Genomics of varicose veins and chronic venous insufficiency

Genomics of varicose veins and chronic venous insufficiency

SE M I N A R S I N V A S C U L A R SU R G E R Y 26 (2013) 2–13 Available online at www.sciencedirect.com www.elsevier.com/locate/semvascsurg ...

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Available online at www.sciencedirect.com

www.elsevier.com/locate/semvascsurg

Genomics of varicose veins and chronic venous insufficiency Jovan N. Markovicn, and Cynthia K. Shortell Department of Vascular Surgery, Duke University School of Medicine, Box 3538, Durham, NC 27710

abstract Recent sequencing of the human genome has opened up new areas of investigation for genetic aberrations responsible for the pathogenesis of many human diseases. To date, there have been no studies that have investigated the entire human genome for the genetic underpinnings of chronic venous insufficiency (CVI). Utilizing Gene Chip Arrays we analyzed the relative expression levels of more than 47,000 transcripts and variants and approximately 38,500 well-characterized genes from each of 20 patients (N (CVI)=10; N (Control Group)=10). Relative gene expression profiles significantly differed between patients with CVI and patients unaffected by CVI. Regulatory genes of mediators of the inflammatory reaction and collagen production were up-regulated and down-regulated, respectively in CVI patients. DNA microarray analysis also showed that relative gene expression of multiple genes which function remains to be elucidated was significantly different in CVI patients. Fundamental advancements in our knowledge of the human genome and understanding of the genetic basis of CVI represents an opportunity to develop new diagnostic, prognostic, preventive and therapeutic modalities in the management of CVI. & 2013 Elsevier Inc. All rights reserved.

1.

Introduction

Chronic venous insufficiency (CVI) affects approximately 10% to 35% of the US adult population [1]. An estimated 25% and 15% of women and men, respectively, have varicose veins, and approximately 1% to 4% of the population is affected either by a C5 or C6 (CEAP [2] classification) disease [3,4]. Manifestations of venous insufficiency range from mild discomfort to chronic ulceration, and disability from this disorder varies accordingly from mild to severe [5–7]. When it progresses to the advanced stage, superficial reflux disease not only reduces quality of life, but is associated with considerable health care cost. In the United States, the estimated annual cost for the management of venous ulcers is between $1.9 and $2.5 billion [8]. Current experimental investigations attempt to develop a unifying theory regarding the pathophysiology of venous reflux n

Corresponding author. E-mail address: [email protected] (J.N. Markovic).

0895-7967/$ - see front matter & 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1053/j.semvascsurg.2013.04.003

and the manifestations of venous hypertension and CVI [9,10]. It has been shown that once a condition of venous hypertension has developed, the consequent hemodynamic disruption causes the failure of the venous valves via a blood flow −mediated inflammatory reaction, with activation of leukocytes, diapedesis into the venous parenchyma, release of enzymes, and remodeling of the vascular wall, ending in venous valve destruction and incompetence [11–13]. Data from several histochemical studies have demonstrated that extensive extracellular matrix remodeling with decreased elastin content and altered molecular differentiation of collagen and glycosoaminglycan, as well as increased vascular endothelium growth factor expression, were associated with venous insufficiency [14–16]. Other studies have suggested that overexpression of acidic fibroblast grow factors in varicose veins wall via fibroblast grow factors receptor and the mitogen-activated protein kinase pathways can influence expression of enzymes

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involved in extensive extracellular matrix remodeling and play an important role in pathogenesis of CVI [17]. Overexpression of inducible nitric oxide synthase and transforming growth factor−β1, as well as increased presence of CD68þ monocyte/ macrophages, has also been documented in patients with varicose veins [18]. Although these molecular mechanism and genetic aberrations are suspected to be the underlying etiology, the exact molecular mechanism responsible for development of CVI remains to be elucidated. Relatively recent sequencing of the human genome has opened up new areas of investigation for pathophysiological processes at the cellular and molecular levels for many human diseases. To date, limited information has been available about the genetic underpinnings of CVI. In our most recent study, our objective was to identify a distinct genomic profile of superficial reflux disease that can serve as the first step toward development of true genetic-based diagnostic and/or therapeutic modalities for CVI patients and to identify metabolic pathways regulated by differently expressed genes [19,20]. Our hypothesis is that the difference between the normal and varicose veins can be defined in terms of differential gene expression. From the best of our knowledge, this was the first study that screened all genes of the entire human genome in order to indentify genetic aberrations responsible for pathogenesis of CVI.

