Frontiers in nutrigenomics, proteomics, metabolomics and cancer prevention

Mutation Research 551 (2004) 51–64

Review

Frontiers in nutrigenomics, proteomics, metabolomics and cancer prevention Cindy D. Davis∗ , John Milner NIH/NCI, Nutritional Sciences Research Group, 6130 Executive Blvd, MSC 7328, Rockville, MD 20892-7328, USA Received 15 October 2003; received in revised form 20 January 2004; accepted 20 January 2004 Available online 4 June 2004

Abstract While dietary habits continue to surface as a significant factor that may influence cancer incidence and tumor behavior, there is considerable scientific uncertainty about who will benefit most. Inadequate knowledge about how the responses depend on an individual’s genetic background (nutrigenetic effects), the cumulative effects of food components on genetic expression profiles (nutritional transcriptomics and nutritional epigenomics effects), the occurrence and activity of proteins (proteomic effects) and/or the dose and temporal changes in cellular small molecular weight compounds (metabolomics effects) may assist in identifying responders and non-responders. Expanding the information about similarities and differences in the “omic” responses across tissues will not only provide clues about specificity in response to bioactive food components but assist in the identification of surrogate tissues and biomarkers that can be used for predicting a response. Deciphering the importance of each of these potential sites of regulation will be particularly challenging but does hold promise in explaining many of the inconsistencies in the literature. © 2004 Elsevier B.V. All rights reserved. Keywords: Nutrigenetics; Nutritional transcriptomics; Proteomics; Metabolomics; Cancer prevention

Abbreviations: DADS, dialllyl disulfide; 1,25 D3, 25dihydroxyvitamin D3 ; 2D-PAGE, two-dimensional polyacrylamide gel electrophoresis; ERK, extracellular signal-regulated kinase; GST, glutathione-S-transferase; MnSOD, manganese dependent superoxide dismutase; MS, mass spectrometer; MTHFR, methylenetetrahydrofolate reductase; NAT, N-acetyltransferase; NMBA, N-nitrosomethylbenzylamine; NMR, nuclear magnetic resonance; nrf2, nuclear factor E2 p45-related factor 2; PPAR␣, peroxisome proliferator-activated receptor ␣; PUFA, polyunsaturated fatty acids; SELDI-TOF, surface-enhanced laser desorption/ionization time of flight; SNP, single nucleotide polymorphism; VDR, Vitamin D receptor ∗ Corresponding author. Tel.: +1 301 594 9692; fax: +1 301 480 3925. E-mail address: [email protected] (C.D. Davis).

Diet has been implicated in the incidence of 6 out of 10 of the leading causes of death of Americans [1]. Although the literature is replete with evidence that breast, prostate, colon, lung and liver cancers are associated with dietary patterns, numerous inconsistencies are also evident [2]. These inconsistencies may reflect the multi-factorial and complex nature of cancer and the specificity that individual dietary constituents have in modifying genetic pathways. While excess calories is generally linked to enhanced cancer risk, a large number of bioactive food components occurring in food may provide protection at several stages of the cancer process [2]. Representative bioactive components that have been identified in food that are protective against cancer in model

0027-5107/$ – see front matter © 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.mrfmmm.2004.01.012

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systems include essential nutrients (i.e., calcium, zinc, selenium, folate, Vitamins C, D and E) as well as non-essential food components (i.e., carotenoids, flavonoids, indoles, allyl sulfur compounds, conjugated linoleic acid, and N-3 fatty acids) [2]. Bioactive food components that may modify simultaneously more than one cancer process including such diverse events as carcinogen metabolism, hormonal balance, cell signaling, cell-cycle control, apoptosis, and angiogenesis [3]. Variation in cancer incidence among and within populations with similar dietary patterns suggests that an individual’s response may reflect interactions with genetic factors, which may have ramifications in gene, protein and metabolite expression patterns (Fig. 1). Nutrigenomics or nutritional genomics, defined as the interaction between nutrition and an individual’s genome or the response of an individual to different diets, will likely provide important clues about responders and non-responders. Techniques used to unravel nutritional genomics are no different than those used in modern molecular genetic research. An integrated framework that simultaneously examines genetics and associated poly-

DNA Transcription RNA Bioactive Food Components

Translation Protein

Metabolism Metabolite

Fig. 1. Bioactive food components can modify transcription, translation and metabolism.

