Novel Candidate Genes in Unstable Areas of Human Atherosclerotic Plaques
Objective— Comparison of gene expression in stable versus unstable atherosclerotic plaque may be confounded by interpatient variability. The aim of this study was to identify differences in gene expression between stable and unstable segments of plaque obtained from the same patient.
Methods and Results— Human carotid endarterectomy specimens were segmented and macroscopically classified using a morphological classification system. Two analytical methods, an intraplaque and an interplaque analysis, revealed 170 and 1916 differentially expressed genes, respectively using Affymetrix gene chip analysis. A total of 115 genes were identified from both analyses. The differential expression of 27 genes was also confirmed using quantitative-polymerase chain reaction on a larger panel of samples. Eighteen of these genes have not been associated previously with plaque instability, including the metalloproteinase, ADAMDEC1 (≈37-fold), retinoic acid receptor responder-1 (≈5-fold), and cysteine protease legumain (≈3-fold). Matrix metalloproteinase-9 (MMP-9), cathepsin B, and a novel gene, legumain, a potential activator of MMPs and cathepsins, were also confirmed at the protein level.
Conclusions— The differential expression of 18 genes not previously associated with plaque rupture has been confirmed in stable and unstable regions of the same atherosclerotic plaque. These genes may represent novel targets for the treatment of unstable plaque or useful diagnostic markers of plaque instability.
Atherosclerosis is a chronic inflammatory disease that remains a major cause of morbidity in the Western world. The composition and vulnerability of the atherosclerotic plaque are considered to be important factors in the development of arterial thrombus and embolic complications.1 However, the precise mechanisms by which plaque ruptures remain to be determined2,3
Gene expression techniques such as microarrays and representational difference analysis are powerful tools that can be used to probe the complexities underlying atherosclerotic plaque initiation and progression.4–6 These techniques have already been used to show altered gene expression between normal and diseased arteries,6,7 between different stages in disease progression8,9 and differential expression in samples of atherosclerotic plaque classified according to patient symptomatology.10 However, there are drawbacks to these types of comparisons. The differences in the cellular composition and morphology between plaque and normal arterial wall may lead to differences in gene expression that simply reflect this variation. In addition, the high degree of variability in plaque composition and gene expression in different patients may confound comparative analysis in studies that use pooled samples.11–13 Features of unstable plaque such as surface ulceration and rupture occur in both symptomless and symptomatic patients,14 and this can also confound studies that classify samples according to patient symptomatology.
The aim of this study was to use a whole transcriptome analysis to characterize the gene expression signature of unstable regions of carotid endarterectomy (CEA) specimens using a stable region of the same specimen as an internal control. It was hoped that this approach might disclose novel diagnostic markers and therapeutic targets.
Details of the experimental protocols used and patient demographics are given in the online data supplement, available at http://atvb.ahajournals.org.
Collection of Human Specimens
Forty-six human CEA specimens were collected. Twenty-seven samples were used for mRNA, 9 for protein, and 10 for histological analysis. Plaques were divided longitudinally and the luminal aspect of both halves of the plaque photographed. Cross-sectional segments (5 mm) were taken along the course of each plaque. Segments were immediately snap-frozen.
Plaque Segment Classification
Segments of plaque were classified into 2 main groups after macroscopic examination by 4 vascular surgeons. Stable segments were covered by a smooth luminal surface indicative of an intact fibrous cap, and unstable segments had an ulcerated surface with or without thrombosis or hemorrhage. A segment was classified as either stable or unstable based on agreement in the score given by at least 3 of the 4 observers (Figure 1).
RNA Isolation and Quantification
RNA was isolated from 79 plaque segments (27 plaques) using an adaptation of the Trizol method (Life Technologies) and the RNeasy mini column method (Qiagen). RNA quality was assessed using the RNA 6000 Nano LabChip Kit (Agilent Bioanalyser 2100; Agilent Technologies).
Affymetrix Hybridization and Analysis
Total RNA from 11 segments from 3 atherosclerotic plaques was processed and hybridized to 11 U133 GeneChip Array sets. These segments were classified as follows: patient 1 stable (×1), unstable (×3); patient 2 stable (×1), unstable (×2); and patient 3 stable (×2), unstable (×2).
