
phases of atherogenesis, and transendothelial migration of T-
cells may be central in later phases . Indeed, leukocyte
migration has been suggested as a therapeutic target . The
identified module was enriched in genes involved in TEML
and thus may be causally involved in the development of
clinically significant atherosclerotic lesions (as indicated by the
extent of coronary stenosis and IMT). However, most of the
identified A-module genes lack pathway annotations but may
in future studies be proven important to leukocyte migration or
The STAGE study was designed as a ‘‘top-down’’ systems
biological approach to identify gene networks or groups of
otherwise functionally associated genes (modules) of importance
for disease severity . The term ‘‘top-down’’ refers to our belief
that these modules must first be identified in clinical studies as the
most disease relevant and then be consecutively detailed by studies
in animal and cellular models to reveal high-resolution networks
. In contrast, ‘‘bottom-up’’ systems biology approaches first
identify full biological networks in prokaryotic or yeast cells and
then examine their roles in more disease-relevant systems. Systems
biological approaches have advantages over traditional gene-
expression profiling studies, which usually focus on identifying
individual genes differentially expressed as a result of disease. Such
gene-by-gene analyses generate many false positives due to a vast
‘‘multiple testing’’ problem. In contrast, the two-way clustering
approach first focuses on identifying functionally associated genes
(which in the current study reduced the number of genes from
12,621 to 3958 represented in 60 tissue clusters) and then
investigate whether the generated clusters (not individual genes)
are related to a given disease phenotype.
Using a multi-organ approach , we hypothesized the liver,
skeletal muscle, or fat deposits would harbour functionally related
genes (e.g., clusters, modules, networks) reflecting molecular
processes in those organs affecting the levels of inflammatory
mediators, blood lipids, glucose or unknown blood constituents
that contribute to coronary atherosclerosis development. There
were no clusters relating to the extent of coronary atherosclerosis
Table 2. mRNA levels measured by real-time PCR from the aortic arch of 6-week-old mice deficient in Ldb2 (Ldb22/2) and
littermate wild-type controls (Ldb2wt/wt).
Category Gene SymbolLdb2wt/wt
Ldb22 2/ /2 2
A-module genes associated to TEML
Claudin 5Cldn5 30761083976271 0.47
Phospholipase C gamma 2Plcg2 461665 72662190.019
Cadherin 5 Cdh535261146036179 0.011
Chemokine (C-X-C motif) ligand 12Cxcl12498610371561680.015
Platelat/endothelial cell adhesion molecule Pecam13456122 5646157 0.016
Angiotensin II receptor-like 1Aplnr 4356253 84664040.069
Kinase insert domain receptorKdr3866224 9646555 0.043
Protocadherin 12Pcdh12 491618878563390.10
Protein Kinase N3Pkn34106193 107666970.050
Protein kinase C etaPrkch5476199 10456369 0.019
Protein tyrosine phosphatase receptor type BPtprb 486616711156575 0.030
Tek tyrosine kinase (endothelial) Tek4306122106865510.021
Tyrosine kinase with immunoglobulin-like and EGF-like domains 1Tie1 5246170 89563740.056
Other TEML genes
Intercellular adhesion molecule 1 Icam1 405654533673 0.0042
F11 receptor F11r 388659 6146151 0.0037
Junction adhesion molecule 2Jam245267061661370.018
Junction adhesion molecule 3Jam3567653 7416163 0.022
Vascular cell adhesion molecule 1Vcam1 492683 7306134 0.0025
Thymus cell antigen 1Thy1 556615870762640.23
CDC42 effector protein (Rho GTPase binding) 5 Cdc42ep55406127 62261190.26
Values are mean 6 SD. p-Values are calculated with unpaired t-test.
Values are normalized to acidic ribosomal phosphoprotien P0 and TATA box binding protein.
Ldb22/2, n=5–6; Ldb2wt/wt, n=6–7.
PLoS Genetics | www.plosgenetics.org10 December 2009 | Volume 5 | Issue 12 | e1000754
in the liver and skeletal muscle. This was surprising given the
importance of these organs for CAD risk factors, such as plasma
cholesterol and diabetes. However, therapies to reduce plasma
lipid and glucose levels (Table 1) might have normalized disease-
promoting activities in CAD-modules in these organs. In contrast,
we identified one part of the A-module in visceral fat that
segregated patients according to the degree of coronary stenosis.
Although the relation of visceral fat to CAD risk factors in blood is
less clear, a high waist-hip ratio—an indicator of increased visceral
fat mass in the abdomen—is a strong predictor of CAD . An
interesting aspect of the visceral fat in the mediastinum is its
anatomic location and the possibility that it is a source of local
macrophages releasing inflammatory mediators . Another
possible cellular source for the presence of the TEML-enriched
atherosclerosis module in visceral fat may be endothelial cells,
which are relatively enriched in this tissue. Although our study
does not directly address the subcellular origin of the A-module in
visceral fat or how it contributes to atherosclerosis, it might be a
local source of inflammatory mediators that increase the rate of
atherosclerosis progression .
