Cardiovascular Research

Cardiovascular Research 2002 54(1):16-24; doi:10.1016/S0008-6363(01)00516-8
© 2002 by European Society of Cardiology

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Copyright © 2002, European Society of Cardiology

Application of gene expression profiling to cardiovascular disease

P.A Henriksen and Y Kotelevtsev^*

Division of Bio-Medical Sciences, Room 323a, Hugh Robson Building, University of Edinburgh, George Square, Edinburgh EH8 9XD, UK

^* Corresponding author. Tel.: +44-131-651-1194; fax: +44-131-650-6527 yuri.kotelevtsev{at}ed.ac.uk

Received 27 June 2001; accepted 18 October 2001

	Abstract

Top Abstract 1. Introduction 2. Methods used for... 3. Applications of gene... 4. Conclusions: cautious... References

 
The number of cardiovascular publications featuring gene expression profiling technologies is growing rapidly. This article introduces four profiling techniques; serial analysis of gene expression, differential display, subtractive hybridisation and DNA microarrays. Illustrations of their application towards cardiovascular research are given and their potential for gene discovery and improving our understanding of gene function is discussed.

KEYWORDS Gene expression; Sequence (DNA/RNA/prot)

	1. Introduction

Top Abstract 1. Introduction 2. Methods used for... 3. Applications of gene... 4. Conclusions: cautious... References

 
Despite intensive studies, the mechanisms behind common cardiovascular diseases such as hypertension and atherosclerosis are poorly understood. The precise combination of environmental and genetic factors responsible for these disorders may vary between patients, producing phenotypically similar manifestations but requiring different interventions to correct them. More detailed characterisation of pathological processes at the molecular and cellular levels will enhance understanding of underlying mechanisms. Cellular phenotype is determined to a large extent (although not completely) by the set of proteins that are expressed in the cell. Ideally a description of cellular phenotype would include information on every protein expressed, its intracellular localisation, and biological activity. This task is beyond the reach of current experimental techniques. New methodologies have provided the opportunity to analyse and in some cases to quantify simultaneously, thousands of messenger RNA (mRNA) transcripts. This type of analysis has been termed gene expression profiling [1]. Databases generated by genome sequencing programs, have facilitated the identification of genes by relatively short sequences within their transcripts and global expression profiling in simple organisms like yeast has yielded information on the regulation of gene expression in response to different stimuli [2]. Medical researchers are now using expression profiling to systematically characterise molecular events pertaining to complex multifactorial diseases. Serial analysis of gene expression (SAGE), differential display, subtraction suppression hybridisation, and DNA microarrays are four major methods used for expression profiling.

This review aims to provide an introduction to these methods and gives illustrations of their application towards cardiovascular research. We examine the potential for gene discovery and highlight the limitations. The number of cardiovascular publications describing expression profiling strategies is growing rapidly and some of these have been reviewed recently [3–5].

	2. Methods used for expression profiling

Top Abstract 1. Introduction 2. Methods used for... 3. Applications of gene... 4. Conclusions: cautious... References

 
2.1 Serial analysis of gene expression
SAGE is a technique for determining mRNA expression levels by sequencing cDNA molecules produced by reversed transcription [6]. The method is based on tagging sequences inside cDNA with specific oligonucleotides bearing recognition sequences for a restriction enzyme that cuts DNA 9–13 bases away from its recognition site. The next step involves joining these short fragments together in concatamers. Sequencing several thousand cloned concatamers comprising 20–30 tags and scoring the sequences corresponding to each mRNA provides a direct measure of mRNA abundance in a given sample.

For known genes, the 9–10 base pair oligos are often sufficient for identifying the cDNA of origin using Gen-Bank. Although SAGE can be performed with small amounts of starting mRNA, the quantity of sequencing required and difficulty in reproducing protocols for concatamer formation have limited its uptake as an expression profiling technique. A detailed review of SAGE methodology is given elsewhere [7].