Cell intensity files were analyzed using the Rosetta Resolver Gene Expression Data Analysis System, v3.2. (Agilent Technologies).
Unsupervised and Supervised Analysis
All GeneChip data were checked for quality and consistency and analyzed using Principal Component Analysis (PCA; SIMCA-P v10.2; Umetrics Software).
The supervised analysis aim to reduce the interpatient variability from the analysis and identify the changes that were truly attributable to morphological differences. Two analyses were used to identify genes consistently differentially expressed between unstable and stable regions of CEA specimens (ANOVA). First, an intraplaque comparison assessing differences in gene expression between unstable segments and stable segments from the same specimen. Second, an interplaque comparison was made in which gene expression values (not samples) in unstable segments were compared with expression in stable segments obtained from all plaques. For details, please see the online supplement (and supplemental Figure I), available at http://atvb.ahajournals.org.
Genes from the GeneChip analysis were selected for confirmation of differential expression based both on the magnitude of the difference in the expression or the function and potential involvement in plaque instability, with less emphasis on the fold difference measured by the GeneChip analysis. This approach was taken to support evaluation of genes from the entire range (supplemental Figure I).
The expression of selected genes was confirmed on a larger panel of samples that consisted of 34 stable and unstable segments from 12 independent plaques using real-time quantitative polymerase chain reaction (QT-PCR; Sybr Green). Segment numbers used in this analysis were as follows: stable segments ×12; unstable segments ×22. Gene expression data were analyzed by normalization against the geometric mean of the expression of the 3 housekeepers (GAPDH, β-actin, and β2-microglobulin) showing the most stable expression as described by Vandesompele et al.16
Macrophage and Smooth Muscle Cell Content of Stable and Unstable Segments
The levels of CD68 and α-actin mRNA were measured by SYBR Green across 24 pairs of stable and unstable segments to confirm the macroscopic classification of the specimens.
Correlation of Expression Patterns With Macrophage, Smooth Muscle, Endothelial, and T-Cell Markers
The expression of the confirmed genes was correlated with the expression of relevant cell markers such as CD68 (macrophages), α-actin (smooth muscle cells), CD31 (endothelial cells), and CD3 (T cells) to determine whether differences in gene expression correlated with changes in cellular composition across the plaque segments.
Ten-micron cryosections of segments from 10 specimens that had been previously macroscopically classified were stained using Masson’s trichrome as described previously.15
In situ hybridization for the asparaginyl endopeptidase legumain was carried on using digoxigenin-labeled probes on 10-μm CEA specimen cryosections as described previously.17 Immunostaining for the macrophage marker CD68 and negative control was performed on 10-μm cryosections adjacent to those used for in situ hybridization using the ChemMate EnVision Detection Kit (DAKO).
Nine CEA specimens (stable segments × 9; unstable segments ×9) were used for protein extraction. Pro–matrix metalloproteinase-9 (MMP-9) and cathepsin B protein were measured by ELISA (Biotrak, Amersham, and KRKA d.d., Novo mesto) and expressed as per milligram soluble protein (quantified using the Pierce BCA [bicinchoninic acid] Assay Kit; Pierce Chemical Company).
Gelatin zymography was carried on using Novex Zymogram gelatin gels (Invitrogen). Western blotting was performed using a monoclonal antibody against human legumain (R & D Systems). Band intensity was determined using densitometric analysis (Image J version 1.33u; NIH) and expressed in arbitrary densitometric units (ADU).
Interobserver variability was low because a majority agreement between observers (at least 3 of 4) was obtained in >97% of the segments analyzed using this system of classification (Figure 1A).
Macroscopic features of unstable segments such as surface ulceration, with or without thrombosis or hemorrhage, and the presence of an intact fibrous cap was also confirmed by the histological analysis in 10 of the 10 specimens that were processed. Representative examples of the histological analysis are shown in Figure 1B.
Macrophage and Smooth Muscle Cell Content of Stable and Unstable Segments
Unstable segments contained 3.2-fold higher levels of the macrophage marker CD68 (78.36±SEM 9.304, n=24, versus 24.40±SEM 4.361, n=24; P<0.0001) and 2.5-fold lower levels of the smooth muscle cell marker α-actin (20.11±2.350, n=24 versus 51.33±SEM 6.764, n=24; P<0.0001) compared with their paired stable segments (Figure 2).