free of surrounding GSV. To achieve maximal homogeneity among analyzed groups (excluding the disease that is analyzed) and in order to assist in controlling for confounding variables, only RNA from the tributary veins from both patient cohorts was extracted and used for analysis. Vein samples were weighed and cut to obtain approximately 15 to 20 mg of RNAlater-stabilized tissue, minced, and homogenized with Omni TH Tissue Homogenizer (Omni International, Inc, Marietta, GA). Total RNA was extracted using the standardized RNeasy Mini Kit (Qiagen, Germantown, MD) protocol. Purified RNA was stored at −701C in RNAse-free water. Under these conditions, according to published data and manufacturer specifications, no degradation of RNA can be detectable for as long as 1 year or longer. Before gene expression analysis, 2 μL from each sample was used to assess the RNA concentration and quality using NanoDrop Spectrophotometery ND-8000 (Thermo Fisher Scientific, Wilmington, DE) and Agilent 2100 Bioanalyzer (Agilent Technologies, Inc, Santa Clara, CA). For quality, RNA samples were analyzed using Agilent 2100 Expert Software. As an indication of RNA quality, an RNA Integrity Number (RIN score) was generated for each sample on a scale of 1 to 10 (1 ¼ lowest; 10 ¼ highest). Tissue samples with RIN score >6.0 were used for microarray hybridization and statistical analysis. Figure 1 is a graphic representation of the RIN scores.

2.

Materials and methods

2.3. Microarray hybridization and statistical gene expression analysis

2.1.

Human tissue collection

The Duke University Health System Institutional Review Board approved the collection of the vein tissue samples and patient specific clinical data for all aspects of this study. One to 5-cm− long sections of residual varicose veins (the great saphenous vein [GSV] tributary) were collected from CVI patients undergoing the incisional phlebectomy of varicosities (n ¼ 10). Normal vein samples (for control group; n ¼ 10) were collected from patients unaffected by superficial reflux disease, undergoing coronary artery bypass graft (CABG) surgery (n ¼ 8) or femoropopliteal bypass surgery (n ¼ 2). CABG involved endoscopic harvesting of the GSV (used as a bypass conduit) and femoropopliteal bypass was performed using traditional, open harvest of the GSV. One to 2-cm−long segments of the GSV with tributary branches were collected from each CABG and femoropopliteal case. Directly after harvesting, vein samples were immediately placed in RNAlater reagent (Ambion, Austin, TX) to preserve the gene expression pattern and to protect cellular RNA from specific and nonspecific degradation in situ. From the operating room, RNAlater-stabilized samples were transported to the laboratory in RNAse-free test tubes and stored at 41C for 24 hours and then placed at −801C, where they remained until all samples were collected and the initiation of RNA extraction and analysis.

The RNA isolates were analyzed at the Microarray Facility of Duke Institute for Genome Science and Policy (Durham, NC). Gene expression data was obtained using the Affymetrix Human Genome U133 Plus 2.0 Arrays (Affymetrix, Santa Clara, CA). We used the newest generation of Gene Chip arrays, which enabled us to analyze the gene expression across the entire human genome for each vein sample. Currently, the U133 Plus 2.0 Arrays has >54,000 probe sets and analyzes the relative expression level of >47,000 transcripts and variants and approximately 38,500 well-characterized genes. Partek Genomics Suite 6.5 software (Partek Inc., St Louis, MO) was utilized to perform statistical data analysis. Expression values were computed from raw CEL files by applying the Robust Multi-Chip Average background correction algorithm. The Robust Multi-Chip Average normalization was done on the entire data set and included the following: values background correction; quantile normalization; log2 transformation; and median polish summarization. Multiway analysis of variance and fold change were performed to select target genes that were differentially expressed between patients with CVI and control group. Top differentially expressed genes were selected with a P value cutoff of o.05 based on analysis of variance test and a fold-change cutoff of 1.5. Hierarchical Clustering was performed on differentially expressed genes based on average linkage with Pearson dissimilarity.

