International Journal of Nephrology

Volume 2013 (2013), Article ID 980923, 8 pages

http://dx.doi.org/10.1155/2013/980923

Research Article

Gene Expression Changes in Venous Segment of Overflow Arteriovenous Fistula

Yasuhiro Hashimoto,1,2 Akiko Okamoto,¹ Hisao Saitoh,¹ Shingo Hatakeyama,² Takahiro Yoneyama,² Takuya Koie,² and Chikara Ohyama²

¹Oyokyo Kidney Research Institute, Hirosaki Hospital, Hirosaki 036-8243, Japan

²Department of Urology, Hirosaki University Graduate School of Medicine, 5 Zaifucho, Hirosaki, Aomori 036-8562, Japan

Received 11 February 2013; Revised 6 April 2013; Accepted 6 April 2013

Academic Editor: Alejandro Martín-Malo

Copyright © 2013 Yasuhiro Hashimoto et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Aim. The objective of this study was to characterize coordinated molecular changes in the structure and composition of the walls of venous segments of arteriovenous (AV) fistulas evoked by overflow. Methods. Venous tissue samples were collected from 6 hemodialysis patients with AV fistulas exposed to overflow and from the normal cephalic veins of 4 other hemodialysis patients. Total RNA was extracted from the venous tissue samples, and gene expression between the 2 groups was compared using Whole Human Genome DNA microarray 44 K. Microarray data were analyzed by GeneSpring GX software and Ingenuity Pathway Analysis. Results. The cDNA microarray analysis identified 397 upregulated genes and 456 downregulated genes. Gene ontology analysis with GeneSpring GX software revealed that biological developmental processes and glycosaminoglycan binding were the most upregulated. In addition, most upregulation occurred extracellularly. In the pathway analysis, the TGF beta signaling pathway, cytokines and inflammatory response pathway, hypertrophy model, and the myometrial relaxation and contraction pathway were significantly upregulated compared with the control cephalic vein. Conclusion. Combining microarray results and pathway information available via the Internet provided biological insight into the structure and composition of the venous wall of overflow AV fistulas.

1. Introduction

Arteriovenous (AV) fistulas are very useful for determining optimal blood flow for dialysis, but AV fistulas exposed to overflow are thought to increase cardiac output and cause high-output cardiac failure [1, 2].

Measurement of blood flow via an internal shunt was first developed by Krivitski et al., and the monitoring of blood flow via a shunt has since become widespread [3]. We use this technique to monitor the blood flow of AV fistulas at our hospital and correct overflow AV fistulas with surgery.

It is thought that the outflow vein of overflow AV fistulas bears a heavy load: as the vein is exposed to increased arterial flow, the wall dilates, triggering a vascular remodeling process. However, the molecular mechanisms by which the outflow vein is remodeled into a mature fistula remain unclear. By investigating venous remodeling in overflow AV fistulas, candidate genes important to the remodeling process can be discovered and their functional significance investigated. Thus, the identification of relevant genes involved in this process should provide insight into AV fistula maturation.

In this study, we performed a cDNA microarray analysis and compared segments of the venous walls of overflow AV fistulas from 6 hemodialysis patients with the normal cephalic veins of 4 other hemodialysis patients to determine whether there was any difference in their gene expression patterns.

2. Study Population

From June 2009 to September 2010, 548 patients underwent hemodialysis at the Oyokyo Kidney Research Institute in Hirosaki, Japan. During that period, 10 patients underwent surgical ligation to correct an overflow AV fistula. When the operation was performed, we retained a sample of the wall of the overflow AV fistula (Figure 1). The AV fistula specimens were resected from the wall of the vein close to the AV fistula anastomosis. The study was approved by the Bioethics Committee of Oyokyo Kidney Research Institute, and all patients provided their informed consent to the procedure prior to it being performed.

(a)

(b)

Figure 1: (a) Photograph of overflow AV fistula. (b) Schematic of overflow AV fistula.