2.2 Differential display
Differential display will detect mRNA sequences that are absent or expressed at very low levels in one sample in comparison with another. This technique is therefore limited to detecting large variations in the amount of mRNA present. The first step involves reverse transcription with an oligo (dT) primer ending with C, G or A at the 3' end [8]. This splits the resulting cDNA sample into three pools according to the final primer nucleotide and these are amplified in a polymerase chain reaction (PCR) with a mix of arbitrary 5' primers designed to yield several hundred PCR products. Radionucleotides are incorporated during the reaction and the PCR products are resolved by electrophoresis on a polyacrylamide gel. Bands present in one sample that are absent from another are candidates for differentially expressed genes. This method is relatively simple and has the advantage of facilitating comparison of most cDNAs from a given cell type using several 5' arbitrary primer combinations and small samples of mRNA. Detected bands are anonymous and therefore require purification, subcloning and sequencing. The PCR step generates a significant rate of false positives based on Northern blot analysis and although modifications may reduce this problem [9], differential display has largely been superseded by the development of more sensitive techniques below.

2.3 Subtraction suppression hybridisation
This approach improves sensitivity for low abundance transcripts by ‘equalising’ their concentration with high abundance transcripts through a hybridisation step [10,11]. Two cDNA populations termed ‘tester’ and ‘driver’ are made using reverse transcription from the two mRNA samples to be compared. The tester cDNA is split further into two pools and two different oligos containing motifs that can be recognised by special primers are ligated onto the 5' ends. Through a serious of hybridisation steps, sequences equally represented in the ‘tester’ and ‘driver’ populations are subtracted out leaving differentially expressed sequences that can be amplified by PCR using primers complementary to the attached oligos. As with differential display the final PCR product may be subcloned for sequencing and compared to Gen-Bank deposited sequences. The technique is not demanding in terms of resources and can lead to identification of novel genes. It is not however, suited to systematic profiling owing to difficulties in standardising the hybridisation steps and the high rate of false positives [12].

2.4 Microarrays
Microarrays facilitate parallel quantification of thousands of specific mRNAs in a sample through hybridisation to complementary sequences placed at specified positions on glass or silicon supports [1,13]. In contrast to differential display and subtractive hybridisation the sequence of the target gene or expressed sequence tag (EST) is known. Recently, two different techniques for high density spotting of DNA molecules on glass or silicone surfaces became available. One format involves robotic deposition of PCR fragments amplified from cDNA clones and was developed at Stanford University [13]; 10 000 genes may be arrayed on a compact area of 3.6 cm². Affymetrix apply an alternative approach based on photolithographic synthesis of oligonucleotides in situ. Ultraviolet light is used to direct base by base synthesis, in parallel, for up to 400 000 oligos on one silicone chip [14]. Specific oligo design allows the user to avoid regions of repetitive or homologous sequence between different genes. Microarrays work on a reverse principle to Northern blot analysis. Instead of labelling a specific cDNA probe and hybridising to a pool of mRNAs immobilised on a membrane, a pool of labelled mRNAs is hybridised to numerous cDNA probes immobilised on a solid support in specific positions. The cDNA populations from two experimental samples are labelled with different fluorescent dyes and hybridised to the same microarray allowing direct comparison of signal intensity. Smaller amounts of RNA are required for the analysis of a greater number of genes than is possible with Northern blotting without a big loss of sensitivity. In one study comparing the two methods, a microarray was able to detect 90% of the expression changes demonstrated in one sample by Northern blotting [15]. Microarray technology is expensive to set up but expression profiling has become possible for many research groups through the development of centralised facilities within academic institutions and the availability of commercial microarrays.

