DCM (dilated cardiomyopathy) versus normal (non-failing) heart samples

DCM I DCM 2 DCM Î DCM 4 DCM 5 DCM 6 DCM 7 Nonnal I Normal 2 Normal 3 Normal 4 Normal 5

Real "I ime RT-PCR analysis:


Phuaphulamdiin GaJcequesöin Troponin I SERCA

S 4 i B Id la M Id IS >3 II M X M H 11 H SC H A Cywh

SNP analysis:

Fig. 3.2 a) Microarray analysis; b) hierarchical clustering; c) verification of individual genes.

of base pairs), they contain enough information to identify the transcript corresponding to the cDNA clone using simple nucleotide and protein database searching algorithms (e.g., BLAST). EST technology is convenient for large-scale transcript profiling through the generation of partial DNA sequences (ESTs) from randomly selected cDNA clones. Using these clones, we can generate an expression profile useful for performing genome-wide comparisons between two or more transcript populations and allow the identification of expression differences at the level of the single gene [24]. Moreover, identifying cDNA clones through EST generation provides a valuable genomic resource that is useful for the identification of protein homologs, chromosome mapping, exon identification in genomic sequences, single nucleotide polymorphism identification and as a substrate for cDNA microarrays [25].

With the publication of the complete sequence of the human genome, the number of genes encoded by the genome is estimated to be 32,000-38,000 [17, 18]. The next step is to determine how many genes are involved in specific cells, tissues, organs. In a recent publication, the first to describe the number of genes expressed in a single organ system, our team took a global approach to address the issue. Using three approaches (a current EST database, the complete nucleotide sequences of chromosomes 21 and 22 and cDNA microarray hybridization) we estimated that the number of genes expressed in the cardiovascular system ranges between 21,000 and 27,000 [26]. The high end of the estimate is a number close to the total number of genes in the human genome, suggesting that the majority of genes in the human genome function in the normal maintenance and function of an organ regardless of its specific function, while only a small proportion are allocated to cell-specific functions.

With access to the human genome map and with the aid of ESTs, we can investigate simultaneously the genes involved in physiologic or pathologic states and begin to develop a modern approach to puzzles of genomics. Our laboratory has applied the EST approach in cardiovascular disease and heart failure. Utilizing EST technology, our laboratory has generated a compendium of genes expressed in the human cardiovascular system, with the ultimate goal of assembling the intricacies of development and of disease, particularly the pathways leading to heart failure [9]. Through a computer-based in silico strategy, we have been able to identify on a large scale both known and previously-unsuspected genetic modulators contributing to the growth of the myocardium from fetal through adult, and from normal to a perturbed hypertrophic phenotype. Our laboratory employed a whole-genome approach using ESTs to characterize gene transcription and to identify new genes overexpressed in cardiac hypertrophy [24]. A detailed comparison of individual gene expression identified 64 genes potentially overexpressed in hypertrophy, and analysis of general transcription patterns revealed a proportional increase in transcripts related to cell/organism defense and a decrease in transcripts related to cell structure and motility in the hypertrophic heart. This approach is in striking contrast to the earlier time consuming and cumbersome gene-by-gene approach in elucidating the genes and mechanisms involved in complexities of development and disease.

Another genomic advance is the DNA microarray chip. DNA microarray chip technology makes use of EST data and represents a major advance in genomics research. In microarray analysis, individual cDNA clones are amplified by polymerase chain reaction (PCR). A micro-sample of each cDNA clone is then chemically bonded onto a glass surface or nylon membrane in an array format. The chip can also be made from oligonucleotides synthesized in situ on the surface of the array. Probes labeled with different fluorophores can be used to identify differential gene expression. The strength and specificity of the interaction can be increased or decreased by altering the length of the oligonucleotides or by varying the conditions of the hybridization reactions. Fluorescence signals representing hybridization to each arrayed gene are then analyzed to determine the relative abundance in the two studied samples of mRNA corresponding to each gene. Currently, there are two other techniques available for large-scale monitoring of gene expression, or transcript profiling: differential display and serial analysis of gene expression which are discussed elsewhere [21].

