Published online before print October 15, 2007, doi: 10.1161/HYPERTENSIONAHA.107.093138
(Hypertension. 2007;50:1126.)
© 2007 American Heart Association, Inc.
Original Articles |
Cross-Talk of Expression Quantitative Trait Loci Within 2 Interacting Blood Pressure Quantitative Trait Loci
From the Department of Pharmacology and Physiology (N.H.L., N.E.L., B.C.F., T.V.L., C.D.H.), George Washington University, Washington, DC; Department of Functional Genomics (N.H.L., B.J.H., Q.S.), Institute for Genomic Research, Rockville Md; Physiological Genomics Laboratory (S.Y.-W., P.F., E.M., B.J.), Department of Physiology and Pharmacology, University of Toledo College of Medicine, Toledo, Ohio.
Correspondence to Bina Joe, Physiological Genomics Laboratory, Department of Physiology and Pharmacology, University of Toledo College of Medicine, 3035 Arlington Ave, Toledo, OH 43614-5804. E-mail bina.joe{at}utoledo.edu or Norman H. Lee, Department of Pharmacology and Physiology, The George Washington University Medical Center, 2300 I St NW, Washington, DC 20037. E-mail phmnhl@gwumc.edu
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Abstract |
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Genetic dissection of the S rat genome has provided strong evidence for the presence of 2 interacting blood pressure quantitative trait loci (QTLs), termed QTL1 and QTL2, on rat chromosome 5. However, the identities of the underlying interacting genetic factors remain unknown. Further experiments targeted to identify the interacting genetic factors by the substitution mapping approach alone are difficult because of the interdependency of natural recombinations to occur at the 2 QTLs. We hypothesized that the interacting genetic factors underlying these 2 QTLs may interact at the level of gene transcription and thereby represent expression QTLs or eQTLs. To detect these interacting expression QTLs, a custom QTL chip containing the annotated genes within QTL1 and QTL2 was developed and used to conduct a transcriptional profiling study of S and 2 congenic strains that retain either 1 or both of the QTLs. The results uncovered an interaction between 2 transcription factor genes, Dmrta2 and Nfia. Furthermore, the "biological signature" elicited by these 2 transcription factors was differential between the congenic strain that retained Lewis alleles at both QTL1 and QTL2 compared with the congenic strain that retained Lewis alleles at QTL1 alone. A network of transcription factors potentially affecting blood pressure could be traced, lending support to our hypothesis.
Key Words: hypertension • gene • congenic • Dahl salt-sensitive rat • inbred
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Introduction |
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Genetic studies of complex polygenic traits such as blood pressure (BP) are primarily conducted with the view to map, identify, and ascertain the magnitude of change imparted by each of the underlying contributing genetic factors. The scope of such studies conducted in humans alone is thwarted by several confounding factors including genetic heterogeneity, low penetrance, and uncontrolled environmental factors.1 Inbred rat models of hypertension have served as tools to study the genetic control of BP with minimal interferences from these confounding factors.2 Allelic mosaics of hypertensive and normotensive strains are built in the form of recombinant inbred strains, consomic strains, or congenic strains to facilitate querying the rat genome for BP quantitative trait loci (QTLs).1–9 In our laboratory, we have located and fine mapped several independent QTLs to small (a few megabases or kilobases) regions of the rat genome.10–14 Unlike these loci, which exert their phenotypic effect independent of the other BP QTLs, a region on rat chromosome 5 (RNO5) was ascertained previously to contain 2 closely linked interacting BP QTLs.15 Because normotensive alleles at both of the QTLs are required to exert a BP-lowering effect, further genetic dissection using substitution mapping as the sole approach becomes complex. Hence, there is a need to apply a complimentary approach to prioritize identification of the 2 interacting BP QTLs on RNO5.
In recent years, whole-genome transcriptome analysis has been applied to genetic studies of inbred strains, recombinant inbred strains, consomic strains, and congenic strains.16–24 In particular, such approaches have resulted in cataloging the dynamics of transcriptomes under different rat genetic backgrounds and various experimental conditions. We have previously used a targeted transcriptome analysis of a chromosome 1 QTL region coupled with whole-genome transcriptome and promoter analyses as a standardized method to identify a transcription factor (TF) as a potential underlying causative genetic factor.25 In the current study, we have applied this method to the genetic dissection of 2 contiguous and interacting BP QTLs (herein named QTL1 and QTL2; Figure 1) on RNO5. For many of the QTL transcriptome analyses performed to date, rat strains are exposed to the same environmental stressor (eg, a high-salt diet that unmasks deficiencies in the normal homeostatic and idiopathic mechanism contributing to cardiovascular disease) that was used in the phenotypic QTL analysis. The challenge of such an approach has always been to differentiate gene expression changes that are causative rather than consequential of the observed BP differences. An alternative and complimentary strategy is to search for causative genes (or predisposition genes) by expression profiling a panel of select recombinant strains in the absence of any environmental stimuli.18,26 The results of 1 such "baseline" approach, which has uncovered a distinct, heritable transcriptional network regulated in a concerted manner by the 2 interacting RNO5 BP QTLs, are presented.
