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Physiol. Genomics 30: 335-341, 2007. First published May 22, 2007; doi:10.1152/physiolgenomics.00076.2007
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Received 2 April 2007; accepted in final form 15 May 2007.
Physiological Genomics 30:335-341 (2007)
1094-8341/07 $8.00 © 2007 American Physiological Society

Dead or alive: gene expression profiles of advanced atherosclerotic plaques from autopsy and surgery

Judith C. Sluimer 1,*, Natasja Kisters 1,*, Kitty B. Cleutjens 1, Oscar L. Volger 2, Anton J. Horrevoets 2, Luc H. van den Akker 3, Ann-Pascale J. Bijnens 1 and Mat J. Daemen 1

1 University of Maastricht, Department of Pathology, Cardiovascular Research Institute Maastricht, Maastricht
2 Academic Medical Center, Department of Medical Biochemistry, Amsterdam
3 Maasland Hospital, Department of Surgery, Sittard, The Netherlands


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Since inclusion of atherosclerotic tissues from different sources is often indispensable to study the full atherogenic spectrum, we investigated to what extent the expression profiles of advanced, stable atherosclerotic lesions obtained during autopsy and surgery are comparable. The gene expression profiles of human carotids with advanced atherosclerosis obtained at autopsy and at vascular surgery were studied by microarray analysis. Expression analysis was performed both at the single gene (Rosetta, Gene Ontology) and at the pathway level using Ingenuity and Gene Set Enrichment Analysis. In addition, mRNA and protein expression levels were validated using quantitative (q) RT-PCR and immunohistochemistry on unrelated advanced carotid lesions from autopsy and surgery. Microarray analysis indicated that the 97.2% of genes showed similar expression levels in advanced atherosclerotic lesions from autopsy and surgery. While the expression data revealed no differences in common atherosclerotic related pathways such as lipid metabolism and inflammation, the differentially expressed genes were mainly involved in basal cell metabolism and hypoxia driven pathways. qRT-PCR confirmed the differential expression of hypoxia-driven genes VEGF-A (2.3-fold {uparrow}), glucose transporter (GLUT)-1 (2.5-fold {uparrow}), GLUT3 (8.3-fold {uparrow}), and hexokinase 1 (2.4-fold {uparrow}) in autopsy vs. surgical specimens. Immunohistochemistry revealed that the transcriptional differences in these hypoxia-related genes were not reflected at the protein level. The gene expression profiles of advanced atherosclerotic lesions from autopsy and surgery are largely similar. However, >500 genes, mostly involved in basal cell metabolism and hypoxia were differentially expressed at mRNA, but not at the protein level.

carotid atherosclerosis; basal cell metabolism; hypoxia


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
A MYRIAD OF EXPRESSION PROFILING studies have been conducted over the past few years to unravel the molecular pathways involved in the initiation and progression of human atherosclerosis (reviewed in Ref. 5). These studies compared complete atherosclerotic plaques (13, 14, 24) or specific plaque regions (1, 19, 28) to nondiseased arteries/regions. Transcript levels were analyzed to study either the early phases of atherosclerosis (2, 14, 24, 26) or the progression from stable to ruptured lesions (9, 21). However, none of these studies have investigated the full spectrum of atherogenesis ranging from a nondiseased artery to early, advanced, stable, and advanced, ruptured lesions. In the optimal profiling study, tissue would be used that represents all these stages obtained from one type of artery and one source, i.e., autopsy or surgery, to ensure reliable and reproducible results. However, in practice there is a lack of available tissue from the same source. For instance, carotid arteries with early lesions are only available from autopsy, while ruptured carotid lesions are hardly available from this source. To overcome this obstacle different sites (9, 12, 28) and/or sources have combined (28). Obviously, these approaches may introduce a high degree of variability and may even obscure the expression profile of genes or pathways associated with atherogenesis.

