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. 2010 Sep 24:3:42.
doi: 10.1186/1755-8794-3-42.

Polymorphisms in folate-metabolizing genes, chromosome damage, and risk of Down syndrome in Italian women: identification of key factors using artificial neural networks

Fabio Coppedè  1 , Enzo Grossi, Francesca Migheli, Lucia Migliore
Affiliations

Polymorphisms in folate-metabolizing genes, chromosome damage, and risk of Down syndrome in Italian women: identification of key factors using artificial neural networks

Fabio Coppedè et al. BMC Med Genomics. .

Abstract

Background: Studies in mothers of Down syndrome individuals (MDS) point to a role for polymorphisms in folate metabolic genes in increasing chromosome damage and maternal risk for a Down syndrome (DS) pregnancy, suggesting complex gene-gene interactions. This study aimed to analyze a dataset of genetic and cytogenetic data in an Italian group of MDS and mothers of healthy children (control mothers) to assess the predictive capacity of artificial neural networks assembled in TWIST system in distinguish consistently these two different conditions and to identify the variables expressing the maximal amount of relevant information to the condition of being mother of a DS child.The dataset consisted of the following variables: the frequency of chromosome damage in peripheral lymphocytes (BNMN frequency) and the genotype for 7 common polymorphisms in folate metabolic genes (MTHFR 677C>T and 1298A>C, MTRR 66A>G, MTR 2756A>G, RFC1 80G>A and TYMS 28bp repeats and 1494 6bp deletion). Data were analysed using TWIST system in combination with supervised artificial neural networks, and a semantic connectivity map.

Results: TWIST system selected 6 variables (BNMN frequency, MTHFR 677TT, RFC1 80AA, TYMS 1494 6bp +/+, TYMS 28bp 3R/3R and MTR 2756AA genotypes) that were subsequently used to discriminate between MDS and control mothers with 90% accuracy. The semantic connectivity map provided important information on the complex biological connections between the studied variables and the two conditions (being MDS or control mother).

Conclusions: Overall, the study suggests a link between polymorphisms in folate metabolic genes and DS risk in Italian women.

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Figures

Figure 1
Figure 1
Overview of the folate metabolic pathway. Folates require several transport systems to enter the cells, the best characterized being the reduced folate carrier (RFC1). Methylenetetrahydrofolate reductase (MTHFR) reduces 5,10-methylenetetrahydrofolate (5,10-MTHF) to 5-methyltetrahydrofolate (5-MTHF). Subsequently, methionine synthase (MTR) transfers a methyl group from 5-MTHF to homocysteine (Hcy) forming methionine (Met) and tetrahydrofolate (THF). Methionine is then converted to S-adenosylmethionine (SAM) in a reaction catalyzed by methionine adenosyltransferase (MAT). Most of the SAM generated is used in transmethylation reactions, whereby SAM is converted to S-adenosylhomocysteine (SAH) by DNA methyltransferases (DNMTs) that transfer the methyl group to the DNA. Vitamin B12 (or cobalamin) is a cofactor of MTR, and methionine synthase reductase (MTRR) is required for the maintenance of MTR in its active state. If not converted into methionine, Hcy can be condensed with serine to form cystathionine in a reaction catalyzed by cystathionine β-synthase (CBS), which requires vitamin B6 as a cofactor. Cystathionine can be then utilized to form the antioxidant compound glutathione (GSH). Another important function of tetrahydrofolate derivatives is in the de novo synthesis of DNA and RNA precursors, where they are used by thymidylate synthase (TYMS) and methylenetetrahydrofolate dehydrogenase (MTHFD) for the synthesis of nucleic acid precursors. MTHFD is a trifunctional enzyme that interconverts tetrahydrofolate derivatives for purine, methionine and thymidylate synthesis. TYMS requires 5,10-MTHF and deoxyuridine monophosphate (dUMP) for the production of to deoxythymine monophospate (dTMP) and dihydrofolate (DHF) in the de novo synthesis of pyrimidines. Other enzymes participate in folate metabolism, among them phosphoribosylglycinamide transformylase (GART) which is a protein required for purine synthesis.
Figure 2
Figure 2
Method of coding the polymorphisms in the database. The code assigned to the polymorphisms transformed each polymorphism in three genotype classes: major homozygous, heterozygous and minor homozygous. For each class a binary coding was applied: 0 if variable absent; 1 if variable present. So for example considering the polymorphism MTRR 66A>G which can exist in three variants: AA (major homozygous), AG (heterozygous) and GG (minor homozygous). Supposing that three records are AA, GG and AG, the coding has been applied as shown in the figure.
Figure 3
Figure 3
Area Under the Curve (AUC) of Receiver Operator Characteristic (ROC) relative to ten classification tasks performed with Back Propagation ANN models. The ROC represents therelationship between sensitivity and specificity for the prediction of each of the considered outcomes. The average ROC is depicted in red
Figure 4
Figure 4
Semantic connectivity map obtained with Auto-Cm System. The figures on the arches of the graph refer to the strength of the association Between two adjacent nodes. The range of this value is from 0 to 1.
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