Germ-Line Variants in Methyl-Group Metabolism Genes and Susceptibility to DNA Methylation in Normal Tissues and Human Primary Tumors¹

Disruption of the normal DNA methylation patterns is an established common hallmark of human cancer cells. In a healthy cell, the DNA methylation patterns are conserved through cell divisions, allowing the expression of the particular set of cellular genes necessary for that cell type and blocking the expression of exogenous-inserted sequences (1, 2, 3). Cancer cells often exhibit the dual phenomenon of global hypomethylation accompanied by hypermethylation of several small regions rich in CpGs called CpG islands (1, 2, 3). The generalized loss of 5-methylcytosine in malignant cells occurs mainly in the CpGs scattered in the bodies of the genes and also in repetitive sequences (4). The aberrant methylation of the CpG island located in the 5′-promoter region of several tumor suppressor genes such as hMLH1, BRCA1, VHL, CDH1, p16^INK4a, and APC shuts down the expression of these contiguous genes (1, 2, 3). Although many tumors share this change for a given gene, unique profiles of promoter hypermethylation do exist for each tumor type with important biological and clinical consequences (5, 6).

However, one of several questions remain unanswered: is there a susceptibility factor that predisposes certain genes and/or particular tumors to possessing different degrees of global hypomethylation or local hypermethylation? This question has been approached from different experimental angles in the past. From the study of the detailed structure of the CpG island, it has been proposed that Sp1 binding sites may serve as protective factors against methylation (7, 8); however, Sp1 knockout mice show no evident alteration in the CpG island methylation patterns (9). On the other hand, certain CpG islands may be more prone to being methylated because they are located near or between regions that are normally methylated, such as Alu sequences and other repetitive elements, from where the methylation may be propagated (10). A similar propagation hypothesis has also recently been postulated as affecting methylation of the borders of CpG islands in an age-dependent manner (11). However, to date, the factors that have gained the widest acceptance as affecting DNA methylation are genetically based. First, germ-line mutations in DNMT3b⁴, which occurs in the Immunodeficiency-Centromeric Instability-Facial anomalies syndrome, cause hypomethylation of pericentromeric satellites of chromosomes 1, 9, and 16 (reviewed in Refs. 12, 13). Second, germ-line mutations in the chromatin-remodeling factor ATRX, which occurs in the ATRX syndrome (X-linked α-thalassemia/mental retardation), cause methylation changes in ribosomal DNA arrays, a Y-specific satellite and subtelomeric repeats (14). Third, the generation of a somatic knockout of the DNMT1 in a cancer cell line causes demethylation of juxtacentromeric satellites (15). Finally, knockout mice of the three most recognized DNMTs, DNMT1, DNMT3a, and DNMT3b, suffer several degrees of hypomethylation (reviewed in Refs. 12, 13).

Nevertheless, are there any other more common and naturally occurring genetic factors that may affect the degree of methylation of normal and cancer cells? The genes involved in the metabolism of the methyl group may represent good candidates and allow the interaction of environmental factors in the process. For example, it has long been known that diets deficient in methyl-group donors such as choline and methionine or in coenzymes of methyl-group metabolism such as folate and vitamin B12 disrupt the levels of intracellular SAM causing DNA hypomethylation (16, 17, 18, 19). Fig. 1 illustrates the enzymatic components and metabolic pathways of the methyl-group network. DNA methylation patterns depend on a sufficient cellular supply of the methyl-group donor SAM, which is synthesized using dietary methionine, but also using methionine recycled from the methylation reaction product S-adenosylhomocysteine. Three candidate genes emerge from this picture: MTHFR that supplies methyltetrahydrofolate as a methyl-group donor; MS that remethylates homocysteine to generate methionine; and CBS that conjugates homocysteine to serine. We have chosen these three enzymes because they have been found to be relatively commonly polymorphic in the general population because the germ-line variants generate less active alleles that lead to higher levels of homocysteine and a deficit in methyl-group donors, and because their putative relevance to the development of human diseases have been suggested previously (20, 21, 22, 23).

In this study, we have analyzed how germ-line genetic variants in MTHFR, MS, and CBS relates to the patterns of DNA methylation observed in normal tissues and human primary tumors. These primary neoplasms (n = 233) included the three most common tumor types in western countries: colorectal; breast; and lung tumors. Our data demonstrates that the MTHFR-677T and MS-2756G variants are associated with constitutive global genomic hypomethylation in normal tissues and low levels of CpG island hypermethylation of tumor suppressor genes in the tumors, respectively. These preliminary results provide evidence of the existence of a putative inherited differential susceptibility to methylate DNA between individuals in healthy and cancer populations.

