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I provided a definition of nutritional genetics and nutrigenomics earlier. Most of the information detailed in this book is hung on a framework of one or the other of these important areas. However, although my theme throughout this book has been on the role of nutrition in human evolution, scientists around the world have tackled these important areas largely to help understand and attenuate human disease and suffering.

Muller and Kersten (198) describe nutrigenomics as the application of high-throughput genomics tools in nutrition research. With this in mind, one can immediately see how important the Human Genome Project has been in illuminating the molecular signature of those genes that have particular relevance to our diet. The Human Genome Project has provided the world's scientists with enormous amounts of accessible bioinformatics data. In the West, the Internet has given consumers the same level of access to information for attaining nutritional optima that maintains health and well-being. Increased awareness by consumers on matters of diet and health has helped to forge today's marketing strategies. Already we are seeing the public responding to personal nutrigenomic information, and this is a trend that is only going to increase. Although this obviates any evolutionary forces that may be at work, it is likely to have profound future effects on population health.

Two caveats may be added to this: (1) Market forces can be used to distort reality. I worry about products that are sold with a singular marketing ploy that oversells a nutrient's health benefits. There are several products with health benefit upsides that may possibly also have some downsides. It is always best to emphasize moderation and balance when talking dietary regimes. (2) Some (not so small) sectors of the population are slow to adapt to public health messages.

In essence, nutrigenomics considers dietary components as signals detected by cellular sensing systems that transduce these into the expression of proteins that regulate metabolite levels (Figure 11.1). Nutrigenomics therefore looks at how nutrients influence the cell's homeostatic signature. Inherent in this global perspective is the search for genes and their

Figure 11.1. Simple scheme showing nutrigenomic aspects of cellular function.

polymorphic forms that predispose us to disease, but that can be modified in their effect by dietary nutrients.

Earlier, Table 3.1 showed a range of important micronutrient-gene interactions that are important elements in the study of nutrigenomics. Figure 11.2 shows some products that are encoded by vitamin A and D responsive genes. Macronutrients are also important and can similarly act through transcription factor pathways that mediate nutrient-gene interactions (see Figure 11.3):

Carbohydrates such as glucose interact with transcription factors like upstream stimulatory factor, sterol-responsive-element binding protein, and carbohydrate responsive binding protein. Fats like cholesterol also act with sterol-responsive-element binding protein, and with the liver X receptor and farnesoid X receptor (bile salt receptor [FXR]). Fatty acids interact with PPARs and hepatocyte nuclear factor among others, and amino acids interact with CCAAT/enhancer-binding protein.

Transcription factors are the most important mechanism by which nutrients influence the expression of genes. To place this in context, there are 48 members of the nuclear hormone receptor superfamily of transcription factors. Many of these have already been mentioned: PPARs, RAR, RXR, VDR, LXR, and FXR.

Without doubt, the future challenge in nutrigenomics is to elucidate further nutrient-modulated molecular pathways and determine downstream effects. Only then will we be

Figure 11.2. Examples of some products that are encoded by vitamin A and D responsive genes.

able to comprehend the true influence of nutrition on human well-being. When that time arrives, human molecular nutrition will reach full maturity as a life sciences discipline.


Random or stochastic variations in the natural environment such as food availability and mutagenic agents confound life processes. However, despite the many factors that impact on the fidelity of DNA, and orchestration of our metabolome, our biological systems remain extraordinarily stable.

The balance between phenotypic stability and instability is regulated by genetic buffering mechanisms that interact with environmental variables. These buffering mechanisms are an integral part of the human nutrigenomic profile and include gene duplication mechanisms, the complex regulatory mesh of epistatic (gene-gene) interactions, and protein-protein interactions that confer molecular stability in a relatively nonspecific manner. More obvious

•Sterol regulatory element binding protein •Hepatocyte nuclear factor

•Sterol regulatory element binding protein •Hepatocyte nuclear factor

Literacy Work Sheet Year

Figure 11.3. Gene expression is mediated by a range of nutrient-regulated transcription factors.

Transcription factor —Nutrient ligand

Figure 11.3. Gene expression is mediated by a range of nutrient-regulated transcription factors.

