Nutrigenomics Opportunities in Asia: 1st ILSI International Conference on Nutrigenomics, Singapore, December 2005 (Forum of Nutrition)

Nutrigenomics – Opportunities in Asia

Forum of Nutrition Vol. 60

Series Editor

Ibrahim Elmadfa, Vienna

Nutrigenomics – Opportunities in Asia

Volume Editors

E. Shyong Tai, Singapore Peter J. Gillies, Newark, Del.

26 figures, 1 in color, and 10 tables, 2007

Basel · Freiburg · Paris · London · New York · Bangalore · Bangkok · Singapore · Tokyo · Sydney

Dr. E. Shyong Tai

Dr. Peter J. Gillies

Department of Endocrinology Singapore General Hospital Singapore

DuPont Haskell Laboratory for Health and Environmental Sciences Newark, Del. (USA)

Library of Congress Cataloging-in-Publication Data ILSI International Conference on Nutrigenomics (1st: 2005: Singapore) Nutrigenomics : opportunities in Asia / volume editors, E.S. Tai, P.J. Gillies. p. ; cm. – (Forum of nutrition, ISSN 1660–0347 ; v. 60) Includes bibliographical references and indexes. ISBN-13: 978–3–8055–8216–2 (hard cover : alk. paper) 1. Nutrition–Genetic aspects–Congresses. 2. Nutrition–Asia–Congresses. 3. Genomics–Asia–Congresses. I. Tai, E.S. (E. Shyong) II. Gillies, P. J. (Peter J.) III. Title. IV. Series. [DNLM: 1. Genomics–Asia–Congresses. 2. Nutrition Index Physiology–genetics–Asia–Congresses. 3. Nutritional Sciences–Asia–Congresses. W1 BI422 v.60 2007 / QU 145 I29n 2007] QP144.G45I47 2005 612.3–dc22 2007012335

Bibliographic Indices. This publication is listed in bibliographic services, including Current Contents® and Index Medicus. Disclaimer. The statements, options and data contained in this publication are solely those of the individual authors and contributors and not of the publisher and the editor(s). The appearance of advertisements in the book is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements. Drug Dosage. The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any change in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug. All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher. © Copyright 2007 by S. Karger AG, P.O. Box, CH–4009 Basel (Switzerland) and ILSI Southeast Asia Region, Singapore www.karger.com Printed on acid-free paper ISSN 1660–0347 ISBN 978–3–8055–8216–2

This book is dedicated to the international community of scientists who believe in the promise of nutrigenomics!

Contents

XI Preface Tai, E.S. (Singapore); Gillies, P.J. (Newark, Del.) Concepts and Methods in Nutrigenomics

1 Nutrition in the ‘Omics’ Era Milner, J.A. (Rockville, Md.)

25 Nutrigenetics El-Sohemy, A. (Toronto, Ont.)

31 Epigenomics and Nutrition Cobiac, L. (Adelaide)

42 Early Nutrition: Impact on Epigenetics Mathers, J.C. (Newcastle)

49 Nutrition and Genome Health Fenech, M. (Adelaide)

66 Nutrition: Ethics and Social Implications Slamet-Loedin, I.H.; Jenie, U.A. (Jakarta)

80 Proteomics Thongboonkerd, V. (Bangkok)

91 Diet and Genomic Stability Young, G.P. (Adelaide)

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97 High-Throughput Genotyping Lee, J.-E. (Seoul) Nutrigenomics and Health

102 Nutrient-Gene Interactions in Lipoprotein Metabolism – An Overview Ordovas, J.M. (Boston, Mass.); Corella, D. (Boston, Mass./Valencia); Kaput, J. (Chicago, Ill.)

110 The Genetics of Lipoprotein Metabolism and Heart Disease Tai, E.S. (Singapore)

118 Gene-Environment Interactions and the Diabetes Epidemic in India Mohan, V.; Sudha, V.; Radhika, G.; Radha, V.; Rema, M.; Deepa, R. (Chennai)

127 Gene Expression in Low Glycemic Index Diet – Impact on Metabolic Control Takeda, E.; Arai, H.; Muto, K.; Matsuo, K.; Sakuma, M.; Fukaya, M.; Yamanaka-Okumura, H.; Yamamoto, H.; Taketani, Y. (Tokushima)

140 Genetic Polymorphisms in Folate-Metabolizing Enzymes and Risk of Gastroesophageal Cancers: A Potential Nutrient-Gene Interaction in Cancer Development Lin, D.; Li, H.; Tan, W.; Miao, X.; Wang, L. (Beijing)

146 Dietary Quercetin Inhibits Proliferation of Lung Carcinoma Cells Hung, H. (Singapore)

158 Osteoporosis: The Role of Genetics and the Environment Ongphiphadhanakul, B. (Bangkok)

168 Application of Nutrigenomics in Eye Health Delcourt, C. (Bordeaux) Nutrigenomics – Applications to the Food Industry

176 Nutrigenomics of Taste – Impact on Food Preferences and Food Production El-Sohemy, A.; Stewart, L.; Khataan, N.; Fontaine-Bisson, B.; Kwong, P.; Ozsungur, S.; Cornelis, M.C. (Toronto, Ont.)

183 Prospects for Improving the Nutritional Quality of Dairy and Meat Products Coffey, S.G. (St. Lucia)

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196 Functionality of Probiotics – Potential for Product Development Dekker, J.; Collett, M.; Prasad, J.; Gopal, P. (Palmerston North) Conclusion

209 Developing the Promise of Nutrigenomics through Complete Science and International Collaborations Kaput, J. (Chicago, Ill./Davis, Calif.) Executive Summary

224 ILSI’s First International Conference on Nutrigenomics: Opportunities in Asia Florentino, R.F. (Metro Manila)

242 Author Index 243 Subject Index

Contents

IX

Preface

Nutrition plays an important role in optimizing human health and managing disease. Unfortunately, the human response to diet is so incredibly variable that nutritional counseling beyond that of general advice is a complex and challenging task. Nutrigenomics seeks to understand the variability of the individual’s response to food and the underlying mechanisms whereby foods exert their health-promoting activities. The promise of nutrigenomics is that with a deeper molecular understanding of nutrition we may some day be able to design diets that truly maximize an individual’s potential for health and wellness. Asia is home to two thirds of the world’s population. Many societies within Asia are undergoing rapid socioeconomic development and are experiencing an attendant transition in diet-related morbidity and mortality. Paradoxically, the problem of under- and overnutrition coexists in Asia. This, combined with the tremendous diversity in diet, dietary intake patterns, local culture, and nutritional needs, makes the identification and provision of an optimal diet relevant to all the people living in Asia an extraordinary challenge. This same diversity, however, provides opportunities to ask and answer scientific questions which cannot be investigated elsewhere in the world. Recognizing the special nutrition science research opportunities afforded in Asia, the International Life Sciences Institute (ILSI) hosted an exciting 3-day meeting in Singapore on December 7–9, 2005. This conference enjoyed the support and guidance of the Commonwealth Scientific and Industrial Research Organization of Australia, the National Institutes of Health in the United States, and the Genome Institute of Singapore. The first ILSI international conference on nutrigenomics, with a focus on opportunities in Asia, was an international

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gathering of scientists from the academia, government and industry that attracted speakers and attendees from around the world with everyone coming to share their experience and knowledge in the area of nutrigenomics. This book is a culmination of the efforts of all those who organized and participated in this conference. The book includes an elegant and articulate summary of the conference that Rodolfo Florentino was kind enough to provide and closes with an invited article by Jim Kaput that provides a road map for international collaboration in nutrigenomics. The core of the book starts off with concepts and methods in nutrigenomics designed to give those interested in this field a general overview; this is followed by specific examples of the applications of these concepts and methods to specific disease states. Unfortunately, it was not possible to include all the presentations from the meeting. Respectful apologies are offered to those speakers and presenters whose work could not be included, but without whose participation the meeting could not have been such a success! For those of you who were able to attend the meeting we hope this book reinforces your memories of the exciting science and collegiality of the conference; for everyone else we hope the book encourages you to engage in nutrigenomic research and to attend the next ILSI conference on nutrigenomics. Dr. E. Shyong Tai, Singapore Dr. Peter J. Gillies, Newark, Del.

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Concepts and Methods in Nutrigenomics Tai ES, Gillies PJ (eds): Nutrigenomics – Opportunities in Asia. Forum Nutr. Basel, Karger, 2007, vol 60, pp 1–24

Nutrition in the ‘Omics’ Era J.A. Milner Nutritional Science Research Group, Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Rockville, Md., USA

Abstract Consumers throughout the world are increasingly questioning the quality and safety of their diets and how the foods they eat are influencing their health. Much of this interest stems from mounting evidence that bioactive food components cannot only influence one’s ability to achieve one’s genetic potential, but can also have a significant influence on the quality of life as measured by both physical and cognitive performance, and modify the risk and/or severity of a variety of disease conditions. During the past century, a wealth of evidence has pointed to dietary habits as a determinant of premature death, including that associated with heart disease, stroke, diabetes, liver disease, atherosclerosis and cancer, although considerable variability in response is evident. Several factors may account for these discrepancies including individual variability due to genetic and epigenetic regulation of cellular proteins and associated small-molecular-weight compounds. This interrelationship between the food components and the ‘omics’ (genomics, epigenomics, transcriptomics, proteomics and metabolomics) will be briefly reviewed as a factor contributing to the variability among studies. Expanded knowledge about these omics interrelationships will not only define the molecular target for food components but will also assist in identifying those individuals who are likely to respond maximally. Copyright © 2007 S. Karger AG, Basel

Belief in the medicinal powers of foods and their components is not a new concept, but has been handed down for generations. Historically, Hippocrates is often quoted as suggesting almost 2,500 years ago to ‘let food be thy medicine and medicine be thy food’. Today, consumers throughout the world are bombarded by a host of health messages about the benefits of foods and/or food components for promoting health and/or reducing the risk of a variety of chronic diseases. Undeniably, evidence continues to mount that the use of foods and/or dietary supplements can assist in achieving one’s ‘genetic potential’,

improve physical and cognitive performance, and reduce chronic diseases [1]. It is logical that such attributes could have profound societal and economic consequences by not only reducing premature deaths but also by reducing overall health care cost. The societal implications are reflected in a 2005 report by the World Health Organization which suggested that at least 80% of premature heart disease, stroke and type 2 diabetes, and 40% of cancer in Southeast Asia could be prevented through a healthy diet, regular physical exercise and avoiding tobacco products [2]. In this same report, it was estimated that a 2% annual reduction in deaths due to chronic disease might save over 8 million lives during the next 10 years. Within India alone, it was estimated this reduction in mortality could result in an economic gain of USD 15 billion during the upcoming decade [2]. While the richness of the scientific literature makes it difficult to discount an involvement of dietary habits in health promotion, it is less obvious who might benefit most, which foods are most important, the circumstances which dictate benefits or risk, and if anyone might be placed at risk by dietary change. These along with other issues are of paramount importance in moving the science of nutrition forward. Unquestionably, it will not be simple to define the conditions and circumstance(s) for achieving the greatest benefit from foods or their isolated components. Nevertheless, the accumulating science makes it believable that such a personalized approach is feasible. Inherent to this concept is that individuals will vary in their response to a food, food component or dietary pattern. Thus, the overriding assumption of this concept is that unique preemptive strategies exist for the use of diet to retard or reverse the progression of a disease which are dependent on the genetics and lifestyle of the consumer. This article is devoted to providing basic principles about how increasing knowledge about genomics can assist with the unraveling of inconsistencies in the scientific literature about the importance of the diet in health promotion and disease prevention, and for predicting who will benefit most or be placed at risk by dietary change.

Controversies Involving Nutrition and Health

Several recent meta-analyses illustrate the mixed messages that can surface about what role, if any, diet has in health promotion [3–6]. While these findings frequently reflect disagreements in interpretation among scientists, they also lead to confusion among consumers and can erode the trust that they have for the scientific enterprise. Thus, greater attention needs to be given to the totality of the information rather than to individual studies in defining the health significance of the diet. It would be truly disappointing if a simple summation of evidence-based nutrition studies became the ‘gold standard’ since the

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majority of case-control and cohort studies are not simple repeats of previous undertaking and thus vary enormously in experimental design, tested populations, and outcome measures; all of this can surely influence overall interpretations. Unfortunately, conclusions based on a meta-analysis may even depend on the method the authors used to select trials for inclusion in the analysis. Thus, summaries of evidence which do not consider the biological response, plausibility and consequences are doomed to create more confusion than they resolve. What is increasingly clear is that inadequate long-term intervention studies exist for making definitive conclusions about who will benefit and who might be placed at risk by dietary change. Indisputably, well-controlled long-term intervention studies which incorporate the newest technologies hold the greatest promise for unraveling the complex interplay between diet and health. Future clinical studies must incorporate genomics in the study design, and not just use it as an analytic approach to confounders to data interpretation. Considerable preclinical evidence linking diet to health outcomes centers on the response to a single bioactive component as a modulator of a key cellular process or series of critical processes [7, 8]. Both in vitro and in vivo studies suggest that multiple targets are likely responsible for the phenotypic response to foods and/or dietary supplements [8, 9]. These targets may be involved in cell division, inflammation, apoptosis, compound bioactivation or a host of other biological processes which influence the phenotype. Focusing on a process which can be modified by one or more bioactive food components will help with a systematic approach to understanding the role of diet in health promotion. However, in some cases, research suggests that whole foods may offer advantages over isolated components, possibly indicating that multiple food components or multiple targets are needed to bring about a desired effect. Nutrient-nutrient, as well as nutrient-drug interactions can be significant determinants of the overall phenotypic response. For example, the ability of n–3 fatty acids to increase the sensitivity to anthracyclines is dependent on vitamin E intake [10] or the benefits of calcium are generally dependent on the intake of vitamin D [11]. While not as frequently examined, negative interactions among food components or nutrient-drug interactions are also possible and such lines of investigation deserve added attention to assist with the identification of potentially vulnerable individuals. Since the quantity of exposure can markedly influence the outcome, it is imperative that nutrition studies use physiologically relevant concentrations and consider the totality of the diet as a factor influencing the overall response [7, 8]. Sadly, multiple exposures in humans are hampered by the availability of definitive biomarkers that reflect a long-term health outcome. Finally, it is worth noting that studies which examine the impact of dietary change through more than one phase of life are exceedingly rare, yet are desperately needed if

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Inactive metabolite Absorbed dose

Biologically effective dose

Susceptibility factors Dietary exposure

Health consequences ‘positive or negative’

Molecular target

Early biologic effect

Altered structure/function

Fig. 1. Three types of biomarkers are needed to evaluate the response to foods and dietary supplements.

sense is to be made out of the diet-health conundrum and the opportune time for intervention to bring about a desired change. A narrow and simplistic view of dietary patterns is obviously problematic and may contribute to misconceptions and thus the confusion that exist today about the importance of functional foods and health promotion and disease prevention. Clearly, predicative biomarkers which can evaluate long-term consequences are fundamental to resolving dietary issues which cannot be addressed for practical or ethical reasons. At present, few validated biomarkers are available for assessing the impact of diet in health promotion. Similar to environmental toxicity research, it is likely that at least 3 different types of biomarkers will be needed to assess the impact of diet (fig. 1) [12]. Foremost among these is the need to accurately identify exposures to foods and their components. Obviously, if the effective concentration does not reach the target tissue, there is little hope that it will be effective in bringing about a desired effect. Likewise, sensitive and reliable biomarkers for identifying the impact of bioactive food components are in short supply. These ‘effect’ biomarkers should provide sensitive and time-/dose-dependent information about how the food component(s) modifies/modify one or more specific cellular processes [6–8]. Assuming this/ these molecular target(s) can be analyzed in the affected or surrogate tissue, it can ideally be an effective biomarker for assessing the response to physiologically relevant exposures to foods or their components. Finally, it is clear that we

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need to assess susceptibility factors including genetic and epigenetic markers which can reflect an individual’s responsiveness to the biological response to a food, food patterns or dietary supplements. The use of such information may assist with improving the usefulness of standard 24-hour recall and food frequency questionnaires by developing predictive models that take into account genetic factors influencing absorption, metabolism and excretion. These susceptibility biomarkers (fig. 1) will again provide important clues about responders, both positive and negative, in the molecular target to diet-induced phenotypic change.

Health Implication of Individual Genomic Variability

The human genome is a complicated blueprint of information. While all DNA has four relatively simple bases (adenine, guanine, cytosine, and thymidine), their sequence can have a pronounced effect on what ultimately evolves. The nearly 3 billion base pairs (3.2 Gb) in the human genome constitute what is sometimes affectionately called the ‘genome encyclopedia’. If a gene is analogous to a word, then a chromosome must be a chapter and the genome the whole book. Similar to a word, a gene may have a single or multiple meanings, and can be influenced by the context in which it is expressed. Like a chapter, a chromosome is a large collection of genes organized into a linear string of information. The complete set of chapters is necessary to form the ‘book’ of information that comprises the genetic blueprint of each and every organism. The size of this blueprint is illustrated by assuming each DNA basis creates a series of words each of which contain 5 characters. Thus, about 600 million words could be generated from the human genome. If these words were compiled to an average of 12 words per line then an equivalent of about 50,000 text lines would be generated. Since an average page only has about 70 lines, this would mean the human genome would contribute about 700,000 pages. If these pages were assembled into an encyclopedia with 1,000 pages in each volume, there would be about 700 volumes for late-night reading and enjoyment! Even this analogy is overly simplistic, since it does not take into consideration genetics, epigenetics, proteomics and metabolomics variations that occur within and among individuals. Human genetic predictions are exceedingly complicated by the presence of comparatively long and variable intron sequences [13]. These intron sequences (noncoding DNA regions) interrupt the sequences containing instructions for making a protein (exons). The panoramic views of the human genome have already begun to reveal a wealth of information and some early surprises. While much remains to be deciphered in this vast information source, several fundamental principles have emerged. It is safe to conclude

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Essential and nonessential food components

Transcription factor

Nucleus

DNA target

Gene

Protein Change mRNA (Change in activity or abundance) Biological response in cell process(es)

Fig. 2. Relationship between dietary components and genomic regulation.

that the more we learn about the human genome, the more there will be to learn. Interestingly, the coding region of a gene (exons) which is the portion of DNA that is transcribed into mRNA and translated into proteins only constitutes about 1.5% of the human genome [14]. Furthermore, transcription units, consisting of exons, introns, and the regulatory region, constitute about 20% of the entire human genome. One must wonder if the remainder is simply a filler or has a yet to be defined role. Evidence already exists that multiple gene messages can be derived from a single stretch of DNA based on alternative uses of promoters, exons, and termination sites. Adding to these overlapping transcription units, somatic recombination events and the existence of highly similar gene families and pseudogenes make it difficult to identify and categorize genes. Regardless, the fundamental premise of genomics is that DNA reading results in the formation of messenger RNA which then codes from proteins which ultimately bring about changes in small-molecular-weight compounds and in cellular processes (fig. 2). It is already known that human genome variability can arise for several reasons including single nucleotide changes (polymorphisms), deletions, insertions, and translocations. Translocations and gross deletions are important causes of both cancer and inherited disease. Such gene rearrangements are nonrandomly distributed in the human genome as a consequence of selection for growth advantage and/or the inherent potential of some DNA sequences to be frequently involved in breakage and recombination. Alu insertional elements, the most abundant class of short interspersed nucleotide elements in humans, are dimeric sequences approximately 300 bp in length derived from the 7SL RNA gene. About 500,000 to 1 ⫻ 106 Alu units are dispersed throughout the

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human haploid genome primarily in AT-rich neighborhoods located within larger GC dense chromosomal regions. These sequences contain a bipartite RNA polymerase III promoter, a central poly-A tract, a 3⬘ poly-A tail, numerous CpG islands and are bracketed by short direct repeats. Such insertions are associated with a number of disease states [15]. Restriction fragment length polymorphisms, short tandem repeats, and variable-number tandem repeats are also present in the genome. Intraspecies variation in the length of DNA fragments generated by the action of restriction enzymes or caused by mutations that alter the sites at which these enzymes act can change the length, number, or production of fragments. Restriction fragment length polymorphism is a term used in two related contexts: as a characteristic of DNA molecules (arising from their differing nucleotide sequences) by which they may be distinguished, and as the laboratory technique which uses this characteristic to compare DNA molecules. The short tandem repeats are tandemly repeated DNA sequences of a pattern of length from 2 to 10 bp [for example (CA)n(TG)n in a genomics region] and the total size is lower than 100 bp. Repeated sequences represent a large part of eukaryotic genomes. Single nucleotide polymorphisms (SNPs) are the most common DNA sequence variation. They occur when a single nucleotide in the genome is altered. A variation in the incidence must occur in at least 1% of the population to be considered an SNP. Huntington’s disease, cystic fibrosis, and muscular dystrophy are examples of diseases which are linked to a single gene polymorphism [16]. While we have known about the genetics of these diseases for a number of years, reliable and effective therapies have remained largely elusive. Cancer and possibly several other chronic diseases are likely a result of multiple genetic shifts and thus present an even more daunting task for understanding the disease but are important for developing strategies for prevention and/or therapy. Fortunately, the majority of SNPs do not appear to cause disease; however, they may assist in determining the likelihood that a particular abnormality may occur [17]. Nevertheless, some SNPs have been linked to an increased disease risk. For example, a gene associated with Alzheimer’s disease is apolipoprotein E. This gene can contain two SNPs which may result in three possible alleles: E2, E3, and E4. Each allele differs by one DNA base, and the protein product of each gene differs by one amino acid. Typically an individual inherits one maternal and one paternal copy of a gene. Research has shown that an individual who inherits at least one E4 allele has a greater risk of developing Alzheimer’s disease, presumably as a result of the one amino acid substation in the E4 protein which influences its structure and function. Inheriting the E2 allele, on the other hand, appears to protect against Alzheimer’s disease. Of course, SNPs are not absolute since those inheriting two E4 alleles do not always develop Alzheimer’s

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disease [18]. Thus, other factors or events, including the environment (diet), may affect the disease risk. It is certainly possible that either internal or external stressors set the stage for when bioactive food components are most effective. Thus, expanded knowledge about genetic and environmental interactions is fundamental to unraveling global variation in the incidence and/or severity of several disease states. Evidence is already surfacing that genetic variation can influence the propensity for the initiating event, the progression to a clinical disease state, and the trajectory of several diseases. For example, the interleukin 1 family of cytokines has a critical role in mediating inflammation, which is considered a factor in many chronic diseases, including coronary artery disease, rheumatoid arthritis and cancer. Recent research has identified several sequence variations in the regulatory DNA of the genes coding for important members of the interleukin 1 family, and these variations are associated with differential effects on the inflammatory response [17]. While inconclusive, evidence is beginning to surface that the physiological relevance of such genetic variation can be modified by the foods that are consumed.

Nutrigenomics and Health

Nutrigenomics represents the dynamic interface between nutrition and genomic-regulated processes [18, 19]. A set of fundamental principles exist which underpins the nutrigenomics concept. The first is that the genotype can influence the ability of a food component to influence cellular processes associated with health and/or disease. Second, numerous dietary components are capable of influencing, singly or in combination, the gene expression patterns involved in multiple cellular processes. Third, the observed cellular response is dependent on the amount and duration of exposure to a specific or blend of food components. Finally, the ability of a bioactive food component to influence cellular processes will depend, in some cases, on the stage of the life cycle. Collectively, nutrigenomics embodies the interrelationships occurring among variation in DNA base sequences, epigenetics events and transcriptomics. Such interactions may influence not only the magnitude, but sometimes the direction, of the response to specific bioactive food components [6, 19–22]. Inappropriate dietary habits may tip the scale from a healthy condition to a state of disease progression. Thus, appropriate dietary intake of food components is fundamental to regulating normal physiological processes, as well as the squelching of potential pathologic conditions. The scientific literature already provides evidence that the response to food components can vary from tissue to tissue, as well as a function of the time and duration of intervention [23–25]. Undeniably, the capturing of this genomic-diet information is critical to the identification of

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Bioactive food component(s)

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

Nutrigenetics

DNA

Nutritional epigenetics RNA Nutritional transcriptomics

Phenotype

Protein Proteomics Metabolomics

Metabolite

Fig. 3. The influence of nutrigenetics, epigenetics, transcriptomics, proteomics, and metabolomics on the phenotypic response to food components.

those individuals who will benefit from intervention strategies and those who might be placed at risk because of a dietary change. The incorporation of this information will allow for nutritional preemption strategies which utilize foods or their components to enhance normal processes and/or to retard or reverse cellular events that lead to aberrant conditions including those associated with chronic disease. The 30,000 genes in the human genome are responsible for more than 100,000 functionally distinct proteins, and likely 3–5 times that number of small-molecular-weight cellular constituents (such as metabolic intermediates, hormones and other signaling molecules, and secondary metabolites) which collectively can enhance or suppress a number of physiological processes. Understanding how foods and their components influence each step in the cascade of events leading to a phenotype (fig. 3) is a daunting task, but holds great promise in helping improve the quality of life and reduce the risk of diseases.

Numerous Bioactive Components Are Involved

A host of dietary components, encompassing both essential and nonessential constituents, are reported to influence both genetic and epigenetic processes. Phytochemicals arising from plants, along with zoochemicals occurring in animal products, fungochemicals from mushrooms, and bacterochemicals from

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bacteria may be physiologically relevant modifiers of health. Compounds encompassing such diverse categories as minerals, amino acids, carbohydrates, fatty acids, carotenoids, dithiolthiones, flavonoids, glucosinolates, isothiocyanates, and allyl sulfurs may influence multiple pathways associated with growth, development and disease resistance [1, 6–8]. Some have estimated that typical diets may contain more than 25,000 bioactive food components. If this is the case, there will likely be many additional compounds which will be identified with physiological relevance. Future research must focus on the effective dose required to bring about a response and when during the life span the maximum response is achievable. Likewise, it will be important to tease apart the effective intake of multiple foods where active components are influencing a common molecular target. Is it possible that consumption of one food can mask the protection provided by another and possibly account for the confusion in findings which attempt to relate a food to a specific health condition?

DNA Polymorphisms Influence Response to Food Components

The metabolism of folate serves as an appropriate example of nutrigenetics or how genetic polymorphisms may cause individual responsiveness to the diet. Folate is recognized to be an important factor in DNA synthesis, stability, and integrity. Its availability can also modulate DNA methylation, which is an important epigenetic determinant of gene expression (an inverse relationship), and the maintenance of DNA integrity and stability. Several SNPs in the folate metabolic pathway have been identified and characterized [26]. The complexity of understanding the importance of SNPs in folate homeostasis comes from their involvement in folate absorption (glutamate carboxypeptidase II), intracellular folate uptake (folate receptors and reduced folate carriers), intracellular folate retention (folylpolyglutamate synthetase) and catabolism and efflux (glutamyl hydrolase), methionine cycle (methionine synthase, methionine synthase reductase), maintenance of the intracellular folate pool (dihydrofolate reductase, serine hydroxymethyltransferase), and nucleotide biosynthesis (thymidylate synthase) [27]. Nevertheless, one or more of these SNPs may influence the requirements for this vitamin B and thereby modify its regulatory effects on epigenetic processes. While much remains to be learned about the functional ramifications of these SNPs, there is evidence that its occurrence in 5,10-methylenetetrahydrofolate reductase may increase disease risk, including cardiovascular disease and certain kinds of birth defects [28]. This variant is relatively common worldwide and is characterized by a replacement of the nucleotide cytosine with thymine at position 677 in the 5,10-methylenetetrahydrofolate reductase gene. This change leads the 5,10-methylenetetrahydrofolate reductase

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gene to produce a form of methylenetetrahydrofolate reductase which has reduced activity at higher temperatures (thermolabile). Individuals with the thermolabile form of the enzyme have increased levels of blood homocysteine, a biomarker which has been identified as a risk factor for heart disease. While low serum or red cell folate does not appear to be a requirement for neural tube defects in humans, it is possible that the response to folate fortification reflects tissue variability in the uptake and/or utilization of this vitamin B [29]. An expanded mechanistic understanding of the role of SNP-regulated folate status in both DNA methylation and DNA stability warrants greater attention [27–30]. Another nutrigenetic example of a dietary link with SNPs arises from studies on the intake of calcium and vitamin D associated with cancer risk. Decreased dietary exposures to calcium and vitamin D have been suggested as risk factors for human colon cancer. Feeding a Western-style diet containing reduced calcium, vitamin D and increased fat to an animal model of familial adenomatous polyposis, a common genetic alteration in colon cancer [31], decreased survival compared to an ideal semipurified diet. This effect was magnified when the p21 gene, which is an important regulator of the cell cycle, was inactivated [32]. More recently, this group has found that feeding this diet to normal C57/Bl6 mice led to hyperproliferation, hyperplasia and whole-crypt dysplasias in the colon [33]. Further modification of folic acid, methionine, choline and vitamin B12 content of the diet was found to result in adenoma and carcinoma development in normal mouse colon [34]. The results indicate that a semipurified rodent diet designed to mimic the human Western diet can induce colonic tumors in normal mice without carcinogen exposure. Likewise, these studies clearly demonstrate the modifying role that genes [Apc, p21, WAF1/ cip1, and p27 (Kip1)] can have in influencing the magnitude of the tumor problem. During the past few years, vitamin D has received increasing attention as a possible deterrent to cancer. At least part of the response to vitamin D may depend on the vitamin D receptor (VDR) and the downstream events that it influences. The VDR, which is known to dimerize with the retinoid X receptor, binds to 1␣,25(OH)2D3 promoter sites to regulate the transcription of many genes in more than 30 tissues. When bound to the hormonally active form of vitamin D, 1,25(OH)2D3, VDR transactivates genes that inhibit proliferation, or promote differentiation and apoptosis [35–37]. VDR has also been shown to bind carcinogenic bile acids and thereby transactivates the CYP3A gene. The CYP3A gene product mediates bile acid degradation, thus anticarcinogenic effects of VDR might also be mediated through bile acid ligands [38]. Whatever the ligand VDR, transactivation efficiency may be influenced by a start codon polymorphism (FokI) which affects the length of the N-terminal VDR transactivation domain. The FokI f allelle results in a VDR protein that is three amino

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acids longer than the protein produced from the F allele [39], and the latter has been shown to be more efficient in transactivating target genes at least in some studies. In a case-control study nested within a large cohort of Singapore Chinese it was noted that individuals carrying the FF FokI genotype had a 51% increased risk of colorectal cancer compared with the Ff genotype and a 84% increase compared to those with the ff genotype [40]. The risk appeared to be modified by both dietary calcium and fat. Among those with a low calcium or low fat intake (below the median values in controls), the risk for colorectal cancer increased in a gene-dose-dependent manner such that individuals possessing the ff genotype displayed an approximately 2.5-fold increased risk. There was little evidence of a VDR genotype-colorectal cancer association among subjects with higher than median values of either dietary fat or calcium. Polymorphisms in manganese superoxide dismutase (MnSOD) is another example of how genetics can be modified by the foods consumed. This enzyme is essential for life as evident by the neonatal lethality in knockout mice. Mice expressing only 50% of the normal complement of MnSOD demonstrate increased susceptibility to oxidative stress and severe mitochondrial dysfunction resulting from elevation of reactive oxygen species. Numerous studies have shown that MnSOD can be induced to protect against pro-oxidant insults resulting from cytokine treatment, ultraviolet light, irradiation, certain tumors, amyotrophic lateral sclerosis, and ischemia/reperfusion. In addition, overexpression of MnSOD has been shown to protect against proapoptotic stimuli as well as ischemic damage. Conversely, several studies have reported declines in MnSOD activity during diseases including cancer, aging, progeria, asthma, and transplant rejection. The molecular mechanisms involved in this loss in activity are not well understood. Certainly, MnSOD gene expression or other defects could play a role in such inactivation. Based on recent studies of the susceptibility of MnSOD to oxidative inactivation, it is also likely that post-translational modification may be an important determinant of the response to dietary components [41]. A polymorphism in MnSOD [valine (V) to alanine (A)] has been reported to be associated with increased prostate cancer risk. This polymorphism becomes a significantly modified risk when prediagnostic plasma antioxidants are considered [42]. In men with the AA genotype, a high selenium concentration (4th versus 1st quartile) was found to be associated with a relative risk of 0.3 [95% confidence interval (CI) 0.2–0.7]. In contrast, in men with the VV/VA genotype, the relative risks were 0.6 (95% CI 0.4–1.0) and 0.7 (95% CI 0.4–1.2) for total and clinically aggressive prostate cancer. These patterns were similar for lycopene and ␣-tocopherol and were particularly strong when combined with information about selenium status. Thus, nutrigenetic information may provide important clues about those who should be particularly vigilant in assuring adequate antioxidant intake. Collectively, evidence from this and

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several other studies suggests that nutrigenomic information can be useful for identifying those who may benefit most from adequate intake of specific nutrients.

Genotypes and Haplotypes

Slight variations in our DNA sequences can have a major impact on whether or not a disease develops and on the response to environmental factors such as infectious microbes, toxins, and diet consumed. Literally millions of SNPs are known to occur within the human genome making it unlikely that a single base change will be found that is sufficient to account for a number of chronic diseases. Thus, examining commonly linked genomic changes seems to be a reasonable solution. Genetic variants are often inherited together in segments of DNA called haplotypes which are shared by a majority of the human population. Thus, they may be useful in deciphering the genetic differences that make some people more susceptible to disease than others and likewise how diet will impact their susceptibility [43]. The International HapMap Project (http://www.hapmap.org/) may be particularly useful in teasing out genetic differences that determine the response to specific foods and their components. This consortium of scientists from six countries is devoted to constructing a map of the patterns of SNPs that occur across populations in Africa, Asia, and the United States. It is hoped that dramatically decreasing the number of individual SNPs by using haplotyping will provide a shortcut for discovery of the DNA regions associated with common complex diseases such as cancer, heart disease, diabetes, and some forms of mental illness. The new map may be particularly useful in providing clues about how genetic variation can be incorporated into understanding the response to environmental factors, including diet.

Dietary Modulation of Epigenomics

Epigenetics refers to the study of heritable changes in gene expression that occur without a change in DNA sequence. Basically it is the study of heritable changes in gene expression resulting from mitotic (the process in cell division by which the nucleus divides through prophase, metaphase, anaphase, and telophase, normally resulting in two new nuclei, each of which contains a complete copy of the parental chromosomes) and meiotic processes (the process of cell division in sexually reproducing organisms that reduces the number of chromosomes in reproductive cells from diploid to haploid, leading to the production of gametes in animals and spores in plants). Thus, epigenetic mechanisms

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provide an ‘extra’ layer of control in gene expression regulation. These regulation processes are critical components in the normal development and growth of cells. Evidence continues to arise that epigenetic abnormalities are causative factors in cancer, genetic disorders and pediatric syndromes as well as contributing factors in autoimmune diseases and aging. Abnormal methylation patterns are a nearly universal finding in cancer, as changes in DNA methylation have been observed in many cancer tissues (e.g. colon, stomach, uterine cervix, prostate, thyroid, and breast). Site-specific alterations in DNA methylation have also been observed in cancer and are thought to have a significant role in gene regulation and tumor behavior. Hypermethylation is often observed at some 5⬘ gene or promoter regions of neoplastic cells that are largely unmethylated in normal somatic tissues [44]. For many of these genes, this hypermethylation has been linked to transcriptional silencing. Three distinct mechanisms are intricately related to epigenetics: DNA methylation, histone modification and RNA-associated silencing [45]. The observation in human cells that silence RNA can function to suppress gene expression at the level of transcription has created an interesting new paradigm shift in thoughts about mammalian RNA regulation [46]. Because epigenetic events can be changed, they offer another explanation for how environmental factors, including diet, can influence biological processes and phenotypes. Collectively, nutritional epigenomics refers to the ability of dietary components to influence each of these distinct mechanisms of regulation. Both essential and nonessential dietary components have been reported to influence DNA methylation patterns [47]. The effects of these food components can occur at four different sites. First, dietary factors are undeniably important in providing and regulating the supply of methyl groups available for the formation of S-adenosylmethionine, the universal methyl donor. Second, dietary factors may modify the utilization of methyl groups by processes including shifts in DNA methyltransferase activity. A third plausible mechanism relates to DNA demethylation activity. Finally, the DNA methylation patterns themselves may influence the response by regulating genes which influence absorption, metabolism or the site of action for the bioactive food component. The importance of maternal methyl donor supply in the diet has been examined in terms of DNA methylation and methylation-dependent epigenetic phenotypes in the offspring. Evidence in the agouti mouse model that supplementation of choline, betaine, folic acid, vitamin B12, methionine, and zinc to the maternal diet increases the level of DNA methylation in the agouti gene and induces a change in the color pattern of the hair coat is particularly striking [48]. This phenotypic change, along with that caused by genistein, coincides with a lower susceptibility to obesity, diabetes, and cancer [49]. These types of studies suggest that in utero exposure to dietary components may not only

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influence embryonic development but may also have profound and long-term health consequences. A process that regulates chromatin structure is through its attachment to histones. It is increasingly apparent that a diverse array of enzymes modify histones through acetylation, phosphorylation, methylation, and ubiquitylation. Perturbations in histones can influence gene expression patterns. Indeed, aberrant histone acetylation is commonly observed in cancer. In general, histone acetylation leads to chromatin remodeling and a derepression of transcription. Naturally occurring histone deacetylase inhibitors such as butyrate can arise from foods that are consumed or from bacterial fermentation of carbohydrate within the gastrointestinal tract. Likewise, ingestion of bioactive components such as sulforaphane from broccoli or genistein from soy [50, 51] can influence histone deacetylase activity. While histones can be modified in a variety of ways, most current information about the role of diet as a modifier comes from studies involving acetylation. Clearly other types of modification deserve attention.

Diet and Transcriptomics

Genomic and epigenomic shifts do not totally account for the role that dietary factors can have on a person’s phenotype since changes in the rate of transcription of genes (nutritional transcriptomics) can also be exceedingly important [52]. Several bioactive food components have been reported to be important regulators of gene expression patterns both in vitro and in vivo. Vitamins, minerals, and various phytochemicals have been reported to significantly influence gene transcription and translation in a dose- and time-dependent manner. These changes are likely key to the ability of food components to influence one or more biological processes including cellular energetics, cell growth, apoptosis, and differentiation, all of which are important in regulating disease risk and consequences. The development of microarray technology continues to provide a powerful tool for examining potential sites of action of food components. One merit of this approach is that it allows for a genome-wide monitoring of expression for the simultaneous assessment of tens of thousands of genes and their relative expression. While microarray technologies provide an important tool to discover expression changes that are linked to cellular processes, it must be remembered that any response is likely cellular dependent and may vary from health to diseased conditions. Greater attention needs to be given to why shifts in multiple genes are occurring simultaneously. Could it be that these changes are a reflection of mRNA stability, pH, intracellular calcium or some other intracellular signal? Regardless, transcriptomics holds promise to assist with the discovery of sensitive biomarkers.

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Animal models may also provide important clues about the physiological importance of shifts in the expression of specific genes. Knockouts have already been used to identify specific sites of action of bioactive food components such as the nuclear factor E2 p45-related factor 2 (Nrf2) and the Kelch domain-containing partner Keap1 which are modified by sulforaphane [53]. Gene expression profiles from wild-type and Nrf2-deficient mice fed sulforaphane have shown several novel downstream events and thus more clues about the true biological response to this food component. The upregulation of glutathione S-transferase, nicotinamide adenine dinucleotide phosphate:quinone reductase, ␥-glutamylcysteine synthetase, and epoxide hydrolase, occurring because of changes in Nrf2, likely explains the ability of sulforaphane to influence multiple processes including those involving xenobiotic-metabolizing enzymes, antioxidants, and biosynthetic enzymes of the glutathione and glucuronidation conjugation pathways. Nrf2 also appears to provide protection against oxidative stress and influences inflammatory processes, both of which contribute to several disease conditions. Observations that Nrf2-deficient mice are refractory to the protective actions of some food components and drugs highlight the importance of the Keap1-Nrf2-ARE signaling pathway as a potentially important molecular target. Because microarray technologies provide only a single snapshot, overinterpretation of data is certainly possible. Mammals are known to be adaptive to excess exposure to foods or components through shifts in absorption or metabolism. Thus, the quantity and duration of exposure must be considered when evaluating the response in gene expression patterns following exposure to foods or components. Molecular studies have already shown that specific events in cell cycle progression that are modified by energy restriction can rather quickly be reversed by refeeding [54]. Likewise, the ability of diallyl disulfide, a bioactive component in garlic, to retard cell proliferation can be reversed by its removal from incubation media [55]. Again, the ability of a cell or host to adapt will dictate the frequency by which interventions will be needed to bring about a desired effect. Another challenge with microarray analysis is how to analyze the massive amounts of data that are generated. Because the number of genes whose expression can be modified by dietary components may be enormous, a hierarchical cluster analysis might be particularly useful. Although most studies use a 50% change in gene expression patterns as a cutoff point for statistical significance, a shift in mRNA expression in much lower amounts may have physiological significance. As advances in bioinformatics occur, the importance of subtle changes in mRNA expression may help with predicting health and disease risk and identifying responders from nonresponders to diet change. Another new technology is RNA interference, which can be used to stop the expression of a particular gene [56, 57]. This technology has recently been

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used to investigate which genes are involved in explaining the actions of bioactive food components and characteristics of diseases and conditions. The use of RNA interference has allowed for the discovery of genes in the worm model Caenorhabditis elegans which are associated with body fat and leanness [58]. Likewise, this technology has been used to identify sites of action of isothiocyanates from broccoli compounds [59]. These tools will surely help to delineate the roles of various cellular factors in health and diseased conditions [56, 59]. They may also provide a new and exciting foundation for gene-nutrient discovery.

Diet and Proteomics

The constellation of proteins in a cell is referred to as the proteome. Unlike the relatively stable genome, the dynamic proteome changes from minute to minute in response to tens of thousands of intra- and extracellular environmental signals, including ingested nutrients. A protein’s chemistry and behavior are specified by the gene sequence and by the number and identities of other proteins made in the same cell at the same time and with which it associates and reacts. Studies to explore protein structure and activities, known as proteomics, will likely be the focus of much research for decades to come and will help elucidate the molecular basis of health and disease. Proteomics is an integral part and key player in the family of ‘omics’ disciplines as there are genomics (gene analysis), transcriptomics (gene expression analysis) and metabolomics (metabolite profiling). Considering the complexity, dynamics and protein concentration range of any given proteome, proteomics is an exceedingly challenging ‘omics’ discipline and generally requires the most sophisticated analytical approaches. It should also be noted that proteomics does not always correlate with transcriptomics. Several factors may account for this disconnect including alternative splicing producing multiple proteins from a single gene. Post-translational modification (i.e. glycosylation, phosphorylation, oxidation, reduction) may also generate multiple protein products originating from a single gene or a single transcript. These modified proteins can vary tremendously in their biological activities. Furthermore, protein expression inside a cell is not only regulated by transcription of mRNA but also by translation efficiencies and degradation rates. While dietary inadequacy has long been recognized to influence protein synthesis and degradation, recent evidence suggests that the nutritional proteomics areas hold promise in explaining a number of subtle changes brought about by slight shifts in eating behaviors. Preclinical studies have already shown that dietary fish oil, conjugated linoleic acid, or elaidic acid can influence

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lipoprotein metabolism and insulin levels as indicated by changes in proteomics [60]. Overall, this approach identified 65 cytosolic and 8 membrane proteins that were modified by dietary components, many of which were related to lipid and glucose metabolism and to oxidative stress [60]. The importance of these findings is in the merging of proteomics and physiologic measurements which will in the future likely provide key insights into the mechanisms by which dietary components regulate several metabolic processes and ultimately change phenotypes [61]. Several other dietary components have also been shown to modify the proteome through changes in post-translational events. For example, dietary selenium has been shown to alter post-translationally proteins and thus influence a number of metabolic pathways presumably by initially altering extracellular signal-regulated kinase activity [62]. Western blot analysis revealed that the response to selenium was not because of a change in protein content per se, but resulted from an increase in extracellular signal-regulated kinase phosphorylation [63]. Likewise, adding allyl sulfur to cells in culture has been shown to modify the phosphorylation of selected proteins and possibly accounts for the ability of it to block the cell in the G2/M phase of the cell cycle [55]. Thus shifts in phosphorylation may be the result of subtle changes in the activity of both kinases and phosphatases which regulate a host of cellular events. The transitory nature of these proteomic changes may indicate that some food components will be needed at more frequent intervals in the diet to achieve a desired outcome.

Diet and Metabolomics

Metabolomics is the study of the metabolome, which is the entire metabolic content of a cell or organism at a given moment [64, 65]. Metabolomics researchers have generally focused their attention on biofluids, including serum and urine, and have paid far less attention to tissues and/or cells. The use of exfoliated cells in the feces or urine as well as buccal cells may offer unique insights into the specific role that diet has in changing small-molecular-weight components and thereby cellular processes. Recent metabolomics studies have utilized a number of sophisticated analytical tools including nuclear magnetic resonance spectroscopy, mass spectrometry, chromatographic analysis, and metabolic network analysis to estimate cellular metabolic fluxes. Metabolomics has been studied in microorganisms and in plants, but the literature lacks systematic studies with animals or humans [64, 66]. Quantitative lipid metabolome data are beginning to reveal differential effects of dietary fats on cardiac and liver phospholipid metabolism and hold promise for predicting

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the response to other foods and their components [67, 68]. This approach mapped changes in the concentration of lipid metabolites to their biochemical pathways and the impact of drugs on their distribution and metabolism [68]. Because many 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 between individuals. Metabolomic approaches have been used for years to understand the dynamic relationships between plasma amino acid inadequacy and cellular processes. Noguchi et al. [69] 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 adequate and excessive intakes of amino acids. More recently, Shi et al. [70] indicated the merits of using highperformance liquid chromatography separations coupled with coulometric array detectors to detect low-molecular-weight, redox-active compounds that differ between dietary-restricted and ad-libitum-fed states. Thus, metabolomic approaches hold promise to detect subtle differences in the biological consequences of consuming too little or too much of individual food constituents. One study that did use a metabolomic approach found that plasma profiles of healthy premenopausal women before and after consumption of 60 g of soy provided clues about shifts in energetics [71]. Despite the presence of substantial intersubject variability, the metabolomic analysis revealed that soy intervention changed the plasma lipoprotein, amino acid, and carbohydrate profiles suggesting soy-induced alterations in fat, protein, and carbohydrate metabolism. It is certainly possible to expand this approach to allow for the identification of individuals, based on their metabolic abilities, who would benefit from the ingestion of a variety of individual foods and/or specific food patterns. The use of knockout and transgenetic models for defining the importance of metabolomic events will surely provide critical clues about cell regulation as influenced by dietary habits. For example, Griffiths and Stubbs [72] used a mutant cell with a transcription factor for hypoxia-inducible factor 1, a crucial mediator of tumor progression because it upregulates a number of genes involved in the formation and regulation of blood vessels, iron metabolism, glucose and energy metabolism, cellular proliferation, differentiation, apoptosis and matrix metabolism, to detect an upregulation in anabolic synthesis of purine rings required to make adenosine triphosphate. More recently, these scientists [73] have demonstrated the merits of using knockout mice to evaluate proteomic and metabolomic changes during atherogenesis as they relate to immune-inflammatory responses, oxidative stress, and energy metabolism. Thus, metabolomic approaches can help elucidate and establish conclusions about gene-nutrient interactions and the resulting shift in small-molecular-weight

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cellular constituents which bring about physiologically relevant events involved in disease processes.

Summary and Conclusions

Genomics is increasingly recognized as a determinant of the biological consequences of the foods and supplements that are consumed. Each of the omics disciplines offers unique opportunities for understanding how bioactive food components might be used to improve health and decrease the risk of disease. While the complexity of the interrelationships between food components and the omics disciplines cannot be overemphasized, a greater understanding of this dynamic interrelationship will help identify those who will benefit most from dietary change. The future road map for nutrition research must incorporate omics signals for understanding the twists and turns needed for health and the cautions and stops necessary for disease prevention.

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Myzak MC, Karplus PA, Chung FL, Dashwood RH: A novel mechanism of chemoprotection by sulforaphane: inhibition of histone deacetylase. Cancer Res 2004;64:5767–5774. Ye R, Goodarzi AA, Kurz EU, Saito S, Higashimoto Y, Lavin MF, Appella E, Anderson CW, LeesMiller SP: The isoflavonoids genistein and quercetin activate different stress signaling pathways as shown by analysis of site-specific phosphorylation of ATM, p53 and histone H2AX. DNA Repair (Amst) 2004;3:235–244. Davis CD, Hord NG: Nutritional ‘omics’ technologies for elucidating the role(s) of bioactive food components in colon cancer prevention. J Nutr 2005;135:2694–2697. Yu X, Kensler T: Nrf2 as a target for cancer chemoprevention. Mutat Res 2005;591:93–102. Hursting SD, Lavigne JA, Berrigan D, Donehower LA, Davis BJ, Phang JM, Barrett JC, Perkins SN: Diet-gene interactions in p53-deficient mice: insulin-like growth factor-1 as a mechanistic target. J Nutr 2004;134:2482S–2486S. Knowles LM, Milner JA: Diallyl disulfide induces ERK phosphorylation and alters gene expression profiles in human colon tumor cells. J Nutr 2003;133:2901–2906. Behlke MA: Progress towards in vivo use of siRNAs. Mol Ther 2006;13:644–670. Gartel AL, Kandel ES: RNA interference in cancer. Biomol Eng 2006;23:17–34. Ashrafi K, Chang FY, Watts JL, Fraser AG, Kamath RS, Ahringer J, Ruvkun G: Genome-wide RNAi analysis of Caenorhabditis elegans fat regulatory genes. Nature 2003;421:268–272. Singh SV, Herman-Antosiewicz A, Singh AV, Lew KL, Srivastava SK, Kamath R, Brown KD, Zhang L, Baskaran R: Sulforaphane-induced G2/M phase cell cycle arrest involves checkpoint kinase 2-mediated phosphorylation of cell division cycle 25C. J Biol Chem 2004;279: 25813–25822. de Roos B, Duivenvoorden I, Rucklidge G, Reid M, Ross K, Lamers RJ, Voshol PJ, Havekes LM, Teusink B: Response of apolipoprotein E*3-Leiden transgenic mice to dietary fatty acids: combining liver proteomics with physiological data. FASEB J 2005;19:813–815. de Roos B, Rucklidge G, Reid M, Ross K, Duncan G, Navarro MA, Arbones-Mainar JM, Guzman-Garcia MA, Osada J, Browne J, Loscher CE, Roche HM: Divergent mechanisms of cis9, trans11 and trans10, cis12-conjugated linoleic acid affecting insulin resistance and inflammation in apolipoprotein E knockout mice: a proteomics approach. FASEB J 2005;19: 1746–1748. Hu H, Jiang C, Li G, Lu J: PKB/AKT and ERK regulation of caspase-mediated apoptosis by methylseleninic acid in LNCaP prostate cancer cells. Carcinogenesis 2005;26:1374–1381. Adams A: Metabolomics: small molecule ‘omics’. Scientist 2003;18:38–40. Watkins SM, German JB: Toward the implementation of metabolomic assessments of human health and nutrition. Curr Opin Biotechnol 2002;13:512–516. German JB, Bauman DE, Burrin DG, Failla ML, Freake HC, King JC, Klein S, Milner JA, Pelto GH, Rasmussen KM, Zeisel SH: Metabolomics in the opening decade of the 21st century: building the roads to individualized health. J Nutr 2004;134:2729–2732. Fiehn O: Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks. Comp Funct Genom 2001;2:155–168. Watkins SM, Lin TY, Davis RM, Ching JR, DePeters EJ, Halpern GM, Walzem RL, German JB: Unique phospholipid metabolism in mouse heart in response to dietary docosahexaenoic or ␣-linolenic acids. Lipids 2001;36:247–254. Fitzgerald DA: Drug discovery: lipid profiling for studying the metabolome. Genet Eng News 2001;21:32–36. Noguchi Y, Sakai R, Kimura T: Metabolomics and its potential for assessment of adequacy and safety of amino acid intake. J Nutr 2003;133(suppl 1):2097S–2100S. Shi H, Vigneau-Callahan KE, Shestopalov AI, Milbury PE, Matson WR, Kristal BS: Characterization of diet-dependent metabolic serotypes: primary validation of male and female serotypes in independent cohorts of rats. J. Nutr 2002;132:1039–1046. Solanky KS, Bailey NJ, Beckwith-Hall BM, Davis A, Bingham S, Holmes E, Nicholson JK, Cassidy A: Application of biofluid 1H nuclear magnetic resonance-based metabonomic techniques for the analysis of the biochemical effects of dietary isoflavones on human plasma profile. Anal Biochem 2003;323:197–204. Griffiths JR, Stubbs M: Opportunities for studying cancer by metabolomics: preliminary observations on tumors deficient in hypoxia-inducible factor 1. Adv Enzyme Regul 2003;43:67–76.

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Mayr M, Chung YL, Mayr U, Yin X, Ly L, Troy H, Fredericks S, Hu Y, Griffiths JR, Xu Q: Proteomic and metabolomic analyses of atherosclerotic vessels from apolipoprotein E-deficient mice reveal alterations in inflammation, oxidative stress, and energy metabolism. Arterioscler Thromb Vasc Biol 2005;25:2135–2142.

Dr. John A. Milner Nutritional Science Research Group, Division of Cancer Prevention National Cancer Institute, 6130 Executive Boulevard, Suite 3164 Rockville, MD 20892 (USA) Tel. ⫹1 301 496 0118, Fax ⫹1 301 480 3925, E-Mail [email protected]

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Nutrigenetics Ahmed El-Sohemy Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Ont., Canada

Abstract Nutrients interact with the human genome to modulate molecular pathways that may become disrupted, resulting in an increased risk of developing various chronic diseases. Genetic polymorphisms affect the metabolism of dietary factors, which in turn affects the expression of genes involved in a number of important metabolic processes. Genetic polymorphisms affecting nutrient metabolism may explain some of the inconsistencies among epidemiological studies relating diet to chronic diseases such as cancer, diabetes, rheumatoid arthritis, osteoporosis and cardiovascular disease. Understanding how genetic variations influence nutrient digestion, absorption, transport, biotransformation, uptake and elimination will provide a more accurate measure of exposure to the bioactive food ingredients ingested. Furthermore, genetic polymorphisms in the targets of nutrient action such as receptors, enzymes or transporters could alter molecular pathways that influence the physiological response to dietary interventions. Among the candidate genes with functional variants that affect nutrient metabolism are those that code for xenobiotic-metabolizing enzymes (also called drug-metabolizing enzymes). These enzymes are involved in the phase I and II biotransformation reactions that produce metabolites with either increased or decreased biological activity compared to the parent compound. A number of dietary factors are known to alter the expression of these genes that, in turn, metabolize a vast array of foreign chemicals including dietary factors such as antioxidants, vitamins, phytochemicals, caffeine, sterols, fatty acids and alcohol. Knowledge of the genetic basis for the variability in response to these dietary factors should result in a more accurate measure of exposure of target tissues of interest to these compounds and their metabolites. Examples of how ‘slow’ and ‘fast’ metabolizers respond differently to the same dietary exposures will be discussed. Identifying relevant diet-gene interactions will benefit individuals seeking personalized dietary advice as well as improve public health recommendations by providing sound scientific evidence linking diet and health. Copyright © 2007 S. Karger AG, Basel

Background

Nutrition plays an important role in the development of chronic diseases such as osteoporosis, diabetes, rheumatoid arthritis, cancer and cardiovascular disease. Nutrigenomics is an emerging branch of nutritional science that uses genomic information along with techniques in molecular biology and genetics to address issues important to nutrition and health [1, 2]. One approach used to explore how genetic and dietary factors interact to influence various health outcomes is to examine how diet alters the expression of genes that regulate important cellular processes. Another is to examine how sequence variations in genes that regulate metabolic pathways affect responsiveness to specific dietary factors. Depending on the study design, a ‘response’ could be risk of disease, biomarkers of disease, or even biochemical indicators of nutrient intake. Nutritional genomics is another term that has been used to describe the complex interactions between nutrition and the genome [2]. Knowledge of the genetic basis for the variability in response to certain dietary factors should result in a more accurate measure of exposure of target tissues of interest to these compounds and their metabolites. Single nucleotide polymorphisms are the most common form of genetic variation and occur at about 1 in every 500–2,000 bases throughout the human genome [3, 4]. They are normally found in at least 1% of the population, although common polymorphisms that occur in 10–50% of the population may be more relevant from a public health perspective. Other types of genetic alterations such as gene deletions or insertions can also occur and have significant phenotypic effects. The complex chronic diseases that occur commonly in developed countries usually take many years (decades) to develop, and their multifactorial etiologies make it difficult to unravel the role of specific dietary factors. Thus, the combined contribution of in vitro, animal, clinical and epidemiologic studies is necessary to understand the role of specific bioactive food ingredients in maintaining optimal health. Epidemiologic studies are of particular interest because they examine the effects of a dietary factor or genetic variant in a human population. The etiology of complex chronic diseases clearly involves both environmental and genetic factors, with environmental influences such as diet likely having a greater influence on individuals with a genetic predisposition. Genetic association studies that link genotype frequencies to health outcomes have been limited by the failure to reproduce many results in subsequent studies that are conducted in different populations [5]. The apparent inconsistencies between studies, however, highlight the critical role that the environment contributes towards the expression of a genetic variant. Despite the limitations of genetic association studies, unexpected findings can sometimes point to environmental

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Dietary factor

Digestion Transport Metabolism

Genome

Uptake Biotransformation

Health outcome

Fig. 1. The effects of diet on health outcomes could occur directly or indirectly through interactions with the genome (e.g. DNA methylation, gene expression). Genetic variations affecting nutrient digestion, absorption, metabolism, uptake or biotransformation can modify the effects of dietary factors on various health outcomes.

exposures that might previously have been overlooked [6]. Furthermore, if a polymorphism that decreases the expression levels of a metabolic enzyme is associated with a decreased risk of a disease, then identifying dietary factors that inhibit the enzyme or decrease the expression levels of its gene in target tissues might be a worthwhile strategy for optimizing health and preventing disease (fig. 1). Functionally significant polymorphisms that affect the absorption, metabolism or disposition of nutrients or other substances found in the diet will ultimately affect the level of exposure of target tissues to the dietary compound and its metabolites. The polymorphic xenobiotic-metabolizing enzymes include the phase I and II biotransformation reactions that metabolize a vast array of chemicals into products that have either increased or decreased biological activity compared to the parent compound. Some dietary factors are known to alter the expression of genes that code for these enzymes that, in turn, transform them into metabolites with altered biological activity. In addition to dietary substances that might be harmful, a number of nutrients and bioactive food ingredients with purported health benefits are also metabolized by specific isoforms. Genetic polymorphisms have been associated with altered rates of enzymatic activities that affect circulating concentrations and ultimately the effectiveness of dietary chemicals and their metabolites. Cytochrome P450s (CYP) are a diverse group of enzymes that play an essential role in the oxidative biotransformation of steroids, prostaglandins, nutrients, drugs, chemicals and carcinogens. Despite their known function in the metabolism of dietary factors and the wide variability in activity associated with specific genetic variants, the use of CYP genotypes in nutritional epidemiologic studies has been limited. Several dietary factors affect the expression of CYP isoforms [7], which display wide individual variability in inducibility and

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activity because of their polymorphic sequences. CYP1A2 plays an important role in the metabolism of a wide range of drugs as well as chemical substances found in the diet. CYP1A2 is known to activate dietary carcinogens such as aromatic amines, but also detoxifies compounds such as caffeine [8]. A recent study linking the low-activity CYP1A2 genotype to an increased risk of myocardial infarction [9] suggests that a substance that is detoxified, rather than activated, by this enzyme may be an important risk factor in that population. Indeed, a subsequent study revealed that individuals with a low-activity CYP1A2 genotype are at a greater risk of coffee-associated heart disease [10]. Since caffeine is the only major substance in coffee that is known to be detoxified by CYP1A2, this observation suggests that caffeine may be an important risk factor for heart disease in certain populations. Glutathione S-transferases (GSTs) are a superfamily of enzymes that play a central role in the detoxification of several dietary compounds [11]. GSTs are divided into several distinct classes with partially overlapping substrate specificities. GSTM1, GSTT1, and GSTP1 are isoforms of the mu, theta, and pi class, respectively. The GSTM1 and GSTT1 null genotypes have been associated with both an increased as well as a decreased risk of certain forms of cancer [12]. Inconsistencies among genetic association studies may be related to the dual role of these enzymes in eliminating both harmful mutagens as well as potentially beneficial compounds such as dietary isothiocyanates that are found in cruciferous vegetables [13]. Indeed, a protective effect of the GSTM1 null genotype on colon and lung cancer has been related to lower urinary excretion of glutathione-conjugated phytochemicals, suggesting that they are not rapidly excreted [6, 14]. GSTT1 plays a similar role to GSTM1 in eliminating potentially beneficial phytochemicals found in cruciferous vegetables [15, 16]. Furthermore, vegetables rich in certain phytochemicals such as isothiocyanates increase the expression of GSTs [17], which conjugate them to more water-soluble forms that are more readily excreted. Although genetic variants in xenobiotic-metabolizing enzymes are unlikely to have clinical significance on their own, they may shed light on the role of potential substrates in disease development. Genetic polymorphisms have also been identified in catechol-O-methyltransferase, sulfotransferase and UDP-glucuronosyltransferase that result in marked differences in enzyme activity [18–20]. These enzymes metabolize a number of dietary compounds which may be more strongly associated with various health outcomes among individuals who ‘detoxify’ them less efficiently. For example, the intake of green tea was associated with a lower risk of breast cancer only in women with the low-activity allele for catechol-O-methyltransferase [21]. This enzyme catalyzes the methylation of catechins found in green tea, which makes them more rapidly eliminated.

