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Human Molecular Genetics, 2003, Vol. 12, Review Issue 2 R135-R144
DOI: 10.1093/hmg/ddg278
© 2003 Oxford University Press

Proteomics of heart disease

Emma McGregor1 and Michael J. Dunn2,*

1Proteome Sciences plc and 2Department of Neuroscience, Institute of Psychiatry, Kings College, London SE5 8AF, UK

Received July 3, 2003; Accepted August 11, 2003


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 GENOMES AND COMPLEXITY
 GENE EXPRESSION
 PROTEOMICS
 PROTEOMIC TECHNOLOGIES
 PROTEOMICS OF HEART DISEASE
 PROTEOMICS VERSUS...
 CONCLUDING REMARKS
 REFERENCES
 
Heart diseases resulting in heart failure are among the leading causes of morbidity and mortality in developed countries. The underlying molecular causes of cardiac dysfunction in most heart diseases are still largely unknown, but are likely to result from underlying alterations in gene and protein expression. Proteomics now allows us to examine global alterations in protein expression in the diseased heart and will provide new insights into cellular mechanisms involved in cardiac dysfunction and should also result in the generation of new diagnostic and therapeutic markers. In this article we review the current status of proteomic technologies and describe how these are being applied to studies of human heart disease.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
GENOMES AND COMPLEXITY
 GENE EXPRESSION
 PROTEOMICS
 PROTEOMIC TECHNOLOGIES
 PROTEOMICS OF HEART DISEASE
 PROTEOMICS VERSUS...
 CONCLUDING REMARKS
 REFERENCES
 
This year has marked the 50th anniversary of the publication of the paper describing the DNA double helix (1). More than 40 years were to pass before the publication of the first complete genome of Haemophilus influenzae (2). However, intense efforts over the last few years have resulted in the availability at the time of writing (June 2003) of complete genome sequences for 143 organisms (16 archael, 108 bacterial, 19 eukaryotic) with many more in progress (GOLD, Genomes OnLine Database, http://igweb.integratedgenomics.com/GOLD/). It is anticipated that the vast amount of information that is accumulating will be an invaluable resource for gaining new insights into cellular functions that determine biologically relevant phenotypes in health and disease.


   GENOMES AND COMPLEXITY
TOP
 ABSTRACT
 INTRODUCTION
 GENOMES AND COMPLEXITY
GENE EXPRESSION
 PROTEOMICS
 PROTEOMIC TECHNOLOGIES
 PROTEOMICS OF HEART DISEASE
 PROTEOMICS VERSUS...
 CONCLUDING REMARKS
 REFERENCES
 
Perhaps one surprising outcome of the draft sequence of the human genome (3,4) is that the human genome contains fewer open reading frames (between 30 000 and 40 000) than had been previously predicted. This number of genes is not that much higher than that predicted in many ‘simpler’ organisms and this poses the question of what is responsible for generating the level of diversity and complexity that distinguishes different organisms. The understanding that one gene can encode more than a single protein as a result of processes including alternative mRNA splicing, RNA editing, and co- and post-translational protein modification has led to a realization that the functional complexity of an organism far exceeds that indicated by its genome sequence alone. It is now appreciated that, at least in eukaryotes, the number of functional gene products that can be expressed by an organism far exceeds the number of genes that encode them. It is, therefore, clear that the ‘omic’ approaches to the global study of the products of gene expression, including transcriptomics, proteomics and metabolomics, will play a major role in elucidating the functional role of gene products and in understanding their involvement in biologically relevant phenotypes in health and disease.


