Mediators of Inflammation

Mediators of Inflammation / 2015 / Article
Special Issue

Basic and Clinical Advances in Chronic Liver Inflammation

View this Special Issue

Research Article | Open Access

Volume 2015 |Article ID 349215 | 10 pages | https://doi.org/10.1155/2015/349215

Comprehensive Screening of Gene Function and Networks by DNA Microarray Analysis in Japanese Patients with Idiopathic Portal Hypertension

Academic Editor: Ekihiro Seki
Received10 Jun 2015
Revised05 Aug 2015
Accepted11 Aug 2015
Published04 Oct 2015

Abstract

The functions of genes involved in idiopathic portal hypertension (IPH) remain unidentified. The present study was undertaken to identify the functions of genes expressed in blood samples from patients with IPH through comprehensive analysis of gene expression using DNA microarrays. The data were compared with data from healthy individuals to explore the functions of genes showing increased or decreased expression in patients with IPH. In cluster analysis, no dominant probe group was shown to differ between patients with IPH and healthy controls. In functional annotation analysis using the Database for Annotation Visualization and Integrated Discovery tool, clusters showing dysfunction in patients with IPH involved gene terms related to the immune system. Analysis using network-based pathways revealed decreased expression of adenosine deaminase, ectonucleoside triphosphate diphosphohydrolase 4, ATP-binding cassette, subfamily C, member 1, transforming growth factor-β, and prostaglandin E receptor 2; increased expression of cytochrome P450, family 4, subfamily F, polypeptide 3, and glutathione peroxidase 3; and abnormalities in the immune system, nucleic acid metabolism, arachidonic acid/leukotriene pathways, and biological processes. These results suggested that IPH involved compromised function of immunocompetent cells and that such dysfunction may be associated with abnormalities in nucleic acid metabolism and arachidonic acid/leukotriene-related synthesis/metabolism.

1. Introduction

Idiopathic portal hypertension (IPH) is characterized by portal hypertension due to obstruction or stenosis of the intrahepatic peripheral portal branches [1, 2]. Patients with IPH present with splenomegaly, anemia, and portal hypertension and are free of obstruction of the extrahepatic portal vein or hepatic vein, hematological disease, parasitic disease, granulomatous liver disease, or congenital hepatic fibrosis. IPH does not usually lead to liver cirrhosis [35]. IPH resembles a disease called noncirrhotic portal fibrosis in India or hepatoportal sclerosis/noncirrhotic portal hypertension in Western countries [5, 6]. The etiology of IPH remains unclear. To date, researchers have suggested that IPH may be attributed to intrahepatic peripheral portal vein thrombosis, splenic factors, abnormal autoimmunity, and related factors [711].

Our research team has shown that the connective tissue growth factor (CTGF) gene is expressed specifically in patients with IPH. Additionally, we previously reported that patients with IPH exhibit high levels of CTGF in the blood and overexpression of CTGF mRNA in liver tissues [12]. However, in rats with transient overexpression of CTGF induced by recombinant adenovirus, no changes were observed in liver tissues, despite the expression of fibrosis-related genes [13]. Thus, we aimed to examine the specific expression of other genes in patients with IPH.

Recently, comprehensive gene expression analysis with DNA microarrays has been used in a variety of diseases, such as cancer and immunological disease, and has been employed for pathophysiological analysis of these diseases [1417]. Such analysis enables detection of genes or biological pathways showing significant increases or decreases in expression in the target specimens; this is useful for estimation of the features of the target disease. To the best of our knowledge, no studies have reported comprehensive analysis of gene expression using DNA microarrays in patients with IPH. Thus, in this study, we aimed to perform comprehensive gene expression analysis using DNA microarrays in blood samples from patients with IPH to identify genes showing significant changes in expression. Moreover, we performed biological pathway analysis using the data obtained from DNA microarrays.

2. Materials and Methods

2.1. Patients

Analysis was conducted using blood samples from four patients with IPH satisfying the IPH diagnostic criteria (prepared under the Intractable Hepatobiliary Disease Program of the Ministry of Health, Labour and Welfare, Japan) and four healthy volunteers as controls [18, 19]. The diagnosis of IPH was based on the following criteria: (1) general findings: one or more cytopenia, normal function or mild dysfunction of the liver, or collateral circulation such as upper gastrointestinal varix, (2) imaging analysis: splenomegaly, atrophy of peripheral parenchyma and enlargement of central parenchyma in the liver, irregular hepatic surface, increase in splenic venous flow, abnormality of intrahepatic peripheral portal branches, mutual binding of hepatic veins, or increase of hepatic venous pressure, (3) pathological findings: collapse or stenosis of peripheral branches of intrahepatic portal veins, fibrosis of splenic sinus, or Gamma-Gandy bodies formation in splenic trabeculae, (4) exclusion of other diseases causing portal hypertension such as cirrhosis, extrahepatic occlusion, Budd-Chiari syndrome, blood disease, parasitic disease, granulomatous liver disease, congenital liver fibrosis, chronic viral hepatitis, and primary biliary cirrhosis, among others. These blood samples had been stored without identifying information at the Sample Storage Center of Kyushu University installed by the Portal Hemodynamics Abnormalities Study Group within the framework of the Intractable Hepatobiliary Disease Program of the Ministry of Health, Labour and Welfare, Japan. The samples were randomly chosen for use in this study. This study was conducted in compliance with the ethical principles of  the Declaration of  Helsinki (1964) and was approved by the ethics committees of Osaka City University and Kyushu University. Informed consent was obtained from all participants.

