International Journal of Nephrology

International Journal of Nephrology / 2013 / Article

Research Article | Open Access

Volume 2013 |Article ID 980923 | https://doi.org/10.1155/2013/980923

Yasuhiro Hashimoto, Akiko Okamoto, Hisao Saitoh, Shingo Hatakeyama, Takahiro Yoneyama, Takuya Koie, Chikara Ohyama, "Gene Expression Changes in Venous Segment of Overflow Arteriovenous Fistula", International Journal of Nephrology, vol. 2013, Article ID 980923, 8 pages, 2013. https://doi.org/10.1155/2013/980923

Gene Expression Changes in Venous Segment of Overflow Arteriovenous Fistula

Academic Editor: Alejandro Martín-Malo
Received11 Feb 2013
Revised06 Apr 2013
Accepted06 Apr 2013
Published27 Apr 2013

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.

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.


Over flow AVFAge Gender Cause of CRFPatency period of AV fistula (months)Blood flow (mL/min)

148MCGN563790
283FCGN932760
357MCGN193280
446MCGN222710
575MCGN1043520
657FIgA882340

Control     
167MCGN
268FCGN
356MCGN
480FCGN

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).


Probe name valueFCAbsoluteGene symbol

A_23_P1061940.04565324638.85FOS
A_23_P4299980.02151416627.22FOSB
A_24_P338950.00120479725.26ATF3
A_23_P46936 24.77EGR2
A_23_P961580.00294370724.55KRT17
A_23_P349150.00112075823.39ATF3
A_23_P710370.00462957221.98IL6
A_23_P46429 21.04CYR61
A_24_P8827320.03620505719.08 
A_23_P971410.021173217.66RGS1
A_23_P323751 17.26FAM83D
A_33_P33162730.0036959815.81CCL3
A_23_P2162250.021992515.72EGR3
A_33_P32952030.00100895515.65HAS1
A_23_P131208 14.26NR4A2
A_23_P2140800.0022460413.9EGR1
A_33_P3214105 13.44ATF3
A_33_P33907930.00406313813.41TRIM36
A_33_P33546070.00123431113.09CCL4
A_23_P795180.00624124912.95IL1B
A_23_P13310.00114630111.08COL13A1
A_23_P110569 10.2TRIM36
A_23_P1664080.00369839810.04OSM
A_32_P76627 10.02 
A_23_P2075640.001514829.94CCL4
A_33_P32990660.0010365579.61NR4A2
A_33_P32143930.0082765919.56 
A_33_P34137410.0321122859.53OXTR
A_33_P32715940.0014510459.49TRIM54
A_24_P1580890.0033575528.93SERPINE1


Probe nameFCAbsolute valueGene symbol

A_23_P2378318.990.009408315MYOC
A_23_P12154514.63 GPM6A
A_33_P336819310.96 PNLIPRP3
A_32_P924898.790.004908097PKD1L2
A_24_P406268.150.011296479GREM2
A_33_P32214088.140.004285622NTNG1
A_23_P1435267.150.004383178S100B
A_23_P1367777.14 APOD
A_23_P1023317.100.003490915SCN7A
A_33_P34219237.030.001119926CADM3
A_23_P1403847.000.026459113CTSG
A_33_P33637996.940.002682039NCAM1
A_24_P2031346.800.024163503DCAF12L1
A_24_P2806846.690.03005707FBXO40
A_23_P555446.510.004269639CCBE1
A_23_P735716.450.03981546MUM1L1
A_23_P2120506.220.021448081BCHE
A_33_P33365576.12  
A_23_P1216766.070.014616995CXXC4
A_23_P2048856.010.007333652PCDH20
A_23_P649195.920.012492463RERGL
A_23_P4229115.81 HS6ST3
A_23_P1462335.790.01808302LPL
A_23_P1106245.760.003615111CTNND2
A_23_P451855.690.00549277FIGF
A_23_P1107645.650.009343005MYOT
A_23_P1148625.410.039056532ANGPTL7
A_23_P392515.31 PLIN5
A_23_P1114025.280.008291814RSPO3
A_33_P34007635.260.038730744PLIN4

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).


