Proteomics Strategies in Cardiovascular Research Hans-Reinhard Zerkowski,† Thomas Grussenmeyer,† Peter Matt,† Martin Grapow,† Stefan Engelhardt,‡ and Ivan Lefkovits*,† Division of Cardio-Thoracic Surgery, Kantonsspital Basel and Department of Research, University of Basel, Switzerland, and the Institute for Pharmacology and Toxicology, Department of Pharmacology, University Wu ¨ rzburg, Germany Received September 29, 2003
Cardiovascular research of the past decades dealt with classical pathophysiological descriptions, then shifted toward the identification of relevant receptors, and then proceeded to the analysis of signal transduction pathways. Most recently, hand in hand with the achievements of the human genome project, the research has gone down the road toward molecular biological “disease gene(s) mapping”. The application of proteome research will attempt to close the gap between genomic (and genetic) analysis and the physiological research. The rich source of heart surgery specimens represents an excellent starting point in data acquisition of proteomic context. Furthermore, animal models of cardiovascular diseases and deficiencies are considered, and will be explored. Examples of results from feasibility studies are given, with the emphasis on quantitative evaluation of proteomic components, hoping to discover co-regulated sets of proteins that are involved in any particular disease state. Identification of new, not yet discovered proteins will be pursued, though the emphasis of this work will be on the definition of characteristic sets of expressed proteins, which in turn might be able to delimit the state of disease and prognosis of therapy outcome. Besides the systematic issues, this paper refers to a number of methodological questions, like the comparison of the proteins detected by staining procedures and proteins detected in models in which biosynthetic labeling is applicable. Keywords: heart disease • aortic disease • cardiac tissue • blood sample • reverse remodeling • 2D gel electrophoresis • charge separation • size separation • image analysis • systems biology
Introduction For many decades, studies of diseases were based upon the search for well-defined causative agents. The science of genomics and proteomics opened the possibility of viewing the disease in a broader context. Cardiovascular research has been one of the first successful applications of proteomic research. The pioneering work of Dunn et al.1-4 provides one of the examples of a global approach in studying molecular mechanisms in cardiovascular biology and medicine. The longstanding clinical interest of our group5 fits well with the challenge of characterizing alterations in global protein expression in various experimental models used in cardiovascular biology. Chronic heart failure (CHF) has become the only cardiovascular pathology that is increasing in incidence and mortality in industrialized countries. Despite improved primary prevention, and notwithstanding effective therapy and surgical intervention, the incidence of heart failure is expected to further increase because of the anticipated growth of the elderly * To whom correspondence should be addressed. E-mail: ivan.lefkovits@ unibas.ch. † Division of Cardio-Thoracic Surgery, Kantonsspital Basel and Department of Research, University of Basel. ‡ Institute for Pharmacology and Toxicology, Department of Pharmacology, University Wu ¨ rzburg.
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population in these countries. This will undoubtedly lead to significant economic strains on the health service. The underlying molecular and (patho)physiological mechanisms responsible for the progression of heart failure remain poorly defined. It is a special value of our endeavor that we share and combine experiences from basic6-8 and clinical9 research to address pathophysiological problems. Although the title of this work implies the description of “strategies”, the actual work includes a number of experiments, some of which were more successful then others; some are ready for further scrutiny, while others are at the exploratory stage. The firm part of our strategy rests on the combination of human specimen analysis with relevant animal models. Here and in our future work, we will try to define areas of cardiovascular research that are unduly neglected.
Materials and Methods The core technology for the proteomic project is twodimensional gel electrophoresis. The actual identification of proteins is then performed either by matching spot positions to known master patterns, or by mass spectrometry of material eluted from individual spots. Two-Dimensional Gel Electrophoresis: Isodalt System. The technique of two-dimensional gel electrophoresis was developed independently in the laboratories of O’Farrell10 and 10.1021/pr034079t CCC: $27.50
2004 American Chemical Society
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Klose.11
The first dimension of separation is based on differences in the charge of polypeptides, the second dimension is a molecular separation according to mass of polypeptide molecules. Isoelectric focusing (IEF) is an electrophoretic method that separates proteins according to their charge. For many years, the charge separation matrix was based on the use of carrier ampholytes (small, soluble, amphoteric molecules) with a high buffering capacity near their pI,12 and usually it was a broad range of ampholytes covering pI values between 3 and 9 that were used. The era of ampholytes (the LKB brand was named ampholines) was followed by development of immobilized pH gradients.13-15 There are several definite advantages of the use of immobilinesstwo of them mentioned here. First, due to commercial availability of the immobiline strips a rigorous comparison of results among various laboratories became possible. Second, the loading capacity of the immobiline strips is considerably higher than of ampholine gels. A disadvantage of immobilines is in the relatively high cost and limited shelf life of the immobilines strips, and also various technical inconveniences such as the requirement for soaking the strips for a relatively long separation time. The size separation is performed in polyacrylamide matrix in an SDS milieu. The most common procedure utilizes an acrylamide gradient of 10 to 20%. In the Isodalt system, the analysis is performed simultaneously for all 20 samples.16-19 The ampholine IEF gradient as well as the gels for the second dimension are established (casted) in a single step for all 20 samples. For charge separation, the tubes are immersed in a large container (2 L) of acidic buffer, for size separation, a tank with 30 L of cooled electrolyte contents is used. The emphasis of the Isodalt system is not only to achieve reproducibility within the studied set of 20 samples, but also to obtain stable and robust experimental comparisons for consecutive experiments.20,21
