Abstract

Impaired folate-mediated 1-carbon metabolism has been linked to multiple disease outcomes. A better understanding of the nutritional and genetic influences on this complex biochemical pathway is needed to comprehend their impact on human health. To this end, we created a mathematical model of folate-mediated 1-carbon metabolism. The model uses published data on folate enzyme kinetics and regulatory mechanisms to simulate the impact of genetic and nutritional variation on critical aspects of the pathway. We found that the model predictions match experimental data, while providing novel insights into pathway kinetics. Our primary observations were as follows: 1) the inverse association between folate and homocysteine is strongest at very low folate concentrations, but there is no association at high folate concentrations; 2) the DNA methylation reaction rate is relatively insensitive to changes in folate pool size; and 3) as folate concentrations become very high, enzyme velocities decrease. With regard to polymorphisms in 5,10-methylenetetrahydrofolate reductase (MTHFR), the modeling predicts that decrease MTHFR activity reduces concentrations of S-adenosylmethionine and 5-methyltetrahydrofolate, as well as DNA methylation, while modestly increasing S-adenosylhomocysteine and homocysteine concentrations and thymidine or purine synthesis. Decreased folate together with a simulated vitamin B-12 deficiency results in decreases in DNA methylation and purine and thymidine synthesis. Decreased MTHFR activity superimposed on the B-12 deficiency appears to reverse the declines in purine and thymidine synthesis. These mathematical simulations of folate-mediated 1-carbon metabolism provide a cost-efficient approach to in silico experimentation that can complement and help guide laboratory studies.

Introduction

Folate plays a critical role in 1-carbon metabolism, biochemical reactions that are related to amino-acid metabolism, nucleotide synthesis, and numerous methyltransferase reactions, including DNA methylation. Folate and nutrients involved in folate-mediated 1-carbon metabolism (FOCM)10 are involved in the etiology of neural-tube defects (1), colorectal and other types of cancer (26), and cardiovascular disease (79). Further, chemotherapeutic agents targeting FOCM play a central role in cancer treatment (10,11). FOCM is highly complex; genetic factors (i.e., polymorphisms in folate-dependent enzymes) and dietary influences interact in intricate ways that ultimately influence folate status and disease risk.

Considerable research over the past 20 y has identified important details about FOCM and its regulation. However, a limiting factor of these critical studies is that they have primarily focused on single pathways and/or single reactions, and thus provide no means for understanding the overall functioning of the system. FOCM comprises a complex nonlinear system, which is difficult to capture using purely experimental methods. Mathematical modeling is an approach that has been particularly useful in the study of complex nonlinear biological systems (12,13) and which has been used for individual components of FOCM (1416).

Building on initial modeling of specific cycles of this pathway (14,15), we developed a mathematical model of the complete cytosolic FOCM, which uses information on folate-enzyme kinetics and regulatory mechanisms to predict the impact of genetic and nutritional variation. We used the model presented here to illustrate how our predictions match experimentally obtained data and provide information on metabolic processes. Throughout the study, we used exactly the same model, and tested the effects of altering particular parameter values or inputs that correspond to these specific biological situations or experiments. Our predictions were generally consistent with those from the experimental literature, which suggests that this simulation model is a valid tool for in silico investigations of FOCM.

Methods

Our objective was to develop a mathematical model that simulates the multiple, interconnected biochemical reactions in folate-mediated 1 carbon metabolism (Fig. 1). Our general approach to the model building used integrated information from 3 sources of published mammalian data: 1) intracellular concentrations of folate-related substrates [e.g., tetrahydrofolate (THF), 5-methyltetrahydrofolate (5mTHF), S-adenosylmethionine (SAM)]; 2) kinetics of enzymes in the folate and methionine cycles; and 3) folate enzyme regulatory mechanisms. The values of the kinetic constants reported in the literature were obtained from a variety of species and tissues, including cell lines, and were obtained under a variety of experimental conditions. Thus, it is not surprising that the reported ranges for individual parameters are quite large; we chose values within the published ranges (Table 1).

Figure 1

The reaction scheme for FOCM modeled in this article. The substrates are enclosed in rectangular boxes and the enzymes in ellipses; vitamin cofactors are enclosed in shaded circles.

TABLE 1

Kinetic parameters used in the mathematical model of FOCM1

EnzymeParameterValueUnits
CBS Vmax 90,000 μmol/(L · h) 
BHMT Vmax 375 μmol/(L · h) 
MS Vmax 525 μmol/(L · h) 
MTHFR Vmax 5000 μmol/(L · h) 
DNMT Ki,SAH 0.84 μmol/L 
GNMT Ki,SAH 0.84 μmol/L 
 Vmax 288 μmol/(L · h) 
TS Vmax 5000 μmol/(L · h) 
DHFR Vmax 5000 μmol/(L · h) 
SHMT "Gly → Ser"   
 Vmax 320,000 μmol/(L · h) 
 Km,5;10-CH2-THF 3000 μmol/L 
CH2 [H2C = O] 500 μmol/L 
FTS [HCOOH] 500 μmol/L 
EnzymeParameterValueUnits
CBS Vmax 90,000 μmol/(L · h) 
BHMT Vmax 375 μmol/(L · h) 
MS Vmax 525 μmol/(L · h) 
MTHFR Vmax 5000 μmol/(L · h) 
DNMT Ki,SAH 0.84 μmol/L 
GNMT Ki,SAH 0.84 μmol/L 
 Vmax 288 μmol/(L · h) 
TS Vmax 5000 μmol/(L · h) 
DHFR Vmax 5000 μmol/(L · h) 
SHMT "Gly → Ser"   
 Vmax 320,000 μmol/(L · h) 
 Km,5;10-CH2-THF 3000 μmol/L 
CH2 [H2C = O] 500 μmol/L 
FTS [HCOOH] 500 μmol/L 
1

The other kinetic parameters are as described in Nijhout et al. (15,17). For complete details, see the online supporting material.

TABLE 1

Kinetic parameters used in the mathematical model of FOCM1

EnzymeParameterValueUnits
CBS Vmax 90,000 μmol/(L · h) 
BHMT Vmax 375 μmol/(L · h) 
MS Vmax 525 μmol/(L · h) 
MTHFR Vmax 5000 μmol/(L · h) 
DNMT Ki,SAH 0.84 μmol/L 
GNMT Ki,SAH 0.84 μmol/L 
 Vmax 288 μmol/(L · h) 
TS Vmax 5000 μmol/(L · h) 
DHFR Vmax 5000 μmol/(L · h) 
SHMT "Gly → Ser"   
 Vmax 320,000 μmol/(L · h) 
 Km,5;10-CH2-THF 3000 μmol/L 
CH2 [H2C = O] 500 μmol/L 
FTS [HCOOH] 500 μmol/L 
EnzymeParameterValueUnits
CBS Vmax 90,000 μmol/(L · h) 
BHMT Vmax 375 μmol/(L · h) 
MS Vmax 525 μmol/(L · h) 
MTHFR Vmax 5000 μmol/(L · h) 
DNMT Ki,SAH 0.84 μmol/L 
GNMT Ki,SAH 0.84 μmol/L 
 Vmax 288 μmol/(L · h) 
TS Vmax 5000 μmol/(L · h) 
DHFR Vmax 5000 μmol/(L · h) 
SHMT "Gly → Ser"   
 Vmax 320,000 μmol/(L · h) 
 Km,5;10-CH2-THF 3000 μmol/L 
CH2 [H2C = O] 500 μmol/L 
FTS [HCOOH] 500 μmol/L 
1

The other kinetic parameters are as described in Nijhout et al. (15,17). For complete details, see the online supporting material.

