Polymorphisms in the DNA repair genes XRCC1 and ERCC2 and biomarkers of DNA damage in human blood mononuclear cells
Eric J. Duell,
John K. Wiencke2,
Tsun-Jen Cheng3,
Andrea Varkonyi2,
Zheng Fa Zuo,
Tara Devi S. Ashok,
Eugene J. Mark4,
John C. Wain5,
David C. Christiani1,6 and
Karl T. Kelsey7
Department of Cancer Cell Biology and
1 Occupational Health Program, Harvard School of Public Health, 665 Huntington Avenue, Boston, MA 02115,
2 Laboratory for Molecular Epidemiology, Department of Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, CA 941430560, USA,
3 National Taiwan University, College of Public Health, Institute of Occupational Medicine and Industrial Hygiene, Taipei, Taiwan and
4 Department of Pathology,
5 Thoracic Surgery Unit, Department of Surgery, and
6 Pulmonary and Critical Care Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
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Abstract
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Polymorphisms in several DNA repair genes have recently been identified, but little is known about their phenotypic significance. To determine whether variation in DNA repair genes is related to host DNA damage, we studied the association between polymorphisms in XRCC1 (codon 399) and ERCC2 (codon 751) and two markers of DNA damage, sister chromatid exchange (SCE) frequencies (n = 76) and polyphenol DNA adducts (n = 61). SCE frequencies were determined using a modified fluorescenceGiemsa method and polyphenol DNA adducts were determined using a P1-enhanced 32P-post-labeling procedure. XRCC1 and ERCC2 genotypes were identified using PCRRFLP. Mean SCE frequencies among current smokers who were homozygous carriers of the 399Gln allele in XRCC1 were greater than those in 399Arg/Arg current smokers. We also observed a possible gene-dosage effect for XRCC1 399Gln and detectable DNA adducts, and significantly more adducts among older subjects who were carriers of the 399Gln allele than in younger subjects with the 399Arg/Arg genotype. The polymorphism in ERCC2 was unrelated to SCE frequency or DNA adduct level. Our results suggest that carriers of the polymorphic XRCC1 399Gln allele may be at greater risk for tobacco- and age-related DNA damage.
Abbreviations: BER, base excision repair; ERCC2, excision repair cross-complementing group 2; NER, nucleotide excision repair; PARP, poly(ADP-ribose) polymerase; SCE, sister chromatid exchange; XRCC1, X-ray repair cross-complementing group 1.
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Introduction
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Individuals deficient in the repair of DNA damage are known to be hypersensitive to ultraviolet (UV) light and at increased risk of developing neoplasms (1). Polymorphisms in several DNA repair genes have been reported (2), but the role of these variants in generating DNA damage phenotypes in human populations has been less well studied. Recently, Lunn et al. (3) reported that there were more placental aflatoxin B1 adducts and glycophorin A variants among carriers of a polymorphism in exon 10 (Arg
Gln at codon 399) of XRCC1 (X-ray repair cross-complementing group 1) than in 399Arg/Arg carriers of the gene. XRCC1 is involved in the repair of single-strand breaks following base excision repair (BER) resulting from exposure to endogenously produced active oxygen, ionizing radiation or alkylating agents (46). Codon 399 in XRCC1 is located within a BRCT (BRCA1 C-terminus) domain which is believed to be a proteinprotein interface that interacts with poly(ADP-ribose) polymerase (PARP) (7,8). PARP is a zinc finger-containing enzyme that detects DNA strand breaks and is involved in BER (9). Chinese hamster cell mutants (EM9 and EM-C11), which have high background sister chromatid exchange (SCE) frequencies and defective single-strand break repair following exposure to ionizing radiation or alkylating agents, revert after transfection of human XRCC1 (5,6). The ERCC2 (excision repair cross-complementing group 2, also known as XPD) gene codes for a protein involved in transcription-coupled nucleotide excision repair (NER). NER removes and corrects oligonucleotide fragments containing a variety of lesions such as UV-induced lesions, chemical adducts and crosslinks (10,11). Reduction of photosensitivity in xeroderma pigmentosum (XP) and trichothiodystrophy (TTD) cells by cloned ERCC2 has been reported (1114) and several polymorphisms in ERCC2 have recently been identified (2).
