Article in press - uncorrected proof Clin Chem Lab Med 2007;45(3):288–300 2007 by Walter de Gruyter • Berlin • New York. DOI 10.1515/CCLM.2007.071 2006/394 Review How to comprehensively analyse proteins and how this influences nutritional research1) Martin Kussmann* BioAnalytical Science Department, Nestle´ Research Centre, Nestec Ltd., Lausanne, Switzerland Abstract Proteomics, the comprehensive analysis of a protein complement in a cell, tissue or biological fluid at a given time, is a key platform within the ‘‘omic’’ technologies that also encompass genomics (gene analysis), transcriptomics (gene expression analysis) and metabolomics (metabolite profiling). This review summarises protein pre-separation, identification, quantification and modification/interaction analysis and puts them into perspective for nutritional R&D. Mass spectrometry has progressed with regard to mass accuracy, resolution and protein identification performance. Separation, depletion and enrichment techniques can increasingly cope with complexity and dynamic range of proteomic samples. Hence, proteomic studies currently provide a broader, albeit still incomplete, coverage of a given proteome. Proteomics adapted and applied to nutrition and health should demonstrate ingredient efficacy, deliver biomarkers for health and disease disposition, help in differentiating dietary responders from non-responders, and discover bioactive food components. Clin Chem Lab Med 2007;45:288–300. Keywords: biomarker; health; mass spectrometry; nutrition; proteomics. Introduction Proteomics is developing into an indispensable analytical platform in modern nutritional research. The technology is employed to address health, sensory and general quality, as well as safety aspects of food. Mass spectrometry, the proteomics workhorse, is implicated in three health-related application areas in nutritional research: the discovery of biomarkers for pre-disposition, exposure and efficacy. In previous articles, our group has described how proteomics can help in the discovery of biomarkers for pre-disposition of nutritionally actionable health and disease 1) This article is based on a contribution at the 3rd Santorini Biologie Prospective Conference, Sep 29–Oct 2, 2006. *Corresponding author: Martin Kussmann, BioAnalytical Science Department, Nestle´ Research Centre, Nestec Ltd., Vers-chez-les-Blanc, 1000 Lausanne 26, Switzerland Phone: q41-21-7859572, Fax: q41-21-7859486, E-mail: [email protected] conditions (1) and has discussed the impact of all three ‘‘omic’’ technologies (gene, protein and metabolite profiling/identification) on nutritional research (2). The present paper delivers a detailed update on proteomic technology with a special emphasis on protein pre-separation, identification, quantification and modification/interaction analysis, and discusses the present impact and future perspectives of proteomics in nutrition. Review of proteomic technology Mass spectrometry Mass spectrometry has clearly revolutionised biology by providing access to huge sets of biomolecules, which have traditionally been studied in small numbers only. The invention of mass spectrometers, enabling a breakthrough for analysis of large biomolecules, was acknowledged by the award of the Nobel Prize to John B. Fenn and Koichi Tanaka for the development of electrospray ionisation (ESI) and matrixassisted laser desorption/ionisation (MALDI) mass spectrometry (MS), respectively (3, 4). These machines can identify proteins by comparing sets of peptide masses and by sequencing individual peptides (5–7). The following characteristics have rendered modern mass spectrometers powerful tools for protein identification: tandem mass spectrometry (MS/MS) for peptide sequencing; high mass accuracy (low to subppm range) in both MS and MS/MS mode; and highresolution power from 10,000 up to several 100,000. Many ion sources can now be combined with different analysers, leading to a variety of (hybrid) mass spectrometers. Quadrupole (Q) analysers, if aligned in a sequential fashion, allow acquisition of data on diagnostic fragment ions and mass losses (8). Their high specificity is particularly useful for highthroughput analysis of post-translational modifications (PTMs), but they are compromised by limited sensitivity (9). The ion-trap (IT) allows for sensitive MS/MS and multiple-stage (MSn) experiments (10, 11). Time-of-flight (ToF) analysers benefit from high speed and sensitivity. These three major types of mass analysers can be combined to triple-Q, Q-IT, QToF, IT-ToF and ToF-ToF tandem mass spectrometers, which are all deployed for protein and peptide analysis. A different way of analysing peptide and protein ions is the Fourier-transform ion cyclotron resonance (FT-ICR) technique (12, 13): ions are analysed by their mass-dependent frequency, resulting in unsurpassed mass accuracy (sub-ppm) and resolution ()100,000). Article in press - uncorrected proof Kussmann: Influence of comprehensive analysis of proteins on nutritional research 289 FT-MS opens the way for ‘‘top-down’’ protein analysis (14, 15): intact proteins are characterised directly, without previous proteolytic or chemical processing. FT-ICR is particularly powerful for peptide sequencing when combined with electron-capture dissociation (ECD), which generates clean c- and z-ion series (16). The reduction of fragmentation complexity is now also possible by electron-transfer dissociation (ETD), which can be combined with standard ion traps (17, 18) and hence bypasses the necessity for investment in an ECD-FT-ICR system. Moreover, the recently developed Orbitrap mass spectrometer (19, 20) also determines peptide and protein molecular masses