Proteomics at the center of nutrigenomics

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Review

Proteomics at the center of nutrigenomics: Comprehensive molecular understanding of dietary health effects

Martin Kussmann, Ph.D.* and Michael Affolter, Ph.D.

Functional Genomics Group, Department of BioAnalytical Sciences, Nestle´ Research Center, Lausanne, Switzerland Manuscript received May 29, 2009; accepted May 31, 2009.

Abstract Apart from the air we breathe, food is the only physical matter we take into our body during our life.

Nutrition exhibits therefore the most important life-long environmental impact on human health. Food components interact with our body at system, organ, cellular, and molecular levels. These dietary components come in complex mixtures, in which not only the presence and concentrations of a single compound but also interactions of multiple compounds determine ingredient bioavailability and bioefficacy. Modern nutritional and health research focuses on promoting health, preventing or delaying the onset of disease, and optimizing performance. Deciphering the molecular interplay between food and health requires therefore holistic approaches because nutritional improvement of certain health aspects must not be compromised by deterioration of others. In other words, in nutrition, we have to get everything right. Proteomics is a central platform in nutrigenomics that describes how our genome expresses itself as a response to diet. Nutrigenetics deals with our genetic predisposition and susceptibility toward diet and helps stratify subject cohorts and discern responders from non-responders. Epigenetics represent DNA sequence-unrelated biochemical modifications of DNA itself and DNA-binding proteins and appears to provide a format for life-long or even transgeneration imprinting of metabolism. Proteomics in nutrition can identify and quantify bioactive proteins and peptides and addresses questions of nutritional bioefficacy. In this review, we focus on these latter aspects, update the reader on technologic developments, and review major applications. Ó 2009 Published by Elsevier Inc.

Keywords: Proteomics; Nutrition; Health; Biomarker; Nutrigenomics; Nutrigenetics

Introduction

Food components interact with our body at system, organ, cellular, and molecular levels. These dietary components come in complex mixtures, in which not only the presence and concentrations of a single compound but also interactions of multiple compounds determine ingredient bioavailability and bioefficacy[1].

Modern nutritional and health research focuses on promoting health, preventing or delaying the onset of disease, and optimizing performance[2]. Deciphering the molecular interplay between food and health requires therefore holistic approaches because nutritional improvement of certain health aspects must not be compromised by deterioration of others[3].

Proteomics is a central platform in nutrigenomics that describes how our genome expresses itself as a response to diet[4]. Nutrigenetics deals with our genetic predisposition and susceptibility toward diet [5]and helps stratify subject cohorts and discern responders from non-responders[6]. Epi- genetics represent DNA sequence-unrelated biochemical modifications of DNA itself and DNA-binding proteins and appears to provide a format for life-long or even transgeneration imprinting of metabolism[7,8]. Proteomics in nutrition can identify and quantify bioactive proteins and peptides and addresses questions of nutritional bioefficacy[9,10].

The interplay between nutrition and health has been known for centuries: the Greek doctor Hippocrates (fourth century B.C.) can be seen as the father of ‘‘functional food,’’ because he recommended using food as medicine and vice versa. Another example of such long-term experi- ence is the record of traditional Chinese medicine: Sun Si- Miao, a famous doctor of the Tang dynasty (seventh century

*Corresponding author. Tel.:þ41-21-785-9572; fax:þ41-21-785-9486.

E-mail address:martin.kussmann@rdls.nestle.com(M. Kussmann).

0899-9007/09/$ – see front matterÓ2009 Published by Elsevier Inc.

doi:10.1016/j.nut.2009.05.022

www.nutritionjrnl.com

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A.D.), stated that, ‘‘When a person is sick, the doctor should first regulate the patient’s diet and lifestyle.’’

The new era of nutritional research translates this rather empirical knowledge to evidence-based molecular science, because food components interact with our body at system, organ, cellular, and molecular levels[11]. Modern nutritional and health research focuses on promoting health, preventing or delaying the onset of disease, and optimizing performance [11].

Dietary components come in complex mixtures, in which not only the presence and concentrations of a single compound but also interactions of multiple compounds influence food compound bioavailability and bioefficacy [1]. Hence, the necessity of developing and applying comprehensive analytical methods to reveal bioactive ingredients and their action becomes evident[12].