Bioactive Food Components

N u t r i g e n o m i c s

Nutrigenetics

DNA

Nutritional Epigenomics RNA Nutritional Transcriptomics

Proteomics

Metabolomics

Protein

Metabolite

Fig. 2. Nutrigenetics, nutritional transcriptomics, nutritional epigenomics, proteomics and metabolomics are necessary to understand the role of nutrition in carcinogenesis.

morphisms with diet-related diseases (nutrigenetics), nutrient induced changes in DNA methylation and chromatin alterations (nutritional epigenomics), nutrient induced changes in gene expression (nutritional transcriptomics), and altered formation and/or bioactivation of proteins (proteomics) will allow for a greater understanding of the interrelationships between diet and cancer risk and tumor behavior (Fig. 2). Since the response to a bioactive food component may be subtle, careful attention will need to be given to characterizing how the quantity and timing of exposure influence small molecular weight cellular constituents (metabolomics). Managing this enormous amount of information will necessitate new and expanded approaches to bioinformatics. The important roles that nutrigenetics, nutritional transcriptomics, proteomics and metabolomics can play in deciphering the role of diet in cancer prevention will be the focus of this review. The potential for nutritional modulation of DNA methylation and other epigenetic events in cancer prevention has been recently reviewed in a conference proceedings [4] and will not be a focus of this article.

1. Tissue and cellular assessment A fundamental issue at the frontier of nutrition and cancer prevention research is which biological samples are most predictive of the response to a bioactive food component in the target tissue. A limitation to validation of biomarkers in target tissue is in many

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cases their inaccessibility. Although blood and blood constituents have frequently been used to evaluate the response to bioactive food components, the concentration and molecular/biochemical effects of these agents in the blood and in the target tissue of interest may not be related. Exfoliated or sloughed cells hold potential for monitoring human exposure to bioactive food components in target tissues but have not been adequately evaluated [5]. Examples include colonic epithelial cells obtained from stool samples, bladder urothelial cells present in urine samples, airway epithelial cells in sputum or bronchoalveolar lavage, buccal mucosal cells obtained by rinsing the mouth and mammary epithelial cells obtained from ductal lavage or nipple aspirate fluid. DNA, RNA and protein isolated from exfoliated cells have been analyzed for various types of genetic and epigenetic changes [5]. Although there are collection limitations, the limited access to some cancer sites, raises the intriguing possibility that some exfoliated cells may serve as surrogate indicators of the response to diet both in terms of cellular uptake and shifts in gene, protein and metabolome expression profiles.

2. Nutrigenetics Genetic polymorphisms may be partially responsible for variations in individual response to bioactive food components. Single nucleotide polymorphisms (SNPs) are becoming increasingly recognized to have an important influence on disease risk [6], for example, inherited polymorphisms in BRCA1 and breast cancer susceptibility [7]. Some common SNPs in genes involved in nutrient metabolism, metabolic activation and/or detoxification could establish the magnitude or whether there is a positive or negative response to a food component [8]. For example, women below the median consumption of fruits and vegetables (<764 g/day), ascorbic acid (<155 mg/day) and ␣-tocopherol (<7.5 mg/day) were reported to be at the greatest risk of breast cancer as a result of a polymorphism that causes a valine to alanine change in the 9 position in the signal sequence for the enzyme manganese dependent superoxide dismutase [9]. Consumption of well-done meat was correlated with increased breast and colon cancer susceptibility among individuals with a rapid/intermediate

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N-acetyltransferase genotype but not among individuals with the slow acetylator genotype [10,11]. An interaction between glutathione-S-transferase (GST) genotype and dietary isothiocyanates, occurring in cruciferous vegetables, was linked to a lower risk for colorectal cancer among individuals who had both GSTM1 and T1-null phenotypes, presumably because of slower rates of metabolism and excretion of this bioactive food component [12]. A severe limitation of many of the current studies relating diet and polymorphisms is the lack of information about the cellular consequences accompanying the polymorphisms and thus if the observed relationships are consistent with a cause and effect relationship or not. Shifts in nutrient metabolism have been observed between individuals with different methylenetetrahydrofolate reductase (MTHFR) genotypes. Substituting C to T at nucleotide 677 results in reduced conversion of 5,10-methylenetetrahydrofolate to 5-methlenetetrahydrofolate, the form of folate that circulates in plasma. Individuals with this polymorphism appear to have an increased dietary folate requirement [13–15]. Dietary habits may influence the effects of this polymorphism has on cancer risk. For example, subjects with the TT genotype for MTHFR were at a decreased risk of colorectal adenomas when they had high (>5.5 ng/mL) plasma folate and an increased risk when they had low (≤5.5 ng/mL) plasma folate concentrations [16]. Since there was no clear relationship between plasma folate and colorectal adenomas among those with the CC or CT genotype, only a subset of the population may benefit from exaggerated folate intakes [16]. However, this relationship may depend on the intake of other nutrients. Recent studies have demonstrated that the iron storage protein ferritin can catabolize folate in vitro and in vivo, and increased heavy-chair ferritin syntheses decreases intracellular folate concentrations independent of exogenous folate concentrations [17]. Such nutrigenetic relationships may also explain recognized associations between the intake of several food components such as Vitamin E and selenium, or calcium and Vitamin D. It remains unclear if nutrigenetic effects are constant across all tissues. For example. the TT polymorphism of MTHFR has been linked to enhanced endometrial, ovarian and breast cancers [18–20]. It also remains unclear what these gene-nutrient interactions mean phys-