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Animals
All of the male rats used for this study were from our breeding colony at the University of Toledo Health Science Campus (former Medical College/University of Ohio), maintained and studied as per the institutional review committee’s approved protocols. The available congenic strains used for this study, S.L(5)x6x9 (Dahl salt-sensitive [S] background with Lewis [L] alleles in both QTL1 and QTL2) and S.L(5)x6x11 (S background with L alleles in QTL1), were generated in our laboratory as described elsewhere.15 The strain with L alleles at QTL2 alone, S.L(5)x5, was euthanized and, therefore, unavailable for study. The "L" in the names of congenic strains is abbreviated to "L" throughout the article. Parental S and L rats and congenic strains were maintained on a low salt (0.3% NaCl)–containing diet No. 7034 from Harlan (http://www.teklad.com/standardrodentdiet/r7034.asp).
Genotyping
DNA was extracted from a tail biopsy using the Promega Wizard (Promega Inc). PCR genotyping with microsatellite markers was done as described earlier.14 New polymorphic microsatellite markers between S and L with D5Mco as prefixes were developed using a rat genomic DNA sequence obtained from the ENSEMBL Web site (www.ensembl.org). These can be accessed on our Web site (http://hsc.utoledo.edu/depts/physiology/research/rat/marker.html).
RNA Extraction and Gene Expression Profiling
Male S, S.L(5)x6x9, and S.L(5)x6x11 congenic rats born on the same day were selected, weaned at 30 days of age, and euthanized at 40 days of age. Whole kidney was isolated in RNAlater (Ambion) as per the manufacturer’s procedures. Total RNA was extracted in TRIzol reagent (Invitrogen) and purified using RNeasy (Qiagen). Fabrication of the custom 70-mer oligonucleotide and cDNA microarrays, microarray statistics, RT-PCR, promoter network, and interactome analyses was conducted as per previously published procedures.27,28 Details are provided in the online Supplemental Methods section (available at http://hyper.ahajournals.org). Primers used for validation by RT-PCR are given in Table S1 (please see http://hyper.ahajournals.org).
Targeted Knockdown of Gene Expression
Dmrta2 mRNA (GenBank accession No. XM_233363) was targeted for knockdown by using RNA interference.29 Small interfering RNA duplexes were designed using the software siRNA Target Finder (Ambion, please see http://www.ambion.com/techlib/misc/siRNA_finder.html) and chemically synthesized (Dharmacon). The Dmrta2 mRNA target sequence was 5'-AAGTTGCAGAAGTTTGATCTG-3', and a corresponding scrambled sequence 5'-AAAGCTAAGGTGTGATTTTCG-3' served as a negative control. Small interfering RNA duplexes were transfected into NRK-52E kidney epithelial cells (American Type Culture Collection catalog No. CRL 1571) with Lipofectamine 2000 as described previously.30
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Refinement of the Congenic Interval
The congenic strains used as genetic tools are represented in Figure 1. Note that the existence of 2 interacting QTLs cannot be deduced from Figure 1 alone, but depends on conclusive evidence provided in the previously published data.15 Using new polymorphic markers, QTL1 was located between D5Mco39 and D5Mco41, and QTL2 was located between D5Mco43 and D5Mco44 (Figure 1). Therefore, the critical regions of interest for the present study were 10 885 789 bp spanning QTL1 and 4 667 028 bp spanning QTL2.
Transcriptional Profiling of S Versus S.L(5)x6x9 Congenic Strain
Using a custom 70-mer oligonucleotide microarray to interrogate QTL1 and QTL2 genes, 26 (11%) of 231 positional candidate genes were found to be significantly differentially expressed between S and S.L(5)x6x9 kidneys. Thirteen of the differentially expressed renal genes exhibited a change of 
1.4-fold, and 2 of these genes encode the TFs doublesex- and mab-3–related TF-like family A2 (Dmrta2) and nuclear factor 1/A (Nfia; Table 1 and Table S2; please see http://hyper.ahajournals.org for the complete gene list and significance analysis of microarrays statistical parameters). The differential expression of both TFs was validated with quantitative RT-PCR (Table 2 and Table S3, for real time RT-PCR P values).