Interestingly, there are no data available comparing the gene expression profiles of atherosclerotic tissue obtained at autopsy and surgery. The aim of this study was to compare, by microarray analysis, gene expression profiles of atherosclerotic tissue with a stable advanced plaque phenotype from autopsy and surgery. Expression analysis was performed at both single gene and pathway levels to study biological functions and pathways associated with expression profiles of autopsy and surgery. In addition, results were validated on mRNA and protein expression levels using quantitative real-time PCR and immunohistochemistry.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Tissue collection.
A total of 26 atherosclerotic carotid artery segments were obtained at autopsy (n = 11 donors; Department of Pathology, University Hospital Maastricht) or from patients undergoing vascular surgery (n = 11 donors; Department of Surgery, Maasland Hospital Sittard). The postmortem interval was <24 h. The difference in mean age of the donors, 82 ± 4 yr and 65 ± 4 yr for autopsy and surgical donors respectively, was not significant (P = 0.06). The sex distribution was also not significantly different between autopsy [72% (n = 8) male] and surgery [64% (n = 7) male; P = 0.65]. Detailed patient characteristics are presented in Supplemental Table S1.1 The tissue was obtained from the Maastricht Pathology Tissue Collection, and collection, storage, and use of tissue and patient data were performed in agreement with the "Code for Proper Secondary Use of Human Tissue." Immediately after resection the atherosclerotic tissue was divided into parallel segments of 5 mm. Snap-frozen segments for RNA isolation were alternated by formalin-fixed segments for histology. The plaque stage was classified based on hematoxylin-eosin (HE)-stained sections (4 µm) according to Virmani et al. (29). Snap-frozen samples were only included when both adjacent HE-stained sections were classified as stable, advanced atherosclerotic lesions.

RNA isolation.
Total RNA was isolated from advanced, stable carotid lesions collected at autopsy (n = 4) or surgery (n = 3) by the guanidine isothiocyanate/CsCl method (6) followed by RNeasy extraction according to the manufacturer (Qiagen, Hilden, Germany). RNA quantity and quality were determined with a Nanodrop spectrophotometer (Witec, Littau, Switzerland) and a 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA), respectively. All samples included had an RNA integrity number ≥5.

Microarray hybridization and data analysis.
Human oligonucleotide libraries (catalog no. HUMLIB384) were obtained from Sigma-Compugen. Technical support was supplied by LabOnWeb (http://www.labonweb.com/cgi-bin/chips/full_loader.cgi). The libraries represent in total 18,600 LEADS clusters plus 231 controls. The oligonucleotide library was printed with a 2 x 12 pin Lucidea Array Spotter (GEHealthcare, Piscataway, NJ) on commercial UltraGAPS slides (aminosilane-coated slides, Corning 40017) and processed according to the manufacturer's instructions. The slides contained 60-mer oligonucleotides, and the batch was checked for the quality of spotting by hybridizing with SpotCheck Cy3-labeled nonamers (Genetix, New Milton, Hampshire, UK).

We amplified 1 µg of total RNA a single round using the Ambion MessageAmp kit (catalog no. 1750; Ambion, Huntingdon, UK), with 50% of rUTP ribonucleotides replaced by aminoallyl-rUTP (catalog no. A5660; Sigma-Aldrich, Zwijndrecht, The Netherlands). Next, labeled cRNA probes were fragmented followed by purification using the RNeasy mini kit (Qiagen). Aminoallyl-modified amplified RNA was labeled with either Cy3 (common reference sample) or Cy5 (samples) monoreactive dyes (GE Healthcare, Uppsala, Sweden). RNA concentration as well as dye incorporation was measured using the Nanodrop spectrophotometer. Equal amounts of labeled cRNAs (typically 1 µg) were applied in duplicate to oligonucleotide arrays and were hybridized for 16 h at 40°C (7). All samples were hybridized against a common reference sample composed of a pool of RNA isolated from human umbilical vein endothelial cells, the monocytic cell-line THP-1, and whole-mount human carotid and aortic lesions to allow comparison between different hybridizations. The complete data set has been made available at http://www.ebi.ac.uk/arrayexpress (accession no. E-MEXP-1004).