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iologically. Does this indicate that the primary effects of folate in the cancer process involves uracil misincorporation during DNA synthesis, leading to DNA instability, during breast, ovarian and endometrial but not during colon cancer? Does it indicate that low concentrations of 5-methyltetrahydrofolate, which lower synthesis of methionine and thus cause hypomethylation of DNA and consequent abnormal gene expression, are more important during colon than during breast, ovarian and endometrial tumor development? Based on available literature both are plausible [21,22]. Multiple polymorphisms within and across genes may contribute to phenotypic changes brought about by food components. For example, polymorphisms in the 5 (the FokI restriction site) and in the 3 (BsmI, TaqI restriction sites and polyA tail) end of the gene for Vitamin D nuclear receptor (VDR) may influence the response to 1,25 D3 [23,24]. The Fok1 polymorphism has been associated with a decrease in the intracellular activity of 1,25 D3 and the 3 polymorphisms are associated with decreased transcription of the gene [24–27]. All of these polymorphisms are associated with cancer susceptibility [27–31]. Wong et al. [27] found that in a Chinese cohort, compared with individuals carrying the FF genotype, those with the Ff genotype had a 51% increase and those with the ff genotype had an 84% increased risk of colorectal cancer. The variation in risk associated with the VDR genotype appeared to be confined to those individuals with relatively low fat or calcium intakes (<41.54 and <387.74 g/day, respectively). Their relatively low calcium and fat consumption may have contributed to the unmasking of this genotype interaction with colorectal cancer risk [27] since previous studies in Western populations where there is higher calcium and fat intakes were unable to observe this relationship [28,29]. Data by Kim et al. [30] suggest that dietary Vitamin D and calcium intake modify the relationship between BsmI VDR genotype and colorectal adenoma risk. Slattery et al. [31] found that the variants of the 3 VDR polymorphisms (BsmI, TaqI and polyA), but not the 5 VDR polymorphism (FokI), were associated with a significant reduced risk of colon cancer. Dozens of additional polymorphic variations within the VDR gene could each have different biological consequences [32]. Future studies investigating the relationship between VDR polymorphisms, diet and cancer susceptibility need to

simultaneously investigate multiple polymorphisms, as well as the functional consequences of these polymorphisms, on specific 1,25 D3 regulated genes. The frontiers will involve the identification of combinations of functional sequence variants, i.e. haploytype patterns, that modulate the Vitamin D endocrine system and confer risk of cancer. It is certainly possible that the response to several of the bioactive food components will depend on multiple polymorphisms. Animal studies have already begun to document the physiologic consequences of gene polymorphisms. The p21WAF1/cip1 gene product, a downstream effector of p53 [33], is an inhibitor of cyclin-dependent kinase activity [34] and is therefore an important regulator of the cell cycle and potentially, of apoptosis and cell differentiation [35]. Targeted inactivation of the p21WAF1/cip1 gene has been found to enhance tumor formation and decrease survival in the multiple intestinal neoplasia (Min) mouse, a genetic model for human colon cancer susceptibility [35]. This effect was magnified when mice consumed a Western-style high risk diet which contains high fat and phosphate and low calcium and Vitamin D [35]. Similarly, inactivation of the cyclin-dependent kinase inhibitor p27kip1 and feeding a Western-style diet were additive in terms of tumor incidence, frequency and size, and in reducing the life span of mice [36]. The inactivation of p27kip1 and the consequent disruption of normal maturation in the colonic mucosa were associated with modestly elevated c-myc, cdk4 and cyclin D1 expression [36]. The finding that the absence of p21WAF 1/cip1 or p27kip1 , when combined with feeding a Western-style diet can significantly increase tumor formation to a greater extent than either factor does alone, demonstrates the importance of considering both dietary and genetic factors in tumor formation and in chemoprevention. The data also emphasize the critical role that dietary factors can have in both tumor initiation and progression, through interaction with pathways that normally maintain intestinal homeostasis [36]. Recent studies indicated that the alelle directing synthesis of leucine at position 198 of glutathione peroxidase was less responsive to selenium than the proline containing protein [37]. Since the leucine allele has been correlated with increased lung [37] and breast cancer [38], alterations in selenium requirements for maximal enzyme activity may explain the increased cancer susceptibility in individuals with the

