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(Circulation. 2003;108:1845.)
© 2003 American Heart Association, Inc.

Clinical Investigation and Reports

Physiological Genomics of Human Arteries

Quantitative Relationship Between Gene Expression and Arterial Stiffness

Séverine Durier, PhD^*; Céline Fassot, PhD^*; Stéphane Laurent, MD, PhD; Pierre Boutouyrie, MD, PhD; Jean-Paul Couetil, MD; Erika Fine, MD; Patrick Lacolley, MD, PhD; Victor J. Dzau, MD; Richard E. Pratt, PhD

From the Laboratory of Physiological Genomics, Department of Medicine (V.J.D., R.E.P.), Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass; the Department of Pharmacology and INSERM EMI 107 (S.D., C.F., S.L., P.B., E.F., P.L.) and the Department of Cardiac Surgery (J.-P.C.), Hôpital Européen Georges Pompidou, Paris, France

Correspondence to Richard E. Pratt, PhD, Department of Medicine, Brigham and Women’s Hospital, 75 Francis St, Boston, MA 02115. E-mail rpratt{at}rics.bwh.harvard.edu

Received November 18, 2002; de novo received May 22, 2003; revision received July 24, 2003; accepted July 26, 2003.

	Abstract

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Background— Previous genomic studies with human tissues have compared differential gene expression between 2 conditions (ie, normal versus diseased) to identify altered gene expression in a binary manner; however, a potentially more informative approach is to correlate the levels of gene expression with quantitative physiological parameters.

Methods and Results— In this study, we have used this approach to examine genes whose expression correlates with arterial stiffness in human aortic specimens. Our data identify 2 distinct groups of genes, those associated with cell signaling and those associated with the mechanical regulation of vascular structure (cytoskeletal–cell membrane–extracellular matrix). Although previous studies have concentrated on the contribution of the latter group toward arterial stiffness, our data suggest that changes in expression of signaling molecules play an equally important role. Alterations in the profiles of signaling molecules could be involved in the regulation of cell cytoskeletal organization, cell–matrix interactions, or the contractile state of the cell.

Conclusion— Although the influence of smooth muscle contraction/relaxation on arterial stiffness could be controversial, our provocative data would suggest that further studies on this subject are indicated.

Key Words: arteries • extracellular matrix • phosphoprotein phosphatase • proteins, cytoskeletal • genomics

	Introduction

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Cardiovascular disease is incompletely predicted by classic risk factors. Increased arterial stiffness can increase cardiovascular morbidity and mortality because of an elevation of systolic blood pressure, which raises left ventricular afterload, and because of a decrease in diastolic blood pressure, which alters coronary perfusion.¹ Arterial stiffness is a major determinant of pulse pressure, which predicts coronary disease, stroke, and cardiovascular events² independently of mean blood pressure, and is also an independent predictor of cardiovascular mortality.³

The molecules underlying the process of arterial stiffening and, specifically, the identification of molecules that can contribute quantitatively to arterial stiffness are still largely unknown. Components of the extracellular matrix, the structure of the matrix, and cell–matrix interactions are thought to be major determinants of arterial stiffness. In contrast, the contribution of various intracellular signaling cascades toward the regulation of arterial stiffness is controversial; however, it is known that treatment with various reagents that influence signaling, such as NO donors,⁴ inhibitors of the renin angiotensin system,⁵ or calcium channel blockers,⁶ can lead to changes in arterial stiffness.

Arterial stiffness can be assessed quantitatively by measurement of pulse wave velocity (PWV³). In the present study, we determined the expression profiles of the human aorta and have identified several genes that were differentially expressed between patients with increased aortic stiffness and patients with distensible aorta. Importantly, we have identified transcripts whose abundance correlates significantly either positively or negatively with PWV.

	Methods

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Subjects
Patients scheduled for bypass surgery for multivessel coronary disease were recruited at Hôpital Broussais. All subjects gave prior informed consent, and the study was conducted according to the guidelines of the ethics committee of the institution. We excluded patients having confounding disease such as dilated cardiomyopathy, valvular disease, connective tissue disease, or aortic aneurysm. A puncture of the ascending aorta was collected during bypass surgery (one sample per patient). Seven samples, chosen randomly, were used for pilot studies to assess RNA yield and quality and for morphological examination to determine the degree of atherosclerosis at the sampling site.

