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Journal of Lipid Research, Vol. 46, 1450-1456, July 2005
Copyright © 2005 by American Society for Biochemistry and Molecular Biology




* Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, TX 78245
Southwest National Primate Research Center, Southwest Foundation for Biomedical Research, San Antonio, TX 78245
Division of Cardiothoracic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX 77030
Published, JLR Papers in Press, April 16, 2005. DOI 10.1194/jlr.M400473-JLR200
1 To whom correspondence should be addressed. e-mail: david{at}darwin.sfbr.org
| ABSTRACT |
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70% of total variation in baboon PON. Although the generation of free radicals is influenced primarily by environmental factors, our findings suggest strong genetic regulation of one component in the antioxidant defense system that plays a major role in susceptibility to atherosclerosis.
Supplementary key words PON1 genetic linkage analysis
| INTRODUCTION |
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Recently, our group has begun to characterize the components of the antioxidant system that play a role in the process of atherosclerosis. A large number of proteins that have antioxidant activity are known to be associated with lipoproteins and are hypothesized to play key roles in protection from lipid oxidation and its consequences (8). Among these, paraoxonase (PON; aryldialkylphosphatase, EC 3.1.8.1) is a well-known enzyme associated specifically with HDL particles. Although, the actual physiological role of PON is controversial, several studies have demonstrated that it is capable of preventing and reversing LDL oxidation in vitro. It is speculated that the presence of PON on HDLs accounts in large part for the cardioprotective benefit of having high levels of HDL (9).
Variation in the gene encoding PON (PON1) has been well studied in several human populations. Most consistently, the Q192R polymorphism is associated with major differences in enzyme activity, with the 192R form having higher and the 192Q form having lower PON activity. Other mutations in human PON1, such as M55L and C108T, also are associated with variation in enzyme activity levels (10).
Baboons are a well studied model of human atherosclerosis and, in particular, of the genetic and dietary effects on risk factors for cardiovascular disease (11). In the present study, we have investigated the determinants of variation in serum PON activity in pedigreed baboons fed diets differing in levels of fat and cholesterol. We determined that PON activity levels are strongly heritable, and we obtained evidence from a genome screen for at least two distinct genes that influence baboon PON activity.
| MATERIALS AND METHODS |
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PON activity assay
PON activity was measured at 30°C by adding serum to 100 mM Tris-HCl, pH 8.5, containing 2 mM CaCl and 3 mM paraoxon (Aldrich Chemical Co.), based on previously published protocols (14, 15). The production of p-nitrophenol was monitored at 405 nm using a Biotek ELx808 microplate reader running in kinetic data acquisition mode, as suggested previously (16). Rates were calculated from 15 min of readings (16 absorbance readings) in the linear phase and converted to micromoles per minute per liter of serum using an extinction coefficient value of 18.05 mM1 cm1; units are given as micromoles per minute per liter of serum. Each sample was run in duplicate wells, and the average value was used. The average coefficient of variation for duplicates was 1.3% (n = 2,266 samples); the across-plate coefficient of variation, based on a control sample run on each plate, was 7.0% (n = 50 plates).
A concern with measurements of activity is the stability of the enzyme. To assess stability, we compared enzyme activity levels in two aliquots from each of eight serum samples; one aliquot was freshly thawed, and the other was left at room temperature for 30 h before assay. We found an average 3% decrease in enzyme activity (from 18.5 to 18.0 µmol/min/l) with incubation at room temperature; this decrease was not significant by ANOVA.
Baboon genotypes
Microsatellite markers were amplified from baboon genomic DNA by PCR. Amplification reactions included 2 units of TaKaRa Taq Polymerase (Shiga, Japan), 1x TaKaRa buffer, 0.33 mM deoxynucleotide triphosphate mix, 1 µM forward and reverse primers, and 50 ng of baboon genomic DNA. PCR was performed with an initial denaturing step of 5 min at 94°C, 35 cycles of 40 s denaturation at 94°C and 30 s extension at 72°C, and a final 5 min extension at 72°C. MgCl2 concentrations and annealing temperatures were optimized for each reaction. Genotypes were determined by gel electrophoresis of fluorescently labeled PCR products in ABI 377 automated sequencers with Gene Scan software and analyzed using Genotyper software (Perkin-Elmer).
Data from nuclear families were used within the laboratories to identify for retyping of genotypes inconsistent with Mendelian inheritance. Genotype data were then further checked, using Markov-Chain Monte Carlo (MCMC) and descent path analysis algorithms implemented in the program SimWalk2 (17), to identify and blank two classes of genotypes: those inconsistent with Mendelian inheritance when data from the full pedigrees were considered and those that appeared to be highly unlikely (e.g., apparent double recombinants occurring within a narrow interval).
