Impact of assisted reproduction, infertility, sex and paternal factors on the placental DNA methylome

Abstract

Children conceived using Assisted Reproductive Technologies (ART) have a higher incidence of growth and birth defects, attributable in part to epigenetic perturbations. Both ART and germline defects associated with parental infertility could interfere with epigenetic reprogramming events in germ cells or early embryos. Mouse models indicate that the placenta is more susceptible to the induction of epigenetic abnormalities than the embryo, and thus the placental methylome may provide a sensitive indicator of 'at risk' conceptuses. Our goal was to use genome-wide profiling to examine the extent of epigenetic abnormalities in matched placentas from an ART/infertility group and control singleton pregnancies (n = 44/group) from a human prospective longitudinal birth cohort, the Design, Develop, Discover (3D) Study. Principal component analysis revealed a group of ART outliers. The ART outlier group was enriched for females and a subset of placentas showing loss of methylation of several imprinted genes including GNAS, SGCE, KCNQT1OT1 and BLCAP/NNAT. Within the ART group, placentas from pregnancies conceived with in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) showed distinct epigenetic profiles as compared to those conceived with less invasive procedures (ovulation induction, intrauterine insemination). Male factor infertility and paternal age further differentiated the IVF/ICSI group, suggesting an interaction of infertility and techniques in perturbing the placental epigenome. Together, the results suggest that the human placenta is sensitive to the induction of epigenetic defects by ART and/or infertility, and we stress the importance of considering both sex and paternal factors and that some but not all ART conceptuses will be susceptible.

Figures

Figure 1

PCA of the whole placenta dataset. PCA was done on all 44 ART and 44 controls over all 414 320 CpGs. A threshold along PC1 was selected using a change-point detection algorithm to determine outliers. Outlier IDs are labeled as follows: 11 in ART, 4 in control. Red circles represent ART and blue circles represent controls.

Figure 2

Association with outlier DNA methylation profile. Clinical attributes, ART status (A) and sex (B) are ranked by association with outlier DNA methylation. For each attribute value, number of data samples is shown above each bar. Light gray represents the portion of outliers within each clinical attribute and black represents the non-outliers. P-values of chi-square test (χ2), no correction, df = degrees of freedom, missing values excluded.

Figure 3

(A) PCA analysis of imprinted regions. We selected 602 CpG sites that overlap known differentially methylated regions associated with imprinted genes or domains. Red color represents ART and blue color represents controls. (B) Heatmap analysis of imprinted regions. Heatmap was generated using DNA methylation levels for 602 CpG sites overlapping known differentially methylated regions associated with imprinted genes or domains. Samples are sorted based on principal component 1 (PC1) as per Figure 3A. Variables are sorted based on gene symbols. Sample annotation is displayed at the bottom of the heatmap. Black arrows represent outlier samples identified in Figure 1. Red color represents ART and blue color represents controls. For the heatmap, data are normalized for visualization (mean = 0, variance = 1).

Figure 4

Pyrosequencing validation of targeted imprinted region and LINE-1 elements. We selected an imprinted region GNAS1A (A) for pyrosequencing validation in 44 ART and 44 controls. The scatter plot is showing the distribution of the DNA methylation levels across the different samples. Outliers represent the samples that deviate in their DNA methylation values from the overall DNA methylation levels within each group of samples. (B) Pyrosequencing analysis of the repeat elements LINE-1 in controls and ART samples. Gray color represents outliers in both ART and controls.

Figure 5

Comparison between in vivo and in vitro groups of ART. Heatmap (A) and PCA graph (B) using the discriminating 436 variables between 21 in vivo and 23 in vitro samples at q = 0.05. Orange color represents in vitro group and turquoise color represents in vivo group. For the heatmap, data are normalized for visualization (mean = 0, variance = 1).

Figure 6

Pyrosequencing validation of three candidate regions identified between in vivo and in vitro comparison. Validation by bisulfite pyrosequencing on two CG sites overlapping WBSCR17 (A); five CG sites overlapping MIR663A including cg18291941 and cg03940098 (B) and six CG sites overlapping SLITRK5 including cg09083627 (C). Comparison was done on the same 21 in vivo and 23 in vitro samples. Statistical analysis was performed using unpaired two tailed t-test.