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1 Department of Social and Preventive Medicine and Kinesiology, and 2 Laval Hospital, Laval University, Ste. Foy, Quebec, Canada G1K 7P4; 3 Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri 63110; 4 Molecular Endocrinology Laboratory, CHUL Research Center, Ste. Foy, Quebec, Canada G1V 4G2; 5 School of Kinesiology and Leisure Studies, University of Minnesota, Minneapolis, Minnesota 55455; 6 Department of Kinesiology, Indiana University, Bloomington, Indiana 47405 - 7000; 7 Department of Health and Kinesiology, Texas A&M University, College Station, Texas 77843; and 8 Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisana 70803
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ABSTRACT |
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 TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES
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An autosomal genomewide
search for genes related to body composition and its changes after a
20-wk endurance-exercise training program has been completed in the
HERITAGE Family Study. Phenotypes included body mass index
(BMI), sum of eight skinfold thicknesses, fat mass (FM), fat-free mass,
percent body fat (%Fat), and plasma leptin levels. A maximum of 364 sib-pairs from 99 Caucasian families was studied with the use of 344 markers with single-point and multipoint linkage analyses. Evidence of
significant linkage was observed for changes in fat-free mass with the
S100A and the insulin-like growth factor I genes (P = 0.0001). Suggestive evidence (2.0 
Lod < 3.0; 0.0001 < P 0.001) was also observed for the changes in FM and
%Fat at 1q31 and 18q21-q23, in %Fat with the uncoupling protein 2 and
3 genes, and in BMI at 5q14-q21. At baseline, suggestive evidence was
observed for BMI at 8q23-q24, 10p15, and 14q11; for FM at 14q11; and
for plasma leptin levels with the low-density lipoprotein receptor
gene. This is the first genomic scan on genes involved in
exercise-training-induced changes in body composition that could
provide information on the determinants of weight loss.
genomewide search; linkage; genetic; body mass index; body fat; fat-free mass
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INTRODUCTION |
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TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES
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EXERCISE TRAINING CAN FAVORABLY alter body composition by reducing body weight and fatness, but there are individual differences in responsiveness. Our laboratory has observed small but significant changes in body composition among families trained with endurance exercise for 20 wk in the HERITAGE Family Study (36). Body composition is determined by complex phenotypes for which multiple genetic and nongenetic factors are expected to be involved. A recent review of the genes potentially related to human adiposity found evidence for several candidates pertaining to regulation of adipose tissue, body mass, energy intake, and energy expenditure (7). Probing the genome of selected individuals with informative markers can identify candidate genes. Evidence for linkage between markers and a phenotype defines chromosomal regions potentially involved in the phenotype variance and thus suggests that candidate genes be localized in these regions.
Five genomewide scans for genes related to obesity, body composition, and energy expenditure have been reported. In Pima Indians, Norman et al. (23, 24) presented such data for percent body fat (%Fat), waist-to-hip circumference ratio, 24-h metabolic rate, sleeping metabolic rate, and respiratory quotient, whereas Hanson et al. (16) showed results for body mass index (BMI). From these studies, evidence of linkages was found for BMI and %Fat at 11q24.1-q24.3 and for 24-h respiratory quotient at 20q11.2. A genomic scan was also conducted on Mexican-American subjects based on fat mass (FM) and blood leptin levels (Lep). Evidence of linkage was observed with Lep, and to a lesser extent with FM, at 2p21 near the proopiomelanocortin gene (11). Another genomic scan (13) was conducted on a French cohort using Lep, FM, and BMI status (>27 kg/m2), and it confirmed previous linkage at 2p21 with leptin and has provided evidence of linkage at 10p12 with BMI. In another study, Lee et al. (19) analyzed a mixed sample of Caucasian and African-American subjects for %Fat and BMI status (BMI < 27 kg/m2 vs. BMI > 27 kg/m2), and linkage was observed at 20q13 for BMI. Finally, based on data from the Québec Family Study, our laboratory recently reported evidence of linkage for fat-free mass (FFM) at 15q25-q26 and 18q12 (5).
Here, we now report the results of the genomic scan for body-composition phenotypes in the sedentary state and in response to 20 wk of exercise-endurance training in the HERITAGE Family Study cohort. This is the first genomic scan reporting results on body-composition changes after exercise training.