In all, 60 tissue clusters were identified, two of which—one in
atherosclerotic lesion and one in visceral fat—related to the extent
of coronary atherosclerosis. This might appear to be a small
fraction (2/60, ,3%). However, since the first clustering step takes
no phenotypic data into consideration but is entirely based on the
mRNA signals in each tissue, these 60 clusters may relate to tissue
physiology or subtraits of CABG patients (Table 1). Examining the
latter possibility, we found that as many as 41 of the tissue clusters
(besides the two related to extent of coronary atherosclerosis)
segregated the patients into groups with significant difference in
the levels of subtraits (not shown).
The gene expression clustering was done with the absolute value
of Spearman rank correlation as distance measure. Thus, we also
included inverse correlated genes which could be implicated in the
same pathway and functionally related. Moreover, Spearman rank
correlation is a non-parametric measure stable against outliers and
in this sense a better distance measure than commonly used
Euclidean and Manhattan distances, where the magnitude in
expression levels are important. Of note, a clustering algorithm
could produce different clusters depending on the distance
measure used and the A-module could therefore have been
different or even lost by other metric clustering choices.
We used atherosclerotic aortic wall/internal mammary artery
(IMA) ratios to highlight atherosclerosis gene expression in the
aortic wall because both aortic wall and IMA samples contain
normal wall gene expression. Unlike the aortic wall, however, the
IMA has no atherosclerosis . This notion was supported by
macro- and microscopic examinations of randomly chosen sets of
aortic wall and IMA samples. Moreover, two-way clustering of
mRNA signals from the aortic wall samples alone did not generate
any cluster that segregated patients by stenosis scores (not shown),
which may be due to a relative large portion of normal vascular
wall gene expression in this tissue. However, we cannot entirely
exclude the possibility that using the aortic wall/IMA ratios
resulted in some false-positive genes (nonatherosclerosis genes
related to normal vascular wall gene expression) that should have
been excluded from the A-module or false-negative genes that
otherwise should have been included.
We decided to use two different atherosclerosis cohorts—
coronary for the exploration and carotid for the confirmatory step.
In doing so, we added more credibility to the confirmatory step
that would have been lost if we instead had used identical cohort
for exploration and confirmation. The validation in the carotid
cohort indicates a general importance of the A-module in
atherosclerosis and at the same time rules out the possible risk
that any of the tissue clusters identified in the STAGE cohort was a
result of the exploratory study design (e.g. choice of sample
locations and/or using ratios instead of straight expression) rather
than related to atherosclerosis. The extents of coronary and
carotid atherosclerosis (as judge from the surrogate measurements
of stenosis score and IMT [8,29]) have repeatedly been shown to
be highly correlated . This observation is not entirely
surprising since atherosclerosis development and the principal
molecular processes underlying this development have been found
to be very similar in all major arteries, regardless of location .
Currently, GWAS are given much attention in leading scientific
journals. However, such studies have some limitations, since they
are primarily designed to identify the relatively few DNA variants
that influence the risk of developing complex diseases, like CAD,
independently of other risk factors . In the current study, we
used a recently published GWAS  to further validate the A-
module genes by calculating the relative enrichment of genetic
CAD risk in the module. Unlike today’s GWAS, which link DNA
variation directly to clinical phenotypes, future studies that also
include intermediate expression phenotypes have the potential to
extract much more disease-relevant information on DNA variation
that contributes to the development of complex diseases. For now,
this information remains hidden in the data generated by GWAS.
Genes encoding LIM domain-binding factors such as LDB2
were initially isolated in a screen for proteins that physically
interact with the LIM domains of nuclear proteins. These proteins
bind to a variety of TFs and are likely to function as enhancers,
bringing together diverse TFs to form higher-order activation
complexes [32–33]. Our screen of LDB2-associated TFs identified
ISL-1alpha, Lmo2, Lhx3a, Lhx3b, LHX2, LHX4, and BRCA1.
ISL-1alpha enhances HNF4 activity and thus insulin signaling
[34–35]. Lmo2 is involved in angiogenesis [36–37]. Lhx3 and
Lhx4 regulate proliferation and differentiation of pituitary-specific
cell lineages  and are expressed in subsets of lymphocytes 
and thymocyte tumor cell lines . BRCA1 is associated with a
selective deficiency in spontaneous and LPS-induced production of
tumor necrosis factor (TNF)-a and of TNF-alpha-induced
expression of intercellular adhesion molecule-1 (ICAM1) on
peripheral blood monocytes  and in controlling the life cycle
of T-lymphocytes . LDB2 has not previously been related to
CAD or atherosclerosis. Because of its high-hierarchy regulatory
role and involvement in diverse biological processes, LDB2 is an
interesting target for further evaluation in complex diseases.
