Predicted Value of MicroRNAs, Vascular Endothelial Growth Factor, and Intermediate Monocytes in the Left Adverse Ventricular Remodeling in Revascularized ST-Segment Elevation Myocardial Infarction Patients

Background Primary percutaneous coronary intervention (PPCI) in patients with ST-segment elevation myocardial infarction (STEMI) improves the survival of patients; nevertheless, some patients develop left ventricular adverse remodeling (LVAR) a few months after the intervention. The main objective of this study was to characterize the role of pro-inflammatory cell populations, related cytokines, and microRNAs (miRNAs) released after PPCI as reliable prognostic biomarkers for LVAR in patients with STEMI. Methods We evaluated the level of pro-inflammatory subsets, before and after revascularization, 1 and 6 months after PPCI, using flow cytometry. We also performed a miRNA microarray in isolated peripheral blood mononuclear cells (PBMCs) and examined the levels of 27 cytokines in patients’ serum of patients by multiplex ELISA. Results We observed that the levels of classical and intermediate monocytes increased 6 h after PPCI in patients who developed LVAR later. Multivariate regression analysis and ROC curves indicated that intermediate monocytes, after PPCI, were the best monocyte subset that correlated with LVAR. Within the 27 evaluated cytokines evaluated, we found that the increase in the level of vascular endothelial growth factor (VEGF) correlated with LVAR. Furthermore, the microarray analysis of PBMCs determined that up to 1,209 miRNAs were differentially expressed 6 h after PPCI in LVAR patients, compared with those who did not develop LVAR. Using RT-qPCR we confirmed a significant increase in miR-16, miR-21-5p, and miR-29a-3p, suggested to modulate the expression of different cytokines, 6 h post-PPCI in LVAR patients. Interestingly, we determined that the combined analysis of the levels of the intermediate monocyte subpopulation, VEGF, and miRNAs gave a better association with LVAR appearance. Similarly, combined ROC analysis provided high accurate specificity and sensibility to identify STEMI patients who will develop LVAR. Conclusion Our data suggest that the combined analysis of intermediate monocytes, VEGF, and miRNAs predicts LVAR in STEMI patients.


Study Subject
All the subjects of this study signed an informed consent form and are voluntary participants. As shown in the STROBE (Supplemental Figure 1), a cohort of 72 patients, were prospectively recruited between March 11 th 2016 and February 10 th 2020. The participants were divided into two groups: 28 healthy volunteers without diagnosed coronary disease and 44 who underwent coronary artery angiography at the University Hospital "Virgen del Rocio" of Seville and were diagnosed with STEMI and treated with PPCI. Clinical and demographical information were collected at the admission in the hospital and during each follow-up visit, scheduled at 1 and 6 months after the hospital discharge. In the STEMI group 4 patients (9.09%) died from adverse cardiovascular events, 2 missed the followup, and 38 (86.36%) completed the 6-months follow-up until September 2020.
The inclusion criteria were: age less than 75 years; patients with STEMI due to occlusion of the left descending artery with an epicardial blood flow TIMI (Thrombolysis in Myocardial Infarction) grade 0-1 in the initial angiogram treated with PPCI, with the onset of symptoms less than 12 hours before the angioplasty. Patients with ischemic heart disease history, a <30 ml/min glomerular filtration rate, and a TIMI flow grade over 1 were excluded.
All the patients were selected for a primary percutaneous coronary intervention through the right radial artery (100% of the STEMI patients). The procedure was performed, at the beginning of the intervention, with the administration of a heparin bolus of 10,000 units. Judkins large-lumen guiding catheter and guidewire were used. Coronary arteries were visualized using angiogram. Before accessing through the radial artery, palmar arch good circulation was confirmed to avoid hand ischemia. After that, a manual thrombus aspiration was systematically performed. To implant the stent the guidewire was appropriately positioned in the stenotic segment of the coronary artery; the balloon was later expanded and the stent was delivered in the corresponding position, relieving the stenosis. All patients in this study were treated with drug-eluting coronary stents with antiproliferative effects. After the intervention all the patients had a TIMI 3, meaning totally blood flow recovery. During the period at the hospital and before patient discharge, 100% of the patients were treated with an oral anticoagulant (aspirin) for 2 months, and antiaggregant (clopidrogel) for 6 months

