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J Thorac Cardiovasc Surg 2008;136:360-369
© 2008 The American Association for Thoracic Surgery

Cardiopulmonary Support and Physiology

Gene expression profiling from endomyocardial biopsy tissue allows distinction between subentities of dilated cardiomyopathy

^a Department of Cardiology, University of Marburg, Baldingerstrasse, Marburg, Germany
^b SIRS-Lab GmbH, Jena, Germany

Received for publication December 17, 2007; revisions received February 13, 2008; accepted for publication March 13, 2008.

^_* Address for reprints: Prof Thomas Meyer, Department of Cardiology, University of Marburg, Baldingerstrasse 1, 35043 Marburg/Lahn, Germany. (Email: meyert{at}med.uni-marburg.de).

	Abstract

Top Abstract Introduction Patients and Methods Results Discussion Table E1 References

 
Objective: Expression profile analysis using endomyocardial biopsy specimens from patients with cardiomyopathies promises to improve the differential diagnosis of heart failure.

Methods: In this study, left ventricular endomyocardial biopsy specimens were obtained from 50 patients and histopathologically classified according to the World Heart Federation Task Force criteria as having dilated cardiomyopathy (n = 17), inflammatory cardiomyopathy (n = 11), myocarditis (n = 15), or pericarditis (n = 7). Microarrays were performed by hybridization of synthesized complementary DNA against a Lab-Arraytor60-combi microarray (SIRS-Lab, Jena, Switzerland). Differentially expressed genes were clustered hierarchically according to their variation in hybridization signals.

Results: In samples from patients with dilated cardiomyopathy, two different types of gene expression profiles were distinguishable. One pattern was unique for dilated cardiomyopathy and inflammatory cardiomyopathy, respectively, and the other more closely resembled that seen in samples from inflammatory heart disease. Additionally, we confirmed the microarray data by showing that dilated cardiomyopathy is associated with a reduced myocardial toll-like receptor 9 expression that resulted from progressive loss of functional cardiomyocytes. Taken together, our data demonstrate the utility and validity of microarrays from endomyocardial biopsy specimens in detecting subentities of dilated cardiomyopathy that do not differ histopathologically, but transcriptionally, from each other. The gene expression profile observed in one subgroup of patients with dilated cardiomyopathy is indicative of ongoing immune activation, albeit infiltrating immunocompetent cells were not detected histopathologically.

Conclusion: Thus, our transcriptional data indicate that dilated cardiomyopathy constitutes a heterogeneous disease with an broad overlap to inflammatory heart disease.

	Introduction

Top Abstract Introduction Patients and Methods Results Discussion Table E1 References

 
Heart transplantation is the sole surgical option for selected patients with severe cardiomyopathy who do not respond to drug therapy. Accurate clinical assessment is obligatory to decide which patients will benefit from heart transplantation. Several studies have been carried out to determine unfavorable prognoses and to provide an early indication for cardiac transplantation. Despite recent advances in our pathophysiologic knowledge of end-stage heart disease, the differential diagnosis of nonischemic cardiomyopathies still remains a challenge for the clinician, particularly in early stages of the disease.^1,2

Dilated cardiomyopathy (DCM), a leading cause of heart failure and heart transplantation, is characterized by dilatation and impaired contraction of the left or both ventricles; it may be idiopathic, familial/genetic, viral, and/or immune. Various pathogenic human viruses have been identified as causative agents for myocardial damage, in particular parvovirus B19, enteroviruses (especially Coxsackie B virus), adenoviruses, and herpesviruses including cytomegalovirus.^3-6 Replication of viral genomes resulting in a transient phase of myocytolysis is usually controlled by an appropriate immune reaction directed at viral clearance. However, a complete elimination of the replicating virus is not achieved in all cases, whereas in others the viral genome persists without productive replication, resulting in a period of disease inactivity in which the patient often improves or completely recovers. Most patients experience exclusively this transient phase of viral infection, as the virus is being cleared with little or no sequelae. Patients usually seek medical care at this stage because of congestive heart failure and the occurrence of ventricular arrhythmias. However, even in these patients the harmful immune activation usually dissipates. Tissue remodeling continues in only a minority of affected individuals, resulting in the ongoing loss of contractile myocardium.² In the latter case, the disease progresses into DCM and clinical signs of chronic heart failure develop owing to impaired systolic and diastolic function of the left ventricle. According to this postulated model of disease progression, the acute inflammation with infiltrating immunocompetent cells finally leads to a remodeled heart organ with predominant reparative fibrosis and, if any, only spare and focally distributed persisting cellular infiltrates.

