Transcriptome analysis of human heart failure reveals dysregulated cell adhesion in dilated cardiomyopathy and activated immune pathways in ischemic heart failure

Abstract

Background: Current heart failure (HF) treatment is based on targeting symptoms and left ventricle dysfunction severity, relying on a common HF pathway paradigm to justify common treatments for HF patients. This common strategy may belie an incomplete understanding of heterogeneous underlying mechanisms and could be a barrier to more precise treatments. We hypothesized we could use RNA-sequencing (RNA-seq) in human heart tissue to delineate HF etiology-specific gene expression signatures.

Results: RNA-seq from 64 human left ventricular samples: 37 dilated (DCM), 13 ischemic (ICM), and 14 non-failing (NF). Using a multi-analytic approach including covariate adjustment for age and sex, differentially expressed genes (DEGs) were identified characterizing HF and disease-specific expression. Pathway analysis investigated enrichment for biologically relevant pathways and functions. DCM vs NF and ICM vs NF had shared HF-DEGs that were enriched for the fetal gene program and mitochondrial dysfunction. DCM-specific DEGs were enriched for cell-cell and cell-matrix adhesion pathways. ICM-specific DEGs were enriched for cytoskeletal and immune pathway activation. Using the ICM and DCM DEG signatures from our data we were able to correctly classify the phenotypes of 24/31 ICM and 32/36 DCM samples from publicly available replication datasets.

Conclusions: Our results demonstrate the commonality of mitochondrial dysfunction in end-stage HF but more importantly reveal key etiology-specific signatures. Dysfunctional cell-cell and cell-matrix adhesion signatures typified DCM whereas signals related to immune and fibrotic responses were seen in ICM. These findings suggest that transcriptome signatures may distinguish end-stage heart failure, shedding light on underlying biological differences between ICM and DCM.

Keywords: Cardiomyopathy; Gene expression; Heart failure; RNA-seq; Transcriptome.

Fig. 1

Schematic of RNA-seq analyses. a mRNA from 64 human hearts was extracted, sequenced, and adjusted for covariates. By comparing DEGs at an FDR of 5%, three pathway analyses were conducted. Analysis 1 considered all shared DEGs between DCM vs NF and ICM vs NF as HF-DEGs (green-blue). Analysis 2 considered non-overlapping DEGs as DCM-specific (green) or ICM-specific (blue). Analysis 3 directly compares diseases (pink). b Principal component analysis of all three cohorts, ICM (blue), DCM (green), and NF (grey). On the first two principal components, each of the three groups clusters together with overlap between ICM and DCM. ICM clusters further away from NF than DCM

Fig. 2

Correlation matrix between samples. The heatmap matrix shows the Spearman correlation coefficient between samples for all expressed genes following adjustment. Samples cluster by phenotype. Cooler colors (blues, greens) represent relationships between samples that are most similar; warmer colors (reds, oranges) represent samples that are more dissimilar with lower coefficients

Fig. 3

Pathway analysis in HF-DEGs. a Venn diagram of DCM vs NF and ICM vs NF DEGs highlighting 2934 overlapping genes used in this analysis. b Top 20 enriched pathways. Bars are filled according to z-score: teal indicates higher (activated), orange indicates lower (inhibited). Pathways without a z-score are grey, and pathways with a z-score of zero are white. The ratio of the number of enriched genes to the number of total genes in the pathway is listed on the right side. c Circos plot of enriched biofunctions and their corresponding DEGs according to IPA. DEGs are colored by mean fold change from DCM or ICM vs NF. d Scatter plot of mean RPKM values of DCM against ICM logarithmically (R² = 0.98) for the 2934 HF-DEGs

Fig. 4

Pathway analysis in disease-specific DEGs. a Venn diagram of DCM vs NF and ICM vs NF highlighting 561 DCM-specific (green) and 1203 ICM-specific (blue) DEGs in this analysis. b Unsupervised clustering heatmap of DCM- and ICM-specific DEGs. Samples cluster according to etiology. c Enriched pathways (p ≤ 0.005). DCM-specific (left) and ICM-specific (right)

Fig. 5

Pathway analysis in disease-specific DEGs. a Network of genes involved in the predicted decrease of extracellular matrix adhesion in DCM. The absolute fold change of each gene is indicated by the size of its oval. b Circos plot of predicted activated biofunctions in ICM for three categories: quantity, infection, and migration. Connection sizes correlate to the number of genes involved in each sub-category, which are listed on the outside of the circle

Fig. 6

Pathways enriched in ICM vs DCM. a Diagram highlighting 535 DEGs from ICM vs DCM. b Top 20 enriched pathways. Teal indicates higher (activated) in ICM relative to DCM, and orange indicates lower (inhibited) in ICM relative to DCM. c Graded activation of Antigen Presentation Pathway from relative low expression in NF, moderate expression in DCM, and high expression in ICM

Fig. 7

Disease-specific and shared HF pathways. Specific events can lead NF hearts towards DCM or ICM, and both diseases have common HF responses. Dysregulated cell-cell and decreased cell-matrix adhesion contributes to DCM. An activated innate immune response, activation of proinflammatory cytokines, and increases in immune cell quantity, movement, and migration are characteristic of ICM. Both DCM and ICM have responses common to HF, including reduced translation, increased fetal gene expression and antigen presentation, and dysregulated mitochondria and protein degradation