A total of 48 consecutive UVH patients who were regularly seen in our outpatient clinic between 02/05/2015 and 18/06/2018 were enrolled in the present study. 42/48 (87.5%) patients had a complete Fontan palliation and 6/48 (12.5%) patients an incomplete or no Fontan palliation. Regarding the morphology of the dominant systemic ventricle, 32/48 (66.7%) patients had a morphological dominant left and 16/32 (50%) patients a morphological dominant right systemic ventricle. In the left systemic ventricle UVH group, 12 patients presented with tricuspid atresia, 12 patients with double inlet left ventricle and 8 patients with pulmonary atresia with or without ventricular septal defect. In the right systemic ventricle UVH group, 8 patients had hypoplastic left heart syndrome or mitral atresia and 8 patients double outlet right ventricle with pulmonary stenosis. The mean age was 22.8 ± 10.1 years (range 11− 46 years) including 17 females and 31 males. At enrollment, a structured protocol including a 12-lead surface electrocardiogram, a physical examination, measurement of blood pressure and transcutaneous oxygen saturation at rest, two-dimensional echocardiography as well as a venous blood draw for routine laboratory parameters and blood sampling were performed. Additionally, ultrasonographic parameters of liver congestion such as the diameter of the inferior caval vein (IVC) at deep inspiration and expiration were measured to calculate the collapsibility index . Clinical characteristics of the patient cohort and UVH-specific data have already been presented in a previous study . Patients’ specific liver parameters are illustrated in Table 1. Thirty-two healthy volunteers served as controls and were matched to UVH patients according to age and gender. In all volunteers, a physical examination, two-dimensional echocardiography to verify the absence of any heart abnormality, ultrasonography of the inferior caval vein to rule out liver congestion and venous blood sampling were performed on the same day. The study complies with the Declaration of Helsinki, was approved by the local ethics committee and all participants or their guardians gave written and informed consent before enrollment.
Sample preparation and RNA isolation
In all patients and controls, blood samples for miRNA detection were collected in PAXgene™ blood tubes (Becton–Dickinson, Heidelberg, Germany) shortly after echocardiographic evaluation. All PAXgene™ blood tubes were stored at room temperature for at least 24 hours to ensure complete lysis of the blood cells, then stored at -20°C for several days and finally transferred to -80°C for long-term storage until RNA isolation. Total RNA including miRNAs was isolated from blood samples using PAXgene™ Blood miRNA Kit on the QIAcube™ robot (Qiagen, Hilden, Germany) following the manufacturer’s recommendations and included DNase I treatment (Qiagen). To confirm the absence of genomic DNA contamination, a conventional PCR with exon spanning primers for Glyceraldehyde 3-Phosphate Dehydrogenase (GAPDH) as previously described . The concentration of isolated total RNA was measured using the NanoDrop ND-2000 spectrophotometer (Thermo Fisher Scientific, Massachusetts, United States). RNA purity was estimated by examining the OD 260/280 and the OD 260/230 ratios. The qualities of total RNA were assessed using the Agilent Bioanalyser 2100 Eukaryote Total RNA Nano Series II (Agilent Technologies, California, United States).
Analysis of miRNAs by microarray
The miRNA abundance profile from the 48 patients with UVH and 32 age- and gender-matched healthy controls was obtained from our previously generated and uploaded raw data to the NCBI GEO database (Accession ID: GSE136547) using SurePrint™ 8X60K Human v21 miRNA platforms (Agilent Technologies) . These platforms contain probes for the detection of 2,549 human miRNAs. An input amount of 100 ng of isolated RNA including miRNAs were labeled and subsequently hybridized to the miRNA microarray chip and the procedure was completed as previously described .
Analysis of miRNAs by RT-qPCR
Real-time quantitative PCR (RT-qPCR) validation analysis was performed using the StepOnePlus™ Real-Time PCR System (Applied Biosystems, Foster City, CA, United States) and the miScript PCR System (Qiagen) as previously described . For the validation step, we used the same samples that were used for the microarray analysis (48 UVH patients and 32 healthy controls) . Fifteen miRNAs (miR-101-3p, miR-144-5p, miR-18a-5p, miR-15a-5p, miR-17-3p, miR-96-5p, miR-18b-5p, miR-140-5p, miR-148a-3p, miR-29b-3p, miR-29c-3p, miR-210-3p, miR-125a-5p, miR-150-5p, and miR-324-3p) were chosen for RT-qPCR validation based on the significant abundance changes on the microarray. Of these miRNAs, miR-29b-3p and miR-29c-3p were significantly correlated to the prognostic scores of advanced liver fibrosis/cirrhosis (MELD-Albumin and ALBI score). For miRNA abundance level detection, an amount of 250 ng of the total RNA was converted into complementary DNA (cDNA). The resulting cDNA was then diluted to have 0.5 ng/μL input material for miRNA detection. All RT-qPCR experiments were carried out using the Liquid Handling Robot QIAgility™ (Qiagen) before performing RT-qPCR. All primer assays used in the current study were provided by Qiagen. Moreover, miRNA reverse transcription control (miRTC) (Qiagen) was performed to assess the performance of the reverse transcription reaction. The melting curve analysis was used to control the specificity of RT-qPCR products. The specificity of amplicons was further confirmed by agarose gel electrophoresis.
Clinical data of the patients were collected from medical records. Echocardiography and ultrasonography were performed using a Vivid™ E9 Ultrasound System (GE Healthcare, Horten, Norway). The echocardiographic loops and ultrasonographic images were stored digitally and analyzed on an Echopac server (Echopac Version 6, GE Healthcare). The collapsibility index of the IVC was calculated according to the formula: the difference of maximum and minimum diameter of the IVC divided by the maximum diameter of the IVC . The formulas used for calculating MELD and modified MELD scores have been described previously . ALBI score was calculated according to the equation: ALBI score = (log10 bilirubin x 0.66) + (albumin x −0.085) and has been previously published . Echocardiographic and ultrasonographic data sets were assessed by investigators blinded to the laboratory results. Investigators of miRNA signatures were blinded to the clinical, echocardiographic, ultrasonographic and laboratory data of the patients.
DataAssist™ Software v3.0 (Applied Biosystems) was used to calculate the relative abundance changes of miRNAs by the equation 2−ΔCt with RNU6B serving as an endogenous control as previously used and validated for this type of sample [22, 23, 25-30]. Clinical data were analyzed using standard statistical software (SPSS version 19; SPSS Inc., Chicago, Illinois). Continuous variables are expressed as mean ± standard deviation or median (interquartile interval) as appropriate. Differences between unpaired groups were analyzed using a Mann-Whitney-U test for continuous variables and a chi-square test (or Fisher exact test, if numbers were small) for nominal variables. Correlations were evaluated using Spearman’s regression coefficient. Receiver-operating characteristic (ROC) curve analysis was used for the performance of individual parameters as well as selected miRNAs to predict advanced liver fibrosis/cirrhosis and the area under the curve (AUC) was calculated. Multivariate analysis was performed using logistic regression analysis in a stepwise forward manner to identify independent predictors of advanced liver fibrosis/cirrhosis. Variables entered into the multivariate model were those that gave statistically significant results in the univariate analysis and didn’t show any multicollinearity. A two-tailed P-value <0.05 was considered statistically significant.