PCA of the microarray data showed that there were no outliers in the analysis. In addition, the analysis produced very similar patterns of expression for Genechip A and B, indicating a high level of consistency between the 2 data sets (supplemental Figure II; PCA).
Individual Intraplaque Analysis
There was consistent differential expression of 170 genes (75 upregulated and 95 downregulated) between stable and unstable regions of the same plaque (P<0.05). Fourteen genes showed a >2.5-fold difference in expression (supplemental Table II), and 4 genes were differentially expressed by >5-fold (retinoic acid receptor responder [7.6-fold], junctional adhesion molecule [JAM-A; 6.6-fold], the hemoglobin scavenger receptor CD163 [6.0-fold], and the ganglioside activator protein GM2A [5.7-fold]).
Expression was compared between 7 unstable segments and 4 stable segments. A total of 1916 genes (1012 upregulated and 904 downregulated) were differentially expressed in areas of stable compared with unstable plaque (P<0.0001). A total of 295 genes (170 upregulated and 125 downregulated) had a >5-fold difference of expression (supplemental Table III). The greatest fold differential expression in the interplaque analysis was 84-fold upregulation of the MMP-1 gene and a 34.2-fold downregulation of signal peptide SCUBE3 (signal peptide, CUB domain, epidermal growth factor-like 3), in unstable compared with stable segments. Examples of the 115 genes in common with the intraplaque analysis (supplemental Table IV) include the genes encoding the endopeptidase, legumain, CD163 antigen, the chemokine CCL18 (pulmonary and activation-regulated chemokine), the adhesion molecule, JAM-A, and the platelet-activating factor acetylhydrolase lipoprotein-associated phospholipase A2 (Lp-PLA2).
Confirmation of Candidate Genes
Twenty-seven of the 32 genes that were selected were found to be differentially expressed, giving a confirmation rate of ≈84% (Table 1). Examples of confirmed upregulated genes in unstable plaque segments include the proteases legumain and MMP-9 (3.11- and 13.6-fold, respectively) and the adhesion molecule JAM-A (2.2-fold). Examples of downregulated genes include the enzymes superoxide dismutase-3 (−2.6-fold) and the epidermal growth factor receptor (−3.3-fold).
Correlation of Expression Patterns With Macrophage, Smooth Muscle, Endothelial, and T-Cell Markers
The expression of 7 of the 27 confirmed genes that were assessed had a significant correlation with CD68 expression (P>0.01), with cathepsin S having the most significant correlation (R2=0.376; P=0.0002). The expression of another 7 genes had a significant correlation with smooth muscle cell α-actin expression, with Ras-related associated with diabetes (R2=0.592; P=<0.0001) and epidermal growth factor receptor (R2=0.502; P=<0.0001; P>0.01) having the most significant correlation. Four genes correlated significantly with CD3 levels, whereas 2 genes correlated with the CD31 marker (Table 2).
Localization of Legumain mRNA Within the Lesion
Legumain mRNA was detected in the shoulder region of the plaque where expression was colocalized with the presence of macrophages (CD68 antigen; Figure 3).
MMP-9,Cathepsin B, and Legumain Protein and MMP-9 Activity in Stable and Unstable Plaque
Unstable segments contained ≈5-fold higher pro–MMP-9 levels than the corresponding stable segments from the same patient (15.16 ng/mg of soluble protein±SEM 4.41, n=9, versus 3.21 ng/mg of soluble protein±SEM 0.94, n=9, respectively; P=0.02; Figure 4A). Significantly greater amounts of cathepsin B were also found in unstable segments compared with stable segments (428.7 ng/mg of soluble protein±SEM 62.3, versus 206.3 ng/mg of soluble protein±SEM 49.9; P=0.0006; Figure 4B). Densitometric analysis of gelatin zymography showed a 6.9-fold increase in pro–MMP-9 (supplemental Figure IIIA) and 11.3-fold active MMP-9 (supplemental Figure IIIB) in unstable compared with stable segments, respectively. In contrast, levels of the gelatinase MMP-2 remained relatively unchanged (Figure 4D). Densitometric analysis of Western blots using an antilegumain antibody showed a 3.1-fold increase in the mature form of the enzyme18 (12 310 ADU±1774 SEM versus 3990 ADU±SEM 1774, respectively; P<0.0001; Figure 4C) in unstable compared with stable segments.