2.2.

2.4.

RNA extraction and processing

Frozen vein samples from both groups of patients were removed from −801C storage, thawed, and prepared for analysis. In the control group, tributary veins were dissected

Gene set enrichment analysis

MetaCore (GeneGo, St Joseph, MI) was used to analyze function and pathways of differentially expressed genes. MetaCore is a manually curated web-based program for identification of Gene

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Fig. 1 – As an indication of RNA quality an RNA Integrity Number (RIN score) was generated for each sample. Sample # C2 (RIN 2.4) was excluded from the statistical analysis.

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Fig. 2 – Based on Principal Component Analysis (PCA) samples # C5 and #C6 were excluded from analysis because they deviated ignorantly from other samples of control group. The exclusion was done to control for potential confounding variables. Samples #C5 and #C6 were harvested from patients with peripheral arterial disease who underwent femoropopliteal bypass procedure.

Ontology relevant pathways, processes, and diseases. Pathways and processes are linked to relevant molecular data and are annotated with known information regarding disease involvement. GeneGo diseases ontology is created based on the classification in Medical Subject Headings. Each disease in GeneGo diseases ontology has its corresponding biomarker gene or a set of genes. Gene set enrichment analysis was performed for pathways, processes, and diseases and the output was ranked based on the P value (P o .05). Gene Ontology Enrichment analysis on the gene lists were performed with χ2 test and limited to functional groups with more than two genes.

2.5.

Exclusion criteria

A total of four samples were excluded from statistical analysis. Based on the Principal Component Analysis, two samples from the control group (#C5 and #C6) appeared to deviate markedly from other members of the control group (Fig 2) and were excluded as outliers. Interestingly, both of these two samples were harvested during two femoropopliteal bypass procedures. One vein sample (#C2) was excluded

based on the RIN criteria (RIN ¼ 2.4). Because two samples were collected from the same patient (different leg), we excluded one of them (#S3) from the statistical analysis after microarray gene analysis revealed almost identical genetic profile between these two vein samples.

3.

Results

The seven (43%) males and nine (57 %) females ranging in age from 47 to 71 years (mean 60.5 7 6.3 years) were included in the statistical analysis of the study. Demographic and clinical characteristics of the patients in CVI and control group are summarized in Table 1. Five (55.6%), three (33.3%), and one (11.1%) patient(s) had C3, C2, and C4 (CEAP) [2] class of CVI, respectively. All patients from the control group that were included in analysis underwent CABG (see exclusion criteria). There was no statistically significant difference in the age and sex among groups (P > .05) and sex and age did not influence gene expression profiles (Fig 3). Nineteen of of 20 (95%) samples met RIN > 6.0 criteria (Table 2).

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Table 1 – Patient’s demographics and clinical characteristics. Group

Patient

Age

Sex

Diagnosis

Procedure

CVI

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10

57 59 47 47 69 71 58 66 58 58

M M F F F F M M F F

CEAP CEAP CEAP CEAP CEAP CEAP CEAP CEAP CEAP CEAP

Incisional phlebectomy

C

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10

65 61 55 56 68 59 70 57 63 59

M F F F M M F M M M

CAD CAD CAD CAD PAD PAD CAD CAD CAD CAD

3 2 2 2 3 2 4 3 3 3

CABG CABG CABG CABG Fem-pop Fem-pop CABG CABG CABG CABG

C, control; CABG, coronary artery bypass graft; CAD, coronary artery disease; CVI, chronic venous insufficiency; F, female; Fem-pop: femoropopliteal bypass graft; M, male; PAD, peripheral arterial disease.

We found 6,501 genes that were differently expressed (P o .05) among two cohorts of patients (material available online). Further analysis within groups of differently expressed genes

revealed a subgroup of 34 genes that had 50% (1.5-fold) or more change in the gene expression levels between CVI and control group. Table 3 shows a detailed description (including

Fig. 3 – Based on Principal Component Analysis (PCA) demographic characteristic of patients included in the analysis did not influence different gene relative expression levels among groups.