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The same kidney RNA samples used for the custom oligonucleotide array experiment were profiled with a custom rat cDNA microarray containing 
27 000 probes. A total of 541 "non-QTL" transcripts were found to be significantly differentially expressed between S and S.L(5)x6x9 kidneys by 1.4-fold (see Table S4 for a complete gene list and SAM statistical parameters). Of the 541 differentially expressed genes, 283 could be assigned a Gene Ontology molecular function or role category, 112 were upregulated in the congenic strain relative to S, 429 were downregulated in the congenic strain relative to S, and 15 corresponded with TFs. Of the 15 TFs identified by microarray analysis, quantitative RT-PCR validated the differential expression of 13 (Table 2). There was good concordance for the 44 (of 51) QTL genes that were successfully interrogated by both the oligonucleotide and cDNA microarrays (Table S5). Thirty-nine genes were in agreement (34 genes not differentially expressed, 4 genes upregulated, and 1 gene downregulated), whereas only 5 genes exhibited conflicting results (ie, a gene exhibiting differential regulation in one but not the other platform).
Transcriptional and Interactome Networks Portend Cardiovascular Function
Transcriptional cross-talk analysis was performed on the TFs listed in Table 2 (see the online Supplemental Methods). TRANSFAC binding matrices (available for 9 of the 15 TFs; see Figure S1) were used by the Motif Alignment and Search Tool to search the TF promoter sequences, revealing a complex regulatory TF network (Figure 2). Many of the TFs appear to regulate the expression of one another through highly conserved network architectural motifs,29 such as feedback (eg, HIF1A and NFYB), feedforward (eg, ATF5, RARA, and HIF1A), and regulatory chain motifs (eg, RXRA, NFYB, and FOXE3; see Table 2 footnote for TF abbreviations). Although a potential Nfia 
Dmrta2 network connection could be established, the unavailability of a TRANSFAC binding matrix for DMRTA2 precluded computational assignment of a reciprocal interaction between QTL TF genes (ie, Dmrta2 Nfia). Consequently, we applied a functional approach to investigate the possibility of such a connection, albeit indirect. Targeted knockdown of Dmrt2a mRNA via RNA interference29 in the rat kidney epithelial cell line NRK-52E resulted in a repression of Nfia transcript levels (1.78±0.19-fold decrease; P=0.0198, unpaired t test), suggesting the possible existence of a feedback regulatory loop between QTL TF genes (Figure 2).
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Of the 526 differentially regulated non-TF/non-QTL genes, 427 promoter sequences were extracted, whereas the remaining 99 promoters were poorly defined (because of sequencing gaps and/or an inability to assign transcription start site) and, hence, were excluded from further analysis. Motif Alignment and Search Tool searches with the available TF matrices indicated that each of the 427 gene promoters could be assigned (binned) to 1 of 8 TFs (Figure 2). Our results highlight a complex network architecture where, eg, 422 promoters contained 2 different sites. The presence of cis-regulatory elements for NFIA and NFYB was the most prevalent, being found in 309 promoters, followed by sites for NFYB and RXRA in 303 promoters and sites for NFIA and RXRA in 222 promoters (data not shown). Least common was the ATF5 and XBP1 element combination, which was found in only 2 promoters. Higher-order permutations could be identified where the combination of any 5 sites (of 8) was present en masse in 39 promoters, and promoters for microfibrillar-associated protein 2 and hypothetical protein LOC361985 had all 8 TF binding sites (data not shown).
Computational analysis of binned genes through ingenuity pathway analysis and EASE (see online Supplemental Methods) identified interactome modules that could be assigned to functions involved in gene expression and cell signaling, in addition to cardiovascular-related functions, such as the small molecule metabolism, mineral metabolism, lipid metabolism, inflammation, cardiovascular function, endocrine system, and hematologic system (Figure 2). On average, each interactome module contained 18 differentially regulated genes. In some extreme instances, as seen in the HIF1A hematologic and NFYB cardiovascular modules, the vast majority of genes were downregulated in the kidneys of S.L(5)x6x9 congenic rats compared with their S rat counterpart (Figure 2). Conversely, instances where the vast majority of genes were upregulated could be seen, eg, in the RXRA small molecule module (Figure 2). The TFs RARA and RXRA were grouped together by ingenuity pathway analysis as potential heterodimerization partners regulating interactome modules involved in small-molecule biochemistry, energy production, and lipid metabolism (Figure 2).
Transcriptional Analysis of S Versus the Congenic Strain S.L(5)x6x11
Renal expression of TF genes in S.L(5)x6x11 relative to S rats was assessed by quantitative RT-PCR (Table 2). Transcript levels for Dmrta2 and Nfia were not found to be differentially expressed between the 2 rat strains. Moreover, the 8 non-QTL network TFs that were differentially expressed in S.L(5)x6x9 (Figure 2 and Table 2) were no longer differentially expressed in S.L(5)x6x11 relative to S (Table 2). By comparison, 9 of the 10 TF genes depicted in Figure 2 (exception being Hif1a) were differentially expressed in parental L relative to S, analogous to the S.L(5)x6x9 findings (Table 2). Next, we determined the relative expression of 427 non-QTL, non-TF network genes in the kidneys of S.L(5)x6x11 compared with S rats. To recall, these genes were differentially expressed between S and the congenic strain with lower BP than S, ie, S.L(5)x6x9. Remarkably, only 10 of the 427 genes were differentially expressed, suggesting that these non-QTL/non-TF genes are likely to be regulated by the proposed network of TFs (and this regulation specifically requires the L alleles of Dmrta2 and Nfia). Quantitative RT-PCR validation was performed on 14 non-QTL, non-TF network genes, including hydroxysteroid (11-ß) dehydrogenase 2, ß-adrenergic receptor kinase 1, superoxide dismutase 2, and the glucagon receptor (Tables S3 and S6). In agreement with microarray data, genes that were differentially expressed in S.L(5)x6x9 versus S rat kidneys were no longer differentially expressed in S.L(5)x6x11 versus S rat kidneys.