3. Inclusion Criteria

The inclusion criteria were as follows: (1) blood access flow greater than 2.0 L/min measured by the color Doppler ultrasound (2) an AV fistula in the lower arm with a distal radio-cephalic anastomosis. In total, 6 patients had overflow AV fistulas that met these criteria. The backgrounds of these patients are detailed in Table 1. We also obtained tissue samples from the lower arm distal cephalic veins of 4 new hemodialysis patients and used these as a control.

Table 1: Patient characteristics.

Over flow AVF
Age
Gender
Cause of CRF
Patency period of AV fistula (months)
Blood flow (mL/min)

1
48
M
CGN
56
3790

2
83
F
CGN
93
2760

3
57
M
CGN
19
3280

4
46
M
CGN
22
2710

5
75
M
CGN
104
3520

6
57
F
IgA
88
2340

Control

1
67
M
CGN

2
68
F
CGN

3
56
M
CGN

4
80
F
CGN

4. Methods

As noted above, venous tissues were resected from a venous segment of an overflow AV fistula from 6 patients and from a normal cephalic vein from 4 other patients. The surgical specimens were immediately placed in test tubes containing RNAlater (see below for details).

Total RNA was extracted from the venous tissue samples, and gene expression between the 2 groups was compared using Whole Human Genome DNA microarray 44 K (Agilent Technologies, Santa Clara, California). The microarray data were analyzed with GeneSpring GX software and Ingenuity Pathway Analysis.

5. RNA Isolation

Surgical specimens were 0.5 cm or smaller in size and were initially stored in RNA later (Ambion, Austin, TX) overnight at °C then at –80°C until RNA extraction. Total RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA) according to the manufacturer’s instructions. The total RNA was further purified using the Qiagen RNeasy Mini Kit (Qiagen, Valencia, CA) and then extracted. The quantity and quality of the RNA were determined using a Nanodrop ND-1000 spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA) and an Agilent Bioanalyzer (Agilent Technologies, Palo Alto, CA).

6. cRNA Amplification and Labeling

Total RNA was amplified and labeled with Cyanine 3 (Cy3) as instructed by the manufacturer of the Agilent Low Input Quick Amp Labeling Kit, one-color (Agilent Technologies, Palo Alto, CA). Briefly, 100 ng of total RNA was reverse transcribed to double-strand cDNA using a poly dT-T7 promoter primer. The primer, template RNA, and quality-control transcripts of known concentration and quality were then denatured at 65°C for 10 min and incubated for 2 hours at 40°C with 5X First-Strand Buffer, 0.1 M DTT, 10 mM dNTP mix, and Affinity Script RNase Block Mix. The Affinity Script enzyme was inactivated at 70°C for 15 min. The resulting cDNA products were then used as templates for in vitro transcription to generate fluorescent cRNA. They were mixed with a transcription master mix in the presence of T7 RNA polymerase and Cy3-labeled CTP and incubated at 40°C for 2 hours. Labeled cRNAs were purified using Qiagen’s RNeasy Mini spin columns and eluted in 30 μL of nuclease-free water. After amplification and labeling, cRNA quantity and cyanine incorporation were determined using a Nanodrop ND-1000 spectrophotometer and an Agilent Bioanalyzer.

7. Sample Hybridization

For each hybridization, 1.65 μg of Cy3-labeled cRNA was fragmented and hybridized onto an Agilent Human GE 4x44K v2 Microarray (Design ID: 026652) for 17 hours at 65°C. After washing, the microarrays were scanned using an Agilent DNA microarray scanner.

