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

Transcription

1 Nutrition 25 (2009) 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, Nestlé Research Center, Lausanne, Switzerland Manuscript received May 29, 2009; accepted May 31, Abstract Keywords: 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. 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]. *Corresponding author. Tel.: þ ; fax: þ address: martin.kussmann@rdls.nestle.com (M. Kussmann). 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]. 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 [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 experience is the record of traditional Chinese medicine: Sun Si- Miao, a famous doctor of the Tang dynasty (seventh century /09/$ see front matter Ó 2009 Published by Elsevier Inc. doi: /j.nut

2 1086 M. Kussmann and M. Affolter / Nutrition 25 (2009) 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 framework, 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 1 outlines 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 Fourier-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 in human blood) [17]. Current MS-based proteomic platforms can deliver a dynamic range of This means that the remaining, as such inaccessible, low-abundant proteome has to be addressed 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). Figure 1 summarizes 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 demand for throughput and speed, (multi)dimensional LC setups have been coupled online to MS analysis, with simple reversed-phase columns and combined strong cation exchange-reversed phase systems being the most frequently applied. These workflows run under the terms MudPIT (multidimensional protein identification technology) or shotgun proteomics [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 subsequent 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

3 M. Kussmann and M. Affolter / Nutrition 25 (2009) Fig. 1. Discovery workflow in mass spectrometry based proteomics. Gel-LC, gel-lc-based separation; LC, liquid chromatography. 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 under PROTEIN 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 2 summarizes 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., 13 C, 2 H, or 15 N 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, advantageous to not label at all to maximize sample integrity and compare samples directly as they are. Therefore, labelfree 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

4 1088 M. Kussmann and M. Affolter / Nutrition 25 (2009) 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. 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 called AQUA (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 structurebased 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 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 the accurate mass tag approach. 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 confidence 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 measured 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

5 M. Kussmann and M. Affolter / Nutrition 25 (2009) Fig. 3. Targeted workflow in mass spectrometry based proteomics with relative and absolute quantification options. IS, internal standard; MRM, multiplereaction monitoring; Q, quadrupole; SRM, selected-reaction monitoring. 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 ( 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 appropriate 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 incorrect 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 [ ingenuity.com products/pathways_analysis.html], BioBase Explain module [ 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 immunerelated 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.

6 1090 M. Kussmann and M. Affolter / Nutrition 25 (2009) 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 neonatal 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]. Barceló-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 occurs 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 proteolysis 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 malignant 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 protein-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% reduction 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 signal-regulated protein kinase (ERK) protein changes after exposure 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 transcriptional 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

7 M. Kussmann and M. Affolter / Nutrition 25 (2009) fragmentary data [52]. If these network-reconstructing and motif-elucidating tools prove to be successful, they may also shed a different light on the terms reproducibility and comparability of 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 concerns 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 snapshots, interpreting protein abundance changes as a result of the interplay between changing protein synthesis and degradation [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 silencing) 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 interventions often resulting in many small rather than a few large molecular changes, making data interpretation most difficult. Improved definition of human cohorts undergoing dietary interventions 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 standardized 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. References [1] Kussmann M, Affolter M, Nagy K, Holst B, Fay LB. Mass spectrometry in nutrition: understanding dietary health effects at the molecular level. Mass Spectrom Rev 2007;26: [2] Kussmann M, Raymond F, Affolter M. OMICS-driven biomarker discovery in nutrition and health. J Biotechnol 2006;124: [3] Kussmann M, Fay LB. Nutrigenomics and personalized nutrition: science and concept. Pers Med 2008;5: [4] Ordovas JM, Corella D. Nutritional genomics. Annu Rev Genomics Hum Genet 2004;5: [5] Kaput J. Nutrigenomics research for personalized nutrition and medicine. Curr Opin Biotechnol 2008;19: [6] Fay LB, German JB. Personalizing foods: is genotype necessary? Curr Opin Biotechnol 2008;19: [7] Gallou-Kabani C, Vige A, Gross MS, Junien C. Nutri-epigenomics: lifelong remodelling of our epigenomes by nutritional and metabolic factors and beyond. Clin Chem Lab Med 2007;45: [8] Junien C, Gallou-Kabani C, Vige A, Gross MS. [Nutritionnal epigenomics: consequences of unbalanced diets on epigenetics processes of programming during lifespan and between generations]. Ann Endocrinol (Paris) 2005;66(pt 3):2S [9] Kussmann M, Affolter M. Proteomic methods in nutrition. Curr Opin Clin Nutr Metab Care 2006;9: [10] Kussmann M, Affolter M, Fay LB. Proteomics in nutrition and health. Comb Chem High Throughput Screen 2005;8: [11] Kussmann M, Daniel H. Editorial overview. Curr Opin Biotechnol 2008;19:63 5. [12] Kussmann M, Rezzi S, Daniel H. Profiling techniques in nutrition and health research. Curr Opin Biotechnol 2008;19: [13] Fenn JB. Electrospray wings for molecular elephants (Nobel lecture). Angew Chem Int Ed Engl 2003;42: [14] Tanaka K. The origin of macromolecule ionization by laser irradiation (Nobel lecture). Angew Chem Int Ed Engl 2003;42: [15] Makarov A, Denisov E, Kholomeev A, Balschun W, Lange O, Strupat K, Horning S. Performance evaluation of a hybrid linear ion trap/orbitrap mass spectrometer. Anal Chem 2006;78: [16] Nielsen ML, Savitski MM, Zubarev RA. Improving protein identification using complementary fragmentation techniques in Fourier transform mass spectrometry. Mol Cell Proteomics 2005;4: [17] Jacobs JM, Adkins JN, Qian WJ, Liu T, Shen Y, Camp DG, Smith RD. Utilizing human blood plasma for proteomic biomarker discovery. J Proteome Res 2005;4: [18] Gong Y, Li X, Yang B, Ying W, Li D, Zhang Y, et al. Different immunoaffinity fractionation strategies to characterize the human plasma proteome. J Proteome Res 2006;5:

8 1092 M. Kussmann and M. Affolter / Nutrition 25 (2009) [19] Thingholm TE, Jensen ON, Larsen MR. Analytical strategies for phosphoproteomics. Proteomics 2009;9: [20] Mechref Y, Madera M, Novotny MV. Glycoprotein enrichment through lectin affinity techniques. Methods Mol Biol 2008; 424: [21] Wollscheid B, Bausch-Fluck D, Henderson C, O Brien R, Bibel M, Schiess R, et al. Mass-spectrometric identification and relative quantification of N-linked cell surface glycoproteins. Nat Biotechnol 2009; 27: [22] Sellers KF, Miecznikowski J, Viswanathan S, Minden JS, Eddy WF. Lights, camera, action! Systematic variation in 2-D difference gel electrophoresis images. Electrophoresis 2007;28: [23] Swanson SK, Washburn MP. The continuing evolution of shotgun proteomics. Drug Discov Today 2005;10: [24] Moresco JJ, Dong MQ, Yates JR III. Quantitative mass spectrometry as a tool for nutritional proteomics. Am J Clin Nutr 2008;88: [25] Panchaud A, Hansson J, Affolter M, Bel RR, Piu S, Moreillon P, Kussmann M. ANIBAL, stable isotope-based quantitative proteomics by aniline and benzoic acid labeling of amino and carboxylic groups. Mol Cell Proteomics 2008;7: [26] Gygi SP, Rist B, Gerber SA, Turecek F, Gelb MH, Aebersold R. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol 1999;17: [27] Ross PL, Huang YN, Marchese JN, Williamson B, Parker K, Hattan S, et al. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics 2004;3: [28] Thompson A, Schafer J, Kuhn K, Kienle S, Schwarz J, Schmidt G, et al. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal Chem 2003; 75: [29] de Godoy LM, Olsen JV, de Souza GA, Li G, Mortensen P, Mann M. Status of complete proteome analysis by mass spectrometry: SILAC labeled yeast as a model system. Genome Biol 2006;7:R50. [30] Mueller LN, Brusniak MY, Mani DR, Aebersold R. An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomics data. J Proteome Res 2008;7: [31] Wong JW, Sullivan MJ, Cagney G. Computational methods for the comparative quantification of proteins in label-free LCn-MS experiments. Brief Bioinform 2008;9: [32] Brun V, Masselon C, Garin J, Dupuis A. Isotope dilution strategies for absolute quantitative proteomics. J Proteomics Article in press; doi: /j.jprot [33] Gerber SA, Rush J, Stemman O, Kirschner MW, Gygi SP. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc Natl Acad Sci U S A 2003;100: [34] Pratt JM, Simpson DM, Doherty MK, Rivers J, Gaskell SJ, Beynon RJ. Multiplexed absolute quantification for proteomics using concatenated signature peptides encoded by QconCAT genes. Nat Protoc 2006; 1: [35] Dupuis A, Hennekinne JA, Garin J, Brun V. Protein standard absolute quantification (PSAQ) for improved investigation of staphylococcal food poisoning outbreaks. Proteomics 2008;8: [36] Mallick P, Schirle M, Chen SS, Flory MR, Lee H, Martin D, et al. Computational prediction of proteotypic peptides for quantitative proteomics. Nat Biotechnol 2007;25: [37] Lange V, Picotti P, Domon B, Aebersold R. Selected reaction monitoring for quantitative proteomics: a tutorial. Mol Syst Biol 2008; 4:222. [38] Yocum AK, Chinnaiyan AM. Current affairs in quantitative targeted proteomics: multiple reaction monitoring mass spectrometry. Brief Funct Genomic Proteomic 2009;8: [39] Schiess R, Wollscheid B, Aebersold R. Targeted proteomic strategy for clinical biomarker discovery. Mol Oncol 2009;3: [40] Anderson NL, Anderson NG, Pearson TW, Borchers CH, Paulovich AG, Patterson SD, et al. A human proteome detection