Human Molecular Genetics, 2000, Vol. 9, No. 19 2909-2918
© 2000 Oxford University Press
Factors involved in the initial mutation of the fragile X CGG repeat as determined by sperm small pool PCR
Department of Genetics, Emory University School of Medicine, 1462 Clifton Road NE, Atlanta, GA 30322, USA
Received 16 August 2000; Revised and Accepted 18 September 2000.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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The fragile X syndrome is one of more than a dozen genetic diseases attributed to the amplification of a trinucleotide repeat. Despite the number of these disease loci, relatively little is known about the mechanism(s) that cause a stable allele to become unstable. Population and family studies of the fragile X CGG repeat have identified a number of factors that may play a role in repeat instability including the number of AGG interruptions, purity of the 3' and 5' end of the repeat and cis-acting factors as related to haplotype background. However, studies that assess whether these factors have an impact on the rate and magnitude of change of the repeat are lacking, mainly due to the lack of an appropriate model system. Therefore, in order to dissect the factors involved in the initial mutations of the CGG repeat, small pool (SP)-PCR was performed on DNA derived from sperm and blood from seven unaffected males whose repeat sizes range from 20 to 33. Using the SP-PCR-derived data, regression analyses suggested that components of the repeat structure such as the number of interruptions and purity of the 3' end of the repeat are important determinants of germline repeat instability. In contrast, elements other than repeat structure, such as haplotype background, seemed to have an impact on somatic repeat instability. The factors identified for either cell type, however, explained only a small portion of the variance, suggesting that other factors may be involved in this process.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Trinucleotide repeat expansion is the hallmark of many diseases including the fragile X and E syndromes, myotonic dystrophy, Huntington’s disease, the spinocerebellar ataxias, spinal and bulbar muscular atrophy, dentatorubral-pallidoluysian atrophy and Friedreich’s ataxia (1–4). In the case of the fragile X syndrome, the amplification of the CGG repeat located in the 5' UTR of the gene FMR1 at Xq27.3 to >200 repeats (the full mutation) causes a mental retardation phenotype (5). In the unaffected population, the CGG repeat ranges from 6 to
55 repeats and is most often transmitted in a stable manner from parent to offspring (5). The alleles of asymptomatic carriers of the fragile X syndrome, however, have repeat sizes between the upper limit of the unaffected population and the lower limit of the fragile X population. These alleles, known as premutation alleles, range from 55 repeats to <200 repeats and are often transmitted unstably from parent to offspring (5). Even before the cloning of the fragile X CGG repeat, the fragile X syndrome was known to exhibit unique inheritance patterns (6,7). Examination of fragile X pedigrees revealed that only females transmit the disease-causing allele to their offspring. Normal transmitting males (NTMs) carry the mutation yet never pass the disease-causing allele to their daughters, although such daughters are at risk of having a child with the fragile X syndrome. Characterizing the CGG repeat in these families revealed that risk of expansion to the full mutation is correlated with the size of the premutation allele when passed through a female germline (5,8–13). Premutation alleles of >90 repeats almost always expand to the full mutation in the next generation whereas alleles of <90 repeats often expand to a larger premutation size. In contrast to carrier females, the premutation allele in carrier males is relatively stable in that it usually only expands a few repeats in the next generation. Interestingly, increasing paternal repeat size (>80 repeats) is correlated with increasing probability for contraction in repeat size in the next generation (11,13,14). The difference in expansion/contraction patterns may be due to different levels of selection against the full mutation in eggs compared with sperm. Support for this hypothesis stems from the fact that only premutation alleles are found among the sperm of a full mutation male (15). Furthermore, it was also shown that a full mutation male fetus at 13 weeks of gestation had only full mutation pro-spermatogonia whereas a male fetus at 17 weeks of gestation exhibited both full and premutation pro-spermatogonia (16). In contrast, examination of full mutation female fetuses at 16 and 17 weeks of gestation only revealed full mutations in the ovaries (16).