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Discussion |
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The identity of causative elements is the single biggest unknown aspect of the genetics of hypertension. The search for high BP-causative genes using animal models can be confounded whenever animals have been exposed to an environmental challenge, such as the administration of a high salt–containing diet for extended periods. The S rats, although well known for their salt sensitivity, develop hypertension even when their salt intake is low.31 This indicates that there are S rat alleles that confer susceptibility to high BP regardless of the dietary salt. In the present study, rat strains maintained on a low salt–containing diet were studied to unmask the underlying genetic factors (ie, gene expression differences) that may predispose S rats to hypertension compared with their congenic counterparts with relatively lower BP. We also reasoned that studying young rats at a developmental stage where critical physiological changes related to BP regulation are documented to occur32 will provide the opportunity to differentiate chronologically between gene expression changes that are potentially causative versus those that are consequential to changes in BP. Other than the well-known role of the kidney as a vital organ regulating BP through the renin-angiotensin system and maintenance of pressure natriuresis,33 early renal transplantation experiments have demonstrated that kidneys from the S rat had a prohypertensive effect.34 These effects on BP were most clear cut in rats maintained on a low-sodium diet.34 Therefore, the kidney was chosen as a primary target organ for study. Our study design incorporated several important features: (1) the use of a custom-made long oligonucleotide chip containing probes for all of the known and predicted genes in the BP QTLs with particular emphasis on TF genes; (2) use of a cDNA microarray to interrogate genes outside of the BP QTLs; (3) computational analyses and identification of TF binding domains within the promoter region of differentially expressed genes; and (4) transcriptional profiling of 2 phenotypically informative rat strains: the S.L(5)x6x9 congenic strain with lower BP than S, which contains L alleles of Dmrta2 in QTL1 and Nfia in QTL2, and the S.L(5)x6x11 congenic strain with no BP effect compared with S containing the L allele for Dmrta2 and the S allele for Nfia.
Our findings suggest that the L alleles of Dmrta2 and Nfia genes may interact with each other by an as-yet-unidentified mechanism and that L alleles of Dmrta2 in combination with S alleles of Nfia is insufficient for the manifestation of differential gene expression that was seen in S.L(5)x6x9 versus S rats (Figure 2). The highlight of the differential gene expression in S.L(5)x6x9 was the concerted regulation of 8 TF genes (RXRA, RARA, HIF1A, XBP1, NFYB, FOXE3, ATF5, and POU3F4). This observation suggests cross-talk between DMRTA2 and NFIA, because the differential expression of these 8 TFs was not observed when L alleles of Nfia in BP QTL2 were not available to cross-talk with the L alleles of Dmrta2 in BP QTL1. Overall, our data are consistent with the hypothesis that the physiological BP QTL1 harboring the cis-acting expression QTL (eQTL) Dmrta2 requires allelic interactions from the physiological BP QTL2 harboring the cis-acting eQTL Nfia for demonstrating the lowering effect on BP and maintaining the transcriptional cross-talk. Of note, the ENSEMBL single nucleotide polymorphism database for L and S strains lists 63 known single nucleotide polymorphisms in the Nfia gene (http://www.ensembl.org/index.html). The role of these single nucleotide polymorphisms in differential gene expression remains to be elucidated.
The central question that remains, however, is whether this cross-talk between Dmrta2 and Nfia is responsible for the observed interaction between the 2 BP QTLs. Similar to any gene expression analysis using models of allelic mosaics obtained from animals of contrasting phenotypes,17–20,35 mere detection of the cis-acting eQTLs does not by any means allow for attesting it as causal to the underlying physiological QTL, which, in our case, is the control of BP. Ideally, further proof will be required to pinpoint the link between the eQTL and the physiological QTL in the context of an absolutely identical genetic background. There is a practical difficulty to attain this goal using any form of genetic model that is currently feasible for the rat. Another way of addressing this issue is to look for perturbations within the "biological signatures" that are downstream of the 2 eQTLs. It is interesting to note that the 8 TFs downstream of Dmrta2 and Nfia appear to act in concert to differentially regulate the expression of at least 500 non-TF genes in S.L(5)x6x9 relative to S. Many of the resulting gene products mapped into interactome networks with cardiovascular-related themes (Figure 2). Remarkably, the vast majority of the non-TF genes were no longer differentially expressed in S.L(5)x6x11 compared with S rats. Therefore, it is reasonable to hypothesize that this lack of differential gene regulation may be responsible for the hypertensive phenotype of S.L(5)x6x11. This reasoning is supported by the results presented in Table S6, wherein a select list of non-TF genes with known or inferred roles in BP control36–57 were significantly different in expression between S and the congenic strain with L alleles at both BP QTLs but not between S and the congenic strain with L alleles in QTL1 only. Maintenance of differential expression of these known role players of BP control between the congenic strain that contains L alleles at both QTLs but not the congenic strain that contains L alleles only at QTL1 lends support to the view that the cross-talk between the eQTLs, Dmrta2 and Nfia, is important for maintaining transcriptional regulation of multiple physiological processes related to BP homeostasis.