2.2. Sample Adjustment and Microarray Data

All blood samples were stored at −80°C. Total RNA was extracted by ISOGEN-LS (NIPPON GENE Co., Ltd., Tokyo, Japan). RNA levels were measured with a spectrophotometer SmartSpec 3000 (Bio-Rad Laboratories, Inc., Hercules, CA, USA), and the quality of each RNA sample was checked with an Agilent 2100 Bioanalyzer (Agilent Technologies, Inc., Santa Clara, CA, USA). Three samples from patients with IPH and three samples from healthy volunteers (with RNA integrity numbers of ≥ 6) were subjected to microarray analysis. The WT-Ovation Pico RNA Amplification System (NuGEN Technologies, Inc., San Carlos, CA, USA) was employed for cDNA synthesis, conversion into sense chain, fragmentation, and biotin labeling, and a Human Gene ST 1.0 Array (Affymetrix, Inc., Santa Clara, CA, USA) was then used for hybridization of the sense chain cDNA. Array scanning was conducted with a GeneChip 3000 Scanner (Affymetrix, Inc.) to yield image data. The array image data from each sample were converted into files in a format enabling numerical extraction using GeneChip Operating Software (GCOS), a standard data analysis system included with the GeneChip system. Microarray data in this paper have been submitted to National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) and are accessible through the GEO series accession number GSE69601.

2.3. Cluster Analysis

The signal intensity values for data from the three patients with IPH and the three healthy controls were divided by the mean signal intensity values for all six samples, and the quotient was converted into a logarithm. The calculated relative signal intensity values were presented on a heat map and subjected to cluster analysis using Cluster 3.0 software with the following parameters: k-means; number of clusters, 12; correlation (uncentered) selected for similarity metric; and number of runs, 1000. The results were visualized with Java TreeView software [20].

2.4. Functional Annotation Analysis for Classification of Gene Function

To extract information on the functions of genes identified as differentially expressing using the GeneChip numerical data, the Database for Annotation Visualization and Integrated Discovery (DAVID) tool was used; this database includes the Gene Ontology Database (http://geneontology.org/) and the Kyoto Encyclopedia of Genes and Genomes (http://www.genome.jp/kegg/) [2123]. For each gene, the ratio between the mean values of the IPH group and the healthy control group, that is, the expression ratio (fold-change), and value obtained using -test were calculated. We used the volcano plot method, which reflects both biological significance and statistical significance. By changing the cut-off value of the expression ratio and value to include gene function terms that may be important, four gene groups were defined, including three groups showing a significant reduction in expression in the presence of IPH (Group A: healthy control/IPH > 2.0, ; Group B: healthy control/IPH > 1.5, ; Group C: healthy control/IPH > 1.5, ) and one group showing significant elevation of expression in the presence of IPH (Group D: IPH/healthy control > 1.5, ). For each group, functional annotation chart analysis and functional annotation clustering analysis were performed.

2.5. Biological Interpretation Using Gene Ontology and Network-Based Pathway Analysis

Gene network analysis was performed using Ingenuity iReport (Ingenuity Systems, http://www.ingenuity.com/, Redwood City, CA, USA). This analysis enabled us to identify differentially expressed genes and molecular interactions for the target disease. On the basis of iReport data, differentially expressed genes were focused using -tests and fold changes, followed by biological interpretation with Ingenuity ontology and canonical pathways. For each pathway, biological process, and disease classification, categories were arranged in the order of involvement rate ranking. Furthermore, relevant genes and networks were also explored by Ingenuity Pathways Analysis (IPA; Ingenuity Systems), with reference to the top 10 significant pathways selected by IPA. Biological interpretation was made from these results.

3. Results

3.1. Cluster Analysis

Figure 1 represents the results of cluster analysis. Samples from all three healthy controls showed similar trends (increases or decreases) in gene expression, while samples from the three patients with IPH showed differing trends. No dominant probe group showing a clear difference between the IPH group and the healthy control group was obtained.

3.2. Functional Annotation Analysis of Differentially Expressed Genes

Table 1 shows the number of charts extracted with the functional annotation chart and the number of clusters extracted by functional annotation clustering. The number of charts was smallest (three) in Group D. Among all groups, clusters for Group C (exhibiting the highest enrichment score (3.37)) contained many gene terms related to the immune system, including “lymphocyte activation” and “leukocyte activation” (Figure 2(a)). This finding suggested that immunological abnormalities were involved in IPH. In Group D, the enrichment score was low (0.94), and no gene function terms were found to be specifically increased in the presence of IPH (Figure 2(b)).


