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| Mini Review |
Proteomic
Technology and Its Biomedical Application
LAU Andy T. Y.1,2,
HE Qing-Yu2,3, CHIU Jen-Fu1,2*
(
1Institute of Molecular Biology, 2Open Laboratory
of Chemical Biology of the Institute of Molecular Technology for Drug Discovery
and Synthesis, 3Department of Chemistry, The University of Hong
Kong, Pokfulam Road, Hong Kong, China )
Abstract Proteomics
has its origins in two-dimensional gel electrophoresis (2-DE), a technique
developed more than twenty years ago. 2-DE has a high-resolution capacity,
and was initially used primarily for separating and characterizing proteins
in complex mixtures. 2-DE remains an important tool for protein identification,
but is now normally coupled with mass spectrometry (MS), a technique which
has advanced considerably in recent years. The recent completion of human
genome project has produced a large DNA database which can be utilized through
bioinformatics, and the next challenge for scientists is to uncover the entire
proteome of a particular organism. The integration of genomic and proteomic
data will help to elucidate the functions of proteins in the pathogenesis
of diseases and the ageing process, and could lead to the discovery of novel
drug target proteins and biomarkers of diseases. This review describes recent
advances in proteomic technology and discusses the potential applications
of proteomics in biomedical research.
Key
words proteomics; two-dimensional
gel electrophoresis (2-DE); matrix assisted laser desorption/ionization-time
of flight-mass spectrometry (MALDI-TOF-MS); biomarkers
The
term ‘proteomics’ seems to have been coined in 1995, to describe the large-scale
characterization of the entire protein components of a cell type, tissue or
whole organism［1－3 ］. Proteomics studies the global protein expression profile
instead of the behaviour of single proteins.
The
study of genes cannot provide much information on the properties of proteins,
because the molecules responsible for cellular functions (e.g. signal transduction)
are proteins. Proteins may undergo more than 200 different types of post-translational
modification, including phosphorylation, glycosylation, acetylation, deamination,
farnesylation, myristolation, palmitoylation, and proteolysis［4］. Such a wide
range of modifications cannot be predicted purely from DNA sequences. Only
through the study of proteins themselves can their characteristics and functions
be elucidated. From the data gathered in the genome project, we now have an
estimated number of proteins encoded by the genome. However, it is difficult
to predict the actual numbers of proteins encoded based on genomic data, for
a number of reasons［5］. Firstly, the exon-intron cannot be accurately predicted
from genomic DNA［6］, i.e. genomic information needs to be integrated with
data obtained from protein studies to confirm the existence of a particular
gene. Secondly, alternative splicing of a transcript can yield more than one
protein product［7］. Therefore, the direct analysis of mRNA or genome does
not reflect the exact number of protein products in a cell. Thirdly, as a
result of compartmentalization and translocation, the same protein can be
found with different properties and functions in different locations (Fig.1)［8］.
These problems can only be solved by proteomics, which can directly identify
the proteins and provide the genomic information by the appropriate integration
of genomic and proteomic data (Fig. 2). Proteomics is a growing new discipline.
Scientists worldwide are applying proteomic technology to solve problems which
cannot be resolved by traditional methods. There is little doubt that the
next decade will be the era of proteomics.
In
this article, we describe recent advances in proteomic technology, and discuss
the potential applications of proteomics in biomedical research.
Fig.1
The flow of genomic information to protein products
The concept of one-gene to one-protein is over-simplified since an RNA can
be differentially spliced and can produce various protein products. Furthermore,
the protein may be affected by more than 200 different types of post-translational
modifications. Finally, as a result of compartmentalization and translocation,
the same protein can be found with different properties and functions in different
locations.
Fig.
2 Categories, potential applications of proteomics, and the benefits of integrating
proteomic and genomic data
1
Types of proteomics
Technically, proteomics
can be classified into three types (Fig. 2).
The first type is protein
expression proteomics. It is the quantitative study of protein expressions
between samples. In this approach, protein expressions of the entire proteome
(ideally) or of subset proteomes can be compared. Novel proteins (e.g. disease-specific
biomarkers) can also be identified. We recently used protein expression proteomics
to study serum samples from hepatitis B virus (HBV) infected individuals［9］
and tissues from primary oral tongue squamous cell carcinoma patients (in
press, Proteomics, 2003), our results identified a number of biomarkers
that were altered in diseased individuals.