Being the only transcriptional regulator among the six genes
relating to severity of atherosclerosis present in all three tissue
clusters (Figure 6A), LDB2 was chosen for functional validation in
atherosclerosis. However, despite the fact that none of the other
five genes were transcriptional regulators, they might still be of
functional importance for atherosclerosis development, which
remains to be determined. In nonatherosclerotic arterial wall and
in early lesions, LDB2 was mainly expressed by the endothelium.
In late lesions, LDB2 expression was more intense and mainly seen
in macrophages/foam cells but also in SMCs. The TEML
pathway has been implicated in both early and late atherosclerosis
. This pathway is also active in lesion SMCs accompanying
endothelial cells in recruiting monocytes from the blood to the
atherosclerotic plaque [43–44]. The pattern of LDB2 expression
seen in early and late lesions has been observed for other key
TEML genes (Vcam1, Icam1, Cxcl1, -14, and -16, and Cdc20) .
The notion that LDB2 is an important regulator of TEML is
further supported by the fact that 13 key genes in TEML were
differentially expressed in the arterial wall of Ldb22/2mice
already at 6 weeks of age. Five of those genes have previously been
PLoS Genetics | www.plosgenetics.org11December 2009 | Volume 5 | Issue 12 | e1000754
shown to affect atherosclerosis in mouse model studies [16–20]. In
addition, a very recent study demonstrated that LDB2 regulates
cell migration both in vitro and in vivo . However, the final
verdict on LDB2 as an important regulator of atherosclerosis
development remains to be determined.
Although it cannot be excluded that the A-module also will be
of importance for early stage of atherosclerosis (e.g., by promoting
early lesion development through activating TEML in the
atherosclerosis-free endothelium), the current study mainly
supports a role of the A-module in late stages of coronary
atherosclerosis. If the activity of this cassette of genes is mirrored,
at least in part, by gene expression in blood (i.e., in leukocytes) or
by plasma protein levels, the A-module may be helpful as a
complement to semi-invasive investigations (e.g., angiography) as
markers of degree of coronary and carotid stenosis.
In conclusion, by adopting a new strategy for functional analysis
of expression profiles isolated from multiple CAD-relevant organs,
we identified a module that is genetically enriched with CAD
risk and important for TEML and atherosclerosis development.
The clinical usefulness, and exact role in CAD of this module
and its high-hierarchy regulator [32–33] LDB2, merit further
Study Patients, Biopsy Collection, and Follow-Up
The STAGE study enrolled 124 patients undergoing CABG at
Karolinska University Hospital, Solna. Forty-two patients under-
going carotid surgery at Stockholm So ¨der Hospital were recruited
as a confirmatory cohort. The studies were approved by the Ethics
Committee of Karolinska University Hospital. All patients gave
written informed consent.
Tissue samples from the distal IMA, wall of the ascending aorta
(aortic root) at the site of proximal vein anastomosis, anterior
hepatic edge (liver), skeletal muscle, and visceral fat in the
mediastinum were preserved in RNAlater (Qiagen) and frozen at
280uC. Lesions in aortic wall samples [47–48] and the absence of
lesions in the IMA  were confirmed by macroscopic and
microscopic examinations (not shown). Carotid plaques were
embedded in OCT (Histolab Products), frozen in liquid isopentane
and dry ice, and stored at 280uC.
One hundred fourteen CABG and 39 carotid stenosis patients
came to a 3-month follow-up visit. Using a standard questionnaire,
a research nurse obtained a medical history and lifestyle
information (e.g., smoking, alcohol consumption, and physical
activity). A physical examination was performed including venous
blood sampling (Text S1).
Coronary and Carotid Atherosclerosis Measurements
All CABG patients underwent preoperative biplane coronary
angiography (Judkins technique). Angiograms were evaluated with
QCA techniques (Medis). The left and right coronary arteries and
their branches were divided into segments . Each segment was
measured during end-diastole. A stenosis score was calculated
from all major lesions in the coronary arteries (1 point, 20–50%
luminal obstruction; 2 points .50% obstruction). In some
patients, right coronary artery occlusion prohibited QCA
evaluation. Before surgery, carotid arteries were examined with
B-mode ultrasound. The far wall of the common carotid artery
was used to measure IMT from the endarterectomy side .
RNA Isolation and Expression Profiling
We performed gene expression profiling on three tissues (liver,
skeletal muscle, visceral fat) in 66 of 114 STAGE patients, and also
in 40 of these 66 patients, on atherosclerotic arterial wall and IMA.
