Circulating proteomic signature of early death in heart failure patients with reduced ejection fraction.

Heart failure (HF) remains a main cause of mortality worldwide. Risk stratification of patients with systolic chronic HF is critical to identify those who may benefit from advanced HF therapies. The aim of this study is to identify plasmatic proteins that could predict the early death (within 3 years) of HF patients with reduced ejection fraction hospitalized in CHRU de Lille. The subproteome targeted by an aptamer-based technology, the Slow Off-rate Modified Aptamer (SOMA) scan assay of 1310 proteins, was profiled in blood samples from 168 HF patients, and 203 proteins were significantly modulated between patients who died of cardiovascular death and patients who were alive after 3 years of HF evaluation (Wilcoxon test, FDR 5%). A molecular network was built using these 203 proteins, and the resulting network contained 2281 molecules assigned to 34 clusters annotated to biological pathways by Gene Ontology. This network model highlighted extracellular matrix organization as the main mechanism involved in early death in HF patients. In parallel, an adaptive Least Absolute Shrinkage and Selection Operator (LASSO) was performed on these 203 proteins, and six proteins were selected as candidates to predict early death in HF patients: complement C3, cathepsin S and F107B were decreased and MAPK5, MMP1 and MMP7 increased in patients who died of cardiovascular causes compared with patients living 3 years after HF evaluation. This proteomic signature of 6 circulating plasma proteins allows the identification of systolic HF patients with a risk of early death.

Integrative System Biology Analyses Identify Seven MicroRNAs to Predict Heart Failure.

Heart failure (HF) has several etiologies including myocardial infarction (MI) and left ventricular remodeling (LVR), but its progression remains difficult to predict in clinical practice. Systems biology analyses of LVR after MI provide molecular insights into this event such as modulation of microRNA (miRNA) that could be used as a signature of HF progression. To define a miRNA signature of LVR after MI, we use 2 systems biology approaches, integrating either proteomic data generated from LV of post-MI rat induced by left coronary artery ligation or multi-omics data (proteins and non-coding RNAs) generated from plasma of post-MI patients from the REVE-2 study. The first approach predicted that 13 miRNAs and 3 of these miRNAs would be validated to be associated with LVR in vivo: miR-21-5p, miR-23a-3p and miR-222-3p. The second approach predicted that 24 miRNAs among 1310 molecules and 6 of these miRNAs would be selected to be associated with LVR in silico: miR-17-5p, miR-21-5p, miR-26b-5p, miR-222-3p, miR-335-5p and miR-375. We identified a signature of 7 microRNAs associated with LVR after MI that support the interest of integrative systems biology analyses to define a miRNA signature of HF progression.

Publisher Correction: MicroRNAs regulating superoxide dismutase 2 are new circulating biomarkers of heart failure.

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MicroRNAs regulating superoxide dismutase 2 are new circulating biomarkers of heart failure.

Although several risk factors such as infarct size have been identified, the progression of heart failure (HF) remains difficult to predict in clinical practice. Using an experimental rat model of post-myocardial infarction (MI), we previously identified 45 proteins differentially modulated during HF by proteomic analysis. This study sought to identify microRNAs (miRNAs) able to regulate these proteins and to test their relevance as biomarkers for HF. In silico bioinformatical analysis selected 13 miRNAs related to the 45 proteins previously identified. These miRNAs were analyzed in the rat and in cohorts of patients phenotyped for left ventricular remodeling (LVR). We identified that 3 miRNAs, miR-21-5p, miR-23a-3p and miR-222-3p, and their target Mn superoxide dismutase (SOD2) were significantly increased in LV and plasma of HF-rats. We found by luciferase activity a direct interaction of miR-222-3p with 3'UTR of SOD2. Transfection of human cardiomyocytes with miR-222-3p mimic or inhibitor induced respectively a decrease and an increase of SOD2 expression. Circulating levels of the 3 miRNAs and their target SOD2 were associated with high LVR post-MI in REVE-2 patients. We demonstrated for the first time the potential of microRNAs regulating SOD2 as new circulating biomarkers of HF.

Integrative network analysis reveals time-dependent molecular events underlying left ventricular remodeling in post-myocardial infarction patients.

To elucidate the time-resolved molecular events underlying the LV remodeling (LVR) process, we developed a large-scale network model that integrates the 24 molecular variables (plasma proteins and non-coding RNAs) collected in the REVE-2 study at four time points (baseline, 1month, 3months and 1year) after MI. The REVE-2 network model was built by extending the set of REVE-2 variables with their mechanistic context based on known molecular interactions (1310 nodes and 8639 edges). Changes in the molecular variables between the group of patients with high LVR (>20%) and low LVR (<20%) were used to identify active network modules within the clusters associated with progression of LVR, enabling assessment of time-resolved molecular changes. Although the majority of molecular changes occur at the baseline, two network modules specifically show an increasing number of active molecules throughout the post-MI follow up: one involved in muscle filament sliding, containing the major troponin forms and tropomyosin proteins, and the other associated with extracellular matrix disassembly, including matrix metalloproteinases, tissue inhibitors of metalloproteinases and laminin proteins. For the first time, integrative network analysis of molecular variables collected in REVE-2 patients with known molecular interactions allows insight into time-dependent mechanisms associated with LVR following MI, linking specific processes with LV structure alteration. In addition, the REVE-2 network model provides a shortlist of prioritized putative novel biomarker candidates for detection of LVR after MI event associated with a high risk of heart failure and is a valuable resource for further hypothesis generation.