8. Microarray Data Analysis

The intensity values of each scanned feature were quantified using Agilent feature extraction software (version 10.7.3.1), which performs background subtractions. We only used features flagged as having no errors (present flags) and excluded features that were not positive, not significant, not uniform, not above background levels, saturated, or population outliers (marginal and absent flags). Normalization was performed using Agilent GeneSpring GX version 11.0.2. (per chip: normalization to the 75 percentile shift; per gene: normalization to median across all samples). There are 34,127 probes in total on the Agilent Human GE 4x44K v2 Microarray (Design ID: 026652), excluding control probes. The microarray data were submitted to NCBI GEO (http://www.ncbi.nlm.nih.gov/geo/), sample number [GSE39488].

The altered transcripts were quantified using a comparative method. We applied a value < 0.05 combined with a >2-fold change in normalized intensity to identify genes with significantly different expression patterns.

9. Gene Ontology Analysis and Pathway Analysis

The gene ontology analysis was performed using Agilent Technologies GeneSpring GX software (11.0.2). Pathway analysis was performed with GenMAPP 2.1 (http://www.genmapp.org/).

10. Results

The cDNA microarray analysis revealed that 397 genes were upregulated and 456 were downregulated (Tables 2 and 3).

Table 2: Genes significantly upregulated in the remodeled vein compared to the control vein (top 30).

Probe name
value
FCAbsolute
Gene symbol

A_23_P106194
0.045653246
38.85
FOS

A_23_P429998
0.021514166
27.22
FOSB

A_24_P33895
0.001204797
25.26
ATF3

A_23_P46936

24.77
EGR2

A_23_P96158
0.002943707
24.55
KRT17

A_23_P34915
0.001120758
23.39
ATF3

A_23_P71037
0.004629572
21.98
IL6

A_23_P46429

21.04
CYR61

A_24_P882732
0.036205057
19.08

A_23_P97141
0.0211732
17.66
RGS1

A_23_P323751

17.26
FAM83D

A_33_P3316273
0.00369598
15.81
CCL3

A_23_P216225
0.0219925
15.72
EGR3

A_33_P3295203
0.001008955
15.65
HAS1

A_23_P131208

14.26
NR4A2

A_23_P214080
0.00224604
13.9
EGR1

A_33_P3214105

13.44
ATF3

A_33_P3390793
0.004063138
13.41
TRIM36

A_33_P3354607
0.001234311
13.09
CCL4

A_23_P79518
0.006241249
12.95
IL1B

A_23_P1331
0.001146301
11.08
COL13A1

A_23_P110569

10.2
TRIM36

A_23_P166408
0.003698398
10.04
OSM

A_32_P76627

10.02

A_23_P207564
0.00151482
9.94
CCL4

A_33_P3299066
0.001036557
9.61
NR4A2

A_33_P3214393
0.008276591
9.56

A_33_P3413741
0.032112285
9.53
OXTR

A_33_P3271594
0.001451045
9.49
TRIM54

A_24_P158089
0.003357552
8.93
SERPINE1

Table 3: Genes significantly downregulated in the remodeled vein compared to the control vein (top 30).