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MATERIALS AND METHODS |
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TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES
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Subjects and Phenotypes
The HERITAGE Family Study cohort has been previously described (4). The HERITAGE Family Study includes Black and Caucasian nuclear family volunteers with no obvious major diseases from the greater Québec City, Canada; Phoenix, AZ; Minneapolis, MN; Austin, TX; and Indianapolis, IN areas. Subjects were tested for a battery of anthropometric and physiological variables before and after a 20-wk exercise program. The Institutional Review Board of each participating institution previously approved the study protocol. Informed, written consent was obtained from each subject. Data from the 522 Caucasian subjects are used in the present study (192 parents and 330 offspring from 99 families), providing a maximum of 364 sib-pairs.Blood samples were obtained for various biochemical assays, and
permanent lymphoblastoid cell lines were established for the extraction
of DNA. Dependent variables include baseline levels and changes (
)
with the 20-wk training period in BMI, %Fat, FM, FFM, sum of eight
skinfold thicknesses (SF8), and Lep. BMI was estimated as the weight in
kilograms divided by height in square meters. %Fat was calculated from
body density measurements obtained after underwater weighing
(3) and the equations of Siri (32) and Lohman
(20) for men and women, respectively, with corrections for
pulmonary residual volume assessed by the helium dilution (17) or oxygen dilution (35, 37) techniques.
FM and FFM (in kg) were calculated from %Fat and body weight. SF8 (in
mm) was estimated by the values in the abdominal, subscapular,
suprailiac, medial calf, triceps, biceps, midaxillary, and thigh
(36). Lep (ng/ml) was evaluated in pre- and posttraining
blood samples obtained before and after, respectively, completion of a
maximal exercise test on the cycle ergometer (25). A
radioimmunoassay (Linco, St. Charles, MO) was used in which the lowest
quantity detectable in the plasma was 0.5 ng/ml.
Molecular Analysis
Choice of markers. Microsatellite markers were selected from various sources, but mainly the Marshfield panel version 8a and the Genethon panel, using maps of the Location Database (LDB) from Southampton, UK (http://cedar.genetics.soton.ac.uk). Microsatellite and restriction fragment length polymorphism (RFLP) markers for candidate genes of obesity and comorbidities, and some microsatellites that have generated positive results in other relevant studies, were also included. The LDB summary maps were also used to identify candidate genes 10 cM on each side of the markers yielding the strongest results.
PCR conditions. Genomic DNA was prepared from permanent lymphoblastoid cells by the proteinase K and phenol-chloroform technique. DNA was dialysed four times against Tris · EDTA buffer (10 mM Tris, 1 mM EDTA, pH 8.0) for 6 h at 4°C, and ethanol was precipitated. PCR conditions and genotyping of the markers have been described in detail elsewhere (8). Briefly, PCR reactions were conducted using 100-ng genomic DNA, 0.5 pmol each of a forward universal M13 primer coupled to infrared tag IRD800 or IRD700 (LICOR) and of the specific forward M13 tailed and reverse untagged primers, 125 µM dNTPs, and 0.3 units Taq polymerase (Perkin-Elmer, Pharmacia, Quiagen) in PCR buffer (100 mM Tris · HCl, pH 8.3, 15 mM MgCl2, 0.5 M KCl, 0.01% gelatin) for a final volume of 10 µl. PCR cycles followed a two-step procedure: 1 cycle at 93°C for 5 min, 10 cycles at 93°C for 20 s and 57°C for 60 s, and 24 cycles at 93°C for 20 s and 52°C for 60 s. Annealing temperature was defined according to the melting temperature of the primers used and ranged between 52 and 67°C, with a difference of 5°C being maintained between the two annealing temperatures.
Marker analysis. Infrared LICOR DNA sequencers were used to detect the PCR products. Automatic genotyping was performed simultaneously for a given marker on all of the study subjects using the computer software SAGA (LICOR). Results were exported directly to a local dBase IV database (GENEMARK) in which a procedure was used to check for Mendelian inheritance incompatibilities within families. Subjects with Mendelian incompatibilities (between 5 and 10%, depending on marker) were retyped completely, i.e., from the PCR reaction to the genotyping.