Gene expression ratioNumber of chartsNumber of clusters

Group AControl/IPH > 2.0, 2817
Group BControl/IPH > 1.5, 12445
Group CControl/IPH > 1.5, 16789
Group DIPH/Control > 1.5, 314

3.3. Biological Interpretation Using Network-Based Pathway Analysis

Supplementary Table in Supplementary Material available online at http://dx.doi.org/10.1155/2015/349215 lists all genes selected on the basis of the iReport. For each pathway, biological process, and disease classification, we extracted categories in the top 25 positions of the involvement rate ranking (Tables 24). The highest ranked target in pathway classification was reduced adenosine deaminase (ADA) expression. Additionally, the target at rank 21 in the pathway classification was abnormal purine metabolism. Targets 8 and 15 in pathway classification and targets 1, 2, and 11 in biological process classification were associated with abnormalities in the synthesis/metabolism of arachidonic acid- (AA-) prostaglandin- (PG-) leukotriene (LT). Expression levels of cytochrome P450, family 4, subfamily F, polypeptide 3 (CYP4F3), and glutathione peroxidase 3 (GPX3), which are involved in leukotriene B4 (LTB4) metabolism, were increased, while the expression levels of the PG E receptor 2 (PTGER2) were reduced. Furthermore, target 22 in pathway classification and target 13 in disease classification were associated with abnormal endothelin (ET) signals. The expression of the ET receptor type A (EDNRA) was reduced, while the expression of natriuretic peptide receptor 3 (NPR3) was increased. Additionally, the expression levels of cluster of differentiation 44 (CD44) and transforming growth factor (TGF)-β were reduced. Figure 3(a) shows combination of the first pathway with the second pathway, and Figure 3(b) shows combination of the first pathway with the third and fifth pathways; each of these plots was prepared with reference to the top 10 significant pathways selected by IPA. Reduced expression of the ectonucleoside triphosphate diphosphohydrolase 4 (ENTPD4), ATP-binding cassette, subfamily C, member 1 (ABCC1), ADA, and TGF-β and increased expression of CYP4F3 were noted. Thus, the results from the iReport were also observed within the IPA network.


NumberPathwaysDEGs valueGenes

1Primary immunodeficiency signaling40.000120085ADA, CD79A, IGHD, IGLL1/IGLL5
2Altered T-cell and B-cell signaling in rheumatoid arthritis40.000772903CD28, SLAMF1, TGFB1, CD79A
3T-helper cell differentiation30.004440048CD28, TGFB1, TNFRSF11B
4Cyclins and cell cycle regulation30.006251755TGFB1, CDK7, E2F5
5B-cell development20.010284999CD79A, IGHD
6Leukocyte extravasation signaling40.013183985CXCR4, CD44, MLLT4, MMP12
7Atherosclerosis signaling30.019146088TGFB1, CXCR4, PLA2G10
8Arachidonic acid metabolism30.021798453GPX3, PLA2G10, CYP4F3
9Colorectal cancer metastasis signaling40.027547337RHOQ, TGFB1, PTGER2, MMP12
10Aryl hydrocarbon receptor signaling30.028201448TGFB1, NFIA, GSTA5
11Hepatic fibrosis/hepatic stellate cell activation30.029806364TGFB1, EDNRA, TNFRSF11B
12Glioma invasiveness signaling20.030646264RHOQ, CD44
13Glutathione metabolism20.031643803GPX3, GSTA5
14Cell cycle: G1/S checkpoint regulation20.03367696TGFB1, E2F5
15Eicosanoid signaling20.03367696PLA2G10, PTGER2
16Induction of apoptosis by HIV120.03367696CXCR4, TNFRSF11B
17Hypoxia signaling in the cardiovascular system20.037891658UBE2D2, HIF1AN
18Tight junction signaling30.038540606TGFB1, MLLT4, TNFRSF11B
19Germ cell-sertoli cell junction signaling30.039164578RHOQ, TGFB1, MLLT4
20Mitotic roles of Polo-like kinase20.040070987TGFB1, PTTG1
21Purine metabolism40.043235613ENTPD4, ABCC1, ADA, SEPT1
22Endothelin-1 signaling30.046366635PLA2G10, EDNRA, PTGER2
23Regulation of IL-2 expression in activated and anergic T Lymphocytes20.05532422CD28, TGFB1
24Chronic myeloid leukemia signaling20.073630475TGFB1, E2F5
25HMGB1 signaling20.075004378RHOQ, TNFRSF11B

Underlined genes indicate increases in expression. Not underlined genes indicate decreases in expression.
DEGs: differentially expressed genes.