by measuring their frequencies and offers mass accuracy comparable to FT-ICR combined with high resolution (80,000). Since the ion frequencies are measured in an electromagnetic field, the expensive high-field magnet of the FT instrument becomes obsolete. It seems that FT-MS instruments developed as high-end metabolite analysers still outperform Orbitraps in terms of ultimate resolution and mass accuracy, which may become a decisive advantage for deconvoluting metabolite mixtures and identifying the components by elemental analysis. The Orbitraps seem to be more robust and more compatible with the liquid chromatography (LC) time scale, rendering them an excellent choice for high-end protein and proteome analysis. Protein pre-separation With these impressive mass spectrometric tools at hand, it is tempting to forget that the complex protein and peptide mixtures of a given proteomic sample need to be separated to some extent before they become amenable to mass spectral analysis. In other words, a reduction in sample complexity must precede protein identification, quantification and characterisation. Two principal approaches towards protein pre-separation and identification have been developed and are in use: (a) the classical ‘‘gel approach’’, i.e., protein display on a two-dimensional gel (2DE), followed by protein spot excision, in-spot protein digestion and protein identification by (tandem) mass spectrometry (21); and (b) ‘‘shotgun proteomics’’ or multidimensional protein identification technology (MudPIT) (22, 23), i.e., early digestion of the protein mixture and multi-dimensional chromatographic separation of the peptides followed by on-line mass spectrometric peptide detection and sequencing. The gel strategy offers the advantage of visualisation of proteins and some of their modifications (‘‘true’’ protein images) and of preserving the protein context. On the other hand, early digestion generates peptides upstream in the workflow, which behave more uniformly in LC and MS than proteins. Proteins with extreme physicochemical properties tend to give poor 2DE results and, therefore, one-dimensional gels combined with multi-dimensional protein chromatography are alternatively employed (24). In the same light of overcoming 2DE limitations but with the objective of preserving the superior separation power of isoelectric focusing (IEF), continuous-flow liquidphase IEF or free-flow electrophoresis (FFE) (25) and multi-compartment off-gel isoelectric focusing (26) have been developed, with the latter also applied to the peptide level (27). More recently, IEF has been used before LC separation and reflects the trend towards the merging of complementary gel- and LCbased methods. LC pre-separation is typically coupled to ESI mass spectrometry by interfaces allowing continuous infusion of the eluting peptides into the ESI source (28). However, switching between MS and MS/MS mode during elution of a chromatographic peak containing several peptides may place constraints on comprehensive analysis. Pre-separation, instrument speed, and acceleration and refinement of data acquisition have alleviated, but not entirely removed, the peak capacity problem (29). Examples are ultra-high-performance LC (UPLC) plus total MS/MS per chromatographic peak (30), mass exclusion lists (every peptide ideally only sequenced once), and gas-phase fractionation (alternating acquisition of adjacent mass ranges) (31). Given the remaining LC-MS/MS limitations, MALDI mass spectrometry has also been combined with upstream LC separation in an automated but off-line mode (32). Thanks to sample immobilisation (eluting peptides are spotted onto the MALDI plate) and the characteristics of MALDI-MS/MS technology, LC is now time-wise uncoupled from MS and the latter from MS/MS. This allows for more directed user intervention and more selective MS/MS experiments, but comes at the expense of reduced throughput. Comparative proteomic studies employing LCESI-MS/MS and LC-MALDI-MS/MS have revealed results that are comparable with regard to proteome coverage and largely overlapping and partly complementary in terms of proteins identified (33). Quantitative proteomics How does one quantify very little of something in the presence of very much of something else? The challenge of quantitative proteomics lies in the complexity and dynamic range of the samples used. Current approaches trying to meet the enormous challenge of quantitative proteomics follow four main avenues that are summarised in Figure 1: a) Differential protein labelling with dyes, twodimensional gel electrophoresis (2DE) and relative quantification by imaging (34, 35); b) Differential isotope-coded labelling (ICL), chromatography and relative quantification by mass spectrometry (36–38); c) Label-free relative peptide and protein quantification by direct comparison of LC-MS/MS data and display of three-dimensional peptide maps (m/z, retention time and peak intensity) (39) (40); and d) Absolute protein quantification by spiking ‘‘proteotypic’’ peptides representative of each protein of interest as internal standards (41). (a) The ‘‘traditional’’ approach to differential, quantitative proteomics is ‘‘differential imaging gel electro- Article in press - uncorrected proof 290 Kussmann: Influence of comprehensive analysis of proteins on nutritional research Figure 1 Current approaches in quantitative proteomics split into gel and off-gel techniques; stable-isotope and label-free methods; and absolute vs. relative quantification. phoresis’’ (DIGE) (42). This strategy offers high information content resulting from two-dimensional protein patterns. However, gel-based techniques show limitations in