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Physiol. Genomics 25: 121-133, 2006. First published January 3, 2006; doi:10.1152/physiolgenomics.00235.2005
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Received 23 September 2005; accepted in final form 1 January 2006.
Physiological Genomics 25:121-133 (2006)
American Physiological Society © 2006 American Physiological Society

Genetic dissection of proteinuria in the Sabra rat

Chana Yagil1, Marina Sapojnikov1, Alexander Wechsler1, Abraham Korol2 and Yoram Yagil1

1 Laboratory for Molecular Medicine and Israeli Rat Genome Center, Faculty of Health Sciences, Ben-Gurion University, Barzilai Medical Center Campus, Ashkelon
2 Laboratory of Mathematical and Population Genetics, Institute of Evolution, Haifa University, Haifa, Israel


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
 DISCLOSURES
 REFERENCES
 
The pathophysiology underlying proteinuria remains incompletely understood and warrants further research. We currently initiated the investigation of the genetic basis of proteinuria in the Sabra rat, a model of salt susceptibility that we showed previously to be also a model of spontaneous proteinuria that is unrelated to salt loading or development of hypertension. We applied the total genome scan strategy in 75 F2 male animals derived from a cross between SBH/y, which are prone to develop proteinuria, and SBN/y, which are relatively resistant to the development of proteinuria. Animals were subjected to uninephrectomy (UNx) to accelerate the development of proteinuria and were provided chow with a low salt content, thus avoiding the development of hypertension. Urinary protein excretion was monitored before UNx and monthly thereafter for 8 mo. The genotype of F2 was determined with microsatellite markers. The data were analyzed for cosegregation by ANOVA and for genetic linkage with a novel multifaceted statistical genetic paradigm. We detected three proteinuria-related quantitative trait loci (QTL) that were associated with the salt sensitivity (H) alleles from SBH/y: SUP2, SUP17, and SUP20 on rat chromosomes (Chr) 2, 17, and 20. We detected an additional QTL on Chr 3, SUP3, that was associated with the salt resistance (N) alleles from SBN/y. A temporal effect was noted: QTL SUP2 and SUP17 surfaced at months 7–8, QTL SUP20 at months 6–8, and QTL SUP3 at months 5–6. The QTL emerging from this study lead us a step closer to identifying the genes associated with and elucidating the pathophysiology of proteinuria.

SBH/y; SBN/y; linkage analysis; quantitative trait locus


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS
 DISCUSSION
 GRANTS
 DISCLOSURES
 REFERENCES
 
THE PATHOPHYSIOLOGY UNDERLYING proteinuria is complex and remains incompletely understood. The traditional explanations for the appearance of protein in the urine have been an excessive leak from the glomerulus into Bowman's space, an inability of the renal tubules to completely reabsorb the normal leak from the glomerulus, direct leak from the renal tubules, or a combination of the above. The molecular pathways underlying each of these mechanisms are most likely heterogeneous, and multiple pathways may be simultaneously involved. At the molecular level, a complex interaction is thought to occur between multiple genes and the environment, causing the glomerular and/or tubular pathology that culminates in proteinuria (1, 2, 5, 6, 9, 20, 25, 32). Additional research is warranted to further elucidate the mechanisms involved in proteinuria.

The focus of the present study was the development of proteinuria in the Sabra rat model of salt susceptibility. This model stands out in that hypertension does not develop spontaneously in the salt-sensitive SBH/y strain, which thus remains normotensive when not salt loaded (35). Proteinuria, on the other hand, develops spontaneously in SBH/y, without the need to salt load the animal or render it hypertensive, thus dissociating proteinuria from salt sensitivity and from hypertension per se (35). This feature of the Sabra model of proteinuria is reminiscent of the appearance of proteinuria in salt-sensitive human populations that are not salt loaded and thus not hypertensive, thereby invoking the involvement of proteinuria-related mechanisms other than hypertension (4, 7, 8, 11, 30, 31).

To identify the pathophysiological pathways involved in the development of proteinuria in SBH/y, we initiated the dissection of the genetic basis of proteinuria in this model. Our immediate aim was to identify the chromosomal loci (quantitative trait loci, QTL) that incorporate the genes involved. To achieve this goal, we applied the total genome scan strategy. We used an interactive multifaceted genetic linkage scheme that has been previously applied in the mapping of genes in plants and insects but not in the search for the genetic basis of complex diseases in mammalians (21, 38). We report here on the application of this scheme in our rodent model and on the steps that led to the detection of QTL related to proteinuria in the Sabra rat model of salt sensitivity.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS
 DISCUSSION
 GRANTS
 DISCLOSURES
 REFERENCES
 
Animals
Sabra hypertension-prone (SBH/y) and hypertension-resistant (SBN/y) rats from the colony inbred at the Israeli Rat Genome Center (34) were housed in compliance with institutional regulations and in accordance with the Guide for the Care and Use of Laboratory Animals (NIH Pub. No. 85-23, revised 1985) and the guidelines of the American Physiological Society for the care of laboratory animals. Climate-controlled conditions were maintained, with temperature set at 22–24°C, and regular timed diurnal light cycles were kept. The study protocol was submitted to and approved by the Institutional Review Committee for Animal Experimentation.