NumberBiological processDEGs valueGenes

1Synthesis of leukotriene B44TGFB1, ABCC1, ADA, PTGER2
2Synthesis of leukotriene5TGFB1, PLA2G10, ABCC1, ADA, PTGER2
3Quantity of pro-B lymphocytes5CD28, CXCR4, CD79A, IGLL1/IGLL5, TNFRSF11B
4T-cell migration8CD28, TGFB1, CXCR4, GNLY, RASGRP1, PLA2G10, ABCC1, CD44
5Arrest in cell cycle progression of keratinocyte cancer cell lines2TGFB1, MELK
6Function of leukocytes12CD28, SLAMF1, TGFB1, CXCR4, RASGRP1, PLA2G10, ABCC1, ADA, CD44, PTGER2, MMP12, LAX1
7Transcytosis of HIV-12CXCR4, CD79A
8Cell movement of hairy leukemia cells2TGFB1, CD44
9Invasion of keratinocyte cancer cell lines2TGFB1, PTGER2
10Signaling of T lymphocytes3CD28, SLAMF1, CD44
11Metabolism of eicosanoid70.000109678TGFB1, PLA2G10, ABCC1, ADA, CYP4F3, EDNRA, PTGER2
12Reorganization of membrane rafts20.000135709CD28, CD44
13Response of memory T lymphocytes20.000135709CD28, TGFB1
14Fusion of leukocytes30.000153367CXCR4, CD44, TNFRSF11B
15Lymphopoiesis of cells30.000153367TGFB1, CXCR4, CD44
16Migration of mammary tumor cells30.000153367TGFB1, CXCR4, CD44
17Cell viability of leukocytes70.000162768CD28, TGFB1, CXCR4, CD44, LAX1, CD79A, TNFRSF11B
18Transmigration of mononuclear leukocytes40.000170867CD28, TGFB1, CXCR4, CD44
19Lymphopoiesis40.000182632TGFB1, CXCR4, CD44, IGLL1/IGLL5
20Adhesion of prostate cancer cell lines30.000200182TGFB1, CXCR4, CD44
21Invasion of breast cell lines30.000200182TGFB1, CXCR4, PTGER2
22TH1 immune response of naive T lymphocytes20.000225478CD28, TGFB1
23Adhesion of hyaluronic acid20.000225478TGFB1, CD44
24Morphology of cardiovascular tissue40.000235591TGFB1, CXCR4, CD44, TNFRSF11B
25Migration of tumor cell lines100.000252381TNFAIP8, TGFB1, CXCR4, RASGRP1, CDK7, PTTG1, SERPINA5, CD44, E2F5, MLLT4

Underlined genes indicate increases in expression. Not underlined genes indicate decreases in expression.
DEGs: differentially expressed genes.

NumberDiseaseDEGs valueGenes

1Immunodeficiency6CD28, CXCR4, RASGRP1, ADA, CD79A, IGLL1/IGLL5
2Leprosy5CD28, SLAMF1, TGFB1, CD79A, IGLL1/IGLL5
3Transcytosis of HIV-12CXCR4, CD79A
4Genital tumor150.000101439CXCR4, CDK7, FOLH1, STAP1, MKI67, GPX3, ABCA8, TGFB1, ABCC1, SERPINA5, CD44, EDNRA, PTGER2, TNFRSF11B, PEG3
5Agammaglobulinemia20.000135709CD79A, IGLL1/IGLL5
6Metastasis of tumor50.000161747CD28, TGFB1, CXCR4, CD44, PTGER2
7Digestive organ tumor200.000177474ERAP2, HIST1H4A, CXCR4, CDK7, PTTG1, FOLH1, TPD52L1, MELK, MKI67, IGLL1/IGLL5, GPX3, TGFB1, OIT3, ABCC1, C7, E2F5, CD44, MMP12, PEG3, TNFRSF11B
8Colorectal cancer140.000211188ERAP2, HIST1H4A, CXCR4, PTTG1, CDK7, FOLH1, TPD52L1, MELK, MKI67, GPX3, TGFB1, CD44, TNFRSF11B, PEG3
9Tuberculoid leprosy30.000255384CD28, SLAMF1, CD79A
10Metastasis100.000432136CD28, ERAP2, TGFB1, CXCR4, PTTG1, CD44, MLLT4, PTGER2, PEG3, TNFRSF11B
11Granulocyte colony stimulating factor-induced psoriasiform dermatitis20.000470563TGFB1, MKI67
12Rectum cancer30.0005718GPX3, CD44, MKI67
13Vascular disease110.00075445NPR3, SERPINA5, MARC1, TGFB1, EDNRA, PTGER2, CD44, MMP12, CD28, MKI67, CXCR4
14Infection by bacteria80.00090366CD28, SLAMF1, TGFB1, SERPINA5, CD44, MMP12, CD79A, IGLL1/IGLL5
15Replication of HIV-140.00121203CD28, TGFB1, CXCR4, PLA2G10
16Pulmonary hypertension30.001305595NPR3, CXCR4, EDNRA
17Prostatic tumor90.001615688GPX3, TGFB1, CXCR4, FOLH1, CD44, EDNRA, STAP1, MKI67, TNFRSF11B
18Uterine serous papillary cancer60.001802487ABCA8, P2RY14, PTTG1, C7, EDNRA, PEG3
19Fibrosis of lung50.001814056TGFB1, PLA2G10, ADA, EDNRA, MMP12
20Cancer330.001947463ERAP2, HIST2H2BE, PTTG1, FOLH1, TPD52L1, MLLT4, MELK, MKI67, CD79A, IGLL1/IGLL5, GPX3, CD28, SARNP, TGFB1, ABCC1, C7, E2F5, ADA, MMP12, TNFRSF11B, PEG3, HIST1H4A, CXCR4, CDK7, STAP1, ABCA8, P2RY14, RASGRP1, OIT3, SERPINA5, CD44, EDNRA, PTGER2
21Endometrial carcinoma80.002077475GPX3, ABCA8, P2RY14, TGFB1, PTTG1, C7, EDNRA, PEG3
22Neuroblastoma30.002212756CXCR4, ABCC1, CD44
23Edema of tissue20.002294982TGFB1, SPRED2
24Carcinoma280.002351502ERAP2, HIST2H2BE, PTTG1, FOLH1, TPD52L1, MELK, MKI67, IGLL1/IGLL5, GPX3, TGFB1, ABCC1, C7, E2F5, MMP12, TNFRSF11B, PEG3, HIST1H4A, CXCR4, CDK7, STAP1, ABCA8, P2RY14, RASGRP1, OIT3, SERPINA5, CD44, EDNRA, PTGER2
25Polyuria30.002583567NPR3, PTTG1, ABCC1