Probe name
FCAbsolute
value
Gene symbol

A_23_P23783
18.99
0.009408315
MYOC

A_23_P121545
14.63

GPM6A

A_33_P3368193
10.96

PNLIPRP3

A_32_P92489
8.79
0.004908097
PKD1L2

A_24_P40626
8.15
0.011296479
GREM2

A_33_P3221408
8.14
0.004285622
NTNG1

A_23_P143526
7.15
0.004383178
S100B

A_23_P136777
7.14

APOD

A_23_P102331
7.10
0.003490915
SCN7A

A_33_P3421923
7.03
0.001119926
CADM3

A_23_P140384
7.00
0.026459113
CTSG

A_33_P3363799
6.94
0.002682039
NCAM1

A_24_P203134
6.80
0.024163503
DCAF12L1

A_24_P280684
6.69
0.03005707
FBXO40

A_23_P55544
6.51
0.004269639
CCBE1

A_23_P73571
6.45
0.03981546
MUM1L1

A_23_P212050
6.22
0.021448081
BCHE

A_33_P3336557
6.12

A_23_P121676
6.07
0.014616995
CXXC4

A_23_P204885
6.01
0.007333652
PCDH20

A_23_P64919
5.92
0.012492463
RERGL

A_23_P422911
5.81

HS6ST3

A_23_P146233
5.79
0.01808302
LPL

A_23_P110624
5.76
0.003615111
CTNND2

A_23_P45185
5.69
0.00549277
FIGF

A_23_P110764
5.65
0.009343005
MYOT

A_23_P114862
5.41
0.039056532
ANGPTL7

A_23_P39251
5.31

PLIN5

A_23_P111402
5.28
0.008291814
RSPO3

A_33_P3400763
5.26
0.038730744
PLIN4

The gene ontology analysis revealed that biological developmental processes and glycosaminoglycan binding were the most upregulated. In addition, most upregulation occurred extracellularly (Tables 4, 5, and 6).

Table 4: Statistically overrepresented GO terms in the biological process category .

Biological process

GO accession (with AmiGO link)
GO term
Corrected value
Count in selection
% count in selection
Count in total
% count in total