Study Protocol
Female SBH/y were cross-bred with male SBN/y rats, generating F1 that were mated brother to sister. F2 animals were weaned at age 1 mo and subsequently provided standard rat chow containing 0.25% NaCl (Koffolk, Tel-Aviv, Israel) and tap water ad libitum.

The phenotype of F1 and F2 animals was studied initially at age 2 mo (baseline). Animals were then subjected to right uninephrectomy (UNx), the rationale being that although this procedure increases the magnitude of proteinuria in both SBH/y and SBN/y, the increment in protein excretion in SBH/y is significantly larger than in SBN/y, thus increasing the gap between the two strains and facilitating our genetic studies (35). Nephrectomy was performed under anesthesia (ketamine-xylazine ip) through a small flank incision. The phenotype was studied subsequently at monthly intervals until age 9 mo in F1 or 10 mo in F2 (7 and 8 mo after UNx, respectively), at which time the animals were killed.

Phenotype
The phenotype consisted of monthly measurements of urinary protein excretion (UPE), as a surrogate of the underlying pathology in the kidneys of the salt-sensitive SBH/y rats. We collected urine for 24 h in metabolic cages and derived the amount of protein excreted from urinary protein concentration and 24-h excretion rate. Total protein concentration was determined colorimetrically by the microprotein-PR method (Sigma Diagnostics).

Genotype
DNA extraction.
Genomic DNA was extracted from the tip of the tail by salt precipitation, followed by phenol-chloroform cleaning, as previously described (34). Purity and quantity of DNA were assessed spectrophotometrically.

Microsatellite markers.
The entire rat genome [chromosomes (Chr) 1–20 and X] was screened with 143 microsatellite markers that had been found polymorphic between SBH/y and SBN/y, aiming at using markers spaced no more than 10–20 cM apart on each chromosome. The microsatellite markers were custom synthesized by Genosys (Sigma), using primer sequences provided by RGD (http://www.rgd.mcw.edu). When a QTL was detected, the density of the markers within the area of the QTL was increased.

Genotyping.
Genotyping was carried out by PCR amplification of genomic DNA, as previously described (36).

Data Analysis
The data were initially analyzed for cosegregation of UPE in F2 with the salt sensitivity (H) and salt resistance (N) alleles, also allowing us to investigate the effect of genotype on UPE. The data were analyzed by one-way ANOVA, and, if significant, the analysis was followed by a post hoc least significant difference test. The data were analyzed in parallel for genetic linkage with the MultiQTL software package (version 2.5, www.multiqtl.com). QTL detection was sought with a structured multistep scheme that is embedded within the software (13–16, 23, 24). The first step in the analysis was to screen the entire genome for genetic linkage, using single-trait analysis (STA), one trait at a time. Each time period (in months after UNx) was analyzed separately, the monthly measurement of UPE representing the "single trait." Multitrait analysis (MTA), a joint analysis of multiple traits and a powerful option that allows investigation of the dependence of two or more distinct yet biologically related traits on the QTL, could not be applied in the present project as our data consisted of only a single trait. The second step in the scheme therefore consisted of multienvironment analysis (MEA), which considers different time points after intervention as different "environments." We applied MEA in the present project by taking two or more different time points as separate environments. In the third and final step of the analysis, we applied multiple interval mapping (MIM), which incorporates into the model interfering effects of other QTL on separate chromosomes, thereby reducing the residual variation (12). MIM, which was possible within each of the time periods during which more than one QTL was detected, was applied to the STA as well as to the MEA. The default model that was utilized in each step of the analysis was unrestricted. At each step of the analysis, an attempt was made to fit the QTL with the simplest and statistically justified model (dominant, recessive, or additive effect) by comparing it with the nonrestricted model and replacing it if the difference was nonsignificant. The algorithm embedded within MultiQTL calculated further at every step of the analysis the statistical significance of each QTL by permutation testing (PT), providing a P value. The traditional threshold definition of a QTL in an F2 cross that is "significant" when the logarithm of the odds (LOD) score is >4.3 and "suggestive" if the LOD score is between 2.8 and 4.3 (18) is valid for STA but invalid for MTA and MEA, at which instances no threshold values have been defined, requiring us to rely on PT. After PT, the MultiQTL algorithm allowed bootstrap analysis (BA), providing the power of detection (PoD) of the QTL and the confidence interval of the estimated QTL position (19), which is presented henceforth as the 95% confidence interval of the QTL span. The overall scheme of the genetic linkage analysis is illustrated in Fig. 1.


Figure 1
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  		Fig. 1. Multi-quantitative trait locus (QTL) genetic mapping analysis scheme.

 

   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
DISCUSSION
 GRANTS
 DISCLOSURES
 REFERENCES
 
Urinary Protein Excretion
Twenty-four-hour UPE rates in parental SBH/y and SBN/y and in F1 and F2 from before UNx and at monthly intervals thereafter for 7–8 mo are shown in Fig. 2. In the parental strains, UPE was higher in SBH/y than in SBN/y from months 3–5 after UNx; in months 6 and 7, some overlap occurred between the two strains. In F1, UPE was similar to that observed in SBN/y, suggesting an overall recessive pattern of inheritance. In F2, UPE ranged between SBH/y and SBN/y.