Proteomics is a central platform in elucidating these molecular events in nutrition: it can identify and quantify bioactive proteins and peptides and addresses questions of nutritional bioefficacy[9]. In this article, we focus on these latter aspects, update the reader on technologic developments, and review major applications: we summarize mass spectrometry (MS)-rooted proteomic techniques for protein identification and quantification and go through a selection of nutritional intervention and bioefficacy studies assessed by proteomic means.

Proteins are the key actors in virtually all biological processes in the human body—they are the ‘‘molecular robots’’

that do all the work. Hence, because we want to gain a more comprehensive understanding of this machinery and further develop the concept of nutritional systems biology, proteomics is at the center of this concerted action.

Proteomics technology Protein identification

Any proteomic study, be it in a nutritional or other frame- work, commences with a protein survey of what can be

‘‘seen’’ in a given sample and condition. Identifying proteins at a large scale and with high throughput is a mass spectrometric business.Figure 1outlines proteomic workflows for the ‘‘discovery mode.’’

Mass spectrometers can identify proteins and peptides by determination of their exact masses and generating information on the amino acid sequences. Today, the main ionization methods deployed are electrospray[13]and matrix-assisted laser desorption[14], which can put large, fragile biomolecules such as proteins and peptides rapidly and gently into gas phase and ionize them while preserving their integrity. These ion sources come in various combinations with different mass analyzers that separate the ions by mass over charge. The most popular analyzers in proteomics are ion traps, triple-quadrupole (triple-Q), time-of-flight (ToF) tubes, orbitrap, and Four- ier-transform ion cyclotron resonance (FT-ICR) cells, with their specific advantages, which are: high sensitivity and

multiple-stage fragmentation for ion traps; high selectivity for triple-Q; high sensitivity and speed for ToF; and very high mass accuracy and resolution for orbitrap and FT-ICR.

Current top-end proteomic machines are orbitrap [15] and FT-ICR instruments[16], which rely on frequency readout of oscillating ions rather than ToF- or scanning-based analysis.

The major remaining analytical challenge is not mass accuracy (today down to subparts per million), mass resolution (today up to several hundred thousand), or absolute sensitivity (today down to a picomolar range), but the dynamic range of protein concentrations (e.g., estimated 10¹² in human blood)[17]. Current MS-based proteomic platforms can deliver a dynamic range of 10⁴. This means that the remaining, as such inaccessible, low-abundant proteome has to be ad- dressed by depletion of the most abundant proteins (e.g., by the commercially available multiple affinity removal system that specifically removes the top 7 or even 14 plasma proteins) [18] or by selective enrichment of low-abundant proteins (e.g., by the immobilized metal affinity chromatography or titanium dioxide techniques for phosphoproteins [19]or lectins[20]or the cell-surface capture technique for glycoproteins [21]). All these biochemical depletion and enrichment resins and columns have matured a great deal and come now in robust formats.

After depletion and/or enrichment, usually further preseparation measurements are taken at the protein or peptide level, based on two-dimensional (2D) gels or on liquid chromatography (LC), or on hybrid approaches (Gel-LC). Fig- ure 1summarizes these proteomic workflows.

Gel-based protein separation methods have the advantage of physically preserving the protein context and generating real protein images. However, they have limited dynamic range, bias toward the more easily soluble proteins, and a low degree of automation with, in consequence, low throughput. The most advanced method for 2D protein separation is differential imaging gel electrophoresis[22], which relies on multiplexed staining and coprocessing of one control plus a maximum of two case samples. Protein spots have then to be detected, excised, digested with trypsin, and amended to LC-MS/MS.

Complementary to the gels and in view of an increasing de- mand for throughput and speed, (multi)dimensional LC setups have been coupled online to MS analysis, with simple reversed-phase columns and combined strong cation ex- change-reversed phase systems being the most frequently applied. These workflows run under the terms MudPIT (multidimensional protein identification technology) orshot- gunproteomics[23]. One major difference compared with gel approaches is that the protein context is physically sacrificed by upstream tryptic digestion of the protein mixture and sub- sequent separation and analysis at the peptide level. The protein context is then reconstructed in silico by reassigning the peptide identification to the same parent protein.

With enrichment and/or depletion, gel- and/or LC-based further preseparation and electrospray ionization–Q/Q-ToF/

ion traps/FT-ICR (online workflow) or matrix-assisted laser

M. Kussmann and M. Affolter / Nutrition 25 (2009) 1085–1093 1086

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desorption ionization–ToF/ToF (offline workflow) based MS, powerful platforms are available for fast and comprehensive assessment of proteomes from body fluids, tissues, cells, or organelles.