Given that the prognosis of patients varies substantially depending on the underlying etiology, early assessment of an accurate diagnosis for patients with new-onset heart failure is of considerable therapeutic importance. The use of cardiac biomarkers or modern noninvasive imaging techniques including magnetic resonance imaging is limited for diagnostic evaluation because of their relatively low sensitivity and specificity as compared with biopsy-proven methods.^2,3,7 Endomyocardial biopsy has been shown to refine the diagnostic procedure, especially in patients with rapidly progressive heart failure that is refractory to conventional therapeutic strategies or if progressive conduction abnormalities or life-threatening ventricular arrhythmias occur.⁷ Despite the often patchy nature of acute myocardial inflammation, which contributes to the underdiagnosis owing to sampling error, endomyocardial biopsy is still regarded as the gold standard for the antemortem diagnosis of myocarditis.^3,7,8 The immunohistochemical assessment of endomyocardial biopsy samples has ameliorated the early detection of inflammatory tissue reactions in the heart.^4,6,9-11 However, the number of cardiac-specific epitopes to be tested is restricted depending on the avidity and availability of the corresponding antibodies. Recent advances in microarray technology allow measuring the expression of thousands of genes simultaneously in a defined small tissue sample.^12-14 Thus, we asked whether microchip technology is principally suitable to distinguish myocarditis from other heart diseases in a diagnostic setting. In particular, we were interested to determine whether gene expression profiling from endomyocardial biopsy tissue adds further information for the identification of patients with cardiomyopathies.

	Patients and Methods

Top Abstract Introduction Patients and Methods Results Discussion Table E1 References

 
Patient Selection and Diagnosis of Heart Disease
Patients with signs of heart failure, who had been referred to our hospital for further diagnostic investigation because of suspected inflammatory heart disease, were enrolled in our study. The patients usually had a history of recent onset of cardiac arrhythmias, undefined electrocardiographic abnormalities, and atypical chest pain suggesting nonischemic cardiomyopathy and/or myocardial inflammation. Inclusion criteria for participating in the study were a histologically proven diagnosis of one of the following four cardiac diagnoses: pericarditis, myocarditis, and dilated cardiomyopathy with or without inflammation (DCM and DCMi, respectively). For all study patients the accurate cardiologic diagnosis was obtained before the microchip assays were performed. Patients with pericarditis usually had a hemodynamically relevant pericardial effusion and/or typical electrocardiographic alterations. Myocarditis was defined as an inflammation of the heart according to the World Heart Federation criteria (14 lymphocytes and macrophages per square millimeter) independent of the clinical phenotype or the presence of heart failure or ventricular dilatation.^15,16 DCMi was diagnosed if, in addition to histologic signs of inflammation, impaired left ventricular (LV) function was proven by echocardiography (LV end-diastolic diameter > 55 mm, LV end-diastolic volume index > 100 mL/m², or LV ejection fraction < 50%).¹⁶ If no inflammation was detectable, patients with impaired LV function were classified as having DCM.

In each patient, coronary angiography was performed to exclude significant coronary artery disease with a luminal narrowing of major arteries of 50% or more. For all patients, the prevalence of the parvovirus B19 genome was tested as described before.⁴ Patients with suspected inflammatory heart disease who underwent endomyocardial biopsy for therapeutic and/or diagnostic reasons were asked to participate in our study. If they agreed, written informed consent was obtained. The investigation conforms with the principles outlined in the Declaration of Helsinki. This study protocol was approved by the local ethical review board.