Figure 2
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  		Fig. 2. Proteinuria in parental SBH/y ({blacksquare}) and SBN/y ({square}) (top), F1 (middle), and F2 (bottom) strains at baseline (B) at 2 mo of age and 1 to 7–8 mo after right uninephrectomy (UNx). UPE, urinary protein excretion.

 
Cosegregation Analysis
The F2 progeny were studied for cosegregation between 24-h UPE and genotype before UNx and at months 1, 3, and 5–8 thereafter. Significant cosegregation was detected on Chr 2, 3, 17, and 20. The levels of UPE by genotype for representative microsatellite markers are shown in Table 1.


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  		Table 1. Twenty-four-hour urinary protein excretion by genotype in F2 cohort for microsatellite markers with maximum LOD scores on chromosomes 2, 3, 17, and 20

 
A temporal effect was detected. In Chr 2, differences in UPE between the genotypes tended to be significant (approaching the P < 0.05 statistical threshold) at months 3–6 after UNx, achieving statistical significance at months 7 and 8. In Chr 3, differences in UPE between genotypes tended to be significant at month 5 and became significant at month 6 but did not persist thereafter. In Chr 17, significant differences in UPE between the genotypes were detected as early as month 3 after UNx, persisting through month 8. In Chr 20, differences in UPE between the various genotypes were significant at months 5–8.

The pattern of cosegregation suggested a recessive mode of inheritance in Chr 2, 17, and 20, the H allele (from SBH/y) being associated with an increase in UPE. A recessive mode of inheritance was also suggested on Chr 3, but contrary to the other chromosomes, the N (from SBN/y) and not the H allele was associated with an increase in UPE.

Genetic Linkage
Primary screening of entire rat genome for linkage.
The entire genome scan was initially screened for genetic linkage before nephrectomy and at months 1, 3, and 5–8 thereafter. Table 2 provides the maximal LOD scores (MLS) as detected by STA. Suggestive (LOD > 2.8) or significant (LOD > 4.3) linkage was detected on Chr 2 and 17 at months 7 and 8 after UNx and on Chr 20 at months 6, 7, and 8. LOD scores approaching or achieving suggestive linkage were also detected on Chr 3 at months 5 and 6. No additional linkage was detected on any of the other autosomes or on Chr X. Chr 2, 3, 17, and 20 were therefore selected for more extensive analyses, the details of which are provided below, by chromosome, timing, and mode of analysis as shown in Fig. 3GoGo–6 and summarized in Table 3.


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  		Table 2. Total genome scan for proteinuria (single-trait algorithm analysis), showing maximal LOD scores for each autosome and chromosome X

 

Figure 3
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  		Fig. 3. Logarithm of the odds (LOD) score tracing for linkage of UPE for chromosome (Chr) 2 using single-trait analysis (STA) for months 7 and 8 after UNx and multienvironment analysis (MEA) for months 3 and 5–8, before and after multiple interval mapping (MIM). Horizontal bar indicates the span of the QTL.

 

Figure 4
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  		Fig. 4. LOD score tracing for linkage of UPE and Chr 3 using STA for months 5 and 6 after UNx and MEA for months 3 and 5–8, before and after MIM. Horizontal bar indicates the span of the QTL.

 

Figure 5
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  		Fig. 5. LOD score tracing for linkage of UPE and Chr 17 using STA for months 7 and 8 after UNx and MEA for months 3 and 5–8, before and after MIM. Horizontal bar indicates the span of the QTL.

 

Figure 6
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  		Fig. 6. LOD score tracing for linkage of UPE and Chr 20 using STA for months 6–8 after UNx and MEA for months 3 and 5–8, before and after MIM. Horizontal bar indicates the span of the QTL.

 

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  		Table 3. Results of multifaceted linkage analysis

 
SINGLE-TRAIT ANALYSIS. For Chr 2, suggestive linkage by the "traditional threshold criteria" was detected first at month 7 after UNx, becoming significant at month 8 (Table 2). Details of the analyses are provided below and are shown in Fig. 3.

Month 7 STA detected a QTL with a LOD score of 2.8 or above between markers D2Rat30 and D2Mgh9, with MLS 4.12 (Fig. 3). The shape of the LOD tracing suggested the presence of two quantitative trait genes (QTG) within this QTL. PT (dominant model) indicated that the QTL was highly significant (P = 0.0027). BA confirmed the presence of the QTL with 84.5% PoD at a significance level of P = 0.01, placing the MLS in position 63.8 ± 10.2 cM and determining thereby that the QTL spans over 39.9 cM and contributes to 34.0% of the phenotypic variation of the trait (PVT). MIM, which was subsequently applied to investigate joint effects from the QTL on Chr 17 and 20, altered the shape of the LOD tracing, removing one of the two peaks and rendering it closer to that observed at 8 mo (Fig. 3). Thus the possibility of two QTGs within QTL SUP2 became less likely. PT (dominant model) indicated that the QTL was highly significant (P = 0.0007). BA confirmed the QTL with 83.9% PoD (P = 0.01), shifting the MLS to position 72.4 ± 6.5 cM near marker D2Rat39 and narrowing the QTL span by ~37% to 25.5 cM.