Tissue, Sera, Cell Lines. There are two separate environments of applications in the proteomic research of our laboratory. In the first one, unlabeled extracts of tissues of various origin are employed, whereas in the second one, metabolically labeled cells are used. In the former usage, images of stained gels22,23 are obtained, whereas in the latter application, the readouts are based on evaluation of autoradiographic or radiofluorographic films (images).24 It has been recognized that one of the prerequisites for successful analysis is an adequate preparation of samples applied on the separation matrix. A standard sample buffer (referred to also as solubilization buffer), containing NP-40, high pI ampholines and urea16,17 is used in most instances, though a number of recipes were worked out for the solubilization of various “difficult” tissues. Several papers from Rabilloud’s laboratory describe recipes for many special uses.25-27 The art of sample preparation (solubilization of solid tissue, admixture of detergents to body fluid samples) remains the least standardized part of the entire separation system. We pay a special attention to preparation and concentration of samples of secreted proteins, and to dialyzed and freeze-dried samples. Processing of Un-Labeled Material. (a) Samples from Human Donors. Surgical specimen: myocardial tissue (atria, ventricles), aortic wall, venous and arterial vessels, pericardial liquid. In the operation theater, specimens are collected and placed in liquid nitrogen. Samples are thawed, minced, and placed in
reviews solubilizing buffer. Specimen for organ cultures are collected in RPMI 1640 media. Sera are obtained from patients according to a set schedule and stored frozen until use. Then, solubilizing buffer is admixed to the thawed serum (1 part serum + 2 parts sample buffer) and applied to the first dimension for IEF separation. (b) Samples from Animal Models. Rat or mouse heart (or other tissue) is dissected, and a portion of the tissue solubilized (10 mm3 tissue + 100 µL solubilizing buffer) and applied to the IEF separation. Processing of Labeled Material. (a) Samples from Human Donors. Sterile biopsy material from sources where proteinsynthetic ability is assumed to be retained, is labeled in organ culture28,29 setups (on floating filters overnight at high 35Smethionine label (250 µCi/ml). The samples are solublized and applied to IEF separation. (b) Samples from Animal Models. Rat or mouse heart is dissected, portions of the tissue are labeled in organ cultures28,29 as indicated above (both short [4 h] and prolonged [overnight] labeling is possible). The samples are then solubilized and applied to the IEF separation. When short labeling is required, often methionine-free medium is used, whereas overnight cultures are usually performed in full medium (i.e., in medium containing the complete equivalent of all essential and nonessential amino acids). Very sensitive cultures (in terms of cell viability) are supposed to be labeled for shorter time, whereas robust cultures can be labeled overnight. Results, in terms of the number of detected 2D gel spots, might differ considerably depending on the labeling protocol.24 Body fluids (serum, tissue exudates) can be sampled from experimental animals or can be collected upon labeling the entire animal; in vivo labeling is done only for small animals, e.g., mice and rats.30,31 Solubilizing buffer is admixed to serum samples (1 part serum + 2 parts solubilizing buffer) and applied to the IEF separation. Protein Detection by Staining and by Autoradiography or Radiofluorography. Staining. (a) Coomassie blue staining was developed some 60 years ago by Fazekas et al.,32 and it is used either in its original recipe or as a staining protocol using colloidal Coomassie staining.33 The clear advantage of the colloidal staining is the fact that excessive destaining is not necessary. As one could easily guess, colloidal Coomassie staining is considerably more expensive than straight Coomassie Blue. It should be noted that, as a rule of thumb, only those spots that are stainable by Coomassie contain enough protein to enable successful mass spectroscopy analysis. (b) Silver-staining is considerably more sensitive than Coomassie blue staining. In our case, silver-staining gives about an order of magnitude less material applied to the separation system and a better readout than Coomassie staining.22,34 Both Coomassie stained gels and silver-stained gels are fixed and thereafter scanned on a “ImageScanner” (Amersham, Zurich). Whenever required, Coomassie stained gels are sealed in plastic folders, and further processed for mass spectrometry. Autoradiography and Radiofluorography. Metabolic labeling, followed by autoradiography or radiofluorography is the most common readout in proteomic studies involving animal models, cell lines, and in some instances biopsy material, whereas unlabeled material is used when human material is analyzed. Autoradiography is performed by bringing dried gels in direct contact with XAR-8 films in casettes at room temperature. Journal of Proteome Research • Vol. 3, No. 2, 2004 201
reviews Radiofluorography is based on the impregnation of gels with diphenyloxazole (PPO), and exposure of films at temperature of -70 °C. The sensitivity of detection via radiofluorography is considerably higher than with standard autoradiography, thus the exposure time can be shortened about 8-fold. The OD saturation curve for radiofluorography is different from autoradiography, and the two detection systems cannot be combined to evaluate gels within one experiment. Radiography detection has a gratuitous advantage that another exposure (another contact dried gel + film) prolonged or shortened can yield images of required intensities, which is especially useful when one sample of a series has been inappropriately loaded (too little or too much). In some instances data acquisition is achieved by “PhosphoImager”, which we intend to employ in the future experiments. For drying, we use an apparatus constructed in the workshop of the Basel Institute for Immunology. The apparatus enables simultaneous drying of all 20 gels from the Isodalt system; it is constructed as a system of 10 drawers, each platform accommodates two gels, and during the (overnight) drying cycle, it regulates heat and vacuum. Blueprints of the system are available from the corresponding author upon request.16 Image Analysis, Master Pattern, and Spot Matching. There are many sophisticated software systems for the evaluation of 2D gel images. We are using a software package originally