Nutritional genomics is a highly innovative and fast-growing field, linking genome research, plant biotechnology and molecular nutritional research. It covers nutrigenomics, which explores the effects of nutrients on the genome, proteome and metabolome, as well as nutrigenetics, the major goal of which is to elucidate the effect of genetic variation on the interaction between diet and disease. Nutritional genomics thus paves the way for novel applications in medicine and human nutrition.
This book presents the latest data on how genetic variation is associated with dietary response and how nutrients influence gene expression, bringing together the various disciplines involved in research. The result is essential reading for nutritionists, biochemists and molecular biologists.

Professor Regina Brigelius-Flohe is the head of the Department of Biochemistry of Micronutrients in the German Institute of Human Nutrition, Potsdam, Germany. Prior to her position in this institute, she worked in industry in the Center of Research of Gruenenthal GmbH and in the Institute for Molecular Pharmacology in Hannover, Germany. Her work has focused on understanding the mechanisms by which small nutrient molecules such as selenium and vitamin E contribute to human health

Professor Hans-Georg Joost was made scientific director of the German Institute of Human Nutrition in 2002. Throughout his career he has published more than 100 peer-reviewed papers, with a primary focus on obesity research.

Nutritional Genomics

Impact on Health and Disease

John Wiley & Sons

Copyright © 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
All right reserved.
ISBN: 978-3-527-31294-8

Chapter One

Nutritional Genomics: Concepts, Tools and Expectations

Hannelore Daniel and Uwe Wenzel

1.1 Nutrigenomics: Just Another “omic”?

The age of nutrigenomics is already upon us. Various new programs in molecular nutrition research have been launched in Europe, Asia, and the US under the heading of nutrigenomics. We may for this review consider nutrigenomics as the science that seeks to provide a molecular understanding for how diets and common dietary constituents affect mammalian metabolism and health by altering gene/protein expression on basis of an individual’s genetic makeup.

Although nutrigenomics represents in the first place just another “omic,” it clearly induces a conceptual shift in nutritional sciences by moving the genome into the center of all the processes that essentially determine mammalian metabolism in health and disease. Moreover, for the first time, nutritional science speaks the same language and uses the same tools as the other biomedical sciences and this is going to change the face of nutrition research. Nutritional sciences is functional genomics “par excellence” and will thereby move the discipline into the heart of biological sciences. Unlike other environmental factors, nutrients, non-nutrient components of foods, and xenobiotics in foods have huge variability in dose and time and hit a rather static genome, affecting the function of a large number of proteins encoded by the respective mRNA molecules that are expressed in a certain cell, organ or organism. Alterations of mRNA levels and in turn of the corresponding protein levels are critical parameters in controlling the flux of a nutrient or metabolite through a biochemical pathway. Nutrients and non-nutrient components of foods, diets, and lifestyle can affect essentially every step in the flow of genetic information from gene expression control to protein synthesis, protein degradation, and allosteric control and consequently alter metabolic functions in the most complex ways (Fig. 1.1).

The advent of high-throughput technologies has led to the rapid accumulation of biological data, ranging from complete genomic sequences, transcripts, proteome and metabolome profiles as well as the first protein-protein interaction maps. Referred to as “omics”, these parallel approaches are usually classified by the measured target molecules. Transcriptomics determines the transcript levels or patterns of subclasses or even of all expressed genes of a given genome. Likewise, proteomics refers to the analysis of the protein complement and metabolomics (also called metabonomics) determines in parallel the accessible metabolites in a cell, tissue, organ, or organism. The data output of these approaches is enormous and often overwhelms our ability to understand the underlying biological processes.

Nutritional science in the past was characterized by well-defined experimental studies based on the experience and knowledge that there is hardly anything else as difficult to standardize as mammalian nutrition. In terms of the biological readouts of nutritional studies, in most cases only a few parameters could be determined simultaneously. The conceptual shift in biological science towards application of high-throughput profiling technologies poses a particular challenge to nutrition researchers as they now have additionally to handle huge data sets derived from the “omic” approaches. How can we use this information to build metabolic topology maps that are easy to comprehend and to interpret and that allow us to navigate to the specific information that we need? Here nutritional science clearly relies on the new systems biology tools of pathway construction that are based on concepts of control theory, numerical analysis, and stochastic processes. Although systems biology is dependent on “omics” and technologies for data input, it really encompasses the design and use of new analysis tools, and the development of new ways to represent data in a meaningful manner. Nutritional systems biology is the high end of systems biology when it comes to describing the highly diverse changes in metabolism occurring at the same time in different organs or even within an organ in its different cell populations.