Month 8 STA detected a QTL with a LOD score of 2.8 and above between markers D2Rat30 and D2Rat223, with MLS 5.19 in the vicinity of marker D2Rat147 (Fig. 3). The shape of the LOD tracing suggested the presence of only one QTG within the QTL. PT (dominant model) indicated that the QTL was highly significant (P = 0.0009). BA confirmed the presence of the QTL with 89.0% PoD (P = 0.01), placing the MLS in position 57.9 ± 5.9 cM and determining that the QTL spans over 23.0 cM and contributes to 34.9% of the PVT. MIM was then applied to investigate the joint effects from the QTL on Chr 17 and 20. PT (dominant model) indicated that the QTL was highly significant (P = 0.0027). BA confirmed the presence of the QTL with 87.2% PoD (P = 0.05), leaving the MLS in position 57.7 ± 7.2 cM and increasing the QTL span by 22.6% to 28.2 cM.

For Chr 3, analysis was applied at 5 and 6 mo after UNx, during which linkage approached or achieved the suggestive level (Table 2). The results of this analysis are shown in Fig. 4.

Month 5 PT (unrestricted model) indicated that the QTL was significant (P = 0.0202). BA confirmed the presence of the QTL with 90.4% PoD (P = 0.05), placing the MLS in position 39.0 ± 17.5 cM between markers D3Rat5 and D3Mgh14 and determining that the QTL spans over 61.3 cM and contributes to 44.7% of the PVT. MIM was not applied at month 5, as no other QTL was detected during this time period.

Month 6 PT (unrestricted model) indicated that the QTL was significant (P = 0.0335). BA confirmed the presence of the QTL with 88.6% PoD (P = 0.05), placing the MLS 2.93 in position 35.9 ± 15.7 cM and determining that the QTL spans over 71.3 cM and contributes to 51.8% of the PVT. MIM was subsequently applied to investigate the effect from the QTL on Chr 20. PT (unrestricted model) indicated that the QTL became highly significant (P = 0.0004). BA confirmed the presence of the QTL with 95.9% PoD (P = 0.01), shifting the MLS to position 51.7 ± 8.3 cM and reducing the QTL span by 54.6% to 32.4 cM.

For Chr 17, linkage was detected first at months 7 and 8 after UNx (Table 2).

Month 7 STA detected a QTL with a LOD score of 2.8 or above between markers D17Rat1 and D17Rat145 with MLS 3.71 between markers D17Rat76 and D17Rat61 (Fig. 5). PT (dominant model) indicated that the QTL was highly significant (P = 0.0002). BA confirmed the presence of the QTL with 94.4% PoD (P = 0.01), placing the MLS in position 9.6 ± 7.7 cM and determining that the QTL spans over 24.7 cM and contributes to 32.7% of the PVT. MIM was then applied to investigate the joint effects from the QTL on Chr 2 and 20. PT (dominant model) indicated that the QTL was highly significant (P = 0.0005). BA confirmed the presence of the QTL with 86.3% PoD (P = 0.01), shifting the MLS to position 13.0 ± 11.0 cM between markers D17Rat142 and D17Rat136 and increasing the span of the QTL by 39.7% to 34.5 cM.

Month 8 STA detected a QTL with a LOD score of 2.8 or above between markers D17Rat1 and D17Mgh4, with MLS 5.30 between markers D17Rat76 and D17Rat61 (Fig. 5). PT (dominant model) indicated that the QTL was highly significant (P < 0.0001). BA confirmed the presence of the QTL with 88.4% PoD (P = 0.001), placing the MLS in position 9.3 ± 8.4 cM near marker D17Rat61 and determining that the QTL spans over 25.7 cM and contributes to 36.7% of the PVT. MIM was then applied to investigate the joint effects from the QTL on Chr 2 and 20. PT (dominant model) indicated that the QTL remained highly significant (P < 0.0001). BA confirmed the presence of the QTL with 95.0% PoD (P = 0.01), determining the position of the MLS at 10.7 ± 8.3 cM near marker D17Rat142 and increasing the span of the QTL by 5.1% to 27.0 cM.

For Chr 20, linkage was detected at months 6, 7, and 8 after UNx (Table 2).

Month 6 STA detected a QTL with a LOD score of 2.8 or above between markers D20Rat32 and D20Rat27 with MLS of 3.65 between markers D20Rat5 and D20Rat27 (Fig. 6). PT (dominant model) indicated that the QTL was highly significant (P = 0.0005). BA confirmed the presence of the QTL with 85.4% PoD (P = 0.01), placing the MLS in position 7.0 ± 5.3 cM and determining that the QTL spans over 17.4 cM and contributes to 23.4% of the PVT. MIM was subsequently applied to investigate the joint effects from the QTL on Chr 3. PT (dominant model) indicated that the QTL was significant (P = 0.0012). BA confirmed the presence of the QTL with 90.0% PoD (P = 0.001), thereby shifting the position of the MLS to 9.4 ± 4.2 cM also between markers D20Rat5 and D20Rat27 and diminishing the span of the QTL by 5.7% to 16.4 cM.

