Genetic analysis of genes causing hypertension and stroke in spontaneously hypertensive rats: Gene expression profiles in the kidneys
- Authors:
- Published online on: July 10, 2015 https://doi.org/10.3892/ijmm.2015.2281
- Pages: 712-724
Abstract
Introduction
Studies have been conducted in an attempt to identify the genes causing hypertension using 2 strains of hypertensive rats: spontaneously hypertensive rats (SHRs) and a substrain derived from the SHRs, stroke-prone SHRs (SHRSP) (1,2). Normotensive Wistar-Kyoto (WKY) rats are normally used as controls in these studies (1). Since SHRs and SHRSP are not only used as models of essential hypertension and stroke, but also as models of attention-deficit hyperactivity disorder (ADHD), it is expected that using these rats, it is possible identify the genes related not only to hypertension and stroke, but also to ADHD (3).
In our previous studies, we investigated gene expression profiles in the adrenal glands (4), and subsequently in the brain (5). Since the kidneys are logical candidate organs for studying hypertension due to their direct influence on body fluids and on the functions of the endocrine, cardiovascular and sympathetic nervous systems, in the present study, we aimed to investigate gene expression profiles in the kidneys. Since the association between kidney function and blood pressure is known to be influenced by numerous intrinsic and extrinsic factors, such as the renin-angiotensin system and catecholamine and aldosterone hormones (6), we compared gene expression profiles in the kidneys of SHRs and WKY rats and also between SHRSP and SHRs, when the rats were at 3 and 6 weeks old, a period in which rats are considered to be in a pre-hypertensive state. We isolated a total of 232 unique genes showing more than a 4-fold increase or less than a 4-fold decrease in expression.
After classifying these 232 genes into 4 groups according to their expression profiles, candidate genes were selected as significantly enriched genes, and categorized with Gene Ontology (GO) terms using the Database for Annotation, Visualization and Integrated Discovery (DAVID) web tools (7,8). Candidate genes were also selected using Ingenuity Pathway Analysis (IPA). The IPA path explorer tool revealed that B-cell CLL/lymphoma 6 (Bcl6) (9–13) and SRY (sex determining region Y)-box 2 (Sox2) (14,15) were possible candidate genes that trigger hypertension in SHRs. Moreover, our findings revealed that angiotensinogen (Agt), angiotensin II receptor-associated protein (Agtrap) (16–18) and apolipoprotein H (Apoh) (19) played pivotal roles among SHRSP-specific genes.
Materials and methods
Animals, RNA extraction, microarray design, microarray analysis and microarray data analysis, reverse transcription-quantitative polymerase chain reaction (RT-qPCR), DAVID and IPA
The details of these procedures have been described in our previous studies [Yamamoto et al (4) and Yoshida et al (5)].
Animals
Three strains of rat, SHR/Izm, SHRSP/Izm and WKY/Izm, were provided by the Disease Model Cooperative Research Association, Kyoto, Japan. Three-week-old rats were purchased and maintained for 2 days in our animal facility and were used as 3-week-old rats. Five-week-old rats were purchased and, after being maintained for 1 week in our animal facility, were used as 6-week-old rats.
RNA extraction
Briefly, total RNA was purified using a miRNeasy kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions.
Microarray design
Expression profiling was generated using a 4x44K whole rat genome oligo microarray version 3.0 G2519F (Agilent Technologies Inc., Santa Clara, CA, USA). Eighteen microarray analyses as 1 color experiment were performed using the WKY rats, SHRs, and SHRSP at 3 and 6 weeks old as biological triplicates. Each gene expression profile was compared between the SHRs and WKY rats and also between the SHRSP and SHRs.
Microarray analysis
Total RNA (200 ng) was reverse transcribed into double-stranded cDNA by the AffinityScript Multiple Temperature Reverse Transcriptase (Agilent Technologies Inc.) and amplified. The resulting cDNA was used for in vitro transcription by T7-polymerase and labeled with cyanine-3-labeled cytosine triphosphate (Perkin-Elmer, Wellesley, MA, USA) using a Low Input Quick Amp Labeling kit (Agilent Technologies Inc.). After being labeled and fragmented, each cRNA sample was hybridized on Agilent 4×44K whole rat genome arrays (Agilent Design #028282). After washing, the slides were scanned using an Agilent Microarray Scanner (G2505C; Agilent Technologies Inc.). Feature Extraction software (version 10.5.1.1) was used to convert the images into gene expression data.
Microarray data analysis
Raw data were imported into Subio Platform version 1.12 (Subio Inc., Kagoshima, Japan) and raw intensity data were normalized to the 75th percentile intensity of probes above background levels (gIsWellAbove=1). SHR- and SHRSP-specific genes were defined as those with signal ratios with more than a 4.0-fold increase or less than a 4.0-fold decrease in expression. Raw data have been accepted in the Gene Expression Omnibus (GEO, accession no. GSE41453).
RT-qPCR
To validate the results obtained by the microarray analysis, 11 enriched genes were randomly selected from 39 enriched unique genes, and RT-qPCR was performed under 15 different experimental conditions. Statistical comparisons between the microarray and RT-qPCR data were performed using Spearman's rank correlation test.