1.1.1 What makes Nutrigenomic Research Exceptional?

In contrast to applications of the profiling technologies in drug discovery or toxicology testing, nutrigenomics deals with some exceptional problems. Drug and xenobiotic testing usually determines the consequences of just one compound on the background of a limited number of relevant genes but an otherwise fairly stable environment. Of course, the test compounds may undergo extensive metabolism and the bioactivity could as an integrated read-out result from both the parent compound and the metabolite(s). However, assessing the metabolic response to complex foods is like looking at hundreds of test compounds at the same time and a highly diverse response over time and spatial location (i.e. organ, cell type in an organ).

The human genome and the genetic variation within the human population are the result of high and persistent evolutionary pressures via processes of gene mutation, selection, and random drift. Nutrition has thereby shaped the human genome like no other environmental factor. Individual dietary components can affect gene mutation rates and nutrient availability affects, for example, fetal viability and modifies the penetrance of deleterious genetic lesions.

As part of the evolutionary pressure it was essential for life that mammals can adapt quickly to changes in their nutritional environment while maintaining metabolism to satisfy the needs of a high rate of ATP production and the production of all building blocks required for cell and tissue renewal and maintenance. Adaptation to food availability in terms of energy as well as individual (essential) nutrients requires very fast but also sustained responses that simultaneously change a huge set of interconnected metabolic processes. This is mainly achieved by hormones that can be classified by their chemical nature (i.e. peptide hormones, amino acid derivatives, or steroids) and/or the mode and time frame of their action. When looking at the effects of a diet on the genetic response, individual nutrients such as carbohydrates, lipids, proteins, or minerals such as calcium directly affect hormone secretion and these hormones adjust cellular functions via specific receptors and a multitude of intracellular signaling events for allowing the required metabolic changes to occur within milliseconds and/or by sustained responses over hours. Moreover, certain nutrients and metabolites directly affect gene expression via interaction with specific cellular targets, including nuclear receptors and response elements, and thereby mediate the integration of extracellular signals (hormones) and signals from the intracellular environment. Allosteric control mechanisms of protein functions are also an integral part of this synchronization of signal inputs from the extracellular and intracellular environment. Figure 1.2 provides a simplified view of the integrative nature of the input signals for adjusting metabolism to alterations in the nutritional environment. They key question is whether the “omics” technologies combined with advanced data analysis and interpretation tools allow us to reconstruct and understand the underlying sensing and signal integration mechanisms and their multidimensional wiring that in the end permit cells to regulate rates of nutrient transport and storage capacity, to fine-tune the flux of intermediates through metabolic routes and branching points, and to restructure the cellular transcriptome and proteome.

1.1.2 Transcript Profiling in Nutrition Research

For historical reasons, transcript profiling has dominated high-throughput genomic studies in mammalian systems since this technology has been around for quite some time. Moreover, various commercial systems for easy-to-handle array-based screening applications are available. Transcript-profiling experiments so far have often followed a simple experimental design in which, for example, cells or organisms are exposed to an altered nutritional environment (absence or presence of a particular compound) and are then assayed for changes in gene expression. These first-generation experiments led to the general conclusion that the cells often respond to quite different environmental conditions with an overlapping response of a battery of genes, although these outputs most probably originate from multiple signaling pathways.