A whole transcriptome approach was used to identify differentially expressed genes in areas of stable and unstable carotid artery atherosclerotic plaque. Two analytical methods, an intraplaque and an interplaque analysis, revealed 170 and 1916 differentially expressed genes, respectively. The greater number of differentially expressed genes found by the interplaque analysis probably reflects the interspecimen variability. A total of 115 differentially expressed genes were identified by both methods of analysis. Differential expression of 27 of the 32 selected genes was confirmed by QRT-PCR analysis of paired stable and unstable segments. Eighteen of these genes have not been linked previously with plaque instability. The expression of MMP-9, cathepsin B, and a novel gene, legumain, a potential activator of MMPs and cathepsins, was confirmed subsequently at the protein level.
Plaques have been classified previously based on symptoms,10 the degree of stenosis of the vessel,19 macroscopically,20 and histologically.4,8 Classification based on symptoms was considered unsuitable for this study because there was often clear macroscopic evidence of plaque instability (such as the presence of thrombus and hemorrhage) in CEA specimens from asymptomatic patients, confirming the findings of others.21 A simple, rapid macroscopic method of classification, based on the definitions of plaque progression and instability described by Stary et al,22 was therefore used to differentiate stable from unstable segments of the same plaque, thereby providing an internal control for each sample and removing interpatient variability from the analysis. Histological analysis on a separate cohort of macroscopically classified segments showed that surface morphology is indicative of the histological morphology of each segment. All segments with a smooth surface were found to contain an intact fibrous cap and were therefore considered to be stable, at least at the time of the operation. Erosion of the intima, disruption of the fibrous cap, exposure of the core of the plaque and intraplaque thrombus, or hemorrhage were histologically identified in all specimens of plaque that were macroscopically classified as unstable.
The classification of atherosclerotic plaques used in this study is also supported by recent data from Lovett et al,23 who found strong associations between carotid plaque surface morphology and histological features such as rupture of the fibrous cap, intraplaque hemorrhage, and lipid core size, as well as with American Heart Association grade in a cohort of 128 patients. Analysis of the levels of expression of macrophage and smooth muscle cell markers also correlated with the macroscopic classification. Unstable segments contained significantly higher levels of macrophages (CD68), whereas the levels of expression of the smooth muscle cell marker α-actin were significantly lower in unstable segments. This is in agreement with the notion that high macrophage numbers and low smooth muscle cell content are associated with plaque vulnerability.24
The Affymetrix analysis on 11 segments from 3 plaques used in this study was not intended to be exhaustive but simply to reveal candidate markers that could then be confirmed in a larger panel of plaques. Unsupervised analysis of the microarray data using PCA identified interpatient variability as the largest source of variability in the data set. A robust intraplaque confirmation analysis, using real-time PCR, on a panel of 32 genes selected (based on consistency and fold difference in expression) from the Affymetrix analyses confirmed differential expression of 27 genes (84% confirmation rate). Eighteen of the 27 confirmed genes have not been linked previously with plaque instability. These include the genes for the metalloproteinase ADAMDEC-1 (≈37-fold upregulated in unstable regions), the cysteine protease legumain (≈3-fold upregulated), and the retinoic acid receptor responder-1 (≈5-fold upregulated).
Affymetrix analysis, real-time PCR, and western blot studies showed that the asparaginyl endopeptidase legumain is consistently upregulated in regions of unstable plaque at the transcript and protein levels. This enzyme has not been associated previously with atherosclerosis. Legumain is expressed by macrophages25 in vitro and has been implicated in the activation of both cathepsins26 and MMPs.27 This prompted our in situ hybridization studies and immunohistochemistry studies, which demonstrated that legumain mRNA was colocalized with macrophages in the shoulder regions of the lesions. The expression of legumain correlated significantly with the expression of CD68, also suggesting that legumain was expressed largely by macrophages28 in the atherosclerotic lesion. The high level of expression of legumain in the human atherosclerotic lesion suggests a possible role for this enzyme in the destabilization of the atherosclerotic plaque, possibly by modulating protease activity and subsequent extracellular matrix turnover.