Markers of DNA damage such as SCE and DNA or protein adduct levels have been successfully used to measure human exposure to genotoxic substances such as cigarette smoke (15). Markers of genotoxicity such as SCEs have been found to correlate with inherited predisposition to cancer and defective DNA repair in a limited number of cases (16). In a relatively recent study of baseline SCE frequencies among 136 monozygotic and 88 dizygotic twins, the authors concluded that genetic influences play a significant role (~30%) in the observed variation in baseline SCE frequency (17). The glutathione S-transferase T1 and M1 polymorphisms (18,19), but not CYP1A1 or CYP2D6 polymorphisms (19), have explained a portion of this variation. Additional genetic factors may contribute to baseline SCE variability in human cells. We have addressed the potential role of genetic variation on SCE frequencies by analyzing the associations between polymorphisms in XRCC1 (codon 399) and ERCC2 (codon 751) with variations in lymphocyte SCE frequencies from 76 healthy subjects.
The earlier study that reported higher aflatoxin DNA adducts in carriers of the XRCC1 399Gln allele (3) postulated that this variant affects a BER-dependent pathway for repair of aflatoxin damage. BER and NER are distinctly different pathways in human cells; only the XPG protein has thus far been shown to carry out overlapping functions within both systems (20). XRCC1, through its interaction with DNA ligase III and polymerase ß, is thought to be important in BER specifically (46). BER characteristically acts on endogenous oxidative DNA damage in which key intermediates include the formation of abasic sites [apurinic/apyrimidinic (AP) sites]. Recently, several benzene-derived DNA adducts have been identified as good substrates for human AP endonuclease (HAP1) (21), making the BER pathway highly relevant to DNA lesions produced by benzene-related phenols and quinones. We previously developed a 32P-post-labeling assay to detect polyphenol DNA adducts, which we postulate are endogenous in nature and arise through oxidative processes (2124). Because of the potential importance of XRCC1 in BER-mediated removal of phenolic adducts, we explored the possible associations of polymorphisms in XRCC1 with polyphenol DNA adducts in mononuclear cells from 61 healthy subjects; the possible involvement of ERCC2 in these adducts was also investigated.
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Materials and methods
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Study population
The study population has been previously described (19). Briefly, the study population consisted of control subjects enrolled in a case-control study of lung cancer at Massachusetts General Hospital (December 1992 to April 1994), and were friends or spouses of enrolled cases. Information on smoking history and occupational/environmental exposures was gathered using an interviewer-administered questionnaire. Since there were many more former smokers than current smokers or non-smokers in this population, former smokers were under-sampled for these analyses (19). Eighty-one of 141 control subjects were sampled for SCE analysis in a previous study (19). Of these 81 subjects, five were excluded as a result of technical problems and incomplete questionnaire or genotype information, leaving 76 for this analysis of SCE frequencies. The 61 subjects for the polyphenol DNA adduct analysis were a previous random sample from the same study population. These study subjects were sampled for the measurement of polyphenol DNA adducts and analysis of associations with lung cancer risk. For six subjects, both SCE frequency and DNA adduct data were available.
Sister chromatid exchange assay
The SCE assay has been described previously (19). Briefly, heparinized blood was processed within 24 h and lymphocytes were cultured using 1% phytohemagglutinin P (BurroughsWellcome). 5-Bromo-2-deoxyuridine (50 µM final conc.) was added after 24 h of culture to achieve differential chromatid staining. Cultured lymphocytes were fixed to slides and air-dried. Chromosomes were stained using a modified fluorescenceGiemsa staining technique. For each study subject, 50 metaphases were scored to estimate the mean SCE frequency. High frequency SCE means were also scored from the highest five and the highest 10 SCE counts available from the 50 metaphases scored.