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leucine allele. Additional studies that reveal a functional consequence of gene polymorphisms not only on enzymatic activity but also phenotypic expression are desperately needed. Transgenic animal studies are revealing tissue specificity in gene-nutrient interactions. Zinc deficiency in p53 knockout mice was found to accelerate the induction and progression of N-nitrosomethylbenzylamine (NMBA)-induced tumors in the forestomach more than in the esophagus [39]. In contrast, mice that are either p53 deficient or zinc deficient but not both, did not have esophageal tumors after treatment [39]. The importance of this observation is that mutations in the p53 tumor suppressor gene are highly relevant to the study of nutrigenetics because mutations of the p53 tumor suppressor gene is the most frequently observed genetic lesion in human cancer [40] and germline inactivation of one allele of the p53 gene is a hallmark of Li–Fraumeni syndrome, a familial cancer syndrome [41]. These data emphasize the importance of gene-nutrient interactions and that diet can ameliorate some genetic predisposition to cancer. It is likely that other similar relationships will emerge but continue to be inadequately examined. Although there are many dietary components that can reduce cancer risk, increased food consumption is not always beneficial. Increased energy consumption and decreased physical activity have been linked to obesity. Obesity has been associated with increased risk of death from cancer [42]. Caloric restriction is probably the best-documented experimental manipulation for decreasing tumor development in rodents including transgenic and knockout models [43–47]. p53-Deficient mice have been a useful model for studying the effects of caloric restriction on spontaneous cancer development. Caloric restriction, begun after weaning increased the latency of spontaneous tumor development in p53-deficient mice by ∼75% [44]. Adult-onset caloric restriction and fasting have also been found to delay spontaneous tumorigenesis in p53-deficient mice [46]. Reductions in circulating IGF-1 concentrations appear to mediate many of the anticancer effects of caloric restriction in this model [45]. Polymorphisms in many genes involved in energy homeostasis, including ␤-2 and ␤-3 adrenoreceptor genes [48,49] and PPAR␥ [50,51], have been associated with cancer susceptibility. It can be expected that other gene variants associated with energy

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balance regulation are going to emerge as players in the field of cancer susceptibility [52]. Focusing on interactions between single gene polymorphisms and a bioactive food component may be overly simplistic. Experimental animals provide some of the most compelling evidence that genetic backgrounds can influence the response to food components [53]. For example, fat resistant A/J mice, but not fat sensitive C57BL/6J mice, increase thermogenic capacity in response to high fat diets [54]. Different strains of mice have different susceptibility to spontaneous and estrogen-or carcinogen-induced mammary cancer [55,56]. Understanding the dynamics of gene–gene interactions as determininants of the response to bioactive food components represent a critical void in nutrigenetics. In summary, subdividing participants in a study by genotype has been found to reveal substantial diet-related risks that are otherwise obscured. However, the examination of single gene polymorphisms may provide a false sense of cause and effect interrelationships. The simultaneous examination of multiple polymorphisms in multiple genes will likely be needed to understand risk and potential benefits of food components. Incorporating studies of SNPs into human investigations should help to define optimal dietary intervention strategies.

3. Nutritional transcriptomics The use of high-throughput genomics technologies to identify molecular pathways influenced by food components is becoming increasingly commonplace [57–65]. As suggested by Muller and Kersten these technologies serve three different purposes for gene expression profiling in nutrition research [57]. They can provide clues about the mechanism that underlies the beneficial or adverse affects of a certain dietary component, identify important genes that are altered in the pre-disease state and therefore possibly serve as “molecular biomarkers” and/or assist in identifying and characterizing the basic molecular pathways influenced by food components [57]. Energy restriction is recognized to be a potent inhibitor of cancer. Recently, macroarray analysis have demonstrated that energy restriction down-regulates the cell cycle, possibly by decreasing the transcript