analysis of proteins with extreme physicochemical properties (43, 44). Moreover, the technique is costly and automation is difficult (45). (b) This category splits further into (i) in vivo metabolic labelling, e.g., stable-isotope labelling of amino acids in cell culture (SILAC), and (ii) in vitro derivatisation of proteins and peptides after recovery and/or digestion (isotope-coded affinity tag, ICAT; isobaric tags for relative accurate quantification (iTRAQ); isotope-coded protein labelling (ICPL) etc. (29). Isotope-coded-labelling (ICL)-related procedures and subsequent MS-based quantification rely on early digestion of the protein mixture and separation at the peptide level (46, 47). While gel-associated drawbacks can be circumvented, ICL approaches also have limitations, such as the risk of insufficient derivatisation yields, alteration of the sample composition and possible protein bias due to tagging post-digestion, amino acid-targeted reagents (48) and chromatographic separation of heavy and light labels (37, 49). Primary amine coding at the peptide level as a path to comparative proteomics has recently been summarised by Regnier and Julka (50). In view of the need for complementary protein quantification techniques, our laboratory has come up with a differential tagging strategy, which combines protein identification with quantification through specific labelling of both the protein N- and C-terminus prior to digestion (51, 52). Conventionally, quantitative mass spectrometric read-out is derived from MS signals or extracted ion chromatograms of differentially labelled peptides. A new strategy based on multiple precursor ions has been recently reported (53): after metabolic labelling of one protein set, combining it with an unlabelled control set, and tryptic digestion of this mixture, a wide precursor-ion window had been defined to include both light and heavy versions of each peptide. The multiplexed MS/MS data were used for both protein identification and quantification. For proteome-level comparisons between different biological conditions, the respective lists of protein identifications are usually recruited. Prakash et al. have now suggested performing such comparisons directly at the mass spectral feature level and have coined the term ‘‘signal maps’’ for comparative proteomics at the level of raw MS data (54). (c) LC-MS/MS-based relative protein quantification without any tagging depends on high resolution and high retention time reproducibility of the chromatography (55). Apart from the multiple ‘‘wet lab’’ and data treatment developments discussed below, a computational approach toward label-free protein quantification making use of predicted peptide detectability has recently been published (56): machinelearning procedures demonstrated that peptide response in MS can be predicted from the sequences of the peptide and flanking regions. Ono et al. described an integrated platform for label-free quantification at the proteome scale under the term ‘‘two-dimensional image-converted analysis of LC and MS’’ (2DICAL) (40): a continuously and rapidly alternating MS vs. MS/MS mode switching between high- and low-energy collision without specific parent ion selection has been proposed (57). The reproducibility, linearity and performance in complex proteomes of label-free quantification situations have been investigated in an LC-IT and LC-FT-MS setting Article in press - uncorrected proof Kussmann: Influence of comprehensive analysis of proteins on nutritional research 291 (58). Both set-ups delivered reproducible quantification data and protein depletion further improved the reproducibility and linearity. Such label-free LC-MS/ MS approaches have also been extended to quantitative phosphoproteomics (59). Bublitz et al. introduced a method to evaluate protein concentrations using the height sum of all MALDI-MS signals that unequivocally matched tryptic peptides of the protein under scrutiny (60). The method was validated with albumin, transferrin, a1-antitrypsin and immunoglobulin G within chromatographic fractions of control and case serum samples. Similarly, the averaged LC-MS/MS intensities of the three most abundant tryptic peptides of a given protein seem to correlate with the absolute amount of this protein and may therefore serve not only for relative but also for (d) absolute quantification of proteins in complex mixtures (61). Apart from this possibly useful correlation, absolute protein quantification in mixtures is – as most other MS-related absolute quantification techniques – mainly based on internal standards to date. In the ‘‘classical’’ AQUA (absolute quantification) approach, the most promising peptide in terms of MS amenability of a given protein is synthesised with an isotope tag and spiked into the sample (62). This strategy was proposed on the global scale by Aebersold and colleagues under the name of ‘‘proteotypic’’ peptides (63). Following the quantification by concatenated peptides (QCAT) variant of quantitative proteomics, a set of such tagged proteotypic peptides is incorporated into an artificial, chemically synthesised protein, which is spiked into the sample and codigested with the unknown proteins, giving rise to defined tryptic peptides of known concentrations (64). The original ICAT technique has also been extended to absolute quantification by Jenkins et al. (65). Finally, two absolute protein quantification methods without internal standards are the visible ICAT approach (vICAT, biotin-containing ICAT reagent providing LCtraceability and quantification by non-MS techniques) (66), and a combination of acid hydrolysis and amino acid analysis by MALDI-MS (67). Focusing