Mapping individual cardiac cell types and their contribution in the development of heart failure in a multiple comorbidity porcine model

Abstract

Abstract Background Heart failure with preserved ejection fraction (HFpEF) is characterized by impaired left ventricular (LV) relaxation and filling due to increased myocardial stiffness and hypertrophy. The development of HFpEF is associated with multiple, interrelated risk factors including obesity, chronic kidney disease (CKD), diabetes mellitus (DM), hypertension. Mechanistic studies have implied that comorbidity-driven chronic inflammation, oxidative stress, and endothelial dysfunction act as contributors to the onset and development of HFpEF. Multiple studies that support this HFpEF paradigm highlight the complexity of the disease with different pathways active in and across different cell types. A recently developed multiple comorbidity swine (MCS) model with DM, CKD hypercholesterolemia, demonstrate inflammation, microvascular endothelial dysfunction and LV diastolic dysfunction. This model mimics the slow progression of disease in patients and is therefore ideal to study the earliest mechanisms underlying HFpEF. Purpose Here we hypothesize that single nuclei RNA (snRNA) sequencing aid in mapping the contribution of each individual cardiac cell cluster to disease development, to improve our mechanistic understanding of HFpEF. Methods We performed snRNA sequencing to analyze the transcriptional landscape of the cardiac environment in the well validated MCS model. Frozen left ventricle myocardial samples of MCS (n=4) and healthy control (n=3) swine were used. Results The analysis included ∼100.000 individual nuclei and offers new insights in cell type specific mechanisms during HFpEF onset and development against the background of common comorbidities. We identified 34 different cell clusters, which together represent 6 major cell types. Besides cardiomyocytes (CMs), endothelial cells (ECs) and fibroblasts, a large population of immune cells and smaller populations of mural cells and neurons were present in both MCS and control swine. IPA pathway analysis predicts transcriptomic changes within the cell clusters in response to comorbidities including activation of semaphoring and estrogen signaling in the largest CM cluster (CM2) and activation of EIF2 signaling in ECs (Fig. 1). Gene set enrichment analysis confirmed the activation of HFpEF paradigm pathways in the MCS dataset. Overlap of human HFpEF heart bulk RNA sequencing data and the MCS dataset showed overlapping disease processes including interferon gamma (IFNG) signaling. Moreover, IFNG signaling and IFNG associated transcription factor STAT1 were activated in most of the CM and EC clusters. Conclusion These findings suggest an essential role of multiple pathways during the onset and development of HFpEF. Further improvement of our understanding of underlying molecular mechanisms and crosstalk between cell populations, could pinpoint drug targets and aid the development of new treatments for HFpEF.Top 5 IPA predicted canonical pathways