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levels of cyclin D and E2F family of genes [63]. Interestingly, the majority of molecular changes induced by energy restriction were reversed by 1 week of ad libitum feeding [65]. Thus, these types of studies provide not only fundamental information about the molecular pathways but also point to the need to understand time of ingestion and gene expression interrelationships. Microarray analysis has been utilized to identify potential molecular targets of bioactive food components such as selenium. Selenium deficiency in mice resulted in increased expression of genes involved in DNA damage repair, oxidative stress and cell-cycle control, yet decreased the expression of genes involved with drug detoxification [66]. In the premalignant MCF10AT human breast cancer cell line, a 200-gene membrane-based cDNA array was used to investigate the effect of selenium on expression of genes associated with apoptosis and cell-cycle regulation [67]. Genes whose expression was modified by selenium included GADD153, cyclin A, CDK1, CDK2, CDK4, CDC25, E2Fs, as well as the MAPK/JNK and phosphoinositide-3 kinase pathways. Many of these gene expression changes have also been observed in prostate cancer cells demonstrating a similarity in response to selenium among different tissues [66]. Of a total of 12,000 genes screened, over 2500 gene were identified as responsive to selenium treatment in human prostate cancer cells [68]. Because of the large number of genes whose expression was modified by selenium, the data were analyzed by cluster analysis (Fig. 3). Overall, their studies suggested the response occurred in 12 clusters of distinct kinetic patterns [68]. Thus, selenium likely affects many more that

Growth Factors

Protein Synthesis

Cell Cycle

Tumor Suppressor

Apoptosis

Signal Transduction

Selenium Angiogenesis Transcription Factor

Cytoskeleton DNA Repair

Adhesion/ Invasion

Fig. 3. Categories of genes that are mediated by dietary selenium in human prostate cancer cells as determined by microarray analysis. Data were obtained from Dong et al. [61].

one key molecular target. The massiveness of the data generated through microarrays makes it imperative that bioinformatics is effectively used, although it is a scientific discipline in its infancy [69]. Gene expression profiling can also be used to study similarities and differences in the molecular response to chemopreventive agents. Mariadason et al. [70] compared changes in gene expression in response to the short chain fatty acid butyrate and curcumin and to changes caused by two drugs (trichostatin A and sulindac) in the colon cancer cell line SW620. Similarities in transcriptional responses demonstrated that sulindac and curcumin, and butyrate and trichostatin A, respectively, have similar mechanisms of action [70]. Comparison of the effects of butyrate and trichostatin A, both known to inhibit histone deacetylase, on gene expression and kinetics of histone acetylation identified subsets of induced and repressed genes that are likely to be coordinately regulated by altered histone acetylation [70]. Thus, gene expression profiling can be useful for comparing and contrasting the response to nutrients and drugs. To raise nutrigenomics above the level of purely descriptive data, we must understand how food components regulate gene expression [57]. For this purpose, mutant mice, particularly knockout mice, are increasingly becoming an invaluable tool [57]. Using knockout mice, we can unambiguously establish how a particular transcription factor mediates the effect of a specific nutrient [57]. Knockout animals have assisted in identifying nuclear factor E2 p45-related factor 2 (Nrf2)-regulated genes induced by the chemopreventive agent sulforaphane [71,74]. Gene expression profiles from wild type and nrf2-deficient mice fed sulforaphane have revealed several novel downstream events and thus more clues about the true biological response. Similar studies with peroxisome proliferator-activated receptor-␣ (PPAR-␣)-null mice have revealed its role in regulating various sites of lipid metabolism [72] and that PPAR␣ is not the nutrient sensor that mediates the lowering of plasma triglyceride concentrations induced by fish oil [73]. A greater understanding of the downstream molecular events will facilitate the effective use of bioactive food components. Overall, several food components have been found to modify the expression of a number of genes. It

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remains unclear what cellular signal accounts for such a wide and varied response in gene expression and how quantity and timing influences the response. More attention to factors influencing mRNA synthesis and stability may provide important clues into this perplexing issue. A greater understanding about why multiple genes are modified simultaneously is needed to adequately interpret nutritional transcriptomics.

4. Proteomics The term proteome refers to all the proteins produced by a species, much as the genome is the entire set of genes [75]. However, unlike the genome, the proteome is dynamic and varies according to the cell type and the functional state of the cell [76]. Proteomic analysis allows a point in time comparison after a dietary intervention or other intervention that influences the proteome. Protein modifications can then be identified in response to the intervention. It is recognized that protein expression does not always correlate with mRNA expression [77]. Several factors may account for this inconsistency including alternative splicing resulting in multiple proteins from a single gene, post-translational modifications (i.e. glycosylation, phosphorylation, oxidation, reduction) or simply shifts in rates of synthesis and degradation [78]. Since these modified proteins can have different biological activities, more attention to protein expression is needed [78]. Several dietary components are recognized that post-translationally modify proteins and thus influence their activity. For example, shifts in phoshorylation occur after exposure to diallyl disulfide (DADS), a compound found in processed garlic [79]. Western blot analysis revealed that DADS exposure did not affect ERK protein content per se but increased its phosphorylation resulting in cell-cycle arrest [79]. Another example of postranslational regulation of proteins by dietary components involves modification of thiol groups in the cytoplasmic protein Keap1 [80]. The binding of Keap1 to Nrf2 is under oxidation/reduction (and alkylation) control via its highly reactive thiol groups. Modification of these thiols has been proposed to account for the ability of sulforaphane to induce glutathione transferase (GST) and quinone reductase [80].