Images were acquired using the Agilent II scanner (Agilent Technologies, Palo Alto, CA), and feature extraction was done using ArrayVision 8.0 software (GE Healthcare Europe, Diegem, Belgium). Background subtracted intensities were LOESS normalized (LIMMA package, Bioconductor software, http://www.bioconductor.org) and imported into Rosetta Resolver (Rosetta Biosoftware, Seattle, WA). The Benjamini-Hochberg method to correct for multiple testing (4) was used to identify genes significantly differentially expressed between advanced atherosclerotic lesions from autopsy and surgery. A P value < 0.01 was considered as statistically significant.

The differentially expressed genes were analyzed using gene ontology (GO) analysis (DAVID version 1, National Institutes of Health). Ingenuity pathway analysis (Ingenuity Systems, Mountain View, CA; https://analysis.ingenuity.com/pa) was performed on genes with an intensity level >50 (2.5 x background) in either of the phenotypes, a fold change >1.4 or < –1.4, and a P value <0.01. In addition, the expression data were analyzed with Gene Set Enrichment Analysis (GSEA) (25). This method analyzes expression data at the level of predefined gene sets instead of individual genes to detect significant, concordant differences in biological processes between two phenotypes. We acknowledge the use of the GSEA 1.0 software and Molecular Signature Database of gene sets (MSigDB) C2 release 1 (http://www.broad.mit.edu/gsea/). All genes in the data set were ranked based on their correlation to the autopsy phenotype, and the rank positions of all members of a given gene set were used to calculate an enrichment score. Subsequently, 1,000 permutations were used to determine which gene sets were significantly enriched in autopsy or surgery [false discovery rate (FDR) <25%].

Quantitative real-time PCR.
Total RNA from additional samples from autopsy (n = 5) and surgery (n = 4) was reverse transcribed and quantitative real-time PCR (qRT-PCR) performed as described (8). Primers directed against hypoxia-inducible transcription factor (HIF)-2{alpha} (10), vascular endothelial growth factor (VEGF)-A (27), VEGFB (27), and housekeeping gene 18S (20) were used in combination with SYBR Green. FAM-TAMRA-labeled primer-probe pairs were obtained for HIF1{alpha}, glucose transporter (GLUT)-1, and GLUT3, hexokinase (HK)-1 and HK2, and housekeeping gene GAPDH (Applied Biosystems, Foster City, CA). Samples and runs were performed in duplicate. RNA copy numbers were calculated using a standard curve and normalized to housekeeping gene mRNA expression.

Immunohistochemistry.
Immunohistochemistry was performed on paraffin-embedded carotid arteries obtained at autopsy (n = 5) or surgery (n = 5). Expanded immunohistochemical methods can be found online (Supplemental Table S2). Sections were stained with primary antibodies against HIF1{alpha}, HIF2{alpha}, VEGF, GLUT1, GLUT3, and HK1 diluted in Tris-buffered saline with 0.1% Tween and 1% bovine serum albumin. Sections were then incubated with appropriate secondary antibodies, and staining was visualized as a brown precipitate using 3,3'-diaminobenzidine tetrachloride (ChemMate Envision detection kit; DAKO, Glostrup, Denmark). Sections incubated without the primary antibody served as a negative control. The sections were quantitatively assessed for a difference in immunoreactivity between autopsy and surgery by computer-assisted color image analysis (Leica QWin V3, Cambridge, UK). Immunoreactivity was quantified within five random fields at x100 magnification. The percentage of positive staining as a function of total tissue area was determined.