GO:0032502
Developmental process

77
29.8
3077
17.9

GO:0007275
Multicellular organismal development

67
26.0
2810
16.3

GO:0010033
Response to organic substance

40
15.5
698
4.1

GO:0001568
Blood vessel development

21
8.1
231
1.3

GO:0048514
Blood vessel morphogenesis

19
7.4
198
1.1

GO:0001944
Vasculature development

21
8.1
238
1.4

GO:0048545
Response to steroid hormone stimulus

23
8.9
183
1.1

GO:0001525
Angiogenesis

18
7.0
139
0.8

GO:0009653
Anatomical structure morphogenesis

30
11.6
1125
6.5

GO:0016265
Death

39
15.1
663
3.8

GO:0048646
Anatomical structure formation involved in morphogenesis

18
7.0
306
1.8

GO:0008219
Cell death

37
14.3
658
3.8

GO:0042221
Response to chemical stimulus

53
20.5
1264
7.3

GO:0048856
Anatomical structure development

42
16.3
2437
14.1

GO:0006950
Response to stress

51
19.8
1642
9.5

GO:0048731
System development

38
14.7
2284
13.3

GO:0032570
Response to progesterone stimulus

9
3.5
21
0.1

GO:0006915∣GO:0008632
Apoptosis

31
12.0
541
3.1

GO:0012501∣GO:0016244
Programmed cell death

31
12.0
549
3.2

GO:0042981
Regulation of apoptosis

35
13.6
796
4.6

GO:0032501∣GO:0050874
Multicellular organismal process

70
27.1
4154
24.1

GO:0043067∣GO:0043070
Regulation of programmed cell death

35
13.6
804
4.7

GO:0010941
Regulation of cell death

35
13.6
807
4.7

GO:0009887
Organ morphogenesis

26
10.1
685
4.0

GO:0009628
Response to abiotic stimulus

16
6.2
357
2.1

GO:0009725
Response to hormone stimulus

23
8.9
358
2.1

GO:0048519∣GO:0043118
Negative regulation of biological process

39
15.1
1756
10.2

GO:0009719
Response to endogenous stimulus

24
9.3
391
2.3

GO:0009605
Response to external stimulus

32
12.4
869
5.0

GO:0048513
Organ development

34
13.2
1682
9.8

GO:0009607
Response to biotic stimulus

23
8.9
385
2.2

GO:0070482
Response to oxygen levels

14
5.4
137
0.8

GO:0009266
Response to temperature stimulus

9
3.5
86
0.5

GO:0050896∣GO:0051869
Response to stimulus

89
34.5
3356
19.5

GO:0048523∣GO:0051243
Negative regulation of cellular process

38
14.7
1606
9.3

GO:0009408∣GO:0006951
Response to heat

9
3.5
61
0.4

GO:0006928
Cellular component movement

14
5.4
450
2.6

GO:0050793
Regulation of developmental process

8
3.1
670
3.9

GO:0048869
Cellular developmental process

24
9.3
1641
9.5

GO:0022603
Regulation of anatomical structure morphogenesis

5
1.9
228
1.3

GO:0051239
Regulation of multicellular organismal process

7
2.7
924
5.4

GO:0007565
Female pregnancy

13
5.0
104
0.6

GO:0030154
Cell differentiation

24
9.3
1576
9.1

GO:0042127
Regulation of cell proliferation

29
11.2
773
4.5

GO:0001666
Response to hypoxia

14
5.4
131
0.8

GO:0008284
Positive regulation of cell proliferation

16
6.2
410
2.4

GO:0048522∣GO:0051242
Positive regulation of cellular process

34
13.2
1806
10.5

GO:0051789
Response to protein stimulus

11
4.3
96
0.6

GO:0048518∣GO:0043119
Positive regulation of biological process

35
13.6
1982
11.5

GO:0043627
Response to estrogen stimulus

12
4.7
98
0.6

GO:0009991
Response to extracellular stimulus

6
2.3
204
1.2

GO:0042493∣GO:0017035
Response to drug

17
6.6
213
1.2

GO:0043066
Negative regulation of apoptosis

19
7.4
345
2.0

GO:0043069∣GO:0043072
Negative regulation of programmed cell death

19
7.4
350
2.0

GO:0051384
Response to glucocorticoid stimulus

10
3.9
70
0.4

GO:0050789∣GO:0050791
Regulation of biological process

109
42.2
7200
41.8

GO:0060548
Negative regulation of cell death

19
7.4
354
2.1

GO:0051707∣GO:0009613∣GO:0042828
Response to other organism

17
6.6
300
1.7

GO:0040011
Locomotion

16
6.2
415
2.4

GO:0009611∣GO:0002245
Response to wounding

21
8.1
507
2.9

GO:0031960
Response to corticosteroid stimulus

10
3.9
75
0.4

GO:0050794∣GO:0051244
Regulation of cellular process

108
41.9
6938
40.3

GO:0014070
Response to organic cyclic substance

12
4.7
114
0.7

GO:0051128
Regulation of cellular component organization

6
2.3
466
2.7

GO:0065007
Biological regulation

109
42.2
7592
44.1

GO:0051704∣GO:0051706
Multiorganism process

27
10.5
706
4.1

GO:0031099
Regeneration

6
2.3
65
0.4

GO:0007610
Behavior

12
4.7
449
2.6

Table 5: Statistically overrepresented GO terms in the molecular function category .

Molecular function

GO accession (with AmiGO link)
GO term
Corrected value
Count in selection
% count in selection
Count in total
% count in total

GO:0005539
Glycosaminoglycan binding

14
5.4
149
0.9

GO:0005515∣GO:0045308
Protein binding

170
65.9
8104
47.0

GO:0001871
Pattern binding

14
5.4
164
1.0

GO:0030247
Polysaccharide binding

14
5.4
164
1.0

GO:0008201
Heparin binding

13
5.0
112
0.7

GO:0005126
Cytokine receptor binding

4
1.6
177
1.0

GO:0005125
Cytokine activity

12
4.7
193
1.1

GO:0005114
Type II transforming growth factor beta receptor binding

4
1.6
7
0.0

GO:0008083
Growth factor activity

13
5.0
160
0.9

GO:0005102
Receptor binding

24
9.3
873
5.1

GO:0030246
Carbohydrate binding

14
5.4
354
2.1

Table 6: Statistically overrepresented GO terms in the cellular component category .