and quantitation project. Mol Cell Proteomics 2009;8: [41] Zubarev RA, Hakansson P, Sundqvist B. Accuracy requirements for peptide characterization by monoisotopic molecular mass measurements. Anal Chem 1996;68: [42] Conrads TP, Anderson GA, Veenstra TD, Pasa Tolic L, Smith RD. Utility of accurate mass tags for proteome-wide protein identification. Anal Chem 2000;72: [43] Pappin DJ. Peptide mass fingerprinting using MALDI-TOF mass spectrometry. Methods Mol Biol 1997;64: [44] Schuerenberg M, Luebbert C, Eickhoff H, Kalkum M, Lehrach H, Nordhoff E. Prestructured MALDI-MS sample supports. Anal Chem 2000;72: [45] Mann M, Wilm M. Error-tolerant identification of peptides in sequence databases by peptide sequence tags. Anal Chem 1994;33: [46] Eng JK, McCormack AL, Yates JR. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J Am Soc Mass Spectrom 1994;5: [47] Perkins DN, Pappin DJ, Creasy DM, Cottrell JS. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 1999;20: [48] Keller A, Nesvizhskii AI, Kolker E, Aebersold R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/ MS and database search. Anal Chem 2002;74: [49] Nesvizhskii AI, Keller A, Kolker E, Aebersold R. A statistical model for identifying proteins by tandem mass spectrometry. Anal Chem 2003;75: [50] Urfer W, Grzegorczyk M, Jung K. Statistics for proteomics: a review of tools for analyzing experimental data. Proteomics 2006;6: [51] Elias JE, Gygi SP. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat Methods 2007;4: [52] Staab CA, Ceder R, Jagerbrink T, Nilsson JA, Roberg K, Jornvall H, et al. Bioinformatics processing of protein and transcript profiles of normal and transformed cell lines indicates functional impairment of transcriptional regulators in buccal carcinoma. J Proteome Res 2007; 6: [53] 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: [54] Kussmann M, Blum-Sperisen S. OMICS-derived targets for inflammatory gut disorders: opportunities for the development of nutrition related biomarkers. Endocr Metab Immune Disord Drug Targets 2007; 7: [55] Kussmann M, Affolter M. Proteomics and metabonomics routes towards obesity. In: Sorensen T, Clément K, editors. Obesity genomics and postgenomics. New York: Informa Health Care; 2008, p [56] Fuchs D, Piller R, Linseisen J, Daniel H, Wenzel U. The human peripheral blood mononuclear cell proteome responds to a dietary flaxseed-intervention and proteins identified suggest a protective effect in atherosclerosis. Proteomics 2007;7: [57] Breikers G, van Breda SG, Bouwman FG, van Herwijnen MH, Renes J, Mariman EC, et al. Potential protein markers for nutritional health effects on colorectal cancer in the mouse as revealed by proteomics analysis. Proteomics 2006;6: [58] tom-dieck H, Doring F, Fuchs D, Roth HP, Daniel H. Transcriptome and proteome analysis identifies the pathways that increase hepatic lipid accumulation in zinc-deficient rats. J Nutr 2005;135: [59] Hansen MB. The enteric nervous system I: organisation and classification. Pharmacol Toxicol 2003;92: [60] Marvin-Guy L, Lopes LV, Affolter M, Courtet-Compondu MC, Wagniere S, Bergonzelli GE, et al. Proteomics of the rat gut: analysis of the myenteric plexus-longitudinal muscle preparation. Proteomics 2005;5: [61] Lopes LV, Marvin-Guy LF, Fuerholz A, Affolter M, Ramadan Z, Kussmann M, et al. Maternal deprivation affects the neuromuscular protein profile of the rat colon in response to an acute stressor later in life. J Proteomics 2008;71:80 8.

