Sample collection
Ethical approval was obtained from the Medical University Graz (25-008ex12/13 and 27-268ex14/15). All women participating in the study provided written informed consent. Control placentas were collected from pregnancies of non-smoking (self-reported) women who had a negative 75 g OGTT performed at 25–28 weeks of gestation and were free from medical disorders or pregnancy complications. GDM was diagnosed according to WHO/IADPSG criteria [16]. The women who had a positive OGTT were either recommended a diet and classified as GDM A1 or were additionally treated with insulin (NovoRapid plus Insulatard; Novo Nordisk Pharma, Vienna, Austria) and classified as GDM A2. Control and GDM samples were matched for ethnicity and fetal sex, but not for maternal BMI, since being overweight is major risk factor for GDM. However, neither BMI nor gestational weight gain differed between control and GDM groups. In addition, the mean and median cell passage of primary cells from control and GDM women was similar. We also performed covariate analysis on multiple factors (gestational age, cord blood insulin, fetal weight and length, fetal ponderal index, placental weight, maternal C-reactive protein [CRP], maternal height, maternal weight and BMI before pregnancy and before birth and maternal gestational weight gain) using Bioconductor package limma (https://bioconductor.org/packages/release/bioc/html/limma.html) and identified an effect on a total of 27 CpG sites at 21 genes (electronic supplementary material [ESM] Table 1), representing only a small subset of total differentially methylated positions and suggesting that observed effects are a result of GDM. We should note, however, that the sample size is too small for such covariate analysis.
We used different samples for expression and methylation analysis. Analysis of RNA and DNA from the same sample would have resulted in better correlation but analysis of different samples, though matched for clinical variables and passages, gives more robust results. Table 1 shows maternal, neonatal and placental characteristics of expression and methylation analyses.
Table 1 Maternal, neonatal and placental clinical variables for gene expression and DNA methylation analyses
Cell culture
Primary AEC and VEC were isolated from third-trimester human placentas after healthy and GDM-complicated pregnancies following a standard protocol [15]. Cells were characterised by immunocytochemical analysis [17] and cultured on 1% (vol./vol.) gelatin-coated flasks (75 cm2) using endothelial basal medium (EBM; Cambrex Clonetics, Baltimore, MD, USA) supplemented with the EGM-MV BulletKit (Cambrex Clonetics). Cell isolations were used up to passage ten as no phenotypical change or altered responses of the cells to culture were observed.
DNA methylation analysis
DNA (1 μg) isolated from fetoplacental AEC (n = 9), VEC (n = 9), AEC from GDM pregnancies (dAEC) (n = 5) and VEC from GDM pregnancies (dVEC) (n = 9), obtained from nine control and nine GDM placentas in total, was bisulphite converted using MethylEasy Bisulphite Modification Kit (Human Genetic Signatures, Sydney, NSW, Australia). Conversion efficiency was assessed by bisulphite-specific PCR (not shown). Hybridisation of bisulphite-treated samples to Illumina Infinium Human Methylation450 (HM450) BeadChips (Illumina, San Diego, CA, USA) was performed according to the manufacturer’s instructions. Based on power calculations for the HM450 array, our sample size would allow us to detect changes of Δβ > 0.2 and p value <0.05 [18]. The BeadChips were scanned using Illumina iScan (Illumina) and raw data were exported as IDAT files. Minfi Bioconductor package (https://bioconductor.org/packages/release/bioc/html/minfi.html) [19] imported data into R (version R 2.15.1), performed quality control, pre-processing and normalisation using the subset-quantile within array normalisation (SWAN) method [20]. The limma package [21] was used to fit a linear model to compare dAEC and dVEC vs control samples, with patient as random effect and allowing for batch effects. False discovery rate (FDR) was calculated by the Benjamini–Hochberg method. M values were calculated after removing probes on the sex chromosomes to eliminate potential sex bias and poor-performing probes. β values were derived from intensities defined by the ratio of methylated (M) to unmethylated (U) probes given by β = M / (U + M + 100). For details of quality control, outlier identification and HM450 platform validation using locus-specific SEQUENOM MassARRAY EpiTYPER (Agena Bioscience, San Diego, CA, USA) [22], see ESM Methods and ESM Figs 1–3. For information on whether CpGs are located in differentially methylated regions (DMRs), see ESM Table 2 and for a discussion of advantages vs disadvantages of the HM450 platform, see ESM Methods. Unadjusted and adjusted p values and information on whether CpGs are located at potential SNPs, within topological domains (TADs) published in HUVECs [23] or part of a DMR, are given in the lists of significantly methylated CpGs available on Gene Expression Omnibus (GEO) database account GSE106099 (www.ncbi.nlm.nih.gov/geo).
RNA isolation
Total RNA was isolated with RNeasy mini Kit (Qiagen, Hilden, Germany) and quality was assessed using a BioAnalyzer BA2100 (Agilent, Foster City, CA, USA) with the RNA 6000 Nano LabChip Kit (Agilent). Samples with an RNA Integrity Number ≥8.5 were further used.