The genes encoding MMPs 1, 8, and 9 that have been associated previously with plaque instability29–31 were among the most differentially expressed genes. Analysis by ELISA and gelatin zymography showed increased levels of both the proactive and active forms of MMP-9 in unstable segments of human plaque. Zymography revealed no difference in the activity of the other major gelatinase, MMP2, which confirms the results of the whole transcriptome scan.
The expression of the potent elastases cathepsins S (mRNA) and B (mRNA and protein) that was found to be upregulated in unstable regions of plaque in this study confirms the finding of other studies that have found increased levels of these enzymes in atherosclerotic plaques32,33 and in plaques from in apolipoprotein E–deficient mice.34 Other genes such as interleukin-8 (IL-8), the scavenger receptor CD36, CD32, and CD86, and the urokinase plasminogen activator that were found to be differential expressed in this study have also been identified by others investigating symptomatic coronary atherosclerotic plaques using microarray technology.10,13
The disparity in the compositions of the gene lists between this study and those of others10,12,13 supports the concept that variation in sample composition and study design can have profound effects on the results of gene expression studies.35 Relatively few genes identified in this study showed a high degree of correlation with any of the cell markers assessed despite reports suggesting that they are exclusively expressed by 1 particular cell type. For example, although CD163 has been shown to be expressed exclusively by cells of the monocyte–macrophage lineage,36 there was a lack of correlation between the expression of CD163 and CD68 levels in the plaque samples. Similarly, there was lack of association of JAM-A, a cell adhesion molecule, with CD31. This lack of expected associations may have been the result of modulation of expression by stimulatory molecules within the plaque. CD163 expression is known to be suppressed by proinflammatory mediators such as interferon-γ, and is strongly upregulated by IL-10 and IL-6 in monocytes and macrophages.37 Because interferon-γ and IL-6 are both known to be expressed in atherosclerotic lesions,38 we would expect them to have an effect in the expression levels of CD163.39 Stimulation of the expression of CD163 by cytokines produced by T cells, such as IL-6, could be responsible for the correlation observed between T-cell expression and CD163. The complex level of regulation of these genes and the fact that some proteins such as JAM-A are not exclusively expressed on specific cells such as endothelial cells,40 could be responsible for the lack of expected correlations.
Several genes known to be associated with the recruitment of cells to sites of injury or inflammation were also identified in this study. These include the chemokines CXCL-2 (macophage inhibitory protein-2) and CCL-18 (pulmonary and activation-regulated chemokine) and the adhesion molecule JAM-A. Previous in vitro studies have shown that JAM-A is involved in platelet aggregation and adhesion to cytokine-stimulated endothelial cells.41 Our finding that JAM-A is upregulated in unstable regions of the atherosclerotic plaque suggests that this factor may influence the formation of platelet aggregates for which fragmentation is responsible for many transient ischemic attacks.
A number of genes known previously to be involved in lipid metabolism were upregulated in unstable plaque. These include the scavenger receptors CD36 and scavenger receptor-A42 and Lp-PLA2. The association between Lp-PLA2 expression and unstable plaque identified in this and other studies indicates that its inhibition with agents that are currently in clinical development43 might stabilize vulnerable plaque.
Whole transcriptome analysis has identified a number of novel genes of which expression is altered between stable and unstable regions of the same plaque. These include the protease legumain, an enzyme that can activate MMPs and cathepsins, that has not been associated previously with atherosclerosis. Functional analysis of the genes identified in this study is now required to determine whether the differential expression of these genes is the cause or a result of plaque instability.
The authors would like to thank Joanne Cox, Tracy Roberts, Rebecca Woollard, Ted Cook, and Stewart Bates for technical assistance and helpful advice.
Sources of Funding
This work was supported by the Biotechnology and Biological Sciences Research Council and GlaxoSmithKline PLC.
Original received August 6, 2005; final version accepted May 17, 2006.
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