Statistical analysis of patient characteristics and qRT-PCR.
All qRT-PCR results are presented as means ± SE. Groups were compared using a Mann-Whitney rank-sum test for continuous variables and {chi}2 test for dichotomous variables (SPSS 11.0, Chicago, IL) and were considered statistically different when P < 0.05.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
DISCUSSION
 GRANTS
 REFERENCES
 
Expression profiles in autopsy and surgery.
Expression analysis using Rosetta Resolver and the Benjamini-Hochberg correction for multiple testing showed that 97.2% of genes showed similar expression profiles in advanced, stable lesions from autopsy and surgery. Gene expression data were subjected to GSEA, which also showed no significant differences (FDR >25%) in expression level between autopsy and surgery samples for the predefined gene sets in the GSEA application.

However, expression analysis showed that 515 genes (2.8%) were significantly differentially expressed between autopsy and surgery. These genes showed a fold change autopsy/surgery ranging from –8.5 to 7.6 (Supplemental Table S3), and the majority of differential genes (n = 343) was upregulated in autopsy samples.

Differentially expressed genes are involved in hypoxia-driven pathways.
The differentially expressed genes were further analyzed using Ingenuity pathway analysis to identify the involved biological functions and pathways and revealed significant differential expression of only two canonical pathways: VEGF signaling (P < 0.0001) and hypoxia in cardiovascular disease (P < 0.01) (Supplemental Fig. S1). Both pathways are associated with oxygen homeostasis.

Expression of mRNA as determined by microarray was validated with qRT-PCR for several genes of these hypoxia-driven pathways on an independent set of atherosclerotic samples (Fig. 1). qRT-PCR showed the significantly higher expression of GLUT1 (2.5-fold up), GLUT3 (8.3-fold up), and HK1 (2.4-fold up), in advanced lesions from autopsy compared with surgery samples. These differences in expression level were even higher than the differences determined in the microarray analysis. Expression of HIF1{alpha}, HIF2{alpha}, VEGFB, and HK2 was not differentially expressed when assessed by real-time PCR. In addition, qRT-PCR showed that VEGFA, for which no probe was present on the microarray, was 2.4-fold higher expressed in autopsy samples.


Figure 1
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  		Fig. 1. Expression ratio between advanced lesions from autopsy and surgery of genes involved in hypoxia and VEGF signaling. Ratio (autopsy/surgery) of mRNA expression level on microarray (white bars) and quantitative real-time PCR (qRT-PCR) (black bars) of genes involved in hypoxia and VEGF signaling. qRT-PCR of an independent set of samples showed the differential expression of VEGFA, glucose transporter (GLUT)-1, GLUT3, and hexokinase (HK)-1 between advanced lesions from autopsy and surgery. The data are represented as ratios of autopsy/surgery. *P < 0.05 for qRT-PCR; N/A, VEGFA probe not available on microarray.

 
Quantitative immunohistochemical analysis of the hypoxia-driven genes, HIF1{alpha}, -2{alpha}, VEGFA, GLUT1, GLUT3, and HK1 showed that immunoreactivity and cellular distribution of these proteins were similar in advanced atherosclerotic lesions from autopsy and surgery (Figs. 2 and 3). Macrophage foam cells of the atherosclerotic lesion were the most prominent cell type showing immunoreactivity of HIF1{alpha} (data not shown), -2{alpha} (data not shown), VEGFA (Fig. 2, B–G), GLUT1 (Fig. 2, C–H), GLUT3 (Fig. 2, D–J), and HK1 (Fig. 2, E–J). In addition, smooth muscle and endothelial cells in the atherosclerotic lesion showed less intense staining of HIF1{alpha}, -2{alpha}, and VEGFA compared with macrophages (Fig. 2).


Figure 2
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  		Fig. 2. Immunohistochemical (IHC) staining of proteins involved in hypoxia-driven pathways. Advanced lesions from autopsy (A–E) and surgery (F–J) were immunohistochemically stained, and quantitative analysis showed similar immunoreactivity of VEGFA (B–G), GLUT1 (C–H), GLUT3 (D–I), and HK1 (E, J) between autopsy and surgery. H&E, hematoxylin and eosin.