In the validation cohort, 25 carotid lesions from 39 patients were
randomly selected for RNA isolation and gene expression
profiling. Aortic arches (third rib to aortic root) were isolated in
RNA later (Ambion) from 6-week-old mice deficient in Ldb2
(Ldb22/2; Mutant Mouse Regional Resource Center, University
of California, Davis), heterozygous and wildtype littermates, and
20- and 40-week-old atherosclerosis-prone mice deficient in the
low density lipoprotein receptor and expressing exclusively
apolipoprotein B100 (Ldlr2/2Apob100/100mice). Total RNA was
isolated from all biopsies with Trizol (BRL-Life Technologies) and
FastPrep (MP Biomedicals) and purified with RNeasy Mini kit
using DNase1 treatment (Qiagen). Sample quality was assessed
with an Agilent Bioanalyzer 2100. cRNA yield was assessed with a
spectrophotometer (ND-1000, NanoDrop Technologies) before
hybridization to HG-U133 Plus 2.0 arrays (Affymetrix). The
arrays were processed with a Fluidics Station 450, scanned with a
GeneArray Scanner 3000, and analyzed with GeneChip Opera-
tional Software 2.0.
Mouse aortic roots (aortic valve level) and human carotid lesions
were isolated and frozen in liquid nitrogen, embedded in OCT
compound (Histolab Products), cryosectioned (5 mm), and fixed in
acetone. Endogenous peroxidase activity was quenched with 0.3%
hydrogen peroxide/0.01% NaN3in water for 10 minutes, and
sections were incubated with 5% blocking serum. Consecutive
sections were incubated with goat anti-LDB2 (Santa Cruz
Biotechnology) , rat monoclonal anti-mouse CD68 (Serotec),
mouse monoclonal anti-human CD68 (Novocastra Laboratories),
rabbit polyclonal anti-mouse SM22 alpha (transgelin, Abcam), or
rabbit polyclonal anti-human VWF (DakoCytomation) at 4uC
overnight. In negative controls, primary antibody was replaced
with serum. After rinsing in Tris-buffered saline, sections were
incubated with secondary biotinylated bovine anti-goat, anti-
mouse, or anti-rat (Vector Laboratories) or anti-rabbit IgG
(DakoCytomation). Avidin-biotin peroxidase complexes (Vectas-
tain ABC Elite, Vector Laboratories) were added followed by
visualization with DAB (Vector Laboratories). All sections were
counterstained with Gill hematoxylin (Histolab Products).
THP-1 monocytes were plated in 10% fetal calf serum/
RPMI-1640 with L-glutamine (2 mM) and HEPES buffer
(25 mM) (Gibco-Invitrogen) supplemented with penicillin
(100 U/ml) and streptomycin (100 mg/ml) and differentiated
(50 ng/ml) (Sigma) for 72 hours. To generate foam cells,
macrophages were incubated with acetylated low density
lipoproteins (50 mg/ml) for 48 hours. Human monocytes were
isolated from blood with Ficoll/Hypaque as described ,
placed in six-well dishes, and allowed to adhere overnight in
RPMI-1640 supplemented with penicillin (100 U/ml), strep-
tomycin (100 mg/ml), and 10% pooled human AB serum. After
washing, fresh serum-containing medium was added, and cells
were cultured for 6 days and harvested. EAHY926 cells were
cultured in DMEM containing high glucose, penicillin (100 U/
ml), streptomycin (100 mg/ml), 10% fetal calf serum, hypo-
xanthine (100 mmol/l), aminopterin (0.4 mmol/l), and thymi-
dine (16 mmol/l). HUVECs were obtained by collagenase
treatment, cultivated, and identified as described . SMCs
from human pulmonary artery (Clonetics) were cultured in
SmGm2 medium containing growth factors (Clonetics) as
PLoS Genetics | www.plosgenetics.org 12 December 2009 | Volume 5 | Issue 12 | e1000754
Total RNA (0.15 mg) was reverse transcribed with Superscript
III (Invitrogen). After threefold dilution, cDNA (3 ml) was
amplified by real-time PCR with 1xTaqMan universal PCR
master mix (Applied Biosystems) on an ABI Prism 7000 (PE
Biosystems) using Assay-On-Demand kits containing correspond-
ing primers and probes (Applied Biosystems). mRNA levels were
normalized to acidic ribosomal phosphoprotein P0 and TATA-
box binding protein. Samples were analyzed in duplicate.
Pre-Processing of Gene Expression Data
Gene-expression values were pre-processed with the robust
multichip average  procedure in three steps (background
604,258 perfect-match Affymetrix probe signals, 423,636 were
mapped to transcripts using RefSeq numbers as identifiers
, generating 15,042 RefSeq transcripts corresponding to
12,621 genes. Straight expression values (i.e., mRNA signals
obtained from one microarray) were used for data analyses of
all tissue biopsies (including the carotid lesion biopsy in the
confirmatory cohort) except for the atherosclerotic arterial wall
and IMA. The latter two biopsies were combined in
atherosclerotic arterial wall/IMA mRNA ratios before data
analysis. mRNA signals in the atherosclerotic arterial wall
biopsy reflect gene activity in the atherosclerotic lesion and in
normal arterial wall, whereas mRNA signals in the IMA
mainly reflect normal arterial wall gene activity (the IMA is
almost entirely devoid of atherosclerotic lesions) . Thus,
the use of atherosclerotic arterial wall/IMA ratios highlights
gene activity related to atherosclerotic lesions in arterial wall
and excludes that relating to normal arterial wall.