   GENE EXPRESSION
TOP
 ABSTRACT
 INTRODUCTION
 GENOMES AND COMPLEXITY
 GENE EXPRESSION
PROTEOMICS
 PROTEOMIC TECHNOLOGIES
 PROTEOMICS OF HEART DISEASE
 PROTEOMICS VERSUS...
 CONCLUDING REMARKS
 REFERENCES
 
Powerful techniques such as DNA micro-arrays and serial analysis of gene expression (SAGE) make it possible to undertake rapid, global transcriptomic profiling of mRNA expression. However, there is often a poor correlation between mRNA abundance and the quantity of the corresponding functional protein present within a cell (5,6). In addition co- and post-translational modification (PTM) events result in a diversity of protein products from a single open reading frame (7). These modifications can include phosphorylation, sulphation, glycosylation, hydroxylation, N-methylation, carboxymethylation, acetylation, prenylation and N-myristolation. Events such as processing of mRNA transcripts and post-translational modifications of proteins are processes that cannot be examined at the level of mRNA, although a recently developed method of intron-specific microarrays does make it possible to examine RNA splicing (8). In addition, protein maturation and degradation are dynamic processes that can control the amount of functionally active protein within a cell.


   PROTEOMICS
TOP
 ABSTRACT
 INTRODUCTION
 GENOMES AND COMPLEXITY
 GENE EXPRESSION
 PROTEOMICS
PROTEOMIC TECHNOLOGIES
 PROTEOMICS OF HEART DISEASE
 PROTEOMICS VERSUS...
 CONCLUDING REMARKS
 REFERENCES
 
For the foregoing reasons it is now generally accepted that there is a need for the direct and large-scale analysis of protein expression. The concept of global mapping of human protein expression was first proposed more than 20 years ago (9,10) with the development of a technique where large numbers of proteins could be separated simultaneously by two-dimensional polyacrylamide gel electrophoresis (2-DE) (11,12). However, it was not until 1995, that the term ‘proteome’, defined as the protein complement of a genome, was first coined by Wilkins working as part of a collaborative team at Macquarie (Australia) and Sydney Universities (Australia) (13,14). In this article we will review the current status of proteomic technologies and describe how these are being applied to studies of human heart disease.


   PROTEOMIC TECHNOLOGIES
TOP
 ABSTRACT
 INTRODUCTION
 GENOMES AND COMPLEXITY
 GENE EXPRESSION
 PROTEOMICS
 PROTEOMIC TECHNOLOGIES
PROTEOMICS OF HEART DISEASE
 PROTEOMICS VERSUS...
 CONCLUDING REMARKS
 REFERENCES
 
Protein separation
The first requirement for proteome analysis is the separation of complex protein mixtures such as are represented by total protein extracts of human heart tissue. The basic technique of 2-DE in which proteins are separated in the first dimension according to their charge properties (isoelectric point, pI) under denaturing conditions, followed by their separation in the second dimension according to their relative molecular mass (Mr) by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS–PAGE), was developed more than 25 years ago (11,12). Nevertheless, it remains the core technology of choice for the majority of applied proteomic projects (15,16) due to its ability to separate simultaneously thousands of proteins and to indicate post-translational modifications that result in alterations in protein pI and/or Mr. Additional advantages are the high-sensitivity visualization of the resulting two-dimensional separations, compatibility with quantitative computer analysis to detect differentially regulated proteins, and the relative ease with which proteins from two-dimensional gels can be identified and characterized by mass spectrometry.

For proteome analysis, it is essential that 2-DE should generate highly reproducible two-dimensional protein separations. Developments over the last few years, particularly those involving the use of immobilized pH gradients (IPG) for the first-dimension isoelectric focusing (IEF) separation, have resulted in the current 2-DE method that combines increased resolving power and high reproducibility with relative simplicity of use. Details of these developments can be found in recent reviews (15,17–19).

The problem of proteomic coverage
Using standard large-format two-dimensional gels (18 cm wide range pH 3–10 IPG IEF strips and 20 cm SDS–PAGE gels), it is possible to routinely separate around 2000 proteins from a total cardiac protein extract. Nevertheless, this gives incomplete proteomic coverage for a complex tissue such as the human heart, which may express more than 10 000 proteins. The number of protein species separated can be increased significantly simply by increasing the total gel area available for the separation. Large format (40x40 cm) two-dimensional gels have been shown to resolve as many as 10 000 proteins (20), but difficulties in handling such large gels preclude their routine use. Alternatively, simply increasing the length of the IPG IEF strips to 24 cm (21) or 48 cm (22) in combination with standard SDS–PAGE gels can result in significantly enhanced protein separation. Even so, several protein species are often found to co-migrate in the same spot on two-dimensional gels using wide-range pH 3–10 IPG IEF (23). Intermediate (e.g. pH 4–7, 6–9) and narrow (e.g. pH 4.0–5.0, 4.5–5.5, 5.0–6.0, 5.5–6.7) range IPG IEF gels are now available and have the capability to ‘pull apart’ this protein profile and increase the resolution in particular regions. This ‘zoom gel’ approach results in enhanced proteomic coverage (24,25), but at the expense of increased workload if large numbers of samples are to be investigated.