Primary colorectal carcinoma samples (118) were obtained from surgical patients at the Hospital de Sant Pau in Barcelona between July 1991 and July 1993 under the supervision of Dr. Gabriel Capella. The Ethics Committee approved the study protocol. Eighty-four primary breast carcinomas and 31 primary lung adenocarcinomas were obtained from surgical resection specimens of patients at the Clinica Dexeus of Barcelona and the Johns Hopkins Hospital (Baltimore, MD). The Joint Committee on Clinical Investigation of the Johns Hopkins University School of Medicine approved specimen collection procedures. All of the samples were frozen in liquid nitrogen immediately after resection and stored at −80°C until processing. DNA was extracted by standard methods.

Genotyping was performed according to previously described PCR/RFLP methods (24, 25, 26). For the MTHFR C677T polymorphism, the primers 5′-TGAAGGAGAAGGTGTCTGCGGGA-3′ (sense) and the 5′-AGGACGGTGCGGTGAGAGTG-3′ (antisense) were used. For the MTHFR A1298C polymorphism, the primers 5′-GCAAGTCCCCCAAGGAGG-3′ (sense) and 5′-GGTCCCCACTTCCAGCATC-3′ (antisense) were used. PCR products were subjected to digestion with HinfI (New England Biolabs) for nucleotide 677 or MboII (New England Biolabs) for nucleotide 1298 for at least 2 h at 37°C, followed by electrophoresis on a 3% agarose Metaphor gel with ethidium bromide. For the MTHFR-C677T, wild-type allele produces a 198-bp band and mutant allele produces 175- and 23-bp bands. For the MTHFR-A1298C, three fragments of 29, 37, and 79 bp indicate the wild-type allele, and two fragments of 37 and 108 bp indicate the variant allele. For the insertion allele in exon 8 of the CBS gene, DNA was amplified with the primers F 5′CTGGCCTTGAGCCCTGAA3′ and R 5′GGCCGGGCTCTGGACTC3′. Wild-type samples showed a single band of 184 bp when tested on a 3% agarose gel electrophoresis, whereas the insertion allele yielded a 252-bp fragment. Genotyping for the MS-A2756G polymorphism was performed as described previously by PCR amplification with the primers 5′-GAACTAGAAGACAGAAATTCTCTA-3′ (sense) and 5′-CATGGAAGAATATCAAGATATTAGA-3′ (antisense; Kimura 00). Digestion was carried out with HaeIII (New England Biolabs); wild-type allele yields a 189-bp fragment, whereas the band pattern for the mutant allele yields 159- and 30-bp bands.

DNA methylation patterns in the CpG islands of tumor suppressor genes were determined by chemical conversion of the unmethylated but not the methylated cytosines to uracil, and subsequent PCRs using primers specific for either the methylated or the modified unmethylated DNA (27). The primers and PCR conditions for the methylation-specific PCR analysis have been previously described for p16^INK4a, p14^ARF, hMLH1, CDH1, BRCA1, APC, LKB1, MGMT, and DAPK (5, 27, 28). Primer sequences for RARβ2 were for the unmethylated reaction 5′-TTGGGATGTTGAGAATGTGAGTGATTT-3′ (upper primer) and 5′-CTTACTCAACCAATCCAACCAAAACAA-3′ (lower primer) and for the methylated reaction 5′-TGTCGAGAACGCGAGCGATTC-3′ (upper primer) and 5′-CGACCAATCCAACCGAAACGA-3′ (lower primer). The annealing temperature was 60°C. Primer sequences for RASSF1A were for the unmethylated reaction 5′-GGGGTTTGTTTTGTGGTTTTGTTT-3′ (upper primer) and 5′-AACATAACCCAATTAAACCCATACTTCA-3′ (lower primer) and for the methylated reaction 5′-GGGTTCGTTTTGTGGTTTCGTTC-3′ (upper primer) and 5′-TAACCCGATTAAACCCGT ACTTCG-3′ (lower primer). The annealing temperature was 60°C. Placental DNA treated in vitro with SssI methyltransferase was used for positive control for methylated alleles, and DNA from normal lymphocytes was used as negative control for methylated alleles. A total of 12 μl of each PCR reaction was directly loaded onto nondenaturing 6% polyacrylamide gels, stained with ethidium bromide, and visualized under UV illumination.