PWV Measurements
PWV (meters/second) was measured along the descending thoraco-abdominal aorta using the foot-to-foot velocity method, as previously published and validated.³ In a multivariate analysis of the 27 patients of the study, age and mean blood pressure were independently related to PWV (P=0.017 and P=0.052, respectively; probability value of the model: 0.009). In stepwise regression analysis, age explained 20.6% (R-squared increment) and mean blood pressure explained 11.7% of the variance of PWV. We adjusted PWV on age and mean blood pressure using a general linear model to determine in a stepwise manner the multivariate ß coefficients and to calculate the predicted values based on the model.

The adjusted PWV values were split into tertiles. Quantitative variables were compared between tertiles by means of an analysis of variance, and categorical variables by means of a ² test. A value of P<0.05 was considered significant. Data are expressed as mean±SD. The statistical analysis was performed using NCSS 6.0 package software.

RNA Extraction and Analysis
RNA was extracted by TRIzol reagent. Transcript profiling with Affymetrix GeneChip (Affymetrix, Santa Clara, Calif) was performed using U95Av2 arrays according to standard Affymetrix protocols.^7–9 Nine samples were isolated with sufficient yield and integrity for complete analysis.

Fluorescent intensities were analyzed with Affymetrix MAS 4.01. The raw data have been deposited at the Gene Expression Omnibus (GEO) web site¹⁰ (accession number GSE420). After global scaling to a target intensity of 2500, all average difference values below zero were brought to zero. Each gene was graded as "present," "marginal," or "absent" by the MAS 4.01. For these studies, marginal calls were regarded as present. The percent transcripts called present in the stiff and distensible groups were not different (27.6±3.2%, distensible; 31.4±2.1% stiff; P=not significant). Analysis was performed using the Affymetrix software package MAS 4.01 and Microsoft Excel 2000 (Microsoft Inc., Redmond, Wash) as described in Figure 1, A and B.

View larger version (18K): [in this window] [in a new window]   Figure 1. Scheme for analysis of profiling data. A, Updated probe descriptions and Gene Ontogeny classifications were obtained from the Affymetrix web site.46 This collection was then filtered for gene annotations indicating potential involvement in mechanical properties (eg, cytoskeleton, membrane adhesion proteins, and extracellular matrix). Further filtering was performed as indicated and as described in Methods. B, The filtering scheme for the entire dataset. Filtering was performed as indicated and as described in Methods.

	Results

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Patients
In multivariate analysis of the study population, age and mean blood pressure were significantly and positively associated with PWV (P<0.05). After adjustment of PWV values on these confounding factors, the 27 patients were split into tertiles: lowest PWV ("distensible" aorta), highest PWV ("stiff" aorta), and medium values (Data Supplement Table 1). As expected, none of the parameters differed between groups, except PWV. Then, according to the quantity and quality of the extracted RNA and the labeled targets for the microarray hybridization, 9 patients were selected, including 4 patients with distensible aorta, 3 patients with stiff aorta, and 2 patients with medium values.

Analysis of Genes Involved in Mechanical Regulation of Vessel Structure
We first tested the hypothesis that genes encoding cytoskeletal proteins, transmembrane proteins (eg, integrins), and extracellular matrix proteins (Figure 1A) would be related to PWV. The results (Data Supplement Table 2) indicated that 32 distinct probe sets passed one of the three filtering criteria. Of these, the majority were related to cytoskeleton (n=14), with the remainder distributed between matrix (n=9) and membrane proteins (n=9). Eighteen transcripts were greater in the stiff arteries, whereas 14 were greater in the distensible arteries. Differentially expressed genes involved in mechanical regulation of vessel structure included integrins (_2b, ₆, ß₃, and ß₅), proteoglycans (decorin, osteomodulin, aggrecan-1, and chondroitin sulfate proteoglycan-5 [neuroglycan-C]), fibulin-1, fascin, and thrombospondin.

