Abstract

Introduction: Heart Failure with Preserved Ejection Fraction (HFpEF) constitutes more than half of all heart failure but has few effective therapies. Recent myocardial transcriptomics and metabolomics have revealed major differences between HFpEF and controls and HF with a reduced ejection fraction (HFrEF); however, how this translates at the protein level is unknown. Methods: Endomyocardial biopsies from patients with HFpEF, nonfailing donor controls and analyzed by data-dependent (n=10 HFpEF, n=9 non-failing controls) and data-independent (n=44 HFpEF, n=5 non-failing controls) mass-spectrometry-based proteomics. Results: The results were integrated with reported RNAseq and clinical characteristics. Proteomics data were analyzed with machine learning and gene ontology (GO) pathway analysis. Both HFpEF and HFrEF proteomes exhibited remarkable similarities, showing upregulation of cytoskeletal pathways and down regulation of oxidative phosphorylation pathways. Notably, structural ribosomal proteins and proteins associated with EIF 2 signaling were downregulated in HFpEF but not HFrEF. Integration with prior transcriptome data found a striking discrepancy between gene (up) and protein (down) expression for oxidative phosphorylation pathways in HFpEF, versus concordant reduction in HFrEF. Agnostic clustering of the HFpEF proteome identified two sub-groups, one with marked downregulation of oxidative phosphorylation and translational initiation pathways versus the other. This sub-group also had the highest proportion of HFpEF patients above the median BMI (40 kg/m 2 in our cohort). Conclusions: To our surprise, no hemodynamic and echocardiographic differences were observed between these groups. Despite marked differences in clinical presentation and pathophysiology, both HFpEF and HFrEF had very similar changes in protein abundance related to cytoskeletal and oxidative phosphorylation related pathways. Integration of proteomics, transcriptomics, and pathway analysis identifies a major deficit in protein translation in HFpEF that is not predictable based on clinical indices. These data reveal a unique proteomic HFpEF signature and identify potential proteins and pathways for therapeutic intervention.

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