Protein quantification

After a protein survey, the first and foremost question asked in proteomic studies comparing multiple conditions is: Which proteins are differentially expressed? Today’s means for protein quantification are gel or MS based[24].

As mentioned underPROTEIN IDENTIFICATION, the gold standard for 2D-rooted proteomics is quantification by differential imaging gel electrophoresis, i.e., by differential staining of the separated protein spots and image analysis.

Figure 2summarizes the principal strategies and steps for gel-free global protein quantification, i.e., stable isotope rooted or label free, metabolic or chemical labeling, and providing relative or absolute quantitative information.

Alternatively to differential imaging gel electrophoresis and compatible with online shotgun LC-MS/MS workflows, stable isotopes can be introduced into the conditions in question, meaning that reagents tagging amino acid side chains at a protein or peptide level can introduce a differential isotopic signature so that the conditions can be quantitatively compared at the MS level; e.g., the ‘‘control’’ sample can be labeled with the ‘‘light,’’ unlabeled form of the reagent, whereas the ‘‘case’’ sample can be derivatized with the

‘‘heavy’’ version (e.g.,¹³C,²H, or¹⁵N label). These tagging techniques can be executed at the protein (e.g., aniline-benzoic acid labeling (AniBAL) [25]) or peptide (e.g., isotope coded affinity tag (ICAT)[26], isobaric tag for relative and absolute quantitation (iTRAQ), [27], tandem mass tag (TMT) [28]) level and can be introduced chemically into the sample (e.g., ICAT, iTRAQ, TMT, AniBAL) or metabolically by feeding cells or even small animals (mice, rats) with isotopically labeled essential amino acids stable isotope labeling of amino acids in cell culture (SILAC)[29]. The quantification readout can be obtained at the MS (ICAT, SILAC, AniBAL) or MS/MS (iTRAQ, TMT) level.

Although it is, on the one hand, preferable to introduce a label as upstream as possible in the workflow to maximize coprocessing of case(s) and control(s) and minimize bias (e.g., achieved in the cases of the metabolic SILAC and the chemical AniBAL methods), it is, on the other hand, advan- tageous to not label at all to maximize sample integrity and compare samples directly as they are. Therefore, ‘‘label- free’’ approaches (Fig. 2) have been developed that deploy spectral counting of peptide assignments for semiquantitative analysis or compare the peak intensities of the very same peptide by overlaying LC-MS runs of control and case samples [30,31].

All previously described methodologies provide information on relative changes in protein abundance. Especially in nutrition it is desirable to also generate information on absolute amounts of proteins present in a given sample. This

Fig. 1. Discovery workflow in mass spectrometry–based proteomics. Gel-LC, gel-LC-based separation; LC, liquid chromatography.

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comes into play in the context of bioavailability and bioefficacy studies: the bases of proven ingredient bioavailability and bioefficacy are absolute values of its amounts in the original food matrix and in the relevant body fluids or tissues.

Therefore, proteins and peptides must be absolutely quantified from ingredient and biomarker perspectives. Such absolute quantitative information can be obtained by spiking defined amounts of stable isotope-labeled peptides or entire proteins into the sample of interest and comparing the corresponding mass signals of the sample peptides with those of these internal standards[32]. The targeted, multiplexed peptide-level version of such a strategy is calledAQUA(absolute quantification)[33], the protein level variant is described as QConCat(artificial, expressed proteins consisting of stable isotope-coded peptides representing the proteins to be quantified and coprocessed with the sample) [34] or PSAQ (protein standard absolute quantification, i.e., spiking of the labeled protein of interest and coprocessing with the sample) [35]. The strategy applied at a proteomic scale with determination and synthesis of labeled unique peptide identifiers for virtually all proteins to be analyzed is known under the concept of proteotypic peptides [36]. The labeled proteotypic peptide standard and its unlabeled, natural counterpart are typically monitored, identified, and quantified by a targeted MS/MS acquisition mode called selected-reaction or multiple-reaction monitoring (Fig. 3) [37,38]: quantitative analysis of selected ion transitions specific for each peptide enable, e.g., validation of clinical biomarkers in plasma [39]. Independent of the variations of the same quantification principle, these methods can be understood as highly sensitive, multiplexed ‘‘MS-based enzyme-linked immunosorbent assays’’ that do not depend on three-dimensional structure- based recognition of protein epitopes. An initiative (Human Proteome Detection and Quantification Project [40]) has been recently proposed, based on this strategy, to develop a complete suite of assays, e.g., two peptides from the protein

product of each of the approximately 20 500 human genes, enabling rapid and systematic verification of candidate biomarkers and laying a quantitative foundation for comprehensive human proteome studies. These assays could serve as an excellent toolset in future nutritional studies to better understand and correlate the effects of diet and food compounds on protein expression and regulation in humans.