Biological process
GO accession (with AmiGO link)GO termCorrected valueCount in selection% count in selectionCount in total% count in total

GO:0032502Developmental process 77 29.8 3077 17.9
GO:0007275Multicellular organismal development 67 26.0 2810 16.3
GO:0010033Response to organic substance 40 15.5 698 4.1
GO:0001568Blood vessel development 21 8.1 231 1.3
GO:0048514Blood vessel morphogenesis 19 7.4 198 1.1
GO:0001944Vasculature development 21 8.1 238 1.4
GO:0048545Response to steroid hormone stimulus 23 8.9 183 1.1
GO:0001525Angiogenesis 18 7.0 139 0.8
GO:0009653Anatomical structure morphogenesis 30 11.6 1125 6.5
GO:0016265Death 39 15.1 663 3.8
GO:0048646Anatomical structure formation involved in morphogenesis 18 7.0 306 1.8
GO:0008219Cell death 37 14.3 658 3.8
GO:0042221Response to chemical stimulus 53 20.5 1264 7.3
GO:0048856Anatomical structure development 42 16.3 2437 14.1
GO:0006950Response to stress 51 19.8 1642 9.5
GO:0048731System development 38 14.7 2284 13.3
GO:0032570Response to progesterone stimulus 9 3.5 21 0.1
GO:0006915∣GO:0008632Apoptosis 31 12.0 541 3.1
GO:0012501∣GO:0016244 Programmed cell death 31 12.0 549 3.2
GO:0042981Regulation of apoptosis 35 13.6 796 4.6
GO:0032501∣GO:0050874Multicellular organismal process 70 27.1 4154 24.1
GO:0043067∣GO:0043070Regulation of programmed cell death 35 13.6 804 4.7
GO:0010941Regulation of cell death 35 13.6 807 4.7
GO:0009887Organ morphogenesis 26 10.1 685 4.0
GO:0009628Response to abiotic stimulus 16 6.2 357 2.1
GO:0009725Response to hormone stimulus 23 8.9 358 2.1
GO:0048519∣GO:0043118Negative regulation of biological process 39 15.1 1756 10.2
GO:0009719Response to endogenous stimulus 24 9.3 391 2.3
GO:0009605Response to external stimulus 32 12.4 869 5.0
GO:0048513Organ development 34 13.2 1682 9.8
GO:0009607Response to biotic stimulus 23 8.9 385 2.2
GO:0070482Response to oxygen levels 14 5.4 137 0.8
GO:0009266Response to temperature stimulus 9 3.5 86 0.5
GO:0050896∣GO:0051869Response to stimulus 89 34.5 3356 19.5
GO:0048523∣GO:0051243Negative regulation of cellular process 38 14.7 1606 9.3
GO:0009408∣GO:0006951Response to heat 9 3.5 61 0.4
GO:0006928Cellular component movement 14 5.4 450 2.6
GO:0050793Regulation of developmental process 8 3.1 670 3.9
GO:0048869Cellular developmental process 24 9.3 1641 9.5
GO:0022603Regulation of anatomical structure morphogenesis 5 1.9 228 1.3
GO:0051239Regulation of multicellular organismal process 7 2.7 924 5.4
GO:0007565Female pregnancy 13 5.0 104 0.6
GO:0030154Cell differentiation 24 9.3 1576 9.1
GO:0042127Regulation of cell proliferation 29 11.2 773 4.5
GO:0001666Response to hypoxia 14 5.4 131 0.8
GO:0008284Positive regulation of cell proliferation 16 6.2 410 2.4
GO:0048522∣GO:0051242Positive regulation of cellular process 34 13.2 1806 10.5
GO:0051789Response to protein stimulus 11 4.3 96 0.6
GO:0048518∣GO:0043119Positive regulation of biological process 35 13.6 1982 11.5
GO:0043627Response to estrogen stimulus 12 4.7 98 0.6
GO:0009991Response to extracellular stimulus 6 2.3 204 1.2
GO:0042493∣GO:0017035Response to drug 17 6.6 213 1.2
GO:0043066Negative regulation of apoptosis 19 7.4 345 2.0
GO:0043069∣GO:0043072Negative regulation of programmed cell death 19 7.4 350 2.0
GO:0051384Response to glucocorticoid stimulus 10 3.9 70 0.4
GO:0050789∣GO:0050791Regulation of biological process 109 42.2 7200 41.8
GO:0060548Negative regulation of cell death 19 7.4 354 2.1
GO:0051707∣GO:0009613∣GO:0042828Response to other organism 17 6.6 300 1.7
GO:0040011Locomotion 16 6.2 415 2.4
GO:0009611∣GO:0002245Response to wounding 21 8.1 507 2.9
GO:0031960Response to corticosteroid stimulus 10 3.9 75 0.4
GO:0050794∣GO:0051244Regulation of cellular process 108 41.9 6938 40.3
GO:0014070Response to organic cyclic substance 12 4.7 114 0.7
GO:0051128Regulation of cellular component organization 6 2.3 466 2.7
GO:0065007Biological regulation 109 42.2 7592 44.1
GO:0051704∣GO:0051706Multiorganism process 27 10.5 706 4.1
GO:0031099Regeneration 6 2.3 65 0.4
GO:0007610Behavior 12 4.7 449 2.6