Underlined genes indicate increases in expression. Not underlined genes indicate decreases in expression.
DEGs: differentially expressed genes.

4. Discussion

IPH is a rare disease. In Japan, IPH was officially added to the list of specific intractable diseases covered by the medical expense subsidy program in January of 2015. IPH has been reported to be associated with autoimmune abnormalities [10, 11, 24]; however, the exact pathophysiology of IPH remains unclear. In this study, we attempted, for the first time, to explore the features of IPH through comprehensive gene analysis with DNA microarrays.

The results of cluster analysis showed that there were no dominant increases/decreases in gene expression in the IPH group as compared to the healthy control group. IPH has been conventionally viewed as a syndrome observed in individuals free of any other disease that can elevate portal pressure. The diagnosis of IPH generally involves ruling out other potential diagnoses, and the pathophysiological features of IPH are not uniform. Thus, these characteristics of IPH may explain the above-mentioned results.

In the analysis of functional annotation charts, many genes showed significantly reduced expression in the IPH group, but only three charts in the IPH group showed significantly increased expression. In functional annotation clustering analysis, the enrichment score of the Group C cluster was the highest (3.37), and this cluster contained many gene terms related to the immune system. Coupled with the observation that some particular trends are found when the enrichment score rises to 2.0 or higher, our data suggested that immunological abnormalities are involved in the pathophysiology of IPH. We also found that the enrichment score in Group D was relatively low (0.94), with no gene function terms showing increased expression in the presence of IPH.

We attempted further clarification of the IPH pathophysiology through network analysis. First, canonical pathway analysis was conducted using the Ingenuity iReport. In pathway classification, reduced expression of ADA was noted. Severe immunodeficiency has been shown to be induced by accumulation of hazardous adenosine metabolites resulting from ADA defects [25, 26]. Thus, reduced expression of ADA may cause induction of autoimmune disease from immunodeficiency, resulting in attack of specific organs. This theory is consistent with the view that immune system abnormalities, particularly suppressor T-cell dysfunction and accompanying autoimmunity, are candidate factors responsible for IPH [24, 27, 28]. ADA dysfunction has also been shown to affect liver differentiation and function [2931]. It is likely that reduced ADA activity serves as a core factor, and combination of ADA with autoimmunity-dependent injury and direct liver injury leads to manifestation of IPH. In this analysis, abnormal purine metabolism was selected. Based on this connection, ADA deficiency induces accumulation of intermediate metabolites due to abnormal nucleic acid metabolism, thereby exerting cytotoxic effects and disrupting lymphocyte differentiation, leading to onset of severe immunodeficiency [32]. Similar to the effects of ADA deficiency, patients with IPH also have abnormal systemic nucleic acid metabolism as a primary feature; this may lead to induction of abnormal lymphocyte differentiation and abnormal function.

The results of the current study showed that the expression levels of CYP4F3 and GPX3, which are known to be involved in degradation of LTB4 and the LTB4 precursor 5-HPETE, were increased. In contrast, the expression of PTGER2 was decreased. LTB4 is known to activate neutrophils [33], and PGE2 is known to have vasodilatory effects [34]. These results may be viewed as compensatory reactions, corresponding to the increased production of LTB4 and PGE2; elevation of these components in the blood may compensate for leukocyte dysfunction and increased blood pressure caused by other factors. Furthermore, analysis of ET signals revealed reduced EDNRA expression and increased NPR3 expression. Decreased expression of the ET receptor leads to vascular contraction [35], while increased expression of NPR3 leads to vascular dilation [36]. Taken together, these findings suggest that NPR3 expression increases as a compensatory reaction to vascular contraction, possibly causing dilation of blood vessels.

When viewed as a whole, the expression of CD44 and TGF-β was reduced. CD44 is known to induce T-cell homing and activate T-helper 1 (Th1) cells, T-helper 2 (Th2) cells, cytotoxic T lymphocytes (CTLs), and natural killer (NK) cells and is thus involved in stem cell proliferation and differentiation [3739]. TGF-β suppresses the proliferation of blood cells and induces the proliferation and activation of mesenchymal cells involved in blood cell differentiation [40, 41]. Thus, in patients with IPH, immune system responses and the differentiation-inducing environment appear to be disturbed.

Furthermore, we explored the network pathway involved in IPH using IPA network analysis. Although the IPA network results did not reveal any additional finding, many genes selected by iReport were also observed in the high-ranked IPA pathways, thus strongly supporting the iReport results.