The second type is structural
proteomics. The main goal of this approach is to map out the structure of
protein complexes or the proteins present in a subcellular localization or
an organelle［10］. This approach can identify all the structural proteins or
protein species within a compartment such as mitochondria, chloroplast and
nuclei, or protein-protein interactions in a complex such as the transcriptome,
where many proteins work as a gigantic complex during transcription.
The third type is functional
proteomics. Analyzing protein profiles at subcellular sites is an important
approach in understanding the functional organization of cells at the molecular
level. In this respect, information about the specific subcellular localization
of a protein may help to elucidate its function. The combination of protein
identification by mass spectrometry with fractionation techniques such as
immunoprecipitation or chromatography for the enrichment of particular subcellular
structures is a profitable avenue of research, and this approach has been
termed ‘subcellular proteomics’［11］. In addition, the analysis of proteins
at the subcellular level is the basis for monitoring important aspects of
dynamic changes in the proteome such as protein translocation.
The aim of all types of
proteomics is not only to identify all the proteins in a cell but also to
create a complete three-dimensional (3-D) map of protein localizations in
a cell or an organism. The completion of this map will certainly require contributions
from various disciplines such as biochemistry, molecular biology, biophysics
and bioinformatics. However, it should be noted that the proteome of a particular
cell is in a dynamic state, and is likely to change at any moment upon external
stimuli. Thus, studying the proteome is similar to taking a snapshot of the
global expression pattern at a particular time.
2
Technology of proteomics
2.1
Separation and isolation of proteins
In
order to study the global protein expression profile and characterize or identify
proteins of interest, the proteins must be separated and then identified by
techniques such as Western blot or mass spectrometry. Initially, 1-DE was
the method of choice to resolve proteins. 1-DE can separate proteins ranging
from ten to several hundred kilodaltons on the basis of molecular mass, and
is easy to manipulate and highly reproducible. In the past, partial protein
purification by chromatography followed by preparative 1-DE was the preferred
method of purifying proteins of interest in a quantity suitable for further
characterization or for analysis by techniques such as enzyme kinetics, amino
acid sequencing by Edman degradation, and amino acid composition analysis［12］.
However, 1-DE technology has its limitations. It can only be used to separate
proteins in certain applications, as it is only able to distinguish proteins
by mass difference. In a cell extract, many proteins have an identical or
very similar molecular mass. The resolved single band in 1-DE may not consist
of homogenous proteins, as more than one type of protein may migrate to the
same distance on the gel. As a result of the limited resolution capability
of 1-DE, alternative methods must be employed to separate complex or crude
samples. 2-DE specifically addresses this problem, since it separates proteins
based on two properties: their sizes, and their individual unique isoelectric
points. This combination of properties amplifies the resolution capability
of 2-DE tremendously, and no other method can replace it. The most useful
application of 2-DE is to resolve protein isoforms of the same size, or proteins
that have undergone various post-translational modifications which changes
their electric charges, such as phosphorylation. Usually, the phosphorylated
forms of a protein can be resolved from the nonphosphorylated counterpart
and appear as a chain of spots horizontally on the 2-D gel［13］. The ability
of 2-DE to resolve thousands of proteins in one gel makes 2-DE an unbeatable
technology, and it is expected to remain in use for another decade.
The
main objective of 2-DE is to obtain protein profiling. In this regard, the
protein expression of different samples, i.e. control and treatment, can be
qualitatively and quantitatively compared. The appearance or disappearance
of specific protein spots indicates differential protein expression, while
the degree of spot intensity provides quantitative information about protein
expression levels. Another objective of 2-DE is proteomic mapping in cells.
2-DE is used to map proteins from various microorganisms［14, 15］, organelles［16］,
protein complexes,［17］ and even subproteomes. As a result, many 2-DE databases
have been established, and are accessible on the Internet.
2-DE
has recently been improved with the introduction of immobilized pH gradients.
Originally the pH gradient for isoelectric focusing in 2-DE was created by
carrier ampholytes. These are mixtures of a few hundred different homologues
of amphoteric buffers, synthesized together in one reaction flask. The mixtures
contain buffers with isoelectric points evenly distributed over a wide spectrum
from pH 3 to pH 10, and they have high buffering power at their isoelectric
points. When an electric field is applied, they start to migrate according
to their charges toward the anode or the cathode respectively, and form automatically
stable pH gradients. The pH gradient works well when protein separation is
performed in native conditions. However, in order to gain a high-resolution
2D protein separation, electrophoresis is usually performed under denaturing
conditions, which prolongs the running time and destabilizes the pH gradient.