DAVID web tool analysis
Annotation enrichment analysis was performed using the DAVID (http://david.abcc.ncifcrf.gov/) web tool (version 6.7, 2010) (7,8) with GenBank IDs bearing Entrez Gene ID (Table I, unique genes identified). This web-based resource provides a set of functional annotation tools for the statistical enrichment of genes categorized into GO terms. We used the GO FAT category, which filtered out very broad GO terms to identify statistically enriched functional groups. The annotated gene and protein symbols were written in italic and regular fonts, respectively.
 | Table IComparison of the number and classification of SHR- and SHRSP-specific probes between the 2 pairs of rat strains. |
IPA
IPA software (IPA®; Qiagen Redwood City, CA, USA, http://www.qiagen.com/ingenuity) was applied to microarray analyses that were conducted to provide functionality for the interpretation of the gene expression data. IPA was performed with Agilent probe IDs bearing Entrez Gene ID as an input for data (Table I, mapped probes). This web tool was used to overlay functions and diseases, and to categorize SHR- and SHRSP-specific genes according to disease-related or functional annotations. It identified the biological functions and/or diseases in the Ingenuity Knowledge Base (Spring 2014 version) that were the most significant to each of the category sets. The probability of the assignment was expressed by a P-value calculated using the right-tailed Fisher's exact test. The path explorer tool was also used to identify relevant interactions among SHR- and SHRSP-specific genes and to identify the shortest literature-supported paths between genes.
IPA was performed using the IPA database (Spring 2014 release of IPA) and the probe IDs of each gene. The data obtained with DAVID were based on the database (version 6.7, 2010) and GenBank IDs of each gene. Since the renewal dates of these two databases were different, small differences were observed between these two annotation results.
Results
Isolation and classification of SHR- and SHRSP-specific genes
We compared gene expression profiles between the SHRs and WKY rats and also between the SHRSP and SHRs, at 3 and 6 weeks of age, and isolated SHR- and SHRSP-specific genes using genome-wide microarray technology. Since we expected the expression of candidate genes to be regulated before elevations in blood pressure (BP), i.e., in the pre-hypertensive period, we examined the expression profiles of each probe using RNA samples prepared from the kidneys, and isolated a total of 353 SHR- and SHRSP-specific probes showing more than a 4-fold increase or less than a 4-fold decrease in expression (Table I).
We classified the 353 probes into 4 groups, from G-1 to G-4 (Table I). G-1 probes were isolated from the rats at 3 weeks of age and contained 87 SHR-specific probes. Their expression profiles were displayed as a heatmap using the Subio Platform (Fig. 1). These 87 probes corresponded to 69 unique genes, 44 of which showed more than a 4-fold increase and 25 showed less than a 4-fold decrease in expression (Table I). G-2 contained 96 SHR-specific genes isolated from the rats at 6 weeks of age, G-3 contained 35 SHRSP-specific genes isolated from the rats at 3 weeks of age, and G-4 contained 32 SHRSP-specific genes isolated from the rats at 6 weeks of age (Table I).
Categorization and enrichment of SHR- and SHRSP-specific genes
Using the DAVID web tools, the candidate genes causing hypertension, stroke and ADHD were selected from each group as significantly enriched genes. We isolated a total of 61 enriched genes consisting of 39 unique genes (Table II).
In order to verify the results obtained from microarray analysis, we randomly selected 11 out of the 39 genes (Table III-A), performed 15 real-time RT-qPCR experiments (Table III-B), and compared the results obtained with those of the micro-array experiments by applying Spearman's rank correlation test. The results supported a correlation between the reults of these two different experiments as rs=0.814 with a two-tailed P-value <0.001.
A total of 69 G-1 genes included 26 enriched genes categorized with 3 GO terms: i) GO:0005576 (extracellular region); ii) GO:0008289 (lipid binding); and iii) GO:0055114 (oxidation reduction) (Table II, G-1). A total of 96 G-2 genes included 24 enriched genes categorized with 4 GO terms: i) GO:0003013 (circulatory system process); ii) GO:0055114 (oxidation reduction); iii) GO:0010817 (regulation of hormone levels); and iv) GO:0006775 (fat-soluble vitamin metabolic process) (Table II, G-2).
A total of 35 G-3 genes included 6 enriched genes categorized with 2 GO terms: i) GO:0003013 (circulatory system process); and ii) GO:0051918 (negative regulation of fibrinolysis) (Table II, G-3). A total of 32 G-4 genes included 5 enriched genes categorized with 2 GO terms: i) GO:0051918 (negative regulation of fibrinolysis) and ii) GO:0030097 (hemopoiesis) (Table II, G-4).
Although 26 enriched G-1 genes and 5 G-4 genes did not include genes categorized with circulatory system process, 24 enriched G-2 genes included 7 genes, and 6 enriched G-3 genes included 4 genes categorized with circulatory system process, respectively (Table II).