Data processing

Apart from the ‘‘wet laboratory’’ equipment to generate large proteomic datasets, it takes sophisticated software to acquire, store, retrieve, process, validate, and interpret these data and to eventually transform them into useful biological information.

The best case scenario in terms of identification would be to use only the mass information of a peptide as a unique signature. Such approaches have been described by Zubarev et al.[41]and later on by Conrads et al.[42]as theaccurate mass tagapproach. In this technique, identification is based only on the peptide mass and high-resolution instruments are needed to provide subpart-per-million mass accuracy (0.1 ppm). But even with such accuracy, high levels of con- fidence in protein identifications can be achieved only in small eukaryotic systems (e.g., yeast).

Proteins can furthermore be identified with good throughput[43]and high sensitivity [44]based on the set of mea- sured proteolytic peptide masses. This process is known as peptide mass fingerprinting. The experimental mass profile is matched against those generated in silico from the protein sequences in the database using the same enzyme cleavage sites. The proteins are then ranked according to the number of peptide masses matching their sequence within a certain mass error tolerance.

In contrast, MS/MS provides access to sequence data, which enables more confident peptide identifications. In an

Metabolic Labeling

Chemical modification of

proteins

Chemical modification of

peptides

Internal standard

Label free strategies

Cells/

tissues

Proteins

Peptides

Mass spec

Heavy label Light label No label

Fig. 2. Strategies for relative and absolute protein quantification in mass spectrometry–based proteomics.

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MS/MS experiment, a precursor ion with a known mass is selected from the previous MS scan and isolated for further collision to produce daughter ions with unique signature.

This process is described as peptide mass sequencing as opposed to peptide mass fingerprinting. Identification of proteins using MS/MS data is currently performed using three different approaches: 1) peptide sequence tagging [45], 2) cross-correlation method [46], and 3) probability-based matching[47].

Although peptide and protein identification and database search programs such as Mascot[47]and Sequest[46]are well established, new software infrastructures for data processing and validation have been built such as the SBEAMS architecture (http://www.sbeams.org) housing the Trans-Proteomic Pipeline and microarray modules that cover gene and protein expressions. The PeptideProphet [48]and ProteinProphet[49]modules in the Trans-Proteomic Pipeline are based on a robust and accurate statistical model to assess the validity of peptide identifications made by MS/

MS and database search. The idea behind such resources is to provide the researcher with means to assess the quality of the data in a dataset-dependent manner and to control the tradeoff between false positives (specificity) and false negatives (sensitivity) [50]. The second strategy to elucidate the false-positive/false-negative tradeoff relies on a database search using a target-decoy database[51]: first, an appropri- ate ‘‘target’’ protein sequence database is generated and then a ‘‘decoy’’ database preserving the general composition of the target database while minimizing the number of peptide sequences in common (generally done by reversing the target

protein sequence) is created. The search is done against the target and the composite database. Assuming that no correct peptides are found in the target and decoy entities and that in- correct assignments from target or decoy sequences are equally likely, one can estimate the total number of false positives.

Apart from the progress in proteomics data processing, software platforms enabling cross-correlation with other

‘‘omics’’ sources (e.g., SBEAMS, Genedata Expressionist, Rosetta Elucidator, etc.) and supporting pathway interpretation (e.g., Ingenuity Pathway Analysis [http://www.

ingenuity.com products/pathways_analysis.html], BioBase Explain module [http://www.biobase-international.com/

pages/index], AffyAnnotator[52]) are maturing rapidly.

Proteomics in nutrition—major applications

Our group and others have contributed to the introduction and adaptation of proteomics to the field of nutrition and health [53]: applications were summarized under topics such as nutritional intervention[1], elucidation of immune- related gut disorders[54], characterization of functional ingredients such as probiotics or milk and soy proteins[10], or the investigation of perturbed energy metabolism as in diabetes and obesity[55]. Moreover, numerous articles on nutritional intervention studies [56,57] and mechanistic elucidation of nutrient action[58]were published from our side and others. The following citations focus on knowledge building in nutritionally relevant biological pathways and on dietary intervention.