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Functional proteomic studies, which characterize protein modifications along with functional data from established biochemical and physiological methods, should lead to a better understanding of the interplay between dietary habits and cancer [81]. The practical application of proteomics will depend on the ability to identify and analyze each of the protein products in a cell or tissue, and this is dependent on the application of several key technologies. Most proteomic tools are new and rapidly developing. The translation of proteomics will likely depend on new and sensitive technologies that allow the rapid analysis and identification of protein products or groups of related proteins [82]. While DNA/RNA analysis using the polymerase chain reaction can be employed to amplify very small signals, there is no comparable methodology for amplifying proteins. Consequently, protein analysis is often sample and detection limited. The conventional proteomic approach of two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) analysis while technically challenging, is not comprehensive and does not lend itself to high-throughput approaches [82]. Subjecting peptide fragments to matrix-assisted laser desorption/ionization time-of-flight mass spectrometry analysis offers interesting possibilities for determining the shifts in the finger print of peptide masses caused by dietary manipulation. A ProteinChip technology that centers around using specifically modified slides with various surface chemistries (i.e. ion exchange, hydrophobic interaction, metal chelation, etc.) to bind and selectively purify proteins from a complex biological sample also may provide clues about the role of bioactive food components and other modifiers of protein. There have been relatively few studies utilizing proteomic techniques to examine the relationship between nutrition and cancer. He et al. [83] demonstrated that low levels of arsenite induces B[a]P-treated lung cell transformation. They then utilized ProteinChip-based technology to identify protein peaks that appeared in lung cells after transformation or that were only present in control cells. They identified several unique protein peaks that were only present in the transformed cells. In the future, identification and characterization of these proteins may reveal the molecular basis of arsenite-induced cell transformation and provide insight into the mechanisms by which arsenic induces carcinogenesis [83].

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Functional proteomic studies have been useful for elucidating the mechanisms for growth inhibition of HT-29 colon cancer cells exposed to sodium butyrate [84]. Butyrate-treated cells exhibited growth inhibition accompanied by proteome alterations in the cells. Using 2D electrophoresis, the authors were able to resolve more than 1000 protein spots each in both treated and untreated HT-29 cells and have identified 35 differentially expressed protein spots as a result of butyrate treatment [84]. Butyrate treatment altered various components of the ubiquitin-proteasome system, suggesting that proteolysis could be a means by which butyrate may regulate key players in the control of cell cycle, apoptosis and differentiation [84]. Also, the authors demonstrated that butyrate simultaneously upregulated both proapoptotic (caspase-4 and cathepsin D) and antiapoptotic proteins (hsp27, antioxidant protein-2 and pyruvate dehydrogenase E1) which may account for the relative resistance of HT-29 cells to butyrate-induced apoptosis [84]. Functional proteomic studies in animals and humans are needed. Since the rat is a useful, widely used animal model for nutrition and cancer studies, the recent completion of a proteomic analysis of the rat liver [85] may be useful in future nutrition studies. This proteomic analysis identified 273 different gene products, of which approximately 60% were enzymes with a broad spectrum of catalytic activities [85]. On average approximately 5–10 protein spots corresponded to one gene product. Evardsson et al. [86] provide an interesting example of how proteome analysis in rat liver has been applied to obesity research. They found that livers from obese (ob/ob) mice displayed higher levels of enzymes involved in fatty acid oxidation and lipogenesis compared to lean mice and these differences were further amplified by treatment with the PPAR␣ agonist, WY14,643. Since WY14,643 normalized the expression levels of several enzymes involved in glycolysis, gluconeogenesis and amino acid metabolism in the obese mice to the levels of lean mice, bioactive food components with an inhibitory effect on PPAR␣ may bring about similar changes. Proteomic approaches have been successfully utilized for the identification of serum biomarkers of cancer. Petricoin et al. [87] identified a proteomic pattern in serum that is diagnostic for ovarian cancer. Using the ProteinChip/SELDI technology and a custom algorithm to recognize protein patterns in