Another problem associated with 2-DE is the difficulty of obtaining good separations of basic proteins, which represent a significant proportion of the total predicted proteome of most eukaryotic organisms. Basic proteins have a tendency to form pronounced streaks rather than discrete spots due to electroendosmotic effects, the migration of reducing agents such as dithiothreitol (DTT) and the potential hydrolysis of acrylamide at basic pH values (26). These effects can be partially overcome using DTT replenishment during IEF combined with the inclusion of glycerol or isopropanol to suppress electroendosmosis (27,28) or using an agent that prevents the reformation of disulfide bonds (29).

A major problem in global proteomics using 2-DE is the very high dynamic range of protein abundance, estimated at 106 for cells and tissues (30) and 1012 for plasma (31). This is beyond the dynamic range of 2-DE, with an estimated maximum dynamic range of 104 (15). Reproducible sample fractionation methods will therefore be essential to enrich low-abundance proteins for proteomic studies. Two general strategies can be employed: cell/sub-cellular fractionation and protein fractionation. The former methods include immuno-isolation, electromigration (e.g. free flow electrophoresis), flow cytometry, density gradient isolation of organelles such as mitochondria (32,33), isolation of membranes (16) and sequential differential extraction using a series of reagents with increasing solubilising power (34). A particular problem in proteomic analysis of the heart is the diversity of cell types that are present. Proteomic profiles of total myocardial lysates are dominated by the proteins present in cardiac myocytes, but such samples will also contain lower amounts of proteins derived from other cell types including fibroblasts, smooth muscle and endothelial cells. Recently we have started to apply the technique of laser capture microdissection (LCM) in which a laser beam is used to isolate specific regions of interest from microscope sections of tissue. While this technique generally results in the isolation of relatively small amounts of material, it has been shown to be possible to perform proteomic studies of the resulting protein samples (35–37). In preliminary studies we have been able to generate sufficient material by LCM of human cardiac tissue sections to produce large-format two-dimensional gels of proteins from isolated cardiac myocytes and microvessels (A. De Souza, unpublished data).

Methods for fractionation of solubilized proteins include electrophoretic separation of the proteins in solution by techniques including continuous free flow electrophoresis (38), recycling IEF (39) and IEF using multi-compartment electrolysers (40). The latter approach seems particularly promising for use in conjunction with narrow range pH gradient IPG IEF for 2-DE (41). Protein mixtures can also be subfractionated by traditional chromatographic techniques such as reverse phase, ion exchange, size exclusion, affinity chromatography and chromatofocusing.

Alternatives to 2-DE
The simplest alternative is the use of SDS–PAGE followed by protein identification by MS/MS, so that several proteins co-migrating in a single band can be identified. This method is limited by the complexity of the protein mixture that can be analysed and has been most successfully applied to the study of protein complexes (42). Other approaches avoid the use of gels altogether by combining liquid chromatography (LC) and MS. In these so-called ‘shotgun’ approaches, a tryptic digest of the sample is separated by one or more dimensions of LC to reduce the complexity of peptide fractions. These are subsequently introduced (either on- or off-line) into a tandem mass spectrometer for sequence-based identification. For example, the so-called ‘MudPIT’ approach of Yates (43) identified around 1500 yeast proteins in a single analysis (44). However, a major limitation of this approach is that it provides no information on quantitative abundance or post-translational modifications of the proteins.