Table 2 – Total RNA spectrometry, concentration, and quality assessment. Patient

QC from Agilent Bioanalyzer

Concentration fold difference

RIN

Dilution factor

Concentration (ng/uL)

Final concentration (ng/uL)

271.5 179.3 148.3 57.01 77.56 42.53 83.55 62.65 76.77 128.9 168.7 193.3 280.6 243.9 76.57 185.5 319.2 212.7 309.1 106.6

6.788 4.484 3.709 1.425 1.939 1.063 2.089 1.566 1.919 3.223 4.217 4.831 7.014 6.098 1.914 4.637 7.981 5.316 7.728 2.664

3.242 2.16 1.828 0.732 0.958 0.535 0.999 0.789 0.944 1.563 2.024 2.335 3.392 2.909 0.929 2.236 3.823 2.572 3.699 1.314

2.09 2.08 2.03 1.95 2.02 1.99 2.09 1.99 2.03 2.06 2.08 2.07 2.07 2.1 2.06 2.07 2.09 2.07 2.09 2.03

0.9 1.79 2 1.47 0.66 1.53 1.76 1.66 1.62 1.96 0.27 2.08 0.4 2.08 1.7 0.97 2.09 1.51 0.96 1.97

40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40

7.7 7.7 7.2 7.4 7.2 7.5 7.2 7.3 NA 8.5 6.6 2.4 6.4 6.7 7.6 7.6 7.5 7.5 7.4 6.4

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

118 80 76 21 33 19 51 26 34 58 71 NA 105 152 36 114 209 125 219 63

118 80 76 21 33 19 51 26 34 58 71 91 105 152 36 114 209 125 219 63

2.3 2.2 2.0 2.7 2.4 2.2 1.6 2.4 2.3 2.2 2.4 2.1 2.7 1.6 2.1 1.6 1.5 1.7 1.4 1.7

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Constant

R G E R Y

260/230

SU

260/280

A S C U L A R

A280

V

A260

M I N A R S I N

Concentration (ng/uL)

SE

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10

QC from NanoDrop Spectrophotometery

C, Control; NA, Not Available; RIN, RNA Integrity Number; S, sample (varicose veins).

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Table 3 – The relative gene expression analysis of patients with chronic venous insufficiency (P o .05; FC > 1.5). P Value

203913_s_at 211548_s_at 206100_at 210258_at 204337_at 1558101_at 235885_at 230550_at 220330_s_at 206638_at 214053_at 223541_at 217388_s_at 203504_s_at 231669_at 202236_s_at 203474_at 214693_x_at 208610_s_at 239609_s_at 202718_at 225288_at 202861_at 235496_at 211343_s_at 207570_at 223623_at 231628_s_at AFFX-DapX-5_at AFFX-r2-Bs-thr-5_s_at AFFX-r2-Bs-dap-5_at 1564378_a_at 242932_at 1568732_at

HPGD HPGD CPM RGS13 RGS4 NFIA P2RY12 MS4A6A SAMSN1 HTR2B ERBB4 HAS3 KYNU ABCA1 SEPP1 SLC16A1 IQGAP2 NBPF10 SRRM2 LPCAT4 IGFBP2 COL27A1 PER1 HRCT1 COL13A1 SHOX C2orf40 — — — — — — —

Hydroxyprostaglandin dehydrogenase 15 (NAD) Hydroxyprostaglandin dehydrogenase 15 (NAD) Carboxypeptidase M Regulator of G-protein signaling 13 Regulator of G-protein signaling 4 Nuclear factor I/A Purinergic receptor P2Y, G-protein coupled, 12 Membrane-spanning 4-domains, subfamily A, member 6A SAM domain, SH3 domain and nuclear localization signals 1 5-hydroxytryptamine (serotonin) receptor 2B V-erb-a erythroblastic leukemia viral oncogene homolog 4 (avian) Hyaluronan synthase 3 Kynureninase (L-kynurenine hydrolase) ATP-binding cassette, sub-family A (ABC1), member1 Selenoprotein P, plasma, 1 Solute carrier family 16, member 1 monocarboxylic acid transporter 1 IQ motif containing gtpase activating protein 2 Neuroblastoma breakpoint family, member 10 Serine/arginine repetitive matrix 2 Lysophosphatidylcholine acyltransferase 4 Insulin-like growth factor binding protein 2, 36kda Collagen, type XXVII, alpha 1 Period homolog 1 (Drosophila) Histidine rich carboxyl terminus 1 Collagen, type XIII, alpha 1 Short stature homeobox Chromosome 2 open reading frame 40 — — — — — — —