Tissue Collection
From each patient undergoing cardiac catheterization, multiple LV endomyocardial specimens were obtained with a flexible bioptome. Each tissue specimen was then divided into two parts: one for histopathologic examination and the other for RNA analyses. The biopsy specimens were immediately snap-frozen in liquid nitrogen and banked at –80°C for storage until further use.

Immunohistochemical Stainings
Frozen sections of the banked endomyocardial tissue samples were cut (5 µm) and stained immunohistochemically for the detection of infiltrating cells. Monoclonal antibodies against CD3, CD4, and CD11c, all obtained from Dako Diagnostics Ltd (Copenhagen, Denmark), were used in this study. Avidin–biotin double sandwich technique (Vectastain Elite ABC Kit; Vector Laboratories, Inc, Burlingame, Calif) was used in combination with a monoclonal antibody against the endothelial antigen EN 4 (Sanbio BV, AmUden, The Netherlands) to distinguish infiltrating lymphocytes (red staining) adjacent to myocytes from those located nearby or inside intramyocardial vessels and capillaries (blue staining). Toll-like receptor 9 (TLR9) was stained with a rabbit polyclonal antibody (H-100) purchased from Santa Cruz Biotechnology (Santa Cruz, Calif). Detection of bound immunoglobulins was achieved with the ABC method employing biotinylated secondary antibodies and avidin–horseradish peroxidase complexes (Vectastain Elite ABC Kit from Vector Laboratories). Diaminobenzidine producing a brown reaction product was used as a substrate for visualization of the enzyme reaction. Finally, the sections were counterstained with Mayer's hematoxylin.

RNA Isolation, Complementary DNA Synthesis and Microarray Hybridization
Total RNA was extracted from endomyocardial biopsy specimens or autoptic heart tissue using the Qiagen RNeasy Kit and eluted in a volume of 30 µL of sterile water as recommended by the manufacturer. RNA concentrations were determined spectrometrically at a wavelength of 260 nm. Because of minimal total RNA in the samples, complementary DNA (cDNA) synthesis was performed by the BD Super SMART cDNA synthesis technology (BD Biosciences, Franklin Lakes, NJ) according to the manufacturer's instructions. Reverse transcription was in the presence of aminoallyl-dUTP and cDNA was labeled by the use of the Alexa Fluor 647 system (Invitrogen, Carlsbad, Calif). Alexa Fluor 647–labeled cDNA was cohybridized with pooled Alexa Fluor 555–labeled cDNA obtained from 6 autoptic hearts from patients who died of noncardiac diseases and served as control samples. Synthesis of cDNA included a DNase I treatment (Promega Corporation, Madison, Wis) to prevent cellular DNA contamination in the polymerase chain reaction (PCR). Each RNA pair (sample vs reference) was hybridized against a Lab-Arraytor60-combi microarray, comprising 578 probes for 556 human genes involved in inflammatory processes and 22 positive/negative controls (SIRS-Lab, Jena, Switzerland). After incubation in a formamide-based hybridization buffer for 10 hours at 42°C, microarrays were washed and dried. Hybridization signal intensities were measured with a GenePix scanner (Axon Instruments, division of Molecular Dynamics, Sunnyvale, Calif).

Microarray Data Pre-processing
Data pre-processing included the following 4 steps: (1) spot detection and quantification, (2) spot flagging according to the defined signal-to-noise threshold value, (3) correction of systematic bias including the normalization and the variance-stabilized transformation of raw signals, and (4) averaging of printing replicates. For the first 2 steps, the GenePix Analysis Software was used. The raw expression signals for each spot were quantified as the median spot intensity in the red and green channel, respectively. The spots were flagged corresponding to the settings of the GenePix Software. For the third step the approach of Huber and colleagues¹⁷ was used and the additive and multiplicative errors were estimated. The normalized signals were transformed by the arcsinh function. Per gene, replicates with the highest flag value were selected and the corresponding signal intensities were averaged.