Specimen and data collection, DNA isolation and 32P-post-labeling
Blood samples (30 ml heparinized whole blood) were obtained from each subject and applied to FicollHypaque density gradients to separate mononuclear cells from erythrocytes and granulocytes. Frozen mononuclear cells were homogenized in 0.1 M Tris, 0.1 M NaCl, 50 mM EDTA pH 8.0 and 1% (v/v) SDS on ice and then extracted twice with equal volumes of chloroformisoamyl alcohol (24:1). The aqueous supernatant was incubated with RNase A and RNase T1 (250 µg/ml, Sigma, St Louis, MO) at 37°C for 60 min followed by digestion with proteinase K (10 µg/ml, Merck) (37°C for 60 min). The digest was extracted twice with chloroformisoamyl alcohol, after which sodium acetate (4.0 M final concentration) was added to the aqueous supernatant. DNA was precipitated with ethanol at 20°C and dissolved in 0.1x SSC. The quantity of DNA was determined by a fluorimetric method (Hoescht 33258; Hoefer, San Francisco, CA). Four micrograms of purified DNA were enzymatically digested to deoxynucleotide 3'-monophosphates (3'-dNps) with micrococcal nuclease (Worthington Biochemicals, Lakewood, NJ) and spleen phosphodiesterase. Samples were then treated with P1 nuclease. The modified nucleotides were converted into 32P-labeled deoxynucleotide 3',5'-diphosphates (3',5'-dpNps) by incubation with 150 µCi [32P]ATP (6000 Ci/mmol; NEN) and 2.5 µl T4 polynucleotide kinase. The total volume of 32P-labeled 3',5'-dpNps was applied to each 10x10 cm polyethyleneimine (PEI) cellulose plate.
Chromatographic conditions
Chromatograms were developed overnight with 0.4 M NaH2PO4 (pH 6.8) 16 cm into an attached paper wick, and an additional 56 h development in the same direction with 1.0 M lithium formate, 4.5 M urea (pH 3.5) without a wick. For development in the second dimension (D2, at 90° to the first), 0.36 M lithium chloride, 0.22 M TrisHCl, 3.8 M urea (pH 8.0) were used for ~56 h, with 6 cm into a wick. A final chromatographic development, D3 with 1.7 M NaH2PO4 (pH 6.0) was carried out for 23 h and until the solvent front had migrated 6 cm into the wick.
Autoradiography and adduct quantification
Polyphenol DNA adducts were located by autoradiography using Kodak (Rochester, NY) XAR-5 film and a Dupont (Boston, MA) Chronex-Lightning Plus intensifying screen. The films were exposed at 70°C for 34 days. The areas of the radioactive spots on the PEI cellulose sheets were measured and the spots were then scraped into liquid scintillation vials containing 5 ml scintillation cocktail (Safety Solve; Research Products, Mt Prospect, IL); radioactivity was determined by liquid scintillation counting. Regions adjacent to the radioactive spots of equal area were scraped, placed into scintillation vials and counted for background determination. Adduct levels were corrected for background counts after adjusting for the area of the TLC sample. The level of modification was calculated as described. For example, assuming that 4 µg of DNA is 1.21x104 pmol of 3'-dNp and that the specific activity of the 32P-ATP is 9.36x106 c.p.m./pmol, adduct levels were calculated as follows: relative adduct level = c.p.m. in adducts/11.32x1010 c.p.m. For each experiment we ran a positive control sample of catechol- or benzenetriol-treated HL60 DNA. Each sample was run at least twice on different days and the relative adduct levels for all experiments were combined to obtain an average adduct level. All samples were coded and assayed blindly. Mean adduct levels were calculated for each individual and expressed as adducts per 1010 nucleotides.
XRCC1 genotyping
DNA was extracted from peripheral lymphocytes by standard methods (Qiagen, Valencia, CA). XRCC1 genotypes were assayed using a PCRRFLP assay. An Arg
Gln substitution in exon 10 (codon 399) was amplified to form an undigested fragment of 242 bp using the primer pair 5'-CCCCAAGTACAGCCAGGTC-3' and 5'-TGTCCCGCTCCTCTCAGTAG-3'. Hot-start PCR was performed under the following conditions: denaturation at 94°C for 4 min followed by 35 cycles of 30 s at 94°C, 30 s at 60°C and 30 s at 72°C, followed by 10 min at 72°C. XRCC1 PCR product was digested with MspI at 37°C overnight and resolved on 2% agarose. Arg/Arg genotypes were digested to form 94 and 148 bp fragments. The Arg
Gln change abolishes the MspI restriction site.