Molecular function
GO accession (with AmiGO link)GO termCorrected valueCount in selection% count in selectionCount in total% count in total

GO:0005539Glycosaminoglycan binding 14 5.4 149 0.9
GO:0005515∣GO:0045308Protein binding 170 65.9 8104 47.0
GO:0001871Pattern binding 14 5.4 164 1.0
GO:0030247Polysaccharide binding 14 5.4 164 1.0
GO:0008201Heparin binding 13 5.0 112 0.7
GO:0005126Cytokine receptor binding 4 1.6 177 1.0
GO:0005125Cytokine activity 12 4.7 193 1.1
GO:0005114Type II transforming growth factor beta receptor binding 4 1.6 7 0.0
GO:0008083Growth factor activity 13 5.0 160 0.9
GO:0005102Receptor binding 24 9.3 873 5.1
GO:0030246Carbohydrate binding 14 5.4 354 2.1


Cellular component
GO accession (with AmiGO link)GO termCorrected valueCount in selection% count in selectionCount in total% count in total

GO:0044421Extracellular region part 49 19.0 937 5.4
GO:0031012Extracellular matrix 25 9.7 339 2.0
GO:0005576Extracellular region 69 26.7 1923 11.2
GO:0005615Extracellular space 32 12.4 673 3.9
GO:0005578Proteinaceous extracellular matrix 20 7.8 309 1.8
GO:0060205Cytoplasmic membrane-bounded vesicle lumen 7 2.7 44 0.3
GO:0031983Vesicle lumen 7 2.7 46 0.3
GO:0031093Platelet 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).


