Korean Circ J.  2024 Aug;54(8):468-481. 10.4070/kcj.2024.0033.

Proteome-wide Characterization and Pathophysiology Correlation in Nonischemic Cardiomyopathies

• Lee S
• Jang DG
• Kyoung YJ
• Kim J
• Kim ES
• Hwang I
• Youn JC
• Kim JS
• Kim IC

• KMID: 2558538
• DOI: http://doi.org/10.4070/kcj.2024.0033

Abstract

Background and Objectives
Although the clinical consequences of advanced heart failure (HF) may be similar across different etiologies of cardiomyopathies, their proteomic expression may show substantial differences in relation to underlying pathophysiology. We aimed to identify myocardial tissue–based proteomic characteristics and the underlying molecular pathophysiology in non-ischemic cardiomyopathy with different etiologies.
Methods
Comparative extensive proteomic analysis of the myocardium was performed in nine patients with biopsy-proven non-ischemic cardiomyopathies (3 dilated cardiomyopathy [DCM], 2 hypertrophic cardiomyopathy [HCM], and 4 myocarditis) as well as five controls using tandem mass tags combined with liquid chromatography–mass spectrometry. Differential protein expression analysis, Gene Ontology (GO) analysis, and Ingenuity Pathway Analysis (IPA) were performed to identify proteomic differences and molecular mechanisms in each cardiomyopathy type compared to the control. Proteomic characteristics were further evaluated in accordance with clinical and pathological findings.
Results
The principal component analysis score plot showed that the controls, DCM, and HCM clustered well. However, myocarditis samples exhibited scattered distribution. IPA revealed the downregulation of oxidative phosphorylation and upregulation of the sirtuin signaling pathway in both DCM and HCM. Various inflammatory pathways were upregulated in myocarditis with the downregulation of Rho GDP dissociation inhibitors. The molecular pathophysiology identified by extensive proteomic analysis represented the clinical and pathological properties of each cardiomyopathy with abundant proteomes.
Conclusions
Different etiologies of non-ischemic cardiomyopathies in advanced HF exhibit distinct proteomic expression despite shared pathologic findings. The benefit of tailored management strategies considering the different proteomic expressions in non-ischemic advanced HF requires further investigation.

Keyword

Cardiomyopathy; Heart failure; Proteomics; Pathology

Figure

• 

  Figure 1 Proteomic analysis of cardiomyopathies and control group. (A) Principal component analysis score plot of proteomics data from cardiomyopathies and control group showing good clustering of controls (Ctrl 1, 2, 3, 4, and 5), dilated cardiomyopathy (DCM 1, 2, and 3), and hypertrophic cardiomyopathy (HCM 1 and 2). Myocarditis (MC 1, 2, 3, and 4) samples exhibit scattered distribution. Each dot represents one patient. Each group is delineated by ellipses that were estimated using the Khachiyan algorithm. (B) Scatter matrix showing the pairwise Pearson correlation values of proteomic expression between individuals (top right), histograms of log2 intensity distributions (diagonal), and corresponding scatter plots (bottom left) with linear fit line (red line). Each group is highlighted by a red border. (C) Volcano plots showing results from the 2-sided Student’s t-test of the 5,775 quantified proteins. Each disease group was individually compared with the control group. In DCM, increased and decreased proteins were noted similarly, whereas increased proteins were predominant in HCM. Protein upregulation was more pronounced in myocarditis, with few proteins being downregulated. The x-axis shows the log2 FC of each identified protein, and the y-axis shows the corresponding −log10 p value. Significantly upregulated proteins in the disease group (FC >1.5 and adjusted p value <0.05) are shown in red; significantly downregulated proteins in the disease group (FC <−1.5 and adjusted p value <0.05) are shown in blue. Non-significant proteins that did not pass the threshold are shown in grey. The top 10 significant proteins in each comparison were labelled with corresponding protein names. All volcano plots were generated using the VolcaNoseR program.Ctrl = control; DCM = dilated cardiomyopathy; FC = fold change; HCM = hypertrophic cardiomyopathy; MC = myocarditis; PC = principal components.

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  Figure 2 Hierarchical clustering of proteins that passed the cut-off of one-way ANOVA (q-value <0.05) was performed in Perseus on Z-scored logarithmized abundances using Euclidean distance and average linkage. The top 4 GO:BP terms in each cluster are represented. DEPs in the control group and in each cardiomyopathy group were clustered into 4 groups according to functional annotation.ANOVA = analysis of variance; BP = Biological Process; Ctrl = control; DCM = dilated cardiomyopathy; GO = Gene Ontology; HCM = hypertrophic cardiomyopathy; MC = myocarditis.

• 

  Figure 3 Dot plots showing the results of GO enrichment analysis in three categories (BP, MF, CC) in differentially expressed proteins from EnrichR webtool. Protein upregulation and downregulation were observed in DCM; protein upregulation was predominant in HCM; and upregulation with limited downregulation of protein was noted in myocarditis. Dots are color-coded from blue to red based on the adjusted p value. Dot size is proportional to the number of proteins in each GO term. The Rich factor shown in the x-axis indicates the ratio of the number of enriched proteins to the number of total annotated proteins.BP = Biological Process; CC = Cellular Compartment; DCM = dilated cardiomyopathy; GO = Gene Ontology; HCM = hypertrophic cardiomyopathy; MF = Molecular Function.The asterisks (*) indicate the statistical significance of each GO term (adjusted p values *<0.05, **< 0.01, ***<0.001).

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