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Conclusion

Genetic polymorphisms can affect the rate of digestion, absorption, metabolism, and uptake of dietary factors, which could explain some of the inconsistent findings relating diet to various health outcomes. In addition to providing a more rational basis for giving personalized dietary advice, the knowledge gained by applying genomic information to nutrition research will also improve the quality of evidence used for making population-based dietary recommendations. Discoveries made using genomic information should translate into more effective dietary strategies to improve overall health by identifying unique targets for prevention. Genetic polymorphisms of xenobiotic-metabolizing enzymes can affect the biological activity of numerous dietary substances, and single nucleotide polymorphisms in genes that code for these enzymes should be included in nutritional epidemiologic studies to provide a more accurate measure of exposure of target cells to the dietary compounds or their metabolites. Incorporating genetic markers in the design of nutritional epidemiologic studies will help clarify the role of both genetic and lifestyle factors in the development of chronic diseases. Acknowledgements This research was supported by a grant from the Canadian Institutes of Health Research (No. MOP-77741) and the Advanced Foods and Materials Network (M&E-B-4). A. El-Sohemy holds a Canada Research Chair in Nutrigenomics.

References 1 2 3 4 5 6 7 8

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Muller M, Kersten S: Nutrigenomics: goals and strategies. Nat Rev 2003;4:315–322. Ordovas JM, Corella D: Nutritional genomics. Annu Rev Genomics Hum Genet 2004;5:71–118. Sachidanandam R, Weissman D, Schmidt SC, et al: A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms. Nature 2001;409:928–933. Thorisson GA, Stein LD: The SNP Consortium website: past, present and future. Nucleic Acids Res 2003;31:124–127. Ioannidis JP, Ntzani EE, Trikalinos TA, Contopoulos-Ioannidis DG: Replication validity of genetic association studies. Nat Genet 2001;29:306–309. Lin HJ, Probst-Hensch NM, Louie AD, et al: Glutathione transferase null genotype, broccoli, and lower prevalence of colorectal adenoma. Cancer Epidemiol Biomarkers Prev 1998;7:647–652. Riddick DS, Lee C, Bhathena A, et al: Transcriptional suppression of cytochrome P450 genes by endogenous and exogenous chemicals. Drug Metab Dispos 2004;32:367–375. Gu L, Gonzalez FJ, Kalow W, Tang BK: Biotransformation of caffeine, paraxanthine, theobromine and theophylline by cDNA-expressed human CYP1A2 and CYP2E1. Pharmacogenetics 1992;2: 73–77. Cornelis MC, El-Sohemy A, Campos H: Genetic polymorphism of CYP1A2 increases risk of myocardial infarction. J Med Genet 2004;41:758–762. Cornelis MC, El-Sohemy A, Kabagambe EK, Campos H: Coffee, CYP1A2 genotype and risk of myocardial infarction. JAMA 2006;295:1135–1141.

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Armstrong RN: Structure, catalytic mechanism and evolution of the glutathione S-transferases. Chem Res Toxicol 1997;10:2–18. Cotton SC, Sharp L, Little J, Brockton N: Glutathione S-transferase polymorphisms and colorectal cancer: a HuGe review. Am J Epidemiol 2000;151:7–32. Kolm RH, Danielson UH, Zhang Y, Talalay P, Mannervik B: Isothiocyanates as substrates for human glutathione transferases: structure-activity studies. Biochem J 1995;311:453–459. London SJ, Yuan J-M, Chung FL, et al: Isothiocyanates, glutathione S-transferase M1 and T1 polymorphisms, and lung-cancer risk: a prospective study of men in Shanghai, China. Lancet 2000;356: 724–729. Seow A, Shi CY, Chung FL, et al: Urinary total isothiocyanate (ITC) in a population-based sample of middle-aged and older Chinese in Singapore: relationship with dietary total ITC and glutathione S-transferase M1/T1/PI genotypes. Cancer Epidemiol Biomarkers Prev 1998;7:775–781. Lin HJ, Zhou H, Dai A, et al: Glutathione transferase GSTT1, broccoli, and prevalence of colorectal adenomas. Pharmacogenetics 2002;12:175–179. Lampe JW, Chen C, Li S, et al: Modulation of human glutathione S-transferase by botanically defined vegetable diets. Cancer Epidemiol Biomarkers Prev 2000;9:787–793. Coughtrie MW, Johnston LE: Interactions between dietary chemicals and human sulfotransferasesmolecular mechanisms and clinical significance. Drug Metab Dispos 2001;29:522–528. Miners JO, McKinnon RA, Mackenzie PI: Genetic polymorphisms of UDP-glucuronosyltransferases and their functional significance. Toxicology 2002;181–182:453–456. Mannisto PT, Kaakkola S: Catechol-O-methyltransferase (COMT): biochemistry, molecular biology, pharmacology, and clinical efficacy of the new selective COMT inhibitors. Pharmacol Rev 1999;51: 593–628. Wu A, Tseng C-C, Van Den Berg D, Yu MC: Tea intake, COMT genotype and breast cancer in Asian-American women. Cancer Res 2003;63:7526–7529.

Dr. A. El-Sohemy Department of Nutritional Sciences, Faculty of Medicine University of Toronto, 150 College St. Toronto, Ont. M5S 3E2 (Canada) Tel. ⫹1 416 946 5776, Fax ⫹1 416 978 5882, E-Mail [email protected]

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Epigenomics and Nutrition Lynne Cobiac Preventative Health National Research Flagship, CSIRO, Adelaide, Australia

Abstract Epigenomics or epigenetics refers to the modification of DNA that can influence the phenotype through changing gene expression without altering the nucleotide sequence of the DNA. Two examples are methylation of DNA and acetylation of the histone DNA-binding proteins. Dietary components – both nutrients and nonnutrients – can influence these epigenetic events, altering genetic expression and potentially modifying disease risk. Some of these epigenetic changes appear to be heritable. Understanding the role that diet and nutrition play in modifying genetic expression is complex given the range of food choices, the diversity of nutrient intakes, the individual differences in genetic backgrounds and intestinal physiological environments where food is metabolized, as well as the impact on and acceptance of new technologies by consumers. Copyright © 2007 S. Karger AG, Basel

Nutrigenomics is an all-encompassing term that covers the interaction of diet with DNA, chromatin or RNA expression. Our diet is a complex mixture of nutrients with known functions and nonnutritive bioactives or food components, not all of which are known, with varying levels of bioactivity and bioavailability, and which can be either protective or nonprotective against the risk of developing disease or ill-health. The study of nutrigenomics thus extends beyond the traditional perspective of nutritional science. Just as there is diversity in the diet, there is considerable diversity in the genome (polymorphisms) and in its expression. Genomic variation can occur in single nucleotides [either as single nucleotide polymorphisms or as point mutations], through insertion or deletion of a few up to several hundred bases or through gross chromosomal rearrangements like those occurring in Down’s syndrome. ‘Nutrigenetics’ is used to refer to the nutrient response at the level of the single gene and is concerned with the effects of specific gene variants on

an individual’s response to diet, and ‘nutrigenomics’ refers to the response to nutrients at the level of the whole genome or how diet affects genetic expression across the whole genome. Nutrigenetics takes into consideration an individual’s genetic background with recognition that there are allelic variations in genes that respond differently to bioactives from foods. Genetic polymorphisms may influence the nutrient or bioactive response in several ways. • Genetically based differences in absorption, disposition, metabolism and excretion (e.g. the ability to remove the dietary potentially carcinogenic heterocyclic amines is influenced by the presence of polymorphisms in the CYP1A2 gene and/or the N-acetyltransferase gene) [1]. • Nutrient-gene interactions – an individual’s genetic background can determine which gene products are expressed that modulate the physiological response to specific nutrients (e.g. polymorphisms in the angiotensinogen or angiotensin-converting enzyme gene may influence the effectiveness of changing dietary intake to modify blood pressure) [2]. • Nutrients may affect gene expression and thus the tissue or cellular level of a protein that is part of the causal pathway of a disease and, as such, could modify an individual’s susceptibility to that disease (e.g. dietary fatty acids appear to play a role in the regulation of COX-2 expression and thus influence the inflammatory response which may eventually lead to diseases such as arthritis, cardiovascular disease and cancer) [3, 4].

Epigenomics

Traditionally it was thought that phenotypic traits were determined solely by genetic mutations and recombinations and that the nucleotide sequence was the sole driver of heredity. However, the study of epigenetics/genomics has dramatically changed this point of view. Epigenomics/genetics refers to modification of the DNA and DNA-binding proteins that can influence the phenotype without altering the nucleotide sequence of the DNA. Methylation of cytosine residues in DNA and modification of the histone proteins are two common examples. The other major epigenetic mechanism is RNA-associated interference with gene expression (e.g. small interfering RNAs [5]). The key issues are that some of these epigenetic changes appear to be heritable, associated with different disease states and modifiable by dietary factors. This short paper will provide an overview of DNA methylation, histone modification and genomic instability, linking to dietary factors and some broader food- and diet-related issues.

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DNA Methylation DNA methylation generally occurs with the transfer of a methyl group from S-adenosylmethionine to the carbon-5 position of cytosine in a CpG dinucleotide sequence catalyzed by DNA methyltransferase. The overall frequency of CpG dinucleotides in the genome is lower than expected from the base composition and they tend to be more common in the promoter regions of genes and in repeat sequences. In general, they tend to be heavily methylated except for those in the promoter regions of transcriptionally active genes where they tend to be less methylated. Methyl-CpG-binding proteins bind specifically to methylated DNA and cause chromatin remodelling. Thus the level of DNA methylation can affect the level of genetic expression with hypermethylation generally having a net silencing effect. Cancer has been associated with disregulation of epigenetic control [6] with resultant changes to DNA methylation patterns such as global hypomethylation [7], hypermethylation of tumor suppressor genes (thus silencing the suppressive effect) [8] or hypomethylation in the promoter region of an oncogene (allowing the oncogene to express) which may be an early event for cancer if this occurs or hypomethylation of repeat sequences that are rich in CpG islands. Changes in methylation (such as global hypomethylation) can also result in chromosome instability [9, 10]. With aging, we also see an epigenetic drift with aberrant methylation of CpG islands in the promoter regions of DNA repair and tumor suppressor genes, which is likely to increase tissue vulnerability to neoplastic transformation and may in some way account for the increased incidence of several cancers, such as colorectal cancer, with advancing age. Furthermore, Alzheimer’s disease has been associated with different patterns of DNA methylation. DNA hypomethylation has been reported in the genetic regions associated with the expression of amyloid precursor protein, presenilin 1 and ␤-secretase [11–13]. Such hypomethylation could theoretically lead to increased transcription with the net result being elevated expression of these proteins that appear to be central to the development of Alzheimer’s disease. Diet and DNA Methylation Dietary folate, methionine and choline are important methyl donors feeding into the methionine cycle, but pyridoxine, vitamin B12, riboflavin and zinc are also important cofactors for the cycle to operate efficiently. Looking at individual nutrients alone may be too simplistic an approach – dietary deficiency of combinations of nutrients may be important [10]. Other nonnutrient dietary components such as tea polyphenols may directly affect the activity of DNA methyltransferase or its gene [14]. Selenium can also affect methylation

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status [15]. Thus differences in dietary intakes of these compounds may affect the methylation status of our DNA, influence genetic expression and hence our predisposition towards disease. Different polymorphisms in the 5,10-methylenetetrahydrofolate reductase affect cancer risk, providing additional evidence of the importance of methylation [16] and the relationship between diet and the methionine cycle [17]. However, there is now also evidence that epigenetic events such as DNA methylation can be determined prior to birth, so-called fetal programming or genetic imprinting, and can even be transmitted over generations [18, 19]. Programming of DNA methylation can occur in utero through maternal diet, the methylation state is then transmitted through mitosis and meiosis with further interaction possible with postnatal environmental factors causing additional epigenetic events. The net effect is to ‘reset’ the basal expression of genes and thus affect disease risk. Fetal malnutrition is associated with an increased risk of diabetes, metabolic syndrome and heart disease – the Barker hypothesis – and epigenetic programming during critical times such as germ cell and early embryo development [20–22] may play a key part in this. The dietary environment of our ancestors may thus be influencing our genetic expression today. Histone Modification The second common epigenomic mechanism relates to modification of histone proteins that mediate the folding of DNA into chromatin which supports and influences genomic transcription. Modifications such as acetylation, methylation, phosphorylation, ubiquitination, polyADP-ribosylation, sumoylation and biotinylation appear to determine the transcription of genes and are referred to as the histone or chromatin code. DNA forms nucleosome structures where 146 bp of DNA are wrapped around an octamer of small basic proteins called histones consisting of two copies of the histone proteins H2A, H2B, H3 and H4. The remaining bases – the linker DNA – link the nucleosomes, and can vary in length from 8 to 114 bp. This variation is species specific, but variation in linker DNA length has also been associated with the developmental stage of the organism or specific regions of the genome. Histone proteins consist of a globular C-terminal domain and a flexible N-terminal tail. The amino terminus protrudes from the nucleosomal surface – lysines, arginines, serines and glutamates in the N-terminus are targets for the modifications mentioned above. Multiple residues can be modified on one histone and one residue can be modified in different ways. For example, lysine can be either acetylated or methylated. Although considerable research has been undertaken on modifications to the histone tails, there is also evidence that the globular domains can be modified [23]. Within a histone, different modifications

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can prevent or enhance further modifications and modifications on different histones can interact. Chromosomes consist of sections that are transcriptionally active or nonactive. If the chromatin is highly condensed (heterochromatin), these areas are generally transcriptionally inactive whereas if the chromatin is more open, less condensed (euchromatin), it tends to be transcriptionally active. Modification of the histones can alter the chromatin structure and influence transcription activity either positively or negatively. Acetylation of histones is associated with transcription and methylation with transcription repression. The histones act as binding sites for effector proteins (such as heterochromatin protein 1) and modification will thus alter the ability of the effector proteins to facilitate DNA transcription – the locus of the histones on the chromatin and the specific patterns of histone modifications will influence what transcription complexes are recruited which will in turn determine the expression profile of specific genes. The modifications of the histones appear to be reversible and enzymically driven with many enzymes only recently discovered and probably many more still to be identified. Some examples include acetyltransferase and deacetyltransferase, kinases and phosphatases, ubiquitin ligases, poly(ADP-ribose) polymerases, biotinidase and holocarboxylase, methyltransferase and demethylase, and SUMO (small ubiquitin-related modifier)-specific ubiquitin-like ligase. Histones (and their modifications) are also important for the regulation of chromosome segregation during cell division (both meiosis and mitosis) and DNA repair [24]. The two most common epigenetic events, DNA methylation and histone modification, can interact and appear to be interdependent in some instances such that methylation of DNA can trigger local histone modifications [6, 25]. Evidence is accumulating on the role of the histone or chromatin code in the etiology of several chronic diseases. Cancer is associated with global changes in histone modifications, for example loss of monoacetylation and trimethylation of H4 [26] and clinical trials with histone deacetylase (HDAC) inhibitors as anticancer agents show promise [27]. Furthermore, there is evidence that histone modifications may play a role in the development of Alzheimer’s disease and some mental disorders [28, 29] and diabetes [30]. Diet and Histone Modification Examples of dietary influences on the histone code include HDAC inhibitors such as the short-chain fatty acid butyrate from resistant starch or fiber fermentation, diallyl sulfide from garlic and sulforaphane [31]. These dietary compounds may thus result in hyperacetylation of histones, altering chromatin structure by opening it up and allowing transcription which may lead to re-expression of silenced genes such as tumor suppressor genes and activation of proapoptotic genes so that the cells can reduce damaged cells exposed to

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environmental insults. Although the exact mechanisms are still to be delineated, some HDAC inhibitors, via hyperacetylation of histones, can activate the transcription factors such as NF␬B which can derepress such genes as p21 and BAX and thus potentiate apoptosis selectively in cancer cells which appear to be more sensitive to HDAC inhibitors than noncancerous cells [31]. Other nutrients that may influence histone modification include folate, choline, methionine, vitamins B6 and B12, riboflavin and zinc which are all necessary for the production of methyl donors for histone methylation; biotin which is a cofactor in fatty acid synthesis by carboxylase enzymes and is required for histone biotinylation, and tryptophan and niacin which supply NAD⫹ for polyADP-ribosylation of histones (NAD⫹ is also required for class III HDACs). Genomic Instability From conception, our DNA is potentially a target for damage which may lead to genomic instability and to genome damage which can increase the risk of some diseases, for example colorectal cancer [9]. Dietary factors may play a role in the prevention of DNA damage and its repair. For example, when methyl group donors are low or unavailable, uracil is incorporated into DNA instead of thymine which can lead to a mutagenic lesion by predisposing that region to the formation of double-strand breaks in the DNA [32]. In other words, there is damage to the DNA. There are many forms of genome damage mainly caused by faulty DNA metabolism and repair which can be linked to nutrient deficiency, oxidative stress and excess calories. Strand breaks in DNA, DNA misrepair and mitotic malfunction result from chromosomal breakage, loss, translocation, amplification, apoptosis and necrosis [32]. Genomic damage markers such as micronuclei have been associated with increased cancer incidence as well as with other degenerative diseases such as Alzheimer’s disease, Parkinson’s disease, diabetes and vascular disease [33–35]. Micronutrients such as folate, nicotinic acid and calcium appear to protect against genome damage whereas others (e.g. biotin) may not be protective and may even elevate genomic damage [32]. More research is needed to fully understand the associations between the different measures of genomic damage, the actual risk of developing specific diseases and the identification of food components that are potentially protective.

Diet and Nutrition Considerations/Impacts

In order to identify foods, nutrients or bioactives that can prevent or delay the onset of disease in population groups, it will be critical to understand the

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effect of food components on gene expression and genome stability as well as the impact of genetic diversity within that population. If we wish to personalize a dietary approach for an individual, we will need to determine the levels of genetic damage, the capability and extent of genetic repair, as well as obtain a measure of that person’s genetic background and the extent of epigenetic changes [36]. However, nutrition or diet is an exceedingly complex environmental factor even before we consider the complex interactions with the genome. Not only is there a vast range of foods and beverages consumed both across and within populations, but there are differences in food preparation and processing, varying amounts of protective or potentially damaging bioactives present with varying bioavailabilities in foods and nutrient-nutrient interactions within the human body. Nutrients or bioactives can affect carcinogen metabolism (e.g. fiber), DNA damage (e.g. heterocyclic amines) and repair (e.g. folate), cell cycle and apoptosis (e.g. butyrate, n–3 fatty acids), proliferation (e.g. resveratrol) and signal transduction (e.g. phytoestrogens) [32, 37–40]. Add to this the activity of 1–2 kg of bacteria per person of around 500 species of colonic microflora, many of which have not yet been identified, with their nutrient-gene and potential gene-gene interactions and it becomes a significant science challenge to understand the genetics of the human host and the profile of the gut microflora, interspecies interaction, cross-feeding, quorum sensing, and their interaction with dietary substrates, the products released and their relationships with the host’s mucosal layer and immune system to develop dietary approaches to improve health and lower disease risk. One example of the link between microflora and epigenetic effects is that there are butyrateproducing bacteria in the colon. Butyrate has been shown to affect histone acetylation and promote apoptosis thus influencing gene expression and elimination of cells with DNA damage, respectively. There are different pathways to producing butyrate and it is feasible that not all butyrate-producing bacteria have yet been identified. As we think strategically about developing foods that may be protective against disease, we may need to become more innovative with delivery mechanisms, in order to deliver bioactives to targeted sites of the intestine where their release will maximize the physiological response. Two examples are (1) microencapsulation [41] that can protect the bioactive until a trigger releases its payload at the desired site and (2) butyrate starch ester that resists some upper intestinal digestion and reaches the colon where the butyrate is released where it may help to promote apoptosis and so potentially minimize the risk of developing colorectal cancer [42]. We really cannot consider diet or food without considering cultural issues and these are also linked to health outcomes. We need to understand dietary

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behaviors, lifestyle factors and food choices. There may be genetic differences in sensory sensitivities that will influence what is consumed. We need to understand attitudes and beliefs that might impinge on the acceptance of these new biomic technologies and foods developed from our greater understanding of nutrition at the molecular level. Ethical issues are likely to be raised as well as whether or not there will be public health benefits from this approach in addition to privacy issues around acquiring genetic information on individuals and who has access to this information [43]. Education and communication to the consumer will be important. Most countries are establishing evidence-based dietary guidelines and recommended nutrient reference values. By their very nature, they are targeted towards general recommendations and populations. How do we integrate the finding that there are specific subgroups in the population that may be more or less sensitive to nutrients or bioactives in the diet? Do we need to revisit some of the nutrient reference values where in some instances it appears that there are much higher levels of for example folate needed to maintain genome stability [44]? Nutrients may have a protective effect on one pathway and a nonprotective effect on another and these complex interplays need to be understood. Furthermore, nutrients or bioactives may be protective at one level and nonprotective at a higher level and this effect may depend on the genetic background of the individual. What is key for all of this is that the appropriate human clinical trials must be undertaken before we can make clear recommendations. With clinical trials we need the right biomarkers that are meaningful, that are valid, measurable and changed with dietary intervention. They need to be suitably specific for different disease states, validated to represent an increased risk of disease or illhealth, and sensitive enough to be perturbed by relatively modest changes in dietary intakes. If we are to begin to tailor diets for specific genetic backgrounds of individuals, we need to be able to have the appropriate cost-effective diagnostics and the accompanying capabilities and technologies to predict disease susceptibility and identify individuals who need more or less of particular nutrients or bioactives. The inherent complexity of this approach is significant and potentially problematic. One enormous challenge is to understand holistically the role of nutrition and epigenetics in polygenic diseases such as obesity, cancer, diabetes – this increases the complexity significantly and is one of the challenges of the future. There is a range of advanced technologies and the emergence of new knowledge that will increase our understanding. Advanced imaging – including

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molecular imaging – will provide new insights [45], as will understanding the role of miRNAs or siRNAs in controlling gene expression in obesity and colorectal cancer for example and the links to nutrition [46]. We need to ensure there is an appropriate use of bioinformatics on the array data being generated which can be highly variable, produce thousands of variables, the results of which are highly dependent on experimental technique.

Conclusion

Dietary intake is a complex social and bioactive mixture where more of a potentially protective nutrient is not always better. Different genetic polymorphisms affect disease risk and dietary recommendations may need to be modified accordingly. Individual measures of genetic variation, epigenetic changes and genome stability, nutrient metabolism and gut flora populations may mean that we should have tailored preventative diets, especially if our diet is likely to affect gene expression and hence health outcomes of our successors. We may need specific nutrigenomic approaches for the prevention and even treatment of different disease states. One thing that is emerging is that it is increasingly unlikely that one set of dietary recommendations is going to suit all of us in the future.

Acknowledgements Thanks must go to Trevor Lockett and Shelly Hope for their help with the manuscript.

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Fenech M: The Genome Health Clinic and genome health nutrigenomics concepts: diagnosis and nutritional treatment of genome and epigenome damage on an individual basis. Mutagenesis 2005;20:255–269. Bonassi S, Hagmar L, Stromberg U, et al: Chromosomal aberrations in lymphocytes predict human cancer independently of exposure to carcinogens. Cancer Res 2000;60:1619–1625. Migliore L, Botto N, Scarpato R, Petrozzi L, Cipriani G, Bonuccelli U: Preferential occurrence of chromosome 21 malsegregation in peripheral blood lymphocytes of Alzheimer’s disease patients. Cytogenet Cell Genet 1999;87:41–46. Migliore L, Scarpato R, Coppede F, Petrozzi L, Bonucelli U, Rodilla V: Chromosome and oxidative damage biomarkers in lymphocytes of Parkinson’s disease patients. Int J Hyg Environ Health 2001;204:61–66. Mathers JC: The biological evolution – towards a mechanistic understanding of the impact of diet on cancer risk. Mutat Res 2004;551:43–49. Reddy L, Odhav B, Bhoola KD: Natural products for cancer prevention: a global perspective. Pharmacol Ther 2003;99:1–13. Schneider Y, Vincent F, Duranton B, Badolo L, Gosse F, Bergmann C, Seiler N, Raul F: Antiproliferative effect of resveratrol, a natural component of grapes and wine, on human colonic cancer cells. Cancer Lett 2000;158:85–91. Augenlicht LH, Mariadason JM, Wilson A, Arango D, Yang W, Heerdt BG, Velcich A: Short chain fatty acids and colon cancer. J Nutr 2002;132:3804S–3808S. Ng Y, Barhoumi R, Tjalkens RB, Fan YY, Kolar S, Wang NY, Lupton JT, Chapkin RS: The role of docosahexaenoic acid in mediating mitochondrial membrane lipid oxidation and apoptosis in colonocytes. Carcinogenesis 2005;26:1914–1921. Augustin MA, Sanguansri L, Bode O: Maillard reaction products as encapsulants for fish oil powders. J Food Sci 2006;71:E25–E32. Annison G, Illman RJ, Topping DL: Acetylated, proprionylated and butyrylated starches raise large bowel short-chain fatty acids preferentially when fed to rats. J Nutr 2003;133:3523–3528. Chadwick R: Nutrigenomics, individualism and public health. Proc Nutr Soc 2004;63:161–166. Fenech M: Recommended dietary allowances (RDAs) for genomic stability. Mutat Res 2001;480: 51–54. Moskaug JO, Carlsen H, Myhrstad M, Blomhoff R: Molecular imaging of the biological effects of quercetin and quercetin-rich foods. Mech Ageing Dev 2004;125:315–324. Wynter CV: The dialectics of cancer: a theory of the initiation and development of cancer through errors in RNAi. Med Hypotheses 2006;66:612–635.

Dr. Lynne Cobiac Dept. of Nutrition and Dietetics Flinders University GPO Box 2100, Adelaide 5001 (Australia) Tel. ⫹61 8 8204 4645, Fax ⫹61 8 8204 6406, E-Mail [email protected]

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Tai ES, Gillies PJ (eds): Nutrigenomics – Opportunities in Asia. Forum Nutr. Basel, Karger, 2007, vol 60, pp 42–48

Early Nutrition: Impact on Epigenetics John C. Mathers Human Nutrition Research Centre, School of Clinical Medical Sciences, University of Newcastle, Newcastle, UK

Abstract Background/Aims: (1) To outline the findings that alterations in nutrition in utero and in early postnatal life influence health in later life. (2) To review the evidence that alterations in epigenetic markings may be a means by which the genome records environmental (including nutritional) exposure resulting in changes in gene expression and cell function which underlie susceptibility to disease. Methods: Literature review. Results: There is strong evidence that low birth weight, especially when followed by accelerated growth in childhood and greater central adiposity in adulthood, is a risk factor for a range of common diseases including cardiovascular disease and type 2 diabetes. Such observations provide the basis for the ‘programming’ hypothesis and present a challenge to discover the mechanisms by which nutritional insults in early life are received, recorded, remembered and then revealed in later life. Emerging evidence suggests that alterations in epigenetic marking of the genome may be a key mechanism by which nutritional exposure in utero can influence gene expression, and therefore, phenotype. Conclusion: Early life nutrition has the potential to change chromatin structure, to alter gene expression and to modulate health throughout the life course. Whether later interventions can reverse adverse epigenetic markings remains to be discovered. Copyright © 2007 S. Karger AG, Basel

The Developmental Origins of Adult Disease Hypothesis

McCance [1] was the pioneer in this field with his observations that undernutrition in the early life of experimental animals (rats and pigs) had lifetime effects. However, these observations had little impact on human nutrition until Barker [2] and others found that low birth weight (LBW) is associated with increased risk of coronary heart disease, stroke, hypertension and type 2 diabetes (T2D). Whilst LBW may also increase the risk of other diseases, e.g. osteoporosis [3], and appears to reduce longevity [4], there is support for the idea that higher birth weight may increase the risk of some cancers [5].

However, postnatal events may modify the influence of poor prenatal development. For example, evidence is accumulating that the increased risk of coronary heart disease, metabolic syndrome and associated diseases in those born small is greater in those who show childhood catch-up growth [6] or who become obese in later life [7]. In addition, Lucas et al. [8] have shown that both the quantity and quality of infant nutrition may have long-term effects on cognitive function. From studies in rural Gambia, Moore et al. [9] reported that the season of birth (a surrogate for the extent of maternal undernutrition) predicted mortality several years later and suggested that undernutrition in utero may have profound long-term effects on immune defences. Such research has reinvigorated interest in the impact of early life nutrition on health throughout the life course and has initiated a paradigm shift in understanding of the role of nutrition in determining health. Importantly, aspects of these epidemiological observations have been reproduced in animal models exposed to various forms of undernutrition in utero including low-protein [10] and low-iron [11] maternal diets. These and other observations underpin the concept of ‘programming’, a term which describes the impact of a stimulus or insult during a critical or sensitive time window resulting in long-term structural and/or functional changes in the organism [12]. Programming is an example of developmental plasticity, i.e. that a given genotype can give rise to different phenotypes depending on environmental conditions [13]. If the impact on long-term health of poor nutrition during development resulting in LBW is exacerbated by overnutrition in childhood, then the populations at greatest risk will be those with a high prevalence of LBW which are undergoing a nutritional transition to overnutrition. Several countries of Southeast Asia have a high LBW prevalence (defined as birth weight less than 2.5 kg) but also an increasing incidence of T2D associated with increasing obesity rates [14]. The risk of T2D appears to be amplified in those born small who experience accelerated childhood growth and who have greater abdominal adiposity in adulthood [14].

Overview of Epigenetics

Epigenetics describes changes to the genome which are inherited from one cell generation to the next which alter gene expression but which do not involve changes in the primary DNA sequence. The main features of epigenetic marking of the genome are (1) DNA methylation and (2) histone ‘decoration’. Changes in DNA methylation are an essential part of normal development. In the early embryo, there is a wave of DNA demethylation after fertilization followed by a wave of de novo methylation upon embryo implantation [15–17].

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DNA methylation is responsible for Y chromosome inactivation, for determining which of the parental alleles is expressed in the case of imprinted genes [18] and for the regulation of expression of particular genes in particular cell types [19]. A proportion of the cytosine residues is modified after translation by attachment of a methyl group to position 5 on the cytosine ring. Such methylated cytosines are usually found where the cytosine is next to a guanine residue, i.e. in a CpG dinucleotide. In about half the genes in the human genome, unmethylated CpGs are found clustered at the 5⬘ ends of genes in domains known as CpG islands. When the CpGs in such islands are unmethylated, gene transcription proceeds normally but when some or all of the CpGs become methylated, the genes are switched off. There is extensive covalent modification (by methylation, acetylation, phosphorylation and ubiquitination) of the amino-terminal tails of histones that protrude from the globular nucleosome core. Emerging evidence suggests that this histone ‘decoration’ is the basis for a histone code which extends considerably the information potential of the genetic (DNA) code [20]. Current hypotheses suggest that the pattern of histone modifications in any region of the genome together with the recruitment of several proteins alter the chromatin structure and DNA methylation and thus control the access to the associated DNA by the proteins necessary for transcription [16]. A range of nutrients may regulate gene expression by altering histone post-translational modifications and/or DNA methylation [21].

Malleability of Epigenetic Markings

Although monozygotic twins are genetically identical, in many cases there are considerable phenotypic differences between members of such monozygotic pairs including differences in disease experience [22]. In a recent elegant study of epigenetic markings in monozygotic twins, Fraga et al. [23] observed that in early life the twins are epigenetically indistinguishable as assessed by global and locus-specific differences in DNA methylation and in histone acetylation. However, with age there was increasing discordance in epigenetic marking and, equally important, associated divergence in gene expression patterns [23]. The nature of the environmental signals which are capable of inducing changes in epigenetic markings is very poorly understood, but it seems likely that a wide range of exposures can have such effects. For example, they may include a range of maternal behaviours. Liu et al. [24] observed that the offspring of rat dams which were more attentive to their pups through licking, grooming and arched-back nursing show more modest hypothalamic-pituitaryadrenal responses to stress. More recently, Weaver et al. [25] demonstrated that

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these differences in stress response were due to stable alterations in DNA methylation (altered cytosine methylation within the promoter of the glucocorticoid receptor) and chromatin structure in the hippocampus of the offspring and that the epigenomic markings were reversible by treatment with the histone deacetylase inhibitor trichostatin A. These findings provide the first evidence that maternal behaviour in the early postnatal period may have profound effects on stress responses throughout life in the offspring and that these phenotypic changes are mediated by changes in gene expression due to stable alterations in DNA methylation and chromatin structure [25]. A number of dietary components may influence DNA methylation and gene expression. For example, molecular modelling studies have shown that the tea polyphenol (⫺)-epigallocatechin-3-gallate (EGCG) fits into the catalytic pocket of DNA methyltransferase 1 (DNMT1) [26] and in doing so acts as a competitive inhibitor. DNMT1 is the ‘house-keeping’ enzyme which ensures that patterns of DNA methylation are copied to the daughter strands each time DNA is replicated. Fang et al. [26] have shown that EGCG suppresses DNMT1 activity with a Ki of 6.89 ␮M and that treatment of tumour cell lines with EGCG results in a time- and dose-dependent demethylation of CpG islands in the promoters of several cancer-related genes. Importantly this reversal of aberrant methylation resulted in re-expression of the silenced genes [26].

Impact of Dietary Factors in Early Life on Epigenetic Markings

Intra-uterine growth retardation (IUGR) can be induced experimentally in rats by bilateral uterine artery ligation at day 19 of gestation and results in animals born at term which are significantly smaller than normal but without any change in litter size [27]. The IUGR rats have altered tissue structure and function including decreases in kidney glomeruli number [28] and reduced small intestinal growth [29]. Although IUGR is a transient prenatal insult, the effects persist into adult life and have been shown to be associated with altered onecarbon metabolism (as illustrated by significantly increased concentrations of homocysteine and S-adenosylhomocysteine) which may be responsible for changes in DNA methylation and histone acetylation [27]. More direct evidence of the effects of alterations in one-carbon metabolism during pregnancy was provided by the study by Waterland and Jirtle [30] in which they gave dietary supplements of methyl group donors and cofactors (folic acid, vitamin B12, choline and betaine) to viable yellow agouti (Avy) mice for 2 weeks before mating and throughout pregnancy and lactation. Dietary supplementation of the dams resulted in phenotypic alterations in the offspring with a higher proportion having a darker coat colour (mottled and pseudoagouti) [30]. This

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coat colour change was due to silencing of the expression of the agouti gene as a result of increased CpG methylation at the Avy locus [30]. The authors speculated that this shift in epigenotype may have occurred during early embryonic development and demonstrated that the altered epigenetic marking observed at weaning (21 days) was stable for several months [30]. Rats fed a low-protein (90 g casein/kg) diet during pregnancy produce smaller offspring which develop increased blood pressure in adulthood [10]. Such maternal undernutrition significantly increased the expression of the glucocorticoid receptor and of the peroxisome proliferator-activated receptor-␣ in the liver of offspring, which was associated with significantly reduced methylation of CpGs in the promoter regions of the same genes [31]. Interestingly, these alterations in epigenetic marking and gene expression were prevented if the low-protein diet was supplemented with folic acid [31]. The limited evidence to date suggests that these effects of altered maternal nutrition on DNA methylation and gene expression in the offspring appeared to be gene specific since the nutritional manipulations had no effect on peroxisome proliferator-activated receptor-␥ [31]. These observations suggest that altered one-carbon metabolism may play a central role in the molecular mechanisms through which IUGR is sensed by the cell. In addition, the emerging evidence suggests that cellular ‘memory’ of such developmental insults may be encoded in altered epigenetic marking of the genome and may be manifested as altered phenotype later in life through changes in the pattern of gene expression.

Conclusions and Suggestions for Further Research

It is now clear that LBW, especially when followed by accelerated growth in childhood and greater central adiposity in adulthood, is a risk factor for a range of common diseases. Whilst maternal undernutrition remains a serious issue throughout the world, an increasing proportion of mothers are overweight or obese and research is needed to ascertain whether the offspring of such mothers carry a long-term health penalty. In addition to the public health implications, the demonstration of ‘programming’ by nutrition in early life raises many fundamental biological questions. These include the mechanisms by which the early life experiences are received, recorded, ‘remembered’ and then revealed in later life. If, as seems likely, changes in epigenetic marking of the genome play a role in this process then it will be important to discover (1) the critical ‘windows’ when epigenetic markings are most susceptible to perturbation, (2) which nutrients or other food constituents can influence epigenetic markings, (3) what DNA domains are susceptible to altered epigenetic marking by nutritional factors, (4) whether alterations in epigenetic markings occurring

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in early life are reversible by nutritional or other interventions in later life and (5) the molecular mechanisms by which nutrition can influence epigenetic markings. The latter will require a much better understanding of the linkages between nutrient-sensitive signalling pathways and chromatin structure and function.

Acknowledgements Research in my laboratory on early nutrition and its impact on epigenetics is funded by the World Cancer Research Fund (2001/37) and by the Biotechnology and Biological Sciences Research Council through the Centre for Integrated Systems Biology of Ageing and Nutrition (CISBAN) (BB/C008200/1).

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Dr. John C. Mathers Human Nutrition Research Centre, School of Clinical Medical Sciences William Leech Building, University of Newcastle Newcastle, NE2 4HH (UK) Tel. ⫹44 1912226912, Fax ⫹44 1912228943, E-Mail [email protected]

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Tai ES, Gillies PJ (eds): Nutrigenomics – Opportunities in Asia. Forum Nutr. Basel, Karger, 2007, vol 60, pp 49–65

Nutrition and Genome Health Michael Fenech CSIRO Human Nutrition, Adelaide, Australia

Abstract The link between genome damage and adverse health outcomes is compelling. There is increasing evidence indicating that genome instability, in the absence of overt exposure to genotoxins, is itself a sensitive marker of nutritional deficiency. We have shown that aboveaverage intake of certain micronutrients (i.e. calcium, vitamin E, retinol, folate, vitamin B12 and nicotinic acid) is associated with a reduced genome damage rate measured using the micronucleus assay. Genome health nutrigenomics is an emerging and important new field of nutritional science because it is increasingly evident that optimal concentration of micronutrients for the prevention of genome damage is dependent on genetic polymorphisms that alter the function of genes involved directly or indirectly in DNA repair and metabolism. Essentially this also means that the dietary ‘nutriome’ (i.e. nutrient profile and composition) recommendations should be matched to an individual’s functional genome to optimise genome health maintenance. Development of functional foods and dietary patterns that are specifically designed to improve genome health maintenance in humans with specific genetic backgrounds are expected to provide an important contribution to a new health strategy based on the diagnosis and individualised nutritional treatment of genome instability (i.e. Genome Health Clinics). Copyright © 2007 S. Karger AG, Basel

The central role of the genetic code in determining health outcomes such as developmental defects and degenerative diseases such as cancer is well established. In addition, it is evident that DNA metabolism and repair is dependent on a wide variety of dietary factors that act as cofactors or substrates in these fundamental metabolic pathways [Ames, 2001; Ames and Wakimoto, 2002; Fenech and Ferguson, 2001]. DNA is continuously under threat of major mutations from conception onwards by a variety of mechanisms which include point mutation, base modification due to reactive molecules such as the hydroxyl radical, chromosome breakage and rearrangement, chromosome loss or gain, gene silencing due to inappropriate methylation of CpG at promoter

sequences, activation of parasitic DNA expression due to reduced methylation of CpG as well as accelerated telomere shortening [Egger et al., 2004; Fenech, 2002, 2005; Rajagopalan and Lengauer, 2004]. It is true to say that all of the above mechanisms of genome damage occur spontaneously due to the effects of endogenously generated mutagens and/or due to deficiency in cofactors required for DNA metabolism and repair and/or exposure to environmental genotoxins. However, it is also true that genetic defects in DNA metabolism and repair, the latter involving more than 100 genes in humans [Lindahl and Wood, 1991; Thompson and Schild, 2002], are also a key factor. While much has been learnt of the genes involved in DNA metabolism and repair and their role in a variety of pathologies, such as defects in BRCA1 and BRCA2 genes that cause increased risk for breast cancer [Nathanson et al., 2001; Thompson and Schild, 2002], much less is known of the impact of cofactor and/or micronutrient deficiency on DNA repair. Put simply, a deficiency in a micronutrient required as a cofactor or as an integral part of the structure of a DNA repair gene (e.g. Zn as a component of the DNA repair glycosylase OGG1 involved in removal of oxidised guanine or Mg as a cofactor for several DNA polymerases) could mimic the effect of a genetic polymorphism that reduces the activity of that enzyme [Ames, 2001, 2003]. Therefore, nutrition has a critical role in DNA metabolism and repair and this awareness is leading to the development of the new field of genome health nutrigenomics [Fenech, 2004, 2005].

Evidence Linking Genome Damage with Adverse Health Outcomes

Genome damage impacts on all stages of life. There is good evidence to show that infertile couples exhibit a higher rate of genome damage than fertile couples [Trkova et al., 2000] when their chromosomal stability is measured in lymphocytes using the micronucleus (MN) assay [Fenech, 2000] (fig. 1). Infertility may be due to a reduced production of germ cells because genome damage effectively causes programmed cell death or apoptosis which is one of the mechanisms by which grossly mutated cells are normally eliminated [Hsia et al., 2003; Narula et al., 2002; Ng et al., 2002]. When the latter mechanism fails, reproductive cells with genomic abnormalities may survive leading to serious developmental defects [Liu et al., 2002; Vinson and Hales, 2002]. That an elevated rate of chromosomal damage is a cause of cancer has been demonstrated by ongoing prospective cohort studies in Italy and the Scandinavian countries which showed a 2- to 3-fold increased risk of cancer in those whose chromosomal damage rate in lymphocytes was in the highest tertile when measured 10–20 years before cancer incidence was measured [Bonassi et al., 2000].

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MN formation – chromosome breakage or loss

Cytochalasin B block cytokinesis block

Nucleoplasmic bridge – chromosome translocation

Fig. 1. Expression of MNs and nucleoplasmic bridges during nuclear division. MNs originate from either (a) lagging whole chromosomes (top panel) that are unable to engage with the mitotic spindle due to a defect in the spindle, or a defect in the centromere/kinetochore complex required to engage with the spindle or (b) an acentric chromosome fragment originating from a chromosome break (top and bottom panel) which lags behind at anaphase because it lacks a centromere/kinetochore complex. Misrepair of two chromosome breaks may lead to an asymmetrical chromosome rearrangement producing a dicentric (i.e. two centromeres) chromosome and an acentric fragment (bottom panel) – frequently the centromeres of the dicentric chromosome are pulled to opposite poles of the cell at anaphase resulting in the formation of a nucleoplasmic bridge between the daughter nuclei. Nucleoplasmic bridges are frequently accompanied by an MN originating from the associated acentric chromosome fragment. Because MNs and nucleoplasmic bridges are only expressed in cells that have completed nuclear division, it is necessary to score these genome instability biomarkers specifically in once-divided cells. This is readily accomplished by blocking cytokinesis using cytochalasin B (for more detailed explanation refer to Thomas et al. [2003] and Fenech [2002]).

Chromosomal damage is also associated with accelerated ageing and neurodegenerative diseases. Several studies have shown that chromosomal abnormalities, including MN frequency (fig. 2), increase progressively with age in somatic cells [Bonassi et al., 2001; Fenech, 1998]. Accelerated ageing and cancer-prone syndromes, such as progeria, Bloom’s syndrome, Fanconi’s anaemia and Werner’s syndrome, exhibit increased chromosomal instability and/or accelerated telomere shortening due to defects in a variety of genes essential for DNA repair and telomere maintenance such as ATM, PARP, BRCA1, BRCA2 and DNA helicases [Joenje and Patel, 2001; Lansdorp, 2000; Shen and Loeb, 2001; Thompson and Schild, 2002]. Equally interesting is the observation that neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease

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Age (years)

b

76–90

66–75

56–65

46–55

36–45

76–90

66–75

56–65

46–55

36–45

26–35

10

26–35

20

100 90 80 70 60 50 40 30 20 10 0

18–25

30

0

a

MN per 1,000 BN cells

40

18–25

MN per 1,000 BN cells

50

Age (years)

Fig. 2. Variation in chromosome DNA damage rates of healthy non-smoking males (n ⫽ 495) (a) and females (n ⫽ 511) (b) within and between age groups measured using the cytokinesis block MN assay. BN ⫽ Binucleated; MN ⫽ micronuclei.

exhibit much higher rates of MN frequency in human peripheral blood lymphocytes [Migliore et al., 1999, 2001]. Those individuals with accelerated ageing syndromes or suboptimal DNA repair may be particularly susceptible to the genome damaging effects of moderate micronutrient deficiency.

The Concept of Genome Damage as a Marker of Nutritional Deficiency

Spontaneous chromosomal rates (more than one cell in a thousand exhibiting a major chromosomal mutation) are high in humans even in the absence of overt exposure to known carcinogens and there is a wide variation in rates of mutation even among individuals of the same age (fig. 2). One therefore has to consider whether genetic factors and diet may be the main determinants of variation in background mutation rate. There is overwhelming evidence that several micronutrients (vitamins and minerals) are required as cofactors for enzymes or as part of the structure of proteins (metalloenzymes) involved in DNA synthesis and repair, prevention of oxidative damage to DNA as well as maintenance methylation of DNA. The role of micronutrients in maintenance of genome stability has recently been extensively reviewed [Ames and Wakimoto, 2002; Fenech, 2003, 2005; Fenech and Ferguson, 2001]. Examples of micronutrients involved in various genome stability processes are given in table 1. The main point is that genome damage caused by moderate micronutrient deficiency is of the same order of magnitude as the genome damage levels caused by exposure

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Table 1. Examples of the role and the effect of deficiency of specific micronutrients on genomic stability Micronutrient/s

Role in genomic stability

Consequence of deficiency

Vitamins C and E

Prevention of oxidation of DNA and lipid oxidation [Claycombe and Meydani, 2001; Halliwell, 2001]

Increased baseline level of DNA strand breaks, chromosome breaks and oxidative DNA lesions and lipid peroxide adducts on DNA [Claycombe and Meydani, 2001; Halliwell, 2001]

Folate and vitamins B2, B6 and B12

Maintenance methylation of DNA; synthesis of dTMP from dUMP and efficient recycling of folate [Fenech, 2001]

Uracil misincorporation in DNA, increased chromosome breaks and DNA hypomethylation [Fenech, 2001]

Niacin, nicotinic acid

Required as substrate for poly(ADPribose) polymerase which is involved in cleavage and rejoining of DNA and telomere length maintenance [Boyonoski et al., 1999; Hageman and Stierum, 2001]

Increased level of unrepaired nicks in DNA, increased chromosome breaks and rearrangements, and sensitivity to mutagens [Boyonoski et al., 1999; Hageman and Stierum, 2001]

Zinc, manganese and selenium

Zn, required as a cofactor for Cu/Zn superoxide dismutase, endonuclease IV, function of p53, Fapy glycosylase and in Zn finger proteins such as poly(ADPribose) polymerase [Dreosti, 2001; Ho and Ames, 2003]; Mn, required as a component of mitochondrial Mn superoxide dismutase [Ambrosone et al., 1999; Keen and Zidenberg-Cherr, 1996]; Se required as a component of peroxidases, e.g. glutathione peroxidase [El-Bayoumy, 2001]

Increased DNA oxidation, DNA breaks and elevated chromosome damage rate [Ambrosone et al., 1999; Dreosti, 2001; El-Bayoumy, 2001; Ho and Ames, 2003; Keen and Zidenberg-Cherr, 1996]

Iron

Required as a component of ribonucleotide reductase and mitochondrial cytochromes [Walter et al., 2002]

Reduced DNA repair capacity and increased propensity for oxidative damage to mitochondrial DNA [Walter et al., 2002]

Calcium and magnesium

Mg, required as cofactor for a variety of DNA polymerases, in nucleotide excision repair, base excision repair and mismatch repair; essential for microtubule polymerisation and chromosome

Reduced fidelity of DNA replication; reduced DNA repair capacity; chromosome segregation errors; survival of genomically aberrant cells

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Table 1. (continued) Micronutrient/s

Role in genomic stability

Consequence of deficiency

segregation [Hartwig, 2001]; Ca, plays an important role in chromosome segregation and is required for apoptosis [Honda et al., 2004; Xu et al., 2003]

[Hartwig, 2001; Honda et al., 2004; Xu et al., 2003]

MNed BN cells per 1,000 BN cells

dTMP ⫽ Deoxythymidylic acid; dUMP ⫽ deoxyuridylic acid. For information on other micronutrients (e.g. carotenoids, vitamin D, polyphenols, and copper) refer to other papers in Fenech and Ferguson [2001].

30

X-rays Folic acid

20

10

0

0

5 120

10 60

20 24

X-rays (rad) 12

Folic acid (nM)

Fig. 3. A comparison of the dose-response effect on MN induction in cytokinesisblocked cultured lymphocytes caused by (a) acute exposure to X-rays up to a maximum dose of 20 rad, equivalent to 10 times the annual exposure safety limit for the general public [IAEA, 2001] and (b) folic acid deficiency within the ‘normal’ physiological range of 12–120 nM concentration. Data are from Fenech and Morley [1986] and Crott et al. [2001a, b]. MNed ⫽ Micronucleated; BN ⫽ binucleated. Results represent the mean ⫾1 SEM; n ⫽ 6 for X-rays and n ⫽ 20 for folic acid experiments. 1 rad ⫽ 0.01 Gy.

to significant doses of environmental genotoxins such as chemical carcinogens, ultraviolet radiation and ionising radiation. An example from our laboratory is the observation that the chromosomal damage in cultured human lymphocytes caused by reducing folate concentration from 120 to 12 nmol/l is equivalent to that induced by an acute exposure to 0.2 Gy of low linear energy transfer ionising radiation (e.g. X-rays), a dose of radiation which is approximately ten times greater than the annual allowed safety limit of exposure for the general population [International Atomic Energy Agency, 1986] (fig. 3). The normal folate concentration in plasma is only 10–30 nmol/l which may be adequate to prevent anaemia but insufficient to minimise chromosomal damage.

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Results from a Recent Epidemiological Study Suggest that There Are at Least Nine Micronutrients That Affect Genome Health Maintenance

We have recently reported the results of an epidemiological study on 190 healthy individuals (mean age 47.8 years, 46% males) designed to determine the association between dietary intake, measured using a food frequency questionnaire, and genome damage in lymphocytes, measured using the MN assay (fig. 1). Multivariate analysis of baseline data showed that (a) the highest tertile of intake of vitamin E, retinol, folic acid, nicotinic acid (preformed) and calcium was associated with significant reductions in MN frequency, i.e., ⫺28, ⫺31, ⫺33, ⫺46, and ⫺49%, respectively (all p values ⬍0.005), relative to the lowest tertile of intake and (b) the highest tertile of intake of riboflavin, pantothenic acid and biotin was associated with significant increases in MN frequency, i.e., ⫹36% (p ⫽ 0.054), ⫹51% (p ⫽ 0.021), and ⫹65% (p ⫽ 0.001), respectively, relative to the lowest tertile of intake (fig. 4). Mid-tertile ␤-carotene intake was associated with an 18% reduction in MN frequency (p ⫽ 0.038); however, the highest tertile of intake (⬎6,400 ␮g/day) resulted in an 18% increment in MN frequency. We were interested in investigating the combined effects of calcium or riboflavin with folate consumption because epidemiological evidence suggests that these dietary factors tend to interact in modifying the risk of cancer [Giovannucci, 2003; Lamprecht and Lipkin, 2003; Willet, 2001; Xu et al., 2003] and they are also associated with reduced risk of osteoporosis and hip fracture [Cagnacci et al., 2003; MacDonald et al., 2004; Sato et al., 2005]. Interactive additive effects were observed such as the protective effect of increased calcium intake (⫺46%) and the exacerbating effect of riboflavin (⫹42%) on increased genome damage caused by low folate intake. The results from this study illustrate the strong impact of a wide variety of micronutrients and their interactions on genome health depending on the level of intake. The results concerning folate are consistent with (a) studies showing that folate deficiency leads to hypomethylation of DNA and excessive incorporation of uracil into DNA which are two of the known underlying molecular events that cause chromosomal instability and MN formation [Choi and Mason, 2002; Crott et al., 2001a and 2001b; Fenech, 2003; Mashiyama et al., 2004] and (b) the observation from controlled intervention studies that folate intakes greater than 200 ␮g/day are required for chromosomal stability [Fenech et al., 1998; Fenech, 2001]. To our knowledge, there are no previous data showing an association between dietary calcium and chromosomal instability. However, calcium plays an important role in chromosome segregation [Honda et al., 2004; Xu et al., 2003]; it restrains cell proliferation, and induces apoptosis and cell differentiation, which may explain, in part, why reduced calcium intake is associated with

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75

Mid-tertile Highest tertile

*

* *

50

* 25

0

*

⫺25

* *

Biotin

Pantothenic acid

Riboflavin

Folate

Calcium

␤-Carotene

*

* Vitamin E

*

Nicotinic acid

⫺50

*

Retinol

Percent variation in genome damage

*p⬍ 0.006

Fig. 4. Percent variation in genome damage rate for the mid-tertile and highest tertile of intake of vitamin E, calcium, folate, retinol, nicotinic acid, ␤-carotene, riboflavin, pantothenic acid and biotin relative to the lowest tertile of intake. Genome damage rate was measured in peripheral blood lymphocytes using the cytokinesis block MN assay. For more information, refer to Fenech et al. [2005].

increased colorectal cancer risk [Cho et al., 2004; Giovannucci, 2003; Lamprecht and Lipkin, 2003].

The Concept of a Genome Health ‘Nutriome’

As shown in table 2, the concentration of genome-protective micronutrients varies greatly between foods and, therefore, one has to start considering foods and diets in terms of their ‘nutriome’, i.e. their content of genome-protective nutrients because this will determine which foods and combinations of foods are likely to be most beneficial for genome health maintenance. For example, certain cereal, vegetable and dairy foods are particularly rich in those micronutrients that have been shown in our epidemiological study to be protective against genome damage. A preliminary food group analysis of the data from the same study suggests that above-average intake of dairy foods, cereals and vegetables are independently associated with a 10–20% reduction in genome damage (data not

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Table 2. Examples of commonly consumed foods and their content of micronutrients that were found to be associated with improved genome stability [Fenech et al., 2005] Calcium mg/100 g Cereals Wheat bran 110 Soya flour 210 Wholemeal wheat flour 40

Folate ␮g/100 g

Niacin mg/100 g

Vitamin E mg/100 g

␤-Carotene ␮g/100 g

Retinol ␮g/100 g

260 345 57

29 2 6

1.6 1.5 1.4

0 0 0

0 0 0

0.8 0.1 0.9

205 20 195

310 35 325

Dairy Cheddar cheese Fresh whole milk Parmesan cheese

800 120 1,220

20 5 20

Nuts Almonds Peanuts Walnuts

250 40 60

96 76 66

2 11 1

20 5.6 0.8

0 0 0

0 0 0

Fish Sardines (canned) Cod (baked) Tuna (canned)

500 11 7

7 12 15

7 1.5 13

1.1 0.6 6.3

Tr Tr Tr

Tr Tr Tr

Meat Beef (mince cooked) Chicken (boiled) Lamb liver (cooked)

20 10 10

16 8 240

5 6.5 15

0.3 0.1 0.3

Tr Tr Tr

Tr Tr 26,780

Fruit Banana Orange Strawberry

7 30 20

22 28 20

0.6 0.2 0.4

0.2 0.2 0.2

200 40 30

0 0 0

Vegetables Broccoli (boiled) Spinach (boiled) Peas (frozen)

80 600 30

110 140 78

0.6 0.4 1.5

1.1 2 Tr

2,500 6,000 300

0 0 0

0.1 0.1 0.3

Tr ⫽ Trace amounts only. Micronutrient content data from Paul and Southgate [1978].

shown, paper in preparation). However, further detailed analyses and placebocontrolled trials are required to identify those specific foods (and levels of intake) with greatest potential for optimising genome health. It is interesting to note that increased intake of fruits, which are relatively poor in nutrients required for genome health maintenance (table 2), is associated with increased MN frequency

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in the same study, suggesting that a strong reliance on fruit within a dietary pattern may actually deplete the body of genome-protective nutrients.

Genome Health – A New Paradigm for Recommended Dietary Allowances

Current recommended dietary allowances (RDAs) for vitamins and minerals are based largely on the prevention of diseases of deficiency such as scurvy in the case of vitamin C, anaemia in the case of folic acid and pellagra in the case of niacin. However, these diseases of deficiency are rare in the developed world but degenerative disease and developmental disease are very important. The dietary allowance for folic acid for the prevention of neural tube defects has been revised to more than double the original RDA [Centers for Disease Control, 1992]. There is a strong international awareness that it is also necessary to redefine RDAs for the prevention of degenerative disease (such as cancer, cardiovascular disease and Alzheimer’s disease) and compression of the morbidity phase during old age. Because diseases of development, degenerative disease and ageing itself are partly caused by damage to DNA [Ames, 1998; Holliday, 1995], it seems logical that we should rather focus our attention on defining optimal requirements of key minerals and vitamins for preventing damage to both nuclear and mitochondrial DNA. To date, our knowledge on optimal micronutrient levels for genomic stability is scanty and disorganised. Table 1 lists some of the most important minerals and vitamins required for DNA maintenance and prevention of DNA damage and the DNA lesions that could be induced by inadequate intake of these antimutagenic vitamins. Supplementation of diet with appropriate minerals and vitamins could, in some cases, help overcome inherited metabolic blocks in key DNA maintenance pathways. A good example are the recent studies on optimisation of folate and vitamin B12 status for genome health maintenance. Both in vitro and in vivo studies with human cells clearly show that folate deficiency, vitamin B12 deficiency and elevated plasma homocysteine are associated with expression of chromosomal fragile sites, chromosome breaks, excessive uracil in DNA, MN formation and DNA hypomethylation (table 2) [Blount and Ames, 1995; Blount et al., 1997; Cravo et al., 1994; Crott et al., 2001a, b; Duthie and Hawdon, 1998; Fenech et al., 1998; Jacky et al., 1983; Jacob et al., 1998; Titenko-Holland et al., 1998]. It is notable that four of eight known human glycosylases are dedicated to the removal of uracil from DNA, the mutation caused by folate deficiency [Lindahl and Wood, 1999]. In vitro experiments indicate that DNA breaks in human cells are minimised when folic acid

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concentration in culture medium is greater than 180 nmol/l [Duthie and Hawdon, 1998; Jacky et al., 1983]. Recently, we have shown that uracil in DNA, chromosome breakage, chromosome rearrangement and gene amplification in human lymphocytes cultured for 9 days are minimised at a folic acid concentration of 120 nmol/l [Crott et al., 2001a, b]. Intervention studies in humans taking folate and/or vitamin B12 supplements show that DNA hypomethylation, chromosome breaks, uracil misincorporation and MN formation are minimised when plasma concentration of vitamin B12 is greater than 300 pmol/l, plasma folate concentration is greater than 34 nmol/l, red cell folate concentration is greater than 700 nmol/l folate and plasma homocysteine is less than 7.5 ␮mol/l [Blount and Ames, 1995; Blount et al., 1997; Cravo et al., 1994; Fenech et al., 1998; Jacob et al., 1998; Titenko-Holland et al., 1998]. These concentrations are only achievable at intake levels in excess of current RDAs, i.e. more than 400 ␮g folic acid per day and more than 2 ␮g vitamin B12 per day. Dietary intakes above the current RDA may be particularly important in those with defects in the absorption and metabolism of these vitamins, for which aging is a contributing factor. For example, it has recently been shown that vitamin B12 (active corrinoid) bioavailability is significantly reduced in Alzheimer’s disease patients suggesting a higher requirement for vitamin B12 in these individuals [McCaddon et al., 2001]. The defect in utilising vitamin B12 could explain the significantly elevated DNA damage rate (MN frequency) observed in both sporadic and familial Alzheimer’s disease patients [Trippi et al., 2001] because MN frequency is significantly related to vitamin B12 status [Fenech et al., 1998; Fenech, 2001]. It is important to note the conflict between the traditional RDA and the new genome health nutrigenomics concepts. The former is designed as a recommendation for 95% of the population to prevent deficiency of a particular nutrient while the latter is intended to provide recommendations on an individual basis by matching the nutriome to the genome to optimise genome health with the expected outcome of improved well-being throughout life and compressing morbidity in the ‘twilight’ years of life.