Pathway nameLS_vs_control

Alpha6 beta4 integrin signaling pathway0.793
Androgen receptor signaling pathway0.528
Apoptosis mechanisms0.124
B-cell receptor signaling pathway0.023
G1 to S cell cycle control1
Cell cycle0.487
Delta-Notch signaling pathway0.226
DNA replication1
EGFR1 signaling pathway0.856
FAS pathway and stress induction of HSP regulation1
Focal Adhesion0.003
G13 signaling pathway 0.269
G protein signaling pathways0.258
Hedgehog signaling pathway1
Apoptosis modulation by HSP701
Id signaling pathway1
IL-1 signaling pathway1
IL-2 signaling pathway0.327
IL-3 signaling pathway0.371
IL-4 signaling pathway0.589
IL-5 signaling pathway0.576
IL-6 signaling pathway1
IL-7 signaling pathway0.498
IL-9 signaling pathway1
Human insulin signaling0.387
Integrin-mediated cell adhesion0.363
Kit receptor signaling pathway0.051
MAPK cascade1
MAPK signaling pathway0.011
mRNA processing (Homo sapiens)0.014
Notch signaling pathway0.191
Ovarian infertility genes1
p38 MAPK signaling pathway (BioCarta)0.108
Regulation of actin cytoskeleton0.834
Eukaryotic transcription initiation0.511
Signal transduction of S1P0.384
Signaling of hepatocyte growth factor receptor1
T cell receptor signaling pathway0.243
TGF-beta receptor signaling pathway0.095
TGF beta signaling pathway0
TNF-alpha/NF- B signaling pathway0.752
Translation factors0.368
Wnt signaling pathway0.15
Wnt signaling pathway0.051
Acetylcholine synthesis1
Alanine and aspartate metabolism
Biogenic amine synthesis1
Cholesterol biosynthesis0.644
Eicosanoid synthesis1
Electron transport chain0.013
Fatty acid beta oxidation 10.403
Fatty acid beta oxidation 21
Fatty acid beta oxidation 31
Beta oxidation meta MAPP0.264
Fatty acid omega oxidation0.687
Fatty acid biosynthesis0.426
Glucocorticoid and mineralcorticoid metabolism1
Glutathione metabolism0.399
Glycogen metabolism0.261
Glycolysis and gluconeogenesis0.235
Heme biosynthesis1
TCA cycle0.24
Mitochondrial LC-fatty acid beta-oxidation0.635
Nuclear receptors in lipid metabolism and toxicity0.389
Nucleotide metabolism0.622
Pentose phosphate pathway1
Prostaglandin synthesis and regulation1
Statin pathway (PharmGKB)1
Steroid biosynthesis1
Synthesis and degradation of ketone bodies1
Triacylglyceride synthesis0.419
Tryptophan metabolism0.501
Beta oxidation of unsaturated fatty acids1
Urea cycle and metabolism of amino groups
GPCRs, class A rhodopsin-like0.317
GPCRs, class B secretin-like1
GPCRs, class C metabotropic glutamate, pheromone1
GPCRs, other1
Matrix metalloproteinases0.652
Monoamine GPCRs1
Nuclear receptors1
Nucleotide GPCRs1
Peptide GPCRs0.081
Cytoplasmic ribosomal proteins0.025
Small ligand GPCRs1
ACE inhibitor pathway (Homo sapiens)0.254
Adipogenesis human0.101
Blood clotting cascade0.155
Calcium regulation in the cardiac cell0.431
Circadian exercise0.754
Complement activation and classical pathway0.646
Complement activation and classical pathway0.022
Cytokines and inflammatory response (BioCarta)0
Hypertrophy model0
Inflammatory response pathway0.054
Irinotecan pathway (Homo sapiens)0.685
Oxidative stress0.402
Proteasome degradation0.278
Myometrial relaxation and contraction pathways0
Striated muscle contraction0.427

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 [1013]. 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.