Echocardiographic studies
Before patient discharge (72 hours post-STEMI) and 6 months after PPCI procedure, an echocardiography was completed to measure left ventricular ejection fraction (EF) systolic and diastolic left ventricular diameters and volumes in order to evaluate the development of left ventricular adverse remodelling (LVAR). A non-invasive ultrasound scan was performed to analyze cardiac volumes and function. The study was carried out with an iE33 (Philips, Amsterdam, Netherlands) echo system with images recorded in DICOM format at the Xcelera station for analysis. Dynamic sequences of three consecutive heartbeats were acquired with image optimization in 2D and doppler-color in several views: pure long axis parasternal, short axis parasternal at mitral valve level, papillary muscles and apex. Moreover, plane apical 2, 3, 4 and 5 chambers views were acquired to visualize segmental contractility defects and to calculate left ventricle ejection fraction, and end-diastolic and end-systolic left ventricle volume (Simpson 4C) (1). Subcostal view was acquired to visualize the inferior vena cava and the pericardial effusion. Continuous or pulsed Doppler (as appropriate) at aortic valve level and left ventricular outflow tract (LVOT), mitral filling, pulmonary veins, tricuspid filling and pulmonary valve were routinely evaluated. In case of valve insufficiency, appropriate measurements were made as established in valvulopathies clinical practice guidelines (2). Pulse Doppler and tissue Doppler in mitral septal and lateral ring were measured to estimate diastolic function and end-diastolic left ventricle pressure. Tricuspid annular systolic displacement (TAPSE) was used to calculate right ventricular systolic function, and tricuspid regurgitation was used to estimate pulmonary artery pressure.
Patients with an increase > 20% in left ventricular end-diastolic volume (LVEDV) and a left ventricular ejection fraction less than 50% were considered as LVAR patients, according to current clinical indications (3,4).

Blood samples: extraction and preparation
Before initiation of the PPCI procedure, patients' blood samples were extracted after catheter insertion in the radial or femoral arteries. These samples represented the time point before revascularization (0h). Moreover, we collected additional blood samples at 6-12 h after culprit vessel opening at 1 and 6 months after the ischemic event. We obtained serum from the blood samples collected without antiserum (in BD Vacutainer ® SST ™ II Advance tubes; Becton, Dickinson and Company, NJ, USA) using centrifugation at 2500 rpm at 4°C for 15 minutes. Fresh peripheral blood samples were collected into ethylenediaminetetraacetic acid (EDTA)-coated tubes (BD Vacutainer® K2E; Becton, Dickinson and Company, NJ, USA) and processed within 6h post-collection. From these tubes, 100 µl of blood were incubated with 10 µl of CD11b (APC conjugated, BD Pharmingen™ APC Mouse Anti-Human CD11b/Mac-1 clon ICRF; BD Pharmingen, CA, USA), 10 µl of CD14 (FITC conjugated, BD Pharmingen™ FITC Mouse Anti-Human CD14 clon M5E2; BD Pharmingen, CA, USA), 10 µl of CD16 (PE conjugated, BD Pharmingen™ PE Mouse Anti-Human CD16 clon 3G8; BD Pharmingen, CA, USA) and 2.5 µl of CD66b (PerCP-Cy5 Tandem dye conjugated, BD Pharmingen™ PerCP-Cy™5.5 Mouse Anti-Human CD66b clon G10F5; BD Pharmingen, CA, USA) during 30 minutes at room temperature (RT) protected from the light. Red blood cells were then lysed with 2 ml of a 10-fold dilution of BD FACS™ Lysing Solution 10X Concentrate (BD Biosciences, NJ, USA) with distilled water. After 10 min at RT, the resulting cell suspension was washed with phosphatebuffered saline (PBS 1X; GIBCO, Paisley, UK) and centrifuged at 1800 rpm during 8 min. The pellet was resuspended in 300 µl of PBS 1X and then analysed with flow cytometry.
Next, we checked the levels of IFN-γ, IL-1β, VEGF and IL-17A in patients' serum using ELISA kits. Thus, levels of IFN-γ were measured using the solid-phase sandwich IFN-γ Human ELISA Kit (BMS228; ThermoFisher Scientific,Waltham, MA, US) according to the manufacturer's protocol. The assay range was 1.6-100 pg/mL. The minimal detectable concentration was 0.99 pg/mL with an intraassay coefficient of variation (CV) of 4.5% and interassay 5.7%. Serum levels of IL-1β were detected using the solid-phase sandwich IL-1β Human ELISA Kit (BMS224-2; ThermoFisher Scientific, Waltham, MA, US) according to the manufacturer's protocol. The assay range was 3.9-250 pg/mL. The minimal detectable concentration was 0.3 pg/mL with an intra-assay CV of 5.1% and interassay 8.6%. Next, levels of VEGF were measured using the solid-phase sandwich VEGF-A Human ELISA Kit (BMS277-2; ThermoFisher Scientific, Waltham, MA, US) according to the manufacturer's protocol. The assay range was 15.6-1000 pg/mL. The minimal detectable concentration was 7.9 pg/mL with an CV of 6.2% and interassay 4.3%. Finally, serum levels of IL-17 were detected using the solidphase sandwich IL-17A Human ELISA Kit (BMS2017; ThermoFisher Scientific, Waltham, MA, US) according to the manufacturer's protocol. The assay range was 1.6-100 pg/mL.The minimal detectable concentration was 0.5 pg/mL with an intra-assay CV) of 7.1% and interassay 9.1%. All ELISAs were done in 96-well plate and were measured on the microplate reader CLARIOstar Plus (BMG LabtechOrtenberg, Germany).