Functions and disease-related annotations of SHR- and SHRSP-specific genes
As described above, the SHR- and SHRSP-specific genes were classified into 4 groups (Table I), and then categorized based on disease-related or functional annotations using IPA. The results obtained are summarized in Table IV, and identified among other significantly enriched functional categories, such as ‘endocrine system disorders', ‘cardiovascular disease', ‘cardiovascular system development and function' and ‘hereditary disorder' (Table IV).
| Table IVSHR- and SHRSP-specific genes classified based on disease-related or functional annotations. |
G-1 genes included 2 genes, cystic fibrosis transmembrane conductance regulator (Cftr) and serine peptidase inhibitor, Kazal type 3 (Spink3) categorized as ‘endocrine system disorders (idiopathic pancreatitis)' (Table IV, G-1) (20,21). G-2 genes included 8 genes: angiotensin I converting enzyme (Ace), deiodinase, iodothyronine, type II (Dio2), acyl-Coenzyme A oxidase 2 (Acox2), fin bud initiation factor homolog (Fibin), flavin-containing monooxygenase 2 (Fmo2), indolethylamine N-methyltransferase (Inmt), myosin XVI (Myo16) and zinc finger and BTB domain containing 16 (Zbtb16) categorized as ‘cardiovascular disease (hypertension)' (Table IV, G-2) (22–24). G-3 genes included 6 genes: Agt, Apoh, epoxide hydrolase 2 (Ephx2), histidine-rich glycoprotein (Hrg), ryanodine receptor 1 (Ryr1) and vascular endothelial growth factor B (Vegfb) categorized as ‘cardiovascular system development and function (development of cardiovascular system)' (Table IV, G-3) (25–30). G-4 genes included 6 genes: Btg3 associated nuclear protein (Banp), Ephx2, retinoid X receptor gamma (Rxrg), Ryr1, RNA-binding protein fox-1 homolog 1 (Rbfox1) and Zbtb16 categorized as ‘hereditary disorder (Huntington's disease)' (Table IV, G-4) (31–33).
Interactions among SHR-specific G-1 and G-2 genes
Since our working hypothesis is that G-1 genes include genes that regulate the expression of G-2 genes, we examined the interactions between 69 G-1 and 96 G-2 genes using IPA, and found 5 direct and 3 indirect interactions (Table I and Fig. 2): Rxrg and group-specific component (Gc) interacted with cytochrome P450 subfamily 24 (Cyp24a1) (34,35); Bcl6 interacted with the following 3 genes: Zbtb16 (9,10), protocadherin 9 (Pcdh9) (11) and Spi-B transcription factor (Spib) (12); Cftr interacted with Ephx2 (36); tumor protein p73 (Tp73) interacted with tetraspanin 1 (Tspan1) (37); and Sox2 interacted with Tp73 (14).
Other than the 8 interactions between the G-1 and G-2 genes, we identified 3 interactions among the G-1 genes: Tp73 interacted with Tspan1; Sox2 interacted with Tp73; Sox2 interacted with connective tissue growth factor (Ctgf) (38); and among the G-2 genes: Gc interacted with Cyp24a1; Cftr interacted with Ephx2; Tp73 interacted with Tspan1, respectively. We also found 12 and 16 self-control genes among the SHR-specific G-1 and G-2 genes, respectively (Fig. 2).
However, we did not detect any interactions between the G-1 genes and the majority of BP-controlling G-2 genes, such as Ace, Agtrap, Cftr, glucagon-like peptide 1 receptor (Glp1r), kininogen 2 (Kng2), myosin light chain, phosphorylatable, fast skeletal muscle (Mylpf), Acox2, Dio2, Fibin, Fmo2, Inmt and Myo16 (Table II, G-2; GO:0003013, circulatory system process and Table IV, G-2; cardiovascular disease: hypertension).
Interactions among SHRSP-specific G-3 and G-4 genes
Since the enriched G-3 genes were expected to regulate the expression of the G-4 genes, we examined the interactions between 35 G-3 and 32 G-4 genes using IPA, and found that Agt interacted not only with Agtrap (16,17) expressed in the rats at 3 and 6 weeks of age, but also indirectly interacted with Zbtb16 (39) expressed in the rats at 6 weeks of age (Fig. 3). In addiiton, a total of 5 self-control genes, such as Agtrap, Ephx2, Apoh, Ryr1 and zinc finger protein 597 (Zfp597) were found to be expressed in the SHRSP at 3 and 6 weeks of age (Fig. 3).
The description and reference of each gene are summarized in Table V.
Discussion
General considerations
The first aim of the present study was to identify candidate genes that triggered hypertension in SHRs, the second was to identify genes related to stroke-prone symptoms, and the third was to identify genes related to ADHD. We compared gene expression profiles between SHRs and WKY rats and also between SHRSP and SHRs at 3 and 6 weeks of age, and isolated a total of 232 unique genes showing more than a 4-fold increase or less than a 4-fold decrease in expression as SHR- or SHRSP-specific genes (Table I). We expected a number of these genes to be related to hypertension, susceptibility to stroke and ADHD.
Interactions among SHR-specific G-1 and G-2 genes
The IPA path explorer tool suggested the presence of 5 direct interactions between 69 G-1 and 96 G-2 genes (Fig. 2): i) Rxrg interacted with Cyp24a1 (34); Bcl6 interacted with the following 3 genes: ii) Zbtb16 (9,10), iii) Pcdh9 (11) and iv) Spib (12); and v) Sox2 interacted with Tp73 (14).
Rxrg and Cyp24a1: Rxrg encodes a member of the retinoid X receptor (Rxr) family of nuclear receptors, which are involved in mediating the antiproliferative effects of retinoic acid. This receptor forms dimers with retinoic acid, thyroid hormone and vitamin D receptors, increasing both DNA binding and transcriptional function on their respective response elements. Cyp24a1 encodes a member of the cytochrome P450 superfamily of enzymes. Cytochrome P450 proteins are monooxygenases that catalyze a number of reactions involved in drug metabolism and the synthesis of cholesterol, steroids and other lipids. By regulating vitamin D3 levels, this enzyme plays a role in calcium homeostasis and the vitamin D endocrine system.