Taken together, these findings suggest that abnormal systemic nucleic acid metabolism is the first factor involved in the onset of IPH and that this abnormality induces abnormal differentiation and function of immunocompetent cells, although our results are preliminary and require further analysis. In this context, abnormalities in AA-related synthesis/metabolism and ET signals can be viewed as compensatory reactions of blood cells corresponding to changes in the external environment of blood cells, for example, increases/decreases in the levels of certain factors in the serum.

The present study enrolled four IPH patients and three of four samples were used for gene analyses. We cannot deny the possibility that our results did not cover the patterns of whole IPH patients because the sample number was small. In addition, we conducted comprehensive gene expression analysis of blood samples. Splenomegaly is a characteristic symptom in patients with IPH and is often accompanied by histological abnormalities, such as splenic sinus hyperplasia and increased collagen fibers. Thus, analysis of splenic tissue specimens may be useful for clarifying the pathophysiology of IPH. However, unlike the liver, in which percutaneous biopsy is relatively easy, the spleen is difficult to be biopsied or resected in healthy individuals and in patients with IPH during routine clinical practice. To date, no reports have described comprehensive gene expression analysis in patients with IPH. Therefore, additional genetic studies are needed to confirm the biological roles of the molecular pathology of IPH determined in the present study. The identification of genetic abnormalities in IPH may lead to the development of genetic therapy.

5. Conclusion

Blood samples from patients with IPH and healthy individuals as controls were subjected to comprehensive gene analysis with DNA microarrays. In cluster analysis, we did not observe any dominant probe groups showing clear differences between the IPH group and the healthy control group. In functional annotation analysis, the cluster of compromised function among patients with IPH included many gene terms primarily related to the immune system. Network analysis revealed suppression of immune function, abnormal nucleic acid metabolism, and abnormal AA-related synthesis/metabolism. These results suggested that abnormal systemic nucleic acid metabolism was a key factor involved in the onset of IPH and that this abnormality induced abnormal differentiation and function in immunocompetent cells. The abnormalities in AA-related synthesis/metabolism and ET signals may represent compensatory reactions in blood cells, corresponding to changes in the external environment of blood cells, such as increases/decreases in the levels of certain factors in the serum.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

The authors thank Dr. Yoshiyuki Takahara for his assistance of constructive discussion. This study is partially supported by the Health and Labour Sciences Research Grant from the Ministry of Health, Labour and Welfare, Japan, regarding Research on Intractable Diseases, Portal Hemodynamics Abnormalities.

Supplementary Materials

Supplementary Table 1: All genes showing significantly increased or decreased expression in patients with IPH selected on the basis of the iReport.