developed by John Taylor,18 now named “Kepler”.19 All relevant information on each and all spots is stored in a relational database. The program keeps track of all images, spot lists, and spot identities, and maintains congruence in the whole system. The image files are processed for noise and streak removal and background correction, and then converted into spot files by Gaussian spot modeling and fitting. The final ‘spot lists’, in which each spot is defined by five parameters (the x and y coordinates and the spot volume parameters sx, sy, and amplitude) are compared to each other. At the end of the matching process, the master pattern contains all the spots occurring in each of the images. Information on each and all spots is stored in a relational database. Since the master image contains besides the x-, y- coordinate positions also structural information on the spots (although not in all instances), the result of the matching provides structural identity to the newly matched spots. Statistical Significance of the Data and Correlation of Data Sets. For modeling purposes, the shape of a spot is best described as a three-dimensional Gaussian curve. Some spots, especially those in which no interference with nearby spots occurs, fulfill this theoretical consideration perfectly. Many other spots are modeled ‘as-best-they-could’ applying sophisticated algorithms, as implemented by the Kepler-software developers. Such spot models are in most instances satisfactory, though a difficulty might become apparent when comparing congruent spots across several gels: some spots that happen to migrate slightly differently, are modeled by the software in such a manner that illegitimate spot matching results. In such an instance, improper correlation is obtained. This is the reason we insist (wherever agreed by the publisher) on presenting both the images and the spot models. We assume that quantitation is correct if the following holds: • upon visual inspection, no interference with nearby spots is detected; and • the spot volume (as defined by Kepler software) does not differ more than 30%. 202
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It should be noted that a comparison of two high-quality gels yields highly significant results both in terms of spot identity and differences, although this statement is true when considering the entire set, but might be false for a given spot pair.
Results Although the aim of our experimental work is as follows: (a) to obtain data through which we might be able to unravel the molecular basis of cardiovascular diseases; (b) to develop reliable diagnostic tools, based on noninvasive sampling; and (c) to acquire results that would have predictive value for disease development, we realize that to achieve this, we must choose adequate disease models. Disease Models Amenable for Proteomic Analysis. Since we have access to biopsy material and surgical specimens, the emphasis of our work is on elucidating those disease states that are in some causal relationship with the available sampling sets. Wherever possible, we seek to establish animal models where more rigorously controlled conditions can be instituted. We list below diseases that we encounter in our department and which we know to be amenable for proteomic analysis. Heart Failure. Heart failure is induced or maintained by different pathophysiologic conditions, i.e., ischemic heart disease (coronary artery disease), advanced heart valve disease, arterial hypertension, dilative cardiomyopathy, and myocarditis. Additional causal factors are viral infections, chronic alcohol abuse, toxic agents, and genetic factors. The complex pathogenesis of heart failure still remains to be determined. In almost all forms of heart disease, the initial step is the compensatory left ventricular hypertrophy, which can besif treated properlys still reversed. Beyond this stage, an “inadequate hypertrophy” develops which goes hand in hand with progressive exhaustion of the myocardial energetic resources, alterations of the cardiomyocytes, and changes of the extracellular matrix, and finally, these conditions yield a terminal heart failure. On the basis of these considerations, the optimal time interval for a surgical intervention can be defined as the point in time in the natural course of a chronic heart valve disease at which all changes of myocardial adaptation are (upon surgery) still completely reversible. The major problem in this setting is that the clinical, echocardiographic, or hemodynamic parameters that define the severity of the condition (e.g., of a heart valve lesion) do not offer an early and precise determination of the transition from “adequate” to “inadequate” hypertrophy35. The proteomic approach might provide an important clue toward the precise molecular pathways in the development and time course of these diseases. Heart Transplantation. For decades, the “gold standard” therapy for patients suffering from severe CHF (in almost all age groups) is heart transplantation. Nevertheless, due to shortage of donor organs, only a small number of patients receive this therapeutical option. Long-term success of heart transplantation is limited by the development of transplantassociated coronary artery disease, as well as chronic rejection and malignancies. The proteomic approach might become a powerful instrument for monitoring allograft rejection. The right ventricular endomyocardial biopsy serves as a source for diagnostic analysis, though this is an invasive procedure. We will attempt to generate a proteomic approach, using peripheral blood
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samples with the aim toward avoiding biopsy and defining a proteomic pattern usable as a grading score for the detection of chronic or acute rejection. Transplatation-associated coronary artery disease (TxCAD) is a serious complication of heart transplantation. It implies a major threat to the survival of the allograft, because the disease affects not only the large, epicardial arteries, but also the intramyocardial branches, as well as the capillary network and the venous system.36 Tissue samples of hearts (vessels, myocardium) explanted due to cardiac rejection will be inspected with this technique in order to scrutinize the yet undefined pathogenetic alterations. Reverse Remodeling after Mechanical Cardiac Support. Shortage of donor organs underscores the burning need for mechanical left ventricular assist devices (LVAD). The mechanical LVAD has become a reliable tool toward stabilizing the conditions of medically refractory patients with end-stage heart failure who are awaiting heart transplantation. A growing body of evidence indicates that LVAD support triggersswithin the failing human heartsa multitude of adaptational mechanisms that are activated by hemodynamic unloading of the left ventricle and by changes in intracardiac and systemic neurohumoral activity. Several studies have demonstrated that mechanical relief of the heart with an LVAD decreases the heart size,37 improves cardiac function,38,39 and decreases the concentration of plasma neurohormones and cytokines.40 Furthermore, such an intervention normalizes the expression of cellsurface receptors,41 inhibits the apoptotic pathway,42,43 and decreases ventricular expression of natriuretic peptides and of tumor necrosis factor-R. It has been suggested that mechanical unloading provides, in some instances, enough betterment to make heart transplantation superfluous. However, predictors for cardiac reverse remodeling are still lacking. Our goal is to compare the proteome of cardiac tissue removed at LVAD implantation, combined with analysis of blood samples taken during the time course of the LVAD support. Aortic Disease. Arteriosclerosis and degenerative changes are present in most patients undergoing cardiac surgery. Aortic aneurysm (dilated aorta) and aortic dissection are less common; however, if present, they are acutely life-threatening due to the risk of sudden aortic rupture. The actual molecular basis of the pathophysiological mechanisms is largely unknown. Investigations using proteomics to determine these pathways have rarely been reported. Proteomic Patterns. In the presented exploratory phase of this project, we are looking at establishing models utilizing three species: mice, rats, and humans. In many instances, especially when using human specimens, the comparison of proteomic patterns is based on spot visualization upon staining (Coomassie, silver, Cypro Ruby), whereas in other cases, especially with mice and rats, metabolic labeling with radiofluorographic readout is utilized. Proteomic patterns presented in this work are all based on silver-staining. The Kepler image analysis system, as we have utilized it in previous projects, allows for quantitative evaluation of spot intensities. In the presented set of experiments, we proceeded “as if” quantitative analysis would be justified, but we know that silver-stained gels allow only evaluation of the relative proportion of spot intensities (as indicated below in the Discussion section). Transgenic Mice. Proteomic patterns from heart samples of three β1-adrenergic receptor transgenic mice44,45 and three wildtype mice were obtained as described in the Material and
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Figure 1. Silver stained proteomic pattern of left ventricular tissue of the wild-type counterpart of the β1-ardenergic receptor transgenic strain. The proteomic pattern is based on wide range IEF separation (3-9 pI range) and size separation in 10-20% acrylamide gradient gels in SDS milieu.
Methods sections. Silver-stained gels were scanned and the images were submitted to Kepler image analysis system. The resulting spot lists were matched to the master pattern. The choice of the mastern pattern and its construction are indicated in the Material and Methods section and in the respective figure legends. Figure 1 represents an image of a silver stained gel with a selected frame on which spot models obtained upon Kepler analysis are superimposed. In this work, the modeling is shown only for a portion of gel. The database will contain complete information on all spots from all patterns. Solubilized sample has been submitted to isoelectric focusing separation followed by polyacrylamide gradient size separation in an SDS milieu. The gel has been silver stained and scanned. Portion of the image is superimposed with Gaussian spot model representations. The pattern of the image analysis is worked out for the entire image, but here only that portion which is discussed in this work is shown. The mentioned frame will be seen in Figure 2a. The image shown in Figure 1 has been selected for the construction of the master pattern (any other gel of the series could be chosen, though usually the “nicest” image is taken). Upon the matching procedure with other images, the master pattern will undergo an alteration, such that spots which are present in other patterns but absent in the “starting” master are electronically transferred to the list (i.e., to the master). The final master cumulatively acquires all of the spots from all of the compared images. The spots shown in the mentioned frame (with the prominent myosin light chain spot) have been evaluated in all six gels (three wild types and three transgenes) and the relevant gel patterns are shown in Figure 2a. There are 28 spots modeled in the Gaussian fashion, matched for obtaining congruent master numbers (Figure 2b), and since this modeling is of quantitative character (for this issue see also the Discussion section), the abundances are compared for every spot in all six patterns. Figure 2c displays 28 bar graphs. Journal of Proteome Research • Vol. 3, No. 2, 2004 203
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Figure 2. Comparison of the proteomic patterns of the wild type and β1-ardenergic receptor transgenic mice. In “panel a” six frames (numbered 1-6) are displayed. The frames are selected areas of images as depicted in Figure 1. The frames 1-3 are from wild-type samples (M301, M307, M309), frames 4-6 from transgenic mice (M305, M306, M308). For clarity, the patterns 1-3 in the upper set are labeled wt (wild type), whereas patterns 4-6 are labeled trg (transgenic). In each pattern, a frame is drawn, and the detected spots are supperimposed by ellipses obtained by the image analysis. The same set of six pattern is shown then in the “panel b”, now upon completion of the matching procedure. Next to each spot the master number is printed. The size of the ellipse reflects the spot abundance, though it should be noted that the third dimension, i.e., the “spot amplitude” is not perceived. In the “panel c” quantitative comparison for all displayed spots is shown as a set of bar diagrams. For clarity, one spot (we have chosen spot 37) is shown in an enlarged fashion, to visualize that the six bars reflect the “spot volume” of the six compared patterns. Six bars (note the numbering 1-6 on the x-axis of the enlarged spot) refer to the above-mentioned six frames. The y-axis gives the spot volume (spot abundance) in arbitrary units. It should be noted that not all spots are present in all six patterns (e.g., the graph for spot 380 shows an extreme case where only in the first and third graph position there is a bar; see “panel b” at the lower left corner of the patterns 1 and 3 [missing in all others]). Note that the ‘master spot numbers’ (msn) are spot descriptors that are congruent for all compared patterns.
