2006/394 Article in press - uncorrected proof

Transcription

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).
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-
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
(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
292
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
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
294
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
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.
296
• Powerful data generation is followed up by
advanced data processing, mining and interpretation tools thanks to progress in bioinformatics.
• Despite all this progress, views on ‘‘proteomes’’
remain incomplete.
• Proteomics needs to be translated and adapted
from the pharmaceutical to the nutritional context.
• Proteomics in nutrition will reveal and characterise
more bioactive peptides/proteins in foodstuffs and
has the potential to deliver biomarkers for ingredient efficacy and individual disposition towards
diet and health.
• Standardisation efforts in proteomics need to be
complemented by standardised nutritional intervention studies.
17.
18.
19.
20.
21.
22.
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Received October 9, 2006, accepted December 15, 2006

2006/394 Article in press - uncorrected proof

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