on a subset of proteins facilitates absolute quantification assisted by targeted MS monitoring. The Anderson group has described such an approach, in which approximately 50 high- and medium-abundant plasma proteins have been absolutely quantified by combining multiple reaction monitoring (MRM) with internal peptide standards (68). A new experimental set-up for internal standardisation has been introduced by Ishihama et al. (69): they performed quantitative mouse-brain proteomics using cell culture-derived isotope tags to compare absolute protein levels across biological samples. For this purpose, the in-culture labelled protein complement of neuronal cells was mixed with brain protein extracts. In view of this multitude of quantitative proteomic approaches, research groups have embarked on systematic comparisons of such techniques with real-life samples. Wu et al. compared DIGE, cICAT (cleavable isotope-coded affinity tag; quantification at MS level) and iTRAQ (multiplexed isobaric tagging; quantification at MS/MS level) for differential proteomics (70). These protein chemistries were combined with either 2DE- or LC-MALDI-ToF-ToF analysis. The sensitivity of the methods was assessed at the peptide level: iTRAQ was found to be superior to ICAT, which in turn equalled DIGE in performance. The limited overlap of proteins identified suggests the complementarity of these methods. Heck’s group has benchmarked DIGE and stable-isotope labelling of amino acids in cell culture (SILAC) against each other (71). Their assessment focused on a small but representative subset (in terms of physicochemical properties) of Saccharomyces cerevisiae proteins and revealed good correlation between the two techniques, which both delivered reproducible quantification results. Finally, a recent article by Mann and co-workers reports on the status and performance of SILAC/FTMS-based quantitative proteomics (72). The paper presents hard facts and figures on what can currently be achieved in terms of proteome coverage and protein limit of detection/limit of quantification (LOD/LOQ) at global scale. It also suggests areas for method and instrument improvements. All quantitative approaches discussed so far address relative or absolute changes in protein abundance when comparing two biological conditions. Most recently, two methodologies have been presented that specifically identify newly synthesised proteins and synthesis rates of selected proteins, respectively. Dieterich et al. have developed the socalled BONCAT approach, which stands for bioorthogonal non-canonical amino acid tagging (73). The technology is based on co-translational introduction of azide groups into proteins and the chemoselective labelling of these azide-labelled proteins with an affinity tag to separate and specifically identify the newly synthesised proteins in mammalian cells. The second paper by Jaleel et al. reports a technique to measure the incorporation rate of amino acids from ingested, stable-isotope-labelled protein into individual plasma proteins (74). The approach involves three steps: (i) production of stable-isotope-labelled protein, oral administration and blood collection; (ii) plasma protein fractionation; and (iii) plasma protein identification by MS/MS and determination of the isotope enrichment by GC-MS. Phospho- and glycoproteomics Once a protein profile has been established and proteins of interest have been identified, PTMs are likely to be addressed in a follow-up study. In fact, differences between cases and controls may not always be manifest in protein quantities, but in their modifications, i.e., protein isoforms may be present in different concentrations. A number of in silico attempts to predict post-translational glycosylation and phosphorylation have been made (75, 76). As protein phosphorylation and O-glycosylation do not ‘‘obey’’ a consensus sequence such as N-glycosylation, the prediction of Ser/Thr-O-phosphoryl/GlcNAc sites remains a challenge. Hjerrild Article in press - uncorrected proof 292 Kussmann: Influence of comprehensive analysis of proteins on nutritional research et al. have trained an artificial neural network with a validated real-life protein phosphorylation MS data set to improve the prediction performance (77). Apart from these naturally abundant PTMs, the large diversity of other biological and chemical modifications is now also being addressed in a systematic manner with specifically designed bioinformatics tools. These programs allow for unrestricted PTM searches in mass spectra data without compromising the peptide identification power (78, 79). When it comes to analysing PTMs, enrichment of modified peptides and proteins is a key pre-requisite, because PTMs may be sub-stoichiometric, modified peptides are often in low abundance, and these analytes may yield poorer mass spectrometric signals. Several analytical properties of phosphopeptides contributing to their LOD and LOQ, which may be lower than for ‘‘naked’’ peptides, have been investigated by Steen et al. using defined pairs of peptides and their phosphorylated analogues (80). Increased hydrophilicity and possibly decreased retention on reversed phase (RP) material, selective ionisation suppression and lower ionisation/detection efficiency of phosphovs. normal peptides are often cited as factors deteriorating the MS-based detection and identification efficiency of phosphopeptides. Surprisingly, the authors could not confirm any of these suspicions with their model peptide set analysed by ESI-MS/MS. Protein phosphorylation is an abundant PTM and of highest importance for regulation and signalling. Consequently, phosphoproteomics has greatly advanced in terms