The 5-methylcytosine DNA content from the primary tumors and normal counterparts were determined by high-performance capillary electrophoresis in 57 samples where DNA was available as described previously (28, 29). Between 0.5–1 μg of DNA was incubated in 20 μl of 88% (v/v) formic acid at 140°C during 90 min. After hydrolysis, samples were reduced to dryness by speed-vac concentration (Savant SC-200). Finally, dried hydrolyzed were redissolved in 2 μl of H₂O Milli-Q grade and stored at −20°C until their analysis. An uncoated fused-silica capillary (Waters Chromathography S.A.; 600 mm × 0.075 inside diameter, effective length 540 mm) was used in a capillary electrophoresis system (Capillary Ion Analyzer; Waters Chromathography S.A.) connected to a processing data station Millennium (Waters Chromathography S.A.). The running buffer used was 24 mm NaHCO₃ (pH 9.6) plus 36 mm SDS. The running conditions were 25°C and operating voltages of 20 kV. On-column absorbance was monitored at 256 nm. Before each run, capillary was conditioned by washing with 1 mm NaOH for 1 min, followed by 0.1 m NaOH for 3 min and equilibrated with the running buffer for 3 min. Buffers and washing solutions were prepared with Milli-Q water and filtered throughout 0.45-μm pore size filters. Hydrolyzed samples were injected hydrostatically at 9.8 cm for 15 s, previously filtered throughout 0.45-μm pore filters. Three replicates of each sample analyzed were carried out. The quantification of the relative methylation in the DNA samples was performed as the percentage of the 5mdC (5-methylcytosine) of the total cytosines, calculated as follows: 5mdC peak area × 100/(dC peak area + 5mdC peak area).

The association between the different variables measuring the methylation parameters and the studied polymorphisms was performed at three levels: observed allelotypes; genotypes; and haplotypes. The CpG island hypermethylation frequency was represented as a percentage and obtained by dividing the number of CpG islands hypermethylated by the number of CpG islands analyzed. It was collapsed into two levels: >0 versus 0. The 5-methylcytosine DNA content in normal tissues, tumors, and the corresponding ratios was considered either as continuous variables and also categorized as follows. For normal, it was considered to be hipomethylated if the concentration was lower than the 25^th percentile of the whole distribution (value of 3.7), whereas tumors were considered to be hypermethylated if the concentration was higher than the 75^th percentile (value of 4.7). For the Normal/Tumor ratio a cutoff value of 1 was used. Statistically significant differences in the distribution of continuos variables in different genotypes were sought using the Kruskal-Wallis’ test. Fisher’s exact test was used to compare the distribution of categorical variables in the observed genotypes, and odds ratios were computed taking always the homozygous wild-type genotype as reference.

Haplotype analysis required phase uncertainty to be solved. We used a Stata program that computes expected frequencies using an expectation maximization (EM) algorithm, under the assumption of Hardy-Weinberg equilibrium (30). This assumption was tested in our samples using the likelihood ratio test and neither of the four polymorphisms proved to be in disequilibrium, although the P for MTHFR-C677T was rather small (P = 0.083). Haplotype analysis allowed us to estimate the association between pairs of polymorphisms and their joint effect on the different variables regarding DNA methylation.

We screened 233 cancer patients (118 colorectal, 84 breast, and 31 lung tumors) for four different germ-line variants in the MTHFR, MS, and CBS genes as described in “Materials and Methods.” Illustrative examples are shown in Fig. 2 A. For the MTHFR-C677T polymorphism, 47.6% (111 of 233) were CC homozygous, 39.1% (91 of 233) were CT heterozygous and 13.3% (31 of 233) were TT homozygous. For the MTHFR-A1298C polymorphism, 43.3% (101 of 233) were AA homozygous, 46.3% (108 of 233) were AC heterozygous and 10.3% (24 of 233) were CC homozygous. For the MS-A2756G polymorphism, 76.4% (178 of 233) were AA homozygous, 21.5% (50 of 233) were AG heterozygous and 2.1% (31 of 233) were GG homozygous. For the CBS 68-bp insertion variant, 83.3% (194 of 233) were homozygous for the absence of insertion, 15.9% (37 of 233) were heterozygous for the insertion, and 0.9% (2 of 233) were homozygous for the insertion.

The levels of 5-methylcytosine in the genome of 57 randomly selected human primary tumors (27 lung, 18 colorectal, and 12 breast carcinomas) and their corresponding normal tissue counterpart was determined by high performance capillary electrophoresis as described in “Materials and Methods.” Illustrative examples are shown in Fig. 2 B. The average content of 5-methylcytosine/5-methylcytosine + cytosine (5mdC/5mdC + dC) in the normal tissues was 5.50%, whereas it was 4.31% in the tumors, an average loss of 22% of 5-methylcytosines in the tumoral genome. Considering each cancer patient individually, 70% (40 of 57) of them had their tumoral genome hypomethylated compared with the corresponding normal tissue, 18% (10 of 57) were equally methylated, and 12% (7 of 57) had higher levels of 5-methylcytosine in the tumors than in the normal tissue.