Roman et al58 have recently provided evidence for the involvement of a genomic segment on RNO5 encompassing a family of Cyp4a genes in controlling BP of the S rat. Their data suggest that there may be additional QTLs on RNO5, which operate independent of the QTLs reported in our study. The observations by Roman et al58 lead them to hypothesize that the change in BP noted in their study is either because of sequence variants within the CYP4A genes or because of factors within the markers D5Rat130 to D5Rat31 (32.6 to 139.9 Mb) on RNO5 that alter the expression of the Cyp4a genes. The S.L(5)x6x9 containing L alleles from 116.6 Mb to 134.9 Mb did not result in differential expression of the Cyp4a genes compared with the S rat. These data suggest that genetic factors within 116.6 to 134.9 Mb of RNO5 are not responsible for the observed changes in Cyp4a genes reported by Roman et al,58 thus lending further support to the view that deficiency of renal formation of 20-HETE contributing to increased BP may be because of functional polymorphisms within the Cyp genes in rats58 and in humans.59
Perspectives
This study has defined a chromosome 5 QTL gene network that synchronizes well with BP differences seen in 2 congenic strains compared with the S, ie, S.L(5)x6x9 and S.L(5)x6x11. At this stage of the study, we are cautious to interpret the detection of cis-eQTLs within the interacting BP QTLs as parallel observations that require further proof to be designated as factors governing the observed BP effects. At the very least, our study provides a novel basis to conduct further elaborate substitution mapping and/or transgenic experiments to test the interactive roles of each of the eQTLs in BP, QTL1 and QTL2 (Table 2) in BP control. In any case, this is the first report of a possible mechanistic explanation for interacting QTLs occurring through a cross-talk between TFs.
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Acknowledgments |
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We give sincere thanks to Drs John P. Rapp and George T. Cicila for critical reading.
Source of Funding
This work was supported by National Institutes of Health grant RO1-HL075414 to B.J. and N.H.L.
Disclosures
None.
Received May 2, 2007; first decision May 30, 2007; accepted September 18, 2007.
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References |
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TopAbstractIntroductionMethodsResultsDiscussionReferences |
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1. Cowley AW Jr. The genetic dissection of essential hypertension. Nat Rev Genet. 2006; 7: 829–840.[Medline] [Order article via Infotrieve]
2. Rapp JP. Genetic analysis of inherited hypertension in the rat. Physiol Rev. 2000; 80: 135–172.
3. Joe B, Garrett MR. Substitution mapping: using congenic strains to detect genes controlling blood pressure. In: Raizada MK, Payton JFR, Kasparov S, Katovich MJ, ed. Cardiovascular Genomics. Humana Press Inc; 2005: 41–58.
4. Joe B, Garrett MR. Genetic analysis of inherited hypertension in the rat. In: Dominiczak A, Connell J, eds. Genetics of Hypertension. Amerstam, The Netherlands: Elsevier Science; 2006; 24: 177–200.
5. Pravenec M, Kurtz TW. Molecular genetics of experimental hypertension and the metabolic syndrome: from gene pathways to new therapies. Hypertension. 2007; 49: 941–952.
6. Deng AY. Positional cloning of quantitative trait loci for blood pressure: how close are we?: a critical perspective. Hypertension. 2007; 49: 740–747.
7. Jacob HJ, Kwitek AE. Rat genetics: attaching physiology and pharmacology to the genome. Nat Rev Genet. 2002; 3: 33–42.[CrossRef][Medline] [Order article via Infotrieve]
8. McBride MW, Carr FJ, Graham D, Anderson NH, Clark JS, Lee WK, Charchar FJ, Brosnan MJ, Dominiczak AF. Microarray analysis of rat chromosome 2 congenic strains. Hypertension. 2003; 41: 847–853.
9. McBride MW, Brosnan MJ, Mathers J, McLellan LI, Miller WH, Graham D, Hanlon N, Hamilton CA, Polke JM, Lee WK, Dominiczak AF. Reduction of Gstm1 expression in the stroke-prone spontaneously hypertension rat contributes to increased oxidative stress. Hypertension. 2005; 45: 786–792.