 

Figure 3
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  		Fig. 3. Quantitative IHC analysis of proteins involved in hypoxia-driven pathways. Quantitative IHC analysis showed a similar expression of VEGFA, GLUT1, GLUT3, and HK1 in advanced lesions from autopsy (white bars) and surgery (black bars).

 
Differentially expressed genes are involved in basal cell metabolism.
In addition to changes related to hypoxia pathways, Ingenuity Pathway analysis also listed highly significant networks involved in functions such as cell signaling, DNA replication, nucleic acid metabolism, cardiovascular disease, cellular growth and differentiation, cell cycle, and cell death (Table 1). Likewise, GO analysis showed that the differential genes were mainly involved in cell growth and maintenance (99 genes, 19%), signal transduction (79 genes, 15%), nucleic acid metabolism (77 genes, 15%), and protein metabolism (66 genes, 13%) and to a smaller extent in cell death (15 genes, 3%) and cell signaling (14 genes, 3%) (Table 2).


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  		Table 1. Top 6 differential networks between advanced lesions from autopsy and surgery demonstrated by Ingenuity Pathway analysis

 

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  		Table 2. GO classification of differential genes after Benjamini-Hochberg correction

 
Differentially expressed genes are not related to atherosclerosis.
The gene expression data were also analyzed to establish whether the differences in expression were related to atherosclerosis. All 515 differentially expressed genes were manually compared with a recently published list of 92 genes related to processes involved in atherosclerotic disease, such as lipid metabolism, inflammation, and matrix degradation (11). Probes representing these 92 genes were indeed present on our microarray, and the expression of 87 genes (95%) was similar between autopsy and surgery. Only the expression levels of five genes were different after Benjamini-Hochberg correction for multiple testing (Table 3). Lamin A/C, Fas, thrombin, and fibrillin were downregulated 1.4-, 1.5-, 1.7-, and 1.9-fold, respectively, in autopsy, whereas hydroxysteroid (11-beta) dehydrogenase 1 was upregulated 2.1-fold in samples obtained at autopsy. Thus, the expression of the majority of atherosclerosis-related genes was similar between autopsy and surgery, and consequently the combination of autopsy and surgery samples for transcriptional analysis might not obscure the atherosclerosis-specific expression profile.


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  		Table 3. Genes previously related to atherosclerosis and differentially expressed between atherosclerotic samples from autopsy and surgery

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
GRANTS
 REFERENCES
 
Microarray analysis combined with GO analysis and extensive pathway profiling (Ingenuity and GSEA), qRT-PCR, and immunohistochemistry were used to study the expression profiles of advanced atherosclerotic lesions from autopsy and surgery. Our results clearly showed that >97% of genes (including several genes known to be involved in atherosclerosis) were unaffected by the source of the tissue, i.e., autopsy or surgery. However, 515 genes primarily involved in basal cell metabolism and hypoxia-driven pathways were differentially expressed and mainly associated with active postmortem transcription. Nevertheless, immunohistochemistry showed that the transcriptional differences in hypoxia-driven genes were not reflected at the protein level.

Sanoudou et al. (22) compared the transcriptome of skeletal muscle from autopsy and surgery and also showed the differential expression of only a minority of genes (1.1%). Remarkably, also in human skeletal muscle, only genes involved in basal cell metabolism, i.e., cell growth/maintenance, nucleic acid and protein metabolism, and cell communication showed differential expression between autopsy and surgical samples. In their study, all differential transcripts showed increased expression in postmortem samples. It seems likely that these processes represent a cellular survival response triggered by the complex environmental changes associated with the cessation of life.