Month 7 STA detected a QTL with a LOD score of 2.8 or above between markers D20Rat32 and D20Rat27, with MLS 4.38 between markers D20Rat49 and D20Rat5 (Fig. 6). PT (dominant model) indicated that the QTL was highly significant (P = 0.0002). BA confirmed the presence of the QTL with 94.2% PoD (P = 0.01), placing the MLS in position 2.0 ± 3.2 cM and determining that the QTL spans over 8.3 cM and contributes to 23.6% of the PVT. MIM was then applied to investigate the joint effects from the QTL on Chr 2 and 17. PT (dominant model) indicated that the QTL was significant (P = 0.0012). BA confirmed the presence of the QTL with 84.4% PoD (P = 0.001), thereby leaving the position of the MLS at 2.9 ± 4.2 cM near marker D20Rat59 and increasing the span of the QTL by 34.9% to 11.2 cM.

Month 8 STA detected a QTL with a LOD score of 2.8 or above between markers D20Rat32 and D20Rat27, with MLS 3.58 between markers D20Rat49 and D20Rat5 (Fig. 6). PT (dominant model) indicated that the QTL was highly significant (P = 0.0006). BA confirmed the presence of the QTL with 87.6% PoD (P = 0.01), placing the MLS in position 2.6 ± 3.6 cM and determining that the QTL spans over 9.6 cM and contributes to 22.0% of the PVT. MIM was applied thereafter to investigate the joint effects from the QTL on Chr 2 and 17. PT (dominant model) indicated that the QTL remained highly significant (P < 0.0001). BA confirmed the presence of the QTL with 93.7% PoD (P = 0.05), determining the position of the MLS to 3.8 ± 4.8 cM near marker D20Rat5 and increasing the span of the QTL by 38.5% to 13.3 cM.

MULTIENVIRONMENT ANALYSIS. Scrutiny of cosegregation data in Table 1 revealed effects as early as month 3 after nephrectomy, even though they did not necessarily achieve statistical significance. This led to a more detailed investigation of Chr 2, 3, 17, and 20 using MEA and incorporating proteinuria at months 3 and 5–8 after UNx as separate environments. Single-chromosome MEA analysis was followed by multienvironment multiple interval mapping analyses (MEA-MIM). Details of the analyses are provided below by chromosome.

For Chr 2, the QTL detected by STA was redefined by MEA, using a dominant model that fit the data for all environments (Fig. 3). PT indicated that the QTL was highly significant (P = 0.0005). BA confirmed the presence of the QTL with 99.0% PoD (P = 0.01), placing the MLS at position 57.4 ± 5.1 cM near marker D2Rat47 and determining that the span of the QTL is 20.1 cM.

MEA-MIM refined further the definition of the QTL (Fig. 3), PT indicating that the QTL remained highly significant (P < 0.0001) and BA confirming the presence of the QTL with 99.0% PoD (P = 0.001), leaving the MLS at position 56.2 ± 2.6 cM near marker D2Rat47 but thereby markedly narrowing the span of the QTL by 48.8% to 10.3 cM.

For Chr 3, the QTL was redefined by MEA, using a model that fit a recessive pattern for months 7 and 8 and an unrestricted pattern for the remaining months (Fig. 4). PT indicated that the QTL was highly significant (P = 0.0012). BA confirmed the presence of the QTL with 84.1% PoD (P = 0.001), placing the MLS at position 39.8 ± 9.8 cM between markers D3Rat5 and D3Mgh14 and determining that the span of the QTL is 38.6 cM.

MEA-MIM redefined the QTL further (Fig. 4), PT indicating that the QTL remained highly significant (P < 0.0001) and BA confirming the presence of the QTL with 100% PoD (P = 0.001), shifting the MLS to position 55.9 ± 1.6 cM next to marker D3Mit6 and thereby strikingly narrowing the span of the QTL by 83.4% to 6.4 cM.

For Chr 17, the QTL was redefined with an additive model (Fig. 5). PT indicated that the QTL was highly significant (P < 0.0001). BA confirmed the presence of the QTL with 100% PoD (P = 0.001), placing the MLS at position 12.1 ± 6.8 cM near marker D17Rat142 and determining that the span of the QTL is 24.8 cM.

MEA-MIM redefined the QTL further (Fig. 5), PT indicating that the QTL remained highly significant (P < 0.0001) and BA confirming the presence of the QTL with 99.9% PoD (P = 0.001), shifting the MLS to position 4.1 ± 4.7 cM near marker D17Rat76 and thereby markedly reducing the span of the QTL by 46.3% to 13.3 cM.

For Chr 20, the QTL was redefined with a dominant model that fit the data for all environments (Fig. 6). PT indicated that the QTL was highly significant (P < 0.0001). BA confirmed the presence of the QTL with 99.9% PoD (P = 0.001), placing the MLS at position 3.7 ± 2.7 cM between markers D20Rat59 and D20Rat5 and determining that the span of the QTL is 9.0 cM (95% confidence interval).