Genome Health Nutrigenomics

One of the important emerging areas of nutrition science is the field of nutrigenomics, i.e. the effect of diet on gene expression and chromosomal structure and the extent to which genetic differences between individuals influence response to a specific dietary pattern, functional food or supplement in terms of a specific health outcome. The specific field of genome health nutrigenomics [Fenech, 2005] has been proposed on the premise that a more useful

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approach to prevention of diseases caused by genome damage is to take into consideration the genotype of individuals with a focus on common genetic polymorphisms that alter the bioavailability of micronutrients and/or the affinity of key enzymes involved in DNA metabolism for their micronutrient cofactor. Supplementation of diet with appropriate minerals and vitamins could, in some cases, help overcome inherited metabolic blocks in key DNA maintenance pathways [Ames, 2003, 2004]. Increasing concentration of a cofactor by supplementation is expected to be particularly effective when a mutation (polymorphism) in a gene decreases the binding affinity for its cofactor resulting in a lower reaction rate. The interaction between genotype and diet in modulating risk is emerging as an exciting area of research as regards micronutrient effects on DNA. This is illustrated by recent research on the common mutations in the methylenetetrahydrofolate reductase (MTHFR) gene and other genes in the folate/methionine cycle with regard to developmental defects and cancer risk [Brody et al., 2002; Skibola et al., 1999]. The product of the MTHFR gene determines the availability of folate for the synthesis of thymidylic acid from deoxyuridylic acid. Polymorphisms in the MTHFR gene, such as the C677T mutation, that reduce activity of the MTHFR enzyme are predicted to minimise uracil misincorporation into DNA whilst making less methylfolate available for the synthesis of S-adenosyl methionine, the common methyl donor [Ames, 1999; Fenech, 2001]. Epidemiological studies have suggested that individuals homozygous for the C677T polymorphism (i.e. TT genotype, which causes reduced MTHFR activity) may be protected against colorectal cancer and acute lymphocytic leukaemia relative to those with the wild-type CC genotype [Chen et al., 1999; Skibola et al., 1999]. Recent results from our laboratory have shown that there are important significant interactions between the MTHFR C677T polymorphism, its cofactor riboflavin and folic acid with respect to chromosomal instability [Kimura et al., 2004]. This is illustrated by (a) the reduction in nuclear bud frequency (a biomarker of gene amplification) in TT homozygotes relative to CC homozygotes for the MTHFR C677T mutation and (b) the observation that high riboflavin concentration increases nuclear bud frequency under low folic acid conditions (12 nM folic acid) probably by increasing MTHFR activity which diverts folate away from dTTP synthesis, increasing the odds for uracil incorporation into DNA synthesis, the generation of breakagefusion-bridge cycles and subsequent gene amplification and nuclear bud formation. Other common polymorphisms, such as the manganese superoxide dismutase alanine-to-valine change in the ⫺9 position, which disables transport of this enzyme to the mitochondrion where it is normally located [Ambrosone et al., 1999], increase susceptibility to oxidative stress and breast cancer risk. Individuals with this manganese superoxide dismutase mutation appear to benefit more than controls from a higher intake of fruits and vegetables and/or

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vitamin C-rich foods in terms of protection against breast cancer [Ambrosone et al., 1999]. In the past, considerable attention has been given to gene-environment interaction as it relates to mutagen/carcinogen exposure and genotoxicity or cancer risk. However, it is probable that gene-diet interaction as it relates to efficacy of DNA repair/DNA metabolism and antioxidant response may be equally important in determining genomic stability and its consequent impact on fertility, development, cancer risk and the rate of ageing.

The Genome Health Clinic Concept – A Paradigm Shift in Disease Prevention Based on the Diagnosis and Nutritional Treatment of Genome and Epigenome Damage

The advances in our knowledge described above have opened up a new opportunity in disease prevention based on the concepts that (a) excessive genome damage is the most fundamental cause of developmental and degenerative disease, (b) genome damage caused by micronutrient deficiency is preventable, (c) accurate diagnosis of genome instability using DNA damage biomarkers that are sensitive to micronutrient deficiency is technically feasible and (d) it is possible to optimise nutritional status and verify efficacy by diagnosis of a reduction in genome and epigenome damage rate after intervention. Given the emerging evidence that dietary requirements of individuals may depend on their inherited genes, we can anticipate (a) important scientific developments in the understanding of the relationships between dietary requirement and genetic background to optimise genome stability and (b) that the accumulated knowledge on dietary requirements for specific genetic subgroups will be used to guide decisions by the practitioners of this novel preventive medicine in what might be called ‘Genome Health Clinics’. In other words, one can envisage that instead of diagnosing and treating diseases caused by genome and/or epigenome damage, health/medical practitioners will be trained, in the near future, to diagnose and nutritionally prevent a most fundamental initiating cause of developmental and degenerative disease, i.e. genome and epigenome damage. This novel approach also opens up the possibility for the massive numbers of health-conscious consumers to be able to assess directly the effect of their dietary and nutritional supplement choices on their genome and that of their children. In addition, there will be scope to develop new functional foods and supplements for genome health that can be mixed and matched so that the dietary intake nutriome is appropriately tailored to an individual’s genotype and genome status. The conceptual framework of the diagnostics and databases required to implement this complementary preventive medicine are described in more detail in Fenech [2003, 2005].

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Dr. Michael Fenech CSIRO Human Nutrition PO Box 10041 Adelaide BC, 5000 (Australia) Tel. ⫹61 8 8303 8880, Fax ⫹61 8 8303 8899, E-Mail [email protected]

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Tai ES, Gillies PJ (eds): Nutrigenomics – Opportunities in Asia. Forum Nutr. Basel, Karger, 2007, vol 60, pp 66–79

Nutrition: Ethics and Social Implications Inez H. Slamet-Loedin, Umar A. Jenie Indonesian Institute of Sciences, National Bioethics Commission, Jakarta, Indonesia

Abstract In October 2003, the general conference of UNESCO adopted the International Declaration on Human Genetic Data, followed by the adoption of the Universal Declaration on Bioethics and Human Rights in October 2005 to ensure the respect of human dignity and the protection of human rights and fundamental freedoms in the collection, processing, use and storage of human genetic data with the requirement of equality, justice and solidarity. Nutrigenomics studies the relationship between specific nutrients or diet and polymorphisms and gene expression; therefore, eventually diet can be tailored for each individual. The dietary intervention is based on collected human genetic data that eventually build knowledge of nutritional requirements, and the nutritional status of different human genotypes. This knowledge can be used to prevent, mitigate or cure chronic diseases. As in another branch of posthuman genome science, it is a global concern that the collected data should not be misused or create inequity. Some ethical issues raised and discussed in this paper are: (1) consent and confidentiality issues in the collection and storage of data, (2) genetic screening and how to prevent inequity, (3) regulatory oversight and in a wider context the need to improve public confidence in biotechnology-related science, (4) other social issues. The ethical issues in nutrigenomics need clear and concise guidelines developed in accordance with the universally adopted declarations and ethical concern needs to be integrated in the scientific design. Efforts to improve the public awareness, public participation and consultation need to be made at the early stage of the development of nutrigenomics. Copyright © 2007 S. Karger AG, Basel

Nutrigenomics has emerged as a new ‘omics’ technology developed as a more complex functional analysis compared to the basic sequence information provided by the Human Genome Projects. This branch of science in the area of genomics allows us to understand the relationship between specific nutrients or diet and gene expression which can eventually facilitate the development of a better strategy for the prevention or treatment of diseases and a personalized individual diet. The science of nutrigenomics allows a better molecular understanding of

how common dietary chemicals affect health by altering the expression of an individual’s genetic makeup [Mathers, 2005]. Like in other areas developed to seek further functional studies of genomic research, such as pharmacogenomics, the use of samples from identified populations resulted in the inextricable linkage of social, ethical and scientific issues. Ethical concerns have become a major global issue in the development of postgenomic science in humans. As a later branch of postgenomic science, the scientific community should learn from other experiences that ethical issues need to be addressed at an early stage of the development of this technology. The experience of pharmacogenetics will therefore give an indication of whether people are prepared to allow the use of their genetic information for the purposes of research and, ultimately, treatment [Burton and Steward, 2005]. Although the integration of ethical concerns into a scientific approach can be a serious challenge due to the complexity of the issues and the fact that the approach may be different in various societies, nowadays it is inevitable that the ethical concerns should be identified and addressed even in the experimental design process. The International HapMap Project is one example of this approach [International HapMap Consortium, 2004]. This HapMap Consortium Project raised many ethical issues because it allowed researchers to compare patterns of variation among both individuals and populations. They decided to have a separate team of ethics advisers that worked collaboratively throughout the project with geneticists to address the ethical issues. In a smaller project, it is still advisable to address ethical issues from early on in conjunction with the scientific decision to avoid a legal implication afterwards. Regarding nutrigenomics, the general public would most probably support the view that obtaining knowledge about the observation that, under certain circumstances and in some individuals, diet can be a serious risk factor for the onset and incidence of a number of diseases is a useful effort for the benefit of society. The paradigm of a preventive approach to health has been promoted even from the Hippocrates era on (‘leave your drugs in the chemist’s pot if you can heal the patient with food’). However, the fears that the collected genetic data will be misused and that the results of the genomic research based on the collected data may generate social discrimination have raised a major concern. The way the data are collected for a specific purpose and the possible misuse and misinterpretation of the collected data can lead to the violation of basic human rights such as privacy, equality and justice. The challenge is how to conduct genetic variation research that uses identified populations in an ethical way, including how to involve members of a population in evaluating the risks and benefits for everyone who shares that identity [International HapMap Consortium, 2004]. The Universal Declaration on Bioethics and Human Rights adopted in 2005 gave a general principle, i.e. ethical issues raised by the rapid science and

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technological applications should be examined with universal respect for the inherent dignity of the human person and with universal respect for, and observance of, human rights and fundamental freedoms [UNESCO, 2005]. In a global context, ethical issues in human genetics have been discussed by the general conferences of UNESCO and resulted in the adoption of the Universal Declaration on the Human Genome and Human Rights on November 11, 1997, followed by the International Declaration of Human Genetic Data on October 16, 2003, and recently the adoption of the Universal Declaration on Bioethics and Human Rights in October, 2005. Chadwick [2004] defined nutrigenomics as the relationship between specific nutrients or diet and gene expression and it is envisaged that it will facilitate the prevention of diet-related common diseases, while nutrigenetics is concerned with the effects of individual genetic variation (single nucleotide polymorphisms) in response to diet, which in the longer term may lead to personalized dietary guidance. The Nuffield report on nutrigenomics [2004] stated that the distinction between nutrigenomics and nutrigenetics provided a useful basis for discussion about what the new technologies could do and future problems. In this paper, the discussion of ethical issues of nutrigenomics will not be separated from nutrigenetics. The major issues discussed here are (1) the data collection process and storage, (2) how to prevent inequity and (3) regulatory oversight and in a wider context the need to improve public confidence in biotechnology-related science.

Data Collection: Consent, Privacy and Confidentiality

To a certain extent, there are similarities in ethical issues between nutrigenomics and pharmacogenomics, mainly with regard to privacy and confidentiality. In order to reveal an association between either diseases or nutrient response and a genetic polymorphism, we need population genetic data. The major difference between nutrigenomics and pharmacogenomics is the fact that pharmaceuticals are well-defined compounds aimed at specific targets; foods, however, are complex substances that have multiple effects on different pathways in the body [Muller and Kersten, 2003]; therefore, the size of population data for nutrigenomics studies may need to be even greater [Chadwick, 2004]. Consent, privacy and confidentiality are considered to be the principal issues in relation to the genetic database. The most commonly expressed fear is that genetic information will be used in ways that could deny people access to health insurance, employment, education, and even loans. This concern is partly increased by the growing recognition that health information is not entirely

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private [Clayton, 2003]. One of the issues to be resolved would be who ought to have access to nutrigenomics information and under what conditions. The fear is also related to the misconception of genetic determinism that the genetic ‘blueprint will tell you all about your future’, while in reality human characteristics are the product of complex interactions over time between genes and the environment. As is true for so many conditions in medicine, clinicians have a variable but usually limited ability to predict when, how severely, and even whether a person with a genetic predisposition to a certain illness is going to become ill [Guttmacher, 2001]. The reality shows that individuals carrying certain genes may be prone to the onset of the disease but far from guaranteed to develop the illness. Collection and management of nutrigenomics data involves data collection starting from obtaining consent, processing, use and storage of a massive amount of data of individuals and populations. The more individualized the promises of genetics, the more collective action is required in the form of population-based research to enable discernment of the differences at the genetic level between individuals that will affect susceptibility [Chadwick, 2004]. Consent In the summary of the Nuffield report on pharmacogenetics ethical issues [Nuffield Council on Bioethics, 2003], it was stated that there are numerous codes of practice and guidance regarding the conduct of clinical research which includes consent. It is a common practice to require consent for the collection and banking of tissue and DNA samples of participants in research, especially if it is intended to combine genetic information with other information from the patient’s medical record. As mentioned above, the nutrigenomics paradigm, as the paradigm of other genomic-based sciences, is proactive, rather than reactive, and will provide advice that is predictive, preventive, and personalized. Standardization of the method that will allow genetic screening in the future will require a large number of populations and nations, including eastern nations, as well as individual consent. Individual consent is obligatory in most of the available guidelines and regulations. There have been arguments that in the case of public interest, individual consent could be overruled. Another argument is that different societies have different concepts of individual freedom and autonomy; the values of individuality are interwoven with the cultural consciousness. In some countries, it is acceptable that local domestic authorities or local leaders give consent on behalf of their people (community consent) in accordance with prevailing local cultural norms, but still in any case domestic law should be upheld. Consent has to be voluntary in nature. The Universal Declaration on Bioethics and Human Rights [UNESCO, 2005] requires informed consent (article 10) and

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the International Declaration on Human Genetic Data [UNESCO, 2003] strongly states that prior, free, informed and expressed consent, without inducement by financial or other personal gains, should be obtained for the collection of human genetic data, human proteomic data or biological samples, whether through invasive or noninvasive procedures, and for their subsequent processing, use and storage, whether carried out by a public or private institution (article 8). Limitations on this principle of consent should only be prescribed for compelling reasons by domestic law consistent with the international law of human rights. The last sentence in the stipulation leaves room to a certain degree of flexibility in domestic regulation, but the next section strengthens that authorization should be obtained from the legal representative in accordance with domestic law; this legal representative should act to the best interest of the person concerned. It was also stated in the International Declaration on Human Genetic Data [UNESCO, 2003] that consent can be withdrawn without any penalty. Individual consent increasingly becomes an important human right issue. Consent has to be free without inducement by financial or other personal gains. The recent legal case of stem cell research in Korea initially started with an issue of consent, due to the fact that the donor was one of the junior researchers in the project, in which power may have played a role, before it later on also developed in other directions. In the case of pharmacogenetics, the Nuffield report [Nuffield Council on Bioethics, 2003] stated that there is a serious question regarding whether voluntary consent to pharmacogenetic testing can truly be obtained in the context of clinical trials or in clinical practice. If researchers require genotyping as a condition of enrolment in a study, patients might not feel able to refuse, especially if they think it is possible that they may get some personal benefit. In some cases, taking part in a clinical trial may be the only way for a patient to have a chance of obtaining a particular medicine, while actually, as stated by Corrigan [2005], the pharmacogenetics study is of no direct benefit to the patient and thus compromises in the consent process cannot be offset against the potential therapeutic benefit. For nutrigenomics, the issue of data collection might be slightly different. First the nutritional information to prevent the onset of a disease can be directly beneficial for the patients or at least the possible benefit is more direct, and second the public tends to think that health problems related to food are less daunting compared to other types of diseases. Regarding informed consent and avoiding the issue of financial gain, the International HapMap Project [International HapMap Consortium, 2004], in which samples were taken from the Yoruba people (from Nigeria), Japanese and Chinese individuals, and from residents of Utah in the United States, could serve as an example. Blood donors had to be adults (as legally defined in each

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country) and competent to provide informed consent. Although donors in Nigeria were each given an equivalent of approximately USD 8.00 and multivitamins worth approximately USD 4.00 to compensate them for their time and travel – a standard amount for the participation in research involving blood draws in that part of Africa – the prospective donors were not told that they would be compensated until after they had arrived to donate, to guard against the possibility that they would be induced to participate by the prospect of material benefit. Although this may not be entirely correct, it would also be unjust not to give any compensation to the people for their time and travel. Another important point is that the consent issue cannot be separated from the public awareness of what the consent is for and more generally the local public opinion on the research aims. Castro [2005] studied the International Ethical Guidelines for Biomedical Research Involving Human Subjects published by the Council for International Organizations of Medical Sciences and found 26 items related to consent in these guidelines that needed to be understood by the participating individuals. This long list suggests the amount of detail that has to be understood before an individual gives his/her consent. It is also very important to include local communities in the public consultation process. Whether consent has to be classified as ‘broad’ or ‘limited’ is another public issue. Limited or narrow consent refers to instances where a sample is only to be used for a restricted range of purposes, as for example a single research project, while broad consent entails that patients agree that their sample may be used for a variety of future studies which it may not be possible to specify in any detail at the time of consent. Allowing broad consent may be of significant benefit to researchers and to society’s interest in the acquisition of knowledge about health and disease, but it may not be beneficial for the individual. The Nuffield pharmacogenomics ethical team recommended/considered that it is permissible to request broad consent to the use of samples which are anonymous or anonymized; however, where samples collected for a particular study are coded or identified, broad consent to future research may also be permissible, but should be sought separately from consent to the initial study. This separate consent may be obtained when the samples are originally taken, or at a later date [Nuffield Council on Bioethics, 2004]. In any case of requesting a broad consent to future research, the prospective donors should be given a comprehensive explanation regarding the risks and benefits to allow them to have a clear understanding of the possible future implications. Privacy and Confidentiality Although confidentiality is often used interchangeably with privacy, according to Anderlik and Rothstein [2001], privacy subsumes at least four

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categories: (1) access to persons and to personal spaces; (2) access to information by third parties; (3) third-party interference with personal choices, especially in intimate spheres such as procreation, and (4) ownership of materials and information derived from persons. Privacy is also a term with deep emotional resonance, while confidentiality describes the duties that accompany the disclosure of nonpublic information to a third party within a professional, fiduciary, or contractual relationship. By law, social norm, or contract, and usually by some combination of these, the third party entrusted with the information is prohibited from redisclosing it or discussing it outside the confines of the relationship except under very restricted circumstances. Security refers to the measures taken to prevent unauthorized access to persons, places, or information. The measures used to achieve the goal of security vary according to the context and the state of technology. As regards privacy, the value of privacy in eastern society may differ from that in western society; however, generally the public favors protecting privacy. Anderlik and Rothstein [2001] explained that privacy has both an intrinsic and instrumental value. The intrinsic value is linked with the ethical principle of autonomy of individual self-governance. The genetic information is connected to personal and group identity, and protecting the privacy of genetic information is an important individual and social priority. The instrumental value of privacy is usually termed as utilitarianism. In the approach to ethics most closely associated with the philosopher Immanuel Kant, each person has the duty to respect the autonomy of others. This means that it is wrong to treat a fellow human being solely as a means to an end, even if that end is something noble like the advancement of science or the cure of disease and the prevention of suffering. In addition, the rules that govern society should be consistent with the principle of respect for autonomy. How to ensure confidentiality is a challenge. Most participants in a genetic data collection worry that employers and insurance companies might get access to the data. Article 14 of the International Declaration on Human Genetic Data [UNESCO, 2003] on privacy and confidentiality stipulates that states should endeavor to protect the privacy of individuals; genetic and proteomics data should not be disclosed to third parties, in particular employers, insurance companies, and educational institutions. Article 11 of the Universal Declaration on Bioethics and Human Rights [2005] regarding privacy and confidentiality also states that any decision/practice should respect privacy. It is in fact important to know and acknowledge set differences in order to be able to enhance assets and reduce liabilities, not only in the field of health care, but also in several other fields. Human interaction is not only marked by rationality, cooperation and complimentarily, but also by fierce competition, rivalry and misuse of other people’s weaknesses. It is therefore absolutely

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necessary when collecting extensive genetic data to safeguard the rights to privacy and confidentiality both of individuals and groups, and to respect the right of individuals to withhold and withdraw consent. The Nuffield report on pharmacogenetic ethics [Nuffield Council on Bioethics, 2003] mentioned that the implications for patients of DNA samples being used in research differ depending on how easily their samples can be traced back to them, and whether the research is likely to give rise to information that may be of personal clinical relevance. The report’s view is that it is generally possible to obtain genetic and clinical information about a patient during a clinical trial and then to anonymize the samples so that the code linking the sample with the patient is destroyed. It was also stated that researchers should explain to prospective participants the implications of the manner in which samples will be stored for those participants. In the International HapMap Projects [International HapMap Consortium, 2004], for example, no personal identifiers or medical information about sample donors were included, but each sample was identified by the population from which it came. The scientific rationale for identifying the populations is that differences in haplotype frequencies and lengths among populations will be important for how data are used in the research project. They decided to name the population since from an ethical point of view, removing population identifiers could create a false sense of protection from collective risks, because it would be easy to guess the populations from which donors were recruited. It would not be difficult either to discern from previously collected data sets the identity of these populations. Rather than allowing donors to assume that their population identities were protected or allowing other researchers to infer those identities, they concluded that naming the populations was thought to be more ethically appropriate. As mentioned above, security refers to the measures taken to prevent unauthorized access to persons, places, or information. The challenge is the fact that this technology deals with massive amounts of data; therefore, storage of data that can guarantee confidentiality needs to be carefully designed. Another challenge is who legally is allowed to have access to the individual data: key researchers, private doctors, parents of underage children? All these issues need to be made clear in the ethical guidelines. Large amounts of the data were collected by private sectors and in many examples these companies were then sold to a bigger company or merged. It should be made sure that changes in the personnel do not jeopardize the data security. To keep the public confidence, care should be taken to ensure the security of the data. Another concern is the potential for accidental release through system malfunction or access by hackers as well as a lapse of vigilance on the part of

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a range of persons, including data processors and researchers as well as health care professionals.

Genetic Screening, How to Prevent Inequity and Some Other Social Issues

Genetic Screening and How to Prevent Inequity Genetic screening is typically defined as the determination of the prevalence of a gene in an asymptomatic population or population group [Chadwick, 2004]. Genetic screening entails identifying individuals affected by a disease, at risk for developing a disease, or at risk for having a child affected by a disease. It involves a population-based approach, and populations are selected with specific goals and strategies in mind [McCabe and McCabe, 2004]. Newborn screening serves as a model for all genetic screening. Newborn screening identifies affected individuals and carriers [McCabe and McCabe, 2004]. There is also an example of the possible population screening beyond the newborn period. Prevention is one goal of screening for adult-onset disorders such as type 2 diabetes mellitus. One form of type 2 diabetes is maturity-onset diabetes of the young (MODY) (27). Diagnosing MODY relies on three generations with autosomal dominant diabetes and 2 patients with onset at ⱕ25 years of age. Thirteen percent of screened family members of Scandinavian patients had a mutation in one of the four MODY genes [Lehto et al., 1999]. If screening could be performed before adolescence, it might be possible to prevent obesity associated with MODY through diet and exercise. Many of the interventions for disorders such as diabetes mellitus, obesity, heart disease, and even some forms of cancer involve lifestyle changes including exercise and diet. Genetic screening in some instances may easily lead to prejudice and discrimination against a whole group or population. The same things can happen in the case of nutrigenetics. The example of public reactions to a publication by Rushton [1995] that suggests Asians in general have a higher IQ compared to Caucasians shows how sensitive this kind of issue is, although this example is not a result of genetic screening. The same kind of reaction may occur when a certain conclusion based on extensive genetic survey reveals significant differences not only between individuals but also between groups and populations. McCabe and McCabe [2004] stated that informed population screening applies the technology for the benefit of populations and the individuals composing those populations, and avoids health care disparities and there must be adequate information for each ethnic or cultural group regarding mutation frequency and penetrance. However, the dilemma is that this could lead to the stigmatization of certain ethnic groups concerning certain diseases. One of the

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differences within food and medicine is that individuals make food choices for a variety of reasons which are nonmedical and are more related to social issues such as expressing their sense of identity. The issue of prevention of inequity is also very much interwoven with privacy. Anderlik and Rothstein [2001] explained that rules protecting the privacy of genetic information are intended to prevent, lessen, or eliminate negative consequences of the new genetics. For example, recent policy statements from the American Society of Human Genetics and other organizations have addressed the appropriate use of predictive genetic testing of children. These statements discourage testing of children for adult-onset disorders and disorders that cannot be ameliorated or cured. It was also written in the same paper that adopting rules that restrict access to genetic information by third parties greatly reduces the potential for genetic discrimination in insurance and employment and a range of negative outcomes, including the destabilization of the insurance market, the sidelining of productive employees, and an erosion of social solidarity, culminating in the creation of a ‘genetic underclass’ of uninsurables and unemployables. Prohibitions against genetic discrimination are virtually meaningless without limitations on the access to genetic information. Laws protecting the privacy of health information and prohibiting genetic discrimination are included in most jurisdictions, but there are gaps in these laws and in the social safety net. McCabe and McCabe [2004] suggested avoiding health care inequalities; furthermore, screening should be offered at a reasonable price or as part of a free public health program. Other Social Issues Data interpretation, drawing conclusions and giving a balanced recommendation are also a major issue. As mentioned earlier, nutrigenomics in certain respects is more complex compared to pharmacogenomics; therefore, the amount of data has to be greater, and consequently interpretation and drawing conclusions of statistic data has to be done very carefully. Genetic screening can help individuals to take certain preventions including choosing a certain type of diet, but this knowledge may also lead individuals to take drastic preventions that may not be necessary such as the case of BRCA1 testing in families with BRCA1-linked hereditary breastovarian cancer. Genetic screening clearly requires a proper consultation and balanced recommendation. Certain types of diet may increase the chances of onset of a particular disease in a certain genetic makeup, but the chances that this happens is actually very small; however, the same diet may be important for other health reasons. In relation to health risks, Suzuki and Knudtson [1989] gave examples in the case of occupational genetic screening; individual differences in DNA often

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reveal statistical risks for occupational diseases that are inconsequential compared to the routine health risks faced by workers every day at home or on the job. A genotype that makes a worker 10 times more likely to suffer from an occupational disorder that only occurs in 1 out of 10,000 people may be intriguing, but in fact far less risky than commuting in busy traffic to a factory. Regarding public health issues, there is a concern that the interest in this new field of research could well overshadow other important necessities in the field of nutrition worldwide [Chadwick, 2004]. The question of whether it is ethical to allocate resources for a certain nutrigenomics research should be answered at the beginning of the project. Resources of developing countries should first of all be allocated to the tackling of basic public health problems addressed in the millennium goals such as malnutrition, diet-related diseases as well as contaminated food; this, however, should not exclude research in the field of nutrigenomics in developing countries. It only means that allocation of resources should be balanced. A way of assessing when applications of nutrigenomics in public health would be worthwhile is needed [Chadwick, 2004]. From a public health perspective, one might even go one step further and ask whether people will actually use the test results to alter their behavior in ways that improve health [Clayton, 2003]. Another concern is how the commercialization of this technology affects the type of applications that will be developed, and who will have access to them. It has to be shown that the results of this technology will not only benefit the developing countries or small groups of people. An example of nutrigenomics research such as green tea consumption that can reduce the chances of developing breast cancer shows the type of information that is useful for the general public. This confirms that this technology does not have to be applicable to the privileged few only.

Regulatory Oversight and Public Awareness

International ethics committees in medical research such as the Human Genome Organization Ethics Committee have already released a set of ethical guidelines for conducting research in these areas. As mentioned above, the general conferences of UNESCO have adopted three declarations, i.e. the Universal Declaration on the Human Genome and Human Rights [UNESCO, 1997], the International Declaration of Human Genetic Data [UNESCO, 2003] and the Universal Declaration on Bioethics and Human Rights [UNESCO, 2005], while at present in many countries there are no specific ethical guidelines available yet for the implementation of ethical principles in nutrigenomics research. As far as possible, domestic regulations and guidelines should be

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made available in accordance with the UNESCO declarations. However, certain adaptations to the local culture and legal framework cannot always be avoided. Efforts to improve the public awareness, public consultation and participation need to be made at the early level of development of nutrigenomics, by giving balanced information and allowing the public to be involved in the decisionmaking process. The example of the current public reactions in some countries to genetically modified food shows how important food issues are for many individuals; these issues are not only centering on the food safety but also include the freedom of choice. In general, there is an urgent need to improve public confidence in biotechnology-related science. Public communication of the emerging science with the public should be pursued. Without public participation, there is a very real risk that the public will turn against this emerging genetic research if projects come to light that violate public expectations of the protection of privacy and autonomy [Anderlik and Rothstein, 2001]. When conducting public consultation at different stages of the research and policy setup, researchers and policy makers need to recognize the importance of local cultures and social systems, values and beliefs. The International HapMap Consortium [2004] shared their experience that asking people respectfully about participating in projects of this type, providing complete, balanced and accurate information, giving them a chance to express their views, and, where possible, incorporating their input need not unduly impede research. Indeed, it can create a climate in which research proceeds in an atmosphere of openness and trust. Information has to be relayed to the patients, donors, health care workers and the general public. The Internet may be an important means apart from others since it can reach various lay audiences [Guttmacher, 2001]. Information about the ‘central dogma’ of genetics to those seeking to inform concerned citizens about ethical, legal, and social aspects has to be easily accessible. The Web has been seen as an egalitarian way to relay information, but on the other hand it has to be realized that the Internet should not be the only means to inform the public since there is still limited Internet access in some localities in the developing and less developed countries.

Concluding Remarks

Ethical issues raised by rapid advances in science and technology applications should be examined with respect for the inherent dignity of the human person and with universal respect for, and observance of, human rights and fundamental freedoms, as mentioned in the Universal Declaration on Bioethics and Human Rights [UNESCO, 2005].

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Consent, privacy and confidentiality as well as equitable access to the technology are the major ethical issues that need to be considered when countries develop their guidelines, regulation and laws related to nutrigenomics. Public consultations at different stages of research and the development of a nutrigenomic project involving a large population need to be carried out to fulfill the right to know and to create an atmosphere of openness and trust. A concise and clear set of guidelines to undertake research in nutrigenomics needs to be developed consistent with the Universal Declaration on Bioethics and Human Rights [UNESCO, 2005] and the International Declaration on Human Genetic Data [UNESCO, 2003]. A way of assessing when applications of nutrigenomics in public health would be worthwhile is needed. Efforts to improve the public awareness and public participation in nutrigenomics need to be made in this new branch of science right from the start. Nutrigenomics research that is beneficial for the general public needs to be undertaken to prove that this technology does not have to be applicable to privileged people only.

References Anderlik MR, Rothstein MA: Privacy and confidentiality of genetic information: what rules for the new science? Annu Rev Genomics Hum Genet 2001;2:401–433. Burton H, Steward A: Nutrigenomics: Report of a Workshop Hosted by The Nuffield Trust and Organized by The Public Health Genetics Unit in February 2004. London, The Nuffield Trust, 2005. Castro LD: Informed consent: what information? Whose consent? Proc Int Joint Bioethics Congr on Intercult Bioethics Asia and the West, Sanliurfa, November 2005. Chadwick R: Nutrigenomics, individualism and public health. Proc Nutr Soc 2004;63:161–166. Clayton EW: Ethical, legal, and social implications of genomic medicine. N Engl J Med 2003;349: 562–569. Corrigan OP: Pharmacogenetics, ethical issues: review of the Nuffield Council on Bioethics Report. J Med Ethics 2005;31:144–148. Guttmacher AE: Human genetics on the web. Annu Rev Genomics Hum Genet 2001;2:213–233. International HapMap Consortium: Integrating ethics and science in the International HapMap Project. Nat Rev Genet 2004;5:467–475. Kaput J: Decoding the pyramid: a systems-biological approach to nutrigenomics. Ann NY Acad Sci 2005;1055:64–79. Lehto M, Wipemo C, Ivarsson SA, Lindgren C, Lipsaren-Nyman M, et al: High frequency of mutations in MODY and mitochondrial genes in Scandinavian patients with familial early-onset diabetes. Diabetologia 1999;42:1131–1137. Mathers J: The science of nutrigenomics; in Burton H, Steward A (eds): Nutrigenomics: Report of a Workshop Hosted by The Nuffield Trust and Organized by The Public Health Genetics Unit on 5 February 2004. London, The Nuffield Trust, 2005. McCabe LL, McCabe ERB: Genetic screening: carriers and affected individuals. Annu Rev Genomics Hum Genet 2004;5:57–69. Muller M, Kersten S: Nutrigenomics: goals and strategies. Nat Rev Genet 2003;4:315–322. Nuffield Council on Bioethics: Pharmacogenetics: ethical issues. Summary and recommendations. 2003. www.nuffieldbioethics.org/file/library/pdf/pharm_summary_chapter.pdf.

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Rushton P: Race, Evolution and Behaviour. A Life History Perspectives. New Brunswick, Transaction Publishers, 1995. Suzuki D, Knudtson P: Genethics. The Ethics of Engineering Life. Cambridge, Harvard University Press, 1989. UNESCO: Universal Declaration on the Human Genome and Human Rights. 1997. UNESCO: International Declaration on Human Genetic Data. 2003. UNESCO: Universal Declaration on Bioethics and Human Rights. 2005.

Dr. Inez H. Slamet-Loedin Indonesian Institute of Sciences, National Bioethics Commission Sasana Widya Sarwono, Jl, Gatot Subroto 10 Jakarta 12710 (Indonesia) Tel. ⫹62 875 4627/5873, Fax ⫹62 875 4588, E-Mail [email protected]

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Tai ES, Gillies PJ (eds): Nutrigenomics – Opportunities in Asia. Forum Nutr. Basel, Karger, 2007, vol 60, pp 80–90

Proteomics Visith Thongboonkerd Siriraj Proteomics Facility, Medical Molecular Biology Unit, Office for Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand

Abstract Proteomics has been widely applied to several biomedical fields in recent years. The high-throughput capability of proteomics allows simultaneous examination of numerous proteins and offers the possibility of a global analysis of proteins in cells, tissues or biofluids. The rapid progress in the field of proteomics is based primarily on the success of protein separation sciences (either gel-based or gel-free techniques) and recent advances of mass spectrometry. Unlike the genome, the proteome is dynamic and varies according to cell type and functional state of the cell. In addition, gene expression does not always correlate with protein expression as one gene can be modified to be several products or proteins that directly govern cellular function. Thus, proteome analysis is expected to provide a wealth of useful information in nutrition research on the effects of nutrients or food components on metabolic pathways. Such research allows experts to explore the regulatory mechanisms for maintaining normal homeostasis during nutritional imbalance, to better understand the pathogenic mechanisms and pathophysiology of nutritional disorders, to define molecular targets of bioactive food components and to identify biomarkers that can be used as diagnostic, predictive or prognostic factors. This paper will provide a brief overview of proteomics, a summary of current proteomic technologies and an example of proteomic application to nutrition research. Finally, the concept of systems biology, which involves integrative ‘omics’ (i.e., combining genomics, transcriptomics, proteomics, lipomics and metabolomics) as well as bioinformatics and modeling, will be discussed. Due to the extent of information that can be obtained from systems biology, this ideal approach holds great promise for future nutrition research. Copyright © 2007 S. Karger AG, Basel

It is well known that nutrition is closely related to health status and illness. Dietary habits and bioactive food components can modulate some medical diseases. Current knowledge on how these food components can modulate health status or diseases remains unclear. Because enzymes in metabolic pathways and

most molecular targets for nutrients are proteins, it is most likely that extensive analysis on these proteins will lead to a better understanding of molecular effects of nutrients or bioactive food components on health status and diseases. Traditional study of proteins involved in nutritional sciences has relied on conventional biochemical methods. This approach is based primarily on hypothesisdriven research, focusing on a specific pathway of nutritional metabolism. Although successful, it is time-consuming and the molecular targets to be studied require a priori assumption. Thus, the number of molecular targets currently known is underestimated and additional methods are required to screen for a large number of novel targets simultaneously. In the postgenomic era, when the Human Genome Project has been completed, several biotechnologies have been developed to utilize the genomic information to examine other cellular compositions (e.g. proteins, transcripts, metabolites, and lipids) on the genomic scale. Since then, respective ‘omics’ fields (e.g. proteomics, transcriptomics, metabolomics, and lipomics) have been defined and widely applied to biomedical research. The successfulness of these omics fields is mainly due to advances in separation sciences (either gel-based or gel-free methods) and mass spectrometry (MS). The great contribution of MS technology to life science has been confirmed as the 2002 Nobel prize in chemistry went to John B. Fenn (who developed electrospray ionization; ESI) and Koichi Tanaka (who developed matrix-assisted laser desorption/ionization; MALDI) [1].

Brief Overview of Proteomics

Since the term ‘proteome’ was coined for the first time in the public by Marc Wilkins in 1994, proteomics, which is a subject or an area of sciences to study the proteome, has been widely applied to several biomedical fields. For human studies, proteomics has been used with five main objectives: (1) to better understand human physiology; (2) to explore and better understand the complexity of pathogenic mechanisms and the pathophysiology of diseases; (3) to define new therapeutic targets for better therapeutic outcome; (4) to identify novel biomarkers for earlier diagnosis, and (5) to identify molecular targets for vaccine development for disease prevention. From 1995 through the end of November 2005, approximately 11,000 proteomics-related articles can be found in PubMed (using the key words ‘proteomics’, ‘proteome’ or ‘proteomic’). Interestingly, half of them appear in the last 2 years. These numbers in a wide variety of international journals imply the rapid progress of the proteomics field and its utility, as well as applicability. The concept of proteomics can be differentiated from that of conventional protein chemistry. Protein chemistry aims to extensively examine the protein

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structure and physicochemical properties of each protein in detail, whereas proteomics aims to simultaneously examine a large number of proteins or the entire proteome in a complex mixture of biological samples (more details for protein chemistry, whereas broader contents for proteomics). Both of them are aimed to better understand the cellular biology and physiology. There are some overlaps between these two fields, e.g. the area of structural proteomics. Therefore, these two fields are different but closely related and complementary. Although they can be distinguished based on their concepts, these two fields cannot be completely separated in practice or in a study on a large number of proteins. When should proteomics be applied? First, the investigators need to have clear specific aims and future plans for the proteomics project. Otherwise, the technology can be misused. The investigators need to design whether they really need the high-throughput analysis. Second, the nature of the study project can determine the type of technologies to be applied. Proteomics is suitable for fishing expedition or screening for candidate proteins, whereas the conventional methods are suitable for hypothesis-driven research in which some leads or prior results are required before proceeding. Third, funding support should be sufficient for a proteomic study as it is costly. Finally, specific instrumentations such as two-dimensional polyacrylamide gel electrophoresis (2D-PAGE), liquid chromatography (LC), capillary electrophoresis (CE), protein chip instrumentation, as well as a mass spectrometric system are needed. ‘Proteome analysis’ (or ‘proteomic analysis’, which will be used interchangeably in this article) can be divided into two categories based upon analytical strategies. The ‘classical approach’ involves extensively and systematically examining proteins for their expression and function, whereas the ‘alternative approach’ bypasses the complicated analytical procedures in the classical approach and involves proteome profiling just to differentiate the types of biological samples (e.g. normal versus disease; a specific disease versus other diseases). The former is suitable for unraveling the pathophysiology of diseases, whereas the latter is suitable for clinical diagnostics and biomarker screening. Techniques that are used in the alternative approach are microarrays, surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF), and CE coupled to MS.

Current Proteomic Technologies

Gel-Based Methods Currently, 2D-PAGE is the most commonly used method in proteomic studies. The principles of protein separation by 2D-PAGE are based on differential pH or isoelectric point (pI) for the first dimension and differential molecular

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size (Mr) for the second dimensional separation [2]. Resolved proteins in 2D gel can be visualized by various stains (e.g. Coomassie brilliant blue, silver, fluorescence). Visualized protein spots can then be excised, in-gel digested with proteolytic enzymes (e.g. trypsin, chymotrypsin, Arg-C, Asp-N, Lys-C, pepsin A, V8-E, V8-DE), and identified by MALDI-MS followed by peptide mass fingerprinting. The most common type of mass analyzer employed in MALDI analysis is time-of-flight (TOF). MALDI-TOF-MS provides a high-throughput manner of protein identification; hundreds of proteins can be identified within a day [3–5]. Consequently, MALDI-TOF-MS has become an integral part of today’s modus operandi in proteome analysis. Even with lots of advantages, the gel-based approach has some limitations. 2D-PAGE procedures are timeconsuming, and low-abundant, transmembrane, and highly hydrophobic proteins may not be detectable in a 2D gel.

Gel-Free Methods Liquid Chromatography Coupled to Tandem Mass Spectrometry Coupling of high-performance LC to ESI-tandem MS (MS/MS) has gained a wide acceptance for gel-free proteomic analysis and become a method of choice for the analysis of membrane and low-abundant proteins [6–8]. ESI is the process of ionization from the electrospray source, whereas MS/MS refers to the strategy of multistep mass analyses. When compared to the 2D-PAGE approach, LC-based methods are more effective for the analysis of small proteins and peptides, as well as membrane and highly hydrophobic proteins. Recently, a high-throughput LC approach has been developed, namely ‘multidimensional protein identification technology’ or 2D-LC-MS/MS [9]. This approach involves proteolytic digestion of the total protein mixture to obtain a set of protein-derived peptides that are then separated by strong cation exchange chromatography (‘bottom-up’ approach). Peptides present in fractions from this strong cation exchange step are separated further by reversedphase LC and then sequenced by MS/MS. Several thousand of peptides can be sequenced in this way in a relatively short time. In another methodology (‘topdown’ approach), which is in contrast to the bottom-up approach, the undigested proteins in the complex mixture are separated by high-performance LC prior to digestion with proteolytic enzymes and MS/MS sequencing [10, 11]. Surface-Enhanced Laser Desorption/Ionization or Protein Chip Technology This method is suitable for proteome profiling. SELDI-TOF-MS combines MALDI-TOF-MS with surface retentate chromatography. A protein sample is

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applied onto a chip surface carrying a functional group (e.g. normal phase, hydrophobic, cation or anion exchange). After incubation, proteins that do not bind to the surface are removed by a simple washing step and bound peptides/ proteins are analyzed by a TOF mass spectrometer. The detection of a protein by SELDI-TOF-MS is critically determined by its concentration in the sample, its binding to the chromatographic surface and its ionization process within the mass spectrometer. This approach reduces the complexity of the sample being analyzed by selecting only a subset of proteins. Only 5–10 ␮l of sample are needed for a single analysis and this method can be readily automated, making it particularly useful for high-throughput studies. Capillary Electrophoresis Coupled to Mass Spectrometry CE-MS is another method suitable for proteome profiling. It offers some advantages as it is fairly robust, uses inexpensive capillaries and is compatible with essentially all buffers and analytes [12–15]. In contrast to LC, CE generally has no flow rate but requires a closed electric circuit. Various MS coupling techniques can be applied to CE [16, 17]. The predominant ionization method for CE-MS is ESI, while MALDI has also been used extensively [18, 19]. The main advantages of MALDI appear to be the enhanced stability as well as easier handling compared to ESI. Additionally, once the analytes are deposited on the target, they can be reanalyzed several times without the need of a new CE run. Moreover, the deposited analytes can be subsequently manipulated. The disadvantages of MALDI are certainly the decreased dynamic range in comparison to ESI and the higher sensitivity towards signal suppression. For the detection of the narrow CE-separated analyte zones, a fast and sensitive mass spectrometer is required. Both ion trap and TOF systems appear adequate. While ion trap MS acquires data over a suitable mass range with the rate of several spectra per second, the resolution is generally too low to resolve the single isotope peaks of ⬎3-fold charged molecules. Consequently, assignment of charge to these spectra is hampered. Modern ESI-TOF mass analyzers record up to 20 spectra per second and provide the resolution of more than 10,000 and a mass accuracy of better than 5 ppm. Therefore, the most suitable mass spectrometer for this type of analysis, to date, is ESI-TOF-MS. Mass Spectrometric Immunoassay Mass spectrometric immunoassay combines immunoassays with MALDITOF-MS [20–33]. Proteins are first captured by microscale affinity techniques and are subsequently examined qualitatively and quantitatively using MALDITOF-MS [34]. This approach has the potential for greatly extending the range, utility and speed of biological research and clinical assays. In the initial phase of development, agarose beads (derivatized with an affinity ligand) were used

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to create a microliter-volume column inside a micropipettor tip (thus, creating an affinity pipette) [20, 22]. More recently, tailored affinity micropipettes with a high flow rate and a high binding capacity have been manufactured and used in combination with robotic platforms for the preparation of up to 96 samples in parallel [24, 28, 32]. Using this approach, the proteins of interest are selectively retained and concentrated by repeat flowing through the affinity pipette. After washing to remove unspecified compounds, the retained proteins are eluted, mixed with a MALDI matrix (i.e. ␣-cyano-4-hydroxycinnamic acid), and targeted onto the MALDI plate. The eluted proteins are then analyzed with a mass spectrometer (TOF-MS).

Applications of Proteomics to Nutrition Research

Proteomics applied to nutrition research or ‘nutriproteomics’ is now at its infancy phase as compared to proteomics applied to other biomedical fields. Even with several review articles on nutriproteomics during the past few years [35–45], the number of original research on nutriproteomics is small [46, 47]. Herein, I provide an example of proteomic application to nutrition research using an animal model of chronic potassium (K⫹) depletion induced by inadequate K⫹ intake [fed with a K⫹-depleted (KD) diet] [46]. K⫹ is one of the most important electrolytes that are crucial for maintaining the normal homeostasis of living cells, tissues and organs. The kidney is the major organ responsible for regulating normal K⫹ homeostasis. Abnormal K⫹ balance may occur when K⫹ intake and its output are imbalanced. Inadequate dietary K⫹ intake, renal K⫹ loss (excessive urinary K⫹ excretion) and/or extrarenal K⫹ loss (e.g. diarrhea and vomiting) can lead to hypokalemia, which is widely defined as a serum K⫹ level of less than 3.5 mmol/l. Hypokalemia may be asymptomatic if the deficit is temporary and the degree of the deficit is modest to mild (3.0–3.5 mmol/l), but can be a cause of death when the degree of the deficit is severe (⬍3.0 mmol/l) and the dysregulation is left untreated. Prolonged K⫹ deficiency can affect several organ systems, especially hemodynamic, cardiovascular, muscular, gastrointestinal and renal systems [48–50]. Renal involvement of prolonged K⫹ depletion has been defined as ‘hypokalemic nephropathy’, a disease known for half a century [51–53], and is associated with metabolic alkalosis, growth retardation, hypertension, polydipsia, polyuria, enlarged kidney, progressive tubulointerstitial injury and ultimately renal failure or end-stage renal disease [54–59]. Even with a long history of the disease, the pathophysiology of hypokalemic nephropathy remains unclear. Although K⫹ repletion can reverse renal ultrastructural changes that occur in an acute K⫹ deficiency state [60], these changes and renal dysfunction remain

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in some cases of chronic K⫹ deficiency, even with the repletion therapy [54, 61]. A recent study by our group has applied proteomic technology to discover previously unknown changes in renal protein expression that are associated with hypokalemic nephropathy [46]. Hypokalemia was induced by giving ad libitum KD diet to BALB/c mice for 8 weeks, whereas the control mice received normal K⫹ chow. The KD mice displayed many characteristics of human hypokalemic nephropathy, including severe hypokalemia, growth retardation, polydipsia, polyuria, markedly enlarged kidneys, severe tubular dilatation, intratubular deposition of amorphous and laminated hyaline materials, as well as tubular atrophy. Gel-based, differential proteome analysis of the kidney (using 2DPAGE and quantitative intensity analysis) revealed altered expression of 33 renal proteins in KD mice. Using MALDI-TOF-MS and quadrupole-TOF-MS/MS, 30 of the altered proteins were identified, including metabolic enzymes (e.g. carbonic anhydrase II, aldose reductase, glutathione S-transferase GT41A), signaling proteins (14-3-3␧, 14-3-3␨ and cofilin 1) and cytoskeletal proteins (␥-actin and tropomyosin) [46]. Some of these altered proteins, particularly metabolic enzymes and signaling proteins, have been demonstrated to be involved in metabolic alkalosis, polyuria and renal tubular injury. Our findings may lead to a new road map for research on hypokalemic nephropathy and to a better understanding of the pathophysiology of this medical disease. In addition to dietary-induced K⫹ depletion, nutriproteomics is expected to provide a wealth of useful information in nutrition research to study effects of nutrients or food components on metabolic pathways, to explore the regulatory mechanisms for maintaining normal homeostasis during nutritional imbalance, to better understand the pathogenic mechanisms and pathophysiology of nutritional disorders, to define molecular targets of bioactive food components and to identify biomarkers that can be used as diagnostic, predictive or prognostic factors.

Integrative Omics and Systems Biology

It is unlikely that the complexity of nutrition science will be completely understood only by proteomics or by any other single omics approach. Integrating all of them is required for future nutrition research. Recently, the concept of ‘systems biology’ has been emerging for the global evaluation of biological systems and has included ‘integrative omics’ as one of analytical processes [62, 63]. Systems biology has been defined by Weston and Hood [64] as ‘the analysis of the relationships among the elements in a system in response to genetic or environmental perturbations, with the goal of understanding the

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system or the emergent properties of the system’. A system may be a few protein molecules carrying out a particular task such as galactose metabolism (termed a biomodule), a complex set of proteins and other molecules working together as a molecular machine such as the ribosome, a network of proteins operating together to carry out an important cellular function such as giving the cell shape (protein network), or a cell or group of cells carrying out particular phenotypic functions. Thus, a biological system may encompass molecules, cells, organs, individuals, or even ecosystems [64]. Advances in the highthroughput platforms of biotechnologies have allowed the simultaneous study of a large complement of genes, transcripts, proteins, lipids or other elements. Systems biology or integrative omics is, thus, the ideal approach for future nutrition research.

Conclusions

The current knowledge in nutrition science can be enhanced by recent advances in the postgenomic biotechnologies, particularly proteomics. Using gel-based and/or gel-free proteomic methodologies, a large number of proteins can be examined simultaneously in a single experiment. The high-throughput capability of proteomics, thus, holds a potential promise in nutrition research. However, it is unlikely that the complete dynamic image of nutrition science will be obtained by a single omics approach. With the concept of systems biology, integrating proteomics into the other omics sciences is, therefore, the ideal approach for future nutrition research.

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Kiernan UA, Tubbs KA, Nedelkov D, Niederkofler EE, Nelson RW: Comparative phenotypic analyses of human plasma and urinary retinol binding protein using mass spectrometric immunoassay. Biochem Biophys Res Commun 2002;297:401–405. Kiernan UA, Tubbs KA, Nedelkov D, Niederkofler EE, Nelson RW: Detection of novel truncated forms of human serum amyloid A protein in human plasma. FEBS Lett 2003;537:166–170. Nedelkov D, Tubbs KA, Niederkofler EE, Kiernan UA, Nelson RW: High-throughput comprehensive analysis of human plasma proteins: a step toward population proteomics. Anal Chem 2004;76: 1733–1737. Nelson RW, Nedelkov D, Tubbs KA, Kiernan UA: Quantitative mass spectrometric immunoassay of insulin like growth factor 1. J Proteome Res 2004;3:851–855. Nelson RW, McLean MA, Hutchens TW: Quantitative determination of proteins by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Anal Chem 1994;66:1408–1415. Daniel H: Genomics and proteomics: importance for the future of nutrition research. Br J Nutr 2002;87(suppl 2):S305–S311. Go VL, Butrum RR, Wong DA: Diet, nutrition, and cancer prevention: the postgenomic era. J Nutr 2003;133:3830S–3836S. Milner JA: Molecular targets for bioactive food components. J Nutr 2004;134:2492S–2498S. Kim H, Page GP, Barnes S: Proteomics and mass spectrometry in nutrition research. Nutrition 2004;20:155–165. Arab L: Individualized nutritional recommendations: do we have the measurements needed to assess risk and make dietary recommendations? Proc Nutr Soc 2004;63:167–172. Ross SA, Srinivas PR, Clifford AJ, Lee SC, Philbert MA, Hettich RL: New technologies for nutrition research. J Nutr 2004;134:681–685. Barnes S, Kim H: Nutriproteomics: identifying the molecular targets of nutritive and non-nutritive components of the diet. J Biochem Mol Biol 2004;37:59–74. Davis CD, Milner J: Frontiers in nutrigenomics, proteomics, metabolomics and cancer prevention. Mutat Res 2004;551:51–64. Davis CD, Hord NG: Nutritional ‘omics’ technologies for elucidating the role(s) of bioactive food components in colon cancer prevention. J Nutr 2005;135:2694–2697. Kim H: New nutrition, proteomics, and how both can enhance studies in cancer prevention and therapy. J Nutr 2005;135:2715–2718. Fuchs D, Winkelmann I, Johnson IT, Mariman E, Wenzel U, Daniel H: Proteomics in nutrition research: principles, technologies and applications. Br J Nutr 2005;94:302–314. Thongboonkerd V, Chutipongtanate S, Kanlaya R, Songtawee N, Sinchaikul S, Parichatikanond P, Chen ST, Malasit P: Proteomic identification of alterations in metabolic enzymes and signaling proteins in hypokalemic nephropathy. Proteomics 2006;6:2273–2285. Mitchell BL, Yasui Y, Lampe JW, Gafken PR, Lampe PD: Evaluation of matrix-assisted laser desorption/ionization-time of flight mass spectrometry proteomic profiling: identification of alpha 2-HS glycoprotein B-chain as a biomarker of diet. Proteomics 2005;5:2238–2246. Gennari FJ: Hypokalemia. N Engl J Med 1998;339:451–458. Gennari FJ: Disorders of potassium homeostasis. Hypokalemia and hyperkalemia. Crit Care Clin 2002;18:273–288, vi. Antes LM, Kujubu DA, Fernandez PC: Hypokalemia and the pathology of ion transport molecules. Semin Nephrol 1998;18:31–45. Relman AS, Schwartz WB: The nephropathy of potassium depletion: a clinical and pathological entity. N Engl J Med 1956;255:195–203. Hollander W Jr: The effect of potassium depletion on the kidneys. N C Med J 1957;18:505–509. Weissmann G, Ludwig AM: Potassium depletion nephropathy. J Mt Sinai Hosp NY 1958;25: 454–458. Tolins JP, Hostetter MK, Hostetter TH: Hypokalemic nephropathy in the rat. Role of ammonia in chronic tubular injury. J Clin Invest 1987;79:1447–1458. Berl T, Linas SL, Aisenbrey GA, Anderson RJ: On the mechanism of polyuria in potassium depletion. The role of polydipsia. J Clin Invest 1977;60:620–625. Ray PE, Suga S, Liu XH, Huang X, Johnson RJ: Chronic potassium depletion induces renal injury, salt sensitivity, and hypertension in young rats. Kidney Int 2001;59:1850–1858.

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Dr. Visith Thongboonkerd Siriraj Proteomics Facility, Medical Molecular Biology Unit Office for Research and Development Faculty of Medicine Siriraj Hospital, Mahidol University Bangkok (Thailand) Tel./Fax ⫹66 2 4184793, E-Mail [email protected]

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Diet and Genomic Stability Graeme P. Young Department of Medicine, Flinders University, Adelaide, Australia

Abstract Cancer results from a disordered and unstable genome – the degree of abnormality progresses as the process of oncogenesis proceeds. Such genomic instability appears to be subject to control by environmental factors as evidenced by the number of cancers that are either caused by specific environmental agents (lung, skin, cervix) or else regulated by a broader range of agents such as effect of diet on gastric and colorectal cancers. Dietary factors might interact in several ways with the genome to protect against cancer. An agent might interact directly with the genome and regulate expression (as a genetic or epigenetic regulator) or indirectly by influencing DNA ‘repair’ responses and so improve genomic stability. Research now shows that diet-genomic interactions in cancer go beyond interactions with the normal genome and involve enhancement of normal cellular responses to DNA damage such that genome stability is more effectively maintained. Activation of apoptosis may be a key to protection. Copyright © 2007 S. Karger AG, Basel

Cancer results from a disordered and unstable genome – the degree of abnormality progresses as the process of oncogenesis proceeds [1, 2]. Such genomic instability appears to be subject to control by environmental factors as evidenced by the number of cancers that are either caused by specific environmental agents (lung, skin, cervix) or else regulated by a broader range of agents such as effect of diet on gastric and colorectal cancers. How might such environmental regulators interact with the genomic events that give rise to cancer? Furthermore, what is the nature of such interactions in the context of the abnormal genome that arises during oncogenesis? The model for oncogenesis termed ‘multistep carcinogenesis’ appears to better explain the genomic instability inherent in cancer [1, 2]. The process is largely driven by a broad range of genetic alterations, randomly accumulating in no given sequence, at multiple sites on DNA. It could be thought of as multiple, superimposed, initiation-promotion models, but such would not adequately

allow for the biological complexity or widespread genomic instability characteristic of oncogenesis in the colon [1]. Changes in DNA thus drive the process of oncogenesis and these might arise in several ways [3]. They might arise as a result of chance events or mistakes especially at the time of DNA replication. Alternatively, they might be caused by an environmental carcinogen where an adduct forms from interaction of the carcinogen with the DNA base or by irradiation. The consequence of such events will then depend on several factors but most important is whether or not the cell can act to ‘repair’ the damage [4]. If damage occurs to DNA, and provided that cellular recognition and surveillance systems detect this, the cell responds in two main ways. One is cell cycle arrest to allow DNA repair [5] through enzymes such as the alkylguanine alkyl transferases (e.g. MGMT). The other involves activation of apoptosis if the mutation cannot be repaired [6]. If both fail, further checkpoint repair systems may come into play when the cell attempts to proliferate (S-phase). This is shown diagrammatically in figure 1. If repair is effective, it would abort any downstream consequences while an unrepaired and thus mutated cell resulting from failed repair might develop into a pro-oncogenic clone. Dietary factors might therefore interact in several ways with the genome to protect against cancer. An agent might interact directly with the genome (as a genetic or epigenetic regulator) or indirectly by influencing ‘repair’ responses. Furthermore, dietary factors might interact with normal DNA to keep it stable or to create a biological setting where progression to oncogenesis is less likely. But it is also possible that stabilizing an already unstable genome might be a mechanism of protection. The purpose of this review is to explore this latter possibility.

Potential Mechanisms for the Role of Diet in Maintaining Genome Stability

A cell reacts to a chance mistake or an induced DNA adduct in an effort to ‘repair’ the damage. Some proteins, such as p53, are critical in the surveillance mechanisms that detect such abnormalities. Others, such as MLH1 and Bub1, form an essential function of coupling DNA repair to surveillance for mutations. As a result of these processes, two main events occur. To effect repair of a DNA base, cell cycle arrest is triggered, through proteins such as p21, and repair enzymes, such as DNA alkyl transferases, restore a gene to normal. The alternative is destruction of the DNA-damaged cell through activation of programmed cell death (apoptosis) pathways involving the caspases [7–9]. If DNA repair is error prone or apoptosis fails, a viable cell may remain that

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Normal DNA

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Error-free repair

Surveillance for damage and/or cell cycle checkpoints Apoptotic death Error-prone or failed repair

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Fig. 1. Diagrammatic representation of a model that could account for the control of mutations contributing to colorectal oncogenesis. The three shaded boxes represent key events in the process that act to control the consequences of DNA adduct formation. The three heavy arrows indicate the major outcomes of inherent surveillance mechanisms for controlling DNA fidelity in response to adduct formation. Failed repair results in adduct ‘fixation’ as a mutation that is passed on to cell progeny. Genomic instability can itself compromise all control mechanisms. Epigenetic regulation can apply at all stages.

carries a mutation – such might create a biotype that is more prone to progression to cancer. Cell cycle arrest/DNA repair and apoptosis have been thought to be sequential with the latter being activated in mitosis if error-prone repair is detected. Recent evidence points to apoptosis also being a primary response to DNA damage [4]. Is it possible then that dietary factors might act to enhance these inherent homeostatic responses to damage? To answer this question, it would be necessary to study the impact of protective agents on the various components of the cellular response to DNA damage. Despite the strong evidence for dietary factors protecting against cancers such as colorectal cancer, there has been little work in this area.