Coupled two-way clustering [4–6] was performed to identify
small and stable clusters of related signals of importance for CAD.
In the first step, clusters were defined using superparamagnetic
clustering , with the absolute value of Spearman rank
correlation as a distance measure between genes. Spearman rank
is a non-parametric measure which is robust to outliers and by
using absolute values we also put together anti-correlated genes.
The analysis was done without using any predefined conceptions
(i.e., phenotypes of the patients). Genes that did not belong to a
cluster were excluded. Then, in the second step, the identified
clusters were related to coronary atherosclerosis by hierarchical
clustering  of the patients, using Manhattan distance and
average linkage as distance measures, based on the mRNA signals
in each of the clusters defined in the first step (Text S1).
To assess the repeatability and reliability of these clusters,
resampling using Jackknife analysis was performed  (Text S1).
Genetic Enrichment Analysis
A-module genes were mapped to eSNPs (Text S1) using two
GGE studies  and tested for enrichment of association with
CAD using the results from the WTCCC study . Different
SNP panels were used in the GGE and WTCCC studies, therefore
we included eSNPs and all SNPs in strong LD (R.0.84) with the
eSNPs. In the 128 A-module genes, there were 97 eSNPs and 387
LD SNPs of the eSNPs, resulting in an expanded set of 484 eSNPs.
Random sampling strategy was used to assess whether the
expanded eSNP set was more likely to associate with CAD than
randomly selected sets of SNPs of equal number. In each random
sample, 97 SNPs located within 1 megabase of human gene
regions were selected to ensure the location of the random SNP
sets matched that of the eSNP set in the A-module. The randomly
selected SNP sets were then expanded by including SNPs in strong
LD (R.0.84) with any of the randomly identified SNPs. We
required the final size of the expanded random set of SNPs to be
within 610% of the expanded set of eSNPs in the A-module.
Therefore, the random sampling scheme produced sets of SNPs in
which the LD, set size, and location with respect to protein coding
genes matched those of the expanded eSNP sets in the A-module.
The process was repeated 100,000 times. For each random SNP
set, we counted the percentage of SNPs with association P-value to
CAD,0.05, and constructed the null distribution. The enrich-
ment P-value was calculated as the number of times that the
percentage exceeds 10.3% from random sampling divided by
Clinical and metabolic characteristics are given as continuous
variables with means 6 SD and as categorical variables with
percentages and numbers of subjects. P-values were calculated
with unpaired t tests; skewed values were log-transformed.
Statistical significances in Venn diagrams were computed using
hypergeometric distributions (Text S1). GO and pathway analyses
were performed with DAVID (Database for Annotation, Visual-
ization and Integration Discovery) software . Mathematica 5.2
or StatView 5.0.1 was used for all other calculations. Text mining
was used to define transcripts previously related to CAD and
atherosclerosis (Text S1, Table S9). For promoter analysis,
TRANSFAC (v11.2)  was used (Text S1).
The superparamagnetic clustering (SPC) algorithm uses a cost
function with a temperature parameter (T) to assign genes into
different clusters. Genes could belong to many clusters (right) or to
no cluster at all (left). At a certain temperature the clusters are
robust and stable against noise (middle).
Found at: doi:10.1371/journal.pgen.1000754.s001 (1.21 MB EPS)
Principles of the cost function in the SPC algorithm.
human carotid plaques. Consecutive human carotid plaque
sections were incubated with goat anti-LDB2 antibody and rat
monoclonal anti-mouse CD68 at 4uC overnight. LDB2 is co-
localized with CD68.
Found at: doi:10.1371/journal.pgen.1000754.s002 (3.17 MB EPS)
LDB2 proteins and CD68 staining in serial sections of
surements of atherosclerosis (QCA and IMT).
Found at: doi:10.1371/journal.pgen.1000754.s003 (0.04 MB
Gene expression cluster relation to surrogate mea-
atherosclerotic arterial wall/IMA cluster in Figure 2.
Found at: doi:10.1371/journal.pgen.1000754.s004 (0.02 MB
49 RefSeqs corresponding to 48 genes of the
Found at: doi:10.1371/journal.pgen.1000754.s005 (0.03 MB
59 RefSeqs/genes of the visceral fat cluster in Figure 3.
lesion cluster in Figure 4.
Found at: doi:10.1371/journal.pgen.1000754.s006 (0.02 MB
55 RefSeqs corresponding to 54 genes of the carotid
129 RefSeqs corresponding to 128 genes in the A-
PLoS Genetics | www.plosgenetics.org13 December 2009 | Volume 5 | Issue 12 | e1000754
Found at: doi:10.1371/journal.pgen.1000754.s007 (0.04 MB
union of all three clusters.
Found at: doi:10.1371/journal.pgen.1000754.s008 (0.03 MB
GO and pathway analysis of the three clusters and the
Found at: doi:10.1371/journal.pgen.1000754.s009 (0.03 MB
TEML pathway genes in DAVID (n=117).
the atherosclerosis module (http://www.pantherdb.org/).