of global mass spectrometric profiling and is increasingly based on complementary approaches wreviewed in (81)x. Commonly, phosphoproteins and -peptides are enriched using affinity-based methods such as immobilised-metal affinity chromatography (IMAC) (82) or, more recently, titanium dioxide material (83). These techniques have resulted in large-scale phosphoproteome exploration (84) in both qualitative and, if combined with differential tagging or label-free techniques, quantitative terms (85). Phosphopeptide capture on IMAC and titanium dioxide resins is currently often preceded by strong cation exchange (SCX) pre-fractionation of the proteolytic peptide mixture, which pre-concentrates phosphopeptides (82). A pI-selective, non-adsorptive phosphopeptide sampling method has been proposed by Zhang et al.: they succeeded in selectively migrating phosphopeptides into a capillary by applying a voltage, because phosphopeptides are negatively charged, even at acidic pH (86). Pure mass spectrometric means to improve phosphopeptide detection have also been used: high-sensitivity MRM has been reported for this purpose by Unwin et al. (87). Brian Chait’s group coined the term ‘‘hypothesis-driven multi-stage mass spectrometry’’ (HMS) (88): the method takes advantage of the dominant loss of phosphoric acid during MS/MS of singly charged, MALDI-generated phosphopeptide ions. Quantification is achieved by combining this detection strategy with stable-isotope labelling. Element mass spectrometry (more specifically: inductively coupled plasma MS, ICP-MS) has also been used to characterise protein phosphorylation stoichiometry: a combination of 1D-PAGE, in-gel digestion and microLC-ICP-MS has been described (89). After enrichment, phosphopeptides can be chemically modified with multiple reagents to introduce a specific and characteristic tag, which facilitates phosphopeptide detection and identification. ‘‘Traditionally’’ this has been achieved through b-elimination and Michael-addition-type reactions (90) and the protocols have been recently adapted to on-line RP-LC-MS/MS (91). Moreover, the global internal standard technology (GIST) propagated by Regnier and co-workers has been extended to phosphoproteomics (92). Glycoprotein enrichment and analysis is more complicated because of the immense variety of natural protein glycosylation. Owing to their non-templatebased synthesis, glycans are more complex in structure than DNA and proteins. They typically come in ‘‘structure ensembles’’ that mediate and fine-tune function in a more ‘‘analogue’’ fashion compared to the more ‘‘digital’’ manner of protein-protein and protein-DNA interactions (93). In vitro and in silico technologies to integrate information on structurefunction relationships of glycans have recently been reviewed (93). Lectins (94, 95), phenylboronates (96, 97) and hydrazine resins (98) are established chemical means for enriching glycoproteins and -peptides. The more complex N-glycans can be tackled either by top-down mass spectrometry (FT-ICR-MS analysis of intact glycoproteins) (15, 99), or by combining sequential exoglycosidase digestion of glycopeptides or glycans with MS monitoring of the truncated structures (100), or by hyphenation of existing methodologies (e.g., chemical derivatisation and affinity-based enrichment) to increase sensitivity and specificity (101). Direct MS/MS of glycans released from proteins is also feasible, although MS-based ‘‘glycomics’’ is less straightforward in interpretation than peptide sequencing owing to the branched structures and the variable stereochemistry (102). However, with improving structural and mass spectral databases, this undertaking is becoming easier. Tryptic peptides bearing large N-glycans are amenable to size-exclusion-based enrichment because of the large glycan moieties, which distinguish these glycopeptides from non-glycopeptides (103). Wuhrer et al. reported on an unconventional approach to protein glycosylation analysis (104): they combined nonspecific digestion with pronase with normal-phase (NP) LC-MS. Owing to the short peptide moieties left after pronase digestion, the NP retention of the glycopeptides is largely dominated by the glycan part and allows for separation of identical peptide moieties with different N-glycans. With similar objectives in mind, Larsen et al. performed sequential specific and non-specific enzymatic treatment with subsequent glycopeptide purification on graphite columns before MS characterisation (105). The more transient but structurally much simpler O-glycosylation can be addressed in a fashion similar Article in press - uncorrected proof Kussmann: Influence of comprehensive analysis of proteins on nutritional research 293 to phosphopeptide analysis, i.e., through detection of diagnostic mass losses (106, 107). A fundamentally different approach to the analysis of protein O-glycosylation has been published under the name TAS (tagging-via-substrate) (108): the technique utilises an azide analogue of O-GlcNAc for metabolic labelling of O-GlcNAc-modified proteins, which can be chemoselectively conjugated for subsequent enrichment and detection in proteomic studies. Protein interaction analysis Protein-protein interaction analysis is the ‘‘bread and butter’’ of systems biology. The rapidly growing research on protein networks is driven by the interest in generating clues to protein function on the global scale. Protein interactions have been successfully mapped on the global scale using various approaches (109). The following comprehensive methods are capable of dealing with high-throughput demands and of generating large-scale data sets: a) Yeast two-hybrid assays (Y2H), i.e., pairs of potentially interacting proteins are expressed in yeast as fusion proteins, one fused to a DNA-binding domain, one connected to a transcriptional activator domain (110); b) ‘‘Reverse’’ two-hybrid systems (rY2H) for the identification of peptides and smaller ligands that disrupt protein-protein interactions (111); c) Yeast-three-hybrid (Y3H) assays for the demonstration of RNA-protein interactions (112); d) In vivo library-vs.