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Apoptotic cells/crypt

5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0

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Fig. 2. Effects of fermentable fiber wheat bran (WB) and nonfermentable fiber methylcellulose (MC) on genotoxin-induced apoptosis in response to azoxymethane, in the distal colon, at 8 and 12 h after administration of azoxymethane [7]. The difference in the effects of fiber on the distal colon was significant (p ⬍ 0.01).

A range of dietary factors have been shown to be proapoptotic in vitro. Some of these are protective in vivo. Butyrate is strongly proapoptotic in vitro [10]. It achieves this by epigenetic regulation (inhibition of histone deacetylase) of a gene that leads to activation of the caspase cascade. When dietary fiber is fed to rodents, active colonic fermentation generates high levels of butyrate which in turn are associated with an augmentation of the colonic apoptotic response to a methylating carcinogen (azoxymethane) and protection against cancer [11–15]. Feeding fermentable wheat bran to rats increases the acute apoptotic response to genotoxic carcinogen (AARGC) while feeding nonfermentable methylcellulose does not (fig. 2) [7]. Feeding type 2 resistant starch (such as high-amylose maize starch) also enhances AARGC; furthermore, butyrate levels in the feces have been shown to correlate significantly with AARGC in distal colonic crypts suggesting that butyrate is the mediator of this effect [15]. A model of defective apoptotic response to a methylating carcinogen has also been developed based on the p53⫹/⫺ mouse [16]. It has been shown that a nonsteroidal anti-inflammatory agent (sulindac) restores this defective response to damage and reduces the risk of cancer caused by the methylating carcinogen. This same principle is now under test with various dietary agents. These findings raise the possibility that a proapoptotic effect characterizes one class of protective agents found in the diet. The possibility that dietary agents might act to enhance other aspects of the normal cellular response to DNA damage is unclear. It is not clear if alkyl transferases can be regulated. Some protective agents serve to slow proliferation, e.g. calcium in colorectal cancer [17]. This is thought to be effective

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because DNA is less subject to exogenous damage in a more slowly proliferating cell. On the other hand, DNA repair mechanisms might also become more effective.

Conclusions

Diet-genomic interactions in cancer seem likely to go beyond interactions with the normal genome and involve enhancement of normal cellular responses to DNA damage such that genome stability is more effectively maintained. Activation of apoptosis may be a key to protection.

References 1

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Boland CR: Malignant tumors of the colon; in Yamada T, Alpers DH, Kaplowitz N, Laine L, Owyang C, Powell DW (eds): Textbook of Gastroenterology, ed 4. Philadelphia, Lippincott, Williams and Wilkins, 2003, pp 1940–1989. Carethers JM, Boland CR: Neoplasia of the gastrointestinal tract; in Yamada T, Alpers DH, Kaplowitz N, Laine L, Owyang C, Powell DW (eds): Textbook of Gastroenterology, ed 4. Philadelphia, Lippincott, Williams and Wilkins, 2003, pp 557–583. Grady WM: Genomic instability and colon cancer. Cancer Metastasis Rev 2004;23:11–27. Young GP, Ying Hu, Le Leu R, Nyskohus L: Dietary fibre and colorectal cancer: a model for environment-gene interactions. Mol Nutr Food Res 2005;49:571–584. Lane DP: Cancer. p53, guardian of the genome. Nature 1992;358:15–16. Hong MY, Chapkin RS, Wild CP, Morris JS, et al: Relationship between DNA adduct levels, repair enzyme, and apoptosis as a function of DNA methylation by azoxymethane. Cell Growth Differ 1999;10:749–758. Hu Y, Martin J, Le Leu R, Young GP: The colonic response to genotoxic carcinogens in the rat: regulation by dietary fibre. Carcinogenesis 2002;23:1131–1137. Potten CS, Grant HK: The relationship between ionizing radiation-induced apoptosis and stem cells in the small and large intestine. Br J Cancer 1998;78:993–1003. Renehan AG, Bach SP, Potten CS: The relevance of apoptosis for cellular homeostasis and tumorigenesis in the intestine. Can J Gastroenterol 2001;15:166–176. Medina V, Edmonds B, Young GP, James R, et al: Induction of caspase-3 protease activity and apoptosis by butyrate and trichostatin A (inhibitors of histone deacetylase): dependence on protein synthesis and synergy with a mitochondrial/cytochrome c-dependent pathway. Cancer Res 1997;57:3697–3707. Cassidy A, Bingham SA, Cummings JH: Starch intake and colorectal cancer risk: an international comparison. Br J Cancer 1994;69:937–942. Whitehead RH, Young GP, Bhathal PS: Effects of short chain fatty acids on a new human colon carcinoma cell line (LIM1215). Gut 1987;27:1457–1463. Stephen AM, Cumming JH: Mechanism of action of dietary fibre in the human colon. Nature 1980;284:283–284. Boffa LC, Lupton JR, Mariani MR: Modulation of colonic epithelial cell proliferation, histone acetylation, and luminal short chain fatty acids by variation of dietary fiber in rats. Cancer Res 1992;52:5906–5912. Le Leu RK, Hu Y, Young GP: Effects of resistant starch and nonstarch polysaccharides on colonic lumenal environment and genotoxin-induced apoptosis in the rat. Carcinogenesis 2002;23: 713–719.

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Hu Y, Le Leu RK, Young GP: Absence of acute apoptotic response to genotoxic carcinogens in p53-deficient mice is associated with increased susceptibility to azoxymethane-induced colon tumours. Int J Cancer 2005;115:561–567. Bonithon-Kopp C, Kronborg O, Giacosa A, Rath U, Faivre J: Calcium and fibre supplementation in prevention of colorectal adenoma recurrence: a randomised intervention trial. Lancet 2000;356: 1300–1306.

Dr. Graeme P. Young Department of Medicine, Flinders University Bedford Park Adelaide 5042 (Australia) Tel. ⫹61 8 8204 4964, Fax ⫹61 8 8204 3943, E-Mail [email protected]

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High-Throughput Genotyping Jong-Eun Lee DNA Link Inc., Seoul, Korea

Abstract There are many genetic variations in the human genome. The most abundant form of genetic variation is the single nucleotide polymorphism (SNP). SNPs are thought to be responsible for observable differences in biological processes among individuals of a population. Genetic association studies utilizing SNP markers are expected to allow identification of genetic factors responsible for complex phenotypes like chronic diseases and responses to various nutritional elements. Success of such studies relies on detecting genetic markers either directly responsible for the phenotype or the markers with a close relationship with causative markers. There are over 10 million SNPs reported and each SNP contains limited genetic information due to the limited number of alleles. To cover these limitations, researchers have to genotype many SNP markers to find appropriate associations. As a result, the need for efficient high-throughput SNP genotyping technologies is high and many efficient high-throughput SNP genotyping technologies have been developed. Highly efficient systems that can handle as many as 500,000 SNPs at a time have been developed and technological advances have transformed genome-wide association studies into reality. Copyright © 2007 S. Karger AG, Basel

Background

Genetic epidemiology is a new and rapidly expanding field of epidemiology. Recent genetic epidemiologic research has increasingly focused on complex, multifactorial disorders. Due to the development of the human genome map and advances in molecular technology, the importance of genetic epidemiologic applications has been enlarged. Large-scale population-based studies requiring close integrative cooperation of genetic and epidemiologic research will play a key role in near-future research. Single nucleotide polymorphisms (SNPs) are single-base differences in the DNA sequences on the genome that can be observed among individuals of a population and are the most abundant form of DNA variation in the human

genome. SNPs are thought to be associated with many phenotypes like disease susceptibility, drug responses and differential responses against environmental factors such as food intake. Each individual has a large number of SNPs on his/her genome. On average, every 1 kb has an SNP which can be used as a marker on the genome. There are over 10 million SNPs deposited in the SNP database (www.ncbi.nlm.nih.gov/SNP/) which provides valuable information about human diversity [1]. Detection methods for SNPs are amenable to automation. Recent developments of high-throughput SNP genotyping technologies have enabled researchers to build high-density SNP maps of the human genome. Through the use of these high-density maps, researchers will be able to find subtle genetic factors which contribute to the phenotype of interest.

Approaches in Association Studies Using Single Nucleotide Polymorphism Markers

There are two general approaches for association studies using SNP markers: the candidate gene approach and whole-genome scan. The candidate gene approach is the most widely used method for exploring an association between a certain gene and a phenotype. The candidate gene approach is a hypothesisbased approach aimed to investigate the role of a particular susceptibility gene in disease etiology, where gene polymorphisms are considered as a risk factor. Under this method, studies are usually based on assumptions about the role of specific known polymorphisms in the candidate gene and their effects on the pathophysiology of a disease. This approach is considered the method of choice where there is a lack of information about SNPs or affordable genotyping methods are lacking. Many reliable SNP genotyping technologies were developed for this type of study design. Technologies like SNaPShot, TaqMan, SNP-IT, Mass Array, and Invader assays were developed [2–6]. Most of these technologies rely on polymerase chain reactions to amplify signals except the Invader assay. These technologies are designed to genotype a single SNP on a single sample at a time except the Mass Array assay which can genotype up to 10 SNPs on a single sample simultaneously. Due to the abundant nature of the SNPs and as more SNPs are discovered and deposited in the SNP database even on a specific gene region, the need for multiplex genotyping technologies which can handle multiple SNPs at a single reaction grew. Also the low information content on a single SNP compared to the other types of markers like short tandem repeats forced researchers to interrogate more SNPs to find genetic signals associated with the phenotype of interest. To meet these needs, many clever multiplex genotyping systems were developed. Degrees of multiplexing range from a few SNPs (e.g. 10 for Mass Array, 12 for SNPstream) to thousands of SNPs (1,536 for Illumina’s BeadArray

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and 25,000 for Molecular Inversion Probes from ParAllele) [7–10]. Low-end multiplexing systems like SNPstream and Mass Array are based on single-base extension assays while the others use allele-specific extension and ligation as the main biochemical reactions. All of the technologies depend on polymerase chain reactions to amplify signals for proper detection. Most of these multiplex genotyping assays except the Mass Array employ a form of tag arrays to sort out individual SNP genotypes from a pool of multiplexed assay results. These tags are: microarrayed tags on glass plates (SNPstream), tags on microbeads (BeadArray), and tag array chips (ParAllele). However, a major disadvantage of this candidate gene approach is that prior knowledge of the pathogenesis of the disease is required. When knowledge of the function and location of genes involved in a certain disease is limited, this approach cannot provide a complete identification of genetic variants. An alternative approach is the whole-genome scan which scans for many SNP markers distributed across different genes in the human genome. The wholegenome scan approach makes no prior assumptions about the identity of the genes to be found. Recent developments of new genotyping technologies and international efforts like the HapMap Project enabled researchers to perform genome-wide association studies by genotyping hundreds of thousands of SNP markers at the same time at reasonable costs [11]. There are two major commercial products on the market currently. One platform is the GeneChip system developed by Affymetrix. There are several chips which can genotype from 10,000 up to 500,000 SNPs at a time [12]. The SNPs on the chip are randomly selected SNPs which cover the whole genome at a density as high as 1 SNP every 5 kb of the genome. This assay is based on hybridization of amplified genomic DNA onto the SNP chips. Discrimination of single-base differences is based on the hybridization strength between the oligonucleotides on the chip and the genomic DNA. The other platform is the Infinium assay developed by Illumina [13]. There are two whole-genome products, the 100,000-SNP chip and the 300,000-SNP chip. The major difference between the Illumina assay and the GeneChip system is that it does the allele-specific extension and signal amplification on the chip instead of hybridization. Both assays deliver highquality genotype data at a reasonable cost per SNP.

Potential Problems in Genetic Association Studies

Although the ability to genotype multiple SNPs at a reasonable cost offers obvious advantages, it can also present serious problems during data analysis. To find which genes are major candidate genes among genes genotyped, multiple testing is usually performed. When performing multiple testing and given

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the large number of genes, adjustment of the significance level is very important. This is due to the large number of false-positive findings that will be obtained by conducting thousands of tests. The debate over adjustment for multiple testing is ongoing. Also, computational power required to handle the large volume of genotype information is very high. Mining pieces of information from many SNPs throughout the genome to get the whole picture of the genetic makeup of a complex phenotype is very challenging. New genetic analysis algorithms which can handle these kinds of data sets and figure out interactions among networks of genes are in great need to identify genetic factors involved in complex diseases.

Conclusions

Most of the SNPs in the human genome have been identified and their relationship to each other on a genomic level have been elucidated. A linkage disequilibrium map of the human genome and the tagging SNPs identified from the HapMap Project have led researchers to try genome-wide association studies. Currently available marker panels on whole-genome genotyping systems cover as much as 70% of genetic variation information of the human genome. Even though it is still quite expensive to perform genome-wide association studies, it is likely that genotyping cost will go down with time and we would expect to see more results generated from such genome-wide association studies. Recent developments in genotyping technologies have lifted the bottleneck on the generation of genotype data and shifted the pressure onto developing new analysis algorithms to elucidate the network of subtle genetic factors which are responsible for the phenotypes of interest.

References 1 2 3 4 5

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Kruglyak L, Nickerson DA: Variation is the spice of life. Nat Genet 2001;27:234–236. Kwok PY: Methods for genotyping single nucleotide polymorphisms. Annu Rev Genomics Hum Genet 2001;2:235–258. Livak KJ: Allelic discrimination using fluorogenic probes and the 5? nuclease assay. Genet Anal 1999;14:143–149. Miller RD, Phillips MS, Jo I, et al; The SNP Consortium Allele Frequency Project: High-density single-nucleotide polymorphism maps of the human genome. Genomics 2005;86:117–126. Buetow KH, Edmonson M, MacDonald R, et al: High-throughput development and characterization of a genome-wide collection of gene-based single nucleotide polymorphism markers by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Proc Natl Acad Sci USA 2001;98:581–584. Kwiatkowski RW, Lyamichev V, de Arruda M, Neri B: Clinical, genetic, and pharmacogenetic applications of the Invader assay. Mol Diagn 1999;4:353–364.

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Syvanen AC: Toward genome-wide SNP genotyping. Nat Genet 2005;37(suppl):S5–S10. Bell PA, Chaturvedi S, Gelfand CA, et al: SNPstream UHT: ultra-high throughput SNP genotyping for pharmacogenomics and drug discovery. Biotechniques 2002;(suppl):70–72, 74, 76–77. Fan JB, Oliphant A, Shen R, et al: Highly parallel SNP genotyping. Cold Spring Harb Symp Quant Biol 2003;68:69–78. Hardenbol P, Yu F, Belmont J, et al: Highly multiplexed molecular inversion probe genotyping: over 10,000 targeted SNPs genotyped in a single tube assay. Genome Res 2005;15:269–275. The International HapMap Consortium: The International HapMap Project. Nature 2003,426: 789–796. Matsuzaki H, Dong S, Loi H, et al: Genotyping over 100,000 SNPs on a pair of oligonucleotide arrays. Nat Methods 2004;1:109–111. Gunderson KL, Sreemers FJ, Lee G, et al: A genome-wide scalable SNP genotyping assay using microarray technology. Nat Genet 2005;37:549–554.

Dr. Jong-Eun Lee Seodaemun-Gu Yonhee-Dong 15–1 Yonsei Milk Bldg. No. 106 Seoul 120–110 (Korea) Tel. ⫹82 2 364 4700, Fax ⫹82 2 364 4778, E-Mail [email protected]

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Nutrigenomics and Health Tai ES, Gillies PJ (eds): Nutrigenomics – Opportunities in Asia. Forum Nutr. Basel, Karger, 2007, vol 60, pp 102–109

Nutrient-Gene Interactions in Lipoprotein Metabolism – An Overview Jose M. Ordovasa, Dolores Corellaa,c, James Kaputb a

Nutrition and Genomics Laboratory, USDA Human Nutrition Research Center on Aging at Tufts University, Boston, Mass., and bLaboratory of Nutrigenomic Medicine, University of Illinois Chicago, Chicago, Ill., USA; cGenetic and Molecular Epidemiology Unit, School of Medicine, University of Valencia, Valencia, Spain

The effect of dietary changes on phenotypes (i.e., plasma lipid measures, body weight and blood pressure) differs significantly between individuals [1–3]. Some individuals appear to be relatively insensitive (hyporesponders) to dietary intervention, whereas others (hyperresponders) have enhanced sensitivity [2]. This phenomenon has been more extensively researched in relation to changes in dietary fat and plasma lipid concentrations for the prevention of cardiovascular disease (CVD) compared to other pathological conditions. Although common knowledge associates low-fat diets with reductions in total and plasma low-density lipoprotein cholesterol (LDL-C), the clinical evidence shows dramatic interindividual differences in response which may be one of the underlying causes of the limited success of dietary recommendations in the prevention of CVDs observed by randomized clinical trials [4]. A growing body of data supports the hypothesis that the interindividual variability in response to dietary modification is determined by genetic factors, especially for lipid and lipoprotein phenotypes [5]. Indirect evidence comes from the general observation that the phenotypic response to diet is determined partly by the baseline value of the phenotype that is itself affected by genetic factors [2]. The main challenges are (1) how to uncover and elucidate the many potential gene-diet interactions and (2) how potential epistatic interactions (gene-gene) caused by differing ancestral backgrounds affect these gene-diet interactions. Several studies have found specific genes to be associated with the variability in response of LDL-C levels responding to changes in dietary fat but

so far, the findings have been highly inconsistent. These conflicting outcomes probably reflect the complexity of the mechanisms involved in dietary responses as well as the limitations of the experimental designs used to address this problem. In addition to their effects on plasma LDL-C levels, low-fat diets can result in reduced plasma high-density lipoprotein and/or increased triacylglycerol (TAG) concentrations [3] that may be particularly harmful for some persons. For example, it has been shown that individuals with a predominance of small, dense LDL particles (subclass pattern B), a phenotype that is associated with an increased risk of coronary heart disease, benefit more from a low-fat diet [6] than do those with the subclass pattern A (larger LDL particles). A significant proportion of the latter group unexpectedly exhibited a more atherogenic pattern B subclass after consuming a lowfat diet. Intervention studies are increasingly focusing on the interindividual differences in response to diet rather than on the mean effect analyzed for a population. Moreover, new evidence indicates that the variability in response is an intrinsic characteristic of the individual, rather than being the result of different dietary compliance with the experimental protocols. Jacobs et al. [7] found that individual TAG responses to a high-fat or to a low-fat diet were vastly different, suggesting that many patients with hypertriglyceridemia are not treated optimally if general advice for either a low-fat or a high-fat diet is given. Studying the reasons for this variation will allow us to better identify individuals who can benefit from a particular dietary intervention. Obviously, this is not an easy task and some authors have already proposed different statistical algorithms in attempts to better predict the response of individuals to different diets [8].

How Nutrients Communicate with Genes

Before presenting some of the current nutrigenetic evidences in the area of lipid metabolism and CVD, it is helpful to gain an understanding of how nutrients and other chemicals in the diet may influence gene expression and drive gene-diet interactions. This, in fact, is the subject of nutrigenomics, which seeks to understand gene-diet interactions in the context of the total genetic makeup of each individual. Technological limitations in the past restricted the investigator to a piecemeal approach: one gene, one gene product and one nutrient at a time. Conceptual and technological advances are changing the playing field. For the first time, researchers can cast a wide net in the form of microarrays that can potentially capture the information about each one of the genes expressed in a specific cell or tissue of interest. Despite these advances, the challenges are not trivial given the chemical complexity of food, our incomplete

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knowledge about the various bioactive components present in food grown in different climates at different times of the year, and our inability to assay gene expression in the most appropriate target tissues in humans. Regulation of the expression of genes involved in fatty acid metabolism occurs when a dietary fat or metabolite binds to and activates specific fatty acid transcription factors. These dietary chemically regulated transcription factors are members of the nuclear receptor superfamily. This gene family consists of 48 mammalian transcription factors that regulate nearly all aspects of development, inflammation, and metabolism. Two subclasses, the peroxisome proliferatoractivated receptors (PPARs) and liver X receptors, are lipid-sensing receptors that have critical roles in lipid and glucose metabolism [9–11]. PPARs are among the best-studied fatty acid-regulated nuclear receptors [12]. After uptake into target cells, a subset of them are transported to the nucleus in association with fatty acid-binding proteins, which facilitates their interaction with PPARs. Several PPAR subtypes have been described. PPAR-␣ plays a key role in lipid oxidation and inflammation, whereas PPAR-␥ is involved in cell (adipocyte) differentiation, glucose lipid storage and inflammation. PPAR-␦ (also known as PPAR-␤), may play an important role in development, lipid metabolism and inflammation. In addition to fatty acids, pharmacological agonists have been developed for each receptor: PPAR-␣ binds fibrates, PPAR-␦ binds lipophilic carboxylic acids, and PPAR-␥ binds glitazones. The fibrates are used to treat hyperlipidemia. The glitazones are used to manage plasma glucose levels in patients with insulin resistance [13]. Many of the previously published nutrigenetic [i.e., single gene/single nucleotide polymorphism (SNP)] studies focused on genes that are regulated by PPARs and other nuclear receptors [14]. Polymorphisms in promoter regions of these genes may disrupt or at least alter the communication with these transcription factors which would have significant consequences in a person’s response to dietary factors that are ligands (i.e., polyunsaturated fatty acids) of the transcription factors. It is also obvious that polymorphisms within the transcription factors themselves will have a significant impact on the way that each one of us responds to dietary factors. The evidence for gene-diet interactions between common SNPs in candidate genes and dietary factors related to lipid metabolism is increasing. However, caution is needed before applying these results to clinical practice for three primary reasons: (1) the meaning of ‘statistically significant results’ is subject to differing interpretations and often depends upon the study design, (2) many initial gene-nutrient-phenotype associations are not replicated in subsequent studies, and (3) gene variations may influence phenotypes differently in individuals from different ancestral backgrounds due to gene-gene (epistatic) interactions.

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Results from Interventional Studies Interventional studies in which subjects receive a controlled dietary intake provide the best approach for conducting gene-nutrient-phenotype association studies. However, these well-controlled feeding studies have several important logistical limitations, most importantly the small number of participants and the brief duration of the interventions. Scores of interventional studies examining gene-diet interactions on different parameters of lipid metabolism have been published. However, the level of replication among studies analyzing the same genetic variation tends to be low. The lack of replication is most likely due to the different characteristics (ethnicity, physical condition, age, lifestyle differences) of study subjects, length of intervention, sample size, and heterogeneity in the design. In a systematic review (from 1966 to 2002), Masson et al. [15] identified 74 relevant articles including dietary intervention studies that had measured the lipid and lipoprotein response to diet in different genotype groups and 17 reviews on gene-diet interactions. After a comparative analysis of the individual findings, they concluded that there is evidence to suggest that (1) variations in the APOA1, APOA4, APOB, and APOE genes contribute to the heterogeneity in the lipid response to dietary intervention and (2) all of these genes are regulated directly or indirectly by PPAR-␣ or other nuclear receptors. However, the evidence suggested by Masson et al. [15] in relation to the above genes comes from meta-analyses of the published data and describes the average effect. It should be noted that there is not total consistency of results among individual studies. More recently, one of our groups [16–18] reviewed this topic extensively and included additional studies reported after 2002. The median for the sample sizes in these more recent studies was in the range of 60 subjects. These small sample sizes highlight one of the traditional problems for the lack of reproducibility, specifically, the statistical power is low. In addition, the composition of the dietary intervention in these studies varied considerably. We propose that the design of future intervention studies should be standardized for key dietary intake variables and phenotype measurements. A minimum set of variables would include patients’ physical and genetic characteristics, medications, composition and length of the dietary treatment, and sample size. Such standardization would allow better comparison among studies and the possibility of conducting metaanalyses, which is not possible under current experimental conditions.

Results from Observational Studies Observational studies have the advantage of large numbers of subjects and the ability to estimate long-term dietary habits. However, the level of evidence of the results obtained from these studies has traditionally been considered to be

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lower than that of experimental studies. Nevertheless, the level of confidence in such studies can be increased by taking into consideration the principle of Mendelian randomization [19]. This concept reflects the random assortment of alleles at the time of gamete formation. Such randomization results in population distributions of genetic variants that are generally independent of behavioral and environmental factors that confound epidemiological associations between potential risk factors and disease. This topic has been extensively reviewed [16–18]. The median population size for recent observational studies is approximately 850. This sample size may be informative for traditional genotypephenotype association studies but, considering the higher measurement error of dietary intake in comparison with experimental studies, it may not have enough statistical power to address properly the complexity of gene-environment interactions. As pointed out for intervention studies, replication of results is still very low. In addition, these findings need the synergy of those studies examining the effects of nutrients on gene expression (nutrigenomics) to provide the mechanistic knowledge that will support the reported statistical associations. Genotype-nutrient-phenotype analyses may be improved by determining ancestral backgrounds of each study participant. These additional data are necessary since SNPs may be expressed differently among individuals of differing ethnicities because of varying gene-gene and gene-nutrient interactions. Determining the genetic architecture (that is, geographical origin of chromosomal regions) in each study participant may reduce statistical noise caused by mismatching cases and controls [20]. Gene-Diet Interactions in the Postprandial State Human beings living in industrialized societies spend most of the waking hours in a nonfasting state because of meal consumption patterns and the amounts of food ingested. Postprandial lipemia, characterized by a rise in TAG after eating, is a dynamic, nonsteady-state condition [21]. Over 25 years ago, Zilversmit [22] proposed that atherogenesis was a postprandial phenomenon since high concentrations of lipoproteins and their remnants following food ingestion could deposit into the arterial wall and accumulate in atheromatous plaques. Several studies have investigated the potential interaction between some polymorphisms in candidate genes and diet on postprandial lipids (for a review, see Ordovas and Corella [17]). In postprandial studies, subjects usually receive a fat-loading test meal that has differing compositions depending on the nutrient(s) to be tested. After the test meal, blood samples are taken to measure postprandial lipids which are then compared with preprandial levels [21]. Consistency among studies is still very low and replication of findings is a major necessity. Postprandial studies often have low numbers of subjects (usually ⬍50) with added complexity inducing even more bias than for other experimental approaches.

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The Road Map to Solidifying the Nutrigenomics Field Despite the excitement brought up by an increasing number of findings related to nutritional genomics, the progress of the field is hampered by the inadequacy of the current experimental approaches to efficiently deal with the biological complexity of the phenotype(s), the complexity of dietary intakes, differing genetic backgrounds among participants, and the limitations of low statistical power of the studies. We and others have proposed that only a comprehensive, international nutritional genomics approach [23, 24] will yield shortand long-term benefits to human health by: (1) revealing novel nutrient-gene interactions, (2) developing new diagnostic tests for adverse responses to diets, (3) identifying specific populations with special nutrient needs, (4) improving the consistency of current definitions and methodology related to dietary assessment, and (5) providing the information for developing more nutritious plant and animal foods and food formulations that promote health and prevent, mitigate, or cure disease. Achieving these goals will require an extensive dialogue between scientists and the public about the nutritional needs of the individual versus groups, local food availability and customs, analysis and understanding of genetic differences between individuals and populations, and serious commitment of funds from the public and private sectors. Nutritional genomics researchers are seeking collaborations of scientists, scholars, and policy makers to maximize the collective impact on global poverty and health by advancing our knowledge of how genetics and nutrition can promote health or cause disease.

Conclusions

Although the current evidence from both experimental and observational nutrigenetics studies is not enough to start making specific personalized nutritional recommendations based on genetic information, there are a large number of examples of common SNPs modulating the individual response to diet as proof of concept of how gene-diet interactions can influence lipid metabolism. It is critical that these preliminary studies go through further replication and that subsequent studies be properly designed with sufficient statistical power and careful attention to phenotype and genotype. The many challenges that lay ahead are evident. This review has examined the vast world of nutrigenetics and nutrigenomics only through the small keyhole of PPAR-␣ and dietary fat. Analogous to the use of the X-ray diffraction patterns 50 years ago to determine the structure of DNA, which led to today’s progress in sequencing the entire human genome, these initial steps in understanding nutrigenomics will likely lead to fundamental breakthroughs that will both clarify today’s mysteries and pave the way for clinical applications. Hopefully, bringing nutrigenetics to the

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state of becoming a practical and useful tool will not take 50 years. However, to arrive at the point where it is possible to assess the modulation by specific SNPs of the effects of dietary interventions on lipid metabolism, well-designed, adequately powered, and adequately interpreted randomized controlled studies (or their equivalent) of greater duration than current studies are needed, with careful consideration given to which patients to include in such studies. Moreover, research must also investigate the potential mechanisms involved in the genediet interactions reported by nutrigenetic studies [23]. These imperative needs can be achieved only through the collaboration of experts in the different fields involved, which must include nutrition professionals [24]. One of the first situations where personalized nutrition is likely to be beneficial is with dyslipidemic patients that require special intervention with dietary treatment. It is known that these individuals will display dramatic heterogeneity in response to the currently recommended therapeutic diets and that the recommendations will need to be adjusted individually. This process could be more efficient and efficacious if the recommendations were carried out based on genetic and molecular knowledge. Moreover, adherence to dietary advice may increase when it is supported with information based on nutritional genomics, and the patient feels that the advice is personalized. However, a number of important changes in the provision of health care are needed in order to achieve the potential benefits associated with this concept, including a teamwork approach, with greater integration among physicians and nutrition professionals. Once more experience is gained from patients and/or individuals at high risk, these approaches could be applied towards primary prevention.

Acknowledgments This study was supported by NIH/NHLBI grant No. HL54776 (J.M.O.), NIH/NHLBI contract No. 1-38038 (J.M.O.), and contracts 53-K06-5-10 and 58-1950-9-001 from the US Department of Agriculture Research Service (J.M.O.) and National Center for Minority Health and Health Disparities Center of Excellence in Nutritional Genomics (MD00222).

References 1 2 3 4

Jacobs DR Jr, Anderson JT, Hannan P, Keys A, Blackburn H: Variability in individual serum cholesterol response to change in diet. Arteriosclerosis 1983;3:349–356. Katan MB, Beynen AC, De Vries JH, Nobels A: Existence of consistent hypo- and hyperresponders to dietary cholesterol in man. Am J Epidemiol 1986;123:221–234. Katan MB, Grundy SM, Willett WC: Should a low-fat, high-carbohydrate diet be recommended for everyone? Beyond low-fat diets. N Engl J Med 1997;337:563–566. Prentice RL, Caan B, Chlebowski RT, Patterson R, Kuller LH, Ockene JK, Margolis KL, Limacher MC, Manson JE, Parker LM, Paskett E, Phillips L, Robbins J, Rossouw JE, Sarto GE,

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Shikany JM, Stefanick ML, Thomson CA, Van Horn L, Vitolins MZ, Wactawski-Wende J, Wallace RB, Wassertheil-Smoller S, Whitlock E, Yano K, Adams-Campbell L, Anderson GL, Assaf AR, Beresford SA, Black HR, Brunner RL, Brzyski RG, Ford L, Gass M, Hays J, Heber D, Heiss G, Hendrix SL, Hsia J, Hubbell FA, Jackson RD, Johnson KC, Kotchen JM, LaCroix AZ, Lane DS, Langer RD, Lasser NL, Henderson MM: Low-fat dietary pattern and risk of invasive breast cancer: the Women’s Health Initiative Randomized Controlled Dietary Modification Trial. JAMA 2006;295:629–642. Loktionov A: Common gene polymorphisms and nutrition: emerging links with pathogenesis of multifactorial chronic diseases. J Nutr Biochem 2003;14:426–451. Krauss RM: Dietary and genetic effects on low-density lipoprotein heterogeneity. Annu Rev Nutr 2001;21:283–295. Jacobs B, De Angelis-Schierbaum G, Egert S, Assmann G, Kratz M: Individual serum triglyceride responses to high-fat and low-fat diets differ in men with modest and severe hypertriglyceridemia. J Nutr 2004;134:1400–1405. Parks EJ, Rutledge JC, Davis PA, Hyson DA, Schneeman BO, Kappagoda CT: Predictors of plasma triglyceride elevation in patients participating in a coronary atherosclerosis treatment program. J Cardiopulm Rehabil 2001;21:73–79. Li AC, Glass CK: PPAR- and LXR-dependent pathways controlling lipid metabolism and the development of atherosclerosis. J Lipid Res 2004;45:2161–2173. Pegorier JP, Le May C, Girard J: Control of gene expression by fatty acids. J Nutr 2004;134: 2444S–2449S. Jump DB: Fatty acid regulation of gene transcription. Crit Rev Clin Lab Sci 2004;41:41–78. Clarke SD: The multi-dimensional regulation of gene expression by fatty acids: polyunsaturated fats as nutrient sensors. Curr Opin Lipidol 2004;15:13–18. Berger JP, Akiyama TE, Meinke PT: PPARs: therapeutic targets for metabolic disease. Trends Pharmacol Sci 2005;26:244–251. Mandard S, Müller M, Kersten S: Peroxisome proliferator-activated receptor alpha target genes. Cell Mol Life Sci 2004;61:393–416. Masson LF, McNeill G, Avenell A: Genetic variation and the lipid response to dietary intervention: a systematic review. Am J Clin Nutr 2003;77:1098–1111. Corella D, Ordovas JM: Single nucleotide polymorphisms that influence lipid metabolism: interaction with dietary factors. Annu Rev Nutr 2005;11;25:341–390. Ordovas JM, Corella D: Nutritional genomics. Annu Rev Genomics Hum Genet 2004;5:71–118. Ordovas JM, Corella D: Genes, diet and plasma lipids: the evidence from observational studies. World Rev Nutr Diet 2004;93:41–76. Tobin MD, Minelli C, Burton PR, Thompson JR: Development of Mendelian randomization: from hypothesis test to ‘Mendelian deconfounding’. Int J Epidemiol 2004;33:26–29. Campbell CD, Ogburn EL, Lunetta KL, Lyon HN, Freedman ML, Groop LC, Altshuler D, Ardlie KG, Hirschhorn JN: Demonstrating stratification in a European American population. Nat Genet 2005;37: 868–872. Ordovas JM: Genetics, postprandial lipemia and obesity. Nutr Metab Cardiovasc Dis 2001;11: 118–133. Zilversmit DB: Atherogenesis: a postprandial phenomenon. Circulation 1979;60:473–485. van Ommen B, Stierum R: Nutrigenomics: exploiting systems biology in the nutrition and health arena. Curr Opin Biotechnol 2002;13:517–521. Kaput J, Ordovas JM, Ferguson L, Ommen BV, Rodriguez RL: The case for strategic international alliances to harness nutritional genomics for public and personal health. Br J Nutr 2005;94: 623–632.

Dr. Jose M. Ordovas Nutrition and Genomics Laboratory USDA Human Nutrition Research Center on Aging at Tufts University 711 Washington St., Boston, MA 02111 (USA) Tel. ⫹1 617 556 3102, Fax ⫹1 617 556 3103, E-Mail [email protected]

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The Genetics of Lipoprotein Metabolism and Heart Disease E. Shyong Tai Singapore General Hospital, Singapore

Abstract Blood lipids are major determinants of risk for cardiovascular disease. Lipid-lowering therapies have been demonstrated to reduce the risk of cardiovascular disease in humans. Genetic variants in many candidate genes are associated with blood lipids. In some instances, such as the association between APOE variants and low-density lipoprotein cholesterol, the associations are similar from population to population. However, for others, the associations may differ between populations. In some instances, these differences related to interactions between the genetic variants and environmental factors. The examination of such associations/interactions tells us something about the biology of human lipoprotein metabolism. However, the utility of genetic variants for predicting cardiovascular disease is currently limited. To date, none of these genetic variants have been shown to improve the ability of predictive functions to discriminate between those at high and low risk of heart disease. To do this, the genetic variants should connote some aspect of risk that is not included in existing predictive functions. Alternatively, they should modify the risk associated with the risk factors in existing functions. Research to determine the impact of these genetic markers as predictors of disease is an important area that is currently underexplored. Copyright © 2007 S. Karger AG, Basel

Blood lipid levels are among the most important, modifiable risk factors for coronary artery disease (CAD). Of those commonly measured, high levels of low-density lipoprotein cholesterol (LDL-C) are associated with increased risk of CAD. In contrast, high concentrations of high-density lipoprotein cholesterol (HDL-C) are protective. Pharmacological treatment to lower LDL-C [1] and raise HDL-C [2–4] have been shown to reduce the risk of CAD.

Genetic Variation and Ethnic Differences in Blood Lipid Levels

In Singapore, we have observed significant ethnic differences in both the levels of lipids in the blood and the risk of CAD [5–7]. Indians living in Singapore have approximately a threefold higher risk of CAD than Chinese people [8, 9]. They also exhibit lower serum levels of HDL-C. Malays, on the other hand, have elevated serum levels of LDL-C and their risk of CAD is intermediate between that of the Chinese and Indians. It is tempting, when comparing ethnic groups, to think that genetic differences could explain differences between ethnic groups. The most common genetic variants in the human genome comprise single nucleotide polymorphisms (SNPs) [10]. These represent a single-base change in the DNA sequence. Such changes can result in changes in the level or function of the protein encoded by the genetic locus. Alternatively, associations between the presence of particular SNPs and a disease could result from linkage disequilibrium between the SNP examined and a causative mutation in another part of the locus. SNPs at the APOE locus have been studied extensively in relation to blood lipids and CAD. Two common polymorphisms occur at sites in codons 112 and 158 of the APOE gene locus. The ␧2 allele contains a cysteine at position 158, and the ␧4 contains an arginine at position 112. When we examined the association between polymorphisms at the APOE locus in the three ethnic groups living in Singapore, the frequency of the APOE4 allele was highest in Malays compared to Chinese and Indians [11]. The presence of the APOE4 allele was also associated with a higher LDL-C concentration, irrespective of the ethnic group. As such, the APOE4 allele contributes to the higher serum concentrations of LDL-C in Malays in Singapore. However, this explanation for the ethnic differences seems overly simplistic for several reasons. Firstly, the effect of the APOE4 allele is relatively small and the differences in LDL-C concentration between the ethnic groups remained even after accounting for the differences in the APOE4 allele frequency. Secondly, when it comes to other genetic loci, the frequency of the ‘at risk’ allele is not always higher in those ethnic groups with greater risk. In an attempt to understand the reasons for low HDL-C concentrations in Asian Indians, we also genotyped Chinese, Malays and Indians for the presence of the TaqIB polymorphism at the CETP locus [12] (encoding cholesteryl ester transfer protein). The B2 allele has been associated with higher concentrations of HDL-C in other populations. In our population, the same association with HDL-C was observed in the various ethnic groups [12]. However, contrary to our expectations, the frequency of the B2 allele was highest in Indians, the ethnic group with the lowest HDL-C concentration.

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Genes, Diet and Ethnic Differences in Blood Lipids

It is important to remember that ethnicity is a construct that encompasses both genetic and cultural differences. This means that, over and above any genetic differences, the different ethnic groups are subject to different environmental exposures. Diet is one of the environmental exposures that differs between ethnic groups. However, dietary factors do not appear to act in isolation either. Differences in the dietary intake of various macronutrients, including dietary fat, failed to explain the ethnic differences in blood lipids in Singapore [13]. Furthermore, family and twin studies suggest that plasma lipid concentrations show significant hereditability assuming that, although genetic factors are unlikely to explain the ethnic differences observed, they may still play a role [14]. In fact, it appears that the effect of diet on blood lipid levels appears to be partially determined by an individual’s genetic makeup. The evidence for this is as follows. Although relationships between dietary changes and serum lipid changes are well founded and predictable for groups, a striking variability in the response of serum cholesterol to diet between subjects has been reported [15–17]. In some individuals, plasma cholesterol levels dramatically decrease following consumption of a low-fat diet, while they remain unchanged in others [16–19]. It has been shown in elegant studies in nonhuman primates that the serum lipoprotein response to dietary manipulation has a significant genetic component [20–22]. In this regard, several variants at various genetic loci seem to modulate the association between dietary fat and serum lipid concentrations [23]. In line with this hypothesis, we have shown that the association with the presence of the B2 allele at the CETP locus (described above) is not a straightforward one. We have shown that this polymorphism interacts with dietary cholesterol intake and that these jointly modulate HDL-C concentration [12]. Other studies have shown that this polymorphism interacts with other environmental factors including obesity/insulin resistance [24], smoking [25] and alcohol intake [26] enhancing the effect of these environmental factors on HDL-C concentration. It has been suggested that, over and above its direct effect on HDLC, the B2 allele connotes an increased susceptibility to the effect of environmental exposure on HDL-C concentration. As another example, we have examined the ⫺514C → T polymorphism at the hepatic lipase (LIPC) locus. The presence of the T allele is associated with elevated serum HDL-C concentration in many studies [27–30]. This was also the case in Singapore [31]. More recently, investigators from the Framingham Offspring Study have shown that this polymorphism modulated the association between dietary fat intake and serum HDL-C concentration [32]. In those with the TT genotype, high dietary fat intake was associated with low HDL-C

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concentration whereas in those with the CC genotype, the opposite was true. The investigators suggested that the TT genotype may identify a group of individuals who are maladapted to a high-fat diet in relation to cardiovascular disease risk. The findings in Singapore were similar. We found that a high-fat diet had an adverse effect on the serum lipid profile in the form of hypertriglyceridemia in all three ethnic groups and that this occurred primarily in those with the TT genotype [31]. In addition, our findings in Asian Indians replicated those in the Framingham Offspring Study in relation to dietary fat intake and low HDL-C concentration.

The Relevance of These Findings to the Risk of Coronary Artery Disease

Gene nutrient interactions of this nature also alter the risk of CAD. For example, ADH3 encodes a protein which metabolizes ethanol. A polymorphism at this locus reduces the catabolism of alcohol resulting in higher HDL-C levels in persons who drink alcohol regularly and carry the polymorphism [33]. These individuals were also shown to have a reduced risk of CAD [33, 34]. Unfortunately, few studies have demonstrated that the gene-diet interactions in relation to blood lipids apply to the risk of CAD as might be expected by the association with blood lipids. The findings related to blood lipids may not translate directly into risk of CAD. For example, the T allele at position ⫺514 at the LIPC locus is associated with increased blood concentrations of HDL-C, and would be expected to be associated with a reduced risk of CAD. However, in a study that examined the association of the ⫺514C → T polymorphism at the LIPC locus with the degree of coronary artery stenosis, the T allele was associated with an increase in coronary stenosis whereas those carrying the C allele experienced less stenosis [35]. This suggests that the T allele connotes an increased risk of CAD. Therefore, based on current knowledge, the relevance of many of the described gene-diet interactions, as they pertain to CAD, is unclear. Many more studies incorporating the assessment of dietary intake, genetic analysis and an assessment of CAD events are required. It has also been suggested that genetic markers may be useful predictors of chronic disease. There are several reasons why this is not likely, at least in the near future. Firstly, as described in this study, the association between a genetic marker and disease is not a straightforward one. Instead the data suggest significant modifications of the effect of genetic variants by environmental exposures. Therefore, to fully utilize a genetic marker to predict disease, we would also have to take into account the environmental exposure. As discussed above,

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there are insufficient data to allow this at this time. Secondly, as predictors of chronic disease, genetic markers should not be considered in isolation. Rather, they need to be considered in the context of the other known risk factors for the disease [36]. For CAD, several risk factors such as blood glucose, lipids, blood pressure, age and gender are well established. In fact, these risk factors, when combined into a predictive function, have been shown to operate well in several different populations. To be clinically useful, these variables should improve the ability of existing predictive functions, such as the Framingham predictive function, to discriminate between individuals with differing risks of CAD. This was carried out for the APOE variants. Although it is fairly well established that the APOE4 allele is associated with increased risk of CAD, when information on the APOE genotype was added to an existing predictive function for CAD, it provided little, if any, additional discriminatory value. What then is the relevance of the study of gene-diet interactions? I believe that this lies in two areas. Firstly, knowledge of gene-diet interactions may facilitate the development of dietary recommendations which are individualized to optimize the benefit obtained. While this is unlikely to be useful for large segments of the populations, in those who do not respond to conventional recommendations for reducing CAD risk, it may be a useful adjunct to current treatment and may reduce the need to resort to drug treatment. Secondly, the elucidation of gene-diet interactions can help us understand the biological pathways through which diet alters the risk of chronic disease. For example, in relation to HDL-C concentration, several dietary factors have been shown to alter HDL metabolism. However, the pathways through which they act are not clear. Whilst it is possible to carry out experiments in cells and animals to try to elucidate these pathways, these findings may not always be relevant to human physiology. The examination of gene-environment interactions offers us an opportunity to understand the pathways involved in humans. Genetic factors which interact with dietary factors to determine a particular phenotypic trait are likely to lie along the metabolic pathway by which the dietary factor acts on the phenotypic trait. As an example, it is known that the substitution of saturated fat in the diet with unsaturated fat results in a decrease in serum HDL-C concentration [37]. However, the pathways involved are unclear. We have found that polymorphisms at the peroxisome proliferator-activated receptor-␣ (PPAR-␣) locus interact with dietary polyunsaturated fatty acid intake to determine HDL-C concentrations in Chinese women [38]. This suggests that polyunsaturated fatty acid may act on HDL-C through PPAR-␣ and genes that are regulated by this PPAR-␣. There is some hope that the elucidation of these pathways in humans may identify molecular targets through which we could mimic the beneficial effects of nutrients in those who require additional pharmacological treatment.

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Conclusion

Dyslipidemia represents an example of a chronic disorder which appears to relate to a combination of genetic and environmental factors. These genetic and environmental factors interact to produce the ultimate phenotype. However, the relevance to CAD risk is not clear at this time. More studies incorporating sufficient numbers of clinical events are required to establish this link. To be useful for the prediction of CAD risk, we also need to evaluate the gain in discrimination resulting from the addition of these new genetic markers to existing predictive functions. Nevertheless, the elucidation of gene-diet interactions may facilitate (1) the development of individualized dietary recommendations for individuals who fail to response to conventional dietary recommendations, and (2) help us identify the biological pathways involved in the beneficial effects of certain nutrients on human physiology. However, these studies need to be large, and include significant numbers of individuals who experience clinical events. For these reasons, there is an urgent need to develop collaborative studies with standardized methodology to ensure adequate sample sizes and statistical power.

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Briel M, Nordmann AJ, Bucher HC: Statin therapy for prevention and treatment of acute and chronic cardiovascular disease: update on recent trials and metaanalyses. Curr Opin Lipidol 2005;16:601–605. Brown BG, Zhao XQ, Chait A, et al: Simvastatin and niacin, antioxidant vitamins, or the combination for the prevention of coronary disease. N Engl J Med 2001;345:1583–1592. Canner PL, Berge KG, Wenger NK, et al: Fifteen year mortality in Coronary Drug Project patients: long-term benefit with niacin. J Am Coll Cardiol 1986;8:1245–1255. Rubins HB, Robins SJ, Collins D, et al: Gemfibrozil for the secondary prevention of coronary heart disease in men with low levels of high-density lipoprotein cholesterol. Veterans Affairs High-Density Lipoprotein Cholesterol Intervention Trial Study Group. N Engl J Med 1999;341: 410–418. Hughes K, Yeo PP, Lun KC, et al: Cardiovascular diseases in Chinese, Malays, and Indians in Singapore. 2. Differences in risk factor levels. J Epidemiol Community Health 1990;44:29–35. Tai ES, Emmanuel SC, Chew SK, Tan BY, Tan CE: Isolated low HDL cholesterol: an insulin-resistant state only in the presence of fasting hypertriglyceridemia. Diabetes 1999;48:1088–1092. Tan CE, Emmanuel SC, Tan BY, Jacob E: Prevalence of diabetes and ethnic differences in cardiovascular risk factors. The 1992 Singapore National Health Survey. Diabetes Care 1999;22: 241–247. Lee J, Heng D, Chia KS, Chew SK, Tan BY, Hughes K: Risk factors and incident coronary heart disease in Chinese, Malay and Asian Indian males: the Singapore Cardiovascular Cohort Study. Int J Epidemiol 2001;30:983–988. Mak KH, Chia KS, Kark JD, et al: Ethnic differences in acute myocardial infarction in Singapore. Eur Heart J 2003;24:151–160. Syvanen AC: Accessing genetic variation: genotyping single nucleotide polymorphisms. Nat Rev Genet 2001;2:930–942.

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Tan CE, Tai ES, Tan CS, et al: APOE polymorphism and lipid profile in three ethnic groups in the Singapore population. Atherosclerosis 2003;170:253–260. Tai ES, Ordovas JM, Corella D, et al: The TaqIB and ⫺629C → A polymorphisms at the cholesteryl ester transfer protein locus: associations with lipid levels in a multiethnic population. The 1998 Singapore National Health Survey. Clin Genet 2003;63:19–30. Deurenberg-Yap M, Li T, Tan WL, van Staveren WA, Chew SK, Deurenberg P: Can dietary factors explain differences in serum cholesterol profiles among different ethnic groups (Chinese, Malays and Indians) in Singapore? Asia Pac J Clin Nutr 2001;10:39–45. Heller DA, de Faire U, Pedersen NL, Dahlen G, McClearn GE: Genetic and environmental influences on serum lipid levels in twins. N Engl J Med 1993;328:1150–1156. Cobb MM, Teitlebaum H: Determinants of plasma cholesterol responsiveness to diet. Br J Nutr 1994;71:271–282. Jacobs DR, Anderson JT, Hannan P, Keys A, Blackburn H: Variability in individual serum cholesterol response to change in diet. Arteriosclerosis 1983;3:349–356. Katan MB, Beynen AC, de Vries JH, Nobels A: Existence of consistent hypo- and hyperresponders to dietary cholesterol in man. Am J Epidemiol 1986;123:221–234. Cobb MM, Risch N: Low-density lipoprotein cholesterol responsiveness to diet in normolipidemic subjects. Metabolism 1993;42:7–13. MA OH, Rosner B, Bishop LM, Sacks FM: Effects of inherent responsiveness to diet and day-today diet variation on plasma lipoprotein concentrations. Am J Clin Nutr 1996;64:53–59. Mahaney MC, Blangero J, Rainwater DL, et al: Pleiotropy and genotype by diet interaction in a baboon model for atherosclerosis: a multivariate quantitative genetic analysis of HDL subfractions in two dietary environments. Arterioscler Thromb Vasc Biol 1999;19:1134–1141. Rainwater DL, Kammerer CM, Hixson JE, et al: Two major loci control variation in beta-lipoprotein cholesterol and response to dietary fat and cholesterol in baboons. Arterioscler Thromb Vasc Biol 1998;18:1061–1068. Rainwater DL, Kammerer CM, VandeBerg JL: Evidence that multiple genes influence baseline concentrations and diet response of Lp(a) in baboons. Arterioscler Thromb Vasc Biol 1999;19:2696–2700. Ordovas JM: Gene-diet interaction and plasma lipid responses to dietary intervention. Biochem Soc Trans 2002;30:68–73. Vohl MC, Lamarche B, Pascot A, et al: Contribution of the cholesteryl ester transfer protein gene TaqIB polymorphism to the reduced plasma HDL-cholesterol levels found in abdominal obese men with the features of the insulin resistance syndrome. Int J Obes Relat Metab Disord 1999;23:918–925. Freeman DJ, Griffin BA, Holmes AP, et al: Regulation of plasma HDL cholesterol and subfraction distribution by genetic and environmental factors. Associations between the TaqI B RFLP in the CETP gene and smoking and obesity. Arterioscler Thromb 1994;14:336–344. Fumeron F, Betoulle D, Luc G, et al: Alcohol intake modulates the effect of a polymorphism of the cholesteryl ester transfer protein gene on plasma high density lipoprotein and the risk of myocardial infarction. J Clin Invest 1995;96:1664–1671. Deeb SS, Peng R: The C-514T polymorphism in the human hepatic lipase gene promoter diminishes its activity. J Lipid Res 2000;41:155–158. Guerra R, Wang J, Grundy SM, Cohen JC: A hepatic lipase (LIPC) allele associated with high plasma concentrations of high density lipoprotein cholesterol. Proc Natl Acad Sci USA 1997;94: 4532–4537. Jansen H, Verhoeven AJ, Weeks L, et al: Common C-to-T substitution at position –480 of the hepatic lipase promoter associated with a lowered lipase activity in coronary artery disease patients. Arterioscler Thromb Vasc Biol 1997;17:2837–2842. Murtomaki S, Tahvanainen E, Antikainen M, et al: Hepatic lipase gene polymorphisms influence plasma HDL levels. Results from Finnish EARS participants. European Atherosclerosis Research Study. Arterioscler Thromb Vasc Biol 1997;17:1879–1884. Tai ES, Corella D, Deurenberg-Yap M, et al: Dietary fat interacts with the ⫺514C → T polymorphism in the hepatic lipase gene promoter on plasma lipid profiles in a multiethnic Asian population: the 1998 Singapore National Health Survey. J Nutr 2003;133:3399–3408.

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Ordovas JM, Corella D, Demissie S, et al: Dietary fat intake determines the effect of a common polymorphism in the hepatic lipase gene promoter on high-density lipoprotein metabolism: evidence of a strong dose effect in this gene-nutrient interaction in the Framingham Study. Circulation 2002;106:2315–2321. Hines LM, Stampfer MJ, Ma J, et al: Genetic variation in alcohol dehydrogenase and the beneficial effect of moderate alcohol consumption on myocardial infarction. N Engl J Med 2001;344: 549–555. Younis J, Cooper JA, Miller GJ, Humphries SE, Talmud PJ: Genetic variation in alcohol dehydrogenase 1C and the beneficial effect of alcohol intake on coronary heart disease risk in the Second Northwick Park Heart Study. Atherosclerosis 2005;180:225–232. Zambon A, Deeb SS, Brown BG, Hokanson JE, Brunzell JD: Common hepatic lipase gene promoter variant determines clinical response to intensive lipid-lowering treatment. Circulation 2001;103: 792–798. Humphries SE, Ridker PM, Talmud PJ: Genetic testing for cardiovascular disease susceptibility: a useful clinical management tool or possible misinformation? Arterioscler Thromb Vasc Biol 2004;24:628–636. Lichtenstein AH, Ausman LM, Jalbert SM, Schaefer EJ: Effects of different forms of dietary hydrogenated fats on serum lipoprotein cholesterol levels. N Engl J Med 1999;340:1933–1940. Chan E, Tan CS, Deurenberg-Yap M, Chia KS, Chew SK, Tai ES: The V227A polymorphism at the PPARA locus is associated with serum lipid concentrations and modulates the association between dietary polyunsaturated fatty acid intake and serum high density lipoprotein concentrations in Chinese women. Atherosclerosis 2006;187:309–315.

Dr. E. Shyong Tai Singapore General Hospital 1, Hospital Drive Singapore 169608 (Singapore) Tel. ⫹65 321 3654, Fax ⫹65 227 3576, E-Mail [email protected]

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Tai ES, Gillies PJ (eds): Nutrigenomics – Opportunities in Asia. Forum Nutr. Basel, Karger, 2007, vol 60, pp 118–126

Gene-Environment Interactions and the Diabetes Epidemic in India V. Mohan, V. Sudha, G. Radhika, V. Radha, M. Rema, R. Deepa Madras Diabetes Research Foundation and Dr. Mohan’s Diabetes Specialities Centre, Gopalapuram, Chennai, India

Abstract The prevalence of diabetes is rising rapidly in all developing countries and India already has the largest number of people with diabetes. Evidence for the rising prevalence of diabetes in India comes from recent population-based studies such as the Chennai Urban Population Study (n
1,262) and the Chennai Urban Rural Epidemiology Study (n
26,001). These two studies revealed that the current age-standardized prevalence of diabetes in Chennai in adults 20 years of age is 14.3%, which is 70% higher than that seen in the year 1989 (8.3%). In the Chennai Urban Population Study, we observed that the higher-income group who consumed excess fat and calorie-rich food had an increased prevalence of diabetes compared to the lowerincome group. There was also a linear increase in the prevalence of diabetes with an increase in visible fat consumption. In addition, we observed that visible fat consumption and physical inactivity showed a cumulative effect on increasing the prevalence of diabetes. We carried out gene-diet interaction studies, which revealed that the adiponectin gene polymorphism (10211T → G) contributed to insulin resistance and diabetes and this was exaggerated in those consuming diets with higher glycemic loads. These subjects also had an increased risk for hypoadiponectinemia. Similarly, the Ala54Thr polymorphism of the fatty acid-binding protein 2 gene showed a synergistic effect with a high glycemic load increasing the risk for hypertriglyceridemia. These studies indicate that gene-diet interactions could play a major role in increasing the risk for diabetes. However, given the imprecision in measuring dietary intake, very large sample sizes would be needed for meaningful conclusions to be drawn. Copyright © 2007 S. Karger AG, Basel

The problem of diabetes is growing in epidemic proportions and is taking a toll of millions of lives worldwide. Recent statistics from the World Health Organization show that in the year 2000, 171 million people had diabetes globally and these numbers are expected to increase to 366 million by the year 2030 [1]. The top three countries in terms of numbers of people with diabetes are India,

China, and the USA and it is predicted that these countries would continue to top the list even by the year 2030 [1]. India currently has about 40 million people with diabetes and by the year 2030, this would increase to nearly 80 million. In this paper, we first show evidence for the epidemic in India and then demonstrate evidence for both genetic variations and environment in its causation. Many of the studies conducted in India documenting the prevalence of diabetes mellitus have been conducted in different geographical locations, which limits our ability to look at trends in disease prevalence over time. We conducted two population-based studies in the Chennai city which has a history of well-conducted epidemiological studies that have documented the prevalence of diabetes. The Chennai Urban Population Study (CUPS) focused on the intraurban difference in the prevalence of diabetes, while the Chennai Urban Rural Epidemiology Study (CURES) was aimed at determining the prevalence of diabetes and its complications.

The Chennai Urban Population Study

The CUPS was conducted in two residential areas representing the lowerand middle-income group in Chennai (formerly Madras) in South India. All individuals aged over 20 years living in these two colonies were requested to participate in the study. Of the total of 1,399 eligible subjects (age 20 years), 1,262 (90.2%) participated in the study. The study subjects underwent a glucose tolerance test and were categorized as having normal glucose tolerance, impaired glucose tolerance (IGT) or diabetes. Twelve-lead ECG was also performed and coronary artery disease was diagnosed based on a previous medical history of coronary artery disease and/or Minnesota coding of ECGs [2]. The overall prevalence of diabetes in the CUPS was 12%, which included 91 subjects (7.2%) with known diabetes and 61 (4.8%) with undiagnosed diabetes [3]. An additional 74 (5.9%) subjects were detected to have IGT. The prevalence of diabetes among the high-income group was twofold higher than that observed in the middle-income group (12.4 vs. 6.5%). Similarly, all the metabolic abnormalities were higher in the middle-income group compared to the low-income group [3]. The study thus suggested that the risk for diabetes increased with increase in income.

The Chennai Urban Rural Epidemiology Study

The CURES is an ongoing epidemiological study conducted on a representative population (aged 20 years) of Chennai (formerly Madras), the

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fourth-largest city in India [4]. The sampling for the CURES was based on the model of systematic random sampling, wherein, of the 155 wards, 46 wards were selected to represent all the 10 corporation zones. The total sample size of 26,001 individuals was selected from these 46 wards. The sample distribution in each ward within these zones was based on the proportion of the population in that particular zone. Further, within each ward, every third lane or road, following the right-hand rule, was surveyed. Such a sampling approach was chosen as it enabled an equitable distribution of the entire Chennai population while ensuring that the sampling error was kept to a minimum. All men and women 20 years of age were considered eligible for the study. Fasting capillary blood glucose was determined using a One Touch Basic glucose meter in all subjects. Blood tests were done in all but 184 subjects (i.e. in 99.3% of the study population). Subjects were categorized as having normal fasting glucose, impaired fasting glucose or diabetes based on the American Diabetes Association capillary blood glucose criteria [5]. Subjects were also classified as ‘known diabetic subjects’ if they stated that they had diabetes and were on treatment. In phase 2 of the CURES, all the known diabetic subjects (n
1,529) were invited to the center for detailed studies on vascular complications. In phase 3, every tenth study subject in phase 1 was invited to the center for more detailed studies.

Evidence for Rising Prevalence of Diabetes in the Indian Subcontinent

The CURES gave us a unique opportunity to determine the prevalence of diabetes in Chennai, and compare the secular trends in the same city over the last two decades. The crude prevalence rate obtained in the study was age standardized to the 1991 census of India and compared with other studies which provided prevalence standardized to the same census. In 1989, the agestandardized prevalence of diabetes was 8.3%; this rose to 11.6% in 1995 and to 13.5% in 2000, while in the present study (2003–2004), it is 14.3%. Thus, within a span of 14 years, the prevalence of diabetes had increased by over 70% (p  0.001). From 1989 to 1995, it increased by 39.8%, between 1995 to 2000 by 16.3%, and between 2000 to 2004 by 6.0% [6].