chr4q34-q35 chr4q34-q35 chr12q14.3 chr1q31.2 chr1q23.3 chr1p31.3-p31.2 chr3q24-q25 chr11q12.1 chr21q11 chr2q36.3-q37.1 chr2q33.3-q34 chr16q22.1 chr2q22.2 chr9q31.1 chr5q31 chr1p12 chr5q13.3 chr1q21.1 chr16p13.3 chr15q14 chr2q33-q34 chr9q32 chr17p13.1-p12 chr9p13.3 chr10q22 chrXp22.33;Yp11.3 chr2q12.2 — — — — — — —

1.79439 1.73692 1.72491 1.70264 1.68598 1.65325 1.63869 1.57244 1.56622 1.56485 1.53157 1.52434 1.52054 1.5177 1.51087 1.5104 1.50341 0.659434 0.657509 0.652814 0.652397 0.647327 0.624414 0.594792 0.580116 0.578779 3.32E-01 3.2779 1.77808 1.76057 1.74742 0.659418 0.638191 0.601486

1.79439 1.73692 1.72491 1.70264 1.68598 1.65325 1.63869 1.57244 1.56622 1.56485 1.53157 1.52434 1.52054 1.5177 1.51087 1.5104 1.50341 −1.51645 −1.52089 −1.53183 −1.53281 −1.54482 −1.6015 −1.68126 −1.72379 −1.72778 −3.0129 3.2779 1.77808 1.76057 1.74742 −1.51649 −1.56693 −1.66255

↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↑ ↑ ↑ ↑ ↓ ↓ ↓

.0338583 .0399245 .0077399 .0290644 .0491628 .00019 .0252026 .0139156 .0220866 .040767 .0119671 .0389574 .0321114 .0202106 .0077436 .0264602 .0145555 .00353 .0017223 .0164269 .0254208 .031299 .0217281 .0059104 .0289762 .0266798 4.62E-02 .0232543 .019176 .02906 .0265293 .0332289 .0160852 .0029386

↑, up-regulated; ↓, down-regulated; C, control group; FC, fold change; S, sample-chronic venous insufficiency group.

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↑↓

R G E R Y

FC

SU

Ratio (S/C)

A S C U L A R

Chromosomal zlocation

V

Gene title

M I N A R S I N

Gene symbol

SE

Probeset ID

SE

M I N A R S I N

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A S C U L A R

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Fig. 4 – Hierarchical clustering performed on differentially expressed genes (P o .05, fold change >1.5 criteria) based on average linkage with Pearson dissimilarity. Each column represents one patient. Rows represent genes. According to a log2 pseudocolor scale (bottom), red indicates a high level of messenger RNA expression, and blue indicates a low level of expression.

probe ID, gene symbol and name, chromosome locus, fold change) for genes that met P o .05 and >1.5-fold−change criteria. The relative gene expression analysis showed that 21 (61.8%) and 13 (38.2%) genes were up-regulated and downregulated, respectively in CVI patients (Fig 4). Results for seven genes were presented as expression sequence tags because the function of these genes is unknown. They are identified by Affymetrix Probeset ID and their relative expression levels. In this subgroup, four (57.1%) expression sequence tags were up-regulated and three (42.9%) expression sequence tags were down-regulated. Hydroxyprostaglandin dehydrogenase-15 (HPGD) gene with locus on chromosome 4 (q34-35) was detected to be up-regulated on two probes (1.79 and 1.74 relative expression fold change; mean 1.77 7 0.035). HPGD was also the highest ranked upregulated gene. Chromosome 2 Open Reading Frame 40 (C2orf40) gene was the highest ranked down-regulated gene, with 3.01 relative expression fold change. Using the GeneGo software program, pathway ontologic analysis was performed and demonstrated that arachidonic acid metabolic pathway was associated with the highest

enrichment score. This was followed by catabolic pathways of tryptophan, indole derivative and indol-alkylamine, and biosynthetic pathways of quinolinate and hyaluronan synthase activity. Detailed results of the top ranked metabolic pathways are shown in Table 4.

4.