-library screening based on leucine zipper interactions (113); e) Mass spectrometry of protein complexes, i.e., tagging proteins as baits to pull out binding partners and subsequent mass spectrometric identification of complex components (114); f) Correlated mRNA expression, i.e., grouping of genes showing similar transcriptional response to a range of stimuli (115); g) Genetic interactions, i.e., identification of nonessential genes that cause lethality when mutated at the same time (116); and h) In silico predictions through genome analysis (117–119). Most advanced among the MS-based technologies for global-scale protein complex analysis (120) is the socalled tandem-affinity purification (TAP), in which a tagged protein is used as a bait to pull out the complex of interest and the second affinity tag serves to enrich and purify this complex (121, 122). This strategy is now increasingly complemented by the protein chip approach, in which protein extracts are typically exposed to microarrays of antibodies (or fragments thereof) (123). Recent advances in the protein microarray field have been reviewed by the Lehrach group (124). Protein arrays can be designed according to various ligand-receptor combinations such as peptide-protein, protein-protein, antibody-antigen, enzyme-substrate, membrane receptor-ligand or protein- DNA/RNA (125). Extended to tissue microarrays (TMAs), this approach can also yield ‘‘histological’’ information, meaning that the subcellular localisation of proteins and their interactions can be revealed (126). Uhlen et al. interrogated these TMAs with antibodies and thereby assembled a human protein atlas for 48 normal and 20 cancerous human tissues. This immense histological resource encompasses ;400,000 high-resolution images, each annotated by a certified pathologist (127). RP protein microarrays have recently been developed to probe protein interaction with a slightly different concept: in contrast to conventional protein (antibody) chips, on which known proteins are spotted and interrogated with the sample protein, on RP protein microarrays, the whole mixture of sample proteins is immobilised to capture the proteome fluctuations among different cell populations within a small area of a tissue (128, 129). Natural protein microarrays have been successfully applied to detect auto-antibodies in sera of patients with lung cancer for early detection of the immune response to tumours (130). An interesting variant of protein microarrays for elucidation of the kinase interactome has recently been presented (131): the authors report a method for densely archiving compounds contained in nanodroplets on peptide or protein substrate-coated microarrays. In these reaction centres of several 1000 nL, phosphorylation progress can be monitored by immunofluorescence. Technological advances in the global assessment of protein interactions have resulted in impressive mass spectrometrically generated ‘‘draft maps’’ of interaction machinery at the organism level, such as in yeast (132, 133). Marc Vidal and his team (134), as well as a German research alliance (135), deploy the Y2H system to even chart the proteome-scale map of the human interactome. The Pandey group and partners have compared the human protein interactome to those of yeast, worm and fly (136). Strikingly, of over 70,000 binary interactions, only approximately 40 were common to all four data sets. The work largely profits from the Human Protein Reference Database (HPRD), which contains approximately 25,000 experimentally verified, binary protein-protein interactions obtained from a manual search of the literature (137). Ramani et al. have undertaken an effort to consolidate the large set of known human protein-protein interactions (138): they developed and applied natural language-processing and literature-mining algorithms to recover interactions from the literature. With established tests, the relative accuracy of the literatureretrieved data was assessed. It should be noted that projection from approximately 15 interactions per protein in the best-sampled interaction set to the estimated 25,000 human genes implies 375,000 interactions, i.e., approximately ten-fold more than those mined in the paper (ca. 30,000 interactions between ;7500 proteins). In addition to the global ‘‘wet-chemistry’’ approaches to protein interaction networks, there have been several efforts to (cross-)validate protein-interaction data derived from different platforms by statistical and computational methods (109, 139). Moreover, the information retrieved from Y2H experiments can be Article in press - uncorrected proof 294 Kussmann: Influence of comprehensive analysis of proteins on nutritional research used to optimise bait selection with the objective of maximising interaction coverage by concentrating on highly connected nodes (hubs) (140). Bioinformatics resources for further processing and interpretation of proteomics-derived protein networks have been summarised by Armstrong et al. (141). The highly connected proteins in a network, the so-called ‘‘hubs’’, deserve special attention and hence Ekman et al., for example, have looked at the particular properties of yeast hub proteins (142). Interactions of proteins with small