Human Molecular Genetics Advance Access originally published online on November 8, 2005
Human Molecular Genetics 2005 14(24):3877-3884; doi:10.1093/hmg/ddi412
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A loss of genome buffering capacity of Dahl salt-sensitive model to modulate blood pressure as a cause of hypertension
Research Centre-CHUM, Montréal, Québec H2W 1T8, Canada
* To whom correspondence should be addressed at: Research Centre, Centre Hospitalier de l'Université de Montréal (CHUM), 7-132 Pavillon Jeanne Mance, 3840, rue St Urbain, Montreal, Quebec H2W 1T8, Canada. Tel: +1 5148908000 ext. 15522; Fax: +1 5144127152; Email: alan.deng{at}umontreal.ca
Received September 20, 2005; Accepted October 31, 2005
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Essential hypertension is a complex trait influenced by multiple genes known as quantitative trait loci (QTLs) for blood pressure (BP). It is not clear, however, what roles these QTLs play in maintaining normotension. Insights gained toward the maintenance of normotension will shed light on how hypertension can result from a deficiency or malfunctioning of this maintenance. Currently, congenic strains were systematically constructed using Dahl salt-sensitive (DSS) and Lewis (LEW) rats not only to define QTLs (i.e. in DSS background), but also to ascertain effects of the same QTLs in preserving normotension (i.e. in LEW background), a first such study. Results showed that although LEW alleles for two QTLs on Chromosome (Chr) 18 lowered BP on the DSS background, their BP-increasing DSS alleles failed to influence BP in the LEW background. To further prove that the LEW background is resistant and the DSS background is susceptible to the effects of QTLs, BP-increasing alleles of a QTL on Chr 2 were introgressed into the DSS background, and its BP-decreasing alleles into the LEW background. Indeed, there was no BP-decreasing effect on the LEW background while demonstrating a BP-increasing effect on the DSS background. Thus, a genetic regulation of BP QTLs in the LEW genome inhibits BP changes by nullifying the effects of BP-altering QTLs. In comparison, the DSS genome must have lost the buffering capacity for stabilizing BP. The current work presents good evidence that a lack of regulation for functions of BP QTLs is a potential underlying cause of hypertension.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Complex traits such as hypertension are controlled by multiple genes (1
). It has been believed that each individual gene known as quantitative trait locus (QTL) exerts a certain amount of blood pressure (BP) effect and multiple of them will accumulatively contribute to BP (1,2). An added complexity among BP QTLs was woven by epistatic QTL–QTL interactions (3–6). However, genetic regulations of BP QTLs themselves are little noticed, and even less understood.
A reason for this lack of information on QTL regulations comes from the fact that a majority of QTLs were localized by the use of animal models especially the rat employing the congenic strategy (1,7). So far, almost all congenic strains have been generated by replacing a chromosome segment of Dahl salt-sensitive (DSS) rats with its homologue of a normotensive strain (8). Few congenic constructions in the opposite direction have been attempted, i.e. replacing QTL alleles from a normotensive strain by those of DSS.
There are certain values attributable to congenic strains so constructed. First, if an insight into the pathogenesis of hypertension can be gained by studying individual QTLs of a hypertensive model, the opposite should also be informative, i.e. an insight into the preservation of BP by the same QTL in a normotensive model. Secondly, genetic research of hypertension to this day has been mostly confined to locating QTLs causing hypertension. Mechanisms of retaining normotension can provide invaluable insights into what can resist hypertension, and if missing or malfunctioning, can lead to hypertension.
With these considerations in mind, we designed current experiments. The goal is to define QTL intervals, to establish their interactions and, more importantly, to analyze the roles that certain QTLs play in normotension of the Lewis (LEW) strain. The results demonstrated that the hypertensive DSS has lost the buffering capacity to BP changes in its genome. As such, the data give birth to a new concept on genetic determination of hypertension and add a new dimension of QTL regulations to the complexity of the polygenic control of hypertension.
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RESULTS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Extensive coverage by congenic strains to define a cluster of BP QTLs
Congenic strains C18S.L1–C18S.L8 in Figure 1 were designed to define BP QTLs on Chr 18. Congenic strain, C18L.S, was created to investigate the function of QTLs in the LEW background.
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Figure 2 shows the actual tracings of mean arterial pressures (MAP) of DSS and congenic strains by telemetry. Because they were consistent with their MAPs, the systolic and diastolic pressures of congenic strains are not presented.
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In defining a QTL and separating those that are close linked, attentions were paid to produce non-overlapping congenic strains. As shown in Figure 1, C18S.L2, C18S.L3 and C18S.L4 carry no overlaps in reference to one another, and each shows a BP effect (Fig. 2). Therefore, three BP QTLs were discovered (Fig. 1) with the caveat of assuming that there is one QTL present in each of the QTL intervals. In size, the QTL intervals are 13 Mb for C18QTL1, 18 Mb for C18QTL2 and 32 Mb for C18QTL3.