the spectra, they first used sera from 50 unaffected women and 50 patients with ovarian cancer to identify a proteomic pattern that completely discriminated cancer from noncancer patients. This pattern consisted of a cluster of five distinguishing ions representing yet-to-be identified proteins. Using this cluster as the ‘biomarker’ pattern for a set of 116 masked serum samples, the investigators correctly identified 50/50 ovarian cancer cases and correctly identified 63/66 noncancerous samples as noncancerous [87]. These data are very encouraging for the establishment of a diagnostic method for a cancer normally extremely difficult to detect early. Similarly, Li and colleagues [88] used a proteomic and bioinformatic approach to identify serum biomarkers to detect breast cancer with SELDI-TOF-MS. The high sensitivity and specificity achieved by the combined use of selected biomarkers show great potential for the early detection of breast cancer and for detecting subtle changes caused by dietary habits. Thus, proteomic approaches, in conjunction with bioinformatics tools, could greatly facilitate the discovery of new and better biomarkers. It remains to be seen whether nutritional interventions, known to decrease breast cancer tumorigienesis in animal models, can be seen to reverse the altered protein profile.

5. Metabolomics Metabolomics is the study of the metabolome, which is the entire metabolic content of a cell or organism at a given moment. While metabolomics researchers generally concentrate on biofluids, including serum and urine [89] a greater attention to cells is needed. In animals and humans, metabolic profiling of blood and urine components to characterize toxicity and disease states such as inborn errors of metabolism has been ongoing since the introduction of gas-chromatography mass-spectrometry in the mid 1960s [90]. Blood and urine samples already are routinely tested for metabolites, such as cholesterol and glucose, which are used as indicators of health. What makes metabolomics new is the attempt to tabulate and quantify all the small molecules within a sample, to find new markers for disease or metabolite patterns as indicators of nutritional status [91]. The inadequate success of single biomarkers in predicting chronic diseases such as cancer attests to the need for

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more global and integrated approaches for assessing metabolism. Both noninvasive techniques, such as nuclear magnetic resonance (NMR) spectroscopy and more invasive techniques, such as the analysis of cell extracts are used in metabolomic research [92]. Recent metabolomic advances incorporate NMR spectroscopy, mass spectrometry, chromatographic analysis, and metabolic network analysis models to estimate cellular metabolic fluxes [92]. Metabolomics has been studied in microorganisms and in plants but little systematic work has so far been performed on the metabolomics of animals or humans [93]. Quantitative lipid metabolome data revealed differential effects of dietary fats on cardiac and liver phospholipid metabolism [94,95]. This approach mapped changes in the concentration of lipid metabolites to their biochemical pathways [95]. This approach was also employed to evaluate the effects of the insulin-sensitizing drug rosigliatazone on liver, plasma, heart and adipose lipid metabolism in mice [96] and is currently being used to build a large database of lipid metabolite concentrations in humans [97]. Because many of the effects of dietary macromolecules on tissue metabolism are reflected in the plasma lipid metabolome, metabolomics has excellent potential for evaluating subtle differences in the metabolic response to diet among individuals. Metabolomic data is also being utilized to understand the relationships among plasma amino acid levels in animals. Nogochi et al. [98] have suggested that correlation-based analyses could be useful in the analysis of metabolomic data to determine which metabolites may be responsible for the biological effects of excessive intakes of amino acids. Furthermore, the use of correlation-based analyses would allow for the relationship between all known and unknown small molecular weight metabolites in cells to be monitored [98]. Few studies have attempted to quantitate the plethora of small molecular weight compounds in biological samples following consumption of a specific bioactive food component. Solansky et al. [99] used 1 H NMR spectroscopy and principal component analysis to determine the metabolites present in urine of rats given a single bolus of epicatechin, a bioactive component present in tea. The biochemical effects associated with epicatechin dosing included decreased urinary concentrations of taurine, citrate,

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dimethylamine and 2-oxoglutarate [99]. Although these metabolomic data on their own do not provide information as to whether the effects of epicatechin are beneficial to health, they do provide a useful comparison of altered metabolism for studies investigating tea consumption in humans [99]. Furthermore, they highlight the potential of NMR-based metabolomic analysis as a tool for in vivo investigation and the possibility that integrated metabolomic studies of other biofluids (i.e. plasma) and tissues could provide a clearer metabolic picture [99]. One study has used a metabolomic approach to evaluate all of the biochemical changes following dietary intervention with soy in humans [100]. In this study, plasma profiles were analyzed from five healthy premenopausal women before and after consumption of 60 g soy/day (containing 45 mg isoflavones) in controlled diets [100]. The plasma metabolite profiles were characteristic and unique for each individual, reflecting the complex interactions of such factors, such as genetic variability [100]. Despite the presence of substantial intersubject variation, the metabolomic analysis enabled the identification of biomarkers related to the dietary intervention. Clear differences in the plasma lipoprotein, amino acid, and carbohydrate profiles were observed following soy intervention, suggesting a soy-induced alteration in energy metabolism [100]. The extent of the metabolic response to soy intervention was subject dependent but the nature of the response was consistent across subjects [100]. It is possible that these types of metabolomic studies would allow for the identification of individuals, based on their metabolic abilities, that would benefit from soy or other types of supplementation for cancer prevention. Historically, energy restricted animals have a lower incidence of cancer than when ad libitum fed. Shi et al. [101,102] have shown that HPLC separations coupled with coulometric array detectors could be used to detect a number of low-molecular-weight, redox-active compounds that differ between dietary-restricted and ad libitum fed rats. The authors reported that, in principle, this technique is capable of detecting nearly 1200 compounds in plasma, and that 250 of these are analytically robust enough to discriminate diet-dependent metabolism. Furthermore, the identified components of the serum metabolome can be used to distinguish ad libitum and dietary restricted rats in independent

