Dilated cardiomyopathy (DCM) is a condition characterized by unexplained left ventricle dilatation, impaired systolic function and nonspecific histologic abnormalities dominated by myocardial fibrosis Citation[1–3]. The condition has an estimated prevalence of 36.5 out of 100,000 in the US population, and is the most common cause of heart failure and cardiac transplantation in the young Citation[4,5]. Patients may experience severe disease complications including arrhythmia, thromboembolic events and sudden death due to ventricular arrhythmia.
Recent developments in medical treatment have diminished symptoms and improved prognosis considerably [6–10]. In addition, novel biventricular pacing modalities appear promising in the treatment of patients with severe heart failure Citation[11–14]. Despite the fairly uniform clinical expression of DCM, the etiology is poorly understood and appears to be very heterogeneous . Viral infections, autoimmune disease and toxic substances are believed to be causative in a proportion of DCM cases, although definitive proof has often been difficult to obtain [2,15]. Recent studies have suggested that genetic factors may account for as many as 30–50% of cases Citation[16,17]. A large number of disease genes have been identified that encode proteins involved in a variety of cell functions, ranging from force generation within the sarcomere to regulation of myocyte ion channels [18–35]. Most affected families present with a ‘pure’ cardiac phenotype and, in these families, autosomal dominant transmission is most frequently followed by recessive and X-linked inheritance. In addition to impaired cardiac function, variable degrees of skeletal muscle dystrophy and cardiac conduction disease have been reported with mutations in genes such as dystrophin, desmin and emerin. Less frequently, affected individuals present with involvement of other organ systems, such as in Barth syndrome, which is characterized by DCM, neutropenia, abnormal mitochondrial function as well as skeletal myopathy Citation[36,37]. Disease-responsible mutations for this condition have been identified in the gene G4.5, which encodes the protein taffazin and in which mutations may also lead to pure DCM, endocardial fibrosis or left ventricle noncompaction without any features of Barth syndrome. Mitochondrial mutations may be suspected in DCM patients with neurologic deficits and skeletal muscle involvement in addition to symptoms from other organ systems Citation[38].
The results of previous drug trials of heart-failure patients have indicated a beneficial effect of prophylactic pharmacologic therapy in asymptomatic individuals with impaired systolic function Citation[6,9]. Therefore, it is reasonable to assume that early diagnosis and treatment of asymptomatic patients with hereditary DCM may also improve their prognosis Citation[8]. Genetic diagnosis would be helpful in identifying unaffected mutation carriers who require follow-up and would enable termination of clinical screening of relatives with a normal genotype. The potential of genetic diagnosis in clinical management of DCM is large, but, at present, only a few studies have investigated the frequency and clinical expression of recognized DCM genes in large patient cohorts. More research is required to clarify the clinical implication of genetic diagnosis in DCM.
The discovery of novel DCM genes has provided valuable new insight into the potential pathophysiologic basis of the condition. However, the variable disease expression observed, even in families affected by identical mutations, suggests that environmental factors, as well as the entire genetic constitution of individual patients, influence disease expression. A variety of pathways modifying the phenotype are likely to be involved in disease development, necessitating detailed analysis of the failing myocardium from affected individuals using multiple methods including proteomic, microarray and metabolic analyses. Each patient is the product of a complex set of genetic variations, different degrees of influence of diets, lifestyles and drug therapy. The genomic profile of any single patient can be assessed using gene arrays (complementary [c]DNA or oligonucleotide based). The proteins expressed by failing hearts can be investigated using 2D polyacrylamide gel electrophoresis (PAGE), which can resolve several thousand proteins of the most abundant proteins in a single sample. 2D-PAGE struggles to identify proteins expressed at low levels. Technical advances in 2D-PAGE and mass spectroscopy may solve some of these issues. A combined approach integrating microarray, proteomic, metabolite analysis and detailed clinical investigations is the most promising way towards understanding the subtleties of heart failure. Proteomic approaches are valuable in that they can identify changes in protein isoform expression, and even changes in phosphorylation state. Other post-translational modifications, such as glycosylation, can also be detected. Identifying these subtle changes allows the investigator to examine or identify signaling pathways involved in disease progression. This approach may also lead to the identification of pathways not previously known to be involved in DCM. Microarray analysis provides entirely different data that may not always correlate to the proteomic data, since changes in gene expression, evident at the level of mRNA, do not always mean changes in protein levels. These data may be more useful in providing a fingerprint or pattern that identifies a particular disease state. Microarrays are also customizable and easily adapted to high-throughput approaches, so it may be possible to identify a subset of genes that have altered expression in DCM, and use these on a chip to quickly assess or predict a patient’s disease state. This would be especially useful in a clinical setting and as part of a more sensitive phenotypic assessment of otherwise healthy gene carriers.
Examination of the metabolome (metabolite analysis) also provides detailed information about the state of a patient’s heart. Certain disease states (for instance, ischemic heart failure) are associated with marked shifts in metabolic profiles. Usually, the heart uses fatty acids to generate ATP, but glucose utilization for ATP production increases as heart failure progresses. It is relatively easy to assess levels of metabolic intermediates, such as fumarate, succinate, creatine, phosphocreatine, reduced NADH, ATP, ADP and amino acids, to create a metabolic profile for each patient. This allows an assessment of which substrates (fatty acids or glucose) are being used for energy production, and to what extent those substrates are being utilized. This will provide extra information for characterizing the disease state or stage of individual patients.
All of these approaches provide a large amount of information. One of the biggest challenges will first be to identify which subsets of information are useful, and then how to integrate the data to provide meaningful and useful information for the clinician and research scientist alike. Bioinformatics is the key to integrating these data, but it is also the weak point in many scientists’ and clinicians’ training. To take advantage of all of these data, a good working knowledge of bioinformatics is needed.
No studies have yet combined all these techniques to investigate cardiac disease, but some have used either proteomic or microarray approaches to investigate patients or animal models of heart failure. It is striking that each study group (either DCM, ischemic or pressure overload heart failure) has different patters of gene and/or protein expression Citation[39,40]. It will be interesting to see how the proteomes and gene expression profiles of patients with the same disease mutation, but with phenotypically different disease, are altered. It was once believed that there was a final common pathway for heart failure, but it is more likely that there are multiple pathways to a common heart-failure phenotype.
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