Studies based on fragile X families have helped to characterize the hyperexpansion event from the premutation to full mutation allele; however, relatively little is known about the mechansim(s) of instability among normal alleles. Population studies as well as family studies have provided clues about factors that may be involved in causing a stable allele to become unstable. One factor implicated in this process of instability is the presence of AGG interruptions among the repeated CGG sequence. Among normal, stable alleles, the CGG repeat is usually interspersed with 1–3 AGGs usually every 9–10 CGG repeats (17–21). Loss or lack of the distal-most interruption and subsequent purity (>24 repeats) of the 3' end of the repeat has been proposed as a factor that affects CGG repeat instability (19,20). Also, the presence or absence of the 5'-most AGG has recently been implicated as a determinant in eventual expansion of the repeat (22,23). Thus, it seems that purity at either end of the repeat may play an important role for CGG repeat instability.
Beyond CGG repeat size and structure, some evidence exists for other factors that affect repeat stability. Based on the observation that premutation/full mutation repeat sizes within sibships were more similar than those among sibships, a familial factor was proposed that is independent of parental sex, CGG repeat size and structure (11), or haplotype background (14). Possibly, inherited cis-acting factors could play a role. Specific CGG repeat structures among unaffected Caucasians are associated with specific haplotype backgrounds common with full mutation alleles (24). Based on these observations, Eichler et al. (24) proposed at least two mutational mechanisms with different mutational rates: (i) the CGG repeat structure is resistant to the loss of AGGs and consequently gains repeats slowly to the 3' end of the repeat; and (ii) the CGG repeat structure is prone to the loss of the 3'-most repeat and is at risk for rapidly expanding to the premutation state. Further population studies in Caucasians demonstrated that unaffected alleles with the 9+n structure were associated with the same haplotype found at a high frequency among intermediate (41–60 repeats), premutation (61–199 repeats) and full mutation alleles (25). These results led investigators to postulate that 9+n structures may be prone to eventual instability compared with 10+n structures. Studies in a large unaffected and fragile X African-American population, however, have suggested that these associations observed in Caucasians may be related to the history of the mutation and may not be indicative of a mutational mechanism (22,23). Nevertheless, the existence of cis-acting factors remains a possibility as repeat size and structure do not seem to account for the variability in stability observed among the different CGG repeat structures.
Although many factors have been implicated in affecting CGG repeat stability, the role of each putative factor has yet to be defined. To date, family studies have been inadequate in assessing the roles of putative factors mainly due to the relatively small sample size of offspring available for transmission studies. Also, the few model systems that have been developed for the CGG repeat locus have yet to mimic the characteristics of repeat instability observed in humans (26).
Because of these limitations, several investigators have sought alternative experimental systems with the power to accurately assess CGG repeat stability. For several of the trinucleotide repeat diseases and minisatellite loci, one such alternative experimental system is examining repeat stability in sperm using small pool polymerization chain reaction (SP-PCR) (27–29). SP-PCR is the method of choice for examining the repeat stability among usually stable CGG repeat alleles because of the large sample sizes necessary to observe changes in repeat size. Although only changes in overall repeat size will be detected using this technique, the results from SP-PCR will provide the rates and distribution of the changes detected, both of which can then be used to dissect factors that are implicated in CGG repeat stability.
We present here the SP-PCR sperm and leukocyte results of seven males within the unaffected CGG repeat range (20–33 repeats) who differ in age, CGG repeat structure and haplotype background. We demonstrate that most of the estimated germline variant rates were higher than the estimated somatic variant rates. Also, the variances among sperm variant distributions tended to be higher than among leukocyte variant distributions. Regression analyses suggested that factors related to repeat structure were more important for explaining the variance among sperm whereas those unrelated to repeat structure appeared more important in the variation among leukocytes. However, much of the variation observed in sperm and leukocytes seemed to stem from individual differences rather than the putative repeat stability factors. These data provide important information regarding the actual impact of putative factors on the initial rate of change of the CGG repeat and the resulting distribution.