Gradient drifting with prolonged isoelectric focusing time causes the loss
of almost all basic and some of the acidic proteins. As a result, the patterns
were insufficiently reproducible. Today, with the development of immobilized
pH gradients［18］, the pH gradients in these gels are prepared by co-polymerizing
acrylamide monomers with acrylamide derivatives containing carboxylic and
tertiary amino groups. Because the buffering groups that form the pH gradient
are fixed, the gradient cannot drift and is not influenced by the sample composition.
The use of immobilized pH gradients has allowed many methodical innovations
for 2-DE. Pre-manufactured gel strips and instruments are available as commercial
products. This is a prerequisite for the development of 2-DE into a highly
reproducible and reliable method. The immobilized strips are of various lengths
(7 cm, 13 cm, 18 cm, and 24 cm), and cover various pH ranges, including broad
range pH 3－10, narrow ranges pH 4－7 and pH 6－11, and a range of a single pH
increment. The method can be applied to different experimental purposes and
conditions as the degree of separation can be enhanced by using larger gel
format and narrow pH range immobilized strips.
2-DE
has also been improved recently with the introduction of DIGE (differences
gel electrophoresis) technology. This technology utilizes fluorescent tagging
of two protein samples with two different dyes. These dyes are amine reactive
and have the same molecular mass in order to eliminate mass differences between
tagged samples. The tagged proteins are mixed together and run on the same
2-D gel. After image acquisition by a fluorescent scanner using different
excitation wavelength of each dye, the gel images are superimposed to identify
differences［19］. This technique can be used for experiments with large sample
sizes (e.g. clinical samples from normal and diseased patients). It minimizes
gel-to-gel variation and reduces workload, as the number of gels needed to
be run is reduced.
2-DE
has also been improved recently with the introduction of DIGE (differences
gel electrophoresis) technology. This technology utilizes fluorescent tagging
of two protein samples with two different dyes. These dyes are amine reactive
and have the same molecular mass in order to eliminate mass differences between
tagged samples. The tagged proteins are mixed together and run on the same
2-D gel. After image acquisition by a fluorescent scanner using different
excitation wavelength of each dye, the gel images are superimposed to identify
differences［19］. This technique can be used for experiments with large sample
sizes (e.g. clinical samples from normal and diseased patients). It minimizes
gel-to-gel variation and reduces workload, as the number of gels needed to
be run is reduced.
Recently, a novel method for protein expression profiling has been invented,
which does not require the separation of proteins by 2-DE. This method is
called isotope-coded affinity tags (ICAT). Protein samples from two different
sources are labeled by two chemically identical reagents that differ only
in mass as a result of isotope composition［20］. Differential labeling of samples
by mass allows the relative amount of proteins between two samples to be quantitated
in the mass spectrometer. The major advantage of this method is that it obviates
the need to perform 2-DE, enabling a larger sample to be loaded for low copy
number proteins. The main disadvantages are that this method works only for
cysteine containing proteins, and peptides must contain appropriately spaced
protease cleavage sites flanking the cysteine residues［21］. Moreover, the
ICAT label is large and remains with each peptide throughout the analysis.
This can make database searching more complicated, especially for small peptides
with limited sequences[22, 23].
Although
the resolution capability of 2-DE is high, the technique has its limitations.
Firstly, it is a labor-intensive and tedious procedure. In our laboratory
two days are needed to carry out IEF, electrophoresis, silver staining and
image acquisition. Secondly, many large or hydrophobic proteins do not enter
the gel during the first dimension. In addition, proteins with extreme pH
(below 3 or above 10) are not separated［24］, but focused as vertical lines
on both sides. Finally, low-copy number proteins either cannot or can hardly
be detected on 2-D gel. Although this problem can be overcome by increasing
the sample loading, there may be a risk of overloading the system and reducing
the resolution［25］.
2.2
Methods of protein detection, image analysis and documentation
There
are many ways to detect proteins in 2-D gels. The method used is very dependent
on protein loading on the gel (analytical or preparative), the purpose of
the gel (for protein quantitation or for blotting), and the sensitivity required.
The most common methods are silver staining［26, 27］ and Coomassie blue staining［28－31］.