CpG island promoter hypermethylation was analyzed in the primary tumors by methylation-specific PCR as described in “Materials and Methods.” Three different sets of genes were studied for each tumor type according to the CpG island hypermethylation profile described for human neoplasms (5). For colorectal tumors were p16^INK4a, p14^ARF, MGMT, APC, LKB1, and hMLH1; breast tumors were p16^INK4a, BRCA1, CDH1, RARβ2, and GSTP1; and lung tumors were p16^INK4a, p14^ARF, DAPK, RARβ2, RASSF1, and MGMT. Illustrative examples are shown in Fig. 2 C. The percentage of CpG island hypermethylation was calculated as the number of CpG islands that showed hypermethylation divided by the number of CpG islands analyzed. The average percentage of CpG island hypermethylation overall tumor types was 20%. The following distribution was observed: 11% (25 of 233) of the tumors had ≥50 hypermethylated CpG islands; 59% (138 of 233) of the tumors had <50% hypermethylated CpG islands; and 30% (70 of 233) of the tumors had no hypermethylated CpG islands. No significant differences in the global rates of CpG island hypermethylation were observed according to the tumor type.

We confronted the four different genotypes in the methyl-group genes MTHFR, MS, and CBS with the 5-methylcytosine DNA content in normal and tumoral tissue and the percentage of CpG island hypermethylation in all malignancies. All single genotypes did not show any statistical association with any DNA methylation parameter (summarized in Tables 1 and 2), except in two important and significant cases:

(a) The homozygous genotype MTHFR-677TT and the heterozygous genotype MTHFR-677CT were strongly associated with a low content of 5-methylcytosine in the DNA of the normal tissue of the cancer patients (Fisher’s exact test, P = 0.002; and Kruskal-Wallis’ test, P = 0.007). Homozygous MTHFR-677TT and heterozygous MTHFR-677CT patients had mean values of 4.47 and 4.26% 5-methylcytosine DNA content (5mdC/5mdC + mdC), respectively, compared with an observed value of 6.85% in the homozygous MTHFR-677CC. Considering single alleles, the presence of the MTHFR-677T allele was also associated with the lower 5-methylcytosine DNA content than the 25^th percentile of the overall distribution (Fisher’s exact test, P = 0.049). Furthermore, those tumors that underwent a less severe global genomic hypomethylation, identified for harboring the same or higher content of 5-methylcytosine than the normal tissues (the minority, ∼30% of all tumors analyzed), tended to be in those patients that had the MTHFR-677T allele (Fischer’s exact test, P = 0.049; 95% confidence interval, odds ratio, 2.37). Fig. 3 illustrates the associations described above.

(b) The carriers of the homozygous genotype MS-2756GG had tumors with a significantly lower number of hypermethylated CpG islands of tumor suppressor genes (Fisher’s exact test, P = 0.029; Table 2). The median rate of CpG island hypermethylation in the tumors harboring homozygous MS-2756GG alleles was 6.6%, whereas in heterozygous MS-2756AG plus homozygous MS-2756AA, the value was 20%. Fig. 3 illustrates the data.

We next addressed the issue of the putative interaction between the different germ-line variants in the three methyl-metabolism genes. Thus, we compared all of the different haplotypes that were generated by the combination of each separate genotype of the four alleles. Among the haplotypes found in our samples, only those that had the single alleles previously associated with DNA methylation alterations, as described above (the MTHFR-677T allele and global genomic hypomethylation and the MS-2756G allele and the low rate of CpG island hypermethylation), again demonstrated significant associations, and these were in the same direction. Thus, for example, the double genotype MTHFR-677CT + MS-2756GG and the tetra-genotype MTHFR-677CT + MS-2756AG + MTHFR-1298AA + CBS-No Insertion were strongly associated with the low percentage of CpG island hypermethylation (both with P = 0.029, Fisher’s exact test) because of the presence of the MS-2756GG genotype. In summary, the association was caused solely by the risk alleles that we found in our screening of single genotypes, and the contribution of the haplotype was minimal.

Whether there exist interindividual differences in the susceptibility to methylate, our DNA remains to be one of the most unsolved questions in the field of epigenetics. In recent years, the presence of aberrations in the DNA methylation patterns, exemplified in the CpG island hypermethylation of tumor suppressor genes in the context of global genomic hypomethylation, has been established as a common hallmark of human cancer (1, 2, 3). However, we do not know how many of these epigenetic lesions are induced by the environment alone, by our genetic background, or by the two acting together.

