Association of genetically predicted human metabolomic gene expression with incident heart failure

Abstract

Abstract Background Heart failure (HF) represents a significant challenge both for patients and healthcare systems worldwide. Despite ongoing efforts, therapeutic options are limited. The underlying pathophysiology involves intricate changes in cardiac metabolism that strongly influence the clinical phenotype, thus potentially offering novel options for therapeutic targeting. Systematic prioritisation of metabolic genes regarding their phenotypic impact on HF is outstanding. Purpose We investigate the association of genetically predicted metabolic gene expression with the onset of HF within UK Biobank (UKB). We followed up on identified candidate genes using human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) models of cardiomyocyte hypertrophy. Methods Utilising genomic data of 484,372 UKB participants and left ventricle-specific expression quantitative trait loci (eQTL) information from Genotype-Tissue Expression (GTEx) project, we predicted genetically regulated gene expression using established methods (PrediXcan) for all metabolic genes. Subsequently, we employed per-gene Cox proportional hazards (COX-PH) models to assess the association of genetically predicted metabolic gene expression with incident HF. We adjusted models for clinical risk using established risk models (Pooled Cohort Equations to Prevent HF (PCP-HF)). To validate our findings in vitro, we subjected hiPSC-CMs to a sympathomimetic stimulus via treatment with endothelin-1 (ET-1; 10 nM). We evaluate alterations in expression via real-time quantitative polymerase chain reaction (RT-qPCR) and establish methods for assessing hypertrophic remodelling. Results COX-PH models revealed the predicted expression of 104/1515 metabolic genes to be independently associated with the onset of HF. Subsequent refinement with a meta-analysis of commonly dysregulated genes in end-stage HF narrowed our findings to 46 candidate genes, of which several were known for their role in HF pathophysiology. We further traced nine genes for validation in hiPSC-CM models and showed significant dysregulation of several genes following the treatment with ET-1. Flow cytometry measured hiPSC-CM hypertrophy following sympathomimetic stimulation (p=0.036). Extracellular flux analysis revealed canonical changes following treatment with ET-1. Conclusions The study revealed several metabolic genes associated with incident HF, thus confirming previous findings and highlighting novel, putative therapeutic target genes. We confirmed transcriptional changes in an in vitro model. Ongoing work includes further validation via silencing candidate genes in hiPSC-CMs and subjection to the established phenotypic in vitro models.