RNA extraction and quantification
RNA extraction was done using mirVana™ miRNA Isolation Kit (Life Technologies, Grand Island, NY, US) to extract small RNAs from peripheral blood mononuclear cells (PBMCs) following manufacturer's instruction. Briefly, after a low-speed centrifugation, PBMCs were washed with PBS 1X and lysed in Lysis/Binding solution supplied with the kit. Then, Acid-Phenol:Chloroform (ThermoFisher Scientific, Waltham, MA, US) were added to the lysate. After purification steps, RNA were eluted in 60 µl of RNase-free water. Finally, PBMCs' RNA was measured using Qubit miRNA assay kit (ThermoFisher Scientific, Waltham, MA, US) and fluorometric quantification (Qubit™ Scientific; ThermoFisher Scientific, Waltham, MA, US).

miRNA Arrays Analysis
The total RNA was labeled using the FlashTag® Biotin HSR labeling Kit (Thermo Fisher Scientific, Inc.) following instructions supplied in the user manual, and we used GeneChip® miRNA 4.0 arrays (ThermoFisher Scientific, Waltham, MA, US) to analysis miRNAs. Washing, staining (GeneChip® Fluidics Station 450; ThermoFisher Scientific, Waltham, MA, US), and scanning (GeneChip® Scanner 3000; ThermoFisher Scientific, Waltham, MA, US) were done following manufacturer's protocol. Briefly, importing CEL file, the analysis of miRNA level RMA+DABG-All and the exporting of the results were done using Transcriptome Analysis Console (TAC) 4.0 software (ThermoFisher Scientific, Waltham, MA, US). A comparative analysis between LVAR patients, non-LVAR patients and controls samples was carried out using fold-change of over ±2.5 and an ANOVA analysis with a p-value < 0.05. Hierarchical clustering was performed using complete linkage and Euclidean distance as a measure of similarity for the differentially expressed with TAC 4.0 software.

In silico targeted gene searching
In order to identify miRNAs targets gene of inflammatory pathways and biological processes, we performed an analysis using the Gene Ontology (GO) browser PANTHER (Protein Analysis THrough Evolutionary Relationships, Version 15.0, http://pantherdb.org/genelistanalysis.do).

Statistical analysis
Statical analyses were performed using SPSS (SPSS Inc. version 25.0 IBM, Armonk, NY, USA) and GraphPad (GraphPad Software Inc., San Diego, CA, USA). Data are expressed as means ± standard error of the means (SEM). We removed the outliers and determined the normality of the distributions using GraphPad. For Gaussian distributions, we did an ordinary One-way ANOVA where we compared all the groups between them (Fisher's LSD test). For nonparametric distributions, we used the Kruskal-Wallis test with multiple comparisons corrected by Dunn's test. Multivariate regression analysis was conducted using SPSS. LVAR was selected as the binary dependent variable and creatine kinase (CK), troponin-T, sex, age, and classical and intermediate monocytes as the covariates for the adjustment. Linear regression analysis was performed using the level of monocytes and the percentage of change in LVEDV as independent and dependent variable, respectively.
In order to categorize a multi panel for LVAR detection, we stratified STEMI patients according to the level of intermediate monocytes, four cytokines and the expression of miRNAs. The level of each biomarker was divided in 4 ranges and each range had a score value from 1 to 4 assigned. The total score was made by the sum of the score reached by each biomarker, (see below algorithm and Supplemental Table 3). In this categorization, the linear combination of intermediate monocytes levels (in cells/ l), the level of secretion for cytokines (in pg/ml) and log10 fold change values for miRNAs were used to calculate the prediction score, where the higher the score, the higher the likelihood for the patient to develop LVAR. To evaluate the specificity and accuracy of the different biomarkers to predict the appearance of LVAR in revascularized STEMI patients, we used the receiver operating characteristic (ROC) curve.