ERCC2 genotyping
ERCC2 genotypes were detected using a PCRRFLP assay. A Lys
Gln in exon 23 (codon 751) was amplified to form an undigested fragment of 184 bp using the primer pair 5'-CCCCCTCTCCCTTTCCTCTG-3' and 5'-AACCAGGGCCAGGCAAGAC-3'. Hot-start PCR was performed under the following conditions: denaturation at 94°C for 4 min, followed by 30 cycles of 30 s at 94°C, 45 s at 59°C and 30 s at 72°C, followed by 10 min at 72°C. ERCC2 PCR products were digested with MboII at 37°C for 1 h and resolved on 4% agarose. Lys/Lys genotypes were digested to form 72 and 112 bp fragments. The Lys
Gln change abolishes the MboII restriction site.
GST-µ genotyping and smoking history
The GST-µ genotyping assay has been described previously (19). Smoking variables used in analyses were based on active smoking (never smoked, former smoker, current smoker), duration of smoking in years, pack-years of smoking, years since quitting smoking, and number of cigarettes smoked per day (among current smokers).
Statistical analysis
Bivariate analyses were used to screen variables as potential confounders or effect modifiers of mean SCE counts and detectable/non-detectable DNA adducts. Linear regression analyses of mean SCE frequency were performed on log-transformed data. The SCE ratio was calculated by exponentiation of the parameter estimate from a linear regression model of log-transformed mean SCE frequency. The SCE ratio compares the geometric mean SCE frequency in an exposed group with that in a referent (unexposed) group, and can be thought of as the adjusted proportional change in the geometric mean SCE frequency between the two comparison groups. All final linear regression models of mean SCE frequency were evaluated for model fit using regression diagnostics including graphical displays of Cook's influence and Studentized residuals. The final multiple linear regression model of mean SCE was compared with and without one influential observation and found not to differ materially. Interaction was evaluated using joint variables (with a common referent group) for exposure and genotype (additive model), as well as interaction terms and stratification (multiplicative model).
A Wilcoxon rank sum test was used to compare mean polyphenol DNA adduct levels between genotypes. Presented geometric means of DNA adducts were calculated from loge-transformed data. In bivariate analyses of DNA adducts, Fisher exact tests were used if cell sizes were less than five. In multivariable logistic regression models, DNA adducts were analyzed as a dichotomous outcome variable (detected/undetected). Covariates (potential confounders) were kept in multivariable regression models if the ß-coefficient for the exposure changed by >10% in a model without the covariate compared with a model with the covariate. Tests of trend were conducted by ordinal coding of explanatory variables and calculation of the P value for the ß-coefficient from a logistic or linear regression model. Separate models of SCE frequencies and detectable polyphenol DNA adducts were run for XRCC1 and ERCC2.
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Results
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Mean SCE frequency among all study subjects did not significantly depart from normality (skewness, 0.27; kurtosis, 0.21; ShapiroWilk W, 0.99; P, 0.6) but natural log transformation gave a slight improvement (skewness, 0.09; kurtosis, 0.42; Shapiro-Wilk W, 0.99; P, 0.9). Demographic characteristics of the 76 study subjects who participated in the SCE analysis have been described previously (22). Of the 76 study subjects, 71 were white and five were non-white. Arithmetic mean SCE frequencies (with 95% confidence intervals) and adjusted SCE ratios according to DNA repair genotypes and demographic variables of interest are presented in Table 1
. All SCE ratios are mutually adjusted for the other variables in the model. Subjects who were homozygous 399Gln carriers in XRCC1 had slightly higher mean SCE frequencies. Mean SCE frequencies and ratios did not differ by ERCC2 genotype. GST-µ null genotype was associated with higher mean SCE levels, as previously reported by Cheng et al. (19). Smoking variables (including active smoking, pack-years and years since quitting) were the strongest predictors of higher mean SCE; these variables remained important even after adjustment for gender, age, GST-µ and XRCC1. Age, race (not shown) and family history of cancer (not shown) were unrelated to mean SCE frequency. Female gender was weakly associated with higher mean SCE frequency. Analyses of log mean SCE frequencies were also conducted using the mean SCE frequency derived from the five or 10 metaphases with the highest SCE counts (high frequency cells). No differences in the results (parameter estimates and mean SCE ratios) were seen (data not shown). In all analyses of SCE frequencies, unadjusted and adjusted ratios did not differ materially, so only adjusted ratios are presented in the tables.
