Multi-omics analyses identify molecular signatures with prognostic values in different heart failure aetiologies

Heart failure (HF) is the leading cause of morbidity and mortality worldwide, and there is an urgent need for more global studies and data mining approaches to uncover its underlying mechanisms. Multiple omics techniques provide a more holistic molecular perspective to study pathophysiological events involved in the development of HF. In this study, we used a label-free whole myocardium multi-omics characterization from three commonly used mouse HF models: transverse aortic constriction (TAC), myocardial infarction (MI), and homozygous Phospholamban-R14del (PLN-R14Δ/Δ). Genes, proteins, and metabolites were analysed for differential expression between each group and a corresponding control group. The core transcriptome and proteome datasets were used for enrichment analysis. For genes that were upregulated at both the RNA and protein levels in all models, clinical validation was performed by means of plasma level determination in patients with HF from the BIOSTAT-CHF cohort. Cell death and tissue repair-related pathways were upregulated in all preclinical models. Fatty acid oxidation, ATP metabolism, and Energy derivation processes were downregulated in all investigated HF aetiologies. Putrescine, a metabolite known for its role in cell survival and apoptosis, demonstrated a 4.9-fold (p