A directed network analysis of the cardiome identifies molecular pathways contributing to the development of HFpEF.

AIMS

The metabolic syndrome and associated comorbidities, like diabetes, hypertension and obesity, have been implicated in the development of heart failure with preserved ejection fraction (HFpEF). The molecular mechanisms underlying the development of HFpEF remain to be elucidated. We developed a cardiome-directed network analysis and applied this to high throughput cardiac RNA-sequencing data from a well-established rat model of HFpEF, the obese and hypertensive ZSF1 rat. With this novel system biology approach, we explored the mechanisms underlying HFpEF.

METHODS AND RESULTS

Unlike ZSF1-Lean, ZSF1-Obese and ZSF1-Obese rats fed with a high-fat diet (HFD) developed diastolic dysfunction and reduced exercise capacity. The number of differentially expressed genes amounted to 1591 and 1961 for the ZSF1-Obese vs. Lean and ZSF1-Obese+HFD vs. Lean comparison, respectively. For the cardiome-directed network analysis (CDNA) eleven biological processes related to cardiac disease were selected and used as input for the STRING protein-protein interaction database. The resulting STRING network comprised 3.460 genes and 186.653 edges. Subsequently differentially expressed genes were projected onto this network. The connectivity between the core processes within the network was assessed and important bottleneck and hub genes were identified based on their network topology. Classical gene enrichment analysis highlighted many processes related to mitochondrial oxidative metabolism. The CDNA indicated high interconnectivity between five core processes: endothelial function, inflammation, apoptosis/autophagy, sarcomere/cytoskeleton and extracellular matrix. The transcription factors Myc and Peroxisome Proliferator-Activated Receptor-α (Ppara) were identified as important bottlenecks in the overall network topology, with Ppara acting as important link between cardiac metabolism, inflammation and endothelial function.

CONCLUSIONS

This study presents a novel systems biology approach, directly applicable to other cardiac disease-related transcriptome data sets. The CDNA approach enabled the identification of critical processes and genes, including Myc and Ppara, that are putatively involved in the development of HFpEF.