Found at: doi:10.1371/journal.pgen.1000754.s010 (0.03 MB
Panther family classification of genes in TEML and
Found at: doi:10.1371/journal.pgen.1000754.s011 (0.38 MB
2,832 genes previously associated to CAD.
among the upstream sequences of the 128 genes in Table S5 as
compared to a background set of sequences.
Found at: doi:10.1371/journal.pgen.1000754.s012 (0.04 MB
Binding sites of transcription factors related to LDB2
Found at: doi:10.1371/journal.pgen.1000754.s013 (0.04 MB PDF)
We thank Stephen Ordway for editorial assistance, Cecilia So ¨derberg-
Naucler for human pulmonary artery SMCs, and Anne-Sofie Johansson
Conceived and designed the experiments: S Ha ¨gg, J Skogsberg, J Lundstro ¨m,
H Zhong, VB Bajic, LM Kaplan, U de Faire, S Rostors, EE Schadt, T Ivert,
J Tegne ´r, J Bjo ¨rkegren. Performed the experiments: S Ha ¨gg, J Skogsberg,
J Lundstro ¨m, P Noori, R Nilsson, H Zhong, S Maleki, MM Shang,
M Bradshaw, VB Bajic, A Silveria, B Gigante, K Leander, S Rosfors,
U Lockowandt, J Liska, P Konrad, R Takolander, A Franco-Cereceda,
EE Schadt, T Ivert, J Bjo ¨rkegren. Analyzed the data: S Ha ¨gg, J Skogsberg,
J Lundstro ¨m, R Nilsson, H Zhong,B Brinne, VB Bajic, S Rosfors,EESchadt,
J Tegne ´r, J Bjo ¨rkegren. Contributed reagents/materials/analysis tools:
J Lundstro ¨m, A SamnegI´rd, LM Kaplan, U der Faire, P Konrad,
R Takolander, A Franco-Cereceda, EE Schadt, T Ivert, A Hamsten,
J Tegne ´r, J Bjo ¨rkegren. Wrote the paper: S Ha ¨gg, J Skogsberg, J Lundstro ¨m,
H Zhong, EE Schadt, T Ivert, A Hamsten, J Tegne ´r, J Bjo ¨rkegren.
1. Ginsburg GS, Donahue MP, Newby LK (2005) Prospects for personalized
cardiovascular medicine: the impact of genomics. J Am Coll Cardiol 46:
2. Schadt EE, Sachs A, Friend S (2005) Embracing complexity, inching closer to
reality. Sci STKE 2005: pe40.
3. Tegner J, Skogsberg J, Bjorkegren J (2007) Thematic review series: systems
biology approaches to metabolic and cardiovascular disorders. Multi-organ
whole-genome measurements and reverse engineering to uncover gene networks
underlying complex traits. J Lipid Res 48: 267–277.
4. Blatt M, Wiseman S, Domany E (1996) Superparamagnetic clustering of data.
Phys Rev Lett 76: 3251–3254.
5. Getz G, Levine E, Domany E (2000) Coupled two-way clustering analysis of
gene microarray data. Proc Natl Acad Sci U S A 97: 12079–12084.
6. Tetko IV, Facius A, Ruepp A, Mewes HW (2005) Super paramagnetic clustering
of protein sequences. BMC Bioinformatics 6: 82.
7. Lusis AJ (2000) Atherosclerosis. Nature 407: 233–241.
8. Bots ML, Grobbee DE (2002) Intima media thickness as a surrogate marker for
generalised atherosclerosis. Cardiovasc Drugs Ther 16: 341–351.
9. Dennis G, Jr., Sherman BT, Hosack DA, Yang J, Gao W, et al. (2003) DAVID:
Database for Annotation, Visualization, and Integrated Discovery. Genome Biol
10. Schadt EE, Molony C, Chudin E, Hao K, Yang X, et al. (2008) Mapping the
genetic architecture of gene expression in human liver. PLoS Biol 6: e107.
11. (2007) Genome-wide association study of 14,000 cases of seven common diseases
and 3,000 shared controls. Nature 447: 661–678.
12. Bateman A, Coin L, Durbin R, Finn RD, Hollich V, et al. (2004) The Pfam
protein families database. Nucleic Acids Res 32: D138–141.
13. von Mering C, Jensen LJ, Snel B, Hooper SD, Krupp M, et al. (2005) STRING:
known and predicted protein-protein associations, integrated and transferred
across organisms. Nucleic Acids Res 33: D433–437.
14. Matys V, Kel-Margoulis OV, Fricke E, Liebich I, Land S, et al. (2006)
TRANSFAC and its module TRANSCompel: transcriptional gene regulation in
eukaryotes. Nucleic Acids Res 34: D108–110.