We have already published mathematical models of the folate cycle (15) and the methionine cycle (14,17). For this study, we combined our models for the folate and methionine cycles to build a combined model for intracellular hepatic FOCM. We investigated how the mutual influences of the folate and methionine cycles affect the overall functioning of FOCM (Fig. 1). The 2 cycles are connected, not only by the methionine synthase (MS) reaction, but also by 2 “long-range” interactions: 5,10-methylenetetrahydrofolate reductase (MTHFR) is inhibited by SAM, and glycine N-methyltransferase (GNMT) is inhibited by 5mTHF. The other long-range interactions in the model are the inhibition of betaine-homocysteine methyltransferase (BHMT) by SAM and the activation of cystathionine β-synthase (CBS) by SAM. The kinetics of these long-range interactions were taken from the literature as described in (17). There is contradictory evidence about the inhibition of BHMT by SAM, as there are positive (18) and negative (19) reports. We followed the evidence of (18), but found that, because the inhibition is only effective at very high SAM concentrations, the presence or absence of the inhibition had little effect on our results, except for exceptionally high methionine loads.

Because the combined model contained new interactions among enzymes and substrates in the folate and methionine cycles that were absent from the earlier models, some of the substrates developed aberrant concentrations. We therefore modified somewhat, within the published ranges, the Vmax and Ki values for MTHFR, CBS, MS, GNMT, and BHMT, so that the resulting concentrations of substrates at equilibrium corresponded to those obtained experimentally. We also adjusted the inhibition of DNA-methyltransferase (DNMT) and GNMT by S-adenosylhomocysteine (SAH), i.e., lowered the DNMT- and GNMT-specific Ki values (20), and increased the Vmax of the backward reaction of serine hydroxymethyltransferase (SHMT; glycine to serine). The Vmax values of thymidylate synthase (TS) and dihydrofolate reductase (DHFR) were increased 100-fold to mimic the conditions observed during cell division (15,21,22). More information about the details of the model is available as Online Supporting Material.

Some substrates and products of FOCM are able to regulate distant enzymes in the network; these mechanisms were modeled as in Nijhout et al. (17). The regulation of MTHFR by SAM and SAH concentrations was obtained by multiplying the Vmax of MTHFR by 10/(10 + [SAM] − [SAH]), corresponding to the experiments by Jencks and Matthews (23) and Yamada et al. (24). We scaled these allosteric interactions so that they equal 1 when the methionine input is 100 μmol/(L · h).

The model calculations assume that the normal mean methionine input to the folate pool is 100 μmol/(L · h), based on the work of Storch et al. (25). Their work demonstrated that, during fasting, the net input to the methionine pool is ∼4 μmol/(kg · h) (from protein breakdown), which is approximately equivalent to an input of 50 μmol/(L · h) to the liver. Conversely, during and immediately after feeding, the methionine input to the liver is ∼4 times as high, or ∼200 μmol/(L · h). Assuming that the fasting state lasts for 16 h/d and the postprandial state lasts for 8 h/d in humans, then over a 24-h period the mean methionine input to the liver is ∼100 μmol/(L · h).

Our prediction of the DNA methylation reaction rate (= methylation rate) was based on the kinetic characteristics published for the maintenance DNA methyltransferase (DNMT1) (20). Multiple other methyltransferase reactions run in parallel to the DNMT1 reaction. The majority of methyltransferases have low Km values for SAM and low Ki values for SAH and, therefore, the reactions they catalyze will behave similarly to DNMT1. DNA methylation depends on the availability of methyl groups, the regulation of the DNMT reaction, and the accessibility of cytosine substrates that is controlled by histones and other DNA-binding proteins. Here we were solely concerned with the first 2 mechanisms and assumed that the accessibility of methylation sites was constant. In the context of our kinetic model, methylation rate represents the reaction flux, whereas most DNA methylation assays refer to relative genome-wide DNA methylation level or the number of cytosine methyl groups at specific locations.

In the folate pool experiments, we changed the total folate pool and computed how the concentrations and reaction rates change. An input parameter was the total folate concentration of the cell; the model then determined the steady-state values of each of the individual folate metabolites. The model showed that, after alteration of the folate pool, the folate metabolites reached their new equilibrium values within an hour. To test the effects of polymorphisms and vitamin deficiencies, we altered the Vmax values of the corresponding enzymes. To simulate methionine loading we varied the methionine input as a function of time and computed the time courses of the concentrations and reaction rates. All calculations were completed using MatLab (version 7.0, The Mathworks).

Results and Discussion

Consequences of variation in the total folate pool.

We computed how the substrate concentrations and reaction velocities at equilibrium change when the total amount of folate is changed (Fig. 2). We used 20 μmol/L as a standard cellular folate concentration in the simulations (15,26). As expected, the Hcy concentration increased as total folate fell below this normal concentration, but at higher folate concentrations, Hcy was not influenced by the folate pool. Intracellular Met concentrations are relatively insensitive to variation in total folate, but the concentration of SAM had a strong linear association with total folate concentration (Fig. 2A). Because SAM is the principal substrate for cellular methylation reactions, one might expect that the overall methylation rate would be a function of SAM concentration and, therefore, the total folate pool. However, the simulations suggested instead that allosteric interactions between the folate and methionine cycles stabilize the DNMT reaction rate against variations in methionine input and folate pool size (17). Here, as previously (17), we assumed that the availability of CpG methylation sites was constant, and the DNMT reaction rate thus represents the rate of the DNMT reaction when it is unconstrained by the accessibility of methylation sites on the DNA. There were only modest changes in the DNMT reaction rate (Fig. 2A), with an increase in folate concentration from 10 μmol/L to 40 μmol/L, whereas SAM increased linearly ∼3-fold. Note that as the total folate pool decreased from 20 μmol/L to 1 μmol/L, the methylation rate declined only by 50%. The DNMT reaction rate remained stable at both high folate and SAM concentrations because most methyl-transferase reactions have low Km values and therefore saturate as SAM increases.

Figure 2

Effect of total folate pool on concentrations and reaction velocities. Metabolites in the methionine cycle and the rate of the DNA methylation reaction (DNMT in Fig. 1), as functions of the total folate pool, are shown in A. The concentration of SAM is a linear function of total folate, but the DNA methylation rate was quite stable as total folate varied. The reaction rates in the folate cycle, as functions of the total folate pool, are shown in B. Because of the the inhibitory binding of folates to folate enzymes, the reaction velocities were relatively stable as total folate varied between 10 and 30 μmol/L.

Understanding and modeling these interrelated reactions is complicated because many of the substrates in the folate cycle are also tight-binding inhibitors of enzymes in the folate cycle (2628). In the absence of such binding, reaction velocities and substrate concentrations would vary in direct proportion to folate pool size (15). Thus, the inhibitory binding causes a remarkable homeostasis in the enzyme velocities of the folate cycle; as total folate decreases, the dissociation of folate-enzyme complexes increases both the amount of active enzyme and additional free folate (15). This mechanism has an important consequence: namely, as folate concentrations become very high, enzyme inhibition increases, and reaction velocities decrease.

Figure 2B illustrates the simulated reaction velocities of enzymes in the folate cycle as a function of total folate concentration. Note that there is a fairly wide range of folate concentrations centered at 20 μmol/L, in which the velocities are relatively stable. As the folate pool decreased, some enzyme velocities remained relatively unchanged [SHMT, TS, and 10-formyltetrahydrofolate dehydrogenase (FTD)], whereas MTHFR, aminoimidazolecarboxamide ribonucleotide transferase (AICART), and 10-formyltetrahydrofolate synthase (FTS) showed a sharp increase in velocity. As folate concentrations fell below 5 μmol/L, all enzyme velocities declined precipitously. The folate concentrations associated with both peak velocity and the rate of decline depend on the Ki of the inhibition and on the Km of the substrate. Although substrate concentrations are well known, there is little information on the Ki values; in these simulations we set all Ki values at 1. All of the velocity curves started at 0 when total folate was 0, rose as folate rose, and then continued to decline as folate concentrations became larger. The shapes of the velocity curves were different because the Km values of the substrates differed. For more details see our discussion in (17).

MTHFR C677T polymorphism.