10. Saad Y, Garrett MR, Manickavasagam E, Yerga-Woolwine S, Farms P, Radecki T, Joe B. Fine-mapping and comprehensive transcript analysis reveals nonsynonymous variants within a novel 1.17 Mb blood pressure QTL region on rat chromosome 10. Genomics. 2007; 89: 343–353.[CrossRef][Medline] [Order article via Infotrieve]
11. Garrett MR, Meng H, Rapp JP, Joe B. Locating a blood pressure quantitative trait locus within 117 kb on the rat genome: substitution mapping and renal expression analysis. Hypertension. 2005; 45: 451–459.
12. Garrett MR, Rapp JP. Defining the blood pressure QTL on chromosome 7 in Dahl rats by a 177kb congenic segment containing Cyp11b1. Mamm Genome. 2003; 14: 268–273.[CrossRef][Medline] [Order article via Infotrieve]
13. Joe B, Garrett MR, Dene H, Rapp JP. Substitution mapping of a blood pressure quantitative trait locus to a 2.73 Mb region on rat chromosome 1. J Hypertens. 2003; 21: 2077–2084.[CrossRef][Medline] [Order article via Infotrieve]
14. Saad Y, Yerga-Woolwine S, Saikumar J, Farms P, Manickavasagam E, Toland EJ, Joe B. Congenic interval mapping of RNO10 reveals a complex cluster of closely-linked genetic determinants of blood pressure. Hypertension. In press.
15. Garrett MR, Rapp JP. Two closely linked interactive blood pressure QTL on rat chromosome 5 defined using congenic Dahl rats. Physiol Genomics. 2002; 8: 81–86.
16. Malek RL, Wang HY, Kwitek AE, Greene AS, Bhagabati N, Borchardt G, Cahill L, Currier T, Frank B, Fu X, Hasinoff M, Howe E, Letwin N, Luu TV, Saeed A, Sajadi H, Salzberg SL, Sultana R, Thiagarajan M, Tsai J, Veratti K, White J, Quackenbush J, Jacob HJ, Lee NH. Physiogenomic resources for rat models of heart, lung and blood disorders. Nat Genet. 2006; 38: 234–239.[CrossRef][Medline] [Order article via Infotrieve]
17. Petretto E, Mangion J, Dickens NJ, Cook SA, Kumaran MK, Lu H, Fischer J, Maatz H, Kren V, Pravenec M, Hubner N, Aitman TJ. Heritability and tissue specificity of expression quantitative trait loci. PLoS Genet. 2006; 2: e172.[CrossRef][Medline] [Order article via Infotrieve]
18. Petretto E, Mangion J, Pravanec M, Hubner N, Aitman TJ. Integrated gene expression profiling and linkage analysis in the rat. Mamm Genome. 2006; 17: 480–489.[CrossRef][Medline] [Order article via Infotrieve]
19. Aitman TJ, Glazier AM, Wallace CA, Cooper LD, Norsworthy PJ, Wahid FN, Al-Majali KM, Trembling PM, Mann CJ, Shoulders CC, Graf D, St Lezin E, Kurtz TW, Kren V, Pravenec M, Ibrahimi A, Abumrad NA, Stanton LW, Scott J. Identification of Cd36 (Fat) as an insulin-resistance gene causing defective fatty acid and glucose metabolism in hypertensive rats. Nat Genet. 1999; 21: 76–83.[CrossRef][Medline] [Order article via Infotrieve]
20. Pravenec M, Wallace C, Aitman TJ, Kurtz TW. Gene expression profiling in hypertension research: a critical perspective. Hypertension. 2003; 41: 3–8.
21. Liang M, Yuan B, Rute E, Greene AS, Zou AP, Soares P, McQuestion GDK, Slocum GR, Jacob HJ, Cowley AW Jr. Renal medullary genes in salt-sensitive hypertension: a chromosomal substitution and cDNA microarray study. Physiol Genomics. 2002; 8: 139–149.
22. Liang M, Yuan B, Rute E, Greene AS, Olivier M, Cowley AW Jr. Insights into Dahl salt-sensitive hypertension revealed by temporal patterns of renal medullary gene expression. Physiol Genomics. 2002; 12: 229–237.
23. Liang M, Cowley AW Jr, Greene AS. High throughput gene expression profiling: A molecular approach to integrative physiology. J Physiol. 2003; 554.1: 22–30.
24. Lee SJ, Liu J, Qi N, Guarnera RA, Lee SY, Cicila GT. Use of a panel of congenic strains to evaluate differentially expressed genes as candidate genes for blood pressure quantitative trait loci. Hypertens Res. 2003; 26: 75–87.[CrossRef][Medline] [Order article via Infotrieve]
25. Joe B, Letwin NE, Garrett MR, Dhindaw S, Frank B, Sultana R, Verratti K, Rapp JP, Lee NH. Transcriptional profiling with a blood pressure QTL interval-specific oligonucleotide array. Physiol Genomics. 2005; 23: 318–326.