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RESULTS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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The SP-PCR technique
Because we are characterizing mutations among normal sized repeat alleles (i.e. those with low mutation rates), we were limited to using the SP-PCR technique instead of the more accurate single sperm experiments. SP-PCR has been successfully employed to assess the rate and direction of change detected as a difference in size from the progenitor allele in several mini- and microsatellite repeat loci (27–29). However, several limitations of this technique need to be described, as they relate to the interpretation of the data. First, this technique can potentially underestimate the true number of expansion events since PCR is known to favor the amplification of the smaller allele (30). We have several lines of evidence that show that our variant distributions are not seriously affected by this PCR limitation. First, we determined that our PCR protocol adapted from Mornet et al. (29) is able to amplify at least 69 repeats (data not shown). Because we are examining CGG repeats in the unaffected range, we would not expect variants in the upper permutation range. Second, we performed reconstruction experiments to assess the impact that allele competition during PCR may have on the results. For these experiments, alleles with repeat sizes of 23 and 41 were co-amplified to mimic variants that may be found among the pool that have close to a 20 repeat difference from the progenitor allele. This experimental situation is probably more extreme than most mutational events in transmission to an offspring since the largest expansion reported among alleles of <40 repeats was a 29 expanding to a 39 repeat allele in a paternal transmission (31). In brief, genomic DNA extracted from blood samples from males with a 23 and a 41 repeat allele were serially diluted to concentrations of 1.05 ng/ml and 6 pg/ml, respectively. We found that a single molecule of the 41 repeat allele co-amplified with a pool of the 23 repeat allele was detected as frequently as a single molecule of the 41 repeat allele amplified alone (Fisher’s exact test, P > 0.05; data not shown). Third, we detected several large expansions among the seven individuals examined here, including several variants in the upper normal and lower permutation range. Collectively, these data suggest that our resulting variant distributions were not seriously affected by PCR size-dependent competition.
A second major criticism of the SP-PCR method is that the variants detected in this assay may be PCR artifacts rather than true variants in repeat size. We have addressed this possibility in our assay by examining a fibroblast clone from a fibroblast cell line of an unaffected male of 28 repeats. Previously, the CGG repeat in the context of fibroblast cell lines were shown to be stable by Southern blot analysis (32,33). We analyzed 24 500 cell equivalents of the fibroblast clone using the SP-PCR method and detected no variants, suggesting that the variants detected in our assay were not due to PCR artifacts (data not shown). Although we cannot rule out the possibility of some PCR artifacts detected in this system, these data support recent experiments suggesting that PCR artifacts are an uncommon occurrence in SP-PCR experiments (34).
A third criticism of the SP-PCR technique relates to the accuracy in determining the rates of change. Indeed, compared with single sperm typing, SP-PCR is less precise in controlling for the number of cell equivalents analyzed. Because the number of equivalent cells in each pool is the denominator in determining the rates of change, much care was taken in accurately diluting and pipetting the DNA samples for SP-PCR to minimize variation. However, the possibility of slight experimental errors cannot be discounted. In addition, efficiency of amplification of all the cell equivalents within a pool is probably not 100% (27). Due to these uncorrectable limitations, we caution that the rates of change presented here are rather crude estimates in the relative rates among the seven males and between tissue types. However, in this study to identify factors involved in repeat stability, we have assumed that the relative rates are unaffected by these limitations.
CGG repeat variation determined by SP-PCR
To examine the impact that CGG repeat structure, purity of the 3' end of the repeat and STR-based haplotype have on CGG repeat instability, SP-PCR was performed on both sperm- and leukocyte-derived DNA for the seven individuals described in Table 1. Overall repeat sizes ranged from 20 to 33 repeats, and purity of the 3' end of the repeat ranged from 9 to 28 repeats (Table 1). CGG structures varied among individuals: two individuals had a 5'-most AGG interruption (10+9 and 9+23), one had a 3'-most AGG interruption (20+9), two had asymmetrical structures (9+10+9 and 9+12+9) and one had two symmetrical interruptions (9+9+9).
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The results of the SP-PCR for all seven individuals for both sperm and leukocytes are summarized in Table 1 and Figure 1. An example of the variants detected by the SP-PCR system is given in Figure 2. Because of the limitations of this technique noted previously (29), we were not able to score variants with a difference greater than or less than four repeats from the progenitor allele. In general, the SP-PCR technique employed here detected variants in all seven of the sperm and leukocyte samples examined. In total, we detected 211 variants among 268 625 sperm equivalents examined and 111 variants among 231 175 leukocyte cell equivalents examined (Figure 1a; Table 1). The overall estimated germline rate of detectable variants was 7.85 x 10–4,
1.6-fold higher than the estimated somatic rate of detectable variants (Table 1). Within the population of variants detected, contractions and expansions were almost equally prevalent for both sperm and leukocytes. The variance calculated for the distribution of sperm variants was lower than the variance calculated for the distribution of leukocyte variants (Table 1). Examination of the individual leukocyte distributions (Fig. 1b–h) revealed that this result was mainly driven by the unusually large expansions found in the leukocytes of the 20+9 individual (Fig. 1e).