The 677TT genotype of the MTHFR gene has been associated with an increase in homocysteine (Hcy) (2938) and an increased risk for neural tube defects (3947) and cardiovascular disease (7,40,4851), but possibly a reduced risk for colorectal and hematopoietic malignancies (2,6,52,53), with strong evidence for gene-environment interactions (4,54). The variant allele decreases the activity of MTHFR by ∼30% in heterozygotes (CT) and 70% in homozygote variants (TT) (55), and these effects may depend on folate status (56). Figure 3 illustrates the predicted relative changes at 20 μmol/L folate in the concentrations of SAM, SAH, and Hcy and in the rates of DNA methylation, thymidine synthesis, and purine synthesis as a consequence of these genotypic changes in enzyme function. The simulations suggest that the variant allele decreases the concentrations of SAM and 5mTHF and the DNA methylation rate, and modestly increases the concentrations of SAH, Hcy, and the rates of thymidine and purine synthesis.

Figure 3

Effect of MTHFR C677T polymorphism on modeled hepatic concentrations and reaction rates. The Y axis shows the % change relative to the MTHFR C/C genotype (wildtype), assuming 70% enzyme activity for C/T (gray bars), and 30% enzyme activity for T/T (black bars). For a comparison of these model results to epidemiological and experimental results, see the discussion in the text.

The model-predicted changes are comparable to those observed for MTHFR genotypes in human populations. For example, homocysteine concentrations have been studied in a variety of populations, including Americans (31,32), Singapore Chinese (33), Europeans (29,3437), and Japanese (38). These studies have consistently shown that Hcy levels are higher in individuals with the variant (TT) genotype compared with those with the wild type (CC) genotype. However, the magnitude of difference varies across studies, and probably reflects variation in folate (or B-vitamin) status and genetic factors. Most studies report that those with the TT genotype have Hcy concentrations that are 25–35% higher than CC individuals (3133,36). The association between the TT genotype and Hcy levels is even more pronounced under conditions of folate deficiency, because the thermolability of the MTHFR is increased. We simulated this decrease in MTHFR activity using the data of Guenther et al. (56), and confirmed with the model that the effect of the TT genotype was indeed larger under conditions of folate deficiency: cellular free Hcy concentrations increased by 29% among those with TT genotypes (in relation to CC) when modeled at 10 μmol/L folate, compared with an increase of 15% at 20 μmol/L folate. Note that the model predictions refer to intracellular free homocysteine.

Other biomarkers of folate metabolism were also examined in relation to the MTHFR genotypes. For example, studies have found an increase in purine synthesis (57) and a decrease in genomic DNA methylation (5860) among those with the variant genotype, compared with wild type, which is consistent with the results predicted by our model.

The model predictions, with respect to MTHFR, are generally supported by recent empirical data. For example, Quinlivan and colleagues reported that reduction in the MTHFR reaction rate reduced the concentration of the product 5mTHF and increased the concentrations of the other folate substrates. Together, these events increased the rates of thymidine and purine synthesis (57). Our model predicted that the reduction in the 5mTHF concentration slows the remethylation of homocysteine to methionine, thereby increasing the concentration of Hcy and SAH, and decreasing the concentration of SAM. This is inconsistent with observations of Davis et al. who found that homocysteine was elevated but total remethylation was unchanged in a study of human folate depletion (61). However, effects on transsulfuration were not measured. Certainly, further work needs to be done to establish the exact relation between MTHFR, remethylation, and homocysteine production as a product of transmethylation. More extensive simulations of the effects of polymorphisms in FOCM under varying folate status will be the subject of a future investigation.

Vitamin B-12 deficiency.

Vitamin B-12 is a critical cofactor of methionine synthase. Vitamin B-12 is obtained from animal products in the diet and its absorption is dependent upon proper gastric-acid secretion, as well as an intact and functioning small intestine, because the site of absorption is very specific. Vegans and the elderly are at particular risk of vitamin B-12 deficiency (62). The primary clinical manifestations of vitamin B-12 deficiency are megaloblastic anemia, neurological deficits, vascular disease and gastrointestinal disturbances (6365). Details about the synergistic relation between folate and vitamin B-12 and the health consequences of vitamin B-12 deficiency, particularly in the elderly, have been published (8,66,67).

We modeled the interaction of vitamin B-12 with folate status by simulating a reduction of the Vmax of MS to 10% of its normal value, which is shown in scenarios 1 and 2 of Table 2. The reduction to 10% is consistent with observations in patients with severe vitamin B-12 deficiency (68) and it reduced the overall velocity of the MS reaction to 25–30% of its normal value. As expected, the concentration of 5mTHF increased nearly 4-fold, which would cause an effective methyl trap in vivo. The reduction in the other folate substrates was associated with 50% and 73% decreases in purine and thymidine synthesis, respectively. The model predicted that the concentrations of methionine and SAM decreased, albeit modestly, likely because fewer methyl groups were being recirculated. The DNA methylation rate, however, was not changed substantially by variation in MS activity, perhaps because the methylation is stabilized by long-range interactions between the folate and methionine cycles (17).

TABLE 2

Interactions between vitamin B-12 and folate status

Scenario1
12345
DescriptionNormalDecreased MS activityDecreased folate input, decreased MS activityDecreased MS and MTHFR activityIncreased folate input, decreased MS activity
Model inputs      
    Folate, μmol/L 20 20 10 20 40 
    MS activity Normal 10% 10% 10% 10% 
    MTHFR activity Normal Normal Normal 10% Normal 
Model outputs      
    Methionine 48.0 31.6 30.7 30.4 32.3 
    SAM 64.4 62.0 46.1 41.4 86.3 
    Hcy 1.11 1.12 1.21 1.25 1.06 
    5,10-CH2-THF 0.90 0.24 0.11 0.78 0.49 
    5mTHF 4.02 15.3 7.8 6.06 30.7 
    THF 8.01 2.20 0.99 6.87 4.48 
    5mTHF: THF 0.50 6.95 7.88 0.88 6.85 
    DNA methylation 132.4 132.0 118.1 112.5 144.3 
    Purine synthesis 463.9 229.6 128.9 436.3 350.5 
    Thymidylate synthesis 230.2 62.9 28.5 200.6 127.9 
Scenario1
12345
DescriptionNormalDecreased MS activityDecreased folate input, decreased MS activityDecreased MS and MTHFR activityIncreased folate input, decreased MS activity
Model inputs      
    Folate, μmol/L 20 20 10 20 40 
    MS activity Normal 10% 10% 10% 10% 
    MTHFR activity Normal Normal Normal 10% Normal 
Model outputs      
    Methionine 48.0 31.6 30.7 30.4 32.3 
    SAM 64.4 62.0 46.1 41.4 86.3 
    Hcy 1.11 1.12 1.21 1.25 1.06 
    5,10-CH2-THF 0.90 0.24 0.11 0.78 0.49 
    5mTHF 4.02 15.3 7.8 6.06 30.7 
    THF 8.01 2.20 0.99 6.87 4.48 
    5mTHF: THF 0.50 6.95 7.88 0.88 6.85 
    DNA methylation 132.4 132.0 118.1 112.5 144.3 
    Purine synthesis 463.9 229.6 128.9 436.3 350.5 
    Thymidylate synthesis 230.2 62.9 28.5 200.6 127.9 
1

Scenario 1 shows the values of selected concentrations and reaction rates when total folate, MS, and MTHFR were normal. In scenario 2, vitamin B-12 deficiency is simulated by lowering the Vmax of the MS reaction to 10% of its normal value. Scenario 3 describes the effect of vitamin B-12 deficiency in the presence of low folate. Scenarios 4 and 5 show that the resulting methyl trap could be partly alleviated if one added a MTHFR deficiency or increased total folate.