Baboon linkage map
Genotype data were used previously to develop a first-generation genetic linkage map of the baboon genome (18). To produce the most recent version of this map, genotype data for 325 human microsatellite loci plus six novel baboon microsatellites were used in marker-to-marker linkage analyses, facilitated by the expert system program Multimap (19). Multipoint linkage analyses are highly dependent upon the validity of the linkage map. Therefore, estimates of multipoint identity-by-descent (IBD) coefficients used in the analyses described below were based on a map containing 283 of these marker loci, 275 of which were placed in unique positions at 1,000:1 odds and 8 at 100:1 odds (the latter being the standard for genetic map construction). The average heterozygosity index for the markers was 0.73 (range, 0.260.92), and the mean intermarker interval in this map was 8.6 centimorgans (cM).
To allow multipoint linkage analyses to detect and localize quantitative trait loci (QTLs) for PON activity, we used the genotype data and information in the baboon whole genome linkage map to produce multipoint IBD matrices using the stochastic MCMC approach implemented in the computer package Loki (20, 21). This MCMC method yields multipoint IBD estimates that more closely approximate those obtained by exact methods that cannot be applied conveniently to large extended pedigrees, such as those in this study. Using the multipoint IBD matrices, we could perform a log of the odds (LOD) score evaluation at 1 cM intervals along each chromosome.
Baboon pedigree
The baboons were assigned to 11 extended pedigrees that included the following relative pair classes: 547 parent-offspring, 533 sibling, 34 grandparent-grandchild, 77 avuncular, 5,786 half sibling, 1,535 half avuncular, 2 first cousin, 33 half first cousin, 20 half first cousin once removed, 475 half sibling-half first cousin, 7 half sibling-half avuncular, and 30 double half avuncular.
Statistical analyses
Before analyses, PON activity levels were loge-transformed to reduce skewness and kurtosis. One animal had a PON value more than 4 SDs below the mean, and this animal was dropped from further study.
We conducted all statistical genetic analyses using a maximum likelihood-based variance decomposition approach (22) implemented in the computer program SOLAR (23). Basic univariate quantitative genetic analyses were used to simultaneously assess the additive effects of genes and the mean effects of selected covariates on PON activity data obtained from animals on each of the three diets. We estimated heritability as the proportion of the residual phenotypic variance (i.e., after accounting for the mean effects of covariates) attributable to the additive effects of genes. Using multivariate extensions to this approach (2426), we also estimated six additional parameters: the additive genetic and random environmental correlations between PON activity levels assayed in samples from animals on the three diets. Significance in all models was assessed by likelihood ratio tests (27).
We used the variance decomposition approach to test for evidence of QTLs that affect variation in PON activity (23). This method entails specification of the genetic covariance between arbitrary relatives as a function of the IBD relationships at a given locus. The covariance matrix for a pedigree is modeled as the sum of the additive genetic covariance attributable to the QTL, the additive genetic covariance attributable to the effects of other loci, and the variance attributable to unmeasured environmental factors. We tested the hypothesis of linkage by comparing the likelihood of a restricted model in which variance attributable to the QTL was constrained to zero (i.e., no linkage) with that of a model in which it was estimated. The LOD score of classical linkage analysis was obtained as the quotient of the difference between the two loge-likelihoods divided by loge 10 (28).
We hypothesize that the genetic contribution to variation in many complex traits is oligogenic (i.e., resulting from the actions of two or more genes with individually measurable effects, expressed over a polygenic background). Consequently, we performed whole genome linkage screens in a sequential manner to facilitate the detection of multiple QTLs for PON activity. That is, after a first screen that detected a putative QTL, we sequentially performed a series of screens in which the previously detected QTLs were fixed in each subsequent model. This was done until a screen failed to detect a putative QTL (defined as LOD > 1.9). A more detailed description of the theory and method underlying oligogenic linkage analysis was presented previously (23).
To control for the overall false-positive rate given the finite marker locus density in the baboon genome linkage map, we estimated the genome-wide P value by the method of Feingold, Brown, and Siegmund (29). In this population, a LOD of 2.696 corresponded to a genome-wide P value of 0.05, which is considered significant. Using the same approach, the threshold for "suggestive" evidence of linkage, as proposed by Lander and Kruglyak (30), was a LOD of 1.461.