Discussion

In addition to valvular incompetence and subsequent hemodynamic alterations that result in venous hypertension as a cause of various clinical manifestations of CVI, a generally accepted theory is that inflammatory process and weakness or alteration of the matrix components of the vein wall play an important role in the pathogenesis of CVI [21–23]. Historically, the majority of these studies, which investigated etiology of CVI, focused on hemodynamics, cellular, and molecular mechanism without trying to reveal genomics as the highest currently known regulatory mechanism for the disease development in general [24]. To this end, we analyzed the genes of the entire human genome in hopes of revealing

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Table 4 – Gene ontology enrichment analysis. S vs C score

No. of genes in list, in group

No. of genes not in list, in group

No. of genes in list, not in group

No. of genes not in list, not in group

GO ID

Arachidonic acid metabolic process Tryptophan catabolic process Quinolinate biosynthetic process Indole derivative catabolic process Indolalkylamine catabolic process Hyaluronan synthase activity Intracellular sterol transport Intracellular cholesterol transport Cdc42 protein signal transduction Lipid translocation Phospholipid translocation Phagocytic vesicle Cytokine secretion Phospholipid homeostasis Platelet dense granule organization Positive regulation of ion transport Secondary active monocarboxylate transmembrane transporter activity Insulin-like growth factor I binding Positive regulation of camp biosynthetic process Prostaglandin E receptor activity Endochondral ossification Response to interferon-gamma Quinolinate metabolic process Phosphotyrosine binding Adenosine receptor activity, Gprotein coupled Entrainment of circadian clock Secretory granule organization Apolipoprotein binding Indole and derivative metabolic Indole derivative metabolic process Prostanoid metabolic process Prostaglandin metabolic process Parturition NAD biosynthetic process Hemostasis

109.419

3.02E-48

33.33

1.43455

1.43455

1

2

24

16071

19369

109.419 109.419 109.419 109.419 109.419 82.4038 82.4038 82.4038 82.4038 82.4038 82.4038 82.4038 82.4038 82.4038

3.02E-48 3.02E-48 3.02E-48 3.02E-48 3.02E-48 1.63E-36 1.63E-36 1.63E-36 1.63E-36 1.63E-36 1.63E-36 1.63E-36 1.63E-36 1.63E-36

33.33 33.33 33.33 33.33 33.33 25 25 25 25 25 25 25 25 25

1.49334 1.49334 1.49334 1.49334 1.40941 1.69442 1.69442 1.69442 1.69442 1.69442 1.69442 1.69442 1.69442 1.69442

1.49334 1.49334 1.49334 1.49334 1.40941 1.69442 1.69442 1.69442 1.69442 1.69442 1.69442 1.69442 1.69442 1.69442

1 1 1 1 1 1 1 1 1 1 1 1 1 1

2 2 2 2 2 3 3 3 3 3 3 3 3 3

24 24 24 24 24 24 24 24 24 24 24 24 24 24

16071 16071 16071 16071 16071 16070 16070 16070 16070 16070 16070 16070 16070 16070

6569 19805 42436 46218 50501 32366 32367 32488 34204 45332 45335 50663 55091 60155

