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

Abstract

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 (n = 16/17, P<10(-27 and-30)). Genes in the transendothelial migration of leukocytes (TEML) pathway were overrepresented in all three clusters, referred to as the atherosclerosis module (A-module). In a second validation step, using three independent cohorts, the A-module was found to be genetically enriched with CAD risk by 1.8-fold (P<0.004). The transcription co-factor LIM domain binding 2 (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.

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 individual genes.

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 ± SD and average ratios in each group and P-values for comparing groups. EVA1 is represented by two RefSeqs.

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 ± 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.

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 ± 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 RefSeqs.

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 = 10⁻¹⁰), 17 in the atherosclerotic arterial wall and carotid stenosis clusters (P = 10⁻³⁰), and 16 in the visceral and carotid stenosis clusters (P = 10⁻²⁷). Six genes were found in all three clusters (P = 10⁻²³). 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 = 6.6×10⁻⁵). 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 .

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 4°C 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-α, and HUVECs isolated with collagenase. (A) Mouse LDB2 and VWF protein expression in serial sections of aortic roots from Ldlr ^−/− Apob ^100/100 mice 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 Ldlr ^−/− Apob ^100/100 mice 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 Ldlr ^−/− Apob ^100/100 mice.