Other methods, including ［35S]-Met or 14C radiolabelling［32,
33］, colloidal gold［28, 34, 35］, zinc imidazole［36, 37］, ponceau S, amido
black［28, 31］, and India ink［28, 38, 39］, can also be used in different applications
to achieve better sensitivity and performance in particular cases. However,
some drawbacks are obvious. For example, glycoproteins are not stained by
Coomassie blue［30］, and many organic dyes are unsuitable for protein detection
on PVDF membranes if samples are to be used for MALDI-TOF［28］. In addition,
although most means of protein detection can give some indication of the quantities
of protein present, in general they cannot be used for global quantitation.
This is because no protein stain is able to detect proteins over a wide range
of concentrations, isoelectric points and amino acid compositions, especially
given the variety of post-translational modifications［30, 38］.
After
2-D electrophoresis and protein visualization by staining, images of gels
are digitized for computer analysis by an image scanner or fluorescent scanner,
and are then subjected to analysis by special image analysis software (either
ImageMaster from Amersham Biosciences or PDQUEST from BioRad). The 2-D patterns
are very complex, and special software tools are required to find differentially
expressed proteins in a series of gels, such as up and down-regulated proteins,
post-translational modified proteins. Image spots on the gels are initially
detected, manually edited and then matched. Each spot’s intensity (volume)
is processed by background subtraction and total spot volume normalization,
and the resulting spot volume percentage is used for comparison. The reliability
of quantitative determinations of protein amounts in spots is largely dependent
on the protein detection technique applied. Usually, only significantly up/down-regulated
spots or appearing/disappearing spots are selected for analysis with mass
spectrometry.
2.3
Analysis of protein using mass spectrometry
After
resolving the protein mixtures and image analysis, the next step is protein
identification. The protein spots are excised and in-gel digested with an
enzyme (e.g. trypsin or chymotrypsin). The digest is then applied onto a sample
plate and coated with matrix. If necessary, the in-gel digest is extracted
with acetonitrile and then concentrated and desalted by a Ziptip prior to
application on the sample plate. The matrix is typically a small energy-absorbing
molecule such as 2,5-dihydroxybenzoic acid or α-cyano-4-hydroxycinnamic acid.
The analyte is spotted, along with the matrix, on the sample plate and allowed
to evaporate, resulting in the formation of crystals. The plate is then put
into the MALDI-TOF mass spectrometer, and the laser is automatically targeted
to specific places on the plate and peptide mass spectra are then obtained.
If the protein is digested with trypsin, the trypsin autolytic fragment peaks
(906.5049, 1153.5741 and 2163.0570) can serve as internal standards for
mass calibration. Several software packages are available to perform database
matching such as MASCOT at www.matrixscience.com［40］, ProFound at www.prowl.com［41］
and Protein Prospector at www.prospector.ucsf.edu/［42］. The degree of accuracy,
reliability and speed differs from software to software, depending on user
preferences. However, regardless of which software is used, four variables
are normally required for a peptide mass fingerprinting (PMF) search: (1)
peptide mass list; (2) specification of the cleavage agent; (3) error tolerance,
i.e., the accuracy of mass measurement; and (4) peptide modifications (e.g.
N-terminal acetylation). The criteria for matching can be made more stringent
by setting a smaller error tolerance or better mass accuracy, a greater number
of matching peptide masses, and a narrow molecular weight and pI ranges. Also,
the species origin of the unknown protein is important during the matching.
Analogous to the DNA chip technologies, the ProteinChip technology coupled
with SELDI-TOF-MS (surface-enhanced laser desorption/ionization-time of flight-mass
spectrometry) has recently been developed by Ciphergen Biosystems, Inc. to
facilitate protein profiling of complex biological mixtures［43, 44］. This
technology utilizes patented ProteinChip arrays to capture individual proteins
from complex mixtures, which are subsequently resolved by mass spectrometry
using the same principle as MALDI-TOF. The efficacy of the SELDI technology
for discovery of prostate cancer protein markers in serum, seminal plasma,
and cell extracts was demonstrated long ago［45, 46］. In a recent study, we
also utilized ProteinChip SELDI-TOF-MS system to detect potential alteration
of protein expression in rat lung epithelial cells (LEC) during arsenic-induced
malignant cell transformation［47, 48］.
2.4 Protein identification
Proteins can usually be identified by peptide mass fingerprinting (PMF)［37,
49－52］ and database searching. However, it is sometimes necessary to use post
source decay (PSD) to confirm the search result, when an insufficient number
of proteolytic peptides is available for confident matching. Because PSD can
deduce the amino acid sequence of peptides from normal, post-translational
modified or novel proteins of interest (Fig.3), this can greatly enhance
the accuracy of the protein identification process, and sometimes leads to
the discovery of new proteins.