From the genetic standpoint, germ-line mutations in DNMT3b have been found in patients with the very rare Immunodeficiency-Centromeric Instability-Facial anomalies syndrome in association with hypomethylation of localized genomic regions (reviewed in Refs. 12, 13). However, mutations in the major maintenance DNMT, DNMT1, or in the other de novo DNMT, DNMT3b, have not been reported in any human disease, including cancer (12, 13). However, to exercise their function of methylating DNA, the DNMTs need a correct supply of the universal methyl-donor SAM. Thus, mutations and germ-line variants in enzymes involved in the complex SAM metabolism (illustrated in Fig. 1) are excellent candidates for affecting the patterns of DNA methylation in health and disease. Our findings that germ-line variants in the MTHFR and MS genes are statistically associated with the 5-methylcytosine DNA content and the number of hypermethylated CpG islands, respectively, support this hypothesis.

The possibility that germ-line variants in methyl-metabolism genes may affect the patterns of DNA methylation may also have consequences for our understanding of diseases associated with these genes. For example, the MTHFR-677T allele, which we have found to be associated with constitutive low levels of 5-methylcytosine DNA, has been related with the appearance of neural tube defects (i.e., spina bifida) and atherosclerotic lesions (i.e., coronary artery disease; Refs. 20, 31, 32, 33). In light of our findings, it is reasonable to propose that disruptions in the DNA methylation patterns may be behind the genesis of some of these neurological and vascular diseases. For example and supporting this concept, hypermethylation of the estrogen receptor gene is associated with atherosclerosis (34), and germ-line mutations in the methyl-CpG-binding protein MeCP2 cause Rett syndrome, a common neurodevelopmental disorder (reviewed in Ref. 35). MTHFR knockout mice were recently shown to have neuropathological lesions, aortic lipid deposition, and global DNA hypomethylation (36). In a pilot study of 19 normal lymphocytes from healthy volunteers, the MTHFR-667T allele was also associated with DNA hypomethylation (37). With respect to human tumors, it has been suggested that the MTHFR-677T allele modulates the risk of developing colorectal, gastric, and endometrial neoplasms and leukemia (24, 38, 39, 40, 41). Our findings lend weight to the proposal that disruption of DNA methylation levels is a mechanism that is likely to underlie this association.

The MS-2756G variant provides us with another excellent example. In human disease, MS deficiency causes megaloblastic anemia with or without some degree of neural dysfunction and mental retardation (42, 43), and aberrations in the DNA methylation profile could now also be invoked. In human tumors, the presence of the MS-2756G variant is associated with a lower colorectal cancer risk (44). After our observation that the MS-GG cancer patients have the lowest ratios of CpG island hypermethylation, it is very tempting to speculate that the lower risk of the MS-2756G variant carriers to develop neoplasms is because of the lesser capacity for aberrant hypermethylation of the CpG islands in the tumor suppressor genes. On the other hand, we should bear in mind the crucial role of methionine, one of the products generated by MS, in cancer. Several cancer cell lines and human primary tumors are unable to grow if they do not receive methionine from external sources such as the media supplement or the diet (45). Our discovery of the putative relation between the M-2756G variant and CpG island hypermethylation suggests that this methionine dependence to survive observed in cancer cells could be mediated in part by the necessity to keep the CpG islands of the tumor suppressor genes hypermethylated.

These genetic data do not detract in any way from the contribution of environmental factors to DNA methylation. It has been demonstrated that oligoelements such as nickel (46) or selenium (47) affect DNA methylation levels. Concerning the methyl-donor pathway, low consumption of folate and methionine in the diet has been associated with global genomic hypomethylation (reviewed in Ref. 48). This dependence of external agents is less critical in vitro in cultured cancer cells lines that are grown in a media that has an excess in methyl-donor molecules but can be extremely critical in vivo for the normal tissues and primary tumors in the patient.

Our finding of an association between certain genetic variants in methyl-group genes and certain DNA methylation patterns in normal and tumor cells opens up several potential avenues of research. For example, large scale studies of healthy populations and cancer patients need to be conducted. These would involve gathering epidemiological dietary data (consumption of folate, methionine, and so on) in combination with the description of haplotypes in methyl-metabolism and DNMT genes to corroborate their putative association with DNA methylation patterns. The elucidation of how dietary and environmental factors, acting as methyl-donors or methyl-acceptors, interacts with our own genetic background of DNA methylation genes may have important consequences for our understanding of how aberrant DNA methylation patterns are early established in human cancer and how we can modulate or prevent this process.