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Factors involved in CGG repeat variation
The estimated variant rates and distribution of the sperm and leukocyte data for each individual were examined. To determine factors that may be involved in causing instability, logistic regression was used to dissect the factors driving the rate of CGG repeat size change (i.e. paternal age, purity of the 3' repeat, purity of the 5' repeat, number of interruptions and haplotype background; see Statistical methods). Using a stepwise procedure, only three of the variables were statistically significant in predicting the rate of the variants for the sperm-derived data: purity of the 3' end of the repeat, purity of the 5' end of the repeat and number of interruptions (Table 2). As expected, the individuals with the greatest number of 3' pure repeats (e.g. 28 pure and 9+23) had the highest rates of change (Table 1). The effect of the number of interruptions on the rate of sperm variants seemed to be coupled with the position of the interruptions. For example, the model predicts that if there is one interruption, the rate of change will be higher if it is in the 5' position compared with the 3' position (e.g. 10+9 versus 20+9). Adding in the length of the 3' pure repeat and using this same example of having one interruption, the model explains the relative decrease in rate of the individuals with 9+23, 10+9 and 20+9 (1.10 x 10–3, 0.32 x 10–3 and 0.29 x 10–3, respectively). However, taken together, these three factors accounted for only a small percentage of the variance as reflected in the low odds ratios.
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Using the difference between the size of the variant and the constitutional size of the repeat, referred to as delta, as the dependent variable, a stepwise linear regression was performed. Again, 3' purity of the repeat and the number of interruptions were identified as important factors that explained the variance in delta (Table 2). These data suggest that increased length of the 3' pure repeat and the increased number of AGG interruptions increase the probability of larger contractions (e.g. 9+23; Fig. 1h). However, as noted in the logistic regression to assess factors involved in rates of change, the two statistically significant factors in this model explained only a small fraction of the variance (16%) observed in the sperm-derived data (Table 2).
In contrast to the sperm-derived data, the stepwise logistic regression performed on leukocyte-derived data revealed that AC1 allele status, purity of the 5' repeat and age of the donor were statistically significant in predicting the rate of change. Inspection of the odds ratios suggested that AC1 allele status has the most impact on rate of change compared with the other two variables (Table 2). Likewise, the stepwise linear regression performed on leukocyte-derived data using delta as the dependent variable implicated AC1 and the number of AGG interruptions explained the size and direction of change of the variants (Table 2). These results suggested that repeat structures with interruptions on the AC1 allele 3 background experience more expansions in leukocytes than those that lack interruptions or on the allele 4 background (i.e. 10+9 versus 9+23; Fig. 1b and h). Similar to the linear regression performed on the sperm-derived data, these two factors accounted for only 16% of the variance (Table 2). Unlike that seen for the sperm data, the purity of the 3' end of the repeat was not a major determinant in predicting the observed leukocyte data.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Although population and family studies have yielded important clues as to the factors involved in CGG repeat stability, definitive experimental data on the impact and relative importance of these factors on the rates and characteristics of changes have not yet been collected. Unfortunately, the usual genetic tools used for such testing are limited, as attempts to model the CGG repeat expansion in transgenic mice have been unsuccessful to date (26,35,36). Studies of CGG repeat tracts in Saccharomyces cerevisiae (37) and Escherichia coli (38) have been more successful in imitating CGG repeat instability, but have been less successful in imitating the bias to expansion seen for large CGG repeats in humans. Family studies have also been unsuccessful in characterizing the factors involved in the initial mutation(s) most often because, relative to the presumed initial mutation rate, the number of meioses examined is small and the number of ancestors is limited (5,10,11,14,17,20,39–44).