Fig.3
A general scheme for protein identification by mass spectrometry using either
PMF, peptide amino acid sequencing/PSD, or both, to improve the success rate
or throughput of protein identification
2.4.1
Peptide mass fingerprinting (PMF) by database matching In PMF, the peptide
masses of unknown proteins are compared to the predicted masses of peptides
from the theoretical digestion of proteins in a database (Fig.4). The more
numbers of peptides match to a protein in database, the more likely the unknown
protein is. The advantage of using PMF is that the protein identification
process is fast and user-friendly. We can routinely identify a hundred unknown
protein spots in a single day’s work. But the success of the method can be
compromised by several factors: (1) insufficient peptides are obtained in
the peptide mass fingerprint to submit to the database search, i.e. there
is insufficient data to identify the unknown protein; (2) PMF cannot analyze
samples containing a mixture of proteins since they generate mixtures of peptides
after the digestion; (3) mass redundancy of peptides with the same masses
but different amino acid compositions, can cause ambiguity in protein identification.
PMF cannot identify post-translationally modified peptides since there is
no such information available from the database. When such problems occur,
peptide amino acid sequencing/post source decay (PSD) is used for further
validation.
2.4.2 Peptide amino acid sequencing/Post source decay (PSD) With peptide
amino acid sequencing, the amino acid sequence of unknown peptides can be
identified (Fig. 4), and then used to search the database to identify the
protein from which it was derived. Unlike PMF, PSD can be used for gels containing
more than one protein. This advantage greatly enhances the protein identification
process since protein bands from 1-D gel can be identified whether homogenous
or not. The PMF data can be supplemented with partial amino acid sequence
information along the result of database search, i.e. an unsuccessful search
of the protein database with the PMF data may be reversed with an additional
partial sequence. The drawback of PSD is that it is not user-friendly, since
the process is not easily automated. As a result, MS analysis and database
searching takes considerable time, and must be performed by an experienced
operator.
Fig.
4 PMF and PSD in mass spectrometric analysis
For PMF, total ions are transmitted through quadrupoles for mass determination.
For PSD, selected parent ion is transmitted into the collision chamber and
then fragmented, resulting in numerous daughter ions for amino acid sequence
determination.
3 Biomedical applications
3.1 Study on the pathogenesis of human diseases–arsenic carcinogenesis
Our
recent studies demonstrated that a low level (1.5 μmol/L) of arsenite induces
B[a]P-treated lung cell transformation［47, 48］(Fig.5). We used ProteinChips
to identify different protein expression, which could potentially be important
for cell transformation induced by this toxic agent. The protein profiles
of cell extracts from all samples, including the control, B[a]P-treated,
and (B[a]P+As)-treated cells, were similar. However, surface-enhanced
laser desorption/ionization time of flight (SELDI-TOF) analysis with Cu-ProteinChips
and WCX-ProteinChips revealed several dramatically different protein peaks
that appeared in lung cells after transformation by treatment of 1.5 μmol/L
arsenite for 12 weeks. Some of these proteins were found to present in mitochondria
and participate in mitochondrial respiratory chain and ATP production. Results
from this study also suggested that the expression of the pro-apoptotic protein
Bax was suppressed in arsenite-induced transformed cells. SAX2 ProteinChip
also identified prominent protein peaks that were preferentially expressed
in control cells. Interestingly, by using SAX2 chip, we were able to detect
several protein peaks whose expression was increased in lung cells treated
only with B[a]P. Identification and characterization of these proteins
may reveal the molecular basis of arsenite-induced cell transformation and
help to elucidate the mechanisms by which arsenic induces carcinogenesis.
Fig.
5 Protein spectra and gel views of SELDI analysis of cellular proteins bound
to copper ProteinChip array[48]
Cellular extract of control LEC cells, cells treated with B[a]P, and cells
treated with B[a]P+arsenite were spotted onto Cu-ProteinChip array. Two protein
peaks with Mr of 4099.3 and 8175.5 were present in the As-transformed
LEC cells, but absent in the control and B[a]P-treated LEC cells.