The array fabrication technique is critical for producing high resolution and minimising background ‘noise’ and cross hybridisation signals. For cDNA arrays, signals vary depending on the slide surface and spotting buffer and the temperature and humidity during array printing [16]. Robotic systems for microarraying are commercially available and the design of the original robot at Stanford University is available online (http://cmgm.stanford.edu.pbrown/mguide/index.html). Following hybridisation and image processing the data must be normalised to adjust for labelling and detection efficiencies for different fluorescent labels and differences in the quantity of starting RNA between samples. Normalisation strategies (reviewed in Ref. [16]) include correcting for total intensity by assuming that although fluorescence varies between individual transcripts, this should even out over many thousands and be identical for the same quantity of RNA labelled with two different fluorescent markers. A second approach utilises the signal ratio of the gene of interest to that of housekeeping genes (the expression levels of which are assumed not to vary between samples). A third uses the fact that the predicted slope of a scatterplot of housekeeping and added equimolar controls for the two probes should be unity. The data can be rescaled using this slope with regression analysis.

The data of hybridisation experiments with cDNA arrays can be presented as a matrix of fluorescent intensities, each value corresponding to a spot on the microarray. In a given matrix, rows may represent genes and columns may represent cDNA samples. In a simple analysis there will be only two columns comparing gene expression in two different samples. This is the commonest form of data presentation, particularly for clinical specimens. Experiments in cell culture may involve different treatments with extended time points. More detailed analysis of the resulting vast data sets may help to uncover common mechanisms of gene regulation or improve functional understanding by grouping expression changes in terms of time and magnitude or according to designated gene function groups. Algorithms comparing the data between rows (genes) and columns (samples) have been developed. Once a measure of similarity (or distance) between individual gene profiles has been assigned, these may be divided into groups or clusters. Brazma et al. [17] have provided a comprehensive review of clustering algorithms in the analysis of expression data. Clustering was first described by DeRisi et al. [2], who discovered that genes with similar expression profiles during metabolic shift in yeasts were functionally related and shared transcription factor binding sites in their promoter regions.

	3. Applications of gene expression profiling to cardiovascular disease

Top Abstract 1. Introduction 2. Methods used for... 3. Applications of gene... 4. Conclusions: cautious... References

 
An overview of recent cardiovascular publications using gene expression profiling is provided in Table 1 where studies are classified according to disease model, experimental sample and profiling method.

View this table: [in this window] [in a new window]   Table 1 Summary of recent applications of expression profiling to cardiovascular disease

 
3.1 Atherosclerosis and endothelial dysfunction
3.1.1 Cell culture models
The above techniques have wide applications in the study of developmental and pathophysiological processes in the cardiovascular system. Difficulties may arise in the interpretation of gene profiles from tissues containing multiple cell types and this may be circumvented in part by using cell culture models described below.

Gimbrone's group used differential display to isolate genes that are up-regulated in cultured endothelial cells (ECs) in response to laminar or turbulent flow shear stress [18]. The genes for manganese superoxide dismutase and cyclooxygenase-2 (COX-2) were up-regulated in cells exposed to laminar shear stress and these findings were confirmed by Northern blotting. Importantly, the investigators confirmed that enhanced gene expression was translated into increased protein synthesis by immunoblotting. The contribution of turbulent flow to endothelial dysfunction and atheromatous plaque development at ‘lesion prone areas’, such as arterial bifurcations and curvatures, is well established. The putative protective effects of an antioxidant enzyme and prostacyclin (the major product of endothelial COX-2), induced by laminar shear stress are particularly interesting in this regard. The failure of differential display to detect enhanced expression of endothelial cell nitric oxide synthase (demonstrated by Northern analysis) in ECs exposed to laminar flow illustrated the limited sensitivity of this technique. The influence of mechanical stimuli has also been studied on vascular smooth muscle cells grown on fibronectin coated supports using cDNA microarrays [19]. Only a handful of the 5000 genes monitored at two time points after the onset of mechanical stretch varied by more than the 2.5-fold threshold change in expression set by the investigators. Applying this threshold may facilitate more reproducible results although the majority of genes varied by less than 2-fold in their expression and quantitatively small but functionally important changes in expression may have been overlooked. Plasminogen activator inhibitor-1 (PAI-1) and Tenascin-C were induced following stretch. These findings were confirmed with Northern blotting, ELISA and Western blotting for corresponding changes in mRNA and protein production. PAI-1 secreted within the vascular wall may regulate extracellular matrix proteolysis and vascular repair. Tenascin-C is also prominent in remodelling tissues and has anti-adhesive properties. The functional impact of these mediators on the atherosclerotic plaque is harder to predict. Expression of PAI-1 may act to strengthen the surrounding extracellular matrix and could render a fibrous cap less prone to rupture. It may also favour the accumulation of matrix and subsequent plaque growth. Whether a stretch stimulus applied to ECs growing in a monolayer reproduces the mechanical environment of a vessel wall is also questionable and studies on ex vivo vessel preparations would be instructive.