Bcl6 and Zbtb16: Bcl6 encodes a zinc finger transcription factor and contains an N-terminal POZ domain. This protein acts as a sequence-specific repressor of transcription, and has been shown to modulate the transcription of START-dependent IL-4 responses in B cells. This protein can interact with various POZ-containing proteins that function as transcription corepressors. Zbtb16 is a member of the Kruppel C2H2-type zinc-finger protein family and encodes a zinc finger transcription factor that contains nine Kruppel-type zinc finger domains at the carboxyl terminus. This protein is located in the nucleus, is involved in cell cycle progression, and interacts with a histone deacetylase.
Bcl6 and Pcdh9: Pcdh9 encodes a member of the protocadherin family, and of transmembrane proteins containing cadherin domains. These proteins mediate cell adhesion in neural tissues in the presence of calcium. The encoded protein may be involved in signaling at neuronal synaptic junctions.
Bcl6 and Spib: Spib encodes a transcriptional activator that binds to the PU-box (5′-GAGGAA-3′) and acts as a lymphoid-specific enhancer.
Sox2 and Tp73: Sox2 encodes a member of the SRY-related HMG-box (SOX) family of transcription factors involved in the regulation of embryonic development and in the determination of cell fate. The product of this gene is required for stem-cell maintenance in the central nervous system, and also regulates gene expression in the stomach. Tp73 encodes tumor protein p53, which responds to diverse cellular stresses to regulate the target genes that induce cell cycle arrest, apoptosis, senescence, DNA repair, and changes in metabolism. The p53 protein is expressed at low levels in normal cells and at high levels in various transformed cell lines, in which it has been suggested to contribute to transformation and malignancy. p53 is a DNA-binding protein that contains transcription activation, DNA-binding and oligomerization domains. It has been postulated to bind to a p53-binding site and activate the expression of downstream genes that inhibit growth and/or invasion, thereby functioning as a tumor suppressor.
Other than these 5 direct interactions between G-1 and G-2 genes, we identified one direct interaction between G-1 genes; Sox2 interacted with Ctgf (38), which encodes a mitogen that is secreted by vascular endothelial cells. This encoded protein plays a role in chondrocyte proliferation and differentiation, cell adhesion in many cell types, and is related to platelet-derived growth factor. Ctgf has been linked to the development and progression of diabetic vascular and renal disease. Low-density lipoproteins (LDL) have previously been shown to induce the expression of Ctgf in aortic endothelial cells (40) (Fig. 2).
SHR-specific G-1 and G-2 genes related to hypertension
Even based on the interactions, described above, we were unable to pinpoint the candidate gene(s) causing hypertension. Although these predicted interactions included the hypertension-related G-2 genes, Ephx2 and Zbtb16, they did not include other hypertension-related genes, such as Ace, Agtrap, Cftr, Glp1r, Kng2, Mylpf, Acox2, Dio2, Fibin, Fmo2, Inmt and Myo16 (Table II, G-2; GO:0003013, circulatory system process and Table IV, G-2; cardiovascular disease: hypertension). In order to identify further interactions between SHR-specific G-1 and G-2 genes, we applied the IPA path explorer tool, suggested the presence of one gene that assisted in these interactions, and found such a condition when we proposed the Jun proto-oncogene (Jun) (41), which interacts directly with specific target DNA sequences to regulate gene expression. The presence of Jun has been shown to facilitate interactions between 3 G-1 genes: Bcl6 (13), Sox2 (15) and ankyrin repeat domain 35 (Ankrd35) (42), and 8 G-2 genes: Spib (43), Ephx2 (44), Tp73 (45), Dio2 (46), cytochrome P450, family 8, subfamily b, polypeptide 1 (Cyp8b1) (47), Mylpf (48), glial cell derived neurotrophic factor (Gdnf) (49) and neurofilament, heavy polypeptide (Nefh) (50) (Fig. 2).
These findings suggested that Bcl6 and Sox2 were the candidate genes responsible for causing hypertension in SHRs.
Interactions among SHRSP-specific G-3 and G-4 genes
Since the candidate gene(s) found to cause stroke in SHRSP were expected to be included in the G-3 genes, we focused on the interaction between G-3 and G-4 genes (Fig. 3). Our results revealed that G-3 genes included 3 typical blood pressure-related genes, Ephx2, Kng2 and Agtrap (Table II, G-3; GO:0003013, circulatory system process). These 3 genes isolated from the SHRSP at 3 weeks of age were not isolated from the SHRs at 3 weeks of age, but were isolated from the SHRs at 6 weeks of age (Table II). These results indicated that the expression of genes related to BP control proceeds more rapidly in SHRSP than in SHRs during their development.
The IPA path explorer tool revealed one interaction among G-3 genes and 8 self-controlling genes (Fig. 3). One of the G-3 genes, Agt, interacted with another G-3 gene, Agtrap, and Agt also interacted with 2 G-4 genes, Agtrap and Zbtb16 (Fig. 3). These results suggest that Agt, Agtrap and Zbtb16 play pivotal roles in causing stroke-prone symptoms. Moreover, G-4 genes including 9 self-controlling genes (Fig. 3), and self-control genes, such as Agtrap, Ephx2, Apoh, Ryr1 and Zfp597, were expressed in the 3- and 6-week-old SHRSP.