molecules, such as ligands in the case of receptors and substrates in the case of enzymes, are another case of ‘‘functional proteomics’’ and have also been analysed globally by proteomic means. In these studies, the small molecule itself was used as a chemical probe to react with or bind to the receptor protein (143, 144). An interesting example of such a strategy has been published by the Heck group: they analysed the ‘‘cGMP/cAMP interactome’’ by an MS-coupled affinity pull-down assay employing these nucleotide monophosphates as immobilised baits (145). In addition to these global mapping strategies, there are several ways of probing protein interactions at a more individual level: a) Direct observation of complexes in the gas phase by ESI (146) or MALDI mass spectrometry (147); b) Protein cross-linking and/or surface labelling and differential mass spectrometric peptide mapping (148–150); in this context, Petrotchenko et al. have recently described an isotopically coded cleavable cross-linker (151); c) Hydrogen-deuterium (H/D) exchange and differential peptide analysis to determine isotope incorporation (152); and d) Native SDS-PAGE, which preserves non-covalent protein interactions, and mass spectrometric analysis of in-gel digested protein complexes (153). Surface plasmon resonance (SPR), a technique based on (macro-)molecular interaction, binding to a gold chip and polarised light reflection, allows for quantification of biomolecular interactions in real time (biomolecular interaction analysis, BIA). This instrumentation has been successfully coupled to direct off-chip MALDI-MS (154) or subsequent elution of the binding partners, especially proteins, and identification of the latter by mass spectrometry (155, 156). Chait and co-workers have published a comprehensive proteomic study of the nuclear pore complex (NPC), in which molecular biology, protein chemistry and mass spectrometry were combined to elucidate complex localisation, protein complex partners, complex stoichiometry, complex architecture and complex mechanism (157). This is an impressive example of how proteomics, in conjunction with other disciplines and techniques, can lead from protein identification to biological function. Proteomics in nutrition While already well established in pharmaceutical research, proteomics is now increasingly being recruited for nutrition-related analysis to address general quality and safety, authenticity and health aspects of food. Whereas mass spectrometry has been long standing as a tool to characterise food matter itself, the proteomics potential for discovering and characterising nutritional biomarkers is increasingly being recognised and, as a consequence, the deployment of mass spectrometric protein analysis for health-related nutrition aspects such as the bioavailability and bioefficacy of peptide and protein ingredients and the pre-disposition of consumer groups is emerging. Markers for bioavailability, bioefficacy and pre-disposition can be addressed in a hypothesis-driven, targeted fashion or at global scale without any preassumption. Nutritional research is now taking advantage of proteomics to discover biologically active food components, to assess their function, quality and safety, and to demonstrate their biological efficacy. Independent of the context of application, proteomics represents the only ‘‘omics’’ platform that delivers not only markers for disposition and efficacy, but also targets of intervention. Expanding to general food quality aspects, it is clear that since proteins are one of the three macronutrients, protein-rich sources such as milk (158) represent gold mines for the discovery, characterisation and utilisation of nutritionally beneficial, bioactive peptides and proteins. Obviously, proteomics is the platform that will shed more light on the protein/peptide content and function of such foodstuffs. Apart from being food ingredients with health benefits, proteins and peptides are also natural carriers of biophysical properties and can serve to modulate not only food content, but also food structure (159). Expert opinion Proteomics has experienced a quantum leap in performance over recent years, mainly thanks to dramatically improved performance of mass spectrometers (increased sensitivity and specificity for protein identification), but also due to largely improved nonMS tools for pre-separation, quantification and characterisation of proteins, namely improved gel and chromatographic techniques and intelligent protein chemistry approaches. This article places particular emphasis on reviewing the latest technologies for protein identification, quantification and modification/interaction analysis. Clearly, the former two have the most immediate impact on nutritional research, as this discipline is developing into a molecular science: we are identifying and quantifying the peptides and proteins in foodstuffs, be they deleterious or beneficial for health, and are establishing sets of (candidate) protein/peptide biomarkers that indicate or even explain food ingredient efficacy on the one hand and individual disposition towards dietary actionable health/disease conditions on the other hand. However, assessment of protein modifications and protein interactions will also become more important for food-related research. Diet influences not only Article in press - uncorrected proof Kussmann: Influence of comprehensive analysis of proteins on nutritional research 295 protein expression, but also protein modifications (160). Moreover, nutritional intervention is much more indirect and subtle than pharmaceutical treatment and, therefore, many small rather than a few abundant protein expression