Most microarray studies in the nutrigenomics area so far have the character of snapshots. Based on the high costs of the arrays, pooled RNA samples and/or only a few arrays have been used for analysis. To come from the snapshot approach to more consistent and reliable data, time-series of changes in gene expression as well as repeated and statistically valid measurements are required. As the costs of commercial arrays are expected to drop considerably in the future and as more small-scale targeted and cheaper arrays become available, better microarray data are expected to be produced. It is also essential that the procedures of how the study was conducted and how the array experiments have been performed are well described and data need to be collected and deposited in a standardized format. ArrayExpress (www.ebi.ac.uk/arrayexpress) is the database for collecting information about microarray experiments and is provided by the European Bioinformatics Institute (EBI). ArrayExpress is the world’s first database for storage of microarray information that conforms agreed community standards of MIAME (Minimum Information About a Microarray Experiment) devised by the Microarray Gene Expression Data (MGED) Society (www.mged.org). Since the nutritional science community has no tradition yet in using transcriptome analysis tools it is advised to adopt these standards quickly. Under the umbrella of the European Nutrigenomics Organization (www.nugo.org) a first nutrigenomics-specified MIAME version has been developed and this should in the future allow via ArrayExpress the sharing of vast amounts of microarray-based data with the global science community. In addition, many journals require or recommend authors of microarray data-based papers to submit their data to a MIAME-compliant database.

In its application as a screening approach to nutrition- or nutrient-dependent gene expression analysis, transcript profiling may lead to numerous newly identified genes/mRNA species that respond – not necessarily as expected – to the particular treatment. Before starting to bring a biological meaning into the observation it is highly recommended to use an independent method such as reverse transcriptase polymerase chain reaction (RT-PCR) or Northern blotting to check the magnitude of the changes in the mRNA level of the identified target gene(s), as the reliability of gene expression changes depends on a variety of parameters and particularly on the applied normalization method. In most cases, array data slightly underestimate the changes in transcript levels but there is also often a considerable number of transcripts that are not confirmed as significantly changed in level when assayed by other methods. Nevertheless, global transcript profiling can be seen as an expedition into the terra incognita of molecular nutrition by identifying novel genes, mechanisms and/or pathways by which a dietary maneuver changes cell physiology. The downside of transcriptomics is that one can get lost in the attempt to understand why the changes happen and although hours of scanning of the relevant literature is a rewarding learning exercise it may not provide the answer.

Although currently mainly used in basic science applications, global gene expression analysis is beginning to move from the laboratories to large-scale clinical trials as a tool in diagnostics. In the field of human nutrition, signatures or unique patterns of gene expression profiles are expected to be used to describe a nutritional condition or may even allow disease states – even preclinical ones – to be determined. The potential of this technology to improve diagnosis and tailored treatment of human diseases becomes obvious in the area of cancer diagnosis. Several comprehensive studies have demonstrated the utility of gene expression profiles for the classification of tumors into characteristic and clinically relevant subtypes and the prediction of clinical outcomes. Applied to human nutrition, gene expression profiling is of course limited (a) by the available cells that should preferentially be obtained by non-invasive techniques, (b) by the genetic heterogeneity of the human population, and (c) by the highly diverse dietary habits and lifestyles. Nevertheless, transcriptome analysis studies for exploring whether characteristic patterns or signatures reflecting the nutritional status in a human population can be obtained need to be performed to explore the scientific and diagnostic value of this technology.

1.1.3 Proteome Profiling in Nutritional Sciences

The term “proteome” was introduced as the complement of the genome and relates to the goal of determining all transcribed and translated open reading frames from a given genome. Analysis of the proteome is beginning to emerge as a second high-throughput tool for nutrition research. The revival of two-dimensional gel electrophoresis (2D-PAGE) but with high resolution, the advanced instrumentation and elegant software tools now available for gel analysis, and the enormous advancements in mass spectrometry have made proteomics applications a practical alternative screening method in the nutrigenomics tool box.

2D-PAGE separates proteins according to charge (isoelectric point: pI) by isoelectric focusing (IEF) in the first dimension and according to size (molecular mass) by sodium dodecyl sulfate PAGE (SDS-PAGE) in the second dimension. It therefore has a unique capacity for the resolution of complex mixtures of proteins, permitting the simultaneous analysis of hundreds or even thousands of gene products. However, not all proteins are resolved and separated equally well by 2DPAGE. Very alkaline, hydrophobic, and integral membrane proteins as well as high molecular weight proteins are still a problem. In some cases, a prefractionation according to cellular compartment (membranes, microsomes, cytosol, mitochondria) or according to protein solubility by classical means may be necessary. In addition, proteins of low cellular abundance, which may be particularly important in view of their cellular functions for example in signaling pathways, are still very difficult to be resolved in the presence of large quantities of housekeeping proteins. However, new concepts are constantly being developed that employ for example tagging techniques or enrich the minor proteins prior to separation in 2D gels.

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