82.4038 66.1699

1.63E-36 1.83E-29

25 20

1.59855 1.57741

1.59855 1.57741

1 1

3 4

24 24

16070 16069

43270 15355

66.1699

1.83E-29

20

1.59481

1.59481

1

4

24

16069

31994

66.1699

1.83E-29

20

1.69442

1.69442

1

4

24

16069

30819

66.1699 66.1699 66.1699 66.1699 66.1699 66.1699

1.83E-29 1.83E-29 1.83E-29 1.83E-29 1.83E-29 1.83E-29

20 20 20 20 20 20

1.43455 1.53796 1.49334 1.49334 1.65587 1.59855

1.43455 1.53796 1.49334 1.49334 1.65587 1.59855

1 1 1 1 1 1

4 4 4 4 4 4

24 24 24 24 24 24

16069 16069 16069 16069 16069 16069

4957 1958 34341 46874 1784 1609

55.3308 55.3308 55.3308 55.3308 55.3308

9.34E-25 9.34E-25 9.34E-25 9.34E-25 9.34E-25

16.67 16.67 16.67 16.67 16.67

1.66298 1.69442 1.69442 1.49334 1.49334

1.66298 1.69442 1.69442 1.49334 1.49334

1 1 1 1 1

5 5 5 5 5

24 24 24 24 24

16068 16068 16068 16068 16068

9649 33363 34185 42430 42434

47.5768 47.5768 47.5768 47.5768 47.5768

2.18E-21 2.18E-21 2.18E-21 2.18E-21 2.18E-21

14.28 14.28 14.28 14.28 14.28

1.43455 1.43455 1.43455 1.49334 1.59855

1.43455 1.43455 1.43455 1.49334 1.59855

1 1 1 1 1

6 6 6 6 6

24 24 24 24 24

16067 16067 16067 16067 16067

6692 6693 7567 9435 7599

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GO, Gene Ontology.

SU

Group score

A S C U L A R

% Genes in group that are present

V

Enrichment P value

M I N A R S I N

Enrichment score

SE

Metabolic function

SE

M I N A R S I N

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the genes involved in the pathophysiology of CVI and to provide data that can serve as a reference for the future genetic research efforts. Data from this study demonstrated that the gene expression profiles differ significantly between CVI patients and patients unaffected by superficial reflux disease. Of 6,501 differently expressed genes (P o .05), we focused on the genes that expressed 1.5-fold change and genes that were involved in metabolic processes, which were previously investigated as the underling etiology of CVI. Our data showed that the relative expression of the HPGD gene was significantly higher in patients with CVI (Table 3). This gene is an important regulator in multiple biological pathways; from the development of cardiovascular system to inflammation homeostasis and prostaglandins (PGs) synthesis pathway [25–27]. By catalyzing the conversion of the 15-hydroxyl group of PGs into keto group, the HPGD enzyme significantly reduces the biological activity of PGs and reduces inflammatory reaction in vivo [28]. In addition, prostanoids prostacyclin (PGI2) and thromboxane A2 (TXA2) play an essential role in the maintenance of vascular tone. As a consequence of their opposing functions (PGI2 is a vasodilator and TXA2 is a vasoconstrictor), an imbalance in PGI2 and TXA2 production has been implicated in the pathophysiology of many thrombotic and cardiovascular disorders and inflammation [29]. One possible explanation for up-regulated HPGD in CVI patients in our study is that, under the influence of the increased amounts of inflammatory mediators (including PGs), the expression of the HPGD is induced as venous intrinsic anti-inflammatory mechanism, because, as mentioned, the HPGD gene (and its product enzyme prostaglandin dehydrogenase) reduces PGs activity and consequently reduces the inflammatory reaction. In addition, observed overexpression of the HPGD gene could be induced by increased venous pressure via altered balance of PGI2/TXA2, a potent vasoactive arachidonic acid metabolites that control vascular tone. It would be interesting to quantify the expression of the HPGD gene in C5-C6 disease to test the theory that in more advanced stages of CVI, anti-inflammatory and vasoactive mechanism, regulated by the HPGD, becomes insufficient to balance the PGs biosynthesis, which in turn can lead to the damage of surrounding tissue, which is pathognomonic for C5-C6 disease. This could be done by comparing the relative expression levels presented in this study and the C5-C6 relative expression levels data from future studies, because our study included patients from C2C4 class. In 2009, Gemmati et al demonstrated a number of single nucleotide polymorphism in HFE, FPN1, MMP12, and FXIII genes in C6 patients [30]. Despite the fact that the authors suggested that identified specific gene variants could be used as diagnostic modalities, we could not utilize their results because the previously mentioned study evaluated genetic mutations for specific gene targets rather than evaluating the entire genome for relative gene expression profile of C6 patients. Thus, as mentioned, further studies can be helpful to investigate and compare whether any of genes detected in our study become suppressed (or overexpressed) with the progression of CVI. Our study also showed that another important group of genes involved in the biosynthesis of collagen were down-