Statistical Data Analysis
In the analysis, the gene expression profiles of 50 patients with sufficient hybridization quality were included. The statistical analysis was performed for 481 reliable genes, that is, genes with detectable signal intensity and sufficient variation throughout the experiment. Missing values were imputed employing the k-nearest neighbor algorithm with k = 15.¹⁸ A vector of p values was obtained from a multivariate permutation test based on 1-way analysis of variance statistics, where the effect of the disease type (DCM, DCMi, myocarditis, and pericarditis) was investigated for each gene. Genes with statistically significant differences were identified by tuning the q value, thus controlling the positive false discovery rate.¹⁹ The corresponding approach also allowed for estimation of the proportion of differentially expressed genes. The gene expression patterns of selected genes were ordered by hierarchical cluster algorithm with Euclidean distance and averaged linkage method.

Reverse-transcriptase PCR (RT-PCR)
Verification of the microarray results was confirmed by real-time PCR using the SYBR-Green method (Invitrogen). Gene-specific primers were designed using Primer 3 software (Applied Biosystems, Foster City, Calif) to amplify fragments of about 200 base pairs in length. The reverse-transcriptase (RT) PCR reactions were carried out in a total volume of 25 µL, containing 25 ng messenger RNA (mRNA), 7.5 µmol/L of each specific primer pair, and 2.5 µL of SYBR Green. The following protocol was applied: reverse transcription at 50°C for 30 minutes followed by a denaturation step at 95°C 15 minutes, and 40 cycles of denaturation at 95°C for 45 seconds, annealing at 60°C for 45 seconds, and extension at 72°C for 45 seconds. A melting curve analysis was run after final amplification via a temperature gradient from 55°C to 94°C in 0.5°C increment steps measuring fluorescence at each temperature for a period of 10 seconds. All reactions were carried out in at least duplicate for each sample. The relative expression of a transcript was calculated as the ratio between the specific transcript level and the level of gapdh as determined for each sample. Using the Bio-Rad iQ-iCycler system software (Bio-Rad Laboratories, Hercules, Calif), the threshold (Ct) at which the cycle numbers were measured was adjusted to areas of exponential amplification of the traces. The -method was used to determine comparative relative expression levels by applying the formula 2^{–(Ct target – Ct reference sample)}, as described previously.²⁰

	Results

Top Abstract Introduction Patients and Methods Results Discussion Table E1 References

 
Characterization of the Study Population
The total study population included 50 patients. Among them, DCM was diagnosed in 17 study participants and DCMi in 11 subjects. The diagnosis of myocarditis was confirmed in 15 patients. A fourth group consisting of 7 subjects with pericardial effusion was included in the study, in which endomyocardial biopsy had revealed no pathologic signs on histologic examination. This group of patients was therefore diagnosed to have pericarditis. With the exception of the latter group, the majority of all study participants in each group were men (DCM, 89.9%; DCMi, 90.0%; and myocarditis, 82.3%). As expected, the echocardiographically measured LV end-diastolic diameter was significantly elevated in both DCM and DCMi patients (67.3 ± 6.9 mm and 64.5 ± 7.0 mm, respectively) as compared with patients with myocarditis or pericarditis (50.2 ± 5.3 mm and 45.1 ± 4.2 mm; P < .05). Similarly, LV ejection fraction differed significantly between DCM and DCMi patients on the one hand (27.2% ± 10.6% and 27.8% ± 9.6%, respectively) and patients with myocarditis or pericarditis on the other (69.1% ± 14.8% and 65.1% ± 22.5%; P < .05). Further details on the study population are presented in Table 1.