Overall Analysis of Gene Expression
The complete dataset was filtered to remove transcripts present in fewer than 3 samples and those with a coefficient of variation of <0.6, yielding 925 transcripts. Among the genes selected, a significant correlation coefficient (>0.6 corresponding to P<0.05) was observed for 24 transcripts (Figure 1B; Data Supplement Table 3). Nineteen transcripts were positively correlated (r0.6) with PWV, whereas 5 transcripts were negatively correlated (r-0.6) (Figure 2). Of the 19 genes with known or suspected biological functions, 7 were classified as "signaling/communication" and 7 were classified as "gene/protein expression." Examples of the former groups are protein phosphatase-1, catalytic subunit, ß isoform (PPP1CB), Yotiao, and A kinase (PRKA) anchor protein that targets PPP1CB. Both of these were negatively correlated with PWV and were dramatically downregulated in the stiff vessel wall (PPP1CB, 72% decrease; Yotiao, 83% decrease). Another transcript, phosphoinositide-3-kinase, regulatory subunit, polypeptide-1 (p85-), was positively correlated (CC=0.86) and upregulated >3-fold.

View larger version (20K): [in this window] [in a new window]   Figure 2. Demonstration of correlations between PWV and expression values. The correlation coefficient between expression values and PWV was calculated and those genes exhibiting coefficients >0.6 or <-0.06 are indicated in Data Supplement Tables 2 and 3. Several examples were plotted in panels A through G.

We examined the gene list for transcripts exhibiting an "all-or-none" phenomenon. Eighty-three transcripts were shown to be 100% absent in one condition and present in more than two thirds of the samples in the other condition; of these, 67 were "absent–>present" (comparing distensible to stiff) and 16 were "present–>absent" (comparing distensible to stiff). Of the 65 genes with known function, those classified as signaling/communication (27 genes) and gene expression (16 genes) again made up approximately two thirds of the samples. Within the former group, three transcripts with increased expression in the stiff vessels could be classified as being within the G protein–coupled receptor signaling pathways, synaptojanin, PKC ß₁, and regulator of G protein signaling-16 (RGS16).

Functional Classification of Differentially Expressed Genes
We analyzed the functional categories of the genes present in Data Supplement Tables 2 and 3. One hundred forty-six transcripts were differentially expressed between the groups, by virtue of being correlated, having a P<0.05, or showing an all-or-none phenomenon. Of these, 121 could be classified into 9 different functional categories¹¹ as shown in Figure 3. Nearly half of the annotated transcripts could be classified as signaling/communication (28%) or gene expression (19%). As a group, transcripts encoding proteins potentially involved in the mechanical regulation of vascular function (cytoskeleton, matrix, membranes, and so on) constituted 29% of the annotated transcripts.

View larger version (52K): [in this window] [in a new window]   Figure 3. Categorization of genes exhibiting differential expression with arterial stiffness. The genes from Data Supplement Tables 2 and 3 were combined and the distribution of genes into the indicated categories was calculated.

The filtering stringency used to generate Data Supplement Table 3 (Figure 1B) was more stringent than that used for Data Supplement Table 2 (Figure 1A). Four genes were found in both tables, A kinase anchor protein, aggrecan-1, retinoschisis-1, and chondroitin sulfate proteoglycan. If the distribution of gene classifications were calculated on the basis of Data Supplement Table 3 alone, the percent representation in "signaling/communication," "gene expression," "metabolism," "defense," and "cell growth" would increase proportionally.

Limitations of the Study
In the Affymetrix GeneChip system, a given gene is represented by more than one oligonucleotide on the chip.⁸ This provides a number of repeat experiments within a single chip to ensure the validity of hybridizations between probe and target, providing a high degree of precision. The small size of the aortic biopsy sample precluded further investigation of genes differentially expressed between the groups because the entire RNA sample was used for profiling. Nevertheless, we are confident in the validity of the composition of the gene lists. In another study using human vascular samples from failed peripheral grafts (J. Lehtonen et al, Brigham and Women’s Hospital, Boston, Mass, unpublished data, 2002), we demonstrated correlation coefficients of 0.95 to 0.97 with a single RNA sample assayed 3 times. Moreover, of 4 genes that were assayed across 4 conditions (16 comparisons) by real-time polymerase chain reaction, all exhibited profiles of expression consistent with the profiles observed by Affymetrix GeneChips. This high degree of accuracy and precision of Affymetrix GeneChip analysis has been reported by others.^7,12