TABLE 2

Interactions between vitamin B-12 and folate status

Scenario1
12345
DescriptionNormalDecreased MS activityDecreased folate input, decreased MS activityDecreased MS and MTHFR activityIncreased folate input, decreased MS activity
Model inputs      
    Folate, μmol/L 20 20 10 20 40 
    MS activity Normal 10% 10% 10% 10% 
    MTHFR activity Normal Normal Normal 10% Normal 
Model outputs      
    Methionine 48.0 31.6 30.7 30.4 32.3 
    SAM 64.4 62.0 46.1 41.4 86.3 
    Hcy 1.11 1.12 1.21 1.25 1.06 
    5,10-CH2-THF 0.90 0.24 0.11 0.78 0.49 
    5mTHF 4.02 15.3 7.8 6.06 30.7 
    THF 8.01 2.20 0.99 6.87 4.48 
    5mTHF: THF 0.50 6.95 7.88 0.88 6.85 
    DNA methylation 132.4 132.0 118.1 112.5 144.3 
    Purine synthesis 463.9 229.6 128.9 436.3 350.5 
    Thymidylate synthesis 230.2 62.9 28.5 200.6 127.9 
Scenario1
12345
DescriptionNormalDecreased MS activityDecreased folate input, decreased MS activityDecreased MS and MTHFR activityIncreased folate input, decreased MS activity
Model inputs      
    Folate, μmol/L 20 20 10 20 40 
    MS activity Normal 10% 10% 10% 10% 
    MTHFR activity Normal Normal Normal 10% Normal 
Model outputs      
    Methionine 48.0 31.6 30.7 30.4 32.3 
    SAM 64.4 62.0 46.1 41.4 86.3 
    Hcy 1.11 1.12 1.21 1.25 1.06 
    5,10-CH2-THF 0.90 0.24 0.11 0.78 0.49 
    5mTHF 4.02 15.3 7.8 6.06 30.7 
    THF 8.01 2.20 0.99 6.87 4.48 
    5mTHF: THF 0.50 6.95 7.88 0.88 6.85 
    DNA methylation 132.4 132.0 118.1 112.5 144.3 
    Purine synthesis 463.9 229.6 128.9 436.3 350.5 
    Thymidylate synthesis 230.2 62.9 28.5 200.6 127.9 
1

Scenario 1 shows the values of selected concentrations and reaction rates when total folate, MS, and MTHFR were normal. In scenario 2, vitamin B-12 deficiency is simulated by lowering the Vmax of the MS reaction to 10% of its normal value. Scenario 3 describes the effect of vitamin B-12 deficiency in the presence of low folate. Scenarios 4 and 5 show that the resulting methyl trap could be partly alleviated if one added a MTHFR deficiency or increased total folate.

Long-term B-12 deficiency reduces tissue levels of folate (69). Scenario 3 in Table 2 shows a simulation of a typical B-12 deficiency in conjunction with a low-folate status, which was induced as a result of the methyl trap. As expected, a low-folate status exacerbated the vitamin B-12 induced decrease in thymidine and purine synthesis.

Scenario 4 in Table 2 simulated the influence of a reduction of MTHFR activity to 10% of normal in the presence of the 10% MS deficiency. This is a notable simulation because the effect of a loss of MS activity, whether it is caused by a mutation or a dietary deficiency of vitamin B-12, can be partly alleviated by a mutation that reduces the activity of MTHFR (B. Shane, unpublished data). The simulation result shows that 1) the methyl trap was alleviated; 2) there was a nearly normal balance among the folate substrates; and 3) thymidine and purine synthesis returned to concentrations comparable to the normal conditions. Note that under this scenario of decreased MS activity, Hcy increased ∼12%, whereas the concentrations of methionine, SAM and DNA methylation decreased by 63%, 64%, and 15%, respectively.

Elevated circulating concentrations of folate can mask a vitamin B-12 deficiency (70). We simulated a scenario mimicking high intracellular folate concentrations, a situation that may arise from use of high-dose dietary supplements containing folate. We raised the total folate level from the normal 20 μmol/L to 40 μmol/L (Table 2, scenario 2 compared with scenario 5), which alleviated some of the adverse consequences of induction of vitamin B-12 deficiency. Thus, for cellular processes that depend on these reactions and substrates, the effect of the vitamin B-12 deficiency can indeed be partially masked by high intakes of folate. Further, comparing scenarios 2 and 5, only the methionine concentration and the ratio of 5mTHF to THF were not changed by folate supplementation in the presence of vitamin B-12 deficiency.

The effect of betaine on homocysteine.

Several studies have examined the relation among betaine concentration, homocysteine concentration, and folate status (7173). Betaine, a choline degradation product, is a normal dietary constituent and also arises from phosphotidylcholine turnover (74). Betaine is also used, together with folate, to treat the severe hyperhomocysteinemia of CBS deficiency. In experimental rodents, the BHMT reaction, although restricted to the liver, is quantitatively more important than folate-dependent remethylation in the reconversion of homocysteine to methionine (B. Shane, unpublished data). The extent to which the BHMT reaction, which typically occurs in liver and kidney in humans, is responsible for homocysteine remethylation in humans is not well understood. Recent studies in mouse models with disruptions in methyltransferases involved in phosphatidylcholine synthesis suggest that methyl group utilization by these reactions may have been greatly underestimated (75,76). In fact, they may represent the major methyl group sinks in metabolism and the major producers of whole body homocysteine (75,76). Under steady state conditions, the choline degradation pathway allowed the possibility of recovery of these methyl groups, with one arising from the BHMT reaction and 2 coming from folate-dependent reactions via dimethylglycine and sarcosine dehydrogenases (77). The extent to which the latter can provide substrates for remethylation would be dependent on folate status.

Published studies have reported the following general findings with regard to the relation of betaine to other substrates in FOCM: 1) homocysteine concentration is inversely related to betaine concentration; 2) homocysteine concentration is sensitive to betaine status at low folate status but relatively insensitive at normal and high folate status; and 3) folic acid supplementation increases betaine concentration (7173). The results of simulations with our model and plots of homocysteine concentration as a function of betaine status (0.5 = low, 1 = normal, 2 = high) for different relative folate concentrations are shown in Figure 4A. As expected, higher folate status was always inversely associated with homocysteine concentrations (Fig. 4A, negative slopes). Moreover, the slopes of the curves became progressively more negative as folate status decreased, confirming observation 2) (Fig. 4A). We tested observation 3) with our model by raising folate status and found that the reaction rate for BHMT reaction decreased and the betaine concentration increased in a dose-dependent manner (simulations not shown). The explanation for this effect is that high folate status drove the methionine synthase reaction faster, which increased SAM concentration (Fig. 4B). This in turn inhibited BHMT so less betaine was metabolized, thus increasing the betaine concentration.

Figure 4

Effect of betaine and folate status on the hepatic concentration of homocysteine and SAM. The amount of betaine affected the velocity of the BHMT reaction. We simulated variation in betaine availability by altering the Vmax of the BHMT reaction. Betaine status = 1.0 is normal. Relative betaine status indicates the increase or decrease in Vmax from its normal value.

Methionine loading.

Methionine loading causes an increase in hepatic SAM and plasma homocysteine, and a relative stimulation of the transsulfuration pathway. The increase in plasma homocysteine following a methionine load is primarily influenced by vitamin B-6 status, rather than folate or vitamin B-12, as the enzymes of the transsulfuration pathway depend on pyridoxal phosphate (PLP). Thus, the methionine load test has been used to assess vitamin B-6 status. The transsulfuration pathway also is stimulated following a protein containing meal (25,78).

To simulate the effect of methionine loading on folate and methionine metabolism, we doubled the normal rate of methionine input [METin = 100 μmol/(L · h)] to 200 μmol/(L · h) for a 5-h period (Fig. 5). Fig. 5A shows the effect of the methionine load on the transsulfuration and remethylation reactions. As expected, Hcy rose (Fig. 5B). However, VMS declined dramatically, because its other substrate, 5mTHF, declined dramatically (Fig. 5A). This is likely due to the steep rise in SAM (Fig. 5C) that inhibited the synthesis of 5mTHF by inhibiting MTHFR. There were 2 competing effects on VBHMT: the rise in its substrate, Hcy, and inhibition of the enzyme by SAM. The data suggest that the former has a larger magnitude of effect than the latter (Fig. 5A).