Proteomic Pattern of Rat Heart Specimen. In the preliminary set of experiments with rat heart samples, we concentrated 204
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on testing the reproducibility, both when comparing samples form different animals, and also when running samples from
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Figure 3. Proteomic pattern of rat heart. Comparison of a portion of silver-stained gels from rat heart samples. Besides spots with silver deposition a cluster of “negatively” stained spots is displayed.
the same animal in parallel. In Figure 3a,b, portions of two silver stained proteomic images are displayed. It can be seen that both the overall patterns as well as the details are well reproducible. Furthermore, in this particular staining, the regions containing negatively stained spots are visible. We intend to explore also other staining procedures (e.g., with Cypro Ruby) in order to visualize these spots in a meaningful manner. Heart and Aorta in the Human Model. A prerequisite for successful proteomic analysis is the ability to distinguish the spot pattern of various tissue sources. The patterns might differ in many quantitative aspects but beside that there are often some “cornerstone” protein spots, which through their position and high abundance are “diagnostic” for the given pattern. In Figure 4 we present the proteomic pattern of human heart specimen and for illustration a frame has been chosen that leads to Figure 5 in which the left frame displays the pattern of aorta proteins, and the right frame the heart proteins.
Figure 4. Proteomic image of a human heart surgical specimen. Silver-stained pattern was scanned and processed as described in Figure 1. A frame indicates the portion of the gel that will be shown in Figure 5 in comparison with other tissue.
Black ellipses indicate (for adequate orientation) spots that are common for both specimen, whereas red and blue ellipses show those spots that are present only in one of the tissues (details are given in the figure captions). In the upper left corner, a cluster of spots of the tropomyosin family is displayed showing that aorta and the left ventricle contain tropomyosin components in different proportions. Clearly, if through the disease, tissue damage would cause protein secretion into the circulation, we would find it in the proteomic pattern of the serum. Conversely, new proteins found in the serum might indicate the tissue origin, and thus the place of pathological changes. All of the above experiments were based on silver-staining readout, and at present 35Smethionine labeling is being performed (in rat model) in order to obtain an insight in metabolic activity in the treated and untreated animals.
Discussion On one hand, this work is supposed to indicate proteomic research directions that might yield “added value” to all those existing sophisticated reports on the proteomics of heart diseases.1-3,46-48 On the other hand, this work is meant to be a strategy paper, such that we describe a path toward: (a) obtaining data relevant to the molecular basis of cardiovascular diseases,
Figure 5. Examples of prominent polypeptide spots in human heart and aorta. Frame from Figure 4 displays in the left panel a portion of the proteomic pattern of aorta, in the right panel the same portion of the proteomic pattern of the left ventricle. Circled spots indicate some protein spots as discussed in the text.
(b) acquiring results with predictive value for disease development, and (c) developing noninvasive diagnostic tools. Genomics, Transcriptomics, Proteomics. It is well-known that not every molecular species of mRNA is involved in the protein synthetic machinery of a cell. Although genes are Journal of Proteome Research • Vol. 3, No. 2, 2004 205
reviews subject of transcriptinal control, mRNA molecules obey the rules of a translational control.49 Inasmuch, transcriptomic analysis allows insights into the ‘readiness’ of the cell to synthesize given polypeptides, it is the proteomic analysis that elucidate the actual set of expressed proteins. In other words, transcriptomic analysis provides only part of the information, the other, more diverse, portion is achieved by proteomic analysis. Metaphorically speaking, the gray reality of the transcriptome is complemented with the colorful kaleidoscope of the proteome). Post-translational modifications of the newly synthesized polypeptides determine to a large extent the function, fate, and destination of the protein. Improper modifications might alter the function of the cell, and might be characteristic of the disease. Examining changes in the proteome might provide insights into understanding molecular mechanisms of the disease. There are numerous examples of cardiovascular functions whose molecular pathways are mediated through posttranslational processes such as phosphorylation, glycosylation, and myristylation, as well as ubiquitination and proteolytic cleavage. If conditions of cardiac hypertrophy are of multiple genesis, we need to knowsin order to ensure adequate therapysthe molecular cause and etiology. If a fast proteomic differential diagnosis allows us to classify a cardiac disease, outcomes such as heart failure might be prevented. Genomic approaches (clarifying sequence homologies or mutations), transcriptomic approaches (indicating transcriptional regulations, alternate splicing, or mRNA half-lives), and proteomics-based research, taken together, will greatly increase our understanding of cardiovascular pathophysiology. Human Material and Animal Models. Proteomic comparisons of patients suffering the same disease symptoms are often difficult, because differences in medical history, therapeutic interventions, genetic polymorphism, tissue inhomogeneity, and so forth might mask the disease-induced alterations. Nevertheless, sequential comparison of body fluids (sera, urin) on a single individual will always be of great value. The main animal model until recently was a rat (although one should not underestimate the usefulness of bovine and canine species). The availabilty of various strains of knock-out mice allows a fresh approach toward the study of molecular mechanisms of certain gene disfunctions. For studying ischaemic heart disease (e.g., induced by coronary artery ligation), the rat will remain the animal of choice. Drawing conclusions for the human species will remain problematic, because a large number of proteomic components is characterized by different isoforms of proteins. Proteomic and Subproteomic Sample Preparation. The rule of the thumb is to work with total protein extracts, to avoid multistep fractionation procedures. If intermediate steps toward obtaining less complex pattern are required, we prefer to isolate subcellular compartments (organelles) rather than to fractionate cell lysates. The reason is that subcellular compartments can be precisely defined and reproducibly characterized. An extract of a membrane fraction, or endosomes, lysosomes, mitochondria is of highly reproducible quality, whereas a fraction via, say, affinity chromatography or immunoprecipitation depends on many physicochemical characteristics, and as a rule it differs from lab to lab. Fractionation of proteins with certain characteristics (e.g., basic proteins, histones), or special solubilization procedure for some tissue is often unavoidable. Thus, the original wide pI range separation (pI 3-10) turns out to be not of universal 206
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use, and other separation protocols, and separation matrixes become necessary. Often IPG instead of ampholines must be used and also alterations in the solubilizing buffers become necessary. In the case of IPG, the narrow range pI strips are very useful, and due to commercial availabilty highly reproducible gels (comparable from lab to lab) can be obtained. The bottleneck of the entire system remains adequate sample preparation. This is especially true for cardiac tissue from human biopsies. Sequential extraction with increased solubilizing power50 turns out to be a useful method. Proteome Detection by Staining and Upon Biosynthetic Labeling. The dichotomy of the two detection methods is apparent. The staining procedures detect the molecular components that are present in the sample irrespective of the time interval of synthesis. Radiofluorography visualizes protein components that were synthesized in the time period of biosynthetic labeling (35S-methionine). Furthermore, not every protein spot is stainable by silver and not every protein contains in its sequence a methionine residue. The resulting pattern of a silver-stained image and radiofluorographic image is surprisingly similar, though the details are in many instances indeed different. Often a strong silverstained spot has a weak radiographic counterpart and vice versa. One should be aware of the fact that the information content of the two kinds of spots is different. Wherever possible, we prefer to obtain both silver-images and radiolabeled patterns. Silver staining reveals typically a pattern of some 10001200 spots, while radiofluorography reveals up to 2000 spots. Overloaded gels or prolonged exposures of the films yield additional spots in some areas of images, while confluence of spots and streaks occur in other parts of the pattern. Such patterns could not be analyzed anymore by Kepler system. As indicated in the result section, silver-stained spots can be quantitated, but the spot intensities cannot be compared “across the gel”. Thus, for example, if matched spots of myosin light chain on one gel display a 3-fold higher intensity than on another gel, the comparison is probably a valid one. If, however, spot 133 is of 3-fold intensity to spot 134 on the same gel, then the mentioned ratio should be taken with caution. The reason is that the molecular properties, in terms of the ability to bind silver, might be in the mentioned polypeptides different. This is especially apparent when attempting to compare “normal” fully stained silver spot with a “negatively stained” spot. Abundant and Rare Proteins. A proteomic pattern based on a wide range pI separation procedure displays polypeptide components of high and intermediate abundance, whereas low abundance proteins are present below detection limit. Silverstaining will miss most of the protein species that are present in the cell at less than 10 000 copies (per cell), whereas radiofluorography will miss all of those protein species that have a slow turnover (and, of course, those that do not have a methionine residue in their sequence); protein species with an abundance of 1000 copies (per cell) will fall short of detection. The 100 most abundant proteins comprise about 90% of the mass of cellular proteins, whereas several thousand remaining molecular species of proteins account for the rest 10% of mass. The most prominent of the high abundance polypeptide is actin, and it is present at 107 copies per cell. Taking into account all of the visualized spots in a proteomic pattern (say 1200 spots) they encompass some 97-99% of protein mass (disregarded are those proteins that fail to enter the separation system), but the ecliptic 1-3% of protein mass might relate to several thousand very low abundance proteins (especially
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Proteomics Strategies in Cardiovascular Research
regulatory proteins that might be present at say 10-50 copies per cell). Even if we neglect the protein isoforms by virtue of co- and post-translational modifications (e.g., phosphorylation, glycosylation), and also by metabolic processing (activation cleavage), the undetected polypeptide species will remain a major challenge for the future identification effort. The above considerations were taken from our results on lymphocyte proteomics.51 Heart-related tissue might be even more extremely biased, such that maybe twenty most prominent proteins make up the mentioned 90% of protein mass. The missing very low abundance proteins might be of a special concern to the research related to “structure discovery” but are of lesser concern in our strategy. As it will be discussed in the next paragraph, we will be able to achieve our goal with those molecular species of proteins that are visualizable. Cluster Analysis and Numerical Taxonomy. If one is told that the expression of a given molecule has been “shut down”, or “switched off”, it is almost certain that in the strict sense, this is not the case. Even in a “knock-out” situation, in which the gene product should not be present at all, leakiness is often reportedsmeaning that a certain number of molecules is present (leakiness occurs in instances where a regulatory gene is defunct while the structural gene is intact). Many events for which we assume an all or none quality, are in fact based on quantitative changes. If we accept the notion that discovery of a single regulatory changeseven if it represents the main componentsdoes not stand for the entire process, we have to accept that new methods of descriptions, aiming at analyzing “sets of changes” will have to be applied. Principal component analysis, cluster analysis, numerical taxonomy are the terms, we have to get acquainted with51 even if they are not intuitively descriptive. Part of our strategy in cardiovascular research will be to describe “sets of changes” diagnostic for the given disease or even for a given stage of disease. Mass Spectrometry and Diminishing Returns. We will be submitting some gels, (or more precisely some spots from some of the gels) to structure analysis by mass spectrometry, whereas for the rest we focus our attention to define “sets of characteristic spot alterations”. As has been mentioned above, all of our efforts refer to polypeptide species present at high or intermediate abundance. In many research institutes, molecular structures of low abundance proteins are now under scrutiny. Special enrichment procedures are being worked out for analysis of abundances of 10-1000 polypeptide copies per cell. Since each step in the increase of sensitivity is more and more cumbersome, the invested effort gives steadily diminishing returns. Large Scale Cohort Studies. The Isodalt system is ideally suited for running a large number of gels, i.e., to compare many samples. We intend to embark on studies in which serum from a large number of donors (and for a long term follow period) will be analyzed for proteomic alterations. Physiology and Pathology. The pioneering efforts of several research groups in establishing protein databases of human cardiac proteins (HSC, HEART, and HP)52-54 have formed a good basis for further exploration.55 Part of our efforts will go toward defining molecular mechanisms of physiologically intact cardiovascular systems. The remaining part of our activity will be geared toward studying pathological conditions, as well as alterations of the proteomic patterns during the recovery phase upon medical treatments.