3.2 Identification, characterization and clinical application of biomarkers
of human diseases–serum biomarkers in HBV-infected patients
Hepatitis B virus (HBV), a serious infectious and widespread human pathogen,
represents a major health problem worldwide. Chronic HBV infection has a very
high chance of evolving into hepatocellular carcinoma. Although considerable
progress has been made in the past few years, the pathogenesis of HBV infection
is still elusive and a definite diagnosis of HBV-infected liver inflammation
still relies on biopsy histological test. Our recent studies used proteomic
technology to globally examine HBV-infected serum samples in a search for
disease-associated proteins that can be used as serological biomarkers for
diagnosis and/ or target proteins for pathogenetic study［9］(Fig.6). After
a comparison with normal serum samples, we found that at least seven proteins
were significantly changed in HBV-infected sera. These greatly altered proteins
were identified as haptoglobin β and α2 chain, apolipoprotein A-I and A-IV,
α1-antitrypsin, transthyretin and DNA topoisomerase IIβ. The alteration of
these proteins presents not only in their quantities but also in their patterns
(or specificity), some of which can be correlated with the necroinflammatory
scores. In particular, apolipoprotein A-I displays heterogeneous change in
expression level with different isoforms, and α1-antitrypsin produces evidently
different fragments, implying diverse cleavage pathways. These phenomena are
unparalleled, and appear to be specific to HBV infection. A combination simultaneously
considering the quantities and isoforms of these proteins could be useful
serum biomarkers for HBV diagnosis and therapy.
Fig.
6 Three representative 2D-gel images for normal, low NIS and high NIS serum
samples, respectively (A), and an enlarged low NIS gel displaying the common
features of human serum proteins (B)
Area 1, haptoglobinβ & cleaved β chain; area 2, haptoglobinα2 chain; area
3, apolipoprotein A-I; area 4, apolipoprotein A-I & A-IV; area 5 &
6, α1-antitrypsin; area 7, DNA topoisomerase IIβ (NIS: necroinflammatory score)[9]
3.3
Proteomic studies on chemotherapeutic agents
3.3.1 To study the mechanisms of drug actions Studies to investigate
the protein changes in rat livers have been conducted by using various agents
including hepatotoxicants, methapyrilene, cyproterone acetate and dexamethasone［53］.
Two-dimensional polyacrylamide gel electrophoresis and mass spectrometry were
used for the identification of compound specific biomarkers. The different
treatments caused distinct changes in the rat liver proteome. Many of the
protein changes could be associated with the known pharmacological and toxicological
mechanisms of action of these drugs. This approach could open up new avenues
for the exploration of molecular mechanisms of toxicity, and is a good illustration
of how proteomics can provide valuable information on the biochemical consequences
elicited by hepatotoxic drugs.
In another study, a single dose of puromycin aminonucleoside (PAN) given peritoneally
to rats induced ultrastructural glomerular changes and a nephrotic syndrome
similar in many respects to human minimal change nephropathy［54］. Increased
plasma protein excretion in urine is a consequence of nephritic syndrome and
nephropathy. 2-DE has therefore been used to profile urinary proteins during
PAN-induced nephrotoxicity and subsequent recovery in rats. In addition, urinary
high performance liquid chromatography (HPLC) profiles and high resolution
proton nuclear magnetic resonance (NMR) spectroscopy have also been used to
detect toxin-induced changes in the relative concentrations of a number of
metabolites. This demonstrates that a proteomic approach, used in conjunction
with other techniques, has the potential to provide valuable information which
traditional clinical chemistry is unable to supply.
3.3.2
To monitor drug effectiveness in clinical trials Recent technological
progress in genomics and proteomics has created a unique opportunity for significantly
improving the pharmaceutical drug development processes. The fact that cells
and whole organisms express specific inducible responses to drug treatment
implies that unique expression patterns and molecular fingerprints indicating
a drug’s efficacy and potential toxicity are accessible［55］.
Bodily fluids such as cerebrospinal fluid (CSF) and serum can be analyzed
throughout the course of a disease. Changes in the protein composition of
CSF may be indicative of altered protein expression in the central nervous
system (CNS), which may be used for causative study or diagnostic biomarkers［56］.
These findings can be strengthened through subsequent proteomic analysis of
specific brain areas implicated in the pathology. This may facilitate pre-clinical
and clinical development of more specific disease markers and new selective
fast acting therapeutics.
Severe adverse drug reactions occur in approximately 7% of hospital patients.
In some cases the side effect is difficult to predict or elucidate because
the pharmacology of the causative agent is unknown. Proteomics may have some
predictive value, but is likely to be of greater use in diagnosis, e.g. by
recognizing a drug signature in an accessible tissue［57］. This may be possible
on a blood sample or biopsy taken at presentation. Alternatively an in
vitro assay that replaced rechallenging the patient with a drug would
be helpful. The goal is to identify target proteins of the causative drug
permitting the development of a safer alternative.
Clinical proteomics, as a new and most exciting sub-discipline of proteomics,
involves the bench-to-bedside clinical application of proteomic tools. Unlike
the genome, there are potentially thousands of proteomes: each cell type has
its own unique proteome. Moreover, each cell type can alter its proteome depending
on the unique tissue microenvironment in which it resides, giving rise to
multiple permutations of a single proteome. Since proteomics has nothing equivalent
to a polymerase chain reaction, identifying and discovering human diseased
cell in a biopsy specimen remains a daunting challenge. New micro-proteomic
technologies are being, and still need to be, developed into the clinical
proteomes. Cancer, as a model disease, provides an excellent environment for
the study of the application of proteomics at the bedside. The promise of
clinical proteomics and related technological development is that cancer can
be identified earlier through the discovery of biomarkers, that the next generation
of targets can also be identified, and that we can then apply this knowledge
to patient-tailored therapy［58］. The ultimate goals of personalized medicine
are to take advantage of a molecular understanding of disease, both to optimize
drug development and direct preventive resources and therapeutic agents at
individuals at risk while they are still well［59］. The benefits will improve
lead selection, and optimized monitoring of drug efficacy and safety in pre-clinical
and clinical studies based on biologically relevant tissue and surrogate markers［55］.
3.4
Development of a tool for therapy
3.4.1 To accelerate the advance of gene therapy Antisense oligonucleotides
are synthetic stretches of DNA, which hybridize with specific mRNA strands
that correspond to target genes. They are a new class of therapeutic agent.
By binding to the mRNA, the antisense oligonucleotides prevent the target
gene being translated into a protein, thereby blocking the action of the gene.
Several genes known to be important in the regulation of apoptosis, cell growth,
metastasis, and angiogenesis, have proved feasible as molecular targets for
gene therapy. Furthermore, new targets are rapidly being uncovered through
integration of functional genomics and proteomics effects［60］. By using the
proteomic approach, proteins that are altered in diseased individuals compared
with normal individuals can be applied for gene therapy. Since the antisense
oligonucleotides can be designed based on the target protein sequence. This
can effectively advance the gene therapy by pinpointing the specific defects
by a ‘reverse genetic’ basis.
3.4.2
To identify disease-associated membrane targets for development of antibody
based therapy Membrane proteins are responsible for some of the most important
functions in cells, including the regulation of cell signaling through surface
receptors, cell-to-cell interactions, and the intracellular compartmentalization
of organelles. Recently, proteomic techniques have focused on high-throughput
analyses of membrane proteins using liquid chromatography coupled to mass
spectrometry (LC/MS). This can identify large numbers of membrane proteins
and modifications, and may also provide insights into protein topology and
orientation in membranes［61］.
HER2 (erbB2/neu) is a member of the erbB family of receptor tyrosine kinases
and is involved in regulating the growth of several types of human carcinomas.
The discoveries that this gene is amplified in breast tumors and its protein
product is overexpressed at the cell surface have led to an effective form
of therapy for breast cancer which utilizes an antibody that targets HER2［62］.
The elucidation of the role of growth factor receptors expressed on the cell
surface in signaling and in uncontrolled cell proliferation, as in epidermal
growth factor receptor, has led to the development of new anticancer therapies
that target specific components of the epidermal growth factor receptor signal
transduction pathway. Selective compounds have been developed to target the
extracellular ligand-binding region of epidermal growth factor receptor. Thus,
the protein profiling of the cell surface proteome would have important implications.
In cancer, cell surface proteins that are restricted in their expression to
specific cancer(s) could be utilized for antibody-based therapy, as in the
case of HER2 or for vaccine development or other forms of immunotherapy.
3.5
Studies on cellular processes including protein-protein interactions and signal
transduction pathway
Post-translational modification on proteins is a fundamental cellular regulatory
mechanism. Protein phosphorylation is the main mechanism by which cells modulate
enzyme activity and protein-protein interactions. The study of protein kinases
is the key to the identification of signal transduction pathways. So far 518
human protein kinases have been identified, and termed the ‘human kinome’.
They control protein activity by catalyzing the addition of a negatively charged
phosphate group to other proteins. Protein kinases modulate a wide variety
of biological processes, especially those that carry signals from the cell
membrane to intracellular targets and coordinate complex biological functions.
Protein-protein interactions play a central role in numerous processes in
the cell and are one of the main fields that functional proteomic study［63］.