View larger version (146K): [in this window] [in a new window]   Figure 1. Immunohistochemical detection of infiltrating immune cells in endomyocardial biopsy samples. Shown are the localization of CD3- (A) and CD4-positive T cells (B and C) in patients with myocarditis (A and B) and DCM (C), respectively, as well as CD11c-positive dendritic cells in a patient with myocarditis (D). Double staining using a monoclonal antibody against endothelial antigen EN 4 was applied to distinguish infiltrating lymphocytes (red staining) from intramyocardial vessels and capillaries (blue staining). Note the absence of a positive CD4 immunostaining in heart tissue obtained from a DCM patient (C).

View larger version (45K): [in this window] [in a new window]   Figure 2. Gene expression characteristics in patients with different clinical diagnoses. From statistical comparison, 42 genes were identified that significantly differed between the patient groups. The gene expression patterns of these genes were ordered by hierarchical cluster algorithm with Euclidian distance and averaged linkage method. Each gene is represented by a single row of colored boxes and each sample by a single column. The associated colors represent variance-normalized expression for individual genes. While the intensity of red color indicates a relative expression greater than the mean, green indicates a decreased expresion level compared with the mean. (B) Distribution of the gene expression throughout the experiment. To minimize the number of false positives, we excluded such genes from the statistical analysis whose mean expression intensity were less than those of any the negative control or whose expression variation throughout the experiment was less than those of the most positive controls. (C) Statistical comparison of the gene expression patterns. Left panel, Histogram of p values obtained from the statistical test. While the line at the height of ho=12 is the histogram expected portion of null p values if none gene was differentially expressed, the line at the height of h1=6.9 is the histogram of the estimated portion of null p values. Middle panel, The q values versus their respective p values. Right panel, The number of genes considered as being differentially expressed versus the respective q values. For a q value closed to 0.17 a particular increase in the curve can be observed.

View larger version (11K): [in this window] [in a new window]   Figure 3. The gene expression profiles between two subgroups of DCM patients differ significantly depending on the severity of impaired LV systolic function. Echocardiographically measured shortening fractions (A) and ejection fractions (EF) (B) are presented separately for both subgroups of DCM patients with distinct gene expression patterns.

Validation of Differentially Expressed Genes Using RT-PCR
The differential gene expression observed in the microarrays was verified by two independent measurement methods. First, we performed real-time RT-PCR from the same set of tissue samples and compared these results with the findings from the microarrays. On the basis of biologic considerations and statistical significance, two genes were selected for RT-PCR measurements: serca and tlr9. The sarcoplasmic reticulum Ca²⁺-ATPase Serca was included in the microarray and chosen for RT-PCR validation, because it is widely known that Serca expression is down-regulated in the hearts of patients with DCM.²¹ The second gene included for validation of the microarray results by quantitative RT-PCR was tlr9. Expression of gapdh was not significantly changed between the samples and therefore gapdh served as a control for normalization of the RT-PCR measurements.

Our RT-PCR results confirmed that serca expression in DCM and DCMi patients was down-regulated as compared with that in patients with pericarditis or myocarditis ( Figure 4, B). The decreased serca expression in the former patient groups as measured by RT-PCR correlated well with the results from the microchip experiment. Similarly, expression of tlr9 coding for TLR9 was significantly reduced in DCM patients as judged by both RT-PCR and microarrays (Figure 4, A). The expression level of tlr9 was intermediate in DCMi patients and high in patients with acute inflammatory heart disease. Thus, our data showed that the RT-PCR results generally paralleled those seen by microarray analysis.

View larger version (23K): [in this window] [in a new window]   Figure 4. Comparison of expression data between microarrays and RT-PCR measurements. Shown are the microarray (left) and RT-PCR results (right) for the expression of tlr9 coding for toll-like receptor 9 (A) and serca (B) in patients grouped with respect to the underlying heart disease. Note that serca and tlr9 mRNA are both down-regulated in DCM and DCMi as compared with patients with myocarditis or pericarditis and that these findings were independent of the particular technique used.

View larger version (117K): [in this window] [in a new window]   Figure 5. Decreased expression of toll-like receptor 9 (TLR9) in DCM and DCMi patients as shown by immunohistochemistry. Endomyocardial biopsy specimens were stained for the presence of TLR9 in patients diagnosed as myocarditis (A), pericarditis (B), DCMi (C), and DCM (D), respectively.

	Discussion

Top Abstract Introduction Patients and Methods Results Discussion Table E1 References

Additionally, our microarray results indicate that there is a substantial overlap in gene expression profiles between the subgroup of DCM patients with only slightly impaired LV systolic function and those diagnosed as DCMi. In the subgroup of DCM patients exhibiting a unique gene expression pattern, the shortening fraction was significantly more reduced as compared with that of patients with a transcription pattern resembling that of myocarditis. Thus, belonging to a transcriptionally defined DCM subgroup seems to be associated with a reduced systolic function of the left ventricle. The different myocardial gene expression profiles seen in both DCM subgroups appear to more likely reflect the severity of LV impairment than the presence of a still ongoing immune reaction, which is undetectable in the diverse immunohistochemical techniques applied here for the detection of infiltrating cells.

The expression levels of the selected genes were generally more decreased in DCM and DCMi patients as compared with those diagnosed as either myocarditis or pericarditis. This finding was not unexpected inasmuch as the genes selected for the microarrays were chosen with respect to their engagement in inflammatory processes. However, it is unclear whether the reduced activation level found here for numerous genes simply reflects the nosologic heterogeneity and clinical severity of the disease or whether it indicates a more profound alteration in transcriptional activity that accompanies the phenotypic dedifferentiation process of cardiomyocytes. Nevertheless, our microarray results allow DCM patients to be divided into two distinct subgroups that differ considerably with respect to the transcriptional activation of inflammatory genes. Thus, microarrays obtained from endomyocardial biopsy tissue add further information for the subclassification of DCM patients that are not covered by conventional methods of gene expression measurements.

Additionally, we tested the microarray technique for screening novel markers whose expression is specifically changed in DCM. Recently, this approach has been successfully applied in numerous studies.^14,22-31 Here we focused on a gene product that has not been related to the pathogenesis of DCM so far, but whose expression was gradually reduced as determined in our microarrays. The gene coding for TLR9 was identified as such a marker that is significantly down-regulated in DCM patients. Interestingly, our RT-PCR results confirmed that the induction of the tlr9 gene is critically impaired in DCM and DCMi patients, whereas significantly higher expression levels were measured in myocarditis and pericarditis patients. Expression of TLR9 was restricted to differentiated cardiomyocytes as observed immunohistochemically, whereas nonmyocytes usually expressed much lower, if any, immunodetectable TLR9. With increasing amounts of fibrotic components, the signal intensity in the anti-TLR9 stained tissue specimens was critically decreased, as was observed in DCM patients. Thus, three independent techniques demonstrated down-regulation of the tlr9 gene as was first revealed in the microarray analyses and later confirmed both at the mRNA and protein level. Members of the toll-like receptor family such as TLR9 have been implicated in the recognition of exogenous and endogenous ligands and activate the nuclear factor B pathway.³² The high expression level of TLR9 in morphologically intact cardiomyocytes suggests a functional role of these cells in the recognition of the cognate ligand, which is unmethylated CpG DNA.

Our data suggest that implementing microarray-based information on gene expression broadens our understanding for the pathogenesis and subclassification of DCM and related diseases. RNA amplification protocols combined with gene expression profiling appear to refine the clinical diagnosis of cardiomyopathies. Novel disease-associated markers may be identified that are suitable for the screening of patients with otherwise unexplained heart disease. Advanced DNA microchip technology promises to identify target genes of potential therapeutic interest that are involved in diverse pathologic pathways such as remodeling and apoptosis. The expression of these genes may then be specifically modulated by gene-targeting approaches that are currently under development.