15. Lieu HD, Withycombe SK, Walker Q, Rong JX, Walzem RL, et al. (2003)
Eliminating Atherogenesis in Mice by Switching Off Hepatic Lipoprotein
Secretion. Circulation 107: 1315–1321.
16. Hauer AD, Habets KL, van Wanrooij EJ, de Vos P, Krueger J, et al. (2009)
Vaccination against TIE2 reduces atherosclerosis. Atherosclerosis 204: 365–371.
17. Hauer AD, van Puijvelde GH, Peterse N, de Vos P, van Weel V, et al. (2007)
Vaccination against VEGFR2 attenuates initiation and progression of
atherosclerosis. Arterioscler Thromb Vasc Biol 27: 2050–2057.
18. Koni PA, Joshi SK, Temann UA, Olson D, Burkly L, et al. (2001) Conditional
vascular cell adhesion molecule 1 deletion in mice: impaired lymphocyte
migration to bone marrow. J Exp Med 193: 741–754.
19. Stevens HY, Melchior B, Bell KS, Yun S, Yeh JC, et al. (2008) PECAM-1 is a
critical mediator of atherosclerosis. Dis Model Mech 1: 175–181; discussion 179.
20. Zernecke A, Liehn EA, Fraemohs L, von Hundelshausen P, Koenen RR, et al.
(2006) Importance of junctional adhesion molecule-A for neointimal lesion
formation and infiltration in atherosclerosis-prone mice. Arterioscler Thromb
Vasc Biol 26: e10–13.
21. Bradley JR (2008) TNF-mediated inflammatory disease. J Pathol 214: 149–160.
22. Hansson GK (2005) Inflammation, atherosclerosis, and coronary artery disease.
N Engl J Med 352: 1685–1695.
23. Braunersreuther V, Mach F (2006) Leukocyte recruitment in atherosclerosis:
potential targets for therapeutic approaches? Cell Mol Life Sci 63: 2079–2088.
24. Tegner J, Bjorkegren J (2007) Perturbations to uncover gene networks. Trends
Genet 1: 34–41.
25. Thompson CJ, Ryu JE, Craven TE, Kahl FR, Crouse JR, 3rd (1991) Central
adipose distribution is related to coronary atherosclerosis. Arterioscler Thromb
26. Berg AH, Scherer PE (2005) Adipose tissue, inflammation, and cardiovascular
disease. Circ Res 96: 939–949.
27. Mazurek T, Zhang L, Zalewski A, Mannion JD, Diehl JT, et al. (2003) Human
epicardial adipose tissue Is a source of inflammatory mediators. Circulation 108:
28. Sims FH (1983) A comparison of coronary and internal mammary arteries and
implications of the results in the etiology of arteriosclerosis. Am Heart J 105:
29. Moise A, Clement B, Saltiel J (1988) Clinical and angiographic correlates and
prognostic significance of the coronary extent score. Am J Cardiol 61: 1255–1259.
30. Hallerstam S, Larsson PT, Zuber E, Rosfors S (2004) Carotid atherosclerosis is
correlated with extent and severity of coronary artery disease evaluated by
myocardial perfusion scintigraphy. Angiology 55: 281–288.
31. Gibson G (2008) The environmental contribution to gene expression profiles.
Nat Rev Genet 9: 575–581.
32. Agulnick AD, Taira M, Breen JJ, Tanaka T, Dawid IB, et al. (1996) Interactions
of the LIM-domain-binding factor Ldb1 with LIM homeodomain proteins.
Nature 384: 270–272.
33. Jurata LW, Gill GN (1997) Functional analysis of the nuclear LIM domain
interactor NLI. Mol Cell Biol 17: 5688–5698.
34. Eeckhoute J, Briche I, Kurowska M, Formstecher P, Laine B (2006) Hepatocyte
nuclear factor 4 alpha ligand binding and F domains mediate interaction and
transcriptional synergy with the pancreatic islet LIM HD transcription factor
Isl1. J Mol Biol 364: 567–581.
35. Kojima H, Nakamura T, Fujita Y, Kishi A, Fujimiya M, et al. (2002) Combined
expression of pancreatic duodenal homeobox 1 and islet factor 1 induces
immature enterocytes to produce insulin. Diabetes 51: 1398–1408.
36. Yamada Y, Pannell R, Forster A, Rabbitts TH (2000) The oncogenic LIM-only
transcription factor Lmo2 regulates angiogenesis but not vasculogenesis in mice.
Proc Natl Acad Sci U S A 97: 320–324.
37. Yamada Y, Warren AJ, Dobson C, Forster A, Pannell R, et al. (1998) The T cell
leukemia LIM protein Lmo2 is necessary for adult mouse hematopoiesis. Proc
Natl Acad Sci U S A 95: 3890–3895.
38. Sheng HZ, Moriyama K, Yamashita T, Li H, Potter SS, et al. (1997) Multistep
control of pituitary organogenesis. Science 278: 1809–1812.