Kurtosis can inflate LOD scores, but in no case did kurtosis exceed ±0.31 in the starting data or ±0.20 for the residual data in the null models for linkage analysis. In addition, we estimated a robust LOD score that was based on 10,000 simulations of the same trait and pedigree data with an unlinked marker (31). The simulations, implemented in SOLAR, yielded a correction factor that could be used to adjust the LOD score. However, for the three PON traits, we estimated adjustment factors >1, indicating that no adjustment was necessary.
| RESULTS |
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G2 = 9.61) was 27% greater than that estimated for the HCHF diet (
G2 = 7.56; P = 0.055) but 44% greater than that for the basal diet (
G2 = 6.66; P = 0.039); the differences in genetic variance between the basal and HCHF diets were not significant. These latter results are consistent with a genotype-by-diet interaction in which the magnitude of the genetic effect on serum PON activity is influenced by the level of dietary fat and cholesterol.
Linkage analyses
We performed a genome screen using multipoint variance components methods to search for QTLs that affect PON activity on the various diets. Table 3 gives the noteworthy multipoint LOD scores for putative PON QTLs. On each of the diets, the strongest evidence was found for a QTL occurring on the baboon homolog of human chromosome 7 (multipoint LODs were 2.02, 9.06, and 4.05 on the basal, LCHF, and HCHF diets, respectively; genome-wide P values were 1 x 108 for LCHF and 0.0018 for HCHF). The microsatellite marker nearest these peaks was D7S821, a marker that maps to human 7q21-22. In the second screen, the strongest evidence was found for a QTL occurring on the baboon homolog of human chromosome 12 (multipoint LODs were 1.79, 1.95, and 2.88 for the basal, LCHF, and HCHF diets, respectively; genome-wide P value was 0.032 for HCHF). The microsatellite markers nearest these signals were D12S375 and D12S75, which map to human 12q13. One other signal (LOD = 2.26) was detected on the baboon homolog of human chromosome 7, but more than 70 cM distant from the first signal. However, this putative QTL was detected only on the HCHF diet. Figure 1A shows a plot of multipoint LOD scores for PON activity on the three diets together with the locations of the 19 microsatellite markers mapped on the baboon chromosome representing a fusion of genomic material found on human chromosomes 7 and 21. Figure 1B shows a similar plot of LOD scores and markers for the baboon homolog of human chromosome 12; the plots were taken from the second screen in which the dominant QTL near D7S821 was fixed in the model. Two-point LOD scores, estimated for the individual markers, showed patterns similar to those presented from the multipoint analyses (data not shown).
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70% of PON activity variance.
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| DISCUSSION |
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7%. Baboon PON activity averaged
26 µmol/min/l, lower than we have observed for human samples (
130 µmol/min/l; n = 1,284; data not shown) using the same assay approach.
By far, the predominant effect on PON activity was genetic, accounting for
53% of total variance. We further partitioned the genetic variance into at least three components. One component was the locus mapping to the region of the baboon genome homologous to human chromosome 7q21-22, where the structural gene for human PON (PON1) is located (32). This QTL was in the significant range on the LCHF and HCHF diets but only in the suggestive range on the basal diet. The nearest microsatellite marker to the QTL was D7S821, which, in the human genome assembly (May 2004), is
1.2 megabases from PON1 (33). A number of studies have demonstrated polymorphisms in the human structural gene that exert significant effects on enzyme activity (10), further supporting the notion that PON1 is responsible for this QTL. The impact of this locus on PON activity varied across the three diet samples, but on average it explained
22% of total trait variance. Surprisingly, linkage analyses identified a second locus, on the baboon homolog of human chromosome 12, that also significantly influenced PON activity on the HCHF diet; suggestive evidence for the QTL was detected on the basal and LCHF diets. On average, this second locus explained
11% of total trait variance. We are not aware of any other gene, whether in the vicinity of this signal on chromosome12 or otherwise, that has previously been proposed to influence PON activity, although there are several genes in the same general vicinity, such as SOAT2, APOF, and LRP, that might be expected to affect lipid and lipoprotein metabolism. Further studies will be required to identify this QTL.
The remaining genetic variance (19%) was not accounted for by any single locus and is designated a "residual additive" genetic effect in Table 5. Given the fact that PON is one of the many antioxidant enzymes that help maintain a delicate balance between free radicals and antioxidants, it is not surprising that other genes affecting redox pathways might also exert significant influence on PON expression.
Of the residual, nongenetic variance, we detected effects by several measured covariates that explained
17% of total trait variance. The significant covariates that were detected in this study included age, sex, apoA-I concentrations, and diet.