MEA-MIM redefined the QTL (Fig. 6), PT indicating that the QTL remained highly significant (P < 0.0001) and BA confirming the presence of the QTL with 100% PoD (P = 0.001), shifting the MLS to position 8.7 ± 2.1 cM between markers D20Rat5 and D20Rat27 and thereby only mildly reducing the span of the QTL by 10.0% to 8.1 cM.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
GRANTS
 DISCLOSURES
 REFERENCES
 
In the current study, we investigated the genetic basis of proteinuria in the Sabra rat as means of identifying the pathogenic mechanisms involved. We used a classic cross between the two contrasting SBH/y and SBN/y strains, a relatively small cohort of F2 animals, but multiple measurements of UPE at timed interval and a comprehensive novel linkage analysis scheme. Our major finding was the detection of three proteinuria-related QTL on autosomes 2, 17, and 20, which we termed SUP2, SUP17, and SUP20, respectively (Sabra urinary protein) and which account for proteinuria that preferentially develops in the parental SBH/y strain. A second finding was a fourth QTL that we detected on Chr 3, which we termed SUP3 and which accounts, at least in part, for the proteinuria observed in the parental SBN/y strain. A third finding of interest was the identification of a temporal effect that characterized the appearance of the proteinuria-related QTL, the QTL surfacing as early as 5 mo after UNx when investigated by cosegregation analysis and at 6–7 mo when determined by linkage analysis and persisting thereafter in all but one chromosome. All these findings were greatly facilitated by the application of a multifaceted genetic linkage scheme which incorporates genetic and statistical algorithms that markedly enhanced our ability to detect and demarcate the QTL and minimize their chromosomal span.

In the present investigation, we studied the genetic basis of proteinuria in the Sabra rat model that had been originally inbred on the basis of its susceptibility to develop hypertension during salt loading (3). Although proteinuria develops preferentially in SBH/y over SBN/y in the presence of two kidneys, we opted to utilize the UNx model in which the magnitude of proteinuria is markedly amplified and the gap in protein excretion between the two strains is increased (35), thereby facilitating our genetic studies. These two latter features of our model must be commented on. With regard to uninephrectomy, it must be kept in mind that the model we utilized in our present investigation of proteinuria was one of decreased renal mass, a potentially confounding yet in our view necessary factor in the genetic analysis. Our rationale for using the UNx model was that, although differences in protein excretion between the two parental strains with two indwelling kidneys have been unequivocally demonstrated, the amount of proteinuria and the differences between the two strains were of smaller magnitude than after UNx (35), which would have necessitated a very large F2 cohort to achieve the power required for QTL detection. UNx allowed us to significantly reduce the size of the F2 cohort, without reducing our ability to detect the four QTL. With regard to the relationship of proteinuria and salt sensitivity, the detection of proteinuria had been an incidental finding during characterization of the phenome of the Sabra model of salt susceptibility (35). It must be emphasized, however, that as far as can be established at our present level of understanding, the development of proteinuria in this model is unrelated to salt loading or to the development of hypertension (35). The development of proteinuria in the salt-sensitive SBH/y strain can thus not be attributed at this stage to salt sensitivity per se. It must nonetheless be recognized that this phenotype (proteinuria) evolved in parallel with and alongside salt sensitivity during the selective inbreeding of this model. In this context, it is of interest that the development of proteinuria in a salt-susceptible strain is not unique to the Sabra rat and has been described and investigated in other strains as well, in particular in the Dahl model, which also carries a genetic background of salt sensitivity (30). A major confounder with other models has been that hypertension develops spontaneously along with salt sensitivity, as the pathophysiology underlying proteinuria in the presence of hypertension is likely to be different and more complex than during normotension. The Sabra model thus carries a major advantage over the Dahl and other models in that the salt-sensitive SBH/y strain remains normotensive throughout its natural lifetime, unless it is salt loaded. The Sabra rat is therefore unique in that it is the only strain with a genetic background of salt sensitivity in which proteinuria can be investigated without hypertension interfering as a confounding factor. If and how this genetic background affects the development of proteinuria remains unresolved.