Figure 5

Effect of methionine loading. We simulated a bolus of methionine by doubling the methionine input (METin) for 5 h (A). As methionine input went up, the increase in SAM activated CBS and inhibited BHMT (A), thus increasing the removal of excess methyl groups from the methionine cycle. The dramatic increase in SAM (C) caused inhibition of MTHFR, resulting in a decline of 5mTHF (B). Because the MS reaction slowed (A), the concentration of homocysteine rose (B). The drop in 5mTHF released the inhibition of GNMT, allowing the GNMT reaction to carry most of the excess methyl groups. Thus, the DNA methylation rate (vDNMT) changed only slightly (C). Concentrations are in brackets and reaction velocities are indicated by the prefix v.

Although the rate of the MS reaction decreased during the methionine load, the concentration of its product, THF, rose (Fig. 5B). In the model simulations, this was because the inhibition of MTHFR caused the concentration of 5,10-CH2-THF to rise, which directed folate toward THF through the 5,10-methylenetetrahydrofolate dehydrogenase (MTD)-5,10-methylenetetrahydrofolate cyclohydrolase (MTCH) pathway.

One might expect that the dramatic increase in SAM during methionine loading would result in an equally dramatic increase in the rate of DNA methylation (Fig. 5C). However, methylation remained remarkably stable. This may be due to the long-range interactions between the folate and methionine cycles (17) and because the increased flux of methyl groups was almost entirely carried by VGNMT (Fig. 5C), a mechanism first proposed by Wagner et al. (79).

General discussion.

We found that our mathematical model of FOCM, which is based on established physiology and biochemistry, can reliably reproduce experimental data from both humans and rodents. Model predictions help explain clinical observations about folate and nutrients relevant to folate metabolism, as well as experimental data from both humans and rodents. Initial modeling has given insights on the mechanisms behind folate homeostasis (15) and the stability of DNA methylation in the face of methionine fluctuations (17). The availability of this functioning mathematical model allows us to easily perform in silico experiments to test hypotheses and provide guidance for experimental studies.

Our model will be particularly useful for providing initial predictions on the combined effects of variation in genetic and nutritional factors on biomarkers of disease risk. Genotype-targeted human feeding studies are expensive and labor intensive, and may be limited by small sample size (57,61,80,81). Whereas these feeding studies provide a gold standard design for understanding mechanisms related to gene-nutrient interactions, the mathematical model will help identify which specific interactions may be most promising for these costly investigations in the human experimental setting. Mathematical models are not intended to replace experimental methods. Rather, these models can be a valuable tool for laboratory researchers and may be used to provide pilot data for human studies.

Our current model is based on data from rats and humans and reflects hepatic FOCM. One of our next steps will be to adapt the model to nonhepatic FOCM, because FOCM in epithelia, such as colonic mucosa, will be important for disease risk estimation. Folate status and folate-metabolizing polymorphisms are clearly linked to colorectal carcinogenesis. We anticipate that the modeling will help us understand some of the gene-gene and gene-environment interactions that have been observed in studies of colorectal cancer or its precursors (4,82). In addition, we anticipate that FOCM modeling outputs will enable us to integrate more biologic information in epidemiologic data analyses, as part of hierarchical modeling structures (83,84).

Further, we plan to extend the model to include mitochondrial compartmentalization. Others have shown that the exchange between mitochondrial and cytosolic folate metabolites and amino acids is an important aspect of intracellular folate metabolism, and that polymorphisms in mitochondrial enzymes should also be considered (26,49,85). We also plan to create a detailed model of folate and amino acid transport between the blood and liver cells (86). This will enable us to infer cellular FOCM status from measurements made in the blood.

We recognize that the biochemical input parameters used in the model often were determined from a wide range of experimental values. Model-derived information can be used to identify components that need to be measured with high accuracy in experimental settings. By conducting a sensitivity analysis using the mathematical model, we are able to determine how sensitive any specific flux, metabolite concentration, or other biomarker is to changes in inputs or enzyme activities. We found, for instance, that some properties of FOCM are extremely insensitive to changes in input parameters (e.g., DNA methylation rate in Figs. 2 and 5), whereas others are sensitive. The model can be used to determine which parameter changes affect biologically relevant processes, such as nucleotide synthesis. This will help to identify components that easily alter the system's functioning as promising targets for epidemiological investigation and therapeutic interventions.

In summary, we developed an initial model of FOCM that can reproduce experimental conditions. We anticipate that in silico predictions will complement, and help direct, experimental or epidemiologic investigations and enhance our understanding of this important biologic pathway.

Literature Cited

1.

Mitchell
LE
.
Epidemiology of neural tube defects
.
Am J Med Genet C Semin Med Genet.
2005
;
135
:
88
94
.

2.

Little
J
,
Sharp
L
,
Duthie
S
,
Narayanan
S
.
Colon cancer and genetic variation in folate metabolism: the clinical bottom line
.
J Nutr.
2003
;
133
:
3758S
66S
.

3.

Giovannucci
E
.
Epidemiologic studies of folate and colorectal neoplasia: a review
.
J Nutr.
2002
;
132
:
2350S
5S
.

4.

Ulrich
CM
.
Nutrigenetics in cancer research–folate metabolism and colorectal cancer
.
J Nutr.
2005
;
135
:
2698
702
.

5.

Rampersaud
GC
,
Bailey
LB
,
Kauwell
GP
.
Relationship of folate to colorectal and cervical cancer: Review and recommendations for practitioners
.
J Am Diet Assoc.
2002
;
102
:
1273
82
.

6.

Robien
K
,
Ulrich
CM
.
5,10-methylenetetrahydrofolate reductase polymorphisms and leukemia risk
.
Am J Epidemiol.
2003
;
157
:
571
82
.

7.

Lewis
SJ
,
Ebrahim
S
,
Davey Smith
G
.
Meta-analysis of MTHFR 677C→T polymorphism and coronary heart disease: does totality of evidence support causal role for homocysteine and preventive potential of folate?
BMJ.
2005
;
331
:
1053
.

8.

Stover
PJ
.
Physiology of folate and vitamin B12 in health and disease
.
Nutr Rev.
2004
;
62
:
S3
12
.

9.

Moat
SJ
,
Lang
D
,
McDowell
IF
,
Clarke
ZL
,
Madhavan
AK
,
Lewis
MJ
,
Goodfellow
J
.
Folate, homocysteine, endothelial function and cardiovascular disease
.
J Nutr Biochem.
2004
;
15
:
64
79
.

10.

Ulrich
CM
,
Robien
K
,
McLeod
HL
.
Cancer pharmacogenetics: polymorphisms, pathways and beyond
.
Nat Rev Cancer.
2003
;
3
:
912
20
.

11.

Robien
K
,
Boynton
A
,
Ulrich
CM
.
Pharmacogenetics of folate-related drug targets in cancer treatment
.
Pharmacogenomics.
2005
;
6
:
673
89
.

12.

Murray
JD
.
Mathematical Biology.
Berlin
:
Springer Verlag
;
1989
.

13.

Edelstein-Keshet
L
.
Mathematical models in biology.
New York
:
Random House
;
1988
.

14.

Reed
MC
,
Nijhout
HF
,
Sparks
R
,
Ulrich
CM
.
A mathematical model of the methionine cycle
.
J Theor Biol.
2004
;
226
:
33
43
.

15.

Nijhout
HF
,
Reed
MC
,
Budu
P
,
Ulrich
CM
.
A mathematical model of the folate cycle: New insights into folate homeostasis
.
J Biol Chem.
2004
;
279
:
55008
16
.

16.

Prudova
A
,
Martinov
MV
,
Vitvitsky
VM
,
Ataullakhanov
FI
,
Banerjee
R
.
Analysis of pathological defects in methionine metabolism using a simple mathematical model
.
Biochim Biophys Acta.
2005
;
1741
:
331
8
.

17.

Nijhout
HF
,
Reed
M
,
Anderson
D
,
Mattingly
J
,
James
SJ
,
Ulrich
CM
.
Long-range allosteric interactions between the folate and methionine cycles stabilize DNA methylation rate
.
Epigenetics.
2006
;
1
:
81
7
.

18.