In our study, females had
20% higher PON activity levels than did males. Studies in human populations have also detected significant effects of sex on PON activity, with women having higher levels than men (3436). There is disagreement among studies with regard to whether there are significant effects of age on PON activity (3437). In none of these studies, however, was the size of the effect for sex or age estimated as we have done in the present study, which indicates that together, sex and age account for
12% of total variance. There also was a significant positive effect of two markers of HDL metabolism, apoA-I and HDL-cholesterol, on PON activity in this study. Of these, apoA-I levels were more strongly associated than HDL-cholesterol, and they explained
5% of total variance. The finding that an HDL measure explains a significant amount of the variance in PON is not surprising given the specific localization of PON on HDL particles. Recently, Kontush, Chantepie, and Chapman (38) reported that PON activity levels tend to increase with increasing HDL density (and decreasing particle size) and that they correlate directly with the ability of different HDL density fractions to prevent LDL oxidation. However, in our study, HDL median diameter, a measure of cholesterol distributions among size-resolved HDLs (39), was at best marginally significant, suggesting that the effect of HDL metabolism on PON activity is related to the amount of HDL rather than to its size distributions.
Finally, increases in dietary cholesterol caused significant increases in PON activity in this set of animals, but we were unable to estimate the size of the effect of diet. Because increasing dietary levels of fat and cholesterol increase HDL levels in baboons (40), it is possible that the dietary category served as a surrogate indicator of HDL metabolism. Studies of diet effects in humans have identified a number of dietary components that can significantly affect PON activity, including ethanol, certain types of fat, and fruits and vegetables (4144). Perhaps accounting for some of the reported effects of diet is a xenobiotic responsive element-like sequence within the human PON1 promoter region that appears to mediate the effects of polyphenols on PON expression in vitro (45). In contrast to the present observations, rabbits fed an atherogenic diet showed reduced PON activity. However, the atherogenic diet also caused a 70% reduction in HDL-cholesterol levels, again suggesting that some diet effects are actually exerted through effects on HDL metabolism.
A possible concern with this report of two QTLs for PON activity is that they were not consistently significant on all diets (i.e., even though the diet means and variances were quite similar, the magnitude of the statistical support for each QTL differed noticeably). However, we can assert that the most significant signals on chromosomes 7 and 12 were replicated in analyses of data from each of the other diets by signals that were significant or suggestive at the genome-wide level (30). Although potential sources of error that could affect our level of confidence in a QTL include seemingly random biological variation and assay error, we believe that the apparent differences in linkage results were attributable, at least in part, to the effects of diet on gene expression.
Evidence for genotype-by-diet interaction is accepted if we can determine that different genes influence the trait or if the genetic variances differ according to diet (26, 40). All three possible pair-wise genetic correlations were indistinguishable from unity, suggesting that the same set of genes influences PON variation on each diet. However, our comparisons of the diet-specific variances provided evidence that the genetic contribution to the variance in PON activity on the LCHF diet was significantly greater than on the other two diets. Thus, the results of our analyses are consistent with a genotype-by-diet interaction and, therefore, a biological basis for some of the differences in genetic models observed in this study.
These results highlight the value of an animal model for characterizing gene-environment interactions that are virtually impossible to detect in free-living populations (such as humans) and yet are likely to be quite important individually.
There are, however, limitations in our ability to extrapolate results from the baboon model to the human situation. First is the fact the our observations of a QTL on the homolog of human chromosome 12 have not yet been replicated in another population or species (although we report replication in the same animals fed different diets). Such replication would increase confidence that the locus might also contribute to genetic variance in humans. Another limitation of this study is that we have assessed PON activity using only the single substrate, paraoxon. It is well documented that various isoforms of human PON can show dramatically different substrate-dependent specific activities (10, 16), so it is conceivable that our results might not be true for other substrates or that we have missed critical genetic effects by choosing to assay with a single substrate. We do not anticipate that baboons will have the same polymorphisms as exert important effects on enzyme activity in humans. Therefore, until we can identify the relevant functional polymorphisms in the baboon structural gene, we cannot determine whether our results regarding baboon PON will apply directly to the human enzyme. Given the many metabolic similarities between the two species, however, it is likely that this report of a number of factors that determine PON variation will be relevant to humans.
In summary, our study has demonstrated significant genetic regulation of an important HDL-associated antioxidant, PON. Effects of at least two genes plus several covariates (age, sex, and HDL metabolism) together account for
70% of the total variation in PON activity. The demonstrated importance of HDL and oxidative stress to the onset and progression of atherosclerosis makes it vital to better understand the determinants of variation in the key HDL antioxidant components. Thus, characterization of the determinants of PON will not only help increase our understanding of the genetic basis of cardiovascular disease but may also create new opportunities for the development of therapeutic interventions aimed at increasing antioxidant capacity via the upregulation of PON expression.
| ACKNOWLEDGMENTS |
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Manuscript received December 1, 2004 and in revised form March 8, 2005 and in re-revised form April 6, 2005.
| REFERENCES |
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