Fig. 3. Targeted workflow in mass spectrometry–based proteomics with relative and absolute quantification options. IS, internal standard; MRM, multiple- reaction monitoring; Q, quadrupole; SRM, selected-reaction monitoring.

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Proteomic investigations of the enteric nervous system, the nervous system of the gastrointestinal tract [59], have the potential to deliver insights into gut functionality. For example, it is recognized that early life events (such as neona- tal–maternal separation) predispose adults to develop visceral pain and enhanced colonic motility in response to acute stress. To better understand the molecular basis for these functional gut disorders induced by environmental stress, our group has established a proteomic catalog of the rat intestine[60]and assessed stress effects on intestinal protein expression[61]. Barcelo´-Batllori et al.[62]investigated implications of cytokine-induced proteins in human intestinal epithelial cells that are related to the irritable bowel syndrome [63]. The motivation behind this study is that cytokine-regulated proteins in intestinal epithelial cells have been associated with the pathogenesis of inflammatory bowel disease [59]. Hang et al. [64]studied by a gel-based approach the molecular mechanism of necrotizing enterocolitis, a serious gastrointestinal inflammatory disease, which frequently oc- curs in preterm neonates who do no adapt to enteral nutrition.

The functional assignment of the differentially expressed proteins revealed that important cellular functions, such as the heat shock response, protein processing, and purine, nitrogen, and energy metabolism, were involved in the early progression of necrotizing enterocolitis[64].

Five studies have employed proteomics, as such or combined with gene expression analysis, to address biomarkers for protection against cancer. Breikers et al.[57]identified 30 proteins differentially expressed in the colonic mucosa of healthy mice with increased vegetable intake. Six proteins identified with altered expression levels could be associated with a protective role in colorectal cancer. The second study integrated DNA microarrays with proteomics to investigate the effects of nutrients with suggested anticolorectal cancer properties and to develop a colon–epithelial cell line–based screening assay for such nutrients [65]. Tan et al. [66]

assessed the sodium butyrate effects on growth inhibition of human HT-29 cancer cells in vitro by employing a 2D- MS–based proteomic strategy. Butyrate treatment altered the expression of various proteins, in particular those of the ubiquitin–proteasome pathway, a result suggesting that pro- teolysis could be an important mechanism by which butyrate regulates key proteins in the control of the cell cycle, apoptosis, and differentiation. Combining gene and protein expression profiling in colonic cancer cells, Herzog et al. [67]

identified the flavonoid flavone, present in a variety of fruits and vegetables, as a potent apoptosis inducer in human cancer cells. Flavone displayed a broad spectrum of effects on gene and protein expression that related to apoptosis induction and cellular metabolism. Aalinkeel et al.[68]evaluated the effect(s) of the flavonoid quercetin on normal and malig- nant prostate cells and identified possible target(s) of quercetin action. Their findings demonstrated that quercetin treatment of prostate cancer cells resulted in decreased cell proliferation and viability. Quercetin promoted cancer cell apoptosis by downregulating the levels of heat shock pro-

tein-90 but exerted no quantifiable effect on normal prostate epithelial cells.

The Daniel group also investigated the consequences of nutrient deficiencies by force-feeding rats with a zinc-deficient diet and analyzing the hepatic transcriptome, proteome, and lipidome. By the combined ‘‘omics’’ analysis prime metabolic pathways of hepatic glucose and lipid metabolism and their changes in zinc deficiency could be identified that cause liver lipid accumulation and hepatic inflammation[58]. In the same context, Fong et al.[69]showed that alleviation of zinc deficiency by zinc supplementation resulted in an 80% reduc- tion of cyclo-oxygenase–2 mRNA, a key enzyme involved in inflammation.

Altered protein expression levels of fructose-induced fatty liver in hamsters have been studied by Zhang et al.[70]. High fructose consumption is associated with the development of fatty liver and dyslipidemia. Matrix-assisted laser desorption ionization–MS-based proteomic analysis of the liver tissue from those hamsters revealed a number of proteins whose expression levels were altered more than two-fold. The identified proteins have been grouped into categories such as fatty acid metabolism, cholesterol and triacylglycerol metabolism, molecular chaperones, enzymes in fructose catabolism, and proteins with housekeeping functions.