Finkelstein
JD
,
Martin
JJ. Methionine metabolism in mammals.
Distribution of homocysteine between competing pathways
.
J Biol Chem.
1984
;
259
:
9508
13
.

19.

Bose
N
,
Greenspan
P
,
Momany
C
.
Expression of recombinant human betaine: homocysteine S-methyltransferase for x-ray crystallographic studies and further characterization of interaction with S-adenosylmethionine
.
Protein Expr Purif.
2002
;
25
:
73
80
.

20.

Clarke
S
,
Banfield
K
.
S-Adenosylmethionine-dependent methyltransferases
. In:
Carmel
R
,
Jacobsen
DW
editors.
Homocysteine in Health and Disease.
Cambridge
:
Cambridge University Press
;
2001
. p.
63
78
.

21.

Bjarnason
GA
,
Jordan
RC
,
Wood
PA
,
Li
Q
,
Lincoln
DW
,
Sothern
RB
,
Hrushesky
WJ
,
Ben-David
Y
.
Circadian expression of clock genes in human oral mucosa and skin: association with specific cell-cycle phases
.
Am J Pathol.
2001
;
158
:
1793
801
.

22.

Wade
M
,
Blake
MC
,
Jambou
RC
,
Helin
K
,
Harlow
E
,
Azizkhan
JC
.
An inverted repeat motif stabilizes binding of E2F and enhances transcription of the dihydrofolate reductase gene
.
J Biol Chem.
1995
;
270
:
9783
91
.

23.

Jencks
DA
,
Mathews
RG. Allosteric inhibition of methylenetetrahydrofolate reductase by adenosylmethionine.
Effects of adenosylmethionine and NADPH on the equilibrium between active and inactive forms of the enzyme and on the kinetics of approach to equilibrium
.
J Biol Chem.
1987
;
262
:
2485
93
.

24.

Yamada
K
,
Chen
Z
,
Rozen
R
,
Matthews
RG. Effects of common polymorphisms on the properties of recombinant human methylenetetrahydrofolate reductase.
[comment]
Proc Natl Acad Sci USA.
2001
;
98
:
14853
8
.

25.

Storch
KJ
,
Wagner
DA
,
Burke
JF
,
Young
VR
.
Quantitative study in vivo of methionine cycle in humans using [methyl-2H3]- and [1–13C]methionine
.
Am J Physiol.
1988
;
255
:
E322
31
.

26.

Cook
RJ
.
Folate metabolism
. In:
Carmel
R
,
Jacobsen
DW
editors.
Homocysteine in Health and Disease.
Cambridge
:
Cambridge University Press
;
2001
. p.
113
34
.

27.

Min
H
,
Shane
B
,
Stokstad
EL
.
Identification of 10-formyltetrahydrofolate dehydrogenase-hydrolase as a major folate binding protein in liver cytosol
.
Biochim Biophys Acta.
1988
;
967
:
348
53
.

28.

Wagner
C
.
Symposium on the subcellular compartmentation of folate metabolism
.
J Nutr.
1996
;
126
:
1228S
34S
.

29.

Kluijtmans
LA
,
Young
IS
,
Boreham
CA
,
Murray
L
,
McMaster
D
,
McNulty
H
,
Strain
JJ
,
McPartlin
J
,
Scott
JM
,
Whitehead
AS
.
Genetic and nutritional factors contributing to hyperhomocysteinemia in young adults
.
Blood.
2003
;
101
:
2483
8
.

30.

Blom
HJ
.
Mutated 5,10-methylenetetrahydrofolate reductase and moderate hyperhomocysteinaemia
.
Eur J Pediatr.
1998
;
157
:
S131
4
.

31.

McNulty
H
,
McKinley
MC
,
Wilson
B
,
McPartlin
J
,
Strain
JJ
,
Weir
DG
,
Scott
JM
.
Impaired functioning of thermolabile methylenetetrahydrofolate reductase is dependent on riboflavin status: implications for riboflavin requirements
.
Am J Clin Nutr.
2002
;
76
:
436
41
.

32.

Ma
J
,
Stampfer
MJ
,
Hennekens
CH
,
Frosst
P
,
Selhub
J
,
Horsford
J
,
Malinow
MR
,
Willett
WC
,
Rozen
R
.
Methylenetetrahydrofolate reductase polymorphism, plasma folate, homocysteine, and risk of myocardial infarction in US physicians. [see comments]
Circulation.
1996
;
94
:
2410
6
.

33.

Trinh
BN
,
Ong
C-N
,
Coetzee
GA
,
Yu
MC
,
Laird
PW
.
Thymidylate synthase: a novel genetic determinant of plasma homocysteine and folate levels
.
Hum Genet.
2002
;
111
:
299
302
.

34.

Dekou
V
,
Gudnason
V
,
Hawe
E
,
Miller
GJ
,
Stansbie
D
,
Humphries
SE
.
Gene-environment and gene-gene interaction in the determination of plasma homocysteine levels in healthy middle-aged men
.
Thromb Haemost.
2001
;
85
:
67
74
.

35.

Dedoussis
GV
,
Panagiotakos
DB
,
Chrysohoou
C
,
Pitsavos
C
,
Zampelas
A
,
Choumerianou
D
,
Stefanadis
C
.
Effect of interaction between adherence to a Mediterranean diet and the methylenetetrahydrofolate reductase 677C→T mutation on homocysteine concentrations in healthy adults: the ATTICA Study
.
Am J Clin Nutr.
2004
;
80
:
849
54
.

36.

de Bree
A
,
Verschuren
WM
,
Bjorke-Monsen
AL
,
van der Put
NM
,
Heil
SG
,
Trijbels
FJ
,
Blom
HJ
.
Effect of the methylenetetrahydrofolate reductase 677C→T mutation on the relations among folate intake and plasma folate and homocysteine concentrations in a general population sample
.
Am J Clin Nutr.
2003
;
77
:
687
93
.

37.

Chango
A
,
Potier De Courcy
G
,
Boisson
F
,
Guilland
JC
,
Barbe
F
,
Perrin
MO
,
Christides
JP
,
Rabhi
K
,
Pfister
M
, et al. 
5,10-methylenetetrahydrofolate reductase common mutations, folate status and plasma homocysteine in healthy French adults of the Supplementation en Vitamines et Mineraux Antioxydants (SU.VI.M AX) cohort
.
Br J Nutr.
2000
;
84
:
891
6
.

38.

Moriyama
Y
,
Okamura
T
,
Kajinami
K
,
Iso
H
,
Inazu
A
,
Kawashiri
M
,
Mizuno
M
,
Takeda
Y
,
Sakamoto
Y
, et al. 
Effects of serum B vitamins on elevated plasma homocysteine levels associated with the mutation of methylenetetrahydrofolate reductase gene in Japanese
.
Atherosclerosis.
2002
;
164
:
321
8
.

39.

De Marco
P
,
Calevo
MG
,
Moroni
A
,
Arata
L
,
Merello
E
,
Finnell
RH
,
Zhu
H
,
Andreussi
L
,
Cama
A
,
Capra
V
.
Study of MTHFR and MS polymorphisms as risk factors for NTD in the Italian population
.
J Hum Genet.
2002
;
47
:
319
24
.

40.

Ueland
PM
,
Hustad
S
,
Schneede
J
,
Refsum
H
,
Vollset
SE
.
Biological and clinical implications of the MTHFR C677T polymorphism
.
Trends Pharmacol Sci.
2001
;
22
:
195
201
.

41.

De Marco
P
,
Moroni
A
,
Merello
E
,
de Franchis
R
,
Andreussi
L
,
Finnell
RH
,
Barber
RC
,
Cama
A
,
Capra
V
.
Folate pathway gene alterations in patients with neural tube defects
.
Am J Med Genet.
2000
;
95
:
216
23
.

42.

Christensen
B
,
Arbour
L
,
Tran
P
,
Leclerc
D
,
Sabbaghian
N
,
Platt
R
,
Gilfix
BM
,
Rosenblatt
DS
,
Gravel
RA
, et al. 
Genetic polymorphisms in methylenetetrahydrofolate reductase and methionine synthase, folate levels in red blood cells, and risk of neural tube defects
.
Am J Med Genet.
1999
;
84
:
151
7
.