Cellular component

GO accession (with AmiGO link)
GO term
Corrected value
Count in selection
% count in selection
Count in total
% count in total

GO:0044421
Extracellular region part

49
19.0
937
5.4

GO:0031012
Extracellular matrix

25
9.7
339
2.0

GO:0005576
Extracellular region

69
26.7
1923
11.2

GO:0005615
Extracellular space

32
12.4
673
3.9

GO:0005578
Proteinaceous extracellular matrix

20
7.8
309
1.8

GO:0060205
Cytoplasmic membrane-bounded vesicle lumen

7
2.7
44
0.3

GO:0031983
Vesicle lumen

7
2.7
46
0.3

GO:0031093
Platelet alpha granule lumen

7
2.7
41
0.2

The pathway analysis revealed that the TGF beta signaling pathway, cytokines and inflammatory response pathway, hypertrophy model, and the myometrial relaxation and contraction pathway were upregulated (Table 7).

Table 7: Pathway analysis results.

Pathway name
LS_vs_control

Alpha6 beta4 integrin signaling pathway
0.793

Androgen receptor signaling pathway
0.528

Apoptosis mechanisms
0.124

B-cell receptor signaling pathway
0.023

G1 to S cell cycle control
1

Cell cycle
0.487

Delta-Notch signaling pathway
0.226

DNA replication
1

EGFR1 signaling pathway
0.856

FAS pathway and stress induction of HSP regulation
1

Focal Adhesion
0.003

G13 signaling pathway
0.269

G protein signaling pathways
0.258

Hedgehog signaling pathway
1

Apoptosis modulation by HSP70
1

Id signaling pathway
1

IL-1 signaling pathway
1

IL-2 signaling pathway
0.327

IL-3 signaling pathway
0.371

IL-4 signaling pathway
0.589

IL-5 signaling pathway
0.576

IL-6 signaling pathway
1

IL-7 signaling pathway
0.498

IL-9 signaling pathway
1

Human insulin signaling
0.387

Integrin-mediated cell adhesion
0.363

Kit receptor signaling pathway
0.051

MAPK cascade
1

MAPK signaling pathway
0.011

mRNA processing (Homo sapiens)
0.014

Notch signaling pathway
0.191

Ovarian infertility genes
1

p38 MAPK signaling pathway (BioCarta)
0.108

Regulation of actin cytoskeleton
0.834

Eukaryotic transcription initiation
0.511

Signal transduction of S1P
0.384

Signaling of hepatocyte growth factor receptor
1

T cell receptor signaling pathway
0.243

TGF-beta receptor signaling pathway
0.095

TGF beta signaling pathway
0

TNF-alpha/NF-B signaling pathway
0.752

Translation factors
0.368

Wnt signaling pathway
0.15

Wnt signaling pathway
0.051

Acetylcholine synthesis
1

Alanine and aspartate metabolism
—

Biogenic amine synthesis
1

Cholesterol biosynthesis
0.644

Eicosanoid synthesis
1

Electron transport chain
0.013

Fatty acid beta oxidation 1
0.403

Fatty acid beta oxidation 2
1

Fatty acid beta oxidation 3
1

Beta oxidation meta MAPP
0.264

Fatty acid omega oxidation
0.687

Fatty acid biosynthesis
0.426

Glucocorticoid and mineralcorticoid metabolism
1

Glutathione metabolism
0.399

Glycogen metabolism
0.261

Glycolysis and gluconeogenesis
0.235

Heme biosynthesis
1

TCA cycle
0.24

Mitochondrial LC-fatty acid beta-oxidation
0.635

Nuclear receptors in lipid metabolism and toxicity
0.389

Nucleotide metabolism
0.622

Pentose phosphate pathway
1

Prostaglandin synthesis and regulation
1

Statin pathway (PharmGKB)
1

Steroid biosynthesis
1

Synthesis and degradation of ketone bodies
1

Triacylglyceride synthesis
0.419

Tryptophan metabolism
0.501

Beta oxidation of unsaturated fatty acids
1

Urea cycle and metabolism of amino groups
—

GPCRs, class A rhodopsin-like
0.317

GPCRs, class B secretin-like
1

GPCRs, class C metabotropic glutamate, pheromone
1

GPCRs, other
1

Matrix metalloproteinases
0.652

Monoamine GPCRs
1

Nuclear receptors
1

Nucleotide GPCRs
1

Peptide GPCRs
0.081

Cytoplasmic ribosomal proteins
0.025

Small ligand GPCRs
1

ACE inhibitor pathway (Homo sapiens)
0.254

Adipogenesis human
0.101

Blood clotting cascade
0.155

Calcium regulation in the cardiac cell