Taken together, our microarray analysis based on genes involved in immune regulation allows for the distinction between different entities of DCM. In a subgroup of DCM patients with impaired LV function, we detected evidence of continuous immune activation, as judged by the elevated expression of inflammatory genes. Thus, despite the absence of infiltrating immunocompetent cells, our transcriptional data demonstrate ongoing activation of the immune system in this DCM subgroup. The robustness of the gene expression signatures shown here can be successfully applied to identify novel disease-associated markers, such as TLR9, that are dysregulated in specific cardiac entities.

	Table E1

Top Abstract Introduction Patients and Methods Results Discussion Table E1 References

 

For genes with q < .17 (in ascending) the data are presented as the means ± standard deviations of normalized and transformed expression signals

Mean ± SD Acc.-No Gene symbol DCM DCMi Myocarditis Pericarditis p value q value NM_003879 CFLAR 0.33 ± 0.24 0.16 ± 0.28 0.63 ± 0.21 0.7 ± 0.42 .000 .029 NM_000064 C3 0.06 ± 0.1 0.05 ± 0.2 0.27 ± 0.16 0.24 ± 0.17 .000 .029 NM_001681.2 SERCA2 0.88 ± 0.41 0.69 ± 0.42 1.24 ± 0.22 1.37 ± 0.36 .000 .029 NM_000062 SERPING1 0.03 ± 0.18 0.12 ± 0.21 0.35 ± 0.22 0.24 ± 0.22 .001 .075 NM_001734 C1S 0.18 ± 0.17 0.17 ± 0.17 0.43 ± 0.22 0.3 ± 0.04 .002 .113 NM_000153 GALC 0.18 ± 0.21 0.02 ± 0.21 0.3 ± 0.17 0.32 ± 0.22 .004 .152 NM_016562 TLR7 0.01 ± 0.1 0.0 ± 0.09 –0.12 ± 0.09 –0.08 ± 0.14 .005 .152 NM_013261.1 PGC-1 0.12 ± 0.09 0.07 ± 0.1 0.14 ± 0.11 0.22 ± 0.09 .005 .152 NM_006573 TNFSF13B –0.23 ± 0.12 –0.08 ± 0.09 –0.24 ± 0.16 –0.22 ± 0.09 .005 .152 NM_004633 IL1R2 0.03 ± 0.05 0.1 ± 0.13 –0.01 ± 0.06 –0.03 ± 0.11 .006 .152 NM_005252 FOS 0.08 ± 0.11 0.09 ± 0.09 0.02 ± 0.06 –0.07 ± 0.16 .006 .152 NM_000657 BCL2.2 –0.21 ± 0.09 –0.13 ± 0.07 –0.27 ± 0.09 –0.2 ± 0.07 .007 .154 NM_080738 EDARADD 0.27 ± 0.19 0.22 ± 0.19 0.46 ± 0.14 0.37 ± 0.17 .008 .154 NM_002521.1 NPPB 0.11 ± 0.6 –0.07 ± 0.62 –0.26 ± 0.57 –0.82 ± 0.65 .009 .154 NM_006301 MAP3K12 –0.07 ± 0.07 –0.01 ± 0.07 –0.03 ± 0.06 –0.18 ± 0.22 .010 .154 NM_000201.1 ICAM1 –0.08 ± 0.09 0.02 ± 0.08 –0.08 ± 0.1 0.02 ± 0.09 .010 .154 NM_000874 IFNAR2 0.59 ± 0.43 0.36 ± 0.28 0.82 ± 0.29 0.82 ± 0.33 .010 .154 NM_003805 CRADD 0.02 ± 0.12 –0.04 ± 0.12 0.11 ± 0.09 0.1 ± 0.13 .011 .154 NM_002745 MAPK1.1 0.22 ± 0.18 0.15 ± 0.13 0.32 ± 0.11 0.33 ± 0.16 .011 .154 NM_002755 MAP2K1 –0.15 ± 0.19 –0.03 ± 0.15 –0.02 ± 0.28 0.17 ± 0.41 .012 .154 NM_019846 CCL28 –0.04 ± 0.11 0.01 ± 0.08 0.07 ± 0.16 0 ± 0.06 .012 .154 NM_006538 BCL2L11 –0.07 ± 0.06 –0.1 ± 0.1 0.01 ± 0.08 –0.02 ± 0.08 .013 .154 NM_017442 TLR9 0.02 ± 0.14 0.06 ± 0.08 0.14 ± 0.09 0.16 ± 0.12 .013 .154 NM_001242 TNFRSF7 –0.13 ± 0.06 –0.06 ± 0.06 –0.07 ± 0.09 –0.13 ± 0.09 .013 .154 NM_000043 TNFRSF6 –0.04 ± 0.05 –0.01 ± 0.08 –0.06 ± 0.1 –0.18 ± 0.23 .014 .154 NM_000655.2 SELL –0.15 ± 0.13 0 ± 0.18 –0.18 ± 0.1 –0.15 ± 0.16 .014 .154 NM_000543 SMPD1 0.13 ± 0.18 0.02 ± 0.2 0.26 ± 0.15 0.2 ± 0.21 .017 .155 NM_002720 PPP4C –0.11 ± 0.15 0 ± 0.09 –0.06 ± 0.11 0.04 ± 0.04 .017 .155 NM_002188 IL13 0.23 ± 0.24 0.11 ± 0.17 0.37 ± 0.19 0.3 ± 0.14 .018 .155 NM_021975 RELA –0.14 ± 0.14 –0.03 ± 0.07 –0.11 ± 0.11 –0.02 ± 0.09 .018 .155 NM_004834 MAP4K4 0.34 ± 0.29 0.22 ± 0.25 0.52 ± 0.19 0.53 ± 0.31 .018 .155 NM_001350 DAXX –0.01 ± 0.09 –0.04 ± 0.12 0.08 ± 0.09 0.05 ± 0.14 .018 .155 NM_022740 HIPK2 –0.11 ± 0.08 –0.03 ± 0.09 –0.16 ± 0.1 –0.09 ± 0.13 .019 .155 NM_004315 ASAH1 –0.09 ± 0.1 –0.06 ± 0.13 0.03 ± 0.1 0.02 ± 0.16 .019 .156 NM_003376 VEGF 0.61 ± 0.42 0.43 ± 0.29 0.83 ± 0.31 0.85 ± 0.33 .020 .161 NM_004131 GZMB –0.23 ± 0.11 –0.14 ± 0.06 –0.22 ± 0.1 –0.13 ± 0.1 .022 .162 NM_000256.2 MYBPC3 1.02 ± 0.43 0.92 ± 0.3 1.22 ± 0.21 1.35 ± 0.17 .022 .162 NM_001225 CASP4 –0.14 ± 0.11 –0.06 ± 0.06 –0.03 ± 0.08 –0.12 ± 0.12 .023 .162 NM_001824.2 CKM 0.79 ± 0.4 0.78 ± 0.44 1.1 ± 0.28 1.2 ± 0.39 .023 .162 NM_002704 PPBP 0.02 ± 0.15 0.17 ± 0.45 0.06 ± 0.16 –0.35 ± 0.69 .025 .170 NM_032964 CCL15.2 –0.03 ± 0.1 –0.05 ± 0.09 0.03 ± 0.07 0.05 ± 0.06 .025 .170 NM_000611 CD59 0.09 ± 0.1 0.04 ± 0.12 0.17 ± 0.12 0.17 ± 0.08 .026 .170

SD, Standard deviation; DCM, dilated cardiomyopathy; DCMi, inflammatory dilated cardiomyopathy.

	Acknowledgments

 
We gratefully acknowledge the expert support by Markus Bläss and Stefan Rußwurm from SIRC Lab for performing microarray analysis and the excellent technical assistance by Marlies Crombach and Heike Eckhardt from the University of Marburg.

	Footnotes

 
The research on this subject is in part funded by grants from the Deutsche Forschungsgemeinschaft to T. Meyer. V. Ruppert is supported by the BMBF German Heart Failure Network.

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Top Abstract Introduction Patients and Methods Results Discussion Table E1 References

 

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