43.

Shaw
GM
,
Rozen
R
,
Finnell
RH
,
Wasserman
CR
,
Lammer
EJ
.
Maternal vitamin use, genetic variation of infant methylenetetrahydrofolate reductase, and risk for spina bifida
.
Am J Epidemiol.
1998
;
148
:
30
7
.

44.

Rizzari
C
,
Valsecchi
MG
,
Conter
V. MTHFR 677C→T mutation and neural-tube defects.
[letter; comment]
Lancet.
1997
;
350
:
1479
80
.

45.

Posey
DL
,
Khoury
MJ
,
Mulinare
J
,
Adams
MJ
, Jr.
,
Ou
CY
.
Is mutated MTHFR a risk factor for neural tube defects? [letter]
Lancet.
1996
;
347
:
686
7
.

46.

Botto
LD
,
Yang
Q
.
5,10-Methylenetetrahydrofolate reductase gene variants and congenital anomalies: a HuGE review
.
Am J Epidemiol.
2000
;
151
:
862
77
.

47.

Boyles
AL
,
Hammock
P
,
Speer
MC
.
Candidate gene analysis in human neural tube defects
.
Am J Med Genet C Semin Med Genet.
2005
;
135
:
9
23
.

48.

Rozen
M
.
Polymorphisms of folate and cobalamin metabolism
. In:
Carmel
R
,
Jacobsen
DW
editors.
Homocysteine in Health and Disease.
Cambridge
:
Cambridge University Press
;
2001
. p. 259–69.

49.

Lim
U
,
Peng
K
,
Shane
B
,
Stover
PJ
,
Litonjua
AA
,
Weiss
ST
,
Gaziano
JM
,
Strawderman
RL
,
Raiszadeh
F
, et al. 
Polymorphisms in cytoplasmic serine hydroxymethyltransferase and methylenetetrahydrofolate reductase affect the risk of cardiovascular disease in men
.
J Nutr.
2005
;
135
:
1989
94
.

50.

Frederiksen
J
,
Juul
K
,
Grande
P
,
Jensen
GB
,
Schroeder
TV
,
Tybjaerg-Hansen
A
,
Nordestgaard
BG
.
Methylenetetrahydrofolate reductase polymorphism (C677T), hyperhomocysteinemia, and risk of ischemic cardiovascular disease and venous thromboembolism: prospective and case-control studies from the Copenhagen City Heart Study
.
Blood.
2004
;
104
:
3046
−51.

51.

Schwartz
SM
,
Siscovick
DS
,
Malinow
MR
,
Rosendaal
FR
,
Beverly
RK
,
Hess
DL
,
Psaty
BM
,
Longstreth
WT
, Jr.
,
Koepsell
TD
, et al. 
Myocardial infarction in young women in relation to plasma total homocysteine, folate, and a common variant in the methylenetetrahydrofolate reductase gene
.
Circulation.
1997
;
96
:
412
7
.

52.

Le Marchand
L
,
Wilkens
LR
,
Kolonel
LN
,
Henderson
BE
.
The MTHFR C677T polymorphism and colorectal cancer: the multiethnic cohort study
.
Cancer Epidemiol Biomarkers Prev.
2005
;
14
:
1198
203
.

53.

Curtin
K
,
Bigler
J
,
Slattery
ML
,
Caan
B
,
Potter
JD
,
Ulrich
CM
.
MTHFR C677T and A1298C polymorphisms: diet, estrogen, and risk of colon cancer
.
Cancer Epidemiol Biomarkers Prev.
2004
;
13
:
285
92
.

54.

Ulrich
CM
,
Kampman
E
,
Bigler
J
,
Schwartz
SM
,
Chen
C
,
Bostick
R
,
Fosdick
L
,
Beresford
SA
,
Yasui
Y
,
Potter
JD
.
Colorectal adenomas and the C677T MTHFR polymorphism: evidence for gene-environment interaction?
Cancer Epidemiol Biomarkers Prev.
1999
;
8
:
659
68
.

55.

Frosst
P
,
Blom
HJ
,
Milos
R
,
Goyette
P
,
Sheppard
CA
,
Matthews
RG
,
Boers
GJ
,
den Heijer
M
,
Kluijtmans
LA
, et al. 
A candidate genetic risk factor for vascular disease: a common mutation in methylenetetrahydrofolate reductase
.
[letter] Nat Genet.
1995
;
10
:
111
3
.

56.

Guenther
BD
,
Sheppard
CA
,
Tran
P
,
Rozen
R
,
Matthews
RG
,
Ludwig
ML
.
The structure and properties of methylenetetrahydrofolate reductase from Escherichia coli suggest how folate ameliorates human hyperhomocysteinemia
.
Nat Struct Biol.
1999
;
6
:
359
65
.

57.

Quinlivan
EP
,
Davis
SR
,
Shelnutt
KP
,
Henderson
GN
,
Ghandour
H
,
Shane
B
,
Selhub
J
,
Bailey
LB
,
Stacpoole
PW
,
Gregory, 3rd
JF
.
Methylenetetrahydrofolate reductase 677C→T polymorphism and folate status affect one-carbon incorporation into human DNA deoxynucleosides
.
J Nutr.
2005
;
135
:
389
96
.

58.

Stern
LL
,
Mason
JB
,
Selhub
J
,
Choi
SW
.
Genomic DNA hypomethylation, a characteristic of most cancers, is present in peripheral leukocytes of individuals who are homozygous for the C677T polymorphism in the methylenetetrahydrofolate reductase gene
.
Cancer Epidemiol Biomarkers Prev.
2000
;
9
:
849
53
.

59.

Castro
R
,
Rivera
I
,
Ravasco
P
,
Camilo
ME
,
Jakobs
C
,
Blom
HJ
,
de Almeida
IT
.
5,10-methylenetetrahydrofolate reductase (MTHFR) 677C→T and 1298A→C mutations are associated with DNA hypomethylation
.
J Med Genet.
2004
;
41
:
454
8
.

60.

Friso
S
,
Choi
SW
,
Girelli
D
,
Mason
JB
,
Dolnikowski
GG
,
Bagley
PJ
,
Olivieri
O
,
Jacques
PF
,
Rosenberg
IH
, et al. 
A common mutation in the 5,10-methylenetetrahydrofolate reductase gene affects genomic DNA methylation through an interaction with folate status
.
Proc Natl Acad Sci USA.
2002
;
99
:
5606
11
.

61.

Davis
SR
,
Quinlivan
EP
,
Shelnutt
KP
,
Ghandour
H
,
Capdevila
A
,
Coats
BS
,
Wagner
C
,
Shane
B
,
Selhub
J
, et al. 
Homocysteine synthesis is elevated but total remethylation is unchanged by the methylenetetrahydrofolate reductase 677C→T polymorphism and by dietary folate restriction in young women
.
J Nutr.
2005
;
135
:
1045
50
.

62.

Wolters
M
,
Strohle
A
,
Hahn
A
.
Cobalamin: a critical vitamin in the elderly
.
Prev Med.
2004
;
39
:
1256
66
.

63.

Institute of MedicineDietary Reference Intakes: Thiamin, riboflavin, niacin, vitamin B6, folate, vitamin B12, pantothenic acid, biotin, and choline.
Washington, DC
:
National Academy Press
.;
1998
.

64.

Morris
MC
,
Evans
DA
,
Bienias
JL
,
Tangney
CC
,
Hebert
LE
,
Scherr
PA
,
Schneider
JA
.
Dietary folate and vitamin B12 intake and cognitive decline among community-dwelling older persons
.
Arch Neurol.
2005
;
62
:
641
5
.

65.

Bleie
O
,
Refsum
H
,
Ueland
PM
,
Vollset
SE
,
Guttormsen
AB
,
Nexo
E
,
Schneede
J
,
Nordrehaug
JE
,
Nygard
O
.
Changes in basal and postmethionine load concentrations of total homocysteine and cystathionine after B vitamin intervention
.
Am J Clin Nutr.
2004
;
80
:
641
8
.