Analyses of QTL–QTL interactions
Figure 3 analyzes the relationship among the three C18QTLs. As demonstrated by a 2x2 factorial ANOVA, C18QTL1 and C18QTL2 act epistatically to each other, so do C18QTL2 and C18QTL3 (P-interaction <0.0001). No test for an interaction between C18QTL1 and C18QTL3 was conducted. But because an epistatic interaction has been established between C18QTL1 and C18QTL2 as well as between C18QTL2 and C18QTL3, by a logical extension, C18QTL1 and C18QTL3 should have an epistatic relationship also. Based on their mutual epistatic relationships, the three QTLs appear to share a common pathway/cascade leading to BP determination (8).
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Lack of BP effects in congenic strains constructed in the LEW background regardless of the QTL alleles increasing or decreasing BP
The last two vertical lines to the right of Figure 1 represent C18L.S and LEW. The genome difference between them is only the chromosome segment marked in hatched and open bars. In terms of phenotype, there is no difference (P>0.91) in MAP between them (Figs 1 and 4). Because C18L.S spans beyond the region harboring the C18QTL1 and C18QTL2 (Fig. 1), its lack of BP effect (Fig. 4) could not be due to an absence of either of the two QTLs. It is not clear if C18L.S also contains C18QTL3, as only a part of segment harboring C18QTL3 is covered by C18L.S (Fig. 1). However, the issue of how many of the three QTLs are included in C18L.S is not relevant to the influence of C18L.S on BP. This is because C18QTL1, C18QTL2 and C18QTL3 act epistatically to one another (Fig. 3). As a consequence, a numerical aggregation of them will not have an accumulative BP effect beyond that of a single QTL among them (8
).
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In contrast to C18QTLs, BP-raising alleles of C2QTL4 (9
) originate from LEW. Logically, one expects that the alleles from DSS would have a BP-decreasing effect. It was with such an expectation that congenic C2L.S was constructed. As shown in Figure 4, MAP of congenic C2L.S is not different from that of LEW (P>0.59), even though the chromosome coverage of C2L.S ensures that C2QTL4 was included. Therefore, the BP-decreasing alleles of C2QTL4 cannot, and do not, influence BP while in the LEW background. |
DISCUSSION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Major findings from the current studies are (a) three BP QTLs exist on Chr 18 that act epistatically to one another in the DSS background. At least two of these QTLs had no BP-increasing effect on BP in the LEW genetic background. (b) BP-decreasing QTL alleles of C2QTL4 carried by C2L.S had no influence on BP in the LEW background. (c) genetic regulations in LEW inhibit not only functions of BP-increasing alleles of C18QTL1 and C18QTL2, but also BP-decreasing alleles of C2S.L4. By inference, the same regulation must be either absent or dysfunctional in DSS rats.
The current work produces the important insight that QTLs alone are not adequate to modulate BP in preserving normotension in LEW. Genome regulations are necessary. Due to its deficiency or malfunctioning, the hypertensive DSS strain has lost the regulatory capability of buffering BP changes in its genome. Therefore, hypertension is caused by individual BP QTLs as well as by a lack of regulations on these QTLs.
Stringent genome regulations of BP by the LEW genome
It is apparent (Figs 1 and 4) that the LEW alleles of all three QTLs, C18QTL1, C18QTL2 and C18QTL3 lowered BP when introgressed into the DSS genome. This fact indicates that the BP-decreasing alleles of these QTLs originate from LEW and conversely their BP-increasing alleles are from DSS. Although, QTLs act epistatically to each other, the alleles of each QTL can function independently of the rest of other QTLs and other genes in the DSS genome. No combination with each other or with any of other QTLs is required for the individual C18QTL1, C18QTL2 or C18QTL3 to influence BP.
Surprisingly, the same QTLs do not affect BP when their DSS alleles were introgressed into the LEW genome (Fig. 1), not because these QTL alleles have no effects themselves. In fact, the BP-lowering effect of C18QTL1 and C18QTL2 accounts for over 60% of total BP of LEW (i.e. 53 of C18S.L5/88 of LEW). An inevitable interpretation will have to be that BP-increasing effects of DSS QTL alleles, although considerable, were nullified by the LEW genome. QTLs alone are not adequate in changing BP in LEW. Consequently, there must exist a genome regulation probably in the form of hypertension ‘suppressors’ in LEW. This strict regulation of BP changes is in sharp contrast to the DSS genome in which BP-decreasing alleles of any of the three QTLs function, as it were, without restraint.