Why the Diabetes Epidemic in India?

Although there are several reports that highlight the high prevalence of diabetes in Indians, the exact reasons for the epidemic of diabetes in this ethnic

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group are still not clear. Population-based studies conducted by us [3, 6] point to the role of both genetic and environmental factors in contributing to the diabetes epidemic in India.

Heritability and Diabetes

Earlier studies on migrant Indians and Europeans conducted in the UK showed that 10% of Asian Indians with diabetes reported that both parents were diabetic compared to 1% of Europeans, suggesting that the inheritance is stronger among Indians [7]. In the CUPS, the prevalence of diabetes was higher among subjects who had a positive family history of diabetes (18.2%) compared to subjects without a family history of diabetes (10.6%, p
0.0015). When subjects with diabetes and IGT were grouped together as glucose intolerance, the prevalence of glucose intolerance among subjects whose parents were both diabetic (55%) was significantly higher than the prevalence among those with one diabetic parent (22.1%, p
0.005), which in turn was higher than the prevalence among those with no parental history of diabetes (15.6%, p  0.0001) [8]. Studies have also shown Asian Indians to be more insulin resistant compared to Europeans [9]. Indeed, hyperinsulinemia has been demonstrated even among Indian neonates in contrast to white Caucasians [10]. Over 10 studies from different parts of the world have confirmed that Indians have a higher degree of insulin resistance [11]. Furthermore, Asian Indians also tend to have increased abdominal obesity compared to other ethnic populations. These studies suggest that there could be a genetic predisposition to insulin resistance and diabetes.

Genetic Polymorphism and Diabetes

In order to answer the question whether any variants at specific genetic loci contribute to increased susceptibility to insulin resistance and diabetes in India, we undertook a systematic search of genes along the insulin action pathway. In the CURES population, we studied several candidate genes for diabetes and insulin resistance. Some showed an association with diabetes similar to that seen in other ethnic groups while a few failed to show an association indicating ethnic differences in genetic susceptibility to diabetes. Three candidate genes implicated in insulin resistance and type 2 diabetes, namely plasma cell glycoprotein 1 (PC-1), peroxisome proliferator-activated receptor- coactivator 1 (PGC-1) and peroxisome proliferator-activated receptor- (PPAR-) were examined in detail in the CURES population.

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Table 1. Genetic polymorphism studied in the CURES [12–14, 16, 17] Gene

Polymorphism

Type of association with diabetes

Reference

PC-1

Lys121Gln (K121Q)

susceptible

[12]

PGC-1

Thr394Thr (G → A) Gly482Ser (G → A) A2962G

susceptible no association no association

[13]

PPAR-

Pro12Ala

no association

[14]

Insulin receptor substrate 2

Gly1057Asp

gene-obesity interaction in diabetes

[16]

Hepatocyte nuclear factor 2

Ala/Val

association with lower age at onset of type 2 diabetes

[17]

The Lys121Gln (K121Q) polymorphism of the PC-1 gene was studied in South Asians living in India (n
679) and this was compared with South Asians living in Dallas (n
1,083) and Caucasians (n
858). The study result suggests that the Lys121Gln (K121Q) polymorphism of the PC-1 gene predicts genetic susceptibility to type 2 diabetes in both South Asians and Caucasians [12]. The prevalence of the Lys121Gln (K121Q) polymorphism was 25% in the nondiabetic group and 34% in the diabetic group of South Asians living in Chennai (p
0.01). The prevalence in the nondiabetic and diabetic groups was 33 and 45% (p
0.01) for the South Asians living in Dallas and 26 and 39% (p
0.003) for the Caucasians [12]. We also observed that the ‘A’ allele of the Thr394Thr (G → A) silent polymorphism of the PGC-1 gene was associated with type 2 diabetes in Asian Indians and that the ‘XA’ genotype confers 1.6 (95% CI 1.264–2.241, p
0.0004) times higher risk for type 2 diabetes compared to the ‘GG’ genotype in this population [13]. The Pro12Ala polymorphism of the PPAR- gene had a protective effect against diabetes in Caucasians (20% in nondiabetic subjects vs. 9% in diabetic subjects, p
0.006), whereas there were no significant differences between diabetic and nondiabetic subjects among the South Asians living in Dallas (20 vs. 23%) and in India (19 vs. 19.3%) [14], showing that this polymorphism does not protect South Asians against diabetes (table 1).

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Though these studies indicate that some genetic variants at key candidate genetic loci are associated with diabetes mellitus, genetic factors alone cannot fully explain such a rapid rise in the prevalence of diabetes in India, as it is highly unlikely that the frequency of genetic variation has changed significantly in this population during the last 30 years. This points to the role of environmental factors in the causation of diabetes.

Epidemiological Transition

India is undergoing rapid epidemiological transition with increasing urbanization. Presently 35% of India is urbanized in contrast to 15% in the 1950s. Urbanization has led to rapid changes in lifestyle, with more white-collar jobs leading to decreased physical activity and affluence associated with consumption of fast foods rich in fat, sugar and calories. This epidemiological transition has lead to a paradigm shift in the health patterns in the country, from communicable to noncommunicable diseases. Of the latter, diabetes is one of the most frequent. The CUPS showed that only 5% of the Chennai residents exercised regularly. Further, studies on physical activity showed that among subjects who performed light-grade activity, the prevalence of diabetes was 17.0%, which was significantly higher than that observed in subjects who performed heavy-grade activity (5.6%) [15]. This association persisted even after adjusting for age. As it is well known that economic transition results in nutritional changes with increased consumption of fatty foods, we examined the relation between visible fat (visible fats are fats and oils derived from animal and vegetable fats that are added during cooking/processing – e.g. butter, ghee, vegetable cooking oil and hydrogenated fat) in diet and the prevalence of diabetes. With an increase in quartiles of visible fat consumption, the prevalence of diabetes and IGT increased indicating a strong relation between diabetes and visible fat intake (fig. 1). These findings implicate reduced physical activity and increased fat intake as some of the environmental factors that may be involved in the pathogenesis of the diabetes epidemic in India.

Gene and Environment Interaction

It is likely that both genes and environment act together and have a cumulative effect on the prevalence of diabetes. To look at this aspect, we studied the combined effect of dietary (physical inactivity) factors and genetic factors in increasing the risk of diabetes.

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9

Diabetes* IGT*

8 Prevalence (%)

7 6 5 4 3 2 1 0 1st quartile

2nd quartile

3rd quartile

4th quartile

Visible fat consumption

Fig. 1. Visible fat consumption and prevalence of newly diagnosed diabetes and IGT – the CUPS. *p  0.05 for trend.

Prevalence of glucose intolerance (%)

25 20 20.8 15 13

10 5

9.4 6.6

0 Family history negative  visible fat (median)

Family history positive  visible fat (median)

Family history negative  visible fat (median)

Family history positive  visible fat (median)

Fig. 2. Synergistic effect of heritability and visible fat on the prevalence of glucose intolerance – the CURES.

Figure 2 shows that the prevalence of glucose intolerance was 13.0% among subjects who consumed excess visible fat but had no family history of diabetes compared to 6.6% among those with no family history of diabetes and who consumed less visible fat (50% median). However, the highest prevalence of diabetes occurred among subjects who consumed increased visible fat and had a positive family history of diabetes. Logistic regression analysis revealed that this effect was additive. Since family history involves both heritability and environmental effects, to examine the interaction of heritability with diet, we further studied specific gene-diet interactions.

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Gene-Diet Interactions

Gene-diet interaction was examined by conducting studies on adiponectin gene polymorphism and fatty acid-binding protein 2 polymorphism. We found that the adiponectin gene polymorphism (10211T → G) contributes to insulin resistance and diabetes and in addition also showed a cumulative effect with dietary glycemic load. Subjects in the highest tertile of glycemic load who also had the 10211T → G polymorphism of the adiponectin gene had an increased risk for hypoadiponectinemia compared to those without (n
26; OR: 51.7, 95% CI: 6.00–445.9, p  0.0001). Similarly, the Ala54Thr polymorphism in fatty acid-binding protein 2 showed a synergistic effect with high glycemic load and increased the risk for hypertriglyceridemia. When obesity (BMI 25) was introduced into the model, the risk for hypertriglyceridemia was further increased (OR: 23.26, 95% CI: 2.38–226.43, p  0.007). These studies indicate that gene-diet interactions could play a major role in increasing the risk for diabetes in Asian Indians. Further work is required to explore whether similar gene-environment interactions occur in individuals of different ethnic origin. Moreover, as the number of studies is small, the confidence intervals are very wide. Much larger studies of nutrigenomics are needed to draw meaningful conclusions given the imprecision in measuring dietary intakes. In summary, the present study shows that a combination of genetic susceptibility (family history of diabetes) along with lifestyle changes with consumption of higher amounts of visible fats combined with physical inactivity is responsible for the diabetes epidemic in India. Preventive strategies aimed at increasing physical activity and decreasing calorie and fat intake could be the key for prevention of diabetes in India and other developing countries.

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Wild S, Roglic G, Green A, Sicree R, King H: Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care 2004;27:1047–1053. Mohan V, Deepa R, Shanthi Rani S, Premalatha G: Prevalence of coronary artery disease and its relationship to lipids in a selected population in south India. The Chennai Urban Population Study (CUPS No 5). J Am Coll Cardiol 2001;38:682–687. Mohan V, Shanthirani S, Deepa R, et al: Intra-urban differences in the prevalence of the metabolic syndrome in southern India – The Chennai Urban Population Study (CUPS No 4). Diabet Med 2001;18:280–287. Deepa M, Pradeepa R, Rema M, Mohan A, Deepa R, Shanthirani S, Mohan V: The Chennai Urban Rural Epidemiology Study (CURES) – study design and methodology (urban component) (CURES-I). J Assoc Physicians India 2003;51:863–870. Expert Committee on the Diagnosis and Classification of Diabetes Mellitus: Report of the expert committee on the diagnosis and classification of diabetes mellitus. Diabetes Care 1997;20: 1183–1197.

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Mohan V, Deepa M, Deepa R, Shanthirani CS, Farooq S, Ganesan A, Datta M: Secular trends in the prevalence of diabetes and impaired glucose tolerance in urban South India – the Chennai Urban Rural Epidemiology Study (CURES-17). Diabetologia 2006;49:1175–1178. Mohan V, Sharp PS, Aber VR, Mather HM, Kohner EM: Family histories of Asian Indian and Europeans non-insulin-dependent diabetic patients. Pract Diabetes 1986;3:254–256. Mohan V, Shanthirani CS, Deepa R: Glucose intolerance (diabetes and IGT) in a selected South Indian population with special reference to family history, obesity and life style factors – the Chennai Urban Population Study (CUPS 14). J Assoc Physicians India 2003;51:771–777. Sharp PS, Mohan V, Levy JC, Mather HM, Kohner EM: Insulin resistance in patients of Asian Indian and European origin with non-insulin dependent diabetes. Horm Metab Res 1987;19: 84–85. Mohan V, Sharp PS, Cloke HR, Burrin JM, Schumer B, Kohner EM: Serum immunoreactive insulin responses to a glucose load in Asian Indian and European type 2 (non-insulin-dependent) diabetic patients and control subjects. Diabetologia 1986;29:235–237. Misra A, Vikram NK: Insulin resistance syndrome (metabolic syndrome) and Asian Indians. Curr Sci 2002;83:1483–1496. Abate N, Chandalia M, Satija P, Adams-Huet B, Grundy SM, Sandeep S, Radha V, Deepa R, Mohan V: ENPP1/PC-1 K121Q polymorphism and genetic susceptibility to type 2 diabetes. Diabetes 2005;54:1207–1213. Vimaleswaran KS, Radha V, Ghosh S, Majumder PP, Deepa R, Babu HN, Rao MR, Mohan V: Peroxisome proliferator-activated receptor-gamma co-activator-1alpha (PGC-1alpha) gene polymorphisms and their relationship to type 2 diabetes in Asian Indians. Diabet Med 2005;22: 1516–1521. Radha V, Vimaleswaran KS, Babu HN, Abate N, Chandalia M, Satija P, Grundy SM, Ghosh S, Majumder PP, Deepa R, Rao SM, Mohan V: Role of genetic polymorphism peroxisome proliferatoractivated receptor-gamma2 Pro12Ala on ethnic susceptibility to diabetes in South-Asian and Caucasian subjects: evidence for heterogeneity. Diabetes Care 2006;29:1046–1051. Mohan V, Gokulakrishnan K, Deepa R, Shanthirani CS, Manjala D: Association of physical inactivity with components of metabolic syndrome and coronary artery disease – the Chennai Urban Population Study (CUPS No 15). Diabet Med 2005;22:1206–1211. Bodhini D, Radha V, Deepa R, Ghosh S, Majumder PP, Rao MR, Mohan V: The G1057D polymorphism of IRS-2 gene and its relationship with obesity in conferring susceptibility to type 2 diabetes in Asian Indians. Int J Obes (Lond) 2007;31:97–102. Anuradha S, Radha V, Deepa R, Hansen T, Carstensen B, Pedersen O, Mohan V: A prevalent amino acid polymorphism at codon 98 (Ala98Val) of the hepatocyte nuclear factor-1alpha is associated with maturity-onset diabetes of the young and younger age at onset of type 2 diabetes in Asian Indians. Diabetes Care 2005;28:2430–2435.

Dr. V. Mohan Madras Diabetes Research Foundation and Dr. Mohan’s Diabetes Specialities Centre 4, Conran Smith Road, Gopalapuram Chennai 600 086 (India) Tel. 91 44 4396 8888, Fax 91 44 2835 0935, E-Mail [email protected]

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Gene Expression in Low Glycemic Index Diet – Impact on Metabolic Control Eiji Takeda, Hidekazu Arai, Kazusa Muto, Kaoru Matsuo, Masae Sakuma, Makiko Fukaya, Hisami Yamanaka-Okumura, Hironori Yamamoto, Yutaka Taketani Department of Clinical Nutrition, Institute of Health Biosciences, University of Tokushima Graduate School, Tokushima, Japan

Abstract Background: Correcting postprandial hyperglycemia forms an important part of the prevention and management of type 2 diabetes. Methods: A low-glycemic-index liquid formula designated as Inslow was prepared by replacing dextrin in the standard balanced formula (SBF) with 55.7% palatinose. Long-term administration of Inslow prevented fatty liver and improved insulin resistance in rats. Expressions of mRNA of factors involved in glucose and lipid metabolism were determined to clarify its mechanism. Results: Analysis of mRNA expressions revealed that Inslow increased the expression of enzymes involved in ␤-oxidation and peroxisome proliferator-activated receptor-␣ (PPAR-␣) in the liver, and increased PPAR-␥, adiponectin and uncoupling protein 2 as well as decreased tumor necrosis factor ␣ in adipose tissue in comparison with those of SBF. Conclusions: Inslow may induce improvement of insulin resistance by accelerated ␤-oxidation through increased expression of the hepatic PPAR-␣ gene and adipocyte PPAR-␥ gene. Therefore, Inslow is a functional food which prevents and treats type 2 diabetes. Copyright © 2007 S. Karger AG, Basel

Postprandial Hyperglycemia and Diabetic Complications

Excessive energy intake with concomitant obesity and physical inactivity are the main risk factors for type 2 diabetes mellitus. In addition, diets high in fat and saturated fatty acids, but low in dietary fiber, and diets with a high glycemic index (GI) increase the risk for type 2 diabetes. Type 2 diabetes develops after years of insulin resistance and eventual pancreatic ␤-cell failure and loss.

The GI was introduced by Jenkins et al. [1, 2] as a quantitative assessment of foods based on postprandial blood glucose response, expressed as a percentage of the response to an equivalent carbohydrate portion of a reference food, such as white bread or glucose [3]. A high-GI food with an equivalent carbohydrate content as a low-GI food induces a larger area under the glucose curve over the postprandial period. As a consequence of the induced insulin response, intake of a high-GI food may result in lower blood glucose concentrations over the late (2–3 h) postprandial period than intake of a low-GI food [4]. The profile of postprandial hyperglycemia is determined by many factors including the timing, quantity and composition of the meal, carbohydrate content, insulin and glucagon secretion. The effects of carbohydrate on health may be better described on the basis of the ability to raise blood glucose levels, which depend on the type of the constituent sugars, the physical form of the carbohydrate, the nature of the starch and other food components [5]. There is a considerable body of evidence from epidemiological studies supporting the concept that postprandial glucose excursions are strongly associated with the development of macrovascular disease, the chief cause of morbidity and mortality in patients with type 2 diabetes. A meta-analysis of 20 studies in over 80,000 subjects found a progressive relationship between fasting plasma glucose (PG) and 2-hour glucose and cardiovascular disease mortality [6]. A meta-analysis of randomized controlled trials investigating the effect of low-GI versus high-GI diets on markers for carbohydrate and lipid metabolism with a crossover or parallel design published in English between 1981 and 2003 has recently been reported [7]. Literature searches identified 16 studies that met the strict inclusion criteria. Low-GI diets significantly reduced fructosamine by ⫺0.1 mmol/l (95% CI ⫺0.20 to 0.00; p ⫽ 0.05), HbA1c by 0.27% (95% CI ⫺0.5 to ⫺0.03; p ⫽ 0.03), total cholesterol by ⫺0.33 mmol/l (95% CI ⫺0.47 to ⫺0.18; p ⬍ 0.0001) and tended to reduce low-density lipoprotein cholesterol in type 2 diabetic subjects by ⫺0.15 mmol/l (95% CI ⫺0.31 to ⫺0.00; p ⫽ 0.06) compared with high-GI diets. No changes were observed in highdensity lipoprotein cholesterol and triacylglycerol concentrations. This analysis supports the use of the GI as a scientifically based tool to enable selection of carbohydrate-containing foods to reduce total cholesterol and improve overall metabolic control of diabetes [2]. The GI of foods is now considered to be an important feature in the development of insulin resistance as determined by the homeostasis model assessment of insulin resistance. After adjustment for potential confounding variables, total but also fruit and cereal dietary fiber intakes were inversely associated with the homeostasis model assessment of insulin resistance in the Framingham Offspring Study [8].

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Mechanisms Linking Postprandial Hyperglycemia and the Risk of Diabetes Mellitus and Its Complications

The precise mechanisms linking hyperglycemia and its associated complications are unclear at this time. However, in what follows, two potential mechanisms are described. Oxidative Stress Inflammation is known to be involved not only in acute illnesses, but also in chronic conditions such as obesity, diabetes, or atherosclerotic disorders [25]. Repeated mental stress may lead to chronic alterations in cortisol and catecholamine concentrations, and to insulin resistance. Furthermore, chronically elevated cortisol concentrations may favor the development of abdominal obesity and metabolic syndrome [26, 27]. The link between mental stress and metabolic disorders may be an inflammatory response that may contribute, over the long term, to the development of central obesity and insulin resistance. The stress response is characterized by a stimulation of the sympathetic nervous system and increased secretion of both epinephrine from the adrenal medulla and glucocorticoids from the adrenal cortex. All these stimuli can be expected to reduce insulin sensitivity. In addition, increased glucocorticoid levels may be linked to central obesity, an essential feature of the metabolic syndrome [26, 34]. Hyperglycemia can increase oxidative stress through several pathways (fig. 1). A major mechanism appears to be the hyperglycemia-induced intracellular reactive oxygen species (ROS), produced by the proton electromechanical gradient generated by the mitochondrial electron transport chain and resulting in an increased production of superoxide [28]. These results suggest that adipose tissue is the major source of the elevated plasma ROS levels. Oxidative stress is known to impair both insulin secretion by pancreatic ␤-cells and glucose transport in muscle and adipose tissue [29–31]. Increased oxidative stress in vascular walls is involved in the pathogenesis of hypertension and atherosclerosis [32]. Oxidative stress also underlies the pathophysiology of hepatic steatosis [33]. Thus, oxidative stress locally produced in each of the above tissues seems to be involved in the pathogenesis of these diseases. Rapid increases in glucose and lipid levels after ingestion of a meal are also likely to trigger carbonyl stress which, either independently or by potentiation of oxidative stress, contributes to the development of both microvascular and macrovascular complications [35]. Changes in Serotonin Metabolism Under conditions of acute stress, increases in brain serotonin may improve stress adaptation and thus may contribute to the initiation as well as termination

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0 min

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Fig. 1. ROS production by high glucose exposure in endothelial cells.

of a cortisol response by way of different serotonergic pathways in the brain [36]. If an increased serotonin level constitutes a biological condition to improve stress adaptation in stress-prone subjects, serotonin activity might be continuously increased after chronic stress experiences. Ultimately, this may lead to a functional shortage in serotonin, causing a subsequent deficiency of brain serotonin activity [37]. Dietary carbohydrate enhances the uptake of circulating tryptophan into the brain mediated by modifying the plasma amino acid pattern. Insulin has little or no effect on plasma tryptophan levels, but it markedly lowers the plasma levels of the large neutral amino acids (LNAAs), which compete with tryptophan for passage across the blood-brain barrier. This decrease allows more tryptophan to enter the brain and resolves the paradox of why dietary carbohydrates, which lack tryptophan, should increase brain levels of this amino acid while protein-rich foods fail to do so. Dietary proteins raise plasma tryptophan levels. However, since tryptophan tends to be the least abundant of the 22 amino acids in proteins, this rise is small relative to the increases in other, more abundant LNAAs, such as leucine, isoleucine, and valine. The carbohydraterich, protein-poor diet caused a significant 42% increase in plasma tryptophan/⌺LNAAs compared with the protein-rich, carbohydrate-poor diet [37]. Young adult mice with a targeted mutation of the serotonin 5-HT2c receptor gene consume more food despite normal responses to exogenous leptin

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administration [38]. Chronic hyperphagia leads to a ‘middle-aged’-onset obesity associated with a partial leptin resistance of late onset [38]. In addition, older mice develop insulin resistance and impaired glucose tolerance. Levels of food intake do not change during obesity development, indicating that 5-HT2c receptor mutant mice undergo an age-dependent reduction in their ability to compensate for chronic moderate hyperphagia. Whichever of these, or other, mechanisms operates, correcting postprandial hyperglycemia forms an important part of the strategy for the prevention and management of complications in patients with type 2 diabetes.

Suppression of Postprandial Hyperglycemia by the Food

Postprandial hyperglycemia can be reduced by altering the carbohydrate content of the diet. In the remainder of this paper, I will discuss the role of palatinose as a source of carbohydrates in the diet. Preparation of Palatinose-Based Food Palatinose is a naturally occurring disaccharide composed of ␣-1,6-linked glucose and fructose. Commercial palatinose is produced from sucrose by enzymatic rearrangement and has been used as a sugar in Japan since 1985. In vivo studies with rats and pigs indicate that palatinose is completely hydrolyzed and absorbed in the small intestine. This is supported by in vitro studies showing that intestinal disaccharidases from various species (including man) can hydrolyze palatinose. The rate of hydrolysis, however, is very slow compared with sucrose and maltose. Blood glucose and insulin levels in humans after oral administration rise more slowly and reach lower maxima than after sucrose administration. Thus, palatinose is completely cleaved and absorbed, and the hydrolysis of palatinose by a homogenate of human intestinal mucosa is one fourth that of sucrose [9, 10]. A previous study demonstrated that the increase in PG and immunoreactive insulin (IRI) after palatinose ingestion was significantly smaller than that after sucrose [11]. The novel enteral liquid formula designated as Inslow was prepared by replacing dextrin in the standard balanced formula (SBF) with 55.7% palatinose (table 1). Inslow contains palatinose, branched dextrin, xylitol and other dietary fiber carbohydrates, and mixed carbohydrates from raw material as the principal carbohydrates, and the percentages of protein, fat, and carbohydrate in the formula are 20, 29.7 and 50.3%, respectively. The commercially available SBF that was used for comparison contains dextrin and sucrose as the principal carbohydrates, and the percentages of protein, fat, and carbohydrate are 16, 25 and 59%, respectively.

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Table 1. Composition of Inslow and standard balanced formula (SBF)

Energy, kcal/ml Protein, % Fat, % SFA MUFA PUFA Carbohydrate, % Maltodextrin Xylitol Palatinose

Inslow

SBF

1 20.0 29.7 9.5 68.5 16.8 50.3 22.8 8.9 68.3

1 16.0 25.0 9.0 45.0 40.0 59.0 2.8 97.2

Sucrose Dextrin

SFA ⫽ Saturated fatty acid; MUFA ⫽ monounsaturated fatty acid; PUFA ⫽ polyunsaturated fatty acid.

Effect of Palatinose-Based Food on Glucose and Lipid Metabolism in Men In the human study, peak PG and IRI levels at 30 min after Inslow loading were lower than after SBF loading. Postprandial fat oxidation rates in the Inslow group were higher than those in the SBF group. The free fatty acid concentration in the Inslow group immediately before lunch was significantly lower than that in the SBF group. PG and IRI levels in the Inslow group after standard lunch were lower than those in the SBF group, though the peak levels in these groups were not different [12]. Effect of Palatinose-Based Food on Glucose and Lipid Metabolism in Rats The effect of Inslow on carbohydrate and lipid metabolism in SpragueDawley rats was compared with that of SBF [13]. After a bolus intragastric injection of each formula equivalent to 0.9 g/kg carbohydrate, the peak levels of PG and IRI in the femoral vein of the Inslow group were significantly smaller than those of the SBF group (fig. 2). The values of the total incremental area (area under the curve) of PG and IRI from the basal level 120 min after Inslow ingestion were significantly smaller than after SBF ingestion. From 20 to 27 weeks of age, daily food intake and body weight did not differ significantly among the Inslow and SBF groups. After ingestion of Inslow or

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160 Plasma insulin (pmol/l)

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Fig. 2. Changes in plasma glucose (a) and plasma insulin (b) levels in the femoral vein after oral administration of Inslow (white circles) and SBF (black squares). Values are means ⫾ SE for n ⫽ 10. *p ⬍ 0.001 (vs. Inslow).

SBF for 2 months, fasting PG levels were not different among the two groups, but the IRI level in the Inslow group was significantly lower than that in the SBF group. Serum triglyceride (TG) level markedly decreased by 34% in the Inslow group and increased by 23% in the SBF group. The TG level of the Inslow group was significantly lower than that of the SBF group. The concentrations of serum free fatty acid and total cholesterol did not differ among the two groups. The weights of epididymal, mesenteric, and retroperitoneal adipose tissues were significantly lower in the Inslow group than in the SBF group (table 2). The concentration of TG in the liver in the Inslow group was significantly lower than that in the SBF group. Insulin sensitivity in the Inslow and SBF groups was evaluated by the hyperinsulinemic euglycemic clamp test with oral glucose load. The glucose infusion rate, which reflected the insulin sensitivity in peripheral tissues, of the Inslow group was significantly higher than that of the SBF group. The rate of hepatic glucose uptake, which might reflect insulin sensitivity in the liver, was significantly higher in the Inslow group than in the SBF group. Expressions of mRNA of Genes Involved in Glucose and Lipid Metabolism After the acclimatization period, rats were divided into two groups: spraydried Inslow powder-fed rats and spray-dried SBF powder-fed rats as reported previously [13]. Both groups were administrated each diet (80 kcal/day) with a pair-feeding condition and water ad libitum for 8 weeks. All rats were sacrificed after 8 weeks on the experimental diets to extract total RNA. To assess dietinduced changes in gene expressions, mRNA levels of genes involved in

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Table 2. Effects of long term administration of Inslow on amounts of body fat and hepatic TG in rats Fat, g/kg bodyweight

SBF Inslow

Hepatic TG mmol/g tissue

epididymal

mesenteric

retroperitoneal

23.8 ⫾ 2.4 14.5 ⫾ 1.2**

22.0 ⫾ 1.3 11.6 ⫾ 0.6**

29.7 ⫾ 1.9 19.6 ⫾ 1.2**

136.7 ⫾ 20.3 77.9 ⫾ 10.1*

*p ⬍ 0.05 (vs. SBF); **p ⬎ 0.01 (vs. SBF).

Table 3. Effect of long-term administration of Inslow on gene expression in rat liver and adipose tissue Liver Fat oxidation: increased PPAR-␣, carnitine palmitoyl transferase 1, acyl-CoA oxidase, 3,2-trans-enoyl-CoA isomerase Fatty acid synthesis: no change Sterol response element-binding protein 1c, fatty acid synthase, glucokinase, pyruvate kinase, glucose 6-phosphatase, phosphoenolpyruvate carboxykinase Energy expenditure, antioxidant activity: increased Uncoupling protein 2 Adipose tissue Energy expenditure, antioxidant activity: increased PPAR-␥, adiponectin, carnitine palmitoyl transferase 1, 3,2-trans-enoyl-CoA isomerase, uncoupling protein 2 Inflammation: decreased TNF-␣

glucose and lipid homeostasis were determined by reverse transcription of total RNA followed by PCR analysis [14]. Analysis of mRNA expressions in the liver revealed that Inslow did not change the level of expression of sterol response element-binding protein 1c but increased the expression of peroxisome proliferator-activated receptor-␣ (PPAR-␣) in fasting conditions (table 3). The mRNA levels of 3,2-trans-enoylCoA isomerase, carnitine palmitoyl transferase 1, acyl-CoA oxidase, and uncoupling protein 2 in the liver of the Inslow group were significantly higher than those of the SBF group. In contrast, there were no differences in the mRNA levels of hepatic glucokinase, pyruvate kinase, glucose 6-phosphatase,

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phosphoenolpyruvate carboxykinase and fatty acid synthetase (FAS) in both groups. Administration of Inslow increased the expression of PPAR-␥, 3,2trans-enoyl-CoA isomerase, carnitine palmitoyl transferase 1, adiponectin and uncoupling protein 2 mRNAs and decreased tumor necrosis factor ␣ (TNF-␣) mRNA in adipose tissue in comparison with those of the SBF group. In a previous report, the chronic effects of high-GI diet and low-GI diet on the lipogenic enzymes, FAS and lipoprotein lipase have been evaluated in normal and diabetic (streptozotocin-injected on day 2 of life) male SpragueDawley rats [15]. After 3 weeks, neither body weights nor relative epididymal fat pad weights differed between the normal and diabetic rats, and high-GI diet induced high basal plasma insulin levels. Plasma TGs were not significantly affected by diet in either normal or diabetic rats. Adipose tissue and liver lipoprotein lipase activities were not modified by the types of GI diet. In normal rats, FAS activity and gene expression in epididymal adipose tissue but not in the liver were greater in rats consuming high-GI diet than in those consuming low-GI diet. High-GI diet compared with low-GI diet resulted in lower hepatic phosphoenolpyruvate carboxykinase mRNA in both normal and diabetic rats. Therefore, high-GI diet is implicated in stimulating FAS activity and lipogenesis and might have undesirable long-term metabolic effects. Functions of Palatinose-Based Food PPAR-␣ is an important lipid sensor and regulator of cellular energy metabolism. It was shown to be a critical player in the regulation of cellular uptake and ␤-oxidation of fatty acid. PPAR-␣ triggers the expression of two proteins that transport fatty acids across the cell membrane: the fatty acid transport protein and fatty acid translocase [16], suggesting a role in cellular uptake and lipid homeostasis. Activation of PPAR-␣ also directly upregulates the transcription of the long-chain fatty acid acyl-CoA synthetase and of various enzymes of the peroxisomal ␤-oxidation pathways, such as acyl-CoA oxidase, acyl-CoA hydratase and dehydrogenase, and keto-acyl-CoA thiolase [17, 18]. The adipose tissue-derived cytokine TNF-␣ could be involved in the development of diabetes through several mechanisms, and elevated levels of this cytokine have been shown to be linked to the risk of diabetes [19]. Uncoupling proteins have also been linked to the development of both obesity and type 2 diabetes [20, 21]. Adiponectin is believed to improve insulin resistance, since it inhibits the expression of TNF-␣ [22] and decreases the content of TG in tissues by enhancing oxidation of fatty acids in skeletal muscles [23]. Furthermore, plasma adiponectin levels are inversely associated with several risk factors for the metabolic syndrome, including adiposity, insulin resistance, diastolic blood pressure, TG concentrations and TNF-␣ receptor concentrations [24].

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High energy intake, high fat diet, high-GI diet, secretion of 1st phase insulin defect

Low-GI diet

Postprandial hyperglycemia

ROS production

Prevention and management of cardiovascular disease, stroke and type 2 diabetes

Fig. 3. Effects of low-GI diet on the prevention and the management of cardiovascular disease, stroke and type 2 diabetes.

These findings suggest that Inslow is a functional food that is effective for the prevention and treatment of obesity, diabetes and metabolic syndrome because it regulates gene expression and consequently glucose and lipid homeostasis.

Conclusions

A diet based on low-GI foods may contribute to the prevention of diabetes mellitus and its complications. Reducing the rate of carbohydrate absorption by lowering the GI of the diet may have several health benefits, such as a reduced insulin demand, improved blood glucose control and reduced blood lipid concentrations [5]. Our data suggest that these may be mediated through pathways involved in fatty acid metabolism and regulated by PPAR-␣. The metabolic changes and the subsequent physiological processes evoked by Inslow suggest that the substitution of carbohydrates in foods by palatinose may play a role in the prevention and the management of cardiovascular disease, stroke and type 2 diabetes (fig. 3). Noteworthy of the emerging evidence is that, in most studies, it is not only the consumption of fruit and vegetables that is associated with a reduced risk of type 2 diabetes, but also the consumption of whole-grain foods. The latter, however, does not appear to play a major role in the regulation of postprandial glucose metabolism, which would suggest that the protective effect of whole grain

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against type 2 diabetes is mediated by as yet complex and incompletely elucidated mechanisms [39, 40].

Acknowledgement This study was supported by a Grant-in-Aid for Scientific Research from the Ministry of Education, Science, and Culture, Japan, and the 21st Century COE Program, Human Nutritional Science on Stress Control, Tokushima, Japan.

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Motojima K, Passilly P, Peters J M, Gonzalez F J, Latruffe N: Expression of putative fatty acid transporter genes are regulated by peroxisome proliferator-activated receptor alpha and gamma activators in a tissue- and inducer-specific manner. J Biol Chem 1998;273:16710–16714. Schoonjans K, Watanabe M, Suzuki H, Mahfoudi A, Krey G, Wahli W, Grimaldi P, Staels B, Yamamoto T, Auwerx J: Induction of the acyl-coenzyme A synthetase gene by fibrates and fatty acids is mediated by a peroxisome proliferator response element in the C promoter. J Biol Chem 1995;270:19269–19276. Tugwood JD, Issemann I, Anderson RG, Bundell KR, McPheat WL, Green S: The mouse peroxisome proliferator-activated receptor recognizes a response element in the 5⬘ flanking sequence of the rat acyl CoA oxidase gene. EMBO J 1992;11:433–439. Fasshauer M, Paschke R: Regulation of adipocytokines and insulin resistance. Diabetologia 2003;46:1594–1603. Ricquier D, Bouillaud F: The uncoupling protein homologues: UCP1, UCP2, UCP3, StUCP and AtUCP. Biochem J 2000;345:161–179. Dalgaard LT, Pedersen O: Uncoupling proteins: functional characteristics and role in the pathogenesis of obesity and type II diabetes. Diabetologia 2001;44:946–965. Maeda N, Shimomura I, Kishida K, Nishizawa H, Matsuda M, Nagaretani H, Furuyama N, Kondo H, Takahashi M, Arita Y, Komuro R, Ouchi N, Kihara S, Tochino Y, Okutomi K, Horie M, Takeda S, Aoyama T, Funahashi T, Matsuzawa Y: Diet-induced insulin resistance in mice lacking adiponectin/ACRP30. Nat Med 2002;8:731–737. Yamauchi T, Kamon J, Waki H, Terauchi Y, Kubota N, Hara K, Mori Y, Ide T, Murakami K, Tsuboyama- Kasaoka N, Ezaki O, Akanuma Y, Gavrilova O, Vinson C, Reitman ML, Kagechika H, Shudo K, Yoda M, Nakano Y, Tobe K, Nagai R, Kimura S, Tomita M, Froguel P, Kadowaki T: The fat-derived hormone adiponectin reverses insulin resistance associated with both lipoatrophy and obesity. Nat Med 2001;7:941–946. Fernandez-Real JM, Lopez-Bermejo A, Casamitjana R, Ricart W: Novel interactions of adiponectin with the endocrine system and inflammatory parameters. J Clin Endocrinol Metab 2003;88: 2714–2718. Grimble RF: Inflammatory status and insulin resistance. Curr Opin Clin Nutr Metab Care 2002;5: 551–559. Bjorntorp P: The regulation of adipose tissue distribution in humans. Int J Obes Relat Metab Disord 1996;20:291–302. Masuzaki H, Paterson J, Shinyama H, Morton NM, Mullins JJ, Seckl JR, Flier JS: A transgenic model of visceral obesity and the metabolic syndrome. Science 2001;294:419–422. Nishikawa T, Edelstein D, Du XL, Yamagishi S, Matsumura T, Kaneda Y, Yorek MA, Beebe D, Oates PJ, Hammes HP, Giardino I, Brownlee M: Normalizing mitochondrial superoxide production blocks three pathways of hyperglycaemic damage. Nature 2000;404:787–790. Matsuoka T, Kajimoto Y, Watada H, Kaneto H, Kishimoto M, Umayahara Y, Fujitani Y, Kamada T, Kawamori R, Yamasaki Y: Glycation-dependent, reactive oxygen species-mediated suppression of the insulin gene promoter activity in HIT cells. J Clin Invest 1997;99:144–150. Maddux BA, See W, Lawrence JC Jr, Goldfine AL, Goldfine ID, Evans JL: Protection against oxidative stress-induced insulin resistance in rat L6 muscle cells by micromolar concentrations of ␣-lipoic acid. Diabetes 2001;50:404–410. Rudich A, Tirosh A, Potashnik R, Hemi R, Kanety H, Bashan N: Prolonged oxidative stress impairs insulin-induced GLUT4 translocation in 3T3-L1 adipocytes. Diabetes 1998;47: 1562–1569. Ohara Y, Peterson TE, Harrison DG: Hypercholesterolemia increases endothelial superoxide anion production. J Clin Invest 1993;91:2546–2551. Roskams T, Yang SQ, Koteish A, Durnez A, DeVos R, Huang X, Achten R, Verslype C, Diehl AM: Oxidative stress and oval cell accumulation in mice and humans with alcoholic and nonalcoholic fatty liver disease. Am J Pathol 2003;163:1301–1311. Bjorntorp P: Growth hormone, insulin-like growth factor-I and lipid metabolism: interactions with sex steroids. Horm Res 1996;46:188–191. Baynes JW, Thorpe SR: Role of oxidative stress in diabetic complications: a new perspective on an old paradigm. Diabetes 1999;48:1–9.

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Anisman H, Zacharko RM: Depression as a consequence of inadequate neurochemical adaptation in response to stressors. Br J Psychiatry 1992;160(suppl 15):36–43. Markus CR, Panhuysen C. Tuiten A, Koppeschaar H, Fekkes D, Peters M: Does carbohydrate-rich, protein-poor food prevent a deterioration of mood and cognitive performance of stress-prone subjects when subjected to a stressful task? Appetite 1998;31:49–65. Nonogaki K, Strack A, Dallman M, Tecott L: Leptin-independent hyperphagia and type 2 diabetes in mice with a mutated serotonin 5-HT2c receptor gene. Nat Med 1998;4:1152–1156. Parillo M, Riccardi G: Diet composition and the risk of type 2 diabetes: epidemiological and clinical evidences. Br J Nutr 2004;92:7–19. Steyn NP, Mann J, Bennett PH, Temple N, Zimmet P, Tuomilehto J, Lindstrom J, Louheranta A: Diet, nutrition and the prevention of type 2 diabetes. Public Health Nutr 2004;7:147–165.

Dr. Eiji Takeda Department of Clinical Nutrition, Institute of Health Biosciences University of Tokushima Graduate School, Kuramoto-cho 3–18–15 Tokushima 770–8503 (Japan) Tel. ⫹81 88 633 7093, Fax ⫹81 88 633 7094, E-Mail [email protected]

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Genetic Polymorphisms in FolateMetabolizing Enzymes and Risk of Gastroesophageal Cancers: A Potential Nutrient-Gene Interaction in Cancer Development Dongxin Lina, Hui Lib, Wen Tana, Xiaoping Miaoa, Li Wangb a

Department of Etiology and Carcinogenesis, Cancer Institute/Hospital and Department of Epidemiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

b

Abstract Folate deficiency has been associated with certain types of human cancer. We therefore investigated the effects of genetic polymorphisms in folate-metabolizing enzymes on the risk of developing gastroesophageal cancers in a Chinese population where folate deficiency is common. We found that functional polymorphisms in methylenetetrahydrofolate reductase (MTHFR) and thymidylate synthase (TS), two key enzymes involved in folate and methyl group metabolism, were significantly associated with increased risk of esophageal squamous cell carcinoma, gastric cardia carcinoma, and pancreatic carcinoma. The polymorphisms modulate risk of these cancers associated with low folate status. Our results suggest that MTHFR and TS genotypes may be determinant of gastroesophageal cancers in this at-risk Chinese population. Copyright © 2007 S. Karger AG, Basel

Folate deficiency resulting from low consumption of vegetables and fruits is associated with an increased risk of several cancers, including gastroesophageal cancer and pancreatic cancer [1–3]. As an essential cofactor for the de novo biosynthesis of purines and thymidylate, folate plays a crucial role in DNA synthesis, repair, and integrity [4]. Folate is also an essential nutrient to provide methyl groups for intracellular DNA methylation reactions [5]. Although the exact mechanism and extent of the relationship between

folate deficiency and risk of these cancers have not been established, aberrant DNA synthesis, repair, and methylation, which result in abnormal gene expression, genome instability, and mutagenesis, may be involved in carcinogenesis [4, 5]. To serve as a methylation mediator, folate requires metabolism catalyzed by several enzymes. It has been suggested that genetic variants resulting in changes in the expression or function of these enzymes may contribute to cancer risk, over and above that associated with folate deficiency. Methylenetetrahydrofolate reductase (MTHFR) and thymidylate synthase (TS) are two key enzymes involved in folate and methyl group metabolism. MTHFR catalyzes the irreversible reduction of 5,10-methylenetetrahydrofolate to 5-methyltetrahydrofolate, the carbon donor for the final formation of the cellular universal methyl donor, S-adenosyl methionine. TS catalyzes the reductive methylation of dUMP by 5,10-methylenetetrahydrofolate to form dTMP, a rate-limiting step in DNA synthesis. Accordingly, variation in MTHFR and TS functions may contribute to the susceptibility to carcinogenesis through aberrant DNA methylation and/or diminished thymidylate synthesis [4, 5]. Two single nucleotide polymorphisms in MTHFR, 677C → T and 1298A → C, have been associated with a phenotype that presents significant reduction in enzyme activity [6, 7]. It was reported that individuals carrying the variant MTHFR genotypes, especially in the context of inadequate folate intake, have significantly reduced levels of global DNA methylation, compared with those carrying the wild-type genotype [8–10]. The 5⬘-UTR of TS contains a variable number of 28-bp tandem repeats, mainly 2 repeats (2R) and 3 repeats (3R), and has been associated with the efficiency of the gene expression [11]. Individuals with the 3R/3R genotype have higher TS RNA levels compared with those with the 2R/2R genotype [12]. A G → C polymorphism within the second repeat of the 3R allele has been found and this mutation disrupts the USF-1-binding consensus element and consequently downregulates TS expression [13, 14]. Because of the key roles of MTHFR and TS in folate biotransformation linked to normal DNA methylation and genome integrality, we hypothesized that genetic polymorphisms resulting in impaired expression or activity of these two enzymes might confer individual susceptibility to cancer. To examine this hypothesis, we have analyzed the associations between risks of developing esophageal squamous cell carcinoma (ESCC), gastric cardia adenocarcinoma, and pancreatic cancer and the polymorphisms in MTHFR and TS in case-control studies in a Chinese population, where folate deficiency has been shown to be common and 60% of the men are plasma folate deficient in spring [15].

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Esophageal Squamous Cell Carcinoma

Hospital-based case-control studies were conducted to examine the associations between two functional genetic polymorphisms in MTHFR and the risk of developing ESCC and gastric cardia adenocarcinoma. For the ESCC study, 240 patients and 360 sex- and age-frequency-matched controls were recruited. Genotypes of the MTHFR 677C → T and 1298A → C polymorphic sites were analyzed by using PCR-based restriction fragment length polymorphism methods. We observed that the allele frequency of MTHFR 677T was significantly higher in patients than in controls (63% vs. 41%, p ⬍ 0.001). Subjects with the 677TT genotype had a more than 6-fold increased risk for developing ESCC [odds ratio (OR) ⫽ 6.18, 95% confidence interval (CI) ⫽ 3.32–11.51] compared with those with the 677CC genotype. Moreover, the increased ESCC risk associated with the polymorphism was in an allele-dose relationship (trend test, p ⫽ 0.0001), with ORs of 1.00, 3.14 (95% CI ⫽ 1.94–5.08), and 6.18 (95% CI ⫽ 3.32–11.51) for the CC, CT, and TT genotype, respectively, after adjustment for age, sex, and smoking status. The 1298CC genotype was extremely rare in both controls (1.4%) and cases (2.9%) and was also associated with an increased risk of ESCC (OR ⫽ 4.43, 95% CI ⫽ 1.23–16.02) compared with the 1298AA genotype [16]. The results were similar for gastric cardia adenocarcinoma, a common cancer and more prevalent in areas of high risk of esophageal cancer in China, in a case-control study consisting of 217 patients and 468 frequency-matched controls (matched for age and sex). We found that subjects with the MTHFR 677TT variant genotype had a 2-fold increased risk for cancer (95% CI ⫽ 1.28–3.26). Furthermore, a significantly elevated risk was also seen among the 677CT heterozygotes (OR ⫽ 1.56, 95% CI ⫽ 1.03–2.36). However, the 1298 polymorphism had no effect on the risk [17]. These findings suggest that the MTHFR genotype may be a genetic determinant of gastroesophageal cancers among this at-risk population where folate intake is low.

Gastroesophageal Cancer

We also performed an independent case-control study to investigate the association between risk of gastroesophageal cancers and TS polymorphisms along, and in interaction with serum folate status. A total of 555 patients (324 with ESCC and 231 with gastric cardia adenocarcinoma) and 492 controls were analyzed for their TS genotypes of the 28-bp tandem repeats and G → C single nucleotide polymorphism in the 5⬘-UTR and serum folate concentration. We found that compared with the normal-expression TS genotype (3Rg/3Rg ⫹ 3Rg/3Rc ⫹ 3Rg/2R), the low-expression TS genotype (3Rc/3Rc ⫹ 3Rc/

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2R ⫹ 2R/2R) alone was significantly associated with an increased risk of ESCC (OR ⫽ 1.47, 95% CI ⫽ 1.03–2.10) but not gastric cardia adenocarcinoma (OR ⫽ 0.98, 95% CI ⫽ 0.68–1.40). More importantly, a significant interaction between the TS polymorphisms and serum folate status in relation to the risk of esophageal cancer and gastric cardia cancer was observed (p ⫽ 0.002 and 0.029, respectively) [18]. Compared to individuals with normal folate (⬎3 mg/dl) and the high-expression genotype, those with normal folate and the low-expression genotype had an increased risk of ESCC (OR ⫽ 1.35, 95% CI ⫽ 0.85–2.14). Low folate was associated with an increased risk of ESCC in the presence of the high-expression genotype (OR ⫽ 9.97, 95% CI ⫽ 5.67–17.53). However, the combination of low folate with the lowexpression phenotype was associated with the highest risk of ESCC (OR ⫽ 22.64, 95% CI ⫽ 10.44–49.05), greater than that expected if both genotype and folate status were simply independent risk factors. A similar interaction was noted for gastric cardia cancer. The combination of low folate and the low-expression genotype was associated with a higher risk of gastric cardia cancer (OR ⫽ 4.08, 95% CI ⫽ 1.94–8.59) than either genotype (OR ⫽ 0.84, 95% CI ⫽ 0.56–1.27) or folate status (OR ⫽ 1.88, 95% CI ⫽ 1.18–3.24) alone.

Pancreatic Cancer

Although the results from epidemiologic studies are not all consistent, prospective studies showed that low folate intake or low serum folate concentration is associated with risk of pancreatic cancer. We therefore examined the contribution of functional polymorphisms in MTHFR and TS to the risk of this cancer in a case-control study consisting of 163 patients with pancreatic ductal adenocarcinoma and 337 controls frequency-matched to the patients by sex and age (⫾5 years). We observed a significantly increased risk of pancreatic cancer associated with the MTHFR 677CT (OR ⫽ 2.60, 95% CI ⫽ 1.61–4.29, p ⫽ 0.005) or 677TT (OR ⫽ 5.12, 95% CI ⫽ 2.94–9.10, p ⬍ 0.001) genotype compared with the 677CC genotype. An increased risk of pancreatic cancer was also associated with the TS 3Rc/3Rc genotype (OR ⫽ 2.19, 95% CI ⫽ 1.13–4.31, p ⫽ 0.022) compared with the TS 3Rg/3Rg genotype. Furthermore, we found a significant interaction between the MTHFR C677T polymorphism and smoking (which depletes systemic and intracellular folate) or drinking (a well-known folate antagonist) intensifying the risk of pancreatic cancer. The ORs for smoking, the polymorphism and both factors combined were 0.70 (95% CI ⫽ 0.30–1.63), 2.17 (95% CI ⫽ 1.17–4.21) and 3.10 (95% CI ⫽ 1.54–6.51), respectively. This effect was much stronger in heavy smokers (OR ⫽ 6.69, 95% CI ⫽ 3.39–13.63, p ⬍ 0.0001). The ORs for drinking, the polymorphism and

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both factors combined were 0.98 (95% CI ⫽ 0.40–2.30), 2.81 (95% CI ⫽ 1.65–4.98) and 4.39 (95% CI ⫽ 2.25–8.78), respectively [19].

Conclusions

In summary, these findings demonstrate a significant association between genetic polymorphisms in the folate-metabolizing genes MTHFR and TS and risks of gastroesophageal and pancreatic cancers in the Chinese population. These genetic variants modulate the risk of these cancers associated with low folate status. Increased folate intake may overcome the effects of genetically determined reduction of MTHFR or TS activity, and suggests a potential role of enhanced folate intake in the prevention of gastroesophageal and pancreatic cancers in an at-risk population of individuals carrying the variant MTHFR and TS alleles. References 1

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Chang-Claude JC, Wahrendorf J, Liang QS, Rei YG, Munoz N, Crespi M, Raedsch R, Thurnham DI, Correa P: An epidemiological study of precursor lesions of esophageal cancer among young persons in a high-risk population in Huixian, China. Cancer Res 1990;50:2268–2274. Mayne ST, Risch HA, Dubrow R, Chow WH, Gammon MD, Vaughan TL, Farrow DC, Schoenberg JB, Stanford JL, Ahsan H, West AB, Rotterdam H, Blot WJ, Fraumeni JF Jr: Nutrient intake and risk of subtypes of esophageal and gastric cancer. Cancer Epidemiol Biomarkers Prev 2002;10:1055–1062. Howe GR, Burch JD: Nutrition and pancreatic cancer. Cancer Causes Control 1996;7:69–82. Duthie SJ: Folic acid deficiency and cancer: mechanisms of DNA instability. Br Med Bull 1999;55: 578–592. Choi SW, Mason JB: Folate and carcinogenesis: an integrated scheme. J Nutr 2000;130:129–132. Frosst P, Blom HJ, Milos R, Goyette P, Sheppard CA, Matthews RG, Boers GJ, den Heijer M, Kluijtmans LA, van den Heuvel LP, Roers GJ: A candidate genetic risk factor for vascular disease: a common mutation in methylenetetrahydrofolate reductase. Nat Genet 1995;10:111–113. Weisberg I, Tran P, Christensen B, Sibani S, Rozen R: A second genetic polymorphism in methylenetetrahydrofolate reductase (MTHFR) associated with decreased enzyme activity. Mol Genet Metab 1998;64:169–172. Stren 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;9:849–853. 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;41:454–458. Friso S, Choi S-W, Girelli D, Mason JB, Dolnikowski GG, Bagley PJ, Olivieri O, Jacques PF, Rosenberg IH, Corrocher R, Selhub J: 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;99:5605–5611. Horie N, Aiba H, Oguro K, Hojo H, Takeishi K: Functional analysis and DNA polymorphism of the tandemly repeated sequences in the 5⬘-terminal regulatory region of the human gene for thymidylate synthase. Cell Struct Funct 1995;20:191–197.

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Pullarkat ST, Stoehlmacher J, Ghaderi V, Xiong Y-P, Ingles SA, Sherrod A, Warren R, Tsao-Wei D, Groshen S, Lenz H-J: Thymidylate synthase gene polymorphism determines response and toxicity of 5-FU chemotherapy. Pharmacogenomics J 2001;1:65–70. Mandola MV, Stoehlmacher J, Muller-Weeks S, Cesarone G, Yu MC, Lenz HJ, Ladner RD: A novel single nucleotide polymorphism within the 5⬘ tandem repeat polymorphism of the thymidylate synthase gene abolishes USF-1 binding and alters transcriptional activity. Cancer Res 2003;63: 2898–2904. Kawakami K, Watanabe G: Identification and functional analysis of single nucleotide polymorphism in the tandem repeat sequence of thymidylate synthase gene. Cancer Res 2003;63: 6004–6007. Hao L, Ma J, Stampfer MJ, Ren A, Tian Y, Tang Y, Willett WC, Li Z: Geographical, seasonal and gender differences in folate status among Chinese adults. J Nutr 2003;133:3630–3635. Song C, Xing D, Tan W, Wei Q, Lin D: Methylenetetrahydrofolate reductase polymorphisms increase risk of esophageal squamous cell carcinoma in a Chinese population. Cancer Res 2001; 61:3272–3275. Miao X, Xing D, Tan W, Qi J, Lu W, Lin D: Susceptibility to gastric cardia adenocarcinoma and genetic polymorphisms in methylenetetrahydrofolate reductase in an at-risk Chinese population. Cancer Epidemiol Biomarkers Prev 2002;11:1454–1458. Tan W, Miao X, Wang L, Yu C, Xiong P, Liang G, Sun T, Zhou Y, Zhang X, Li H, Lin D: Significant increase in risk of gastroesophageal cancer is associated with interaction between promoter polymorphisms in thymidylate synthase and serum folate status. Carcinogenesis 2005;26: 1430–1435. Wang L, Miao X, Tan W, Lu X, Zhao P, Zhao X, Shan Y, Li H, Lin D: Genetic polymorphisms in methylenetetrahydrofolate reductase and thymidylate synthase and risk of pancreatic cancer. Clin Gastroenterol Hepatol 2005;3:743–751.

Dr. Dongxin Lin Department of Etiology and Carcinogenesis, Chinese Academy of Medical Sciences Cancer Hospital/Institute, 17 Panjiayuan Nanli, Caoyang District Beijing 100021 (China) Tel. ⫹86 10 8778 8491, Fax ⫹86 10 6772 2460, E-Mail [email protected]

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Dietary Quercetin Inhibits Proliferation of Lung Carcinoma Cells Huynh Hung Laboratory of Molecular Endocrinology, Division of Cellular and Molecular Research, National Cancer Centre, Singapore, Singapore

Abstract Regular consumption of fruits and vegetables is strongly associated with reduced risk of developing chronic diseases. It is estimated that one third of all cancer deaths in the USA could be avoided through appropriate dietary modification. Several studies have indicated that fruits, vegetables and whole grains contain significant amounts of bioactive phytochemicals that have antiproliferative and antineoplastic properties. The bioactive phytochemicals may help protect cellular systems from oxidative damage as well as reduce the risk of chronic diseases. Quercetin and other related flavonoids have been shown to inhibit carcinogen-induced tumors in rodents. In humans, the total average intake of quercetin and kaempferol is estimated at 20 mg/day and consumption of quercetin from onions and apples was inversely correlated with lung cancer risk. In this study, we report that quercetin-inhibited A549 lung carcinoma cell proliferation was associated with activation of the extracellular signal-regulated kinase (ERK). Inhibition of MEK1/2 but not PI3 kinase, p38 kinase or JNK abolished quercetin-induced apoptosis suggesting MEK-ERK activation was required to trigger apoptosis. Copyright © 2007 S. Karger AG, Basel

The relationship between diet and cancers has been implicated in several epidemiological studies [Block et al., 1992]. The cancer incidence is significantly lower in people whose diets consist largely of fruits and vegetables to those whose diets consist mainly of animal products [Block et al., 1992]. The results from several studies indicate that vegetables and fruits contain large amounts of bioactive phytochemicals that may help protect cellular systems from oxidative damage as well as lower the risk of chronic diseases [Leighton et al., 1992; Messina et al., 1994]. The current study of nutrient-modulated carcinogenesis involves exploring the effects of flavonoids on target receptors and signal transduction pathways;

cell cycle control and checkpoint; apoptosis and antiangiogenic processes. Flavonoids have been found to arrest cell cycle progression either at G1/S or at G2/M boundaries (reviewed in Casagrande and Darbon [2001]). However, the precise mechanism responsible for the cell cycle effect of these compounds is not clearly understood yet. It is proposed that the 3⬘-OH of the phenyl ring might be important for the level at which the cell cycle arrests. The presence of the 3⬘-OH in quercetin and luteolin correlates with a G1 block while its absence in kaempferol and apigenin correlates with a G2 block [Casagrande and Darbon, 2001]. Flavonoids that upregulate both p21CIP1 and p27KIP1 (quercetin, luteolin and daidzein) lead to G1 arrest. By contrast, the flavonoids which poorly upregulate p27KIP1 and not p21CIP1 (kaempferol and apigenin) or which induce only p21CIP1 (genistein) are unable to arrest cells in G1 [Casagrande and Darbon, 2001]. The most common flavonoids found in the diet are quercetin, kaempferol, rutin and robinin [Anton, 1988]. The total average intake of quercetin and kaempferol is estimated at 16 mg and 4 mg/day, respectively. In the gastrointestinal tract, robinin is hydrolyzed to kaempferol by the ␤-glucosidase activity of microorganisms [Bokkenheuser and Winter, 1988]. Among the dietary flavonoids, quercetin has been studied extensively [Aligiannis et al., 2001; Constantinou et al., 1995; Lee et al., 1998]. In addition, consumption of quercetin from onions and apples is inversely correlated with lung cancer risk [Le Marchand et al., 2000]. In the present study, we report that quercetin inhibits human A549 lung cancer cell proliferation and induces apoptosis. In addition to the inhibition of Akt activation and upregulation of Bax and Bad, extracellular signal-regulated kinase (ERK) activation plays an important role in mediating quercetin-induced apoptosis in A549 cells and ERK functions upstream of the caspase activation to initiate the apoptotic signal. Materials and Methods Reagents U0126, LY294002 and antibodies against phospho-MEK1/2 (Ser217/221), cleaved caspase 7 (20 kDa), caspase 3, phospho-Akt (Ser473), phospho-p44/42 ERK (Thr202/Tyr204), anti-Akt, anti-ERK1 and cleaved poly(ADP-ribose) polymerase (PARP) were from New England Biolabs, Beverly, Mass., USA. Antibodies against Bax, phospho-c-Jun (Ser63), phospho-JNK (Thr183/Tyr185), ␣-tubulin, Bcl-2, Bcl-xL, and Bad were obtained from Santa Cruz Inc., Santa Cruz, Calif., USA. Horseradish peroxidase-conjugated secondary antibodies were purchased from Pierce, Rockford, Ill., USA. Chemiluminescent detection system was supplied by Amersham, Pharmacia Biotech, Arlington Heights, Ill., USA. Disposal tissue culture plates and dishes were purchased from Nunc Inc., Naperville, Ill., USA. Quercetin and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) was purchased

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Fig. 1. Effects of quercetin on the viability and proliferation of A549 cells. Cells were grown and treated with serum-free RPMI-1640 medium containing either 0.1% DMSO or indicated doses of quercetin for 24 and 48 h as described in Materials and Methods. Cell proliferation (a) and cell viability (b) were determined by BrdU incorporation and MTT assay, respectively. Bars with different letters are significantly different from one another at p ⬍ 0.01.

from Sigma, Saint Louis, Mo., USA. Cell proliferation ELISA and in situ cell death detection kits were supplied by Roche Diagnostics Corporation, Indianapolis, Ind., USA. RPMI1640 medium, fetal bovine serum, TRIzol and penicillin-streptomycin were from Gibco-BRL, Grand Island, N.Y., USA. Cell Proliferation and Thymidine Incorporation Assays Human A549 lung epithelial cells were obtained from American Type Culture Collection (Rockville, Md., USA). Cells were treated with indicated concentrations of quercetin in minimum essential medium. Hemocytometric counts and thymidine incorporation of triplicate cultures were performed as previously described [Huynh et al., 1996]. Cell proliferation and cell viability were determined at 24 and 48 h after treatment using the cell proliferation ELISA kit and the MTT assay, respectively, as described in Huynh et al. [1996]. Experiments were repeated at least 3 times, and the data were expressed as means ⫾ SD.

