International Journal of Molecular Sciences Article Systems Biology in Chronic Heart Failure—Identification of Potential miRNA Regulators Alba Vilella-Figuerola 1,2 , Alex Gallinat 1,2 , Rafael Escate 1,3 , Sònia Mirabet 3,4 , Teresa Padró 1,3,† and Lina Badimon 1,3,5, *,† 1 Cardiovascular-Program-ICCC, IR-Hospital Santa Creu i Sant Pau, IIB-Sant Pau, 08025 Barcelona, Spain 2 Biochemistry and Molecular Biology Department, Universitat Autònoma de Barcelona (UAB), 08193 Cerdanyola del Vallès, Spain 3 Centro de Investigación Biomédica en Red Cardiovascular (CIBER-CV), Instituto de Salud Carlos III, 28029 Madrid, Spain 4 Heart Failure Group, Cardiology Department, Hospital Santa Creu i Sant Pau, 08025 Barcelona, Spain 5 Chair Cardiovascular Research, Universitat Autònoma de Barcelona (UAB), 08025 Barcelona, Spain * Correspondence: lbadimon@santpau.cat; Tel.: +34-935-565-882; Fax: +34-935-565-559 † These authors contributed equally to this work. Abstract: Heart failure (HF) is a complex disease entity with high clinical impact, poorly understood pathophysiology and scantly known miRNA-mediated epigenetic regulation. We have analysed miRNA patterns in patients with chronic HF (cHF) and a sex- and age-matched reference group and pursued an in silico system biology analysis to discern pathways involved in cHF pathophysiology. Twenty-eight miRNAs were identified in cHF that were up-regulated in the reference group, and eight of them were validated by RT-qPCR. In silico analysis of predicted targets by STRING protein-protein interaction networks revealed eight cluster networks (involving seven of the identified miRNAs) enriched in pathways related to cell cycle, Ras, chemokine, PI3K-AKT and TGF-β signaling. By ROC Citation: Vilella-Figuerola, A.; curve analysis, combined probabilities of these seven miRNAs (let-7a-5p, miR-107, miR-125a-5p, miR- Gallinat, A.; Escate, R.; Mirabet, S.; 139-5p, miR-150-5p, miR-30b-5p and miR-342-3p; clusters 1–4 [C:1–4]), discriminated between HF with Padró, T.; Badimon, L. Systems Biology in Chronic Heart preserved ejection fraction (HFpEF) and HF with reduced ejection fraction (HFrEF), and ischaemic Failure—Identification of Potential and non-ischaemic aetiology. A combination of miR-107, miR-139-5p and miR-150-5p, involved in miRNA Regulators. Int. J. Mol. Sci. clusters 5 and 7 (C:5+7), discriminated HFpEF from HFrEF. Pathway enrichment analysis of miRNAs 2022, 23, 15226. https://doi.org/ present in C:1–4 (let-7a-5p, miR-125a-5p, miR-30b-5p and miR-342-3p) revealed pathways related to HF 10.3390/ijms232315226 pathogenesis. In conclusion, we have identified a differential signature of down-regulated miRNAs Academic Editors: Michael T. Chin in the plasma of HF patients and propose novel cellular mechanisms involved in cHF pathogenesis. and Claudio de Lucia Keywords: miRNA; chronic heart failure; network; enrichment pathways; pathophysiology Received: 11 October 2022 Accepted: 1 December 2022 Published: 3 December 2022 Publisher’s Note: MDPI stays neutral 1. Introduction with regard to jurisdictional claims in Heart failure (HF), affecting 1–2% of the world’s population over the age of 65, is published maps and institutional affil- one of the leading causes of mortality and morbidity [1,2]. It is a complex disease where iations. the heart is unable to meet the metabolic demands of the body [3,4]. Chronic HF (cHF) has several underlying aetiologies (ischaemic disease, dilated cardiomyopathy, hypertro- phy cardiomyopathy, hypertension, heart valve disease, etc.) that produce alterations in heart structure (left ventricle hypertrophy or dilation, cardiac fibrosis, heart remodelling) Copyright: © 2022 by the authors. and function (abnormal cardiac output, diastolic dysfunction), leading to common symp- Licensee MDPI, Basel, Switzerland. This article is an open access article tomatology [3]. The underlying pathological mechanisms that conduce to cHF are not distributed under the terms and completely understood, but several biological pathways are involved in the progression of conditions of the Creative Commons the disease [5]. Attribution (CC BY) license (https:// Plasma sample analysis allows the easy acquisition of biologic material, such as mi- creativecommons.org/licenses/by/ croRNAs (miRNAs) that may assist in disease characterisation and patient risk stratification. 4.0/). Int. J. Mol. Sci. 2022, 23, 15226. https://doi.org/10.3390/ijms232315226 https://www.mdpi.com/journal/ijms Int. J. Mol. Sci. 2022, 23, 15226 2 of 22 Further, some plasma miRNAs provide prognosis information [6–8] (such as HF devel- opment after AMI [6]), or help in disease diagnosis [9,10], (as biomarkers of childhood dilated cardiomyopathy [9]). miRNAs are short RNA sequences (18–22 nucleotides long) that are involved in the regulation and control of RNA expression [11]. Given their role as transcriptional regulators, miRNAs are being thoroughly studied in several conditions [12]. Because of their stability and accessibility in blood, there is a great interest to determine the miRNA role in HF. Some miRNAs have been related to cardiovascular (CV) disease pathogenesis [13,14] and the potential of others (e.g., miR-423-5p) as biomarkers for HF has been explored [15]. In that sense, we have previously determined that, in familial hypercholesterolaemia patients, up-regulated expression of miRNA miR-133a was associated with CV disease clinical event presentation. Further, we detected that this miRNA was mechanistically associated with atherosclerosis development [16]. Using a similar approach, we have investigated whether plasma miRNAs could show a differential pattern of expression in patients with cHF. Further, we performed an in silico systems biology analysis to predict protein targets of the identified differential miRNA signature and highlight those proteins involved in biological processes and molecular pathways with a higher probability to contribute to the pathophysiology of chronic heart failure. 2. Results 2.1. Differential miRNA Signature in cHF We comparatively analysed the plasma levels of 377 miRNAs in cHF patients and reference subjects (RS). Of these 377 miRNAs, 167 were consistently detected in both groups. Differential expression analyses between cHF and RS showed that 28 of these miRNAs presented statistically significant differences and a fold-change (log2 ) higher than ±0.38 between groups. Of these, the top 15 miRNAs presenting an area under the curve (AUC) ≥0.7 after C-statistics analysis and a fold-change >1.7 (0.75 in log2 ) were selected for further analysis. Three of the top-fifteen miRNAs were of the same family (let-7a-5p, let-7f-5p and let-7g-5p) and have been reported to be involved in the regulation of common pathways [17]. Therefore, only miRNA let-7a-5p, with the best AUC among them, was further analysed. In addition, and considering the probability of a low detection level, we excluded those miRNAs that according to miRBase presented a number of reads in the lowest 25% range (Figure 1 and Supplemental Table S1). Thus, nine miRNAs (let-7a-5p, miR-107, miR-125a-5p, miR-139-5p, miR-150-5p, miR-224- 5p, miR-30b-5p, miR-335-5p and miR-342-3p) were further selected for validation. Addi- tionally, we selected two miRNAs (miR-16-5p and miR-223-3p) that had similar expression values between groups as negative controls. These selected miRNAs were further validated by RT-qPCR using customised cards in larger cHF (n = 46) and RS (n = 26) groups. Differences between groups reached statistical power in eight miRNAs (let-7a-5p, miR-107, miR-125a-5p, miR-139-5p, miR-150-5p, miR- 