There have been several previous attempts to dissect the genetic basis of proteinuria in strains other than the Sabra rat. Four reports provide us with information on the genetic basis of proteinuria in crosses between salt-sensitive and salt-resistant populations fed low-salt-containing diets, similar to the conditions under which we carried out our present investigation in the Sabra model. Poyan Meyr et al. (22) studied a cross between Dahl salt-sensitive (SS) and spontaneously hypertensive (SHR) rats maintained on a low-sodium (0.2% NaCl) diet, the SHR being the "proteinuria-resistant" strain and the Dahl SS serving as the "proteinuria-sensitive" strain. The phenotype in that cross consisted of weekly measurements of urinary albumin excretion in animals aged 4–8 wk. The investigators detected significant QTL on Chr 2, 6, 9, and 19 and suggestive QTL on Chr 8, 10, and 11. The QTL conveyed sensitivity in terms of the development of proteinuria, except for that on Chr 11, which conveyed resistance. Blood pressure (BP) of F2 animals was not reported. Garrett et al. (10) studied a backcross of Dahl SS with SHR held on low-salt diet (0.3%), focusing on ages 8, 12, and 16 wk. They found albuminuria-related QTL on Chr 1, 2, 6, 8, 10, 11, 13, and 19. BP was in the hypertensive range in some but not all animals. Schulz et al. (26) backcrossed Munich Wistar Frömter (MWF) onto Lewis rats. The MWF rat develops spontaneous hypertension and proteinuria with aging and is salt sensitive, whereas the Lewis rat remains normotensive, has a contrasting low protein excretion rate, and is salt resistant (17). Animals were studied at 8, 14 and 24 wk of age. Linkage analysis revealed significant QTL on Chr 1, 12 and 17 and a suggestive QTL on chromosome 6. Systolic BP in the F2 population ranged from 118 to 217 mmHg. Schulz et al. (27) performed an additional backcross between MWF and SHR in which animals maintained on a low-sodium diet (0.2% NaCl) were studied at 8, 14, and 24 wk. Significant protein-related QTL were detected on Chr 1, 6, 8, and 9 and suggestive QTL on Chr 4, 7, 15, and X. BP ranged from normotensive to hypertensive. Another report provides us with information on the genetic basis of the relationship between proteinuria and salt-sensitive hypertension per se. Siegel et al. (29) studied a cross between Dahl SS and SHR maintained on a high-salt diet (4% NaCl). Suggestive linkage for proteinuria was found on Chr 6, 8, and 9 and significant linkage on Chr 6 (a second locus) and 19. There was a wide range of BP values in F2, some becoming severely hypertensive. Additional reports relate to studies that have been carried out in populations that are not salt sensitive. Shiozawa et al. (28) investigated the genetic basis of proteinuria in a cross between fawn-hooded hypertensive (FHH) and August Copenhagen Irish (ACI) rats. Animals were subjected to UNx at 5–6 wk of age and thereafter were provided standard rat chow. At 8 wk after UNx, UPE was determined. Significant proteinuria- and albuminuria-related QTL were detected on Chr 1 in two locations (Rf-1 and Rf-2), Chr 3 (Rf-3), and Chr 14 (Rf-4), and a suggestive QTL was detected on Chr 17 (rf-5). Table 4 provides a summary of all the above-mentioned studies and the resulting QTL.


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  		Table 4. Summary of proteinuria/albuminuria-related QTL

 
How do our data compare with the results of similar studies in other strains? The QTL we detected in the Sabra rats on Chr 2 (SUP2) is concordant with the proteinuria-related QTL described by Garrett et al. (10) and Poyan Meyr et al. (22) and thus lends support to their findings. The QTL that we detected on Chr 17 and 20 (SUP17 and SUP20, respectively) do not coincide with those previously reported by Schulz et al. (27) or Shiozawa et al. (28) on the same chromosomes and are thus novel proteinuria-related QTL. What are the possible explanations for the diverging results? First, it has been repeatedly shown that crosses between different strains can yield different QTL, thereby demonstrating the genetic and pathophysiological heterogeneity of complex traits such as proteinuria. Second, differences in phenotype can account for differing QTL. Our model was normotensive throughout the study, whereas in several of the other studies hypertension may have been a confounding factor. Third, our model utilized the UNx procedure to amplify the level of proteinuria, whereas in several other studies the investigators were content with low levels of proteinuria in the presence of both kidneys. Finally, linkage analysis at different time points (age or time after UNx) is likely to yield different QTL. In the present study we performed linkage analysis from 2 to 10 mo of age, whereas in most other studies, linkage analyses were performed at younger ages.

The proteinuria-related QTL that we detected in the present study on Chr 3 (SUP3) deserves particular attention. Albeit only suggestive, this QTL stands out as it was associated with SBN/y N alleles that conferred susceptibility to development of proteinuria, as opposed to the other three QTL that were associated with the SBH/y H alleles that conferred similar susceptibility to proteinuria. Another way of interpreting the results is that SUP3 is associated with the SBH/y H allele that confers relative resistance to proteinuria and is thus protective, which is equivalent to the interpretation provided for the QTL detected on Chr 11 by Poyan Meyr et al. (22). Congenics introgressing QTL SUP3 from the SBH/y background onto the contrasting SBN/y genetic background should allow us to further clarify this latter possibility. Irrespective of our interpretations, the SUP3 QTL is most likely related to the proteinuria that we observed in SBN/y and unrelated to the large amount of proteinuria found in SBH/y. It is concordant with the proteinuria-related QTL Rf3 previously reported in a cross between FHH and ACI (28), which raises an interesting question as to the pathogenic significance of Rf3. QTL SUP3 may in fact eventually help shed light on the pathophysiology of proteinuria that develops spontaneously in certain rodent strains with aging.

A question may arise as to why we detected the QTL only in the later months of the study and as to the biological significance of the "temporal" effect, which has been previously reported by other investigators as well (10, 27). Proteinuria was expressed in the present study as a dynamic phenotype that developed over several months. We suggest, therefore, two possible explanations for the temporal effect. One explanation is that the genes within these QTL were significantly expressed only in the later months of the study. The second explanation is that the sensitivity of the analysis allowed detection of these QTL only when the level of proteinuria in SBH/y reached a certain threshold level. The results of the cosegregation analysis, which detected the QTL at earlier time periods than linkage analysis, suggest that the "late" detection of the QTL may indeed be related to a threshold level of proteinuria and to a matter of sensitivity of the analysis, i.e., that with the present methodologies, QTL can be detected only when the genes embedded within them are phenotypically expressed beyond a threshold level.