Statistical Analysis
Phenotypic variables were adjusted within gender for the effects of age, age2, and age3, with Lep further adjusted for FM (Lep-FM). Phenotype changes were also adjusted for baseline values, andLep was adjusted for FM. When the regression
parameters were estimated, a regression procedure with outliers
(greater than ±3 SD) excluded was used. Only significant effects
(P < 0.05) were included in the model. Residuals were
computed for all subjects, including outliers, and were then
standardized to a mean of 0 and a SD of 1. SAS (version 6.08) for
personal computer was used for the regression analysis. Single-point
[SIBPAL (28)] and multipoint [MAPMAKER/SIBS
(18)] linkage analyses were performed by using sibships
from the nuclear families, where non-independence of sib-pairs was
taken into account in the analyses. The data were also analyzed with
the multipoint variance components routine implemented in SEGPATH
(26), in which the correlation among siblings within a
family is modeled as a function of the allele sharing at a marker
locus, as well as a residual polygenic component. The proportion of
marker alleles shared that were identical by descent was estimated by
using MAPMAKER/SIBS (18) and was input as data into the
SEGPATH model. In addition to the trait locus and polygenic
heritabilities, the spouse correlation and excess sibling resemblance
beyond that predicted under the genetic model were estimated by maximum
likelihood in SEGPATH. Linkage was tested by a likelihood ratio test
distributed as a 1:1 mixture of a 
2 with 1 df and a
point mass at 0 or by Lod score. LDB maps complemented by those of the
Marshfield were used to define marker distances for multipoint
analyses. LDB maps compared well with other genetic maps as with the
Genethon and the Marshfield ones. For instance, the majority of
the markers showed the same relative position across the different
maps, which is the more important criterion for accurate multipoint
analyses in contrast to absolute distances on the chromosome.
Significance level for the genomewide analysis was set at 0.0001 or to
the equivalent Lod score of 3.0, as estimated by nominal P
value targeted (0.05) divided by the number of markers used (344 markers). In fact, this value is conservative because the real value
should be Meff = M{1...[V(
)/M]}, where
Meff is the effective marker number, M is the number of
markers scored, and V() is the variance of the eigen values of the
intermarker correlation matrix for each chromosome (9).
Meff should be less than M because of intercorrelations
among linked markers.
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RESULTS |
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TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES
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A total of 344 markers (291 microsatellites; 53 RFLPs) from the 22 autosomal chromosomes were typed. The mean spacing of the markers was
9.7 cM (range <0.1-25 cM), and the mean heterozygosity in
HERITAGE was 0.69 (0.01-0.97). The mean age, BMI, FFM, FM, %Fat,
SF8, and Lep values by generation and gender of the subjects are
presented in Table 1. The mean BMI for
both genders from the parental generation was 28.0 kg/m2
(n = 192; range 19-48 kg/m2). In the
parental generation, 27, 42, and 37% of the subjects were normal
weight (BMI < 25 kg/m2), overweight (25 BMI < 30 kg/m2), and obese (BMI 
30 kg/m2), respectively (22). Offspring were
leaner than their parents with a mean BMI of 24.6 kg/m2
(n = 330; range 17-44 kg/m2) and a BMI
distribution of 63, 24, and 13% as normal weight, overweight, and
obese, respectively.
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The results from multipoint linkage analyses using either MAPMAKER/SIBS
(Fig. 1)
or SEGPATH (Fig.
2) for
the 22 autosomal chromosomes are presented for BMI, FM, and %Fat. FFM,
SF8, Lep, and Lep-FM results were not included in Figs. 1 and 2 for
clarity and because no significant results were observed with these
phenotypes using multipoint analytic strategies.
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Significant (P 0.0001; Lod score 3.0) and
suggestive (0.0001 < P 0.001; 2.0 Lod score < 3.0) linkages from single-point and multipoint analyses are
summarized in Tables 2 and
3, respectively. Significant linkages
were observed for the FFM with candidate genes S100A
(P = 0.0001) and insulin-like growth factor I (IGF-I) (P = 0.0001). Suggestive evidence of linkage was
observed for BMI at 5q14-q21, FM at 9q34, FM and %Fat at
1q31 and 18q21-q23, and %Fat with the ATPase 
2 and
uncoupling protein 2 (UCP2) genes.
