Gene expression in giant cell myocarditis: Altered expression of immune response genes.

BACKGROUND: Giant cell myocarditis is a rapidly progressive and often fatal condition without a clear etiology or treatment. A better understanding of giant cell myocarditis pathogenesis is critical to developing treatments to prevent progression and reverse damage. We compared the gene expression of giant cell myocarditis with that of nonfailing hearts. METHODS: Left ventricular samples from two giant cell myocarditis patients harvested during ventricular assist device placement and six unused donor hearts were examined using Affymetrix U133A microarrays. Differential gene expression was defined with a Bonferroni-adjusted p value < or = 0.05 from a Student's t-test and an absolute fold change > or = 2.0. Select gene expression was confirmed with quantitative PCR. RESULTS: Of 115 differentially expressed genes, most were upregulated in giant cell myocarditis and involved in immune response, transcriptional regulation, and metabolism. T-cell activation genes included chemokine receptor 4; chemokine ligands 5, 9, 13, and 18; interleukin-10 receptor alpha; and beta-2 integrin. CONCLUSIONS: Gene expression analysis of giant cell myocarditis offers novel insights into its pathogenesis, namely the role of T-cell activators of the Th1 subset and immune response genes previously implicated in heart failure. This forms the basis for future work aimed at defining novel therapeutic targets for giant cell myocarditis.

Gene expression analysis of ischemic and nonischemic cardiomyopathy: shared and distinct genes in the development of heart failure.

Cardiomyopathy can be initiated by many factors, but the pathways from unique inciting mechanisms to the common end point of ventricular dilation and reduced cardiac output are unclear. We previously described a microarray-based prediction algorithm differentiating nonischemic (NICM) from ischemic cardiomyopathy (ICM) using nearest shrunken centroids. Accordingly, we tested the hypothesis that NICM and ICM would have both shared and distinct differentially expressed genes relative to normal hearts and compared gene expression of 21 NICM and 10 ICM samples with that of 6 nonfailing (NF) hearts using Affymetrix U133A GeneChips and significance analysis of microarrays. Compared with NF, 257 genes were differentially expressed in NICM and 72 genes in ICM. Only 41 genes were shared between the two comparisons, mainly involved in cell growth and signal transduction. Those uniquely expressed in NICM were frequently involved in metabolism, and those in ICM more often had catalytic activity. Novel genes included angiotensin-converting enzyme-2 (ACE2), which was upregulated in NICM but not ICM, suggesting that ACE2 may offer differential therapeutic efficacy in NICM and ICM. In addition, a tumor necrosis factor receptor was downregulated in both NICM and ICM, demonstrating the different signaling pathways involved in heart failure pathophysiology. These results offer novel insight into unique disease-specific gene expression that exists between end-stage cardiomyopathy of different etiologies. This analysis demonstrates that transcriptome analysis offers insight into pathogenesis-based therapies in heart failure management and complements studies using expression-based profiling to diagnose heart failure of different etiologies.

Identification of a gene expression profile that differentiates between ischemic and nonischemic cardiomyopathy.

BACKGROUND: Gene expression profiling refines diagnostic and prognostic assessment in oncology but has not yet been applied to myocardial diseases. We hypothesized that gene expression differentiates ischemic and nonischemic cardiomyopathy, demonstrating that gene expression profiling by clinical parameters is feasible in cardiology. METHODS AND RESULTS: Affymetrix U133A microarrays of 48 myocardial samples from Johns Hopkins Hospital (JHH) and the University of Minnesota (UM) obtained (1) at transplantation or left ventricular assist device (LVAD) placement (end-stage; n=25), (2) after LVAD support (post-LVAD; n=16), and (3) from newly diagnosed patients (biopsy; n=7) were analyzed with prediction analysis of microarrays. A training set was used to develop the profile and test sets to validate the accuracy of the profile. An etiology prediction profile developed in end-stage JHH samples was tested in independent samples from both JHH and UM with 100% sensitivity and 100% specificity in end-stage samples and 33% sensitivity and 100% specificity in both post-LVAD and biopsy samples. The overall sensitivity was 89% (95% CI 75% to 100%), and specificity was 89% (95% CI 60% to 100%) over 210 random partitions of end-stage samples into training and test sets. Age, gender, and hemodynamic differences did not affect the profile's accuracy in stratified analyses. Select gene expression was confirmed with quantitative polymerase chain reaction. CONCLUSIONS: Gene expression profiling accurately predicts cardiomyopathy etiology, is generalizable to samples from separate institutions, is specific to disease stage, and is unaffected by differences in clinical characteristics. This strongly supports ongoing efforts to incorporate expression profiling-based biomarkers in determining prognosis and response to therapy in heart failure.