References

  1. S. Ohira, H. Naito, I. Amano et al., “2005 Japanese Society for Dialysis Therapy guidelines for vascular access construction and repair for chronic hemodialysis,” Therapeutic Apheresis and Dialysis, vol. 10, no. 5, pp. 449–462, 2006. View at: Publisher Site | Google Scholar
  2. C. Basile, C. Lomonte, L. Vernaglione, F. Casucci, M. Antonelli, and N. Losurdo, “The relationship between the flow of arteriovenous fistula and cardiac output in haemodialysis patients,” Nephrology Dialysis Transplantation, vol. 23, no. 1, pp. 282–287, 2008. View at: Publisher Site | Google Scholar
  3. N. M. Krivitski, “Theory and validation of access flow measurement by dilution technique during hemodialysis,” Kidney International, vol. 48, no. 1, pp. 244–250, 1995. View at: Google Scholar
  4. T. Lee and P. Roy-Chaudhury, “Advances and new frontiers in the pathophysiology of venous neointimal hyperplasia and dialysis access stenosis,” Advances in Chronic Kidney Disease, vol. 16, no. 5, pp. 329–338, 2009. View at: Publisher Site | Google Scholar
  5. P. Roy-Chaudhury, Y. Wang, M. Krishnamoorthy et al., “Cellular phenotypes in human stenotic lesions from haemodialysis vascular access,” Nephrology Dialysis Transplantation, vol. 24, no. 9, pp. 2786–2791, 2009. View at: Publisher Site | Google Scholar
  6. D. Abeles, S. Kwei, G. Stavrakis, Y. Zhang, E. T. Wang, and G. García-Cardeña, “Gene expression changes evoked in a venous segment exposed to arterial flow,” Journal of Vascular Surgery, vol. 44, no. 4, pp. 863–870, 2006. View at: Publisher Site | Google Scholar
  7. K. B. Boyle, D. Hadaschik, S. Virtue et al., “The transcription factors Egr1 and Egr2 have opposing influences on adipocyte differentiation,” Cell Death and Differentiation, vol. 16, no. 5, pp. 782–789, 2009. View at: Publisher Site | Google Scholar
  8. J. Kumbrink, K. H. Kirsch, and J. P. Johnson, “EGR1, EGR2, and EGR3 activate the expression of their coregulator NAB2 establishing a negative feedback loop in cells of neuroectodermal and epithelial origin,” Journal of Cellular Biochemistry, vol. 111, no. 1, pp. 207–217, 2010. View at: Publisher Site | Google Scholar
  9. L. Marconcini, S. Marchio, L. Morbidelli et al., “c-fos-Induced growth factor/vascular endothelial growth factor D induces angiogenesis in vivo and in vitro,” Proceedings of the National Academy of Sciences of the United States of America, vol. 96, no. 17, pp. 9671–9676, 1999. View at: Publisher Site | Google Scholar
  10. V. Schettler, K. Völker, E. G. Schulz, and E. Wieland, “Impact of lipid apheresis on Egr-1, c-Jun, c-Fos, and Hsp70 gene expression in white blood cells,” Therapeutic Apheresis and Dialysis, vol. 15, no. 1, pp. 105–112, 2011. View at: Publisher Site | Google Scholar
  11. K. Maehara, K. Oh-Hashi, and K. I. Isobe, “Early growth-responsive-1-dependent manganese superoxide dismutase gene transcription mediated by platelet-derived growth factor,” The FASEB Journal, vol. 15, no. 11, pp. 2025–2026, 2001. View at: Google Scholar
  12. J. Wenk, P. Brenneisen, M. Wlaschek et al., “Stable overexpression of manganese superoxide dismutase in mitochondria identifies hydrogen peroxide as a major oxidant in the AP-1-mediated induction of matrix-degrading metalloprotease-1,” The Journal of Biological Chemistry, vol. 274, no. 36, pp. 25869–25876, 1999. View at: Publisher Site | Google Scholar
  13. T. Kondo, F. R. Sharp, J. Honkaniemi, S. Mikawa, C. J. Epstein, and P. H. Chan, “DNA fragmentation and prolonged expression of c-fos, c-jun, and hsp70 in kainic acid-induced neuronal cell death in transgenic mice overexpressing human CuZn-superoxide dismutase,” Journal of Cerebral Blood Flow and Metabolism, vol. 17, no. 3, pp. 241–256, 1997. View at: Google Scholar
  14. R. Pecoits-Filho, B. Lindholm, and P. Stenvinkel, “The malnutrition, inflammation, and atherosclerosis (MIA) syndrome—the heart of the matter,” Nephrology Dialysis Transplantation, vol. 17, supplement 11, pp. 28–31, 2002. View at: Google Scholar

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.


More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views3151
Downloads1031
Citations

Related articles

We are committed to sharing findings related to COVID-19 as quickly as possible. We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. Review articles are excluded from this waiver policy. Sign up here as a reviewer to help fast-track new submissions.