26. Lee NH. Physiogenomic strategies and resources to associate genes with rat models of heart, lung and blood disorders. Exp Physiol. 2007; 1–11.
27. Malek RL, Irby RB, Guo QM, Lee K, Wong S, Ruffy M, Tsai J, Kwong KY, Frank B, Liu ET, Quackenbush J, Jove R, Yeatman T, Lee NH. Identification of Src transformation fingerprint in human colon cancer. Oncogene. 2002; 21: 7256–7265.[CrossRef][Medline] [Order article via Infotrieve]
28. Wang HY, Malek RL, Luu TV, Behbahani B, Frank B, Quackenbush J, Lee NH. Assessing unmodified 70-mer oligonucleotide probe performance against traditional PCR amplicons on glass slide microarrays. Genome Biol. 2003; 4: R5.[CrossRef][Medline] [Order article via Infotrieve]
29. Lee NH. Genomic approaches for reconstructing gene networks. Pharmacogenomics. 2005; 6: 245–258.[CrossRef][Medline] [Order article via Infotrieve]
30. Irby RB, Malek RL, Bloom G, Tsai J, Letwin N, Frank BC, Verratti K, Yeatman TJ, Lee NH. Iterative microarray and RNA interference-based interrogation of the SRC-induced invasive phenotype. Cancer Res. 2005; 65: 1814–1821.
31. Rapp JP, Dene H. Development and characteristics of inbred strains of Dahl salt-sensitive and salt-resistant rats. Hypertension. 1985; 7: 340–349.
32. Zicha J, Kunes J. Ontogenetic aspects of hypertension development: analysis in the rat. Physiol Rev. 1999; 79: 1227–1282.
33. Cowley AW Jr, Roman RJ. The role of the kidney in hypertension. JAMA. 1996; 275: 1581–1589.
34. Dahl LK, Knudsen KD, Iwai J. Genetic influence of the kidney in hypertension-prone rats. Circ Res. 1970; 27 (suppl 2): 277+.
35. Hubner N, Wallace CA, Zimdahl H, Petretto E, Schulz H, Maciver F, Mueller M, Hummel O, Monti J, Zidek V, Musilova A, Kren V, Causton H, Game L, Born G, Schmidt S, Muller A, Cook SA, Kurtz TW, Whittaker J, Pravenec M, Aitman TJ. Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nat Genet. 2005; 37: 243–253.[CrossRef][Medline] [Order article via Infotrieve]
36. Arthur JW, Sanchez-Perez A, Cook DI. Scoring of predicted GRK2 phosphorylation sites in Nedd4-2. Bioinformatics. 2006; 22: 2192–2195.
37. Semela D, Langer DA, Shah V. GRK2 makes trouble: a no-NO in portal hypertension. Gastroenterology. 2006; 130: 1001–1003.[Medline] [Order article via Infotrieve]
38. Liu S, Premont RT, Kontos CD, Zhu S, Rockey DC. A crucial role for GRK2 in regulation of endothelial cell nitric oxide synthase function in portal hypertension. Nat Med. 2005; 11: 952–958.[CrossRef][Medline] [Order article via Infotrieve]
39. Gros R, Chorazyczewski J, Meek MD, Benovic JL, Ferguson SS, Feldman RD. G-protein-coupled receptor kinase activity in hypertension: increased vascular and lymphocyte G-protein receptor kinase-2 protein expression. Hypertension. 2000; 35: 38–42.
40. Barbato A, Russo P, Venezia A, Strazzullo V, Siani A, Cappuccio FP. Analysis of Gly40Ser polymorphism of the glucagon receptor (GCGR) gene in different ethnic groups. J Hum Hypertens. 2003; 17: 577–579.[CrossRef][Medline] [Order article via Infotrieve]
41. Strazzullo P, Iacone R, Siani A, Barba G, Russo O, Russo P, Barbato A, D’Elia L, Farinaro E, Cappuccio FP. Altered renal sodium handling and hypertension in men carrying the glucagon receptor gene (Gly40Ser) variant. J Mol Med. 2001; 79: 574–580.[CrossRef][Medline] [Order article via Infotrieve]
42. Rutherford S, Boatwright SD, Samwell GA, Morris BJ, Griffiths LR. A linkage and cross-sectional study of hypertension and obesity using a poly (A) Alu-repeat polymorphism at the glucagon receptor gene locus (17q25). Clin Exp Pharmacol Physiol. 1998; 25: 627–629.[CrossRef][Medline] [Order article via Infotrieve]
43. Behrends S, Vehse K, Scholz H, Bullerdiek J, Kazmierczak B. Assignment of GUCY1A3, a candidate gene for hypertension, to human chromosome bands 4q31.1–>q31.2 by in situ hybridization. Cytogenet Cell Genet. 2000; 88: 204–205.[CrossRef][Medline] [Order article via Infotrieve]
44. Hammer F, Stewart PM. Cortisol metabolism in hypertension. Best Pract Res Clin Endocrinol Metab. 2006; 20: 337–353.[CrossRef][Medline] [Order article via Infotrieve]
45. Mariniello B, Ronconi V, Sardu C, Pagliericcio A, Galletti F, Strazzullo P, Palermo M, Boscaro M, Stewart PM, Mantero F, Giacchetti G. Analysis of the 11beta-hydroxysteroid dehydrogenase type 2 gene (HSD11B2) in human essential hypertension. Am J Hypertens. 2005; 18: 1091–1098.[CrossRef][Medline] [Order article via Infotrieve]
46. Morineau G, Sulmont V, Salomon R, Fiquet-Kempf B, Jeunemaitre X, Nicod J, Ferrari P. Apparent mineralocorticoid excess: report of six new cases and extensive personal experience. J Am Soc Nephrol. 2006; 17: 3176–3184.