9 M. Kussmann and M. Affolter / Nutrition 25 (2009) [62] Barceló-Batllori S, André M, Servis C, Lévy N, Takikawa O, Michetti P, et al. Proteomic analysis of cytokine-induced proteins in human intestinal epithelial cells: implications for inflammatory bowel diseases. Proteomics 2002;2: [63] Wood JD, Alpers DH, Andrews PL. Fundamentals of neurogastroenterology. Gut 1999;45(suppl 2):II6 16. [64] Hang P, Sangild PT, Sit WH, Ngai HH, Xu R, Siggers JL, Wan JM. Temporal proteomic analysis of intestine developing necrotizing enterocolitis following enteral formula feeding to preterm pigs. J Proteome Res 2009;8: [65] Stierum R, Burgemeister R, van Helvoort A, Peijnenburg A, Schutze K, Seidelin M, et al. Functional food ingredients against colorectal cancer. An example project integrating functional genomics, nutrition and health. Nutr Metab Cardiovasc Dis 2001;11(suppl):94 8. [66] Tan S, Seow TK, Liang RCMY, Koh S, Lee CPC, Chung MCM, Hooi SC. Proteome analysis of butyrate-treated human colon cancer cells (HT-29). Int J Cancer 2002;98: [67] Herzog A, Kindermann B, Doring F, Daniel H, Wenzel U. Pleiotropic molecular effects of the pro-apoptotic dietary constituent flavone in human colon cancer cells identified by protein and mrna expression profiling. Proteomics 2004;4: [68] Aalinkeel R, Bindukumar B, Reynolds JL, Sykes DE, Mahajan SD, Chadha KC, Schwartz SA. The dietary bioflavonoid, quercetin, selectively induces apoptosis of prostate cancer cells by down-regulating the expression of heat shock protein 90. Prostate 2008; 68: [69] Fong LY, Zhang L, Jiang Y, Farber JL. Dietary zinc modulation of COX-2 expression and lingual and esophageal carcinogenesis in rats. J Natl Cancer Inst 2005;97: [70] Zhang L, Perdomo G, Kim DH, Qu S, Ringquist S, Trucco M, Dong HH. Proteomic analysis of fructose-induced fatty liver in hamsters. Metabolism 2008;57: [71] Davis CD, Milner J. Frontiers in nutrigenomics, proteomics, metabolomics and cancer prevention. Mutat Res 2004;551: [72] Knowles LM, Milner JA. Diallyl disulfide induces ERK phosphorylation and alters gene expression profiles in human colon tumor cells. J Nutr 2003;133: [73] Dinkova-Kostova AT, Holtzclaw WD, Cole RN, Itoh K, Wakabayashi N, Katoh Y, et al. Direct evidence that sulfhydryl groups of Keap1 are the sensors regulating induction of phase 2 enzymes that protect against carcinogens and oxidants. Proc Natl Acad Sci USA 2002;99: [74] Panchaud A, Affolter M, Moreillon P, Kussmann M. Experimental and computational approaches to quantitative proteomics: status quo and outlook. J Proteomics 2008;71: [75] Mancone C, Amicone L, Fimia GM, Bravo E, Piacentini M, Tripodi M, Alonzi T. Proteomic analysis of human very low-density lipoprotein by two-dimensional gel electrophoresis and MALDI-TOF/TOF. Proteomics 2007;7: [76] Bantscheff M, Eberhard D, Abraham Y, Bastuck S, Boesche M, Hobson S, et al. Quantitative chemical proteomics reveals mechanisms of action of clinical ABL kinase inhibitors. Nat Biotechnol 2007; 25: [77] Mallick P, Schirle M, Chen SS, Flory MR, Lee H, Martin D, et al. Computational prediction of proteotypic peptides for quantitative proteomics. Nat Biotechnol 2007;25: [78] Anderson L, Hunter CL. Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol Cell Proteomics 2006;5: [79] Nicholson JK, Connelly J, Lindon JC, Holmes E. Metabonomics: a platform for studying drug toxicity and gene function. Nat Rev Drug Discov 2002;1: [80] Doherty MK, Beynon RJ. Protein turnover on the scale of the proteome. Expert Rev Proteomics 2006;3: [81] Siffert W. G protein polymorphisms in hypertension, atherosclerosis, and diabetes. Annu Rev Med 2005;56: [82] Chen Y, Rollins J, Paigen B, Wang X. Genetic and genomic insights into the molecular basis of atherosclerosis. Cell Metab 2007;6: [83] Holtmann G, Siffert W, Haag S, Mueller N, Langkafel M, Senf W, et al. G-protein beta 3 subunit 825 CC genotype is associated with unexplained (functional) dyspepsia. Gastroenterology 2004;126: [84] Beck HC, Nielsen EC, Matthiesen R, Jensen LH, Sehested M, Finn P, et al. Quantitative proteomic analysis of post-translational modifications of human histones. Mol Cell Proteomics 2006;5: [85] Bonenfant D, Coulot M, Towbin H, Schindler P, van Oostrum J. Characterization of histone H2A and H2B variants and their post-translational modifications by mass spectrometry. Mol Cell Proteomics 2006;5: