Proteomics to identify novel biomarkers and therapeutic targets in cardiovascular disease
Markus Kubicek, Silvia M. Sanz-González, Francisco Verdeguer and Vicente Andrés*
Laboratory of Vascular Biology, Department of Molecular and Cellular Pathology and Therapy, Instituto de
Biomedicina de Valencia-CSIC, 46010 Valencia, Spain
* Correspondence:
Vicente Andrés
Instituto de Biomedicina de Valencia
Consejo Superior de Investigaciones Científicas
C/Jaime Roig 11, 46010 Valencia (Spain)
Tel: +34-96-3391752
FAX: +34-96-3690800
e-mail: vandres@ibv.csic.es
KEY WORDS: Proteomics, Protein modification, Two-dimensional electrophoresis, Mass spectrometry,
Cardiovascular disease.
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Abstract
The proteome is described as the entirety of all proteins expressed within a cell at a given moment. In
contrast to the stability of the genome, the proteome is highly dynamic and reflects the cell’s current status.
Since proteins carry out almost all biological functions, the proteome stands in direct relation to cellular
functions. Proteomic analysis (i.e., two-dimensional electrophoresis, mass spectrometry and bioinformatics)
aims at identifying changes in the composition of the proteome associated to pathophysiologic events that affect
basic cellular functions. Functional proteomics expands to understanding the connection between proteomic
changes and the state of a cell, taken into account that the observed modifications can be either cause or
consequence of the pathological state. Proteomics will not only improve our basic understanding of the factors
and molecular mechanisms underlying cardiovascular disease, but also will help identifying novel diagnostic
markers and fuel the rational design and discovery of new drugs for medical intervention. This review will
discuss basic proteomic approaches relevant to cardiovascular disease, as well as their applications for the
identification of biomarkers and drug design.
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Outline
1. Potential of proteomic studies in disease research
2. Methodological aspects of proteomic analysis
2.1. Two-dimensional separation of proteins
2.2. Protein visualization and image analysis
2.3. Protein identification by mass spectrometry (MS)
2.4. Recent developments in proteomic technology
3. Applications of proteomics to the pathobiology of the cardiovascular system
3.1. Cardiovascular proteomic databases
3.2. Proteomic studies in cultured cells
3.3. Proteomic studies in animal models of cardiovascular disease
3.3.1. Non transgenic models
3.3.2. Transgenic mouse models
3.4. Proteomic studies relevant to human cardiovascular disease
3.4.1. Proteomic analysis of human arterial tissue
3.4.2. Proteomic analysis of human cardiac tissue
4. Concluding remarks
5. Acknowledgements
6. References
1. Potential of proteomic studies in disease research
The genetic information stored in a cell’s nucleus (the genome) is undoubtedly the underlying factor
determining cellular phenotype. Nevertheless, despite the impact of genomics in molecular biology and
medicine, the genome should not be seen as more than a rough outline of a cell’s body plan. Although a certain
percentage of diseases have been linked to genetic changes, the majority of pathological disorders are associated
with protein alterations. Since proteins not only carry out almost all bio-enzymatic functions, but they also
respond to and integrate extra- and intracellular changes, it comes as no surprise that proteins serve as the main
drug targets and biological disease markers. Thus, understanding proteomic changes in pathological situations
will help to decipher the molecular basis of disease and to elucidate the relevant protein components, pathways
and regulations.
In terms of genome size, humans are not so much superior to nematodes. So in order to understand what
makes up the complexity we inherit we have to look somewhere else than solely at the genome. DNA is a rather
rigid molecule that serves as a template for the expression of mRNA and proteins. Ultimately, it is mainly the
entirety of expressed proteins (the proteome) what accounts for the functional flexibility and complexity of the
human body. Different mechanisms contribute to phenotypic diversity (Fig. 1). First, transcription of a single
gene can give rise to different messenger RNAs (mRNAs) through utilization of alternative transcription
initiation sites and alternative splicing [1]. Specific mRNAs display highly different half-lives depending in part
on their sequence determinants, but also on the state of the cell. Still, the correlation between mRNA abundance
and the abundance of the corresponding protein has been found to be quite low [2]. This discrepancy may arise
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mainly from protein degradation, which is highly specific and subjected to cellular regulation. Additional
complexity arises because protein function depends on the right folding (secondary and tertiary structure), on
their interaction with other biomolecules (quaternary structure), and on reversible post-translational
modifications [3]. To date, more than 200 different protein modifications have been described [4], amongst them
phosphorylation, methylation, glycosilation, prenylation, and sulfatation. In addition, subcellular localization
and compartmentalization greatly affects protein function. For example, a number of proteins exert distinct
functions in different microcellular compartments. Finally, most proteins operate as parts of large protein
complexes or networks. These networks themselves are highly interconnected in complex networks that are
capable of sensing and reacting to intra- and extracellular changes.
Although genomic analysis provides important information towards susceptibility to acquire a certain
disease, it is equally important to elucidate proteomic changes induced by environmental factors which
determine disease severity and prognosis (for example in cardiovascular disease or cancer). Therefore, disease
proteomics will allow us to define the pathological state of a cell or a tissue. From this information we will be
able to derive the molecular mechanisms of the disorder, putative medical intervention targets and diagnostic
markers.
Proteomics encompasses a growing set of different techniques aimed at functionally describing the whole
proteome of a cell or a tissue in a given moment under specific conditions. Protein components of pathological
relevance may be identified by comparing the proteome of patients and unaffected individuals. Consequently,
the relevant questions we have to answer in order to ascertain the pathological relevance of a candidate protein
include the following: i) when, where and to what level is the protein expressed during disease development, and
what are the types of modifications it is subjected to; ii) what is the function of the protein and how is that
function modulated during disease progression; iii) is the candidate protein involved in protein-protein
interactions, and what can we learn from these interactions about the molecular mechanism of the pathologic
process; iv) can functional information be derived from the structure of the protein and how is this linked to the
pathology; and v) can the acquired information be used for diagnostic purpose and/or for the design of
therapeutic agents.
2. Methodological aspects of proteomic analysis
2.1. Two-dimensional separation of proteins
Proteomic technology still is in its infancy days, but methodological advances have made possible to
attack the task of analyzing the whole proteome of a clinical sample, an ambitious project given the enormous
number of expressed proteins and the diversity in potential post-translational modifications they can undergo.
Most proteomic studies aimed at elucidating disease-dependent proteome alterations have so far taken advantage
of two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) [5] (Fig. 2). The first dimension (isoelectric
focusing) is carried out using gels with a fixed pH gradient to separate proteins according to their isoelectric
point (pI). Subsequently, the proteins are resolved according to their relative masses in a second dimension by
SDS-PAGE. After completion, protein spots are visualized and subjected to mass spectrometry (MS) in order to
determine their identity [6]. Despite certain limitations, proteomics has provided useful information on disease
related changes in protein expression as we discuss below.
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In order to obtain the profile of protein expression in disease, proteins have to be extracted from complex
biological samples such as cell populations, tissues, or biological fluids. Sample acquisition and preparation is
the first and very often most critical step in 2D-PAGE. Biological fluids such as whole serum or plasma are
relatively easily accessible, however protocols for sample collection are not usually ideally suited for proteomics
due to possible protein degradation or modifications during handling. The use of serum is complicated by the
high abundance of albumin that interferes with 2D-PAGE. This problem can be overcome by prefractionating
the sample. For example, Zuo et al. described a system that allowed sample prefractionation into a few welldefined pools using microscale solution isoelectric focusing (nusol-IEF) prior to 2D-PAGE [7]. At least 6- to 30fold higher protein loads were possible for nonalbumin fractions on narrow pH range IPG gels. This method
substantially increases the dynamic range of protein detection since higher protein loads can be applied to
narrow pH range 2D-PAGE gels.
Whole tissue analysis from human patients is often required in proteomics. Tissue heterogeneity may
further complicate the analysis of clinical samples obtained by biopsy. Tissue microdissection techniques,
especially laser-capture microdissection, allow the isolation of defined cell types from whole tissue. These
approaches greatly reduce tissue heterogeneity, however the amount of protein retrieved maybe insuficient for
2D-PAGE analysis [8]. Lymphocytes and mononuclear cells can be easily isolated from whole peripheral blood
by differential centrifugation. Tissue recovery of cells can be circumvented by analyzing cultures of primary or
immortalized cell lines, an approach that has been widely used in proteomic research because yields high
amounts of protein and experimental variables can be tightly controlled. Although treatment of cultured cells
with agents that mimic, at least in part, the pathogenic process of interest may shed significant insight into
disease-related intracellular changes, it is noteworthy that the results obtained from cultured cells may not
accurately reflect the situation in the living tissue/organism.
Regardless of their source, proteins need to be extracted using a solubilization protocol that it is suitable
for isoelectric focusing. Extraction is accomplished using a chemical cocktail that has to be optimized for every
different sample. Still, a single buffer will not solubilize all the proteins contained in the sample due to their
chemical and physical heterogeneity (i. e., differences in hydrophobicity, a high range of molecular sizes, etc).
This problem can be solved if differentially extracted portions of the sample are analyzed individually. An
intrinsic limitation of gel-based proteomic techniques is that highly abundant proteins are preferentially
displayed. One approach to overcome this challenge is to reduce sample complexity by analyzing protein subsets
rather than whole cell extracts. The removal of highly abundant constitutive proteins may help detect lowabundance proteins that may be pathologically relevant. In addition, subcellular fractionation allows the
identification of interesting proteins that only exist in certain organelles and may not be visualized in whole cell
lysates. Moreover, “organelle proteomics” allows searching for protein changes in compartments that are of
special pathological interest. For example, surface membrane proteins currently comprise a large fraction of the
therapeutic targets and diagnostic markers due to their key role in disease development (i.e., cancer invasiveness
and inflammatory infiltration in atherosclerotic plaques). Of note in this regard, proteins with extracellular
domains can be biotinylated and easily extracted by affinity-purification [9].
2.2. Protein visualization and image analysis
In order to compare proteomic profiles between pathological and normal control samples, proteins
separated by 2D-PAGE must be visualized by in-gel protein staining protocols. Despite its relatively low
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sensitivity, Coomassie Brilliant Blue (CBB) R-250 is the most conventional approach for protein staining.
However, quantification of protein amount is problematic since proteins may be visualized to a different extent
during destaining. Colloidal CBB G-250 has circumvented this problem by allowing background-free detection
of proteins at a sub-microgram level [10]. Silver staining protocols are at least hundred times more sensitive than
CBB-based techniques [11], but inherent oxidative modification of the stained proteins makes it incompatible
with MS [12]. Recently, commercially available MS-compatible silver staining kits have successfully been
applied in proteomics [13]. Like silver staining, fluorescent methods allow for the visualization of a larger
number of proteins, which is especially crucial for samples available in limited amounts [12]. Fluorescent
staining of proteins combines high sensitivity and compatibility with downstream analysis and additionally
allows staining of proteins that are hardly stained by alternative methods (like glycoproteins, lipoproteins, low
molecular mass proteins and metalloproteases). In addition, alterations between the disease and the control
profiles can be easily detected using differential in-gel electrophoresis (DIGE), in which the two samples are
labeled with different fluorescent dyes and analyzed in the same two-dimensional gel [13]. Conventionally,
pattern comparison between pathological and control samples requires gel scanning and “software-supported”
analysis. Nevertheless, improvement in software for automated spot comparisons is desirable.
2.3. Protein identification by mass spectrometry (MS)
The identity of spots exhibiting different patterns between control and pathological specimens is usually
assessed by MS “fingerprinting” [15]. This technique analyzes the mass-to-charge ratio (m/z) of peptide
fragments produced by proteolytic cleavage with specific proteases (i. e., trypsin, chymotrypsin). Every protein
digested with a specific enzyme will give rise to a unique mass spectrum that can be compared against a
hypothetical genome-derived peptide-mass database. Because MS is a highly sensitive technique, special care
must be taken to prevent sample contamination. In order to be subjected to MS, the spot of interest has to be
excised from the gel and treated with the desired protease. The resulting peptide mixture is volatilized and
ionized by matrix-associated laser desortion (MALDI) or electrospray ionization (ESI) [6]. The real mass
analyzer is usually time of flight (TOF) if coupled to MALDI, or ion trap and quadrouple if ESI is used. In either
case, peptides are separated according to their specific m/z plotted against peak intensities. Computer
comparison to databases will give rise to hit lists, which are the typical informational outputs of proteomic
experiments. In some cases, however, protein identity maybe uncertain due to high background levels or
impurities (spots separated by two-dimensional electrophoresis often contain several proteins). In either case, the
peptides can be further defragmented, producing smaller sized charged fragments that are re-analyzed in a
collision induced (CID) mass spectrometer [16]. In principle, CID results in a peak pattern that also contains
information about the peptide sequence. In this way proteins are identified accurately and additional information
on the localization and identity of post-translational modifications can be gathered. Additional information on
the equipment and techniques used to analyze peptide sequence by fragmentation is provided by Aebersold and
Mann [6].
2.4. Recent developments in proteomic technology
Despite recent improvements, conventional two-dimensional MS-based analysis still holds some
limitations, especially low throughput and the need of a relatively large amount of sample. Both of these
problems are quite inconvenient for disease proteomics, where large-scale assays are desired. Several strategies
have been developed in order to enhance sample automation. For example, direct scanning of 2D-PAGE or
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trypsin containing membranes by MALDI–MS have been reported [17]. Moreover, gel-free approaches using
liquid chromatography (LC) [18] or capillary electrophoresis [19] to achieve protein separation prior to MS have
been developed, thus circumventing the tedious task of 2D-PAGE. Protein samples can be separated by coupling
two different HPLC columns in tandem and connecting them directly to a mass spectrometer. Usually, proteins
in the first chromatography are retained according to their charge using an ion exchange column. Then, in a
coupled reverse phase column, proteins are further separated according to their hydrophobicity [20]. Although
protein mixtures can be analyzed this way, enzymatic digestion of the sample prior to chromatography is more
convenient. In this approach a thousand of different peptides are separated by the HPLC that can readily be
analyzed by MS. Because subpicomolar amounts of sample are sufficient, peptides derived from low abundant
proteins that usually are not detectable by gel-based methods can be identified using this approach. Moreover,
the serial setup of tandem HPLC connected directly to the MS analyzer allows for high throughput automation.
Isotope code affinity tagging (ICAT) of proteins further increases detection sensitivity for low abundance
proteins analyzed by gel-free proteomics [14]. By using different tags, one of natural abundance and the other
isotopically labeled, quantitative differences in protein abundance between two different samples can be readily
detected. The proteome is first digested and the resulting peptides are purified and subjected to LC-ESI.
Imaging MS has been recently developed to perform in situ proteomic analysis of whole tissues without
the need of previous protein separation [21]. In this technique, frozen tissue sections are directly applied to
MALDI-MS analysis in a regular spatial manner to directly obtain mass profiles across the tissue section.
Following the success of DNA microarray chips, protein microarrays are being developed for protein
profiling and medical diagnostics [22-24]. Protein chips contain defined sets of proteins arrayed at high density
onto glass slides. Some approaches use peptides [25] or whole proteins encoded by libraries which are coated
onto the chip in order to screen for interacting molecules, or to carry out functional screens. Recently, reverse
phase protein arrays have been described that immobilize the whole protein content of a tissue on an array [26]
to screen for protein modifications or in search for autoimmune disorders [27]. Ziauddin et al. have reported the
generation of protein microarrays whose features are clusters of live cells that express a defined cDNA at each
location [28].
Protein array techniques are especially appropriate for understanding drug intervention and for finetuning of drug design. For instance, screening a protein array against low molecular weight drug inhibitors may
yield significant insight into both drug targets as well as the drug’s mode-of-action. Protein arrays are also
suitable to test the affinity of new drug derivatives. One example of such use is a recent study on FKBP12
binding to different small molecules in yeast [24]. Another useful application of protein chips is the screening
for specific enzymatic activities. Protein kinases are of high interest in drug intervention due to their key
regulatory role in biological signaling pathways. Zhu et al. have shown that many kinases remained active when
immobilized on polydimethylsiloxan (PDMS) chips and showed genuine substrate specificity [22]. As an
alternative to protein arrays, antibodies that will specifically recognize their respective antigen according to their
abundance can be immobilized onto the microchip surface [23]. Such antibody arrays are suited for protein
quantification (by using a second antibody against the bound protein), and for the detection of specific posttranslational modifications like phosphorylation. The main advantage of protein arrays is their capacity to
simultaneously analyze a high number of proteins even among different cell and tissue types. Chips can be
7
combined with direct MALDI analysis for read-out, thereby generating a highly sensitive and high-throughput
automated system.
Major limitations in current protein chips are the generation of expressing clones for a whole proteome,
the lack of post-translational modifications in bacterially expressed proteins, the short-term stability of some
proteins on synthetic slides, and the difficulties in printing homogeneous spots. Moreover, the shortness of
highly specific antibodies limits the use of antibody arrays. Nevertheless, protein chip automation is a rapidly
evolving technique with a huge potential in molecular medicine. Better and more efficient methods of
quantification need to be developed before protein microarrays will become a standard method for clinical
applications.
3. Applications of proteomics to the pathobiology of the cardiovascular system
3.1. Cardiovascular proteomic databases
Databases of cardiac proteins have been generated from human [29-35], dog [35,36], rat [35,37], and
bovine [38] tissue. The uniform resource location (URL) of some of these databases is given in Table 1.
Proteomic analysis of pathological samples can be directly compared to these databases instead of, or in addition
to, their comparison to experimental control samples. In fact, a large number of proteins extracted from dog, rat
and mouse cardiac tissue has been successfully identified by direct comparison against a human heart protein
database [39]. This provides a useful shortcut in the rough analysis of proteins of specific tissues from different
species, although confirmation of these data will be required.
3.2. Proteomic studies in cultured cells
Several studies have been carried out in primary cultures of cells that are relevant to cardiovascular
disease, including smooth muscle cells (SMCs), endothelial cells (ECs), foam cells (Table 2), and
cardiomyocytes (Table 3).
Cultured cells can be analyzed under multiple conditions and pathophysiological stimuli. For instance,
significant differences in the expression of certain proteins have been reported in proliferating versus nonproliferating primary rabbit SMCs [40]. Likewise, LC-MS/MS analysis of quiescent human umbilical vein ECs
(HUVECs) has allowed the identification of 53 proteins involved in several processes, including cytoskeletal
organization, cellular motility and plasticity, control of apoptosis and senescence, coagulation, and antigen
presentation [41]. Since both hyperplastic and hypertrophic growth play a key role in cardiovascular disease,
Patton et al. investigated alterations in the proteome of rat aortic SMCs stimulated with different growth factors
(FCS, PDGF and angiotensin II) [42]. Compared to unstimulated cells, several proteins could be identified that
underwent growth factor-dependent upregulation (i. e., heat shock proteins hsp60 and hsp70, protein disulfide
isomerase, vimentin, actin, EF-1, and calreticulin) and downregulation (i. e., myosin heavy chain). The authors
concluded that hyperplastic and hypertrophic growth processes are accompanied by similar changes in protein
expression.
Nichols et al. studied the effect of glucocorticoids on the proteome of cultured bovine aortic ECs, rat
SMCs (synthetic- and contractile-state), and rat cardiac muscle and non-muscle cells [43,44]. They observed
direct effects of glucocorticoids on ECs that may be clinically relevant in the setting of cardiovascular disease.
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They also identified an overlapping, but not identical, set of proteins of the so-called glucocorticoid domain that
are induced by glucocorticoids in the various cell lines analyzed.
Changes in the proteome during foam cell formation have been investigated using an in vitro model
consisting of exposure of U937 cells to oxidized LDL (oxLDL) [45]. oxLDL-regulated factors included 28
upregulated and 9 downregulated proteins. Moreover, 11 proteins were exclusively detected in foam cell
cultures, whereas 8 proteins detected in control U937 cultures were absent in foam cells.
Treatment of rabbit ventricular myocytes with adenosine at a concentration sufficient to mimic
pharmacological preconditioning has revealed two novel phosphorylation sites in myosin light chain 1 (MLC1),
both of which are highly conserved in rats and humans [46]. Likewise, treatment of cardiac myocytes with the
hypertrophic agent phenylephrine resulted in changes in MLC, as well as in mitochondrial proteins, hsp27 and
several chaperone cofactors [47].
The effect of blood flow in plaque development has been investigated in a 2D-PAGE study using bovine
aortic ECs subjected in vitro to different flows [48]. The gelsolin family member CapG was undetectable under
pro-atherogenic (oscillating) flow conditions, whereas its level of expression was upregulated under nonatherogenic (unidirectional) flow, in agreement with the protective function of CapG against atheroma
development.
In addition to environmental influences, age is a relevant factor in the pathogenesis of cardiovascular
disorders. Proteome comparison of cultured aortic SMCs that had been obtained from either newborn or aged
rats detected 14 differentially expressed proteins, 4 in newborn and 10 in older animals [49]. One of the protein
specific for old aortic SMCs has been identified as cellular retinol-binding protein.
3.3. Proteomic studies in animal models of cardiovascular disease
3.3.1. Non transgenic models
Using a bovine model of hereditary dilated cardiomyopathy (DCM), Weekes et al. separated over 1125
proteins from ventricular tissue. Amongst these, 24 were found decreased and 11 increased in pathological
versus control tissue, but only 12 have been successfully identified [38]. The authors concluded that
inappropriate protein ubiquitination plays a major role in DCM.
Rapid ventricular pacing in dogs results in a heart failure condition that closely resembles human and
canine DCM. Proteomic analysis of control versus paced myocardial samples indicated that the development of
heart failure in this model involves alterations in proteins involved in mitochondrial energy production,
cytoskeletal architecture, and calcium-regulated processes [50,51]. Particularly, diminished expression of
mitochondrial hydroxylmethyl glutaryl CoA synthase was observed, consistent with the hypothesis of reduced
mitochondrial energy supply during DCM. Furthermore, glycolytic enzymes were detected in increased
amounts, probably as a mechanism to counteract insufficient mitochondrial energy production.
Several animal studies have investigated changes in the protein profile in the myocardium following
ischemia-reperfusion injury. Sakai et al identified rat myocardial proteins altered upon ischemia by using
fluorescence-based 2D-PAGE [52]. Likewise, Schwertz et al. have also found marked differences in protein
expression following myocardial ischemia-reperfusion in rabbits [53]. Additionally, the authors surveyed the
effect of FUT-175, a complement inhibitor that can reduce the amount of necrosis in ischemic tissue, on
ischemia-induced myocardial protein alterations. Specially, superoxide dismutase precursor and B-crystalline
9
were preserved by drug treatment. Importantly, this assay provides an interesting application for proteomics in
order to discover therapeutic targets and to detect specific effects of pharmacological drugs.
In order to gain insight into the regulation of heart function by different blood flow rates, myocardial
tissue from distinct regions of the heart of beagle dogs has been subjected to 2D-PAGE [54]. As compared with
high flow areas, tissue from low flow regions displayed reduced nitric oxide synthase inhibition, suggesting
enhanced nitric oxide formation, a higher glycolytic and a lower fatty acid oxidation capacities.
Chronic alcohol consumption accentuates the severity of acquired immunodeficiency syndrome (AIDS)
and contributes to the development of cardiomyopathy. As concluded from proteomic alterations detected in an
AIDS mouse model, Weekes et al. have suggested that alcohol consumption exacerbates the effects of retroviral
infection on the heart by lowering the stress response, thus leading to de-protection and further cytotoxic effects
[55]. Patel et al. reported proteome alterations in cardiac tissue from rats that had been chronically exposed to
alcohol, including reduced expression of hsp60, hsp70 and desmin, whereas actin, vimentin, albumin, MLC1
and MLC2 did not change [56].
3.3.2. Transgenic mouse models
Transgenic mice have become an invaluable tool to further our understanding of cardiovascular disease.
By analyzing serum from apolipoprotein E*3-Leiden transgenic mice, Skehel et al. have identified haptoglobin
as a putative marker of early atherosclerosis [57]. The epsilon isoform of protein kinase C (PKC-) plays a
critical role in protection against injury in multiple organs, including the heart. Using 2D-PAGE and affinity
pull-down assays, Ping et al. have identified PKC--dependent signaling complexes in PKC- transgenic mice
[58]. Furthermore, complementary separation techniques coupled to LC-MS/MS showed that these regulatory
complexes include metabolism- and transcription/translation-related proteins [59].
3.4. Proteomic studies relevant to human cardiovascular disease
One important goal of proteomic analysis is to identify novel disease biomarkers, which can be used for
diagnostic purposes and to monitor disease progression and the response to therapy. Because severe clinical
manifestations of cardiovascular disease maybe prevented or limited by early medical intervention, identification
of biomarkers associated to early pathological stages is especially crucial. Major cardiovascular risk factors (i.
e., hyperlipidemia, hypertension, smoking, diabetes) are not markers of an existing pathological manifestation
(like plaque formation), nor can disease progression be followed using these indirect blood markers. Moreover,
early diagnostic of atherosclerosis is particularly difficult since disease manifestations like angina pectoris or
thrombosis occur at late phases of the disease. Therefore, the identification of biomarkers of different stages of
the disease will be of great significance. In the next section, we discuss proteomic studies of relevance for
human cardiovascular disease. It is noteworthy that only four of these studies performed mRNA expression
studies, two of them reporting a good correlation between protein and mRNA levels [48,54] and two reporting
lack of correlation between these parameters [60,67]. Enhanced transcription may contribute to increased
expression of a given protein if the corresponding mRNA is induced in a temporally appropriate manner under
the same conditions. In contrast, post-transcriptional and/or post-translational mechanisms may operate when
increased expression of a given protein is not paralleled by augmented mRNA steady-state level. Thus, it is
desirable that mRNA expression studies are performed to shed light on the results of proteomic analysis.
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3.4.1. Proteomic analysis of human arterial tissue
Proteomic studies using human arterial tissue are summarized in Table 2. By examining human coronary
artery tissue by proteomic analysis, You et al. found increased expression of ferritin light chain in coronary
artery disease (CAD) patients when compared to unaffected individuals [60]. Augmented ferritin light chain
expression is postulated to contribute to the pathogenesis of CAD by modulating the oxidation of lipids within
the vessel wall through the generation of reactive oxygen species. In another study, Duran et al. examined the
proteins secreted by cultures of arterial tissue obtained from atherosclerotic and non-atherosclerotic vessels [61].
Pathological arteries showed a much higher number of in vitro excreted proteins as revealed by 2D-PAGE.
Obviously, additional studies are required to determine which of these proteins may be genuine biomarkers of
atherosclerosis. Changes in the distribution of several cytoskeletal proteins, like vimentin, desmin and actin,
have been described within the tunica media of atherosclerotic human arteries [62]. Because some of these
alterations appear to depend on the stage of the lesion, they are candidate markers for early and advanced phases
of atheroma formation. Similarly, protein alterations associated with the initial stages of human atherogenesis
have been detected by comparing plaque-free aortic tissue, fatty streaks and fibro-fatty containing lesions
[63,64].
Age is a major cardiovascular risk factor. Using a 2D-PAGE approach, Song et al have suggested that
alterations in the proteome of atheroma-free aortic intima correlate with histological changes such as the intimal
thickening often found with aging [65]. For instance, certain proteins detected in arteries from old individuals
were rarely found in young individuals, such as 1-antichymotrypsin, haptoglobin -chain and immunoglobin G
chains. Other proteins, such as albumin and -antitrypsin, were present in both groups, although their expression
was augmented with age.
3.4.2. Proteomic analysis of human cardiac tissue
The application of proteomics to heart disease is probably the most developed area of cardiovascular
proteomics (Table 3). Pleissner et al. found a characteristic distribution of MLC by comparing human
myocardium from left ventricular (LV) and right atrial (RA) samples obtained from end-stage, failing explanted
versus healthy donor hearts [66]. In fact, expression of atrial MLC, but not -myosin heavy chain, is correlated
in vivo with increased ventricular function in patients with hypertrophic obstructive cardiomyopathy [67].
Thiede et al. [68] have identified several DCM-associated post-translational modifications as well as
differentially expressed proteins by using HPLC-separation coupled to MALDI-MS. Some alterations in
cytoskeletal proteins associated with DCM have been found in humans, such as MLC1 [69]. In another study, 88
proteins displayed downregulation in cardiac tissue from DCM versus ischemic heart disease (IHD) patients,
while 5 proteins were increased in the DCM group, with the most prominent changes occurring in the contractile
protein MLC2 and in desmins [70]. Recently, Weekes et. al found in the heart of DCM patients a 2-fold and 5fold increase in protein ubiquitination relative to IHD and control hearts, respectively [71], in agreement with
their previous studies in a bovine model [38]. Scheler et al. have investigated by 2D-PAGE the pattern of hsp27
in human myocardial tissue [72,73]. Their studies have demonstrated significant alterations in hsp27 pattern of
expression when comparing normal and cardiomyopathic hearts (DCM and IHD), and suggest that some form of
hsp27 degradation occurs during heart failure.
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4. Concluding remarks
Identification of proteome changes will fuel the establishment of novel diagnostic tests to assess an
individual’s cardiovascular risk, such as the development of atherothrombosis or heart failure. It may as well
become possible to monitor disease progression and the prognosis of cardiovascular patients by identifying
stage-specific disease markers, and to direct therapeutic strategies to the overabundance, deficiency or altered
function of a specific disease-related protein. Proteomics is also likely to facilitate both the development of new
drugs as well as the design of drug derivates of superior effectiveness and reduced toxicity. In particular,
proteomic chip technology may be employed in the fast and accurate screening of therapeutic agents by
assessing both their specificity and potency towards the target protein. Undoubtedly, proteomics is a rapidly
evolving methodology that holds great promise in cardiovascular research through the assessment of disease
related alterations that are not predictable from genomic analysis. However, proteomics still awaits further
advancements in order to satisfy the needs of effective disease diagnostics and drug therapy. Once
improvements in sensitivity, throughput and sample requirement are accomplished, proteomic techniques will
substantially expand and probably become a standard approach both in the research laboratory and in the clinic.
5. Acknowledgements
We apologize to the many colleagues whose work has not been cited due to space constrains. Proteomic
studies in the laboratory of V.A. are supported by Laboratorios INDAS (Spain) and Instituto de Salud Carlos III
(Red de Centros C03/01). M.K. is a fellow of the European Union Marie Curie Programme. S.M.S.-G. and F. V.
are fellows of the Instituto de Salud Carlos III.
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14
Table 1: On line databases of 2D PAGE maps of heart tissue and cell types of relevance in cardiovascular
pathophysiology
Source
Human,
dog
Database
rat HSC-2D PAGE
Web-URL address
http://www.harefield.nthames.nsh.uk/nhli/protein/
HEART-2D PAGE
http://userpage.chemie.fu-berlin.de/~pleiss/dhzb.html
HP-2D PAGE
http://www.mdc-berlin.de/~emu/heart/
TMIG,
Proteome http://www.proteome.jp/2D/XML/Endothel/endothel_menu.html
Database of HUVEC
Human
Rat
SWISS-2D PAGE
(U937 cells)
http://www.expasy.org/ch2dothergifs/publi/u937.gif
SWISS-2D PAGE
(red blood cells)
SWISS-2D PAGE
(platelets)
OGP-WWW database
(platelets)
HUVEC-2D PAGE
http://www.expasy.org/ch2dothergifs/publi/rbc.gif
http://www.expasy.org/ch2dothergifs/publi/platelet.gif
http://proteomewww.glycob.ox.ac.uk/2d/2d.html
http://www.huvec.com (in construction)
RAT HEART-2D PAGE http://www.mpiib-berlin.mpg.de/2D-PAGE/
15
Table 2: Proteomic studies of relevance in vascular pathobiology
SPECIES
Bovine
EXPERIMENTAL APROACH
REFERENCES
Control vs glucocorticoid-treated aortic ECs [43,44]
and SMCs
EC cultures exposed to plaque-free vs plaque- [48]
prone flows.
Rat
Control vs glucocorticoid-treated SMCs and [44]
ECs
Mitogen-starved vs mitogen-stimulated aortic [42]
SMCs
Newborn vs old SMCs
[49]
Mouse
Serum
from
atherosclerosis
prone [57]
apolipoprotein E*3-Leiden mice vs wild-type
mice
Human
Control vs atherosclerotic coronary artery
Atheroma-free thoracic aorta from young vs
old individuals
Media vs atheroma (aorta, femoral and
coronary artery)
Aortic intima from lesion free vs fibro- fatty
lesions
Proteins secreted by normal vs atheroclerotic
tissue
Quiescent HUVECS
Control vs oxLDL-treated U937 cells
Rabbit
[60]
[65]
[62]
[63,64]
[61]
[41]
[45]
Proliferating vs nonproliferating SMCs in [40]
culture
16
Table 3: Proteomic analysis of cultured cardiomyocytes and cardiac tissue
SPECIES
EXPERIMENTAL APROACH
Bovine
Control and glucocorticoid-treated cultured [44]
cardiomyocytes
Control and DCM ventricular myocardium
[38]
Rat
Mouse
Human
Control and glucocorticoid-treated cultured
cardiomyocytes
Control, isquemic and isquemic-reperfusion heart
Cardiac tissue from control and alcohol-treated
animals
Control and phenylephrine-induced hypertrophic
cardiomyocyte
REFERENCES
[44]
[52]
[56]
[47]
Cardiac tissue from PKC- transgenic and wild- [58,59]
type mice
Murine model of AIDS to asses the effect of [55]
chronic alchohol comsumption on cardiac tissue
Identification of cardiac proteins
Left ventricular and right atrial samples from
end-stage explanted and healthy heart
Control
and
hypertrophied
ventricular
myocardium
Control and diseased heart (DCM, IHD)
[29-31]
[66]
[67]
[68,70-73]
Rabbit
Control and pharmacologically preconditioned [46]
ventricular cardiomyocytes
Myocardial protein expression following [53]
ischemia and reperfusion, with or without FUT175
Dog
Cardiac tissue from regions subjected to low and [54]
high blood flow
Control and pacing-induced heart failure
[50,51]
17
GENE
GENOME
Transcription
Introns
I
II
III
IV
V
pre mRNA
Exons
1
2
3a
3b
3c
4
Alternative splicing
mRNA
1
2
3a
4
Translation
Protein 3a
1
2
3b
4
1
2
Translation
Protein 3b
3c
TRANSCRIPTOME
4
Translation
Protein 3c
Post-translational modifications
Expressed
proteins
PROTEOME
Phenotype
Fig.1: Phenotype versus genotype complexity. Gene transcription results in the generation of a
precursor messenger RNA (pre-mRNA) that includes both coding (exon) and non-coding (intron) gene
sequences. Unless alternative transcriptional starting sites are present, each gene is transcribed in a single premRNA transcript. Mature messenger RNAs (mRNA) are generated by splicing of pre-mRNAs, a process that
involves the excision of introns and joining of exon sequences. The example shows a hypothetical gene with 5
introns (I-V), 6 exons (1, 2, 3a, 3b, 3c and 4). Note that alternative splicing can give rise to three distinct
mRNAs each bearing a different variant of exon 3. mRNA translation by the ribosome will generate structural
and functional diverse proteins from the respective mRNA species. Once expressed, proteins undergo posttranslational modifications like three-dimensional folding and chemical protein modifications that can greatly
affect protein function, localization and stability. To date, more than 200 different protein modifications have
been described [4], including phosphorylation, methylation, glycosilation, prenylation, and sulfatation.
Moreover, individual proteins often function as parts of large multiprotein complexes and networks, thus adding
complexity to the regulation of protein function and stability. Taken into account the diversity of posttranslational modifications, the complexity of the proteome greatly exceeds that of the underlying transcriptome
and genome.
18
kDa
TISSUE
SAMPLING
200
115
80
50
35
27
18
Control
Pathologic
“Control”
.…...
..
.
. . . .. .
… … ..
. .
kDa
200
115
80
50
35
27
18
“Pathologic”
.
. .
.…...
.. . .
. . . .. .
… … ..
Protein
extraction and
solubilization
2
-
4 6
8 10 pH
+
Staining and image analysis
1st DIMENSION:
Isoelectrofocusing
(pH gradient)
Excision of differentially
expressed proteins
Partial proteolysis
(i.e. trypsin)
Peptide
digestion mix
kDa
Mass spectrometry
200
115
80
50
35
27
18
Bioinformatics (protein data base)
2nd DIMENSION:
SDS-PAGE
(molecular weight)
Identification of proteins and
post-translational modifications
DIAGNOSTIC MARKERS
THERAPEUTIC TARGETS
Functional
proteomics
Fig. 2: Usual steps in proteomic analysis. Control and pathological tissues are collected for protein
extraction and solubilization prior to bidimensional separation. In the first dimension (isoelectric focusing),
solubilized proteins are applied to an IPG (Immobilized pH Gradient) strip. In the second dimension, IPG strips
are subjected to SDS-PAGE. Gels are then stained and image analysis with suitable software is performed. Spots
that display differential patterns when comparing control and pathological specimens are excised from the gel
and digested with a specific protease (i.e. trypsin). The resulting peptide mixtures are analyzed by mass
spectrometry in order to identify the corresponding proteins by searching in available protein databases.
Conventional proteomic analysis and functional proteomics not only shed significant insight into the
mechanisms underlying basic biological processes, but also help identify diagnostic markers and therapeutic
targets.
19