The lack of a model and the difficulties involved in ascertaining a sufficient number of transmissions for a single study have forced researchers to study the different mutational events in a number of alternative mammalian experimental systems. These systems include studying trinucleotide repeat stability in vitro by serially passaging lymphoblasts, fibroblasts and somatic cell hybrids (32,33,45). Also, researchers have used single sperm typing (46–50) and sperm SP-PCR analysis (27–29) to determine variant rates and distributions for several of the trinucleotide repeat and minisatellite loci.
We used SP-PCR to examine the large number of cells necessary to estimate rates of the variants among normal size alleles where initial mutations occur and to characterize the distribution of the variants detected among these typically stable CGG repeat alleles. To date, only one other study has examined the CGG repeat size (9+9+9) of one male in both sperm and leukocytes comparable to the repeat sizes examined here (29). However, the method for scoring variants in this study does not allow comparison of results. We report here the SP-PCR results of seven males for both sperm and leukocyte samples. Using these data, we systematically examined the impact of the 3' purity of repeat, 5' purity of the repeat, number of interruptions, haplotype background and age of the donor on both the rate and characteristics of the change observed in both sperm and leukocyte populations.
Repeat structure
Purity of the 3' end of the repeat has been implicated as a factor in repeat stability based on the observation that alleles with >24 pure 3' repeats shared a haplotype background common to mutated alleles (19) and that alleles >34–37 pure 3' repeats were transmitted unstably in pedigrees (20). Also, premutation alleles either contained one proximal interruption or none at all, suggesting that the loss of the 3'-most interruption is an important mutational hit in the pathway (14,17,20,21). Experimental data from single sperm analyses also implicated purity of the 3' repeat as a major factor determining repeat stability (46).
There is increasing evidence that the 3' purity of the repeat cannot be discussed separately from the number of interruptions within the repeat structure. Recently, examination of intermediate alleles (35–49 repeats) among African-Americans suggested that the pool of susceptible alleles included those with a single 5' interruption coupled with >24 pure 3' repeats (23). Also, the Tunisian-Jewish population, which is suspected to have an ethnic predisposition for the fragile X syndrome, has a higher frequency of pure alleles compared with the Caucasian population (51). Thus, it seems that there is a hierarchy of susceptible alleles with large pure 3' repeats. For example, those with large 3' pure repeats and no interruptions may be more unstable compared with those that have large 3' pure repeats and one distal interruption. Indeed, single sperm analyses performed in premutation allele sized sperm support this idea of a hierarchy, as a 68 pure repeat individual exhibited the highest rate of variants in sperm compared with the 9+65, 9+68 and 9+9+80 individuals (50). Our data are consistent with these data in that both purity of the 3' end of the repeat and number of interruptions were identified in regression analyses as factors that predicted both the rate and characteristics of the changes detected in sperm (Table 2). For example, the 28 pure repeat individual in this study had the highest rate of variants among all seven sperm samples, although the majority of the variants detected were contractions (79%; Table 1). The higher rate of variants detected in the sperm of the 28 pure repeat individual was expected since this individual had the largest number of pure 3' repeats and a lack of interruptions. Our data support the hierarchy of susceptible alleles hypothesis in that the 9+23 was the individual with the second highest rate of sperm variants, again with the majority of variants detected being contractions (62%; Table 1). Moreover, the position of the single interruption also played a role in this hierarchy of susceptibility: the rate of variants decreased if the single AGG interruption was distally placed compared with proximally placed. This is not consistent with the conclusion from our haplotype association data in the African-American population where we found that the distally located interruption seemed to be a susceptibility factor for expansion. It is interesting to note that for the one male with the 20+9 structure, the majority of the leukocyte variants were large expansions. However, the role of the distally versus proximally placed interruption cannot be defined by these results as we were limited to one male per CGG repeat structure in this study.