Concluding Remarks It would be pleasantsthough somewhat dullsif biological systems would be such that a “single cause” would lead to a “single consequence”. Such simple systems (highly sensitive to all sorts of environmental influences) might have existed early in evolution, but they were replaced by more sophisticated ones. The basic property of a complex biological system is that a failure of any component is compensated by some sort of “fail-safe” mechanism. In our research, we apply this holistic view, and join the effort that is now defined as the so-called “systems biology”. In cardiovascular research, the dichotomy of recognizing multiple changes, while searching for a prime target affecting these multiple changes is of utmost importance. The function of the heart, the principal motor that keeps the body going, is indeed backed up by a large number of fail-safe mechanisms. Proteomic markers mentioned in this study (like tropomyoisin, myosin light chain, or for that matter, actin and tubulin) are considered to be only markers for orientation, while associations with entire sets of co-regulated molecules remain to be discovered. Abbreviations. CHF, chronic heart failure; IEF, isoelectric focusing; IPG, immobilized pH gradient; ISODALT, acronym for 2D gel system; LSB, large scale biology; LVAD, left ventricular assist device; MSN, master spot number; PPO, diphenyloxazole; TxCAD, transplantation associated coronary artery disease
Acknowledgment. I.L. is indebted to the Research Department of the Kantonsspital Basel for providing generous conditions toward establishing a new proteomics laboratory. The entire equipment for proteomics has been transferred from BIIswith the generous consent of F. Hoffmann-La Roche, Baselsto the new facilities. The authors thank Emmanuel Traunecker for his diligent work in the proteomics laboratory. References (1) Dunn, M. J. Biochem. Soc. Trans. 1997, 25, 248-254. (2) Dunn, M. J. Drug Discovery Today 2000, 5, 76-84. (3) Jiang, L.; Tsubakihara, M.; Heinke, M. Y.; Yao, M.; Dunn, M. J.; Phillips, W.; dos Remedios, C. G.; Nosworthy, N. J. Proteomics 2001, 1, 1481-1488. (4) McGregor, E.; Dunn, M. J. Hum. Mol. Genet. 2003. (5) Zerkowski, H. R.; Grapow, M. T.; Todorov, A.; Morawietz, H. Z. Kardiol. 2000, 89 Suppl. 7, 76-84. (6) Lefkovits, I.; Kettman, J. R.; Frey, J. R. Electrophoresis 2000, 21, 2688-2693. (7) Lefkovits, I. Res. Immunol. 1995, 146, 5-10. (8) Lefkovits, I. Folia Microbiol. (Praha) 1995, 40, 405-412. (9) Zerkowski, H. R.; Preiss, M. Z. Kardiol. 2001, 90 Suppl. 6, 6569. (10) O’Farrell, P. H. J. Biol. Chem. 1975, 250, 4007-4021. (11) Klose, J. Humangenetik 1975, 26, 231-243. (12) AmershamBiosciences; http://www.electrophoresis.apbiotech. com. (13) Gorg, A.; Boguth, G.; Obermaier, C.; Posch, A.; Weiss, W. Electrophoresis 1995, 16, 1079-1086. (14) Righetti, P. G.; Gianazza, E.; Bianchi-Bosisio, A.; Sinha, P.; Kottgen, E. J. Chromatogr. 1991, 569, 197-228. (15) Sinha, P.; Kottgen, E.; Westermeier, R.; Righetti, P. G. Electrophoresis 1992, 13, 210-214. (16) Anderson, N. G.; Anderson, N. L. Anal. Biochem. 1978, 85, 331340. (17) Anderson, N. L.; Anderson, N. G. Anal. Biochem. 1978, 85, 341354. (18) Taylor, J.; Anderson, N.; Coulter, B.; Scandora, A.; Anderson, N. In Electrophoresis ‘79; Radola, B., Ed.; W.de Gruyter: Berlin, 1980. (19) Anderson, L. A. Kepler Software Manual; Rockville, 1992.