PHENOTYPIC INSTABILITY Figure 11.4. The balance between phenotypic outcomes with respect to nutrition and genetic buffering.

nutritional interactions clearly also exist between many nutrients and a wide variety of genes, biologically active food-derived molecules and the metabolome, and nutrients with nutrients.

When these buffering mechanisms fail, a less robust cellular integrity ensues, leading to phenotypic instability. This could mean a reduced reproductive potential, a less competitive individual with respect to sequestering food, and/or the occurrence of disease. Figure 11.4 shows a schematic of the balance between phenotypic outcomes with respect to nutrition and genetic buffering.

It is relatively easy to construct a simple mapping of nutritionally relevant interactions that lead to a particular phenotype if one limits the mapping to a fairly small area of the interactome. For example, dietary folate is required for de novo methionine biosynthesis (i.e., DNA methylation regulated gene expression and hundreds of other biomethylations), nucleotide formation (production of thymine for the elaboration of DNA), amino acid interconversions, and the lowering of athero-, neuro-, and embryotoxic homocysteine. Many proteins that facilitate folate-dependent one-carbon transfer reactions, or act as folate carriers, or that simply lead to the addition or removal of polyglutamyl tails that are required for cellular retention/reactivity, are encoded by common polymorphic genes. These variant folate genes do not work in isolation; (199) epistasis occurs with significant interactions. For example, within the potentially deleterious C677T-MTHFR-TT genotype, four other folate SNPs (A1298C-MTHFR, G80A-reduced folate carrier [G80A-RFC], A2756G-methionine synthase [A2756G-MS], and A66G-methionine synthase reductase [A66G-MSR]) seem to determine the balance between homocysteine transsulphuration to cysteine and folate-related homocysteine remethylation to methionine (199). It is already well recognized that a limited permutation of C677T- and A1298C-MTHFR genotypes is possible—three or four mutant alleles within this haplotype are extremely rare if at all possible. The MS and MSR genes that exist as common variants have a close interaction because their gene products are dependent proteins, which along with vitamin B12, interact in the de novo biosynthesis of methionine and the regeneration of tetrahydrofolate for nucleotide biosynthesis. In this reaction, MS-bound vitamin B12 cycles between methylcobalamin and cob(I)alamin forms of vitamin B12. As the cob(I)alamin form is susceptible to oxidation, leading to an inactive cob(II)alamin form of the enzyme, MSR in concert with S-adenosylmethionine is required to salvage this molecular species for further catalytic cycling (200, 201). The occurrence (over-representation within a clinical phenotype) of these and other similar folate SNPs is now associated with risk for several developmental and degenerative conditions. Many of these SNPs also have functional metabolic consequences ascribed to them that are likely to influence the robustness of the clinical phenotype—sometimes for the better, sometimes for the worse.

In addition, vitamins C, B2, and B6 also play important roles in folate metabolism in a similar manner to B12. That is, they exhibit significant direct and indirect nutrient-nutrient interactions with respect to folate: Secretion of vitamin C into the gastric lumen aids the bioavailability of reduced dietary folate, B2 is a cofactor for MTHFR, and B6 is required as a cofactor for serinehydroxymethyl transferase (SHMT) and cystathionine j-synthase (C jS). Therefore, both B2 and B6 impact upon the interconversion of the various pathway specific one-carbon forms of folate.

Folate, and as a consequence, B12 B2, and possibly B6 also provide a good example of direct nutrient-gene interactions. On the one hand, folate provides the one-carbon unit for conversion of uracil to thymine needed in the elaboration of DNA, and it provides methyl groups for gene expression, which is a direct general effect of diet upon the human transcriptome. On the other hand, folate SNPs interact with folate status to influence risk for a variety of diseases and reproductive health. In the latter case it would seem that the abundance of dietary folate may even select folate-related SNPs in the early embryo and offer a survival advantage under the prevailing nutritional environment (25, 37).