changes are expected and the interactions between these proteins need to be taken into consideration. In principle, the concepts of proteomics applied to pharmaceutical research are transposable to nutrition. In fact, nutrition does not have to reinvent the wheel: nutritionists can learn from experience gained with proteomics in drug development and assessment. The tools are there: it is the combination of advanced and complementary separation techniques, high-end mass spectrometers, intelligent protein chemistry and powerful (bio-) informatics that renders today’s proteomics a mature biomarker delivery platform. MS has become so sensitive, specific and highthroughput that it furnishes both fewer falsenegatives (sensitivity) and false-positives (specificity) in a shorter time. Moreover, the vast amount of data generated and deposited in the public domain on protein identities, structures and their interactions can be mined in a way meaningful to nutrition. For example, key nutrients can be correlated with their transporters and converting enzymes and studies can be interrogated in terms of correlated expression changes observed. Outlook Whereas there are plenty of ‘‘classical’’ nutritional (intervention) studies, proteomics-derived nutritional (candidate) biomarkers have remained scarce to date. One major cause of this lack of nutritionally relevant investigations in humans may be the pre-requisite for non- or minimally invasive sampling for the purpose of food research. MS-based protein and peptide characterisation may circumvent this bottleneck because it can analyse more easily accessible body fluids such as serum or plasma, urine, saliva and tears. More recently, even nasal fluids and sweat have been ‘‘tapped’’ as a potential source of non-invasively available biomarkers. Different nutrition studies posing similar questions have not always produced congruent results, resulting in compromised inter-study and inter-laboratory comparability. One reason for this discouraging finding might have been ill-defined cohorts, i.e., groups too heterogeneous for biomarker establishment of subtle nutritional interventions and for deciphering differences between two metabolic conditions. Therefore, genotyping should be included in future nutrition studies to separate static genetic pre-disposition from dynamic environmental imprinting. Moreover, proteomics, with the potential to generate comprehensive sets of biomarkers, may improve study outcomes not only by better read-out of the end points, but also by better-defined subject cohorts. Proteomics can give examples of how to standardise investigations: initiatives such as MIAPE (minimal information about a proteomic experiment), with parallel movements in transcriptomics and metabolomics, attempt to define and harmonise procedures for data generation, processing and reporting to advance inter-study and inter-laboratory comparability. Correspondingly, the definition of standard diets and standardised dietary components has to be implemented. The molecular composition of diets, although applied under the same name (for example ‘‘low-carbohydrate’’ or ‘‘high-fat’’), often differs between studies in terms of calorie distribution between macronutrients, nutrient composition and sources of ingredients. Future nutrigenomic projects should be based on double-blinded, placebocontrolled crossover studies, in which each subject acts as his/her own standard (before and after intervention). An important pre-condition for the establishment of nutrition-relevant biomarkers is a better definition of human metabolic health. The molecular description of health is more difficult than that of disease, as health encompasses a wider biological ‘‘bandwidth’’. We need to understand ‘‘normal metabolism’’ and the magnitude of underlying inter-individual variability. It appears desirable to generate health status information with regard to transcripts, proteins and metabolites and to assess normal variability at all three levels. In this regard, the impact of nutritional MS, with its capability to monitor time-course changes in proteins, peptides, nutrients and metabolites, should and will increase: it can complement global measurements at the other two ‘‘omic’’ levels and may provide access to more visible molecular changes to dietary intervention, as they may occur at the transcript level. Ultimately, sets of biomarkers at the protein, nutrient and metabolite level should indicate responsiveness to diet and eventually even explain the relationship between nutrition and health. As a consequence, the disciplines ‘‘nutrigenetics’’ and ‘‘nutrigenomics’’ have evolved (161). Nutrigenetics asks the question as to how individual genetic disposition affects susceptibility to diet (162). Nutrigenomics addresses the inverse relationship, i.e., how diet influences gene transcription, protein expression and metabolism (162). Proteomics plays a key role in the nutrigenomics field. Future success also depends on the degree of integration, not only between the ‘‘omic’’ levels, but also in terms of merging (nutri-) genomics with (nutri-)genetics: the dynamic, ‘‘omics’’-derived biomarkers that indicate condition and respond to intervention need to be related to the static genetic pre-disposition, which pre-determines to some extent the biomarker readout. Highlights • MS has matured into a sensitive, specific and highthroughput protein identification platform. • Non-MS techniques for pre-separation, derivatisation, quantification and characterisation of proteins and their interactions are increasingly capable of dealing with complexity and a dynamic range. 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