Although the number of samples was small, we used a rather strict set of filtering criteria. Transcripts were called "differentially represented" only if they passed several independent filters—for example, a statistically significant correlation coefficient, or P<0.1 and an all-or-none pattern in which the lower abundance group was required to be 100% absent and the higher abundance group was required to be >66% present. Because the groups were small, this meant that transcripts had to be present in either two thirds or three fourths of the samples. For transcripts that were not all or none or not significantly correlated, we imposed a stricter criteria, P<0.05 and present in at least half of the samples of the higher abundance group. Thus, the probability that these transcripts were associated with aortic stiffness only by chance was low. Nevertheless, we acknowledge that the conclusions drawn must be viewed cautiously because they are based on 9 samples. The correlation study is limited by the fact that only a few samples are present at the ends of the spectrum (very distensible or very stiff). Moreover, we are aware that different statistical analysis could, in fact, yield different conclusions. Finally, we acknowledge that the use of the strict filtering criteria will likely result in false-negatives.

Some heterogeneity in sampling of the aortic punches should be acknowledged. However, this heterogeneity was likely limited, because regions with extensive atherosclerosis were avoided during the sampling. Indeed, in the first 4 patients, the biopsies that we collected were fixed, embedded, and examined histomorphometrically. The results demonstrated that the extent of atherosclerosis in the punch biopsies was small despite extensive atherosclerosis at peripheral arterial sites (data not shown). Although limited, the variations in the extent of atherosclerosis at the sampling site could explain differences in gene expression between patients. Recent studies, indeed, show a strong correlation between PWV and subclinical atherosclerosis.^13,14 Among the identified genes of the present study, several could be directly implicated in atherosclerosis such as decorin,¹⁵ protein kinase ß₁,¹⁶ catenin,¹⁷ and integrins _vß₃, triggering activation of PI3 kinase.¹⁸

	Discussion

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The present study is the first study in humans in which expression profiles in tissue isolated from individual patients are correlated with a clinically relevant cardiovascular phenotype. Thirty-five genes were shown to exhibit patterns of expression that correlated with PWV, a measure of aortic stiffness. Among these are protein phosphatase-1, catalytic subunit, ß isoform (PPP1CB), also known as the catalytic subunit of myosin light chain phosphatase,¹⁹ and Yotiao, an A kinase (PRKA) anchor protein.^20,21 PPP1CB is part of a trimeric complex with MYTP1, a large 130-kDa noncatalytic regulatory/targeting subunit and a protein of unknown function, M20.²² Both of these other components are represented on the Affymetrix U95Av2 chip, were expressed in the vessel, and exhibited a nonsignificant decrease in expression (data not shown). The regulation of activity and cellular location of this heterotrimer is complex and apparently dependent on vasoconstrictor stimulation.^22,23 PPP1CB dephosphorylates myosin light chain, leading to breakdown of cross-bridge formation and reduced contractile force. PCC1CB mediates, in part, NO-dependent vasodilation.¹⁹ Yotiao interacts with PPP1CB, resulting in targeting of PPP1CB to the cell membrane where regulation of receptors or channels can occur.²⁰

Other transcripts that exhibited strong correlations could also be related to PWV. Phosphoinositide-3-kinase, regulatory subunit, polypeptide-1 (p85-) demonstrated a strong, positive correlation and was upregulated over 3-fold. p85 associates with phosphorylated receptor tyrosine kinases, PI3 kinase catalytic subunit p110 and with IRS-1/2, and is thought to act as a scaffolding or adaptor protein to target p110 to the cell membrane.²⁴ Originally implicated in growth regulation, it is now known that PI3K has broader actions and is involved in the signaling pathway of vasoconstrictors such as Ang II,^25,26 activates smooth muscle calcium channels,²⁷ and is involved in cell adhesion and cytoskeletal organization through activation of focal adhesion kinase.^28,29 Moreover, in isolated smooth muscle strips, inhibition of PI3K leads to relaxation.³⁰ Expression of the catalytic subunits of PI3K could also be detected (phosphoinositide-3-kinase, catalytic, polypeptide, and phosphoinositide-3-kinase, catalytic, ß polypeptide), but these were not significantly different between groups.