Oxidised low density lipoprotein (Ox-LDL) is another central influence in atherosclerotic plaque development. Monocytes become engorged with cholesterol within the plaque forming foam cells and this process has been modelled by incubating the monocytic THP-1 cell line with Ox-LDL and comparing expression profiles to untreated cells using microarrays [20]. Of the 6805 genes arrayed, 268 (4%) altered their expression, a minimum of 2-fold, at one of the time points ranging up to 4 days. Data was presented in clusters according to temporal expression patterns and results were confirmed using quantitative real time PCR. In this method, reverse transcribed mRNA is PCR-amplified with gene specific primers in the presence of a probe containing a quenched fluorescent dye. The 5' exonuclease activity of the polymerase is utilised to release fluorescent dye from the target probe facilitating continuous monitoring of the reaction against a reference probe such as 18S RNA. Genes previously demonstrated to be responsive to Ox-LDL loading of macrophages such as thrombomodulin, were induced in the THP-1 cells, offering some validation of the model. The scavenger receptors A and CD 36 involved in Ox-LDL uptake were up-regulated along with nuclear receptors that control lipid metabolism reflecting a lipid storage phenotype similar to maturing adipocytes. Vascular smooth muscle cell (VSMC) behaviour in an atheromatous plaque has been modeled by treating cultured VSMCs with the cytokine, tumor necrosis factor [21]. Eotaxin, a chemokine characterised by its chemotactic properties for eosinophils, was induced more than 20-fold on a cDNA microarray. Eosinophils are not present within atherosclerotic plaques and the finding prompted immunohistochemical studies to localise eotaxin, which was expressed in plaque smooth muscle cells and its receptor, present on plaque macrophages and mast cells. This study demonstrated how transcriptional profiling can complement traditional, hypothesis driven research through producing unexpected findings, in this case the presence of a signalling pathway in atherosclerotic plaques.

The above studies illustrate the contribution expression profiling can make to the molecular dissection of cellular responses to specific stimuli. Comparison between different cell lines or cells grown in different matrix environments has also provided valuable information. Primary rat VSMCs were compared to a transformed proliferating rat VSMC line using differential display [22] in an ingenious model of smooth muscle cell proliferation occurring during restenosis. Differential up-regulation of one of the isolated clones was confirmed by in situ hybridisation in rat carotid arteries following angioplasty but the full gene could not be identified from the 3' sequence alone. Further cloning steps were required to determine the full sequence that had homology with human translational elongation factor, coding for a protein involved in protein synthesis.

Finally, coordinating expression profiling with gene mapping led to the identification of the Tangier disease gene coding for the ABC1 transporter [23]. Cells from subjects with Tangier disease are defective in the process of apolipoprotein mediated removal of cholesterol and phospholipids. Microarrays were used to compare gene expression in fibroblasts from Tangier patients with healthy controls under conditions known to induce cholesterol efflux. Probing of samples from three patients identified several under expressed genes including one that mapped to a large genetic locus independently characterised by positional cloning. The confirmation of point mutations in this gene in Tangier patients, the absence of mutations in healthy controls and the observation that overexpression of ABC1 resulted in enhanced cholesterol efflux indicated that ABC1 is the Tangier genetic defect.