In order to detect further interactions between SHRSP-specific G-3 and G-4 genes, we applied the IPA path explorer tool, suggested the presence of one gene that assisted these interactions, and found such a condition when we proposed tumor protein p53 (Tp53) (51), which interacts directly with specific target DNA sequences to regulate gene expression. The presence of Tp53 facilitated interactions between 2 G-3 genes, Agt (18) and Apoh (19), and 3 G-4 genes, Apoh, Vegfb (52) and Banp (51) (Fig. 3).
SHRSP-specific G-3 and G-4 genes related to stroke
Four enriched G-3 genes were categorized as GO:0003013 (circulatory system process). These genes were expected to participate in blood pressure control and the pathogenesis of stroke. Moreover, 2 enriched G-3 genes, Apoh and Hrg, were categorized as GO:0051918 (negative regulation of fibrinolysis) (Table II, G-3). Since Apoh has been implicated in various physiological pathways, including lipoprotein metabolism, coagulation and the production of antiphospholipid autoantibodies, we hypothesized that it may participate in the genesis of atherosclerosis and stroke. Hrg possesses antimicrobial activity, and the incorporation of Hrg into fibrin clots facilitates bacterial entrapment and killing and promotes inflammation. Since vascular inflammation is known to trigger atherosclerosis, Hrg influences atherosclerosis and susceptibility to strokes.
Two out of the 5 enriched G-4 genes, Apoh and Hrg were categorized as GO:0051918 (negative regulation of fibrinolysis), while the remaining 3 genes, chemokine (C-C motif) receptor 1 (Ccr1), leukocyte immunoglobulin-like receptor B-3-like (Lilrb3l) and Zbtb16 were categorized as GO:0030097 (hemopoiesis) (Table II, G-4): Ccr1 encodes a member of the β-chemokine receptor family. Knockout studies on the mouse homolog suggested roles for this gene in host protection from inflammatory responses, and susceptibility to viruses and parasites. Lilrb3l is a receptor for the major histocompatibility complex class I antigen (MHC-I), and may play a physiological role in the brain for neuronal circuitry stability by inhibiting synaptic plasticity. Zbtb16 encodes a protein which is located in the nucleus. It is involved in cell cycle progression and interacts with a histone deacetylase.
Genes related to ADHD and Huntington's disease
We previously examined gene expression profiles in the brain, and found that 6 SHRSP-specific genes isolated from the rats at 6 weeks of age (Agtr1b, Arc, Egr2, Fos, Hspa1b and Snca) were annotated to ‘behavior' and were suggested to participate in the genesis of ADHD (5). In the present study, we investigated gene expression profiles in the kidneys, and unexpectedly found that 6 SHRSP-specific genes isolated from the rats at 6 weeks of age (Banp, Ephx2, Rbfox1, Rxrg, Ryr1 and Zbtb16) were annotated to ‘Huntington's disease' (Table IV, G-4). Tp53 was also found to be involved in ‘Huntington's disease' (33,52). These findings suggested the participation of common genes in the genesis of symptoms related to ADHD and Huntington's disease (Table IV, G-4).
Conclusion
SHR-specific genes isolated from the kidneys of 3-week-old rats included possible candidate genes that trigger hypertension (Bcl6 and Sox2), and SHRSP-specific genes isolated from the kidneys of 3-week-old rats included possible candidate genes that trigger stroke, such as Agt, Agtrap and Apoh. The results obtained from SHRSP-specific genes isolated from the kidneys of 6-week-old rats included 6 genes that have been functionally annotated to Huntington's disease (Banp, Ephx2, Rbfox1, Rxrg, Ryr1 and Zbtb16). These results implicate these genes in the involuntary movement associated with Huntington's disease as well as ‘attention-deficit hyperactivity' observed in ADHD.
Acknowledgments
We thank Dr Etsuro Yamanishi, President Emeritus of Hirakata General Hospital for Developmental Disorders, and Professor Kazunori Shimada, Professor Emeritus of Osaka University, for their constant support and encouragement, and Miss Fumie Kanazawa for her expert secretarial assistance. We also thank the National Center for Biotechnology Information (USA) for access to the network servers and Medical English Service (Japan) for proofreading our manuscript.