39. Xu Y, Baldassare M, Fisher P, Rathbun G, Oltz EM, et al. (1993) LH-2: a LIM/
homeodomain gene expressed in developing lymphocytes and neural cells. Proc
Natl Acad Sci U S A 90: 227–231.
PLoS Genetics | www.plosgenetics.org14 December 2009 | Volume 5 | Issue 12 | e1000754
40. Wu HK, Heng HH, Siderovski DP, Dong WF, Okuno Y, et al. (1996) Download full-text
Identification of a human LIM-Hox gene, hLH-2, aberrantly expressed in
chronic myelogenous leukaemia and located on 9q33–34.1. Oncogene 12:
41. Zielinski CC, Budinsky AC, Wagner TM, Wolfram RM, Kostler WJ, et al.
(2003) Defect of tumour necrosis factor-alpha (TNF-alpha) production and
TNF-alpha-induced ICAM-1-expression in BRCA1 mutations carriers. Breast
Cancer Res Treat 81: 99–105.
42. Mak TW, Hakem A, McPherson JP, Shehabeldin A, Zablocki E, et al. (2000)
Brcal required for T cell lineage development but not TCR loci rearrangement.
Nat Immunol 1: 77–82.
43. Cai Q, Lanting L, Natarajan R (2004) Interaction of monocytes with vascular
smooth muscle cells regulates monocyte survival and differentiation through
distinct pathways. Arterioscler Thromb Vasc Biol 24: 2263–2270.
44. Cai Q, Lanting L, Natarajan R (2004) Growth factors induce monocyte binding
to vascular smooth muscle cells: implications for monocyte retention in
atherosclerosis. Am J Physiol Cell Physiol 287: C707–714.
45. Skogsberg J, Lundstrom J, Kovacs A, Nilsson R, Noori P, et al. (2008)
Transcriptional profiling uncovers a network of cholesterol-responsive athero-
sclerosis target genes. PLoS Genet 4: e1000036. doi:10.1371/journal.
46. Storbeck CJ, Wagner S, O’Reilly P, McKay M, Parks R, et al. (2009) The Ldb1
and Ldb2 Transcriptional Co-factors Interact with the Ste20-like Kinase SLK
and Regulate Cell Migration. Mol Biol Cell.
47. Adler Y, Fisman EZ, Shemesh J, Schwammenthal E, Tanne D, et al. (2004)
Spiral computed tomography evidence of close correlation between coronary
and thoracic aorta calcifications. Atherosclerosis 176: 133–138.
48. Fazio GP, Redberg RF, Winslow T, Schiller NB (1993) Transesophageal
echocardiographically detected atherosclerotic aortic plaque is a marker for
coronary artery disease. J Am Coll Cardiol 21: 144–150.
49. Austen WG, Edwards JE, Frye RL, Gensini GG, Gott VL, et al. (1975) A
reporting system on patients evaluated for coronary artery disease. Report of the
Ad Hoc Committee for Grading of Coronary Artery Disease, Council on
Cardiovascular Surgery, American Heart Association. Circulation 51: 5–40.
50. Wendelhag I, Liang Q, Gustavsson T, Wikstrand J (1997) A new automated
computerized analyzing system simplifies readings and reduces the variability in
ultrasound measurement of intima-media thickness. Stroke 28: 2195–2200.
51. Mizunuma H, Miyazawa J, Sanada K, Imai K (2003) The LIM-only protein,
LMO4, and the LIM domain-binding protein, LDB1, expression in squamous
cell carcinomas of the oral cavity. Br J Cancer 88: 1543–1548.
52. Stengel D, Antonucci M, Gaoua W, Dachet C, Lesnik P, et al. (1998) Inhibition
of LPL expression in human monocyte-derived macrophages is dependent on
LDL oxidation state: a key role for lysophosphatidylcholine. Arterioscler
Thromb Vasc Biol 18: 1172–1180.
53. Palmblad J, Lerner R, Larsson SH (1994) Signal transduction mechanisms for
leukotriene B4 induced hyperadhesiveness of endothelial cells for neutrophils.
J Immunol 152: 262–269.
54. Gredmark S, Straat K, Homman-Loudiyi M, Kannisto K, Soderberg-Naucler C
(2007) Human cytomegalovirus downregulates expression of receptors for
platelet-derived growth factor by smooth muscle cells. J Virol 81: 5112–5120.
55. Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, et al. (2003) Summaries
of Affymetrix GeneChip probe level data. Nucleic Acids Res 31: e15.
56. Mecham BH, Klus GT, Strovel J, Augustus M, Byrne D, et al. (2004) Sequence-
matched probes produce increased cross-platform consistency and more
reproducible biological results in microarray-based gene expression measure-
ments. Nucleic Acids Res 32: e74.
57. Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and
display of genome-wide expression patterns. Proc Natl Acad Sci U S A 95:
58. Efron B (1979) Bootstrap methods: another look at the jackknife. AS 7: 1–26.
PLoS Genetics | www.plosgenetics.org15 December 2009 | Volume 5 | Issue 12 | e1000754