This problem is currently being addressed by the development of MS-based techniques in which stable isotopes are used to differentiate between two populations of proteins. This approach consists of four steps: (a) differential isotopic labelling of the two protein mixtures; (b) digestion of the combined labelled samples with a protease such as trypsin or Lys-C; (c) separation of the peptides by multidimensional LC; and (d) quantitative analysis and identification of the peptides by MS/MS. The most widely used method is the isotope-coded affinity tag (ICAT) (45), but there are a variety of other approaches (46,47). Although these approaches are promising, caveats are (a) their quantitative reproducibility needs to be established, (b) the dynamic range of the ICAT technique seems to be no better than 2-DE (48), and (c) there is evidence that it can be complementary to a 2-DE approach in identifying a different subset of proteins from a given sample (19,49). Finally, there is much interest in the development of antibody and protein arrays for quantitative expression profiling (50–52), but considerable work remains to be carried out before this approach can be routinely used in proteomic investigations.

Protein detection and visualization
After 2-DE the separated proteins must be visualized at high sensitivity, but there is a need for the detection method to combine the properties of an extended dynamic range, a linear staining response, and if possible to be compatible with downstream protein identification by mass spectrometry. Silver staining with its high sensitivity (around 0.1 ng protein) has until recently been the method of choice, but its limited dynamic range and restricted quantitative capacity have driven the development of alternative detection methods based on the use of fluorescent compounds. The SYPRO dyes, in particular SYPRO Ruby, are currently the most appropriate post-electrophoretic stains as these combine high sensitivity with an extended dynamic range for improved quantitation with compatibility with mass spectrometry (53,54).

A pre-electrophoretic fluorescent staining method based on the labelling of protein samples with N-hydroxy succinimidyl ester derivatives of fluorescent cyanine (Cy) dyes and known as two-dimensional difference gel electrophoresis (DIGE) (55) is currently generating considerable interest. This approach has the advantage that a pair of protein samples can be labelled separately with Cy3 and Cy5 derivatives. The two samples can be mixed and then separated together on the same two-dimensional gel. The resulting two-dimensional gel is then scanned to acquire the Cy3 and Cy5 images separately. Improved quantitative accuracy of comparison of multiple pairs of samples can be achieved using a pooled internal standard labelled with a third dye, Cy2 (56,57).

Protein identification
Mass spectrometry has become the technique of choice for protein identification as these methods are very sensitive, require small amounts of sample (femtomole to attomole concentrations) and have the capacity for high sample throughput (58–60). Peptide mass fingerprinting is typically the primary tool for protein identification. This technique is based on the finding that a set of peptide masses obtained by MS analysis of a protein digest (usually trypsin) provides a characteristic mass fingerprint of that protein. The protein is then identified by comparison of the experimental mass fingerprint with theoretical peptide masses generated in silico using protein and nucleotide sequence databases. This approach proves very effective when trying to identify proteins from species whose genomes are completely sequenced, but is not so reliable for organisms whose genomes have not been completed. This has been a problem in the past for proteomic studies of some animal models of heart disease, for example those involving rats, dogs, pigs and cows. This problem has been shown to be overcome effectively by improving PMF by adopting an orthogonal approach combined with amino acid compositional analysis (61).

If it proves impossible to identify a protein based on PMF alone, it is then essential to obtain amino acid sequence information. This can be generated by conventional automated chemical Edman microsequencing but is most readily accomplished using tandem mass spectrometry (MS/MS). MS/MS takes advantage of two-stage MS instruments, either MALDI-MS with post-source decay (PSD), MALDI-TOF-TOF-MS/MS or ESI-MS/MS triple-quadropole, ion-trap, or Q-TOF machines, to induce fragmentation of peptide bonds. One approach is to generate a short partial sequence or ‘tag’ which is used in combination with the mass of the intact parent peptide ion to provide significant additional information for the homology search (62). A second approach uses a database searching algorithm SEQUEST (63) to match uninterpreted experimental MS/MS spectra with predicted fragment patterns generated in silico from sequences in protein and nucleotide databases.