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The evidence for linkage could be considered as more convincing when
more than one phenotype shows linkage evidence with the same marker,
when different markers within a linkage group are linked with the same
phenotype, or when markers yield positive results for given phenotypes
with different analytic linkage methods (P values for single
point; Lod scores for multipoint). Such regions were found on
chromosomes 1, 5, 8, 10, 11, 14, and 18. On chromosome 1, the marker
D1S1660 at 1q31.1 showed evidence of linkage with both FM and
%Fat, using either SIBPAL (P = 0.0006 and
P = 0.0007, respectively) or MAPMAKER/SIBS (Lod score
of 2.2 with each). At 5q14.1-q21.1 on chromosome 5, two successive
markers, D5S1725 and D5S1462, showed linkages with BMI
(P = 0.0004 and Lod = 2.4, respectively). At
11q13.1-q21, several polymorphisms were tested for the two neighboring
genes, UCP2 and UCP3. UCP2 haplotypes of the exon 4 Ala55Val and exon 8 insertion/deletion polymorphisms and marker D11S2002 yielded suggestive
linkages with %Fat (Lod = 2.2, P = 0.0009, respectively). Moreover, polymorphisms in UCP3 gene also showed some
evidence of linkages with %Fat (P = 0.009; Lod = 1.9) and FM (P = 0.01; Lod = 1.7). Finally,
on chromosome 18, within a region of 16 cM, three markers (D18S38,
D18S878, and D18S1371) generated evidence of linkage with FM and
%Fat, with D18S878 giving the strongest evidence (P = 0.001; Lod scores between 1.3 and 1.9).
At baseline, suggestive evidence was observed for BMI at 9q34 and
10p15, for BMI and Lep at 8q23-q24, for BMI and FM at 12p12 and 14q11,
for Lep with low-density lipoprotein receptor (LDLR) gene, and for
Lep-FM at 10q21-q22. At 8q23.3-q24.12, two neighboring markers,
D8S556 and D8S592, showed suggestive linkage both with BMI
(P = 0.001 and Lod = 2.0, respectively) and D8S592
with Lep (P = 0.001). Two different areas of chromosome
10 yielded evidence of linkage. At 10p15.3-p15.1, neighboring markers
D10S1435 and D10S189 showed suggestive linkages, respectively, with FM
(Lod = 2.1) and BMI (Lod of 2.7 or 2.3 by the two methods). At
10q21-q22, markers GATA121A08 and D10S2327 provided evidence of linkage
with Lep-FM (P = 0.001 for both). Finally, on
chromosome 14q11-q11.2, three consecutive markers (D14S283, D14S742,
and D14S1280) showed linkages with BMI (2.2 Lod 2.4),
whereas D14S283 was also linked with FM (P = 0.0006, Lod = 2.0) and, to a lesser extent, with Lep (P = 0.003).
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DISCUSSION |
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TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES
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A genomewide search for quantitative trait loci influencing body composition before and in response to 20 wk of endurance training was completed in the HERITAGE Family Study. The strongest evidence of linkage, in terms of statistical significance reflected by Lod scores and P values, was with the training changes in FFM on chromosomes 1 and 12. On chromosome 12, positive linkage with IGF-I was observed, whereas on chromosome 1, changes in FFM were linked with a polymorphism in the S100A gene. Both results were supported by multipoint analysis (Lod scores of 2.3 and 1.0, respectively). For IGF-I, this confirmed the positive linkage reported previously using single-point analysis (33). The S100A gene encodes an acidic calcium-binding protein and is located at 1q21 within a cluster of nine calcium-binding protein coding genes (29). The S100A protein is mainly present in the slow-twitch muscle fiber of cardiac and skeletal muscles and, at a much lower level, in nervous tissue (14, 15). S100A was found to be more efficient than calmodulin in antagonizing DNA binding to MyoD, which belongs to a family of myogenic basic helix-loop-helix transcription factors that activates muscle-specific genes (2). Because FFM is composed, in large part, by muscle tissue, we can speculate that the linkage observed here between the S100A gene and the training changes in FFM might come from the S100A protein-antagonizing effects on MyoD by which numerous muscle-specific genes could be activated or inactivated after training.
Single-point and multipoint linkage with training changes in body fat was observed at 1q31 with the marker D1S1660. The genes encoding the two regulators of G-protein signaling 1 and 2 have their genes located at 1q31, <5 cM from D1S1660. On chromosome 5q14.1-q21.1, a region of 18 cM, defined by markers D5S1725 and D5S1462, was linked to training changes in BMI. The cocaine- and amphetamine-regulated transcript and the prohormone convertase 1 (PCSK1) are located in this region. The cocaine- and amphetamine-regulated transcript is a neuropeptide, expressed in the brain, that has an appetite-suppressing effect. PCSK1 is involved in the processing of numerous prehormones, like proinsulin, and one could speculate that regular exercise stimulates PCSK1.
