Network Medicine Approaches to Classify Heart Failure With PReserved Ejection Fraction by Signatures of DNA Methylation and Point-of-carE Risk calculaTors (PRESMET)

Atrial Fibrillation, Heart Failure, Diastolic, Heart Failure, Systolic, Pulmonary Hypertension

HFpEF, Interactome, CD4+ T cells, HFrEF, Liquid biopsy, Network Medicine

Heart failure (HF) is a syndrome, resulting from structural or functional impairment of ventricular filling or ejection of blood. Effective HF management depends on accurate and rapid diagnosis requiring assessment of symptoms and physical signs in combination with advanced and expensive imaging tools. However, several challenges arise from the traditional symptom-based diagnosis because co-morbidities of HF have similar presentations. This implies the need for a deeper knowledge of mechanistic links among genetic and epigenetic events governing the pathophysiology of HF leading to a novel molecular-based system to differentiate HF phenotypes. Now, it is emerging that the pathophysiology of HFpEF and HFrEF is different, it provides an opportunity to identify biomarker candidates that could aid in HF diagnosis and stratification between these two forms of the disease. The aim of PRESMET project is to perform liquid biopsy strategies to identify novel putative non-invasive epigenetic-sensitive biomarkers that could be used either alone or in combination with established diagnostic tests, such as natriuretic peptide, to help differentiate HFpEF from HFrEF. The Investigators will perform DNA methylation analysis on CD4+ T cells isolated from patients versus controls. Remarkably, big data generated from NGS tools will be analyzed by advanced network-oriented algorithms. Our results may provide a useful clinical roadmap in order to improve precision medicine and personalized therapy of HF.

The Investigators will perform the first Network Medicine approach to integrate the DNA methylome of circulating CD4+T cells and clinical parameters in patients with HFpEF and HFrEF. Liquid biopsy strategies will be performed to isolate PBMCs and purificate CD4+ T cells. Successivelly, genomic DNA will be extracted on columns and will be send out for RRBS. Network-oriented algorithms will be used to analyze DNA methylation signatures and to identify specific epigenetic changes in relation to left ventricle ejection fraction. Network-oriented DNA methylation signatures will be integrated to the H2FPEF point-of-care calculator and, then, will be validated by the use of q-RT-PCR, WB, and ELISA.

Italy