The stabilizing effect of the LEW genome can be seen from another angle. One might argue that the LEW genome was resistant to the effect of BP-increasing QTL alleles because a numerical overabundance of BP-decreasing QTLs would have cancelled out the BP-increasing QTLs in its genome. If that were the case, one would expect a decrease in BP when additional BP-decreasing QTL alleles are added into the LEW genome. But actually after BP-increasing alleles of LEW were replaced by those of BP-increasing alleles of DSS of C2QTL4, there was no BP effect either (Fig. 4). This evidence indicates that the LEW genome inhibits BP changes, not only in response to BP-increasing alleles of C18QTL1 and C18QTL2 but also in response to BP-decreasing alleles of C2QTL4. Therefore, the LEW genome has achieved a balanced equilibrium by buffering BP fluctuations.
Another argument is that DSS would produce over abundant stimulators in its genome that could allow the individual QTLs to be freely functional even independently of one another. This scenario is less likely because the heterozygotes of F1(DSSxLEW) have the same BP as LEW (data not shown), indicating that the LEW genome is dominant over that of DSS. This fact favors the interpretation of inhibitors present in the LEW genome, rather than stimulators in the DSS genome that would regulate the functions of C18QTLs and C2QTL4.
There is a similar observation that the genetic background of Sabra hypertension-resistant strain was not permissive to the function of BP QTLs on Chr 1 (10), suggesting that a regulation of the QTL functions may play a role in that model also. In contrast, the normotensive Wistar Kyoto (WKY) strain permits the function of QTLs on Chr 2 (11,12), suggesting that a regulatory system may not be pertinent to functions of QTLs in WKY.
A loss of genome buffering capacity to modulate BP changes in DSS
In contrast to the resistant nature of the LEW genome to BP changes, the DSS genome possesses QTLs that can directly either increase or decrease BP. To allow this to happen, DSS must have lost a regulatory capacity to control these QTLs in stabilizing BP. Further supportive evidence for the loss of such a buffering capacity is that the DSS genome could prohibit neither the effects of BP-increasing alleles of other QTLs (5,13) nor the effects of BP-decreasing alleles of others QTLs [Table 1 in (8); (3,14)].
Another interpretation could be that it might be the numerical counterbalance between BP-decreasing and BP-increasing QTLs that would determine the overall BP of LEW and DSS. As LEW harbors more BP-decreasing QTLs than BP-increasing QTLs (8), in a mere mathematical battle, the overall BP is tilted toward normotension. However, this numerical interpretation lacks a logical cohesion, because contrary to LEW, DSS has more BP-increasing than BP-decreasing QTLs (8), yet BP-decreasing alleles from a single QTL, C18QTL1 or C18QTL2 or C18QTL3, are capable of lowering BP despite the numeral advantage of BP-increasing QTL alleles as a whole would have nullified its effect. It is, therefore, not a numerical balance, but rather, a regulation that plays a role in preserving BP of LEW. In comparison, it must be due to a lack of such a regulation that disturbed the equilibrium of the DSS genome to buffering BP changes.
Another interpretation could be that DSS possesses more BP-raising QTL alleles that are recessive to their homologous BP-decreasing QTL alleles than LEW. As the dominant BP-decreasing alleles are so abundant in LEW, so that a loss of a few of them could not change the balanced threshold, no BP changes overall can be seen. This argument, then, further supports the notion that the buffering capability to prevent BP changes in DSS is non-existent or greatly weakened.
Therefore, a thorough understanding of the pathogenesis of DSS hypertension requires not only identifications of individual QTLs, but also a comprehension of their regulations.
As to the nature of the genetic ‘regulators’ of the QTLs in the LEW genome, it can only be speculated at the present. One possibility could be that another QTL(s) could play the role. It is also possible that genes other than QTLs could function in that capacity. Moreover, factors other than genes such as epigenetic influences could also play a role. Future research needs to address this question in order to identify genetic regulators of BP QTLs.
Implications for understanding genetic bases of human essential hypertension
Our current work can facilitate the discovery of QTLs and the comprehension of the importance of QTL regulations in human hypertension, and potentially other complex traits as well. Up until this point, research in understanding genetic bases of essential hypertension has been concentrated on detecting BP QTLs using total genome scans in a variety of populations (2,15,16). Genome regulations that can completely nullify the effects of the BP QTLs are totally unknown and/or are not considered as a factor impacting on the functions of the BP QTLs. But our current work provided a compelling reason to examine and to comprehend genome regulations of QTLs as an integral part of the genetic determination of BP.