No obvious candidate genes can be proposed for the linkages observed on chromosomes 8q and 10p with baseline values of BMI, FM, and Lep, and on 10q for Lep adjusted for body fat. These regions of chromosome 8 and 10 are not close to those previously reported to be linked to Lep or body fat (11, 13). On the other hand, chromosome 8 linkage was shown to be in a region where the human homologue of several quantitative trait loci related to control of body weight and body fat are predicted to be located (see Ref. 7). On chromosome 11, the two uncoupling protein (UCP2 and UCP3) genes are good candidates for the linkage observed. Positive correlation between UCP2 mRNA levels and BMI and %Fat in skeletal muscle (1) and white adipose tissue (21), as well as a negative correlation between BMI and UCP3 in skeletal muscle (31), has been reported. Moreover, it has been observed that exercise training lowered the level of UCP3 mRNA (30). These observations support the hypothesis that UCP2 and UCP3 might be related to changes in BMI and body fat content observed after the 20-wk endurance-exercise training program in the HERITAGE Family Study.
Evidence of linkage for BMI and body fat at baseline was also detected
with three markers covering a region of 9 cM on chromosome 14. The
angiogenin (ANG) gene encoding a protein involved in neovascularization of tissues is located in the linked region. However, only weak linkages
(Lod scores < 1.5) were observed in HERITAGE for a RFLP located
within ANG. Further association and functional studies are needed to
clarify the possible effect of ANG on the regulation of human body
composition. On chromosome 18, the observed linkages with FM and
%Fat were 4 cM telomeric to the melanocortin 4 receptor (MC4R) gene
(10). Two mutations in human MC4R have been found to be at
the origin of massive obesity (34, 38), whereas our laboratory previously observed an association between MC4R polymorphism and FM and %Fat in female subjects from the Québec Family Study (6). An enhanced stimulation of MC4R produces a decrease
in food intake and an increase in energy expenditure and body
temperature. These observations are concordant with the observation
that there was a mean body fat loss with training in the HERITAGE
Family Study cohort.
Finally, evidence of linkage was observed between the LDLR gene on chromosome 19 and baseline and exercise training-induced changes in Lep and, to a lesser extent, with baseline adiposity phenotypes. LDLR is an important regulator of serum cholesterol that may have implications for the development of both hypertension and obesity. Significant associations of LDLR with obesity have been previously reported in two different normotensive populations (12, 27) with the same LDLR microsatellite marker used in the present study. The linkage observed here is more strongly related to leptin per se than to obesity phenotypes. Further studies of this LDLR polymorphism and its relationships with lipid variables and Lep are needed in the HERITAGE cohort.
In summary, we have observed significant linkages for a body-composition phenotype with changes in response to a 20-wk exercise training period and markers within S100A and IGF-I genes in the Caucasian families from the HERITAGE Family Study. Suggestive results were also observed on genomic regions from chromosomes 1, 5, 8, 10, 11, 14, 18, and 19. Candidate genes could be proposed for all chromosomes except 8 and 10. Linkages with training changes in lean body mass, body fat content, and BMI were generally different from those observed at baseline levels, suggesting that different metabolic pathways are contributing to human variation in body-composition alterations resulting from chronic exposure to exercise. This underlines the usefulness of intervention studies in which a metabolic stress, in this case exercise training, keeps uncovering new links between genes and phenotypes, which would otherwise go undetected in cross-sectional studies.
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ACKNOWLEDGEMENTS |
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The authors thank Monique Chagnon and other people who have been involved in the genotyping of some candidate genes, to Anne-Marie Bricault for cell lines and DNA production, and to Claude Leblanc and Christian Couture for the database management. Thanks are also extended to all of the investigators involved in testing among the five centers and to all subjects who have generously agreed to participate in this study.
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FOOTNOTES |
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The HERITAGE Family Study is supported by National Heart, Lung, and Blood Institute Grants HL-45670 (to C. Bouchard), HL-47323 (to A. S. Leon), HL-47317 (to D. C. Rao), HL-47327 (to J. S. Skinner), and HL-47321 (to J. H. Wilmore). A. S. Leon is partially supported by the Henry L. Taylor endowed Professorship in Exercise Science and Health Enhancement. C. Bouchard is partially supported by the George A. Bray Chair in Nutrition.
Address for reprint requests and other correspondence: Y. C. Chagnon, Physical Activity Sciences Laboratory, Kinesiology, PEPS 0212, Laval Univ., Ste. Foy, Québec, Canada G1K 7P4 (E-mail: Yvon.Chagnon{at}kin.msp.ulaval.ca).
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 27 April 2000; accepted in final form 11 December 2000.
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TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
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