Bioinformatics
Bionformatics plays a central role in proteomics. It is a fundamental tool for quantitative analysis of differential patterns of protein expression in two-dimensional gels (64) and there are a variety of bioinformatic tools for identifying proteins based on MS and other chemical data. Most of these tools with their associated databases are available on the Internet through the World Wide Web, and can be accessed through the ExPASy proteomics server (www.expasy.ch/tools/). It is essential that all the data generated in proteomics projects is stored in annotated and curated databases and that eventually the output from large-scale proteomics projects can be integrated with other theoretical and experimental biological data, an area that has become known as systems biology (65).


   PROTEOMICS OF HEART DISEASE
TOP
 ABSTRACT
 INTRODUCTION
 GENOMES AND COMPLEXITY
 GENE EXPRESSION
 PROTEOMICS
 PROTEOMIC TECHNOLOGIES
 PROTEOMICS OF HEART DISEASE
PROTEOMICS VERSUS...
 CONCLUDING REMARKS
 REFERENCES
 
Heart diseases resulting in heart failure are among the leading causes of morbidity and mortality and can result from either systemic disease (e.g. hypertensive heart disease, ischaemic heart disease) or specific heart muscle disease (e.g. dilated cardiomyopathy). The causes of cardiac dysfunction in most heart diseases are still largely unknown, but are likely to result from underlying alterations in gene and protein expression. Proteomic studies are therefore likely to provide new insights into cellular mechanisms involved in cardiac dysfunction and may also provide new diagnostic and therapeutic markers.

2-DE protein databases
An essential aid to proteomic studies of the heart are the 2-DE gel protein databases of human cardiac proteins that have been established. These databases, known as HSC-2DPAGE (66), HEART-2DPAGE (67), and HP-2DPAGE (68), are accessible through the Internet and conform to the rules for federated 2DE protein databases (69). In addition, 2-DE heart protein databases for other animals, such as the mouse, rat (70), dog (71), pig and cow, are also under construction.

Dilated cardiomyopathy
Proteomic investigations of human heart disease have so far concentrated on dilated cardiomyopathy (DCM), a disease of unknown aetiology in which contributory factors are prior viral infections, cardiac specific autoantibodies, toxic agents, genetic factors and sustained alcohol abuse. As many as 100 cardiac proteins have been observed to significantly alter in their expression in DCM, with the majority of these proteins being less abundant in the diseased heart (72–76). Many of these proteins have been identified (72,75,77–79) and have been classified into three broad functional classes (Table 1).

  1. Cytoskeletal and myofibrillar proteins. This finding is consistent with contractile dysfunction and defects in excitation–contraction coupling in congestive heart failure (80,81).
  2. Proteins associated with mitochondria and energy production. This is consistent with the view that in congestive heart failure myocardial energetics of the myocardium are impaired (82).
  3. Proteins associated with stress responses. This finding is consistent with previous findings by one-dimensional SDS–PAGE of differential expression of heat shock proteins in heart failure (83). The potential complexity of this system and the potential involvement of differential post-translational modifications is highlighted by a study of the 27 kDa heat shock protein (hsp27) in the human heart (84). Nearly 60 protein spots corresponding to hsp27 were detected and there were relative differences in spot intensities between DCM and controls.


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  		Table 1. Protein changes found in proteomic studies of human DCM and animal models
 
Animal models
Proteomic studies of human heart tissue are complicated by factors such as disease state, tissue heterogeneity, genetic variability, medical history and therapeutic interventions. A potentially useful approach to overcome these problems is to apply proteomic analysis to some of the many animal models of human heart disease which have been established. Several models of cardiac hypertrophy, heart failure and disease have been developed in small mammals, especially the rat, but to date these have been under-exploited for proteomic studies. Examples of such studies include changes in cardiac proteins in response to alcohol (85,86), retrovirus infection (87) and lead toxicity (88). More recently we have used a proteomic approach to investigate changes in myocardial protein expression in a rat model of ischaemic heart disease induced by coronary artery ligation. A total of 22 proteins were found to be differentially expressed in response to coronary ligation and the majority of these were either structural/myofibrillar or energy related proteins (J.X. Yan, unpublished data).