Several genes were also expressed in an all-or-none pattern. Protein kinase C ß₁ is found in this group and is among the genes with the highest fold difference between the distensible and stiff vessels. PKC ß₁ plays a role in muscle contraction and has been reported to be involved in long-term, sustained contractions.^31,32 Thus, PKC ß could be an important determinant in arterial stiffness. Another example within this group is synaptojanin-1, polyphosphoinositide phosphatase, which is also among the highest upregulated transcripts. Originally described in presynaptic neurons and responsible for normal synaptic vesicle recycling, it has recently been shown to play a role in actin rearrangement.³³ Expression of synaptojanin in COS-7 cells leads to a decrease in stress fiber assembly,³⁴ suggesting that synaptojanin expression in the stiff vessels could be a compensatory response.

Cell–Matrix Interactions
We tested the hypothesis that the connection between the cytoskeleton, cell surface proteins, and extracellular matrix would play a major role in arterial stiffness; 32 transcripts were called "differentially expressed" by virtue of a strong correlation, a significant probability value, or an all-or-none pattern of expression. Several of these proteins could play a role in arterial stiffness. Indeed, integrins act as mechanoreceptors and transmit mechanical signals to the cytoskeleton.³⁵ Integrins and proteoglycans, through their interaction with extracellular matrix components and growth factors, are important molecular determinants in providing specificity for signaling.^36–39

In the present study, integrins _2b, ₆, ß₃, and ß₅ transcript levels were different between stiff and distensible aortas. The _2b and ₆ integrin transcripts were present in all samples from stiff aortas and absent in all samples from distensible aortas. Integrin ₆ forms heterodimers with a ß₁ or a ß₄ integrin subunit, binds to laminin, and plays a role in the regulation of smooth muscle cell phenotype.³⁷ Interestingly, ₆ integrin has not yet been reported in aortic smooth muscle. The ß₅ integrin transcript was less abundant in stiff aortas than in distensible aortas. The ß₅ subunit of the integrin receptor, generally coupled with the _v subunit, forms a heterodimer involved in cell adhesion to the matrix proteins vitronectin and osteopontin.³⁷ Vitronectin and osteopontin gene transcripts were among the most abundant ones in the present study but were not differentially expressed between stiff and distensible aortas.

Proteoglycans and Related Proteins
Among the transcripts exhibiting differential abundance between stiff and distensible aortas (Data Supplement Table 2), 4 transcripts belonged to the proteoglycan family (decorin, osteomodulin, aggrecan-1, and chondroitin sulfate proteoglycan-5 [neuroglycan-C]) or related proteins (dermatopontin, a decorin-binding proteoglycan). Dermatopontin, neuroglycan-C, and osteomodulin gene transcripts were less abundant in stiff aortas compared with distensible aortas, whereas the decorin and aggrecan transcripts were more abundant.

The mechanisms through which these different profiles could be associated with an increased aortic stiffness are likely very complex. Proteoglycans organize the extracellular matrix, particularly the collagenous network, act as signaling molecules, and are involved in cell migration and proliferation.³⁶ Dermatopontin associates with the small proteoglycan decorin and regulates cell proliferation and collagen assembly.^36,40 Particularly, the decreased expression of dermatopontin in fibroblast cultures from the skin of patients with systemic sclerosis has been related to the pathogenesis of fibrosis in hypertrophic scars.⁴⁰ Interestingly, aortic stiffness is increased in patients with systemic sclerosis.⁴¹ Data on proteoglycans and arterial stiffness are scarce. Carotid distensibility is marginally increased in pseudoxanthoma elasticum (PXE), a disease characterized by an early accumulation of heparan sulfate proteoglycans into large arteries.^42,43 In spontaneously hypertensive rats, the increase in arterial stiffness in response to a high-sodium diet and/or a diuretic was related to the decrease in hyaluronan content.⁴⁴ In rat mesenteric arteries, partial removal of chondroitin–dermatan sulfate-containing glycosaminoglycans from the arterial wall increased vascular stiffness.⁴⁵

In conclusion, we have shown that it was possible to identify genes that are differentially expressed between stiff and distensible human aortas using the microarray procedure. The proteins encoded by these genes such as signaling molecules and proteoglycans could play a role in arterial stiffness through either cell–matrix interactions or enhanced contractile signaling pathways. Moreover, these studies have identified many transcripts of unknown function that could participate in regulation of arterial stiffness, emphasizing the need for further functional genomic studies.

	Footnotes

 
The online-only Data Supplement is available at http://www.circulationaha.org.

*The first 2 authors contributed equally to this work.

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