Myocardial Proteome in Human Heart Failure With Preserved Ejection Fraction

Abstract

Background: Heart failure with preserved ejection fraction (HFpEF) constitutes more than half of all HF but has few effective therapies. Recent human myocardial transcriptomics and metabolomics have identified major differences between HFpEF and controls. How this translates at the protein level is unknown.

Methods and results: Myocardial tissue from patients with HFpEF and nonfailing donor controls was analyzed by data-dependent acquisition (n=10 HFpEF, n=10 controls) and data-independent acquisition (n=44 HFpEF, n=5 controls) mass spectrometry-based proteomics. Differential protein expression analysis, pathway overrepresentation, weighted coexpression network analysis, and machine learning were integrated with clinical characteristics and previously reported transcriptomics. Principal component analysis (data-dependent acquisition-mass spectrometry) found HFpEF separated into 2 subgroups: one similar to controls and the other disparate. Downregulated proteins in HFpEF versus controls were enriched in mitochondrial transport/organization, translation, and metabolism including oxidative phosphorylation. Proteins upregulated in HFpEF were related to immune activation, reactive oxygen species, and inflammatory response. Ingenuity pathway analysis predicted downregulation of protein translation, mitochondrial function, and glucose and fat metabolism in HFpEF. Expression of oxidative phosphorylation and metabolism genes (higher) versus proteins (lower) was discordant in HFpEF versus controls. Data-independent acquisition-mass spectrometry proteomics also yielded 2 HFpEF subgroups; the one most different from controls had a higher proportion of patients with severe obesity and exhibited lower proteins related to fuel metabolism, oxidative phosphorylation, and protein translation. Three modules of correlated proteins in HFpEF that correlated with left ventricular hypertrophy and right ventricular load related to (1) proteasome; (2) fuel metabolism; and (3) protein translation, oxidative phosphorylation, and sarcomere organization.

Conclusions: Integrative proteomics, transcriptomics, and pathway analysis supports a defect in both metabolism and translation in HFpEF. Patients with HFpEF with more distinct proteomic signatures from control more often had severe obesity, supporting therapeutic efforts targeting metabolism and translation, particularly in this subgroup.

Keywords: fatty acids; heart failure; metabolism; myocardium; obesity; oxidative phosphorylation; proteomics.

Figure 1. Proteomic signatures in HFpEF and nonfailing controls.

A, Principal component analysis of DDA‐MS proteomics from 9 nonfailing controls (black) and 10 patients with HFpEF (purple). B, Volcano plot (negative logarithm base 2 of the adjusted P value [−log₂ P _adj] vs the logarithm base 2 of the fold change [log₂FC]) showing proteins significantly lower or higher in HFpEF vs controls (black). C, Dot plot of all nonzero loadings from principal component 1 from all proteins in the data set. The x axis corresponds to the loading. The 20 most positive (blue) and most negative (red) proteins are shown. D, Corresponding enrichment of GO:BP of all proteins correlated to PC1. The x axis is the log transformation of the adjusted P value. Circle size reflects protein ratio, which is the proportion of differentially expressed proteins in the pathway vs all differentially expressed proteins. GO:BP terms were curated using the Jaccard index to avoid redundancy; the full list of GO:BP terms is found in Data S1. Color coding reflects P value after Benjamini–Hochberg adjustment for multiple comparisons. GO:BP indicates Gene Ontology: Biological Processes; HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction; PC1, principal component 1; and PC2, principal component 2.

Figure 2. Pathways enriched by protein differences between HFpEF and respective controls (DDA‐MS).

Significantly enriched pathways were curated to diminish redundancy; the full pathway lists are provided in Data S1. Overrepresentation analysis of differentially expressed proteins is displayed. A, GO:BP enrichment of differentially expressed proteins in HFpEF vs Control. Circle size reflects the proportion of differentially expressed proteins in the pathway vs the total number of differentially expressed proteins. Color coding shows the P value after Benjamini–Hochberg adjustment for multiple comparisons. B, GO:BP pathway enrichment of differentially expressed genes/proteins with concordant or discordant directionality vs controls. Only genes with corresponding protein data were used in this analysis. Proteins and their corresponding genes were divided into 4 groups: (1) both higher expression vs controls, (2) both lower expression vs controls, (3) proteins higher/genes lower vs controls, and (4) proteins lower/genes higher vs controls. Color coding reflects P value after Benjamini–Hochberg adjustment for multiple comparisons. DDA‐MS indicates data‐dependent acquisition; GO:BP, Gene Ontology: Biological Processes; and HFpEF, heart failure with preserved ejection fraction.

Figure 3. Identification of subgroups with HFpEF by agnostic clustering of protein expression measured by DIA‐MS proteomics data set.

Subgroups of HFpEF were identified by K‐means clustering using proteomic data. A, Principal component analysis using all proteins measured identifies 2 distinct HFpEF proteomic signatures, with HFpEF Group 1 (blue) much closer to controls compared with HFpEF Group 2 (red). B, Gene Ontology: Biological ProcessesGO:BP enrichment for each subgroup with HFpEF compared with controls and to each other. Significantly enriched pathways were curated to diminish redundancy; the full pathway lists are provided in Data S1 Circle size reflects protein ratio, which is the proportion of differentially expressed proteins in the pathway vs all differentially expressed proteins. Color coding reflects P value after Benjamini–Hochberg adjustment for multiple comparisons. DIA‐MS indicates data‐independent acquisition; ER, endoplasmic reticulum; HFpEF, heart failure with preserved ejection fraction; PC1, principal component 1; and PC2, principal component 2.

Figure 4. Clinical correlates of HFpEF proteomic phenotypes from DIA‐MS proteomics.

A, Gene ontology enrichment of protein modules identified by weighted gene coexpression network analysis and corresponding cluster dendrogram. Significantly enriched pathways were curated to diminish redundancy; the full pathway lists are provided in Data S1. B, Heatmap of clinical variables that correlated with protein modules in the DIA‐MS data set for HFpEF only. Pearson correlation coefficient between clinical variables and protein eigenvalues (the first principal component of each group of correlated proteins) are displayed. Correlation coefficients for P values <0.05 are shown. Full correlation results are in Data S1. Sex is coded as 1—female, 0—male (ie, negative correlation implies protein module is associated with male sex). DIA‐MS indicates data‐independent acquisition; HFpEF, heart failure with preserved ejection fraction; LVEDD indicates left ventricular end diastolic diameter; LVMi, left ventricular mass index; MAPK, mitogen‐activated protein kinase; mPAP, mean pulmonary artery pressure; NIF/NK‐κB, nuclear factor kappa B‐inducing kinase; and RAP/PCWP, right atrial to pulmonary capillary wedge pressure ratio.