3.1.2 Analysis of atherosclerotic tissue
Expression profiles from cell culture models of restenosis and atherosclerosis have led to the identification of novel genes and the demonstration of their expression in clinical specimens and tissue samples from animal models of these diseases. However, complex interactions between multiple cell types and matrix are lost with in vitro systems and further information may be gained by summative expression profiles obtained directly from animal model tissues or clinical specimens. The complexity of expression profiling of patient tissue samples is compounded by the additional variables of different genetic backgrounds, aetiology of underlying disease and preceding drug treatments.

Profiling of human atherosclerotic plaques is limited by the availability of tissue. To overcome the technical problem of limited mRNA, novel methods of cDNA amplification have been applied [24] to use cDNA arrays to probe neointimal tissue obtained following atherectomy of in-stent restenosis. This study highlighted the problem of choosing an appropriate control tissue to compare against neointimal tissue. Tunica media samples form coronary and gastrointestinal arteries were taken as controls and observed differences may therefore not be specific to the process of restenosis that was the target of the study. Carotid endarterectomy samples consist of larger pieces of tunica media gouged from the arterial wall during revascularisation. cDNA array profiling identified heightened activity of the Early growth response gene-1 (Egr-1) transcriptional pathway in these lesions compared to media from non-diseased arteries [25]. Egr-1 modulates a group of stress responsive genes including platelet-derived growth factor and transforming growth factor β that may contribute to plaque growth and smooth muscle cell recruitment. A fundamental role for this pathway in atheroma development was suggested by the additional finding of Egr-1 activation in early atherosclerotic lesions from cholesterol fed LDL receptor knockout mice.

3.2 Heart failure
The heart is an organ with areas of regional specialisation, composed of multiple cell types including cardiomyocytes, fibroblasts, endothelial and neuroendocrine cells. Expression profiles from diseased myocardium will therefore reflect the response from these different cells types as well as leucocytes recruited during inflammatory processes. Yang et al. [26] compared expression profiles from end stage heart failure patients suffering from ischaemic cardiomyopathy and dilated cardiomyopathy to non-diseased myocardium. The authors argued that comparing end-stage heart failure myocardium with different underlying aetiologies would identify shared expression profiles that may be fundamental to the failing myocardium. Only 12 of 7000 arrayed genes were identified with similar expression changes in both types of heart failure in addition to five genes expressed uniquely in failing and two genes expressed in non-failing hearts. Shared changes included functional themes such as reduced expression of the structural and contractile proteins β-actin and striated muscle LIM protein-1. It is not clear to what degree the lack of similarity between heart failure specimens represented different underlying disease aetiology, severity or other variables. Expression profiles are meaningful only in the context of experimental conditions in which they have been measured. In order to apply this technology to human specimens, new methods of detailed characterisation and annotation of the clinical background will be needed to compare results between centres.

3.3 Cardiac hypertrophy
Cardiac hypertrophy is characterised by alterations in cardiomyocyte metabolism, contractile proteins and the extracellular matrix. It may result from a familial disorder or occur as an adaptive response, following stresses such as haemodynamic overload and myocardial infarction (MI). Hwang et al. [27] compared EST frequency between three pooled human hypertrophic ventricle cDNA libraries with more than 70 000 myocardial ESTs generated from human fetal, adult and hypertrophied hearts. The established association with high levels of natriuretic peptide expression (atrial natriuretic peptide and brain natriuretic peptide) was observed in all three hypertrophic samples. In addition there was a striking up-regulation of genes associated with the cellular response to injury including a heat shock protein, -β-crystallin, not previously implicated in hypertrophy. Only 64 genes were identified as being potentially overexpressed, demonstrating the limitation of large scale EST sequencing to detecting statistically significant changes only in moderately abundant transcripts. The Toronto group used this extensive EST library to create a large ‘Cardiochip’ cDNA array to probe samples from patients with hereditary hypertrophic obstructive cardiomyopathy [28]. Genes for tropomyosin and thymosin beta4, a regulator of actin polymerization, were differentially expressed in keeping with disruption of the sarcomere and cytoskeleton in this disorder. Although the contribution of these expression changes to function and phenotype remains to be elucidated the potential for detailed characterisation of patients with this disorder was demonstrated.