Microarray is increasingly being used to investigate patterns of gene expression. Recently, microarray technology has been employed as a means of large-scale screening of vast numbers of genes - if not whole genomes - that possess differential expression in two distinct conditions. While new and exciting developments have arisen in such fields as cancer [27] and yeast, [28] very few cardiovascular based microarray studies have been published [29]. In animal studies of myocar-dial infarction, Friddle et al. [30] used microarray technology to identify gene expression patterns altered during induction and regression of cardiac hypertrophy induced by administration of angiotensin II and isoproterenol in a mouse model. A total of 55 genes were identified during induction or regression of cardiac hypertrophy. They confirmed 25 genes or pathways previously shown to be altered by hypertrophy, and further identified a larger set of 30 genes whose expression had not previously been associated with cardiac hypertrophy or regression. Among the 55 genes, 32 genes were altered only during induction, and 8 were altered only during regression. This study, using a genome-wide approach, demonstrated that a set of known and novel genes was involved in cardiac remodeling during regression and that these genes were distinct from those expressed during induction of hypertrophy.

In other studies, to examine the host gene expression involved during different phases of viral myocarditis in a coxsackievirus B-infected mouse model, Taylor et al. [31] showed that 169 known genes of about 7,000 clones initially screened had a level of expression significantly different at one or more postinfection time points (days 3, 9 and 30) as compared with baseline. The genes were sorted according to their functional groups and the portrait thus produced showed gene regulatory processes during viremic, inflammatory, and healing phases of the myocarditic process. The same group also utilized differential mRNA display method to assess gene expression at the transcription level in a mouse enteroviral model, and found 2 up-regulated (Mus musculus inducible GTPase, mouse mito-chondrial hydrophobic peptide) and 3 down-regulated (mouse /-globin, Homo sapiens cAMP-regulated response element binding protein binding protein, Mus musculus Nip21 mRNA) candidate genes. Microarray studies in artery and vein have also yielded differential gene expression profiles. In one study using cDNA array analysis to explore the genes in the vasculature system, Adams et al. [32] described a set of 68 genes that were consistently differentially expressed in aortic media as compared with vena cava media.

In the first reported human microarray study in end stage heart failure, Yang et al. [33] examined gene expression in 2 failing human hearts using oligo-based arrays. The investigators used high density oligonucleotide arrays to investigate failing and nonfailing human hearts (end stage ischemic and dilated cardiomyopathy). Similar changes were identified in twelve genes in both types of heart failure, which the authors maintain, indicate that these changes may be intrinsic to heart failure. They found altered expression in cytoskeletal and myofibrillar genes, in genes involved in degradation and disassembly of myocardial proteins, in metabolism, in protein synthesis and genes encoding stress proteins. While the "Affychip" in this study offers a carefully-controlled systematic method of analysis, its current lack of user flexibility in its design hinders novel gene discovery currently available in tissue-specific arrays.

Our laboratory has taken a different approach to microarray technology. Taking advantage of our vast previously-acquired resources, we have constructed a first generation custom-made cardiovascular-based cDNA microarray, which we term the "CardioChip" [34]. Its practicality and flexibility has allowed us to conceptualize the molecular events surrounding end-stage heart failure. The current Cardiochip contains 10,368 redundant and randomly sequenced expressed sequence tags, derived from several human heart and artery cDNA libraries. The Cardiochip has been used to develop a profile of previously-suspected candidates involved in molecular events surrounding the pathology of heart failure; more importantly, this method identifies novel candidates that may, with further verification at the functional level, be responsible for contributing to the demise of myocardial function.

In our recent study of dilated cardiomyopathy (DCM), the Cardiochip verified several expected candidate genes and identified some novel candidates [35]. Atrial naturietic peptide showed an intense level of up-regulation across the DCM patient samples, confirming at the microarray level its pivotal role as a circulating marker of cardiac muscle stress [36]. Indeed, its presence in our analysis lent a degree of credence to the validity of our study. Despite its significant up-regulation versus non-failing samples, the level of atrial naturietic peptide was highly variable among the patients.