Age of the donor
Age of the individual was an attractive factor for germline instability because the number of stem cell divisions in males increases with increasing age (52). If the CGG repeat were biased to expansions once it has attained a certain threshold in size (i.e. premutation alleles), one would expect an increase in expansions with increasing age in the absence of selection against large alleles. Indeed, a study of the CTG repeat in myotonic dystrophy patients using SP-PCR analysis demonstrated an increased ratio of expansions to contractions with increasing age of the donor (28,53). Results from the seven unaffected sized range alleles reported here did not reflect an age effect. Additional stepwise logistic regression analyses categorizing the variants as expansions or contractions for the dependent variable also failed to identify increasing age as a factor predicting the expansions observed in the sperm data (data not shown). It may be that these unaffected alleles have such a low mutation rate compared with the mutated alleles previously studied that the chance of a mutation occurring early during mitotic divisions and being affected by subsequent divisions is low. If this were true, a specific repeat size threshold may exist beyond which age is a factor in repeat stability (i.e. intermediate alleles of 40–60 repeats). Ascertainment of males of different ages with intermediate alleles will be necessary to confirm or refute this hypothesis.
Cis-acting factors
Population studies in Caucasians have suggested that haplotype background related to cis-acting factors have an impact on CGG repeat stability. Specifically, the 6-4-4/5 haplotype background is associated with ‘asymmetrical’ CGG repeat structures (i.e. 9+12+9) and with mutated alleles (24). Because this haplotype background was not associated with intermediate alleles, these structures were postulated to be prone to loss of the distal-most interruption and subsequent rapid expansion (24). We examined here two individuals with the 6-4-4/5 haplotype background and asymmetrical CGG repeat structures and did not find an increased rate of variants nor a bias for expansion (Table 1; Fig. 1f and g). However, our system could not detect the rate of the presumed initial mutation of these alleles, that is, the loss of the 3' AGG without alteration in repeat size. Interestingly, the mean number of repeats for the three observed expansions in the 9+12+9 individual was +21 repeats. This jump in repeat number would create an allele closer to the premutation state rather than the intermediate allele state, which is the exact process suggested from association studies in Caucasians. Irrespective, the data from this study indicate that cis-acting factors, as defined by haplotype background, do not play a significant role in the initial mutation process.
Factors involved in somatic variation
In general, factors involved in somatic variation differed from those identified in the germline. For the rate of variants, most of the identified factors did not involve repeat structure. Thus, the rate of variants may be governed more by environmental events, such as infection, which would lead to rapid cell division. Support for this hypothesis is the high leukocyte variant rate in the 9+9+9 individual compared with his germline variant rate (Table 1). The data from the 9+9+9 individual could be a result of exposure of different environmental factors compared with the six other individuals. Alternatively, this individual’s background unrelated to haplotype could govern these individual differences. At least two lines of evidence in model systems exist that support the idea of individual background differences unrelated to haplotype. First, studies of the CGG repeat in yeast strains suggest that local chromosome structure and mutations in rad27 (the yeast homolog to human FEN1) influence repeat stability (37). Second, a study of somatic mosaicism in transgenic mice with the CTG expansion responsible for myotonic dystrophy demonstrated that the range of mosaicism was dependent on on the transgenic line rather than the size of the repeat and flanking sequences (54). These individual differences suggest that cis- and trans-acting factors within rather than among individuals may play a role in somatic CGG repeat instability.
Conclusion
We present here a study of the factors involved in the initial rates and distribution of the usually stable fragile X CGG repeat locus. Using sperm as an ‘experimental system’, we confirmed that the 3' purity of the repeat and the number of interruptions are important factors involved in both the rate and distribution of the variants. We caution, however, that other factors cannot be excluded as these two factors explained only a small portion of the variance in the observed data. These data, while preliminary due to a small sample size, are important as it is the first step in the understanding of the within population dynamics of the CGG repeat and the creation of ‘susceptible’ alleles that are prone to future expansion.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Laboratory methods
Unaffected males previously genotyped for the fragile X CGG repeat and 3' flanking markers (DXS548, FRAXAC1 and FRAXAC2; see Fig. 3 for location and nomenclature) were contacted for the study. Of a pool of 72 males, 14 participated in the study by providing a blood and sperm sample. DNA was extracted from the blood samples as previously described (55). DNA was extracted from sperm samples from a protocol adapted from Jeffreys et al. (27) and Miller et al. (55). Genomic DNA from lymphocytes or sperm was diluted to a concentration of 1.05 ng/µl equivalent to
175 haploid genomes/µl (27,29). CGG repeat length variation was analyzed by SP-PCR (27) using a PCR protocol adapted from Brown et al. (56) and Mornet et al. (29). In brief, 1 µl aliquots of blood or sperm-derived DNA were amplified by PCR in a 15 µl cocktail mix of 1x assay buffer (Boehringer, Indianapolis, IN), 1.25 mM MgCl2, 10% DMSO, 500 µM dATP, 500 µM dCTP, 500 µM dTTP, 500 µM 7-deaza dGTP,7 µM each of primers 1 and 2 (56) and 1 U of Expand Long Polymerase (Boehringer). The reaction mixture was subjected to the temperatures and cycles as published by Mornet et al. (29). The sequence of primer 1 was 5'-GACGGAGGCGCCGCTGCCAGG-3' and the sequence of primer 2 was 5'-TCCTCCATCTTCTCTTCAGCCCT-3' (56).