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Western Blot Analysis To determine the changes in the expression of indicated proteins, cells were grown and treated with indicated concentrations of quercetin. An equal amount of proteins (100 ␮g per sample) was subjected to Western blot analysis as described in Huynh et al. [2002]. Blots were visualized with a chemiluminescent detection system (Amersham) as described by the manufacturer.

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Fig. 3. Effects of quercetin on the levels of Bcl-2, Bax, Bad and Bcl-xL in A549 cells. Cells were cultured as described in Materials and Methods. Cells were treated with vehicle or indicated concentrations of quercetin for 24 h. Cells were harvested and lysed for Western blot analysis as described in Materials and Methods. Blots were incubated with indicated antibodies. Changes in the levels of Bax, Bad, Bcl-2 and Bcl-xL proteins are shown below each blot. Experiments were repeated 3 times with similar results.

Detection of Apoptosis A549 cells were plated onto 8-chamber slides at a density of 5 ⫻ 103 cells per well and treated with indicated concentrations of quercetin for 48 h. Apoptosis was detected by the terminal deoxynucleotidyl transferase-mediated dUTP nick-end labelling (TUNEL) assay using the in situ cell death detection kit (Roche) as described by the manufacturer. Slides were visualized with a laser confocal microscope (Zeiss) equipped with epifluorescence optics and appropriate filters for FITC. Labelling indices were obtained by counting the number of labelled cells among at least 500 cells per region expressed as a percentage value.

Results

For the time course and dose-response experiments, human A549 lung cancer cells were treated with different concentrations of quercetin for 24 and 48 h. Figure 1 shows that quercetin caused a dose-dependent reduction in DNA synthesis (fig. 1a) and cell viability (fig. 1b) (p ⬍ 0.01). A 50% reduction in cell viability was seen at a dose of 29.0 ␮M after 48 h of incubation (fig. 1b).

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Fig. 4. Effects of quercetin on the cleavage of caspase 3, caspase 7 and PARP in A549 cells. Cells were cultured and treated with quercetin as described in Materials and Methods. Cells were harvested and lysed for Western blot analysis as described in Materials and Methods. Blots were incubated with indicated antibodies. Changes in the levels of cleaved caspase 3, cleaved caspase 7 and cleaved PARP are shown below each blot. Experiments were repeated 3 times with similar results.

DNA fragmentation (fig. 2a) and a dose-dependent increase in apoptotic cells (fig. 2b) were observed in quercetin-treated cells. As shown in figure 3, quercetin induced a significant elevation in the expression of proapoptotic Bax and Bad (p ⬍ 0.01). While Bcl-2 expression was slightly decreased, the antiapoptotic Bcl-xL expression was significantly increased by quercetin (p ⬍ 0.01) (fig. 3d). An 89-kDa cleaved PARP fragment was detected in quercetin-treated samples (fig. 4e). Figure 4c and d shows that the cleaved forms of caspase 3 and 7 fragments were readily detectable at a dose as low as 14.5 ␮M of quercetin and reached high levels at a dose of 29.0 ␮M. Figure 5 shows that quercetin decreased in total antiapoptotic Akt protein (fig. 5d) and its basal phosphorylation (fig. 5c). Treatment of A549 cells with quercetin also led to a dose-dependent activation of MEK1/2 (fig. 6b) and ERK1/2 (fig. 6d). To determine whether quercetin-induced apoptosis was mediated by the activation of ERK, A549 cells were treated with MEK inhibitor to suppress quercetin-induced ERK activation and its downstream effects. The TUNEL assay showed that quercetin and combined quercetin-LY294002 caused apoptosis in

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Fig. 5. Effects of quercetin on the basal levels of p85 subunit of PI3 kinase, Akt-1 and phosphorylated Akt (Ser473) in A549 cells. Cells were cultured and treated with quercetin as described in Materials and Methods. Cells were harvested and lysed for Western blot analysis as described in Materials and Methods. Blots were incubated with indicated antibodies. Changes in the levels of Akt-1 and phospho-Akt-1 are shown below each blot. Experiments were repeated 3 times with similar results.

A549 cells (fig. 7d, f). Co-treatment of A549 cells with quercetin and U0126 completely blocked quercetin-induced apoptosis (fig. 7e). Co-treatment of cells with quercetin and U0126 prevented quercetin-induced phosphorylation of ERK, phosphorylation of c-Jun, and cleavage of caspase 3, caspase 7 and PARP (fig. 8). Blocking PI3 kinase activity neither enhanced nor prevented quercetininduced apoptosis, and cleavage of caspase 3, caspase 7 and PARP (fig. 8). The results suggest that activation of MEK-ERK plays an important role in quercetin-induced apoptosis and ERK acts upstream of caspase 3 and caspase 7 to exert its apoptotic influence in the quercetin-treated A549 cells.

Discussion

In the present study, we have shown that quercetin inhibits human A549 lung cancer cell proliferation and induces apoptosis. In addition to the inhibition of Akt-1 phosphorylation, sustained activation of ERK is required for quercetin-induced apoptosis in A549 cells. Quercetin treatment results in doseand time-dependent activation of ERK. That elevated ERK activity contributes

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Fig. 6. Effects of quercetin on the levels of MEK1, ERK, and phosphorylated MEK1/2 (Ser217/221), phosphorylated ERK (Thr202/Tyr204), phosphorylated JNK (Thr183/ Tyr185), and phosphorylated c-Jun (Ser63) in A549 cells. Cells were cultured and treated with various concentrations of quercetin as described in Materials and Methods. Cells were harvested and lysed for Western blot analysis as described in Materials and Methods. Blots were incubated with indicated antibodies. Changes in the levels of indicated proteins are shown below each blot. Experiments were repeated 3 times with similar results.

to apoptosis by quercetin is supported by the observations that activation of ERK by expression of activated MEK1 induces apoptosis while inhibition of ERK by MEK inhibitors blocks quercetin-induced cell death (data not shown). Quercetin-induced apoptosis is associated with cleavage of caspase 3, caspase 7 and PARP all of which can be reduced by treatment of A549 cells with the MEK1/2 inhibitor. Our findings suggest that in addition to the inhibition of Akt activation, ERK activation plays an important role in mediating quercetininduced apoptosis in A549 cells and ERK functions upstream of the caspase activation to initiate the apoptotic signal. In the present report, we observe that quercetin inhibits Akt expression and Akt phosphorylation. Because Akt is a downstream target of PI3 kinase, the

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f Fig. 7. Effects of MEK1/2 inhibitor U0126 and PI3 kinase inhibitor LY294002 on quercetin-induced apoptosis in A549 cells. Cells were grown and treated with a vehicle (a), 10.0 ␮M of U0126 (b), 10.0 ␮M of LY294002 (c), 58.0 ␮M of quercetin (d), 58.0 ␮M of quercetin plus 10.0 ␮M of U0126 (e), and 58.0 ␮M of quercetin plus 10.0 ␮M of LY294002 (f) for 24 h. Cells were subjected to the TUNEL assay as described in Materials and Methods. Original magnification, ⫻200.

observed inhibition of Akt phosphorylation suggests that quercetin also inhibits PI3 kinase. This argument is supported by previous studies showing that quercetin is an inhibitor of PI3 kinase and serine/threonine protein kinases [Agullo et al., 1997; Gamet-Payrastre et al., 1999; Hagiwara et al., 1988; Yoshizumi et al., 2001]. By suppressing the activation of Akt-1, quercetin can promote apoptosis via several pathways. Inactivation of Akt would prevent Akt-1 from phosphorylating Bad on serine 136. As a result, Bad binds to Bcl-2, and its proapoptotic activity is effectively increased in the death regulation equation.

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Fig. 8. Effects of MEK1/2 inhibitor U0126 and PI3 kinase inhibitor LY294002 on quercetin-induced phosphorylation of ERK, c-Jun and cleavage of caspase 3, caspase 7 and PARP in lung carcinoma cells. A549 cells were grown and treated with SRF medium containing 0.1% DMSO and indicated concentrations of drugs for 24 h. Cells were harvested and lysed for Western blot analysis as described in Materials and Methods. Blots were incubated with indicated antibodies. Experiments were repeated 3 times with similar results.

In this study, we have provided evidence that activation of MEK-ERK plays a dominant role in quercetin-induced activation of caspase 3 and 7 which is necessary for the cleavage of PARP and apoptosis in A549 lung cancer cells. Quercetin treatment results in high and sustained activation of ERK in A549 cells. One important difference between the quercetin- and IGF-1-induced ERK activation is the time and duration of activity (data not shown). In the case of IGF-1, ERK activation is rapid, occurring within minutes of treatment, and transient. With quercetin, significant activation occurs at 3 h, but the activity remains highly elevated throughout the experiment (up to 24 h). Utilizing U0126 to modulate ERK activity, we find that inhibition of MEK-ERK activation abolishes quercetin-induced apoptosis. Our results are similar to other studies which demonstrate that abrogation of the ERK pathway by Taxol delays or fails to prevent Taxol-induced apoptosis [Kalechman et al., 2000; Lieu et al., 1998]. However, it is not a universal feature of mammalian cells as activation of the MEK-ERK pathway has been shown to contribute to cell proliferation and survival [Ballif and Blenis, 2001], migration [Krueger et al., 2001] and transformation [Montesano et al., 1999]. Furthermore, inhibition of stress-induced signalling via the MEK-ERK pathway increases the toxic effects of chemotherapeutic drugs and irradiation [Yano et al., 1992]. Therefore, the ability of the MEK-ERK

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pathway to regulate proliferation versus survival appears to depend on cell types and the amplitude and duration of ERK activation. A short activation of the MEK-ERK cascade by growth factors such as IGF-1 is associated with proliferation while prolonged activation of ERK activity inhibits DNA synthesis. Our data suggest that consumption of diets containing high levels of quercetin may help to reduce the risk of and/or prevent lung cancer.

Acknowledgements This work was supported by grants from the National Medical Research Council of Singapore (NMRC/0541/2001), A*STAR-BMRC (LS/00/017) and A*STAR-BMRC (LS/00/ 019) to Huynh Hung.

References Agullo G, Gamet-Payrastre L, Manenti S, Viala C, Remesy C, Chap H, Payrastre B: Relationship between flavonoid structure and inhibition of phosphatidylinositol 3-kinase: a comparison with tyrosine kinase and protein kinase C inhibition. Biochem Pharmacol 1997;53:1649–1657. Aligiannis N, Mitaku S, Mitrocotsa D, Leclerc S: Flavonoids as cycline-dependent kinase inhibitors: inhibition of cdc 25 phosphatase activity by flavonoids belonging to the quercetin and kaempferol series. Planta Med 2001;67:468–470. Anton R: Flavonoids and Traditional Medicine. New York, Liss, 1988. Ballif BA, Blenis J: Molecular mechanisms mediating mammalian MEK-MAP kinase cell survival signals. Cell Growth Differ 2001;12:397–408. Block G, Patterson B, Subar A: Fruit, vegetables, and cancer prevention: a review of the epidemiological evidence. Nutr Cancer 1992;18:1–29. Bokkenheuser VD, Winter J: Hydrolysis of flavonoids by human intestinal bacteria; in Cody V, Middleton E, Harbourne JB, Beretz A (eds): Plant Flavonoids in Biology and Medicine II. New York, Alan R Liss, Inc, 1988. Casagrande F, Darbon JM: Effects of structurally related flavonoids on cell cycle progression of human melanoma cells: regulation of cyclin-dependent kinases CDK2 and CDK1. Biochem Pharmacol 2001;61:1205–1215. Constantinou A, Mehta R, Runyan C, Rao K, Vaughan A, Moon R: Flavonoids as DNA topoisomerase antagonists and poisons: structure-activity relationships. J Nat Prod 1995;58:217–225. Gamet-Payrastre L, Manenti S, Gratacap MP, Tulliez J, Chap H, Payrastre B: Flavonoids and the inhibition of PKC and PI 3-kinase. Gen Pharmacol 1999;32:279–286. Hagiwara M, Inoue S, Tanaka T, Nunoki K, Ito M, Hidaka H: Differential effects of flavonoids as inhibitors of tyrosine protein kinases and serine/threonine protein kinases. Biochem Pharmacol 1988;37:2987–2992. Huynh H, Chow PK, Ooi LL, Soo KC: A possible role for insulin-like growth factor-binding protein-3 autocrine/paracrine loops in controlling hepatocellular carcinoma cell proliferation. Cell Growth Differ 2002;13:115–122. Huynh H, Yang X, Pollak M: Estradiol and antiestrogens regulate a growth inhibitory insulin-like growth factor binding protein 3 autocrine loop in human breast cancer cells. J Biol Chem 1996;271: 1016–1021. Kalechman Y, Longo DL, Catane R, Shani A, Albeck M, Sredni B: Synergistic anti-tumoral effect of paclitaxel (Taxol)⫹AS101 in a murine model of B16 melanoma: association with ras-dependent signal-transduction pathways. Int J Cancer 2000;86:281–288.

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Krueger JS, Keshamouni VG, Atanaskova N, Reddy KB: Temporal and quantitative regulation of mitogen-activated protein kinase (MAPK) modulates cell motility and invasion. Oncogene 2001; 20:4209–4218. Lee SC, Kuan CY, Yang CC, Yang SD: Bioflavonoids commonly and potently induce tyrosine dephosphorylation/inactivation of oncogenic proline-directed protein kinase FA in human prostate carcinoma cells. Anticancer Res 1998;18:1117–1121. Leighton T, Ginther C, Fluss L, Harter WK, Cansado J, Notario V: Phenolic compounds in food and their effects on health II. Washington, American Chemical Society, 1992, pp 229–238. Le Marchand L, Murphy SP, Hankin JH, Wilkens LR, Kolonel LN: Intake of flavonoids and lung cancer. J Natl Cancer Inst 2000;92:154–160. Lieu CH, Liu CC, Yu TH, Chen KD, Chang YN, Lai YK: Role of mitogen-activated protein kinase in taxol-induced apoptosis in human leukemic U937 cells. Cell Growth Differ 1998;9:767–776. Messina MJ, Persky V, Setchell KD, Barnes S: Soy intake and cancer risk: a review of the in vitro and in vivo data. Nutr Cancer 1994;21:113–131. Montesano R, Soriano JV, Hosseini G, Pepper MS, Schramek H: Constitutively active mitogen-activated protein kinase kinase MEK1 disrupts morphogenesis and induces an invasive phenotype in Madin-Darby canine kidney epithelial cells. Cell Growth Differ 1999;10:317–332. Yano T, Pinski J, Groot K, et al: Stimulation by bombesin and inhibition by bombesin/GRP antagonist RC-3905 of growth of human breast cancer cell lines 1123. Cancer Res 1992;52:4545–4547. Yoshizumi M, Tsuchiya K, Kirima K, Kyaw M, Suzaki Y, Tamaki T: Quercetin inhibits Shc- and phosphatidylinositol 3-kinase-mediated c-Jun N-terminal kinase activation by angiotensin II in cultured rat aortic smooth muscle cells. Mol Pharmacol 2001;60:656–665.

Dr. Huynh Hung Laboratory of Molecular Endocrinology Division of Cellular and Molecular Research National Cancer Centre of Singapore Singapore 169610 (Singapore) Tel. ⫹65 436 8347, Fax ⫹65 226 5694, E-Mail [email protected]

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Osteoporosis: The Role of Genetics and the Environment Boonsong Ongphiphadhanakul Department of Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand

Abstract Osteoporosis is partly genetically determined. The genetics of osteoporosis is polygenic in nature with multiple common polymorphic alleles interacting with each other and environmental factors to determine bone mass. A number of studies have attempted to dissect the genetic factors responsible for the pathogenesis of osteoporosis using genome-wide scanning and the candidate gene approach. However, the results of such studies among different populations have been mostly inconsistent, suggesting genetic heterogeneity of osteoporosis. It is likely that the cohort of genes indicating predisposition to the risk of osteoporosis may be different among populations with different ethnic backgrounds. The successful identification of susceptibility genes for osteoporosis should prove to be helpful in targeting preventive and therapeutic measures to individuals at higher risk and to render the effort more cost-effective. Information with regard to genetic variations is also likely to be useful in targeting preventive or therapeutic measures to subjects genetically determined to have better responsiveness. Intestinal calcium absorption is dependent on vitamin D receptor gene polymorphisms. Skeletal responsiveness to estrogen, particularly at lower doses, is related to polymorphisms in the estrogen receptor-
gene. Recently, circulating homocysteine levels have been shown to be associated with fracture risk. Folate and vitamin B supplements for reducing serum homocysteine and fractures in postmenopausal women have not been fully investigated. However, there is an interaction between folate status and methylenetetrahydrofolate reductase gene polymorphism on bone phenotypes. Due to recent technological advances, whole-genome association study is becoming more feasible. Genomic information with regard to the susceptibility to osteoporosis and the responsiveness to preventive or therapeutic modalities should supplement rather than replace conventional clinical information. Clinical decision should also take into account the social, health and economic perspectives in order to balance the benefit of novel clinical strategies against the associated risks and available resources. Copyright © 2007 S. Karger AG, Basel

Genetic Determinants of Osteoporosis

The risk of osteoporosis is partly genetically determined. Studies performed in families demonstrated a resemblance in bone mineral density (BMD) between child-parent pairs [1, 2]. Moreover, studies carried out in twins also revealed more resemblance in bone mass among monozygotic twins than dizygotic twins [3, 4]. In multigenerational pedigree studies, a genetic contribution to bone mass was also demonstrated [5]. The heritability of bone mass is estimated to be 60–80% and its influence can be demonstrated as early as before puberty [6]. The skeletal quantitative phenotypes for osteoporosis do not conform to a simple monogenic model and the genetics of osteoporosis is polygenic in nature with multiple common polymorphic alleles interacting with each other and environmental factors to determine the quantitative bone phenotypes [7, 8]. A number of studies have attempted to dissect the genetic factors responsible for the pathogenesis of osteoporosis using genome-wide scanning and the candidate gene approach. For genome scans, the results of studies in different populations, however, are still inconsistent, which suggests that osteoporosis may be genetically heterogeneous [9, 10]. Association studies which have examined the association between variants at candidate genetic loci and osteoporosis have investigated a number of genes in various populations including genes encoding the
1-chain of type 1 collagen (COLIA1), vitamin D receptor (VDR), estrogen receptor-
(ESR1) and others. Role of the VDR Gene VDR was the first gene identified to be associated with an osteoporosisrelated phenotype. The VDR gene contains 11 exons and spans approximately 75 kb. There are at least 3 single nucleotide polymorphisms (SNPs) at the 3 end of the VDR gene which have originally been studied in relation to osteoporosis. The SNPs can be identified by BsmI, ApaI and TaqI restriction endonucleases, respectively. In 1992, it was reported that these SNPs, which are in linkage disequilibrium, are associated with BMD. The relation is such that the B allele which denotes the absence of the BsmI restriction site is associated with lower bone mass. Numerous studies then followed with inconsistent results. According to meta-analyses, the effect of the VDR gene on osteoporosis appears to be positive but the magnitude is rather small. Between genotypes, BMD differs by 0.15–0.2 Z score units. Moreover, the effect of the VDR gene is also dependent on age and menopausal status [11, 12]. Role of the COLIA1 Gene Type 1 collagen is one of the major proteins in bone. The type 1 collagen gene has been investigated as a susceptibility gene for osteoporosis. In 1996,

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Grant et al. [13] reported that a G-to-T SNP in the promoter of the gene encoding the COLIA1 gene which affects the binding site to the transcription factor Sp1 is associated with BMD. BMD is higher in the presence of the G allele and a dose-related relationship is also apparent. Besides BMD, the SNP is also associated with osteoporotic fractures. The relationship between the COLIA1 gene and osteoporosis has been investigated in meta-analyses which suggested that the COLIA1 polymorphism is associated with BMD [14, 15]. Nevertheless, the effect is small being 0.15 Z scores per T allele. On the other hand, the effect of the SNP on fractures is out of proportion to that on BMD and it is likely that the polymorphism may be more related to bone quality rather than quantity. Therefore, the increase in the risk of fractures is mainly due to the impairment in bone quality in subjects with the T allele. Role of the ESR1 Gene Since estrogen deficiency is the main cause of osteoporosis and the action of estrogen is mediated through estrogen receptors, a number of studies have also investigated the relation between the ESR1 gene and osteoporosis. Two of the more frequently studied SNPs are located in intron 1 of the ESR1 gene which can be detected by PvuII and XbaI restriction endonucleases, respectively. The results of studies on the ESR1 gene and osteoporosis are inconsistent. However, a meta-analysis showed that the XX allele according to the detection by XbaI restriction endonuclease was related to less BMD and more osteoporotic fractures [16]. An effect of the P allele as assessed by PvuII restriction endonuclease was not found. Role of the Low-Density Lipoprotein Receptor-Related Protein 5 Gene Besides the candidate gene approach, genome scan has also been utilized to locate genes for osteoporosis without a priori knowledge of the function of the genes involved. Chromosomal regions which have been found to be related to bone mass include those on chromosomes 1, 2, 4, 5, 6, and 11. The importance of the chromosomal region 11q12–13 has been substantiated by studies of 2 rare monogenic bone disorders, namely autosomal dominant high bone mass and autosomal recessive osteoporosis pseudoglioma syndrome, which were also mapped to chromosomal region 11q12. The gene responsible in both disorders is the low-density lipoprotein receptor-related protein 5 (LRP5) gene which was previously unknown to play a role in bone metabolism. With regard to the relation between the LRP5 gene and osteoporosis, Ferrari et al. [17] studied the associations of genetic variants at the LRP5 locus and bone mass in adults, adolescents and children. A number of polymorphisms in LRP5 were found and associations demonstrated between these variants and peak bone mass as well

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as bone size. Subsequent studies also demonstrated a relation between various SNPs in the LRP5 gene and bone mass in Caucasians [18, 19] as well as in Asians [20, 21], but not without dispute [22]. Limitations of Studies on the Genetics of Osteoporosis Although association study has higher power than conventional linkage analysis, it can be flawed by population admixture [23]. Using the transmission disequilibrium test to test linkage and association could be an alternative and robust approach [24]. Moreover, most studies regarding genetic susceptibility to osteoporosis have only examined the relation between genetic variations and bone mass. The genetic correlation between the susceptibility to osteoporotic fractures and bone mass variation can be low [25] and genetic susceptibility to low bone mass cannot be readily generalized to osteoporotic fractures. To date, the COLIA1 gene polymorphism appears to be more consistently related to BMD and osteoporotic fractures [13, 26, 27] and the functionality of the polymorphism has been suggested [28]. However, there appear to be marked differences in allele frequency of the COLIA1 polymorphism in populations with different ethnicity. In particular, studies performed in Asian populations revealed that the COLIA1 polymorphism is almost nonexistent which undermines its role, if any, in the risk assessment of osteoporosis in Asians [29–31]. Taken together, it is likely that the cohort of genes predisposing to the risk of osteoporosis can be different among populations with different ethnic backgrounds.

Genetic Determinants of Responsiveness in Bone-Related Phenotypes

Responsiveness to Calcium Calcium is an essential nutrient for bone health. Epidemiological studies have demonstrated that calcium intake affects the risk factors for osteoporotic fractures. Nevertheless, the effect of calcium on bone mass in young adults is dependent on the duration and continuity of increased calcium intake as well as possibly the reproductive hormonal milieu. A short-term study demonstrated increased BMD in prepubertal subjects [32] while another study revealed a beneficial effect only after menarche [33]. Moreover, the beneficial effect of supplemental calcium on bone rapidly dissipated within 1–2 years of stopping the calcium supplements [34]. On the other hand, long-term calcium supplementation appears to exert a minimal effect on bone mass. A 7-year study in adolescents showed that the gain in bone mass during the first 4 years of calcium

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supplementation diminished at the end of the study at 7 years [35]. In a metaanalysis of cross-sectional studies, it was found that in young and middle-aged females, there is a correlation between bone mass and calcium intake. Moreover, it was observed in a meta-analysis of longitudinal studies that increased calcium intake can prevent 1% bone loss per year [36]. Despite its possible beneficial effects on bone mass in children and young adults, the role of calcium alone in the prevention of osteoporotic fractures in postmenopausal women is less clear. Calcium supplementation in women within 5 years postmenopausally does not appear to possess a beneficial effect on bone mass. Nevertheless, in elderly women with osteoporosis, calcium supplementation tends to decrease vertebral fractures [37]. Intestinal absorption of calcium appears to be influenced by genetic factors, particularly VDR gene polymorphism. The increase in intestinal calcium absorption during low calcium intake as a normal physiologic adaptation disappears in subjects with the BB genotype but not in subjects with the bb genotype [38]. Despite the difference in calcium absorption, the influence of VDR polymorphisms on changes in bone mass after calcium supplementation has not been studied, although the effect of calcium or calcitriol in preventing osteoporotic fractures has been found to depend on VDR gene polymorphisms [39]. Responsiveness to Estrogen Since estrogen exerts its effect mainly through estrogen receptors, it is therefore not surprising that ESR1 has been investigated as a target causing variation in individual response to estrogen. Despite the inconsistency in the relation between ESR1 polymorphisms and bone mass, there are relatively more consistent evidences regarding the relation between these polymorphisms and skeletal responsiveness to estrogen. For example, polymorphisms in ESR1 have been demonstrated to be associated with the protective effects of estrogen on BMD [40, 41] and fracture risk [42], although not without dispute [43]. Part of the reasons for the inconsistent results may be that the influence of ESR1 polymorphism on skeletal responsiveness can be more apparent at a lower dose of estrogen [41]. Despite the association between ESR1 polymorphisms and response in BMD, the effect on the fracture rate is as yet unknown. Similar to the studies investigating ESR1 polymorphisms and BMD, the more often studied polymorphisms are the polymorphic sites identifiable by PvuII or XbaI restriction endonucleases in intron 1 which may not be directly involved in the physiology of bone responsiveness. Besides skeletal responsiveness to estrogen, ESR1 has also been found to influence the increase in high-density lipoprotein cholesterol after estrogen [44], which further supports the role of nucleotide variations in the ESR1 gene in tissue responsiveness to estrogen.

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Homocysteine and Osteoporosis Recently, it has been found that homocysteine is a novel risk factor for osteoporotic fractures. It is well established that patients with homocystinuria, a rare autosomal recessive disorder with markedly elevated circulating homocysteine levels, have generalized osteoporosis even at young age. The reason for early osteoporosis is unclear, but may be related to impairment in collagen cross-linking. In elderly subjects, 2 independent studies reported concurrently that high homocysteine levels were associated with fractures. In one of the studies (from the Netherlands), subjects in the highest quartiles of homocysteine levels had twice the risk of a nonvertebral fracture compared to those in the other quartiles [45]. The other study (from the Framingham Study cohort in the USA) demonstrated that men in the highest quartiles had four times the risk of hip fractures while in women the risk increased by a factor of 2 relative to those in the lowest quartile of homocysteine levels [46]. Despite the associations, a causal relation between homocysteine levels and fracture is less clear. However, more recently, a prospective study revealed that the risk of fractures was reduced by half in patients with stroke after folate and mecobalamin supplementation [47]. Nevertheless, the factors more directly responsible for the reduction in fracture risk are homocysteine or folate and the B vitamin status cannot be clearly determined. There is a common allelic polymorphism C677T in the methylenetetrahydrofolate reductase (MTHFR) gene with changes in the amino acid at position 222 from alanine to valine. The substitution results in an enzyme which is more thermolabile and causes higher homocysteine and lower folate levels. The polymorphism has been found to be associated, albeit inconsistently, with BMD or fractures in a number of studies [48–50]. The inconsistency may be explained by the possible interaction between the MTHFR genotype and the intake of B vitamins and folate. Subjects with low folate levels who had the TT genotype tended to have lower broadband ultrasound attenuation and lower BMD at the Ward’s triangle. Subjects with higher folate levels and with the TT genotype had higher, rather than lower, hip BMD [51]. In a longitudinal study with a mean of 6.6 years of follow-up, there was no relation between the MTHFR genotype and BMD at baseline. However, increasing riboflavin intake correlated with femoral BMD both at baseline and the end of the follow-up period in subjects homozygous for the T allele in the MTHFR gene [52]. Taken together, the findings suggest a protective role of B vitamins in osteoporotic fractures which may depend on the MTHFR genotype. A long-term interventional study taking into account the nutrientgene interaction is clearly needed. With the current technological advances in the genomic scale analysis, it is not inconceivable that the cohort of susceptibility genes for osteoporosis as well

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as the skeletal responsiveness to drugs and nutrients will soon be elucidated. The information will be potentially useful in the clinical management of osteoporosis and should supplement rather than replace conventional clinical information. In the face of limited resources, clinical decision should also take into account the social, health and economic perspectives in order to balance the benefit of novel clinical strategies against the associated risks and available resources.

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Dr. Boonsong Ongphiphadhanakul Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University Bangkok (Thailand) Tel. 66 2 201 2416, ext. 1590, Fax 66 2 201 2416, E-Mail [email protected]

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Tai ES, Gillies PJ (eds): Nutrigenomics – Opportunities in Asia. Forum Nutr. Basel, Karger, 2007, vol 60, pp 168–175

Application of Nutrigenomics in Eye Health Cécile Delcourt Inserm, U593 ‘Epidemiology, Public Health and Development’; Université Victor Segalen Bordeaux 2, Bordeaux, France

Abstract This paper reviews recent findings on the implication of nutritional and genetic factors in age-related eye diseases: age-related macular degeneration (AMD; a degenerative disease of the retina) and cataract (opacification of the lens). Because of direct exposure to light, the eye is particularly sensitive to oxidative stress. Antioxidants, such as vitamin E, C or zinc, clearly have a protective effect in AMD and probably in cataract. In addition, two carotenoids, lutein and zeaxanthin, may play a more specific role in the eye: they accumulate in the retina, where they form the macular pigment, and in the lens. Their role is probably to filter out phototoxic blue light and to quench singlet oxygen. Finally, docosahexaenoic acid (an ␻⫺3 polyunsaturated fatty acid) is particularly important for the retina, where it exerts structural, functional and protective actions. Besides, these diseases are strongly influenced by genetics, as demonstrated by familial and twin studies. The apolipoprotein E4 allele is associated with a reduced risk of AMD, while an association of AMD with complement factor H polymorphism has recently been demonstrated. Nutrigenomics, by studying the interactions between genetic variability and nutritional factors, represents a new challenge in order to account for interindividual variations in disease susceptibility. Such potential interactions are presented. Copyright © 2007 S. Karger AG, Basel

There is growing evidence for a major implication of nutrition and genetics in the etiology of age-related eye diseases [age-related macular degeneration (AMD), cataract and glaucoma], which are the major causes of blindness worldwide [1]. However, the interest in nutritional risk factors for these diseases and the identification of the associated genes are still recent. As such, the interactions between nutritional and genetic factors have not yet been studied. Some hypotheses can be drawn from the known interactions between nutrition and specific biological mechanisms.

AMD is a degeneration of the central retina, known as the macula. It is associated with extracellular deposits forming yellow spots on the retina, named drusen. These deposits are probably related to decreased degradation and elimination of cellular components during the process of renewal of the photoreceptors. Late-stage AMD is characterized by the development of choroidal neovascularization (exudative AMD) or by the disappearance of photoreceptors and underlying retinal pigment epithelium (atrophic AMD). Cataract is an opacification of the lens, which focuses the light on the retina. Glaucoma is a neuropathy of the optic nerve, leading to a gradual loss of the peripheric visual field and leading finally to total blindness. The prevalence of these diseases increases sharply with age. They are multifactorial, with both genetic and environmental factors. Some risk factors have been clearly identified, such as apolipoprotein E, complement factor H polymorphisms and smoking for AMD; light exposure, smoking, diabetes and oral corticosteroid use for cataract, and intraocular pressure for glaucoma. Oxidative stress plays an important role in eye ageing. The retina is particularly susceptible to oxidative stress because of its high content of easily peroxidizable long-chain polyunsaturated fatty acids (PUFA), in particular docosahexaenoic acid (DHA; an ␻⫺3 PUFA) [2]. Its susceptibility is also due to the high level of in situ reactive oxygen species production, due in particular to light exposure and high metabolic activity [2]. Opacification of the lens is due to oxidation of the structural proteins of the lens, inducing their aggregation [3]. Three types of nutritional factors offer or may offer protection against eye ageing: antioxidants, such as vitamins C and E or zinc; lutein and zeaxanthin, two carotenoids which accumulate specifically in the retina and lens; ␻⫺3 PUFA, and in particular DHA, which have important structural and protective functions in the retina. Initial epidemiological observations, showing that high vitamin E plasma levels may protect against AMD [4], have been confirmed by a large randomized clinical trial performed in the United States. In this study, performed on nearly 5,000 subjects, supplementation for 6 years with high doses of antioxidants (vitamins E and C, and ␤-carotene) and zinc significantly reduced the risk of developing advanced AMD by 34% in subjects with early AMD [5]. In parallel, numerous studies have evidenced a 20–50% reduction of the risk for nuclear cataract (one of the subtypes of cataract, based on the localization of the opacities) in subjects with high dietary intakes or high plasma concentrations of vitamins C and E [6]. However, in several large randomized clinical trials, the risk for cataract was not reduced with antioxidant supplementation [7–9]. Only the REACT study showed an effect of supplementation with vitamins C and E, and ␤-carotene on cortical cataract [10]. A more recent research domain regards the role of two carotenoids, lutein and zeaxanthin, for the protection of the retina and the lens. These carotenoids

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accumulate in the macula, where they are known as the macular pigment [11], and they are also the only carotenoids found in the lens [12]. Besides their antioxidant properties, they probably act as a filter against the phototoxic effects of blue light [11]. Two clinical studies have shown that eyes at risk of AMD have a lower density of the macular pigment [13, 14]. Epidemiological studies also suggest that a high intake or high plasma levels of lutein and zeaxanthin could protect against AMD and cataract [15–22]. Although all these studies have yielded results in the direction of a protective effect, they were not always significant due to small sample sizes. A small randomized study showed improvement of near visual acuity with lutein supplementation, in subjects with atrophic AMD [23]. Finally, DHA is a major component of the photoreceptors, where it exerts structural (membrane fluidity, interaction with rhodopsin) and protective functions [24]. The protective functions include the systemic anti-inflammatory, antiangiogenic and antiapoptotic functions, but also specific actions such as increase in lysosomal acid lipase, leading to increased lipid degradation in the retinal pigment epithelium [24]. Few epidemiological studies are available concerning the associations of AMD with fat. In two cross-sectional studies [25, 26], weekly fish consumption, which is the main source of DHA, was associated with a 50–60% reduction in the risk for AMD, after multivariate adjustment. In the Eye Disease Case-Control Study, a high dietary intake of ␻⫺6 PUFA was significantly associated with a 2-fold increased risk for exudative AMD, after multivariate adjustment. Consumption of ␻⫺3 PUFA was significantly associated with a 31% reduction in the risk for AMD after age and gender adjustment, but not after multivariate adjustment. Results were similar for fish intake [27]. In a pooled analysis of the Nurses’ Health and Health Professionals’ Cohort Studies, subjects consuming fish had a reduced risk of developing AMD, after multivariate adjustment [28]. High DHA intakes were also associated with a reduced risk of AMD, whereas, surprisingly, high intakes of ␣-linolenic acid were associated with an increased risk. Finally, in a study on 261 patients, initially presenting early AMD, total fat intake, and more specifically, intakes of vegetable fat, monounsaturated fatty acids and PUFA (mainly due to ␻⫺6 PUFA) were positively associated with the risk of developing late AMD [29]. Fish intake was associated with a decreased risk of late AMD only in those with a low dietary intake of linoleic acid. Globally, these results suggest that excessive intake of ␻⫺6 PUFA, and low intake of ␻⫺3 PUFA may be associated with an increased risk for AMD. Recent studies suggest that ␻⫺3 and ␻⫺6 PUFA may also be implicated in other eye conditions, such as glaucoma [30] or dry eye syndrome [31]. Besides the nutritional dimension of AMD and cataract, these diseases are strongly influenced by genetics, as demonstrated by familial and twin studies

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[32–37]. The apolipoprotein E4 allele is associated with a 50% reduced risk of AMD [38–43]. Recently, three independent teams simultaneously demonstrated a significant association between the Y402H polymorphism of the complement factor H gene and AMD in North American subjects [44–46], immediately followed by three other corroborating papers in North American populations [47–49], one study from France [50] and one from Iceland [51]. Y402H is a common variant, with about 30% of the general population bearing the minor (C) allele (at least in Caucasians). In these studies, subjects heterozygotes for C have a 2.5- to 4-fold increased risk for AMD, while subjects homozygotes for C have a 3.5- to 7.5-fold increased risk for AMD [50]. Complement factor H is a key regulator of the complement system of innate immunity [52]. Histologic observations are consistent with inappropriate activation of the complement system in AMD [53]. Finally, several linkage studies show an association of AMD with chromosome 10p26 [54]. With respect to cataract, a recent linkage study identified a major locus on chromosome 6p12–q12 for cortical cataract [55]. Interactions between genetic variability and nutritional factors represent a new challenge in order to account for interindividual variations in disease susceptibility. While some properties of nutritional factors rely on direct effects (such as antioxidant properties, or structural functions of DHA), many nutritional factors also have cellular effects and interact with genes. Nutrigenomics in eye health therefore potentially includes all genes implicated in the metabolism or activities of nutritional factors associated with eye diseases, and all nutrients implicated in the activities of genes associated with eye diseases, thereby opening a vast research domain. Since the genes identified to date are from the lipid metabolism (apolipoprotein E) and innate immunity (complement factor H), interactions with lipids and antioxidants are particularly expected. Recently, in an animal model, the combination of the apolipoprotein E4 allele with a high-fat diet induced modifications of the retina that mimic the pathology associated with human AMD [56]. It is also well known that PUFA and zinc interact with genes of inflammation and immunity [57, 58]. Whether the risk for AMD may be modified by interactions of PUFA and zinc with the complement factor H gene remains to be determined. Zinc has recently been implicated in the binding of complement factor H with its target complement factor (C3b) [59]. In the field of carotenoids, the Pi isoform of the glutathione S-transferase (GSTP1) has recently been identified as a membrane-bound binding protein for zeaxanthin in the macula [60]. The same authors have shown that GSTP1 and zeaxanthin act in synergy for the prevention of membrane lipid peroxidation [61]. Interestingly, GSTP1 polymorphism was associated with the risk of cortical cataract in an Estonian population [62]. These data globally suggest that interaction of the GSTP1 gene with dietary zeaxanthin may be implicated in AMD and cataract.

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In conclusion, age-related eye diseases, which are the major causes of blindness worldwide, are strongly influenced by nutrition and genetics. Nutrigenomics, by studying the interactions of nutritional and genetic factors, opens a new research avenue. Understanding the interaction of nutrients with genes may help target susceptible individuals for nutritional prevention of eye diseases.

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Dr. Cécile Delcourt Inserm, U593, Université Victor Segalen Bordeaux 2 146, rue Léo Saignat FR–33076 Bordeaux Cedex (France) Tel. ⫹33 5 57 57 15 96, Fax ⫹33 5 57 57 14 86, E-Mail [email protected]

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Nutrigenomics of Taste – Impact on Food Preferences and Food Production Ahmed El-Sohemy, Lindsay Stewart, Nora Khataan, Bénédicte FontaineBisson, Pauline Kwong, Stephen Ozsungur, Marilyn C. Cornelis Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Ont., Canada

Abstract Food preferences are influenced by a number of factors such as personal experiences, cultural adaptations and perceived health benefits. Taste, however, is the most important determinant of how much a food is liked or disliked. Based on the response to bitter-tasting compounds such as phenylthiocarbamide (PTC) or 6-n-propylthiouracil (PROP), individuals can be classified as supertasters, tasters or nontasters. Sensitivity to bitter-tasting compounds is a genetic trait that has been recognized for more than 70 years. Genetic differences in bitter taste perception may account for individual differences in food preferences. Other factors such as age, sex and ethnicity may also modify the response to bitter-tasting compounds. There are several members of the TAS2R receptor gene family that encode taste receptors on the tongue, and genetic polymorphisms of TAS2R38 have been associated with marked differences in the perception of PTC and PROP. However, the association between TAS2R38 genotypes and aversion to bitter-tasting foods is not clear. Single nucleotide polymorphisms in other taste receptor genes have recently been identified, but their role in bitter taste perception is not known. Establishing a genetic basis for food likes/dislikes may explain, in part, some of the inconsistencies among epidemiologic studies relating diet to risk of chronic diseases. Identifying populations with preferences for particular flavors or foods may lead to the development of novel food products targeted to specific genotypes or ethnic populations. Copyright © 2007 S. Karger AG, Basel

Background

Food preferences are determined by a number of factors such as taste. Individual differences in the perception of sweet, salty, sour, umami or bitter taste may influence dietary habits, which can affect nutritional status and risk of chronic diseases [1, 2]. The ability to taste bitter compounds such as

NH

NH2 C

S

Fig. 1. Chemical structure of PTC.

phenylthiocarbamide (PTC) or 6-n-propylthiouracil (PROP) is a genetic trait that is due to the presence of a functional TAS2R38 receptor, which is expressed in taste cells on the tongue [3] (fig. 1). PTC and PROP are members of a class of compounds called ‘thioureas’ and carry the chemical group N-C⫽S, which is responsible for their characteristic bitter taste [1, 2, 4]. Although these chemicals are not found in foods or beverages, their structural similarities to naturally occurring chemicals have made them useful tools to study taste preferences and food aversions. Populations have typically displayed bimodality in sensitivity to PTC, with approximately 75% of individuals perceiving this compound as bitter, while the remaining 25% find this compound to be relatively tasteless [3, 5]. Those who perceive the bitter taste can be further divided into tasters and supertasters [3–8]. The TAS2R38 gene consists of a single exon that is 1,002 bp long and encodes a 333-amino-acid 7-transmembrane domain, guanine nucleotidebinding protein-coupled receptor [9]. Single nucleotide polymorphisms (SNPs) that result in amino acid substitutions have been identified in the TAS2R38 gene, and can be used to predict taster status [9–11]. There are two common haplotypes that consist of three SNPs (A49P, V262A, and I296V), which show a strong association with PTC taster status [12]. Tasters have the PAV haplotype whereas nontasters have the AVI haplotype [11]. The A49P substitution has been used as a tag SNP to identify taster status [13]. Only a few studies have explored the relationship between genetic differences in the TAS2R38 gene and taster phenotype [9, 13, 14]. A recent study examined the association between TAS2R38 genotype and PROP sensitivity using a series of PROP solutions given to both children and adults [13]. A gene-dosage effect was observed such that two copies of the P allele conferred greater PROP sensitivity than a single copy. A modest heterozygote effect was also apparent in another study where AP heterozygotes had a higher PTC taste threshold than PP individuals and were described as slightly less sensitive to PTC [9]. Duffy et al. [14] also found that PROP bitterness varied significantly across genotypes with AA homozygotes tasting less bitterness than either AP heterozygotes or PP homozygotes. Using PTC paper, we evaluated the association between taster status and TAS2R38 genotype with the A49P tag SNP. Subjects (n ⫽ 366) were asked to

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Nontaster Taster Supertaster

Frequency (%)

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0

AA

AP TAS2R38 genotype

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Fig. 2. Association between TAS2R38 genotype and taster status.

rate the bitterness of PTC paper on a scale of 1 (not at all bitter) to 9 (extremely bitter), and were then categorized as either nontasters (1–3), tasters (4–6) or supertasters (7–9). DNA was isolated from fasting blood samples and genotyping was performed using real-time PCR analysis for the A49P polymorphism. In this multiethnic population, the frequency of the P allele was 55%. As expected, the presence of the P allele was associated with bitter taste perception (fig. 2). Among those with the AA genotype, 80% were considered nontasters, 17% tasters and 2% supertasters. For subjects who had the PP genotype, 11% were nontasters, 40% were tasters and 49% were supertasters. Most heterozygotes had the intermediate taster phenotype (52%), although some were classified as either nontasters (26%) or supertasters (22%). In addition to the TAS2R38 genotype, other factors such as age, sex and ethnicity have been shown to modify PTC taste perception. Previous studies have revealed that the frequency of PTC nontasters is 6–23% in China, 30% in North American Caucasians and 40% in India [2, 5, 15, 16]. However, the modifying effects of ethnicity that have been reported could be due to population differences in the frequency of the different TAS2R38 alleles. Indeed, in a multiethnic population, we found the frequency of nontasters to be 12% in Asians, 31% in South Asians and 43% in Caucasians, and this was mainly due to differences in the frequencies of the TAS2R38 alleles. Nevertheless, other population or cultural differences might modify the genotype-phenotype association within each of the ethnicities. Such differences could also be responsible for any differences in food preferences between different ethnic groups. The ability to taste PTC or PROP is present in young children and declines gradually with age [17]. Some individuals who are born sensitive to bitter-tasting

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compounds may become less sensitive with age because of experience, aging or disease [18, 19]. In a recent study, the genotype-phenotype association varied by age with the relationship being stronger among children [13]. Moreover, genotype modified their food preferences such that those with the AP or PP genotypes preferred sweeter-tasting foods. Unlike the children, however, there was no association between TAS2R38 genotypes and sweet preferences in the adults [13]. Another study involving a population of women aged 60 years and older found no association between the TAS2R38 genotype and food preference [16]. The decline in taste sensitivity that occurs with age could have masked any effects of genotype. An indirect effect of the ability to taste bitter compounds has been observed in the consequent avoidance of foods containing these bitter-tasting substances [4, 7]. Many of these foods also have antioxidants, and thus the tasting ability that leads to their avoidance has been implicated in the etiology of common disorders. There are different classes of common dietary compounds that are bitter, and might be particularly bitter to certain individuals, such as: polyphenols, methylxanthines, isoflavones, flavonoids, glucosinolates and sulfamides. Previous studies have observed an association between taste responsiveness to PROP and food preferences. Drewnowski et al. [20] found an inverse relationship between PROP sensitivity and acceptance of cruciferous vegetables and tart citrus fruits in young women. A second observational study of young women by the same group reported an association between perception of the bitter taste of PROP and reduced preferences for Brussels sprouts, cabbage, spinach, and coffee [21]. They also observed greater sensitivity to PROP among female breast care patients who had a lower acceptance for bitter taste [22]. Despite the numerous associations reported between PROP sensitivity and acceptance of bitter foods, fewer studies have examined the relationship between PROP sensitivity and food consumption. A cross-sectional study by Kaminski et al. [23] found no direct relationship between PROP taster status and the frequency of consumption of 22 bitter food items. However, Basson et al. [24] observed that men who found PROP to be very bitter consumed fewer vegetables. In a recent study, vegetable preference was found to be a direct predictor of intake and those who perceived PROP as very bitter consumed vegetables less frequently [25]. Thus, the perception of bitter compounds appears to directly influence dietary habits such as vegetable intake. It has been hypothesized that supertasters eat less of the healthy vegetables due to an increased sensitivity to their bitterness, and eat more sweets or fatty foods that are associated with an increased risk of cardiovascular disease. However, there is evidence that individuals who are sensitive to bitter-tasting vegetables are also more sensitive to sweet foods [8, 26, 27]. Thus, tasters and supertasters may have a stronger

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taste acuity in general, and their heightened taste perceptions prevent overconsumption of a variety of foods. Because the TAS2R38 gene has recently been linked to bitterness taster status, only a few studies have examined the link between this genotype and food preferences [13, 16]. Timpson et al. [16] were the first to examine the effect of genetic variation in taste on eating behavior and the risk of diet-related chronic disease. They examined the relationship between TAS2R38 haplotypes, coronary heart disease, coronary heart disease risk factors, and food consumption behaviors in postmenopausal women. No significant associations were observed between TAS2R38 haplotypes and either coronary heart disease traits or food consumption. However, they did observe a marginally lower risk of diabetes among those with the nontaster genotype. This suggests that these individuals may have been more likely to consume a diet rich in bitter-tasting vegetables. In that study, there was no direct association between TAS2R38 genotypes and food preferences. However, the population consisted of elderly women and the effect of age on taste perception may have masked an association between genotype and food preference. We are currently examining the relationship of PTC taster status and TAS2R38 genotype to food preferences and food intake in a multiethnic population of subjects 20–29 years of age. The T2R gene family consists of about 25 different members, each with its unique ability to perceive the different tastes of diverse dietary compounds [3]. Genetic polymorphisms in these other taste receptors may also be important determinants of preferences for particular foods. Recently, an SNP in the TAS2R50 gene was associated with an increased risk of myocardial infarction suggesting that it may be associated with adverse dietary habits [28]. Further studies will be needed to determine whether TAS2R50 genotypes affect food preferences and intake.

Conclusion

Taste is one of the most important determinants of food preferences and genetic differences in taste perception may explain individual differences in eating habits. Sensitivity to bitter-tasting compounds has been associated with a lower preference and consumption of bitter-tasting foods. Although removal of bitter compounds from certain foods may increase their palatability, it could also decrease their nutritional benefit since many bitter-tasting phytochemicals may have health-promoting properties. The bitter taste can also be masked by the addition of salt, sugar or fat, but this leads to the increased consumption of dietary factors that have been associated with an increased risk of chronic disease. The development of ‘bitter blockers’ may be a better strategy to enhance the taste of some vegetables that are perceived to be bitter by certain individuals

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[29]. Identifying populations with preferences for particular flavors or foods may lead to the development of novel food products targeted to specific genotypes or ethnic populations.

Acknowledgements This research was supported by a grant from the Advanced Foods and Materials Network (M&E-B-4). A. E. holds a Canada Research Chair in Nutrigenomics.

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Dr. A. El-Sohemy Department of Nutritional Sciences, Faculty of Medicine University of Toronto, 150 College St., Room 350 Toronto, Ont. M5S 3E2 (Canada) Tel. ⫹1 416 946 5776, Fax ⫹1 416 978 5882, E-Mail [email protected]

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Prospects for Improving the Nutritional Quality of Dairy and Meat Products Shaun G. Coffey CSIRO Livestock Industries, St. Lucia, Australia

Abstract Knowledge of the function of human and animal genes and their interactions is rapidly increasing as a result of the completion of sequencing efforts for the human, bovine and other genomes. Through transcriptomics, proteomics and metabolomics, we have the capacity to study the health effects of food compounds at the molecular level. The same tools that can assist the understanding of nutrigenomics in humans can also be applied to producing animal-derived foods with desired capacities to alter gene expression in humans. This, essentially, represents food taking another major step in value through the personalisation of health and nutrition. In its own right, nutrigenomics offers the potential to improve animal production enterprises through major health and productivity gains. Copyright © 2007 S. Karger AG, Basel

Food derived from animals is an important source of nutrients in diets, and the demand for high-quality meat and dairy products will rise over the next 20 years, accompanied by more stringent quality assurance requirements. Nutrigenomic approaches offer the potential to develop enhanced systems that overcome some of the existing limitations of production [Andersen et al., 2005; Givens, 2005]. Discoveries associated with sequencing of the human and animal genomes, and the emergence of related technologies provide an exciting opportunity to develop a deeper understanding of nutrition at both the molecular and systems level, thus opening the possibility of major transformation in both human nutrition and animal production. As advances in animal genomics start to match pace with human studies, the rate of progress in nutrigenomics can be expected to increase. This paper briefly considers the contribution of animals to human diets, and hence to health and nutrition; outlines recent developments in animal genomics, proteomics, metabolomics and transcriptomics, and explores the

Table 1. Per capita global consumption of meat: actual and projected (kg per capita per year) Region

1964–1966

1997–1999

2030

World Developing countries Transition countries Industrialised countries East Asia South Asia

24.2 10.2 42.5 61.5 8.7 3.9

36.4 25.5 46.2 88.2 37.7 5.3

45.3 36.7 60.7 100.1 58.5 11.7

WHO/FAO [2003].

Table 2. Per capita global consumption of milk: actual and projected (litres per capita per year) Region

1964–1966

1997–1999

2030

World Developing countries Transition countries Industrialised countries East Asia South Asia

73.9 28.0 156.7 185.5 3.6 37.0

78.1 44.6 159.1 212.2 10.0 67.5

89.5 65.8 178.7 221.0 17.8 106.9

WHO/FAO [2003].

possibilities for manipulating the nutrient profile of animal products designed for human consumption as well as the application of nutrigenomics to improving animal production itself. If the promise of personalised food [German and Watzke, 2004] is to be met, a considerable research effort is needed.

Animal Products in Human Diets

Consumption of animal products has grown rapidly over the last 40 years [WHO/FAO, 2003] and is projected to continue to grow (tables 1, 2), especially in developing and transition economies [Delgado et al., 1998]. Animal products are a significant source of high-quality protein, calcium and iron, but have also

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been identified as a major source of excessive saturated fat intakes. Animalderived food contributes around 30% of the total energy input, over 50% of which is derived from fat. Milk and dairy products have made a major contribution to saturated fatty acid consumption, a topic that serves as a useful example of the possibilities for nutrigenomics.

Dietary Fat and Chronic Disease: Exploring the Possibilities for Nutrigenomics

Givens [2005] has summarised the current knowledge of the relationships between dietary fatty acids and chronic disease. Concern over some animalderived foods, especially saturated fatty acids (SFAs), have led to concerns about the contribution of these foods to an increased risk of cardiovascular disease and the metabolic syndrome. Undoubtedly, total consumption of SFAs needs to be reduced, and significant benefits are to be gained by replacing them with monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs). As animal products currently make a contribution to SFA consumption, this situation needs to change. Fortunately, fatty acid composition of animal products is highly responsive to nutrition in the animal, and a sizeable research effort is being made to lower SFA, and raise MUFA and PUFA levels in milk and meat. This effort aims to retain the other inherent nutritional values of these foods. Manipulations possible in milk and meat serve as an illustration of this point. Fatty acid in milk originates through a complex process that responds to several nutritional approaches to enable manipulation of fatty acid composition. For example, supplements of plant oils, or oilseeds (such as canola, soya bean and sunflower) reduce both short- and medium-chain fatty acids in milk, reducing SFAs and raising MUFAs and PUFAs [Givens, 2005] (although it should be noted that PUFAs are not synthesised in any appreciable amounts in ruminant tissues, and therefore, concentrations in milk reflect levels of PUFAs leaving the rumen). Conjugated linoleic acid concentrations in milk have been shown to be higher in milk fat from cows offered fresh forages as compared with conserved forages, and are also enhanced by the use of supplements of oilseeds and fish oils. Conjugated linoleic acid is of interest because of its potential anticancer effects. In meats derived from ruminant animals, efforts have been made to increase the ratio of PUFAs to SFAs and enhancing n–3 PUFA, tasks that can be achieved again through nutrition, using high-forage-based diets or oil supplements rich in PUFAs. The fatty acid profile in non-ruminant meat, in contrast, is essentially a reflection of that in the diet.

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The evidence is clear that the nutrient profile of animal products is highly responsive to nutrition. Feeding strategies offer the possibility of changes in production systems; however, we need a better understanding of the impact of the diet. Andersen et al. [2005] illustrate that feeding strategies have a regulatory effect on the biological processes in muscle, which is directly reflected in the quality of the meat. The fact that specific diet components, inter alia, regulate gene expression is well recognised, but few studies have explored the complex interactions between individual nutrients on the genome. The ongoing mapping of the genomes of farm animals together with advances in bioinformatics and molecular biology will undoubtedly accelerate progress in the future.

The Challenges to Animal Production Systems

Two challenges face animal agriculture: first, the unravelling of the specific interactions of food on health and nutrition at the molecular level and second, the need to study the constellation of changes that take place in the nutritional environment where impacts are not necessarily additive (a systems biology approach). These are equivalent to the challenges in the human domain. Nutrigenomic approaches are being explored to ensure higher standards for the quality of meat and dairy products for consumption. Examples will be discussed in relation to exploiting basic and empirical understandings of physiological and physical processes. These can then be placed in a systems biology context as an aid to decision making by producers. A change of focus in production systems towards an understanding of how feed influences biological mechanisms and impacts quality traits, for example, may lead to the production of a diverse range of products with quite specific nutritional attributes, and targeted market segments. In animal production systems, this requires a change from classical nutrition research (where all test individuals are treated as genetically identical) to one where responses to diet are analysed with an awareness of individual, age and genotype interactions. Nutrigenomics and systems biology combined offer the opportunity to transform both the nutritional value and the overall utility of animal-derived human foods. These will require major changes to farming systems, a topic beyond the scope of this paper. Suffice it to say that as individual diets are selected to cater for individual human dispositions, animal agriculture will experience pressure to continue to move further away from the production of food as a bulk commodity: a prospect with far-reaching impacts.

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Table 3. Progress in sequencing animal genomes Species

Status

Mouse, dog, rat, chicken (red jungle fowl) Bovine, chimpanzee, opossum, rhesus macaque Orangutan, marmoset, elephant, shrew, hedgehog, guinea pig, tenrec, armadillo, rabbit, cat, wallaby, platypus

completed advanced draft draft

Dalrymple [2005] and Moore et al. [2005].

General Progress with Animal Genomes and beyond

The advent of significant progress in genomics, proteomics, metabolomics and transcriptomics also allows a change of focus in our study of animals. The focus is now shifting towards an understanding of how animal diets, feeding regimes, production conditions and handling strategies influence biological mechanisms and the outcome of these in relation to specific meat, milk and other product quality parameters. In turn, the more fundamental understanding of, for example, muscle physiological and physical processes, and their interactions in relation to gene expression and environmental constraints will enable full exploitation of systems biology approaches. These will shape future management strategies in animal production. With the completion of the human genome, science has begun to unravel and understand human biological complexity through the emerging fields of proteomics and pharmacogenetics [Kauwell, 2005]. Progress in animal genome studies is not as well advanced [Hendersen et al., 2005] but is making rapid progress [Dalrymple, 2005]. The mouse, rat and dog genomes are now available; the bovine genome is close to completion, and several others are at advanced stages of sequencing (table 3). Having the genome sequences available is important because they open the way to address research questions, to undertake genome-wide analyses and to allow comparative studies across the increasing number of available genome sequences. This work forms the basic platform on which we can develop nutrigenomics in the animal production enterprise.

Mammalian Genomics and the Bovine Genome

The Commonwealth Scientific and Industrial Research Organisation (CSIRO) has taken a leading role in functional genomics involving a number of

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international research organisations in the International Bovine BAC Mapping Consortium and the International Bovine Genome Project. A significant reason for our decision to participate in the Bovine Genome Project was to ensure that the whole sequence of a major production species be available in the public domain for use by all interested research agencies. The first draft of the bovine sequence was released in October 2004. This first draft already provided access to increased numbers of microsatellite markers and randomly generated single nucleotide polymorphisms (SNPs), facilitating the identification of genetic polymorphisms associated with valuable traits in Qualitative Trait Loci and other studies [Barendse, 2005]. The third draft was due for release in early 2006, and will greatly enhance the quality of information available. CSIRO has built a number of databases from this work, some of which are publicly available (http://www.livestockgenomics.csiro.au). Significant progress has been made as a result of early access to the very large amount of ordered sequence covering most of the protein-coding regions of the genome. Among our early projects was the design and implementation of an interactive bovine in silico SNP database, an activity started in 2001. This project identified the need for the production of SNPs based on the bovine expressed sequence tag collections and thus also for the clustering and annotation of the clustered transcripts. The analysis pipeline and results are described in detail in Hawken et al. [2004]. Significant progress has been made in transcriptomics [Carninici et al., 2005] in recent times. CSIRO has also developed the first bovine cDNA-based microarray for the analysis of muscle and fat. This consisted of 9,600 elements derived from 2,000 expressed sequence tags and 73,000 anonymous cDNA clones [Lehnert et al., 2004]. This array has been used in a variety of experiments with muscle and fat samples from cattle of different genotypes or undergoing a range of treatments [Byrne et al., 2005; Reverter et al., 2003, 2004, 2005a, c; Wang et al., 2005a, b]. Combined, these studies have started us down the track of systems biology [Reverter et al., 2005b]. We have related work in the ovine [Dalrymple, 2005] and chicken domains [Moore et al., 2005] although the latter is focussed on disease and disease management.

Genome Information: Current Limits

The genome sequences of domestic animals and particularly the bovine sequence are incredibly valuable, as they will provide the complete repertoire of genes that are present in these species as well as the means to measure the transcriptional activities of these genes in different tissues and under different nutritional regimes.