C18QTL3 potentially plays a role in human essential hypertension
A comparative mapping demonstrated that the region harboring C18QTL3 also corresponds to that containing QTLs for human essential hypertension (17–19). Therefore, C18QTL3 discovered in our current work as well as those implicated in the work of other investigators (20,21) will have implications in the development of essential hypertension. A revelation of the complex interactions among C18QTLs (Fig. 3) will also facilitate the isolation and identification of homologous QTLs in humans based on their shared pathway/cascade.
In conclusion, the current experimental investigation in studying both hypertensive and normotensive models provides insights that QTLs alone are not adequate in controlling BP. A genome regulation is needed to modulate functions of BP QTLs. In consequence, hypertension is caused not only by genetic variations in individual QTLs, but also by a deficient genetic regulation of these QTLs. The same principle can be potentially applied to the understanding of essential hypertension in humans.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Animals
Protocols for handling as well as maintaining animals were approved by our institutional animal committee (CIPA). All experimental procedures were in accordance with the guidelines of institutional, provincial and federal regulations. DSS and LEW rats were the same as used previously (3
,9,13,14).
Congenic constructions in the DSS genetic background (i.e. DSSxLEW or SxL in short)
The breeding procedure and screening protocol were essentially the same as reported previously (9,13,14). Current studies produced eight congenic strains (Fig. 1) to completely cover the region suspected to harbor a QTL (22) and are designated as follows: DSSxLEW-(D18Chm41–D18Rat45)/Lt (abbreviated as C18S.L1), DSSxLEW-(D18Rat61–D18Rat45)/Lt (C18S.L2), DSSxLEW-(D18Chm41–D18Rat92)/Lt (C18S.L3), DSSxLEW-(D18Chm91–D18Rat67)/Lt (C18S.L4), DSSxLEW-(D18Chm31–D18Rat55)/Lt (C18S.L5), DSSxLEW-(D18Chm56–D18Rat55)/Lt (C18S.L6), DSSxLEW-(D18Rat29–D18Rat55)/Lt (C18S.L7) and DSSxLEW-(D18Rat67–D18Rat55)/Lt (C18S.L8).
Congenic constructions in the LEW genetic background (i.e. LEWxDSS or LxS in short)
The breeding procedure and screening protocol were similar to those in previous constructions of congenic strains in the DSS background (9,13,14) except that the recipient and the donor of a chromosome segment were the opposite. Accordingly, the animal breedings were different after F1 generations. Briefly, rats of F1 progeny were backcrossed to LEW rats to produce the first backcross generation (BC1). Such backcrosses were repeated until BC5 with the aid of genome scan at each BC generation (9,13,14).
To establish a congenic strain, a BC5 rat was mated to a LEW rat to duplicate the segment of interest. Subsequently, a female rat and a male rat were sister–brother bred to generate rats homozygous SS for the region of interest, but homozygous LL for the rest of the genome. Consequently, one congenic strain was produced, LEWxDSS-(D18Chm31–D18Mit8)/Lt (C18L.S for Chr 18).
As BP-decreasing alleles of C2QTL4 came from DSS (9), it was expected that BP would drop in a congenic strain in which C2QTL4 alleles of LEW were replaced by those of DSS. It was for this purpose, another congenic strain was generated, LEWxDSS-(D2Uia5–D2Rat143)/Lt (C2L.S) for Chr 2.
BP measurements
BP studies on the congenic strains were essentially the same as reported previously (9,13,14). In brief, male rats were weaned at 21 days of age, maintained on a low salt diet (0.2% NaCl, Harlan Teklad 7034) and then fed a high salt diet (2% NaCl, Harlan Teklad 94217) starting from 35 days of age until the end of the experiment. Telemetry probes were implanted when rats were 56 days old (i.e. after 3 weeks of the high salt diet) with their body weights between 250–320 g. BPs for all the strains were measured at least at two different times to exclude seasonal as well as environmental influences. Thus, the BP data were pooled from separately reproducible measurements for each strain.
Statistical analysis
Repeated measures' analysis of variance (ANOVA) followed by Dunnett, (which permits a correction for multiple comparisons and sample sizes), was used to compare a parameter between two groups as presented previously (9,13,14). During the BP comparison, ANOVA was, first, used to analyze the data to see if there was any difference among the groups. If the difference is significant, then, the Dunnett test was followed to see which group is different and how much significant from the DSS strain.
The 2x2 ANOVA determines a QTL–QTL interaction (or a lack of it) by evaluating whether the observed BP effect of a congenic strain combining separate QTLs is significantly different from a predicted sum of BP effects from each individual QTL (3).
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ACKNOWLEDGEMENT |
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This work was supported by a grant from Canadian Institutes for Health Research (CIHR) to A.Y.D.
Conflict of Interest statement. None.
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FOOTNOTES |
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These authors contributed equally to the current work. 
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