30b-5p, miR-335-5p and miR-342-3p), being all down-regulated in cHF compared to RS. Differential expression of miR-224-5p, although showing a similar trend, did not reach statistical significance. Negative controls (miR-16-5p and miR-223-3p) maintained similar expression between groups (Figure 2 and Supplemental Table S2). Int. J. Mol. Sci. 2022, 23, 15226 3 of 22 Int. J. Mol. Sci. 2022, 23, 15226 3 of 23 Int. J. Mol. Sci. 2022, 23, 15226 3 of 23 Figure1.1. miRNA profiling Figure profiling in in cHF cHFpatients patientsand andreference referencesubjects. subjects. (a)(a) miRNAmiRNA profiling profiling as aasdota dot plot Figure in ain plot 1. miRNA Cartesian a Cartesian profiling graph. graph.x-axis in x-axis cHF patients represents represents and log2log reference (fold-change) 2 (fold-change) subjects. andand (a) y-axis miRNA represents y-axis profiling represents log10log as a (mediandot 10 (median plot con- in a Cartesian centration concentration graph.miRNAs p-value). p-value). x-axis miRNAs represents with log2 (fold-change) with1.3-fold 1.3-fold changes changesin and y-axis represents inconcentration concentration level (greater log10than (greater (median than con- 0.38[log 0.38 [log22 centration (1.3)] and p-value). less than miRNAs 0.38 [log with 1.3-fold 2 (1/1.3)]) and changes in significant concentration p-value are shown level in (greater solid black than dots. 0.38 [log2 miRNAs (1.3)] and less than 0.38 [log2 (1/1.3)]) and significant p-value are shown in solid black dots. miRNAs (1.3)] and with1.3-fold less than 1.3-fold 0.38 in changes [log 2 (1/1.3)]) andlevel concentration significant (greater p-value are shown than0.38 0.38 in solid (1.3)] [log2(1.3)] andblack dots. lessthan than0.38 miRNAs 0.38[log [log2 with changes in concentration level (greater than [log and less with 1.3-fold (1/1.3)]) changes in concentration but non-significant p-value arelevel shown (greater in solid than 0.38 grey [log2miRNAs dots. 2 (1.3)] and withless than less than0.38 [log22 1.3-fold (1/1.3)]) (1/1.3)]) but non-significant non-significant p-value p-value are are shown shown in in solid grey dots. miRNAs with less than 1.3-fold changesbut in concentration level (lower than 0.38 [logsolid greyand 2 (1.3)] dots. miRNAs higher thanwith0.38 less [log2than 1.3-fold (1/1.3)]) and changes changes in in concentration concentration level level (lower (lower than than0.380.38 [log (1.3)] and [log2(b) 2 (1.3)] andhigher higher than than0.380.38 [log (1/1.3)]) [log2 2in(1/1.3)]) and and non-significant p-value are shown in black circles. Bar graph indicating changes the expres- non-significant non-significant sion of miRNAs p-value p-value betweenare areshown showninin patients black andblack circles. circles. reference (b) Bar (b) graph Bar subjects. graph indicating indicating (c) Schematic changes changes workflow in for the in theexpression miRNAexpres- se- of miRNAs sion of miRNAs between patients between and patients reference and subjects. reference (c) subjects. Schematic (c) workflow Schematic lection. AUC: area under the curve; cHF: chronic heart failure; FC: fold-change; Q1: first (lowest) for workflow miRNA for selection. miRNA se- lection. AUC: quartile;AUC: area under RS: area theunder referencecurve; the cHF: subjects; curve; cHF: chronic ROC: chronic heart receiver heart failure; operating FC:failure; FC: fold-change; fold-change; characteristic Q1: curve. Q1: first first (lowest) (lowest) quartile; RS: quartile; RS: reference reference subjects; ROC: subjects; receiverROC: receiver operating operating characteristic characteristic curve. curve. Figure2.2.Box Figure Boxand andwhisker whiskerplots plotsofofselected selectedmiRNAs miRNAsanalysed analysedinincHF cHF(n(n==46) 46)and andRS RS(n(n==26). 26).UU Figure 2. Box and Mann–Whitney whisker test. plots