3.4 Myocardial infarction
Rodent models of MI result in inflammatory and fibrotic repair responses within necrotic myocardium in addition to compensatory hypertrophy of the remaining viable ventricle. Stanton et al. [29] examined expression of 7000 cDNAs in the infarcted left ventricular (LV) free wall and interventricular septum at five different time points after surgical induction of MI in the rat. Approximately 7000 clones were isolated from a rat LV cDNA library and probed with fluorescently labelled cDNA from both sites, at each time point in infarction and sham operated animals. The authors chose an arbitrary threshold of 1.8-fold increase or decrease in expression and sub-arrayed these genes for repeat analysis. Over 700 genes were shown to have reproducible patterns of differential expression. In addition to identifying genes previously described as having altered expression in myocardial infarction, new genes were implicated in the processes of repair and remodelling. That no gene exhibited unique changes in expression within the interventricular septum compared to the LV free wall was surprising considering that they were subjected to different stress environments. The effectiveness of clustering expression profiles in time and magnitude of response to provide clues to the roles of poorly characterised genes was demonstrated. Osteoblast-specific factor-2 was thought to be unique to osteoblasts. This gene had heightened expression in a cluster with collagen, laminin and fibronectin suggesting a role in the matrix deposition and remodelling occurring after infarction. Finally, the reduced expression of several enzymes involved in the β-oxidation pathway provided a possible insight into the metabolism of injured myocardium, moving away from fatty acid substrates. Models of reversible ischaemia or ischaemia–reperfusion injury allow assessment of early changes occurring before the onset of necrosis. An intriguing theme of cytoprotective gene expression was demonstrated using subtractive hybridisation within the myocardium following transient coronary occlusion in pigs [30]. Anti-apoptotic factors and heat shock proteins were expressed along with growth factors that may act as survival signals. The profile is no more than a catalogue of expression events but it provides insight into the critical balance that exists between life and death following ischaemia–reperfusion and begs the question whether augmentation of survival factors improve functional outcome following ischaemia.

Models of cardiac hypertrophy and failure are characterised by reexpression of fetal genes. However, the consequences of gene expression during development are very different from those initiated following injury. Sehl et al. [31] used microarrays to compare expression profiles during myocardial development and infarction. The arrays consisted mainly of cDNA clones produced by subtractive hybridisation of sham operated and MI samples. This approach has the advantage of enriching the array for differentially expressed genes, decreasing the examination of multiple housekeeping or ‘non varying’ genes [32]. In addition, the subtractive technique generates clones from any part of the mRNA and allows identification of differentially expressed genes that have not been cloned previously. For developmental profiling, embryonic and 1-day old neonatal myocardial samples were compared with healthy adult rat myocardium. Ventricular tissue was harvested at multiple time points after coronary artery ligation and compared to sham operated tissue for stress response profiling. The greatest contrast was seen when comparing neonatal with adult myocardium and, as expected, this reflected higher expression of signal transduction and growth regulatory proteins such as p21 in the developing heart. Twelve genes previously described as being expressed during myocardial development were identified together with ten uncharacterised ESTs and 36 genes not previously associated with cardiac development. Although the array consisted largely of cDNA clones derived by subtractive hybridisation of infarcted myocardium only 63 of the 989 clones representing 14 different genes had demonstrable differential expression following myocardial infarction. Previously described shared gene expression patterns between cardiac development and injury, including increased atrial natriuretic peptide, fibronectin and collagen III expression were confirmed. However, these were in a small subset and the discordant regulation of the majority of genes suggested that a shift to the fetal gene program is not a general characteristic in failing hearts.