0.431

Circadian exercise
0.754

Complement activation and classical pathway
0.646

Complement activation and classical pathway
0.022

Cytokines and inflammatory response (BioCarta)
0

Hypertrophy model
0

Inflammatory response pathway
0.054

Irinotecan pathway (Homo sapiens)
0.685

Oxidative stress
0.402

Proteasome degradation
0.278

Myometrial relaxation and contraction pathways
0

Striated muscle contraction
0.427

11. Discussion

AV fistulas are very useful for determining the optimal blood flow for hemodialysis since satisfactory blood access flow is necessary for adequate hemodialysis. When stenotic lesions occur within the vascular system and blood flow is insufficient, a percutaneous transluminal angioplasty or some other intervention is performed. However, overflow AV fistulas increase cardiac output and cause high-output cardiac failure [1].

In the 2005 Japanese Society for Dialysis Therapy Guidelines for Vascular Access Construction and Repair for Chronic Hemodialysis, vascular access flow is said to lead to heart failure when the blood access flow is greater than 1.0–1.5 L/min or when the vascular access flow/cardiac output ratio is >20% [1]. If the vascular access flow is clearly responsible for a decline in cardiac function, then it is necessary to intentionally constrict or occlude the vascular access [1]. Surveillance of blood flow in internal shunts by the Doppler echocardiography has become widespread and overflow AV fistulas are now actively treated. Several recent studies have noted the importance of histological changes in AV fistulas [4, 5].

Microarrays of vascular access have been reported in experimental animal models, but there have been no such analyses in humans [6]. In the present study, venous tissue samples were resected from overflow AV fistulas from 6 hemodialysis patients and from the normal cephalic veins of 4 other hemodialysis patients, and their gene expression patterns were compared.

It is interesting to note that zinc finger-containing transcription factors such as egr1, egr2, and egr3 and immediate early genes such as fos and jun, were found to be remarkably upregulated in the present study; egr1, egr 2, and egr 3 have been implicated in the proliferation and differentiation of many cell types [7, 8], and fos and jun have been linked to the regulation of angiogenesis [9]. Moreover, egr-1, c-jun, and c-fos have been linked to the regulation of free radical scavenging enzymes [10–13]. We also observed the upregulation of free radical scavenging enzyme activity in the walls of the overflow AV fistulas, which may reflect chronic reactive oxygen species formation in overflow AV fistulas.

The pathway analysis indicated that the TGF beta signaling pathway and cytokines and inflammatory response pathway were upregulated. This suggests that overflow AV fistulas may be implicated in chronic inflammation in hemodialysis patients.

Malnutrition, inflammation, and atherosclerosis (MIA syndrome) are common in end-stage renal disease (ESRD) patients, and inflammation has been identified as playing a key role in atherosclerotic cardiovascular disease. Proinflammatory cytokines are pivotal to the inflammation that is, associated with malnutrition and atherosclerosis in ESRD [14]. Our findings suggest that overflow AV fistulas may be implicated in MIA syndrome.

12. Conclusion

Combining microarray results and pathway information available via the Internet provided biological insight into molecular changes in the venous walls of overflow AV fistulas. Despite the small sample size, our study findings suggest that overflow AV fistulas may be implicated in chronic inflammation in hemodialysis patients.

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