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regulated in CVI patients. Data showed (Table 3) that the relative expression levels of the collagen type 13α1 and collagen type 27α1 genes were 1.72 and 1.54 down-regulated, respectively in CVI patients. Our findings support data from molecular studies that demonstrated altered molecular differentiation of collagen and extensive extracellular matrix remodeling in the wall of varicose veins [31,32]. In 2010, Dennis et al demonstrated that collagen XIII in vascular endothelial cells mediates α1β1-integrin−dependent transmigration of monocytes [33]. However, the function of this gene in CVI remains unknown. Because these are relatively novel genes (collagen type 27α1 was discovered in 2003) [34], we report these findings as the reference for comparison with data from the future research. Chromosome 2 Open Reading Frame-40 (C2orf40) gene was the top down-regulated gene in our study. Despite that C2orf40 is highly a conserved gene, its function still remains unclear. Data from several studies demonstrated that C2orf40 is potentially a tumor suppressor gene and no studies to date have associated this gene with venous pathophysiology [35]. In addition, data from our study revealed seven genes that were detected as expression sequence tags on microarrays. Currently, there are very limited data available for these sequences. Given the fact that some of the genes (with unclear function) identified in our study were significantly up-regulated (up to 3.28-fold change) or down-regulated (3.01-fold change; C2orf40), there is a potential that they represent a novel gene candidates responsible for the pathogenesis of CVI. However, this association is based exclusively on the high fold change that characterized the genes mentioned, and this correlation needs to be confirmed by further investigations. Another complex network of singling pathways, including mitogen-activated protein kinase, was extensively investigated in CVI and described as possible mechanism of transduction of the laminar shear stress (LSS) in vascular endothelial cells [36–38]. It has been demonstrated that changes in LSS can lead to altered expression of multiple genes in endothelial cells [39,40]. Topper et al showed that cyclooxygenase (COX)-2, manganese superoxide dismutase, and endothelial cell nitric oxide synthase were selectively upregulated in endothelial cells exposed to a steady LSS [41]. Data from several other recent studies confirmed that LSS upregulates COX-2 and prostanoids in vascular endothelial cells [42]. COX isozymes (COX-1 and COX-2) are central regulatory enzymes of the synthesis of prostanoids from membranederived arachidonic acid. Our data from gene ontology enrichment analysis (Table 4) showed that multiple inflammatory pathways, including arachidonic acid metabolism, prostanoid metabolism, and prostaglandin biosynthesis (with respective enrichment scores of 109.41, 47.58 and 47.58) were regulated by genes that had significantly different expression profiles (>50%) in CVI patients. These data suggest that regulatory genes of inflammatory reaction, which is demonstrated to be responsible for development of CVI, have a critical role in the CVI pathogenesis. However, the question of whether the statistically significant difference in expression profiles of genes that regulate inflammatory reaction occurs first or as a response to the mediators of inflammatory reaction remains to be answered.

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Conclusion

In our study, based on DNA microarray analysis, we showed that relative gene expression profiles significantly differ between patients with CVI and patients unaffected by superficial reflux disease. Data from our study demonstrate that regulatory genes of arachidonic acid metabolism and mediators of the inflammatory reaction (HPGD) are overexpressed in CVI patients. We also show that regulatory genes of collagen production are down-regulated in veins affected by superficial reflux disease. In addition, our analysis showed that relative gene expression of multiple genes, the functions of which are currently unknown, was significantly different in CVI patients. Based on the magnitude of fold change of gene expression levels, we can hypothesize that this novel group of genes potentially plays an important role in CVI pathogenesis. In order to confirm these results, it is vital to test our gene expression data findings with data from other independent studies. However, as mentioned, to date no study that screened the entire human genome of CVI exists in the literature. Thus, further genetic studies are required to confirm our findings because the fundamental advancements in our knowledge of the human genome and the pathophysiology of CVI represent an opportunity to develop new diagnostic, prognostic, preventive, and therapeutic strategies for CVI. At our institution, at the time when this article was written, we were simultaneously conducting the Institutional Review Board−approved phase II of this study, which analyzes the gene expression post-transcriptional regulatory mechanisms, in hopes of gaining more data and better insight into the genetics that underline development of CVI.

Acknowledgements This research study was funded by the American Collage of Phlebology. The authors gratefully acknowledge the support in collecting vein samples of Peter Smith, MD, Carmelo Milano, MD, Shu Lin, MD, PhD, Jaffrey Gaca, MD from the Duke University Department of Cardiothoracic Surgery and the members of the Duke Endoscopic Vein Harvest Team.

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