In addition, we observed a consistent up-regulation of selected sarcomeric and extracellular matrix proteins (i.e., ^-myosin heavy chain, a-actinin, a-cardiac actin, troponin I, tropomyosin, collagen, etc.). Evidence in knockout mice and human studies has offered insight into the putative role of these proteins in maintaining sarcomeric integrity [34-43]. Mutations of proteins associated with a-actinin, namely MLP, cardiac a-actin, desmin and titin, have been shown to be present in certain forms of human DCM [44-48]. Ambiguities exist in the literature regarding the expression of collagen and other members of the extracellular matrix; nonetheless, regulation of the extracellular matrix is important in the formation of fibrosis and impaired contractile function [49, 50].

Calcium signaling has recently become an important area of interest in the investigation of heart failure [51]. A decrease in calcium cycling genes has been shown to result in reduced contractility in mice whose ^-adrenergic stimulation is blunted leading to decreased phospholamban phosphorylation [33]. Ca2+ATPase is key in regulating contractility, and its ~2-fold average down-regulation in our DCM samples lends credence to its involvement. This is supported by a recent study in which the transfer of the Ca2+ATPase gene into the rat myocardium prevents certain features of heart failure [52]. The presence of Ca2+/calmodulin-de-pendent kinase in our analysis, despite showing only about 1.1-fold down-regulation, is particularly intriguing, as it is known to phosphorylate phospholamban [53]. In addition, inositol 1,4,5-triphosphate receptor (a member of the calcium channel family) which may be responsible for calcium release from intracellular stores, [54] was also significantly down-regulated (1.86-fold). Inositol 1,4,5-trispho-sphate 3-kinase was recently cloned [55] and may be another key component in this regulation (1.86-fold). Our findings suggest that the role of Ca2+ signaling down-regulation may be of crucial significance in the evolution of heart failure and would warrant further investigation.

A number of novel ESTs were also identified from our study to be differentially regulated. Verification with quantitative real-time RT-PCR confirmed this expression. It is an intriguing prospect that these among other transcripts, after full-length sequencing, represent novel cardiac-specific genes encoding proteins that are potentially key to solving the puzzle of the molecular pathophysiology of heart failure. Indeed, our microarray analysis not only serves as a genomic model for a more complete understanding of DCM, but also as a focused target for possible therapeutic interventions specific to the cardiac tissue. Investigations are currently underway to elucidate the function of these candidates.

In a similar study [56] our laboratory developed comparative microarray portraits of DCM and hypertrophic cardiomyopathy (HCM). Overall, our results showed that 192 genes were highly expressed in both DCM and HCM (atrial natriuretic peptide, CD59, decorin, elongation factor 2 and heat shock protein 90) and that 51 genes were downregulated in both conditions (elastin, sarcomeric/reti-culum Ca2+ -ATPase). Differentially expressed genes as determined quantitatively by RT-PCR (Fig. 3.3) included a B-crystallin, antagonizer of myc transcriptional activity, beta dystrobrevin, calsequestrin, lipocortin and lumican). What this study shows is that although having similar clinical features, the gene defects leading to DCM and HCM differ. DCM, a cytoskeletalopathy, and HCM, a sarcomyopathy, are common forms of cardiomyopathy that result in end stage heart failure through different remodelling and molecular pathways. Our microarray portrait of DCM demonstrated that more genes involved in cell and organism defence were upregulated, especially immune system response genes. By contrast, protein and cell expression genes were downregulated. Hypertrophic processes are evident in the increase in ribosomal genes upregulated in HCM, whereas cell signalling and cell structure genes were downregulated.