A potential confounding factor of the small animal models is that the normal pattern of cardiac protein expression, for example the isoforms of several of the myofibrillar proteins, differs from that of larger mammals, including humans. For this reason, in our laboratory we have focussed our proteomic studies on models of heart failure in large animals. In a study of pacing-induced dilated-type heart failure in the dog, we have used both wide range pH 3–10 NL (89) and intermediate range pH 4–7 (90) IPG IEF gels for 2-DE separations of cardiac proteins. A total of 70 proteins were found to be altered in failure after pacing and as in human DCM more of these proteins were found to be reduced (43 proteins) rather than increased (27 proteins) in abundance. Interestingly, subsequent identification of the proteins revealed alterations in the same classes of proteins (mitochondrial and energy production, cytoskeletal and myofibrillar, stress proteins) as in the case of human DCM (Table 1).

We have also applied proteomics to bovine hereditary DCM, an autosomal recessive disease, that is considered to be a good potential model of human DCM (91). A total of 35 myocardial proteins were found to be differentially expressed (24 decreased, 11 increased) in association with the disease (92) and these have been identified (Table 1). The most significant change was a 7-fold increase in ubiquitin carboxyl-terminal hydrolase (UCH). This deubiquitinating enzyme is responsible for maintaining the cytoplasmic pool of free ubiquitin. On this basis we reasoned that an increase in the abundance of UCH could increase the intracellular concentration of ubiquitin and thereby facilitate increased protein ubiquitination in the diseased state, leading to proteolysis of the targeted proteins via the 26S proteasome pathway. Interestingly, inappropriate ubiquitin conjugation had already been proposed to contribute to heart failure (93).

We have subsequently shown that UCH is more than 8-fold elevated at the protein level and more than 5-fold elevated at the mRNA level in human DCM hearts (94). Importantly, the increased expression of UCH was shown by immunocytochemistry to be associated with the myocytes which do not exhibit detectable staining in control hearts. Overall protein ubiquitination was increased 5-fold in DCM relative to control hearts and using a selective affinity purification method we have demonstrated enhanced ubquitination of a specific set of proteins in DCM hearts (Fig. 1). These proteins have been subsequently identified by MS and many of these were found to be concordant with the proteins that we have previously found to be reduced in abundance in DCM (72). These data add support to our hypothesis that inappropriate ubiquitination and proteolysis of a specific set of myocardial proteins occurs in DCM and that this contributes to cardiac dysfunction in the diseased heart.



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  		Figure 1. Selective affinity purification and identification of biotinylated proteins in human DCM. (A) A strategy for selective affinity purification if biotinylated cardiac proteins; (B) two-dimensional gel analysis of unbound (non-biotinylated) and bound biotinylated proteins; (C) Identification of biotinylated proteins by peptide mass finger printing using MALDI-TOF. The red arrows indicate those proteins previously found at reduced abundance in human DCM heart (72).

 

   PROTEOMICS VERSUS TRANSCRIPTOMICS
TOP
 ABSTRACT
 INTRODUCTION
 GENOMES AND COMPLEXITY
 GENE EXPRESSION
 PROTEOMICS
 PROTEOMIC TECHNOLOGIES
 PROTEOMICS OF HEART DISEASE
 PROTEOMICS VERSUS...
CONCLUDING REMARKS
 REFERENCES
 
The availability of cDNA and oligonucleotide microarrays for several thousand genes from the human and other mammalian genomes has made it possible to perform mRNA expression profiling on a truly global scale. Currently these microarrays allow the analysis of up to 12 000 human transcripts simultaneously. It is, therefore, certainly true that this transcriptomic approach allows a far greater coverage of the estimated 30 000–40 000 expressed genes in the human genome (3,4) at the level of mRNAs than is currently possible at the level of proteins, at least using the current generation of proteomic technologies. However, as already discussed, it is likely that in the near future we will see a significant increase in proteomic coverage through the development of improved and alternative methods. In the meantime, analysis at the protein level still has the distinct advantages of analysing the relative abundance of functional proteins that may not correlate with the levels of the corresponding mRNAs and of facilitating the analysis of co- and post-translational events that are not apparent at the level of mRNA. Proteomic studies of heart disease have so far concentrated on studies of the relative abundance of proteins. However, it is certain that post-translational modifications, without concomitant changes in protein abundance, are involved in pathological consequences at that molecular level. Studies of post-translational modification events in the normal and diseased heart are currently in their infancy, but some data is available concerning targets of phosphorylation in the myocardium (95,96).