MS can be used to identify novel phosphoproteins, measure changes in the phosphorylation
state of proteins in response to an effector, and determine phosphorylation
sites in proteins. Identification of phosphorylation sites can provide information
about the mechanism of enzyme regulation and the protein kinases and phosphatases
involved. Proteomics can study global phosphorylation changes in response
to stimuli, by a simultaneous study of the phosphoproteome. A common approach
in studying phosphorylation is the labeling of phosphoproteins with 32P in
vivo . The phosphoproteome of cells (e.g. normal versus diseased cells) can
be analyzed by culturing cells with 32P and then obtaining the labeled cell
lysates. Changes in the phosphorylation state of the proteins can then be
studied by 2-DE and autoradiography. Proteins of interest are excised from
the gel and microsequenced by MS. MALDI-TOF mass spectrometry can also be
used to identify phosphopeptides［64－67］. When phosphorylated peptides are
subjected to ionization by MALDI, phosphate groups are frequently liberated
from the peptides. This is the case for phosphoserine- and phosphothreonine-containing
peptides, which can liberate HPO3 or H3PO4, resulting in a neutral loss of
80 and 98 Daltons respectively. Careful examinations of the spectrum for differences
in peptide masses of 80 Daltons that are not found in the unphosphorylated
peptide control can identify phosphopeptides. Phosphopeptides can also be
identified by treating one of the two identical samples with protein phosphatase
to liberate phosphate groups. Once a phosphopeptide is identified, it can
be again sequenced by MS/MS for identification of the phosphorylation site[67]. During
the last decade, several studies were made with the aim of discovering or
designing small molecules that block protein dimerization or protein (peptide)receptor
interaction or, on the contrary, induce protein dimerization［63］. Mass spectrometry-based
proteomics can reveal protein-protein interactions on a large scale. In a
study that investigated the epidermal growth factor receptor (EGFR) pathway［68］,
stable isotopic amino acids in cell culture (SILAC) were used to differentially
label proteins in EGF-stimulated versus unstimulated cells. Combined cell
lysates were affinity-purified over the SH2 domain of the adapter protein
Grb2 (GST-SH2 fusion protein) that specifically binds phosphorylated EGFR
and Src homologous and collagen (Shc) protein. 228 proteins were identified,
of which 28 were selectively enriched upon stimulation. SILAC combined with
modification-based affinity purification is a useful approach to detect specific
and functional protein-protein interactions. Indeed, a significant number
of human diseases can be attributed to defects in cellular signal transduction
pathways. Proteomics can define critical components of signal transduction
networks, thereby contributing to the development of more effective therapeutic
agents that can specifically target individual disease-altered proteins.4
Future prospects in proteomic technology
Although proteomic technology is certainly capable of characterizing the proteome
of a given cell or organism, both techniques continue to have their limitations.
Although the resolving power of 2-DE remains unchallenged, mass spectrometry
has become more sensitive, faster, and more reproducible. However, examination
of the proteome of a cell or organism is like taking a snapshot of its activity
at a single point in time. This may underestimate or miss the significance
of processes taking place over time. One of the greatest challenges for proteomics
is the study of low-copy number proteins. Many classes of proteins, e.g. transcription
factors and some enzymes, are in low-copy number. These proteins are unlikely
to be detected on 2-D gels unless they are enriched or partially purified.
Therefore, resolution is a bottleneck for proteomics advancement for the time
being, and development of higher resolution techniques for proteomics is an
urgent issue. We believe that technological advances will result in the gradual
maturation of proteomics, and that we will ultimately be able to study the
proteome comprehensively. We forecast that future proteomic technology will
focus on real-time proteomics, i.e. monitoring the proteome in a real time/time-lapse
manner, and this will greatly enhance our knowledge of how proteins behave.
The era of proteomics is on its way.
Acknowledgements
We thank Dr. D. Wilmshurst for reviewing the manuscript, Amersham Biosciences
and Yuan Zhou for technical assistance in our proteomic studies at the University
of Hong Kong.
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Received:
August 12, 2003Accepted: August 25, 2003
This work was supported by University of Hong Kong grants #10204004 and #10204007,
Hong Kong Research Grant Council grants #HKU7218/02M, #HKU7395/03M and #HKU7227/02M,
and a grant from the Hong Kong University Grants Committee under the Area
of Excellence Scheme
*Corresponding author: Tel, (852) 2299-0777; Fax, (852) 2817-1006; e-mail,
[email protected]
Updated
at: 12-18-2003