A very clear nutrient-gene interaction is given by G80A-RFC. This common folate SNP in the reduced folate carrier protein (RFC) is responsible for subtly altering the cellular uptake of the proteins major folyl ligand-5-methyltetrahydrofolate (202). RFC is responsible for the uptake of folate from the jejunum (203) and the subsequent translocation of this trace nutrient across the membranes of a variety of cells (204, 205).

This SNP at position 80 (exon 2) of the RFC gene is represented by the substitution of a guanine for an adenine (G80A RFC), and it leads to an arginine replacing a histidine in the expressed carrier protein. The functional effect of the expressed polymorphic receptor protein is the altered assimilation of folate from dietary sources into red cells (erythrocytes) (202), which is a process that occurs during erythropoiesis in the bone marrow. Interestingly, RFC is particularly abundant in tissues that have a high folate requirement, i.e., reproductive tissues. This means that G80A-RFC may potentially influence reproductive efficacy, although this speculative possibility remains to be proven.

Although modern data sets within nutrigenomics and the whole "omics" revolution tend to have very high dimensionality, leading to a clear need for in silico modeling of such systems biology "omic" supersets, it is possible to focus in on one area and to examine how overall genetic variability and selected nutrient-nutrient interactions might influence cell biology and, hence, phenotype. Using the folate variant genes described above, Figure 11.5


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Methylfolate (MF)

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Reduced folate carrier SNPI

Following deconjugation of polyglutamyl methylfolate, anionic monoglutamyl methylfolate binds to cationic sites on the reduced folate carrier (RFC). Following cellular internalization, a drop in pH due to an increase in proton (H+) concentration liberates methylfolate, which then enters the cytosol

Methylation cycle Methionine^-Homocysteine-

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Methylene-tetrahydro-folate reductase SNPs

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19bp deletion DHFR

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Figure 11.5. With reference to a selected area of folate metabolism, this simplified figure shows how overall genetic variability and selected nutrient-nutrient interactions might influence cell biology and, hence, phenotype. The overall pattern of the genotypes shown (others SNPs exist but are not given), and availability of dependent nutrients, even within this small area of metabolism will modify an individual's potential phenotype with respect to developmental and degenerative disease risk.. C677T-MTHFR is the classic example, but others include a common dihydrofolate reductase 19-base pair deletion allele that acts as a risk factor for preterm delivery (206), whereas A2756G-MS and G80A-RFC modify risk of developing a life-threatening blood clot. C677T-MTHFR and A66G-MSR can act together to increase risk for Down syndrome, whereas folate, B12, C677T-MTHFR, A1298C-MTHFR, and A66G-MSR have all been implicated in the etiology of neural tube defects. Furthermore, the biological availability and/or effectiveness of vitamin B12 and vitamin C may be compromised in atrophic gastritis, and thus may have a secondary impact on folate metabolism. The points of impact and permutations of potential interaction and hence the overall spectrum of variability, even in this small area of metabolism, are clearly immense. Expand the area of interest, and one can readily appreciate that nutrigenomics encompasses a very high dimensionality, leading to the clear need for in silico modeling of systems biology "omic" supersets.

shows a simple mapping to illustrate the bespoke architecture within metabolic pathways. One can liken this mapping to a town plan. As an example, if one assumes that SNPs exist to benefit the organism under certain circumstances, one can envision a simple parallel. Think of a very busy road that has 100 accidents within a notorious one-mile stretch each year. The council then installs a series of speed bumps (sleeping policemen) and chicanes that slow the traffic up. As a consequence, the number of accidents drops to 20 per year. If one thinks of each speed measure as a SNP, one can see how an SNP might confer benefit. One can similarly draw the parallel of diversionary measures to improve traffic flow where access to key urban facilities may be limited. In this context, consider the MTHFR protein. The C677T-MTHFR variant may slow one-carbon unit "traffic" flow to methionine synthesis, but providing overall folate status is good, it leads to a diversion, and hence enhanced one-carbon unit "traffic" flow into thymine, one of the building blocks of DNA. By regulating metabolic traffic in this way, SNPs aid phenotypic stability. One can similarly view nutrient cofactors that act in synergy with folate as payment that has to be provided at tollbooths that permit onward passage of one-carbon units. Without wanting to appear trite, the town plan analogy has much to commend it.

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