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Table I. Mean SCE frequencies and mean SCE ratios according to XRCC1 and ERCC2 genotypes and variables of interest
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Table II
gives means and adjusted mean SCE ratios for the joint (additive) effects of smoking and polymorphic DNA repair genes XRCC1 and ERCC2. In general, results of interactions based on active smoking (never smoked, former smoker, current smoker) did not differ substantially from those based on other measures of smoking (pack-years, duration and years since quitting) (data not shown). Thus, in the interest of precision, we present only results for interactions with active smoking. Among those who had never smoked, heterozygous and homozygous allele carriers were combined to increase sample size. Mean SCE frequencies and ratios were higher among current smokers who were homozygous carriers of the 399Gln allele in XRCC1 (mean 9.8, ratio 1.4) than in Arg/Arg current smokers (mean 8.1, ratio 1.2). Mean SCE frequencies and ratios among current smokers did not vary by ERCC2 genotype. Mean SCE frequencies and ratios by gender, age, race, family history of cancer and GST-µ did not differ by XRCC1 or ERCC2 genotypes (data not shown).
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Table II. Mean SCE frequencies and mean SCE ratios for the joint effects of smoking and DNA repair genes XRCC1 and ERCC2
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To investigate whether the polymorphism in XRCC1 at codon 399 potentiates the effect of the joint exposure of GST-µ genotype and smoking, we evaluated interaction terms between XRCC1 genotype and a variable for the joint exposure of active smoking status and GST-µ genotype [six categories: never smoked/GSTM1+ (referent group), never smoked/GSTM1, former smoker/GSTM1+, former smoker/GSTM1, current smoker/GSTM1+, current smoker/GSTM1]. There were too few subjects to separate the three genotypes for XRCC1, so heterozygous individuals were combined with those homozygous for the 399Gln allele to increase statistical power. Age- and sex-adjusted mean SCE ratios for combined smoking/GSTM1 groups were only slightly higher among XRCC1 399Arg/Gln + Gln/Gln genotypes [1.0 (referent), 1.2, 1.2, 1.2, 1.4, 1.3] than for the Arg/Arg genotype [1.0 (referent), 1.0, 1.1, 1.2, 1.2, 1.2]. We did not observe any differences in mean SCE ratios for combined smoking/GSTM1 stratified by ERCC2 genotypes. Because of sample size constraints, we were unable to evaluate the joint effects of GST-µ null and DNA repair genotypes and biomarkers of DNA damage.
The distribution of polyphenol DNA adducts was skewed in this sample (skewness, 2.5; kurtosis, 7.6; ShapiroWilk W, 0.62; P < 0.0001), with only 38% of subjects (n = 23) having detectable DNA adducts. Of the 61 subjects assayed for DNA adducts, 59 were Caucasian and two were non-Caucasian (data not shown). Geometric means of polyphenol DNA adducts and frequency of undetected and detected adducts are presented in Table III
. Means of polyphenol DNA adducts showed a weak positive trend with the presence of one or more copies of the XRCC1 399Gln allele. Mean adduct levels were slightly higher among GST-µ-null genotypes than among GST-µ-positive genotypes. Smoking (active status, pack-years, years since quitting) was unrelated to mean levels of DNA adducts or to the detection of adducts. Mean adduct levels were slightly higher among those subjects who reported a family history of any cancer. In bivariate analyses of polyphenol DNA adducts as a continuous variable, only greater age (three categories, Wilcoxon rank sum test P = 0.002) and male gender (Wilcoxon rank sum test P = 0.04) were significantly associated with higher levels of this class of DNA adducts.
Unadjusted and adjusted odds ratios for detection of polyphenol DNA adducts and polymorphisms in XRCC1 and ERCC2 are presented in Table IV
. Detection of polyphenol DNA adducts was positively associated with one or more copies of the XRCC1 399Gln allele, but not the ERCC2 751Gln allele. Odds ratios for detection of DNA adducts and XRCC1 genotype became more pronounced after adjustment for the effects of age, gender and family history of cancer. A trend test of odds ratios for detectable DNA adducts and XRCC1 genotype gave a P value of borderline significance (P = 0.07). Because the nature of polyphenol DNA adducts is believed to be endogenous, and because we detected an effect of age on this class of adducts, we evaluated the potential interaction of XRCC1 genotype and age using an additive model. Unadjusted odds ratios and 95% confidence intervals (CI) for detection of polyphenol DNA adducts and the joint effect of age and XRCC1 genotype are presented in Table IV
. Due to small sample size, age was dichotomized at the sample median (<65 years,
65 years) and XRCC1 399Arg/Gln genotypes were combined with 399Gln/Gln genotypes; furthermore, only unadjusted odds ratios are presented. The relationship between age and the XRCC1 399Gln allele was more than additive [11.0 > (1.3 + 5.5 1.0)]. Joint variables for smoking/XRCC1 and smoking/GST-µ were not associated with the presence of detectable polyphenol DNA adducts (data not shown).