In addition to the cellular response of increased basal metabolism, hypoxia-driven pathways were also initiated. The hypoxic response is regulated by the protein stabilization of HIF1 and -2 in hypoxic cells. These oxygen sensors orchestrate the transcription of several hypoxia responsive genes involved in angiogenesis, glucose metabolism, and cell proliferation/survival and include VEGF, GLUT1 and -3, and HK1 and -2 (23). It may be appreciated that cessation of life is undeniably associated with cellular ischemia/hypoxia, as our data clearly show. HIF1{alpha} mRNA expression in postmortem kidney compared with surgery was significantly increased in postmortem human kidney (3, 17), but surprisingly VEGF mRNA was decreased in the same samples. Postmortem skeletal muscle did not show a significant increase in HIF1{alpha} and VEGF mRNA expression (22).

The differences in gene expression we observed were not reflected at protein level. Our findings are corroborated by similar VEGF protein expression in tubuli and arteries of human kidney from deceased and living donors. However, in the same sections, a slightly different expression was shown in glomeruli. It seems that differences in protein expression between postmortem and living tissue, if any, are smaller than the transcriptional differences. Theoretically, perishing cells might be able to start transcription and/or translation, but their evident death will prevent them from continuing any process. In addition, protein turnover is a more time-consuming process than mRNA turnover and therefore less sensitive to detect changes. However, changes in posttranslational modifications such as phosphorylation or glycosylation may occur within minutes but are beyond the scope of this study.

A potential concern using autopsy samples to study gene expression would be the quality and integrity of mRNA derived from this source. However, mRNA from autopsy is fairly resistant to degradation in a wide variety of tissues (15, 16, 22). Total mRNA remained intact up to 48 h postmortem (15, 22), and microarray expression data were found to be reliable even when mRNA was partially degraded (16). More specifically, others using qRT-PCR studied the transcript levels of HIF1{alpha} and VEGF mRNA, which was shown to be resistant to degradation up to 48 h postmortem (30). Therefore, any degradation is unlikely to influence detection of gene expression by microarray and qRT-PCR. Aside from the source possibly influencing RNA integrity, another concern was raised by a recent report on the presence of RNA damage in human atherosclerosis (18). However, an mRNA quality control was applied, ensuring similar mRNA integrity of samples from autopsy and surgery. Therefore, any differences in expression profile of autopsy and surgery are not expected to be caused by a difference in mRNA quality and integrity between these two sources.

In conclusion, the gene expression profiles of advanced atherosclerotic lesions from autopsy and surgery are largely similar. However, specific gene pathways, mostly involved in basal cell metabolism and hypoxia, were differentially expressed at the mRNA level. Despite the transcriptional differences in hypoxia-related genes, protein expression in advanced atherosclerotic lesions from autopsy and surgery was comparable. Nevertheless, human expression profiling studies using a combination of both sources should be analyzed with caution.


   GRANTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
REFERENCES
 
The work described herein was performed in the framework of the European Vascular Genomics Network (EVGN Grant LSHM-CT-2003-503254 to K. B. Cleutjens, A. P. J. Bijnens, and M. J. Daemen) and was supported in part by two grants of the Netherlands Organization of Scientific Research; i.e., the Innovational Research Veni program (grant 916.046.083 to A. P. J. Bijnens) and the Genomics program (grant 050-10-014 to N. Kisters).


   ACKNOWLEDGMENTS
 
We gratefully acknowledge Moniek Faessen for excellent technical assistance and Jack Cleutjens for assistance with colorimetric analysis.


   FOOTNOTES
 
Address for reprint requests and other correspondence: M. Daemen, Dept. of Pathology, Univ. of Maastricht, Dept. of Pathology, PO Box 5800, 6202 AZ Maastricht, The Netherlands (e-mail: Mat.Daemen{at}path.unimaas.nl).

Article published online before print. See web site for date of publication (http://physiolgenomics.physiology.org).

* J. C. Sluimer and N. Kisters contributed equally to this work. Back

1 The online version of this article contains supplemental data. Back


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 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 

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