These reports describe the most informative cDNA microarray-based analysis of end-stage heart failure derived from DCM and HCM currently available. These in-

Fig. 3.3 Real-time RT-PCR confirmed commonly up-regulated or down-regulated (A) and differentially expressed (B) genes in DCM and HCM. The fold change is displayed as relative to normalized normal adult heart samples. * denotes p<0.05, #: p<0.01. The atrial natriuretic peptide was increased more than 20-fold in both DCM and HCM, not shown in the bar graph. Calpain: calcium acti-

Fig. 3.3 Real-time RT-PCR confirmed commonly up-regulated or down-regulated (A) and differentially expressed (B) genes in DCM and HCM. The fold change is displayed as relative to normalized normal adult heart samples. * denotes p<0.05, #: p<0.01. The atrial natriuretic peptide was increased more than 20-fold in both DCM and HCM, not shown in the bar graph. Calpain: calcium acti-

vated neutral protease, EF2: elongation factor 2, HSP 90: heat shock protein 90, SOD: copper/zinc superoxide dismutase, SERCA: sarcoplasmic/reticulum calcium-ATPase, B-cryst: a B-crystallin, CASQ: calsequestrin, MALC: atrial myosin alkali light chain, BDTN: jS-dystrobre-vin, Mad: antagonizer of myc transcriptional activity, and TRR: thioredoxin reductase.

vestigations are not exhaustive in that they do not attempt to fully characterize the molecular basis of heart failure. Their intention is to provide a preliminary portrait of global gene expression in complex cardiovascular disease using cDNA mi-croarray and QRT-PCR technology, and to highlight the effectiveness of our ever-evolving platform for gene discovery.

As these intriguing findings show, microarray data offer a holistic view of the interrelated gene network during disease processes. Genes which are either over-expressed or under-expressed in a diseased tissue or organ present prima facie evidence that they are involved in disease pathogenesis. Using the genomic approach, previously unrecognized alterations in the expression of specific genes can be identified and novel genes can also be discovered, leading to a clearer understanding of the gene pathways. However, nature functions by integration, and proteins do not work in isolation but are instead involved in interrelated networks. The challenge of genomics lies not only in identifying genes, but also in understanding their function, the latter also referred to as "functional genomics". In the short term, the goal is to assign putative function to each of the genes using systematic, high-throughput approaches to the database. As functional information accumulates, the knowledge gap will be filled out in the form of expression profile studies, protein microcharacterization and their post-translational modifications, protein-protein interactions, computational approaches, and the response to loss of function by mutation [57]. Unraveling these networks and their interactions will be vital to an integrated mapping between genotype and pheno-type. Gene chip technology may eventually be used to diagnose, stage or classify clinical conditions by detecting genetic markers associated with disease states in biopsy or blood samples. Indeed, gene expression microarray technology is a powerful tool with enormous potential in the years to come.


Heart failure is a complex syndrome. It may involve either the right or left ventricle, progress from compensated to decompensated stages, result from ischemic or nonischemic etiology, affect mainly systolic or diastolic function, and pertain to high cardiac output or low cardiac output status. The mechanism by which its genetic machinery controls these responses remains to be fully elucidated. The complexity of this disease raises numerous questions. Do all kinds of heart failure share a common final pathway? The transition from compensated cardiac hypertrophy to decompensated heart failure is accompanied by marked changes in the expression of an array of genes in the heart. What is the marker for early myocar-dial decompensation and could we intervene in its progression? The genome-wide approach will help us to integrate our current understanding of the pathophysiolo-gical pathways associated with heart failure. Large-scale DNA sequencing and the use of microarray technology have provided biomedical researchers with powerful tools to handle the vast database of the Human Genome Project. Structural biol ogy and computational technology will further refine the structure prediction method and help decipher the complexity of sequence-structure-function in biological science. With the expanding database of newly discovered novel genes and functional annotations, therapeutic modalities aimed at specific molecular targets may be more effective and closer than previously imagined.


I would especially like to thank Isolde Prince for her help in preparing this manuscript. I would also like to thank David Barrans, Adam Dempsey, Jim Hwang, and Dimitri Stamatiou for their comments and suggestions.


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