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Discussion
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We investigated the relationship between polymorphisms in the BER gene XRCC1 and the NER gene ERCC2 and biomarkers of DNA damage in human blood mononuclear cells from healthy subjects. Our results suggest that XRCC1 (in addition to cigarette smoking and GST-µ) may contribute to baseline SCE frequency. In a small subset of subjects, we found evidence that mean SCE frequencies among current smokers homozygous for the XRCC1 399Gln allele were greater than those in current smokers with the 399Arg/Arg genotype. While these findings are based on small numbers, they are consistent with the results of Lunn et al. (3) who found more DNA damage (aflatoxin DNA adducts and somatic glycophorin A variants) among carriers of the same allele (399Gln) in XRCC1.
Because oxidative DNA damage is repaired via BER-related processes, we analyzed polyphenol DNA adducts and polymorphisms in XRCC1 (as well as ERCC2). Our results suggest that the XRCC1 allele 399Gln may be associated with higher DNA adduct levels and the presence of detectable adducts, a result consistent with the proposed role of XRCC1 in BER of oxidative DNA damage. Lunn et al. (3) found a gene dosage effect with the same XRCC1 allele and (detectable/undetectable) aflatoxin DNA adducts. When, however, the authors evaluated aflatoxin adduct levels, 399Gln allele carriers (homozygous plus heterozygous) were more likely to have intermediate, rather than higher, levels of adducts. In the present study, mean DNA adduct levels among individuals with two copies of the XRCC1 399Gln allele (6.6 adducts/1010 nucleotides) were higher compared with those in heterozygous (2.9 adducts/1010 nucleotides) and Arg/Arg (2.0 adducts/1010 nucleotides) individuals, but differences were not statistically significant (Wilcoxon rank sum test P = 0.17, comparing Gln/Gln with Arg/Arg individuals). The association of age with DNA adducts and the potential interaction of age with XRCC1 genotype supports the endogenous nature of these modifications and a possible role for XRCC1 and BER in clearing DNA adducts at higher endogenous concentrations that may occur with greater age. Together, these findings suggest a role for XRCC1 in removal of DNA damage in human mononuclear blood cells represented by SCEs and polyphenol DNA adducts.
The mechanism responsible for the association of the XRCC1 399Gln allele and higher levels of baseline DNA damage is unknown. However, the 399Gln allele is located within the central BRCT domain of XRCC1, which contains a binding site for PARP and is conserved in several proteins involved in DNA damage repair, cell cycle control, and recombination (8,25). In Chinese hamster ovary cells lines with non-conservative amino acid substitutions within the BRCT domain, reduced repair of single-strand breaks and hypersensitivity to ionizing radiation has been observed (26). Similar cell lines deficient in XRCC1 exhibit elevated spontaneous SCE frequency (5, 6). This is the second report to evaluate DNA damage phenotypes and the 399Gln allele in XRCC1. Our results, together with those of Lunn et al. (3), suggest that this polymorphism in XRCC1 may be important in more than one type of DNA damage phenotype, and that further mechanistic studies of this protein are needed. Our results also suggest that the polymorphism in ERCC2 has no phenotypic effect on SCE frequencies or the presence of polyphenol DNA adducts.
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Acknowledgments
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The authors wish to thank Dr Robert Millikan for helpful comments; Dr Heather Nelson, Kathy Springer and Duk-Hwan Kim for technical assistance; Linda Lineback and Marcia Chertok for recruiting and interviewing study subjects; and Marlys Rogers and Nick Weidemann for data management and computing assistance. This research was supported by NIH grants: T32CA09078 (E.D.), ES06717, ES04705 (J.W.), ES00002, ES08357, ES04705, CA74386, CA09078 and ES/CA06409.
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Notes
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7 To whom correspondence should be addressed Email: kelsey{at}hsph.harvard.edu 
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Received September 22, 1999;
revised December 28, 1999;
accepted December 30, 1999.