47. Watson B Jr. Genetics of the kidney and hypertension. Curr Hypertens Rep. 2003; 5: 273–276.[CrossRef][Medline] [Order article via Infotrieve]
48. Williams TA, Mulatero P, Filigheddu F, Troffa C, Milan A, Argiolas G, Parpaglia PP, Veglio F, Glorioso N. Role of HSD11B2 polymorphisms in essential hypertension and the diuretic response to thiazides. Kidney Int. 2005; 67: 631–637.[CrossRef][Medline] [Order article via Infotrieve]
49. Oudit GY, Sun H, Kerfant BG, Crackower MA, Penninger JM, Backx PH. The role of phosphoinositide-3 kinase and PTEN in cardiovascular physiology and disease. J Mol Cell Cardiol. 2004; 37: 449–471.[CrossRef][Medline] [Order article via Infotrieve]
50. Olivier M, Hsiung CA, Chuang LM, Ho LT, Ting CT, Bustos VI, Lee TM, De Witte A, Chen YD, Olshen R, Rodriguez B, Wen CC, Cox DR. Single nucleotide polymorphisms in protein tyrosine phosphatase 1beta (PTPN1) are associated with essential hypertension and obesity. Hum Mol Genet. 2004; 13: 1885–1892.
51. Chan SH, Tai MH, Li CY, Chan JY. Reduction in molecular synthesis or enzyme activity of superoxide dismutases and catalase contributes to oxidative stress and neurogenic hypertension in spontaneously hypertensive rats. Free Radic Biol Med. 2006; 40: 2028–2039.[CrossRef][Medline] [Order article via Infotrieve]
52. Rodriguez-Iturbe B, Sepassi L, Quiroz Y, Ni Z, Wallace DC, Vaziri ND. Association of mitochondrial SOD deficiency with salt-sensitive hypertension and accelerated renal senescence. J Appl Physiol. 2007; 102: 255–260.
53. Dunn SA, Mohteshamzadeh M, Daly AK, Thomas TH. Altered tropomyosin expression in essential hypertension. Hypertension. 2003; 41: 347–354.
54. Kuschnir E. The left ventricle and hemodynamic patterns in essential hypertension. Regul Pept. 2005; 128: 221–225.[CrossRef][Medline] [Order article via Infotrieve]
55. Rizzoni D, Paiardi S, Rodella L, Porteri E, De Ciuceis C, Rezzani R, Boari GE, Zani F, Miclini M, Tiberio GA, Giulini SM, Rosei CA, Bianchi R, Rosei EA. Changes in extracellular matrix in subcutaneous small resistance arteries of patients with primary aldosteronism. J Clin Endocrinol Metab. 2006; 91: 2638–2642.
56. Szmigielski C, Raczkowska M, Styczynski G, Pruszczyk P, Gaciong Z. Metabolism of collagen is altered in hypertensives with increased intima media thickness. Blood Press. 2006; 15: 157–163.[CrossRef][Medline] [Order article via Infotrieve]
57. Matsubara M. Renal sodium handling for body fluid maintenance and blood pressure regulation. Yakugaku Zasshi. 2004; 124: 301–309.[CrossRef][Medline] [Order article via Infotrieve]
58. Roman RJ, Hoagland KM, Lopez B, Kwitek AE, Garrett MR, Rapp JP, Lazar J, Jacob HJ, Sarkis A. Characterization of blood pressure and renal function in chromosome 5 congenic strains of Dahl S rats. Am J Physiol Renal Physiol. 2006; 290: F1463–F1471.
59. Stec DE, Roman RJ, Flasch A, Rieder MJ. Functional polymorphism in human CYP4F2 decreases 20-HETE production. Physiol Genomics. 2007; 30: 74–81.
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