  1. Supplementary Materials

References

  1. Y. Nakanuma, K. Tsuneyama, O. Makoto, and K. Katayanagi, “Pathology and pathogenesis of idiopathic portal hypertension with an emphasis on the liver,” Pathology Research and Practice, vol. 197, no. 2, pp. 65–76, 2001. View at: Publisher Site | Google Scholar
  2. M. Okudaira, M. Ohbu, and K. Okuda, “Idiopathic portal hypertension and its pathology,” Seminars in Liver Disease, vol. 22, no. 1, pp. 59–71, 2002. View at: Publisher Site | Google Scholar
  3. K. Okuda, “Non-cirrhotic portal hypertension versus idiopathic portal hypertension,” Journal of Gastroenterology and Hepatology, vol. 17, supplement 3, pp. S204–S213, 2002. View at: Publisher Site | Google Scholar
  4. S. K. Sarin and D. Kapoor, “Non-cirrhotic portal fibrosis: current concepts and management,” Journal of Gastroenterology and Hepatology, vol. 17, no. 5, pp. 526–534, 2002. View at: Publisher Site | Google Scholar
  5. J. N. L. Schouten, J. C. Garcia-Pagan, D. C. Valla, and H. L. A. Janssen, “Idiopathic noncirrhotic portal hypertension,” Hepatology, vol. 54, no. 3, pp. 1071–1081, 2011. View at: Publisher Site | Google Scholar
  6. R. Krishan Dhiman, Y. Chawla, R. Kumar Vasishta et al., “Non-cirrhotic portal fibrosis (idiopathic portal hypertension): experience with 151 patients and a review of the literature,” Journal of Gastroenterology and Hepatology, vol. 17, no. 1, pp. 6–16, 2002. View at: Publisher Site | Google Scholar
  7. S. Hillaire, E. Bonte, M.-H. Denninger et al., “Idiopathic non-cirrhotic intrahepatic portal hypertension in the West: a re-evaluation in 28 patients,” Gut, vol. 51, no. 2, pp. 275–280, 2002. View at: Publisher Site | Google Scholar
  8. S. Matsutani, H. Maruyama, T. Akiike et al., “Study of portal vein thrombosis in patients with idiopathic portal hypertension in Japan,” Liver International, vol. 25, no. 5, pp. 978–983, 2005. View at: Publisher Site | Google Scholar
  9. K. Tokushige, S. Hirose, H. Nishimura et al., “Abnormal T cell activation and skewed T cell receptor Vβ repertoire usage in Japanese patients with idiopathic portal hypertension,” Clinical Immunology and Immunopathology, vol. 75, no. 3, pp. 206–213, 1995. View at: Publisher Site | Google Scholar
  10. Y. Nakanuma, A. Nonomura, M. Hayashi et al., “Pathology of the liver in ‘idiopathic portal hypertension’ associated with autoimmune disease,” Acta Pathologica Japonica, vol. 39, no. 9, pp. 586–592, 1989. View at: Google Scholar
  11. K. Saito, Y. Nakanuma, K. Takegoshi et al., “Non-specific immunological abnormalities and association of autoimmune diseases in idiopathic portal hypertension. A study by questionnaire,” Hepato-Gastroenterology, vol. 40, no. 2, pp. 163–166, 1993. View at: Google Scholar
  12. H. Morikawa, A. Tamori, S. Nishiguchi et al., “Expression of connective tissue growth factor in the human liver with idiopathic portal hypertension,” Molecular Medicine, vol. 13, no. 5-6, pp. 240–245, 2007. View at: Publisher Site | Google Scholar
  13. “Research on intractable diseases,” in Annual Report of Research Committee on Portal Hemodynamics Abnormalities, M. Hashizume, Ed., vol. 2006, pp. 25–28, The Ministry of Health, Labour and Welfare of Japan, 2006, (Japanese). View at: Google Scholar
  14. J. DeRisi, L. Penland, P. O. Brown et al., “Use of a cDNA microarray to analyse gene expression patterns in human cancer,” Nature Genetics, vol. 14, no. 4, pp. 457–460, 1996. View at: Publisher Site | Google Scholar
  15. J. H. Chen, H. C. He, F. N. Jiang et al., “Analysis of the specific pathways and networks of prostate cancer for gene expression profiles in the Chinese population,” Medical Oncology, vol. 29, no. 3, pp. 1972–1984, 2012. View at: Google Scholar
  16. H.-M. Lee, H. Sugino, C. Aoki et al., “Abnormal networks of immune response-related molecules in bone marrow cells from patients with rheumatoid arthritis as revealed by DNA microarray analysis,” Arthritis Research and Therapy, vol. 13, no. 3, article R89, 2011. View at: Publisher Site | Google Scholar
  17. D. Arasappan, W. Tong, P. Mummaneni, H. Fang, and S. Amur, “Meta-analysis of microarray data using a pathway-based approach identifies a 37-gene expression signature for systemic lupus erythematosus in human peripheral blood mononuclear cells,” BMC Medicine, vol. 9, article 65, 2011. View at: Publisher Site | Google Scholar
  18. “Research on intractable diseases,” in Annual Report of Research Committee on Portal Hemodynamics Abnormalities, M. Hashizume, Ed., vol. 2008, pp. 93–94, The Ministry of Health, Labour and Welfare of Japan, 2008, (Japanese). View at: Google Scholar
  19. “Research on intractable diseases,” in Annual Report of Research Committee on Portal Hemodynamics Abnormalities, F. Moriyasu, Ed., vol. 2012, pp. 161–162, The Ministry of Health, Labour and Welfare of Japan, 2012 (Japanese). View at: Google Scholar
  20. A. J. Saldanha, “Java Treeview—extensible visualization of microarray data,” Bioinformatics, vol. 20, no. 17, pp. 3246–3248, 2004. View at: Publisher Site | Google Scholar
  21. G. Dennis Jr., B. T. Sherman, D. A. Hosack et al., “DAVID: database for annotation, visualization, and integrated discovery,” Genome Biology, vol. 4, no. 5, article P3, 2003. View at: Publisher Site | Google Scholar
  22. D. W. Huang, B. T. Sherman, Q. Tan et al., “The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists,” Genome Biology, vol. 8, no. 9, article R183, 2007. View at: Publisher Site | Google Scholar