The significance of our study does not relate solely to the detection of proteinuria-related QTL but also to the methodology that we used in detecting them. The linkage analysis scheme that we applied in the current study incorporated a multifaceted analytical algorithm that consists of several structured steps that either are not commonly utilized or are not usually referred to or presented in detail in scientific reports. This scheme sets up a new user-friendly interactive platform for the detection of QTL that can be applied to the analysis of other sets of complex traits in mammalian and other organisms. The analysis scheme incorporates three consecutive steps: single-trait analysis, the basis of the algorithm, which is consistent with and comparable to other genetic linkage paradigms, multienvironment analysis (16), which utilizes "repeated measurements" that are embedded in study designs, and multiple interval mapping (37), the final step in the analysis, which reduces the background (noncontrolled) variation by taking into account QTL effects from other chromosomes (http://www.multiqtl.com). It is noteworthy that in the final analyses of the data in the current study, MEA-MIM achieved a striking narrowing of the span of all four QTL to the 10-cM range, an almost unprecedented result, considering the F2 cohort size that we used. Finally, the ease of use of the software and the detailed presentation of each facet of the analysis as we have done in the present report in our view render this analysis scheme advantageous over other commonly used linkage analysis schemes and set a precedent of a new standard in QTL reporting.

The present study leaves us with three QTL for proteinuria in a rodent model. The chromosomal segment defining QTL SUP2 incorporates fewer than 137 of the 1,609 known genes on rat Chr 2; SUP17 incorporates fewer than 201 genes of the 741 genes on Chr 17; and SUP20 incorporates fewer than 57 of the 827 annotated genes on Chr 20. The total number of candidate genes within these QTL is therefore 395 (see Supplemental Material; available at the Physiological Genomics web site).1 Considering the fact that the total number of genes in the rat genome is ~30,000, the first step in the dissection of the genetic basis of the relationship between salt sensitivity and proteinuria has achieved a 98.7% reduction in the number of genes to 1.3% of all identified genes on the rat genome. This number of candidate genes remains, nonetheless, far too large to pursue any particular gene among them.

What are the next steps in our quest to identify the actual genes involved in the development of proteinuria in this model? We are in the process of constructing consomic and congenic strains using the Sabra SBH/y and SBN/y strains, and some of the results are already available (35). We have already reported that introgression of Chr 17 from SBN/y onto the background of SBH/y significantly reduced the amount of proteinuria, thus confirming the presence of a proteinuria-related QTL on that chromosome but not yet reducing the span of the QTL (35). Introgression of Chr 17 from SBN/y onto the genetic background of SBH/y also significantly reduces BP (unpublished data). Because we previously (36) also detected a BP-related QTL on Chr 17, the question may arise whether the two QTL (BP and proteinuria) are not one and the same. Our results indicate, however, that the two QTL are sharply demarcated on different segments of the chromosome, leading us to conclude that they are distinct. Substitution mapping with congenics, the preparation of which is ongoing, should eventually provide us with the biological proof to our conclusion. We have also previously reported (35) that introgressing most of Chr 1 from SBN/y onto the background of SBH/y resulted in a significant decrease in proteinuria, suggesting the presence of a QTL on that chromosome, yet we did not detect a QTL on Chr 1 in the present study, either by cosegregation or by linkage analysis. Although it cannot be ruled out with certainty that our failure to detect a proteinuria-related QTL on Chr 1 was due, at least in part, to the relatively small number of animals that we used in our F2 cross, other yet unknown factors must be considered when linkage analysis and substitution mapping yield such conflicting results. Another avenue that we are currently pursuing is the study of differential gene expression in the kidneys of SBH/y and SBN/y rats, our view being that elucidation of the genetic basis of complex diseases ultimately depends on our ability to apply and judiciously integrate multiple investigative approaches such as linkage analysis, congenic strains, and differential gene expression profiling, as we previously demonstrated in a parallel project (33).


   GRANTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
DISCLOSURES
 REFERENCES
 
This study was supported in part by grants to C. Yagil and Y. Yagil from the Israel Science Foundation, the German-Israeli Foundation for Scientific Research and Development and the Chief Scientist’s Office of the Israeli Ministry of Health.


   DISCLOSURES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
 DISCLOSURES
REFERENCES
 
A. Korol is CEO of MultiQTL Ltd.


   ACKNOWLEDGMENTS
 
We acknowledge the contributions of Marina Grinyok, Svetlana Rosenblum, Gurion Katni, and Dr. Shlomo Fytlovich for technical support.


   FOOTNOTES
 
Article published online before print. See web site for date of publication (http://physiolgenomics.physiology.org).

Address for reprint requests and other correspondence: Y. Yagil, Dept. of Nephrology and Hypertension, Barzilai Medical Center Campus, Ashkelon 78306, Israel (e-mail: labmomed{at}bgumail.bgu.ac.il).

1 The Supplemental Material for this article is available online at http://physiolgenomics.physiology.org/cgi/content/full/00235.2005/DC1. Back


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 GRANTS
 DISCLOSURES
 REFERENCES
 

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