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In addition, it will be increasingly possible to associate individual variation in phenotype with individual variation in genome sequence in a manner that allows selection for optimally performing animals in particular production environments. That being said, we are far from understanding all that is contained within a genome sequence. The gene repertoires of most mammals examined to date are very similar. Intriguingly, the actual protein-encoding genes in a mammalian genome only account for approximately 1.5% of the genome sequence. The remaining 98.5% have not been thoroughly explored but may contain complex gene expression regulatory information. We currently have a poor view of how gene expression is controlled and how multiple genes contribute to complex phenotypes. So what makes a mouse a mouse and a human a human? The answer probably lies with species-specific regulatory mechanisms that control the spatial and temporal expression patterns of genes. A significant component of this system undoubtedly involves species-specific epigenetic modifications of DNA and histones. Perhaps one of the most significant advances in recent years has been the realization that epigenetic modifications controlling gene expression can be influenced by nutrition. At a higher level, it is in the interactions between the gene products that lies hidden-layer regulatory information that we are just starting to appreciate. This presents significant technical challenges, as at present we do not understand the rules governing the non-linear dynamics of these complex biological systems. This is a fertile ground for fundamental investigations that may lead to a better understanding of the ways in which nutrition impacts on gene expression.

Timing of Production Interventions

Perhaps one of the most significant advances in nutrigenomics over the last decade has been the realization that nutritional restrictions on an animal or fetus during critical developmental periods can induce permanent change in gene expression programs and hence the phenotype even on removal of the restriction. One of the best examples of this phenomenon is the observation that nutritional constraint in a fetus in utero can result in a predisposition toward obesity of that individual as an adult according to the Barker hypothesis [Kwong et al., 2004; Wilson, 1999]. Presumably the physiological ‘set points’ of the fetus are altered in utero (fetal programming) to reflect the expected harsh environment that will meet the newborn animal. The animal’s metabolism is therefore geared toward accumulation and storage of energy and in the absence of the harsh environment this results in the accumulation of adipose tissue.

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Another example involves nurturing. A newborn rat that is not sufficiently licked, groomed and nurtured by its mother shows epigenetic alterations of the gene encoding the glucocorticoid receptor. This protein plays a key role in the response of the rat to stress. In the poorly nurtured pups, the gene is partially silenced by these epigenetic modifications resulting in pups with increased levels of stress hormones and a decreased ability to explore new environments. These effects are permanent [Weaver et al., 2004]. This persistence of phenotypic traits related to diet, variously described as nutritional ‘imprinting’ [Levin, 2000] or nutritional programming [Singal et al., 2003], points to the fact that biological organisms have a variety of mechanisms to respond to the environment to which they are exposed. They adapt through a variety of structural, biochemical and regulatory processes, and respond in a complex manner to factors such as physical forces, blood supply, extracellular matrix macromolecules and growth factors [Harper and Pethick, 2004]. For production animals, these findings offer a significant potential for altering production systems to enable specific qualities of animal-derived foods if there are suitable epigenetic pathways for this to happen. By intervening at critical points in the growth/life cycle of the animal, the phenotype might be controlled to the extent that the ultimate product can be specified more closely.

Prospective Targets for Nutrigenomics in Animal Production

Nutritional genomics has many prospects for applications in animal production. The full range of applications goes well beyond just altering the nutrient profile of food for human consumption. Animal scientists are already responding to the consumer demands for high standards of safety, nutritional value, texture and flavour, tenderness, water-holding capacity, colour and lipid content, lipid composition, oxidative stability and uniformity. Included within the CSIRO portfolio, for example, are: • assessment of food safety and food quality [see for example Bettleheim et al., 2005; Gilbert et al., 2005]; • developing new biomarkers of nutrient exposure and disease risk [Donaldson et al., 2005; Ingham et al., 2005; Wang et al., 2005a, 2005b; Strandberg et al., 2005]; • studies of cellular effects of nutrients and bioactive compounds [Krause et al., 2005; Lai et al., 2005; Pan et al., 2005]; • developing better methods to examine data sets related to genomics and nutrigenomics [Reverter et al., 2005b];

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•

understanding the effects of functional foods [Dunshea et al., 2005; Liu and Eady, 2005], and methods to manipulate food composition [Kitessa et al., in press]; • understanding the biology and development of the muscle [Harper and Pethick, 2004; Pethick et al., 2004; Vuocolo et al., 2005]; • using gene expression profiling to examine stress and meat quality [Kerr and Hines, 2005; Moser et al., 2004]; • an expanded understanding of the gut through the application of metagenomics to enable a holistic and mechanistic analysis of the genetic and metabolic potential of entire microbial communities [Morrison et al., 2005]. These, and other areas of applications, will develop quickly over the next 3–5 years largely as a result of the current investment in postgenomic technologies. A significant challenge also lies in developing a more detailed and fuller knowledge of the full range and role of bioactives so that we might understand, for example, the apparent protective effect of increased consumption of milk against cardiovascular disease [Givens, 2005].

Transgenic Animals

One avenue to changing the nutrient profile of animal products is through the use of transgenesis to transfer to food animal species traits from other species. Seymour et al. [2004] consider that transgenics is unlikely to be part of animal production in the near future, but is likely to be important in the longer term. Consumer acceptance currently is a major issue, but may dissipate because of the significant consumer benefit deriving from its use. Table 4 outlines some of the possible targets and benefits including those of nutrigenomic interest. As our targeting of benefits improves, we can be more confident that transgenesis will become increasingly accepted for food products, as it already is in the production of therapeutic drugs. Many groups, including our own [see Adams and Briegel, 2005], have developed and studied transgenic animals, so we know that the technology is assessable. Harper et al. [2003] survey existing knowledge on transgenic food animals and provide an excellent source of information on this topic.

Conclusions

The integrative nature of the discipline of nutrition has long distinguished the field of study. The advent of genomics, and the growing capacity of our new research tools to enable a deeper understanding of function at both the subcellular

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Table 4. Survey of transgenic livestock species, existing or predicted, that should be of interest to Australian or New Zealand animal food producers in the next 5 years Animal

Genes introduced or deleted

Performance criteria (consumer benefit)

Bovine

␤ and ␬ casein

Bovine Bovine Bovine

intestinal lactase lysostaphin ␤-lactoglobulin

Ovine

growth hormone

Ovine

myostatin

Porcine Porcine

insulin-like growth factor 1 bovine ␣-lactalbumin

Porcine Porcine

spinach stearoyl-CoA desaturase phytase expressed in saliva

increased expression of casein proteins (improved protein content of milk) reduction of lactose in the milk (lactose-intolerant people) mastitis resistance (reduced use of antibiotics) increased production of this protein in milk, as well as increased growth and disease resistance in calves feeding on the milk (reduced antibiotic use and improved health benefits) increased growth rates, increased feed conversion efficiency, decreased carcass fatness, and increased lactation (leaner meat) reduced myostatin expression and increased muscle in sheep (leaner meat) increased growth rate and reduced carcass fatness (leaner meat) increased growth rate and improved health of piglets (unknown consumer value) modified lipid composition (increased unsaturated fats)

Caprine

lysostaphin

Caprine

rat stearoyl-CoA desaturase

Caprine

human lysozyme

Common carp Chinook salmon Silver sea bream Japanese abalone Rainbow trout

growth hormone growth hormone growth hormone growth hormone growth hormone

utilisation of phosphorus bound to phytate by the pig, and hence a reduction in waste phosphorous (environmental impact reduced) cure or prevention of Staphylococcus aureus mastitis (reduced antibiotic use) modified milk fat composition (increased unsaturated fatty acid proportion) modified milk fat composition and enhanced immune responses (reduced antibiotic use and modified milk) increased growth rate (cheaper fish) increased growth rate (cheaper fish) increased growth rate (cheaper fish) increased growth rate (cheaper fish) increased growth rate (cheaper fish)

Seymour et al. [2004].

and the whole-system level challenge both animal and human research to understand the complex relationship between genome and diet. Application of nutritional genomics in agriculture is a rapidly growing field and offers the prospect of designing effective dietary and management regimes to improve animal production and better manage disease status. The

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same principles can be applied to design and produce animal-derived food for specific functions in the human diet. As a still-emerging endeavour, nutrigenomics requires a significant research effort, especially at the more basic end of the spectrum. Within animal production, however, excellent potential exists, and early results support the belief that animal products will have an increasingly significant impact on ‘designer’ human health and nutrition.

Acknowledgements I would like to recognise Ross Tellam, Alan Brownlee, Gene Wijffels, Chris Prideaux, Soressa Kitessa, Peter Willadsen, Bill Barendse and Greg Harper for many useful discussions. I also acknowledge our partners in the International Bovine Genome Project, and especially Steve Kappes of the USDA. My many colleagues in CSIRO Livestock Industries continue to pursue the best in bioscience and technology, and are a continuous source of inspiration. The partners in the Bovine Genome Project are: the National Human Genome Research Institute (USA); CSIRO (Australia); the US Department of Agriculture; the State of Texas; Genome Canada; Agritech Investments Limited (New Zealand); Dairy Insight Ltd. (New Zealand); AgResearch Ltd. (New Zealand); the Kleberg Association (USA); the National, Texas and South Dakota Beef Councils (USA). The sequencing of the bovine genome is being undertaken by the Baylor College of Medicine’s Human Genome Sequencing Center (USA) and the British Columbia Cancer Agency (Vancouver, Canada).

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Mr. Shaun G. Coffey Industrial Research Ltd 69 Gracefield Rd Lower Hutt 5040 (New Zealand) Tel. ⫹64 4931 3000, Fax ⫹64 4566 6004, E-Mail [email protected]

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Functionality of Probiotics – Potential for Product Development James Dekker, Michael Collett, Jaya Prasad, Pramod Gopal Fonterra Co-operative Group, Palmerston North, New Zealand

Abstract It is becoming increasingly accepted by consumers that live lactic acid bacteria do exert health benefits when eaten. In addition, it is also becoming recognised that not all probiotic bacteria are equal. It is now no longer just a question of providing sufficient numbers of viable bacteria in a product; industry must also provide proof of efficacy for each strain. In the early 1990s, Fonterra embarked on a programme to develop proprietary probiotic strains, and as a result, commercialised two strains, Bifidobacterium lactis HN019 and Lactobacillus rhamnosus HN001. Over the past decade, Fonterra has developed a significant body of peerreviewed published reports around these strains, including studies showing safety in animal and human trials, protection against pathogens such as Salmonella typhimurium and Escherichia coli O157:H7, modulation of human and animal immune markers at realistic dose rates, and efficacy in human clinical trials. Based on this work, HN019 and HN001 have been applied to several functional foods both by Fonterra (under the DR10™ and DR20™ brands, respectively) and by third parties (e.g. under the HOWARU™ brand by Danisco). While the ‘gold standard’ of proof of efficacy is a phase III clinical trial, ethical considerations as well as expense preclude the use of clinical trials as screening tools for probiotics. Therefore, biomarkers have to be employed to identify strains with probiotic utility, and to define the different positive health benefits of existing probiotic strains. However, as the mechanisms by which most probiotic bacteria exert their health benefits remain unclear, the question of which biomarkers accurately reflect efficacy in vivo remains unresolved. With recent technological advances, and the shift toward probiotics targeted to specific conditions, researchers are beginning to tease out how probiotic bacteria work, and it is this knowledge that will inform biomarker development and improve the ability to offer the market safe and effective probiotic functional foods. Copyright © 2007 S. Karger AG, Basel

If nutrigenomics involves the relationship between nutrition and host gene expression, then a vital component of this interaction is the gut microbiota. It has been estimated that the human gastro-intestinal tract (GIT)

contains around 1014 micro-organisms, outnumbering human cells by a factor of 10:1 [1]. The gut microbial community is not only large, but also extremely diverse, with at least 400 different bacterial strains inhabiting the GIT at any one time. It also appears that the make-up of this microbial community is unique to each individual, in terms of the actual species present and their relative proportions [2, 3]. It has become apparent from microbial ecology studies that both the environment and host genotype influence the composition of gut microbiota. Thus, within the intestinal tract, food substances will come into contact with a range of metabolically active bacterial cells. These bacterial cells are known to metabolize or modify various nutrients and may even play vital roles in human nutrition. However, it is also known that gut bacteria interact with host gut epithelial and immune cells, and interact with other micro-organisms, including pathogens, whereby the growth or impact of other strains may be enhanced or diminished. Given the huge diversity of the gut microbiota, it is obvious that individual strains will differ in their ability to participate in such interactions. Until recently, medical science has largely considered the members of the gut microbial community as falling into one of two camps, either pathogenic micro-organisms (including opportunistic pathogens) that can do harm to the host, or non-pathogenic micro-organisms that are considered benign or neutral. However, there is now increasing evidence that a third class exists, whereby some bacterial strains offer a positive health impact. This concept underlies the definition of probiotic bacteria as being ‘live microorganisms that, when administered in adequate amounts, confer a health benefit on the host’ [4].

Development of Fonterra Probiotic Strains

Fonterra is the world’s leading exporter of dairy products and is responsible for a third of international dairy trade. It is a co-operative company owned by more than 12,000 New Zealand dairy farmers, and exports a wide range of dairy-based commodity and specialty products to over 140 countries. In the mid 1990s, Fonterra decided to develop its own proprietary probiotic strains to specifically target gut health and immune enhancement. A strategy was developed to isolate probiotic bacterial strains useful as functional food ingredients. Strains were screened for their ability to survive in the human GIT, to be safe for human consumption, to produce a positive impact on the human microbiota, to show efficacy in providing a health benefit, and for their suitability for application to consumer products.

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Survival in the GIT As part of an initial screen, over 2,000 bacterial strains of human or dairy origin were tested for the ability to survive passage through the GIT and to colonise the gut epithelium [5, 6]. As the use of in vivo human trials for this was obviously impractical, a series of in vitro assays were applied to predict survival in the GIT. Strains were first tested for survival at low pH and high bile acid concentrations, and then acid- and bile-tolerant strains were tested for adherence to the human intestinal epithelial cell lines Caco-2, HT-29, and HT-29 MTX (a mucus-producing variant of HT-29) as an assay for gut colonisation. Candidate probiotic strains were selected according to the results of these screening tests and preliminary immune efficacy data from mouse studies. Following further efficacy and safety trials, two strains were commercialised: Lactobacillus rhamnosus HN001, which was trademarked as DR20™, and Bifidobacterium lactis HN019, trademarked as DR10™. These strains have also been licensed to Danisco, and are marketed under the HOWARU™ brand. Safety Like other functional foods, probiotic bacteria need to provide a health benefit that is essentially risk-free to the consumer. That is, the probiotic bacterial strain must pose no threat to the consumer even when consumed in large quantities or by those with relatively low immune status. Although lactobacilli and bifidobacteria are normal inhabitants of the human gut, and are generally considered as ‘safe’ bacteria, there is still the requirement to show that each specific strain is non-pathogenic. Several in vitro and animal models were employed to provide sciencebased evidence that the candidate probiotics were safe for human consumption. Three mouse feeding trials were conducted using groups of BALB/c mice orally administered DR10 or DR20 over a range of dose rates, daily for 7–28 days [7–9]. The mice were assessed for various haematological, histological, and growth parameters. The results indicated that even at the highest dose of 2.5 ⫻ 1012 CFU/kg body weight/day, given for 28 days, neither probiotic strain was associated with alterations in food and water intake, live body weight, haematological parameters (including red blood cell and leucocyte counts), blood biochemistry (including levels of plasma total protein, albumin, cholesterol, and glucose), and mucosal histology (such as epithelial cell height, mucosal thickness, and villus height) [8]. In addition, there was no evidence that probiotic bacteria had translocated to the blood, liver, spleen, kidney, or mesenteric lymph nodes. Further in vitro studies have examined specific safety issues. For instance, the mucus layer of the gut is an important defence mechanism that protects the gut epithelia from attack from pathogens. Experiments have shown that DR10

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and DR20 do not degrade mucin, the principal component of the mucus layer, and so are unlikely to encourage pathogenic infection [10]. It has also been shown that DR10 and DR20 cannot aggregate or activate platelets [11], which is a positive finding as platelet aggregation by bacteria in the blood can lead to various pathologies, and neither DR10 nor DR20 exhibited undesirable or atypical antibiotic resistance profiles [12]. Recently, another aspect of safety has been examined in an animal model. Given that probiotic bacteria are thought to modulate the host immune system, there remains a theoretical possibility that probiotic bacteria could exacerbate immune-related disorders, such as an autoimmune disease. However, in a murine model of experimental autoimmune thyroiditis, feeding with DR10 or DR20 for extended periods did not accelerate disease progression [13]. It was concluded from all these studies that DR10 and DR20 are safe for human consumption. Microbial Ecology Screening procedures provided in vitro evidence that DR10 and DR20 could survive within the human GIT. Further studies have been conducted to see whether these bacteria colonised the gut in vivo, and their impact on resident gut microflora was assessed. One dietary intervention study involved feeding 10 healthy human volunteers with a total daily dose of 1.6 ⫻ 109 CFU DR20 using a three-stage study design over a 15-month period [3]. For the first 6 months (run-in period), volunteers consumed a daily portion of low-fat milk. For the next 6 months (treatment period), this milk contained DR20, and for the final 3 months of the trial, the volunteers reverted back to the milk-only portion. Using a variety of detection methods, the study showed that the range of total lactobacillus counts from faeces was less than 102 to 2 ⫻ 108 CFU/g during the run-in phase. Upon the consumption of HN001, this range increased to 105 to 108 CFU/g, which could not be explained by merely the amount of DR20 consumed. Therefore, it appeared that DR20 enhanced the growth of other lactobacilli in the healthy adult gut. Although in most cases DR20 became the dominant lactobacillus strain in the GIT during the feeding period, it did not generally persist in the faeces beyond the treatment period. Thus, consumption of DR20 transiently altered the total lactobacillus and enterococcal counts in most cases, without affecting biochemical or other bacteriological factors. DR10 was the subject of a randomised, double-blind, and placebocontrolled study to examine its ecological impact as well as the effect of galactooligosaccharides (GOS) on the GIT microbiota. GOS are known to act as a ‘prebiotic’, a food compound that supports the growth of healthy bacteria. GOS have been shown to enhance the growth in the GIT of lactobacilli and bifidobacteria species including DR10 and DR20 [14]. In the study, 30 subjects were

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randomly assigned into three groups [15]. After a two-week pre-intervention (run-in) period, subjects in group 1 consumed milk containing the GOS prebiotic, group 2 subjects received a total daily dose of 3 ⫻ 1010 CFU DR10 in milk, while group 3 subjects were given milk without supplementation for a 4-week period. This was followed by a 2-week wash-out period with no dietary intervention. Faecal samples were collected from all subjects at weekly intervals and analysed for bacterial content. The results indicated that while subjects consuming milk supplemented with either GOS or DR10 exhibited significantly increased bifidobacteria faecal counts (p ⬍ 0.0002), subjects that received the control milk showed no significant change compared with the run-in period. Interestingly, both groups 1 and 2 also exhibited increased lactobacilli faecal counts. Thus, similar to DR20, the study indicated that DR10 at least transiently colonised the human gut, and had positive impacts on gut microflora [4]. The question as to how many probiotic bacteria were required to observe the beneficial changes in gut microflora was addressed in a recent randomised, double-blind, placebo-controlled study involving 80 healthy ‘elderly’ (⬎60 years old) subjects [16]. The subjects were divided into 4 groups that received either a high dose of DR10 (5 ⫻ 109 CFU/day), a medium dose (1.0 ⫻ 109 CFU/day), a low dose (6.5 ⫻ 107 CFU/day), or vehicle only. The results indicated that even at the lowest dose, consumption of DR10 significantly increased bifidobacterial counts, but reduced enterobacterial counts, compared with vehicle-only controls. In summary, results of microbial ecology studies showed that both DR10 and DR20 can survive within the gut environment and appeared to exert beneficial effects on gut health through the enhancement of other lactobacilli and bifidobacteria populations, genera associated with gastro-intestinal health in humans. Neither strain permanently colonised the adult human GIT, and therefore had to be taken on a regular basis to maintain significant numbers. Efficacy The defining characteristic of a probiotic strain, apart from its viability in the GIT, is the provision of a health benefit to its host. Based on Fonterra’s aim of producing probiotic strains with immune-enhancing effects, immune assays were used to explore efficacy. The mammalian immune system is large and multifaceted, and is generally considered to consist of two major divisions: first, elements that are ‘hard wired’ to respond to certain immune challenges (the innate immune system) and second, elements that can adapt through various mechanisms to the bewildering array of antigens that the immune system is exposed to (the adaptive immune system). To explore whether DR10 or DR20 could provide a health benefit through immune enhancement, both the adaptive and innate arms of the immune system were tested.

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A mouse trial was conducted in which mice were fed DR10 or DR20 at total doses of 109 CFU/day, and various immune parameters were compared with control mice [17]. The results indicated that aspects of both the innate and acquired arms of the immune system were positively affected by the presence of the probiotic bacteria. One measure involved examining the activity of phagocytes. Phagocytes act as scavenger cells, patrolling the body looking for invading or foreign cells, and engulfing these cells whole. The engulfed cells are then broken down inside the phagocytes and the antigens presented to immune responder cells to drive the immune response. Compared with control mice, mice fed either DR10 or DR20 showed increased phagocytic activity by both blood leucocytes and peritoneal macrophages. Another cell type with a surveillance role is the natural killer (NK) cell. These cells roam the body looking for virally infected or tumour cells, whereupon they kill the affected cell. An assay examining the activity of NK cells against tumour cells in vitro showed that NK cells from DR10- and DR20-fed mice exhibited enhanced tumour cell killing compared with NK cells from control mice. As an example of the adaptive immune response, the production of specific antibodies by mice inoculated with tetanus toxoid and cholera antigens showed that DR10 and DR20 exhibited adjuvant activity, that is, the presence of the bacteria enhanced the production of specific antibodies in response to antigen challenge. Interestingly, it was also shown that the immune-enhancing effects of DR10 and DR20 did not perturb the overall leucocyte cell counts or the relative levels of lymphocyte subpopulations. A related study [18] explored the effect of probiotic dose on phagocytic activity and antibody production, and found that the increased phagocytosis observed was not due to increased numbers of phagocytic cells, but rather to increased activity of phagocytic cells already present. While blood phagocyte activity improved even at the lowest dose (107 CFU DR20/day), higher doses (109 or 1011 CFU DR20/day) were associated with greater improvement in phagocytic activity. Mouse models have also been used to investigate possible mechanisms by which DR10 and DR20 mediate immune enhancement. A previous study [17] showed that immune cells taken from the spleens of mice fed DR20 and then stimulated to non-specifically activate T lymphocytes (immune responder cells) tended to produce more of a cytokine called interferon-␥ than the same stimulated cells taken from control mice. Cytokines are chemical messengers or ‘immune hormones’ that are used to signal other immune cells nearby or at a distance. Current models propose that T helper (Th) lymphocytes, in reacting to antigens presented by antigen-presenting cells, can promote either an immune response that favours immune cells such as phagocytes (a Th1 response) or an immune response that favours antibody production (a Th2 response). This is achieved through expression of characteristic cytokines, and it is believed that

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the balance between Th1 and Th2 responses can have profound impacts on how the immune system reacts to potential disease threats. For instance, one theory suggests that allergic responses may be due at least in part to a skewing of the immune response to the allergen towards a Th2 response [19, 20]. In an experimental model in which mice are immunised with ovalbumin protein, spleen immune cells taken from these mice will respond to the addition of ovalbumin peptides by producing the cytokines interleukin (IL)-4 and IL-5, which are considered indicative of a Th2 response. In mice fed DR20 prior to and during ovalbumin immunisation, isolated stimulated spleen cells produced increased levels of interferon-␥ (a pro-Th1 cytokine) without affecting IL-4 and IL-5 levels [21]. A similar effect on Th1/Th2 balance was observed for peritoneal macrophages. Thus, it seems that although DR20 appears to promote a Th1-type response, it does not do so at the expense of an ongoing Th2 response. As the balance between Th1 and Th2 responses may have consequences for normal immune function, this finding was particularly significant. Apart from enhanced immune parameters, another potential beneficial effect of probiotic bacteria is the protection against infection by pathogenic micro-organisms. Both DR10 and DR20 have been shown to be effective against pathogens in several animal models. For instance, mice fed DR20 showed reduced morbidity, reduced bacterial translocation, and increased markers of innate and acquired immunity toward the pathogenic Escherichia coli strain O157:H7 [22], as well as impressive protection against mortality from Salmonella typhimurium infection [23]. Likewise, DR10 was shown to be effective in protecting weaning piglets against diarrhoea associated with rotavirus or E. coli infection [24], and protected mice against pathogenic E. coli O157:H7 [25] and against pathogenic S. typhimurium challenges designed to mimic both a single exposure to high pathogen levels (as might be encountered after consumption of heavily contaminated food prepared under poor hygiene) or chronic exposure to pathogens over a period of time [26]. Given the positive animal trial results, the probiotic efficacy of DR10 and DR20 was examined in human clinical trials. For DR20, several 3-stage pre- to post-intervention trials have been performed on groups of healthy middle-aged or elderly subjects [27–29], with 3-week run-in periods to establish baseline data, 3-week intervention periods using milk supplemented with DR20 (5 ⫻ 109 or 5 ⫻ 1010 CFU/day), followed by 3-week wash-out periods. In all the studies, analysis of blood samples taken from the subjects showed significant increases in the phagocytic activities of blood polymorphonuclear leucocytes and monocytes, as well as increased tumouricidal activity by NK cells. Similar benefits for human immune function have been shown for DR10, including improved phagocytic capacity [28, 30, 31], interferon-␥ production [31], and NK cell activity [30, 31]. Interestingly, one trial noted that subjects

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with relatively poor phagocytic responses prior to treatment tended to exhibit the largest relative increases in activity in response to DR10 [32]. This finding suggested that DR10 may be effective in protecting against immune senescence in the elderly [33], although this hypothesis requires further study. Although the experimental results so far represent strong evidence that DR10 and DR20 exhibit physiological efficacy, the gold standard of proof remains the demonstration of actual health benefits in a phase III clinical trial. Such a trial has recently been completed for DR10 that investigated the health effects of supplemented milk powder in children [Sazawal et al., in preparation, 2005]. The trial was designed to meet the standards used by the pharmaceutical industry, and consisted of a randomised, double-blind and placebo-controlled trial involving more than 600 children aged 12–36 months. The study was conducted in a lower- to middle-class residential area in Sangam Vihar, India. Healthy children with no chronic illnesses were enrolled in the study, and randomly assigned to either treatment or control groups (n ⫽ 312 each). The treatment group received milk fortified with DR10 and GOS, while the control group received non-supplemented milk, twice daily for a period of 12 months, followed by a 2-year follow-up period. As the trial was double-blinded to prevent possible bias, neither the subjects nor researchers knew which group a child was in during the trial period. The results showed convincing and credible evidence that the group fed the fortified milk displayed significant health benefits over those that consumed the normal milk. For instance, children fed DR10 and GOS were 22% better protected against dysentery, 16% better protected against the burden of severe illness (non-diarrhoeal disease), 32% better protected against sickness with high temperature, 7% better protected against ear infection, and 6% less likely to need antibiotics. All of these findings are consistent with the view that DR10 has immune-enhancing properties. The study also found that children fed the fortified milk displayed a 35% reduction in iron deficiency anaemia as well as significantly improved growth rates more in line with growth charts published by the National Center for Health Statistics, USA, compared with the control group. Another recently completed double-blind, randomised, placebo-controlled trial showed that DR10 and DR20 may be beneficial against allergy [34]. This trial, based in Wellington, New Zealand, studied children aged 1–10 years with a history of atopic dermatitis (eczema). The study subjects (n ⫽ 59) all exhibited a positive skin reaction to at least one common allergen using the skin prick test, and were randomly assigned to control or treatment groups. The control group received a placebo supplement, while the test group consumed a supplement containing DR10 and DR20 daily for 12 weeks. The parents of the subjects (also blinded to whether the child received the probiotic bacteria or placebo) kept symptom diaries. Symptom severity was then tracked using the scoring atopic dermatitis (SCORAD) index. While the treatment group showed significant improvement in eczema

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symptoms compared with baseline SCORAD index values, this improvement was slightly below statistical significance when compared with the placebo effect in the control group. Nonetheless, in a subgroup of children that exhibited at least one positive skin prick test to a food allergen (n ⫽ 37), the group fed DR10 and DR20 had significantly decreased SCORAD values compared with the placebo group. The results indicated that DR10 and DR20 exerted a positive health impact at least on children with allergic dermatitis who exhibit a food allergy. Based on this trial, a second much larger study is now in progress to examine the impact of DR10 and DR20 on childhood allergy. Products A large body of science-based evidence now shows that DR10 and DR20 are safe for human consumption, are able to survive in the human GIT, and exert health benefits on the host both in terms of gut health and immune enhancement. Based on these findings, Fonterra’s probiotic bacterial strains have been applied to various products, including milk powders, yoghurts, cheese, fermented milk drinks, and powdered supplements, both under Fonterra’s own DR10 and DR20 brands, and licensed to third parties, for example under Dansico’s HOWARU brands.

Further Research

Although previous studies have shown that DR10 and DR20 mediate immune-enhancing effects in vitro, and offer demonstrable health benefits to the host, the mechanisms by which these benefits occur remain to be resolved. Likewise, it remains unclear how in vitro assays reflect immune mechanisms in vivo. Knowledge into how probiotic bacteria actually work can be applied to a number of areas, such as the selection of new strains targeted to specific effects (e.g. the ability to stimulate interferon-␥ production or to increase NK cell activity), or to be active against specific diseases, such as inflammatory bowel disease. Mechanistic information can also be used to support the probiotic strains in the market and assist decision-making by regulatory authorities. Therefore, even though DR10 and DR20 have already been successfully commercialised, they remain the subjects of ongoing research programmes at Fonterra and other research organisations. The research is centred around five main areas which are presented in what follows. Genomics A draft genome sequence of DR20 has been obtained. This has allowed the interrogation of the genome for genes involved in probiotic mechanisms. For

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instance, using the sequence data, the DR20 genome has been screened for genes containing the known cell-wall-anchoring motif ‘LPxTG’ to identify proteins expressed on the cell wall. As these proteins interact with the external environment of the bacterium, it is possible that these proteins are involved in mediating probiotic effects. Some of these LPxTG-containing proteins are now the subjects of further study into immune mechanisms. Genome data can also be used to answer specific issues. For example, analysis of genome data strongly suggested that the vancomycin resistance exhibited by DR20 (a trait typical of Lactobacillus species) was encoded on the chromosome, meaning that it cannot be easily passed on to other bacteria. Genome data can also be used to assist proteomic studies, and to reconstruct metabolic pathways in silico to predict how DR20 might operate in different environments. Genetic Tools Knowledge of the DR20 genome has allowed the development of genetic tools (plasmid vectors) that enable the genetic manipulation of DR20. Using our suite of genetic tools, we are now able to overexpress as well as disrupt genes of interest, allowing investigation into the roles of specific genes in different probiotic attributes. In vitro Assays Assays based on cell lines or ex vivo leucocyte populations offer many advantages over the use of animal models and clinical trials in terms of cost, ease of use, and numbers of experimental samples that can be tested. To explore aspects of probiotic function, we have developed several in vitro assays of immune interaction that can be used to identify bacterial components and/or genes important in mediating probiotic effects, and perhaps even identify host receptor molecules. Other in vitro strategies, such as microarrays and single nucleotide polymorphism screening, may be useful in investigating the role of human gene expression and gene polymorphisms in determining immune responses to probiotic bacteria, allowing the application of nutrigenomic approaches to probiotics research. While each in vitro assay may provide a useful result, it will be the ability to integrate data from across multiple assays and combine with data from animal studies and human clinical trials that will provide the greatest benefit. An integrated approach will also help identify in vitro assays that best reflect the mechanisms of in vivo health impacts observed in human trials of DR10 and DR20. Animal Models The main advantages of animal trials are their lower cost (compared with clinical trials) and their ability to mimic human physiological conditions (compared

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with in vitro assays). Animal trials can be used to test specific product formats or encapsulation techniques to ensure that efficacy is maintained, and to provide material to explore probiotic mechanisms that would be unavailable from human trials. Also, there exist a wide variety of animal models of human disease that can be used to identify whether DR10 and DR20 have any activity against specific pathologies or conditions. Finally, DR10 and DR20 may also prove to be effective as probiotics in companion and/or production animals. Fermentation Fonterra has extensive expertise in the large-scale production and handling of dairy cultures and their properties in various formats, including the maintenance of viability during processing [35]. As probiotic bacteria such as DR10 and DR20 can grow, or at least survive, in dairy-based products such as yoghurt and cheese, knowledge of their sensory impacts is vital. DR20 is part of a flavour biotechnology research programme at Fonterra, and has yielded at least one enzyme that may have utility in controlling bitterness in fermented dairy products [36].

Conclusions

The development of a successful functional food is a long and complex process. Such foods must be completely safe for the consumer, yet offer a demonstrable health benefit without unwanted side-effects or risk of overdose. To this end, Fonterra has embarked on a long-term programme to prove that its probiotic bacterial strains DR10 and DR20 are safe and effective using sciencebased studies that withstand the rigour of external peer review. The application of further experimental research programmes will close the gap between health ingredients with proven health benefits and the understanding of the mechanisms responsible for these health benefits at the molecular level.

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Shu Q, Qu F, Gill HS: Probiotic treatment using Bifidobacterium lactis HN019 reduces weanling diarrhea associated with rotavirus and Escherichia coli infection in a piglet model. J Pediatr Gastroenterol Nutr 2001;33:171–177. Shu Q, Gill HS: A dietary probiotic Bifidobacterium lactis (HN019) reduces the severity of Escherichia coli O157:H7 infection in mice. Med Microbiol Immunol 2001;189:147–152. Shu Q, Lin H, Rutherfurd KJ, Fenwick SG, Prasad J, Gopal PK, Gill HS: Dietary Bifidobacterium lactis (HN019) enhances resistance to oral Salmonella typhimurium infection in mice. Microbiol Immunol 2000;44:213–222. Sheih YH, Chiang BL, Wang LH, Liao CK, Gill HS: Systemic immunity-enhancing effects in healthy subjects following dietary consumption of the lactic acid bacterium Lactobacillus rhamnosus HN001. J Am Coll Nutr 2001;20:149–156. Gill HS, Cross ML, Rutherfurd KJ, Gopal PK: Dietary probiotic supplementation to enhance cellular immunity in the elderly. Br J Biomed Sci 2001;58:94–96. Gill HS, Rutherfurd KJ: Probiotic supplementation to enhance natural immunity in the elderly: effects of a newly characterized immunostimulatory strain Lactobacillus rhamnosus HN001 (DR20™) on leucocyte phagocytosis. Nutr Res 2001;21:183–189. Chiang BL, Sheih YH, Wang LH, Liao CK, Gill HS: Enhancing immunity by dietary consumption of a probiotic lactic acid bacterium (Bifidobacterium lactis HN019): optimization and definition of cellular immune responses. Eur J Clin Nutr 2000;54:849–855. Arunachalam K, Gill HS, Chandra RK: Enhancement of natural immune function by dietary consumption of Bifidobacterium lactis (HN019). Eur J Clin Nutr 2000;54:263–267. Gill HS, Rutherfurd KJ, Cross ML, Gopal PK: Enhancement of immunity in the elderly by dietary supplementation with the probiotic Bifidobacterium lactis HN019. Am J Clin Nutr 2001;74: 833–839. Gill HS, Darragh AJ, Cross ML: Optimizing immunity and gut function in the elderly. J Nutr Health Aging 2001;5:80–91. Sistek D, Kelly R, Wickens K, Stanley T, Fitzharris P, Crane J: Is the effect of probiotics on atopic dermatitis confined to food sensitized children? Clin Exp Allergy 2006;36:629–633. Prasad J, McJarrow P, Gopal P: Heat and osmotic stress responses of probiotic Lactobacillus rhamnosus HN001 (DR20) in relation to viability after drying. Appl Environ Microbiol 2003;69: 917–925. Christensson C, Bratt H, Collins LJ, Coolbear T, Holland R, Lubbers MW, O’Toole PW, Reid JR: Cloning and expression of an oligopeptidase, PepO, with novel specificity from Lactobacillus rhamnosus HN001 (DR20). Appl Environ Microbiol 2002;68:254–262.

Dr. James Dekker Fonterra Co-operative Group Palmerston North (New Zealand) Tel. ⫹64 6 350 6323, Fax ⫹74 6 350 4658, E-Mail [email protected]

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Conclusion Tai ES, Gillies PJ (eds): Nutrigenomics – Opportunities in Asia. Forum Nutr. Basel, Karger, 2007, vol 60, pp 209–223

Developing the Promise of Nutrigenomics through Complete Science and International Collaborations Jim Kaput Laboratory of Nutrigenomic Medicine, University of Illinois, Chicago, Ill., and NCMHD Center of Excellence in Nutritional Genomics, University of California, Davis, Calif., USA; the European Nutrigenomics Organisation (http://www.nugo.org)

Abstract Food is economically available to 4 billion of the world’s 6 billion people, a situation that resulted from dramatically improved methods for producing, storing, and distributing food on a mass scale during the last 100 years. Nevertheless, almost 2 billion people are malnourished through either over-consumption of fats and calories or lack of adequate calories and micronutrients. Malnourishment results in chronic diseases, immune dysfunction, and early death. Analyzing and understanding gene – nutrient interactions is therefore a necessary step for designing and producing foods for maintaining the health of populations and individuals. Nutrigenomics is the study of how constituents of the diet interact with genes, and their products, to alter phenotype and conversely, how genes and their products metabolize these constituents into nutrients, antinutrients, and bioactive compounds. However, defining causal gene X nutrient interactions involved in maintaining optimum health are more challenging because of the (i) chemical complexity of food, (ii) genetic heterogeneity of humans, and (iii) the complexity of physiological responses to nutrient intakes in health and disease. Three significant developments will allow progress in nutrition and nutrigenomics: the development of high throughput omic (genomic, transcriptomic, proteomic, and metabolomic) technologies, improved experimental designs, and the development of research collaborations to study complex biological processes. The practical applications of nutrigenomics are the possibility of delivering the right micronutrients in the optimum amount to the food insecure and developing novel foods which are more nutritious, flavourful, storable, and health promoting than many of the products manufactured today. Copyright © 2007 S. Karger AG, Basel

Humans have dramatically improved methods for economically producing, storing, and distributing food on a mass scale during the last 100 years. The

result of those advances is a world population of over 6 billion people covering almost all ecological niches. In no other era has more food been available for more people. Nevertheless, almost 2 billion people are malnourished, through either overconsumption of fats and calories or underconsumption of calories and micronutrients. While these malnourishment problems could theoretically be solved by personal responsibility (eating less) in lands of plenty or distributing food equitably and inexpensively to the undernourished, the realization of the genetic uniqueness of individuals may confound simple-minded solutions of distributing the same types and amounts of foods to all. That is, food produced or formulated for one population may not be optimum for every member of that population or for individuals in populations because of the genetic heterogeneity of the human species. Analyzing and understanding gene-nutrient interactions is therefore a necessary step for designing and producing foods for maintaining the health of populations and individuals. Nutrigenomics is the study of how constituents of the diet interact with genes, and their products, to alter phenotypes and conversely, how genes and their products metabolize these constituents into nutrients, antinutrients, and bioactive compounds. As is well illustrated by the contributions to this volume, the methodologies for analyzing genes (genomics), their RNA products (transcriptomics), proteins (proteomics), and metabolites (metabolomics) are being applied to the study of nutrition in cell culture, animal models, and humans. New analytical methods [Dawson et al., 2005; Moore, 2004; Roweis and Saul, 2000; Tenenbaum et al., 2000] are being developed and applied to these complex, high-dimensional data sets to identify associations among food intake, genetic makeup, and physiological responses in an individual. Focusing on the individual as the unit of analyses for nutrient intakes rather than the population is a profound transformation for the field of nutrition and for the food industry. Dietary guidelines have historically been derived from associating some physiological markers (e.g. cholesterol) with dietary intake as determined from population studies. Although perhaps unintended, dietary guidelines imply that all individuals are genetically, culturally, and physiologically identical. The same foods with the same nutrient compositions can be produced for everyone. Nutrigenomics, on the other hand, may determine the optimal nutrient intakes for an individual as opposed to a population. The concepts of nutritional genomics are therefore a profound transformation from past practices in science and its applications to the food industry. Nutrigenomics has been demonstrated by monogenic diseases such as phenylketonuria (http://www.ncbi.nlm.nih.gov/entrez/dispomim.cgi?id ⫽ 261600) where a mutation causes a susceptibility to phenylalanine. Reduce the amount of phenylalanine in the diet, and many with this genetic condition survive. However, defining causal gene-nutrient interactions involved in

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maintaining optimum health is more challenging because of (a) the chemical complexity of food, (b) the genetic heterogeneity of humans, and (c) the complexity of physiological responses to nutrient intakes in health and disease.

The Diversity Challenges

Chemicals in Foods Numerous studies have demonstrated that chemicals classified as macronutrients, such as certain fatty acids, and micronutrients, such as vitamins, regulate gene expression directly (reviewed in Schuster [2006]) or through changes in signal transduction pathways [Guo and Sonenshein, 2006]. Although many of these studies used single nutrients added to cell culture media, laboratory animal food, or to human diets, most dietary chemicals that are consumed are found in complex mixtures. Oil extracted from corn contains at least 9 fatty acids, 13 different triglycerides, 9 sterols, more than 12 fatty acid sterols, and 5 tocols [Kaput and Rodriguez, 2004]. While many of these nutrients are metabolized for energy, a number of them can be direct (i.e., nonmetabolized) activators of transcription factors (e.g. ␤-sitosterol [Cantafora et al., 2003; Orzechowski et al., 2002]) or be metabolized to activators (certain fatty acids to 15-deoxy-⌬ 12,14-PGJ2 [Kliewer et al., 1995]). Hence, nutrigenomic research requires detailed knowledge of the nutrient composition of foods and effects of cooking [Malfatti and Felton, 2006]. Food composition databases have been or are being developed by the Food and Agriculture Organization of the United Nations (http://www.fao.org/infoods/publications_en.stm), the International Life Sciences Institute (ILSI Crop Composition Database: http://www.cropcomposition.org/), the International Network of Food Data Systems (http://www.fao.org/infoods/directory_en.stm), USDA (Food Composition Database: http://www.nal.usda.gov/fnic/foodcomp/Data/SR18/ sr18.html), and the European Food Information Resource Network (EuroFIR: http://www.eurofir.net/). Other national governments have compiled food composition tables for their countries, but data must often be extracted from unlinked flat files or from publications. Some of these databases may be incomplete, particularly for minor components that may be bioactive. Identifying the bioactive chemicals and the amounts that alter expression of genetic information and physiological processes is a critical part of understanding how diet alters molecular pathways. Nutrient intake assessments are challenging since free-living humans do not regard daily life as a science experiment where the amount and type of food is accurately and precisely recorded. It is generally accepted in the nutrition community that food recall tools are less

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than ideal [Rutishauser, 2005] but are likely to be the most convenient for largescale studies. Ultimately, quantitative analytical methods need to be developed for analyzing nutrient intakes and for gene-nutrient-phenotype studies [Corella and Ordovas, 2005; Ordovas and Corella, 2004]. Our diets are also greatly influenced by food produced by industry. Most individuals in developed countries can inexpensively obtain enough calories and most macro- and micronutrients. However, many foods with varying combinations of macro- and micronutrient compositions were developed primarily for taste and economy without the sophisticated knowledge of how individual humans respond to nutrients. Hence, it is not surprising that our current national and international food systems have not produced the most healthful foods. New knowledge created by nutrition and nutrigenomic research should enable food companies to design and produce healthy and tasty food as economically as is done now. Perhaps most importantly, increased nutrigenomic knowledge may also address a problem resulting from globalization: humanitarian food aid often supplies enough calories but with inadequate and unbalanced nutrient content [Hughes and Lawrence, 2005]. Urban dwellers in developing countries often adopt Western foods and habitats resulting in increases in chronic diseases such as type 2 diabetes mellitus (T2DM) [Epping-Jordan et al., 2005; James et al., 2001; Lieberman, 2003]. A better understanding of the nutrient needs of individuals in genetically diverse populations is needed for targeting the right nutrients to the right individuals. Genetic Diversity During the stepwise migrations from East Africa and subsequent population centers [Jorde and Wooding, 2004; Tishkoff and Verrelli, 2003], groups migrating to new locations carried a subset of the genetic diversity. Migration and subsequent population expansion resulted in individual humans sharing 99.9% of their genomic sequences. More detailed genetic analyses indicated that it is possible to place individuals in groups based on ancestry considering differences in the 0.1% variation in sequence. On average, there is a 12–14% difference between geographically distinct populations [Jorde and Wooding, 2004] – for example, between Africa and Asia. The majority of human variation (estimated range of 86–88%) occurs within a geographic population (e.g. Europe). These differences among ancestral groups resulted from genetic drift and selective pressures from the environment, including food availability in the new environments. An example to illustrate the role of food is lactose tolerance. A mutation in the promoter of the lactase gene provided a selective advantage to individuals living in northern climates (in this case Europe) because a new food source, other mammals’ milk, could be consumed in adulthood [Enattah et al., 2002; Harvey et al., 1998]. Other examples of how

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genetic heterogeneity affects health outcomes can be found in differences in efficacy of drugs in different ancestral populations. Alleles of CYP3A4 differ in metabolism of many prescription drugs and the allele frequencies vary by ethnicity [Xie et al., 2001]. Similar differences are likely to occur in other metabolic pathways, some of which will be involved in metabolism of nutrients and food-derived bioactives. However, one cannot assume the presence of an allele based on ancestry because alleles may be present in a population but at differing frequencies than in another population – that is, one still has a chance to inherit an allele identical to one that predominates in another population. The implications of the genetic similarity of all humans as a species and diversity among individuals are significant for nutrigenomic researchers. Metabolism is a set of interconnected biochemical pathways. Interactions between genes or their proteins are called gene-gene interactions (or epistasis) [Carlborg and Harley, 2004; Chiu et al., 2006; Moore, 2004] and at the protein level ‘biochemical buffering’ [Hartman et al., 2001; Hartman, 2006]. Since allele frequencies of many genes differ among ancestral populations, the chance of inheriting a specific set of gene variants varies depending upon the genetic history of one’s family line. Some polymorphisms, such as single nucleotide polymorphisms (SNPs), alter gene-gene interactions and therefore a single gene variant is not deterministic. Rather, it expresses itself within the background of the individual’s total DNA sequence. The terms genome structure or architecture encapsulate the total set of variants (SNPs and other sequence differences) within one’s genome. Hence, SNP analyses of individual genes must be accompanied by analyses of ancestral chromosomal regions inherited from one’s parents. Several examples of this effect have been found. • The HapK haplotype (a collection of SNPs inherited as a unit) in the leukotriene A4 hydrolase gene (LTA4H). This haplotype confers a relative increased risk of 1.36 of myocardial infarctions plus cardiovascular disease (CVD) in European Americans [Helgadottir et al., 2006] but the risk is almost 5-fold in African Americans (fig. 1). About 27% of the European Americans in the control group inherited at least one copy of HapK but that haplotype was present in only 6% of African American controls. Since preliminary analyses indicated that HapK is very rare in Africa, its occurrence in African Americans is due to the presence of European admixture. Synergistic interactions between the HapK haplotype (European derived) in LTA4H and other genes of African origin (gene-gene interactions or epistasis) may increase risk. Another possibility could be different responses to environmental factors (gene-environment interactions) which manifest themselves in African-European admixtures.

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Frequency of HapK

Ancestral group

Rel. risk

PAR

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control

0.19

0.15

1.35

0.09

0.04

4.94

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LTAH4 HapK

African American 0.18

Fig. 1. Effect of ancestral inheritance on the effect of a haplotype. Inheriting LTAH4 haplotype K increases the relative risk of myocardial infarction (MI) and other CVD symptoms depending upon the presence of non-LTAH4 genomic sequences. European-derived regions are open regions of chromosomes and African-derived are hatched. These other genomic regions are inherited differently depending upon the ancestral background altering the risk of disease because of gene-gene interactions. Each individual from an admixed population may have different chromosomal segments from different ancestral groups. Frequency of HapK ⫽ The frequency of the allele in the cases (MI ⫹ CVD) and controls; Rel. risk ⫽ the relative risk of inheriting the HapK allele; PAR ⫽ the population-attributable risk. Although PAR is population based, it is often used as a measure of the effect of the allele on the total disease risk. The values of 0.09 and 0.22 indicate that other genes and environmental factors contribute to the risk in each population. Adapted from Helgadottir et al. [2006].

•

•

The association of the APOE4 allele with Alzheimer’s disease [Kang et al., 1997]. Even though the APOE4 variant is twice as common in African Americans than Asians, Asians develop Alzheimer’s disease five times more frequently than African Americans [Ntais et al., 2004]. The association between variants in the calpain-10 gene (CAPN10) and T2DM is strongest in Mexican Mestizo and Mexican American populations [del Bosque-Plata et al., 2004].

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Since chromosomal regions are often rearranged during meiosis, each individual may inherit a unique patchwork of chromosomal regions from different ancestral backgrounds, particularly in cultures where several ancestral groups intermarry. Population stratification has been shown to occur in a seemingly homogeneous population such as European Americans [Campbell et al., 2005]. Genetic homogeneity is assumed because of unrelated phenotypic similarities such as skin color, which is a poor substitute for genetic analyses [Kittles and Weiss, 2003; Shriver et al., 2005]. The challenge for geneticists and nutrigenomics researchers, however, is to develop analytical approaches for calculating the effect of gene variants in different genomic (ancestral) backgrounds. Nonlinear dimensionality reduction or other algorithms [Dawson et al., 2005; Moore, 2004] hold some promise for analyzing the complex data sets generated by nutritionists, geneticists, physiologists, and nutrigenomics researchers. Complexity of Health and Disease Processes Health is often thought of as the absence of symptoms of disease and, as importantly, as an all or none phenomenon, i.e. one is either healthy or sick. This dichotomous view of health and disease does not accurately describe biological processes. Complex traits, such as health, or the chronic diseases obesity, diabetes, cardio- or cerebrovascular diseases, Alzheimer’s disease, and certain cancers are caused by the contribution of many genes interacting with multiple environmental factors (reviewed in Kaput [2004, 2006]). Indeed, the list of candidate genes for different chronic diseases attests to the molecular heterogeneity: a large number of genes in different molecular pathways have been linked with T2DM [Parikh and Groop, 2004], CVD [Ordovas and Corella, 2005], and, indeed, all chronic diseases including cancers (http://condor.bcm. tmc.edu/oncogene.html). While the identity and combination of contributing genes (or their variants) to disease in any one individual have not been ascertained for most chronic diseases, one can conclude that the physiological complexity of the same disease in different individuals results from many genes interacting with each other and with environmental factors [Ordovas and Corella, 2005]. These combinations differ among individuals because of human genetic variation.

Addressing the Diversity Challenges

Advances in understanding disease processes, the molecular and genetic workings of biological systems, and increase in health and longevity in

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many countries since the 1950s are a direct result of the worldwide interest in and support for biomedical research. The United States government appropriated USD 334.9 billion to the National Institutes of Health (NIH) for the 55-year period ending in 2005 (http://officeofbudget.od.nih.gov/ui/Appropriations HistoryByIC.htm). Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain, Sweden, and the UK (the original European Union countries) provided EUR 536.3 million (approximately USD 0.9 billion) from 1993 to 2003 (NIH appropriations were USD 137.8 billion in that period). While gains in health measures have been impressive, it is still not possible to describe in full how disease is initiated or how to identify genetic or nutrigenomic susceptibility (that is, diet-related susceptibility to disease). The majority of research funds were earmarked for disease research – i.e., understanding the etiology of disease processes and linking intake of specific nutrients to disease at the population level and substantially less to disease prevention. Only 4% of the US NIH budget is for nutrition research (http://www.scnrc.org/docs/starke-reed_files/frame.htm and http:// hnrim.nih.gov/Report/ Nih02_rpt.pdf). Hence, while our progress in biomedical research has been substantial, it is not possible to develop nutritional recommendations for maintaining optimal health for each individual nor for personalized medicine. Three significant developments will allow progress in nutrition and nutrigenomics: the development of high-throughput ‘omic’ (genomic, transcriptomic, proteomic, and metabolomic) technologies, improved experimental designs, and the development of research collaborations to study complex biological processes. Technologies The human genome project provided more than just the sequence of human DNA: it propelled the development of high-throughput DNA analysis methods and instruments. The success of DNA and RNA methods spawned similar advances in high-throughput analyses of proteins and metabolites. Proteomics and metabolomics provide the additional necessary data sets to understand genes and their expression patterns. While the technical advances were of great importance, the human genome project also initiated a change in the molecular and genetic research culture: the ability and willingness to analyze complex biological processes rather than only the reductionist approach of studying single genes and proteins. While reductionist biology will continue to contribute important insights, whole organisms can now be studied. Coupled with systems biology approaches of monitoring macromolecules (DNA, RNA, and protein) and metabolites, these methodological approaches will provide a more complete analytical picture of complex biological processes.

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Experimental Design The relative ease of analyzing gene variants and transcripts with microarrays (e.g. Affymetrix), beads (e.g. Illumina technology), and quantitative real-time PCR (e.g. Applied Biosystem) allowed the analyses of many candidate genedisease and gene-nutrient-phenotype association studies. Many of the initial studies, however, could not be replicated. This led experts in genetic epidemiology to a critical analysis of association study designs. The most common design flaws are sample sizes that lack appropriate statistical power, control groups that are not appropriately matched to cases, population stratification that occurs because of genetic admixtures among study participants, and overinterpretation of data [Cardon and Bell, 2001; Colhoun et al., 2003; Hopper et al., 2005; Ioannidis, 2005; Lander and Kruglyak, 1995; Newton-Cheh and Hirschhorn, 2005; Risch, 1997; Tabor et al., 2002]. As a means to improve genetic association studies, several international collaborations have been established. HuGENet™ (http://www.cdc.gov/genomics/ hugenet/default.htm) [Khoury, 2004], P3G (the Public Population Project in Genomics, http://www.p3gconsortium.org/), and a ‘network of investigator networks’ [Ioannidis et al., 2006] have all been formed for improving human genetic research by sharing best practices, tools, and methods for analyses of associations between genetic variation and common diseases (http:// www.cdc.gov/ genomics/hugenet/default.htm). Nutrigenomic researchers [Kaput, 2004, 2005; Ordovas and Corella, 2004] added that (1) chronic disease may result from multiple molecular pathways that may obscure gene-disease or gene-nutrient-phenotype association analyses, (2) the physiological response to the presence of a disease may alter expression of genetic information, (3) genotype-environment interactions are rarely taken into account in nutritional or genetic epidemiological experiments, and these interactions are known to affect the expression of genetic information in response to different environments, (4) ancestral background should be included because of epistasis (interaction of genes that are not alleles, especially the suppression of the effect of one gene by another), and (5) laboratory animals and cultured cells may not account for genetic or nutritional variations found in humans. Recommendations to address these issues were proposed as a means to improve study designs and the reliability of conclusions and are based on the biological fact that expression of genetic information results from interaction of DNA (through the organism) and its environment. The International Effort Recognizing and acknowledging the limitations of current nutrition, genetic, and nutrigenomic research designs and strategies, 89 scientists from 22 countries called for strategic international alliances to harness nutrigenomic

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research for personal and public health [Kaput et al., 2005, 2006]. The goals outlined were to (1) create a federation for sharing data from cell culture experiments, laboratory animal studies, and in particular human nutritional intervention and cohort (prospective and retrospective) studies, (2) develop more highly powered human studies, (3) improve analyses and consistency of phenotypes, (4) develop better measurements of food intake, (5) introduce controls for population stratification, (6) analyze a wider array of genetic makeup by recruiting individuals from different ethnic groups, (7) include other environmental variables that alter expression of genetic information, and (8) promote interactions between academia and industry to convert knowledge for the public good. International collaborations have a rich tradition in scientific research and many arose through serendipity and personal contacts. The goal of the international nutrigenomic research community [Kaput et al., 2005] is to provide the means for making these collaborative efforts easier to initiate, maintain, and sustain. The development of best practices and sharing of data will produce more reliable results than can be obtained through individual efforts.

The Next Steps

While many perceive that nutrigenomics has its ‘home’ exclusively in the discipline of nutrition, it encompasses the concepts and technologies of numerous research and application fields including genetics, molecular biology, physiology, food science, agriculture, behavioral science, anthropology, ethics, the food industry, and health care. The integration of these disciplines necessary for analyzing and understanding nutrient (and environment)-gene interactions for health and disease processes, that is, into a biological whole, requires intensive and extensive collaborations. The international research network will aid in focusing the talents and resources of individuals with diverse expertise in concepts and technologies. The initial efforts at forming productive collaborations among researchers are being done through an international network initiated by the European Nutrigenomics Organisation (NuGO) and called the Nutrigenomics Society. NuGO is the ideal foundational organization for this effort since it is the only nutrigenomics organization whose mission is multinational and not explicitly linked to a specific research program. It is funded by the European Union and consists of 23 partner institutions in 10 European countries. The mission of NuGO is to develop, integrate, and facilitate genomic technologies, infrastructure and research for nutritional science, to train a new generation of nutrigenomics scientists, in order to improve the impact of nutrition in health promotion and disease prevention. The mission of NuGO is consistent with the

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many diverse efforts of nutrigenomic centers and researchers around the world, and is essentially identical to that of the international Nutrigenomics Society. Since the NuGO was developed and operated to foster communication among institutes, laboratories, and individuals in Europe, the NuGO website and resources have been made available to the Nutrigenomics Society. Members of the international society are collaborating with NuGO to develop a nutrigenomics information portal for scientists, health care workers, and the public [Kaput et al., 2006], to participate in the development of best practices, and collaborate on the research and applications of the science.

The Role of Nutrigenomics Researchers and Businesses in Asia

Asia has about 25% of the globe’s land mass and about 60% of the world’s population. The ecosystems and human genetic ancestries are highly diverse, providing a vital resource for analyzing nutrient-gene interactions. Comparing the nutrigenomic results across genetic ancestries and food intake habits within Asia and between Asia and other groups throughout the world may illuminate important pathways for maintaining health and preventing disease among all ancestral populations. A prime example is the appearance of obesity-related metabolic disorders that occur at a lower body mass index in Asian (and Mexican [Sanchez-Castillo et al., 2005]) populations compared to European populations [WHO Expert Consultation, 2004]. The genetic and molecular explanations for the differences among Mexicans, Asians, Europeans, and other populations may identify the pathways which contribute most to these disorders in different ancestral backgrounds. Since food types and amounts vary among these populations, nutrigenomics research is likely to provide the information for improving and maintaining health of individuals in Asia and the Pacific region, as well as for others in the world. Nutrigenomics may also provide fundamental and valuable data for implementing food policies. Genetic analyses have demonstrated that all humans belong to one species yet differ among individuals and ancestral groups [Hinds et al., 2005; Jorde and Wooding, 2004; Shriver et al., 2005]. Differential responses to the same food have been observed at the macro level: certain populations that adopt Western diets and activity levels have higher levels of obesity and other chronic diseases than European ancestral populations. One of the best-known examples is type 2 diabetes, which occurs rarely in Pima Indians living in Mexico while the incidence is approximately 50% for those whose ancestors migrated to the USA. The incidence of T2DM in the USA is around 7% (http://diabetes.niddk.nih.gov/dm/pubs/statistics/index. htm#7). Hence, analyzing the responses of different ancestral groups to foods, particularly to manufactured foods, is a public health imperative.

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Economic progress and humanitarian aid has reduced the proportion of undernourished throughout the world, and particularly in China (http:// www.fao.org/docrep/007/y5650e/y5650e03.htm#P1_33). However, at the same time, type 2 diabetes is projected to double in the next 20 years but most of the increases will occur in India, China, Pakistan, Mexico, Brazil, and developing countries [Yach et al., 2006]. Certain ancestral groups appear to be more sensitive to increased calorie consumption and/or physical inactivity. With these significant differences in response to calories and macronutrients, one might also question whether the standard recommended daily intakes of micronutrients is appropriate for all populations. While it is known that micronutrient intakes differ among ethnic groups in developed countries [Arab et al., 2003; Hamrosi et al., 2005; Huang et al., 2002; Manav et al., 2004; Satia-About et al., 2003], whether different ancestral groups require different amounts of micronutrients is, apparently, not known. Nutrigenomics research may provide the answers to these questions and provide the foundation for evidence-based decisions on the types and amounts of foods needed for individuals in developed and developing countries. The Future of Nutrigenomic Science and Applications

Nutrigenomics researchers are advocating that an organism cannot be completely, or even adequately, analyzed or understood in isolation from its environment. That is, the reductionist view that genes or their variants alone will inform us about health or disease processes is incomplete at best, and perhaps provides erroneous results and conclusions. This strong statement is justified: all organisms respond at the molecular level to nutrients in their environment – if they did not, they would not survive that environment. Hence, nutrigenomic research is leading the development of a more complete scientific method, one which includes analyses of genes and the environmental variables with which they interact. The practical applications of nutrigenomics are immense: from delivering the right micronutrients in the optimum amount to the food insecure, to developing novel foods which are more nutritious, flavorful, storable, and health promoting than many of the products manufactured today. Acknowledgements The preparation of the manuscript was supported by the National Center for Minority Health and Health Disparities Center of Excellence in Nutritional Genomics, Grant MD 00222 and by the European Union, EU FP6 NoE Grant, Contract No. CT2004-50594.