  23. P. Takahashi, D. J. Xavier, A. F. Evangelista et al., “MicroRNA expression profiling and functional annotation analysis of their targets in patients with type 1 diabetes mellitus,” Gene, vol. 539, no. 2, pp. 213–223, 2014. View at: Publisher Site | Google Scholar
  24. F. Pulvirenti, I. Pentassuglio, C. Milito et al., “Idiopathic non cirrhotic portal hypertension and spleno-portal axis abnormalities in patients with severe primary antibody deficiencies,” Journal of Immunology Research, vol. 2014, Article ID 672458, 8 pages, 2014. View at: Publisher Site | Google Scholar
  25. G. Cristalli, S. Costanzi, C. Lambertucci et al., “Adenosine deaminase: functional implications and different classes of inhibitors,” Medicinal Research Reviews, vol. 21, no. 2, pp. 105–128, 2001. View at: Publisher Site | Google Scholar
  26. C. J. van de Wiele, J. G. Vaughn, M. R. Blackburn et al., “Adenosine kinase inhibition promotes survival of fetal adenosine deaminase-deficient thymocytes by blocking dATP accumulation,” The Journal of Clinical Investigation, vol. 110, no. 3, pp. 395–402, 2002. View at: Publisher Site | Google Scholar
  27. K. Umeyama, T. Yamashita, and K. Yoshikawa, “Etiology of idiopathic portal hypertension (IPH)—the role of immunological mechanism in IPH,” Nihon Geka Gakkai Zasshi, vol. 93, no. 4, pp. 400–412, 1992. View at: Google Scholar
  28. K. Tokushige, K. Yamauchi, T. Komatsu, K. Takasaki, and N. Hayashi, “Predominant T helper 1 cells in patients with idiopathic portal hypertension,” Journal of Gastroenterology and Hepatology, vol. 15, no. 11, pp. 1312–1317, 2000. View at: Publisher Site | Google Scholar
  29. A. A. J. Migchielsen, M. L. Breuer, M. A. van Roon et al., “Adenosine-deaminase-deficient mice die perinatally and exhibit liver-cell degeneration, atelectasis and small intestinal cell death,” Nature Genetics, vol. 10, no. 3, pp. 279–287, 1995. View at: Publisher Site | Google Scholar
  30. M. Wakamiya, M. R. Blackburn, R. Jurecic et al., “Disruption of the adenosine deaminase gene causes hepatocellular impairment and perinatal lethality in mice,” Proceedings of the National Academy of Sciences of the United States of America, vol. 92, no. 9, pp. 3673–3677, 1995. View at: Publisher Site | Google Scholar
  31. J. S. Kühl, K. Schwarz, A. Münch et al., “Hyperbilirubinemia and rapid fatal hepatic failure in severe combined immunodeficiency caused by adenosine deaminase deficiency (ADA-SCID),” Klinische Padiatrie, vol. 223, no. 2, pp. 85–89, 2011. View at: Publisher Site | Google Scholar
  32. A. Aiuti, F. Cattaneo, S. Galimberti et al., “Gene therapy for immunodeficiency due to adenosine deaminase deficiency,” The New England Journal of Medicine, vol. 360, no. 5, pp. 447–458, 2009. View at: Publisher Site | Google Scholar
  33. W. R. Henderson Jr., “The role of leukotrienes in inflammation,” Annals of Internal Medicine, vol. 121, no. 9, pp. 684–697, 1994. View at: Publisher Site | Google Scholar
  34. A.-M. Hristovska, L. E. Rasmussen, P. B. L. Hansen et al., “Prostaglandin E2 induces vascular relaxation by E-prostanoid 4 receptor-mediated activation of endothelial nitric oxide synthase,” Hypertension, vol. 50, no. 3, pp. 525–530, 2007. View at: Publisher Site | Google Scholar
  35. A. J. Donato, L. A. Lesniewski, D. Stuart et al., “Smooth muscle specific disruption of the endothelin-A receptor in mice reduces arterial pressure, and vascular reactivity and affects vascular development,” Life Sciences, vol. 118, no. 2, pp. 238–243, 2014. View at: Publisher Site | Google Scholar
  36. R. S. Khambata, C. M. Panayiotou, and A. J. Hobbs, “Natriuretic peptide receptor-3 underpins the disparate regulation of endothelial and vascular smooth muscle cell proliferation by C-type natriuretic peptide,” British Journal of Pharmacology, vol. 164, no. 2, pp. 584–597, 2011. View at: Publisher Site | Google Scholar
  37. S. Khaldoyanidi, D. Schnabel, N. Föhr, and M. Zöller, “Functional activity of CD44 isoforms in haemopoiesis of the rat,” British Journal of Haematology, vol. 96, no. 1, pp. 31–45, 1997. View at: Publisher Site | Google Scholar
  38. B. Wittig, S. Seiter, N. Föger, C. Schwärzler, U. Günthert, and M. Zöller, “Functional activity of murine CD44 variant isoforms in allergic and delayed type hypersensitivity,” Immunology Letters, vol. 57, no. 1–3, pp. 217–223, 1997. View at: Publisher Site | Google Scholar
  39. A. Q. Rafi-Janajreh, P. S. Nagarkatti, and M. Nagarkatti, “Role of CD44 in CTL and NK cell activity,” Frontiers in Bioscience, vol. 3, pp. d665–d671, 1998. View at: Google Scholar
  40. E. Sitnicka, F. W. Ruscetti, G. V. Priestley, N. S. Wolf, and S. H. Bartelmez, “Transforming growth factor beta1 directly and reversibly inhibits the initial cell divisions of long-term repopulating hematopoietic stem cells,” Blood, vol. 88, no. 1, pp. 82–88, 1996. View at: Google Scholar
  41. A. Moustakas and C.-H. Heldin, “Signaling networks guiding epithelial-mesenchymal transitions during embryogenesis and cancer progression,” Cancer Science, vol. 98, no. 10, pp. 1512–1520, 2007. View at: Publisher Site | Google Scholar

Copyright © 2015 Kohei Kotani 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.

828 Views | 487 Downloads | 7 Citations
 PDF  Download Citation  Citation
 Download other formatsMore
 Order printed copiesOrder

We are committed to sharing findings related to COVID-19 as quickly and safely as possible. Any author submitting a COVID-19 paper should notify us at help@hindawi.com to ensure their research is fast-tracked and made available on a preprint server as soon as possible. We will be providing unlimited waivers of publication charges for accepted articles related to COVID-19.