Abbreviations:
|
ADHD |
attention-deficit hyperactivity disorder |
|
BP |
blood pressure |
|
DAVID |
Database for Annotation, Visualization and Integrated Discovery |
|
FC |
fold change |
|
GEO |
Gene Expression Omnibus |
|
GO |
Gene Ontology |
|
GS |
gene symbol |
|
IPA |
Ingenuity Pathway Analysis |
|
RT-qPCR |
reverse transcription-quantitative polymerase chain reaction |
|
SHRs |
spontaneously hypertensive rats |
|
SHRSP |
stroke-prone SHRs |
|
WKY rats |
(normotensive) Wistar-Kyoto rats |
References
|
Okamoto K and Aoki K: Development of a strain of spontaneously hypertensive rats. Jpn Circ J. 27:282–293. 1963. View Article : Google Scholar : PubMed/NCBI | |
|
Okamoto K, Yamori Y and Nagaoka A: Establishment of the stroke-prone spontaneously hypertensive rat (SHR). Circ Res. 34–35(Suppl I): I-143–I-153. 1974. | |
|
Ueno KI, Togashi H, Mori K, Matsumoto M, Ohashi S, Hoshino A, Fujita T, Saito H, Minami M and Yoshioka M: Behavioural and pharmacological relevance of stroke-prone spontaneously hypertensive rats as an animal model of a developmental disorder. Behav Pharmacol. 13:1–13. 2002. View Article : Google Scholar : PubMed/NCBI | |
|
Yamamoto H, Okuzaki D, Yamanishi K, Xu Y, Watanabe Y, Yoshida M, Yamashita A, Goto N, Nishiguchi S, Shimada K, et al: Genetic analysis of genes causing hypertension and stroke in spontaneously hypertensive rats. Int J Mol Med. 31:1057–1065. 2013.PubMed/NCBI | |
|
Yoshida M, Watanabe Y, Yamanishi K, Yamashita A, Yamamoto H, Okuzaki D, Shimada K, Nojima H, Yasunaga T, Okamura H, et al: Analysis of genes causing hypertension and stroke in spontaneously hypertensive rats: gene expression profiles in the brain. Int J Mol Med. 33:887–896. 2014.PubMed/NCBI | |
|
Guyton AC: Abnormal renal function and autoregulation in essential hypertension. Hypertension. 18(Suppl): III49–III53. 1991. View Article : Google Scholar : PubMed/NCBI | |
|
Huang W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2009. View Article : Google Scholar | |
|
Huang DW, Sherman BT and Lempicki RA: Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37:1–13. 2009. View Article : Google Scholar : | |
|
Daniel JM and Reynolds AB: The catenin p120(ctn) interacts with Kaiso, a novel BTB/POZ domain zinc finger transcription factor. Mol Cell Biol. 19:3614–3623. 1999.PubMed/NCBI | |
|
Dhordain P, Albagli O, Honore N, Guidez F, Lantoine D, Schmid M, The HD, Zelent A and Koken MH: Colocalization and heteromerization between the two human oncogene POZ/zinc finger proteins, LAZ3 (BCL6) and PLZF. Oncogene. 19:6240–6250. 2000. View Article : Google Scholar | |
|
Miles RR, Crockett DK, Lim MS and Elenitoba-Johnson KS: Analysis of BCL6-interacting proteins by tandem mass spectrometry. Mol Cell Proteomics. 4:1898–1909. 2005. View Article : Google Scholar : PubMed/NCBI | |
|
Wei F, Zaprazna K, Wang J and Atchison ML: PU.1 can recruit BCL6 to DNA to repress gene expression in germinal center B cells. Mol Cell Biol. 29:4612–4622. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Vasanwala FH, Kusam S, Toney LM and Dent AL: Repression of AP-1 function: A mechanism for the regulation of Blimp-1 expression and B lymphocyte differentiation by the B cell lymphoma-6 protooncogene. J Immunol. 169:1922–1929. 2002. View Article : Google Scholar : PubMed/NCBI | |
|
Cox JL, Wilder PJ, Gilmore JM, Wuebben EL, Washburn MP and Rizzino A: The SOX2-interactome in brain cancer cells identifies the requirement of MSI2 and USP9X for the growth of brain tumor cells. PLoS One. 8:e628572013. View Article : Google Scholar : PubMed/NCBI | |
|
Mansukhani A, Ambrosetti D, Holmes G, Cornivelli L and Basilico C: Sox2 induction by FGF and FGFR2 activating mutations inhibits Wnt signaling and osteoblast differentiation. J Cell Biol. 168:1065–1076. 2005. View Article : Google Scholar : PubMed/NCBI | |
|
Fukuyama K, Ichiki T, Takeda K, Tokunou T, Iino N, Masuda S, Ishibashi M, Egashira K, Shimokawa H, Hirano K, et al: Downregulation of vascular angiotensin II type 1 receptor by thyroid hormone. Hypertension. 41:598–603. 2003. View Article : Google Scholar : PubMed/NCBI | |
|
Azuma K, Tamura K, Shigenaga A, Wakui H, Masuda S, Tsurumi-Ikeya Y, Tanaka Y, Sakai M, Matsuda M, Hashimoto T, et al: Novel regulatory effect of angiotensin II type 1 receptor-interacting molecule on vascular smooth muscle cells. Hypertension. 50:926–932. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Liu Q, Wang G, Zhou G, Tan Y, Wang X, Wei W, Liu L, Xue W, Feng W and Cai L: Angiotensin II-induced p53-dependent cardiac apoptotic cell death: its prevention by metallothionein. Toxicol Lett. 191:314–320. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Wang J, Yuan Y, Zhou Y, Guo L, Zhang L, Kuai X, Deng B, Pan Z, Li D and He F: Protein interaction data set highlighted with human Ras-MAPK/PI3K signaling pathways. J Proteome Res. 7:3879–3889. 2008. View Article : Google Scholar : PubMed/NCBI | |