These nutritional intervention studies looked at gene/protein abundance changes in response to a nutritional intervention. However, several food components may not only alter gene and protein expression but also target post-translational modifications[71]. Diet-induced protein modifications can ideally be assessed by proteomic techniques. For instance, the protein phosphorylation status of the extracellular sig- nal-regulated protein kinase (ERK) protein changes after ex- posure to diallyl disulfide, a compound present in processed garlic, an effect resulting in cell cycle arrest[72]. Another example is the modification of thiol groups in the cytoplasmic protein Keap1[73]. This alteration of the protein redox status affects its binding to the protein Nrf2, which acts as a tran- scriptional regulator.

Conclusions and outlook

Nutrition is a young field for proteomics compared with clinical[30]and medical[32]applications. The success of proteomics in nutrition and health will depend on several factors. The proteomic technologic platforms as such, independent of their application, will benefit from further advanced protein/peptide separation techniques, better depletion and enrichment methods, and more sensitive and specific mass analysis techniques.

The second area of platform-related improvements is bioinformatics. The tools to assess data quality and to convert data into interpretable information are improving rapidly [74]. Current ‘‘gaps’’ in ‘‘omic’’ datasets and hidden regulation motifs upstream of the observed gene product regulation may be elucidated by interpretation tools able to reconstruct pathways and regulatory networks even in the presence of

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fragmentary data [52]. If these network-reconstructing and motif-elucidating tools prove to be successful, they may also shed a different light on the termsreproducibilityand comparabilityof ‘‘omic’’ studies: rather than searching for the same transcripts/proteins/metabolites found regulated between related studies, one may focus on the common motifs behind these—often at first glance divergent—datasets to find congruence between them.

The third area of proteomic method improvement con- cerns the analytical strategy. Intelligent focusing on proteome subsets, be it at the level of cell organelles, protein subclasses[75,76], or the mass spectral level (targeted proteomics with proteotypic peptides[77]or selected-reaction monitoring [78]), will yield less complex proteomes but provide deeper insights into molecular networks.

Apart from this expected progress within proteomics, the technology will largely profit from its cross-correlation with gene expression analysis at the mRNA level and metabolite profiling. In the search of the causality and ‘‘wiring’’ among these three observational levels, one must be aware of the fact that the interrelated timing of gene and protein expression and metabolite generation remains to be understood [79].

One possible solution to this is addressing protein turnover at a proteomic scale, i.e., rather than taking proteomic snap- shots, interpreting protein abundance changes as a result of the interplay between changing protein synthesis and degra- dation[80]. Proteome turnover information will add value to nutritional intervention studies performed with stable isotope-labeled amino acids, peptides, and proteins.

What takes proteomics in nutritional and food research beyond the pure technologic developments is that human genetic heterogeneity comes into play. Genetic susceptibility may predispose an individual to a diet-induced disease and this also relates to biomarker profiles in individuals[81,82].

This shall be further illustrated with a nutritionally relevant example: Siffert [81] and Holtmann et al. [83] identified and characterized metabolically relevant single nucleotide polymorphisms in G-proteins, the latter representing an important ‘‘funnel’’ of cellular signaling. These polymorphisms predispose individuals of different ethnicity to having a higher risk of developing hypertension, atherosclerosis, metabolic syndrome, or functional dyspepsia[81,83].

Epigenetic regulation such as DNA methylation (gene si- lencing) and histone acetylation (chromatin structure) should ideally be included in nutritional systems biology, because these mechanisms strongly influence gene transcription and expression. Remarkably, MS-based proteomic methods have started to contribute in this regard: Beck et al.[84]presented a quantitative analysis of human histone post-translational modifications, whereas Bonenfant et al.[85]focused on the histone codes of H2A and H2B variants.

In humans, dietary changes represent rather subtle interven- tions often resulting in many small rather than a few large molecular changes, making data interpretation most difficult.

Improved definition of human cohorts undergoing dietary inter- ventions through proper geno- and phenotyping can be expected

to deliver clearer readouts from ‘‘omics’’ applications. As nutritional science develops into a holistic molecular science with systems biology character, all intervention studies, including those that use proteomic approaches, should be based on stan- dardized diets and ingredients and stratified cohorts and ideally follow the double-blinded, placebo-controlled crossover design.

Proteomics will continue to play a major role in systems biology, because it can not only identify and quantify the

‘‘molecular robots’’ that do all the work in biological systems, but also map the networks of their physical interactions among each other and with nutrients, drugs, and other small molecules. An impressive example of such a thematic network establishment has been given by Bantscheff et al.

[76]who revealed mechanisms of action of clinical kinase inhibitors by MS profiling of small-molecule interactions with hundreds of endogenously expressed protein kinases.

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