Journal of Proteome Research • Vol. 3, No. 2, 2004 207
reviews (20) Anderson, N. L. Two-Dimensional Electrophoresis; Large Scale Biology Press: Washington, 1988. (21) Lefkovits, I.; Young, P.; Kuhn, L.; Kettman, J.; Gemmell, A.; Tollaksen, S.; Anderson, L.; Anderson, N. In Immunological Methods III; Academic Press: Orlando, 1985. (22) Kettman, J.; Lefkovits, I. Eur. J. Immunol. 1984, 14, 778-81. (23) Kettman, J.; Lefkovits, I. Clin. Chem. 1984, 30, 1950-5. (24) Kettman, J.; Burnham, K.; Lefkovits, I. J. Immunol. Methods 1988, 114, 235-241. (25) Santoni, V.; Molloy, M.; Rabilloud, T. Electrophoresis 2000, 21, 1054-1070. (26) Rabilloud, T. Electrophoresis 1998, 19, 758-60. (27) Lucchiari, M. A.; Pereira, C. A.; Kuhn, L.; Lefkovits, I. Res. Virol. 1992, 143, 231-40. (28) Frey, J. R.; Hartmann, K. U.; Lefkovits, I. Dev. Immunol. 1996, 5, 53-66. (29) Gajendran, N.; Frey, J. R.; Lefkovits, I.; Kuhn, L.; Fountoulakis, M.; Krapfenbauer, K.; Brenner, H. R. Proteomics 2002, 2, 16011615. (30) Pluschke, G.; Lefkovits, I. Clin. Chem. 1984, 30, 2043-2047. (31) Pluschke, G.; Jenni, L.; van Alphen, L.; Lefkovits, I. Clin. Exp. Immunol. 1986, 66, 331-339. (32) Fazekas de St. Groth, S.; Webster, R.; Daytner, A. Biochim. Biophys. Acta 1936, 71, 377-391. (33) SLRI Proteomics Database: http://192.197.250.118/sampePreparation.html. (34) Switzer, R. C., 3rd; Merril, C. R.; Shifrin, S. Anal. Biochem. 1979, 98, 231-237. (35) Horstkotte, D.; Piper, C.; Zerkowski, H. R.; Bergemann, R.; Dahm, M.; Hein, S.; Osswald, B.; Riess, F. C.; Steinhoff, G. J. Heart Valve Dis., in press. (36) Labarrere, C. A.; Nelson, D. R.; Park, J. W. Curr. Opin. Cardiol. 2001, 16, 110-117. (37) Levin, H. R.; Oz, M. C.; Chen, J. M.; Packer, M.; Rose, E. A.; Burkhoff, D. Circulation 1995, 91, 2717-2720. (38) Nakatani, S.; McCarthy, P. M.; Kottke-Marchant, K.; Harasaki, H.; James, K. B.; Savage, R. M.; Thomas, J. D. J. Am. Coll. Cardiol. 1996, 27, 894-901. (39) Frazier, O. H.; Benedict, C. R.; Radovancevic, B.; Bick, R. J.; Capek, P.; Springer, W. E.; Macris, M. P.; Delgado, R.; Buja, L. M. Ann. Thorac. Surg. 1996, 62, 675-681; discussion 681-682.
208
Journal of Proteome Research • Vol. 3, No. 2, 2004
Zerkowski et al. (40) Goldstein, D. J.; Moazami, N.; Seldomridge, J. A.; Laio, H.; Ashton, R. C., Jr.; Naka, Y.; Pinsky, D. J.; Oz, M. C. Ann. Thorac. Surg. 1997, 63, 971-974. (41) Morawietz, H.; Szibor, M.; Goettsch, W.; Bartling, B.; Barton, M.; Shaw, S.; Koerfer, R.; Zerkowski, H. R.; Holtz, J. Circulation 2000, 102, III188-193. (42) Milting, H.; Bartling, B.; Schumann, H.; El-Banayosy, A.; Wlost, S.; Ruter, F.; Darmer, D.; Holtz, J.; Korfer, R.; Zerkowski, H. R. Thorac. Cardiovasc. Surg. 1999, 47, 48-50. (43) Bartling, B.; Milting, H.; Schumann, H.; Darmer, D.; Arusoglu, L.; Koerner, M. M.; El-Banayosy, A.; Koerfer, R.; Holtz, J.; Zerkowski, H. R. Circulation 1999, 100, II216-223. (44) Engelhardt, S.; Grimmer, Y.; Fan, G. H.; Lohse, M. J. Mol. Pharmacol. 2001, 60, 712-717. (45) Engelhardt, S.; Hein, L.; Wiesmann, F.; Lohse, M. J. Proc. Natl. Acad. Sci. USA 1999, 96, 7059-7064. (46) Arrell, D. K.; Neverova, I.; Van Eyk, J. E. Circ. Res. 2001, 88, 763773. (47) Scheler, C.; Li, X. P.; Salnikow, J.; Dunn, M. J.; Jungblut, P. R. Electrophoresis 1999, 20, 3623-3628. (48) Jager, D.; Jungblut, P. R.; Muller-Werdan, U. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2002, 771, 131-153. (49) Garcia-Sanz, J. A.; Mikulits, W.; Livingstone, A.; Lefkovits, I.; Mullner, E. W. Faseb J. 1998, 12, 299-306. (50) Molloy, M. P.; Herbert, B. R.; Walsh, B. J.; Tyler, M. I.; Traini, M.; Sanchez, J. C.; Hochstrasser, D. F.; Williams, K. L.; Gooley, A. A. Electrophoresis 1998, 19, 837-844. (51) Lefkovits, I.; Kuhn, L.; Valiron, O.; Merle, A.; Kettman, J. Proc. Natl. Acad. Sci. USA 1988, 85, 3565-3569. (52) Evans, G.; Wheeler, C. H.; Corbett, J. M.; Dunn, M. J. Electrophoresis 1997, 18, 471-479. (53) Pleissner, K. P.; Sander, S.; Oswald, H.; Regitz-Zagrosek, V.; Fleck, E. Electrophoresis 1996, 17, 1386-1392. (54) Muller, E. C.; Thiede, B.; Zimny-Arndt, U.; Scheler, C.; Prehm, J.; Muller-Werdan, U.; Wittmann-Liebold, B.; Otto, A.; Jungblut, P. Electrophoresis 1996, 17, 1700-1712. (55) Pankuweit, S.; Lottspeich, F.; Maisch, B. Eur. Heart J. 1995, 16 Suppl. O, 81-84.
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