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Ioannidis JP: Why most published research findings are false. PLoS Med 2005;2:e124. Ioannidis JP, Gwinn M, Little J, et al: A road map for efficient and reliable human genome epidemiology. Nat Genet 2006;38:3–5. James PT, Leach R, Kalamara E, Shayeghi M: The worldwide obesity epidemic. Obes Res 2001;9(suppl 4): 228S–233S. Jorde LB, Wooding SP: Genetic variation, classification and ‘race’. Nat Genet 2004;36(suppl 1): S28–S33. Kang DE, Saitoh T, Chen X, et al: Genetic association of the low-density lipoprotein receptor-related protein gene (LRP), an apolipoprotein E receptor, with late-onset Alzheimer’s disease. Neurology 1997;49:56–61. Kaput J: Diet-disease gene interactions. Nutrition 2004;20:26–31. Kaput J: An introduction and overview of nutritional genomics: application to type 2 diabetes and international nutrigenomics; in Kaput J, Rodriguez RL (eds): Nutritional Genomics. Discovering the Path to Personalized Nutrition. Hobeken, Wiley, 2006, pp 1–36. Kaput J, Astley S, Renkema M, Ordovas J, van Ommen B: Harnessing nutrigenomics: development of web-based communication, databases, resources, and tools. Genes Nutr 2006;1:5–12. Kaput J, Ordovas JM, Ferguson L, et al: The case for strategic international alliances to harness nutritional genomics for public and personal health. Br J Nutr 2005;94:623–632. Kaput J, Rodriguez RL: Nutritional genomics: the next frontier in the postgenomic era. Physiol Genomics 2004;16:166–177. Khoury MJ: The case for a global human genome epidemiology initiative. Nat Genet 2004;36: 1027–1028. Kittles RA, Weiss KM: Race, ancestry, and genes: implications for defining disease risk. Annu Rev Genomics Hum Genet 2003;4:33–67. Kliewer SA, Lenhard JM, Willson TM, Patel I, Morris DC, Lehmann JM: A prostaglandin J2 metabolite binds peroxisome proliferator-activated receptor gamma and promotes adipocyte differentiation. Cell 1995;83:813–819. Lander E, Kruglyak L: Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet 1995;11:241–247. Lieberman LS: Dietary, evolutionary, and modernizing influences on the prevalence of type 2 diabetes. Annu Rev Nutr 2003;23:345–377. Malfatti, MA, Felton JS: Susceptibility to exposure to heterocyclic amines from cooked food: role of UDP-glucuronosyltransferases; in Kaput J, Rodriguez RL (eds): Nutritional Genomics. Discovering the Path to Personalized Nutrition. Hoboken, Wiley, 2006, pp 331–352. Manav M, Su J, Hughes K, Lee HP, Ong CN: Omega-3 fatty acids and selenium as coronary heart disease risk modifying factors in Asian Indian and Chinese males. Nutrition 2004;20:967–973. Moore JH: Computational analysis of gene-gene interactions using multifactor dimensionality reduction. Expert Rev Mol Diagn 2004;4:795–803. Newton-Cheh C, Hirschhorn JN: Genetic association studies of complex traits: design and analysis issues. Mutat Res 2005;573:54–69. Ntais C, Polycarpou A, Ioannidis JP: Meta-analysis of the association of the cathepsin D Ala224Val gene polymorphism with the risk of Alzheimer’s disease: a HuGE gene-disease association review. Am J Epidemiol 2004;159:527–536. Ordovas JM, Corella D: Nutritional genomics. Annu Rev Genomics Hum Genet 2004;5:71–118. Ordovas JM, Corella D: Genetic variation and lipid metabolism: modulation by dietary factors. Curr Cardiol Rep 2005;7:480–486. Orzechowski A, Ostaszewski P, Jank M, Berwid SJ: Bioactive substances of plant origin in food – Impact on genomics. Reprod Nutr Dev 2002;42:461–477. Parikh H, Groop L: Candidate genes for type 2 diabetes. Rev Endocr Metab Disord 2004;5:151–176. Risch N: Evolving methods in genetic epidemiology. 2. Genetic linkage from an epidemiologic perspective. Epidemiol Rev 1997;19:24–32. Roweis ST, Saul LK: Nonlinear dimensionality reduction by locally linear embedding. Science 2000;290: 2323–2326. Rutishauser IH: Dietary intake measurements. Public Health Nutr 2005;8:1100–1107.

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Dr. Jim Kaput University of Illinois Chicago MC 958 840 South Wood St. Chicago, IL 60611 (USA) Tel. ⫹1 312 371 1540, Fax ⫹1 312 996 0669, E-Mail [email protected]

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Executive Summary Tai ES, Gillies PJ (eds): Nutrigenomics – Opportunities in Asia. Forum Nutr. Basel, Karger, 2007, vol 60, pp 224–241

ILSI’s First International Conference on Nutrigenomics: Opportunities in Asia Rodolfo F. Florentino Philippine Association of Nutrition, Metro Manila, Philippines

Abstract ILSI’s first international conference on nutrigenomics that was held in Singapore in December 2005 highlighted the tremendous opportunities of nutrigenomics and the fast growing ‘omics’ sciences in improving human health. A wide array of topics starting with an overview of genomics and its application to nutritional science, to the influence of genetic control and metabolic programming in chronic disease, and to the implications of nutrigenomics to individuals and populations, was discussed in nine plenary sessions. The conference concluded that the future of nutrigenomics in Asia is bright, given strong support in human resource development, logistical resources, and the participation of the private sector. Two post-conference symposia followed, dealing with the use of genomics technology in nutrition research and the application of nutrigenomics in nutritional food science. Copyright © 2007 S. Karger AG, Basel

The International Life Sciences Institute’s (ILSI) first international conference on nutrigenomics was held at the Raffles City Convention Center in Singapore on December 7–9, 2005, with the theme of ‘opportunities in Asia’. The conference was organized by the ILSI and its Southeast Asia region branch, in collaboration with the Commonwealth Scientific and Industrial Research Organization (CSIRO) of Australia. The organizers also received support and collaboration from other ILSI branches, the National Institutes of Health, USA, and the Genome Institute of Singapore, Singapore. The aim of the 3-day conference was to promote the understanding of this new frontier in nutritional science with the key objective of stimulating research on the application of nutrigenomics in health promotion and disease prevention. Attended by close to 400 participants from 33 countries, the conference brought together over 50 internationally renowned experts and regional scientists to share their knowledge and the latest developments in the ‘omics’ technologies.

Their insightful presentations were key to the success of the conference, and many speakers have kindly contributed papers to this book. In appreciation of the contributions by the other speakers, a summary of their presentations is shared here with the readers.

Summary of Plenary Presentations

Plenary Session 1: New Perspectives on Genes, Nutrients and Health The opening plenary session 1, focusing on new perspectives on genes, nutrients and health, was co-chaired by Dr. Sushila Chang of Ngee Ann Polytechnic, Singapore, and Dr. Graeme Young of Flinders University, Australia. Dr. John Milner, Chief of the Nutritional Science Research Group, National Cancer Institute, National Institutes of Health, USA, was one of the two keynote speakers. He gave a comprehensive overview of genomics and its application to nutritional science, particularly its capacity to offer unprecedented opportunities to achieve genetic potential, increase physical and mental productivity, and reduce the risk and consequences of disease. He pointed out that every individual is different, responding differently to food and food components, as determined by the phenotype. Thus, the appeal of nutrigenomics, as it elucidates the interaction between bioactive components in foods and cellular processes, is its potential for an individualized approach to health and nutrition, to recognize cultural and ethnic differences, and its potential to open new market niches for food and pharmaceutical companies. The study of nutrigenetics could clarify the role of genetic differences in response to various doses of a nutrient, just as the study of transcriptomics could provide clues about molecular targets of specific food components. Apart from nutrigenetics, epigenetic profiling could give us clues as to individuals who might benefit from intervention. On the other hand, proteomics and metabolomics are fresh and exciting areas that could also elucidate the varied responses to bioactive food components. Dr. Milner concluded that we still do not know enough. We need to identify and validate biomarkers for effect and susceptibility; we need to effectively communicate these omics information to the health care community and consumers, and at the same time we need to work within a responsible bioethical framework. The other keynote speaker, Dr. Edison Liu, Executive Director of the Genome Institute of Singapore, Singapore, spoke on how concepts of functional genomics are very relevant today, as illustrated in their studies in the area of cancer. For example, with the modern genomic and computational technologies available today, one can study classes of tumors and collections of genes in a hierarchical cluster at the same time, and answer the question – ‘Can the genetic expression of a tumor determine the expression pattern of the tumor?’ Experiments in

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mice showed that expression profile provides a footprint of the molecular history of the tumor organized along biochemical pathways: the primacy of pathways over individual genes. This principle is apparently applicable in humans as well. In fact, the p53 gene deficiency classifier by microarray technology could outperform the gold standard of DNA sequencing and in addition has prognostic significance in specific therapeutic subgroups. One could also conclude that the p53 signature may reflect the operational configuration of this pathway in breast cancer. The impact of environmental exposure to tumor phenotype was shown by studies on the outcome of breast cancer in patients given hormone replacement therapy. Microarray technology could give a definitive imprint of what tumor will arise as a result of exogenous exposure. In conclusion, Dr. Liu stated that nutritional science will have to move from an epidemiologic science to a therapeutic science and from case-control to cohort studies, with the aid of very comprehensive genomic studies. Furthermore, considering the complexity of the human organism, we need to employ a systems approach, enlisting a combination of clinical, technological, and mathematical skills. Plenary Session 2: Nutritional Influences on Molecular Epidemiology and Diseases This plenary session was co-chaired by Dr. Choon Nam Ong of the National University of Singapore, Singapore, and Dr. Dieter Söll of Yale University, USA. The first speaker was Dr. Carl Keen, Chair of the Department of Nutrition, University of California, Davis, USA, who gave an overview of the broad concepts of the interrelationships among food, genetics and chronic disease. The changing expectations of a healthy diet towards ‘optimal nutrition’, from prevention of nutritional deficiencies to reduction of risk of age-related diseases, has changed the concept of recommended dietary allowances to dietary recommended intakes in some countries, incorporating the concept of disease prevention. At the same time, there is a growing demand to take into account the individual’s or population’s genetic background, lifestyle habits and physical environment in defining nutritional needs. We have seen the influence of nutrition in obesity, diabetes, cardiovascular disease, hyperlipidemia and even some cancers, but the issue to be addressed is the influence of genetic control and metabolic programming in all of these factors. This is where the field of nutritional genomics, which studies the molecular interactions between nutritional stimuli and the genome, and how these interactions promote health and prevent disease, can contribute. Dr. Keen enumerated five key tenets of nutritional genomics: (1) improper diets are risk factors for diseases; (2) dietary chemicals alter gene expression and/or genome structure; (3) influence of diet on health depends upon an individual’s genetic makeup; (4) genes regulated by diet play a role in chronic diseases, and (5) diets based upon genotype, nutritional requirements and status

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can prevent or mitigate chronic disease. The goal then is individualized nutrition and genome-based dietary recommendations to achieve and maintain optimal health, and to prevent, mitigate and treat disease. This would entail, among others, a technology for low-cost, high-throughput single nucleotide polymorphism analysis, sequencing and gene expression profiling; access to data sets for large, diverse human populations; bioinformatic tools and theories for the visualization of large, complex data sets, and a ‘systems biology’ approach to investigating the relationships between nutrition and disease. The genetic influence on obesity was discussed by Dr. Jong Ho Lee, Full Professor of Food and Nutrition at the College of Human Ecology, Yonsei University, Korea. Dr. Lee explained that interindividual variation in obesity results from action of multiple genes and environmental factors. Two types of human studies are used to identify the specific variants that affect obesity: linkage analysis and association studies. Linkage analysis has been successful in mapping genes responsible for single gene disorders, but these conditions are very rare. Most cases of obesity are polygenic, arising from multiple genes with a small contribution to the obesity phenotype, and for these, association studies are required. Candidate genes are selected on the basis of their function in biochemical pathways related to the regulation of energy balance or to the adipose tissue biology. Currently, positive associations with obesity phenotypes have been reported for more than 70 genes. Dr. Lee illustrated these studies with their studies on adinopectin and perilipin. Their data have confirmed that the ⫹276G/T polymorphism of the adiponectin gene modulates circulating adiponectin and insulin resistance, particularly in obese states independently from common environmental factors. They also found that the perilipin locus is a determinant of coronary artery disease risk in Koreans. In addition, the perilipin gene may be involved in lipid metabolism and systemic inflammation in coronary artery disease patients who have high visceral fat accumulation. Dr. Lee concluded that genetic markers of obesity may help in the identification of individuals who are at greater risk of obesity and its comorbidities, so that personalized nutrigenetic programs may help people make the lifestyle and dietary changes best suited to their needs. The topic of healthy aging and its relation to genetics, plasma lipids and diet was discussed by Dr. José Ordovas, Director of the Nutrition and Genomics Laboratory at the Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, USA. The most explored gene in terms of associations with longevity has been the apolipoprotein E (APOE) gene. It has been observed that the presence of the APOE4 allele is associated with a decreased life span in Caucasians but not in Asians. These differences suggest either ethnic-specific gene-gene or gene-environment interactions that modulate the effects of the APOE4 allele on life span. Studies on other lipid candidate genes such as APOA1,

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APOA4, APOB, and CETP have also shown significant gene-diet interactions, suggesting that life span could be affected by the interactions between our genetic makeup and dietary habits. Dr. Ordovas suggested that large prospective studies need to be designed, supported by extensive genotyping and analytical capacities, in order to fully benefit from the contribution of genetics to longevity. Other speakers in this plenary session were: Dr. E. Shyong Tai, Consultant and Clinical Specialist in the Department of • Endocrinology at the Singapore General Hospital, Singapore, who focused on the genetics of lipoprotein metabolism and heart disease. • Dr. Mohan Viswanathan, President and Director of the Madras Diabetes Research Foundation, India, who discussed polymorphisms and dietary influences on diabetes. • Dr. Boonsong Ongphiphadhanakul, Professor in the Division of Endocrinology and Metabolism at the Faculty of Medicine, Mahidol University, Thailand, who discussed the genetic perspectives on osteoporosis. Plenary Session 3: Nutrient-Gene Interactions On the second day of the conference, the third plenary session was cochaired by Dr. Shuhei Kobayashi of the University of Human Arts and Sciences, Japan, and Dr. Michael Fenech of CSIRO Human Nutrition, Australia. Dr. Carl Keen, Professor and Chair of the Department of Nutrition at the University of California, Davis, USA, discussed nutrient-gene interactions, particularly in early development. It is well known that a common factor contributing to pregnancy complications is suboptimal nutrition during embryonic and fetal development. Recent studies now appear to establish the link between nutrition and gene expression during these stages of development. As an example, Dr. Keen cited the effect of maternal dietary manganese intake on the phenotypic expression of the pallid gene or other strain variations in the developing conceptus, resulting in ataxia, otolith development and other defects in various animals, and may perhaps be responsible for various syndromes in humans including epilepsy. Other examples he cited are the effects of maternal dietary deficiency in copper (as in Menke’s syndrome), iron (both short-term and longterm effects), and zinc (developmental defects). Other micronutrient deficiencies during prenatal development can result in behavioral, immunological and biochemical abnormalities. In some cases, these may be secondary to epigenetic or development changes in DNA methylation patterns which may persist into adulthood, influencing the individual’s risk to certain chronic diseases including hypertension, diabetes and cardiovascular disease. On the other hand, there is evidence that certain nutrient-gene interactions during early development may decrease the risk for some chronic diseases. According to Dr. Keen, the challenge for the next decade will be the determination of the epigenetic or persistent

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consequences associated with mild micronutrient deficiencies during early development, and the extent to which the persistent effects contribute to the risk for age-related chronic diseases. Other speakers in this plenary session were: • Dr. John Mathers, Professor of Human Nutrition in the School of Clinical Medical Science at the University of Newcastle, UK, who presented an extensive review of the impact of epigenetics on early nutrition. • Dr. Eiji Takeda, Professor in the Department of Clinical Nutrition at the Institute of Health Biosciences, University of Tokushima Graduate School, Japan. Dr. Takeda presented their study on the effect of Inslow, a low glycemic index liquid formula, in carbohydrate, protein and fatty acid metabolism and in gene expression. • Dr. Huynh The Hung, Associate Professor at the Division of Cellular and Molecular Research at the National Cancer Center, Singapore, who spoke on the effects of dietary quercetin and kaempferol, which are bioactive phytochemicals in some vegetables and fruits, in the regulation of cell proliferation and apoptosis. Plenary Session 4: Nutrigenomics in Cancer Risk Reduction The fourth plenary session was co-chaired by Dr. Edison Liu of the Genome Institute of Singapore, Singapore and Dr. Lynnette Ferguson of the University of Auckland, New Zealand. One of the speakers was Dr. Mimi Yu, a cancer epidemiologist and McKnight Presidential Professor at the Cancer Center of the University of Minnesota, USA. Dr. Yu reviewed studies on tea, particularly green tea and its protective effect against breast cancer. It has been shown that green tea polyphenols are antioxidants, capable of upregulating phase II enzymes and downregulating phase I enzymes. It has also been shown that either black or green tea extracts containing catechins can inhibit chemically induced mammary tumors in animals, and suppress growth of human breast cancer cell lines. More recent epidemiological data have consistently shown green tea (as opposed to black tea) drinkers to be associated with breast cancer protection, primarily women possessing the COMT low-activity genotype. COMT is a key enzyme in tea catechin excretion. The protective effect of green tea against breast cancer is primarily seen in women possessing the angiotensin-converting enzyme high-activity genotype. Angiotensin II is a potent angiogenic factor. Thus, anti-angiogenesis may be a mechanistic pathway behind green tea/breast cancer protection. On the other hand, recent epidemiologic studies have shown no evidence of a protective effect of black tea. In fact, cohort studies such as the Singapore Chinese Health Study suggest a modest increase in the risk of breast cancer with intake of black tea, possibly because of the increase in circulating estrogen.

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Other speakers in this plenary session were: Dr. Graeme Young, Professor of Gastroenterology at Flinders University and Head of Gastrointestinal Services at Flinders Medical Center, Australia. Dr. Young gave a comprehensive discussion of the linkage between dietary factors and genomic stability, particularly as related to cancer development. Dr. Dongxin Lin, Director and Professor in the Department of Cancer Etiology and Carcinogenesis at the Cancer Institute and Hospital of the Chinese Academy of Medicinal Sciences, China, who discussed folate-metabolizing enzymes and their relationship with gastroesophageal cancer risk.

Plenary Session 5: Nutrigenomics and Molecular Immunomodulation The fifth plenary session was co-chaired by Dr. Lee Yuan Kun of the National University of Singapore and Dr. Nico van Belzen of ILSI Europe. The first speaker, Dr. Lynnette Ferguson, Head of the Department of Nutrition and the Center for Mutagen Testing in Auckland, New Zealand, discussed polymorphisms in genes as they affect immune response and susceptibility to disease. It has been advanced that inflammatory processes including the release of proinflammatory cytokines and the formation of reactive oxygen and nitrogen species underlie many chronic diseases including cancer. Dr. Ferguson particularly focused her discussion on the manner in which polymorphisms in genes affecting selenium status interact with dietary selenium to affect the risk of prostate cancer in a high-risk population. Their studies in Auckland among adults 50–75 years of age suggest that high levels of DNA damage in white blood cells could predict a high prostate cancer risk and therefore provide a useful biomarker, and that there is increased white blood cell DNA damage and an increased risk of prostate cancer in GPX1 variants carrying the TT allele, which could be reversed by selenium supplementation. On the other hand, they showed that there was decreased white blood cell DNA damage and a decreased risk of prostate cancer in GPX4 variants carrying the TT allele, which could be increased by selenium supplementation. Dr. Ferguson concluded that selenium requirements should be justified on an individual basis in order to reduce the risk of chronic inflammation and its attendant effect on reactive oxygen species. The next speaker was Dr. Nina Rautonen, Director of Bioscience at Danisco Innovation, Finland, who discussed the role of pre- and probiotics in immune regulation as they interact with the intestinal mucosa, particularly on their interaction at the molecular level. Using the Caco-2 cell model, they studied the effects of pre- and probiotics on cyclooxygenase (COX) expression. COX are enzymes that generate prostaglandins which mediate inflammatory responses: COX-1 is expressed constitutively, while the expression of COX-2 is

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induced in inflammatory reactions and in various cancers. Overexpression of COX-2 has been linked with early stages of colon cancer development. Results of their studies showed that specific probiotic strains can induce different COX expression patterns. For example, Bif420 causes a significant decrease in COX-2 expression, while Lactobacillus acidophilus causes a significant increase. Using an automated continuous multistage simulator, they also showed that prebiotics could alter COX expression in the colonocytes via microbial fermentation. Dr. Rautonen concluded that the use of different in vitro models could be a useful tool for studying the molecular mechanisms of the interactions between pre- and probiotics and the intestinal mucosa. Dr. Michael Müller, Full Professor and Chair of Nutrition, Metabolism and Genomics in the Division of Human Nutrition, Wagenigen University, The Netherlands, spoke on the role of polyunsaturated fatty acids as modulators of the immune system partly mediated by peroxisome proliferator-activated receptor-␣ (PPAR-␣). Dr. Müller reviewed their attempts to characterize the immunemodulatory and anti-inflammatory functions of PPAR-␣ by means of whole-genome microarray analysis. He explained that the anti-inflammatory role of PPAR-␣ has mainly been attributed to its capacity to downregulate proinflammatory genes and to modulate the NF-␬B pathway involved in translocation and activation of the proinflammatory transcription factor. Dr. Müller concluded that PPAR-␣ has an important role in the cellular adaptation to changes in free fatty acid levels during fasting and feeding, in particular by modulating the innate immune response, and that downregulation of proinflammatory genes by polyunsaturated fatty acids is a major part of this PPAR-mediated process. Plenary Session 6: Nutrigenomics in Other Physiological States and Well-Being The sixth plenary session was co-chaired by Dr. Junshi Chen of the ILSI Focal Point in China, and Dr. John Matthers of the School of Clinical Medical Sciences, University of Newcastle, UK. The first speaker was Dr. Lin He, Professor and Head of the Laboratory of Nutrigenomics, Institute of Nutritional Sciences at the Shanghai Institute of Biological Sciences of the Chinese Academy of Medical Sciences. Dr. He first discussed their study on iodine deficiency disorders. It is well known that thyroid hormones, besides their trophic effect on growth and energy metabolism, also affect and regulate over 100 enzymes, affecting transcription of many genes by binding to mitochondria, nuclear and cell membrane receptors. They investigated the interaction between genetic factors and iodine-deficient physical environment in determining the overall risk of iodine deficiency disorders in a iodine-deficient region in China with a high prevalence of iodine deficiency disorders. Haplotype analysis revealed a positive association between the APOE gene and the

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borderline mental retardation group but not the mental retardation group. They also found an association between APOE and metabolic disorders of the thyroid hormones, T3 and T4, and/or the thyroid-stimulating hormone. Dr. He proceeded to discuss the results of their studies in the Anhui region in China, one of the provinces most affected by severe famine during the period from 1959 to 1961, on the rates of schizophrenia before, during and after the famine years. They observed that among the exposed birth cohorts, there was a highly significant twofold increase in the risk of developing schizophrenia in later life. The fact that the proportion of familial to sporadic cases remained the same in the exposed and unexposed birth cohorts suggests that the increased risk may be due to lowering of the genetic risk threshold for schizophrenia and/or an increase in survival of carriers of schizophrenia risk alleles. Dr. He concluded that genome variation is likely to play a major role. Their investigation in this area is presently continuing. The next speaker was Dr. Katsuya Nagai, Vice Director and Professor, Institute of Protein Research at Osaka University, Japan, who discussed genes and circadian rhythm, and their relationship with metabolism and food regulation. He briefly described the mechanism of circadian rhythm in mammals as originating from a master circadian clock in the hypothalamic suprachiasmatic nucleus which integrates circadian rhythms of brain functions, autonomic nerves and the endocrine glands, thus generating circadian rhythms of enzyme activities, hormonal levels and behavior. He discussed the positive-negative feedback loop on transcriptions of clock-related genes such as period 1, 2 and 3 and cryptochrome 1 and 2, and novel genes such as the suprachiasmatic nucleus circadian oscillatory protein and the period-1-interacting protein of the suprachiasmatic nucleus. Apart from involvement in circadian rhythm, Dr. Nagai obtained evidence that the suprachiasmatic nucleus is involved in the mechanism of homeostasis through the control of autonomic activities. Their findings suggest that the molecular mechanism of the circadian clock and histamine nervous system is involved in the maintenance of homeostasis such as energy metabolism, blood glucose and blood pressure. The other speaker in this plenary session was: • Dr. Cecile Delcourt, Researcher at the National Institute of Health and Medical Research (INSERM), Laboratory of Epidemiology, Public Health and Development, France, who discussed the application of nutrigenomics in eye health, particularly in relation to age-related macular degeneration, cataract and glaucoma. Plenary Session 7: Use and Impact of Genomics in the Food Supply This plenary session was co-chaired by Dr. William Padolina of the International Rice Research Institute in the Philippines, and Dr. Lynne Cobiac of CSIRO Preventative Health Flagship, Australia.

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The first speaker was Dr. Peter Weber, Corporate Scientist at the Human Nutrition and Health Department of DSM Nutritional Products. Dr. Weber gave some examples of how DSM is using the nutrigenomic approach in human nutrition, such as in the identification of new bioactive ingredients; understanding the mode of action of new as well as of established ingredients; establishing biomarkers; developing safe bioactive ingredients, and, for the future, developing personalized nutritional solutions. As a take-home message, Dr. Weber emphasized that nutrigenomic technology should not be looked at as a ‘standalone’ tool. Rather, this technology should complement the established (classical) tools required to provide the scientific evidence to identify, understand and develop bioactive food ingredients. The third speaker in this session was Dr. Gerard Barry who is the Harvest Plus Rice Crop Team Leader and Head of the International Rice Research Institute’s Intellectual Property Management Unit in the Philippines. Dr. Barry discussed the work of Harvest Plus with the primary goal of breeding crops for better human nutrition. The biofortification effort is seen as an additional option for alleviating micronutrient deficiency, and currently efforts are focused on increasing the iron, zinc and provitamin A content of major crops, including maize, rice, beans, cassava, wheat and sweet potato. In addition, preliminary work has started on sorghum, peanut and other crops. The majority of approaches rely on germ plasm screening and breeding, but transgenic approaches are also being pursued in cases where no sufficient variation could be found in the crop. Dr. Barry illustrated this with the work on maize where ␤-carotene-rich varieties are being screened; orange-fleshed sweet potatoes which are already being promoted as weaning food in some countries; low-phytate maize to improve the bioavailability of zinc, and the well-known Golden Rice with its high level of ␤-carotene and other provitamin A carotenoids. In fact, UK scientists have developed a new genetically modified strain of the Golden Rice, producing more ␤-carotene. Genomic and transgenic approaches are being used to raise the levels of iron and zinc of cereal crops. Recent feeding trials with the high-iron rice (IR68144) have shown an impact on improving total body iron among nonanemic iron-deficient subjects. Dr. Barry reported that Harvest Plus coverage has been increasing, with the involvement of more centers around the world. Other supporters have come in such as the Gates Foundation, which is supporting work on bananas, cassava, sorghum and rice, all with the aim of increasing the micronutrient density of staple crops for the alleviation of micronutrient deficiency. Other speakers in this plenary session were: • Dr. Ahmed El-Sohemy, Assistant Professor at the Department of Nutritional Sciences at the Faculty of Medicine, University of Toronto, Canada, who discussed the nutrigenomics of taste and its impact on food preferences and food production.

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•

Dr. Shaun Coffey, Chief of CSIRO Livestock Industries, Australia, who discussed how nutrigenomics approaches are being explored to improve the quality of meat and dairy products for consumption.

Plenary Session 8: Nutrigenomics – Individual and Population Implications The eighth plenary session was co-chaired by Dr. Heng Leng Chee of the National University of Singapore, Singapore, and Dr. Aman Wirakartakasumah of the United Nations Educational, Scientific and Cultural Organization (UNESCO). The first speaker was Dr. Ben van Ommen of the TNO Quality of Life and the European Nutrigenomics Organization, The Netherlands. Dr. van Ommen stated that, driven by the unravelling of the human genome and its related technological developments, the so-called omics sciences have advanced insights into the influence of genetic polymorphisms on nutritional metabolism. Systems biology has provided new insights into the broad molecular action of nutrients, as it captures patterns, profiles and complex data sets arising from complex interactions, rather than of single ‘target’ gene responses. Dr. van Ommen cited systemic inflammation, now appearing to be a central component in body homeostasis, and its possible involvement in chronic diseases such as atherosclerosis and the comorbidities of obesity. Thus, we now know the many processes involved in cholesterol metabolism, suggesting points of intervention in the prevention of atherosclerosis, using not just pharmaceuticals but nutrition and diet that could act on these same points of intervention. Using metabolomics in the study of lipid metabolism, we can discern gene expression changes after exposure to dietary cholesterol stress. This becomes clearer using ‘knowledge network’ charts and bioinformatics, enabling us to look at the big picture, in order to understand, e.g., how cholesterol gene expression changes are related to inflammation through their effect on macrophage activation. With this information, we could make a network-based analysis of systemic inflammation. Likewise, we can use metabolomics to understand the effect of inflammation in disorders associated with obesity such as insulin resistance, hyperlipidemia, diabetes, and hypertension. For example, differences in gene expression changes are seen when rats are given a high-carbohydrate or high-protein diet, and these are reflected in the balance between immune effector cells and regulatory cells in the gut. It means that the cellular response to infection is a very complex matter, involving a multitude of enzymes, receptors, adaptors, and transcriptors. Dr. van Ommen concluded that systems biology is needed to merge nutrition research with physiology and biomedical research. This is necessary in order to understand both the complexity of the many processes involved in ‘keeping us healthy’, and to study the many ‘overarching’ processes like inflammation, both in the whole organism (‘systemic’) and in specific/

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local processes (e.g. atherosclerosis, gut health). The goal is to be able to subtly modulate these processes with food bioactives and thus establish a new concept of a balanced nutrition targeted to tailor-made health. Dr. Umar Jenie, Head of the Indonesian Institute of Sciences and Professor of Organic Medicinal Chemistry at the Gadjah Mada University, Indonesia, spoke on the ethical and social implications of nutrigenomics. While nutrigenomics offers great promise in benefiting human health, there is the concern that genomic data could be misused with the ultimate result of creating inequity. Dr. Jenie discussed some ethical issues related to nutrigenomics, such as consent prior to collection of data; access of information; public and individuality issues; prevention of inequity, and regulatory oversight. He reviewed the International Declaration of Human Genomic Data issued by UNESCO in 2003, which forms the basis for ensuring respect for human rights in the collection, processing, use and storage of human genetic data. Dr. Jenie concluded that while there are many bioethical issues, there is rarely just one ‘right answer’. The other speaker in this plenary session was: • Dr. Michael Fenech, Principal Research Scientist and Project Leader for Nutrigenomics and Genome Health, CSIRO Human Nutrition, Australia, who spoke on genome health as a nutrigenomics approach to setting dietary recommendations. Plenary Session 9: Nutrigenomics – The Future The last plenary session of the conference was co-chaired by Dr. Rodolfo Florentino of the Nutrition Foundation of the Philippines, and Dr. Suzanne Harris of ILSI. The first speaker in this session was Dr. Richard Head, Director of CSIRO Preventative Health Flagship and Affiliate Professor in the Department of Clinical and Experimental Pharmacology at the University of Adelaide, Australia. Dr. Head believed that the availability of analytical tools in the area of genomics and proteomics, together with the measurement of biological markers, coupled with appropriate mathematics, have contributed to a better understanding of the health potential of food. However, there are important challenges to be faced. In the area of biomarkers, the focus is on the nature of sampling and the influence of physiological and related influences on approaches to sampling. In the area of proteomics, the key challenge is to both characterize and understand the influence of nutrients on protein expression. In the area of genomics, the challenge is in understanding the role of the influence of nutrients in gene expression. In all of these issues, the role of mathematicalbased science, including bioinformatics, is critical. Dr. Head illustrated these issues with their current work in these areas: how to link single nucleotide polymorphisms to chromosome positioning as in the study of prostate cancer;

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gene profiling in colorectal cancer; 3-dimensional display of mass spectrometer data, and cell-nutrient interactions with colonic bacteria and their byproducts. In conclusion, Dr. Head stated that the future of nutrigenomics is interlinked with addressing the challenges of biomarkers and cell-nutrient interactions, aided by appropriate mathematics. The next speaker was Dr. Ben van Ommen of the TNO Quality of Life and the European Nutrigenomics Organization, The Netherlands, who spoke on the role of nutrigenomics in global health improvement. Dr. van Ommen said that one of the major challenges in nutrition and health is the increasing emphasis on disease prevention. Technology is no longer a limiting factor. There are now many examples of applications of the omics technologies in nutrition. Many examples of genetic differences relevant to nutrition and health have been studied, and there is now a better understanding of the complexity of the interactions between genes and nutrition. However, there is no suitable strategy for tackling multiple small genetic differences, and nutritional systems biology still needs to be developed. In addition, effective communication of this new knowledge to consumers and health professionals must be developed, and new business models must be identified by the food industry. Moving beyond cohorts and statistics, a systems biology approach is needed to model the nutrigenomics complexity in a single person. For example, the dynamics of the whole cholesterol metabolism in a hypothetical person can be modeled using a systems biology markup language. While consumers may be ready for health food, the key issue is whether science and industry are ready to meet the demand. Personalized nutrition might not be there yet, but it is driving us towards ‘community nutrition’: molecular epidemiology, biotech foods, and food for primary prevention. However, Dr. van Ommen concluded that nutrigenomics is too big for any single discipline; multiple primary collaborations involving a merger of analytical, informatics and biological capacities are required. Fortunately, a number of regional and global initiatives (such as the European Nutrigenomics Organization) support these developments, giving a bright future for nutritional systems biology. The last speaker in this session was Dr. Sakarindr Bhumiratana, President of the National Science and Technology Development Agency, Thailand, who spoke on the potential of nutrigenomics, particularly in Asia. He declared that nutrigenomics is an exciting science with its myriad implications in improving human health. With the development in the omics sciences, we have the capacity to better understand, through systems biology, individual responsiveness to food, microbes and the environment. This could lead to the development of new foods for optimum health, not to mention high-value foods and ingredients with their huge economic potential. Dr. Bhumiratana pointed to the broad diversity in Asia, i.e. the region’s rich biodiversity, the high ethnic diversity of the Asian population, the wide usage of traditional foods and herbs with their time-tested functional properties,

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and the rapidly growing developments in science and technology, which all make Asia an extremely fertile area for nutrigenomics research. What is needed is human resource development in research and development, favorable policies to encourage research and development in science and technology, together with the provision of logistical resources and involvement of the private sector. The establishments of Centers of Excellence, regional and international linkages through research and development, collaboration between scientists, and mechanisms for joint funding are also important considerations. In conclusion, Dr. Bhumiratana foresees a bright future for nutrigenomics in Asia, with increasing numbers of academics, researchers, research students and small start-ups to take full advantage of Asian opportunities and niches arising from the area’s biodiversity, ethnic and food diversity. There are already excellent world-class centers in genomics research in a number of countries in Asia, and certainly more will be established in the future. Symposia and Workshop

The conference program also included two symposia, namely ‘Using Genomics Technologies in Nutrition Research’, held on December 7, 2005, and ‘Nutrigenomics and Functional Foods Science’, held on December 9, 2005. A workshop on ‘Intellectual Property in New Product Development’ was held on December 8, 2005. Symposium on Using Genomics Technologies in Nutrition Research The symposium, which aimed to discuss in greater detail the use and application of genomics technologies in nutrition-related research, was chaired by Dr. Sakarindr Bhumiratana of the National Science and Technology Development Agency, Thailand, and Dr. David Mitchell of CSIRO, Australia. Dr. Christopher Wong of the Genome Institute of Singapore, Singapore, presented the different technologies available to study biological events on a genome-wide scale. He highlighted the use of gene transcription profiling technologies, such as microarrays in cancer biology; comparative genomic hybridization array to study chromosomal aberrations, and pathogen detection chip to identify unique DNA sequences in all viral genomes, including those that can be found in the contaminated food supply. He then shared the studies done on caloric restrictions, lymphocyte gene expression and PPAR-␥ in chronic diseases. He also added that the gene identification signature/pairedend diTag sequencing technology may be used to discover novel proteins, while high content screening automated imaging approach to cell-based assays for individual cell data in multiple-cell assays. He stressed the importance of seamless information management, if integrated data derived from different experiment techniques are to be achieved.

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Research in nutrigenomics is not a simple solution. One must take into account the complexity of various factors including nutrients, genotypes, lifestyle and phenotypes, and available technology. Dr. Michael Muller of Wageningen University, the Netherlands, shares the perspective of interpreting the wealth of data generated in nutrigenomics research in a biological lingo. Gene ontology was set up by the Gene Ontology Consortium in 1998 to provide a comprehensive, controlled set of terms that can be used to describe genes in all organisms to assist in the interpretation of the variably regulated gene expression. Dr. Muller also highlighted another challenge in nutrigenomics data interpretation – namely pathway analysis. While there are established databases on known pathways, the challenge lies in the discovery of new pathways, such as regulatory pathways. The use of modeling in the gene set enrichment analysis to determine a statistically significant set of genes between two biological states may assist in identifying gene sets involved in a certain metabolic pathway, signal transduction route, or overexpressed in a specific cancer type. Thus, while databases are readily available, ingenuity is an essential skill for bioinformaticians to interpret nutrigenomics data. One of the great challenges of maintaining health in the 21st century includes the acknowledgement on the use of food-based interventions as a solution. Dr. Laurent B. Fay of the Nestlé Research Center, Switzerland, began his presentation by introducing the concept of metabolomics, which is a technology used in metabolite fingerprinting in the field of metabolomics. Mass spectroscopy and nuclear magnetic resonance act as complementary data sets in metabolomics analysis, as nuclear magnetic resonance provides high throughput over a wide range of chemical classes, and mass spectroscopy data can provide high sensitivity and resolution, and allow comparison in large, available identification databases. Currently, the highest resolving power of all mass spectroscopy techniques available has Fourier transform mass spectrometry. In a study conducted to correlate chocolate consumption to mood uplifting, it was found that chocolate cravers have a different metabolism as compared to noncravers. Using the Fourier transform ion cyclotron resonance mass spectrometry metabolite profiling model, Dr. Fay was able to show strong response following the consumption of chocolate in predictive biomarkers in plasma metabolites. Other speakers in this symposium were: • Dr. Jong-Eun Lee of DNA Link, Inc., Korea, who shared the technology developed to address the rapidly expanding field of molecular epidemiology, which requires a sensitive and specific technique in genotype screening. • Dr. Visith Thongboonkerd of Mahidol University, Thailand, who shared gel-based two-dimensional polyacrylamide gel electrophoresis and gelfree two-dimensional liquid chromatography as techniques for protein separation.

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Symposium on Nutrigenomics and Functional Food Science This symposium aimed to derive the data generated in nutrigenomics research to substantiate efficacy and safety of functional ingredients, through identification and validation of biomarkers. The chairs of the symposium were Dr. E-Siong Tee of the Nutrition Society of Malaysia, Malaysia, and Dr. Richard, Head of CSIRO, Australia. Dr. Keiko Abe of the University of Tokyo, Japan, opened the session with an overview of how nutrigenomics had been used to evaluate functional foods and presented some case studies on soy protein isolate, hyperlipidemia and atherosclerosis; cacao polyphenol, fat deposition and fatty acid metabolism; sesamin and alcohol metabolism; fructo-oligosaccharides and allergenic responses. She argued that nutrigenomics can be used to explain the causal relationships of evoked effects, assess food functionality and safety, identify appropriate biomarkers, and provide a better understanding of physiological processes. She also highlighted the establishment of a national project on systems biology and ILSI Japan’s initiatives on functional food science and nutrigenomics. Dr. Junshi Chen of ILSI Focal Point in China shared the study results of tea and oral cancers in animals and humans, using silver-stained nucleolar organizer region, proliferation cell nuclear antigen and epidermal growth factor receptor as biomarkers. Both the animal model and human subjects showed significant protection afforded by mixed tea in oral mucosa cells and leukoplakia mucosa cells, respectively. These findings demonstrated that the selected biomarkers met the criteria of effective biomarkers for disease risk as agreed at the meeting Functional Foods – Scientific and Global Perspectives, ILSI Europe, 2002. The responses of silver-stained nucleolar organizer region, proliferation cell nuclear antigen and epidermal growth factor receptor may serve as the substantiation of mixed tea functionality and mechanisms of action that may lead to the development of functional food products. In nutritional epidemiology, biomarkers can also be used to substantiate dose-response relationships and suggest potential mechanisms of action. Dr. Adeline Seow of the National University of Singapore, Singapore, presented compelling evidence on the level of sulforaphane metabolites in the plasma correlated to the consumption amount of cruciferous vegetables. Isothiocyanates (ITC) induce phase 2 metabolic enzymes, including glutathione-S-transferase (GST), but GST can also conjugate – and thus inactivate – ITC. M1 and T1 polymorphisms in the genes coding for GST enzymes had been shown to abolish the activity of GST, as shown in the higher ITC urinary output in subjects with polymorphisms compared to those with fully functioning GST. At the tissue level, GST-null individuals are afforded greater protection of ITC against chemical carcinogens. This is apparent in many studies, including those conducted among male smokers in Shanghai and male and female smokers in the

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USA. GST-null individuals were also afforded greater protection from breast cancer as observed among Shanghai women and from colorectal cancer seen among Chinese people in Singapore. Dr. Choon Nam Ong of the National University of Singapore, Singapore, shared the use of cellular proteomics in nutrition research by presenting the study on the effects of ␤-phenylethyl ITC on liver cancer cells. It was shown, through proteomics technologies, that ␤-phenylethyl ITC increases reactive oxygen species production in the liver by 9 differentially expressed proteins – most of which are related to oxidative stress. He argued that the cellular proteomics approach may be used as a tool to better understand the molecular targets of nutrients and to obtain substantiation of the functionality and mechanisms of action. The other speaker in this symposium was: • Dr. James Dekker of Fonterra, New Zealand, who shared the development of probiotic strains as functional ingredients. Workshop on Intellectual Property in New Product Development The protection of intellectual property in the sciences is an increasingly important topic. Facilitated by Dr. Sushila Chang of Ngee Ann Polytechnic, Singapore, and Mr. Martin Schweiger of Schweiger & Partner, the workshop introduced the various types of intellectual properties, addressed the Singapore Copyright and Patent Act, and highlighted that similar regulatory mechanisms may not be available in other countries. Dr. Chang opened the workshop with a brief presentation on the different definitions and nature of copyrights, patents, trademarks, trade secrets and registered designs. She also shared the steps required to protect and exploit the intellectual property relating to a product or a process. Mr. Schweiger explained the patent process in greater detail and discussed the benefits and values of patents. Short case studies were also shared at this meeting.

Conclusion and Recommendations

This conference, being ILSI’s first international meeting to focus on the exciting new field of nutrigenomics, highlighted the tremendous potential that nutrigenomics and the other so-called omics sciences have in improving human health. The conference showed how, with the unravelling of the human genome and its related technological developments, nutrigenomics has widened our understanding of the molecular interactions between nutritional stimuli and the genome, with the goal of achieving our genetic potential, increasing physical and mental productivity, and reducing the risk and consequences of disease. Thus, it was shown how nutrigenomics has increased our understanding of the influence of genetic control

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and metabolic programming in chronic diseases including obesity, diabetes, cardiovascular disease, hyperlipidemia, osteoporosis, and even some forms of cancer. The conference illustrated the application of omics technologies in discerning the wide influence of genetic differences in nutrition including physiological responses to nutrition stimuli and recommended intake of nutrients. A major goal is individualized nutrition and genome-based dietary recommendations not only to achieve and maintain optimum health, but also to prevent and mitigate disease. On the other hand, the conference highlighted the promise of genomic and transgenic approaches in improving our food supply. Biofortification efforts in major crops already hold promise in contributing to the alleviation of micronutrient deficiencies that plague populations in many parts of the world, while nutrigenomics approaches have been utilized to improve the quality of meat, dairy products and other foods for consumption of the general population or for special population needs. At the same time, the conference emphasized that nutrigenomic technologies should not be perceived and applied as a stand-alone tool. Aside from the established ‘classical’ tools, the role of mathematical-based science including bioinformatics is critical. Nutrigenomics is too big for any single discipline; multiple collaborations involving a merger of analytical, informatics and biological capacities are required. Nutrition should move beyond cohorts and statistics. Systems biology is needed in modeling the complexity of the numerous interlinking processes in nutritional metabolism in order to capture patterns, profiles, and complex data sets arising from these complex metabolic interactions. Besides providing a clearer understanding of the influence of nutrients on human health, systems biology could lead to the development of new foods for optimum health, not to mention high-value foods and ingredients with their high economic potential. The conference concluded that the future of nutrigenomics in Asia is bright, considering Asia’s broad diversity not only in culture but in biodiversity and ethnicity, and its wide range of traditional foods and herbs. The rapid developments in science and technology in most of Asia offer many opportunities for nutrigenomics research. What is needed is human resource development in research and development, favorable science and technology policies including the provision of sufficient logistics, and the participation of the private sector. Finally, the need to establish Centers of Excellence as well as international and regional linkages was expressed. At the same time, it is necessary to look for ways to effectively communicate these omics information to the health care community and consumers, while working within a responsible bioethical framework. Dr. Rodolfo F. Florentino Nutrition Foundation of the Philippines 18 May Street, Congressional Village Quezon City, 1106 (Philippines) Tel./Fax ⫹63 2 926 7838, E-Mail [email protected]

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Author Index

Arai, H. 127

Hung, H. 146

Prasad, J. 196

Cobiac, L. 31 Coffey, S.G. 183 Collett, M. 196 Corella, D. 102 Cornelis, M.C. 176

Jenie, U.A. 66

Radha, V. 118 Radhika, G. 118 Rema, M. 118

Deepa, R. 118 Dekker, J. 196 Delcourt, C. 168 El-Sohemy, A. 25, 176 Fenech, M. 49 Florentino, R.F. 224 Fontaine-Bisson, B. 176 Fukaya, M. 127 Gopal, P. 196

Kaput, J. 102, 209 Khataan, N. 176 Kwong, P. 176 Lee, J.-E. 97 Li, H. 140 Lin, D. 140 Mathers, J.C. 42 Matsuo, K. 127 Miao, X. 140 Milner, J.A. 1 Mohan, V. 118 Muto, K. 127 Ongphiphadhanakul, B. 158 Ordovas, J.M. 102 Ozsungur, S. 176

Sakuma, M. 127 Slamet-Loedin, I.H. 67 Stewart, L. 176 Sudha, V. 118 Tai, E.S. 110 Takeda, E. 127 Taketani, Y. 127 Tan, W. 140 Thongboonkerd, V. 80 Wang, L. 140 Yamamoto, H. 127 Yamanaka-Okumura, H. 127 Young, G.P. 91

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Subject Index

Adiponectin, single nucleotide polymorphism 227 Age-related macular degeneration clinical features 169 genetics 171 oxidative stress and nutrient protection 169, 170 Akt, quercetin-induced apoptosis role in lung cancer cells 151–154 Alcohol dehydrogenase, single nucleotide polymorphisms and blood lipid levels 113 Alu, features 6, 7 Apolipoproteins APOE4 allele 214, 228 APOE haplotypes 232 polymorphisms and variability in dietary lipid response 105, 111, 114 Apoptosis, quercetin modulation in lung cancer cells 151–156 Association studies experimental design 217 limitations 161 Bifidobacterium lactis, see Probiotics Biomarkers, nutrition response studies 4, 5 Breast cancer, catechin protection 229 Calcium deficiency and genomic instability 53–55 food content 57

osteoporosis and genetics in response 161, 162 Calpain-10, gene variants 214 Cancer, see specific cancers ␤-Carotene deficiency and genomic instability 55 food content 57 rice engineering 233 Cataract gene polymorphisms 171 oxidative stress and nutrient protection 169, 170 Catechins, breast cancer protection 229 Chennai Urban Population Study heritability of diabetes 121 overview 119 physical activity findings 123 Chennai Urban Rural Epidemiology Study diabetes trends 120 gene-environment interactions in diabetes 123, 124 overview 119, 120 susceptibility gene search 121–123 Cholesterol, see High-density lipoprotein cholesterol, Low-density lipoprotein cholesterol Cholesteryl ester transfer protein gene-diet interactions 228 single nucleotide polymorphisms and blood lipid levels 111, 112 Collagen type 1 ␣1-chain, osteoporosis candidate gene 159, 160

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Complement activation in age-related macular degeneration 171 zinc interactions 171 Confidentiality, data collection 71–74 Consent, data collection 69–71 Coronary artery disease blood lipids genetic variation and ethnic differences 111 nutrient-gene interactions and risk relevance 112–115 nutrient-gene interactions in lipoprotein metabolism interaction mechanisms 103, 104 interventional studies 105, 106 observational studies 105 overview 102 postprandial lipidemia 106 prospects for study 106, 107 pathogenesis 215 postprandial hyperglycemia risks, see Postprandial hyperglycemia Cyclooxygenase, probiotic effects on expression 230 Cytochromes P450, polymorphisms 27, 28 Dairy products animal production system challenges 186 intervention timing 189, 190 nutrigenomics targets 190, 191 bovine genome sequencing and mapping 187, 188 consumption 184, 185 dietary fat and chronic disease 185, 186 genomics limitations 188, 189 transgenic animal prospects 191, 192 Diabetes mellitus epidemiology 118, 119 gene-diet interactions 125 gene-environment interactions 123, 124 India, see Chennai Urban Population Study, Chennai Urban Rural Epidemiology Study pathogenesis 215 postprandial hyperglycemia

Subject Index

mechanisms of diabetic complications oxidative stress 129 serotonin metabolism 129–131 palatinose-based food suppression human studies 132 metabolism gene expression response 133–135 preparation of food 131, 132 rat studies 132, 133 vascular disease risks 128 prevention 136, 137 DNA damage, see Genomic instability DNA methylation aging 33 dietary factor effects in early life 45, 46 diet effects 14, 15, 33, 34, 45 diseases 33 gene silencing 49, 50 mechanism 33 DNA microarray, genotyping 99 Docosahexaenoic acid age-related macular degeneration benefits 170 oxidation 169 DR10, see Probiotics DR20, see Probiotics Epigenetics definition 13, 43 dietary factor effects in early life 45–47 gut microflora effects 37 malleability of markings 44, 45 mechanisms 14, 15, 32–36, 43, 44 Esophageal cancer, see Gastroesophageal cancer Estrogen receptor-␣ osteoporosis candidate gene 160 polymorphism and bone health 162 Ethics, nutrigenomics studies 67–78 Extracellular signal-regulated kinase, quercetin-induced apoptosis role in lung cancer cells 151–153, 155, 156 Fatty acid synthetase, palatinose-based food effects on expression 135 Flavonoids, see Quercetin

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Folate deficiency and genomic instability 53–55 food content 57 functions 140, 141 metabolism 140, 141 Fonterra probiotics, see Probiotics Food preferences, nutrigenomics of taste 176–181 Gastroesophageal cancer methylenetetrahydrofolate reductase polymorphisms in esophageal squamous cell carcinoma 142 thymidylate synthase polymorphisms 142, 143 Genetic screening definition 74 inequity protection 74, 75 occupational screening 75, 76 Genomic instability adverse health outcomes 50–52, 91 diet and DNA repair 92–95 genome health clinic concept 61 genome health nutrigenomics 59–61 nutrient deficiency effects 52–54 nutriome concept 56–58 oncogenesis 91, 92 overview 36 recommended dietary allowance considerations 58, 59 Genomics animal genome sequencing 187, 188 Human Genome Project 216 overview 5, 6 Genotyping, see Single nucleotide polymorphism Glutathione S-transferase cancer studies 28 induction 28, 240 isoforms 28 polymorphisms in cataract 171 Glycemic index low-index food, see Palatinose-based food overview 127, 128 Gut microflora, see also Probiotics abundance 197 epigenetic effects 37

Subject Index

Haplotype, see also Single nucleotide polymorphism HapMap Project 13, 67, 70, 73, 77, 99, 100 Hepatic lipase, single nucleotide polymorphisms and blood lipid levels 112, 113 High-density lipoprotein cholesterol genetic and ethnic variation in blood levels 111 protective effects 110 Histone diet effects on modification 35, 36 dietary factor effects in early life 45, 46 modification 15, 34, 35, 44 structure 34 Homocysteine, osteoporosis risk factor 163 Human Genome Project 216 International Declaration of Human Genetic Data 76, 78 International Life Sciences Institute, international nutrigenomics conference goals 224 intellectual property workshop 240, 241 participants 224 plenary sessions new perspectives on genes, nutrients and health 225, 226 nutrient-gene interactions 228 nutrigenomics and molecular immunomodulation 230, 231 nutrigenomics cancer risk reduction 229, 230 individual and population implications 234, 235 other physiological states and well-being 231–233 the future 235–237 nutritional influences on molecular epidemiology and diseases 226–228 use and impact of genomics in the food supply 233, 234 recommendations 240, 241 symposia genomics technologies in nutrition research 237–239

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International Life Sciences Institute, (continued) symposia (continued) nutrigenomics and functional food science 239, 240 Intervention trials, design 2, 3, 38 Iron deficiency and genomic instability 53 rice engineering 234 Isothiocyanates, phase 2 metabolic enzyme induction 239 Keap1, diet effects 16 Lactobacillus rhamnosus, see Probiotics Leukotriene A4 hydrolase, single nucleotide polymorphisms 213 Low birth weight adult disease risks 42, 43 epigenetic studies 45, 46 Low-density lipoprotein cholesterol cardiovascular risks 102, 110 diet and individual response 102 genetic and ethnic variation in blood levels 111 Low-density lipoprotein receptor-related protein 5, osteoporosis candidate gene 160, 161 Lung cancer, quercetin effects on A549 cells apoptosis assay and effects 150–156 materials 147, 148 overview 147 proliferation assay and effects 148, 150 Western blot analysis 149 Lutein, eye protection 169 Magnesium, deficiency and genomic instability 53, 54 Malnutrition, prevalence 210 Manganese, deficiency and genomic instability 53 Manganese superoxide dismutase, single nucleotide polymorphisms 12, 13 Mass Array assay, genotyping 98, 99 Mass spectrometry, proteomics techniques 83–85

Subject Index

Maturity-onset diabetes of the young, genetic screening 74 Meat animal genome sequencing 187 animal production system challenges 186 intervention timing 189, 190 nutrigenomics targets 190, 191 consumption 184, 185 dietary fat and chronic disease 185, 186 genomics limitations 188, 189 transgenic animal prospects 191, 192 Metabolomics, overview 18–20 Methylenetetrahydrofolate reductase bone health and polymorphisms 163 esophageal squamous cell carcinoma polymorphisms 142 function 141 pancreatic cancer polymorphisms 143, 144 single nucleotide polymorphisms 10, 11, 60, 141 Micronucleus assay, genomic instability studies of nutrient deficiency effects 50–52, 55 Milk, see Dairy products Niacin deficiency and genomic instability 53 food content 57 Nrf2, diet effects 16 Nutrigenetics, overview 31, 32, 68 Nutrigenomics Asia research and business 219, 220, 224, 225 bioactive foods 9, 10 definition 68 diversity challenges food chemical diversity 211, 212 genetic diversity 212–215 health and disease diversity 215 ethics and social implications 67–78 funding of research 216 international conference, see International Life Sciences Institute international research collaborations 217, 218

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overview 8, 9, 66, 67, 210 prospects 220 Nutrigenomics Organisation 218, 219 Nutriome, concept 56–58 Osteoporosis candidate genes association study limitations 161 collagen type 1 ␣1-chain 159, 160 estrogen receptor-␣ 160 low-density lipoprotein receptorrelated protein 5 160, 161 vitamin D receptor 159 genetic determinants of bone response calcium 161, 162 estrogen 162 homocysteine as risk factor 163 heredity 159 Oxidative stress eye disease 169, 170 hyperglycemia 129 Palatinose-based food hyperglycemia suppression human studies 132 metabolism gene expression response 133–135 rat studies 132, 133 preparation 131, 132 Pancreatic cancer, folate-metabolizing enzyme polymorphisms 143, 144 Peroxisome proliferator-activated receptors anti-inflammatory actions 231 function 103 palatinose-based food effects on expression 134, 135 polymorphisms diabetes susceptibility 121 regulated genes 103, 104, 114 types 103 Phenylketonuria, nutrigenomics 210 Phenylthiocarbamide, nutrigenomics of taste 177–181 Plasma cell glycoprotein 1, polymorphisms and diabetes susceptibility 121, 122 Postprandial hyperglycemia mechanisms of diabetic complications

Subject Index

oxidative stress 129 serotonin metabolism 129–131 palatinose-based food suppression human studies 132 metabolism gene expression response 133–135 preparation of food 131, 132 rat studies 132, 133 vascular disease risks 128 Postprandial lipidemia, gene-diet interactions 106 Potassium, nutriproteomics studies 85, 86 Privacy, data collection 71–74 Probiotics cyclooxygenase expression effects 231 definition 197 Fonterra strains efficacy 200–204 microbial ecology studies 199, 200 products 204 prospects for study animal models 205, 206 assays 205 fermentation 206 genetic tools 205 genomics 204, 205 safety 198, 199 viability 198 6-Propylthiouracil, nutrigenomics of taste 177–181 Proteomics capillary electrophoresis/mass spectrometry 84 liquid chromatography/tandem mass spectrometry 83 mass spectrophotometric immunoassay 84, 85 overview 17, 18, 81, 82 potassium nutriproteomics studies 85, 86 protein chip 83, 84 two-dimensional gel electrophoresis 82, 83 Quercetin flavonoid regulation of carcinogenesis 146, 147 intake 147 lung cancer A549 cell effects

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Quercetin (continued) lung cancer A549 cell effects (continued) apoptosis assay and effects 150–156 materials 147, 148 overview 147 proliferation assay and effects 148, 150 Western blot analysis 149 Recommended dietary allowance, genome health considerations 58, 59 Retinol, food content 57 RNA interference, applications 16, 17 RNA silencing, overview 14, 32 Selenium deficiency and genomic instability 53 protein modification regulation 18 Serotonin, hyperglycemia effects on metabolism 129–131 Short tandem repeats, features 7 Single nucleotide polymorphism, see also specific genes abundance 26, 98 association studies using markers 98–100 disease susceptibility 7, 8 features 7, 97, 98 food response effects 10–13 haplotyping 13 HapMap Project 13, 67, 70, 73, 77, 99, 100 high-throughput genotyping 98–100 lipoprotein metabolism and nutrient-gene interactions 102–107 Superoxide dismutase, see Manganese superoxide dismutase

Subject Index

Suprachiasmatic nucleus, autonomic control 232 Systems biology, overview 86, 87 TASR38, nutrigenomics of taste 177–181 Taste, see Food preferences Thymidylate synthase function 141 gastroesophageal cancer polymorphisms 142, 143 pancreatic cancer polymorphisms 143, 144 Transcriptomics, overview 15–17 Transgenic animal, prospects in meat and dairy production 191, 192 Universal Declaration on Bioethics and Human Rights 67, 68, 72, 76–78 Universal Declaration on the Human Genome and Human Rights 76 Vitamin B12, genome health maintenance 58, 59 Vitamin C, deficiency and genomic instability 53 Vitamin D receptor osteoporosis candidate gene 159 single nucleotide polymorphisms 11, 12, 162 Vitamin E deficiency and genomic instability 53 food content 57 Zeaxanthin, eye protection 169 Zinc complement interactions 171 deficiency and genomic instability 53

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