No information was given on the structural or inflammatory cell types contributing to the changes in gene expression within the myocardial tissue in these models of myocardial ischaemia and infarction. In addition, the array used by Stanton et al. [29] was biased against genes expressed in infiltrating leucocytes or genes with very low basal expression levels because the arrayed genes were taken from a normal LV cDNA library that would not represent genes from these cell populations. This could be why monocyte expression of cathepsin B following MI was detected by Sehl et al. [31] but not in the former study.

Expression profiles from specific cell types, harvested from tissues, can be obtained using laser capture microdissection [33,34]. Laser capture allows precise identification, dissection and retrieval of pure cell populations that are more reflective of the disease process in vivo. Luo et al. [34] compared gene expression in large and small neurons within rat dorsal root ganglia with RNA extracted from 1000 captured neurons of each size. The small quantity of RNA generated required further linear amplification with T7 RNA polymerase before hybridisation. The investigators were able to demonstrate reproducible patterns of gene expression, using microarrays that were subsequently confirmed by immunohistochemistry. One possible application in the cardiovascular field would be the selection of macrophage foam cells from atherosclerotic plaque specimens.

	4. Conclusions: cautious optimism

Top Abstract 1. Introduction 2. Methods used for... 3. Applications of gene... 4. Conclusions: cautious... References

 
The interpretation of data from any expression profiling technique requires caution. The risk of false positive signals or bands is high. In microarray studies this may arise through cross hybridisation of homologous or conserved repeat sequences. Lee et al. [35] demonstrated that for a specified cDNA sample the ability of a DNA chip to distinguish correctly the presence of any sequence was 90% per run. Costly repeat analysis of the same sample is therefore required and ideally all findings should be confirmed in a separate system such as Northern blotting. Secondly, attaching functional significance to changes in gene expression level alone is difficult. No information is provided on post-translational modifications or the rate of protein degradation. An alteration in expression and protein synthesis of any given gene may represent an adaptive, compensatory response or contribute to disease progression. Profiling merely provides a further level of characterisation for pathophysiological processes without offering information on the role of individual genes. Finally, levels of mRNA do not always correlate with protein synthesis. Studies in yeast indicate that expression of important growth regulatory genes may not vary between quiescent and proliferative conditions [36]. Expression analysis is complicated further by the fact that functionality and not protein level is the critical issue. If receptors or ligands are expressed but not functional, the value of gene expression and protein analysis is disputable and may be misleading.

Bearing these caveats in mind, the potential uses for systematic expression profiling on a genome wide scale in cardiovascular research are clear. Further studies will enhance our understanding of the relationship between expression level and functional consequence for individual genes. Computational approaches such as clustering, outlined above, will allow some degree of functional interpretation through linking the expression of unknown or poorly characterised genes with better known ones and through identifying patterns of genes regulating certain functions such as signalling or metabolic pathways. Profiling information provides a rich source of additional questions and hypotheses. Further experiments are often required to examine whether expression profiles translate from in vitro to in vivo models or from transgenic mice with a certain disease phenotype to diseased human tissue. Association of particular profiles with specific disease phenotypes may have future diagnostic [37] or prognostic value as well as allowing the design of specific experiments to examine in detail, the functional roles of individual genes within these profiles. The enormous amount of data produced by profiling studies poses challenges for publication and journals frequently use supplemental web pages to fully release data. The development of expression databanks akin to the sequence databanks already available will facilitate more rapid comparisons between cells and tissues in health and disease. The Stanford Microarray Database is endeavouring to provide an online, searchable, public interface for the dissemination of microarray studies [38]. Such enhanced facilities for the comparison and pattern recognition of gene expression profiles will give researchers powerful tools for gaining further insights into gene function.

Time for primary review 28 days.

	Acknowledgements

 
Dr Peter Henriksen is supported by a Wellcome Trust Clinical Training Fellowship. Thanks are due to Dr D. Newby and Dr J.-M. Sallenave for helpful comments during preparation of this manuscript.

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