66.

Huerta
JM
,
Gonzalez
S
,
Vigil
E
,
Prada
M
,
San Martin
J
,
Fernandez
S
,
Patterson
AM
,
Lasheras
C
.
Folate and cobalamin synergistically decrease the risk of high plasma homocysteine in a nonsupplemented elderly institutionalized population
.
Clin Biochem.
2004
;
37
:
904
10
.

67.

Bailey
LB
.
Folate and vitamin B12 recommended intakes and status in the United States
.
Nutr Rev.
2004
;
62
:
S14
20
.

68.

Taylor
RT
,
Hanna
ML
,
Hutton
JJ
.
5-methyltetrahydrofolate homocysteine cobalamin methyltransferase in human bone marrow and its relationship to pernicious anemia
.
Arch Biochem Biophys.
1974
;
165
:
787
95
.

69.

Herbert
V
.
Folic acid
.
Annu Rev Med.
1965
;
16
:
359
70
.

70.

Savage
DG
,
Lindenbaum
J
.
Folate-cobalamin interactions
. In:
Bailey
LB
editor.
Folate in Health and Disease.
New York
:
Marcel Dekker
;
1995
. p.
237
85
.

71.

Holm
PI
,
Bleie
O
,
Ueland
PM
,
Lien
EA
,
Refsum
H
,
Nordrehaug
JE
,
Nygard
O
.
Betaine as a determinant of postmethionine load total plasma homocysteine before and after B-vitamin supplementation
.
Arterioscler Thromb Vasc Biol.
2004
;
24
:
301
7
.

72.

Holm
PI
,
Ueland
PM
,
Vollset
SE
,
Midttun
O
,
Blom
HJ
,
Keijzer
MB
,
den Heijer
M
.
Betaine and folate status as cooperative determinants of plasma homocysteine in humans
.
Arterioscler Thromb Vasc Biol.
2005
;
25
:
379
85
.

73.

Melse-Boonstra
A
,
Holm
PI
,
Ueland
PM
,
Olthof
M
,
Clarke
R
,
Verhoef
P
.
Betaine concentration as a determinant of fasting total homocysteine concentrations and the effect of folic acid supplementation on betaine concentrations
.
Am J Clin Nutr.
2005
;
81
:
1378
82
.

74.

Zeisel
SH
,
Mar
MH
,
Howe
JC
,
Holden
JM
.
Concentrations of choline-containing compounds and betaine in common foods
.
J Nutr.
2003
;
133
:
1302
7
.

75.

Jacobs
RL
,
Stead
LM
,
Devlin
C
,
Tabas
I
,
Brosnan
ME
,
Brosnan
JT
,
Vance
DE
.
Physiological regulation of phospholipid methylation alters plasma homocysteine in mice
.
J Biol Chem.
2005
;
280
:
28299
305
.

76.

Stead
LM
,
Brosnan
JT
,
Brosnan
ME
,
Vance
DE
,
Jacobs
RL
.
Is it time to reevaluate methyl balance in humans?
Am J Clin Nutr.
2006
;
83
:
5
10
.

77.

Shane
B
.
Folate, vitamin B12 and vitamin B6
. In:
Stipanuk
MH
editor.
Biochemical and Physiological Bases of Human Nutrition.
New York
:
Saunders
;
2000
. p.
483
518
.

78.

Storch
KJ
,
Wagner
DA
,
Young
VR
.
Methionine kinetics in adult men: effects of dietary betaine on L-[2H3-methyl-1–13C]methionine
.
Am J Clin Nutr.
1991
;
54
:
386
94
.

79.

Wagner
C
,
Briggs
WT
,
Cook
RJ
.
Inhibition of glycine N-methyltransferase activity by folate derivatives: implications for regulation of methyl group metabolism
.
Biochem Biophys Res Commun.
1985
;
127
:
746
52
.

80.

Kauwell
GP
,
Wilsky
CE
,
Cerda
JJ
,
Herrlinger-Garcia
K
,
Hutson
AD
,
Theriaque
DW
,
Boddie
A
,
Rampersaud
GC
,
Bailey
LB
.
Methylenetetrahydrofolate reductase mutation (677C→T) negatively influences plasma homocysteine response to marginal folate intake in elderly women
.
Metabolism.
2000
;
49
:
1440
3
.

81.

Davis
SR
,
Scheer
JB
,
Quinlivan
EP
,
Coats
BS
,
Stacpoole
PW
,
Gregory, 3rd
JF
.
Dietary vitamin B-6 restriction does not alter rates of homocysteine remethylation or synthesis in healthy young women and men
.
Am J Clin Nutr.
2005
;
81
:
648
55
.

82.

Ulrich
CM
,
Curtin
K
,
Potter
JD
,
Bigler
J
,
Caan
B
,
Slattery
ML
.
Polymorphisms in the reduced folate carrier, thymidylate synthase, or methionine synthase and risk of colon cancer
.
Cancer Epidemiol Biomarkers Prev.
2005
;
14
:
2509
16
.

83.

Ulrich
CM
,
Nijhout
HF
,
Reed
MC
.
Mathematical modeling: epidemiology meets systems biology.
Cancer Epidemiol Biomarkers Prev, in press.

84.

Ulrich
CM
,
Nijhout
HF
,
Reed
MC
.
Mathematical modeling: epidemiology meets systems biology
.
Cancer Epidemiol Biomarkers Prev.
2006
;
15
:
827
9
.

85.

Stover
PJ
,
Chen
LH
,
Suh
JR
,
Stover
DM
,
Keyomarsi
K
,
Shane
B
.
Molecular cloning, characterization, and regulation of the human mitochondrial serine hydroxymethyltransferase gene
.
J Biol Chem.
1997
;
272
:
1842
8
.

86.

Gregory, 3rd
JF
,
Quinlivan
EP
.
In vivo kinetics of folate metabolism
.
Annu Rev Nutr.
2002
;
22
:
199
220
.

Abbreviations

     
  • 5,10-CH2-THF

    5,10-methylenetrahydrofolate

  •  
  • 5mTHF

    5-methyltetrahydrofolate

  •  
  • AICART

    aminoimidazolecarboxamide ribonucleotide transferase

  •  
  • BHMT

    betaine-homocysteine methyltransferase

  •  
  • CBS

    cystathionine β-synthase

  •  
  • DHFR

    dihydrofolate reductase

  •  
  • DNMT

    DNA-methyltransferase

  •  
  • FOCM

    folate-mediated 1-carbon metabolism

  •  
  • FTD

    10-formyltetrahydrofolate dehydrogenase

  •  
  • FTS

    10-formyltetrahydrofolate synthase

  •  
  • GNMT

    glycine N-methyltransferase

  •  
  • Hcy

    homocysteine

  •  
  • Ki

    inhibition constant

  •  
  • Km

    Michaelis-Menten constant

  •  
  • MAT

    methionine adenosyl transferase

  •  
  • METin

    rate of methionine input

  •  
  • MS

    methionine synthase

  •  
  • MTD

    5,10-methylenetetrahydrofolate dehydrogenase

  •  
  • MTCH

    5,10-methylenetetrahydrofolate cyclohydrolase

  •  
  • MTHFR

    5,10-methylenetetrahydrofolate reductase

  •  
  • PLP

    pyridoxal phosphate

  •  
  • SAH

    S-adenosylhomocysteine

  •  
  • SAHH

    S-adenosylhomocysteine hydrolase

  •  
  • SAM

    S-adenosylmethionine

  •  
  • THF

    tetrahydrofolate

  •  
  • TS

    thymidylate synthase

  •  
  • Vmax

    maximum velocity of reaction

Footnotes

1

This research was supported by NIH grant R01 CA 105437 (C.M.U.) and NSF grant DMS 0109872 (M.C.R.)

2

More detailed information on the model parameters and characteristics is available with the online posting of this paper at jn.nutrition.org.

Supplementary data