Microarray gene expression analysis
Total RNA from AEC (n = 8) and VEC (n = 8) isolated from eight placentas from normal pregnancies and dAEC (n = 11) and dVEC (n = 10) isolated from 14 placentas from pregnancies complicated by GDM was labelled using Ambion WT Expression Kit for Affymetrix GeneChip Whole transcript (WT) Expression Arrays (Life Technologies, Carlsbad, CA). The cRNA was hybridised to GeneChip Human 1.0 ST arrays according to the manufacturer’s instructions (Affymetrix, Santa Clara, CA, USA). Washing and staining (GeneChip HT Hybridization, Wash and Stain Kit; Affymetrix) was performed with Affymetrix GeneChip fluidics station 450. Arrays were scanned using Affymetrix GeneChip scanner GCS3000. Labelling and hybridisation controls were evaluated with Affymetrix Expression Console EC 1.1. Data were analysed with RMA (robust multi-chip average), including background correction, quantile normalisation, log2 transformation and median polish summarisation using Genomic Suite v6.5 (Partek, St Louis, MO, USA) [24]. Statistical analysis used one-way ANOVA with fetal sex and mother as random factors. The p values were adjusted for multiple testing using the Benjamini–Hochberg method (R/Bioconductor package ‘multtest’; https://www.bioconductor.org/packages/release/bioc/html/multtest.html) [25, 26].
qPCR
cDNA was synthesized from 50 ng total RNA of different cell isolations (n = 10 per group) then used for microarray analysis according to protocol (SuperScript II Reverse Transcriptase protocol; Invitrogen, Carlsbad, CA, USA). qPCR was performed with TaqMan gene expression assays (Applied Biosystems, Carlsbad, CA, USA) and ABI Prism 5700 Sequence Detection System (Applied Biosystems, Foster City, CA, USA). The mean hypoxanthine-guanine phosphoribosyltransferase 1 (HPRT1) and ribosomal protein L30 (RPL30) expression was used as internal control as their expression was unaffected by GDM (not shown). Data were analysed using the \( {2}^{-\Delta \Delta {\mathrm{C}}_{\mathrm{t}}} \) method [27]. Statistical analysis used Student’s t test in SigmaPlot (Systat Software, San Jose, CA, USA).
Pathway analysis
Gene lists were analysed with Ingenuity Pathway Analysis (IPA, version 2.3) (Qiagen). Cut-off for methylation differences was p < 0.05 and Δβ ≥ 0.2 and for gene expression p < 0.05 and fold change (FC) ≥1.5. When investigating pathways the cut-off was set to Δβ ≥ 0.1 (methylation) and FC ≥1.3 (expression) in order to have sufficient genes for the analysis.
F-actin immunofluorescence staining
AEC (n = 5), VEC (n = 6), dAEC (n = 5) and dVEC (n = 5), each in quadruplicates, were seeded in gelatin-coated chamber slides (50,000 cells/well). Participants’ characteristics are provided in ESM Table 3. After 24 h, slides were transferred to room temperature, washed with HBSS and fixed with 3.7% (wt./vol.) formaldehyde in PBS for 10 min. After washing with PBS, cells were permeabilised with 0.1% (vol./vol.) Triton X-100 in PBS for 25 min, washed with PBS, blocked with 1% (wt./vol.) BSA in PBS for 25 min and incubated phalloidin-488 FITC (1:20, Thermo Fisher, Eugene, OR, USA) with DL550 (DyLight 550 goat-anti mouse, 1:100, Thermo Fisher) for 1 h in the dark. Stained cells were washed with PBS and slides were mounted with Dako fluorescent mounting medium (Dako, Carpinteria, CA, USA) with DAPI (1:2000). After overnight drying, actin organisation was observed using a Zeiss LSM 510 Meta microscope, objective Plan-Apochromat 63×/1.4 Oil DIC, at 495 nm and 518 nm excitation wavelength (Zeiss, Oberkochen, Germany) using Zeiss LSM Image Browser. F-actin staining was performed and photographed by a blinded observer.
Electrical cell-substrate impedance sensing
Impedance measurements were performed using an electrical cell-substrate impedance sensing (ECIS) system (Applied Biophysics, Troy, NY, USA) [28]. AEC (n = 10) and dAEC (n = 6) (80,000 cells/well), VEC (n = 8) and dVEC (n = 4) (110,000 cells/well) were seeded in 400 μl EBM on gelatin-coated gold electrodes (8W10E+ arrays; Applied Biophysics), in duplicates. Participants’ characteristics are provided in ESM Table 3. VEC are smaller in diameter and were seeded in higher density. Thus, both AEC and VEC reached confluency after 12 h. Impedance was then recorded at 4 kHz for 24 h. Linear mixed-effect model using R package nlme (https://CRAN.R-project.org/package=nlme) was fitted for AEC and VEC separately. GDM, time and the interaction between them were used as fixed effects. Time was also used as random effect.