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cohorts [101,102]. Further characterization of the metabolomic changes induced by dietary restriction or during interventions with specific food components should lead to a better understanding of when dietary change may be most appropriate in improving health. Metabolic changes during tumor proliferation has also been studied utilizing metabolomic approaches. The tumor metabolome is characterized by high glycolytic and glutaminolytic capacities, high phosphometabolite levels and a high channeling of glucose carbons to synthetic processes [103]. This allows tumor cells to proliferate over broad variations in oxygen and glucose supply. One key regulator of the tumor metabolome is the glycolytic isoenzyme pyruvate kinase type M2 [103]. These authors identified the metabolic Achilles’ heel of the tumor as its sensitivity to a reduction of NAD levels caused by activation of poly(ADP-ribose) polymerase after DNA damage [103]. This type of research will allow for a better understanding of metabolism in tumor cells and thus approaches that might be effective in altering their rates of proliferation. Coupling knockout animals or cells with metabolomic analysis can yield important clues about cell regulation. For example, the transcription factor, HIF-1, is a crucial mediator of tumor progression because of its role in up-regulating a large number of genes, including those involved in the formation and dynamic regulation of blood vessels, iron metabolism, glucose and energy metabolism, cellular proliferation, differentiation and viability, apoptosis and matrix metabolism [104]. Griffiths and Stubbs [105] used in vivo magnetic resonance spectroscopy and magnetic resonance imaging methods, complemented with in vitro NMR and classical biochemical analysis of tumor extracts, to measure physiological and biochemical parameters in wild-type and HIF-1␤ deficient mutant cells grown as solid tumors in vivo in mice. The main biochemical effect of the mutations was on ATP synthesis: their ATP content was 20% of normal. NMR spectroscopy of tumor extracts showed that the mutant cells had low levels of glycine, betaine and various cholines. This suggested that the primary effect of failure to up-regulate glucose transporter and glycolytic enzyme synthesis was to reduce provision of intermediates for anabolic synthesis of the purine rings required to make ATP [105]. This study demonstrates that metabolomic approaches can be

utilized to draw useful conclusions about metabolic biochemistry in cancer cells.

6. Summary Dietary components continue to surface as likely determinants of cancer risk and tumor behavior. While these linkages are fascinating, the literature is also replete with inconsistencies. While multi-factorial, these inconsistencies probably reflect variation in the ability of food constituents to reach or affect critical molecular targets. Genetic polymorphisms can alter the response to dietary components (nutrigenetic effect) by influencing the absorption, metabolism or site of action. Likewise, variation in DNA methylation patterns and other epigenomic events that influence overall gene expression can modify the response to food components and visa versa. Furthermore, variation in the ability of food components to increase or depress gene expression (nutritional transcriptomic effect) may account for some of the observed inconsistencies in the response to food components. Since a host of dietary constituents are recognized to influence postranslational events, these also likely account for at least part of the response variation. Functional proteomic studies that capture all of the proteins produced by a species and link them to physiological significance within the cell will be fundamental to understanding the relationship between dietary interventions, proteome changes and cancer. While a bioactive food component may influence a number of key molecular events that are involved with cancer prevention, to do so it must achieve an effective concentration within the target site, be in the correct metabolic form and lead to changes in small molecular weight signals in the cellular milleau (metabolomic effects). Elemental to assessing and evaluating the significance of the interrelationships among a dietary component with nutrigenetics, nutritional epigenomics, nutritional transcriptomics, proteomics and metabolomics is knowledge about the appropriate tissue/cell or surrogate to monitor. As the era of molecular nutrition unfolds, a greater understanding of how foods and components influence cancer will surely arise. Such information will be critical in the development of effective delivery of tailored approaches to reduce the cancer burden. As this information unfolds it is crit-

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ical that it is utilized within a responsible bioethical framework.

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