|
Scotet V, De Braekeleer M, Audrézet MP, Lodé L, Verlingue C, Quéré I, Mercier B, Duguépéroux I, Codet JP, Moineau MP, et al: Prevalence of CFTR mutations in hypertrypsinaemia detected through neonatal screening for cystic fibrosis. Clin Genet. 59:42–47. 2001. View Article : Google Scholar : PubMed/NCBI | |
|
Lempinen M, Paju A, Kemppainen E, Smura T, Kylänpää ML, Nevanlinna H, Stenman J and Stenman UH: Mutations N34S and P55S of the SPINK1 gene in patients with chronic pancreatitis or pancreatic cancer and in healthy subjects: a report from Finland. Scand J Gastroenterol. 40:225–230. 2005. View Article : Google Scholar : PubMed/NCBI | |
|
Gonzalez-Villalobos RA, Janjoulia T, Fletcher NK, Giani JF, Nguyen MT, Riquier-Brison AD, Seth DM, Fuchs S, Eladari D, Picard N, et al: The absence of intrarenal ACE protects against hypertension. J Clin Invest. 123:2011–2023. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Gumieniak O, Perlstein TS, Williams JS, Hopkins PN, Brown NJ, Raby BA and Williams GH: Ala92 type 2 deiodinase allele increases risk for the development of hypertension. Hypertension. 49:461–466. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Løset M, Mundal SB, Johnson MP, Fenstad MH, Freed KA, Lian IA, Eide IP, Bjørge L, Blangero J, Moses EK and Austgulen R: A transcriptional profile of the decidua in preeclampsia. Am J Obstet Gynecol. 204:84.e1–84.e27. 2011. View Article : Google Scholar | |
|
Delbosc S, Cristol JP, Descomps B, Mimran A and Jover B: Simvastatin prevents angiotensin II-induced cardiac alteration and oxidative stress. Hypertension. 40:142–147. 2002. View Article : Google Scholar : PubMed/NCBI | |
|
Sakai T, Balasubramanian K, Maiti S, Halder JB and Schroit AJ: Plasmin-cleaved beta-2-glycoprotein 1 is an inhibitor of angiogenesis. Am J Pathol. 171:1659–1669. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Simpkins AN, Rudic RD, Schreihofer DA, Roy S, Manhiani M, Tsai HJ, Hammock BD and Imig JD: Soluble epoxide inhibition is protective against cerebral ischemia via vascular and neural protection. Am J Pathol. 174:2086–2095. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Thulin A, Ringvall M, Dimberg A, Kårehed K, Väisänen T, Väisänen MR, Hamad O, Wang J, Bjerkvig R, Nilsson B, et al: Activated platelets provide a functional microenvironment for the antiangiogenic fragment of histidine-rich glycoprotein. Mol Cancer Res. 7:1792–1802. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
O-Uchi J, Jhun BS, Hurst S, Bisetto S, Gross P, Chen M, Kettlewell S, Park J, Oyamada H, Smith GL, et al: Overexpression of ryanodine receptor type 1 enhances mitochondrial fragmentation and Ca2+- induced ATP production in cardiac H9c2 myoblasts. Am J Physiol Heart Circ Physiol. 305:H1736–H1751. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Bellomo D, Headrick JP, Silins GU, Paterson CA, Thomas PS, Gartside M, Mould A, Cahill MM, Tonks ID, Grimmond SM, et al: Mice lacking the vascular endothelial growth factor-B gene (Vegfb) have smaller hearts, dysfunctional coronary vasculature, and impaired recovery from cardiac ischemia. Circ Res. 86:E29–E35. 2000. View Article : Google Scholar : PubMed/NCBI | |
|
Strand AD, Aragaki AK, Shaw D, Bird T, Holton J, Turner C, Tapscott SJ, Tabrizi SJ, Schapira AH, Kooperberg C and Olson JM: Gene expression in Huntington's disease skeletal muscle: a potential biomarker. Hum Mol Genet. 14:1863–1876. 2005. View Article : Google Scholar : PubMed/NCBI | |
|
Hodges A, Strand AD, Aragaki AK, Kuhn A, Sengstag T, Hughes G, Elliston LA, Hartog C, Goldstein DR, Thu D, et al: Regional and cellular gene expression changes in human Huntington's disease brain. Hum Mol Genet. 15:965–977. 2006. View Article : Google Scholar : PubMed/NCBI | |
|
Thomas EA: Striatal specificity of gene expression dysregulation in Huntington's disease. J Neurosci Res. 84:1151–1164. 2006. View Article : Google Scholar : PubMed/NCBI | |
|
Kephart DD, Walfish PG, DeLuca H and Butt TR: Retinoid X receptor isotype identity directs human vitamin D receptor heterodimer transactivation from the 24-hydroxylase vitamin D response elements in yeast. Mol Endocrinol. 10:408–419. 1996.PubMed/NCBI | |
|
Zella LA, Shevde NK, Hollis BW, Cooke NE and Pike JW: Vitamin D-binding protein influences total circulating levels of 1,25-dihydroxyvitamin D3 but does not directly modulate the bioactive levels of the hormone in vivo. Endocrinology. 149:3656–3667. 2008. View Article : Google Scholar : PubMed/NCBI | |
|