Microarrays are increasingly being used to investigate patterns of gene expression, but few cardiovascular based microarray studies have so far been published (reviewed in 97,98). Nevertheless it is interesting to compare the results of the proteomic studies of heart disease, and heart failure in particular, with the results that have been obtained by transcriptomic analysis. The published transcriptomic studies of heart failure have been carried out on samples from relatively small numbers of human patients (between one and eight pairs of failure and control samples) (99–104). While these transcriptomic studies have screened more gene products as mRNAs (typically up to 12 000) than have been screened as proteins by proteomics (typically up to 3000), very similar results have been obtained. The transcriptomic studies have revealed differential expression of around 100 or so gene products in heart failure; a similar level to that reported in the proteomic studies reviewed here. Moreover, these differentially expressed genes fall into similar functional classes to those reported at the protein level by proteomics, i.e. genes encoding (a) cytoskeletal and myofibrillar proteins, (b) proteins associated with energy metabolism and mitochondria, (c) proteins associated with stress responses, (d) proteins involved in protein synthesis, and (e) proteins associated with protein degradation and disassembly (99–104). In addition, due to the higher sensitivity of microarray analysis compared with the current generation of proteomic technologies, differential changes in the expression of less abundant gene products associated with (a) cell signalling, (b) cell division and (c) apoptosis have also been reported (101–104).

Transcriptomic and proteomic approaches are in general still descriptive, providing inventories of genes and proteins associated with heart disease. Some of these changes are compatible with the known pathophysiology of the disease processes involved, and in some cases they are giving new insights into cellular processes that might be involved in cardiac dysfunction in disease. However, a major challenge will now be to investigate the detailed functional implications of these changes, and this will require a more traditional system-based approach. Ultimate success in functional genomics/proteomics may require the application of approaches now referred to as ‘systems biology’ to integrate the output from transcriptomics and proteomics with both experimental and theoretical biological data (65).


   CONCLUDING REMARKS
TOP
 ABSTRACT
 INTRODUCTION
 GENOMES AND COMPLEXITY
 GENE EXPRESSION
 PROTEOMICS
 PROTEOMIC TECHNOLOGIES
 PROTEOMICS OF HEART DISEASE
 PROTEOMICS VERSUS...
 CONCLUDING REMARKS
REFERENCES
 
The current battery of proteomic technologies make it possible to characterize global alterations in protein expression associated with processes of human disease. Although the application of proteomics to human heart disease is in its infancy, it is already clear from studies of dilated cardiomyopathy, both in human patients and appropriate models, that it complements traditional candidate gene/protein studies and global genomic approaches and promises to provide new insights into the cellular mechanisms involved in cardiac dysfunction. An additional benefit of these proteomic studies should be the discovery of new diagnostic and/or prognostic biomarkers and the identification of potential drug targets for the development of new therapeutic approaches for combating heart disease.


   FOOTNOTES
 
* To whom correspondence should be addressed at: Department of Neuroscience, P045, Institute of Psychiatry, Kings College, University of London, De Crespigny Park, London SE5 8AF, UK. Tel: +44 2078485110; Fax: +44 2078485109; Email: m.dunn{at}iop.kcl.ac.uk Back


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 GENOMES AND COMPLEXITY
 GENE EXPRESSION
 PROTEOMICS
 PROTEOMIC TECHNOLOGIES
 PROTEOMICS OF HEART DISEASE
 PROTEOMICS VERSUS...
 CONCLUDING REMARKS
 REFERENCES
 

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