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PCR products were migrated onto a 17 x 17 cm small gel of 8% polyacrylamide for at least 2 h at 400 V. The DNA was transferred to a positively charged membrane (Boehringer) and prehybridized overnight at 55°C using a standard hybridizing solution (5x SSC, 0.1% N-lauroylsarcosine, 0.02% SDS, 1% Boehringer blocking reagent). The membrane was then hybridized at 55°C for 1 h in the standard hybridizing solution. The probe (CGC)7 was DIG 3'-end labeled according to manufacturer’s instructions (Boehringer). After hybridization, the membrane was subjected to two 30 min washes of 2x SSC, 0.1% SDS followed by two 30 min washes of 0.1x SSC, 0.1% SDS. All four washes were performed at 55°C. After the application of CDP-STAR (Boehringer), the DNA was visualized using autoradiography.
To determine the AGG interspersion pattern, sequencing was performed on lymphocyte-derived DNA from a protocol previously described (22).
Statistical methods
Logistic regression was used to examine factors that had an impact on the rate of the variants detected in sperm and leukocytes. In this analysis, we assumed that each sperm is independent; therefore, we did not group the sperm according to individuals. The dependent variable was the status of the allele detected (‘variant’ as ‘1’ and ‘wild-type’ as ‘0’). The independent variables were number of AGG interruptions, number of pure 3' repeats, number of pure 5' repeats, AC1 allele status (allele 3 coded as ‘1’ and allele 4 coded as ‘0’) and age of the individual at the time of sample donation. For sperm regression analyses, the age of the individual was expressed as the number of estimated mitotic divisions. There are an estimated 34 mitotic divisions before the onset of male puberty and 23 stem cell divisions/year after puberty (52). As in other trinucleotide sperm analyses in the literature (49), we assumed the age of puberty to be 13 years; thus, the number of mitotic divisions (n) can be estimated as n = 34 + 23(a – 13), where a is the age at the time of donation. For leukocyte regression analyses, age was expressed as the age of the individual in years at the time of donation. The logistic regression was performed separately for the sperm data and leukocyte data, and each are weighted in the analysis by the number of equivalent cells examined. Results of the logistic regression are reported as odds ratios (OR) with 95% confidence interval (95% CI) and correspond to the probability of having an allele of a different repeat size within a population of sperm or leukocytes. To examine factors that had an impact on the direction and magnitude in size of the repeat, a linear regression was used. The dependent variable was the change in size of the repeat (e.g. +10 repeats or –15 repeats). The independent variables were the same as in the logistic regression. All statistical analyses were performed with Statistical Analysis System (SAS) software release 6.12.
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ACKNOWLEDGEMENTS |
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We would like to thank Elizabeth F. Scott and Mary L. Leslie for advice and assistance on ascertaining samples as well as Lorri Griffin for the establishment of lymphoblastoid cell lines (M01-RR-00039). We are indebted to Dr Sarah L. Nolin and Jane Iber for technical advice and Aileen Kenneson and Dr Fengzhu Sun for analytical advice. Also, we would like to thank Drs Stephen T. Warren and Fuping Zhang for furnishing the fibroblast clone and providing helpful discussions. Finally, we would like to thank the study subjects whose participation made this work possible. This work was supported by NIH grants HD35576, HD29909 and HD08443.
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FOOTNOTES |
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+ To whom correspondence should be addressed. Tel: +1 404 727 5862; Fax: +1 404 727 3949; Email: ssherman@genetics.emory.edu
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REFERENCES |
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