Norkina O, Kaur S, Ziemer D and De Lisle RC: Inflammation of the cystic fibrosis mouse small intestine. Am J Physiol Gastrointest Liver Physiol. 286:G1032–G1041. 2004. View Article : Google Scholar : PubMed/NCBI | |
|
Fontemaggi G, Kela I, Amariglio N, Rechavi G, Krishnamurthy J, Strano S, Sacchi A, Givol D and Blandino G: Identification of direct p73 target genes combining DNA microarray and chromatin immunoprecipitation analyses. J Biol Chem. 277:43359–43368. 2002. View Article : Google Scholar : PubMed/NCBI | |
|
Seo E, Basu-Roy U, Zavadil J, Basilico C and Mansukhani A: Distinct functions of Sox2 control self-renewal and differentiation in the osteoblast lineage. Mol Cell Biol. 31:4593–4608. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Senbonmatsu T, Saito T, Landon EJ, Watanabe O, Price E Jr, Roberts RL, Imboden H, Fitzgerald TG, Gaffney FA and Inagami T: A novel angiotensin II type 2 receptor signaling pathway: possible role in cardiac hypertrophy. EMBO J. 22:6471–6482. 2003. View Article : Google Scholar : PubMed/NCBI | |
|
El-Shewy HM, Sohn M, Wilson P, Lee MH, Hammad SM, Luttrell LM and Jaffa AA: Low-density lipoprotein induced expression of connective tissue growth factor via transactivation of sphingosine 1-phosphate receptors in mesangial cells. Mol Endocrinol. 26:833–845. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Kyriakis JM and Avruch J: Mammalian mitogen-activated protein kinase signal transduction pathways activated by stress and inflammation. Physiol Rev. 81:807–869. 2001.PubMed/NCBI | |
|
Horisawa K, Tateyama S, Ishizaka M, Matsumura N, Takashima H, Miyamoto-Sato E, Doi N and Yanagawa H: In vitro selection of Jun-associated proteins using mRNA display. Nucleic Acids Res. 32:e1692004. View Article : Google Scholar : PubMed/NCBI | |
|
Rao S, Matsumura A, Yoon J and Simon MC: SPI-B activates transcription via a unique proline, serine, and threonine domain and exhibits DNA binding affinity differences from PU.1. J Biol Chem. 274:11115–11124. 1999. View Article : Google Scholar : PubMed/NCBI | |
|
Ai D, Fu Y, Guo D, Tanaka H, Wang N, Tang C, Hammock BD, Shyy JY and Zhu Y: Angiotensin II up-regulates soluble epoxide hydrolase in vascular endothelium in vitro and in vivo. Proc Natl Acad Sci USA. 104:9018–9023. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Toh WH, Siddique MM, Boominathan L, Lin KW and Sabapathy K: c-Jun regulates the stability and activity of the p53 homologue, p73. J Biol Chem. 279:44713–44722. 2004. View Article : Google Scholar : PubMed/NCBI | |
|
Canettieri G, Franchi A, Guardia MD, Morantte I, Santaguida MG, Harney JW, Larsen PR and Centanni M: Activation of thyroid hormone is transcriptionally regulated by epidermal growth factor in human placenta-derived JEG3 cells. Endocrinology. 149:695–702. 2008. View Article : Google Scholar | |
|
Jahan A and Chiang JY: Cytokine regulation of human sterol 12alpha-hydroxylase (CYP8B1) gene. Am J Physiol Gastrointest Liver Physiol. 288:G685–G695. 2005. View Article : Google Scholar | |
|
Morris JB, Pham TM, Kenney B, Sheppard KE and Woodcock EA: UTP transactivates epidermal growth factor receptors and promotes cardiomyocyte hypertrophy despite inhibiting transcription of the hypertrophic marker gene, atrial natriuretic peptide. J Biol Chem. 279:8740–8746. 2004. View Article : Google Scholar | |
|
Fontana X, Hristova M, Da Costa C, Patodia S, Thei L, Makwana M, Spencer-Dene B, Latouche M, Mirsky R, Jessen KR, et al: c-Jun in Schwann cells promotes axonal regeneration and motoneuron survival via paracrine signaling. J Cell Biol. 198:127–141. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Kinoshita I, Leaner V, Katabami M, Manzano RG, Dent P, Sabichi A and Birrer MJ: Identification of cJun-responsive genes in Rat-1a cells using multiple techniques: Increased expression of stathmin is necessary for cJun-mediated anchorage-independent growth. Oncogene. 22:2710–2722. 2003. View Article : Google Scholar : PubMed/NCBI | |
|
Sinha S, Malonia SK, Mittal SP, Singh K, Kadreppa S, Kamat R, Mukhopadhyaya R, Pal JK and Chattopadhyay S: Coordinated regulation of p53 apoptotic targets BAX and PUMA by SMAR1 through an identical MAR element. EMBO J. 29:830–842. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Izzotti A, Cartiglia C, Longobardi M, Bagnasco M, Merello A, You M, Lubet RA and De Flora S: Gene expression in the lung of p53 mutant mice exposed to cigarette smoke. Cancer Res. 64:8566–8572. 2004. View Article : Google Scholar : PubMed/NCBI | |
|
Steffan JS, Kazantsev A, Spasic-Boskovic O, Greenwald M, Zhu YZ, Gohler H, Wanker EE, Bates GP, Housman DE and Thompson LM: The Huntington's disease protein interacts with p53 and CREB-binding protein and represses transcription. Proc Natl Acad Sci USA. 97:6763–6768. 2000. View Article : Google Scholar : PubMed/NCBI |
